Expert Guidance for MSc, BIT, BSc, HND Projects: PHP, Python Automation, Freelancing and ICT Tuition: Python SECTION 2 – Control Flow Conditionals

SECTION 2 – Control Flow (Weeks 2–3)

2.1 Conditionals – `if`, `elif`, `else`

📚 What: Conditionals let your program make decisions.
🧠 AI/ML Relevance:

Check accuracy thresholds before deployment
Decide model tuning steps based on loss value
Perform different preprocessing for text vs image datasets

💻 Example: Accuracy Evaluation

accuracy = 0.88

if accuracy > 0.9:
    print("Excellent model")
elif accuracy > 0.8:
    print("Good model, but can improve")
else:
    print("Needs more training")

Nested Conditions

📚 What: Condition inside another condition.
🧠 AI/ML Relevance:

Check multiple model performance metrics
Apply rules for different dataset conditions

💻 Example: Accuracy + Loss Check

accuracy = 0.92
loss = 0.15

if accuracy > 0.9:
    if loss < 0.2:
        print("Ready for deployment")
    else:
        print("Accuracy is fine, but reduce loss")

🏋 Exercise:
Write a conditional that:

Checks if accuracy > 0.85
If true, checks if loss < 0.15 → print "Excellent"
Else print "Needs work"

2.2 Loops

For Loops

📚 What: Repeat code for a fixed number of iterations or over a collection.
🧠 AI/ML Relevance:

Iterate over datasets
Process batches in training
Apply transformations to every data point

💻 Example: Processing Dataset Batches

batches = ["Batch 1", "Batch 2", "Batch 3"]

for batch in batches:
    print(f"Processing {batch}")

While Loops

📚 What: Repeat until a condition becomes false.
🧠 AI/ML Relevance:

Continue training until loss is below a threshold
Keep collecting data until dataset size is reached

💻 Example: Training Until Loss Threshold

loss = 1.0
while loss > 0.1:
    print(f"Training... current loss = {loss}")
    loss -= 0.2
print("Training stopped: Loss threshold met")

Break & Continue

📚 What:

break → Stop the loop early
continue → Skip the rest of the current iteration

💻 Example: Stop Training Early

losses = [0.5, 0.3, 0.15, 0.09]

for loss in losses:
    if loss < 0.1:
        print("Loss is low enough, stopping early")
        break
    print(f"Current loss: {loss}")

💻 Example: Skip Bad Data

data_points = [1, None, 3, None, 5]

for point in data_points:
    if point is None:
        continue
    print(f"Processing: {point}")

range()

📚 What: Generates a sequence of numbers for looping.

💻 Example: 5 Epochs Training

for epoch in range(1, 6):
    print(f"Training Epoch {epoch}")

Loop Over Iterables

📚 What: Loop directly over lists, dicts, sets, tuples.
💻 Example: Looping Over Dictionary of Model Parameters

params = {"learning_rate": 0.001, "batch_size": 32, "epochs": 10}

for key, value in params.items():
    print(f"{key}: {value}")

🏋 Exercise:
Loop over a dictionary containing "model", "accuracy", and "loss" and print them in key: value format.

2.3 Comprehensions

📚 What: Shorter, cleaner way to create lists, sets, dicts from loops.
🧠 AI/ML Relevance: Quickly transform datasets without writing long loops.

List Comprehension

💻 Example: Squaring All Loss Values

losses = [0.5, 0.3, 0.15]
squared_losses = [loss**2 for loss in losses]
print(squared_losses)

Tuple Comprehension (Actually Generators in Python)

💻 Example: Generate Accuracy Values on the Fly

accuracies = (round(acc, 2) for acc in [0.82, 0.91, 0.88])
for acc in accuracies:
    print(acc)

Dict Comprehension

💻 Example: Mapping Model Names to Accuracies

models = ["Model_A", "Model_B", "Model_C"]
accs = [0.85, 0.90, 0.88]

model_performance = {model: acc for model, acc in zip(models, accs)}
print(model_performance)

Set Comprehension

💻 Example: Unique Label Extraction

labels = ["cat", "dog", "cat", "bird", "dog"]
unique_labels = {label for label in labels}
print(unique_labels)

🏋 Final Exercise for Section 2:
Given:

models = ["A", "B", "C"]
accuracies = [0.9, 0.85, 0.92]

Create a dictionary mapping each model to its accuracy (dict comprehension)
Create a list of only the accuracies above 0.88 (list comprehension)
Loop over the dictionary and print "Model X has accuracy Y"

Python dictionary methods in action.

