NumPy Indexing and Selection
In this lecture we will discuss how to select elements or groups of elements from an array.
import numpy as np
#Creating sample array
arr = np.arange(0,11)
#Show
arr
Bracket Indexing and Selection
The simplest way to pick one or some elements of an array looks very similar to python lists:
#Get values in a range
arr[1:5]
#Get values in a range
arr[0:5]
Broadcasting
Numpy arrays differ from a normal Python list because of their ability to broadcast:
#Setting a value with index range (Broadcasting)
arr[0:5]=100
#Show
arr
# Reset array, we'll see why I had to reset in a moment
arr = np.arange(0,11)
#Show
arr
#Important notes on Slices
slice_of_arr = arr[0:6]
#Show slice
slice_of_arr
#Change Slice
slice_of_arr[:]=99
#Show Slice again
slice_of_arr
Now notice that the changes also occur in our original array!
arr
The Data is not copied, it's a view of the original array! This avoids memory problems!
#To get a copy, need to be explicit
arr_copy = arr.copy()
arr_copy
Indexing a 2D array (matrices)
The general format is arr_2d[row][col] or arr_2d[row,col]. I recommend usually using the comma notation for clarity.
arr_2d = np.array(([5,10,15],[20,25,30],[35,40,45]))
#Show
arr_2d
#Indexing row
arr_2d[1]
# Format is arr_2d[row][col] or arr_2d[row,col]
# Getting individual element value
arr_2d[1][0]
# Getting individual element value
arr_2d[1,0]
# 2D array slicing
#Shape (2,2) from top right corner
arr_2d[:2,1:]
#Shape bottom row
arr_2d[2]
#Shape bottom row
arr_2d[2,:]
Fancy Indexing
Fancy indexing allows one to select entire rows or columns out of order. To show this, let's quickly build out a NumPy Array:
#Set up matrix
arr2d = np.zeros((10,10))
#Length of array
arr_length = arr2d.shape[1]
#Set up array
for i in range(arr_length):
arr2d[i] = i
arr2d
Fancy indexing allows the following
arr2d[[2,4,6,8]]
#Allows in any order
arr2d[[6,4,2,7]]
More Indexing
Indexing a 2D Matrix can be a bit confusing at first, especially when you start to add in step size.
Selection
Let's briefly go over how to use brackets for selection based off of comparison operators.
arr = np.arange(1,11)
arr
arr > 4
bool_arr = arr>4
bool_arr
arr[bool_arr]
arr[arr>2]
x = 2
arr[arr>x]
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