A library for data summary and analysis from various formats such as CSV, API, URL, etc.
Project description
SheetBuddy
SheetBuddy is a Python library for performing exploratory data analysis (EDA), data summary, and generating comprehensive reports in Excel format. It supports reading data from CSV files, JSON files, and APIs.
Features
- Data Cleaning and Preprocessing
- Load data from CSV, JSON, and APIs
- Generate EDA reports in Excel format
- Summary statistics, null values, standard deviation, and more
- Column information including descriptions ('May not be available for all columns')
- Conditional formatting and styling for Excel sheets
- Summary Statistics
- Visualization (Correlation Matrix, Basic Mathematics)
- Data Export (Excel)
New Features in Version 3.1.0 🚀
Outlier Detection and Visualization 📊
- Feature: Detect outliers in numerical columns using z-score or IQR methods.
- Implementation: New methods
detect_outliers
andadd_outliers_plot
to identify and visualize outliers with boxplots in the EDA sheet.
Enhanced EDA Sheet Visualizations 🖼️
- Feature: Comprehensive visualizations in the EDA sheet:
- Histograms for numerical columns.
- Boxplots for visualizing outliers.
- Correlation heatmaps for understanding relationships.
- Implementation: Integrated methods to create these visualizations in the EDA sheet.
Custom Text Headings for Visualizations 📝
- Feature: Descriptive titles for each visualization section to improve readability.
- Implementation:
add_text_heading
method to add custom text headings to each visualization.
Structured Dataset Summary 🗂️
- Feature: New Dataset Info sheet with a summary of the dataset, including name, format, number of rows and columns, description, and data link.
- Implementation:
add_dataset_info
method to create a structured summary of the dataset.
Requirements 📦
To use SheetBuddy, ensure you have the following dependencies:
pandas==1.3.3
requests==2.26.0
openpyxl==3.0.9
tqdm==4.62.3
matplotlib==3.4.3
seaborn==0.11.2
scipy==1.7.1
Note 📝
This library is designed specifically for numerical data analysis. Ensure your datasets are primarily numerical to make the most of SheetBuddy's capabilities.
Enjoy the new features and improvements! 🎉
Python Version Requirements:
- This version of SheetBuddy requires Python 3.7 or higher.
Upgrade now to leverage these powerful new features and make your data analysis even more insightful! 📈✨
Installation
You can install SheetBuddy using pip
:
pip install sheetbuddy
or
pip install sheetbuddy==3.1.0
Check for the lastest version
pip install sheetbuddy --upgrade
Usage
Example 1: Generating an EDA and Datasummary Report from a CSV File.
from sheetbuddy import SheetBuddy
file_path_or_url = 'https://people.sc.fsu.edu/~jburkardt/data/csv/airtravel.csv'
output_file_name = 'datasummary_report.xlsx'
sb = SheetBuddy(file_path_or_url)
sb.generate_eda_report(output_file_name)
Example 2: Generating an Datasummary & EDA Report from a Local JSON File.
from sheetbuddy import SheetBuddy
file_path = 'path/to/your/data.json'
output_file_name = 'enter_your_desired_name.xlsx'
sb = SheetBuddy(file_path)
sb.generate_eda_report(output_file_name)
Example 3: Generating an Datasummary & EDA Report from a Local CSV File.
from sheetbuddy import SheetBuddy
filename = 'your_local_path.csv'
outputfile = 'enter_your_desired_name.xlsx'
sb = SheetBuddy(filename)
sb.generate_eda_report(outputfile)
How It Works:
1.Data Loading: SheetBuddy loads data from the specified source (CSV, JSON, or API).
2.Data Analysis: It performs various data analyses, including summary statistics, null values analysis, and column descriptions.
3.Report Generation: The results are compiled into an Excel file with conditional formatting and styling for easy interpretation.
Contributing:
Contributions are welcome! If you have any suggestions, bug reports, or feature requests, please open an issue or submit a pull request on GitHub.
License:
SheetBuddy is licensed under the MIT License. See the LICENSE file for more details.
We hope you enjoy these new features and improvements in SheetBuddy v3.1.0 ! 🚀
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