From cf01a03839ed33af1d5f285425fca5ac1885ad72 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Bertrand=20N=C3=A9ron?= <bneron@pasteur.fr>
Date: Thu, 17 Oct 2024 14:22:01 +0200
Subject: [PATCH] fix path and pandas_cours
---
notebooks/Courses/pandas_cours.ipynb | 710 +++++++++++++--------------
1 file changed, 353 insertions(+), 357 deletions(-)
diff --git a/notebooks/Courses/pandas_cours.ipynb b/notebooks/Courses/pandas_cours.ipynb
index f9078e2..7fc46ff 100644
--- a/notebooks/Courses/pandas_cours.ipynb
+++ b/notebooks/Courses/pandas_cours.ipynb
@@ -3,12 +3,14 @@
{
"cell_type": "markdown",
"id": "horizontal-listening",
- "metadata": {},
+ "metadata": {
+ "jp-MarkdownHeadingCollapsed": true
+ },
"source": [
"# <center><b>Course</b></center>\n",
"\n",
"<div style=\"text-align:center\">\n",
- " <img src=\"images/pandas_logo.svg\" width=\"600px\">\n",
+ " <img src=\"../images/pandas_logo.svg\" width=\"600px\">\n",
" <div>\n",
" Bertrand Néron, François Laurent, Etienne Kornobis\n",
" <br />\n",
@@ -351,7 +353,7 @@
{
"data": {
"text/plain": [
- "2"
+ "np.int64(2)"
]
},
"execution_count": 11,
@@ -372,7 +374,7 @@
{
"data": {
"text/plain": [
- "2"
+ "np.int64(2)"
]
},
"execution_count": 12,
@@ -448,7 +450,7 @@
{
"data": {
"text/plain": [
- "2"
+ "np.int64(2)"
]
},
"execution_count": 15,
@@ -480,7 +482,7 @@
{
"data": {
"text/plain": [
- "1"
+ "np.int64(1)"
]
},
"execution_count": 16,
@@ -618,7 +620,7 @@
},
{
"cell_type": "code",
- "execution_count": 20,
+ "execution_count": 19,
"id": "least-cruise",
"metadata": {},
"outputs": [
@@ -631,7 +633,7 @@
"dtype: int64"
]
},
- "execution_count": 20,
+ "execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
@@ -650,7 +652,7 @@
},
{
"cell_type": "code",
- "execution_count": 21,
+ "execution_count": 20,
"id": "better-blame",
"metadata": {},
"outputs": [
@@ -663,7 +665,7 @@
"dtype: int64"
]
},
- "execution_count": 21,
+ "execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
@@ -694,7 +696,7 @@
},
{
"cell_type": "code",
- "execution_count": 22,
+ "execution_count": 21,
"id": "regulated-ready",
"metadata": {},
"outputs": [
@@ -747,7 +749,7 @@
"b 4 5 6"
]
},
- "execution_count": 22,
+ "execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
@@ -762,7 +764,7 @@
},
{
"cell_type": "code",
- "execution_count": 23,
+ "execution_count": 22,
"id": "stable-discharge",
"metadata": {},
"outputs": [
@@ -772,7 +774,7 @@
"Index(['a', 'b'], dtype='object')"
]
},
- "execution_count": 23,
+ "execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
@@ -783,7 +785,7 @@
},
{
"cell_type": "code",
- "execution_count": 24,
+ "execution_count": 23,
"id": "configured-coral",
"metadata": {},
"outputs": [
@@ -793,7 +795,7 @@
"Index(['A', 'B', 'C'], dtype='object')"
]
},
- "execution_count": 24,
+ "execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
@@ -812,7 +814,7 @@
},
{
"cell_type": "code",
- "execution_count": 25,
+ "execution_count": 24,
"id": "facial-curve",
"metadata": {},
"outputs": [
@@ -879,7 +881,7 @@
"3 9 10 11"
]
},
- "execution_count": 25,
+ "execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
@@ -899,7 +901,7 @@
},
{
"cell_type": "code",
- "execution_count": 26,
+ "execution_count": 25,
"id": "suspected-nirvana",
"metadata": {},
"outputs": [
@@ -955,7 +957,7 @@
"2 3 6"
]
},
- "execution_count": 26,
+ "execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
@@ -990,7 +992,7 @@
},
"outputs": [],
"source": [
- "titanic = pd.read_csv(\"data/titanic.csv\")"
+ "titanic = pd.read_csv(\"../data/titanic.csv\")"
]
},
{
@@ -1005,7 +1007,7 @@
},
{
"cell_type": "code",
- "execution_count": 28,
+ "execution_count": 29,
"id": "bridal-development",
"metadata": {},
"outputs": [
@@ -1022,7 +1024,7 @@
}
],
"source": [
- "! head -5 data/bar_data.tsv"
+ "! head -5 ../data/bar_data.tsv"
]
},
{
@@ -1035,7 +1037,7 @@
},
{
"cell_type": "code",
- "execution_count": 194,
+ "execution_count": 30,
"id": "listed-framework",
"metadata": {},
"outputs": [
@@ -1115,13 +1117,13 @@
"4 9.080359 5.629192 18.443504 4.268572"
]
},
- "execution_count": 194,
+ "execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "bar = pd.read_csv(\"data/bar_data.tsv\", sep=\"\\t\", comment=\"#\")\n",
+ "bar = pd.read_csv(\"../data/bar_data.tsv\", sep=\"\\t\", comment=\"#\")\n",
"bar.head()"
]
},
@@ -1135,7 +1137,7 @@
},
{
"cell_type": "code",
- "execution_count": 29,
+ "execution_count": 31,
"id": "allied-artist",
"metadata": {},
"outputs": [
@@ -1152,12 +1154,12 @@
}
],
"source": [
- "! head -5 data/data_for_plt.csv"
+ "! head -5 ../data/data_for_plt.csv"
]
},
{
"cell_type": "code",
- "execution_count": 196,
+ "execution_count": 32,
"id": "limiting-tokyo",
"metadata": {},
"outputs": [
@@ -1225,13 +1227,13 @@
"2 2 2.11 383.40 437.458982 15.040385"
]
},
- "execution_count": 196,
+ "execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "data = pd.read_csv(\"data/data_for_plt.csv\", sep=\"\\t\")\n",
+ "data = pd.read_csv(\"../data/data_for_plt.csv\", sep=\"\\t\")\n",
"data.head(3)"
]
},
@@ -1246,7 +1248,7 @@
},
{
"cell_type": "code",
- "execution_count": 197,
+ "execution_count": 33,
"id": "crucial-flight",
"metadata": {},
"outputs": [
@@ -1326,13 +1328,13 @@
"4 -1.37 361.37 448.864769 5.732690"
]
},
- "execution_count": 197,
+ "execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "data = pd.read_csv(\"data/data_for_plt.csv\", sep=\"\\t\", index_col=0)\n",
+ "data = pd.read_csv(\"../data/data_for_plt.csv\", sep=\"\\t\", index_col=0)\n",
"data.head()"
]
},
@@ -1348,7 +1350,7 @@
},
{
"cell_type": "code",
- "execution_count": 30,
+ "execution_count": 34,
"id": "oriented-bleeding",
"metadata": {},
"outputs": [
@@ -1436,13 +1438,13 @@
"4 -1.37 361.37 448.864769 5.732690"
]
},
- "execution_count": 30,
+ "execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "data = pd.read_csv(\"data/no_header.tsv\", sep=\"\\t\", index_col=0, header=None)\n",
+ "data = pd.read_csv(\"../data/no_header.tsv\", sep=\"\\t\", index_col=0, header=None)\n",
"data.head()"
]
},
@@ -1456,7 +1458,7 @@
},
{
"cell_type": "code",
- "execution_count": 31,
+ "execution_count": 35,
"id": "competent-negative",
"metadata": {},
"outputs": [
@@ -1468,7 +1470,7 @@
" [3, 6]])"
]
},
- "execution_count": 31,
+ "execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
@@ -1479,7 +1481,7 @@
},
{
"cell_type": "code",
