用前几个月的数据替换 nan 值

Question

我有一个 DataFrame 如下。此 DataFrame 包含 NAN 值。我想用上个月的 DataFrame 中较早的非 nan 值替换 nan 值：

date (y-d-m)  | value 
 2022-01-01   | 1  
 2022-02-01   | 2 
 2022-03-01   | 3     
 2022-04-01   | 4  
 ...
 2022-01-02   | nan  
 2022-02-02   | nan
 2022-03-02   | nan
 2022-04-02   | nan
 ... 
 2022-01-03   | nan  
 2022-02-03   | nan
 2022-03-03   | nan
 2022-04-03   | nan

期望的结果

date (y-d-m)  | value 
 2022-01-01   | 1  
 2022-02-01   | 2 
 2022-03-01   | 3     
 2022-04-01   | 4  
 ...
 2022-01-02   | 1  
 2022-02-02   | 2
 2022-03-02   | 3
 2022-04-02   | 4
 ... 
 2022-01-03   | 1  
 2022-02-03   | 2
 2022-03-03   | 3
 2022-04-03   | 4

数据：

{'date (y-d-m)': ['2022-01-01', '2022-02-01', '2022-03-01', '2022-04-01',
                  '2022-01-02', '2022-02-02', '2022-03-02', '2022-04-02',
                  '2022-01-03', '2022-02-03', '2022-03-03', '2022-04-03'],
 'value': [1.0, 2.0, 3.0, 4.0, nan, nan, nan, nan, nan, nan, nan, nan]}

Answer 1

您可以将 "date (y-d-m)" 列转换为日期时间；然后 groupby "day" 并向前填充 ffill （前几个月同一天的值）：

df['date (y-d-m)'] = pd.to_datetime(df['date (y-d-m)'], format='%Y-%d-%m')
df['value'] = df.groupby(df['date (y-d-m)'].dt.day)['value'].ffill()

输出：

   date (y-d-m)  value
0    2022-01-01    1.0
1    2022-01-02    2.0
2    2022-01-03    3.0
3    2022-01-04    4.0
4    2022-02-01    1.0
5    2022-02-02    2.0
6    2022-02-03    3.0
7    2022-02-04    4.0
8    2022-03-01    1.0
9    2022-03-02    2.0
10   2022-03-03    3.0
11   2022-03-04    4.0

用前几个月的数据替换 nan 值

Replace nan values with data from previous months

python

nan

dataframe

pandas

pandas-groupby