python_tips.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-30T17:51:14.925896Z",
     "start_time": "2020-05-30T17:51:14.922015Z"
    }
   },
   "outputs": [],
   "source": [
    "chaine=\"Python est un langage de programmation interprété, multi-paradigme et multiplateformes. Il favorise la programmation impérative structurée, fonctionnelle et orientée objet. l est doté d'un typage dynamique fort, d'une gestion automatique de la mémoire par ramasse-miettes et d'un système de gestion d'exceptions ; il est ainsi similaire à Perl, Ruby, Scheme, Smalltalk et Tcl.\" \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-30T17:51:15.579349Z",
     "start_time": "2020-05-30T17:51:15.573925Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "376"
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(chaine)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-30T17:51:33.709499Z",
     "start_time": "2020-05-30T17:51:33.706058Z"
    }
   },
   "outputs": [],
   "source": [
    "chaine=chaine.lower()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-30T17:51:34.784181Z",
     "start_time": "2020-05-30T17:51:34.778971Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"python est un langage de programmation interprété, multi-paradigme et multiplateformes. il favorise la programmation impérative structurée, fonctionnelle et orientée objet. l est doté d'un typage dynamique fort, d'une gestion automatique de la mémoire par ramasse-miettes et d'un système de gestion d'exceptions ; il est ainsi similaire à perl, ruby, scheme, smalltalk et tcl.\""
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chaine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-30T17:51:38.823886Z",
     "start_time": "2020-05-30T17:51:38.810351Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chaine==chaine[::-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-30T17:51:41.244110Z",
     "start_time": "2020-05-30T17:51:41.240254Z"
    }
   },
   "outputs": [],
   "source": [
    "nbre=len(list(set(chaine)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-30T17:51:42.489389Z",
     "start_time": "2020-05-30T17:51:42.485547Z"
    }
   },
   "outputs": [],
   "source": [
    "from collections import Counter\n",
    "c=Counter(list(chaine))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-30T17:51:43.190611Z",
     "start_time": "2020-05-30T17:51:43.183695Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(' ', 52),\n",
       " ('e', 40),\n",
       " ('t', 34),\n",
       " ('i', 25),\n",
       " ('a', 24),\n",
       " ('r', 20),\n",
       " ('m', 19),\n",
       " ('o', 18),\n",
       " ('n', 18),\n",
       " ('s', 18),\n",
       " ('l', 17),\n",
       " ('u', 12),\n",
       " ('p', 11),\n",
       " ('d', 10),\n",
       " ('g', 8),\n",
       " ('é', 7),\n",
       " (',', 6),\n",
       " ('y', 5),\n",
       " ('c', 5),\n",
       " ('f', 4)]"
      ]
     },
     "execution_count": 205,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c.most_common(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-30T17:51:46.342447Z",
     "start_time": "2020-05-30T17:51:46.335675Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[' ',\n",
       " \"'\",\n",
       " ',',\n",
       " '-',\n",
       " '.',\n",
       " ';',\n",
       " 'a',\n",
       " 'b',\n",
       " 'c',\n",
       " 'd',\n",
       " 'e',\n",
       " 'f',\n",
       " 'g',\n",
       " 'h',\n",
       " 'i',\n",
       " 'j',\n",
       " 'k',\n",
       " 'l',\n",
       " 'm',\n",
       " 'n',\n",
       " 'o',\n",
       " 'p',\n",
       " 'q',\n",
       " 'r',\n",
       " 's',\n",
       " 't',\n",
       " 'u',\n",
       " 'v',\n",
       " 'x',\n",
       " 'y',\n",
       " 'à',\n",
       " 'è',\n",
       " 'é']"
      ]
     },
     "execution_count": 206,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-30T17:51:48.268890Z",
     "start_time": "2020-05-30T17:51:48.011726Z"
    }
   },
   "outputs": [
    {
     "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": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "for i in range(1,nbre):\n",
    "    plt.scatter(x=c.most_common(nbre)[i][0],y=c.most_common(nbre)[i][1],s=c.most_common(nbre)[i][1]*5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-30T13:37:06.419915Z",
     "start_time": "2020-05-30T13:37:06.416165Z"
    }
   },
   "outputs": [],
   "source": [
    "unique=set(chaine)\n",
    "chainebis=''.join(unique)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-30T13:37:07.605939Z",
     "start_time": "2020-05-30T13:37:07.599428Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'uhelpagnsyt ro'"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chainebis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-30T09:18:09.604371Z",
     "start_time": "2020-05-30T09:18:09.598391Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'lavlavlav'"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chainebis*3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-30T13:39:14.455655Z",
