Python in Excel for Beginners: From Zero to Insight

Name: Python in Excel for Beginners: From Zero to Insight
Rating: 4.3 (7 reviews)

Learn Python essentials, apply a 4-step framework to a case study. Practical, concise, and workflow-first. 100% in Excel

Created byCedric Dezitter

Last updated 12/2025

English

What you'll learn

Describe how Python is integrated into Excel and navigate the interface to work with Python
Write and execute basic Python code directly in Excel to perform simple programming tasks
Use the pandas library to clean, filter, and transform Excel data for analysis
Automate routine Excel tasks with Python to increase efficiency and accuracy
Apply Python skills to solve real-world business problems, such as gperforming data analysis within Excel

Course content

8 sections • 44 lectures • 1h 39m total length

Get to Know Your Instructor1:28
Students will be able to understand their instructor background and teaching style.
Course Introduction3:12
Students will know the course structure and content.

Overview of Python Integration2:45
Students will be able to describe how Python runs inside Excel.
The PY() Function2:56
Students will be able to run simple Python statements with =PY().
Output Data & the Python Card5:45
Students will be able to interpret Python card results and choose between keeping Python objects or inserting Excel values.
The Python Group on the Excel Ribbon2:20
Students will be able to navigate Insert Python, Editor, Refresh/Reset, and Initialization to control the Python environment.
The Python Editor in Excel2:28
Students will be able to write, run, and manage multi-line scripts and add comments for maintainability.
Referencing Excel Data in Python4:31
Students will be able to pull ranges, tables, and named ranges with xl() and handle headers and object types.
Introduction to Python in Excel
Cheatsheet0:03

Python & Pandas Mini-Toolkit4:02
Students will be able to recognize variables, core types, lists/dicts/tuples/sets, and basic Pandas objects used in the course.
Explore Your Data3:48
Students will be able to profile datasets with .head(), .shape, .columns, .info(), and .describe() to spot issues early.
Explore Your Data
Make Core Transformations3:06
Students will be able to rename columns, standardize strings, convert types, handle missing values, and remove duplicates.
Make Core Transformations
Make Advanced Transformations & Calculations2:37
Students will be able to create/update columns, perform lookups with .map(), extract date parts, and tidy fields for analysis.
Make Advanced Transformations & Calculations
Provide Insights3:04
Students will be able to summarize data with groupby, totals, and simple tables that answer concrete business questions.
Provide Insights
Cheatsheet0:03

Discover the Case Study1:22
Students will get some information on the available data.
Load Data into Python in Excel1:16
Students will be able to import data using xl(), and validate initial shapes and types.
df.shape()0:47
Students will be able to confirm row/column counts and decide next steps for cleaning or feature creation.
df.describe()1:31
Students will be able to summarize numeric columns and spot outliers, zeros, and suspicious ranges.
df.columns0:35
Students will be able to review column names, detect typos, and plan consistent renames.
df.head()0:29
Students will be able to preview sample records to sanity-check parsing, delimiters, and casing.
df.info()0:50
Students will be able to inspect dtypes and null counts to plan type fixes and missing-data handling.
Reflection on the first step of the Data-Work Framework1:01

Rename Columns2:38
Students will be able to apply a rename map for readable, consistent headers that support joins and grouping.
Fix Data Types3:17
Students will be able to convert text to numbers and dates with pd.to_numeric() and pd.to_datetime() safely.
Drop Duplicates3:19
Students will be able to remove duplicate records using key subsets while preserving the first valid row.
Standardize the Account_Manager Column3:38
Students will be able to clean spacing/casing and correct known name variants to a single standard.
Standardize the Customer_Type Column1:49
Students will be able to trim spaces and unify categories (e.g., Private/Pro) for reliable grouping.
Standardize the Country Column3:24
Students will be able to normalize country labels and aliases (e.g., UK → United Kingdom) consistently.
Standardize the Currency Column3:10
Students will be able to fill missing currency via country lookups and validate against known codes.
Reflection on the second step of the Data-Work Framework0:54

