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"# Explorative Datenanalyse des NYCFlights Datensatzes\n",
"\n",
"### Vorwissen:\n",
"Mit einem gewissen Vorwissen in Statistik (Basics aus MA3) haben Sie mehr von der Veranstaltung. Die wichtigsten Grundlagen finden Sie zum Beispiel in:\n",
"- Kopp-Schneider, Werft “Grundlagen der Statistik” in Medizinische Physik: https://link.springer.com/book/10.1007%2F978-3-662-54801-1\n",
"\n",
"### Literatur zum Nachschlagen während des Labors (Links auf moodle): \n",
"- Bruce et al. Praktische Statistik für Data Scientists, Kapitel 1 bis Seite 31\n",
"- Sauer, Moderne Datenanalyse mit R: Wir machen eine ähnliche Analyse mit Python wie in Kapitel 10 (ohne 10.2.3) mit R beschrieben\n",
"- Python-Dokumentationen, z.B. VanderPlas: Data Science mit Python\n",
"\n",
"### Sie werden hier:\n",
"- Datensatz einlesen/laden\n",
"- Daten als Tabelle anschauen: typische Datenstruktur für Data Science: pandas DataFrame\n",
"- Unterschiedliche Skalentypen der Daten erkennen\n",
"- Kategoriale Daten visualisieren (Welche Datentypen sind kategorial?)\n",
"- Metrische (kontinuierlich oder diskret mit vielen Werten) Daten visualisieren und nach Kategorien sortiert visualisieren\n",
"- Zusammenhänge zwischen metrischen Daten visualisieren und interpretieren\n",
"- den NYCFlights Datensatz erkunden\n",
"\n",
"### Lernziele:\n",
"- Sie haben eine erste Idee wie eine explorative Datenanalyse funktioniert und sich schon einmal in einen Datensatz vertieft.\n",
"- Sie erkennen unterschiedliche Datentypen und können Daten unterschiedlicher Datentypen geeignet visualisieren.\n",
"- Sie können Visualisierungen nach verschiedenen Kategorien in Python nutzen und wissen, wann welche Visualisierung sinnvoll ist.\n",
"\n",
"### Output:\n",
"- 1 Graph Ihrer Wahl (jede/r wird vermutlich etwas anderes finden, was sie/er interessant findet. Zu Beginn der nächsten Vorlesung werden wir alle Graphen anschauen und so unser Wissen über den Datensatz erweitern). Bitte bis nächsten Dienstag auf moodle hochladen. Bitte stellen Sie die Daten so dar, dass sie auf einer Folie erkennbar sind (Größe der Achsenbeschriftung, etc.)."
]
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"#Nötige Module importieren\n",
"\n",
"#Daten einlesen und Daten verarbeiten\n",
"import pandas as pd\n",
"\n",
"#Plotten\n",
"import matplotlib.pyplot as plt\n",
"\n",
"#zum (einfacheren) Erstellen guter statistischer Plots:\n",
"import seaborn as sns\n",
"\n",
"sns.set_theme() # alle Plots (auch von matplotlib) im seaborn-Style\n",
"\n",
"# Numerische und statistisches Werkzeug\n",
"import numpy as np\n",
"from scipy import stats"
]
},
{
"cell_type": "markdown",
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"source": [
"## Anleitung\n",
"\n",
"Im Folgenden sollen Sie versuchen mit Hilfe von Visualisierungen einen (ersten) Überblick über den NYCFlights Datensatz zu bekommen. Für einfache und gute statistische Datenvisualisierungen eignet sich das Paket `seaborn`. Es basiert auf `matplotlib`. Es vereinfacht sehr viele Vorgänge und liefert überaus nützliche und hochwertige Graphen: \n",
"https://seaborn.pydata.org/index.html\n",
"\n",
"Ändern von Darstellung und Beschriftung etc. basiert auf matplotlib. Die Dokumentation finden Sie unter: https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.html\n",
"\n",
"In der letzten Anleitung gab es hierzu schon die ersten Graphen. Nun wollen wir uns etwas genauer mit dem Datensatz beschäftigen. Wir fokussieren uns in diesem Labor darauf, Daten abhängig von verschiedenen Kategorien zu visualisieren. Verteilungen von kontinuierlichen Daten betrachten wir in späteren Laboren."
