Pandas Pivot Titanic: Create a Pivot table with multiple indexes from the data set of titanic.csv

Pandas: Pivot Titanic Exercise-3 with Solution

Write a Pandas program to create a Pivot table with multiple indexes from the data set of titanic.csv. Go to Editor

Sample Solution:

Python Code :

import pandas as pd
import numpy as np
df = pd.read_csv('titanic.csv')
result = pd.pivot_table(df, index = ["sex","age"], aggfunc=np.sum)
print(result)

Sample Output:

              Unnamed: 15  adult_male  alone    ...     pclass  sibsp  survived
sex    age                                      ...                            
female 0.75           0.0         0.0    0.0    ...          6      4         2
       1.00           0.0         0.0    0.0    ...          6      1         2
       2.00           0.0         0.0    0.0    ...         15      9         2
       3.00           0.0         0.0    0.0    ...          5      4         1
       4.00           0.0         0.0    0.0    ...         13      4         5
       5.00           0.0         0.0    1.0    ...         11      7         4
       6.00           0.0         0.0    0.0    ...          5      4         1
       7.00           0.0         0.0    0.0    ...          2      0         1
       8.00           0.0         0.0    0.0    ...          5      3         1
       9.00           0.0         0.0    0.0    ...         12     10         0
       10.00          0.0         0.0    0.0    ...          3      0         0
       11.00          0.0         0.0    0.0    ...          3      4         0
       13.00          0.0         0.0    1.0    ...          5      0         2
       14.00          0.0         0.0    1.0    ...          9      3         3
       14.50          0.0         0.0    0.0    ...          3      1         0
       15.00          0.0         0.0    2.0    ...         10      1         4
       16.00          0.0         0.0    3.0    ...         12      5         5
       17.00          0.0         0.0    3.0    ...         12      6         5
       18.00          0.0         0.0    4.0    ...         31      6         8
       19.00          0.0         0.0    3.0    ...         13      3         7
       20.00          0.0         0.0    1.0    ...          6      1         0
       21.00          0.0         0.0    4.0    ...         16      5         4
       22.00          0.0         0.0    7.0    ...         26      3        10
       23.00          0.0         0.0    3.0    ...         10      4         4
       24.00          0.0         0.0    7.0    ...         31     10        14
       25.00          0.0         0.0    1.0    ...         11      3         2
       26.00          0.0         0.0    3.0    ...         12      2         3
       27.00          0.0         0.0    2.0    ...         15      2         5
       28.00          0.0         0.0    4.0    ...         16      3         5
       29.00          0.0         0.0    2.0    ...         16      3         5
...                   ...         ...    ...    ...        ...    ...       ...
male   42.00          0.0        10.0    6.0    ...         21      3         3
       43.00          0.0         3.0    2.0    ...          8      1         0
       44.00          0.0         6.0    3.0    ...         15      3         1
       45.00          0.0         6.0    5.0    ...         10      1         2
       45.50          0.0         2.0    2.0    ...          4      0         0
       46.00          0.0         3.0    2.0    ...          4      1         0
       47.00          0.0         7.0    7.0    ...         12      0         0
       48.00          0.0         5.0    3.0    ...          8      2         3
       49.00          0.0         4.0    1.0    ...          6      3         2
       50.00          0.0         5.0    2.0    ...          8      4         1
       51.00          0.0         6.0    5.0    ...         13      0         1
       52.00          0.0         4.0    3.0    ...          6      1         1
       54.00          0.0         5.0    3.0    ...          8      1         0
       55.00          0.0         1.0    1.0    ...          1      0         0
       55.50          0.0         1.0    1.0    ...          3      0         0
       56.00          0.0         3.0    3.0    ...          3      0         1
       57.00          0.0         1.0    1.0    ...          2      0         0
       58.00          0.0         2.0    1.0    ...          2      0         0
       59.00          0.0         2.0    2.0    ...          5      0         0
       60.00          0.0         3.0    1.0    ...          4      2         1
       61.00          0.0         3.0    3.0    ...          5      0         0
       62.00          0.0         3.0    3.0    ...          4      0         1
       64.00          0.0         2.0    1.0    ...          2      1         0
       65.00          0.0         3.0    2.0    ...          5      0         0
       66.00          0.0         1.0    1.0    ...          2      0         0
       70.00          0.0         2.0    1.0    ...          3      1         0
       70.50          0.0         1.0    1.0    ...          3      0         0
       71.00          0.0         2.0    2.0    ...          2      0         0
       74.00          0.0         1.0    1.0    ...          3      0         0
       80.00          0.0         1.0    1.0    ...          1      0         1

[145 rows x 8 columns]                                    

Python Code Editor:

Pivot Titanic.csv:

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