Lesson 4. Basic Operators in Python

Learning Objectives

At the end of this activity, you will be able to:

  • Explain how operators are used in Python to execute a specific computation or operation.
  • Write Python code to run different operations on data including math calculations, and conditional subsets.

What are Operators in Python?

Operators are symbols in Python that carry out a specific computation, or operation. The value or condition that the operator operates on is called the operand. The operand can be a variable such as jan_precip_in which has some value, (say, 0.70) or data structure like a list that contains months. The operand can also be a conditional expression or statement.

For example, you might check that the list months contains the value January. If the list contains January, the return on the check is True (January does exist in the months list). If it does not contain January, the return will be False (January does not exist in the months list). There are many different types of operators in Python including:

Arithmeticto complete mathematical calculationsboulder_precip_in * 25.4
Assignmentto assign new values (typically as a result of a arithmetic calculation)boulder_precip_in *= 25.4
Comparison or Relationalto compare operands (e.g. greater than symbol >)boulder_precip_in > phoenix_precip_in
Identityto check whether operands are the sameboulder_precip_in is not phoenix_precip_in
Membershipto check whether one operand is contained within another operand"January" in months
Logicalto check whether operands are true"January" in months AND "Jan" in months

Arithmetic Operators In Python

In Python, there are many arithmetic operations that can be completed, including operators for:

  • addition (+)
  • subtraction (-)
  • multiplication (*)
  • division (/)
  • exponents (**)

Review the cells below to explore mathematical operators in Python.

# Add two values
a = 2
b = 3

a + b
# Subtraction
b - a
# Division 
b / a
# Multiply
a * b
# Exponents
a ** b

Example Applications Of Using Math Focused Operators in Python - Unit Conversion

For scientific workflows, these arithmetic operators are very useful for converting the units of measurements, for example, from inches to millimeters (1 inch = 25.4 mm) for precipitation values. The example belows converts the average precipitation value for Boulder, CO in January from inches to millimeters.

jan_precip_inches = 0.70
inches_to_mm = 25.4

jan_precip_inches * inches_to_mm

Challenge - Data Types & Math

Interactive Activity

Create two variables:

  • march_precip_in that contains the numeric value 1.85 which represents average precipitation in Boulder in March.
  • in_to_mm that contains the value 25.4 which represents the value to convert inches to mm.

Using only these variables and arithmetic operators, create a third variable march_precip_mm, which represents the value for march_precip_in (average precipitation in Boulder) converted to millimeters (mm). Finally, answer the question:

  1. What is the type() of the object: march_precip_mm?

Assignment Operators in Python

While arithmetic operators are very useful for calculations, they do not change the original values of the variables being used.

For example, when you run:

jan_precip_inches = 0.70
inches_to_mm = 25.4

jan_precip_inches * inches_to_mm

the variable jan_precip_inches will still retain the value 0.7 (the measurement in inches), even after the calculation to convert to mm is run.


Combining Assignment with Arithmetic Operators: Arithmetic Assignment

If you want to assign a new value as a result of a calculation, you can use an assignment operator, which combines the arithmetic operator (e.g. *) with the assignment = to set a new value. For example, you can combine * and = to multiply a value and set the result equal to itself plus the new value.

jan_precip = 0.70
inches_to_mm = 25.4

jan_precip *= inches_to_mm


Recall that on the previous page on working with lists, you also used an assignment operator to append items to the end of a list. This works when your list contains strings. This is a special case of the addition assignment operator += because it is not actually completing a mathematical operation on the list. It simply appends the values as new items to the end of the list.

months = ["January", "February"]

['January', 'February']
months += ["March", "April"]

['January', 'February', 'March', 'April']

However, not all assignment operators can be used on all object types. For example, the following code will result in an error because Python does not know how to handle reassigning each value in the list.

boulder_precip_in = [0.70, 0.75, 1.85]
boulder_precip_in *= 25.4

Data Tip: You can review the Python docs on types and operations to see what kinds of operations can be run on different object types.


Create two variables:

  • annual_avg_precip_nyc that is equal to 42.65 Note that this value represents the total annual average precipitation for New York City. However, it is is missing the value for precipitation in December. You are going to fix that!
  • dec_avg_precip_nyc that is equal to 3.58

Using the += operator (addition assignment), add december_precip_nyc to annual_avg_precip_nyc, so that annual_avg_precip_nyc represents the complete annual average precipitation in New York City.

Output of Arithmetic Assignment Operators Does Not Automatically Print

Notice now that the output is not automatically printed when you use arithmatic assignment operators. This is because there is an assignment involved (you are using the equals sign, so the output value is being reassigned to the variable).

Remember that this:

jan_precip = 0.70
jan_precip *= 25.4

is the same as:

jan_precip = 0.70
jan_precip = 25.4 * jan_precip
jan_precip = 0.70
jan_precip *= 25.4

To see the new value, you can call the variable name (e.g. jan_precip), or you can use the print statement (e.g. print(jan_precip)) to display the new value. Using the print statement can be very helpful because then you can print multiple values. For example, notice calling only the variable names (e.g. a, jan_precip, b), you are only shown the value of the last variable.


Using print(), you can print as many things as you want.


You can even combine the variables with a text string in a print statement by including a text string "text" within the print statement. To do this, simply separate the text string from the object that is being printed using a comma ,.

print("January precipitation:", jan_precip)
January precipitation: 17.779999999999998

Notice that the word print does not show up the output. Instead, you simply see the result, without the parentheses or quotations for the text string. You have now used your first Python function - print()! Functions in Python are commands that can take inputs that are used to produce output. You will learn more about functions later in these exercises, and you will use the print function a lot, as it can be very handy for viewing results and for communicating the status of your code.

