Accessing a NumPy-based array by a specific Column index can be achieved by indexing. NumPy follows standard 0-based indexing in Python.
Example:
Given array: 1 13 6
9 4 7
19 16 2
Input: print(NumPy_array_name[ :,2])
Output: [6 7 2]
Explanation: printing 3rd column
Access ith column of a 2D Numpy Array in Python
Printing 1st row and 2nd column.
For column : numpy_Array_name[ : ,column] For row : numpy_Array_name[ row, : ]
Python3
import numpy as nparray = [[1, 13, 6], [9, 4, 7], [19, 16, 2]]# defining arrayarr = np.array(array)print('printing 0th row')print(arr[0, :])print('printing 2nd column')print(arr[:, 2])# multiple columns or rows can be selected as wellprint('selecting 0th and 1st row simultaneously')print(arr[:,[0,1]]) |
Output :
printing 0th row [ 1 13 6] printing 2nd column [6 7 2] selecting 0th and 1st row simultaneously [[ 1 13] [ 9 4] [19 16]]
Access the ith column of a Numpy array using transpose
Transpose of the given array using the .T property and pass the index as a slicing index to print the array.
Python3
import numpy as nparr = np.array([[1, 13, 6], [9, 4, 7], [19, 16, 2]])# Access the ith column of a 2D NumPy arraycolumn_i = arr.T[2]#printing the columnprint(column_i) |
Output:
[6 7 2]
Access the ith column of a Numpy array using list comprehension
Here, we access the ith element of the row and append it to a list using the list comprehension and printed the col.
Python3
import numpy as nparr = np.array([[1, 13, 6], [9, 4, 7], [19, 16, 2]])# Access the ith column of a 2D NumPy arraycol = [row[1] for row in arr]# printing the columnprint(col) |
Output:
[13, 4, 16]
Access the ith column of a Numpy array using Ellipsis
Pass the ith index along with the ellipsis to print the returned array column.
For column : numpy_Array_name[...,column] For row : numpy_Array_name[row,...] where '...' represents no of elements in the given row or column
Note: This is not a very practical method but one must know as much as one can.
Python3
import numpy as np# defining arrayarray = [[1, 13, 6], [9, 4, 7], [19, 16, 2]]# converting to numpy arrayarr = np.array(array)print('selecting 0th column')print(arr[..., 0])print('selecting 1st row')print(arr[1, ...]) |
Output:
selecting 0th column [ 1 9 19] selecting 1st row [9 4 7]
