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Getting Started with PlotSense

This guide will help you get up and running with PlotSense in just a few minutes.

Prerequisites

Before you begin, make sure you have:

  • Python 3.7 or higher
  • pandas library installed
  • A Groq API key (required for AI features)

Step 1: Installation

Install PlotSense using pip:

pip install plotsense

Step 2: Set Up Your API Key

PlotSense requires a Groq API key for its AI-powered features. You can obtain a free API key from Groq's website.

Setting Up Your API Key

You can configure your API key in several ways:

  1. Environment Variable (Recommended):

    export GROQ_API_KEY="your-api-key-here"

  2. In Your Python Code:

    import os
os.environ["GROQ_API_KEY"] = "your-api-key-here"

Step 3: Your First PlotSense Visualization

Let's create your first AI-powered visualization:

from plotsense import recommender
from plotsense import plotgen
from plotsense import explainer
import pandas as pd

# Create sample data
data = {
    'x': [1, 2, 3, 4, 5],
    'y': [2, 5, 3, 8, 7],
    'category': ['A', 'B', 'A', 'B', 'A']
}
df = pd.DataFrame(data)

# Step 1: Get AI recommendations
print("Getting AI recommendations...")
suggestions = recommender(df)
print(f"Found {len(suggestions)} visualization suggestions")

# Step 2: Generate a plot from the first suggestion
print("Generating plot...")
plot = plotgen(df, suggestions.iloc[0])

# Step 3: Get AI explanation
print("Getting AI explanation...")
explanation = explainer(plot)
print(f"Explanation: {explanation}")

Understanding the Core Workflow

PlotSense follows a simple three-step workflow:

1. Recommender (recommender())

  • Analyzes your dataset structure
  • Returns up to 10 visualization suggestions
  • Each suggestion includes plot type and recommended variables

2. Plot Generator (plotgen())

  • Takes your DataFrame and a recommendation
  • Generates the actual visualization
  • Returns a plot object you can display or save

3. Explainer (explainer())

  • Analyzes your generated plot
  • Provides natural language insights
  • Helps you understand patterns in your data

Working with Real Data

Here's how to use PlotSense with your own datasets:

from plotsense import recommender
from plotsense import plotgen
from plotsense import explainer
import pandas as pd

# Load your data
df = pd.read_csv("your_dataset.csv")

# Get recommendations
suggestions = recommender(df)

# Explore different suggestions
for i, suggestion in suggestions.iterrows():
    print(f"Suggestion {i+1}: {suggestion['plot_type']} - {suggestion['description']}")

# Generate your preferred plot
selected_suggestion = suggestions.iloc[0]  # or choose any index
plot = plotgen(df, selected_suggestion)

# Get insights
explanation = explainer(plot)
print(explanation)

Next Steps

Now that you have PlotSense working, explore:

Tips for Better Results

  1. Clean Data: Ensure your data is properly formatted and doesn't have missing values in key columns
  2. Meaningful Column Names: Use descriptive column names to help the AI understand your data
  3. Appropriate Data Size: PlotSense works best with datasets that have between 10-10,000 rows
  4. Mixed Data Types: Include both numerical and categorical columns for more visualization options