     "start_time": "2020-05-30T13:39:14.449608Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'eglesusnt'"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chaine[::-3]#à l'envers par pas de 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-31T13:24:51.832473Z",
     "start_time": "2020-05-31T13:24:51.174919Z"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-31T13:30:40.466062Z",
     "start_time": "2020-05-31T13:30:40.460004Z"
    }
   },
   "outputs": [],
   "source": [
    "d={'col1': [1, 2,'NaN'], 'col2': [3, 4,7],'col3':[9,3,2]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-31T13:34:47.054173Z",
     "start_time": "2020-05-31T13:34:47.048222Z"
    }
   },
   "outputs": [],
   "source": [
    "df=pd.DataFrame(d,index=('A','B','C'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-31T13:34:47.860959Z",
     "start_time": "2020-05-31T13:34:47.850896Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "      <td>-1.822082</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.822082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-1.252031</th>\n",
       "      <td>-0.998455</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.998455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-1.165952</th>\n",
       "      <td>-0.102937</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.102937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-1.044378</th>\n",
       "      <td>-0.501388</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.501388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-0.985532</th>\n",
       "      <td>0.330218</td>\n",
       "      <td>1</td>\n",
       "      <td>0.330218</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                sum  count      mean\n",
       "A                                   \n",
       "-1.339856 -1.822082      1 -1.822082\n",
       "-1.252031 -0.998455      1 -0.998455\n",
       "-1.165952 -0.102937      1 -0.102937\n",
       "-1.044378 -0.501388      1 -0.501388\n",
       "-0.985532  0.330218      1  0.330218"
      ]
     },
     "execution_count": 266,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfbis.groupby('A').D.agg(['sum','count','mean']).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-01T13:43:03.499434Z",
     "start_time": "2020-06-01T13:43:03.494723Z"
    }
   },
   "outputs": [],
   "source": [
    "df_ages=pd.DataFrame({'age':np.random.randint(18,77,10)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-01T13:43:25.937890Z",
     "start_time": "2020-06-01T13:43:25.930684Z"
    }
   },
   "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>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age\n",
       "0   22\n",
       "1   52\n",
       "2   74\n",
       "3   60\n",
       "4   75"
      ]
     },
     "execution_count": 268,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ages.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-01T13:50:27.262090Z",
     "start_time": "2020-06-01T13:50:27.251829Z"
    }
   },
   "outputs": [],
   "source": [
    "df_ages['intervalles']=pd.cut(x=df_ages['age'],bins=[18,25,50,65,100])\n",
    "df_ages['decades']=pd.cut(x=df_ages['age'],bins=[18,25,50,65,100],labels=['jeune','quadra','quinqua','senior'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-01T20:11:38.509357Z",
     "start_time": "2020-06-01T20:11:38.496556Z"
    }
   },
   "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>age</th>\n",
       "      <th>intervalles</th>\n",
       "      <th>decades</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22</td>\n",
       "      <td>(18, 25]</td>\n",
       "      <td>jeune</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>52</td>\n",
       "      <td>(50, 65]</td>\n",
       "      <td>quinqua</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>74</td>\n",
       "      <td>(65, 100]</td>\n",
       "      <td>senior</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>60</td>\n",
       "      <td>(50, 65]</td>\n",
       "      <td>quinqua</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>75</td>\n",
       "      <td>(65, 100]</td>\n",
       "      <td>senior</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>28</td>\n",
       "      <td>(25, 50]</td>\n",
       "      <td>quadra</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>63</td>\n",
       "      <td>(50, 65]</td>\n",
       "      <td>quinqua</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>50</td>\n",
       "      <td>(25, 50]</td>\n",
       "      <td>quadra</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>63</td>\n",
       "      <td>(50, 65]</td>\n",
       "      <td>quinqua</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>67</td>\n",
       "      <td>(65, 100]</td>\n",
       "      <td>senior</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age intervalles  decades\n",
       "0   22    (18, 25]    jeune\n",
       "1   52    (50, 65]  quinqua\n",
       "2   74   (65, 100]   senior\n",
       "3   60    (50, 65]  quinqua\n",
       "4   75   (65, 100]   senior\n",
       "5   28    (25, 50]   quadra\n",
