Conditional Logic on Pandas DataFrame

Question

How to apply conditional logic to a Pandas DataFrame.

See DataFrame shown below,

   data desired_output 0     1          False 1     2          False 2     3           True 3     4           True 

My original data is show in the 'data' column and the desired_output is shown next to it. If the number in 'data' is below 2.5, the desired_output is False.

I could apply a loop and do re-construct the DataFrame... but that would be 'un-pythonic'

Answer 1

In [1]: df Out[1]:    data 0     1 1     2 2     3 3     4 

You want to apply a function that conditionally returns a value based on the selected dataframe column.

In [2]: df['data'].apply(lambda x: 'true' if x <= 2.5 else 'false') Out[2]: 0     true 1     true 2    false 3    false Name: data 

You can then assign that returned column to a new column in your dataframe:

In [3]: df['desired_output'] = df['data'].apply(lambda x: 'true' if x <= 2.5 else 'false')  In [4]: df Out[4]:    data desired_output 0     1           true 1     2           true 2     3          false 3     4          false