The distance formula is a mathematical method that helps us find the distance between two points in a Cartesian coordinate system, just like the X and Y axes on a graph. Imagine you have two points, say (x1, y1) and (x2, y2). To calculate the distance between them, we can use the formula: distance = sqrt ( (x2 - x1)^2 + (y2 - y1)^2).

In this article, we will implement an algorithm to find the least distance in an unweighted, fully connected graph in python using breadth-first graph traversal algorithm. Breadth-first search is a graph traversal algorithm in which we traverse each vertex of the graph exactly once by starting from any single vertex.

You can use numpy broadcasting to create a distance matrix where the entry at (i,j) is the distance from i -th point to j -th point. Try to avoid Python loops if possible as they are comparatively slow. a = np.random.uniform (size= (4,2,1)) # coordinate of i-th point is a [i, :, 0] np.sqrt (np.sum (np.square (a - a.T), axis=1))

Create two arrays of data points for the X and Y axes. For this example, we'll use NumPy to generate random data. 3. Create Scatter Plot Use Matplotlib's scatter () function to create a scatter plot with the generated data points. 4. Customize and Show the Plot Customize the plot by adding a title, labels for the X and Y axes, and a legend.