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
264 lines
9.0 KiB
Python
264 lines
9.0 KiB
Python
# -*- coding:utf-8 -*-
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# !/usr/bin/env python
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"""
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Date: 2026/2/22 13:00
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Desc: 天天基金网-基金数据-分红送配
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https://fund.eastmoney.com/data/fundfenhong.html
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"""
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import pandas as pd
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import requests
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from akshare.utils.tqdm import get_tqdm
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def fund_fh_em(
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year: str = "2025",
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typ: str = "",
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rank: str = "BZDM",
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sort: str = "asc",
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page: int = -1,
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) -> pd.DataFrame:
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"""
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天天基金网-基金数据-分红送配-基金分红
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https://fund.eastmoney.com/data/fundfenhong.html#DJR,desc,1,,,
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:param year: 查询年份
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:type year: str
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:param typ: 基金类型；空串表示全部; choice of {"指数型-其他", "指数型-海外股票", "指数型-固收", "指数型-股票", "债券型-中短债",
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"债券型-长债", "债券型-理财", "债券型-混合债", "债券型-混合一级", "债券型-混合二级", "货币型-普通货币", "货币型-浮动净值",
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"混合型-平衡", "混合型-偏债", "混合型-偏股", "混合型-灵活", "混合型-绝对收益", "股票型", "REITs", "Reits", "QDII-商品",
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"QDII-普通股票", "QDII-混合债", "QDII-混合偏股", "QDII-纯债", "QDII-REITs", "FOF"}
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:type typ: str
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:param rank: 排序字段；choice of {"BZDM", "ABBNAME", "DJR", "FSRQ", "FHFCZ", "FFR"}; "BZDM": 基金代码,
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"ABBNAME": 基金简称, "DJR": 权益登记日, "FSRQ": 除息日期, "FHFCZ": 分红(元/份), "FFR": 分红发放日
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:type rank: str
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:param sort: 排序方向；排序方式; choice of {"asc", "desc"}
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:type sort: str
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:param page: 查询页数；请求第page页数据; -1 表示全部页面
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:type page: int
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:return: 基金分红
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:rtype: pandas.DataFrame
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"""
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def get_df_from_response(response):
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text = response.text
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return pd.DataFrame(eval(text[text.find("[["): text.find(";var jjfh_jjgs")]))
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url = "https://fund.eastmoney.com/Data/funddataIndex_Interface.aspx"
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params = {
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"dt": "8",
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"page": "1" if page == -1 else str(page),
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"rank": rank,
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"sort": sort,
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"gs": "",
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"ftype": typ,
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"year": year,
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}
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r = requests.get(url, params=params)
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data_list = [get_df_from_response(r)]
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if page == -1:
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data_text = r.text
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total_page = eval(data_text[data_text.find("=") + 1: data_text.find(";")])[0]
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tqdm = get_tqdm()
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for p in tqdm(range(2, total_page + 1), leave=False):
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params.update({"page": str(p)})
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r = requests.get(url, params=params)
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data_list.append(get_df_from_response(r))
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big_df = pd.concat(objs=data_list, ignore_index=True)
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big_df.reset_index(inplace=True)
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big_df["index"] = big_df.index + 1
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# 处理空数据时报错的问题
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if big_df.empty:
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big_df = big_df.reindex(
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columns=[
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"序号",
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"基金代码",
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"基金简称",
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"权益登记日",
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"除息日期",
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"分红",
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"分红发放日",
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"-",
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]
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)
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big_df.columns = [
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"序号",
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"基金代码",
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"基金简称",
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"权益登记日",
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"除息日期",
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"分红",
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"分红发放日",
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"-",
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]
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big_df = big_df[
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["序号", "基金代码", "基金简称", "权益登记日", "除息日期", "分红", "分红发放日"]
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]
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big_df["权益登记日"] = pd.to_datetime(big_df["权益登记日"]).dt.date
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big_df["除息日期"] = pd.to_datetime(big_df["除息日期"]).dt.date
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big_df["分红发放日"] = pd.to_datetime(big_df["分红发放日"]).dt.date
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big_df["分红"] = pd.to_numeric(big_df["分红"])
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return big_df
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def fund_cf_em(
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year: str = "2025",
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typ: str = "",
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rank: str = "FSRQ",
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sort: str = "desc",
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page: int = -1,
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) -> pd.DataFrame:
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"""
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天天基金网-基金数据-分红送配-基金拆分
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https://fund.eastmoney.com/data/fundchaifen.html#FSRQ,desc,1,,,
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:param year: 查询年份
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:type year: str
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:param typ: 基金类型；空串表示全部; choice of {"", "指数型-其他", "指数型-海外股票", "指数型-固收", "指数型-股票",
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"债券型-中短债", "债券型-长债", "债券型-可转债", "债券型-混合债", "债券型-混合一级", "债券型-混合二级",
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"商品（不含QDII）", "货币型", "混合型-平衡", "混合型-偏债", "混合型-偏股", "混合型-灵活", "股票型", "QDII", "FOF"}
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:type typ: str
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:param rank: 排序字段；choice of {"BZDM", "ABBNAME", "FSRQ", "FHFCZ"}; "BZDM": 基金代码,
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"ABBNAME": 基金简称, "FSRQ": 拆分折算日, "FHFCZ": 拆分折算(每份)
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:type rank: str
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:param sort: 排序方向；choice of {"asc", "desc"}
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:type sort: str
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:param page: 查询页数；请求第page页数据; -1 表示全部页面
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:type page: int
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:return: 基金拆分
