Comparisons and control flow

1. Comparison operators

Like in MATLAB, Python supports logical operators such as greater than, less than, equal to, and so on, which let you check whether values meet some criteria. In this lesson, you will learn these operators and how to use them to control the flow of your code.

2. Using comparison operators

Comparison operators compare the two value and return the outcome of the comparison. In this example, the variable "meets_criteria" will be "True" or "False" based on the comparison between the variables "value" and "threshold." Since we are using the "greater than" operator, "meets_criteria" will be True if "value" is greater than "threshold," which it is.

3. The comparison operators

There are six comparison operators available in Python. You'll note a strong similarity between the comparison operators in Python and MATLAB. Notably, all are the same except for the "Not equal" operator, which differs between the two languages.

4. If

Comparison operators are especially useful for controlling the flow of your code using if statements. If statements work similarly to if statements in MATLAB, with a few distinct differences. In Python, the statement is separated from the expression to evaluate by a colon; then the expression is indented with four lines. Like for loops, the statement terminates when the code block is no longer indented.

5. Else

You can use "else" to specify an alternative block of code to execute when the conditions of the if statement are not met.

6. Else if

If there are multiple conditions you need to evaluate, you can add "else, if" statements using the expression "elif" after the if statement to add additional conditions. Each statement is evaluated in order, so the first statement, which evaluates "True," will be executed and subsequent statements will not be evaluated.

7. Let's practice

Now you are ready to practice using comparison operators and if statements to control the flow of your code.

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This exercise is part of the course

Python for MATLAB Users

BeginnerSkill Level

4.7+

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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.

Exercise 1: Welcome to Python!Exercise 2: Basic calculations Exercise 3: Forecasting an investment Exercise 4: Types of variables Exercise 5: Methods and packages Exercise 6: Manipulating strings with methods Exercise 7: Using Python packages Exercise 8: Arrays & plotting Exercise 9: Getting started with NumPy arrays Exercise 10: Plotting bicycle traffic Exercise 11: What predicts animal longevity?

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.

Exercise 1: Diving into NumPy arrays Exercise 2: Forecasting over time Exercise 3: How much more do organic avocados cost?Exercise 4: Indexing NumPy arrays Exercise 5: Getting columns from NumPy arrays Exercise 6: Bike traffic throughout the week Exercise 7: Filtering arrays with Boolean indexing Exercise 8: Lists Exercise 9: List exploration Exercise 10: Making NumPy arrays from lists Exercise 11: Operating on lists and arrays Exercise 12: Customizing plots Exercise 13: Colors, linestyles, and legends Exercise 14: Encoding data in color & size Exercise 15: Determine engine types in wildlife strikes

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.

Exercise 1: Dictionaries Exercise 2: Which keys?Exercise 3: Get data out Exercise 4: Modifying dictionaries Exercise 5: Introduction to DataFrames Exercise 6: Explore a pandas DataFrame Exercise 7: Plot data from a DataFrame Exercise 8: Accessing pandas DataFrames Exercise 9: Accessing rows and columns Exercise 10: The many flavors of .iloc Exercise 11: Creating pandas DataFrames Exercise 12: From a CSV file Exercise 13: From a dictionary of lists Exercise 14: From a list of dictionaries

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.

Exercise 1: Looping through data Exercise 2: Looping through lists Exercise 3: Overlaying multiple plots on a figure Exercise 4: Comparisons and control flow

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Exercise 5: Counting different types of aircraft Exercise 6: Finding certain rows in a DataFrame Exercise 7: Filtering data Exercise 8: Boolean indexing for quick stats Exercise 9: Booleans for the win!Exercise 10: Boolean indexing and Matplotlib fun Exercise 11: Well done!