plottable

There are three primary methods for customizing the appearance of a table in plottable:

1. Keyword Arguments: Supply styling keywords directly to the Table constructor.
2. ColumnDefinition: Provide a ColumnDefinition object for specific columns to apply granular styling.
3. Row/Column Access: Access the table's internal rows or columns directly to apply manual modifications.

To contribute to plottable, it is recommended to use an editable installation. This allows you to make changes to the source code and see them reflected immediately without re-installing the package. Clone the repository and install it using pip install -e ..

git clone https://github.com/znstrider/plottable.git
cd plottable

pip install -e .

This project requires all Python code to be formatted using black before committing. You can format the entire project by running black on all .py files, or target a specific file using black <filename.py>. You can also configure your IDE to perform autoformatting with black.

# Format all files in the directory
black .

# Format a specific file
black <filename.py>

There are three primary ways to customize the appearance and behavior of a table in plottable:

1. Keyword Arguments: Supply styling keywords directly to the Table constructor.
2. ColumnDefinition: Provide a ColumnDefinition object for specific columns to apply granular styling.
3. Row and Column Access: Access the table's internal rows or columns directly for manual manipulation.

Install the plottable library using pip to start creating presentation-ready tables in Matplotlib.

pip install plottable

You can create custom formatters by defining a function that accepts a cell value and returns a formatted string or number. This can be a standard function or a lambda expression.

Signature for a custom function:

def my_formatter(cell_content: Any) -> str | Number: ...

# Using a lambda for simple rounding
tab = Table(d, column_definitions=[ColDef(name="A", formatter=lambda x: round(x, 1))])

You can create custom plots by defining a function with a specific signature. The first argument must be a matplotlib.axes.Axes object, and the second must be the cell's value (val). Any additional arguments will be provided via the plot_kw parameter in ColumnDefinition.

For complex data, your function can use the cell value to look up data in a dictionary or call a getter function.

import matplotlib.pyplot as plt
from typing import Any

def custom_plot_fn(ax: plt.Axes, val: Any, **kwargs):
    # Use kwargs passed from plot_kw
    color = kwargs.get("color", "blue")
    
    # Logic to get complex data based on cell value
    data = my_data_dict.get(val)
    
    # Perform matplotlib operations on ax
    ax.bar(data)

You can apply background coloring to the cells of a specific column by providing a cmap argument to its ColumnDefinition. The cmap argument accepts either a Callable or a LinearSegmentedColormap. This allows the background color of each cell to be determined dynamically based on its underlying value.

# Example concept (requires ColumnDefinition context)
ColumnDefinition(
    name="column_name",
    cmap=my_colormap  # Can be a Callable or LinearSegmentedColormap
)

You can format how numeric values are displayed within a plot by passing a formatter key inside the plot_kw dictionary of a ColumnDefinition.

There are two ways to provide a formatter:

1. Python formatting syntax: Pass a standard Python format string (e.g., "{:.0%}").
2. Formatter functions: Pass a callable function (e.g., decimal_to_percent from plottable.formatters).

# Using a format string
ColumnDefinition(
    "C",
    plot_fn=progress_donut,
    plot_kw={
        "is_pct": True,
        "formatter": "{:.0%}"
    },
)

# Using a formatter function
from plottable.formatters import decimal_to_percent
ColumnDefinition(
    "D",
    plot_fn=bar,
    plot_kw={
        "formatter": decimal_to_percent,
    },
)

Use ColumnDefinition to override the display name, adjust the width relative to the default, or change font attributes via textprops.

• Title: Use title="New Name".
• Width: Use width=2.0 to make a column twice as wide as the default.
• Textprops: Pass a dictionary of matplotlib text properties like {"ha": "left", "weight": "bold"}.

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from plottable import ColumnDefinition, Table

d = pd.DataFrame(np.random.random((5, 5)), columns=["A", "B", "C", "D", "E"]).round(2)
fig, ax = plt.subplots(figsize=(6, 5))

# Example: Custom title, width, and text properties
tab = Table(d, column_definitions=[
    ColumnDefinition(name="A", title="Title A", width=2),
    ColumnDefinition(name="index", textprops={"ha": "left", "weight": "bold"})
])

plt.show()