Introduction to Python in Jupyter Notebook/Google Colab
Contents
This notebook contains material from CBE30338; content is available on Github.
< 1.1 Getting Started with Python and Jupyter Notebooks | Contents | Tag Index | 1.3 Python Conditionals and Libraries
Introduction to Python in Jupyter Notebook/Google Colab#
!pip install -q -r requirements.txt
1.1 What is Google Colab#
Free to use for non-commercial use
Easy access to high computational power (CPUs, GPUs)
Easy start (no need to install or tune)
Connected to Google Drive
1.2 Python Basics#
An edit of Tutorial by Jacob Gerace
1.2.1 What I hope you’ll get out of this tutorial#
The feeling that you’ll “know where to start” when you see python code.
(You won’t be a python expert after one hour)
Basics to variables, lists, conditionals, functions, loops, and the numpy package.
Resources to look further
1.2.1.1 Why Python?#
Clean syntax
The same code can run on all Operating Systems
Extensive first and third party libraries (of particular note for our purposes is NumPy)
1.2.1.2 Markdown Sidenote#
This text is written in a Markdown block. Markdown is straightforward way to format writeups in Jupyter, but I won’t cover it here for the sake of brevity.
See if you can use Markdown in your next homework, here’s a link that explains the formatting: https://daringfireball.net/projects/markdown/syntax .
You can also look at existing Markdown examples (i.e. this worksheet) and emulate the style. Double click a Markdown box in Jupyter to show the code.
1.2.1.3 LaTeX Sidenote#
LaTeX (pronounced “La-tech”) is a language itself used widely to write documents with symbolic math
When you add a mathematical formula to these markdown blocks, the math is in LaTeX.
Ex from class: $\(V \frac{dC}{dt} = u(t) - Q C(t)\)$
A good resource: https://en.wikibooks.org/wiki/LaTeX/Mathematics
1.2.2 Python Basics#
1.2.2.1 Variables#
a='1'
print(a)
1
#A variable stores a piece of data and gives it a name
answer = 42
#answer contained an integer because we gave it an integer!
is_it_thursday = True
is_it_wednesday = False
#these both are 'booleans' or true/false values
pi_approx = 3.1415
#This will be a floating point number, or a number containing digits after the decimal point
my_name = "Jacob"
#This is a string datatype, the name coming from a string of characters
#Data doesn't have to be a singular unit
#p.s., we can print all of these with a print command. For Example:
print(answer)
print(pi_approx)
42
3.1415
1.2.2.2 More Complicated Data Types#
#What if we want to store many integers? We need a list!
prices = [10, 20, 30, 40, 50]
#This is a way to define a list in place. We can also make an empty list and add to it.
colors = []
colors.append("Green")
colors.append("Blue")
colors.append("Red")
print(colors)
#We can also add unlike data to a list
prices.append("Sixty")
#As an exercise, look up lists in python and find out how to add in the middle of a list!
print(prices)
#We can access a specific element of a list too:
print(colors[0])
print(colors[2])
#Notice here how the first element of the list is index 0, not 1!
#Languages like MATLAB are 1 indexed, be careful!
#In addition to lists, there are tuples
#Tuples behave very similarly to lists except that you can't change them
# after you make them
#An empty Tuple isn't very useful:
empty_tuple = ()
#Nor is a tuple with just one value:
one_tuple = ("first",)
#But tuples with many values are useful:
rosa_parks_info = ("Rosa", "Parks", 1913, "February", 4)
#You can access tuples just like lists
print(rosa_parks_info[0] + " " + rosa_parks_info[1])
# You cannot modify existing tuples, but you can make new tuples that extend
# the information.
# I expect Tuples to come up less than lists. So we'll just leave it at that.
['Green', 'Blue', 'Red']
[10, 20, 30, 40, 50, 'Sixty']
Green
Red
Rosa Parks
1.2.2.3 Using Variables#
float1 = 5.75
float2 = 2.25
#Addition, subtraction, multiplication, division are as you expect
print(float1 + float2)
print(float1 - float2)
print(float1 * float2)
print(float1 / float2)
#Here's an interesting one that showed up in the first homework in 2017. Modulus:
print(5 % 2)
8.0
3.5
12.9375
2.5555555555555554
1
1.2.2.4 Importing in Python: Math and plotting#
#Just about every standard math function on a calculator has a python equivalent pre made.
#however, they are from the 'math' package in python. Let's add that package!
import math as m
print(m.log(float1))
print(m.exp(float2))
print(m.pow(2,5))
# There is a quicker way to write exponents if you want:
print(2.0**5.0)
#Like in MATLAB, you can expand the math to entire lists
list3 = [1, 2, 3, 4, 5]
print(2 * list3)
1.749199854809259
9.487735836358526
32.0
32.0
[1, 2, 3, 4, 5, 1, 2, 3, 4, 5]
#We can plot easily in Python like in matlab, just import the relevant package!
%matplotlib inline
import matplotlib.pyplot as plt
x_vals = [-2, -1, 0, 1, 2]
y_vals = [-4, -2, 0, 2, 4]
plt.plot(x_vals, y_vals)
[<matplotlib.lines.Line2D at 0x2b1fa2c9cd0>]
1.2.2.5 Loops in Python#
#Repeat code until a conditional statement ends the loop
#Let's try printing a list
fib = [1, 1, 2, 3, 5, 8]
#While loops are the basic type
i = 0
while(i < len(fib)):
print(fib[i])
i = i + 1
#In matlab, to do the same thing you would have the conditional as: counter < (length(fib) + 1)
#This is because matlab starts indexing at 1, and python starts at 0.
#The above type of loop is so common that the 'for' loop is the way to write it faster.
print("Let's try that again")
#This is most similar to for loops in matlab
for i in range(0, len(fib)) :
print(fib[i])
print("One more time:")
#Or you can do so even neater
for e in fib:
print(e)
1
1
2
3
5
8
Let's try that again
1
1
2
3
5
8
One more time:
1
1
2
3
5
8
1.2.3 Additional Resources#
If you still feel VERY lost: Code Academy
If you want a good reference site: Official Python Reference
If you want to learn python robustly: Learn Python the Hard Way
Feel free to contact me at: jgerace (at) nd (dot) edu
< 1.1 Getting Started with Python and Jupyter Notebooks | Contents | Tag Index | 1.3 Python Conditionals and Libraries