Practical Examples of Using Python in Excel (For Everyday Work)

Excel users often reach a point where formulas start feeling heavy. Data cleaning needs multiple helper columns. Logic becomes hard to read. Repeating the same analysis every month wastes time. Python in Excel is useful only at this point. It lets you run small Python scripts directly inside Excel to handle tasks that are slow, repetitive, or messy with formulas alone. You don’t need to learn Python in depth — you use it only where Excel struggles. Below are practical, step-by-step examples showing how to actually use Python in Excel for everyday work.

Practical Examples of Using Python in Excel

How to Use Python in Excel (One-Time Setup)

Open Microsoft Excel
Select a cell
Go to Insert ? Python
Paste the Python code
Run it

The output appears inside Excel as a table or value.

1. Cleaning Messy Data Automatically

The Excel problem

Text data often has extra spaces, inconsistent casing, or blank cells.

How to do this using Python in Excel

Assume your data is in column A

import pandas as pd

data = xl("A:A")
cleaned = data.dropna()
cleaned = cleaned.apply(lambda x: x.strip().title())

cleaned

What this does

Removes empty cells
Trims extra spaces
Standardises text format

Why this helps

What normally needs multiple formulas is done in one clean step, and the result stays in Excel.

2. Removing Duplicates Cleanly

The Excel problem

Excel removes only exact duplicates. Small differences often remain.

How to do this using Python in Excel

import pandas as pd

data = xl("A:B")
unique_data = data.drop_duplicates()

unique_data

What this does

Keeps only unique rows and removes duplicates without changing the original data.

3. Replacing Long IF Formulas with Clear Logic

The Excel problem

Nested IF formulas are hard to read and maintain.

How to do this using Python in Excel

data = xl("A:B")
data["Category"] = data["Sales"].apply(
    lambda x: "High" if x > 50000 else "Low"
)

data

What this does

Creates a new column using simple, readable logic instead of long formulas.

4. Working with Large Datasets Faster

The Excel problem

Large files slow down recalculation and formulas.

How to do this using Python in Excel

data = xl("A:D")
summary = data.groupby("Region")["Sales"].sum()

summary

What this does

Calculates totals efficiently and returns the result as a clean table in Excel.

5. Repeating the Same Monthly Analysis

The Excel problem

Monthly reports require repeating the same steps again and again.

How to do this using Python in Excel

data = xl("A:D")
report = data.groupby("Product")["Revenue"].mean()

report

What this does

Once written, the same code can be reused every month with new data.

6. Combining Data from Multiple Sheets

The Excel problem

Merging sheets manually is time-consuming and error-prone.

How to do this using Python in Excel

sheet1 = xl("Sheet1!A:D")
sheet2 = xl("Sheet2!A:D")

combined = pd.concat([sheet1, sheet2])

combined

What this does

Combines data from multiple sheets into one clean table.

When You Should Use Python in Excel

Use Python in Excel when:

Formulas become unreadable
Data size increases
Tasks repeat regularly

Do not use it for simple calculations where formulas are faster.

Conclusion

Python in Excel is not about replacing Excel formulas. It is about reducing effort where formulas become painful. Used selectively, it makes Excel work cleaner, faster, and easier to maintain.

You don’t need to use Python everywhere — only where it saves time.

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