#!/usr/bin/env python # -*- coding:utf-8 -*- """ Date: 2024/9/15 15:30 Desc: 东方财富网-数据中心-特色数据-商誉 东方财富网-数据中心-特色数据-商誉-A股商誉市场概况: https://data.eastmoney.com/sy/scgk.html 东方财富网-数据中心-特色数据-商誉-商誉减值预期明细: https://data.eastmoney.com/sy/yqlist.html 东方财富网-数据中心-特色数据-商誉-个股商誉减值明细: https://data.eastmoney.com/sy/jzlist.html 东方财富网-数据中心-特色数据-商誉-个股商誉明细: https://data.eastmoney.com/sy/list.html 东方财富网-数据中心-特色数据-商誉-行业商誉: https://data.eastmoney.com/sy/hylist.html """ import pandas as pd import requests from akshare.utils.tqdm import get_tqdm def stock_sy_profile_em() -> pd.DataFrame: """ 东方财富网-数据中心-特色数据-商誉-A股商誉市场概况 https://data.eastmoney.com/sy/scgk.html :return: A股商誉市场概况 :rtype: pandas.DataFrame """ url = "https://datacenter-web.eastmoney.com/api/data/v1/get" params = { "sortColumns": "REPORT_DATE", "sortTypes": "-1", "pageSize": "5000", "pageNumber": "1", "reportName": "RPT_GOODWILL_MARKETSTATISTICS", "token": "894050c76af8597a853f5b408b759f5d", "columns": "ALL", "filter": """((GOODWILL_STATE="1")( | IMPAIRMENT_STATE="1"))(TRADE_BOARD="all")""", } r = requests.get(url, params=params) data_json = r.json() data_df = pd.DataFrame(data_json["result"]["data"]) data_df.columns = [ "_", "报告期", "商誉", "商誉减值", "净资产", "商誉占净资产比例", "商誉减值占净资产比例", "净利润规模", "商誉减值占净利润比例", "_", "_", ] data_df = data_df[ [ "报告期", "商誉", "商誉减值", "净资产", "商誉占净资产比例", "商誉减值占净资产比例", "净利润规模", "商誉减值占净利润比例", ] ] data_df["报告期"] = pd.to_datetime(data_df["报告期"], errors="coerce").dt.date data_df["商誉"] = pd.to_numeric(data_df["商誉"], errors="coerce") data_df["商誉减值"] = pd.to_numeric(data_df["商誉减值"], errors="coerce") data_df["净资产"] = pd.to_numeric(data_df["净资产"], errors="coerce") data_df["商誉占净资产比例"] = pd.to_numeric( data_df["商誉占净资产比例"], errors="coerce" ) data_df["商誉减值占净资产比例"] = pd.to_numeric( data_df["商誉减值占净资产比例"], errors="coerce" ) data_df["净利润规模"] = pd.to_numeric(data_df["净利润规模"], errors="coerce") data_df["商誉减值占净利润比例"] = pd.to_numeric( data_df["商誉减值占净利润比例"], errors="coerce" ) data_df.sort_values(["报告期"], inplace=True, ignore_index=True) return data_df def stock_sy_yq_em(date: str = "20240630") -> pd.DataFrame: """ 东方财富网-数据中心-特色数据-商誉-商誉减值预期明细 https://data.eastmoney.com/sy/yqlist.html :param date: 参考网站指定的数据日期 :type date: str :return: 商誉减值预期明细 :rtype: pandas.DataFrame """ url = "https://datacenter-web.eastmoney.com/api/data/v1/get" params = { "sortColumns": "NOTICE_DATE,SECURITY_CODE", "sortTypes": "-1,-1", "pageSize": "5000", "pageNumber": "1", "columns": "ALL", "token": "894050c76af8597a853f5b408b759f5d", "reportName": "RPT_GOODWILL_STOCKPREDICT", "filter": f"""(REPORT_DATE='{"-".join([date[:4], date[4:6], date[6:]])}')""", } r = requests.get(url, params=params) data_json = r.json() big_df = pd.DataFrame() total_page = int(data_json["result"]["pages"]) tqdm = get_tqdm() for page in tqdm(range(1, total_page + 1), leave=False): params.update({"pageNumber": page}) r = requests.get(url, params=params) data_json = r.json() temp_df = pd.DataFrame(data_json["result"]["data"]) big_df = pd.concat(objs=[big_df, temp_df], ignore_index=True) big_df.reset_index(inplace=True) big_df["index"] = big_df["index"] + 1 big_df.rename( columns={ "index": "序号", "SECUCODE": "-", "SECURITY_CODE": "股票代码", "ORG_CODE": "-", "SECURITY_NAME_ABBR": "股票简称", "TRADE_MARKET": "交易市场", "INDUSTRY_NAME": "-", "INDUSTRY_CODE": "-", "NOTICE_DATE": "公告日期", "REPORT_DATE": "数据日期", "PE_REPORT_DATE": "-", "PREDICT_NETPROFIT_LOWER": "预计净利润-下限", "PREDICT_NETPROFIT_UPPER": "预计净利润-上限", "PERFORM_CHANGE_UPPER": "业绩变动幅度-上限", "PERFORM_CHANGE_LOWER": "业绩变动幅度-下限", "PERFORM_CHANGE": "-", "PERFORM_CHANGE_EXPLAIN": "业绩变动原因", "PREDICT_TYPE": "-", "PREDICT_INDICATOR_CODE": "-", "PREDICT_PERIOD": "-", "PE_SAMEREPORT_NETPROFIT": "上年度同期净利润", "NETPROFIT": "-", "PE_GOODWILL": "上年商誉", "NEWEST_REPORT_DATE": "最新商誉报告期", "NEWEST_GOODWILL": "最新一期商誉", "PE_SAMEREPORT_DATE": "-", }, inplace=True, ) big_df = big_df[ [ "序号", "股票代码", "股票简称", "业绩变动原因", "最新商誉报告期", "最新一期商誉", "上年商誉", "预计净利润-下限", "预计净利润-上限", "业绩变动幅度-下限", "业绩变动幅度-上限", "上年度同期净利润", "公告日期", "交易市场", ] ] big_df["交易市场"] = big_df["交易市场"].map( {"shzb": "沪市主板", "kcb": "科创板", "szzb": "深市主板", "cyb": "创业板"} ) big_df["最新商誉报告期"] = pd.to_datetime( big_df["最新商誉报告期"], 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["最新一期商誉"] = pd.to_numeric(big_df["最新一期商誉"], errors="coerce") big_df["上年商誉"] = pd.to_numeric(big_df["上年商誉"], errors="coerce") big_df["预计净利润-下限"] = pd.to_numeric( big_df["预计净利润-下限"], errors="coerce" ) big_df["预计净利润-上限"] = pd.to_numeric( big_df["预计净利润-上限"], errors="coerce" ) big_df["业绩变动幅度-下限"] = pd.to_numeric( big_df["业绩变动幅度-下限"], errors="coerce" ) big_df["业绩变动幅度-上限"] = pd.to_numeric( big_df["业绩变动幅度-上限"], errors="coerce" ) big_df["上年度同期净利润"] = pd.to_numeric( big_df["上年度同期净利润"], errors="coerce" ) return big_df def stock_sy_jz_em(date: str = "20240630") -> pd.DataFrame: """ 东方财富网-数据中心-特色数据-商誉-个股商誉减值明细 https://data.eastmoney.com/sy/jzlist.html :param date: 参考网站指定的数据日期 :type date: str :return: 个股商誉减值明细 :rtype: pandas.DataFrame """ url = "https://datacenter-web.eastmoney.com/api/data/v1/get" params = { "sortColumns": "GOODWILL_CHANGE", "sortTypes": "-1", "pageSize": "5000", "pageNumber": "1", "columns": "ALL", "token": "894050c76af8597a853f5b408b759f5d", "reportName": "RPT_GOODWILL_STOCKDETAILS", "filter": f"""(REPORT_DATE='{"-".join([date[:4], date[4:6], date[6:]])}')""", } r = requests.get(url, params=params) data_json = r.json() big_df = pd.DataFrame() total_page = int(data_json["result"]["pages"]) tqdm = get_tqdm() for page in tqdm(range(1, total_page + 1), leave=False): params.update({"pageNumber": page}) r = requests.get(url, params=params) data_json = r.json() temp_df = pd.DataFrame(data_json["result"]["data"]) big_df = pd.concat(objs=[big_df, temp_df], ignore_index=True) big_df.reset_index(inplace=True) big_df["index"] = big_df["index"] + 1 big_df.rename( columns={ "index": "序号", "SECUCODE": "-", "SECURITY_CODE": "股票代码", "SECURITY_NAME_ABBR": "股票简称", "ORG_CODE": "-", "LISTING_DATE": "-", "LISTING_STATE": "-", "TRADE_BOARD": "交易市场", "GOODWILL": "商誉", "DATE_TYPE": "-", "REPORT_TYPE_CODE": "-", "DATA_ADJUST_TYPE": "-", "NOTICE_DATE": "公告日期", "REPORT_DATE": "数据日期", "GOODWILL_PRE": "-", "GOODWILL_CHANGE": "商誉减值", "SUMSHEQUITY": "-", "SUMSHEQUITY_RATIO": "商誉占净资产比例", "SE_CHANGE_RATIO": "商誉减值占净资产比例", "PARENTNETPROFIT": "净利润", "PNP_CHANGE_RATIO": "商誉减值占净利润比例", "PNP_YOY_RATIO": "-", "INDUSTRY_CFT": "-", "INDUSTRY_CFTCODE": "-", "IS_SHOW": "-", "MAXREPORTDATE": "-", }, inplace=True, ) big_df = big_df[ [ "序号", "股票代码", "股票简称", "商誉", "商誉减值", "商誉占净资产比例", "商誉减值占净资产比例", "净利润", "商誉减值占净利润比例", "公告日期", "交易市场", ] ] big_df["交易市场"] = big_df["交易市场"].map( {"shzb": "沪市主板", "kcb": "科创板", "szzb": "深市主板", "cyb": "创业板"} ) big_df["公告日期"] = pd.to_datetime(big_df["公告日期"], errors="coerce").dt.date big_df["商誉"] = pd.to_numeric(big_df["商誉"], errors="coerce") big_df["商誉减值"] = pd.to_numeric(big_df["商誉减值"], errors="coerce") big_df["商誉占净资产比例"] = pd.to_numeric( big_df["商誉占净资产比例"], errors="coerce" ) big_df["商誉减值占净资产比例"] = pd.to_numeric( big_df["商誉减值占净资产比例"], errors="coerce" ) big_df["净利润"] = pd.to_numeric(big_df["净利润"], errors="coerce") big_df["商誉减值占净利润比例"] = pd.to_numeric( big_df["商誉减值占净利润比例"], errors="coerce" ) big_df["商誉减值占净利润比例"] = pd.to_numeric( big_df["商誉减值占净利润比例"], errors="coerce" ) return big_df def stock_sy_em(date: str = "20231231") -> pd.DataFrame: """ 东方财富网-数据中心-特色数据-商誉-个股商誉明细 https://data.eastmoney.com/sy/list.html :param date: 参考网站指定的数据日期 :type date: str :return: 个股商誉明细 :rtype: pandas.DataFrame """ url = "https://datacenter-web.eastmoney.com/api/data/v1/get" params = { "sortColumns": "NOTICE_DATE,SECURITY_CODE", "sortTypes": "-1,-1", "pageSize": "5000", "pageNumber": "1", "columns": "ALL", "token": "894050c76af8597a853f5b408b759f5d", "reportName": "RPT_GOODWILL_STOCKDETAILS", "filter": f"""(REPORT_DATE='{"-".join([date[:4], date[4:6], date[6:]])}')""", } r = requests.get(url, params=params) data_json = r.json() big_df = pd.DataFrame() total_page = int(data_json["result"]["pages"]) tqdm = get_tqdm() for page in tqdm(range(1, total_page + 1), leave=False): params.update({"pageNumber": page}) r = requests.get(url, params=params) data_json = r.json() temp_df = pd.DataFrame(data_json["result"]["data"]) big_df = pd.concat(objs=[big_df, temp_df], ignore_index=True) big_df.reset_index(inplace=True) big_df["index"] = big_df["index"] + 1 big_df.rename( columns={ "index": "序号", "SECUCODE": "-", "SECURITY_CODE": "股票代码", "SECURITY_NAME_ABBR": "股票简称", "ORG_CODE": "-", "LISTING_DATE": "-", "LISTING_STATE": "-", "TRADE_BOARD": "交易市场", "GOODWILL": "商誉", "DATE_TYPE": "-", "REPORT_TYPE_CODE": "-", "DATA_ADJUST_TYPE": "-", "NOTICE_DATE": "公告日期", "REPORT_DATE": "数据日期", "GOODWILL_PRE": "上年商誉", "GOODWILL_CHANGE": "商誉减值", "SUMSHEQUITY": "-", "SUMSHEQUITY_RATIO": "商誉占净资产比例", "SE_CHANGE_RATIO": "商誉减值占净资产比例", "PARENTNETPROFIT": "净利润", "PNP_CHANGE_RATIO": "商誉减值占净利润比例", "PNP_YOY_RATIO": "净利润同比", "INDUSTRY_CFT": "-", "INDUSTRY_CFTCODE": "-", "IS_SHOW": "-", "MAXREPORTDATE": "-", }, inplace=True, ) big_df = big_df[ [ "序号", "股票代码", "股票简称", "商誉", "商誉占净资产比例", "净利润", "净利润同比", "上年商誉", "公告日期", "交易市场", ] ] big_df["交易市场"] = big_df["交易市场"].map( {"shzb": "沪市主板", "kcb": "科创板", "szzb": "深市主板", "cyb": "创业板"} ) big_df["公告日期"] = pd.to_datetime(big_df["公告日期"], errors="coerce").dt.date big_df["商誉"] = pd.to_numeric(big_df["商誉"], errors="coerce") big_df["商誉占净资产比例"] = pd.to_numeric( big_df["商誉占净资产比例"], errors="coerce" ) big_df["净利润"] = pd.to_numeric(big_df["净利润"], errors="coerce") big_df["净利润同比"] = pd.to_numeric(big_df["净利润同比"], errors="coerce") big_df["上年商誉"] = pd.to_numeric(big_df["上年商誉"], errors="coerce") return big_df def stock_sy_hy_em(date: str = "20240930") -> pd.DataFrame: """ 东方财富网-数据中心-特色数据-商誉-行业商誉 https://data.eastmoney.com/sy/hylist.html :param date: 参考网站指定的数据日期 :type date: str :return: 个股商誉明细 :rtype: pandas.DataFrame """ url = "https://datacenter-web.eastmoney.com/api/data/v1/get" params = { "sortColumns": "SUMSHEQUITY_RATIO", "sortTypes": "-1", "pageSize": "5000", "pageNumber": "1", "columns": "ALL", "token": "894050c76af8597a853f5b408b759f5d", "reportName": "RPT_GOODWILL_INDUSTATISTICS", "filter": f"""(REPORT_DATE='{"-".join([date[:4], date[4:6], date[6:]])}')""", } r = requests.get(url, params=params) data_json = r.json() big_df = pd.DataFrame() total_page = int(data_json["result"]["pages"]) tqdm = get_tqdm() for page in tqdm(range(1, total_page + 1), leave=False): params.update({"pageNumber": page}) r = requests.get(url, params=params) data_json = r.json() temp_df = pd.DataFrame(data_json["result"]["data"]) big_df = pd.concat(objs=[big_df, temp_df], ignore_index=True) big_df.reset_index(inplace=True, drop=True) big_df.rename( columns={ "REPORT_DATE": "数据日期", "INDUSTRY_NAME": "行业名称", "INDUSTRY_CODE": "-", "ORG_NUM": "公司家数", "GOODWILL": "商誉规模", "GOODWILL_CHANGE": "-", "SUMSHEQUITY": "净资产", "SUMSHEQUITY_RATIO": "商誉规模占净资产规模比例", "SE_CHANGE_RATIO": "-", "PARENTNETPROFIT": "净利润规模", "PNP_CHANGE_RATIO": "-", }, inplace=True, ) big_df = big_df[ [ "行业名称", "公司家数", "商誉规模", "净资产", "商誉规模占净资产规模比例", "净利润规模", ] ] big_df["公司家数"] = pd.to_numeric(big_df["公司家数"], errors="coerce") big_df["商誉规模"] = pd.to_numeric(big_df["商誉规模"], errors="coerce") big_df["净资产"] = pd.to_numeric(big_df["净资产"], errors="coerce") big_df["商誉规模占净资产规模比例"] = pd.to_numeric( big_df["商誉规模占净资产规模比例"], errors="coerce" ) big_df["净利润规模"] = pd.to_numeric(big_df["净利润规模"], errors="coerce") return big_df if __name__ == "__main__": stock_sy_profile_em_df = stock_sy_profile_em() print(stock_sy_profile_em_df) stock_sy_yq_em_df = stock_sy_yq_em(date="20240630") print(stock_sy_yq_em_df) stock_sy_jz_em_df = stock_sy_jz_em(date="20240630") print(stock_sy_jz_em_df) stock_sy_em_df = stock_sy_em(date="20240630") print(stock_sy_em_df) stock_sy_hy_em_df = stock_sy_hy_em(date="20240930") print(stock_sy_hy_em_df)