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Next, we wish to find average, maximum, and minimum price of items in each category, so, we apply aggregate
        functions mean, min, and max on column Price of grouped DataFrame groceryGroupedDF, as shown below:
         >>> groceryGroupedDF['Price'].agg(['mean', 'min', 'max'])
        output:
                                mean   min      max
               Category
                   Food  42.500000        20     70
              Household  80.000000        80     80
                Hygiene  33.333333        30     40
        Execution of
                groceryGroupedDF['Price'].agg(['mean', 'min', 'max'])
        in the Pandas tutor yields the following visualization:
                             groceryGroupedDF['Price'].agg(['mean', 'min', 'max'])

                                  Series                                mean    min   max
                              0    20                   Category
                              1    60                   Food            42.50    20     70

                              2    20                   Household          80    80     80
                              3    70                   Hygiene         33.33    30     40
                              4    80
                              5    40
                              6    30
                              7    30



               We can organise the data in a DataFrame into groups using the function groupby(), based on specific criteria,
               such as values stored in one or more columns. The method returns a DataFrameGroupBy object.
               We can apply more than one aggregate operations by passing the list of operations to be carried out on the groups
               of data as an argument to the function agg.




          C T  04     Write the Pandas statements to group the records of employeeDF DataFrame with respect
                      to department and compute minimum, maximum, and average salary of employees in each
                      department.






             Let's Summarise



              A Pandas DataFrame is a two-dimensional tabular structure that can accommodate objects of various types.
           Ø
              A dictionary can be converted to a Pandas DataFrame using the method pd.DataFrame(). The keys of
           Ø
              the dictionary act as column labels. For each key (acting as a label), the values in the list appear in the
              corresponding column in the DataFrame.

              A list of lists can be converted to a Pandas DataFrame using the method pd.DataFrame(). Each sublist in
           Ø
              the list includes the values in a row of the DataFrame.


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