# Create a dictionary for months
months = {
    "January": 31,
    "February": 28,
    "March": 31,
    "April": 30,
    "May": 31,
    "June": 30
}

print("Original dictionary:", months)

1. Access values

print("Days in February:", months["February"])
print("Days in May (get method):", months.get("May"))
print("Non-existing month with default:", months.get("December", "Not Found"))

2. Add or update

months["July"] = 31  # Add
months["February"] = 29  # Update (Leap year)
print("After add/update:", months)

3. Remove — `pop()`

removed_days = months.pop("June")
print("Removed June (days =", removed_days, "):", months)

4. Remove last item — `popitem()`

last_item = months.popitem()
print("Removed last item:", last_item)
print("After popitem:", months)

5. Delete — `del`

del months["April"]
print("After deleting April:", months)

6. Clear dictionary

temp_months = months.copy()
temp_months.clear()
print("After clear:", temp_months)

7. Copy dictionary

months_copy = months.copy()
print("Copied dictionary:", months_copy)

8. Keys, Values, Items

print("Keys:", months.keys())
print("Values:", months.values())
print("Items:", months.items())

9. Loop through dictionary

for month, days in months.items():
    print(f"{month}: {days} days")

10. Check membership

print("Is 'March' in months?", "March" in months)
print("Is 'December' not in months?", "December" not in months)

11. Update with another dictionary

months.update({"August": 31, "September": 30})
print("After update:", months)

12. Dictionary comprehension

month_lengths = {month: len(month) for month in months}
print("Month name lengths:", month_lengths)

Tuple example similar to the list and set ones.
Tuples are ordered, immutable collections — so you can’t change items after creation (no append(), remove(), etc.), but you can still use many useful built-in functions.

# Create a tuple of fruits
fruits = ("apple", "banana", "cherry", "date", "elderberry", "banana")

print("Original tuple:", fruits)

1. Access elements by index

print("First fruit:", fruits[0])
print("Last fruit:", fruits[-1])

2. Slicing

print("First 3 fruits:", fruits[:3])
print("Fruits from index 2 onwards:", fruits[2:])

3. Count — `count()`

print("Count of 'banana':", fruits.count("banana"))

Counts occurrences of a value.

4. Index — `index()`

print("Index of 'cherry':", fruits.index("cherry"))

Finds the first occurrence of a value.

5. Length — `len()`

print("Number of fruits:", len(fruits))

6. Membership test

print("Is 'apple' in tuple?", "apple" in fruits)
print("Is 'mango' not in tuple?", "mango" not in fruits)

7. Concatenation

more_fruits = ("fig", "grape")
combined = fruits + more_fruits
print("Combined tuple:", combined)

8. Repetition

repeat_fruits = ("apple", "banana") * 2
print("Repeated tuple:", repeat_fruits)

9. Min / Max / Sum (works only if all items are same type)

numbers = (5, 2, 9, 1)
print("Min number:", min(numbers))
print("Max number:", max(numbers))
print("Sum:", sum(numbers))

10. Tuple unpacking

a, b, c, *rest = fruits
print("a:", a)
print("b:", b)
print("c:", c)
print("rest:", rest)

11. Convert to list (to modify) and back

temp_list = list(fruits)
temp_list.append("honeydew")
fruits = tuple(temp_list)
print("Modified tuple:", fruits)

Python set functions/methods with examples.
Remember: a set is unordered, does not allow duplicates, and elements must be immutable.

# Create a set of fruits
fruits = {"apple", "banana", "cherry", "date", "elderberry"}

print("Original set:", fruits)

1. Add — `add()`

fruits.add("fig")
print("After add:", fruits)

Adds a single item.

2. Update — `update()`

fruits.update(["grape", "honeydew"])
print("After update:", fruits)

Adds multiple items.

3. Remove — `remove()`

fruits.remove("banana")
print("After remove:", fruits)

Removes an item; error if not found.

4. Discard — `discard()`

fruits.discard("kiwi")  # No error if not found
print("After discard (kiwi):", fruits)

Removes item without error.

5. Pop — `pop()`

removed = fruits.pop()
print("Popped item:", removed)
print("After pop:", fruits)

Removes a random item (since sets are unordered).

6. Clear — `clear()`

temp_set = fruits.copy()
temp_set.clear()
print("After clear:", temp_set)

Removes all items.

7. Copy — `copy()`

fruits_copy = fruits.copy()
print("Copied set:", fruits_copy)

Shallow copy.

8. Union — `union()` / `|`

set_a = {"apple", "banana", "mango"}
set_b = {"banana", "kiwi", "orange"}
print("Union:", set_a.union(set_b))
print("Union (|):", set_a | set_b)

Combines sets, removes duplicates.

9. Intersection — `intersection()` / `&`

print("Intersection:", set_a.intersection(set_b))
print("Intersection (&):", set_a & set_b)

Items common to both sets.

10. Difference — `difference()` / `-`

print("Difference:", set_a.difference(set_b))
print("Difference (-):", set_a - set_b)

Items in the first set but not the second.

11. Symmetric Difference — `symmetric_difference()` / `^`

print("Symmetric Difference:", set_a.symmetric_difference(set_b))
print("Symmetric Difference (^):", set_a ^ set_b)

Items in either set but not both.

12. isdisjoint()

print("Is disjoint?:", {"pineapple"} .isdisjoint({"mango"}))

True if no elements in common.

13. issubset() / issuperset()

print("Is subset?:", {"apple", "banana"}.issubset(set_a))
print("Is superset?:", set_a.issuperset({"banana"}))

14. len()

print("Number of fruits:", len(fruits))

Counts items in the set.

Python list function / method with examples so you have a ready reference.

