Array Inspection: Program 1

Inspect 1D array

import numpy as np

''' Create a NumPy array '''
arr = np.array([1, 2, 3, 4, 5, 8, 9, 10, 14, 7])

''' Inspect the shape of the array '''
print("\nShape of array:", arr.shape)

''' Inspect the size of the array (total number of elements) '''
print("\nSize of array:", arr.size)

''' Inspect the number of dimensions of the array '''
print("\nNumber of dimensions:", arr.ndim)

''' Inspect the data type of the array '''
print("\nData type of array:", arr.dtype)

''' Inspect the length of the array '''
print("\nLength of array:", len(arr))

''' Inspect if all elements are non-zero '''
print("\nAre all elements non-zero?:", np.all(arr))

''' Inspect if any element is non-zero '''
print("\nIs any element non-zero?:", np.any(arr))

''' Inspect the minimum value in the array '''
print("\nMinimum value:", np.min(arr))

''' Inspect the maximum value in the array '''
print("\nMaximum value:", np.max(arr))

''' Inspect the sum of all elements in the array '''
print("\nSum of elements:", np.sum(arr))

''' Inspect the mean of the array elements '''
print("\nMean of elements:", np.mean(arr))

''' Inspect the standard deviation of the array '''
print("\nStandard deviation of elements:", np.std(arr))

''' Inspect the variance of the array '''
print("\nVariance of elements:", np.var(arr))

''' Inspect the cumulative sum of array elements '''
print("\nCumulative sum:", np.cumsum(arr))

''' Inspect the cumulative product of array elements '''
print("\nCumulative product:", np.cumprod(arr))

''' Inspect the indices of the minimum values '''
print("\nIndex of minimum value:", np.argmin(arr))

''' Inspect the indices of the maximum values '''
print("\nIndex of maximum value:", np.argmax(arr))

''' Inspect sorted array '''
print("\nSorted array:", np.sort(arr))

''' Inspect if the array is sorted '''
print("\nIs array sorted?:", np.all(np.diff(arr) >= 0))

''' Inspect the unique elements in the array '''
print("\nUnique elements:", np.unique(arr))

''' Inspect the number of occurrences of each element '''
(unique, counts) = np.unique(arr, return_counts=True)
print("\nOccurrences of elements:", dict(zip(unique, counts)))

''' Inspect the square root of array elements '''
print("\nSquare root of array elements:", np.sqrt(arr))

''' Inspect the logarithm of array elements '''
print("\nNatural log of array elements:", np.log(arr))

''' Inspect the exponential of array elements '''
print("\nExponential of array elements:", np.exp(arr))

''' Inspect the copy behavior of the array '''
arr_copy = np.copy(arr)
print("\nCopied array:", arr_copy)

''' Inspect array reshaping '''
reshaped_arr = arr.reshape(1, -1)
print("\nReshaped array:\n", reshaped_arr)

''' Inspect repeating of array elements '''
repeated_arr = np.repeat(arr, 3)
print("\nRepeated array:", repeated_arr)

''' Inspect where specific conditions are met in the array '''
condition_arr = np.where(arr > 2)
print("\nIndices where condition is met:", condition_arr)

Output 1

Array Inspection: Program 2

Inspect 2D Array

import numpy as np

''' Create a 2D NumPy array '''
arr_2d = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])

''' Inspect the shape of the 2D array '''
print("\nShape of 2D array:", arr_2d.shape)

''' Inspect the number of rows and columns '''
rows, cols = arr_2d.shape
print("\nNumber of rows:", rows)
print("\nNumber of columns:", cols)

''' Inspect the size (total number of elements) of the 2D array '''
print("\nSize of 2D array:", arr_2d.size)

''' Inspect the number of dimensions of the 2D array '''
print("\nNumber of dimensions:", arr_2d.ndim)

''' Inspect the data type of the 2D array '''
print("\nData type of 2D array:", arr_2d.dtype)

''' Inspect the transpose of the 2D array '''
print("\nTranspose of 2D array:\n", arr_2d.T)

''' Inspect the diagonal elements of the 2D array '''
print("\nDiagonal elements:", np.diag(arr_2d))

''' Inspect the sum of all elements in the 2D array '''
print("\nSum of all elements:", np.sum(arr_2d))

''' Inspect the sum along rows (axis 1) '''
print("\nSum along rows:", np.sum(arr_2d, axis=1))

''' Inspect the sum along columns (axis 0) '''
print("\nSum along columns:", np.sum(arr_2d, axis=0))

''' Inspect the mean of all elements in the 2D array '''
print("\nMean of all elements:", np.mean(arr_2d))

''' Inspect the mean along rows '''
print("\nMean along rows:", np.mean(arr_2d, axis=1))

''' Inspect the mean along columns '''
print("\nMean along columns:", np.mean(arr_2d, axis=0))

Output 2

Array Inspection: Program 3

Inspect 2D Array

import numpy as np

''' Create a 2D NumPy array '''
arr_2d = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])

''' Inspect the maximum value in the 2D array '''
print("\nMaximum value:", np.max(arr_2d))

''' Inspect the minimum value in the 2D array '''
print("\nMinimum value:", np.min(arr_2d))

''' Inspect the index of the maximum value in the 2D array '''
print("\nIndex of maximum value:", np.unravel_index(np.argmax(arr_2d), arr_2d.shape))

