(Python Tutorial – 020)
Python Global Keyword
In this article, you’ll learn about the global keyword, global variable and when to use global keywords.
Before reading this article, make sure you have got some basics of Python Global, Local and Nonlocal Variables.
What is the global keyword
global keyword allows you to modify the variable outside of the current scope. It is used to create a global variable and make changes to the variable in a local context.
Rules of global Keyword
The basic rules for
global keyword in Python are:
- When we create a variable inside a function, it is local by default.
- When we define a variable outside of a function, it is global by default. You don’t have to use
- We use
globalkeyword to read and write a global variable inside a function.
- Use of
globalkeyword outside a function has no effect.
Use of global Keyword
Let’s take an example.
Example 1: Accessing global Variable From Inside a Function
c = 1 # global variable def add(): print(c) add()
When we run the above program, the output will be:
However, we may have some scenarios where we need to modify the global variable from inside a function.
Example 2: Modifying Global Variable From Inside the Function
c = 1 # global variable def add(): c = c + 2 # increment c by 2 print(c) add()
When we run the above program, the output shows an error:
UnboundLocalError: local variable 'c' referenced before assignment
This is because we can only access the global variable but cannot modify it from inside the function.
The solution for this is to use the
Example 3: Changing Global Variable From Inside a Function using global
c = 0 # global variable def add(): global c c = c + 2 # increment by 2 print("Inside add():", c) add() print("In main:", c)
When we run the above program, the output will be:
Inside add(): 2 In main: 2
In the above program, we define c as a global keyword inside the
Then, we increment the variable c by
c = c + 2. After that, we call the
add() function. Finally, we print the global variable c.
As we can see, change also occurred on the global variable outside the function,
c = 2.
Global Variables Across Python Modules
In Python, we create a single module
config.py to hold global variables and share information across Python modules within the same program.
Here is how we can share global variables across the python modules.
Example 4: Share a global Variable Across Python Modules
config.py file, to store global variables
a = 0 b = "empty"
update.py file, to change global variables
import config config.a = 10 config.b = "alphabet"
main.py file, to test changes in value
import config import update print(config.a) print(config.b)
When we run the
main.py file, the output will be
In the above, we have created three files:
config.py stores global variables of a and b. In the
update.py file, we import the
config.py module and modify the values of a and b. Similarly, in the
main.py file, we import both
update.py module. Finally, we print and test the values of global variables whether they are changed or not.
Global in Nested Functions
Here is how you can use a global variable in nested function.
Example 5: Using a Global Variable in Nested Function
def foo(): x = 20 def bar(): global x x = 25 print("Before calling bar: ", x) print("Calling bar now") bar() print("After calling bar: ", x) foo() print("x in main: ", x)
The output is :
Before calling bar: 20 Calling bar now After calling bar: 20 x in main: 25
In the above program, we declared a global variable inside the nested function
foo() function, x has no effect of the global keyword.
Before and after calling
bar(), the variable x takes the value of local variable i.e
x = 20. Outside of the
foo() function, the variable x will take value defined in the
bar() function i.e
x = 25. This is because we have used
global keyword in x to create global variable inside the
bar() function (local scope).
If we make any changes inside the
bar() function, the changes appear outside the local scope, i.e.
Disclaimer: The information and code presented within this recipe/tutorial is only for educational and coaching purposes for beginners and developers. Anyone can practice and apply the recipe/tutorial presented here, but the reader is taking full responsibility for his/her actions. The author (content curator) of this recipe (code / program) has made every effort to ensure the accuracy of the information was correct at time of publication. The author (content curator) does not assume and hereby disclaims any liability to any party for any loss, damage, or disruption caused by errors or omissions, whether such errors or omissions result from accident, negligence, or any other cause. The information presented here could also be found in public knowledge domains.
Latest end-to-end Learn by Coding Projects (Jupyter Notebooks) in Python and R:
There are 2000+ End-to-End Python & R Notebooks are available to build Professional Portfolio as a Data Scientist and/or Machine Learning Specialist. All Notebooks are only $29.95. We would like to request you to have a look at the website for FREE the end-to-end notebooks, and then decide whether you would like to purchase or not.