CODEWITHSUNDEEP
pythonpandaslambdaconditional-statementsapply
gk7
gk7

Reputation: 155

Apply with a condition on a Pandas dataframe elementwise

I am learning Python and I try to understand how apply() method works in Pandas dataframes.

As an exercise I would like to use a one line code to apply the str.upper() method on the elements of a Pandas dataframe only if these elements are strings.

I thought to combine a lambda conditional expression with apply but the problem is that when apply calls the Pandas dataframe, the dataframe --if I have understood well -- returns a Series to apply which then passes it to the function. I wonder how I could get a level deeper and call the function on the elements of the Pandas dataframe.

This is how I do what I intend when apply() calls on a column of the DataFrame (a Series):

df= pd.DataFrame([[1, 'a'],['b',2]], columns = ['A', 'B'] )
df['A'].apply(lambda x: str.upper(x) if type(x) is str else x)

But how I could do that on the entire dataframe with one line of code?

I am looking for a solution that would work with columns that contain both numerics and strings and would leave the numerics intact.

Upvotes: 8

Views: 11815

Answers (1)

piRSquared
piRSquared

Reputation: 294218

Your one-liner

df.applymap(lambda x: x.upper() if isinstance(x, str) else x)

   A  B
0  1  A
1  B  2

Upvotes: 8

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