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Import
2023-08-21 15:12:19 +00:00
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Visualisation de données intégrée avec Pandas \n",
"\n",
"Dans ce notebook, nous en apprendrons davantage sur les capacités intégrées de pandas pour la visualisation des données! C'est construit à partir de matplotlib, mais il fusionne avec pandas pour une utilisation plus facile! \n",
"\n",
"Jetons un coup d'oeil !"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Importations"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Les Données\n",
"\n",
"Il y a deux fichiers de données csv que vous pouvez lire en tant que dataframes:"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"df1 = pd.read_csv('df1',index_col=0)\n",
"df2 = pd.read_csv('df2')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Feuilles de style\n",
"\n",
"Matplotlib a des [feuilles de style](http://matplotlib.org/gallery.html#style_sheets) que vous pouvez utiliser pour rendre vos tracés un peu plus beaux. Ces feuilles de style incluent plot_bmh, plot_fivethirtyeight, plot_ggplot et plus. Ils créent essentiellement un ensemble de règles de style pour vos tracés. Je vous recommande de les utiliser, ils donnent à tous vos tracés le même aspect professionnel. Vous pouvez même créer les vôtres si vous voulez que les tracés de votre entreprise aient toutes le même look (mais c'est un peu fastidieux à créer).\n",
"\n",
"Voici comment les utiliser.\n",
"\n",
"**Avant plt.style.use() vos tracés ressemblaient à ça :**"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<AxesSubplot:>"
]
},
"metadata": {},
"execution_count": 3
},
{
"output_type": "display_data",
"data": {
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},
"metadata": {
"needs_background": "light"
}
}
],
"source": [
"df1['A'].hist()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Appelez le style ggplot:"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"plt.style.use('ggplot')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Maintenant, vos tracés ressemblent à ça :"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<AxesSubplot:>"
]
},
"metadata": {},
"execution_count": 5
},
{
"output_type": "display_data",
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},
"metadata": {}
}
],
"source": [
"df1['A'].hist()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<AxesSubplot:>"
]
},
"metadata": {},
"execution_count": 6
},
{
"output_type": "display_data",
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},
"metadata": {}
}
],
"source": [
"plt.style.use('bmh')\n",
"df1['A'].hist()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<AxesSubplot:>"
]
},
"metadata": {},
"execution_count": 7
},
{
"output_type": "display_data",
"data": {
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},
"metadata": {}
}
],
"source": [
"plt.style.use('dark_background')\n",
"df1['A'].hist()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<AxesSubplot:>"
]
},
"metadata": {},
"execution_count": 8
},
{
"output_type": "display_data",
"data": {
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},
"metadata": {}
}
],
"source": [
"plt.style.use('fivethirtyeight')\n",
"df1['A'].hist()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"plt.style.use('ggplot')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Restons-en là pour le style ggplot et montrons comment utiliser les capacités de traçage intégrées de pandas !"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Types de graphique\n",
"\n",
"Il existe plusieurs types de graphiques intégrés à pandas, la plupart d'entre eux étant des graphiques statistiques par essence:\n",
"\n",
"* df.plot.area \n",
"* df.plot.barh \n",
"* df.plot.density \n",
"* df.plot.hist \n",
"* df.plot.line \n",
"* df.plot.scatter\n",
"* df.plot.bar \n",
"* df.plot.box \n",
"* df.plot.hexbin \n",
"* df.plot.kde \n",
"* df.plot.pie\n",
"\n",
"Vous pouvez aussi simplement appeler df.plot(kind='hist') ou remplacer cet argument par l'un des termes clés indiqués dans la liste ci-dessus (par exemple'box','barh', etc.)\n",
"___"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Commençons à les examiner !\n",
"\n",
"## Surface"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<AxesSubplot:>"
]
},
"metadata": {},
"execution_count": 10
},
{
"output_type": "display_data",
"data": {
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},
"metadata": {}
}
],
"source": [
"df2.plot.area(alpha=0.4)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Diagrammes à barres"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" a b c d\n",
"0 0.039762 0.218517 0.103423 0.957904\n",
"1 0.937288 0.041567 0.899125 0.977680\n",
"2 0.780504 0.008948 0.557808 0.797510\n",
"3 0.672717 0.247870 0.264071 0.444358\n",
"4 0.053829 0.520124 0.552264 0.190008"
],
"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>a</th>\n <th>b</th>\n <th>c</th>\n <th>d</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>0.039762</td>\n <td>0.218517</td>\n <td>0.103423</td>\n <td>0.957904</td>\n </tr>\n <tr>\n <th>1</th>\n <td>0.937288</td>\n <td>0.041567</td>\n <td>0.899125</td>\n <td>0.977680</td>\n </tr>\n <tr>\n <th>2</th>\n <td>0.780504</td>\n <td>0.008948</td>\n <td>0.557808</td>\n <td>0.797510</td>\n </tr>\n <tr>\n <th>3</th>\n <td>0.672717</td>\n <td>0.247870</td>\n <td>0.264071</td>\n <td>0.444358</td>\n </tr>\n <tr>\n <th>4</th>\n <td>0.053829</td>\n <td>0.520124</td>\n <td>0.552264</td>\n <td>0.190008</td>\n </tr>\n </tbody>\n</table>\n</div>"
