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Tutorials - Matt Harrison: Getting Started with Polars
Learn the basics of Polars, a Rust library that uses Arrow under the hood, and discover how to work with data frames, handle large datasets, and optimize performance with debugging techniques and data processing strategies.
- You can work with data frames in pollers, a library that leverages Rust and arrow under the hood.
- When debugging, use a lazy approach with pollers, as it allows for better performance and optimization.
- When working with dates, use the date time library with pollers.
- When doing aggregations, split data into smaller chunks and use chaining to process in parallel.
- When working with large datasets, use pollers to handle the load and reduce memory requirements.
- You can use a pipe in pollers to pass in a list of expressions into the group by function.
- When working with columns, use the describe function to inspect the data and determine the best approach.
- When doing aggregations, you can use a global variable to update the current state of the data frame.
- You can use the group by function in pollers to maximize CPU usage and improve performance.
- When working with data frames, you can use the with columns function to work with reordered columns.
- When using pollers, you can pass in a regular expression to exclude certain columns from the output.
- When working with data, you can use the mean and standard deviation functions to summarize the data.
- When doing plots, you can use pollers to create plots in matplotlib.
- When working with data frames, you can use the pipe function to pass in a list of expressions into the group by function.
- When doing aggregations, you can use a lazy approach to improve performance and reduce memory usage.
- When working with pollers, you can use chaining to process data in parallel and improve performance.
- When working with data frames, you can use the with columns function to work with reordered columns.
- When doing plots, you can use pollers to create plots in matplotlib.