{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Musterlösung zu Projektaufgabe Deskriptive Statistik und offene Fragen\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Grundlage: Datensatz der San Francisco Public Library, s.a. https://zbmed.github.io/2023-2024-ZK_Data_Librarian_Modul_3/organisation/dataset/"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### Frage 1: Wie viele Senioren und Kinder sind Kunden der San Francisco Public Library?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### Frage 2: Wie viele Nutzer möchten per Mail informiert werden?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### Frage 3: Wie alt sind diese Nutzer durchschnittlich im Vergleich zu Nutzern, die per Post informiert werden möchten?"
]
},
{
"cell_type": "markdown",
"metadata": {
"jp-MarkdownHeadingCollapsed": true,
"tags": []
},
"source": [
"##### Frage 4: Wie viele Ausleihen werden im Mittel pro Altersgruppe und pro Jahr getätigt? Ist die Streuung zwischen den Gruppen gleich?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### Frage 5: Welche Altersgruppe verlängert im Mittel wie oft?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### Frage 6: Wie ist die Verteilung der Altersgruppen im Mission District"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### Frage 7: Erklärung von Normalisierung von Kreuztabellen (Kapitel 3 im Skript)"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"import numpy as np\n",
"%matplotlib inline\n",
"sns.set_theme()\n",
"# das was wir brauchen in abgekürzter Form"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"df = pd.read_csv(\n",
" \"../data/Library_Usage.csv\",\n",
" na_values=\"none\",\n",
" low_memory=False\n",
")\n",
"# Einlesen des Datensatzes in das neu definierte DataFrame df mit Überschreibung \n",
"#fehlender Werte"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"
\n",
"\n",
"
\n",
" \n",
"
\n",
"
\n",
"
Patron Type Code
\n",
"
Patron Type Definition
\n",
"
Total Checkouts
\n",
"
Total Renewals
\n",
"
Age Range
\n",
"
Home Library Code
\n",
"
Home Library Definition
\n",
"
Circulation Active Month
\n",
"
Circulation Active Year
\n",
"
Notice Preference Code
\n",
"
Notice Preference Definition
\n",
"
Provided Email Address
\n",
"
Year Patron Registered
\n",
"
Within San Francisco County
\n",
"
\n",
" \n",
" \n",
"
\n",
"
0
\n",
"
5
\n",
"
Staff
\n",
"
53
\n",
"
15
\n",
"
NaN
\n",
"
b2
\n",
"
Bayview
\n",
"
Mar
\n",
"
2023.0
\n",
"
z
\n",
"
Email
\n",
"
True
\n",
"
2003
\n",
"
False
\n",
"
\n",
"
\n",
"
1
\n",
"
5
\n",
"
Staff
\n",
"
480
\n",
"
378
\n",
"
NaN
\n",
"
e9
\n",
"
Excelsior
\n",
"
Jun
\n",
"
2023.0
\n",
"
z
\n",
"
Email
\n",
"
True
\n",
"
2003
\n",
"
False
\n",
"
\n",
"
\n",
"
2
\n",
"
5
\n",
"
Staff
\n",
"
70
\n",
"
33
\n",
"
45 to 54 years
\n",
"
n4
\n",
"
Noe Valley
\n",
"
Jan
\n",
"
2023.0
\n",
"
z
\n",
"
Email
\n",
"
True
\n",
"
2011
\n",
"
False
\n",
"
\n",
"
\n",
"
3
\n",
"
5
\n",
"
Staff
\n",
"
3934
\n",
"
2140
\n",
"
NaN
\n",
"
o2
\n",
"
Ocean View
\n",
"
Jul
\n",
"
2023.0
\n",
"
z
\n",
"
Email
\n",
"
True
\n",
"
2003
\n",
"
False
\n",
"
\n",
"
\n",
"
4
\n",
"
5
\n",
"
Staff
\n",
"
1118
\n",
"
1035
\n",
"
NaN
\n",
"
o7
\n",
"
Ortega
\n",
"
Jul
\n",
"
2023.0
\n",
"
z
\n",
"
Email
\n",
"
True
\n",
"
2003
\n",
"
False
\n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" Patron Type Code Patron Type Definition Total Checkouts Total Renewals \\\n",
"0 5 Staff 53 15 \n",
"1 5 Staff 480 378 \n",
"2 5 Staff 70 33 \n",
"3 5 Staff 3934 2140 \n",
"4 5 Staff 1118 1035 \n",
"\n",
" Age Range Home Library Code Home Library Definition \\\n",
"0 NaN b2 Bayview \n",
"1 NaN e9 Excelsior \n",
"2 45 to 54 years n4 Noe Valley \n",
