Python in Excel Step-by-Step

Name: Python in Excel Step-by-Step
Brand: Wiley
Price: 38.99 EUR
Availability: OnlineOnly

David Langer(Autor*in)

Wiley (Verlag)

1. Auflage

Erschienen am 9. Dezember 2025

275 Seiten

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978-1-394-34078-1 (ISBN)

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Inhalt

Cover
Title Page
Copyright
Contents
ACKNOWLEDGMENTS
ABOUT THE AUTHOR
ABOUT THE TECHNICAL EDITORS
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
INDEX
EULA

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