Update projects/ytmusic.md

projects/ytmusic.md

@@ -1,4 +1,107 @@
-Sure, let's start from the beginning, setting up a Python project using a virtual environment to work with the YTMusic API via OAuth authentication. We'll go through everything step-by-step, from creating the virtual environment to running the OAuth setup, and then using the API to interact with YouTube Music.
+# YouTube Music Data Analysis
+
+## Setup
+
+```python
+from ytmusicapi import YTMusic
+import pandas as pd
+import matplotlib.pyplot as plt
+```
+
+# Initialize YTMusic with OAuth credentials
+```python
+ytmusic = YTMusic('oauth.json')
+```
+
+## Fetch Data
+
+### Liked Songs
+
+```python
+liked_songs = ytmusic.get_liked_songs(limit=100)
+liked_songs_df = pd.DataFrame(liked_songs['tracks'])
+liked_songs_df['artists'] = liked_songs_df['artists'].apply(lambda x: x[0]['name'] if x else None)
+liked_songs_df.head()
+```
+
+### Playlists
+
+```python
+playlists = ytmusic.get_library_playlists(limit=25)
+playlists_df = pd.DataFrame(playlists)
+playlists_df.head()
+```
+
+### History
+
+```python
+history = ytmusic.get_history()
+history_df = pd.DataFrame(history)
+history_df.head()
+```
+
+## Data Visualization
+
+### Liked Songs by Artist
+
+```python
+liked_songs_df['artists'].value_counts().plot(kind='bar', figsize=(10, 5))
+plt.title('Liked Songs by Artist')
+plt.xlabel('Artist')
+plt.ylabel('Number of Liked Songs')
+plt.show()
+```
+
+### History by Title
+
+```python
+history_df['title'].value_counts().plot(kind='bar', figsize=(10, 5))
+plt.title('History by Title')
+plt.xlabel('Title')
+plt.ylabel('Number of Plays')
+plt.show()
+```
+
+## Save Data to CSV
+
+```python
+liked_songs_df.to_csv('liked_songs.csv', index=False)
+playlists_df.to_csv('playlists.csv', index=False)
+history_df.to_csv('history.csv', index=False)
+```
+```
+
+### Full Script Breakdown
+
+1. **Setup:**
+   - Import necessary libraries (`ytmusicapi`, `pandas`, `matplotlib`).
+   - Initialize the YTMusic API with OAuth credentials.
+
+2. **Fetch Data:**
+   - Get the user's liked songs and convert them to a DataFrame.
+   - Get the user's playlists and convert them to a DataFrame.
+   - Get the user's history and convert it to a DataFrame.
+
+3. **Data Visualization:**
+   - Visualize the liked songs by artist using a bar chart.
+   - Visualize the history by title using a bar chart.
+
+4. **Save Data to CSV:**
+   - Save the processed DataFrames to CSV files for further analysis or backup.
+
+### How to Use This Notebook
+
+1. **Ensure you have the `oauth.json` file in your project directory, which contains your OAuth credentials for the YTMusic API.**
+2. **Start Jupyter Notebook:**
+   ```bash
+   jupyter notebook
+   ```
+3. **Create a new notebook or open an existing one and copy the above cells into the notebook.**
+4. **Run the cells step by step to fetch, analyze, visualize, and save your YouTube Music data.**
+
+This setup will provide you with a comprehensive and interactive data analysis report of your YouTube Music telemetry.
+
+---
 
 ### Step 1: Set Up Your Python Virtual Environment
 
@@ -60,7 +163,6 @@ First, ensure you have Python installed on your system. I recommend using Python
    You can create a Python script like `main.py` to start coding with the API:
    ```python
    from ytmusicapi import YTMusic
-
    ytmusic = YTMusic('oauth.json')
    ```