熊猫分组和过滤

Question

I have the following .csv

  Name  Location    Product Type    number
Greg    1       Fruit   grape   1
Greg    1       Fruit   apple   2
Greg    1       Bakery  bread   5
Greg    1       Bakery  roll    8
Greg    2       Fruit   grape   7
Greg    2       Fruit   apple   1
Greg    3       Fruit   grape   2
Greg    4       Bakery  roll    3
Greg    4       Bakery  bread   4
Sam 5       Fruit   apple   7
Sam 5       Fruit   grape   9
Sam 5       Fruit   apple   10
Sam 6       Bakery  roll    11
Sam 6       Bakery  bread   12
Sam 7       Fruit   orange  13
Sam 7       Bakery  roll    14
Tim 8       Fruit   bread   16
Zack    9       Bakery  roll    17
Zack    10      Fruit   apple   19
Zack    10      Fruit   grape   20

I would like to put this into pandas and group by name, location where there is more than one location with more than two products. I would still want to maintain the 'number' for the products

So something Like this as an example since Greg at location 1 has two products

name    location    product     type
Greg    1       Fruit, bakery   grape,apple,bread,roll

I am struggling with the groupby and ultimately getting this back to a data frame that I could .to_csv

Answer 1

IIUC use transform with nunique

df1=df[df.groupby(['Name','Location']).Product.transform('nunique')>1]
    Name  Location Product    Type  number
0   Greg         1   Fruit   grape       1
1   Greg         1   Fruit   apple       2
2   Greg         1  Bakery   bread       5
3   Greg         1  Bakery    roll       8
14   Sam         7   Fruit  orange      13
15   Sam         7  Bakery    roll      14

Answer 2

If you do df.groupby([col_names]) , col_names will become the index.

In order to convert the indexes back to columns, you are required to use the DataFrame.reset_index() method.

Hope that helps.