- "execution_count": 32,
+ "execution_count": 36,
"id": "fantastic-monday",
"metadata": {},
"outputs": [
@@ -1489,7 +1491,7 @@
"[[1, 4], [2, 5], [3, 6]]"
]
},
- "execution_count": 32,
+ "execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
@@ -1510,12 +1512,12 @@
},
{
"cell_type": "code",
- "execution_count": 33,
+ "execution_count": 38,
"id": "simple-luxury",
"metadata": {},
"outputs": [],
"source": [
- "titanic = pd.read_csv(\"data/titanic.csv\")"
+ "titanic = pd.read_csv(\"../data/titanic.csv\")"
]
},
{
@@ -1528,7 +1530,7 @@
},
{
"cell_type": "code",
- "execution_count": 37,
+ "execution_count": 39,
"id": "wound-asbestos",
"metadata": {},
"outputs": [
@@ -1557,7 +1559,7 @@
},
{
"cell_type": "code",
- "execution_count": 203,
+ "execution_count": 40,
"id": "worthy-bridge",
"metadata": {},
"outputs": [
@@ -1699,7 +1701,7 @@
"4 0 373450 8.0500 NaN S "
]
},
- "execution_count": 203,
+ "execution_count": 40,
"metadata": {},
"output_type": "execute_result"
}
@@ -1710,7 +1712,7 @@
},
{
"cell_type": "code",
- "execution_count": 204,
+ "execution_count": 41,
"id": "absent-authorization",
"metadata": {},
"outputs": [
@@ -1798,7 +1800,7 @@
"1 0 PC 17599 71.2833 C85 C "
]
},
- "execution_count": 204,
+ "execution_count": 41,
"metadata": {},
"output_type": "execute_result"
}
@@ -1817,7 +1819,7 @@
},
{
"cell_type": "code",
- "execution_count": 205,
+ "execution_count": 42,
"id": "aboriginal-smith",
"metadata": {},
"outputs": [
@@ -1901,7 +1903,7 @@
"890 0 370376 7.75 NaN Q "
]
},
- "execution_count": 205,
+ "execution_count": 42,
"metadata": {},
"output_type": "execute_result"
}
@@ -1920,7 +1922,7 @@
},
{
"cell_type": "code",
- "execution_count": 39,
+ "execution_count": 43,
"id": "sunset-ballot",
"metadata": {},
"outputs": [
@@ -2061,7 +2063,7 @@
"max 6.000000 512.329200 "
]
},
- "execution_count": 39,
+ "execution_count": 43,
"metadata": {},
"output_type": "execute_result"
}
@@ -2073,7 +2075,7 @@
},
{
"cell_type": "code",
- "execution_count": 43,
+ "execution_count": 44,
"id": "whole-township",
"metadata": {},
"outputs": [
@@ -2100,34 +2102,29 @@
},
{
"cell_type": "code",
- "execution_count": 44,
+ "execution_count": 45,
"id": "furnished-dealing",
"metadata": {},
"outputs": [
{
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/tmp/ipykernel_6604/502188208.py:1: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError. Select only valid columns before calling the reduction.\n",
- " titanic.median()\n"
+ "ename": "TypeError",
+ "evalue": "Cannot convert [['Braund, Mr. Owen Harris'\n 'Cumings, Mrs. John Bradley (Florence Briggs Thayer)'\n 'Heikkinen, Miss. Laina' ... 'Johnston, Miss. Catherine Helen \"Carrie\"'\n 'Behr, Mr. Karl Howell' 'Dooley, Mr. Patrick']\n ['male' 'female' 'female' ... 'female' 'male' 'male']\n ['A/5 21171' 'PC 17599' 'STON/O2. 3101282' ... 'W./C. 6607' '111369'\n '370376']\n [nan 'C85' nan ... nan 'C148' nan]\n ['S' 'C' 'S' ... 'S' 'C' 'Q']] to numeric",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
+ "Cell \u001b[0;32mIn[45], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mtitanic\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmedian\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
+ "File \u001b[0;32m~/Projects/Cours/scientific_python/sci_py312/lib/python3.11/site-packages/pandas/core/frame.py:11706\u001b[0m, in \u001b[0;36mDataFrame.median\u001b[0;34m(self, axis, skipna, numeric_only, **kwargs)\u001b[0m\n\u001b[1;32m 11698\u001b[0m \u001b[38;5;129m@doc\u001b[39m(make_doc(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmedian\u001b[39m\u001b[38;5;124m\"\u001b[39m, ndim\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m2\u001b[39m))\n\u001b[1;32m 11699\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mmedian\u001b[39m(\n\u001b[1;32m 11700\u001b[0m \u001b[38;5;28mself\u001b[39m,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 11704\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[1;32m 11705\u001b[0m ):\n\u001b[0;32m> 11706\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmedian\u001b[49m\u001b[43m(\u001b[49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mskipna\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnumeric_only\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 11707\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(result, Series):\n\u001b[1;32m 11708\u001b[0m result \u001b[38;5;241m=\u001b[39m result\u001b[38;5;241m.\u001b[39m__finalize__(\u001b[38;5;28mself\u001b[39m, method\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmedian\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
+ "File \u001b[0;32m~/Projects/Cours/scientific_python/sci_py312/lib/python3.11/site-packages/pandas/core/generic.py:12431\u001b[0m, in \u001b[0;36mNDFrame.median\u001b[0;34m(self, axis, skipna, numeric_only, **kwargs)\u001b[0m\n\u001b[1;32m 12424\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mmedian\u001b[39m(\n\u001b[1;32m 12425\u001b[0m \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m 12426\u001b[0m axis: Axis \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 12429\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[1;32m 12430\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Series \u001b[38;5;241m|\u001b[39m \u001b[38;5;28mfloat\u001b[39m:\n\u001b[0;32m> 12431\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_stat_function\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 12432\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmedian\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnanops\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnanmedian\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mskipna\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnumeric_only\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\n\u001b[1;32m 12433\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n",
