{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "08977396-ae9a-4c48-9890-e2d3f9bf5c0e",
   "metadata": {},
   "source": [
    "# TP : graphique barplot - nombre d'achats par mois"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "225af1ed-6dcd-4116-99d1-f649dfa8f96f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "import s3fs\n",
    "import re\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import matplotlib.dates as mdates\n",
    "from datetime import datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7fe35156-ea0b-4f9b-b981-1231e26b1baf",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create filesystem object\n",
    "S3_ENDPOINT_URL = \"https://\" + os.environ[\"AWS_S3_ENDPOINT\"]\n",
    "fs = s3fs.S3FileSystem(client_kwargs={'endpoint_url': S3_ENDPOINT_URL})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e0a09bf5-5a96-40c2-93be-ba0a6a130266",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Evolution vente \n",
    "\n",
    "# Importation\n",
    "# Chargement des données temporaires\n",
    "BUCKET = \"projet-bdc2324-team1\"\n",
    "FILE_KEY_S3 = \"0_Temp/Company 1 - Purchases.csv\"\n",
    "FILE_PATH_S3 = BUCKET + \"/\" + FILE_KEY_S3\n",
    "\n",
    "with fs.open(FILE_PATH_S3, mode=\"rb\") as file_in:\n",
    "    purchases = pd.read_csv(file_in, sep=\",\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "0c686793-b760-4013-9f79-f2eeee86cafb",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ticket_id</th>\n",
       "      <th>customer_id</th>\n",
       "      <th>purchase_id</th>\n",
       "      <th>event_type_id</th>\n",
       "      <th>supplier_name</th>\n",
       "      <th>purchase_date</th>\n",
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       "      <th>children</th>\n",
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       "      <th>name_facilities</th>\n",
       "      <th>name_categories</th>\n",
       "      <th>name_events</th>\n",
       "      <th>name_seasons</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>2023-11-08 18:32:18+00:00</td>\n",
       "      <td>Atelier</td>\n",
       "      <td>11.0</td>\n",
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       "    <tr>\n",
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       "      <td>1256137</td>\n",
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       "      <td>vente en ligne</td>\n",
       "      <td>2023-11-08 19:30:28+00:00</td>\n",
       "      <td>Atelier</td>\n",
       "      <td>11.0</td>\n",
       "      <td>pricing_formula</td>\n",
       "      <td>False</td>\n",
       "      <td>offre muséale groupe</td>\n",
       "      <td>mucem</td>\n",
       "      <td>indiv entrées tp</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1826671</th>\n",
       "      <td>20662819</td>\n",
       "      <td>1256137</td>\n",
       "      <td>8007699</td>\n",
       "      <td>5</td>\n",
       "      <td>vente en ligne</td>\n",
       "      <td>2023-11-08 19:30:28+00:00</td>\n",
       "      <td>Atelier</td>\n",
       "      <td>11.0</td>\n",
       "      <td>pricing_formula</td>\n",
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       "      <td>mucem</td>\n",
       "      <td>indiv entrées tp</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2023</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1826672 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         ticket_id  customer_id  purchase_id  event_type_id   supplier_name  \\\n",
       "0         13070859        48187      5107462              4  vente en ligne   \n",
       "1         13070860        48187      5107462              4  vente en ligne   \n",
       "2         13070861        48187      5107462              4  vente en ligne   \n",
       "3         13070862        48187      5107462              4  vente en ligne   \n",
       "4         13070863        48187      5107462              4  vente en ligne   \n",
       "...            ...          ...          ...            ...             ...   \n",
       "1826667   20662815      1256135      8007697              5  vente en ligne   \n",
       "1826668   20662816      1256136      8007698              5  vente en ligne   \n",
       "1826669   20662817      1256136      8007698              5  vente en ligne   \n",
       "1826670   20662818      1256137      8007699              5  vente en ligne   \n",
       "1826671   20662819      1256137      8007699              5  vente en ligne   \n",
       "\n",
       "                    purchase_date type_of_ticket_name  amount  \\\n",
       "0       2018-12-28 14:47:50+00:00             Atelier     8.0   \n",
       "1       2018-12-28 14:47:50+00:00             Atelier     4.0   \n",
       "2       2018-12-28 14:47:50+00:00             Atelier     4.0   \n",
       "3       2018-12-28 14:47:50+00:00             Atelier     4.0   \n",
       "4       2018-12-28 14:47:50+00:00             Atelier     4.0   \n",
       "...                           ...                 ...     ...   \n",
       "1826667 2023-11-08 17:23:54+00:00             Atelier    11.0   \n",