       "6   63    (50, 65]  quinqua\n",
       "7   50    (25, 50]   quadra\n",
       "8   63    (50, 65]  quinqua\n",
       "9   67   (65, 100]   senior"
      ]
     },
     "execution_count": 286,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-01T20:11:41.358763Z",
     "start_time": "2020-06-01T20:11:41.349617Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(18, 25], (50, 65], (65, 100], (25, 50]]\n",
       "Categories (4, interval[int64]): [(18, 25] < (25, 50] < (50, 65] < (65, 100]]"
      ]
     },
     "execution_count": 287,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ages['intervalles'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-01T20:12:11.737550Z",
     "start_time": "2020-06-01T20:12:11.722880Z"
    }
   },
   "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>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>50.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>61.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>66.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>75.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      age\n",
       "min  22.0\n",
       "25%  50.5\n",
       "50%  61.5\n",
       "75%  66.0\n",
       "max  75.0"
      ]
     },
     "execution_count": 290,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ages.describe().loc['min':'max']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-30T14:09:31.943311Z",
     "start_time": "2020-05-30T14:09:31.938391Z"
    }
   },
   "outputs": [],
   "source": [
    "from geopy import GoogleV3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-30T17:38:01.758633Z",
     "start_time": "2020-05-30T17:38:01.755372Z"
    }
   },
   "outputs": [],
   "source": [
    "maison=\"24 rue Chambourdin, 41000 Blois\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-30T17:38:02.892722Z",
     "start_time": "2020-05-30T17:38:02.504930Z"
    }
   },
   "outputs": [],
   "source": [
    "localisation=GoogleV3(api_key='AIzaSyBR7_Kj2QFicAWx9MfctW6uUpZsQlO6-AU',domain='maps.googleapis.com').geocode(maison)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-30T17:38:03.452698Z",
     "start_time": "2020-05-30T17:38:03.439169Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "24 Rue Chambourdin, 41000 Blois, France\n"
     ]
    }
   ],
   "source": [
    "print(localisation.address)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-30T17:38:04.406279Z",
     "start_time": "2020-05-30T17:38:04.401730Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "47.5888783\n"
     ]
    }
   ],
   "source": [
    "print(localisation.latitude)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-30T17:38:05.109822Z",
     "start_time": "2020-05-30T17:38:05.103729Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.3280024\n"
     ]
    }
   ],
   "source": [
    "print(localisation.longitude)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-30T17:38:05.745094Z",
     "start_time": "2020-05-30T17:38:05.740941Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0\n"
     ]
    }
   ],
   "source": [
    "print(localisation.altitude)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-30T17:39:43.934556Z",
     "start_time": "2020-05-30T17:39:43.930971Z"
    }
   },
   "outputs": [],
   "source": [
    "dico=localisation.raw"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-30T17:39:52.961117Z",
     "start_time": "2020-05-30T17:39:52.952545Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'access_points': [{'access_point_type': 'TYPE_SEGMENT',\n",
       "   'location': {'latitude': 47.5888565, 'longitude': 1.3279786},\n",
       "   'location_on_segment': {'latitude': 47.5889072, 'longitude': 1.328034},\n",
       "   'place_id': 'ChIJKS5vtAKo5EcRMoYoVdQIkeM',\n",
       "   'segment_position': 0.3976487219333649,\n",
       "   'unsuitable_travel_modes': []}],\n",
       " 'address_components': [{'long_name': '24',\n",
       "   'short_name': '24',\n",
       "   'types': ['street_number']},\n",
       "  {'long_name': 'Rue Chambourdin',\n",
       "   'short_name': 'Rue Chambourdin',\n",
       "   'types': ['route']},\n",
       "  {'long_name': 'Blois',\n",
       "   'short_name': 'Blois',\n",
       "   'types': ['locality', 'political']},\n",
       "  {'long_name': 'Loir-et-Cher',\n",
       "   'short_name': 'Loir-et-Cher',\n",
       "   'types': ['administrative_area_level_2', 'political']},\n",
       "  {'long_name': 'Centre-Val de Loire',\n",
       "   'short_name': 'Centre-Val de Loire',\n",
       "   'types': ['administrative_area_level_1', 'political']},\n",
       "  {'long_name': 'France',\n",
       "   'short_name': 'FR',\n",
       "   'types': ['country', 'political']},\n",
       "  {'long_name': '41000', 'short_name': '41000', 'types': ['postal_code']}],\n",
       " 'formatted_address': '24 Rue Chambourdin, 41000 Blois, France',\n",
       " 'geometry': {'location': {'lat': 47.5888783, 'lng': 1.3280024},\n",
       "  'location_type': 'ROOFTOP',\n",
       "  'viewport': {'northeast': {'lat': 47.59022728029151,\n",
       "    'lng': 1.329351380291502},\n",
