Python for Accounting: A Modern Guide to Using Python Programming in Accounting

Contents
Introduction
	What this book is about
	Whom this book is for
	Why read this book
	A quick tour of Python's data tools
	How to use this book
	Roadmap
Getting set up
	Installing Python on your computer
	Setting up your local workspace
	Using JupyterLab
	Using Anaconda Navigator
Python ABCs
	A quick look at Python code
	Variables and operators
		Everything is an object
		Point me to the values
		The cycle of a variable's life
		Operators
	Python's built-in data types
		Integers
		Floating-point numbers
		Booleans
		The None type
		Strings
		Associated built-in functions
	Python's built-in collections
		Lists
		Overthinking: Tuples and sets
		Dictionaries
		Membership operators
		Associated built-in functions
	Control flow
		If-else statements
		Loops
		List comprehensions
	Functions
		Defining functions
		Parameters and arguments
		Return values
	Modules, packages, and libraries
		Modules and packages
		Libraries
	How to find help
		Jupyter notebook helpers
		Finding help online
	Overthinking: code style
		Naming variables and functions
		Breaking long lines of code
		Spacing around operators
		Code comments
Working with tables
	Pandas in a nutshell
		Getting started
		Reading data from spreadsheets
		Preparing and transforming data
		Visualizing data
		Writing data to a spreadsheet
	Tables, columns, and values
		Series
		DataFrame
		Index and axes
		Values and types
	Reading and writing Excel files
		Reading Excel files
		Inspecting data
		Writing Excel files
	Slicing, filtering, and sorting tables
		Selecting columns
		Removing columns
		Selecting rows and columns
		Filtering data
		Sorting data
	Project: Organizing sales data by channel
	Adding and modifying columns
		Adding columns
		Renaming columns
		Replacing values
		Assigning new values to a table slice
		Filter and edit
	Summarizing data
		Counting unique values
		Averages and numerical summaries
		High-level table information
	Cleaning data
		Dealing with missing values
		Dealing with duplicate rows
		Converting column data types
	Project: Reading and cleaning a QuickBooks general ledger
	Working with text columns
		Revisiting Python strings
		String methods in pandas
		String data types in pandas
		Overthinking: Regular expressions
	Working with date columns
		Revisiting Python dates
		Date columns
		Pandas date methods
		Pandas date arithmetic
		Overthinking: Timezones
	Applying custom functions
		Applying functions to columns
		Overthinking: Functions without a name
		Applying functions to rows
		Overthinking: Other function parameters
	Project: Mining product reviews
	Concatenating tables
		Row-wise concatenation
		Column-wise concatenation
		Appending rows to a DataFrame
	Joining tables
		How joins work
		Joins in pandas
		Inner, outer, left, and right joins
		More joining options
	Project: Filling missing product names in the sales data
	Groups and pivot tables
		How group operations work
		Group operations in pandas
		Stacking and unstacking
		Pivot tables
	Overthinking: Changing how DataFrames are displayed
		Setting display options
		Styling tables
Visualizing data
	Plotting with matplotlib
		Elements of a matplotlib plot
		Plotting basics
		Adjusting plot details
		Overthinking: Styles, colors, and fonts
	Project: Making a waterfall plot from a cash flow statement
	Other plotting libraries
		Plotting with pandas
		Plotting with seaborn
		Using matplotlib, pandas and seaborn together
		Overthinking: Interactive plots
Sales analysis project
	Setting up your project
		Files and folders
		Documenting your analysis
	Preparing data
		Product and cost data
		Sales data
	Finding answers
		Channel profits
		Category profits
		Channel and category profits
		Product profits
		Sharing results
	Next steps