Webscrapping.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "url = 'https://www.afm-telethon.fr/telethon/bref/parrains-resultats-telethon-1379'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "reponse = requests.get(url)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "from bs4 import BeautifulSoup\n",
    "soup = BeautifulSoup(reponse.content, 'html.parser')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# dans ma compréhension de liste\n",
    "# je veux récupérer que les années=> Année, 1987, 1988, 1989..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "liste = soup.select(\"td\") # => retourne un itérable (comme une liste)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "annees   = [ el.text for el in liste[3::3] ] # slicing: [start, stop, step]\n",
    "parrains = [ el.text for el in liste[4::3] ]\n",
    "montants = [ el.text for el in liste[5::3] ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "annees = [int(annee) for annee in annees]\n",
    "montants = [ int(prix.replace('€', '').replace('\\xa0', '').replace(' ', '')) for prix in montants  ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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4DBgInB4RMyUdC8yIiCl52n9ImgW8CBwREY/0ZcXNzOqmpxv8oPVNfpX60CNiKjC1NO5rhdcBfC4PZmbWD3zrf4dZ3l9oM3v1ckJfSVVN/P6BMHv18LNczMxqwi30FcQtZTPra26hm5nVhBO6mVlNOKGbmdWEE7qZWU04oZuZ1YQTuplZTTihm5nVhBO6mVlNOKGbmdWEE7qZWU04oZuZ1YQTuplZTTihm5nVhBO6mVlN+PG5y8mPxTWzTuEWuplZTTihm5nVxKuyy6WnbhJ3kZjZysotdDOzmnBCNzOrCSd0M7OacEI3M6sJJ3Qzs5pwQjczqwkndDOzmnBCNzOriUoJXdIESbMlzZF0ZA9x75MUksa1r4pmZlZFy4QuaSBwCrA7MBaYKGlsg7i1gM8A17W7kmZm1lqVW/+3B+ZExFwASecBewOzSnHfAI4HjmhrDXvBTz40s1ezKl0uI4F5hffz87iXSdoW2DgiLm5j3czMrBeW+6SopAHA94HDK8QeLGmGpBmLFi1a3lmbmVlBlYS+ANi48H5UHtdlLeDfgKsk3QPsAExpdGI0IiZFxLiIGDd8+PBlr7WZmS2lSh/6dGBzSZuSEvm+wIe6JkbEY8CwrveSrgI+HxEz2lvVFcv98Wa2smnZQo+IF4BDgMuAO4DJETFT0rGS9urrCpqZWTWV/sFFREwFppbGfa1J7Pjlr5aZmfWW7xQ1M6sJJ3Qzs5pwQjczqwkndDOzmnBCNzOrCSd0M7OacEI3M6sJJ3Qzs5pwQjczqwkndDOzmnBCNzOrCSd0M7OacEI3M6sJJ3Qzs5pwQjczq4lKz0PvK/6vQGZm7eMWuplZTfRrC70qt+TNzFpzC93MrCac0M3MasIJ3cysJpzQzcxqwgndzKwmnNDNzGrCCd3MrCac0M3MasIJ3cysJpzQzcxqwgndzKwmnNDNzGqiUkKXNEHSbElzJB3ZYPrnJM2SdKukP0napP1VNTOznrRM6JIGAqcAuwNjgYmSxpbCbgLGRcSbgQuB77a7omZm1rMqLfTtgTkRMTcingPOA/YuBkTElRHxdH57LTCqvdU0M7NWqiT0kcC8wvv5eVwzHwcuaTRB0sGSZkiasWjRouq1NDOzltp6UlTSR4BxwPcaTY+ISRExLiLGDR8+vJ2zNjN71avyH4sWABsX3o/K47qRtBvwFWCniHi2PdUzM7OqqrTQpwObS9pU0irAvsCUYoCkbYCfAntFxEPtr6aZmbXSMqFHxAvAIcBlwB3A5IiYKelYSXvlsO8BQ4ALJN0saUqT4szMrI9U+ifRETEVmFoa97XC693aXC8zM+sl3ylqZlYTTuhmZjXhhG5mVhNO6GZmNeGEbmZWE07oZmY14YRuZlYTTuhmZjXhhG5mVhNO6GZmNeGEbmZWE07oZmY14YRuZlYTTuhmZjXhhG5mVhNO6GZmNeGEbmZWE07oZmY14YRuZlYTTuhmZjXhhG5mVhNO6GZmNeGEbmZWE07oZmY14YRuZlYTTuhmZjXhhG5mVhNO6GZmNeGEbmZWE07oZmY1USmhS5ogabakOZKObDB9VUnn5+nXSRrT9pqamVmPWiZ0SQOBU4DdgbHAREljS2EfBx6NiNcBJwLHt7uiZmbWsyot9O2BORExNyKeA84D9i7F7A38Mr++ENhVktpXTTMza2VQhZiRwLzC+/nAW5vFRMQLkh4D1gceLgZJOhg4OL99EjadXSpnWPEzPfwk9Flcf8yzA+M6uW59GtfJdVuBcZ1ct/6K66S6bdI0OiJ6HIB9gJ8X3u8H/KgUczswqvD+bmBYq7IbzGtGp8Z1ct28rF4nXtZX9zrpGqp0uSwANi68H5XHNYyRNAgYCjxSoWwzM2uTKgl9OrC5pE0lrQLsC0wpxUwBDsiv9wGuiPzzYmZmK0bLPvRIfeKHAJcBA4HTI2KmpGNJhwNTgNOAsyTNARaTkv6ymNTBcZ1ct3bHdXLd+iuuk+vW7rhOrlt/xXVy3V4mN6TNzOrBd4qamdWEE7qZWU04oZuZ1YQTuplZTVS5U9SsT0gaCkwg3WkM6X6GyyJiScXPvzMiLs+v1waGR8TdpZg3R8SthfcbAkTEg5KGA/8OzI6ImS3m9e2I+HKLmE2BbYBZEXFnYfxo4KGIeCY/EuNAYFtgFvCziHghx+0FTIuIZyos+/8DFkbEbEk7Am8D7oiIiwsxQ0jrd2PgReCuXP5LpbK2JD2+o7gdpkTEHa3qkT//0Yj4Ram8kcB1EfFkYfyEiLi08H57ICJien4+1ATgzoiY2mJ+Z0bE/j1MfwfpkSW3R8S0wvi3ktbR45JWB47kle3w7Yh4LMcdClwUEfOWLr3bfLou474/Iv4o6UPA24E7gEkR8Xwh9rXAe+m+Lc6JiMdLZS7ftuivq1z6eaX9f+A/6b7Sflfc2VrU6WsRcWyhrFHAnyLinkLMxyLi9PxawPuBID3rZhfSRrsTOLX8BSvN64qI2KU0blhEFG4H1kfIOzApQUQe/x7gzxGxOCev/yUnHODwiJif474P/DoirumhHusBhwD3ky5T/TI5iZC+DI8WYncG3kf37fDziJhTiNkfOBqYxis3qo0C3gl8PSLObFaXQhn3RcRoSR8ATgIeAgYDB0bE9BxzY0Rsm19/kvQlFukBcgfmdfYO4LsRcVqOO7k8K9Id0mcCRMShOe63EfGf+fXeuQ5XkfbP4yLijDztdmD7iHha0vHAZsBvSfsBEfGxHPcv4CngEuBc0o/biw2W+yTS9h5Eupx41/yZnYCbIuKIvE4+D9wK7Az8jXRE/ibgwxFxWy7ri8BE0jOa5udZjCJ9586LiO803wIv1+e+iBidXx8K/A9pv9ga+ExE/C5PK26Lo0kP/BsEXE56nMiVpO1/WUR8K8eV73lRXp4r8rrbS9L1EbF9jj8oz/8i4D+A33ctg6SZwFb5UuxJwNPkZ0/l8e/NcY/l7XB33g4XRMSiBsv9q1z/NYAlwBDgN7k8RcQBhXXyLuBqYA/gphz/HuC/I+KqHLfc26LyLaXtHoDHSMnhL8B/k1pXjeJ+BZwP/B44K2+o/YAzgF8W4g4lJYejSDvvKcC3SMlrfCHuJGBqXknvyMO+edwPKtb9vvz323kjnZQ3/qcLMTcWXv+YtONMAc4GLsjLcF5xnqQvX3G