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import folium
import pandas as pd

country_geo = 'world-countries.json'

data = pd.read_csv('Indicators.csv')
data.shape

data.head()

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# select Life expectancy for females for all countries in 2013
hist_indicator =  'Life expectancy at birth'
hist_year = 2013

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

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

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# Setup a folium map at a high-level zoom 
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)

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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>')

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# Importing the libraries
import pandas as pd

# read the training data and store data in DataFrame titled housing_data
housing_data = pd.read_csv('../Desktop/iowa_housing/train.csv')

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Step2: Exploring the Data

#display the first five rows of the data
house_data.head()
#display the first five rows of the data
house_data.head()
Id	MSSubClass	MSZoning	LotFrontage	LotArea	Street	Alley	LotShape	LandContour	Utilities	...	PoolArea	PoolQC	Fence	MiscFeature	MiscVal	MoSold	YrSold	SaleType	SaleCondition	SalePrice
0	1	60	RL	65.0	8450	Pave	NaN	Reg	Lvl	AllPub	...	0	NaN	NaN	NaN	0	2	2008	WD	Normal	208500
1	2	20	RL	80.0	9600	Pave	NaN	Reg	Lvl	AllPub	...	0	NaN	NaN	NaN	0	5	2007	WD	Normal	181500
2	3	60	RL	68.0	11250	Pave	NaN	IR1	Lvl	AllPub	...	0	NaN	NaN	NaN	0	9	2008	WD	Normal	223500

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# AOI co-ordinates (created via geojson.io) 
geojson_geometry = {
  "type": "Polygon",
  "coordinates": [
    [ 
      [438.2666015625,22.27893059841188],
      [440.013427734375,22.27893059841188],
      [440.013427734375,23.33216830631147],
      [438.2666015625,23.33216830631147],
      [438.2666015625,22.27893059841188]

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# get images that overlap with our AOI 
geometry_filter = {
  "type": "GeometryFilter",
  "field_name": "geometry",
  "config": geojson_geometry
}

# get images acquired within a date range
date_range_filter = {
  "type": "DateRangeFilter",