by Jeffrey Kantor (jeff at nd.edu). The latest version of this notebook is available at https://github.com/jckantor/CBE30338.

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

- Step 0: Gain Executable Access to Jupyter Notebooks
- Using Jupyter/Python in the Cloud
- Vocareum
- Installing Jupyter/Python on your laptop

- Step 1: Start a Jupyter Notebook Session
- Step 2: Simple Calculations with Python
- Basic Arithmetic Operations
- Python Libraries
- Working with Lists
- Working with Dictionaries
- Plotting with Matplotlib
- Solve Equations with Sympy Library

- Step 3: Where to Learn More

Jupyter notebooks are documents that can be viewed and executed inside 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 Python code in a notebook you will need access to a Python kernal. A kernal is simply a program that runs in the background, maintains workspace memory for variables and functions, and executes Python code. The kernal can be located on the same laptop as your web browser or located in an on-line cloud service.

**Important Note Regarding Versions** There are two versions of Python in widespread use. Version 2.7 released in 2010, which was the last release of the 2.x series. Version 3.5 is the most recent release of the 3.x series which represents the future direction of language. It has taken years for the major scientific libraries to complete the transition from 2.x to 3.x, but it is now safe to recommend Python 3.x for widespread use. So for this course be sure to use latest verstion, currently 3.6, of the Python language.

The easiest way to use Jupyter notebooks is to sign up for a free or paid account on a cloud-based service such as Wakari.io or SageMathCloud. You will need continuous internet connectivity to access your work, but the advantages are there is no software to install or maintain. All you need is a modern web browser on your laptop, Chromebook, tablet or other device. Note that the free services are generally heavily oversubscribed, so you should consider a paid account to assure access during prime hours.

There are also demonstration sites in the cloud, such as tmpnb.org. These start an interactive session where you can upload an existing notebook or create a new one from scratch. Though convenient, these sites are intended mainly for demonstration and generally quite overloaded. More significantly, there is no way to retain your work between sessions, and some python functionality is removed for security reasons.

This course will use a cloud-based service Vocareum to provide on-line access to Jupyter notebooks and Python through the Sakai learn management system. Once you log into Sakai, you can access Vocareum directly and use it to complete and submit course assignments.

For regular off-line use you should consider installing a Jupyter Notebook/Python environment directly on your laptop. This will provide you with reliable off-line access to a computational environment. This will also allow you to install additional code libraries to meet particular needs.

Choosing this option will require an initial software installation and routine updates. For this course the recommended package is Anaconda available from Continuum Analytics. Downloading and installing the software is 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 simplifies startup of the notebook environment and manage the update process.

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 and execute the following statement at the command line:

`> jupyter notebook`

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

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.

- 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 video overview of Jupyter notebooks.

In [1]:

```
from IPython.display import YouTubeVideo
YouTubeVideo("HW29067qVWk",560,315,rel=0)
```

Out[1]:

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 are built into the Python langauge. Here are some examples. In particular, note that exponentiation is done with the ** operator.

In [2]:

```
a = 12
b = 2
print(a + b)
print(a**b)
print(a/b)
```

The Python language has only very basic operations. Most math functions are in various math libraries. The `numpy`

library is convenient library. This next cell shows how to import `numpy`

with the prefix `np`

, then use it to call a common mathematical functions.

In [3]:

```
import numpy as np
# mathematical constants
print(np.pi)
print(np.e)
# trignometric functions
angle = np.pi/4
print(np.sin(angle))
print(np.cos(angle))
print(np.tan(angle))
```

In [4]:

```
xList = [1, 2, 3, 4]
xList
```

Out[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
```

Out[5]:

Sum a list of numbers

In [6]:

```
np.sum(x)
```

Out[6]:

An element-by-element operation between two lists may be performed with

In [7]:

```
print(np.add(x,y))
print(np.dot(x,y))
```

In [8]:

```
for x in xList:
print("sin({0}) = {1:8.5f}".format(x,np.sin(x)))
```

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}
mw
```

Out[9]:

We can a value to an existing dictionary.

In [10]:

```
mw['C8H18'] = 114.23
mw
```

Out[10]:

We can retrieve a value from a dictionary.

In [11]:

```
mw['CH4']
```

Out[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]))
```

Dictionaries can be sorted by key or by value

In [13]:

```
for species in sorted(mw):
print(" {:<8s} {:>7.2f}".format(species, mw[species]))
```

In [14]:

```
for species in sorted(mw, key = mw.get):
print(" {:<8s} {:>7.2f}".format(species, mw[species]))
```

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]:

```
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
x = np.linspace(0,10)
y = np.sin(x)
z = np.cos(x)
plt.plot(x,y,'b',x,z,'r')
plt.xlabel('Radians');
plt.ylabel('Value');
plt.title('Plotting Demonstration')
plt.legend(['Sin','Cos'])
plt.grid()
```

In [16]:

```
plt.plot(y,z)
plt.axis('equal')
```

Out[16]:

In [17]:

```
plt.subplot(2,1,1)
plt.plot(x,y)
plt.title('Sin(x)')
plt.subplot(2,1,2)
plt.plot(x,z)
plt.title('Cos(x)')
```

Out[17]:

In [18]:

```
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')
```

Out[18]:

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.

- A Primer on Scientific Programming with Python (Fourth Edition) by Hans Petter Langtangen. 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.

- pycse - Python Computations in Science and Engineering by John Kitchin at Carnegie Mellon. This is a link into the the github repository for pycse, click on the
`Raw`

button to download the`.pdf`

file.

**Interative learning and on-line tutorials**

- Code Academy on Python
- Khan Academy Videos on Python Programming
- Python Tutorial
- Think Python: How to Think Like a Computer Scientist
- Engineering with Python

**Official documentation, examples, and galleries.**

In [ ]:

```
```