# RDataCompy
Lightning-fast dataframe comparison library, implemented in Rust with Python bindings.
## Overview
RDataCompy is a high-performance library for comparing dataframes, inspired by Capital One's [datacompy](https://github.com/capitalone/datacompy). Built in Rust and leveraging Apache Arrow, it provides **40+ million cells/second** throughput with comprehensive comparison reports.
### Why RDataCompy?
- 🚀 **Blazing Fast**: 40-46M cells/second (100-1000x faster than Python-based solutions)
- 💾 **Memory Efficient**: Zero-copy operations using Apache Arrow columnar format
- 🎯 **Flexible**: Configurable tolerance for numeric comparisons
- 📊 **Comprehensive**: Detailed reports showing exact differences
- 🔧 **Multi-Format**: Works with PyArrow, Pandas, PySpark, and Polars DataFrames
- 💰 **Decimal Support**: Full DECIMAL(p,s) support with cross-precision compatibility
## Installation
```bash
pip install rdatacompy
```
### Optional Dependencies
```bash
# For PySpark support
pip install rdatacompy[spark]
# For Pandas support
pip install rdatacompy[pandas]
# For Polars support
pip install rdatacompy[polars]
# Install everything
pip install rdatacompy[all]
```
## Quick Start
### Basic Usage (PyArrow)
```python
import pyarrow as pa
from rdatacompy import Compare
# Create sample tables
df1 = pa.table({
'id': [1, 2, 3, 4],
'value': [10.0, 20.0, 30.0, 40.0],
'name': ['Alice', 'Bob', 'Charlie', 'David']
})
df2 = pa.table({
'id': [1, 2, 3, 5],
'value': [10.001, 20.0, 30.5, 50.0],
'name': ['Alice', 'Bob', 'Chuck', 'Eve']
})
# Compare dataframes
comp = Compare(
df1,
df2,
join_columns=['id'],
abs_tol=0.01, # Absolute tolerance for floats
df1_name='original',
df2_name='updated'
)
# Print comprehensive report
print(comp.report())
```
**Example Output:**
```
DataComPy Comparison
--------------------
DataFrame Summary
-----------------
DataFrame Columns Rows
original 3 4
updated 3 4
Column Summary
--------------
Number of columns in common: 3
Number of columns in original but not in updated: 0
Number of columns in updated but not in original: 0
Row Summary
-----------
Matched on: id
Any duplicates on match values: No
Absolute Tolerance: 0.01
Relative Tolerance: 0
Number of rows in common: 3
Number of rows in original but not in updated: 1
Number of rows in updated but not in original: 1
Number of rows with some compared columns unequal: 1
Number of rows with all compared columns equal: 2
Column Comparison
-----------------
Number of columns compared with some values unequal: 2
Number of columns compared with all values equal: 0
Total number of values which compare unequal: 2
Columns with Unequal Values or Types
------------------------------------
Column original dtype updated dtype # Unequal Max Diff # Null Diff
value float64 float64 1 0.5000 0
name string string 1 N/A 0
Sample Rows with Unequal Values for 'value'
--------------------------------------------------
id value (original) value (updated)
3 30.000000 30.500000
Sample Rows with Unequal Values for 'name'
--------------------------------------------------
id name (original) name (updated)
3 Charlie Chuck
```
**Understanding the Report:**
- **DataFrame Summary**: Shows the dimensions of both dataframes being compared
- **Column Summary**: Lists columns that exist in both, and columns unique to each dataframe
- **Row Summary**:
- Shows which columns were used for matching (join keys)
- Number of rows that exist in both dataframes (3 in this example)
- Rows unique to each dataframe (id=4 only in original, id=5 only in updated)
- How many matched rows have differences (1 row has differences, 2 are identical)