       "   'southwest': {'lat': 47.58752931970851, 'lng': 1.326653419708498}}},\n",
       " 'place_id': 'ChIJgX8srQKo5EcRbvcdEmQV7d8',\n",
       " 'plus_code': {'compound_code': 'H8QH+H6 Blois, France',\n",
       "  'global_code': '8FV3H8QH+H6'},\n",
       " 'types': ['street_address']}"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dico"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-30T17:45:08.328167Z",
     "start_time": "2020-05-30T17:45:08.322190Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'northeast': {'lat': 47.59022728029151, 'lng': 1.329351380291502},\n",
       " 'southwest': {'lat': 47.58752931970851, 'lng': 1.326653419708498}}"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dico['geometry']['viewport']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-30T14:32:28.048645Z",
     "start_time": "2020-05-30T14:32:27.550903Z"
    }
   },
   "outputs": [],
   "source": [
    "import requests\n",
    "import pprint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-30T14:32:55.369245Z",
     "start_time": "2020-05-30T14:32:55.364932Z"
    }
   },
   "outputs": [],
   "source": [
    "url=\"https://randomuser.me/api/?results=1\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-30T14:33:16.465291Z",
     "start_time": "2020-05-30T14:33:16.226305Z"
    }
   },
   "outputs": [],
   "source": [
    "users=requests.get(url).json()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-30T14:33:27.055419Z",
     "start_time": "2020-05-30T14:33:27.046888Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'info': {'page': 1,\n",
      "          'results': 1,\n",
      "          'seed': 'f7a5bc2f7b2e84e0',\n",
      "          'version': '1.3'},\n",
      " 'results': [{'cell': '0764-752-466',\n",
      "              'dob': {'age': 45, 'date': '1975-01-28T17:41:35.950Z'},\n",
      "              'email': 'jesus.rose@example.com',\n",
      "              'gender': 'male',\n",
      "              'id': {'name': 'NINO', 'value': 'MA 98 30 64 X'},\n",
      "              'location': {'city': 'St Davids',\n",
      "                           'coordinates': {'latitude': '15.3794',\n",
      "                                           'longitude': '-147.2228'},\n",
      "                           'country': 'United Kingdom',\n",
      "                           'postcode': 'I75 4GS',\n",
      "                           'state': 'Nottinghamshire',\n",
      "                           'street': {'name': 'Church Lane', 'number': 4849},\n",
      "                           'timezone': {'description': 'Alaska',\n",
      "                                        'offset': '-9:00'}},\n",
      "              'login': {'md5': '90c00788364053f9d17e2138b370c58c',\n",
      "                        'password': 'bluemoon',\n",
      "                        'salt': 'eJyGQVg8',\n",
      "                        'sha1': 'edcbecced63a45555e8ad9a0a31b0abffcddc8cc',\n",
      "                        'sha256': 'c1b9706d57e357b89e5914fac741348df3e7c2c3cb7887758e42448f02c635b1',\n",
      "                        'username': 'bigcat852',\n",
      "                        'uuid': '7c171dbf-1968-4bc1-b211-d8ec0b5df69a'},\n",
      "              'name': {'first': 'Jesus', 'last': 'Rose', 'title': 'Mr'},\n",
      "              'nat': 'GB',\n",
      "              'phone': '016973 72658',\n",
      "              'picture': {'large': 'https://randomuser.me/api/portraits/men/76.jpg',\n",
      "                          'medium': 'https://randomuser.me/api/portraits/med/men/76.jpg',\n",
      "                          'thumbnail': 'https://randomuser.me/api/portraits/thumb/men/76.jpg'},\n",
      "              'registered': {'age': 2, 'date': '2018-03-21T11:55:03.238Z'}}]}\n"
     ]
    }
   ],
   "source": [
    "pprint.pprint(users)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-30T14:37:45.495724Z",
     "start_time": "2020-05-30T14:37:45.280129Z"
    }
   },
   "outputs": [],
   "source": [
    "import wikipedia"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-30T14:39:21.202456Z",
     "start_time": "2020-05-30T14:39:20.747868Z"
    }
   },
   "outputs": [],
   "source": [
    "resultat=wikipedia.geosearch(localisation.latitude,localisation.longitude)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-30T14:39:34.894919Z",
     "start_time": "2020-05-30T14:39:34.889557Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[\"Hôtel d'Alluye\", 'Château de Blois', 'La Maison de la Magie Robert-Houdin', 'Blois station', \"École nationale d'ingénieurs du Val de Loire\", 'Blois', 'Roman Catholic Diocese of Blois', 'Canton of Blois-2', 'Arrondissement of Blois']\n"
     ]
    }
   ],
   "source": [
    "print(resultat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-30T14:40:15.902987Z",
     "start_time": "2020-05-30T14:40:15.273140Z"
    }
   },
   "outputs": [],
   "source": [
    "resultat=wikipedia.page('Blois')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-30T14:41:08.742689Z",
