<jupyter_start><jupyter_text>Importing Data<jupyter_code>pm25 = pd.read_csv('PM25_monthly_avg_2018.csv')
pm25.head()<jupyter_output><empty_output><jupyter_text>Converting data into numpy arrays<jupyter_code>lat = pm25['Lat'].values
lon = pm25['Long'].values
dec = pm25['Dec'].values
jan = pm25['Jan'].values
feb = pm25['Feb'].values
mar = pm25['Mar'].values
apr = pm25['Apr'].values
may = pm25['May'].values
jun = pm25['Jun'].values
jul = pm25['Jul'].values
aug = pm25['Aug'].values
sep = pm25['Sep'].values
octo = pm25['Oct'].values
nov = pm25['Nov'].values
dec = pm25['Dec'].values<jupyter_output><empty_output><jupyter_text>Manually adding data to show some random locations<jupyter_code>site_lon = [77.175211,77.184967,77.284445]
site_lat = [28.630673,28.546935,28.450554]
slon = np.asarray(site_lon)
slat = np.asarray(site_lat)<jupyter_output><empty_output><jupyter_text>Plotting the data<jupyter_code>%matplotlib inline
map = Basemap(llcrnrlon=76.812,llcrnrlat=28.37,urcrnrlon=77.4,urcrnrlat=28.9)#, resolution = 'h', epsg=24378)
#map.drawmapboundary(fill_color='aqua')
#Fill the continents with the land color
map.readshapefile('delhi_Project_sssss','Delhi')
#map.fillcontinents(color='red',lake_color='aqua')
#map.drawcoastlines()
plt.title('July 2018',fontsize=20)
map.scatter(lon, lat, latlon=True,
          c=jul, s=100,
          cmap='Reds', alpha=0.9)
map.colorbar(label=r'$\ PM_{2.5} Concentration $')
plt.clim(0,350)
map.scatter(slon,slat,latlon=True,s=30,color='green',alpha=1)
plt.show()
plt.savefig('jul',dpi=300)<jupyter_output><empty_output>
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