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"### 1. Analyse der Datentypen und Erstellung eines Codebooks\n",
"Wir nutzen den NYC Flights Datensatz gekürzt um die fehlenden Werte (siehe 0_ExplorativeDatenanalyse_Einführung). Bevor wir mit Visualisierungen starten, müssen wir die Art der Variablen kennen. Schauen Sie sich die Datentabelle an und notieren Sie kurz für jede Variable:\n",
"\n",
"- Bedeutung (können Sie mit dem Namen etwas anfangen? Schauen Sie ggf. in die Dokumentation!)\n",
"- Datentyp und Skalenniveau\n",
"- Möglicher Wertebereich, Einheit etc. \n",
"- evtl. weitere Anmerkungen (z.B. Anzahl fehlender Werte)"
]
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"text": [
"/home/david/DATENV/lib/python3.12/site-packages/nycflights13/__init__.py:2: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n",
" from pkg_resources import resource_filename as _rf\n"
]
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"<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>year</th>\n",
" <th>month</th>\n",
" <th>day</th>\n",
" <th>dep_time</th>\n",
" <th>sched_dep_time</th>\n",
" <th>dep_delay</th>\n",
" <th>arr_time</th>\n",
" <th>sched_arr_time</th>\n",
" <th>arr_delay</th>\n",
" <th>carrier</th>\n",
" <th>flight</th>\n",
" <th>tailnum</th>\n",
" <th>origin</th>\n",
" <th>dest</th>\n",
" <th>air_time</th>\n",
" <th>distance</th>\n",
" <th>hour</th>\n",
" <th>minute</th>\n",
" <th>time_hour</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2013</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>517.0</td>\n",
" <td>515</td>\n",
" <td>2.0</td>\n",
" <td>830.0</td>\n",
" <td>819</td>\n",
" <td>11.0</td>\n",
" <td>UA</td>\n",
" <td>1545</td>\n",
" <td>N14228</td>\n",
" <td>EWR</td>\n",
" <td>IAH</td>\n",
" <td>227.0</td>\n",
" <td>1400</td>\n",
" <td>5</td>\n",
" <td>15</td>\n",
" <td>2013-01-01T10:00:00Z</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2013</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>533.0</td>\n",
" <td>529</td>\n",
" <td>4.0</td>\n",
" <td>850.0</td>\n",
" <td>830</td>\n",
" <td>20.0</td>\n",
" <td>UA</td>\n",
" <td>1714</td>\n",
" <td>N24211</td>\n",
" <td>LGA</td>\n",
" <td>IAH</td>\n",
" <td>227.0</td>\n",
" <td>1416</td>\n",
" <td>5</td>\n",
" <td>29</td>\n",
" <td>2013-01-01T10:00:00Z</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2013</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>542.0</td>\n",
" <td>540</td>\n",
" <td>2.0</td>\n",
" <td>923.0</td>\n",
" <td>850</td>\n",
" <td>33.0</td>\n",
" <td>AA</td>\n",
" <td>1141</td>\n",
" <td>N619AA</td>\n",
" <td>JFK</td>\n",
" <td>MIA</td>\n",
" <td>160.0</td>\n",
" <td>1089</td>\n",
" <td>5</td>\n",
" <td>40</td>\n",
" <td>2013-01-01T10:00:00Z</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2013</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>544.0</td>\n",
" <td>545</td>\n",
" <td>-1.0</td>\n",
" <td>1004.0</td>\n",
" <td>1022</td>\n",
" <td>-18.0</td>\n",
" <td>B6</td>\n",
" <td>725</td>\n",
" <td>N804JB</td>\n",
" <td>JFK</td>\n",
" <td>BQN</td>\n",
" <td>183.0</td>\n",
" <td>1576</td>\n",
" <td>5</td>\n",
" <td>45</td>\n",
" <td>2013-01-01T10:00:00Z</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2013</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>554.0</td>\n",
" <td>600</td>\n",
" <td>-6.0</td>\n",
" <td>812.0</td>\n",
" <td>837</td>\n",
" <td>-25.0</td>\n",
" <td>DL</td>\n",
" <td>461</td>\n",
" <td>N668DN</td>\n",
" <td>LGA</td>\n",
" <td>ATL</td>\n",
" <td>116.0</td>\n",
" <td>762</td>\n",
" <td>6</td>\n",
" <td>0</td>\n",
" <td>2013-01-01T11:00:00Z</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
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"text/plain": [
" year month day dep_time sched_dep_time dep_delay arr_time \\\n",
