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NumPy Indexing and Selection

In this lecture we will discuss how to select elements or groups of elements from an array.



In [1]:

    
import numpy as np



In [2]:

    
#Creating sample array
arr = np.arange(0,11)



In [3]:

    
#Show
arr









    Out[3]:





array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10])

Bracket Indexing and Selection

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



In [4]:

    
#Get a value at an index
arr[8]









    Out[4]:





8



In [5]:

    
#Get values in a range
arr[1:5]









    Out[5]:





array([1, 2, 3, 4])



In [6]:

    
#Get values in a range
arr[0:5]









    Out[6]:





array([0, 1, 2, 3, 4])

Broadcasting

Numpy arrays differ from a normal Python list because of their ability to broadcast:



In [7]:

    
#Setting a value with index range (Broadcasting)
arr[0:5]=100

#Show
arr









    Out[7]:





array([100, 100, 100, 100, 100,   5,   6,   7,   8,   9,  10])



In [8]:

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

#Show
arr









    Out[8]:





array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10])



In [9]:

    
#Important notes on Slices
slice_of_arr = arr[0:6]

#Show slice
slice_of_arr









    Out[9]:





array([0, 1, 2, 3, 4, 5])



In [10]:

    
#Change Slice
slice_of_arr[:]=99

#Show Slice again
slice_of_arr









    Out[10]:





array([99, 99, 99, 99, 99, 99])

Now note the changes also occur in our original array!



In [11]:

    
arr









    Out[11]:





array([99, 99, 99, 99, 99, 99,  6,  7,  8,  9, 10])

Data is not copied, it's a view of the original array! This avoids memory problems!



In [12]:

    
#To get a copy, need to be explicit
arr_copy = arr.copy()

arr_copy









    Out[12]:





array([99, 99, 99, 99, 99, 99,  6,  7,  8,  9, 10])

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.



In [13]:

    
arr_2d = np.array(([5,10,15],[20,25,30],[35,40,45]))

#Show
arr_2d









    Out[13]:





array([[ 5, 10, 15],
       [20, 25, 30],
       [35, 40, 45]])



In [14]:

    
#Indexing row
arr_2d[1]









    Out[14]:





array([20, 25, 30])



In [15]:

    
# Format is arr_2d[row][col] or arr_2d[row,col]

# Getting individual element value
arr_2d[1][0]









    Out[15]:





20



In [16]:

    
# Getting individual element value
arr_2d[1,0]









    Out[16]:





20



In [17]:

    
# 2D array slicing

#Shape (2,2) from top right corner
arr_2d[:2,1:]









    Out[17]:





array([[10, 15],
       [25, 30]])



In [18]:

    
#Shape bottom row
arr_2d[2]









    Out[18]:





array([35, 40, 45])



In [19]:

    
#Shape bottom row
arr_2d[2,:]









    Out[19]:





array([35, 40, 45])

More Indexing Help

Indexing a 2d matrix can be a bit confusing at first, especially when you start to add in step size. Try google image searching NumPy indexing to fins useful images, like this one:

Conditional Selection

This is a very fundamental concept that will directly translate to pandas later on, make sure you understand this part!

Let's briefly go over how to use brackets for selection based off of comparison operators.



In [20]:

    
arr = np.arange(1,11)
arr









    Out[20]:





array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10])



In [21]:

    
arr > 4









    Out[21]:





array([False, False, False, False,  True,  True,  True,  True,  True,
        True])



In [22]:

    
bool_arr = arr>4



In [23]:

    
bool_arr









    Out[23]:





array([False, False, False, False,  True,  True,  True,  True,  True,
        True])



In [24]:

    
arr[bool_arr]









    Out[24]:





array([ 5,  6,  7,  8,  9, 10])



In [25]:

    
arr[arr>2]









    Out[25]:





array([ 3,  4,  5,  6,  7,  8,  9, 10])



In [26]:

    
x = 2
arr[arr>x]