Python - Appending a new Dataframe column that is a function of two separate numerical columns

Question

I have a dataset that gives me the geographic coordinates of residential properties.

My goal is to append a new column to this dataframe that shows the properties' distances to various prominent locations in the city. I plan on doing this by finding the difference in the latitude and longitude to give X and Y values, and using the pythagorean theorem to find the straight-line distance between the two locations.

How do I structure the code so that the new column is a pythagorean function of the differences between latitude and longitude?

Thank you.

Answer 1

Use the distance matrix to know the distance from one point to all other corresponding points.

Here is an example:

def dist_PQ(P,Q):
    PP=P*P
    QQ=Q*Q
    PSquared_Sum = np.sum(PP,axis=1,keepdims=True)
    QSquared_Sum = np.sum(QQ,axis=1,keepdims=True)

    squared_distance = PSquared_Sum + QSquared_Sum.T - 2*np.dot(P,Q.T)

    return(squared_distance)

1. Use the above function to find distances between two sets of points say (P and Q)
2. Say P has 10 points the values are as numpy array [[x11,y11],[x12,y12],[x13,y13]...] and Q has 3 points as [[x21,y21],[x22,y22],[x23,y23]].

3. pass P and Q to the dist_PR function to get the output as (10,3) matrix which gives the distances from all points in array P to all points of Q.

   • Now we can use the matrix of distance in our further calculation.
   • Suppose we want to find the place which is nearest from P1 (a point in P), we can simply find the minimum distance value in row number 1 of that distance matrix.