如何为两列中的每个唯一值计算 Python 中的加权平均值？

Question

The picture below shows a few lines of printed lists I have in Python. I would like to get: a list of unique values of boroughs, a corresponding list of unique values of years, and a list of weighted averages of "averages" with "nobs" as weights but for each borough and each year (the variable "type" indicates if there was just one, two or three types in a specific year in a borough).

I know how to get a weighted average using the entire lists:

weighted_avg = np.average(average, weights=nobs)

But I don't know how to calculate one for each unique borough-year.

I'm new to Python, please help if you know how to do it.

Answer 1

Assuming that the 'type' column doesn't affect your calculations, you can get the average using groupby . Here's the data:

df = pd.DataFrame({'borough': ['b1', 'b2']*6, 'year': [2008, 2009, 2010, 2011]*3,
          'average': np.random.randint(low=100, high=200, size=12), 
          'nobs': np.random.randint(low=1, high=40, size=12)})
print(df):
   borough  year  average  nobs
0       b1  2008      166     1
1       b2  2009      177    35
2       b1  2010      114    27
3       b2  2011      187    18
4       b1  2008      193     2
5       b2  2009      105    27
6       b1  2010      114    36
7       b2  2011      144     3
8       b1  2008      114    39
9       b2  2009      157     6
10      b1  2010      133    17
11      b2  2011      176    12

we add a new column which is the product of the average and nobs columns:

df['average x nobs'] = df['average']*df['nobs']
newdf = pd.DataFrame({'weighted average': df.groupby(['borough', 'year']).sum()['average x nobs']/df.groupby(['borough', 'year']).sum()['nobs']})
print(newdf):
              weighted average
borough year                  
b1      2008        119.000000
        2010        118.037500
b2      2009        146.647059
        2011        179.090909