What is pandas?

No, it’s not the plural form of the large animal with black-and-white fur. pandas is something we first heard about back in 2008, long before it became, along with NumPy, one of the most popular open-source libraries for the programming language Python. 

We have lots to tell you about it, so buckle up. 

By the end of this glossary entry, you’ll know how this Python library works and its benefits and drawbacks. Let’s get started. 

pandas, Explained

pandas, in the simplest terms, is an open-source Python library (or package) for data analysts and scientists. It allows those analysts and scientists to manipulate data and retrieve information from data. 

Here is the website for this Python library. It has a more complicated definition:

“pandas is a fast, powerful, flexible, and easy-to-use open-source data analysis and manipulation tool built on top of the Python programming language.”

pandas dates back to 2008 — six years before The Data Incubator started its data science and engineering bootcamps for people with a passion for data. Its name comes from the term “panel data,” which means data sets that include observations of the same individuals over time. Renowned businessman and software developer Wes McKinney created this Python package and calls himself its “Benevolent Dictator for Life.”

pandas has a BSD license — a ‘low restriction’ license type for open-source software that doesn’t have any redistribution requirements. That means anyone can use, study, share and change it in any way they like. Wes actually built pandas on the NumPy library — the other open-source Python package that data scientists and analysts gravitate toward.

What Does This Python Library Do?

pandas provides various data operations and structures. That helps you manipulate numerical data sets and time series. Because of its easy-to-use data structures, we think you can carry out complex data manipulation tasks using this Python library after a little training. Those tasks include:

• Data cleansing
• Data normalization
• Data inspection
• Data visualization
• Data merges
• Data joins
• Data fill
• Data loading
• Data saving
• Data analysis

That’s just the tip of the iceberg. And because this Python library is open-source (free!), it won’t cost you anything to manipulate data. 

So What About Python?

As you now know, pandas is built around the programming language Python. Its easy-to-use data structures and data manipulation features help you complete Python-related tasks. 

While you don’t have to use pandas for Python, you don’t have to use Python at all. But why should you?

• We think Python is easy to understand as a programming language. It’s great for beginners because it has an English-like syntax.
• Python has so many use cases. Use it to design websites, program databases, and program systems. 
• There are loads of support libraries for Python that can help you master your programming skills.
• Python is also completely open source (free!), so it won’t cost you anything to use.

Combining Python with pandas can make your life even easier. You’ll be able to manipulate numerical data and time series like a pro. To do this successfully, however, you’ll need a little training. Data Science Essentials, for example, is our 8-week, part-time program that helps you extract, clean, and analyze data using Python. Learn more about this program here. 

Benefits of This Python Library

Here are some of the benefits of the pandas library:

• It provides simple forms of data representation. That makes it easier to analyze and get value from your data.
• It handles and manages a large amount of data.
• It helps you customize data just the way you like. 
• It requires less writing than other Python libraries.
• It exclusively supports Python, and all its features serve the programming language.
• You can use it in almost any industry, including finance, retail, and statistics. 

This Python Package Sounds Great! Are There Any Drawbacks?

Of course!

While Python has a simple syntax, pandas has a somewhat different code. You might struggle when manipulating data at first, but we think you’ll become more confident over time. 

There’s also a lack of documentation. While you can find lots of resources for Python, you’ll find little information on the more complex aspects of pandas. 

What Is pandas? Key Takeaways

• pandas is an open-source library for the programming language Python.
• Wes McKinney created this package to make it easier to manipulate numerical data and time series. 
• This Python package dates back to 2008 and remains, along with NumPy, one of the most popular open-source Python libraries. 
• It requires less writing than other Python libraries.
• You can use this Python package for data cleansing, data normalization, data inspection, data visualization, and other data manipulation tasks. 
• pandas has little documentation online and a different syntax to Python, which might complicate matters.