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Learn NumPy for Data Science (Course II)

Learn NumPy for Data Science (Course II)

Introduction to NumPy and NumPy Arrays

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NumPy is an open-source library for performing numerical computation using Python. It is used for working with arrays and performing array operations. NumPy is used in various domains such as Linear Algebra, Fourier Transforms, etc. In fact, NumPy stands for Numerical Python. In Data Science, NumPy is highly used for manipulating numerical data (example: pixel arrays of an image).

Installing NumPy using pip

NumPy can be installed using the python package manager pip by simply running the following command:

$ pip install numpy

It is a general convention to import NumPy as np in our python code. The following block of code illustrates how to import NumPy and check its version:

 # Library import conventions
 import numpy as np

 #Check version of numpy installed
 print(np.__version__) # Printed version:1.18.1

NumPy Arrays

NumPy array is a grid of values, similar to a Python List, where all data are of the same type (integer, float). If you are familiar with Linear Algebra, a NumPy array can simply be thought of as a matrix having homogenous values. The following examples illustrate what NumPy Arrays looks like:

  • \left[[ 1, \ \ 2]\right]  
  • \left[[ 1], \ \ [ 2]\right]      
  • \left[[ 1, \ \ 2] ,\ [ 3, \ \ 4]\right]

A NumPy array can simply be created from a Python list by passing the list into the numpy.array() function as:

# Importing numpy
import numpy as np

# Defining a python list
l = [1, 2, 3, 4]

# Creating a numpy array from the list
arr = np.array(l)

# Printing the array
print(arr)
[1 2 3 4]

Some of the main key features of a NumPy array are: shape, dimension and size.

Shape of a NumPy Array

The shape of a NumPy array is a Python Tuple that denotes the number of elements in each axis of the array.

For example, the array [1, 2, 3, 4] has only one axis with 4 elements. Thus, the shape of this array would be the tuple (4,). Similarly, the array [[ 1], [ 2]] has two axes, one horizontal and one vertical. Thus the shape of this array would be the tuple (2, 1).

The shape() function of NumPy can be used to check the shape of arrays:

# Importing numpy
import numpy as np

# Defining numpy arrays from python lists
arr1 = np.array([1, 2, 3, 4])
arr2 = np.array([[1], [2]])

# Printing the shape of numpy arrays
print("Shape of arr1: ", arr1.shape)
print("Shape of arr2: ", arr2.shape)
Shape of arr1: (4,)
Shape of arr2: (2, 1)
Dimension of a NumPy Array

In NumPy, the number of Axes in an array is called the dimension of the array. A multi-dimensional NumPy array is also referred to as an n-d array, a shorthand name for N-dimensional array.

For example, the array [1, 2, 3, 4, 5] has only one axis, so it is a 1-D array. Likewise, the array[[ 1], [ 2]] has two axes, thus it is a 2-D array.

The ndim() function of NumPy can be used to check the dimension of arrays:

# Importing numpy
import numpy as np

# Defining numpy arrays from python lists
arr1 = np.array([1, 2, 3, 4])
arr2 = np.array([[1], [2]])

# Printing the dimension of numpy arrays
print("Dimension of arr1: ", arr1.ndim)
print("Dimension of arr2: ", arr2.ndim)
Dimension of arr1: 1
Dimension of arr2: 2
Size of a NumPy Array

Size of a NumPy array is simply the number of elements in the array. For example, the array [1, 2, 3, 4] has 4 elements, so its size is 4.

The size() function can be used to get the size of an array:

# Importing numpy
import numpy as np

# Defining numpy arrays from python lists
arr1 = np.array([1, 2, 3, 4])
arr2 = np.array([[1], [2]])

# Printing the size of numpy arrays
print("Size of arr1: ", arr1.size)
print("Size of arr2: ", arr2.size)
Size of arr1: 4
Size of arr2: 2

There you have it! You have installed NumPy on your machine and also learned about the fundamentals of NumPy arrays. Head onto the next chapter to further about different ways of creating NumPy arrays.

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