import folium
import pandas as pd

"""
Explanation: Data Source: https://www.kaggle.com/worldbank/world-development-indicators <br> Folder: 'world-development-indicators'
Using Folium Library for Geographic Overlays
Further exploring CO2 Emissions per capita in the World Development Indicators Dataset
End of explanation
"""


country_geo = r'/Users/Harish/Documents/HK_Work/Python/world-development-indicators/world-countries.json'

# Read in the World Development Indicators Database
data = pd.read_csv(r'/Users/Harish/Documents/HK_Work/Python/world-development-indicators/Indicators.csv')
data.shape

data.head()

"""
Explanation: Country coordinates for plotting
source: https://github.com/python-visualization/folium/blob/master/examples/data/world-countries.json
End of explanation
"""


# select CO2 emissions for all countries in 2011
hist_indicator = 'CO2 emissions \(metric'
hist_year = 2011

mask1 = data['IndicatorName'].str.contains(hist_indicator) 
mask2 = data['Year'].isin([hist_year])

# apply our mask
stage = data[mask1 & mask2]
stage.head()

"""
Explanation: Pull out CO2 emisions for every country in 2011
End of explanation
"""


plot_data = stage[['CountryCode','Value']]
plot_data.head()

# label for the legend
hist_indicator = stage.iloc[0]['IndicatorName']

"""
Explanation: Setup our data for plotting.
Create a data frame with just the country codes and the values we want plotted.
End of explanation
"""


# Setup a folium map at a high-level zoom @Alok - what is the 100,0, doesn't seem like lat long
map = folium.Map(location=[100, 0], zoom_start=1.5)

# choropleth maps bind Pandas Data Frames and json geometries.  This allows us to quickly visualize data combinations
map.choropleth(geo_data=country_geo, data=plot_data,
             columns=['CountryCode', 'Value'],
             key_on='feature.id',
             fill_color='YlGnBu', fill_opacity=0.7, line_opacity=0.2,
             legend_name=hist_indicator)

# Create Folium plot
map.save('plot_data.html')

# Import the Folium interactive html file
from IPython.display import HTML
HTML('<iframe src=plot_data.html width=700 height=450></iframe>')

"""
Explanation: Visualize CO2 emissions per capita using Folium
Folium provides interactive maps with the ability to create sophisticated overlays for data visualization
End of explanation
"""


