This chapter gets you started moving from MATLAB to Python. You'll learn about some of the similarities and differences between MATLAB and Python, how to use methods and packages, and be introduced to the popular NumPy package.

Welcome to Python!

Basic calculations

Forecasting an investment

Types of variables

Methods and packages

Manipulating strings with methods

Using Python packages

Arrays & plotting

Getting started with NumPy arrays

Plotting bicycle traffic

What predicts animal longevity?

From MATLAB to Python

In this chapter, you will build on your new NumPy knowledge. You will dive into NumPy arrays, the Python analog to matrices by performing mathematical operations and indexing. You will also begin to explore another important Python data structure, the list, and then round out the chapter by making customs plots of your arrays using Matplotlib.

Diving into NumPy arrays

Forecasting over time

How much more do organic avocados cost?

Indexing NumPy arrays

Getting columns from NumPy arrays

Bike traffic throughout the week

Filtering arrays with Boolean indexing

Lists

List exploration

Making NumPy arrays from lists

Operating on lists and arrays

Customizing plots

Colors, linestyles, and legends

Encoding data in color & size

Determine engine types in wildlife strikes

NumPy and Matplotlib

This chapter introduces some powerful Python data structures: the dictionary and the pandas DataFrame. You will learn to create dictionaries by setting key-value pairs, and view then how to view and modify your dictionary. Then you will be introduced to one of the most important packages in the Pythonista's toolbox, pandas. Specifically, you'll focus on the pandas' structure, the DataFrame, which organizes tabular data in an easily accessible way. Lastly, you'll learn how to transform different data types into DataFrames to make your data easier to work with.

Dictionaries

Which keys?

Get data out

Modifying dictionaries

Introduction to DataFrames

Explore a pandas DataFrame

Plot data from a DataFrame

Accessing pandas DataFrames

Accessing rows and columns

The many flavors of .iloc

Creating pandas DataFrames

From a CSV file

From a dictionary of lists

From a list of dictionaries

Dictionaries and pandas

You'll finish the course by controlling your  Python flow. You will learn how to iterate through different Python data structures using for loops. You will also learn about Python contingencies using if, else, and elif and the Python comparison operators (greater than, less than, etc.) that will decide which lines of your code will be executed. Lastly, you'll circle back to NumPy arrays by using Python comparison operators to filter your arrays.

Looping through data

Looping through lists

Overlaying multiple plots on a figure

Comparisons and control flow

Counting different types of aircraft

Finding certain rows in a DataFrame

Filtering data

Boolean indexing for quick stats

Booleans for the win!

Boolean indexing and Matplotlib fun

Well done!

Control Flow and Loops

FAA Wildlife Strikes

Fremont Bridge - hourly traffic

Fremont Bridge - daily traffic

Animal taxonomy

Historical avocado data

Python is a versatile programming language that is becoming more and more popular for doing data science. Companies worldwide are using Python to harvest insights from their data and get a competitive edge. This course focuses on helping Matlab users learn to use Python specifically for data science. You will quickly learn how to migrate from Matlab to Python for data analysis and visualization. Learn the fundamentals of Python syntax, how to use numpy arrays to store and manipulate data. You will learn how to use matplotlib to discover trends, correlations, and patterns in real datasets, including bicycle traffic in the city of Seattle and avocado prices across the United States.

<h2>Transition from Matlab to Python</h2>
Python is a versatile programming language that is becoming more and more popular for doing data science. Companies worldwide are using Python to harvest insights from their data and get a competitive edge. This course focuses on helping Matlab users learn to use Python specifically for data science. You will quickly learn how to migrate from Matlab to Python for data analysis and visualization. 
<br><br>
<h2>Explore NumPY and Matplotlib Libraries </h2>
Learn the fundamentals of Python syntax, and how to use NumPy arrays to store and manipulate data. You will learn how to use matplotlib to discover trends, correlations, and patterns in real datasets, including bicycle traffic in the city of Seattle and avocado prices across the United States.
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<h2>Learn to Use If, Else, and Elif in Python</h2>
In the final chapters, you’ll learn how to control your Python flow. You’ll look at how to use a variety of Python contingencies such as if, else, and elif, as well as comparison operators to define which lines of your code will be executed. 
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By the end of this course, you’ll have a fundamental understanding of Python, its popular dictionaries and libraries, and hands-on experience in using both so you can confidently apply your new skills to your work, personal projects, or further learning. 

Transition from Matlab to Python in just four hours by learning some fundamental concepts, and diving into the NumPy and Matplotlib packages. 

Python for MATLAB Users

Transition from MATLAB by learning some fundamental Python concepts, and diving into the NumPy and Matplotlib packages. 

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Control Flow and Loops - Well done!

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