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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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Kursbeschreibung

Transition from Matlab to Python

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.

Explore NumPY and Matplotlib Libraries

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.

Learn to Use If, Else, and Elif in Python

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.

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.
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  1. 1

    From MATLAB to Python

    Kostenlos

    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.

    Kapitel Jetzt Abspielen
    Welcome to Python!
    50 xp
    Basic calculations
    100 xp
    Forecasting an investment
    100 xp
    Types of variables
    100 xp
    Methods and packages
    50 xp
    Manipulating strings with methods
    100 xp
    Using Python packages
    100 xp
    Arrays & plotting
    50 xp
    Getting started with NumPy arrays
    100 xp
    Plotting bicycle traffic
    100 xp
    What predicts animal longevity?
    100 xp
  2. 2

    NumPy and Matplotlib

    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.

    Kapitel Jetzt Abspielen
    Diving into NumPy arrays
    50 xp
    Forecasting over time
    100 xp
    How much more do organic avocados cost?
    100 xp
    Indexing NumPy arrays
    50 xp
    Getting columns from NumPy arrays
    50 xp
    Bike traffic throughout the week
    100 xp
    Filtering arrays with Boolean indexing
    100 xp
    Lists
    50 xp
    List exploration
    100 xp
    Making NumPy arrays from lists
    100 xp
    Operating on lists and arrays
    100 xp
    Customizing plots
    50 xp
    Colors, linestyles, and legends
    100 xp
    Encoding data in color & size
    100 xp
    Determine engine types in wildlife strikes
    100 xp
  3. 3

    Dictionaries and pandas

    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.

    Kapitel Jetzt Abspielen
    Dictionaries
    50 xp
    Which keys?
    50 xp
    Get data out
    100 xp
    Modifying dictionaries
    100 xp
    Introduction to DataFrames
    50 xp
    Explore a pandas DataFrame
    100 xp
    Plot data from a DataFrame
    100 xp
    Accessing pandas DataFrames
    50 xp
    Accessing rows and columns
    100 xp
    The many flavors of .iloc
    100 xp
    Creating pandas DataFrames
    50 xp
    From a CSV file
    100 xp
    From a dictionary of lists
    100 xp
    From a list of dictionaries
    100 xp
  4. 4

    Control Flow and Loops

    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.

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    Looping through data
    50 xp
    Looping through lists
    50 xp
    Overlaying multiple plots on a figure
    100 xp
    Comparisons and control flow
    50 xp
    Counting different types of aircraft
    100 xp
    Finding certain rows in a DataFrame
    100 xp
    Filtering data
    50 xp
    Boolean indexing for quick stats
    100 xp
    Booleans for the win!
    100 xp
    Boolean indexing and Matplotlib fun
    100 xp
    Well done!
    50 xp
Für Unternehmen

Trainierst du 2 oder mehr?

Verschaffen Sie Ihrem Team Zugriff auf die vollständige DataCamp-Plattform, einschließlich aller Funktionen.

Datensätze

FAA Wildlife StrikesFremont Bridge - hourly trafficFremont Bridge - daily trafficAnimal taxonomyHistorical avocado data

Mitwirkende

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Mari Nazary
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Becca Robins
Justin Kiggins HeadshotJustin Kiggins

Product Manager

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