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
Now Reading
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
The Math Module in Python is a powerful toolbox for various math operations in Python. It handles everything from basic arithmetic to advanced functions like trigonometry and logarithms. This article explores its features and constants, showing how it can be used for mathematical tasks in Python.
Math Module in Python is like a magical toolbox for all things mathematical. It's like having a super-smart math assistant in your code, helping with everyday math like addition and subtraction but also handling fancy tricks like trigonometry, logarithms, and unique mathematical constants. This module is your secret weapon whether you're a coding newbie or a seasoned pro working on scientific or engineering projects. It's the math wizard that supercharges Python's number-crunching capabilities.
The Python Math Module is like a toolbox of math tools that come with Python. These tools help you do math stuff, like adding and subtracting, and also fancier things, like trigonometry and logarithms.
Examples:
Mathematical Operations:
The Math Module simplifies complex mathematical operations in Python, making it a powerful tool for scientific and financial applications.
The Python math module comes built-in with Python and is a handy toolbox for doing all sorts of math stuff. It's like a virtual math wizard that helps you crunch numbers and use special math values in your Python programs. Here, we will explore the concept of division in the Python math module and provide examples:
Division in Python Math Module
Division in the Python math module is performed using regular Python division operators (/, //, and %). The math module doesn't introduce any new division operators or functions specifically for division operations. Instead, it relies on Python's built-in division operators for numerical operations.
1. Floating-Point Division (/):
The / operator is used for regular floating-point division in Python. It returns a floating-point result, including the decimal part.
a = 10
b = 3
result = a / b
print("Floating-Point Division:", result)
Output:
Floating-Point Division: 3.3333333333333335
2. Integer Division (//):
The // operator helps with dividing integers. It gives you the biggest whole number that is less than or equal
a = 10
b = 3
result = a // b
print("Integer Division:", result)
Output:
Integer Division: 3
3. Modulus (%):
The % operator calculates the remainder of the division of two numbers.
a = 10
b = 3
result = a % b
print("Modulus (Remainder):", result)
Output:
Modulus (Remainder): 1
In the code examples above, we used standard Python operators to perform division operations. The math module is more focused on providing mathematical functions and constants rather than introducing new division operators.
These division concepts are part of Python's core functionality. They can be used in combination with the functions and constants provided by the math module to perform a wide range of mathematical calculations.
Here are the explanations and examples of the constants provided by the math module in Python with example:
1. Euler’s Number
Euler's number, sometimes written as "math.e," is a fundamental mathematical constant. It's approximately 2.718281828459045. This number is essential for natural logarithms and has a key role in many mathematical and scientific calculations.
Output:
2.718281828459045
2. Pi
Pi, written as math.pi, is a vital mathematical number. It's roughly 3.141592653589793, and it helps us in math when we're dealing with circles and angles. Pi shows us how many times a circle's circumference goes around its diameter. We use it in different math problems involving shapes and angles.
Output
3.141592653589793
3. Tau
Tau, represented by math.tau, is a constant that equals 2 times Pi (2π), approximately equal to 6.283185307179586. It is useful in contexts where one complete rotation or cycle is relevant, such as in physics and engineering.
Output:
6.283185307179586
4. Infinity
Infinity is represented by math.inf in Python. It represents a concept of being unbounded or unlimited. Positive infinity is represented by math.inf, and negative infinity is represented by -math.inf.
Output:
inf
-inf
5. Not a Number (NaN)
NaN, represented by math.nan, is used to indicate that a value is not a valid number. It is often encountered when performing invalid mathematical operations.
Output:
nan
These constants provided by the math module are valuable in a wide range of mathematical and scientific calculations. They help maintain precision and accuracy in your Python programs.
In math, the ceiling finds the smallest whole number greater or equal, while the floor identifies the largest whole number smaller or equal. Python simplifies this with math.ceil() for the ceiling and math.floor() for the floor.
Example 1: Ceiling Value
Output: Ceiling value of 4.346 is 5
Example 2: Floor Value
Output: Floor value of 4.346 is 4
These functions are particularly useful in scenarios where you need to work with integers and round numbers to the nearest whole value.
The Python math module is a standard library module that provides a wide range of mathematical functions and constants. It's useful for performing various mathematical calculations.
Example:
Example
In this illustration, we use the math.factorial() function to find the factorial of 5, which gives us the answer 120. You can swap out the 5 with any different whole number to determine the factorial of that specific number.
Finding the Greatest Common Divisor (GCD) in Python can be done using the math module's gcd() function. The GCD is the largest positive integer that divides two numbers without leaving a remainder.
Example:
Output:
The GCD of 15 and 5 is: 5
Within Python's math module, you'll discover a nifty tool known as math.fabs() For Example:
Output:
You can rely on math.fabs() to ensure a number is positive, no matter its original sign.
Logarithmic Functions:
Within Python's math module, you'll discover a set of tools designed for unraveling the secrets of numbers through logarithmic functions. These functions act like mathematical detectives, and they enable you to unveil the mysteries behind numbers. Use math.log() for natural logs, math.log2() for base-2, and math.log10() for base-10.
