pandas new df from groupby
df = pd.DataFrame(old_df.groupby(['groupby_attribute'])['mean_attribute'].mean())
df = df.reset_index()
dfpandas new df from groupby
df = pd.DataFrame(old_df.groupby(['groupby_attribute'])['mean_attribute'].mean())
df = df.reset_index()
dfgroup 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 as_index=false
When you use as_index=False , you indicate to groupby() that you don't want to set the column ID as the index (duh!). ... Using as_index=True allows you to apply a sum over axis=1 without specifying the names of the columns, then summing the value over axis 0.groupby and sort python
In[34]: df.sort_values(['job','count'],ascending=False).groupby('job').head(3)
Out[35]:
count job source
4 7 sales E
2 6 sales C
1 4 sales B
5 5 market A
8 4 market D
6 3 market Bgroupby 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]Copyright © 2021 Codeinu
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