{
    "info": {
        "author": "Gertjan van den Burg",
        "author_email": "gertjanvandenburg@gmail.com",
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        "classifiers": [
            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: Implementation :: CPython",
            "Programming Language :: Python :: Implementation :: PyPy"
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        "description": "# CleverCSV: A Clever CSV Package\n\n[![Build Status](https://travis-ci.org/alan-turing-institute/CleverCSV.svg?branch=master)](https://travis-ci.org/alan-turing-institute/CleverCSV)\n[![PyPI version](https://badge.fury.io/py/clevercsv.svg)](https://pypi.org/project/clevercsv/)\n[![Documentation Status](https://readthedocs.org/projects/clevercsv/badge/?version=latest)](https://clevercsv.readthedocs.io/en/latest/?badge=latest)\n[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/alan-turing-institute/CleverCSVDemo/master?filepath=CSV_dialect_detection_with_CleverCSV.ipynb)\n\n*CleverCSV provides a drop-in replacement of the Python* ``csv`` *package with \nimproved dialect detection for messy CSV files. It also provides a handy \ncommand line tool that can standardize a messy file or generate Python code to \nimport it.*\n\n**Useful links:**\n\n- [CleverCSV on Github](https://github.com/alan-turing-institute/CleverCSV)\n- [CleverCSV on PyPI](https://pypi.org/project/clevercsv/)\n- [Demo of CleverCSV on Binder (interactive!)](https://mybinder.org/v2/gh/alan-turing-institute/CleverCSVDemo/master?filepath=CSV_dialect_detection_with_CleverCSV.ipynb)\n- [Paper (PDF)](https://gertjanvandenburg.com/papers/VandenBurg_Nazabal_Sutton_-_Wrangling_Messy_CSV_Files_by_Detecting_Row_and_Type_Patterns_2019.pdf)\n- [Paper (HTML)](https://rdcu.be/bLVur)\n- [Reproducible Research Repo](https://github.com/alan-turing-institute/CSV_Wrangling/)\n\n## Introduction\n\n- CSV files are awesome: they are lightweight, easy to share, human-readable, \n  version-controllable, and supported by many systems and tools!\n- CSV files are terrible: they can have many different formats, multiple \n  tables, headers or no headers, escape characters, and there's no support for \n  data dictionaries.\n\nCleverCSV is a Python package that aims to solve many of the pain points of \nCSV files, while maintaining many of the good things. The package \nautomatically detects (with high accuracy) the format (*dialect*) of CSV \nfiles, thus making it easier to simply point to a CSV file and load it, \nwithout the need for human inspection. In the future, we hope to solve some of \nthe other issues of CSV files too.\n\nCleverCSV is [based on \nscience](https://gertjanvandenburg.com/papers/VandenBurg_Nazabal_Sutton_-_Wrangling_Messy_CSV_Files_by_Detecting_Row_and_Type_Patterns_2019.pdf). \nWe investigated thousands of real-world CSV files to find a robust way to \nautomatically detect the dialect of a file. This may seem like an easy \nproblem, but to a computer a CSV file is simply a long string, and every \ndialect will give you *some* table. In CleverCSV we use a technique based on \nthe patterns of the parsed file and the data type of the parsed cells. With \nour method we achieve a 97% accuracy for dialect detection, with a 21% \nimprovement on non-standard (*messy*) CSV files.\n\nWe think this kind of work can be very valuable for working data scientists \nand programmers and we hope that you find CleverCSV useful (if there's a \nproblem, please open an issue!) Since the academic world counts citations, \nplease **cite CleverCSV if you use the package**. Here's a BibTeX entry you \ncan use:\n\n```bib\n@article{van2019wrangling,\n        title = {Wrangling Messy {CSV} Files by Detecting Row and Type Patterns},\n        author = {{van den Burg}, G. J. J. and Nazabal, A. and Sutton, C.},\n        journal = {Data Mining and Knowledge Discovery},\n        year = {2019},\n        month = {Jul},\n        day = {26},\n        issn = {1573-756X},\n        doi = {10.1007/s10618-019-00646-y},\n}\n```\n\nAnd of course, if you like the package please *spread the word!* You can do \nthis by Tweeting about it \n([#CleverCSV](https://twitter.com/hashtag/clevercsv)) or clicking the \u2b50\ufe0f [on \nGitHub](https://github.com/alan-turing-institute/CleverCSV)!\n\n## Installation\n\nThe package is available on PyPI:\n\n```bash\n$ pip install clevercsv\n```\n\n## Usage\n\nCleverCSV consists of a Python library and a command line tool called \n``clevercsv``.\n\n### Library\n\nWe designed CleverCSV to provide a drop-in replacement for the built-in CSV \nmodule, with some useful functionality added to it. Therefore, if you simply \nwant to replace the builtin CSV module with CleverCSV, you can import \nCleverCSV as follows, and use it as you would use the builtin [csv \nmodule](https://docs.python.org/3/library/csv.html).\n\n```python\nimport clevercsv\n```\n\nCleverCSV provides an improved version of the dialect sniffer in the CSV \nmodule, but it also adds some useful wrapper functions. These functions \nautomatically detect the dialect and aim to make working with CSV files \neasier. We currently have the following helper functions:\n\n* [detect_dialect](https://clevercsv.readthedocs.io/en/latest/source/clevercsv.html#clevercsv.wrappers.detect_dialect): \n  takes a path to a CSV file and returns the detected dialect\n* [read_csv](https://clevercsv.readthedocs.io/en/latest/source/clevercsv.html#clevercsv.wrappers.read_csv): \n  automatically detects the dialect and encoding of the file, and returns the \n  data as a list of rows.\n* [csv2df](https://clevercsv.readthedocs.io/en/latest/source/clevercsv.html#clevercsv.wrappers.csv2df): \n  detects the dialect and encoding of the file and then uses Pandas to read \n  the CSV into a DataFrame.\n\nOf course, you can also use the traditional way of loading a CSV file, as in \nthe Python CSV module:\n\n```python\n# importing this way makes it easy to port existing code to CleverCsv\nimport clevercsv as csv\n\nwith open(\"data.csv\", \"r\", newline=\"\") as fp:\n  # you can use verbose=True to see what CleverCSV does:\n  dialect = csv.Sniffer().sniff(fid.read(), verbose=False)\n  fp.seek(0)\n  reader = csv.reader(fp, dialect)\n  rows = list(reader)\n```\n\nThat's the basics! If you want more details, you can look at the code of the \npackage, the test suite, or the [API \ndocumentation](https://clevercsv.readthedocs.io/en/latest/source/modules.html).\n\n### Command-Line Tool\n\nThe ``clevercsv`` command line application has a number of handy features to \nmake working with CSV files easier. For instance, it can be used to view a CSV \nfile on the command line while automatically detecting the dialect. It can \nalso generate Python code for importing data from a file with the correct \ndialect. The full help text is as follows:\n\n```text\nUSAGE\n  clevercsv [-h] [-v] [-V] <command> [<arg1>] ... [<argN>]\n\nARGUMENTS\n  <command>       The command to execute\n  <arg>           The arguments of the command\n\nGLOBAL OPTIONS\n  -h (--help)     Display this help message.\n  -v (--verbose)  Enable verbose mode.\n  -V (--version)  Display the application version.\n\nAVAILABLE COMMANDS\n  code            Generate Python code for importing the CSV file.\n  detect          Detect the dialect of a CSV file\n  help            Display the manual of a command\n  standardize     Convert a CSV file to one that conforms to RFC-4180.\n  view            View the CSV file on the command line using TabView\n```\n\nEach of the commands has further options (for instance, the ``code`` command \ncan generate code for importing a Pandas DataFrame). Use\n``clevercsv help <command>`` for more information. Below are some examples for \neach command:\n\n#### Code\n\nCode generation is useful when you don't want to detect the dialect of the \nsame file over and over again. You simply run the following command and copy \nthe generated code to a Python script!\n\n```text\n$ clevercsv code imdb.csv\n\n# Code generated with CleverCSV\n\nimport clevercsv\n\nwith open(\"imdb.csv\", \"r\", newline=\"\", encoding=\"utf-8\") as fp:\n    reader = clevercsv.reader(fp, delimiter=\",\", quotechar=\"\", escapechar=\"\\\\\")\n    rows = list(reader)\n```\n\nWe also have a version that reads a Pandas dataframe:\n\n```text\n$ clevercsv code --pandas imdb.csv\n\n# Code generated with CleverCSV\n\nimport clevercsv\n\ndf = clevercsv.csv2df(\"imdb.csv\", delimiter=\",\", quotechar=\"\", escapechar=\"\\\\\")\n```\n\n#### Detect\n\nDetection is useful when you only want to know the dialect.\n\n```text\n$ clevercsv detect imdb.csv\nDetected: SimpleDialect(',', '', '\\\\')\n```\n\nThe ``--plain`` flag gives the components of the dialect on separate lines, \nwhich makes combining it with ``grep`` easier.\n\n```text\n$ clevercsv detect --plain imdb.csv\ndelimiter = ,\nquotechar =\nescapechar = \\\n```\n\n#### Standardize\n\nUse the ``standardize`` command when you want to rewrite a file using the \nRFC-4180 standard:\n\n```text\n$ clevercsv standardize --output imdb_standard.csv imdb.csv\n```\n\nIn this particular example the use of the escape character is replaced by \nusing quotes.\n\n#### View\n\nThis command allows you to view the file in the terminal. The dialect is of \ncourse detected using CleverCSV! Both this command and the ``standardize`` \ncommand support the ``--transpose`` flag, if you want to transpose the file \nbefore viewing or saving:\n\n```text\n$ clevercsv view --transpose imdb.csv\n```\n\n\n## Contributors\n\nCode:\n\n* [Gertjan van den Burg](https://gertjan.dev)\n\nScientific work:\n\n* [Gertjan van den Burg](https://gertjan.dev)\n* [Alfredo Nazabal](https://scholar.google.com/citations?user=IanHvT4AAAAJ)\n* [Charles Sutton](https://homepages.inf.ed.ac.uk/csutton/)\n\n\n## Contributing\n\nIf you want to encourage development of CleverCSV, the best thing to do now is \nto *spread the word!*\n\nIf you encounter an issue in CleverCSV, please open an issue or submit a pull \nrequest!\n\n## Notes\n\nLicense: MIT (see LICENSE file).\n\nCopyright (c) 2019 [The Alan Turing Institute](https://turing.ac.uk).",
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