07_analyser_des_donnees.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "e50d5af5-477e-4178-8e14-03f464fc7cb3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "62c58b56-7988-4336-aee2-810a3d73d830",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"data.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "52dc8d9e-3783-44af-9bf4-55ff4fc919e2",
   "metadata": {},
   "source": [
    "# Ajouter des colonnes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "f4a89a16-6f6b-403c-948a-9523c05c055f",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.dropna(subset=[\"tax\"], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "20ae8a2e-3ab9-4a5b-9cb5-53bbcf3ee6f1",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>date</th>\n",
       "      <th>first_name</th>\n",
       "      <th>last_name</th>\n",
       "      <th>email</th>\n",
       "      <th>gender</th>\n",
       "      <th>ip_address</th>\n",
       "      <th>country</th>\n",
       "      <th>price_paid</th>\n",
       "      <th>tax</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>03/17/2021</td>\n",
       "      <td>Erl</td>\n",
       "      <td>Kilminster</td>\n",
       "      <td>ekilminster2@etsy.com</td>\n",
       "      <td>Male</td>\n",
       "      <td>213.8.101.145</td>\n",
       "      <td>United States</td>\n",
       "      <td>$3.73</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>03/19/2021</td>\n",
       "      <td>Grata</td>\n",
       "      <td>Brantl</td>\n",
       "      <td>gbrantl3@umn.edu</td>\n",
       "      <td>Female</td>\n",
       "      <td>5.140.237.101</td>\n",
       "      <td>United States</td>\n",
       "      <td>$3.70</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>03/24/2021</td>\n",
       "      <td>Kanya</td>\n",
       "      <td>Beasant</td>\n",
       "      <td>kbeasant4@jigsy.com</td>\n",
       "      <td>Male</td>\n",
       "      <td>106.252.162.233</td>\n",
       "      <td>Morocco</td>\n",
       "      <td>$9.24</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>03/08/2021</td>\n",
       "      <td>Codi</td>\n",
       "      <td>Feasley</td>\n",
       "      <td>cfeasley7@ed.gov</td>\n",
       "      <td>Male</td>\n",
       "      <td>26.190.191.249</td>\n",
       "      <td>France</td>\n",
       "      <td>$3.01</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>03/09/2021</td>\n",
       "      <td>Waylon</td>\n",
       "      <td>Heersema</td>\n",
       "      <td>wheersema8@tumblr.com</td>\n",
       "      <td>Male</td>\n",
       "      <td>63.242.174.112</td>\n",
       "      <td>Canada</td>\n",
       "      <td>$3.70</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>991</th>\n",
       "      <td>992</td>\n",
       "      <td>03/07/2021</td>\n",
       "      <td>Elihu</td>\n",
       "      <td>Crush</td>\n",
       "      <td>ecrushrj@berkeley.edu</td>\n",
       "      <td>Male</td>\n",
       "      <td>174.38.173.53</td>\n",
       "      <td>Morocco</td>\n",
       "      <td>$5.05</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>994</th>\n",
       "      <td>995</td>\n",
       "      <td>03/17/2021</td>\n",
       "      <td>Carolyne</td>\n",
       "      <td>Bruntjen</td>\n",
       "      <td>cbruntjenrm@reference.com</td>\n",
       "      <td>Female</td>\n",
       "      <td>7.29.168.78</td>\n",
       "      <td>France</td>\n",
       "      <td>$5.68</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>997</td>\n",
       "      <td>03/17/2021</td>\n",
       "      <td>Ame</td>\n",
       "      <td>Rastall</td>\n",
       "      <td>arastallro@toplist.cz</td>\n",
       "      <td>Female</td>\n",
       "      <td>188.219.12.109</td>\n",
       "      <td>NaN</td>\n",
       "      <td>$4.59</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>998</td>\n",
       "      <td>03/13/2021</td>\n",
       "      <td>Betty</td>\n",
       "      <td>Stickels</td>\n",
       "      <td>bstickelsrp@cloudflare.com</td>\n",
       "      <td>Male</td>\n",
       "      <td>199.80.60.87</td>\n",
       "      <td>United States</td>\n",
       "      <td>$7.54</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>1000</td>\n",
       "      <td>03/23/2021</td>\n",
       "      <td>Rochelle</td>\n",
       "      <td>Pringuer</td>\n",
       "      <td>rpringuerrr@house.gov</td>\n",
       "      <td>NaN</td>\n",
       "      <td>170.193.249.83</td>\n",
       "      <td>Canada</td>\n",
       "      <td>$8.01</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>654 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       id        date first_name   last_name                       email  \\\n",
       "2       3  03/17/2021        Erl  Kilminster       ekilminster2@etsy.com   \n",
       "3       4  03/19/2021      Grata      Brantl            gbrantl3@umn.edu   \n",
       "4       5  03/24/2021      Kanya     Beasant         kbeasant4@jigsy.com   \n",
       "7       8  03/08/2021       Codi     Feasley            cfeasley7@ed.gov   \n",
       "8       9  03/09/2021     Waylon    Heersema       wheersema8@tumblr.com   \n",
