{ "cells": [ { "cell_type": "markdown", "id": "9d69fa9a", "metadata": {}, "source": [ "# Deskriptive Statistik\n", "\n", "pandas-Objekte sind mit einer Reihe von gängigen mathematischen und statistischen Methoden ausgestattet. Die meisten von ihnen fallen in die Kategorie der Reduktionen oder zusammenfassenden Statistiken, Methoden, die einen einzelnen Wert (wie die Summe oder den Mittelwert) aus einer Serie oder einer Reihe von Werten aus den Zeilen oder Spalten eines DataFrame extrahieren. Im Vergleich zu ähnlichen Methoden, die sich bei NumPy-Arrays finden, behandeln sie auch fehlende Daten." ] }, { "cell_type": "code", "execution_count": 1, "id": "0185567d", "metadata": { "execution": { "iopub.execute_input": "2026-05-21T14:14:52.066756Z", "iopub.status.busy": "2026-05-21T14:14:52.066607Z", "iopub.status.idle": "2026-05-21T14:14:52.299481Z", "shell.execute_reply": "2026-05-21T14:14:52.299117Z", "shell.execute_reply.started": "2026-05-21T14:14:52.066739Z" } }, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " 0 1 2\n", "2022-02-03 0.139876 -0.730735 -0.467788\n", "2022-02-04 0.561951 1.125547 -0.801495\n", "2022-02-05 0.255746 -0.735519 -0.659993\n", "2022-02-06 0.227345 -1.646032 -0.282293\n", "2022-02-07 1.393767 0.118605 -0.535032\n", "2022-02-08 0.828147 0.473280 0.175779\n", "2022-02-09 NaN NaN NaN" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import numpy as np\n", "import pandas as pd\n", "\n", "\n", "rng = np.random.default_rng()\n", "df = pd.DataFrame(\n", " rng.normal(size=(7, 3)),\n", " index=pd.date_range(\"2022-02-02\", periods=7),\n", ")\n", "new_index = pd.date_range(\"2022-02-03\", periods=7)\n", "df2 = df.reindex(new_index)\n", "\n", "df2" ] }, { "cell_type": "markdown", "id": "e27b6767", "metadata": {}, "source": [ "Der Aufruf der `pandas.DataFrame.sum`-Methode gibt eine Serie zurück, die Spaltensummen enthält:" ] }, { "cell_type": "code", "execution_count": 2, "id": "af24c329", "metadata": { "execution": { "iopub.execute_input": "2026-05-21T14:14:52.300049Z", "iopub.status.busy": "2026-05-21T14:14:52.299932Z", "iopub.status.idle": "2026-05-21T14:14:52.302768Z", "shell.execute_reply": "2026-05-21T14:14:52.302440Z", "shell.execute_reply.started": "2026-05-21T14:14:52.300041Z" } }, "outputs": [ { "data": { "text/plain": [ "0 3.406833\n", "1 -1.394854\n", "2 -2.570822\n", "dtype: float64" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df2.sum()" ] }, { "cell_type": "markdown", "id": "61ac9a11", "metadata": {}, "source": [ "Die Übergabe von `axis='columns'` oder `axis=1` summiert stattdessen über die Spalten:" ] }, { "cell_type": "code", "execution_count": 3, "id": "841d02c8", "metadata": { "execution": { "iopub.execute_input": "2026-05-21T14:14:52.303268Z", "iopub.status.busy": "2026-05-21T14:14:52.303173Z", "iopub.status.idle": "2026-05-21T14:14:52.305954Z", "shell.execute_reply": "2026-05-21T14:14:52.305667Z", "shell.execute_reply.started": "2026-05-21T14:14:52.303260Z" } }, "outputs": [ { "data": { "text/plain": [ "2022-02-03 -1.058647\n", "2022-02-04 0.886003\n", "2022-02-05 -1.139765\n", "2022-02-06 -1.700981\n", "2022-02-07 0.977340\n", "2022-02-08 1.477206\n", "2022-02-09 0.000000\n", "Freq: D, dtype: float64" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df2.sum(axis=\"columns\")" ] }, { "cell_type": "markdown", "id": "67017fab", "metadata": {}, "source": [ "Wenn eine ganze Zeile oder Spalte alle NA-Werte enthält, ist die Summe `0`. Dies kann mit der Option `skipna` deaktiviert werden:" ] }, { "cell_type": "code", "execution_count": 