Download this page as a jupyter notebook at Lesson 6
# Script block to identify host, user, and kernel
import sys
! hostname; ! whoami; ! pwd;
print(sys.executable)
atomickitty sensei /home/sensei/engr-1330-webroot/1-Lessons/Lesson05 /opt/jupyterhub/bin/python3
%%html
<!-- Script Block to set tables to left alignment -->
<style>
table {margin-left: 0 !important;}
</style>
Functions are simply pre-written code fragments that perform a certain task. In older procedural languages functions and subroutines are similar, but a function returns a value whereas a subroutine operates on data. The difference is subtle but important.
More recent thinking has functions being able to operate on data (they always could) and the value returned may be simply an exit code. An analogy are the functions in MS Excel. To add numbers, we can use the sum(range) function and type =sum(A1:A5)
instead of typing =A1+A2+A3+A4+A5
To restate A function is a block of code which only runs when it is called; it is pre-defined (prototyped) using parameters (placeholder variables)
The GIF below animates these ideas:
Things you do using functions:
print()
function) We call a function simply by typing the name of the function or by using a dot notation. Whether we can use the dot notation or not depends on how the function is written, whether it is part of a class, and how it is imported into a program.
Some functions expect us to pass data to them to perform their tasks. These data are known as parameters( older terminology is arguments, or argument list) and we pass them to the function by enclosing their values in parenthesis ( ) separated by commas.
For instance, the print()
function for displaying text on the screen is "called" by typing print('Hello World')
where print is the name of the function and the literal (a string) 'Hello World' is the argument.
A function, whether built-in, or added must be defined before it is called, otherwise the script will fail. Certain built-in functions "self define" upon start (such as print()
and type()
and we need not worry about those funtions). The diagram below illustrates the requesite flow control for functions that need to be defined before use.
An example below will illustrate, change the cell to code and run it, you should get an error. Then fix the indicated line (remove the leading "#" in the import math ... line) and rerun, should get a functioning script.
# reset the notebook using a magic function in JupyterLab
%reset -f
# An example, run once as is then activate indicated line, run again - what happens?
x= 4.
sqrt_by_arithmetic = x**0.5
print('Using arithmetic square root of ', x, ' is ',sqrt_by_arithmetic )
#import math # import the math package ## activate and rerun
sqrt_by_math = math.sqrt(x) # note the dot notation
print('Using math package square root of ', x,' is ',sqrt_by_arithmetic)
Using arithmetic square root of 4.0 is 2.0
--------------------------------------------------------------------------- NameError Traceback (most recent call last) <ipython-input-3-a9bb754eacfd> in <module> 6 print('Using arithmetic square root of ', x, ' is ',sqrt_by_arithmetic ) 7 #import math # import the math package ## activate and rerun ----> 8 sqrt_by_math = math.sqrt(x) # note the dot notation 9 print('Using math package square root of ', x,' is ',sqrt_by_arithmetic) NameError: name 'math' is not defined
An alternate way to load just the sqrt() function is shown below, either way is fine.
# reset the notebook using a magic function in JupyterLab
%reset -f
# An example, run once as is then activate indicated line, run again - what happens?
x= 4.
sqrt_by_arithmetic = x**0.5
print('Using arithmetic square root of ', x, ' is ',sqrt_by_arithmetic )
from math import sqrt # import sqrt from the math package ## activate and rerun
sqrt_by_math = sqrt(x) # note the notation
print('Using math package square root of ', x,' is ',sqrt_by_arithmetic)
Using arithmetic square root of 4.0 is 2.0 Using math package square root of 4.0 is 2.0
The base Python functions and types built into it that are always available, the figure below lists those functions.
Notice all have the structure of function_name()
, except __import__()
which has a constructor type structure, and is not intended for routine use (among other things it allows assigning multiple names for an imported module which usually breaks things badly) but instead we should stick with plain old import module_name
as an expression rather than a function call.
Python is also distributed with a large number of external functions. These functions are saved in files known as modules. To use the built-in codes in Python modules, we have to import them into our programs first. We do that by using the import keyword. There are three ways to import:
# Example 1 of import
%reset -f
import random
low = 1 ; high = 10
random.randrange(low,high) #generate random number in range low to high
3
# Example 2 of import
%reset -f
import random as r
low = 1 ; high = 10
r.randrange(low,high)
7
# Example 3 of import
%reset -f
from random import randrange
low = 1 ; high = 10
randrange(low,high)
6
The modules that come with Python are extensive and listed at https://docs.python.org/3/py-modindex.html. There are also other modules that can be downloaded and used (just like user defined modules below). In these labs we are building primitive codes to learn how to code and how to create algorithms. For many practical cases you will want to load a well-tested package to accomplish the tasks.
That exercise is saved for the end of the document.
We can define our own functions in Python and reuse them throughout the program. The syntax for defining a function is:
def functionName( argument ):
code detailing what the function should do
note the colon above and indentation
...
...
return [expression]
The keyword def
tells the program that the indented code from the next line onwards is
part of the function.
