Working With Python Polars - Real PythonPolars is an emerging high-performance DataFrame library for efficient data manipulation.
How to Work With Polars LazyFrames - Real PythonPolars LazyFrame enhances data processing efficiency through lazy evaluation and optimized query plans.
Polars Plugins: let's make them easier to useBruno Kind shares his transformative internship experience contributing to Polars plugins, highlighting the significance of user defined functions and community engagement.
Polars vs. pandas: What's the Difference? | The PyCharm BlogPolars is a high-performance Python dataframe library for large datasets, outperforming pandas on speed and memory usage.
How Narwhals and scikit-lego came together to achieve dataframe-agnosticismScikit-lego now supports multiple dataframe implementations like Polars alongside pandas with the help of Narwhals.
Working With Python Polars - Real PythonPolars is an emerging high-performance DataFrame library for efficient data manipulation.
How to Work With Polars LazyFrames - Real PythonPolars LazyFrame enhances data processing efficiency through lazy evaluation and optimized query plans.
Polars Plugins: let's make them easier to useBruno Kind shares his transformative internship experience contributing to Polars plugins, highlighting the significance of user defined functions and community engagement.
Polars vs. pandas: What's the Difference? | The PyCharm BlogPolars is a high-performance Python dataframe library for large datasets, outperforming pandas on speed and memory usage.
How Narwhals and scikit-lego came together to achieve dataframe-agnosticismScikit-lego now supports multiple dataframe implementations like Polars alongside pandas with the help of Narwhals.
Dataframes explained: The modern in-memory data science formatDataframes provide efficient and powerful data manipulation in data science, surpassing traditional methods like SQL and Excel.
Episode #224: Narwhals: Expanding DataFrame Compatibility Between Libraries - The Real Python PodcastNarwhals enhances compatibility among Python libraries, enabling modern data handling features.The project mainly supports library maintainers to improve interlibrary functionality.
Dataframe interoperability - what has been achieved, and what comes next?Simple and clear common language enables collaboration among diverse attendees at PyCon Lithuania 2024.
From CSV to Parquet: A Journey Through File Formats in Apache Spark with ScalaSparkSession is used as the entry point to Spark SQL functionality.Different file formats like CSV, Parquet, JSON, and Avro can be read into DataFrames in Spark.
Skrub 0.2.0: tabular learning made easySkrub 0.2.0 simplifies machine learning on complex dataframes using tabular_learner.