Analyzing Data With Polars in Python

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Joram Mutenge

3:08:48

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1. Introduction

1.1 Section 1 jupyter notebooks.zip

1. About Me.mp4

01:11

2. Installing Polars and Other Libraries.mp4

01:11

3. Why Use Polars.mp4

02:08

4. What to Know Upfront.mp4

00:30

5. Contained in this Course.mp4

00:56

6. Testing the Performance of Polars.mp4

03:59

7. Understanding Series.mp4

04:25

8. Understanding DataFrames.mp4

07:53

2. Filtering Rows

1.1 Section 2 jupyter notebooks.zip

1. Filtering Rows with Square Brackets.mp4

03:01

2. Filtering Rows with the Expression API.mp4

04:43

3. Filtering in Lazy Mode.mp4

02:53

4. Filtering Rows based on Values.mp4

03:55

3. Filtering Columns

1.1 Section 3 jupyter notebooks.zip

1. Filtering Columns with Square Brackets.mp4

04:17

2. Filtering Columns with the Expression API.mp4

03:48

4. Modifying DataFrames

1.1 Section 4 jupyter notebooks.zip

1. Updating Columns.mp4

03:26

2. Updating Rows.mp4

06:01

3. Adding Columns.mp4

07:04

4. Adding Rows.mp4

01:07

5. Data Types and Missing Values

1.1 Section 5 jupyter notebooks.zip

1. Missing Values.mp4

07:40

2. Replacing Missing Values.mp4

06:39

3. Numerical Data Types and Precision.mp4

06:24

4. String and Categorical Data Types.mp4

04:33

5. Nested Data Types List, Struct, Object.mp4

03:41

6. Text Transformation

1.1 Section 6 jupyter notebooks.zip

1. Formatting Text.mp4

01:36

2. Replacing Text.mp4

02:33

3. Slicing Text.mp4

04:18

4. Filtering Text.mp4

03:34

5. Splitting Text.mp4

05:24

7. Statistics and Aggregations

1.1 Section 7 jupyter notebooks.zip

1. Statistics.mp4

05:15

2. Counting Values.mp4

03:39

3. Grouping Values.mp4

11:35

4. Grouping Values with Over.mp4

02:54

5. Quantiles.mp4

03:17

8. Combining Dataframes

1.1 Section 8 jupyter notebooks.zip

1. Concatenating DataFrames.mp4

05:11

2. Left and Inner Joins on Dataframes.mp4

04:50

9. Timeseries (Dates and Time)

1.1 Section 9 jupyter notebooks.zip

1. Time Zones.mp4

04:02

2. Parsing Datetime Strings.mp4

02:47

3. Adjusting Datetimes.mp4

07:41

4. Extracting Datetime Components.mp4

03:38

5. Filtering Timeseries.mp4

03:09

6. Grouping on Datetimes.mp4

04:20

10. Input and Output

1.1 Section 10 jupyter notebooks.zip

1. Reading Files from the Web.mp4

01:05

2. Selecting and Naming Columns while Reading.mp4

04:21

3. Reading Excel Files.mp4

03:00

4. Reading JSON Files.mp4

02:17

5. Reading Parquet Files.mp4

00:49

6. Writing Files to Disk.mp4

05:49

11. Conclusion

1.1 Section 11 jupyter notebook.zip

1. Course Complete.mp4

00:19

Description

Speed Up your Data Analysis with the New Lightning-Fast DataFrame Library

What You'll Learn?

Work with large datasets that exceed memory capacity.
Take advantage of parallel and optimized data analysis using Polars.
Utilize Polars expressions syntax that’s easy to read and write.
Load data from various sources, including web-based files, Excel, JSON, and Parquet files.
Combine data from different datasets efficiently using fast join operations.
Perform grouping and parallel aggregations for in-depth analysis.
Derive valuable insights from time series data.

Who is this for?

Data analysts seeking to break free from the constraints of traditional spreadsheets and eager to embrace a more agile and efficient data analysis tool.

Data scientists new to Polars who want to quickly get up and running with this powerful dataframe library.

Users of Pandas or other dataframe libraries who want to explore a faster and more efficient data analysis tool.

What You Need to Know?

A computer running Windows, Linux, or MacOS with Python installed.

A curious mind and willingness to learn.

More details

Description
Speed up your data analysis with Polars, the rapidly growing open-source dataframe library that's gaining popularity among Python data scientists. This comprehensive course is your gateway to harnessing the power of Polars.

What You Will Learn:
- Introduction: Get started with a warm welcome and learn how to set up your environment by installing Polars and essential libraries. Discover why Polars is becoming the preferred choice for data scientists.
- Core Concepts: Gain a solid foundation by exploring key concepts, including Series and DataFrames. Understand how Polars simplifies data analysis and manipulation.
- Data Transformation: Dive into practical data manipulation. Learn how to filter rows and columns, update data, and add new columns and rows efficiently.
- Data Types and Missing Values: Master the art of handling data types, precision, and missing values. Discover how Polars supports various data types, including string and categorical, and tackle nested data structures.
- Text Transformation: Unlock the secrets of text manipulation using Polars. Format, replace, slice, filter, and split text with ease.
- Statistics and Aggregations: Learn how to perform statistical analysis and aggregations. Count values, group data, and calculate quantiles to gain deeper insights.
- Combining Dataframes: Explore the world of data integration by concatenating DataFrames and executing left and inner joins efficiently.
- Timeseries (Dates and Time): Delve into timeseries data with Polars. Understand time zones, parse datetime strings, and extract datetime components. Perform operations on timeseries data and group it effectively.
- Input and Output: Master data import and export with Polars. Read data from various sources, select and name columns, and write data to disk.

Who This Course Is For:
- Data analysts seeking to break free from the constraints of traditional spreadsheets and eager to embrace a more agile and efficient data analysis tool.
- Data scientists new to Polars who want to quickly get up and running with this powerful dataframe library.
- Users of Pandas or other dataframe libraries who want to explore a faster and more efficient data analysis tool.

This course prioritizes hands-on learning through Jupyter notebooks, providing in-depth coverage of each topic. All code shown in the video is included as Jupyter notebooks. By the end of this course, you'll be equipped to optimize data loading, manipulation, and analysis, making you a proficient Polars data analyst ready to tackle real-world data challenges.
Who this course is for:
Data analysts seeking to break free from the constraints of traditional spreadsheets and eager to embrace a more agile and efficient data analysis tool.
Data scientists new to Polars who want to quickly get up and running with this powerful dataframe library.
Users of Pandas or other dataframe libraries who want to explore a faster and more efficient data analysis tool.

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I earned my master's degree in data science from the University of Illinois at Urbana-Champaign.Currently, I work as a Data Scientist in the Manufacturing Industry working on demand forecasting models.I also create tutorial videos on YouTube on data analysis and data science topic. Additionally, I perform data analysis work for prominent YouTube channels to help them better understand their analytics and drive data-driven business decisions.

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