Most beginners treat implied volatility as one number (you need the surface instead)

You’re not alone if you pull an options chain, plot a single expiration, then wonder why the next expiry “looks wrong.”

That process makes it hard to understand what's happening in the market.

Liquid underlyings almost never produce a flat implied volatility curve across strikes and expirations.

Because of this, single-slice charts hide the shape that explains pricing and hedge behavior.

On real desks, people reshape the chain into a strike-by-maturity grid early because it surfaces issues fast and prevents false conclusions.

If your goal is to design, price, backtest, and automate options strategies in Python, you need a repeatable way to turn raw chains into a clean research object.

Once you can build that grid quickly, you can compare days-to-expiration consistently and test ideas without manual expiry switching.

The easiest way to do it is to build an implied volatility surface.

Here's a Python example you can use now.

Assumes you already have a pandas DataFrame named `calls` with columns daysToExpiration, strike, and impliedVolatility. You can use yFinance to get this data.

This produces an implied volatility surface grid where rows represent strikes and columns represent days to expiration.

You can pass surface.values into a 3D plot, but the bigger win is earlier in your workflow because the grid makes missing strikes, broken quotes, and inconsistent expirations obvious.

That helps you avoid the common beginner trap where one bad implied volatility point dominates your chart.

Use the printed ranges to confirm you have sensible strikes and maturities before you interpret anything.

Once this step becomes routine, you can track how the surface moves after events and tie those moves to strategy behavior.

Learn to use Python for options trading.

​Getting Started With Python for Quant Finance helps you take this from a one-off notebook into a repeatable research workflow that you can trust.

Students use it to replace ad hoc option-chain poking with a structured process that leads to testable options strategies and cleaner backtests, so they spend time on research instead of debugging data shapes.

Inside Getting Started With Python for Quant Fianance

• Step-by-step frameworks help you turn option quotes into a consistent dataset you can reuse, so each new strategy starts from a known-good workflow.
• 40 code templates give you production-style starting points for options pricing, surface work, and backtest-ready features.
• Real-time answers keep you moving when your chain filters, pivots, or joins produce confusing edge cases.

You end up with a reliable pipeline from raw data to analysis to strategy testing.

More than 1,700+ students are seeing results.

Your next steps

Start by standardizing your environment and data pulls so you can reproduce results on a fresh machine.

Then build a small set of reusable functions that reshape option chains into grids and generate the same sanity checks every time.

After that, plug those surfaces into option signals and backtests, and you’ll reach the point where each research idea ends with a measured result instead of a messy chart.

​Build options workflows you can trust 👉​

Learn how to analyze, validate, and automate options research in Python with Getting Started With Python for Quant Finance.

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Jason Strimpel, founder of PyQuant News Say 👋 on X/Twitter, LinkedIn, or book a call with me. You can always reply to these emails. I check them all