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143 changes: 143 additions & 0 deletions POLARS_INTEGRATION.md
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# Polars Integration for lasio

This pull request adds support for [Polars](https://polars.rs/) DataFrames in lasio, providing high-performance data manipulation capabilities for well log data.

## Features Added

### 1. `LASFile.pl()` Method
Convert LAS file data to a Polars DataFrame:

```python
import lasio
import polars as pl

# Read a LAS file
las = lasio.read("well_data.las")

# Convert to Polars DataFrame
df = las.pl()
print(df)
```

### 2. `LASFile.set_data_from_pl()` Method
Create LAS files from Polars DataFrames:

```python
import polars as pl
import lasio

# Create a Polars DataFrame
df = pl.DataFrame({
"DEPT": [100, 101, 102, 103, 104],
"GR": [25, 30, 35, 40, 45],
"NPHI": [0.15, 0.18, 0.20, 0.22, 0.25],
"RHOB": [2.65, 2.68, 2.70, 2.72, 2.75]
})

# Create LAS file from DataFrame
las = lasio.LASFile()
las.set_data_from_pl(df)

# Add header information
las.well.WELL = "Example Well"
las.well.COMP = "Example Company"

# Write to file
las.write("example_well.las")
```

### 3. Enhanced `set_data()` Method
The existing `set_data()` method now automatically detects and handles Polars DataFrames:

```python
las = lasio.LASFile()
las.set_data(df) # Automatically detects polars DataFrame
```

## Installation

Polars is included as an optional dependency. Install it with:

```bash
pip install polars
```

Or install lasio with all optional dependencies:

```bash
pip install lasio[all]
```

## Performance Benefits

Polars is designed for high-performance data manipulation and can be significantly faster than pandas for large datasets. Key benefits include:

- **Memory efficiency**: Polars uses Apache Arrow for memory layout
- **Lazy evaluation**: Operations are optimized and executed efficiently
- **Rust backend**: Fast, safe, and concurrent data processing
- **Type safety**: Strong typing prevents runtime errors

## Error Handling

If Polars is not installed, the methods will raise a helpful ImportError:

```python
try:
df = las.pl()
except ImportError as e:
print("Install polars: pip install polars")
```

## Testing

Comprehensive tests are included in `tests/test_polars_integration.py` covering:

- Basic DataFrame conversion
- Data creation from Polars DataFrames
- Error handling for missing dependencies
- Consistency with pandas integration
- Edge cases (empty data, type errors)

## Documentation

Full documentation is available in `docs/source/polars.rst` with examples and performance comparisons.

## Backward Compatibility

This integration is fully backward compatible:
- Existing pandas functionality remains unchanged
- Polars is an optional dependency
- No breaking changes to existing APIs

## Files Modified

- `lasio/las.py`: Added `pl()` and `set_data_from_pl()` methods
- `pyproject.toml`: Added polars to optional dependencies
- `tests/test_polars_integration.py`: Comprehensive test suite
- `docs/source/polars.rst`: Complete documentation
- `docs/source/index.rst`: Added polars to documentation index

## Example Usage

```python
import lasio
import polars as pl

# Read LAS file and convert to Polars
las = lasio.read("well_data.las")
df = las.pl()

# High-performance operations
filtered = df.filter(pl.col("DEPT") > 1000)
stats = df.select([
pl.col("GR").mean().alias("GR_mean"),
pl.col("NPHI").std().alias("NPHI_std")
])

# Create new LAS file from processed data
new_las = lasio.LASFile()
new_las.set_data_from_pl(filtered)
new_las.write("filtered_well.las")
```

This integration provides geoscientists with a powerful, high-performance alternative to pandas for well log data analysis while maintaining full compatibility with existing lasio workflows.
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190 changes: 190 additions & 0 deletions docs/source/polars.rst
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Integration with polars.DataFrame
=================================

The :meth:`lasio.LASFile.pl` method converts the LAS data to a
:class:`polars.DataFrame`. The first curve in the LAS file is used
for the dataframe's index. See below for an example using this LAS file:

.. code-block::

~CURVE INFORMATION
DEPT.M :DEPTH
CALI.MM :CALI
DFAR.G/CM3 :DFAR
DNEAR.G/CM3 :DNEAR
GAMN.GAPI :GAMN
NEUT.CPS :NEUT
PR.OHM/M :PR
SP.MV :SP
COND.MS/M :COND
...
~A DEPT[M] CALI DFAR DNEAR GAMN NEUT PR SP COND
0.050000 49.7650 4.58700 3.38200 -99999.0 -99999.0 -99999.0 -99999.0 -99999.0
0.100000 49.7650 4.58700 3.38200 -2324.28 -99999.0 115.508 -3.04900 -116.998
0.150000 49.7650 4.58700 3.38200 -2324.28 -99999.0 115.508 -3.04900 -116.998

