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
83 lines
2.9 KiB
Python
83 lines
2.9 KiB
Python
#!/usr/bin/env python
|
|
# -*- coding:utf-8 -*-
|
|
"""
|
|
Date: 2024/9/26 18:00
|
|
Desc: Drewry 集装箱指数
|
|
https://www.drewry.co.uk/supply-chain-advisors/supply-chain-expertise/world-container-index-assessed-by-drewry
|
|
https://infogram.com/world-container-index-1h17493095xl4zj
|
|
"""
|
|
|
|
import pandas as pd
|
|
import requests
|
|
from bs4 import BeautifulSoup
|
|
|
|
from akshare.utils import demjson
|
|
|
|
|
|
def drewry_wci_index(symbol: str = "composite") -> pd.DataFrame:
|
|
"""
|
|
Drewry 集装箱指数
|
|
https://infogram.com/world-container-index-1h17493095xl4zj
|
|
:param symbol: choice of {"composite", "shanghai-rotterdam", "rotterdam-shanghai", "shanghai-los angeles",
|
|
"los angeles-shanghai", "shanghai-genoa", "new york-rotterdam", "rotterdam-new york"}
|
|
:type symbol: str
|
|
:return: Drewry 集装箱指数
|
|
:rtype: pandas.DataFrame
|
|
"""
|
|
symbol_map = {
|
|
"composite": 0,
|
|
"shanghai-rotterdam": 1,
|
|
"rotterdam-shanghai": 2,
|
|
"shanghai-los angeles": 3,
|
|
"los angeles-shanghai": 4,
|
|
"shanghai-genoa": 5,
|
|
"new york-rotterdam": 6,
|
|
"rotterdam-new york": 7,
|
|
}
|
|
url = "https://infogram.com/world-container-index-1h17493095xl4zj"
|
|
r = requests.get(url)
|
|
soup = BeautifulSoup(r.text, features="lxml")
|
|
data_text = soup.find_all("script")[-4].string.strip("window.infographicData=")[:-1]
|
|
data_json = demjson.decode(data_text)
|
|
data_json_need = data_json["elements"]["content"]["content"]["entities"][
|
|
"7a55585f-3fb3-44e6-9b54-beea1cd20b4d"
|
|
]["data"][symbol_map[symbol]]
|
|
date_list = [item[0]["value"] for item in data_json_need[1:]]
|
|
try:
|
|
value_list = [item[1]["value"] for item in data_json_need[1:]]
|
|
except TypeError:
|
|
value_list = [item[1]["value"] for item in data_json_need[1:-1]]
|
|
temp_df = pd.DataFrame([date_list, value_list]).T
|
|
temp_df.columns = ["date", "wci"]
|
|
temp_df["date"] = pd.to_datetime(
|
|
temp_df["date"], format="%d-%b-%y", errors="coerce"
|
|
).dt.date
|
|
temp_df["wci"] = pd.to_numeric(temp_df["wci"], errors="coerce")
|
|
return temp_df
|
|
|
|
|
|
if __name__ == "__main__":
|
|
drewry_wci_index_df = drewry_wci_index(symbol="composite")
|
|
print(drewry_wci_index_df)
|
|
|
|
drewry_wci_index_df = drewry_wci_index(symbol="shanghai-rotterdam")
|
|
print(drewry_wci_index_df)
|
|
|
|
drewry_wci_index_df = drewry_wci_index(symbol="rotterdam-shanghai")
|
|
print(drewry_wci_index_df)
|
|
|
|
drewry_wci_index_df = drewry_wci_index(symbol="shanghai-los angeles")
|
|
print(drewry_wci_index_df)
|
|
|
|
drewry_wci_index_df = drewry_wci_index(symbol="los angeles-shanghai")
|
|
print(drewry_wci_index_df)
|
|
|
|
drewry_wci_index_df = drewry_wci_index(symbol="shanghai-genoa")
|
|
print(drewry_wci_index_df)
|
|
|
|
drewry_wci_index_df = drewry_wci_index(symbol="new york-rotterdam")
|
|
print(drewry_wci_index_df)
|
|
|
|
drewry_wci_index_df = drewry_wci_index(symbol="rotterdam-new york")
|
|
print(drewry_wci_index_df)
|