Combining rows with nearly same fields - python-3.x

Question

I have two data frames and want to combine them into a single data frame. I used a common key to merge two frames. The final result was a data frame that some of rows have nearly identical fields except a few columns have different values. I want to combine these nearly identical rows into a single row considering adding appropriate columns.
Here are the data frames:
stores:
Banner - Region - Store ID
Walmart - NC - 66999
TJ - NY - 4698
prices:
Price - Store ID - UPC
3.6 - 66999 - 234565
4.5 - 4698 - 334526
I already merged tow frames and played a little bit to converge to the desired frame.
store_cross = pd.crosstab(stores['Store ID'],stores['Region'],margins=True)
merged_df2 = pd.merge(store_cross,prices,left_on='Store ID', right_on='Store ID')
merged_df2 = pd.merge(merged_df2,stores,left_on='Store ID', right_on='Store ID')
This is the result so far:
NY - NC - Price - UPC - Banner
1 - 0 - 3.6 - 234565 - Walmart
0 - 1 - 4.5 - 334526 - TJ
It is possible to have a UPC at different stores. It means that there are other rows in the frame that have the same UPC and Banner but at different locations.
What I am looking to have is something like this:
Banner - UPC - NC - NY
Walmart - 234565 - 3.9 - 3.6
TJ - 334526 - 4.5 - 4.3

Answer 1

I believe you need first merge and then DataFrame.pivot_table:
df = pd.merge(stores, prices, on='Store ID')
store_cross = df.pivot_table(index=['Banner', 'Store ID','UPC'],
columns='Region',
values='Price',
aggfunc='sum').reset_index()
print (store_cross)
Region Banner Store ID UPC NC NY
0 TJ 4698 334526 NaN 4.5
1 Walmart 66999 234565 3.6 NaN