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2023-08-21 15:12:19 +00:00
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"# Fusionner, Joindre et Concaténer\n",
"\n",
"Il y a 3 façons principales de combiner des DataFrames ensemble : Fusion, assemblage et concaténation. Dans ce notebook, nous discuterons de ces 3 méthodes avec des exemples.\n",
"\n",
"____"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Exemple DataFrames"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"df1 = pd.DataFrame({'A': ['A0', 'A1', 'A2', 'A3'],\n",
" 'B': ['B0', 'B1', 'B2', 'B3'],\n",
" 'C': ['C0', 'C1', 'C2', 'C3'],\n",
" 'D': ['D0', 'D1', 'D2', 'D3']},\n",
" index=[0, 1, 2, 3])"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"df2 = pd.DataFrame({'A': ['A4', 'A5', 'A6', 'A7'],\n",
" 'B': ['B4', 'B5', 'B6', 'B7'],\n",
" 'C': ['C4', 'C5', 'C6', 'C7'],\n",
" 'D': ['D4', 'D5', 'D6', 'D7']},\n",
" index=[4, 5, 6, 7]) "
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"df3 = pd.DataFrame({'A': ['A8', 'A9', 'A10', 'A11'],\n",
" 'B': ['B8', 'B9', 'B10', 'B11'],\n",
" 'C': ['C8', 'C9', 'C10', 'C11'],\n",
" 'D': ['D8', 'D9', 'D10', 'D11']},\n",
" index=[8, 9, 10, 11])"
]
},
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"metadata": {},
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"3 A3 B3 C3 D3"
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"execution_count": 5
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"source": [
"df1"
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{
"output_type": "execute_result",
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"text/plain": [
" A B C D\n",
"4 A4 B4 C4 D4\n",
"5 A5 B5 C5 D5\n",
"6 A6 B6 C6 D6\n",
"7 A7 B7 C7 D7"
],
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},
"metadata": {},
"execution_count": 6
}
],
"source": [
"df2"
]
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"cell_type": "code",
"execution_count": 7,
"metadata": {},
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{
"output_type": "execute_result",
"data": {
"text/plain": [
" A B C D\n",
"8 A8 B8 C8 D8\n",
"9 A9 B9 C9 D9\n",
"10 A10 B10 C10 D10\n",
"11 A11 B11 C11 D11"
],
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"metadata": {},
"execution_count": 7
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],
"source": [
"df3"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Concaténation\n",
"\n",
"La concaténation colle fondamentalement les DataFrames ensemble. Gardez à l'esprit que les dimensions doivent correspondre à l'axe sur lequel vous concaténez. Vous pouvez utiliser **pd.concat** et passer dans une liste de DataFrames à concaténer ensemble :"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" A B C D\n",
"0 A0 B0 C0 D0\n",
"1 A1 B1 C1 D1\n",
"2 A2 B2 C2 D2\n",
"3 A3 B3 C3 D3\n",
"4 A4 B4 C4 D4\n",
"5 A5 B5 C5 D5\n",
"6 A6 B6 C6 D6\n",
"7 A7 B7 C7 D7\n",
"8 A8 B8 C8 D8\n",
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},
"metadata": {},
"execution_count": 8
}
],
"source": [
"pd.concat([df1,df2,df3])"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" A B C D A B C D A B C D\n",
"0 A0 B0 C0 D0 NaN NaN NaN NaN NaN NaN NaN NaN\n",
"1 A1 B1 C1 D1 NaN NaN NaN NaN NaN NaN NaN NaN\n",
"2 A2 B2 C2 D2 NaN NaN NaN NaN NaN NaN NaN NaN\n",
"3 A3 B3 C3 D3 NaN NaN NaN NaN NaN NaN NaN NaN\n",
