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13. Print In Python
15. Python for Loop
19. Break in Python
23. Float in Python
25. List in Python
27. Tuples in Python
29. Set in Python
53. Python Modules
57. Python Packages
59. Class in Python
61. Object in Python
73. JSON Python
79. Python Threading
84. Map in Python
85. Filter in Python
86. Eval in Python
96. Sort in Python
101. Datetime Python
103. 2D Array in Python
104. Abs in Python
105. Advantages of Python
107. Append in Python
110. Assert in Python
113. Bool in Python
115. chr in Python
118. Count in python
119. Counter in Python
121. Datetime in Python
122. Extend in Python
123. F-string in Python
125. Format in Python
131. Index in Python
132. Interface in Python
134. Isalpha in Python
136. Iterator in Python
137. Join in Python
140. Literals in Python
141. Matplotlib
144. Modulus in Python
147. OpenCV Python
149. ord in Python
150. Palindrome in Python
151. Pass in Python
156. Python Arrays
158. Python Frameworks
160. Python IDE
164. Python PIP
165. Python Seaborn
166. Python Slicing
168. Queue in Python
169. Replace in Python
173. Stack in Python
174. scikit-learn
175. Selenium with Python
176. Self in Python
177. Sleep in Python
179. Split in Python
184. Strip in Python
185. Subprocess in Python
186. Substring in Python
195. What is Pygame
197. XOR in Python
198. Yield in Python
199. 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.
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