+ "File \u001b[0;32m~/Projects/Cours/scientific_python/sci_py312/lib/python3.11/site-packages/pandas/core/generic.py:12377\u001b[0m, in \u001b[0;36mNDFrame._stat_function\u001b[0;34m(self, name, func, axis, skipna, numeric_only, **kwargs)\u001b[0m\n\u001b[1;32m 12373\u001b[0m nv\u001b[38;5;241m.\u001b[39mvalidate_func(name, (), kwargs)\n\u001b[1;32m 12375\u001b[0m validate_bool_kwarg(skipna, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mskipna\u001b[39m\u001b[38;5;124m\"\u001b[39m, none_allowed\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[0;32m> 12377\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_reduce\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 12378\u001b[0m \u001b[43m \u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mname\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mskipna\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mskipna\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnumeric_only\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnumeric_only\u001b[49m\n\u001b[1;32m 12379\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
+ "File \u001b[0;32m~/Projects/Cours/scientific_python/sci_py312/lib/python3.11/site-packages/pandas/core/frame.py:11562\u001b[0m, in \u001b[0;36mDataFrame._reduce\u001b[0;34m(self, op, name, axis, skipna, numeric_only, filter_type, **kwds)\u001b[0m\n\u001b[1;32m 11558\u001b[0m df \u001b[38;5;241m=\u001b[39m df\u001b[38;5;241m.\u001b[39mT\n\u001b[1;32m 11560\u001b[0m \u001b[38;5;66;03m# After possibly _get_data and transposing, we are now in the\u001b[39;00m\n\u001b[1;32m 11561\u001b[0m \u001b[38;5;66;03m# simple case where we can use BlockManager.reduce\u001b[39;00m\n\u001b[0;32m> 11562\u001b[0m res \u001b[38;5;241m=\u001b[39m \u001b[43mdf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_mgr\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mreduce\u001b[49m\u001b[43m(\u001b[49m\u001b[43mblk_func\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 11563\u001b[0m out \u001b[38;5;241m=\u001b[39m df\u001b[38;5;241m.\u001b[39m_constructor_from_mgr(res, axes\u001b[38;5;241m=\u001b[39mres\u001b[38;5;241m.\u001b[39maxes)\u001b[38;5;241m.\u001b[39miloc[\u001b[38;5;241m0\u001b[39m]\n\u001b[1;32m 11564\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m out_dtype \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m out\u001b[38;5;241m.\u001b[39mdtype \u001b[38;5;241m!=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mboolean\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n",
+ "File \u001b[0;32m~/Projects/Cours/scientific_python/sci_py312/lib/python3.11/site-packages/pandas/core/internals/managers.py:1500\u001b[0m, in \u001b[0;36mBlockManager.reduce\u001b[0;34m(self, func)\u001b[0m\n\u001b[1;32m 1498\u001b[0m res_blocks: \u001b[38;5;28mlist\u001b[39m[Block] \u001b[38;5;241m=\u001b[39m []\n\u001b[1;32m 1499\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m blk \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mblocks:\n\u001b[0;32m-> 1500\u001b[0m nbs \u001b[38;5;241m=\u001b[39m \u001b[43mblk\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mreduce\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1501\u001b[0m res_blocks\u001b[38;5;241m.\u001b[39mextend(nbs)\n\u001b[1;32m 1503\u001b[0m index \u001b[38;5;241m=\u001b[39m Index([\u001b[38;5;28;01mNone\u001b[39;00m]) \u001b[38;5;66;03m# placeholder\u001b[39;00m\n",
+ "File \u001b[0;32m~/Projects/Cours/scientific_python/sci_py312/lib/python3.11/site-packages/pandas/core/internals/blocks.py:404\u001b[0m, in \u001b[0;36mBlock.reduce\u001b[0;34m(self, func)\u001b[0m\n\u001b[1;32m 398\u001b[0m \u001b[38;5;129m@final\u001b[39m\n\u001b[1;32m 399\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mreduce\u001b[39m(\u001b[38;5;28mself\u001b[39m, func) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28mlist\u001b[39m[Block]:\n\u001b[1;32m 400\u001b[0m \u001b[38;5;66;03m# We will apply the function and reshape the result into a single-row\u001b[39;00m\n\u001b[1;32m 401\u001b[0m \u001b[38;5;66;03m# Block with the same mgr_locs; squeezing will be done at a higher level\u001b[39;00m\n\u001b[1;32m 402\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mndim \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m2\u001b[39m\n\u001b[0;32m--> 404\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvalues\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 406\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvalues\u001b[38;5;241m.\u001b[39mndim \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[1;32m 407\u001b[0m res_values \u001b[38;5;241m=\u001b[39m result\n",
+ "File \u001b[0;32m~/Projects/Cours/scientific_python/sci_py312/lib/python3.11/site-packages/pandas/core/frame.py:11481\u001b[0m, in \u001b[0;36mDataFrame._reduce.<locals>.blk_func\u001b[0;34m(values, axis)\u001b[0m\n\u001b[1;32m 11479\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m np\u001b[38;5;241m.\u001b[39marray([result])\n\u001b[1;32m 11480\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m> 11481\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mop\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mskipna\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mskipna\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n",
+ "File \u001b[0;32m~/Projects/Cours/scientific_python/sci_py312/lib/python3.11/site-packages/pandas/core/nanops.py:147\u001b[0m, in \u001b[0;36mbottleneck_switch.__call__.<locals>.f\u001b[0;34m(values, axis, skipna, **kwds)\u001b[0m\n\u001b[1;32m 145\u001b[0m result \u001b[38;5;241m=\u001b[39m alt(values, axis\u001b[38;5;241m=\u001b[39maxis, skipna\u001b[38;5;241m=\u001b[39mskipna, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwds)\n\u001b[1;32m 146\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 147\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43malt\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mskipna\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mskipna\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 149\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m result\n",
+ "File \u001b[0;32m~/Projects/Cours/scientific_python/sci_py312/lib/python3.11/site-packages/pandas/core/nanops.py:787\u001b[0m, in \u001b[0;36mnanmedian\u001b[0;34m(values, axis, skipna, mask)\u001b[0m\n\u001b[1;32m 785\u001b[0m inferred \u001b[38;5;241m=\u001b[39m lib\u001b[38;5;241m.\u001b[39minfer_dtype(values)\n\u001b[1;32m 786\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m inferred \u001b[38;5;129;01min\u001b[39;00m [\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstring\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmixed\u001b[39m\u001b[38;5;124m\"\u001b[39m]:\n\u001b[0;32m--> 787\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCannot convert \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mvalues\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m to numeric\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 788\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 789\u001b[0m values \u001b[38;5;241m=\u001b[39m values\u001b[38;5;241m.\u001b[39mastype(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mf8\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
