Automate Excel with Python: From Manual Grind to One-Click Workflow

Cover Page....2

Title Page....3

Copyright Page....4

Dedication Page....6

About the Author....7

About the Technical Reviewer....8

BRIEF CONTENTS....9

CONTENTS IN DETAIL....10

ACKNOWLEDGMENTS....21

INTRODUCTION....23

Why This Book?....23

Why Python?....25

How This Book Is Organized....27

Dataframes, Your New Best Friends....31

PART I: FROM SPREADSHEETS TO DATAFRAMES....33

1 GETTING STARTED WITH PYTHON....34

Technical Considerations for Working with Python....34

Python Version....34

Distribution Platform....35

Integrated Development Environment....36

The IDLE Shell: A Simple Interface....36

Spyder: A More Robust Working Environment....38

Summary....40

2 DISPLAYING DATA AND UNDERSTANDING DATA TYPES....43

Printing String Variables and Strings of Strings....44

Data Types....45

Printing Numbers and Numeric Variables....47

Concatenating Different Data Types....49

Learning from Your Mistakes....49

Customizing Functionality with Parameters....52

Printing an Empty Line....53

Escape Characters and Escape Sequences....53

Potential Pitfalls for Python Novices....54

Using Quotation Marks Inconsistently....55

Ignoring Case Sensitivity....57

Not Knowing Your Data....57

Summary....58

3 CREATING AND MANIPULATING DATAFRAMES AND LISTS....61

What Exactly Is a Dataframe?....61

How to Create a Dataframe....62

Importing the pandas Module and Manually Creating a Dataframe....62

Copying a Dataframe....68

Subsetting a Dataframe by Column....68

Common Dataframe Operations....70

Counting Rows Using the len() Function....71

Counting Rows and Columns with the shape Attribute....71

Deleting Rows with a Specific Value....72

Identifying and Dropping Duplicated Rows....73

Concatenating Dataframes....73

Lists: The DNA of Dataframes....78

Creating a List....80

Isolating Unique List Values....81

Appending a Single Item to a List....81

Adding a List to a List....82

Sorting a List....82

Identifying Minimum, Maximum, and Mean List Values....83

Removing a List Element....83

Comparing Lists....84

Creating an Empty Dataframe with a List....84

Converting a Column into a List....85

Another Fragment of Dataframe DNA: The Series Object....85

Summary....87

4 ADDING, MODIFYING, AND CALCULATING COLUMN DATA....90

Defining a Column....90

Changing How Columns Appear in a Dataframe....91

Reordering Columns in a Dataframe....91

Dropping Columns....92

Renaming Columns....94

Sorting Dataframes by Column Values....95

Printing Select Columns....96

Changing Values in Columns....97

Overwriting All Column Values....97

Replacing Particular Values in Dataframes and Target Columns....97

Replacing Substrings....99

Creating New Columns....100

Adding a New Column with a Single Value....100

Duplicating a Column....101

Concatenating Two or More Columns into a New One....102

Math Methods and Operators....103

Applying the sum() Method to a Dataframe Column....104

Returning Average, Maximum, or Minimum Values....104

Calculating Median Values....105

Rounding a Single Column or Full Dataframe....106

Converting to Absolute Values....108

Stringing Together Multiple Methods....109

Storing Calculated Values in a New Column....110

Comparing Values....111

Conditional Logic....112

Controlling Execution Flow with if Logic....113

Iterating Through a List....113

Repeating a Process with the while Loop....114

Iterating Through a Dataframe....115

Replicating SUMIF with the where() Conditional Statement....116

Storing Values Returned from iterrows() in a New Column....117

Storing Conditional Results in a New Column with List Comprehensions....118

Handling Exceptions with a try-except Block....119

Summary....120

5 ACCESSING AND TRANSFORMING INDIVIDUAL CELL VALUES....123

An Overview of Values and Variables....124

Converting Integers, Floats, and Strings....126

Converting Individual Variables....126

Converting an Entire Dataframe Column....128

Converting Objects to Strings....130

Manually Inserting Values with the input() Function....130

Answering a Question and Saving the Answer....131

Selecting a Menu Option....133

Pausing Execution to Review Output....134

Working with NaN Objects....135

Manually Creating NaN Objects....135

Manually Entering NaN Objects into a Dataframe....136

Replacing NaN Objects....137

Slicing Techniques for Strings and Lists....138

