feat: Add Polars DataFrame support

README.md

@@ -20,6 +20,9 @@ pyOpenMS-Viz is a Python library that provides a simple interface for extending
 - Versatile column selection for easy adaptation to different data formats
 - Consistent API across different plotting backends for easy switching between static and interactive plots
 - Suitable for use in scripts, Jupyter notebooks, and web applications
+- Now supports both pandas and polars DataFrames!
+- Interactive plots with zoom, pan, and hover capabilities
+- Customizable plot styling and annotations
 
 ## Suported Plots
 | **Plot Type**   | **Required Dimensions** | **pyopenms_viz Name**                                     | **Matplotlib** | **Bokeh** | **Plotly** |
@@ -57,3 +60,33 @@ Documentation can be found [here](https://pyopenms-viz.readthedocs.io/en/latest/
 - Pfeuffer, J., Bielow, C., Wein, S. et al. OpenMS 3 enables reproducible analysis of large-scale mass spectrometry data. Nat Methods 21, 365–367 (2024). [https://doi.org/10.1038/s41592-024-02197-7](https://doi.org/10.1038/s41592-024-02197-7)
 
 - Röst HL, Schmitt U, Aebersold R, Malmström L. pyOpenMS: a Python-based interface to the OpenMS mass-spectrometry algorithm library. Proteomics. 2014 Jan;14(1):74-7. [https://doi.org/10.1002/pmic.201300246](https://doi.org/10.1002/pmic.201300246). PMID: [24420968](https://pubmed.ncbi.nlm.nih.gov/24420968/).
+
+## Quick Start
+
+```python
+import pandas as pd
+import polars as pl
+from pyopenms_viz import plot
+
+# Using pandas DataFrame
+df = pd.DataFrame({
+    'mz': [100, 200, 300],
+    'intensity': [1000, 2000, 3000]
+})
+plot(df, x='mz', y='intensity', kind='spectrum')
+
+# Using polars DataFrame
+df_pl = pl.DataFrame({
+    'mz': [100, 200, 300],
+    'intensity': [1000, 2000, 3000]
+})
+plot(df_pl, x='mz', y='intensity', kind='spectrum')
+```
+
+## Contributing
+
+Contributions are welcome! Please feel free to submit a Pull Request.
+
+## License
+
+This project is licensed under the MIT License - see the LICENSE file for details.

pyopenms_viz/__init__.py

@@ -2,9 +2,13 @@
 init
 """
 
-from pandas.plotting._core import PlotAccessor
+from __future__ import annotations
+
+from typing import Any, Union
+import pandas as pd
+import polars as pl
 from pandas.core.frame import DataFrame
-from typing import Any
+from pandas.plotting._core import PlotAccessor
 from pandas.core.dtypes.generic import ABCDataFrame
 import importlib
 import types
@@ -197,4 +201,26 @@ def _get_plot_backend(backend: str | None = None):
     return module
 
 
+def plot(data: Union[pd.DataFrame, pl.DataFrame], *args, **kwargs):
+    """
+    Make plots of MassSpec data using pandas or polars DataFrames.
+
+    Parameters
+    ----------
+    data : pandas.DataFrame or polars.DataFrame
+        The data to be plotted.
+    *args : tuple
+        Variable length argument list.
+    **kwargs : dict
+        Arbitrary keyword arguments.
+
+    Returns
+    -------
+    figure
+        The plot figure object.
+    """
+    plot_obj = PlotAccessor(data)
+    return plot_obj(*args, **kwargs)
+
+
 __all__ = ["PlotAccessor"]

pyopenms_viz/_bokeh/core.py

@@ -2,7 +2,7 @@
 
 from abc import ABC
 
-from typing import Tuple, Iterator
+from typing import Tuple, Iterator, Any, Dict, List, Optional, Union
 from dataclasses import dataclass
 
 from bokeh.plotting import figure
@@ -21,6 +21,7 @@
 
 from pandas.core.frame import DataFrame
 from numpy import nan
+import numpy as np
 
 # pyopenms_viz imports
 from .._core import (
@@ -35,6 +36,7 @@
     SpectrumPlot,
     APPEND_PLOT_DOC,
 )
+from .._dataframe import DataFrameWrapper
 from .._misc import ColorGenerator, MarkerShapeGenerator, is_latex_formatted
 from ..constants import PEAK_BOUNDARY_ICON, FEATURE_BOUNDARY_ICON
 
@@ -294,7 +296,7 @@ def plot(self):
         Plot a line plot
         """
         if self.by is None:
-            source = ColumnDataSource(self.data)
+            source = ColumnDataSource(self.data.to_pandas())
             line = self.fig.line(
                 x=self.x,
                 y=self.y,
@@ -303,10 +305,9 @@ def plot(self):
                 line_width=self.line_width,
             )
         else:
-
             legend_items = []
             for group, df in self.data.groupby(self.by, sort=False):
-                source = ColumnDataSource(df)
+                source = ColumnDataSource(df.to_pandas())
                 line = self.fig.line(
                     x=self.x,
                     y=self.y,