       "1826668 2023-11-08 18:32:18+00:00             Atelier    11.0   \n",
       "1826669 2023-11-08 18:32:18+00:00             Atelier    11.0   \n",
       "1826670 2023-11-08 19:30:28+00:00             Atelier    11.0   \n",
       "1826671 2023-11-08 19:30:28+00:00             Atelier    11.0   \n",
       "\n",
       "                children  is_full_price      name_event_types name_facilities  \\\n",
       "0        pricing_formula          False      spectacle vivant           mucem   \n",
       "1        pricing_formula          False      spectacle vivant           mucem   \n",
       "2        pricing_formula          False      spectacle vivant           mucem   \n",
       "3        pricing_formula          False      spectacle vivant           mucem   \n",
       "4        pricing_formula          False      spectacle vivant           mucem   \n",
       "...                  ...            ...                   ...             ...   \n",
       "1826667  pricing_formula          False  offre muséale groupe           mucem   \n",
       "1826668  pricing_formula          False  offre muséale groupe           mucem   \n",
       "1826669  pricing_formula          False  offre muséale groupe           mucem   \n",
       "1826670  pricing_formula          False  offre muséale groupe           mucem   \n",
       "1826671  pricing_formula          False  offre muséale groupe           mucem   \n",
       "\n",
       "           name_categories            name_events  name_seasons  \n",
       "0        indiv prog enfant  l'école des magiciens          2018  \n",
       "1        indiv prog enfant  l'école des magiciens          2018  \n",
       "2        indiv prog enfant  l'école des magiciens          2018  \n",
       "3        indiv prog enfant  l'école des magiciens          2018  \n",
       "4        indiv prog enfant  l'école des magiciens          2018  \n",
       "...                    ...                    ...           ...  \n",
       "1826667   indiv entrées tp                    NaN          2023  \n",
       "1826668   indiv entrées tp                    NaN          2023  \n",
       "1826669   indiv entrées tp                    NaN          2023  \n",
       "1826670   indiv entrées tp                    NaN          2023  \n",
       "1826671   indiv entrées tp                    NaN          2023  \n",
       "\n",
       "[1826672 rows x 15 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "purchases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "84a11cdd-aeb9-457a-bf7b-b2ad1752b99d",
   "metadata": {},
   "outputs": [],
   "source": [
    "purchases['purchase_date'] = pd.to_datetime(purchases['purchase_date'])\n",
    "\n",
    "purchases_filtered = purchases[purchases['event_type_id'] == 5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "bea0e516-ee62-4bb4-bdd9-bb2502972d84",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>month</th>\n",
       "      <th>fake_category</th>\n",
       "      <th>purchase_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013-06-01</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013-07-01</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013-09-01</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013-10-01</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2013-11-01</td>\n",
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       "      <th>196</th>\n",
       "      <td>2023-09-01</td>\n",
       "      <td>1</td>\n",
       "      <td>6900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>2023-10-01</td>\n",
       "      <td>0</td>\n",
       "      <td>3621</td>\n",
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       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>2023-10-01</td>\n",
       "      <td>1</td>\n",
       "      <td>8313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>2023-11-01</td>\n",
       "      <td>0</td>\n",
       "      <td>945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>2023-11-01</td>\n",
       "      <td>1</td>\n",
       "      <td>2268</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>201 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         month  fake_category  purchase_id\n",
       "0   2013-06-01              1            1\n",
       "1   2013-07-01              1            1\n",
       "2   2013-09-01              0            2\n",
       "3   2013-10-01              1            1\n",
       "4   2013-11-01              0            2\n",
       "..         ...            ...          ...\n",
       "196 2023-09-01              1         6900\n",
       "197 2023-10-01              0         3621\n",
       "198 2023-10-01              1         8313\n",
       "199 2023-11-01              0          945\n",
       "200 2023-11-01              1         2268\n",
       "\n",
       "[201 rows x 3 columns]"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# création de la table pr faire le graphique\n",
    "\n",
    "purchases_graph = purchases_filtered[['purchase_id', 'purchase_date']].drop_duplicates()\n",
    "\n",
    "purchases_graph[\"fake_category\"] = np.random.choice([0, 1], size=purchases_graph.shape[0], p = [0.3, 0.7])\n",
