CODEWITHSUNDEEP
pythonpandasbinarycategorical-data
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Reputation: 43

Python: Converting multiple binary columns to single categorical column

I have a csv file dataset that contains 170 columns, the first 5 columns contain unique identifiers (Platform, ID, date, length of call, name). The remaining columns 175 contain binary data covering 10 categories. I want to condense those columns so that the number of columns in my data frame is 15. Including an example below: 

import pandas as pd

df1 = pd.DataFrame({'Platform': ['Telephone', 'Chat', 'Text'], 'ID': [1, 2, 
3], 'Length': [1545,1532,1511], 'Name': ['andy', 'helen', 'peter'], 'Problem: 
A':[0,1,0], 'Problem: B':[1,0,0], 'Problem: C': [0,0,1], 'Solution: A': 
[0,1,0], 'Solution: B':[1,0,0], 'Solution: C': [0,0,1]})

The output is:

df.head()

ID  Date        Length\\
1   2015-10-16    1545
2   2015-10-09    1532
3   2015-10-13    1511 

Name Problem: A Problem: B  Problem: C  Solution: A Solution: B Solution: C
andy         0          1           0            0           1           0
helen        1          0           0            1           0           0
peter        0          0           1            0           0           1

What I want the data frame to look like:

  Platform ID Length  Name   Problem  Solution
  Telephone 1 1545    andy    B        B
  Chat      2 1532    helen   A        A
  Text      3 1511    peter   C        C

FYI this is not the full dataframe. There are a total of 170 colums that I would like to transform into 15.

Upvotes: 2

Views: 2321

Answers (2)

Rohit Nandi
Rohit Nandi

Reputation: 878

I created this custom function which will serve your purpose. I got the idea from this stackoverflow article

def condenseCols(data,finalCol,*cols):
    cols = list(cols)
    x = data[cols] # Slice the cols
    x = x.idxmax(axis=1) 
    # x is now a series, holding column name of the max value in the row i.e one of the column from cols
    x = x.apply(lambda s : s.split(": ")[1]) # extract only the prefix (A,B,C)

    data[finalCol] = x
    data = data.drop(cols, axis=1, inplace=True) # Drop the columns : cols
    return data

Call this method, by passing the column names you want to condense, along with the final name of column

condenseCols(df1,'Problem','Problem: A','Problem: B','Problem: C')
condenseCols(df1,'Solution','Solution: A','Solution: B','Solution: C')

There are other ways too, to do this, as cited in the article stackoverflow article

Upvotes: 0

cs95
cs95

Reputation: 402872

You can use groupby + apply with a dot product on the columns ;

df = df.set_index('Name')
df.groupby(df.columns.str.split(':').str[0], axis=1).apply(
    lambda x: x.dot(x.columns.str.split(': ').str[1])
)

      Problem Solution
Name                  
andy        B        B
helen       A        A
peter       C        C

Upvotes: 1

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