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PySpark 4.0: Native Plotting API for DataFrames
Problem:
Visualizing PySpark DataFrames typically requires converting to Pandas first, adding memory overhead and extra processing steps.
Solution:
PySpark 4.0 adds native Plotly-powered plotting, enabling direct .plot() calls on DataFrames without Pandas conversion.
Full Article:
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Batch Process DataFrames with PySpark Pandas UDF Vectorization
Problem:
Traditional UDFs (User-Defined Functions) run your custom Python function on each row individually, which can significantly slow down DataFrame operations.
Solution:
Pandas UDFs solve this by batching data into chunks and applying vectorized pandas transformations across entire columns, rather than looping through rows.
As a result, they can be 10 to 100 times faster on large DataFrames.
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