Python for Business Analytics – groupby()

Personal Career & Learning Guide for Data Analyst, Data Engineer and Data Scientist

In business analytics, it is often necessary to group data by specific categories or attributes, in order to analyze and understand the underlying trends and patterns in the data. One powerful tool for performing these types of analyses is the groupby() function in Python.

The groupby() function in Python is a powerful tool for grouping data by specific categories or attributes. It works by taking a data set, such as a pandas DataFrame, and grouping the rows of data based on the values of one or more columns.

For example, let’s say you have a data set of sales data, where each row represents a single sale, and includes information such as the product name, the customer name, and the sales amount. Using the groupby() function, you can group the sales data by product name, in order to calculate the total sales for each product.

The groupby() function in Python is a flexible and versatile tool, and can be used for a variety of business analytics tasks, such as data cleaning and transformation, data analysis and modeling, and data visualization.

One key benefit of the groupby() function is its ability to handle large amounts of data with ease. By grouping data into smaller, more manageable pieces, the groupby() function allows you to perform complex analysis on your data, without having to deal with the challenges of working with large, unwieldy data sets.

In addition to its efficiency and speed, the groupby() function is also easy to use. With its simple and intuitive syntax, it allows you to perform complex data grouping operations with just a few lines of code. Whether you’re grouping data by product name, customer name, or any other category, the groupby() function makes it quick and easy to perform these tasks.

In conclusion, the groupby() function in Python is a powerful and versatile tool for business analytics. Whether you’re working with large data sets, or looking for a fast and efficient way to group and analyze your data, the groupby() function provides the solution you need. With its easy-to-use syntax and high performance, it is the ideal choice for business analysts and data scientists looking to take their data analysis and modeling skills to the next level.

 

Python for Business Analytics – groupby()

Loader Loading...
EAD Logo Taking too long?

Reload Reload document
| Open Open in new tab

Download PDF [164.03 KB]

Personal Career & Learning Guide for Data Analyst, Data Engineer and Data Scientist

Applied Machine Learning & Data Science Projects and Coding Recipes for Beginners

A list of FREE programming examples together with eTutorials & eBooks @ SETScholars

95% Discount on “Projects & Recipes, tutorials, ebooks”

Projects and Coding Recipes, eTutorials and eBooks: The best All-in-One resources for Data Analyst, Data Scientist, Machine Learning Engineer and Software Developer

Topics included: Classification, Clustering, Regression, Forecasting, Algorithms, Data Structures, Data Analytics & Data Science, Deep Learning, Machine Learning, Programming Languages and Software Tools & Packages.
(Discount is valid for limited time only)

Disclaimer: The information and code presented within this recipe/tutorial is only for educational and coaching purposes for beginners and developers. Anyone can practice and apply the recipe/tutorial presented here, but the reader is taking full responsibility for his/her actions. The author (content curator) of this recipe (code / program) has made every effort to ensure the accuracy of the information was correct at time of publication. The author (content curator) does not assume and hereby disclaims any liability to any party for any loss, damage, or disruption caused by errors or omissions, whether such errors or omissions result from accident, negligence, or any other cause. The information presented here could also be found in public knowledge domains.

Learn by Coding: v-Tutorials on Applied Machine Learning and Data Science for Beginners

Please do not waste your valuable time by watching videos, rather use end-to-end (Python and R) recipes from Professional Data Scientists to practice coding, and land the most demandable jobs in the fields of Predictive analytics & AI (Machine Learning and Data Science).

The objective is to guide the developers & analysts to “Learn how to Code” for Applied AI using end-to-end coding solutions, and unlock the world of opportunities!

Pandas Example – Write a Pandas program to split the following dataframe into groups based on all columns and calculate GroupBy value counts on the dataframe

Pandas Example – Write a Pandas program to split a given dataframe into groups and list all the keys from the GroupBy object

R for Business Analytics – Chapter 9: Lists

Python for Business Analytics – Chapter 20: List