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
Now Reading
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
Python provides a built-in function known as "filter()" that can generate a new iterator when applied to an iterable, such as a list or dictionary. By specifying criteria, this iterator effectively filters out specific elements. While various methods, like python filter vs list comprehension and different types of for loops, can be used to filter items from a list, the filter python list offers a concise and efficient way to exclude elements, requiring fewer lines of code for the same task. This efficiency becomes particularly advantageous when dealing with large datasets.
The Python built-in `filter()` method operates on iterable objects like lists, tuples, dictionaries, and more. It selectively extracts specific elements by taking two arguments: a function and an iterable. Utilized as an input parameter, this function is responsible for filtering individual elements within the iterable, ultimately yielding an iterator. Python filter Objects are capable of being iterated over and are referred to as iterables.
Python's built-in function, filter(), functions to filter elements from an iterable like lists or tuples. It operates by applying a specified condition and generates an iterator that exclusively includes elements meeting this particular criterion. The general syntax for employing the filter() function is as follows:
filter(function, iterable)
function: A function that takes an element from the iterable as its argument and returns either True or False. The filter() function will include elements for which the function returns True.
iterable: The iterable (e.g., list, tuple) from which elements are filtered.
Let's delve into the operation of the `filter()` function:
By applying the designated function to every element within the iterable,
Any element for which the function yields True is incorporated into the resulting set,
Conversely, any element for which the function yields False is omitted from the outcome,
Ultimately, the `filter()` function furnishes an iterator that holds the elements that passed through the filter.
Illustrated below is a fundamental example that employs the `filter()` function to separate even numbers from a list:
numbers = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
# Define a function to check if a number is even
def is_even(x):
return x % 2 == 0
# Use filter() to filter even numbers
even_numbers = list(filter(is_even, numbers))
print(even_numbers) # Output: [2, 4, 6, 8, 10]
In this example:
Defining a custom function named "is_even(x)," we check whether a number is even, returning True for even numbers and False for odd ones. Subsequently, we employ the filter() function to sift through the numbers list, storing the outcome in the even_numbers variable.
The filter() function is a powerful tool for selectively extracting elements from an iterable based on specific conditions, making it useful for various data processing tasks in Python.
The syntax of filter function Python is as follows,
filter(function, iterable)
Two arguments are required by the filter() method.
function: used to determine whether or not each iterable value is true for this function.
iterable: The iterables on which filtering will be performed, such as sets, lists, tuples, etc.
Return Type: <class 'filter'>
All the input iterable's items that passed the function check are included in the iterator that the filter() method returns.
Following are the Python filter examples:
In this example, vowels from the Python List are being filtered out using the filter object. Python in conjunction with a special function called "fun()".
# function that filters vowels
def fun(variable):
letters = ['a', 'e', 'i', 'o', 'u']
if (variable in letters):
return True
else:
return False
# sequence
sequence = ['g', 'e', 'e', 'j', 'k', 's', 'p', 'r']
# using filter function
filtered = filter(fun, sequence)
print('The filtered letters are:')
for s in filtered:
print(s)
Output:
The filtered letters are:
e
e
When using filter() with a lambda function, you create an iterator that yields only the elements that satisfy the condition specified in the lambda function. Here's how it works:
filter(lambda x: condition, iterable)
lambda x: This is a lambda function that takes an element x from the iterable.
condition: The condition to be checked for each element x. If the condition evaluates to True, the element is included in the result.
Here's an example to illustrate using filter() with a lambda function:
# a list contains both even and odd numbers.
seq = [0, 1, 2, 3, 5, 8, 13]
# result contains odd numbers of the list
result = filter(lambda x: x % 2 != 0, seq)
print(list(result))
# result contains even numbers of the list
result = filter(lambda x: x % 2 == 0, seq)
print(list(result))
Output :
[1, 3, 5, 13]
[0, 2, 8]
You can use the filter() function in Python with both a lambda function and a custom function to filter elements from an iterable.
Here's an example that demonstrates how to do this:
Suppose you have a list of numbers, and you want to filter out the numbers that are divisible by 3 using both a lambda function and a custom filtering function.
# List of numbers
numbers = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
# Define a custom function for filtering
def is_divisible_by_3(x):
return x % 3 == 0
# Using filter with a lambda function
filtered_numbers_lambda = list(filter(lambda x: x % 3 == 0, numbers))
# Using filter with the custom filtering function
filtered_numbers_custom = list(filter(is_divisible_by_3, numbers))
print("Filtered numbers using lambda function:", filtered_numbers_lambda)
print("Filtered numbers using custom function:", filtered_numbers_custom)
In this example:
We have a list of numbers called numbers.