+ "\u001b[0;31mTypeError\u001b[0m: Cannot convert [['Braund, Mr. Owen Harris'\n 'Cumings, Mrs. John Bradley (Florence Briggs Thayer)'\n 'Heikkinen, Miss. Laina' ... 'Johnston, Miss. Catherine Helen \"Carrie\"'\n 'Behr, Mr. Karl Howell' 'Dooley, Mr. Patrick']\n ['male' 'female' 'female' ... 'female' 'male' 'male']\n ['A/5 21171' 'PC 17599' 'STON/O2. 3101282' ... 'W./C. 6607' '111369'\n '370376']\n [nan 'C85' nan ... nan 'C148' nan]\n ['S' 'C' 'S' ... 'S' 'C' 'Q']] to numeric"
]
- },
- {
- "data": {
- "text/plain": [
- "PassengerId 446.0000\n",
- "Survived 0.0000\n",
- "Pclass 3.0000\n",
- "Age 28.0000\n",
- "SibSp 0.0000\n",
- "Parch 0.0000\n",
- "Fare 14.4542\n",
- "dtype: float64"
- ]
- },
- "execution_count": 44,
- "metadata": {},
- "output_type": "execute_result"
}
],
"source": [
@@ -2144,7 +2141,7 @@
},
{
"cell_type": "code",
- "execution_count": 54,
+ "execution_count": 46,
"id": "2a6e3ac6-90fe-4d2a-94c4-0b2a0030a892",
"metadata": {},
"outputs": [
@@ -2161,7 +2158,7 @@
"dtype: float64"
]
},
- "execution_count": 54,
+ "execution_count": 46,
"metadata": {},
"output_type": "execute_result"
}
@@ -2180,7 +2177,7 @@
},
{
"cell_type": "code",
- "execution_count": 55,
+ "execution_count": 47,
"id": "further-circular",
"metadata": {},
"outputs": [
@@ -2197,7 +2194,7 @@
"dtype: float64"
]
},
- "execution_count": 55,
+ "execution_count": 47,
"metadata": {},
"output_type": "execute_result"
}
@@ -2216,19 +2213,20 @@
},
{
"cell_type": "code",
- "execution_count": 57,
+ "execution_count": 48,
"id": "comprehensive-division",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
+ "Sex\n",
"male 577\n",
"female 314\n",
- "Name: Sex, dtype: int64"
+ "Name: count, dtype: int64"
]
},
- "execution_count": 57,
+ "execution_count": 48,
"metadata": {},
"output_type": "execute_result"
}
@@ -2247,17 +2245,17 @@
},
{
"cell_type": "code",
- "execution_count": 58,
+ "execution_count": 49,
"id": "universal-boutique",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "80.0"
+ "np.float64(80.0)"
]
},
- "execution_count": 58,
+ "execution_count": 49,
"metadata": {},
"output_type": "execute_result"
}
@@ -2268,17 +2266,17 @@
},
{
"cell_type": "code",
- "execution_count": 59,
+ "execution_count": 50,
"id": "several-principle",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "0.42"
+ "np.float64(0.42)"
]
},
- "execution_count": 59,
+ "execution_count": 50,
"metadata": {},
"output_type": "execute_result"
}
@@ -2305,7 +2303,7 @@
},
{
"cell_type": "code",
- "execution_count": 82,
+ "execution_count": 51,
"id": "received-editing",
"metadata": {},
"outputs": [
@@ -2372,7 +2370,7 @@
"3 9 10 11"
]
},
- "execution_count": 82,
+ "execution_count": 51,
"metadata": {},
"output_type": "execute_result"
}
@@ -2385,7 +2383,7 @@
},
{
"cell_type": "code",
- "execution_count": 83,
+ "execution_count": 52,
"id": "classified-pittsburgh",
"metadata": {},
"outputs": [
@@ -2395,7 +2393,7 @@
"Index(['A', 'B', 'Z'], dtype='object')"
]
},
- "execution_count": 83,
+ "execution_count": 52,
"metadata": {},
"output_type": "execute_result"
}
@@ -2409,7 +2407,7 @@
},
{
"cell_type": "code",
- "execution_count": 84,
+ "execution_count": 53,
"id": "exceptional-roberts",
"metadata": {},
"outputs": [
@@ -2476,7 +2474,7 @@
"3 9 10 11"
]
},
- "execution_count": 84,
+ "execution_count": 53,
"metadata": {},
"output_type": "execute_result"
}
@@ -2496,7 +2494,7 @@
},
{
"cell_type": "code",
- "execution_count": 85,
+ "execution_count": 54,
"id": "surprised-burns",
"metadata": {},
"outputs": [
@@ -2563,7 +2561,7 @@
"3 9 10 11"
]
},
- "execution_count": 85,
+ "execution_count": 54,
"metadata": {},
"output_type": "execute_result"
}
@@ -2582,7 +2580,7 @@
},
{
"cell_type": "code",
- "execution_count": 86,
+ "execution_count": 55,
"id": "breathing-yeast",
"metadata": {},
"outputs": [
@@ -2649,7 +2647,7 @@
"e 9 10 11"
]
},
- "execution_count": 86,
+ "execution_count": 55,
"metadata": {},
"output_type": "execute_result"
}
@@ -2661,7 +2659,7 @@
},
{
"cell_type": "code",
- "execution_count": 87,
+ "execution_count": 56,
"id": "central-columbus",
"metadata": {},
"outputs": [
@@ -2728,7 +2726,7 @@
"d 9 10 11"
]
},
- "execution_count": 87,
+ "execution_count": 56,
"metadata": {},
"output_type": "execute_result"
}
@@ -2747,7 +2745,7 @@
},
{
"cell_type": "code",
- "execution_count": 88,
+ "execution_count": 57,
"id": "outer-access",
"metadata": {},
"outputs": [
@@ -2819,7 +2817,7 @@
"e 9 10 11 12"
]
},
- "execution_count": 88,
+ "execution_count": 57,
"metadata": {},
"output_type": "execute_result"
}
@@ -2839,7 +2837,7 @@
},
{
"cell_type": "code",
- "execution_count": 89,
+ "execution_count": 58,
"id": "respective-twins",
"metadata": {},
"outputs": [
@@ -2931,7 +2929,7 @@
"e 9 10 11 12 9 10 11 12"
]
},
- "execution_count": 89,
+ "execution_count": 58,
"metadata": {},
"output_type": "execute_result"
}
@@ -2952,7 +2950,7 @@
},
{
"cell_type": "code",
- "execution_count": 90,
+ "execution_count": 59,
"id": "blank-ceiling",
"metadata": {},
"outputs": [
@@ -3026,7 +3024,7 @@
"12 9 10 11"
]
},
- "execution_count": 90,
+ "execution_count": 59,
"metadata": {},
"output_type": "execute_result"
}
@@ -3045,7 +3043,7 @@
},
{
"cell_type": "code",
- "execution_count": 91,
+ "execution_count": 60,
"id": "checked-prototype",
"metadata": {},
"outputs": [
@@ -3117,7 +3115,7 @@
"e 9 10 11 12"
]
},
- "execution_count": 91,
+ "execution_count": 60,
"metadata": {},
"output_type": "execute_result"
}
@@ -3136,7 +3134,7 @@
},
{
"cell_type": "code",
- "execution_count": 92,
+ "execution_count": 61,
"id": "alpine-coast",
"metadata": {},
"outputs": [],
@@ -3146,7 +3144,7 @@
},
{
"cell_type": "code",
- "execution_count": 93,
+ "execution_count": 62,
"id": "gothic-freight",
"metadata": {},
"outputs": [
@@ -3220,7 +3218,7 @@
"12 9 10 11"
]
},
- "execution_count": 93,
+ "execution_count": 62,
