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pandasmatplotlibcartopy

Plotting points on a map with calculated distance

This is my dataframe 

import pandas as pd
from shapely.geometry import Point
import geopandas as gpd
from geopandas import GeoDataFrame
import matplotlib.pyplot as plt

d = {"STATE" : [ "NJ", "NJ", "NJ", "NJ"], 
     "CATEGORY": ["A", "B", "C", "D"],
     "LATITUDE" : [ 40.794856, 40.790176, 40.826762, 40.495150], 
     "LONGITUDE" : [ -74.149086, -74.255100, -74.101990, -74.442890]}
df = pd.DataFrame(data=d)

df.plot(kind="scatter", x="LONGITUDE", y="LATITUDE", alpha=0.4)
plt.show()

enter image description here

I want to calculate the distance between points based on category column:

A -> B

A -> C

A -> D

and connect the dots with distance displayed between them as a label

#creating point object so I can calculate distance bwtween coordinates
df["point"] = [Point(xy) for xy in zip(df['LONGITUDE'], df['LATITUDE'])]
#Formula I use for calculating distance between two points, this works when I have two seperate columns
#df['lat_long_diff'] = df.apply(lambda x : geodesic((x['LATITUDE_A'],x['LONGITUDE_A']),(x['LATITUDE_other_points'],x['LONGITUDE_other_points'])).miles,axis=1)

Also if I could include the Map of New Jersey as background it would be great, just the outline map would do.

Solution 
  • import cartopy
import cartopy.crs as ccrs
import cartopy.feature as cfeature

import matplotlib.pyplot as plt
from matplotlib.text import Text

import geopandas as gpd
import pandas as pd

import geopy.distance


def calc_dist(point_a, point_b):
    return(geopy.distance.geodesic(point_a, point_b).miles)

def draw_lines(x, y, p1 , p2):
    x1, x2 = x[p1], x[p2]
    y1, y2 = y[p1], y[p2]
    ax.plot([x1,x2],[y1,y2])


d = {"STATE" : [ "NJ", "NJ", "NJ", "NJ"], 
     "CATEGORY": ["A", "B", "C", "D"],
     "LATITUDE" : [ 40.794856, 40.790176, 40.826762, 40.495150], 
     "LONGITUDE" : [ -74.149086, -74.255100, -74.101990, -74.442890]}

df = pd.DataFrame(d)

dist_list = []
for i in [1,2,3]:
    dist = calc_dist((df.at[0, 'LATITUDE'], df.at[0, 'LONGITUDE']),
                    (df.at[i, 'LATITUDE'], df.at[i, 'LONGITUDE']))
    dist_list.append(dist)

proj = ccrs.PlateCarree(central_longitude=0)

fig, ax = plt.subplots(subplot_kw=dict(projection=proj), figsize=(16,16))
ax.set_extent([df['LONGITUDE'].min()-1,
               df['LONGITUDE'].max()+ 1,
               df['LATITUDE'].min()- 1,
               df['LATITUDE'].max()+1], 
              crs=ccrs.PlateCarree())

x = df['LONGITUDE'].tolist()
y = df['LATITUDE'].tolist()

ax.scatter(x, y)

draw_lines(x, y, 0, 1)
draw_lines(x, y, 0, 2)
draw_lines(x, y, 0, 3)

ax._add_text(Text(text=str(round(dist_list[0], 2)), x=-74.20, y=40.82))
ax._add_text(Text(text=str(round(dist_list[1], 2)), x=-74.12, y= 40.8))
ax._add_text(Text(text=str(round(dist_list[2], 2)), x=-74.29, y= 40.64))

ax.add_feature(cfeature.STATES.with_scale('10m'), zorder=0)

fig.canvas.draw()
fig.tight_layout()

plt.show()

    enter image description here