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: int64groupby in pandas
>>> df = pd.DataFrame({'Animal': ['Falcon', 'Falcon',
... 'Parrot', 'Parrot'],
... 'Max Speed': [380., 370., 24., 26.]})
>>> df
Animal Max Speed
0 Falcon 380.0
1 Falcon 370.0
2 Parrot 24.0
3 Parrot 26.0
>>> df.groupby(['Animal']).mean()
Max Speed
Animal
Falcon 375.0
Parrot 25.0groupby and list
In [1]: df = pd.DataFrame( {'a':['A','A','B','B','B','C'], 'b':[1,2,5,5,4,6]})
df
Out[1]:
a b
0 A 1
1 A 2
2 B 5
3 B 5
4 B 4
5 C 6
In [2]: df.groupby('a')['b'].apply(list)
Out[2]:
a
A [1, 2]
B [5, 5, 4]
C [6]
Name: b, dtype: object
In [3]: df1 = df.groupby('a')['b'].apply(list).reset_index(name='new')
df1
Out[3]:
a new
0 A [1, 2]
1 B [5, 5, 4]
2 C [6]Pandas groupby
>>> emp.groupby(['dept', 'gender']).agg({'salary':'mean'}).round(-3)groupby
df.groupby(sepal_len_groups)['sepal length (cm)'].agg(count='count')
sum_sep = sep.groupby('Year').agg({'TotalProjects':'sum',
'TotalFunds':'sum',
'TotalFunds':'count',
'SubDistrict':'count'})
sum_sep.stb.subtotal(grand_label='Total').applymap('{:,.0f}'.format)pandas 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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