P_11_Pandas.ipynb

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  {
   "cell_type": "markdown",
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   "source": [
    "### Pandas ###\n",
    "Ein paar einfache Arbeiten mit Pandas.\n",
    "* Einlesen von Daten\n",
    "* Filtern von Daten\n",
    "* Einfache Berechnung\n",
    "* Darstellung als Graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importieren\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Sehr einfaches Einlesen einer CSV Datei\n",
    "data = pd.read_csv('time_series_covid19_confirmed_global.csv') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Das geht alternativ auch über eine URL\n",
    "url=\"https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv\"\n",
    "data=pd.read_csv(url)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Darstellen der Daten\n",
    "# Hinweis: NaN steht für 'Not A Number'\n",
    "print (data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Anzeigen der erten Zeilen\n",
    "print (data.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Anzeigen der letzten Zeilen\n",
    "print (data.tail)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Anzeige der Spaltenköpfe\n",
    "print(data.columns)\n",
    "# Anzeige der Zeilenindizes\n",
    "print(data.index)\n",
    "# Anzeige der Form\n",
    "print (data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Anzeigen einer Zeile nach der Zeilennummer\n",
    "print (data.loc[132])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Auswahl von Werten über Vergleichsoperatoren\n",
    "# Hinweis: data['Country/Region']=='Russia' erstellt einen dataframe mit boolschen Werten\n",
    "boolframe = data['Country/Region']=='Russia'\n",
    "print (boolframe.values)\n",
    "# den kann man prima zur Selektion im DataFrame benutzen\n",
    "russia = data.loc[boolframe]\n",
    "print (russia)\n",
    "# ein spezielles Datum aus dieser Auswahl\n",
    "print (russia['12/18/20'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Einfache Berechnungen**\n",
    "\n",
    "Zeilenweise Berechnung der 7 Tage Inzidenz, der Maximalwerte und des datums des Höchststandes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Neue Spalten berechnen\n",
    "data[\"7Tage\"]=data.iloc[:,-1]-data.iloc[:,-7]\n",
    "data[\"max\"] = data.iloc[:,4:].max(axis=1)\n",
    "data[\"maxDate\"] = data.iloc[:,4:].idxmax(axis=1)\n",
    "print(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Einfache Verwendung der Plotfunktionen** "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "russia.iloc[0,4:-3].plot()"
   ]
  }
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