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Construct new column which gives the average values of the other elements in a group using Pandas


By : HeliChen
Date : October 17 2020, 06:10 AM
I hope this helps . I would like to improve in performance and elegance a code using Pandas for a new column which gives the average values of the other elements in a group. , Here you go:
code :
df['yy'] = (df.groupby('date').xx
              .apply(lambda x: (x.sum() - x)/(len(x) - 1))
           )
                xx  yy
date       type         
2018-01-01  A   1   5.00
            B   5   1.00
2018-02-01  B   3   NaN
2018-03-01  A   2   4.25
            B   7   3.00
            C   3   4.00
            D   1   4.50
            E   6   3.25
2018-05-01  B   3   NaN
2018-06-01  A   5   2.50
            B   2   4.00
            C   3   3.50
2018-07-01  A   1   NaN
2018-08-01  B   9   3.00
            C   3   9.00
2018-09-01  A   2   7.00
            B   7   2.00
2018-10-01  C   3   7.00
            A   6   5.50
            B   8   4.50
2018-11-01  A   2   NaN
2018-12-01  B   7   9.00
            C   9   7.00


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pandas dataframe, add one column as moving average of another column for each group


By : TradeNarK
Date : March 29 2020, 07:55 AM
Hope this helps I have a dataframe df like below. , You could use groupby/rolling/mean:
code :
result = (df.set_index('date')
            .groupby('market')['value']
            .rolling(10).mean()
            .unstack('market'))
market          GOLD    SILVER
date                          
2000-01-01       NaN       NaN
2000-01-02       NaN       NaN
2000-01-03       NaN       NaN
2000-01-04       NaN       NaN
2000-01-05       NaN       NaN
2000-01-06       NaN       NaN
2000-01-07       NaN       NaN
2000-01-08       NaN       NaN
2000-01-09       NaN       NaN
2000-01-10  0.310077  0.582063
2000-01-11  0.312008  0.752218
2000-01-12  0.151159  0.877230
2000-01-13  0.213611  0.742156
2000-01-14  0.440113  0.614720
2000-01-15  0.551360  0.649967
...

Group by column in pandas dataframe and average arrays


By : Stailok Man
Date : March 29 2020, 07:55 AM
should help you out If I understand correctly, to get the component-wise averages you can simply apply np.mean to the 'Word Vector' SeriesGroupBy explicitly.
code :
df.groupby('Genre')['Word Vector'].apply(np.mean)
>>> df = pd.DataFrame({'Title': list('ABCDEFGHIJ'), 
                       'Genre': list('ABCBBDCDED'), 
                       'Word Vector': [np.random.randint(0, 10, 10) 
                                       for _ in range(len('ABCDEFGHIJ'))]})

>>> df

  Genre Title                     Word Vector
0     A     A  [3, 6, 8, 0, 4, 8, 1, 4, 0, 1]
1     B     B  [5, 4, 4, 4, 8, 7, 4, 3, 7, 2]
2     C     C  [1, 7, 6, 7, 3, 3, 8, 1, 8, 1]
3     B     D  [0, 4, 6, 7, 1, 5, 5, 0, 6, 7]
4     B     E  [8, 2, 1, 4, 1, 2, 0, 4, 9, 1]
5     D     F  [7, 9, 7, 8, 8, 7, 2, 9, 1, 3]
6     C     G  [0, 7, 1, 9, 6, 2, 1, 0, 3, 7]
7     D     H  [4, 7, 9, 4, 1, 5, 0, 3, 0, 6]
8     E     I  [5, 1, 5, 1, 8, 1, 1, 4, 5, 6]
9     D     J  [7, 9, 0, 1, 8, 3, 8, 8, 1, 0]

>>> df.groupby('Genre')['Word Vector'].apply(np.mean)

Genre
A    [3.0, 6.0, 8.0, 0.0, 4.0, 8.0, 1.0, 4.0, 0.0, ...
B    [4.33333333333, 3.33333333333, 3.66666666667, ...
C    [0.5, 7.0, 3.5, 8.0, 4.5, 2.5, 4.5, 0.5, 5.5, ...
D    [6.0, 8.33333333333, 5.33333333333, 4.33333333...
E    [5.0, 1.0, 5.0, 1.0, 8.0, 1.0, 1.0, 4.0, 5.0, ...
Name: Word Vector, dtype: object

Taking average of last n elements with group by in pandas


By : Na Ja
Date : March 29 2020, 07:55 AM
I wish this helpful for you I have a pandas dataframe df as: , New way of using nth
code :
df.groupby('Type').nth([-1,-2,-3]).Value.mean(axis=0,level=0)
Out[250]: 
Type
A    110.496667
B    173.763333
Name: Value, dtype: float64

Pandas dataframe: Group by two columns and then average the third column


By : user2780287
Date : March 29 2020, 07:55 AM
I hope this helps . I have a dataframe that looks like the example below: , Given this:
code :
   year category  amount
0  2015        A     200
1  2015        B    1000
2  2015        A     300
3  2016        C    1200
4  2016        A     800
5  2016        A    2500
6  2016        B     100
df.groupby(['year','category'])['amount'].mean()
    year  category
2015  A            250
      B           1000
2016  A           1650
      B            100
      C           1200
Name: amount, dtype: int64
df.groupby(['year','category'])['amount'].mean().reset_index()

   year category  amount
0  2015        A     250
1  2015        B    1000
2  2016        A    1650
3  2016        B     100
4  2016        C    1200

Pandas group values and get their average


By : Sachin Jagtap
Date : March 29 2020, 07:55 AM
fixed the issue. Will look into that further You could lstrip £, cast to integer, and then compute the GroupBy.mean and plot:
code :
df.Price.str.lstrip('£').astype(int).groupby(df.Model).mean().plot.barh()
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