在熊猫数据框中显示具有一个或多个 NaN 值的行

Question

我有一个数据框，其中一些行包含缺失值。

 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

我想在屏幕上显示这些行。如果我尝试 df.isnull() ，它会给出一个带有 True 和 False 的长数据帧。有什么方法可以选择这些行并将它们打印在屏幕上吗？

原文由 Peaceful 发布，翻译遵循 CC BY-SA 4.0 许可协议

Answer 1

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

解释：

 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

原文由 jezrael 发布，翻译遵循 CC BY-SA 4.0 许可协议

Answer 2

使用 df[df.isnull().any(axis=1)] 用于 python 3.6 或更高版本。

原文由 Prateek Nagaria 发布，翻译遵循 CC BY-SA 4.0 许可协议