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Polars Tutorial: Blazingly Fast Exploratory Data Analysis in Python
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Polars is a Python package (written in Rust) for working with DataFrames. Think Pandas, but faster. Much faster! In this tutorial, I'll explore basics of Polars and will compare it against Pandas - both in speed and syntax. This is a notebook walkthrough video, so you'll be able to follow along easily with the links below.
Links:
0:00 Intro
0:08 Pandas vs Polars
1:01 Goal of the video
1:27 Setup
2:43 Notebook structure
4:03 Installing Polars
4:46 Set Polars configs
5:43 Reading data with Polars
6:18 DataFrame basic exploration
7:34 Column selection
10:52 DataFrame filtering
12:42 DataFrame quality checks (checking for NAs and static)
15:55 Data cleaning and pre-processing
18:55 Univariate analysis (value counts, mean, median, etc.)
21:35 Multivariate analysis (groupby, aggregates)
27:14 Custom functions with Polars
32:00 Saving Polars DataFrame
32:20 Summary
Links:
0:00 Intro
0:08 Pandas vs Polars
1:01 Goal of the video
1:27 Setup
2:43 Notebook structure
4:03 Installing Polars
4:46 Set Polars configs
5:43 Reading data with Polars
6:18 DataFrame basic exploration
7:34 Column selection
10:52 DataFrame filtering
12:42 DataFrame quality checks (checking for NAs and static)
15:55 Data cleaning and pre-processing
18:55 Univariate analysis (value counts, mean, median, etc.)
21:35 Multivariate analysis (groupby, aggregates)
27:14 Custom functions with Polars
32:00 Saving Polars DataFrame
32:20 Summary
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