The latest version of this IPython notebook is available at for noncommercial use under terms of the Creative Commons Attribution Noncommericial ShareAlike License.

J.C. Kantor ([email protected])

Getting Started with Jupyter Notebooks and Python

The purpose of this Jupyter Notebook is to get you started using Jupyter Notebooks and Python for routine chemical engineering calculations. This introduction assumes this is your first exposure to Python or Jupyter Notebook.

Step 0: Gain Executable Access to Jupyter Notebooks

Jupyter notebooks are documents that can be viewed and executed using any modern web browser. Since you're reading this notebook, you already know how to view a Jupyter notebook. The next step is to learn how to execute computations that may be embedded in a Jupyter notebook.

To execute code in a notebook you will need access to a computation. You can do this through an on-line cloud-based service, or by installing a suitable computational environment on your laptop.

Using Jupyter/Python in the Cloud

The easiest approach is to sign up for a free account on a cloud-based service such as or SageMathCloud. You will need continuous internet connectivity to access your work, but the advantages are there is nothing to install, no software to maintain. All you need is a modern web browser on your laptop, Chromebook, tablet or other device.

There are also demonstration sites in the cloud, such as These will start up a interactive session where you can upload an existing notebook, or create a new one from scratch. While convenient, these sites are intended mainly for demonstration. There is no way to retain your work between sessions, and not all functionality is available.

Installing Jupyter/Python on your Laptop

Alternatively, to gain off-line access you can install a Jupyter Notebook/Python environment directly on your laptop. This will provide you with reliable off-line access to a computational environment to which you can add additional functionaility to meet your particular needs. This option does require an initial installation and routine updates.

There are at least two excellent and free packages available for free download:

There are differences between these packages, particularly in the methods used to download and maintain additional Python libraries. In both cases the process for downloading and installing the software are well documented and easy to follow. Allow about 10-30 minutes for the installation depending on your connection speed.

After installing, be sure to check for updates before proceeding further. With the Anaconda package this is done by executing the following two commands in a terminal window:

> conda update conda
> conda update anaconda

Anaconda includes an 'Anaconda Navigator' application that simplify startup of the notebook environment and manage the update process.

Step 1: Start a Jupyter Notebook Session

If you are using a cloud-based service a Jupyter session will be started when you log on.

If you have installed a Jupyter/Python distribution on your laptop then you can open a Jupyter session in one of two different ways:

  • Use the Anaconda Navigator App, or
  • open a terminal window on your laptop, then execute the following statement at the command line:

      > ipython notebook

Either way, once you have opened a session you should see a browser window like this:

Screen Shot Jupyter Session

At this point the browser displays a list of directories and files. You can navigate amoung the directories in the usual way by clicking on directory names or on the 'breadcrumbs' located just about the listing.

Jupyter notebooks are simply files in a directory with a .ipynb suffix. They can be stored in any directory including Dropbox or Google Drive. Upload and create new Jupyter notebooks in the displayed directory using the appropriate buttons. Use the checkboxes to select items for other actions, such as to duplicate, to rename, or to delete notebooks and directories.

To start a notebook session, open a terminal window and navigate to the directory where you will be keeping your notebooks. Then execute the following statement at the command line:

> ipython notebook

The terminal window will show information indicating start up of an ipython session, then browser window will open listing notebooks in your current directory. At this point your options are

  • select one of your existing notebooks to work on,
  • start a new notebook by clicking on the New Notebook button, or
  • import a notebook from another directory by dragging it onto the list of notebooks.

An IPython notebook consists of cells that hold headings, text, or python code. The user interface is relatively self-explanatory. Take a few minutes now to open, rename, and save a new notebook.

Here's a quick overview of IPython notebooks prepared by the team that created the software.

In [1]:
from IPython.display import YouTubeVideo
In [ ]:

Step 2: Experiment with Python

Python is an elegant and modern language for programming and problem solving that has found increasing use by engineers and scientists. In the next few cells we'll demonstrate some basic Python functionality.

Basic Arithmetic Operations

Basic arithmetic functions

In [2]:
a = 12
b = 2
print "a + b = ", a + b
print "a**b = ", a**b
print "a/b = ", a/b
a + b =  14
a**b =  144
a/b =  6

Most math functions are in the numpy library. This next cell shows how to import numpy with the prefix np, then use it to call a common function

In [3]:
import numpy as np

Working with Lists

Lists are a versatile way of organizing your data in Python. Here are some examples, more can be found on this Khan Academy video.

In [4]:
xList = [1, 2, 3, 4]
print xList
[1, 2, 3, 4]

Concatentation is the operation of joining one list to another.

In [5]:
# Concatenation
x = [1, 2, 3, 4];
y = [5, 6, 7, 8];

x + y
[1, 2, 3, 4, 5, 6, 7, 8]

A common operation is to apply a binary operation to elements of a single list. For an example such as arithmetic addition, this can be done using the sum function from numpy, or using python's build-in reduce function. The advantage of reduce is that it can extend any pairwise operation to work on a list.

