Counting number of zeros per row by Pandas DataFrame?

Use a boolean comparison which will produce a boolean df, we can then cast this to int, True becomes 1, False becomes 0 and then call count and pass param axis=1 to count row-wise:

In [56]:

df = pd.DataFrame({'a':[1,0,0,1,3], 'b':[0,0,1,0,1], 'c':[0,0,0,0,0]})
df
Out[56]:
   a  b  c
0  1  0  0
1  0  0  0
2  0  1  0
3  1  0  0
4  3  1  0
In [64]:

(df == 0).astype(int).sum(axis=1)
Out[64]:
0    2
1    3
2    2
3    2
4    1
dtype: int64

Breaking the above down:

In [65]:

(df == 0)
Out[65]:
       a      b     c
0  False   True  True
1   True   True  True
2   True  False  True
3  False   True  True
4  False  False  True
In [66]:

(df == 0).astype(int)
Out[66]:
   a  b  c
0  0  1  1
1  1  1  1
2  1  0  1
3  0  1  1
4  0  0  1

EDIT

as pointed out by david the astype to int is unnecessary as the Boolean types will be upcasted to int when calling sum so this simplifies to:

(df == 0).sum(axis=1)

You can count the zeros per column using the following function of python pandas. It may help someone who needs to count the particular values per each column

df.isin([0]).sum(axis=1)

Here df is the dataframe and the value which we want to count is 0