Metadata-Version: 2.1
Name: dataclass-csv
Version: 0.1.1
Summary: Map CSV data into dataclasses
Home-page: https://github.com/dfurtado/dataclass-csv
Author: Daniel Furtado
Author-email: daniel@dfurtado.com
License: BSD license
Description: # Dataclass CSV
        
        Dataclass CSV makes working with CSV files easier and much better than working with Dicts. It uses Python's Dataclasses to store data of every row on the CSV file and also uses type annotations which enables proper type checking and validation.
        
        ## Installation
        
        ```shell
        pipenv install dataclass-csv
        ```
        
        ## Getting started
        
        First, add the necessary imports:
        
        ```python
        from dataclasses import dataclass
        
        from dataclass_csv import DataclassReader
        ```
        
        Assuming that we have a CSV file with the contents below:
        ```text
        firstname,email,age
        Elsa,elsa@test.com, 11
        Astor,astor@test.com, 7
        Edit,edit@test.com, 3
        Ella,ella@test.com, 2
        ```
        
        Let's create a dataclass that will represent a row in the CSV file above:
        ```python
        @dataclass(init=False)
        class User():
            firstname: str
            email: str
            age: int
        ```
        
        The dataclass `User` has 3 properties, `firstname` and `email` is of type `str` and `age` is of type `int`. Note that it is required to add the `init=False` to the dataclass decorator.
        
        To load and read the contents of the CSV file we do the same thing as if we would be using the `DictReader` from the `csv` module in the Python's standard library. After opening the file we create an instance of the `DataclassReader` passing two arguments. The first is the `file` and the second is the dataclass that we wish to use to represent the data of every row of the CSV file. Like so:
        
        ```python
        with open(filename) as users_csv:
            reader = DataclassReader(users_csv, User)
            for row in reader:
                print(row)
        ```
        
        The `DataclassReader` internally uses the `DictReader` from the `csv` module to read the CSV file which means that you can pass the same arguments that you would pass to the `DictReader`. The complete argument list is shown below:
        
        ```python
        dataclass_csv.DataclassReader(f, cls_mapper, fieldnames=None, restkey=None, restval=None, dialect='excel', *args, **kwds)
        ```
        
        If you run this code you should see an output like this:
        
        ```python
        User(firstname='Elsa', email='elsa@test.com', age=11)
        User(firstname='Astor', email='astor@test.com', age=7)
        User(firstname='Edit', email='edit@test.com', age=3)
        User(firstname='Ella', email='ella@test.com', age=2)
        ```
        
        ### Error handling
        
        One of the advantages of using the `DataclassReader` is that it makes it easy to detect when the type of data in the CSV file is not what your application's model is expecting. And, the `DataclassReader` shows errors that will help to identify the rows with problem in your CSV file.
        
        For example, say we change the contents of the CSV file shown in the Getting started section and, modify the `age` of the user Astor, let's change it to a string value:
        
        ```text
        Astor, astor@test.com, test
        ```
        
        Remember that in the dataclass `User` the `age` property is annotated with `int`. If we run the code again an exception will be raised with the message below:
        
        ```python
        ValueError: The field age is of type <class 'int'> but received a value of type <class 'str'>
        ```
        
        ### Default values
        
        The `DataclassReader` also handles properties with default values. Let's modify the dataclass `User` and add a default value for the field `email`:
        
        ```python
        @dataclass(init=False)
        class User():
            firstname: str
            email: str = 'Not specified'
            age: int
        ```
        
        And we modify the CSV file and remove the email for the user Astor:
        
        ```python
        Astor,Furtado,, 7
        ```
        
        If we run the code we should see the output below:
        
        ```text
        User(firstname='Elsa', email='elsa@test.com', age=11)
        User(firstname='Astor', email='Not specified', age=7)
        User(firstname='Edit', email='edit@test.com', age=3)
        User(firstname='Ella', email='ella@test.com', age=2)
        ```
        
        Note that now the object for the user Astor have the default value `Not specified` assigned to the email property.
        
        ### Mapping dataclass fields to columns
        
        The mapping between a dataclass property and a column in the CSV file will be done automatically if the names match, however, there are situations that the name of the header for a column is different. We can easily tell the `DataclassReader` how the mapping should be done using the method `map`. Assuming that we have a CSV file with the contents below:
        
        ```text
        First Name,email,age
        Elsa,elsa@test.com, 11
        ```
        
        Note that now, the column is called **First Name** and not **firstname**
        
        And we can use the method `map`, like so:
        
        ```python
        reader = DataclassReader(users_csv, User)
        reader.map('First name').to('firstname')
        ```
        
        Now the DataclassReader will know how to extract the data from the column **First Name** and add it to the to dataclass property **firstname**
        
        ## Copyright and License
        
        Copyright (c) 2018 Daniel Furtado. Code released under BSD 3-clause license
        
        ## Credits
        
        This package was created with Cookiecutter and the audreyr/cookiecutter-pypackage project template.
        
        
        # History
        
        ### 0.1.0 (2018-11-25)
        
        * First release on PyPI.
        
        ### 0.1.1 (2018-11-25)
        
        * Documentation fixes.
        
Keywords: dataclass dataclasses csv dataclass-csv
Platform: UNKNOWN
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: BSD License
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Programming Language :: Python :: 3.7
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: MacOS :: MacOS X
Classifier: Operating System :: Unix
Classifier: Operating System :: POSIX
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