Slicing a Single String....139

Slicing Within an Entire Column....140

Slicing a List from a List....142

Pulling a Select Element from a List....144

Indexing Techniques for Dataframes and Series Objects....145

Pulling Values by Index Position....145

Pulling Values by Unique Index Label....147

Targeting Single Values....147

Pulling a Value from a Series....148

Modifying Existing Cells....149

Splitting Techniques....151

Splitting a Single String....152

Handling Inconsistent Delimiters....153

Splitting Columns in a Dataframe....155

Summary....157

6 FILTERING AND DISPLAYING DATAFRAMES....160

A Closer Look at the DataFrame() Method....160

Using Optional Arguments....162

Creating an Empty Dataframe with a Column List....163

Working with Dataframe Indexes....164

Naming and Renaming an Index....165

Renumbering or Resetting an Index....165

Sorting by the Index....167

Moving Values Between the Index and a Column....168

Subsetting Dataframes....169

By Column List....170

By Relative Index Location....174

By Index Label....175

By Matching Values in Columns....178

By Excluding Values in Columns....179

By a Substring Value....179

By Column Labels in a List....180

By Inclusion....181

By Exclusion....181

By Mathematical Condition....181

By NaN Objects....182

Controlling the Appearance of Dataframe Output....183

Printing the First or Last Few Rows....184

Printing Specific Rows....184

Printing the Rightmost Columns....185

Customizing Global Display Settings....186

Working with Dictionary Objects....190

Declaring a Single Dictionary....191

Declaring a List of Dictionaries....192

Accessing and Modifying Dictionary Contents....192

Creating New Dictionary Key-Value Pairs on the Fly....193

Storing Dictionaries in Dataframes....194

Summary....195

PART II: TOOLS TO REPLICATE EXCEL FUNCTIONALITY....197

7 COUNTING AND SUMMING VALUES....198

The value_counts() Method....199

Counting Every Value in a Column....199

Counting Specific Values in a Column....200

Counting a List of Specific Values in a Column....201

Normalizing value_counts() Results....202

The crosstab() Method....204

Creating Basic Cross-Tabulations....204

Performing Math Operations....209

Adding Row and Column Totals....216

Handling Missing Values....217

Normalizing crosstab() Data....219

The pivot_table() Method....222

Breaking Down the Basic Form of pivot_table()....223

Grouping Unique Values....228

Calculating Math Values....228

Handling NaN Objects....230

Organizing Dataframe Values....232

Summary....233

8 MERGING DATAFRAMES....235

The Basics of VLOOKUP and merge()....235

How merge() Handles Orphaned Keys....238

The Full merge() Method Syntax....240

Specifying Join Type....243

Analyzing Merge Results....246

Defining Keys....249

Handling Different Key Column Labels....252

Checking Your Match Expectations....253

Quality Control with the shape Attribute....256

Summary....257

9 FORMATTING AND CALCULATING DATES AND TIMES....261

Introducing the Datetime Module and datetime.now()....261

Creating Datetime Objects....262

Isolating Units of Time as Integers....264

Converting Datetime Objects to Strings....265

Transforming Timestamps....266

Converting a Single Datetime Object to a Custom-Formatted String....270

Isolating Units of Time as Strings....271

Removing Leading Zeros from Single-Digit Time Elements....271

Working with Time Durations: The Timedelta Object....272

Comparing and Calculating Dates and Times....274

Datetime Objects in Dataframes....276

Dataframe Datetime Operations....277

Using Directives to Customize to_datetime() Results....278

Calculating Timedeltas in a New Column....279

Subsetting a Dataframe Using Datetime Objects....280

Summary....281

PART III: WORKFLOW TECHNIQUES....284

10 READING EXCEL FILES INTO DATAFRAMES....285

Creating or Downloading Your Excel Spreadsheet....286

Introducing the read_excel() Method....286

Importing a Specific Tab from a Workbook....288

Importing All Tabs at Once....290

Filtering Source Data....291

Parsing Input Spreadsheets....292

Dealing with More Complex Spreadsheets....294

Setting the Column Labels for Your Dataframe....296

Setting an Excel Column as the Dataframe Index....297

Handling Hard Returns in Excel Data....298

Reading in a CSV File....299

Summary....301

11 SAVING DATAFRAMES TO EXCEL....304

Simple Single-Tab Export....304

Exporting a More Complex Dataframe....306

