08_analyser_les_données_et_graphiques.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "c12d5c3d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv(\"data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "bbeaf5f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count      1000\n",
       "unique      528\n",
       "top       $3.73\n",
       "freq          8\n",
       "Name: price_paid, dtype: object"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"price_paid\"].describe() # affiche juste les rangées qui ont des valeurs\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9045841c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# la somme du prix qui a ete payé\n",
    "df.price_paid = df.price_paid.apply(lambda x: x.replace(\"$\", \"\"))\n",
    "df.price_paid = df.price_paid.astype(float)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "92ee9665",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6.4619299999999935"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"price_paid\"].mean() # moyenne"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "cb11f8d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6461.93"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"price_paid\"].sum() # somme"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "907894b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"price_paid\"].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1a4b39a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10.0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"price_paid\"].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "eafc1802",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Canada', 'United States', 'Morocco', 'France', nan], dtype=object)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# affiche toute les valeurs uniques à l'interieur d'une colonne\n",
    "df[\"country\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f5443aa6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 5.36,  8.32,  3.73,  3.7 ,  9.24,  5.82,  6.29,  3.01,  9.05,\n",
       "        5.71,  3.15,  5.3 ,  5.97,  4.53,  4.1 ,  6.44,  3.16,  4.9 ,\n",
       "        5.55,  4.86,  6.72,  9.79,  8.23,  9.9 ,  5.88,  3.06,  9.52,\n",
       "        6.76,  7.81,  6.02,  5.  ,  5.34,  4.67,  6.22,  3.37,  9.5 ,\n",
       "        4.54,  4.04,  3.45,  4.96,  5.29,  8.11,  7.8 ,  4.21,  9.81,\n",
       "        9.7 ,  9.71,  3.88,  3.4 ,  9.23,  6.57,  9.99,  6.94,  4.26,\n",
       "        9.13,  6.66,  9.35,  3.04,  6.17,  7.68,  4.23,  4.69,  7.56,\n",
       "        5.92,  4.65,  6.79,  8.71,  8.96,  4.39,  7.54,  5.83,  6.39,\n",
       "        9.6 ,  9.68,  9.86,  5.53,  3.51,  8.43,  6.89,  6.68,  4.66,\n",
       "        4.95,  6.77,  7.48,  7.23,  8.12,  5.8 ,  6.06,  4.71,  7.78,\n",
       "        9.67,  9.93,  4.56,  4.42,  4.12,  4.32,  8.83,  3.18,  8.86,\n",
       "        7.2 ,  3.72,  7.67,  8.68,  9.58,  7.91,  7.65,  8.77,  7.17,\n",
       "        9.47,  8.97,  6.07,  9.89,  8.85,  9.37,  7.03,  5.9 ,  4.  ,\n",
       "        6.88,  8.75,  3.03,  6.64,  8.65,  5.98,  8.69,  4.46,  4.24,\n",
       "        8.79,  4.82,  8.16,  7.19,  5.87,  7.05,  3.93,  5.33,  6.09,\n",
       "        5.72,  3.12,  8.45,  6.86,  4.74,  7.98,  8.7 ,  4.57,  4.38,\n",
       "        6.8 ,  6.53,  7.27,  7.75,  3.95, 10.  ,  6.96,  6.58,  5.56,\n",
       "        6.31,  5.49,  6.71,  6.93,  4.92,  9.02,  5.91,  9.27,  4.97,\n",
       "        7.02,  3.1 ,  4.25,  6.16,  4.47,  6.73,  9.32,  7.41,  9.51,\n",
       "        8.88,  5.47,  4.2 ,  9.38,  7.44,  9.1 ,  6.24,  4.43,  3.21,\n",
       "        8.82,  5.68,  6.14,  7.32,  8.62,  5.75,  3.96,  8.21,  3.52,\n",
       "        8.22,  9.45,  7.82,  3.05,  4.61,  7.83,  7.88,  4.48,  4.4 ,\n",
       "        8.37,  6.21,  9.16,  3.34,  8.24,  3.23,  9.3 ,  7.62,  8.74,\n",
       "        8.63,  3.78,  8.15,  3.87,  9.33,  8.02,  8.61,  3.17,  9.83,\n",
       "        4.44,  3.29,  6.11,  4.93,  6.05,  5.63,  9.98,  5.84,  8.19,\n",
       "        3.81,  5.86,  8.84,  3.33,  8.89,  8.34,  6.47,  8.66,  9.57,\n",
       "        3.48,  7.71,  8.27,  3.63,  8.28,  7.04,  3.84,  5.21,  8.33,\n",
       "        9.12,  3.09,  9.19,  8.25,  9.92,  3.79,  8.31,  9.43,  7.72,\n",
       "        3.44,  4.18,  7.12,  7.13,  4.84,  7.3 ,  7.89,  9.04,  4.03,\n",
       "        4.98,  6.7 ,  4.34,  6.32,  5.06,  9.31,  4.3 ,  3.56,  8.41,\n",
