mosaiq 0.3.3

A generalized mosaic plotting function for categorical and numerical data.

Mosaiq

The Mosaiq library introduces mosaiq as a Python function that generates a mosaic plot using Altair, supporting both categorical and numeric fields. This versatile visualization tool automatically bins numeric data and consolidates low-frequency categories into a single "TOP_NA" group. The plot is designed to provide a clear overview of the distribution and relationship between two fields, with customizable color schemes.

Features

• Automatic Binning for Numeric Data: Uses histogram binning for numeric fields based on a specified maximum number of bins.
• Top-N Categories for Categorical Data: Keeps only the most frequent categories (up to max_bins), combining all others into a "TOP_NA" bin.
• Customizable Color Scheme: A single color parameter controls the color scheme for both fields, allowing a unified look across all bins.
• Tooltip Support: Hovering over bins displays detailed information, including category labels and counts.

Dependencies

• Altair for visualization.
• Pandas and NumPy for data manipulation.
• Narwhals for handling specific typing.

Installation

Install the required Python packages with:

pip install mosaiq 

Usage

Function Signature

mosaiq(dataframe: FrameT, field1: str, field2: str, max_bins=6, color="category20")

Parameters

• dataframe (FrameT): A pandas DataFrame containing the data to be visualized.
• field1 (str): Name of the first field (categorical or numeric) to display on the x-axis.
• field2 (str): Name of the second field (categorical or numeric) to display as blocks within the mosaic.
• max_bins (int, optional): Maximum number of bins or categories to display. Defaults to 6.
• color (str, optional): Color scheme for all bins. Defaults to "category20".
• top_na_label (str, optional): Provide a custom label for category bins that do not pass max_bins threshold.

Returns

• altair.Chart: A compound Altair chart representing the mosaic plot.

Example Usage

from vega_datasets import data
mosaiq(data.seattle_weather(), "weather", "wind").configure_view(continuousWidth=900)

import pandas as pd
import narwhals as nw
from mosaiq import mosaiq

# Create a sample DataFrame
data = {
    "Category": ["A", "B", "C", "D", "E", "F", "G", "H"],
    "Value": [10, 15, 7, 30, 45, 10, 22, 5]
}
df = pd.DataFrame(data)

# Generate a mosaic plot
chart = mosaiq(df, "Category", "Value", max_bins=5, color="blueorange")
chart.display()

Customization

• Adjust Binning: Control the number of bins for numeric fields with max_bins. If more categories than max_bins are present, the function groups the least frequent categories into a new "TOP_NA" category.
• Color Scheme: Set a color scheme using any valid Altair color scheme name (e.g., "blues", "viridis", "category10"). This single color parameter unifies the plot’s appearance.

Additional Notes

This function is decorated with @nw.narwhalify to handle non-pandas DataFrame input using Narwhals typing. If you’re unfamiliar with Narwhals, check out the Narwhals GitHub repo for further information.

License

MIT License. See LICENSE for more information.

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Enjoy exploring your data with Mosaiq!