Project: Data Filtering with Comprehensions0%

Project: Data Filtering with Comprehensions

Intermediate16 min read•Updated: 2026-10-01

Project: Data Filtering with Comprehensions

Welcome to the Chapter 1 Capstone Project! In the preceding lessons, you mastered advanced list comprehensions, set comprehensions, dictionary comprehensions, and nested multi-dimensional structures.

Now, you will combine all these comprehension paradigms to build a production-grade Domestic Flight Booking Intelligence Engine. You will filter raw aviation manifests, deduplicate routes, aggregate airline pricing, and compute route matrices in declarative, high-speed Python.

Real-World Analogy: MakeMyTrip Flight Search Engine

When a traveler searches for flights between Delhi and Bengaluru on a booking aggregator like MakeMyTrip or Ixigo:

Visual Architecture Blueprint

+-------------------------------------------------------------------------+
|              FLIGHT SEARCH & INTELLIGENCE ENGINE PIPELINE               |
+-------------------------------------------------------------------------+
|                                                                         |
|  Raw Flight Feed (10,000+ flights from DGCA / Airport Radar)            |
|                              │                                          |
|                              ▼                                          |
|  1. Set Comprehension        ──> Extract Unique Airlines & Destination  |
|                                  Hubs for Dropdown Filter Menus         |
|                              │                                          |
|                              ▼                                          |
|  2. List Comprehension       ──> Filter flights by User Budget (₹),     |
|     with Walrus (:=)             Non-Stop status & Departure Window     |
|                              │                                          |
|                              ▼                                          |
|  3. Dict Comprehension       ──> Group Filtered Flights by Airline &    |
|                                  Compute Lowest Fare & Average Price    |
|                              │                                          |
|                              ▼                                          |
|  4. Nested Comprehension     ──> Generate City-to-City Route Availability|
|                                  Matrix for Quick Comparison            |
|                                                                         |
+-------------------------------------------------------------------------+

Every stage of this data pipeline can be executed concisely and expressively using Python's comprehension toolkit.

Project Specification & Raw Dataset

The input data represents flight records received from domestic air traffic coordination:

1flight_manifest = [
  {"flight": "6E-204", "airline": "IndiGo", "origin": "DEL", "dest": "BOM", "price": 4800, "duration": 130, "stops": 0, "dep_hour": 6},
  {"flight": "AI-802", "airline": "Air India", "origin": "DEL", "dest": "BOM", "price": 6200, "duration": 140, "stops": 0, "dep_hour": 8},
  {"flight": "UK-955", "airline": "Vistara", "origin": "DEL", "dest": "BOM", "price": 7500, "duration": 135, "stops": 0, "dep_hour": 18},
  {"flight": "SG-123", "airline": "SpiceJet", "origin": "DEL", "dest": "BOM", "price": 3900, "duration": 210, "stops": 1, "dep_hour": 14},
  {"flight": "6E-551", "airline": "IndiGo", "origin": "DEL", "dest": "BLR", "price": 5400, "duration": 165, "stops": 0, "dep_hour": 9},
  {"flight": "AI-504", "airline": "Air India", "origin": "DEL", "dest": "BLR", "price": 6800, "duration": 170, "stops": 0, "dep_hour": 20},
  {"flight": "UK-812", "airline": "Vistara", "origin": "DEL", "dest": "BLR", "price": 8200, "duration": 160, "stops": 0, "dep_hour": 17},
  {"flight": "6E-901", "airline": "IndiGo", "origin": "BOM", "dest": "BLR", "price": 4200, "duration": 105, "stops": 0, "dep_hour": 11},
  {"flight": "SG-442", "airline": "SpiceJet", "origin": "BOM", "dest": "BLR", "price": 3600, "duration": 115, "stops": 0, "dep_hour": 22},
  {"flight": "AI-631", "airline": "Air India", "origin": "BOM", "dest": "DEL", "price": 5900, "duration": 145, "stops": 0, "dep_hour": 15},
  {"flight": "6E-718", "airline": "IndiGo", "origin": "BOM", "dest": "DEL", "price": 4900, "duration": 135, "stops": 0, "dep_hour": 19}
13]

Complete Production-Grade Implementation

Here is the modular, fully runnable Flight Intelligence Engine:

