This tutorial provides 3 methods to create 2-D Numpy Arrays in Python. The following is the key syntax for these 3 methods. After that, 3 Python Numpy code examples are provided.
Method 1:
np.array[[numbers in the first row ], [numbers in the second row]]
Method 2:
np.zeros(shape(row number, column number))
Method 3:
array_name=np.arange(length number)
array_name=array_name.reshape(row number, column number)
Example 1: Create a 2-D array
# import numpy
import numpy as np
# create 2-D array_a
array_a=np.array([[1,2,4],[2,1,5]])
# print out array_a
print("array_a:\n",array_a)
# check the shape of array_a
print("the dimension of array_a:\n", array_a.shape)Output:
array_a: [[1 2 4] [2 1 5]] the dimension of array_a: (2, 3)
Example 2: Create 2-D arrays of all zero
# import numpy
import numpy as np
# create a 2-D all zero array
array_zero=np.zeros(shape=(4, 2))
# print out the array_zero
print(array_zero)Output:
[[0. 0.] [0. 0.] [0. 0.] [0. 0.]]
Example 3: Create sequential numbers 2-D array
The following is to generate a array with sequential numbers. It has two components, one is before reshaping, and it is 1 dimension. After reshaping, it becomes 2 dimensions.
# import numpy
import numpy as np
# create a range of 10, namely numbers from 0 to 9
array_c=np.arange(10)
# print out array_c
print("array_c:\n",array_c)
print("checking type of array_c:\n",type(array_c))
print("checking shape of array_c:\n", np.shape(array_c))
print("checking the dimension of array c:\n", array_c.ndim)
# reshape it
array_c=array_c.reshape(2,5)
# print out array_c
print("reshaped array_c:\n", array_c)
print("checking shape of reshaped array_c:\n", np.shape(array_c))
print("checking the dimension of reshaped array_c:\n", array_c.ndim)Output:
array_c: [0 1 2 3 4 5 6 7 8 9] checking type of array_c: <class 'numpy.ndarray'> checking shape of array_c: (10,) checking the dimension of array c: 1 reshaped array_c: [[0 1 2 3 4] [5 6 7 8 9]] checking shape of reshaped array_c: (2, 5) checking the dimension of reshaped array_c: 2