1 files changed, 101 insertions, 0 deletions
diff --git a/AllAboutData/getData.py b/AllAboutData/getData.py
new file mode 100644
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+++ b/AllAboutData/getData.py
@@ -0,0 +1,101 @@
+import pandas as pd
+import requests
+from utils import osSpecific  #  Some functions to delete and create directoryes needed for data storing
+from dotenv import load_dotenv
+import os
+
+'''
+This python script fetches NBA teams and its players (currently playing and retired) and some
+additional information about them. 
+The data is stored in the current working directory (any existing "Data" file is overwritten.
+Data is in csv format.
+
+Author: Rasmus Luha
+Created_at: 16.03.2022
+Data fetched from: https://rapidapi.com/theapiguy/api/free-nba/
+'''
+
+# Loading API key from environment variables.
+load_dotenv()
+API_KEY = os.getenv("API_KEY")
+
+# To use this script, you should make .env file in current dir and add there: API_KEY = your API_key
+# You can get the API key from url listed above.
+
+# API request details
+url = "https://free-nba.p.rapidapi.com/"
+headers = {
+            'x-rapidapi-host': "free-nba.p.rapidapi.com",
+            'x-rapidapi-key': API_KEY
+          }
+
+# File name variables to store data in 
+if osSpecific.whichOs() == "windows":
+    teamsFile = "Data/NBAteams.csv"
+    playersDir = "Data\Players\\"
+else:
+    teamsFile = "Data/NBAteams.csv"
+    playersDir = "Data/Players/"
+
+# Create new Data directory in order to avoid duplicates, when data is requested multiple times 
+osSpecific.deleteDataDir()
+osSpecific.addDataDir()
+
+
+
+###### Functions ######
+
+def getTeamsData(url, headers):
+
+    querystring = {"page":"0"}
+    response = requests.request("GET", url+"teams", headers=headers, params=querystring)
+
+    teamsDf = pd.DataFrame(response.json()["data"])
+    teamsDf.set_index("id")
+    teamsDf = teamsDf.drop("id", axis=1)
+    teamsDf.to_csv(teamsFile)
+    print("Teams data stored in Data directory as \"NBAteams.csv\"")
+
+
+def getPlayerData(url, headers):
+
+    print("Stared reading players data")
+
+    # First request is made just to get the amount of pages that must be looped through
+    querystring = {"per_page":"100","page":"0"}
+    response = requests.request("GET", url+"players", headers=headers, params=querystring)
+    pageCount = response.json()["meta"]["total_pages"]
+    
+    print("Pages to read: "+str(pageCount)) 
+    for el in range(1, pageCount+1):
+    
+        # Requesting pages in loop till pageCount
+        querystring = {"per_page":"100","page":el}
+        response = requests.request("GET", url+"players", headers=headers, params=querystring)
+        data = response.json()["data"]
+        
+        for player in data:
+            teamName = player["team"]["full_name"]
+            playerDf = pd.DataFrame(columns=["first_name", "last_name", "position", "height_feet", "height_inches"])
+    
+            playerSeries = pd.Series({"first_name" : player["first_name"],
+                                      "last_name" : player["last_name"],
+                                      "position" : player["position"],
+                                      "height_feet" : player["height_feet"],
+                                      "height_inches" : player["height_inches"]})
+    
+            #add player to dataframe
+            playerDf.loc[len(playerDf)] = playerSeries 
+            #add dataframe to File
+            playerDf.to_csv(playersDir+teamName+".csv", mode='a', index=False, header=False)
+    
+        print("Page "+str(el)+" read.") 
+    print("All done, check \"Data\" Dir.")
+
+
+
+
+# Requesting and storing data
+if __name__ == "__main__":
+    getTeamsData(url, headers)
+    getPlayerData(url, headers)