"0 2013 1 1 517.0 515 2.0 830.0 \n",
"1 2013 1 1 533.0 529 4.0 850.0 \n",
"2 2013 1 1 542.0 540 2.0 923.0 \n",
"3 2013 1 1 544.0 545 -1.0 1004.0 \n",
"4 2013 1 1 554.0 600 -6.0 812.0 \n",
"\n",
" sched_arr_time arr_delay carrier flight tailnum origin dest air_time \\\n",
"0 819 11.0 UA 1545 N14228 EWR IAH 227.0 \n",
"1 830 20.0 UA 1714 N24211 LGA IAH 227.0 \n",
"2 850 33.0 AA 1141 N619AA JFK MIA 160.0 \n",
"3 1022 -18.0 B6 725 N804JB JFK BQN 183.0 \n",
"4 837 -25.0 DL 461 N668DN LGA ATL 116.0 \n",
"\n",
" distance hour minute time_hour \n",
"0 1400 5 15 2013-01-01T10:00:00Z \n",
"1 1416 5 29 2013-01-01T10:00:00Z \n",
"2 1089 5 40 2013-01-01T10:00:00Z \n",
"3 1576 5 45 2013-01-01T10:00:00Z \n",
"4 762 6 0 2013-01-01T11:00:00Z "
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from nycflights13 import flights\n",
"fls = flights.dropna()\n",
"fls.head()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "3d4b4541a7bd605b",
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"outputs": [
{
"ename": "SyntaxError",
"evalue": "unterminated string literal (detected at line 5) (3173771718.py, line 5)",
"output_type": "error",
"traceback": [
" \u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[3]\u001b[39m\u001b[32m, line 5\u001b[39m\n\u001b[31m \u001b[39m\u001b[31mdep_time = \"Abflugszeit, \"integer -> intervallskaliert\", \"Bereich +unendlich\", \"\"\u001b[39m\n ^\n\u001b[31mSyntaxError\u001b[39m\u001b[31m:\u001b[39m unterminated string literal (detected at line 5)\n"
]
}
],
"source": [
"#TODO: Erstellen eines einfachen, kurzen CodeBooks\n",
"year = \"Jahr des Ereignis\", \"integer -> intervallskaliert\", \"Bereich unendlich\", \"\"\n",
"month = \"Monat des Ereignis\", \"integer -> intervallskaliert\", \"Bereich 1-12\", \"\"\n",
"day = \"Tag des Ereignis\", \"integer -> intervallskaliert\", \"Bereich 1-31\", \"\"\n",
"dep_time = \"Abflugszeit, \"integer -> intervallskaliert\", \"Bereich +unendlich\", \"\"\n",
"sched_dep_time = \"Abflugszeit geplant , \"integer -> intervallskaliert\", \"Bereich +unendlich\", \"\"\n",
"dep_delay = \"Abflugszeit verspätung\", \"integer -> intervallskaliert\", \"Bereich unendlich\", \"\"\n",
"arr_time = \"Ankunftszeit\" \"integer -> intervallskaliert\", \"Bereich +unendlich\", \"\"\n",
"sched_arr_time = \"Ankunftszeit geplant\", \"integer -> intervallskaliert\", \"Bereich +unendlich\", \"\"\n",
"arr_delay = \"Ankunftszeit verspätung\", \"integer -> intervallskaliert\", \"Bereich unendlich\", \"\"\n",
"carrier = \"Airline\", \"string -> nominalskaliert\", \"Bereich nicht definiert\", \"\"\n",
"flight = \"Flugnummer\" \"int -> ordinalskaliert\", \"Bereich +unendlich\", \"\"\n",
"tailnum = \"Flugzeugregistrierungsnummer\", \"string -> nominalskaliert\", \"Bereich nicht definiert\", \"\"\n",
"origin = \" Start / Abflugort\", \"string -> nominalskaliert\", \"Bereich nicht definiert\", \"\"\n",
"dest = \"Ziel / Ankunftsort\", \"string -> nominalskaliert\", \"Bereich nicht definiert\", \"\"\n",
"air_time = \"Flugzeit\", \"integer -> intervallskaliert\", \"Bereich +unendlich\", \"\"\n",
"distance = \"Distanz\", \"integer -> intervallskaliert\", \"Bereich +unendlich\", \"\"\n",
"hour = \"Volle Stunden\", \"integer -> intervallskaliert\", \"Bereich +unendlich\", \"\"\n",
"minute = \"Volle Minuten\", \"integer -> intervallskaliert\", \"Bereich 0-59\", \"\"\n",
"time_hour = \"Aktuelle Uhrzeit\", \" string -> intervallskaliert\", \"Bereich nicht definiert\", \"\"\n"
]
},
{
"cell_type": "markdown",
"id": "30614e80a808033e",
"metadata": {},
"source": [
"### 2. Analyse der Kategorien\n",
"\n",
