DataComPy is a package to compare two Pandas DataFrames. Originally started to
be something of a replacement for SAS's PROC COMPARE for Pandas DataFrames
with some more functionality than just Pandas.DataFrame.equals(Pandas.DataFrame)
(in that it prints out some stats, and lets you tweak how accurate matches have to be).
Then extended to carry that functionality over to Spark Dataframes.
pip install datacompyor
conda install datacompyIf you would like to use Spark or any other backends please make sure you install via extras:
pip install datacompy[spark]
pip install datacompy[dask]
pip install datacompy[duckdb]
pip install datacompy[ray]
pip install datacompy[snowflake]
With version v0.12.0 the original SparkCompare was replaced with a
Pandas on Spark implementation. The original SparkCompare implementation differs
from all the other native implementations. To align the API better, and keep behaviour
consistent we are deprecating the original SparkCompare into a new module LegacySparkCompare
Subsequently in v0.13.0 a PySaprk DataFrame class has been introduced (SparkSQLCompare)
which accepts pyspark.sql.DataFrame and should provide better performance. With this version
the Pandas on Spark implementation has been renamed to SparkPandasCompare and all the spark
logic is now under the spark submodule.
If you wish to use the old SparkCompare moving forward you can import it like so:
from datacompy.spark.legacy import LegacySparkCompareStarting with v0.14.1, SparkPandasCompare is slated for deprecation. SparkSQLCompare is the prefered and much more performant.
It should be noted that if you continue to use SparkPandasCompare that numpy 2+ is not supported due to dependency issues.
Different versions of Spark, Pandas, and Python interact differently. Below is a matrix of what we test with. With the move to Pandas on Spark API and compatability issues with Pandas 2+ we will for the mean time note support Pandas 2 with the Pandas on Spark implementation. Spark plans to support Pandas 2 in Spark 4
| Spark 3.2.4 | Spark 3.3.4 | Spark 3.4.2 | Spark 3.5.1 | |
|---|---|---|---|---|
| Python 3.9 | ✅ | ✅ | ✅ | ✅ |
| Python 3.10 | ✅ | ✅ | ✅ | ✅ |
| Python 3.11 | ❌ | ❌ | ✅ | ✅ |
| Python 3.12 | ❌ | ❌ | ❌ | ❌ |
| Pandas < 1.5.3 | Pandas >=2.0.0 | |
|---|---|---|
Compare |
✅ | ✅ |
SparkPandasCompare |
✅ | ❌ |
SparkSQLCompare |
✅ | ✅ |
| Fugue | ✅ | ✅ |
Note
At the current time Python 3.12 is not supported by Spark and also Ray within Fugue.
If you are using Python 3.12 and above, please note that not all functioanlity will be supported.
Pandas and Polars support should work fine and are tested.
- Pandas: (See documentation)
- Spark: (See documentation)
- Polars: (See documentation)
- Snowflake/Snowpark: (See documentation)
- Fugue is a Python library that provides a unified interface for data processing on Pandas, DuckDB, Polars, Arrow, Spark, Dask, Ray, and many other backends. DataComPy integrates with Fugue to provide a simple way to compare data across these backends. Please note that Fugue will use the Pandas (Native) logic at its lowest level (See documentation)
We welcome and appreciate your contributions! Before we can accept any contributions, we ask that you please be sure to sign the Contributor License Agreement (CLA).
This project adheres to the Open Source Code of Conduct. By participating, you are expected to honor this code.
Roadmap details can be found here
