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How-To-Win-Eurovision

Our MIT 6.S080 final project: visualizating and analyzing data relating to Eurovision top-ranking country and song characteristics throughout the years.

Here are some of the files in our repo:

data-wrangling scripts:

  • KOF_globalisation_index_script.ipynb: creates KOF_globalization_modified.csv (our cleaned KOF index dataset) from KOF_Globalisation_Index.xlsx
  • spotify_script.ipynb: creates spotify_audio_features.csv (our spotify datset) by gathering data via Spotify API calls
  • votes_dataset_script.ipynb: creates votes_dataset_clean.csv from eurovision_song_contest_1975_2016.xlsx
  • wrangling_wiki.py: we scraped Wikipedia to extract the top 5 wininning Eurovision songs at each year into a txt file, and then used Wrangle to clean/organize the data set. The wrangling script creates cleaned_wikipedia_songs.csv.

data-integration-scripts:

  • countries_distance.ipynb: adding a geographoc distance column between pairs of countries by integrating the countriesarea.csv and votes datasets.
  • spotify_wiki_integration.py: creating merged_euro.csv, a file with the audio features corresponding to each of the songs in the wikipedia table.
  • integrating_KOF_and_votes.ipnb: to check the correlation between the votes that a country gets with its globaliztion index KOF.

data:

contains both our original and cleaned datasets

visualuzations:

  • world_vs_eurviosion_songs_features.py and histograms.png: a summary of the distributions of different audio features in Eurovision songs compared with general Spotify songs.
  • voting_2_countries_mutual.png: an example of 2 countries that have given each other significanlty more points throughout the years than the average Eurovision country would give them
  • voting_cluster_map.png: a map of counry clusters that tend to vote for each other
  • voting_map.png: an illustration of country voting patterns on a map of Europe

other:

  • votes_vs_country_dist_visualization.ipynb: investigating and visualizing correlation between country distance and voting trends
  • votes_visualization.ipynb: creates some of the visualizations of country pair voting patterns. The interactive visualization on the map is not rendered on Github: to see it, re-run the notebook in Binder. You can also modify parameters to show some visualizations for different pairs of countries.