Sets Unique Elements and Set Operations

Sets Unique Elements and Set Operations#

Party Intro: Welcome to the Data Purge Party, You Duplicate-Dropping Disaster!#

Crank it up, you data-dumping DJ! You’ve crashed the Data Purge Party, where sets are ruthless spinners tossing out duplicate tracks like yesterday’s mixtape! I’m your host, screaming because you’re trying to play “phone” twice in a row—sets don’t mess with that noise. Sets are your VIP pass for unique products, customers, or tags, making lookups 1000x faster than lists and crushing Excel’s duplicate hell in one beat. Union, intersection, and difference are your dance moves for merging catalogs and segmenting customers. Every code snippet’s a banger with a zinger, and the exercises? Dance-offs that’ll roast your repetitive crap while you laugh. Grab your glowsticks (keyboard) and spin clean, or you’re getting ejected with a DuplicateError to the face!

Code Beats with Laughs: Sets Drop the Mic#

Create Sets: The Duplicate Purge Sets are like a DJ who only spins unique tracks—add duplicates, and they’re ghosted faster than a bad SoundCloud rapper.

# Bad: List full of repeats
messy_list = ['laptop', 'phone', 'laptop', 'tablet', 'phone']
print(f"📋 Messy Playlist: {len(messy_list)} tracks")  # 5—Boring repeats!

# Good: Set cleans it up
unique_set = set(messy_list)
print(f"🎵 Clean Playlist: {len(unique_set)} unique tracks ({sorted(unique_set)})")  # 4—Pure bangers!

Try adding “laptop” again? Set just shrugs, “Already spinning, you repetitive rookie!”

Fast Lookups: The VIP Check Sets check membership faster than a bouncer scanning wristbands—perfect for inventory or customer tags.

inventory = {'laptop', 'phone', 'tablet'}
item = 'laptop'
if item in inventory:  # Lightning-fast!
    print(f"✅ {item.title()} on the dancefloor!")
else:
    print(f"❌ {item.title()} not invited!")
# Compare to slow list: Takes 1000x longer for 1M items!

Check a bad item like 42? Set snickers, “Not on the list, you gatecrashing goof!”

Set Operations: The Dancefloor Merge Union, intersection, and difference are your moves to mix catalogs or segment crowds like a pro DJ.

club_a = {'laptop', 'phone', 'tablet'}
club_b = {'phone', 'tablet', 'mouse'}
all_bangers = club_a | club_b  # Union
shared_hits = club_a & club_b  # Intersection
a_exclusive = club_a - club_b  # Difference
print("🎧 Playlist Mashup:")
print(f"   All Tracks: {len(all_bangers)} ({sorted(all_bangers)})")
print(f"   Shared Hits: {len(shared_hits)} ({sorted(shared_hits)})")
print(f"   Club A Exclusives: {len(a_exclusive)}")

Bad merge like {1} | ["phone"]? TypeError drops the mic, “Mixing types? You’re ruining the vibe!”

Segmentation: The Crowd Control Sets slice through customer data like a DJ curating a hype playlist—find unique groups in one spin.

vips = {'alice', 'bob', 'carol'}
promo = {'bob', 'dave', 'carol'}
both = vips & promo
only_vips = vips - promo
print("🎉 Crowd Control:")
print(f"   Both VIP & Promo: {len(both)} ({sorted(both)})")
print(f"   VIP Only: {len(only_vips)}")

Mess up the set? ValueError spins, “Your data’s offbeat—fix it, DJ dropout!”

Dance-Off Exercise: Spin a Set That Roasts Your Lame Tracks#

Time to drop a beat, you duplicate-dropping DJ! Build a Product Playlist Dashboard:

  1. Create your_products = ['laptop', 'phone', 'laptop', 'tablet', 'phone'] with duplicates.

  2. Use set() to clean it. If duplicates remain, print, “Duplicates detected? You’re spinning last week’s tracks, loser!”

  3. Define two sets: store_a = {'laptop', 'phone'} and store_b = {'phone', 'mouse'}. Merge with | and find exclusives with -.

  4. Write a check_stock(product, inventory) function. If item’s missing, print, “Not in stock? You’re off the guest list!”

  5. Bonus: Count shared items (&). If none, print, “No shared tracks? Your stores are a snooze-fest!”

Starter Code:

your_products = ['laptop', 'phone', 'laptop', 'tablet', 'phone']
store_a = {'laptop', 'phone'}
store_b = {'phone', 'mouse'}

unique_catalog = set(your_products)
print(f"🎵 Product Playlist Dashboard:")
print(f"   Raw Tracks: {len(your_products)}")
print(f"   Unique Tracks: {len(unique_catalog)} ({sorted(unique_catalog)})")

# Merge and analyze
all_products = # Union
shared = # Intersection
exclusives = # Difference
print(f"\n🏪 Store Mix:")
print(f"   Total Tracks: {len(all_products)}")
print(f"   Shared: {len(shared)}")
print(f"   Store A Exclusives: {len(exclusives)}")

# Stock check function
def check_stock(product, inventory):
    pass  # Implement me!

hot_item = 'laptop'
print(f"\n🔥 Hot Track '{hot_item}': {check_stock(hot_item, store_a)}")

Test it, screenshot your dashboard for your GitHub party log, and don’t spin a dud, you track-mixing maestro!

Rant About Code Gatecrashers: You Rocked the Party, But Don’t Slack Off!#

Holy bangers, you survived the Data Purge Party, you duplicate-dropping DJ! You’ve cleaned catalogs, spun fast lookups, and mixed sets like a club legend. Sets are the secret weapon behind Walmart’s \(500B inventory and Netflix’s tag system—add one duplicate, and your data’s messier than a dancefloor at 3 AM. This is the shit that lands you \)140K+ data gigs while your buddies are de-duping Excel rows like suckers. Next up, tuples: Get ready to lock down your data, or I’ll kick you out with a TypeError to the speakers!

print("🎧" * 20)
print("SETS = YOUR DUPLICATE-PURGING SUPERPOWER!")
print("💻 Clean data faster than a club remix!")
print("🚀 Next: Tuples—lock it down like a pro!")
print("🎧" * 20)

# Your code here
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