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{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "96226d71-a8c2-4845-8b35-4f3f8d4680a8",
   "metadata": {},
   "source": [
    "# <center>**TP**</center>\n",
    "\n",
    "<img src=\"./img/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": 7,
   "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": 8,
   "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=\"./img/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=\"./img/plot_data1_vs_data2.png\" width=\"300px\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "027476b8-1d47-4373-8709-0fbe03843a70",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f487b8253d0>"
      ]
     },
     "execution_count": 9,
     "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": []
  },
  {
   "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=\"./img/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": 11,
   "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": 12,
   "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": []
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "3686b1f0-b4d4-44a0-9666-0d131a6b64ec",
   "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>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": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "3efa2dda-6aa8-4f34-a14a-b207f3803def",
   "metadata": {},
   "source": [
    "do the bar plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "220d76c0-eb3c-49c7-993f-df78a4dae9a7",
   "metadata": {},
   "outputs": [
    {
     "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": []
  },
  {
   "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=\"./img/histogram_pixels.png\" width=\"300px\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "d04b6458-fa0e-4fb3-a9ad-b6feb5634ce6",
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "d4710fcc-3ca5-496d-bd02-18931aa2e9bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "koala = image.imread('../data/koala.jpeg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "a2cb26e8-3be9-41c4-8b8f-2983184b9039",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "300d1ecd-0017-49df-a01d-3e2c27511758",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(255, 255, 255)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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": 20,
   "id": "dc30676b-4acd-41d3-b8a5-587af64b4441",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f48794fef40>"
      ]
     },
     "execution_count": 20,
     "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": []
  },
  {
   "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": 21,
   "id": "7d937154-5e81-4ffc-a110-82755ca4efa4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4.9112441705122984\n",
      "3.0821900958177255\n"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "2d22c136-80e1-4d32-925b-525f88cd9099",
