1_Array

1. Array

Ex00

zip() function is used to combine multiple iterables (like lists, tuples, etc.) into tuples. It takes in two or more iterables and returns an iterator that produces tuples where the i-th tuple contains the i-th element from each of the input iterables. It stops with the shortest list.

list1 = [1, 2, 3]
list2 = ['a', 'b', 'c']

zipped = zip(list1, list2)

for item in zipped:
    print(item)

Ex01

Numpy usage to load and manage data tables.

• Create numpy array (np.ndarray): np.array([[1, 2], [3, 4]]).
• Get numpy array shape: np_array.shape.
• Array dimensions: len(np_array.shape).
• Slicing: We pass slice instead of index like this: [start:end] or [start:end:step].
  • If we don't pass start its considered 0.
  • If we don't pass end its considered length of array in that dimension.
  • If we don't pass step its considered 1.
• Convert np array to python lists: ndarray.tolist().
• numpy.arange() returns an array with evenly spaced values within a specified range. np.arange(0, 10, 2) outputs [0 2 4 6 8].

Example:

np_array = np.array([[1, 2], [3, 4]])
print("NP Array:", np_array)
"""
NP Array:
[[1 2]
 [3 4]]

"""

np_shape = np_array.shape
print("Shape:", np_shape)
"""
Shape: (2, 2)
"""

np_dimension = len(np_array.shape)
print("Dimension:", np_dimension)
"""
Dimension: 2
"""

np_sliced_array = np_array[0:1]
print("Sliced Array:", np_sliced_array)
"""
Sliced Array: [[1 2]]
"""

list_array = np_sliced_array.tolist()
print("List Array:", list_array)
"""
List Array: [[1, 2]]
"""

Ex02

Pillow image usage to load and manage images.

• Image.open(path) loads image.
• img.format gets the loaded image format.

Ex03

matplotlib usage to show graphics. See graph examples. Most of the Matplotlib utilities lies under the pyplot submodule, and are usually imported under the plt alias: import matplotlib.pyplot as plt.

• plt.imshow(img) puts the image. Example in grayscale: plt.imshow(img, cmap="gray")
• plt.show() to show window.
• plt.xticks() and plt.yticks() to customize the tick locations and labels.

Ex04

Image rotation by transposing it. The transpose of a matrix is a new matrix whose rows are the columns of the original. It can be done iterating the matrix columns and rows or with zip(*<matrix>).

Ex05

More numpy and pillow.

• Image.fromarray(inverted_array).show() creates an image from numpy array and shows it.
• np.subtract(255, array) subtracts the input array from 255.
• np.stack([np.zeros_like(blue_array), np.zeros_like(blue_array), blue_array], axis=2) creates a new array by stacking three arrays, where the first two arrays are filled with zeros and the third array is the blue_array.
• np.mean(array, axis=2, keepdims=True) calculates the mean of array values of the axis 2. It keeps the original dims to avoid dimension errors.
• np.squeeze(grey_array, axis=2) removes single-dimensional entry from the shape of the grey_array along the specified axis.