Mean, Median, and mode of a list of values (SCORE) given a certain zip code for every year

问题

I want to find the mean, median and mode value for each year given a specific ZIP code how can I achieve this, I already read the data from CSV file and convert it to json file and define it as DataFrame my data sample is not limited to the following table it's larger

回答1:

Use SciPy.mstats:

In [2295]: df.DATE = pd.to_datetime(df.DATE).dt.year

In [2291]: import scipy.stats.mstats as mstats

In [2313]: def mode(x):
      ...:     return mstats.mode(x, axis=None)[0]
      ...: 

 In [2314]: df.groupby(['DATE', 'ZipCodes']).agg(["mean","median", mode])
Out[2314]: 
              SCORE            
               mean median mode
DATE ZipCodes                  
2017 44        88.0   88.0   88
     55        90.0   90.0   90
     66        92.5   92.5   90
     77        96.0   96.0   96
2018 33        90.0   90.0   90
     55        92.0   92.0   92
     66        97.0   97.0   97
2019 55        96.0   96.0   96
     77        90.0   90.0   90

回答2:

you could use groupby to group the data by date and zipcode and then use the .agg function to apply the mean, median and mode to it. The code would look as follow

groupedData = df.groupby(["DATE","Zip codes"]).agg({"Score" : ["mean","median","mode"]

来源：https://stackoverflow.com/questions/65439932/mean-median-and-mode-of-a-list-of-values-score-given-a-certain-zip-code-for