       "        6.49,  7.18,  6.75,  4.72,  3.68,  3.6 ,  9.85,  9.88,  6.46,\n",
       "        7.93,  3.53,  7.79,  4.16,  5.54,  5.35,  4.91,  6.5 ,  6.84,\n",
       "        8.17,  3.27,  5.27,  5.62,  3.66,  9.4 ,  5.66,  9.96,  4.76,\n",
       "        6.34,  8.64,  5.65,  5.08,  5.5 ,  6.6 ,  5.45,  8.95,  3.08,\n",
       "        9.97,  7.38,  6.98,  6.37,  4.31,  5.16,  7.9 ,  9.2 ,  8.01,\n",
       "        7.31,  7.66,  8.46,  3.14,  6.35,  3.2 ,  8.08,  7.28,  5.57,\n",
       "        3.49,  9.18,  7.22,  9.06,  6.55,  5.79,  3.25,  7.99,  8.18,\n",
       "        5.15,  6.23,  9.36,  5.05,  3.57,  9.21,  7.52,  3.28,  8.54,\n",
       "        3.8 ,  4.51,  9.75,  7.14,  8.4 ,  7.08,  8.49,  3.02,  6.27,\n",
       "        7.36,  4.01,  6.91,  8.38,  4.78,  8.94,  8.35,  7.96,  7.85,\n",
       "        8.1 ,  6.52,  6.2 ,  4.83,  9.08,  5.32,  7.16,  3.38,  5.61,\n",
       "        8.8 ,  4.22,  9.82,  9.29,  5.48,  5.24,  7.5 ,  7.95,  3.98,\n",
       "        3.74,  4.87,  3.86,  4.17,  3.36,  5.6 ,  6.51,  4.55,  4.36,\n",
       "        7.45,  8.55,  3.77,  3.46,  8.92,  6.4 ,  3.92,  5.1 ,  9.42,\n",
       "        9.09,  5.81,  6.43,  6.48,  6.12,  6.18,  3.31,  8.6 ,  6.85,\n",
       "        4.02,  3.35,  8.42,  5.89,  9.72,  3.61,  7.39,  9.84,  3.22,\n",
       "        4.35,  4.62,  4.49,  4.5 ,  8.72,  6.63,  4.7 ,  6.59,  3.75,\n",
       "        5.2 ,  3.59,  7.09,  5.78,  5.26,  6.62,  4.88,  5.4 ,  3.94,\n",
       "        6.99,  3.91,  3.54,  9.53,  9.65,  3.89,  9.77,  5.25,  3.58,\n",
       "        9.62,  3.69,  4.85,  3.55,  5.09,  4.13,  3.  ,  3.41,  5.69,\n",
       "        4.79,  9.95,  5.76,  6.87,  8.44,  6.19,  7.76,  9.63,  7.43,\n",
       "        4.68,  5.73,  5.42,  7.25,  9.11,  5.44,  5.7 ,  9.8 ,  5.03,\n",
       "        6.81,  5.18,  7.34,  4.05,  4.27,  5.37,  8.9 ,  7.51,  8.48,\n",
       "        3.9 ,  9.26,  3.11,  5.19,  5.51,  8.99,  4.07,  9.01,  8.53,\n",
       "        6.67,  6.56,  6.92,  9.15,  5.67,  5.96,  8.09,  5.11,  7.4 ,\n",
       "        7.33,  5.43,  6.3 ,  5.28,  5.02,  5.93,  9.78,  9.28,  3.5 ,\n",
       "        9.22,  6.15,  9.69,  7.97,  5.94,  9.76,  3.83,  4.73,  6.95,\n",
       "        3.32,  9.03,  4.64,  7.59,  3.3 ,  7.37,  6.13,  7.7 ,  3.07,\n",
       "        6.69,  3.71,  9.55,  4.33,  6.1 ,  4.59])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"price_paid\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1c219e9b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "France           408\n",
       "United States    333\n",
       "Canada           161\n",
       "Morocco           54\n",
       "Name: country, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# combien de clients on a dans chaque pays\n",
    "df[\"country\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "282ac9a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Female    493\n",
       "Male      438\n",
       "Name: gender, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# connaitre quel est le genre dominant\n",
    "df[\"gender\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e9336b79",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Female    0.529538\n",
       "Male      0.470462\n",
       "Name: gender, dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"gender\"].value_counts(normalize=True) # pour avoir sous forme de pourcentage\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "4d284b1e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>id</th>\n",
       "      <th>price_paid</th>\n",
       "      <th>tax</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Canada</th>\n",
       "      <td>70365</td>\n",
       "      <td>1017.45</td>\n",
       "      <td>2160.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>214440</td>\n",
       "      <td>2619.61</td>\n",
       "      <td>5100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Morocco</th>\n",
       "      <td>26591</td>\n",
       "      <td>362.97</td>\n",
       "      <td>780.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States</th>\n",
       "      <td>168150</td>\n",
       "      <td>2183.67</td>\n",
       "      <td>4520.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   id  price_paid     tax\n",
       "country                                  \n",
       "Canada          70365     1017.45  2160.0\n",
       "France         214440     2619.61  5100.0\n",
