Pandas, or the Python Data Analysis Library, was created by Wes McKinney in 2008. It’s primary use to manipulate data in DataFrames or 2-dimensional labeled data structure with columns of potentially different types. The insertion, manipulation, and transformation of DataFrames are of significant use to Analysts using Python. Featuring many of the aspects that Excel and other data analysis tools possess, but able to process much larger datasets, Pandas use has grown significantly and is one of the most used libraries for Analysts, Scientists, and Data Engineers.

Pandas has core features which include the following:

  • DataFrames & Series objects
  • Reading & Writing Data
  • Aggregating & Grouping Data
  • Pivoting Tables
  • Time Series Analysis
  • Visualizations in Pandas
  • Merging & Joining data

For more on Pandas see our extensive post on its history, usage, and support within the analytics community.

Using Pandas to explore data in Excel files

Introduction: When it comes to Data Science, we need to talk about data, and data comes in a lot of…

2 days ago

Pandas: An Open Source Library for Python

A Brief Introduction Pandas is an Open Source library built on top of NumPy. It allows for fast analysis and…

2 months ago

Tips for Performing EDA With Python

What is Exploratory Data Analysis (EDA)? EDA with Python is a critical skill for all data analysts, scientists, and even…

3 months ago

Concatenate, Merge, And Join Data with Pandas

Importance of Merging & Joining Data Many need to join data with Pandas, however there are several operations that are…

3 months ago

What is Pandas for Data Analysis?

Pandas is one of the most popular libraries for data analysis in the world and is growing rapidly. But, what…

3 months ago

Transform JSON Into a DataFrame

JSON is one of the most common data formats available in digital and non-digital applications. As a result, there it…

4 months ago

Understanding Pandas Data Types

Challenges with Pandas Data Types When using any software, it's critical to understand the data types that your data will…

4 months ago

An API Based ETL Pipeline With Python – Part 2

A Slimmed Down ETL In this post, we provide a much simpler approach to running a very basic ETL. We…

4 months ago

An API Based ETL Pipeline With Python – Part 1

In this post, we're going to show how to generate a rather simple ETL process from API data retrieved using…

5 months ago

The Growth of the Pandas Library

As data analytics, data science, and data engineering have exploded in popularity and growth as concepts, they've had some support…

5 months ago