Display rows with one or more NaN values in pandas dataframe

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.

You can use DataFrame.any with parameter axis=1 for check at least one True in row by DataFrame.isna with boolean indexing:

df1 = df[df.isna().any(axis=1)]

---

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')

---

print (df)
                             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       NaN  2.593468e+01

Explanation:

print (df.isna())
                            alpha1 alpha2 gamma1 gamma2 chi2min
filename                                                       
M66_MI_NSRh35d32kpoints.dat  False  False  False  False   False
F71_sMI_DMRI51d.dat          False  False   True  False   False
F62_sMI_St22d7.dat           False  False  False  False   False
F41_Car_HOC498d.dat          False  False  False  False   False
F78_MI_547d.dat              False  False  False   True   False

print (df.isna().any(axis=1))
filename
M66_MI_NSRh35d32kpoints.dat    False
F71_sMI_DMRI51d.dat             True
F62_sMI_St22d7.dat             False
F41_Car_HOC498d.dat            False
F78_MI_547d.dat                 True
dtype: bool

df1 = df[df.isna().any(axis=1)]
print (df1)
                     alpha1  alpha2    gamma1  gamma2       chi2min
filename                                                           
F71_sMI_DMRI51d.dat   0.000   0.000       NaN     0.0  1.000000e+25
F78_MI_547d.dat       1.897   5.459  0.095319     NaN  2.593468e+01

df.isna().any() returns the columns status for nan values. Hence, a better way to observe and analyze the nan values would be:

df.loc[:, df.isna().any()]

example