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https://github.com/capitalone/datacompy
Pandas, Polars, and Spark DataFrame comparison for humans and more!
https://github.com/capitalone/datacompy
compare dask data data-science dataframes fugue numpy pandas polars pyspark python spark
Last synced: 3 days ago
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Pandas, Polars, and Spark DataFrame comparison for humans and more!
- Host: GitHub
- URL: https://github.com/capitalone/datacompy
- Owner: capitalone
- License: apache-2.0
- Created: 2018-03-23T13:16:03.000Z (over 6 years ago)
- Default Branch: develop
- Last Pushed: 2024-10-29T13:45:14.000Z (about 2 months ago)
- Last Synced: 2024-10-29T15:19:21.397Z (about 2 months ago)
- Topics: compare, dask, data, data-science, dataframes, fugue, numpy, pandas, polars, pyspark, python, spark
- Language: Python
- Homepage: https://capitalone.github.io/datacompy/
- Size: 9.92 MB
- Stars: 483
- Watchers: 22
- Forks: 127
- Open Issues: 11
-
Metadata Files:
- Readme: README.md
- License: LICENSE
- Codeowners: CODEOWNERS
- Roadmap: ROADMAP.rst
Awesome Lists containing this project
- awesome-datascience - datacompy - DataComPy is a package to compare two Pandas DataFrames. (The Data Science Toolbox / Comparison)
- awesome-data-engineering - datacompy - DataComPy is a Python library that facilitates the comparison of two DataFrames in pandas, Polars, Spark and more. The library goes beyond basic equality checks by providing detailed insights into discrepancies at both row and column levels. (Data Comparison)
README
# DataComPy
[](https://github.com/ambv/black)
[](https://badge.fury.io/py/datacompy)
[](https://anaconda.org/conda-forge/datacompy)
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.
## Quick Installation
```shell
pip install datacompy
```
or
```shell
conda install datacompy
```
### Installing extras
If you would like to use Spark or any other backends please make sure you install via extras:
```shell
pip install datacompy[spark]
pip install datacompy[fugue]
pip install datacompy[snowflake]
```
### Legacy Spark Deprecation
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 PySpark 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:
```python
from datacompy.spark.legacy import LegacySparkCompare
```
### SparkPandasCompare Deprecation
Starting 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.
#### Supported versions and dependncies
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](https://issues.apache.org/jira/browse/SPARK-44101)
| | 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.
## Supported backends
- Pandas: ([See documentation](https://capitalone.github.io/datacompy/pandas_usage.html))
- Spark: ([See documentation](https://capitalone.github.io/datacompy/spark_usage.html))
- Polars: ([See documentation](https://capitalone.github.io/datacompy/polars_usage.html))
- Snowflake/Snowpark: ([See documentation](https://capitalone.github.io/datacompy/snowflake_usage.html))
- 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](https://capitalone.github.io/datacompy/fugue_usage.html))
## Contributors
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)](https://cla-assistant.io/capitalone/datacompy).
This project adheres to the [Open Source Code of Conduct](https://developer.capitalone.com/resources/code-of-conduct/).
By participating, you are expected to honor this code.
## Roadmap
Roadmap details can be found [here](https://github.com/capitalone/datacompy/blob/develop/ROADMAP.rst)