向量化实现可从pandas数据框中的单行创建多行 [英] Vectorized implementation to create multiple rows from a single row in pandas dataframe

问题描述

对于输入表中的每一行，我需要通过基于每月分隔日期范围来生成多行. (请参考下面的示例输出).

For each row in the input table, I need to generate multiple rows by separating the date range based on monthly. (please refer to the below sample output).

有一种简单的迭代方法可以逐行转换，但是在大型数据帧上速度很慢.

There is a simple iterative approach to convert row by row, but it is very slow on large dataframes.

有人可以建议采用矢量化方法，例如使用apply()，map()等实现目标吗?

Could anyone suggest a vectorized approach, such as using apply(), map() etc. to achieve the objective?

输出表是一个新表.

输入:

ID, START_DATE, END_DATE
1, 2010-12-08, 2011-03-01
2, 2010-12-10, 2011-01-12
3, 2010-12-16, 2011-03-07

输出:

ID, START_DATE, END_DATE, NUMBER_DAYS, ACTION_DATE
1, 2010-12-08, 2010-12-31, 23, 201012
1, 2010-12-08, 2011-01-31, 54, 201101
1, 2010-12-08, 2011-02-28, 82, 201102
1, 2010-12-08, 2011-03-01, 83, 201103
2, 2010-12-10, 2010-12-31, 21, 201012
2, 2010-12-10, 2011-01-12, 33, 201101
3, 2010-12-16, 2010-12-31, 15, 201012
4, 2010-12-16, 2011-01-31, 46, 201101
5, 2010-12-16, 2011-02-28, 74, 201102
6, 2010-12-16, 2011-03-07, 81, 201103

推荐答案

我认为您可以使用:

import pandas as pd

df = pd.DataFrame({'ID': {0: 1, 1: 2, 2: 3}, 
'END_DATE': {0: pd.Timestamp('2011-03-01 00:00:00'),
             1: pd.Timestamp('2011-01-12 00:00:00'), 
             2: pd.Timestamp('2011-03-07 00:00:00')}, 
'START_DATE': {0: pd.Timestamp('2010-12-08 00:00:00'), 
               1: pd.Timestamp('2010-12-10 00:00:00'), 
               2: pd.Timestamp('2010-12-16 00:00:00')}}, 
columns=['ID','START_DATE', 'END_DATE'])

print df
   ID START_DATE   END_DATE
0   1 2010-12-08 2011-03-01
1   2 2010-12-10 2011-01-12
2   3 2010-12-16 2011-03-07

#if multiple columns, you can filter them by subset
#df = df[['ID','START_DATE', 'END_DATE']]

#stack columns START_DATE and END_DATE
df1 = df.set_index('ID')
        .stack()
        .reset_index(level=1, drop=True)
        .to_frame()
        .rename(columns={0:'Date'})
#print df1

#resample and fill missing data 
df1 = df1.groupby(df1.index).apply(lambda x: x.set_index('Date').resample('M').asfreq())
         .reset_index()
print df1

   ID       Date
0   1 2010-12-31
1   1 2011-01-31
2   1 2011-02-28
3   1 2011-03-31
4   2 2010-12-31
5   2 2011-01-31
6   3 2010-12-31
7   3 2011-01-31
8   3 2011-02-28
9   3 2011-03-31

Month的最后一天存在问题，因为resample添加了Month的最后一天，所以首先创建period列，然后创建 combine_first 从列中添加缺失值Date并通过 bfill 添加START_DATE列的值缺失.

There is problem with last day of Month, because resample add last day of Month, so first create period columns and then merge them. By combine_first add missing values from column Date and by bfill add missing values of column START_DATE.

df['period'] = df.END_DATE.dt.to_period('M')
df1['period'] = df1.Date.dt.to_period('M')

df2 = pd.merge(df1, df, on=['ID','period'], how='left')

df2['END_DATE'] = df2.END_DATE.combine_first(df2.Date)
df2['START_DATE'] = df2.START_DATE.bfill()
df2 = df2.drop(['Date','period'], axis=1)

最后通过与 dt.days 和 dt.strftime :

Last add new columns by difference with dt.days and dt.strftime:

df2['NUMBER_DAYS'] = (df2.END_DATE - df2.START_DATE).dt.days
df2['ACTION_DATE'] = df2.END_DATE.dt.strftime('%Y%m')

print df2
   ID START_DATE   END_DATE  NUMBER_DAYS ACTION_DATE
0   1 2010-12-08 2010-12-31           23      201012
1   1 2010-12-08 2011-01-31           54      201101
2   1 2010-12-08 2011-02-28           82      201102
3   1 2010-12-08 2011-03-01           83      201103
4   2 2010-12-10 2010-12-31           21      201012
5   2 2010-12-10 2011-01-12           33      201101
6   3 2010-12-16 2010-12-31           15      201012
7   3 2010-12-16 2011-01-31           46      201101
8   3 2010-12-16 2011-02-28           74      201102
9   3 2010-12-16 2011-03-07           81      201103

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