    "\n",
    "purchases_graph['month'] = purchases['purchase_date'].dt.strftime('%Y-%m')\n",
    "\n",
    "# purchases_graph = purchases_graph.groupby('month')['purchase_id'].count().reset_index()\n",
    "purchases_graph = purchases_graph.groupby(['month','fake_category'])['purchase_id'].count().reset_index()\n",
    "\n",
    "purchases_graph['month'] = pd.to_datetime(purchases_graph['month'])\n",
    "\n",
    "purchases_graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "c9b70757-7b80-4e6d-99f0-58b9812f404f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Créer le graphique (a changé ! le bon est le barplot qui vient après)\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(purchases_graph['month'], purchases_graph['purchase_id'])\n",
    "\n",
    "# Définir le format de l'axe des x en fonction des dates\n",
    "plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d'))\n",
    "plt.gca().xaxis.set_major_locator(mdates.DayLocator(interval=5)) # Ajustez l'intervalle selon vos besoins\n",
    "\n",
    "# Rotation des étiquettes de l'axe x pour une meilleure lisibilité\n",
    "plt.xticks(rotation=45)\n",
    "\n",
    "\n",
    "# Titres et labels\n",
    "plt.title('Évolution des données')\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('Valeurs')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0561564e-2c74-4d99-9aa3-26099160520e",
   "metadata": {},
   "source": [
    "## TP : second graphique - barplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "1753d45c-2737-4082-a5b0-461071a03351",
   "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>month</th>\n",
       "      <th>fake_category</th>\n",
       "      <th>purchase_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>2019-03-01</td>\n",
       "      <td>1</td>\n",
       "      <td>102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>2019-05-01</td>\n",
       "      <td>1</td>\n",
       "      <td>140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>2019-06-01</td>\n",
       "      <td>1</td>\n",
       "      <td>131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>2021-06-01</td>\n",
       "      <td>1</td>\n",
       "      <td>157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>2021-07-01</td>\n",
       "      <td>1</td>\n",
       "      <td>145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>2021-09-01</td>\n",
       "      <td>1</td>\n",
       "      <td>123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>2021-10-01</td>\n",
       "      <td>1</td>\n",
       "      <td>220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160</th>\n",
       "      <td>2022-03-01</td>\n",
       "      <td>1</td>\n",
       "      <td>112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162</th>\n",
       "      <td>2022-04-01</td>\n",
       "      <td>1</td>\n",
       "      <td>107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>164</th>\n",
       "      <td>2022-05-01</td>\n",
       "      <td>1</td>\n",
       "      <td>164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166</th>\n",
       "      <td>2022-06-01</td>\n",
       "      <td>1</td>\n",
       "      <td>158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172</th>\n",
       "      <td>2022-09-01</td>\n",
       "      <td>1</td>\n",
       "      <td>178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>2022-10-01</td>\n",
       "      <td>1</td>\n",
       "      <td>218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>2022-11-01</td>\n",
       "      <td>1</td>\n",
       "      <td>137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>2022-12-01</td>\n",
       "      <td>1</td>\n",
       "      <td>107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>2023-01-01</td>\n",
       "      <td>0</td>\n",
       "      <td>2052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>2023-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>5079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>2023-02-01</td>\n",
       "      <td>0</td>\n",
       "      <td>2684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>2023-02-01</td>\n",
       "      <td>1</td>\n",
       "      <td>6350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>2023-03-01</td>\n",
       "      <td>0</td>\n",
       "      <td>2196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>2023-03-01</td>\n",
       "      <td>1</td>\n",
       "      <td>5304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>2023-04-01</td>\n",
       "      <td>0</td>\n",
       "      <td>3595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>2023-04-01</td>\n",
       "      <td>1</td>\n",
       "      <td>8563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>2023-05-01</td>\n",
       "      <td>0</td>\n",
       "      <td>3727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>2023-05-01</td>\n",
       "      <td>1</td>\n",
       "      <td>8653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>2023-06-01</td>\n",
       "      <td>0</td>\n",
       "      <td>2904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>2023-06-01</td>\n",
