How to count the number of rows that have the value 1 for all the columns in a dataframe?

Question

The following is an example dataframe for this issue:

name    gender    address    phone no.
---------------------------------------
1       1         1          1
1       0         0          0
1       1         1          1
1       1         0          1

The desired output here is 2 because the number of rows containing all 1s is 2.

Can anyone please help me with this issue?
Thanks.

Asked By: Vineeth Sai

||

Source

Answer 1

Use eq(1) to identify the values with 1, then aggregate per row with any to have True when all values are True and sum the True taking advantage of the True/1 equivalence:

df.eq(1).all(axis=1).sum()

output: 2

Intermediates:

df.eq(1)
   name  gender  address  phone no.
0  True    True     True       True
1  True   False    False      False
2  True    True     True       True
3  True    True    False       True

df.eq(1).all(axis=1)
0     True
1    False
2     True
3    False
dtype: bool

Answered By: mozway

Answer 2

Let’s do

l = sum(df.eq(1).all(axis=1))

print(l)

2

Answered By: Ynjxsjmh

Answer 3

Assuming above dataframe is a binary table i.e. all values are either 1 or 0, then df.sum(axis=1) equal to 4 should give you all rows where all values are 1.

df[df.sum(axis=1) == len(df.columns)]

   name  gender  address  phone no.
0     1       1        1          1
2     1       1        1          1

Answered By: Azhar Khan

How to count the number of rows that have the value 1 for all the columns in a dataframe?

Question:

Answers: