From 85e79b6c7c28a161aab0e1747ff3148d43e7d720 Mon Sep 17 00:00:00 2001
From: Bertrand Neron <bneron@pasteur.fr>
Date: Mon, 27 Sep 2021 09:21:13 +0200
Subject: [PATCH] :fire: remove old notebooks
---
.../cours_matplotlib.ipynb | 3036 -------
.../np_pd_mplt_bertrand/matplotlib_TP.ipynb | 526 --
.../matplotlib_TP_solutions.ipynb | 1057 ---
.../np_pd_mplt_bertrand/numpy_TP.ipynb | 334 -
.../numpy_TP_solutions.ipynb | 606 --
.../numpy_cours_solutions.ipynb | 3140 --------
.../np_pd_mplt_bertrand/pandas_TP.ipynb | 286 -
.../pandas_TP_solution.ipynb | 596 --
.../np_pd_mplt_bertrand/pandas_cours.ipynb | 7127 -----------------
9 files changed, 16708 deletions(-)
delete mode 100644 previous_materials/np_pd_mplt_bertrand/cours_matplotlib.ipynb
delete mode 100644 previous_materials/np_pd_mplt_bertrand/matplotlib_TP.ipynb
delete mode 100644 previous_materials/np_pd_mplt_bertrand/matplotlib_TP_solutions.ipynb
delete mode 100644 previous_materials/np_pd_mplt_bertrand/numpy_TP.ipynb
delete mode 100644 previous_materials/np_pd_mplt_bertrand/numpy_TP_solutions.ipynb
delete mode 100644 previous_materials/np_pd_mplt_bertrand/numpy_cours_solutions.ipynb
delete mode 100644 previous_materials/np_pd_mplt_bertrand/pandas_TP.ipynb
delete mode 100644 previous_materials/np_pd_mplt_bertrand/pandas_TP_solution.ipynb
delete mode 100644 previous_materials/np_pd_mplt_bertrand/pandas_cours.ipynb
diff --git a/previous_materials/np_pd_mplt_bertrand/cours_matplotlib.ipynb b/previous_materials/np_pd_mplt_bertrand/cours_matplotlib.ipynb
deleted file mode 100644
index 016d489..0000000
--- a/previous_materials/np_pd_mplt_bertrand/cours_matplotlib.ipynb
+++ /dev/null
@@ -1,3036 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": 1,
- "id": "631fea4f-3022-471d-97c0-0888a78d2bea",
- "metadata": {},
- "outputs": [],
- "source": [
- "%matplotlib widget\n",
- "#%matplotlib inline"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "7425925e-b520-4d87-bf6b-3152f60c19ca",
- "metadata": {},
- "source": [
- "# <center>**Cours**</center>\n",
- "\n",
- "<img src=\"./images/logo2_matplotlib.svg\" style=\"margin:0 auto;\">\n",
- "<div style=\"text-align:center\">\n",
- " Bertrand Néron\n",
- " <br>\n",
- " <a src=\" https://research.pasteur.fr/en/team/bioinformatics-and-biostatistics-hub/\">Bioinformatics and Biostatistiqucs HUB</a>\n",
- " <br />\n",
- " © Institut Pasteur, 2021\n",
- "</div>"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "9f7ef4ec-9a15-4fbb-8c91-7784d6838499",
- "metadata": {},
- "source": [
- "# Motivation"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "90fe735b-9570-4b4b-8f1f-e6116de7b4de",
- "metadata": {},
- "source": [
- "# Installation\n",
- "\n",
- "* open a shell \n",
- "* activate the virtualenv (*source <prefix>/bin/activate*)\n",
- "and type:\n",
- " \n",
- "```\n",
- "pip install matplotlib\n",
- "```\n",
- "\n",
- "*You may need root permission if you do not use a virtualenv.*"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "d2fcbe0d-6c5d-4cd5-a7cb-1de3c6168a1d",
- "metadata": {},
- "source": [
- "# Using matplotlib"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "id": "abbbacce-63d9-435f-bb08-f54ff1c9143a",
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "import matplotlib\n",
- "import matplotlib.pyplot as plt"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "ee76e2e5-e737-451f-bd60-055f41923b93",
- "metadata": {},
- "source": [
- "# Concepts and Terminology"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "8aa36c70-6bd7-4264-aa57-f83781c1c108",
- "metadata": {},
- "source": [
- "<img src=\"images/mplt_concept.png\" width=\"600px\">\n",
- "\n",
- "> https://matplotlib.org/stable/tutorials/introductory/usage.html#parts-of-a-figure"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "2fa77d5e-74ae-435f-b28a-a9dd8880dc5c",
- "metadata": {},
- "source": [
- "### Figure\n",
- "\n",
- "The figure is like a canvas where all you Axes (plots) where drawn.\n",
- "A figuer acn containes several Axes (plots) but to be useful should have at least one.\n",
- "The easiest way to create a new figure is with pyplot:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "id": "99a6eba9-6e9f-4697-9778-143163d2280a",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "b6d97bb2eefe4f49a607d5a6d3ab842e",
- "version_major": 2,
- "version_minor": 0
- },
- "text/plain": [
- "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "fig = plt.figure() # an empty figure with no axes"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "id": "b16c62d1-dd07-488a-a018-3a81b7a2860d",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "c2090e31620c44d7a9e168543d63c139",
- "version_major": 2,
- "version_minor": 0
- },
- "text/plain": [
- "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "fig, ax_lst = plt.subplots(2, 2) # a figure with a 2x2 grid of Axes"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "bc55f943-cca6-4522-aec2-ca766995f8db",
- "metadata": {},
- "source": [
- "### Axes\n",
- "\n",
- "This is what you think of as ‘a plot’.\n",
- "* The Axes contains two (or three in the case of 3D) **Axis** objects\n",
- "* Each Axes has a title \n",
- "* Each Axes can contain a legend \n",
- "\n",
- "### Axis\n",
- "\n",
- "These are the number-line-like objects.\n",
- "\n",
- "### Labels\n",
- "\n",
- "This the \"legend\" of Axis. There is 2 labels for 2D plots the ``x_label`` and ``y_label``\n",
- "\n",
- "### Ticks\n",
- "\n",
- "The ticks arethe marks on the axis and ticklabels (strings labeling the ticks).\n",
- "the is two kind of ticks, major and minor ticks.\n",
- "by default they are automaticaly generated by the axis.\n",
- "but they can be configured."
- ]
- },
- {
- "cell_type": "markdown",
- "id": "bf8fce4d-0271-4b5d-8c7a-f2d2a310aed8",
- "metadata": {},
- "source": [
- "# Coding styles\n",
- "\n",
- "When viewing matplotlib code, you will find different coding styles and usage patterns. \n",
- "* matlab style\n",
- "* object-oriented style\n",
- "\n",
- "These styles are perfectly valid and have their pros and cons.\n",
- "The only caveat is to avoid mixing the coding styles for your own code.\n",
- "\n",
- "matlab style is fine for small interface like in notebook,\n",
- "whereas to have even more control in application embeding matplotlib GUI the pyplot level may be dropped completely, leaving a purely object-oriented approach."
- ]
- },
- {
- "cell_type": "markdown",
- "id": "a717bf5e-194f-43a5-8b8c-46e04a129236",
- "metadata": {},
- "source": [
- "## pyplot functional style (*aka matlab style*)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "id": "c61e6d40-84f4-4610-91d7-597a031b307f",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "Text(0, 0.5, 'volts')"
- ]
- },
- "execution_count": 5,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "plt.plot([1, 2, 3])\n",
- "plt.title('hi mom')\n",
- "plt.grid(True)\n",
- "plt.xlabel('time')\n",
- "plt.ylabel('volts')"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "de91852f-663e-4156-b2fb-c4892333825c",
- "metadata": {},
- "source": [
- "## Object oriented style"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "id": "85e712cb-b2d1-4f4b-8d47-8ad388a745b4",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "193fddde8bd748ab83bd4aac9385f0aa",
- "version_major": 2,
- "version_minor": 0
- },
- "text/plain": [
- "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/plain": [
- "Text(0, 0.5, 'volts')"
- ]
- },
- "execution_count": 6,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "fig, ax = plt.subplots() # by default 1 row, 1 column, 1 axe\n",
- "ax.plot([1, 2, 3])\n",
- "ax.set_title('hi mom')\n",
- "ax.grid(True)\n",
- "ax.set_xlabel('time')\n",
- "ax.set_ylabel('volts')"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "524e79fd-17b5-4d19-b417-eb46428b8e87",
- "metadata": {},
- "source": [
- "In this notebook we will use the *pyplot functional coding style*"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "47fb81f9-5bfd-4a97-b603-d6fa06b1084a",
- "metadata": {},
- "source": [
- "## Dive into matplotlib\n",
- "\n",
- "Now we are going to learn to use some compounds.\n",
- "To the demos belows we will need of *numpy* and *pandas* packages"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "id": "57e41359-cf96-4bf3-ac6c-f7778bf46b43",
- "metadata": {},
- "outputs": [],
- "source": [
- "import numpy as np\n",
- "import pandas as pd"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "b7dfc1c5-8602-4d8e-8f75-4994b8508b5f",
- "metadata": {},
- "source": [
- "# Plot\n",
- "\n",
- "> https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.plot.html"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "3f3ce2b1-198f-41a5-80f1-fde2d7272bbd",
- "metadata": {},
- "source": [
- "## One variable"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "id": "11b3d419-688e-47fc-882e-4cc5167283e5",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
- "<style scoped>\n",
- " .dataframe tbody tr th:only-of-type {\n",
- " vertical-align: middle;\n",
- " }\n",
- "\n",
- " .dataframe tbody tr th {\n",
- " vertical-align: top;\n",
- " }\n",
- "\n",
- " .dataframe thead th {\n",
- " text-align: right;\n",
- " }\n",
- "</style>\n",
- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>City</th>\n",
- " <th>Year</th>\n",
- " <th>Tmp</th>\n",
- " <th>std</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>0</th>\n",
- " <td>Barcelona</td>\n",
- " <td>1995</td>\n",
- " <td>62.019178</td>\n",
- " <td>9.569756</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1</th>\n",
- " <td>Barcelona</td>\n",
- " <td>1996</td>\n",
- " <td>61.125956</td>\n",
- " <td>9.420765</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2</th>\n",
- " <td>Barcelona</td>\n",
- " <td>1997</td>\n",
- " <td>62.612329</td>\n",
- " <td>9.827235</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>3</th>\n",
- " <td>Barcelona</td>\n",
- " <td>1998</td>\n",
- " <td>60.273973</td>\n",
- " <td>19.750126</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4</th>\n",
- " <td>Barcelona</td>\n",
- " <td>1999</td>\n",
- " <td>61.204658</td>\n",
- " <td>13.904526</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>...</th>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>177</th>\n",
- " <td>Rome</td>\n",
- " <td>2016</td>\n",
- " <td>61.185246</td>\n",
- " <td>15.914193</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>178</th>\n",
- " <td>Rome</td>\n",
- " <td>2017</td>\n",
- " <td>61.377808</td>\n",
- " <td>11.916595</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>179</th>\n",
- " <td>Rome</td>\n",
- " <td>2018</td>\n",
- " <td>60.821370</td>\n",
- " <td>20.327932</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>180</th>\n",
- " <td>Rome</td>\n",
- " <td>2019</td>\n",
- " <td>59.215068</td>\n",
- " <td>23.514064</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>181</th>\n",
- " <td>Rome</td>\n",
- " <td>2020</td>\n",
- " <td>52.676119</td>\n",
- " <td>6.224294</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "<p>182 rows × 4 columns</p>\n",
- "</div>"
- ],
- "text/plain": [
- " City Year Tmp std\n",
- "0 Barcelona 1995 62.019178 9.569756\n",
- "1 Barcelona 1996 61.125956 9.420765\n",
- "2 Barcelona 1997 62.612329 9.827235\n",
- "3 Barcelona 1998 60.273973 19.750126\n",
- "4 Barcelona 1999 61.204658 13.904526\n",
- ".. ... ... ... ...\n",
- "177 Rome 2016 61.185246 15.914193\n",
- "178 Rome 2017 61.377808 11.916595\n",
- "179 Rome 2018 60.821370 20.327932\n",
- "180 Rome 2019 59.215068 23.514064\n",
- "181 Rome 2020 52.676119 6.224294\n",
- "\n",
- "[182 rows x 4 columns]"
- ]
- },
- "execution_count": 8,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "temp = pd.read_csv(\"data/fr_sp_it_temp.tsv\", sep=\"\\t\", header=0, index_col=0)\n",
- "temp"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "id": "99d54202-c825-4dbc-8c25-b7631cb814e4",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
- "<style scoped>\n",
- " .dataframe tbody tr th:only-of-type {\n",
- " vertical-align: middle;\n",
- " }\n",
- "\n",
- " .dataframe tbody tr th {\n",
- " vertical-align: top;\n",
- " }\n",
- "\n",
- " .dataframe thead th {\n",
- " text-align: right;\n",
- " }\n",
- "</style>\n",
- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>City</th>\n",
- " <th>Year</th>\n",
- " <th>Tmp</th>\n",
- " <th>std</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>130</th>\n",
- " <td>Paris</td>\n",
- " <td>1995</td>\n",
- " <td>53.742192</td>\n",
- " <td>20.406326</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>131</th>\n",
- " <td>Paris</td>\n",
- " <td>1996</td>\n",
- " <td>52.293169</td>\n",
- " <td>15.207325</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>132</th>\n",
- " <td>Paris</td>\n",
- " <td>1997</td>\n",
- " <td>55.580000</td>\n",
- " <td>12.745185</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>133</th>\n",
- " <td>Paris</td>\n",
- " <td>1998</td>\n",
- " <td>50.317534</td>\n",
- " <td>27.794295</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>134</th>\n",
- " <td>Paris</td>\n",
- " <td>1999</td>\n",
- " <td>54.565753</td>\n",
- " <td>13.990209</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>135</th>\n",
- " <td>Paris</td>\n",
- " <td>2000</td>\n",
- " <td>54.337705</td>\n",
- " <td>10.345685</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>136</th>\n",
- " <td>Paris</td>\n",
- " <td>2001</td>\n",
- " <td>53.944932</td>\n",
- " <td>12.074808</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>137</th>\n",
- " <td>Paris</td>\n",
- " <td>2002</td>\n",
- " <td>52.743014</td>\n",
- " <td>18.722075</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>138</th>\n",
- " <td>Paris</td>\n",
- " <td>2003</td>\n",
- " <td>54.562192</td>\n",
- " <td>13.721165</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>139</th>\n",
- " <td>Paris</td>\n",
- " <td>2004</td>\n",
- " <td>53.585246</td>\n",
- " <td>11.761756</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>140</th>\n",
- " <td>Paris</td>\n",
- " <td>2005</td>\n",
- " <td>53.407671</td>\n",
- " <td>14.983372</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>141</th>\n",
- " <td>Paris</td>\n",
- " <td>2006</td>\n",
- " <td>54.199726</td>\n",
- " <td>13.030266</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>142</th>\n",
- " <td>Paris</td>\n",
- " <td>2007</td>\n",
- " <td>53.572055</td>\n",
- " <td>12.962494</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>143</th>\n",
- " <td>Paris</td>\n",
- " <td>2008</td>\n",
- " <td>52.381694</td>\n",
- " <td>15.655254</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>144</th>\n",
- " <td>Paris</td>\n",
- " <td>2009</td>\n",
- " <td>53.061096</td>\n",
- " <td>14.640168</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>145</th>\n",
- " <td>Paris</td>\n",
- " <td>2010</td>\n",
- " <td>51.648219</td>\n",
- " <td>13.601742</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>146</th>\n",
- " <td>Paris</td>\n",
- " <td>2011</td>\n",
- " <td>55.001918</td>\n",
- " <td>10.339226</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>147</th>\n",
- " <td>Paris</td>\n",
- " <td>2012</td>\n",
- " <td>53.256557</td>\n",
- " <td>11.453752</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>148</th>\n",
- " <td>Paris</td>\n",
- " <td>2013</td>\n",
- " <td>52.088493</td>\n",
- " <td>14.922241</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>149</th>\n",
- " <td>Paris</td>\n",
- " <td>2014</td>\n",
- " <td>53.650411</td>\n",
- " <td>11.968851</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>150</th>\n",
- " <td>Paris</td>\n",
- " <td>2015</td>\n",
- " <td>53.434973</td>\n",
- " <td>15.680118</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>151</th>\n",
- " <td>Paris</td>\n",
- " <td>2016</td>\n",
- " <td>51.122951</td>\n",
- " <td>21.198085</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>152</th>\n",
- " <td>Paris</td>\n",
- " <td>2017</td>\n",
- " <td>54.367945</td>\n",
- " <td>11.949198</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>153</th>\n",
- " <td>Paris</td>\n",
- " <td>2018</td>\n",
- " <td>45.773151</td>\n",
- " <td>36.811172</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>154</th>\n",
- " <td>Paris</td>\n",
- " <td>2019</td>\n",
- " <td>52.208219</td>\n",
- " <td>22.722815</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>155</th>\n",
- " <td>Paris</td>\n",
- " <td>2020</td>\n",
- " <td>49.320149</td>\n",
- " <td>7.458857</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " City Year Tmp std\n",
- "130 Paris 1995 53.742192 20.406326\n",
- "131 Paris 1996 52.293169 15.207325\n",
- "132 Paris 1997 55.580000 12.745185\n",
- "133 Paris 1998 50.317534 27.794295\n",
- "134 Paris 1999 54.565753 13.990209\n",
- "135 Paris 2000 54.337705 10.345685\n",
- "136 Paris 2001 53.944932 12.074808\n",
- "137 Paris 2002 52.743014 18.722075\n",
- "138 Paris 2003 54.562192 13.721165\n",
- "139 Paris 2004 53.585246 11.761756\n",
- "140 Paris 2005 53.407671 14.983372\n",
- "141 Paris 2006 54.199726 13.030266\n",
- "142 Paris 2007 53.572055 12.962494\n",
- "143 Paris 2008 52.381694 15.655254\n",
- "144 Paris 2009 53.061096 14.640168\n",
- "145 Paris 2010 51.648219 13.601742\n",
- "146 Paris 2011 55.001918 10.339226\n",
- "147 Paris 2012 53.256557 11.453752\n",
- "148 Paris 2013 52.088493 14.922241\n",
- "149 Paris 2014 53.650411 11.968851\n",
- "150 Paris 2015 53.434973 15.680118\n",
- "151 Paris 2016 51.122951 21.198085\n",
- "152 Paris 2017 54.367945 11.949198\n",
- "153 Paris 2018 45.773151 36.811172\n",
- "154 Paris 2019 52.208219 22.722815\n",
- "155 Paris 2020 49.320149 7.458857"
- ]
- },
- "execution_count": 9,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "paris = temp[temp.City == 'Paris']\n",
- "paris"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "id": "b13ce1af-fcba-41a4-8270-15b18bbe6f9b",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "[<matplotlib.lines.Line2D at 0x7f0790b4c310>]"
- ]
- },
- "execution_count": 10,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "plt.plot(paris['Tmp'])"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "ebb4469b-dd16-4047-8833-f9c01b234f46",
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "markdown",
- "id": "ae6a2a70-d851-49a1-a8dc-3c73d6041b51",
- "metadata": {},
- "source": [
- "## Two variables"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "id": "f9d20f59-c49b-442a-a882-c899590ce5ec",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "[<matplotlib.lines.Line2D at 0x7f07e04215e0>]"
- ]
- },
- "execution_count": 11,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "plt.plot(paris['Year'],paris['Tmp'])"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 12,
- "id": "bb79414d-cce0-4fa3-9ae1-4cbf55207e66",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "[<matplotlib.lines.Line2D at 0x7f0790b64370>]"
- ]
- },
- "execution_count": 12,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "plt.plot(paris['Year'],paris['Tmp'],\n",
- " marker='o')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 13,
- "id": "6e5b03a8-1502-4ecc-8a0c-623a4bc0932e",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "[<matplotlib.lines.Line2D at 0x7f0790b64ca0>]"
- ]
- },
- "execution_count": 13,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "plt.plot(paris['Year'],paris['Tmp'],\n",
- " marker='o',\n",
- " linestyle=''\n",
- " )"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 14,
- "id": "5942e686-e65c-40d4-a58e-54828f4186b3",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "[<matplotlib.lines.Line2D at 0x7f0790b6f2e0>]"
- ]
- },
- "execution_count": 14,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "plt.plot(paris['Year'],paris['Tmp'],\n",
- " marker='s',\n",
- " color='red',\n",
- " linestyle='--'\n",
- " )"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 15,
- "id": "540467bb-46ff-444c-b575-6abe145a20b3",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "[<matplotlib.lines.Line2D at 0x7f0790b6fca0>]"
- ]
- },
- "execution_count": 15,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "plt.plot(paris['Year'],paris['Tmp'],\n",
- " marker='v',\n",
- " color='brown',\n",
- " linestyle='-.'\n",
- " )"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "ece2ad49-59c4-4f40-b65d-bd8e228598b8",
- "metadata": {},
- "source": [
- "all available linestyles, markers and colors are described in https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.plot.html"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "08767e1e-c7e1-48d6-908a-72d91fafb7d2",
- "metadata": {},
- "source": [
- "## Several plots"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 16,
- "id": "bac9a02e-8301-4885-b34a-8d91fff234e4",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<matplotlib.legend.Legend at 0x7f0790af93a0>"
- ]
- },
- "execution_count": 16,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "bdx = temp[temp.City == 'Bordeaux']\n",
- "plt.plot(paris['Year'], paris['Tmp'], label='Paris')\n",
- "plt.plot(bdx['Year'], bdx['Tmp'], label='Bordeaux')\n",
- "\n",
- "plt.legend()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 17,
- "id": "7e9383b8-3ace-4042-a0f5-91071e56257b",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "Text(0.5, 1.0, 'Average Temperature')"
- ]
- },
- "execution_count": 17,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "for city, df in temp.groupby('City'):\n",
- " plt.plot(df['Year'], df['Tmp'], label=city)\n",
- "\n",
- "plt.legend(ncol=2)\n",
- "plt.xlabel(\"Year\")\n",
- "plt.ylabel(\"Tp in °F\")\n",
- "plt.title(\"Average Temperature\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 18,
- "id": "70924d07-7d98-4f8d-8bcf-de595c44be3e",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<matplotlib.legend.Legend at 0x7f0790b0a970>"
- ]
- },
- "execution_count": 18,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "paris_max = paris['Tmp'] + paris['std']\n",
- "paris_min = paris['Tmp'] - paris['std']\n",
- "\n",
- "plt.plot(paris['Year'], paris['Tmp'], label= \"Paris\" )\n",
- "\n",
- "plt.fill_between(paris['Year'], \n",
- " paris_max, \n",
- " y2=paris_min,\n",
- " alpha=0.5 \n",
- " )\n",
- "\n",
- "plt.legend()"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "aa620f3b-ced6-4ed3-9f4c-73abbdc86841",
- "metadata": {},
- "source": [
- "## xlabel and ylabel\n",
- "\n",
- "> https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.xlabel.html\n",
- "> https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.ylabel.html"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 19,
- "id": "d241268f-7265-424c-9d5c-aa554cb2aa16",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<matplotlib.legend.Legend at 0x7f0790b64670>"
- ]
- },
- "execution_count": 19,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "paris_max = paris['Tmp'] + paris['std']\n",
- "paris_min = paris['Tmp'] - paris['std']\n",
- "\n",
- "plt.plot(paris['Year'], paris['Tmp'], label= \"Paris\" )\n",
- "\n",
- "plt.fill_between(paris['Year'], \n",
- " paris_max, \n",
- " y2=paris_min,\n",
- " color=\"orange\", \n",
- " alpha=0.5 \n",
- " )\n",
- "plt.xlabel('Year')\n",
- "plt.ylabel('Temp in °F')\n",
- "plt.legend()"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "48c27e9b-6a4c-4751-8d65-2b18ebc4bce6",
- "metadata": {},
- "source": [
- "## Legend\n",
- "\n",
- "> https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.legend.html"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 20,
- "id": "6c674b4b-0107-44ab-92f0-30800cce309e",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<matplotlib.legend.Legend at 0x7f0790a75b80>"
- ]
- },
- "execution_count": 20,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "plt.plot(paris['Year'], paris['Tmp'], label='Paris')\n",
- "plt.plot(bdx['Year'], bdx['Tmp'], label='Bordeaux')\n",
- "plt.legend(['T°F at Paris', 'T°F at Bordeaux'], ncol=2)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 21,
- "id": "f3d98bc4-0ab2-4ee4-87bf-7cafe9e96954",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<matplotlib.legend.Legend at 0x7f0790a45340>"
- ]
- },
- "execution_count": 21,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "plt.plot(paris['Year'], paris['Tmp'], label='Paris')\n",
- "plt.plot(bdx['Year'], bdx['Tmp'], label='Bordeaux')\n",
- "plt.legend(['T°F at Paris', 'T°F at Bordeaux'], loc=\"upper right\")"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "8c8d4209-6ca9-4ff8-b7dd-45960257ab50",
- "metadata": {},
- "source": [
- "## Title\n",
- "\n",
- "> https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.title.html"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 22,
- "id": "8a912d1e-12d9-48d1-b77b-620ac69c7cec",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "Text(0.5, 1.0, 'average temp in France')"
- ]
- },
- "execution_count": 22,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "plt.plot(paris['Year'], paris['Tmp'], label='Paris')\n",
- "plt.plot(bdx['Year'], bdx['Tmp'], label='Bordeaux')\n",
- "plt.legend(['T°F at Paris', 'T°F at Bordeaux'])\n",
- "plt.title(\"average temp in France\")"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "6a84d787-3591-4e9f-b46d-709f0df54c30",
- "metadata": {},
- "source": [
- "## Grid\n",
- "\n",
- "> https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.grid.html"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 23,
- "id": "39f2e186-c45c-4068-a93a-13c99ee102b0",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "Text(0.5, 1.0, 'average temp in France')"
- ]
- },
- "execution_count": 23,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "plt.plot(paris['Year'], paris['Tmp'], label='Paris')\n",
- "plt.plot(bdx['Year'], bdx['Tmp'], label='Bordeaux')\n",
- "plt.legend(['T°F at Paris', 'T°F at Bordeaux'])\n",
- "plt.grid(ls=':') # ls = linestyle\n",
- "plt.title(\"average temp in France\")"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "d611387d-2153-4f0f-aa6a-f31cf2c8ce43",
- "metadata": {},
- "source": [
- "## xlim, ylim, axvline, axhline\n",
- "\n",
- "> https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.xlim.html\n",
- "\n",
- "> https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.lim.html\n",
- "\n",
- "> https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.axvline.html\n",
- "\n",
- "> https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.axhline.html"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 24,
- "id": "f256b620-8b59-477e-afc4-9f5dc65817eb",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "(50.0, 60.0)"
- ]
- },
- "execution_count": 24,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "plt.plot(paris['Year'], paris['Tmp'], label='Paris')\n",
- "plt.plot(bdx['Year'], bdx['Tmp'], label='Bordeaux')\n",
- "\n",
- "plt.axvline(2008, color=\"red\", linestyle=\"--\")\n",
- "plt.axhline(55, linestyle=\"-.\")\n",
- "\n",
- "plt.xticks(np.arange(1998, 2019, 2))\n",
- "plt.xlim([1998,2018])\n",
- "plt.ylim(50,60)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "3dffdb47-04cd-409c-b23b-cb7090c24286",
- "metadata": {},
- "source": [
- "## loglog and semilog\n",
- "\n",
- "> https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.loglog.html\n",
- "\n",
- "> https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.semilogx.html\n",
- "\n",
- "> https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.semilogy.html"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 25,
- "id": "cfcf593b-53d9-431b-bf04-7094b345d6e6",
- "metadata": {},
- "outputs": [],
- "source": [
- "plt.semilogy(paris['Year'], paris['Tmp'],\n",
- " marker='*',\n",
- " markersize=15,\n",
- " markeredgecolor=\"k\" # short option = mec\n",
- " )\n",
- "plt.grid()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 26,
- "id": "07bf81ea-93e7-4f50-aa3c-45ec748fcbea",
- "metadata": {},
- "outputs": [],
- "source": [
- "plt.loglog(paris['Year'], paris['Tmp'], marker='X')\n",
- "plt.grid(True, which=\"both\", linestyle='--')"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "d11439d9-b1b9-42fc-9be3-82e22dbd3d2c",
- "metadata": {},
- "source": [
- "# Histogram\n",
- "\n",
- "> https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.hist.html\n",
- "\n",
- "A histogram is a plot that lets you discover, and show, the underlying frequency distribution (shape) of a set of continuous data. This allows the inspection of the data for its underlying distribution (e.g., normal distribution), outliers, skewness, etc. \n",
- "\n",
- "Histograms are based on area, not height of bars\n",
- "\n",
- "In a histogram, it is the area of the bar that indicates the frequency of occurrences for each bin. This means that the height of the bar does not necessarily indicate how many occurrences of scores there were within each individual bin. It is the product of height multiplied by the width of the bin that indicates the frequency of occurrences within that bin. One of the reasons that the height of the bars is often incorrectly assessed as indicating frequency and not the area of the bar is due to the fact that a lot of histograms often have equally spaced bars (bins), and under these circumstances, the height of the bin does reflect the frequency.\n",
- "What is the difference between a bar chart and a histogram?\n",
- "\n",
- "The major difference is that a histogram is only used to plot the frequency of score occurrences in a **continuous** data set that has been divided into classes, called bins. Bar charts, on the other hand, can be used for a great deal of other types of variables including ordinal and nominal data sets.\n",
- "\n",
- "https://statistics.laerd.com/statistical-guides/understanding-histograms.php"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 27,
- "id": "297366ed-d133-460c-be02-7753f52a640e",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
- "<style scoped>\n",
- " .dataframe tbody tr th:only-of-type {\n",
- " vertical-align: middle;\n",
- " }\n",
- "\n",
- " .dataframe tbody tr th {\n",
- " vertical-align: top;\n",
- " }\n",
- "\n",
- " .dataframe thead th {\n",
- " text-align: right;\n",
- " }\n",
- "</style>\n",
- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>SepalLengthCm</th>\n",
- " <th>SepalWidthCm</th>\n",
- " <th>PetalLengthCm</th>\n",
- " <th>PetalWidthCm</th>\n",
- " <th>Species</th>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>Id</th>\n",
- " <th></th>\n",
- " <th></th>\n",
- " <th></th>\n",
- " <th></th>\n",
- " <th></th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>1</th>\n",
- " <td>5.1</td>\n",
- " <td>3.5</td>\n",
- " <td>1.4</td>\n",
- " <td>0.2</td>\n",
- " <td>Iris-setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2</th>\n",
- " <td>4.9</td>\n",
- " <td>3.0</td>\n",
- " <td>1.4</td>\n",
- " <td>0.2</td>\n",
- " <td>Iris-setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>3</th>\n",
- " <td>4.7</td>\n",
- " <td>3.2</td>\n",
- " <td>1.3</td>\n",
- " <td>0.2</td>\n",
- " <td>Iris-setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4</th>\n",
- " <td>4.6</td>\n",
- " <td>3.1</td>\n",
- " <td>1.5</td>\n",
- " <td>0.2</td>\n",
- " <td>Iris-setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>5</th>\n",
- " <td>5.0</td>\n",
- " <td>3.6</td>\n",
- " <td>1.4</td>\n",
- " <td>0.2</td>\n",
- " <td>Iris-setosa</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " SepalLengthCm SepalWidthCm PetalLengthCm PetalWidthCm Species\n",
- "Id \n",
- "1 5.1 3.5 1.4 0.2 Iris-setosa\n",
- "2 4.9 3.0 1.4 0.2 Iris-setosa\n",
- "3 4.7 3.2 1.3 0.2 Iris-setosa\n",
- "4 4.6 3.1 1.5 0.2 Iris-setosa\n",
- "5 5.0 3.6 1.4 0.2 Iris-setosa"
- ]
- },
- "execution_count": 27,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "iris = pd.read_csv('data/Iris.csv', sep=',' , header=0, index_col='Id' )\n",
- "iris.head()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 28,
- "id": "ea14337d-06c1-4c90-aaa1-3e9012a05fc4",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "Text(0, 0.5, 'frequency')"
- ]
- },
- "execution_count": 28,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "sepal_len = iris.iloc[:, 0]\n",
- "bins = 20\n",
- "n , plt_bins,patches = plt.hist(sepal_len, bins)\n",
- "plt.title(\"Histogram sepal Length\")\n",
- "plt.xlabel(\"sepal length in cm\")\n",
- "plt.ylabel(\"frequency\")"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "81d0d71a-629d-4478-9415-061311282839",
- "metadata": {},
- "source": [
- "## Influence of bins"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 29,
- "id": "194c61a8-b22f-4c06-89b8-1e255b818ef5",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "Text(0, 0.5, 'frequency')"
- ]
- },
- "execution_count": 29,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "bins = 200\n",
- "n , plt_bins,patches = plt.hist(sepal_len, bins)\n",
- "plt.title(\"too many bins\")\n",
- "plt.xlabel(\"sepal length in cm\")\n",
- "plt.ylabel(\"frequency\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 30,
- "id": "d9847d87-63be-441e-909c-6404809008f4",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "Text(0, 0.5, 'frequency')"
- ]
- },
- "execution_count": 30,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "bins = 4\n",
- "n , plt_bins,patches = plt.hist(sepal_len, bins)\n",
- "plt.title(\"not enought bins\")\n",
- "plt.xlabel(\"sepal length in cm\")\n",
- "plt.ylabel(\"frequency\")"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "1431708b-f346-418c-8969-b6b1b2c9f6fc",
- "metadata": {},
- "source": [
- "## several Histograms on the same figure"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 31,
- "id": "11ade1ad-9733-4b0f-86e9-edb8e9a6a2e7",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "Text(0, 0.5, 'frequency')"
- ]
- },
- "execution_count": 31,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "sepal_len = iris.iloc[:, 0]\n",
- "petal_len = iris.iloc[:, 2]\n",
- "\n",
- "bins = 20\n",
- "\n",
- "n , plt_bins,patches = plt.hist(sepal_len, bins, \n",
- " edgecolor='k', \n",
- " alpha=0.5,\n",
- " label='sepal length'\n",
- " )\n",
- "n , plt_bins,patches = plt.hist(petal_len, bins, \n",
- " edgecolor='k', \n",
- " alpha=0.5,\n",
- " label='petal length'\n",
- " )\n",
- "plt.legend()\n",
- "plt.title(\"Histogram sepal Length\")\n",
- "plt.xlabel(\"sepal length in cm\")\n",
- "plt.ylabel(\"frequency\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 32,
- "id": "bf6c136d-337a-4dac-9212-be743c07e1fe",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "Text(0, 0.5, 'frequency')"
- ]
- },
- "execution_count": 32,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "sepal_len = iris.iloc[:, 0]\n",
- "petal_len = iris.iloc[:, 2]\n",
- "\n",
- "bins = 20\n",
- "\n",
- "n , plt_bins,patches = plt.hist([sepal_len, petal_len],\n",
- " bins, \n",
- " edgecolor='k',\n",
- " density=True\n",
- " )\n",
- "plt.legend(['sepal length', 'petal length'])\n",
- "plt.title(\"Histogram sepal Length\")\n",
- "plt.xlabel(\"sepal length in cm\")\n",
- "plt.ylabel(\"frequency\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 33,
- "id": "128b2fe5-2de6-4844-b6c1-e318df38f964",
- "metadata": {},
- "outputs": [],
- "source": [
- "n , plt_bins,patches = plt.hist(sepal_len,\n",
- " bins, \n",
- " edgecolor='k',\n",
- " density=True\n",
- " )"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "d6673305-c6df-4053-9b6a-4b63bf882bdf",
- "metadata": {},
- "source": [
- "# Histogram 2D\n",
- "\n",
- "> https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.hist2d.html\n",
- "\n",
- "2D histograms are useful when you need to analyse the relationship between 2 numerical variables that have a huge number of values. It is useful for avoiding the over-plotted scatterplots. \n",
- "\n",
- "Given a set of ordered pairs describing data points, you can count the number of points with similar values to construct a two-dimensional histogram. This is similar to a one-dimensional histogram, but it describes the joint variation of two random variables rather than just one."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 34,
- "id": "ae5735f7-4a8a-471c-b8e6-7ae7bdede2b7",
- "metadata": {},
- "outputs": [],
- "source": [
- "petal_len = iris.loc[:, 'PetalLengthCm']"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 35,
- "id": "cf74efc2-5322-46df-a8ec-363adfe67f4f",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "Text(0.5, 1.0, 'sepal lenght vs petal length')"
- ]
- },
- "execution_count": 35,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "_ = plt.hist2d(sepal_len, petal_len, bins=(25, 25), cmap=plt.cm.jet)\n",
- "plt.xlabel('Sepal lenght (cm)')\n",
- "plt.ylabel('Petal length (cm)')\n",
- "plt.title('sepal lenght vs petal length' )"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "4a4380ee-5b86-4a54-8ba8-b7e4aeccb141",
- "metadata": {},
- "source": [
- "# Bar plot\n",
- "\n",
- "> https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.bar.html"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "02f41b9b-5d55-4b37-9dd1-de88cb702d7e",
- "metadata": {},
- "source": [
- "## Vertical bar plot"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 36,
- "id": "10543a41-2d3e-416b-abcb-0a92f7fd429b",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
- "<style scoped>\n",
- " .dataframe tbody tr th:only-of-type {\n",
- " vertical-align: middle;\n",
- " }\n",
- "\n",
- " .dataframe tbody tr th {\n",
- " vertical-align: top;\n",
- " }\n",
- "\n",
- " .dataframe thead th {\n",
- " text-align: right;\n",
- " }\n",
- "</style>\n",
- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>SepalLengthCm</th>\n",
- " <th>SepalWidthCm</th>\n",
- " <th>PetalLengthCm</th>\n",
- " <th>PetalWidthCm</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>count</th>\n",
- " <td>150.000000</td>\n",
- " <td>150.000000</td>\n",
- " <td>150.000000</td>\n",
- " <td>150.000000</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>mean</th>\n",
- " <td>5.843333</td>\n",
- " <td>3.054000</td>\n",
- " <td>3.758667</td>\n",
- " <td>1.198667</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>std</th>\n",
- " <td>0.828066</td>\n",
- " <td>0.433594</td>\n",
- " <td>1.764420</td>\n",
- " <td>0.763161</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>min</th>\n",
- " <td>4.300000</td>\n",
- " <td>2.000000</td>\n",
- " <td>1.000000</td>\n",
- " <td>0.100000</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>25%</th>\n",
- " <td>5.100000</td>\n",
- " <td>2.800000</td>\n",
- " <td>1.600000</td>\n",
- " <td>0.300000</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>50%</th>\n",
- " <td>5.800000</td>\n",
- " <td>3.000000</td>\n",
- " <td>4.350000</td>\n",
- " <td>1.300000</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>75%</th>\n",
- " <td>6.400000</td>\n",
- " <td>3.300000</td>\n",
- " <td>5.100000</td>\n",
- " <td>1.800000</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>max</th>\n",
- " <td>7.900000</td>\n",
- " <td>4.400000</td>\n",
- " <td>6.900000</td>\n",
- " <td>2.500000</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " SepalLengthCm SepalWidthCm PetalLengthCm PetalWidthCm\n",
- "count 150.000000 150.000000 150.000000 150.000000\n",
- "mean 5.843333 3.054000 3.758667 1.198667\n",
- "std 0.828066 0.433594 1.764420 0.763161\n",
- "min 4.300000 2.000000 1.000000 0.100000\n",
- "25% 5.100000 2.800000 1.600000 0.300000\n",
- "50% 5.800000 3.000000 4.350000 1.300000\n",
- "75% 6.400000 3.300000 5.100000 1.800000\n",
- "max 7.900000 4.400000 6.900000 2.500000"
- ]
- },
- "execution_count": 36,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "iris_desc = iris.describe()\n",
- "iris_desc"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 37,
- "id": "5114531d-9a90-4480-9d67-248afbf4c8ad",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "(4,)"
- ]
- },
- "execution_count": 37,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "iris_means = iris_desc.loc['mean', :]\n",
- "iris_means.shape"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 38,
- "id": "873e3079-951d-46f8-9f8f-5a65951fa5cf",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "Index(['SepalLengthCm', 'SepalWidthCm', 'PetalLengthCm', 'PetalWidthCm'], dtype='object')"
- ]
- },
- "execution_count": 38,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "iris_means.index"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 39,
- "id": "5ebd9556-230b-4ced-96d0-6a5af7e7c348",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "SepalLengthCm 5.843333\n",
- "SepalWidthCm 3.054000\n",
- "PetalLengthCm 3.758667\n",
- "PetalWidthCm 1.198667\n",
- "Name: mean, dtype: float64"
- ]
- },
- "execution_count": 39,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "iris_means"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 40,
- "id": "55098bbf-f6c9-4491-a5e9-5e7570f6f387",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "Text(0, 0.5, 'Average (in cm)')"
- ]
- },
- "execution_count": 40,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "recs = plt.bar(iris_desc.columns, iris_means, yerr=iris_desc.loc['std', :])\n",
- "for idx, data in enumerate(iris_means):\n",
- " plt.text(x=idx, y=data , s=f\"{data:.2f}\")\n",
- "plt.ylabel(\"Average (in cm)\")\n",
- " "
- ]
- },
- {
- "cell_type": "markdown",
- "id": "da95aa03-236e-4a62-b31b-f6a1a0e4d506",
- "metadata": {},
- "source": [
- " now we want to compare each species"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 41,
- "id": "029be9d0-034c-4424-9ca8-957109a55fe5",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
- "<style scoped>\n",
- " .dataframe tbody tr th:only-of-type {\n",
- " vertical-align: middle;\n",
- " }\n",
- "\n",
- " .dataframe tbody tr th {\n",
- " vertical-align: top;\n",
- " }\n",
- "\n",
- " .dataframe thead th {\n",
- " text-align: right;\n",
- " }\n",
- "</style>\n",
- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>SepalLengthCm</th>\n",
- " <th>SepalWidthCm</th>\n",
- " <th>PetalLengthCm</th>\n",
- " <th>PetalWidthCm</th>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>Species</th>\n",
- " <th></th>\n",
- " <th></th>\n",
- " <th></th>\n",
- " <th></th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>Iris-setosa</th>\n",
- " <td>5.006</td>\n",
- " <td>3.418</td>\n",
- " <td>1.464</td>\n",
- " <td>0.244</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>Iris-versicolor</th>\n",
- " <td>5.936</td>\n",
- " <td>2.770</td>\n",
- " <td>4.260</td>\n",
- " <td>1.326</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>Iris-virginica</th>\n",
- " <td>6.588</td>\n",
- " <td>2.974</td>\n",
- " <td>5.552</td>\n",
- " <td>2.026</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " SepalLengthCm SepalWidthCm PetalLengthCm PetalWidthCm\n",
- "Species \n",
- "Iris-setosa 5.006 3.418 1.464 0.244\n",
- "Iris-versicolor 5.936 2.770 4.260 1.326\n",
- "Iris-virginica 6.588 2.974 5.552 2.026"
- ]
- },
- "execution_count": 41,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "averages = iris.groupby(\"Species\").mean()\n",
- "averages"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 42,
- "id": "5eea5d45-4aed-4ce0-9169-65f99b8e9a40",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
- "<style scoped>\n",
- " .dataframe tbody tr th:only-of-type {\n",
- " vertical-align: middle;\n",
- " }\n",
- "\n",
- " .dataframe tbody tr th {\n",
- " vertical-align: top;\n",
- " }\n",
- "\n",
- " .dataframe thead th {\n",
- " text-align: right;\n",
- " }\n",
- "</style>\n",
- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>SepalLengthCm</th>\n",
- " <th>SepalWidthCm</th>\n",
- " <th>PetalLengthCm</th>\n",
- " <th>PetalWidthCm</th>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>Species</th>\n",
- " <th></th>\n",
- " <th></th>\n",
- " <th></th>\n",
- " <th></th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>Iris-setosa</th>\n",
- " <td>0.352490</td>\n",
- " <td>0.381024</td>\n",
- " <td>0.173511</td>\n",
- " <td>0.107210</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>Iris-versicolor</th>\n",
- " <td>0.516171</td>\n",
- " <td>0.313798</td>\n",
- " <td>0.469911</td>\n",
- " <td>0.197753</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>Iris-virginica</th>\n",
- " <td>0.635880</td>\n",
- " <td>0.322497</td>\n",
- " <td>0.551895</td>\n",
- " <td>0.274650</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " SepalLengthCm SepalWidthCm PetalLengthCm PetalWidthCm\n",
- "Species \n",
- "Iris-setosa 0.352490 0.381024 0.173511 0.107210\n",
- "Iris-versicolor 0.516171 0.313798 0.469911 0.197753\n",
- "Iris-virginica 0.635880 0.322497 0.551895 0.274650"
- ]
- },
- "execution_count": 42,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "std = iris.groupby(\"Species\").std()\n",
- "std"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 43,
- "id": "97dc5299-ce2e-4900-85de-f2e5819904bc",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<matplotlib.legend.Legend at 0x7f0790a63fd0>"
- ]
- },
- "execution_count": 43,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "width = 0.25 # the bar width \n",
- "x = np.arange(len(averages.columns)) # the xticks\n",
- "\n",
- "for shift, specie in enumerate(averages.index, -1):\n",
- " spec_avs = averages.loc[specie, :]\n",
- " abscisses = x + (shift * width)\n",
- " plt.bar(abscisses, \n",
- " spec_avs, \n",
- " width,\n",
- " yerr=std.loc[specie, :],\n",
- " label=specie)\n",
- " for one_abcisse, av in zip(abscisses, spec_avs) :\n",
- " plt.text(x=one_abcisse, y=av , s=f\"{av:.2f}\")\n",
- "\n",
- "plt.xticks(x, averages.columns, rotation=-45)\n",
- "plt.ylabel(\"Average (in cm)\")\n",
- "plt.legend()"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "7aba3fd5-9836-4a4e-9969-14a3fc0c076e",
- "metadata": {},
- "source": [
- "### xticks"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "0013fa09-2b05-412f-ac8a-da87c3c3b038",
- "metadata": {},
- "source": [
- "In the example above we don not use the labels as first parameter of pyplot.bar.\n",
- "But we use positions that allow us \n",
- "* to center tick on the midel bar\n",
- "* put one bar on the left (tick - bar width)\n",
- "* one bar on the the right (tick + bar width)\n",
- "\n",
- "and to put the name on the ticks we use the pyplot.xticks function\n",
- "with the location of the ticks and the names to display."