"metadata": {},
"output_type": "execute_result"
}
@@ -3240,7 +3238,7 @@
},
{
"cell_type": "code",
- "execution_count": 94,
+ "execution_count": 63,
"id": "signal-disabled",
"metadata": {},
"outputs": [
@@ -3312,7 +3310,7 @@
"3 12 9 10 11"
]
},
- "execution_count": 94,
+ "execution_count": 63,
"metadata": {},
"output_type": "execute_result"
}
@@ -3334,7 +3332,7 @@
},
{
"cell_type": "code",
- "execution_count": 96,
+ "execution_count": 64,
"id": "western-roots",
"metadata": {},
"outputs": [],
@@ -3352,7 +3350,7 @@
},
{
"cell_type": "code",
- "execution_count": 97,
+ "execution_count": 65,
"id": "sophisticated-speaking",
"metadata": {},
"outputs": [
@@ -3433,7 +3431,7 @@
"1 42 43 44"
]
},
- "execution_count": 97,
+ "execution_count": 65,
"metadata": {},
"output_type": "execute_result"
}
@@ -3452,7 +3450,7 @@
},
{
"cell_type": "code",
- "execution_count": 99,
+ "execution_count": 66,
"id": "associate-lodge",
"metadata": {},
"outputs": [
@@ -3547,7 +3545,7 @@
"7 9 10 11"
]
},
- "execution_count": 99,
+ "execution_count": 66,
"metadata": {},
"output_type": "execute_result"
}
@@ -3576,7 +3574,7 @@
},
{
"cell_type": "code",
- "execution_count": 116,
+ "execution_count": 67,
"id": "dying-hepatitis",
"metadata": {},
"outputs": [
@@ -3718,7 +3716,7 @@
"4 0 373450 8.0500 NaN S "
]
},
- "execution_count": 116,
+ "execution_count": 67,
"metadata": {},
"output_type": "execute_result"
}
@@ -3729,7 +3727,7 @@
},
{
"cell_type": "code",
- "execution_count": 117,
+ "execution_count": 68,
"id": "authentic-winter",
"metadata": {},
"outputs": [
@@ -3779,7 +3777,7 @@
"2 female 26.0"
]
},
- "execution_count": 117,
+ "execution_count": 68,
"metadata": {},
"output_type": "execute_result"
}
@@ -3790,7 +3788,7 @@
},
{
"cell_type": "code",
- "execution_count": 118,
+ "execution_count": 69,
"id": "partial-trading",
"metadata": {},
"outputs": [
@@ -3867,7 +3865,7 @@
"4 male 35.0 0 0 373450"
]
},
- "execution_count": 118,
+ "execution_count": 69,
"metadata": {},
"output_type": "execute_result"
}
@@ -3878,7 +3876,7 @@
},
{
"cell_type": "code",
- "execution_count": 119,
+ "execution_count": 70,
"id": "electrical-force",
"metadata": {},
"outputs": [
@@ -3928,7 +3926,7 @@
"1 female 38.0"
]
},
- "execution_count": 119,
+ "execution_count": 70,
"metadata": {},
"output_type": "execute_result"
}
@@ -3939,7 +3937,7 @@
},
{
"cell_type": "code",
- "execution_count": 120,
+ "execution_count": 71,
"id": "after-giving",
"metadata": {},
"outputs": [
@@ -4007,7 +4005,7 @@
"2 female 26.0 0 0 STON/O2. 3101282"
]
},
- "execution_count": 120,
+ "execution_count": 71,
"metadata": {},
"output_type": "execute_result"
}
@@ -4034,7 +4032,7 @@
},
{
"cell_type": "code",
- "execution_count": 124,
+ "execution_count": 72,
"id": "homeless-debut",
"metadata": {
"tags": []
@@ -4051,7 +4049,7 @@
"Name: Sex, dtype: object"
]
},
- "execution_count": 124,
+ "execution_count": 72,
"metadata": {},
"output_type": "execute_result"
}
@@ -4070,7 +4068,7 @@
},
{
"cell_type": "code",
- "execution_count": 180,
+ "execution_count": 73,
"id": "c0edb00d-037e-45b1-9003-d16bef62258e",
"metadata": {},
"outputs": [
@@ -4085,7 +4083,7 @@
"Name: Sex, dtype: object"
]
},
- "execution_count": 180,
+ "execution_count": 73,
"metadata": {},
"output_type": "execute_result"
}
@@ -4104,7 +4102,7 @@
},
{
"cell_type": "code",
- "execution_count": 125,
+ "execution_count": 74,
"id": "operating-rehabilitation",
"metadata": {},
"outputs": [
@@ -4184,7 +4182,7 @@
"4 male 35.0 3 0"
]
},
- "execution_count": 125,
+ "execution_count": 74,
"metadata": {},
"output_type": "execute_result"
}
@@ -4211,7 +4209,7 @@
},
{
"cell_type": "code",
- "execution_count": 131,
+ "execution_count": 75,
"id": "realistic-liberal",
"metadata": {},
"outputs": [
@@ -4346,7 +4344,7 @@
"170 61.0 0 0 111240 33.5000 B19 S "
]
},
- "execution_count": 131,
+ "execution_count": 75,
"metadata": {},
"output_type": "execute_result"
}
@@ -4373,7 +4371,7 @@
},
{
"cell_type": "code",
- "execution_count": 136,
+ "execution_count": 76,
"id": "charming-debate",
"metadata": {},
"outputs": [
@@ -4435,7 +4433,7 @@
"871 Beckwith, Mrs. Richard Leonard (Sallie Monypeny) 47.0"
]
},
- "execution_count": 136,
+ "execution_count": 76,
"metadata": {},
"output_type": "execute_result"
}
@@ -4457,7 +4455,7 @@
},
{
"cell_type": "code",
- "execution_count": 137,
+ "execution_count": 77,
"id": "extensive-sense",
"metadata": {},
"outputs": [
@@ -4498,118 +4496,118 @@
" </thead>\n",
" <tbody>\n",
" <tr>\n",
- " <th>320</th>\n",
- " <td>321</td>\n",
+ " <th>138</th>\n",
+ " <td>139</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
- " <td>Dennis, Mr. Samuel</td>\n",
+ " <td>Osen, Mr. Olaf Elon</td>\n",
" <td>male</td>\n",
- " <td>22.0</td>\n",
+ " <td>16.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
- " <td>A/5 21172</td>\n",
- " <td>7.2500</td>\n",
+ " <td>7534</td>\n",
+ " <td>9.2167</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>59</th>\n",
- " <td>60</td>\n",
- " <td>0</td>\n",
- " <td>3</td>\n",
- " <td>Goodwin, Master. William Frederick</td>\n",
- " <td>male</td>\n",
- " <td>11.0</td>\n",
- " <td>5</td>\n",
+ " <th>211</th>\n",
+ " <td>212</td>\n",
+ " <td>1</td>\n",
" <td>2</td>\n",
- " <td>CA 2144</td>\n",
- " <td>46.9000</td>\n",
+ " <td>Cameron, Miss. Clear Annie</td>\n",
+ " <td>female</td>\n",
+ " <td>35.0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>F.C.C. 13528</td>\n",
+ " <td>21.0000</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>176</th>\n",
- " <td>177</td>\n",
- " <td>0</td>\n",
- " <td>3</td>\n",
- " <td>Lefebre, Master. Henry Forbes</td>\n",
+ " <th>547</th>\n",
+ " <td>548</td>\n",
+ " <td>1</td>\n",
+ " <td>2</td>\n",
+ " <td>Padro y Manent, Mr. Julian</td>\n",
" <td>male</td>\n",
" <td>NaN</td>\n",
- " <td>3</td>\n",
- " <td>1</td>\n",
- " <td>4133</td>\n",
- " <td>25.4667</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>SC/PARIS 2146</td>\n",
+ " <td>13.8625</td>\n",
" <td>NaN</td>\n",
- " <td>S</td>\n",
+ " <td>C</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>117</th>\n",
- " <td>118</td>\n",
+ " <th>482</th>\n",
+ " <td>483</td>\n",
" <td>0</td>\n",
- " <td>2</td>\n",
- " <td>Turpin, Mr. William John Robert</td>\n",
+ " <td>3</td>\n",
+ " <td>Rouse, Mr. Richard Henry</td>\n",
" <td>male</td>\n",
- " <td>29.0</td>\n",
- " <td>1</td>\n",