       "..    ...         ...        ...         ...                         ...   \n",
       "991   992  03/07/2021      Elihu       Crush       ecrushrj@berkeley.edu   \n",
       "994   995  03/17/2021   Carolyne    Bruntjen   cbruntjenrm@reference.com   \n",
       "996   997  03/17/2021        Ame     Rastall       arastallro@toplist.cz   \n",
       "997   998  03/13/2021      Betty    Stickels  bstickelsrp@cloudflare.com   \n",
       "999  1000  03/23/2021   Rochelle    Pringuer       rpringuerrr@house.gov   \n",
       "\n",
       "     gender       ip_address        country price_paid   tax  \n",
       "2      Male    213.8.101.145  United States      $3.73  20.0  \n",
       "3    Female    5.140.237.101  United States      $3.70  20.0  \n",
       "4      Male  106.252.162.233        Morocco      $9.24  20.0  \n",
       "7      Male   26.190.191.249         France      $3.01  20.0  \n",
       "8      Male   63.242.174.112         Canada      $3.70  20.0  \n",
       "..      ...              ...            ...        ...   ...  \n",
       "991    Male    174.38.173.53        Morocco      $5.05  20.0  \n",
       "994  Female      7.29.168.78         France      $5.68  20.0  \n",
       "996  Female   188.219.12.109            NaN      $4.59  20.0  \n",
       "997    Male     199.80.60.87  United States      $7.54  20.0  \n",
       "999     NaN   170.193.249.83         Canada      $8.01  20.0  \n",
       "\n",
       "[654 rows x 10 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "ffc09c83-4884-4f26-b524-438eeba68744",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id              int64\n",
       "date           object\n",
       "first_name     object\n",
       "last_name      object\n",
       "email          object\n",
       "gender         object\n",
       "ip_address     object\n",
       "country        object\n",
       "price_paid    float64\n",
       "tax           float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.price_paid = df.price_paid.apply(lambda x: x.replace(\"$\", \"\"))\n",
    "df.price_paid = df.price_paid.astype(float)\n",
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "45dcbced-3dc8-4c09-ba1f-f8cea34c7418",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"price_total\"] = df[\"price_paid\"] * (1 - df[\"tax\"] / 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "6af470d6-f294-49d0-a4fc-3fde15e74f23",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>03/17/2021</td>\n",
       "      <td>Erl</td>\n",
       "      <td>Kilminster</td>\n",
       "      <td>ekilminster2@etsy.com</td>\n",
       "      <td>Male</td>\n",
       "      <td>213.8.101.145</td>\n",
       "      <td>United States</td>\n",
       "      <td>3.73</td>\n",
       "      <td>20.0</td>\n",
       "      <td>2.984</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>03/19/2021</td>\n",
       "      <td>Grata</td>\n",
       "      <td>Brantl</td>\n",
       "      <td>gbrantl3@umn.edu</td>\n",
       "      <td>Female</td>\n",
       "      <td>5.140.237.101</td>\n",
       "      <td>United States</td>\n",
       "      <td>3.70</td>\n",
       "      <td>20.0</td>\n",
       "      <td>2.960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>03/24/2021</td>\n",
       "      <td>Kanya</td>\n",
       "      <td>Beasant</td>\n",
       "      <td>kbeasant4@jigsy.com</td>\n",
       "      <td>Male</td>\n",
       "      <td>106.252.162.233</td>\n",
       "      <td>Morocco</td>\n",
       "      <td>9.24</td>\n",
       "      <td>20.0</td>\n",
       "      <td>7.392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>03/08/2021</td>\n",
       "      <td>Codi</td>\n",
       "      <td>Feasley</td>\n",
       "      <td>cfeasley7@ed.gov</td>\n",
       "      <td>Male</td>\n",
       "      <td>26.190.191.249</td>\n",
       "      <td>France</td>\n",
       "      <td>3.01</td>\n",
       "      <td>20.0</td>\n",
       "      <td>2.408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>03/09/2021</td>\n",
       "      <td>Waylon</td>\n",
       "      <td>Heersema</td>\n",
       "      <td>wheersema8@tumblr.com</td>\n",
       "      <td>Male</td>\n",
       "      <td>63.242.174.112</td>\n",
       "      <td>Canada</td>\n",
       "      <td>3.70</td>\n",
       "      <td>20.0</td>\n",
       "      <td>2.960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>991</th>\n",
       "      <td>992</td>\n",
       "      <td>03/07/2021</td>\n",
       "      <td>Elihu</td>\n",
       "      <td>Crush</td>\n",
       "      <td>ecrushrj@berkeley.edu</td>\n",
       "      <td>Male</td>\n",
       "      <td>174.38.173.53</td>\n",
       "      <td>Morocco</td>\n",
       "      <td>5.05</td>\n",
       "      <td>20.0</td>\n",
       "      <td>4.040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>994</th>\n",
       "      <td>995</td>\n",
       "      <td>03/17/2021</td>\n",
       "      <td>Carolyne</td>\n",
       "      <td>Bruntjen</td>\n",
       "      <td>cbruntjenrm@reference.com</td>\n",
       "      <td>Female</td>\n",
       "      <td>7.29.168.78</td>\n",
       "      <td>France</td>\n",
