{
 "cells": [
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   "id": "d7bda38c",
   "metadata": {},
   "source": [
    "# Datenpartner mit Anzahl der Objekte pro Bundesland\n",
    "\n",
    "Dieses Notebook ruft Statistiken aus der API der Deutschen Digitalen Bibliothek ab und bereitet sie für eine Auswertung nach Bundesland auf."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2e7adfe2",
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    {
     "data": {
      "text/markdown": [
       "**Letzte Aktualisierung:** 03.04.2026 um 05:10:08 Uhr"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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     "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>Einrichtungen</th>\n",
       "      <th>Liefernde Einrichtungen</th>\n",
       "      <th>Objekte</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bundesland</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Baden-Württemberg</th>\n",
       "      <td>589</td>\n",
       "      <td>57</td>\n",
       "      <td>11002569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hessen</th>\n",
       "      <td>382</td>\n",
       "      <td>57</td>\n",
       "      <td>10031337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nordrhein-Westfalen</th>\n",
       "      <td>883</td>\n",
       "      <td>158</td>\n",
       "      <td>9333116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Berlin</th>\n",
       "      <td>306</td>\n",
       "      <td>108</td>\n",
       "      <td>7199813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sachsen</th>\n",
       "      <td>385</td>\n",
       "      <td>54</td>\n",
       "      <td>7073104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bayern</th>\n",
       "      <td>601</td>\n",
       "      <td>59</td>\n",
       "      <td>6776615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rheinland-Pfalz</th>\n",
       "      <td>165</td>\n",
       "      <td>29</td>\n",
       "      <td>4553607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sachsen-Anhalt</th>\n",
       "      <td>144</td>\n",
       "      <td>21</td>\n",
       "      <td>1973574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Schleswig-Holstein</th>\n",
       "      <td>337</td>\n",
       "      <td>44</td>\n",
       "      <td>1793351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Niedersachsen</th>\n",
       "      <td>386</td>\n",
       "      <td>58</td>\n",
       "      <td>1745139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brandenburg</th>\n",
       "      <td>199</td>\n",
       "      <td>41</td>\n",
       "      <td>1468534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hamburg</th>\n",
       "      <td>98</td>\n",
       "      <td>20</td>\n",
       "      <td>1366747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thüringen</th>\n",
       "      <td>282</td>\n",
       "      <td>72</td>\n",
       "      <td>355308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mecklenburg-Vorpommern</th>\n",
       "      <td>102</td>\n",
       "      <td>16</td>\n",
       "      <td>349094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ausland</th>\n",
       "      <td>13</td>\n",
       "      <td>4</td>\n",
       "      <td>167669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Saarland</th>\n",
       "      <td>43</td>\n",
       "      <td>15</td>\n",
       "      <td>66712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bremen</th>\n",
