Introduction to Python for Data Analysis

In this class, we are moving from SQL to Python. As Data Analysts, you won’t just write code; you will explain it to your colleagues. Today, we focus on the “Anatomy of Data.” We will learn the names of the symbols and structures that make up every Python script so you can speak about your work with confidence.

Lead-In – The Visual Challenge

Before we look at our Quizlet today, I want to see how much ‘English for IT’ you already know. I will show you a few symbols that are common in Python and SQL and just tell me, in English, what do we call these marks?”

1. _

   • Target: Underscore (Most students say “low line” or “bottom dash”).

2. ' '

   • Target: Quotes / Quotation marks (Listen for “brackets” or “commas”—correct this early).

3. =

   • Target: Equals / Assign (Ask: “What do we do with this? We assign a value”).

4. #

   • Target: Hash / Pound / Octothorpe (Explain that in Python, this is for comments).

5. .

   • Target: Dot / Period (Reinforce that in tech, we almost always say dot).

6. ()

   • Technical Name: Parentheses (pa-REN-the-sees).

   • Common Name: Round brackets.

   • The Job: Used for calling methods (like .head()) or grouping math.

7. []

   • Technical Name: Square brackets.

   • The Job: Used for indexing (looking inside a DataFrame) or creating a list.

8. {}

   • Technical Name: Curly brackets or Braces.

   • The Job: Used for dictionaries (mapping one value to another).

Vocabulary Practice

Python Fluency: Naming Symbols & Data Structures

Code Snippets Practice

The “Blind Coder” Activity

Level 1: The Basics (Focus: Dots & Parentheses)

This is for building speed with the most common Python symbols.

• Snippet: df.info()

• Architect’s Script: “Write d f, then dot, then info, then open parenthesis, then close parenthesis.”

• Manager Explanation: “I’m calling the info method to check for missing values and data types.”

Level 2: The Selection (Focus: Square Brackets & Quotes)

This is where students usually make mistakes with “brackets” vs “parentheses.”

• Snippet: df['City']

• Architect’s Script: “d f, open square bracket, single quote, capital C i t y, single quote, close square bracket.”

• Manager Explanation: “I am indexing the ‘City’ column to see where our customers are located.”

Level 3: The Calculation (Focus: Assignment & Underscores)

The “Final Boss” of Lesson 1. It combines everything.

• Snippet: avg_price = df['price'].mean()

• Architect’s Script: “a v g, underscore, price, space, equals, space, d f, open square bracket, single quote, price, single quote, close square bracket, dot, mean, open parenthesis, close parenthesis.”

• Manager Explanation: “I am assigning the average of the ‘price’ series to a new variable called ‘avg_price’.”

Extra Snippet Practice

Part 1: From Snippet to Script (Writing the “How-to”)

Task: For each code snippet below, write the Technical Spelling (exactly how you would dictate it to a colleague).

1. import pandas as pd

2. df.info()

3. id_column = df['id']

4. df_test = df.head(5)

5. results_df['price'].sum()

Part 2: From Script to Snippet (The “Coder” Challenge)

Task: Read the “Technical Spelling” scripts below and write the actual Python code snippet that they describe.

Situation 6

“Write d f, then dot, then tail, then open parenthesis, then five, then close parenthesis.”

• Your Code: ____________________

Situation 7

“total underscore cost, space, equals, space, one thousand.”

• Your Code: ____________________

Situation 8

“d f, open square bracket, single quote, capital N a m e, single quote, close square bracket, dot, unique, open parenthesis, close parenthesis.”

• Your Code: ____________________

Situation 9

“d f, underscore, raw, equals, p d, dot, read underscore csv, open parenthesis, single quote, data dot c s v, single quote, close parenthesis.”

• Your Code: ____________________

Situation 10

“d f, open square bracket, single quote, salary, single quote, close square bracket, dot, mean, open parenthesis, close parenthesis.”

• Your Code: ____________________