''' Inspect the index of the minimum value in the 2D array '''
print("\nIndex of minimum value:", np.unravel_index(np.argmin(arr_2d), arr_2d.shape))

''' Inspect the standard deviation of the 2D array '''
print("\nStandard deviation of all elements:", np.std(arr_2d))

''' Inspect the variance of the 2D array '''
print("\nVariance of all elements:", np.var(arr_2d))

''' Inspect the cumulative sum along rows '''
print("\nCumulative sum along rows:\n", np.cumsum(arr_2d, axis=1))

''' Inspect the cumulative sum along columns '''
print("\nCumulative sum along columns:\n", np.cumsum(arr_2d, axis=0))

''' Inspect reshaping the 2D array into a 1D array '''
reshaped_arr = arr_2d.reshape(-1)
print("\nReshaped to 1D array:", reshaped_arr)

''' Inspect flattening the 2D array into a 1D array '''
flattened_arr = arr_2d.flatten()
print("\nFlattened array:", flattened_arr)

Output 3

Array Inspection: Program 4

import numpy as np

''' Create a 2D NumPy array '''
arr_2d = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])

''' Inspect converting a 1D array back into a 2D array '''
arr_1d = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9])
reshaped_back = arr_1d.reshape(3, 3)
print("\nReshaped back to 2D array:\n", reshaped_back)

''' Inspect slicing a submatrix from the 2D array '''
submatrix = arr_2d[0:2, 1:3]
print("\nSliced submatrix:\n", submatrix)

''' Inspect accessing a specific row in the 2D array '''
row_1 = arr_2d[1, :]
print("\nRow 1 of the 2D array:", row_1)

''' Inspect accessing a specific column in the 2D array '''
col_2 = arr_2d[:, 2]
print("\nColumn 2 of the 2D array:", col_2)

''' Inspect stacking two 2D arrays vertically '''
arr_2d_2 = np.array([[10, 11, 12], [13, 14, 15], [16, 17, 18]])
stacked_vertically = np.vstack((arr_2d, arr_2d_2))
print("\nVertically stacked arrays:\n", stacked_vertically)

''' Inspect stacking two 2D arrays horizontally '''
stacked_horizontally = np.hstack((arr_2d, arr_2d_2))
print("\nHorizontally stacked arrays:\n", stacked_horizontally)

''' Inspect splitting the 2D array into sub-arrays along rows '''
split_row = np.split(arr_2d, 3, axis=0)
print("\nRow-wise split arrays:", split_row)

''' Inspect splitting the 2D array into sub-arrays along columns '''
split_col = np.split(arr_2d, 3, axis=1)
print("\nColumn-wise split arrays:", split_col)

''' Inspect repeating elements of the 2D array '''
repeated_arr = np.repeat(arr_2d, 2, axis=0)
print("\nRepeated array along rows:\n", repeated_arr)

''' Inspect tiling the 2D array '''
tiled_arr = np.tile(arr_2d, (2, 1))
print("\nTiled array:\n", tiled_arr)

Output 4

Array Inspection: Program 5

Inspect 2D Array

import numpy as np

''' Create a 2D NumPy array '''
arr_2d = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])

''' Inspect converting the 2D array into a 3D array '''
arr_3d = arr_2d.reshape(3, 3, 1)
print("\nReshaped to 3D array:\n", arr_3d)

''' Inspect generating an identity matrix '''
identity_matrix = np.eye(3)
print("\nIdentity matrix:\n", identity_matrix)

''' Inspect where a condition is true in the 2D array '''
condition_indices = np.where(arr_2d > 5)
print("\nIndices where elements are greater than 5:", condition_indices)

''' Inspect masking elements based on a condition '''
masked_array = np.ma.masked_where(arr_2d > 5, arr_2d)
print("\nMasked array:\n", masked_array)

''' Inspect sorting each row of the 2D array '''
sorted_rows = np.sort(arr_2d, axis=1)
print("\nRows sorted individually:\n", sorted_rows)

''' Inspect sorting each column of the 2D array '''
sorted_cols = np.sort(arr_2d, axis=0)
print("\nColumns sorted individually:\n", sorted_cols)

''' Inspect finding the unique elements of the 2D array '''
unique_elements = np.unique(arr_2d)
print("\nUnique elements in the 2D array:", unique_elements)

''' Inspect counting the occurrences of each unique element in the 2D array '''
(unique_vals, counts) = np.unique(arr_2d, return_counts=True)
print("\nOccurrences of unique elements:", dict(zip(unique_vals, counts)))

''' Inspect changing the data type of the 2D array '''
arr_float = arr_2d.astype(float)
print("\n2D array with float data type:\n", arr_float)

''' Inspect clipping (limiting) the values of the 2D array '''
clipped_arr = np.clip(arr_2d, 3, 7)
print("\nClipped 2D array (values between 3 and 7):\n", clipped_arr)

Output 5

Array Inspection: Program 6

Complex Array

Introduction to Numpy

Array Inspection: Program 1

Output 1

Array Inspection: Program 2

Output 2

Array Inspection: Program 3

Output 3

Array Inspection: Program 4

Output 4

Array Inspection: Program 5

Output 5

Array Inspection: Program 6

Output 6