#!/usr/bin/env python # -*- coding:utf-8 -*- """ Date: 2024/10/2 9:00 Desc: 股票基本面数据 新浪财经-财务报表-财务摘要 https://vip.stock.finance.sina.com.cn/corp/go.php/vFD_FinanceSummary/stockid/600004.phtml 新浪财经-财务分析-财务指标 https://money.finance.sina.com.cn/corp/go.php/vFD_FinancialGuideLine/stockid/600004/displaytype/4.phtml 新浪财经-发行与分配 https://money.finance.sina.com.cn/corp/go.php/vISSUE_ShareBonus/stockid/600004.phtml """ from datetime import datetime from io import StringIO import pandas as pd import requests from bs4 import BeautifulSoup from akshare.utils.tqdm import get_tqdm def stock_financial_report_sina( stock: str = "sh600600", symbol: str = "资产负债表" ) -> pd.DataFrame: """ 新浪财经-财务报表-三大报表 https://vip.stock.finance.sina.com.cn/corp/go.php/vFD_FinanceSummary/stockid/600600/displaytype/4.phtml?source=fzb&qq-pf-to=pcqq.group :param stock: 股票代码 :type stock: str :param symbol: choice of {"资产负债表", "利润表", "现金流量表"} :type symbol: str :return: 新浪财经-财务报表-三大报表 :rtype: pandas.DataFrame """ symbol_map = {"资产负债表": "fzb", "利润表": "lrb", "现金流量表": "llb"} url = "https://quotes.sina.cn/cn/api/openapi.php/CompanyFinanceService.getFinanceReport2022" params = { "paperCode": f"{stock}", "source": symbol_map[symbol], "type": "0", "page": "1", "num": "1000", } r = requests.get(url, params=params) data_json = r.json() df_columns = [ item["date_value"] for item in data_json["result"]["data"]["report_date"] ] big_df = pd.DataFrame() temp_df = pd.DataFrame() for date_str in df_columns: temp_df = pd.DataFrame( data_json["result"]["data"]["report_list"][date_str]["data"] ) temp_df = temp_df[["item_title", "item_value"]] temp_df["item_value"] = pd.to_numeric(temp_df["item_value"], errors="coerce") temp_tail_df = pd.DataFrame.from_dict( data={ "数据源": data_json["result"]["data"]["report_list"][date_str][ "data_source" ], "是否审计": data_json["result"]["data"]["report_list"][date_str][ "is_audit" ], "公告日期": data_json["result"]["data"]["report_list"][date_str][ "publish_date" ], "币种": data_json["result"]["data"]["report_list"][date_str][ "rCurrency" ], "类型": data_json["result"]["data"]["report_list"][date_str]["rType"], "更新日期": datetime.fromtimestamp( data_json["result"]["data"]["report_list"][date_str]["update_time"] ).isoformat(), }, orient="index", ) temp_tail_df.reset_index(inplace=True) temp_tail_df.columns = ["item_title", "item_value"] temp_df = pd.concat(objs=[temp_df, temp_tail_df], ignore_index=True) temp_df.columns = ["项目", date_str] big_df = pd.concat(objs=[big_df, temp_df[date_str]], axis=1, ignore_index=True) big_df = big_df.T big_df.columns = temp_df["项目"] big_df = pd.concat(objs=[pd.DataFrame({"报告日": df_columns}), big_df], axis=1) # 此处有 '国内票证结算' 和 '内部应收款'字段重复 big_df = big_df.loc[:, ~big_df.columns.duplicated(keep="first")] return big_df def stock_financial_abstract(symbol: str = "600004") -> pd.DataFrame: """ 新浪财经-财务报表-关键指标 https://vip.stock.finance.sina.com.cn/corp/go.php/vFD_FinanceSummary/stockid/600004.phtml :param symbol: 股票代码 :type symbol: str :return: 新浪财经-财务报表-关键指标 :rtype: pandas.DataFrame """ url = "https://quotes.sina.cn/cn/api/openapi.php/CompanyFinanceService.getFinanceReport2022" params = { "paperCode": f"sh{symbol}", "source": "gjzb", "type": "0", "page": "1", "num": "1000", } r = requests.get(url, params=params) data_json = r.json() key_list = list(data_json["result"]["data"]["report_list"].keys()) temp_df = pd.DataFrame( data_json["result"]["data"]["report_list"][key_list[0]]["data"] ) big_df = temp_df["item_title"] for item in key_list: temp_df = pd.DataFrame(data_json["result"]["data"]["report_list"][item]["data"]) big_df = pd.concat( objs=[big_df, temp_df["item_value"]], axis=1, ignore_index=True ) big_df.index = big_df.iloc[:, 0] big_df = big_df.iloc[:, 1:] big_one_df = big_df.loc["常用指标":"每股指标"] big_one_df = big_one_df.iloc[1:-1, :] big_one_df.reset_index(inplace=True) big_one_df.insert(0, "选项", "常用指标") big_two_df = big_df.loc["每股指标":"盈利能力"] big_two_df = big_two_df.iloc[1:-1, :] big_two_df.reset_index(inplace=True) big_two_df.insert(0, "选项", "每股指标") big_three_df = big_df.loc["盈利能力":"成长能力"] big_three_df = big_three_df.iloc[1:-1, :] big_three_df.reset_index(inplace=True) big_three_df.insert(0, "选项", "盈利能力") big_four_df = big_df.loc["成长能力":"收益质量"] big_four_df = big_four_df.iloc[1:-1, :] big_four_df.reset_index(inplace=True) big_four_df.insert(0, "选项", "成长能力") big_five_df = big_df.loc["收益质量":"财务风险"] big_five_df = big_five_df.iloc[1:-1, :] big_five_df.reset_index(inplace=True) big_five_df.insert(0, "选项", "收益质量") big_six_df = big_df.loc["财务风险":"营运能力"] big_six_df = big_six_df.iloc[1:-1, :] big_six_df.reset_index(inplace=True) big_six_df.insert(0, "选项", "财务风险") big_seven_df = big_df.loc["营运能力":] big_seven_df = big_seven_df.iloc[1:-1, :] big_seven_df.reset_index(inplace=True) big_seven_df.insert(0, "选项", "营运能力") big_df = pd.concat( objs=[ big_one_df, big_two_df, big_three_df, big_four_df, big_five_df, big_six_df, big_seven_df, ], ignore_index=True, ) key_list.insert(0, "选项") key_list.insert(1, "指标") big_df.columns = key_list for item in big_df.columns[2:]: big_df[item] = pd.to_numeric(big_df[item], errors="coerce") return big_df def stock_financial_analysis_indicator_em( symbol: str = "301389.SZ", indicator: str = "按报告期" ) -> pd.DataFrame: """ 东方财富-A股-财务分析-主要指标 https://emweb.securities.eastmoney.com/pc_hsf10/pages/index.html?type=web&code=SZ301389&color=b#/cwfx :param symbol: 股票代码（带市场标识） :type symbol: str :param indicator: choice of {"按报告期", "按单季度"} :type indicator: str :return: 东方财富-A股-财务分析-主要指标 :rtype: pandas.DataFrame """ if indicator == "按报告期": url = "https://datacenter.eastmoney.com/securities/api/data/get" params = { "type": "RPT_F10_FINANCE_MAINFINADATA", "sty": "APP_F10_MAINFINADATA", "quoteColumns": "", "filter": f"""(SECUCODE="{symbol}")""", "p": "1", "ps": "200", "sr": "-1", "st": "REPORT_DATE", "source": "HSF10", "client": "PC", } else: url = "https://datacenter.eastmoney.com/securities/api/data/v1/get" params = { "reportName": "RPT_F10_QTR_MAINFINADATA", "columns": "ALL", "quoteColumns": "", "filter": f"""(SECUCODE="{symbol}")""", "pageNumber": "1", "pageSize": "200", "sortTypes": "-1", "sortColumns": "REPORT_DATE", "source": "HSF10", "client": "PC", } r = requests.get(url, params=params) data_json = r.json() temp_df = pd.DataFrame(data_json["result"]["data"]) return temp_df def stock_financial_analysis_indicator( symbol: str = "600004", start_year: str = "1900" ) -> pd.DataFrame: """ 新浪财经-财务分析-财务指标 https://money.finance.sina.com.cn/corp/go.php/vFD_FinancialGuideLine/stockid/600004/ctrl/2019/displaytype/4.phtml :param symbol: 股票代码 :type symbol: str :param start_year: 开始年份 :type start_year: str :return: 新浪财经-财务分析-财务指标 :rtype: pandas.DataFrame """ url = ( f"https://money.finance.sina.com.cn/corp/go.php/vFD_FinancialGuideLine/" f"stockid/{symbol}/ctrl/2020/displaytype/4.phtml" ) r = requests.get(url) soup = BeautifulSoup(r.text, features="lxml") year_context = soup.find(attrs={"id": "con02-1"}).find("table").find_all("a") year_list = [item.text for item in year_context] if start_year in year_list: year_list = year_list[: year_list.index(start_year) + 1] else: return pd.DataFrame() out_df = pd.DataFrame() tqdm = get_tqdm() for year_item in tqdm(year_list, leave=False): url = ( f"https://money.finance.sina.com.cn/corp/go.php/vFD_FinancialGuideLine/" f"stockid/{symbol}/ctrl/{year_item}/displaytype/4.phtml" ) r = requests.get(url) temp_df = pd.read_html(StringIO(r.text))[12].iloc[:, :-1] temp_df.columns = temp_df.iloc[0, :] temp_df = temp_df.iloc[1:, :] big_df = pd.DataFrame() indicator_list = [ "每股指标", "盈利能力", "成长能力", "营运能力", "偿债及资本结构", "现金流量", "其他指标", ] for i in range(len(indicator_list)): if i == 6: inner_df = temp_df[ temp_df.loc[ temp_df.iloc[:, 0].str.find(indicator_list[i]) == 0, : ].index[0] : ].T else: inner_df = temp_df[ temp_df.loc[ temp_df.iloc[:, 0].str.find(indicator_list[i]) == 0, : ].index[0] : temp_df.loc[ temp_df.iloc[:, 0].str.find(indicator_list[i + 1]) == 0, : ].index[0] - 1 ].T inner_df = inner_df.reset_index(drop=True) big_df = pd.concat(objs=[big_df, inner_df], axis=1) big_df.columns = big_df.iloc[0, :].tolist() big_df = big_df.iloc[1:, :] big_df.index = temp_df.columns.tolist()[1:] out_df = pd.concat(objs=[out_df, big_df]) out_df.dropna(inplace=True) out_df.reset_index(inplace=True) out_df.rename(columns={"index": "日期"}, inplace=True) out_df.sort_values(by=["日期"], ignore_index=True, inplace=True) out_df["日期"] = pd.to_datetime(out_df["日期"], errors="coerce").dt.date for item in out_df.columns[1:]: out_df[item] = pd.to_numeric(out_df[item], errors="coerce") return out_df def stock_history_dividend() -> pd.DataFrame: """ 新浪财经-发行与分配-历史分红 https://vip.stock.finance.sina.com.cn/q/go.php/vInvestConsult/kind/lsfh/index.phtml :return: 所有股票的历史分红数据 :rtype: pandas.DataFrame """ url = "https://vip.stock.finance.sina.com.cn/q/go.php/vInvestConsult/kind/lsfh/index.phtml" params = {"p": "1", "num": "50000"} r = requests.get(url, params=params) temp_df = pd.read_html(StringIO(r.text))[0] temp_df["代码"] = temp_df["代码"].astype(str).str.zfill(6) temp_df.columns = [ "代码", "名称", "上市日期", "累计股息", "年均股息", "分红次数", "融资总额", "融资次数", "详细", ] temp_df["上市日期"] = pd.to_datetime(temp_df["上市日期"], errors="coerce").dt.date temp_df["累计股息"] = pd.to_numeric(temp_df["累计股息"], errors="coerce") temp_df["年均股息"] = pd.to_numeric(temp_df["年均股息"], errors="coerce") temp_df["分红次数"] = pd.to_numeric(temp_df["分红次数"], errors="coerce") temp_df["融资总额"] = pd.to_numeric(temp_df["融资总额"], errors="coerce") temp_df["融资次数"] = pd.to_numeric(temp_df["融资次数"], errors="coerce") del temp_df["详细"] return temp_df def stock_history_dividend_detail( symbol: str = "000002", indicator: str = "分红", date: str = "" ) -> pd.DataFrame: """ 新浪财经-发行与分配-分红配股详情 https://vip.stock.finance.sina.com.cn/corp/go.php/vISSUE_ShareBonus/stockid/300670.phtml :param indicator: choice of {"分红", "配股"} :type indicator: str :param symbol: 股票代码 :type symbol: str :param date: 分红配股的具体日期, e.g., "1994-12-24" :type date: str :return: 指定 indicator, stock, date 的数据 :rtype: pandas.DataFrame """ if indicator == "分红": url = f"https://vip.stock.finance.sina.com.cn/corp/go.php/vISSUE_ShareBonus/stockid/{symbol}.phtml" r = requests.get(url) temp_df = pd.read_html(StringIO(r.text))[12] temp_df.columns = [item[2] for item in temp_df.columns.tolist()] temp_df.columns = [ "公告日期", "送股", "转增", "派息", "进度", "除权除息日", "股权登记日", "红股上市日", "查看详细", ] del temp_df["查看详细"] if temp_df.iloc[0, 0] == "暂时没有数据！": return pd.DataFrame() temp_df["公告日期"] = pd.to_datetime( temp_df["公告日期"], errors="coerce" ).dt.date temp_df["送股"] = pd.to_numeric(temp_df["送股"], errors="coerce") temp_df["转增"] = pd.to_numeric(temp_df["转增"], errors="coerce") temp_df["派息"] = pd.to_numeric(temp_df["派息"], errors="coerce") temp_df["除权除息日"] = pd.to_datetime( temp_df["除权除息日"], format="%Y-%m-%d", errors="coerce" ).dt.date temp_df["股权登记日"] = pd.to_datetime( temp_df["股权登记日"], format="%Y-%m-%d", errors="coerce" ).dt.date temp_df["红股上市日"] = pd.to_datetime( temp_df["红股上市日"], format="%Y-%m-%d", errors="coerce" ).dt.date if date: url = "https://vip.stock.finance.sina.com.cn/corp/view/vISSUE_ShareBonusDetail.php" params = { "stockid": symbol, "type": "1", "end_date": date, } r = requests.get(url, params=params) temp_df = pd.read_html(StringIO(r.text))[12] temp_df.columns = ["item", "value"] return temp_df else: return temp_df else: url = f"https://vip.stock.finance.sina.com.cn/corp/go.php/vISSUE_ShareBonus/stockid/{symbol}.phtml" r = requests.get(url) temp_df = pd.read_html(StringIO(r.text))[13] temp_df.columns = [item[1] for item in temp_df.columns.tolist()] temp_df.columns = [ "公告日期", "配股方案", "配股价格", "基准股本", "除权日", "股权登记日", "缴款起始日", "缴款终止日", "配股上市日", "募集资金合计", "查看详细", ] del temp_df["查看详细"] if temp_df.iloc[0, 0] == "暂时没有数据！": return pd.DataFrame() temp_df["公告日期"] = pd.to_datetime( temp_df["公告日期"], errors="coerce" ).dt.date temp_df["配股方案"] = pd.to_numeric(temp_df["配股方案"], errors="coerce") temp_df["配股价格"] = pd.to_numeric(temp_df["配股价格"], errors="coerce") temp_df["基准股本"] = pd.to_numeric(temp_df["基准股本"], errors="coerce") temp_df["募集资金合计"] = pd.to_numeric( temp_df["募集资金合计"], errors="coerce" ) temp_df["除权日"] = pd.to_datetime( temp_df["除权日"], format="%Y-%m-%d", errors="coerce" ).dt.date