pandas Grouping Data Grouping numbers
Example
For the following DataFrame:
import numpy as np
import pandas as pd
np.random.seed(0)
df = pd.DataFrame({'Age': np.random.randint(20, 70, 100),
'Sex': np.random.choice(['Male', 'Female'], 100),
'number_of_foo': np.random.randint(1, 20, 100)})
df.head()
# Output:
# Age Sex number_of_foo
# 0 64 Female 14
# 1 67 Female 14
# 2 20 Female 12
# 3 23 Male 17
# 4 23 Female 15
Group Age into three categories (or bins).
Bins can be given as
- an integer
nindicating the number of bins—in this case the dataframe's data is divided intonintervals of equal size - a sequence of integers denoting the endpoint of the left-open intervals in which the data is divided into—for instance
bins=[19, 40, 65, np.inf]creates three age groups(19, 40],(40, 65], and(65, np.inf].
Pandas assigns automatically the string versions of the intervals as label. It is also possible to define own labels by defining a labels parameter as a list of strings.
pd.cut(df['Age'], bins=4)
# this creates four age groups: (19.951, 32.25] < (32.25, 44.5] < (44.5, 56.75] < (56.75, 69]
Name: Age, dtype: category
Categories (4, object): [(19.951, 32.25] < (32.25, 44.5] < (44.5, 56.75] < (56.75, 69]]
pd.cut(df['Age'], bins=[19, 40, 65, np.inf])
# this creates three age groups: (19, 40], (40, 65] and (65, infinity)
Name: Age, dtype: category
Categories (3, object): [(19, 40] < (40, 65] < (65, inf]]
Use it in groupby to get the mean number of foo:
age_groups = pd.cut(df['Age'], bins=[19, 40, 65, np.inf])
df.groupby(age_groups)['number_of_foo'].mean()
# Output:
# Age
# (19, 40] 9.880000
# (40, 65] 9.452381
# (65, inf] 9.250000
# Name: number_of_foo, dtype: float64
Cross tabulate age groups and gender:
pd.crosstab(age_groups, df['Sex'])
# Output:
# Sex Female Male
# Age
# (19, 40] 22 28
# (40, 65] 18 24
# (65, inf] 3 5