1"""
2MSK Python Capstone: Domestic Flight Search & Intelligence Engine
3Author: MSK Institute
4"""
5 
6# Raw Aviation Data Feed
7FLIGHT_MANIFEST = [
  {"flight": "6E-204", "airline": "IndiGo", "origin": "DEL", "dest": "BOM", "price": 4800, "duration": 130, "stops": 0, "dep_hour": 6},
  {"flight": "AI-802", "airline": "Air India", "origin": "DEL", "dest": "BOM", "price": 6200, "duration": 140, "stops": 0, "dep_hour": 8},
  {"flight": "UK-955", "airline": "Vistara", "origin": "DEL", "dest": "BOM", "price": 7500, "duration": 135, "stops": 0, "dep_hour": 18},
  {"flight": "SG-123", "airline": "SpiceJet", "origin": "DEL", "dest": "BOM", "price": 3900, "duration": 210, "stops": 1, "dep_hour": 14},
  {"flight": "6E-551", "airline": "IndiGo", "origin": "DEL", "dest": "BLR", "price": 5400, "duration": 165, "stops": 0, "dep_hour": 9},
  {"flight": "AI-504", "airline": "Air India", "origin": "DEL", "dest": "BLR", "price": 6800, "duration": 170, "stops": 0, "dep_hour": 20},
  {"flight": "UK-812", "airline": "Vistara", "origin": "DEL", "dest": "BLR", "price": 8200, "duration": 160, "stops": 0, "dep_hour": 17},
  {"flight": "6E-901", "airline": "IndiGo", "origin": "BOM", "dest": "BLR", "price": 4200, "duration": 105, "stops": 0, "dep_hour": 11},
  {"flight": "SG-442", "airline": "SpiceJet", "origin": "BOM", "dest": "BLR", "price": 3600, "duration": 115, "stops": 0, "dep_hour": 22},
  {"flight": "AI-631", "airline": "Air India", "origin": "BOM", "dest": "DEL", "price": 5900, "duration": 145, "stops": 0, "dep_hour": 15},
  {"flight": "6E-718", "airline": "IndiGo", "origin": "BOM", "dest": "DEL", "price": 4900, "duration": 135, "stops": 0, "dep_hour": 19}
19]
20 
21# -------------------------------------------------------------
22# Module 1: Extract Unique Hubs & Airlines (Set Comprehensions)
23# -------------------------------------------------------------
24def get_unique_network_metadata(manifest):
  """Uses set comprehensions to extract unique operational hubs and airlines."""
  origins = {f["origin"] for f in manifest}
  destinations = {f["dest"] for f in manifest}
  all_airports = origins | destinations  # Mathematical set union!
  airlines = {f["airline"] for f in manifest}
  return sorted(all_airports), sorted(airlines)
31 
32# -------------------------------------------------------------
33# Module 2: User Search Query Filter (Advanced List Comprehension)
34# -------------------------------------------------------------
35def search_flights(manifest, origin, dest, max_budget=7000, non_stop_only=True):
  """Filters flights matching route, budget, and non-stop criteria."""
  # List comprehension: applies multi-predicate filters and formats display record
  matches = [
      {
          "flight_no": f["flight"],
          "carrier": f["airline"],
          "route": f"{f['origin']} ➔ {f['dest']}",
          "departure": f"{f['dep_hour']:02d}:00 hrs",
          "duration": f"{f['duration'] // 60}h {f['duration'] % 60}m",
          "total_fare": f["price"] + 450,  # Adding ₹450 airport passenger service fee
          "type": "Non-Stop" if f["stops"] == 0 else f"{f['stops']} Stop(s)"
      }
      for f in manifest
      if f["origin"] == origin
      if f["dest"] == dest
      if f["price"] <= max_budget
      if (not non_stop_only or f["stops"] == 0)
  ]
  return matches
55 
56# -------------------------------------------------------------
57# Module 3: Airline Price Analytics (Dictionary Comprehension)
58# -------------------------------------------------------------
59def compute_airline_summary(manifest, origin, dest):
  """Uses dict comprehensions to compute lowest fare and average price per airline."""
  # Step A: Filter flights for this route
  route_flights = [f for f in manifest if f["origin"] == origin and f["dest"] == dest]
  
