How to scrape time-series chart data from poocoin.app with Python(如何用Python从pocoin.app中抓取时间序列图数据)

本文介绍了如何用Python从pocoin.app中抓取时间序列图数据的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！

问题描述

我正在尝试刮token info from poocoin。所有其他信息都可用，但我无法从图表中获取时间序列数据。

import requests, re 
from bs4 import BeautifulSoup
import pandas as pd

url = 'https://poocoin.app/tokens/0x7606267a4bfff2c5010c92924348c3e4221955f2'
response = requests.get(url)
soup = BeautifulSoup(response.text, 'html.parser')

URL

更新：新方法将是对data推荐答案参数：

进行反向工程

当我有解决方案时，我会更新答案。

上一种方法：

您可以通过直接请求他们的API(我相信)来使其工作，通过requests.json() decoder将其转换为JSON，并以访问字典的方式获取所需的数据：["some_key"]。

定位发送请求的位置：Dev tools -> Network -> Fetch/XHR -> find name and click on it(本例中：Candles-bsc？..)-> Preview(查看响应是否为您想要的)-> Headers -> copy Request URL -> make a request -> optional: add additional request headers if response != 200。

可以使用Insomnia to test a response。在Fetch/XHR -> right click -> copy as cURL (bash) -> place inside Insomnia -> see the reponse下查找名称。

此时只需传入user-agent to request headers即可收到200状态码，否则会抛出403或503状态码。选中what's your user-agent。

通过user-agent：

headers = {
    "user-agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/96.0.4664.45 Safari/537.36",
}

response = requests.get("URL", headers=headers)

编码和example in the online IDE：

import requests

headers = {
    "user-agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/96.0.4664.45 Safari/537.36",
}

params = {
    "to":"2021-11-29T09:15:00.000Z",
    "limit":"321",
    "lpAddress":"0xd8b6A853095c334aD26621A301379Cc3614f9663", 
    "interval":"15m",
    "baseLp":"0x58F876857a02D6762E0101bb5C46A8c1ED44Dc16"
}


response = requests.get("https://api2.poocoin.app/candles-bsc", params=params, headers=headers).json()


# whole response from API call for a particular token (i believe)
# some data needs to be adjusted (open/close price, etc.)
for result in response:
    count = result["count"]
    _time = result["time"]
    open_price = result["open"]
    close_price = result["close"]
    high = result["high"]
    low = result["low"]
    volume = result["volume"]
    base_open = result["baseOpen"]
    base_close = result["baseClose"]
    base_high = result["baseHigh"]
    base_low = result["baseLow"]

    print(f"{count}
"
          f"{_time}
"
          f"{open_price}
"
          f"{close_price}
"
          f"{high}
"
          f"{low}
"
          f"{volume}
"
          f"{base_open}
"
          f"{base_close}
"
          f"{base_high}
"
          f"{base_low}
")


# part of the output:
'''
194
2021-11-29T06:00:00.000Z
6.6637177e-13
6.5189422e-13
6.9088173e-13
5.9996067e-13
109146241968737.17
610.0766516756873
611.1764494818917
612.3961994618185
606.7446709385977

1
2021-11-25T16:15:00.000Z
1.7132448e-13
1.7132448e-13
1.7132448e-13
1.7132448e-13
874858231833.1771
643.611707269882
642.5014860521045
644.5105804619558
638.9447353699617

# ...
'''

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