- ]
- },
- {
- "cell_type": "markdown",
- "id": "3df1f2e0-9475-4656-8108-5cffb1ea4867",
- "metadata": {},
- "source": [
- "## Horizontal bar plot\n",
- "\n",
- "Same as *pyplot.bar* but at horizontal.\n",
- "But do not forget to use *xerr* instesd of *yerr* to display *std*\n",
- "and switch *idx* and *data* for *x* and *y* for labels coordinates."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 44,
- "id": "551754f2-888b-4c3e-8e05-3fe4d0ab58f9",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "Text(0, 0.5, 'Average (in cm)')"
- ]
- },
- "execution_count": 44,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "recs = plt.barh(iris_desc.columns, iris_means, xerr=iris_desc.loc['std', :])\n",
- "for idx, data in enumerate(iris_means):\n",
- " plt.text(x=data, y= idx + 0.1 , s=f\"{data:.2f}\")\n",
- "plt.ylabel(\"Average (in cm)\")\n",
- " "
- ]
- },
- {
- "cell_type": "markdown",
- "id": "37938ef4-fa71-48a0-9344-5811fb84ba33",
- "metadata": {},
- "source": [
- "# Boxplot\n",
- "\n",
- "> https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.boxplot.html\n",
- "\n",
- "Box plots visually show the distribution of numerical data and skewness through displaying the data quartiles (or percentiles) and averages.\n",
- "\n",
- "Box plots show the five-number summary of a set of data: including the minimum score, first (lower) quartile, median, third (upper) quartile, and maximum score.\n",
- "\n",
- "<div>\n",
- " <img src=\"images/boxplot_explanation.png\" />\n",
- "</div>\n",
- "\n",
- "for more explanation visithttps://www.simplypsychology.org/boxplots.html"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 45,
- "id": "e6a7c6be-f094-4fc3-bcb8-e036fb71f363",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "Index(['SepalLengthCm', 'SepalWidthCm', 'PetalLengthCm', 'PetalWidthCm'], dtype='object')"
- ]
- },
- "execution_count": 45,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "iris.columns[:-1]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 46,
- "id": "15540dfc-8955-4736-983f-8290c53ec96e",
- "metadata": {},
- "outputs": [],
- "source": [
- "recs = plt.boxplot(iris.iloc[:, :-1],\n",
- " labels=iris.columns[:-1])\n"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "608af516-89cc-46a8-b1c2-f67317d50ee1",
- "metadata": {},
- "source": [
- "With a notch at the median"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 47,
- "id": "7e1a9570-dca5-41e3-8541-3003113a9571",
- "metadata": {},
- "outputs": [],
- "source": [
- "recs = plt.boxplot(iris.iloc[:, :-1],\n",
- " labels=iris.columns[:-1],\n",
- " notch=True)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "d05085c7-afed-4d76-8113-27356056ba94",
- "metadata": {},
- "source": [
- "with colored box"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 48,
- "id": "7853d478-1d90-4dad-97af-3954ddd994fb",
- "metadata": {},
- "outputs": [],
- "source": [
- "res_boxes = plt.boxplot(iris.iloc[:, :-1],\n",
- " labels=iris.columns[:-1],\n",
- " notch=True,\n",
- " patch_artist=True)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 49,
- "id": "f5a33448-1785-4697-893a-9e121a4b7ed4",
- "metadata": {},
- "outputs": [],
- "source": [
- "color = (\"orangered\", \"darkorange\", \"limegreen\", \"aqua\")\n",
- "\n",
- "res_boxes = plt.boxplot(iris.iloc[:, :-1],\n",
- " labels=iris.columns[:-1],\n",
- " notch=True,\n",
- " patch_artist=True)\n",
- "for i, c in enumerate(color):\n",
- " res_boxes['boxes'][i].set_facecolor(c)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "c1526170-5092-44c6-b71a-67c464087df7",
- "metadata": {},
- "source": [
- "# Violin Plot\n",
- "\n",
- "> https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.violinplot.html#matplotlib.pyplot.violinplot\n",
- "\n",
- "Sometimes the median and mean aren't enough to understand a dataset. Are most of the values clustered around the median? Or are they clustered around the minimum and the maximum with nothing in the middle? When you have questions like these, distribution plots are your friends.\n",
- "\n",
- "The box plot is an old standby for visualizing basic distributions. It's convenient for comparing summary statistics (such as range and quartiles), but it doesn't let you see variations in the data. For multimodal distributions (those with multiple peaks) this can be particularly limiting.\n",
- "\n",
- "But fret not—this is where the violin plot comes in. A violin plot is a hybrid of a box plot and a kernel density plot, which shows peaks in the data.\n",
- "\n",
- "formore explanation visit: https://mode.com/blog/violin-plot-examples/"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "49989052-9d0b-4d13-8136-b2579fa66005",
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "code",
- "execution_count": 50,
- "id": "0b21ac5f-a20c-402b-b4a3-8da33bbb245e",
- "metadata": {},
- "outputs": [],
- "source": [
- "ticks = np.arange(len(iris.columns[:-1]))\n",
- "violin_res = plt.violinplot(iris.iloc[:, :-1], \n",
- " ticks,\n",
- " )"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "37be476b-2d22-4ef0-8f3b-5193e4bfaaef",
- "metadata": {},
- "source": [
- "With small customization"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 51,
- "id": "11886a4b-d412-4975-a4c1-10621f4f7222",
- "metadata": {},
- "outputs": [],
- "source": [
- "ticks = np.arange(len(iris.columns[:-1]))\n",
- "violin_res = plt.violinplot(iris.iloc[:, :-1], \n",
- " ticks,\n",
- " showmeans=True,\n",
- " )\n",
- "_= plt.xticks(ticks, iris.columns[:-1], rotation=45)\n",
- "\n",
- "for i, c in enumerate(color):\n",
- " violin_res['bodies'][i].set_facecolor(c)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "a0d205b0-f9cc-4a16-bfa6-d79ac9908cfa",
- "metadata": {},
- "source": [
- "> for more customization: https://matplotlib.org/stable/gallery/statistics/customized_violin.html#sphx-glr-gallery-statistics-customized-violin-py"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "cdbd7179-6c53-4662-a9b8-aabb385805bb",
- "metadata": {},
- "source": [
- "# Scatter plot\n",
- "\n",
- "> https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.scatter.html\n",
- "\n",
- "A scatter plot (aka scatter chart, scatter graph) uses dots to represent values for two different numeric variables. The position of each dot on the horizontal and vertical axis indicates values for an individual data point. Scatter plots are used to observe relationships between variables.\n",
- "\n",
- "https://chartio.com/learn/charts/what-is-a-scatter-plot/"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 52,
- "id": "7c9deaef-7f5a-421c-a847-200da9846288",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "Index(['SepalLengthCm', 'SepalWidthCm', 'PetalLengthCm', 'PetalWidthCm',\n",
- " 'Species'],\n",
- " dtype='object')"
- ]
- },
- "execution_count": 52,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "iris.columns"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 53,
- "id": "cfab318e-1428-4b55-9869-d917e1df2521",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "Text(0.5, 1.0, 'Scatter plot Iris sepal')"
- ]
- },
- "execution_count": 53,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "plt.scatter(iris['SepalLengthCm'], iris['SepalWidthCm'])\n",
- "plt.xlabel('sepal length in (cm)')\n",
- "plt.ylabel('sepal width in (cm)')\n",
- "plt.title('Scatter plot Iris sepal')"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "c8ef0536-f98c-4ac3-99fa-b59e41136207",
- "metadata": {},
- "source": [
- "It's nice but we have nothing very evident appear.\n",
- "\n",
- "But there is several sepcies in dataset so we ca colored diferently each specie "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 54,
- "id": "56403c92-c573-4c49-befe-fb96f5cb4034",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "9385c4b9e43345b286347c05ab97c308",
- "version_major": 2,
- "version_minor": 0
- },
- "text/plain": [
- "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "plt.figure(figsize=(8,6)) #in inches\n",
- "\n",
- "for specie, data in iris.groupby('Species'):\n",
- " plt.scatter(data['SepalLengthCm'], data['SepalWidthCm'], label=specie )\n",
- " \n",
- "plt.xlabel('sepal length in (cm)')\n",
- "plt.ylabel('sepal width in (cm)')\n",
- "plt.legend()\n",
- "plt.title('Scatter plot Iris sepal')\n",
- "plt.grid()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 55,
- "id": "8277527e-8912-4d9c-8506-2d5899fcdf1e",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "de9e00d1ed2f4ab8a1be4732a66c70d8",
- "version_major": 2,
- "version_minor": 0
- },
- "text/plain": [
- "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "plt.figure(figsize=(8,6))\n",
- "for specie, data in iris.groupby('Species'):\n",
- " plt.scatter(data['SepalLengthCm'], data['SepalWidthCm'],\n",
- " s=150, # the dot size\n",
- " label=specie )\n",
- " "
- ]
- },
- {
- "cell_type": "markdown",
- "id": "0aecda7b-598a-4154-bda7-4ea33fee9f62",
- "metadata": {},
- "source": [
- "Let's get a new data set with more data"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 56,
- "id": "f6b9c462-b1f0-4636-91bf-a786b4a62a02",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
- "<style scoped>\n",
- " .dataframe tbody tr th:only-of-type {\n",
- " vertical-align: middle;\n",
- " }\n",
- "\n",
- " .dataframe tbody tr th {\n",
- " vertical-align: top;\n",
- " }\n",
- "\n",
- " .dataframe thead th {\n",
- " text-align: right;\n",
- " }\n",
- "</style>\n",
- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>MW</th>\n",
- " <th>AlogP</th>\n",
- " <th>PSA</th>\n",
- " <th>HBA</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>0</th>\n",
- " <td>0.00</td>\n",
- " <td>1.00</td>\n",
- " <td>72.731113</td>\n",
- " <td>1.141668</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1</th>\n",
- " <td>3.63</td>\n",
- " <td>544.59</td>\n",
- " <td>391.427565</td>\n",
- " <td>0.984864</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2</th>\n",
- " <td>2.11</td>\n",
- " <td>383.40</td>\n",
- " <td>437.458982</td>\n",
- " <td>15.040385</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>3</th>\n",
- " <td>1.24</td>\n",
- " <td>162.23</td>\n",
- " <td>480.111263</td>\n",
- " <td>11.401907</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4</th>\n",
- " <td>-1.37</td>\n",
- " <td>361.37</td>\n",
- " <td>448.864769</td>\n",
- " <td>5.732690</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " MW AlogP PSA HBA\n",
- "0 0.00 1.00 72.731113 1.141668\n",
- "1 3.63 544.59 391.427565 0.984864\n",
- "2 2.11 383.40 437.458982 15.040385\n",
- "3 1.24 162.23 480.111263 11.401907\n",
- "4 -1.37 361.37 448.864769 5.732690"
- ]
- },
- "execution_count": 56,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "data_for_plt = pd.read_csv(\"data/data_for_plt.csv\", sep=\"\\t\", header=0, index_col=0)\n",
- "data_for_plt.columns = ['MW', 'AlogP', 'PSA', 'HBA']\n",
- "data_for_plt.head()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 57,
- "id": "72b392ba-459b-4e00-a363-f72f589d0b9a",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<matplotlib.collections.PathCollection at 0x7f079048afa0>"
- ]
- },
- "execution_count": 57,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "x = data_for_plt['MW']\n",
- "y = data_for_plt['AlogP']\n",
- "plt.scatter(x, y)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "3a3d9d8b-93f0-4f9b-bd1d-28c92eb1fb6a",
- "metadata": {},
- "source": [
- "When the is lot of point it could be useful to use transparency to have a better data visualization"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 58,
- "id": "8473712b-f23a-4702-be31-218041abb88e",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<matplotlib.collections.PathCollection at 0x7f07904bc460>"
- ]
- },
- "execution_count": 58,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "plt.scatter(x, y,\n",
- " alpha=0.5)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "549f8e74-dfd7-4a94-a107-f2f03155072c",
- "metadata": {},
- "source": [
- "The size can be set for each dot\n",
- "\n",
- "use an array like (size of the x/y)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 59,
- "id": "6b62354f-276c-403b-8780-92badbfb87c7",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "493294f7a6d249e1a809110a6761fc15",
- "version_major": 2,
- "version_minor": 0
- },
- "text/plain": [
- "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/plain": [
- "<matplotlib.collections.PathCollection at 0x7f0790421a60>"
- ]
- },
- "execution_count": 59,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "fig = plt.figure(figsize=(8,6))\n",
- "plt.scatter(x, y,\n",
- " s=data_for_plt['PSA'],\n",
- " alpha=0.5)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "0c11267a-82a4-41f9-b652-67fa705f2fd8",
- "metadata": {},
- "source": [
- "And now we want to colored each dot in function of the *HBA* value "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 60,
- "id": "114fc0a3-a7db-4022-8b66-ea3f14d85580",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "3dc521d1217f478fb80d60f0586973b4",
- "version_major": 2,
- "version_minor": 0
- },
- "text/plain": [
- "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/plain": [
- "Text(0, 0.5, 'Hydrogen Bond Acceptor')"
- ]
- },
- "execution_count": 60,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "fig = plt.figure(figsize=(12,7))\n",
- "plt.scatter(data_for_plt['MW'],\n",
- " data_for_plt['AlogP'],\n",
- " s=data_for_plt['PSA'],\n",
- " c=data_for_plt['HBA'],\n",
- " edgecolors=\"k\",\n",
- " alpha=0.5,\n",
- " cmap=\"bwr\")\n",
- "cb = plt.colorbar()\n",
- "plt.xlabel('Molecular Weight')\n",
- "plt.ylabel('Hydrophobicity')\n",
- "cb.ax.set_ylabel(\"Hydrogen Bond Acceptor\", rotation=-90, va=\"bottom\")"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "e8b5c24d-9993-447c-9b1e-cd8473acc131",
- "metadata": {},
- "source": [
- "# Color map\n",
- "\n",
- "> https://matplotlib.org/stable/tutorials/colors/colormaps.html\n",
- "\n",
- "> https://matplotlib.org/stable/gallery/color/named_colors.html"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 61,
- "id": "8329e2ea-645a-419d-a158-0687ad21fcbf",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "bbdd224ca5d14b8e96d03f946cb4e489",
- "version_major": 2,
- "version_minor": 0
- },
- "text/plain": [
- "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/plain": [
- "Text(0, 0.5, 'Hydrogen Bond Acceptor')"
- ]
- },
- "execution_count": 61,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "fig = plt.figure(figsize=(12,7))\n",
- "plt.scatter(data_for_plt['MW'],\n",
- " data_for_plt['AlogP'],\n",
- " s=data_for_plt['PSA'], # marker size accept an array\n",
- " c=data_for_plt['HBA'], # marker color accept an array \n",
- " edgecolors=\"k\", \n",
- " alpha=0.5,\n",
- " cmap=\"hot\")\n",
- "cb = plt.colorbar()\n",
- "plt.xlabel('Molecular Weight')\n",
- "plt.ylabel('Hydrophobicity')\n",
- "cb.ax.set_ylabel(\"Hydrogen Bond Acceptor\", rotation=-90, va=\"bottom\")"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "cda0efa7-45c7-4b2e-85b0-aed8c44e60d9",
- "metadata": {},
- "source": [
- "# 3D Plots"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "dc458d14-e159-4170-8640-eb897c43288d",
- "metadata": {},
- "source": [
- "## 3D scatter plot"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 62,
- "id": "bce60bb6-22d4-43dd-b276-955a55caee5c",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "636ebc84e6124b1790877bf63b45405c",
- "version_major": 2,
- "version_minor": 0
- },
- "text/plain": [
- "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/plain": [
- "<matplotlib.legend.Legend at 0x7f079032ebb0>"
- ]
- },
- "execution_count": 62,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "fig = plt.figure()\n",
- "ax = fig.add_subplot(111, projection='3d')\n",
- "\n",
- "for specie, data in iris.groupby('Species'):\n",
- " ax.scatter(data['SepalLengthCm'], \n",
- " data['SepalWidthCm'], \n",
- " data['PetalLengthCm'], \n",
- " s = data['PetalWidthCm'] * 10,\n",
- " label=specie)\n",
- "\n",
- "ax.set_xlabel('Sepal Lenght')\n",
- "ax.set_ylabel('Sepal Width')\n",
- "ax.set_zlabel('Petal Length')\n",
- "plt.title('Iris 3d scatter plot')\n",
- "plt.legend()"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "37ea32ed-6651-4e26-bcae-31b134693a25",
- "metadata": {},
- "source": [
- "# Heatmap\n",
- "\n",
- "It is often desirable to show data which depends on two independent \n",
- "variables as a color coded image plot. \n",
- "This is often referred to as a heatmap. \n",
- "If the data is categorical, this would be called a categorical heatmap.\n",
- "\n",
- "> https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.imshow.html#matplotlib.pyplot.imshow\n",
- "\n",
- "> https://matplotlib.org/stable/gallery/images_contours_and_fields/image_annotated_heatmap.html"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 63,
- "id": "11fbb4fa-49d7-4978-ba12-8936dc3ae614",
- "metadata": {},
- "outputs": [
- {
- "ename": "NotImplementedError",
- "evalue": "Axes3D currently only supports the aspect argument 'auto'. You passed in 'equal'.",
- "output_type": "error",
- "traceback": [
- "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
- "\u001b[0;31mNotImplementedError\u001b[0m Traceback (most recent call last)",
- "\u001b[0;32m<ipython-input-63-1489787dad6a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 18\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 19\u001b[0m \u001b[0;31m# create the heatmap\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 20\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mharvest\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 21\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 22\u001b[0m \u001b[0;31m# We want to show all ticks...\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;32m~/Projects/MNE/lib/python3.8/site-packages/matplotlib/pyplot.py\u001b[0m in \u001b[0;36mimshow\u001b[0;34m(X, cmap, norm, aspect, interpolation, alpha, vmin, vmax, origin, extent, filternorm, filterrad, resample, url, data, **kwargs)\u001b[0m\n\u001b[1;32m 2870\u001b[0m \u001b[0mfilternorm\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfilterrad\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m4.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresample\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2871\u001b[0m data=None, **kwargs):\n\u001b[0;32m-> 2872\u001b[0;31m __ret = gca().imshow(\n\u001b[0m\u001b[1;32m 2873\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcmap\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcmap\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnorm\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnorm\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maspect\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maspect\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2874\u001b[0m \u001b[0minterpolation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minterpolation\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malpha\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0malpha\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvmin\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvmin\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;32m~/Projects/MNE/lib/python3.8/site-packages/matplotlib/__init__.py\u001b[0m in \u001b[0;36minner\u001b[0;34m(ax, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1350\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0minner\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1351\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1352\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msanitize_sequence\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1353\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1354\u001b[0m \u001b[0mbound\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnew_sig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbind\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;32m~/Projects/MNE/lib/python3.8/site-packages/matplotlib/axes/_axes.py\u001b[0m in \u001b[0;36mimshow\u001b[0;34m(self, X, cmap, norm, aspect, interpolation, alpha, vmin, vmax, origin, extent, filternorm, filterrad, resample, url, **kwargs)\u001b[0m\n\u001b[1;32m 5582\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0maspect\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5583\u001b[0m \u001b[0maspect\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrcParams\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'image.aspect'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5584\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_aspect\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maspect\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 5585\u001b[0m im = mimage.AxesImage(self, cmap, norm, interpolation, origin, extent,\n\u001b[1;32m 5586\u001b[0m \u001b[0mfilternorm\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfilternorm\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfilterrad\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfilterrad\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;32m~/Projects/MNE/lib/python3.8/site-packages/mpl_toolkits/mplot3d/axes3d.py\u001b[0m in \u001b[0;36mset_aspect\u001b[0;34m(self, aspect, adjustable, anchor, share)\u001b[0m\n\u001b[1;32m 321\u001b[0m \"\"\"\n\u001b[1;32m 322\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0maspect\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;34m'auto'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 323\u001b[0;31m raise NotImplementedError(\n\u001b[0m\u001b[1;32m 324\u001b[0m \u001b[0;34m\"Axes3D currently only supports the aspect argument \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 325\u001b[0m \u001b[0;34mf\"'auto'. You passed in {aspect!r}.\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;31mNotImplementedError\u001b[0m: Axes3D currently only supports the aspect argument 'auto'. You passed in 'equal'."
- ]
- }
- ],
- "source": [
- "# create a dataset\n",
- "\n",
- "vegetables = [\"cucumber\", \"tomato\", \"lettuce\", \"asparagus\",\n",
- " \"potato\", \"wheat\", \"barley\"]\n",
- "farmers = [\"Farmer Joe\", \"Upland Bros.\", \"Smith Gardening\",\n",
- " \"Agrifun\", \"Organiculture\", \"BioGoods Ltd.\", \n",
- " \"Cornylee Corp.\"]\n",
- "\n",
- "# harvest is the production intons/year for each vegetable (row)\n",
- "# by each farmer (columns)\n",
- "harvest = np.array([[0.8, 2.4, 2.5, 3.9, 0.0, 4.0, 0.0],\n",
- " [2.4, 0.0, 4.0, 1.0, 2.7, 0.0, 0.0],\n",
- " [1.1, 2.4, 0.8, 4.3, 1.9, 4.4, 0.0],\n",
- " [0.6, 0.0, 0.3, 0.0, 3.1, 0.0, 0.0],\n",
- " [0.7, 1.7, 0.6, 2.6, 2.2, 6.2, 0.0],\n",
- " [1.3, 1.2, 0.0, 0.0, 0.0, 3.2, 5.1],\n",
- " [0.1, 2.0, 0.0, 1.4, 0.0, 1.9, 6.3]])\n",
- "\n",
- "# create the heatmap\n",
- "plt.imshow(harvest)\n",
- "\n",
- "# We want to show all ticks...\n",
- "# ... and label them with the respective list entries\n",
- "xticks = plt.xticks(np.arange(len(farmers)), farmers, \n",
- " rotation=45, ha=\"right\", rotation_mode=\"anchor\")\n",
- "yticks = plt.yticks(np.arange(len(vegetables)), vegetables)\n",
- "\n",
- "# Loop over data vegetables and farmers and annotate the heatmap.\n",
- "for i in range(len(vegetables)):\n",
- " for j in range(len(farmers)):\n",
- " text = plt.text(j, i, # the coordinate of the cell\n",
- " harvest[i, j], # the intensiy of the cell\n",
- " ha=\"center\", # horizontal alignment\n",
- " va=\"center\", # vertical alignment\n",
- " color=\"w\" if harvest[i, j] < 5 else \"k\")\n",
- "\n",
- "# add a color bar \n",
- "cbar = plt.colorbar()\n",
- "\n",
- "# add a ylabel to the colorbar\n",
- "# va = vertical alignment \n",
- "cbar.ax.set_ylabel(\"Harvest (tons/year)\", rotation=-90, va=\"bottom\")\n",
- " \n",
- "plt.title(\"Harvest of local farmers (in tons/year)\")\n"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "e3574873-a57b-4b24-8828-d10442eb80e0",
- "metadata": {},
- "source": [
- "# Subplots"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "12249845-cb33-4af6-9b05-4665c2a7452d",
- "metadata": {},
- "source": [
- "We can pack several plots in a figure.\n",
- "There is several way to do that, here we describe the *pyplot.subplots* function\n",
- "> https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.subplots.html"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "2f8b607d-d4e2-4e59-b71a-6c36feb759dc",
- "metadata": {},
- "outputs": [],
- "source": [
- "# in this case we use the oop coding style\n",
- "\n",
- "fig, axs = plt.subplots(2,2, figsize=(9,7)) # 2 rows, 2 columns\n",
- "\n",
- "_ = axs[0,0].hist(iris.iloc[:, 0], color='blue', edgecolor='k')\n",
- "axs[0,0].set(xlabel='sepal length', ylabel='Frequency')\n",
- "\n",
- "_ = axs[0,1].hist(iris.iloc[:, 1], color='orange')\n",
- "axs[0,1].set(xlabel='sepal width', ylabel='Frequency')\n",
- "\n",
- "_ = axs[1,0].hist(iris.iloc[:, 2], color='green', edgecolor='k')\n",
- "axs[1,0].set(xlabel='petal length', ylabel='Frequency')\n",
- "\n",
- "_ = axs[1,1].hist(iris.iloc[:, 3], color='red')\n",
- "# an other way to set the label\n",
- "axs[1,1].set_xlabel('petal length')\n",
- "axs[1,1].set_ylabel('Frequency')\n",
- "fig.suptitle('Iris Histograms')\n",
- "\n",
- "plt.tight_layout()\n"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "903c98ca-f294-4b8a-8c43-c39d5900c628",
- "metadata": {},
- "source": [
- "# Save figure\n",
- "\n",
- "> https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.savefig.html"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "84ec4abe-85e8-4ba0-a045-5fc2bf185a70",
- "metadata": {},
- "outputs": [],
- "source": [
- "########## Create a figure ##########\n",
- "\n",
- "fig, axs = plt.subplots(2,2, figsize=(9,7)) # 2 rows, 2 columns\n",
- "\n",
- "_ = axs[0,0].hist(iris.iloc[:, 0], color='blue', edgecolor='k')\n",
- "axs[0,0].set(xlabel='sepal length', ylabel='Frequency')\n",
- "\n",
- "_ = axs[0,1].hist(iris.iloc[:, 1], color='orange')\n",
- "axs[0,1].set(xlabel='sepal width', ylabel='Frequency')\n",
- "\n",
- "_ = axs[1,0].hist(iris.iloc[:, 2], color='green', edgecolor='k')\n",
- "axs[1,0].set(xlabel='petal length', ylabel='Frequency')\n",
- "\n",
- "_ = axs[1,1].hist(iris.iloc[:, 3], color='red')\n",
- "axs[1,1].set(xlabel='petal length', ylabel='Frequency')\n",
- "\n",
- "fig.suptitle('Iris Histograms')\n",
- "\n",
- "plt.tight_layout()\n",
- "\n",
- "####### Save the figure in png format #########\n",
- "\n",
- "plt.savefig('images/iris_histograms.png')"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "547a8527-d169-4c0d-98ed-88001322550d",
- "metadata": {},
- "source": [
- "# Conclusion\n",
- "\n",
- "We have seen\n",
- "\n",
- "* figure, axes, subplots notions \n",
- "* plot\n",
- "* hist\n",
- "* hist2d\n",
- "* bar plot\n",
- "* boxplot\n",
- "* scatter plot\n",
- "* heatmap\n",
- "* colormap\n",
- "* xticks, xlabels, yticks, ylabels\n",
- "* plot parameters: alpha, fontsize, marker, markersize, ...\n",
- " \n",
- "But ther are so many others plots and functions in matplotlib\n",
- "\n",
- "* meshgrid\n",
- "* 3D plots\n",
- "* pie chart\n",
- "* violin plot\n",
- "* ...\n",
- "\n",
- "check the matplotlib galerie: https://matplotlib.org/stable/gallery/index.html#pyplot\n",
- "\n",
- "and the cheat sheet: https://github.com/matplotlib/cheatsheets"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "a149f323-a3b8-4747-9be3-32f911f66d25",
- "metadata": {},
- "source": [
- "> the examples of this course are largely inspired from: https://towardsdatascience.com/matplotlib-tutorial-with-code-for-pythons-powerful-data-visualization-tool-8ec458423c5e"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.8.9"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/previous_materials/np_pd_mplt_bertrand/matplotlib_TP.ipynb b/previous_materials/np_pd_mplt_bertrand/matplotlib_TP.ipynb
deleted file mode 100644
index f55b681..0000000
--- a/previous_materials/np_pd_mplt_bertrand/matplotlib_TP.ipynb
+++ /dev/null
@@ -1,526 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "id": "96226d71-a8c2-4845-8b35-4f3f8d4680a8",
- "metadata": {},
- "source": [
- "# <center>**TP**</center>\n",
- "\n",
- "<img src=\"./images/logo2_matplotlib.svg\">\n",
- "<div style=\"text-align:center\">\n",
- " Bertrand Néron\n",
- " <br />\n",
- " <a src=\" https://research.pasteur.fr/en/team/bioinformatics-and-biostatistics-hub/\">Bioinformatics and Biostatistiqucs HUB</a>\n",
- " <br />\n",
- " © Institut Pasteur, 2021\n",
- "</div>"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "id": "8db20ce6-572a-4bd9-ab90-7e2b00b4c27c",
- "metadata": {},
- "outputs": [],
- "source": [
- "import numpy as np\n",
- "import matplotlib.pyplot as plt\n",
- "import pandas as pd"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "e321f59e-15c6-4d72-afd3-eaad525afc79",
- "metadata": {
- "tags": []
- },
- "source": [
- "# Plot"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "ccd81dfe-c3a9-47fb-a4cc-42c86b34ee4e",
- "metadata": {},
- "source": [
- "We provide 3 data sets which can be loaded as below"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "d8504f2c-f1d7-4440-8c90-fb2ad98e152d",
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "id": "86453109-e6a5-49fe-821e-511d19753a89",
- "metadata": {},
- "outputs": [],
- "source": [
- "data1 = np.load(\"data/data_1.npy\")\n",
- "data2 = np.load(\"data/data_2.npy\")\n",
- "freqs = np.load(\"data/freqs.npy\")"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "ee2fbfa7-2ec4-4458-940e-d5673a9f843d",
- "metadata": {},
- "source": [
- "check the data structure of these data"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "9a590d32-0f7c-4e47-8932-15ccb75a99f1",
- "metadata": {},
- "source": [
- "<img src=\"./images/data.png\">"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "1a9dacfd-00e7-473f-9490-77cfa2023934",
- "metadata": {},
- "source": [
- "We want to compare more in details a subset of *data_1* and *data_2*\n",
- "we have to extract from the 4th row, 3rd columns all data in z\n",
- "for data1 and data2\n",
- "\n",
- "* these values will be plotted on the y abscisse using the freqs data as x abcisse\n",
- "* plot data1 as plain line and data 2 in dashed line\n",
- "* add a legend\n",
- "* add tile to the figure and abscisses\n",
- "\n",
- "the resulting figure should looklike below\n",
- "\n",
- "<img src=\"./images/plot_data1_vs_data2.png\" width=\"300px\">"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "027476b8-1d47-4373-8709-0fbe03843a70",
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "markdown",
- "id": "dae8f60d-5c83-40a1-8642-358233fa15af",
- "metadata": {},
- "source": [
- "# Bar plot\n",
- "\n",
- "We have generated a file with different experimental conditions and a control. For each conditions several mesures where taken.\n",
- "We want to display these results as a bar plot.\n",
- "\n",
- "* each bar represent the mean for each condition\n",
- "* the mean for each condition must be write on the plot\n",
- "* each bar must display the standard deviation\n",
- "* and the name of the condition must be display on the x abcsisse\n",
- " as in sceenshot below\n",
- "\n",
- "<img src=\"./images/barplot.png\" width=\"300px\">\n"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "c7e38daa-9697-46dc-a9e0-9471772940a5",
- "metadata": {},
- "source": [
- "before to open the file with pandas have a loook on it."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "885b3bf2-de72-4b6e-9925-f049a9832020",
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "markdown",
- "id": "0dd7611e-bd88-460e-b589-8e627ff313a9",
- "metadata": {},
- "source": [
- "check the structure of your data"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "d1d532f0-e41d-4ab1-9ef1-3b1bd6ad390a",
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "cdd3c1dd-52df-4165-afb7-bbc4e5264622",
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "3686b1f0-b4d4-44a0-9666-0d131a6b64ec",
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "markdown",
- "id": "3efa2dda-6aa8-4f34-a14a-b207f3803def",
- "metadata": {},
- "source": [
- "do the bar plot"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "220d76c0-eb3c-49c7-993f-df78a4dae9a7",
- "metadata": {},
- "outputs": [],
- "source": [
- "\n"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "da6ae784-19f0-4916-abdc-2d17eed5ba7b",
- "metadata": {},
- "source": [
- "# Histogram\n",
- "\n",
- "We want to study the pixel intensity distributon of the koala image.\n",
- "We want to do this for each layer separately.\n",
- "so we decide to create an histogram like below.\n",
- "\n",
- "<img src=\"./images/histogram_pixels.png\" width=\"300px\">"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "id": "d04b6458-fa0e-4fb3-a9ad-b6feb5634ce6",
- "metadata": {},
- "outputs": [],
- "source": [
- "from matplotlib import image"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "id": "d4710fcc-3ca5-496d-bd02-18931aa2e9bc",
- "metadata": {},
- "outputs": [],
- "source": [
- "koala = image.imread('images/koala.jpeg')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "a2cb26e8-3be9-41c4-8b8f-2983184b9039",
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "300d1ecd-0017-49df-a01d-3e2c27511758",
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "markdown",
- "id": "e72a866c-3940-4e7d-89f4-bcbcee1d5606",
- "metadata": {},
- "source": [
- "to know which colored are available in matplotlib visit:\n",
- "https://matplotlib.org/stable/gallery/color/named_colors.html"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "dc30676b-4acd-41d3-b8a5-587af64b4441",
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "markdown",
- "id": "e4b9800b-692b-4e16-8346-d4ac29834f08",
- "metadata": {},
- "source": [
- "## standardization\n",
- "\n",
- "create a data set with 2000 samples\n",
- "* with a normal distribution \n",
- "* centered on 5.0 \n",
- "* with 3.0 as standard deviation "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "e77f53bd-f00d-4e7a-9e0d-8eba0bd05d98",
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "markdown",
- "id": "b255aeee-58f7-4e2f-8d6a-f5bf96a6f9be",
- "metadata": {},
- "source": [
- "Standardized this dataset, and compare the 2 distributions."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "255607ac-d232-451a-98b8-80dcb9318153",
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "c8ca950f-9ee6-4d54-9fc9-6762af9141a2",
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "markdown",
- "id": "999ac4af-2a84-4da1-aa87-5240d7d23c44",
- "metadata": {},
- "source": [
- "Now, do the same operation on the 2D array below, and standardized each column.\n",
- "check the results with histograms and compare them to the ones belows."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "id": "7a38957e-cc70-4a1d-829f-045d4e1b646d",
- "metadata": {},
- "outputs": [],
- "source": [
- "data = np.array([np.random.normal(loc=5.0, scale=3.0, size=2000),\n",
- " np.random.normal(loc=2.0, scale=2.0, size=2000)]\n",
- " ).T"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "id": "45eb377d-0a84-45ca-8c82-c939e08dbeaa",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<matplotlib.legend.Legend at 0x7f1a819a8730>"
- ]
- },
- "execution_count": 7,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 864x432 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "fig = plt.figure(figsize=(12,6))\n",
- "_ = plt.hist(data[:, 0], bins=75, color='blue', alpha=0.5, ec='k', label='col 0')\n",
- "_ = plt.hist(data[:, 1], bins=75, color='orange', alpha=0.5, ec='k', label='col 1')\n",
- "plt.legend()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "d0327f88-f7f9-420a-b3ce-0e5bc524c86d",
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "dbd88c6a-e642-43c4-bb10-fe5c02ae4295",
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "markdown",
- "id": "f8173833-3709-4916-94d9-418cd1a8da0d",
- "metadata": {},
- "source": [
- "# Boxplot\n",
- "\n",
- "We are not very satisfed by the first vizualization of *data_bar* data.\n",
- "We decide to use a boxplot to view this data\n",
- "\n",
- "* create a 10, 8 inch figure containing a boxplot\n",
- "* add a grid\n",
- "* *bonus* we will colored each box with different colors as below\n",
- "\n",
- "<img src=\"images/boxplot.png\" width=\"300px\">\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "77b7cad3-16ba-40a3-953c-231dfb68b00b",
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "7470a4ec-72bf-436a-9481-29ec8a225973",
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "4d93309c-3ce6-4220-8d80-c95c58c0c0df",
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "markdown",
- "id": "e820d1ff-c6ca-42a9-a1c6-450dea31f3e3",
- "metadata": {},
- "source": [
- "# Histogram 2D\n",
- "\n",
- "We want to analyse the relationship between 2 numerical variables.\n",
- "These two variables are in a numpy 2D array\n",
- "To do that we plan to vizualize data with an 2D histogram as below\n",
- "\n",
- "<img src=\"./images/histogram_2D.png\" width=\"300px\" />\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "id": "b61b2420-fc72-40db-9cdd-2435b480946f",
- "metadata": {},
- "outputs": [],
- "source": [
- "linked_vars = np.load('data/linked_vars.npy')"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "74a25028-5dc2-44bd-b5ee-71ca6018cc9c",
- "metadata": {},
- "source": [
- "how many samples are contained in the data set"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "6303918b-b17b-4f1f-91df-abbb2d2012c0",
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "markdown",
- "id": "3a7b4457-4bc5-4959-a9a0-fa13484730bc",
- "metadata": {},
- "source": [
- "what are the min, max and mean value for these two variables"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "49d4e338-e548-4802-a23d-417654bdeb42",
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "markdown",
- "id": "bf182d06-7947-4f7d-b2aa-a8c7da3df6a4",
- "metadata": {},
- "source": [
- "create the histogram with a colorbar use the *\"jet\"* colormap"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "6cd41a4c-e741-46df-8538-d6d6da06cfbf",
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "e59f58e8-f4d0-49cc-b11f-923787314ae4",
- "metadata": {},
- "outputs": [],
- "source": []
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.8.9"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/previous_materials/np_pd_mplt_bertrand/matplotlib_TP_solutions.ipynb b/previous_materials/np_pd_mplt_bertrand/matplotlib_TP_solutions.ipynb
deleted file mode 100644
index 3a2f75e..0000000
--- a/previous_materials/np_pd_mplt_bertrand/matplotlib_TP_solutions.ipynb
+++ /dev/null
@@ -1,1057 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "id": "96226d71-a8c2-4845-8b35-4f3f8d4680a8",
- "metadata": {},
- "source": [
- "# <center>**TP**</center>\n",
- "\n",
- "<img src=\"./images/logo2_matplotlib.svg\">\n",
- "<div style=\"text-align:center\">\n",
- " Bertrand Néron\n",
- " <br />\n",
- " <a src=\" https://research.pasteur.fr/en/team/bioinformatics-and-biostatistics-hub/\">Bioinformatics and Biostatistiqucs HUB</a>\n",
- " <br />\n",
- " © Institut Pasteur, 2021\n",
- "</div>"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 24,
- "id": "8db20ce6-572a-4bd9-ab90-7e2b00b4c27c",
- "metadata": {},
- "outputs": [],
- "source": [
- "import numpy as np\n",
- "import matplotlib.pyplot as plt\n",
- "import pandas as pd"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "e321f59e-15c6-4d72-afd3-eaad525afc79",
- "metadata": {
- "tags": []
- },
- "source": [
- "# Plot"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "ccd81dfe-c3a9-47fb-a4cc-42c86b34ee4e",
- "metadata": {},
- "source": [
- "We provide 3 data sets which can be loaded as below"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 25,
- "id": "86453109-e6a5-49fe-821e-511d19753a89",
- "metadata": {},
- "outputs": [],
- "source": [
- "data1 = np.load(\"data/data_1.npy\")\n",
- "data2 = np.load(\"data/data_2.npy\")\n",
- "freqs = np.load(\"data/freqs.npy\")"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "ee2fbfa7-2ec4-4458-940e-d5673a9f843d",
- "metadata": {},
- "source": [
- "check the data structure of these data"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "9a590d32-0f7c-4e47-8932-15ccb75a99f1",
- "metadata": {},
- "source": [
- "<img src=\"./images/data.png\">"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "1a9dacfd-00e7-473f-9490-77cfa2023934",
- "metadata": {},
- "source": [
- "We want to compare more in details a subset of *data_1* and *data_2*\n",
- "we have to extract from the 4th row, 3rd columns all data in z\n",
- "for data1 and data2\n",
- "\n",
- "* these values will be plotted on the y abscisse using the freqs data as x abcisse\n",
- "* plot data1 as plain line and data 2 in dashed line\n",
- "* add a legend\n",
- "* add tile to the figure and abscisses\n",
- "\n",
- "the resulting figure should looklike below\n",
- "\n",
- "<img src=\"./images/plot_data1_vs_data2.png\" width=\"300px\">"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 26,
- "id": "027476b8-1d47-4373-8709-0fbe03843a70",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<matplotlib.legend.Legend at 0x7f28ed3e2820>"
- ]
- },
- "execution_count": 26,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 576x432 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "fig = plt.figure(figsize=(8,6))\n",
- "plt.plot(freqs, data1[3,4,:], label=\"data 1\")\n",
