Please create a new project and name your project as CCE$\_6960.$

Then, define the function $($named as DistMatrix$)$ to calculate Euclidean distances between

DataFrame A and DataFrame B$.$ The function should be able to deal with different inputs $($e$.$g$.,$

number of points and dimensions of points$)$ without changing your codes.

a$)$ Document the codes in your report.

b$)$ Submit your Python files and the e$-$copy of your report to ELearning.

Note: A portion of the codes are included in the Appendix.

Hints:

DistMatrix $($A$,$ B$)$

$\%$ It returns distances between points in DataFrame A and DataFrame B $($each row is one point$)$

$\%$ The results are organized into a new DataFrame as explained in the example

$\%$ Example:

$\%$

$456$ ; Coordinates of A$2$

$246$ Coordinates of A$3$

$1233]$; Coordinates of A$4$

$620]$ Coordinates of B$2$

$\%$ The distance between A $1$ and B $1$ is $4\mathrm{.}69$

$\%$ The distance between $A1$ and $B2$ is $5\mathrm{.}83$

$\%$ The distance between $A2$ and $B1$ is $5\mathrm{.}00-$

$\%$ The distance between A$2$ and B$2$ is $7\mathrm{.}00,$ and so on

The standard Euclidean distance can be calculated as:

distance $=\sqrt[\phantom{2}]{(x_1-y_1{)}^2+(x_2-y_2{)}^2+(x_3-y_3{)}^2}$