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.
- 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)
We are excited to announce the release of SheetBuddy v2.1.0, which brings several new features and enhancements to improve your data analysis experience:
-
Expanded Column Descriptions 📝:
- Added a comprehensive dictionary for column descriptions.
-
Enhanced Data Ingestion 🚀:
- Improved methods for reading CSV, JSON, and API data.
- Better error handling for smoother data ingestion.
-
Advanced Data Summarization 📊:
- New
get_column_info
method for detailed column information. - Enhanced summary statistics for all data types.
- Methods to calculate null values, percentages, standard deviation, unique values, and most frequent values.
-
get_basic_math
method for basic calculations (mean, median, mode, range).
- New
-
Improved Excel Formatting and Styling ✨:
- Consistent formatting and styling for all Excel sheets.
- New methods for conditional formatting and adding text headings.
-
Visualization Enhancements 📈:
- New methods for histograms, correlation heatmaps, and bar charts in Excel sheets.
-
Comprehensive Dataset Info Sheet 🗂️:
- Summary sheet with dataset name, format, rows, columns, description, and data link.
-
Robust Report Generation 📝:
- Comprehensive EDA report with multiple detailed sheets.
- Improved progress indicators and logging.
You can install SheetBuddy using pip
:
pip install sheetbuddy
pip install sheetbuddy==2.1.0
pip install sheetbuddy --upgrade
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)
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.
Contributions are welcome! If you have any suggestions, bug reports, or feature requests, please open an issue or submit a pull request on GitHub.
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 v2.1.0! 🚀