Pandas Groupby and Computing Median Last Updated : 23 May, 2024 Comments Improve Suggest changes Like Article Like Report The Pandas in Python is known as the most popular and powerful tool for performing data analysis. It because of the beauty of Pandas functionality and the ability to work on sets and subsets of the large dataset. So in this article, we are going to study how pandas Group By functionality works and saves tons of effort while working on a large dataset. Also, we will solve real-world problems using Pandas Group By and Median functionalities. Pandas groupby() The groupby() method in pandas splits the dataset into subsets to make computations easier. Generally, groupby() splits the data, applies the functionalities, and then combine the result for us. Let's take an example if we have data on alcohol consumption of different countries and we want to perform data analysis continent-wise, this problem can be minimized using groupby() method in pandas. It splits the data continent-wise and calculates median using the median() method. Syntax : DataFrame.groupby(by=None, axis=0, level=None, as_index=True, sort=True, group_keys=True, squeeze=<object object>, observed=False, dropna=True) Example 1: Find the median of alcohol consumption continent-wise on a given dataset. Python3 # import the packages import pandas as pd # read Dataset data = pd.read_csv("drinksbycountry.csv") data.head() # perform groupby on continent and find median # of total_litres_of_pure_alcohol data.groupby(["continent"])["total_litres_of_pure_alcohol"].median() # perform groupby on continent and find median # of wine_serving data.groupby(["continent"])["wine_servings"].median() Output : median of total_litres_of_pure_alcoholmedian of wine_serving Example 2: Find the median of the total population group by age on a given dataset. Python3 # import packages import pandas as pd # read Dataset data = pd.read_csv("WorldPopulationByAge2020.csv") data.head() # perform group by AgeGrp and find median data.groupby(["AgeGrp"])["PopTotal"].median() Output : Group by Age Comment More infoAdvertise with us Next Article Pandas Groupby and Computing Median abhijitmahajan772 Follow Improve Article Tags : Technical Scripter Python Technical Scripter 2020 Python-pandas Python pandas-groupby +1 More Practice Tags : python Similar Reads Pandas Groupby and Computing Mean Pandas is an open-source library that is built on top of NumPy library. It is a Python package that offers various data structures and operations for manipulating numerical data and time series. It is mainly popular for importing and analyzing data much easier. Pandas is fast and it has high-perform 2 min read Max and Min date in Pandas GroupBy Prerequisites: Pandas Pandas GroupBy is very powerful function. This function is capable of splitting a dataset into various groups for analysis. Syntax: dataframe.groupby([column names]) Along with groupby function we can use agg() function of pandas library. Agg() function aggregates the data tha 1 min read Pandas dataframe.groupby() Method Pandas groupby() function is a powerful tool used to split a DataFrame into groups based on one or more columns, allowing for efficient data analysis and aggregation. It follows a "split-apply-combine" strategy, where data is divided into groups, a function is applied to each group, and the results 6 min read 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 u 5 min read Pandas - GroupBy One Column and Get Mean, Min, and Max values We can use Groupby function to split dataframe into groups and apply different operations on it. One of them is Aggregation. Aggregation i.e. computing statistical parameters for each group created example - mean, min, max, or sums. Let's have a look at how we can group a dataframe by one column and 2 min read Pandas Groupby and Sum It's a simple concept but it's an extremely valuable technique that's widely used in data science. It is helpful in the sense that we can : Compute summary statistics for every groupPerform group-specific transformationsDo the filtration of data The dataframe.groupby() involves a combination of spli 2 min read Groupby without aggregation in Pandas Pandas is a great python package for manipulating data and some of the tools which we learn as a beginner are an aggregation and group by functions of pandas. Groupby() is a function used to split the data in dataframe into groups based on a given condition. Aggregation on other hand operates on se 4 min read Pandas Groupby Average GroupBy operations are powerful tools for summarizing and aggregating data. One common operation is calculating the average (mean) of groups within a DataFrame. Whether you're analyzing sales data by region, customer behavior by age group, or any other grouped data, groupby() method combined with ag 3 min read How to combine Groupby and Multiple Aggregate Functions in Pandas? Pandas is an open-source Python library built on top of NumPy. It allows data structures and functions to manipulate and analyze numerical data and time series efficiently. It is widely used in data analysis for tasks like data manipulation, cleaning and exploration. One of its key feature is to gro 3 min read How to do groupby on a multiindex in Pandas? In this article, we will be showing how to use the groupby on a Multiindex Dataframe in Pandas. In Data science when we are performing exploratory data analysis, we often use groupby to group the data of one column based on the other column. So, we are able to analyze how the data of one column is g 5 min read Like