如何获取下载按钮的url并读取Python中的CSV文件？

Question

I am using Python Google Colab and trying to read the csv file from this link: https://www.macrotrends.net/stocks/charts/AAPL/apple/stock-price-history

If you scroll little bit down, you will be able to see download button. I'd like to get the link by using selenium or bs and read the csv file. I am trying to do something like this,

# install packages
!pip install selenium
!apt-get update # to update ubuntu to correctly run apt install
!apt install chromium-chromedriver
!cp /usr/lib/chromium-browser/chromedriver /usr/bin

# load packages
import pandas as pd
from selenium import webdriver
import sys

# run selenium and read the csv file
sys.path.insert(0,'/usr/lib/chromium-browser/chromedriver')
chrome_options = webdriver.ChromeOptions()
chrome_options.add_argument('--headless')
chrome_options.add_argument('--no-sandbox')
chrome_options.add_argument('--disable-dev-shm-usage')
driver = webdriver.Chrome('chromedriver',chrome_options=chrome_options)
driver.get('https://www.macrotrends.net/stocks/charts/AAPL/apple/stock-price-history')#put here the adress of your page
btn = driver.find_element_by_tag_name('button')
btn.click()
df = pd.read_csv('##.csv')

It seems to be working until btn.click() part but getting error after as it doesn't tell me the link of the download button nor the file name. Could you please assist? That would be much appreciated.

Answer 1

No need for selenium. The data is embedded in the <script> tags.

import requests
from bs4 import BeautifulSoup
import json
import pandas as pd

t = 'AAPL'
url = 'https://www.macrotrends.net/assets/php/stock_price_history.php?t={}'.format(t)

response = requests.get(url)
soup = BeautifulSoup(response.text, 'html.parser')

scripts = soup.find_all('script',{'type':'text/javascript'})
for script in scripts:
    if 'var dataDaily' in str(script):
        jsonStr = '[' + str(script).split('[',1)[-1].split('];')[0] + ']'
        jsonData = json.loads(jsonStr)
        
df = pd.DataFrame(jsonData)
df = df.rename(columns={'o':'open','h':'high','l':'low','c':'close','d':'date','v':'volume'})
df.to_csv('MacroTrends_Data_Download_{}.csv'.format(t), index=False)

Output:

print(df)
             date      open      high  ...   volume     ma50    ma200
0      1980-12-12    0.1012    0.1016  ...  469.034      NaN      NaN
1      1980-12-15    0.0964    0.0964  ...  175.885      NaN      NaN
2      1980-12-16    0.0893    0.0893  ...  105.728      NaN      NaN
3      1980-12-17    0.0910    0.0915  ...   86.442      NaN      NaN
4      1980-12-18    0.0937    0.0941  ...   73.450      NaN      NaN
          ...       ...       ...  ...      ...      ...      ...
10135  2021-02-25  124.6800  126.4585  ...  148.200  131.845  112.241
10136  2021-02-26  122.5900  124.8500  ...  164.560  131.838  112.460
10137  2021-03-01  123.7500  127.9300  ...  116.308  131.840  112.716
10138  2021-03-02  128.4100  128.7200  ...  102.261  131.790  112.957
10139  2021-03-03  124.8100  125.7100  ...  111.514  131.661  113.184

[10140 rows x 8 columns]