Python

This page provides an overview of different techniques and skill levels related to Python, including basic, intermediate, and advanced techniques.

Basic

These are the basic skills, helpful even for beginning courses and activities.

Intermediate

These would be considered intermediate skills, applied in higher-level courses and activities.

Advanced

These are advanced skills, useful for more experienced users and advanced projects.

Subsections of Python

Basics

The following Python skills and techniques may be considered basic level in the context of data analysis.

Data Structures

  • Lists: Know how to create and manipulate lists, and use them to store and organize data.

  • Dictionaries: Know how to create and manipulate dictionaries, and use them to store and organize data in key-value pairs.

Control Structures

  • Conditional Statements: Know how to use if-else statements to conditionally execute code.

  • Loops: Know how to use for and while loops to iterate over data.

Functions

  • Defining Functions: Know how to define functions to organize and reuse code.

  • Lambda Functions: Know how to define and use lambda functions for short and simple functions.

File I/O

  • Reading and Writing Files: Know how to read and write data from files using Python.

External Libraries

  • NumPy: Know how to use NumPy to perform numerical operations and calculations.

  • pandas: Know how to use pandas to work with structured data and perform data analysis tasks.

  • Matplotlib: Know how to use Matplotlib to create basic plots and visualizations.

These skills and the associated techniques provide a strong foundation for data analysis in Python, and can be built upon with more advanced topics and libraries as needed.

Intermediate

This page provides an overview of intermediate skills for working with Python in the context of data analysis.

External Libraries

  • NumPy: Know how to work with arrays, manipulate data, and perform mathematical operations.

  • pandas: Know how to work with data frames and manipulate data for exploratory data analysis.

  • Matplotlib: Know how to create customized visualizations for data analysis.

Data Cleaning

  • Merging and joining data frames: Know how to combine data from multiple sources.

  • Handling missing data: Know how to identify missing data and impute it using various methods.

  • Data normalization and scaling: Know how to standardize data and scale it to compare across different variables.

Data Analysis

  • Descriptive statistics: Know how to calculate basic summary statistics like mean, median, and standard deviation.

  • Inferential statistics: Know how to perform hypothesis testing and confidence intervals.

  • Regression analysis: Know how to perform linear regression and interpret regression coefficients.

Workflow and Collaboration

  • Version control with Git: Know how to use Git for version control and collaborate with others on code.

  • Unit testing and debugging: Know how to write and run unit tests and debug code.

  • Code organization and project structure: Know how to structure a Python project for scalability and reproducibility.

Type Hints

  • Type hints: Know how to use type hints in Python to specify function argument types, return types, and class attributes.

Employing important new features such as type hints shows a deeper understanding of Python and a commitment to writing clean, maintainable, and efficient code.

By using type hints, developers improve the documentation of their code, catch errors more easily, and help other developers understand how to use their code.

With the increasing adoption of type hints in the Python community, it is becoming an essential intermediate to advanced skill for those working on larger projects or collaborating with other developers.

def add_numbers(x: int, y: int) -> int:
    return x + y

The type hints are specified using the : syntax, where x: int means that x is of type int. The -> int syntax after the function arguments specifies the return type of the function as int.

Type hints are not enforced by the Python interpreter, but are used by static analysis tools and linters to catch type-related errors early in the development process.

Advanced

Advanced Python Skills

These skills are considered advanced and will be useful for more advanced data analysis tasks.

Object-Oriented Programming

  • Understand the basics of object-oriented programming (OOP) and how to apply it in Python.
  • Create and use classes to encapsulate related data and functionality.
  • Use inheritance and polymorphism to extend existing classes and create new ones.

Functional Programming

  • Understand the principles of functional programming and how to use functional programming concepts in Python.
  • Use lambda functions and higher-order functions to create more expressive and powerful code.
  • Apply functional programming techniques to data processing and analysis tasks.

Decorators

  • Understand what decorators are and how to use them to modify the behavior of functions and methods.
  • Use built-in Python decorators like @property, @staticmethod, and @classmethod.
  • Create custom decorators to add functionality to your code.

Generators and Iterators

  • Understand the difference between generators and iterators and how to use them in Python.
  • Use generators to lazily generate and process data without creating large in-memory data structures.
  • Implement custom iterators to provide custom ways of iterating over data.

Concurrency and Parallelism

  • Understand the difference between concurrency and parallelism and how to achieve both in Python.
  • Use threads and processes to perform multiple tasks simultaneously.
  • Use asynchronous programming techniques to handle I/O-bound tasks efficiently.

Performance Optimization

  • Understand how to optimize Python code for performance.
  • Use profiling tools to identify performance bottlenecks in your code.
  • Apply performance optimization techniques like caching, memoization, and vectorization to speed up your code.

Independent Study

Books remain a surprisingly cost-effective investment.

When you’re ready to truly master this powersful language, consider investing in a top-rated book like “Fluent Python” by Luciano Ramalho. The second edition is current, published in March 2022 covering up to Python 3.10 for the newest features.

Or High Performance Python: Practical Performant Programming for Humans by Micha Gorelick and Ian Ozsvald covering high-performance options for processing big data, multiprocessing, and more.

GitHub Resouces

Participate in Open Source