Skip to main content

2D NumPy Array

Updated Aug 17, 2021 ·

Overview​

NumPy arrays are of type numpy.ndarray, indicating they are n-dimensional arrays.

  • Arrays like np_height and np_weight are 1D arrays.
  • NumPy supports higher dimensions, including 2D, 3D, or more.

Example:

import numpy as np  
np_height = np.array([1.56, 1.75, 1.89])  
print(type(np_height))

Output:

<class 'numpy.ndarray'>

Working with 2D Arrays​

A 2D NumPy array is like a grid or a table.

  • Create it from a list of lists.
  • Each sublist corresponds to a row.
  • The shape attribute shows the array's dimensions.

Example:

np_2d = np.array([[1.56, 65.3], [1.75, 72.4], [1.89, 89.1]])  
print(np_2d.shape)

This will return the following output, indicating the array has 3 rows with 2 columns each.

(3, 2)
info

All elements must be of the same type. If one element is a string, all will convert to strings.

Accessing Specific Data​

You can subset a 2D array to access specific rows, columns, or elements.

Syntax: Use np_2d[row][column] or np_2d[row, column].

Using the previous example, we can get the 2nd row's 2nd element by:

print(np_2d[1][1])     # Output: 72.4  
print(np_2d[1, 1])     # Output: 72.4

Slicing Rows and Columns​

  • Use np_2d[:, 1] for all rows in column 1.

Using the Numpy array below:

np_2d = np.array([
          [1.56, 65.3], 
          [1.75, 72.4], 
          [1.89, 89.1]])

To select column 1 for all rows:

subset = np_2d[:, 1]  
print(subset)

Output:

[65.3 72.4 89.1]

To select rows 0-2, column 1:

subset = np_2d[0:2, 0]  
print(subset)

Output:

[1.56 1.75]

Feedback