     "start_time": "2020-05-30T14:41:08.689025Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A10 autoroute\n",
      "Agen\n",
      "Ain\n",
      "Aisne\n",
      "Ajaccio\n",
      "Albert Ronsin\n",
      "Albi\n",
      "Alençon\n",
      "Alexandre Dumas\n",
      "Allier\n",
      "Alpes-Maritimes\n",
      "Alpes-de-Haute-Provence\n",
      "Aly Cissokho\n",
      "Ambloy\n",
      "Amboise\n",
      "Amiens\n",
      "Angers\n",
      "Angoulême\n",
      "Angé\n",
      "Annecy\n",
      "Archbishop of Reims\n",
      "Ardennes (department)\n",
      "Ardèche\n",
      "Areines\n",
      "Ariège (department)\n",
      "Arras\n",
      "Arrondissement of Blois\n",
      "Arrondissements of France\n",
      "Artins\n",
      "Athos (fictional character)\n",
      "Aube\n",
      "Auch\n",
      "Aude\n",
      "Aurillac\n",
      "Autainville\n",
      "Authon, Loir-et-Cher\n",
      "Auxerre\n",
      "Avaray\n",
      "Averdon\n",
      "Aveyron\n",
      "Avignon\n",
      "Avoine, Indre-et-Loire\n",
      "Azay-le-Rideau\n",
      "Azé, Loir-et-Cher\n",
      "BNF (identifier)\n",
      "Baillou\n",
      "Ballan-Miré\n",
      "Bar-le-Duc\n",
      "Bas-Rhin\n",
      "Basse-Terre\n",
      "Bastia\n",
      "Baule\n",
      "Bauzy\n",
      "Beauce-la-Romaine\n",
      "Beauchêne, Loir-et-Cher\n",
      "Beaugency\n",
      "Beaumont-en-Véron\n",
      "Beauvais\n",
      "Belfort\n",
      "Bernard Onanga Itoua\n",
      "Berthenay\n",
      "Besançon\n",
      "Billy, Loir-et-Cher\n",
      "Binas\n",
      "Blaison-Saint-Sulpice\n",
      "Blois (disambiguation)\n",
      "Blois Cathedral\n",
      "Blood libel\n",
      "Bobigny\n",
      "Boisseau\n",
      "Bonneveau\n",
      "Bordeaux\n",
      "Bou\n",
      "Bouchemaine\n",
      "Bouches-du-Rhône\n",
      "Bouffry\n",
      "Bourg-en-Bresse\n",
      "Bourges\n",
      "Boursay\n",
      "Bracieux\n",
      "Brain-sur-l'Authion\n",
      "Briou\n",
      "Bréhémont\n",
      "Brévainville\n",
      "Busloup\n",
      "Béhuard\n",
      "Caen\n",
      "Cahors\n",
      "Calvados (department)\n",
      "Calvinism\n",
      "Candes-Saint-Martin\n",
      "Candé-sur-Beuvron\n",
      "Cangey\n",
      "Cantal\n",
      "Canton of Blois-1\n",
      "Canton of Blois-2\n",
      "Canton of Blois-3\n",
      "Canton of Vineuil\n",
      "Cantons of France\n",
      "Carcassonne\n",
      "Catherine de' Medici\n",
      "Cayenne\n",
      "Cellettes, Loir-et-Cher\n",
      "Cellé\n",
      "Central European Summer Time\n",
      "Central European Time\n",
      "Centre-Val de Loire\n",
      "Cergy\n",
      "Chailles\n",
      "Chaingy\n",
      "Chalonnes-sur-Loire\n",
      "Chambord, Loir-et-Cher\n",
      "Chambéry\n",
      "Champigny-en-Beauce\n",
      "Chaon, Loir-et-Cher\n",
      "Charente\n",
      "Charente-Maritime\n",
      "Chargé\n",
      "Charles, duc d'Orléans\n",
      "Charleville-Mézières\n",
      "Chartres\n",
      "Chaumont, Haute-Marne\n",
      "Chaumont-sur-Loire\n",
      "Chaumont-sur-Tharonne\n",
      "Chauvigny-du-Perche\n",
      "Cheillé\n",
      "Chenonceaux\n",
      "Cher (department)\n",
      "Cheverny\n",
      "Chinon\n",
      "Chissay-en-Touraine\n",
      "Chitenay\n",
      "Choue\n",
      "Choussy\n",
      "Chouzy-sur-Cisse\n",
      "Chouzé-sur-Loire\n",
      "Châlons-en-Champagne\n",
      "Château\n",
      "Château de Blois\n",
      "Châteauneuf-sur-Loire\n",
      "Châteauroux\n",
      "Châteauvieux, Loir-et-Cher\n",
      "Châtillon-sur-Cher\n",
      "Châtres-sur-Cher\n",
      "Chécy\n",
      "Chémery\n",
      "Chênehutte-Trèves-Cunault\n",
      "Cinais\n",
      "Cinq-Mars-la-Pile\n",
      "Claudine Doury\n",
      "Clermont-Ferrand\n",
      "Cléry-Saint-André\n",
      "Coat of arms of Blois\n",
      "Colmar\n",
      "Combleux\n",
      "Communes of France\n",
      "Communes of the Loir-et-Cher department\n",
      "Conan, Loir-et-Cher\n",
      "Concriers\n",
      "Corentin Jean\n",
      "Cormenon\n",
      "Cormeray\n",
      "Corrèze\n",
      "Corse-du-Sud\n",
      "Couddes\n",
      "Couffy\n",
      "Coulommiers-la-Tour\n",
      "Count of Blois\n",
      "Cour-Cheverny\n",
      "Cour-sur-Loire\n",
      "Courbouzon, Loir-et-Cher\n",
      "Courmemin\n",
      "Couëtron-au-Perche\n",
      "Creuse\n",
      "Crouy-sur-Cosson\n",
      "Crucheray\n",
      "Créteil\n",
      "Côte-d'Or\n",
      "Côtes-d'Armor\n",
      "Danzé\n",
      "Darvoy\n",
      "Daylight saving time\n",
      "Denis Papin\n",
      "Denée, Maine-et-Loire\n",
      "Departments of France\n",
      "Deux-Sèvres\n",
      "Dhuizon\n",
      "Digne-les-Bains\n",
      "Dijon\n",
      "Dordogne\n",
      "Doubs\n",
      "Droué\n",
      "Dry, Loiret\n",
      "Drôme\n",
      "Essonne\n",
      "Eure\n",
      "Eure-et-Loir\n",
      "Fabrice Moireau\n",
      "Faverolles-sur-Cher\n",
      "Faye, Loir-et-Cher\n",
      "Finistère\n",
      "Foix\n",
      "Fondettes\n",
      "Fontaine-Raoul\n",
      "Fontaine-les-Coteaux\n",
      "Fontaines-en-Sologne\n",
      "Fontevraud-l'Abbaye\n",
