基于 pandas 中另一列的 group by 删除行

Question

Having a data set as below:

I need to do the cartesian of product based on month and location. Need an output as below:

I created a new dataframe-with the unique values of product. Then cross merged the df with dataset.need to drop the rows based on the month,location and product

Answer 1

You can try groupby then cross merge on Product column

out = (df.groupby(['Month', 'Location'])
       .apply(lambda g: g[['Product']].merge(g[['Product']], how='cross'))
       .droplevel(2)
       .reset_index()
       .rename(columns={'Product_x': 'Product', 'Product_y': 'Destination'}))

print(out)

   Month  Location Product Destination
0     17  Banglore       A           A
1     17  Banglore       A           B
2     17  Banglore       B           A
3     17  Banglore       B           B
4     18  Banglore       C           C
5     18       GOA       D           D
6     18       GOA       D           B
7     18       GOA       B           D
8     18       GOA       B           B

Answer 2

You can use itertools.product :

prods = [list(product(df[df.Month == month].location, df[df.Month == month].Product)) for month in df.Month.unique()]

Note that then I applied itertools.chain to prods as to 'flatten' it as it is a nested list,

prods = list(chain(*prods))

month_prod = [[m for i in range(len(df[df.Month == m]))] for m in df.Month]
months = list(chain(*month_prod))

df = pd.DataFrame({'Month': months, 'location': [item[0] for id, item in enumerate(prods)], 'Product': [item[1] for id, item in enumerate(prods)]} )