       "      <td>40</td>\n",
       "      <td>11</td>\n",
       "      <td>65789</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        Einrichtungen  Liefernde Einrichtungen   Objekte\n",
       "Bundesland                                                              \n",
       "Baden-Württemberg                 589                       57  11002569\n",
       "Hessen                            382                       57  10031337\n",
       "Nordrhein-Westfalen               883                      158   9333116\n",
       "Berlin                            306                      108   7199813\n",
       "Sachsen                           385                       54   7073104\n",
       "Bayern                            601                       59   6776615\n",
       "Rheinland-Pfalz                   165                       29   4553607\n",
       "Sachsen-Anhalt                    144                       21   1973574\n",
       "Schleswig-Holstein                337                       44   1793351\n",
       "Niedersachsen                     386                       58   1745139\n",
       "Brandenburg                       199                       41   1468534\n",
       "Hamburg                            98                       20   1366747\n",
       "Thüringen                         282                       72    355308\n",
       "Mecklenburg-Vorpommern            102                       16    349094\n",
       "Ausland                            13                        4    167669\n",
       "Saarland                           43                       15     66712\n",
       "Bremen                             40                       11     65789"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import requests\n",
    "import pandas as pd\n",
    "from datetime import datetime\n",
    "from IPython.display import Markdown, display\n",
    "\n",
    "# Zuerst wird aus der DDB-Suche eine Facet-Statistik zu `dataprovider_id` geladen. \n",
    "# Ergebnis dieser Zelle ist `df` mit zwei Spalten (`dataprovider_id`, `count`). \n",
    "# Zusätzlich wird auf 32-stellige Provider-IDs gefiltert, damit im DataFrame nur echte Provider-IDs stehen.\n",
    "\n",
    "# API-Call: https://api.deutsche-digitale-bibliothek.de/2/search/index/search/select?q=*%3A*&rows=0&facet=true&facet.field=dataprovider_id&facet.limit=-1&facet.sort=count&wt=json\n",
    "\n",
    "url = \"https://api.deutsche-digitale-bibliothek.de/2/search/index/search/select\"\n",
    "params = {\n",
    "    \"q\": \"*:*\",\n",
    "    \"rows\": 0,\n",
    "    \"facet\": \"true\",\n",
    "    \"facet.field\": \"dataprovider_id\",\n",
    "    \"facet.limit\": -1,\n",
    "    \"facet.sort\": \"count\",\n",
    "    \"wt\": \"json\",\n",
    "}\n",
    "\n",
    "resp = requests.get(url, params=params, timeout=300)\n",
    "resp.raise_for_status()\n",
    "data = resp.json()\n",
    "\n",
    "facet_list = data[\"facet_counts\"][\"facet_fields\"][\"dataprovider_id\"]  # [\"id1\", count1, \"id2\", count2, ...]\n",
    "pairs = list(zip(facet_list[0::2], facet_list[1::2]))\n",
    "\n",
    "df = pd.DataFrame(pairs, columns=[\"dataprovider_id\", \"count\"])\n",
    "df[\"dataprovider_id\"] = df[\"dataprovider_id\"].astype(str)\n",
    "df[\"count\"] = pd.to_numeric(df[\"count\"], errors=\"coerce\").fillna(0).astype(\"int64\")\n",
    "\n",
    "df = df[df[\"dataprovider_id\"].str.len() == 32].reset_index(drop=True)\n",
    "# df\n",
    "\n",
    "# Als nächstes werden Organisationsdaten (Einrichtungen und Aggregatoren) aus dem Organisation-Index geladen. \n",
    "# Felder, die mal als Liste und mal als Einzelwert kommen, werden vereinheitlicht. \n",
    "# Ergebnis ist `df_org` mit Metadaten wie `label`, `type`, `sector_fct`, `city_de_fct` und `state_de_fct`.\n",
    "# \n",
    "# API-Call: https://api.deutsche-digitale-bibliothek.de/2/search/index/organization/select?q=type:ddb-institution%20OR%20type:ddb-aggregator&fl=id,label,type,sector_fct,city_de_fct,state_de_fct&rows=10000&wt=json\n",
    "\n",
    "url_org = \"https://api.deutsche-digitale-bibliothek.de/2/search/index/organization/select\"\n",
    "params_org = {\n",
    "    \"q\": \"type:ddb-institution OR type:ddb-aggregator\",\n",
    "    \"fl\": \"id,label,type,sector_fct,city_de_fct,state_de_fct\",\n",
    "    \"rows\": 10000,\n",
    "    \"wt\": \"json\",\n",
    "}\n",
    "\n",
    "resp_org = requests.get(url_org, params=params_org, timeout=300)\n",
    "resp_org.raise_for_status()\n",
    "data_org = resp_org.json()\n",
    "\n",
    "docs = data_org.get(\"response\", {}).get(\"docs\", [])\n",
    "\n",
    "def _as_list(value):\n",
    "    if value is None:\n",
    "        return []\n",
    "    # Solr liefert bei *_fct i.d.R. Listen; zur Sicherheit wird auch ein Scalar zu einer Liste normalisiert\n",
    "    return value if isinstance(value, list) else [value]\n",
    "\n",
    "df_org = pd.DataFrame({\n",
    "    \"id\": [d.get(\"id\") for d in docs],\n",
    "    \"label\": [_as_list(d.get(\"label\")) for d in docs],\n",
    "    \"type\": [d.get(\"type\") for d in docs],\n",
    "    \"sector_fct\":  [d.get(\"sector_fct\") for d in docs],\n",
    "    \"city_de_fct\": [_as_list(d.get(\"city_de_fct\")) for d in docs],\n",
    "    \"state_de_fct\": [_as_list(d.get(\"state_de_fct\")) for d in docs],\n",
    "})\n",
    "# df_org\n",
    "\n",
    "# Anschließend werden die Provider-Statistik (`df`) und die Organisationsdaten (`df_org`) zusammengeführt.\n",
    "# Damit stehen pro Einrichtung/Provider sowohl Metadaten als auch die Objektanzahl (`count`) zur Verfügung.\n",
    "# Das Ergebnis kann als Excel-Datei mit aktuellem Datum gespeichert werden (`df_join`).\n",
    "\n",
    "# Join: Provider-Facets (df) + Organisationsdaten (df_org)\n",
    "df_org[\"id\"] = df_org[\"id\"].astype(str)\n",
    "\n",
    "df_join = df.merge(df_org, left_on=\"dataprovider_id\", right_on=\"id\", how=\"outer\")\n",
    "\n",
    "# Für Excel: Array-Spalten lesbar als Strings serialisieren (Listen -> '; '-String)\n",
    "for col in [\"label\", \"city_de_fct\", \"state_de_fct\"]:\n",
    "    if col in df_join.columns:\n",
    "        df_join[col] = df_join[col].apply(lambda xs: \"; \".join(map(str, xs)) if isinstance(xs, list) else (\"\" if xs is None else str(xs)))\n",
    "\n",
    "# Optional: doppelte Join-Spalte entfernen und Spaltenordnung setzen\n",
    "if \"dataprovider_id\" in df_join.columns:\n",
    "    df_join = df_join.drop(columns=[\"dataprovider_id\"])\n",
    "\n",
    "df_join = df_join[[\"id\", \"label\", \"type\", \"sector_fct\", \"city_de_fct\", \"state_de_fct\", \"count\"]]\n",
    "\n",
    "# Ergebnis in Excel-Datei speichern mit Datum\n",
    "# out_path = \"statistic-federal_state_\" + pd.Timestamp.now().strftime(\"%Y-%m-%d\") + \".xlsx\"\n",
    "# df_join.to_excel(out_path, index=False)\n",
    "\n",
    "# df_join\n",
    "\n",
    "# Im nächsten Schritt wird `df_join` nach Bundesland ausgewertet. Dafür wird das (ggf. mehrfach belegte)\n",
