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import numpy as np

# Create the following rank 2 array with shape (3, 4).
a = np.array([[1, 3, 5, 7],
              [2, 4, 6, 8],
              [9, 10, 11, 12]]) 

# Get all columns in the second row.
row_r1 = a[1, :]    # [2 4 6 8] (4,)

# Get all columns from second until third row.
row_r2 = a[1:3, :]  # [[2 4 6 8]] (2, 4)

# Get the second colunm of all rows
col_r1 = a[:, 1]    # [3 4 10] (3,)

# Get from second until third column of all rows
col_r2 = a[:, 1:3]  # [[ 3  5]
                    #  [ 4  6] 
                    #  [10 11]] (3, 2)

import numpy as np

a = np.array([[1, 3, 5, 7],
              [2, 4, 6, 8],
              [9, 10, 11, 12]])

# Find the odd numbers.
a[(a % 2 != 0)] # [1 3 5 7 9 11]

import numpy as np

# Create the following rank 2 array with shape (3, 4).
a = np.array([[1, 3, 5, 7],
              [2, 4, 6, 8],
              [9, 10, 11, 12]]) 

# Use slicing to pull out the subarray consisting of the first 2 rows
# and columns 1 and 2
# b is the following array of shape (2, 2) 
b = a[:2, 1:3]  # [[3, 5],
                #  [4, 6]]

# A slice of an array is a view into the same data, so modifying it
# will modify the original array.
a[0, 1]         # "5"

# b[0, 0] is the same piece of data as a[0, 1]
b[0, 0] = 123   # "123"

import numpy as np

a = np.array([1, 3, 5])     # Create a rank 1 array
type(a)                     # <class 'numpy.ndarray'>
a.shape                     # (3,)


b = np.array([[1, 3, 5],
              [2, 4, 6]])   # Create a rank 2 array
b.shape                     # (2, 3) Two lines and 3 columns


c = np.array((2, 2))        # Create an array of all zeros
                            # [[0. 0.],
                            #  [0. 0.]]