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pythonpandasencoding
Carlos Mougan
Carlos Mougan

Reputation: 811

Pandas split column based on category of the elements

I have a pandas.DataFrame in it I have a column. The columns contains, integers, strings, time...

I want to create columns (containing [0,1]) that tells if the value in that column is a string or not, a time or not... in an efficient way.

          A
0  Hello 
1  Name
2  123
3  456
4  22/03/2019

And the output should be

          A    A_string A_number A_date
0  Hello       1        0        0
1  Name        1        0        0 
2  123         0        1        0
3  456         0        1        0
4  22/03/2019  0        0        1

Upvotes: 2

Views: 2356

Answers (2)

Umar.H
Umar.H

Reputation: 23099

We can use the native pandas .to_numeric, to_datetime to test for dates & numbers. Then we can use .loc for assignment and fillna to match your target df. 

df.loc[~pd.to_datetime(df['A'],errors='coerce').isna(),'A_Date'] = 1

df.loc[~pd.to_numeric(df['A'],errors='coerce').isna(),'A_Number'] = 1

df.loc[(pd.to_numeric(df['A'],errors='coerce').isna()) 
       & pd.to_datetime(df['A'],errors='coerce').isna()
       ,'A_String'] = 1

df = df.fillna(0)


print(df)

            A  A_Date  A_Number  A_String
0       Hello     0.0       0.0       1.0
1        Name     0.0       0.0       1.0
2         123     0.0       1.0       0.0
3         456     0.0       1.0       0.0
4  22/03/2019     1.0       0.0       0.0

Upvotes: 2

sammywemmy
sammywemmy

Reputation: 28689

Using pandas str methods to check for the string type could help:

df = pd.read_clipboard()

df['A_string'] = df.A.str.isalpha().astype(int)

df['A_number'] = df.A.str.isdigit().astype(int)

#naive assumption
df['A_Date'] = (~df.A.str.isalnum()).astype(int)

df.filter(['A','A_string','A_number','A_Date'])

    A       A_string    A_number    A_Date
0   Hello       1          0           0
1   Name        1          0           0
2   123         0          1           0
3   456         0          1           0
4   22/03/2019  0          0           1

Upvotes: 4

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