Data Analytics to Streamlit

This tutorial demonstrates how you can quickly analyze CSV data and create interactive visualizations using the Alph Editor. You’ll learn how to upload a dataset, ask Agent Alph for insights, and generate a Streamlit application—all within minutes.

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Prerequisites

• Access to the Alph Editor
• A CSV dataset for analysis

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Step 1: Uploading Your CSV Dataset

The Alph Editor makes it easy to import your data:

1. Open the Alph Editor workspace

2. Simply drag and drop your CSV file into the editor’s file panel

   Or alternatively:

   Use the file upload button in the top navigation bar

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Step 2: Asking Agent Alph for Initial Analysis

Once your CSV file is uploaded, you can immediately start interacting with Agent Alph to analyze your data:

1. In the chat panel, ask:

   Please analyze my CSV file and give me a summary of the dataset including basic statistics, missing values, and key observations.
   

2. Agent Alph will instantly provide an analysis that includes:

   • Dataset shape and structure
   • Summary statistics
   • Missing value assessment
   • Initial observations about patterns in the data

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Step 3: Requesting Visualizations

To better understand your data, ask Agent Alph to create visualizations:

1. In the chat panel, ask:

   Can you create some visualizations to help me understand the patterns in this data?
   

2. Agent Alph will generate code to create these visualizations and execute it for you, displaying the results in the chat panel

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Step 4: Generating a Streamlit App

Now, let’s create an interactive dashboard with Streamlit:

1. In the chat panel, ask:

   Please create a Streamlit app that loads this CSV file and provides interactive visualizations and a simple prediction model. Make it run on port 5000.
   

2. Agent Alph will generate a complete app.py file with the Streamlit dashboard code:

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Step 5: Running Your Streamlit App

To run the generated Streamlit app:

1. In the chat panel, ask:

   How do I run this Streamlit app?
   

2. Agent Alph will provide instructions, which typically include:

   pip install streamlit pandas matplotlib seaborn scikit-learn
   streamlit run app.py --server.port 5000
   

3. Execute these commands in the Alph Editor’s integrated terminal

4. Use the port forwarding feature to access your Streamlit app:

   • Go to the Port Forward tab in the bottom panel
   • Enter port 5000
   • Click “Add Port”
   • Open the provided URL in your browser

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Step 6: Refining Your Analysis

Need to adjust your analysis or dashboard? Simply ask Agent Alph:

1. In the chat panel, request specific changes:

   Can you modify the Streamlit app to include a feature importance plot and allow users to input custom values for prediction?
   

2. Agent Alph will suggest the necessary code changes and help you implement them

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Conclusion

You’ve now seen how easy it is to perform end-to-end data analysis with the Alph Editor:

1. Upload your CSV data with a simple drag and drop
2. Ask Agent Alph for immediate insights and visualizations
3. Generate an interactive Streamlit dashboard with a single prompt
4. Run and access your dashboard using port forwarding
5. Refine your analysis iteratively through natural conversation

This workflow eliminates the traditional complexity of data analysis setup, allowing you to focus on deriving insights and making decisions based on your data.

Try it with your own datasets to experience how Agent Alph can transform your data analysis workflow!