halport_evol_annuel_depots.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "976d4c12",
   "metadata": {},
   "source": [
    "# Représenter l'évolution annuelle et les types de dépôts dans le portail HAL-ENS"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4ac160c",
   "metadata": {},
   "source": [
    "Import de bibliothèque"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c08252bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd, json, requests"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac8ae206",
   "metadata": {},
   "source": [
    "Définition des fonctions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c0de82a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "def req_hal(year, subtype) :\n",
    "    \"\"\"nombre de publication pour telle année pour tel type de dépôt\"\"\"\n",
    "    req = requests.get(f\"https://api.archives-ouvertes.fr/search/ens/?q=submittedDateY_i:{year}&fq=submitType_s:{subtype}&rows=0\")\n",
    "    req = req.json()\n",
    "    num = req['response']['numFound']\n",
    "    return num"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "b45e752b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def req_total(year, st) :\n",
    "    \"\"\"nombre de publication pour telle année\"\"\"\n",
    "    req = requests.get(f\"https://api.archives-ouvertes.fr/search/ens/?q=submittedDateY_i:{year}&rows=0\")\n",
    "    req = req.json()\n",
    "    tot = req['response']['numFound']\n",
    "    return tot"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd618994",
   "metadata": {},
   "source": [
    "Détermination de la période"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "188f135d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Année de départ : 2001\n",
      "Année de fin : 2016\n"
     ]
    }
   ],
   "source": [
    "year1 = int (input(\"Année de départ : \"))\n",
    "year2 = int (input(\"Année de fin : \"))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a41544c8",
   "metadata": {},
   "source": [
    "Calculs des statistiques"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "c6193753",
   "metadata": {},
   "outputs": [],
   "source": [
    "totaux = {}\n",
    "subtypes = [\"total\"]\n",
    "for year in range(year1, year2+1):\n",
    "    totaux[year] = {}\n",
    "    for st in subtypes :\n",
    "        tot = req_total(year, st)\n",
    "        totaux[year][st] = tot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e2ce525f",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = {}\n",
    "subtypes = [\"file\", \"notice\", \"annex\"]\n",
    "for year in range(year1, year2+1):\n",
    "    data[year] = {}\n",
    "    for st in subtypes : \n",
    "        num = req_hal(year, st)\n",
    "        data[year][st] = num"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca9a209f",
   "metadata": {},
   "source": [
    "Transformation en tableau et graphes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "c11df341",
   "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>2001</th>\n",
       "      <th>2002</th>\n",
       "      <th>2003</th>\n",
       "      <th>2004</th>\n",
       "      <th>2005</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>file</th>\n",
       "      <td>7</td>\n",
       "      <td>212</td>\n",
       "      <td>146</td>\n",
       "      <td>242</td>\n",
       "      <td>200</td>\n",
       "      <td>615</td>\n",
       "      <td>460</td>\n",
       "      <td>506</td>\n",
       "      <td>524</td>\n",
       "      <td>547</td>\n",
       "      <td>928</td>\n",
       "      <td>613</td>\n",
       "      <td>635</td>\n",
       "      <td>788</td>\n",
       "      <td>1077</td>\n",
       "      <td>1278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>notice</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>220</td>\n",
       "      <td>358</td>\n",
       "      <td>694</td>\n",
       "      <td>969</td>\n",
       "      <td>913</td>\n",
       "      <td>1174</td>\n",
       "      <td>856</td>\n",
       "      <td>983</td>\n",
       "      <td>2731</td>\n",
       "      <td>2939</td>\n",
       "      <td>1292</td>\n",
       "      <td>1306</td>\n",
       "      <td>1376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>annex</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total</th>\n",
       "      <td>7</td>\n",
       "      <td>212</td>\n",
       "      <td>149</td>\n",
       "      <td>462</td>\n",
       "      <td>558</td>\n",
       "      <td>1309</td>\n",
       "      <td>1429</td>\n",
       "      <td>1419</td>\n",
       "      <td>1698</td>\n",
       "      <td>1403</td>\n",
       "      <td>1911</td>\n",
       "      <td>3344</td>\n",
       "      <td>3574</td>\n",
       "      <td>2081</td>\n",
       "      <td>2394</td>\n",
       "      <td>2673</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        2001  2002  2003  2004  2005  2006  2007  2008  2009  2010  2011  \\\n",
       "file       7   212   146   242   200   615   460   506   524   547   928   \n",
       "notice     0     0     3   220   358   694   969   913  1174   856   983   \n",
       "annex      0     0     0     0     0     0     0     0     0     0     0   \n",
       "total      7   212   149   462   558  1309  1429  1419  1698  1403  1911   \n",
       "\n",
       "        2012  2013  2014  2015  2016  \n",
       "file     613   635   788  1077  1278  \n",
       "notice  2731  2939  1292  1306  1376  \n",
       "annex      0     0     1    11    19  \n",
       "total   3344  3574  2081  2394  2673  "
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dftot = pd.DataFrame.from_dict(totaux)\n",
    "df = pd.DataFrame.from_dict(data)\n",
    "dffin = [df, dftot]\n",
    "dffin = pd.concat(dffin)\n",
    "dffin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "0df3a0d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = df.T # pour inverser colonnes et lignes\n",
    "df.plot.bar(stacked = True)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}