2.3. Using WITS DATA to build a bar graph

1. Read, clean, and prepare data to build maps

1.1. Create your first choropleth world map

1.2. Create dictionaries: JSON and GeoJSON

1.3. Read and clean immigration data

2. Problem & Solution

2.1. Fusion of GeoJson and immigration data

2.2. Create your first map with pinpoints

2.3. Using WITS data to build a bar graph

2.4. Insert a bar graph in a pinpoint map

2.5. Obtaining the risk of each country

2.6. Fusion of risk and geometry data

2.7. Drawing route between two ports

Preparing statistical data to be inserted on a map

Let's extract data from WITS. To do so, first install WITS package.

!pip install world_trade_data --upgrade

Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/ Collecting world_trade_data Downloading world_trade_data-0.1.1.tar.gz (11 kB) Preparing metadata (setup.py) ... Successfully built world_trade_data Installing collected packages: xmltodict, world_trade_data Successfully installed world_trade_data-0.1.1 xmltodict-0.13.0

Now it is possible to read WITS data. The next code obtains the values of Zambia (ZMB) exportation to Rwanda (RWA).

import pandas as pd

import world_trade_data as wits

zmb_exports_2010 = wits.get_indicator('XPRT-TRD-VL', partner='rwa', reporter='zmb', year='2010')

zmb_exports_2010 = zmb_exports_2010.reset_index()

zmb_exports_2010

This will result in:

Filtering values

The next code filter products which are lower than a threshold value in column 'Value'.

filter = zmb_exports_2010['Value'] > 10

zmb_exports_2010_filtered = zmb_exports_2010[filter]

zmb_exports_2010_filtered

A related bar graph

The next code will generated the bar graph related with the values in the filtered data table.

fig = plt.figure()

# gca stands for 'get current axis'

ax = plt.gca()

zmb_exports_2010_filtered[['ProductCode', 'Value']].plot.barh(x='ProductCode', y='Value', rot=0, ax = ax)

ax.set_yticklabels(zmb_exports_2010_filtered['ProductCode'])

plt.show()

To enable this bar graph to be inserted into a map, it is necessary to convert it to a html file. To do this, it is necessary to employ the command fig_to_html from the package mpld3. But, first, let's install it.

Installing mpld3 package

!pip install mpld3

Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/ Collecting mpld3 Downloading mpld3-0.5.9-py3-none-any.whl (201 kB) ... Installing collected packages: mpld3 Successfully installed mpld3-0.5.9

Converting a bar graph to an HTML file

import matplotlib.pyplot as plt, mpld3

html_graph = mpld3.fig_to_html(fig)

html_graph

Visualization of the content of the generated HTML file

import folium

from ipywidgets import HTML

page='<html>'+html_graph+'</html>'

iframe = folium.IFrame(page,

width=300,

height=300)

popup = folium.Popup(iframe)

display(HTML(page))

The Python code with all the steps is summarized in this Google Colab (click on the link):

https://colab.research.google.com/drive/1w0d5xSKCT9XFB5ADHsurutDSyRK78p-E?usp=sharing

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