diff options
Diffstat (limited to 'AllAboutData/getData.py')
| -rw-r--r-- | AllAboutData/getData.py | 82 |
1 files changed, 48 insertions, 34 deletions
diff --git a/AllAboutData/getData.py b/AllAboutData/getData.py index 4cea9af..7b503fa 100644 --- a/AllAboutData/getData.py +++ b/AllAboutData/getData.py @@ -1,32 +1,34 @@ -import pandas as pd +'''NBA data reciever + +This python script fetches NBA teams and its players (including retired) +with some additional information about them. +The data is stored in the current working directory and thus, +any existing "Data" file is overwritten. Data will be in csv format. + +To use this script, "pandas" and "python-dotenv" must be installed +You also have to make .env file in current dir and add there: API_KEY = your API_key +You can get the API key from url below. + +Used API: https://rapidapi.com/theapiguy/api/free-nba/ +''' + +import os import requests -from utils import osSpecific # Some functions to delete and create directoryes needed for data storing +import pandas as pd +from utils import osSpecific # Some functions to delete and create directories for data 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 + "x-rapidapi-host": "free-nba.p.rapidapi.com", + "x-rapidapi-key": API_KEY } # File name variables to store data in @@ -37,7 +39,7 @@ else: teamsFile = "Data/NBAteams.csv" playersDir = "Data/Players/" -# Create new Data directory in order to avoid duplicates, when data is requested multiple times +# Createubg new Data dir to avoid duplicates (due appending) osSpecific.deleteDataDir() osSpecific.addDataDir() @@ -46,8 +48,12 @@ osSpecific.addDataDir() ###### Functions ###### def getTeamsData(url, headers): +''' +Requests Data about NBA teams and stores it. +Takes API url as first and its headers as second argument. +''' - querystring = {"page":"0"} + querystring = {"page": "0"} response = requests.request("GET", url+"teams", headers=headers, params=querystring) teamsDf = pd.DataFrame(response.json()["data"]) @@ -58,36 +64,44 @@ def getTeamsData(url, headers): def getPlayerData(url, headers): +''' +Requests Data about NBA players and stores it, based on teams +Takes API url as first and its headers as second argument. +''' - 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"} + print("Stared reading players data") + # First request is made to get the page count to loop + querystring = {"per_page": "100","page":"0"} response = requests.request("GET", url+"players", headers=headers, params=querystring) - pageCount = response.json()["meta"]["total_pages"] + pageCount = response.json()["meta"]["total_pages"] # Got the page count here + 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} + # Requesting pages in loop till pageCount is reached + querystring = {"per_page": "100","page": el} response = requests.request("GET", url+"players", headers=headers, params=querystring) data = response.json()["data"] + # Making dataframe for each player to store it suitable file for player in data: teamName = player["team"]["full_name"] - playerDf = pd.DataFrame(columns=["first_name", "last_name", "position", "height_feet", "height_inches"]) + 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"]}) + 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 + # Add dataframe to File, if first to be added, then also add column names hdr = False if os.path.isfile(playersDir+teamName+".csv") else True playerDf.to_csv(playersDir+teamName+".csv", mode='a', index=False, header=hdr) @@ -96,7 +110,7 @@ def getPlayerData(url, headers): - if __name__ == "__main__": getTeamsData(url, headers) getPlayerData(url, headers) + |