Task$1$: Nearest Neibhour search using python in big data

There is a datasets called restuarents the data set is consisted of $2$D data points stored in a text file in following format:

id$\_1$ x$1$ y$1$

id$\_2$ x$2$ y$2$

$.$

$.$

$.$

id$\_$n xn yn

each line includes a unique id for a point and its geographical coordinates, longititude and latitud. for example the entry in the dataset, precisely indicates the location of the restaurent. with $"$x$"$ representing ongitude and $"$y$"$ representing latitude.

Queries: we have $200$ users interested in finding the nearest shop. Their locations are provided in a text file formatted identical to the datasets.

id$\_1$ x$\_1$ y$\_1$

id$\_2$ x$\_2$ y$\_2$

$.$

$.$

$.$

id$\_200$ x$\_200$ y$\_200$

for example id$\_1=1,$ x$\_1=31\mathrm{.}45,$ y$\_1=150\mathrm{.}44$ indicate's a user's location.

find the nearest restaurent for each query using the following algorithm:

$1.$ sequential scan based method: calculate the distance between a query point to every point in the selected dataset to find the nearest neibhor.

The output should display in the following format:

The ID$,$ x and y coordinates of the nearest neibhor identified for each query point

$"$id$=56,$ x$=34\mathrm{.}15,$ y$=149\mathrm{.}21,$ for query $1"$

the total running time for processing all $200$ queries and the average time per query $($divide the total running time by $200)$

i want python code for the above requirements, assume that you have two data text files which is in the above format.