Metadata-Version: 2.1
Name: sheetbuddy
Version: 2.2.0
Summary: A library for data summary and analysis from various formats such as CSV, API, URL, etc.
Home-page: https://github.com/AshishRogannagari/sheetbuddy
Author: Ashish Rogannagari
Author-email: Ashishrogannagari98@gmail.com
Keywords: data analysis,data summary,CSV,API,URL,pandas,requests,openpyxl,tqdm,excel,eda,data,data science,data engineering,data analytics,data visualization,data manipulation,data cleaning,data wrangling,data preprocessing,data transformation,data exploration,data mining,data modeling,data interpretation,data presentation,data reporting,data storytelling
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: License :: OSI Approved :: MIT License
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Education
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Mathematics
Classifier: Topic :: Scientific/Engineering :: Visualization
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Software Development
Classifier: Topic :: Software Development :: Libraries
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Classifier: Natural Language :: English
Requires-Python: >=3.7
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: pandas
Requires-Dist: numpy
Requires-Dist: requests
Requires-Dist: openpyxl
Requires-Dist: tqdm
Requires-Dist: matplotlib
Requires-Dist: seaborn

# <a name="top"></a>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.

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## 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)

## Release Notes for SheetBuddy v2.0.0 🚀

### New Features:

1. **📊 Correlation Matrix Sheet**:
   - Automatically generate and save a correlation matrix for numerical columns in your Excel report. Easily spot relationships and dependencies between variables.

2. **🧮 Basic Mathematics Sheet**:
   - Get essential statistics like mean, median, mode, and range for your numerical data. Now included in a separate sheet for quick insights into data distribution and central tendencies.

#### 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`:

```bash
pip install sheetbuddy
```
# or

```bash
pip install sheetbuddy==2.0.0
```

## Check for the lastest version


```bash
pip install sheetbuddy --upgrade
```
### Usage

Example 1: Generating an  EDA and Datasummary Report from a CSV File.


```python

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.

```python

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.

```python

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 v2.0.0 ! 🚀

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