chap10


Modeling and Simulation in Python

Chapter 10

Copyright 2017 Allen Downey

License: Creative Commons Attribution 4.0 International


In [1]:
# Configure Jupyter so figures appear in the notebook
%matplotlib inline

# Configure Jupyter to display the assigned value after an assignment
%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'

# import functions from the modsim.py module
from modsim import *

from pandas import read_html

Under the hood

To get a DataFrame and a Series, I'll read the world population data and select a column.

DataFrame and Series contain a variable called shape that indicates the number of rows and columns.


In [2]:
filename = 'data/World_population_estimates.html'
tables = read_html(filename, header=0, index_col=0, decimal='M')
table2 = tables[2]
table2.columns = ['census', 'prb', 'un', 'maddison', 
                  'hyde', 'tanton', 'biraben', 'mj', 
                  'thomlinson', 'durand', 'clark']
table2.shape

In [3]:
census = table2.census / 1e9
census.shape

In [4]:
un = table2.un / 1e9
un.shape

A DataFrame contains index, which labels the rows. It is an Int64Index, which is similar to a NumPy array.


In [5]:
table2.index

And columns, which labels the columns.


In [6]:
table2.columns

And values, which is an array of values.


In [7]:
table2.values

A Series does not have columns, but it does have name.


In [8]:
census.name

It contains values, which is an array.


In [9]:
census.values

And it contains index:


In [10]:
census.index

If you ever wonder what kind of object a variable refers to, you can use the type function. The result indicates what type the object is, and the module where that type is defined.

DataFrame, Int64Index, Index, and Series are defined by Pandas.

ndarray is defined by NumPy.


In [11]:
type(table2)

In [12]:
type(table2.index)

In [13]:
type(table2.columns)

In [14]:
type(table2.values)

In [15]:
type(census)

In [16]:
type(census.index)

In [17]:
type(census.values)

Optional exercise

The following exercise provides a chance to practice what you have learned so far, and maybe develop a different growth model. If you feel comfortable with what we have done so far, you might want to give it a try.

Optional Exercise: On the Wikipedia page about world population estimates, the first table contains estimates for prehistoric populations. The following cells process this table and plot some of the results.


In [18]:
filename = 'data/World_population_estimates.html'
tables = read_html(filename, header=0, index_col=0, decimal='M')
len(tables)

Select tables[1], which is the second table on the page.


In [19]:
table1 = tables[1]
table1.head()

Not all agencies and researchers provided estimates for the same dates. Again NaN is the special value that indicates missing data.


In [20]:
table1.tail()

Again, we'll replace the long column names with more convenient abbreviations.


In [21]:
table1.columns = ['PRB', 'UN', 'Maddison', 'HYDE', 'Tanton', 
                  'Biraben', 'McEvedy & Jones', 'Thomlinson', 'Durand', 'Clark']

Some of the estimates are in a form Pandas doesn't recognize as numbers, but we can coerce them to be numeric.


In [22]:
for col in table1.columns:
    table1[col] = pd.to_numeric(table1[col], errors='coerce')

Here are the results. Notice that we are working in millions now, not billions.


In [23]:
table1.plot()
decorate(xlim=[-10000, 2000], xlabel='Year', 
         ylabel='World population (millions)',
         title='Prehistoric population estimates')
plt.legend(fontsize='small');

We can use xlim to zoom in on everything after Year 0.


In [24]:
table1.plot()
decorate(xlim=[0, 2000], xlabel='Year', 
         ylabel='World population (millions)',
         title='CE population estimates')
plt.legend(fontsize='small');

See if you can find a model that fits these data well from Year 0 to 1950.

How well does your best model predict actual population growth from 1950 to the present?


In [25]:
# Solution goes here

In [26]:
# Solution goes here

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