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  3. Python for MATLAB Users
  • 1

    From MATLAB to Python

    Free

    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.

  • 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.

  • 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.

  • 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.

Initializing

Exercise

Bike traffic throughout the week

In 2012, the City of Seattle installed meters to count bicycles crossing the Fremont Bridge, a major thoroughfare for cyclists commuting to downtown Seattle from the northern neighborhoods.

A 2D NumPy array called bike_traffic is loaded, which contains the average number of cyclists that were counted crossing the Fremont Bridge during each hour of the day for each day of the week from Oct 2012 to mid-June 2018.

  • Each column is a day of the week, starting on Monday.
  • Each row is an hour of the day, beginning with the 12am-1am hour.

Don't forget that indexing in Python is a bit different from MATLAB:

  • Python indexing starts at 0, not 1
  • In Python, you can use negative indices: -1 is equivalent to MATLAB's end

Instructions 1/4

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  • 1

    To confirm which axis is hours and which is days, print the shape of the bike_traffic array.

  • 2

    Print traffic on Friday (the fifth column) during the 4 pm hour.

  • 3

    Print total traffic on a typical Monday.

  • 4

    Print the average hourly traffic on weekends between 10 am and 1 pm.