Popcorn Hacks

Popcorn Hack 1

movies = ["The Matrix", "Captain Marvel", "The Hunger Games", "Harry Potter and the Goblet of Fire"]
movies[1] = "Interstellar"
movies.append("Inside Out")

print("Updated movie list:", movies)


Updated movie list: ['The Matrix', 'Interstellar', 'The Hunger Games', 'Harry Potter and the Goblet of Fire', 'Inside Out']

Popcorn Hack 2

ages = [15, 20, 34, 16, 18, 21, 14, 19]
ages.sort()
eligible_to_vote = [age for age in ages if age >= 18]
print("Sorted ages eligible for voting:", eligible_to_vote)


Sorted ages eligible for voting: [18, 19, 20, 21, 34]

Homework Hack

Homework Hack 1

Video Notes:

#1:

  • Python lists can store multiple items, even of different data types (numbers, strings, etc.)
  • Can create a list using square brackets, like my_list = [1, 2, 3]
  • Lists are ordered, so can access items by their index—starting from 0. Negative indexing counts from the end
  • Lists are mutable, which means I can change, add, or remove elements anytime
  • Methods like .append(), .insert(), .remove(), and .sort() make it super easy to manipulate lists
  • Can slice a list using [start:stop] to get a portion of it without touching the original
  • Nested lists (lists inside lists) are possible, and I can access inner elements with double indexing like list[0][1]

#2: Python Comprehensions

  • List comprehensions offer a concise way to create lists by embedding a for loop directly within square brackets.​
  • Can include conditional logic, allowing for filtering elements during list creation.​
  • Set comprehensions use curly braces {} and are similar to list comprehensions but automatically eliminate duplicate elements.​
  • Dictionary comprehensions allow for constructing dictionaries using a syntax like {key: value for item in iterable}.​
  • Comprehensions can be nested, enabling the creation of complex data structures in a readable manner.​
  • Using comprehensions can lead to more efficient and readable code compared to traditional loops.​
  • Use comprehensions sparingly to maintain code clarity

Homework Hack 2

numbers = list(range(1, 31))

filtered_numbers = [num for num in numbers if num % 3 == 0 and num % 5 != 0]
print("Original List:")
print(numbers)

print("\nFiltered List (Divisible by 3 but not by 5):")
print(filtered_numbers)


Original List:
[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30]

Filtered List (Divisible by 3 but not by 5):
[3, 6, 9, 12, 18, 21, 24, 27]

Homework Hack 3

import pandas as pd

def filter_spotify_data(file_path):
    data = pd.read_csv(file_path)

    # Filter for songs with more than 10 million total streams
    filtered = data[data['Total Streams (Millions)'] > 10]
    print("🎧 Songs with Over 10 Million Total Streams:\n")
    return filtered
    
filter_spotify_data("Spotify_2024_Global_Streaming_Data.csv")

🎧 Songs with Over 10 Million Total Streams:
Country Artist Album Genre Release Year Monthly Listeners (Millions) Total Streams (Millions) Total Hours Streamed (Millions) Avg Stream Duration (Min) Platform Type Streams Last 30 Days (Millions) Skip Rate (%)
0 Germany Taylor Swift 1989 (Taylor's Version) K-pop 2019 23.10 3695.53 14240.35 4.28 Free 118.51 2.24
1 Brazil The Weeknd After Hours R&B 2022 60.60 2828.16 11120.44 3.90 Premium 44.87 23.98
2 United States Post Malone Austin Reggaeton 2023 42.84 1425.46 4177.49 4.03 Free 19.46 4.77
3 Italy Ed Sheeran Autumn Variations K-pop 2018 73.24 2704.33 12024.08 3.26 Premium 166.05 25.12
4 Italy Ed Sheeran Autumn Variations R&B 2023 7.89 3323.25 13446.32 4.47 Free 173.43 15.82
... ... ... ... ... ... ... ... ... ... ... ... ...
495 Brazil Karol G MAÑANA SERÁ BONITO Jazz 2018 18.80 2947.97 12642.83 3.59 Premium 83.30 18.58
496 Canada Dua Lipa Future Nostalgia Classical 2023 89.68 4418.61 11843.46 3.15 Free 143.96 5.82
497 Germany Karol G MAÑANA SERÁ BONITO Rock 2023 36.93 2642.90 8637.46 4.08 Free 76.36 15.84
498 Canada SZA SOS Indie 2022 87.26 4320.23 12201.40 2.79 Free 84.50 13.07
499 Sweden BTS Proof Reggaeton 2018 89.96 4804.15 12044.32 4.03 Free 92.27 34.36

500 rows × 12 columns

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