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is a data processing pipeline able to handle Pandas Dataframes. In many cases\nDataframes are used to clean-up data, pre-processing it and to perform feature engineering, \n`pipesnake` tries to simplify these steps, creating complex pipelines.\n\n[documentation](https://pierluigi-failla.github.io/pipesnake/); [examples](examples/README.md);\n\n## Why?\n\nTwo easy reasons:\n* in many cases Pandas DataFrame is super easy to build _feature extractor_ or _data preocessors_\n* in many cases it is useful to have a pipeline that can process both `x` and `y` at the same time\n\n# How can you use `pipesnake` ?\n\n## Install\n\nThe easy way:\n\n`pip install --upgrade https://github.com/pierluigi-failla/pipesnake/tarball/master`\n\nto get the latest version available on GitHub, or:\n\n`pip install pipesnake` \n\nto install the latest stable version on [PyPi](https://pypi.python.org).\n\n## Coding\n\nYou can build your own pipelines combining `SeriesPipe` and `ParallelPipe`, both of them can handle list \nof `Transformer`. \n\nAn inherited `Transformer` object is a class which implements the abstract \n`base.Transformer` methods:\n\n```python\nfrom pipesnake.base import Transformer\n\nclass MyTransformer(Transformer):\n    def __init__(self, name=None, \u003cyour params\u003e):\n        Transformer.__init__(self, name=name, ...)\n        \n    def fit_x(self, x):\n        \u003cyour implementation\u003e\n\n    def fit_y(self, y):\n        \u003cyour implementation\u003e\n\n    def transform_x(self, x):\n        \u003cyour implementation\u003e\n\n    def transform_y(self, y):\n        \u003cyour implementation\u003e\n\n    def inverse_transform_x(self, x):\n        \u003cyour implementation\u003e\n\n    def inverse_transform_y(self, y):\n        \u003cyour implementation\u003e\n```\n\nYou can find some `Transformers` already implemented in `pipesnake.transformers`. \n\nOnce you have all the needed `Transformers` you can create pipelines for feature engineering or data \nprocessing using `SeriesPipe` or `ParallelPipe`:\n\n```python\nfrom pipesnake.pipe import ParallelPipe\nfrom pipesnake.pipe import SeriesPipe\n\npipe = SeriesPipe(transformers=[\n    ParallelPipe(transformers=[\n        MyTransformer1(\u003cparams\u003e),\n        MyTransformer2(\u003cparams\u003e),\n    ]),\n    MyTransformer3(\u003cparams\u003e),\n])\n```\n\nMore info in the [documentation]() and in the [examples](examples/README.md).\n\n# Batteries included\n\n`pipesnake` comes with several transformers included:\n\nModule | Name | Short Description\n--- | --- | ---\n`pipenskae.transformers.combiner` | `Combiner` | Apply user function to a column or a set of columns\n`pipenskae.transformers.combiner` | `Roller` | Apply the provided function rolling within a given window\n`pipenskae.transformers.converter` | `Category2Number` | Convert categorical to number\n`pipenskae.transformers.deeplearning` | `LSTMPacker` | Pack rows in order to be used as input for LSTM networks\n`pipenskae.transformers.dropper` | `DropDuplicates` | Drop duplicated rows and/or cols\n`pipenskae.transformers.dropper` | `DropNanCols` | Drop cols with nans\n`pipenskae.transformers.dropper` | `DropNanRows` | Drop rows with nans\n`pipenskae.transformers.financial` | `ToReturn` | Convert columns to `financial return`: r_t = (x_t - x_{t-1}) / x_{t-1}\n`pipenskae.transformers.imputer` | `ReplaceImputer` | Impute NaNs replacing them\n`pipenskae.transformers.imputer` | `KnnImputer` | Impute NaNs using K-nearest neighbors\n`pipenskae.transformers.misc` | `ToNumpy` | Convert `x` and `y` to a particular numpy type\n`pipenskae.transformers.misc` | `ColumnRenamer` | Rename `x` and `y` columns\n`pipenskae.transformers.misc` | `Copycat` | Copy the datasets forward\n`pipenskae.transformers.scaler` | `MinMaxScaler` | Min max scaler\n`pipenskae.transformers.scaler` | `StdScaler` | Standard deviation scaler\n`pipenskae.transformers.scaler` | `MadScaler` | Median absolute deviation scaler\n`pipenskae.transformers.scaler` | `UnitLenghtScaler` | Scale the feature vector to have norm 1.0\n`pipenskae.transformers.selector` | `ColumnSelector` | Select a given list of column names to keep\n`pipenskae.transformers.stats` | `Digitize` | Digitize values in columns based on their `pdf`\n`pipenskae.transformers.stats` | `ToSymbolProbability` | Convert values in columns to their probabilities\n\n# How can you contribute to `pipesnake` ?\n\nFirst of all grab a copy of the repository: \n\n`git clone https://github.com/scikit-learn/scikit-learn.git`\n\nyou can run tests just running `run_tests.py`. \n\nThere is a bunch of things you can contribute as far as `pipesnake` is at its early stages:\n\n* **improvements**: make the library bugfixed, faster, parallel, nicer, cleaner...;\n* **documentation**: this library uses Sphinx to generate documentation, so feel free to enrich it;\n* **samples**: create examples about using the library;\n* **transformers**: develop new-general-purpose transformers to share with the community;\n* **tests**: code better tests to extend the coverage and reduce code regression;\n\nor whatever you may thing is relevant to make `pipesnake` better.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpierluigi-failla%2Fpipesnake","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fpierluigi-failla%2Fpipesnake","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpierluigi-failla%2Fpipesnake/lists"}