From d2ca1a0787f39b6658c604bbad29dbad5cdd9e39 Mon Sep 17 00:00:00 2001 From: Albert King Date: Thu, 10 Aug 2023 22:12:23 +0800 Subject: [PATCH] Date (#4040) * update date * add version * fix macro_china_gyzjz --- akshare/__init__.py | 3 +- akshare/economic/macro_china.py | 121 +++++++++++++++++--------------- docs/changelog.md | 6 ++ docs/data/macro/macro.md | 26 +++---- 4 files changed, 88 insertions(+), 68 deletions(-) diff --git a/akshare/__init__.py b/akshare/__init__.py index d2274c581b6..31e6789f184 100644 --- a/akshare/__init__.py +++ b/akshare/__init__.py @@ -2494,9 +2494,10 @@ 1.10.77 add: add bond_cb_profile_sina interface 1.10.78 fix: fix get_cffex_rank_table interface 1.10.79 add: add stock_hold_management_detail_em interface +1.10.80 fix: fix macro_china_gyzjz interface """ -__version__ = "1.10.79" +__version__ = "1.10.80" __author__ = "AKFamily" import sys diff --git a/akshare/economic/macro_china.py b/akshare/economic/macro_china.py index 2f6aad66471..e3fde4b8d06 100644 --- a/akshare/economic/macro_china.py +++ b/akshare/economic/macro_china.py @@ -263,7 +263,7 @@ def macro_china_gdp_yearly() -> pd.DataFrame: str(int(round(t * 1000))), str(int(round(t * 1000)) + 90) ) ) - json_data = json.loads(r.text[r.text.find("{") : r.text.rfind("}") + 1]) + json_data = json.loads(r.text[r.text.find("{"): r.text.rfind("}") + 1]) date_list = [item["date"] for item in json_data["list"]] value_list = [item["datas"]["中国GDP年率报告"] for item in json_data["list"]] value_df = pd.DataFrame(value_list) @@ -322,7 +322,7 @@ def macro_china_cpi_yearly() -> pd.DataFrame: str(int(round(t * 1000))), str(int(round(t * 1000)) + 90) ) ) - json_data = json.loads(res.text[res.text.find("{") : res.text.rfind("}") + 1]) + json_data = json.loads(res.text[res.text.find("{"): res.text.rfind("}") + 1]) date_list = [item["date"] for item in json_data["list"]] value_list = [item["datas"]["中国CPI年率报告"] for item in json_data["list"]] value_df = pd.DataFrame(value_list) @@ -380,7 +380,7 @@ def macro_china_cpi_monthly() -> pd.DataFrame: str(int(round(t * 1000))), str(int(round(t * 1000)) + 90) ) ) - json_data = json.loads(res.text[res.text.find("{") : res.text.rfind("}") + 1]) + json_data = json.loads(res.text[res.text.find("{"): res.text.rfind("}") + 1]) date_list = [item["date"] for item in json_data["list"]] value_list = [item["datas"]["中国CPI月率报告"] for item in json_data["list"]] value_df = pd.DataFrame(value_list) @@ -441,7 +441,7 @@ def macro_china_ppi_yearly() -> pd.DataFrame: str(int(round(t * 1000))), str(int(round(t * 1000)) + 90) ) ) - json_data = json.loads(res.text[res.text.find("{") : res.text.rfind("}") + 1]) + json_data = json.loads(res.text[res.text.find("{"): res.text.rfind("}") + 1]) date_list = [item["date"] for item in json_data["list"]] value_list = [item["datas"]["中国PPI年率报告"] for item in json_data["list"]] value_df = pd.DataFrame(value_list) @@ -501,7 +501,7 @@ def macro_china_exports_yoy() -> pd.DataFrame: res = requests.get( f"https://cdn.jin10.com/dc/reports/dc_chinese_exports_yoy_all.js?v={str(int(round(t * 1000))), str(int(round(t * 1000)) + 90)}" ) - json_data = json.loads(res.text[res.text.find("{") : res.text.rfind("}") + 1]) + json_data = json.loads(res.text[res.text.find("{"): res.text.rfind("}") + 1]) date_list = [item["date"] for item in json_data["list"]] value_list = [item["datas"]["中国以美元计算出口年率报告"] for item in json_data["list"]] value_df = pd.DataFrame(value_list) @@ -561,7 +561,7 @@ def macro_china_imports_yoy() -> pd.DataFrame: res = requests.get( f"https://cdn.jin10.com/dc/reports/dc_chinese_imports_yoy_all.js?v={str(int(round(t * 1000))), str(int(round(t * 1000)) + 90)}" ) - json_data = json.loads(res.text[res.text.find("{") : res.text.rfind("}") + 1]) + json_data = json.loads(res.text[res.text.find("{"): res.text.rfind("}") + 1]) date_list = [item["date"] for item in json_data["list"]] value_list = [item["datas"]["中国以美元计算进口年率报告"] for