       "      <td>5.68</td>\n",
       "      <td>20.0</td>\n",
       "      <td>4.544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>997</td>\n",
       "      <td>03/17/2021</td>\n",
       "      <td>Ame</td>\n",
       "      <td>Rastall</td>\n",
       "      <td>arastallro@toplist.cz</td>\n",
       "      <td>Female</td>\n",
       "      <td>188.219.12.109</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.59</td>\n",
       "      <td>20.0</td>\n",
       "      <td>3.672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>998</td>\n",
       "      <td>03/13/2021</td>\n",
       "      <td>Betty</td>\n",
       "      <td>Stickels</td>\n",
       "      <td>bstickelsrp@cloudflare.com</td>\n",
       "      <td>Male</td>\n",
       "      <td>199.80.60.87</td>\n",
       "      <td>United States</td>\n",
       "      <td>7.54</td>\n",
       "      <td>20.0</td>\n",
       "      <td>6.032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>1000</td>\n",
       "      <td>03/23/2021</td>\n",
       "      <td>Rochelle</td>\n",
       "      <td>Pringuer</td>\n",
       "      <td>rpringuerrr@house.gov</td>\n",
       "      <td>NaN</td>\n",
       "      <td>170.193.249.83</td>\n",
       "      <td>Canada</td>\n",
       "      <td>8.01</td>\n",
       "      <td>20.0</td>\n",
       "      <td>6.408</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>654 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       id        date first_name   last_name                       email  \\\n",
       "2       3  03/17/2021        Erl  Kilminster       ekilminster2@etsy.com   \n",
       "3       4  03/19/2021      Grata      Brantl            gbrantl3@umn.edu   \n",
       "4       5  03/24/2021      Kanya     Beasant         kbeasant4@jigsy.com   \n",
       "7       8  03/08/2021       Codi     Feasley            cfeasley7@ed.gov   \n",
       "8       9  03/09/2021     Waylon    Heersema       wheersema8@tumblr.com   \n",
       "..    ...         ...        ...         ...                         ...   \n",
       "991   992  03/07/2021      Elihu       Crush       ecrushrj@berkeley.edu   \n",
       "994   995  03/17/2021   Carolyne    Bruntjen   cbruntjenrm@reference.com   \n",
       "996   997  03/17/2021        Ame     Rastall       arastallro@toplist.cz   \n",
       "997   998  03/13/2021      Betty    Stickels  bstickelsrp@cloudflare.com   \n",
       "999  1000  03/23/2021   Rochelle    Pringuer       rpringuerrr@house.gov   \n",
       "\n",
       "     gender       ip_address        country  price_paid   tax  price_total  \n",
       "2      Male    213.8.101.145  United States        3.73  20.0        2.984  \n",
       "3    Female    5.140.237.101  United States        3.70  20.0        2.960  \n",
       "4      Male  106.252.162.233        Morocco        9.24  20.0        7.392  \n",
       "7      Male   26.190.191.249         France        3.01  20.0        2.408  \n",
       "8      Male   63.242.174.112         Canada        3.70  20.0        2.960  \n",
       "..      ...              ...            ...         ...   ...          ...  \n",
       "991    Male    174.38.173.53        Morocco        5.05  20.0        4.040  \n",
       "994  Female      7.29.168.78         France        5.68  20.0        4.544  \n",
       "996  Female   188.219.12.109            NaN        4.59  20.0        3.672  \n",
       "997    Male     199.80.60.87  United States        7.54  20.0        6.032  \n",
       "999     NaN   170.193.249.83         Canada        8.01  20.0        6.408  \n",
       "\n",
       "[654 rows x 11 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "d6f76a3e-1f50-449e-b818-ba89ef5b3535",
   "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>gender</th>\n",
       "      <th>country</th>\n",
       "      <th>price_paid</th>\n",
       "      <th>tax</th>\n",
       "      <th>price_total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>03/17/2021</td>\n",
       "      <td>Male</td>\n",
       "      <td>United States</td>\n",
       "      <td>3.73</td>\n",
       "      <td>20.0</td>\n",
       "      <td>2.984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>03/19/2021</td>\n",
       "      <td>Female</td>\n",
       "      <td>United States</td>\n",
       "      <td>3.70</td>\n",
       "      <td>20.0</td>\n",
       "      <td>2.960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>03/24/2021</td>\n",
       "      <td>Male</td>\n",
       "      <td>Morocco</td>\n",
       "      <td>9.24</td>\n",
       "      <td>20.0</td>\n",
       "      <td>7.392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>03/08/2021</td>\n",
       "      <td>Male</td>\n",
       "      <td>France</td>\n",
       "      <td>3.01</td>\n",
       "      <td>20.0</td>\n",
       "      <td>2.408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>03/09/2021</td>\n",
       "      <td>Male</td>\n",
       "      <td>Canada</td>\n",
       "      <td>3.70</td>\n",
       "      <td>20.0</td>\n",
       "      <td>2.960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>991</th>\n",
       "      <td>03/07/2021</td>\n",
       "      <td>Male</td>\n",
       "      <td>Morocco</td>\n",
       "      <td>5.05</td>\n",
       "      <td>20.0</td>\n",
       "      <td>4.040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>994</th>\n",
       "      <td>03/17/2021</td>\n",
       "      <td>Female</td>\n",
       "      <td>France</td>\n",
       "      <td>5.68</td>\n",
       "      <td>20.0</td>\n",
       "      <td>4.544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>03/17/2021</td>\n",
       "      <td>Female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.59</td>\n",
       "      <td>20.0</td>\n",
       "      <td>3.672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>03/13/2021</td>\n",
       "      <td>Male</td>\n",
       "      <td>United States</td>\n",
       "      <td>7.54</td>\n",
       "      <td>20.0</td>\n",
       "      <td>6.032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>03/23/2021</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Canada</td>\n",
       "      <td>8.01</td>\n",
       "      <td>20.0</td>\n",
       "      <td>6.408</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>654 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           date  gender        country  price_paid   tax  price_total\n",
       "2    03/17/2021    Male  United States        3.73  20.0        2.984\n",
       "3    03/19/2021  Female  United States        3.70  20.0        2.960\n",
       "4    03/24/2021    Male        Morocco        9.24  20.0        7.392\n",
       "7    03/08/2021    Male         France        3.01  20.0        2.408\n",
       "8    03/09/2021    Male         Canada        3.70  20.0        2.960\n",
       "..          ...     ...            ...         ...   ...          ...\n",
       "991  03/07/2021    Male        Morocco        5.05  20.0        4.040\n",
       "994  03/17/2021  Female         France        5.68  20.0        4.544\n",
       "996  03/17/2021  Female            NaN        4.59  20.0        3.672\n",
       "997  03/13/2021    Male  United States        7.54  20.0        6.032\n",
       "999  03/23/2021     NaN         Canada        8.01  20.0        6.408\n",
       "\n",
       "[654 rows x 6 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop([\"first_name\", \"last_name\", \"email\", \"ip_address\", \"id\"], axis=1, inplace=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "342ac69c-9def-41e3-b65e-f0bb0dc9c373",
   "metadata": {},
   "source": [
    "# Analyser des données"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "40c06511-278e-4675-8318-0fd262d9670d",
   "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>price_paid</th>\n",
       "      <th>tax</th>\n",
       "      <th>price_total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>654.000000</td>\n",
       "      <td>654.0</td>\n",
       "      <td>654.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>6.371789</td>\n",
       "      <td>20.0</td>\n",
       "      <td>5.097431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.053502</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.642802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>20.0</td>\n",
       "      <td>2.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4.542500</td>\n",
       "      <td>20.0</td>\n",
       "      <td>3.634000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>6.210000</td>\n",
       "      <td>20.0</td>\n",
       "      <td>4.968000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>8.140000</td>\n",
       "      <td>20.0</td>\n",
       "      <td>6.512000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>10.000000</td>\n",
       "      <td>20.0</td>\n",
       "      <td>8.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       price_paid    tax  price_total\n",
       "count  654.000000  654.0   654.000000\n",
       "mean     6.371789   20.0     5.097431\n",
       "std      2.053502    0.0     1.642802\n",
       "min      3.000000   20.0     2.400000\n",
       "25%      4.542500   20.0     3.634000\n",
       "50%      6.210000   20.0     4.968000\n",
       "75%      8.140000   20.0     6.512000\n",
       "max     10.000000   20.0     8.000000"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "dd8d869e-6214-4199-9681-3f73f46b1e3d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.097431192660551"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"price_total\"].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "d8debaa0-ddfd-4fe0-b671-4b36683ded07",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3333.7200000000003"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"price_total\"].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "e747f8df-8c10-4950-a1f1-5e794b8b06fe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.4000000000000004\n",
      "8.0\n"
     ]
    }
   ],
   "source": [
    "print(df[\"price_total\"].min())\n",
