在熊猫dataframe中显示带有一个或多个NaN值的行。

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

我有一个dataframe，其中有些行包含缺失的值。

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 on 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?

我想在屏幕上显示这些行。如果我尝试使用df.isnull()，它会给出一个带有True和False的长数据aframe。有什么办法可以让我选择这些行并在屏幕上打印它们吗?

1 个解决方案

#1

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

您可以使用任何带有参数轴=1的值来检查至少一行中的一个真值和布尔索引:

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

---

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

print (df.isnull())
                            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.isnull().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.isnull().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