One-way ANOVA in PySpark

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This is my one-way ANOVA implementation from scratch using PySpark. The reason why I did it is that at this moment neither Spark's library MLLib either Spark 2.4.4 itself have a function that allows to perform One-Way ANOVA.

Information returned by the function

The following values are returned by this function:

• sswg: Sum Of Squares withing groups
• ssbg: Sum of Squares between groups
• f_statistic: The F-statistic computed for the given variables
• df_1: Degrees of freedom 1, equal to m - 1 where m is the number of levels (i.e. number of distinct categorical values in the grouping variables)
• df_2: Deegrees of freedom 2, equal to n - m where n is the number of observations and m is the number of levels (as specified in the previous point)

How to import and use one-way ANOVA

(Optional): install libraries

The requirements.txt file is related to the "Demo notebook" setup: however you can use it for reference, removing pandas and scipy libraries if not needed.

pip install -r requirements.txt

Import the function in your code or notebook

from anova_in_spark import one_way_anova
sswg, ssbg, f_statistic, df_1, df_2 = one_way_anova(df_of_anova, 'categorical_variable_column_name','continuous_variable_column_name')

How to run the example

For helping the users familiarise with this function and see how it works I included a small Jupyter Notebook where I will apply one-way ANOVA to the test dataset and compare its results with the ones obtained by the function stats.f_oneway(...) contained in the SciPy package.

Instructions for running the example notebook

Load the required libraries for running the Jupyer Notebook

pip install -r requirements.txt

(Optional but recommended if you are using a virtual environment)

ipython kernel install --user --name=anova_spark'

Start Jupyter notebook (and have fun!)

jupyter notebook

How to collaborate

If you find any error or have any suggestion on how to improve this function please feel free to open a Pull Request or an Issue!