- "plt.plot(freqs, data2[3,4,:], linestyle=\"--\", label=\"data 1\")\n",
- "plt.xlabel(\"Frequence\")\n",
- "plt.ylabel(\"Intensity\")\n",
- "plt.title(\"comparison Data1 vs Data2\")\n",
- "plt.legend()"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "dae8f60d-5c83-40a1-8642-358233fa15af",
- "metadata": {},
- "source": [
- "# Bar plot\n",
- "\n",
- "We have generated a file with different experimental conditions and a control. For each conditions several mesures where taken.\n",
- "We want to display these results as a bar plot.\n",
- "\n",
- "* each bar represent the mean for each condition\n",
- "* the mean for each condition must be write on the plot\n",
- "* each bar must display the standard deviation\n",
- "* and the name of the condition must be display on the x abcsisse\n",
- " as in sceenshot below\n",
- "\n",
- "<img src=\"./images/barplot.png\" width=\"300px\">\n"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "c7e38daa-9697-46dc-a9e0-9471772940a5",
- "metadata": {},
- "source": [
- "before to open the file with pandas have a loook on it."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 27,
- "id": "885b3bf2-de72-4b6e-9925-f049a9832020",
- "metadata": {},
- "outputs": [],
- "source": [
- "data_bar = pd.read_csv(\"data/bar_data.tsv\", sep=\"\\t\", comment='#')"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "0dd7611e-bd88-460e-b589-8e627ff313a9",
- "metadata": {},
- "source": [
- "check the structure of your data"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 28,
- "id": "d1d532f0-e41d-4ab1-9ef1-3b1bd6ad390a",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "(20, 4)\n",
- "Index(['cond1', 'cond2', 'cond3', 'control'], dtype='object')\n"
- ]
- }
- ],
- "source": [
- "print(data_bar.shape)\n",
- "print(data_bar.columns)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 29,
- "id": "cdd3c1dd-52df-4165-afb7-bbc4e5264622",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
- "<style scoped>\n",
- " .dataframe tbody tr th:only-of-type {\n",
- " vertical-align: middle;\n",
- " }\n",
- "\n",
- " .dataframe tbody tr th {\n",
- " vertical-align: top;\n",
- " }\n",
- "\n",
- " .dataframe thead th {\n",
- " text-align: right;\n",
- " }\n",
- "</style>\n",
- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>cond1</th>\n",
- " <th>cond2</th>\n",
- " <th>cond3</th>\n",
- " <th>control</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>0</th>\n",
- " <td>14.644417</td>\n",
- " <td>2.945309</td>\n",
- " <td>24.811719</td>\n",
- " <td>5.114340</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1</th>\n",
- " <td>12.071043</td>\n",
- " <td>4.406424</td>\n",
- " <td>21.574601</td>\n",
- " <td>2.507118</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2</th>\n",
- " <td>8.227469</td>\n",
- " <td>3.185252</td>\n",
- " <td>20.651623</td>\n",
- " <td>4.449593</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>3</th>\n",
- " <td>8.980799</td>\n",
- " <td>9.233560</td>\n",
- " <td>24.859737</td>\n",
- " <td>4.127919</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4</th>\n",
- " <td>9.080359</td>\n",
- " <td>5.629192</td>\n",
- " <td>18.443504</td>\n",
- " <td>4.268572</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " cond1 cond2 cond3 control\n",
- "0 14.644417 2.945309 24.811719 5.114340\n",
- "1 12.071043 4.406424 21.574601 2.507118\n",
- "2 8.227469 3.185252 20.651623 4.449593\n",
- "3 8.980799 9.233560 24.859737 4.127919\n",
- "4 9.080359 5.629192 18.443504 4.268572"
- ]
- },
- "execution_count": 29,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "data_bar.head()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 30,
- "id": "3686b1f0-b4d4-44a0-9666-0d131a6b64ec",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
- "<style scoped>\n",
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- " .dataframe tbody tr th {\n",
- " vertical-align: top;\n",
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- " .dataframe thead th {\n",
- " text-align: right;\n",
- " }\n",
- "</style>\n",
- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>cond1</th>\n",
- " <th>cond2</th>\n",
- " <th>cond3</th>\n",
- " <th>control</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>count</th>\n",
- " <td>20.000000</td>\n",
- " <td>20.000000</td>\n",
- " <td>20.000000</td>\n",
- " <td>20.000000</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>mean</th>\n",
- " <td>11.200424</td>\n",
- " <td>4.914373</td>\n",
- " <td>19.729708</td>\n",
- " <td>3.882791</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>std</th>\n",
- " <td>3.126661</td>\n",
- " <td>2.315690</td>\n",
- " <td>3.684203</td>\n",
- " <td>0.739996</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>min</th>\n",
- " <td>7.022382</td>\n",
- " <td>0.717714</td>\n",
- " <td>10.613455</td>\n",
- " <td>2.507118</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>25%</th>\n",
- " <td>8.792467</td>\n",
- " <td>3.670328</td>\n",
- " <td>18.001254</td>\n",
- " <td>3.273711</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>50%</th>\n",
- " <td>11.145291</td>\n",
- " <td>4.685109</td>\n",
- " <td>19.962329</td>\n",
- " <td>3.915296</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>75%</th>\n",
- " <td>12.980845</td>\n",
- " <td>5.847649</td>\n",
- " <td>22.332247</td>\n",
- " <td>4.313827</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>max</th>\n",
- " <td>17.400218</td>\n",
- " <td>9.868423</td>\n",
- " <td>24.859737</td>\n",
- " <td>5.255738</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " cond1 cond2 cond3 control\n",
- "count 20.000000 20.000000 20.000000 20.000000\n",
- "mean 11.200424 4.914373 19.729708 3.882791\n",
- "std 3.126661 2.315690 3.684203 0.739996\n",
- "min 7.022382 0.717714 10.613455 2.507118\n",
- "25% 8.792467 3.670328 18.001254 3.273711\n",
- "50% 11.145291 4.685109 19.962329 3.915296\n",
- "75% 12.980845 5.847649 22.332247 4.313827\n",
- "max 17.400218 9.868423 24.859737 5.255738"
- ]
- },
- "execution_count": 30,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "desc = data_bar.describe()\n",
- "desc"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "3efa2dda-6aa8-4f34-a14a-b207f3803def",
- "metadata": {},
- "source": [
- "do the bar plot"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 31,
- "id": "220d76c0-eb3c-49c7-993f-df78a4dae9a7",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": "iVBORw0KGgoAAAANSUhEUgAAAeAAAAFlCAYAAAAzqTv+AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/Z1A+gAAAACXBIWXMAAAsTAAALEwEAmpwYAAAWwUlEQVR4nO3de3SU9Z3H8c+XRKwIKUaSEESMZRHCHcx6aTE7kGKRWigitIg1iBxWT92tddVi3XpbLVkvW+/ddVcqR7nssqChJUXZ0Gq71XVDjYBxlR4bJYoxgMpF0JD89o8MkUBCQjIzXyZ5v86Zw8zzPDPzfXwOvvPMTAYLIQgAACRWN+8BAADoiggwAAAOCDAAAA4IMAAADggwAAAOCDAAAA5SE/lkffr0CTk5OYl8SgAA3GzYsGF7CCGjuXUJDXBOTo7KysoS+ZQAALgxs3daWsdL0AAAOCDAAAA4IMAAADggwAAAOCDAAAA4IMAAADggwAAAOCDAAAA4IMAAADggwAAAOCDAAAA4IMAAADggwAAAOCDAAJJSJBJRJBLxHgNoNwIMAIADAgwAgAMCDACAAwIMAIADAgwAgAMCDACAAwIMAIADAgwAgAMCDACAAwIMAIADAgwAgAMCDACAAwIMAIADAgwAgAMCDACAAwIMAIADAgwAgAMCDACAAwIMAIADAgwAgAMCDACAAwIMAIADAgwAgAMCDACAAwIMAIADAgwAgAMCDACAAwIMAIADAgwAgAMCDACAAwIMAIADAgwAgAMCDACAAwIMAIADAgwAgAMCDACAAwIMAIADAgwAgAMCDACAAwIMAIADAgwAgINWA2xmp5vZb8yswsxeN7MfRJenm9k6M9sS/fOU+I8LAE3NnTtXmZmZGj58eOOy1157Teeff75GjBihb33rW9q1a9cR93vzzTc1evToxktaWpoeeOABSdJPfvITjRw5UqNHj9aFF16o999/P1G7gy6kLWfAByT9XQhhqKTzJH3fzIZKWiCpNIQwSFJp9DYAJNScOXO0du3aJsvmzZunoqIibdq0SdOmTdO99957xP0GDx6s8vJylZeXa8OGDerRo4emTZsmSbrxxhu1ceNGlZeX6+KLL9add96ZkH1B19JqgEMI20IIf4xe3y3pDUmnSZoqaXF0s8WSvh2nGQGgRfn5+UpPT2+y7K233lJ+fr4kaeLEiVq5cuVRH6O0tFQDBw7UGWecIUlKS0trXLd3716ZWYynBo7xPWAzy5E0RtL/SMoKIWyLrvpAUlYL95lvZmVmVlZTU9ORWQGgTYYNG6bi4mJJ0ooVK7R169ajbr98+XLNmjWrybJbbrlFp59+upYsWcIZMOKizQE2s56SVkq6LoTQ5A2VEEKQFJq7Xwjh8RBCXgghLyMjo0PDAkBbLFq0SI899pjOPvts7d69W927d29x288//1yrV6/WjBkzmiy/++67tXXrVs2ePVuPPPJIvEdGF9SmAJvZCWqI75IQwqro4mozy46uz5b0YXxGBIBjM2TIED3//PPasGGDZs2apYEDB7a47a9//WuNHTtWWVnNvoin2bNnt/oSNtAebfkUtEl6QtIbIYR/OmTVakmF0euFkopjPx4AHLsPP2w4H6ivr9ddd92lq6++usVtly1bdsTLz1u2bGm8XlxcrCFDhsRnUHRp1vDq8VE2MBsn6XeSNkmqjy7+sRreB/4PSQMkvSNpZghh59EeKy8vL5SVlXV0ZgBxlLNgjfcIbfLB0oZfvEjpma7P3t2kun27lNKjt748brZC7T7t/mPDfvQ466vq/VeFMjMd2L1DO9Y+pKwZd0iS6j/fr/d+fqVOu/rf1O3Ekxsfu+aZn6p2Z5Vk3ZSalqH0b3xfqb36tHvWyqJvdmBPkczMbEMIIa+5damt3TmE8HtJLX0EsKAjgwFAR2VMuanZ5Wl5U49Yltrr1Mb4SlK37l/S6T9YduRjTvtx7AYEWsA3YQEA4IAAAwDggAADAOCAAAMA4IAAAwDggAADAOCAAAMA4IAAAwDggAADAOCAAAMA4IAAAwDggAADAOCAAAMA4IAAAwDggAADAOCAAAMA4IAAAwDggAADAOCAAAMA4IAAAwDggAADAOCAAAMA4IAAAwDggAADAOCAAAMA4IAAAwDggAADAOCAAAMA4IAAAwDggAADAOCAAAMA4IAAAwDggAADAOCAAAMA4IAAAwDggAADAOCAAAMA4IAAAwDggAADAOCAAAMA4IAAAwDggAADAOCAAAMA4IAAAwDggAADAOCAAAMA4IAAAwDgINV7AABoj76XFXmPAHQIZ8AAADggwAAAOCDAAAA4IMAAADggwAAAOCDAAAA4IMAAADggwAAAOCDAAAA4aDXAZrbIzD40s82HLLvdzN4zs/LoZXJ8xwQAoHNpyxnwk5ImNbP8ZyGE0dFLSWzHAgCgc2s1wCGEFyXtTMAsAAB0GR15D/haM9sYfYn6lJhNBABAF9DeAP9c0kBJoyVtk3R/Sxua2XwzKzOzspqamnY+HQAAnUu7AhxCqA4h1IUQ6iX9q6RzjrLt4yGEvBBCXkZGRnvnBACgU2lXgM0s+5Cb0yRtbmlbAABwpNTWNjCzZZIikvqYWZWk2yRFzGy0pCCpUtJfx29EAAA6n1YDHEKY1cziJ+IwCwAAXQbfhAUAgAMCDACAAwIMAIADAgwAgAMCnACRSESRSMR7DADAcYQAAwDggAADAOCAAAMA4IAAAwDggAADAOCAAAMA4IAAAwDggAADAOCAAAMA4IAAAwDggAADAOCAAAMA4IAAAwDggAADAOCAAAMA4IAAAwDggAADAOCAAAMA4IAAAwDggAADAOCAAAMA4IAAAwDggAADAOCAAAMA4IAAAwDggAADAOCAAAMA4IAAAwDggAADAOCAAAMA4IAAAwDggAADAOCAAAMA4IAAJ9DcuXOVmZmp4cOHNy5bsWKFhg0bpm7duqmsrKzZ+23dulXjx4/X0KFDNWzYMD344ION63bu3KmJEydq0KBBmjhxoj766KO47wcAoOMIcALNmTNHa9eubbJs+PDhWrVqlfLz81u8X2pqqu6//35VVFTo5Zdf1qOPPqqKigpJUlFRkQoKCrRlyxYVFBSoqKgorvsAAIgNApxA+fn5Sk9Pb7IsNzdXgwcPPur9srOzNXbsWElSr169lJubq/fee0+SVFxcrMLCQklSYWGhnn322dgPDgCIOQKcZCorK/Xqq6/q3HPPlSRVV1crOztbktS3b19VV1d7jgcAaCMCnET27Nmj6dOn64EHHlBaWtoR681MZuYwGQDgWBHgJFFbW6vp06dr9uzZuuSSSxqXZ2Vladu2bZKkbdu2KTMz02tEAMAxIMBJIISgq666Srm5ubr++uubrJsyZYoWL14sSVq8eLGmTp3qMSIA4BhZCCFhT5aXlxda+lWb9shZsCZmjxVPHyxdIElK6Zmuz97dpLp9u5TSo7e+PG62Uk7qqZ3r/kV1+z5RtxN7qnvmmcr6zj/owO4d2rH2IWXNuEP7q15X9ZIf6YSMHCn6EvMp+VfopIF/qbp9u7S9uEgHdtUoNS1TfaYuUMpJvTo0b2XRNzu6y0hiyfL3Kpnwd6rrMrMNIYS85talJnqYrixjyk3NLu9x1lePWJba61RlzbhDkvSl/sN0xo9+1ex9U05KU9Z3fxq7IQEACcFL0AAAOCDAAAA4IMAAADggwAAAOCDAAAA4IMAAADggwAAAOCDAAAA4IMAAADggwAAAOCDAAAA4aDXAZrbIzD40s82HLEs3s3VmtiX65ynxHRMAgM6lLWfAT0qadNiyBZJKQwiDJJVGbwMAgDZqNcAhhBcl7Txs8VRJi6PXF0v6dmzHAgCgc2vve8BZIYRt0esfSMpqaUMzm29mZWZWVlNT086nAwCgc+nwh7BCCEFSOMr6x0MIeSGEvIyMjI4+HQAAnUJ7A1xtZtmSFP3zw9iNBABA59feAK+WVBi9XiipODbjAADQNbTl15CWSXpJ0mAzqzKzqyQVSZpoZlskfT16GwAAtFFqaxuEEGa1sKogxrMAANBl8E1YAAA4IMAAADggwAAAOCDAAAA4IMAAADggwAAAOCDAAAA4IMAAADggwAAAOCDAAAA4IMAAADggwAAAOCDAAAA4IMAAADggwAAAOCDAAAA4IMAAADggwAAAOCDAAAA4IMAAADhI9R6gK+h7WZH3CACA4wxnwAAAOCDAAAA4IMAAADggwAAAOCDAAAA4IMAAADggwAAAOCDAAAA4IMAAADggwAAAOCDAwCEikYgikYj3GAC6AAIMAIADAgwAgAMCDACAAwIMAIADAgwAgAMCDACAAwIMAIADAgwAgAMCDACAAwIMAIADAgwAgAMCDACAAwIMAIADAgwAgAMCDACAAwIMAIADAgwAgAMCDACAAwIMAIADAgwAgAMCDACAAwIMAIADAgy0oK6uTmPGjNHFF198xLp33nlHBQUFGjlypCKRiKqqqhrXTZo0Sb179272fgBwEAEGWvDggw8qNze32XU33HCDrrjiCm3cuFG33nqrbr755sZ1N954o5566qlEjQkgSRFgoBlVVVVas2aN5s2b1+z6iooKTZgwQZI0fvx4FRcXN64rKChQr169EjIngOTVoQCbWaWZbTKzcjMri9VQgLfrrrtO99xzj7p1a/6vyKhRo7Rq1SpJ0jPPPKPdu3drx44diRwRQJKLxRnw+BDC6BBCXgweC3C3Y8cOZWZm6uyzz25xm/vuu08vvPCCxowZoxdeeEGnnXaaUlJSEjglgGSX6j0AcLz55JNPtHr1apWUlGj//v3atWuXLr/8cj399NON2/Tr16/xDHjPnj1auXKlevfu7TQxgGTU0TPgIOl5M9tgZvOb28DM5ptZmZmV1dTUdPDpgPj7yle+oqqqKlVWVmr58uWaMGFCk/hK0vbt21VfXy9JWrhwoebOnesxKoAk1tEAjwshjJV0kaTvm1n+4RuEEB4PIeSFEPIyMjI6+HSAn1tvvVWrV6+WJP32t7/V4MGDddZZZ6m6ulq33HJL43YXXHCBZsyYodLSUvXv31/PPfec18gAjmMWQojNA5ndLmlPCOG+lrbJy8sLZWWx+6xWzoI1MXssfKGy6Jsxf8xkOVYfLF0gSep7WZHzJK2Lx3GSkudYJZN4HSsc/8xsQ0ufkWr3GbCZnWxmvQ5el3ShpM3tfTwAALqSjnwIK0vSM2Z28HGWhhDWxmQqAAA6uXYHOITwtqRRMZwFANDJRCIRSQ2fm0BTfBMWAAAOCDAAAA4IMAAg7vbv369zzjlHo0aN0rBhw3Tbbbcdsc27776r8ePHa8yYMRo5cqRKSkokSbW1tSosLNSIESOUm5urhQsXJnr8uOCbsAAAcXfiiSdq/fr16tmzp2prazVu3DhddNFFOu+88xq3ueuuuzRz5kxdc801qqio0OTJk1VZWakVK1bos88+06ZNm/Tpp59q6NChmjVrlnJycvx2KAY4AwYAxJ2ZqWfPnpIazmhra2sV/S2aJtvs2rVLUsNXwvbr169x+d69e3XgwAHt27dP3bt3V1paWmJ3IA4IMAAgIerq6jR69GhlZmZq4sSJOvfcc5usv/322/X000+rf//+mjx5sh5++GFJ0qWXXqqTTz5Z2dnZGjBggG644Qalp6d77EJMEWAAQEKkpKSovLxcVVVVeuWVV7R5c9Pvblq2bJnmzJmjqqoqlZSU6Hvf+57q6+v1yiuvKCUlRe+//77+/Oc/6/7779fbb7/ttBexQ4ABAAnVu3dvjR8/XmvXNv3upieeeEIzZ86UJJ1//vnav3+/tm/frqVLl2rSpEk64YQTlJmZqa997WuK5dcaeyHAAIC4q6mp0ccffyxJ2rdvn9atW6chQ4Y02WbAgAEqLS2VJL3xxhvav3+/MjIyNGDAAK1fv16StHfvXr388stH3DcZ8SloAEhSyfAPZ3zw9g5J0qgf/bu2r/mZFOqlUK8eQy7Qtb83Xb5wlrr3HaQeg87V56dN0W/+/h7Nv+kOyUynRK7RmTeXqP7zgdrx0i+1pM8ZkoJOHvF1TVm6VVq6NebzJvIfziDAAIC46555pvpd+dARy3tfcPkX2/QZoL6X33vENt26n6SMb98c1/k88BI0AAAOCDAAAA4IMAAADggwAAAOCDAAAA4IMAAADggwAAAOCDAAAA74Ig4AQNz0vazIe4TjFmfAAAA4IMAAADggwAAAOCDAAAA4IMAAADggwAAAOODXkIBD8CsTABKFM2AAABwQYAAAHBBgAAAcEGAAABwQYAAAHBBgAAAcEGAAABwQYAAAHBBgAAAcEGAAABwQYAAAHBBgAAAcEGAAABwQYAAAHBBgAAAcEGAAABwQYAAAHBBgAAAcEGAAABwQYAAAHBBgAAAcEGAAABwQYAAAHBBgAAAcEGAAABwQYAAAHBBgAAAcEGAAABwQYAAAHBBgAAAcdCjAZjbJzN40sz+Z2YJYDQUAQGfX7gCbWYqkRyVdJGmopFlmNjRWgwEA0Jl15Az4HEl/CiG8HUL4XNJySVNjMxYAAJ1bRwJ8mqSth9yuii4DAACtSI33E5jZfEnzozf3mNmb8X7O41QfSdu9h2gL+0fvCdwlxbHiOEniWCWLpDhOUlyO1RktrehIgN+TdPoht/tHlzURQnhc0uMdeJ5OwczKQgh53nOgdRyr5MGxSg4cp+Z15CXo/5U0yMzONLPukr4raXVsxgIAoHNr9xlwCOGAmV0r6TlJKZIWhRBej9lkAAB0Yh16DziEUCKpJEazdHZd/mX4JMKxSh4cq+TAcWqGhRC8ZwAAoMvhqygBAHBAgJ2Z2ZNmdmn0+rXRr/UMZtbHezY0ddixWhL9GtbNZrbIzE7wng9fOOxYPWFmr5nZRjP7TzPr6T1fV2RmOWZ2WTvuN8fMHonHTN4I8PHlvyV9XdI73oOgVUskDZE0QtJJkub5joOj+GEIYVQIYaSkdyVd6z1QF5UjqdkAm1ncv5PieESAj5GZXRH9Sfo1M3sq+lPd+uiyUjMbEN3uSTN7yMz+YGZvH/LTuJnZI9Gzp/+SlHnwsUMIr4YQKn32rPOJ87EqCVGSXlHD78GjneJ8rHYd3EYNPyzxwZd26OgxklQk6QIzKzezH0bPbFeb2XpJpWaWbmbPRh/vZTMb6baziRJC4NLGi6Rhkt6S1Cd6O13SLyUVRm/PlfRs9PqTklao4YecoWr43mxJukTSOjX86lY/SR9LuvSw56k8+BxcjvtjdYKkP0q6wHufk/WSiGMl6ReSqiX9RlIP731OtkuMjlFE0q8Oecw5avgK4/To7Ycl3Ra9PkFS+SHbPeL93yAeF86Aj80ESStCCNslKYSwU9L5kpZG1z8ladwh2z8bQqgPIVRIyoouy5e0LIRQF0J4X9L6xIze5STqWD0m6cUQwu/isRNdRNyPVQjhSjWE+Q1J34nbnnResThGzVkXfSxF7/9U9PHXSzrVzNJiuA/HHQIcX58dct3cpkBbHPOxMrPbJGVIuj4uE6El7fp7FUKoU8O/2jY95hPhcG09RnvjPcjxjAAfm/WSZpjZqZJkZumS/qCGr+GUpNmSWjsTelHSd8wsxcyyJY2P17BdXFyPlZnNk/QNSbNCCPWxHr6Liduxir43/BcHr0uaIun/Yr8LnV4sjtFuSb2Osv530ceRmUUkbQ/R9+87qy75ybP2CiG8bmZ3S3rBzOokvSrpbyT9wsxulFQj6cpWHuYZNbycU6GGT2S+dHCFmf2tpJsk9ZW00cxKQgh8urYd4n2sJP2zGj6t/lLD/9e1KoRwZ2z3omuI87EySYujL2WapNckXRP7vejcYnSMNkqqM7PX1PA+8UeHrb9d0iIz2yjpU0mFsduD4xPfhAUAgANeggYAwAEBBgDAAQEGAMABAQYAwAEBBgDAAQEGAMABAQYAwAEBBgDAwf8DJ/oZNgG4X0MAAAAASUVORK5CYII=\n",
- "text/plain": [
- "<Figure size 576x432 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "fig = plt.figure(figsize=(8,6))\n",
- "means = desc.loc['mean', :]\n",
- "recs = plt.bar(desc.columns, means, yerr=desc.loc['std', :])\n",
- "for idx, data in enumerate(means):\n",
- " plt.text(x=idx, y=data , s=f\"{data:.2f}\")\n"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "da6ae784-19f0-4916-abdc-2d17eed5ba7b",
- "metadata": {},
- "source": [
- "# Histogram\n",
- "\n",
- "We want to study the pixel intensity distributon of the koala image.\n",
- "We want to do this for each layer separately.\n",
- "so we decide to create an histogram like below.\n",
- "\n",
- "<img src=\"./images/histogram_pixels.png\" width=\"300px\">"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 32,
- "id": "d04b6458-fa0e-4fb3-a9ad-b6feb5634ce6",
- "metadata": {},
- "outputs": [],
- "source": [
- "from matplotlib import image"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 33,
- "id": "d4710fcc-3ca5-496d-bd02-18931aa2e9bc",
- "metadata": {},
- "outputs": [],
- "source": [
- "koala = image.imread('images/koala.jpeg')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 34,
- "id": "a2cb26e8-3be9-41c4-8b8f-2983184b9039",
- "metadata": {},
- "outputs": [],
- "source": [
- "red = koala[:, :, 0]\n",
- "green = koala[:, :, 1]\n",
- "blue = koala[:, :, 2]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 35,
- "id": "300d1ecd-0017-49df-a01d-3e2c27511758",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "(255, 255, 255)"
- ]
- },
- "execution_count": 35,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "red.max(), green.max(), blue.max()"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "e72a866c-3940-4e7d-89f4-bcbcee1d5606",
- "metadata": {},
- "source": [
- "to know which colored are available in matplotlib visit:\n",
- "https://matplotlib.org/stable/gallery/color/named_colors.html"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 36,
- "id": "dc30676b-4acd-41d3-b8a5-587af64b4441",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<matplotlib.legend.Legend at 0x7f28edaaec40>"
- ]
- },
- "execution_count": 36,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 864x432 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "fig = plt.figure(figsize=(12,6))\n",
- "_ = plt.hist([red.flatten(), green.flatten(), blue.flatten()], bins=255, \n",
- " label=[f'{c} layer'for c in ('red', 'green', 'blue')], \n",
- " color=['red', 'green', 'cornflowerblue'],\n",
- " density=True\n",
- " )\n",
- "\n",
- "plt.xlabel(\"pixel intensity distribution\")\n",
- "plt.legend(loc=\"upper right\")"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "83043f63-0b2f-4c6a-9376-819c03c11170",
- "metadata": {},
- "source": [
- "## standardization\n",
- "\n",
- "create a data set with 2000 samples\n",
- "* with a normal distribution \n",
- "* centered on 5.0 \n",
- "* with 3.0 as standard deviation "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 74,
- "id": "7d937154-5e81-4ffc-a110-82755ca4efa4",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "4.95063694306622\n",
- "3.0396582574110975\n"
- ]
- }
- ],
- "source": [
- "data = np.random.normal(loc=5.0, scale=3.0, size=2000)\n",
- "print(data.mean())\n",
- "print(data.std())"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "2d22c136-80e1-4d32-925b-525f88cd9099",
- "metadata": {},
- "source": [
- "Standardized this dataset, and compare the 2 distributions"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 75,
- "id": "726c4bb3-b4e1-4cfd-ab6b-5c5297bafc17",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "2.1316282072803006e-17\n",
- "1.0\n"
- ]
- }
- ],
- "source": [
- "d_std = (data - data.mean()) / data.std()\n",
- "print(d_std.mean())\n",
- "print(d_std.std())"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "c6ec47f3-dc60-4423-af20-ac981c0f5131",
- "metadata": {},
- "source": [
- "Normalized the dataset, and compare the 2 distributions"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 78,
- "id": "2a3a5a0e-97a5-40df-935a-6e45a2326894",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<matplotlib.legend.Legend at 0x7f28ec778040>"
- ]
- },
- "execution_count": 78,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 864x432 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "fig = plt.figure(figsize=(12,6))\n",
- "_ = plt.hist(data, color='blue', alpha=0.5, bins=75, ec='k', label='data')\n",
- "_ = plt.hist(d_std, color='orange', alpha=0.5, bins=75, ec='k', label='standardized data')\n",
- "plt.legend()"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "1015843e-aa4f-4d3e-ab6c-954984c66df4",
- "metadata": {},
- "source": [
- "Now do the same operation on 2D array, and standardized each column."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 93,
- "id": "ee8c51fe-145e-4ee0-b688-61b9344e3def",
- "metadata": {},
- "outputs": [],
- "source": [
- "data = np.array([np.random.normal(loc=5.0, scale=3.0, size=2000),\n",
- " np.random.normal(loc=2.0, scale=2.0, size=2000)]\n",
- " ).T"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 94,
- "id": "b4bd660f-2821-4e48-8e46-0aab364e80d1",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<matplotlib.legend.Legend at 0x7f28ec1544c0>"
- ]
- },
- "execution_count": 94,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 864x432 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "fig = plt.figure(figsize=(12,6))\n",
- "_ = plt.hist(data[:, 0], bins=75, color='blue', alpha=0.5, ec='k', label='col 0')\n",
- "_ = plt.hist(data[:, 1], bins=75, color='orange', alpha=0.5, ec='k', label='col 1')\n",
- "plt.legend()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 95,
- "id": "539df0d5-98ae-47f6-b3cf-e1c455b22650",
- "metadata": {},
- "outputs": [],
- "source": [
- "data_std = (data - data.mean(axis=0)) / data.std(axis=0)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 90,
- "id": "a98efb3e-77b6-423d-8614-60fee982475b",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<matplotlib.legend.Legend at 0x7f28ec49b700>"
- ]
- },
- "execution_count": 90,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 864x432 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "fig = plt.figure(figsize=(12,6))\n",
- "_ = plt.hist(data_std[:, 0], bins=75, color='blue', alpha=0.5, ec='k', label='col 0')\n",
- "_ = plt.hist(data_std[:, 1], bins=75, color='orange', alpha=0.5, ec='k', label='col 1')\n",
- "plt.legend()"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "f8173833-3709-4916-94d9-418cd1a8da0d",
- "metadata": {},
- "source": [
- "# Boxplot\n",
- "\n",
- "We are not very satisfed by the first vizualization of *data_bar* data.\n",
- "We decide to use a boxplot to view this data\n",
- "\n",
- "* create a 10, 8 inch figure containing a boxplot\n",
- "* add a grid\n",
- "* *bonus* we will colored each box with different colors as below\n",
- "\n",
- "<img src=\"images/boxplot.png\" width=\"300px\">\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 37,
- "id": "77b7cad3-16ba-40a3-953c-231dfb68b00b",
- "metadata": {},
- "outputs": [],
- "source": [
- "desc = data_bar.describe()\n",
- "means = desc.loc['mean', :]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 38,
- "id": "7470a4ec-72bf-436a-9481-29ec8a225973",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "Index(['cond1', 'cond2', 'cond3', 'control'], dtype='object')"
- ]
- },
- "execution_count": 38,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "data_bar.columns"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 39,
- "id": "4d93309c-3ce6-4220-8d80-c95c58c0c0df",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 720x576 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "fig = plt.figure(figsize=(10,8))\n",
- "results = plt.boxplot([data_bar[f'{col}'] for col in data_bar.columns], \n",
- " labels=['cond1', 'cond2', 'cond3', 'ctrl'],\n",
- " patch_artist=True)\n",
- "\n",
- "color = (\"red\", \"orange\", \"yellow\", \"green\")\n",
- "for i, c in enumerate(color):\n",
- " results['boxes'][i].set_facecolor(c)\n",
- " \n",
- "plt.grid()"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "e820d1ff-c6ca-42a9-a1c6-450dea31f3e3",
- "metadata": {},
- "source": [
- "# Histogram 2D\n",
- "\n",
- "We want to analyse the relationship between 2 numerical variables.\n",
- "These two variables are in a numpy 2D array\n",
- "To do that we plan to vizualize data with an 2D histogram as below\n",
- "\n",
- "<img src=\"./images/histogram_2D.png\" width=\"300px\" />\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 40,
- "id": "b61b2420-fc72-40db-9cdd-2435b480946f",
- "metadata": {},
- "outputs": [],
- "source": [
- "linked_vars = np.load('data/linked_vars.npy')"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "74a25028-5dc2-44bd-b5ee-71ca6018cc9c",
- "metadata": {},
- "source": [
- "how many samples are contained in the data set"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 41,
- "id": "6303918b-b17b-4f1f-91df-abbb2d2012c0",
- "metadata": {},
- "outputs": [],
- "source": [
- "x = linked_vars[0, :]\n",
- "y = linked_vars[1, :]"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "3a7b4457-4bc5-4959-a9a0-fa13484730bc",
- "metadata": {},
- "source": [
- "what are the min, max and mean value for these two variables"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 42,
- "id": "49d4e338-e548-4802-a23d-417654bdeb42",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "-5.040819200223366 3.941423634240521 -0.0006741395329023279\n",
- "-15.120104816222625 19.86580328914713 3.002658699844036\n"
- ]
- }
- ],
- "source": [
- "for v in (x,y):\n",
- " print(v.min(), v.max(), v.mean())"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "bf182d06-7947-4f7d-b2aa-a8c7da3df6a4",
- "metadata": {},
- "source": [
- "create the histogram with a colorbar use the *\"jet\"* colormap"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 43,
- "id": "6cd41a4c-e741-46df-8538-d6d6da06cfbf",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "Text(0, 0.5, 'Intensity')"
- ]
- },
- "execution_count": 43,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 432x288 with 2 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "_ = plt.hist2d(x, y, bins=(250, 250), cmap=plt.cm.jet)\n",
- "cbar = plt.colorbar()\n",
- "cbar.ax.set_ylabel(\"Intensity\", rotation=-90, va=\"bottom\")"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "fc82dcfd-c4aa-4681-ac50-6baa9a4d5c9c",
- "metadata": {},
- "source": [
- "# Scatter plot\n",
- "\n",
- "Compare The *Petal length* vs *Petal width*\n",
- "for the 3 iris species: *setosa*, *versicolor* and *virginica*"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 44,
- "id": "9931f8b0-a239-4752-a2e8-0ba376cc49fb",
- "metadata": {},
- "outputs": [],
- "source": [
- "iris = pd.read_csv('data/Iris.csv', sep=',' , header=0, index_col='Id' )"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 45,
- "id": "b6b1505f-819a-4cb4-b171-453afb715e51",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "Text(0.5, 1.0, 'Scatter plot Iris sepal')"
- ]
- },
- "execution_count": 45,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 576x432 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "plt.figure(figsize=(8,6))\n",
- "\n",
- "for specie, data in iris.groupby('Species'):\n",
- " plt.scatter(data['PetalLengthCm'], data['PetalWidthCm'], label=specie )\n",
- "\n",
- "plt.xlabel('sepal length in (cm)')\n",
- "plt.ylabel('sepal width in (cm)')\n",
- "plt.legend()\n",
- "\n",
- "plt.title('Scatter plot Iris sepal')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "b7c86dc3-a042-490e-b79e-70bbc77d82a0",
- "metadata": {},
- "outputs": [],
- "source": []
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.8.9"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/previous_materials/np_pd_mplt_bertrand/numpy_TP.ipynb b/previous_materials/np_pd_mplt_bertrand/numpy_TP.ipynb
deleted file mode 100644
index 3744c12..0000000
--- a/previous_materials/np_pd_mplt_bertrand/numpy_TP.ipynb
+++ /dev/null
@@ -1,334 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "id": "0e466858-db98-4ed1-814d-e0e67e87c29e",
- "metadata": {},
- "source": [
- "# <center>**TP**</center>\n",
- "\n",
- "<img src=\"images/NumPy_logo_2020.png\" style=\"margin:0 auto;width:400px\">\n",
- "<div style=\"text-align:center\">\n",
- " Bertrand Néron\n",
- " <br />\n",
- " <a src=\" https://research.pasteur.fr/en/team/bioinformatics-and-biostatistics-hub/\">Bioinformatics and Biostatistiqucs HUB</a>\n",
- " <br />\n",
- " © Institut Pasteur, 2021\n",
- "</div>"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "id": "0284b06d-60af-4928-b141-2ebb94f42409",
- "metadata": {},
- "outputs": [],
- "source": [
- "from matplotlib import image\n",
- "from matplotlib import pyplot"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "a93a7d94-eab4-45e7-a557-bc1c1cbed382",
- "metadata": {},
- "source": [
- "To open an image as a numpy ndarray, use the matplotlib.image package "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "id": "ce686f82-93c8-439b-bb83-1b7a7a7809bb",
- "metadata": {},
- "outputs": [],
- "source": [
- "koala = image.imread('images/koala.jpeg')"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "40b7d3cf-d071-4e5b-9e8c-1dccc637350f",
- "metadata": {},
- "source": [
- "To display a numpy.ndarray as an image, use the pyplot.imshow function"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "id": "3c758a0b-1379-4b4f-aaa6-eaef7fbdd39c",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<matplotlib.image.AxesImage at 0x7f050ab99fa0>"
- ]
- },
- "execution_count": 5,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "pyplot.imshow(koala)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "85773df1-55e8-41e5-99c1-a77e343c5af2",
- "metadata": {},
- "source": [
- "have a look on the data, what is the structure of the data?"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "472fbc43-fed9-4c4b-af6c-387310abb27b",
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "markdown",
- "id": "69b45d27-a427-48be-bdbc-4206f5b944cb",
- "metadata": {},
- "source": [
- "What is the size (height, width) of the image?"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "99c6ba96-5f58-4cbd-861c-0ee9933e621c",
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "markdown",
- "id": "525a50b8-82da-461e-9733-6f637f6f0bb2",
- "metadata": {},
- "source": [
- "what is the maximum, value of the pixel for each layer (red,green, blue)?"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "3c081098-f814-41fa-8146-b504e6bbdeba",
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "markdown",
- "id": "c8b39e9e-5d9b-4918-bc49-76f7baa5cd29",
- "metadata": {},
- "source": [
- "what is the min value of the pixel for each layer (red,green, blue)?"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "78b692ab-6396-48b6-842b-0e11a2cb4575",
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "markdown",
- "id": "2c53f07b-c36f-4818-b20a-a86befa8e50e",
- "metadata": {},
- "source": [
- "what is the mean value of the pixel for each layer (red,green, blue)?"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "46e6795f-84af-487e-91e2-c786c71eb2ab",
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "markdown",
- "id": "54afb033-ca4c-4a52-8510-b4a35d431a7b",
- "metadata": {},
- "source": [
- "create a new image which contains only the head of the koala (check the axis on koala image) and display it."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "15751610-ec1c-4d11-9db2-111878b7f0bb",
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "markdown",
- "id": "013c9f50-3169-4f94-bf0c-6d11ea0577d5",
- "metadata": {},
- "source": [
- "from the original koala image, create a B&W image by averaging the pixel values of the 3 layers: red, green, blue.\n",
- "> to display the image use\n",
- "```python\n",
- "pyplot.imshow(<my_array>, cmap='gray', vmin=<the pixel min value>, vmax=<the pixel max value>)\n",
- "```"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "4cffca41-de2c-45c2-85dc-f9dfb1b5988e",
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "markdown",
- "id": "11aa6248-055e-4540-a29e-ab2e626905a9",
- "metadata": {},
- "source": [
- "Filter the B&W image set each pixel which intensity is < 120 to 0\n",
- "\n",
- "display the 2 images (the B&W and filtered B&W)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "26dc894f-bc21-4512-929d-5f2cf3de0adc",
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "b1a978e8-4f9d-4e0f-b485-7248ce7c359e",
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "markdown",
- "id": "63b3b906-703f-48bc-8e41-5178cd79f8f8",
- "metadata": {},
- "source": [
- "display the resulting image with\n",
- "```python\n",
- "pyplot.imshow(<filtered_image>, cmap='gray', vmin=0, vmax=255)\n",
- "```"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "ead66b8b-7d42-4232-9d86-062e7559936e",
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "markdown",
- "id": "319b22c8-3512-4aa9-9ae6-9160018ba365",
- "metadata": {},
- "source": [
- "Create a new image from koala, but where the intensity of blue is half than in the original koala picture.\n",
- "*Hint*\n",
- "> check the type of the original & newly computed pixels in blue layer"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "d5b5320d-da7b-4cab-9a35-43b256f6da0e",
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "150f79b9-c514-4f1d-96e4-f6283083d8d3",
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "ecc8f8b8-fee7-40a8-8fbe-04cd392ae386",
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "markdown",
- "id": "7bec41b7-d754-42aa-8bed-f9efc2057e82",
- "metadata": {},
- "source": [
- "> check the structure of the new array vs koala"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "65d613aa-3064-4323-bde3-88d569af903d",
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "markdown",
- "id": "b04021e6-5e22-40d0-9de9-6a9a99d94d18",
- "metadata": {},
- "source": [
- "display the resultin image with\n",
- "```python\n",
- "pyplot.imshow(koala_blue)\n",
- "```"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.8.8"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/previous_materials/np_pd_mplt_bertrand/numpy_TP_solutions.ipynb b/previous_materials/np_pd_mplt_bertrand/numpy_TP_solutions.ipynb
deleted file mode 100644
index 6069aa0..0000000
--- a/previous_materials/np_pd_mplt_bertrand/numpy_TP_solutions.ipynb
+++ /dev/null
@@ -1,606 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "id": "30009376-e2fd-4708-96b9-33f1e6693b66",
- "metadata": {},
- "source": [
- "# <center>**TP**</center>\n",
- "\n",
- "<img src=\"images/NumPy_logo_2020.png\" style=\"margin:0 auto;width:400px\">\n",
- "<div style=\"text-align:center\">\n",
- " Bertrand Néron\n",
- " <br />\n",
- " <a src=\" https://research.pasteur.fr/en/team/bioinformatics-and-biostatistics-hub/\">Bioinformatics and Biostatistiqucs HUB</a>\n",
- " <br />\n",
- " © Institut Pasteur, 2021\n",
- "</div>"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 20,
- "id": "0284b06d-60af-4928-b141-2ebb94f42409",
- "metadata": {},
- "outputs": [],
- "source": [
- "import numpy as np\n",
- "from matplotlib import image\n",
- "from matplotlib import pyplot"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "a93a7d94-eab4-45e7-a557-bc1c1cbed382",
- "metadata": {},
- "source": [
- "To open an image as a numpy *ndarray*, use the *matplotlib.image* package "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 21,
- "id": "ce686f82-93c8-439b-bb83-1b7a7a7809bb",
- "metadata": {},
- "outputs": [],
- "source": [
- "koala = image.imread('images/koala.jpeg')"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "40b7d3cf-d071-4e5b-9e8c-1dccc637350f",
- "metadata": {},
- "source": [
- "To display a *numpy.ndarray* as an image, use the *pyplot.imshow* function"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 22,
- "id": "3c758a0b-1379-4b4f-aaa6-eaef7fbdd39c",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<matplotlib.image.AxesImage at 0x7f4beb73c280>"
- ]
- },
- "execution_count": 22,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "pyplot.imshow(koala)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "85773df1-55e8-41e5-99c1-a77e343c5af2",
- "metadata": {},
- "source": [
- "have a look on the data, what is the structure of the data?"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 23,
- "id": "472fbc43-fed9-4c4b-af6c-387310abb27b",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "3\n",
- "(450, 800, 3)\n"
- ]
- }
- ],
- "source": [
- "print(koala.ndim)\n",
- "print(koala.shape)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "69b45d27-a427-48be-bdbc-4206f5b944cb",
- "metadata": {},
- "source": [
- "What is the size (height, width) of the image?"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 24,
- "id": "99c6ba96-5f58-4cbd-861c-0ee9933e621c",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "koala image have 450px height x 800px width\n"
- ]
- }
- ],
- "source": [
- "height, width = koala.shape[:2]\n",
- "print(f\"koala image have {height}px height x {width}px width\")"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "525a50b8-82da-461e-9733-6f637f6f0bb2",
- "metadata": {},
- "source": [
- "what is the maximum, value of the pixel for each layer (red,green, blue)?"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 25,
- "id": "3c081098-f814-41fa-8146-b504e6bbdeba",
- "metadata": {},
- "outputs": [],
- "source": [
- "red_layer = koala[:, :, 0]\n",
- "green_layer = koala[:, :, 1]\n",
- "blue_layer = koala[:, :, 2]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 26,
- "id": "4cfb1372-d17e-4b6e-b421-0c1164286379",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "(255, 255, 255)"
- ]
- },
- "execution_count": 26,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "red_layer.max(), green_layer.max(), blue_layer.max()"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "c8b39e9e-5d9b-4918-bc49-76f7baa5cd29",
- "metadata": {},
- "source": [
- "what is the min value of the pixel for each layer (red,green, blue)?"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 27,
- "id": "78b692ab-6396-48b6-842b-0e11a2cb4575",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "(0, 0, 0)"
- ]
- },
- "execution_count": 27,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "red_layer.min(), green_layer.min(), blue_layer.min()"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "2c53f07b-c36f-4818-b20a-a86befa8e50e",
- "metadata": {},
- "source": [
- "what is the mean value of the pixel for each layer (red,green, blue)?"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 28,
- "id": "46e6795f-84af-487e-91e2-c786c71eb2ab",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "(105.20358611111111, 110.28186388888889, 100.34698611111111)"
- ]
- },
- "execution_count": 28,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "red_layer.mean(), green_layer.mean(), blue_layer.mean()"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "54afb033-ca4c-4a52-8510-b4a35d431a7b",
- "metadata": {},
- "source": [
- "create a new image which contains only the head of the koala (check the axis on koala image). and display it"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 29,
- "id": "15751610-ec1c-4d11-9db2-111878b7f0bb",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<matplotlib.image.AxesImage at 0x7f4beb6ae4c0>"
- ]
- },
- "execution_count": 29,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "head = koala[0:220, 300:580, :]\n",
- "pyplot.imshow(head)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "013c9f50-3169-4f94-bf0c-6d11ea0577d5",
- "metadata": {},
- "source": [
- "from the original koala image, create a B&W image by averaging the pixel values of the 3 layers: red, green, blue.\n",
- "> to display the image use\n",
- "```python\n",
- "pyplot.imshow(<my_array>, cmap='gray', vmin=<the pixel min value>, vmax=<the pixel max value>)\n",
- "```"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 30,
- "id": "4cffca41-de2c-45c2-85dc-f9dfb1b5988e",
- "metadata": {},
- "outputs": [],
- "source": [
- "nb = koala.mean(axis=2)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 31,
- "id": "fb4adc6e-1c87-4e4a-8bce-e3ac7e101a5f",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "(450, 800)"
- ]
- },
- "execution_count": 31,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "nb.shape"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 32,
- "id": "7b46155c-4da5-40b7-a790-012b376adc1f",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<matplotlib.image.AxesImage at 0x7f4bf05e2700>"
- ]
- },
- "execution_count": 32,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "pyplot.imshow(nb, cmap='gray', vmin=0, vmax=255)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "39fb8e5c-ef27-4314-bf1a-df6aaa73f2a8",
- "metadata": {},
- "source": [
- "Filter the B&W image set each pixel which intensity is < 120 to 0\n",
- "\n",
- "display the 2 images (the B&W and filtered B&W)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 33,
- "id": "0ac82651-85e1-443e-91ee-074de97ad394",
- "metadata": {},
- "outputs": [],
- "source": [
- "nb2 = nb.copy()\n",
- "mask = nb2 < 120"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 34,
- "id": "061699ef-b71a-44a8-ab24-498c2272031e",
- "metadata": {},
- "outputs": [],
- "source": [
- "nb2[mask] = 0"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 35,
- "id": "2bfd95b9-df7b-4c15-8396-934d5d491beb",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "(0.0, 255.0)"
- ]
- },
- "execution_count": 35,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "nb2.min(), nb2.max()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 36,
- "id": "6c59e056-7ba5-4d29-9c6c-7fc3594d4bd7",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<matplotlib.image.AxesImage at 0x7f4bf055b310>"
- ]
- },
- "execution_count": 36,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "pyplot.imshow(nb2, cmap='gray', vmin=0, vmax=255)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "48f7770b-1041-4b0e-a05c-8d2a83b78d6b",
- "metadata": {},
- "source": [
- "Create a new image from koala, but where the intensity of blue is half than in the original koala picture.\n",
- "*Hint*\n",
- "> check the type of the original & newly computed pixels in blue layer"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 54,
- "id": "7f013e30-1d48-4e79-b82d-a94a6bb16b6e",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "uint8 float64\n"
- ]
- }
- ],
- "source": [
- "blue2 = blue_layer / 2\n",
- "blue2 = blue2.round()\n",
- "print(blue_layer.dtype, blue2.dtype)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "cb7437b7-2ab3-472d-bd1a-26f92c2d9cfa",
- "metadata": {},
- "outputs": [],
- "source": [
- "blue2 = blue2.astype(int)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 51,
- "id": "fd19a630-5db4-44ef-ac35-10dd6afe942d",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "(450, 800, 3)\n",
- "(3, 450, 800)\n"
- ]
- }
- ],
