Python Basics for Analysts

Why Python?

• General-purpose: used for analysis, automation, web apps, and machine learning
• Readable syntax: approachable for beginners
• Huge ecosystem: libraries for almost every data task
• Free and open-source
• Industry demand: commonly listed in analyst and data science job descriptions

Variables and data types

Variables are names that hold values.

Choose a type that reflects how the value will be used. A postal code like '600001' is better stored as text than as a number.

Example

orders = 12
revenue = 18450.75
region = 'South'
is_returning_customer = True
average_order_value = revenue / orders
print(average_order_value) # 1537.5625
print(round(average_order_value, 2)) # 1537.56
# f-strings make readable output
print(f"AOV for {region}: ₹{average_order_value:,.2f}")

Lists and dictionaries:

monthly_sales = [12000, 15000, 13500]
print(sum(monthly_sales) / len(monthly_sales)) # average
store = {'name': 'Store A', 'city': 'Chennai', 'sales': 45000}
print(store['city']) # Chennai

How it works

• = is the assignment operator: Python evaluates the right side, then stores it under the name on the left.
• print() displays a value so you can inspect it.
• type(x) tells you a variable's data type.
• In notebooks, the last expression in a cell is displayed automatically.

Common mistakes