Mish :
I have the following data frame:
import pandas as pd
import numpy as np
df = pd.DataFrame({'Q1': ['A,B', 'A,C', 'A,B', 'B,C', 'A,B,C','C,B,A','B,C,A'],
'Q2': ['B,A', 'C,A', 'B,C,A', 'A,B', 'A,C', 'B,C','C,B'],
'Q3': ['C,A', 'C,B', 'A,B', 'C,B', 'A,B,C','A,B,C','C,A']})
df['Q1'] = df['Q1'].apply(lambda x: x.split(','))
df['Q2'] = df['Q2'].apply(lambda x: x.split(','))
df['Q3'] = df['Q3'].apply(lambda x: x.split(','))
colQ1 = df["Q1"].explode().unique()
colQ1df = pd.DataFrame(columns = colQ1)
df = pd.concat([df, colQ1df], sort=False)
print(df)
I want to fill the new column 'A' with a '1' if column 'Q1' contains 'A' and 0 if it does not.
jezrael :
Dont explode values, better is use Series.str.get_dummies with concat if need processing each column same way:
df = pd.DataFrame({'Q1': ['A,B', 'A,C', 'A,B', 'B,C', 'A,B,C','C,B,A','B,C,A'],
'Q2': ['B,A', 'C,A', 'B,C,A', 'A,B', 'A,C', 'B,C','C,B'],
'Q3': ['C,A', 'C,B', 'A,B', 'C,B', 'A,B,C','A,B,C','C,A']})
df = pd.concat([df[x].str.get_dummies(',') for x in df], keys=df.columns, axis=1)
df.columns = df.columns.map('_'.join)
print (df)
Q1_A Q1_B Q1_C Q2_A Q2_B Q2_C Q3_A Q3_B Q3_C
0 1 1 0 1 1 0 1 0 1
1 1 0 1 1 0 1 0 1 1
2 1 1 0 1 1 1 1 1 0
3 0 1 1 1 1 0 0 1 1
4 1 1 1 1 0 1 1 1 1
5 1 1 1 0 1 1 1 1 1
6 1 1 1 0 1 1 1 0 1
If want each column to separate Dataframe:
df1 = df['Q1'].str.get_dummies(',')
print (df1)
A B C
0 1 1 0
1 1 0 1
2 1 1 0
3 0 1 1
4 1 1 1
5 1 1 1
6 1 1 1
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