"3 NaN o2 Ocean View \n",
"4 NaN o7 Ortega \n",
"\n",
" Circulation Active Month Circulation Active Year Notice Preference Code \\\n",
"0 Mar 2023.0 z \n",
"1 Jun 2023.0 z \n",
"2 Jan 2023.0 z \n",
"3 Jul 2023.0 z \n",
"4 Jul 2023.0 z \n",
"\n",
" Notice Preference Definition Provided Email Address \\\n",
"0 Email True \n",
"1 Email True \n",
"2 Email True \n",
"3 Email True \n",
"4 Email True \n",
"\n",
" Year Patron Registered Within San Francisco County \n",
"0 2003 False \n",
"1 2003 False \n",
"2 2011 False \n",
"3 2003 False \n",
"4 2003 False "
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#df\n",
"df.head()\n",
"#Überblick über das DataFrame"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Frage 1: Wie viele Senioren und Kinder sind Kunden der San Francisco Public Library?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Um diese Frage zu beantworten, kommen die Spalten \"Patron Type Definition\" oder \"Age Range\" in Frage, also schauen wir uns die Einträge (Merkmalsausprägungen) an: "
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Adult 274682\n",
"Juvenile 53281\n",
"Senior 49332\n",
"Teen 40561\n",
"Welcome 9966\n",
"Digital Access Card 3714\n",
"Teacher Card 3234\n",
"Staff 806\n",
"Retired Staff 215\n",
"Visitor 148\n",
"Library By Mail 120\n",
"At User Adult 118\n",
"At User Senior 78\n",
"At User Welcome 13\n",
"At User Teen 8\n",
"Business 7\n",
"At User Juvenile 7\n",
"Name: Patron Type Definition, dtype: int64"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df['Patron Type Definition'].value_counts()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Man sieht, dass es sich um nominale Werte handelt."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"25 to 34 years 92669\n",
"35 to 44 years 81719\n",
"10 to 19 years 66857\n",
"45 to 54 years 48319\n",
"0 to 9 years 32692\n",
"65 to 74 years 31714\n",
"20 to 24 years 25381\n",
"75 years and over 19297\n",
"55 to 59 years 19076\n",
"60 to 64 years 17654\n",
"Name: Age Range, dtype: int64"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df['Age Range'].value_counts()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Man sieht, dass es sich um ordinale Werte handelt."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In der Beschreibung des Datensatzes steht, dass sich die Spalte 'Age Range' nach dem Geburtsdatum richtet. D.h. es ist davon auszugehen, dass die Nutzer der Bibliothek zum Zeitpunkt der Bereitstellung des Datensatzes (2023) in die Kategorien von 'Age Range' einsortiert werden. In der Spalte 'Patron Type Definition' sind verschiedene Merkmalsausprägungen zu finden, die nicht unbedingt etwas mit dem Alter zu tun haben (z.B. VISITOR oder SPECIAL). Daher ist davon auszugehen, dass wir mit 'Age Range' am nähesten Fragestellungen zu Alter beantworten zu können. Richtige Angaben zu tatsächlichem Alter der Nutzer liegen bei diesem Datensatz nicht vor."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"An dieser Stelle ist also zu definieren, was Senioren und Kinder sind. Eine Möglichkeit ist, Senioren als Age Range = 65 to 74 years und Age Range = 75 years and over zu definieren, man könnte aber auch Age Range = 60 to 64 years dazunehmen. Für das weitere Vorgehen, definieren wir Senioren als über 65-jährige und Kinder als bis 19-jährige. Um die Frage 1 zu beantworten, reicht es also, die entsprechenden Einträge zu summieren."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"kinder=df.loc[df['Age Range'] == \"0 to 9 years\"] \n",
"kinder=df.loc[\n",
" (df['Age Range'] == \"0 to 9 years\") | \n",
" (df['Age Range'] == \"10 to 19 years\")\n",
"] \n",