+ " <td>50.0</td>\n",
" <td>0</td>\n",
- " <td>11668</td>\n",
- " <td>21.0000</td>\n",
+ " <td>0</td>\n",
+ " <td>A/5 3594</td>\n",
+ " <td>8.0500</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>714</th>\n",
- " <td>715</td>\n",
+ " <th>243</th>\n",
+ " <td>244</td>\n",
" <td>0</td>\n",
- " <td>2</td>\n",
- " <td>Greenberg, Mr. Samuel</td>\n",
+ " <td>3</td>\n",
+ " <td>Maenpaa, Mr. Matti Alexanteri</td>\n",
" <td>male</td>\n",
- " <td>52.0</td>\n",
+ " <td>22.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
- " <td>250647</td>\n",
- " <td>13.0000</td>\n",
+ " <td>STON/O 2. 3101275</td>\n",
+ " <td>7.1250</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>61</th>\n",
- " <td>62</td>\n",
- " <td>1</td>\n",
- " <td>1</td>\n",
- " <td>Icard, Miss. Amelie</td>\n",
- " <td>female</td>\n",
- " <td>38.0</td>\n",
+ " <th>861</th>\n",
+ " <td>862</td>\n",
" <td>0</td>\n",
+ " <td>2</td>\n",
+ " <td>Giles, Mr. Frederick Edward</td>\n",
+ " <td>male</td>\n",
+ " <td>21.0</td>\n",
+ " <td>1</td>\n",
" <td>0</td>\n",
- " <td>113572</td>\n",
- " <td>80.0000</td>\n",
- " <td>B28</td>\n",
+ " <td>28134</td>\n",
+ " <td>11.5000</td>\n",
" <td>NaN</td>\n",
+ " <td>S</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
- " PassengerId Survived Pclass Name \\\n",
- "320 321 0 3 Dennis, Mr. Samuel \n",
- "59 60 0 3 Goodwin, Master. William Frederick \n",
- "176 177 0 3 Lefebre, Master. Henry Forbes \n",
- "117 118 0 2 Turpin, Mr. William John Robert \n",
- "714 715 0 2 Greenberg, Mr. Samuel \n",
- "61 62 1 1 Icard, Miss. Amelie \n",
- "\n",
- " Sex Age SibSp Parch Ticket Fare Cabin Embarked \n",
- "320 male 22.0 0 0 A/5 21172 7.2500 NaN S \n",
- "59 male 11.0 5 2 CA 2144 46.9000 NaN S \n",
- "176 male NaN 3 1 4133 25.4667 NaN S \n",
- "117 male 29.0 1 0 11668 21.0000 NaN S \n",
- "714 male 52.0 0 0 250647 13.0000 NaN S \n",
- "61 female 38.0 0 0 113572 80.0000 B28 NaN "
+ " PassengerId Survived Pclass Name Sex \\\n",
+ "138 139 0 3 Osen, Mr. Olaf Elon male \n",
+ "211 212 1 2 Cameron, Miss. Clear Annie female \n",
+ "547 548 1 2 Padro y Manent, Mr. Julian male \n",
+ "482 483 0 3 Rouse, Mr. Richard Henry male \n",
+ "243 244 0 3 Maenpaa, Mr. Matti Alexanteri male \n",
+ "861 862 0 2 Giles, Mr. Frederick Edward male \n",
+ "\n",
+ " Age SibSp Parch Ticket Fare Cabin Embarked \n",
+ "138 16.0 0 0 7534 9.2167 NaN S \n",
+ "211 35.0 0 0 F.C.C. 13528 21.0000 NaN S \n",
+ "547 NaN 0 0 SC/PARIS 2146 13.8625 NaN C \n",
+ "482 50.0 0 0 A/5 3594 8.0500 NaN S \n",
+ "243 22.0 0 0 STON/O 2. 3101275 7.1250 NaN S \n",
+ "861 21.0 1 0 28134 11.5000 NaN S "
]
},
- "execution_count": 137,
+ "execution_count": 77,
"metadata": {},
"output_type": "execute_result"
}
@@ -4630,7 +4628,7 @@
},
{
"cell_type": "code",
- "execution_count": 138,
+ "execution_count": 78,
"id": "enormous-dublin",
"metadata": {},
"outputs": [
@@ -4731,7 +4729,7 @@
"456 male 65.0 0 0 13509 26.550 E38 S "
]
},
- "execution_count": 138,
+ "execution_count": 78,
"metadata": {},
"output_type": "execute_result"
}
@@ -4755,7 +4753,7 @@
},
{
"cell_type": "code",
- "execution_count": 139,
+ "execution_count": 79,
"id": "piano-chance",
"metadata": {},
"outputs": [
@@ -4823,7 +4821,7 @@
"4 4 5"
]
},
- "execution_count": 139,
+ "execution_count": 79,
"metadata": {},
"output_type": "execute_result"
}
@@ -4835,7 +4833,7 @@
},
{
"cell_type": "code",
- "execution_count": 140,
+ "execution_count": 80,
"id": "minute-printer",
"metadata": {},
"outputs": [
@@ -4903,7 +4901,7 @@
"4 0 0"
]
},
- "execution_count": 140,
+ "execution_count": 80,
"metadata": {},
"output_type": "execute_result"
}
@@ -4914,7 +4912,7 @@
},
{
"cell_type": "code",
- "execution_count": 141,
+ "execution_count": 81,
"id": "polar-offering",
"metadata": {},
"outputs": [
@@ -4982,7 +4980,7 @@
"4 4 5"
]
},
- "execution_count": 141,
+ "execution_count": 81,
"metadata": {},
"output_type": "execute_result"
}
@@ -5005,7 +5003,7 @@
},
{
"cell_type": "code",
- "execution_count": 142,
+ "execution_count": 82,
"id": "designing-capacity",
"metadata": {},
"outputs": [
@@ -5073,7 +5071,7 @@
"4 4 5"
]
},
- "execution_count": 142,
+ "execution_count": 82,
"metadata": {},
"output_type": "execute_result"
}
@@ -5084,7 +5082,7 @@
},
{
"cell_type": "code",
- "execution_count": 143,
+ "execution_count": 83,
"id": "breeding-radio",
"metadata": {},
"outputs": [
@@ -5152,7 +5150,7 @@
"4 4 5"
]
},
- "execution_count": 143,
+ "execution_count": 83,
"metadata": {},
"output_type": "execute_result"
}
@@ -5175,7 +5173,7 @@
},
{
"cell_type": "code",
- "execution_count": 145,
+ "execution_count": 84,
"id": "systematic-hawaii",
"metadata": {},
"outputs": [
@@ -5317,7 +5315,7 @@
"9 0 237736 30.0708 NaN C "
]
},
- "execution_count": 145,
+ "execution_count": 84,
"metadata": {},
"output_type": "execute_result"
}
@@ -5336,7 +5334,7 @@
},
{
"cell_type": "code",
- "execution_count": 147,
+ "execution_count": 85,
"id": "foster-customs",
"metadata": {},
"outputs": [
@@ -5478,7 +5476,7 @@
"9 0 237736 30.0708 NaN C "
]
},
- "execution_count": 147,
+ "execution_count": 85,
"metadata": {},
"output_type": "execute_result"
}
@@ -5497,7 +5495,7 @@
},
{
"cell_type": "code",
- "execution_count": 151,
+ "execution_count": 86,
"id": "eligible-breath",
"metadata": {},
"outputs": [],
@@ -5507,22 +5505,22 @@
},
{
"cell_type": "code",
- "execution_count": 152,
+ "execution_count": 87,
"id": "alpine-residence",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "24 Palsson, Miss. Torborg Danira\n",
- "200 Vande Walle, Mr. Nestor Cyriel\n",
- "630 Barkworth, Mr. Algernon Henry Wilson\n",
- "80 Waelens, Mr. Achille\n",
- "410 Sdycoff, Mr. Todor\n",
+ "359 Mockler, Miss. Helen Mary \"Ellie\"\n",
+ "64 Stewart, Mr. Albert A\n",
+ "864 Gill, Mr. John William\n",
+ "389 Lehmann, Miss. Bertha\n",
+ "181 Pernot, Mr. Rene\n",
"Name: Name, dtype: object"
]
},
- "execution_count": 152,
+ "execution_count": 87,
"metadata": {},
"output_type": "execute_result"
}
@@ -5533,7 +5531,7 @@
},
{
"cell_type": "code",
- "execution_count": 153,
+ "execution_count": 88,
"id": "therapeutic-sudan",
"metadata": {},
"outputs": [
@@ -5574,78 +5572,78 @@
" </thead>\n",
" <tbody>\n",
" <tr>\n",
- " <th>24</th>\n",
- " <td>25</td>\n",
+ " <th>64</th>\n",
+ " <td>65</td>\n",
" <td>0</td>\n",
- " <td>3</td>\n",
- " <td>Palsson, Miss. Torborg Danira</td>\n",
