semvec/pypi-packages huggingface dataset:
mongodbautomation-ravi 0.0.5
A python package for connecting with database.
License: MIT
Requires Python: >=3.9

# requirements_dev.txt we use for the testing
It makes it easier to install and manage dependencies for development and testing, separate from the dependencies required for production.

# difference between requirements_dev.txt and requirements.txt

requirements.txt is used to specify the dependencies required to run the production code of a Python project, while requirements_dev.txt is used to specify the dependencies required for development and testing purposes.

# tox.ini
We use if for the testing in the python package testing against different version of the python 

## how tox works tox enviornment creation
1. Install depedencies and packages 
2. Run commands
3. Its a combination of the (virtualenvwrapper and makefile)
4. It creates a .tox


# pyproject.toml
it is being used for configuration the python project it is a alternative of the setup.cfg file. its containts configuration related to the build system
such as the build tool used package name version author license and dependencies

# setup.cfg
In summary, setup.cfg is used by setuptools to configure the packaging and installation of a Python projec

# Testing python application
*types of testing*
1. Automated testing 
2. Manual testing

*Mode of testing*
1. Unit testing
2. Integration tests

*Testing frameworks*

1. pytest
2. unittest
3. robotframework
4. selenium
5. behave
6. doctest

# check with the code style formatting and syntax(coding standard)

1. pylint
2. flake8(it is best because it containt 3 library pylint pycodestyle mccabe)
3. pycodestyle
acs-explorer 0.1.9
A python package that helps user to search for and analyze ACS data.
License: MIT
Requires Python: <4.0,>=3.12

# ACS Explorer: Exploring the Digital Divide

The **acs_explorer** is a Python-based tool designed to simplify interaction with the U.S. Census Bureau's American Community Survey (ACS) data. This project provides policymakers, researchers, and academics with an intuitive way to explore variables, analyze trends, and generate meaningful insights.

Key features include:

- Topic Search: Search and shortlist variables related to your topic of interest (e.g., internet access, education levels) across multiple datasets and years.
- Data Retrieval: Fetch detailed Census data for specific variables, geographic levels (state, county, or tract), and time periods.
- Trend Analysis: Visualize and analyze temporal trends to uncover meaningful patterns.
- Geographic Integration: Use geocoding to retrieve Census information for specific addresses.
- Custom Reports: Generate PDF or HTML reports summarizing data insights and trends.

This project focuses on digital policy indicators and addresses gaps in analyzing the digital divide across regions in the U.S., making it a unique and valuable tool for research and policymaking.

## Installation

```bash
$ pip install acs_explorer
```

## Usage

- TODO

## Contributing

Interested in contributing? Check out the contributing guidelines. Please note that this project is released with a Code of Conduct. By contributing to this project, you agree to abide by its terms.

## License

`acs_explorer` was created by Yiqi Ling. It is licensed under the terms of the MIT license.

## Credits

`acs_explorer` was created with [`cookiecutter`](https://cookiecutter.readthedocs.io/en/latest/) and the `py-pkgs-cookiecutter` [template](https://github.com/py-pkgs/py-pkgs-cookiecutter).