01_topic-modeling_dataset-reorganize.ipynb

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   "source": [
    "# Einführung in Topic Modeling mit Python - Step 1: Datensatz organisieren\n",
    "\n",
    "## 1. Datenkorpus laden\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# read csv into dataframe object\n",
    "corpus = pd.read_csv(\"data/speeches-bundesregierung.csv\", encoding=\"UTF-8\") # update path\n",
    "print(f\"Der Datzensatz enthält {corpus.shape[0]} Reden mit {corpus.shape[1]} Attributen.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "corpus.head()"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# show columns and their datatypes\n",
    "corpus.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.1 Datensatz chronologisch sortieren"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# change dtype in data to pandas datetime-format\n",
    "corpus[\"date\"] = pd.to_datetime(corpus.date)\n",
    "\n",
    "# check datatypes\n",
    "corpus.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def order_by_year(dataframe, date):\n",
    "    \"\"\"Order dataframe object by year based \n",
    "    on column date.\"\"\"\n",
    "    df = dataframe.set_index(str(date))\n",
    "    df[\"year\"] = df.index.year\n",
    "    df = df.sort_index()\n",
    "    df = df.reset_index()\n",
    "    df = df.set_index(\"year\")\n",
    "    return df\n",
    "\n",
    "corpus = order_by_year(corpus, \"date\") # apply function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# view sorted dataframe\n",
    "corpus.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save sorted dataframe\n",
    "# corpus.to_csv(\"data/speeches-bundesregierung_sorted.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.2 Beitragszahlen pro Jahr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# group corpus by year\n",
    "speeches_per_year = corpus.groupby(\"year\")\n",
    "speeches_per_year.size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# plot speeches per year\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.figure(figsize=(10,7), dpi=360) # create new figure with dimensions=figsize and specific resolution\n",
    "\n",
    "speeches_per_year.size().plot.bar()\n",
    "\n",
    "plt.title(\"Anzahl der Reden pro Jahr\", fontsize=22, fontstyle=\"oblique\")\n",
    "plt.xlabel(\"Jahr\", fontsize=20)\n",
    "plt.ylabel(\"Anzahl\", fontsize=20)\n",
    "plt.xticks(fontsize=15, rotation=60)\n",
    "plt.yticks(fontsize=15)\n",
    "#plt.grid(True) # show grid\n",
    "plt.tight_layout() # for optimal fit\n",
    "#plt.savefig(\"PATH\") # save figure to disk\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Übung 1: Datensatz nach Personen gruppieren\n",
    "\n",
    "Gruppieren Sie den Datensatz so, dass Ihnen ausgegeben wird, wie viele Reden pro Redner:in im Datensatz enthalten sind."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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