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 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]groupby
df['frequency'] = df['county'].map(df['county'].value_counts())
county frequency
1 N 5
2 N 5
3 C 1
4 N 5
5 S 1
6 N 5
7 N 5groupby
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)Copyright © 2021 Codeinu
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