pandas 在系列上重置索引以删除多索引 [英] Pandas reset index on series to remove multiindex

问题描述

当我用计数重新采样一些数据时，我从DataFrame创建了一个Series 像这样:H2是DataFrame:

I created a Series from a DataFrame, when I resampled some data with a count like so: where H2 is a DataFrame:

H3=H2[['SOLD_PRICE']]
H5=H3.resample('Q',how='count')
H6=pd.rolling_mean(H5,4)

这产生了一个看起来像这样的系列:

This yielded a series that looks like this:

1999-03-31  SOLD_PRICE     NaN
1999-06-30  SOLD_PRICE     NaN
1999-09-30  SOLD_PRICE     NaN
1999-12-31  SOLD_PRICE    3.00
2000-03-31  SOLD_PRICE    3.00

具有类似以下内容的索引:

with an index that looks like:

MultiIndex
[(1999-03-31 00:00:00, u'SOLD_PRICE'), (1999-06-30 00:00:00, u'SOLD_PRICE'), (1999-09-30 00:00:00, u'SOLD_PRICE'), (1999-12-31 00:00:00, u'SOLD_PRICE'),.....

我不希望第二列作为索引.理想情况下，我将使用DataFrame，其中第1列为日期"，第2列为销售"(删除索引的第二级).我不太清楚如何重新配置​​索引.

I don't want the second column as an index. Ideally I'd have a DataFrame with column 1 as "Date" and column 2 as "Sales" (dropping the second level of the index). I don't quite see how to reconfigure the index.

推荐答案

只需调用reset_index():

In [130]: s
Out[130]:
0           1
1999-03-31  SOLD_PRICE   NaN
1999-06-30  SOLD_PRICE   NaN
1999-09-30  SOLD_PRICE   NaN
1999-12-31  SOLD_PRICE     3
2000-03-31  SOLD_PRICE     3
Name: 2, dtype: float64

In [131]: s.reset_index()
Out[131]:
            0           1   2
0  1999-03-31  SOLD_PRICE NaN
1  1999-06-30  SOLD_PRICE NaN
2  1999-09-30  SOLD_PRICE NaN
3  1999-12-31  SOLD_PRICE   3
4  2000-03-31  SOLD_PRICE   3

有很多删除列的方法:

调用两次reset_index()并指定一列:

In [136]: s.reset_index(0).reset_index(drop=True)
Out[136]:
            0   2
0  1999-03-31 NaN
1  1999-06-30 NaN
2  1999-09-30 NaN
3  1999-12-31   3
4  2000-03-31   3

重置索引后删除该列:

In [137]: df = s.reset_index()

In [138]: df
Out[138]:
            0           1   2
0  1999-03-31  SOLD_PRICE NaN
1  1999-06-30  SOLD_PRICE NaN
2  1999-09-30  SOLD_PRICE NaN
3  1999-12-31  SOLD_PRICE   3
4  2000-03-31  SOLD_PRICE   3

In [139]: del df[1]

In [140]: df
Out[140]:
            0   2
0  1999-03-31 NaN
1  1999-06-30 NaN
2  1999-09-30 NaN
3  1999-12-31   3
4  2000-03-31   3

重置后呼叫drop():

In [144]: s.reset_index().drop(1, axis=1)
Out[144]:
            0   2
0  1999-03-31 NaN
1  1999-06-30 NaN
2  1999-09-30 NaN
3  1999-12-31   3
4  2000-03-31   3

然后，在重置索引后，只需重命名列

Then, after you've reset your index, just rename the columns

In [146]: df.columns = ['Date', 'Sales']

In [147]: df
Out[147]:
         Date  Sales
0  1999-03-31    NaN
1  1999-06-30    NaN
2  1999-09-30    NaN
3  1999-12-31      3
4  2000-03-31      3

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