   "metadata": {},
   "source": [
    "Standardized this dataset, and compare the 2 distributions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "726c4bb3-b4e1-4cfd-ab6b-5c5297bafc17",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-8.881784197001253e-17\n",
      "0.9999999999999999\n"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "c6ec47f3-dc60-4423-af20-ac981c0f5131",
   "metadata": {},
   "source": [
    "Normalized the dataset, and compare the 2 distributions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "2a3a5a0e-97a5-40df-935a-6e45a2326894",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f48794fefa0>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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kSSq8vIJwjLEVWDLKpvMLWo0kSZJUIk6xLEmSpEQyCEuSJCmRDMKSJElKJIOwJEmSEskgLEmSpEQyCEuSJCmRDMKSJElKJIOwJEmSEskgLEmSpEQyCEuSJCmRDMKSJElKJIOwJEmSEskgLEmSpEQyCEuSJCmRDMKSJElKJIOwJEmSEumwchcgCRrXNtDb08HOHS1AqtzlSJPS0NBIR0dv1u0tLTtJpUpWjiRlZRCWKkBvTwfplSlqr24udynSpHV09JJKpbNub26uLVktkjQWWyMkSZKUSAZhSZIkJZJBWJIkSYlkEJYkSVIiGYQlSZKUSAZhSZIkJZJBWJIkSYlkEJYkSVIiGYQlSZKUSAZhSZIkJZJTLEtZtLW2kL6mDoCdO1qA1JQ6viampaWNurr0GNt3kkqVrJyK5DmSNF0YhKUs4kAf6ZUpAGqvbp5yx9fE9PVFUql01u3NzbUlq6VSeY4kTRe2RkiSJCmRDMKSJElKJIOwJEmSEskgLEmSpETK62K5EEI78BLwKjAQY1wSQjgWuJPBS93bgUtijC8Wp0xJkiSpsMbzifB5McbFMcYlmefXAQ/EGE8CHsg8lyRJkqaEybRGrADWZ5bXA7WTrkaSJEkqkXzvIxyB74YQIvB/Y4zrgPkxxq7M9ueA+aPtGEJYDawGqKmpmWS5UrKNnISjel4N9deuKW9BUoXKNelHTU01a9bUl6weSZUp3yB8TozxmRDC64DvhRCeGLkxxhgzIfkQmdC8DmDJkiWjjpGUn5GTcKRvby9rLVIlyzXpR3t79m2SkiOv1ogY4zOZr88DdwNnAN0hhAUAma/PF6tISZIkqdByBuEQwpEhhKOGloF3AzuAe4BVmWGrgI3FKlKSJEkqtHxaI+YDd4cQhsb/W4zxOyGELcDXQwiXA08DlxSvTEmSJKmwcgbhGOMuYNEo63cD5xejKEmSJKnYnFlOkiRJiWQQliRJUiIZhCVJkpRIBmFJkiQlkkFYkiRJiWQQliRJUiIZhCVJkpRIBmFJkiQlkkFYkiRJiWQQliRJUiIZhCVJkpRIBmFJkiQlkkFYkiRJiWQQliRJUiIZhCVJkpRIBmFJkiQl0mHlLkDS6NpaW0hfU8fOHS1AqtzlSCXT0tJGXV06x5idpFIlKUfSNGYQlipUHOgjvTJF7dXN5S5FKqm+vkgqlR5zTHNzbUlqkTS92RohSZKkRDIIS5IkKZFsjZCmqKEe4up5NdRfu6bc5UhTSq4+5JqaatasqS9ZPZLKwyAsTVFDPcTp29vLXYo05eTqQ25vz75N0vRha4QkSZISySAsSZKkRLI1QlNa49oGens6cvbJDo0DEn1f3oaGRjo6erNuL0VfZK4avD+sJKlUDMKa0np7OvLqkx0aByT6vrwdHb1l74vMVYP3h5UklYqtEZIkSUokg7AkSZISySAsSZKkRLJHWNPO0IVxTz61i1NOWggk+wK5Qsp1oduuXU+ycOEpYx7Di+EkSZXCIKxpZ+jCuNqrm0mvXAYk+wK5QsrnQrdly7JvHxojSVIlsDVCkiRJiWQQliRJUiLZGiEBba0tpK+pA6ZfP/HIyUSe/snPx2xtmA5aWtqoq0uPsd0eZZVfJUxuI2kcQTiEMAPYCjwTY7wohHAi8DVgDvAo8Mcxxv3FKVMqrjjQN20n3Bg5mcjvN7eWtZZS6OuLTtihilcJk9tIGl9rxCeBx0c8Xwv8U4zxTcCLwOWFLEySJEkqpryCcAjhBOA9wBczzwOwDLgrM2Q9UFuE+iRJkqSiyLc1ohH4K+CozPM5QG+McSDzvBM4frQdQwirgdUANTU1Ey5UGst07vEdj5H9wNXzaqi/ds249re/VsqPPb7S9JAzCIcQLgKejzE+GkJYOt4XiDGuA9YBLFmyJI53fykf07nHdzxG9gOnb28f9/7210r5scdXmh7y+UT4bODiEMKFwEzgvwH/DFSHEA7LfCp8AvBM8cqUJEmSCitnj3CM8a9jjCfEGFPAZcCDMcY/AjYBH8gMWwVsLFqVkiRJUoFNZkKNa4E/DyH8lMGe4S8VpiRJkiSp+MY1oUaMsQloyizvAs4