      "Fort-de-France\n",
      "Fortan\n",
      "Forêt de Russy\n",
      "Fossé, Loir-et-Cher\n",
      "France\n",
      "Françay\n",
      "French Guiana\n",
      "French States-General\n",
      "Fresnes, Loir-et-Cher\n",
      "Fréteval\n",
      "GND (identifier)\n",
      "Gap, Hautes-Alpes\n",
      "Gard\n",
      "Gare de Blois\n",
      "Gaston, Duke of Orléans\n",
      "Gennes, Maine-et-Loire\n",
      "Geographic coordinate system\n",
      "Germany\n",
      "Germigny-des-Prés\n",
      "Gers\n",
      "Gironde\n",
      "Gièvres\n",
      "Gombergean\n",
      "Gregory of Tours\n",
      "Grenoble\n",
      "Guadeloupe\n",
      "Guéret\n",
      "Gy-en-Sologne\n",
      "Haut-Rhin\n",
      "Haute-Corse\n",
      "Haute-Garonne\n",
      "Haute-Loire\n",
      "Haute-Marne\n",
      "Haute-Savoie\n",
      "Haute-Saône\n",
      "Haute-Vienne\n",
      "Hautes-Alpes\n",
      "Hautes-Pyrénées\n",
      "Hauts-de-Seine\n",
      "Henry I, Duke of Guise\n",
      "Henry III of France\n",
      "Henry IV of France\n",
      "Herbault\n",
      "Houssay, Loir-et-Cher\n",
      "Huismes\n",
      "Huisseau-en-Beauce\n",
      "Huisseau-sur-Cosson\n",
      "Huế\n",
      "Hérault\n",
      "INSEE code\n",
      "Ille-et-Vilaine\n",
      "Illusionist\n",
      "Indre\n",
      "Indre-et-Loire\n",
      "Ingrandes-de-Touraine\n",
      "Institut national de la statistique et des études économiques (France)\n",
      "Isère\n",
      "Italian Wars\n",
      "Italy\n",
      "Jacques Nicolas Augustin Thierry\n",
      "Jargeau\n",
      "Jean Eugène Robert-Houdin\n",
      "Jean Marie Pardessus\n",
      "Jean Morin (theologian)\n",
      "Joan of Arc\n",
      "Josnes\n",
      "Joué-lès-Tours\n",
      "Juigné-sur-Loire\n",
      "Jura (department)\n",
      "LCCN (identifier)\n",
      "La Bohalle\n",
      "La Chapelle-Enchérie\n",
      "La Chapelle-Montmartin\n",
      "La Chapelle-Saint-Martin-en-Plaine\n",
      "La Chapelle-Saint-Mesmin\n",
      "La Chapelle-Vendômoise\n",
      "La Chapelle-Vicomtesse\n",
      "La Chapelle-aux-Naux\n",
      "La Chapelle-sur-Loire\n",
      "La Chaussée-Saint-Victor\n",
      "La Daguenière\n",
      "La Ferté-Beauharnais\n",
      "La Ferté-Imbault\n",
      "La Ferté-Saint-Cyr\n",
      "La Fontenelle, Loir-et-Cher\n",
      "La Madeleine-Villefrouin\n",
      "La Maison de la Magie Robert-Houdin\n",
      "La Marolle-en-Sologne\n",
      "La Ménitré\n",
      "La Possonnière\n",
      "La Riche\n",
      "La Roche-Clermault\n",
      "La Roche-sur-Yon\n",
      "La Rochelle\n",
      "La Ville-aux-Clercs\n",
      "La Ville-aux-Dames\n",
      "Lailly-en-Val\n",
      "Lamotte-Beuvron\n",
      "Lancé\n",
      "Lancôme, Loir-et-Cher\n",
      "Landes-le-Gaulois\n",
      "Landes (department)\n",
      "Langeais\n",
      "Langon-sur-Cher\n",
      "Laon\n",
      "Larçay\n",
      "Lassay-sur-Croisne\n",
      "Laval, Mayenne\n",
      "Lavardin, Loir-et-Cher\n",
      "Le Controis-en-Sologne\n",
      "Le Gault-du-Perche\n",
      "Le Mans\n",
      "Le Plessis-Dorin\n",
      "Le Plessis-l'Échelle\n",
      "Le Poislay\n",
      "Le Puy-en-Velay\n",
      "Le Temple, Loir-et-Cher\n",
      "Le Thoureil\n",
      "Lerné\n",
      "Les Essarts, Loir-et-Cher\n",
      "Les Hayes\n",
      "Les Montils\n",
      "Les Ponts-de-Cé\n",
      "Les Roches-l'Évêque\n",
      "Les Rosiers\n",
      "Lestiou\n",
      "Lewes\n",
      "Lignières, Loir-et-Cher\n",
      "Lignières-de-Touraine\n",
      "Lille\n",
      "Limeray\n",
      "Limoges\n",
      "Lisle, Loir-et-Cher\n",
      "List of English monarchs\n",
      "List of French monarchs\n",
      "List of communes of the Loire valley\n",
      "List of museums in France\n",
      "List of twin towns and sister cities in France\n",
      "Loir-et-Cher\n",
      "Loire-Atlantique\n",
      "Loire (department)\n",
      "Loire River\n",
      "Loire Valley\n",
      "Loiret\n",
      "Lons-le-Saunier\n",
      "Loreux\n",
      "Lorges\n",
      "Lot-et-Garonne\n",
      "Lot (department)\n",
      "Louis II, Cardinal of Guise\n",
      "Louis XIII of France\n",
      "Louis XII of France\n",
      "Lozère\n",
      "Lunay\n",
      "Lussault-sur-Loire\n",
      "Luynes, Indre-et-Loire\n",
      "Lyon\n",
      "Magic (illusion)\n",
      "Maine-et-Loire\n",
      "Mamoudzou\n",
      "Manche\n",
      "Maray\n",
      "Marchenoir\n",
      "Marcilly-en-Beauce\n",
      "Marcilly-en-Gault\n",
      "Mardié\n",
      "Mareau-aux-Prés\n",
      "Mareuil-sur-Cher\n",
      "Marie Louise of Austria\n",
      "Marie de' Medici\n",
      "Marne (department)\n",
      "Marolles, Loir-et-Cher\n",
      "Marseille\n",
      "Martinique\n",
      "Maslives\n",
      "Mathematician\n",
      "Maves\n",
      "Mayenne\n",
      "Mayotte\n",
      "Mazangé\n",
      "Medievalist\n",
      "Melun\n",
      "Menars\n",
      "Mende, Lozère\n",
      "Mennetou-sur-Cher\n",
      "Mer, Loir-et-Cher\n",
      "Mesland\n",
      "Meslay, Loir-et-Cher\n",
      "Metz\n",
      "Meung-sur-Loire\n",
