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Reaching a serious milestone in collaboration with the Fugue mission
Because the final time we wrote about DataComPy in October of 2021, quite a bit has modified. In response to pepy.tech, which tracks downloads from the Python package deal index (PyPI), the package deal has been downloaded over 12 million instances. DataComPy was additionally granted important standing on PyPI because of the giant variety of downloads as outlined within the PyPI 2FA Safety Key Giveaway submit. It is a enormous milestone and a testomony to its applicability of a easy but well-defined instrument, making it a breeze to know detailed variations between two Pandas or Spark DataFrames. Extra importantly, our choice to open supply the package deal has been emphatically validated by the neighborhood.
Throughout PyData Seattle 2023, I had the chance to attach with the maintainers of Fugue, a mission that defines an abstraction layer so customers can scale their native Python code to work in opposition to distributed information sorts like Spark or Dask.
After studying extra in regards to the mission it grew to become evident that DataComPy would profit from adopting Fugue; with assist from Han and Kevin, the maintainers of Fugue, we recognized two foremost enhancements that we may make to DataComPy:
- Extending the performance to the backends that Fugue helps (Spark, Dask, Ray, Polars, DuckDB, Arrow, and many others.)
- Comparability throughout dataset sorts (e.g. Pandas DataFrame vs Spark)
The utilization is similar to the present expertise utilizing Pandas. The one distinction is there isn’t a Class instantiation of the Examine class like for Pandas:
from io import StringIO
import pandas as pd
import datacompydata1 = """acct_id,dollar_amt,title,float_fld,date_fld
10000001234,123.45,George Maharis,14530.1555,2017-01-01
10000001235,0.45,Michael Bluth,1,2017-01-01
10000001236,1345,George Bluth,,2017-01-01
10000001237,123456,Bob Loblaw,345.12,2017-01-01
10000001239,1.05,Lucille Bluth,,2017-01-01
"""
data2 = """acct_id,dollar_amt,title,float_fld
10000001234,123.4,George Michael Bluth,14530.155
10000001235,0.45,Michael Bluth,
10000001236,1345,George Bluth,1
10000001237,123456,Robert Loblaw,345.12
10000001238,1.05,Free Seal Bluth,111
"""
df1 = pd.read_csv(StringIO(data1))
df2 = pd.read_csv(StringIO(data2))
datacompy.is_match(
df1,
df2,
join_columns='acct_id', #You can even specify an inventory of columns
abs_tol=0, #Elective, defaults to 0
rel_tol=0, #Elective, defaults to 0
df1_name='Authentic', #Elective, defaults to 'df1'
df2_name='New' #Elective, defaults to 'df2'
)
# False
# This technique prints out a human-readable report summarizing and sampling variations
print(datacompy.report(
df1,
df2,
join_columns='acct_id', #You can even specify an inventory of columns
abs_tol=0, #Elective, defaults to 0
rel_tol=0, #Elective, defaults to 0
df1_name='Authentic', #Elective, defaults to 'df1'
df2_name='New' #Elective, defaults to 'df2'
))
DataComPy makes use of Fugue to partition the 2 DataFrames into chunks and examine every chunk in parallel utilizing the Pandas-based Examine. The comparability outcomes are then aggregated to provide the ultimate end result. Completely different from the be part of operation utilized in Examine and SparkCompare, the Fugue model makes use of the cogroup -> map-like semantic (not precisely the identical as Fugue adopts a rough model to attain nice efficiency), which ensures full information comparability with constant outcomes in comparison with Pandas-based Examine.
With a view to examine DataFrames of various backends, you simply want to interchange df1 and df2 with DataFrames of various backends. Simply go in DataFrames reminiscent of Pandas DataFrames, DuckDB relations, Polars DataFrames, Arrow tables, Spark DataFrames, Dask DataFrames or Ray datasets. For instance, to check a Pandas dataframe with a Spark dataframe:
from pyspark.sql import SparkSessionspark = SparkSession.builder.getOrCreate()
spark_df2 = spark.createDataFrame(df2)
datacompy.is_match(
df1,
spark_df2,
join_columns='acct_id',
)
To make use of a selected backend, you might want to have the corresponding library put in. For instance, if you wish to examine Ray datasets, you will need to set up the ray further:
pip set up datacompy[ray]
Implementing this kind of performance natively inside DataComPy would have been a big effort, however Fugue provides us this functionality free of charge! To not point out the brand new performance we’ll obtain as Fugue continues to mature! This, in my thoughts, is the true energy of open supply. Collaboration like this may unlock alternatives the place they didn’t exist earlier than!
The following goal is to make sure now we have technique parity between the Fugue performance and our core library (see concern #214). We additionally wish to examine whether or not or not we are able to deprecate our native Spark performance in favor of the Fugue-based various.
In the end we wish this to be a package deal for customers and the route shall be closely influenced by person enter. If in case you have ideas,
ideas and contributions, we extremely encourage you to take part. You will discover the repository on GitHub — with directions on the right way to contribute and open discussions.
The strategic collaboration with the Fugue mission has propelled DataComPy to new heights, introducing enhanced performance and alternatives for customers. As DataComPy continues to evolve, it exemplifies the ability of open supply collaboration and stands prepared to fulfill the information evaluation wants of a dynamic and ever-changing panorama. Lastly, an enormous thanks to all of the contributors who’ve helped us attain 12 million downloads. Right here is trying on the subsequent 12 million!
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