"#Hilfsvariable"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"99549"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(kinder) "
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"senioren=df.loc[\n",
" (df['Age Range'] == \"65 to 74 years\") | \n",
" (df['Age Range'] == \"75 years and over\")\n",
"] \n",
"#Hilfsvariable\n"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"51011"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(senioren)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Antwort auf Frage 1:"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Es sind 99549 Kinder (bis 19 Jahre) und 51011 Senioren (ab 65 Jahren) registriert.\n"
]
}
],
"source": [
"print('Es sind ' + str(len(kinder)) + ' Kinder (bis 19 Jahre) und ' + str(len(senioren)) + ' Senioren (ab 65 Jahren) registriert.' )\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"-----"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Frage 2: Wie viele Nutzer möchten per Mail informiert werden?"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"393301"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(df.loc[(df['Notice Preference Definition'] == \"Email\")])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Antwort auf Frage 2:"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"393301 Nutzer möchten per Mail informiert werden.\n"
]
}
],
"source": [
"print(str(len(df.loc[(df['Notice Preference Definition'] == \"Email\")])) + ' Nutzer möchten per Mail informiert werden.')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Frage 3: Wie alt sind diese Nutzer durchschnittlich im Vergleich zu Nutzern, die per Post informiert werden möchten?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Die relevante Spalte über die gewünschte Benachrichtigungsart schauen wir uns genauer an:"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Email 393301\n",
"None 42989\n",
"Name: Notice Preference Definition, dtype: int64"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df['Notice Preference Definition'].value_counts()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Es handelt sich um ein nominales Merkmal."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Auch hier stoßen wir auf eine Interpretationsfrage. Da der Datensatz kein Alter ausgibt, sondern nur Altersstufen (Age Range) bzw. Kategorien, die nicht immer etwas mit dem Alter zu tun haben sondern eher mit dem Nutzerstatus (Patron Type Definition), müssen wir überlegen, was ein Durchschnitt bedeuten kann. Age Range ist ein ordinales Merkmal, d.h. wir können keinen Erwartungswert berechnen, aber uns dennoch Häufigkeitstabellen anschauen."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Um diese beiden Merkmale (nominal und ordinal) zu verbinden, können wir uns einen Plot angucken, dafür definieren wir eine neue Variable indem wir nach den Merkmalen filtern, die uns interessieren:"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"benachrichtigung=df.loc[\n",
" (df['Notice Preference Definition'] == \"Email\") | \n",
" (df['Notice Preference Definition'] == \"None\")\n",
"] #Hilfsvariable für den Plot, damit nur die Merkmale 'email' und 'print' angezeigt werden"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.catplot(x='Age Range', kind='count', hue='Notice Preference Definition', data=benachrichtigung, aspect=3, order=[\"0 to 9 years\",\"10 to 19 years\",\"20 to 24 years\",\"25 to 34 years\",\"35 to 44 years\",\"45 to 54 years\",\"55 to 59 years\",\"60 to 64 years\",\"65 to 74 years\",\"75 years and over\"])\n",
"#wenn data=df gewählt wird, würden wir auch die Werte für 'print' sehen"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Um besser die Zahlen zu verstehen, hilft uns eine Kreuztabelle:"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"
