Module 1: Python Fundamentals

Module Overview

This module introduces you to essential Python programming concepts for data science. You'll learn how to navigate Google Colab, understand Python's fundamental data types, work with conditional statements, implement loops, create functions, and use imports. These foundational skills will prepare you for working with data in Python and using various data science tools and libraries.

Learning Objectives

Navigate and effectively use Google Colab for Python development
Understand and work with Python's fundamental data types
Write conditional statements and if statements for decision making
Implement loops for repetitive tasks and data iteration
Define and use functions to organize and reuse code
Work with imports to utilize Python modules and libraries

Detailed Objective: Understanding Python Data Types

Different Python types may have different definitions and, therefore, different behaviors. Understanding the subtle differences between data types will help you become a better coder.

Value Types

Name	Type	Description	Code
String	`str`	Used to represent a text value	`"bloomtech"`
Integer	`int`	Discrete numerical values	`42`
Float	`float`	Continuous numerical values	`3.14`
Boolean	`bool`	Binary logical propositions	`True`

Container Types

Name	Type	Description	Code
List	`list`	Indexed, mutable order, and allow duplicate values	`[1, 2, 3]`
Tuple	`tuple`	Indexed, immutable order, and allow duplicate values	`(1, 2, 3)`
Set	`set`	Unindexed, unordered, and do not allow duplicate values	`{1, 2, 3}`
Dictionary	`dict`	Keyed index, immutable order, changeable values	`{"a": 1, "b": 2, "c": 3}`

Container Types: Pandas (pd) and Numpy (np)

Name	Type	Definition
Numpy Array	`np.array`	One-dimensional sequence of data (like a list of values)
Numpy Matrix	`np.matrix`	Two-dimensional sequence of data (like a list of lists)
Pandas Series	`pd.Series`	One-dimensional sequence of data (like a list of values)
Pandas DataFrame	`pd.DataFrame`	Multi-dimensional sequence of data (like a list of lists)

Type Methods

Each data type has different methods that represent the behavior of an object. Methods are associated with a specific object type. For example, a list has the .append() method associated with it for adding values to the end of the list.

my_list = [1, 2, 3]
my_list.append(4)
print(my_list)
# Output: [1, 2, 3, 4]

There are many more data types you will be studying and using throughout this course.

Guided Project

Open DS_111_Intro_to_Python.ipynb in the GitHub repository below to follow along with the guided project:

GitHub: Intro to Python