There is 33 Keywords(reserved words) in the Python Programming Language. Below you will find a list of the 33 Python Keywords.
| and | as | assert |
| break | class | continue |
| def | del | elif |
| else | except | False |
| finally | for | from |
| global | if | import |
| in | is | lambda |
| None | nonlocal | not |
| or | pass | raise |
| return | True | try |
| while | with | yield |
The keyword module gives us the ability to list out Python keywords or check if a word is a Python Keyword.
Step 1 – Import Keyword Module
#import keyword module
>>> import keyword
>>>
Step 2 – List out Python Keywords
#List Out Python Keywords
>>> keyword.kwlist
['False', 'None', 'True', 'and', 'as', 'assert', 'break', 'class', 'continue', 'def', 'del', 'elif', 'else', 'except', 'finally', 'for', 'from', 'global', 'if', 'import', 'in', 'is', 'lambda', 'nonlocal', 'not', 'or', 'pass', 'raise', 'return', 'try', 'while', 'with', 'yield']
Step 3 – Is Keyword?
#Is Keyword?
>>> keyword.iskeyword('not')
True
>>> keyword.iskeyword('dog')
False
Step 4 – Remove Keyword Module
#Remove Keyword Module
>>> del keyword
If you have any questions about the Python Keyword Module leave a comment below.
Below you will find the full list of Python Keywords we will not explain what each one does in this tutorial but if one of the words is highlighted in blue you can either hover over the word or click on it for a better explanation.
| and | as | assert |
| break | class | continue |
| def | del | elif |
| else | except | False |
| finally | for | from |
| global | if | import |
| in | is | lambda |
| None | nonlocal | not |
| or | pass | raise |
| return | True | try |
| while | with | yield |
Take a look at what happens when assign a variable a keyword. We will get an error. We will look at couple different examples here.
#Trying To Create A Variable using def
>>> def = 3
File "<stdin>", line 1
def = 3
^
SyntaxError: invalid syntax
#Trying To Create A Variable using else
>>> else = "Why doesn't this work"
File "<stdin>", line 1
else = "Why doesn't this work"
^
SyntaxError: invalid syntax
#Trying To Create A Variable using False
>>> False = 3
File "<stdin>", line 1
SyntaxError: can't assign to keyword
We can gain access to the keywords in Python by importing a module called keyword. We have not covered importing of modules yet, but we will in a later tutorial. For now just follow a long this module is easy to use and pretty straight forward.
First We Need To Import The Keyword Module
Open your Python interpreter and we are about import the keyword module. To import a module we simply type import and then the modules name. Follow the example below to import keyword module.
#Import Module
>>> import keyword
>>>
#note we do not get any response when we import a module
Get The Python Keyword List
Here we will use the keyword module to get a list of the Python Keywords in our interpreter. This command only works if you have imported the module.
#Get The Python Keyword List
>>> keyword.kwlist
['False', 'None', 'True', 'and', 'as', 'assert', 'break', 'class', 'continue', 'def', 'del', 'elif', 'else', 'except', 'finally', 'for', 'from', 'global', 'if', 'import', 'in', 'is', 'lambda', 'nonlocal', 'not', 'or', 'pass', 'raise', 'return', 'try', 'while', 'with', 'yield']
Check If A Word Is A Python Keyword Using The Keyword Module
In this example we will use the keyword module again this time we can check is a certain word is a keyword.
#Check If A Word Is A Python Keyword Using The Keyword Module
>>> keyword.iskeyword('else')
True
#note the word needs to be a string
#if we do not use a string we get an error
>>> keyword.iskeyword(else)
File "<stdin>", line 1
keyword.iskeyword(else)
You do not need to memorize the Python Keywords as we move on with our Python tutorials you will pick up on the keywords as you learn Python. If you have any questions or comments leave them below.
The id() built-in function returns a unique integer which is the identifier of an object in the memory. We can use id() to check if two variables are using the same object.