\n",
"\n",
"
\n",
" \n",
"
\n",
"
Age Range
\n",
"
0 to 9 years
\n",
"
10 to 19 years
\n",
"
20 to 24 years
\n",
"
25 to 34 years
\n",
"
35 to 44 years
\n",
"
45 to 54 years
\n",
"
55 to 59 years
\n",
"
60 to 64 years
\n",
"
65 to 74 years
\n",
"
75 years and over
\n",
"
All
\n",
"
\n",
"
\n",
"
Notice Preference Definition
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
" \n",
" \n",
"
\n",
"
Email
\n",
"
28740
\n",
"
54936
\n",
"
22701
\n",
"
88200
\n",
"
77618
\n",
"
45165
\n",
"
17336
\n",
"
15539
\n",
"
27170
\n",
"
15069
\n",
"
392474
\n",
"
\n",
"
\n",
"
None
\n",
"
3952
\n",
"
11921
\n",
"
2680
\n",
"
4469
\n",
"
4101
\n",
"
3154
\n",
"
1740
\n",
"
2115
\n",
"
4544
\n",
"
4228
\n",
"
42904
\n",
"
\n",
"
\n",
"
All
\n",
"
32692
\n",
"
66857
\n",
"
25381
\n",
"
92669
\n",
"
81719
\n",
"
48319
\n",
"
19076
\n",
"
17654
\n",
"
31714
\n",
"
19297
\n",
"
435378
\n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
"Age Range 0 to 9 years 10 to 19 years 20 to 24 years \\\n",
"Notice Preference Definition \n",
"Email 28740 54936 22701 \n",
"None 3952 11921 2680 \n",
"All 32692 66857 25381 \n",
"\n",
"Age Range 25 to 34 years 35 to 44 years 45 to 54 years \\\n",
"Notice Preference Definition \n",
"Email 88200 77618 45165 \n",
"None 4469 4101 3154 \n",
"All 92669 81719 48319 \n",
"\n",
"Age Range 55 to 59 years 60 to 64 years 65 to 74 years \\\n",
"Notice Preference Definition \n",
"Email 17336 15539 27170 \n",
"None 1740 2115 4544 \n",
"All 19076 17654 31714 \n",
"\n",
"Age Range 75 years and over All \n",
"Notice Preference Definition \n",
"Email 15069 392474 \n",
"None 4228 42904 \n",
"All 19297 435378 "
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.crosstab(\n",
" benachrichtigung['Notice Preference Definition'],\n",
" benachrichtigung['Age Range'],\n",
" margins=True\n",
")\n",
"# Beachte, dass wir hier eine Teilmenge des Datensatzes betrachten über die Variable \"benachrichitigung\". \n",
"# D.h. wir blenden die Ereignisse aus, wo in der Spalte 'Notice Preference Definition' nicht \n",
"# 'Email' oder 'Print' steht."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"----\n",
"###### WICHTIG:\n",
"Beachte, dass die Summe der Nutzer, die per Mail informiert werden hier 392474 ergibt. Wir haben aber bei Frage 2 festgestellt, dass eigentlich 393301 Nutzer per Mail informiert werden möchten.\n",
"Dies liegt daran, dass offensichtlich in einigen Einträgen (Zeilen des Datensatzes) zwar im Feld \"Notice Preference Definition\" der Wert \"Email\" steht, aber offensichtlich im Feld \"Age Range\" kein Eintrag steht. In der Kreuztabelle werden die beiden Merkmale 'Age Range' und 'Notice Preference Definition' betrachtet und somit nur die Einträge, wo entsprechednd beide Felder ausgefüllt sind.\n",
"\n",
"(Das ist übrigens unabhängig ob man die Kreuztabelle über die \"große\" Variable \"df\" oder \"benachrichtigung\" berechnet, probiere es gerne aus!)\n",
"\n",
"----"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"
\n",
"\n",
"
\n",
" \n",
"
\n",
"
Age Range
\n",
"
0 to 9 years
\n",
"
10 to 19 years
\n",
"
20 to 24 years
\n",
"
25 to 34 years
\n",
"
35 to 44 years
\n",
"
45 to 54 years
\n",
"
55 to 59 years
\n",
"
60 to 64 years
\n",
"
65 to 74 years
\n",
"
75 years and over
\n",
"
\n",
"
\n",
"
Notice Preference Definition
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
" \n",
" \n",
"
\n",
"
Email
\n",
"
0.073228
\n",
"
0.139974
\n",
"
0.057841
\n",
"
0.224728
\n",
"
0.197766
\n",
"
0.115078
\n",
"
0.044171
\n",
"
0.039592
\n",
"
0.069228
\n",
"
0.038395
\n",
"
\n",
"
\n",
"
None
\n",
"
0.092113
\n",
