Enumerate_Position.py

# Enumeration is used in loops to return the position and the data in that position while looping
# Enumeration in deep learning: https://github.com/SciComp8/Python_Programming/blob/main/Artificial_Intelligence/AI_Medicine/Recurrent_Neural_Network.ipynb


list1 = ['cake1', 'cake2', 'cake3']
list2 = ['Vanilla Cake', 'Red Velvet Cake', 'Black Forest Cake']
full_list = list(zip(list1, list2))
for idx, item in enumerate(full_list):
    cake_idx, cake_name = item
    print(idx, cake_idx, cake_name)
    
# 0 cake1 Vanilla Cake
# 1 cake2 Red Velvet Cake
# 2 cake3 Black Forest Cake

for idx, item in enumerate(full_list):
    print(idx, item)

# 0 ('cake1', 'Vanilla Cake')
# 1 ('cake2', 'Red Velvet Cake')
# 2 ('cake3', 'Black Forest Cake')

# enumerate() is a function that takes any iterable as argument, and returns an enumerate object, which consists of pairs containing the elements of the original iterable, along with their index within the iterable. 
print(list(enumerate(range(100, 1001, 100))))
# [(0, 100), (1, 200), (2, 300), (3, 400), (4, 500), (5, 600), (6, 700), (7, 800), (8, 900), (9, 1000)]`

programming_language_list = ['Python', 'JavaScript', 'Julia', 'R', 'C']
for index, value in enumerate(programming_language_list):
    print(index, value)
# 0 Python
# 1 JavaScript
# 2 Julia
# 3 R
# 4 C

for index, value in enumerate(programming_language_list, start=1): # Change the starting index
    print(index, value)
# 1 Python
# 2 JavaScript
# 3 Julia
# 4 R
# 5 C

programming_language_enumerate = enumerate(programming_language_list)
print(list(programming_language_enumerate))
# [(0, 'Python'), (1, 'JavaScript'), (2, 'Julia'), (3, 'R'), (4, 'C')]

# Map categorical labels to numerical indices
atac_train = pd.read_parquet('../single_cell_data/atac_train.parquet', engine='pyarrow')
row_labels = df['id'].values
row_label_to_index = {label: index for index, label in enumerate(row_labels)}
type(row_label_to_index)
# <class 'dict'>