如何遍历 Pandas DataFrameGroupBy 并选择特定列的每个分组变量的所有条目？
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How to iterate over pandas DataFrameGroupBy and select all entries per grouped variable for specific column?
提问by Server Khalilov
Let's assume, there is a table like this:
让我们假设，有一个这样的表：
Id | Type | Guid
I perform on such a table the following operation:
我在这样的表上执行以下操作：
df = df.groupby('Id')
Now I would like to iterate through first nrows and for each specific Idas a listprint all the corresponding entries from column Guid.
Please, help me with a solution.
现在，我将通过首先要迭代n行对于每一个特定Id的list打印全部来自列相应的条目Guid。请帮我解决一个问题。
回答by Scott Boston
I think I would do it like this:
我想我会这样做：
Create some data for testing
创建一些数据进行测试
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
选择 n 作为要返回的记录数和 groupby
n = 5
df_groups = df.groupby('Id')
Iterate through df_group with for loop and print
使用 for 循环遍历 df_group 并打印
for name,group in df_groups:
print('ID: ' + str(name))
print(group.head(n))
print("\n")
Output:
输出：
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
.
.
.
EDIT To print all the Guids in a list for each ID you can use the following:
编辑要打印每个 ID 的列表中的所有 Guid，您可以使用以下内容：
for name,group in df_groups:
print('ID: ' + str(name))
print(group.Guid.tolist())
print("\n")
Output:
输出：
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]
.
.
.
回答by Andy Hayden
I like to use get_groupfor this. First you can pull out the keys:
我喜欢用get_group这个。首先你可以拔出钥匙：
In [11]: df
Out[11]:
A B
0 1 2
1 1 4
2 2 6
3 3 8
In [12]: g = df.groupby("A")
In [13]: g.groups.keys()
Out[13]: dict_keys([1, 2, 3])
You can iterate through the keys:
您可以遍历键：
In [14]: for k in g.groups.keys():
print(g.get_group(k))
print("\n")
A B
0 1 2
1 1 4
A B
2 2 6
A B
3 3 8
To get the first nitems of a DataFrame you can use head:
要获取nDataFrame的第一项，您可以使用head：
In [21]: df.head(3) # or g.get_group(k).head(n)
Out[21]:
A B
0 1 2
1 1 4
2 2 6
Note: The groupby also has a head method which takes the first n of each group:
注意：groupby 也有一个 head 方法，它取每组的前 n 个：
In [21]: g.head(1)
Out[21]:
A B
0 1 2
2 2 6
3 3 8
