根据不同的行和列拆分行答案

【问题标题】：Split rows based on different rows and columns根据不同的行和列拆分行
【发布时间】：2020-12-03 08:43:19
【问题描述】：

非常感谢您在这方面的帮助。

我有一张包含产品、日期和金额的表格。这就是初始表格的样子。

Product ID     goliveyear     endyear     Revenue   
    1            2020-10       2022-02       90
    1            2020-10       2022-02       140
    1            2020-10       2022-02       60

目的是将每一行拆分为到年底剩余的月数如果是第一年，则从第一年的月份开始拆分，直到年底如果年份是年末，则拆分到年末的月份。收入需要按月的行数进行拆分，因为第一个表中的收入是指整个期间。其间的所有年份将分为 12 行，以及每个月的收入。

  Product ID     goliveyear     endyear     Year    Month   Revenue
    1            2020-10       2022-02       2020     10      90/3=30
    1            2020-10       2022-02       2020     11       30
    1            2020-10       2022-02       2020     12       30
    1            2020-10       2022-02       2021     01     140/12 =11.67
    1            2020-10       2022-02       2021     02       11.67
    1            2020-10       2022-02       2021     03       11.67
    1            2020-10       2022-02       2021     04       11.67
   ...            ...            ...         ...      ...      ... 
    1            2020-10       2022-02       2022     01      60/2 = 30
    1            2020-10       2022-02       2022     02       30

非常感谢大家。

【问题讨论】：

我认为您需要提供试用代码来解决您的问题。

标签： python pandas

【解决方案1】：

相当多的步骤。

首先设置df

from io import StringIO
import pandas as pd
from datetime import datetime,timedelta


df = pd.read_csv(StringIO(
"""
Product_ID     goliveyear     endyear     Revenue   
    1            2020-10       2022-02       90
    1            2020-10       2022-02       140
    1            2020-10       2022-02       60
"""), delim_whitespace=True)
df['goliveyear'] = pd.to_datetime(df['goliveyear'])
df['endyear'] = pd.to_datetime(df['endyear'])
df

然后添加 year_start、year_end、period_start、period_end 列

df['ys'] = df['goliveyear'].dt.year + df.groupby('Product_ID').cumcount()
df['ye'] = df['ys'] + 1
df['ys'] = pd.to_datetime(df['ys'], format = '%Y')
df['ye'] = pd.to_datetime(df['ye'], format = '%Y')+ timedelta(days=-1)
df['ps'] = df[['goliveyear','ys']].max(axis=1)
df['pe'] = df[['endyear','ye']].min(axis=1)

生产


      Product_ID  goliveyear           endyear                Revenue  ys                   ye                   ps                   pe
--  ------------  -------------------  -------------------  ---------  -------------------  -------------------  -------------------  -------------------
 0             1  2020-10-01 00:00:00  2022-02-01 00:00:00         90  2020-01-01 00:00:00  2020-12-31 00:00:00  2020-10-01 00:00:00  2020-12-31 00:00:00
 1             1  2020-10-01 00:00:00  2022-02-01 00:00:00        140  2021-01-01 00:00:00  2021-12-31 00:00:00  2021-01-01 00:00:00  2021-12-31 00:00:00
 2             1  2020-10-01 00:00:00  2022-02-01 00:00:00         60  2022-01-01 00:00:00  2022-12-31 00:00:00  2022-01-01 00:00:00  2022-02-01 00:00:00

然后首先添加months作为列表

df['months'] = df.apply(lambda r: [d.month for d in pd.date_range(r['ps'], r['pe'], freq='MS', closed = None).to_pydatetime()], axis=1)

输出：

      Product_ID  goliveyear           endyear                Revenue  ys                   ye                   ps                   pe                   months
--  ------------  -------------------  -------------------  ---------  -------------------  -------------------  -------------------  -------------------  ---------------------------------------
 0             1  2020-10-01 00:00:00  2022-02-01 00:00:00         90  2020-01-01 00:00:00  2020-12-31 00:00:00  2020-10-01 00:00:00  2020-12-31 00:00:00  [10, 11, 12]
 1             1  2020-10-01 00:00:00  2022-02-01 00:00:00        140  2021-01-01 00:00:00  2021-12-31 00:00:00  2021-01-01 00:00:00  2021-12-31 00:00:00  [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]
 2             1  2020-10-01 00:00:00  2022-02-01 00:00:00         60  2022-01-01 00:00:00  2022-12-31 00:00:00  2022-01-01 00:00:00  2022-02-01 00:00:00  [1, 2]

然后我们分解months 对收入进行所需的计算并删除不需要的列

df = df.explode('months')
df['Revenue'] = df['Revenue'] / df.groupby(['Product_ID','ys'])['months'].transform('count')
df = df.drop(columns = ['goliveyear','endyear','ye','ps','pe'])
df['ys'] = df['ys'].dt.year

得到


      Product_ID    Revenue    ys    months
--  ------------  ---------  ----  --------
 0             1    30       2020        10
 0             1    30       2020        11
 0             1    30       2020        12
 1             1    11.6667  2021         1
 1             1    11.6667  2021         2
 1             1    11.6667  2021         3
 1             1    11.6667  2021         4
 1             1    11.6667  2021         5
 1             1    11.6667  2021         6
 1             1    11.6667  2021         7
 1             1    11.6667  2021         8
 1             1    11.6667  2021         9
 1             1    11.6667  2021        10
 1             1    11.6667  2021        11
 1             1    11.6667  2021        12
 2             1    30       2022         1
 2             1    30       2022         2

【讨论】：

【解决方案2】：

试试这个：

import pandas as pd
from io import StringIO

s = """
Product ID,goliveyear,endyear,Revenue
1,2020-10,2022-02,90
1,2020-10,2022-02,140
1,2020-10,2022-02,60"""

df = pd.read_csv(StringIO(s))

# generate the months list between thest two months
df['rng'] = df.apply(lambda x: pd.date_range(x['goliveyear'], x['endyear'], 
freq='MS'), axis=1)

# explode the dataframe by months list
df_exploded = df.explode('rng')

df_exploded['Year'] = df_exploded['rng'].dt.year
df_exploded['Month'] = df_exploded['rng'].dt.month

# the (index,year) pair to fliter rows
filter_year = list(zip(df.index, df_exploded.Year.unique()))

# used columns
use_cols = ['Product ID', 'goliveyear', 'endyear', 'Revenue', 'Month']

# filter rows
df_filter = df_exploded.set_index([df_exploded.index, 
df_exploded.Year]).loc[filter_year, 
use_cols].reset_index().drop(columns='level_0')

# calculate the average Revenue
result = df_filter.set_index(['Year', "Month"]).assign(
    Revenue=(df_filter.groupby(['Year', 'Month'])['Revenue'].sum() / 
     df_filter.groupby('Year')['Month'].count())
).reset_index()

result

输出

    Year    Month   Product ID  goliveyear  endyear Revenue
0   2020    10  1   2020-10 2022-02 30.000000
1   2020    11  1   2020-10 2022-02 30.000000
2   2020    12  1   2020-10 2022-02 30.000000
3   2021    1   1   2020-10 2022-02 11.666667
4   2021    2   1   2020-10 2022-02 11.666667
5   2021    3   1   2020-10 2022-02 11.666667
6   2021    4   1   2020-10 2022-02 11.666667
7   2021    5   1   2020-10 2022-02 11.666667
8   2021    6   1   2020-10 2022-02 11.666667
9   2021    7   1   2020-10 2022-02 11.666667
10  2021    8   1   2020-10 2022-02 11.666667
11  2021    9   1   2020-10 2022-02 11.666667
12  2021    10  1   2020-10 2022-02 11.666667
13  2021    11  1   2020-10 2022-02 11.666667
14  2021    12  1   2020-10 2022-02 11.666667
15  2022    1   1   2020-10 2022-02 30.000000
16  2022    2   1   2020-10 2022-02 30.000000

【讨论】：