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Tools for working with CSV files

Project Description

CSV Toolkit Overview


CSV Toolkit is a Python package that provides validation tooling and processing of CSV files. The validation tooling is based on the fantastic package Vladiate. The interface and extension mechanisms are similarly implemented as the rex.core extension mechanisms.

Example Usage

This packace comes equipped with validation tooling, a CLI, a tooling interface, a logging mechaism, and a loader mechanism. All are extensible, allow for future additions of new tools to this package, and the instroduction of custom tools depending on this package. This package comes with implementations built in as well.

Validation Tooling

This application comes with a validation tooling mechanism buil-tin. It allows for defining a validation schema to run against a CSV file. This was implemented due to the severe lack of strict validation mechanisms in the Python standard library’s csv module. While it does implement the csv module to some extent, it allows for strict validation with an extensible validation mechanism. Furthermore, the validation mechanism may be used via the CLI or as a standard, internal validation mechanism for your pacakge.

Built-In Simple CSV Validator

Included with this package is a simple CSV file validation mechanism to use to validate simple CSV structures where fields may contain any values or may be empty. This is also a good example of how to implement a CSV validation schema as an internal tool available to the CLI.

New Implementations

Subclass the BaseFileValidator class to create a new CSV validation tool. The required fields validators, delimeter, default_validator, check_duplicate_headers, and logger attributes must be defined. Creating a new logger for each CSV validating tool is recommended, but not necessary.

An example bare-bones implementation would be:

>>> class YourFirstValidatorLogger(Logger):
>>>     pass
>>> class YourFirstValidator(BaseFileValidator):
>>>     validators = {
>>>         "Field1": [],
>>>         "Field2": [],
>>>         "Field3": [],
>>>     }
>>>     delimiter = ","
>>>     default_validator = AnyVal
>>>     check_duplicate_headers = True
>>>     logger = YourFirstValidatorLogger
>>>     def validate(self):
>>>         ... validation mechanism here...
>>> validator = YourFirstValidator(LocalFileLoader('/path/to/example.csv'))
>>> print validator.validate()
>>> result = validator()
>>> print result.validation
>>> print result.log
... validation log text...

Obviously, you may call the validate property directly without a logger, but you may also call the validator instance, which returns a named tuple Result with validation and log attributes.

Please note, att this time the BaseFileValidator only supports loggers of the built-in type. Pull requests and contributions to change this are more than welcome.

Validator Attribute Definition

The validators attribute must define the validation schema for your type of CSV. It must be a dictionary with string keys defining the available columns and list values specifying the validator (with any initialization parameters the validator requires).

An example validation schema would look like:

>>> validators = {
>>>     "Foo": [
>>>         UniqueVal(),
>>>     ],
>>>     "Bar": [
>>>         RegexVal(r'^baz$'),
>>>     ],
>>>     "hello world": [
>>>         IntVal(empty_ok=True),
>>>     ],
>>> }

This schema corresponds to a CSV with headers Foo, Bar, and hello world. The Foo column must contain unique values, the Bar column must contain fields matching the regular expression ^baz$, and the hello world column must contain integer values, but allows for empty fields as well.

Built-In Validators

This package comes with built-in validators. For example:

  • IntVal: Integer values (allows empty values)

  • FloatVal: Float values (allows empty values)

  • BoolVal: Boolean values (allows empty values)

  • EnumVal: Enumerated values:

    EnumVal(['a', 'list', 'of', 'enumerations',])
  • UniqueVal: Unique values only

  • RegexVal: Fields must match supplied regex value (or no fields are matched)

  • EmptyVal: All fields must be empty

  • AnyVal: Any allowed values, but not empty

NOTE: Inclusion of a JSON validator has not been made at this time, but pull requests and contributions of an implementation are welcome.


The logging mechanism is simple, and records logs to an internal dictionary per instantiation. This allows for easy storage and retrieval of logs and logging information pertinent to your CSV tool.

One may use the global logging instance logger_main, the logging context manager logger_context, or subclass the logging implementation Logger to create custom logging instances.


The loader mechanism provides an easy tool to work with files and string objects. A simple wrapper around a specified loader, working with file-like objects becomes much simpler when working with CSV data.

A user may work with the StringLoader or LocalFileLoader classes by instantiating them with a source string or directory. For example:

>>> mystring = StringLoader(StringIO("A test string."))
>>> teststring =
>>> print teststring
"A test string."

To create new loaders, simply subclass the Loader class, specify a loader and any args or kwargs that are necessary for that loader to operate.


This package provides a tooling interface to allow automatic discovery of new tooling commands for the CLI. Simply subclass the Tool class to create a new tool, which will be usable via the CLI. Make sure to specify the required name attribute. A description atrribute is very useful, and if your tool/command requires it, specify the arguments attribute.

The implementation method must be overriden to tell the application what to do when the command is run or the tool is used internally to an application. The function must return a 0 if successful and a 1 or other if not. The returned value is passed to stdout for successes and stderror for failures.


The arguments must be a list of tuples with each touple containing the parameters usually passed to the argparse.add_argument() function. For example, a typical implementation looks like:

>>> self.parser.add_argument(
>>>     "filename",
>>>     type=argparse.FileType('r'),
>>>     help="A file."
>>> )

which, for a tool implementation, should be converted too:

>>> arguments = [
>>>     (
>>>         'filename',
>>>         {'type': argparse.FileType('r')},
>>>         {'help': 'A file.'},
>>>     ),
>>> ]

Please note that the file (the entry point for the CLI) will parse known arguments from the command line, and pass the rest to your tooling implementation.


The command line interface automatically discovers all tooling implementations subclassed from the interface Tool super class. The base command line argument is csvtoolkit with a named parameter. The named parameter is any of the available tooling implementations’ name attribute.

For example:

>>> class MyTool(Tool):
>>>     name = "my-super-awesome-tool"
>>>     ... and so on...

This tooling implementation is available via the CLI with the command:

$ csvtoolkit my-super-awesome-tool

Again, please note that the file (the entry point for the CLI) will parse known arguments from the command line, and pass the rest to your tooling implementation.


Contributions and/or fixes to this package are more than welcome. Please submit them by forking this repository and creating a Pull Request that includes your changes. We ask that you please include unit tests and any appropriate documentation updates along with your code changes. Code must be PEP 8 compliant.

This project will adhere to the Semantic Versioning methodology as much as possible, so when building dependent projects, please use appropriate version restrictions.

A development environment can be set up to work on this package by doing the following:

$ virtualenv csvtools
$ cd csvtools
$ . ./bin/activate
$ git clone
$ pip install -e ./csvtools[dev]
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