Metadata-Version: 2.1
Name: marshmallow-numerical
Version: 0.0.2
Summary: Marshmallow Schema generator for pandas and numpy
Home-page: https://github.com/zblz/marshmallow-numerical
Author: Víctor Zabalza
Author-email: vzabalza@gmail.com
License: MIT
Description: # marshmallow-numerical
        
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        `marshmallow-numerical` is a library that helps you generate
        [marshmallow](https://marshmallow.readthedocs.io/) Schemas for Pandas and Numpy
        data structures.
        
        # Usage
        
        Let's start by creating an example dataframe for which we want to create a
        `Schema`. This dataframe has four columns: two of them are of string type, one
        is a float, and the last one is an integer.
        
        ```python
        import pandas as pd
        import numpy as np
        from marshmallow_numerical import SplitDataFrameSchema
        
        animal_df = pd.DataFrame(
            [
                ("falcon", "bird", 389.0, 2),
                ("parrot", "bird", 24.0, 2),
                ("lion", "mammal", 80.5, 4),
                ("monkey", "mammal", np.nan, 4),
            ],
            columns=["name", "class", "max_speed", "num_legs"],
        )
        ```
        
        You can then create a marshmallow schema that will validate and load dataframes
        that follow the same structure as the one above and that have been serialized
        with `DataFrame.to_json` with the [`orient=split`
        format](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.to_json.html#pandas.DataFrame.to_json):
        
        ```python
        class AnimalSchema(SplitDataFrameSchema):
            """Automatically generated schema for animal dataframe"""
        
            dtypes = animal_df.dtypes
        ```
        
        When passing a valid payload for a new animal, this schema will validate it and
        build a dataframe:
        
        ```python
        animal_schema = AnimalSchema()
        
        new_animal = {
            "data": [("leopard", "mammal", 58.0, 4), ("ant", "insect", 0.288, 6)],
            "columns": ["name", "class", "max_speed", "num_legs"],
            "index": [0, 1],
        }
        
        new_animal_df = animal_schema.load(new_animal)
        
        print(type(new_animal_df))
        # <class 'pandas.core.frame.DataFrame'>
        print(new_animal_df)
        #       name   class  max_speed  num_legs
        # 0  leopard  mammal     58.000         4
        # 1      ant  insect      0.288         6
        ```
        
        However, if we pass a payload that doesn't conform to the schema, it will raise
        a marshmallow `ValidationError` exception with informative message about errors:
        
        ```python
        invalid_animal = {
            "data": [("leopard", "mammal", 58.0, "four")],  # num_legs is not an int
            "columns": ["name", "class", "num_legs"],  # missing  max_speed column
            "index": [0],
        }
        
        animal_schema.load(invalid_animal)
        
        # Raises:
        # marshmallow.exceptions.ValidationError: {'columns': ["Must be equal to ['name', 'class', 'max_speed', 'num_legs']."], 'data': {0: {3: ['Not a valid integer.']}}}
        ```
        
        `marshmallow_numerical` can also generate Schemas for the `orient=records`
        format by following the above steps but using
        `marshmallow_numerical.RecordsDataFrameSchema` as the superclass for
        `AnimalSchema`.
        
        # Installation
        
        marshmallow-numerical requires Python >= 3.6 and marshmallow >= 3.0. You can
        install it with pip:
        
        ```
        pip install marshmallow-numerical
        ```
        
        # Contributing
        
        Contributions are welcome!
        
        You can report a problem or feature request in the [issue
        tracker](https://github.com/zblz/marshmallow-numerical/issues). If you feel that
        you can fix it or implement it, please submit a pull request referencing the
        issues it solves.
        
        Unit tests written using the [`pytest`](https://pytest.org) framework are in the
        `tests` directory, and are run using
        [tox](https://tox.readthedocs.io/en/latest/) on Python 3.6 and 3.7. You can run
        the tests by installing tox:
        ```
        pip install tox
        ```
        and running the linters and tests for all Python versions by running `tox`, or
        for a specific Python version by running:
        ```
        tox -e py36
        ```
        
        We format the code with [black](https://github.com/python/black), and you can
        format your checkout of the code before commiting it by running:
        ```
        tox -e black -- .
        ```
        
Platform: UNKNOWN
Requires-Python: >=3.6
Description-Content-Type: text/markdown
