How to use GroupBy & Aggregate function In Python Pandas
In this approach, the user needs to call the DataFrame.groupby() function to demonstrate how to get the count of the last value in the group using pandas in the python language.
Example:
In this example, we create a sample dataframe with car names and prices as shown and apply groupby function on cars, setting as_index false doesn’t create a new index then aggregate the grouped function by the last price of the cars using the ‘last’ parameter in the aggregate function and name the column ‘Price_last’.Followed by that add another lambda function to get the number of times the car got the last price.
The dataframe used in the below example:
cars Price_in_million 0 benz 15 1 benz 12 2 benz 23 3 benz 23 4 bmw 63 5 bmw 34 6 bmw 63
Python3
# import python pandas package import pandas as pd # create a sample dataframe data = pd.DataFrame({ 'cars' : [ 'benz' , 'benz' , 'benz' , 'benz' , 'bmw' , 'bmw' , 'bmw' ], 'Price_in_million' : [ 15 , 12 , 23 , 23 , 63 , 34 , 63 ]}) # use groupby function to groupby cars, setting # as_index false doesnt create an index. # use aggregate function with 'last; parameter # to get the last price in the group of cars. # apply lambda function to get the number of # times the car got the last price. data.groupby( 'cars' , as_index = False ).agg(Price_last = ( 'Price_in_million' , 'last' ), Price_last_count = ( 'Price_in_million' , lambda x: sum (x = = x.iloc[ - 1 ]))) |
Output:
Pandas GroupBy – Count last value
A groupby operation involves grouping large amounts of data and computing operations on these groups. It is generally involved in some combination of splitting the object, applying a function, and combining the results. In this article let us see how to get the count of the last value in the group using pandas.
Syntax:
DataFrame.groupby(by, axis, as_index)
Parameters:
- by (datatype- list, tuples, dict, series, array): mapping, function, label, or list of labels. The function passed is used as-is to determine the groups.
- axis (datatype int, default 0): 1 – splits columns and 0 – splits rows.
- as_index (datatype bool, default True.): Returns an object with group labels as the index, for all aggregated output,