Drop rows with all zeros in pandas data frame

One-liner. No transpose needed:

df.loc[~(df==0).all(axis=1)]

And for those who like symmetry, this also works...

df.loc[(df!=0).any(axis=1)]

It turns out this can be nicely expressed in a vectorized fashion:

> df = pd.DataFrame({'a':[0,0,1,1], 'b':[0,1,0,1]})
> df = df[(df.T != 0).any()]
> df
   a  b
1  0  1
2  1  0
3  1  1

I think this solution is the shortest :

df= df[df['ColName'] != 0]

I look up this question about once a month and always have to dig out the best answer from the comments:

df.loc[(df!=0).any(1)]

Thanks Dan Allan!

Replace the zeros with nan and then drop the rows with all entries as nan. After that replace nan with zeros.

import numpy as np
df = df.replace(0, np.nan)
df = df.dropna(how='all', axis=0)
df = df.replace(np.nan, 0)

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