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First and foremost: This is a project I'm using to refine my craft utilizing Python, Cython and Rust together in a high-performance and efficient fashion. Aimed at replacing the core analysis functionality found in Pandas; only really fast and memory efficient.

This package is in Alpha and in no-way can you expect this to be functional or reliable.

Project outlook (...the long story):

The (long term) goal for this project is to provide a light-weight alternative to the fantastic pandas. I love and use pandas all the time, so this is what has inspired me to making something similar; but excelling in a few areas such as minimizing memory footprint and speeding up certain computations via Rust's speed and safety guarantees.

This project shall have the same concept of dataframe & series; these objects will be stored as Rust structures. When "displayed" in Python it will merely be some meta-data description of the vector. Most computations will take place there, those which can't or perhaps better implemented in numpy will continue to be done in numpy (via pointer transferals, thus free of copies).

The point is, I'm striving to practice efficiency, parallelism, safety & speed with this project while maintaining some of the most valuable functionality of pandas.

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Install checklist

• Rustup
  • rustup install nightly
  • rustup default nightly
• gcc >= 7.x.x
• g++ >= 7.x.x

NOTE Only Python 3.5 is being tested against on Unix platforms

#####Installing from command line:

# Clone repo:
git clone https://github.com/milesgranger/lumber-jack.git && cd lumber-jack

# Run tests
LD_LIBRARY_PATH=$(pwd)/lumberjack/rust:$LD_LIBRARY_PATH python setup.py test

# Install
python setup.py build_ext && python setup.py install

# Uninstall
pip uninstall lumber-jack

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Working Pandas & Numpy like operations:

There isn't much, but check back soon! ;)

import lumberjack as lj

# lj.Series is a drop-in replacement for pandas.Series, overriding these methods:
series = lj.Series.arange(0, 10000)  # ~8x  faster than numpy
series.sum()                         # ~40x faster than pandas & ~3x  faster than numpy
series.cumsum()                      # ~4x  faster than pandas & ~0x  faster than numpy
series.mean()                        # ~98x faster than pandas & ~20x faster than numpy
series * 2                           # ~8x  faster than pandas & ~0x  faster than numpy
series *= 2                          # ~10x faster than pandas & ~0x  faster than numpy
series + 2                           # ....
series += 2                          # ....

Working Alteration Tools:

Split arrays of text which are separated by < something > into a one-hot encoded ndarray

from lumberjack import alterations
raw_texts = ['hello, there', 'hi, there']
alterations.split_n_one_hot_encode(raw_texts, sep=',', cutoff=0)
(['hello', 'there', 'hi'], [[1, 1, 0], [0, 1, 1]])