temp_df["股权登记日"] = pd.to_datetime( temp_df["股权登记日"], format="%Y-%m-%d", errors="coerce" ).dt.date temp_df["缴款起始日"] = pd.to_datetime( temp_df["缴款起始日"], format="%Y-%m-%d", errors="coerce" ).dt.date temp_df["缴款终止日"] = pd.to_datetime( temp_df["缴款终止日"], format="%Y-%m-%d", errors="coerce" ).dt.date temp_df["配股上市日"] = pd.to_datetime( temp_df["配股上市日"], format="%Y-%m-%d", errors="coerce" ).dt.date if date: url = "https://vip.stock.finance.sina.com.cn/corp/view/vISSUE_ShareBonusDetail.php" params = { "stockid": symbol, "type": "1", "end_date": date, } r = requests.get(url, params=params) temp_df = pd.read_html(StringIO(r.text))[12] temp_df.columns = ["item", "value"] return temp_df else: return temp_df def stock_ipo_info(stock: str = "600004") -> pd.DataFrame: """ 新浪财经-发行与分配-新股发行 https://vip.stock.finance.sina.com.cn/corp/go.php/vISSUE_NewStock/stockid/600004.phtml :param stock: 股票代码 :type stock: str :return: 返回新股发行详情 :rtype: pandas.DataFrame """ url = f"https://vip.stock.finance.sina.com.cn/corp/go.php/vISSUE_NewStock/stockid/{stock}.phtml" r = requests.get(url) temp_df = pd.read_html(StringIO(r.text))[12] temp_df.columns = ["item", "value"] return temp_df def stock_add_stock(symbol: str = "688166") -> pd.DataFrame: """ 新浪财经-发行与分配-增发 https://vip.stock.finance.sina.com.cn/corp/go.php/vISSUE_AddStock/stockid/600004.phtml :param symbol: 股票代码 :type symbol: str :return: 返回增发详情 :rtype: pandas.DataFrame """ url = f"https://vip.stock.finance.sina.com.cn/corp/go.php/vISSUE_AddStock/stockid/{symbol}.phtml" r = requests.get(url) temp_df = pd.read_html(StringIO(r.text))[12] if temp_df.at[0, 0] == "对不起，暂时没有相关增发记录": raise f"股票 {symbol} 无增发记录" big_df = pd.DataFrame() for i in range(int(len(temp_df.at[0, 1]) / 10)): temp_df = pd.read_html(StringIO(r.text))[13 + i].iloc[:, 1] big_df[temp_df.name.split(" ")[1].split("：")[1][:10]] = temp_df big_df = big_df.T big_df.reset_index(inplace=True) big_df.columns = [ "公告日期", "发行方式", "发行价格", "实际公司募集资金总额", "发行费用总额", "实际发行数量", ] big_df["公告日期"] = pd.to_datetime(big_df["公告日期"], errors="coerce").dt.date return big_df def stock_restricted_release_queue_sina(symbol: str = "600000") -> pd.DataFrame: """ 新浪财经-发行分配-限售解禁 https://vip.stock.finance.sina.com.cn/q/go.php/vInvestConsult/kind/xsjj/index.phtml?symbol=sh600000 :param symbol: 股票代码 :type symbol: str :return: 返回限售解禁数据 :rtype: pandas.DataFrame """ url = f"https://vip.stock.finance.sina.com.cn/q/go.php/vInvestConsult/kind/xsjj/index.phtml?symbol={symbol}" r = requests.get(url) temp_df = pd.read_html(StringIO(r.text))[0] temp_df.columns = [ "代码", "名称", "解禁日期", "解禁数量", "解禁股流通市值", "上市批次", "公告日期", ] temp_df["解禁日期"] = pd.to_datetime(temp_df["解禁日期"], errors="coerce").dt.date temp_df["公告日期"] = pd.to_datetime(temp_df["公告日期"], errors="coerce").dt.date temp_df["解禁数量"] = pd.to_numeric(temp_df["解禁数量"], errors="coerce") temp_df["解禁股流通市值"] = pd.to_numeric( temp_df["解禁股流通市值"], errors="coerce" ) temp_df["上市批次"] = pd.to_numeric(temp_df["上市批次"], errors="coerce") temp_df["上市批次"] = pd.to_numeric(temp_df["上市批次"], errors="coerce") return temp_df def stock_circulate_stock_holder(symbol: str = "600000") -> pd.DataFrame: """ 新浪财经-股东股本-流通股东 P.S. 