CODEWITHSUNDEEP
pythonpandasnumpydataframe
avogadro
avogadro

Reputation: 41

Looking for a way to produce a table of statistics from columns in a data frame

I have a data set with categories/codes e.g. male/female, state of service, code of service and I have a column of paid claims.

I am looking for a way to create a table/pivot using Python to generate outputs where I only have the top 10 highest column of average paid claims by code of service (i.e. what are the top 10 codes with highest average paid claims). I also wanted to append with median, stdev, counts so the output looks something like

Table:

gender, code, state, paid claim
F, 1234, TX, $300
F, 2345, NJ, $120
F, 3456, NJ, $30
M, 1234, MN, $250
M, 4567, CA, $50
F, 1234, MA, $70
F, 8901, CA, $150
F, 23457, NY, $160
F, 4567, SD, $125

Output I am trying to generate (top 10 ave paid claim by code):

code, average claim, median claim, count claim
1234,  206, xxx, 3

So, I did something like:

service_code_average=df.groupby('service_code', as_index=False)['paid claim'].mean().sort_values(by='paid claim')

I was not able to limit to top 10 and I was struggling to append the media and count.

Upvotes: 0

Views: 480

Answers (1)

YOLO
YOLO

Reputation: 21749

Here you can leverage agg function where you can specify multiple aggregation function in one go. You can do the following:

# convert string to integer
df['paid claim'] = df['paid claim'].str.extract('(\d+)')
df['paid claim'] = df['paid claim'].astype(int)

# set n value
top_n = 2 ## set this to 10 

# apply aggregation 
df1 = df.groupby('code').agg({'paid claim':{'average': lambda x: x.nlargest(top_n).mean(),
                                      'counts': lambda x: x.count(),
                                      'median': lambda x: x.median()}})

# reset column names
df1.columns = df1.columns.droplevel()
df1 = df1.reset_index()

print(df1)

    code  average  counts  median
0   1234    275.0       3   250.0
1   2345    120.0       1   120.0
2   3456     30.0       1    30.0
3   4567     87.5       2    87.5
4   8901    150.0       1   150.0
5  23457    160.0       1   160.0

Upvotes: 1

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