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Packages for Data Analysis Activities

Learn about the two important and core packages for data analytics activities in Python; NumPy and Pandas.

After modules, you should learn about the various Python packages you would be exploring throughout the course.

There are two important and core packages for data analytics activities in Python; they are NumPy and Pandas.

Here is a sneak peek before we delve any deeper:

NumPy

NumPy is the core package for numerical computing. It contains many high-level packages. Pandas is one of the most heavily used of these high-level packages. Pandas provides spreadsheet functionality in Python. It supports data with labelled rows and columns.

It contains methods and functions for:

  • data loading
  • data manipulations
  • data cleaning
  • data exploration.

Pandas

Pandas is a package that you will use if you want to leverage a variety of ready-to-use methods instead of writing the low-level programming logic yourself. On the other hand, you use NumPy when you want to program at a low-level.

For example, you want some functionality that Pandas doesn’t have, so you need to create it yourself using NumPy. You would also use NumPy if you are planning to create your own package. Although Pandas is often more practical, it’s very important and useful to learn NumPy. Many core features of Pandas and other packages use NumPy functionality.

GitHub pulled some data on contributions from the developers on their website. As a result of the analysis conducted, Python was the leading language the developers used and NumPy and Pandas were some of the leading libraries they used. Read more in the Octoverse article below.

Read: The State of the Octoverse [1]

Next, you will learn more about using NumPy and Pandas.

References

1. The State of the Octoverse. [Internet]. GitHub; 2019. Available from: https://github.blog/2019-01-24-the-state-of-the-octoverse-machine-learning/

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Python Packages: NumPy and Pandas Dataframe

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