How to use GroupBy, pandas Merge & Aggregate function In Python Pandas
The count of the last value in the group using pandas can also be got using the pandas merge function as follows.
Syntax:
DataFrame.merge(right, how='inner', on=None)
Parameters:
- right – Object to merge with. (dataframe or series object).
- how – left join, right join, outer join, Default – Inner join
- on – (label or list). Specify the Column names to join on.
Example:
In this example, we create a sample dataframe with car names and prices as shown and apply groupby function on cars, and use tail() function to compute the final value of the group. Now, perform an inner merge with the grouped dataset and the original dataset. Finally, apply a count aggregated groupby function to get the no. of occurrences of the last value.
Python3
# import pandas packageimport pandas as pd# create a sample datasetdata = pd.DataFrame({'cars': ['benz', 'benz', 'benz', 'benz', 'bmw', 'bmw', 'bmw'], 'Price_in_million': [15, 12, 23, 23, 63, 34, 63]})# perform inner merge with the grouped and original datasetmerged = pd.merge(data.groupby('cars').tail(1), data, how='inner')# apply a count aggregated groupby function to # get the no. of. occurrences of last value.result = merged.groupby(['cars', 'Price_in_million'])[ 'Price_in_million'].agg('count')print(result) |
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,
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