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A map utility function that caches results in case of failures and reruns, processes pandas Series, and supports parallelization

Project description

maputil

A powerful map function that improves on Python's built-in map function by adding caching and support for both lists and pandas Series.

Features

  • Caches results in a SQLite database to avoid recomputing values if the code fails or reruns
  • Works with both Python lists and pandas Series
  • Preserves Series indexes in the output, making it easy to add results as a new column in a DataFrame
  • Supports concurrent execution with multiple threads
  • Optional progress bar

Example

from maputil import map

# With a pandas Series
inputs = pd.Series([1, 2, 3, 4, 5], index=["a", "b", "c", "d", "e"])

def f(x):
    return x * 2

outputs = map(f, inputs, run="demo")

Usage

The map() function requires a run parameter to identify cached results. For the same run name, the function will use cached results for inputs it has seen before.

Optional parameters:

  • concurrency: Number of threads to use for parallel execution
  • progress: Set to True to display a progress bar

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