item in json_data["list"]] value_df = pd.DataFrame(value_list) @@ -621,7 +621,7 @@ def macro_china_trade_balance() -> pd.DataFrame: res = requests.get( f"https://cdn.jin10.com/dc/reports/dc_chinese_trade_balance_all.js?v={str(int(round(t * 1000))), str(int(round(t * 1000)) + 90)}" ) - json_data = json.loads(res.text[res.text.find("{") : res.text.rfind("}") + 1]) + json_data = json.loads(res.text[res.text.find("{"): res.text.rfind("}") + 1]) date_list = [item["date"] for item in json_data["list"]] value_list = [item["datas"]["中国以美元计算贸易帐报告"] for item in json_data["list"]] value_df = pd.DataFrame(value_list) @@ -681,7 +681,7 @@ def macro_china_industrial_production_yoy() -> pd.DataFrame: res = requests.get( f"https://cdn.jin10.com/dc/reports/dc_chinese_industrial_production_yoy_all.js?v={str(int(round(t * 1000))), str(int(round(t * 1000)) + 90)}" ) - json_data = json.loads(res.text[res.text.find("{") : res.text.rfind("}") + 1]) + json_data = json.loads(res.text[res.text.find("{"): res.text.rfind("}") + 1]) date_list = [item["date"] for item in json_data["list"]] value_list = [item["datas"]["中国规模以上工业增加值年率报告"] for item in json_data["list"]] value_df = pd.DataFrame(value_list) @@ -742,7 +742,7 @@ def macro_china_pmi_yearly() -> pd.DataFrame: str(int(round(t * 1000))), str(int(round(t * 1000)) + 90) ) ) - json_data = json.loads(res.text[res.text.find("{") : res.text.rfind("}") + 1]) + json_data = json.loads(res.text[res.text.find("{"): res.text.rfind("}") + 1]) date_list = [item["date"] for item in json_data["list"]] value_list = [item["datas"]["中国官方制造业PMI报告"] for item in json_data["list"]] value_df = pd.DataFrame(value_list) @@ -803,7 +803,7 @@ def macro_china_cx_pmi_yearly() -> pd.DataFrame: str(int(round(t * 1000))), str(int(round(t * 1000)) + 90) ) ) - json_data = json.loads(res.text[res.text.find("{") : res.text.rfind("}") + 1]) + json_data = json.loads(res.text[res.text.find("{"): res.text.rfind("}") + 1]) date_list = [item["date"] for item in json_data["list"]] value_list = [item["datas"]["中国财新制造业PMI终值报告"] for item in json_data["list"]] value_df = pd.DataFrame(value_list) @@ -864,7 +864,7 @@ def macro_china_cx_services_pmi_yearly() -> pd.DataFrame: str(int(round(t * 1000))), str(int(round(t * 1000)) + 90) ) ) - json_data = json.loads(res.text[res.text.find("{") : res.text.rfind("}") + 1]) + json_data = json.loads(res.text[res.text.find("{"): res.text.rfind("}") + 1]) date_list = [item["date"] for item in json_data["list"]] value_list = [item["datas"]["中国财新服务业PMI报告"] for item in json_data["list"]] value_df = pd.DataFrame(value_list) @@ -925,7 +925,7 @@ def macro_china_non_man_pmi() -> pd.DataFrame: str(int(round(t * 1000))), str(int(round(t * 1000)) + 90) ) ) - json_data = json.loads(res.text[res.text.find("{") : res.text.rfind("}") + 1]) + json_data = json.loads(res.text[res.text.find("{"): res.text.rfind("}") + 1]) date_list = [item["date"] for item in json_data["list"]] value_list = [item["datas"]["中国官方非制造业PMI报告"] for item in json_data["list"]] value_df = pd.DataFrame(value_list) @@ -986,7 +986,7 @@ def macro_china_fx_reserves_yearly() -> pd.DataFrame: str(int(round(t * 1000))), str(int(round(t * 1000)) + 90) ) ) - json_data = json.loads(res.text[res.text.find("{") : res.text.rfind("}") + 1]) + json_data = json.loads(res.text[res.text.find("{"): res.text.rfind("}") + 1]) date_list = [item["date"] for item in json_data["list"]] value_list = [item["datas"]["中国外汇储备报告"] for item in json_data["list"]] value_df = pd.DataFrame(value_list) @@ -1047,7 +1047,7 @@ def macro_china_m2_yearly() -> pd.DataFrame: str(int(round(t * 1000))), str(int(round(t * 1000)) + 90) ) ) - json_data = json.loads(res.text[res.text.find("{") : res.text.rfind("}") + 1]) + json_data = json.loads(res.text[res.text.find("{"): res.text.rfind("}") + 1]) date_list = [item["date"] for item in json_data["list"]] value_list = [item["datas"]["中国M2货币供应年率报告"] for