[英]Filling in missing data using "ffill"
I have the following data我有以下数据
4/23/2021 493107
4/26/2021 485117
4/27/2021 485117
4/28/2021 485117
4/29/2021 485117
4/30/2021 485117
5/7/2021 484691
I want it to look like the following:我希望它看起来像下面这样:
4/23/2021 493107
4/24/2021 485117
4/25/2021 485117
4/26/2021 485117
4/27/2021 485117
4/28/2021 485117
4/29/2021 485117
4/30/2021 485117
5/1/2021 484691
5/2/2021 484691
5/3/2021 484691
5/4/2021 484691
5/5/2021 484691
5/6/2021 484691
5/7/2021 484691
So it uses date below to fill in the missing data.所以它使用下面的日期来填写缺失的数据。 I tried the following code:
我尝试了以下代码:
df['Date']=pd.to_datetime(df['Date'].astype(str), format='%m/%d/%Y')
df.set_index(df['Date'], inplace=True)
df = df.resample('D').sum().fillna(0)
df['crude'] = df['crude'].replace({ 0:np.nan})
df['crude'].fillna(method='ffill', inplace=True)
However, this results in taking the data above and getting the following:但是,这会导致获取上述数据并获得以下结果:
4/23/2021 493107
4/24/2021 493107
4/25/2021 493107
4/26/2021 485117
4/27/2021 485117
4/28/2021 485117
4/29/2021 485117
4/30/2021 485117
5/1/2021 485117
5/2/2021 485117
5/3/2021 485117
5/4/2021 485117
5/5/2021 485117
5/6/2021 485117
5/7/2021 969382
Which does not match what I need the output to be.这与我需要的 output 不匹配。
Set the index of the dataframe to Date
, then using asfreq
conform/reindex the index of the dataframe to daily frequency providing fill method as backward fill将 dataframe 的索引设置为
Date
,然后使用asfreq
将 dataframe 的索引设置为每日频率,提供填充方法作为反向填充
df.set_index('Date').asfreq('D', method='bfill')
crude
Date
2021-04-23 493107
2021-04-24 485117
2021-04-25 485117
2021-04-26 485117
2021-04-27 485117
2021-04-28 485117
2021-04-29 485117
2021-04-30 485117
2021-05-01 484691
2021-05-02 484691
2021-05-03 484691
2021-05-04 484691
2021-05-05 484691
2021-05-06 484691
2021-05-07 484691
Try replace 0 with bfill instead of ffill :尝试用bfill而不是ffill替换 0 :
import pandas as pd
df = pd.DataFrame({
'crude': {'4/23/2021': 493107, '4/26/2021': 485117,
'4/27/2021': 485117, '4/28/2021': 485117,
'4/29/2021': 485117, '4/30/2021': 485117,
'5/7/2021': 484691}
})
df.index = pd.to_datetime(df.index)
df = df.resample('D').sum()
df['crude'] = df['crude'].replace(0, method='bfill')
print(df)
df
: df
:
crude
2021-04-23 493107
2021-04-24 485117
2021-04-25 485117
2021-04-26 485117
2021-04-27 485117
2021-04-28 485117
2021-04-29 485117
2021-04-30 485117
2021-05-01 484691
2021-05-02 484691
2021-05-03 484691
2021-05-04 484691
2021-05-05 484691
2021-05-06 484691
2021-05-07 484691
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