4, "id": "1848d330", "metadata": { "execution": { "iopub.execute_input": "2026-05-21T14:14:52.306554Z", "iopub.status.busy": "2026-05-21T14:14:52.306473Z", "iopub.status.idle": "2026-05-21T14:14:52.309582Z", "shell.execute_reply": "2026-05-21T14:14:52.309335Z", "shell.execute_reply.started": "2026-05-21T14:14:52.306546Z" } }, "outputs": [ { "data": { "text/plain": [ "2022-02-03 -1.058647\n", "2022-02-04 0.886003\n", "2022-02-05 -1.139765\n", "2022-02-06 -1.700981\n", "2022-02-07 0.977340\n", "2022-02-08 1.477206\n", "2022-02-09 NaN\n", "Freq: D, dtype: float64" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df2.sum(axis=\"columns\", skipna=False)" ] }, { "cell_type": "markdown", "id": "d2108f1b", "metadata": {}, "source": [ "Einige Aggregationen, wie z.B. `mean`, erfordern mindestens einen Nicht-`NaN`-Wert, um ein wertvolles Ergebnis zu erhalten:" ] }, { "cell_type": "code", "execution_count": 5, "id": "ca53df06", "metadata": { "execution": { "iopub.execute_input": "2026-05-21T14:14:52.310032Z", "iopub.status.busy": "2026-05-21T14:14:52.309962Z", "iopub.status.idle": "2026-05-21T14:14:52.312817Z", "shell.execute_reply": "2026-05-21T14:14:52.312562Z", "shell.execute_reply.started": "2026-05-21T14:14:52.310025Z" } }, "outputs": [ { "data": { "text/plain": [ "2022-02-03 -0.352882\n", "2022-02-04 0.295334\n", "2022-02-05 -0.379922\n", "2022-02-06 -0.566994\n", "2022-02-07 0.325780\n", "2022-02-08 0.492402\n", "2022-02-09 NaN\n", "Freq: D, dtype: float64" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df2.mean(axis=\"columns\")" ] }, { "cell_type": "markdown", "id": "b353433d", "metadata": {}, "source": [ "## Optionen für Reduktionsmethoden\n", "\n", "Methode | Beschreibung\n", ":------ | :-----------\n", "`axis` | die Achse der zu reduzierenden Werte: `0` für die Zeilen des DataFrame und `1` für die Spalten\n", "`skipna` | fehlende Werte ausschließen; standardmäßig `True`\n", "`level` | nach Ebene gruppiert reduzieren, wenn die Achse hierarchisch indiziert ist (MultiIndex)\n", "\n", "Einige Methoden, wie `idxmin` und `idxmax`, liefern indirekte Statistiken wie den Indexwert, bei dem der Mindest- oder Höchstwert erreicht wird:" ] }, { "cell_type": "code", "execution_count": 6, "id": "2709e81e", "metadata": { "execution": { "iopub.execute_input": "2026-05-21T14:14:52.314248Z", "iopub.status.busy": "2026-05-21T14:14:52.314089Z", "iopub.status.idle": "2026-05-21T14:14:52.316907Z", "shell.execute_reply": "2026-05-21T14:14:52.316606Z", "shell.execute_reply.started": "2026-05-21T14:14:52.314240Z" } }, "outputs": [ { "data": { "text/plain": [ "0 2022-02-07\n", "1 2022-02-04\n", "2 2022-02-08\n", "dtype: datetime64[ns]" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df2.idxmax()" ] }, { "cell_type": "markdown", "id": "55b12d7a", "metadata": {}, "source": [ "Andere Methoden sind Akkumulationen:" ] }, { "cell_type": "code", "execution_count": 7, "id": "cff100f1", "metadata": { "execution": { "iopub.execute_input": "2026-05-21T14:14:52.317239Z", "iopub.status.busy": "2026-05-21T14:14:52.317142Z", "iopub.status.idle": "2026-05-21T14:14:52.320221Z", "shell.execute_reply": "2026-05-21T14:14:52.320030Z", "shell.execute_reply.started": "2026-05-21T14:14:52.317226Z" } }, "outputs": [ { "data": { "text/html": [ "
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count6.0000006.0000006.000000
