sol: lab7/songs

lab7/songs/1.sql

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+SELECT
+  name
+FROM
+  songs;

lab7/songs/2.sql

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+SELECT
+  name
+FROM
+  songs
+ORDER BY
+  tempo;

lab7/songs/3.sql

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+SELECT
+  name
+FROM
+  songs
+ORDER BY
+  duration_ms DESC
+LIMIT
+  5;

lab7/songs/4.sql

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+SELECT
+  name
+FROM
+  songs
+WHERE
+  danceability > 0.75
+  AND energy > 0.75
+  AND valence > 0.75;

lab7/songs/5.sql

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+SELECT
+  AVG(energy)
+FROM
+  songs;

lab7/songs/6.sql

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+SELECT
+  songs.name
+FROM
+  songs
+  JOIN artists ON artists.id = songs.artist_id
+WHERE
+  artists.name = 'Post Malone';

lab7/songs/7.sql

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+SELECT
+  AVG(songs.energy)
+FROM
+  songs
+  JOIN artists ON songs.artist_id = artists.id
+WHERE
+  artists.name = 'Drake';

lab7/songs/8.sql

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+SELECT
+  songs.name
+FROM
+  songs
+WHERE
+  songs.name LIKE '%feat%';

lab7/songs/README.md

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+https://cs50.harvard.edu/x/2023/labs/7/

lab7/songs/answers.txt

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+If songs.db contains the top 100 songs of one listener from 2018, how would you characterize their audio aura?
+- With a 0.65 energy, 0.48 valence, 0.71 in danceability, and with the top 5 songs and artists that are part of the popular music of 2018, I'd characterize the listener's audio aura as BASIC.
+
+Hypothesize about why the way you’ve calculated this aura might not be very representative of the listener. What better ways of calculating this aura would you propose?
+- Maybe we can include the average tempo as a feature of the characterization of a listener but I still find that to be too raw and would probably require more refinement.