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python-pour-finance/04-Visualisation-Matplotlib.../04-02-Pandas Visualisation/Pandas Visualisation Exerci...

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Import
2023-08-21 15:12:19 +00:00
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Exercices Visualisation Pandas\n",
"\n",
"C'est un exercice rapide pour vous permettre de passer en revue les différentes graphiques que nous avons montrées plus tôt. Utilisez **df3** pour reproduire les tracés suivants. "
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"df3 = pd.read_csv('df3')\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 500 entries, 0 to 499\nData columns (total 4 columns):\n # Column Non-Null Count Dtype \n--- ------ -------------- ----- \n 0 a 500 non-null float64\n 1 b 500 non-null float64\n 2 c 500 non-null float64\n 3 d 500 non-null float64\ndtypes: float64(4)\nmemory usage: 15.8 KB\n"
]
}
],
"source": [
"df3.info()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" a b c d\n",
"0 0.336272 0.325011 0.001020 0.401402\n",
"1 0.980265 0.831835 0.772288 0.076485\n",
"2 0.480387 0.686839 0.000575 0.746758\n",
"3 0.502106 0.305142 0.768608 0.654685\n",
"4 0.856602 0.171448 0.157971 0.321231"
],
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},
"metadata": {},
"execution_count": 3
}
],
"source": [
"df3.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Recréez ce nuage de points de b en fonction de a. Notez la couleur et la taille des points. Notez également la taille des chiffres. Voyez si vous pouvez trouver un moyen de l'étirer de la même façon. Si besoin revoyez le notebook Matplotlib...**"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<AxesSubplot:xlabel='a', ylabel='b'>"
]
},
"metadata": {},
"execution_count": 7
},
{
"output_type": "display_data",
"data": {
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},
"metadata": {
"needs_background": "light"
}
}
],
"source": [
"df3.plot.scatter('a', 'b', c='r', figsize=(12,3))"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x11b398390>"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
},
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"data": {
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"text/plain": [
"<Figure size 864x216 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Créer un histogramme de la colonne 'a'.**"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<AxesSubplot:ylabel='Frequency'>"
]
},
"metadata": {},
"execution_count": 9
},
{
"output_type": "display_data",
"data": {
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},
"metadata": {
"needs_background": "light"
}
}
],
"source": [
"df3['a'].plot.hist()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x11b4b3410>"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Ces graphiques sont biens, mais les finitions ne sont pas extras. Utilisez les feuilles de style pour définir le style sur 'ggplot' et refaire le même histogramme qu'au-dessus. Trouvez aussi comment y ajouter des bandes supplémentaires.**"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<AxesSubplot:ylabel='Frequency'>"
]
},
"metadata": {},
"execution_count": 19
},
{
"output_type": "display_data",
"data": {
"text/plain": "<Figure size 432x288 with 1 Axes>",
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},
"metadata": {}
}
],
"source": [
"plt.style.use('ggplot')\n",
"df3['a'].plot.hist(bins=20, alpha=0.5)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x11b7e2150>"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Créer une boîte à moustache comparant les colonnes 'a' et 'b'.**"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<AxesSubplot:>"
]
},
"metadata": {},
"execution_count": 21
},
{
"output_type": "display_data",
"data": {
"text/plain": "<Figure size 432x288 with 1 Axes>",
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},
"metadata": {}
}
],
"source": [
"df3[['a', 'b']].plot.box()"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x11b908910>"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Créer un diagramme d'estimation de la densité du noyau (kde) de la colonne 'd'**"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<AxesSubplot:ylabel='Density'>"
]
},
"metadata": {},
"execution_count": 25
},
{
"output_type": "display_data",
"data": {
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},
"metadata": {}
}
],
"source": [
"df3['d'].plot.kde()"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x1a1d6e6c90>"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Déterminez comment augmenter la largeur de la ligne et faire en sorte que le style de ligne soit en pointillés. (Note: En général, on ne trace pas de kde)**"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<AxesSubplot:ylabel='Density'>"
]
},
"metadata": {},
"execution_count": 36
},
{
"output_type": "display_data",
"data": {
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},
"metadata": {}
}
],
"source": [
"df3['d'].plot.kde(lw=5, ls='--')"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x1a1d7d6ed0>"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Créer un graphique de surface de toutes les colonnes pour les lignes seulement jusqu'à la 30ème.**"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<AxesSubplot:>"
]
},
"metadata": {},
"execution_count": 58
},
{
"output_type": "display_data",
"data": {
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},
"metadata": {}
}
],
"source": [
"df3.loc[:31].plot.area(alpha=.4)\n",
"# plt.tight_layout()"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x1a1d7a8110>"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Défi Bonus !\n",
"Il est possible que cela vous semble compliqué, référez-vous aux solutions si vous n'arrivez pas à trouver !\n",
"**Remarquez comment la légende de notre figure précédente recouvrait une partie du diagramme. Savez-vous comment afficher la légende à l'extérieur du tracé comme indiqué ci-dessous?**\n",
"\n",
"**Essayez de faire une recherche sur Google pour trouver un bon lien sur stackoverflow à ce sujet. Si vous n'arrivez pas à en trouver, utilisez celui-ci comme indice. - [Lien Stackoverflow.](http://stackoverflow.com/questions/23556153/how-to-put-legend-outside-the-plot-with-pandas)**"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {},
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": "<Figure size 432x288 with 0 Axes>"
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
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},
"metadata": {}
}
],
"source": [
"f = plt.figure()\n",
"\n",
"df3.loc[:31].plot.area(alpha=.4)\n",
"plt.legend(loc='center left', bbox_to_anchor=(1.0, 0.5))\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<Figure size 432x288 with 0 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Bon travail!"
]
}
],
"metadata": {
"kernelspec": {
"name": "python3",
"display_name": "Python 3.7.9 64-bit ('pyfinance': conda)",
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