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
Now Reading
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
An Armstrong number in Python is one where the sum of all its digits, each raised to the power of the number of digits, equals the number itself.
To put it another way, if a number has "n" digits and you raise each one to the power of "n," add their values and the result is equal to the original number, the number is an Armstrong number.
Understanding Armstrong numbers can be a great way to deepen your programming skills and enhance your understanding of number theory and mathematical properties. In this tutorial, we will aim to understand the different topics related to Armstrong numbers in Python.
The fascinating mathematical idea of Armstrong numbers is applied in a variety of exercises and programming problems. They offer an engaging way to investigate mathematical properties and coding strategies in many programming languages. In this tutorial, we will delve into the topic of Armstrong numbers in Python and learn what are the different ways we can implement them in Python.
Armstrong numbers in Python are numbers that are the sums of the very digits they are composed of, each raised to the power of the number of digits. It is also referred to as a narcissistic number, a pluperfect digital invariant, or a pluperfect digital number. Fundamentally, the sum of the digits of any n-digit number with all the numbers raised to the powers of n must be equal to the n-digit number to be Armstrong numbers.
For example, 8208 is a 4-digit Armstrong number. The prevalence of Armstrong numbers decreases as the number of digits rises. The largest known Armstrong number in base 10 has 39 digits and is 115,132,219,018,763,992,565,095,597,973,971,522,401.
Let us understand this concept with the help of an example.
Code:
num = 153
#num is the number we want to find if it is an armstrong number or not
digits_num = len(str(num))
temp = num
add_sum = 0
while temp != 0:
# get the last digit in the number
k = temp % 10
add_sum += k**digits_num
# floor division
# updates second last digit as last digit.
temp = temp//10
if add_sum == num:
print('Given number is an Armstrong Number')
else:
print('Given number is not a Armstrong Number')
In this approach, we will check if a number is an Armstrong number or not without using the power function. Let us have a look at an example of how to find if a number is an Armstrong number or not without using the power function.
Code:
def count_digits(number):
count = 0
while number > 0:
number //= 10
count += 1
return count
def is_armstrong(number):
original_num = number
num_of_digits = count_digits(number)
sum_of_digits = 0
while number > 0:
digit = number % 10
product = 1
for _ in range(num_of_digits):
product *= digit
sum_of_digits += product
number //= 10
return sum_of_digits == original_num
# Input from the user
number = int(input("Enter a number: "))
if is_armstrong(number):
print(number, "is an Armstrong number.")
else:
print(number, "is not an Armstrong number.")
In this program, the count_digits function counts the digits in the inputted number, and the is_armstrong function determines whether the number is an Armstrong number by manually computing each digit's power using a loop, and then adding the powered digits together.
In this section, we will take a look at how to check if a number is an Armstrong number or not using string manipulation with the help of this Python program example.
Code:
def is_armstrong(number):
num_str = str(number)
num_of_digits = len(num_str)
sum_of_digits = 0
for digit_char in num_str:
digit = int(digit_char)
sum_of_digits += digit ** num_of_digits
return sum_of_digits == number
# Input from the user
number = int(input("Enter a number: "))
if is_armstrong(number):
print(number, "is an Armstrong number.")
else:
print(number, "is not an Armstrong number.")
The is_armstrong method in this program changes the integer to a string so that you can loop through each of its digits. The sum of the digits raised to the power of the number of digits is then calculated using a loop. An Armstrong number is one where the total is equal to the original number.
Let us understand how to check for an Armstrong number using the digit-by-digit approach with the help of this example below.
Code:
def count_digits(number):
count = 0
while number > 0:
number //= 10
count += 1
return count
def is_armstrong(number):
original_num = number
num_of_digits = count_digits(number)
sum_of_digits = 0
while number > 0:
digit = number % 10
sum_of_digits += digit ** num_of_digits
number //= 10
return sum_of_digits == original_num
# Input from the user
number = int(input("Enter a number: "))
if is_armstrong(number):
print(number, "is an Armstrong number.")
else:
print(number, "is not an Armstrong number.")
The is_armstrong function iterates through the digits of the number while the count_digits function counts the number of digits in the inputted number. To determine whether a number is an Armstrong number, it computes the sum of the digits raised to the power of the number of digits and compares it to the original number. The output is boolean in nature, as in true or false.
Let us understand how to check if a number is an Armstrong number in the return statement with the help of an example shown below.
Code:
def is_armstrong(number):
original_num = number
num_str = str(number)
num_of_digits = len(num_str)
sum_of_digits = sum(int(digit_char) ** num_of_digits for digit_char in num_str)
return sum_of_digits == original_num
# Taking input from the user
number = int(input("EPlease enter a number: "))
result = is_armstrong(number)
if result:
print(number, "is an Armstrong number.")
else:
print(number, "is not an Armstrong number.")
The is_armstrong function in this program uses a generator expression from the sum function to compute the sum of the digits raised to the power of the number of digits. The comparison's outcome is immediately returned as a boolean. Depending on whether the input number is an Armstrong number or not, the main portion of the code takes the result and outputs the relevant message.
Let us understand this concept with the help of this example of a Python program to find the Armstrong number in an interval.
Code:
def count_digits(number):
count = 0
while number > 0:
number //= 10
count += 1
return count
def is_armstrong(number):
original_num = number
num_of_digits = count_digits(number)
sum_of_digits = 0
while number > 0:
digit = number % 10
sum_of_digits += digit ** num_of_digits
number //= 10
return sum_of_digits == original_num
# Input from the user
lower_limit = int(input("Enter the lower limit of the interval: "))
upper_limit = int(input("Enter the upper limit of the interval: "))
armstrong_numbers = []
for number in range(lower_limit, upper_limit + 1):
if is_armstrong(number):
armstrong_numbers.append(number)
print("Armstrong numbers in the interval:", armstrong_numbers)
This program iterates through each number in the interval after receiving the interval's bottom and upper bounds as input. Using the is_armstrong function previously defined, it determines whether each number is an Armstrong number and, if so, adds it to the list of Armstrong numbers. The list of Armstrong numbers that were discovered during the specified timeframe is then printed by the program.
Armstrong numbers, also known as narcissistic numbers or pluperfect numbers, are a topic we covered in this tutorial. We also went through how to tell if a given number is an Armstrong number, using various different methods and techniques. If you wish to learn more about Armstrong numbers and other Python concepts, checking out courses from upGrad might be of great help.
1. Can an Armstrong number in Python have only one digit?
Yes, single-digit numbers are regarded as Armstrong numbers since they meet the criteria for such a number. Armstrong numerals, for instance, range from 0 to 9. 0 to 9 can also be called an Armstrong number list.
2. Are there any Armstrong numbers in the negative range?
For positive integers, Armstrong numbers are frequently taken into account. Because negative numbers' distinctive characteristics are based on digit manipulation, the Armstrong number notion cannot be directly applied to them.
3. Do the Armstrong numbers in Python have any practical uses?
Armstrong numbers are employed in programming and mathematical exercises to teach ideas like loops, conditional statements, and number theory even if they have no immediate practical applicability in real-world situations.
4. Do any effective algorithms exist to locate huge Armstrong numbers?
The prevalence of Armstrong numbers decreases as the number of digits rises. There isn't a single effective algorithm for finding huge Armstrong numbers; instead, it's usual practice to look at each number's properties.
5. Can data science or cryptography employ Armstrong numbers?
Due to their unique mathematical quality, Armstrong numbers are not frequently employed in cryptography or data science, but they can act as a jumping-off point for more complicated number-related concepts.
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...