"
0.277853
\n",
"
0.062465
\n",
"
0.104163
\n",
"
0.095585
\n",
"
0.073513
\n",
"
0.040556
\n",
"
0.049296
\n",
"
0.105911
\n",
"
0.098546
\n",
"
\n",
"
\n",
"
All
\n",
"
0.075089
\n",
"
0.153561
\n",
"
0.058296
\n",
"
0.212847
\n",
"
0.187697
\n",
"
0.110982
\n",
"
0.043815
\n",
"
0.040549
\n",
"
0.072842
\n",
"
0.044322
\n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
"Age Range 0 to 9 years 10 to 19 years 20 to 24 years \\\n",
"Notice Preference Definition \n",
"Email 0.073228 0.139974 0.057841 \n",
"None 0.092113 0.277853 0.062465 \n",
"All 0.075089 0.153561 0.058296 \n",
"\n",
"Age Range 25 to 34 years 35 to 44 years 45 to 54 years \\\n",
"Notice Preference Definition \n",
"Email 0.224728 0.197766 0.115078 \n",
"None 0.104163 0.095585 0.073513 \n",
"All 0.212847 0.187697 0.110982 \n",
"\n",
"Age Range 55 to 59 years 60 to 64 years 65 to 74 years \\\n",
"Notice Preference Definition \n",
"Email 0.044171 0.039592 0.069228 \n",
"None 0.040556 0.049296 0.105911 \n",
"All 0.043815 0.040549 0.072842 \n",
"\n",
"Age Range 75 years and over \n",
"Notice Preference Definition \n",
"Email 0.038395 \n",
"None 0.098546 \n",
"All 0.044322 "
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Nun möchten wir das prozentual betrachten:\n",
"pd.crosstab(\n",
" benachrichtigung['Notice Preference Definition'],\n",
" benachrichtigung['Age Range'],\n",
" margins=True, normalize=0\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Antwort auf Frage 3:\n",
"\n",
" Die Frage können wir wie folgt beantworten. \n",
" Von allen Nutzern, die per Mail informiert werden möchten, sind\n",
" - ca. 7% in der Alterklasse 0 bis 9 Jahre,\n",
" - ca. 14% in der Altersklasse 10 bis 19 Jahre,\n",
" \n",
" ...\n",
" \n",
" - ca. 7% in der Altersklasse 65 bis 74 Jahre,\n",
" - ca. 4% in der Altersklasse 75 Jahre und älter.\n",
"\n",
"Im Vergleich dazu, sind von allen Nutzern, die nicht informiert werden möchten\n",
"- ca. 9% in der Altersklasse 0 bis 9 Jahre,\n",
"- ca. 28% in der Altersklasse 10 bis 19 Jahre,\n",
"\n",
"...\n",
"\n",
"- ca. 11% in der Altersklasse 65 bis 74 Jahre,\n",
"- ca. 10% in der Altersklasse 75 Jahre und älter.\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Frage 4: Wie viele Ausleihen werden im Mittel pro Altersgruppe und pro Jahr getätigt? Ist die Streuung zwischen den Gruppen gleich?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Für diese Fragestellung sind die Spalten 'Total Checkouts' und 'Age Range' relevant. Bei der ersten handelt es sich um ein metrisches Merkmal, die zweite ist ordinal.\n",
"Wir definieren eine neue Spalte, die die durchschnittliche jährliche Ausleihe eines Nutzers ausgibt.\n"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"df['Circulation Active Year'] = pd.to_numeric(df['Circulation Active Year'], errors='coerce')\n",
"df['Membership Duration Years'] = (df['Circulation Active Year'] - df['Year Patron Registered'])+1 \n",
"# hier werden der Einfachheit ganze Jahre angenommen\n",
"df[\"Average Checkouts per Year\"] =(df['Total Checkouts']/df['Membership Duration Years'])\n"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
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"text/plain": [
" Patron Type Code Patron Type Definition Total Checkouts Total Renewals \\\n",
"0 5 Staff 53 15 \n",
"1 5 Staff 480 378 \n",
"2 5 Staff 70 33 \n",
"3 5 Staff 3934 2140 \n",
"4 5 Staff 1118 1035 \n",
"\n",
" Age Range Home Library Code Home Library Definition \\\n",
"0 NaN b2 Bayview \n",
"1 NaN e9 Excelsior \n",
"2 45 to 54 years n4 Noe Valley \n",
"3 NaN o2 Ocean View \n",
"4 NaN o7 Ortega \n",
"\n",
" Circulation Active Month Circulation Active Year Notice Preference Code \\\n",
