Q.34: What is Negative Indexing in NumPy Arrays?
Negative indexing in NumPy arrays allows individuals to access elements from the end of an array by using negative integers as indices. This feature offers a convenient way to retrieve elements relative to the array’s end, without the need for precise knowledge of its length. In NumPy, -1 corresponds to the last element, -2 refers to the second-to-last element, and so forth.
Q.35: Can You Create a Plot in NumPy?
Using NumPy and Matplotlib together, you can create a simple plot. NumPy is primarily a library for numerical computations with arrays, while Matplotlib is a popular Python library for creating various plots and charts. To create a plot, first import NumPy and Matplotlib, then use the functions from both libraries.
Python
import numpy as np
import matplotlib.pyplot as plt
# Create any sample data using NumPy
x = np.linspace(0, 2 * np.pi, 100) # generate 100 points between 0 and 2*pi
y = np.sin(x) # compute the sine of each point
plt.plot(x, y, label='Sine Wave') # plotting the sine wave
plt.xlabel('X-axis')
plt.ylabel('Y-axis')
plt.legend()
plt.grid(True)
# Show the plot
plt.show()
Q.36: Discuss Uses of vstack() and hstack() Functions
The vstack() and hstack() functions in NumPy are used to stack or concatenate arrays vertically and horizontally, respectively. These functions are essential for combining arrays in different dimensions and are widely used in various data processing and manipulation tasks.
vstack() (Vertical Stack): np.vstack() is used to vertically stack or concatenate arrays along the vertical axis (axis 0). This means that it stacks arrays on top of each other.
hstack() (Horizontal Stack): np.hstack() is used to horizontally stack or concatenate arrays along the horizontal axis (axis 1). This means that it stacks arrays side by side.
Q.42: How to Compare Two NumPy Arrays?
Here, we’ll concentrate on the array comparison performed with NumPy. When two NumPy arrays are compared, the presence of the same element at each corresponding index indicates whether the arrays are comparable.
Method 1: We generally use the == operator to compare two NumPy arrays to generate a new array object. Call ndarray.all() with the new array object as ndarray to return True if the two NumPy arrays are equivalent.
Method 2: Using array_equal()
This array_equal() function checks if two arrays have the same elements and same shape.
Python
numpy.array_equal(arr1, arr2)