Metadata-Version: 2.1
Name: dataclass-csv
Version: 1.1.2
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
Keywords: dataclass dataclasses csv dataclass-csv
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
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
Classifier: Environment :: Console
Description-Content-Type: text/markdown

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# 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.


## Main features

- Use `dataclasses` instead of dictionaries to represent the rows in the CSV file.
- Take advantage of the `dataclass` properties type annotation. `DataclassReader` use the type annotation to perform validation of the data of the CSV file.
- Automatic type conversion. `DataclassReader` supports `str`, `int`, `float`, `complex`, `datetime` and `bool`.
- Helps you troubleshoot issues with the data in the CSV file. `DataclassReader` will show exactly in which line of the CSV file contain errors.
- Extract only the data you need. It will only parse the properties defined in the `dataclass`
- Familiar syntax. The `DataclassReader` is used almost the same way as the `DictReader` in the standard library.
- It uses `dataclass` features that let you define metadata properties so the data can be parsed exactly the way you want.
- Make the code cleaner. No more extra loops to convert data to the correct type, perform validation, set default values, the `DataclassReader` will do all this for you.


## 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
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`.

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,
    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:

```text
dataclass_csv.exceptions.CsvValueError: The field `age` is defined as <class 'int'> but
received a value of type <class 'str'>. [CSV Line number: 3]
```

Note that apart from telling what the error was, the `DataclassReader` will also show which line of the CSV file contain the data with errors.

## 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
from dataclasses import dataclass


@dataclass
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,, 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.

Default values can also be set using `dataclasses.field` like so:

```python
from dataclasses import dataclass, field


@dataclass
class User():
    firstname: str
    email: str = field(default='Not specified')
    age: int
```

## 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**

## Supported type annotation

At the moment the `DataclassReader` support `int`, `str`, `float`, `complex`, `datetime`, and `bool`. When defining a `datetime` property, it is necessary to use the `dateformat` decorator, for example:

```python
from dataclasses import dataclass
from datetime import datetime

from dataclass_csv import DataclassReader, dateformat


@dataclass
@dateformat('%Y/%m/%d')
class User:
    name: str
    email: str
    birthday: datetime


if __name__ == '__main__':

    with open('users.csv') as f:
        reader = DataclassReader(f, User)
        for row in reader:
            print(row)
```

Assuming that the CSV file have the following contents:

```text
name,email,birthday
Edit,edit@test.com,2018/11/23
```

The output would look like this:

```text
User(name='Edit', email='edit@test.com', birthday=datetime.datetime(2018, 11, 23, 0, 0))
```

## Fields metadata

It is important to note that the `dateformat` decorator will define the date format that will be used to parse date to all properties
in the class. Now there are situations where the data in a CSV file contains two or more columns with date values in different formats. It is possible
to set a format specific for every property using the `dataclasses.field`. Let's say that we now have a CSV file with the following contents:

```text
name,email,birthday, create_date
Edit,edit@test.com,2018/11/23,2018/11/23 10:43
```

As you can see the `create_date` contains time information as well.

The `dataclass` User can be defined like this:

```python
from dataclasses import dataclass, field
from datetime import datetime

from dataclass_csv import DataclassReader, dateformat


@dataclass
@dateformat('%Y/%m/%d')
class User:
    name: str
    email: str
    birthday: datetime
    create_date: datetime = field(metadata={'dateformat': '%Y/%m/%d %H:%M'})
```

Note that the format for the `birthday` field was not speficied using the `field` metadata. In this case the format specified in the `dateformat`
decorator will be used.

## Handling values with empty spaces

When defining a property of type `str` in the `dataclass`, the `DataclassReader` will treat values with only white spaces as invalid. To change this
behavior, there is a decorator called `@accept_whitespaces`. When decorating the class with the `@accept_whitespaces` all the properties in the class
will accept values with only white spaces.

For example:

```python
from dataclass_csv import DataclassReader, accept_whitespaces

@accept_whitespaces
@dataclass
class User:
    name: str
    email: str
    birthday: datetime
    created_at: datetime
```

If you need a specific field to accept white spaces, you can set the property `accept_whitespaces` in the field's metadata, like so:

```python
@dataclass
class User:
    name: str
    email: str = field(metadata={'accept_whitespaces': True})
    birthday: datetime
    created_at: datetime
```

## Copyright and License

Copyright (c) 2018 Daniel Furtado. Code released under BSD 3-clause license

## Credits

This package was created with [Cookiecutter](https://github.com/audreyr/cookiecutter) and the [audreyr/cookiecutter-pypackage](https://github.com/audreyr/cookiecutter-pypackage) project template.


# History

### 0.1.0 (2018-11-25)

* First release on PyPI.

### 0.1.1 (2018-11-25)

* Documentation fixes.

### 0.1.2 (2018-11-25)

* Documentation fixes.

### 0.1.3 (2018-11-26)

* Bug fixes
* Removed the requirement of setting the dataclass init to `True`

### 0.1.5 (2018-11-29)

* Support for parsing datetime values.
* Better handling when default values are set to `None`

### 0.1.6 (2018-12-01)

* Added support for reader default values from the default property of the `dataclasses.field`.
* Added support for allowing string values with only white spaces in a class level using the `@accept_whitespaces` decorator or through the `dataclasses.field` metadata.
* Added support for specifying date format using the `dataclasses.field` metadata.

### 0.1.7 (2018-12-01)

* Added support for default values from `default_factory` in the field's metadata. This allows adding mutable default values to the dataclass properties.

### 1.0.0 (2018-12-16)

* When a data does not pass validation it shows the line number in the CSV file where the data contain errors.
* Improved error handling.
* Changed the usage of the `@accept_whitespaces` decorator.
* Updated documentation.

### 1.0.1 (2019-01-29)

* Fixed issue when parsing headers on a CSV file with trailing white spaces.

### 1.1.0 (2019-02-17)

* Added support for boolean values.
* Docstrings

### 1.1.1 (2019-02-17)

* Documentation fixes.

### 1.1.2 (2019-02-17)

* Documentation fixes.


