<\/span><\/h2>\nTo find which column per row has the highest value, you can iterate over each row of your dataset and compare the values in each column. By keeping track of the highest value and its corresponding column index for each row, you can determine the desired result.<\/p>\n
Let’s consider the following example:<\/p>\n
“`python
\nimport numpy as np<\/p>\n
# Assuming you have a numpy array called ‘data’
\nnum_rows, num_columns = data.shape<\/p>\n
highest_column_per_row = np.zeros(num_rows, dtype=int)<\/p>\n
for i in range(num_rows):
\n max_value = data[i, 0]
\n max_column = 0
\n for j in range(num_columns):
\n if data[i, j] > max_value:
\n max_value = data[i, j]
\n max_column = j
\n highest_column_per_row[i] = max_column<\/p>\n
print(highest_column_per_row)
\n“`<\/p>\n
After executing this code, the resulting array ‘highest_column_per_row’ will contain the column index with the highest value for each row of the dataset. <\/p>\n
The code outlined above provides a straightforward solution to finding which column per row has the highest value.<\/strong><\/p>\n<\/p>\n