optimask 0.1.8

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OptiMask: Efficient NaN Data Removal in Python

Introduction

OptiMask is a Python package designed to facilitate the process of removing NaN (Not-a-Number) data from matrices, while efficiently computing the largest submatrix without NaN values. This tool prioritizes practicality, compatibility with Numpy arrays and Pandas DataFrames, and user-friendliness.

Key Features

• NaN Data Removal: OptiMask simplifies NaN data removal from matrices, preserving data integrity.
• Largest Submatrix: OptiMask calculates the largest submatrix without NaN, enhancing data analysis accuracy.
• Efficient Computation: With optimized computation, OptiMask provides rapid results without undue delays.
• Numpy and Pandas Compatibility: OptiMask seamlessly adapts to both Numpy and Pandas data structures.
• User-Friendly Interface: OptiMask offers an intuitive Python interface, ensuring accessibility for users of varying expertise.

Utilization

To employ OptiMask, install the optimask package via pip:

::

pip install optimask

Usage Example

Import the OptiMask class from the optimask package and utilize its methods for efficient data masking:

.. code-block:: python

from optimask import OptiMask import numpy as np

m = 120 n = 7 data = np.zeros(shape=(m, n)) data[24:72, 3] = np.nan data[95, :5] = np.nan

rows, cols = OptiMask.solve(data) len(rows) * len(cols) / data.size, np.isnan(data[rows][:, cols]).any()

Output: (0.85, False)

Further Information

Additional details about the algorithm are available in this notebook <notebooks/Optimask.ipynb>_.

Contributions

Contributions to the optimask project are encouraged. For bug reports, feature requests, or code contributions, please open an issue or submit a pull request.