Understanding the logic of using the any() arguement

Question

I have a dataframe that contains only 1s and 0s and looks like this (in reality I have several more columns):

Test A B C D E
0    1 1 0 1 0
0    0 1 0 1 0
0    0 0 0 1 0
1    1 0 1 1 0

I first look at just the first column and check each row to return True if there is a one and False if there is a zero. I then look at the first two columns and check each row to return True if at least one of the values is a 1 and False if not. I continue in this manner, adding a column each time.

My code is:

for i in range(0,len(df.columns)):
    print(df.iloc[:,0:0+i].any(axis=1))

For the first column, it returns False, False, False, False. I don’t understand why this is considering that my final value in the Test column is a 1. Why is it not returning False, False, False, True?

Reproducible data:

data = {'Test': [0, 0, 0, 1],
    'A': [1, 0, 0, 1],
   'B':[1,1,0,0],
   'C':[0,0,0,1],
   'D':[1,1,1,1],
   'E':[0,0,0,0]}

df = pd.DataFrame(data)

Asked By: Niam45

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Source

Answer 1

The problem is that you started your column slicing at 1 instead of 0, you are skipping the test column.

df.iloc[:,0:1+i].any(axis=1) (notice it starts at 0 now)

This small correction should make it works as intended.

Answered By: Freemin

Understanding the logic of using the any() arguement

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