doublerun 0.0.3

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Introduction

This repository was created to provide reusable tools for validating "double-runs" in the context of the Cockpit migration project, but also for other refactoring projects (MLOps, LACI, etc.).

Usage

Install the doublerun package using pip:

In Databricks:

%pip install doublerun

If you are using an old databricks runtime and you use outdated pandas or pyspark versions, run this instead to avoid updating pandas and pyspark (at your own risk):

%pip install --no-deps doublerun

Locally:

pip install --proxy http://127.0.0.1:3128 doublerun

Pandas Usage

Import the comparison functions:

import pandas as pd
from doublerun.pandas import (
    comparison_count,
    comparison_schema,
    comparison_columns,
    comparison_records,
    global_comparison
)

df1 = pd.DataFrame({
        'Column1' : [1, 2, 3],
        'Column2' : ['A', 'B', 'C']
    })

df2 = pd.DataFrame({
    'Column1' : [1, 2, 3, 4],
    'Column3' : ['E', 'B', 'C', 'F']
})

Compare the number of rows between the two DataFrames.

df1_count, df2_count = comparison_count(df1, df2)

Output:

************************COMPARAISON DU NOMBRE DE LIGNES*************************

    Le nombre de lignes des deux DataFrames ne sont pas égaux :
        - Pour le DataFrame 1 on a 3 lignes.
        - Pour le DataFrame 2 on a 4 lignes.

Compare the schema of the two DataFrames and return the missing columns in each.

missing_cols_in_1, missing_cols_in_2 = comparison_schema(df1, df2)

Output:

*****************************COMPARAISON DU SCHEMA******************************

    Les schémas ne sont pas équivalents.
    Dans le DataFrame 1 il y a ces colonnes manquantes par rapport au DataFrame 2:
        ['Column3']
    
    Dans le DataFrame 2 il y a ces colonnes manquantes par rapport au DataFrame 1:
        ['Column2']

Compare the number of columns in the two DataFrames and return the columns in common.

common_columns = comparison_columns(df1, df2)

Output:

****************************COMPARAISON DES COLONNES****************************

    Le nombre de colonnes est identiques entre les deux DataFrames.
        Nombre de colonnes communes entre les deux DataFrames: 1
        Colonnes communes entre les deux DataFrames: 
            ['Column1']

Compare the records of the two DataFrames and return:

• common : Records present in both DataFrames.
• left_only, right_only : Records present only in df1 or df2.

common, left_only, right_only = comparison_records(df1, df2)

Output:

***************COMPARAISON DES DONNÉES ENTRE LES DEUX DATAFRAMES****************

    Certaines lignes ne sont pas présentes dans les deux DataFrames:
            0 lignes sont uniquement dans le DataFrame 1.
            
            1 lignes sont uniquement dans le DataFrame 2.
            
            3 lignes sont présentes dans les deux DataFrames.

Global Comparison

You can also run the global_comparison function and print all of the above at once.

common, left_only, right_only = global_comparison(df1, df2)

Spark Usage

Usage with spark DataFrames is exactly the same but functions need to be imported from doublerun.spark instead:

import pandas as pd
from doublerun.spark import (
    comparison_count,
    comparison_schema,
    comparison_columns,
    comparison_records,
    global_comparison
)

df1 = pd.DataFrame({
        'Column1' : [1, 2, 3],
        'Column2' : ['A', 'B', 'C']
    })

df2 = pd.DataFrame({
    'Column1' : [1, 2, 3, 4],
    'Column3' : ['E', 'B', 'C', 'F']
})

df1 = spark.createDataFrame(df1)
df2 = spark.createDataFrame(df2)

comparison_count(df1, df2)
comparison_schema(df1, df2)
comparison_columns(df1, df2)
common, left_diff, right_diff = comparison_records(df1, df2)
# common, left_diff, right_diff = global_comparison(df1, df2)

Contributing

In order to contribute, create your branch with a meaningful title representing a feature you would like to develop (Examples: pandas_visualisation_mismatches, pandas_high_perf_dask, spark_notebooks, etc.). Please, have a look at existing branches before creating a new one.

Then, make a pull request to the dev branch to make sure no conflicts are created when we will be merging multiple branches together.

Credits

Thanks to Bilel BOUACHA of the HyperVision Team for providing the basis for the code contained in this package. This code was slightly refactored to be used as a general comparison tool between two spark or pandas DataFrames.