Pandas Pivot Titanic: Count survival by gender, categories wise age of various classes
8. Pivot Table: Count Survival by Gender and Age Categories
Write a Pandas program to create a Pivot table and count survival by gender, categories wise age of various classes. Go to Editor
Note: Age categories (0, 10), (10, 30), (30, 60), (60, 80)
Sample Solution:
Python Code :
import pandas as pd
import numpy as np
df = pd.read_csv('titanic.csv')
age = pd.cut(df['age'], [0, 10, 30, 60, 80])
result = df.pivot_table('survived', index=['sex',age], columns='pclass', aggfunc='count')
print(result)
Sample Output:
pclass 1 2 3
sex age
female (0, 10] 1.0 8.0 22.0
(10, 30] 34.0 36.0 57.0
(30, 60] 48.0 30.0 22.0
(60, 80] 2.0 NaN 1.0
male (0, 10] 2.0 9.0 22.0
(10, 30] 24.0 43.0 151.0
(30, 60] 63.0 44.0 76.0
(60, 80] 12.0 3.0 4.0
For more Practice: Solve these Related Problems:
- Write a Pandas program to create a pivot table from titanic.csv that counts survivors by gender within each defined age category.
- Write a Pandas program to generate a pivot table that aggregates survival counts by gender and the four age partitions.
- Write a Pandas program to build a pivot table that displays the number of survivors grouped by gender and custom age intervals.
- Write a Pandas program to create a pivot table showing survival counts for each combination of gender and age group in titanic.csv.
Python Code Editor:
Pivot Titanic.csv:
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