{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Données manquantes\n",
"\n",
"Montrons quelques méthodes pratiques pour traiter les données manquantes dans pandas :"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"df = pd.DataFrame({'A':[1,2,np.nan],\n",
" 'B':[5,np.nan,np.nan],\n",
" 'C':[1,2,3]})"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" A B C\n",
"0 1.0 5.0 1\n",
"1 2.0 NaN 2\n",
"2 NaN NaN 3"
],
"text/html": "
\n\n
\n \n \n | \n A | \n B | \n C | \n
\n \n \n \n 0 | \n 1.0 | \n 5.0 | \n 1 | \n
\n \n 1 | \n 2.0 | \n NaN | \n 2 | \n
\n \n 2 | \n NaN | \n NaN | \n 3 | \n
\n \n
\n
"
},
"metadata": {},
"execution_count": 3
}
],
"source": [
"df"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" A B C\n",
"0 1.0 5.0 1"
],
"text/html": "\n\n
\n \n \n | \n A | \n B | \n C | \n
\n \n \n \n 0 | \n 1.0 | \n 5.0 | \n 1 | \n
\n \n
\n
"
},
"metadata": {},
"execution_count": 4
}
],
"source": [
"df.dropna()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" C\n",
"0 1\n",
"1 2\n",
"2 3"
],
"text/html": "\n\n
\n \n \n | \n C | \n
\n \n \n \n 0 | \n 1 | \n
\n \n 1 | \n 2 | \n
\n \n 2 | \n 3 | \n
\n \n
\n
"
},
"metadata": {},
"execution_count": 5
}
],
"source": [
"df.dropna(axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" A B C\n",
"0 1.0 5.0 1\n",
"1 2.0 NaN 2"
],
"text/html": "\n\n
\n \n \n | \n A | \n B | \n C | \n
\n \n \n \n 0 | \n 1.0 | \n 5.0 | \n 1 | \n
\n \n 1 | \n 2.0 | \n NaN | \n 2 | \n
\n \n
\n
"
},
"metadata": {},
"execution_count": 6
}
],
"source": [
"df.dropna(thresh=2)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" A B C\n",
"0 1 5 1\n",
"1 2 Valeur de remplacement 2\n",
"2 Valeur de remplacement Valeur de remplacement 3"
],
"text/html": "\n\n
\n \n \n | \n A | \n B | \n C | \n
\n \n \n \n 0 | \n 1 | \n 5 | \n 1 | \n
\n \n 1 | \n 2 | \n Valeur de remplacement | \n 2 | \n
\n \n 2 | \n Valeur de remplacement | \n Valeur de remplacement | \n 3 | \n
\n \n
\n
"
},
"metadata": {},
"execution_count": 7
}
],
"source": [
"df.fillna(value='Valeur de remplacement')"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"0 1.0\n",
"1 2.0\n",
"2 1.5\n",
"Name: A, dtype: float64"
]
},
"metadata": {},
"execution_count": 8
}
],
"source": [
"df['A'].fillna(value=df['A'].mean())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Bon travail!"
]
}
],
"metadata": {
"kernelspec": {
"name": "python3",
"display_name": "Python 3.7.9 64-bit ('pyfinance': conda)",
"metadata": {
"interpreter": {
"hash": "e89404a230d8800c54ad520c7b67d1bd9bb833a07b37dd3e521a178a3dc34904"
}
}
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.9-final"
}
},
"nbformat": 4,
"nbformat_minor": 1
}