csvcomparer 0.1.0

Compare delimited files that share a common key.

csvcomparer

Compare delimited files that share a common key.
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Table of Contents

1. Overview
2. Example
3. Installation
4. Roadmap
5. Contributing
6. License
7. Contact
8. Acknowledgments

Overview

csvcomparer is an open-source Python project used for determining differences between two delimited files (referred to here as "left" and "right" files) that share a common key, or index. Specifically, csvcomparer determines:

• Columns exclusive to the left and right files, respectively.
• Rows exclusive to the left and right files, respectively.
• Field-level differences for rows/columns in common between files.

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Basic Usage

As a python module:

from csvcomparer import CsvCompare

diffs = CsvCompare(
  "<path/to/left_file.csv>",
  "<path/to/right_file.csv>",
  "<key>").diffs

As a command line utility:

> python csvcomparer left_csv_filepath right_csv_filepath key

Examples

Provided the following file data:
menu_l.csv

id	name	pic	price	score	togo
1A	beer	🍺	$6.00	3.9	N
1B	wine	🍷	$7.25	4.5	N
2A	cheese	🧀	$4.10	4.0	Y
3A	bacon	🥓	$3.33	4.9	Y

menu_r.csv

id	name	pic	price	stars
1A	beer	🍻	$5.25	3.9
1B	wine	🍷	$7.25	4.8
2A	cheese	🧀	$3.95	4.1
4C	taco	🌮	$8.33	3.1
5B	pizza	🍕	$9.99	2.4

Usage as a Python module...

>>> from csvcomparer import CsvCompare
>>> CsvCompare("menu_l.csv", "menu_r.csv", "id").diffs

... or as a command-line utility:

> python csvcomparer menu_l.csv menu_r.csv id

Returns:

{'cols_added': ['stars'],
 'cols_removed': ['score', 'togo'],
 'rows_added': {'4C': {'name': 'taco',
                       'pic': '🌮',
                       'price': '$8.33',
                       'stars': 3.1},
                '5B': {'name': 'pizza',
                       'pic': '🍕',
                       'price': '$9.99',
                       'stars': 2.4}},
 'rows_changed': {'1A': [('pic', '🍺', '🍻'), ('price', '$6.00', '$5.25')],
                  '2A': [('price', '$4.10', '$3.95')]},
 'rows_removed': {'3A': {'name': 'bacon',
                         'pic': '🥓',
                         'price': '$3.33',
                         'score': 4.9,
                         'togo': 'Y'}}}

Multi-column keys are also supported. So for the same file data:

>>> CsvCompare("menu_l.csv", "menu_r.csv", ["id", "name"]).diffs

Returns:

{'cols_added': ['stars'],
 'cols_removed': ['score', 'togo'],
 'rows_added': {('4C', 'taco'): {'pic': '🌮', 'price': '$8.33', 'stars': 3.1},
                ('5B', 'pizza'): {'pic': '🍕', 'price': '$9.99', 'stars': 2.4}},
 'rows_changed': {('1A', 'beer'): [('pic', '🍺', '🍻'),
                                   ('price', '$6.00', '$5.25')],
                  ('2A', 'cheese'): [('price', '$4.10', '$3.95')]},
 'rows_removed': {('3A', 'bacon'): {'pic': '🥓',
                                    'price': '$3.33',
                                    'score': 4.9,
                                    'togo': 'Y'}}}

See the docs for detailed usage and examples.

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Installation

Prerequisites

• Python v3.6 or greater
• Poetry

Then simply run:

poetry install csvcomparer

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Roadmap

csvcomparer is in its infancy, and there are high hopes for this project!

The ultimate goal is being able to compare any two data sets that can be consumed as a "dataframe", regardless of size, efficiently as possible. This comes with a great deal of challenges, but I'm confident it will get there.

See the open issues for a full list of proposed features (and known issues).

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Contributing

Any contributions are greatly appreciated.

If you have a suggestion that would make this better, please fork the repo and create a pull request. You can also simply open an issue with the tag "enhancement". Don't forget to give the project a star! Thanks again!

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
3. Commit your Changes (git commit -m 'Add some AmazingFeature')
4. Push to the Branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

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License

Distributed under the MIT License. See LICENSE.txt for more information.

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Contact

Ryan Bergsmith - LinkedIn - [e-mail]
Project Link: Github

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Acknowledgments

• Robin Zaubeerer for being a great PDM coach and providing roadmap inspiration.

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