Mastering Polars: The Beginner's Guide

Install python via homebrew, create and activate a virtual environment, install and verify jupyter notebook, then install polars and pandas and test imports in notebooks and PyCharm.

Create polars data frames from dictionaries and lists in Python using an efficient, easy-to-use API, and see how polars outperforms traditional libraries.

Explore how Polars interoperates with NumPy and pandas, converting data between Polars data frames, NumPy arrays, and pandas frames, with emphasis on 64-bit floats and zero-copy efficiency.

Learn to read csv, json, and parquet files with polars in a Python Jupyter notebook, using parallel reads and lazy loading for large datasets.

Read json files with polars using read_json and handle nested structures in a Python notebook. Explode the friends column and create columns with with_columns to reveal friend names and hobbies.

Select specific columns from data frames in Polars using square brackets, improving performance and readability; explore with read_csv, head, and select operations.

Learn to select multiple columns in Polars using regex patterns and dtype-based selectors. Use the selectors API with df.select, pl.all, and exclude to streamline data frame workflows.

Learn how to add and transform multiple columns in polars dataframes using with columns, select, and round, creating profit, discount, and sales columns with aliases and expressions.

Transform data frames with Polars, enabling fast filtering, grouping, and aggregating with memory efficiency and an intuitive API for data engineers and scientists.

Explore how polars enables fast data frame iteration through lazy evaluation and parallel processing. Compare rows and iterator rows, and practice multiple column iterations to understand memory use and performance.

Master distinct values in Polars with the unique method, optimized for performance, and accelerate analysis using lazy evaluation and parallel processing on large data.

Learn to apply numeric filters in Polars on integer columns by filtering a data frame with comparisons, multiple conditions, isin, and between methods to extract relevant rows and columns.

Explore how to filter string columns in Polars using equals, contains, and isin, then refine results with and/or conditions and select relevant columns.

Master date-based filtering in Polars by using equals, not equals, and is between with proper date casting and parsed dates, demonstrated on order date and ship date columns.

Master Polars group by aggregations to process large data sets quickly and memory efficiently. Apply simple and complex aggregations—min, max, sum, mean—across multiple columns, with expressions and optional maintain order.

Master the group by with having in polars by performing category and region aggregations on profit using max and sum, then filtering totals above 2000 to emulate SQL having.

Explore how to group by category in polars, iterate over groups, and apply aggregations like min and max, then rename and reorder results.

Identify and count null values in Polars data frames, create is null indicators, filter rows by nulls, and drop nulls from specific or all columns.