group by pandas examples
>>> n_by_state = df.groupby("state")["state"].count()
>>> n_by_state.head(10)
state
AK 16
AL 206
AR 117
AS 2
AZ 48
CA 361
CO 90
CT 240
DC 2
DE 97
Name: last_name, dtype: int64group by pandas examples
>>> n_by_state = df.groupby("state")["state"].count()
>>> n_by_state.head(10)
state
AK 16
AL 206
AR 117
AS 2
AZ 48
CA 361
CO 90
CT 240
DC 2
DE 97
Name: last_name, dtype: int64pandas groupby
data.groupby('month', as_index=False).agg({"duration": "sum"})create a colun in pandas using groupby
In [13]: df.groupby(["item", "color"])["id"].transform("count")
Out[13]:
0 2
1 2
2 2
3 1
4 2
dtype: int64pandas group by column
>> df = pd.read_excel(r"C:path_to_filedataset_test.xlsx")
>> print(df)
'''
name number
0 p1 64
1 p2 98
2 p1 93
3 p2 57
4 p1 89
5 p2 83
6 p1 58
7 p2 73
8 p1 64
9 p2 24
'''
>> data = df.groupby("name").number.apply(list)
>> print(data)
'''
name
p1 [64, 93, 89, 58, 64]
p2 [98, 57, 83, 73, 24]
Name: number, dtype: object
'''
>> print(data.p1)
'''
[64, 93, 89, 58, 64]
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