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.
To use fletcher in Pandas DataFrames, all you need to do is to wrap your data
in a FletcherArray 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.FletcherArray(['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
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 \
black=18.5b0 \
codecov \
flake8 \
numba \
pandas \
pip \
pyarrow \
pytest \
pytest-cov \
pytest-flake8 \
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
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.