<!-- generated by epythet -->

# sung

Music data access. Sources from chords&lyrics dataset, spotify and wikipedia.
[pypi](https://pypi.org/project/sung/)
[Documentation](https://thorwhalen.github.io/sung)

# Install

To install:	`pip install sung`

# Chords and Lyrics Processing

The `sung` package provides powerful tools for processing chord sheets and lyrics, including parsing, filtering, formatting, and rendering to various formats.

## Basic Usage

```python
from sung import render_chords_and_lyrics, search_songs

# Search for songs in the dataset
songs = search_songs(title="wonderwall", artist="oasis")
print(f"Found {len(songs)} songs")

# Get the raw chord sheet text
raw_text = songs.iloc[0]["chords&lyrics"]

# Render to text (reconstruct the original format)
formatted_text = render_chords_and_lyrics(raw_text, to="text")
print(formatted_text)

# Render to PDF
render_chords_and_lyrics(raw_text, to="pdf", output_path="song.pdf")
```

## Advanced Features

### Filter Non-Lyrics Content

Remove extraneous text that isn’t part of the actual song. The keyword is `apply_filter_non_lyrics` – `filter_non_lyrics` is the standalone function it delegates to, and passing *that* name as a keyword is silently ignored:

```python
from sung import remove_non_lyrics

# Remove non-lyrics content (section headers, metadata, etc.)
clean_lyrics = render_chords_and_lyrics(
    raw_text, to="text", apply_filter_non_lyrics=True
)

# Keep metadata lines like [Verse], [Chorus]
clean_with_metadata = render_chords_and_lyrics(
    raw_text, to="text", apply_filter_non_lyrics=True, keep_metadata_lines=True
)

# Standalone function
clean_text = remove_non_lyrics(raw_text)
```

### Pack Lines for Better Space Usage

Combine short lines to make better use of horizontal space:

```python
from sung import pack_song_text

# Pack lines up to 80 characters
packed_text = render_chords_and_lyrics(
    raw_text, to="text", pack_lines=True, max_line_length=80
)

# Standalone function
packed = pack_song_text(raw_text, max_length=80)
```

### Combine Features

Use both filtering and packing together:

```python
# Clean and optimized output
optimized = render_chords_and_lyrics(
    raw_text,
    to="text",
    apply_filter_non_lyrics=True,
    pack_lines=True,
    max_line_length=80,
)
```

## PDF Rendering with Custom Styling

```python
# Custom fonts and spacing
render_chords_and_lyrics(
    raw_text,
    to="pdf",
    output_path="styled_song.pdf",
    page_size="LETTER",  # Can use strings like 'A4', 'LETTER', 'LEGAL'
    lyrics_font={"name": "Times-Roman", "size": 14, "color": "black"},
    chords_font={"name": "Helvetica-Bold", "size": 12, "color": "blue"},
    title_font={"name": "Helvetica-Bold", "size": 18, "color": "darkred"},
    margin=50,
    spacing_chord_lyrics=15,
    spacing_group=20,
)
```

## Dataset Access

The package provides access to a large dataset of songs with chords and lyrics:

```python
from sung import get_lyrics_and_chords_dataset

# Load the full dataset (cached after first load)
df = get_lyrics_and_chords_dataset()
print(f"Dataset contains {len(df)} songs")

# A local copy of the zip is read directly, with no Kaggle credentials:
# pass zip_path=, or set SUNG_CHORDS_AND_LYRICS_ZIP, or keep it where haggle
# downloads it ($HAGGLE_ROOTDIR/zips/eitanbentora/chords-and-lyrics-dataset.zip).
# usecols= loads only the columns you need (the CSV is ~650 MB).
df = get_lyrics_and_chords_dataset(
    usecols=["artist_name", "song_name", "chords&lyrics"]
)

# Search by multiple criteria
results = search_songs(
    title="hotel california", artist="eagles", lyrics="welcome to the hotel"
)
```

## Core Functions

- `render_chords_and_lyrics()`: Main function for rendering chord sheets
- `search_songs()`: Search the song database
- `parse_chord_lyrics()`: Parse raw chord/lyrics text into structured data
- `filter_non_lyrics()`: Remove non-lyrics content
- `pack_song_lines()`: Combine short lines for better formatting
- `extract_title()`: Extract song title from chord sheet
- `get_lyrics_and_chords_dataset()`: Access the full dataset

# Spotify

For spotify tools, you’ll also need spotify API access info:

```default
export SPOTIFY_API_CLIENT_ID="your_api_client_id"
export SPOTIFY_API_CLIENT_SECRET="your_api_client_secrete"
export SPOTIPY_REDIRECT_URI="http://127.0.0.1:8000/callback"
export SPOTIPY_CLIENT_ID="$SPOTIFY_API_CLIENT_ID"
export SPOTIPY_CLIENT_SECRET="$SPOTIFY_API_CLIENT_SECRET"
```

## Spotify API credentials?

To obtain Spotify API credentials, follow these steps:

```none
1.	Create a Spotify Developer Account:

•	Visit the [Spotify Developer Dashboard](https://developer.spotify.com/dashboard/).

•	Log in with your Spotify account or create a new one.

2.	Register a New Application:

•	Click on “Create an App.”

•	Provide an App Name and App Description.

•	Agree to the Developer Terms of Service.

•	Click “Create.”

3.	Retrieve Client ID and Client Secret:

•	After creating the app, you’ll be directed to the app’s dashboard.

•	Here, you’ll find your Client ID and Client Secret.

•	Keep these credentials secure; they are essential for API authentication.

4.	Set Redirect URIs (if applicable):

•	In your app settings, click “Edit Settings.”

•	Under “Redirect URIs,” add the URIs where Spotify should redirect after authentication.

•	This is crucial for certain authorization flows.
```

For detailed information on authorization flows and using your credentials, refer to
[Spotify’s Authorization Guide](https://developer.spotify.com/documentation/web-api/concepts/authorization).

Ensure you handle your Client Secret securely and adhere to
[Spotify’s Developer Terms of Service](https://developer.spotify.com/terms/).

> **Note on redirect URIs.** Spotify rejects `http://localhost` as
> “Insecure” since April 2025. Use `http://127.0.0.1:PORT/callback` and
> add that exact value (no trailing slash, exact case) in your app
> dashboard’s Redirect URIs.
> **Note on audio features.** Spotify [removed `/audio-features`
> access](https://developer.spotify.com/blog/2024-11-27-changes-to-the-web-api)
> for apps registered after Nov 2024. `sung` degrades gracefully:
> `Tracks.audio_features` returns `{}` with a one-time warning, and
> `Tracks.data` / `Playlist.data` fall back to metadata-only.

# Building a Spotify Playlist from a List of Song Names

Got a list of song names (from a setlist, a chat, a markdown doc) and want
a playlist? Use `playlist_from_songs` — it searches Spotify for each
song, picks the best match (preferring exact title + artist matches and
filtering out covers/karaoke), and creates the playlist in one call.

```python
from sung import playlist_from_songs

songs = [
    ("Clocks", "Coldplay"),
    "Fix You — Coldplay",  # also: "Title - Artist", "Title by Artist"
    {"name": "Believer", "artist": "Imagine Dragons"},
    "Radioactive",  # title only is fine, but match quality drops
]

playlist, matches = playlist_from_songs(
    songs,
    playlist_name="My Mix",
    # public=True,           # default
    # market="US",           # optional ISO market code
    # search_limit=10,       # candidates to consider per song
    # skip_missing=True,     # drop unresolved instead of raising
)

print(playlist.playlist_url)
for m in matches:
    print(m.summary())
    if m.ambiguous:
        for c in m.candidates[:3]:
            print(
                "   alt:",
                c["name"],
                "—",
                ", ".join(c["artists"]),
                "pop=",
                c["popularity"],
            )
```

`matches` is a `list[SongMatch]`. Each match exposes `track_id`,
`track_name`, `artist_names`, `album_name`, `popularity`, plus
`ambiguous: bool` (top two candidates are different songs/artists scoring
within 10 points — worth showing alternatives) and `not_found: bool`.

## CLI

For one-shot use from a file (or stdin), the same logic is wrapped as
`python -m sung.playlists`:

```bash
# songs.txt — one song per line; "Title - Artist" or just "Title".
# Comment lines (#), markdown bullets (-, *, 1.), and **bold** are tolerated.
python -m sung.playlists songs.txt --name "My Mix"

# Inspect matches without creating the playlist:
python -m sung.playlists songs.txt --dry-run

# JSON report (for chaining):
python -m sung.playlists songs.txt --name "My Mix" --json

# stdin works too:
cat songs.txt | python -m sung.playlists - --name "My Mix"
```

Other flags: `--private` (default is public), `--market US`.

## Lower-level building blocks

If you want to inspect matches before creating anything (or hand-tune
ranking), the helper is a thin layer over:

- `parse_song_descriptor(...)` — turn a descriptor into `(name, artist)`
- `resolve_song(...)` / `resolve_songs(...)` — search + rank, return `SongMatch`(es)
- `Playlist.create_from_track_list(...)` — actually creates the playlist
- `delete_playlist(playlist_id, ask_confirmation=False)` — clean up

# Using the sung.base Module to Search Tracks and Create a Playlist

In this example, we’ll:

* Search for tracks using `Tracks.search`.
* See the search results from the dict-like `tracks` instance
* Display the search results in a pandas DataFrame.
* Create a playlist called “my_test_playlist” with the selected tracks.
* Get the URL of the newly created playlist.
* Delete a playlist
* Instantiate a `Playlist` object using a URL.
* Look at audio features aof this playlist

Import the necessary classes from the sung.base module

```python
from sung.base import Tracks, Playlist
```

Search for tracks with the query ‘Love’, limiting the results to 7
This will return a Tracks object containing the search results

```python
tracks = Tracks.search(query="Love", limit=7)
```

```default
list(tracks)
```

```none
['1vrd6UOGamcKNGnSHJQlSt',
 '3CeCwYWvdfXbZLXFhBrbnf',
 '1dGr1c8CrMLDpV6mPbImSI',
 '0u2P5u6lvoDfwTYjAADbn4',
 '6nGeLlakfzlBcFdZXteDq7',
 '6dBUzqjtbnIa1TwYbyw5CM',
 '7hR22TOX3RorxJPcsz5Wbo']
```

You can also make a `tracks` object by passing a list of track IDs or urls

```python
track_ids = [
    "1vrd6UOGamcKNGnSHJQlSt",
    "3CeCwYWvdfXbZLXFhBrbnf",
    "1dGr1c8CrMLDpV6mPbImSI",
    "0u2P5u6lvoDfwTYjAADbn4",
    "https://open.spotify.com/track/6nGeLlakfzlBcFdZXteDq7",  # url
    "https://open.spotify.com/track/6dBUzqjtbnIa1TwYbyw5CM",  # url
    "spotify:track:7hR22TOX3RorxJPcsz5Wbo",  # uri
]

tracks = Tracks(track_ids)
```

`tracks` is a `Mapping` (that means “dict-like”), so you can do what you do with dicts…

Like listing the tracks’ keys (IDs)

```python
list(tracks)
```

```none
['1vrd6UOGamcKNGnSHJQlSt',
 '3CeCwYWvdfXbZLXFhBrbnf',
 '1dGr1c8CrMLDpV6mPbImSI',
 '0u2P5u6lvoDfwTYjAADbn4',
 '6nGeLlakfzlBcFdZXteDq7',
 '6dBUzqjtbnIa1TwYbyw5CM',
 '7hR22TOX3RorxJPcsz5Wbo']
```

Like Accessing the value of a track for a given key.
The value is a bunch of metadata about the track.

```python
track_metadata = tracks["1dGr1c8CrMLDpV6mPbImSI"]  # get metadata of track via it's id
assert isinstance(track_metadata, dict)
sorted(track_metadata)
```

```none
['album',
 'artists',
 'available_markets',
 'disc_number',
 'duration_ms',
 'explicit',
 'external_ids',
 'external_urls',
 'href',
 'id',
 'is_local',
 'name',
 'popularity',
 'preview_url',
 'track_number',
 'type',
 'uri']
```

But we also have extras over normal dicts.

We can get metadata of a track via it’s index:

```python
track_metadata = tracks[2]  # get metadata of track via it's id
```

We can get a sublist of track metadatas from a list of ids.

```python
list_of_track_metadatas = tracks[
    ["6dBUzqjtbnIa1TwYbyw5CM", "1vrd6UOGamcKNGnSHJQlSt"]
]  # get metadata of tracks via a list of ids
```

We can also get a sublist using slicing.

```python
list_of_track_metadatas = tracks[2:4]  # get metadata of tracks via a slice of ids
```

## Display the search results in a pandas DataFrame

The dataframe method converts the track metadata into a DataFrame for easy viewing.

