[英]Getting no data when scraping a table
I am trying to scrape historical data from a table in coinmarketcap.我正在尝试从 coinmarketcap 的表中抓取历史数据。 However, the code that I run gives back "no data."
但是,我运行的代码返回“无数据”。 I thought it would be fairly easy, but not sure what I am missing.
我认为这会很容易,但不确定我错过了什么。
url = "https://coinmarketcap.com/currencies/bitcoin/historical-data/"
data = requests.get(url)
bs=BeautifulSoup(data.text, "lxml")
table_body=bs.find('tbody')
rows = table_body.find_all('tr')
for row in rows:
cols=row.find_all('td')
cols=[x.text.strip() for x in cols]
print(cols)
Output: Output:
C:\Users\Ejer\anaconda3\envs\pythonProject\python.exe C:/Users/Ejer/PycharmProjects/pythonProject/CloudSQL_test.py
['No Data']
Process finished with exit code 0
Your problem basically is you're trying to get a table but this table is dynamically created by JS in this case you need to call an interpreter for this JS.您的问题基本上是您试图获取一个表,但该表是由 JS 动态创建的,在这种情况下您需要为此 JS 调用解释器。 But however you just can check the.network monitor on your browser and you can get the requests and probably contains a full JSON or XML raw data and you don't need to scrape.
但无论如何,您只需在浏览器上查看 .network 监视器,就可以获得请求,并且可能包含完整的 JSON 或 XML 原始数据,您不需要抓取。 I did it and I got this request:
我做到了,我收到了这个请求:
https://web-api.coinmarketcap.com/v1/cryptocurrency/ohlcv/historical?id=1&convert=USD&time_start=1604016000&time_end=1609286400
Check it out and I hope help you!检查一下,希望对您有所帮助!
You don't need to scrape the data, you can get
request it:你不需要抓取数据,你
get
请求它:
import time
import requests
def get_timestamp(datetime: str):
return int(time.mktime(time.strptime(datetime, '%Y-%m-%d %H:%M:%S')))
def get_btc_quotes(start_date: str, end_date: str):
start = get_timestamp(start_date)
end = get_timestamp(end_date)
url = f'https://web-api.coinmarketcap.com/v1/cryptocurrency/ohlcv/historical?id=1&convert=USD&time_start={start}&time_end={end}'
return requests.get(url).json()
data = get_btc_quotes(start_date='2020-12-01 00:00:00',
end_date='2020-12-10 00:00:00')
import pandas as pd
# making A LOT of assumptions here, hopefully the keys don't change in the future
data_flat = [quote['quote']['USD'] for quote in data['data']['quotes']]
df = pd.DataFrame(data_flat)
print(df)
Output: Output:
open high low close volume market_cap timestamp
0 18801.743593 19308.330663 18347.717838 19201.091157 3.738770e+10 3.563810e+11 2020-12-02T23:59:59.999Z
1 19205.925404 19566.191884 18925.784434 19445.398480 3.193032e+10 3.609339e+11 2020-12-03T23:59:59.999Z
2 19446.966422 19511.404714 18697.192914 18699.765613 3.387239e+10 3.471114e+11 2020-12-04T23:59:59.999Z
3 18698.385279 19160.449265 18590.193675 19154.231131 2.724246e+10 3.555639e+11 2020-12-05T23:59:59.999Z
4 19154.180593 19390.499895 18897.894072 19345.120959 2.529378e+10 3.591235e+11 2020-12-06T23:59:59.999Z
5 19343.128798 19411.827676 18931.142919 19191.631287 2.689636e+10 3.562932e+11 2020-12-07T23:59:59.999Z
6 19191.529463 19283.478339 18269.945444 18321.144916 3.169229e+10 3.401488e+11 2020-12-08T23:59:59.999Z
7 18320.884784 18626.292652 17935.547820 18553.915377 3.442037e+10 3.444865e+11 2020-12-09T23:59:59.999Z
8 18553.299728 18553.299728 17957.065213 18264.992107 2.554713e+10 3.391369e+11 2020-12-10T23:59:59.999Z
声明:本站的技术帖子网页,遵循CC BY-SA 4.0协议,如果您需要转载,请注明本站网址或者原文地址。任何问题请咨询:yoyou2525@163.com.