4DXi2632Tso8ifakPyOWeWJg2q/D6fOCzeQc5ELi8MG0RMAO4F/gusE2DZZ5KSoI/ISWtH5Jat8eSfgy74o4DfgF8JC/z94CDSDvx+wtxs4F1GsxnXeCuwvspTYbfA0/lmJuBjfLr7Uk/lO/J728qlHUb6cu3PvAksGFhnjcX4ubl7bR/Xq8H5HV0AHBAIa5Y9t+ATfPrYcAtTbbDDcCAwvti3E25LgcBfwIWAqcCO5XW0UxSYlsDeBRYI48fTGqVdu1LaxTqc1l+/Wbgb4Wy7gIGN9gOqwD/6GHf7LaPltbxkPx6TN6vPtNkWwzMy/A4sHYevzqlfT1vi/GkH6zxwAP59U4Nyp1OziXAmsBthWl3NPoOde1Dpe0wgPSDcFre9pfm7b9WcZ3kv4PythqY36u0DLcVpq0BXJVfjy7VvdK26DE3VQnqi6E/V1qT+qi0Az/eZHgCeKEwz0H59TqkpHdio5238IV7BFilsEzFZehK+FuSHsAzhpRcNgE2Ka670g6/ZqH84g48u/D6hp524Px3C+CrpIRxJ6kFvUUxPq+nBT2UVZz/IOCa/HpdcrIp7LxDG2yHoaXt8CiwJ/kLXBjGk7ocus0zv9+IlDgPpfuPX/H1LaXPFNfpWqQf6XOAEXnc3AZ1LZZ3fQ/lXQbskl//umtbkn5YbmlUXn6/YV6GvwPzCuO7kvZqef2snt8PJP94kPbNriPw1Uv1KW6HO4v7VmH8JqX9ZyGpxb1JaRhDOnp++cemVM4Q0vf6+432ufLrBvvTAFJD5HJg60bbArgl71/rU3r2SWk+FwAfza9/AYwr7PfTe9gOg4G9SK31RcX1SEq265LywnqF7VL88bgNWLXwPZhRLKO326KnoT/70CNSV8M0YJqkwaRDsInACcDwHDcgd7usSUrUQ0l3o65KWtFFg0iH+KuSdiQi4r5cdpdnJL0l8iF5wVuAYt/lEuAtEbGwXHFJXd1EgyL3f0bEEknvBiZJuoC0obt0xTwvaXqkxxAT6dDv5e6WSIeP7yHdHXZCREyR9HxE3FuqwuqStiHt7AMj4qlC+cVD9KvyHb3H5dfviYiLcpfIY4W4yJ+/C/gG8A1JbyZti6nA60jbYV1SshsiaUxE3CNp/dKyviRpvYhYDIwgJRki4tHSI5W/BdwoaRqvPM1zNOmQ+xuFuGuBpyPiz6V1gKSup3U+IWmzyP3nEfGApPGkbo03FpdT0uBI3XR7FspZjcIFAhHxBHCYpO2AX0m6mMYXEGwl6XHSj9yqkjbK816la7mzTwBnSjqGtN5vlnQzqRHwueIiFQuPiAeBk4GTJW1SmHSxpL+QEsfPgcmSriX90F2dY6YCl0q6mtQ3fUFe1vVK8zkM+JOkf9B9O7yO1MXW5Q+klvfN5ZUg6arC24WStu6Ki4gnJb0LOJ3U3dPlOUlrRMTTwHaFsoYCxe/ES8CJ+Tt1oqSFLH3ubyjpB1ykbdy1HYaUlvUTwA8kHUV6guHf83d5Xp72cjWKhef9ZQowRdIahUmnkZLwQOArwAWS5gI7kI6+u/wcmC7pOtJR7fF5WYeTclmXw6i2LZqrkvX7YqD0q1yatkbh9WeBuaTugENJh6I/I/3qHV2I+wzpEPBneSV3/RIPB64uxG0LXEfqipmWhztIiWO7Qtw3Sf2ejep3fP77B0qHw4XPvlR4fwn5MLQUtyGlll0evyapRfM7YH6D6VeWhq7uhm4tFNIP3jHAfXl4idSSOAcYXWVbFGImklppC0n9438ktZoWAAcX4j6Yt9XleZ57FrbDOaUy1yV1dx2eh32BdZdhX9oK2LzB+MGk/uKu96NpfEg7EtitSdki9cme3Yv6rAO8rcH4N5DOnbyP1Gc8oDR9fC/m8TZgh/x6M1J/+Qfo3p2zRx7/zsK4AeTWYmncDrle78uvB/Z2O+SyRpG7shpM27HwetUmMcOAN/VQ/p6kczZV6rIGuRusNH7tvM9sB2zQYPoWvVjeEbxyFLcO6VlWS+UNUsNiH2DLFuUt17boz5OiW0RqEVaJHQEQEfdLWgfYjdSPfX0p7o2kL83tUbjKoEmZG1I4KRqpNdQr+Uw5EfGvBtNGRkT5qZTlmDVJ3SUPNZm+FSkxnFqxPgNJX5SnG0wbSjqiWOopmJKGROFqhBblK9KRxSDSIfiCiHigFLce8FrSP0ZZ0qLMDei