Data Analysis with Polars and Python

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Free Download Data Analysis with Polars and Python
Published 1/2026
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 22h 11m | Size: 14 GB
Master data analysis with the powerful Polars library! Up-to-date for 2026. All datasets included --- beginners welcome!

What you'll learn
Master data manipulation operations in Polars including sorting, filtering, grouping, pivoting, joining and more!
Understand Polar's functional, expression-based syntax for building up complex chains of logic
Use LazyFrames to create complex query plans that Polars can optimize for efficiency
Work with a variety of data including text, temporal, numeric, nested structures, and more
Requirements
Basic/intermediate experience with a spreadsheet software like Microsoft Excel/Google Sheets (common functions, vlookups, countif, pivot tables etc)
Basic experience with the Python programming language (we'll cover the basics if you're brand new!)
Strong knowledge of data types (strings, integers, floating points, booleans) etc
Description
Welcome to the most comprehensive Polars course on Udemy! Data Analysis with Polars and Python offers 22+ hours of in-depth video tutorials on the powerful Polars data analysis library. The course also includes a wide collection of datasets, quizzes, and coding challenges to aid your learning.Why Polars?The core of Polars is written in Rust, one of the fastest programming languages in the world. At the same time, the library enables us to write our code in Python, the most popular language in the world. We gain the best of both worlds -- the speed and efficiency of Rust and the simplicity and elegance of Python.Who is this Course For?The course is designed for learners of all skill levels, from experienced data analysts to students who have never programmed before. Lessons include:installing Python and Polars on your computerunderstanding the core mechanics of Pythonworking with the Jupyter Lab coding environmentWhether you've spent time in a spreadsheet software like Microsoft Excel/Google Sheets or another data analysis library like Pandas, Polars can help take your data analysis skills to the next level.What Topics Will We Cover?We'll cover the core objects of Polars including:SeriesDataFramesLazyFramesMost of our work will focus on the DataFrame, a 2-dimensional table of rows and columns. We'll cover data manipulation operations including:sortingfilteringgroupingaggregatingde-duplicatingpivotingdeletingjoiningreplacingworking with text dataworking with temporal/datetime dataWe'll also cover some of Polar's unique column data types including:listsarraysstructsand more!Data Analysis with Polars and PythonI'm excited to share everything I've learned about Polars, a powerful library that is quickly emerging as a dominant competitor in Python's data science ecosystem. I look forward to seeing you in the course!
Who this course is for
Data analysts and business analysts
Excel/Google Sheets users who looking to learn a more powerful software for data analysis
Developers familiar with Pandas who want to explore the rising entrant in the Python data science ecosystem
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[0dayddl]

Data Analysis with Polars and Python
.MP4, AVC, 1920x1080, 30 fps | English, AAC, 2 Ch | 22h 11m | 14.1 GB
Instructor: Boris Paskhaver​

Master data analysis with the powerful Polars library! Up-to-date for 2026. All datasets included -- beginners welcome!

What you'll learn

• Master data manipulation operations in Polars including sorting, filtering, grouping, pivoting, joining and more!
• Understand Polar's functional, expression-based syntax for building up complex chains of logic
• Use LazyFrames to create complex query plans that Polars can optimize for efficiency
• Work with a variety of data including text, temporal, numeric, nested structures, and more

Requirements

• Basic/intermediate experience with a spreadsheet software like Microsoft Excel/Google Sheets (common functions, vlookups, countif, pivot tables etc)
• Basic experience with the Python programming language (we'll cover the basics if you're brand new!)
• Strong knowledge of data types (strings, integers, floating points, booleans) etc

Description

Welcome to the most comprehensive Polars course on Udemy!

Data Analysis with Polars and Python offers 22+ hours of in-depth video tutorials on the powerful Polars data analysis library. The course also includes a wide collection of datasets, quizzes, and coding challenges to aid your learning.

Why Polars?

The core of Polars is written in Rust, one of the fastest programming languages in the world. At the same time, the library enables us to write our code in Python, the most popular language in the world. We gain the best of both worlds - the speed and efficiency of Rust and the simplicity and elegance of Python.

Who is this Course For?

The course is designed for learners of all skill levels, from experienced data analysts to students who have never programmed before. Lessons include:

• installing Python and Polars on your computer
• understanding the core mechanics of Python
• working with the Jupyter Lab coding environment

Whether you've spent time in a spreadsheet software like Microsoft Excel/Google Sheets or another data analysis library like Pandas, Polars can help take your data analysis skills to the next level.