  # Step B: Get unique airlines on this route via set comprehension
  operating_airlines = {f["airline"] for f in route_flights}
  
  # Step C: Dict comprehension computing min fare per airline
  cheapest_by_airline = {
      airline: min(f["price"] for f in route_flights if f["airline"] == airline)
      for airline in operating_airlines
  }
  
  # Step D: Dict comprehension computing average fare per airline
  avg_by_airline = {
      airline: round(
          sum(f["price"] for f in route_flights if f["airline"] == airline) / 
          len([f for f in route_flights if f["airline"] == airline]), 
          2
      )
      for airline in operating_airlines
  }
  
  return cheapest_by_airline, avg_by_airline
84 
85# -------------------------------------------------------------
86# Module 4: Route Frequency Matrix (Nested Comprehension)
87# -------------------------------------------------------------
88def build_route_matrix(manifest, hubs):
  """Constructs a 2D matrix counting flights between all airport pairs."""
  # Nested comprehension: Outer loop builds origin rows, inner loop counts flights to dest
  matrix = [
      [
          len([f for f in manifest if f["origin"] == orig and f["dest"] == dst])
          if orig != dst else "-"
          for dst in hubs
      ]
      for orig in hubs
  ]
  return matrix
100 
101 
102# --- Execution & Verification ---
103if __name__ == "__main__":
  hubs, airlines = get_unique_network_metadata(FLIGHT_MANIFEST)
  print("=== 1. Aviation Network Metadata (Set Comprehensions) ===")
  print("Active Airport Hubs :", hubs)
  print("Operating Carriers  :", airlines)
108 
  print("\n=== 2. Flight Search: DEL to BOM (Budget <= ₹7,000, Non-Stop) ===")
  search_results = search_flights(FLIGHT_MANIFEST, origin="DEL", dest="BOM", max_budget=7000, non_stop_only=True)
  
  print(f"{'FLIGHT':<10} | {'AIRLINE':<12} | {'ROUTE':<12} | {'DEP TIME':<12} | {'DURATION':<8} | {'FINAL FARE'}")
  print("-" * 75)
  for r in search_results:
      print(f"{r['flight_no']:<10} | {r['carrier']:<12} | {r['route']:<12} | {r['departure']:<12} | {r['duration']:<8} | ₹{r['total_fare']}")
116 
  print("\n=== 3. Carrier Price Intelligence for DEL ➔ BOM (Dict Comprehensions) ===")
  min_fares, avg_fares = compute_airline_summary(FLIGHT_MANIFEST, "DEL", "BOM")
  for carrier in sorted(min_fares.keys()):
      print(f"  {carrier:<12} : Starting at ₹{min_fares[carrier]:<5} | Route Avg: ₹{avg_fares[carrier]:,.2f}")
121 
  print("\n=== 4. Hub-to-Hub Flight Frequency Matrix (Nested Comprehensions) ===")
  matrix_hubs = ["DEL", "BOM", "BLR"]
  matrix = build_route_matrix(FLIGHT_MANIFEST, matrix_hubs)
  
  # Print formatted matrix table
  header = f"{'ORIGIN':<8} | " + " | ".join(f"{h:^5}" for h in matrix_hubs)
  print(header)
  print("-" * len(header))
  for hub, row in zip(matrix_hubs, matrix):
      row_str = " | ".join(f"{str(cell):^5}" for cell in row)
      print(f"{hub:<8} | {row_str}")

