This project focused on data wrangling techniques applied to song characteristic data acquired from the Spotify API. The goal is to gather, clean, merge, aggregate, and visualize the data.
Data Acquisition: The Spotify API is utilized to acquire song characteristic data. This involves authenticating with the Spotify API using your credentials and making API calls to fetch the desired data. Data Cleaning: The acquired data may contain missing values, duplicates, or inconsistencies. This step involves cleaning the data by handling missing values, removing duplicates, standardizing formats, and resolving any other data quality issues. Data Merging: Additional data sources (if available) can be merged with the song characteristic data to enhance the analysis. This step involves merging the data based on common keys or indices. Data Aggregation: The data can be aggregated to create meaningful summaries or insights. This can involve grouping the data by certain attributes, calculating statistical measures, or deriving new features. Data Visualization: Visualizations are created to better understand the data and communicate insights effectively. This step involves using libraries like matplotlib and seaborn to plot various charts, graphs, and plots.
Clone or download the project repository to your local machine. Install the required Python packages mentioned in the data_wrangling.ipynb file. Open the data_wrangling.ipynb file in Jupyter Notebook or any compatible environment. Follow the instructions provided in the notebook to execute the code step by step. Modify and experiment with the code as needed to suit your requirements. Review the generated visualizations and analysis to gain insights into the song characteristics.
Ensure that you protect your Spotify API credentials and do not share them publicly. Feel free to modify the code or expand the project to explore additional aspects of the Spotify API or incorporate more data sources. If you encounter any issues or have questions, please feel free to reach out for assistance.
Major thanks to Data Wrangling '22/23 Group 10 at Vrije Univesiteit Amsterdam for their contributions.
Happy data wrangling!