Python In Excel: Zero To Hero

Learn how to run Python in Excel using the Python formula, print and return strings like hello world, and concatenate strings while understanding none type and basic diagnostics.

Create python variables for revenue, cost of goods sold, admin expenses, payroll, advertising, and interest expense; illustrate assignment order and derive gross margin as revenue minus cost of goods sold.

Explore true and false in Python and Excel, showing that True equals one and False equals zero, and how to assign revenue for gross margin calculations.

Master Python comparisons by evaluating whether two values are equal or not, returning true or false. Explore how the exclamation point signals not equal when comparing pi to other values.

Explore how to create and manipulate Python strings using double and single quotes, including apostrophes, print with variables, use f-strings for dynamic output, and measure length with len.

Explore Python lists, including empty, single-item, numbers, and mixed lists, and understand their role as a foundational data structure for later data frames and printing results.

Learn Python indexing with zero-based indices, access list elements by their index, print results, handle index errors, and use comments to annotate code.

Learn to create a list of lists, access items with positive and negative indices, and print nested elements in Python, using descriptive variable naming.

Explore how to use the type function in python to inspect values, printing types like list, int, and string to reveal an object's class.

Explore python data types by distinguishing integer, float, and list types; learn how a trailing period creates a float, print values, and identify the list type or list class.

Master slicing and splicing in Python to extract parts of SKUs from strings and lists using the colon operator and index positions.

Explore lists in Python by tracing indexing and slicing, including reversing with extended slicing, and build truncated SKU strings to reveal major type and version.

Explore Python tuples, their immutability and use of parentheses versus lists, learn indexing and len, and work with lists of tuples, including single-item tuple syntax.

Explore Python sets, created with curly brackets, which store unique items, lack order, and cannot be accessed by index. Compare sets with lists and tuples, and learn how duplicates behave.

Explore Python dictionaries, create key-value pairs with curly brackets, access and update values by their keys, inspect keys, values, and items, and understand keys must be immutable.

Master the basics of Python functions: define with def, name and call a function, pass a required argument, and use indentation to control scope while printing the result.

Compare how the sum function works in Excel and Python: in Excel, use =sum(...) with arguments. In Python, use sum(list) after creating a list, illustrating the functional difference.

Explore how the if function mirrors a three-argument test in Python, returning true or false based on an argument check and printing outcomes.

Explore building and calling Python functions in Excel, using arguments and default values, returning results to cells while mastering function calls.

Learn to use *args and **kwargs to accept varying arguments, accumulate totals, and follow the rule that args precede kwargs and required arguments precede defaults.

Learn how data frames work in Python and Excel, create and inspect df objects, access columns as series, and use headers and the Pandas library to manage dynamic data.

Learn to filter dataframes in Python for Excel workflows, using not equals, greater than, less than, and other comparisons, and create a new filtered dataframe for prices above $100.

Explore how to select rows and columns in a DataFrame using loc by index and date fields, including examples of row zero, row one, and accessing date values.

Learn to import the date time module in Python for Excel, create date and date time objects, and specify year, month, day, plus hour, minute, second, and microsecond.

Explore python date and time math with timedelta and relativedelta to add days, weeks, hours, minutes, milliseconds, and microseconds, and see why months and years require relativedelta.

Explore how pandas handles time in data frames using timestamps, including parsing strings to timestamps, distinguishing timestamp from datetime, and nanosecond precision with pd.timestamp.

Explore parsing irregular date strings with Python’s date time formatting in Excel, using format codes like %B, %Y, %I, %M, %p to obtain a usable date time.

Convert sales orders to an Excel table, summarize columns, and plot trends using Seaborn. Adjust the order date to day for clearer visuals, and explore Power Query within Excel.

Explore how if, for, and while statements fit within code blocks and scope, and see how statements form the building blocks of Python code.

Learn how to write Python if statements with indentation-based blocks, else and elif branches, and how true and false paths execute using comparison, logical, identity operators, and in/not in tests.

Master Python for statements by iterating over lists and tuples, printing elements, and unpacking data frame rows into index and color.

Learn Python while statements, starting i at zero, updating it with i += 1 until i <= 10, and using while true with if and break to exit.

Explore using Python with Power Query in Excel to fetch external data, load CSV files, and configure query names for reliable Python execution in Azure.

Explore transforming data with Power Query and Python, then build a Python pivot table from a sales order data frame using amount as values and reset index in the pivot.

Learn to parse sales order items with Python, extract version numbers from complex item codes in a data frame using loops and functions, and count shipping and handling across variants.

Define a shipping and rush handler and parse item names with dashes to populate a version column in the sales order data frame, enabling downstream analysis and pivoting.

Parse and split strings by single and double dashes to extract version numbers as Python objects for Excel analysis, then build a pivot table of sales orders by version.

Explains using the handy_stuff.xlsx workbook to explore installed Python packages, view their attributes and methods, update selections with F9, and access package documentation for learning pandas and more.