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
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
Now Reading
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
Arrays, in the expansive universe of programming, are more than mere data containers; they're the bedrock of efficient computation. In Python, a language renowned for its versatility, arrays take on a unique significance. They not only enable organized storage but also facilitate swift data operations. This tutorial aims to provide professionals with an in-depth exploration of Python arrays, detailing their nuances, applications, and pivotal roles in data manipulation and algorithmic processes.
Python, revered for its clean syntax and scalability, offers myriad data structures, among which arrays stand out distinctively. Python arrays serve as a fundamental bridge between abstract programming constructs and real-world applications, such as data analysis, algorithm implementation, and system design.
While Python does not have native arrays in the traditional sense, its list structures emulate them, and the dedicated 'array' module augments their functionality. Coupled with third-party libraries like NumPy, Python's array capabilities elevate to meet complex computational needs. This tutorial embarks on a comprehensive journey, covering the essence, manipulation, and inherent advantages of arrays in Python.
Arrays serve as one of the foundational data structures in computer science. Their consistency in type and systematic memory storage makes them highly efficient, especially for operations that demand rapid access to elements. When diving into Python, understanding arrays becomes vital, given their nuanced differences from the more flexible lists. Let’s explore deeper.
Arrays:
These are structured collections wherein every element adheres to a uniform data type. Positioned sequentially in memory, their organized structure facilitates swift access and modification.
Lists:
Unlike arrays, lists in Python are versatile containers capable of holding varied data types. This flexibility, while advantageous, lacks the strict type uniformity arrays maintain.
Static Array:
Once declared, its size remains unchanged throughout its lifecycle. However, Python generally eschews static arrays in favor of its more dynamic counterparts.
Dynamic Array:
Contrary to static arrays, dynamic arrays can adjust their size according to the data they hold. As elements are added or removed, they can expand or shrink accordingly. Python's lists essentially operate as dynamic arrays.
Fixed Size:
A hallmark of static arrays, their sizes are set during their declaration and remain unchanged.
Homogeneity:
Arrays’ strength lies in their consistent element type. This homogeneity ensures predictability and optimized performance in various operations.
Memory Storage:
One of arrays’ distinguishing features is their allocation in sequential memory locations. This contiguity enhances data retrieval speeds.
While Python lists offer flexibility, there are times when a stricter data structure is desired. Enter Python’s 'array' module. This module facilitates the creation of arrays closer to those in languages like C or Java. By focusing on type consistency, it provides a more traditional array experience for those situations demanding uniformity.
Example:
import array as arr
a = arr.array('i', [1,2,3])
This line demonstrates the declaration of an integer array using Python's 'array' module. Here, 'i' signifies the type (integer), and the subsequent list contains the array's initial values.
Here is an array in python example:
Code:
# Creating an array
my_array = [10, 20, 30, 40, 50]
# Accessing elements
print(my_array[0]) # Output: 10
print(my_array[2]) # Output: 30
# Modifying elements
my_array[1] = 25
print(my_array) # Output: [10, 25, 30, 40, 50]
# Array length
length = len(my_array)
print(length) # Output: 5
# Adding elements to the end
my_array.append(60)
print(my_array) # Output: [10, 25, 30, 40, 50, 60]
# Removing elements
my_array.pop(2)
print(my_array) # Output: [10, 25, 40, 50, 60]
Code:
# Creating arrays
array1 = [1, 2, 3, 4, 5]
array2 = [6, 7, 8, 9, 10]
# Concatenation
concatenated_array = array1 + array2
print(concatenated_array) # Output: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
# Slicing
sliced_array = array1[2:5]
print(sliced_array) # Output: [3, 4, 5]
# Finding maximum and minimum
max_value = max(array1)
min_value = min(array2)
print(max_value, min_value) # Output: 5 6
# Sorting
sorted_array = sorted(array1)
print(sorted_array) # Output: [1, 2, 3, 4, 5]
# Reversing
reversed_array = array2[::-1]
print(reversed_array) # Output: [10, 9, 8, 7, 6]
# Searching for an element
element_to_search = 8
if element_to_search in array2:
print(f"{element_to_search} found in the array.")
else:
print(f"{element_to_search} not found in the array.")
