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
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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
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 this tutorial, we will dive deep into the rich world of data types in Python. As Python has gained traction in multiple domains, from web development to data science, understanding its data types becomes crucial. These data types form the foundational elements of Python, and mastery over them paves the way for advanced programming.
Let’s embark on this enlightening journey!
Python, a dynamic and multifaceted programming language, boasts a comprehensive assortment of data types tailored to cater to diverse applications and user needs. Whether you are working on web development, data analysis, artificial intelligence, or just basic scripting, understanding these data types in Python becomes imperative. Ranging from the simple numeric and sequence types to the advanced binary and memory types, Python furnishes programmers with the tools needed for efficient coding and problem-solving.
Dive in to explore these data building blocks!
Python, being a versatile and modern language, introduces an assortment of standard data types. These data types in Python form the building blocks of the language, empowering developers to craft efficient and diverse applications. Grasping these is pivotal for any Python enthusiast. Let's delve deep into each of these data types.
Python data types are fundamental and cater to various mathematical needs:
Sequences in Python are ordered collections. These are quintessential and exhibit different properties:
Captures the essence of logical operations:
Dealing with unique collection items:
For associating keys with values:
Manipulate binary data:
Code:
x = 5
y = "Hello, World!"
z = [1, 2, 3]
print(type(x)) # Output: <class 'int'>
print(type(y)) # Output: <class 'str'>
print(type(z)) # Output: <class 'list'>
Code:
# Integer
x = 5
print("Integer:", x)
print("Type of x:", type(x))
# Floating-point number
y = 3.14
print("Float:", y)
print("Type of y:", type(y))
# Complex number
z = 2 + 3j
print("Complex:", z)
print("Type of z:", type(z))
Code:
# String
string_sequence = "Hello, World!"
print("String:", string_sequence)
print("Type of string_sequence:", type(string_sequence))
# List
list_sequence = [1, 2, 3, 4, 5]
print("List:", list_sequence)
print("Type of list_sequence:", type(list_sequence))
# Tuple
tuple_sequence = (10, 20, 30, 40, 50)
print("Tuple:", tuple_sequence)
print("Type of tuple_sequence:", type(tuple_sequence))
# Range
range_sequence = range(5)
print("Range:", list(range_sequence))
print("Type of range_sequence:", type(range_sequence))
Code:
string = "Hello, World!"
# Accessing individual characters using positive indexing
first_character = string[0]
second_character = string[1]
last_character = string[-1] # Equivalent to string[len(string) - 1]
print("First character:", first_character)
print("Second character:", second_character)
print("Last character:", last_character)
substring = string[7:12] # Extracts "World"
every_other_character = string[::2] # Extracts "Hlo ol!"
reversed_string = string[::-1] # Reverses the string
print("Substring:", substring)
print("Every other character:", every_other_character)
print("Reversed string:", reversed_string)
Code:
L = []
print("Initial blank List: ")
print(L)
L = ['upGradTutorial!']
print("\nList with the use of String: ")
print(L)
L = ["up", "Grad", "Tutorial!"]
print("\nList with multiple values: ")
print(L[0])
print(L[1])
L = [['up', 'Grad'], ['Tutorial!']]
print("\nMulti-Dimensional List: ")
print(L)
Code:
List = ["up", "Grad", "Tutorial!"]
print("Accessing element from the list")
print(List[0])
print(List[1])
print("Accessing element using negative indexing")
print(List[-1])
print(List[-2])
Code:
# Creating a tuple
fruits = ("apple", "banana", "cherry", "date", "elderberry")
# Accessing elements by index
first_fruit = fruits[0]
second_fruit = fruits[1]
# Accessing elements using negative indexing
last_fruit = fruits[-1]
# Counting the occurrences of an element
num_cherries = fruits.count("cherry")
# Getting the index of an element
index_of_date = fruits.index("date")
# Creating a new tuple by slicing
selected_fruits = fruits[1:4]
# Printing the results
print("Fruits:", fruits)
print("First fruit:", first_fruit)
print("Second fruit:", second_fruit)
print("Last fruit:", last_fruit)
print("Number of cherries:", num_cherries)
print("Index of 'date':", index_of_date)
print("Selected fruits:", selected_fruits)
Code:
# Creating a tuple
fruits = ("apple", "banana", "cherry", "date", "elderberry")
# Accessing elements by index
first_fruit = fruits[0]
second_fruit = fruits[1]
third_fruit = fruits[2]
# Accessing elements using negative indexing
last_fruit = fruits[-1]
second_last_fruit = fruits[-2]
# Printing the results
print("First fruit:", first_fruit)
print("Second fruit:", second_fruit)
print("Third fruit:", third_fruit)
print("Last fruit:", last_fruit)
print("Second last fruit:", second_last_fruit)
Code:
# Boolean values
is_true = True
is_false = False
# Logical operations
result_and = is_true and is_false # Logical AND
result_or = is_true or is_false # Logical OR
result_not = not is_true # Logical NOT
# Comparisons
greater_than = 5 > 3
less_than = 2 < 1
equal_to = 10 == 10
not_equal = 5 != 8
# Conditional statements
if is_true:
print("It's true!")
else:
print("It's false.")
if greater_than:
print("5 is greater than 3.")
if less_than:
print("2 is less than 1.")
else:
print("2 is not less than 1.")
