S3Q1 · Railway Ticket Booking Analysis¶

⚡ Quick Reference

Function: analyze_bookings(bookings: dict, task: str)

berth_types = ["UB", "MB", "LB", "SU", "SL", None]

def analyze_bookings(bookings, task):
    if task == "ticket_prices":
        return {pnr: b["ticket_rate"] * len(b["passengers"])
                for pnr, b in bookings.items()}

    if task == "total_revenue":
        return sum(b["ticket_rate"] * len(b["passengers"])
                   for b in bookings.values())

    if task == "berth_preference_count":
        counts = {bt: 0 for bt in berth_types}
        for b in bookings.values():
            for _, pref in b["passengers"]:
                counts[pref] += 1
        return counts

    if task == "coach_type_count":
        counts = {}
        for b in bookings.values():
            ct = b["coach_type"]
            counts[ct] = counts.get(ct, 0) + len(b["passengers"])
        return counts

Key rules: - Ticket price per PNR = ticket_rate × number_of_passengers - berth_preference_count includes all 6 berth types (even with 0 count), including None - coach_type_count only includes coach types present in the data

Problem Statement¶

Problem

Implement analyze_bookings(bookings, task) - four analytics operations on nested booking data.

Sample data:

bookings = {
    "PNR112": {"coach_type": "3AC", "ticket_rate": 900,
               "passengers": [("Rahul","UB"), ("Baskar",None)]},
    "PNR123": {"coach_type": "2AC", "ticket_rate": 1500,
               "passengers": [("Amit","LB"), ("Ravi","UB")]},
    "PNR456": {"coach_type": "3AC", "ticket_rate": 800,
               "passengers": [("Priya","MB"), ("Neha",None)]},
    "PNR789": {"coach_type": "SL",  "ticket_rate": 500,
               "passengers": [("Vikram","UB"),("Kiran","MB"),("Surya","SU")]},
}

Operation 1 - `ticket_prices`¶

Total price per PNR = ticket_rate × number of passengers:

return {pnr: b["ticket_rate"] * len(b["passengers"])
        for pnr, b in bookings.items()}

PNR	rate	passengers	total
PNR112	900	2	1800
PNR123	1500	2	3000
PNR456	800	2	1600
PNR789	500	3	1500

→ {'PNR112': 1800, 'PNR123': 3000, 'PNR456': 1600, 'PNR789': 1500} ✓

Operation 2 - `total_revenue`¶

Sum all PNR totals:

return sum(b["ticket_rate"] * len(b["passengers"]) for b in bookings.values())

1800 + 3000 + 1600 + 1500 = 7900 ✓

Operation 3 - `berth_preference_count`¶

Count passengers per berth preference. All 6 types must appear in output (zero if absent):

counts = {bt: 0 for bt in berth_types}
for b in bookings.values():
    for _, pref in b["passengers"]:
        counts[pref] += 1
return counts

Berth	Passengers	Count
`UB`	Rahul, Ravi, Vikram	3
`MB`	Priya, Kiran	2
`LB`	Amit	1
`SU`	Surya	1
`SL`	-	0
`None`	Baskar, Neha	2

→ {'UB':3, 'MB':2, 'LB':1, 'SU':1, 'SL':0, None:2} ✓

Operation 4 - `coach_type_count`¶

Count total passengers per coach type (only types present in data):

counts = {}
for b in bookings.values():
    ct = b["coach_type"]
    counts[ct] = counts.get(ct, 0) + len(b["passengers"])
return counts

Coach	PNRs	Passengers
`3AC`	PNR112(2), PNR456(2)	4
`2AC`	PNR123(2)	2
`SL`	PNR789(3)	3

→ {'3AC': 4, '2AC': 2, 'SL': 3} ✓

Complete solution approaches¶

PythonicExplanatory (loops)Using lambda dispatch

berth_types = ["UB", "MB", "LB", "SU", "SL", None]

def analyze_bookings(bookings, task):
    if task == "ticket_prices":
        return {pnr: b["ticket_rate"] * len(b["passengers"])
                for pnr, b in bookings.items()}

    if task == "total_revenue":
        return sum(b["ticket_rate"] * len(b["passengers"])
                   for b in bookings.values())

    if task == "berth_preference_count":
        counts = {bt: 0 for bt in berth_types}
        for b in bookings.values():
            for _, pref in b["passengers"]:
                counts[pref] += 1
        return counts

    if task == "coach_type_count":
        counts = {}
        for b in bookings.values():
            ct = b["coach_type"]
            counts[ct] = counts.get(ct, 0) + len(b["passengers"])
        return counts

berth_types = ["UB", "MB", "LB", "SU", "SL", None]

def analyze_bookings(bookings, task):
    if task == "ticket_prices":
        result = {}
        for pnr, b in bookings.items():
            result[pnr] = b["ticket_rate"] * len(b["passengers"])
        return result

    if task == "total_revenue":
        total = 0
        for b in bookings.values():
            total += b["ticket_rate"] * len(b["passengers"])
        return total

    if task == "berth_preference_count":
        counts = {bt: 0 for bt in berth_types}
        for b in bookings.values():
            for passenger in b["passengers"]:
                pref = passenger[1]
                counts[pref] += 1
        return counts

    if task == "coach_type_count":
        counts = {}
        for b in bookings.values():
            ct = b["coach_type"]
            n  = len(b["passengers"])
            if ct in counts:
                counts[ct] += n
            else:
                counts[ct] = n
        return counts

from collections import defaultdict

berth_types = ["UB", "MB", "LB", "SU", "SL", None]

def analyze_bookings(bookings, task):
    all_passengers = [(pnr, b["coach_type"], b["ticket_rate"], p, pref)
                      for pnr, b in bookings.items()
                      for p, pref in b["passengers"]]

    if task == "ticket_prices":
        prices = defaultdict(int)
        for pnr, _, rate, _, _ in all_passengers:
            prices[pnr] += rate
        return dict(prices)

    if task == "total_revenue":
        return sum(rate for _, _, rate, _, _ in all_passengers)

    if task == "berth_preference_count":
        counts = {bt: 0 for bt in berth_types}
        for _, _, _, _, pref in all_passengers:
            counts[pref] += 1
        return counts

    if task == "coach_type_count":
        counts = defaultdict(int)
        for _, ct, _, _, _ in all_passengers:
            counts[ct] += 1
        return dict(counts)

Key takeaways¶

01

Pre-initialise berth_preference_count with zeros

{bt: 0 for bt in berth_types} ensures all 6 berth types appear in the result, even those with no passengers. Without this, absent berth types would simply not appear as keys.

02

None is a valid dictionary key

Passengers with no berth preference have None as their second tuple element. Python dicts accept None as a key - counts[None] += 1 works correctly.

03

ticket_rate × len(passengers) - rate is per person

ticket_rate is the price for one passenger. Multiply by len(passengers) to get the total for the entire PNR booking.