import pandas as pd
import numpy as np

df = pd.DataFrame(np.random.randn(10,6))
# Make a few areas have NaN values
df.iloc[1:3,1] = np.nan
df.iloc[5,3] = np.nan
df.iloc[7:9,5] = np.nan

 0   1   2   3   4   5
0 0.520113 0.884000 1.260966 -0.236597 0.312972 -0.196281
1 -0.837552  NaN 0.143017 0.862355 0.346550 0.842952
2 -0.452595  NaN -0.420790 0.456215 1.203459 0.527425
3 0.317503 -0.917042 1.780938 -1.584102 0.432745 0.389797
4 -0.722852 1.704820 -0.113821 -1.466458 0.083002 0.011722
5 -0.622851 -0.251935 -1.498837  NaN 1.098323 0.273814
6 0.329585 0.075312 -0.690209 -3.807924 0.489317 -0.841368
7 -1.123433 -1.187496 1.868894 -2.046456 -0.949718  NaN
8 1.133880 -0.110447 0.050385 -1.158387 0.188222  NaN
9 -0.513741 1.196259 0.704537 0.982395 -0.585040 -1.693810

  0  1  2  3  4  5
0 False False False False False False
1 False True False False False False
2 False True False False False False
3 False False False False False False
4 False False False False False False
5 False False False True False False
6 False False False False False False
7 False False False False False True
8 False False False False False True
9 False False False False False False

0 False
1  True
2 False
3  True
4 False
5  True
dtype: bool

df[df.isnull().values==True]

Out[126]: 
   0   1   2   3   4   5
1 1.090872  NaN -0.287612 -0.239234 -0.589897 1.849413
2 -1.384721  NaN -0.158293 0.011798 -0.564906 -0.607121
5 -0.477590 -2.696239 0.312837  NaN 0.404196 -0.797050
7 0.369665 -0.268898 -0.344523 -0.094436 0.214753  NaN
8 -0.114483 -0.842322 0.164269 -0.812866 -0.601757  NaN