The keyword return
tells the program to return an answer from the
function.
There can be multiple return statements in a function.
Once the function executes
a return statement, the program exits the function and continues with its next executable
statement.
If the function does not need to return any value, you can omit the return
statement, the exit code will be none which is a special constant with logical value false
Its probably a good habit to have a return statement anyway, and just return null
Functions can be pretty elaborate; they can search for things in a list, determine variable types, open and close files, read and write to files.
To get started we will build a few really simple mathematical functions; we will need this skill in the future anyway, especially in scientific programming contexts.
For our first function we will code $$f(x) = x\sqrt{1 + x}$$ into a function named dusty()
.
When you run the next cell, all it does is prototype the function (defines it), nothing happens until we use the function.
def dusty(x) :
temp = x * ((1.0+x)**(0.5)) # don't need the math package
return temp
# the function should make the evaluation
# store in the local variable temp
# return contents of temp
# wrapper to run the dusty function
yes = 0
while yes == 0:
xvalue = input('enter a numeric value')
try:
xvalue = float(xvalue)
yes = 1
except:
print('enter a bloody number! Try again \n')
# call the function, get value , write output
yvalue = dusty(xvalue)
print('f(',xvalue,') = ',yvalue) # and we are done
f( 3.0 ) = 6.0
Sum of numbers from 0 to N
def sum_of_n(n):
mysum = 0 # initialize the accumulator
for i in range(n+1): # construct an accumulation loop
mysum = mysum + i
return mysum
how_many = 3
print(sum_of_n(how_many))
6
Sum of numbers from 0 to N, but forget the return statement
def sum_of_n(n):
mysum = 0 # initialize the accumulator
for i in range(n+1): # construct an accumulation loop
mysum = mysum + i
how_many = 6
print(sum_of_n(how_many))
21
Sum of numbers from 0 to N, and including defaults.
def sum_of_n(n=3):
mysum = 0 # initialize the accumulator
for i in range(n+1): # construct an accumulation loop
mysum = mysum + i
return mysum
print(sum_of_n()) # if we have no argument, uses the default
6
Create a Fahrenhiet to Celsius converter and test it for these values:
*hint: Formula-(°F − 32) × 5/9 = °C
Define the problem (problem statement):
Gather information (identify known and unknown values, and governing equations)
Generate and evaluate potential solutions
def FC(x) : # convert F to C
C = (x - 32)*5/9
return C
print(FC(99))
37.22222222222222
Verify and test the solution.
print(FC(-40))
-40.0
print(FC(32))
0.0
print(FC(212))
100.0
def F2C(x) : # convert F to C
C = (x - 32)*5/9
return C
# wrapper to run the F2C function
yes = 0
while yes == 0:
xvalue = input('Enter a temperature in Fairyheight')
try:
xvalue = float(xvalue)
yes = 1
except:
print('Enter a bloody number! Try again \n')
# call the function, get value , write output
yvalue = F2C(xvalue)
print('Temp: ',xvalue,'F = ',yvalue,' C') # and we are done
Temp: -40.0 F = -40.0 C
An important concept when defining a function is the concept of variable scope. Variables defined inside a function are treated differently from variables defined outside. Firstly, any variable declared within a function is only accessible within the function. These are known as local variables.
In the dusty()
function, the variables x
and temp
are local to the function.
Any variable declared outside a function in a main program is known as a program variable
and is accessible anywhere in the program.
In the example, the variables xvalue
and yvalue
are program variables (global to the program; if they are addressed within a function, they could be operated on.)
Generally we want to protect the program variables from the function unless the intent is to change their values.
The way the function is written in the example, the function cannot damage xvalue
or yvalue
.
If a local variable shares the same name as a program variable, any code inside the function is accessing the local variable. Any code outside is accessing the program variable
In this section we will invent the neko()
function, export it to a file, so we can reuse it in later notebooks without having to retype or cut-and-paste. The neko()
function evaluates:
Its the same as the dusty() function, except operates on the absolute value in the wadical.
Copy the neko() function script below into that file.
def neko(input_argument) :
import math #ok to import into a function
local_variable = input_argument * math.sqrt(abs(1.0+input_argument))
return local_variable
# wrapper to run the neko function
import mylibrary
yes = 0
while yes == 0:
xvalue = input('enter a numeric value')
try:
xvalue = float(xvalue)
yes = 1
except:
print('enter a bloody number! Try again \n')
# call the function, get value , write output
yvalue = mylibrary.neko(xvalue)
print('f(',xvalue,') = ',yvalue) # and we are done
In JupyterHub environments, you may discover that changes you make to your external python file are not reflected when you re-run your script; you need to restart the kernel to get the changes to actually update. The figure below depicts the notebook, external file relatonship
Functions can contain themselves as part of definition, this is called recursion. Use the provilige with care, its easy to clobber the memory stack using recursive structures. Consider the factorial function definition from math class:
$$ 0! = 1 \\ 1! = 1 \\ 2! = 0! * 1! * 2 \\ \dots \\ N! = (N-1)! * N $$def factorial(n=0):
if n <= 1:
return 1
else:
return n*factorial(n-1)
factorial(6)
720
Graphing values is part of the broader field of data visualization, which has two main goals:
In this subsection we will concentrate on introducing skills to start exploring data and to produce meaningful visualizations we can use throughout the rest of this notebook. Data visualization is a rich field of study that fills entire books. The reason to start visualization here instead of elsewhere is that with mathematical functions plotting is a natural activity and we have to import the matplotlib module to make the plots.
