The Geometry of Maps - Twitter Trend

Below assignment is taken from here.


Introduction


In this project, you will develop a geographic visualization of
twitter data across the USA. You will need to use dictionaries, lists,
and data abstraction techniques to create a modular program. Below
assignment is phase 2 of this project. For reference, phase 1 of
this project is available
here.


Phase 2: The Geometry of Maps


Positions


We will use the position abstract data type to represent
geographic latitude-longitude positions on the Earth. The data
abstraction, defined at the top of geo.py, has the constructor
make_position and the selectors latitude and longitude.


In this phase, you will write two functions that together determine
the centers of U.S. states. The shape of a state is represented as a
list of polygons. Some states (e.g. Hawaii) consist of multiple
polygons, but most states (e.g. Colorado) consist of only one polygon
(still represented as a length-one list).


Problem 6 (2 pt). Implement find_centroid, which takes a polygon and returns three values: the coordinates of its centroid and
its area. The input polygon is represented as a list of position
abstract data types, which are the consecutive vertices of its
perimeter. The first vertex is always identical to the last.


The centroid of a two-dimensional shape is its center of balance,
defined as the intersection of all straight lines that evenly divide
the shape into equal-area halves. find_centroid returns the centroid
and area of an individual polygon.


The formula for computing the centroid of a polygon appears on
Wikipedia. The formula relies on vertices being consecutive (either
clockwise or counterclockwise; both give the same answer), a property
that you may assume always holds for the input.


When you complete this problem, the doctest for find_centroid should
pass.

python3 trends.py -t find_centroid

Problem 7

A few notes about your code:

-
Whenever possible, try to use object-based loops instead of index-based loops. It makes the code cleaner and lowers the cognitive overhead. For example, turn this loop:

centroid_and_area_of_all_polygons = [] 
for index in range(len(polygons)):
    centroid_and_area_of_all_polygons.append(find_centroid(polygons[index]))

Into this one:

centroid_and_area_of_all_polygons = [] 
for polygon in polygons:
    centroid_and_area_of_all_polygons.append(find_centroid(polygon))

You could even simplify this with the built-in function map:

centroid_and_area_of_all_polygons = list(map(find_centroid, polygons))

Or with a list comprehension:

centroid_and_area_of_all_polygons = [find_centroid(polygon) for polygon in polygons]

-
The following piece of code:

if area_of_polygon < 0:
    return (centroid_latitude, centroid_longitude, -area_of_polygon)
else:
    return (centroid_latitude, centroid_longitude, area_of_polygon)

...would benefit from the built-in function abs:

return (centroid_latitude, centroid_longitude, abs(area_of_polygon))

-
You are importing waaaayyyyy too many modules and features that you don't use. Please try to only include what you will use, it will make it simpler for you (and other people reading your code) to know what your code really relies on.

StackExchange Code Review Q#91279, answer score: 4