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pipesnake 0.1

# `pipesnake`*a pandas sklearn-inspired pipeline data processor*`pipesnake` is a data processing pipeline able to handle Pandas Dataframes. In many casesDataframes are used to clean-up data, pre-processing it and to perform feature engineering, `pipesnake` tries to simplify these steps, creating complex pipelines.[documentation](docs/source/index.rst); [examples](examples/README.md);## Why?Two easy reasons:* in many cases Pandas DataFrame is super easy to build _feature extractor_ or _data preocessors_* in many cases it is useful to have a pipeline that can process both `x` and `y` at the same time# How can you use `pipesnake` ?## InstallThe easy way:`pip install --upgrade https://github.com/pierluigi-failla/pipesnake/tarball/master`to get the latest version available on GitHub, or:`pip install pipesnake` to install the latest stable version on [PyPi](https://pypi.python.org).## CodingYou can build your own pipelines combining `SeriesPipe` and `ParallelPipe`, both of them can handle list of `Transformer`. An inherited `Transformer` object is a class which implements the abstract `base.Transformer` methods:```pythonfrom pipesnake.base import Transformerclass MyTransformer(Transformer): def __init__(self, name=None, <your params>): Transformer.__init__(self, name=name, ...) def fit_x(self, x): <your implementation> def fit_y(self, y): <your implementation> def transform_x(self, x): <your implementation> def transform_y(self, y): <your implementation> def inverse_transform_x(self, x): <your implementation> def inverse_transform_y(self, y): <your implementation>```You can find some `Transformers` already implemented in `pipesnake.transformers`. Once you have all the needed `Transformers` you can create pipelines for feature engineering or data processing using `SeriesPipe` or `ParallelPipe`:```pythonfrom pipesnake.pipe import ParallelPipefrom pipesnake.pipe import SeriesPipepipe = SeriesPipe(transformers=[ ParallelPipe(transformers=[ MyTransformer1(<params>), MyTransformer2(<params>), ]), MyTransformer3(<params>),])```More info in the [documentation]() and in the [examples](examples/README.md).# Batteries included`pipesnake` comes with several transformers included:Module | Name | Short Description--- | --- | ---`pipenskae.transformers.combiner` | `Combiner` | Apply user function to a column or a set of columns`pipenskae.transformers.combiner` | `Roller` | Apply the provided function rolling within a given window`pipenskae.transformers.converter` | `Category2Number` | Convert categorical to number`pipenskae.transformers.deeplearning` | `LSTMPacker` | Pack rows in order to be used as input for LSTM networks`pipenskae.transformers.dropper` | `DropDuplicates` | Drop duplicated rows and/or cols`pipenskae.transformers.dropper` | `DropNanCols` | Drop cols with nans`pipenskae.transformers.dropper` | `DropNanRows` | Drop rows with nans`pipenskae.transformers.financial` | `ToReturn` | Convert columns to `financial return`: r_t = (x_t - x_{t-1}) / x_{t-1}`pipenskae.transformers.imputer` | `ReplaceImputer` | Impute NaNs replacing them`pipenskae.transformers.imputer` | `KnnImputer` | Impute NaNs using K-nearest neighbors`pipenskae.transformers.misc` | `ToNumpy` | Convert `x` and `y` to a particular numpy type`pipenskae.transformers.misc` | `ColumnRenamer` | Rename `x` and `y` columns`pipenskae.transformers.misc` | `Copycat` | Copy the datasets forward`pipenskae.transformers.scaler` | `MinMaxScaler` | Min max scaler`pipenskae.transformers.scaler` | `StdScaler` | Standard deviation scaler`pipenskae.transformers.scaler` | `MadScaler` | Median absolute deviation scaler`pipenskae.transformers.scaler` | `UnitLenghtScaler` | Scale the feature vector to have norm 1.0`pipenskae.transformers.selector` | `ColumnSelector` | Select a given list of column names to keep`pipenskae.transformers.stats` | `ToSymbolProbability` | Convert values in columns to their probabilities# How can you contribute to `pipesnake` ?First of all grab a copy of the repository: `git clone https://github.com/scikit-learn/scikit-learn.git`you can run tests just running `run_tests.py`. There is a bunch of things you can contribute as far as `pipesnake` is at its early stages:* **improvements**: make the library bugfixed, faster, parallel, nicer, cleaner...;* **documentation**: this library uses Sphinx to generate documentation, so feel free to enrich it;* **samples**: create examples about using the library;* **transformers**: develop new-general-purpose transformers to share with the community;* **tests**: code better tests to extend the coverage and reduce code regression;or whatever you may thing is relevant to make `pipesnake` better.