- "source": [
- "koala2 = np.array([red_layer, green_layer, blue2])\n",
- "print(koala.shape)\n",
- "print(koala2.shape)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "40e13039-919a-4325-8d82-ae46378d1782",
- "metadata": {},
- "source": [
- "We saw that the axes in koala2 are not in same order as in original image\n",
- "\n",
- "In the image the last axis must match to the layers. So we have to sort the axis"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 52,
- "id": "6b870e0d-f378-49f2-b610-d10c91e15412",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "(3, 450, 800)\n",
- "(800, 450, 3)\n",
- "(450, 800, 3)\n"
- ]
- }
- ],
- "source": [
- "print(koala2.shape)\n",
- "koala2 = np.swapaxes(koala2, 0,2)\n",
- "print(koala2.shape)\n",
- "koala2 = np.swapaxes(koala2, 0, 1)\n",
- "print(koala2.shape)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 53,
- "id": "861d60a3-470c-4609-993f-c4df092b425d",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<matplotlib.image.AxesImage at 0x7f4beaa2f130>"
- ]
- },
- "execution_count": 53,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "pyplot.imshow(koala2)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "a6356842-1e5a-45c5-8c34-c899f0f99330",
- "metadata": {},
- "outputs": [],
- "source": []
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.8.8"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/previous_materials/np_pd_mplt_bertrand/numpy_cours_solutions.ipynb b/previous_materials/np_pd_mplt_bertrand/numpy_cours_solutions.ipynb
deleted file mode 100644
index 6ed5977..0000000
--- a/previous_materials/np_pd_mplt_bertrand/numpy_cours_solutions.ipynb
+++ /dev/null
@@ -1,3140 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "id": "24c3d590-2420-444f-8628-bf77a175fa0b",
- "metadata": {
- "tags": []
- },
- "source": [
- "<div style=\"text-align:center;display:block\">\n",
- "\n",
- "<img src=\"images/numpy.jpg\" style=\"margin:0 auto;width:600px\">\n",
- "\n",
- "<br>\n",
- "<div style=\"text-align:center\">\n",
- "TC, JBM, BN, AZ\n",
- "\n",
- "<br>\n",
- "© Institut Pasteur, 2021\n",
- "</div>\n",
- "</div>"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "6fcc5f15-b7bf-415f-b714-645bfbc63317",
- "metadata": {},
- "source": [
- "# installation\n",
- "\n",
- "```python\n",
- "pip install numpy\n",
- "```"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "d1c677bf-8fa3-4fa0-9e69-315e45476533",
- "metadata": {},
- "source": [
- "# Convention"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "id": "bcee0cf5-4c99-4100-88a8-ec3e1ecd2837",
- "metadata": {},
- "outputs": [],
- "source": [
- "import numpy as np"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "id": "044ab6d8-b09c-459e-99d8-8f9133747361",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "array([1, 2, 3])"
- ]
- },
- "execution_count": 2,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "x = np.array([1,2,3])\n",
- "x"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "id": "4a6596da-ddfa-42e9-8dfc-72f90a5f0ef3",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "numpy.ndarray"
- ]
- },
- "execution_count": 3,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "type(x)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "deb37026-83f1-4b42-a469-7300d953d2b7",
- "metadata": {},
- "source": [
- "*x* is an instance of the object **numpy.ndarray**. The constructor takes as argument a sequence. Here we provided a list hence the ([ ]) syntax.\n",
- "\n",
- "NB2: Following the previous nota bene about the syntax we have:\n",
- "\n",
- "```python\n",
- "a = np.array(1, 2, 3, 4) # WRONG\n",
- "a = np.array([1, 2, 3, 4]) # RIGHT\n",
- "```\n"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "50834fdb-a7b0-4be4-aa0e-1bfe0a26947a",
- "metadata": {},
- "source": [
- "## NumPy provides fast and memory efficient data structures"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "id": "ecccc640-d531-4c54-81b3-f85d34a8a827",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "CPU times: user 27.2 ms, sys: 345 µs, total: 27.5 ms\n",
- "Wall time: 27.6 ms\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "499999500000"
- ]
- },
- "execution_count": 4,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "l = range(1000000)\n",
- "%time sum(l)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "id": "db3afb63-be2a-4724-b69f-041054fd7d41",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "CPU times: user 1.59 ms, sys: 0 ns, total: 1.59 ms\n",
- "Wall time: 907 µs\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "499999500000"
- ]
- },
- "execution_count": 5,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "x = np.array(l)\n",
- "%time x.sum()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "id": "333448ab-5b14-4ca9-ab2f-2fecbdadeaf9",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "numpy is ~ 18 faster than pure python\n"
- ]
- }
- ],
- "source": [
- "print(f\"numpy is ~ {26.4/1.43:.0f} faster than pure python\")"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "fc6aea51-6d56-4c8b-adbf-c54b91cf49a2",
- "metadata": {},
- "source": [
- "### Example 2\n",
- "we want to compute the $\\sum X_i^2$ given $X$"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "id": "b94502d7-c31d-4db7-8ad8-595ab4ad7029",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "CPU times: user 286 ms, sys: 8.72 ms, total: 295 ms\n",
- "Wall time: 294 ms\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "333332833333500000"
- ]
- },
- "execution_count": 7,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "l = range(1000000)\n",
- "%time sum([x**2 for x in l])"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "id": "e942f0d5-b41d-48be-86fe-2207581db30e",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "CPU times: user 2.27 ms, sys: 186 µs, total: 2.46 ms\n",
- "Wall time: 1.76 ms\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "333332833333500000"
- ]
- },
- "execution_count": 8,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "x = np.array(l)\n",
- "%time (x**2).sum()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "id": "212d4ec5-bc87-4907-92c0-6aae8ec53d76",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "numpy is ~ 116 faster than pure python\n"
- ]
- }
- ],
- "source": [
- "print(f\"numpy is ~ {265/2.28:.0f} faster than pure python\")"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "1bc9282c-fb8f-4e4e-a3d6-5a8fe09c6681",
- "metadata": {},
- "source": [
- "## Creates N-D arrays"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "31fd728d-8c04-4ae8-9327-b331cc39dcab",
- "metadata": {},
- "source": [
- "## 1-D array"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "id": "4d0d92c4-89bb-4d1a-bd5b-79c9061c7828",
- "metadata": {},
- "outputs": [],
- "source": [
- "one_d = np.array([1, 2, 10, 2, 1 ])"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "id": "98111601-9c39-4a7c-a313-c862397c698d",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "5"
- ]
- },
- "execution_count": 11,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "len(one_d)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "a14a0991-3494-4ee3-bdca-5a18e8ee21c3",
- "metadata": {},
- "source": [
- "Indexing/slicing works like Python sequences"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 12,
- "id": "cc434831-4126-4f8a-aa0a-5eabfb99b493",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "10"
- ]
- },
- "execution_count": 12,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "one_d[2]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 13,
- "id": "9719751d-eb51-40c7-9f02-2e28ae78d2d4",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "array([ 2, 10, 2, 1])"
- ]
- },
- "execution_count": 13,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "one_d[1:]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 14,
- "id": "2349c0be-d24c-4184-9d1d-0d840738284c",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "array([10, 2])"
- ]
- },
- "execution_count": 14,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "one_d[2:4]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 15,
- "id": "32f093f2-bce7-4591-bb3b-ebfc794dc09d",
- "metadata": {},
- "outputs": [],
- "source": [
- "### 2-D arrays"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "cd1caf41-0081-4e3a-8c92-1357f331bf5f",
- "metadata": {},
- "source": [
- "Here is a naive way to build a 2D matrix with values going from 1 to 12. Later, we will use more power full\n",
- "method to do this (arange, reshape, ...)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 16,
- "id": "7a8c6bcb-1304-4108-9311-131af5b3ded7",
- "metadata": {},
- "outputs": [],
- "source": [
- "n1 = [1, 2, 3]\n",
- "n2 = [4, 5, 6]\n",
- "n3 = [7, 8, 9]\n",
- "n4 = [10, 11, 12]\n",
- "two_d = np.array([n1, n2, n3, n4])"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 17,
- "id": "b0f7a780-971e-4587-9457-793934dea9fd",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "2"
- ]
- },
- "execution_count": 17,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "two_d.ndim"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 18,
- "id": "8bc4629e-ad79-4d27-a336-be1159b248d0",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "1"
- ]
- },
- "execution_count": 18,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "one_d.ndim"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 19,
- "id": "87f4a252-b7d9-44bc-b0f1-a0b032bbfd65",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "(4, 3) (5,)\n"
- ]
- }
- ],
- "source": [
- "print(two_d.shape, one_d.shape)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "a852f3c6-c47c-4716-ac22-235a8f5ab4c9",
- "metadata": {},
- "source": [
- "### Indexing: LC convention (Line / Column)\n",
- "\n",
- "For a 5x5 matrix, the indexing works as follows\n",
- "\n",
- "<img src=\"./images/matrix.png\">"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 20,
- "id": "ccc4dd58-d503-4c51-9269-50b15ba9e41c",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "array([[ 1, 2, 3],\n",
- " [ 4, 5, 6],\n",
- " [ 7, 8, 9],\n",
- " [10, 11, 12]])"
- ]
- },
- "execution_count": 20,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "two_d"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 21,
- "id": "24f76bdb-c003-4ec9-be44-35fd464fa575",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "11"
- ]
- },
- "execution_count": 21,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# To get 11, last row, second column:\n",
- "two_d[3, 1]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 22,
- "id": "0627549d-0732-47b5-b1f5-70b634bb59c0",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "11"
- ]
- },
- "execution_count": 22,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# equivalent but a bit slower:\n",
- "two_d[3][1]"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "be724779-6f50-4b06-8df1-b0c51b76804e",
- "metadata": {},
- "source": [
- "### 3-D arrays?\n",
- "\n",
- "you manipulate 3D arrays almost every day "
- ]
- },
- {
- "cell_type": "markdown",
- "id": "3902efeb-e719-4734-a13d-28e02044b249",
- "metadata": {},
- "source": [
- "A black and white image is a 2D matrix with a value between 0 (black) and 255 (white) for each pixel (0-255 for 8 bits encoded image)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "402ad8ce-b753-4583-8fa6-db1465e418aa",
- "metadata": {},
- "source": [
- "<img src=\"images/image_BW_numpy.png\" style=\"margin:0 auto;width:200px\">"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "18e8fcc2-0a82-4f12-9045-53961c4576c2",
- "metadata": {},
- "source": [
- "A color image is a 3D matrix, it's the supperposition of 3 2D matrix one for the Red, one for the Green and one for the Blue"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "4a1379cd-8ab4-4dd0-8911-bff59b247466",
- "metadata": {},
- "source": [
- "<img src=\"./images/colored_image_numpy.png\" style=\"margin:0 auto;width:400px\">"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "3a351fc3-635b-4513-ada2-f71ae2a9b485",
- "metadata": {},
- "source": [
- "#### axis\n",
- "in numpy when we do operation on matrix we have to specify on which direction you wnat to do the operation\n",
- "\n",
- "for instance you have a 2D matrix and you have the operator sum.\n",
- "but you need to tell numpy if you want to sum along the columns or the rows.\n",
- "for that numpy have a parameter axis\n",
- "in 2D matrix axis=0 is the row axis axis=1 is the columns axis"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "84dab023-ccb1-43e6-86a4-b94c72a8bfa9",
- "metadata": {},
- "source": [
- "<img src=\"images/axis.png\" style=\"margin:0 auto;width:600px\">"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "d76492fc-71fe-44a0-ba67-7b407220e24d",
- "metadata": {},
- "source": [
- "### Can you imagine a 4D array?\n",
- "\n",
- "> Yes a film can be view as a sequence of colored image, so it's a 4D array"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "dd9901f1-016b-4874-9586-a8fdd10fe98c",
- "metadata": {},
- "source": [
- "<img src=\"./images/4D_array.png\">"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "03e4057d-bbe3-4a52-8412-a30549f79a0a",
- "metadata": {},
- "source": [
- "Volume of air and at each position we measure the pressure and temperature. \n",
- "To simplify, we decompose the volume in 2x2x3 smaller cubes"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 23,
- "id": "5a609b66-da1a-45e6-a2b1-c27e9ca2dfa4",
- "metadata": {},
- "outputs": [],
- "source": [
- "c1 = [1,2,3]; c2 = [1,2,3]; c3 = [1,2,3]; c4 = [1,2,3]; \n",
- "c5 = [1,2,3]; c6 = [1,2,3]; c7 = [1,2,3]; c8 = [1,2,3]; \n",
- "x = np.array(\n",
- " [ # first dimension (2 slices)\n",
- " [ # second dimension (2 rows)\n",
- " [ # third dimension (2 columns)\n",
- " c1, c2\n",
- " ], \n",
- " [\n",
- " c3, c4\n",
- " ]\n",
- " ],\n",
- " [\n",
- " [\n",
- " c5, c6\n",
- " ],\n",
- " [\n",
- " c7, c8\n",
- " ]\n",
- " ]\n",
- " ]) "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 24,
- "id": "71b2219a-5d91-42ac-93f2-cb89d6aad801",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "(2, 2, 2, 3)"
- ]
- },
- "execution_count": 24,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "x.shape"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "533fde0b-fb1d-4bf3-b258-7e588e51e29e",
- "metadata": {},
- "source": [
- "## Function to create arrays"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 25,
- "id": "7b8555b9-80c6-4558-9aa1-dfe7de7c4c62",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "array([[1., 1., 1.],\n",
- " [1., 1., 1.]])"
- ]
- },
- "execution_count": 25,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# 2D array \n",
- "np.ones((2,3))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 26,
- "id": "7d11d736-9a38-4bcd-a067-b8edd94bd66b",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "array([[[1., 1., 1., 1., 1.],\n",
- " [1., 1., 1., 1., 1.],\n",
- " [1., 1., 1., 1., 1.],\n",
- " [1., 1., 1., 1., 1.]],\n",
- "\n",
- " [[1., 1., 1., 1., 1.],\n",
- " [1., 1., 1., 1., 1.],\n",
- " [1., 1., 1., 1., 1.],\n",
- " [1., 1., 1., 1., 1.]],\n",
- "\n",
- " [[1., 1., 1., 1., 1.],\n",
- " [1., 1., 1., 1., 1.],\n",
- " [1., 1., 1., 1., 1.],\n",
- " [1., 1., 1., 1., 1.]]])"
- ]
- },
- "execution_count": 26,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# 3 D array\n",
- "np.ones((3,4,5))"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "7b0e8c5a-d5c3-45ee-9c95-66363a79c2ce",
- "metadata": {},
- "source": [
- "### The **arange** function\n",
- "\n",
- "> Evenly spaced values within a given interval based on a **step**"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 27,
- "id": "5660a98d-c9b0-4117-a869-8257592d82ce",
- "metadata": {},
- "outputs": [],
- "source": [
- "a = np.arange(1, 10) # not that the end is exclusive and the step is 1 by default"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 28,
- "id": "1b090b77-4113-4290-8c48-ca10c5207752",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "array([0, 2, 4, 6, 8])"
- ]
- },
- "execution_count": 28,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "np.arange(0, 10, step=2)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "73efa449-2b29-43b6-8fb9-ca03fd639750",
- "metadata": {},
- "source": [
- "The **reshape** methode\n",
- "\n",
- "> Gives a new shape to an array without changing its data."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 29,
- "id": "8d43a13e-9283-490e-b8f5-32291128bbd0",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "array([[1, 2, 3],\n",
- " [4, 5, 6],\n",
- " [7, 8, 9]])"
- ]
- },
- "execution_count": 29,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "a2 = a.reshape(3,3)\n",
- "a2"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "fcf73865-bc24-4bb9-b8b9-24ff264a8a9c",
- "metadata": {},
- "source": [
- "**NB** the product of dimensions = number of values\n",
- "```python\n",
- "len(a) = 9 \n",
- "3 * 3 = 9\n",
- "```"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 30,
- "id": "47bd6cf0-8cb3-4f99-acb4-cca110dc67d0",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "array([[1, 2, 3],\n",
- " [4, 5, 6],\n",
- " [7, 8, 9]])"
- ]
- },
- "execution_count": 30,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# equivalent to\n",
- "\n",
- "a2 = np.reshape(a, (3,3))\n",
- "a2"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "f6f81aa8-d6ad-483d-9be8-e4525ae579eb",
- "metadata": {},
- "source": [
- "### the **linspace** function\n",
- "\n",
- "> Evenly spaced values within a given interval based on a **number of points**"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 31,
- "id": "1dc1c67c-df2b-409f-acd9-ba69ccee7c47",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "array([0. , 0.11111111, 0.22222222, 0.33333333, 0.44444444,\n",
- " 0.55555556, 0.66666667, 0.77777778, 0.88888889, 1. ])"
- ]
- },
- "execution_count": 31,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "np.linspace(0, 1, 10)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "33fbe07c-0166-49b3-a4b1-d4fb0fa9c379",
- "metadata": {},
- "source": [
- "### ones, zeros, diag, eye, empty"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 32,
- "id": "4ba5244e-ffbe-40d8-9e4b-bb1669fefbd2",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "array([[5, 0, 0, 0],\n",
- " [0, 5, 0, 0],\n",
- " [0, 0, 1, 0],\n",
- " [0, 0, 0, 1]])"
- ]
- },
- "execution_count": 32,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "np.diag((5,5,1,1))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 33,
- "id": "c2dfdfe1-f90c-4dc7-9708-cd776b8700d9",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "array([[1., 1.],\n",
- " [1., 1.]])"
- ]
- },
- "execution_count": 33,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "np.ones((2,2))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 34,
- "id": "5d89026e-6b57-4264-979b-26fe0479a3a4",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "array([[0., 0.],\n",
- " [0., 0.]])"
- ]
- },
- "execution_count": 34,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "np.zeros((2,2))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 35,
- "id": "f9e8e9af-5c5b-4509-932a-25313f6c8095",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "array([[1., 0., 0.],\n",
- " [0., 1., 0.],\n",
- " [0., 0., 1.]])"
- ]
- },
- "execution_count": 35,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "np.eye(3)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "7844105b-26b8-4cf1-9742-ca38db857695",
- "metadata": {},
- "source": [
- "# Random values\n",
- "\n",
- "> Python language has its own random module but numpy has more functionalities."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 36,
- "id": "adff2440-1323-4e47-a7c1-05b2298b9c1f",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "0.2374135561970464"
- ]
- },
- "execution_count": 36,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# uniform random values between 0 and 1\n",
- "np.random.rand()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 37,
- "id": "89a1f130-0549-4127-87cc-bca1e6d8fcf0",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "-2.181325840579963"
- ]
- },
- "execution_count": 37,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# normal distribution\n",
- "np.random.randn()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 38,
- "id": "96e4cd87-5d98-4e27-ba8f-3a7573975224",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "array([-1.16043015, -0.41323816, 0.26145112, -0.72910492, 0.74809394,\n",
- " -0.44404394, -0.65350114, -0.37594278, -1.27568323, -1.25554752])"
- ]
- },
- "execution_count": 38,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# array of normally distributed values\n",
- "# with mean=0 (loc) and std=1.0 (scale) (defaults)\n",
- "np.random.normal(loc=0.0, scale=1.0, size=10)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 39,
- "id": "c1119efa-0d17-424d-8d76-7e0047988143",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "array([[ 1.83459533, 1.34741056],\n",
- " [ 1.04856601, 0.36270561],\n",
- " [-1.07733443, 2.26669404],\n",
- " [-0.14518838, 1.30587942],\n",
- " [-1.52924688, 3.78152616]])"
- ]
- },
- "execution_count": 39,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# a 2Darray of normally distributed values\n",
- "np.random.normal(loc=1.0, scale=2.0, size=(5,2))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 40,
- "id": "8c6345c7-01e5-4bde-8624-be7567fe79f3",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "array([1.95882936, 1.55143615, 1.23730791, 1.21954068, 0.87052116,\n",
- " 1.84664919, 1.96556124, 0.39348725, 0.77651197, 0.18411425])"
- ]
- },
- "execution_count": 40,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# array of uniform distributed values\n",
- "np.random.uniform(low=0, high=2, size=10)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "7d993c9b-4872-4354-9da5-b573f4b14e84",
- "metadata": {},
- "source": [
- "# Exercices\n",
- "\n",
- "> - Create a 4x4 matrix with 2's on the diagonal.\n",
- "> - Create a 100x100 matrix with 2's on the diagonal.\n",
- "> - Create a matrix 5x5 with random number uniformly distributed\n",
- "> - Create this matrix:\n",
- "```\n",
- "1 0 0 0 0\n",
- "0 1 0 0 0\n",
- "0 0 5 0 0\n",
- "0 0 0 1 0\n",
- "0 0 0 0 1\n",
- "```\n",
- "and \n",
- "```\n",
- "0 1 1 1 1\n",
- "1 0 1 1 1\n",
- "1 1 -4 1 1\n",
- "1 1 1 0 1\n",
- "1 1 1 1 0\n",
- "```"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 41,
- "id": "94cdfb63-7ef2-4297-8791-2183f3d71a30",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "array([[2, 0],\n",
- " [0, 2]])"
- ]
- },
- "execution_count": 41,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "x = np.diag([2,2])\n",
- "x"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 42,
- "id": "c13f20f0-e90d-4241-864e-e5d7612efc4a",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "array([[2, 0, 0, ..., 0, 0, 0],\n",
- " [0, 2, 0, ..., 0, 0, 0],\n",
- " [0, 0, 2, ..., 0, 0, 0],\n",
- " ...,\n",
- " [0, 0, 0, ..., 2, 0, 0],\n",
- " [0, 0, 0, ..., 0, 2, 0],\n",
- " [0, 0, 0, ..., 0, 0, 2]])"
- ]
- },
- "execution_count": 42,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "x = np.diag([2]*100)\n",
- "x"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 43,
- "id": "47603231-d84f-46c3-ad3b-594901fc273d",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "array([[1, 0, 0, 0, 0],\n",
- " [0, 1, 0, 0, 0],\n",
- " [0, 0, 1, 0, 0],\n",
- " [0, 0, 0, 1, 0],\n",
- " [0, 0, 0, 0, 1]])"
- ]
- },
- "execution_count": 43,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "A = np.diag([1]*5)\n",
- "A"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 44,
- "id": "3374247d-1802-47e3-9827-4e1bc0531c7d",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "array([[1, 0, 0, 0, 0],\n",
- " [0, 1, 0, 0, 0],\n",
- " [0, 0, 5, 0, 0],\n",
- " [0, 0, 0, 1, 0],\n",
- " [0, 0, 0, 0, 1]])"
- ]
- },
- "execution_count": 44,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "A[2,2]=5\n",
- "A"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 45,
- "id": "d9b2ac6f-5ae7-4d0f-a284-cb3925b043e8",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "array([[1., 1., 1., 1., 1.],\n",
- " [1., 1., 1., 1., 1.],\n",
- " [1., 1., 1., 1., 1.],\n",
- " [1., 1., 1., 1., 1.],\n",
- " [1., 1., 1., 1., 1.]])"
- ]
- },
- "execution_count": 45,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "B = np.ones((5,5))\n",
- "B"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 46,
- "id": "30bd7abb-0ceb-42e2-a3e4-1adb93c3ed1b",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "array([[ 0., 1., 1., 1., 1.],\n",
- " [ 1., 0., 1., 1., 1.],\n",
- " [ 1., 1., -4., 1., 1.],\n",
- " [ 1., 1., 1., 0., 1.],\n",
- " [ 1., 1., 1., 1., 0.]])"
- ]
- },
- "execution_count": 46,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "B = B - A\n",
- "B"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "8f1555d9-55cd-4b03-811c-f2224822d518",
- "metadata": {},
- "source": [
- "# data types"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 47,
- "id": "bb4a7d5b-d2a3-42a7-8bce-7c3ecab1f30f",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "dtype('int64')"
- ]
- },
- "execution_count": 47,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "x = np.array([1,2,3])\n",
- "x.dtype"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 48,
- "id": "b6761fbb-7a96-4244-a433-3d9d4861f6ee",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "dtype('float64')"
- ]
- },
- "execution_count": 48,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "x = np.array([1., 2, 3.5])\n",
- "x.dtype"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 49,
- "id": "24b99d8b-fa40-4450-9571-0e9039daa568",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "dtype('float64')"
- ]
- },
- "execution_count": 49,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "x = np.array([1,2,3], dtype=float)\n",
- "x.dtype"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 50,
- "id": "de2cde1e-5dc6-40b7-8367-68bc4b6a13a5",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "array([1., 2., 3.])"
- ]
- },
- "execution_count": 50,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "x = np.array([1, 2, 3])\n",
- "x = x.astype(float)\n",
- "x"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "e091dae2-3205-4644-9657-cd813039726e",
- "metadata": {},
- "source": [
- "<div class=\"alert alert-warning\">\n",
- "If you mix types, the most complex type is used\n",
- "</div>"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 51,
- "id": "2b335534-e646-4660-a67c-a8ad965ddfef",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "array(['1.0', '1', 'oups'], dtype='<U32')"
- ]
- },
- "execution_count": 51,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "np.array([1.0, 1, \"oups\"])"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "2b9be850-666f-4e96-bdf3-30bc53741a77",
- "metadata": {},
- "source": [
- "<div class=\"practical\">\n",
- "<h1>Basic indexing and slicing </h1>\n",
- "</div>\n",
- "\n",
- "# Example in 2D\n",
- "\n",
- "Syntax. First axis is for rows and second for columns:"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "e870925c-e520-43c1-85c0-3450af31ca26",
- "metadata": {},
- "source": [
- "<img src=\"images/exo_table1.png\">"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "a718bf74-be4b-4464-9d6d-7d0f26f6f984",
- "metadata": {},
- "source": [
- "Create the array shown above. Then, with slicing and indexing, \n",
- "> - extract first row, \n",
- "> - extract first column (orange cells)\n",
- "> - extract even values only, odd values only\n",
- "> - extract the 4 blue cells\n",
- "> - extract the 2 green cells\n",
- "> - extract the 2x2 sub-matrix in bottom right corner"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 52,
- "id": "fe54df35-dd1d-44eb-8087-7122b1e07265",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "array([[ 0, 1, 2, 3, 4],\n",
- " [10, 11, 12, 13, 14],\n",
- " [20, 21, 22, 23, 24],\n",
- " [30, 31, 32, 33, 34],\n",
- " [40, 41, 42, 43, 44]])"
- ]
- },
- "execution_count": 52,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "r = np.arange(5)\n",
- "m = np.array([r, r+10, r+20, r+30, r+40])\n",
- "m"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 53,
- "id": "9c204b45-396d-4b43-acd2-00a84f57a33a",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "array([0, 1, 2, 3, 4])"
- ]
- },
- "execution_count": 53,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# first row\n",
- "m[0, :]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 119,
- "id": "8369f926-2572-4afc-a89b-6d35ff8b1921",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "(11, 13, 31, 33)"
- ]
- },
- "execution_count": 119,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# blue cells\n",
- "m[1,1], m[1,3], m[3,1], m[3,3]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 55,
- "id": "22448595-9e9b-4e1d-9328-2ab409b83077",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "array([[11, 13],\n",
- " [31, 33]])"
- ]
- },
- "execution_count": 55,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# even values\n",
- "m[1::2, 1::2]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 56,
- "id": "12cadf07-283f-4437-8586-846f94fd8721",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "array([ 0, 10, 20, 30, 40])"
- ]
- },
- "execution_count": 56,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# orange column\n",
- "m[:,0]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 57,
- "id": "48e14271-99bb-4089-b761-d338c7975ede",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "array([23, 24])"
- ]
- },
- "execution_count": 57,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# green cells\n",
- "m[2, -2:]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 58,
- "id": "98a2ff9b-1906-4b2b-9fff-4a735597606b",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "array([[33, 34],\n",
- " [43, 44]])"
- ]
- },
- "execution_count": 58,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# blue sub corner\n",
- "m[-2:, -2:]"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "cd1049f2-8936-4d61-a26b-38fea3684a2b",
- "metadata": {},
- "source": [
- "# Copies and views\n",
- "\n",
- "We are manipulating objects. So be careful with the references"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 59,
- "id": "05e69472-aa21-4f51-8989-e759ee7a1159",
- "metadata": {},
- "outputs": [],
- "source": [
- "## views"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 60,
- "id": "3da2f5f2-c302-4e35-b5c3-3d4f3f8d860f",
- "metadata": {},
- "outputs": [],
- "source": [
- "a = np.array([1,2,3,4,5])\n",
- "b = a.view()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 61,
- "id": "503d63fa-58b2-4bfe-af4c-d08e54f41103",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "(array([1, 2, 3, 4, 5]), array([1, 2, 3, 4, 5]))"
- ]
- },
- "execution_count": 61,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "a, b"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 62,
- "id": "f4664618-a1aa-40e7-b2e3-19221bd7954a",
- "metadata": {},
- "outputs": [],
- "source": [
- "a[2] = 30"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 63,
- "id": "e4a1c3f9-8a02-46be-bb51-14db61cb7755",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "(array([ 1, 2, 30, 4, 5]), array([ 1, 2, 30, 4, 5]))"
- ]
- },
- "execution_count": 63,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "a, b"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 64,
- "id": "58efbf90-4a57-46ed-bb97-c998f4f9d025",
- "metadata": {},
- "outputs": [],
- "source": [
- "b[-2:] = 0"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 65,
- "id": "16178b9b-f7f7-4805-9493-ac6c55fb013f",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "(array([ 1, 2, 30, 0, 0]), array([ 1, 2, 30, 0, 0]))"
- ]
- },
- "execution_count": 65,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "a, b"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "53fea18b-23b6-4d62-9394-b4bdbb6f0d19",
- "metadata": {},
- "source": [
- "<div class=\"alert alert-warning\">\n",
- "[:] does nt make a shallow copy as for python list it's equivalent to a view\n",
- "</div>"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 120,
- "id": "44a0929f-fb62-4f50-bf00-a0eabcfaf387",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "(array([ 1, 2, 300, 4, 5]), array([ 1, 2, 300, 4, 5]))"
- ]
- },
- "execution_count": 120,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "a = np.array([1,2,3,4,5])\n",
- "b = a[:]\n",
- "a[2] = 300\n",
- "a, b"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 122,
- "id": "aebba68e-5679-4a73-8b79-e355eb630dee",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "array([ 1, 2, 300, 4, 5])"
- ]
- },
- "execution_count": 122,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "c = a.copy()\n",
- "c"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 123,
- "id": "287123c1-5ab2-4ede-8371-2699fc8aedb0",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "(array([ 1, 2, 150, 4, 5]), array([ 1, 2, 300, 4, 5]))"
- ]
- },
- "execution_count": 123,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "c[2] = 150\n",
- "c, a"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "6fde6353-b338-4f83-8b57-b336df3dcf12",
- "metadata": {},
- "source": [
- "# Fancy indexing \n",
- "\n",
- "> As we have seen before, standard Python slicing and indexing works on NumpPy array.\n",
- "> Yet, NumPy provides more indexing, which can be performed with boolean or integer arrays, also called **masked**"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "1af388f7-8270-45c6-952a-5513bf2216eb",
- "metadata": {},
- "source": [
- "## Indexing with boolean masks\n",
- "\n",
- "> Boolean mask is a very powerful feature in NumPy.\n",
- "> It can be used to index an array, and assign new values to a sub-array. \n",
- "> Note also that it creates copies not views"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 67,
- "id": "df226fb1-d572-42b7-993a-e5710cfe7abd",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15])"
- ]
- },
- "execution_count": 67,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "data = np.arange(16)\n",
- "data"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "632306f0-25a3-4b98-b8c6-ff20599aa866",
- "metadata": {},
- "source": [
- "Find all multiple of 7"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 68,
- "id": "fb5b4ee3-49dd-423e-a6c1-e6b04887ef65",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "array([ True, False, False, False, False, False, False, True, False,\n",
- " False, False, False, False, False, True, False])"
- ]
- },
- "execution_count": 68,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "data % 7 == 0"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 69,
- "id": "bdbfa641-41f3-47df-b701-9e1bb53585a1",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "array([ 0, 7, 14])"
- ]
- },
- "execution_count": 69,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "mask = (data % 7 == 0)\n",
- "data[mask]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 70,
- "id": "70a9d62a-6652-41dd-9757-bed6bd523939",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "array([-100, 1, 2, 3, 4, 5, 6, -100, 8, 9, 10,\n",
- " 11, 12, 13, -100, 15])"
- ]
- },
- "execution_count": 70,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# Replaces values: \n",
- "data[mask] = -100\n",
- "data"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 71,
- "id": "3e83e3cb-89b9-42a5-af67-5925c2b6f6ec",
- "metadata": {},
- "outputs": [],
- "source": [
- "## Indexing with an array of integers"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 72,
- "id": "dbcc4a1e-20e1-4dcc-99b2-80d18eaeb1f2",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "array([-1, 2, -3, -4])"
- ]
- },
- "execution_count": 72,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "data = np.array([-1, 2, -3, -4, -5, 10, 20])\n",
- "indices = [0, 1, 2, 3]\n",
- "data[indices]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 73,
- "id": "7a2cb72a-5ea3-45a9-a428-bb9f91b38267",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "array([-1, -3, -4, -5])"
- ]
- },
- "execution_count": 73,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "data[data<0]"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "7fdcb734-ae19-445e-9ac9-09227185799c",
- "metadata": {},
- "source": [
- "<img src=\"images/exo_table2.png\">"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "e6503506-a1a5-450b-abb4-97d7b665c44a",
- "metadata": {},
- "source": [
- "Create the array above and extract the following data sets:\n",
- "> - the 9 blue cells\n",
- "> - the 5 orange cells \n",
- "> - the green cells"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 74,
- "id": "4aa35130-ab00-4454-9e95-dd1b83c0ef65",
- "metadata": {},
- "outputs": [],
- "source": [
- "m = np.array([[i+j for i in range(6)] for j in range(0, 60, 10)])"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 75,
- "id": "004c6b0a-b746-440b-b0de-768105c5f8ff",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "array([[ 0, 1, 2, 3, 4, 5],\n",
- " [10, 11, 12, 13, 14, 15],\n",
- " [20, 21, 22, 23, 24, 25],\n",
- " [30, 31, 32, 33, 34, 35],\n",
- " [40, 41, 42, 43, 44, 45],\n",
- " [50, 51, 52, 53, 54, 55]])"
- ]
- },
- "execution_count": 75,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "m"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 76,
- "id": "fd6ffaa9-51ae-4c5f-9df5-fee28c60c8ce",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "array([ 1, 12, 23, 34, 45])"
- ]
- },
- "execution_count": 76,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# orange\n",
- "m[(0,1,2,3,4), (1,2,3,4,5)]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 77,
- "id": "138b542e-7600-4a44-857e-ff22196248f8",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "array([[30, 32, 35],\n",
- " [40, 42, 45],\n",
- " [50, 52, 55]])"
- ]
- },
- "execution_count": 77,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# blue:\n",
- "m[3:, [0,2,5]]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 78,
- "id": "9f46cd0e-1091-45d8-bef6-0556ebd4de87",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "array([ 4, 24, 54])"
- ]
- },
- "execution_count": 78,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# green\n",
- "m[np.array([True, False,True,False,False,True]),4]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 79,
- "id": "db0028dc-5cf2-4bc0-a104-d861bed4cb1e",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "array([ 4, 24, 54])"
- ]
- },
- "execution_count": 79,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# green\n",
- "m[np.array([1, 0, 1, 0, 0, 1], dtype=bool), 4]"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "de6caebb-8bf9-4494-bd9e-1e3f01e6c3d1",
- "metadata": {},
- "source": [
- "# Numerical operations"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 80,
- "id": "d514d0e2-d739-4f46-a04b-423b65222ab0",
- "metadata": {},
- "outputs": [],
- "source": [
- "a = np.array([[4, 7], \n",
- " [2, 6]])\n",
- "\n",
- "b = np.array([[0.6, -0.7],\n",
- " [-0.2, 0.4]])"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 81,
- "id": "a9a1823e-3d4b-46d8-85c6-131d9e8a24ed",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "array([[4.6, 6.3],\n",
- " [1.8, 6.4]])"
- ]
- },
- "execution_count": 81,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "a + b"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "126f72e2-85f6-4fc7-a9cd-1de1ab33d390",
- "metadata": {},
- "source": [
- "<div class=\"alert alert-warning\">\n",
- "Again be careful with copies and views\n",
- "</div>"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 82,
- "id": "abaeea51-55b6-4bd7-806a-af9a5b7c7bc6",
- "metadata": {},
- "outputs": [],
- "source": [
- "c = b.copy()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 83,
- "id": "df03e4ad-5ab2-41e9-a4ab-ebf5ce95c81a",
- "metadata": {},
- "outputs": [],
- "source": [
- "c *= 2"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 84,
- "id": "cda76afb-1fc7-4163-a0b0-f3073c84a6a3",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "(array([[ 1.2, -1.4],\n",
- " [-0.4, 0.8]]),\n",
- " array([[ 0.6, -0.7],\n",
- " [-0.2, 0.4]]))"
- ]
- },
- "execution_count": 84,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "c, b"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 85,
- "id": "4225c892-6cb0-4646-b77a-6f6048b6b6ed",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "array([[ 2.4, -4.9],\n",
- " [-0.4, 2.4]])"
- ]
- },
- "execution_count": 85,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# elementwise product\n",
- "a * b"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "94a02111-0279-4a87-ba0e-ae478609efed",
- "metadata": {},
- "source": [
- "<div class=\"alert alert-warning\">\n",
- "This is not a matrix product\n",
- "</div>"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 86,
- "id": "1b473d9c-0829-4126-865c-02868e3cbd9e",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "array([[ 1.00000000e+00, 3.33066907e-16],\n",
- " [-1.11022302e-16, 1.00000000e+00]])"
- ]
- },
- "execution_count": 86,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# matrix product\n",
- "a.dot(b)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 87,
- "id": "7801f83c-97af-4914-be29-4cebd6600901",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "array([[ 1., 0.],\n",
- " [-0., 1.]])"
