Data Engineering with Polars in Python

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Jayden Rasband

1:43:22

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1. Getting Started

1. Welcome to the Course!.mp4

01:15

2. Installing Python.mp4

03:05

3. Installing Visual Studio Code.mp4

02:23

4. Extensions for Visual Studio Code.mp4

02:31

5.1 Course Materials.zip

5. Download Course Materials.html

2. Python Fundamentals

1. Data Types.mp4

02:17

2. Data Types int.html

3. Data Types float.html

4. Data Types str.html

5. Data Types NoneType.html

6. Data Structures.mp4

01:58

7. Data Structures Lists.html

8. Data Structures Tuples.html

9. Data Structures Dictionaries.html

10. Data Structures Sets.html

11. Casting.mp4

03:57

12. Casting int to str.html

13. Mathematical Operators.mp4

03:58

14. Mathematical Operators Addition.html

15. Mathematical Operators Subtraction.html

16. Mathematical Operators Multiplication.html

17. Mathematical Operators Division.html

18. Mathematical Operators Modulo.html

19. Mathematical Operators Exponentiation.html

20. Comparison Operators.mp4

03:09

21. Comparison Operators Equal.html

22. Comparison Operators Not Equal.html

23. Logical Operators.mp4

03:57

24. Logical Operators.html

25. Membership Operators.mp4

03:37

26. If Statements.mp4

03:08

3. DataFrame Inputs & Outputs

1. Installing Polars.mp4

01:02

2. Read CSV.mp4

07:41

3. Write CSV - Part 1.mp4

03:12

4. Write CSV - Part 2.mp4

05:20

5. Write Database.mp4

05:28

6. Read Database.mp4

03:28

7. Read Excel.mp4

02:36

8. From Pandas.mp4

01:30

4. Selecting From DataFrames

1. Select.mp4

06:57

2. Filter.mp4

02:24

5. Joining & Appending to DataFrames

1. Inner Joins.mp4

02:58

2. Anti Joins.mp4

02:31

3. Left Joins.mp4

04:13

6. Aggregating DataFrames

1. Min & Max.mp4

03:45

2. Mean, Median, & Mode.mp4

04:12

3. Quantiles.mp4

04:13

4. Sum.mp4

01:55

5. Rank.mp4

04:42

Description

Transform and Manipulate Data

What You'll Learn?

Wrangle Data Like a Pro
Master the Basics of Polars
Perform Aggregation Functions on Tabular Data
Extract, Transform, and Manipulate Data

Who is this for?

Data Engineers looking to Expand Their Toolkit

Data Cleaning Professionals

What You Need to Know?

No prior experience necessary

More details

Description
Whether you're looking to launch a career in data engineering or aiming to enhance your existing data manipulation skills, this course offers a comprehensive toolkit for success in today's data-driven landscape. This course is meticulously crafted to provide you with an in-depth understanding of data engineering using Polars, a blazingly fast DataFrame library for Python that empowers you to handle large datasets effortlessly.
Data Extraction
Dive into the world of data by learning how to extract data from various sources, including databases, CSV files, and APIs. Discover the art of handling different data formats and structures, ensuring data integrity and completeness.
Data Transformation
Master the art of data transformation as you explore techniques to clean, reshape, and restructure raw data into a usable format. Learn to handle missing values, perform data imputation, and apply advanced transformation operations to make your data analysis-ready.
Data Analysis
Unleash the full potential of the Polars library as you delve into its powerful capabilities for data manipulation. From filtering and aggregating to joining and grouping, you'll learn how to perform complex operations with incredible speed and efficiency.
Real-World Scenarios
Make your learning experience applicable using real world scenarios that simulate the challenges data engineers face daily. We've carefully crafted our lectures to help you conquer essential data engineering tasks. You'll gain job ready skills and knowledge on how to address common data problems.
Who this course is for:
Data Engineers looking to Expand Their Toolkit
Data Cleaning Professionals

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Jayden holds a bachelors in Finance and a Masters in Business Administration. He currently works as a Business Intelligence Manager where he works with various stakeholders to build reporting and analytical solutions. His technical expertise includes: Python, SQL, CSS, HTML, and JavaScript. Jayden also maintains a python library on PyPI called pandas-geojson.

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