# Python in Excel Step by Step > Learn Python in Excel, pandas, data cleaning, DataFrames, and visualisation with David Langer's practical guide. ## Details - **Author:** David Langer - **Publisher:** Wiley - **Language:** English - **Year:** 2026 - **Pages:** 272 - **ISBN:** 9781394340767 - **File Size:** 14.7 MB - **Difficulty:** beginner - **Price:** Free - **URL:** https://www.allcompetitionclasses.com/books/python-in-excel-step-by-step ## Exam Relevance - GATE Computer Science and Information Technology (supplement - GATE Data Science and Artificial Intelligence (supplementary - SSC CGL (indirect digital skills only) - SSC CHSL (indirect digital skills only) - IBPS PO (indirect digital skills only) - IBPS Clerk (indirect digital skills only) - SBI PO (indirect digital skills only) - SBI Clerk (indirect digital skills only) - RRB NTPC (indirect digital skills only) - RRB JE (indirect digital skills only) - RRB Group D (indirect digital skills only) - UPSC Civil Services Examination (indirect analytical skills - State PSC examinations (indirect analytical skills only) - NDA (indirect digital skills only) - CDS (indirect digital skills only) - NEET UG (indirect study-data analysis only) - JEE Main (indirect study-data analysis only) - JEE Advanced (indirect study-data analysis only) ## Subjects & Topics - Python Programming - Python in Excel - Microsoft Excel - Python Data Types - Lists and Dictionaries - Tuples and Sets - Control Flow and Loops - Python Functions - Lambda Expressions - Pandas - DataFrames - Data Loading - Data Cleaning - Data Manipulation - Data Filtering - Combining DataFrames - Pivoting Data - Numeric Data Analysis - String Processing - Datetime Analysis - Data Visualization - Plotnine - Time-Series Visualization - Data Science Fundamentals - Copilot in Excel - Computer Science - Programming - Data Science - Data Analytics - Business and Productivity Software - Educational Technology ## Tags #Python in Excel book #Python in Excel Step by Step David Langer #David Langer Python book #Python Excel tutorial #learn Python in Excel #Python for beginners #Excel data analysis #Python aur Excel kaise sikhe #Excel me Python ka use #Python in Excel Hindi #Python in Excel English book #pandas tutorial for beginners #DataFrame in Python #Python data cleaning #Excel data visualization #plotnine tutorial #Python programming book 2026 #best book to learn Python with Excel #Python for data analytics #data science beginner book #Excel se data analysis #Python loops and functions #Python lists dictionaries tuples sets #Python in Excel PDF #Python Excel notes ## Full Description Want to analyze data in Excel without getting stuck doing repetitive calculations, complicated formulas, or manual data cleaning? Python in Excel Step by Step by David Langer shows you how to combine the familiarity of Microsoft Excel with the analytical power of Python. Published by Wiley in 2026, this practical book helps Excel users develop the programming and data analysis skills needed to work with real-world datasets. You can begin with little or no Python programming experience and gradually progress towards manipulating tables, analyzing information, creating visualizations, and exploring data science workflows. The book is especially useful for students, working professionals, researchers, and aspiring data analysts who want to move beyond conventional spreadsheet techniques. Rather than requiring you to abandon Excel and learn an entirely separate programming environment first, it builds on your existing Excel knowledge. You learn the Python fundamentals that are directly useful for analysing data, followed by techniques for working with structured datasets and creating meaningful visual representations of information. WHAT YOU WILL LEARN Chapter 1 introduces Python in Excel, explains how the feature works, and discusses why it can be useful for spreadsheet-based analysis. You learn how Python can extend Excel's analytical capabilities and how the book's learning approach connects your existing spreadsheet skills with programming. This introduction helps you understand the purpose of Python in Excel before moving into code. Chapter 2 covers Python data types, including integers, floating-point numbers, strings, and Boolean values. These concepts are essential for understanding how Python represents numbers, text, and logical conditions. Learning to recognise and work with different data types helps prevent common errors when analyzing information or performing calculations. Chapter 3 explores Python data structures, including lists, dictionaries, tuples, and sets. It also introduces slicing, which allows you to select portions of a sequence. These concepts provide the foundation for organising, accessing, and manipulating collections of information efficiently. Chapter 4 explains control flow and loops. You learn about if/else statements, for loops, while loops, and comprehensions. These tools allow programs to make decisions, repeat operations, and process multiple values without requiring you to perform every step manually. They are particularly useful when working with datasets that contain many records. Chapter 5 introduces functions and lambda expressions. Functions help organise reusable operations into manageable units, while lambda expressions provide a compact way to define certain operations. Understanding these concepts makes your code easier to structure and helps you build more flexible data analysis workflows. Chapter 6 introduces data table fundamentals through the pandas library. You learn how to load data and explore DataFrames, which are structured tables used extensively in Python-based data analysis. This chapter establishes the foundation for examining spreadsheet data programmatically and understanding how Python represents rows, columns, and datasets. Chapter 7 focuses on working with columns. Its topics include exploring columns and handling numeric, string, and datetime data. These skills are useful when cleaning inconsistent values, examining dates, processing text, and preparing information for further analysis. They also help you understand how different types of information behave within a DataFrame. Chapter 8 develops your ability to work with complete data tables. Using an AdventureWorks data analysis example, the chapter covers changing DataFrames, filtering records, combining DataFrames, and pivoting data. These techniques help you select relevant information, bring datasets together, reshape tables, and prepare data for answering practical business questions. Chapter 9 introduces data visualisation using plotnine. The chapter covers categorical visualisations and time-series visualisations, helping you turn tabular information into charts that are easier to interpret. Visualisation is useful for recognising patterns, comparing groups, examining changes over time, and communicating findings to other people. Chapter 10 concludes with a do-it-yourself data science roadmap. It encourages you to continue developing your skills, outlines a path for further learning, and introduces AI with Copilot in Excel. This final chapter connects the skills learned throughout the book with possible next steps towards more independent data analysis and data science work. Together, these ten chapters provide a learning path from basic Python concepts to practical data manipulation and visualisation inside Excel. The focus is on useful programming foundations, pandas-based table analysis, plotnine charts, and continued learning rather than an exhaustive survey of every --- - **URL:** https://www.allcompetitionclasses.com/books/python-in-excel-step-by-step [Download / View Book](https://www.allcompetitionclasses.com/books/python-in-excel-step-by-step) [Browse All Books](https://www.allcompetitionclasses.com/books)