Expected Output

1=== 1. Aviation Network Metadata (Set Comprehensions) ===
2Active Airport Hubs : ['BLR', 'BOM', 'DEL']
3Operating Carriers  : ['Air India', 'IndiGo', 'SpiceJet', 'Vistara']
4 
5=== 2. Flight Search: DEL to BOM (Budget <= ₹7,000, Non-Stop) ===
6FLIGHT     | AIRLINE      | ROUTE        | DEP TIME     | DURATION | FINAL FARE
7---------------------------------------------------------------------------
86E-204     | IndiGo       | DEL ➔ BOM    | 06:00 hrs    | 2h 10m   | ₹5250
9AI-802     | Air India    | DEL ➔ BOM    | 08:00 hrs    | 2h 20m   | ₹6650
10 
11=== 3. Carrier Price Intelligence for DEL ➔ BOM (Dict Comprehensions) ===
12  Air India    : Starting at ₹6200 | Route Avg: ₹6,200.00
13  IndiGo       : Starting at ₹4800 | Route Avg: ₹4,800.00
14  SpiceJet     : Starting at ₹3900 | Route Avg: ₹3,900.00
15  Vistara      : Starting at ₹7500 | Route Avg: ₹7,500.00
16 
17=== 4. Hub-to-Hub Flight Frequency Matrix (Nested Comprehensions) ===
18ORIGIN   |  DEL  |  BOM  |  BLR 
19--------------------------------
20DEL      |   -   |   4   |   3  
21BOM      |   2   |   -   |   2  
22BLR      |   0   |   0   |   -  

Best Practices & Comparison: Do's and Don'ts

Practice	Bad Implementation	Gold-Standard Implementation
Deduplication	Looping over items and checking `if city not in list`	Set comprehension: `{f['origin'] for f in manifest}`
Price Filtering	Manual accumulator list with multiple nested `if`s	Single multi-condition list comprehension
Aggregations	Iterating 10 times to find minimums	Dict comprehension with `min()` and `sum()` generators
Matrix Setup	Manual nested loops appending row by row	`[[expr for col in cols] for row in rows]`
Readability	Giant single-line expressions without formatting	Format comprehensions with clear vertical indentation

Quick Revision Summary Cheat Sheet

Set Comprehensions: Deduplicate unique identifiers (airports, airlines, user IDs) in $O(n)$ time.
List Comprehensions: Filter and transform search results with multi-predicate conditions (if f['origin'] == origin if f['price'] <= budget).
Dictionary Comprehensions: Compute group-level summaries and analytical indexes ({k: min(...) for k in unique_keys}).
Nested Comprehensions: Generate cross-tabulation and adjacency matrices ([[count for dst in hubs] for orig in hubs]).

Practice Challenge

Scenario: Red-Eye Night Flight Discount Filter

Airlines offer promotional discounts on late-night Red-Eye flights (flights departing between 21:00 (9 PM) and 05:00 (5 AM)):

1
Write a list comprehension that filters only Red-Eye flights from FLIGHT_MANIFEST (f["dep_hour"] >= 21 or f["dep_hour"] <= 5).
2
Apply a 20% promotional discount to the base price: price * 0.80.
3
Format each output record as: "[DISCOUNTED] <FlightNo> (<Airline>) - New Price: ₹<Price> (Dep: <Hour>:00 hrs)".

Starter Code

1# Using FLIGHT_MANIFEST from the main lesson
2# TODO: Filter red-eye flights with 20% discount using list comprehension

Complete Solution

1red_eye_deals = [
2    f"[DISCOUNTED] {f['flight']} ({f['airline']}) - New Price: ₹{f['price'] * 0.80:.0f} (Dep: {f['dep_hour']:02d}:00 hrs)"
3    for f in FLIGHT_MANIFEST
4    if f["dep_hour"] >= 21 or f["dep_hour"] <= 5
5]
6 
7print("=== Red-Eye Promotional Deals (20% Off) ===")
8for deal in red_eye_deals:
9    print(deal)

Expected Output

1=== Red-Eye Promotional Deals (20% Off) ===
2[DISCOUNTED] SG-442 (SpiceJet) - New Price: ₹2880 (Dep: 22:00 hrs)

Interactive Quiz• Knowledge Check

Practice Quiz

Question 1

In the flight search engine, why is a set comprehension chosen to extract airport codes from the raw manifest?

Question 2

How does the operator | function between two sets: origins | destinations?

Question 3

What does the expression min(f["price"] for f in route_flights if f["airline"] == airline) inside the dictionary comprehension compute?

Question 4

In the route matrix comprehension [[len(...) for dst in hubs] for orig in hubs], what does the outer loop represent?

Question 5

Why is {f["flight_no"]: f for f in flights} a valid dictionary comprehension?

🚀 What's Next?

In the next lesson, we will continue your intermediate Python journey with *Arguments Recap (args, kwargs) (2: Functions Deep Dive).

