Mean, Median, And Mode Of A List Of Values (score) Given A Certain Zip Code For Every Year

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

Solution 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                  
20174488.088.0885590.090.0906692.592.5907796.096.09620183390.090.0905592.092.0926697.097.09720195596.096.0967790.090.090

Solution 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"]