Tips and Traps¶

1. It is suggested that you use multiprocessing (e.g., pool_size=8) to speed up data profiling. Note: It seems to me that currently multiprocessing only works when minimal=True.

2. minimal=True helps reuce consumed memory.

    profile = ProfileReport(
        df, title="Data Profiling Report", 
        explorative=True, minimal=True, pool_size=8
    )

Things on this page are fragmentary and immature notes/thoughts of the author. Please read with your own judgement!

Things on this page are fragmentary and immature notes/thoughts of the author. Please read with your own judgement!

Tips and Traps¶

Alternatives to pandas for Small Data¶

1. Polars is a blazingly fast DataFrames library implemented in Rust using Apache Arrow as memory model. It is the best replacement of pandas for small data at this time. Notice that Polars support multithreading and lazy computation but it cannot handle data larger than memory at this time.

References¶

https://github.com/databricks/koalas

https://databricks.com/blog/2020/08/11/interoperability-between-koalas-and-apache-spark.html

https://notebooks.gesis.org/binder/jupyter/user/databricks-koalas-mxw72n1l/notebooks/docs/source/getting_started/10min.ipynb

https://spark.apache.org/docs/latest/api/python/pyspark.sql.html

https://spark.apache.org/docs/latest/api/python/pyspark.sql.html#pyspark.sql.DataFrame

https://spark.apache.org/docs/latest/api/python/pyspark.sql.html#pyspark.sql.Column

https://spark.apache.org/docs/latest/api/python/pyspark.sql.html#module-pyspark.sql.functions