特定股票特定时间只有前 5 个; e.g., 000002 https://vip.stock.finance.sina.com.cn/corp/go.php/vCI_CirculateStockHolder/stockid/600000.phtml :param symbol: 股票代码 :type symbol: str :return: 新浪财经-股东股本-流通股东 :rtype: pandas.DataFrame """ pd.set_option("future.no_silent_downcasting", True) url = f"https://vip.stock.finance.sina.com.cn/corp/go.php/vCI_CirculateStockHolder/stockid/{symbol}.phtml" r = requests.get(url) temp_df = pd.read_html(StringIO(r.text))[13].iloc[:, :5] temp_df.columns = [*range(5)] big_df = pd.DataFrame() need_range = temp_df[ temp_df.iloc[:, 0].str.find("截止日期") == 0 ].index.tolist() + [len(temp_df)] tqdm = get_tqdm() for i in tqdm(range(len(need_range) - 1), leave=False): truncated_df = temp_df.iloc[need_range[i] : need_range[i + 1], :] truncated_df = truncated_df.dropna(how="all") temp_truncated = truncated_df.iloc[2:, :] temp_truncated.reset_index(inplace=True, drop=True) concat_df = pd.concat( objs=[temp_truncated, truncated_df.iloc[0, :], truncated_df.iloc[1, :]], axis=1, ) concat_df.columns = concat_df.iloc[0, :] concat_df = concat_df.iloc[1:, :] try: # try for pandas >= 2.1.0 concat_df["截止日期"] = concat_df["截止日期"].ffill() concat_df["公告日期"] = concat_df["公告日期"].ffill() except: # noqa: E722 try: # try for pandas < 2.1.0 concat_df["截止日期"] = concat_df["截止日期"].fillna(method="ffill") concat_df["公告日期"] = concat_df["公告日期"].fillna(method="ffill") except Exception as e: print("Error:", e) big_df = pd.concat(objs=[big_df, concat_df], axis=0, ignore_index=True) big_df = big_df[ [ "截止日期", "公告日期", "编号", "股东名称", "持股数量(股)", "占流通股比例(%)", "股本性质", ] ] big_df.columns = [ "截止日期", "公告日期", "编号", "股东名称", "持股数量", "占流通股比例", "股本性质", ] big_df["截止日期"] = pd.to_datetime(big_df["截止日期"], errors="coerce").dt.date big_df["公告日期"] = pd.to_datetime(big_df["公告日期"], errors="coerce").dt.date big_df["编号"] = pd.to_numeric(big_df["编号"], errors="coerce") big_df["持股数量"] = pd.to_numeric(big_df["持股数量"], errors="coerce") big_df["占流通股比例"] = pd.to_numeric(big_df["占流通股比例"], errors="coerce") big_df.reset_index(inplace=True, drop=True) return big_df def stock_fund_stock_holder(symbol: str = "600004") -> pd.DataFrame: """ 新浪财经-股本股东-基金持股 https://vip.stock.finance.sina.com.cn/corp/go.php/vCI_FundStockHolder/stockid/600004.phtml :param symbol: 股票代码 :type symbol: str :return: 新浪财经-股本股东-基金持股 :rtype: pandas.DataFrame """ url = f"https://vip.stock.finance.sina.com.cn/corp/go.php/vCI_FundStockHolder/stockid/{symbol}.phtml" r = requests.get(url) temp_df = pd.read_html(StringIO(r.text))[13].iloc[:, :6] temp_df.columns = [*range(6)] big_df = pd.DataFrame() need_range = temp_df[ temp_df.iloc[:, 0].str.find("截止日期") == 0 ].index.tolist() + [len(temp_df)] tqdm = get_tqdm() for i in tqdm(range(len(need_range) - 1), leave=False): # pass