>>> a = 2
>>> a
2
>>> id(a)
4297366528
>>> b = 3
>>> b
3
>>> id(b)
4297366560
>>> id(a)==id(b)
False
>>> c = 2
>>> c
2
>>> id(c)
4297366528
>>> id(a)==id(c)
True
>>> d = "string"
>>> d
'string'
>>> id(d)
4328793624
>>> e = "String"
>>> e
'String'
>>> id(d)==id(e)
False
Updated: March 9th, 2015
a = 2
In this case, the variable would point to the object. The variable is “a” and the object is “2”. So, “a” points to the object that contains “2”. Take a look at our diagram below.
a = 2
b = 2
Here we have two variables with the same value. In this case, Python would use the same object since “2” is the same for both variables. Have a look at another diagram how Python would reference the objects.
In the above case, you can see both variables are referencing the same object even tho they are different variables.
a = 2
b = a
In this case, we set a = 2 and then we assign the variable “b” to the variable “a”. In this case the variable “b” does not actually reference “a”. “b” references “a’s” object not the variable. We will use the same diagram from above since it is actually the same.
a = 2
b = 2
The reassignment
b = 3
In this scenario, we have the variables “a” and “b” set to the same object then we reassign the variable “b” to a new object that contains the value of 3. How would this work? Let’s take a look at another awesome diagram.
a = 2
b = a
The Reassignment
a = 3
In this scenario, we originally assign the variable “a” to the value of 2 and then we assign the variable “b” to the object of “a”. Then later, on we reassign the variable “a” to a new object. In this case, “b” would still reference the object that contains the value 2 and the variable “a” would reference a new object that contains the value 3. Check out another one of my awesome drawings to better understand how this works.
We can actually gain access to the identifier with some coding. I will show how we can see the identifier and how we can also see that we are using the same object when referencing the same value with different variables. The returned integer is the actual location of objects in the memory of the underlying C language.
Access Python Objects Reference Example
#Acces Objects Reference
>>> a = 78
>>> id(78)
4297368960
#Access Another Reference
>>> b = "String"
>>> id(b)
4328969864
#How about two variables accessing the same object
>>> c = 2
>>> id(c)
4297366528
>>> d = 2
>>> id(d)
4297366528
#Easier way to compare the two
>>> id(c) == id(d)
True
#How about using above example and the reference is not the same what would happen?
>>> id(c) == id(a)
False
In this tutorial, we really went deep into the workings of the Python programming language. We now see how Python variables reference objects. We also looked into how to find the memory location of an object using the built-in function id(). If you have any questions about how to Python variables reference objects leave a comment below.
The type() built-in Python function gives us the ability to check the type information of an object via the variable.
#Create a variable
>>> a = 9
#Call the variable
>>> a
9
#Check the type of the object
>>> type(a)
<class 'int'>
#create a variable containing a string
>>> b = "string"
#check the type of the object
>>> type(b)
<class 'str'>
#Create a variable
>>> c = "apple": 5, "banana": 3
#check the type
>>> type(c)
<class 'dict'>
Why There is No Type Declaration in PythonIf you have learned other programming languages like Java or C you may a bit confused why we can declare a variable without giving that variable a type. If Python is your first language, then you may not. Python is smart enough to find out our types when we declare a variable in our programs. If Python can find out what type of data we are using in our code then why would we waste our time declaring types of variables?
The reason why we do not have to give a variable a type declaration is because the variable does not contain any information. In the previous tutorial, we discussed that variables are just pointers or they just reference a section of memory called an object. The object contains the type information, the value that was given and a reference counter.
The object holds the type information to save memory and accelerate your programs. If we created several variables with the exact same value then these variables are just pointing to one memory space. If the variables held the type information and values then we would be using a lot more memory in our programs which would slow down the program. In this day and age everyone wants programs to run at high speed. So when an object holds the information we can cut down on memory used.
We can access the type information in our objects by using a built-in function called type(). Let’s take a look at some examples.
Access Python Objects Type Information Examples
#Type integer
>>> a = 9
>>> type(a)
<class 'int'>
#Type Float
>>> b = 6.7
>>> type(b)
<class 'float'>
#Type String
>>> c = "String"
>>> type(c)
<class 'str'>
#Type List
>>> d = [123, 456, 789]
>>> type(d)
<class 'list'>
#How about first object contained in the list
>>> type(d[0])
<class 'int'>
Now that we have seen Why There is No Type Declaration in Python the understanding of how variables work should become clear as we move on with our Python Variable tutorials. If you have any questions about Why There is No Type Declaration in Python leave a comment below we will be sure to help you out.