We define a custom filtering function called is_divisible_by_3(x) that checks if a number is divisible by 3.
We use filter() with a lambda function to filter numbers that are divisible by 3, and the result is stored in filtered_numbers_lambda.
We also use filter() with the custom filtering function is_divisible_by_3, and the result is stored in filtered_numbers_custom.
Both approaches achieve the same result, filtering out numbers that are divisible by 3 from the numbers list. The use of a custom function allows for more complex and reusable filtering logic, while the lambda function provides a concise way to define the filtering condition inline.
Python's filter() function is used to pick out specific data from a vast collection of data. Additionally, it is an alternative for list comprehension since filters have low memory and execution time requirements. The filter() function in Python, a built-in feature, allows for the selection of elements based on specific conditions, whether through the utilization of lambda functions or predefined functions. It operates on iterable data structures such as lists, tuples, or filter list python string contains. It yields an iterator comprising elements for which the condition evaluates as True. Below are some typical use cases of the filter() function:
Filtering Elements by Condition:
The primary use of filter() is to filter elements from a sequence that meet a specified condition.
numbers = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
even_numbers = list(filter(lambda x: x % 2 == 0, numbers))
print(even_numbers) # Output: [2, 4, 6, 8, 10]
Removing Duplicates:
data = [1, 2, 2, 3, 4, 4, 5]
unique_values = list(filter(lambda x: x not in seen.add(x) and (x not in seen or False), data))
print(unique_values) # Output: [1, 2, 3, 4, 5]
Filtering based on Custom Criteria:
You can use a custom function as the filtering criterion.
def is_positive(number):
return number > 0
numbers = [-2, -1, 0, 1, 2]
positive_numbers = list(filter(is_positive, numbers))
print(positive_numbers) # Output: [1, 2]
Traditional for loops and the filter() function in Python are both used for iterating over sequences (e.g., lists, tuples) and processing elements based on certain conditions. However, they have different use cases and characteristics. Let's compare them with examples to illustrate their differences.
Traditional for Loop:
A traditional for loop allows you to iterate over elements in a sequence one by one, and you can apply custom logic within the loop to filter or process elements as needed. It provides full control over the iteration process and allows for more complex logic.
Example using a traditional for loop:
numbers = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
even_numbers = []
for num in numbers:
if num % 2 == 0:
even_numbers.append(num)
print(even_numbers) # Output: [2, 4, 6, 8, 10]
In this example, we use a for loop to iterate through the numbers list and filter even numbers by checking the remainder when dividing by 2.
Pros of Traditional for Loop:
Cons of Traditional for Loop:
filter() Function:
The filter() function, a native Python feature, is purpose-built for selectively sifting elements from an iterable according to a defined condition, ultimately yielding an iterator comprising elements that meet the specified criteria. It excels in succinctly handling straightforward filtering assignments.
Example using the filter() function:
numbers = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
even_numbers = list(filter(lambda num: num % 2 == 0, numbers))
print(even_numbers) # Output: [2, 4, 6, 8, 10]
In this example, we use the filter() function to create a new list containing even numbers from the numbers list.
Pros of filter() Function:
Cons of filter() Function:
Use a traditional for loop when you need fine-grained control over the iteration process and when you want to apply complex logic or modify the original sequence. On the other hand, use the filter() function when you have a simple filtering task and want a more concise and functional programming-style approach.
A valuable tool in Python for handling iterable data structures is the `filter()` function. It simplifies the tasks of data manipulation, analysis, and data extraction that pertain to your programming needs. By segregating the filtering logic from the iteration process, it enables you to craft code that is both more expressive and reusable. Whether you use a custom function or a lambda function as the filtering criterion, filter() provides a convenient way to extract the elements that meet your criteria, creating a filtered result.
1. What does Python's filter () do?
Python's built-in filter() function allows you to iterate through an iterable and retrieve the components that satisfy a given condition. A filtering method is what is being used here.
2. How do I write a Python filter?
'filter()' is a Python function that is built-in for filtering list elements. 'filter(fn, list)' is required, which calls for a Python filter array. In this instance, a filter_height function will be written. When the height is less than 150, True is returned; otherwise, False.
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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...