Locating Maps using Python
In this post, we will be looking to get and print some of the maps using the jupyter notebook and python. The purpose of this notebook is to have fun with the Jupyter and python — to know how powerful python is. Moreover, this post is for fun and as for a learning part.
Various map plotting has been done using basic python coding in the jupyter notebook. Follow to try, implement and practice to match the best results possible out of this notebook.
Run all the commands and coding snippets in the python jupyter notebook and get the surprisingly outputs.
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Let’s Go step by step to implement and Execute the Coding part👨💻
Note: Run and Execute each code block in the new cell of the Jupyter notebook.
Importing Pandas and Numpy
import numpy as np
import pandas as pdWe will be doing such plotting and generating maps using Folium. Folium is not available by default. So, we first need to install it before we start with the Map Plotting.
What is Folium?
Folium is a Python library used for visualizing geospatial data. It is easy to use and yet a powerful library. Folium is a Python wrapper for Leaflet.js which is a leading open-source JavaScript library for plotting interactive maps.
Let’s install Folium
!conda install -c conda-forge folium=0.5.0 --yes #installing folium through conda library
import foliumprint('Folium installed and imported!')
The following packages will be downloaded:
package | build
— — — — — — — — — — — — — -| — — — — — — — — -
altair-4.1.0 | py_1 614 KB conda-forge
branca-0.4.1 | py_0 26 KB conda-forge
brotlipy-0.7.0 |py36h8c4c3a4_1000 346 KB conda-forge
chardet-3.0.4 |py36h9f0ad1d_1006 188 KB conda-forge
cryptography-3.0 | py36h45558ae_0 640 KB conda-forge
folium-0.5.0 | py_0 45 KB conda-forge
pandas-1.0.5 | py36h830a2c2_0 10.1 MB conda-forge
pysocks-1.7.1 | py36h9f0ad1d_1 27 KB conda-forge
toolz-0.10.0 | py_0 46 KB conda-forge
urllib3–1.25.10 | py_0 92 KB conda-forge
vincent-0.4.4 | py_1 28 KB conda-forge
— — — — — — — — — — — — — — — — — — — — — — — — — — — — — —
Total: 12.1 MB
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Now coming on to display a simple map.
Display simple map
# define the world map
world_map = folium.Map()# display world map
world_map
Output for simple map
Go ahead. Try zooming in and out of the rendered map above.
All locations on a map are defined by their respective Latitude and Longitude values. So you can create a map and pass in a center of Latitude and Longitude values of [0,0].
Let’s create a map centered around India and play with the zoom level to see how it affects the rendered map
Let’s create a map centered around India and play with the zoom level to see how it affects the rendered map
# define the world map centered around India with a low zoom level
world_map = folium.Map(location=[20, 78], zoom_start=4)# display world map
world_map
Output for world map-centered around India.
You can change the various latitude and longitude to go through the different locations and countries.
You can also change to zoom level for initial visualization and then zoom in and out accordingly.
Let’s create a Stamen Toner map of India with a zoom level of 4.
# create a Stamen Toner map of the world centered around India
world_map = folium.Map(location=[20, 77], zoom_start=4, tiles='Stamen Toner')# display map
world_map
The output of Stamen Toner Map
These are high-contrast B+W (black and white) maps. They are perfect for data mashups and exploring river meanders and coastal zones.
Let’s create a Stamen Terrain map of India with zoom level 4.
# create a Stamen Toner map of the world centered around India
world_map = folium.Map(location=[20, 77], zoom_start=4, tiles='Stamen Terrain')# display map
world_map
The output of Stamen Terrain Map
Now let us import data from an online source using the URL and display the Choropleth map
These are maps that feature hill shading and natural vegetation colors. They showcase advanced labeling and linework generalization of dual-carriageway roads.
Feel free to zoom in and find the legal roadways and rivers.
This data is the immigrants to Canada from different parts of countries.
What is a Choropleth Map ❓
A Choropleth map is a thematic map in which areas are shaded or patterned in proportion to the measurement of the statistical variable being displayed on the map, such as population density or per-capita income. The choropleth map provides an easy way to visualize how a measurement varies across a geographic area or it shows the level of variability within a region.
df_can = pd.read_excel('https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/DV0101EN/labs/Data_Files/Canada.xlsx',
sheet_name='Canada by Citizenship',
skiprows=range(20),
skipfooter=2)print('Data downloaded and read into a dataframe!')# Show the dataframe as downloaded
df_can.head()# print the dimensions of the dataframe
print(df_can.shape)
Clean up data — We will make some modifications to the original dataset to make it easier to create our visualizations. Refer to Introduction to Matplotlib and Line Plots and Area Plots, Histograms, and Bar Plots notebooks for a detailed description of this preprocessing.
# 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)# 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)
In order to create a Choropleth map, we need a GeoJSON file that defines the areas/boundaries of the state, county, or country that we are interested in.
In our case, since we are endeavoring to create a world map, we want a GeoJSON that defines the boundaries of all world countries. For your convenience, we will be providing you with this file, so let’s go ahead and download it. Let’s name it world_countries.json.
# download countries geojson file
!wget --quiet https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/DV0101EN/labs/Data_Files/world_countries.json -O world_countries.json
print('GeoJSON file downloaded!')Now that we have the GeoJSON file, let’s create a world map, centered around [0, 0] latitude and longitude values, with an initial zoom level of 2, and using Mapbox Bright style.
world_geo = r'world_countries.json' # geojson file# create a plain world map
world_map = folium.Map(location=[0, 0], zoom_start=2, tiles='Mapbox Bright')# generate choropleth map using the total immigration of each country to Canada from 1980 to 2013
world_map.choropleth(
geo_data=world_geo,
data=df_can,
columns=['Country', 'Total'],
key_on='feature.properties.name',
fill_color='YlOrRd',
fill_opacity=0.7,
line_opacity=0.2,
legend_name='Immigration to Canada'
)# display map
world_map
The output of Choropleth Map
Thank you for viewing this notebook and article. Just give it a try to run cells of code in Jupyter Notebook. You will love 💚 to play with this amazing stuff.
You can find the same set of codes executed on Jupyter Notebook By Visiting this Repository — Locating_maps_using_ipython
I hope you found this lab interesting and educational. Feel free to contact me if you have any questions!
If you liked the post and find it useful then please visit this Repository in Github and Drop a star ⭐
Thanks, from my side, this is Mayank, keep learning and exploring!!
Originally published at The Coding Bro