"4 NaN NaN NaN NaN A4 B4 C4 D4 NaN NaN NaN NaN\n",
"5 NaN NaN NaN NaN A5 B5 C5 D5 NaN NaN NaN NaN\n",
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"10 NaN NaN NaN NaN NaN NaN NaN NaN A10 B10 C10 D10\n",
"11 NaN NaN NaN NaN NaN NaN NaN NaN A11 B11 C11 D11"
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},
"metadata": {},
"execution_count": 9
}
],
"source": [
"pd.concat([df1,df2,df3],axis=1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"_____\n",
"## Exemple DataFrames"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"left = pd.DataFrame({'key': ['K0', 'K1', 'K2', 'K3'],\n",
" 'A': ['A0', 'A1', 'A2', 'A3'],\n",
" 'B': ['B0', 'B1', 'B2', 'B3']})\n",
" \n",
"right = pd.DataFrame({'key': ['K0', 'K1', 'K2', 'K3'],\n",
" 'C': ['C0', 'C1', 'C2', 'C3'],\n",
" 'D': ['D0', 'D1', 'D2', 'D3']}) "
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" key A B\n",
"0 K0 A0 B0\n",
"1 K1 A1 B1\n",
"2 K2 A2 B2\n",
"3 K3 A3 B3"
],
"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>key</th>\n <th>A</th>\n <th>B</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>K0</td>\n <td>A0</td>\n <td>B0</td>\n </tr>\n <tr>\n <th>1</th>\n <td>K1</td>\n <td>A1</td>\n <td>B1</td>\n </tr>\n <tr>\n <th>2</th>\n <td>K2</td>\n <td>A2</td>\n <td>B2</td>\n </tr>\n <tr>\n <th>3</th>\n <td>K3</td>\n <td>A3</td>\n <td>B3</td>\n </tr>\n </tbody>\n</table>\n</div>"
},
"metadata": {},
"execution_count": 11
}
],
"source": [
"left"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" key C D\n",
"0 K0 C0 D0\n",
"1 K1 C1 D1\n",
"2 K2 C2 D2\n",
"3 K3 C3 D3"
],
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"metadata": {},
"execution_count": 12
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"source": [
"right"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"___"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Fusion\n",
"\n",
"La fonction **merge** vous permet de fusionner des DataFrames ensemble en utilisant une logique similaire à celle de la fusion de Tables SQL. Par exemple :"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" key A B C D\n",
"0 K0 A0 B0 C0 D0\n",
"1 K1 A1 B1 C1 D1\n",
"2 K2 A2 B2 C2 D2\n",
"3 K3 A3 B3 C3 D3"
],
"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>key</th>\n <th>A</th>\n <th>B</th>\n <th>C</th>\n <th>D</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>K0</td>\n <td>A0</td>\n <td>B0</td>\n <td>C0</td>\n <td>D0</td>\n </tr>\n <tr>\n <th>1</th>\n <td>K1</td>\n <td>A1</td>\n <td>B1</td>\n <td>C1</td>\n <td>D1</td>\n </tr>\n <tr>\n <th>2</th>\n <td>K2</td>\n <td>A2</td>\n <td>B2</td>\n <td>C2</td>\n <td>D2</td>\n </tr>\n <tr>\n <th>3</th>\n <td>K3</td>\n <td>A3</td>\n <td>B3</td>\n <td>C3</td>\n <td>D3</td>\n </tr>\n </tbody>\n</table>\n</div>"
},
"metadata": {},
"execution_count": 13
}
],
"source": [
"pd.merge(left,right,how='inner',on='key')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Exemple plus compliqué :"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"left = pd.DataFrame({'key1': ['K0', 'K0', 'K1', 'K2'],\n",
" 'key2': ['K0', 'K1', 'K0', 'K1'],\n",
" 'A': ['A0', 'A1', 'A2', 'A3'],\n",
" 'B': ['B0', 'B1', 'B2', 'B3']})\n",
" \n",
"right = pd.DataFrame({'key1': ['K0', 'K1', 'K1', 'K2'],\n",
" 'key2': ['K0', 'K0', 'K0', 'K0'],\n",
" 'C': ['C0', 'C1', 'C2', 'C3'],\n",
" 'D': ['D0', 'D1', 'D2', 'D3']})"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" key1 key2 A B C D\n",