(Note, you can also use the tracks.dataframe(keys, front_columns=…) to retrieve a data table with more control.)

If you have `pandas` installed, you can get the meta data as a table (dataframe).

```python
tracks.data
```

<div>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>name</th>
      <th>duration_ms</th>
      <th>popularity</th>
      <th>explicit</th>
      <th>id</th>
      <th>album</th>
      <th>artists</th>
      <th>available_markets</th>
      <th>disc_number</th>
      <th>external_ids</th>
      <th>external_urls</th>
      <th>href</th>
      <th>is_local</th>
      <th>preview_url</th>
      <th>track_number</th>
      <th>type</th>
      <th>uri</th>
    </tr>
    <tr>
      <th>id</th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>1vrd6UOGamcKNGnSHJQlSt</th>
      <td>Love Story</td>
      <td>235266</td>
      <td>62</td>
      <td>False</td>
      <td>1vrd6UOGamcKNGnSHJQlSt</td>
      <td>{'album_type': 'album', 'artists': [{'external...</td>
      <td>[{'external_urls': {'spotify': 'https://open.s...</td>
      <td>[CA, US]</td>
      <td>1</td>
      <td>{'isrc': 'USCJY0803275'}</td>
      <td>{'spotify': 'https://open.spotify.com/track/1v...</td>
      <td>https://api.spotify.com/v1/tracks/1vrd6UOGamcK...</td>
      <td>False</td>
      <td>https://p.scdn.co/mp3-preview/7bc39c6033766fc8...</td>
      <td>3</td>
      <td>track</td>
      <td>spotify:track:1vrd6UOGamcKNGnSHJQlSt</td>
    </tr>
    <tr>
      <th>3CeCwYWvdfXbZLXFhBrbnf</th>
      <td>Love Story (Taylor’s Version)</td>
      <td>235766</td>
      <td>76</td>
      <td>False</td>
      <td>3CeCwYWvdfXbZLXFhBrbnf</td>
      <td>{'album_type': 'single', 'artists': [{'externa...</td>
      <td>[{'external_urls': {'spotify': 'https://open.s...</td>
      <td>[AR, AU, AT, BE, BO, BR, BG, CA, CL, CO, CR, C...</td>
      <td>1</td>
      <td>{'isrc': 'USUG12100342'}</td>
      <td>{'spotify': 'https://open.spotify.com/track/3C...</td>
      <td>https://api.spotify.com/v1/tracks/3CeCwYWvdfXb...</td>
      <td>False</td>
      <td>https://p.scdn.co/mp3-preview/b2c1ed4794591a62...</td>
      <td>1</td>
      <td>track</td>
      <td>spotify:track:3CeCwYWvdfXbZLXFhBrbnf</td>
    </tr>
    <tr>
      <th>1dGr1c8CrMLDpV6mPbImSI</th>
      <td>Lover</td>
      <td>221306</td>
      <td>84</td>
      <td>False</td>
      <td>1dGr1c8CrMLDpV6mPbImSI</td>
      <td>{'album_type': 'album', 'artists': [{'external...</td>
      <td>[{'external_urls': {'spotify': 'https://open.s...</td>
      <td>[AR, AU, AT, BE, BO, BR, BG, CA, CL, CO, CR, C...</td>
      <td>1</td>
      <td>{'isrc': 'USUG11901473'}</td>
      <td>{'spotify': 'https://open.spotify.com/track/1d...</td>
      <td>https://api.spotify.com/v1/tracks/1dGr1c8CrMLD...</td>
      <td>False</td>
      <td>https://p.scdn.co/mp3-preview/aad996e106de5278...</td>
      <td>3</td>
      <td>track</td>
      <td>spotify:track:1dGr1c8CrMLDpV6mPbImSI</td>
    </tr>
    <tr>
      <th>0u2P5u6lvoDfwTYjAADbn4</th>
      <td>lovely (with Khalid)</td>
      <td>200185</td>
      <td>86</td>
      <td>False</td>
      <td>0u2P5u6lvoDfwTYjAADbn4</td>
      <td>{'album_type': 'single', 'artists': [{'externa...</td>
      <td>[{'external_urls': {'spotify': 'https://open.s...</td>
      <td>[AR, AU, AT, BE, BO, BR, BG, CA, CL, CO, CR, C...</td>
      <td>1</td>
      <td>{'isrc': 'USUM71804190'}</td>
      <td>{'spotify': 'https://open.spotify.com/track/0u...</td>
      <td>https://api.spotify.com/v1/tracks/0u2P5u6lvoDf...</td>
      <td>False</td>
      <td>https://p.scdn.co/mp3-preview/18b3cbbad11e488c...</td>
      <td>1</td>
      <td>track</td>
      <td>spotify:track:0u2P5u6lvoDfwTYjAADbn4</td>
    </tr>
    <tr>
      <th>6nGeLlakfzlBcFdZXteDq7</th>
      <td>Love Story</td>
      <td>316280</td>
      <td>74</td>
      <td>False</td>
      <td>6nGeLlakfzlBcFdZXteDq7</td>
      <td>{'album_type': 'album', 'artists': [{'external...</td>
      <td>[{'external_urls': {'spotify': 'https://open.s...</td>
      <td>[AR, AU, AT, BE, BO, BR, BG, CA, CL, CO, CR, C...</td>
      <td>1</td>
      <td>{'isrc': 'FRUM71400048'}</td>
      <td>{'spotify': 'https://open.spotify.com/track/6n...</td>
      <td>https://api.spotify.com/v1/tracks/6nGeLlakfzlB...</td>
      <td>False</td>
      <td>https://p.scdn.co/mp3-preview/677f771b1fc30024...</td>
      <td>3</td>
      <td>track</td>
      <td>spotify:track:6nGeLlakfzlBcFdZXteDq7</td>
    </tr>
    <tr>
      <th>6dBUzqjtbnIa1TwYbyw5CM</th>
      <td>Lovers Rock</td>
      <td>213920</td>
      <td>85</td>
      <td>False</td>
      <td>6dBUzqjtbnIa1TwYbyw5CM</td>
      <td>{'album_type': 'album', 'artists': [{'external...</td>
      <td>[{'external_urls': {'spotify': 'https://open.s...</td>
      <td>[AR, AU, AT, BE, BO, BR, BG, CA, CL, CO, CR, C...</td>
      <td>1</td>
      <td>{'isrc': 'USHM21438143'}</td>
      <td>{'spotify': 'https://open.spotify.com/track/6d...</td>
      <td>https://api.spotify.com/v1/tracks/6dBUzqjtbnIa...</td>
      <td>False</td>
      <td>https://p.scdn.co/mp3-preview/922a42db5aa8f8d3...</td>
      <td>9</td>
      <td>track</td>
      <td>spotify:track:6dBUzqjtbnIa1TwYbyw5CM</td>
    </tr>
    <tr>
      <th>7hR22TOX3RorxJPcsz5Wbo</th>
      <td>Love Somebody</td>
      <td>204828</td>
      <td>86</td>
      <td>False</td>
      <td>7hR22TOX3RorxJPcsz5Wbo</td>
      <td>{'album_type': 'single', 'artists': [{'externa...</td>
      <td>[{'external_urls': {'spotify': 'https://open.s...</td>
      <td>[AR, AU, AT, BE, BO, BR, BG, CA, CL, CO, CR, C...</td>
      <td>1</td>
      <td>{'isrc': 'USUG12406387'}</td>
      <td>{'spotify': 'https://open.spotify.com/track/7h...</td>
      <td>https://api.spotify.com/v1/tracks/7hR22TOX3Ror...</td>
      <td>False</td>
      <td>https://p.scdn.co/mp3-preview/00b94e332ed40625...</td>
      <td>1</td>
      <td>track</td>
      <td>spotify:track:7hR22TOX3RorxJPcsz5Wbo</td>
    </tr>
  </tbody>
</table>
</div>

## Make a playlist

Create a new playlist named ‘my_test_playlist’ with the selected tracks
The create_from_track_list class method creates a new playlist with the given tracks

```python
playlist = Playlist.create_from_track_list(
    track_list=selected_track_ids, playlist_name="my_test_playlist"
)
print(f"\nPlaylist '{playlist.playlist_id}' created successfully.")
```

```none
Playlist '7BZcFvIWUnVzvZ5wpVt9cD' created successfully.
```

Get the playlist URL of the newly created playlist (go check it out!)

```python
playlist.playlist_url
```

```none
'https://open.spotify.com/playlist/7BZcFvIWUnVzvZ5wpVt9cD'
```

## Delete a playlist

We purposely tried to make deleting a playlist not as easy as the other actions.
So we didn’t attach a delete method to the playlist instance, but put this in a
separate function you have to import.
Also, we made that function verbose, and asking for confirmation by default.
(But there’s arguments to control that, so you can use `functools.partial` to
make your own cowboy (not speaking and not asking for permission) version).

```python
from sung import delete_playlist

delete_playlist(playlist.playlist_id)
```

Instantiate a Playlist object using a URL.
This allows you to interact with the playlist, such as accessing its tracks.

```python
top50_global_url = (
    "https://open.spotify.com/playlist/37i9dQZEVXbMDoHDwVN2tF?si=d6e0c7bc8f59473b"
)
top50_playlist = Playlist(top50_global_url)
df = top50_playlist.data
df["first_artist"] = df["artists"].apply(lambda x: x[0]["name"])
df["name_and_first_artist"] = df["name"] + " - " + df["first_artist"]
top_5_tracks = top50_playlist.data.iloc[:5].name_and_first_artist
top_5_tracks
```

```none
id
2plbrEY59IikOBgBGLjaoe          Die With A Smile - Lady Gaga
5vNRhkKd0yEAg8suGBpjeY                           APT. - ROSÉ
6dOtVTDdiauQNBQEDOtlAB    BIRDS OF A FEATHER - Billie Eilish
7ne4VBA60CxGM75vw0EYad        That’s So True - Gracie Abrams
7tI8dRuH2Yc6RuoTjxo4dU                           Who - Jimin
Name: name_and_first_artist, dtype: object
```

## Audio features

```python
import pandas as pd

print(f"{top50_playlist.audio_features_df.shape=}")
top50_playlist.audio_features_df.iloc[0]
```

```none
top50_playlist.audio_features_df.shape=(50, 17)


danceability                                                    0.521
energy                                                          0.592
key                                                                 6
loudness                                                       -7.777
mode                                                                0
speechiness                                                    0.0304
acousticness                                                    0.308
instrumentalness                                                  0.0
liveness                                                        0.122
valence                                                         0.535
tempo                                                         157.969
type                                                   audio_features
uri                              spotify:track:2plbrEY59IikOBgBGLjaoe
track_href          https://api.spotify.com/v1/tracks/2plbrEY59Iik...
analysis_url        https://api.spotify.com/v1/audio-analysis/2plb...
duration_ms                                                    251668
time_signature                                                      3
Name: 2plbrEY59IikOBgBGLjaoe, dtype: object
```

```python
import seaborn as sns
from matplotlib import pyplot as plt

df = pd.merge(
    top50_playlist.data,
    top50_playlist.audio_features_df,
    left_index=True,
    right_index=True,
)
# scatter plot with danceability and energy, colored by popularity, with size as loudness
sns.scatterplot(
    data=df, x="danceability", y="energy", hue="popularity", size="loudness"
)

# Move the legend outside the plot
plt.legend(bbox_to_anchor=(1.05, 1), loc="upper left")
```

![png](https://raw.githubusercontent.com/thorwhalen/sung/main/misc/sung_demo_files/sung_demo_31_0.png)

```python
import seaborn as sns

sns.pairplot(
    top50_playlist.audio_features_df[
        [
            "danceability",
            "energy",
            "loudness",
            "speechiness",
            "acousticness",
            "instrumentalness",
            "liveness",
            "valence",
            "tempo",
        ]
    ]
)
```

```none
<seaborn.axisgrid.PairGrid at 0x10ac069b0>
```

![png](https://raw.githubusercontent.com/thorwhalen/sung/main/misc/sung_demo_files/sung_demo_32_1.png)

