Countries by Lowest Point of Altitude on Land¶

IPython Notebook to retrieve, process, display and plot data on lowest point of altitude on land for 80 countries and Antarctica from photius.com, which compiled the data from the CIA World Fact Book 2014.

First import necessary libraries, setup geonames mapper, download data and turn it into a pandas DataFrame.

In [1]:

import pandas as pd
from lxml import html
from geonamescache.mappers import country

mapper = country(from_key='name', to_key='iso3')

url ='http://www.photius.com/rankings/geography/elevation_extremes_lowest_point_2014_0.html'
xpath = '//table'

tree = html.parse(url)
tables = tree.xpath(xpath)
raw_html = html.tostring(tables[4])
df = pd.read_html(raw_html, header=0)[0][['Country', 'Value']]

Process data

Add an iso3 column using the geonamescache mapper function created above, needed for rendering the d3 based map and set more meaningful column names.

In [2]:

df['iso3'] = df['Country'].apply(lambda x: mapper(x))
df.dropna(subset=['iso3', 'Value'], inplace=True)
df.set_index('Country', inplace=True)
df.columns = ['Altitude', 'iso3']
df.head()

Out[2]:

	Altitude	iso3
Country
Lesotho	1400	LSO
Rwanda	950	RWA
Andorra	840	AND
Burundi	772	BDI
Uganda	621	UGA

Display and plot highest low points

First set some common plotting properties.

In [3]:

footer = 'CC BY-SA 2014 Ramiro Gómez - ramiro.org • Data: www.photius.com/rankings/geography/elevation_extremes_lowest_point_2014_0.html'
mpl.rcParams['font.size'] = 11
mpl.rcParams['font.family'] = 'Ubuntu'
mpl.rcParams['axes.color_cycle'] = 'a6cee3, 1f78b4, b2df8a, 33a02c, fb9a99, e31a1c, fdbf6f, ff7f00, cab2d6'

Show highest low points

In [4]:

df.sort('Altitude', inplace=True)
df.tail(10)

Out[4]:

	Altitude	iso3
Country
Central African Republic	335	CAF
Armenia	400	ARM
Liechtenstein	430	LIE
Botswana	513	BWA
Mongolia	560	MNG
Uganda	621	UGA
Burundi	772	BDI
Andorra	840	AND
Rwanda	950	RWA
Lesotho	1400	LSO

Plot highest low points

In [5]:

s = df['Altitude'][-10:]
s.plot(kind='barh', figsize=(10, 6), title='Countries by Highest Low Point of Altitude on Land\n', fontsize='large')

ax = plt.axes()
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.spines['bottom'].set_visible(False)
ax.spines['left'].set_color((1, 1, 1))
ax.xaxis.set_label_text(footer)
ax.xaxis.set_ticklabels('')
ax.yaxis.set_label_text('')

for i, x in enumerate(s):
    ax.text(x + 10, i - .1, int(x), ha='left', fontsize='large')

plt.savefig('../static/img/graphs/highest-low-point-altitude-countries.png', bbox_inches='tight')

Show lowest low points

In [6]:

df.head(10)

Out[6]:

	Altitude	iso3
Country
Antarctica	-2540	ATA
Israel	-408	ISR
Jordan	-408	JOR
Syria	-200	SYR
Djibouti	-155	DJI
China	-154	CHN
Egypt	-133	EGY
Kazakhstan	-132	KAZ
Kyrgyzstan	-132	KGZ
Ethiopia	-125	ETH

Plot lowest low points

In [7]:

s = df['Altitude'][:10].apply(lambda x: x * -1)
s.plot(kind='barh', figsize=(10, 6), title='Countries by Lowest Low Point of Altitude on Land\n', fontsize='large')

ax = plt.axes()
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.spines['bottom'].set_visible(False)
ax.spines['left'].set_color((1, 1, 1))
ax.xaxis.set_label_text(footer)
ax.xaxis.set_ticklabels('')
ax.yaxis.set_label_text('')

for i, x in enumerate(s):
    ax.text(x + 20, i - .1, int(-x), ha='left', fontsize='large')

plt.savefig('../static/img/graphs/lowest-low-point-altitude-countries.png', bbox_inches='tight')

In [8]:

df.to_csv('../static/data/csv/lowest-altitude-countries.csv', encoding='utf-8', index=False)

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