Setting Up Dataframe.py

# Importing Data -->
digits = load_digits()

# Display Number Of Images -->
print("Image data shape: ", digits.data.shape)
print()
print("Label data shape: ", digits.target.shape)

# Show images -->
plt.figure(figsize=(20,8))
for index, (image, label) in enumerate(zip(digits.data[0:16], digits.target[0:16])):
    plt.subplot(2,8, index+1)
    plt.imshow(np.reshape(image,(8,8)), cmap=plt.cm.gray)
    plt.title('Labeled %i\n' % label, fontsize = 20)

'''
1. create a blank fig.
2. run a loop calling the first 16 entries in digits.data, digits.target as "image" and "label" respectively.
3. create a subplot inside the blank figure of 2 rows and 8 cols.
4. place images in them (8,8) long and gray.
Algo credit: 'https://towardsdatascience.com/logistic-regression-using-python-sklearn-numpy-mnist-handwriting-recognition-matplotlib-a6b31e2b166a'
'''