pandas data frame transform INT64 columns to boolean

Question

Some column in dataframe df, df.column, is stored as datatype int64.

The values are all 1s or 0s.

Is there a way to replace these values with boolean values?

Solution 1

df['column_name'] = df['column_name'].astype('bool')

For example:

import pandas as pd
import numpy as np
df = pd.DataFrame(np.random.random_integers(0,1,size=5), 
                  columns=['foo'])
print(df)
#    foo
# 0    0
# 1    1
# 2    0
# 3    1
# 4    1

df['foo'] = df['foo'].astype('bool')
print(df)

yields

     foo
0  False
1   True
2  False
3   True
4   True

---

Given a list of column_names, you could convert multiple columns to bool dtype using:

df[column_names] = df[column_names].astype(bool)

If you don't have a list of column names, but wish to convert, say, all numeric columns, then you could use

column_names = df.select_dtypes(include=[np.number]).columns
df[column_names] = df[column_names].astype(bool)

Solution 2

Reference: Stack Overflow unutbu (Jan 9 at 13:25), BrenBarn (Sep 18 2017)

I had numerical columns like age and ID which I did not want to convert to Boolean. So after identifying the numerical columns like unutbu showed us, I filtered out the columns which had a maximum more than 1.

# code as per unutbu
column_names = df.select_dtypes(include=[np.number]).columns 

# re-extracting the columns of numerical type (using awesome np.number1 :)) then getting the max of those and storing them in a temporary variable m.
m=df[df.select_dtypes(include=[np.number]).columns].max().reset_index(name='max')

# I then did a filter like BrenBarn showed in another post to extract the rows which had the max == 1 and stored it in a temporary variable n.
n=m.loc[m['max']==1, 'max']

# I then extracted the indexes of the rows from n and stored them in temporary variable p.
# These indexes are the same as the indexes from my original dataframe 'df'.
p=column_names[n.index]

# I then used the final piece of the code from unutbu calling the indexes of the rows which had the max == 1 as stored in my variable p.
# If I used column_names directly instead of p, all my numerical columns would turn into Booleans.
df[p] = df[p].astype(bool)