I have a pandas data frame with data like this...
df0
| Customer | Product | Price |
|---|---|---|
| A | x | $12 |
| B | y | $15 |
| B | y | $10 |
| C | x | $19 |
| C | y | $9 |
| D | x | $16 |
| D | y | $8 |
| D | y | $10 |
I need to separate this data set into two data frames:
One data frames where customers only have one product. Another data frame where customers have multiple products like this...
df1
| Customer | Product | Price |
|---|---|---|
| A | x | $12 |
| B | y | $15 |
| B | y | $10 |
df2
| Customer | Product | Price |
|---|---|---|
| C | x | $19 |
| C | y | $9 |
| D | x | $16 |
| D | y | $8 |
| D | y | $10 |
I then will do some analysis on prices as a follow-on step. I know how to filter on one column but I can't figure out a filter which needs to use both columns like this. Would anyone please help with the code which would split the data into two data frames? Many thanks
CodePudding user response：
If you use
df0.Product.groupby(df0.Customer).transform('nunique')
Will return the number of unique products for the customer in the row. So
df0[df0.Product.groupby(df0.Customer).transform('nunique') > 1]
will create a DataFrame with all those with more than one product, for example.