我有一个包含一些缺失值的DataFrame。我想使用ffill()
来填充var1
和var2
中按date
和building
分组的缺失值。我可以逐个变量地执行此操作,但是当我尝试同时对两个变量执行此操作时,代码崩溃了。如何同时对这两个变量执行此操作,并且不修改但保留var3
或var4
?
df = pd.DataFrame({
'date': ['2019-01-01','2019-01-01','2019-01-01','2019-01-01','2019-02-01','2019-02-01','2019-02-01','2019-02-01'],
'building': ['a', 'a', 'b', 'b', 'a', 'a', 'b', 'b'],
'var1': [1.5, np.nan, 2.1, 2.2, 1.2, 1.3, 2.4, np.nan],
'var2': [100, 110, 105, np.nan, 102, np.nan, 103, 107],
'var3': [10, 11, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan],
'var4': [1, 2, 3, 4, 5, 6, 7, 8]
})
df
date building var1 var2 var3 var4
0 2019-01-01 a 1.5 100.0 10.0 1
1 2019-01-01 a NaN 110.0 11.0 2
2 2019-01-01 b 2.1 105.0 NaN 3
3 2019-01-01 b 2.2 NaN NaN 4
4 2019-02-01 a 1.2 102.0 NaN 5
5 2019-02-01 a 1.3 NaN NaN 6
6 2019-02-01 b 2.4 103.0 NaN 7
7 2019-02-01 b NaN 107.0 NaN 8
# This works
df['var1'] = df.groupby(['date', 'building'])['var1'].ffill()
df['var2'] = df.groupby(['date', 'building'])['var2'].ffill()
df
date building var1 var2 var3 var4
0 2019-01-01 a 1.5 100.0 10.0 1
1 2019-01-01 a 1.5 110.0 11.0 2
2 2019-01-01 b 2.1 105.0 NaN 3
3 2019-01-01 b 2.2 105.0 NaN 4
4 2019-02-01 a 1.2 102.0 NaN 5
5 2019-02-01 a 1.3 102.0 NaN 6
6 2019-02-01 b 2.4 103.0 NaN 7
7 2019-02-01 b 2.4 107.0 NaN 8
# This doesn't work
df[['var1', 'var2']] = df.groupby(['date', 'building'])[['var1', 'var2']].ffill()
ValueError: Columns must be same length as key
var1
和var2
被保留了。我修改了我的问题,包括其他不应该被删除或修改的变量。 - Gaurav Bansal