Relational Operators in Python

Often in Python, you need to compare two values against each other. To do this, you can check a statement, such as 3 < 4, and get returned one of two values from Python: True or False. These are called boolean values and can be very powerful in scripting workflows. A boolean is a value that is either 1 (True), or 0 (False). Like strings or integers, booleans are their own data type.

In Python, there are many relational operations that can be used, including operators for:

  • equal (==)
  • not equal (!=)
  • greater than (>)
  • greater than or equal (>=)
  • less than (<)
  • less than or equal (<=)

Review the cells below to see what these operations return in different circumstances.

# What type of object is `True`?
# Note that the T needs to be capitalized! type(true) won't work!

Relational operations return a boolean value.

# Is the value 3 less then 4?
3 < 4
# Is the value 3 greater than 4?
3 > 4
# Does 3 equal 3?
3 == 3
# Does 3 equal 4?
3 == 4
# Does 3 NOT equal 4?
3 != 4
# Is 3 less than or equal to 4?
3 <= 4
# Is 3 less than or equal to 3?
3 <= 3
# Is 3 greater than or equal to 4?
3 >= 4

Similar to other types of variable types, bool values can be assigned to a variable.

Data Tip: You do not need to put the operation below (3 > 2) in parenthesis, as is done below. However, doing so makes the code a bit easier to read. {: .notice–success }

is_greater = (3 > 2)


Relational operators can be extremely powerful as you begin to develop more complex scripts. For example you may test whether a variable has a specific value. If it does (the condition is true), then you tell the script to run a particular operation.


rainfall = 3

if rainfall > 2:
    # Perform some calculation 

You will learn more about conditions statements in chapter 17 of the introduction to earth data science textbook.

Membership Operators in Python

A membership operator, such as in, will check if one item contains another item. This can be useful with strings, lists, or other data storage objects that you will learn about in later lessons, such as dataframes.

precip = "Precipitation"

# Are the characters `Precip` in the object called precip?
"Precip" in precip
temp_1 = [70, 68, 74]

68 in temp_1
# You can also combine in with not to check for non-membership
69 not in temp_1

Logical Operators

Logical operators can be used to check combinations of booleans. The most common logical operators are and and or. and will check that both of the statements being checked are true. True and True will return True, but True and False will return False. or will check that one of the statements being checked are true. Unlike and, True or True will return True, and True or False will return True as well. Both False and False and False or False will return False.

# True and True
68 in temp_1 and 70 in temp_1
# True and False
68 in temp_1 and 69 in temp_1
# True or True
68 in temp_1 or 70 in temp_1
# True or False
68 in temp_1 or 69 in temp_1

Identity Operators in Python

An identity operator, such as is, will check if two variables are referring to the same object. It is similar to the == operator, except that it will not only check that the values of two variables are identical, but it will check that they are referring to the exact same thing in Python. It’s a subtle distinction, but can be very useful.

Data Tip: Memory Allocation in Python

To understand the difference between is and ==, first you need to know a little bit about how Python stores data. In Python, when a variable is declared, there’s a certain place on your computer that is reserved to store that data. This place has a memory address.

In Python, you can see the memory id of a variable using the id() function. This will print out a long integer that is how Python identifies the object. It’s what Python uses to find the variable in the computer’s memory.

When you create a variable, let’s say a = 3, if you run id(a) or id(3), you’ll get the same output.

For me, it was 94641000121024. This is the memory address where Python stores the variable 3 for your script.

When you create a list, a new id is created for your list, as it is a new variable in Python. So let’s say you make a list like so: list1 = [1, 2, 3]. This list would be given a new id, such as 140657632719088.

If you create an identical list like this: list2 = [1, 2, 3] and check its id, it’s stored at a different location, such as 140657632694912. This is because the list is a new variable that’s stored in a new place in the memory.

Because of this, list1 == list2 will return True, because their values are equal. However, list1 is list2 will return False, since they are stored in different parts of your computer’s memory.

However, if you make a list by assigning it to the old list, a.k.a list1 = list2, they will both have the memory address of list1, 140657632719088. This is because you were not creating a new variable, and thus making a new memory address.

You were instead pointing to an old memory address when creating list2. Because of this, list1 == list2 will return True, because their values are equal, AND list1 is list2 will also return True, since they are stored at the same location.

# Create variables to compare
temp_1 = [70, 68, 74]
temp_2 = [70, 68, 74]

# Create a new variable called temp_3 from temp_1
temp_3 = temp_1
# Test that temp_3 the same as temp_1
temp_1 is temp_3
# While temp_1 and temp_2 contain the same values...
temp_1 == temp_2
# They have been created independently
temp_1 is temp_2

With this example, you can easily see the distinction between == and is. Even though temp_1 and temp_2 contain identical values, they are technically not the same list. That is to say they are not stored in the same memory location on your computer. However, since temp_3 was set to equal temp_1, they are exactly the same.

You can also combine is with not to check that two variables are NOT the same.

temp_1 is not temp_2

Like all other types of boolean values, the outputs of these operations can be assigned to variables as well.

is_the_same = (temp_1 is temp_2)


Challenge: Test Your Knowledge

Below, there are variables assigned to the output of either relational, identity, membership, or logical operations. Currently, each operation is returning False. Modify the operations so that they will all return True.

# Modify relational operation so the assigned variable returns True
relational = (3 <= 2)

# Modify identity operation so the assigned variable returns True
identity = (4 is 3)

# Modify membership operation so the assigned variable returns True
membership = (72 in temp_1)

# Modify logical operation so the assigned variable returns True
temp_1 = [70, 68, 74]
logical = (68 in temp_1 and 69 in temp_1)
# Currently all of these objects return True. 
# Modify the code above so they all return True!
print(relational, identity, membership, logical)
False False False False

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