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TODO 11
Using the axis_array defined below complete the following TODOs.
Hint: Think about which axis is being collapsed or the direction of the arrow for each axis in the above picture.
- Compute the average using np.average for each row. Store the output into the variable row_avg.
- Find the minimum value using np.min for each column. Store the output into the variable col_min.
axis_array = np.vstack([np.full((1, 5), 2), np.full((1, 5), 5)])
print(f"axis_array output: \n {axis_array}")
print(f"axis_array shape: {axis_array.shape}")
# TODO 11.1
row_avg =
print(f"row_avg output: \n {row_avg}")
print(f"row_avg shape: {row_avg.shape}")
todo_check([
(row_avg.shape == (2,), 'row_avg does not have the correct shape of (2,)'),
(np.all(row_avg == np.array([2, 5])), 'row_avg does not have the correct values')
])
# TODO 11.2
col_min =
print(f"col_min output: \n {col_min}")
print(f"col_min shape: {col_min.shape}")
todo_check([
(col_min.shape == (5,), "col_min does not have the correct shape of (5,)"),
(np.all(col_min == np.array([2, 2, 2, 2, 2])), 'col_min does not have the correct values')
])
Introduction
In Python, the term "for each row" and "for each column" refers to the concept of looping over the elements in a 2-dimensional data structure, such as a matrix or a 2-dimensional array.
-
"For each row" means to iterate over the rows of a 2-dimensional data structure, where each iteration of the loop represents a row, and access is given to the elements in that row.
-
"For each column" means to iterate over the columns of a 2-dimensional data structure, where each iteration of the loop represents a column, and access is given to the elements in that column.
Here is an example of how you might use these terms in a for loop:
import numpy as np
# Create a 2-dimensional array
matrix = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
# Loop over each row
for row in matrix:
print(row)
# Loop over each column
for col in matrix.T:
print(col)
OUTPUT:
[1 2 3]
[4 5 6]
[7 8 9]
[1 4 7]
[2 5 8]
[3 6 9]
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