Lab 5: World Progress¶
Welcome to lab 5!
This final lab in Data 8.1x brings together many of the topics so far, including data table manipulation,
visualization, and iteration. The content of the lab is based on a ser
...
Lab 5: World Progress¶
Welcome to lab 5!
This final lab in Data 8.1x brings together many of the topics so far, including data table manipulation,
visualization, and iteration. The content of the lab is based on a series of talks by Hans Rosling, a
statistician who advised many world leaders about the changing state of the world's population.
(Optional) For a video introduction to the topic of Global population change, you can watch Hans
Rosling's video, Don't Panic: The Facts About Population.
First, set up the tests and imports by running the cell below.
In [15]:
# Run this cell to set up the notebook, but please don't change it.
# These lines import the Numpy and Datascience modules.
import numpy as np
from datascience import *
# These lines do some fancy plotting magic.
import matplotlib
%matplotlib inline
import matplotlib.pyplot as plots
plots.style.use('fivethirtyeight')
from ipywidgets import interact, interactive, fixed, interact_manual
import ipywidgets as widgets
from client.api.notebook import Notebook
ok = Notebook('lab05.ok')
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Assignment: World Progress: Global Population
OK, version v1.13.11
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The global population of humans reached 1 billion around 1800, 3 billion around 1960, and 7 billion
around 2011. The potential impact of exponential population growth has concerned scientists,
economists, and politicians alike.
The UN Population Division estimates that the world population will likely continue to grow throughout
the 21st century, but at a slower rate, perhaps reaching 11 billion by 2100. However, the UN does not
rule out scenarios of more extreme growth.In this section, we will examine some of the factors that influence population growth and how they are
changing around the world.
The first table we will consider is the total population of each country over time. Run the cell below.
In [16]:
# The population.csv file can also be found online here:
# https://github.com/open-numbers/ddf--gapminder--systema_globalis/raw/master/ddf--
datapoints--population_total--by--geo--time.csv
# The version in this project was downloaded in February, 2017.
population = Table.read_table('population.csv')
population.show(3)
geo time population_total
abw 1800 19286
abw 1801 19286
abw 1802 19286
... (87792 rows omitted)
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