Python Bootcamp for Data Analysis #2: Functions

Name: Python Bootcamp for Data Analysis #2: Functions
Rating: 4.2 (35 reviews)

From Zero to Hero: The Second Module of Miuul's Python Bootcamp

Created byMiuul Data Science & Deep Learning

Last updated 6/2024

English

What you'll learn

Understand and articulate the concept of functions in Python and their importance in simplifying and managing code complexity
Write functions using proper syntax, including docstrings to document the purpose and parameters of the function
Utilize predefined arguments
Identify the appropriate scenarios for defining functions

Course content

1 section • 9 lectures • 51m total length

Course Materials0:03
Introduction12:49
Learn how to define and call functions in python, and master function literacy, arguments, and docstrings. Explore print usage, arguments vs parameters, and basic formatting with pep 8 guidelines.
Docstring7:25
Learn to write clear docstrings for Python functions, detailing purpose, parameters, and return values, in NumPy or Google styles, and view them with help.
Statements8:41
Explore the function body, defined with def, and how it executes statements, uses parameters, prints outputs, and updates lists via append, while understanding local and global scopes.
Predefined Arguments4:06
Define functions with default values and parameters, illustrated by a division function with A and B that runs even when inputs are omitted.
When to Write a Function3:01
Learn how the DRY principle prompts writing functions in Python to automate repetitive calculations with temperature, humidity, and battery data, using arguments to run a single calculate function.
Return Functions6:01
Explore how the return statement allows using a function's outputs as inputs, demonstrate single and multiple outputs (tuples), storing results, and handling type and scope notes in Python.
Calling Functions within Functions6:44
Master calling functions within other functions by shaping external arguments in a main function, invoking pre-existing operations, and managing outputs in a Python data analysis workflow.
Local and Global Variables2:35
Explore local and global variables by applying a function that appends to a list, showing how local scope can influence global data while c remains local.

Requirements

A curious mind and the enthusiasm to explore new concepts are crucial.
A text editor or an Integrated Development Environment (IDE) such as PyCharm, VSCode, or even a simple editor like IDLE provided by Python.

Description

Step into Miuul's Python Bootcamp for Data Analysis, a beginner-friendly course tailored to transform newcomers into adept programmers. Embark on your programming journey with Miuul!

Miuul's Python Bootcamp is designed not just to teach but to inspire creativity and innovation in coding. Each module in this series is constructed with a hands-on approach, allowing you to directly apply what you learn in real-world scenarios.

In this module, we will delve into Python functions, teaching you how to define and use functions to make your code more modular, reusable, and manageable. You'll learn to write functions with proper syntax, utilize arguments and return values effectively, and understand how functions can streamline your coding process, especially in data analysis.

Moreover, as you progress, you'll tackle more complex concepts and techniques, preparing you for advanced topics in future courses. This bootcamp is your gateway to becoming a proficient Python programmer, equipped with the knowledge to tackle data analysis challenges and beyond.

Join us at Miuul's Python Bootcamp for Data Analysis, where learning to code becomes an adventure, empowering you to write, analyze, and innovate. Here, every line of code you write brings you one step closer to mastering the art of Python programming.

Who this course is for:

Those interested in entering the field of data analysis and want to build a strong foundation in Python
Professionals aiming to transition into tech roles
Undergraduate and graduate students who require Python programming skills for research, projects, or coursework in computer science, engineering, statistics, or related fields.
Anyone with an interest in programming and data analysis