当数据在序列中时，在Pandas中填充缺失值
[英]Filling missing values in Pandas when the data is in a sequence
问题描述
当数据有一些序列时，我在查找如何填充pandas中的缺失值方面遇到了一些麻烦。
例如，在下面的示例中，我想补HHC_2019_02_03_53.png与B9和HHC_2019_02_03_54.png与B10 。 fillna方法中有没有让我增加pandas系列的东西，或者有没有办法通过使用同一列的前一个值和下一个值来填充缺失值
df = pd.DataFrame(np.array([['HHC_2019_02_03_0.png', 'A1'],
['HHC_2019_02_03_1.png', 'A2'],
['HHC_2019_02_03_2.png', 'A3'],
['HHC_2019_02_03_3.png', 'A4'],
['HHC_2019_02_03_4.png', 'A5'],
['HHC_2019_02_03_5.png', 'A6'],
['HHC_2019_02_03_6.png', 'A7'],
['HHC_2019_02_03_7.png', 'A8'],
['HHC_2019_02_03_8.png', 'A9'],
['HHC_2019_02_03_9.png', 'A10'],
['HHC_2019_02_03_10.png', 'A11'],
['HHC_2019_02_03_11.png', 'A12'],
['HHC_2019_02_03_12.png', 'A13'],
['HHC_2019_02_03_13.png', 'A14'],
['HHC_2019_02_03_14.png', 'A15'],
['HHC_2019_02_03_15.png', 'A16'],
['HHC_2019_02_03_16.png', 'A17'],
['HHC_2019_02_03_17.png', 'A18'],
['HHC_2019_02_03_18.png', 'A19'],
['HHC_2019_02_03_19.png', 'A20'],
['HHC_2019_02_03_20.png', 'A21'],
['HHC_2019_02_03_21.png', 'A22'],
['HHC_2019_02_03_22.png', 'A23'],
['HHC_2019_02_03_23.png', 'A24'],
['HHC_2019_02_03_24.png', 'A25'],
['HHC_2019_02_03_25.png', 'A26'],
['HHC_2019_02_03_26.png', 'A27'],
['HHC_2019_02_03_27.png', 'A28'],
['HHC_2019_02_03_28.png', 'A29'],
['HHC_2019_02_03_29.png', 'A30'],
['HHC_2019_02_03_30.png', 'A31'],
['HHC_2019_02_03_31.png', 'A32'],
['HHC_2019_02_03_32.png', 'A33'],
['HHC_2019_02_03_33.png', 'A34'],
['HHC_2019_02_03_34.png', 'A35'],
['HHC_2019_02_03_35.png', 'A36'],
['HHC_2019_02_03_36.png', 'Z3'],
['HHC_2019_02_03_37.png', 'Z2'],
['HHC_2019_02_03_38.png', 'Z1'],
['HHC_2019_02_03_39.png', 'Z4'],
['HHC_2019_02_03_40.png', 'Z5'],
['HHC_2019_02_03_41.png', 'Z6'],
['HHC_2019_02_03_42.png', 'Z7'],
['HHC_2019_02_03_43.png', 'Z8'],
['HHC_2019_02_03_44.png', 'Z9'],
['HHC_2019_02_03_45.png', 'B5'],
['HHC_2019_02_03_46.png', 'B2'],
['HHC_2019_02_03_47.png', 'B4'],
['HHC_2019_02_03_48.png', 'B4'],
['HHC_2019_02_03_49.png', 'B5'],
['HHC_2019_02_03_50.png', 'B6'],
['HHC_2019_02_03_51.png', 'B7'],
['HHC_2019_02_03_52.png', 'B8'],
['HHC_2019_02_03_53.png', np.nan],
['HHC_2019_02_03_54.png', np.nan],
['HHC_2019_02_03_55.png', 'C1'],
['HHC_2019_02_03_56.png', 'C2'],
['HHC_2019_02_03_57.png', 'C3'],
['HHC_2019_02_03_58.png', 'C4'],
['HHC_2019_02_03_59.png', 'C5'],
['HHC_2019_02_03_60.png', 'C6'],
['HHC_2019_02_03_61.png', 'C7'],
['HHC_2019_02_03_62.png', 'C8'],
['HHC_2019_02_03_63.png', 'C9'],
['HHC_2019_02_03_64.png', 'C10'],
['HHC_2019_02_03_65.png', 'C11'],
['HHC_2019_02_03_66.png', 'C12'],
['HHC_2019_02_03_67.png', 'C13'],
['HHC_2019_02_03_68.png', 'C14'],
['HHC_2019_02_03_69.png', 'C15'],
['HHC_2019_02_03_70.png', 'C16'],
['HHC_2019_02_03_71.png', 'C17'],
['HHC_2019_02_03_72.png', 'C18'],
['HHC_2019_02_03_73.png', 'EE1'],
['HHC_2019_02_03_74.png', 'EE2'],
['HHC_2019_02_03_75.png', 'EE3'],
['HHC_2019_02_03_76.png', 'EE4'],
['HHC_2019_02_03_77.png', 'EE5'],
['HHC_2019_02_03_78.png', 'EE6'],
['HHC_2019_02_03_79.png', 'F1'],
['HHC_2019_02_03_80.png', 'F2'],
['HHC_2019_02_03_81.png', 'F3'],
['HHC_2019_02_03_82.png', 'F4'],
['HHC_2019_02_03_83.png', 'F5'],
['HHC_2019_02_03_84.png', 'F6'],
['HHC_2019_02_03_85.png', 'F7'],
['HHC_2019_02_03_86.png', 'F8'],
['HHC_2019_02_03_87.png', 'G1'],
['HHC_2019_02_03_88.png', 'G2'],
['HHC_2019_02_03_89.png', 'G3'],
['HHC_2019_02_03_90.png', 'G4'],
['HHC_2019_02_03_91.png', 'G5'],
['HHC_2019_02_03_92.png', 'G6'],
['HHC_2019_02_03_93.png', 'G7'],
['HHC_2019_02_03_94.png', 'G8'],
['HHC_2019_02_03_95.png', 'G9'],
['HHC_2019_02_03_96.png', 'G10'],
['HHC_2019_02_03_97.png', 'G11'],
['HHC_2019_02_03_98.png', 'G12'],
['HHC_2019_02_03_99.png', 'G13'],
['HHC_2019_02_03_100.png', 'G14'],
['HHC_2019_02_03_101.png', 'G15'],
['HHC_2019_02_03_102.png', 'G16'],
['HHC_2019_02_03_103.png', '1'],
['HHC_2019_02_03_104.png', '2'],
['HHC_2019_02_03_105.png', 3],
['HHC_2019_02_03_106.png', 4],
['HHC_2019_02_03_107.png', 5],
['HHC_2019_02_03_108.png', 6],
['HHC_2019_02_03_109.png', 7],
['HHC_2019_02_03_110.png', 'R0'],
['HHC_2019_02_03_111.png', 'R1'],
['HHC_2019_02_03_112.png', 'R2'],
['HHC_2019_02_03_113.png', 'R3'],
['HHC_2019_02_03_114.png', 'R4'],
['HHC_2019_02_03_115.png', 'R5'],
['HHC_2019_02_03_116.png', 'R6'],
['HHC_2019_02_03_117.png', 'R7'],
['HHC_2019_02_03_118.png', 'R8'],
['HHC_2019_02_03_119.png', 'R9'],
['HHC_2019_02_03_120.png', 'R10'],
['HHC_2019_02_03_121.png', 'R11'],
['HHC_2019_02_03_122.png', 'R12'],
['HHC_2019_02_03_123.png', 'R13'],
['HHC_2019_02_03_124.png', 'R14'],
['HHC_2019_02_03_125.png', 'R15'],
['HHC_2019_02_03_126.png', 'R16'],
['HHC_2019_02_03_127.png', 'R17'],
['HHC_2019_02_03_128.png', 'R18'],
['HHC_2019_02_03_129.png', 'R19'],
['HHC_2019_02_03_130.png', 'R20'],
['HHC_2019_02_03_131.png', 'R21'],
['HHC_2019_02_03_132.png', 'R22'],
['HHC_2019_02_03_133.png', 'U1'],
['HHC_2019_02_03_134.png', 'U2'],
['HHC_2019_02_03_135.png', 'U3'],
['HHC_2019_02_03_136.png', 'U4'],
['HHC_2019_02_03_137.png', 'U5'],
['HHC_2019_02_03_138.png', 'U5'],
['HHC_2019_02_03_139.png', 'U6'],
['HHC_2019_02_03_140.png', 'U7']]), columns=['col1', 'col2'])
1 个解决方案
解决方案1
0 2019-03-30 18:08:59
由于你有多个条件，我会使用np.select ：
# Show dataframe used.
print(df.head())
col1 col2
0 HHC_2019_02_03_0.png A1
1 HHC_2019_02_03_1.png A2
2 HHC_2019_02_03_2.png A3
3 HHC_2019_02_03_3.png A4
4 HHC_2019_02_03_4.png A5
应用np.select
conditions = [df['col1'] == 'HHC_2019_02_03_53.png',
df['col1'] == 'HHC_2019_02_03_54.png']
choices = ['B9', 'B10']
df['col2'] = np.select(conditions, choices, default=df['col2'])
print(df[(df['col1'] == 'HHC_2019_02_03_53.png') | (df['col1'] == 'HHC_2019_02_03_54.png')])
col1 col2
53 HHC_2019_02_03_53.png B9
54 HHC_2019_02_03_54.png B10
Pandas索引插值在最后一个数据点之后填充缺失值
[英]Pandas index interpolation filling in missing values after the last data point
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