How to use GroupBy, pandas Merge, and Sum function In Python Pandas
We can also get the same result by slightly altering the above approach, using the last() function instead of tail(), as shown below,
Example:
In this example, we create a sample dataframe with car names and prices as shown, apply groupby function on cars, and use the last() function to find the final element of every group and inner Merge the grouped dataset with the original dataset. Now compare the two prices in the merged columns and create a new column of bool data type, where the prices match. Now use the groupby function to get the number of times the last value of the group is repeated.
Python3
# import pandas package import pandas as pd # create a sample dataset data = pd.DataFrame({ 'cars' : [ 'benz' , 'benz' , 'benz' , 'benz' , 'bmw' , 'bmw' , 'bmw' ], 'Price_in_million' : [ 15 , 12 , 23 , 23 , 63 , 34 , 63 ]}) # computes the final value of each group grouped = data.groupby( 'cars' ).last() # Merge dataset named "data" with this result data = data.merge(grouped, left_on = 'cars' , right_index = True , how = 'inner' ) # Now compare the merged columns for same price # and create a new column of boolean values # where prices match data[ 'count' ] = data[ 'Price_in_million_x' ] = = data[ 'Price_in_million_y' ] # Use groupby function to return the aggregated # sum of count column where the price matches data.groupby( 'cars' )[ 'count' ]. sum () |
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,