👉 *[Continue to Next Lesson: Arguments Recap (args, kwargs) →](/tutorials/python-for-intermediate/arguments-recap-args-kwargs)

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Arguments Recap (*args, **kwargs)

Continue learning with hands-on practice, examples, and exercises in the upcoming topic.

Related Lessons

Previous Lesson	Next Lesson
Advanced Structural Pattern Matching with Guards and Class Patterns	Arguments Recap (args, *kwargs)

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Test your understanding of this lesson with 5 questions. Each question has one correct answer.

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Arguments Recap (*args, **kwargs)

Project: Data Filtering with Comprehensions

Intermediate16 min read•Updated: 2026-10-01

Study Materials Cheatsheet

Project: Data Filtering with Comprehensions

Welcome to the Chapter 1 Capstone Project! In the preceding lessons, you mastered advanced list comprehensions, set comprehensions, dictionary comprehensions, and nested multi-dimensional structures.

Real-World Analogy: MakeMyTrip Flight Search Engine

When a traveler searches for flights between Delhi and Bengaluru on a booking aggregator like MakeMyTrip or Ixigo:

Visual Architecture Blueprint

+-------------------------------------------------------------------------+
|              FLIGHT SEARCH & INTELLIGENCE ENGINE PIPELINE               |
+-------------------------------------------------------------------------+
|                                                                         |
|  Raw Flight Feed (10,000+ flights from DGCA / Airport Radar)            |
|                              │                                          |
|                              ▼                                          |
|  1. Set Comprehension        ──> Extract Unique Airlines & Destination  |
|                                  Hubs for Dropdown Filter Menus         |
|                              │                                          |
|                              ▼                                          |
|  2. List Comprehension       ──> Filter flights by User Budget (₹),     |
|     with Walrus (:=)             Non-Stop status & Departure Window     |
|                              │                                          |
|                              ▼                                          |
|  3. Dict Comprehension       ──> Group Filtered Flights by Airline &    |
|                                  Compute Lowest Fare & Average Price    |
|                              │                                          |
|                              ▼                                          |
|  4. Nested Comprehension     ──> Generate City-to-City Route Availability|
|                                  Matrix for Quick Comparison            |
|                                                                         |
+-------------------------------------------------------------------------+

Every stage of this data pipeline can be executed concisely and expressively using Python's comprehension toolkit.

Project Specification & Raw Dataset

The input data represents flight records received from domestic air traffic coordination:

1flight_manifest = [
  {"flight": "6E-204", "airline": "IndiGo", "origin": "DEL", "dest": "BOM", "price": 4800, "duration": 130, "stops": 0, "dep_hour": 6},
  {"flight": "AI-802", "airline": "Air India", "origin": "DEL", "dest": "BOM", "price": 6200, "duration": 140, "stops": 0, "dep_hour": 8},
  {"flight": "UK-955", "airline": "Vistara", "origin": "DEL", "dest": "BOM", "price": 7500, "duration": 135, "stops": 0, "dep_hour": 18},
  {"flight": "SG-123", "airline": "SpiceJet", "origin": "DEL", "dest": "BOM", "price": 3900, "duration": 210, "stops": 1, "dep_hour": 14},
  {"flight": "6E-551", "airline": "IndiGo", "origin": "DEL", "dest": "BLR", "price": 5400, "duration": 165, "stops": 0, "dep_hour": 9},
  {"flight": "AI-504", "airline": "Air India", "origin": "DEL", "dest": "BLR", "price": 6800, "duration": 170, "stops": 0, "dep_hour": 20},
  {"flight": "UK-812", "airline": "Vistara", "origin": "DEL", "dest": "BLR", "price": 8200, "duration": 160, "stops": 0, "dep_hour": 17},
  {"flight": "6E-901", "airline": "IndiGo", "origin": "BOM", "dest": "BLR", "price": 4200, "duration": 105, "stops": 0, "dep_hour": 11},
  {"flight": "SG-442", "airline": "SpiceJet", "origin": "BOM", "dest": "BLR", "price": 3600, "duration": 115, "stops": 0, "dep_hour": 22},
  {"flight": "AI-631", "airline": "Air India", "origin": "BOM", "dest": "DEL", "price": 5900, "duration": 145, "stops": 0, "dep_hour": 15},
  {"flight": "6E-718", "airline": "IndiGo", "origin": "BOM", "dest": "DEL", "price": 4900, "duration": 135, "stops": 0, "dep_hour": 19}
13]

