使用 groupby 滚动年初至今的百分比变化

发布于 2025-01-09 09:24:07 字数 477 浏览 4 评论 0原文

我有一个大数据框。您将在下面找到其摘录：

lst=[['31122020','A',12],['31012021','A',14],['28022021','A',15],['31032021','A',17]]
df2=pd.DataFrame(lst, columns=['Date','FN','AuM'])

我想计算 AuM 列的年初至今 (YTD)。新专栏应该是这样的：

lst=[['31122020','A',12,'NaN'],['31012021','A',14,0.167],['28022021','A',15,0.25],['31032021','A',17,0.417]]
df2=pd.DataFrame(lst, columns=['Date','FN','AuM','AuM_YTD_%Change'])

你知道有什么pandas函数可以达到我的目标吗？

原文

I have got a big data frame. Below you will find an extract of it:

lst=[['31122020','A',12],['31012021','A',14],['28022021','A',15],['31032021','A',17]]
df2=pd.DataFrame(lst, columns=['Date','FN','AuM'])

I would like to calculate the Year to date (YTD) of the column AuM. The new column should look like this:

lst=[['31122020','A',12,'NaN'],['31012021','A',14,0.167],['28022021','A',15,0.25],['31032021','A',17,0.417]]
df2=pd.DataFrame(lst, columns=['Date','FN','AuM','AuM_YTD_%Change'])

Do you know any pandas function which can reach my goal?

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冷夜 2025-01-16 09:24:07

您可以为一年内的日期创建一个掩码，然后使用 diff + cumsum 进行更改，使用 div 表示变化率：

df2['Date'] = pd.to_datetime(df2['Date'], format='%d%m%Y')
msk = df2['Date'] < df2.loc[0, 'Date'] + pd.to_timedelta(365, unit='D')
df2['AuM_YTD_%Change'] = df2.loc[msk, 'AuM'].diff().cumsum().div(df2.loc[0,'AuM'])

输出：

        Date FN  AuM  AuM_YTD_%Change
0 2020-12-31  A   12              NaN
1 2021-01-31  A   14         0.166667
2 2021-02-28  A   15         0.250000
3 2021-03-31  A   17         0.416667

You can create a mask for dates inside one year, then use diff + cumsum for the changes, and div for the change rates:

df2['Date'] = pd.to_datetime(df2['Date'], format='%d%m%Y')
msk = df2['Date'] < df2.loc[0, 'Date'] + pd.to_timedelta(365, unit='D')
df2['AuM_YTD_%Change'] = df2.loc[msk, 'AuM'].diff().cumsum().div(df2.loc[0,'AuM'])

Output:

        Date FN  AuM  AuM_YTD_%Change
0 2020-12-31  A   12              NaN
1 2021-01-31  A   14         0.166667
2 2021-02-28  A   15         0.250000
3 2021-03-31  A   17         0.416667

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