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Articles by AmitDiwan
Page 7 of 840
How Can You Copy Objects in Python?
In Python, if you want to copy an object, the assignment operator won't fulfill the purpose. It creates bindings between a target and an object, meaning it never creates a new object — it only creates a new variable sharing the reference of the original object. To fix this, Python provides the copy module with generic shallow and deep copy operations. Assignment vs Copy Operations Let's first understand why assignment doesn't create a copy ? original = [[1, 2], [3, 4]] assigned = original # Modifying assigned affects original assigned[0][0] = 99 print("Original:", original) print("Assigned:", ...
Read MoreDifference Between Matrices and Arrays in Python?
The arrays in Python are ndarray objects from NumPy. The matrix objects are strictly 2-dimensional whereas the ndarray objects can be multi-dimensional. To create arrays in Python, use the NumPy library. Matrices in Python A matrix is a special case of two-dimensional array where each data element is of strictly the same size. Matrices are a key data structure for many mathematical and scientific calculations. Every matrix is also a two-dimensional array but not vice versa. Matrix objects are a subclass of ndarray, so they inherit all the attributes and methods of ndarrays. Creating a Matrix with ...
Read MorePython - Display the Contents of a Text File in Reverse Order?
We will display the contents of a text file in reverse order. Python provides several methods to reverse text content, including slicing and looping approaches. Using String Slicing The most Pythonic way to reverse text content is using slice notation with a negative step ? # Create sample text content text_content = "This is it!" # Write to file first with open("sample.txt", "w") as file: file.write(text_content) # Read and reverse the file content with open("sample.txt", "r") as myfile: my_data = myfile.read() # Reversing the data ...
Read MoreDifference Between Del and Remove() on Lists in Python?
Python lists provide two primary ways to remove elements: the del keyword and the remove() method. Both serve different purposes and work differently based on whether you know the index or the value. Del Keyword in Python List The del keyword removes elements by index position. It can delete single elements, multiple elements using slicing, or the entire list ? Delete a Single Element # Create a List car_brands = ["Toyota", "Benz", "Audi", "Bentley"] print("List =", car_brands) # Delete element at index 2 del car_brands[2] print("Updated List =", car_brands) List ...
Read MoreHow to Merge Elements in a Python Sequence?
Python sequences include strings, lists, tuples, and other iterable data structures. You can merge elements within a sequence using various methods like join(), reduce() with lambda functions, list comprehensions, and zip(). Using join() Method The join() method concatenates string elements in a sequence − # List of characters letters = ['H', 'O', 'W', 'A', 'R', 'E', 'Y', 'O', 'U'] # Display the original list print("List =", letters) # Merge first 3 elements using join() letters[0:3] = [''.join(letters[0:3])] # Display the result print("Result =", letters) List = ['H', 'O', 'W', 'A', ...
Read MoreWhat Does the // Operator Do?
In Python, the // operator is the floor division operator that divides two numbers and rounds the result down to the nearest integer. This is different from regular division (/) which returns a float result. Syntax The basic syntax for floor division is ? a // b Where a is the dividend and b is the divisor. Basic Floor Division Example Let's see how floor division works with positive numbers ? a = 37 b = 11 print("First Number:", a) print("Second Number:", b) # Floor division result = ...
Read MoreNumpy Array advantage over a Nested List
In this article, we will learn about the advantages of NumPy arrays over nested lists in Python. NumPy arrays offer significant performance and memory benefits that make them ideal for numerical computations and data analysis. Key Advantages of NumPy Arrays Speed: NumPy arrays execute faster than nested lists due to optimized C implementations Memory Efficiency: NumPy arrays consume less memory than nested lists Vectorized Operations: Element-wise operations without explicit loops Broadcasting: Operations between arrays of different shapes Mathematical Functions: Built-in mathematical and statistical functions NumPy Arrays NumPy provides an N-dimensional array type called ndarray. ...
Read MoreConvert a polynomial to Hermite_e series in Python
To convert a polynomial to a Hermite_e series, use the hermite_e.poly2herme() method in Python NumPy. This function converts an array representing polynomial coefficients (ordered from lowest to highest degree) to an array of coefficients for the equivalent Hermite_e series. Syntax numpy.polynomial.hermite_e.poly2herme(pol) Parameters The pol parameter is a 1-D array containing the polynomial coefficients ordered from lowest degree to highest degree. Example Let's convert a polynomial with coefficients [1, 2, 3, 4, 5] to its equivalent Hermite_e series ? import numpy as np from numpy.polynomial import hermite_e as H # ...
Read MoreConvert a Hermite_e series to a polynomial in Python
To convert a Hermite_e series to a polynomial, use the hermite_e.herme2poly() method in NumPy. This function converts an array representing the coefficients of a Hermite_e series (ordered from lowest degree to highest) to an array of coefficients of the equivalent polynomial in the standard basis. Syntax numpy.polynomial.hermite_e.herme2poly(c) Parameters The parameter c is a 1-D array containing the Hermite_e series coefficients, ordered from lowest order term to highest. Example Let's convert a Hermite_e series with coefficients [1, 2, 3, 4, 5] to its polynomial form ? import numpy as np from ...
Read MoreGenerate a Vandermonde matrix of the Hermite_e polynomial in Python
To generate a Vandermonde matrix of the Hermite_e polynomial, use the hermite_e.hermevander() function in Python NumPy. This method returns a pseudo-Vandermonde matrix where each row corresponds to evaluating Hermite_e polynomials of increasing degrees at a given point. The shape of the returned matrix is x.shape + (deg + 1, ), where the last index represents the degree of the corresponding Hermite_e polynomial. The dtype will match the converted input array x. Parameters The function accepts two main parameters: x − Array of points where polynomials are evaluated. Converted to float64 or complex128 depending on element ...
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