Tutorial Playlist
200 Lessons1. Introduction to Python
2. Features of Python
3. How to install python in windows
4. How to Install Python on macOS
5. Install Python on Linux
6. Hello World Program in Python
7. Python Variables
8. Global Variable in Python
9. Python Keywords and Identifiers
10. Assert Keyword in Python
11. Comments in Python
12. Escape Sequence in Python
13. Print In Python
14. Python-if-else-statement
15. Python for Loop
16. Nested for loop in Python
17. While Loop in Python
18. Python’s do-while Loop
19. Break in Python
20. Break Pass and Continue Statement in Python
21. Python Try Except
22. Data Types in Python
23. Float in Python
24. String Methods Python
25. List in Python
26. List Methods in Python
27. Tuples in Python
28. Dictionary in Python
29. Set in Python
30. Operators in Python
31. Boolean Operators in Python
32. Arithmetic Operators in Python
33. Assignment Operator in Python
34. Bitwise operators in Python
35. Identity Operator in Python
36. Operator Precedence in Python
37. Functions in Python
38. Lambda and Anonymous Function in Python
39. Range Function in Python
40. len() Function in Python
41. How to Use Lambda Functions in Python?
42. Random Function in Python
43. Python __init__() Function
44. String Split function in Python
45. Round function in Python
46. Find Function in Python
47. How to Call a Function in Python?
48. Python Functions Scope
49. Method Overloading in Python
50. Method Overriding in Python
51. Static Method in Python
52. Python List Index Method
53. Python Modules
54. Math Module in Python
55. Module and Package in Python
56. OS module in Python
57. Python Packages
58. OOPs Concepts in Python
59. Class in Python
60. Abstract Class in Python
61. Object in Python
62. Constructor in Python
63. Inheritance in Python
64. Multiple Inheritance in Python
65. Encapsulation in Python
66. Data Abstraction in Python
67. Opening and closing files in Python
68. How to open JSON file in Python
69. Read CSV Files in Python
70. How to Read a File in Python
71. How to Open a File in Python?
72. Python Write to File
73. JSON Python
74. Python JSON – How to Convert a String to JSON
75. Python JSON Encoding and Decoding
76. Exception Handling in Python
77. Recursion in Python
78. Python Decorators
79. Python Threading
80. Multithreading in Python
81. Multiprocеssing in Python
82. Python Regular Expressions
83. Enumerate() in Python
84. Map in Python
85. Filter in Python
86. Eval in Python
87. Difference Between List, Tuple, Set, and Dictionary in Python
88. List to String in Python
89. Linked List in Python
90. Length of list in Python
91. Reverse a List in Python
92. Python List remove() Method
93. How to Add Elements in a List in Python
94. How to Reverse a List in Python?
95. Difference Between List and Tuple in Python
96. List Slicing in Python
97. Sort in Python
98. Merge Sort in Python
99. Selection Sort in Python
100. Sort Array in Python
101. Sort Dictionary by Value in Python
102. Datetime Python
103. Random Number in Python
104. 2D Array in Python
105. Abs in Python
106. Advantages of Python
107. Anagram Program in Python
108. Append in Python
109. Applications of Python
110. Armstrong Number in Python
111. Assert in Python
112. Binary Search in Python
113. Binary to Decimal in Python
114. Bool in Python
115. Calculator Program in Python
116. chr in Python
117. Control Flow Statements in Python
118. Convert String to Datetime Python
119. Count in python
120. Counter in Python
121. Data Visualization in Python
122. Datetime in Python
123. Extend in Python
124. F-string in Python
125. Fibonacci Series in Python
126. Format in Python
127. GCD of Two Numbers in Python
128. How to Become a Python Developer
129. How to Run Python Program
130. In Which Year Was the Python Language Developed?
131. Indentation in Python
132. Index in Python
133. Interface in Python
134. Is Python Case Sensitive?
135. Isalpha in Python
136. Isinstance() in Python
137. Iterator in Python
138. Join in Python
139. Leap Year Program in Python
140. Lexicographical Order in Python
141. Literals in Python
Now Reading
142. Matplotlib
143. Matrix Multiplication in Python
144. Memory Management in Python
145. Modulus in Python
146. Mutable and Immutable in Python
147. Namespace and Scope in Python
148. OpenCV Python
149. Operator Overloading in Python
150. ord in Python
151. Palindrome in Python
152. Pass in Python
153. Pattern Program in Python
154. Perfect Number in Python
155. Permutation and Combination in Python
156. Prime Number Program in Python
157. Python Arrays
158. Python Automation Projects Ideas
159. Python Frameworks
160. Python Graphical User Interface GUI
161. Python IDE