      "Meurthe-et-Moselle\n",
      "Meuse (department)\n",
      "Meusnes\n",
      "Middle Ages\n",
      "Millançay\n",
      "Moisy\n",
      "Mondoubleau\n",
      "Mont-de-Marsan\n",
      "Mont-près-Chambord\n",
      "Montauban\n",
      "Monteaux\n",
      "Monthou-sur-Bièvre\n",
      "Monthou-sur-Cher\n",
      "Montlivault\n",
      "Montlouis-sur-Loire\n",
      "Montoire-sur-le-Loir\n",
      "Montpellier\n",
      "Montrichard-Val-de-Cher\n",
      "Montrieux-en-Sologne\n",
      "Montrouveau\n",
      "Montsoreau\n",
      "Morbihan\n",
      "Morée\n",
      "Moselle (department)\n",
      "Mosnes\n",
      "Moulins, Allier\n",
      "Muides-sur-Loire\n",
      "Mulsans\n",
      "Mur-de-Sologne\n",
      "MusicBrainz\n",
      "Mâcon\n",
      "Méhers\n",
      "Mûrs-Erigné\n",
      "Nancy, France\n",
      "Nanterre\n",
      "Nantes\n",
      "Napoleon I\n",
      "National Library of the Czech Republic\n",
      "Naveil\n",
      "Nazelles-Negron\n",
      "Neung-sur-Beuvron\n",
      "Neuvy, Loir-et-Cher\n",
      "Neuvy-en-Sullias\n",
      "Nevers\n",
      "Nice\n",
      "Nicolas Vogondy\n",
      "Niort\n",
      "Nièvre\n",
      "Noizay\n",
      "Nord (French department)\n",
      "Nouan-le-Fuzelier\n",
      "Nourray\n",
      "Noyers-sur-Cher\n",
      "Nîmes\n",
      "Oise\n",
      "Oisly\n",
      "Olivet, Loiret\n",
      "Onzain\n",
      "Orléans\n",
      "Orne\n",
      "Orçay\n",
      "Oucques la Nouvelle\n",
      "Ouvrouer-les-Champs\n",
      "Ouzouer-le-Doyen\n",
      "Overseas department\n",
      "Paris\n",
      "Parnay, Maine-et-Loire\n",
      "Pas-de-Calais\n",
      "Pau, Pyrénées-Atlantiques\n",
      "Perpignan\n",
      "Pezou\n",
      "Philippe Ariès\n",
      "Philippe Gondet\n",
      "Philosopher\n",
      "Physicist\n",
      "Pierrefitte-sur-Sauldre\n",
      "Pocé-sur-Cisse\n",
      "Poitiers\n",
      "Pontlevoy\n",
      "Pontoise\n",
      "Pouillé, Loir-et-Cher\n",
      "Pray, Loir-et-Cher\n",
      "Prefectures in France\n",
      "Prefectures of France\n",
      "Privas\n",
      "Prunay-Cassereau\n",
      "Pruniers-en-Sologne\n",
      "Puy-de-Dôme\n",
      "Pyrénées-Atlantiques\n",
      "Pyrénées-Orientales\n",
      "Périgny, Loir-et-Cher\n",
      "Périgueux\n",
      "Quimper\n",
      "Rahart\n",
      "Regions of France\n",
      "Renaissance\n",
      "Renay\n",
      "Rennes\n",
      "René Guénon\n",
      "Rhodon\n",
      "Rhône (department)\n",
      "Rigny-Ussé\n",
      "Rilly-sur-Loire\n",
      "Rivarennes, Indre-et-Loire\n",
      "Rivière, Indre-et-Loire\n",
      "Rochecorbon\n",
      "Rochefort-sur-Loire\n",
      "Roches, Loir-et-Cher\n",
      "Rocé\n",
      "Rodez\n",
      "Romania\n",
      "Romilly, Loir-et-Cher\n",
      "Romorantin-Lanthenay\n",
      "Rouen\n",
      "Rougeou\n",
      "Ruan-sur-Egvonne\n",
      "Réunion\n",
      "Saint-Aignan, Loir-et-Cher\n",
      "Saint-Amand-Longpré\n",
      "Saint-Arnoult, Loir-et-Cher\n",
      "Saint-Avertin\n",
      "Saint-Ay\n",
      "Saint-Benoit-sur-Loire\n",
      "Saint-Bohaire\n",
      "Saint-Brieuc\n",
      "Saint-Claude-de-Diray\n",
      "Saint-Clément-des-Levées\n",
      "Saint-Cyr-du-Gault\n",
      "Saint-Cyr-sur-Loire\n",
      "Saint-Denis, Réunion\n",
      "Saint-Denis-de-l'Hôtel\n",
      "Saint-Denis-en-Val\n",
      "Saint-Denis-sur-Loire\n",
      "Saint-Dyé-sur-Loire\n",
      "Saint-Firmin-des-Prés\n",
      "Saint-Genouph\n",
      "Saint-Georges-sur-Cher\n",
      "Saint-Germain-sur-Vienne\n",
      "Saint-Gervais-la-Forêt\n",
      "Saint-Gourgon\n",
      "Saint-Hilaire-Saint-Mesmin\n",
      "Saint-Hilaire-la-Gravelle\n",
      "Saint-Jacques-des-Guérets\n",
      "Saint-Jean-Froidmentel\n",
      "Saint-Jean-de-Braye\n",
      "Saint-Jean-de-la-Croix\n",
      "Saint-Jean-de-la-Ruelle\n",
      "Saint-Jean-des-Mauvrets\n",
      "Saint-Jean-le-Blanc, Loiret\n",
      "Saint-Julien-de-Chédon\n",
      "Saint-Julien-sur-Cher\n",
      "Saint-Laurent-Nouan\n",
      "Saint-Laurent-des-Bois, Loir-et-Cher\n",
      "Saint-Loup, Loir-et-Cher\n",
      "Saint-Lubin-en-Vergonnois\n",
      "Saint-Léonard-en-Beauce\n",
      "Saint-Lô\n",
      "Saint-Marc-du-Cor\n",
      "Saint-Martin-d'Abbat\n",
      "Saint-Martin-de-la-Place\n",
      "Saint-Martin-des-Bois\n",
      "Saint-Mathurin-sur-Loire\n",
      "Saint-Michel-sur-Loire\n",
      "Saint-Ouen, Loir-et-Cher\n",
      "Saint-Patrice\n",
      "Saint-Pierre-des-Corps\n",
      "Saint-Pryvé-Saint-Mesmin\n",
      "Saint-Père-sur-Loire\n",
      "Saint-Rimay\n",
      "Saint-Romain-sur-Cher\n",
      "Saint-Rémy-la-Varenne\n",
      "Saint-Saturnin-sur-Loire\n",
      "Saint-Sulpice-de-Pommeray\n",
      "Saint-Viâtre\n",
      "Saint-Étienne\n",
      "Saint-Étienne-de-Chigny\n",
      "Saint-Étienne-des-Guérets\n",
      "Sainte-Anne, Loir-et-Cher\n",
      "Sainte-Gemmes-sur-Loire\n",
      "Salbris\n",
      "Sambin\n",
      "Sandillon\n",
      "Santenay, Loir-et-Cher\n",
      "Sargé-sur-Braye\n",
      "Sarthe\n",
      "Sasnières\n",
      "Sassay\n",