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:rtype: pandas.DataFrame
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"""
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def get_df_from_response(response):
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text = response.text
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code = text[text.find("[["): text.find(";var jjcf_jjgs")]
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if code:
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return pd.DataFrame(eval(code))
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return pd.DataFrame()
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url = "https://fund.eastmoney.com/Data/funddataIndex_Interface.aspx"
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params = {
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"dt": "9",
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"page": "1" if page == -1 else str(page),
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"rank": rank,
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"sort": sort,
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"gs": "",
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"ftype": typ,
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"year": year,
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}
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r = requests.get(url, params=params)
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data_list = [get_df_from_response(r)]
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if page == -1:
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data_text = r.text
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total_page = eval(data_text[data_text.find("=") + 1: data_text.find(";")])[0]
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tqdm = get_tqdm()
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for p in tqdm(range(2, total_page + 1), leave=False):
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params.update({"page": str(p)})
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r = requests.get(url, params=params)
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data_list.append(get_df_from_response(r))
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big_df = pd.concat(objs=data_list, ignore_index=True)
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big_df.reset_index(inplace=True)
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big_df.loc[:, "index"] = big_df["index"] + 1
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# 处理空数据时报错的问题
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if big_df.empty:
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big_df = big_df.reindex(
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columns=[
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"序号",
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"基金代码",
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"基金简称",
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"拆分折算日",
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"拆分类型",
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"拆分折算",
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"-",
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]
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)
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big_df.columns = [
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"序号",
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"基金代码",
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"基金简称",
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"拆分折算日",
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"拆分类型",
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"拆分折算",
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"-",
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]
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big_df = big_df[
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["序号", "基金代码", "基金简称", "拆分折算日", "拆分类型", "拆分折算"]
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]
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big_df["拆分折算日"] = pd.to_datetime(big_df["拆分折算日"]).dt.date
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big_df["拆分折算"] = pd.to_numeric(big_df["拆分折算"], errors="coerce")
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return big_df
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def fund_fh_rank_em() -> pd.DataFrame:
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"""
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天天基金网-基金数据-分红送配-基金分红排行
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https://fund.eastmoney.com/data/fundleijifenhong.html
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:return: 基金分红排行
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:rtype: pandas.DataFrame
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"""
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url = "https://fund.eastmoney.com/Data/funddataIndex_Interface.aspx"
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params = {
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"dt": "10",
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"page": "1",
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"rank": "FHFCZ",
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"sort": "desc",
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"gs": "",
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"ftype": "",
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}
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r = requests.get(url, params=params)
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data_text = r.text
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total_page = eval(data_text[data_text.find("=") + 1: data_text.find(";")])[0]
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big_df = pd.DataFrame()
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tqdm = get_tqdm()
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for page in tqdm(range(1, total_page + 1), leave=False):
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params.update({"page": str(page)})
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r = requests.get(url, params=params)
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data_text = r.text
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temp_list = eval(
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data_text[data_text.find("[["): data_text.find(";var fhph_jjgs")]
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)
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temp_df = pd.DataFrame(temp_list)
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big_df = pd.concat(objs=[big_df, temp_df], ignore_index=True)
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big_df.reset_index(inplace=True)
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big_df["index"] = big_df.index + 1
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# 处理空数据时报错的问题
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if big_df.empty:
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big_df = big_df.reindex(
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columns=[
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"序号",
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"基金代码",
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"基金简称",
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"累计分红",
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"累计次数",
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"成立日期",
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"-",
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]
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)
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big_df.columns = [
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"序号",
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"基金代码",
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"基金简称",
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"累计分红",
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"累计次数",
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"成立日期",
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"-",
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]
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big_df = big_df[
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["序号", "基金代码", "基金简称", "累计分红", "累计次数", "成立日期"]
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]
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big_df["成立日期"] = pd.to_datetime(big_df["成立日期"]).dt.date
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big_df["累计分红"] = pd.to_numeric(big_df["累计分红"], errors="coerce")
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big_df["累计次数"] = pd.to_numeric(big_df["累计次数"], errors="coerce")
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return big_df
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if __name__ == "__main__":
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fund_fh_em_df = fund_fh_em(year="2025")
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print(fund_fh_em_df)
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fund_cf_em_df = fund_cf_em(year="2025")
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print(fund_cf_em_df)
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fund_fh_rank_em_df = fund_fh_rank_em()
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print(fund_fh_rank_em_df)
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