Data analysts, data scientists, and engineers are used to experimenting. IPython was created by scientists with experimentation in mind. The interactive environment that IPython provides is comparable to an interactive computing environment provided by Matlab, Mathematica, and Maple.

The following is a list of features of the IPython shell:

The following list describes how to use the IPython shell:

We saw a number of so-called magic functions in action. These functions start with the % character. If the magic function is used on a line by itself, the % prefix is optional.

When the libraries are imported in IPython, we can open manual pages for library functions with the help command. It is not necessary to know the name of a function. We can type a few characters and then let the tab completion do its work. Let's, for instance, browse the available information for the arange() function.

We can browse the available information in either of the following two ways:

Jupyter Notebook, previously known as IPython Notebooks, provides a tool to create and share web pages with text, charts, and Python code in a special format. Have a look at these notebook collections at the following links:

Often, the notebooks are used as an educational tool, or to demonstrate Python software. We can import or export notebooks either from plain Python code or from the special notebook format. The notebooks can be run locally, or we can make them available online by running a dedicated notebook server. Certain cloud computing solutions, such as Wakari and PiCloud, allow you to run notebooks in the cloud. Cloud computing is one of the topics of Chapter 11, Environments Outside the Python Ecosystem and Cloud Computing.

To start a session with Jupyter Notebook,enter the following instruction on the command line:

$ jupyter-notebook

This will start the notebook server and open a web page showing the contents of the folder from which the command will execute. You can then select New | Python 3 to start a new notebook in Python 3.

You can also open ch-01.ipynb, provided in the code package for this book. The ch-01 notebook file has the code for the simple applications that we will describe shortly.

After going through the installation of NumPy, it's time to have a look at NumPy arrays. NumPy arrays are more efficient than Python lists when it comes to numerical operations. NumPy arrays are, in fact, specialized objects with extensive optimizations. NumPy code requires less explicit loops than equivalent Python code. This is based on vectorization.

If we go back to high school mathematics, then we should remember the concepts of scalars and vectors. The number 2, for instance, is a scalar. When we add 2 to 2, we are performing scalar addition. We can form a vector out of a group of scalars. In Python programming terms, we will then have a one-dimensional array. This concept can, of course, be extended to higher dimensions. Performing an operation on two arrays, such as addition, can be reduced to a group of scalar operations. In straight Python, we will do that with loops going through each element in the first array and adding it to the corresponding element in the second array. However, this is more verbose than the way it is done in mathematics. In mathematics, we treat the addition of two vectors as a single operation. That's the way NumPy arrays do it too, and there are certain optimizations using low-level C routines that make these basic operations more efficient. We will cover NumPy arrays in more detail in the Chapter 2, NumPy Arrays.

Imagine that we want to add two vectors called a and b. The word vector is used here in the mathematical sense, which means a one-dimensional array. We will learn about specialized NumPy arrays that represent matrices in Chapter 4, Statistics and Linear Algebra. The vector a holds the squares of integers 0 to n; for instance, if n is equal to 3, a contains 0, 1, or 4. The vector b holds the cubes of integers 0 to n, so if n is equal to 3, then the vector b is equal to 0, 1, or 8. How would you do that using plain Python? After we come up with a solution, we will compare it to the NumPy equivalent.

The following function solves the vector addition problem using pure Python without NumPy:

def pythonsum(n): 
   a = list(range(n)) 
   b = list(range(n)) 
   c = [] 
 
   for i in range(len(a)): 
       a[i] = i ** 2 
       b[i] = i ** 3 
       c.append(a[i] + b[i]) 
 
   return c 

The following is a function that solves the vector addition problem with NumPy:

def numpysum(n): 
  a = numpy.arange(n) ** 2 
  b = numpy.arange(n) ** 3 
  c = a + b 
  return c 

Note that numpysum() does not need a for loop. We also used the arange() function from NumPy, which creates a NumPy array for us with integers from 0 to n. The arange() function was imported; that is why it is prefixed with numpy.

Now comes the fun part. We mentioned earlier that NumPy is faster when it comes to array operations. How much faster is Numpy, though? The following program will show us by measuring the elapsed time in microseconds for the numpysum() and pythonsum() functions. It also prints the last two elements of the vector sum. Let's check that we get the same answers using Python and NumPy:

#!/usr/bin/env/python 
 
import sys 
from datetime import datetime 
import numpy as np 
 
""" 
This program demonstrates vector addition the Python way. 
Run the following from the command line: 
 
  python vectorsum.py n 
 
Here, n is an integer that specifies the size of the vectors. 
 