What Topics Will We Cover?

We'll cover the core objects of Polars including:

• Series
• DataFrames
• LazyFrames

Most of our work will focus on the DataFrame, a 2-dimensional table of rows and columns. We'll cover data manipulation operations including:

• sorting
• filtering
• grouping
• aggregating
• de-duplicating
• pivoting
• deleting
• joining
• replacing
• working with text data
• working with temporal/datetime data

We'll also cover some of Polar's unique column data types including:

• lists
• arrays
• structs

and more!

Data Analysis with Polars and Python

I'm excited to share everything I've learned about Polars, a powerful library that is quickly emerging as a dominant competitor in Python's data science ecosystem. I look forward to seeing you in the course!

Who this course is for:

• Data analysts and business analysts
• Excel/Google Sheets users who looking to learn a more powerful software for data analysis
• Developers familiar with Pandas who want to explore the rising entrant in the Python data science ecosystem

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[0dayddl]

6.28 GB | 8min 45s | mp4 | 1920X1080 | 16:9
Genre:eLearning |Language:English​

Files Included :

Spoiler

FileName :001 Welcome to Polars.mp4 | Size: (90.56 MB)
FileName :002 [macOS] Intro to Terminal.mp4 | Size: (56.19 MB)
FileName :003 [macOS] Install uv, a Python package and project manager.mp4 | Size: (20.1 MB)
FileName :004 [macOS] Download Course Materials and Setup Project.mp4 | Size: (12.63 MB)
FileName :005 [Windows] Intro to PowerShell.mp4 | Size: (30.63 MB)
FileName :006 [Windows] Install uv, a Python package and project manager.mp4 | Size: (17.23 MB)
FileName :007 [Windows] Download Course Materials and Setup Project.mp4 | Size: (17.99 MB)
FileName :008 Jupyter Lab Startup and Shutdown.mp4 | Size: (31.41 MB)
FileName :009 Intro to Jupyter Lab.mp4 | Size: (43.8 MB)
FileName :010 Setting Up Ruff Formatter in Jupyter Lab.mp4 | Size: (5.63 MB)
FileName :011 Import Libraries into Jupyter Lab.mp4 | Size: (13.15 MB)
FileName :001 Comments.mp4 | Size: (19.08 MB)
FileName :002 Data Types.mp4 | Size: (29.32 MB)
FileName :003 Operators.mp4 | Size: (38.47 MB)
FileName :004 Equality and Inequality Operators.mp4 | Size: (27.4 MB)
FileName :005 Variables.mp4 | Size: (21.1 MB)
FileName :006 Built-In Functions.mp4 | Size: (52.92 MB)
FileName :007 Custom Functions.mp4 | Size: (61.2 MB)
FileName :008 String Methods.mp4 | Size: (67.24 MB)
FileName :009 Lists.mp4 | Size: (39.65 MB)
FileName :010 Index Positions and Slicing.mp4 | Size: (53 MB)
FileName :011 Tuples.mp4 | Size: (22.05 MB)
FileName :012 Dictionaries.mp4 | Size: (43.68 MB)
FileName :013 Classes and Objects.mp4 | Size: (27.37 MB)
FileName :014 Importing Modules.mp4 | Size: (42.51 MB)
FileName :015 Importing Libraries.mp4 | Size: (11.34 MB)
FileName :016 Unsigned and Signed Integers.mp4 | Size: (73.5 MB)
FileName :001 Import the Polars Series.mp4 | Size: (18.08 MB)
FileName :002 Create a Series.mp4 | Size: (36.97 MB)
FileName :003 Data Type Inference.mp4 | Size: (36.88 MB)
FileName :004 Attributes.mp4 | Size: (11.14 MB)
FileName :005 Missing Values.mp4 | Size: (21.22 MB)
FileName :006 The alias Method.mp4 | Size: (14.56 MB)
FileName :007 Import a CSV File with the read csv Function.mp4 | Size: (39.37 MB)
