pandas Reshaping and pivoting Cross Tabulation
Example
import pandas as pd
df = pd.DataFrame({'Sex': ['M', 'M', 'F', 'M', 'F', 'F', 'M', 'M', 'F', 'F'],
'Age': [20, 19, 17, 35, 22, 22, 12, 15, 17, 22],
'Heart Disease': ['Y', 'N', 'Y', 'N', 'N', 'Y', 'N', 'Y', 'N', 'Y']})
df
Age Heart Disease Sex
0 20 Y M
1 19 N M
2 17 Y F
3 35 N M
4 22 N F
5 22 Y F
6 12 N M
7 15 Y M
8 17 N F
9 22 Y F
pd.crosstab(df['Sex'], df['Heart Disease'])
Hearth Disease N Y
Sex
F 2 3
M 3 2
Using dot notation:
pd.crosstab(df.Sex, df.Age)
Age 12 15 17 19 20 22 35
Sex
F 0 0 2 0 0 3 0
M 1 1 0 1 1 0 1
Getting transpose of DF:
pd.crosstab(df.Sex, df.Age).T
Sex F M
Age
12 0 1
15 0 1
17 2 0
19 0 1
20 0 1
22 3 0
35 0 1
Getting margins or cumulatives:
pd.crosstab(df['Sex'], df['Heart Disease'], margins=True)
Heart Disease N Y All
Sex
F 2 3 5
M 3 2 5
All 5 5 10
Getting transpose of cumulative:
pd.crosstab(df['Sex'], df['Age'], margins=True).T
Sex F M All
Age
12 0 1 1
15 0 1 1
17 2 0 2
19 0 1 1
20 0 1 1
22 3 0 3
35 0 1 1
All 5 5 10
Getting percentages :
pd.crosstab(df["Sex"],df['Heart Disease']).apply(lambda r: r/len(df), axis=1)
Heart Disease N Y
Sex
F 0.2 0.3
M 0.3 0.2
Getting cumulative and multiplying by 100:
df2 = pd.crosstab(df["Age"],df['Sex'], margins=True ).apply(lambda r: r/len(df)*100, axis=1)
df2
Sex F M All
Age
12 0.0 10.0 10.0
15 0.0 10.0 10.0
17 20.0 0.0 20.0
19 0.0 10.0 10.0
20 0.0 10.0 10.0
22 30.0 0.0 30.0
35 0.0 10.0 10.0
All 50.0 50.0 100.0
Removing a column from DF (one way):
df2[["F","M"]]
Sex F M
Age
12 0.0 10.0
15 0.0 10.0
17 20.0 0.0
19 0.0 10.0
20 0.0 10.0
22 30.0 0.0
35 0.0 10.0
All 50.0 50.0