"0 Mar 2023.0 z \n",
"1 Jun 2023.0 z \n",
"2 Jan 2023.0 z \n",
"3 Jul 2023.0 z \n",
"4 Jul 2023.0 z \n",
"\n",
" Notice Preference Definition Provided Email Address \\\n",
"0 Email True \n",
"1 Email True \n",
"2 Email True \n",
"3 Email True \n",
"4 Email True \n",
"\n",
" Year Patron Registered Within San Francisco County \\\n",
"0 2003 False \n",
"1 2003 False \n",
"2 2011 False \n",
"3 2003 False \n",
"4 2003 False \n",
"\n",
" Membership Duration Years Average Checkouts per Year \n",
"0 21.0 2.523810 \n",
"1 21.0 22.857143 \n",
"2 13.0 5.384615 \n",
"3 21.0 187.333333 \n",
"4 21.0 53.238095 "
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
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\n",
"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"\n",
"%matplotlib inline\n",
"sns.set_theme()\n",
"plt.figure(figsize=(16, 6))\n",
"my_order =['0 to 9 years', '10 to 19 years', '20 to 24 years', '25 to 34 years', '35 to 44 years', '45 to 54 years', '55 to 59 years', '60 to 64 years', '65 to 74 years', '75 years and over']\n",
"\n",
"plt = sns.boxplot(y='Average Checkouts per Year', x='Age Range', data=df[df['Average Checkouts per Year']"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"my_order =['0 to 9 years', '10 to 19 years', '20 to 24 years', '25 to 34 years', '35 to 44 years', '45 to 54 years', '55 to 59 years', '60 to 64 years', '65 to 74 years', '75 years and over']\n",
"sns.catplot(x='Age Range', kind=\"count\", data=mission_district, order=my_order, aspect=3)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Die obere Grafik beantwortet die Frage schon ganz gut, dennoch können wir uns auch die genauen Werte über eine Kreuztabelle ausgeben lassen:"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"
\n",
"\n",
"
\n",
" \n",
"
\n",
"
Age Range
\n",
"
0 to 9 years
\n",
"
10 to 19 years
\n",
"
20 to 24 years
\n",
"
25 to 34 years
\n",
"
35 to 44 years
\n",
"
45 to 54 years
\n",
"
55 to 59 years
\n",
"
60 to 64 years
\n",
"
65 to 74 years
\n",
"
75 years and over
\n",
"
\n",
"
\n",
"
Home Library Definition
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
" \n",
" \n",
"
\n",
"
Mission
\n",
"
0.054378
\n",
"
0.173489
\n",
"
0.05183
\n",
"
0.218813
\n",
"
0.229385
\n",
"
0.123936
\n",
"
0.040607
\n",
"
0.034698
\n",
"
0.049119
\n",
"
0.023746
\n",
"
\n",
"
\n",
"
All
\n",
"
0.054378
\n",
"
0.173489
\n",
"
0.05183
\n",
"
0.218813
\n",
"
0.229385
\n",
"
0.123936
\n",
"
0.040607
\n",
"
0.034698
\n",
"
0.049119
\n",
"
0.023746
\n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
"Age Range 0 to 9 years 10 to 19 years 20 to 24 years \\\n",
"Home Library Definition \n",
"Mission 0.054378 0.173489 0.05183 \n",
"All 0.054378 0.173489 0.05183 \n",
"\n",
"Age Range 25 to 34 years 35 to 44 years 45 to 54 years \\\n",
"Home Library Definition \n",
"Mission 0.218813 0.229385 0.123936 \n",
"All 0.218813 0.229385 0.123936 \n",
"\n",
"Age Range 55 to 59 years 60 to 64 years 65 to 74 years \\\n",
"Home Library Definition \n",
"Mission 0.040607 0.034698 0.049119 \n",
"All 0.040607 0.034698 0.049119 \n",
"\n",
"Age Range 75 years and over \n",
"Home Library Definition \n",
"Mission 0.023746 \n",
"All 0.023746 "
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.crosstab(\n",
" mission_district['Home Library Definition'],\n",
" mission_district['Age Range'],\n",
" margins=True, normalize=0\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"###### Antwort auf Frage 6\n",
"Die Verteilung der Altersgruppen im Mission District ist wie folgt:\n",
"- ca. 5% der Nutzer sind zwischen 0 und 9 Jahren,\n",
"- ca. 17% der Nutzer sind zwischen 10 und 19 Jahren,\n",
" \n",
"...\n",
"\n",