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" ], "text/plain": [ " 0 1 2\n", "count 6.000000 6.000000 6.000000\n", "mean 0.567805 -0.232476 -0.428470\n", "std 0.478841 0.996497 0.344133\n", "min 0.139876 -1.646032 -0.801495\n", "25% 0.234445 -0.734323 -0.628752\n", "50% 0.408849 -0.306065 -0.501410\n", "75% 0.761598 0.384611 -0.328667\n", "max 1.393767 1.125547 0.175779" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df2.describe()" ] }, { "cell_type": "markdown", "id": "f041660e", "metadata": {}, "source": [ "Bei nicht-numerischen Daten erzeugt `describe` alternative zusammenfassende Statistiken:" ] }, { "cell_type": "code", "execution_count": 9, "id": "6347c7dd", "metadata": { "execution": { "iopub.execute_input": "2026-05-21T14:14:52.326978Z", "iopub.status.busy": "2026-05-21T14:14:52.326830Z", "iopub.status.idle": "2026-05-21T14:14:52.331282Z", "shell.execute_reply": "2026-05-21T14:14:52.331079Z", "shell.execute_reply.started": "2026-05-21T14:14:52.326970Z" } }, "outputs": [ { "data": { "text/html": [ "
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CodeOctal
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" ], "text/plain": [ " Code Octal\n", "count 6 6\n", "unique 6 5\n", "top U+0000 004\n", "freq 1 2" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data = {\n", " \"Code\": [\"U+0000\", \"U+0001\", \"U+0002\", \"U+0003\", \"U+0004\", \"U+0005\"],\n", " \"Octal\": [\"001\", \"002\", \"003\", \"004\", \"004\", \"005\"],\n", "}\n", "df3 = pd.DataFrame(data)\n", "\n", "df3.describe()" ] }, { "cell_type": "markdown", "id": "26533260", "metadata": {}, "source": [ "Deskriptive und zusammenfassende Statistiken:\n", "\n", "Methode | Beschreibung\n", ":------ | :-----------\n", "`count` | Anzahl der Nicht-NA-Werte\n", "`describe` | Berechnung einer Reihe von zusammenfassenden Statistiken für Serien oder jede DataFrame-Spalte\n", "`min`, `max` | Berechnung der Mindest- und Höchstwerte\n", "`argmin`, `argmax` | Berechnung der Indexstellen (ganze Zahlen), an denen der Mindest- bzw. Höchstwert erreicht wurde\n", "`idxmin`, `idxmax` | Berechnung der Indexbeschriftungen, an denen der Mindest- bzw. Höchstwert erreicht wurde\n", "`quantile` | Berechnung des Stichprobenquantils im Bereich von 0 bis 1\n", "`sum` | Summe der Werte\n", "`mean` | Arithmetisches Mittel der Werte\n", "`median` | Arithmetischer Median (50%-Quantil) der Werte\n", "`mad` | Mittlere absolute Abweichung vom Mittelwert\n", "`prod` | Produkt aller Werte\n", "`var` | Stichprobenvarianz der Werte\n", "`std` | Stichprobenstandardabweichung der Werte\n", "`skew` | Stichprobenschiefe (drittes Moment) der Werte\n", "`kurt` | Stichprobenwölbung (viertes Moment) der Werte\n", "`cumsum` | Kumulierte Summe der Werte\n", "`cummin`, `cummax` | Kumuliertes Minimum bzw. Maximum der Werte\n", "`cumprod` | Kumuliertes Produkt der Werte\n", "`diff` | Berechnung der ersten arithmetischen Differenz (nützlich für Zeitreihen)\n", "`pct_change` | Berechnung der prozentualen Veränderungen" ] }, { "cell_type": "markdown", "id": "6114cc66", "metadata": {}, "source": [ "## `ydata_profiling`\n", "\n", "[ydata-profiling](https://docs.profiling.ydata.ai/latest/) erzeugt Profilberichte aus einem pandas DataFrame. Die Funktion `pandas df.describe()` ist praktisch, aber ein wenig einfach für die explorative Datenanalyse. ydata-profiling erweitert pandas DataFrame mit `df.profile_report()`, die automatisch einen standardisierten Bericht zum Verständnis der Daten erzeugt." ] }, { "cell_type": "markdown", "id": "180e34de", "metadata": {}, "source": [ "### Installation\n", "\n", "```bash\n", "$ uv add standard-imghdr legacy-cgi \"ydata-profiling[notebook, unicode]\"\n", "Resolved 251 packages in 2.53s\n", "Prepared 1 package in 106ms\n", "Installed 24 packages in 155ms\n", " + annotated-types==0.7.0\n", " + dacite==1.9.2\n", " + htmlmin==0.1.12\n", " + imagehash==4.3.1\n", " + legacy-cgi==2.6.3\n", " + llvmlite==0.44.0\n", " + multimethod==1.12\n", " + networkx==3.5\n", " + numba==0.61.0\n", " + patsy==1.0.1\n", " + phik==0.12.4\n", " + puremagic==1.29\n", " + pydantic==2.11.7\n", " + pydantic-core==2.33.2\n", " + pywavelets==1.8.0\n", " + seaborn==0.13.2\n", " + standard-imghdr==3.13.0\n", " + statsmodels==0.14.4\n", " + tangled-up-in-unicode==0.2.0\n", " + typeguard==4.4.2\n", " + typing-inspection==0.4.1\n", " + visions==0.8.1\n", " + wordcloud==1.9.4\n", " + ydata-profiling==4.16.1\n", "$ uv run jupyter notebook\n", "```\n", "\n", "In Python 3.13 wurden die Module `imghdr` und `cgi` entfernt, siehe auch [PEP 594](https://peps.python.org/pep-0594/). Als Abhilfe für diese Legacy-Produkte wurden jedoch [standard-imghdr](https://pypi.org/project/standard-imghdr/) und [legacy-cgi](https://pypi.org/project/legacy-cgi/) im Python Package Index bereitgestellt." ] }, { "cell_type": "markdown", "id": "bc8f6229", "metadata": {}, "source": [ "### Beispiel" ] }, { "cell_type": "code", "execution_count": 10, "id": "502c406e", "metadata": { "execution": { "iopub.execute_input": "2026-05-21T14:14:52.331929Z", "iopub.status.busy": "2026-05-21T14:14:52.331795Z", "iopub.status.idle": "2026-05-21T14:14:55.253888Z", "shell.execute_reply": "2026-05-21T14:14:55.253508Z", "shell.execute_reply.started": "2026-05-21T14:14:52.331921Z" }, "scrolled": true }, "outputs": [ { "data": { "text/html": [ "\n", "
\n", " Upgrade to ydata-sdk\n", "

\n", " Improve your data and profiling with ydata-sdk, featuring data quality scoring, redundancy detection, outlier identification, text validation, and synthetic data generation.\n", "

\n", "
\n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "7b8bb0f9130b4d6b971869052f74eca1", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Summarize dataset: 0%| | 0/5 [00:00" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from ydata_profiling import ProfileReport\n", "\n", "\n", "profile = ProfileReport(df2, title=\"Pandas Profiling Report\")\n", "\n", "profile.to_notebook_iframe()" ] }, { "cell_type": "markdown", "id": "dd493572", "metadata": {}, "source": [ "### Konfiguration für große Datensätze\n", "\n", "Standardmäßig fasst ydata-profiling den Datensatz so zusammen, dass er die meisten Erkenntnisse für die Datenanalyse liefert. Wenn die Berechnungszeit der Profilerstellung zu einem Engpass wird, bietet ydata-profiling mehrere Alternativen, um diesen zu überwinden. Für die folgenden Beispiele lesen wir zunächst einen größeren Datensatz in pandas ein:" ] }, { "cell_type": "code", "execution_count": 11, "id": "7a17d0a1", "metadata": { "execution": { "iopub.execute_input": "2026-05-21T14:14:55.254688Z", "iopub.status.busy": "2026-05-21T14:14:55.254465Z", "iopub.status.idle": "2026-05-21T14:14:55.651214Z", "shell.execute_reply": "2026-05-21T14:14:55.650190Z", "shell.execute_reply.started": "2026-05-21T14:14:55.254677Z" } }, "outputs": [], "source": [ "titanic = pd.read_csv(\n", " \"https://raw.githubusercontent.com/datasciencedojo/datasets/master/titanic.csv\"\n", ")" ] }, { "cell_type": "markdown", "id": "c4f81b0a", "metadata": {}, "source": [ "#### 1. Minimaler Modus\n", "\n", "ydata-profiling enthält eine minimale Konfigurationsdatei [config_minimal.yaml](https://github.com/ydataai/ydata-profiling/blob/master/src/ydata_profiling/config_minimal.yaml), in der die teuersten Berechnungen standardmäßig ausgeschaltet sind. Dies ist die empfohlene Ausgangsbasis für größere Datensätze." ] }, { "cell_type": "code", "execution_count": 12, "id": "935e789f", "metadata": { "execution": { "iopub.execute_input": "2026-05-21T14:14:55.653144Z", "iopub.status.busy": "2026-05-21T14:14:55.652728Z", "iopub.status.idle": "2026-05-21T14:14:58.171935Z", "shell.execute_reply": "2026-05-21T14:14:58.171535Z", "shell.execute_reply.started": "2026-05-21T14:14:55.653106Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "367b653cad454e18874077fe542581c7", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Summarize dataset: 0%| | 0/5 [00:00" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "profile = ProfileReport(\n", " titanic, title=\"Minimal Pandas Profiling Report\", minimal=True\n", ")\n", "\n", "profile.to_notebook_iframe()" ] }, { "cell_type": "markdown", "id": "8486784c", "metadata": {}, "source": [ "Weitere Details zu Einstellungen und Konfiguration findet ihr unter [Available settings](https://docs.profiling.ydata.ai/latest/advanced_settings/available_settings/)." ] }, { "cell_type": "markdown", "id": "be3c36b0", "metadata": {}, "source": [ "#### 2. Stichprobe\n", "\n", "Eine alternative Möglichkeit bei sehr großen Datensätzen besteht darin, nur einen Teil davon für die Erstellung des Profiling-Berichts zu verwenden:" ] }, { "cell_type": "code", "execution_count": 13, "id": "8398e57f", "metadata": { "execution": { "iopub.execute_input": "2026-05-21T14:14:58.172684Z", "iopub.status.busy": "2026-05-21T14:14:58.172605Z", "iopub.status.idle": "2026-05-21T14:15:00.748317Z", "shell.execute_reply": "2026-05-21T14:15:00.747935Z", "shell.execute_reply.started": "2026-05-21T14:14:58.172673Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "2497cd6c5b5744d6a8308ee1f91c5b26", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Summarize dataset: 0%| | 0/5 [00:00" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sample = titanic.sample(frac=0.05)\n", "profile = ProfileReport(sample, title=\"Sample Pandas Profiling Report\")\n", "\n", "profile.to_notebook_iframe()" ] }, { "cell_type": "markdown", "id": "7167085a", "metadata": {}, "source": [ "#### 3. Teure Berechnungen deaktivieren\n", "\n", "Um den Rechenaufwand in großen Datensätzen zu verringern, aber dennoch einige interessante Informationen zu erhalten, können einige Berechnungen nur für bestimmte Spalten gefiltert werden:" ] }, { "cell_type": "code", "execution_count": 14, "id": "f4eb6960", "metadata": { "execution": { "iopub.execute_input": "2026-05-21T14:15:00.748960Z", "iopub.status.busy": "2026-05-21T14:15:00.748854Z", "iopub.status.idle": "2026-05-21T14:15:03.253754Z", "shell.execute_reply": "2026-05-21T14:15:03.253349Z", "shell.execute_reply.started": "2026-05-21T14:15:00.748951Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "6abb77a2016145a3bdd96edd297190a4", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Summarize dataset: 0%| | 0/5 [00:00" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "profile = ProfileReport()\n", "profile.config.interactions.targets = [\"Sex\", \"Age\"]\n", "profile.df = titanic\n", "\n", "profile.to_notebook_iframe()" ] }, { "cell_type": "markdown", "id": "18b20626", "metadata": {}, "source": [ "Die Einstellung `interactions.targets`, kann sowohl über Konfigurationsdateien wie auch über Umgebungsvariablen geändert werden; Einzelheiten hierzu findet ihr unter [Changing settings](https://docs.profiling.ydata.ai/latest/advanced_settings/changing_settings/)." ] }, { "cell_type": "markdown", "id": "d6f6656b", "metadata": {}, "source": [ "#### 4. Nebenläufigkeit\n", "\n", "Aktuell wird an einem skalierbaren Spark-Backend für ydata-profiling gearbeitet, siehe [Spark Profiling Development](https://github.com/orgs/ydataai/projects/16/views/2)." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3.13 Kernel", "language": "python", "name": "python313" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.13.0" }, "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { "00331b1a978f4e268979ad37281cfa43": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", "state": { "description_width": "", "font_size": null, "text_color": null } }, "02e0b3aa32e34f49bd4133ccfebc8784": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": 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