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Data Analysis with Polars in Python
Last updated 11/2024
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English + subtitle | Duration: 4h 2m | Size: 1.04 GB
Master Data Manipulation with Polars - The High Performance DataFrame Library
What you'll learn
How to Read CSV Files into Polars DataFrames
How to Push Data from Polars into a Database
How to Read Excel Files into Polars DataFrames
How to Aggregate Data
How to Join DataFrames
How to Take Advantage of Polars' Superior Processing Speed
Requirements
No Prior Experience Required
Description
Who Should Take This Course?
- Aspiring Data Analysts seeking to learn data discovery practices
- Beginner Data Engineers looking to improve data manipulation skills
- Data Engineers looking to utilize polars in their data pipelines
- Pandas users looking to make the switch to Polars
Why Learn Polars
Over the last decade Python has become more utilized in Data Pipelines. However, most pipelines faced performance issues when processing large datasets in Python. This limitation hindered Python's ability to manage "Big Data".
But in recent years, Polars unlocked the door to processing large datasets with its high performance data structures. It uses parallel processing to quickly read data into DataFrames and Series.
And its performance doesn't stop there! Not only can Polars read and write data quickly, it can also manipulate vast amounts data faster than Pandas.
After Finishing the Course, you'll be able to
- Read CSV files into Polars DataFrames
- Know how to push data directly from Polars into a database
- Export DataFrames to Excel
- Aggregate complex datasets
- Join DataFrames together
- Utilize Polars' superior processing speed
FAQs
Q: Is the switch from Pandas difficult?A: No. The basic concepts are the same. There are definitely differences between the two libraries, but functionality between the two are very similar. If you can do it in Pandas, you can do it in Polars!
Q: I'm already learning Pandas, would you say I'm wasting my time?A:No. My first exposure to DataFrames was using Pandas. Many of the concepts I learned in Pandas helped me understand Polars. They are definitely different in terms of performance. Pandas may at some point release a faster version, but as for now Polars is much faster when working with large datasets.
Q: Pandas has integrations with many more libraries than Polars. Won't I be missing out on these if I make the switch?A:Absolutely not. Its true that Polars does not have as many integrations with other python libraries, but switching from a polars DataFrame to a Pandas DataFrame is easy. Polars has a function that allows you to convert to and from a Pandas DataFrame. This allows you to get the performance of Polars while also getting the integrations of Pandas. Other libraries have also begun to build integrations with Polars so that may change altogether.
Q: What kind of bear is best?A:There are basically two schools of thought... Pandas and Polars are indeed competing DataFrame libraries. Its probably for you to decide the answer to this question!
Who this course is for
Beginner Data Engineers looking to improve data manipulation skills
Data Engineers looking to utilize polars in their data pipelines
Pandas users looking to make the switch to Polars
Aspiring Data Analysts
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