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View exploring_data.py
import folium
import pandas as pd
country_geo = 'world-countries.json'
data = pd.read_csv('Indicators.csv')
data.shape
data.head()
View indicators.py
# 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()
View creating_folium.py
# 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)
View html.py
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>')
View European Soccer data.ipynb
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View soccer.ipynb
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View loading_data.py
# 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')
View exploring_data.py
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
View AOI_co-ordinates.py
# 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]
View FIlters.py
# 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",
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