      "Saumur\n",
      "Savennières\n",
      "Savigny-en-Véron\n",
      "Savigny-sur-Braye\n",
      "Savoie\n",
      "Savonnières\n",
      "Saône-et-Loire\n",
      "Seigy\n",
      "Seine-Maritime\n",
      "Seine-Saint-Denis\n",
      "Seine-et-Marne\n",
      "Selles-Saint-Denis\n",
      "Selles-sur-Cher\n",
      "Selommes\n",
      "Seuilly\n",
      "Seur\n",
      "Siege of Orléans\n",
      "Sighişoara\n",
      "Sigloy\n",
      "Socialist Party (France)\n",
      "Soings-en-Sologne\n",
      "Somme (department)\n",
      "Sonia Bompastor\n",
      "Souesmes\n",
      "Sougé, Loir-et-Cher\n",
      "Souvigny-en-Sologne\n",
      "Souzay-Champigny\n",
      "Stephen of England\n",
      "Strasbourg\n",
      "Sully-sur-Loire\n",
      "Suèvres\n",
      "Séris\n",
      "Talcy, Loir-et-Cher\n",
      "Tarbes\n",
      "Tarn-et-Garonne\n",
      "Tarn (department)\n",
      "Tavers\n",
      "Ternay, Loir-et-Cher\n",
      "Territoire de Belfort\n",
      "The Three Musketeers\n",
      "The Vicomte de Bragelonne\n",
      "Theillay\n",
      "Thizay, Indre-et-Loire\n",
      "Thomas de Mahy, Marquis de Favras\n",
      "Thoré-la-Rochette\n",
      "Thoury\n",
      "Thubois, Father of Hugh Depayan, founder of the Knights Templar\n",
      "Thésée, Loir-et-Cher\n",
      "Time zone\n",
      "Toulon\n",
      "Toulouse\n",
      "Tour-en-Sologne\n",
      "Tourailles\n",
      "Tours\n",
      "Traditionalist School\n",
      "Treaty of Blois (1504)\n",
      "Troo\n",
      "Troyes\n",
      "Trélazé\n",
      "Tulle\n",
      "Turquant\n",
      "Twenty Years After\n",
      "Twin towns and sister cities\n",
      "UNESCO\n",
      "UTC+01:00\n",
      "UTC+02:00\n",
      "United Kingdom\n",
      "Urbino\n",
      "VIAF (identifier)\n",
      "Val-d'Oise\n",
      "Val-de-Marne\n",
      "Valaire\n",
      "Valence (city)\n",
      "Valencisse\n",
      "Vallières-les-Grandes\n",
      "Valloire-sur-Cisse\n",
      "Vallères\n",
      "Vallée-de-Ronsard\n",
      "Vannes\n",
      "Var (department)\n",
      "Varennes-sur-Loire\n",
      "Vaucluse\n",
      "Veilleins\n",
      "Vendée\n",
      "Vendôme\n",
      "Vernou-en-Sologne\n",
      "Vernou-sur-Brenne\n",
      "Versailles, Yvelines\n",
      "Vesoul\n",
      "Veuves\n",
      "Veuzain-sur-Loire\n",
      "Vienne\n",
      "Vietnam\n",
      "Vievy-le-Rayé\n",
      "Villandry\n",
      "Villavard\n",
      "Villebarou\n",
      "Villebernier\n",
      "Villebout\n",
      "Villechauve\n",
      "Villedieu-le-Château\n",
      "Villefranche-sur-Cher\n",
      "Villefrancœur\n",
      "Villeherviers\n",
      "Villemardy\n",
      "Villeneuve-Frouville\n",
      "Villeny\n",
      "Villeporcher\n",
      "Villerable\n",
      "Villerbon\n",
      "Villermain\n",
      "Villeromain\n",
      "Villetrun\n",
      "Villexanton\n",
      "Villiers-sur-Loir\n",
      "Villiersfaux\n",
      "Vineuil, Loir-et-Cher\n",
      "Vosges (department)\n",
      "Vouvray\n",
      "Vouzon\n",
      "Véretz\n",
      "Waldshut-Tiengen\n",
      "Weimar\n",
      "WorldCat Identities\n",
      "Yonne\n",
      "Yvelines\n",
      "Yvoy-le-Marron\n",
      "Épiais\n",
      "Épinal\n",
      "Épuisay\n",
      "Évreux\n",
      "Évry-Courcouronnes\n"
     ]
    }
   ],
   "source": [
    "for link in resultat.links:\n",
    "    print(link)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-30T14:41:26.538557Z",
     "start_time": "2020-05-30T14:41:26.246004Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Blois (, bl-WAH; French pronunciation: \\u200b[blwa]) is a city and the capital of Loir-et-Cher department in central France, situated on the banks of the lower river Loire between Orléans and Tours.'"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resultat.summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-30T17:13:09.842735Z",
     "start_time": "2020-05-30T17:13:09.715455Z"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from skimage import data\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-30T17:13:20.227397Z",
     "start_time": "2020-05-30T17:13:19.446818Z"
    }
   },
   "outputs": [],
   "source": [
    "image=data.camera()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-30T17:13:34.415222Z",
     "start_time": "2020-05-30T17:13:34.410359Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-30T17:13:51.226263Z",
     "start_time": "2020-05-30T17:13:51.215777Z"
    }
   },
   "outputs": [],
   "source": [
    "mask=image<87"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-30T17:14:01.843068Z",
     "start_time": "2020-05-30T17:14:01.839442Z"
    }
   },
   "outputs": [],
   "source": [
    "image[mask]=255"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-30T17:14:25.216486Z",
     "start_time": "2020-05-30T17:14:25.013765Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x126f63f50>"
      ]
     },
     "execution_count": 99,
     "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": [
    "plt.imshow(image,cmap='gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}