The first vector to be added contains the squares of 0 up to n. 
The second vector contains the cubes of 0 up to n. 
The program prints the last 2 elements of the sum and the elapsed  time: 
""" 
 
def numpysum(n): 
   a = np.arange(n) ** 2 
   b = np.arange(n) ** 3 
   c = a + b 
 
   return c 
 
def pythonsum(n): 
   a = list(range(n)) 
   b = list(range(n)) 
   c = [] 
 
   for i in range(len(a)): 
       a[i] = i ** 2 
       b[i] = i ** 3 
       c.append(a[i] + b[i]) 
 
   return c 
 
size = int(sys.argv[1]) 
 
start = datetime.now() 
c = pythonsum(size) 
delta = datetime.now() - start 
print("The last 2 elements of the sum", c[-2:]) 
print("PythonSum elapsed time in microseconds", delta.microseconds) 
 
start = datetime.now() 
c = numpysum(size) 
delta = datetime.now() - start 
print("The last 2 elements of the sum", c[-2:]) 
print("NumPySum elapsed time in microseconds", delta.microseconds) 

The output of the program for 1000, 2000, and 3000 vector elements is as follows:

$ python3 vectorsum.py 1000
The last 2 elements of the sum [995007996, 998001000]
PythonSum elapsed time in microseconds 976
The last 2 elements of the sum [995007996 998001000]
NumPySum elapsed time in microseconds 87
$ python3 vectorsum.py 2000
The last 2 elements of the sum [7980015996, 7992002000]
PythonSum elapsed time in microseconds 1623
The last 2 elements of the sum [7980015996 7992002000]
NumPySum elapsed time in microseconds 143
$ python3 vectorsum.py 4000
The last 2 elements of the sum [63920031996, 63968004000]
PythonSum elapsed time in microseconds 3417
The last 2 elements of the sum [63920031996 63968004000]
NumPySum elapsed time in microseconds 237

Clearly, NumPy is much faster than the equivalent normal Python code. One thing is certain; we get the same results whether we are using NumPy or not. However, the result that is printed differs in representation. Note that the result from the numpysum() function does not have any commas. How come? Obviously, we are not dealing with a Python list, but with a NumPy array. We will learn more about NumPy arrays in the Chapter 2, NumPy Arrays.

The following table lists documentation websites for the Python data analysis libraries we discussed in this chapter.

The popular Stack Overflow software development forum has hundreds of questions tagged NumPy, SciPy, Pandas, Matplotlib, IPython, and Jupyter Notebook. To view them, go to http://stackoverflow.com/questions/tagged/<your-tag-word-here>.

If you are really stuck with a problem, or you want to be kept informed of the development of these libraries, you can subscribe to their respective discussion mailing list(s). The number of e-mails per day varies from list to list. Developers actively involved with the development of these libraries answer some of the questions asked on the mailing lists.

For IRC users, there is an IRC channel on irc://irc.freenode.net. The channel is called #scipy, but you can also ask NumPy questions since SciPy users also have knowledge of NumPy, as SciPy is based on NumPy. There are at least 50 members on the SciPy channel at all times.

The ch-01.ipynb file contains the code for looking at the modules inside the NumPy, SciPy, Pandas, and Matplotlib libraries. Don't worry about understanding the code just trying to run it for now. You can modify this code to look at the modules inside other libraries as well.

We shall learn about visualizing the data in a later chapter. For now, let's try loading two sample datasets and building a basic plot. First, install the sklearn library from which we shall load the data using the following command:

$ pip3 install scikit-learn 

Import the datasets using the following command:

from sklearn.datasets import load_iris 
from sklearn.datasets import load_boston 

Import the Matplotlib plotting module:

from matplotlib import pyplot as plt 
%matplotlib inline 

Load the iris dataset, print the description of the dataset, and plot column 1 (sepal length) as x and column 2 (sepal width) as y:

iris = load_iris() 
print(iris.DESCR) 
data=iris.data 
plt.plot(data[:,0],data[:,1],".") 

The resulting plot will look like the following image:

Load the boston dataset, print the description of the dataset and plot column 3 (proportion of non-retail business) as x and column 5 (nitric oxide concentration) as y, each point on the plot marked with a + sign:

boston = load_boston()
print(boston.DESCR)
data=boston.data
plt.plot(data[:,2],data[:,4],"+")

The resulting plot will look like the following image:

In this chapter, we installed NumPy, SciPy, Pandas, Matplotlib, IPython, and Jupyter Notebook, all of which we will be using in this book. We got a vector addition program working, and learned how NumPy offers superior performance. In addition, we explored the available documentation and online resources. We executed code to find the modules inside the libraries and loaded some sample datasets to draw some basic plots using Matplotlib.

In the next chapter, Chapter 2, NumPy Arrays, we will take a look under the hood of NumPy and explore some fundamental concepts, including arrays and data types.