Jefferson County Animal Services Intake Data Analysis

Overview:

The project analyses Jefferson County Animal Services intakes based on indate by focusing on the years surrounding March 2020, when Covid-19 was detected in the U.S. The indate indicates the date the animal enters the Jefferson County Animal Services system. The years for the visualization graph are 2019, 2020, and 2021.

Question:

Did the animal intakes in 2020 reflect the upsurge of animal adoptions during Covid-19?

Data Source:

https://data.louisvilleky.gov/dataset/animal-service-intake-and-outcome Disclaimer The data source continues to update its information often, if data is retrieved directly from the source it will not match with the indate date used for this project. You can still use the project and update the data as needed. This project was intended for educational purpose only and new update in the future are expected. That will include a visual with indate and outdate numbers.

This project contains the following, as expectations for a passing status:

Uploaded your GitHub repository:

Via Git and with a minimum of 5 separate commits https://github.com/Vicky-Kaeser/cats-dogs-

Special Instructions:

Follow each link to install Git and/or Git Hub on your local computer https://github.com/git-guides/install-git https://desktop.github.com/

To clone this repo into your local computer:

Open Git(Bash) and run the following git clone https://github.com/Vicky-Kaeser/cats-dogs-

Include a README.md files:

This link is a guide to markdown https://guides.github.com/features/mastering-markdown/

The README.md must include:

An overview of what the project is about

Packages installations to run the project:

requirements.txt file contain the following:

notebook

pandas

numpy

matplotlib

The local computer contains Python version 3.9.7 in this project https://www.python.org/downloads/ also, Windows11 version 21H2 and Anaconda https://docs.anaconda.com/anaconda/install/windows/

3+ features/categories to meet passing status:

Category 1:

Create a list Populate the list with several values, retrieve at least one value, and use it in your program

Category 2:

Utilize External Data Read data from an external file, such as CSV

Category 3:

Data Display Visualize data in a graph

Category 4:

Best Practices Utilize a virtual environment and a requirements.txt file(to document library dependencies)

"Stretch" feature/categories 5:

Use a Jupyter notebook to document your data analysis

Any special instructions required to run the project

Python environments https://docs.python.org/3/tutorial/venv.html

On macOS or Unix

Create a virtual environment: python3 -m venv env

Activate virtual environment: source env/bin/activate

Install requirements: pip install -r requirements.txt

Run the following code to start the program: python3 viewing.py

On Windows

Create a virtual environment: py -m venv env

Activate virtual environment: env\Scripts\activate

Install requirements: py -m pip install -r requirements.txt

Run the following code to start the program: py viewing.py

Anaconda environment.yml https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html