#### Overview of this book

Today's world of science and technology is all about speed and flexibility. When it comes to scientific computing, NumPy is on the top of the list. NumPy will give you both speed and high productivity. "NumPy Cookbook" will teach you all about NumPy, a leading scientific computing library. NumPy replaces a lot of the functionality of Matlab and Mathematica, but in contrast to those products, it is free and open source. "Numpy Cookbook" will teach you to write readable, efficient, and fast code that is as close to the language of Mathematics as much as possible with the cutting edge open source NumPy software library. You will learn about installing and using NumPy and related concepts. At the end of the book, we will explore related scientific computing projects. This book will give you a solid foundation in NumPy arrays and universal functions. You will also learn about plotting with Matplotlib and the related SciPy project through examples. "NumPy Cookbook" will help you to be productive with NumPy and write clean and fast code.
NumPy Cookbook
Credits
www.PacktPub.com
Preface
Free Chapter
Winding Along with IPython
Get to Grips with Commonly Used Functions
Connecting NumPy with the Rest of the World
Audio and Image Processing
Special Arrays and Universal Functions
Profiling and Debugging
Quality Assurance
Speed Up Code with Cython
Index

## Importing a web notebook

Python scripts can be imported as a web notebook. Obviously, we can also import previously exported notebooks.

### How to do it...

The following steps show how a python script can be imported as a web notebook:

1. Import a python script by dragging it from Explorer or Finder into the notebook page. The following screenshot is an example of what we see after dragging the `vectorsum.py` from NumPy Beginner's Guide into the notebook page:

2. Click the Upload button to import the program. IPython does a decent job of importing the code. Unfortunately, as shown in the following screenshot, the code is all placed in one cell. At least that is how it worked at the time of writing:

3. Tag the script for multiple cells.

In order to split the code into multiple cells we need to use special tags. These tags are in fact Python comments, but they look a bit like XML tags. The code has to start with the following tag:

`# <nbformat>2</nbformat>`

This indicates the format of the notebook. Each new code cell is indicated with the following tag:

`# <codecell>`

The following is the tagged code:

```# <nbformat>2</nbformat>
#!/usr/bin/env/python

from datetime import datetime
import numpy
"""
Chapter 1 of NumPy Beginners Guide.
This program demonstrates vector addition the Python way.
Run from the command line as follows

python vectorsum.py n

where 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 = numpy.arange(n) ** 2
b = numpy.arange(n) ** 3
c = a + b

return c

def pythonsum(n):
a = range(n)
b = 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

# <codecell>
size = int(50)

# <codecell>
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

# <codecell>
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 code is split into several cells according to the tags, as shown in the following screenshot: