Python_Code_File.txt

!pip install cartopy
import cartopy
import numpy as np  # useful for many scientific computing in Python
import pandas as pd # primary data structure library

#!pip3 install folium==0.5.0
import folium

print('Folium installed and imported!')

# define the world map
world_map = folium.Map()

# display world map
world_map

# define the world map centered around Canada with a higher zoom level
world_map = folium.Map(location=[56.130, -106.35], zoom_start=8)

# display world map
world_map

# create a Stamen Toner map of the world centered around Canada
world_map = folium.Map(location=[56.130, -106.35], zoom_start=4, tiles='Stamen Terrain')

# display map
world_map
from js import fetch
import io

URL = 'https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork/Data%20Files/Canada.xlsx'
resp = await fetch(URL)
text = io.BytesIO((await resp.arrayBuffer()).to_py())

df_can = pd.read_excel(
    text,
    sheet_name='Canada by Citizenship',
    skiprows=range(20),
    skipfooter=2)

print('Data downloaded and read into a dataframe!')
import requests
import io

URL = 'https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork/Data%20Files/Canada.xlsx'

# Fetch the data using requests library
response = requests.get(URL)
response.raise_for_status()  # Raise an exception for bad responses (4xx or 5xx)

# Read the data into a BytesIO object
text = io.BytesIO(response.content)

df_can = pd.read_excel(
    text,
    sheet_name='Canada by Citizenship',
    skiprows=range(20),
    skipfooter=2)

print('Data downloaded and read into a dataframe!')

df_can.head()

# print the dimensions of the dataframe
print(df_can.shape)

# clean up the dataset to remove unnecessary columns (eg. REG) 
df_can.drop(['AREA','REG','DEV','Type','Coverage'], axis=1, inplace=True)

# let's rename the columns so that they make sense
df_can.rename(columns={'OdName':'Country', 'AreaName':'Continent','RegName':'Region'}, inplace=True)

# for sake of consistency, let's also make all column labels of type string
df_can.columns = list(map(str, df_can.columns))

# add total column
df_can['Total'] = df_can.sum(axis=1, numeric_only=True)

# check how the dataset now looks like
df_can.head()

# years that we will be using in this lesson - useful for plotting later on
years = list(map(str, range(1980, 2014)))
print ('data dimensions:', df_can.shape)

df_can.head()

import requests
import io
import json

# ... (previous code remains the same) ...

# download countries geojson file
URL = 'https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork/Data%20Files/world_countries.json'

# Fetch the data using requests library
response = requests.get(URL)
response.raise_for_status()  # Raise an exception for bad responses (4xx or 5xx)

# Read the data into a BytesIO object
data = io.BytesIO(response.content)
world_geo = json.load(data)

print('GeoJSON file loaded!')

import folium

# ... (previous code remains the same) ...

# create a plain world map
world_map = folium.Map(location=[0, 0], zoom_start=2)

# generate choropleth map using the total immigration of each country to Canada from 1980 to 2013
folium.GeoJson(
    world_geo,
    style_function=lambda feature: {
        'fillColor': 'YlOrRd',
        'fillOpacity': 0.7,
        'color': 'black',
        'weight': 0.2,
    }
).add_to(world_map)

# add a legend (optional)
# ...

# display map
world_map

import folium

# ... (previous code remains the same) ...

# let Folium determine the scale.
world_map = folium.Map(location=[0, 0], zoom_start=2)

# Use folium.Choropleth instead of world_map.choropleth
folium.Choropleth(
    geo_data=world_geo,
    data=df_can,
    columns=['Country', 'Total'],
    key_on='feature.properties.name',
    threshold_scale=threshold_scale,
    fill_color='YlOrRd', 
    fill_opacity=0.7, 
    line_opacity=0.2,
    legend_name='Immigration to Canada',
    reset=True
).add_to(world_map)  # Add the choropleth to the map

world_map