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.
From MATLAB to PythonFree
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.
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.
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.
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.
While getting his Ph.D. in Neuroscience, Justin Kiggins abandoned MATLAB in favor of Python to run
experiments and analyze data. Since then, he's used Python to perform machine
learning on brain activity, automate the boring parts of scientific experiments,
create video games for mice, build web applications for storing scientific
data, and scrape avocado prices from the internet to figure out where Millenials
can best afford a house. You can find him on Twitter at
his blog at justinkiggins.com