如何根据 Pandas dataframe 中的日期值和条件创建新列

Question

Table 1:

Item Type Order Date Ship Date Purchase Cost

0 Example 2014-08-10 2014-08-10 850.7544

1 Snacks 2014-08-10 2014-08-10 NaN

2 Cosmetics 2/22/2015 2/22/2015 NaN

3 Fruits 2015-09-12 2015-09-12 NaN

4 Personal Care 9/17/2014 9/17/2014 NaN

5 Household 2010-04-02 2010-04-02 NaN

6 Clothes 2/20/2013 2/20/2013 NaN

Table 2:

Item Type   Purchase Start Date     Purchase End Date   Cost Per Unit 

0 Baby Food 2010-01-01 2010-05-01 158.2736

1 Beverages 2010-01-01 2010-05-01 36.0620

2 Cereal 2010-01-01 2010-05-01 160.4460

3 Clothes 2010-01-01 2010-05-01 66.6608

4 Cosmetics 2010-01-01 2010-05-01 266.6920

5 Fruits 2010-01-01 2010-05-01 5.5980

6 Household 2010-01-01 2010-05-01 467.7890

7 Meat 2010-01-01 2010-05-01 274.2285

Here I need to fill the Purchase Cost Column In Table 1 Based in Table 2 v Date & Cost Per Unit Column

For example In table 1 Household Date Values between (2010-04-02,2010-04-02) so in Table 2 Household Values between (2010-01-01,2010-05-01),so from Table 1 Order Date & Ship Date are in the Date range of 'Purchase start Date'& 'Purchase End Date',so we can fill the value of Purchase cost as '467.789',So how to fill the Purchase cost?

Answer 1

I assume that all "date" columns have been converted to datetime type. Otherwise start from converting them.

Generate an auxiliary Series :

wrk = pricing.assign(year=pricing['Start Date'].dt.year)\
    .drop_duplicates(subset=['Item', 'year'])\
    .set_index(['Item', 'year'])['(USD)dollar'].rename('price'); wrk

It contains first price in each year and product. Product name ( Item ) and year are levels of MultiIndex and the price is the value.

For your sample data, completed with one row for Cosmetics for year 2014 , the result is:

Item       year
Snacks     2010    68
           2011    72
Cosmetics  2014    50
Name: price, dtype: int64

Then, to fill price column, run:

product.price = wrk[product.set_index(['Product',
    product['Date (USD)'].dt.year]).index].tolist()

The result is:

     Product Date (USD)  price
0     Snacks 2010-02-03   68.0
1     Snacks 2010-02-06   68.0
2     Snacks 2014-02-03    NaN
3     Snacks 2012-02-03    NaN
4  Cosmetics 2012-02-03    NaN
5  Cosmetics 2013-02-03    NaN
6  Cosmetics 2013-02-08    NaN
7  Cosmetics 2014-02-06   50.0
8  Cosmetics 2014-02-09   50.0