Designating the Tab Name....308

Excluding the Dataframe Index....309

Freezing the View....310

The Six Steps to Exporting and Formatting Excel Files....311

Step 1: Creating a Writer Object and Excel File....313

Step 2: Adding Multiple Dataframes to One Excel Workbook....315

Step 3: Closing the Writer Object and Excel File....316

Step 4: Creating and Populating the Workbook Object and Excel File....318

Step 5: Formatting the Excel File....319

Step 6: Closing the Workbook Object and Excel File....333

Emailing from Python....335

Sending a Basic Email....335

Converting Dataframes to HTML Code....336

Sending an Email Containing HTML....337

Summary....338

12 THERE AND BACK AGAIN: AN EXCEL–PYTHON–EXCEL WORKFLOW....342

The Scenario....342

Analyzing the Vets Workbook....343

Flowcharting the Manual Process....345

Coding with Modularity....345

Writing and Calling UDFs....346

Defining Required and Optional Parameters....347

Returning One or More Values....348

Saving UDFs in a Separate Script....349

Writing Your First Script: Rolling Over a File....351

Importing Your Favorite Modules....351

Importing the Ducks UDFs....352

Printing a Header....352

Setting Your Dataframe Display Preferences....352

Setting the Date....353

Creating a New File for Todays Date....355

Automating Exception Reports and Data Management Tasks....357

Importing the Vets Workbook Tabs....357

Generating and Sending an Email....358

Automating the Overdue Invoice Process....359

Filtering Todays Appointments....362

Pausing the Programs Execution....362

Automating Daily Appointment Email....363

Recording New Appointments....366

Exporting Dataframes to Excel Tabs....366

Updating and Formatting the Excel File in Spyder....368

Automating Updates in Excel Files....374

Analyzing Trends with Dynamic Pivot Tables....379

Using Spyder as Your Daily Workflow GUI....382

Summary....382

A WORKING WITH FOLDERS, FILES, AND PATHNAMES....384

Pathnames in File Explorer vs. Python....384

Viewing and Changing Your Working Directory....386

Listing the Contents of a Folder....387

Creating a New Directory....388

Checking Whether a Pathname Exists....389

Copying a File....390

Renaming or Moving a File or Folder....391

Deleting a File....392

Deleting Folders....393

File Management Quick Reference....394

B CLEANING UP A MESSY SPREADSHEET....395

Reading a Messy Spreadsheet into a Dataframe....395

Customizing Column Labels....397

Specifying a Column as the Index....398

Dropping a Record by Index Label....398

Sorting by Index Label....399

Splitting Columns: Converting Full Names to First and Last....400

Renaming Columns....401

Reordering Columns....401

Changing a Specific Cell Value....402

Converting Strings to Datetime Objects....402

Exporting the Cleaned-Up Dataframe Back to Excel....403

Formatting the Clean Excel File....404

The Final Product....405

C THE DUCKS MODULE....407

PYTHON QUICK REFERENCE....413

INDEX....417

This practical guide will help spreadsheet pros save time and boost productivity using beginner Python tools – without having to become a programmer. Automate Excel with Python bridges the gap between Excel expertise and Python programming without forcing you to start from scratch. Unlike other programming guides, it’s specifically designed for Excel users who need more power and automation, not for professional programmers. This book focuses on enhancing your existing Excel skills rather than replacing them. You’ll learn how Python’s dataframes work like virtual spreadsheets, extending what you already know while automating repetitive tasks. You’ll be able to import and export data seamlessly between Excel and Python to integrate coding into your current workflow. Concise, practical examples throughout will get you up and running quickly. Each topic addresses real problems Excel users face, with solutions you can implement immediately. The structure is deliberately flexible. Chapters progress logically but stand independently, so you can jump to exactly what you need without reading everything that came before. This makes it perfect for solving urgent problems or learning at your own pace. You’ll apply the techniques you learn to automate reports, clean messy data, perform complex calculations, and handle large datasets that would overwhelm Excel alone—all while leveraging your existing spreadsheet knowledge.