Automate Data Analysis and Visualization with ChatGPT and Notable Plugin

Table of Contents

• Introduction
• Installing and Setting Up Notable Plugin
• Generating Basic Data Analysis and Visualization
• Customizing Analysis with Detailed Prompts
• Advanced Analysis with Multiple Datasets
• Conclusion

Introduction to Notable Plugin for Automated Data Analysis

I recently discovered an excellent GPT-3 plugin called Notable that automates data analysis in seconds. This plugin generates complete data analysis reports from datasets with just a simple prompt. Notable can produce quick overviews like summary statistics and visualizations, or highly customized analysis if provided with more details in the prompt.

In this post, we'll explore how to use Notable with ChatGPT to automatically generate data analysis code and visualizations for any dataset. We'll walk through examples of using Notable for a basic population demographic analysis, custom player analysis for FIFA video game datasets, and even multi-dataset analysis over time.

Overview of Notable Plugin Capabilities

Notable allows users to get an automated data analysis report that can include:

• Summary statistics of datasets
• Customized data visualizations like plots, charts, graphs
• Underlying Python code for analysis in Jupyter notebook
• Ability to analyze multiple datasets
• Analysis can be generic or customized with additional prompt details

Benefits of Automating Data Analysis with Notable

Automating data analysis workflow with Notable provides several benefits:

• Fast way to get overview understanding of any dataset
• Allows focus on insights rather than coding
• Great for accelerating machine learning model iterations
• Customizable to get specific visualizations as needed
• Saves time on exploratory analysis phase

Installing and Setting Up Notable Plugin

In order to use Notable, you first need access to ChatGPT Plus plan. Then go to the Plugin section and install Notable. After installing, you'll need to connect your Notable account, which is where generated analysis code notebooks will be saved.

The key steps are:

1. Install Notable plugin in ChatGPT

2. Create Notable account when prompted

3. Create Notable project for saving notebooks

4. Copy project URL to use in ChatGPT prompts

How to Install Notable Plugin

Follow these steps to install Notable:

1. Go to ChatGPT site and ensure you are subscribed to Plus plan
2. Click on "No Plugins Enabled" button
3. Go to Plugin Store and search for Notable
4. Click on Install for Notable plugin

Linking Notable Project for Analysis

Once Notable account is connected, you need to link a Notable project for the analysis:

1. In Notable account, create a new project
2. Give project a name like "Automated Analysis"
3. Copy the project URL from Notable
4. Paste this project URL in your ChatGPT prompts

Generating Basic Data Analysis and Visualization

Let's go through an example of using Notable on a population demographics dataset to automatically generate some basic analysis and visualizations.

Using Sample Population Dataset

For the first example, we'll use the following publically available population dataset: https://github.com/datasets/population/blob/master/data/population.csv This dataset provides population statistics for countries from 1950 to 2020. We want to analyze high level worldwide population growth trends.

Reviewing Automatically Generated Analysis

With just the basic prompt details of:

• Linking population dataset
• Linking Notable project
• "Analyze data and make charts of population trends" Here is what Notable automatically generated:
• Overview of dataset columns
• Sample table preview rows
• Line chart of top 5 most populous countries
• High level observations of population growth This took seconds to produce without any coding! The full Python code for the analysis is available in the linked Notable project notebook.

Customizing Analysis with Detailed Prompts

Now let's see how we can customize the analysis by providing more details in our prompt. We'll use the FIFA video game player datasets.

By specifying the required plots, restricted country set, and variables to analyze, Notable creates a tailored analysis report.

Using FIFA Player Dataset

For this example, we will use the FIFA 20 player dataset: https://www.kaggle.com/stefanoleone992/fifa-20-complete-player-dataset This dataset provides attributes like ratings, salary, height, weight, etc. for soccer video game players.

Specifying Required Visualizations

In our prompt, we provide the following custom analysis details:

• Only analyze players from USA, Canada, England, Brazil, Argentina
• Create bar plot for average 'Overall' player rating by country
• Generate histogram and box plots for height data
• Scatter plot for weight distribution per country
• Pie chart for top 10 highest paid USA players

Reviewing Customized Analysis and Plots

Based on those specific prompts, Notable generated the exact visualizations requested across the restricted country set:

• Custom rating analysis by country
• Height distribution analysis
• Weight scatter plot
• Top USA player values This is the power of Notable to build tailored analysis simply using a detailed text prompt, without needing to write any custom data exploration code!

Advanced Analysis with Multiple Datasets

One of the most powerful features of Notable is the ability to analyze multiple datasets at once. Let's walk through an example.

Loading Multiple FIFA Datasets

For this example, we will load 5 FIFA player datasets from 2017 to 2021: https://www.kaggle.com/stefanoleone992/fifa-21-complete-player-dataset This will allow us to analyze how key player attributes have evolved over multiple years.

Generating Line Plot for Players Over Time

We want to see how the official player 'Overall' ratings have changed for top players over 5 years, so we specify in the prompt to:

• Load and join the 5 datasets
• Analyze just 5 top players: Messi, Ronaldo, Neymar Jr, Mbappe, Mane
• Generate line plot of Overall ratings per player through time

Reviewing Multi-Dataset Analysis

Notable automatically handles matching players across years, joining the datasets, restricting to just those 5 players, and producing the line plot showing the rating trends over time without needing to write any complex join/analysis code! This demonstrates the power of Notable plugin to instantly perform multi-dataset analytics simply using a text prompt.

Conclusion

The Notable plugin for ChatGPT allows auto-generating data analysis code, plots, and notebooks through a simple text prompt interface.

It handles dataset ingestion, data wrangling, analysis, and visualization automatically based on details provided in prompt.

Notable enables fast, flexible, and fully-customizable automated data analytics without needing to write any code!

It can be used for rapid data exploration, machine learning feature analysis, and even production-level reporting.

FAQ

Q: How do I get started with the Notable plugin?
A: You need to install the Notable plugin from the ChatGPT plugin store. Then create a Notable account and project to link to ChatGPT for generating your analysis notebooks.

Q: What kind of analysis can I automate with Notable?
A: You can generate data visualizations, statistical analysis, and machine learning modeling by providing detailed prompts about the required analysis.

Q: Can I customize the analysis and plots?
A: Yes, you can provide specifics in your prompt about the visualizations, datasets, and parameters you want to analyze. This allows detailed customization.

Q: Does the plugin work for multiple datasets?
A: Absolutely, you can include links to multiple datasets in your prompt andNotable will automatically combine and analyze them based on your specifications.

Q: What skills do I need to use Notable effectively?
A: You'll need basic data analysis and Python coding skills. Notable generates the code, but you need to understand how to refine the analysis and implement in Python yourself.

Q: Can I view and edit the Python code?
A: Yes, Notable provides Jupyter notebooks with all the generated Python code you can view, run, and edit as needed for your analysis.

Q: Is there a limit to how much data Notable can process?
A: There are some practical limits, but it can handle large datasets for visualization and modeling. You may need to sample or filter your data for very large sets.

Q: Does Notable work with other types of files besides CSV?
A: It primarily works with CSV data, but also supports JSON, Excel, Parquet and other tabular data formats. Unstructured data requires more custom handling.

Q: Can I collaborate with others on Notable notebooks?
A: Yes, you can add collaborators to your Notable projects for sharing and editing notebooks.

Q: Are there security concerns with automating data analysis?
A: You should be careful about exposing sensitive data. But the code generation is secure and you retain control over your analysis notebooks.