- **Column Comparison**: Summary of which columns have differences across all matched rows
- **Columns with Unequal Values**: Detailed breakdown per column showing:
- Data types in each dataframe
- Number of unequal values
- Max difference (for numeric columns)
- Number of null mismatches
- **Sample Rows**: Shows actual examples of differences with **join key values** displayed first (not row index), making it easy to identify exactly which records differ
### Using with Pandas
```python
import pandas as pd
from rdatacompy import Compare
df1 = pd.DataFrame({
'id': [1, 2, 3],
'amount': [100.50, 200.75, 300.25]
})
df2 = pd.DataFrame({
'id': [1, 2, 3],
'amount': [100.51, 200.75, 300.24]
})
# Directly compare Pandas DataFrames (auto-converted to Arrow)
comp = Compare(df1, df2, join_columns=['id'], abs_tol=0.01)
print(comp.report())
```
### Using with PySpark
```python
from pyspark.sql import SparkSession
from rdatacompy import Compare
# For Spark 3.5, enable Arrow for better performance
spark = SparkSession.builder \
.config("spark.sql.execution.arrow.pyspark.enabled", "true") \
.getOrCreate()
df1 = spark.createDataFrame([(1, 100), (2, 200)], ['id', 'value'])
df2 = spark.createDataFrame([(1, 100), (2, 201)], ['id', 'value'])
# Directly compare Spark DataFrames (auto-converted to Arrow)
# Works with Spark 3.5+ (via toPandas) and 4.0+ (via toArrow)
comp = Compare(df1, df2, join_columns=['id'])
print(comp.report())
```
### Decimal Support
```python
from decimal import Decimal
import pyarrow as pa
from rdatacompy import Compare
# Compare DECIMAL columns with different precision/scale
df1 = pa.table({
'id': [1, 2, 3],
'price': pa.array([
Decimal('123.456789012345'),
Decimal('999.999999999999'),
Decimal('42.123456789012')
], type=pa.decimal128(28, 12)) # High precision
})
df2 = pa.table({
'id': [1, 2, 3],
'price': pa.array([
Decimal('123.456789'),
Decimal('999.999998'),
Decimal('42.123457')
], type=pa.decimal128(18, 6)) # Lower precision
})
# Compare with tolerance - handles different precision automatically
comp = Compare(df1, df2, join_columns=['id'], abs_tol=0.00001)
print(comp.report())
```
## Features
### Comparison Report
The report includes:
- **DataFrame Summary**: Row and column counts
- **Column Summary**: Common columns, unique to each dataframe
- **Row Summary**: Matched rows, unique rows, duplicates
- **Column Comparison**: Which columns have differences
- **Sample Differences**: Example rows with unequal values
- **Statistics**: Number of differences, max difference, null differences
### API Methods
```python
comp = Compare(df1, df2, join_columns=['id'])
# Get full comparison report
report = comp.report()
# Check if dataframes match
matches = comp.matches() # Returns bool
# Get common columns
common_cols = comp.intersect_columns()
# Get columns unique to each dataframe
df1_only = comp.df1_unq_columns()
df2_only = comp.df2_unq_columns()
```
### Supported Data Types
- ✅ Integers: int8, int16, int32, int64, uint8, uint16, uint32, uint64
- ✅ Floats: float32, float64
- ✅ Decimals: decimal128, decimal256 (with cross-precision compatibility)
- ✅ Strings: utf8, large_utf8
- ✅ Booleans
- ✅ Dates: date32, date64
- ✅ Timestamps (with timezone support)
### Cross-Type Compatibility
RDataCompy is designed for real-world data migration scenarios:
```python
# Compare different numeric types (int vs float vs decimal)
df1 = pa.table({'id': [1], 'val': pa.array([100], type=pa.int64())})
df2 = pa.table({'id': [1], 'val': pa.array([100.0], type=pa.float64())})
comp = Compare(df1, df2, join_columns=['id'])
comp.matches() # True - types are compatible!
