How to iterate over pandas DataFrameGroupBy and select all entries per grouped variable for specific column?

Question

Let's assume, there is a table like this:

Id | Type | Guid

I perform on such a table the following operation:

df = df.groupby('Id')

Now I would like to iterate through first n rows and for each specific Id as a list print all the corresponding entries from column Guid. Please, help me with a solution.

Answer 1

I think I would do it like this:

Create some data for testing

df = pd.DataFrame({'Id':np.random.randint(1,10,100),'Type':np.random.choice(list('ABCD'),100),'Guid':np.random.randint(10000,99999,100)})

print(df.head()
   Id Type   Guid
0   2    A  89247
1   4    B  39262
2   3    C  45522
3   1    B  99724
4   4    C  51322

Choose n for number of records to return and groupby

n = 5
df_groups = df.groupby('Id')

Iterate through df_group with for loop and print

for name,group in df_groups:
    print('ID: ' + str(name))
    print(group.head(n))
    print("\n")

Output:

ID: 1
    Id Type   Guid
3    1    B  99724
5    1    B  74182
37   1    D  49219
47   1    B  81464
65   1    C  84925


ID: 2
    Id Type   Guid
0    2    A  89247
6    2    A  16499
7    2    A  79956
34   2    C  56393
40   2    A  49883
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EDIT To print all the Guids in a list for each ID you can use the following:

for name,group in df_groups:
    print('ID: ' + str(name))
    print(group.Guid.tolist())
    print("\n")

Output:

ID: 1
[99724, 74182, 49219, 81464, 84925, 67834, 43275, 35743, 36478, 94662, 21183]


ID: 2
[89247, 16499, 79956, 56393, 49883, 97633, 11768, 14639, 88591, 31263, 98729]


ID: 3
[45522, 13971, 75882, 96489, 58414, 22051, 80304, 46144, 22481, 11278, 84622, 61145]


ID: 4
[39262, 51322, 76930, 83740, 60152, 90735, 42039, 22114, 76077, 83234, 96134, 93559, 87903, 98199, 76096, 64378]


ID: 5
[13444, 55762, 13206, 94768, 19665, 75761, 90755, 45737, 23506, 89345, 94912, 81200, 91868]
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