Code:
# Creating an array
my_array = [10, 20, 30, 40, 50]
# Accessing individual elements
first_element = my_array[0]
second_element = my_array[1]
print(first_element) # Output: 10
print(second_element) # Output: 20
# Accessing elements using negative indices (counting from the end)
last_element = my_array[-1]
second_last_element = my_array[-2]
print(last_element) # Output: 50
print(second_last_element) # Output: 40
Changing Elements:
Code:
my_array = [10, 20, 30, 40, 50]
my_array[2] = 35 # Changing the element at index 2 to 35
print(my_array) # Output: [10, 20, 35, 40, 50]
Adding Elements:
Code:
my_array = [10, 20, 30, 40, 50]
# Appending an element
my_array.append(60)
print(my_array) # Output: [10, 20, 30, 40, 50, 60]
# Inserting an element at index 2
my_array.insert(2, 25)
print(my_array) # Output: [10, 20, 25, 30, 40, 50, 60]
When programming, choosing the right data structure can greatly influence performance, clarity, and efficiency. Arrays, in this context, are not mere placeholders for data but are vital tools tailored for optimization. While Python has several data structures, arrays stand out for their unique combination of efficiency, flexibility, and safety.
Here, we'll dive deep into the intrinsic value of arrays in Python.
1. Efficiency: Speedy Access and Operations
Arrays store elements in contiguous memory locations. This contiguous storage means that when the starting memory location (base address) is known, fetching any element becomes a matter of simple arithmetic. This results in constant time (1)O(1) retrieval, making operations exceptionally fast.
2. Flexibility: Adaptable Size
While static arrays have a fixed size, dynamic arrays (similar to Python lists) can change their size dynamically. As elements are added, the array expands, and as they're removed, it contracts. This dynamic resizing ensures optimal memory utilization.
3. Type Safety: Uniformity with the 'array' Module
Python is dynamically typed, but there are scenarios where strict type adherence becomes crucial. The 'array' module in Python ensures that all elements within the array conform to a specific type, thereby eliminating unexpected type errors and enhancing performance due to uniformity.
4. Utility in Algorithms: Core to Computational Techniques
Arrays play a pivotal role in algorithm design. Many foundational algorithms, from binary search to various sorting techniques like quicksort, are structured around arrays. Their predictable structure and fast access times make arrays an ideal choice for algorithmic problem-solving.
Linear Search:
Code:
def linear_search(arr, target):
for i in range(len(arr)):
if arr[i] == target:
return i # Return the index of the target element if found
return -1 # Return -1 if the target element is not found in the array
# Example usage:
my_array = [1, 2, 3, 4, 5]
target_element = 3
result = linear_search(my_array, target_element)
if result != -1:
print(f"Element {target_element} found at index {result}.")
else:
print(f"Element {target_element} not found in the array.")
Binary Search:
Code:
def binary_search(arr, target):
left, right = 0, len(arr) - 1
while left <= right:
mid = left + (right - left) // 2 # Calculate the middle index
if arr[mid] == target:
return mid # Return the index of the target element if found
elif arr[mid] < target:
left = mid + 1 # Adjust the search range to the right half
else:
right = mid - 1 # Adjust the search range to the left half
return -1 # Return -1 if the target element is not found in the sorted array
# Example usage (requires a sorted array):
sorted_array = [1, 2, 3, 4, 5]
target_element = 3
result = binary_search(sorted_array, target_element)
if result != -1:
print(f"Element {target_element} found at index {result}.")
else:
print(f"Element {target_element} not found in the array.")