# Compare different decimal precisions
df1 = pa.table({'val': pa.array([Decimal('123.45')], type=pa.decimal128(28, 12))})
df2 = pa.table({'val': pa.array([Decimal('123.45')], type=pa.decimal128(18, 6))})
# Compares successfully - precision difference handled automatically
```
## Performance
Benchmarked on a dataset with 150,000 rows × 200 columns (58.8M data points):
- **Comparison time**: 1.3 seconds
- **Throughput**: 46 million cells/second
- **Memory overhead**: 16 MB (only stores differences)
### vs datacompy
RDataCompy is significantly faster than Python-based solutions:
- **Columnar processing**: Uses SIMD-optimized Arrow compute kernels
- **Zero-copy**: Works directly on Arrow arrays without data duplication
- **Hash-based joins**: O(n) row matching vs O(n²) pandas merge
- **No type inference**: Arrow types known upfront (no runtime checks per column)
## Development
### Prerequisites
- Rust 1.70+
- Python 3.8+
- maturin
### Building from Source
```bash
# Clone repository
git clone https://github.com/yourusername/rdatacompy
cd rdatacompy
# Create virtual environment
python -m venv .venv
source .venv/bin/activate
# Install maturin
pip install maturin
# Build and install in development mode
maturin develop --release
# Run examples
python examples/basic_usage.py
```
### Running Tests
```bash
# Rust tests
cargo test
# Python examples
python examples/test_multi_dataframe_types.py
python examples/test_decimal_types.py
python examples/benchmark_large.py
```
## Contributing
Contributions are welcome! Please feel free to submit a Pull Request.
## License
Apache-2.0
## Acknowledgments
- Inspired by [Capital One's datacompy](https://github.com/capitalone/datacompy)
- Built with [Apache Arrow](https://arrow.apache.org/) and [PyO3](https://pyo3.rs/)
## Roadmap
See [TODO.md](TODO.md) for planned features and improvements.
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"description": "# RDataCompy\n\nLightning-fast dataframe comparison library, implemented in Rust with Python bindings.\n\n## Overview\n\nRDataCompy is a high-performance library for comparing dataframes, inspired by Capital One's [datacompy](https://github.com/capitalone/datacompy). Built in Rust and leveraging Apache Arrow, it provides **40+ million cells/second** throughput with comprehensive comparison reports.\n\n### Why RDataCompy?\n\n- \ud83d\ude80 **Blazing Fast**: 40-46M cells/second (100-1000x faster than Python-based solutions)\n- \ud83d\udcbe **Memory Efficient**: Zero-copy operations using Apache Arrow columnar format\n- \ud83c\udfaf **Flexible**: Configurable tolerance for numeric comparisons\n- \ud83d\udcca **Comprehensive**: Detailed reports showing exact differences\n- \ud83d\udd27 **Multi-Format**: Works with PyArrow, Pandas, PySpark, and Polars DataFrames\n- \ud83d\udcb0 **Decimal Support**: Full DECIMAL(p,s) support with cross-precision compatibility\n\n## Installation\n\n```bash\npip install rdatacompy\n```\n\n### Optional Dependencies\n\n```bash\n# For PySpark support\npip install rdatacompy[spark]\n\n# For Pandas support\npip install rdatacompy[pandas]\n\n# For Polars support\npip install rdatacompy[polars]\n\n# Install everything\npip install rdatacompy[all]\n```\n\n## Quick Start\n\n### Basic Usage (PyArrow)\n\n```python\nimport pyarrow as pa\nfrom rdatacompy import Compare\n\n# Create sample tables\ndf1 = pa.table({\n 'id': [1, 2, 3, 4],\n 'value': [10.0, 20.0, 30.0, 40.0],\n 'name': ['Alice', 'Bob', 'Charlie', 'David']\n})\n\ndf2 = pa.table({\n 'id': [1, 2, 3, 5],\n 'value': [10.001, 20.0, 30.5, 50.0],\n 'name': ['Alice', 'Bob', 'Chuck', 'Eve']\n})\n\n# Compare dataframes\ncomp = Compare(\n df1, \n df2,\n join_columns=['id'],\n abs_tol=0.01, # Absolute tolerance for floats\n df1_name='original',\n df2_name='updated'\n)\n\n# Print comprehensive report\nprint(comp.report())\n```\n\n**Example Output:**\n\n```\nDataComPy Comparison\n--------------------\n\nDataFrame Summary\n-----------------\n\nDataFrame Columns Rows\noriginal 3 4\nupdated 3 4\n\nColumn Summary\n--------------\n\nNumber of columns in common: 3\nNumber of columns in original but not in updated: 0\nNumber of columns in updated but not in original: 0\n\nRow Summary\n-----------\n\nMatched on: id\nAny duplicates on match values: No\nAbsolute Tolerance: 0.01\nRelative Tolerance: 0\nNumber of rows in common: 3\nNumber of rows in original but not in updated: 1\nNumber of rows in updated but not in original: 1\n\nNumber of rows with some compared columns unequal: 1\nNumber of rows with all compared columns equal: 2\n\nColumn Comparison\n-----------------\n\nNumber of columns compared with some values unequal: 2\nNumber of columns compared with all values equal: 0\nTotal number of values which compare unequal: 2\n\nColumns with Unequal Values or Types\n------------------------------------\n\nColumn original dtype updated dtype # Unequal Max Diff # Null Diff\nvalue float64 float64 1 0.5000 0\nname string string 1 N/A 0\n\nSample Rows with Unequal Values for 'value'\n--------------------------------------------------\n\nid value (original) value (updated) \n3 30.000000 30.500000 \n\nSample Rows with Unequal Values for 'name'\n--------------------------------------------------\n\nid name (original) name (updated) \n3 Charlie Chuck \n```\n\n**Understanding the Report:**\n\n- **DataFrame Summary**: Shows the dimensions of both dataframes being compared\n- **Column Summary**: Lists columns that exist in both, and columns unique to each dataframe\n- **Row Summary**: \n - Shows which columns were used for matching (join keys)\n - Number of rows that exist in both dataframes (3 in this example)\n - Rows unique to each dataframe (id=4 only in original, id=5 only in updated)\n - How many matched rows have differences (1 row has differences, 2 are identical)\n- **Column Comparison**: Summary of which columns have differences across all matched rows\n- **Columns with Unequal Values**: Detailed breakdown per column showing:\n - Data types in each dataframe\n - Number of unequal values\n - Max difference (for numeric columns)\n - Number of null mismatches\n- **Sample Rows**: Shows actual examples of differences with **join key values** displayed first (not row index), making it easy to identify exactly which records differ\n\n### Using with Pandas\n\n```python\nimport pandas as pd\nfrom rdatacompy import Compare\n\ndf1 = pd.DataFrame({\n 'id': [1, 2, 3],\n 'amount': [100.50, 200.75, 300.25]\n})\n\ndf2 = pd.DataFrame({\n 'id': [1, 2, 3],\n 'amount': [100.51, 200.75, 300.24]\n})\n\n# Directly compare Pandas DataFrames (auto-converted to Arrow)\ncomp = Compare(df1, df2, join_columns=['id'], abs_tol=0.01)\nprint(comp.report())\n```\n\n### Using with PySpark\n\n```python\nfrom pyspark.sql import SparkSession\nfrom rdatacompy import Compare\n\n# For Spark 3.5, enable Arrow for better performance\nspark = SparkSession.builder \\\n .config(\"spark.sql.execution.arrow.pyspark.enabled\", \"true\") \\\n .getOrCreate()\n\ndf1 = spark.createDataFrame([(1, 100), (2, 200)], ['id', 'value'])\ndf2 = spark.createDataFrame([(1, 100), (2, 201)], ['id', 'value'])\n\n# Directly compare Spark DataFrames (auto-converted to Arrow)\n# Works with Spark 3.5+ (via toPandas) and 4.0+ (via toArrow)\ncomp = Compare(df1, df2, join_columns=['id'])\nprint(comp.report())\n```\n\n### Decimal Support\n\n```python\nfrom decimal import Decimal\nimport pyarrow as pa\nfrom rdatacompy import Compare\n\n# Compare DECIMAL columns with different precision/scale\ndf1 = pa.table({\n 'id': [1, 2, 3],\n 'price': pa.array([\n Decimal('123.456789012345'),\n Decimal('999.999999999999'),\n Decimal('42.123456789012')\n ], type=pa.decimal128(28, 12)) # High precision\n})\n\ndf2 = pa.table({\n 'id': [1, 2, 3],\n 'price': pa.array([\n