Posted onDec 31, 2022

Numpy Indexing and Selection

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 arrayarr = np.arange(0,11)#Showarr

The simplest way to pick one or some elements of an array looks very similar to python lists:

#Get values in a rangearr[1:5]#Get values in a rangearr[0:5]

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#Showarr

# Reset array, we'll see why I had to reset in  a momentarr = np.arange(0,11)#Showarr

#Important notes on Slicesslice_of_arr = arr[0:6]#Show sliceslice_of_arr

#Change Sliceslice_of_arr[:]=99#Show Slice againslice_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 explicitarr_copy = arr.copy()arr_copy

The general format isarr_2d[row][col] orarr_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]))#Showarr_2d

#Indexing rowarr_2d[1]

# Format is arr_2d[row][col] or arr_2d[row,col]# Getting individual element valuearr_2d[1][0]

# Getting individual element valuearr_2d[1,0]

# 2D array slicing#Shape (2,2) from top right cornerarr_2d[:2,1:]

#Shape bottom rowarr_2d[2]

#Shape bottom rowarr_2d[2,:]

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 matrixarr2d = np.zeros((10,10))

#Length of arrayarr_length = arr2d.shape[1]

#Set up arrayfor i in range(arr_length):    arr2d[i] = iarr2d

Fancy indexing allows the following

arr2d[[2,4,6,8]]

#Allows in any orderarr2d[[6,4,2,7]]

Indexing a 2D Matrix can be a bit confusing at first, especially when you start to add in step size.

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 = 2arr[arr>x]

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