Are you looking for my non-technical blog?

This is now my technical-only blog, my non-technical blog is here.

30 September 2013

A Quick Intro. to NumPy

For me, NumPy is a Python list on steroids. You can use it to create multidimensional arrays and matrices.

The convention is to import it as follows:

import numpy as np

To create an array of numbers between 0 and 9, you could use the following command:

x = range(9)

To convert that list into a NumPy array, you can write:

x = np.array(range(9))

And to make you life easier, there is a shorthand for the above command:

x = np.arange(9)

So far, we have been creating one dimensional array. However, there are ways to reshape the arrays. The reshape() method when applied on an array, it returns a reshaped version of it without changing the original object. To reshape the original object itself, then use resize() instead.

y = x.reshape(2,5)

The above command create a 2-dimensional array of 2 rows and 5 columns. You can create a much dimensional arrays as you want. See the command below for a 3*4*5 array.

y = np.arange(3*4*5).reshape(3,4,5)

The mathematical operations '+', '-', '/' and '*' are applied elementwise.

x = np.arange(10)

# To multiply each element of x by 10
y = x + 10

# To multiply each element of x by itself
y = x + x

To do a Matrix Multiplication though:

# Create a 3 * 5 Matrix
A = np.arange(15).reshape(3,5)

# Create a 5 * 2 Matrix
B = np.arange(10).reshape(5,2)

# Dot product gives you a 3 * 2 Matrix
y = y = np.dot(A, B)

Just like lists, you can get parts of arrays

For original lists:

A = range(10)
A[2:5] # [2, 3, 4]

For NumPy Arrays

B =  arange(10)
B[2:5] # array([2, 3, 4])

However, you can set some elements of the array as follows

B[2:5] = 1337

But, you cannot do the same to lists.

A[2:5] = 1337 # TypeError: can only assign an iterable

For statisticians, there are also the following functions

x = np.arange(5) + 1
x.mean() # 3.0
x.max() # 5
x.min() # 1
x.std() # 1.414

You can also access elements of the array using start, stop and a step:

x = np.arange(10)
x[2:7:2] # array([2, 4, 6])

Or access specific elements, let's say elements 1, 5 and 6

x[[1,5,6]] # array([1, 5, 6])

Similar to reshape() and resize(), ravel() converts a multidimensional array into a one-dimensional array, while transpose() turns rows into columns and vice versa.

If you program in R, you will not miss their way of accessing elements of array that meet a certain condition.

x = np.arange(10)
x[x>4] # array([5, 6, 7, 8, 9])
x[x%2 == 1] # array([1, 3, 5, 7, 9])

If you are having an array of elements that are either True or False.

x = np.array([True, False, True, True])

x.all() # Only True if all elements are True
x.any() # Only True if any elements are True

Finally, there is a repeat() that repeats each element of the array n times

x = np.array([1, 2])
x.repeat(3) # array([1, 1, 1, 2, 2, 2])

That's all folks for today.
Check the following tutorial for more information.