Plot latitude, longitude, elevation and EMF data from CSV in Python

Question

I'm trying to plot a large number of latitude, longitude, elevation and EMF values from a CSV file. The CSV file looks like this

dat,latitude,longitude,EMF,Elevation
1/20/2022 7:18:17,59.39556688,18.12773272,0,18.17260262
1/20/2022 7:18:18,59.39556685,18.12773267,0,18.17260262
1/20/2022 7:18:19,59.39556684,18.12773265,0,18.17260262
1/20/2022 7:18:20,59.39556693,18.1277326,4.1,18.17260262
1/20/2022 7:18:21,59.39556698,18.12773191,4,18.17260262
1/20/2022 7:18:22,59.39556714,18.1277315,4.1,18.17260262
1/20/2022 7:18:23,59.39556728,18.12773191,4.1,18.17260262
1/20/2022 7:18:24,59.39556718,18.12773088,4,18.17260262
1/20/2022 7:18:25,59.39556755,18.12773013,4.1,18.17260262
1/20/2022 7:18:26,59.39556755,18.1277296,131,18.17260262
1/20/2022 7:18:27,59.39556729,18.12772922,125.9,18.17260262
1/20/2022 7:18:28,59.39556682,18.1277278,9,18.17260262
1/20/2022 7:18:29,59.39556684,18.1277263,4.1,18.17260262

I want to represent them by diferent colors depending on the EMF value.

My code is this

from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
import pandas

points = pandas.read_csv('data.csv')


fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')


latitude = points['latitude'].values
longitude = points['longitude'].values
EMF = points['EMF'].values

plt.ticklabel_format(useOffset=False)

ax.scatter(latitude, longitude, EMF, c='r', marker='o')

plt.show()

How can i do that?

Answer 1

Edit: per Mr. T's comment, you can just plug EMF into c

import pandas as pd
from io import StringIO
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt

s = """1/20/2022 7:18:17,59.39556688,18.12773272,0,18.17260262
1/20/2022 7:18:18,59.39556685,18.12773267,0,18.17260262
1/20/2022 7:18:19,59.39556684,18.12773265,0,18.17260262
1/20/2022 7:18:20,59.39556693,18.1277326,4.1,18.17260262
1/20/2022 7:18:21,59.39556698,18.12773191,4,18.17260262
1/20/2022 7:18:22,59.39556714,18.1277315,4.1,18.17260262
1/20/2022 7:18:23,59.39556728,18.12773191,4.1,18.17260262
1/20/2022 7:18:24,59.39556718,18.12773088,4,18.17260262
1/20/2022 7:18:25,59.39556755,18.12773013,4.1,18.17260262
1/20/2022 7:18:26,59.39556755,18.1277296,131,18.17260262
1/20/2022 7:18:27,59.39556729,18.12772922,125.9,18.17260262
1/20/2022 7:18:28,59.39556682,18.1277278,9,18.17260262
1/20/2022 7:18:29,59.39556684,18.1277263,4.1,18.17260262"""

df = pd.read_csv(StringIO(s), header=None)
df.columns = pd.Index(['dat','latitude','longitude','EMF','Elevation'])


fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
plt.ticklabel_format(useOffset=False)

x, y, z = df['latitude'], df['longitude'], df['EMF']
ax.scatter(x, y, z, c=z)


plt.show()

Original answer Without resorting to fancier methods, you could pick a few thresholds and use pandas convenient indexing capabilities to plot different thresholds with different colors.

import pandas as pd
from io import StringIO
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt

s = """1/20/2022 7:18:17,59.39556688,18.12773272,0,18.17260262
1/20/2022 7:18:18,59.39556685,18.12773267,0,18.17260262
1/20/2022 7:18:19,59.39556684,18.12773265,0,18.17260262
1/20/2022 7:18:20,59.39556693,18.1277326,4.1,18.17260262
1/20/2022 7:18:21,59.39556698,18.12773191,4,18.17260262
1/20/2022 7:18:22,59.39556714,18.1277315,4.1,18.17260262
1/20/2022 7:18:23,59.39556728,18.12773191,4.1,18.17260262
1/20/2022 7:18:24,59.39556718,18.12773088,4,18.17260262
1/20/2022 7:18:25,59.39556755,18.12773013,4.1,18.17260262
1/20/2022 7:18:26,59.39556755,18.1277296,131,18.17260262
1/20/2022 7:18:27,59.39556729,18.12772922,125.9,18.17260262
1/20/2022 7:18:28,59.39556682,18.1277278,9,18.17260262
1/20/2022 7:18:29,59.39556684,18.1277263,4.1,18.17260262"""

df = pd.read_csv(StringIO(s), header=None)
df.columns = pd.Index(['dat','latitude','longitude','EMF','Elevation'])


fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
plt.ticklabel_format(useOffset=False)

below = df[df['EMF'] < 2]
x, y, z = below['latitude'], below['longitude'], below['EMF']
ax.scatter(x, y, z, c='r')

above = df[df['EMF'] >= 2]
x, y, z = above['latitude'], above['longitude'], above['EMF']
ax.scatter(x, y, z, c='b')

plt.show()