Complete Production-Grade Implementation

Here is the modular, fully runnable Flight Intelligence Engine:

1"""
2MSK Python Capstone: Domestic Flight Search & Intelligence Engine
3Author: MSK Institute
4"""
5 
6# Raw Aviation Data Feed
7FLIGHT_MANIFEST = [
  {"flight": "6E-204", "airline": "IndiGo", "origin": "DEL", "dest": "BOM", "price": 4800, "duration": 130, "stops": 0, "dep_hour": 6},
  {"flight": "AI-802", "airline": "Air India", "origin": "DEL", "dest": "BOM", "price": 6200, "duration": 140, "stops": 0, "dep_hour": 8},
  {"flight": "UK-955", "airline": "Vistara", "origin": "DEL", "dest": "BOM", "price": 7500, "duration": 135, "stops": 0, "dep_hour": 18},
  {"flight": "SG-123", "airline": "SpiceJet", "origin": "DEL", "dest": "BOM", "price": 3900, "duration": 210, "stops": 1, "dep_hour": 14},
  {"flight": "6E-551", "airline": "IndiGo", "origin": "DEL", "dest": "BLR", "price": 5400, "duration": 165, "stops": 0, "dep_hour": 9},
  {"flight": "AI-504", "airline": "Air India", "origin": "DEL", "dest": "BLR", "price": 6800, "duration": 170, "stops": 0, "dep_hour": 20},
  {"flight": "UK-812", "airline": "Vistara", "origin": "DEL", "dest": "BLR", "price": 8200, "duration": 160, "stops": 0, "dep_hour": 17},
  {"flight": "6E-901", "airline": "IndiGo", "origin": "BOM", "dest": "BLR", "price": 4200, "duration": 105, "stops": 0, "dep_hour": 11},
  {"flight": "SG-442", "airline": "SpiceJet", "origin": "BOM", "dest": "BLR", "price": 3600, "duration": 115, "stops": 0, "dep_hour": 22},
  {"flight": "AI-631", "airline": "Air India", "origin": "BOM", "dest": "DEL", "price": 5900, "duration": 145, "stops": 0, "dep_hour": 15},
  {"flight": "6E-718", "airline": "IndiGo", "origin": "BOM", "dest": "DEL", "price": 4900, "duration": 135, "stops": 0, "dep_hour": 19}
19]
20 
21# -------------------------------------------------------------
22# Module 1: Extract Unique Hubs & Airlines (Set Comprehensions)
23# -------------------------------------------------------------
24def get_unique_network_metadata(manifest):
  """Uses set comprehensions to extract unique operational hubs and airlines."""
  origins = {f["origin"] for f in manifest}
  destinations = {f["dest"] for f in manifest}
  all_airports = origins | destinations  # Mathematical set union!
  airlines = {f["airline"] for f in manifest}
  return sorted(all_airports), sorted(airlines)
31 
32# -------------------------------------------------------------
33# Module 2: User Search Query Filter (Advanced List Comprehension)
34# -------------------------------------------------------------
35def search_flights(manifest, origin, dest, max_budget=7000, non_stop_only=True):
  """Filters flights matching route, budget, and non-stop criteria."""
  # List comprehension: applies multi-predicate filters and formats display record
  matches = [
      {
          "flight_no": f["flight"],
          "carrier": f["airline"],
          "route": f"{f['origin']} ➔ {f['dest']}",
          "departure": f"{f['dep_hour']:02d}:00 hrs",
          "duration": f"{f['duration'] // 60}h {f['duration'] % 60}m",
          "total_fare": f["price"] + 450,  # Adding ₹450 airport passenger service fee
          "type": "Non-Stop" if f["stops"] == 0 else f"{f['stops']} Stop(s)"
      }
      for f in manifest
      if f["origin"] == origin
      if f["dest"] == dest
      if f["price"] <= max_budget
      if (not non_stop_only or f["stops"] == 0)
  ]
  return matches
55 
56# -------------------------------------------------------------
57# Module 3: Airline Price Analytics (Dictionary Comprehension)
58# -------------------------------------------------------------
59def compute_airline_summary(manifest, origin, dest):
  """Uses dict comprehensions to compute lowest fare and average price per airline."""
  # Step A: Filter flights for this route
  route_flights = [f for f in manifest if f["origin"] == origin and f["dest"] == dest]
  
