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👋 Hi there, I am Nirmal. I am a former software engineer with a keen interest in data science and analytics domains. Besides, I love to contribute to open source and help others to understand various tech stuffs.
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This series will provides the core concepts of python programming starting from variables, data types, control flow, and functions to some problem solving.
File handling is a fundamental aspect of programming, allowing us to read and write data to and from files. Opening and Closing Files Before performing any file operation, we need to open the file. Python provides the open() function for this purpose...
Bias in Machine Learning Bias refers to the simplifying assumptions made by a model to make the target function easier to learn. High bias can lead to underfitting Represents the error introduced by approximating a real-world problem Example: A li...
When it comes to developing software applications, choosing the appropriate architectural style is a critical decision. Among the popular options are microservices and monolithic architecture. By understanding the trade-offs, readers can make well-i...
In a world where information is everywhere, businesses have tons of data at their fingertips. This data is like a goldmine waiting to be used for smart decisions. That's where Big Data analytics and Data Science come in. They're like the experts who ...
Python is a powerful tool for data professionals, largely due to its extensive collection of open-source libraries and packages. In this blog, we'll explore the fundamentals of pandas, with a special focus on its core data structures: Series and Data...
NumPy is a powerful library for numerical computing in Python, offering robust support for large, multi-dimensional arrays and an extensive collection of mathematical functions. Creating Arrays To begin using NumPy, import the library and create arra...
Higher-order functions play a crucial role in functional programming paradigms. Higher-order functions like filter(), map(), and reduce() enable you to manipulate functions as first-class objects, allowing for more flexible and expressive programming.
The filter function selects elements from an iterable based on a given condition, returning a new iterable containing only the elements that satisfy the condition.
# Example 2: Using filter() with lambda function
def is_even(x):
return x % 2 == 0
numbers = [1, 2, 3, 4, 5, 6]
even_numbers = list(filter(lambda x: x % 2 == 0, numbers))
print(even_numbers) # Output: [2, 4, 6]
The map function applies a given function to each element of an iterable and returns an iterable containing the results.
def is_even(x):
return x % 2 == 0
numbers = [1, 2, 3, 4, 5, 6]
even_numbers = list(map(lambda x: x % 2 == 0, numbers))
print(even_numbers)
# Output: [False, True, False, True, False, True]
The reduce function repeatedly applies a binary function to the elements of an iterable, reducing it to a single value by performing the specified operation cumulatively.
Syntax : reduce(function, list)
import functools
#Using normal function
def sum(a, b):
return a + b
list = [2, 5, 9, 11, 13]
val = functools.reduce(sum, list)#func takes two two element
print(val)
#Using lambda function
list = [2, 5, 9, 11, 13]
val = functools.reduce(lambda a, b: a+b, list)#func takes two two element
print(val)
How it works in above example of reduce()
Step:1 = First pass 2,5 as arguments and return 2 + 5 = 7
Step:2 = pass 7, 9 as arguments and return 7 + 9 = 16
Step:3 = pass 16, 11 arguments and return 16 + 11 = 27
Step:4 = pass 27 , 13 27 + 13 = 40 (Final results)
Partial functions in Python allow you to create new functions with fewer parameters by fixing some values from an existing function.
It simplifies function calls and promotes code reusability by creating specialized versions of functions with pre-filled values.
from functools import partial
def sum(a,b,c,d):
return a+b+c+d
#Passing a = 1, b = 2, c = 3
add_portion = partial(sum,1,2,3)
#Now passing d = 4
result = add_portion(4)
print(result)# output = 10