pandas 中的多索引fillna [英] Multi-Indexed fillna in Pandas

问题描述

我有一个多索引数据框，我正在寻找回填组中缺失值的方法.我目前拥有的数据框如下所示:

I have a multi-indexed dataframe and I'm looking to backfill missing values within a group. The dataframe I have currently looks like this:

df = pd.DataFrame({
                'group': ['group_a'] * 7 + ['group_b'] * 3 + ['group_c'] * 2,
                'Date': ["2013-06-11",
                        "2013-07-02",
                        "2013-07-09",
                        "2013-07-30",
                        "2013-08-06",
                        "2013-09-03",
                        "2013-10-01",
                        "2013-07-09",
                        "2013-08-06",
                        "2013-09-03",
                        "2013-07-09",
                        "2013-09-03"],
                 'Value': [np.nan, np.nan, np.nan,  9,  4, 40, 18, np.nan, np.nan, 5, np.nan, 2]})

df.Date = df['Date'].apply(lambda x: pd.to_datetime(x).date())
df = df.set_index(['group', 'Date'])

我正在尝试获取一个数据框，该数据框会回填该组中缺少的值. 像这样:

I'm trying to get a dataframe that backfills the missing values within the group. Like this:

Group   Date        Value
group_a 2013-06-11      9
        2013-07-02      9
        2013-07-09      9
        2013-07-30      9
        2013-08-06      4
        2013-09-03     40
        2013-10-01     18
group_b 2013-07-09      5
        2013-08-06      5
        2013-09-03      5
group_c 2013-07-09      2
        2013-09-03      2

我尝试使用pd.fillna('Value', inplace=True)，但是收到关于在副本上设置值的警告，此后我就发现与多索引的存在有关.有没有一种方法可以使fillna适用于多索引行?另外，理想情况下，我只能将fillna应用于一列，而不是整个数据框.

I tried using pd.fillna('Value', inplace=True), but I get a warning on setting a value on copy, which I've since figured out is related to the presence of the multi-index. Is there a way to make fillna work for multi-indexed rows? Also, ideally I'd be able to apply the fillna to only one column and not the entire dataframe.

任何对此的见识都会很棒.

Any insight on this would be great.

推荐答案

使用groupby(level=0)然后使用bfill和update:

df.update(df.groupby(level=0).bfill())
df

注意:update更改df的位置.

df = df.groupby(level='group').bfill()

df = df.unstack(0).bfill().stack().swaplevel(0, 1).reindex_like(df)

特定于列

df.Value = df.groupby(level=0).Value.bfill()

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