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.

• 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.

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

Install the required Python packages with:

pip install mosaiq 

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

• 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.

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

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()

• 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.

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.

MIT License. See LICENSE for more information.

Enjoy exploring your data with Mosaiq!