- ]
- },
- "execution_count": 87,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "a.dot(b).round()"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "e81611c8-de34-4c8c-97b9-baa2bf5740f9",
- "metadata": {},
- "source": [
- "# Reductions"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 88,
- "id": "d18a1e4d-5bf7-46a8-a4f7-968b692e2ce8",
- "metadata": {},
- "outputs": [],
- "source": [
- "x = np.array([1,2,3,4,-1,-2,-3,-4])"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 89,
- "id": "2d537f9c-454b-4b75-879a-626cd97fc9ee",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "sum = 0\n",
- "min = -4\n",
- "max = 4\n",
- "position of the max = 3\n"
- ]
- }
- ],
- "source": [
- "print(\"sum =\", x.sum())\n",
- "print(\"min =\", x.min())\n",
- "print(\"max =\", x.max())\n",
- "print(\"position of the max =\", x.argmax())"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 90,
- "id": "e501a139-9e25-40b4-8c24-ee830f8ddfe1",
- "metadata": {},
- "outputs": [],
- "source": [
- "a = np.array([\n",
- " [1,10,1],\n",
- " [2,8,3]\n",
- "])"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 91,
- "id": "67bb7f69-c474-4fc8-bf05-9ea39a886425",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "25"
- ]
- },
- "execution_count": 91,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "a.sum()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 92,
- "id": "7c4cdca1-e81b-4eea-b7dd-5a7a490f9da0",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "array([ 3, 18, 4])"
- ]
- },
- "execution_count": 92,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "a.sum(axis=0) # sum along the axis 0 or rows"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 93,
- "id": "53413090-49ba-4ecb-96de-6edc48169bff",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "array([12, 13])"
- ]
- },
- "execution_count": 93,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "a.sum(axis=1) # sum along the axis 1 or columns"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "9411347d-fb05-413f-95f4-b65397766f27",
- "metadata": {},
- "source": [
- "# data standardization and Normalization"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "363f44e8-f4e1-4b6c-aed6-97401f3c4a32",
- "metadata": {},
- "source": [
- "Standardization formula $X_{standardized}= \\frac{X - \\mu}{\\sigma}$\n",
- "\n",
- "So in numpy its write "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 124,
- "id": "0b53a378-69fe-40fc-b882-9fa3a4daada3",
- "metadata": {},
- "outputs": [],
- "source": [
- "data_standardized = (data - data.mean()) / data.std()"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "e578c237-14c3-4bb8-86ef-4cec7853e34f",
- "metadata": {},
- "source": [
- "Normalization formula $X_{normalized} = \\frac{X - X_{min}}{X_{max} - X_{min}}$"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "65f7da4e-18c1-4c85-ad5b-fe34fd6e703a",
- "metadata": {},
- "source": [
- "# Iterations"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 94,
- "id": "671bd896-5aa0-4ebf-b894-addf94453844",
- "metadata": {},
- "outputs": [],
- "source": [
- "x = np.random.normal(size=12).reshape(6,2)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 95,
- "id": "9ba4ce3a-51ab-4284-97df-0d0c87625783",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "array([[-0.49113682, 0.28880782],\n",
- " [-1.89906736, -0.58253771],\n",
- " [ 1.0224148 , -0.11296469],\n",
- " [ 1.14279032, 0.00567871],\n",
- " [-1.03474836, 1.44914073],\n",
- " [ 1.30421041, 1.60822144]])"
- ]
- },
- "execution_count": 95,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "x"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 96,
- "id": "7402047a-c8ef-4015-be8e-16e72c0e9bd4",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[ 1.0224148 -0.11296469]\n",
- "[1.14279032 0.00567871]\n",
- "[-1.03474836 1.44914073]\n",
- "[1.30421041 1.60822144]\n"
- ]
- }
- ],
- "source": [
- "for row in x:\n",
- " if row.sum() > 0 :\n",
- " print(row)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 97,
- "id": "5d844860-7ff0-459b-b9ac-6f2064fc9433",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "1.0224148000050624\n",
- "1.1427903227607883\n",
- "1.4491407331363357\n",
- "1.304210408041745\n",
- "1.608221438002454\n"
- ]
- }
- ],
- "source": [
- "for item in x.flat:\n",
- " if item >1:\n",
- " print(item)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 98,
- "id": "126c649a-2d22-474a-b296-79780879bfd2",
- "metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "<ipython-input-98-396fc74286c3>:1: RuntimeWarning: invalid value encountered in sqrt\n",
- " res = np.sqrt(-1)\n"
- ]
- }
- ],
- "source": [
- "res = np.sqrt(-1)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 99,
- "id": "de9dcc1b-50fa-4c36-ae0c-949c6feec993",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "nan"
- ]
- },
- "execution_count": 99,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "res"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 100,
- "id": "4baa648b-3dac-4b6f-8bcb-ec412bc70588",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "True"
- ]
- },
- "execution_count": 100,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "np.isnan(res)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 101,
- "id": "5e7cca16-f5c3-42fc-8000-8d51a9a8a306",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "1.7976931348623157e+308"
- ]
- },
- "execution_count": 101,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "import sys\n",
- "sys.float_info.max"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 102,
- "id": "60ae8823-eea4-4fa4-8143-c28363cc057f",
- "metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "<ipython-input-102-5f50193860e4>:1: RuntimeWarning: overflow encountered in multiply\n",
- " res = np.array([1e308,1e300]) * 10\n"
- ]
- }
- ],
- "source": [
- "res = np.array([1e308,1e300]) * 10"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 103,
- "id": "0f1d4df4-f241-4631-9cbd-adf47a59f1a7",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "array([ inf, 1.e+301])"
- ]
- },
- "execution_count": 103,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "res"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 104,
- "id": "cc4c4bdc-2bbd-451f-9697-b031706b609d",
- "metadata": {},
- "outputs": [],
- "source": [
- "# Resizing"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 105,
- "id": "7c3004c5-76a7-467b-9439-ecc01c32e65f",
- "metadata": {},
- "outputs": [],
- "source": [
- "a = np.diag([1,2,3,4])"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 106,
- "id": "21f0e588-628a-4c28-872f-6f1057950b8e",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "array([[1, 0, 0, 0],\n",
- " [0, 2, 0, 0],\n",
- " [0, 0, 3, 0],\n",
- " [0, 0, 0, 4],\n",
- " [0, 0, 0, 0]])"
- ]
- },
- "execution_count": 106,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# Can be used to add a column or a row\n",
- "a.resize((5,4))\n",
- "a"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "5539cdf8-c6ff-4e69-a489-4fcc4081987f",
- "metadata": {},
- "source": [
- "# transpose"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 107,
- "id": "43e34be1-6075-4358-97ea-b1f317b8fa8e",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "array([[ 0, 1, 2, 3],\n",
- " [ 4, 5, 6, 7],\n",
- " [ 8, 9, 10, 11]])"
- ]
- },
- "execution_count": 107,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "a = np.arange(12).reshape((3,4))\n",
- "a"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 108,
- "id": "f377af8c-b264-4bdc-bce1-43cc5f7d7788",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "array([[ 0, 4, 8],\n",
- " [ 1, 5, 9],\n",
- " [ 2, 6, 10],\n",
- " [ 3, 7, 11]])"
- ]
- },
- "execution_count": 108,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "b = a.T\n",
- "b"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "8cdcb8a7-899d-41d6-a34a-aad8d985a9e1",
- "metadata": {},
- "source": [
- "# swapaxes\n",
- "\n",
- "Interchange two axes of an array.\n",
- "(https://numpy.org/doc/stable/reference/generated/numpy.swapaxes.html)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 109,
- "id": "1c44286f-0a9c-4943-9288-6797ecebfb90",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "array([[[ 0, 1],\n",
- " [ 2, 3],\n",
- " [ 4, 5],\n",
- " [ 6, 7]],\n",
- "\n",
- " [[ 8, 9],\n",
- " [10, 11],\n",
- " [12, 13],\n",
- " [14, 15]],\n",
- "\n",
- " [[16, 17],\n",
- " [18, 19],\n",
- " [20, 21],\n",
- " [22, 23]]])"
- ]
- },
- "execution_count": 109,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "a = np.arange(24).reshape((3,4,2))\n",
- "a"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 110,
- "id": "669aacba-f92b-417c-bac2-a03bcb90c529",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "array([[[ 0, 8, 16],\n",
- " [ 2, 10, 18],\n",
- " [ 4, 12, 20],\n",
- " [ 6, 14, 22]],\n",
- "\n",
- " [[ 1, 9, 17],\n",
- " [ 3, 11, 19],\n",
- " [ 5, 13, 21],\n",
- " [ 7, 15, 23]]])"
- ]
- },
- "execution_count": 110,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "b = a.swapaxes(0,2)\n",
- "b"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 111,
- "id": "62303e1d-5192-4112-9809-b0fcdd3b053b",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "((3, 4, 2), (2, 4, 3))"
- ]
- },
- "execution_count": 111,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "a.shape, b.shape"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "eebd0afa-3a38-41e5-8dbc-ac9a8b4d1460",
- "metadata": {},
- "source": [
- "# array concatenation"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 127,
- "id": "41f5d5e0-6734-4cc4-a34b-124080bca6ee",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "array([[1, 2, 3],\n",
- " [4, 5, 6]])"
- ]
- },
- "execution_count": 127,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "a = np.array([1, 2, 3])\n",
- "b = np.array([4, 5, 6])\n",
- "c = np.vstack([a, b])\n",
- "c"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 113,
- "id": "5dfb4a39-bea4-4fee-96b9-b705a04d4fe1",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "array([1, 2, 3, 4, 5, 6])"
- ]
- },
- "execution_count": 113,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# equivalent of the + operator with list\n",
- "np.hstack([a,b])"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 132,
- "id": "cd14e23e-bb95-4270-ae9b-bbaf65940498",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "array([[ 0, 1, 2, 10, 11, 12],\n",
- " [ 3, 4, 5, 13, 14, 15]])"
- ]
- },
- "execution_count": 132,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "a = np.arange(6).reshape(2,3)\n",
- "b = np.arange(10,16).reshape(2,3)\n",
- "np.hstack([a,b])"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "1345c700-b01a-4f49-9976-07fcd582cfd8",
- "metadata": {},
- "source": [
- "# Sorting"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 115,
- "id": "62861c8f-3128-4a06-b23b-4908fbfeea60",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "array([ 1, 2, 5, 7, 8, 10])"
- ]
- },
- "execution_count": 115,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "a = np.array([5,1,10,2,7,8])\n",
- "a.sort() # inplace\n",
- "a"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 116,
- "id": "0dd2dedc-3b44-4ac3-9fa9-f7211f2bbc5e",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "array([ 1, 2, 5, 7, 8, 10])"
- ]
- },
- "execution_count": 116,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "a = np.array([5,1,10,2,7,8])\n",
- "sorted_a = np.sort(a) # new array\n",
- "sorted_a"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 117,
- "id": "7a3842bf-e445-4203-9674-ec6d2fd50586",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "array([ 5, 1, 10, 2, 7, 8])"
- ]
- },
- "execution_count": 117,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "a"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 118,
- "id": "2f8c81b1-fac2-4073-a965-166e1d144170",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "array([1, 3, 0, 4, 5, 2])"
- ]
- },
- "execution_count": 118,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "a = np.array([5,1,10,2,7,8])\n",
- "a.argsort()"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "12dc5a6c-a439-45df-bde5-03f63bf6111d",
- "metadata": {},
- "source": [
- "# Loading data\n",
- "\n",
- "Numpy has its own reader of tabulated data sets\n",
- "\n",
- "np.genfromtxt, np.loads, ...\n",
- "\n",
- "However, we will use pandas read_csv function that is far better"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "438224df-8e68-4666-9e45-b55917be05bf",
- "metadata": {},
- "outputs": [],
- "source": []
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.8.8"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/previous_materials/np_pd_mplt_bertrand/pandas_TP.ipynb b/previous_materials/np_pd_mplt_bertrand/pandas_TP.ipynb
deleted file mode 100644
index 92705d1..0000000
--- a/previous_materials/np_pd_mplt_bertrand/pandas_TP.ipynb
+++ /dev/null
@@ -1,286 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "id": "3731abb9-a087-4a54-bdf2-d8046145ad30",
- "metadata": {},
- "source": [
- "# <center>**TP**</center>\n",
- "\n",
- "<img src=\"./images/pandas_logo.svg\">\n",
- "<div style=\"text-align:center\">\n",
- " Bertrand Néron\n",
- " <br />\n",
- " <a src=\" https://research.pasteur.fr/en/team/bioinformatics-and-biostatistics-hub/\">Bioinformatics and Biostatistiqucs HUB</a>\n",
- " <br />\n",
- " © Institut Pasteur, 2021\n",
- "</div>"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "07a7668b-c3a8-4e1b-94cd-1c6e5cbca62e",
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "57795cc2-9ce8-4000-970f-731ffe80aac5",
- "metadata": {},
- "outputs": [],
- "source": [
- "import pandas as pd"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "6a65859a-1bd8-4073-bf48-6798cc00ae99",
- "metadata": {},
- "source": [
- "read the 'data/city_temperature.csv'\n",
- "\n",
- "force the City datatype to string by passing\n",
- "```\n",
- "dtype={'City': str}\n",
- "```\n",
- "As argument to the function to read the file.<br />\n",
- "Don't worry to the warning, it is due to State wich contains Nan for non US contry, but we do not use these data"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "f7ca7b5c-488b-4b3e-b96f-0818b79ee5b3",
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "1be5c2d5-f3cc-477e-ae55-0638a203ec4e",
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "markdown",
- "id": "f271729c-5c7a-451b-99df-54a884043a88",
- "metadata": {},
- "source": [
- "We will work only on Europe Region. so creat data named europe with only these data.\n",
- "explore it a lit bit\n",
- "* how many data?\n",
- "* which columns? \n",
- "* index? "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "6b69163d-f147-4d48-bb0e-7129c57b9057",
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "eeb1ae09-f0ae-4f6e-862c-581aac9b09f8",
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "1d1065bd-092f-4042-af0e-dae8b78d79d5",
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "markdown",
- "id": "3d0ca497-c0ad-4582-b54b-d26557cf30eb",
- "metadata": {},
- "source": [
- "wich country are in europe?"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "fb49d50b-cc16-409e-8d7f-8500f077a596",
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "markdown",
- "id": "1211fd0a-4879-4169-984f-e604341c82c3",
- "metadata": {},
- "source": [
- "remove columns 'Region' and 'State' from the data"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "f6e17279-a0e0-4082-93c8-52c80ccfaa4c",
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "markdown",
- "id": "1be6e117-e807-4910-8faf-4b09f08ee810",
- "metadata": {},
- "source": [
- "from europe data create a new dataset containing countries: 'France', 'Spain', 'Italy'"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "5e4e5959-f5f4-498a-aa9f-e3e4d8505086",
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "markdown",
- "id": "7f49bcf4-884d-4d55-beb5-7f9b238f6333",
- "metadata": {},
- "source": [
- "group the data on 'City' and 'Year' compute the mean of each group and keep only the 'AvgTemperature' column."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "774ce218-9d3a-4306-a6c8-67c55e3b4f83",
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "markdown",
- "id": "3a5e3e0e-b49c-4c7a-974a-12dd41e846a3",
- "metadata": {},
- "source": [
- "do the same but compute the standard deviation"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "f76fc4d8-3de7-479f-a797-8bffea0459d7",
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "markdown",
- "id": "3d0ab3ad-6ba3-4fdd-b6ce-1393965d080c",
- "metadata": {},
- "source": [
- "* reset the index fo the mean data and std data\n",
- "* rename the column AvgTemperature to Tmp on the mean data\n",
- "* rename the column AvgTemperature to std on the std data"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "9ad90621-eba1-4ae8-8d61-853c7884dc01",
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "markdown",
- "id": "d300f6b7-6a98-42e0-b6e1-d7eea277443a",
- "metadata": {},
- "source": [
- "merge the two tables data_mean and data_std"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "68fc7eb5-e9a9-4757-a470-553bd9dd81ee",
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "markdown",
- "id": "5c0a9ad6-7ba5-4061-8a1f-b852b934cc8f",
- "metadata": {},
- "source": [
- "save the data in a file"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "a0be61a1-0d08-4ccf-b1dc-761ab7ea0fc3",
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "markdown",
- "id": "05668aae-b68b-48de-a70f-4fe269125611",
- "metadata": {},
- "source": [
- "# Teasing\n",
- "\n",
- "a quick data plotting. we will improve it in matplotlib course"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "6b588f07-3103-403e-beab-0cdc23bb7d8d",
- "metadata": {},
- "outputs": [],
- "source": [
- "for city, df in clean_data.groupby('City'):\n",
- " df.plot('Year', 'Tmp', label=city)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "fdc3ad9c-9984-4390-85ba-de36860db524",
- "metadata": {},
- "outputs": [],
- "source": []
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.8.8"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/previous_materials/np_pd_mplt_bertrand/pandas_TP_solution.ipynb b/previous_materials/np_pd_mplt_bertrand/pandas_TP_solution.ipynb
deleted file mode 100644
index 0cfacca..0000000
--- a/previous_materials/np_pd_mplt_bertrand/pandas_TP_solution.ipynb
+++ /dev/null
@@ -1,596 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "id": "3731abb9-a087-4a54-bdf2-d8046145ad30",
- "metadata": {},
- "source": [
- "# <center>**TP**</center>\n",
- "\n",
- "<img src=\"./images/pandas_logo.svg\">\n",
- "<div style=\"text-align:center\">\n",
- " Bertrand Néron\n",
- " <br />\n",
- " <a src=\" https://research.pasteur.fr/en/team/bioinformatics-and-biostatistics-hub/\">Bioinformatics and Biostatistiqucs HUB</a>\n",
- " <br />\n",
- " © Institut Pasteur, 2021\n",
- "</div>"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "id": "57795cc2-9ce8-4000-970f-731ffe80aac5",
- "metadata": {},
- "outputs": [],
- "source": [
- "import pandas as pd"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "bb483977-82ba-42fa-bea7-2cb3da9ca864",
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "markdown",
- "id": "6a65859a-1bd8-4073-bf48-6798cc00ae99",
- "metadata": {},
- "source": [
- "read the 'data/city_temperature.csv'\n",
- "\n",
- "force the City datatype to string by passing\n",
- "```\n",
- "dtype={'City': str}\n",
- "```\n",
- "As argument to the function to read the file.<br />\n",
- "Don't worry to the warning, it is due to State wich contains Nan for non US contry, but we do not use these data"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "id": "f7ca7b5c-488b-4b3e-b96f-0818b79ee5b3",
- "metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/home/bneron/Projects/MNE/lib/python3.8/site-packages/IPython/core/interactiveshell.py:3165: DtypeWarning: Columns (2) have mixed types.Specify dtype option on import or set low_memory=False.\n",
- " has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\n"
- ]
- }
- ],
- "source": [
- "world = pd.read_csv('data/city_temperature.csv' , sep=',', dtype={'City': str})"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "id": "1be5c2d5-f3cc-477e-ae55-0638a203ec4e",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "Index(['Region', 'Country', 'State', 'City', 'Month', 'Day', 'Year',\n",
- " 'AvgTemperature'],\n",
- " dtype='object')"
- ]
- },
- "execution_count": 3,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "world.columns"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "f271729c-5c7a-451b-99df-54a884043a88",
- "metadata": {},
- "source": [
- "We will work only on Europe Region. so creat data named europe with only these data"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "id": "6b69163d-f147-4d48-bb0e-7129c57b9057",
- "metadata": {},
- "outputs": [],
- "source": [
- "europe = world[world['Region'] == 'Europe']"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "3d0ca497-c0ad-4582-b54b-d26557cf30eb",
- "metadata": {},
- "source": [
- "wich country are in europe?"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "id": "fb49d50b-cc16-409e-8d7f-8500f077a596",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "array(['Albania', 'Austria', 'Belarus', 'Belgium', 'Bulgaria', 'Croatia',\n",
- " 'Cyprus', 'Czech Republic', 'Denmark', 'Finland', 'France',\n",
- " 'Germany', 'Georgia', 'Greece', 'Hungary', 'Iceland', 'Ireland',\n",
- " 'Italy', 'Latvia', 'Macedonia', 'The Netherlands', 'Norway',\n",
- " 'Poland', 'Portugal', 'Romania', 'Russia', 'Serbia-Montenegro',\n",
- " 'Slovakia', 'Spain', 'Sweden', 'Switzerland', 'Ukraine',\n",
- " 'United Kingdom', 'Yugoslavia'], dtype=object)"
- ]
- },
- "execution_count": 5,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "europe.Country.unique()"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "1211fd0a-4879-4169-984f-e604341c82c3",
- "metadata": {},
- "source": [
- "remove columns 'Region' and 'State' from the data"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "id": "f6e17279-a0e0-4082-93c8-52c80ccfaa4c",
- "metadata": {},
- "outputs": [],
- "source": [
- "europe = europe[['Country', 'City', 'Month', 'Day', 'Year', 'AvgTemperature']]"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "1be6e117-e807-4910-8faf-4b09f08ee810",
- "metadata": {},
- "source": [
- "from europe data create a new dataset containing countries: 'France', 'Spain', 'Italy'"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "id": "5e4e5959-f5f4-498a-aa9f-e3e4d8505086",
- "metadata": {},
- "outputs": [],
- "source": [
- "fr_sp_it = europe[europe['Country'].isin(['France', 'Spain', 'Italy'])]"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "7f49bcf4-884d-4d55-beb5-7f9b238f6333",
- "metadata": {},
- "source": [
- "group the data on 'City' and 'Year' compute the mean of each group and keep only the 'AvgTemperature' column."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "id": "774ce218-9d3a-4306-a6c8-67c55e3b4f83",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "City Year\n",
- "Barcelona 1995 62.019178\n",
- " 1996 61.125956\n",
- " 1997 62.612329\n",
- " 1998 60.273973\n",
- " 1999 61.204658\n",
- " ... \n",
- "Rome 2016 61.185246\n",
- " 2017 61.377808\n",
- " 2018 60.821370\n",
- " 2019 59.215068\n",
- " 2020 52.676119\n",
- "Name: AvgTemperature, Length: 182, dtype: float64"
- ]
- },
- "execution_count": 8,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "fr_sp_it_mean = fr_sp_it.groupby(['City', 'Year']).mean()['AvgTemperature']\n",
- "fr_sp_it_mean"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "3a5e3e0e-b49c-4c7a-974a-12dd41e846a3",
- "metadata": {},
- "source": [
- "do the same but compute the standard deviation"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "id": "f76fc4d8-3de7-479f-a797-8bffea0459d7",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "City Year\n",
- "Barcelona 1995 9.569756\n",
- " 1996 9.420765\n",
- " 1997 9.827235\n",
- " 1998 19.750126\n",
- " 1999 13.904526\n",
- " ... \n",
- "Rome 2016 15.914193\n",
- " 2017 11.916595\n",
- " 2018 20.327932\n",
- " 2019 23.514064\n",
- " 2020 6.224294\n",
- "Name: AvgTemperature, Length: 182, dtype: float64"
- ]
- },
- "execution_count": 9,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "fr_sp_it_std = fr_sp_it.groupby(['City', 'Year']).std()['AvgTemperature']\n",
- "fr_sp_it_std"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "3d0ab3ad-6ba3-4fdd-b6ce-1393965d080c",
- "metadata": {},
- "source": [
- "* reset the index fo the mean data and std data\n",
- "* rename the column AvgTemperature to Tmp on the mean data\n",
- "* rename the column AvgTemperature to std on the std data"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "id": "9ad90621-eba1-4ae8-8d61-853c7884dc01",
- "metadata": {},
- "outputs": [],
- "source": [
- "data_mean = fr_sp_it_mean.reset_index()\n",
- "data_mean.columns = ['City', 'Year', 'Tmp']\n",
- "data_std = fr_sp_it_std.reset_index()\n",
- "data_std.columns = ['City', 'Year', 'std']"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "d300f6b7-6a98-42e0-b6e1-d7eea277443a",
- "metadata": {},
- "source": [
- "merge the two table data_mean and data_std"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "id": "68fc7eb5-e9a9-4757-a470-553bd9dd81ee",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
- "<style scoped>\n",
- " .dataframe tbody tr th:only-of-type {\n",
- " vertical-align: middle;\n",
- " }\n",
- "\n",
- " .dataframe tbody tr th {\n",
- " vertical-align: top;\n",
- " }\n",
- "\n",
- " .dataframe thead th {\n",
- " text-align: right;\n",
- " }\n",
- "</style>\n",
- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>City</th>\n",
- " <th>Year</th>\n",
- " <th>Tmp</th>\n",
- " <th>std</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>0</th>\n",
- " <td>Barcelona</td>\n",
- " <td>1995</td>\n",
- " <td>62.019178</td>\n",
- " <td>9.569756</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1</th>\n",
- " <td>Barcelona</td>\n",
- " <td>1996</td>\n",
- " <td>61.125956</td>\n",
- " <td>9.420765</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2</th>\n",
- " <td>Barcelona</td>\n",
- " <td>1997</td>\n",
- " <td>62.612329</td>\n",
- " <td>9.827235</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>3</th>\n",
- " <td>Barcelona</td>\n",
- " <td>1998</td>\n",
- " <td>60.273973</td>\n",
- " <td>19.750126</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4</th>\n",
- " <td>Barcelona</td>\n",
- " <td>1999</td>\n",
- " <td>61.204658</td>\n",
- " <td>13.904526</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>...</th>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>177</th>\n",
- " <td>Rome</td>\n",
- " <td>2016</td>\n",
- " <td>61.185246</td>\n",
- " <td>15.914193</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>178</th>\n",
- " <td>Rome</td>\n",
- " <td>2017</td>\n",
- " <td>61.377808</td>\n",
- " <td>11.916595</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>179</th>\n",
- " <td>Rome</td>\n",
- " <td>2018</td>\n",
- " <td>60.821370</td>\n",
- " <td>20.327932</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>180</th>\n",
- " <td>Rome</td>\n",
- " <td>2019</td>\n",
- " <td>59.215068</td>\n",
- " <td>23.514064</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>181</th>\n",
- " <td>Rome</td>\n",
- " <td>2020</td>\n",
- " <td>52.676119</td>\n",
- " <td>6.224294</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "<p>182 rows × 4 columns</p>\n",
- "</div>"
- ],
- "text/plain": [
- " City Year Tmp std\n",
- "0 Barcelona 1995 62.019178 9.569756\n",
- "1 Barcelona 1996 61.125956 9.420765\n",
- "2 Barcelona 1997 62.612329 9.827235\n",
- "3 Barcelona 1998 60.273973 19.750126\n",
- "4 Barcelona 1999 61.204658 13.904526\n",
- ".. ... ... ... ...\n",
- "177 Rome 2016 61.185246 15.914193\n",
- "178 Rome 2017 61.377808 11.916595\n",
- "179 Rome 2018 60.821370 20.327932\n",
- "180 Rome 2019 59.215068 23.514064\n",
- "181 Rome 2020 52.676119 6.224294\n",
- "\n",
- "[182 rows x 4 columns]"
- ]
- },
- "execution_count": 11,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "clean_data = pd.merge(data_mean, data_std, on=['City', 'Year'])\n",
- "clean_data"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "5c0a9ad6-7ba5-4061-8a1f-b852b934cc8f",
- "metadata": {},
- "source": [
- "save the data in a file"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 12,
- "id": "a0be61a1-0d08-4ccf-b1dc-761ab7ea0fc3",
- "metadata": {},
- "outputs": [],
- "source": [
- "clean_data.to_csv('data/fr_sp_it_temp.tsv', sep='\\t')"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "05668aae-b68b-48de-a70f-4fe269125611",
- "metadata": {},
- "source": [
- "# Teasing\n",
- "\n",
- "a quick data plotting. we will improve it in matplotlib course"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 13,
- "id": "6b588f07-3103-403e-beab-0cdc23bb7d8d",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- },
- {
- "data": {
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r0iVpsI2Lsjna1EVbj6azQkEDuBpV2+bwewn9xTKS49j26Y288jeb2FSSE5DnnEhqQiwZSXG6mCeEKpt7LriA6bGxJBuAd2p0Fh4KXgVwEbGKyLMiclxEjonIBhHJFJHXRKTS/bdvKzDUjNLdP0Rrz6DfTazGszI/I2SbDefNkrayded6OdsZ3lTRiNNQbe+54AKmx6q8DNISYjWNEiLezsAfBl42xiwFVgPHgIeA140xJcDr7u9VhBqtQAnQDDzUXBs7RH8A/8wTe/n8MwfCOoaG9j4Ghp0sHieAx8ZYuGJhpja2CpEpA7iIZACbgZ8CGGMGjTEdwM3Az9x3+xlwS3CGqELhVJvv+2DOJPmzYDWm02k4frabPafbGR4JX8+RSncFyuIxFShjXbUom9NtvdSd06qgYPNmBr4AsAOPicg+EfmJiKQAc40xTe77nAXmjvdgEXlARCpEpMJutwdm1CrgPDvRF2UFvkokFHKtiXQPDNPVPxTuoQRNQ4dr5ts3NBLW3tueTRzGm4EDXO3Og+ssPPi8CeCxQBnwA2NMKeDgonSJcXXvH3cZnDHmEWNMuTGmPCcnuBezlP9OtTrIzUgkMW7i3W9mMk9f8Giehde0nu+x7k2zsGCpbO5hbnoCGUnjd6wsmZNKTlqC5sFDwJsAXg/UG2Pec3//LK6A3iwi8wHcf7cEZ4gqFE4FsIQwHDx9waM5D15jd818UxNi2XsmfAG8qqV7wtk3uDpSXrUoi7er2/zemUl5Z8oAbow5C9SJyGXuQ9cAR4EXgPvcx+4Dng/KCFVIBKILYTidX40ZvQG82t5DemIsm0qywzYDN8ZQ1dJzwQrM8WxclE1rzwAn3T3DVXB4W4XyWeBxETkIrAG+CfwLcK2IVAIfcH+vIlBH7yAdvUMBqwEPh+yUBOJjLFEdwGvsDhbmpLK2yEZ9ex8tXaEvJ2zq7McxODLpDBzgqsVZgO5WH2xeBXBjzH53HnuVMeYWY0y7MabNGHONMabEGPMBY4x2sIlQnn0wI3kGbrEIudbEqM6BV9t7WJSTSlmRa8lFONIonguY49WAj5VvS6Y4K1n7ogSZrsRUo02sFkyzC2G4RXMteI+7V/vCnBQuz00nPsbC3jMdIR9HZbO7idXcyVMoAFctzua9U+fCWvIY7TSAK0619mIRgtJoKpRcATw6G1p5LmAuykkhITaGlfkZYcmDV7X0kJkST2bK1LsnbVyUTc/AMAfqO0MwstlJA7iittVBrjWJhNjILCH0yLMm0dzdH5Ub69bYXZ+SFuW4UhdlhVYONXQyMDwS0nFUtfRMmf/22LDIlQfXbdaCRwM4rt4Ov95TP2s/6tW2OSJ2BeZYedYkjIHmMFzcC7Yau2sD4UL3Qqu1RTYGh50caewK2RiMMVS2jN8DZTyZKfFcnpuuATyINIAD20/a+btnDvDmidm3UtQYw6nWKAng7lLC+ii8kFltd1CYmTz6Kams0H0hM4RpFHvPAJ19Q14HcHBts7bvTAd9g6H9pDBbaAAHjjS6cnSei3mzyTnHIN39wxHbxGosT1/waLyQWW3vYWHO+cA5Jz2RfFtSSCtRqpo9S+invoDpcdWiLAZHnOzWbdaCQgM4cLTJ9TF0NjbfqY3wJlZjzc9wrcaMtlpwp9P1KWnRRa1+ywpt7DndHrLVjlXuC6nj9QGfyPoFmcTFCH/UvihBoQEcOOrOI56ZhQHcc3EskmvAPRLjYshOTYi6GbinidXYGTi48uDNXQM0hqg/eGVzD2mJscxJ877He3J8LKUFNs2DB8msD+Dd/UPUtrkC9+lZGMBr2xzEWIR8d/440uXZom9jh2r3zHdh9qUzcAhdHryypZuSOamIiE+P27g4myONXXT0DgZpZLPXrA/gnracJXNSqT/Xh9M5u5rv1Lb2UmBLCtp+laGWZ02MugA+WkJ40cXDpfPTSIqLCVk9uC8lhGNtXJyFMfBOtXYnDLTo+F87DZ70yfUr5jE44uRsFJagTSbSuxBeLM+9GjOauuDVtLqaWGVdtHgmLsbCqvwM9oXgQma7Y5DWnsEpm1iNZ3WBlZT4GM2DB4EG8MYuMlPiWbcgE5hdeXBjjKsLYRRUoHjkWpPoH3JyzhE9H9erWxwsmiB1sbbIxpHGLvqHglum57mAudiHC5gecTEW1i/I1P7gQaABvKmL5fPTKcp0BbHZFMDt3QP0Do5ERQWKR541+trK1rT2sDB7/MBZVmhj2Gk4GOTl6pXN3jWxmsjGxdmcanVE3QXmcJvVAXxoxMmJs91cnpvOfGsiMRbhTNvsCeDR0IXwYtFWC97dP0Rz1wCL5oz/Hnk6EwY7D17Z0k1yfAy5Gf5d7N642LXNmlajBNasDuDV9h4GR5wsz00nLsZCrjUxYmfgTqfxuS/GaA14FKVQ8qNsNabnl+xEM/DMlHgWZKcEfUFPVYurla3F4lsFisdlc9PISonnbb2QGVCzOoB7LmAun58OQFFmSsSWEv77G5Ws/NqrfGnbodENiqdyqrWXuBgZ3Y4sGmQkxZEcHxM1XQmrx3QhnEhZoY29QV7QU+VDD5TxWCzChkVZ/LGqNaouMIfbrA/gCbGW0RxwQWZyRK7GHBx28st3T5OdEs+zFfW8/7tv8uDjezk0RV60ttVBQWYysVFSQgiu/RjzrEk0dETe+zieGrurTt/TxGo8ZUVW2hyDQfv02N0/RFNnv18XMMfauDiblu4Bqlp0m7VAiZ7/uX440tjF0vnpowGsKCvZ3RtkKMwj880bx5tp7Rnk67euYOcXtvLA5kVsP2nnpv/cyUd//C7bT9rHnfXUtjmiKn3iEU19wavtPRTYJm/1uzbIefCq0V14fC8hHOtqzYMH3KwN4MaY0QoUj0L3hgaRlgd/cncd89IT2VySw5z0RB66filvf/H9fPH6pVS19HDvo7u48d938vz+htGWuZ7+GtFUgeKRZ4uenXlq7I7RHuATKZmTFtSd6j3bqPmziGesgsxkCjKT+KPmwQNm1gbwxs5+OvuGWJ57aQCPpDRKY0cfb520c0d5/gWpkLTEOP5yyyJ2fGEr3759Ff3DI3zuyf1s/e6b/PydWmrbHAwMO6OqAsUjz5pEm2Mw4luYjrh/yS6cJP8NEGMRSgut7DndEZRxVLX0EB9roSAA7RY2Lsrm3Zq2Wdt7P9C8CuAiUisih0Rkv4hUuI+tFpF33Mf/V0TSp3qemcRzAfPysQHcnWc8HUGlhM9U1GMM3FleMO7tCbEx3LmugN//7RZ+dM9aslMT+Mfnj3D9wzuA6OhCeDFPLXhjZ2TPwhvdTaymmoEDlBbaOHG2i56B4YCPo6qlh4XZKQG5VnLV4my6+4c51KDbrAWCL+/IVmPMGmNMufv7nwAPGWNWAtuA/xvw0QXRkcZORGDpvPN5vfTEOKzJcRGTQhlxGp6uqOPqxdlT7mdpsQh/cvk8nvvUVTz9lxvYuDib7NR4ls2PqN+7XvHUgkf6DvWjTay8COBri2w4DRyo6wj4OCpbur3axNgbV7m3WdtZqXnwQJjOr9QlwHb3168Bt09/OKFztLGLBdkpJMfHXnC8KDM5YgL4zqpWGjr6uGvd+LPv8YgI6xdk8uj966j4h2u92pw20nh25on0PHi1u4nVVCkUgDUFViDwnQl7B4epb+9jsRe/RLyRnZrAyrwM3jo5+3a/CgZvA7gBXhWRPSLygPvYEeBm99d3AONGERF5QEQqRKTCbvfvTRsecXpd2+ytiy9gehREUAB/avcZbMlxXHf53HAPZUaZm5aARSJ/OX2NvYeMpLhLmliNJyMpjpI5qewJ8IXMGrsDY3zbxGEqW5bksPdMO529kVXtNRN5G8CvNsaUAdcDD4rIZuATwKdFZA+QBozbPcgY84gxptwYU56Tk+PXIL/w60Pc8aN3ArYAoLNviPr2Pi7PzbjktqKsZBra+2b8RZbWngFeO9rMbWX5Eb+bfKDFxliYlx75bWVd26ileN1/e22RjX1nOgLaErmy5Xy75UDZclkOToN2JwwArwK4MabB/XcLrnz3emPMcWPMdcaYtcCvgOpgDfLKhZnYuwdGe3dP1+gKzNxLZ+CFmckMOw1NIdrlxF/b9jYwNGJ8Sp/MJnm2pIjPgXtTQjhWWaGNzr4haloDt1CmqqWHWItQFMD1AqUFVtISY3lrFm4iHmhTBnARSRGRNM/XwHXAYRGZ4z5mAf4B+GGwBrmpxDVz3x6gvJlnD8yJUigws2vBjTE8ufsMZYVWlgTo4lK0ybUmRXQVSnf/EC3dA17lvz08ja32BrCcsLK5h+LsFOJjA1dxHBtjYVNJNm9NsMBMec+bd2UusFNEDgC7gBeNMS8DfyoiJ4HjQCPwWLAGOS8jkcvmprEjQFeujzZ2kZOWQM44e/t5ZhozuZRwz+l2qu0O7l5XGO6hzFh51iSaOvoZidAdlkZ34fFhBr4wO4WMpLiArsisaukJ2AXMsbYsyeFsVz8nmgPzqXqs//pDFd955UTAn3cmip3qDsaYGmD1OMcfBh4OxqDGs6kkm5+/e5q+wRGS4qeX8z3a1HVB/fdY89ITiYuRGT0Df3J3HakJsdy4an64hzJj5VqTGHYa7N0DzMuIvGZdnjTIZE2sLmaxCGWF1oCtyBwYHqG2zRGUn7PNS1yfqt86YWfpvMCVsvYPjfDff6jCMTjC1qU5rC3KDNhzz0QRsxJz85IcBoedvHdqestwB4ZHqGzuHjd9Aq5VbQW2ZM6cC2zVS6B09Q/x24ON3LQ6l5SEKX//zlqeUsJIbWpV3eJuYpXpW+65rNBGZUtPQCo8alt7cZrpL6Efz/yMJJbOSwt4OeFbJ+04BkdIiLXw1ReOROwnMG9FTABfvyCThFgL209OL41S2dzDsNOMewHTYyaXEr6wv5H+ISd368XLSZ3fmWdmX4yeSE1rD4WZyT7nnj2NrfbVTX8Wfr4CJTjXWbYsyWF37bmArh596VATtuQ4vnXbSg43dPF0RV3AnnsmipgAnhgXw/oFmeyonN5v7MkuYHoUZSXP2J15ntpdx9J5aazKv7QEUp0X6asxq1scLPSjzcHqAisWCcyCnsrmHkS8W0jkjy1LchgaMQHbrb5/aITfH23mTy6fx62leawvzuRfXzkR1fXmERPAATaX5FDZ0jOtFXZHG7tIjo+ZdCPfwsxkuvqH6eidWRvjHm7o5FBDJ3evK/C6Nni2Sk2IJSMpLiJXY444DafaXBsZ+yolIZal89LZe6Zj2uOoanF9CkiMC846g/LiTJLjY3jrZEtAns+TPrlh5XxEhK9+eDkdvYN87/cnA/L8M1FkBXD3hY/p9FE42tjFsvnpk24NNVNLCZ+uqCM+1sItpXnhHkpEcG3sEHkBvKG9j8Fhp18zcHBt8LDvTPu087+VLd0BXcBzsfhYC1ctyubNE4EpJ3zpUBPW5Dg2uPutXJ6bwUevKOQX757mRIDWkMw0ERXAl8xNZW56Am/5mUZxOi/tAT6eohnYlbB/aIRt+xq4YcU8rMnR178kGFwbO0ReAK/2VKD4GTzXFtlwDI5wcholesMjTk61OlgcpPy3x5bLcqhv76Nmmq0y+odGeP1YCx+8fB5xY7om/t21l5GWGMs//e+RqKw5j6gALiJsKsnhj1Wtfs0u6tv76BkYnvQCJkCBbebNwH93uInu/mHu0tpvr+XbInMG7qkB93sGXjj9HXpOn+tlaMQEpQJlrPeNKSecju0n7fQMDHPDygtLHm0p8fzddZfxdnUbvzt8dlqvMRNFVAAHVz14R++QX/2Ejza5HjNRDbhHSkIs2akJM+pC5pO76ijOSubKhdFd1xpIudZEuvuH6YqwLfKq3U2s/O0UWZiZTHZq/LTqwSubPduoBTeAF2QmszAnZdrlhC9elD4Z66PrC1k2P51vvHgs4jf5uFjEBfCrF2cjAjv8eMOPNHYRYxGvlp8XZibNmBl4jb2H906d4069eOmTPKvrk1SkpVFq7D0s8qGJ1cVEhFL3TvX+qnKXEPqbxvHFliU5vFvTRv+Qf8HVkz75k+UXpk88YizC125aTkNHHz98K2gtm8Ii4gJ4VmoCK3Iz2O5HHvxoYxeLclK8uqpeOINqwZ+qqCPGInykLD/cQ4kouVbXCsxIKyWstju82sRhMmuLbNS29dLWM+DX46taesizJpEagsViW5bkMDDs5N0a/8oJPemTyVaMXrEwi5tW5/LDt6ojasvEqURcAAdXGmXvmQ6fd4/35gKmR2FWCk2drmqAcBoacfLrPfVcs3QOc9Ijb0l4OEXixg5d/UPYuwd86oEyHk8e3N9ywsqWnqDnvz2uXJhFQqzF7zTKxdUnE/nSDUuxiPDNl4759TozUUQG8M1LchhxGt72YQHAOccgTZ394/YAH09hZjJOE/5NAV4/1kJrzyB3r9eVl77KTkkgPsZCfQQF8BofduGZzKr8DGIt4lcefMRpXE2sQhTAE+NiuHJhll8BvH9ohN9Pkj4Za35GEp95/2J+d/gsf6yKjl7kERnAywptpMTH+LQqc7Ie4OM5X0oY3p4oT+0+w7z0RDaX+LcZxmxmsQi51kQaQ7Sc3uk0vHSoid5B/5eG19g9TaymFzwT42K4PDfdr0qUhnbXZsrBvoA51pYlOdTYHT6nN0arT7xsuPXJqxdQmJnM1144wtAM37TFGxEZwONjLWxYlOVTXxRPBYrXKRT3Yp5w5ssaO/p466SdO8rzA7Ij+GyUa02ioT007+HTFXV8+vG9/PCtGr+fo8buaWI1+SbV3igrsnGwvsPnQDXaAyWA26hNZctlrgnKmz7Owj3pk6umSJ94JMbF8JUPLaeypYdfvHPa53HONBEbFTaV5HDmXK/XM+SjjV3kZiRi87I0a05aAgmxlrBeyHx2Tz1OA3eWa/rEX3nWpJDMwO3dA6O51V/tOuP37K7a3kORH02sxlNWaKN/yMkxd/8fb1W1uD4FLM4J3WYhC7NTKMhM8qke3Jf0yVgfWDaHzUty+N7vT9Lq50XemSJiA7hnWb23u/QcberyOn0CrlKswszksK3GdDoNT+2u4+rF2aNL+5Xvcq1JNHf3B/1i9DdePErf0AhfumEp9u4BXjni36KRGrsjYM2j1o7u0ONbGqWypYectAQykuMCMg5viAhbluTwdnWr1++Vr+mTsa/1jx9aTt/gSMRv/BCxAbw4K5l8WxLbveiL0j80QrXd4XX6xCOcpYQ7q1pp6OjTi5fTlGdLwhho7greLHxHpZ3f7G/kU1sW8cmrF1KQmcTP/fh47mliNd0SQo9caxLz0hPZ42MlSmVLT0jz3x5blsyhd3CEitpzXt3f1/TJWIvnpPLxjcU8VVHHwfoOnx8/U0RsABcRNi/J4Z3qtik/rp44283IFD3Ax1OY5Qrgoe6h0NTZx7dfOY4tOY5rl88N6WtHG09f8Pog1YL3D43wD785zILsFD69dTExFuHPrihi16lzHD/rW+rC08TKl114plJebON/DzTywe9v56vPH+bFg03YuydOGxhjqA5TAN+wKIu4GPGqGsXf9MlYf31NCVkpCXz1hSM4I3Tjh4gN4ACbS7LpGRhm3xQzDE8PcG9LCD0KM5PpHRyhzRG6trKvH2vmhod3UGN38K3bVpIQG5xWnrOFpy94sGrB//ONKk639fKNW1aMLhC7s7yAhFiLzxfJqt0VKIGagQN86YZl/N21S8hJS+DpinoefGIv677xe6757pt88blDPL+/gaYxmz+f7eqnZ2CYxWHYLDs1IZZ1xZleBfAdla1+pU/GSkuM46Hrl7LvTAfb9jX4/TzhFNF7cm1YlE2MRdhRaWf9gol7hBxt7CItIZZ898IOb43tSpideukGyIE0OOzk2y8f5yc7T7F8fjr/+dHSgP5Hnq3mu/fDDEY9/8nmbn60vZrbyvK4anH26HFbSjw3rc5l274GvnD9UtITvcslVweohHCsXGsSn72mBHAtCjvc0Ml7p86x69Q5fnugkV/tOgO4JivrF2RiTXKNNRgbGXtjy5IcvvW745zt7J90L9MXDzb6nT4Z67bSPH757mm+/cpxPrwm1+/ZfLh4NVoRqRWRQyKyX0Qq3MfWiMi7nmMisj64Q71URlIcawqsU17IPNLYybLcdJ97S4SqlPBMWy93/PBtfrLzFPduKOK5T1+lwTtAEuNiyE5NCPgM3Ok0fOm5Q6QkxPLlG5Zdcvu9G4roHRzhuT31Xj9nTasDa7L/TaymEhdjobTQxl9tWcSj969j/1ev47efvZqvfGg5y+an8fqxZn6y8xQWcbVuDgdPOeFkmzx40ifXLZ877YBrsQifff9imrsGeP1YYDaWCCVfZuBbjTFjrxh+G/gnY8zvROQG9/fvC+TgvLG5JIfvv36SdsfguCWCI07D8bPdfpXi5YegrexLh5r4wrMHQeAHHyvj+pW603yg5QWhrexTFXVUnG7n2x9ZRdY4n85W5VtZXWDlF++e5r6rir2aPFS39AR09j2VGIuwIi+DFXkZfPLqBTidhip7Dz0Dw+OeUyhcNjeNeemJvHXSPmHr5NH0SYD+r2xZksPc9ASe2n2GD66YF5DnDJXp/PoygOeqYAbQOP3h+G7TkmyMcVVtjOd0m4PewZEpW8iOJzEuhnnpiUEpJXRd/DrEpx/fy8I5qbz015s0eAdJnjUxoAHc3j3At146xhULMrlj7cQNxu69sohqu8Prlg81rf7tgxkoFnenTk8flXDwlBPuqGxleILihJcONZGRFMfGMWmr6YiNsXBneQFvnbRHVN8c8D6AG+BVEdkjIg+4j/0N8K8iUgd8B/jieA8UkQfcKZYKu316PX/HszrfSnpi7ITL6kc3MfYjgIMrjRLoFEq1vYdb/uuP/PLdMzyweSHP/OUGrfUOojz3zjyBqib6+otH6R9y8o1bV046s75x1XwyU+L5+Tu1Uz6np4mVps5caZTu/mH213Vcctv5jYunnz4Z687yApwGnqnwPuU1E3j7L3C1MaYMuB54UEQ2A58C/tYYUwD8LfDT8R5ojHnEGFNujCnPyQl8P48Yi3B1STbbT7aO+x/0SGMXcTFCiZ9bQxVmJXP6XOD6oTy3t56b/mMnzV39PHp/OV+6YVlAVt2pieVak+gfcnIuANVE20/aeX5/I59636Ipmz0lxsVwZ3kBrx1tnnJm52liFcgSwki1cbGrOOHNcVZl7qhspTuA6ROPgsxkrl6czdMVddPeSzSUvIocxpgG998twDZgPXAf8Jz7Ls+4j4XF5pIcznb1jy4BHutoYxeL56T5HSQLM5Np7hrwu9m8R+/gMJ9/5gD/5+kDrMjN4KXPbeL9S7XGOxTyRksJp7eYx1PzvTA7hU+9b5FXj/nYFYUY4In3zkx6v+qWwJcQRqqMpDjKCq3jlhMGOn0y1t3rC2jo6JswHTsTTRnVRCRFRNI8XwPXAYdx5by3uO/2fqAyWIOcyibPvnrjvOFHm7r8yn97eEoJ66fZEOn+R3fz6731/PX7F/PEX1zB/AzfShqV/zy14A0d03sP/+ONSs6c6+Xrt67walMQcM3srlk6hyd3n2FgeOJJQE1rD7EWGf15m+22LMnhUEPnBb1KBoaDkz7xuHb5XGzJcTy1e/JftjOJN/8Kc4GdInIA2AW8aIx5GfgL4Lvu498EHpjkOYIqz5rEopwUdly0rL6lux9794DPS+jH8uSmp3Mh81Srg1215/jCB5fyf667TDsLhpin/r9hGjPwk83d/OitGm4vy+eqRb7N/u7ZUExrzyAvT7KpbnWLg8LM5IirQw6WLUvmABf2OtpxMjjpE4+E2BhuK8vntaPNEdPkasqfFmNMjTFmtfvP5caYb7iP7zTGrHUfv8IYsyf4w53YppIc3jt14b56vvYAH4+nFnw6pYSvHXX9x71pda7fz6H8l5EUR3J8jN9bq3lqvtMSY/nyjZfWfE9l0+JsirOSJ+2PUtPao+mTMS7PTSc7Nf6CT9UvBjF94nH3ugKGRgzP7Y2Mi5lR8+t+y5Ic+oecVNSe77zmqUBZNo0ZeFZKPCnxMdOagb96pJnLc9NHc7EqtERktBLFH0/udtV8f/nG5X4tsrFYhD+7sog9p9s50th5ye0jTkNta69ewBzDYhE2l+Sw/aSdEacZTZ8EYvHOZErmprG2yMaTu+tC3gPJH1ETwK9YmEl8jOWCzY6PNnZRkJlERpL/bTFFhIJplBLauwfYc6ad65ZH1gKBaJNr9W8xT0t3P//yu2NcuTCT28vy/H79O9YWkBg3fn+U+vZeBkecAWsjGy22XJZDe+8Qhxs6R9Mnk21cHCh3rSugxu6gwo/djEItagJ4cnws5cW2C3JmvmxiPJmirGRO+xnAXz/WjDFw3eVacRJOeTb/ZuBf/+0xr2q+p5KRHMcta/L4zf4GOnsv3Iz7fAmhplDG2lSSgwi8ecIe1OqTi31o1XxSE2JH+8TMZFETwMH1hh8/201LVz+OgWFOtTpYPt+3DoTj8Szm8afl5KtHmynITGLpvNB3d1Pn5VmTaHMM0jfoXTnowPAI3331BC8caOTTWxcFJLjes6GI/iEnz+ypu+B4MLoQRoPMlHhW5Vt57dhZXgtB+sQjOT6WD6/J5aVDTXT2DU39gDCKsgDu+u28o7KV42e7MWZ6FzA9CrNSGBh2YvfxyrRjYJidVa1ct3zetGZvavpGa8E7p56F76/r4Kb/2Ml/vFHFbWV5Xtd8T+Xy3AzWFtn45bunL5gMVNsd2ILYxCqSbVmSw+GGLlf1SQjSJx53ryugf8jJCwfC0iHEa1EVwJfPd1253l5pH9MDPAAB3M9Swu0n7QwOO7lON2UIu9Fa8EkqUfoGR/jmS8e47b//SHf/MI/dv45/u3NNQHuy33NlEbVtvRcsFqm2awXKRLa413hkJMWx0cfyzelYmZfB8vnpPDnD0yhRFcAtFuHqxdnsrGzlSEMn1uS40X7Q0+FvKeGrR5vJTIkf3ZtQhU+ebfKNHd6raeP6h7fzyPYa7l5fyKt/u5mtS+cEfBzXr5xHVkr8BSWFNXaHVqBMYE2BlZy0BG5cNT+kLSdEhLvXF3CksYvDDZdWDs0UURXAwbXZcZtjkJcONbF8vu89wMeTZ03CInCmzfueKEMjTl4/1sz7l87RhTszwNy0BGIsckklSs/AMF/5zWHueuRdnAae+Isr+OatK0nzchMGXyXExnD3+gLeON5MfXsvnX1DtPZoE6uJxFiEl/56E//4oeUhf+2b1+SREGvhyRm8MjPqIsvV7jx4V/9wQNInAPGxFuZnJPk0A9916hxd/cOaPpkhYmMszEu/sK3s9pN2/uR72/nle6f5xMYFvPw3m3xeZemPj15RBMDj752hxnMBM4xtZGe6nLQEr1sXBFJGUhw3rpzP8/sa6R0cDvnreyPqAvictMTRhTuBuIDp4Wsp4atHzpIYZ2FTSeA7MCr/5FoTaWjvo7N3iP/7zAHufXQXiXEWnv2rDfzjTctJjg/NDoN51iQ+sGwuT+2u41hTNwCLwrCJsJraXesK6B4Y5qVDE7dBCKeoC+Dg2uwYCEgJoYcvfcGNMbx6tJnNJTkkxeumxDNFnjWJY01dfOB7b/HcvgYe3LqIF/96E2uLJt5PNVju3VDMOccgP9peTaxFRq+zqJll/YJMFmanzNgGVxG9qfFE7t9YTHpSHCUBnNUUZiXT2jOIY2CYlITJ/9kON3TR1NnP3113WcBeX01fvi2Zrv5hltmSeez+dazIC9wveF9tXJzFwpwUauwOFuakaBOrGUpEuGtdAd/63XGqWrpZ7Oe+AsESlT818zOSeHDrYiyWwNVe+1KJ8trRs1gErglCFYPy331XFfP9u9bwwmc2hjV4gysw3HOlKxe+MFvTJzPZbWX5xFqEp3bXTX3nEIvKAB4MvgTwV482s35B5ribLKvwyUlL4JbSvBkz2719bT4ZSXGsDPMvEzW5nLQErl0+l1/vbWBwePx9OsNlZvwkR4CiTFeVwJkpFvOcbnNw/Gw312rzKjWF9MQ4/vD59wVspacKnrvWFXDOMchrR5vDPZQLaAD3UkZyHOmJsVPOwD1vsJYPKm9kpsTrnqgRYFNJDnnWpBlXE64/OT4oykqZMoC/eqSZZfPTdZd5paJIjEW4ozyfnVWtfreWDgYN4D4ozEyeNIC39gxQcfqczr6VikJ3lBcA8EzFzLmYqQHcBwWZydS39zIyQVvZN4614NTe30pFpTxrEptLcni6on7CGBBqXgVwEakVkUMisl9EKtzHnnJ/v999+/6gjnQGKMpKZmjE0DRBS9JXj54lz5oUkE0klFIzz5+uL+BsV/8FG8eEky8z8K3GmDXGmHIAY8xd7u/XAL8GngvGAGeSyUoJeweH2VHZynWXz9Xe30pFqfcvnUt2avyM2a1n2ikUcUWrO4FfTX84M9toAB+nlHD7yVYGhp1cq/lvpaJWfKyF28vyef14C+2OwXAPx+sAboBXRWSPiDxw0W2bgGZjTGVghzbzzM9IJNYi487AXz16loykONYXh76vhlIqdN6/dA4jTsPeM+Hf9NjbAH61MaYMuB54UEQ2j7ntT5lk9i0iD4hIhYhU2O0zI2/kr9gYC/m2S9vKDo84ef1YC9cs097fSkW7lfkZxFgkcgK4MabB/XcLsA1YDyAiscBtwFOTPPYRY0y5MaY8JyfyW6sWjFNKuKv2HJ19Q1ynqy+VinrJ8bEsm5/GvjMd4R7K1AFcRFJEJM3zNXAdcNh98weA48aY+uANcWYZrxb81SPNJMRa2LwkdHv2KaXCp7TAxoG6jrCXE3ozA58L7BSRA8Au4EVjzMvu2+5mFly8HKsoK5mO3iE6+4YAV+/v1442s6kkJ2QbAiilwqusyIpjcISTzd1hHceUEccYUwOsnuC2+wM9oJnOU4lSd66XjLwMjjR20dDRx+c+UBLmkSmlQqW0wLVR+b4zHaM7gIWDXnHzUaGnK6E7jfLa0Wbt/a3ULFOUlUxmSnzYL2RqAPdRYZZrBn7aXQv+6tFmyosyyUpNCOewlFIhJCKUFljZpwE8sqQmxJKVEs+Zc73UnevlWFOX9j5RahYqK7JRbXfQ0Ru+BT0awP3gKiV08Kq797euvlRq9iktsAKwv64jbGPQAO4HTynhq0fOsnReGkVZKeEeklIqxFYVWLEI7A1jPbgGcD8UZSXT0N7H7lrt/a3UbJWaEMuSuWlhzYNrAPdDQWYyToO797euvlRqtiorsrG/rgNnmBb0aAD3Q5G7Fjw3I5HLc7X3t1KzVWmBle7+YartPWF5fQ3gfvCUEl67XHt/KzWblRW5FvSEqx5cA7gf5mck8fVbVvCp9y0O91CUUmG0ICuFjKS4sDW20uYdfvqzK4vCPQSlVJhZLEJpoTVsAVxn4EopNQ2lBTZOtnTT1T8U8tfWAK6UUtNQWmjFGDhY1xny19YArpRS07Cm0IpIeC5kagBXSqlpSE+MY3FOalgW9GgAV0qpaSortLGvrgNjQrugRwO4UkpNU2mhlY7eIU61OkL6uhrAlVJqms4v6OkI6etqAFdKqWlanJNKWkJsyPPgGsCVUmqaLBZhTaF1Zs7ARaRWRA6JyH4RqRhz/LMiclxEjojIt4M3TKWUmtlKC6ycONuFY2A4ZK/py1L6rcaYVs83IrIVuBlYbYwZEBHd1VcpNWuVFtlwGjhQ38FVi7JD8prTSaF8CvgXY8wAgDGmJTBDUkqpyOPZYi2UfVG8DeAGeFVE9ojIA+5jS4BNIvKeiLwlIuvGe6CIPCAiFSJSYbfbAzFmpZSacazJ8SzMSQlpAPc2hXK1MabBnSZ5TUSOux+bCVwJrAOeFpGF5qJKdmPMI8AjAOXl5eHZtkIppUKgtMDGmydaMMaEZK8Ar2bgxpgG998twDZgPVAPPGdcdgFOIDSJH6WUmoHKiqy0OQapO9cXktebMoCLSIqIpHm+Bq4DDgO/Aba6jy8B4oHWCZ5GKaWiXmlBaHfo8SaFMhfY5v44EAs8YYx5WUTigUdF5DAwCNx3cfpEKaVmk8vmpZEcH8O+M+3cUpoX9NebMoAbY2qA1eMcHwT+LBiDUkqpSBRjEVbnh25Bj67EVEqpACottHKsqYu+wZGgv5YGcKWUCqCyQhvDTsOhhuDv0KMBXCmlAmhNoRUgJI2tNIArpVQAZacmUJSVHJJKFA3gSikVYKUFrguZwS7M0wCulFIBVlZkw949QGNnf1BfRwO4UkoF2OiCntPBTaNoAFdKqQBbOj+NxDhL0BtbaQBXSqkAi4uxsCrPGvQLmRrAlVIqCEqLrBxt7GJgOHgLejSAK6VUEJQW2BgccXK4oStor6EBXCmlgqAsBAt6NIArpVQQzElPJM+aFNQLmRrAlVIqSMqKbDoDV0qpSFRaYKWxs5+zQVrQowFcKaWCpDTIeXAN4EopFSSX52YQH2sJWj24BnCllAqS+FgLK3LTg3YhUwO4UkoFUVmhjUMNnQwOOwP+3BrAlVIqiEoLbQwMOznWFPgFPV4FcBGpFZFDIrJfRCrcx74mIg3uY/tF5IaAj04ppSLc2iIb1y6fi0jgn3vKXenH2GqMab3o2PeMMd8J5ICUUiqazMtI5Mf3lgfluTWFopRSEcrbAG6AV0Vkj4g8MOb4Z0TkoIg8KiK28R4oIg+ISIWIVNjt9mkPWCmllIu3AfxqY0wZcD3woIhsBn4ALALWAE3Ad8d7oDHmEWNMuTGmPCcnJwBDVkopBV4GcGNMg/vvFmAbsN4Y02yMGTHGOIEfA+uDN0yllFIXmzKAi0iKiKR5vgauAw6LyPwxd7sVOBycISqllBqPN1Uoc4Ft4qqBiQWeMMa8LCK/EJE1uPLjtcBfBmuQSimlLjVlADfG1ACrxzl+T1BGpJRSyitaRqiUUhFKjDGhezERO3Daz4dnAxcvJIp2es6zg57z7DCdcy4yxlxSxhfSAD4dIlJhjAnOcqYZSs95dtBznh2Ccc6aQlFKqQilAVwppSJUJAXwR8I9gDDQc54d9Jxnh4Cfc8TkwJVSSl0okmbgSimlxtAArpRSESqsAdzdhrZFRA6PObZaRN5x7wD0vyKS7j4eLyKPuY8fEJH3jXnMmyJyYszuQHNCfzZTE5ECEfmDiBwVkSMi8jn38UwReU1EKt1/29zHRUT+XUSq3G17y8Y8133u+1eKyH3hOqepBPicR8a8xy+E65ym4sc5L3X/zA+IyOcveq4Pun+2q0TkoXCcjzcCfM6X7AA2E/lxzh9z/0wfEpG3RWT1mOfy7302xoTtD7AZKAMOjzm2G9ji/voTwD+7v34QeMz99RxgD2Bxf/8mUB7Oc/HyfOcDZe6v04CTwHLg28BD7uMPAf/P/fUNwO8AAa4E3nMfzwRq3H/b3F/bwn1+wTxn92094T6fIJ3zHGAd8A3g82OeJwaoBhYC8cABYHm4zy+Y5+y+rRbIDvc5BeGcr/L8P8XVmtvz/9nv9zmsM3BjzHbg3EWHlwDb3V+/Btzu/no58Ib7cS1ABxBRCwGMMU3GmL3ur7uBY0AecDPwM/fdfgbc4v76ZuDnxuVdwCquLpB/ArxmjDlnjGnH9e/0wdCdifcCeM4Rw9dzNsa0GGN2A0MXPdV6oMoYU2OMGQSedD/HjBPAc44Yfpzz2+7/rwDvAvnur/1+n2diDvwI5wd/B1Dg/voA8GERiRWRBcDaMbcBPOb+yPUVkWBsHxpYIlIMlALvAXONMU3um87i6gAJrh+GujEPq3cfm+j4jDbNcwZIFNfuTu+KyC3BH/H0eXnOE4nm93kyE+0ANmP5cc6fxPVJE6bxPvuyqXGofAL4dxH5CvACMOg+/iiwDKjA1U/lbWDEfdvHjDEN4upb/mvgHuDnIR21D0QkFdc4/8YY0zX2940xxohI1NV2Buici9zv80LgDRE5ZIypDtKQp03fZ7/P+Wr3+zwHeE1Ejrs/rc9Ivp6ziGzFFcCvnu5rz7gZuDHmuDHmOmPMWuBXuHJDGGOGjTF/a4xZY4y5GbDiyjlhzu8Y1A08wQzeHUhE4nC92Y8bY55zH272pAncf7e4jzdw4aeMfPexiY7PSAE657Hvcw2u6x6lQR+8n3w854lE8/s8ITPODmDBGfH0+XrOIrIK+AlwszGmzX3Y7/d5xgVw929dRMQC/APwQ/f3yeLaEQgRuRYYNsYcdadUst3H44APMUN3B3Kndn4KHDPG/NuYm14APJUk9wHPjzl+r7hcCXS6P5q9AlwnIjb3Fe7r3MdmnECds/tcE9zPmQ1sBI6G5CR85Mc5T2Q3UCIiC0QkHrjb/RwzTqDOWSbYASzwI54+X89ZRAqB54B7jDEnx9zf//c50FdmffmDa4bdhOtCRj2ujxWfwzWzPgn8C+dXixYDJ3BdKPg9ro/TACm4KlIO4sqfPwzEhPO8Jjnfq3Hl9w4C+91/bgCygNeBSve5ZbrvL8B/4foUcogxlTa4Uk1V7j8fD/e5BfuccV3BP4TrWsgh4JPhPrcAnvM8989/F66L8/VAuvu2G9z/F6qBL4f73IJ9zrgqMQ64/xyJsnP+CdA+5r4VY57Lr/dZl9IrpVSEmnEpFKWUUt7RAK6UUhFKA7hSSkUoDeBKKRWhNIArpVSE0gCuopq7nnyniFw/5tgdIvJyOMelVCBoGaGKeiKyAngG18rNWGAf8EHjxzJ8EYk1xgwHeIhK+UUDuJoVROTbgAPXwi8HUASsAOKArxljnnc3JPqF+z4AnzHGvC2u3vP/jGsRxlJjzJLQjl6p8WkAV7OCe1n2XlzN0X4LHDHG/FJErMAuXLNzAziNMf0iUgL8yhhT7g7gLwIrjDGnwjF+pcYzE7sRKhVwxhiHiDwF9AB3AjfJ+Z1gEoFCoBH4TxFZg6vT5diZ9i4N3mqm0QCuZhOn+48AtxtjToy9UUS+BjQDq3Fd4O8fc7MjRGNUymtahaJmo1eAz3o2/hART1vaDKDJGOPE1VM+JkzjU8orGsDVbPTPuC5eHhSRI+7vAf4buE9EDgBL0Vm3muH0IqZSSkUonYErpVSE0gCulFIRSgO4UkpFKA3gSikVoTSAK6VUhNIArpRSEUoDuFJKRaj/D5lDAZBEJl6SAAAAAElFTkSuQmCC\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
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- },
- {
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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
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- },
- "output_type": "display_data"
- },
- {
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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- },
- {
- "data": {
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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- },
- {
- "data": {
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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- },
- {
- "data": {
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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "for city, df in clean_data.groupby('City'):\n",
- " df.plot('Year', 'Tmp', label=city)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "fdc3ad9c-9984-4390-85ba-de36860db524",
- "metadata": {},
- "outputs": [],
- "source": []
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.8.9"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/previous_materials/np_pd_mplt_bertrand/pandas_cours.ipynb b/previous_materials/np_pd_mplt_bertrand/pandas_cours.ipynb
deleted file mode 100644
index fb5a3b8..0000000
--- a/previous_materials/np_pd_mplt_bertrand/pandas_cours.ipynb
+++ /dev/null
@@ -1,7127 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "id": "aee76635-5b42-41dc-a731-f6fa3a99761a",
- "metadata": {},
- "source": [
- "# <center>**Cours**</center>\n",
- "\n",
- "<div style=\"text-align:center\">\n",
- " <img src=\"./images/pandas_logo.svg\" width=\"600px\">\n",
- " <div>\n",
- " Bertrand Néron\n",
- " <br />\n",
- " <a src=\" https://research.pasteur.fr/en/team/bioinformatics-and-biostatistics-hub/\">Bioinformatics and Biostatistiqucs HUB</a>\n",
- " <br />\n",
- " © Institut Pasteur, 2021\n",
- " </div> \n",
- "</div>"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "b580a070-acfd-42d5-bcd1-2edf2ba5356b",
- "metadata": {},
- "source": [
- "# Intro\n",
- "\n",
- "Pandas is designed to manipualted tabulated data, Numpy is designed to do computation on arrays. So is the differences: \n",
- "\n",
- "**Numpy** \n",
- "* handle one structure: the ndarray.\n",
- "* an *array* can have 1, 2 or more dimension.\n",
- "* An *ndarray* handle homogenous data, only one datatype for data in an array.\n",
- "* So numpy is mostly uses to do math on arrays.\n",
- "\n",
- "**Pandas** \n",
- "* *Series* have 1 dimension, *DataFrame* have 2 dimensions\n",
- "* *Pandas* does **not** handle structures with more than 2 dimensions\n",
- "* But *DataFrame* can contain heterogenous data, each column can have a different datatype\n",
- "* *Pandas* is more power full to request data or manipulate them\n",
- "\n",
- "So *Numpy* is mostly used to do math, *Pandas* to explore data structured in tables. "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "150c48f1-0409-40ee-a5c0-ab21a9793c8e",
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "markdown",
- "id": "261181bc-fb16-4186-8e6f-712395679df6",
- "metadata": {},
- "source": [
- "# Installation\n",
- "\n",
- "For *conda* users\n",
- "\n",
- "```shell\n",
- "conda install pandas\n",
- "```\n",
- "\n",
- "for *pip* users\n",
- "```shell\n",
- "pip install pandas\n",
- "```"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "e43b65cd-5c23-48b5-8192-e573c1659a1a",
- "metadata": {},
- "source": [
- "# Import Convention"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "id": "0864cd25-4512-44de-a32c-86f2952380a7",
- "metadata": {},
- "outputs": [],
- "source": [
- "import numpy as np\n",
- "import pandas as pd"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "31f940ee-d534-4668-a942-5d7ce3395281",
- "metadata": {},
- "source": [
- "# DataFrame Terminology\n",
- "\n",
- "<img src=\"images/pandas_dataframe.png\" width=\"300px\" />\n",
- "\n",
- "Each column in a DataFrame is a Series"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "f8b7def7-e41c-4dd0-9896-1d60f81dd9cb",
- "metadata": {},
- "source": [
- "# Create DataFrame\n",
- "\n",
- "> https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "43dcf2b4-e6ae-4abd-b09c-768005b785be",
- "metadata": {},
- "source": [
- "## from list of lists"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "id": "ff5891e5-831c-4653-88f8-133b038ab27c",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
- "<style scoped>\n",
- " .dataframe tbody tr th:only-of-type {\n",
- " vertical-align: middle;\n",
- " }\n",
- "\n",
- " .dataframe tbody tr th {\n",
- " vertical-align: top;\n",
- " }\n",
- "\n",
- " .dataframe thead th {\n",
- " text-align: right;\n",
- " }\n",
- "</style>\n",
- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>A</th>\n",
- " <th>B</th>\n",
- " <th>C</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>0</th>\n",
- " <td>1</td>\n",
- " <td>2</td>\n",
- " <td>3</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1</th>\n",
- " <td>4</td>\n",
- " <td>5</td>\n",
- " <td>6</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " A B C\n",
- "0 1 2 3\n",
- "1 4 5 6"
- ]
- },
- "execution_count": 3,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "df = pd.DataFrame([[1,2,3],\n",
- " [4,5,6]],\n",
- " columns=['A', 'B', 'C']\n",
- " )\n",
- "df"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "id": "e218bbb0-5ae9-4b1c-800f-2a748353c80b",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "RangeIndex(start=0, stop=2, step=1)"
- ]
- },
- "execution_count": 4,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "df.index"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "id": "e3b8ca30-9b64-4cd7-b285-307d175aced8",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "Index(['A', 'B', 'C'], dtype='object')"
- ]
- },
- "execution_count": 5,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "df.columns"
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- },
- {
- "cell_type": "code",
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- " A B C\n",
- "a 1 2 3\n",
- "b 4 5 6"
- ]
- },
- "execution_count": 6,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "df = pd.DataFrame([[1,2,3],\n",
- " [4,5,6]],\n",
- " columns=['A', 'B', 'C'],\n",
- " index= ['a', 'b'])\n",
- "df"
- ]
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- {
- "cell_type": "code",
- "execution_count": 7,
- "id": "ca74b45e-8a21-4d1a-8224-c552eb3c160c",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "Index(['a', 'b'], dtype='object')"
- ]
- },
- "execution_count": 7,
- "metadata": {},
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- }
- ],
- "source": [
- "df.index"
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- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "id": "df090f78-eb28-445c-a547-7ca641486a8b",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "Index(['A', 'B', 'C'], dtype='object')"
- ]
- },
- "execution_count": 8,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "df.columns"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "e1fe8d4e-9646-49e5-97e2-1cb78c92395f",
- "metadata": {},
- "source": [
- "## from dict"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "id": "62ef022e-e9bc-4d25-9916-73c391970830",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
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- " A B\n",
- "0 1 4\n",
- "1 2 5\n",
- "2 3 6"
- ]
- },
- "execution_count": 9,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "df = pd.DataFrame({'A': [1,2,3],\n",
- " 'B': np.arange(4,7),\n",
- " })\n",
- " \n",
- "df"
- ]
- },
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- "id": "0b057f1e-1c8b-40ad-897e-04626d61b32c",
- "metadata": {},
- "source": [
- "## from numpy ndarray"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "id": "12604805-f078-4e9b-8502-0675040a77cc",
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- " 0 1 2\n",
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- ]
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- "execution_count": 10,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "df = pd.DataFrame(np.arange(12).reshape(4,3))\n",
- "df"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "id": "045f232c-f191-40e1-b907-78b023bd6e1d",
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- "outputs": [
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- "<div>\n",
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- " <tr>\n",
- " <th>3</th>\n",
- " <td>9</td>\n",
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- " <td>11</td>\n",
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- "text/plain": [
- " A B C\n",
- "0 0 1 2\n",
- "1 3 4 5\n",
- "2 6 7 8\n",
- "3 9 10 11"
- ]
- },
- "execution_count": 11,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "df = pd.DataFrame(np.arange(12).reshape(4,3),\n",
- " columns=['A', 'B', 'C'])\n",
- "df"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "5aa36c20-ed23-4a77-a8bb-6b3984a1bfbc",
- "metadata": {},
- "source": [
- "## from file\n",
- "\n",
- "> https://pandas.pydata.org/docs/reference/api/pandas.read_csv.html"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 12,
- "id": "bd473619-6619-481b-bdde-ee3ee8469398",
- "metadata": {},
- "outputs": [],
- "source": [
- "titanic = pd.read_csv(\"data/titanic.csv\")"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "b89c340c-0dfc-4c8c-88e8-53add4af6ac3",
- "metadata": {},
- "source": [
- "We want to open *data/bar_data.tsv* file\n",
- "\n",
- "but the 2 first lines are comments\n",
- "\n",
- "and the separator between fields is *tab*\n",
- "\n",
- "See below the first lines"
- ]
- },
- {
- "cell_type": "raw",
- "id": "06ec5158-93a1-40b8-9e06-b4095a284954",
- "metadata": {},
- "source": [
- "# generated with fooo software version 12bis\n",
- "# 2021/02/31\n",
- "cond1 cond2 cond3 control\n",
- "14.644417316782045 2.9453091400880465 24.81171864537413 5.114340165446571\n",
- "12.071043262601615 4.406424332565544 21.574601309211538 2.5071180945299716\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 13,
- "id": "63c6498c-d404-405a-9d6e-357ed9c6b835",
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- " <tbody>\n",
- " <tr>\n",
- " <th>0</th>\n",
- " <td>14.644417</td>\n",
- " <td>2.945309</td>\n",
- " <td>24.811719</td>\n",
- " <td>5.114340</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1</th>\n",
- " <td>12.071043</td>\n",
- " <td>4.406424</td>\n",
- " <td>21.574601</td>\n",
- " <td>2.507118</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2</th>\n",
- " <td>8.227469</td>\n",
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- " <td>4.449593</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>3</th>\n",
- " <td>8.980799</td>\n",
- " <td>9.233560</td>\n",
- " <td>24.859737</td>\n",
- " <td>4.127919</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4</th>\n",
- " <td>9.080359</td>\n",
- " <td>5.629192</td>\n",
- " <td>18.443504</td>\n",
- " <td>4.268572</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " cond1 cond2 cond3 control\n",
- "0 14.644417 2.945309 24.811719 5.114340\n",
- "1 12.071043 4.406424 21.574601 2.507118\n",
- "2 8.227469 3.185252 20.651623 4.449593\n",
- "3 8.980799 9.233560 24.859737 4.127919\n",
- "4 9.080359 5.629192 18.443504 4.268572"
- ]
- },
- "execution_count": 13,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "bar = pd.read_csv(\"data/bar_data.tsv\", sep=\"\\t\", comment=\"#\")\n",
- "bar.head()"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "0e604a16-4960-4b8e-9444-95df497efa9f",
- "metadata": {},
- "source": [
- "the data in the file are already indexed like below"
- ]
- },
- {
- "cell_type": "raw",
- "id": "a71af9d2-17c0-4d02-904b-709026f59c77",
- "metadata": {},
- "source": [
- " MW AlogP PSA HBA\n",
- "0 0.0 1.0 72.73111270481336 1.1416684150966834\n",
- "1 3.63 544.59 391.4275648686457 0.9848635571682688\n",
- "2 2.11 383.4 437.4589821943501 15.040385372412596\n",
- "3 1.24 162.23 480.1112629835199 11.401906578750385\n"
- ]
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- {
- "cell_type": "code",
- "execution_count": 14,
- "id": "1c196b24-1c6a-4fd5-9571-afb315a624e4",
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- " <td>1.141668</td>\n",
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- " <tr>\n",
- " <th>1</th>\n",
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- " <td>3.63</td>\n",
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- " <td>391.427565</td>\n",
- " <td>0.984864</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2</th>\n",
- " <td>2</td>\n",
- " <td>2.11</td>\n",
- " <td>383.40</td>\n",
- " <td>437.458982</td>\n",
- " <td>15.040385</td>\n",
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- "</div>"
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- "text/plain": [
- " Unnamed: 0 MW AlogP PSA HBA\n",
- "0 0 0.00 1.00 72.731113 1.141668\n",
- "1 1 3.63 544.59 391.427565 0.984864\n",
- "2 2 2.11 383.40 437.458982 15.040385"
- ]
- },
- "execution_count": 14,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "data = pd.read_csv(\"data/data_for_plt.csv\", sep=\"\\t\")\n",
- "data.head(3)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "a1d8ab31-73cc-42ec-b172-c2263a0f5ae1",
- "metadata": {},
- "source": [
- "To avoiding to have an extra column, you can specify which columns to use as index.\n",
- "This column **must** have distincts values."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 15,
- "id": "eeacf68e-13b6-4df8-afc1-5a0a1dedb980",
- "metadata": {},
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- " <th>AlogP</th>\n",
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- " <th>HBA</th>\n",
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- " <td>3.63</td>\n",
- " <td>544.59</td>\n",
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- " <td>0.984864</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2</th>\n",
- " <td>2.11</td>\n",
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- " <td>437.458982</td>\n",
- " <td>15.040385</td>\n",
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- " <tr>\n",
- " <th>3</th>\n",
- " <td>1.24</td>\n",
- " <td>162.23</td>\n",
- " <td>480.111263</td>\n",
- " <td>11.401907</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4</th>\n",
- " <td>-1.37</td>\n",
- " <td>361.37</td>\n",
- " <td>448.864769</td>\n",
- " <td>5.732690</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " MW AlogP PSA HBA\n",
- "0 0.00 1.00 72.731113 1.141668\n",
- "1 3.63 544.59 391.427565 0.984864\n",
- "2 2.11 383.40 437.458982 15.040385\n",
- "3 1.24 162.23 480.111263 11.401907\n",
- "4 -1.37 361.37 448.864769 5.732690"
- ]
- },
- "execution_count": 15,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "data = pd.read_csv(\"data/data_for_plt.csv\", sep=\"\\t\", index_col=0)\n",
- "data.head()"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "6c428aee-a589-4f2c-93ff-eb606e3c2c2c",
- "metadata": {},
- "source": [
- "The first line is used as header.<br />\n",
- "So you can specify the number of the row which represent the header,\n",
- "or you can set this parameter to None if the data have no header"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 16,
- "id": "6a9a0426-36da-4d00-902b-f2133f1c8ac2",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
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- " .dataframe tbody tr th:only-of-type {\n",
- " vertical-align: middle;\n",
- " }\n",
- "\n",
- " .dataframe tbody tr th {\n",
- " vertical-align: top;\n",
- " }\n",
- "\n",
- " .dataframe thead th {\n",
- " text-align: right;\n",
- " }\n",
- "</style>\n",
- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>1</th>\n",
- " <th>2</th>\n",
- " <th>3</th>\n",
- " <th>4</th>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>0</th>\n",
- " <th></th>\n",
- " <th></th>\n",
- " <th></th>\n",
- " <th></th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>0</th>\n",
- " <td>0.00</td>\n",
- " <td>1.00</td>\n",
- " <td>72.731113</td>\n",
- " <td>1.141668</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1</th>\n",
- " <td>3.63</td>\n",
- " <td>544.59</td>\n",
- " <td>391.427565</td>\n",
- " <td>0.984864</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2</th>\n",
- " <td>2.11</td>\n",
- " <td>383.40</td>\n",
- " <td>437.458982</td>\n",
- " <td>15.040385</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>3</th>\n",
- " <td>1.24</td>\n",
- " <td>162.23</td>\n",
- " <td>480.111263</td>\n",
- " <td>11.401907</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4</th>\n",
- " <td>-1.37</td>\n",
- " <td>361.37</td>\n",
- " <td>448.864769</td>\n",
- " <td>5.732690</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " 1 2 3 4\n",
- "0 \n",
- "0 0.00 1.00 72.731113 1.141668\n",
- "1 3.63 544.59 391.427565 0.984864\n",
- "2 2.11 383.40 437.458982 15.040385\n",
- "3 1.24 162.23 480.111263 11.401907\n",
- "4 -1.37 361.37 448.864769 5.732690"
- ]
- },
- "execution_count": 16,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "data = pd.read_csv(\"data/no_header.tsv\", sep=\"\\t\", index_col=0, header=None)\n",
- "data.head()"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "b9f75188-5b3c-4eaf-80b3-abd17ebac0c0",
- "metadata": {},
- "source": [
- "# basic operations"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 17,
- "id": "4179facb-d0cc-4ab4-9f4e-9462abd9c6a7",
- "metadata": {},
- "outputs": [],
- "source": [
- "titanic = pd.read_csv(\"data/titanic.csv\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 18,
- "id": "96717b3a-d9fe-4abe-bc7b-b78ed8260788",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "The titanic dataset is 891 length\n",
- "The titanic dataset contains 891 rows x 12 columns\n"
- ]
- }
- ],
- "source": [
- "print(f\"The titanic dataset is {len(titanic)} length\")\n",
- "rows, cols = titanic.shape\n",
- "print(f\"The titanic dataset contains {rows} rows x {cols} columns\")"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "bf7459dc-1977-4b7f-82c6-f22e7035e8bd",
- "metadata": {},
- "source": [
- "## head\n",
- "\n",
- "> https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.head.html"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 19,
- "id": "9f4f2ac6-d9eb-4f34-ab88-4b2dbed64629",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
- "<style scoped>\n",
- " .dataframe tbody tr th:only-of-type {\n",
- " vertical-align: middle;\n",
- " }\n",
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- " .dataframe tbody tr th {\n",
- " vertical-align: top;\n",
- " }\n",
- "\n",
- " .dataframe thead th {\n",
- " text-align: right;\n",
- " }\n",
- "</style>\n",
- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>PassengerId</th>\n",
- " <th>Survived</th>\n",
- " <th>Pclass</th>\n",
- " <th>Name</th>\n",
- " <th>Sex</th>\n",
- " <th>Age</th>\n",
- " <th>SibSp</th>\n",
- " <th>Parch</th>\n",
- " <th>Ticket</th>\n",
- " <th>Fare</th>\n",
- " <th>Cabin</th>\n",
- " <th>Embarked</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>0</th>\n",
- " <td>1</td>\n",
- " <td>0</td>\n",
- " <td>3</td>\n",
- " <td>Braund, Mr. Owen Harris</td>\n",
- " <td>male</td>\n",
- " <td>22.0</td>\n",
- " <td>1</td>\n",
- " <td>0</td>\n",
- " <td>A/5 21171</td>\n",
- " <td>7.2500</td>\n",
- " <td>NaN</td>\n",
- " <td>S</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1</th>\n",
- " <td>2</td>\n",
- " <td>1</td>\n",
- " <td>1</td>\n",
- " <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
- " <td>female</td>\n",
- " <td>38.0</td>\n",
- " <td>1</td>\n",
- " <td>0</td>\n",
- " <td>PC 17599</td>\n",
- " <td>71.2833</td>\n",
- " <td>C85</td>\n",
- " <td>C</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2</th>\n",
- " <td>3</td>\n",
- " <td>1</td>\n",
- " <td>3</td>\n",
- " <td>Heikkinen, Miss. Laina</td>\n",
- " <td>female</td>\n",
- " <td>26.0</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>STON/O2. 3101282</td>\n",
- " <td>7.9250</td>\n",
- " <td>NaN</td>\n",
- " <td>S</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>3</th>\n",
- " <td>4</td>\n",
- " <td>1</td>\n",
- " <td>1</td>\n",
- " <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
- " <td>female</td>\n",
- " <td>35.0</td>\n",
- " <td>1</td>\n",
- " <td>0</td>\n",
- " <td>113803</td>\n",
- " <td>53.1000</td>\n",
- " <td>C123</td>\n",
- " <td>S</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4</th>\n",
- " <td>5</td>\n",
- " <td>0</td>\n",
- " <td>3</td>\n",
- " <td>Allen, Mr. William Henry</td>\n",
- " <td>male</td>\n",
- " <td>35.0</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>373450</td>\n",
- " <td>8.0500</td>\n",
- " <td>NaN</td>\n",
- " <td>S</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " PassengerId Survived Pclass \\\n",
- "0 1 0 3 \n",
- "1 2 1 1 \n",
- "2 3 1 3 \n",
- "3 4 1 1 \n",
- "4 5 0 3 \n",
- "\n",
- " Name Sex Age SibSp \\\n",
- "0 Braund, Mr. Owen Harris male 22.0 1 \n",
- "1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 \n",
- "2 Heikkinen, Miss. Laina female 26.0 0 \n",
- "3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 \n",
- "4 Allen, Mr. William Henry male 35.0 0 \n",
- "\n",
- " Parch Ticket Fare Cabin Embarked \n",
- "0 0 A/5 21171 7.2500 NaN S \n",
- "1 0 PC 17599 71.2833 C85 C \n",
- "2 0 STON/O2. 3101282 7.9250 NaN S \n",
- "3 0 113803 53.1000 C123 S \n",
- "4 0 373450 8.0500 NaN S "
- ]
- },
- "execution_count": 19,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "titanic.head()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 20,
- "id": "ac2ff2b5-a97e-4c87-80c5-cccf17383ade",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
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- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>PassengerId</th>\n",
- " <th>Survived</th>\n",
- " <th>Pclass</th>\n",
- " <th>Name</th>\n",
- " <th>Sex</th>\n",
- " <th>Age</th>\n",
- " <th>SibSp</th>\n",
- " <th>Parch</th>\n",
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- " <th>0</th>\n",
- " <td>1</td>\n",
- " <td>0</td>\n",
- " <td>3</td>\n",
- " <td>Braund, Mr. Owen Harris</td>\n",
- " <td>male</td>\n",
- " <td>22.0</td>\n",
- " <td>1</td>\n",
- " <td>0</td>\n",
- " <td>A/5 21171</td>\n",
- " <td>7.2500</td>\n",
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- " <tr>\n",
- " <th>1</th>\n",
- " <td>2</td>\n",
- " <td>1</td>\n",
- " <td>1</td>\n",
- " <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
- " <td>female</td>\n",
- " <td>38.0</td>\n",
- " <td>1</td>\n",
- " <td>0</td>\n",
- " <td>PC 17599</td>\n",
- " <td>71.2833</td>\n",
- " <td>C85</td>\n",
- " <td>C</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " PassengerId Survived Pclass \\\n",
- "0 1 0 3 \n",
- "1 2 1 1 \n",
- "\n",
- " Name Sex Age SibSp \\\n",
- "0 Braund, Mr. Owen Harris male 22.0 1 \n",
- "1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 \n",
- "\n",
- " Parch Ticket Fare Cabin Embarked \n",
- "0 0 A/5 21171 7.2500 NaN S \n",
- "1 0 PC 17599 71.2833 C85 C "
- ]
- },
- "execution_count": 20,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "titanic.head(n=2)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "7d174422-36bf-4f3a-b80b-f20a8870f4c8",
- "metadata": {},
- "source": [
- "## tail\n",
- "\n",
- "> https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.tail.html"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 21,
- "id": "34f2946f-ba89-43df-bdfd-da9eb8e21e69",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
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- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>PassengerId</th>\n",
- " <th>Survived</th>\n",
- " <th>Pclass</th>\n",
- " <th>Name</th>\n",
- " <th>Sex</th>\n",
- " <th>Age</th>\n",
- " <th>SibSp</th>\n",
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- " <th>Cabin</th>\n",
- " <th>Embarked</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>889</th>\n",
- " <td>890</td>\n",
- " <td>1</td>\n",
- " <td>1</td>\n",
- " <td>Behr, Mr. Karl Howell</td>\n",
- " <td>male</td>\n",
- " <td>26.0</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>111369</td>\n",
- " <td>30.00</td>\n",
- " <td>C148</td>\n",
- " <td>C</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>890</th>\n",
- " <td>891</td>\n",
- " <td>0</td>\n",
- " <td>3</td>\n",
- " <td>Dooley, Mr. Patrick</td>\n",
- " <td>male</td>\n",
- " <td>32.0</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>370376</td>\n",
- " <td>7.75</td>\n",
- " <td>NaN</td>\n",
- " <td>Q</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " PassengerId Survived Pclass Name Sex Age SibSp \\\n",
- "889 890 1 1 Behr, Mr. Karl Howell male 26.0 0 \n",
- "890 891 0 3 Dooley, Mr. Patrick male 32.0 0 \n",
- "\n",
- " Parch Ticket Fare Cabin Embarked \n",
- "889 0 111369 30.00 C148 C \n",
- "890 0 370376 7.75 NaN Q "
- ]
- },
- "execution_count": 21,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "titanic.tail(2)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "36c00695-8370-4954-af0f-45e3182f66dc",
- "metadata": {},
- "source": [
- "## describe\n",
- "\n",
- "To have basic descriptive statistics.\n",
- "The columns on which pandas cannot do statistics are omitted (Name, Sex, ...)\n",
- "\n",
- "> https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.describe.html"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 22,
- "id": "a8d82741-6c8f-4914-bd01-883787742eb7",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
- "<style scoped>\n",
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- " }\n",
- "\n",
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- "</style>\n",
- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>PassengerId</th>\n",
- " <th>Survived</th>\n",
- " <th>Pclass</th>\n",
- " <th>Age</th>\n",
- " <th>SibSp</th>\n",
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- " <th>Fare</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>count</th>\n",
- " <td>891.000000</td>\n",
- " <td>891.000000</td>\n",
- " <td>891.000000</td>\n",
- " <td>714.000000</td>\n",
- " <td>891.000000</td>\n",
- " <td>891.000000</td>\n",
- " <td>891.000000</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>mean</th>\n",
- " <td>446.000000</td>\n",
- " <td>0.383838</td>\n",
- " <td>2.308642</td>\n",
- " <td>29.699118</td>\n",
- " <td>0.523008</td>\n",
- " <td>0.381594</td>\n",
- " <td>32.204208</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>std</th>\n",
- " <td>257.353842</td>\n",
- " <td>0.486592</td>\n",
- " <td>0.836071</td>\n",
- " <td>14.526497</td>\n",
- " <td>1.102743</td>\n",
- " <td>0.806057</td>\n",
- " <td>49.693429</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>min</th>\n",
- " <td>1.000000</td>\n",
- " <td>0.000000</td>\n",
- " <td>1.000000</td>\n",
- " <td>0.420000</td>\n",
- " <td>0.000000</td>\n",
- " <td>0.000000</td>\n",
- " <td>0.000000</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>25%</th>\n",
- " <td>223.500000</td>\n",
- " <td>0.000000</td>\n",
- " <td>2.000000</td>\n",
- " <td>20.125000</td>\n",
- " <td>0.000000</td>\n",
- " <td>0.000000</td>\n",
- " <td>7.910400</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>50%</th>\n",
- " <td>446.000000</td>\n",
- " <td>0.000000</td>\n",
- " <td>3.000000</td>\n",
- " <td>28.000000</td>\n",
- " <td>0.000000</td>\n",
- " <td>0.000000</td>\n",
- " <td>14.454200</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>75%</th>\n",
- " <td>668.500000</td>\n",
- " <td>1.000000</td>\n",
- " <td>3.000000</td>\n",
- " <td>38.000000</td>\n",
- " <td>1.000000</td>\n",
- " <td>0.000000</td>\n",
- " <td>31.000000</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>max</th>\n",
- " <td>891.000000</td>\n",
- " <td>1.000000</td>\n",
- " <td>3.000000</td>\n",
- " <td>80.000000</td>\n",
- " <td>8.000000</td>\n",
- " <td>6.000000</td>\n",
- " <td>512.329200</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " PassengerId Survived Pclass Age SibSp \\\n",
- "count 891.000000 891.000000 891.000000 714.000000 891.000000 \n",
- "mean 446.000000 0.383838 2.308642 29.699118 0.523008 \n",
- "std 257.353842 0.486592 0.836071 14.526497 1.102743 \n",
- "min 1.000000 0.000000 1.000000 0.420000 0.000000 \n",
- "25% 223.500000 0.000000 2.000000 20.125000 0.000000 \n",
- "50% 446.000000 0.000000 3.000000 28.000000 0.000000 \n",
- "75% 668.500000 1.000000 3.000000 38.000000 1.000000 \n",
- "max 891.000000 1.000000 3.000000 80.000000 8.000000 \n",
- "\n",
- " Parch Fare \n",
- "count 891.000000 891.000000 \n",
- "mean 0.381594 32.204208 \n",
- "std 0.806057 49.693429 \n",
- "min 0.000000 0.000000 \n",
- "25% 0.000000 7.910400 \n",
- "50% 0.000000 14.454200 \n",
- "75% 0.000000 31.000000 \n",
- "max 6.000000 512.329200 "
- ]
- },
- "execution_count": 22,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "desc = titanic.describe()\n",
- "desc"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 23,
- "id": "16e79cc5-13d6-4147-812c-c067c75a11ed",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "titanic have 12 cols\n",
- "desc have 7 cols\n"
- ]
- }
- ],
- "source": [
- "print(f\"titanic have {len(titanic.columns)} cols\\ndesc have {len(desc.columns)} cols\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 24,
- "id": "a2e27411-4645-4f74-8ca6-74b212002f19",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "<class 'pandas.core.series.Series'>\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "0 22.0\n",
- "1 38.0\n",
- "2 26.0\n",
- "Name: Age, dtype: float64"
- ]
- },
- "execution_count": 24,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "ages = titanic[\"Age\"]\n",
- "print(type(ages))\n",
- "ages.head(3)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "e3e4a814-4bf3-4a44-a2f9-cebb4aa440e8",
- "metadata": {},
- "source": [
- "## rename columns"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 25,
- "id": "7a2ab243-9344-45f1-afb0-bfd1392e75cc",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
- "<style scoped>\n",
- " .dataframe tbody tr th:only-of-type {\n",
- " vertical-align: middle;\n",
- " }\n",
- "\n",
- " .dataframe tbody tr th {\n",
- " vertical-align: top;\n",
- " }\n",
- "\n",
- " .dataframe thead th {\n",
- " text-align: right;\n",
- " }\n",
- "</style>\n",
- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>A</th>\n",
- " <th>B</th>\n",
- " <th>C</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>0</th>\n",
- " <td>0</td>\n",
- " <td>1</td>\n",
- " <td>2</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1</th>\n",
- " <td>3</td>\n",
- " <td>4</td>\n",
- " <td>5</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2</th>\n",
- " <td>6</td>\n",
- " <td>7</td>\n",
- " <td>8</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>3</th>\n",
- " <td>9</td>\n",
- " <td>10</td>\n",
- " <td>11</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " A B C\n",
- "0 0 1 2\n",
- "1 3 4 5\n",
- "2 6 7 8\n",
- "3 9 10 11"
- ]
- },
- "execution_count": 25,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "df = pd.DataFrame(np.arange(12).reshape(4,3),\n",
- " columns=['A', 'B', 'C'])\n",
- "df"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 26,
- "id": "ac099473-4ea9-4c0f-9bb1-01fcd9f1110c",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "Index(['A', 'B', 'Z'], dtype='object')"
- ]
- },
- "execution_count": 26,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "cols = list(df.columns)\n",
- "cols[2] = 'Z'\n",
- "df.columns = cols\n",
- "df.columns"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 27,
- "id": "77352321-0d23-4164-8628-ee5351879c55",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
- "<style scoped>\n",
- " .dataframe tbody tr th:only-of-type {\n",
- " vertical-align: middle;\n",
- " }\n",
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- " .dataframe tbody tr th {\n",
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- " </tr>\n",
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- " <th>3</th>\n",
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- " <td>11</td>\n",
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- " </tbody>\n",
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- "text/plain": [
- " X Y Z\n",
- "0 0 1 2\n",
- "1 3 4 5\n",
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- ]
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- "execution_count": 27,
- "metadata": {},
- "output_type": "execute_result"
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- ],
- "source": [
- "df.columns = ['X', 'Y', 'Z']\n",
- "df"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "9444fdbd-3025-4fe1-a4e2-ba726d271a64",
- "metadata": {},
- "source": [
- "## rename index"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 28,
- "id": "eb6bf438-9a84-4585-9bf7-b15ae549da29",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
- "<style scoped>\n",
- " .dataframe tbody tr th:only-of-type {\n",
- " vertical-align: middle;\n",
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- " .dataframe tbody tr th {\n",
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- " }\n",
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- " <td>5</td>\n",
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- " <tr>\n",
- " <th>c</th>\n",
- " <td>6</td>\n",
- " <td>7</td>\n",
- " <td>8</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>e</th>\n",
- " <td>9</td>\n",
- " <td>10</td>\n",
- " <td>11</td>\n",
- " </tr>\n",
- " </tbody>\n",
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- "</div>"
- ],
- "text/plain": [
- " X Y Z\n",
- "a 0 1 2\n",
- "b 3 4 5\n",
- "c 6 7 8\n",
- "e 9 10 11"
- ]
- },
- "execution_count": 28,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "df.index = ['a', 'b', 'c', 'e']\n",
- "df"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "b4cf5d79-1f08-45e6-8b95-6f9c24f94a36",
- "metadata": {},
- "source": [
- "## add column"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 29,
- "id": "1d39be28-733e-44c9-be53-9dc21985dd37",
- "metadata": {},
- "outputs": [],
- "source": [
- "_id = [0, 400, 3,12]\n",
- "df['id'] = _id"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 30,
- "id": "1635c90d-b3bd-4fa0-bed5-156bb2efc206",
- "metadata": {},
- "outputs": [
- {
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- "text/html": [
- "<div>\n",
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- " X Y Z id\n",
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- "source": [
- "df"
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- },
- {
- "cell_type": "code",
- "execution_count": 31,
- "id": "465513e0-fdd9-4cc0-854a-8cdda25ef660",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
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- " <td>7</td>\n",
- " <td>8</td>\n",
- " <td>3</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>e</th>\n",
- " <td>9</td>\n",
- " <td>10</td>\n",
- " <td>11</td>\n",
- " <td>12</td>\n",
- " <td>9</td>\n",
- " <td>10</td>\n",
- " <td>11</td>\n",
- " <td>12</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " X Y Z id X Y Z id\n",
- "a 0 1 2 0 0 1 2 0\n",
- "b 3 4 5 400 3 4 5 400\n",
- "c 6 7 8 3 6 7 8 3\n",
- "e 9 10 11 12 9 10 11 12"
- ]
- },
- "execution_count": 31,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "pd.concat([df, df], axis=1)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "c1d7d5b7-a09d-4e0c-985e-97778a2f7b11",
- "metadata": {},
- "source": [
- "## reorder columns"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 32,
- "id": "0b18fd40-3462-4c35-b700-28cd2f908406",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
- "<style scoped>\n",
- " .dataframe tbody tr th:only-of-type {\n",
- " vertical-align: middle;\n",
- " }\n",
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- " .dataframe tbody tr th {\n",
- " vertical-align: top;\n",
- " }\n",
- "\n",
- " .dataframe thead th {\n",
- " text-align: right;\n",
- " }\n",
- "</style>\n",
- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>X</th>\n",
- " <th>Y</th>\n",
- " <th>Z</th>\n",
- " <th>id</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>a</th>\n",
- " <td>1</td>\n",
- " <td>0</td>\n",
- " <td>2</td>\n",
- " <td>0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>b</th>\n",
- " <td>4</td>\n",
- " <td>3</td>\n",
- " <td>5</td>\n",
- " <td>400</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>c</th>\n",
- " <td>7</td>\n",
- " <td>6</td>\n",
- " <td>8</td>\n",
- " <td>3</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>e</th>\n",
- " <td>10</td>\n",
- " <td>9</td>\n",
- " <td>11</td>\n",
- " <td>12</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " X Y Z id\n",
- "a 1 0 2 0\n",
- "b 4 3 5 400\n",
- "c 7 6 8 3\n",
- "e 10 9 11 12"
- ]
- },
- "execution_count": 32,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "df[['X', 'Y']] = df[['Y', 'X']]\n",
- "df"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "bda93a31-325f-4a91-a3b8-9ae5c40c552f",
- "metadata": {},
- "source": [
- "## set column as index\n",
- "\n",
- "> https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.set_index.html"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 33,
- "id": "83243ef0-9ec1-4da1-8941-d8316a41d7ae",
- "metadata": {},
- "outputs": [],
- "source": [
- "df2 = df.set_index(\"id\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 34,
- "id": "41eb445c-7d09-4161-9c2d-1cf5afdc58b2",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
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- "source": [
- "df"
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- {
- "cell_type": "code",
- "execution_count": 35,
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- " <td>8</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>12</th>\n",
- " <td>10</td>\n",
- " <td>9</td>\n",
- " <td>11</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
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- " X Y Z\n",
- "id \n",
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- "400 4 3 5\n",
- "3 7 6 8\n",
- "12 10 9 11"
- ]
- },
- "execution_count": 35,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "df2"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 36,
- "id": "da1c2427-e0ce-4dd1-8016-6d338432dc7d",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
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- " <td>8</td>\n",
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- " X Y Z\n",
- "id \n",
- "0 1 0 2\n",
- "400 4 3 5\n",
- "3 7 6 8\n",
- "12 10 9 11"
- ]
- },
- "execution_count": 36,
- "metadata": {},
- "output_type": "execute_result"
- }
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- "source": [
- "df.set_index(\"id\", inplace=True)\n",
- "df"
- ]
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- {
- "cell_type": "markdown",
- "id": "bf269b66-70a2-4c16-8f05-563f3df91b28",
- "metadata": {},
- "source": [
- "## add row\n",
- "\n",
- "> https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.append.html\n",
- "> https://pandas.pydata.org/docs/reference/api/pandas.concat.html"
- ]
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- "id": "a45bc690-d7d1-4987-8923-9b5c7228e06f",
- "metadata": {},
- "outputs": [],
- "source": [
- "rows = pd.DataFrame([[30, 31, 32], [42, 43, 44]], columns=['X', 'Y', 'Z']) "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 38,
- "id": "efe1c6e5-e780-4754-b61e-57489adb0452",
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- " <th>1</th>\n",
- " <td>42</td>\n",
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- " X Y Z\n",
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- "1 42 43 44"
- ]
- },
- "execution_count": 38,
- "metadata": {},
- "output_type": "execute_result"
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- "source": [
- "extended_df = df.append(rows)\n",
- "extended_df"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 39,
- "id": "2cc7683b-cc59-49ab-8a7b-33b3c9b2652d",
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- " X Y Z\n",
- "0 1.0 0.0 2.0\n",
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- "3 10.0 9.0 11.0\n",
- "4 3.0 6.0 9.0"
- ]
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- "extended_df = df.append({'X': 3, 'Y':6.0 , 'Z':9.0}, ignore_index=True)\n",
- "extended_df"
- ]
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- "cell_type": "code",
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- " <th>6</th>\n",
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- " <td>5.0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>7</th>\n",
- " <td>7.0</td>\n",
- " <td>6.0</td>\n",
- " <td>8.0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>8</th>\n",
- " <td>10.0</td>\n",
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- "text/plain": [
- " X Y Z\n",
- "0 1.0 0.0 2.0\n",
- "1 4.0 3.0 5.0\n",
- "2 7.0 6.0 8.0\n",
- "3 10.0 9.0 11.0\n",
- "4 3.0 6.0 9.0\n",
- "5 1.0 0.0 2.0\n",
- "6 4.0 3.0 5.0\n",
- "7 7.0 6.0 8.0\n",
- "8 10.0 9.0 11.0"
- ]
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- "execution_count": 40,
- "metadata": {},
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- "source": [
- "pd.concat([extended_df, df], ignore_index=True)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "051f6a5b-6c74-46fc-8c78-26ba5f44ab8b",
- "metadata": {},
- "source": [
- "# filtering Table\n",
- "\n",
- "> https://pandas.pydata.org/docs/user_guide/indexing.html#indexing"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 41,
- "id": "3befe110-59d3-432b-9fda-7d5a29b1018e",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
- "<style scoped>\n",
- " .dataframe tbody tr th:only-of-type {\n",
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- "</style>\n",
- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>PassengerId</th>\n",
- " <th>Survived</th>\n",
- " <th>Pclass</th>\n",
- " <th>Name</th>\n",
- " <th>Sex</th>\n",
- " <th>Age</th>\n",
- " <th>SibSp</th>\n",
- " <th>Parch</th>\n",
- " <th>Ticket</th>\n",
- " <th>Fare</th>\n",
- " <th>Cabin</th>\n",
- " <th>Embarked</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>0</th>\n",
- " <td>1</td>\n",
- " <td>0</td>\n",
- " <td>3</td>\n",
- " <td>Braund, Mr. Owen Harris</td>\n",
- " <td>male</td>\n",
- " <td>22.0</td>\n",
- " <td>1</td>\n",
- " <td>0</td>\n",
- " <td>A/5 21171</td>\n",
- " <td>7.2500</td>\n",
- " <td>NaN</td>\n",
- " <td>S</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1</th>\n",
- " <td>2</td>\n",
- " <td>1</td>\n",
- " <td>1</td>\n",
- " <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
- " <td>female</td>\n",
- " <td>38.0</td>\n",
- " <td>1</td>\n",
- " <td>0</td>\n",
- " <td>PC 17599</td>\n",
- " <td>71.2833</td>\n",
- " <td>C85</td>\n",
- " <td>C</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2</th>\n",
- " <td>3</td>\n",
- " <td>1</td>\n",
- " <td>3</td>\n",
- " <td>Heikkinen, Miss. Laina</td>\n",
- " <td>female</td>\n",
- " <td>26.0</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>STON/O2. 3101282</td>\n",
- " <td>7.9250</td>\n",
- " <td>NaN</td>\n",
- " <td>S</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>3</th>\n",
- " <td>4</td>\n",
- " <td>1</td>\n",
- " <td>1</td>\n",
- " <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
- " <td>female</td>\n",
- " <td>35.0</td>\n",
- " <td>1</td>\n",
- " <td>0</td>\n",
- " <td>113803</td>\n",
- " <td>53.1000</td>\n",
- " <td>C123</td>\n",
- " <td>S</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4</th>\n",
- " <td>5</td>\n",
- " <td>0</td>\n",
- " <td>3</td>\n",
- " <td>Allen, Mr. William Henry</td>\n",
- " <td>male</td>\n",
- " <td>35.0</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>373450</td>\n",
- " <td>8.0500</td>\n",
- " <td>NaN</td>\n",
- " <td>S</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " PassengerId Survived Pclass \\\n",
- "0 1 0 3 \n",
- "1 2 1 1 \n",
- "2 3 1 3 \n",
- "3 4 1 1 \n",
- "4 5 0 3 \n",
- "\n",
- " Name Sex Age SibSp \\\n",
- "0 Braund, Mr. Owen Harris male 22.0 1 \n",
- "1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 \n",
- "2 Heikkinen, Miss. Laina female 26.0 0 \n",
- "3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 \n",
- "4 Allen, Mr. William Henry male 35.0 0 \n",
- "\n",
- " Parch Ticket Fare Cabin Embarked \n",
- "0 0 A/5 21171 7.2500 NaN S \n",
- "1 0 PC 17599 71.2833 C85 C \n",
- "2 0 STON/O2. 3101282 7.9250 NaN S \n",
- "3 0 113803 53.1000 C123 S \n",
- "4 0 373450 8.0500 NaN S "
- ]
- },
- "execution_count": 41,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "titanic.head()"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "5885d425-efa6-409f-a764-37ae9dbf9f88",
- "metadata": {},
- "source": [
- "## selecting columns"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 42,
- "id": "874ea459-0744-4e35-b5d1-9ac2716bfd2d",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "0 male\n",
- "1 female\n",
- "2 female\n",
- "3 female\n",
- "4 male\n",
- "Name: Sex, dtype: object"
- ]
- },
- "execution_count": 42,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "titanic['Sex'].head()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 43,
- "id": "ea4fe1c3-2e57-4a7b-b298-adc9477bfeb8",
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- "outputs": [
- {
- "data": {
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- "<div>\n",
- "<style scoped>\n",
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- " <th></th>\n",
- " <th>Sex</th>\n",
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- " <tr>\n",
- " <th>1</th>\n",
- " <td>female</td>\n",
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- " <td>1</td>\n",
- " <td>1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2</th>\n",
- " <td>female</td>\n",
- " <td>26.0</td>\n",
- " <td>3</td>\n",
- " <td>1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>3</th>\n",
- " <td>female</td>\n",
- " <td>35.0</td>\n",
- " <td>1</td>\n",
- " <td>1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4</th>\n",
- " <td>male</td>\n",
- " <td>35.0</td>\n",
- " <td>3</td>\n",
- " <td>0</td>\n",
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- "text/plain": [
- " Sex Age Pclass Survived\n",
- "0 male 22.0 3 0\n",
- "1 female 38.0 1 1\n",
- "2 female 26.0 3 1\n",
- "3 female 35.0 1 1\n",
- "4 male 35.0 3 0"
- ]
- },
- "execution_count": 43,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "titanic[['Sex', 'Age', 'Pclass', 'Survived']].head()"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "727d9757-3948-4e4a-96ca-0a1a0a2a7f1f",
- "metadata": {},
- "source": [
- "## Selecting on a condition"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 44,
- "id": "70d22f9b-986c-48dc-b908-84ce12d3f2d5",
- "metadata": {},
- "outputs": [
- {
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- "<div>\n",
- "<style scoped>\n",
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- " .dataframe tbody tr th {\n",
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- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>PassengerId</th>\n",
- " <th>Survived</th>\n",
- " <th>Pclass</th>\n",
- " <th>Name</th>\n",
- " <th>Sex</th>\n",
- " <th>Age</th>\n",
- " <th>SibSp</th>\n",
- " <th>Parch</th>\n",
- " <th>Ticket</th>\n",
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- " <th>Embarked</th>\n",
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- " <tbody>\n",
- " <tr>\n",
- " <th>1</th>\n",
- " <td>2</td>\n",
- " <td>1</td>\n",
- " <td>1</td>\n",
- " <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
- " <td>female</td>\n",
- " <td>38.0</td>\n",
- " <td>1</td>\n",
- " <td>0</td>\n",
- " <td>PC 17599</td>\n",
- " <td>71.2833</td>\n",
- " <td>C85</td>\n",
- " <td>C</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2</th>\n",
- " <td>3</td>\n",
- " <td>1</td>\n",
- " <td>3</td>\n",
- " <td>Heikkinen, Miss. Laina</td>\n",
- " <td>female</td>\n",
- " <td>26.0</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>STON/O2. 3101282</td>\n",
- " <td>7.9250</td>\n",
- " <td>NaN</td>\n",
- " <td>S</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>3</th>\n",
- " <td>4</td>\n",
- " <td>1</td>\n",
- " <td>1</td>\n",
- " <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
- " <td>female</td>\n",
- " <td>35.0</td>\n",
- " <td>1</td>\n",
- " <td>0</td>\n",
- " <td>113803</td>\n",
- " <td>53.1000</td>\n",
- " <td>C123</td>\n",
- " <td>S</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4</th>\n",
- " <td>5</td>\n",
- " <td>0</td>\n",
- " <td>3</td>\n",
- " <td>Allen, Mr. William Henry</td>\n",
- " <td>male</td>\n",
- " <td>35.0</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>373450</td>\n",
- " <td>8.0500</td>\n",
- " <td>NaN</td>\n",
- " <td>S</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>6</th>\n",
- " <td>7</td>\n",
- " <td>0</td>\n",
- " <td>1</td>\n",
- " <td>McCarthy, Mr. Timothy J</td>\n",
- " <td>male</td>\n",
- " <td>54.0</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>17463</td>\n",
- " <td>51.8625</td>\n",
- " <td>E46</td>\n",
- " <td>S</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " PassengerId Survived Pclass \\\n",
- "1 2 1 1 \n",
- "2 3 1 3 \n",
- "3 4 1 1 \n",
- "4 5 0 3 \n",
- "6 7 0 1 \n",
- "\n",
- " Name Sex Age SibSp \\\n",
- "1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 \n",
- "2 Heikkinen, Miss. Laina female 26.0 0 \n",
- "3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 \n",
- "4 Allen, Mr. William Henry male 35.0 0 \n",
- "6 McCarthy, Mr. Timothy J male 54.0 0 \n",
- "\n",
- " Parch Ticket Fare Cabin Embarked \n",
- "1 0 PC 17599 71.2833 C85 C \n",
- "2 0 STON/O2. 3101282 7.9250 NaN S \n",
- "3 0 113803 53.1000 C123 S \n",
- "4 0 373450 8.0500 NaN S \n",
- "6 0 17463 51.8625 E46 S "
- ]
- },
- "execution_count": 44,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "titanic[titanic['Age'] > 25].head()"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "47db1f49-85e3-4a42-a36b-efc13c795a86",
- "metadata": {},
- "source": [
- "## Indexing/Slicing\n",
- "\n",
- "> https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html\n",
- "\n",
- "### loc vs iloc\n",
- "\n",
- "**.loc** is primarily label based, but may also be used with a boolean array. <br />\n",
- "\n",
- "**.iloc** is primarily integer position based (from 0 to length-1 of the axis), but may also be used with a boolean array. \n",
- "\n",
- "The both methods use the same syntax as numpy indexing/slicing"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 45,
- "id": "053d851a-b3be-4b08-8fae-b702c690654e",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
- "<style scoped>\n",
- " .dataframe tbody tr th:only-of-type {\n",
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- " .dataframe tbody tr th {\n",
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- " }\n",
- "\n",
- " .dataframe thead th {\n",
- " text-align: right;\n",
- " }\n",
- "</style>\n",
- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>Sex</th>\n",
- " <th>Age</th>\n",
- " </tr>\n",
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- " <tbody>\n",
- " <tr>\n",
- " <th>1</th>\n",
- " <td>female</td>\n",
- " <td>38.0</td>\n",
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- " <tr>\n",
- " <th>2</th>\n",
- " <td>female</td>\n",
- " <td>26.0</td>\n",
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- "text/plain": [
- " Sex Age\n",
- "1 female 38.0\n",
- "2 female 26.0"
- ]
- },
- "execution_count": 45,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "titanic.loc[[1,2], ['Sex', 'Age']]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 46,
- "id": "8c1b161b-08e8-467d-b4c5-a24d1d9741d0",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
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- " <th>Sex</th>\n",
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- " <th>1</th>\n",
- " <td>female</td>\n",
- " <td>38.0</td>\n",
- " <td>1</td>\n",
- " <td>0</td>\n",
- " <td>PC 17599</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2</th>\n",
- " <td>female</td>\n",
- " <td>26.0</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>STON/O2. 3101282</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>3</th>\n",
- " <td>female</td>\n",
- " <td>35.0</td>\n",
- " <td>1</td>\n",
- " <td>0</td>\n",
- " <td>113803</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4</th>\n",
- " <td>male</td>\n",
- " <td>35.0</td>\n",
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- ],
- "text/plain": [
- " Sex Age SibSp Parch Ticket\n",
- "1 female 38.0 1 0 PC 17599\n",
- "2 female 26.0 0 0 STON/O2. 3101282\n",
- "3 female 35.0 1 0 113803\n",
- "4 male 35.0 0 0 373450"
- ]
- },
- "execution_count": 46,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "titanic.loc[1:4, 'Sex':'Ticket'] # Ticket column is included"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 47,
- "id": "00ed9540-5843-4160-8c1c-65201b248ae4",
- "metadata": {},
- "outputs": [
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- "data": {
- "text/html": [
- "<div>\n",
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- " }\n",
- "\n",
- " .dataframe thead th {\n",
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- "</style>\n",
- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>Sex</th>\n",
- " <th>Age</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>0</th>\n",
- " <td>male</td>\n",
- " <td>22.0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1</th>\n",
- " <td>female</td>\n",
- " <td>38.0</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " Sex Age\n",
- "0 male 22.0\n",
- "1 female 38.0"
- ]
- },
- "execution_count": 47,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "titanic.iloc[[0,1], [4, 5]]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 48,
- "id": "e9c95e97-debb-46e3-bbf5-d2981b807369",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
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- " <th>0</th>\n",
- " <td>male</td>\n",
- " <td>22.0</td>\n",
- " <td>1</td>\n",
- " <td>0</td>\n",
- " <td>A/5 21171</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1</th>\n",
- " <td>female</td>\n",
- " <td>38.0</td>\n",
- " <td>1</td>\n",
- " <td>0</td>\n",
- " <td>PC 17599</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2</th>\n",
- " <td>female</td>\n",
- " <td>26.0</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>STON/O2. 3101282</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " Sex Age SibSp Parch Ticket\n",
- "0 male 22.0 1 0 A/5 21171\n",
- "1 female 38.0 1 0 PC 17599\n",
- "2 female 26.0 0 0 STON/O2. 3101282"
- ]
- },
- "execution_count": 48,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "titanic.iloc[0:3, 4:9] # the 9th column is exclude"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 49,
- "id": "b99f6265-355c-439e-8820-7e74f833da24",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
- "<style scoped>\n",
- " .dataframe tbody tr th:only-of-type {\n",
- " vertical-align: middle;\n",
- " }\n",
- "\n",
- " .dataframe tbody tr th {\n",
- " vertical-align: top;\n",
- " }\n",
- "\n",
- " .dataframe thead th {\n",
- " text-align: right;\n",
- " }\n",
- "</style>\n",
- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>Name</th>\n",
- " <th>Age</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>183</th>\n",
- " <td>Becker, Master. Richard F</td>\n",
- " <td>1.0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>248</th>\n",
- " <td>Beckwith, Mr. Richard Leonard</td>\n",
- " <td>37.0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>618</th>\n",
- " <td>Becker, Miss. Marion Louise</td>\n",
- " <td>4.0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>871</th>\n",
- " <td>Beckwith, Mrs. Richard Leonard (Sallie Monypeny)</td>\n",
- " <td>47.0</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " Name Age\n",
- "183 Becker, Master. Richard F 1.0\n",
- "248 Beckwith, Mr. Richard Leonard 37.0\n",
- "618 Becker, Miss. Marion Louise 4.0\n",
- "871 Beckwith, Mrs. Richard Leonard (Sallie Monypeny) 47.0"
- ]
- },
- "execution_count": 49,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "mask = titanic['Name'].str.contains('^Bec')\n",
- "titanic[mask][['Name', 'Age']]"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "72ce37a8-65f0-471e-896e-8d5dbeaf51ab",
- "metadata": {},
- "source": [
- "## Selecting random samples\n",
- "\n",
- "> https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.sample.html"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 50,
- "id": "cf240646-4782-49e3-8a49-86915d95188b",
- "metadata": {},
- "outputs": [
- {
- "data": {
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- "<div>\n",
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- "</style>\n",
- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>PassengerId</th>\n",
- " <th>Survived</th>\n",
- " <th>Pclass</th>\n",
- " <th>Name</th>\n",
- " <th>Sex</th>\n",
- " <th>Age</th>\n",
- " <th>SibSp</th>\n",
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- " <tbody>\n",
- " <tr>\n",
- " <th>533</th>\n",
- " <td>534</td>\n",
- " <td>1</td>\n",
- " <td>3</td>\n",
- " <td>Peter, Mrs. Catherine (Catherine Rizk)</td>\n",
- " <td>female</td>\n",
- " <td>NaN</td>\n",
- " <td>0</td>\n",
- " <td>2</td>\n",
- " <td>2668</td>\n",
- " <td>22.3583</td>\n",
- " <td>NaN</td>\n",
- " <td>C</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>810</th>\n",
- " <td>811</td>\n",
- " <td>0</td>\n",
- " <td>3</td>\n",
- " <td>Alexander, Mr. William</td>\n",
- " <td>male</td>\n",
- " <td>26.0</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>3474</td>\n",
- " <td>7.8875</td>\n",
- " <td>NaN</td>\n",
- " <td>S</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>319</th>\n",
- " <td>320</td>\n",
- " <td>1</td>\n",
- " <td>1</td>\n",
- " <td>Spedden, Mrs. Frederic Oakley (Margaretta Corn...</td>\n",
- " <td>female</td>\n",
- " <td>40.0</td>\n",
- " <td>1</td>\n",
- " <td>1</td>\n",
- " <td>16966</td>\n",
- " <td>134.5000</td>\n",
- " <td>E34</td>\n",
- " <td>C</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>874</th>\n",
- " <td>875</td>\n",
- " <td>1</td>\n",
- " <td>2</td>\n",
- " <td>Abelson, Mrs. Samuel (Hannah Wizosky)</td>\n",
- " <td>female</td>\n",
- " <td>28.0</td>\n",
- " <td>1</td>\n",
- " <td>0</td>\n",
- " <td>P/PP 3381</td>\n",
- " <td>24.0000</td>\n",
- " <td>NaN</td>\n",
- " <td>C</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>244</th>\n",
- " <td>245</td>\n",
- " <td>0</td>\n",
- " <td>3</td>\n",
- " <td>Attalah, Mr. Sleiman</td>\n",
- " <td>male</td>\n",
- " <td>30.0</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>2694</td>\n",
- " <td>7.2250</td>\n",
- " <td>NaN</td>\n",
- " <td>C</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>408</th>\n",
- " <td>409</td>\n",
- " <td>0</td>\n",
- " <td>3</td>\n",
- " <td>Birkeland, Mr. Hans Martin Monsen</td>\n",
- " <td>male</td>\n",
- " <td>21.0</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>312992</td>\n",
- " <td>7.7750</td>\n",
- " <td>NaN</td>\n",
- " <td>S</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " PassengerId Survived Pclass \\\n",
- "533 534 1 3 \n",
- "810 811 0 3 \n",
- "319 320 1 1 \n",
- "874 875 1 2 \n",
- "244 245 0 3 \n",
- "408 409 0 3 \n",
- "\n",
- " Name Sex Age SibSp \\\n",
- "533 Peter, Mrs. Catherine (Catherine Rizk) female NaN 0 \n",
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- "351 male NaN 0 0 113510 35.000 C128 S \n",
- "456 male 65.0 0 0 13509 26.550 E38 S "
- ]
- },
- "execution_count": 55,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "titanic[titanic['PassengerId'].isin([457, 352, 174])]"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "27f7d2b6-b831-413b-ad45-a9083ab37bc4",
- "metadata": {},
- "source": [
- "## where\n",
- "\n",
- "Where cond is **True**, **keep the original** value.<br />\n",
- "Where **False**, **replace** with corresponding value from other.\n",
- "\n",
- "> https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.where.html"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 56,
- "id": "16c93799-d95c-4bf0-801e-a8296c4b53bd",
- "metadata": {},
- "outputs": [
- {
- "data": {
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- "<div>\n",
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- " A B\n",
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- ]
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- "execution_count": 56,
- "metadata": {},
- "output_type": "execute_result"
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- "source": [
- "df = pd.DataFrame(np.arange(-4, 6).reshape(-1, 2), columns=['A', 'B'])\n",
- "df"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 57,
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- "source": [
- "df.where(df < 0 , 0)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 58,
- "id": "7ca9826a-df8e-4dc7-a401-eb6b1ecaece7",
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- " A B\n",
- "0 4 3\n",
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- },
- "execution_count": 58,
- "metadata": {},
- "output_type": "execute_result"
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- ],
- "source": [
- "df.where(df > 0 , -df)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "02dca349-2f89-46ca-a217-b014a6ebc2ba",
- "metadata": {},
- "source": [
- "## mask\n",
- "\n",
- "Replace values where the condition is False.\n",
- "\n",
- "> https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.where.html"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 59,
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- " A B\n",
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- "execution_count": 59,
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- ],
- "source": [
- "df.mask(df < 0, 0)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 60,
- "id": "fdac6aa6-6be5-4748-a15f-31cccdf96c05",
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- {
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- " <td>2</td>\n",
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- " A B\n",
- "0 4 3\n",
- "1 2 1\n",
- "2 0 1\n",
- "3 2 3\n",
- "4 4 5"
- ]
- },
- "execution_count": 60,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "df.mask(df < 0, -df)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "ebdbb1c1-3b8f-4ed8-baf8-52ef3f902a60",
- "metadata": {},
- "source": [
- "## query\n",
- "\n",
- "Query the columns of a DataFrame with a boolean expression.\n",
- "\n",
- "> https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.query.html"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 61,
- "id": "190ce486-2e50-40c5-9492-b8242fc9fe7e",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
- "<style scoped>\n",
- " .dataframe tbody tr th:only-of-type {\n",
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- " .dataframe tbody tr th {\n",
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- "\n",
- " .dataframe thead th {\n",
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- "</style>\n",
- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>MW</th>\n",
- " <th>AlogP</th>\n",
- " <th>PSA</th>\n",
- " <th>HBA</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>count</th>\n",
- " <td>4742.000000</td>\n",
- " <td>4742.000000</td>\n",
- " <td>4742.000000</td>\n",
- " <td>4742.000000</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>mean</th>\n",
- " <td>2.789844</td>\n",
- " <td>349.476255</td>\n",
- " <td>251.191263</td>\n",
- " <td>4.931060</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>std</th>\n",
- " <td>2.334380</td>\n",
- " <td>131.676331</td>\n",
- " <td>144.644436</td>\n",
- " <td>5.005337</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>min</th>\n",
- " <td>-11.100000</td>\n",
- " <td>1.000000</td>\n",
- " <td>0.096189</td>\n",
- " <td>-12.056136</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>25%</th>\n",
- " <td>1.460000</td>\n",
- " <td>261.360000</td>\n",
- " <td>126.591665</td>\n",
- " <td>1.573914</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>50%</th>\n",
- " <td>2.830000</td>\n",
- " <td>332.350000</td>\n",
- " <td>251.855772</td>\n",
- " <td>4.901533</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>75%</th>\n",
- " <td>4.170000</td>\n",
- " <td>420.565000</td>\n",
- " <td>374.808373</td>\n",
- " <td>8.251959</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>max</th>\n",
- " <td>15.490000</td>\n",
- " <td>992.860000</td>\n",
- " <td>499.851862</td>\n",
