Python Crash Course | Data Analysis with Polars and Python

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Data Analysis with Polars and Python

By : Boris Paskhaver

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Data Analysis with Polars and Python

By: Boris Paskhaver

Buy this Video

Overview of this book

This course teaches efficient, scalable data analysis with Polars and Python, a modern alternative to traditional DataFrame libraries. You begin by setting up your Python environment and revisiting key Python concepts. Early lessons introduce Polars Series and DataFrames, emphasizing performance and memory efficiency. You work in Jupyter Lab to gain hands-on experience with vectorized, declarative logic, replacing row-by-row thinking with expressions. As you progress, you'll learn to filter, sort, reshape, and join large datasets, working with text, categorical, datetime, and nested data. Advanced topics like GroupBy, window functions, and selectors help you create concise, scalable analysis pipelines. The course concludes with LazyFrames, teaching optimized query plans for large data without memory overload. You'll also explore Polars' integration with other libraries and how to extend its functionality. Ideal for analysts and developers, this course enhances your ability to work with large datasets more efficiently, reinforcing key concepts with quizzes throughout.

Introduction

Welcome to Polars

[macOS] Intro to Terminal

[macOS] Install uv, a Python package and project manager

[macOS] Download Course Materials and Setup Project

[Windows] Intro to PowerShell

[Windows] Install uv, a Python package and project manager

[Windows] Download Course Materials and Setup Project

Jupyter Lab Startup and Shutdown

Intro to Jupyter Lab

Setting Up Ruff Formatter in Jupyter Lab

Import Libraries into Jupyter Lab

Python Crash Course

Comments

Data Types

Operators

Equality and Inequality Operators

Variables

Built-In Functions

Custom Functions

String Methods

Lists

Index Positions and Slicing

Tuples

Dictionaries

Classes and Objects

Importing Modules

Importing Libraries

Unsigned and Signed Integers

Series

Import the Polars Series

Create a Series

Data Type Inference

Attributes

Missing Values

The alias Method

Import a CSV File with the read_csv Function

The head and tail Methods

Memory Optimization and the schema_overrides Parameter

Sorting a Series

Mathematical Methods

Rounding Methods

How Polars Differs from Pandas

DataFrames I

Intro to DataFrames

Create a DataFrame from Scratch

Read a DataFrame from CSV

No Index, No Problem

Intro to Expressions

The select Method I

Renaming Columns

The select Method II

The select Method III: Targeting by Data Type

Expressions as Building Blocks

Expressions that Count Values

Extracting One or More Rows

List Slicing Syntax

Expressions that Target Row Values

Extracting a Single Value from DataFrame with the item Method

Extracting Rows by Index Positions with the gather and gather_every Methods

Extracting a Random Set of Values

Casting Columns to Different Types

Customizing the DataFrame Schema

Renaming Columns

The name Attribute

Dropping Columns

Replacing Values

Mathematical Operations I

Mathematical Operations II

Cumulative Mathematical Operations

The with_columns Method

The all and exclude Functions

DataFrames II

The fill_null Method

Interpolation

Dropping Missing Data

Sorting by a Single Column

Sorting by Multiple Columns I

Sorting by Multiple Columns II

Characters vs Bytes

Sorting based on Expressions

The top_k and bottom_k Methods

The rank Method

The shuffle Method

Counting and Extracting Unique Values

The value_counts Method

DataFrames III - Filtering

Introducing the Dataset

The filter Method

Filtering with Mathematical Operators

Filtering with Missing Values

Filtering with Boolean Columns

Applying And Logic (Multiple Boolean Expressions)

Keyword Argument Filtering

Applying Or Logic

Operator Precedence

Applying Exclusive OR (xor) Logic

Filtering for Unique and Duplicate Values

Filtering with Datetimes

The is_between Method

The is_in Method

The remove Method

Negation with Tilde Symbol

When, Then, Otherwise

Partitioning DataFrames

Joins

Introducing the Datasets

Inner Joins

The on Parameter

Full Joins

Left and Right Joins

Semi Join

Anti Join

Cross Joins/Cartesian Products

Joining on Multiple Columns

The validate Parameter

The join_asof Method I

The join_asof Method II: Tolerance

The join_asof Method III: The by Parameter

Concatenation

Vertical Concatenation

Horizontal Concatenation

Diagonal Concatenation

Align Concatenation

Relaxed Concatenation

Rechunking

The vstack Method

The extend Method

The hstack Method

Reshaping

Wide vs. Long DataFrames

The unpivot Method to Convert a Wide DataFrame to a Long DataFrame

The pivot Method to Convert a Long DataFrame to a Wide DataFrame

Pivot Tables I

Pivot Tables II

The transpose Method

Arrays and Lists

The str.split Method

The list Namespace

Sorting the Lists

The explode Method

Exploding with Multiple Columns of Lists

Mathematical Operations

The list.eval, list.any, and list.all Methods

Concatenating Column Values

Arrays

The arr Attribute

Structs

Intro to Structs

The struct.field and unnest Methods

The value_counts Method

Rename Struct Fields

Using Structs to Work with Nested Data

Using Structs to Identify Duplicates across Columns

Working with Text Data

Introducing the Dataset

Case Conversion

Removing Whitespace

Removing Prefix and Suffix

Characters vs. Bytes

String Slicing

Filtering Methods

Regular Expressions

Capture Groups

Replacing Values

Find Longest Employee Names

Categoricals and Enums

Categorical Data

The cat Namespace

Enums

Enums, Categoricals, and Sorting

Working with Datetimes

Import a Dataset with Datetimes

Parse Datetimes with the strptime Method

Parse Dates and Times

Converting from Date to String

Extracting Datetime Components

Filtering by Date, Time, and Datetime

Adding and Subtracting Time I

Adding and Subtracting Time II

Durations

Timezones I

Timezones II - Conversions

Selectors

A Review of Targeting Strategies

Introducing Selectors

Selecting by Data Type

Selecting by Column Position

Set Operations with Selectors

GroupBy

Introducing our Dataset

Intro to Grouping

GroupBy Methods

Largest and Smallest Values per Group

Aggregations with the agg Method

Using Selectors in Aggregations

Grouping with Multiple Columns

Grouping Temporal Data

Window Functions and the over Method

LazyFrames

LazyFrames and Eager vs. Lazy Evaluation

The scan_csv Function and the collect Method

A Matter of Time

Convert a DataFrame to a LazyFrame

LazyFrame Limitations

Data Analysis with Polars and Python

By : Boris Paskhaver

Data Analysis with Polars and Python

By: Boris Paskhaver

Overview of this book

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