I have te following pandas dataframe:
data_df = pd.DataFrame({‘ind’:[‘la’,’p’,’la’,’la’,’p’,’g’,’g’,’la’],
‘dist’:[10.,5.,7.,8.,7.,2.,5.,3.],
‘diff’:[0.54,3.2,8.6,7.2,2.1,1.,3.5,4.5],
‘cas’:[1.,2.,3.,4.,5.,6.,7.,8.]})
that is
cas diff dist ind
0 1 0.54 10 la
1 2 3.20 5 p
2 3 8.60 7 la
3 4 7.20 8 la
4 5 2.10 7 p
5 6 1.00 2 g
6 7 3.50 5 g
7 8 4.50 3 la
I need to compute the weighted average of all the columns where the weights are in the ‘dist’ column and group the values by ‘ind’.
For example for ‘ind’=’la’ and the ‘diff’ column:
((100.54)+(8.607)+(7.208)+(4.503))/(10+7+8+3) = 4.882143
The result I want to obtain is the following
cas diff
ind
g 6.714286 2.785714
la 3.107143 4.882143
p 3.750000 2.558333
which is obtained by multiplying each value of each colums by the corrisponding value in the ‘dist’ column, sum the results with the same ‘ind’ and then dividing the result by the sum of all the ‘dist’ values corrisponding to the same ind.
I thought this would have been an easy task done by the dataframe ‘groupby’ method, but actually it’s kind of tricky.
Can someone please help me?
解决方案
You may obtain within groups normalized weights by using transform:
df[‘weight’] = df[‘dist’] / df.groupby(‘ind’)[‘dist’].transform(‘sum’)
df[‘weight’]
0 0.357143
1 0.416667
2 0.250000
3 0.285714
4 0.583333
5 0.285714
6 0.714286
7 0.107143
Name: weight, dtype: float64
Then, you just need to multiply these weight by the values, and take the sum:
df[‘wcas’], df[‘wdiff’] = (df[n] * df[‘weight’] for n in (‘cas’, ‘diff’))
df.groupby(‘ind’)[[‘wcas’, ‘wdiff’]].sum()
wcas wdiff
ind
g 6.714286 2.785714
la 3.107143 4.882143
p 3.750000 2.558333
Edit: with in-place mutation:
backup = df.copy() # make a backup copy to mutate in place
cols = df.columns[:2] # cas, diff
df[cols] = df[‘weight’].values[:, None] * df[cols]
df.groupby(‘ind’)[cols].sum()
cas diff
ind
g 6.714286 2.785714
la 3.107143 4.882143
p 3.750000 2.558333
Original: https://blog.csdn.net/weixin_30357183/article/details/112947098
Author: 小女巫诺诺
Title: pandas算加权平均值_使用Pandas数据框计算加权平均值
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