将会计年度日期切片,合并并映射到日历年度日期到新列

时间:2018-09-27 19:40:39

标签: python pandas date dictionary

我有以下熊猫数据框:

Shortcut_Dimension_4_Code     Stage_Code
10225003                      2
8225003                       1
8225004                       3
8225005                       4

它是一个更大的数据集的一部分,我需要能够按月和年进行过滤。我需要从Shortcut_Dimension_4_Code列中大于9999999的值的前两位数字中提取会计年度,而对于小于或等于9999999的值从第一位数中提取财务值。该值需要加到“ 20”才能产生年份“ 20” +“ 8” = 2008 | “ 20” +“ 10” =2010。

该年份“ 2008、2010”需要与阶段代码值(1-12)结合起来,以产生一个月/年,即02/2010。

然后需要将日期02/2010从会计年度日期转换为日历年度日期,即会计年度日期:02/2010 =日历年度日期:08/2009。结果日期需要在新列中显示。最终的df最终看起来像这样:

Shortcut_Dimension_4_Code     Stage_Code     Date
10225003                      2              08/2009
8225003                       1              07/2007
8225004                       3              09/2007
8225005                       4              10/2007

我是熊猫和python的新手,可以使用一些帮助。我从这里开始:

Shortcut_Dimension_4_Code   Stage_Code  CY_Month    Fiscal_Year
    0   10225003                 2           8.0        10
    1   8225003                  1           7.0        82
    2   8225003                  1           7.0        82
    3   8225003                  1           7.0        82
    4   8225003                  1           7.0        82

我使用.map和.str方法来生成此df,但在2008-2009财政年度,我一直无法弄清楚如何获得财政年度的权利。

1 个答案:

答案 0 :(得分:0)

在下面的代码中,我假设Shortcut_Dimension_4_Code是一个整数。如果是字符串,则可以将其转换或切片,如下所示:df['Shortcut_Dimension_4_Code'].str[:-6]。在代码旁的注释中有更多解释。

只要您不必处理空值,该方法就应该起作用。

import pandas as pd
import numpy as np
from datetime import date
from dateutil.relativedelta import relativedelta

fiscal_month_offset = 6

input_df = pd.DataFrame(
    [[10225003, 2],
    [8225003, 1],
    [8225004, 3],
    [8225005, 4]],
    columns=['Shortcut_Dimension_4_Code', 'Stage_Code'])

# make a copy of input dataframe to avoid modifying it
df = input_df.copy()

# numpy will help us with numeric operations on large collections
df['fiscal_year'] = 2000 + np.floor_divide(df['Shortcut_Dimension_4_Code'], 1000000)

# loop with `apply` to create `date` objects from available columns
# day is a required field in date, so we'll just use 1
df['fiscal_date'] = df.apply(lambda row: date(row['fiscal_year'], row['Stage_Code'], 1), axis=1)

df['calendar_date'] = df['fiscal_date'] - relativedelta(months=fiscal_month_offset)
# by default python dates will be saved as Object type in pandas. You can verify with `df.info()`
# to use clever things pandas can do with dates we need co convert it
df['calendar_date'] = pd.to_datetime(df['calendar_date'])

# I would just keep date as datetime type so I could access year and month
# but to create same representation as in question, let's format it as string
df['Date'] = df['calendar_date'].dt.strftime('%m/%Y')

# copy important columns into output dataframe
output_df = df[['Shortcut_Dimension_4_Code', 'Stage_Code', 'Date']].copy()
print(output_df)