Decimal('123.456789'),\n Decimal('999.999998'),\n Decimal('42.123457')\n ], type=pa.decimal128(18, 6)) # Lower precision\n})\n\n# Compare with tolerance - handles different precision automatically\ncomp = Compare(df1, df2, join_columns=['id'], abs_tol=0.00001)\nprint(comp.report())\n```\n\n## Features\n\n### Comparison Report\n\nThe report includes:\n- **DataFrame Summary**: Row and column counts\n- **Column Summary**: Common columns, unique to each dataframe\n- **Row Summary**: Matched rows, unique rows, duplicates\n- **Column Comparison**: Which columns have differences\n- **Sample Differences**: Example rows with unequal values\n- **Statistics**: Number of differences, max difference, null differences\n\n### API Methods\n\n```python\ncomp = Compare(df1, df2, join_columns=['id'])\n\n# Get full comparison report\nreport = comp.report()\n\n# Check if dataframes match\nmatches = comp.matches() # Returns bool\n\n# Get common columns\ncommon_cols = comp.intersect_columns()\n\n# Get columns unique to each dataframe\ndf1_only = comp.df1_unq_columns()\ndf2_only = comp.df2_unq_columns()\n```\n\n### Supported Data Types\n\n- \u2705 Integers: int8, int16, int32, int64, uint8, uint16, uint32, uint64\n- \u2705 Floats: float32, float64\n- \u2705 Decimals: decimal128, decimal256 (with cross-precision compatibility)\n- \u2705 Strings: utf8, large_utf8\n- \u2705 Booleans\n- \u2705 Dates: date32, date64\n- \u2705 Timestamps (with timezone support)\n\n### Cross-Type Compatibility\n\nRDataCompy is designed for real-world data migration scenarios:\n\n```python\n# Compare different numeric types (int vs float vs decimal)\ndf1 = pa.table({'id': [1], 'val': pa.array([100], type=pa.int64())})\ndf2 = pa.table({'id': [1], 'val': pa.array([100.0], type=pa.float64())})\ncomp = Compare(df1, df2, join_columns=['id'])\ncomp.matches() # True - types are compatible!\n\n# Compare different decimal precisions\ndf1 = pa.table({'val': pa.array([Decimal('123.45')], type=pa.decimal128(28, 12))})\ndf2 = pa.table({'val': pa.array([Decimal('123.45')], type=pa.decimal128(18, 6))})\n# Compares successfully - precision difference handled automatically\n```\n\n## Performance\n\nBenchmarked on a dataset with 150,000 rows \u00d7 200 columns (58.8M data points):\n\n- **Comparison time**: 1.3 seconds\n- **Throughput**: 46 million cells/second\n- **Memory overhead**: 16 MB (only stores differences)\n\n### vs datacompy\n\nRDataCompy is significantly faster than Python-based solutions:\n- **Columnar processing**: Uses SIMD-optimized Arrow compute kernels\n- **Zero-copy**: Works directly on Arrow arrays without data duplication\n- **Hash-based joins**: O(n) row matching vs O(n\u00b2) pandas merge\n- **No type inference**: Arrow types known upfront (no runtime checks per column)\n\n## Development\n\n### Prerequisites\n\n- Rust 1.70+\n- Python 3.8+\n- maturin\n\n### Building from Source\n\n```bash\n# Clone repository\ngit clone https://github.com/yourusername/rdatacompy\ncd rdatacompy\n\n# Create virtual environment\npython -m venv .venv\nsource .venv/bin/activate\n\n# Install maturin\npip install maturin\n\n# Build and install in development mode\nmaturin develop --release\n\n# Run examples\npython examples/basic_usage.py\n```\n\n### Running Tests\n\n```bash\n# Rust tests\ncargo test\n\n# Python examples\npython examples/test_multi_dataframe_types.py\npython examples/test_decimal_types.py\npython examples/benchmark_large.py\n```\n\n## Contributing\n\nContributions are welcome! Please feel free to submit a Pull Request.\n\n## License\n\nApache-2.0\n\n## Acknowledgments\n\n- Inspired by [Capital One's datacompy](https://github.com/capitalone/datacompy)\n- Built with [Apache Arrow](https://arrow.apache.org/) and [PyO3](https://pyo3.rs/)\n\n## Roadmap\n\nSee [TODO.md](TODO.md) for planned features and improvements.\n\n\n",
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