An assignment statement in a function creates a local variable for the variable on the left hand side of the assignment operator. It is called local because this variable only exists inside the function and you cannot use it outside. For example, consider again the square function:
If you press the ‘last >>’ button you will see an error message. When we try to use y on line 6 (outside the function) Python looks for a global variable named y but does not find one. This results in the error:Name Error: 'y' is not defined.
The variable y only exists while the function is being executed — we call this its lifetime. When the execution of the function terminates (returns), the local variables are destroyed. Codelens helps you visualize this because the local variables disappear after the function returns. Go back and step through the statements paying particular attention to the variables that are created when the function is called. Note when they are subsequently destroyed as the function returns.
Formal parameters are also local and act like local variables. For example, the lifetime of x begins whensquare is called, and its lifetime ends when the function completes its execution.
So it is not possible for a function to set some local variable to a value, complete its execution, and then when it is called again next time, recover the local variable. Each call of the function creates new local variables, and their lifetimes expire when the function returns to the caller.
On the other hand, it is legal for a function to access a global variable. However, this is considered bad form by nearly all programmers and should be avoided. Look at the following, nonsensical variation of the square function.
Although the badsquare function works, it is silly and poorly written. We have done it here to illustrate an important rule about how variables are looked up in Python. First, Python looks at the variables that are defined as local variables in the function. We call this the local scope. If the variable name is not found in the local scope, then Python looks at the global variables, or global scope. This is exactly the case illustrated in the code above. power is not found locally in badsquare but it does exist globally. The appropriate way to write this function would be to pass power as a parameter. For practice, you should rewrite the badsquare example to have a second parameter called power.
There is another variation on this theme of local versus global variables. Assignment statements in the local function cannot change variables defined outside the function. Consider the following codelens example.
Now step through the code. What do you notice about the values of variable power in the local scope compared to the variable power in the global scope?
The value of power in the local scope was different than the global scope. That is because in this example power was used on the left hand side of the assignment statement power = p. When a variable name is used on the left hand side of an assignment statement Python creates a local variable. When a local variable has the same name as a global variable we say that the local shadows the global. A shadow means that the global variable cannot be accessed by Python because the local variable will be found first. This is another good reason not to use global variables. As you can see, it makes your code confusing and difficult to understand.
To cement all of these ideas even further lets look at one final example. Inside the square function we are going to make an assignment to the parameter x There’s no good reason to do this other than to emphasize the fact that the parameter x is a local variable. If you step through the example in codelens you will see that although x is 0 in the local variables for square, the x in the global scope remains 2. This is confusing to many beginning programmers who think that an assignment to a formal parameter will cause a change to the value of the variable that was used as the actual parameter, especially when the two share the same name. But this example demonstrates that that is clearly not how Python operates.