I have the following data:
Invoice NoStockCode Description Quantity CustomerID Country
536365 85123A WHITE HANGING HEART T-
Try using a variation of the following:
df.groupby('company').product.agg([('count', 'count'), ('NoStockCode', ', '.join), ('Descrption', ', '.join), ('Quantity', ', '.join)])
I can't reproduce your code right now, but I think that:
print (df.groupby(['InvoiceNo','CustomerID','Country'],
as_index=False)['NoStockCode','Description','Quantity']
.agg(lambda x: list(x)))
would give you the expected output
You could use pd.pivot_table with aggfunc=list
:
import pandas as pd
df = pd.DataFrame({'Country': ['United Kingdom', 'United Kingdom', 'United Kingdom'],
'CustomerID': [17850, 17850, 17850],
'Description': ['WHITE HANGING HEART T-LIGHT HOLDER',
'WHITE METAL LANTERN',
'CREAM CUPID HEARTS COAT HANGER'],
'Invoice': [536365, 536365, 536365],
'NoStockCode': ['85123A', '71053', '84406B'],
'Quantity': [6, 6, 8]})
result = pd.pivot_table(df, index=['Invoice','CustomerID','Country'],
values=['NoStockCode','Description','Quantity'],
aggfunc=lambda x: ', '.join(map(str, x)))
print(result)
yields
Description NoStockCode Quantity
Invoice CustomerID Country
536365 17850 United Kingdom WHITE HANGING HEART T-LIGHT HOLDER, WHITE META... 85123A, 71053, 84406B 6, 6, 8
Note that if Quantity
are int
s, you will need to convert them to str
s before calling ', '.join
. That is why map(str, x)
was used above.
IIUC
df.groupby(['Invoice','CustomerID'],as_index=False)['Description','NoStockCode'].agg(','.join)
Out[47]:
Invoice CustomerID Description \
0 536365 17850 WHITEHANGINGHEARTT-LIGHTHOLDER,WHITEMETALANTER...
NoStockCode
0 85123A,71053,84406B