Alternatives to count and know what columns have missing values in Pandas

Question

I tried this, but I'm not sure if this is the best way to get the information about columns with missing values. For example, I use the target labels to reduce information over missing values and see much better its distribution

cols = dataframe.columns.values.tolist()
dfnas = pd.DataFrame()
for col in cols:
    dfnas[col] = dataframe.label[dataframe[col].isnull()].value_counts()

[Edited]

This is the result of that snippet

In [6]:

dfnas Out[6]:

Out[64]:

 id f1 f2 f3 f4 f5 f6  

0 NaN NaN NaN 180 100 NaN NaN

1 NaN NaN NaN 1 1 NaN NaN

Answer 1

You could use np.sum to get the counts for each column:

import numpy as np
import pandas as pd

df = pd.DataFrame({'c1':[1, np.nan, np.nan], 'c2':[2, 2, np.nan]})
np.sum(df.isnull())
Out[4]: 
c1    2
c2    1
dtype: int64