Metadata-Version: 2.1 Name: BasicTextMetrics Version: 0.2.1 Summary: Analyze textual data and extract useful metrics such as word count, character count, average word length, and most common words Author-email: Famosa Muyiwa License: MIT License Copyright (c) 2022 LearnOpenCV Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. Project-URL: Source, https://github.com/famosamuyiwa/TextMetrics.git Keywords: Text analysis,Text metrics,Word counting,Character counting,Word length,Frequency analysis,Linguistic analysis,Natural language processing (NLP),Textual data processing,Textual insights,Textual statistics,Textual analytics,Language metrics,Language analysis,Text readability,Sentiment analysis (potentially),Tokenization,Part-of-speech tagging (PoS tagging),Textual summarization,Lexical analysis Classifier: Development Status :: 3 - Alpha Classifier: Operating System :: OS Independent Classifier: License :: OSI Approved :: MIT License Classifier: Programming Language :: Python Classifier: Programming Language :: Python :: 3 Requires-Python: >=3.6 Description-Content-Type: text/markdown License-File: LICENSE Requires-Dist: nltk TextMetrics TextMetrics is a Python package for basic text analysis and statistics. It provides simple yet efficient functions to analyze textual data and extract useful metrics such as word count, character count, average word length, and most common words. Features Word Count: Count the number of words in a text. Character Count: Count the number of characters in a text. Average Word Length: Calculate the average length of words in a text. Most Common Words: Find the most common words in a text. Installation You can install Basictextmetrics using pip: pip install BasicTextMetrics Usage Here's a quick example demonstrating the usage of BasicTextMetrics: python Copy code from basictextmetrics import core text = "This is a sample text for demonstrating the BasicTextMetrics package." print("Word count:", core.word_count(text)) print("Character count:", core.char_count(text)) print("Average word length:", core.avg_word_length(text)) print("Most common words:", core.most_common_words(text)) Contributing Contributions are welcome! If you find any issues or have suggestions for improvement, feel free to open an issue or submit a pull request on the GitHub repository. License This project is licensed under the MIT License - see the LICENSE file for details. This README provides a brief overview of the BasicTextMetrics package, its features, installation instructions, usage examples, guidelines for contributing, and information about the license. Feel free to customize it further based on your preferences and specific details of the package.