The example below is code adapted from Grus (2015) that illustrates simple generic plots. I added a single line (label the x-axis), and corrected some transcription errors (not the original author's mistake, just the consequence of how the API handled the cut-and-paste), but otherwise the code is unchanged.
# python script to illustrate plotting
# CODE BELOW IS ADAPTED FROM:
# Grus, Joel (2015-04-14). Data Science from Scratch: First Principles with Python
# (Kindle Locations 1190-1191). O'Reilly Media. Kindle Edition.
#
import matplotlib.pyplot # import the plotting library from matplotlib.pyplot
# build a couple of lists
years = [1950, 1960, 1970, 1980, 1990, 2000, 2010] # define one list for years
gdp = [300.2, 543.3, 1075.9, 2862.5, 5979.6, 10289.7, 14958.3] # and another one for Gross Domestic Product (GDP)
# build the plot
matplotlib.pyplot.plot( years, gdp, color ='green', marker ='o', linestyle ='solid') # create a line chart, years on x-axis, gdp on y-axis
# what if "^", "P", "*" for marker?
# what if "red" for color?
# what if "dashdot", '--' for linestyle?
matplotlib.pyplot.title("Nominal GDP")# add a title
matplotlib.pyplot.ylabel("Billions of $")# add a label to the x and y-axes
matplotlib.pyplot.xlabel("Year")# add a title
matplotlib.pyplot.show() # display the plot
Now lets put the plotting script into a function so we can make line charts of any two numeric lists
def plotAline(list1,list2,strx,stry,strtitle): # plot list1 on x, list2 on y, xlabel, ylabel, title
import matplotlib.pyplot # import the plotting library from matplotlib.pyplot
matplotlib.pyplot.plot( list1, list2, color ='green', marker ='o', linestyle ='solid') # create a line chart, years on x-axis, gdp on y-axis
matplotlib.pyplot.title(strtitle)# add a title
matplotlib.pyplot.ylabel(stry)# add a label to the x and y-axes
matplotlib.pyplot.xlabel(strx)
matplotlib.pyplot.show() # display the plot
return #null return
# wrapper script
years = [1950, 1960, 1970, 1980, 1990, 2000, 2010] # define two lists years and gdp
gdp = [300.2, 543.3, 1075.9, 2862.5, 5979.6, 10289.7, 14958.3]
print(type(years[0]))
print(type(gdp[0]))
plotAline(years,gdp,"Year","Billions of $","Nominal GDP")
<class 'int'> <class 'float'>
Use the plotting script and create a function that draws a straight line between two points.
def MakeALine():
import matplotlib.pyplot # import the plotting library from matplotlib.pyplot
x1 = input('Please enter x value for point 1')
y1 = input('Please enter y value for point 1')
x2 = input('Please enter x value for point 2')
y2 = input('Please enter y value for point 2')
xlist = [x1,x2]
ylist = [y1,y2]
matplotlib.pyplot.plot( xlist, ylist, color ='orange', marker ='*', linestyle ='solid')
#plt.title(strtitle)# add a title
matplotlib.pyplot.ylabel("Y-axis")# add a label to the x and y-axes
matplotlib.pyplot.xlabel("X-axis")
matplotlib.pyplot.show() # display the plot
return #null return
MakeALine()
Grus, Joel (2015-04-14). Data Science from Scratch: First Principles with Python (Kindle Locations 1190-1191). O'Reilly Media. Kindle Edition.
Call Expressions in "Adhikari, A. and DeNero, J. Computational and Inferential Thinking The Foundations of Data Science" https://www.inferentialthinking.com/chapters/03/3/Calls.html
Functions and Tables in "Adhikari, A. and DeNero, J. Computational and Inferential Thinking The Foundations of Data Science" https://www.inferentialthinking.com/chapters/08/Functions_and_Tables.html
Visualization in "Adhikari, A. and DeNero, J. Computational and Inferential Thinking The Foundations of Data Science" https://www.inferentialthinking.com/chapters/07/Visualization.html
Documentation; The Python Standard Library; 9. Numeric and Mathematical Modules https://docs.python.org/2/library/math.html
Code.org; Chris Bosh of Miami Heat and Jess Lee CEO of Polyvore. Let's use code to join Anna and Elsa as they explore the magic and beauty of ice. https://youtu.be/0eo0ESEX9DE
ENGR-1330 Functions and Variable Scope (Fall 2020) https://3.137.111.182/engr-1330-webroot/1-Lessons/Lesson05/OriginalPowerpoint/PythonFunctions-VariableScope-ENGR1330-CTwithDS.pdf