## Audio analysis

```python
audio_analysis = top50_playlist.audio_analysis(next(iter(top50_playlist)))
```

```python
type(audio_analysis)
```

```none
dict
```

```python
list(audio_analysis)
```

```none
['meta', 'track', 'bars', 'beats', 'sections', 'segments', 'tatums']
```

```python
audio_analysis["meta"]
```

```none
{'analyzer_version': '4.0.0',
 'platform': 'Linux',
 'detailed_status': 'OK',
 'status_code': 0,
 'timestamp': 1723785476,
 'analysis_time': 5.78793,
 'input_process': 'libvorbisfile L+R 44100->22050'}
```

```python
audio_analysis["track"]
```

```none
{'num_samples': 5549270,
 'duration': 251.66757,
 'sample_md5': '',
 'offset_seconds': 0,
 'window_seconds': 0,
 'analysis_sample_rate': 22050,
 'analysis_channels': 1,
 'end_of_fade_in': 0.2034,
 'start_of_fade_out': 241.98096,
 'loudness': -7.863,
 'tempo': 158.005,
 'tempo_confidence': 0.501,
 'time_signature': 3,
 'time_signature_confidence': 1.0,
 'key': 6,
 'key_confidence': 0.253,
 'mode': 0,
 'mode_confidence': 0.452,
 'codestring': 'eJxVmgcC...twbhuw==',
 'code_version': 3.15,
 'echoprintstring': '...',
 'echoprint_version': 4.12,
 'synchstring': 'eJxVWYmR6zoMa8UlSDx09N...EsF7ZvNGhvn5DzGSYaU=',
 'synch_version': 1.0,
 'rhythmstring': 'eJxdnAmSWzkSQ6..._8hOM4=',
 'rhythm_version': 1.0}
```

```python
from lkj import truncate_dict_values

print(f"{len(audio_analysis['bars'])=})")
truncate_dict_values(audio_analysis["bars"])
```

```none
len(audio_analysis['bars'])=211)


[{'start': 1.36936, 'duration': 1.1373, 'confidence': 0.506},
 {'start': 2.50666, 'duration': 1.14286, 'confidence': 0.037}]
```

```python
print(f"{len(audio_analysis['beats'])=})")
truncate_dict_values(audio_analysis["beats"])
```

```none
len(audio_analysis['beats'])=639)


[{'start': 0.598, 'duration': 0.39454, 'confidence': 0.786},
 {'start': 0.99254, 'duration': 0.37682, 'confidence': 0.461}]
```

```python
print(f"{len(audio_analysis['sections'])=})")
truncate_dict_values(audio_analysis["sections"])
```

```none
len(audio_analysis['sections'])=10)


[{'start': 0.0,
  'duration': 9.72348,
  'confidence': 1.0,
  'loudness': -22.269,
  'tempo': 158.348,
  'tempo_confidence': 0.353,
  'key': 1,
  'key_confidence': 0.03,
  'mode': 0,
  'mode_confidence': 0.567,
  'time_signature': 3,
  'time_signature_confidence': 1.0},
 {'start': 9.72348,
  'duration': 29.24311,
  'confidence': 0.606,
  'loudness': -11.712,
  'tempo': 158.114,
  'tempo_confidence': 0.364,
  'key': 9,
  'key_confidence': 0.217,
  'mode': 1,
  'mode_confidence': 0.472,
  'time_signature': 3,
  'time_signature_confidence': 1.0}]
```

```python
print(f"{len(audio_analysis['segments'])=})")
truncate_dict_values(audio_analysis["segments"])
```

```none
len(audio_analysis['segments'])=780)


[{'start': 0.0,
  'duration': 0.2034,
  'confidence': 0.0,
  'loudness_start': -60.0,
  'loudness_max_time': 0.0,
  'loudness_max': -60.0,
  'loudness_end': 0.0,
  'pitches': [1.0, 1.0],
  'timbre': [0.0, 171.13]},
 {'start': 0.2034,
  'duration': 0.41234,
  'confidence': 1.0,
  'loudness_start': -60.0,
  'loudness_max_time': 0.05445,
  'loudness_max': -22.196,
  'loudness_end': 0.0,
  'pitches': [0.082, 0.554],
  'timbre': [33.758, 53.096]}]
```

```python
print(f"{len(audio_analysis['tatums'])=})")
truncate_dict_values(audio_analysis["tatums"])
```

```none
len(audio_analysis['tatums'])=1278)


[{'start': 0.598, 'duration': 0.19727, 'confidence': 0.786},
 {'start': 0.79527, 'duration': 0.19727, 'confidence': 0.786}]
```

```python
from lkj import truncate_dict_values

truncate_dict_values(audio_analysis, max_list_size=2)
```

```none
{'meta': {'analyzer_version': '4.0.0',
  'platform': 'Linux',
  'detailed_status': 'OK',
  'status_code': 0,
  'timestamp': 1476616359,
  'analysis_time': 109.6869,
  'input_process': 'libvorbisfile L+R 44100->22050'},
 'track': {'num_samples': 15594936,
  'duration': 707.25336,
  'sample_md5': '',
  'offset_seconds': 0,
  'window_seconds': 0,
  'analysis_sample_rate': 22050,
  'analysis_channels': 1,
  'end_of_fade_in': 2.82703,
  'start_of_fade_out': 693.6381,
  'loudness': -10.355,
  'tempo': 106.396,
  'tempo_confidence': 0.595,
  'time_signature': 4,
  'time_signature_confidence': 0.904,
  'key': 5,
  'key_confidence': 0.049,
  'mode': 0,
  'mode_confidence': 0.228,
  'codestring': 'eJw1nQmS5LqOBK9SRxB38v4X63BHtv2...y2bmBs1qBYvtiECKLDlx_oH4Y3DGg==',
  'code_version': 3.15,
  'echoprintstring': 'eJzcnQ2u7DhzZLckUaRILkf82_8S5oT...av23MzLxruBTa5AgsAdSL_-A1nrtTE=',
  'echoprint_version': 4.12,
  'synchstring': 'eJx9mAuS5DgIRK_iI1h_6_4XW_IlclV...OPN79PfjNMgH2A-QVgPFZR_AHMBU_o=',
  'synch_version': 1.0,
  'rhythmstring': 'eJyNnVmSJEmOQ68SR9B9uf_FxogHqme...EXgaMRt5h7-gy4yclWmWjb8_wC55QRT',
  'rhythm_version': 1.0},
 'bars': [{'start': 2.64511, 'duration': 2.23768, 'confidence': 0.825},
  {'start': 4.88279, 'duration': 2.16451, 'confidence': 0.352}],
 'beats': [{'start': 0.9181, 'duration': 0.58698, 'confidence': 0.683},
  {'start': 1.50508, 'duration': 0.57369, 'confidence': 0.572}],
 'sections': [{'start': 0.0,
   'duration': 11.51788,
   'confidence': 1.0,
   'loudness': -20.409,
   'tempo': 108.121,
   'tempo_confidence': 0.728,
   'key': 6,
   'key_confidence': 0.162,
   'mode': 1,
   'mode_confidence': 0.478,
   'time_signature': 4,
   'time_signature_confidence': 0.1},
  {'start': 11.51788,
   'duration': 30.61314,
   'confidence': 0.731,
   'loudness': -12.171,
   'tempo': 107.882,
   'tempo_confidence': 0.499,
   'key': 10,
   'key_confidence': 0.429,
   'mode': 0,
   'mode_confidence': 0.387,
   'time_signature': 4,
   'time_signature_confidence': 0.467}],
 'segments': [{'start': 0.0,
   'duration': 0.60957,
   'confidence': 0.0,
   'loudness_start': -60.0,
   'loudness_max_time': 0.5863,
   'loudness_max': -56.103,
   'loudness_end': 0.0,
   'pitches': [0.8, 0.511],
   'timbre': [0.116, 169.016]},
  {'start': 0.60957,
   'duration': 0.60376,
   'confidence': 1.0,
   'loudness_start': -55.895,
   'loudness_max_time': 0.03504,
   'loudness_max': -36.652,
   'loudness_end': 0.0,
   'pitches': [0.434, 0.38],
   'timbre': [13.111, 157.189]}],
 'tatums': [{'start': 0.9181, 'duration': 0.29349, 'confidence': 0.683},
  {'start': 1.21159, 'duration': 0.29349, 'confidence': 0.683}]}
```

# sung

Music data access. Mainly sources from spotify and wikipedia.

# Install

To install:	`pip install sung`

For most tools, you’ll also need a spotify

```default
export SPOTIFY_API_CLIENT_ID="your_api_client_id"
export SPOTIFY_API_CLIENT_SECRET="your_api_client_secrete"
export SPOTIPY_REDIRECT_URI="http://127.0.0.1:8000/callback"
export SPOTIPY_CLIENT_ID="$SPOTIFY_API_CLIENT_ID"
export SPOTIPY_CLIENT_SECRET="$SPOTIFY_API_CLIENT_SECRET"
```

## Spotify API credentials?

To obtain Spotify API credentials, follow these steps:
1.	Create a Spotify Developer Account:
•	Visit the [Spotify Developer Dashboard](https://developer.spotify.com/dashboard/).
•	Log in with your Spotify account or create a new one.
2.	Register a New Application:
•	Click on “Create an App.”
•	Provide an App Name and App Description.
•	Agree to the Developer Terms of Service.
•	Click “Create.”
3.	Retrieve Client ID and Client Secret:
•	After creating the app, you’ll be directed to the app’s dashboard.
•	Here, you’ll find your Client ID and Client Secret.
•	Keep these credentials secure; they are essential for API authentication.
4.	Set Redirect URIs (if applicable):
•	In your app settings, click “Edit Settings.”
•	Under “Redirect URIs,” add the URIs where Spotify should redirect after authentication.
•	This is crucial for certain authorization flows.

For detailed information on authorization flows and using your credentials, refer to
[Spotify’s Authorization Guide](https://developer.spotify.com/documentation/web-api/concepts/authorization).

Ensure you handle your Client Secret securely and adhere to
[Spotify’s Developer Terms of Service](https://developer.spotify.com/terms/).

# Using the sung.base Module to Search Tracks and Create a Playlist

In this example, we’ll:

* Search for tracks using `Tracks.search`.
* See the search results from the dict-like `tracks` instance
* Display the search results in a pandas DataFrame.
* Create a playlist called “my_test_playlist” with the selected tracks.
* Get the URL of the newly created playlist.
* Delete a playlist
* Instantiate a `Playlist` object using a URL.
* Look at audio features aof this playlist

Import the necessary classes from the sung.base module

```python
from sung.base import Tracks, Playlist
```

Search for tracks with the query ‘Love’, limiting the results to 7
This will return a Tracks object containing the search results

```python
tracks = Tracks.search(query="Love", limit=7)
```

```default
list(tracks)
```

```none
['1vrd6UOGamcKNGnSHJQlSt',
 '3CeCwYWvdfXbZLXFhBrbnf',
 '1dGr1c8CrMLDpV6mPbImSI',
 '0u2P5u6lvoDfwTYjAADbn4',
 '6nGeLlakfzlBcFdZXteDq7',
 '6dBUzqjtbnIa1TwYbyw5CM',
 '7hR22TOX3RorxJPcsz5Wbo']
```

You can also make a `tracks` object by passing a list of track IDs or urls

```python
track_ids = [
    "1vrd6UOGamcKNGnSHJQlSt",
    "3CeCwYWvdfXbZLXFhBrbnf",
    "1dGr1c8CrMLDpV6mPbImSI",
    "0u2P5u6lvoDfwTYjAADbn4",
    "https://open.spotify.com/track/6nGeLlakfzlBcFdZXteDq7",  # url
    "https://open.spotify.com/track/6dBUzqjtbnIa1TwYbyw5CM",  # url
    "spotify:track:7hR22TOX3RorxJPcsz5Wbo",  # uri
]

tracks = Tracks(track_ids)
```

`tracks` is a `Mapping` (that means “dict-like”), so you can do what you do with dicts…

Like listing the tracks’ keys (IDs)

```python
list(tracks)
```

```none
['1vrd6UOGamcKNGnSHJQlSt',
 '3CeCwYWvdfXbZLXFhBrbnf',
 '1dGr1c8CrMLDpV6mPbImSI',
 '0u2P5u6lvoDfwTYjAADbn4',
 '6nGeLlakfzlBcFdZXteDq7',
 '6dBUzqjtbnIa1TwYbyw5CM',
 '7hR22TOX3RorxJPcsz5Wbo']
```

Like Accessing the value of a track for a given key.
The value is a bunch of metadata about the track.

```python
track_metadata = tracks["1dGr1c8CrMLDpV6mPbImSI"]  # get metadata of track via it's id
assert isinstance(track_metadata, dict)
sorted(track_metadata)
```

```none
['album',
 'artists',
 'available_markets',
 'disc_number',
 'duration_ms',
 'explicit',
 'external_ids',
 'external_urls',
 'href',
 'id',
 'is_local',
 'name',
 'popularity',
 'preview_url',
 'track_number',
 'type',
 'uri']
```

But we also have extras over normal dicts.

We can get metadata of a track via it’s index:

```python
track_metadata = tracks[2]  # get metadata of track via it's id
```

We can get a sublist of track metadatas from a list of ids.

```python
list_of_track_metadatas = tracks[
    ["6dBUzqjtbnIa1TwYbyw5CM", "1vrd6UOGamcKNGnSHJQlSt"]
]  # get metadata of tracks via a list of ids
```

We can also get a sublist using slicing.

```python
list_of_track_metadatas = tracks[2:4]  # get metadata of tracks via a slice of ids
```

## Display the search results in a pandas DataFrame

The dataframe method converts the track metadata into a DataFrame for easy viewing.

(Note, you can also use the tracks.dataframe(keys, front_columns=…) to retrieve a data table with more control.)

If you have `pandas` installed, you can get the meta data as a table (dataframe).

```python
tracks.data
```

<div>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>name</th>
      <th>duration_ms</th>
      <th>popularity</th>
      <th>explicit</th>
      <th>id</th>
      <th>album</th>
      <th>artists</th>
      <th>available_markets</th>
      <th>disc_number</th>
      <th>external_ids</th>
      <th>external_urls</th>
      <th>href</th>
      <th>is_local</th>
      <th>preview_url</th>
      <th>track_number</th>
      <th>type</th>
      <th>uri</th>
    </tr>
    <tr>
      <th>id</th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>1vrd6UOGamcKNGnSHJQlSt</th>
      <td>Love Story</td>
      <td>235266</td>
      <td>62</td>
      <td>False</td>
      <td>1vrd6UOGamcKNGnSHJQlSt</td>
      <td>{'album_type': 'album', 'artists': [{'external...</td>
      <td>[{'external_urls': {'spotify': 'https://open.s...</td>
      <td>[CA, US]</td>
      <td>1</td>
      <td>{'isrc': 'USCJY0803275'}</td>
      <td>{'spotify': 'https://open.spotify.com/track/1v...</td>
      <td>https://api.spotify.com/v1/tracks/1vrd6UOGamcK...</td>
      <td>False</td>
      <td>https://p.scdn.co/mp3-preview/7bc39c6033766fc8...</td>
      <td>3</td>
      <td>track</td>
      <td>spotify:track:1vrd6UOGamcKNGnSHJQlSt</td>
    </tr>
    <tr>
      <th>3CeCwYWvdfXbZLXFhBrbnf</th>
      <td>Love Story (Taylor’s Version)</td>
      <td>235766</td>
      <td>76</td>
      <td>False</td>
      <td>3CeCwYWvdfXbZLXFhBrbnf</td>
      <td>{'album_type': 'single', 'artists': [{'externa...</td>
      <td>[{'external_urls': {'spotify': 'https://open.s...</td>
      <td>[AR, AU, AT, BE, BO, BR, BG, CA, CL, CO, CR, C...</td>
      <td>1</td>
      <td>{'isrc': 'USUG12100342'}</td>
      <td>{'spotify': 'https://open.spotify.com/track/3C...</td>
      <td>https://api.spotify.com/v1/tracks/3CeCwYWvdfXb...</td>
      <td>False</td>
      <td>https://p.scdn.co/mp3-preview/b2c1ed4794591a62...</td>
      <td>1</td>
      <td>track</td>
      <td>spotify:track:3CeCwYWvdfXbZLXFhBrbnf</td>
    </tr>
    <tr>
      <th>1dGr1c8CrMLDpV6mPbImSI</th>
      <td>Lover</td>
      <td>221306</td>
      <td>84</td>
      <td>False</td>
      <td>1dGr1c8CrMLDpV6mPbImSI</td>
      <td>{'album_type': 'album', 'artists': [{'external...</td>
      <td>[{'external_urls': {'spotify': 'https://open.s...</td>
      <td>[AR, AU, AT, BE, BO, BR, BG, CA, CL, CO, CR, C...</td>
      <td>1</td>
      <td>{'isrc': 'USUG11901473'}</td>
      <td>{'spotify': 'https://open.spotify.com/track/1d...</td>
      <td>https://api.spotify.com/v1/tracks/1dGr1c8CrMLD...</td>
      <td>False</td>
      <td>https://p.scdn.co/mp3-preview/aad996e106de5278...</td>
      <td>3</td>
      <td>track</td>
      <td>spotify:track:1dGr1c8CrMLDpV6mPbImSI</td>
    </tr>
    <tr>
      <th>0u2P5u6lvoDfwTYjAADbn4</th>
      <td>lovely (with Khalid)</td>
      <td>200185</td>
      <td>86</td>
      <td>False</td>
      <td>0u2P5u6lvoDfwTYjAADbn4</td>
      <td>{'album_type': 'single', 'artists': [{'externa...</td>
      <td>[{'external_urls': {'spotify': 'https://open.s...</td>
      <td>[AR, AU, AT, BE, BO, BR, BG, CA, CL, CO, CR, C...</td>
      <td>1</td>
      <td>{'isrc': 'USUM71804190'}</td>
      <td>{'spotify': 'https://open.spotify.com/track/0u...</td>
      <td>https://api.spotify.com/v1/tracks/0u2P5u6lvoDf...</td>
      <td>False</td>
      <td>https://p.scdn.co/mp3-preview/18b3cbbad11e488c...</td>
      <td>1</td>
      <td>track</td>
      <td>spotify:track:0u2P5u6lvoDfwTYjAADbn4</td>
    </tr>
    <tr>
      <th>6nGeLlakfzlBcFdZXteDq7</th>
      <td>Love Story</td>
      <td>316280</td>
      <td>74</td>
      <td>False</td>
      <td>6nGeLlakfzlBcFdZXteDq7</td>
      <td>{'album_type': 'album', 'artists': [{'external...</td>
      <td>[{'external_urls': {'spotify': 'https://open.s...</td>
      <td>[AR, AU, AT, BE, BO, BR, BG, CA, CL, CO, CR, C...</td>
      <td>1</td>
      <td>{'isrc': 'FRUM71400048'}</td>
      <td>{'spotify': 'https://open.spotify.com/track/6n...</td>
      <td>https://api.spotify.com/v1/tracks/6nGeLlakfzlB...</td>
      <td>False</td>
      <td>https://p.scdn.co/mp3-preview/677f771b1fc30024...</td>
      <td>3</td>
      <td>track</td>
      <td>spotify:track:6nGeLlakfzlBcFdZXteDq7</td>
    </tr>
    <tr>
      <th>6dBUzqjtbnIa1TwYbyw5CM</th>
      <td>Lovers Rock</td>
      <td>213920</td>
      <td>85</td>
      <td>False</td>
      <td>6dBUzqjtbnIa1TwYbyw5CM</td>
      <td>{'album_type': 'album', 'artists': [{'external...</td>
      <td>[{'external_urls': {'spotify': 'https://open.s...</td>
      <td>[AR, AU, AT, BE, BO, BR, BG, CA, CL, CO, CR, C...</td>
      <td>1</td>
      <td>{'isrc': 'USHM21438143'}</td>
      <td>{'spotify': 'https://open.spotify.com/track/6d...</td>
      <td>https://api.spotify.com/v1/tracks/6dBUzqjtbnIa...</td>
      <td>False</td>
      <td>https://p.scdn.co/mp3-preview/922a42db5aa8f8d3...</td>
      <td>9</td>
      <td>track</td>
      <td>spotify:track:6dBUzqjtbnIa1TwYbyw5CM</td>
    </tr>
    <tr>
      <th>7hR22TOX3RorxJPcsz5Wbo</th>
      <td>Love Somebody</td>
      <td>204828</td>
      <td>86</td>
      <td>False</td>
      <td>7hR22TOX3RorxJPcsz5Wbo</td>
      <td>{'album_type': 'single', 'artists': [{'externa...</td>
      <td>[{'external_urls': {'spotify': 'https://open.s...</td>
      <td>[AR, AU, AT, BE, BO, BR, BG, CA, CL, CO, CR, C...</td>
      <td>1</td>
      <td>{'isrc': 'USUG12406387'}</td>
      <td>{'spotify': 'https://open.spotify.com/track/7h...</td>
      <td>https://api.spotify.com/v1/tracks/7hR22TOX3Ror...</td>
      <td>False</td>
      <td>https://p.scdn.co/mp3-preview/00b94e332ed40625...</td>
      <td>1</td>
      <td>track</td>
      <td>spotify:track:7hR22TOX3RorxJPcsz5Wbo</td>
    </tr>
  </tbody>
</table>
</div>

## Make a playlist

Create a new playlist named ‘my_test_playlist’ with the selected tracks
The create_from_track_list class method creates a new playlist with the given tracks

```python
playlist = Playlist.create_from_track_list(
    track_list=selected_track_ids,
    playlist_name='my_test_playlist'
)
print(f"
Playlist '{playlist.playlist_id}' created successfully.")

```

```none
Playlist '7BZcFvIWUnVzvZ5wpVt9cD' created successfully.
```

Get the playlist URL of the newly created playlist (go check it out!)

```python
playlist.playlist_url
```

```none
'https://open.spotify.com/playlist/7BZcFvIWUnVzvZ5wpVt9cD'
```

## Delete a playlist

We purposely tried to make deleting a playlist not as easy as the other actions.
So we didn’t attach a delete method to the playlist instance, but put this in a
separate function you have to import.
Also, we made that function verbose, and asking for confirmation by default.
(But there’s arguments to control that, so you can use `functools.partial` to
make your own cowboy (not speaking and not asking for permission) version).

```python
from sung import delete_playlist

delete_playlist(playlist.playlist_id)
```

Instantiate a Playlist object using a URL.
This allows you to interact with the playlist, such as accessing its tracks.

```python
top50_global_url = (
    "https://open.spotify.com/playlist/37i9dQZEVXbMDoHDwVN2tF?si=d6e0c7bc8f59473b"
)
top50_playlist = Playlist(top50_global_url)
df = top50_playlist.data
df["first_artist"] = df["artists"].apply(lambda x: x[0]["name"])
df["name_and_first_artist"] = df["name"] + " - " + df["first_artist"]
top_5_tracks = top50_playlist.data.iloc[:5].name_and_first_artist
top_5_tracks
```

```none
id
2plbrEY59IikOBgBGLjaoe          Die With A Smile - Lady Gaga
5vNRhkKd0yEAg8suGBpjeY                           APT. - ROSÉ
6dOtVTDdiauQNBQEDOtlAB    BIRDS OF A FEATHER - Billie Eilish
7ne4VBA60CxGM75vw0EYad        That’s So True - Gracie Abrams
7tI8dRuH2Yc6RuoTjxo4dU                           Who - Jimin
Name: name_and_first_artist, dtype: object
```

## Audio features

```python
import pandas as pd

print(f"{top50_playlist.audio_features_df.shape=}")
top50_playlist.audio_features_df.iloc[0]
```

```none
top50_playlist.audio_features_df.shape=(50, 17)


danceability                                                    0.521
energy                                                          0.592
key                                                                 6
loudness                                                       -7.777
mode                                                                0
speechiness                                                    0.0304
acousticness                                                    0.308
instrumentalness                                                  0.0
liveness                                                        0.122
valence                                                         0.535
tempo                                                         157.969
type                                                   audio_features
uri                              spotify:track:2plbrEY59IikOBgBGLjaoe
track_href          https://api.spotify.com/v1/tracks/2plbrEY59Iik...
analysis_url        https://api.spotify.com/v1/audio-analysis/2plb...
duration_ms                                                    251668
time_signature                                                      3
Name: 2plbrEY59IikOBgBGLjaoe, dtype: object
```

```python
import seaborn as sns
from matplotlib import pyplot as plt

df = pd.merge(
    top50_playlist.data,
    top50_playlist.audio_features_df,
    left_index=True,
    right_index=True,
)
# scatter plot with danceability and energy, colored by popularity, with size as loudness
sns.scatterplot(
    data=df, x="danceability", y="energy", hue="popularity", size="loudness"
)

# Move the legend outside the plot
plt.legend(bbox_to_anchor=(1.05, 1), loc="upper left")
```

![png](https://raw.githubusercontent.com/thorwhalen/sung/main/misc/sung_demo_files/sung_demo_31_0.png)

```python
import seaborn as sns

sns.pairplot(
    top50_playlist.audio_features_df[
        [
            "danceability",
            "energy",
            "loudness",
            "speechiness",
            "acousticness",
            "instrumentalness",
            "liveness",
            "valence",
            "tempo",
        ]
    ]
)
```

```none
<seaborn.axisgrid.PairGrid at 0x10ac069b0>
```

![png](https://raw.githubusercontent.com/thorwhalen/sung/main/misc/sung_demo_files/sung_demo_32_1.png)

## Audio analysis

```python
audio_analysis = top50_playlist.audio_analysis(next(iter(top50_playlist)))
```

```python
type(audio_analysis)
```

```none
dict
```

```python
list(audio_analysis)
```

```none
['meta', 'track', 'bars', 'beats', 'sections', 'segments', 'tatums']
```

```python
audio_analysis["meta"]
```

```none
{'analyzer_version': '4.0.0',
 'platform': 'Linux',
 'detailed_status': 'OK',
 'status_code': 0,
 'timestamp': 1723785476,
 'analysis_time': 5.78793,
 'input_process': 'libvorbisfile L+R 44100->22050'}
```

```python
audio_analysis["track"]
```

```none
{'num_samples': 5549270,
 'duration': 251.66757,
 'sample_md5': '',
 'offset_seconds': 0,
 'window_seconds': 0,
 'analysis_sample_rate': 22050,
 'analysis_channels': 1,
 'end_of_fade_in': 0.2034,
 'start_of_fade_out': 241.98096,
 'loudness': -7.863,
 'tempo': 158.005,
 'tempo_confidence': 0.501,
 'time_signature': 3,
 'time_signature_confidence': 1.0,
 'key': 6,
 'key_confidence': 0.253,
 'mode': 0,
 'mode_confidence': 0.452,
 'codestring': 'eJxVmgcC...twbhuw==',
 'code_version': 3.15,
 'echoprintstring': '...',
 'echoprint_version': 4.12,
 'synchstring': 'eJxVWYmR6zoMa8UlSDx09N...EsF7ZvNGhvn5DzGSYaU=',
 'synch_version': 1.0,
 'rhythmstring': 'eJxdnAmSWzkSQ6..._8hOM4=',
 'rhythm_version': 1.0}
```

```python
from lkj import truncate_dict_values

print(f"{len(audio_analysis['bars'])=})")
truncate_dict_values(audio_analysis["bars"])
```

```none
len(audio_analysis['bars'])=211)


[{'start': 1.36936, 'duration': 1.1373, 'confidence': 0.506},
 {'start': 2.50666, 'duration': 1.14286, 'confidence': 0.037}]
```

```python
print(f"{len(audio_analysis['beats'])=})")
truncate_dict_values(audio_analysis["beats"])
```

```none
len(audio_analysis['beats'])=639)


[{'start': 0.598, 'duration': 0.39454, 'confidence': 0.786},
 {'start': 0.99254, 'duration': 0.37682, 'confidence': 0.461}]
```

```python
print(f"{len(audio_analysis['sections'])=})")
truncate_dict_values(audio_analysis["sections"])
```

```none
len(audio_analysis['sections'])=10)


[{'start': 0.0,
  'duration': 9.72348,
  'confidence': 1.0,
  'loudness': -22.269,
  'tempo': 158.348,
  'tempo_confidence': 0.353,
  'key': 1,
  'key_confidence': 0.03,
  'mode': 0,
  'mode_confidence': 0.567,
  'time_signature': 3,
  'time_signature_confidence': 1.0},
 {'start': 9.72348,
  'duration': 29.24311,
  'confidence': 0.606,
  'loudness': -11.712,
  'tempo': 158.114,
  'tempo_confidence': 0.364,
  'key': 9,
  'key_confidence': 0.217,
  'mode': 1,
  'mode_confidence': 0.472,
  'time_signature': 3,
  'time_signature_confidence': 1.0}]
```

```python
print(f"{len(audio_analysis['segments'])=})")
truncate_dict_values(audio_analysis["segments"])
```

```none
len(audio_analysis['segments'])=780)


[{'start': 0.0,
  'duration': 0.2034,
  'confidence': 0.0,
  'loudness_start': -60.0,
  'loudness_max_time': 0.0,
  'loudness_max': -60.0,
  'loudness_end': 0.0,
  'pitches': [1.0, 1.0],
  'timbre': [0.0, 171.13]},
 {'start': 0.2034,
  'duration': 0.41234,
  'confidence': 1.0,
  'loudness_start': -60.0,
  'loudness_max_time': 0.05445,
  'loudness_max': -22.196,
  'loudness_end': 0.0,
  'pitches': [0.082, 0.554],
  'timbre': [33.758, 53.096]}]
```

```python
print(f"{len(audio_analysis['tatums'])=})")
truncate_dict_values(audio_analysis["tatums"])
```

```none
len(audio_analysis['tatums'])=1278)


[{'start': 0.598, 'duration': 0.19727, 'confidence': 0.786},
 {'start': 0.79527, 'duration': 0.19727, 'confidence': 0.786}]
```

```python
from lkj import truncate_dict_values

truncate_dict_values(audio_analysis, max_list_size=2)
```

```none
{'meta': {'analyzer_version': '4.0.0',
  'platform': 'Linux',
  'detailed_status': 'OK',
  'status_code': 0,
  'timestamp': 1476616359,
  'analysis_time': 109.6869,
  'input_process': 'libvorbisfile L+R 44100->22050'},
 'track': {'num_samples': 15594936,
  'duration': 707.25336,
  'sample_md5': '',
  'offset_seconds': 0,
  'window_seconds': 0,
  'analysis_sample_rate': 22050,
  'analysis_channels': 1,
  'end_of_fade_in': 2.82703,
  'start_of_fade_out': 693.6381,
  'loudness': -10.355,
  'tempo': 106.396,
  'tempo_confidence': 0.595,
  'time_signature': 4,
  'time_signature_confidence': 0.904,
  'key': 5,
  'key_confidence': 0.049,
  'mode': 0,
  'mode_confidence': 0.228,
  'codestring': 'eJw1nQmS5LqOBK9SRxB38v4X63BHtv2...y2bmBs1qBYvtiECKLDlx_oH4Y3DGg==',
  'code_version': 3.15,
  'echoprintstring': 'eJzcnQ2u7DhzZLckUaRILkf82_8S5oT...av23MzLxruBTa5AgsAdSL_-A1nrtTE=',
  'echoprint_version': 4.12,
  'synchstring': 'eJx9mAuS5DgIRK_iI1h_6_4XW_IlclV...OPN79PfjNMgH2A-QVgPFZR_AHMBU_o=',
  'synch_version': 1.0,
  'rhythmstring': 'eJyNnVmSJEmOQ68SR9B9uf_FxogHqme...EXgaMRt5h7-gy4yclWmWjb8_wC55QRT',
  'rhythm_version': 1.0},
 'bars': [{'start': 2.64511, 'duration': 2.23768, 'confidence': 0.825},
  {'start': 4.88279, 'duration': 2.16451, 'confidence': 0.352}],
 'beats': [{'start': 0.9181, 'duration': 0.58698, 'confidence': 0.683},
  {'start': 1.50508, 'duration': 0.57369, 'confidence': 0.572}],
 'sections': [{'start': 0.0,
   'duration': 11.51788,
   'confidence': 1.0,
   'loudness': -20.409,
   'tempo': 108.121,
   'tempo_confidence': 0.728,
   'key': 6,
   'key_confidence': 0.162,
   'mode': 1,
   'mode_confidence': 0.478,
   'time_signature': 4,
   'time_signature_confidence': 0.1},
  {'start': 11.51788,
   'duration': 30.61314,
   'confidence': 0.731,
   'loudness': -12.171,
   'tempo': 107.882,
   'tempo_confidence': 0.499,
   'key': 10,
   'key_confidence': 0.429,
   'mode': 0,
   'mode_confidence': 0.387,
   'time_signature': 4,
   'time_signature_confidence': 0.467}],
 'segments': [{'start': 0.0,
   'duration': 0.60957,
   'confidence': 0.0,
   'loudness_start': -60.0,
   'loudness_max_time': 0.5863,
   'loudness_max': -56.103,
   'loudness_end': 0.0,
   'pitches': [0.8, 0.511],
   'timbre': [0.116, 169.016]},
  {'start': 0.60957,
   'duration': 0.60376,
   'confidence': 1.0,
   'loudness_start': -55.895,
   'loudness_max_time': 0.03504,
   'loudness_max': -36.652,
   'loudness_end': 0.0,
   'pitches': [0.434, 0.38],
   'timbre': [13.111, 157.189]}],
 'tatums': [{'start': 0.9181, 'duration': 0.29349, 'confidence': 0.683},
  {'start': 1.21159, 'duration': 0.29349, 'confidence': 0.683}]}
```

# Analyze a playlist

```python
from sung import TracksAnalysis, ensure_playlist_id
```

## Initialize the Class with a Playlist ID

```python
# Let's analyze my daughter's playlist...

playlist = (
    "https://open.spotify.com/playlist/4nEeS47ineUShHK2iAVeO0?si=be16c62b664f43f3"
)

ta = TracksAnalysis(playlist)
```

Note that, alternatively, if you already have a dataframe, you can just give `TracksAnalysis` that.
Note that it must have been prepared by `TracksAnalysis`, or at least satisfy the dataframe conditions on the columns.

```python
# import pandas as pd
# df = pd.read_excel("~/Dropbox/_odata/ai_contexts/misc/music/encore_playlist.xlsx")
# ta = TracksAnalysis(df)
```

## Access the Processed Dataframe

```python
ta.df.head()
```

<div>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>name</th>
      <th>first_artist</th>
      <th>duration_ms</th>
      <th>popularity</th>
      <th>explicit</th>
      <th>album_release_date</th>
      <th>album_release_year</th>
      <th>added_at_date</th>
      <th>url</th>
      <th>first_letter</th>
      <th>...</th>
      <th>artists</th>
      <th>disc_number</th>
      <th>track_number</th>
      <th>external_ids</th>
      <th>external_urls</th>
      <th>href</th>
      <th>uri</th>
      <th>is_local</th>
      <th>added_at_datetime</th>
      <th>id_y</th>
    </tr>
    <tr>
      <th>id</th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>62HY7V5hRKtfIZ7uCYqYqu</th>
      <td>The Imperial March (Darth Vader's Theme)</td>
      <td>John Williams</td>
      <td>182973</td>
      <td>0</td>
      <td>False</td>
      <td>1997-01-01</td>
      <td>1997</td>
      <td>NaN</td>
      <td>https://open.spotify.com/track/62HY7V5hRKtfIZ7...</td>
      <td>T</td>
      <td>...</td>
      <td>[{'external_urls': {'spotify': 'https://open.s...</td>
      <td>2</td>
      <td>1</td>
      <td>{'isrc': 'USSM10411815'}</td>
      <td>{'spotify': 'https://open.spotify.com/track/62...</td>
      <td>https://api.spotify.com/v1/tracks/62HY7V5hRKtf...</td>
      <td>spotify:track:62HY7V5hRKtfIZ7uCYqYqu</td>
      <td>False</td>
      <td>NaN</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>0mVL6TMwrRqFsLRgoKoAfS</th>
      <td>Wimoweh (Mbube)</td>
      <td>Yma Sumac</td>
      <td>158440</td>
      <td>0</td>
      <td>False</td>
      <td>2015-01-28</td>
      <td>2015</td>
      <td>NaN</td>
      <td>https://open.spotify.com/track/0mVL6TMwrRqFsLR...</td>
      <td>W</td>
      <td>...</td>
      <td>[{'external_urls': {'spotify': 'https://open.s...</td>
      <td>1</td>
      <td>4</td>
      <td>{'isrc': 'FR0W61497652'}</td>
      <td>{'spotify': 'https://open.spotify.com/track/0m...</td>
      <td>https://api.spotify.com/v1/tracks/0mVL6TMwrRqF...</td>
      <td>spotify:track:0mVL6TMwrRqFsLRgoKoAfS</td>
      <td>False</td>
      <td>NaN</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>3N7aUWtL9EvKrBtmSCxuYW</th>
      <td>Mah Na Mah Na</td>
      <td>Mahna Mahna and The Two Snowths</td>
      <td>125906</td>
      <td>10</td>
      <td>False</td>
      <td>2011-01-01</td>
      <td>2011</td>
      <td>NaN</td>
      <td>https://open.spotify.com/track/3N7aUWtL9EvKrBt...</td>
      <td>M</td>
      <td>...</td>
      <td>[{'external_urls': {'spotify': 'https://open.s...</td>
      <td>1</td>
      <td>30</td>
      <td>{'isrc': 'USWD11158969'}</td>
      <td>{'spotify': 'https://open.spotify.com/track/3N...</td>
      <td>https://api.spotify.com/v1/tracks/3N7aUWtL9EvK...</td>
      <td>spotify:track:3N7aUWtL9EvKrBtmSCxuYW</td>
      <td>False</td>
      <td>NaN</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>1XieuY2bxCfXNHwSDJQrPs</th>
      <td>My Baby Just Cares For Me</td>
      <td>Nina Simone</td>
      <td>215706</td>
      <td>36</td>
      <td>False</td>
      <td>2007-01-01</td>
      <td>2007</td>
      <td>NaN</td>
      <td>https://open.spotify.com/track/1XieuY2bxCfXNHw...</td>
      <td>M</td>
      <td>...</td>
      <td>[{'external_urls': {'spotify': 'https://open.s...</td>
      <td>1</td>
      <td>8</td>
      <td>{'isrc': 'USPR38700001'}</td>
      <td>{'spotify': 'https://open.spotify.com/track/1X...</td>
      <td>https://api.spotify.com/v1/tracks/1XieuY2bxCfX...</td>
      <td>spotify:track:1XieuY2bxCfXNHwSDJQrPs</td>
      <td>False</td>
      <td>NaN</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>40Fr7DB1j6RB2DqygQI4LI</th>
      <td>Holocaust</td>
      <td>Ceza</td>
      <td>207746</td>
      <td>57</td>
      <td>False</td>
      <td>2004-07-01</td>
      <td>2004</td>
      <td>NaN</td>
      <td>https://open.spotify.com/track/40Fr7DB1j6RB2Dq...</td>
      <td>H</td>
      <td>...</td>
      <td>[{'external_urls': {'spotify': 'https://open.s...</td>
      <td>1</td>
      <td>3</td>
      <td>{'isrc': 'TR0640600079'}</td>
      <td>{'spotify': 'https://open.spotify.com/track/40...</td>
      <td>https://api.spotify.com/v1/tracks/40Fr7DB1j6RB...</td>
      <td>spotify:track:40Fr7DB1j6RB2DqygQI4LI</td>
      <td>False</td>
      <td>NaN</td>
      <td>NaN</td>
    </tr>
  </tbody>
</table>
<p>5 rows × 29 columns</p>
</div>

## Audio Features Analysis

```python
ta.plot_features_histogram()
```

![png](https://raw.githubusercontent.com/thorwhalen/sung/main/misc/playlist_analysis_files/playlist_analysis_10_0.png)

```python
ta.plot_features_scatter()
```

![png](https://raw.githubusercontent.com/thorwhalen/sung/main/misc/playlist_analysis_files/playlist_analysis_11_0.png)

You can choose what fields to map to what plot features (x, y, size and hue).

The names of the audio feature fields with their descriptions, are

```python
from sung.util import spotify_features_fields

spotify_features_fields
```

```none
{'name': 'The name of the track.',
 'artist': 'The name of the primary artist associated with the track.',
 'album': 'The name of the album in which the track appears.',
 'release_date': 'The release date of the album or single. Format can vary (e.g., YYYY-MM-DD).',
 'duration_ms': "The track's duration in milliseconds.",
 'popularity': 'The popularity of the track, with values ranging from 0 to 100. Higher values indicate greater popularity.',
 'explicit': 'A boolean indicating whether the track contains explicit content.',
 'external_url': 'A dictionary of external URLs, including a link to the track on Spotify.',
 'preview_url': 'A URL to a 30-second preview of the track, if available.',
 'track_number': "The track's position within its album or single. The first track is 1.",
 'album_total_tracks': 'The total number of tracks in the album that contains this track.',
 'available_markets': 'A list of country codes where the track is available.',
 'album_images': 'A list of album cover art images in various sizes, with URLs to access them.',
 'acousticness': 'A confidence measure from 0.0 to 1.0 indicating the likelihood that the track is acoustic. Higher values denote a higher probability. Range: 0.0 to 1.0.',
 'danceability': 'Reflects how suitable a track is for dancing, based on tempo, rhythm stability, beat strength, and overall regularity. Higher values indicate greater danceability. Range: 0.0 to 1.0.',
 'energy': 'Measures the intensity and activity of a track. Energetic tracks feel fast, loud, and noisy. Higher values represent more energy. Range: 0.0 to 1.0.',
 'instrumentalness': 'Predicts whether a track contains no vocals. Higher values suggest a greater likelihood of the track being instrumental. Range: 0.0 to 1.0.',
 'liveness': 'Detects the presence of an audience in the recording. Higher values indicate a higher probability of the track being performed live. Range: 0.0 to 1.0.',
 'loudness': 'The overall loudness of a track in decibels (dB), averaged across the entire track. Useful for comparing the relative loudness of tracks. Typical range: -60 to 0 dB.',
 'speechiness': 'Measures the presence of spoken words in a track. Higher values indicate more speech-like content. Values above 0.66 suggest tracks made entirely of spoken words; values between 0.33 and 0.66 may contain both music and speech; values below 0.33 likely represent music and other non-speech-like tracks. Range: 0.0 to 1.0.',
 'valence': 'Describes the musical positiveness conveyed by a track. Higher values sound more positive (e.g., happy, cheerful), while lower values sound more negative (e.g., sad, angry). Range: 0.0 to 1.0.',
 'tempo': 'The estimated tempo of a track in beats per minute (BPM). Range: 0 to 250 BPM.',
 'key': 'The estimated overall key of the track, represented as an integer corresponding to standard Pitch Class notation (e.g., 0 = C, 1 = C♯/D♭, ..., 11 = B). If no key was detected, the value is -1. Range: -1 to 11.',
 'mode': 'Indicates the modality (major or minor) of a track. Major is represented by 1 and minor by 0. Range: 0 or 1.',
 'time_signature': 'An estimated overall time signature of a track, indicating how many beats are in each bar. Range: 3 to 7.'}
```

```python
ta.plot_features_scatter(x="valence", y="tempo", hue="key")
```

![png](https://raw.githubusercontent.com/thorwhalen/sung/main/misc/playlist_analysis_files/playlist_analysis_14_0.png)

```python

```

```python
ta.plot_dataframe_distributions()
```

![png](https://raw.githubusercontent.com/thorwhalen/sung/main/misc/playlist_analysis_files/playlist_analysis_16_0.png)

```python
ta.plot_features_pairs()
```

```none
<seaborn.axisgrid.PairGrid at 0x3291ac1f0>
```

![png](https://raw.githubusercontent.com/thorwhalen/sung/main/misc/playlist_analysis_files/playlist_analysis_17_1.png)

```python

```

## Metadata Analyses

```python
print(f"Number of songs: {ta.number_of_songs}")
print(f"Number of unique names: {ta.number_of_unique_names}")
```

```none
Number of songs: 182
Number of unique names: 178
```

### Print Duplicates

```python
ta.print_duplicates()
```

```none
### Duplicates
|                              |   count |
|:-----------------------------|--------:|
| Houdini                      |       2 |
| Anything You Can Do          |       2 |
| Somebody That I Used to Know |       2 |
| The Bare Necessities         |       2 |
```

### Print Most Popular Songs

```python
ta.print_most_popular_songs(n=20)
```

```none
| name                                            | first_artist      |   popularity |
|:------------------------------------------------|:------------------|-------------:|
| Espresso                                        | Sabrina Carpenter |           90 |
| exes                                            | Tate McRae        |           83 |
| Sweet Dreams (Are Made of This) - 2005 Remaster | Eurythmics        |           83 |
| Zombie                                          | The Cranberries   |           82 |
| Here Comes The Sun - Remastered 2009            | The Beatles       |           82 |
| Before You Go                                   | Lewis Capaldi     |           81 |
| bad guy                                         | Billie Eilish     |           81 |
| ...Baby One More Time                           | Britney Spears    |           81 |
| Tainted Love                                    | Soft Cell         |           79 |
| Wrecking Ball                                   | Miley Cyrus       |           78 |
| New Rules                                       | Dua Lipa          |           78 |
| Rolling in the Deep                             | Adele             |           77 |
| Kings & Queens                                  | Ava Max           |           77 |
| Easy On Me                                      | Adele             |           77 |
| Roar                                            | Katy Perry        |           77 |
| bellyache                                       | Billie Eilish     |           76 |
| Jolene                                          | Dolly Parton      |           75 |
| Symphony (feat. Zara Larsson)                   | Clean Bandit      |           75 |
| Nice For What                                   | Drake             |           74 |
| bury a friend                                   | Billie Eilish     |           74 |
```

### Print Top Artists

```python
ta.print_top_artists(n=25)
```

```none
| first_artist       |   count |
|:-------------------|--------:|
| Dua Lipa           |       6 |
| Eminem             |       5 |
| Jacob Collier      |       4 |
| Norris Nuts        |       4 |
| The Beatles        |       4 |
| Hugh Jackman       |       4 |
| Music Together     |       3 |
| Leonard Bernstein  |       3 |
| Julie Andrews      |       3 |
| Walk off the Earth |       3 |
| John Williams      |       3 |
| Billie Eilish      |       3 |
| Anna Kendrick      |       3 |
| Zac Efron          |       2 |
| Clean Bandit       |       2 |
| Adele              |       2 |
| Shakira            |       2 |
| Stromae            |       2 |
| Michael Jackson    |       2 |
| Lin-Manuel Miranda |       2 |
| Caravan Palace     |       2 |
| Queen              |       2 |
| Ynairaly Simo      |       2 |
| Édith Piaf         |       2 |
| Dolly Parton       |       1 |
```

### Plot Number of Songs per Release Year

```python
ta.plot_songs_per_year()
```

```none
/Users/thorwhalen/.pyenv/versions/3.10.13/envs/p10/lib/python3.10/site-packages/IPython/core/pylabtools.py:170: UserWarning: Glyph 44053 (강) missing from font(s) DejaVu Sans.
  fig.canvas.print_figure(bytes_io, **kw)
/Users/thorwhalen/.pyenv/versions/3.10.13/envs/p10/lib/python3.10/site-packages/IPython/core/pylabtools.py:170: UserWarning: Glyph 45224 (남) missing from font(s) DejaVu Sans.
  fig.canvas.print_figure(bytes_io, **kw)
/Users/thorwhalen/.pyenv/versions/3.10.13/envs/p10/lib/python3.10/site-packages/IPython/core/pylabtools.py:170: UserWarning: Glyph 49828 (스) missing from font(s) DejaVu Sans.
  fig.canvas.print_figure(bytes_io, **kw)
/Users/thorwhalen/.pyenv/versions/3.10.13/envs/p10/lib/python3.10/site-packages/IPython/core/pylabtools.py:170: UserWarning: Glyph 53440 (타) missing from font(s) DejaVu Sans.
  fig.canvas.print_figure(bytes_io, **kw)
/Users/thorwhalen/.pyenv/versions/3.10.13/envs/p10/lib/python3.10/site-packages/IPython/core/pylabtools.py:170: UserWarning: Glyph 51068 (일) missing from font(s) DejaVu Sans.
  fig.canvas.print_figure(bytes_io, **kw)
```

![png](https://raw.githubusercontent.com/thorwhalen/sung/main/misc/playlist_analysis_files/playlist_analysis_28_1.png)

### Plot Added Date vs. Release Date

```python
ta.plot_added_vs_release_dates()
```

![png](https://raw.githubusercontent.com/thorwhalen/sung/main/misc/playlist_analysis_files/playlist_analysis_30_0.png)

```python
ta.plot_added_vs_release_kde()
```

![png](https://raw.githubusercontent.com/thorwhalen/sung/main/misc/playlist_analysis_files/playlist_analysis_31_0.png)

```python
ta.plot_added_vs_release_kde_boundary()
```

![png](https://raw.githubusercontent.com/thorwhalen/sung/main/misc/playlist_analysis_files/playlist_analysis_32_0.png)

