Natassha Selvaraj’s March 28, 2023, KDnuggets article Automate the Boring Stuff with GPT-4 and Python is a practical tutorial comparing GPT-3.5- and GPT-4-generated Python examples. It covers charting a diabetes dataset, extracting PDF text, and sending email. It is not a GPT-4 edition of the book Automate the Boring Stuff with Python, nor a current guide to ChatGPT plans or model access.
What the tutorial demonstrates
Selvaraj asks both models to produce code for practical data-science and automation tasks, then describes how the examples behaved. The article is useful as a look at prompting and debugging, not as a controlled comparison proving that one model is generally more accurate.
Visualizing a diabetes dataset
One prompt asks for Python code to visualize independent variables by outcome in a diabetes dataset. The GPT-3.5 response, according to Selvaraj, made an incorrect assumption about a dataframe. The GPT-4 example used a dataframe named df and included plotting setup. The author also notes that the quality of the dataset description affects the result. A generated chart script is only as suitable as its assumptions about the data columns, structure, and plotting environment.
Extracting text from a PDF
The PDF example asks the models to extract text and write it to a text file. Selvaraj reports an encoding error in the initial GPT-3.5 example, then says the code worked after the output encoding was changed to UTF-8. The GPT-4 example specified UTF-8. This is a useful illustration of a fix to try when writing text, but the reported outcome applies to the article’s examples rather than every PDF, operating system, or Python setup.
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Sending an automated email
The email example ran into an authentication problem. Selvaraj discusses an app password after noting that Google’s former “less secure apps” approach was unavailable due to security changes. That advice belongs to the article’s 2023 context: email-provider authentication rules can change, so consult the provider’s current documentation before configuring a script. Do not treat the old approach as current setup instructions.
How to use generated Python code safely
The examples point to a practical workflow: describe the task and inputs precisely, inspect the code, run it in a suitable environment, and use the actual error or output to guide corrections. A plausible-looking answer is not evidence that a script fits your files or will run as written.
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- Specify the input. Name the file or dataframe, describe its structure and relevant columns, and state the desired output. For a chart, clarify which variables should be compared and how the outcome is represented.
- Check assumptions and dependencies. Look for assumed variable names, file paths, libraries, credentials, and output formats. Confirm that they match your data and environment before execution.
- Run a small, inspectable test. Use a copy of the input where practical. Check that the result is sensible, not merely that the script completes without an error.
- Debug from the evidence. If the code fails, provide the exact error message and relevant context, then review any revised code before running it. The PDF example shows how an encoding issue can require a specific adjustment.
- Protect sensitive information. Do not paste passwords, tokens, or private data into a prompt or hard-code credentials in a script. Follow your email provider’s current authentication guidance.
What the GPT-3.5 and GPT-4 comparison can—and cannot—tell you
The article gives a handful of descriptive examples; it does not report a controlled benchmark, measured accuracy rate, or time-saved statistic. Its observations are specific to the prompts and code shown. They can illustrate ways generated code may make incorrect assumptions or need debugging, but they do not establish a general performance ranking between GPT-3.5 and GPT-4.
For a task-specific comparison, judge each answer by whether it matches the data and requested outcome, runs in your environment, makes its dependencies and assumptions clear, handles errors sensibly, and requires a manageable amount of human correction. The tutorial provides examples relevant to those questions, but no systematic measurements across them.
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Is it a current guide to GPT-4 access or Python learning?
No. The page describes product access and interface details from March 2023, including a plan price quoted at that time. Those details should not be used as current pricing or availability guidance. The article’s enduring value is its demonstration of practical prompting and iterative code correction.
If you want to rely on generated scripts, learning Python fundamentals remains useful: it helps you recognize what code is doing, identify risky assumptions, and diagnose failures. Automate the Boring Stuff with Python is a separate book focused on Python fundamentals and practical automation tasks; Suhail Patel describes that learning orientation in an InfoQ podcast transcript. Check the current edition and listing before choosing a copy.
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