[python] Display rows with one or more NaN values in pandas dataframe

I have a dataframe in which some rows contain missing values.

In [31]: df.head()
Out[31]: 
                             alpha1  alpha2    gamma1    gamma2       chi2min  
filename                                                                        
M66_MI_NSRh35d32kpoints.dat  0.8016  0.9283  1.000000  0.074804  3.985599e+01   
F71_sMI_DMRI51d.dat          0.0000  0.0000       NaN  0.000000  1.000000e+25   
F62_sMI_St22d7.dat           1.7210  3.8330  0.237480  0.150000  1.091832e+01   
F41_Car_HOC498d.dat          1.1670  2.8090  0.364190  0.300000  7.966335e+00   
F78_MI_547d.dat              1.8970  5.4590  0.095319  0.100000  2.593468e+01 

I want to display those rows on the screen. If I try df.isnull(), it gives a long dataframe with True and False. Is there any way by which I can select these rows and print them on the screen?

Suppose gamma1 and gamma2 are two such columns for which df.isnull().any() gives True value , the following code can be used to print the rows.

bool1 = pd.isnull(df['gamma1'])
bool2 = pd.isnull(df['gamma2'])
df[bool1]
df[bool2]

Use df[df.isnull().any(axis=1)] for python 3.6 or above.

Can try this too, almost similar previous answers.

    d = {'filename': ['M66_MI_NSRh35d32kpoints.dat', 'F71_sMI_DMRI51d.dat', 'F62_sMI_St22d7.dat', 'F41_Car_HOC498d.dat', 'F78_MI_547d.dat'], 'alpha1': [0.8016, 0.0, 1.721, 1.167, 1.897], 'alpha2': [0.9283, 0.0, 3.833, 2.809, 5.459], 'gamma1': [1.0, np.nan, 0.23748000000000002, 0.36419, 0.095319], 'gamma2': [0.074804, 0.0, 0.15, 0.3, np.nan], 'chi2min': [39.855990000000006, 1e+25, 10.91832, 7.966335000000001, 25.93468]}
    df = pd.DataFrame(d).set_index('filename')

Count of null values in each column.

df.isnull().sum()

df.isnull().any(axis=1)