### Plot First Letter Distribution

```python
ta.plot_first_letter_distribution(sort_by="lexicographical")
```

![png](https://raw.githubusercontent.com/thorwhalen/sung/main/misc/playlist_analysis_files/playlist_analysis_34_0.png)

```python
ta.plot_first_letter_distribution(sort_by="count")
```

![png](https://raw.githubusercontent.com/thorwhalen/sung/main/misc/playlist_analysis_files/playlist_analysis_35_0.png)

### Print Top Tracks by Starting Letter

```python
ta.print_top_names_by_letter(n=5)
```

```none
L (5 tracks):
  - (74) Levitating
  - (73) Livin' la Vida Loca
  - (63) Lucy In The Sky With Diamonds - Remastered 2009
  - (58) La Vie en rose
  - (50) Lose Yourself
W (5 tracks):
  - (78) Wrecking Ball
  - (71) We Don't Talk About Bruno
  - (69) Wellerman - Sea Shanty
  - (69) We Will Rock You - Remastered 2011
  - (64) Wuthering Heights
T (5 tracks):
  - (79) Tainted Love
  - (74) Training Season
  - (70) The Other Side
  - (70) The Greatest Show
  - (70) This Is Me
H (5 tracks):
  - (82) Here Comes The Sun - Remastered 2009
  - (74) Happy Together
  - (57) Houdini
  - (57) Houdini
  - (57) Holocaust
S (5 tracks):
  - (83) Sweet Dreams (Are Made of This) - 2005 Remaster
  - (75) Symphony (feat. Zara Larsson)
  - (74) Somebody That I Used To Know
  - (74) Señorita
  - (71) Super Trouper
N (4 tracks):
  - (78) New Rules
  - (74) Nice For What
  - (63) Night Falls
  - (44) Non, je ne regrette rien
R (5 tracks):
  - (77) Rolling in the Deep
  - (77) Roar
  - (70) Rewrite The Stars
  - (59) Ring My Bell
  - (48) Running Out Of Time
E (4 tracks):
  - (90) Espresso
  - (83) exes
  - (77) Easy On Me
  - (53) Eating the Cats (Donald Trump Remix)
A (5 tracks):
  - (73) Ain't Nobody (Loves Me Better) (feat. Jasmine Thompson)
  - (72) A Million Dreams
  - (53) A Spoonful of Sugar - From "Mary Poppins" / Soundtrack Version
  - (0) Anything You Can Do
  - (29) Away We Go
1 (1 tracks):
  - (19) 10:35
I (5 tracks):
  - (68) I Like To Move It
  - (66) Illusion
  - (65) I'll Be There for You - Theme From "Friends"
  - (47) It's Oh So Quiet
  - (40) Indiana Jones Theme
C (5 tracks):
  - (69) Chandelier
  - (67) Come Alive
  - (62) Crab Rave
  - (53) Coconut
  - (46) Cups - Movie Version
V (1 tracks):
  - (25) Viens
U (1 tracks):
  - (14) Unity
D (5 tracks):
  - (72) Don't Stop Believin'
  - (66) Dance Monkey
  - (62) Drive My Car - Remastered 2009
  - (53) Do-Re-Mi
  - (41) Don't Rain on My Parade
M (5 tracks):
  - (74) MIDDLE OF THE NIGHT
  - (56) Material Girl
  - (49) Mellow Yellow
  - (46) Monster
  - (36) My Baby Just Cares For Me
B (5 tracks):
  - (81) Before You Go
  - (81) bad guy
  - (76) bellyache
  - (74) bury a friend
  - (67) Bad - 2012 Remaster
F (4 tracks):
  - (70) From Now On
  - (65) Friend Like Me
  - (63) Fat Bottomed Girls - Remastered 2011
  - (58) Fever
G (5 tracks):
  - (74) Ghostbusters
  - (73) Get Into It (Yuh)
  - (72) Gangnam Style (강남스타일)
  - (62) Get Back Up Again
  - (59) Greased Lightnin' - From “Grease”
P (3 tracks):
  - (64) Peaches
  - (30) Pink Panther Theme - Remix Version
  - (0) Papaoutai
Y (3 tracks):
  - (65) You Can't Always Get What You Want
  - (49) You Can't Stop The Beat
  - (31) You're No Good - From 'Minions: The Rise of Gru' Soundtrack
Z (1 tracks):
  - (82) Zombie
K (3 tracks):
  - (77) Kings & Queens
  - (58) Karma Chameleon
  - (0) Kung Fu Fighting (From "Kung Fu Panda 3")
O (5 tracks):
  - (64) Oye Como Va
  - (43) One Of A Kind
  - (41) On My Way
  - (40) One More Song
  - (0) Omg - Radio Edit
J (3 tracks):
  - (75) Jolene
  - (43) Je Me Suis Fait Tout Petit
  - (33) Jolie coquine
5 (1 tracks):
  - (30) 5:55
. (1 tracks):
  - (81) ...Baby One More Time
" (1 tracks):
  - (3) "Ellens Gesang III", D839: Ave Maria
```

```python
# To print all of them, just make n really big
ta.print_top_names_by_letter(n=10_000)
```

```none
L (12 tracks):
  - (74) Levitating
  - (73) Livin' la Vida Loca
  - (63) Lucy In The Sky With Diamonds - Remastered 2009
  - (58) La Vie en rose
  - (50) Lose Yourself
  - (47) Love The Way You Lie
  - (47) Le café
  - (44) Les cités d'or
  - (39) Little Boxes
  - (35) Lost and Found
  - (34) Lemon Boy - Acappella Version
  - (33) Laisse aller
W (17 tracks):
  - (78) Wrecking Ball
  - (71) We Don't Talk About Bruno
  - (69) We Will Rock You - Remastered 2011
  - (69) Wellerman - Sea Shanty
  - (64) Wuthering Heights
  - (64) Who Let The Dogs Out
  - (48) What Time Is It
  - (46) Without Me
  - (43) West Side Story: Act I: America
  - (35) West Side Story: Act I: Something's Coming
  - (34) West Side Story: Act II: I Feel Pretty
  - (32) We the #Legends
  - (30) We Play All Night Long
  - (22) Winter Ducks Play on Water
  - (6) Waka Waka (This Time for Africa) [The Official 2010 FIFA World Cup (TM) Song] (feat. Freshlyground)
  - (0) Windy (Re-Recorded)
  - (0) Wimoweh (Mbube)
T (16 tracks):
  - (79) Tainted Love
  - (74) Training Season
  - (70) The Other Side
  - (70) The Greatest Show
  - (70) This Is Me
  - (68) Truth Hurts
  - (67) Try Everything
  - (66) The Family Madrigal
  - (57) True Colors - Film Version
  - (56) The Magic Key
  - (54) The Real Slim Shady
  - (48) Time Alone With You (feat. Daniel Caesar)
  - (37) The Bare Necessities
  - (39) The Lonely Goatherd
  - (37) The Bare Necessities
  - (0) The Imperial March (Darth Vader's Theme)
H (8 tracks):
  - (82) Here Comes The Sun - Remastered 2009
  - (74) Happy Together
  - (57) Houdini
  - (57) Houdini
  - (57) Holocaust
  - (31) Here Comes The Sun (feat. dodie)
  - (26) Hello
  - (0) Hello Song
S (21 tracks):
  - (83) Sweet Dreams (Are Made of This) - 2005 Remaster
  - (75) Symphony (feat. Zara Larsson)
  - (74) Señorita
  - (74) Somebody That I Used To Know
  - (71) Super Trouper
  - (70) Solo (feat. Demi Lovato)
  - (68) Scatman (ski-ba-bop-ba-dop-bop)
  - (68) Somewhere Over The Rainbow_What A Wonderful World
  - (68) Surface Pressure
  - (65) Single Ladies (Put a Ring on It)
  - (46) Somebody That I Used to Know
  - (46) Somebody That I Used to Know
  - (36) Scarborough Fair / Canticle - Extended Version
  - (36) Strawberry Fields Forever - Remastered 2009
  - (30) Shape of You
  - (26) Stay Shrimpy
  - (24) Superman March - Alternate Version
  - (24) Sing, Sing, Sing
  - (18) Senza un perchè
  - (0) Suite No.3 In D Major, BWV 1068: 2. Air
  - (0) She Sells Sea Shells
N (4 tracks):
  - (78) New Rules
  - (74) Nice For What
  - (63) Night Falls
  - (44) Non, je ne regrette rien
R (8 tracks):
  - (77) Rolling in the Deep
  - (77) Roar
  - (70) Rewrite The Stars
  - (59) Ring My Bell
  - (48) Running Out Of Time
  - (47) Running with the Wolves - WolfWalkers Version
  - (23) Running Outta Love (feat. Tori Kelly)
  - (0) Ridin' in the Car
E (4 tracks):
  - (90) Espresso
  - (83) exes
  - (77) Easy On Me
  - (53) Eating the Cats (Donald Trump Remix)
A (8 tracks):
  - (73) Ain't Nobody (Loves Me Better) (feat. Jasmine Thompson)
  - (72) A Million Dreams
  - (53) A Spoonful of Sugar - From "Mary Poppins" / Soundtrack Version
  - (0) Anything You Can Do
  - (29) Away We Go
  - (28) All Norris Nuts Songs About Themselves
  - (2) Alors On Danse - Radio Edit
  - (0) Anything You Can Do
1 (1 tracks):
  - (19) 10:35
I (9 tracks):
  - (68) I Like To Move It
  - (66) Illusion
  - (65) I'll Be There for You - Theme From "Friends"
  - (47) It's Oh So Quiet
  - (40) Indiana Jones Theme
  - (35) I Wan'na Be Like You (2016)
  - (34) In My Bones (feat. Kimbra & Tank and The Bangas)
  - (11) I Got You - 1964 Smash Version
  - (0) It's Only A Paper Moon
C (8 tracks):
  - (69) Chandelier
  - (67) Come Alive
  - (62) Crab Rave
  - (53) Coconut
  - (46) Cups - Movie Version
  - (36) C'est de l'eau
  - (22) Chill Jazz
  - (0) Coco Made Me Do It
V (1 tracks):
  - (25) Viens
U (1 tracks):
  - (14) Unity
D (13 tracks):
  - (72) Don't Stop Believin'
  - (66) Dance Monkey
  - (62) Drive My Car - Remastered 2009
  - (53) Do-Re-Mi
  - (41) Don't Rain on My Parade
  - (38) Drop It Like It's Hot - Radio Edit
  - (37) Do The Bartman
  - (35) Dramophone
  - (25) Drive My Car
  - (11) Djevojka Sa Čardaš Nogama
  - (4) Do Your Ears Hang Low?
  - (1) Dragostea Din Tei
  - (0) Duel Of The Fates
M (9 tracks):
  - (74) MIDDLE OF THE NIGHT
  - (56) Material Girl
  - (49) Mellow Yellow
  - (46) Monster
  - (36) My Baby Just Cares For Me
  - (30) My Own Drum (Remix) [with Missy Elliott]
  - (27) Malambo No. 1
  - (10) Mah Na Mah Na
  - (2) Mozart: Horn Concerto No. 4 in E-Flat Major, K. 495: III. Rondo (Allegro vivace)
B (9 tracks):
  - (81) Before You Go
  - (81) bad guy
  - (76) bellyache
  - (74) bury a friend
  - (67) Bad - 2012 Remaster
  - (60) Blood on the Dance Floor
  - (46) Blue (Da Ba Dee)
  - (40) Bizet: Carmen, WD 31, Act 1: Habanera. "L'amour est un oiseau rebelle" (Carmen, Chœur)
  - (39) Baby Mine
F (4 tracks):
  - (70) From Now On
  - (65) Friend Like Me
  - (63) Fat Bottomed Girls - Remastered 2011
  - (58) Fever
G (7 tracks):
  - (74) Ghostbusters
  - (73) Get Into It (Yuh)
  - (72) Gangnam Style (강남스타일)
  - (62) Get Back Up Again
  - (59) Greased Lightnin' - From “Grease”
  - (40) Grand Finale
  - (32) Ghost in the Keys
P (3 tracks):
  - (64) Peaches
  - (30) Pink Panther Theme - Remix Version
  - (0) Papaoutai
Y (3 tracks):
  - (65) You Can't Always Get What You Want
  - (49) You Can't Stop The Beat
  - (31) You're No Good - From 'Minions: The Rise of Gru' Soundtrack
Z (1 tracks):
  - (82) Zombie
K (3 tracks):
  - (77) Kings & Queens
  - (58) Karma Chameleon
  - (0) Kung Fu Fighting (From "Kung Fu Panda 3")
O (6 tracks):
  - (64) Oye Como Va
  - (43) One Of A Kind
  - (41) On My Way
  - (40) One More Song
  - (0) Omg - Radio Edit
  - (0) ooh la la (feat. Greg Nice & DJ Premier)
J (3 tracks):
  - (75) Jolene
  - (43) Je Me Suis Fait Tout Petit
  - (33) Jolie coquine
5 (1 tracks):
  - (30) 5:55
. (1 tracks):
  - (81) ...Baby One More Time
" (1 tracks):
  - (3) "Ellens Gesang III", D839: Ave Maria
```

