How I use Python to go beyond basic analysis

How I use Python to go beyond basic analysis

When I first started learning Python, I thought it would become my main tool for data analysis.

In reality, that didn’t happen.

I still use Excel, SQL, and Power BI much more often.

But over time, I realised that Python has a very specific role in my workflow.

I don’t use it for everything.

I use it when other tools stop being enough.

When Excel starts to feel limiting

Excel is great for exploring data and building an initial understanding.

But there are situations where it becomes difficult to manage:

• larger datasets

• repeated calculations

• more complex logic

• scenario testing

  
  

This is usually the point where I move to Python.

When I need to test “what if” scenarios

One of the things I find most useful in Python is the ability to simulate different scenarios.

Instead of just analysing what already happened, I can test:

• what happens if we change a key variable

• how different assumptions affect outcomes

• where the tipping points are

  
  

This was particularly useful in my discount strategy project, where I tested how different discount limits impact overall profitability.

When I need more control over the analysis

Python gives me flexibility that is hard to achieve in Excel.

I can:

• structure the analysis step by step

• reuse logic across the project

• make the process reproducible

• keep everything transparent and easy to follow

  
  

This makes it easier to trust the results.

When statistical methods are needed

For more advanced analysis, Python allows me to apply statistical techniques that go beyond basic aggregation.

For example:

• regression analysis

• correlation analysis

• distribution analysis

  
  

These methods help me understand relationships between variables, not just describe them.

Why I don’t use Python for everything

Even though Python is powerful, it is not always the best tool.

For example:

• quick exploration is often faster in Excel

• data extraction is easier in SQL

• communicating results is more effective in Power BI or Tableau

  
  

For me, Python is not a replacement for other tools.

It is a complement.

Final thought

Python became much more useful to me when I stopped trying to use it everywhere.

Now I use it in specific situations:

when I need more flexibility, more control, or deeper analysis.

And that’s when it adds real value.