- " <td>female</td>\n",
- " <td>8.0</td>\n",
- " <td>3</td>\n",
" <td>1</td>\n",
- " <td>349909</td>\n",
- " <td>21.0750</td>\n",
- " <td>NaN</td>\n",
- " <td>S</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>80</th>\n",
- " <td>81</td>\n",
- " <td>0</td>\n",
- " <td>3</td>\n",
- " <td>Waelens, Mr. Achille</td>\n",
+ " <td>Stewart, Mr. Albert A</td>\n",
" <td>male</td>\n",
- " <td>22.0</td>\n",
+ " <td>NaN</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
- " <td>345767</td>\n",
- " <td>9.0000</td>\n",
+ " <td>PC 17605</td>\n",
+ " <td>27.7208</td>\n",
" <td>NaN</td>\n",
- " <td>S</td>\n",
+ " <td>C</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>200</th>\n",
- " <td>201</td>\n",
+ " <th>181</th>\n",
+ " <td>182</td>\n",
" <td>0</td>\n",
- " <td>3</td>\n",
- " <td>Vande Walle, Mr. Nestor Cyriel</td>\n",
+ " <td>2</td>\n",
+ " <td>Pernot, Mr. Rene</td>\n",
" <td>male</td>\n",
- " <td>28.0</td>\n",
+ " <td>NaN</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
- " <td>345770</td>\n",
- " <td>9.5000</td>\n",
+ " <td>SC/PARIS 2131</td>\n",
+ " <td>15.0500</td>\n",
" <td>NaN</td>\n",
- " <td>S</td>\n",
+ " <td>C</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>410</th>\n",
- " <td>411</td>\n",
- " <td>0</td>\n",
+ " <th>359</th>\n",
+ " <td>360</td>\n",
+ " <td>1</td>\n",
" <td>3</td>\n",
- " <td>Sdycoff, Mr. Todor</td>\n",
- " <td>male</td>\n",
+ " <td>Mockler, Miss. Helen Mary \"Ellie\"</td>\n",
+ " <td>female</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
- " <td>349222</td>\n",
- " <td>7.8958</td>\n",
+ " <td>330980</td>\n",
+ " <td>7.8792</td>\n",
" <td>NaN</td>\n",
- " <td>S</td>\n",
+ " <td>Q</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>630</th>\n",
- " <td>631</td>\n",
+ " <th>389</th>\n",
+ " <td>390</td>\n",
" <td>1</td>\n",
- " <td>1</td>\n",
- " <td>Barkworth, Mr. Algernon Henry Wilson</td>\n",
+ " <td>2</td>\n",
+ " <td>Lehmann, Miss. Bertha</td>\n",
+ " <td>female</td>\n",
+ " <td>17.0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>SC 1748</td>\n",
+ " <td>12.0000</td>\n",
+ " <td>NaN</td>\n",
+ " <td>C</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>864</th>\n",
+ " <td>865</td>\n",
+ " <td>0</td>\n",
+ " <td>2</td>\n",
+ " <td>Gill, Mr. John William</td>\n",
" <td>male</td>\n",
- " <td>80.0</td>\n",
+ " <td>24.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
- " <td>27042</td>\n",
- " <td>30.0000</td>\n",
- " <td>A23</td>\n",
+ " <td>233866</td>\n",
+ " <td>13.0000</td>\n",
+ " <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" </tbody>\n",
@@ -5653,22 +5651,22 @@
"</div>"
],
"text/plain": [
- " PassengerId Survived Pclass Name \\\n",
- "24 25 0 3 Palsson, Miss. Torborg Danira \n",
- "80 81 0 3 Waelens, Mr. Achille \n",
- "200 201 0 3 Vande Walle, Mr. Nestor Cyriel \n",
- "410 411 0 3 Sdycoff, Mr. Todor \n",
- "630 631 1 1 Barkworth, Mr. Algernon Henry Wilson \n",
- "\n",
- " Sex Age SibSp Parch Ticket Fare Cabin Embarked \n",
- "24 female 8.0 3 1 349909 21.0750 NaN S \n",
- "80 male 22.0 0 0 345767 9.0000 NaN S \n",
- "200 male 28.0 0 0 345770 9.5000 NaN S \n",
- "410 male NaN 0 0 349222 7.8958 NaN S \n",
- "630 male 80.0 0 0 27042 30.0000 A23 S "
+ " PassengerId Survived Pclass Name Sex \\\n",
+ "64 65 0 1 Stewart, Mr. Albert A male \n",
+ "181 182 0 2 Pernot, Mr. Rene male \n",
+ "359 360 1 3 Mockler, Miss. Helen Mary \"Ellie\" female \n",
+ "389 390 1 2 Lehmann, Miss. Bertha female \n",
+ "864 865 0 2 Gill, Mr. John William male \n",
+ "\n",
+ " Age SibSp Parch Ticket Fare Cabin Embarked \n",
+ "64 NaN 0 0 PC 17605 27.7208 NaN C \n",
+ "181 NaN 0 0 SC/PARIS 2131 15.0500 NaN C \n",
+ "359 NaN 0 0 330980 7.8792 NaN Q \n",
+ "389 17.0 0 0 SC 1748 12.0000 NaN C \n",
+ "864 24.0 0 0 233866 13.0000 NaN S "
]
},
- "execution_count": 153,
+ "execution_count": 88,
"metadata": {},
"output_type": "execute_result"
}
@@ -5691,7 +5689,7 @@
},
{
"cell_type": "code",
- "execution_count": 154,
+ "execution_count": 89,
"id": "extended-usage",
"metadata": {},
"outputs": [
@@ -5765,7 +5763,7 @@
"4 Indomie pack 5.0"
]
},
- "execution_count": 154,
+ "execution_count": 89,
"metadata": {},
"output_type": "execute_result"
}
@@ -5789,7 +5787,7 @@
},
{
"cell_type": "code",
- "execution_count": 155,
+ "execution_count": 90,
"id": "administrative-partition",
"metadata": {},
"outputs": [
@@ -5856,7 +5854,7 @@
"4 Indomie pack 5.0"
]
},
- "execution_count": 155,
+ "execution_count": 90,
"metadata": {},
"output_type": "execute_result"
}
@@ -5875,7 +5873,7 @@
},
{
"cell_type": "code",
- "execution_count": 156,
+ "execution_count": 91,
"id": "english-parallel",
"metadata": {},
"outputs": [
@@ -5928,7 +5926,7 @@
"2 Indomie cup 3.5"
]
},
- "execution_count": 156,
+ "execution_count": 91,
"metadata": {},
"output_type": "execute_result"
}
@@ -5947,7 +5945,7 @@
},
{
"cell_type": "code",
- "execution_count": 157,
+ "execution_count": 92,
"id": "corresponding-owner",
"metadata": {},
"outputs": [
@@ -6007,7 +6005,7 @@
"4 Indomie pack 5.0"
]
},
- "execution_count": 157,
+ "execution_count": 92,
"metadata": {},
"output_type": "execute_result"
}
@@ -6026,7 +6024,7 @@
},
{
"cell_type": "code",
- "execution_count": 160,
+ "execution_count": 93,
"id": "serial-omaha",
"metadata": {},
"outputs": [
@@ -6090,7 +6088,7 @@
},
{
"cell_type": "code",
- "execution_count": 161,
+ "execution_count": 94,
"id": "exclusive-madison",
"metadata": {},
"outputs": [
@@ -6185,7 +6183,7 @@
},
{
"cell_type": "code",
- "execution_count": 162,
+ "execution_count": 95,
"id": "institutional-promotion",
"metadata": {},
"outputs": [],
@@ -6198,7 +6196,7 @@
},
{
"cell_type": "code",
- "execution_count": 163,
+ "execution_count": 96,
"id": "upset-joyce",
"metadata": {},
"outputs": [
@@ -6254,7 +6252,7 @@
"2 3 HORSE"
]
},
- "execution_count": 163,
+ "execution_count": 96,
"metadata": {},
"output_type": "execute_result"
}
@@ -6265,7 +6263,7 @@
},
{
"cell_type": "code",
- "execution_count": 164,
+ "execution_count": 97,
"id": "hidden-attitude",
"metadata": {},
"outputs": [
@@ -6321,7 +6319,7 @@
"2 1 45"
]
},
- "execution_count": 164,
+ "execution_count": 97,
"metadata": {},
"output_type": "execute_result"
}
@@ -6340,7 +6338,7 @@
},
{
"cell_type": "code",
- "execution_count": 165,
+ "execution_count": 98,
"id": "separated-extreme",
"metadata": {},