Create the Sales_EUR Column2:44
Students will be able to convert local currency to EUR using mapped rates and produce an integer/decimal output.
Create Month and Year Columns1:57
Students will be able to extract month and year from dates for time-based analysis.
Drop Unneeded Columns1:50
Students will be able to remove helper or irrelevant fields to keep a lean, analysis-ready dataset.
Run Final Checks2:04
Students will be able to re-profile the cleaned table and confirm dtypes, nulls, and duplicates are resolved.
Reflection on the third step of the Data-Work Framework1:05

How Many New Customers Have We Contracted in 2025 to Date?1:33
Students will be able to count acquisitions for a given year using filters or groupby().size().get().
Can We Get a List of the Customers Acquired Last Year?1:38
Students will be able to filter by year and return customer IDs with .loc[...] and .tolist().
What Are Sales YTD by Country and Customer Type?1:19
Students will be able to aggregate totals with groupby(["Country","Customer_Type"])["Sales"].sum().
In Which Month Do We Typically Acquire the Most Customers?1:08
Students will be able to rank acquisition volume by month with groupby().size().sort_values().
Which of the Three Account Managers Is Selling the Most?2:18
Students will be able to sum sales by manager, sort descending, and present the top performer(s).
How Many Active Customers Were Acquired in 2015?5:19
Students will be able to compute the 2015 cohort size and its share of total customers as a percentage.
Reflection on the fourth step of the Data-Work Framework0:41

Requirements

Basic Excel proficiency like formulas, formatting, charts, filters
Understanding of common Excel use cases like reports, data entry, and tables
Familiarity with Excel workbooks, cells, ranges, and how to work with data in Excel
No prior Python experience required: you will learn some Python in this course
Curiosity and an appetite to learn
Microsoft 365 with Python in Excel enabled

Description

Python in Excel for Beginners: From Zero to Insight is a practical, project-driven course for Excel users who want to unlock Python—without leaving Excel. You’ll start in the Python-in-Excel environment: how the =PY() function works, what the Python card shows, how to reference ranges with xl(), and how to use the Ribbon tools and the Editor. You’ll see exactly how Python and Excel talk to each other, and how to convert Python outputs to native Excel cells.

Next, you’ll get a focused crash course in just enough Python and pandas to be productive: variables and core types, DataFrames and Series, quick inspection (.head(), .shape, .describe(), .info()), and fixes for real-world issues (casing, spaces, types, missing values, duplicates).

Finally, everything comes together in a guided case study. You’ll clean a messy sales extract, standardize text and dates, convert currencies, drop duplicates, create helpful columns, and answer real business questions.

By the end, you’ll be able to clean and analyze data directly inside Excel using Python and pandas—producing repeatable, trustworthy results faster than with formulas alone. No prior Python required; every step is demo-first, with downloadable files and concise, bite-sized lessons you can follow at your own pace—though some familiarity with Python will help you move faster.

Note: This is not a full Python/pandas course; it’s a practical toolkit for Excel workflows.

Who this course is for:

Excel power users who would like to explore Python
Professionals looking at streamlining data cleaning and automate repetitive calculations
Professionals looking to filter, reshape, and visualize data without leaving Excel
Anyone curious to understand how Python and Excel work together
Anyone willing to transition to more data-driven workflows (especially in finance, operations, or BI roles)
Anyone willing to discover Python without having to setup an environment

Python in Excel for Beginners: From Zero to Insight

What you'll learn

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Course content

Introduction2 lectures • 5min

Introduction to Python in Excel7 lectures • 21min

Python and Pandas Essentials (for Excel)6 lectures • 17min

Case Study - Explore Your Data8 lectures • 8min

Case Study - Make Core Transformations8 lectures • 22min

Case Study - Make Advanced Transformations & Calculations5 lectures • 10min

Case Study - Provide Insights7 lectures • 14min

Conclusion1 lecture • 4min

Requirements

Description

Who this course is for: