知微
fa45d8aa5f
- health_checklist.json: 192.168.1.122→node122
- ocr_client.py: docstring IP→node122
- docs/market-data-requirements.md: IP→node122
- 所有API调用通过ProxyHandler({})绕过系统代理
Privoxy对node122:18003返回500，直连正常
113 lines
3.9 KiB
Python
113 lines
3.9 KiB
Python
#!/usr/bin/env python
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# -*- coding:utf-8 -*-
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"""
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Date: 2024/1/20 23:00
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Desc: 东方财富-数据中心-中国油价
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https://data.eastmoney.com/cjsj/oil_default.html
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"""
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import pandas as pd
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import requests
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def energy_oil_hist() -> pd.DataFrame:
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"""
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汽柴油历史调价信息
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https://data.eastmoney.com/cjsj/oil_default.html
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:return: 汽柴油历史调价信息
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:rtype: pandas.DataFrame
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"""
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url = "https://datacenter-web.eastmoney.com/api/data/v1/get"
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params = {
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"reportName": "RPTA_WEB_YJ_BD",
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"columns": "ALL",
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"sortColumns": "dim_date",
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"sortTypes": "-1",
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"token": "894050c76af8597a853f5b408b759f5d",
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"pageNumber": "1",
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"pageSize": "1000",
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"source": "WEB",
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"p": "1",
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"pageNo": "1",
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"pageNum": "1",
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}
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r = requests.get(url, params=params)
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data_json = r.json()
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temp_df = pd.DataFrame(data_json["result"]["data"])
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temp_df.columns = ["调整日期", "汽油价格", "柴油价格", "汽油涨跌", "柴油涨跌"]
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temp_df["调整日期"] = pd.to_datetime(temp_df["调整日期"], errors="coerce").dt.date
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temp_df["汽油价格"] = pd.to_numeric(temp_df["汽油价格"], errors="coerce")
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temp_df["柴油价格"] = pd.to_numeric(temp_df["柴油价格"], errors="coerce")
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temp_df["汽油涨跌"] = pd.to_numeric(temp_df["汽油涨跌"], errors="coerce")
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temp_df["柴油涨跌"] = pd.to_numeric(temp_df["柴油涨跌"], errors="coerce")
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temp_df.sort_values(by=["调整日期"], inplace=True)
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temp_df.reset_index(inplace=True, drop=True)
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return temp_df
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def energy_oil_detail(date: str = "20220517") -> pd.DataFrame:
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"""
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全国各地区的汽油和柴油油价
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https://data.eastmoney.com/cjsj/oil_default.html
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:param date: 可以调用 ak.energy_oil_hist() 得到可以获取油价的调整时间
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:type date: str
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:return: oil price at specific date
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:rtype: pandas.DataFrame
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"""
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date = "-".join([date[:4], date[4:6], date[6:]])
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url = "https://datacenter-web.eastmoney.com/api/data/v1/get"
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params = {
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"reportName": "RPTA_WEB_YJ_JH",
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"columns": "ALL",
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"filter": f"(dim_date='{date}')",
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"sortColumns": "cityname",
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"sortTypes": "1",
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"token": "894050c76af8597a853f5b408b759f5d",
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"pageNumber": "1",
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"pageSize": "1000",
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"source": "WEB",
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}
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r = requests.get(url, params=params)
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data_json = r.json()
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temp_df = pd.DataFrame(data_json["result"]["data"]).iloc[:, 1:]
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temp_df.columns = [
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"日期",
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"地区",
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"V_0",
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"V_92",
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"V_95",
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"V_89",
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"ZDE_0",
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"ZDE_92",
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"ZDE_95",
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"ZDE_89",
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"QE_0",
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"QE_92",
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"QE_95",
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"QE_89",
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"首字母",
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]
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del temp_df["首字母"]
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temp_df["日期"] = pd.to_datetime(temp_df["日期"], errors="coerce").dt.date
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temp_df["V_0"] = pd.to_numeric(temp_df["V_0"], errors="coerce")
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temp_df["V_92"] = pd.to_numeric(temp_df["V_92"], errors="coerce")
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temp_df["V_95"] = pd.to_numeric(temp_df["V_95"], errors="coerce")
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temp_df["V_89"] = pd.to_numeric(temp_df["V_89"], errors="coerce")
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temp_df["ZDE_0"] = pd.to_numeric(temp_df["ZDE_0"], errors="coerce")
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temp_df["ZDE_92"] = pd.to_numeric(temp_df["ZDE_92"], errors="coerce")
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temp_df["ZDE_95"] = pd.to_numeric(temp_df["ZDE_95"], errors="coerce")
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temp_df["ZDE_89"] = pd.to_numeric(temp_df["ZDE_89"], errors="coerce")
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temp_df["QE_0"] = pd.to_numeric(temp_df["QE_0"], errors="coerce")
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temp_df["QE_92"] = pd.to_numeric(temp_df["QE_92"], errors="coerce")
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temp_df["QE_95"] = pd.to_numeric(temp_df["QE_95"], errors="coerce")
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temp_df["QE_89"] = pd.to_numeric(temp_df["QE_89"], errors="coerce")
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return temp_df
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if __name__ == "__main__":
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energy_oil_hist_df = energy_oil_hist()
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print(energy_oil_hist_df)
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energy_oil_detail_df = energy_oil_detail(date="20240118")
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print(energy_oil_detail_df)
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