The time complexities for searching elements in arrays depend on the specific search algorithm used. Here are some common search algorithms and their corresponding time complexities:
1. Linear Search:
2. Binary Search (for Sorted Arrays):
3. Hash Table (for Unordered Arrays):
4. Binary Search Tree (BST) (for Sorted Arrays):
5. Other Search Algorithms (e.g., Interpolation Search, Exponential Search):
Here's a general algorithm for counting elements in an array:
Code:
def count_elements(arr, target):
count = 0 # Initialize a count variable to 0
# Iterate through the entire array
for element in arr:
if element == target:
count += 1 # Increment the count if the element matches the target
return count
# Example usage:
my_array = [1, 2, 3, 2, 4, 2, 5]
target_element = 2
result = count_elements(my_array, target_element)
print(f"Element {target_element} appears {result} times in the array.")
Reversing the elements in an array typically involves rearranging the order of elements so that they appear in the reverse order. The time complexity for reversing elements in an array is O(n), where "n" is the number of elements in the array. This is because you need to visit and swap every element in the array exactly once.
Here's a general algorithm for reversing elements in an array and its time complexity:
Code:
def reverse_array(arr):
left = 0
right = len(arr) - 1
while left < right:
# Swap elements at the left and right positions
arr[left], arr[right] = arr[right], arr[left]
left += 1
right -= 1
# Example usage:
my_array = [1, 2, 3, 4, 5]
reverse_array(my_array)
print(my_array) # Output: [5, 4, 3, 2, 1]
Code:
import array as ar
x = ar.array('i', [1, 2, 3,4,5])
print("The before array extend : ", end =" ")
for i in range (0, 4):
print (x[i], end =" ")
print()
x.extend([6,7,8,9,10])
print("\nThe array after extend :",end=" ")
for i in range(0,10):
print(x[i],end=" ")
print()
Python arrays are rich and multifaceted, and become an indispensable tool for any developer. Through this tutorial, we've uncovered the intricacies of arrays, their adeptness in data handling, and the flexibility Python grants in their utilization. As modern challenges in technology demand more efficient solutions, understanding these structures becomes very important.
While the journey of learning never truly ends, this tutorial has laid a robust foundation. We urge professionals to further their expertise, continually seeking avenues to apply and expand their knowledge. And for those aiming to upskill even further, upGrad offers courses tailored to the evolving needs of the industry. Dive deeper, and keep the flame of learning to burn.
1. What distinguishes a list from an array in Python?
In Python, lists serve as dynamic data structures that can store items of varying data types, be they integers, strings, or even other lists. Arrays, on the other hand, when utilized via the 'array' module, insist on type consistency, ensuring that all elements are of a singular pre-defined type.
2. How to declare array in Python?
In Python, arrays can be initiated using the 'array' module. For instance, after importing the module (import array as arr), one can declare an integer array with the syntax a = arr.array('i', [1,2,3]). Lists, however, are Python's default sequence data structure.
3. Method to create a Python empty array?
Lists, which often double as arrays in Python, can be made empty as empty_list = []. For a stricter array definition using the 'array' module, you can establish an empty integer array using: import array as arr; a = arr.array('i').
4. What justifies the use of the 'array' module over lists?
While lists are versatile, the 'array' module is distinct due to its emphasis on type consistency. This characteristic ensures that every element within the array adheres to the same data type. Such consistency is beneficial for specific applications requiring strict data type conformity.
5. Can you add element to array in Python NumPy?
Absolutely! NumPy, a powerful library for numerical computations in Python, introduces the numpy.append() method. This function allows users to seamlessly add elements to arrays, granting them an efficient tool to augment their data structures.
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
Popular
Talk to our experts. We’re available 24/7.
Indian Nationals
1800 210 2020
Foreign Nationals
+918045604032
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 enrolling. upGrad does not make any representations regarding the recognition or equivalence of the credits or credentials awarded, unless otherwise expressly stated. Success depends on individual qualifications, experience, and efforts in seeking employment.
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...