Merge pull request #42 from UBC-MDS/add_coverage_report

feat(badge): Add badges and test coverage token; update readme

README.md

@@ -1,4 +1,8 @@
+[![ci-cd](https://github.com/UBC-MDS/pyeda/actions/workflows/ci-cd.yml/badge.svg)](https://github.com/UBC-MDS/pyeda/actions/workflows/ci-cd.yml)
 [![Documentation Status](https://readthedocs.org/projects/pyeda31/badge/?version=latest)](https://pyeda31.readthedocs.io/en/latest/?badge=latest)
+[![Project Status: Active](https://www.repostatus.org/badges/latest/active.svg)](https://www.repostatus.org/)
+![PyPI - Python Version](https://img.shields.io/pypi/pyversions/pyeda31)
+![PyPI - Version](https://img.shields.io/pypi/v/pyeda31)
 
 # pyeda31
 
@@ -18,7 +22,7 @@ Catherine Meng, Jessie Zhang, Zheng He
     Generate summary statistics for specified columns or all columns if none are provided.
 
 ## Contributing to the Python Ecosystem
-The `pyeda` package complements the Python ecosystem by providing simple and efficient tools for users to implement quick EDA in the first step of their analysis. While there are some other similar Python packages such as [Sweetviz](https://pypi.org/project/sweetviz/) (in-depth EDA with a focus on visualization) and [perform-eda](https://pypi.org/project/perform-eda/) (providing comprehensive EDA analysis), these tools can be too heavyweight for quick analysis. Instead, our `pyeda` package aims for simplicity and efficiency, enabling users to quickly complete the most basic and important steps, including validating dataset formats, checking for missing values, and generating statistical summaries for columns of interest. It is a lightweight alternative for small-scale tasks or the initial understanding of the dataset for in_depth research. Users can also combine `pyeda` with other visualization packages for deeper insights.
+The `pyeda31` package complements the Python ecosystem by providing simple and efficient tools for users to implement quick EDA in the first step of their analysis. While there are some other similar Python packages such as [Sweetviz](https://pypi.org/project/sweetviz/) (in-depth EDA with a focus on visualization) and [perform-eda](https://pypi.org/project/perform-eda/) (providing comprehensive EDA analysis), these tools can be too heavyweight for quick analysis. Instead, our `pyeda31` package aims for simplicity and efficiency, enabling users to quickly complete the most basic and important steps, including validating dataset formats, checking for missing values, and generating statistical summaries for columns of interest. It is a lightweight alternative for small-scale tasks or the initial understanding of the dataset for in_depth research. Users can also combine `pyeda31` with other visualization packages for deeper insights.
 
 ## Installation
 
@@ -28,7 +32,7 @@ $ pip install pyeda31
 
 ## Usage
 
-`pyeda` can be used to verify the format of data files and perform basic exploratory data analysis as follows:
+`pyeda31` can be used to verify the format of data files and perform basic exploratory data analysis as follows:
 ```python
 from pyeda31.check_csv import check_csv
 from pyeda31.pymissing_values_summary import missing_values_summary
@@ -60,8 +64,8 @@ Interested in contributing? Check out the contributing guidelines. Please note t
 
 ## License
 
-`pyeda` was created by Catherine Meng, Jessie Zhang, Zheng He. It is licensed under the terms of the MIT license.
+`pyeda31` was created by Catherine Meng, Jessie Zhang, Zheng He. It is licensed under the terms of the MIT license.
 
 ## Credits
 
-`pyeda` was created with [`cookiecutter`](https://cookiecutter.readthedocs.io/en/latest/) and the `py-pkgs-cookiecutter` [template](https://github.com/py-pkgs/py-pkgs-cookiecutter).
+`pyeda31` was created with [`cookiecutter`](https://cookiecutter.readthedocs.io/en/latest/) and the `py-pkgs-cookiecutter` [template](https://github.com/py-pkgs/py-pkgs-cookiecutter).