    "print(df[\"price_total\"].max())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "4ab09ed9-bd0a-4ff4-ae5f-49eea9d5f322",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['United States', 'Morocco', 'France', 'Canada', nan]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"country\"].unique().tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "2d8b0751-33c5-449f-949b-93ae2c929a1d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "France           255\n",
       "United States    226\n",
       "Canada           108\n",
       "Morocco           39\n",
       "Name: country, dtype: int64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"country\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "9aad28f4-e303-4448-a917-d99086b32ac3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Female    323\n",
       "Male      289\n",
       "Name: gender, dtype: int64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"gender\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "ecd6202c-3bb1-4b4a-b64e-7d91ba9fa3ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Female    0.527778\n",
       "Male      0.472222\n",
       "Name: gender, dtype: float64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"gender\"].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "758c8459-c7c2-4ade-b8d9-c3a8f68a4bf5",
   "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>price_paid</th>\n",
       "      <th>tax</th>\n",
       "      <th>price_total</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>670.95</td>\n",
       "      <td>2160.0</td>\n",
       "      <td>536.760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>1611.71</td>\n",
       "      <td>5100.0</td>\n",
       "      <td>1289.368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Morocco</th>\n",
       "      <td>263.81</td>\n",
       "      <td>780.0</td>\n",
       "      <td>211.048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States</th>\n",
       "      <td>1473.60</td>\n",
       "      <td>4520.0</td>\n",
       "      <td>1178.880</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               price_paid     tax  price_total\n",
       "country                                       \n",
       "Canada             670.95  2160.0      536.760\n",
       "France            1611.71  5100.0     1289.368\n",
       "Morocco            263.81   780.0      211.048\n",
       "United States     1473.60  4520.0     1178.880"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(\"country\").sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "b5a5e4d1-c2db-4641-8537-b4497622e358",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "gender\n",
       "Female    5.119628\n",
       "Male      5.023502\n",
       "Name: price_total, dtype: float64"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(\"gender\")[\"price_total\"].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "2d230fa7-5e65-45e4-99bc-1eaf109b2c13",
   "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></th>\n",
       "      <th>price_paid</th>\n",
       "      <th>tax</th>\n",
       "      <th>price_total</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>6.428600</td>\n",
       "      <td>20.0</td>\n",
       "      <td>5.142880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>6.396167</td>\n",
       "      <td>20.0</td>\n",
       "      <td>5.116933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Morocco</th>\n",
       "      <td>6.824348</td>\n",
       "      <td>20.0</td>\n",
       "      <td>5.459478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States</th>\n",
       "      <td>6.381043</td>\n",
       "      <td>20.0</td>\n",
       "      <td>5.104835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">Male</th>\n",
       "      <th>Canada</th>\n",
       "      <td>5.821961</td>\n",
       "      <td>20.0</td>\n",
       "      <td>4.657569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>6.272458</td>\n",
       "      <td>20.0</td>\n",
       "      <td>5.017966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Morocco</th>\n",
       "      <td>6.307143</td>\n",
       "      <td>20.0</td>\n",
       "      <td>5.045714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States</th>\n",
       "      <td>6.594845</td>\n",
       "      <td>20.0</td>\n",
       "      <td>5.275876</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      price_paid   tax  price_total\n",
       "gender country                                     \n",
       "Female Canada           6.428600  20.0     5.142880\n",
       "       France           6.396167  20.0     5.116933\n",
       "       Morocco          6.824348  20.0     5.459478\n",
       "       United States    6.381043  20.0     5.104835\n",
       "Male   Canada           5.821961  20.0     4.657569\n",
       "       France           6.272458  20.0     5.017966\n",
       "       Morocco          6.307143  20.0     5.045714\n",
       "       United States    6.594845  20.0     5.275876"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby([\"gender\", \"country\"]).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1875d956-c9c1-4543-b59f-be454a963e8d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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