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alofro committed Mar 18, 2024
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2 changes: 1 addition & 1 deletion content/descriptive_statistics/_index.md
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Expand Up @@ -51,7 +51,7 @@ Für eine erste Demo bist Du verantwortlich:
- Berechne 2-3 Statistiken und erstelle 2-3 Visualisierungen basierend auf den Informationen im [Datensatz](/2023-2024-ZK_Data_Librarian_Modul_3/organisation/dataset/).
- Nutze `pandas` zur Berechnung der Statistiken und `seaborn` für die Visualisierungen.

Lade bis spätestens **24.03.23** Deinen Report in Form eines *Jupyter Notebooks* in der [Dateiablage](https://zbiw.th-koeln.de/moodle/mod/assign/view.php?id=12281&forceview=1) in Moodle hoch.
Lade bis spätestens **18.03.24** Deinen Report in Form eines *Jupyter Notebooks* in der [Dateiablage](https://zbiw.th-koeln.de/moodle/mod/assign/view.php?id=12281&forceview=1) in Moodle hoch.

{{% /customnotice %}}

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130 changes: 130 additions & 0 deletions content/solutions.files/2.8.solutions_na.ipynb
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Expand Up @@ -80,6 +80,136 @@
"df.isna().sum()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 keine Angabe\n",
"1 keine Angabe\n",
"2 45 to 54 years\n",
"3 keine Angabe\n",
"4 keine Angabe\n",
" ... \n",
"436285 35 to 44 years\n",
"436286 10 to 19 years\n",
"436287 60 to 64 years\n",
"436288 35 to 44 years\n",
"436289 75 years and over\n",
"Name: Age Range, Length: 436290, dtype: object"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df['Age Range'].fillna(\"keine Angabe\")"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Patron Type Code 0\n",
"Patron Type Definition 0\n",
"Total Checkouts 0\n",
"Total Renewals 0\n",
"Age Range 912\n",
"Home Library Code 2\n",
"Home Library Definition 0\n",
"Circulation Active Month 39513\n",
"Circulation Active Year 39513\n",
"Notice Preference Code 42989\n",
"Notice Preference Definition 0\n",
"Provided Email Address 0\n",
"Year Patron Registered 0\n",
"Within San Francisco County 1207\n",
"dtype: int64"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.isna().sum()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"2 45 to 54 years\n",
"5 45 to 54 years\n",
"6 55 to 59 years\n",
"7 45 to 54 years\n",
"9 45 to 54 years\n",
" ... \n",
"436285 35 to 44 years\n",
"436286 10 to 19 years\n",
"436287 60 to 64 years\n",
"436288 35 to 44 years\n",
"436289 75 years and over\n",
"Name: Age Range, Length: 435378, dtype: object"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df['Age Range'].dropna()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Patron Type Code 0\n",
"Patron Type Definition 0\n",
"Total Checkouts 0\n",
"Total Renewals 0\n",
"Age Range 912\n",
"Home Library Code 2\n",
"Home Library Definition 0\n",
"Circulation Active Month 39513\n",
"Circulation Active Year 39513\n",
"Notice Preference Code 42989\n",
"Notice Preference Definition 0\n",
"Provided Email Address 0\n",
"Year Patron Registered 0\n",
"Within San Francisco County 1207\n",
"dtype: int64"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.isna().sum()"
]
},
{
"cell_type": "code",
"execution_count": 4,
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