numpy Array slicing and indexing

Python Numpy

numpy Array (1D and 2’D) creation and operation

numpy array itreating

numpy array join

numpy array sort and Filter

numpy array split and search

numpy shape and reshape

numpy Array slicing and indexing

Bracket Indexing and Selection

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

Code :

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

Bracket Indexing and Selection

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

Code :

#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:

Code :

 #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.

Code :

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]
# 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:

Code :

#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:

Code :

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