"Sie haben nun geschaut welche Variablen kategorisch vorliegen. Finden Sie heraus, wie viele verschiedene Kategorien es jeweils gibt. Starten Sie auch hier mit Visualisierungen durch geeignete Balkendiagramme (sns.countplots). Für Details können Sie dann immer noch rechnen. Einen ersten Plot sollten Sie schon mit der letzten Anleitung erstellt haben. Schauen Sie sich jetzt alle Kategorien an. Evtl. stellen Sie dabei fest, dass eine der Kategorien, die Sie als kategorial einsortiert hatten, eigentlich nicht kategorial ist, sondern z.B. numerisch vorliegt, aber nur wenige verschiedene Werte annehmen kann.\n",
"\n",
"Um auf ein Element einer Variable zuzugreifen: `'variablenname'[[index]]` (index beginnt bei 0) (z.B. `fls.year[[0]]`)"
]
},
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"text/plain": [
"<Figure size 6000x2000 with 3 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#TODO: Analyse der Kategorien\n",
"cols = fls.shape[1]\n",
"dNames = list(fls)\n",
"fig, axs = plt.subplots(1, 3, figsize=(60, 20))\n",
"sns.countplot(data=fls, x=\"carrier\", ax=axs[0]).set_title(\"carrier\")\n",
"sns.countplot(data=fls, x=\"dest\", ax=axs[1]).set_title(\"dest\")\n",
"sns.countplot(data=fls, x=\"origin\", ax=axs[2]).set_title(\"origin\")\n",
"\n",
"plt.show()\n",
"#sns.countplot(data=fls, x='origin', ax=axs[0]).set_title(\"Abfuhr\")\n",
"\n",
"#plt.show() # zeigt die Grafik an, ohne die Objektinformationen auszugeben"
]
},
{
"cell_type": "markdown",
"id": "c4a08071adcea7c4",
"metadata": {},
"source": [
"### 3. Einfluss der kategorialen Variablen auf die kontinuierlichen Variablen\n",
"\n",
"Sie haben nun einen Überblick über die kategorialen Variablen. Nun wollen wir explorieren, welche Kategorien Einfluss auf welche kontinuierlichen Variablen haben. Dazu nutzt man am besten Visualisierungen. Sie können mit den vorgeschlagenen Möglichkeiten aus der letzten Anleitung starten und auch andere testen:\n",
"\n",
"- Boxplot: grafische Übersicht der wichtigsten Quantile. Praktischerweise dann auch für verschiedene Kategorien einer Variable getrennt möglich\n",
"- Violinplot: kombiniert Dichteschätzer und Boxlot (Box nimmt Form der geschätzten Dichtefunktion an)\n",
"- Kategoriale Barplots verschiedener Metriken (Mittelwert, Median, etc.)\n",
"- diese und weitere Möglichkeiten z.B. auf: https://seaborn.pydata.org/tutorial/categorical.html\n",
"\n",
"Nehmen Sie sich genug Zeit den Datensatz zu erkunden und erstellen Sie dann einen Graph zu einem Aspekt, den Sie interessant finden.\n",
"Sie dürfen natürlich auch Zusammenhänge zwischen kontinuierlichen Variablen untersuchen. Die Visualisierung in Streudiagrammen ist Ihnen aber vermutlich deutlich bekannter als der Einsatz kategorialer Variablen in Visualisierungen, achten Sie daher darauf, dass in Ihrem Graphen auch kategoriale Variablen vorkommen."
]
},
{
"cell_type": "code",
"execution_count": 29,
"id": "2d87d3b5fc03b219",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 2000x800 with 5 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# TODO: Exploration des Datensatzes durch Visualisierungen.\n",
"fig, axs = plt.subplots(1, 4, figsize=(20, 8))\n",
"\n",
"\n",
"sns.barplot(data=fls, y=\"carrier\",x= \"dep_delay\",hue=\"origin\",mulitple ax=axs[0], errorbar=None)\n",
"sns.barplot(data=fls, hue=\"carrier\",x= \"arr_delay\", ax=axs[1], errorbar=None)\n",
"sns.boxplot(data=fls, x=\"origin\", y=\"distance\", ax=axs[2])\n",
"\n",
"\n",
"sns.histplot(data=fls, x= \"origin\", y=\"carrier\", cbar=True, ax=axs[3])\n",
"\n",
"\n",
"\n",
"plt.show()\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3f4df820-b04c-47a0-aa60-a3ad5bbcf681",
"metadata": {},
"outputs": [],
"source": []
}
],
"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": "3.12.3"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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