Data Analysis with Polars

Data Analysis with Polars, available at $84.99, has an average rating of 4.54, with 102 lectures, 14 quizzes, based on 348 reviews, and has 2538 subscribers.

You will learn about 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, Seaborn, Altair & Plotly Using Polars with Scikit-learn This course is ideal for individuals who are Data scientists with no familiarity with Polars and want to get up and running or Data scientists with some familiarity with Polars but want a deeper understanding or Pandas or other dataframe library users It is particularly useful for Data scientists with no familiarity with Polars and want to get up and running or Data scientists with some familiarity with Polars but want a deeper understanding or Pandas or other dataframe library users.

Enroll now: Data Analysis with Polars

Summary

Title: Data Analysis with Polars

Price: $84.99

Average Rating: 4.54

Number of Lectures: 102

Number of Quizzes: 14

Number of Published Lectures: 66

Number of Published Quizzes: 12

Number of Curriculum Items: 116

Number of Published Curriculum Objects: 78

Original Price: £19.99

Quality Status: approved

Status: Live

What You Will 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, Seaborn, Altair & Plotly
Using Polars with Scikit-learn

Who Should Attend

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
Pandas or other dataframe library users

Target Audiences

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
Pandas or other dataframe library users

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 and I keep this course up-to-date with new releases of Polars – updated to version 1.5

“A thorough introduction to Polars” – Ritchie Vink, creator of Polars

“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 for learners and comes from my time examining the Polars source code.

An important note about videos: this is a primarily a notebook course and not a video course. Not all of the lectures have videos and some of the videos may have components that are not up-to-date. Why? Because the Polars API has changed too often to allow me to keep videos up-to-date. Instead I focus on keeping the notebooks up-to-date with an extensive automated testing system that alerts me to changes in the API. I release an updated version of the course about twice a month in response to changes in Polars.

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 optimised ways to:

load and transform your data from CSV, Excel, Parquet, cloud storage or a database
run your analysis in parallel
understand optimal patterns for building queries
work with larger-than-memory datasets
carry out aggregations on your data
combine your datasets with joins and concatenations
work with nested dtypes including lists and structs
optimise the speed and memory usage of your queries
work with string and categorical data
visualise your outputs with Matplotlib, Seaborn, Plotly, hvPlot & Altair
prepare your data for machine learning pipelines with sklearn

Course Curriculum

Chapter 1: Up and running with Polars

Lecture 1: Course introduction

Lecture 2: Why use Polars instead of Pandas?

Lecture 3: How can you make best use of the course materials?

Lecture 4: Course materials

Lecture 5: Polars quickstart

Lecture 6: Lazy mode: Introducing lazy mode

Lecture 7: Lazy mode: evaluating queries

Lecture 8: Introduction to Data types

Lecture 9: Series and DataFrame

Lecture 10: Converting to and from Pandas & Numpy

Lecture 11: Visualisation

Chapter 2: Filtering rows

Lecture 1: Filtering rows I: Filtering rows with square brackets

Lecture 2: Filtering rows 2: Using `filter` and the Expression API

Lecture 3: Filtering rows 3: using `filter` in lazy mode

Lecture 4: Filtering rows based on values from another DataFrame

Chapter 3: Selecting columns and transforming dataframes

Lecture 1: Selecting columns 1: using square brackets

Lecture 2: Selecting columns 2: using select and expressions

Lecture 3: Selecting columns 3: choosing multiple columns

Lecture 4: Selecting columns 4: transforming and adding columns

Lecture 5: Selecting columns 5: Transforming and adding multiple columns

Lecture 6: Selecting columns 6: Adding a column based on a condition or mapping

Lecture 7: Sorting and fast-track algorithms

Lecture 8: Transforming a DataFrame

Lecture 9: Iterating through a DataFrame

Chapter 4: Data types and missing values

Lecture 1: Missing values

Lecture 2: Replacing missing values

Lecture 3: Replacing missing values with expressions

Lecture 4: Numerical dtypes and precision

Lecture 5: Introducing categorical data

Lecture 6: Categoricals and the string cache

Lecture 7: Introduction to nested dtypes: List, Struct and Object

Lecture 8: List dtype 1: Creating and transforming List columns

Lecture 9: List dtype 2: using expressions on List columns

Lecture 10: Text transformation

Chapter 5: Grouping and aggregation

Lecture 1: Statistics

Lecture 2: Value counts

Lecture 3: Groupby 1: Key concepts

Lecture 4: Groupby 2: Iterating and group values

Lecture 5: Quantiles and histograms

Lecture 6: Introduction to group operations with over()

Lecture 7: Pivot & melt

Chapter 6: Combining dataframes

Lecture 1: Concatenating DataFrames

Lecture 2: Concatenating DataFrames

Lecture 3: Joins

Lecture 4: Joins on string and categorical data

Lecture 5: Filtering a DataFrame by another DataFrame

Lecture 6: Extending, stacking and concatenating

Chapter 7: Time series analysis

Lecture 1: Introduction to time series dtypes

Lecture 2: Time zones

Lecture 3: Parsing datetime strings

Lecture 4: Adjusting datetimes

Lecture 5: Extracting datetime components

Lecture 6: Filtering time series

Lecture 7: Temporal groupby – introduction to groupby_dynamic

Lecture 8: Controlling the `groupby_dynamic` window

Chapter 8: Input/Output

Lecture 1: Read a single CSV file

Lecture 2: CSV files 2: multiple CSV files

Lecture 3: Read an Excel file

Lecture 4: Read JSON and newline delimited JSON

Lecture 5: CSV files 3: reading larger-than-memory CSV files in batches

Lecture 6: CSV files 4: streaming larger-than-memory datasets

Lecture 7: Parquet files 1: single Parquet files

Lecture 8: Reading from a database

Chapter 9: Nested dtypes

Lecture 1: Visualisations with Plotly

Lecture 2: Visualisations with Matplotlib

Lecture 3: Visualisations with Seaborn

Instructors

Liam Brannigan
Data science instructor and communicator

Rating Distribution

1 stars: 3 votes
2 stars: 6 votes
3 stars: 21 votes
4 stars: 107 votes
5 stars: 211 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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