In [6]:
# Two ways to sum a list of numbers
print np.sum(x)
print reduce(np.add,x)

An element-by-element operation between two lists may be performed with python's built-in map function.

In [7]:
# Two ways to add a two lists of numbers
print np.add(x,y)
print map(np.add,x,y)
[ 6  8 10 12]
[6, 8, 10, 12]

A for loop is a means for iterating over the elements of a list. The colon marks the start of code that will be executed for each element of a list. Indenting has meaning in Python. In this case, everything in the indented block will be executed on each iteration of the for loop.

In [8]:
for x in xList:
    print "x =", x, "    sin(x) = ", np.sin(x)
x = 1     sin(x) =  0.841470984808
x = 2     sin(x) =  0.909297426826
x = 3     sin(x) =  0.14112000806
x = 4     sin(x) =  -0.756802495308

Working with dictionaries

Dictionaries are useful for storing and retrieving data as key-value pairs. For example, here is a short dictionary of molar masses. The keys are molecular formulas, and the values are the corresponding molar masses.

In [9]:
mw = {'CH4': 16.04, 'H2O': 18.02, 'O2':32.00, 'CO2': 44.01}
print mw
{'H2O': 18.02, 'CO2': 44.01, 'CH4': 16.04, 'O2': 32.0}

We can a value to an existing dictionary.

In [10]:
mw['C8H18'] = 114.23
print mw
{'H2O': 18.02, 'CO2': 44.01, 'CH4': 16.04, 'O2': 32.0, 'C8H18': 114.23}

We can retrieve a value from a dictionary.

In [11]:

A for loop is a useful means of interating over all key-value pairs of a dictionary.

In [12]:
for species in mw.keys():
    print "The molar mass of {:<s} is {:<7.2f}".format(species, mw[species])
The molar mass of H2O is 18.02  
The molar mass of CO2 is 44.01  
The molar mass of CH4 is 16.04  
The molar mass of O2 is 32.00  
The molar mass of C8H18 is 114.23 

Dictionaries can be sorted by key or by value

In [13]:
for species in sorted(mw):
    print " {:<8s}  {:>7.2f}".format(species, mw[species])
 C8H18      114.23
 CH4         16.04
 CO2         44.01
 H2O         18.02
 O2          32.00
In [14]:
for species in sorted(mw, key = mw.get):
    print " {:<8s}  {:>7.2f}".format(species, mw[species])
 CH4         16.04
 H2O         18.02
 O2          32.00
 CO2         44.01
 C8H18      114.23


Importing the matplotlib.pyplot library gives IPython notebooks plotting functionality very similar to Matlab's. Here are some examples using functions from the

In [15]:
import matplotlib.pyplot as plt
import numpy as np
%matplotlib inline

x = np.linspace(0,10)
y = np.sin(x)
z = np.cos(x)

plt.title('Plotting Demonstration')
In [16]:
(-1.0, 1.0, -1.0, 1.0)
In [17]:

<matplotlib.text.Text at 0x108041610>

Step 3: Solve Equations using Sympy

One of the best features of Python is the ability to extend it's functionality by importing special purpose libraries of functions. Here we demonstrate the use of a symbolic algebra package Sympy for routine problem solving.

In [19]:
import sympy as sym

sym.var('P V n R T');

# Gas constant
R = 8.314        # J/K/gmol
R = R * 1000     # J/K/kgmol

# Moles of air
mAir = 1         # kg
mwAir = 28.97    # kg/kg-mol
n = mAir/mwAir   # kg-mol

# Temperature
T = 298

# Equation
eqn = sym.Eq(P*V,n*R*T)

# Solve for P 
f = sym.solve(eqn,P)
print f[0]

# Use the sympy plot function to plot
sym.plot(f[0],(V,1,10),xlabel='Volume m**3',ylabel='Pressure Pa')
<sympy.plotting.plot.Plot at 0x107eddc10>

Step 4: Learn More

Python offers a full range of programming language features, and there is a seemingly endless range of packages for scientific and engineering computations. Here are some suggestions on places you can go for more information on programming for engineering applications in Python.

Introduction to Python for Science

This excellent introduction to python is aimed at undergraduates in science with no programming experience. It is free and available at the following link.

Tutorial Introduction to Python for Science and Engineering.

The following text is licensed by the Hesburgh Library for use by Notre Dame students and faculty only. Please refer to the library's acceptable use policy. Others can find it at Springer or Amazon. Resources for this book are available on github.

pycse is a package of python functions, examples, and document prepared by John Kitchin at Carnegie Mellon University. It is a recommended for its coverage of topics relevant to chemical engineers, including a chapter on typical chemical engineering computations.

Interative learning and on-line tutorials

Official documentation, examples, and galleries.

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