Google has a basic Python class available to begin with:
https://developers.google.com/edu/python/
Happy Scripting!!!
UPDATE: Playing with Python..
With the recent Earthquake in the Philippines last January 11, 2015 at around 3:30AM (Phil. Time) that reach 5.9 Magnitude, I though maybe I can create an image (Data Visualisation) on where is the centre of the earthquake. So here's python to the rescue. Since I been reading for awhile regarding what other modules can do, I ended up using Basemap. The script below has 2 parts. First is to grab dataset from the cvs file. The columns are Latitude, Longitude and Magnitude. The second part is the engine for the script where Baseman is being used to create the image.
For the result, the red dot will got bigger if the magnitude is higher.
----------------------------------------------------------------
# Import Dataset
import csv
filename ='DS.csv'
lats, lons = [], []
mags = []
with open(filename) as f:
# Create a csv reader object.
reader = csv.reader(f)
# Ignore the header row.
#next(reader)
# Store the latitudes and longitudes in the appropriate lists.
for row in reader:
lats.append(float(row[0]))
lons.append(float(row[1]))
mags.append(float(row[2]))
#----------------------------------------------------------------
from mpl_toolkits.basemap import Basemap
import matplotlib.pyplot as plt
import numpy as np
## Philippine Map coordinates
m = Basemap(resolution='f',projection='merc',
lat_0=13, lon_0=122,
llcrnrlat=5.0,
urcrnrlat=19.0,
llcrnrlon=114.,
urcrnrlon=130.0,
)
m.drawmapboundary(fill_color='white')
m.fillcontinents(color='#F5DEB3',lake_color='#85A6D9')
m.drawcoastlines(color='black', linewidth=.4)
m.drawcountries(color='#6D5F47', linewidth=.4)
min_marker_size= 2.5
for lon, lat, mag in zip(lons, lats, mags):
x,y = m(lon, lat)
msize = mag * min_marker_size
m.plot(x,y, 'ro', markersize=msize)
figsize=(50, 18)
plt.show()
DS.csv
09.97,124.17,2.6
11.64,126.10,3.2
11.55,126.31,3.1
11.59,126.23,4.9
04.87,127.19,3.7
06.17,126.02,2.5
14.79,120.00,2.3
05.69,126.26,4.2
14.74,119.91,5.9
Final image:
