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tests Update boolean value parsing to follow convention used by other types Feb 12, 2019
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README.md

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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

pipenv install dataclass-csv

Getting started

First, add the necessary imports:

from dataclasses import dataclass

from dataclass_csv import DataclassReader

Assuming that we have a CSV file with the contents below:

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:

@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:

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:

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:

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:

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:

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:

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:

Astor,, 7

If we run the code we should see the output below:

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:

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:

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:

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:

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:

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

The output would look like this:

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:

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:

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:

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:

@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 and the audreyr/cookiecutter-pypackage project template.

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