Popcorn Hacks
Popcorn Hack 1
fav_movies = ["Ratatoullie", "The Lion King", "The Incredibles", "Finding Nemo", "Toy Story"]
fav_movies[1] = "The Incredibles 2"
fav_movies.append("Inside Out")
print(fav_movies)
['Ratatoullie', 'The Incredibles 2', 'The Incredibles', 'Finding Nemo', 'Toy Story', 'Inside Out']
Popcorn Hack 2
ages = [15, 20, 34, 16, 18, 21, 14, 19]
eligible = [num for num in ages if num >= 18]
print(eligible)
[20, 34, 18, 21, 19]
Homework Hacks
Homework Hack 1
Complete Python List Tutorial
- Two ways to create a list: example = list(), or, more commonly used, example = []
- Can input prepopulated values and add values at the end of the list using append
- Order is very important for lists
- Not required to look at whole list to access certain value (order starts with 0, 1, 2, 3,…)
- To check end of the list, -1 represents end value
- Slicing is another way to access certain values: primes[2:5], accessing the items starting from 2 but excluding 5
- To combine two lists, use the + sign: numbers + letters
How to Filter Lists in Python
- A list comprehension is an easier and more readable way to create a list
- Best taught using a for loop: I want n for each n in nums –> my_list = [n for n in nums]
- Map runs everything throughout the function, lambda is anonymous function: compared to list comprehension, less understood by someone completely new to Python
- Using mod with if statements n for n in nums
- I want a (letter, num) pair for each letter in ‘abcd’ for each number in ‘0123’: my_list = [(letter, num) for letter in ‘abcd’ for num in range(4)]
- Zip functions creates tuples that matches up lists for us, can be used for data frame correlation in projects
- I want a dict{‘name’: ‘hero’} for each name, hero in zip(names, heros) with dictionary comprenehsion: my_dict = {name: hero for name,, hero in zip (names, heros)}
Homework Hack 2
nums = list(range(1, 31))
filtered = [n for n in nums if n % 3 == 0 and n % 5 != 0]
print("Original:", nums)
print("Filtered (div by 3, not by 5):", filtered)
Original: [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 (div by 3, not by 5): [3, 6, 9, 12, 18, 21, 24, 27]
Homework Hack 3
import pandas as pd
# Load the dataset
data = pd.read_csv("Spotify_2024_Global_Streaming_Data.csv")
# Drop rows with missing values
data_cleaned = data.dropna()
# Sort the data by 'Price'
# (If you don't need sorting by 'Price', you can omit this line)
# data_cleaned = data_cleaned.sort_values(by="Price")
# Filter the data for songs with more than 10 million streams
data_filtered = data_cleaned[data_cleaned["Total Streams (Millions)"] > 10]
def filter_spotify_data():
"""
Displays songs with more than 10 million streams from the cleaned dataset.
"""
if not data_filtered.empty:
print("\nSongs with more than 10 million streams:\n")
print(data_filtered[['Artist', 'Total Streams (Millions)']].to_string(index=False))
return data_filtered
else:
print("No songs found with more than 10 million streams.")
return pd.DataFrame() # Return empty DataFrame
# Calling the function to filter and display the songs
filter_spotify_data()
Songs with more than 10 million streams:
Artist Total Streams (Millions)
Taylor Swift 3695.53
The Weeknd 2828.16
Post Malone 1425.46
Ed Sheeran 2704.33
Ed Sheeran 3323.25
Billie Eilish 1087.06
Bad Bunny 4630.69
Doja Cat 377.63
Bad Bunny 2828.77
Ariana Grande 4312.46
Dua Lipa 4310.86
BLACKPINK 3624.36
Doja Cat 1900.87
SZA 1242.31
Post Malone 4736.90
Karol G 3266.90
Ed Sheeran 85.59
Ariana Grande 1110.47
The Weeknd 1803.77
Taylor Swift 4315.28
The Weeknd 4287.04
Bad Bunny 4330.02
BTS 4977.34
Ed Sheeran 332.97
Drake 1214.26
BLACKPINK 4676.80
Dua Lipa 3124.74
SZA 830.85
SZA 579.83
Ariana Grande 4238.21
Billie Eilish 2780.82
Ariana Grande 783.06
Billie Eilish 4426.47
Post Malone 3695.99
BLACKPINK 4092.10
Olivia Rodrigo 1431.67
Bad Bunny 3003.03
SZA 3558.49
Ed Sheeran 3612.78
Bad Bunny 1926.77
BTS 3052.97
Billie Eilish 3183.06
Bad Bunny 2401.92
Dua Lipa 3453.73
Dua Lipa 4249.26
Doja Cat 2761.97
Post Malone 1409.26
Bad Bunny 806.39
Doja Cat 2941.19
Post Malone 3686.44
BLACKPINK 2051.37
Ed Sheeran 3231.61
Olivia Rodrigo 4758.46
The Weeknd 203.60
Ed Sheeran 3846.15
Drake 2900.19
Drake 649.22
Ariana Grande 4120.54
Billie Eilish 2713.96
BTS 2870.76
Olivia Rodrigo 3000.97
Ariana Grande 3573.62
Billie Eilish 2678.06
BTS 1611.96
Karol G 4903.53
Bad Bunny 2789.13
Dua Lipa 4829.21
Ed Sheeran 2836.06
Post Malone 1932.87
Karol G 1360.00
Dua Lipa 3549.43
Billie Eilish 1162.81
Ed Sheeran 2370.05
Ed Sheeran 3038.38
Ed Sheeran 3274.97
Dua Lipa 3367.16
Taylor Swift 1650.21
Taylor Swift 3794.39
Dua Lipa 3168.61
The Weeknd 3256.16
Dua Lipa 1948.75
Olivia Rodrigo 1450.05
Olivia Rodrigo 1917.19
Taylor Swift 4571.48
Ed Sheeran 2229.52
Drake 3478.89
Doja Cat 1212.11
Post Malone 1532.36
The Weeknd 3972.97
Bad Bunny 2324.87
Drake 1530.81
Doja Cat 2881.02
BTS 3902.64
Ed Sheeran 3123.41
BTS 3675.18
SZA 1399.01
Post Malone 746.75
Drake 661.76
Drake 4426.96
SZA 741.90
Bad Bunny 4749.43
Ed Sheeran 1884.12
SZA 989.55
Taylor Swift 673.32
Olivia Rodrigo 4299.11
Bad Bunny 1227.76
Karol G 1842.09
BTS 351.09
SZA 375.13
Dua Lipa 3975.83
Olivia Rodrigo 520.67
Drake 527.99
Billie Eilish 3343.44
Drake 3300.84
Post Malone 4101.21
Taylor Swift 1579.47
Karol G 1114.91
Ed Sheeran 3784.46
BLACKPINK 613.40
BLACKPINK 1542.03
Karol G 2686.28
BLACKPINK 4317.44
Bad Bunny 4602.86
BLACKPINK 1698.69
Olivia Rodrigo 1317.04
Dua Lipa 4624.56
Dua Lipa 1548.96
Olivia Rodrigo 4887.97
Dua Lipa 3044.78
Karol G 243.75
BLACKPINK 3956.67
Karol G 2934.56
BTS 1675.96
Taylor Swift 1110.25
Doja Cat 4183.84
BLACKPINK 3479.59
Dua Lipa 3929.81
Dua Lipa 2539.39
Taylor Swift 557.82
SZA 503.98
Ariana Grande 453.95
Bad Bunny 2388.65
Taylor Swift 2766.58
Taylor Swift 3132.91
Ed Sheeran 2174.73
Olivia Rodrigo 3942.74
BTS 1756.77
Doja Cat 3492.18
BTS 4434.66
Taylor Swift 3915.17
Karol G 974.75
BLACKPINK 1640.00
Karol G 3647.73
Drake 3762.00
Olivia Rodrigo 809.52
Olivia Rodrigo 1285.58
BTS 4982.01
Dua Lipa 484.56
Ariana Grande 3573.46
Post Malone 180.11
BLACKPINK 2076.70
Olivia Rodrigo 950.72
Bad Bunny 1166.08
BTS 2690.72
Olivia Rodrigo 190.45
Ed Sheeran 2750.61
Dua Lipa 2226.05
BLACKPINK 575.61
BLACKPINK 4333.27
Dua Lipa 495.74
BTS 3288.59
Post Malone 303.23
BTS 686.88
The Weeknd 249.26
Bad Bunny 1995.13
Karol G 132.35
Ed Sheeran 3925.37
Post Malone 1248.87
BLACKPINK 3076.82
Olivia Rodrigo 4785.59
Ed Sheeran 1431.08
Olivia Rodrigo 4675.70
Post Malone 4778.80
BTS 590.67
Taylor Swift 2388.40
Dua Lipa 3247.19
SZA 920.15
SZA 3033.78
SZA 1004.42
Karol G 3798.55
BLACKPINK 2413.40
BTS 1344.69
SZA 4584.85
Karol G 2768.71
Doja Cat 3533.64
Ed Sheeran 4279.71
Dua Lipa 2841.36
Karol G 2147.30
Taylor Swift 720.37
Doja Cat 4822.51
Ariana Grande 4182.37
BLACKPINK 817.85
Post Malone 549.44
The Weeknd 3891.51
Ed Sheeran 1222.67
Ed Sheeran 2801.46
BLACKPINK 3784.36
SZA 2111.71
Doja Cat 4155.56
Karol G 4353.90
Karol G 3533.65
Billie Eilish 2588.44
Olivia Rodrigo 4327.22
BLACKPINK 3290.31
Dua Lipa 887.87
Doja Cat 4220.60
Ed Sheeran 127.87
Taylor Swift 4040.53
Ariana Grande 2014.64
Billie Eilish 1916.99
Ed Sheeran 1187.34
SZA 937.80
SZA 1240.34
Ariana Grande 1199.11
Post Malone 2787.54
Karol G 1931.00
BTS 556.05
Drake 4325.40
Ed Sheeran 889.03
BTS 4723.90
BLACKPINK 4317.88
Dua Lipa 4845.19
BLACKPINK 4663.73
Karol G 4164.84
The Weeknd 2144.48
Ariana Grande 4070.87
Bad Bunny 3242.90
Drake 2618.05
Karol G 171.59
Olivia Rodrigo 1073.99
BTS 4253.44
Bad Bunny 2160.53
Taylor Swift 2176.29
Ed Sheeran 1663.63
BLACKPINK 1772.23
Olivia Rodrigo 956.07
Bad Bunny 4599.85
Doja Cat 1827.24
BLACKPINK 2247.07
BTS 3874.11
Ariana Grande 1131.78
BTS 4970.09
Bad Bunny 4076.97
BTS 2596.35
BTS 1285.90
Billie Eilish 3186.46
Dua Lipa 3881.97
Post Malone 2176.73
Doja Cat 3823.16
Dua Lipa 2167.61
Drake 3339.32
Billie Eilish 1106.43
Doja Cat 400.60
Bad Bunny 3401.83
SZA 4306.81
Olivia Rodrigo 467.03
The Weeknd 3751.83
Billie Eilish 4889.60
Olivia Rodrigo 2381.52
Post Malone 3577.51
Bad Bunny 2580.96
BTS 1231.26
Billie Eilish 729.20
Bad Bunny 4373.63
Drake 3156.29
Bad Bunny 3386.90
Taylor Swift 792.92
BTS 2535.10
Doja Cat 2787.80
Ed Sheeran 2755.47
Olivia Rodrigo 3703.51
Karol G 1195.79
Drake 2002.04
SZA 1664.95
Bad Bunny 1604.46
Drake 3538.29
BLACKPINK 1337.83
The Weeknd 3979.36
Billie Eilish 4591.27
Drake 2331.92
Bad Bunny 1411.16
Dua Lipa 729.32
BLACKPINK 2449.16
The Weeknd 4958.08
BTS 4982.14
Bad Bunny 3703.62
The Weeknd 2477.66
SZA 1736.76
Post Malone 4804.77
Bad Bunny 3969.62
Post Malone 4081.55
BLACKPINK 1625.17
The Weeknd 4432.34
Karol G 584.77
SZA 552.07
BTS 568.39
Olivia Rodrigo 3537.30
The Weeknd 4631.59
Ariana Grande 68.89
Billie Eilish 621.77
Olivia Rodrigo 270.85
Drake 3794.46
Billie Eilish 2407.50
Billie Eilish 3798.08
Drake 3154.77
Olivia Rodrigo 4373.66
Ed Sheeran 2835.28
Ed Sheeran 1413.30
Karol G 4608.13
Doja Cat 3731.43
The Weeknd 2606.68
Bad Bunny 1004.18
Karol G 2347.88
Bad Bunny 1754.03
Doja Cat 3254.53
Dua Lipa 4014.28
BLACKPINK 1130.13
Ariana Grande 3437.85
Billie Eilish 3839.10
Billie Eilish 2121.90
Billie Eilish 1535.96
Karol G 4502.98
Olivia Rodrigo 4263.51
Doja Cat 317.61
Bad Bunny 450.02
The Weeknd 2473.41
BLACKPINK 3666.76
The Weeknd 1948.57
Ed Sheeran 3449.56
Taylor Swift 699.47
BLACKPINK 2989.92
BLACKPINK 3524.25
BTS 2123.19
The Weeknd 4165.92
SZA 1686.49
Drake 551.39
Doja Cat 3399.99
Olivia Rodrigo 1378.13
BTS 4009.50
Drake 2866.27
Dua Lipa 2231.41
Karol G 3365.73
Olivia Rodrigo 2813.80
Doja Cat 1637.14
Drake 2268.09
Drake 2768.04
Olivia Rodrigo 4229.37
Drake 1739.72
Taylor Swift 3598.44
The Weeknd 583.44
Ariana Grande 3694.26
Karol G 685.49
SZA 1747.15
BTS 4680.78
Karol G 3202.54
Billie Eilish 2140.81
BTS 774.82
Drake 4330.97
Karol G 4785.67
Doja Cat 3537.91
Dua Lipa 4814.25
Bad Bunny 2358.34
Doja Cat 2773.29
Bad Bunny 505.13
Ariana Grande 4906.61
The Weeknd 1937.79
Doja Cat 2255.51
Bad Bunny 374.09
Billie Eilish 167.58
Drake 2067.78
Taylor Swift 2948.76
Drake 4119.51
BTS 4575.14
Bad Bunny 3003.44
The Weeknd 755.15
Doja Cat 2770.84
Olivia Rodrigo 545.22
Karol G 1854.24
BTS 1691.24
Drake 1022.34
Doja Cat 2970.68
The Weeknd 712.71
Billie Eilish 838.15
Taylor Swift 1358.57
Taylor Swift 2630.80
Billie Eilish 4496.22
Dua Lipa 4416.07
Ed Sheeran 4065.30
Taylor Swift 2982.12
Ariana Grande 3206.23
Dua Lipa 2083.30
Karol G 390.77
Billie Eilish 928.24
Ariana Grande 2634.12
Olivia Rodrigo 4194.09
BTS 985.65
Karol G 816.15
Post Malone 2809.94
Post Malone 4325.96
Post Malone 3548.61
SZA 1352.31
BTS 4110.13
BTS 2577.21
Drake 2005.96
SZA 4086.80
Bad Bunny 408.31
BTS 3861.34
Dua Lipa 3186.63
Ariana Grande 1353.58
Olivia Rodrigo 1381.93
Doja Cat 4182.96
Ed Sheeran 4247.65
BTS 684.82
Dua Lipa 1612.47
Doja Cat 1223.84
Karol G 1434.17
Ariana Grande 4010.19
Ariana Grande 2832.59
Doja Cat 1335.86
Doja Cat 3266.83
Post Malone 1468.29
Billie Eilish 4540.01
Ed Sheeran 3915.82
Bad Bunny 3732.18
Ariana Grande 2643.71
BTS 2243.64
Doja Cat 2725.89
BLACKPINK 1582.82
Bad Bunny 4086.13
Post Malone 2180.65
BLACKPINK 3698.73
Post Malone 1459.15
BTS 2291.20
Taylor Swift 668.94
BLACKPINK 2345.24
Ariana Grande 4215.62
Post Malone 215.88
SZA 258.82
SZA 609.00
Post Malone 2926.33
Dua Lipa 4657.70
Post Malone 1617.86
Taylor Swift 1290.95
Bad Bunny 2368.55
Doja Cat 53.56
Post Malone 363.54
Bad Bunny 3222.47
Doja Cat 998.50
BTS 3603.33
Post Malone 4694.31
Karol G 1118.71
Billie Eilish 935.54
Karol G 942.96
SZA 1657.04
Ed Sheeran 2773.68
BTS 4967.39
SZA 4633.32
The Weeknd 504.93
Dua Lipa 1536.95
Olivia Rodrigo 646.68
BTS 2846.32
Dua Lipa 1149.32
Ariana Grande 2534.02
Billie Eilish 4985.54
Drake 1858.07
Ed Sheeran 272.14
Drake 4637.31
Dua Lipa 3713.06
Karol G 3334.47
Taylor Swift 379.66
The Weeknd 543.04
Ariana Grande 2670.61
Billie Eilish 4894.01
Ariana Grande 4491.02
BLACKPINK 4854.16
Billie Eilish 3540.43
Olivia Rodrigo 3611.23
BLACKPINK 4117.41
Taylor Swift 2856.86
Bad Bunny 3265.36
BLACKPINK 1151.64
Doja Cat 4340.54
BLACKPINK 2703.99
SZA 4919.96
Post Malone 1507.38
Karol G 2947.97
Dua Lipa 4418.61
Karol G 2642.90
SZA 4320.23
BTS 4804.15
| 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
Review Questions
-
Explain what lists are in Python, including how to modify and manipulate them.
Lists are datasets organized in either list constructors or brackets, and they are used to display values or strings. To modify and manipulate them, list comprehensions can be performed and using functions such as append, for, and if can help you add or analyze your data. - Provide a real-world scenario where a filtering algorithm might be applied.
In a media analyzer project, if, for example, we wanted to be provided with data that is filtered together, such as corresponding posted pictures and the acquired number of likes, filtering algorithms can provide these as tuples to be analyzed for ML prediction of how future pictures might perform. - Discuss why analyzing the efficiency of filtering algorithms is important for software development.
Often times, filtering through exceedingly long lists take an enormous amount of time. Analyzing and enhancing filtering algorithms can be time-saving and effcient when in need of filtered data for certain projects, such as the ML we’re currently working on in class.