How do I count specific values across multiple columns in pandas

Question

I have the DataFrame

df = pd.DataFrame({
    'colA':['?',2,3,4,'?'],
    'colB':[1,2,'?',3,4],
    'colC':['?',2,3,4,5]
})

I would like to get the count the the number of '?' in each column and return the following output -

colA - 2
colB - 1
colC - 1

Is there a way to return this output at once. Right now the only way I know how to do it is write a for loop for each column.

Answer 1

looks like the simple way is

df[df == '?'].count()

the result is

colA    2
colB    1
colC    1
dtype: int64

where df[df == '?'] give us DataFrame with ? and Nan

  colA colB colC
0    ?  NaN    ?
1  NaN  NaN  NaN
2  NaN    ?  NaN
3  NaN  NaN  NaN
4    ?  NaN  NaN

and the count non-NA cells for each column.

Please, look on the other solutions: good readable and the most faster

Answer 2

You can use numpy.count_nonzero here.

pd.Series(np.count_nonzero(df.to_numpy()=='?', axis=0), index=df.columns)
# pd.Series((df.values == '?').sum(0), index=df.columns)

colA    2
colB    1
colC    1
dtype: int64

Timeit results:

Benchmarking with df of shape (1_000_000, 3)

big_df = pd.DataFrame(df.to_numpy().repeat(200_000,axis=0))
big_df.shape
(1000000, 3)

---

In [186]: %timeit pd.Series(np.count_nonzero(big_df.to_numpy()=='?', axis=0), index=big_df.columns)
53.1 ms ± 231 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)

In [187]: %timeit big_df.eq('?').sum()
171 ms ± 7.42 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)

In [188]: %timeit big_df[big_df == '?'].count()
314 ms ± 4.24 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

In [189]: %timeit pd.Series(np.apply_along_axis(lambda x: Counter(x)['?'], 0, big_df.values), index=big_df.columns)
174 ms ± 3.05 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)