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
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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
As Python continues its upward trajectory in the tech domain, the knowledge of multithreading is indispensable for professionals eager to extract maximum efficiency from their Python applications. This concept allows professionals to harness the power of concurrent execution, leading to optimized application performance and efficient CPU resource utilization. Aimed at those familiar with Python's basics, this tutorial delves into the intricate details of multithreading in Python.
Multithreading, a facet of multiprocessing, serves as a potent tool to optimize CPU usage. It encompasses the concurrent running of multiple threads, allowing for a judicious resource allocation and an accelerated execution of tasks, primarily in I/O-bound scenarios.
In Python programming, a process is perceived as a self-sufficient unit of execution. Distinct from threads, processes are quite robust, equipped with their own dedicated memory space, ensuring that the resources they utilize aren't easily tampered with by other processes. This makes processes stable and less prone to interference. When a program is initiated, a process is born, guiding the execution. Each process possesses a unique process ID, distinguishing it from others and affirming its autonomy.
Python's threading capability is often hailed as a significant boon for optimizing the efficiency of applications. It offers an avenue for concurrent operations, especially in scenarios where parallelization can boost performance without necessitating separate memory spaces as processes do.
In the computing ecosystem, a thread is visualized as a diminutive of a process. It is a lighter execution unit that operates within the confines of a parent process. Multiple threads can coexist within a single process, executing concurrently and enhancing the throughput of applications.
Multithreading is a technique in Python that allows you to execute multiple threads concurrently within a single process. Each thread represents an independent unit of execution that can run in parallel with other threads. Python's threading module is commonly used for implementing multithreading. Here's an example and some explanations:
Here is an example of multithreading in Python:
Code:
import threading
import time
# Function to simulate a task
def worker_function(thread_id):
print(f"Thread {thread_id} started.")
time.sleep(2) # Simulate some work
print(f"Thread {thread_id} finished.")
# Create multiple threads
threads = []
for i in range(3):
thread = threading.Thread(target=worker_function, args=(i,))
threads.append(thread)
# Start the threads
for thread in threads:
thread.start()
# Wait for all threads to finish
for thread in threads:
thread.join()
print("All threads have finished.")
In this example, three threads are created, each simulating work using the worker_function. The threads execute concurrently, and the main program waits for all threads to finish using the join() method.
Explanation:
We import the threading module to work with threads and the time module to simulate some work. The worker_function is defined to simulate a task that each thread will execute. It takes a thread_id parameter to identify the thread. We create three threads and add them to the threads list. Each thread is assigned the worker_function as its target, and a unique thread_id is passed as an argument.
We start each thread using the start() method. This initiates their execution. To ensure that the main program waits for all threads to finish, we use the join() method for each thread. This blocks the main program until each thread has completed. Finally, we print "All threads have finished" to indicate that all threads have completed their tasks.
Multithreading can be useful for tasks that can be parallelized to improve program performance, such as concurrent data processing, I/O operations, or running multiple tasks simultaneously.
Python's concurrent.futures module provides a ThreadPoolExecutor class that allows you to easily create and manage a pool of threads for concurrent execution of tasks. This is an alternative to the lower-level threading module and provides a higher-level interface for working with threads. Here's an example of using ThreadPoolExecutor:
Code:
import concurrent.futures
# Function to simulate a task
def worker_function(thread_id):
print(f"Thread {thread_id} started.")
return f"Thread {thread_id} result"
# Create a ThreadPoolExecutor with 3 threads
with concurrent.futures.ThreadPoolExecutor(max_workers=3) as executor:
# Submit tasks to the executor
futures = [executor.submit(worker_function, i) for i in range(3)]
# Retrieve results as they become available
for future in concurrent.futures.as_completed(futures):
result = future.result()
print(result)
print("All threads have finished.")
In this example, the ThreadPoolExecutor efficiently manages a pool of threads, executes tasks concurrently, and returns results as they become available. This approach simplifies the management of threads and is particularly useful for parallelizing tasks in a controlled and scalable way.
Explanation:
We import the concurrent.futures module, which contains the ThreadPoolExecutor class. The worker_function is defined to simulate a task. It takes a thread_id parameter and returns a result. We create a ThreadPoolExecutor with a specified number of maximum workers (in this case, 3). Tasks are submitted to the executor using the executor.submit() method. Each submit call schedules the execution of the worker_function with a specific thread_id.
We collect the resulting futures (representing the asynchronous results of the tasks) in a list called futures. We use concurrent.futures.as_completed(futures) to iterate through the futures as they are completed. The result() method retrieves the result of each future. Finally, we print the results and indicate when all threads have finished.
Python's support for multithreading offers several perks:
The judicious deployment of multithreading can make or break application performance.
Multithreading has undeniably transformed how developers approach concurrency in their applications. By efficiently leveraging CPU resources and optimizing execution times, multithreading offers a competitive edge in high-performance computing. It's vital, however, to understand its nuances and implement it judiciously. Overuse or misuse can lead to complications such as race conditions or deadlocks.
For professionals eager to elevate their Python skills, mastering multithreading becomes imperative. Moreover, as applications become complex and user demands increase, a deep comprehension of concurrency principles will be a cornerstone of robust software development. For those committed to continuous learning, platforms like upGrad provide courses that delve into advanced topics like these, ensuring professionals stay ahead in their careers.
1. Multithreading vs. multiprocessing in Python?
Multithreading involves multiple threads operating within one process, utilizing shared memory. It's suitable for tasks that require quick context switches. Multiprocessing, however, employs separate processes with individual memory spaces, ensuring true parallelism. This becomes especially vital for CPU-intensive operations where bypassing Python's Global Interpreter Lock (GIL) becomes crucial.
2. Advantages and disadvantages of multithreading in Python?
Multithreading offers numerous advantages such as parallel execution, improved application responsiveness, and efficient CPU and memory utilization. However, it's not without pitfalls. Drawbacks include potential race conditions due to shared memory access and the complexities in synchronization, which can introduce unexpected behaviors
3. What is the Python thread class?
Python's threading module provides a Thread class, used to create and handle threads. By instantiating the Thread class and providing a target function, one can spawn a new thread. The class offers various methods like start(), join(), and is_alive() to manage and monitor thread execution.
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...