### The dates table

Just a table with the added_at and album_release dates, along with other metadata, for convenience

```python
ta.dates
```

<div>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>album_release_date</th>
      <th>added_at_date</th>
      <th>name</th>
      <th>first_artist</th>
      <th>name_and_artist</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>181</th>
      <td>2010-06-18</td>
      <td>2024-11-06</td>
      <td>Love The Way You Lie</td>
      <td>Eminem</td>
      <td>Love The Way You Lie -- Eminem</td>
    </tr>
    <tr>
      <th>180</th>
      <td>2002-05-26</td>
      <td>2024-11-03</td>
      <td>Without Me</td>
      <td>Eminem</td>
      <td>Without Me -- Eminem</td>
    </tr>
    <tr>
      <th>179</th>
      <td>2014-11-24</td>
      <td>2024-11-03</td>
      <td>Lose Yourself</td>
      <td>Eminem</td>
      <td>Lose Yourself -- Eminem</td>
    </tr>
    <tr>
      <th>178</th>
      <td>2005-12-06</td>
      <td>2024-11-03</td>
      <td>The Real Slim Shady</td>
      <td>Eminem</td>
      <td>The Real Slim Shady -- Eminem</td>
    </tr>
    <tr>
      <th>177</th>
      <td>2024-05-30</td>
      <td>2024-11-03</td>
      <td>Houdini</td>
      <td>Eminem</td>
      <td>Houdini -- Eminem</td>
    </tr>
    <tr>
      <th>...</th>
      <td>...</td>
      <td>...</td>
      <td>...</td>
      <td>...</td>
      <td>...</td>
    </tr>
    <tr>
      <th>2</th>
      <td>2011-01-01</td>
      <td>2016-09-07</td>
      <td>Mah Na Mah Na</td>
      <td>Mahna Mahna and The Two Snowths</td>
      <td>Mah Na Mah Na -- Mahna Mahna and The Two Snowths</td>
    </tr>
    <tr>
      <th>5</th>
      <td>2012-10-29</td>
      <td>2016-09-07</td>
      <td>Sing, Sing, Sing</td>
      <td>The Andrews Sisters</td>
      <td>Sing, Sing, Sing -- The Andrews Sisters</td>
    </tr>
    <tr>
      <th>3</th>
      <td>2007-01-01</td>
      <td>2016-09-07</td>
      <td>My Baby Just Cares For Me</td>
      <td>Nina Simone</td>
      <td>My Baby Just Cares For Me -- Nina Simone</td>
    </tr>
    <tr>
      <th>1</th>
      <td>2015-01-28</td>
      <td>2016-08-14</td>
      <td>Wimoweh (Mbube)</td>
      <td>Yma Sumac</td>
      <td>Wimoweh (Mbube) -- Yma Sumac</td>
    </tr>
    <tr>
      <th>0</th>
      <td>1997-01-01</td>
      <td>2016-08-14</td>
      <td>The Imperial March (Darth Vader's Theme)</td>
      <td>John Williams</td>
      <td>The Imperial March (Darth Vader's Theme) -- Jo...</td>
    </tr>
  </tbody>
</table>
<p>182 rows × 5 columns</p>
</div>
```python
ta.tracks_grouped_by_year
```

<div>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>name_and_artist</th>
      <th>number_of_songs</th>
    </tr>
    <tr>
      <th>album_release_year</th>
      <th></th>
      <th></th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>1954</th>
      <td>[Malambo No. 1 -- Moises Vivanco]</td>
      <td>1</td>
    </tr>
    <tr>
      <th>1961</th>
      <td>[West Side Story: Act II: I Feel Pretty -- Leo...</td>
      <td>3</td>
    </tr>
    <tr>
      <th>1965</th>
      <td>[The Lonely Goatherd -- Julie Andrews, Do-Re-M...</td>
      <td>3</td>
    </tr>
    <tr>
      <th>1967</th>
      <td>[Lucy In The Sky With Diamonds - Remastered 20...</td>
      <td>3</td>
    </tr>
    <tr>
      <th>1968</th>
      <td>[Scarborough Fair / Canticle - Extended Versio...</td>
      <td>2</td>
    </tr>
    <tr>
      <th>1969</th>
      <td>[You Can't Always Get What You Want -- The Rol...</td>
      <td>2</td>
    </tr>
    <tr>
      <th>1970</th>
      <td>[Oye Como Va -- Santana]</td>
      <td>1</td>
    </tr>
    <tr>
      <th>1971</th>
      <td>[Coconut -- Harry Nilsson]</td>
      <td>1</td>
    </tr>
    <tr>
      <th>1973</th>
      <td>[Strawberry Fields Forever - Remastered 2009 -...</td>
      <td>1</td>
    </tr>
    <tr>
      <th>1974</th>
      <td>[Jolene -- Dolly Parton]</td>
      <td>1</td>
    </tr>
    <tr>
      <th>1977</th>
      <td>[We Will Rock You - Remastered 2011 -- Queen]</td>
      <td>1</td>
    </tr>
    <tr>
      <th>1978</th>
      <td>[Greased Lightnin' - From “Grease” -- John Tra...</td>
      <td>3</td>
    </tr>
    <tr>
      <th>1980</th>
      <td>[Super Trouper -- ABBA]</td>
      <td>1</td>
    </tr>
    <tr>
      <th>1981</th>
      <td>[Tainted Love -- Soft Cell]</td>
      <td>1</td>
    </tr>
    <tr>
      <th>1983</th>
      <td>[Les cités d'or -- Le Groupe Apollo, Sweet Dre...</td>
      <td>2</td>
    </tr>
    <tr>
      <th>1984</th>
      <td>[Material Girl -- Madonna]</td>
      <td>1</td>
    </tr>
    <tr>
      <th>1988</th>
      <td>[Drive My Car -- Bobby McFerrin]</td>
      <td>1</td>
    </tr>
    <tr>
      <th>1989</th>
      <td>[Je Me Suis Fait Tout Petit -- Georges Brassen...</td>
      <td>2</td>
    </tr>
    <tr>
      <th>1990</th>
      <td>[Do The Bartman -- The Simpsons]</td>
      <td>1</td>
    </tr>
    <tr>
      <th>1991</th>
      <td>[Mozart: Horn Concerto No. 4 in E-Flat Major, ...</td>
      <td>1</td>
    </tr>
    <tr>
      <th>1993</th>
      <td>[Somewhere Over The Rainbow_What A Wonderful W...</td>
      <td>2</td>
    </tr>
    <tr>
      <th>1994</th>
      <td>[Zombie -- The Cranberries]</td>
      <td>1</td>
    </tr>
    <tr>
      <th>1995</th>
      <td>[Scatman (ski-ba-bop-ba-dop-bop) -- Scatman Jo...</td>
      <td>3</td>
    </tr>
    <tr>
      <th>1996</th>
      <td>[It's Only A Paper Moon -- Ella Fitzgerald]</td>
      <td>1</td>
    </tr>
    <tr>
      <th>1997</th>
      <td>[Blood on the Dance Floor -- Michael Jackson, ...</td>
      <td>3</td>
    </tr>
    <tr>
      <th>1999</th>
      <td>[Livin' la Vida Loca -- Ricky Martin, Anything...</td>
      <td>4</td>
    </tr>
    <tr>
      <th>2000</th>
      <td>[Indiana Jones Theme -- John Williams, Who Let...</td>
      <td>3</td>
    </tr>
    <tr>
      <th>2001</th>
      <td>[Don't Stop Believin' -- Journey]</td>
      <td>1</td>
    </tr>
    <tr>
      <th>2002</th>
      <td>[Without Me -- Eminem, Dragostea Din Tei -- O-...</td>
      <td>2</td>
    </tr>
    <tr>
      <th>2004</th>
      <td>[Drop It Like It's Hot - Radio Edit -- Snoop D...</td>
      <td>2</td>
    </tr>
    <tr>
      <th>2005</th>
      <td>[The Real Slim Shady -- Eminem, Winter Ducks P...</td>
      <td>6</td>
    </tr>
    <tr>
      <th>2006</th>
      <td>[Pink Panther Theme - Remix Version -- Henry M...</td>
      <td>1</td>
    </tr>
    <tr>
      <th>2007</th>
      <td>[What Time Is It -- Zac Efron, You Can't Stop ...</td>
      <td>8</td>
    </tr>
    <tr>
      <th>2008</th>
      <td>[C'est de l'eau -- Les Enfantastiques, I Like ...</td>
      <td>6</td>
    </tr>
    <tr>
      <th>2010</th>
      <td>[Love The Way You Lie -- Eminem, Waka Waka (Th...</td>
      <td>3</td>
    </tr>
    <tr>
      <th>2011</th>
      <td>[Rolling in the Deep -- Adele, Lost and Found ...</td>
      <td>4</td>
    </tr>
    <tr>
      <th>2012</th>
      <td>[Gangnam Style (강남스타일) -- PSY, Somebody That I...</td>
      <td>6</td>
    </tr>
    <tr>
      <th>2013</th>
      <td>[Roar -- Katy Perry, Wrecking Ball -- Miley Cy...</td>
      <td>5</td>
    </tr>
    <tr>
      <th>2014</th>
      <td>[Lose Yourself -- Eminem, I Got You - 1964 Sma...</td>
      <td>4</td>
    </tr>
    <tr>
      <th>2015</th>
      <td>[Cups - Movie Version -- Anna Kendrick, Unity ...</td>
      <td>4</td>
    </tr>
    <tr>
      <th>2016</th>
      <td>[Chill Jazz -- Simon Leonard Thorpe, Ghostbust...</td>
      <td>10</td>
    </tr>
    <tr>
      <th>2017</th>
      <td>[New Rules -- Dua Lipa, The Other Side -- Hugh...</td>
      <td>14</td>
    </tr>
    <tr>
      <th>2018</th>
      <td>[Solo (feat. Demi Lovato) -- Clean Bandit, We ...</td>
      <td>6</td>
    </tr>
    <tr>
      <th>2019</th>
      <td>[We Play All Night Long -- Norris Nuts, Viens ...</td>
      <td>14</td>
    </tr>
    <tr>
      <th>2020</th>
      <td>[All Norris Nuts Songs About Themselves -- Nor...</td>
      <td>10</td>
    </tr>
    <tr>
      <th>2021</th>
      <td>[Stay Shrimpy -- Norris Nuts, Monster -- YOASO...</td>
      <td>14</td>
    </tr>
    <tr>
      <th>2022</th>
      <td>[Don't Rain on My Parade -- Lea Michele, Laiss...</td>
      <td>3</td>
    </tr>
    <tr>
      <th>2023</th>
      <td>[10:35 -- Tiësto, exes -- Tate McRae, Houdini ...</td>
      <td>5</td>
    </tr>
    <tr>
      <th>2024</th>
      <td>[Houdini -- Eminem, Eating the Cats (Donald Tr...</td>
      <td>5</td>
    </tr>
  </tbody>
</table>
</div>
```python

```

# Play with more playlists now

```python
from sung import TracksAnalysis

liked_songs_as_of_nov_2024 = (
    "https://open.spotify.com/playlist/0TR0PpkMt37afbzNuexYEc?si=4ba4ec8221d84e94"
)

ta = TracksAnalysis(liked_songs_as_of_nov_2024)
```

```python
ta.plot_dataframe_distributions()
```

![png](https://raw.githubusercontent.com/thorwhalen/sung/main/misc/playlist_analysis_files/playlist_analysis_45_0.png)

```python
ta.plot_features_pairs()
```

![png](https://raw.githubusercontent.com/thorwhalen/sung/main/misc/playlist_analysis_files/playlist_analysis_46_0.png)

<p class="epythet-aggregates">This documentation as a single file: <a href="sung.md">sung.md</a> (Markdown, for agents).</p>