What Is Pandas in Python? Everything You Need to Know

Everything about Pandas library

What is Pandas in Python?

Pandas is an open source Python package that is most widely used for data science/data analysis and machine learning tasks. It is built on top of another package named Numpy, which provides support for multi-dimensional arrays. As one of the most popular data wrangling packages, Pandas works well with many other data science modules inside the Python ecosystem, and is typically included in every Python distribution, from those that come with your operating system to commercial vendor distributions like ActiveState’s ActivePython.

What Can you Do with DataFrames using Pandas?

Pandas makes it simple to do many of the time consuming, repetitive tasks associated with working with data, including:

  • Data cleansing
  • Data fill
  • Data normalization
  • Merges and joins
  • Data visualization
  • Statistical analysis
  • Data inspection
  • Loading and saving data
  • And much more

In fact, with Pandas, you can do everything that makes world-leading data scientists vote Pandas as the best data analysis and manipulation tool available.

With this series we will go through reading some data, analyzing it , manipulating it, and finally storing it. These are all things that you are able to be done with the Pandas library. There are many more functionalities that can be explored but that would simply take too much time and for people who are interested in the library and want to dive deeper into it the documentation for it is a great start: https://pandas.pydata.org/docs/user_guide/index.html#user-guide
With deep roots in open source, and as a founding member of the Python Foundation, ActiveState actively contributes to the Python community. We offer the convenience, security and support that your enterprise needs while being compatible with the open source distribution of Python.
Download ActiveState Python to get started or contact us to learn more about using ActiveState Python in your organization.

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