+HZY6Iqoa186y6hJXtawm5VfdLzo2rpPr1pu4qvqtyyUi7lK6nOvxSN0VY4BxpMuWbi+FDyL1X0P6FYR0hrrsCeCPFcqD1JLougrjKaBhQpc0rhB3V/GHoiuRN4pplMyblPVUi7irGtWrh/IaJfOX4yR1W4a8HE+2WtYc96KkcZKKMd2SeY5brHTF0c65C2ipsiRtTTrhN5R0Rl/AKElLSGf+b8xx25BOxA6lcDVMMa5UVjnmvyLipgbzXNa4qnVblvLaFle1rBZmkY5qVua4Tq5bt7jczTmJ9AN8CfDFyFePqXAlT0/6LaEr/bPpTwLPSjqBdGh4DfB1SadFxPf7KG4n0uV7S0iHXNcA60p6Htgv8mWUVeLaWVanx7V7nqSrlD4ZEdeV9osdSCestsqjflEhrqeyziiU1Y64qnVblvLaGVepLEnFPvxuoeTzUJ0e18l1600c6Wq4Y0jdv58A/ippr0jnhsrnCxvqzztF9yP90+kdSf9Y+t8j4uOky84+1odxJwG7R8RupP705yNiR9JJutN6GdfOsjo9rt3zXLOcbAAi4lrSOYTexLWzrLrEVS3r26RzGWuVhiF0zw+dHNfJdetN3FoRcWlELImIE0gnQi/NP8LV+sZjGU58tGPglcsRB5JuCCmezLm9r+MKscVLz2b2Jq6dZXV6XB/M82TgYtJJ1Lfn4YN53I96E9fOsuoS14uy/kbhYoDSd3TeyhDXyXXrZdwtlC7lJd038A/gkUafLw/9eVL0DNLlbmuS+n1fIF2vugvpl+oDfRR3OunX7grStaULIuJzSpcj3RgRW1aNa2dZnR7X7nnm2N1pfJtzt1u/q8S1s6y6xFWMeT2wOBrfCblB5JOonRzXyXXrZdyHSNfYX1uKGQ18NSIOKn9+qfL6MaEPovvt8G8lXRp3H3BK5Gur+yBuMOlOvLGkX8TTI53sWx14TeRrvqvEtbOsTo9r9zzNrA9UacZ78NDugXT1xXdI9wAsJt1Be0cet05v4tpZVl3ilqGsO1fWuE6uW1/E9TT020lRSUMkHStppqTHJC2SdK2kA1ZQ3O2luAN7G9fOsjo9rt3zBCaTblvfOSLWi4j1gZ1JV8dM7mVcO8uqS1xvyxpfint0JYrr5Lr1RVxz/dhC+x3pIVGjSLc/fxXYHPglhTvBOjmuk+u2Eixr02dT0P0ZIi3j2llWXeI6uW5e1uWL62loGdBXA0s/HGl6/juAdDNQx8d1ct1WgmWdBnyBwq3XwAbAF0k3h1WOa2dZdYnr5Lp5WZcvrqehP69Df0rpQfQoPdh/Mbz8MB6tJHGdXLdOX9YPkp4982dJj0paTLordj3SM0l6E9fOsuoS18l187IuX1xzVbJ+Xwyk6yuvJ/UP/ZVXHtM6HDh0ZYjr5Lp1+rLmcVuSnsszpDR+Qm/j2llWXeI6uW5e1uWLazb0SzJvWan8pMSVOa6T69YJy0p6cuZs0iNu7wH2Lky7sTdx7SyrLnGdXDcv6/LF9fgdqxK0ogfyfwRameM6uW6dsKz07j/b9BjXzrLqEtfJdfOyLl9cT0N/Ppzr1maTSCcCOj6uk+vW7rh2z5P0aIYnASL9o4zxwIVK/8hBvYxrZ1l1ievkunlZly+uuSpZvy8Gqv9Lq46N6+S6rQTLegX5X4oVxg0i/SPmF3sT186y6hLXyXXzsi5fXE9DvyTzXNHTgHc0mXbOyhDXyXVbCZa16n+2aRnXzrLqEtfJdfOyLl9cT0O/PcvFzMzaqz+vQzczszZyQjczqwkndDOzmnBCNzOrCSd0M7Oa+D8oSbqGKPJ2tQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = seaborn.barplot(x=annees, y=montants, color='b')\n",
    "ax.set_title('montants du téléthons donations')\n",
    "ax.tick_params(axis='x', rotation=90)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "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": "3.8.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}