Most Data Scientists/Analysts using Python are familiar with Pandas. And if you are in the data science field, you probably have invested quite a significant amount of time learning how to use them to manipulate your data. However, one of the main complaints about Pandas is its speed and inefficiencies when dealing with large datasets. Fortunately, there is a new dataframe library that attempts to address this main complaint about Pandas — Polars. Polars is a DataFrame library that is completely written in Rust. In this workshop, you will learn the basics of using Polars in Python and how it can be used in place of Pandas.

Topics:

• Comparing Pandas and Polars
  Comparing the performances of using Pandas and Polars DataFrames
• Loading a Polars DataFrame
  Learn how to load a Polars DataFrame from a data structure (such as list or
  dictionary), as well from CSV files
• Selecting columns and rows
  Learn how to select rows and columns from a Polars DataFrame
• Lazy Evaluation in Polars
  Learn the unique features of Polars –lazy evaluation, and how it helps to optimize your query
• Manipulating values in dataframe
  Learn the best practices of how to manipulate the values in your Polars DataFrame
• Data cleansing
  Learn the various ways to clean your data (replacing null values, substituting
  with other values, etc)
• Performing GroupBy functions
  Learn how to use the GroupBy function to perform exploratory data analytics