numpy Array slicing and indexing

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Bracket Indexing and Selection

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

				
					#Get a value at an index
arr[8]
				
			
				
					#Get values in a range
arr[1:5]
				
			
				
					#Get values in a range
arr[2:]
				
			

Broadcasting

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

				
					#Setting a value with index range (Broadcasting)
arr = np.arange(10,21)
arr[0:5]=200

#Show
arr
				
			
				
					# Reset array, we'll see why I had to reset in  a moment
arr = np.arange(10,21)

#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 note the changes also occur in our original array!

				
					arr
				
			

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  [row,column]
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]
arr_2d
				
			
				
					# 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[2,1]
				
			

Fancy Indexing

Fancy indexing allows you 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))
arr2d
				
			
				
					#Length of array
arr_length = arr2d.shape[1]
arr_length
				
			
				
					#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 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:

Selection

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

				
					arr = np.arange(1,11)
arr
				
			
				
					arr[arr > 4]
				
			
				
					bool_arr = arr>4
				
			
				
					bool_arr
				
			
				
					arr[bool_arr]
				
			
				
					arr[arr>2]
				
			
				
					x = 5
arr[arr>x]
				
			

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