ofEmSJElS8TnFsiRJkhLJICxJkqREMghLkiQpkQzCkiRJSiSDsCRJkhLJICxJkqREMghLkiQpkcZ1H2GpEjSubaC3pwOAnTtagFRZ66kkLa072XB0OwDNzb3U1aXpfKJ1eN3uPb1lq02SpEpjENaU09vTQXplCoDaq5vLW0yF6Xt5P9XVFwIwe3Y7qVSa3s52qqtTAAwMfLmM1UmSVFlsjZAkSVIiGYQlSZKUSAZhSZIkJZJBWJIkSYlkEJYkSVIiGYQlSZKUSAZhSZIkJZJBWJIkSYlkEJYkSVIiGYQlSZKUSAZhSZIkJdJh5S5A0uS0tbaQvqYOgJd/2TW8vru7hdbmOva80AKkylKbNFW1tLRRV5ceY/tOUqni7S+pNAzC0hQXB/pIr0wB8J//r394fVXoo35FioZbm8tUmTR19fVFUql01u3NzbVF3V9SadgaIUmSpEQyCEuSJCmRbI2QEqR//15am+sACIfXsOiMNeUtSNKocvUYA9TUVLNmTX1J6pGmK4OwlCAzqwaoX5ECoHFje1lrkZRdrh5jgPb2sbdLys3WCEmSJCWSQViSJEmJZBCWJElSIhmEJUmSlEgGYUmSJCWSQViSJEmJZBCWJElSInkfYUnD4iuP09pc52QbUgI0NDTS0dGbdbsTdigJcgbhEMJM4GHgiMz4u2KMN4QQTgS+BswBHgX+OMa4v5jFSiquWVW/pn5Fysk2pATo6Ogdc9IOJ+xQEuTTGvEKsCzGuAhYDCwPIZwFrAX+Kcb4JuBF4PKiVSlJkiQVWM4gHAe9nHlalXlEYBlwV2b9eqC2GAVKkiRJxZDXxXIhhBkhhFbgeeB7wM+A3hjjQGZIJ3B8ln1XhxC2hhC29vT0FKBkSZIkafLyCsIxxldjjIuBE4AzgDfn+wIxxnUxxiUxxiXz5s2bWJWSJElSgY3r9mkxxl5gE/B2oDqEMHSx3QnAM4UtTZIkSSqenEE4hDAvhFCdWf5t4F3A4wwG4g9khq0CNhapRkmSJKng8rmP8AJgfQhhBoPB+esxxv8IIewEvhZC+AzQAnypiHVKkiRJBZUzCMcYtwNvHWX9Lgb7hSVJkqQpxymWJUmSlEgGYUmSJCWSQViSJEmJZBCWJElSIhmEJUmSlEgGYUmSJCWSQViSJEmJZBCWJElSIhmEJUmSlEgGYUmSJCWSQViSJEmJZBCWJElSIhmEJUmSlEgGYUmSJCWSQViSJEmJZBCWJElSIhmEJUmSlEgGYUmSJCWSQViSJEmJZBCWJElSIhmEJUmSlEgGYUmSJCWSQViSJEmJZBCWJElSIhmEJUmSlEgGYUmSJCWSQViSJEmJZBCWJElSIhmEJUmSlEgGYUmSJCWSQViSJEmJZBCWJElSIhmEJUmSlEiHlbsA6WCNaxvo7ekAoHpeDfXXrilzRYWze08vGzY2AdDdveeQ5d17estWmyRJSZMzCIcQXg/cDswHIrAuxvjPIYRjgTuBFNAOXBJjfLF4pSopens6SK9MAZC+vb2stRTaQP8BqquXAlBV1XnI8kD/9vIVJ0lSwuTTGjEA/EWM8TTgLODPQginAdcBD8QYTwIeyDyXJEmSpoScQTjG2BVj3JZZfgl4HDgeWAGszwxbD9QWqUZJkiSp4MZ1sVwIIQW8FXgEmB9j7Mpseo7B1glJkiRpSsj7YrkQwmzgm0B9jPGXIYThbTHGGEKIWfZbDawGqKmpmVy1SqyRF9Dt3NHCYGt68ox2sd3Ii+5e2b+/fMVJkjTF5BWEQwhVDIbgO2KM38qs7g4hLIgxdoUQFgDPj7ZvjHEdsA5gyZIlo4ZlKZeRF9DVXt1c3mLKaLSL7UZedHfgwJbyFSdJ0hSTszUiDH70+yXg8RjjP47YdA+wKrO8CthY+PIkSZKk4sjnE+GzgT8GHgshtGbW/S/gc8DXQwiXA08DlxSlQkmSJKkIcgbhGGMzELJsPr+w5Ugqle7uFlqb6wiH17DojOkzaYmUFC0tbdTVpbNur6mpZs2a+pLVI01FziwnJVRV6KN+RYrGje3lLkXSBPT1RVKpdNbt7e3Zt0kaNK7bp0mSJEnThUFYkiRJiWRrhCpaW2sL6WvqEnPv4Ff2v1IR9wke6h8G7CGWEsoeZCWBQVgVLQ70kV6ZSsy9gw8coCLuEzzUPwzYQywllD3ISgJbIyRJkpRIBmFJkiQlkq0RqgiNaxvo7ekAmLL9wJuaNr+mn3e05VL1+I7HyH5gXu0dc3t85fGS1SVpcnL1+La07CSVKt7x7SHWVGAQVkXo7ekgvTIFMGX7gffu3UdV1bGH9PiOXC5Vj+94jOwHrv/HV8fcftU/PVjCyiRNRq4e3+bm2qIe3x5iTQW2RkiSJCmRDMKSJElKJIOwJEmSEskgLEmSpEQyCEuSJCmRDMKSJElKJIOwJEmSEsn7CKvkRk6eUT2vhvpr15S5okGbmjazd+++4ckvDp4QY1PT5vIWKEmSCsogrJIbOXlG+vb2stYy0t69+6iuXjo8+cXBE2Ls3buvvAVKkqSCsjVCkiRJiWQQliRJUiLZGqGyamttIX1NHTt3tACpcpczpq6u5w7pGx653NV1RPmKkyRJ4+YnwiqrONBHemWK/fv6yl1KTv39UFV1LNXVSzM9xK9d7u9/tdwlSpKkcTAIS5IkKZEMwpIkSUokg7AkSZISySAsSZKkRDIIS5IkKZEMwpIkSUokg7AkSZISyQk1NCmNaxvo7ekAoHpeDfXXrsk6tqGhkY6OXjqfaGXD0e3Abyal6O7ew6amzZy39KxSlK0J6t+/l9bmOrpf2MX8uQsBCIfXsOiM7P/dJSVTS0sbdXXprNt37XqShQtPybq9pqaaNWvqC1+YNIJBWJPS29NBemUKgPTt7WOO7ejoJZVK09vZTnX14D5VVZ2ZCSk62bt3X3GL1aTNrBqgfkWKhlubqV+xDIDGje3lLUpSRerri6RS6azbm5trWbYs+/b29uzbpEKxNUKSJEmJZBCWJElSItkaoZJoXNtA5xP30NvZzp4XWoBUyWvY1LSZvXv3vaYvecPGJmCwV7mr6wiqq0teliRJKpOcnwiHEL4cQng+hLBjxLpjQwjfCyE8lfl6THHL1FTX29PBJ95TTf2KFBzoK0sNe/fuy/QjH/uar0PL/f2vlqUuSZJUHvm0RtwGLD9o3XXAAzHGk4AHMs8lSZKkKSNnEI4xPgzsOWj1CmB9Znk9UFvYsiRJkqTimujFcvNjjF2Z5eeA+QWqR5IkSSqJSV8sF2OMIYSYbXsIYTWwGqCmpmayLyepwnR3t9DaXAdAfOXx8hYjadrINSGHE26oECYahLtDCAtijF0hhAXA89kGxhjXAesAlixZkjUwS5qaqkLf4EWQwFX/9GB5i5E0beSakMMJN1QIE22NuAdYlVleBWwsTDmSJElSaeRz+7R/B/4LOCWE0BlCuBz4HPCuEMJTwO9nnkuSJElTRs7WiBjjh7JsOr/AtUiaJtp+1EDc3wFAOLyGRWesKXNFkpKooaGRjo7erNvtM5Yzy0kquLi/Y7hvuHFje1lrkZRcHR299hlrTBPtEZYkSZKmNIOwJEmSEsnWCAHZ+6ie/skmZhzYy5GzD+e8dy2n/trsvZ5trS2kr6mjel7NmOOy6ep6jg0bm+ju3sOGjU0Aw8vd3XvY1LSZ85aeNe7jqryG7jNsr7CkSuO9imUQFpC9j6q3s536FSl6e5to7ekY8xhxoI/0yhTp29snVEN/P1RXL6WqqpPq6qUAw8tVVZ3s3btvQsdVeQ3dZ9heYUmVxnsVy9YISZIkJZJBWJIkSYlkEJYkSVIi2SOsvHR1Pce9DzxBc/NiXv2to3nDyecB0PlEKxuObgdg955e4DcXzQETvnAuWw1eTFfZ+vfvpbW5jj0vtACprOOccEPSZOW60G1wzE5SqeLV4IQdU59BWHnp74cjj5jN9X9US+PG9uGLC3o726muTgEw0L8d+M1Fc8CEL5zLVoMX01W2mVUD1K9I0XBr85jjnHBD0mTlutANoLm5tqg1OGHH1GdrhCRJkhLJICxJkqREMghLkiQpkQzCkiRJSiSDsCRJkhLJICxJkqRE8vZpKqqhewrv3NHC4rOPKeprHXyf4YPvN9zVdQTV1UUtQZIkTSF+IqyiGrqn8P59fUV/rd/cZ/jY13wdWu7vf7XoNUiSpKnDICxJkqREMghLkiQpkQzCkiRJSiQvlpNUcdp+1EDc3wFAOLyGRWesGV732we20fajBhadsabMVUpScTU0NNLR0Zt1e01NNWvW1Bf9GNOZQVhSxYn7O6hfkQKgcWP7a9Zt3z6LB3/eUb7iJKlEOjp6SaXSWbe3t2ffVshjTGe2RkiSJCmRDMKSJElKJFsj8pCrvwYqr8emcW0DvT0dVM+rof7aNTn/DS0tO0mlBpdH9mfueaEFSBW7XCVYd3cLrc11w73A49kHGNd+klRKLS1t1NWls26vhOwwFWosJoNwHnL110Dl9dj09nSQXpkifXs7kPvf0NxcO7w8sj+z4dbm4hUpAVWhj/oVqeFe4PHsA4xrP0kqpb6+WPH9uVOhxmKyNUKSJEmJZBCWJElSItkaUSK5enR37XqShQtPybo9V4/OwcfvfKKVDUe309zcS11dmpaWnex9frD3t/uFXcyfuxB4bX/lUG9wrr7gkf2ZI8e+sv8VNmxsort7Dxs2NmXG7hle19V1BNXVWQ+raW7offPbB7ax54VjOPg9lm370Hr71SWVWq7+2ZHX15Tj+Ln2z+cYSWcQLpF8enSXLcu+PVePzsHH7+1sp7o6xezZ7aRSaZqba4d7fxtubaZ+xTLgtf2VI7ePZWR/5sixBw5AdfVSqqo6qa5eOjg2s1xV1Ul//6tjHlfT29D7Zvv2Wdz1SF/e24fW268uqdRy9c+OvL6mHMfPtX8+x0g6WyMkSZKUSAZhSZIkJVIiWiOmwzzb2fqAnv7JJmYc2MvuPb3sff7AuO+nmqtvU6p0Q+/hod733z6wjbYfNXhvYUkqgFx9yJO9xqncJhWEQwjLgX8GZgBfjDF+riBVFdh0mGc7Wx9Qb2d7pq/yazz4845xHzdX36ZU6Ub2ENevWMb27bMm9L0gSTpUPn3Mk7nGqdwm3BoRQpgBfAH4A+A04EMhhNMKVZgkSZJUTJPpET4D+GmMcVeMcT/wNWBFYcqSJEmSimsyQfh44Bcjnndm1kmSJEkVL8QYJ7ZjCB8AlscY/zTz/I+BM2OMnzho3GpgdebpKcCTEy933OYCL5Tw9aYTz93Eee4mx/M3cZ67ifPcTZznbuI8dxOX69y9IcY4L9dBJnOx3DPA60c8PyGz7jVijOuAdZN4nQkLIWyNMS4px2tPdZ67ifPcTY7nb+I8dxPnuZs4z93Eee4mrlDnbjKtEVuAk0IIJ4YQDgcuA+6ZbEGSJElSKUz4E+EY40AI4RPAfzJ4+7Qvxxh/XLDKJEmSpCKa1H2EY4z3AfcVqJZiKEtLxjThuZs4z93keP4mznM3cZ67ifPcTZznbuIKcu4mfLGcJEmSNJVNpkdYkiRJmrKmVRAOIaRDCM+EEFozjwuzjFseQngyhPDTEMJ1pa6zEoUQ/iGE8EQIYXsI4e4QQnWWce0hhMcy53dricusKLneRyGEI0IId2a2PxJCSJWhzIoTQnh9CGFTCGFnCOHHIYRPjjJmaQhh74jv5YZy1FqJcn0PhkE3Z95320MIbytHnZUohHDKiPdUawjhlyGE+oPG