FileName :008 The head and tail Methods.mp4 | Size: (22.81 MB)
FileName :009 Memory Optimization and the schema overrides Parameter.mp4 | Size: (42.73 MB)
FileName :010 Sorting a Series.mp4 | Size: (21.3 MB)
FileName :011 Mathematical Methods.mp4 | Size: (21.58 MB)
FileName :012 Rounding Methods.mp4 | Size: (10.78 MB)
FileName :013 How Polars Differs from Pandas.mp4 | Size: (48.43 MB)
FileName :001 Intro to DataFrames.mp4 | Size: (10.76 MB)
FileName :002 Create a DataFrame from Scratch.mp4 | Size: (9.02 MB)
FileName :003 Read a DataFrame from CSV.mp4 | Size: (31.62 MB)
FileName :004 No Index, No Problem.mp4 | Size: (19.21 MB)
FileName :005 Intro to Expressions.mp4 | Size: (25.72 MB)
FileName :006 The select Method I.mp4 | Size: (23.74 MB)
FileName :007 Renaming Columns.mp4 | Size: (33.76 MB)
FileName :008 The select Method II.mp4 | Size: (40.55 MB)
FileName :009 The select Method III - Targeting by Data Type.mp4 | Size: (15.68 MB)
FileName :010 Expressions as Building Blocks.mp4 | Size: (18.65 MB)
FileName :011 Expressions that Count Values.mp4 | Size: (27.88 MB)
FileName :012 Extracting One or More Rows.mp4 | Size: (24.63 MB)
FileName :013 List Slicing Syntax.mp4 | Size: (23.76 MB)
FileName :014 Expressions that Target Row Values.mp4 | Size: (20.42 MB)
FileName :015 Extracting a Single Value from DataFrame with the item Method.mp4 | Size: (13.09 MB)
FileName :016 Extracting Rows by Index Positions with the gather and gather every Methods.mp4 | Size: (16.29 MB)
FileName :017 Extracting a Random Set of Values.mp4 | Size: (9.73 MB)
FileName :018 Casting Columns to Different Types.mp4 | Size: (21.2 MB)
FileName :019 Customizing the DataFrame Schema.mp4 | Size: (24.15 MB)
FileName :020 Renaming Columns.mp4 | Size: (15.85 MB)
FileName :021 The name Attribute.mp4 | Size: (22.63 MB)
FileName :022 Dropping Columns.mp4 | Size: (13.32 MB)
FileName :023 Replacing Values.mp4 | Size: (10.15 MB)
FileName :024 Mathematical Operations I.mp4 | Size: (42.61 MB)
FileName :025 Mathematical Operations II.mp4 | Size: (14.25 MB)
FileName :026 Cumulative Mathematical Operations.mp4 | Size: (26.2 MB)
FileName :027 The with columns Method.mp4 | Size: (36.2 MB)
FileName :028 The all and exclude Functions.mp4 | Size: (21.87 MB)
FileName :001 The fill null Method.mp4 | Size: (46.02 MB)
FileName :002 Interpolation.mp4 | Size: (18.07 MB)
FileName :003 Dropping Missing Data.mp4 | Size: (41.09 MB)
FileName :004 Sorting by a Single Column.mp4 | Size: (51.06 MB)
FileName :005 Sorting by Multiple Columns I.mp4 | Size: (29.9 MB)
FileName :006 Sorting by Multiple Columns II.mp4 | Size: (33.23 MB)
FileName :007 Characters vs Bytes.mp4 | Size: (35.56 MB)
FileName :008 Sorting based on Expressions.mp4 | Size: (29.56 MB)
FileName :009 The top k and bottom k Methods.mp4 | Size: (13.34 MB)
FileName :010 The rank Method.mp4 | Size: (22.1 MB)
FileName :011 The shuffle Method.mp4 | Size: (19.22 MB)
FileName :012 Counting and Extracting Unique Values.mp4 | Size: (25.84 MB)
FileName :013 The value counts Method.mp4 | Size: (28.69 MB)
FileName :001 Introducing the Dataset.mp4 | Size: (16.98 MB)
FileName :002 The filter Method.mp4 | Size: (39.59 MB)
FileName :003 Filtering with Mathematical Operators.mp4 | Size: (24.88 MB)
FileName :004 Filtering with Missing Values.mp4 | Size: (38.11 MB)
FileName :005 Filtering with Boolean Columns.mp4 | Size: (11.64 MB)
FileName :006 Applying And Logic (Multiple Boolean Expressions).mp4 | Size: (58.94 MB)
FileName :007 Keyword Argument Filtering.mp4 | Size: (15.28 MB)
FileName :008 Applying Or Logic.mp4 | Size: (26.51 MB)
FileName :009 Operator Precedence.mp4 | Size: (24.21 MB)
FileName :010 Applying Exclusive OR (xor) Logic.mp4 | Size: (12.77 MB)
FileName :011 Filtering for Unique and Duplicate Values.mp4 | Size: (27.27 MB)
FileName :012 Filtering with Datetimes.mp4 | Size: (42.84 MB)
FileName :013 The is between Method.mp4 | Size: (32.54 MB)
FileName :014 The is in Method.mp4 | Size: (13.15 MB)
FileName :015 The remove Method.mp4 | Size: (29.31 MB)
FileName :016 Negation with Tilde Symbol.mp4 | Size: (30.39 MB)
FileName :017 When, Then, Otherwise.mp4 | Size: (33.33 MB)
FileName :018 Partitioning DataFrames.mp4 | Size: (50.12 MB)
FileName :001 Introducing the Datasets.mp4 | Size: (20.94 MB)
FileName :002 Inner Joins.mp4 | Size: (58.2 MB)
FileName :003 The on Parameter.mp4 | Size: (10.75 MB)
FileName :004 Full Joins.mp4 | Size: (36.11 MB)
FileName :005 Left and Right Joins.mp4 | Size: (24.73 MB)
FileName :006 Semi Join.mp4 | Size: (10.59 MB)
FileName :007 Anti Join.mp4 | Size: (9.69 MB)
FileName :008 Cross JoinsCartesian Products.mp4 | Size: (14.21 MB)
FileName :009 Joining on Multiple Columns.mp4 | Size: (33.69 MB)
FileName :010 The validate Parameter.mp4 | Size: (52.01 MB)
FileName :011 The join asof Method I.mp4 | Size: (47.64 MB)
FileName :012 The join asof Method II - Tolerance.mp4 | Size: (29.27 MB)
FileName :013 The join asof Method III - The by Parameter.mp4 | Size: (50.6 MB)
FileName :001 Vertical Concatenation.mp4 | Size: (15.12 MB)
FileName :002 Horizontal Concatenation.mp4 | Size: (16.43 MB)
FileName :003 Diagonal Concatenation.mp4 | Size: (15.59 MB)
FileName :004 Align Concatenation.mp4 | Size: (37.39 MB)
FileName :005 Relaxed Concatenation.mp4 | Size: (39.33 MB)
FileName :006 Rechunking.mp4 | Size: (66.27 MB)
FileName :007 The vstack Method.mp4 | Size: (51.16 MB)
FileName :008 The extend Method.mp4 | Size: (29.15 MB)
FileName :009 The hstack Method.mp4 | Size: (8.79 MB)
FileName :001 Wide vs Long DataFrames.mp4 | Size: (23.53 MB)
FileName :002 The unpivot Method to Convert a Wide DataFrame to a Long DataFrame.mp4 | Size: (26.65 MB)
FileName :003 The pivot Method to Convert a Long DataFrame to a Wide DataFrame.mp4 | Size: (16.47 MB)
FileName :004 Pivot Tables I.mp4 | Size: (27.41 MB)
FileName :005 Pivot Tables II.mp4 | Size: (17.72 MB)
FileName :006 The transpose Method.mp4 | Size: (14.69 MB)
FileName :001 Arrays and Lists.mp4 | Size: (35.05 MB)
FileName :002 The str split Method.mp4 | Size: (16.2 MB)
FileName :003 The list Namespace.mp4 | Size: (20.32 MB)
FileName :004 Sorting the Lists.mp4 | Size: (9.17 MB)
FileName :005 The explode Method.mp4 | Size: (12.57 MB)
FileName :006 Exploding with Multiple Columns of Lists.mp4 | Size: (28.1 MB)
FileName :007 Mathematical Operations.mp4 | Size: (20.91 MB)
FileName :008 The list eval, list any, and list all Methods.mp4 | Size: (31.79 MB)
FileName :009 Concatenating Column Values.mp4 | Size: (35.17 MB)
FileName :010 Arrays.mp4 | Size: (23.18 MB)
FileName :011 The arr Attribute.mp4 | Size: (15.61 MB)
