Python Refresher (For Data Science)

1. Introduction

Data-science libraries are easier to use when core Python concepts are familiar. Values, functions, collections, conditions, and file handling explain the building blocks behind notebook code and help distinguish a Python issue from a library issue.

2. Core concepts

Python names refer to objects with types. Lists and dictionaries organize records, loops express repeated work, and functions isolate transformations. Comprehensions and lambda expressions can be concise, but clarity matters more than compressing a calculation into one line.

3. Topics covered in this module

• Variables, types, conversion, strings, methods, operators, and precedence
• Input/output, if/else, match, loops, functions, and formatted strings
• Lists, tuples, sets, dictionaries, comprehensions, and lambdas
• Files, JSON, object-oriented basics, and exceptions

4. Syntax and how to read it

def clean_score(raw: str) -> float:
    value = float(raw.strip())
    if not 0 <= value <= 100:
        raise ValueError('score must be between 0 and 100')
    return value

The function converts text into a number and enforces a meaningful range. A clear exception tells the caller why the input is invalid.

5. Practical workflow

Trace a small example by hand, inspect values with type() and repr(), then extract repeated work into a function. Use explicit conversion and handle expected parsing errors at the boundary.

6. Common mistakes

• Using == None instead of is None or assuming empty containers are the same as missing values.
• Writing one dense expression whose intermediate values and assumptions cannot be checked.

7. Best practices

• Use descriptive snake_case names and functions with explicit inputs and outputs.
• Prefer readable control flow and small tests over clever one-liners.

8. Practice task

Write a function that takes a list of strings and returns lowercase, trimmed, non-empty values.

9. One possible solution

def clean_labels(values: list[str]) -> list[str]:
    return [value.strip().lower() for value in values if value.strip()]

10. Summary

Review the Python language concepts that underpin notebook analysis and scientific libraries. Use descriptive snake_case names and functions with explicit inputs and outputs. Prefer readable control flow and small tests over clever one-liners.

11. What comes next

The next module, Claim your Free Developer Tools, builds on this foundation and applies it to a new part of the analytics workflow.