Creating Speedometer Charts Using CSV Files and Python with Plotly Library

Here's a post with detailed installation instructions, including how to set up the environment and install the necessary libraries:

If you're looking to visualize KVus data using a gauge chart, follow these steps to set up the KVus Gauge Visualization project on your local machine.

Prerequisites

Make sure you have Python 3 installed. You can check your Python version by running:

python --version

If Python is not installed, download it from the official website.

Step 1: Clone the Repository

First, you need to clone the repository to your local machine. Open your terminal and run:

git clone https://github.com/your-username/your-repository.git
cd your-repository

Step 2: Set Up a Virtual Environment (Optional)

To avoid conflicts between dependencies, it's a good idea to create a virtual environment. You can do this with the following commands:

python -m venv venv
source venv/bin/activate  # On Windows, use `venv\Scripts\activate`

Step 3: Install Required Libraries

Next, install the necessary libraries using pip. Run the following command:

pip install pandas plotly matplotlib

Step 4: Prepare Your Data

Ensure you have a CSV file named dados.csv in the data directory. This file should contain the appropriate columns for Data and Kvus.

Step 5: Run the Script

Once everything is set up, you can run the script to generate the gauge chart:

python your_script.py

Make sure to replace your_script.py with the name of your Python script.

Step 6: View the Result

The gauge chart will be displayed in your web browser. You can interact with it to visualize the KVus data over time.

Conclusion

Following these steps will help you set up the KVus Gauge Visualization project easily. If you have any questions or run into issues, feel free to reach out!

Feel free to modify any sections to better suit your style or add any additional details!

Repo Github to download