For multi-dimensional NumPy arrays, you can access the elements as below:
for a NumPy array multi_arr, you can use below syntax:
multi_arr[1, 2] - to access value at row 1 and column 2
multi_arr[1, :] - to access value at row 1 and all columns
multi_arr[:, 1] - to access value at all rows and column 1
multi_arr[3:7, 2:10] - to access values at row numbers from 3 to 7 (row index 3 to 6) and at column numbers from 2 to 10 (column index 2 to 9)
Syntax used is
array[ row_start_index : row_end_index, column_start_index : column_end_index]
We can also index NumPy arrays using a NumPy array of boolean values on one axis to specify the indices that we want to access.
multi_arr = np.arange(12).reshape(3,4)
This will create a NumPy array of size 3x4 (3 rows and 4 columns) with values from 0 to 11 (value 12 not included).
print(multi_arr)
This will output
array( [ [ 0, 1, 2, 3],
[ 4, 5, 6, 7],
[ 8, 9, 10, 11] ] )
rows_wanted = np.array( [True, False, True] )
Here, we are saying that we want first row (True) and the 3rd row (True) values, and we don't want 2nd-row value (False).
Let us use this boolean NumPy array rows_wanted in the above multi-dimensional array (multi_arr), to extract the desired portion of this multi_arr array.
multi_arr_portion = multi_arr[rows_wanted, : ]
Here, we are saying - get all columns :, but get only row numbers 1 and 3 ('0' and '2' index rows)
print(multi_arr_portion)
This will print
array( [ [ 0, 1, 2, 3],
[ 8, 9, 10, 11] ] )
Please follow the below steps:
(1) Import numpy as np
(2) Create a 4x5 (4 rows, 5 columns) NumPy array called my_multi_arr
my_multi_arr = np.arange(20).reshape(<<your code comes here>>)
(3) Extract values from row index numbers 2 to 4 and from column index numbers 2 to 5, and store it in a variable called my_multi_arr_portion
my_multi_arr_portion = my_multi_arr[<<your code comes here>>]
(4) Print the my_multi_arr_portion array using print() function to see its values
print(<<your code comes here>>)
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