Mastering Data Analysis with Polars in Python: Crash Course

Define and use Python functions with default parameters, return values, and prints. Iterate over names, handle dictionaries, and square numbers to illustrate basic function syntax and usage.

Explore Python modules and packages by demonstrating whole-module imports, attribute imports, and aliases, plus creating a custom module and handling import errors, with built-in modules like os.

Explore how to design Python classes with attributes and methods, implement inheritance with a bird subclass, and use class and static methods to model rectangles and squares.

Explore series in Polars, a single-column data structure, and perform indexing, slicing, arithmetic, and aggregations like sum and min, along with filtering, missing data handling, and casting.

Create a Polars data frame in Python with name, age, and city columns, view data using head, and add a boolean over 30 column to practice basic data manipulation.

Learn to read and write csv files with Polars in Python, using data frames, lazy evaluation, and filtering to handle large datasets efficiently.

Learn how to read and write excel files with polars in Python, including importing excel files, handling sheets, and converting data to a polars dataframe with integer columns.

Convert pandas data frames to polars to boost performance and memory efficiency, leverage polars' functional API, and seamlessly integrate polars into your workflow without csv or excel transfers.

Master data selection and filtering in Polars by selecting columns and filtering rows with conditions and logical operations to subset data frames efficiently in Python.

Learn to filter data with Polars by applying single and complex conditions to isolate relevant rows, using and/or logic and the tilde operator for negation.

Learn to select single and multiple columns, index-based and range-based rows, and conditionally filter data with Polars, including expression-based column aliasing such as age and selected ages.

Learn to slice data frames and sample data efficiently in Polars, using offset, length, random and deterministic sampling, to create focused subsets for model training and analysis.

Master sorting data with polars in python by learning single and multi-column sorts, controlling ascending and descending order, and applying those techniques to real data frames.

Explore aggregation and grouping in Polars to summarize data with sum and mean by department, using group by operations and basic dataframe creation.

Learn how to perform basic aggregations with Polars in Python, computing min, max, mean, median, and sum by groups, with practical examples and outputs.

Rank data with Polars in Python by score, using mean, max, dense, and ordinal methods to handle ties, with a sample data frame of names and scores.

Group data with Polars by department to compute mean and max salaries, then extend to multi-column groupings and total salary calculations for pivot-table style insights.

Explore pivot tables and cross tabulation with polars to summarize data across department and region. Learn aggregation using sum and Len alias for counts, and reveal relationships between variables.

Learn to join and concatenate data frames in Polars, covering inner, left, and outer joins, and practice vertical and horizontal concatenation with matching and mismatched columns.

Master memory management with Polars by selecting necessary columns, downcasting to int 32, and using lazy evaluation and scan to efficiently handle large data sets.

Explore how Polars uses parallel processing to speed up data analysis by leveraging multiple CPU cores. Batch processing with 100 slices optimizes a calc score on a large data frame.

analyze Tesla stock data with polars and python by fetching three months of yfinance data, converting to polars, and visualizing closing prices and daily percent changes with matplotlib.

Master the essentials of data analysis with polars in Python, from setting up tools and data structures to aggregation, joining, and optimizing performance for large datasets, with finance examples.