14_introduction_to_numpy.md

Introduction to NumPy

NumPy (Numerical Python) is an important Python library for scientific computation. Libraries such as Pandas and GeoPandas are built on top of NumPy.

It provides a fast and efficient ways to work with Arrays. In the domain of spatial data analysis, it plays a critical role in working with Raster data - such as satellite imagery, aerial photos, elevation data etc. Since the underlying structure of raster data is a 2D array for each band - learning NumPy is critical in processing raster data using Python.

By convention, numpy is commonly imported as np

import numpy as np

Arrays

The array object in NumPy is called ndarray. It provides a lot of supporting functions that make working with arrays fast and easy. Arrays may seem like Python Lists, but ndarray is upto 50x faster in mathematical operations. You can create an array using the array() method. As you can see, the rsulting object is of type numpy.ndarray

a = np.array([1, 2, 3, 4])
print(type(a))

Arrays can have any dimensions. We can create a 2D array like below. ndarray objects have the property ndim that stores the number of array dimensions. You can also check the array size using the shape property.

b = np.array([[1, 2, 4], [3, 4, 5]])
print(b)
print(b.ndim)
print(b.shape)

You can access elements of arrays like Python lists using [] notation.

print(b[0])

print(b[0][2])

Array Operations

Mathematical operations on numpy arrays are easy and fast. NumPy as many built-in functions for common operations.

print(np.sum(b))

You can also use the functions operations on arrays.

c = np.array([[2, 2, 2], [2, 2, 2]])
print(np.divide(b, c))

If the objects are numpy objects, you can use the Python operators as well

print(b/c)

You can also combine array and scalar objects. The scalar operation is applied to each item in the array.

print(b)
print(b*2)
print(b/2)

An important concept in NumPy is the Array Axes. Similar to the pandas library, In a 2D array, Axis 0 is the direction of rows and Axis 1 is the direction of columns. The diagram below show the directions.

Let's see how we can apply a function on a specific axis. Here when we apply sum function on axis-0 of a 2D array, it gives us a 1D-array with values summed across rows.

print(b)
row_sum = b.sum(axis=0)
print(row_sum)

Exercise

Sum the array b along Axis-1. What do you think will be the result?

import numpy as np

b = np.array([[1, 2, 4], [3, 4, 5]])
print(b)

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