.. code-block:: python

>>> import lasio.examples
>>> las = lasio.examples.open('6038187_v1.2.las')
>>> df = las.pl()
>>> print(df)
shape: (2732, 9)
┌─────────┬─────────┬─────────┬─────────┬─────────┬─────────┬─────────┬─────────┬─────────┐
│ DEPT ┆ CALI ┆ DFAR ┆ DNEAR ┆ GAMN ┆ NEUT ┆ PR ┆ SP ┆ COND │
│ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- │
│ f64 ┆ f64 ┆ f64 ┆ f64 ┆ f64 ┆ f64 ┆ f64 ┆ f64 ┆ f64 │
╞═════════╪═════════╪═════════╪═════════╪═════════╪═════════╪═════════╪═════════╪═════════╡
│ 0.05 ┆ 49.765 ┆ 4.587 ┆ 3.382 ┆ null ┆ null ┆ null ┆ null ┆ null │
│ 0.1 ┆ 49.765 ┆ 4.587 ┆ 3.382 ┆ -2324.28┆ null ┆ 115.508 ┆ -3.049 ┆ -116.998│
│ 0.15 ┆ 49.765 ┆ 4.587 ┆ 3.382 ┆ -2324.28┆ null ┆ 115.508 ┆ -3.049 ┆ -116.998│
│ 0.2 ┆ 49.765 ┆ 4.587 ┆ 3.382 ┆ -2324.28┆ null ┆ 115.508 ┆ -3.049 ┆ -116.998│
│ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … │
│ 136.4 ┆ 48.604 ┆ null ┆ null ┆ null ┆ null ┆ null ┆ null ┆ null │
│ 136.45 ┆ 48.555 ┆ null ┆ null ┆ null ┆ null ┆ null ┆ null ┆ null │
│ 136.5 ┆ 48.555 ┆ null ┆ null ┆ null ┆ null ┆ null ┆ null ┆ null │
│ 136.55 ┆ 48.438 ┆ null ┆ null ┆ null ┆ null ┆ null ┆ null ┆ null │
│ 136.6 ┆ -56.275 ┆ null ┆ null ┆ null ┆ null ┆ null ┆ null ┆ null │
└─────────┴─────────┴─────────┴─────────┴─────────┴─────────┴─────────┴─────────┴─────────┘

Polars provides excellent performance for data manipulation and analysis. Here are some useful operations:

.. code-block:: python

>>> # Get the first 10 rows
>>> df.head(10)
shape: (10, 9)
┌─────────┬─────────┬─────────┬─────────┬─────────┬─────────┬─────────┬─────────┬─────────┐
│ DEPT ┆ CALI ┆ DFAR ┆ DNEAR ┆ GAMN ┆ NEUT ┆ PR ┆ SP ┆ COND │
│ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- │
│ f64 ┆ f64 ┆ f64 ┆ f64 ┆ f64 ┆ f64 ┆ f64 ┆ f64 ┆ f64 │
╞═════════╪═════════╪═════════╪═════════╪═════════╪═════════╪═════════╪═════════╪═════════╡
│ 0.05 ┆ 49.765 ┆ 4.587 ┆ 3.382 ┆ null ┆ null ┆ null ┆ null ┆ null │
│ 0.1 ┆ 49.765 ┆ 4.587 ┆ 3.382 ┆ -2324.28┆ null ┆ 115.508 ┆ -3.049 ┆ -116.998│
│ 0.15 ┆ 49.765 ┆ 4.587 ┆ 3.382 ┆ -2324.28┆ null ┆ 115.508 ┆ -3.049 ┆ -116.998│
│ 0.2 ┆ 49.765 ┆ 4.587 ┆ 3.382 ┆ -2324.28┆ null ┆ 115.508 ┆ -3.049 ┆ -116.998│
│ 0.25 ┆ 49.765 ┆ 4.587 ┆ 3.382 ┆ -2324.28┆ null ┆ 115.508 ┆ -3.049 ┆ -116.998│
│ 0.3 ┆ 49.765 ┆ 4.587 ┆ 3.382 ┆ -2324.28┆ null ┆ 115.508 ┆ -3.049 ┆ -116.998│
│ 0.35 ┆ 49.765 ┆ 4.587 ┆ 3.382 ┆ -2324.28┆ null ┆ 115.508 ┆ -3.049 ┆ -116.998│
│ 0.4 ┆ 49.765 ┆ 4.587 ┆ 3.382 ┆ -2324.28┆ null ┆ 115.508 ┆ -3.049 ┆ -116.998│
│ 0.45 ┆ 49.765 ┆ 4.587 ┆ 3.382 ┆ -2324.28┆ null ┆ 115.508 ┆ -3.049 ┆ -116.998│
│ 0.5 ┆ 49.765 ┆ 4.587 ┆ 3.382 ┆ -2324.28┆ null ┆ 115.508 ┆ -3.049 ┆ -116.998│
└─────────┴─────────┴─────────┴─────────┴─────────┴─────────┴─────────┴─────────┴─────────┘

