Drop rows with all zeros in pandas data frame

Question

I can use pandas dropna() functionality to remove rows with some or all columns set as NA\'s. Is there an equivalent function for drop

Answer 1

df = df [~( df [ ['kt'  'b'   'tt'  'mky' 'depth', ] ] == 0).all(axis=1) ]

Try this command its perfectly working.

Answer 2

import pandas as pd

df = pd.DataFrame({'a' : [0,0,1], 'b' : [0,0,-1]})

temp = df.abs().sum(axis=1) == 0      
df = df.drop(temp)

Result:

>>> df
   a  b
2  1 -1

Answer 3

For me this code: df.loc[(df!=0).any(axis=0)] did not work. It returned the exact dataset.

Instead, I used df.loc[:, (df!=0).any(axis=0)] and dropped all the columns with 0 values in the dataset

The function .all() droped all the columns in which are any zero values in my dataset.

Answer 4

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)

Answer 5

I think this solution is the shortest :

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

Answer 6

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)]