Find the price from a pricelist with scales

In python I am trying to lookup the relevant price depending on qty from a list of scale prices. For example when getting a quotation request:

  Product  Qty  Price
0       A    6
1       B  301
2       C    1
3       D  200
4       E   48

Price list with scale prices:

   Product  Scale Qty  Scale Price
0        A          1           48
1        A          5           43
2        A         50           38
3        B          1           10
4        B         10            9
5        B         50            7
6        B        100            5
7        B        150            2
8        C          1          300
9        C          2          250
10       C          3          200
11       D          1            5
12       D        100            3
13       D        200            1
14       E          1          100
15       E         10           10
16       E        100            1

Output that I would like:

  Product  Qty  Price
0       A    6     43
1       B  301      2
2       C    1    300
3       D  200      1
4       E   48     10

Answers:

Thank you for visiting the Q&A section on Magenaut. Please note that all the answers may not help you solve the issue immediately. So please treat them as advisements. If you found the post helpful (or not), leave a comment & I’ll get back to you as soon as possible.

Method 1

Try with merge_asof:

output = (pd.merge_asof(df2.sort_values("Qty"),df1.sort_values("Scale Qty"),left_on="Qty",right_on="Scale Qty",by="Product")
          .sort_values("Product", ignore_index=True)
          .drop("Scale Qty", axis=1)
          .rename(columns={"Scale Price":"Price"}))

>>> output
  Product  Qty  Price
0       A    6     43
1       B  301      2
2       C    1    300
3       D  200      1
4       E   48     10

Inputs:

df1 = pd.DataFrame({'Product': ['A','A','A','B','B','B','B','B','C','C','C','D','D','D','E','E','E'],
                   'Scale Qty': [1, 5, 50, 1, 10, 50, 100, 150, 1, 2, 3, 1, 100, 200, 1, 10, 100],
                   'Scale Price': [48, 43, 38, 10, 9, 7, 5, 2, 300, 250, 200, 5, 3, 1, 100, 10, 1]})

df2 = pd.DataFrame({"Product": list("ABCDE"),
                    "Qty": [6,301,1,200,48]})

Method 2

Assuming df1 and df2, use merge_asof:

pd.merge_asof(df1.sort_values(by='Qty'),
              df2.sort_values(by='Scale Qty').rename(columns={'Scale Price': 'Price'}),
              by='Product', left_on='Qty', right_on='Scale Qty')

output:

  Product  Qty  Scale Qty  Price
0       C    1          1    300
1       A    6          5     43
2       E   48         10     10
3       D  200        200      1
4       B  301        150      2

All methods was sourced from stackoverflow.com or stackexchange.com, is licensed under cc by-sa 2.5, cc by-sa 3.0 and cc by-sa 4.0