"outputs": [
@@ -6400,7 +6398,7 @@
"2 3 HORSE 33"
]
},
- "execution_count": 165,
+ "execution_count": 98,
"metadata": {},
"output_type": "execute_result"
}
@@ -6411,7 +6409,7 @@
},
{
"cell_type": "code",
- "execution_count": 168,
+ "execution_count": 99,
"id": "impressed-copper",
"metadata": {},
"outputs": [
@@ -6467,7 +6465,7 @@
"2 1 45"
]
},
- "execution_count": 168,
+ "execution_count": 99,
"metadata": {},
"output_type": "execute_result"
}
@@ -6488,7 +6486,7 @@
},
{
"cell_type": "code",
- "execution_count": 167,
+ "execution_count": 100,
"id": "identified-posting",
"metadata": {},
"outputs": [
@@ -6552,7 +6550,7 @@
"2 3 HORSE 3 33"
]
},
- "execution_count": 167,
+ "execution_count": 100,
"metadata": {},
"output_type": "execute_result"
}
@@ -6571,7 +6569,7 @@
},
{
"cell_type": "code",
- "execution_count": 169,
+ "execution_count": 101,
"id": "logical-alfred",
"metadata": {},
"outputs": [
@@ -6633,7 +6631,7 @@
"3 42 MONKEY"
]
},
- "execution_count": 169,
+ "execution_count": 101,
"metadata": {},
"output_type": "execute_result"
}
@@ -6646,7 +6644,7 @@
},
{
"cell_type": "code",
- "execution_count": 170,
+ "execution_count": 102,
"id": "progressive-blogger",
"metadata": {},
"outputs": [
@@ -6708,7 +6706,7 @@
"3 35 100"
]
},
- "execution_count": 170,
+ "execution_count": 102,
"metadata": {},
"output_type": "execute_result"
}
@@ -6731,7 +6729,7 @@
},
{
"cell_type": "code",
- "execution_count": 171,
+ "execution_count": 103,
"id": "stock-attachment",
"metadata": {},
"outputs": [
@@ -6803,7 +6801,7 @@
"3 42 MONKEY NaN NaN"
]
},
- "execution_count": 171,
+ "execution_count": 103,
"metadata": {},
"output_type": "execute_result"
}
@@ -6822,7 +6820,7 @@
},
{
"cell_type": "code",
- "execution_count": 172,
+ "execution_count": 104,
"id": "equivalent-conservative",
"metadata": {},
"outputs": [
@@ -6894,7 +6892,7 @@
"3 NaN NaN 35 100"
]
},
- "execution_count": 172,
+ "execution_count": 104,
"metadata": {},
"output_type": "execute_result"
}
@@ -6913,7 +6911,7 @@
},
{
"cell_type": "code",
- "execution_count": 173,
+ "execution_count": 105,
"id": "seasonal-publisher",
"metadata": {},
"outputs": [
@@ -6977,7 +6975,7 @@
"2 3 HORSE 3 33"
]
},
- "execution_count": 173,
+ "execution_count": 105,
"metadata": {},
"output_type": "execute_result"
}
@@ -6996,7 +6994,7 @@
},
{
"cell_type": "code",
- "execution_count": 174,
+ "execution_count": 106,
"id": "neural-christianity",
"metadata": {},
"outputs": [
@@ -7037,31 +7035,31 @@
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
- " <td>12.0</td>\n",
- " <td>RAT</td>\n",
- " <td>12.0</td>\n",
- " <td>12.0</td>\n",
- " </tr>\n",
- " <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>2</th>\n",
+ " <td>12.0</td>\n",
+ " <td>RAT</td>\n",
+ " <td>12.0</td>\n",
+ " <td>12.0</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",
+ " <td>35.0</td>\n",
+ " <td>100.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
+ " <td>42.0</td>\n",
+ " <td>MONKEY</td>\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",
@@ -7070,13 +7068,13 @@
"text/plain": [
" gene_ID species 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"
+ "1 3.0 HORSE 3.0 33.0\n",
+ "2 12.0 RAT 12.0 12.0\n",
+ "3 NaN NaN 35.0 100.0\n",
+ "4 42.0 MONKEY NaN NaN"
]
},
- "execution_count": 174,
+ "execution_count": 106,
"metadata": {},
"output_type": "execute_result"
}
@@ -7099,7 +7097,7 @@
},
{
"cell_type": "code",
- "execution_count": 181,
+ "execution_count": 107,
"id": "appropriate-astrology",
"metadata": {},
"outputs": [
@@ -7225,7 +7223,7 @@
"[88 rows x 3 columns]"
]
},
- "execution_count": 181,
+ "execution_count": 107,
"metadata": {},
"output_type": "execute_result"
}
@@ -7284,7 +7282,7 @@
},
{
"cell_type": "code",
- "execution_count": 185,
+ "execution_count": 108,
"id": "corresponding-natural",
"metadata": {},
"outputs": [
@@ -7337,7 +7335,7 @@
"1 4 42 6"
]
},
- "execution_count": 185,
+ "execution_count": 108,
"metadata": {},
"output_type": "execute_result"
}
@@ -7359,7 +7357,7 @@
},
{
"cell_type": "code",
- "execution_count": 186,
+ "execution_count": 109,
"id": "stunning-retrieval",
"metadata": {},
"outputs": [
@@ -7412,7 +7410,7 @@
"1 4 5 6"
]
},
- "execution_count": 186,
+ "execution_count": 109,
"metadata": {},
"output_type": "execute_result"
}
@@ -7436,30 +7434,28 @@
},
{
"cell_type": "code",
- "execution_count": 187,
+ "execution_count": 110,
"id": "relevant-sentence",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "<AxesSubplot:>"
+ "<Axes: >"
]
},
- "execution_count": 187,
+ "execution_count": 110,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
- "image/png": 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jz5WZT1Wv1XXAVuB/gQdHnS0iNgG/D0xm5jUsHORxx1ByZWYtv4C3Al9sWr4buHvEmTYDh5uWnwI2Vvc3Ak+NON9DwG/XMNergK8Db6lDNhbOWZgFbgIersvPEjgKXL5k3UhzAa8BnqE64KEuuZbJ+XbgX+uQDdgEfBfYwMKRfg9X+Qaeq7YjcM6/CIuerdbVyXhmPg9Q3V45qiARsRl4E/DvdclVTVM8DpwEHsnMumT7OPAR4CdN6+qQK4EvRcTB6hIUdcj1euB7wN9WU06fjIi1Nci11B3AfdX9kWbLzOPAXwLHgOeBM5n5pWHkqnOBd3SqviAixoDPAx/OzB+MOs+izHwpF369vQq4PiKuGXEkIuKdwMnMPDjqLMu4MTPfzMK04Qci4rdGHYiFEeSbgb/OzDcBZxnd9NKyqhMJ3wX846izAFRz2zuA1wG/BKyNiPcO47nqXOAlnKp/IiI2AlS3J1c7QES8koXy/kxmPlCXXM0y8zTQYOEzhFFnuxF4V0QcZeEKmjdFxN/XIBeZ+Vx1e5KFudzra5DrWeDZ6rcngM+xUOijztXsHcDXM/NEtTzqbG8DnsnM72Xmj4EHgN8YRq46F3gJp+ofAHZW93eyMAe9aiIigE8BRzLzY3XJVWW7IiLWVffXsPCmfnLU2TLz7sy8KjM3s/Ce+nJmvnfUuSJibUS8evE+C3Omh0edKzP/G/huRFxdrdoG/Meocy3xbs5Pn8Dosx0DboiIV1X/Rrex8MHv4HON8oOHDj4MuBn4T+C/gD8ZcZb7WJjP+jELo5I7gV9k4cOwp6vbDauc6TdZmFb6FvB49XXzqHNV2X4N+EaV7TDwp9X6kWdryjjF+Q8xR/2zfD3wzerricX3+6hzVRmuA+aqn+U/AevrkKvK9irgBeCypnUjzwZ8lIUBy2Hg74BfGEYuT6WXpELVeQpFktSCBS5JhbLAJalQFrgkFcoCl6RCWeCSVCgLXJIK9f+G8SKnugrwJQAAAABJRU5ErkJggg==\n",
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",
"text/plain": [
- "<Figure size 432x288 with 1 Axes>"
+ "<Figure size 640x480 with 1 Axes>"
]
},
- "metadata": {
- "needs_background": "light"
- },
+ "metadata": {},
"output_type": "display_data"
}
],
@@ -7488,9 +7484,9 @@
],
"metadata": {
"kernelspec": {
- "display_name": "Python [conda env:dev]",
+ "display_name": "Python 3 (ipykernel)",
"language": "python",
- "name": "conda-env-dev-py"
+ "name": "python3"
},
"language_info": {
"codemirror_mode": {
@@ -7502,7 +7498,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.10.4"
+ "version": "3.11.10"
}
},
"nbformat": 4,
--
GitLab