       "      <td>1</td>\n",
       "      <td>6641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>2023-07-01</td>\n",
       "      <td>0</td>\n",
       "      <td>4247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>2023-07-01</td>\n",
       "      <td>1</td>\n",
       "      <td>10022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>2023-08-01</td>\n",
       "      <td>0</td>\n",
       "      <td>6146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>2023-08-01</td>\n",
       "      <td>1</td>\n",
       "      <td>14593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>2023-09-01</td>\n",
       "      <td>0</td>\n",
       "      <td>2954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>2023-09-01</td>\n",
       "      <td>1</td>\n",
       "      <td>6900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>2023-10-01</td>\n",
       "      <td>0</td>\n",
       "      <td>3621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>2023-10-01</td>\n",
       "      <td>1</td>\n",
       "      <td>8313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>2023-11-01</td>\n",
       "      <td>0</td>\n",
       "      <td>945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>2023-11-01</td>\n",
       "      <td>1</td>\n",
       "      <td>2268</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         month  fake_category  purchase_id\n",
       "96  2019-03-01              1          102\n",
       "100 2019-05-01              1          140\n",
       "102 2019-06-01              1          131\n",
       "142 2021-06-01              1          157\n",
       "144 2021-07-01              1          145\n",
       "148 2021-09-01              1          123\n",
       "150 2021-10-01              1          220\n",
       "160 2022-03-01              1          112\n",
       "162 2022-04-01              1          107\n",
       "164 2022-05-01              1          164\n",
       "166 2022-06-01              1          158\n",
       "172 2022-09-01              1          178\n",
       "174 2022-10-01              1          218\n",
       "176 2022-11-01              1          137\n",
       "178 2022-12-01              1          107\n",
       "179 2023-01-01              0         2052\n",
       "180 2023-01-01              1         5079\n",
       "181 2023-02-01              0         2684\n",
       "182 2023-02-01              1         6350\n",
       "183 2023-03-01              0         2196\n",
       "184 2023-03-01              1         5304\n",
       "185 2023-04-01              0         3595\n",
       "186 2023-04-01              1         8563\n",
       "187 2023-05-01              0         3727\n",
       "188 2023-05-01              1         8653\n",
       "189 2023-06-01              0         2904\n",
       "190 2023-06-01              1         6641\n",
       "191 2023-07-01              0         4247\n",
       "192 2023-07-01              1        10022\n",
       "193 2023-08-01              0         6146\n",
       "194 2023-08-01              1        14593\n",
       "195 2023-09-01              0         2954\n",
       "196 2023-09-01              1         6900\n",
       "197 2023-10-01              0         3621\n",
       "198 2023-10-01              1         8313\n",
       "199 2023-11-01              0          945\n",
       "200 2023-11-01              1         2268"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "purchases_graph[purchases_graph[\"purchase_id\"]>100] "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4113b464-1349-4e6e-a8c0-8a327eb7ef58",
   "metadata": {},
   "source": [
    "à partir de 2023, rupture : passage de plusieurs centaines à + de 7k ventes (et 3k en nov 2023) - on prend slt 2023"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "161efc2b-8439-4fe7-b136-cc70b9e83267",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# début du graphique\n",
    "\n",
    "purchases_graph_used = purchases_graph[purchases_graph[\"month\"] >= datetime(2023,1,1)]\n",
    "purchases_graph_used_0 = purchases_graph_used[purchases_graph_used[\"fake_category\"]==0]\n",
    "purchases_graph_used_1 = purchases_graph_used[purchases_graph_used[\"fake_category\"]==1]\n",
    "\n",
    "\n",
    "# Création du barplot\n",
    "plt.bar(purchases_graph_used_0[\"month\"], purchases_graph_used_0[\"purchase_id\"], width=12, label = \"categorie 0\")\n",
    "plt.bar(purchases_graph_used_0[\"month\"], purchases_graph_used_1[\"purchase_id\"], \n",
    "        bottom = purchases_graph_used_0[\"purchase_id\"], width=12, label = \"categorie 1\")\n",
    "\n",
    "\n",
    "# commande pr afficher slt\n",
    "plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%b'))\n",
    "\n",
    "\n",
    "# Ajout de titres et d'étiquettes\n",
    "plt.xlabel('Mois')\n",
    "plt.ylabel('Nombre d achats')\n",
    "plt.title('Nombre d achats au cours de l année 2023 pour l offre muséale groupe')\n",
    "plt.legend()\n",
    "\n",
    "# Affichage du barplot\n",
    "plt.show()"
   ]
  }
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