Learning guide: Python for Excel users, half-day workshop

Data is data, so why should it be / Python and Excel go awkwardly?

OK, I’m no lyricist, but there is a beauty in build one skill set off of another.

I believe that every data analyst should have in their “stack” of tools a spreadsheet application, BI/dashboarding program, database and programming language. For that last category, Python is a solid choice.

Data analysts generally come to programming from spreadsheets. Too often, programming languages are seen as the spreadsheet-killer. This should not be the case: after all, spreadsheets are a valued plank of the analytics stack!

Not only that, but “spreadsheet smarts” put the analyst at an advantage for Python mastery. After all, data is data — once the tasks and method become second nature to you, it’s easy to shift production into another tool.

I built the below learning guide with this shift in mind. By starting with the “mental model” of data in Excel, my hope is that analysts can augment their knowledge by picking up Python.

Lesson 1: Python and Excel for data analytics

Objective: Student can compare and contrast uses of Exc­el and Python for data analytics

Description:

• Welcome to Planet Python
• What is Python and when would you use it?
• Working in Jupyter

Exercises: “Hello world” in Jupyter

Assets needed: None

Time: 30 minutes

Lesson 2: From Excel cells to Python lists and dictionaries

Objective: Student can create, inspect and manipulate lists and dictionaries

Description:

• Variable assignment
• Data types
• Indexing and subsetting lists
• Indexing and accessing dictionaries

Exercises: Drills on lists and dictionaries

Assets needed: None

Time: 45 minutes

Lesson 3: From Excel tables to Python DataFrames

Objective: Student can create, inspect and manipulate DataFrames

Description:

• From lists to NumPy arrays
• From NumPy arrays to Pandas DataFrames
• Importing and inspecting a DataFrame

Exercises: Drills

Assets needed: Baseball records

Time: 45 minutes

Lesson 4: From Excel lookups and PivotTables to Pandas manipulation

Objective: Student can manipulate tabular data with Pandas

Description:

• Sorting, filtering, summarizing, renaming
• Merging
• Un-pivoting and re-shaping
• Exporting results

Exercises: Drills

Assets needed: Baseball records

Time: 50 minutes

Lesson 5: Data visualization with seaborn

Objective: Student can visualize univariate distributions

Description:

• Bar charts
• Line charts
• Histograms
• Custom plots & themes

Exercises: Drills

Assets needed: Baseball records

Time: 30 minutes

Lesson 6: From “That’s hard in Excel” to “That’s easy in Python!”

Objective: Student can conduct end-to-end data analysis project

Description:

• Append, transpose, summarize and visualize a set of csv files

Exercises: Drills

Assets needed: Retail sales dataset

Time: 30 minutes

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