Reads the 10-year historical prices from Nasdaq into a Pandas DataFrame

10y_historical_prices_nasdaq_dataframe.py

import requests
import pandas as pd
import csv

def historical_prices(symbol:str):
    symbol = symbol.lower()
    url = "https://www.nasdaq.com/symbol/{0}/historical".format(symbol)
    headers = {'content-type' : 'application/json'}
    data = "10y|true|{0}".format(symbol)
    
    historical_prices_df = pd.DataFrame(columns=('date', 'close', 'volume', 'open', 'high', 'low'))
    resp = requests.post(url, data=data, headers=headers)
    reader = csv.reader(resp.text.split('\n'), delimiter=',')

    # Skip the first two rows
    next(reader)
    next(reader)
    
    for row in reader:
        if row:
            historical_prices_df.loc[len(historical_prices_df)] = row  
     
    # Create a date index
    historical_prices_df.set_index('date', inplace=True)
    historical_prices_df.index = pd.to_datetime(historical_prices_df.index, format = '%Y/%m/%d')
    
    return historical_prices_df


def main():
    prices_df = historical_prices('aapl')
    print(prices_df.head(5))
    

if  __name__ =='__main__':
	main()