  # Step B: Get unique airlines on this route via set comprehension
  operating_airlines = {f["airline"] for f in route_flights}
  
  # Step C: Dict comprehension computing min fare per airline
  cheapest_by_airline = {
      airline: min(f["price"] for f in route_flights if f["airline"] == airline)
      for airline in operating_airlines
  }
  
  # Step D: Dict comprehension computing average fare per airline
  avg_by_airline = {
      airline: round(
          sum(f["price"] for f in route_flights if f["airline"] == airline) / 
          len([f for f in route_flights if f["airline"] == airline]), 
          2
      )
      for airline in operating_airlines
  }
  
  return cheapest_by_airline, avg_by_airline
84 
85# -------------------------------------------------------------
86# Module 4: Route Frequency Matrix (Nested Comprehension)
87# -------------------------------------------------------------
88def build_route_matrix(manifest, hubs):
  """Constructs a 2D matrix counting flights between all airport pairs."""
  # Nested comprehension: Outer loop builds origin rows, inner loop counts flights to dest
  matrix = [
      [
          len([f for f in manifest if f["origin"] == orig and f["dest"] == dst])
          if orig != dst else "-"
          for dst in hubs
      ]
      for orig in hubs
  ]
  return matrix
100 
101 
102# --- Execution & Verification ---
103if __name__ == "__main__":
  hubs, airlines = get_unique_network_metadata(FLIGHT_MANIFEST)
  print("=== 1. Aviation Network Metadata (Set Comprehensions) ===")
  print("Active Airport Hubs :", hubs)
  print("Operating Carriers  :", airlines)
108 
  print("\n=== 2. Flight Search: DEL to BOM (Budget <= ₹7,000, Non-Stop) ===")
  search_results = search_flights(FLIGHT_MANIFEST, origin="DEL", dest="BOM", max_budget=7000, non_stop_only=True)
  
  print(f"{'FLIGHT':<10} | {'AIRLINE':<12} | {'ROUTE':<12} | {'DEP TIME':<12} | {'DURATION':<8} | {'FINAL FARE'}")
  print("-" * 75)
  for r in search_results:
      print(f"{r['flight_no']:<10} | {r['carrier']:<12} | {r['route']:<12} | {r['departure']:<12} | {r['duration']:<8} | ₹{r['total_fare']}")
116 
  print("\n=== 3. Carrier Price Intelligence for DEL ➔ BOM (Dict Comprehensions) ===")
  min_fares, avg_fares = compute_airline_summary(FLIGHT_MANIFEST, "DEL", "BOM")
  for carrier in sorted(min_fares.keys()):
      print(f"  {carrier:<12} : Starting at ₹{min_fares[carrier]:<5} | Route Avg: ₹{avg_fares[carrier]:,.2f}")
121 
  print("\n=== 4. Hub-to-Hub Flight Frequency Matrix (Nested Comprehensions) ===")
  matrix_hubs = ["DEL", "BOM", "BLR"]
  matrix = build_route_matrix(FLIGHT_MANIFEST, matrix_hubs)
  
  # Print formatted matrix table
  header = f"{'ORIGIN':<8} | " + " | ".join(f"{h:^5}" for h in matrix_hubs)
  print(header)
  print("-" * len(header))
  for hub, row in zip(matrix_hubs, matrix):
      row_str = " | ".join(f"{str(cell):^5}" for cell in row)
      print(f"{hub:<8} | {row_str}")

Expected Output

1=== 1. Aviation Network Metadata (Set Comprehensions) ===
2Active Airport Hubs : ['BLR', 'BOM', 'DEL']
3Operating Carriers  : ['Air India', 'IndiGo', 'SpiceJet', 'Vistara']
4 
5=== 2. Flight Search: DEL to BOM (Budget <= ₹7,000, Non-Stop) ===
6FLIGHT     | AIRLINE      | ROUTE        | DEP TIME     | DURATION | FINAL FARE
7---------------------------------------------------------------------------
86E-204     | IndiGo       | DEL ➔ BOM    | 06:00 hrs    | 2h 10m   | ₹5250
9AI-802     | Air India    | DEL ➔ BOM    | 08:00 hrs    | 2h 20m   | ₹6650
10 
11=== 3. Carrier Price Intelligence for DEL ➔ BOM (Dict Comprehensions) ===
12  Air India    : Starting at ₹6200 | Route Avg: ₹6,200.00
13  IndiGo       : Starting at ₹4800 | Route Avg: ₹4,800.00
14  SpiceJet     : Starting at ₹3900 | Route Avg: ₹3,900.00
15  Vistara      : Starting at ₹7500 | Route Avg: ₹7,500.00
16 
17=== 4. Hub-to-Hub Flight Frequency Matrix (Nested Comprehensions) ===
18ORIGIN   |  DEL  |  BOM  |  BLR 
19--------------------------------
20DEL      |   -   |   4   |   3  
21BOM      |   2   |   -   |   2  
22BLR      |   0   |   0   |   -  

Best Practices & Comparison: Do's and Don'ts

Practice	Bad Implementation	Gold-Standard Implementation
Deduplication	Looping over items and checking `if city not in list`	Set comprehension: `{f['origin'] for f in manifest}`
Price Filtering	Manual accumulator list with multiple nested `if`s	Single multi-condition list comprehension
Aggregations	Iterating 10 times to find minimums	Dict comprehension with `min()` and `sum()` generators
Matrix Setup	Manual nested loops appending row by row	`[[expr for col in cols] for row in rows]`
Readability	Giant single-line expressions without formatting	Format comprehensions with clear vertical indentation

Quick Revision Summary Cheat Sheet

Set Comprehensions: Deduplicate unique identifiers (airports, airlines, user IDs) in $O(n)$ time.
List Comprehensions: Filter and transform search results with multi-predicate conditions (if f['origin'] == origin if f['price'] <= budget).
Dictionary Comprehensions: Compute group-level summaries and analytical indexes ({k: min(...) for k in unique_keys}).
Nested Comprehensions: Generate cross-tabulation and adjacency matrices ([[count for dst in hubs] for orig in hubs]).

Practice Challenge

Scenario: Red-Eye Night Flight Discount Filter

Airlines offer promotional discounts on late-night Red-Eye flights (flights departing between 21:00 (9 PM) and 05:00 (5 AM)):

1
Write a list comprehension that filters only Red-Eye flights from FLIGHT_MANIFEST (f["dep_hour"] >= 21 or f["dep_hour"] <= 5).
2
Apply a 20% promotional discount to the base price: price * 0.80.
3
Format each output record as: "[DISCOUNTED] <FlightNo> (<Airline>) - New Price: ₹<Price> (Dep: <Hour>:00 hrs)".

Starter Code

1# Using FLIGHT_MANIFEST from the main lesson
2# TODO: Filter red-eye flights with 20% discount using list comprehension

Complete Solution

1red_eye_deals = [
2    f"[DISCOUNTED] {f['flight']} ({f['airline']}) - New Price: ₹{f['price'] * 0.80:.0f} (Dep: {f['dep_hour']:02d}:00 hrs)"
3    for f in FLIGHT_MANIFEST
4    if f["dep_hour"] >= 21 or f["dep_hour"] <= 5
5]
6 
7print("=== Red-Eye Promotional Deals (20% Off) ===")
8for deal in red_eye_deals:
9    print(deal)

Expected Output

1=== Red-Eye Promotional Deals (20% Off) ===
2[DISCOUNTED] SG-442 (SpiceJet) - New Price: ₹2880 (Dep: 22:00 hrs)

Interactive Quiz• Knowledge Check

Practice Quiz

Question 1

In the flight search engine, why is a set comprehension chosen to extract airport codes from the raw manifest?

Question 2

How does the operator | function between two sets: origins | destinations?

Question 3

What does the expression min(f["price"] for f in route_flights if f["airline"] == airline) inside the dictionary comprehension compute?

Question 4

In the route matrix comprehension [[len(...) for dst in hubs] for orig in hubs], what does the outer loop represent?

Question 5

Why is {f["flight_no"]: f for f in flights} a valid dictionary comprehension?

🚀 What's Next?

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