Spaces:
Running on Zero
Running on Zero
| # See | |
| ## Using NHL API for player in game level statistics | |
| #https://towardsdatascience.com/nhl-analytics-with-python-6390c5d3206d | |
| #[NHL API pull via python](https://gitlab.com/dword4/nhlapi) | |
| #!pip3 install pickle5 | |
| import pickle5 as pickle | |
| import requests | |
| import numpy as np | |
| import pandas as pd | |
| # Set up the API call variables | |
| year_last = '2020' | |
| season_type = '02' | |
| max_game_ID = 3000; # just to be conservative in case there are data not accounted | |
| yr = list(map(str, list(range(1917, year_last))))[::-1]; #reverse the list order | |
| # Loop over the counter and format the API call | |
| for year in yr: | |
| for season_type in ssn: | |
| game_data = [] | |
| for i in range(0,max_game_ID): | |
| r = requests.get(url='https://api-web.nhle.com/v1/' | |
| + year + season_type +str(i).zfill(4)+'/feed/live') | |
| data = r.json() | |
| game_data.append(data) | |
| # Save as pickle file | |
| with open('/content/drive/My Drive/Learning/sports/nhl/'+year+'_'+ season_type + 'FullDataset.pkl', 'wb') as f: | |
| pickle.dump(game_data, f, pickle.HIGHEST_PROTOCOL) | |
| # Data pull done, let me know. | |
| print('Done! - year ' + year + ' and season type: ' + season_type) |