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 and add_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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