    "# Bundeslandfeld auf einen Wert reduziert und anschließend je Bundesland aggregiert:\n",
    "# - Anzahl eindeutiger Einrichtungen\n",
    "# - Anzahl liefernder Einrichtungen (`count > 0`)\n",
    "# - Summe der Objekte (`count`)\n",
    "# \n",
    "# Ergebnis ist der DataFrame `agg`.\n",
    "\n",
    "df_bl = df_join.loc[df_join[\"id\"].notna(), [\"id\", \"state_de_fct\", \"count\"]].copy()\n",
    "df_bl[\"count\"] = pd.to_numeric(df_bl[\"count\"], errors=\"coerce\").fillna(0).astype(\"int64\")\n",
    "\n",
    "# state_de_fct ist nach dem Join serialisiert (\"; \"-getrennt). Für die Zuordnung wird der erste Wert verwendet.\n",
    "df_bl[\"Bundesland\"] = (\n",
    "    df_bl[\"state_de_fct\"]\n",
    "    .fillna(\"\")\n",
    "    .astype(str)\n",
    "    .str.split(\"; \")\n",
    "    .str[0]\n",
    "    .str.strip()\n",
    "    .replace({\"\": \"Ausland\"})\n",
    ")\n",
    "\n",
    "einrichtungen = df_bl.groupby(\"Bundesland\")[\"id\"].nunique().rename(\"Einrichtungen\")\n",
    "liefernde = (\n",
    "    df_bl.loc[df_bl[\"count\"] > 0]\n",
    "    .groupby(\"Bundesland\")[\"id\"]\n",
    "    .nunique()\n",
    "    .rename(\"Liefernde Einrichtungen\")\n",
    ")\n",
    "objekte = df_bl.groupby(\"Bundesland\")[\"count\"].sum().rename(\"Objekte\")\n",
    "\n",
    "agg = (\n",
    "    pd.concat([einrichtungen, liefernde, objekte], axis=1)\n",
    "    .fillna(0)\n",
    "    .astype({\"Einrichtungen\": \"int64\", \"Liefernde Einrichtungen\": \"int64\", \"Objekte\": \"int64\"})\n",
    "    .sort_values([\"Objekte\", \"Liefernde Einrichtungen\", \"Einrichtungen\"], ascending=False)\n",
    ")\n",
    "\n",
    "# Stand: Datum/Uhrzeit der Notebook-Ausführung (lokale Zeitzone)\n",
    "stand = datetime.now().astimezone().strftime(\"%d.%m.%Y um %H:%M:%S Uhr\")\n",
    "display(Markdown(f\"**Letzte Aktualisierung:** {stand}\"))\n",
    "\n",
    "# Anzeige\n",
    "agg"
   ]
  },
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",
      "text/plain": [
       "<Figure size 1400x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.ticker import FuncFormatter\n",
    "\n",
    "# Zum Schluss werden die aggregierten Werte aus `agg` als horizontale Balkendiagramme dargestellt:\n",
    "# - Objekte je Bundesland\n",
    "# - Einrichtungen vs. liefernde Einrichtungen je Bundesland\n",
    "# Die Achsenbeschriftung wird so formatiert, dass große Zahlen ohne wissenschaftliche Notation gut lesbar bleiben.\n",
    "\n",
    "plot_df = agg.iloc[::-1]  # für barh: klein -> groß\n",
    "\n",
    "fig, axes = plt.subplots(1, 2, figsize=(14, max(6, 0.35 * len(plot_df))))\n",
    "\n",
    "def _format_int_no_sci(x, pos):\n",
    "    _ = pos  # required by FuncFormatter signature\n",
    "    return f\"{x:,.0f}\".replace(\",\", \".\")\n",
    "\n",
    "# Objekte\n",
    "plot_df[\"Objekte\"].plot(kind=\"barh\", ax=axes[0], color=\"#4C78A8\")\n",
    "axes[0].set_title(\"Objekte\")\n",
    "axes[0].set_xlabel(\"Anzahl\")\n",
    "axes[0].grid(axis=\"x\", linestyle=\":\", alpha=0.4)\n",
    "axes[0].xaxis.set_major_formatter(FuncFormatter(_format_int_no_sci))\n",
    "\n",
    "plot_df[[\"Einrichtungen\", \"Liefernde Einrichtungen\"]].plot(kind=\"barh\", ax=axes[1])\n",
    "axes[1].set_title(\"Einrichtungen\")\n",
    "axes[1].set_xlabel(\"Anzahl\")\n",
    "axes[1].grid(axis=\"x\", linestyle=\":\", alpha=0.4)\n",
    "axes[1].legend(loc=\"lower right\")\n",
    "\n",
    "fig.tight_layout()\n",
    "plt.show()"
   ]
  }
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