"- ca. 5% der Nutzer sind zwischen 65 und 74 Jahren,\n",
"- ca. 2% der Nutzer sind 75 Jahre und älter.\n",
"\n",
"\n",
"----"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Frage 7: Erklärung von Normalisierung von Kreuztabellen (Kapitel 3 im Skript)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Wir nutzen das Beispiel von oben (Alterskategorien und Benachrichtigungsart aus Frage 3).\n",
"Zuerst einmal eine nicht normalisierte Kreuztabelle:"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"
\n",
"\n",
"
\n",
" \n",
"
\n",
"
Age Range
\n",
"
0 to 9 years
\n",
"
10 to 19 years
\n",
"
20 to 24 years
\n",
"
25 to 34 years
\n",
"
35 to 44 years
\n",
"
45 to 54 years
\n",
"
55 to 59 years
\n",
"
60 to 64 years
\n",
"
65 to 74 years
\n",
"
75 years and over
\n",
"
All
\n",
"
\n",
"
\n",
"
Notice Preference Definition
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
" \n",
" \n",
"
\n",
"
Email
\n",
"
28740
\n",
"
54936
\n",
"
22701
\n",
"
88200
\n",
"
77618
\n",
"
45165
\n",
"
17336
\n",
"
15539
\n",
"
27170
\n",
"
15069
\n",
"
392474
\n",
"
\n",
"
\n",
"
None
\n",
"
3952
\n",
"
11921
\n",
"
2680
\n",
"
4469
\n",
"
4101
\n",
"
3154
\n",
"
1740
\n",
"
2115
\n",
"
4544
\n",
"
4228
\n",
"
42904
\n",
"
\n",
"
\n",
"
All
\n",
"
32692
\n",
"
66857
\n",
"
25381
\n",
"
92669
\n",
"
81719
\n",
"
48319
\n",
"
19076
\n",
"
17654
\n",
"
31714
\n",
"
19297
\n",
"
435378
\n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
"Age Range 0 to 9 years 10 to 19 years 20 to 24 years \\\n",
"Notice Preference Definition \n",
"Email 28740 54936 22701 \n",
"None 3952 11921 2680 \n",
"All 32692 66857 25381 \n",
"\n",
"Age Range 25 to 34 years 35 to 44 years 45 to 54 years \\\n",
"Notice Preference Definition \n",
"Email 88200 77618 45165 \n",
"None 4469 4101 3154 \n",
"All 92669 81719 48319 \n",
"\n",
"Age Range 55 to 59 years 60 to 64 years 65 to 74 years \\\n",
"Notice Preference Definition \n",
"Email 17336 15539 27170 \n",
"None 1740 2115 4544 \n",
"All 19076 17654 31714 \n",
"\n",
"Age Range 75 years and over All \n",
"Notice Preference Definition \n",
"Email 15069 392474 \n",
"None 4228 42904 \n",
"All 19297 435378 "
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Nun möchten wir das prozentual betrachten:\n",
"pd.crosstab(\n",
" benachrichtigung['Notice Preference Definition'],\n",
" benachrichtigung['Age Range'],\n",
" margins=True\n",
")\n",
"# die Reihenfolge der betrachteten Spalten 'Notice Preference Definition' und 'Age Range' spielt keine Rolle, \n",
"# probiere es ruhig aus\""
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"
\n",
"\n",
"
\n",
" \n",
"
\n",
"
Age Range
\n",
"
0 to 9 years
\n",
"
10 to 19 years
\n",
"
20 to 24 years
\n",
"
25 to 34 years
\n",
"
35 to 44 years
\n",
"
45 to 54 years
\n",
"
55 to 59 years
\n",
"
60 to 64 years
\n",
"
65 to 74 years
\n",
"
75 years and over
\n",
"
\n",
"
\n",
"
Notice Preference Definition
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
" \n",
" \n",
"
\n",
"
Email
\n",
"
0.073228
\n",
"
0.139974
\n",
"
0.057841
\n",
"
0.224728
\n",
"
0.197766
\n",
"
0.115078
\n",
"
0.044171
\n",
"
0.039592
\n",
"
0.069228
\n",
"
0.038395
\n",
"
\n",
"
\n",
"
None
\n",
"
0.092113
\n",
"
0.277853
\n",
"
0.062465
\n",
"
0.104163
\n",
"
0.095585
\n",
"
0.073513
\n",
"
0.040556
\n",
"
0.049296
\n",
"
0.105911
\n",
"
0.098546
\n",
"
\n",
"
\n",
"
All
\n",
"
0.075089
\n",
"
0.153561
\n",
"
0.058296
\n",
"
0.212847
\n",
"
0.187697
\n",
"
0.110982
\n",
"
0.043815
\n",
"
0.040549
\n",
"
0.072842
\n",
"
0.044322
\n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
"Age Range 0 to 9 years 10 to 19 years 20 to 24 years \\\n",
"Notice Preference Definition \n",
