LOVEY’S SUPER PYTHON STUDY GUIDE!

Lovey is intrigued by Python!

General Exam Preparation Advice

  • Review Readings and Lectures: Focus on key terms like “def” or “infinite loop”.
  • Understand Code Application: Know how to write and interpret Python code, especially for file handling, lists, dictionaries, requests, and pandas.
  • Practice with Examples: Use the FEQT from large group sessions as a model for exam questions.
  • Group Study: Discuss and quiz each other on key concepts and potential exam questions.
  • Note Sheet: Prepare a comprehensive 8.5x11 sheet with crucial notes and summaries.

Python Programming Study Guide: Units 07 - 12

Key Concepts

Unit 07: File Handling

  • Open and Read Files: Using open(), read(), readlines() for accessing file contents.
  • Write to Files: Using write() to create or modify files.
  • Closing Files: Importance of closing files or using with statement for automatic closure.

Unit 08: Lists

  • Mutable Sequence Types: Understanding that lists can be changed after creation.
  • List Operations: append(), index(), insert(), pop(), remove(), reverse(), count().
  • Indexing and Slicing: Accessing elements using indices and slicing for sublists.

Unit 09: Dictionaries

  • Key-Value Pairs: Understanding the structure of dictionaries.
  • Dictionary Functions: keys(), values(), get() for accessing dictionary elements.

Unit 10: HTTP

  • Making HTTP Requests: Using requests.get() to make GET requests.
  • Handling JSON: Using json() for parsing JSON responses.
  • Query Parameters: Understanding and using query strings/params in requests.

Unit 11: APIs

  • API: A set of functions and procedures that allow the creation of applications accessing features or data of an operating system, application, or other services.
  • HTTP Protocol: The foundation for data communication on the Web, used by Web APIs.
  • JSON Responses: Most Web APIs return data in JSON (JavaScript Object Notation) format.

Unit 12: Pandas Library

  • DataFrames and Series: Understanding the basic structures in Pandas.
  • Reading Data: Using read_csv() for reading CSV files.
  • Data Selection: Using loc, iloc for row/column selection and filters.
  • Data Conversion: Converting data using to_records().