Counting number of zeros per row by Pandas DataFrame?

Question

Given a DataFrame I would like to compute number of zeros per each row. How can I compute it with Pandas?

This is presently what I ve done, this returns indices of zeros

def is_blank(x):     return x == 0   indexer = train_df.applymap(is_blank) 

Answer 1

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)