truncated_df = temp_df.iloc[need_range[i] : need_range[i + 1], :] truncated_df = truncated_df.dropna(how="all") temp_truncated = truncated_df.iloc[2:, :] temp_truncated.reset_index(inplace=True, drop=True) concat_df = pd.concat(objs=[temp_truncated, truncated_df.iloc[0, 1:]], axis=1) concat_df.columns = truncated_df.iloc[1, :].tolist() + ["截止日期"] try: # try for pandas >= 2.1.0 concat_df["截止日期"] = concat_df["截止日期"].ffill() concat_df["截止日期"] = concat_df["截止日期"].ffill() except: # noqa: E722 try: # try for pandas < 2.1.0 concat_df["截止日期"] = concat_df["截止日期"].fillna(method="ffill") concat_df["截止日期"] = concat_df["截止日期"].fillna(method="bfill") except Exception as e: print("Error:", e) big_df = pd.concat(objs=[big_df, concat_df], axis=0, ignore_index=True) big_df.dropna(inplace=True) big_df.reset_index(inplace=True, drop=True) big_df.columns = [ "基金名称", "基金代码", "持仓数量", "占流通股比例", "持股市值", "占净值比例", "截止日期", ] big_df["持仓数量"] = pd.to_numeric(big_df["持仓数量"], errors="coerce") big_df["占流通股比例"] = pd.to_numeric(big_df["占流通股比例"], errors="coerce") big_df["持股市值"] = pd.to_numeric(big_df["持股市值"], errors="coerce") big_df["占净值比例"] = pd.to_numeric(big_df["占净值比例"], errors="coerce") big_df["截止日期"] = pd.to_datetime(big_df["截止日期"], errors="coerce").dt.date return big_df def stock_main_stock_holder(stock: str = "600004") -> pd.DataFrame: """ 新浪财经-股本股东-主要股东 P.S. 特定股票特定时间只有前 5 个; e.g., 000002 https://vip.stock.finance.sina.com.cn/corp/go.php/vCI_StockHolder/stockid/600004.phtml :param stock: 股票代码 :type stock: str :return: 新浪财经-股本股东-主要股东 :rtype: pandas.DataFrame """ url = f"https://vip.stock.finance.sina.com.cn/corp/go.php/vCI_StockHolder/stockid/{stock}.phtml" r = requests.get(url) temp_df = pd.read_html(StringIO(r.text))[13].iloc[:, :5] temp_df.columns = [*range(5)] big_df = pd.DataFrame() need_range = temp_df[ temp_df.iloc[:, 0].str.find("截至日期") == 0 ].index.tolist() + [len(temp_df)] for i in range(len(need_range) - 1): truncated_df = temp_df.iloc[need_range[i] : need_range[i + 1], :] truncated_df = truncated_df.dropna(how="all") temp_truncated = truncated_df.iloc[5:, :] temp_truncated.reset_index(inplace=True, drop=True) concat_df = pd.concat( objs=[ temp_truncated, truncated_df.iloc[0, :], truncated_df.iloc[1, :], truncated_df.iloc[2, :], truncated_df.iloc[3, :], truncated_df.iloc[4, :], ], axis=1, ) concat_df.columns = concat_df.iloc[0, :] concat_df = concat_df.iloc[1:, :].copy() try: # try for pandas >= 2.1.0 concat_df["截至日期"] = concat_df["截至日期"].ffill() concat_df["公告日期"] = concat_df["公告日期"].ffill() concat_df["股东总数"] = concat_df["股东总数"].ffill() concat_df["平均持股数"] = concat_df["平均持股数"].ffill() except: # noqa: E722 try: # try for pandas < 2.1.0 concat_df["截至日期"] = concat_df["截至日期"].fillna(method="ffill") concat_df["公告日期"] = concat_df["公告日期"].fillna(method="ffill") concat_df["股东总数"] = concat_df["股东总数"].fillna(method="ffill") concat_df["平均持股数"] = concat_df["平均持股数"].fillna(method="ffill") except Exception as e: