Metadata-Version: 2.1
Name: fletcher
Version: 0.5.0
Summary: Pandas ExtensionDType/Array backed by Apache Arrow
Home-page: https://github.com/xhochy/fletcher
Author: Uwe L. Korn
Author-email: fletcher@uwekorn.com
License: MIT
Description: # fletcher
        
        [![CircleCI](https://circleci.com/gh/xhochy/fletcher/tree/master.svg?style=svg)](https://circleci.com/gh/xhochy/fletcher/tree/master)
        [![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/ambv/black)
        [![FOSSA Status](https://app.fossa.io/api/projects/git%2Bgithub.com%2Fxhochy%2Ffletcher.svg?type=shield)](https://app.fossa.io/projects/git%2Bgithub.com%2Fxhochy%2Ffletcher?ref=badge_shield)
        
        A library that provides a generic set of Pandas ExtensionDType/Array
        implementations backed by Apache Arrow. They support a wider range of types
        than Pandas natively supports and also bring a different set of constraints and
        behaviours that are beneficial in many situations.
        
        ## Usage
        
        To use `fletcher` in Pandas DataFrames, all you need to do is to wrap your data
        in a `FletcherChunkedArray` object. Your data can be of either `pyarrow.Array`,
        `pyarrow.ChunkedArray` or a type that can be passed to `pyarrow.array(…)`.
        
        
        ```
        import fletcher as fr
        import pandas as pd
        
        df = pd.DataFrame({
            'str': fr.FletcherChunkedArray(['a', 'b', 'c'])
        })
        
        df.info()
        
        # RangeIndex: 3 entries, 0 to 2
        # Data columns (total 1 columns):
        # str    3 non-null fletcher[string]
        # dtypes: fletcher[string](1)
        # memory usage: 100.0 bytes
        ```
        
        ## Development
        
        While you can use `fletcher` in pip-based environments, we strongly recommend
        using a `conda` based development setup with packages from `conda-forge`.
        
        ```
        # Create the conda environment with all necessary dependencies
        conda create -y -q -n fletcher python=3.6 \
            pre-commit \
            asv \
            numba \
            pandas \
            pip \
            pyarrow \
            pytest \
            pytest-cov \
            six \
            -c conda-forge
        
        # Activate the newly created environment
        source activate fletcher
        
        # Install fletcher into the current environment
        pip install -e .
        
        # Run the unit tests (you should do this several times during development)
        py.test
        
        # Install pre-commit hooks
        # These will then be automatically run on every commit and ensure that files
        # are black formatted, have no flake8 issues and mypy checks the type consistency.
        pre-commit install
        ```
        
        Code formatting is done using black. This should keep everything in a
        consistent styling and the formatting can be automatically adjusted using
        `black .`. Note that we have pinned the version of `black` to ensure that
        the formatting is reproducible.
        
        ### Benchmarks
        
        In `benchmarks/` we provide a set of benchmarks to compare the performance of
        `fletcher` against `pandas` and ensure that `fletcher` itself stays performant.
        The benchmarks are written using
        [airspeed velocity](https://asv.readthedocs.io/en/stable/). When developing
        the benchmarks you can run them using `asv dev` (use `-b <pattern>` to only
        run a selection of them) only once. To get real benchmark values, you should
        use `asv run --python=same` to run the benchmarks multiple times and get
        meaningful average runtimes.
        
Platform: UNKNOWN
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Description-Content-Type: text/markdown
