ipy-compare 0.4.1

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ipy-compare

ipy-compare is an interactive tool designed for use in Jupyter notebooks and Google Colab, enabling users to visually compare rows and columns of a DataFrame. Ideally used for tagging or for labeling data in a pandas DataFrame.

Features

• Row and Column Comparison: Supports both overall row-level measures and column-specific measures.
• Pagination: Navigate through rows using custom indices or iterators.
• Repeatable Sampling: Use a fixed random seed for consistent sampling.
• Branding: Add customizable branding to the footer, including a linkable logo and text.
• Interactive Radio Buttons: Visual indicators for selected measures.
• Colab Compatible: Works seamlessly in Google Colab notebooks.

Example:

We support two different ways to use ipy-compare.

1. Each Column: Use a measure for each column.
2. Overall Row: Use a measure for the entire row.

Where you can specify the questions you want to ask for each column or for the entire row.

Each Column

Overall Row

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Installation

pip install ipy-compare

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Usage

Import and Initialize

from ipy_compare import Compare
import pandas as pd
import panel as pn  # Required for notebook/colab display

# Sample DataFrame
data = {
    'Column1': ['A', 'B', 'C'],
    'Column2': ['X', 'Y', 'Z'],
    'Column3': ['Apple', 'Fish swimming', 'Vrooom car']
}
df = pd.DataFrame(data)

# Define measures
measures = {
    "overall": ["Good", "Bad"],
    "each": ["Better", "Worse", "Neutral"]
}

# Initialize Compare
tool = Compare(df, columns=["Column1", "Column3"], measures=measures)

# Display the tool
tool.servable()

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Navigation and Interaction

1. Navigate between rows using the Previous and Submit & Next buttons.
2. Use the radio buttons to select measures for each column or for the overall row.
3. Submit measurements using either the Submit button or Submit & Next to move to the next row.
4. All measurements are captured and can be retrieved programmatically.

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Custom Pagination

To specify a subset of rows or custom order:

# Custom row order
custom_order = [2, 0, 1]

# Initialize Compare with custom pagination
tool = Compare(df, columns=["Column1", "Column3"], measures=measures, pagination=custom_order)
tool.servable()

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Repeatable Sampling

To use random sampling with a fixed seed:

# Get a random sample of rows
sampled_indices = Compare.sample_indices(df, n=2, seed=42)

# Initialize Compare with sampled indices
tool = Compare(df, columns=["Column1", "Column3"], measures=measures, pagination=sampled_indices)
tool.servable()

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Retrieve Measurements

Once interactions are complete, retrieve the captured measurements:

# Get measurements as a DataFrame
measurements = tool.get_measurements()
print(measurements)

Example Output:

row_index	column	value	measure	type
0	None	None	Good	overall
0	Column1	A	Better	column
0	Column3	Apple	Neutral	column
1	None	None	Bad	overall
1	Column1	B	Worse	column
1	Column3	Fish swimming	Neutral	column

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Google Colab Usage

When using in Google Colab, make sure to run these commands first:

!pip install ipy-compare panel

The tool is fully compatible with Google Colab and will work the same way as in Jupyter notebooks.

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Advanced Options

Custom Measures

Define your own measure categories:

measures = {
    "overall": ["Excellent", "Poor"],
    "each": ["Correct", "Incorrect", "Not Sure"]
}

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Contributing

Contributions are welcome! Please fork the repository and submit a pull request with your changes.

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License

This project is licensed under the MIT License.