Python Projects for Beginners a ten-Week Bootcamp Approach to Python Programming

Handling Naming Errors

All programmers make mistakes, so it’s not a problem if you run into errors. It just comes with the job. Let’s look at a common mistake that occurs with variables (no need to write this):
>>> Sport = 'baseball' # capital 'S'
>>> print(sport) # lowercase 'S'
If we try to run this code, we’ll get the following error/output:
NameError: name 'sport' is not defined
This is because the names are completely different. We referenced a variable with a lowercase “s” but declared one with capital “S.” To fix this we would capitalize the “s” in sport within print.

Integer and Float Variables

To store an integer or float in a variable, we give a name to the left of the operator and write a number on the right side. In the next cell, let’s go ahead and write the following code:

num1 = 5 # storing an integer into a variable num2 = 8.4 # storing a float into a variable print(num1, num2) # you can print multiple items using commas

Go ahead and run that cell. Notice the output is 5 and 8.4, even though we print out “num1” and “num2.” We’re printing out the value that is stored in those variables.

Boolean Variables

Remember that booleans are True or False values, so storing them is as simple as typing in one of those two words. Let’s write the following:

# storing a boolean into a variable switch = True print(switch)

Go ahead and run that cell. The resulted output is “True”. Notice that in Jupyter Notebook, the value of True or False will glow green. This is a good indication if we wrote it correctly.

String Variables

Strings are as easy to store as the previous three data types. Just keep in mind that the use of single or double quotes matters. Let’s go ahead and write the following code in a new cell:

# storing strings into a variable name = 'John Smith' fav_number = '9' print(name, fav_number) # will print 9 next to the name

Go ahead and run that. Remember that the string “9” is not the same as the integer 9.
These two data types act differently, even though the output looks similar.