Applying lambda on Python DataFrame

I am having a Python Pandas DataFrame like

>>> df
classification like
0 flower 1
1 flower 0
2 flower 0
3 adventure 1
4 adventure 1


I want to create an output DataFrame like

>>> df
classification like liked
0 flower 1 True
1 flower 0 False
2 flower 0 False
3 adventure 1 True
4 adventure 1 True


I am "apply"ing the Python lambda function on the input DataFrame as follows:

>>> df['like'].apply(lambda x: x == 1)


But I am getting all 'False' under the 'liked' column

>>> df
classification like liked
0 flower 1 False
1 flower 0 False
2 flower 0 False
3 adventure 1 False
4 adventure 1 False


Any quick suggestions will be helpful.

>>> df['like'].astype(int)
0 1
1 0
2 0
3 1
4 1
Name: like, dtype: int32


@jezrael

>>> df['liked'] = df['like'].astype(bool)
>>> df
classification like liked
0 flower 1 True
1 flower 0 True
2 flower 0 True
3 adventure 1 True
4 adventure 1 True


@jezrael : DTypes

>>> df.dtypes
classification object
like object
liked bool
dtype: object


try df['like'].astype(int)
– Rakesh
Jun 29 at 13:13

df['like'].astype(int)

If you can avoid apply which is a hidden loop as your needs can be vectorized.
– Parfait
Jun 29 at 13:14

apply

1 Answer
1

Convert integer column to boolean:

print (type(df.loc[0, 'like']))
<class 'numpy.int64'>

df['liked'] = df['like'].astype(bool)


Or assign comparison with 1:

df['liked'] = df['like'] == 1


If 1 is string compare by string '1':

1

'1'

print (type(df.loc[0, 'like']))
<class 'str'>

df['liked'] = df['like'] == '1'
print (df)
classification like liked
0 flower 1 True
1 flower 0 False
2 flower 0 False
3 adventure 1 True
4 adventure 1 True


When I compared using df['like'] == '1' it is working. But the problem is when I checked the data type for the 'like' column it is showing as int32
– somnathchakrabarti
Jun 29 at 13:24

Do you check it by print (type(df.loc[0, 'like'])) ?
– jezrael
Jun 29 at 13:25

print (type(df.loc[0, 'like']))

No I checked by df['like'].astype(int)
– somnathchakrabarti
Jun 29 at 13:26

Here is possible check it by print (df.dtypes), it return object for string column
– jezrael
Jun 29 at 13:27

print (df.dtypes)

object

ok, so it is string that's the problem
– somnathchakrabarti
Jun 29 at 13:29

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