"0 K0 K0 A0 B0 C0 D0\n",
"1 K1 K0 A2 B2 C1 D1\n",
"2 K1 K0 A2 B2 C2 D2"
],
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},
"metadata": {},
"execution_count": 15
}
],
"source": [
"pd.merge(left, right, on=['key1', 'key2'])"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" key1 key2 A B C D\n",
"0 K0 K0 A0 B0 C0 D0\n",
"1 K0 K1 A1 B1 NaN NaN\n",
"2 K1 K0 A2 B2 C1 D1\n",
"3 K1 K0 A2 B2 C2 D2\n",
"4 K2 K1 A3 B3 NaN NaN\n",
"5 K2 K0 NaN NaN C3 D3"
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"metadata": {},
"execution_count": 16
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"source": [
"pd.merge(left, right, how='outer', on=['key1', 'key2'])"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" key1 key2 A B C D\n",
"0 K0 K0 A0 B0 C0 D0\n",
"1 K1 K0 A2 B2 C1 D1\n",
"2 K1 K0 A2 B2 C2 D2\n",
"3 K2 K0 NaN NaN C3 D3"
],
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"metadata": {},
"execution_count": 17
}
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"source": [
"pd.merge(left, right, how='right', on=['key1', 'key2'])"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" key1 key2 A B C D\n",
"0 K0 K0 A0 B0 C0 D0\n",
"1 K0 K1 A1 B1 NaN NaN\n",
"2 K1 K0 A2 B2 C1 D1\n",
"3 K1 K0 A2 B2 C2 D2\n",
"4 K2 K1 A3 B3 NaN NaN"
],
"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>key1</th>\n <th>key2</th>\n <th>A</th>\n <th>B</th>\n <th>C</th>\n <th>D</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>K0</td>\n <td>K0</td>\n <td>A0</td>\n <td>B0</td>\n <td>C0</td>\n <td>D0</td>\n </tr>\n <tr>\n <th>1</th>\n <td>K0</td>\n <td>K1</td>\n <td>A1</td>\n <td>B1</td>\n <td>NaN</td>\n <td>NaN</td>\n </tr>\n <tr>\n <th>2</th>\n <td>K1</td>\n <td>K0</td>\n <td>A2</td>\n <td>B2</td>\n <td>C1</td>\n <td>D1</td>\n </tr>\n <tr>\n <th>3</th>\n <td>K1</td>\n <td>K0</td>\n <td>A2</td>\n <td>B2</td>\n <td>C2</td>\n <td>D2</td>\n </tr>\n <tr>\n <th>4</th>\n <td>K2</td>\n <td>K1</td>\n <td>A3</td>\n <td>B3</td>\n <td>NaN</td>\n <td>NaN</td>\n </tr>\n </tbody>\n</table>\n</div>"
},
"metadata": {},
"execution_count": 18
}
],
"source": [
"pd.merge(left, right, how='left', on=['key1', 'key2'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Assemblage\n",
"Join est une méthode pratique pour combiner les colonnes de deux DataFrames potentiellement indexées différemment résultant en un seul DataFrame."
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"left = pd.DataFrame({'A': ['A0', 'A1', 'A2'],\n",
" 'B': ['B0', 'B1', 'B2']},\n",
" index=['K0', 'K1', 'K2']) \n",
"\n",
"right = pd.DataFrame({'C': ['C0', 'C2', 'C3'],\n",
" 'D': ['D0', 'D2', 'D3']},\n",
" index=['K0', 'K2', 'K3'])"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" A B C D\n",
"K0 A0 B0 C0 D0\n",
"K1 A1 B1 NaN NaN\n",
"K2 A2 B2 C2 D2"
],
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},
"metadata": {},
"execution_count": 20
}
],
"source": [
"left.join(right)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" A B C D\n",
"K0 A0 B0 C0 D0\n",
"K1 A1 B1 NaN NaN\n",
"K2 A2 B2 C2 D2\n",
"K3 NaN NaN C3 D3"
],
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},
"metadata": {},
"execution_count": 21
}
],
"source": [
"left.join(right, how='outer')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Bon travail!"
]
}
],
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