# Printing the results
print("Result of AND:", result_and)
print("Result of OR:", result_or)
print("Result of NOT:", result_not)
print("Greater than:", greater_than)
print("Less than:", less_than)
print("Equal to:", equal_to)
print("Not equal:", not_equal)
Code:
s = set()
print("Initial blank Set: ")
print(s)
s = set("upGradTutorial!")
print("\nSet with the use of String: ")
print(s)
s = set(["up", "Grad", "Tutorial!"])
print("\nSet with the use of List: ")
print(s)
s = set([1, 2, 'up', 4, 'Grad', 6, 'Tutorial!'])
print("\nSet with the use of Mixed Values")
print(s)
Code:
s1 = set(["up", "Grad", "Tutorial!"])
print("Initial set")
print(s1)
print("\nElements of set: ")
for i in s1:
print(i, end=" ")
print("up" in s1)
Code:
Dict = {}
print("Empty Dictionary: ")
print(Dict)
Dict = {1: 'up', 2: 'Grad', 3: 'Tutorial!'}
print("\nDictionary with the use of Integer Keys: ")
print(Dict)
Dict = {'Name': 'up', 1: [1, 2, 3, 4]}
print("\nDictionary with the use of Mixed Keys: ")
print(Dict)
Dict = dict({1: 'up', 2: 'Grad', 3: 'Tutorial!'})
print("\nDictionary with the use of dict(): ")
print(Dict)
Dict = dict([(1, 'up'), (2, 'Grad')])
print("\nDictionary with each item as a pair: ")
print(Dict)
Code:
D = {1: 'up', 'name': 'Grad', 3: 'Tutorial!'}
print("Accessing a element using key:")
print(D['name'])
print("Accessing a element using get:")
print(D.get(3))
Code:
# Numeric data types
integer_number = 5
float_number = 3.14
complex_number = 2 + 3j
# String data type
text = "Hello, World!"
# Boolean data type
is_true = True
is_false = False
# List data type
fruits = ["apple", "banana", "cherry"]
# Tuple data type
coordinates = (10, 20)
# Set data type
unique_numbers = {1, 2, 3, 4, 5}
# Dictionary data type
person = {"name": "John", "age": 25, "city": "New York"}
# Printing the values and their types
print(integer_number, type(integer_number))
print(float_number, type(float_number))
print(complex_number, type(complex_number))
print(text, type(text))
print(is_true, type(is_true))
print(is_false, type(is_false))
print(fruits, type(fruits))
print(coordinates, type(coordinates))
print(unique_numbers, type(unique_numbers))
print(person, type(person))
Diving into Python's data types, we've covered the essentials that shape our coding projects. Now, with this foundation, navigating Python becomes much smoother. As this tutorial concludes, remember that the world of programming is ever-evolving, urging us to keep learning. For those keen on enhancing their Python expertise, consider exploring courses by upGrad. Every step in your learning journey is significant, and having the right guidance makes all the difference.
1. Are lists mutable in Python?
In Python, lists are indeed mutable. This characteristic allows programmers to modify, add, or delete items from a list even after its initial creation. This adaptability makes lists a versatile data structure, especially when data needs constant updates or modifications.
2. What's the difference between tuples and lists?
Both tuples and lists are sequence types in Python. The primary distinction lies in their mutability. Lists can be altered after creation, offering dynamic modification capabilities. In contrast, tuples are immutable, meaning once they are created, their content remains static and cannot be changed, which can be advantageous for ensuring data consistency.
3. Can dictionaries hold multiple data types?
Absolutely! Dictionaries in Python are incredibly flexible. They can store key-value pairs where values can be of varied data types, including integers, strings, lists, or even other dictionaries and objects. This multifaceted nature of dictionaries makes them ideal for representing structured data in diverse scenarios.
4. How is the range data type commonly used?
The range data type in Python is predominantly employed in loops, especially the 'for' loop. It generates a sequence of numbers, which can dictate how many times the loop will iterate. This is particularly useful when an action needs repetition over a fixed set of iterations, ensuring code conciseness and efficiency.
5. What is the role of the complex data type in Python?
In Python, the complex data type provides a means to represent numbers that comprise both a real and an imaginary component. This capability is particularly vital in domains like engineering and mathematics, where complex number operations and computations are frequent. Python's innate support for this data type facilitates intricate mathematical tasks with ease.
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...