FileName :001 Intro to Structs.mp4 | Size: (30.24 MB)
FileName :002 The struct field and unnest Methods.mp4 | Size: (13.76 MB)
FileName :003 The value counts Method.mp4 | Size: (22.27 MB)
FileName :004 Rename Struct Fields.mp4 | Size: (9.89 MB)
FileName :005 Using Structs to Work with Nested Data.mp4 | Size: (82.41 MB)
FileName :006 Using Structs to Identify Duplicates across Columns.mp4 | Size: (21.8 MB)
FileName :001 Introducing the Dataset.mp4 | Size: (13.05 MB)
FileName :002 Case Conversion.mp4 | Size: (19.69 MB)
FileName :003 Removing Whitespace.mp4 | Size: (23.55 MB)
FileName :004 Removing Prefix and Suffix.mp4 | Size: (18.41 MB)
FileName :005 Characters vs Bytes.mp4 | Size: (36.72 MB)
FileName :006 String Slicing.mp4 | Size: (20.26 MB)
FileName :007 Filtering Methods.mp4 | Size: (41.08 MB)
FileName :008 Regular Expressions.mp4 | Size: (34.95 MB)
FileName :009 Capture Groups.mp4 | Size: (42.89 MB)
FileName :010 Replacing Values.mp4 | Size: (23.14 MB)
FileName :011 Find Longest Employee Names.mp4 | Size: (36.5 MB)
FileName :001 Categorical Data.mp4 | Size: (37.93 MB)
FileName :002 The cat Namespace.mp4 | Size: (13.44 MB)
FileName :003 Enums.mp4 | Size: (31.39 MB)
FileName :004 Enums, Categoricals, and Sorting.mp4 | Size: (26.48 MB)
FileName :001 Import a Dataset with Datetimes.mp4 | Size: (31.37 MB)
FileName :002 Parse Datetimes with the strptime Method.mp4 | Size: (46.21 MB)
FileName :003 Parse Dates and Times.mp4 | Size: (22.29 MB)
FileName :004 Converting from Date to String.mp4 | Size: (28.09 MB)
FileName :005 Extracting Datetime Components.mp4 | Size: (28.9 MB)
FileName :006 Filtering by Date, Time, and Datetime.mp4 | Size: (49.11 MB)
FileName :007 Adding and Subtracting Time I.mp4 | Size: (29.78 MB)
FileName :008 Adding and Subtracting Time II.mp4 | Size: (24.9 MB)
FileName :009 Durations.mp4 | Size: (23.66 MB)
FileName :010 Timezones I.mp4 | Size: (62.83 MB)
FileName :011 Timezones II - Conversions.mp4 | Size: (22.98 MB)
FileName :001 A Review of Targeting Strategies.mp4 | Size: (38.99 MB)
FileName :002 Introducing Selectors.mp4 | Size: (14.55 MB)
FileName :003 Selecting by Data Type.mp4 | Size: (16.67 MB)
FileName :004 Selecting by Column Position.mp4 | Size: (14.47 MB)
FileName :005 Set Operations with Selectors.mp4 | Size: (42.93 MB)
FileName :001 Introducing our Dataset.mp4 | Size: (23.3 MB)
FileName :002 Intro to Grouping.mp4 | Size: (21.33 MB)
FileName :003 GroupBy Methods.mp4 | Size: (35.27 MB)
FileName :004 Largest and Smallest Values per Group.mp4 | Size: (22.14 MB)
FileName :005 Aggregations with the agg Method.mp4 | Size: (26.04 MB)
FileName :006 Using Selectors in Aggregations.mp4 | Size: (19.17 MB)
FileName :007 Grouping with Multiple Columns.mp4 | Size: (8.52 MB)
FileName :008 Grouping Temporal Data.mp4 | Size: (46.18 MB)
FileName :009 Window Functions and the over Method.mp4 | Size: (34.68 MB)
FileName :001 LazyFrames and Eager vs Lazy Evaluation.mp4 | Size: (23.74 MB)
FileName :002 The scan csv Function and the collect Method.mp4 | Size: (56.54 MB)
FileName :003 A Matter of Time.mp4 | Size: (12.84 MB)
FileName :004 Convert a DataFrame to a LazyFrame.mp4 | Size: (17.4 MB)
FileName :005 LazyFrame Limitations.mp4 | Size: (36.58 MB)

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