Computer Science > Research Paper > University of California, BerkeleyDATA 8 DATA8BerkeleyXData8.1x lab05.html (All)

University of California, BerkeleyDATA 8 DATA8BerkeleyXData8.1x lab05.html

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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') ===================================================================== Assignment: World Progress: Global Population OK, version v1.13.11 ===================================================================== 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) [Show More]

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