},
"metadata": {},
"execution_count": 11
}
],
"source": [
"df2.head()"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<AxesSubplot:>"
]
},
"metadata": {},
"execution_count": 12
},
{
"output_type": "display_data",
"data": {
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},
"metadata": {}
}
],
"source": [
"df2.plot.bar()"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<AxesSubplot:>"
]
},
"metadata": {},
"execution_count": 13
},
{
"output_type": "display_data",
"data": {
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},
"metadata": {}
}
],
"source": [
"df2.plot.bar(stacked=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Histogramess"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<AxesSubplot:ylabel='Frequency'>"
]
},
"metadata": {},
"execution_count": 14
},
{
"output_type": "display_data",
"data": {
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},
"metadata": {}
}
],
"source": [
"df1['A'].plot.hist(bins=50)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Tracés linéaires"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<AxesSubplot:>"
]
},
"metadata": {},
"execution_count": 15
},
{
"output_type": "display_data",
"data": {
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},
"metadata": {}
}
],
"source": [
"df1.plot.line(y='B',figsize=(12,3),lw=1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Diagrammes de dispersion"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<AxesSubplot:xlabel='A', ylabel='B'>"
]
},
"metadata": {},
"execution_count": 16
},
{
"output_type": "display_data",
"data": {
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},
"metadata": {}
}
],
"source": [
"df1.plot.scatter(x='A',y='B')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Vous pouvez utiliser c pour colorier en fonction d'une autre valeur de colonne.\n",
"Utilisez cmap pour indiquer la carte des couleurs à utiliser. \n",
"Pour toutes les cartes de couleurs, allez voir :http://matplotlib.org/users/colormaps.html"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<AxesSubplot:xlabel='A', ylabel='B'>"
]
},
"metadata": {},
"execution_count": 17
},
{
"output_type": "display_data",
"data": {
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},
"metadata": {}
}
],
"source": [
"df1.plot.scatter(x='A',y='B',c='C',cmap='coolwarm')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Ou utiliser s pour indiquer la taille basée sur une autre colonne. Le paramètre s doit être un tableau, pas seulement le nom d'une colonne :"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<AxesSubplot:xlabel='A', ylabel='B'>"
]
},
"metadata": {},
"execution_count": 18
},
{
"output_type": "display_data",
"data": {
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},
"metadata": {}
}
],
"source": [
"df1.plot.scatter(x='A',y='B',s=df1['C']*100)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Boîtes à moustache"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<AxesSubplot:>"
]
},
"metadata": {},
"execution_count": 19
},
{
"output_type": "display_data",
"data": {
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},
"metadata": {}
}
],
"source": [
"df2.plot.box() # Peut aussi passer un argument by= pour groupby"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Tracé de pastilles hexagonales\n",
"\n",
"Utile pour les données bivariées, alternative au nuage de points:"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<AxesSubplot:xlabel='a', ylabel='b'>"
]
},
"metadata": {},
"execution_count": 20
},
{
"output_type": "display_data",
"data": {
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},
"metadata": {}
}
],
"source": [
"df = pd.DataFrame(np.random.randn(1000, 2), columns=['a', 'b'])\n",
"df.plot.hexbin(x='a',y='b',gridsize=25,cmap='Oranges')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"____"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Diagramme d'estimation de la densité du noyau (KDE)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<AxesSubplot:ylabel='Density'>"
]
},
"metadata": {},
"execution_count": 21
},
{
"output_type": "display_data",
"data": {
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},
"metadata": {}
}
],
"source": [
"df2['a'].plot.kde()"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<AxesSubplot:ylabel='Density'>"
]
},
"metadata": {},
"execution_count": 22
},
{
"output_type": "display_data",
"data": {
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},
"metadata": {}
}
],
"source": [
"df2.plot.density()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"C'est fini ! J'espère que vous comprendrez pourquoi cette méthode de traçage sera beaucoup plus facile à utiliser que la méthode matplotlib, elle équilibre la facilité d'utilisation avec le contrôle de la figure. Beaucoup d'appels de tracé acceptent également des arguments supplémentaires de sa maman matplotlib pyplot. \n",
"\n",
"\n",
"# Bon travail!"
]
}
],
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