如何在多索引 pandas 数据帧上将数据滞后x特定天数? [英] How to lag data by x specific days on a multi index pandas dataframe?

问题描述

我有一个数据框，其中包含日期，资产，然后是价格/成交量数据.我正尝试从7天前提取数据，但是问题是我不能使用shift()，因为我的表中缺少日期.

I have a dataframe that has dates, assets, and then price/volume data. I'm trying to pull in data from 7 days ago, but the issue is that I can't use shift() because my table has missing dates in it.

 date   cusip   price   price_7daysago
1/1/2017    a   1   
1/1/2017    b   2   
1/2/2017    a   1.2 
1/2/2017    b   2.3 
1/8/2017    a   1.1         1
1/8/2017    b   2.2         2

我尝试创建一个lambda函数来尝试使用loc和timedelta来创建此移位，但是我只能输出空的numpy数组:

I've tried creating a lambda function to try to use loc and timedelta to create this shifting, but I was only able to output empty numpy arrays:

def row_delta(x, df, days, colname):
    if datetime.strptime(x['recorddate'], '%Y%m%d') - timedelta(days) in [datetime.strptime(x,'%Y%m%d') for x in   df['recorddate'].unique().tolist()]:
        return df.loc[(df['recorddate_date'] == df['recorddate_date'] - timedelta(days)) & (df['cusip'] == x['cusip']) ,colname]
    else:
        return 'nothing'

我还考虑过要做与此类似的操作缺少日期，但是我的问题是我有多个索引，日期和临时表，所以我不能只是为此重新索引.

I also thought of doing something similar to this in order to fill in missing dates, but my issue is that I have multiple indexes, the dates and the cusips so I can't just reindex on this.

我不确定我还能做什么，但是任何帮助将不胜感激！

I'm not really sure what else I can do, but any help would be greatly appreciated!

推荐答案

merge DataFrame 与其自身，同时在右侧Frame的日期列中添加7天.使用后缀参数适当地命名列.

merge the DataFrame with itself while adding 7 days to the date column for the right Frame. Use the suffixes argument to name the columns appropriately.

import pandas as pd

df['date'] = pd.to_datetime(df.date)
df.merge(df.assign(date = df.date+pd.Timedelta(days=7)), 
         on=['date', 'cusip'],
         how='left', suffixes=['', '_7daysago'])

输出: df

        date cusip  price  price_7daysago
0 2017-01-01     a    1.0             NaN
1 2017-01-01     b    2.0             NaN
2 2017-01-02     a    1.2             NaN
3 2017-01-02     b    2.3             NaN
4 2017-01-08     a    1.1             1.0
5 2017-01-08     b    2.2             2.0

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