print("Error:", e) concat_df["股东总数"] = concat_df["股东总数"].str.strip("查看变化趋势") concat_df["平均持股数"] = concat_df["平均持股数"].str.strip( "(按总股本计算) 查看变化趋势" ) big_df = pd.concat(objs=[big_df, concat_df], axis=0, ignore_index=True) big_df.dropna(inplace=True, how="all") big_df.reset_index(inplace=True, drop=True) big_df.rename( columns={"持股数量(股)": "持股数量", "持股比例(%)": "持股比例"}, inplace=True ) big_df.columns.name = None big_df["持股数量"] = pd.to_numeric(big_df["持股数量"], errors="coerce") big_df["持股比例"] = big_df["持股比例"].str.strip("↓") big_df["持股比例"] = pd.to_numeric(big_df["持股比例"], errors="coerce") big_df["截至日期"] = pd.to_datetime(big_df["截至日期"], errors="coerce").dt.date big_df["公告日期"] = pd.to_datetime(big_df["公告日期"], errors="coerce").dt.date big_df["股东总数"] = pd.to_numeric(big_df["股东总数"], errors="coerce") big_df["平均持股数"] = pd.to_numeric(big_df["平均持股数"], errors="coerce") return big_df if __name__ == "__main__": stock_financial_report_sina_df = stock_financial_report_sina( stock="sh600600", symbol="现金流量表" ) print(stock_financial_report_sina_df) stock_financial_report_sina_df = stock_financial_report_sina( stock="sh600600", symbol="资产负债表" ) print(stock_financial_report_sina_df) stock_financial_report_sina_df = stock_financial_report_sina( stock="sh600600", symbol="利润表" ) print(stock_financial_report_sina_df) stock_financial_abstract_df = stock_financial_abstract(symbol="600600") print(stock_financial_abstract_df) stock_financial_analysis_indicator_df = stock_financial_analysis_indicator( symbol="600519", start_year="2019" ) print(stock_financial_analysis_indicator_df) stock_history_dividend_df = stock_history_dividend() print(stock_history_dividend_df) stock_history_dividend_detail_df = stock_history_dividend_detail( symbol="600012", indicator="分红", date="" ) print(stock_history_dividend_detail_df) stock_history_dividend_detail_df = stock_history_dividend_detail( symbol="600012", indicator="分红", date="2019-07-08" ) print(stock_history_dividend_detail_df) stock_history_dividend_detail_df = stock_history_dividend_detail( symbol="000002", indicator="配股" ) print(stock_history_dividend_detail_df) stock_history_dividend_detail_df = stock_history_dividend_detail( symbol="000002", indicator="配股", date="1999-12-22" ) print(stock_history_dividend_detail_df) stock_ipo_info_df = stock_ipo_info(stock="600004") print(stock_ipo_info_df) stock_add_stock_df = stock_add_stock(symbol="600004") print(stock_add_stock_df) stock_restricted_release_queue_sina_df = stock_restricted_release_queue_sina( symbol="600000" ) print(stock_restricted_release_queue_sina_df) stock_circulate_stock_holder_df = stock_circulate_stock_holder(symbol="600000") print(stock_circulate_stock_holder_df) stock_fund_stock_holder_df = stock_fund_stock_holder(symbol="601318") print(stock_fund_stock_holder_df) stock_main_stock_holder_df = stock_main_stock_holder(stock="600000") print(stock_main_stock_holder_df) stock_financial_analysis_indicator_em_df = stock_financial_analysis_indicator_em( symbol="301389.SZ", indicator="按报告期" ) print(stock_financial_analysis_indicator_em_df)