Pandas Pivot或Groupby用于动态生成的列

Question

I have a dataframe with sales information in a supermarket. Each row in the dataframe represents an item, with several characteristics as columns. The original DataFrame is something like this:

In [1]: import pandas as pd
        my_data = [{'ticket_number' : '001', 'item' : 'tomato', 'ticket_price' : '21'},
               {'ticket_number' : '001', 'item' : 'candy', 'ticket_price' : '21'},
               {'ticket_number' : '001', 'item' : 'soup', 'ticket_price' : '21'},
               {'ticket_number' : '002', 'item' : 'soup', 'ticket_price' : '12'},
               {'ticket_number' : '002', 'item' : 'cola', 'ticket_price' : '12'},
               {'ticket_number' : '003', 'item' : 'beef', 'ticket_price' : '56'},
               {'ticket_number' : '003', 'item' : 'tomato', 'ticket_price' : '56'},
               {'ticket_number' : '003', 'item' : 'pork', 'ticket_price' : '56'}]
        df = pd.DataFrame(my_data)

In [2]: df
Out [2]:    
            ticket_number   ticket_price        item
         0        001            21           tomato
         1        001            21           candy
         2        001            21           soup
         3        002            12           soup
         4        002            12           cola
         5        003            56           beef
         6        003            56           tomato
         7        003            56           pork

I need a DataFrame where each row represents a ticket with all the items bought and the ticket price as columns. In this example:

            ticket_number   ticket_price      item1  item2  item3
         0        001            21           tomato candy  soup
         1        002            12           soup   cola
         2        003            56           beef   tomato pork

I tried using df.groupby(ticket_number).item.value_counts() , but that does not create new columns. I have never used pivot_table , maybe it is useful.

Any help would be very appreciated.

Thanks!

Answer 1

One possible way to use groupby to make lists of it that can then be turned into columns:

In [24]: res = df.groupby(['ticket_number', 'ticket_price'])['item'].apply(list).apply(pd.Series)

In [25]: res
Out[25]:
                                 0       1     2
ticket_number ticket_price
001           21            tomato   candy  soup
002           12              soup    cola   NaN
003           56              beef  tomato  pork

Then, after cleaning up this result a bit:

In [27]: res.columns = ['item' + str(i + 1) for i in res.columns]

In [29]: res.reset_index()
Out[29]:
  ticket_number ticket_price   item1   item2 item3
0           001           21  tomato   candy  soup
1           002           12    soup    cola   NaN
2           003           56    beef  tomato  pork

---

Another possible way to create a new column which numbers the items in each group with groupby.cumcount :

In [38]: df['item_number'] = df.groupby('ticket_number').cumcount()

In [39]: df
Out[39]:
     item ticket_number ticket_price  item_number
0  tomato           001           21            0
1   candy           001           21            1
2    soup           001           21            2
3    soup           002           12            0
4    cola           002           12            1
5    beef           003           56            0
6  tomato           003           56            1
7    pork           003           56            2

And then do some reshaping:

In [40]: df.set_index(['ticket_number', 'ticket_price', 'item_number']).unstack(-1)
Out[40]:
                              item
item_number                      0       1     2
ticket_number ticket_price
001           21            tomato   candy  soup
002           12              soup    cola   NaN
003           56              beef  tomato  pork

From here, with some cleaning of the columns names, you can achieve the same as above.

The reshaping step with set_index and untack could also be done with pivot_table : df.pivot_table(columns=['item_number'], index=['ticket_number', 'ticket _price'], values='item', aggfunc='first')