merge dataframes and add price data for each instance of an item ID-CodePudding

I am trying to merge two dataframes so that each instance of an item ID in DF3 displays the pricing data associated with the matching ID from DF1.

DF3 (what I am trying to accomplish)

recipeID	itemID_out	qty_out	buy_price	sell_price	buy_quantity	sell_quantity	id_1_in	qty_id1	buy_price	sell_price	buy_quantity	sell_quantity	id_2_in	qty_id2	buy_price	sell_price	buy_quantity	sell_quantity	id_3_in	qty_id3	buy_price	sell_price	buy_quantity	sell_quantity	id_4_in	qty_id4	buy_price	sell_price	buy_quantity	sell_quantity	id_5_in	qty_id5	buy_price	sell_price	buy_quantity	sell_quantity
1	1986	1	129	167	67267	21637	123	1	10	15	1500	3000	124	1	12	14	550	800	125	1	8	12	124	254	126	1	22	25	1251	890	127	1	64	72	12783	1251515
2	1987	1	1521	1675	654	1245	123	2	10	15	1500	3000
3	1988	1	128376	131429	47	23	123	10	10	15	1500	3000	124	3	12	14	550	800

These are the two dataframes I am trying to merge from.

DF1: Contains 26863 rows; master list of item names, IDs, and price data. Pulled from API, new items can be added and will appear as new rows after an update request from the user.

itemID	name	buy_price	sell_price	buy_quantity	sell_quantity
1986	XYZ	129	167	67267	21637
123	ABC	10	15	1500	3000
124	DEF	12	14	550	800

DF2 (contains 12784 rows; recipes that combine from items in the master list. Pulled from API, new recipes can be added and will appear as new rows after an update request from the user.)

recipeID	itemID_out	qty_out	id_1_in	qty_id1	id_2_in	qty_id2	id_3_in	qty_id3	id_4_in	qty_id4	id_5_in	qty_id5
1	1986	1	123	1	124	1	125	1	126	1	127	1
2	1987	1	123	2
3	1988	1	123	10	124	3

Recipes can contain a combination of 1 to 5 items (null values occur) that consist of IDs from DF1 and/or the itemID_out column in DF2.

The "id_#_in" columns in DF2 can contain item IDs from the "itemID_out" column, due to that recipe using the item that is being output from another recipe.

I have tried to merge it using:

pd.merge(itemlist_modified, recipelist_modified, left_on='itemID', right_on='itemID_out')

But this only ever results in a single column of ideas receiving the pricing data as intended.

I feel like I'm trying to use the wrong function for this, any help would be very much appreciated!

Thanks in advance!

CodePudding user response：

Not a pretty approach, but it first melts the ingredient table into long form and then merges it on the itemlist table

import pandas as pd
import numpy as np

itemlist_modified = pd.DataFrame({
    'itemID': [1986, 123, 124],
    'name': ['XYZ', 'ABC', 'DEF'],
    'buy_price': [129, 10, 12],
    'sell_price': [167, 15, 14],
    'buy_quantity': [67267, 1500, 550],
    'sell_quantity': [21637, 3000, 800],
})

recipelist_modified = pd.DataFrame({
    'RecipeID': [1, 2, 3],
    'itemID_out': [1986, 1987, 1988],
    'qty_out': [1, 1, 1],
    'id_1_in': [123, 123, 123],
    'qty_id1': [1, 2, 10],
    'id_2_in': [124.0, np.nan, 124.0],
    'qty_id2': [1.0, np.nan, 3.0],
    'id_3_in': [125.0, np.nan, np.nan],
    'qty_id3': [1.0, np.nan, np.nan],
    'id_4_in': [126.0, np.nan, np.nan],
    'qty_id4': [1.0, np.nan, np.nan],
    'id_5_in': [127.0, np.nan, np.nan],
    'qty_id5': [1.0, np.nan, np.nan],
})
    
#columns which are not qty or input id cols
id_vars = ['RecipeID','itemID_out','qty_out']

#prepare dict to map column name to ingredient number
col_renames = {}
col_renames.update({'id_{}_in'.format(i 1):'ingr_{}'.format(i 1) for i in range(5)})
col_renames.update({'qty_id{}'.format(i 1):'ingr_{}'.format(i 1) for i in range(5)})

#melt reciplist into longform
long_recipelist = recipelist_modified.melt(
    id_vars=id_vars,
    var_name='ingredient',
).dropna()

#add a new column to specify whether each row is a qty or an id
long_recipelist['kind'] = np.where(long_recipelist['ingredient'].str.contains('qty'),'qty_in','id_in')

#convert ingredient names
long_recipelist['ingredient'] = long_recipelist['ingredient'].map(col_renames)

#pivot on the new ingredient column
reshape_recipe_list = long_recipelist.pivot(
    index=['RecipeID','itemID_out','qty_out','ingredient'],
    columns='kind',
    values='value',
).reset_index()

#merge with the itemlist
priced_ingredients = pd.merge(reshape_recipe_list, itemlist_modified, left_on='id_in', right_on='itemID')

#pivot on the priced ingredients
priced_ingredients = priced_ingredients.pivot(
    index = ['RecipeID','itemID_out','qty_out'],
    columns = 'ingredient',
)

#flatten the hierarchical columns
priced_ingredients.columns = ["_".join(a[::-1]) for a in priced_ingredients.columns.to_flat_index()]
priced_ingredients.columns.name = ''

priced_ingredients = priced_ingredients.reset_index()

priced_ingredients partial output: