minte9

learningjourney

Group By

 
""" Grouping rows by values

Groupby is one of the most powerful feature in pandas.
We can group rows according to some shared value.

We can also group by a first column, the group that grouping 
by a second column.
"""

import pandas as pd
import pathlib

DIR = pathlib.Path(__file__).resolve().parent / '../_data/'
df = pd.read_csv(DIR / 'titanic.csv')


print("Grouped by Sex averages:")
print(df.groupby('Sex').mean(numeric_only=True).to_markdown())

# | Sex    |     Age |   Survived |   SexCode |
# |:-------|--------:|-----------:|----------:|
# | female | 29.3964 |   0.666667 |         1 |
# | male   | 31.0143 |   0.166863 |         0 |


print("Grouped by Sex count:")
print(df.groupby('Sex')['Name'].count().to_markdown())

# | Sex    |   Name |
# |:-------|-------:|
# | female |    462 |
# | male   |    851 |


print("Grouped by Survived count:")
print(df.groupby('Survived')['Name'].count().to_markdown())

# |   Survived |   Name |
# |-----------:|-------:|
# |          0 |    863 |
# |          1 |    450 |


print("Group by two columns (average):")
print(df.groupby(['Sex', 'Survived'])['Age'].mean().to_markdown())

# |               |     Age |
# |:--------------|--------:|
# | ('female', 0) | 24.9014 |
# | ('female', 1) | 30.8671 |
# | ('male', 0)   | 32.3208 |
# | ('male', 1)   | 25.9519 |