pandas 熊猫在 Groupby 中重新索引日期
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Pandas reindex dates in Groupby
提问by clg4
I have a dataframe with sporadic dates as the index, and columns = 'id' and 'num'. I would like to pd.groupbythe 'id' column, and apply the reindex to each group in the dataframe.
我有一个以零星日期为索引的数据框，列 = 'id' 和 'num'。我想要pd.groupby“id”列，并将重新索引应用于数据框中的每个组。
My sample dataset looks like this:
我的示例数据集如下所示：
id num
2015-08-01 1 3
2015-08-05 1 5
2015-08-06 1 4
2015-07-31 2 1
2015-08-03 2 2
2015-08-06 2 3
My expected output once pd.reindexwith ffillis:
我预计，一旦输出pd.reindex用ffill的是：
id num
2015-08-01 1 3
2015-08-02 1 3
2015-08-03 1 3
2015-08-04 1 3
2015-08-05 1 5
2015-08-06 1 4
2015-07-31 2 1
2015-08-01 2 1
2015-08-02 2 1
2015-08-03 2 2
2015-08-04 2 2
2015-08-05 2 2
2015-08-06 2 3
I have tried this, among other things to no avail:
newdf=df.groupby('id').reindex(method='ffill')Which returns error:AttributeError: Cannot access callable attribute 'reindex' of 'DataFrameGroupBy' objects, try using the 'apply' method
我试过这个，除此之外无济于事：
newdf=df.groupby('id').reindex(method='ffill')返回错误：AttributeError: Cannot access callable attribute 'reindex' of 'DataFrameGroupBy' objects, try using the 'apply' method
Any help would be much appreciated
任何帮助将非常感激
回答by JoeCondron
There's probably a slicker way to do this but this works:
可能有一种更巧妙的方法来做到这一点，但这有效：
def reindex_by_date(df):
dates = pd.date_range(df.index.min(), df.index.max())
return df.reindex(dates).ffill()
df.groupby('id').apply(reindex_by_date).reset_index(0, drop=True)
