Skip to content

M
mooc-rr
Commit 4d146606 authored by 0ac65f62d21d982ba39b93c80cd9db24's avatar 0ac65f62d21d982ba39b93c80cd9db24
Browse files
Options

Added a panda part

parent d47e4e23
Hide whitespace changes
Inline Side-by-side
Showing with 249 additions and 11 deletions
module2/exo4/exercice.ipynb
View file @ 4d146606
......@@ -10,13 +10,14 @@
},
{
"cell_type": "code",
"execution_count": 1,
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt"
"import matplotlib.pyplot as plt\n",
"import pandas as pd"
]
},
{
......@@ -28,21 +29,21 @@
},
{
"cell_type": "code",
"execution_count": 2,
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['Year', 'Month', 'Day', 'Number of emails', 'Number that require answers']\n",
"['2023', '8', '2', '3', '0']\n",
"['2023', '8', '3', '2', '0']\n",
"['2023', '8', '4', '2', '0']\n",
"['2023', '8', '5', '0', '0']\n",
"['2023', '8', '6', '1', '0']\n",
"['2023', '8', '7', '3', '2']\n",
"['2023', '8', '8', '2', '0']\n"
"['Date', 'Number of emails', 'Number that require answers']\n",
"['2023-08-02', '3', '0']\n",
"['2023-08-03', '2', '0']\n",
"['2023-08-04', '2', '0']\n",
"['2023-08-05', '0', '0']\n",
"['2023-08-06', '1', '0']\n",
"['2023-08-07', '3', '2']\n",
"['2023-08-08', '5', '3']\n"
]
}
],
......@@ -213,6 +214,243 @@
"np.median(Nemail)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Traitement des données avec pandas"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"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>Date</th>\n",
" <th>Number of emails</th>\n",
" <th>Number that require answers</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2023-08-02</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2023-08-03</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2023-08-04</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2023-08-05</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2023-08-06</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>2023-08-07</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>2023-08-08</td>\n",
" <td>5</td>\n",
" <td>3</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Date Number of emails Number that require answers\n",
"0 2023-08-02 3 0\n",
"1 2023-08-03 2 0\n",
"2 2023-08-04 2 0\n",
"3 2023-08-05 0 0\n",
"4 2023-08-06 1 0\n",
"5 2023-08-07 3 2\n",
"6 2023-08-08 5 3"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"raw_data = pd.read_csv(filename)\n",
"raw_data"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"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>Number of emails</th>\n",
" <th>Number that require answers</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Date</th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2023-08-02</th>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2023-08-03</th>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2023-08-04</th>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2023-08-05</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2023-08-06</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2023-08-07</th>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2023-08-08</th>\n",
" <td>5</td>\n",
" <td>3</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Number of emails Number that require answers\n",
"Date \n",
"2023-08-02 3 0\n",
"2023-08-03 2 0\n",
"2023-08-04 2 0\n",
"2023-08-05 0 0\n",
"2023-08-06 1 0\n",
"2023-08-07 3 2\n",
"2023-08-08 5 3"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"date = pd.to_datetime(raw_data['Date'],format='%Y-%m-%d')\n",
"raw_data['Date'] = date\n",
"data = raw_data.set_index('Date')\n",
"data"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f6799aa65f8>"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"data['Number of emails'].plot()"
]
},
{
"cell_type": "code",
"execution_count": null,
......
Markdown is supported
0% or
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment