The dataset contains information about the songs in Jacob Collier's playlist. Here's a brief description of each column in the dataset:

• Spotify ID: The unique identifier for the track on Spotify.
• Artist IDs: The unique identifiers for the artists of the track.
• Track Name: The name of the track.
• Album Name: The name of the album that the track is from.
• Artist Name(s): The names of the artists of the track.
• Release Date: The release date of the track.
• Duration (ms): The duration of the track in milliseconds.
• Popularity: The popularity of the track on Spotify.
• Added By: The user who added the track to the playlist.
• Added At: The date and time when the track was added to the playlist.
• Danceability: A measure from 0.0 to 1.0 of how suitable a track is for dancing.
• Energy: A measure from 0.0 to 1.0 that represents a perceptual measure of intensity and activity.
• Key: The key the track is in.
• Loudness: The overall loudness of a track in decibels (dB).
• Mode: The modality (major or minor) of a track, the type of scale from which its melodic content is derived.
• Speechiness: Speechiness detects the presence of spoken words in a track.
• Acousticness: A confidence measure from 0.0 to 1.0 of whether the track is acoustic.
• Instrumentalness: Predicts whether a track contains no vocals.
• Liveness: Detects the presence of an audience in the recording.
• Valence: A measure from 0.0 to 1.0 describing the musical positiveness conveyed by a track.
• Tempo: The overall estimated tempo of a track in beats per minute (BPM).
• Time Signature: An estimated overall time signature of a track.

To analyze this data for non-obvious insights into the musical mind of Jacob Collier, we can perform various analyses. For example, we can look at:

1. The distribution of genres in the playlist.
2. The most frequently occurring artists.
3. The distribution of song attributes (e.g., danceability, energy, key, loudness, mode, speechiness, acousticness, instrumentalness, liveness, valence, tempo, and time signature).
4. The distribution of song release years.
5. The correlation between song attributes.

Before we proceed, could you please provide the genre information for each song or artist if available? The current dataset does not have this information.

Understood. Let's proceed with the available data. Here's what we can do:

1. Analyze the most frequently occurring artists in the playlist to understand which artists Jacob Collier might be influenced by.
2. Analyze the distribution of song attributes (e.g., danceability, energy, key, loudness, mode, speechiness, acousticness, instrumentalness, liveness, valence, tempo, and time signature). This will give us insights into the type of music Jacob Collier enjoys in terms of these characteristics.
3. Analyze the distribution of song release years. This will give us an idea of which periods of music history Jacob Collier is most interested in.
4. Analyze the correlation between song attributes. This will give us insights into how different musical characteristics relate to each other in the songs Jacob Collier enjoys.

Let's start with analyzing the most frequently occurring artists in the playlist.

The top 10 artists most frequently appearing in Jacob Collier's playlist are:

1. Stevie Wonder - 20 tracks
2. KNOWER - 18 tracks
3. Deerhoof - 18 tracks
4. Dirty Projectors - 15 tracks
5. Sting - 15 tracks
6. Jacob Collier - 13 tracks
7. Sam Amidon - 12 tracks
8. Laura Mvula - 10 tracks
9. Keith Jarrett - 10 tracks
10. Bon Iver - 10 tracks

It's interesting to note that Jacob Collier's own music appears in this playlist, suggesting that he also enjoys listening to and is perhaps influenced by his own work.

Next, let's analyze the distribution of song attributes such as danceability, energy, key, loudness, mode, speechiness, acousticness, instrumentalness, liveness, valence, tempo, and time signature.

Here are the distributions of various song attributes in Jacob Collier's playlist:

1. Danceability: The distribution is spread out, but there's a peak around 0.4-0.5, indicating that Jacob tends to enjoy songs that have a moderate level of danceability.
2. Energy: The distribution is also spread, with a peak around 0.4-0.6, suggesting that Jacob enjoys songs with moderate energy.
3. Key: The songs are spread across all keys, suggesting that Jacob enjoys music in a variety of keys.
4. Loudness: The distribution is somewhat normal with a peak around -10 dB. This suggests that Jacob tends to enjoy songs that aren't overly loud.
5. Mode: The songs in the playlist are predominantly in the major mode (1), suggesting that Jacob prefers songs in major keys.
6. Speechiness: The distribution is heavily skewed towards lower values, indicating that Jacob prefers songs with less spoken words.
7. Acousticness: The distribution is spread out, suggesting that Jacob enjoys both acoustic and non-acoustic songs.
8. Instrumentalness: The distribution is skewed towards lower values, indicating that Jacob prefers songs with vocals.
9. Liveness: The distribution is heavily skewed towards lower values, suggesting that Jacob prefers studio recordings over live performances.
10. Valence: The distribution is spread out but leans towards higher values, suggesting that Jacob enjoys music that conveys a more positive mood.
11. Tempo: The distribution peaks around 100-120 BPM, indicating a preference for moderately paced songs.
12. Time Signature: The majority of songs are in 4/4 time, which is the most common time signature in popular music.

Next, let's analyze the distribution of song release years.

The distribution of song release years shows that the songs in Jacob Collier's playlist span a wide range of years, from the 1950s to the present. However, there's a clear preference for songs released between the late 1960s and early 2000s, with a peak in the late 1970s and early 1980s. This suggests that Jacob is particularly interested in music from these periods.

Lastly, let's analyze the correlation between song attributes to see how different musical characteristics relate to each other in the songs Jacob enjoys.

The heatmap above shows the correlations between different song attributes in Jacob Collier's playlist. Here are some notable observations:

1. Energy and Loudness: There's a strong positive correlation (0.77) between Energy and Loudness. This is expected as louder songs often sound more energetic.
2. Energy and Acousticness: There's a strong negative correlation (-0.70) between Energy and Acousticness. This suggests that more acoustic songs in the playlist tend to be less energetic, which is consistent with the nature of acoustic music.
3. Danceability and Valence: There's a moderate positive correlation (0.49) between Danceability and Valence. This means that songs that are more danceable tend to convey a more positive mood.
4. Loudness and Acousticness: There's a moderate negative correlation (-0.53) between Loudness and Acousticness. This suggests that more acoustic songs in the playlist tend to be quieter.
5. Speechiness and several attributes: Speechiness has moderate negative correlations with Energy, Danceability, and Valence. This suggests that songs with more spoken words in the playlist tend to be less energetic, less danceable, and convey a less positive mood.

These correlations suggest that the songs in Jacob Collier's playlist often share certain combinations of musical attributes. For example, more energetic songs are often louder and less acoustic, while more danceable songs often convey a more positive mood. These insights provide a glimpse into the musical preferences and influences of Jacob Collier.

Here are some insights based on the analysis of when tracks were added to the playlist:

1. Number of Songs Added Each Year: The number of songs added to the playlist has fluctuated over the years. There was a significant increase in the number of songs added around 2016, followed by a decline in the subsequent years. The number of songs added saw another peak in 2019, followed by a decline in 2020 and 2021. This could be related to various factors such as Jacob Collier's personal and professional activities during these periods.
2. Number of Songs Added Each Month (Regardless of Year): When looking at the months regardless of the year, it appears that the number of songs added to the playlist is quite evenly distributed across all months, with slightly more songs added in the early and middle parts of the year (January to July).

This temporal analysis provides additional insights into Jacob Collier's playlist curation habits over time. It's important to note, however, that these trends may be influenced by a variety of factors beyond Jacob's musical preferences, such as his schedule, musical projects, or other personal or professional commitments.

The line plots above show the average values of different song attributes for the songs added to Jacob Collier's playlist each year. Here are some trends:

1. Danceability: The average danceability of songs added to the playlist has generally increased over the years.
2. Energy: The average energy of songs has fluctuated over the years, but there's a general trend of decrease since around 2018.
3. Key: The average key of songs does not show a clear trend over the years.
4. Loudness: The average loudness of songs seems to have slightly increased over the years.
5. Mode: The average mode of songs shows a slight trend towards minor keys in more recent years.
6. Speechiness: The average speechiness of songs added to the playlist has generally decreased over the years.
7. Acousticness: The average acousticness of songs does not show a clear trend over the years.
8. Instrumentalness: The average instrumentalness of songs has decreased, indicating a preference for songs with more vocals in recent years.
9. Liveness: The average liveness of songs does not show a clear trend over the years.
10. Valence: The average valence of songs has fluctuated over the years, with a general decrease in more recent years.
11. Tempo: The average tempo of songs shows a slight decrease over the years.
12. Time Signature: The average time signature of songs does not show a clear trend over the years.

These trends provide insights into how Jacob Collier's musical preferences in terms of these song attributes have evolved over time.

Next, let's check the distribution of song release years for the songs added to the playlist each year.

The boxplot shows the distribution of song release years for the songs added to Jacob Collier's playlist each year. Here are some trends:

1. The median release year of the songs added to the playlist has generally increased over time. This suggests that Jacob has been adding more recent songs to his playlist in recent years.
2. The interquartile range (IQR, represented by the height of the boxes) represents the middle 50% of the release years for the songs added each year. A larger IQR suggests a wider range of song release years. The IQR has generally decreased over time, suggesting that the range of song release years has become narrower in recent years.
3. The whiskers represent the range of song release years within 1.5 times the IQR from the first and third quartiles. Points outside the whiskers are considered outliers. There are several outliers each year, suggesting that Jacob occasionally adds songs that are much older or newer than the majority of the songs he adds that year.

These trends provide further insights into how Jacob Collier's musical preferences in terms of song release years have evolved over time.

The word cloud above shows the 50 most common words in the track names and album names in Jacob Collier's playlist. The size of each word is proportional to its frequency.

Here are some of the most common words:

1. "Love": This is the most common word, suggesting that many of the songs and albums in the playlist have themes related to love.
2. "Live": This word's prominence suggests that the playlist includes several live performances or tracks from live albums.
3. "Remastered": This word's prominence suggests that the playlist includes several remastered tracks or albums.
4. "Version": This word's prominence suggests that the playlist includes several alternative or specific versions of songs.
5. "Feat": This abbreviation for "featuring" suggests that the playlist includes many collaborative tracks.
6. Music-related words: Words like "song", "music", "mix", and "album" are common, as expected in track and album names.

Please note that this analysis does not consider the context in which words appear, so some words may be part of larger phrases with specific meanings.

To analyze trends over time, we can divide the dataset into time periods (e.g., years or months) and generate word clouds for each period. However, this would require a more detailed text analysis, such as topic modeling, to identify meaningful trends and changes in the themes of the songs and albums over time. Due to the complexity of this analysis and the limitations of this platform, it may be more appropriate to conduct it using specialized text analysis tools or software.

The table shows the average values of various song attributes for popular (Popularity > 0) and unpopular (Popularity = 0) tracks in Jacob Collier's playlist. Here are some observations:

1. Danceability: The average danceability is slightly higher for unpopular tracks (0.55) than for popular tracks (0.55).
2. Energy: Unpopular tracks have a slightly higher average energy (0.51) than popular tracks (0.49).
3. Key: The average key is similar for both popular and unpopular tracks.
4. Loudness: The average loudness is slightly higher (less negative) for unpopular tracks (-11.20 dB) than for popular tracks (-11.30 dB).
5. Mode: The average mode is slightly higher for unpopular tracks (0.73) than for popular tracks (0.72), indicating a slight preference for major keys in both groups.
6. Speechiness: Unpopular tracks have a higher average speechiness (0.10) than popular tracks (0.08).
7. Acousticness: Unpopular tracks are slightly more acoustic on average (0.51) than popular tracks (0.49).
8. Instrumentalness: Unpopular tracks have a slightly higher average instrumentalness (0.23) than popular tracks (0.20).
9. Liveness: The average liveness is similar for both popular and unpopular tracks.
10. Valence: Unpopular tracks have a slightly higher average valence (0.51) than popular tracks (0.49).
11. Tempo: The average tempo is slightly lower for unpopular tracks (113.6 BPM) than for popular tracks (114.7 BPM).
12. Time Signature: The average time signature is slightly lower for unpopular tracks (3.76) than for popular tracks (3.81).

These differences are quite small, suggesting that popular and unpopular tracks in Jacob Collier's playlist are similar in terms of these musical attributes. This implies that Jacob's musical preferences are not strongly influenced by the popularity of the tracks.

Here are the correlations between the Popularity of tracks (with Popularity > 0) and other song attributes in Jacob Collier's playlist:

1. Loudness (0.29): There's a moderate positive correlation between Popularity and Loudness. This suggests that louder tracks tend to be more popular.
2. Danceability (0.24): There's a small positive correlation between Popularity and Danceability. This suggests that more danceable tracks tend to be more popular.
3. Energy (0.18): There's a small positive correlation between Popularity and Energy. This suggests that more energetic tracks tend to be more popular.
4. Valence (0.10), Tempo (0.09), Speechiness (0.06), Mode (0.03), and Key (0.02): These attributes have very small positive correlations with Popularity.
5. Liveness (-0.03): There's a very small negative correlation between Popularity and Liveness. This suggests that tracks with a higher presence of an audience (more "live") tend to be slightly less popular.
6. Acousticness (-0.25): There's a small negative correlation between Popularity and Acousticness. This suggests that more acoustic tracks tend to be less popular.
7. Instrumentalness (-0.30): There's a moderate negative correlation between Popularity and Instrumentalness. This suggests that tracks with more vocals (less instrumental) tend to be more popular.

These correlations provide insights into the characteristics of popular tracks in Jacob Collier's playlist. It's important to note, however, that these are correlation coefficients and do not imply causation. Other factors not included in the dataset may also influence a track's popularity.

I apologize for any confusion, but the current dataset you provided does not contain information about the genres of the tracks or artists. To perform an analysis of genres, we would need this information.

If you have an updated dataset that includes genre information, you can upload it and we can continue with the analysis. Alternatively, if the genre information is not available, we can explore other aspects of the dataset.

I see, apologies for the oversight. The 'Genres' column contains a list of genres associated with each track, separated by commas.

Let's proceed with the genre analysis. We can start by finding the most common genres in the playlist. Then, we can also explore how the genres relate to other attributes such as Popularity, Release Year, or song attributes (e.g., danceability, energy). Let's start with finding the most common genres.

The top 20 most common genres in Jacob Collier's playlist are:

1. Indietronica - 87 tracks
2. Art Pop - 84 tracks
3. Rock - 83 tracks
4. Singer-Songwriter - 77 tracks
5. Soul - 75 tracks
6. Permanent Wave - 70 tracks
7. Chamber Pop - 69 tracks
8. Indie Rock - 66 tracks
9. Pop - 65 tracks
10. Classic Rock - 60 tracks
11. Experimental Pop - 59 tracks
12. Soft Rock - 57 tracks
13. Contemporary Jazz - 48 tracks
14. Alternative R&B - 48 tracks
15. Jazz - 47 tracks
16. Mellow Gold - 45 tracks
17. Indie Soul - 44 tracks
18. Hip Hop - 43 tracks
19. Funk - 43 tracks
20. Noise Pop - 43 tracks

This variety of genres reflects Jacob Collier's eclectic taste in music and his appreciation for a wide range of musical styles.

Next, let's explore how the genres relate to other attributes such as Popularity, Release Year, or song attributes like danceability and energy. For this, we can select a few of the most common genres and compare these attributes across these genres. Let's proceed with this analysis.

Apologies for the confusion. It appears that there might be some missing values in the 'Genres' column, which is causing an error when we try to match the genres. Let's handle these missing values by filling them with an empty string and then proceed with the genre analysis.

Here are the average values of various song attributes and Popularity for the top 5 most common genres in Jacob Collier's playlist:

1. Art Pop: This genre has an average danceability of 0.59, energy of 0.51, and popularity of 23.
2. Indietronica: This genre has an average danceability of 0.59, energy of 0.59, and popularity of 28.
3. Rock: This genre has an average danceability of 0.57, energy of 0.56, and popularity of 33.
4. Singer-Songwriter: This genre has an average danceability of 0.56, energy of 0.33 (the lowest among the top genres), and popularity of 29.
5. Soul: This genre has an average danceability of 0.60 (the highest among the top genres), energy of 0.52, and popularity of 38 (the highest among the top genres).

These averages provide insights into the typical characteristics of songs from each genre in Jacob Collier's playlist. For example, Soul songs tend to have the highest danceability and popularity, while Singer-Songwriter songs tend to have the lowest energy.

It's important to note, however, that these are averages and there can be a lot of variability within each genre. Also, the Popularity values are specific to this playlist and may not reflect the overall popularity of these genres in general.

Lastly, the 'Genres' column in the dataset appears to include multiple genres for each track. This analysis considers a track to belong to a genre if that genre is listed among the track's genres, regardless of the order or number of genres listed. Thus, a track could be included in the averages for multiple genres.

The table shows the count and average values of various song attributes and Popularity for tracks in each key in Jacob Collier's playlist:

1. C#/Db: This is the most common key, with 170 tracks. It has an average danceability of 0.58, energy of 0.53, and popularity of 27.
2. C: This key has 137 tracks. It has an average danceability of 0.55, energy of 0.43 (the lowest among the top keys), and popularity of 25.
3. G: This key has 131 tracks. It has an average danceability of 0.54, energy of 0.47, and popularity of 26.
4. F: This key has 120 tracks. It has an average danceability of 0.54, energy of 0.47, and popularity of 30.
5. D: This key has 116 tracks. It has an average danceability of 0.54, energy of 0.47, and popularity of 26.
6. A: This key has 111 tracks. It has an average danceability of 0.56, energy of 0.50, and popularity of 27.
7. F#/Gb: This key has 103 tracks. It has an average danceability of 0.55, energy of 0.51, and popularity of 28.
8. B: This key has 96 tracks. It has an average danceability of 0.59 (the highest among the top keys), energy of 0.58 (the highest among the top keys), and popularity of 30.
9. G#/Ab: This key has 96 tracks. It has an average danceability of 0.53, energy of 0.53, and popularity of 26.
10. E: This key has 88 tracks. It has an average danceability of 0.51, energy of 0.49, and popularity of 30 (the highest among the top keys).
11. A#/Bb: This key has 84 tracks. It has an average danceability of 0.55, energy of 0.51, and popularity of 25.
12. D#/Eb: This key has 53 tracks. It has an average danceability of 0.48 (the lowest among the top keys), energy of 0.34 (the lowest among the top keys), and popularity of 28.

These averages provide insights into the typical characteristics of tracks in each key in Jacob Collier's playlist. For example, tracks in B tend to have the highest danceability and energy, while tracks in E tend to be the most popular. It's important to note, however, that these are averages and there can be a lot of variability within each key. Also, the Popularity values are specific to this playlist and may not reflect the overall popularity of these keys in general.