[英]python - Pandas: groupby ffill for multiple columns
I have the following DataFrame with some missing values.我有以下 DataFrame 有一些缺失值。 I want to use
ffill()
to fill missing values in both var1
and var2
grouped by date
and building
.我想使用
ffill()
来填充按date
和building
分组的var1
和var2
缺失值。 I can do that for one variable at a time, but when I try to do it for both, it crashes.我可以一次为一个变量执行此操作,但是当我尝试为两个变量执行此操作时,它会崩溃。 How can I do this for both variables at once, while also not modifying but retaining
var3
or var4
?如何同时对两个变量执行此操作,同时也不修改但保留
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
I think you need to add fillna
before your groupby
.我认为您需要在
groupby
之前添加fillna
。
df[["var1", "var2"]] = df[["var1", "var2"]].fillna(df.groupby(['date', 'building'])[["var1", "var2"]].ffill())
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
Do it iteratively:反复执行:
gb = df.groupby(['date', 'building'])
for g in ["var1", "var2"]:
df[g] = gb[g].ffill()
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
@Gaurav Bansal You are just missing a few columns when fitting group by in the dataframe. @Gaurav Bansal 在数据框中拟合 group by 时,您只是缺少几列。
df[['date', 'building','var1', 'var2']] = df.groupby(['date', 'building'])[['var1', 'var2']].ffill()
Group by will return four column data frame which is 'date', building', 'var1' and 'var2' or you can just give a data frame to store the manipulated dataframe. Group by 将返回四列数据框,即“日期”、“建筑物”、“var1”和“var2”,或者您可以只提供一个数据框来存储操作的数据框。
So you need to store it into a four column df to have the perfect match for key-value returned.因此,您需要将其存储到一个四列 df 中,以便与返回的键值完美匹配。
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