162. Python input and output
163. Python Installation on Windows
164. Python Object-Oriented Programming
165. Python PIP
166. Python Seaborn
167. Python Slicing
168. type() function in Python
169. Queue in Python
170. Replace in Python
171. Reverse a Number in Python
172. Reverse a string in Python
173. Reverse String in Python
174. Stack in Python
175. scikit-learn
176. Selenium with Python
177. Self in Python
178. Sleep in Python
179. Speech Recognition in Python
180. Split in Python
181. Square Root in Python
182. String Comparison in Python
183. String Formatting in Python
184. String Slicing in Python
185. Strip in Python
186. Subprocess in Python
187. Substring in Python
188. Sum of Digits of a Number in Python
189. Sum of n Natural Numbers in Python
190. Sum of Prime Numbers in Python
191. Switch Case in Python
192. Python Program to Transpose a Matrix
193. Type Casting in Python
194. What are Lists in Python?
195. Ways to Define a Block of Code
196. What is Pygame
197. Why Python is Interpreted Language?
198. XOR in Python
199. Yield in Python
200. Zip in Python
In the vast ecosystem of Python, literals stand as the cornerstone of syntax, serving as constant values that developers use throughout their code. Literals streamline code by directly denoting data, enhancing readability and performance. As professionals delve deeper into advanced coding or upskilling, understanding literals becomes paramount. In this tutorial, we will explore the essence of literals in Python, shedding light on their significance and the myriad benefits they bring to the table.
Python, a high-level and dynamic programming language, boasts of a diverse range of constructs and features. Among these, literals emerge as a fundamental concept, acting as fixed values that the language processes directly. Unlike variables, which store data that can change, literals remain constant throughout a program's execution. As the name suggests, they offer a "literal" representation of values, be it numbers, text, or other data types.
This tutorial is meticulously designed to provide working professionals a comprehensive insight into Python literals. We'll dissect the types of literals in Python, delve into their importance, and elucidate how their apt utilization can elevate coding efficacy.
In programming, certain terms and constructs form the very backbone of a language, setting the foundation for advanced concepts. Among these foundational elements in Python is the concept of literals. But what exactly are literals? Let's delve deeper.
Literals, in Python, are best understood as the raw building blocks of a program. They are the fixed, unchangeable values you encode directly into your scripts. While variables serve as containers that store and change values, literals are constant, unchanging data elements. When a developer encodes a number like 10 or a text like "Python" in their code, they are essentially using literals.
To get a clearer picture of literals in Python, one needs to discern the various types of literals that exist. Here's a comprehensive comparison showing the types of literals in Python with example:
Type of Literal | Description | Example |
Numeric Literal | Values that represent numeric data types | 123, 10.5 |
String Literal | Sequence of characters surrounded by quotes | "Hello", 'Python' |
Boolean Literal | Represents two truth values: True and False | True, False |
None Literal | Represents the absence of value or a null value | None |
Complex Literal | Combination of real and imaginary parts | 3+4j |
List, Dict, Set, Tuple Literals | Compound data types with their specific literals | [1, 2], {"key":"value"}, {1,2,3}, (1,2) |
In Python, literals are used to represent constant values that are used directly in your code. A literal is a way to express a fixed value directly in the source code of a program, without requiring any calculations or evaluations. It's a fundamental concept in programming languages that allows you to define values for various data types in a concise and readable manner.
Python supports various types of literals to represent different kinds of data, such as:
Literals are used to provide initial or constant values for variables, parameters, and other data structures in your code. They enhance code readability and reduce the need for explicit conversions or computations when working with constants. Using literals also makes it easier to understand the intended data types and values within the code.
Code:
x = (1 == True)
y = (1 == False)
z = True + 2
k = False + 6
print("x is", x)
print("y is", y)
print("z:", z)
print("k:", k)
Code:
s = 'upGradTutorial!'
t = "upGradTutorial!"
m = '''up
Grad
Tutorial!'''
