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
133 lines
4.3 KiB
Python
133 lines
4.3 KiB
Python
#!/usr/bin/env python
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# -*- coding:utf-8 -*-
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"""
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Date: 2021/12/27 15:47
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Desc: 中国公路物流运价、运量指数
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http://index.0256.cn/expx.htm
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"""
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import pandas as pd
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import requests
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def index_price_cflp(symbol: str = "周指数") -> pd.DataFrame:
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"""
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中国公路物流运价指数
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http://index.0256.cn/expx.htm
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:param symbol: choice of {"周指数", "月指数", "季度指数", "年度指数"}
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:type symbol: str
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:return: 中国公路物流运价指数
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:rtype: pandas.DataFrame
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"""
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symbol_map = {
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"周指数": "2",
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"月指数": "3",
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"季度指数": "4",
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"年度指数": "5",
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}
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url = "http://index.0256.cn/expcenter_trend.action"
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params = {
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"marketId": "1",
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"attribute1": "5",
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"exponentTypeId": symbol_map[symbol],
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"cateId": "2",
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"attribute2": "华北",
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"city": "",
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"startLine": "",
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"endLine": "",
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}
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headers = {
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"Origin": "http://index.0256.cn",
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"Referer": "http://index.0256.cn/expx.htm",
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"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) "
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"Chrome/90.0.4430.212 Safari/537.36",
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}
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r = requests.post(url, data=params, headers=headers)
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data_json = r.json()
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temp_df = pd.DataFrame(
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[
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data_json["chart1"]["xLebal"],
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data_json["chart1"]["yLebal"],
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data_json["chart2"]["yLebal"],
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data_json["chart3"]["yLebal"],
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]
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).T
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temp_df.columns = ["日期", "定基指数", "环比指数", "同比指数"]
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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["定基指数"] = pd.to_numeric(temp_df["定基指数"], errors="coerce")
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temp_df["环比指数"] = pd.to_numeric(temp_df["环比指数"], errors="coerce")
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temp_df["同比指数"] = pd.to_numeric(temp_df["同比指数"], errors="coerce")
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return temp_df
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def index_volume_cflp(symbol: str = "月指数") -> pd.DataFrame:
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"""
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中国公路物流运量指数
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http://index.0256.cn/expx.htm
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:param symbol: choice of {"月指数", "季度指数", "年度指数"}
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:type symbol: str
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:return: 中国公路物流运量指数
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:rtype: pandas.DataFrame
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"""
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symbol_map = {
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"月指数": "3",
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"季度指数": "4",
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"年度指数": "5",
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}
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url = "http://index.0256.cn/volume_query.action"
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params = {
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"type": "1",
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"marketId": "1",
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"expTypeId": symbol_map[symbol],
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"startDate1": "",
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"endDate1": "",
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"city": "",
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"startDate3": "",
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"endDate3": "",
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}
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headers = {
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"Origin": "http://index.0256.cn",
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"Referer": "http://index.0256.cn/expx.htm",
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"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) "
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"Chrome/90.0.4430.212 Safari/537.36",
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}
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r = requests.post(url, data=params, headers=headers)
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data_json = r.json()
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temp_df = pd.DataFrame(
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[
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data_json["chart1"]["xLebal"],
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data_json["chart1"]["yLebal"],
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data_json["chart2"]["yLebal"],
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data_json["chart3"]["yLebal"],
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]
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).T
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temp_df.columns = ["日期", "定基指数", "环比指数", "同比指数"]
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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["定基指数"] = pd.to_numeric(temp_df["定基指数"], errors="coerce")
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temp_df["环比指数"] = pd.to_numeric(temp_df["环比指数"], errors="coerce")
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temp_df["同比指数"] = pd.to_numeric(temp_df["同比指数"], errors="coerce")
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return temp_df
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if __name__ == "__main__":
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index_price_cflp_df = index_price_cflp(symbol="周指数")
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print(index_price_cflp_df)
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index_price_cflp_df = index_price_cflp(symbol="月指数")
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print(index_price_cflp_df)
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index_price_cflp_df = index_price_cflp(symbol="季度指数")
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print(index_price_cflp_df)
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index_price_cflp_df = index_price_cflp(symbol="年度指数")
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print(index_price_cflp_df)
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index_volume_cflp_df = index_volume_cflp(symbol="月指数")
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print(index_volume_cflp_df)
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index_volume_cflp_df = index_volume_cflp(symbol="季度指数")
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print(index_volume_cflp_df)
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index_volume_cflp_df = index_volume_cflp(symbol="年度指数")
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print(index_volume_cflp_df)
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