"Email 0.073228 0.139974 0.057841 \n",
"None 0.092113 0.277853 0.062465 \n",
"All 0.075089 0.153561 0.058296 \n",
"\n",
"Age Range 25 to 34 years 35 to 44 years 45 to 54 years \\\n",
"Notice Preference Definition \n",
"Email 0.224728 0.197766 0.115078 \n",
"None 0.104163 0.095585 0.073513 \n",
"All 0.212847 0.187697 0.110982 \n",
"\n",
"Age Range 55 to 59 years 60 to 64 years 65 to 74 years \\\n",
"Notice Preference Definition \n",
"Email 0.044171 0.039592 0.069228 \n",
"None 0.040556 0.049296 0.105911 \n",
"All 0.043815 0.040549 0.072842 \n",
"\n",
"Age Range 75 years and over \n",
"Notice Preference Definition \n",
"Email 0.038395 \n",
"None 0.098546 \n",
"All 0.044322 "
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Nun normalisieren wir nach Zeilen (normalize=0)\n",
"pd.crosstab(\n",
" benachrichtigung['Notice Preference Definition'],\n",
" benachrichtigung['Age Range'],\n",
" margins=True, normalize=0\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Hier sehen wir, dass jede Zeile in der Summe 1 ergibt. D.h. wir können die Tabelle prozentual nach Zeilen interpretieren.\n",
"Also z.B. erste Zeile: Von allen Nutzern, die per Mail informiert werden möchten, befinden sich ca. 12% (0.115078 von 1) in der Altersgruppe 45 bis 54 Jahre.\n"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"
\n",
"\n",
"
\n",
" \n",
"
\n",
"
Age Range
\n",
"
0 to 9 years
\n",
"
10 to 19 years
\n",
"
20 to 24 years
\n",
"
25 to 34 years
\n",
"
35 to 44 years
\n",
"
45 to 54 years
\n",
"
55 to 59 years
\n",
"
60 to 64 years
\n",
"
65 to 74 years
\n",
"
75 years and over
\n",
"
All
\n",
"
\n",
"
\n",
"
Notice Preference Definition
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
" \n",
" \n",
"
\n",
"
Email
\n",
"
0.879114
\n",
"
0.821694
\n",
"
0.894409
\n",
"
0.951775
\n",
"
0.949816
\n",
"
0.934725
\n",
"
0.908786
\n",
"
0.880197
\n",
"
0.856719
\n",
"
0.780899
\n",
"
0.901456
\n",
"
\n",
"
\n",
"
None
\n",
"
0.120886
\n",
"
0.178306
\n",
"
0.105591
\n",
"
0.048225
\n",
"
0.050184
\n",
"
0.065275
\n",
"
0.091214
\n",
"
0.119803
\n",
"
0.143281
\n",
"
0.219101
\n",
"
0.098544
\n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
"Age Range 0 to 9 years 10 to 19 years 20 to 24 years \\\n",
"Notice Preference Definition \n",
"Email 0.879114 0.821694 0.894409 \n",
"None 0.120886 0.178306 0.105591 \n",
"\n",
"Age Range 25 to 34 years 35 to 44 years 45 to 54 years \\\n",
"Notice Preference Definition \n",
"Email 0.951775 0.949816 0.934725 \n",
"None 0.048225 0.050184 0.065275 \n",
"\n",
"Age Range 55 to 59 years 60 to 64 years 65 to 74 years \\\n",
"Notice Preference Definition \n",
"Email 0.908786 0.880197 0.856719 \n",
"None 0.091214 0.119803 0.143281 \n",
"\n",
"Age Range 75 years and over All \n",
"Notice Preference Definition \n",
"Email 0.780899 0.901456 \n",
"None 0.219101 0.098544 "
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Als Vergleich normalisieren wir nach Spalten (normalize=1)\n",
"pd.crosstab(\n",
" benachrichtigung['Notice Preference Definition'],\n",
" benachrichtigung['Age Range'],\n",
" margins=True, normalize=1\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Hier sehen wir, dass jede Spalte in der Summe 1 ergibt. D.h. wir können die Tabelle prozentual nach Spalten interpretieren.\n",
"Also z.B. erste Spalte: Von allen Nutzern, die zwischen 0 und 9 Jahren sind, möchten sich ca. 12% (0.120886 von 1) nicht informieren lassen.\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"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": "3.9.13"
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"nbformat": 4,
"nbformat_minor": 4
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