Data Analysis with Polars

Focused View

Liam Brannigan

50:27

186 View

1 - Introduction up and running with Polars

1 - Course introduction.mp4

01:38

1 - Lazy mode.html

2 - Why use Polars instead of Pandas.mp4

00:45

3 - Course materials.html

3 - course-package.tar.zip

3 - course-package-windows.tar.zip

4 - Installing the software on MacOS.html

5 - Installing the software on Windows.html

6 - How to use the materials on this course.html

7 - Polars up and running.mp4

04:06

8 - Introducing lazy evaluation and query optimization.mp4

05:47

9 - Introduction to Data types.mp4

03:22

10 - Introduction to Series and DataFrame.html

11 - Converting to and from Pandas & Numpy.html

2 - Selecting data and introduction to expressions

3 - Selecting columns.html

12 - Filtering rows I Filtering rows with square brackets.mp4

03:09

13 - Filtering rows II Using filter and the Expression API.mp4

05:36

14 - Filtering rows III using filter in lazy mode.mp4

05:48

15 - Selecting columns I square brackets and select.mp4

07:01

16 - Selecting columns II choosing multiple columns.mp4

03:12

17 - Selecting columns III transforming and adding columns.mp4

04:14

18 - Selecting columns IV Transforming and adding multiple columns.html

19 - Adding a conditional column.mp4

05:49

Description

Transform your data analysis with Polars - the powerful new dataframe library

What You'll Learn?

Taking advantage of parallel and optimised analysis with Polars
Working with larger-than-memory data
Using Polars expressions for analysis that is easy to read and write
Loading data from a wide variety of data sources
Combining data from different datasets using fast joins operations
Grouping and parallel aggregations
Deriving insight from time series
Preparing data for machine learning pipelines
Visualising data with Matplotlib and Plotly

Who is this for?

Data scientists with no familiarity with Polars and want to get up and running

Data scientists with some familiarity with Polars but want a deeper understanding

What You Need to Know?

Computer with Windows/Linux/MacOS and a python installation

A basic familiarity with Pandas for data science. You need to be a Pandas user, not an Pandas expert!

More details

Description
In this course I show you how to take advantage of Polars - the fast-growing open source dataframe library that is becoming the go-to dataframe library for data scientists in python. I am a Polars contributor with a focus on making Polars accessible to new users. IÂ have spent months getting familiar with all the details of the Polars codebase.

"Thank you for your great work with this course - I've optimized some code thanks to it already!" Maiia Bocharova

The course is for data scientists who have some familiarity with a dataframe library like Pandas but who want to move to Polars because it is easier to write and faster to run. The core materials are Jupyter notebooks that examine each topic in depth. Each notebook comes with a set of exercises to help you develop your understanding of the core concepts.

For many key topics this course is the only source of documentation. I have focused on producing Jupyter notebooks to allow anyone taking the course to start using the full power of Polars. As a consequence the video content is limited. More videos that go beyond the notebooks will be added in the coming months once the core functionality has been documented in the notebooks.

The course introduces the syntax of Polars and shows you the many ways that Polars allows you to produce queries that are easy to read and write. However, the course also delves deeper to help you understand and exploit the algorithms that drive the outstanding performance of Polars.

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
run your analysis in parallel
work with larger-than-memory datasets
carry out aggregations on your data
combine your datasets
visualise your outputs and
prepare your data for machine learning pipelines
Who this course is for:
Data scientists with no familiarity with Polars and want to get up and running
Data scientists with some familiarity with Polars but want a deeper understanding

User Reviews

Rating

average 0

Total votes0

Focused display

Data Analysis

Liam Brannigan

Instructor's Courses

I have a PhD in climate physics from the University of Oxford. Before founding my own company I was lead data scientist at a startup working on problems in NLP, time series and geospatial analysis. I am is a data science communicator and I help to spread the work on exciting new technology on my youtube channel and twitter.

Udemy

View courses Udemy

Students take courses primarily to improve job-related skills.Some courses generate credit toward technical certification. Udemy has made a special effort to attract corporate trainers seeking to create coursework for employees of their company.