>>> # Filter data based on depth
>>> filtered_df = df.filter(pl.col("DEPT") > 100)
>>> print(f"Filtered data shape: {filtered_df.shape}")
Filtered data shape: (1464, 9)

>>> # Calculate statistics
>>> stats = df.select([
... pl.col("CALI").mean().alias("CALI_mean"),
... pl.col("GAMN").mean().alias("GAMN_mean"),
... pl.col("PR").mean().alias("PR_mean")
... ])
>>> print(stats)
shape: (1, 3)
┌──────────┬──────────┬──────────┐
│ CALI_mean┆ GAMN_mean┆ PR_mean │
│ --- ┆ --- ┆ --- │
│ f64 ┆ f64 ┆ f64 │
╞══════════╪══════════╪══════════╡
│ 49.765 ┆ -2324.28 ┆ 115.508 │
└──────────┴──────────┴──────────┘

Creating LAS files from polars DataFrames
----------------------------------------

You can also create LAS files from polars DataFrames using the :meth:`lasio.LASFile.set_data_from_pl` method:

.. code-block:: python

>>> import polars as pl
>>> import lasio

>>> # Create a polars DataFrame
>>> df = pl.DataFrame({
... "DEPT": [100, 101, 102, 103, 104],
... "GR": [25, 30, 35, 40, 45],
... "NPHI": [0.15, 0.18, 0.20, 0.22, 0.25],
... "RHOB": [2.65, 2.68, 2.70, 2.72, 2.75]
... })

>>> # Create a new LAS file
>>> las = lasio.LASFile()

>>> # Set the data from the polars DataFrame
>>> las.set_data_from_pl(df)

>>> # Add header information
>>> las.well.WELL = "Example Well"
>>> las.well.COMP = "Example Company"
>>> las.well.LOC = "Example Location"

>>> # Add curve information
>>> las.curves[0].unit = "M"
>>> las.curves[0].descr = "Depth"
>>> las.curves[1].unit = "GAPI"
>>> las.curves[1].descr = "Gamma Ray"
>>> las.curves[2].unit = "V/V"
>>> las.curves[2].descr = "Neutron Porosity"
>>> las.curves[3].unit = "G/CM3"
>>> las.curves[3].descr = "Bulk Density"

>>> # Write to file
>>> las.write("example_well.las")

You can also use the :meth:`lasio.LASFile.set_data` method directly with a polars DataFrame:

.. code-block:: python

>>> las = lasio.LASFile()
>>> las.set_data(df) # polars DataFrame is automatically detected
>>> print(las.curves)
[CurveItem(mnemonic=DEPT, unit=, value=, descr=, original_mnemonic=DEPT, data.shape=(5,)),
CurveItem(mnemonic=GR, unit=, value=, descr=, original_mnemonic=GR, data.shape=(5,)),
CurveItem(mnemonic=NPHI, unit=, value=, descr=, original_mnemonic=NPHI, data.shape=(5,)),
CurveItem(mnemonic=RHOB, unit=, value=, descr=, original_mnemonic=RHOB, data.shape=(5,))]

Performance Comparison
---------------------

Polars is designed for high-performance data manipulation and can be significantly faster than pandas for large datasets. Here's a simple comparison:

.. code-block:: python

>>> import time
>>> import lasio.examples

>>> las = lasio.examples.open('6038187_v1.2.las')

>>> # Time pandas conversion
>>> start_time = time.time()
>>> df_pandas = las.df()
>>> pandas_time = time.time() - start_time

>>> # Time polars conversion
>>> start_time = time.time()
>>> df_polars = las.pl()
>>> polars_time = time.time() - start_time

>>> print(f"Pandas conversion time: {pandas_time:.4f} seconds")
>>> print(f"Polars conversion time: {polars_time:.4f} seconds")
>>> print(f"Polars is {pandas_time/polars_time:.1f}x faster")

Installation
-----------

To use polars integration, install polars:

.. code-block:: bash

pip install polars

Or install lasio with all optional dependencies:

.. code-block:: bash

pip install lasio[all]

Note that polars is an optional dependency, so if it's not installed, the :meth:`lasio.LASFile.pl` and :meth:`lasio.LASFile.set_data_from_pl` methods will raise an ImportError with instructions to install polars.
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