Example 1: Calculating the natural logarithm (base e) of 10:
import math
result = math.log(10)
print(result)
Output:
2.302585092994046
Example 2: Calculating the base 10 logarithm of 1000:
import math
result = math.log10(1000)
print(result)
Output:
3.0
Power functions in Python's math module allow you to compute powers and exponentials. You can use math.exp() to calculate the exponential of a number and math.pow() to raise a number to a specific power.
Example 1: Calculating e raised to the power 2:
import math
result = math.exp(2)
print(result)
Output:
7.38905609893065
Example 2: Calculating 3 raised to the power 4:
import math
result = math.pow(3, 4)
print(result)
Output:
81.0
These functions are useful for various mathematical calculations in Python.
The math module in Python includes the exp() function, which calculates the exponential value of a given number (e^x). It is used to find the value of e raised to a specified power. Here's a brief explanation and examples:
Explanation:
The exp() function calculates the value of e raised to the power of the given number.
In mathematical notation, exp(x) = e^x.
Math module in Python example:
1. Calculating e^4:
import math
result = math.exp(4)
print(result)
Output:
54.598150033144236
2. Calculating e^(-3):
import math
result = math.exp(-3)
print(result)
Output:
0.049787068367863944
3. Calculating e^0:
import math
result = math.exp(0)
print(result)
Output: 1.0
The exp() function is useful for various applications, including exponential growth calculations and probability distributions.
To determine the potency of a number in Python, we can employ the math module and make use of the pow() function. For example:
Output:
81.0
In this illustration, we include the math module, and afterward, we employ the math.pow(x, y) method to compute 3 raised to the 4th power, resulting in 81.0. The pow() function needs two things to work: x is like the starting point, and y is the number that tells us how many times to use it.
Within the realm of Python programming, skilled mathematicians have designed a unique tool known as the "math module." This nifty tool proves incredibly useful when dealing with logarithms, which are essentially like hidden keys unlocking solutions to various mathematical and scientific challenges. To reveal these hidden solutions, all you need to do is utilize the mighty math.log() function.
Remember to import the math module in Python before using these functions.
In this instance, we bring in the math toolkit, establish a number (which happens to be 16), and subsequently employ the math.sqrt() tool to compute the square root. The outcome is showcased on the screen.
In this instance, we bring in the math toolkit, establish a number (which happens to be 16), and subsequently employ the math.sqrt() tool to compute the square root. The outcome is showcased on the screen.
Output of the code:
The square root of 16 is: 4.0
The math.sqrt() function can be used to find the square root of any non-negative number.
Python's math module is like a toolbox for solving math problems, especially when dealing with angles. It's like a compass guiding you through tasks like finding sine, cosine, and tangent, and it's your map for switching between degrees and radians.
1. Finding Sine, Cosine, and Tangent:
Math tools help find sine, cosine, and tangent angles in radians.
Example:
2. Converting Values from Degrees to Radians and Vice Versa:
You can convert values between degrees and radians using these functions:
Example:
In Python, these built-in operations simplify the process of handling angles and trigonometry. They take an angle measured in radians as input and give you the relevant trigonometric results as output. If you ever need to deal with degrees instead of radians, you can effortlessly switch between the two as required.
The Python math module is a toolbox full of mathematical tools and important numbers. In this discussion, we'll delve into the unique functions found within the math module functions in Python and learn how to put them to work.
1. Finding Gamma Value:
The math.gamma(x) function calculates the gamma value of the argument x.
Example:
2. Checking for Infinity or NaN:
You can employ the function math.isinf to determine whether a number is infinite and use math.isnan(x) to ascertain if a number is Not a Number (NaN).
Examples:
Python's Math module simplifies complex math tasks, providing pi, Euler's number, division, ceiling/floor values, factorials, GCD, absolute values, logarithms, powers, and trigonometry functions. This enhances Python's suitability for scientific, engineering, and financial applications, making it a powerful math tool.
1. What is the math module in Python?
The Python math module is like a virtual math toolbox that's already included with Python. It helps you perform mathematical operations in your programs. You can use it for basic tasks, such as adding or subtracting numbers, and for more advanced math, like solving trigonometry problems and working with logarithms.
2. What constants does the math module provide?
The math module provides several constants, including:
3. What is Euler's Number, and how do you access it in Python?
Euler's Number, represented as "e," is approximately 2.71828182846. You can access it in Python using the following syntax:
import math
e_value = math.e
4. How can I calculate the factorial of a number using the math module?
You can get a number's factorial using math.factorial().
Here's an example:
import math
number = 5
factorial = math.factorial(number)
5. What is the purpose of the ceil() and floor() functions in the math module?
The "ceil" function is like a helpful elevator that takes your decimal number up to the next higher whole number. Meanwhile, the "floor" function acts as a floor beneath your decimal, finding the largest whole number just below it or equal to it. These functions come to your rescue when you need to nudge your numbers to the nearest whole figure, whether it's up or down.
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...