Python in Excel step-by-step / David Langer.

Format:

Book

Author/Creator:

Langer, David, author.

Language:

English

Subjects (All):

Microsoft Excel (Computer file)--Handbooks, manuals, etc.

Microsoft Excel (Computer file).

Electronic spreadsheets--Computer programs--Handbooks, manuals, etc.

Electronic spreadsheets.

Python (Computer program language)--Handbooks, manuals, etc.

Python (Computer program language).

Genre:

Handbooks and manuals.

Physical Description:

1 online resource (275 pages) : illustrations

Edition:

1st ed.

Place of Publication:

Newark : John Wiley & Sons, Incorporated, [2026]

Summary:

An intuitive guide for professionals wanting to prepare for the future of Microsoft Excel by building Python in Excel skills and unleashing the power of their data.A hands-on guide to the foundational Python in Excel skills you'll need to understand and use this powerful analytics tool, Python in Excel Step-by-Step is for current Excel users.

Contents:

INTRODUCTION

CHAPTER 1: INTRODUCING PYTHON IN EXCEL

1.1 Introducing Python in Excel

1.2 How Python in Excel Works

1.2.1 The Azure Cloud

1.2.2 Security

1.2.3 Scalability

1.3 Why Python in Excel?

1.3.1 Reproducible Analytics

1.3.2 Advanced Data Visualization

1.3.3 Do-it-Yourself (DIY) Data Science

1.3.4 Copilot in Excel

1.4 Continue Your Learning

CHAPTER 2: DATA TYPES

2.1 Integers

2.1.1 What Are Integers?

2.1.2 Working with Integers

2.2 Floats

2.2.1 What Are Floats?

2.2.2 Working with Floats

2.2.3 Casting

2.3 Strings

2.3.1 What Are Strings?

2.3.2 Working with Strings

2.3.3 Formatting Strings

2.4 Booleans

2.4.1 What Are Booleans?

2.4.2 Checking Equivalence

2.4.3 Logical Comparisons

2.4.4 Zeros and Ones

2.4.5 Logical Operators

2.5 Continue Your Learning

CHAPTER 3: DATA STRUCTURES

3.1 Lists

3.1.1 What Are Lists?

3.1.2 Writing Lists

3.1.3 Nesting Lists

3.1.4 Empty Lists

3.1.5 Changing Lists

3.1.6 Accessing Lists

3.2 Dictionaries

3.2.1 What Are Dictionaries?

3.2.2 Writing Dictionaries

3.2.3 Accessing Dictionaries

3.2.4 Working with Keys

3.2.5 Missing Keys

3.2.6 Working with Values

3.2.7 Changing Dictionaries

3.3 Tuples

3.3.1 Writing Tuples

3.3.2 Accessing Tuples

3.3.3 Tuples Are Immutable

3.4 Sets

3.4.1 Writing Sets

3.4.2 Comparing Sets

3.4.3 Changing Sets

3.5 Slicing Data

3.5.1 Indexing

3.5.2 Slicing

3.5.3 Striding

3.6 Continue Your Learning

CHAPTER 4: CONTROL FLOW AND LOOPS

4.1 if/else Statements

4.1.1 Basic if

4.1.2 Adding else

4.1.3 Nesting if/else

4.1.4 elif

4.1.5 Logical Operators

4.1.6 Comparison Operators

4.2 for Loops.