       "Morocco         26591      362.97   780.0\n",
       "United States  168150     2183.67  4520.0"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(\"country\").sum() # \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "6b228392",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "gender\n",
       "Female    3165.26\n",
       "Male      2833.61\n",
       "Name: price_paid, dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(\"gender\")[\"price_paid\"].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "67690fc6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>price_paid</th>\n",
       "      <th>tax</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gender</th>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">Female</th>\n",
       "      <th>Canada</th>\n",
       "      <td>461.472973</td>\n",
       "      <td>6.419324</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>512.648241</td>\n",
       "      <td>6.350352</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Morocco</th>\n",
       "      <td>479.925926</td>\n",
       "      <td>7.024815</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States</th>\n",
       "      <td>487.282353</td>\n",
       "      <td>6.437118</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">Male</th>\n",
       "      <th>Canada</th>\n",
       "      <td>406.000000</td>\n",
       "      <td>6.117467</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>534.928177</td>\n",
       "      <td>6.498950</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Morocco</th>\n",
       "      <td>483.652174</td>\n",
       "      <td>6.146957</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States</th>\n",
       "      <td>525.284722</td>\n",
       "      <td>6.672500</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              id  price_paid   tax\n",
       "gender country                                    \n",
       "Female Canada         461.472973    6.419324  20.0\n",
       "       France         512.648241    6.350352  20.0\n",
       "       Morocco        479.925926    7.024815  20.0\n",
       "       United States  487.282353    6.437118  20.0\n",
       "Male   Canada         406.000000    6.117467  20.0\n",
       "       France         534.928177    6.498950  20.0\n",
       "       Morocco        483.652174    6.146957  20.0\n",
       "       United States  525.284722    6.672500  20.0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby([\"gender\", \"country\"]).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "6b57e664",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "no numeric data to plot",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Input \u001b[1;32mIn [37]\u001b[0m, in \u001b[0;36m<cell line: 5>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\n\u001b[0;32m      3\u001b[0m \u001b[38;5;66;03m#df.groupby(\"date\")[\"price_paid\"].sum().plot()\u001b[39;00m\n\u001b[0;32m      4\u001b[0m \u001b[38;5;66;03m#df.groupby([\"date\"]).sum().plot()\u001b[39;00m\n\u001b[1;32m----> 5\u001b[0m \u001b[43mdf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgroupby\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mdate\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mprice_paid\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msum\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mplot\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\plotting\\_core.py:972\u001b[0m, in \u001b[0;36mPlotAccessor.__call__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m    969\u001b[0m             label_name \u001b[38;5;241m=\u001b[39m label_kw \u001b[38;5;129;01mor\u001b[39;00m data\u001b[38;5;241m.\u001b[39mcolumns\n\u001b[0;32m    970\u001b[0m             data\u001b[38;5;241m.\u001b[39mcolumns \u001b[38;5;241m=\u001b[39m label_name\n\u001b[1;32m--> 972\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m plot_backend\u001b[38;5;241m.\u001b[39mplot(data, kind\u001b[38;5;241m=\u001b[39mkind, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
      "File \u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\plotting\\_matplotlib\\__init__.py:71\u001b[0m, in \u001b[0;36mplot\u001b[1;34m(data, kind, **kwargs)\u001b[0m\n\u001b[0;32m     69\u001b[0m         kwargs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124max\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(ax, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mleft_ax\u001b[39m\u001b[38;5;124m\"\u001b[39m, ax)\n\u001b[0;32m     70\u001b[0m plot_obj \u001b[38;5;241m=\u001b[39m PLOT_CLASSES[kind](data, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m---> 71\u001b[0m \u001b[43mplot_obj\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgenerate\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     72\u001b[0m plot_obj\u001b[38;5;241m.\u001b[39mdraw()\n\u001b[0;32m     73\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m plot_obj\u001b[38;5;241m.\u001b[39mresult\n",
      "File \u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\plotting\\_matplotlib\\core.py:327\u001b[0m, in \u001b[0;36mMPLPlot.generate\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    325\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mgenerate\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[0;32m    326\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_args_adjust()\n\u001b[1;32m--> 327\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_compute_plot_data\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    328\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_setup_subplots()\n\u001b[0;32m    329\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_make_plot()\n",
      "File \u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\plotting\\_matplotlib\\core.py:506\u001b[0m, in \u001b[0;36mMPLPlot._compute_plot_data\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    504\u001b[0m \u001b[38;5;66;03m# no non-numeric frames or series allowed\u001b[39;00m\n\u001b[0;32m    505\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_empty:\n\u001b[1;32m--> 506\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mno numeric data to plot\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m    508\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdata \u001b[38;5;241m=\u001b[39m numeric_data\u001b[38;5;241m.\u001b[39mapply(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_convert_to_ndarray)\n",
      "\u001b[1;31mTypeError\u001b[0m: no numeric data to plot"
     ]
    }
   ],
   "source": [
    "# afficher pour chaque jour dans le mois le prix total payé\n",
    "import matplotlib\n",
    "#df.groupby(\"date\")[\"price_paid\"].sum().plot()\n",
    "#df.groupby([\"date\"]).sum().plot()\n",
    "df.groupby('date')[\"price_paid\"].sum().plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "ae7e4972",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.price_paid = df.price_paid.apply(lambda x: x.replace(\"$\", \"\"))\n",
    "df.price_paid = df.price_paid.astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "df44b84b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='date'>"
      ]
     },
     "execution_count": 39,
     "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": [
    "df.groupby(\"date\")[\"price_paid\"].sum().plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "756e81a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='date'>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.groupby(\"date\")[\"price_paid\"].sum().plot(figsize=[15,10]) # pour grossir la figure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "717a5ea1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='price_paid'>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.groupby(\"gender\")[\"price_paid\"].sum().plot.pie()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "ac3aa81f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='price_paid'>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# graphique camember\n",
    "df.groupby(\"gender\")[\"price_paid\"].sum().plot.pie(legend=True, figsize=(7,7))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "d530d5b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='country'>"
      ]
     },
     "execution_count": 43,
     "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": [
    "# graphique en barre\n",
    "df.groupby(\"country\")[\"price_paid\"].sum().plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "d0ecfe21",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='country'>"
      ]
     },
     "execution_count": 44,
     "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": [
    "# graphique en barre\n",
    "df.groupby(\"country\")[\"price_paid\"].sum().plot.bar(rot=45, legend=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "278fbc77",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}