- " <td>22.449167</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " MW AlogP PSA HBA\n",
- "count 4742.000000 4742.000000 4742.000000 4742.000000\n",
- "mean 2.789844 349.476255 251.191263 4.931060\n",
- "std 2.334380 131.676331 144.644436 5.005337\n",
- "min -11.100000 1.000000 0.096189 -12.056136\n",
- "25% 1.460000 261.360000 126.591665 1.573914\n",
- "50% 2.830000 332.350000 251.855772 4.901533\n",
- "75% 4.170000 420.565000 374.808373 8.251959\n",
- "max 15.490000 992.860000 499.851862 22.449167"
- ]
- },
- "execution_count": 61,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "data = pd.read_csv(\"data/data_for_plt.csv\", sep=\"\\t\", index_col=0)\n",
- "data.describe()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 62,
- "id": "b14f5cc1-3536-48de-a987-5edad6738662",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
- "<style scoped>\n",
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- " .dataframe tbody tr th {\n",
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- "\n",
- " .dataframe thead th {\n",
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- " }\n",
- "</style>\n",
- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>MW</th>\n",
- " <th>AlogP</th>\n",
- " <th>PSA</th>\n",
- " <th>HBA</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>0</th>\n",
- " <td>0.00</td>\n",
- " <td>1.00</td>\n",
- " <td>72.731113</td>\n",
- " <td>1.141668</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2</th>\n",
- " <td>2.11</td>\n",
- " <td>383.40</td>\n",
- " <td>437.458982</td>\n",
- " <td>15.040385</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>3</th>\n",
- " <td>1.24</td>\n",
- " <td>162.23</td>\n",
- " <td>480.111263</td>\n",
- " <td>11.401907</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4</th>\n",
- " <td>-1.37</td>\n",
- " <td>361.37</td>\n",
- " <td>448.864769</td>\n",
- " <td>5.732690</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>5</th>\n",
- " <td>1.18</td>\n",
- " <td>232.24</td>\n",
- " <td>340.062690</td>\n",
- " <td>-1.295778</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>...</th>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4708</th>\n",
- " <td>0.76</td>\n",
- " <td>178.23</td>\n",
- " <td>305.444063</td>\n",
- " <td>5.792319</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4723</th>\n",
- " <td>5.13</td>\n",
- " <td>368.42</td>\n",
- " <td>487.539129</td>\n",
- " <td>6.255837</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4735</th>\n",
- " <td>2.88</td>\n",
- " <td>230.35</td>\n",
- " <td>434.232441</td>\n",
- " <td>1.938911</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4739</th>\n",
- " <td>4.31</td>\n",
- " <td>348.44</td>\n",
- " <td>413.058441</td>\n",
- " <td>3.298288</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4741</th>\n",
- " <td>3.25</td>\n",
- " <td>298.40</td>\n",
- " <td>405.732817</td>\n",
- " <td>7.153158</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "<p>1501 rows × 4 columns</p>\n",
- "</div>"
- ],
- "text/plain": [
- " MW AlogP PSA HBA\n",
- "0 0.00 1.00 72.731113 1.141668\n",
- "2 2.11 383.40 437.458982 15.040385\n",
- "3 1.24 162.23 480.111263 11.401907\n",
- "4 -1.37 361.37 448.864769 5.732690\n",
- "5 1.18 232.24 340.062690 -1.295778\n",
- "... ... ... ... ...\n",
- "4708 0.76 178.23 305.444063 5.792319\n",
- "4723 5.13 368.42 487.539129 6.255837\n",
- "4735 2.88 230.35 434.232441 1.938911\n",
- "4739 4.31 348.44 413.058441 3.298288\n",
- "4741 3.25 298.40 405.732817 7.153158\n",
- "\n",
- "[1501 rows x 4 columns]"
- ]
- },
- "execution_count": 62,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "data.query('AlogP + HBA < PSA')"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "2d68ce23-76e9-40cf-8fc3-a393e5f0a506",
- "metadata": {},
- "source": [
- "> You can refer to variables in the environment by prefixing them with an ‘@’ character "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 63,
- "id": "71a5d6df-67e2-4b49-8125-e1d7ffc5bc22",
- "metadata": {},
- "outputs": [],
- "source": [
- "# let's create a new dataframe\n",
- "df2 = pd.DataFrame([np.random.normal(loc=6.0, scale=3.0, size=4742),\n",
- " np.random.uniform(low=2, high=5, size=4742)]).T\n",
- "df2.columns = ['HBC', 'Coef_B']"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "ed7eb87d-0a38-4a9c-9b06-84187f9a583e",
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "code",
- "execution_count": 64,
- "id": "2a3ec8bf-7597-4caf-9803-d827a2e5c4c9",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
- "<style scoped>\n",
- " .dataframe tbody tr th:only-of-type {\n",
- " vertical-align: middle;\n",
- " }\n",
- "\n",
- " .dataframe tbody tr th {\n",
- " vertical-align: top;\n",
- " }\n",
- "\n",
- " .dataframe thead th {\n",
- " text-align: right;\n",
- " }\n",
- "</style>\n",
- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>MW</th>\n",
- " <th>AlogP</th>\n",
- " <th>PSA</th>\n",
- " <th>HBA</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>1</th>\n",
- " <td>3.63</td>\n",
- " <td>544.59</td>\n",
- " <td>391.427565</td>\n",
- " <td>0.984864</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>5</th>\n",
- " <td>1.18</td>\n",
- " <td>232.24</td>\n",
- " <td>340.062690</td>\n",
- " <td>-1.295778</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>6</th>\n",
- " <td>4.24</td>\n",
- " <td>357.79</td>\n",
- " <td>444.719542</td>\n",
- " <td>5.821089</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>7</th>\n",
- " <td>-1.27</td>\n",
- " <td>331.34</td>\n",
- " <td>340.070827</td>\n",
- " <td>0.672629</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>8</th>\n",
- " <td>-1.41</td>\n",
- " <td>319.33</td>\n",
- " <td>240.241820</td>\n",
- " <td>8.409007</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>...</th>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4737</th>\n",
- " <td>2.37</td>\n",
- " <td>310.34</td>\n",
- " <td>258.286559</td>\n",
- " <td>-1.405512</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4738</th>\n",
- " <td>7.14</td>\n",
- " <td>654.96</td>\n",
- " <td>381.644852</td>\n",
- " <td>0.070478</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4739</th>\n",
- " <td>4.31</td>\n",
- " <td>348.44</td>\n",
- " <td>413.058441</td>\n",
- " <td>3.298288</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4740</th>\n",
- " <td>3.22</td>\n",
- " <td>284.38</td>\n",
- " <td>7.701971</td>\n",
- " <td>2.080299</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4741</th>\n",
- " <td>3.25</td>\n",
- " <td>298.40</td>\n",
- " <td>405.732817</td>\n",
- " <td>7.153158</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "<p>2744 rows × 4 columns</p>\n",
- "</div>"
- ],
- "text/plain": [
- " MW AlogP PSA HBA\n",
- "1 3.63 544.59 391.427565 0.984864\n",
- "5 1.18 232.24 340.062690 -1.295778\n",
- "6 4.24 357.79 444.719542 5.821089\n",
- "7 -1.27 331.34 340.070827 0.672629\n",
- "8 -1.41 319.33 240.241820 8.409007\n",
- "... ... ... ... ...\n",
- "4737 2.37 310.34 258.286559 -1.405512\n",
- "4738 7.14 654.96 381.644852 0.070478\n",
- "4739 4.31 348.44 413.058441 3.298288\n",
- "4740 3.22 284.38 7.701971 2.080299\n",
- "4741 3.25 298.40 405.732817 7.153158\n",
- "\n",
- "[2744 rows x 4 columns]"
- ]
- },
- "execution_count": 64,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# keep rows where HBA from data is lesser tha columns HBC in df2\n",
- "# note that the 2 data must have the same lenght\n",
- "data.query('HBA < @df2.HBC')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 65,
- "id": "a2918ba9-695c-4d7d-9f21-7ce7baf5b29d",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
- "<style scoped>\n",
- " .dataframe tbody tr th:only-of-type {\n",
- " vertical-align: middle;\n",
- " }\n",
- "\n",
- " .dataframe tbody tr th {\n",
- " vertical-align: top;\n",
- " }\n",
- "\n",
- " .dataframe thead th {\n",
- " text-align: right;\n",
- " }\n",
- "</style>\n",
- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>Name</th>\n",
- " <th>Sex</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>581</th>\n",
- " <td>Thayer, Mrs. John Borland (Marian Longstreth M...</td>\n",
- " <td>female</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>637</th>\n",
- " <td>Collyer, Mr. Harvey</td>\n",
- " <td>male</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>699</th>\n",
- " <td>Humblen, Mr. Adolf Mathias Nicolai Olsen</td>\n",
- " <td>male</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>252</th>\n",
- " <td>Stead, Mr. William Thomas</td>\n",
- " <td>male</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " Name Sex\n",
- "581 Thayer, Mrs. John Borland (Marian Longstreth M... female\n",
- "637 Collyer, Mr. Harvey male\n",
- "699 Humblen, Mr. Adolf Mathias Nicolai Olsen male\n",
- "252 Stead, Mr. William Thomas male"
- ]
- },
- "execution_count": 65,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "vip = titanic.sample(100)[['Name', 'Sex']]\n",
- "vip.head(4)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 66,
- "id": "279b560e-46a0-4107-b8ac-0c0f13d29e66",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "(891, 12)"
- ]
- },
- "execution_count": 66,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "titanic.shape"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 67,
- "id": "408dedf2-df56-4b8e-8fc8-795e8f77e00a",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
- "<style scoped>\n",
- " .dataframe tbody tr th:only-of-type {\n",
- " vertical-align: middle;\n",
- " }\n",
- "\n",
- " .dataframe tbody tr th {\n",
- " vertical-align: top;\n",
- " }\n",
- "\n",
- " .dataframe thead th {\n",
- " text-align: right;\n",
- " }\n",
- "</style>\n",
- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>PassengerId</th>\n",
- " <th>Survived</th>\n",
- " <th>Pclass</th>\n",
- " <th>Name</th>\n",
- " <th>Sex</th>\n",
- " <th>Age</th>\n",
- " <th>SibSp</th>\n",
- " <th>Parch</th>\n",
- " <th>Ticket</th>\n",
- " <th>Fare</th>\n",
- " <th>Cabin</th>\n",
- " <th>Embarked</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>0</th>\n",
- " <td>1</td>\n",
- " <td>0</td>\n",
- " <td>3</td>\n",
- " <td>Braund, Mr. Owen Harris</td>\n",
- " <td>male</td>\n",
- " <td>22.0</td>\n",
- " <td>1</td>\n",
- " <td>0</td>\n",
- " <td>A/5 21171</td>\n",
- " <td>7.2500</td>\n",
- " <td>NaN</td>\n",
- " <td>S</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1</th>\n",
- " <td>2</td>\n",
- " <td>1</td>\n",
- " <td>1</td>\n",
- " <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
- " <td>female</td>\n",
- " <td>38.0</td>\n",
- " <td>1</td>\n",
- " <td>0</td>\n",
- " <td>PC 17599</td>\n",
- " <td>71.2833</td>\n",
- " <td>C85</td>\n",
- " <td>C</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " PassengerId Survived Pclass \\\n",
- "0 1 0 3 \n",
- "1 2 1 1 \n",
- "\n",
- " Name Sex Age SibSp \\\n",
- "0 Braund, Mr. Owen Harris male 22.0 1 \n",
- "1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 \n",
- "\n",
- " Parch Ticket Fare Cabin Embarked \n",
- "0 0 A/5 21171 7.2500 NaN S \n",
- "1 0 PC 17599 71.2833 C85 C "
- ]
- },
- "execution_count": 67,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "titanic_vip = titanic.query('Name in @vip.Name')\n",
- "titanic.head(2)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 68,
- "id": "9e5ea45c-e6c4-422b-8dd6-46c3767768d4",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "(100, 12)"
- ]
- },
- "execution_count": 68,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "titanic_vip.shape"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "cd79d151-6c63-4b47-917c-8118eafdb4be",
- "metadata": {},
- "source": [
- "## drop_duplicate\n",
- "\n",
- "Return DataFrame with duplicate rows removed.\n",
- "\n",
- "> https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.drop_duplicates.html"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 69,
- "id": "9f3f3bc3-d35d-4e6b-902b-988d67029298",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
- "<style scoped>\n",
- " .dataframe tbody tr th:only-of-type {\n",
- " vertical-align: middle;\n",
- " }\n",
- "\n",
- " .dataframe tbody tr th {\n",
- " vertical-align: top;\n",
- " }\n",
- "\n",
- " .dataframe thead th {\n",
- " text-align: right;\n",
- " }\n",
- "</style>\n",
- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>brand</th>\n",
- " <th>style</th>\n",
- " <th>rating</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>0</th>\n",
- " <td>Yum Yum</td>\n",
- " <td>cup</td>\n",
- " <td>4.0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1</th>\n",
- " <td>Yum Yum</td>\n",
- " <td>cup</td>\n",
- " <td>4.0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2</th>\n",
- " <td>Indomie</td>\n",
- " <td>cup</td>\n",
- " <td>3.5</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>3</th>\n",
- " <td>Indomie</td>\n",
- " <td>pack</td>\n",
- " <td>15.0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4</th>\n",
- " <td>Indomie</td>\n",
- " <td>pack</td>\n",
- " <td>5.0</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " brand style rating\n",
- "0 Yum Yum cup 4.0\n",
- "1 Yum Yum cup 4.0\n",
- "2 Indomie cup 3.5\n",
- "3 Indomie pack 15.0\n",
- "4 Indomie pack 5.0"
- ]
- },
- "execution_count": 69,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "df = pd.DataFrame({\n",
- " 'brand': ['Yum Yum', 'Yum Yum', 'Indomie', 'Indomie', 'Indomie'],\n",
- " 'style': ['cup', 'cup', 'cup', 'pack', 'pack'],\n",
- " 'rating': [4, 4, 3.5, 15, 5]\n",
- "})\n",
- "df"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 70,
- "id": "903be0e3-ffa7-4a34-95a1-83397d2ecbc8",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
- "<style scoped>\n",
- " .dataframe tbody tr th:only-of-type {\n",
- " vertical-align: middle;\n",
- " }\n",
- "\n",
- " .dataframe tbody tr th {\n",
- " vertical-align: top;\n",
- " }\n",
- "\n",
- " .dataframe thead th {\n",
- " text-align: right;\n",
- " }\n",
- "</style>\n",
- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>brand</th>\n",
- " <th>style</th>\n",
- " <th>rating</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>0</th>\n",
- " <td>Yum Yum</td>\n",
- " <td>cup</td>\n",
- " <td>4.0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2</th>\n",
- " <td>Indomie</td>\n",
- " <td>cup</td>\n",
- " <td>3.5</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>3</th>\n",
- " <td>Indomie</td>\n",
- " <td>pack</td>\n",
- " <td>15.0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4</th>\n",
- " <td>Indomie</td>\n",
- " <td>pack</td>\n",
- " <td>5.0</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " brand style rating\n",
- "0 Yum Yum cup 4.0\n",
- "2 Indomie cup 3.5\n",
- "3 Indomie pack 15.0\n",
- "4 Indomie pack 5.0"
- ]
- },
- "execution_count": 70,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# By default, it removes duplicate rows based on all columns\n",
- "df.drop_duplicates()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 71,
- "id": "2a4a437f-36fa-483f-8ba1-0a9f5745f516",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
- "<style scoped>\n",
- " .dataframe tbody tr th:only-of-type {\n",
- " vertical-align: middle;\n",
- " }\n",
- "\n",
- " .dataframe tbody tr th {\n",
- " vertical-align: top;\n",
- " }\n",
- "\n",
- " .dataframe thead th {\n",
- " text-align: right;\n",
- " }\n",
- "</style>\n",
- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>brand</th>\n",
- " <th>style</th>\n",
- " <th>rating</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>0</th>\n",
- " <td>Yum Yum</td>\n",
- " <td>cup</td>\n",
- " <td>4.0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2</th>\n",
- " <td>Indomie</td>\n",
- " <td>cup</td>\n",
- " <td>3.5</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " brand style rating\n",
- "0 Yum Yum cup 4.0\n",
- "2 Indomie cup 3.5"
- ]
- },
- "execution_count": 71,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "#To remove duplicates on specific column(s), use subset.\n",
- "df.drop_duplicates(subset=['brand'])"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 72,
- "id": "dd31f31d-7803-4413-ada4-d85b80486ffe",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
- "<style scoped>\n",
- " .dataframe tbody tr th:only-of-type {\n",
- " vertical-align: middle;\n",
- " }\n",
- "\n",
- " .dataframe tbody tr th {\n",
- " vertical-align: top;\n",
- " }\n",
- "\n",
- " .dataframe thead th {\n",
- " text-align: right;\n",
- " }\n",
- "</style>\n",
- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>brand</th>\n",
- " <th>style</th>\n",
- " <th>rating</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>1</th>\n",
- " <td>Yum Yum</td>\n",
- " <td>cup</td>\n",
- " <td>4.0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2</th>\n",
- " <td>Indomie</td>\n",
- " <td>cup</td>\n",
- " <td>3.5</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4</th>\n",
- " <td>Indomie</td>\n",
- " <td>pack</td>\n",
- " <td>5.0</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " brand style rating\n",
- "1 Yum Yum cup 4.0\n",
- "2 Indomie cup 3.5\n",
- "4 Indomie pack 5.0"
- ]
- },
- "execution_count": 72,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# To remove duplicates and keep last occurrences, use keep.\n",
- "df.drop_duplicates(subset=['brand', 'style'], keep='last')"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "673b58f8-af09-428f-94a9-40304e7a346c",
- "metadata": {},
- "source": [
- "# groupby"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 73,
- "id": "85a8024c-cdfc-4cf9-a143-f4032b916cf7",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "##################### 1 #########################\n",
- " PassengerId Survived Pclass Sex Age\n",
- "1 2 1 1 female 38.0\n",
- "3 4 1 1 female 35.0\n",
- "6 7 0 1 male 54.0\n",
- "11 12 1 1 female 58.0\n",
- "23 24 1 1 male 28.0\n",
- ".. ... ... ... ... ...\n",
- "871 872 1 1 female 47.0\n",
- "872 873 0 1 male 33.0\n",
- "879 880 1 1 female 56.0\n",
- "887 888 1 1 female 19.0\n",
- "889 890 1 1 male 26.0\n",
- "\n",
- "[216 rows x 5 columns]\n",
- "##################### 2 #########################\n",
- " PassengerId Survived Pclass Sex Age\n",
- "9 10 1 2 female 14.0\n",
- "15 16 1 2 female 55.0\n",
- "17 18 1 2 male NaN\n",
- "20 21 0 2 male 35.0\n",
- "21 22 1 2 male 34.0\n",
- ".. ... ... ... ... ...\n",
- "866 867 1 2 female 27.0\n",
- "874 875 1 2 female 28.0\n",
- "880 881 1 2 female 25.0\n",
- "883 884 0 2 male 28.0\n",
- "886 887 0 2 male 27.0\n",
- "\n",
- "[184 rows x 5 columns]\n",
- "##################### 3 #########################\n",
- " PassengerId Survived Pclass Sex Age\n",
- "0 1 0 3 male 22.0\n",
- "2 3 1 3 female 26.0\n",
- "4 5 0 3 male 35.0\n",
- "5 6 0 3 male NaN\n",
- "7 8 0 3 male 2.0\n",
- ".. ... ... ... ... ...\n",
- "882 883 0 3 female 22.0\n",
- "884 885 0 3 male 25.0\n",
- "885 886 0 3 female 39.0\n",
- "888 889 0 3 female NaN\n",
- "890 891 0 3 male 32.0\n",
- "\n",
- "[491 rows x 5 columns]\n"
- ]
- }
- ],
- "source": [
- "for p_class, df in titanic.groupby('Pclass'):\n",
- " print(f\"##################### {p_class} #########################\")\n",
- " print(df[['PassengerId', 'Survived', 'Pclass', 'Sex', 'Age']])"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 74,
- "id": "b106defa-9762-4fdf-831a-863b38510c39",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "##################### 1 #########################\n",
- "================== female =================\n",
- " Survived Age\n",
- "count 94.000000 85.000000\n",
- "mean 0.968085 34.611765\n",
- "std 0.176716 13.612052\n",
- "min 0.000000 2.000000\n",
- "25% 1.000000 23.000000\n",
- "50% 1.000000 35.000000\n",
- "75% 1.000000 44.000000\n",
- "max 1.000000 63.000000\n",
- "================== male =================\n",
- " Survived Age\n",
- "count 122.000000 101.000000\n",
- "mean 0.368852 41.281386\n",
- "std 0.484484 15.139570\n",
- "min 0.000000 0.920000\n",
- "25% 0.000000 30.000000\n",
- "50% 0.000000 40.000000\n",
- "75% 1.000000 51.000000\n",
- "max 1.000000 80.000000\n",
- "##################### 2 #########################\n",
- "================== female =================\n",
- " Survived Age\n",
- "count 76.000000 74.000000\n",
- "mean 0.921053 28.722973\n",
- "std 0.271448 12.872702\n",
- "min 0.000000 2.000000\n",
- "25% 1.000000 22.250000\n",
- "50% 1.000000 28.000000\n",
- "75% 1.000000 36.000000\n",
- "max 1.000000 57.000000\n",
- "================== male =================\n",
- " Survived Age\n",
- "count 108.000000 99.000000\n",
- "mean 0.157407 30.740707\n",
- "std 0.365882 14.793894\n",
- "min 0.000000 0.670000\n",
- "25% 0.000000 23.000000\n",
- "50% 0.000000 30.000000\n",
- "75% 0.000000 36.750000\n",
- "max 1.000000 70.000000\n",
- "##################### 3 #########################\n",
- "================== female =================\n",
- " Survived Age\n",
- "count 144.000000 102.000000\n",
- "mean 0.500000 21.750000\n",
- "std 0.501745 12.729964\n",
- "min 0.000000 0.750000\n",
- "25% 0.000000 14.125000\n",
- "50% 0.500000 21.500000\n",
- "75% 1.000000 29.750000\n",
- "max 1.000000 63.000000\n",
- "================== male =================\n",
- " Survived Age\n",
- "count 347.000000 253.000000\n",
- "mean 0.135447 26.507589\n",
- "std 0.342694 12.159514\n",
- "min 0.000000 0.420000\n",
- "25% 0.000000 20.000000\n",
- "50% 0.000000 25.000000\n",
- "75% 0.000000 33.000000\n",
- "max 1.000000 74.000000\n"
- ]
- }
- ],
- "source": [
- "for p_class, df in titanic.groupby('Pclass'):\n",
- " print(f\"##################### {p_class} #########################\")\n",
- " for sex, df2 in df.groupby('Sex'):\n",
- " print(f\"================== {sex} =================\")\n",
- " print(df2[['Survived', 'Age']].describe())"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "138f082f-c6c8-451d-adc0-6c7b700c8aba",
- "metadata": {},
- "source": [
- "# Table Concatenation/Merging\n",
- "\n",
- "> https://pandas.pydata.org/pandas-docs/stable/user_guide/merging.html\n",
- "> https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.merge.html"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 75,
- "id": "39740881-c1de-4d6c-ad25-e4cf354cd5ca",
- "metadata": {},
- "outputs": [],
- "source": [
- "table_1 = pd.DataFrame({'gene_ID':[1,12,3],\n",
- " 'specie': ['HUMAN', 'RAT', 'HORSE']})\n",
- "table_2 = pd.DataFrame({'gene_ID':[12,3,1],\n",
- " 'effect': [12, 33, 45]})\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 76,
- "id": "6c92d995-cca3-45c2-9248-da81c78c4cd4",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
- "<style scoped>\n",
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- " .dataframe tbody tr th {\n",
- " vertical-align: top;\n",
- " }\n",
- "\n",
- " .dataframe thead th {\n",
- " text-align: right;\n",
- " }\n",
- "</style>\n",
- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>gene_ID</th>\n",
- " <th>specie</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>0</th>\n",
- " <td>1</td>\n",
- " <td>HUMAN</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1</th>\n",
- " <td>12</td>\n",
- " <td>RAT</td>\n",
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- " <th>2</th>\n",
- " <td>3</td>\n",
- " <td>HORSE</td>\n",
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- " </tbody>\n",
- "</table>\n",
- "</div>"
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- " gene_ID specie\n",
- "0 1 HUMAN\n",
- "1 12 RAT\n",
- "2 3 HORSE"
- ]
- },
- "execution_count": 76,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "table_1"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 77,
- "id": "4173409a-8476-4cc6-91bc-e508802f7560",
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- "data": {
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- " <th>effect</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>0</th>\n",
- " <td>12</td>\n",
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- " <th>1</th>\n",
- " <td>3</td>\n",
- " <td>33</td>\n",
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- " <th>2</th>\n",
- " <td>1</td>\n",
- " <td>45</td>\n",
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- " gene_ID effect\n",
- "0 12 12\n",
- "1 3 33\n",
- "2 1 45"
- ]
- },
- "execution_count": 77,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "table_2"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 78,
- "id": "ef3b16ba-5fe5-4ce2-84da-80ea0e1a69da",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
- "<style scoped>\n",
- " .dataframe tbody tr th:only-of-type {\n",
- " vertical-align: middle;\n",
- " }\n",
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- " .dataframe tbody tr th {\n",
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- " <th>specie</th>\n",
- " <th>effect</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>0</th>\n",
- " <td>1</td>\n",
- " <td>HUMAN</td>\n",
- " <td>45</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1</th>\n",
- " <td>12</td>\n",
- " <td>RAT</td>\n",
- " <td>12</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2</th>\n",
- " <td>3</td>\n",
- " <td>HORSE</td>\n",
- " <td>33</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " gene_ID specie effect\n",
- "0 1 HUMAN 45\n",
- "1 12 RAT 12\n",
- "2 3 HORSE 33"
- ]
- },
- "execution_count": 78,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "pd.merge(table_1, table_2, on='gene_ID')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 79,
- "id": "9f5a1e4b-2602-44b9-a474-e24051dc3d41",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
- "<style scoped>\n",
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- " .dataframe tbody tr th {\n",
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- " }\n",
- "\n",
- " .dataframe thead th {\n",
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- " }\n",
- "</style>\n",
- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>ref</th>\n",
- " <th>effect</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>0</th>\n",
- " <td>12</td>\n",
- " <td>12</td>\n",
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- " <td>33</td>\n",
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- " <tr>\n",
- " <th>2</th>\n",
- " <td>1</td>\n",
- " <td>45</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " ref effect\n",
- "0 12 12\n",
- "1 3 33\n",
- "2 1 45"
- ]
- },
- "execution_count": 79,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "table_3 = pd.DataFrame({'ref':[12,3,1],\n",
- " 'effect': [12, 33, 45]})\n",
- "table_3"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 80,
- "id": "8a53d320-9abb-4961-b73c-9b230ec7c119",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
- "<style scoped>\n",
- " .dataframe tbody tr th:only-of-type {\n",
- " vertical-align: middle;\n",
- " }\n",
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- " .dataframe tbody tr th {\n",
- " vertical-align: top;\n",
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- "\n",
- " .dataframe thead th {\n",
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- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
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- " <th>gene_ID</th>\n",
- " <th>specie</th>\n",
- " <th>ref</th>\n",
- " <th>effect</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>0</th>\n",
- " <td>1</td>\n",
- " <td>HUMAN</td>\n",
- " <td>1</td>\n",
- " <td>45</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1</th>\n",
- " <td>12</td>\n",
- " <td>RAT</td>\n",
- " <td>12</td>\n",
- " <td>12</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2</th>\n",
- " <td>3</td>\n",
- " <td>HORSE</td>\n",
- " <td>3</td>\n",
- " <td>33</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " gene_ID specie ref effect\n",
- "0 1 HUMAN 1 45\n",
- "1 12 RAT 12 12\n",
- "2 3 HORSE 3 33"
- ]
- },
- "execution_count": 80,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "pd.merge(table_1, table_3, left_on='gene_ID', right_on='ref')"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "d12910ef-19ad-4fd0-a6b7-f2aa1f77cb19",
- "metadata": {},
- "source": [
- "### Effect of *how* parameter"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 81,
- "id": "fac0238f-728a-4343-be46-de027c970830",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
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- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>gene_ID</th>\n",
- " <th>specie</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>0</th>\n",
- " <td>1</td>\n",
- " <td>HUMAN</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1</th>\n",
- " <td>12</td>\n",
- " <td>RAT</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2</th>\n",
- " <td>3</td>\n",
- " <td>HORSE</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>3</th>\n",
- " <td>42</td>\n",
- " <td>MONKEY</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " gene_ID specie\n",
- "0 1 HUMAN\n",
- "1 12 RAT\n",
- "2 3 HORSE\n",
- "3 42 MONKEY"
- ]
- },
- "execution_count": 81,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "table_4 = pd.DataFrame({'gene_ID':[1,12,3, 42],\n",
- " 'specie': ['HUMAN', 'RAT', 'HORSE', 'MONKEY']})\n",
- "table_4"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 82,
- "id": "42478322-d7c1-4db8-8b6d-7c455eeae9fb",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
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- " <td>100</td>\n",
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- "text/plain": [
- " ref effect\n",
- "0 12 12\n",
- "1 3 33\n",
- "2 1 45\n",
- "3 35 100"
- ]
- },
- "execution_count": 82,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "table_5 = pd.DataFrame({'ref':[12,3,1, 35],\n",
- " 'effect': [12, 33, 45, 100]})\n",
- "table_5"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 83,
- "id": "0d5a5b6c-8095-45b0-b732-4fa825945f4f",
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- "data": {
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- " <td>45.0</td>\n",
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- " <th>1</th>\n",
- " <td>12</td>\n",
- " <td>RAT</td>\n",
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- " <td>12.0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2</th>\n",
- " <td>3</td>\n",
- " <td>HORSE</td>\n",
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- " <td>33.0</td>\n",
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- " <th>3</th>\n",
- " <td>42</td>\n",
- " <td>MONKEY</td>\n",
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- "text/plain": [
- " gene_ID specie ref effect\n",
- "0 1 HUMAN 1.0 45.0\n",
- "1 12 RAT 12.0 12.0\n",
- "2 3 HORSE 3.0 33.0\n",
- "3 42 MONKEY NaN NaN"
- ]
- },
- "execution_count": 83,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "pd.merge(table_4, table_5, left_on='gene_ID', right_on='ref', how='left')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 84,
- "id": "9df6048c-fd58-4a37-a6c7-44ae6610a487",
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- " gene_ID specie ref effect\n",
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- "3 NaN NaN 35 100"
- ]
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- "execution_count": 84,
- "metadata": {},
- "output_type": "execute_result"
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- ],
- "source": [
- "pd.merge(table_4, table_5, left_on='gene_ID', right_on='ref', how='right')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 85,
- "id": "04639e16-42ec-4fe9-bf34-3752e56e598a",
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- " gene_ID specie ref effect\n",
- "0 1 HUMAN 1 45\n",
- "1 12 RAT 12 12\n",
- "2 3 HORSE 3 33"
- ]
- },
- "execution_count": 85,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "pd.merge(table_4, table_5, left_on='gene_ID', right_on='ref', how='inner')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 86,
- "id": "d3be2f27-9167-494e-a03e-2687188d1504",
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- "data": {
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- "<div>\n",
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- " <th>gene_ID</th>\n",
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- " <th>ref</th>\n",
- " <th>effect</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>0</th>\n",
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- " <td>RAT</td>\n",
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- " <td>12.0</td>\n",
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- " <tr>\n",
- " <th>2</th>\n",
- " <td>3.0</td>\n",
- " <td>HORSE</td>\n",
- " <td>3.0</td>\n",
- " <td>33.0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>3</th>\n",
- " <td>42.0</td>\n",
- " <td>MONKEY</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4</th>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>35.0</td>\n",
- " <td>100.0</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " gene_ID specie ref effect\n",
- "0 1.0 HUMAN 1.0 45.0\n",
- "1 12.0 RAT 12.0 12.0\n",
- "2 3.0 HORSE 3.0 33.0\n",
- "3 42.0 MONKEY NaN NaN\n",
- "4 NaN NaN 35.0 100.0"
- ]
- },
- "execution_count": 86,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "pd.merge(table_4, table_5, left_on='gene_ID', right_on='ref', how='outer')"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "25f496dc-0c47-438c-bf95-6b5cd9aed119",
- "metadata": {},
- "source": [
- "# Crosstab\n",
- "\n",
- "Compute a simple cross tabulation of two (or more) factors. By default computes a frequency table of the factors \n",
- "\n",
- "> https://pandas.pydata.org/docs/reference/api/pandas.crosstab.html"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 87,
- "id": "2765062a-4e89-4272-90d7-0637454b143b",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
- "<style scoped>\n",
- " .dataframe tbody tr th:only-of-type {\n",
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- " }\n",
- "\n",
- " .dataframe thead th {\n",
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- " }\n",
- "</style>\n",
- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th>Pclass</th>\n",
- " <th>1</th>\n",
- " <th>2</th>\n",
- " <th>3</th>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>Age</th>\n",
- " <th></th>\n",
- " <th></th>\n",
- " <th></th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>0.42</th>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>0.67</th>\n",
- " <td>0</td>\n",
- " <td>1</td>\n",
- " <td>0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>0.75</th>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>2</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>0.83</th>\n",
- " <td>0</td>\n",
- " <td>2</td>\n",
- " <td>0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>0.92</th>\n",
- " <td>1</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>...</th>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>70.00</th>\n",
- " <td>1</td>\n",
- " <td>1</td>\n",
- " <td>0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>70.50</th>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>71.00</th>\n",
- " <td>2</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>74.00</th>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>80.00</th>\n",
- " <td>1</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "<p>88 rows × 3 columns</p>\n",
- "</div>"
- ],
- "text/plain": [
- "Pclass 1 2 3\n",
- "Age \n",
- "0.42 0 0 1\n",
- "0.67 0 1 0\n",
- "0.75 0 0 2\n",
- "0.83 0 2 0\n",
- "0.92 1 0 0\n",
- "... .. .. ..\n",
- "70.00 1 1 0\n",
- "70.50 0 0 1\n",
- "71.00 2 0 0\n",
- "74.00 0 0 1\n",
- "80.00 1 0 0\n",
- "\n",
- "[88 rows x 3 columns]"
- ]
- },
- "execution_count": 87,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "pd.crosstab(index=titanic.Age, columns=titanic.Pclass)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "e7e7b2e8-36af-40a2-b53b-60a74f95bbe2",
- "metadata": {},
- "source": [
- "# Saving data\n",
- "\n",
- "> https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.to_csv.html\n",
- "\n",
- "```python\n",
- "df.to_csv(<path to file>, sep='\\t', index=False)\n",
- "```"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "a7a49995-b459-4a6b-a4e9-8c4e705e4610",
- "metadata": {},
- "source": [
- "# Teasing\n",
- "\n",
- "pandas use matplotlib to display graphics"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 88,
- "id": "2b5810e6-4d5e-4ab1-b178-33b89f625059",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "18541b6c252d4e45a77129e40b687a7a",
- "version_major": 2,
- "version_minor": 0
- },
- "text/plain": [
- "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/plain": [
- "<AxesSubplot:>"
- ]
- },
- "execution_count": 88,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "titanic.Age.hist(bins=40)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "2fdb832c-cf11-49c5-b568-44ff6fb2f460",
- "metadata": {},
- "outputs": [],
- "source": []
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.8.8"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
--
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