print(s)
print(t)
print(m)
Code:
v = 'u'
w = "G"
print(v)
print(w)
Code:
a = 0b10101
b = 51
c = 0o321
d = 0x121
print(a, b, c, d)
Code:
a = 25.9
b = 46.7
print(a, b)
Code:
a = 8 + 6j
b = 6j
print(a, b)
Code:
x = (1 == True)
y = (1 == False)
z = True + 2
k = False + 6
print("x is", x)
print("y is", y)
print("z:", z)
print("k:", k)
Code:
num = [10, 20, 30, 40, 50]
n = ['abc', 'efg', 'pqr', 3]
print(num)
print(n)
Code:
even_num = (20, 40, 60, 80)
odd_num = (10, 30, 50, 70)
print(even_num)
print(odd_num)
Code:
alpha = {'a': 'africa', 'b': 'bali', 'c': 'canada'}
info = {'name': 'Ram', 'age': 25, 'ID': 25}
print(alpha)
print(info)
Code:
vowels = {'a', 'e', 'i', 'o', 'u'}
fruits = {"coconut", "papaya", "berry"}
print(vowels)
print(fruits)
In the intricate world of Python programming, literals shine brightly as both foundational elements and powerful tools. These constants, which are directly coded into scripts, offer several layers of advantages that contribute not just to the code's efficiency but also to its elegance. Let's systematically unpack these layers to understand the underlying benefits.
Using literals directly in your Python code can have some disadvantages:
To address these disadvantages, consider using constants or variables instead of literals. By assigning meaningful names to values and centralizing their definitions, you can improve code readability, maintainability, and flexibility. This practice is especially important when dealing with values that are reused in multiple places or that have special significance in your application.
While on the surface literals might seem like mere data representations, their advantages permeate deeper, impacting the efficiency, clarity, and integrity of the code. The following table provides a summarized overview of the compelling benefits literals bring to Python programming:
Advantage | Description |
Code Clarity | Direct value representation for enhanced transparency. |
Memory Efficiency | Optimized memory allocation for reduced footprint. |
Type Safety | Ensured data type consistency to minimize conversion errors. |
Faster Execution | Quick processing due to immutability of literals. |
Consistent Codebase | Direct representation leading to uniformity in code. |
Fewer Runtime Errors | Reduced ambiguity for predictable script behaviors. |
Optimized Operations | Tailored literals for specific efficient operations. |
Enhanced Debugging | Easier bug identification and resolution. |
Literals, as illustrated, significantly elevate the caliber of Python programming. By understanding and harnessing their inherent advantages, developers can craft robust, efficient, and clear Python scripts.
Literals in Python provide more than just static data placeholders; they are powerful tools that influence various aspects of programming, from code clarity to memory optimization. By leveraging these inherent advantages, developers can craft precise, efficient, and reliable scripts. As the world of programming becomes increasingly complex and collaborative, the benefits of literals shine through, offering a consistent codebase and fewer runtime errors.
For professionals seeking to upskill in Python, understanding the nuances of literals becomes crucial. By doing so, they not only enhance their coding prowess but also prepare themselves for advanced programming challenges. For those dedicated to continuous learning, upGrad offers a plethora of upskilling courses to delve deeper into Python and its intricacies.
1. How many types of literals are allowed in Python?
Python provides a comprehensive set of literals to cater to various data representation needs. This includes simple types like numeric and string literals and extends to complex and compound types such as boolean, complex numbers, lists, dictionaries, sets, and tuples. Together, they allow for versatile coding and data handling in diverse scenarios.
2. What are literals in Python? How many types of literals are allowed in Python?
In Python, literals refer to the constants coded directly into a program's source code. They can be thought of as the raw data that Python uses to operate. Python permits a variety of literals, encompassing simple ones like numeric and string, and extends to the more complex types like boolean, complex numbers, and compound data structures.
3. What is the use of f-string literal Python?
The f-string literal, a notable feature introduced in Python 3.6, offers a mechanism to embed expressions within string literals. By doing so, it presents a more concise and readable approach to integrate values into strings. This helps clean up the code and reduce the need for cumbersome concatenations and format calls.
4. Can you provide an example of a numeric literal in Python?
Absolutely. Numeric literals in Python represent values without any fractional component or with it. As an example, the number 256 stands as an integer numeric literal, signifying a whole number. On the other hand, 10.5 is a floating-point numeric literal, indicative of a number with a decimal component.
5. What is meant by the term Python literal type hint?
The term Python literal hint revolves around Python's type annotation system. Essentially, it provides guidance or a hint regarding the expected type of a literal or a variable within the code. By using type hints, developers can promote clearer code understanding, ease the debugging process, and potentially catch type-related errors earlier in the development cycle.
PAVAN VADAPALLI
Director of Engineering
Director of Engineering @ upGrad. Motivated to leverage technology to solve problems. Seasoned leader for startups and fast moving orgs. Working …Read More
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upGrad does not grant credit; credits are granted, accepted or transferred at the sole discretion of the relevant educational institution offering the diploma or degree. We advise you to enquire further regarding the suitability of this program for your academic, professional requirements and job prospects before enr...