4.2.1 What Are for Loops?

4.2.2 Writing for Loops

4.2.3 Short-circuiting for Loops

4.2.4 Exiting for Loops

4.3 while Loops

4.3.1 Writing while Loops

4.3.2 while Loop Gotchas

4.3.3 Exiting while Loops

4.4 Comprehensions

4.4.1 List Comprehensions

4.4.2 Dictionary Comprehensions

4.5 Continue Your Learning

CHAPTER 5: FUNCTIONS

5.1 Introducing Functions

5.1.1 Defining Functions

5.1.2 Keyword Arguments

5.1.3 Returning Objects

5.1.4 Variable Scope

5.1.5 Why Write Your Own Functions?

5.2 Lambdas

5.2.1 Writing Lambdas

5.2.2 Using Lambdas

5.3 Continue Your Learning

CHAPTER 6: DATA TABLE FUNDAMENTALS

6.1 Introducing Pandas

6.1.1 AdventureWorks Data Analysis

6.1.2 Tables in Microsoft Excel

6.1.3 Tables Are Dataframe Objects

6.1.4 Columns Are Series Objects

6.1.5 Rows Are Series Objects

6.2 Loading Data

6.2.1 Loading Excel Cell Ranges

6.2.2 Loading Excel Tables

6.2.3 Loading from Power Query

6.3 Exploring Dataframes

6.3.1 The info() Method

6.3.2 The head() Method

6.3.3 The tail() Method

6.3.4 The describe() Method

6.3.5 Dataframe Indexes

6.4 The Workbook So Far

6.5 Continue Your Learning

CHAPTER 7: WORKING WITH COLUMNS

7.1 Exploring Columns

7.1.1 Accessing Columns

7.1.2 The info() Method

7.1.3 The head() and tail() Methods

7.1.4 Indexes

7.2 Numeric Columns

7.2.1 The count() Method

7.2.2 The size Attribute

7.2.3 The min() and max() Methods

7.2.4 The sum() Method

7.2.5 The gt() and lt() Methods

7.2.6 The mean() and median() Methods

7.2.7 The std() Method

7.2.8 The describe() Method

7.2.9 The value_counts() Method

7.2.10 The isna() and fillna() Methods

7.3 String Columns

7.3.1 The lower() and upper() Methods

7.3.2 The cat() Method

7.3.3 The isalpha() Method.

7.3.4 The startswith() and endswith() Methods

7.3.5 The contains() Method

7.3.6 The replace() Method

7.3.7 The slice() Method

7.3.8 The split() Method

7.3.9 The len() Method

7.3.10 The value_counts() Method

7.3.11 The isna() and fillna() Methods

7.4 Datetime Columns

7.4.1 Datetime Attributes

7.4.2 The month_name() and day_name() Methods

7.4.3 The is* Attributes

7.4.4 Calculating Elapsed Time

7.5 The Workbook So Far

7.6 Continue Your Learning

CHAPTER 8: WORKING WITH DATA TABLES

8.1 AdventureWorks Data Analysis

8.2 Changing Dataframes

8.2.1 Method Chaining

8.2.2 The assign() Method

8.2.3 Changing Columns with assign ( )

8.2.4 Adding Columns with assign ( )

8.2.5 Data Wrangling with assign ( )

8.2.6 Column Names with Spaces

8.3 Filtering Dataframes

8.3.1 Python Masks

8.3.2 Combining Masks

8.3.3 The isin() Method

8.3.4 The query() Method

8.4 Combining Dataframes

8.4.1 The merge() Method

8.4.2 Left Joins

8.4.3 Inner Joins

8.4.4 Additional Column Handling

8.5 Pivoting Dataframes

8.5.1 Aggregating by One Column

8.5.2 Aggregating by Multiple Columns

8.5.3 The pivot_table() Method

8.6 The Workbook So Far

8.7 Continue Your Learning

CHAPTER 9: DATA VISUALIZATION

9.1 Introducing Plotnine

9.1.1 The Grammar of Graphics

9.1.2 Coding Patterns

9.2 Categorical Visualizations

9.2.1 Initial Data Wrangling

9.2.2 Bar Charts

9.2.3 Proportional Bar Charts

9.2.4 Faceted Bar Charts

9.2.5 Column Charts

9.3 Time Series Visualizations

9.3.1 Time Series Data Wrangling

9.3.2 Line Charts

9.4 The Workbook

9.5 Continue Your Learning

CHAPTER 10: YOUR DIY DATA SCIENCE ROADMAP

10.1 You've Got This

10.2 The Roadmap

10.2.1 Stop #1: Decision Trees

10.2.2 Stop #2: Random Forests

10.2.3 Stop #3: K-means Clustering.

10.2.4 Stop #4: DBSCAN Clustering

10.2.5 Stop #5: Logistic Regression

10.2.6 Stop #6: Linear Regression

10.3 AI with Copilot in Excel

10.4 Continue Your Learning.

Notes:

Description based upon print version of record.

Includes bibliographical references.

Description based on publisher supplied metadata and other sources.

ISBN:

139440638X

9781394406388

1394340788

9781394340781

OCLC:

1561174360