| Name | decyphr JSON |
| Version |
1.1.1
JSON |
| download |
| home_page | None |
| Summary | An all-encompassing, automated toolkit for deep Exploratory Data Analysis and reporting. |
| upload_time | 2025-08-04 20:58:55 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.9 |
| license | MIT License
Copyright (c) 2025 Ayush Singh
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. |
| keywords |
eda
data analysis
profiling
visualization
data science
machine learning
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# Decyphr
An all-encompassing, automated toolkit for generating deep, professional, and interactive Deep Data Analysis reports with a single line of code. It's not just a normal EDA.
Decyphr is designed to accelerate the data science workflow by automating the tedious and time-consuming process of initial data exploration. It goes beyond basic profiling to provide deep statistical insights, advanced machine learning-driven analysis, and a stunning, presentation-ready report that is as beautiful as it is informative.
## Key Features
Decyphr provides a comprehensive suite of analyses, intelligently triggered based on your data's characteristics:
Complete Overview: Instant summary of dataset shape, memory usage, variable types, and data quality warnings.
Deep Univariate Analysis: Detailed statistical profiles, histograms, and frequency charts for every variable.
Multivariate Analysis: Stunning, interactive heatmaps for both linear (Pearson) and non-linear (Phik) correlations.
Advanced Data Quality: Automatically detects constant columns, whitespace issues, and potential outliers using multiple methods (IQR, Isolation Forest).
Statistical Inference: Performs automated Hypothesis Testing (T-Tests, ANOVA, Chi-Squared) to uncover statistically significant relationships.
Machine Learning Insights:
PCA: Analyzes dimensionality reduction possibilities.
Clustering: Automatically finds hidden segments in your data using K-Means.
Feature Importance: Trains a baseline model to identify the most predictive features when a target is provided.
Explainable AI (XAI): Generates SHAP summary plots to explain how your features impact model predictions.
Specialized Analysis: Includes dedicated modules for Deep Text Analysis (Sentiment, NER, Topics), Time-Series Decomposition, and Geospatial Mapping.
Data Drift Detection: Compare two datasets to quantify changes in data distribution over time.
High-End Interactive Report: A beautiful, modern dashboard with a toggleable light/dark theme, responsive charts, and a professional UI/UX.
## Quick Start
1. Installation
!pip install decyphr
2. Importing
import decyphr
3. Running Analysis
decyphr.analyze(filepath="data/your_data.csv")
4. Generating Report (Jupyter Notebook)
import os, webbrowser
path = os.path.abspath('Reports/your_generated_report_name.html')
webbrowser.open(f'file://{path}')
5. Generating Report (Google Colab)
from google.colab import files
files.download("reports/your_report_name.html")
# Generated Your First Report
Create a Python script add the above code. Just point it to your dataset and let Decyphr do the rest.
Your professional, interactive HTML report will be automatically saved in a new decyphr_reports/ directory.
## Project Structure
This project uses a highly modular "plugin" architecture to ensure it is robust, maintainable, and easy to extend. All analysis and visualization logic is separated into self-contained modules located in the src/decyphr/analysis_plugins/ directory.
## Capabilities
While there is no end of this vast ocean but as of now Decyphr can process more than 1 lakh rows with over 1 lakh cells in less than 3 mins. Isn't that amazing !
## Contributing
Contributions are what make the open-source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated. Please feel free to fork the repo and create a pull request, or open an issue with suggestions.
## License
Distributed under the MIT License. See LICENSE file for more information.
Designed and Created by - Ayush Singh
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"description": "# Decyphr\n\nAn all-encompassing, automated toolkit for generating deep, professional, and interactive Deep Data Analysis reports with a single line of code. It's not just a normal EDA.\n\nDecyphr is designed to accelerate the data science workflow by automating the tedious and time-consuming process of initial data exploration. It goes beyond basic profiling to provide deep statistical insights, advanced machine learning-driven analysis, and a stunning, presentation-ready report that is as beautiful as it is informative.\n\n\n\n\n## Key Features\n\nDecyphr provides a comprehensive suite of analyses, intelligently triggered based on your data's characteristics:\n\nComplete Overview: Instant summary of dataset shape, memory usage, variable types, and data quality warnings.\n\nDeep Univariate Analysis: Detailed statistical profiles, histograms, and frequency charts for every variable.\n\nMultivariate Analysis: Stunning, interactive heatmaps for both linear (Pearson) and non-linear (Phik) correlations.\n\nAdvanced Data Quality: Automatically detects constant columns, whitespace issues, and potential outliers using multiple methods (IQR, Isolation Forest).\n\nStatistical Inference: Performs automated Hypothesis Testing (T-Tests, ANOVA, Chi-Squared) to uncover statistically significant relationships.\n\nMachine Learning Insights:\nPCA: Analyzes dimensionality reduction possibilities.\nClustering: Automatically finds hidden segments in your data using K-Means.\nFeature Importance: Trains a baseline model to identify the most predictive features when a target is provided.\nExplainable AI (XAI): Generates SHAP summary plots to explain how your features impact model predictions.\n\nSpecialized Analysis: Includes dedicated modules for Deep Text Analysis (Sentiment, NER, Topics), Time-Series Decomposition, and Geospatial Mapping.\n\nData Drift Detection: Compare two datasets to quantify changes in data distribution over time.\n\nHigh-End Interactive Report: A beautiful, modern dashboard with a toggleable light/dark theme, responsive charts, and a professional UI/UX.\n\n## Quick Start\n\n1. Installation\n\n!pip install decyphr\n\n2. Importing\n\nimport decyphr\n\n3. Running Analysis\n\ndecyphr.analyze(filepath=\"data/your_data.csv\")\n\n4. Generating Report (Jupyter Notebook)\n\nimport os, webbrowser\npath = os.path.abspath('Reports/your_generated_report_name.html')\nwebbrowser.open(f'file://{path}')\n\n5. Generating Report (Google Colab)\n\nfrom google.colab import files\nfiles.download(\"reports/your_report_name.html\")\n\n# Generated Your First Report\n\nCreate a Python script add the above code. Just point it to your dataset and let Decyphr do the rest.\n\nYour professional, interactive HTML report will be automatically saved in a new decyphr_reports/ directory.\n\n## Project Structure\n\nThis project uses a highly modular \"plugin\" architecture to ensure it is robust, maintainable, and easy to extend. All analysis and visualization logic is separated into self-contained modules located in the src/decyphr/analysis_plugins/ directory.\n\n## Capabilities\n\nWhile there is no end of this vast ocean but as of now Decyphr can process more than 1 lakh rows with over 1 lakh cells in less than 3 mins. Isn't that amazing !\n\n## Contributing\n\nContributions are what make the open-source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated. Please feel free to fork the repo and create a pull request, or open an issue with suggestions.\n\n## License\n\nDistributed under the MIT License. See LICENSE file for more information.\nDesigned and Created by - Ayush Singh\n",
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"license": "MIT License\n \n Copyright (c) 2025 Ayush Singh\n \n Permission is hereby granted, free of charge, to any person obtaining a copy\n of this software and associated documentation files (the \"Software\"), to deal\n in the Software without restriction, including without limitation the rights\n to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n copies of the Software, and to permit persons to whom the Software is\n furnished to do so, subject to the following conditions:\n \n The above copyright notice and this permission notice shall be included in all\n copies or substantial portions of the Software.\n \n THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\n SOFTWARE.",
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