+N7LCCF8OYTwfAhhx4h1x4YQvhdCeCrz9Zgs+67KjHkqhLCqdFVXhiznzt+zechy7oqX72KM0+YBpIFrcoyZAfwMWAgcDrQBp5W79nI/gHcDh2WW1wJrs4xrB+aWu95yP/J5HwH/E7gls3wZcGe5666EB7AAeFtm+SjgJ6Ocu6XAf5S71kp85PoeBC4Evg0E4CzgkXLXXImPzPfwcwzea3Tket97vzkX5wJvA3aMWPf3wHWZ5etG+10BHAvsynw9JrN8TLn/PRVw7vw9O/FzV7R8N60+Ec6TU0OPIsb43RjjQObpZgbvC63s8nkfrQDWZ5bvAs4PIYQS1liRYoxdMcZtmeWXgMdxVspCWgHcHgdtBqpDCAvKXVQFOh/4WYzx6XIXUqlijA8Dew5aPfLn2nqgdpRdLwC+F2PcE2N8EfgesLxYdVai0c6dv2fzk+V9l48J5bvpGIQ/kfmzw5ez/MnGqaFz+xMGP1EaTQS+G0J4NDNrYFLl8z4aHpP54bcXmFOS6qaITLvIW4FHRtn89hBCWwjh2yGE3yltZRUt1/egP+Pycxnw71m2+d7Lbn6MsSuz/Bwwf5Qxvgdz8/fs+BUl3025IBxCuD+EsGOUxwrgX4A3AouBLuB/l7PWSpPj3A2N+TQwANyR5TDnxBjfBvwB8GchhHNLULqmoRDCbOCbQH2M8ZcHbd7G4J+sFwGfBzaUuLxK5vfgJIXBSaAuBr4xymbfe3mKg3+P9tZT4+Tv2QkpWr6b1H2EyyHG+Pv5jAsh3Ar8xyib8poaejrKde5CCHXARcD5mR9wox3jmczX50MIdzP4p4iHC1zqVJDP+2hoTGcI4TDgaGB3acqrbCGEKgZD8B0xxm8dvH1kMI4x3hdC+D8hhLkxxrHmlU+EPL4HE/szbhz+ANgWY+w+eIPvvZy6QwgLYoxdmZab50cZ8wyDvdZDTgCaSlBbxfP37MSM/F4tdL6bcp8Ij+WgPrj3AjtGGebU0KMIISwH/gq4OMb4qyxjjgwhHDW0zGDj/2jnOAnyeR/dAwxdLf0B4MFsP/iSJNMn/SXg8RjjP2YZ89+H+qlDCGcw+LMq8f8Tkef34D3AyjDoLGDviD9la9CHyNIW4Xsvp5E/11YBG0cZ85/Au0MIx2T+hP3uzLpE8/fsxBU135X76sBCPoCvAo8B2zP/+AWZ9ccB940YdyGDV6r/DPh0ueuuhAfwUwZ7a1ozj6G7HQyfOwavxGzLPH6c9HM32vsIWMPgDzmAmQz+6fWnwI+AheWuuRIewDkM/jl1+4j324XAFcAVmTGfyLzH2hi8qOQd5a67Eh7ZvgcPOncB+ELmffkYsKTcdVfSAziSwWB79Ih1vvdGP1f/zuCfofsZ7Le8nMHrHB4AngLuB47NjF0CfHHEvn+S+dn3U+Cj5f63VMi58/fsxM9d0fKdM8tJkiQpkaZVa4QkSZKUL4OwJEmSEskgLEmSpEQyCEuSJCmRDMKSJElKJIOwJEmSEskgLEmSpEQyCEuSJCmR/j9jubw8NdKlUgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "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": 24,
   "id": "ee8c51fe-145e-4ee0-b688-61b9344e3def",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "b4bd660f-2821-4e48-8e46-0aab364e80d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f4878c35b80>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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hml8gw52bUXRHJGts286h+hSDC9psRl1bIznjvf3xwGtB8CpdSg0AFiL2AAAAAG/Q/AIAAMAbxB6AMuTLB0tkhAEAaHQ0v0AZ8uWDJTLCAAA0OmIPAAAA8AbNLwAAALxB7AEAUDPF1umNxcYUiQRWDgDQ/AIAaqfYOr1DQ9sCqwUAJGIPAAAA8Ajv/AIAWlZ3d68SiVTBebZHBvxD8wsAaFmJRIrtkQFkIfYAAAAAb9D8AgAAwBs0vwAAAPAGzS8AAAC8QfMLAAAAb7DaA9DADg0e1vT0GUlSMjmlF/sHs+bHT5zMGZt/7KpV7frM5hsDqLRxjPykW+5sQpK04vwRDQ91SpJseVjX3dBTx8oAAI2A5hdoYNPTZxQKbZYktbWNX7g959y5V3PG5h+bSg3WvMZG484m1LU1IkkaHV2pjRvTt3v743WrCQDQOIg9AAAAwBs0vwAAAPAGzS8AAAC8QfMLAAAAb9D8AgAAwBus9oCm17u7W6nJRM74W28f1zUfuSpnfOxoTFKk9oUBDS4WG1FnZ3SR+TFFIoGVAwCBKLn5NbOLJL0m6YRz7nNmtkFSn6Q1kl6XtN05d7Y2ZQKFpSYTiu6I5Ixv2zmk6I4teccBSDMzTpFItOD80NC2wGoBgKAsJfbwx5LenHd/t6Q/d85dLeldSfdXszAAAACg2kpqfs1svaT/KOkbmfsmaYukFzKH7Je0rQb1AQAAAFVTauyhV9KfSLo4c3+NpJRz7lzm/riky/M90MwekPSAJIXD4bILBYD5ksmYVpx/98L2xXOmTpHp9gm5ZQBLVbT5NbPPSXrHOfe6mW1e6jdwzu2TtE+SNm3a5Jb6eADIp81m9OVbf7t98ZzuJ8l0+4TcMoClKuWd309JusPMbpfULulfSdorKWRmyzLv/q6XdKJ2ZQIAAACVK5r5dc79D+fceudcRNKXJA045+6RdEjSnZnDOiT116xKAAAAoAoq2eRil6SHzeyY0hngp6pTEgAAAFAbS9rkwjk3KGkwc/u4pBuqXxIAAABQG2xvDAAAAG/Q/AIAAMAbNL8AAADwBs0vAAAAvEHzCwAAAG8sabUHAGgl7r03c7ZHliRbHtZ1N/QEXxAAoOZofgF4a2Xbb9S1NZIz3tsfD7wWAEAwiD0AAADAGzS/AAAA8AbNLwAAALxB8wsAAABv0PwCAADAG6z2ALSwiYmTerF/8ML9ZHIq6/7ExGTwRQENJBYbUWdntOB8OBxST09XYPUAqD2aX6CFzc5KodDmC/fb2saz7icSfcEXBTSQmRmnSCRacD4eLzwHoDkRewAAAIA3eOcXqJFDg4c1PX0mJ2owJ5mc0qHBw/rM5huDLw4AAE/R/AI1Mj19RqHQ5pyowZy2tnFNT58JvjAAADxG7AEAAADeoPkFAACAN4g9APBCMhnT8FBn9uD7qUWPXXH+SNZjbHlY193QU7MaAQC1R/MLwAttNqOurZGssa6vv7/osaOjK7Vx428f09sfr12BAIBAEHsAAACAN2h+AQAA4A2aXwAAAHiD5hcAAADeoPkFAACAN2h+AQAA4A2aXwAAAHiD5hcAAADeoPkFAACAN9jhDQBKtHCL5Lntj9n2GACaB80vAJRo4RbJc9sfs+0xADQPYg8AAADwBu/8oqn07u5WajKRNTZ2NCYpUpd6AABAc6H5RVNJTSYU3RHJGtu2c6g+xQAAgKZD7AEAAADeoPkFAACAN4g9AGU6NHhY09NnssaSySm92D8oSZqYmFQoFHxdAACgMJpfoEzT02cUCm3OGmtrG78wlkj0BV8UAABYFLEHAAAAeIPmFwAAAN6g+QUAAIA3aH4BAADgDZpfAAAAeIPVHuCthUuVzV+mTJJWrWqvQ1UAAKCWaH7hrYVLlc1fpkySUqnBwGsCAAC1RewBAAAA3qD5BQAAgDeIPaBsvbu7lZpM5IyH1oXVtaunDhU1n4mJk1k5Y4ktkptRMhnT8FBn1pgtD9enGDSV7u5eJRKpgvPhcEg9PV2B1QP4gOYXZUtNJhTdEckZjx6IB15Ls5qdFVskt4A2m1HX1kjWWG9/vC61oLkkEilFItGC8/F44TkA5SH2AAAAAG/wzi8CQUQCAAA0AppfBIKIBAAAaATEHgAAAOANml8AAAB4g9gDGlKhjPDY0ZikSCA1TEyczNnyeE4yOaWJiQ+yDBkKSiZjWnH+3Zwl0JKnjuvStVdJklacP3Jh3paHdd0N5N8BoNZoftGQCmWEt+0cCqyG2VmprW11zlJkUno5stnZ9wOrBc2nzWb05VtXauPGSNZ495ND6tq6RZI0OvrbeZZGA4BgEHsAAACAN3jnFwCAGim2g1ssNqZIJLByAIjmFwCAmim2g9vQ0LbAagGQRuwBAAAA3qD5BQAAgDeKxh7M7ApJByRdKslJ2uec22tmqyU9r/S6U3FJX3DOvVu7UgEACFYsNqLOzmjB+XA4pJ6ersDqAVC5UjK/5yT9d+fcETO7WNLrZvYDSZ2SfuSc+6qZPSrpUUm7alcqAADBmplxi2Z24/HCcwAaU9HYg3Nuwjl3JHP7V5LelHS5pK2S9mcO2y9pW41qBAAAAKpiSas9mFlE0sckvSLpUufcRGbqpNKxiHyPeUDSA5IUDofLLhQAAN8QuwCqr+Tm18w+LOlvJHU5535pZhfmnHPOzFy+xznn9knaJ0mbNm3KewwAAMhF7AKovpJWezCzNqUb3+ecc9/JDCfN7LLM/GWS3qlNiQAAAEB1FG1+Lf0W71OS3nTOfX3e1EuSOjK3OyT1V788AAAAoHpKiT18StJ2ST81s+HM2J9K+qqkb5vZ/ZJ+LukLNakQAAAAqJKiza9zbkiSFZi+tbrlAAAAALXDDm8AAADwBs0vAAAAvLGkdX6BahsZjin6SGfO+NjRmNI7ZwMAAFQPzS/qyp2bUXRHJGd8286h4IsBAAAtj9gDAAAAvME7vwAAlKnY9sOx2JgikcDKydHd3atEIlVwnu2R4SOaXwAAylRs++GhoW2B1ZJPIpFie2RgAWIPAAAA8AbNLwAAALxB7AFVl2/5MpYuA5bOvfemhoc6c8ZteVjX3dATfEEA0AJoflF1+ZYvY+kyYOlWtv1GXVsjOeO9/fHAawGAVkHsAQAAAN7gnV/UzKHBw5qePiNJSian9GL/YNb8qlXtVTl/vnNL0umpVEXnB4KUTMZyIw7vp+pRCgC0NJpf1Mz09BmFQpslSW1t4xduz0mlBqty/nznlqRzs6MVnR8IUpvN5EQcur7+fn2KAYAWRuwBAAAA3qD5BQAAgDeIPaCo3t3dSk0mcsZZvgwA6qvS7ZWLPZ7tj9GKaH5RVGoykbN0mcTyZQBQb5Vur1zs8Wx/jFZE7AEAAADe4J1fAGgRIz/pljv724jSivNHNDzUyY5wADAPzS8AtAh3NpG1XNro6Ept3BhhRzgAmIfYAwAAALxB8wsAAABvEHtAy3rv7Ht5tz2e2w55YmJSoVDgZQEAgDqi+UXLOn9eebc9ntsOOZHoC74oAABQV8QeAAAA4A2aXwAAAHiD2AOy5NvKuF7bGB8aPKzp6TNZY3N5XUlkdoESJZMxDQ91Zo3Z8nDeY+evFTy3TvDc8awV7B+2P0YrovlFlnxbGddrG+Pp6TM5md25vK4kMrtAidpsJmv9X0kF1/6dv1bw3DrBix2P1sb2x2hFxB4AAADgDd75BYAmMxdjmB9LkKSpU/WJKAFAM6H5BYAmMxdjmB9LkKTuJ+sTUQKAZkLsAQAAAN6g+QUAAIA3aH4BAADgDZpfAAAAeIPmFwAAAN5gtYcmlG8XtrfePq5rPnJVzrGhdWF17WrMXZkmJk5m7dg2XzI5pYmJD7KDGwAAqCqa3yZUaBe26I4tOcdGD8SDKaoMs7NSW9vqnF3cpPRObrOz7wdfFAAAaGnEHgAAAOANml8AAAB4g9iDp/LlhiVp7Gjp26OenkoVzOu+2D+oiYlJMrsAAKCh0Px6Kl9uWEpnh0t1bvZ8wbxuKLRZiURfBRUCAABUH7EHAAAAeIN3fhtYNaIJAADUS3d3rxKJVMH5cDiknp6uwOoBJJrfhlaNaAIAAPWSSKQUiUQLzsfjheeAWiH2AAAAAG/Q/AIAAMAbxB4aQKFs78APv6+PrvqdnPG5pcRWrWrXZzbfuOi5R4Zjij7SmTM+djSmQ4MnNT19Ju+5JZV0fgB+SCZjGh7qzB47dVwrzsdzxm15WNfd0JjbqqO6YrERdXZGF5kfUyRSu+9PphjloPltAIWyvd/7+zOLLiWWSg0WPbc7N1MwNzw9nXv+uXNLKun8APzQZjPq2hrJGut+ckhfvnWlNm7MHu/tjwdWF+prZsYtmukdGtpW0+9PphjlIPYAAAAAb9D8AgAAwBvEHhZRiyxRvnxvo67bOzFxUi/2D2blgOd77+zZ4IsC0LRGftItdzb79W/F+SMa+Uk3GWHURLFM8vHjb+mqq65Z9BzFfteTO24+NL+LqEWWKF++t1HX7Z2dlUKhzVk54PnOn381+KIANC13NpGTGx4dXamBf879g1+gGkrJJG/ZUnheKv67ntxx8yH2AAAAAG/wzm8LOzR4uGBkIZmc0sTEBxUKBV4WgAaQTMa04vy7OcuUTZ0KPoaVbxk1lkvzQ7FYQiNEBipdzq0Z/o2VaMbYB81vC5uePqO2ttUFl0ubnX0/+KIANIQ2m8m7TFn3k8HHsPIto8ZyaX4oFktohMhApcu5NcO/sRLNGPsg9gAAAABv0PwCAADAG17HHorlVObnePIt0fPrXw+rd/d5de3KzaX17u7WoR/8o2Z+nb0c2K9/OaGPrvpdSWwfDKA1zc/wrjh/5MLtpeSJ584x//FS4SxwvtdoWx4uo3ogWK2eCW5EXje/xXIq83M8+ZboSaXiGp7Mv0RPajKhe29ZqVDo9qzx7if/mu2DAbS0+Rne0dHf5oqXkieeO8f8x0uFs8D5XqPJDaMZtHomuBERewAAAIA3Knrn18xuk7RX0kWSvuGc+2pVqqqyfLuqSdLI4UNKjcdzxpOnjuvStVcV/bhuYuKkho6k8n5cMf5Pw7r0+tlFlxKrdAe1ucdLynuOiYnJRR8PAM1m4bJoc6/T+V6jCy3nttRl1ApFKqqxFJt7702WeVtEpcuM+aDSpcaacamySpXd/JrZRZL+QtK/lzQu6VUze8k5N1at4qol365qkvS9v/9VzsdkUvqjua6tW4p+XDc7K334wx/N+3FFajyu2dnDi9ZV6Q5qc4+XlPcciUTfoo8HgGazcFm0udfpfK/RhZZzW2ocopaRipVtvyGusYhKlxnzQaVLjTXjUmWVqiT2cIOkY8654865s5L6JG2tTlkAAABA9VXS/F4u6Rfz7o9nxgAAAICGZM658h5odqek25xz/zlzf7ukTzjnHlxw3AOSHsjcvUbSW4ucdq2kU2UV5C+uWXm4bkvHNSsP123puGbl4botHdesPM1w3a50zq3LN1HJH7ydkHTFvPvrM2NZnHP7JO0r5YRm9ppzblMFNXmHa1YertvScc3Kw3VbOq5ZebhuS8c1K0+zX7dKYg+vSvqImW0ws+WSviTppeqUBQAAAFRf2e/8OufOmdmDkr6n9FJn33TOvVG1ygAAAIAqq2idX+fcdyV9t0q1SCXGI5CFa1YertvScc3Kw3VbOq5ZebhuS8c1K09TX7ey/+ANAAAAaDZsbwwAAABv1LX5NbOomZ0ws+HM1+0FjrvNzN4ys2Nm9mjQdTYSM/uamf2TmY2a2d+aWajAcXEz+2nmur4WcJkNo9hzx8w+aGbPZ+ZfMbNIHcpsGGZ2hZkdMrMxM3vDzP44zzGbzWx63s9tdz1qbTTFfuYs7X9nnmujZnZ9PepsFGZ2zbzn0LCZ/dLMuhYcw3NNkpl908zeMbOj88ZWm9kPzOztzH8vKfDYjswxb5tZR3BV11eBa8bvzyIKXLfW69Wcc3X7khSV9EiRYy6S9DNJV0laLmlE0u/Vs+46X7PPSlqWub1b0u4Cx8Ulra13vXW+VkWfO5L+i6S/ytz+kqTn6113na/ZZZKuz9y+WNL/y3PNNkv6u3rX2mhfxX7mJN0u6R8kmaQbJb1S75ob5Svzs3pS6XU554/zXEtfh09Lul7S0Xlj/1PSo5nbj+b7XSBptaTjmf9ekrl9Sb3/PXW8Zvz+LO+6tVyv1gyxB7ZRnsc5933n3LnM3cNKr6+M/Ep57myVtD9z+wVJt5qZBVhjQ3HOTTjnjmRu/0rSm2LnxmrZKumASzssKWRml9W7qAZxq6SfOed+Xu9CGpFz7seSphYMz3/t2i9pW56H/gdJP3DOTTnn3pX0A0m31arORpLvmvH7s7gCz7VSNFWv1gjN74OZjyC+WeBjG7ZRLuw+pd9JysdJ+r6ZvZ7ZZc9HpTx3LhyTeVGclrQmkOoaXCYC8jFJr+SZ/qSZjZjZP5jZtcFW1rCK/czxWlbYlyR9q8Acz7X8LnXOTWRun5R0aZ5jeM4Vxu/PpWmpXq3mza+Z/dDMjub52irpLyX9W0kflTQh6X/Vup5mUOSazR3zmKRzkp4rcJrfd85dL+kPJf1XM/t0AKWjRZjZhyX9jaQu59wvF0wfUfrj6eskPSHpxYDLa1T8zJXB0psk3SHpYJ5pnmslcOnPnVm6qUT8/lyyluvVKlrntxTOuT8o5Tgze1LS3+WZKmkb5VZS7JqZWaekz0m6NfOil+8cJzL/fcfM/lbpjyR+XOVSG10pz525Y8bNbJmkVZJOB1NeYzKzNqUb3+ecc99ZOD+/GXbOfdfM/o+ZrXXONfo+7zVVws+cd69lJfpDSUecc8mFEzzXFpU0s8uccxOZ+Mw7eY45oXRues56SYMB1Naw+P25dPN/NlulV6v3ag/z826fl3Q0z2FsozyPmd0m6U8k3eGc+5cCx3zIzC6eu610yD/ftW11pTx3XpI09xfQd0oaKPSC6INM3vkpSW86575e4Jh/PZeLNrMblH4d8f1/GEr5mXtJ0g5Lu1HS9LyPrX32n1Qg8sBzbVHzX7s6JPXnOeZ7kj5rZpdkPqr+bGbMS/z+LE9L9mr1/Gs7Sc9K+qmkUaUv0mWZ8X8j6bvzjrtd6b86/5mkx+pZc72/JB1TOlcznPmaW6ngwjVT+q8tRzJfb/h8zfI9dyT1KP3iJ0ntSn/cekzSTyRdVe+a63y9fl/pj09H5z3Hbpf0R5L+KHPMg5nn1YjSfzRyU73rrvdXoZ+5BdfNJP1F5rn4U0mb6l13vb8kfUjpZnbVvDGea7nX6VtKf9w8q3SW8n6l/zbhR5LelvRDSaszx26S9I15j70v8/p2TNK99f631Pma8fuzvOvWcr0aO7wBAADAG42w2gMAAAAQCJpfAAAAeIPmFwAAAN6g+QUAAIA3aH4BAADgDZpfAAAAeIPmFwAAAN6g+QUAAIA3/j8+qUn2vCztPwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "539df0d5-98ae-47f6-b3cf-e1c455b22650",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "a98efb3e-77b6-423d-8614-60fee982475b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f4878b35070>"
      ]
     },
     "execution_count": 27,
     "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": []
  },
  {
   "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=\"img/boxplot.png\" width=\"300px\">\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "77b7cad3-16ba-40a3-953c-231dfb68b00b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "7470a4ec-72bf-436a-9481-29ec8a225973",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['cond1', 'cond2', 'cond3', 'control'], dtype='object')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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": [
    "f"
   ]
  },
  {
   "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=\"./img/histogram_2D.png\" width=\"300px\" />\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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": 34,
   "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": 35,
   "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": []
  },
  {
   "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": 36,
   "id": "6cd41a4c-e741-46df-8538-d6d6da06cfbf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Intensity')"
      ]
     },
     "execution_count": 36,
     "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": []
  },
  {
   "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": 37,
   "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": 38,
   "id": "b6b1505f-819a-4cb4-b171-453afb715e51",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Scatter plot Iris sepal')"
      ]
     },
     "execution_count": 38,
     "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": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b7c86dc3-a042-490e-b79e-70bbc77d82a0",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.8.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}