What you'll learn
Explain why Polars is a better tool for data science than Pandas
Be able to efficiently read data from a file or database with Polars
Transform data with Polars including string, time series and categorical data
Do basic descriptive statistics along with groupby aggregations and group operations
Combine data from different datasets using joins and concatenations
Understand lazy evaluation and query optimization
Visualise data with Polars
Prepare data for machine learning pipelines with Polars
Requirements
A basic familiarity with Pandas for data science. You need to be a Pandas user, not an Pandas expert!
Description
This course shows you how to take advantage of Polars - the fast-growing new open source dataframe library that is becoming the go-to dataframe library for data scientists in python. The course is for data scientists who have some familiarity of an existing dataframe library like Pandas or Dask but who want to move to a library that is easier to write and faster to run. The course consists of video lectures, Jupyter notebooks and interactive quizzes. Each notebook comes with a set of exercises to help you develop your understanding of the core concepts. The course introduces the syntax of Polars that many have found to be easier to read and write than existing tools such as Pandas or SQL. In addition, the course shows you how to take advantage of the powerful features built into Polars such as the query optimizer and automatic parallelization.By the end of the course you will have optimized ways to:load and transform your data from CSV, Excel, Parquet, IPC or a database connection carry out aggregations on your datacombine your datasetsvisualise your outputs andprepare your data for machine learning pipelinesThe examples will cover a range of domains from tabular data to time series and NLP.
Overview
Section 1: Introduction - up and running with Polars
Lecture 1 Course introduction
Lecture 2 Why use Polars instead of Pandas?
Lecture 3 Course materials
Lecture 4 Installing the software on MacOS
Lecture 5 Installing the software on Windows
Lecture 6 How to use the materials on this course
Lecture 7 Polars - up and running
Lecture 8 Introducing lazy evaluation and query optimization
Lecture 9 Introduction to Data types
Lecture 10 Introduction to Series and DataFrame
Lecture 11 Converting to and from Pandas & Numpy
Section 2: Selecting data and introduction to expressions
Lecture 12 Filtering rows I: Filtering rows with square brackets
Lecture 13 Filtering rows II: Using `filter` and the Expression API
Lecture 14 Filtering rows III: using `filter` in lazy mode
Lecture 15 Selecting columns I: square brackets and .select
Lecture 16 Selecting columns II: choosing multiple columns
Lecture 17 Selecting columns III: transforming and adding columns
Lecture 18 Selecting columns IV: Transforming and adding multiple columns
Lecture 19 Adding a conditional column
Data scientists who want to process data with a library that offers higher performance,Data scientists who want to process data with a library that requires fewer user optimizations


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