# Create a list of fruits
fruits = ["apple", "banana", "cherry", "date", "elderberry"]

print("Original list:", fruits)

1. Append — `append()`

fruits.append("fig")
print("After append:", fruits)

Adds an item to the end.

2. Insert — `insert()`

fruits.insert(2, "blueberry")
print("After insert:", fruits)

Inserts at a specific index.

3. Extend — `extend()`

fruits.extend(["grape", "honeydew"])
print("After extend:", fruits)

Adds multiple items.

4. Remove — `remove()`

fruits.remove("banana")
print("After remove:", fruits)

Removes first occurrence of a value.

5. Pop — `pop()`

popped = fruits.pop(3)
print("Popped item:", popped)
print("After pop:", fruits)

Removes by index (last item if no index given).

6. Clear — `clear()`

temp_list = fruits.copy()
temp_list.clear()
print("After clear:", temp_list)

Removes all items.

7. Index — `index()`

print("Index of cherry:", fruits.index("cherry"))

Finds the first occurrence of a value.

8. Count — `count()`

print("Count of 'apple':", fruits.count("apple"))

Counts how many times a value appears.

9. Sort — `sort()`

fruits.sort()
print("Sorted list:", fruits)

fruits.sort(reverse=True)
print("Reverse sorted list:", fruits)

Sorts in place (alphabetically by default).

10. Reverse — `reverse()`

fruits.reverse()
print("After reverse:", fruits)

Reverses order.

11. Copy — `copy()`

new_fruits = fruits.copy()
print("Copied list:", new_fruits)

Shallow copy of the list.

12. Slicing

print("First 3 fruits:", fruits[:3])
print("Last 2 fruits:", fruits[-2:])

Access sublists.

13. List Comprehension

long_names = [f for f in fruits if len(f) > 5]
print("Fruits with names longer than 5 letters:", long_names)

Create lists with conditions.

14. Length — `len()`

print("Number of fruits:", len(fruits))

Gets number of items.

15. Min / Max

print("Min fruit (alphabetically):", min(fruits))
print("Max fruit (alphabetically):", max(fruits))

import sys

# Join all arguments into one string

full_text = " ".join(sys.argv[1:])

# Example: full_text = "Arguments passed is <123> from command"

# Extract the part between < and >

start = full_text.find("<") + 1

end = full_text.find(">")

number = full_text[start:end]

print(number)

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Friday, August 15, 2025

Python SECTION 2 – Control Flow Conditionals – if, elif, else Loops Comprehensions

SECTION 2 – Control Flow (Weeks 2–3)

2.1 Conditionals – if, elif, else

Nested Conditions

2.2 Loops

For Loops

While Loops

Break & Continue

range()

Loop Over Iterables

2.3 Comprehensions

List Comprehension

Tuple Comprehension (Actually Generators in Python)

Dict Comprehension

Set Comprehension

1. Access values

2. Add or update

3. Remove — pop()

4. Remove last item — popitem()

5. Delete — del

6. Clear dictionary

7. Copy dictionary

8. Keys, Values, Items

9. Loop through dictionary

10. Check membership

11. Update with another dictionary

12. Dictionary comprehension

1. Access elements by index

2. Slicing

3. Count — count()

4. Index — index()

5. Length — len()

6. Membership test

7. Concatenation

8. Repetition

9. Min / Max / Sum (works only if all items are same type)

10. Tuple unpacking

11. Convert to list (to modify) and back

1. Add — add()

2. Update — update()

3. Remove — remove()

4. Discard — discard()

5. Pop — pop()

6. Clear — clear()

7. Copy — copy()

8. Union — union() / |

9. Intersection — intersection() / &

10. Difference — difference() / -

11. Symmetric Difference — symmetric_difference() / ^

12. isdisjoint()

13. issubset() / issuperset()

14. len()

1. Append — append()

2. Insert — insert()

3. Extend — extend()

4. Remove — remove()

5. Pop — pop()

6. Clear — clear()

7. Index — index()

8. Count — count()

9. Sort — sort()

10. Reverse — reverse()

11. Copy — copy()

12. Slicing

13. List Comprehension

14. Length — len()

15. Min / Max

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2.1 Conditionals – `if`, `elif`, `else`

3. Remove — `pop()`

4. Remove last item — `popitem()`

5. Delete — `del`

3. Count — `count()`

4. Index — `index()`

5. Length — `len()`

1. Add — `add()`

2. Update — `update()`

3. Remove — `remove()`

4. Discard — `discard()`

5. Pop — `pop()`

6. Clear — `clear()`

7. Copy — `copy()`

8. Union — `union()` / `|`

9. Intersection — `intersection()` / `&`

10. Difference — `difference()` / `-`

11. Symmetric Difference — `symmetric_difference()` / `^`

1. Append — `append()`

2. Insert — `insert()`

3. Extend — `extend()`

4. Remove — `remove()`

5. Pop — `pop()`

6. Clear — `clear()`

7. Index — `index()`

8. Count — `count()`

9. Sort — `sort()`

10. Reverse — `reverse()`

11. Copy — `copy()`

14. Length — `len()`