GroupBy

Simplifying data analysis for everyonePhoto by William Felker on UnsplashPivoting and Grouping operations are fundamental to every typical tabular data analysis process. The pivot_table() and groupby() method stands among one of the most commonly used methods in Pandas.Used primarily for understanding categorical data, Grouping lets you compute statistics for individual groups in the data.Representation of Grouping (Image by Author)Pivot tables, on the other hand, allow you to cross-tabulate your data for fine-grained…

To avoid unexpected resultsPhoto by Muhammad Daudy on UnsplashIn statistics, a categorical variable is a variable that can take on one of a limited, and usually fixed, number of possible values. Pandas has a dedicated data type for categorical variables, which is category.In some cases, it is highly useful and efficient to use the category data type. For instance, if you have a string or object column that consists of only a few different values, converting it to category data type will save a substantial amount of…

An efficient tool for exploratory data analysisPhoto by Sharon Pittaway on UnsplashThe groupby is one of the most frequently used Pandas functions in data analysis. It is used for grouping the data points (i.e. rows) based on the distinct values in the given column or columns. We can then calculate aggregated values for the generated groups.If we have a dataset that contains brand and price information for cars, the groupby function can be used for calculating the average price for each brand.(image by author)In this…

Data ScienceAll you need to know about Pandas DataFrame Group By to use it efficientlyPhoto by Steve Johnson on UnsplashPandas Power! 🔋Pandas is widely used Python library for data analytics projects. However, it is never easy to analyze the data as it is to get valuable insights from it. To understand the data better, you need to transform and aggregate it. And that’s when groupby comes into the picture.In Pandas, groupby essentially splits all the records from your dataset into different categories or groups and offers…

Learn how to use the groupby() method in Polars to aggregate data and apply functionsPhoto by Hannah Busing on UnsplashSo far in my previous few articles on Polars, I have introduced you to the basics of the Polars DataFrame, why it is a better dataframe library than Pandas, and how its lazy evaluation feature allows it to optimize your queries. As you explore Polars, you will encounter one very common question — how do I perform a groupby on the Polars DataFrame? Most Pandas users are familiar with the groupby()…

Computing the mean in pandas using Group By expressionsPhoto by Diana Polekhina on UnsplashIntroductionWhen working with pandas DataFrames, we usually need to compute certain metrics for specific groups. The typical way to achieve this is through the traditional group-by expression followed by the relevant calculation we need to compute.In today’s short tutorial we will be showcasing how to perform Group-By operations over pandas DataFrames in order to compute the mean (aka average) and median values per group.First,…

Step-by-step examples and different use cases of Pandas GroupByPhoto by Anthony Intraversato on UnsplashIn the data science community, Pandas has become a very popular framework for processing and manipulating data. It’s based upon Python, a very simple and versatile language we are all familiar with. It offers many useful functions to help us transform data into the format we want. One of them is groupby, a function that can partition DataFrame rows into groups based on the values of certain columns.When I was first…