item in json_data["list"]] value_df = pd.DataFrame(value_list) @@ -1246,7 +1246,7 @@ def macro_china_daily_energy() -> pd.DataFrame: str(int(round(t * 1000))), str(int(round(t * 1000)) + 90) ) ) - json_data = json.loads(res.text[res.text.find("{") : res.text.rfind("}") + 1]) + json_data = json.loads(res.text[res.text.find("{"): res.text.rfind("}") + 1]) date_list = [item["date"] for item in json_data["list"]] value_list = [item["datas"]["沿海六大电厂库存动态报告"] for item in json_data["list"]] value_df = pd.DataFrame(value_list) @@ -1392,7 +1392,7 @@ def macro_china_market_margin_sh() -> pd.DataFrame: str(int(round(t * 1000))), str(int(round(t * 1000)) + 90) ) ) - json_data = json.loads(res.text[res.text.find("{") : res.text.rfind("}") + 1]) + json_data = json.loads(res.text[res.text.find("{"): res.text.rfind("}") + 1]) date_list = [item["date"] for item in json_data["list"]] value_list_1 = [item["datas"]["总量"][0] for item in json_data["list"]] value_list_2 = [item["datas"]["总量"][1] for item in json_data["list"]] @@ -1627,7 +1627,7 @@ def macro_china_lpr() -> pd.DataFrame: # 中国-新房价指数 def macro_china_new_house_price( - city_first: str = "北京", city_second: str = "上海" + city_first: str = "北京", city_second: str = "上海" ) -> pd.DataFrame: """ 中国-新房价指数 @@ -3640,29 +3640,40 @@ def macro_china_gyzjz() -> pd.DataFrame: :return: 工业增加值增长 :rtype: pandas.DataFrame """ - url = "https://datainterface.eastmoney.com/EM_DataCenter/JS.aspx" + url = "https://datacenter-web.eastmoney.com/api/data/v1/get" params = { - "type": "GJZB", - "sty": "ZGZB", - "js": "({data:[(x)],pages:(pc)})", - "p": "1", - "ps": "2000", - "mkt": "0", - "pageNo": "1", - "pageNum": "1", - "_": "1625824314514", + 'columns': 'REPORT_DATE,TIME,BASE_SAME,BASE_ACCUMULATE', + 'pageNumber': '1', + 'pageSize': '2000', + 'sortColumns': 'REPORT_DATE', + 'sortTypes': '-1', + 'source': 'WEB', + 'client': 'WEB', + 'reportName': 'RPT_ECONOMY_INDUS_GROW', + 'p': '1', + 'pageNo': '1', + 'pageNum': '1', + '_': '1691676211803', } r = requests.get(url, params=params) - data_text = r.text - data_json = demjson.decode(data_text[1:-1]) - temp_df = pd.DataFrame([item.split(",") for item in data_json["data"]]) + data_json = r.json() + temp_df = pd.DataFrame(data_json['result']['data']) temp_df.columns = [ + "发布时间", "月份", "同比增长", "累计增长", ] - temp_df["同比增长"] = pd.to_numeric(temp_df["同比增长"]) - temp_df["累计增长"] = pd.to_numeric(temp_df["累计增长"]) + temp_df = temp_df[[ + "月份", + "同比增长", + "累计增长", + "发布时间", + ]] + temp_df["同比增长"] = pd.to_numeric(temp_df["同比增长"], errors="coerce") + temp_df["累计增长"] = pd.to_numeric(temp_df["累计增长"], errors="coerce") + temp_df['发布时间'] = pd.to_datetime(temp_df['发布时间'], errors="coerce").dt.date + temp_df.sort_values(['发布时间'], ignore_index=True, inplace=True) return temp_df @@ -3805,14 +3816,14 @@ def macro_china_society_electricity() -> pd.DataFrame: } r = requests.get(url, params=params) data_text = r.text - data_json = demjson.decode(data_text[data_text.find("{") : -3]) + data_json = demjson.decode(data_text[data_text.find("{"): -3]) page_num = math.ceil(int(data_json["count"]) / 31) big_df = pd.DataFrame(data_json["data"]) for i in range(1, page_num): params.update({"from": i * 31}) r = requests.get(url, params=params) data_text = r.text - data_json = demjson.decode(data_text[data_text.find("{") : -3]) + data_json = demjson.decode(data_text[data_text.find("{"): -3]) temp_df = pd.DataFrame(data_json["data"]) big_df = pd.concat([big_df, temp_df], ignore_index=True) @@ -3856,14 +3867,14 @@ def macro_china_society_traffic_volume() -> pd.DataFrame: } r = requests.get(url, params=params) data_text = r.text - data_json = demjson.decode(data_text[data_text.find("{") : -3]) + data_json = demjson.decode(data_text[data_text.find("{"): -3]) page_num = math.ceil(int(data_json["count"]) / 31) big_df = pd.DataFrame(data_json["data"]["非累计"]) for i in tqdm(range(1, page_num), leave=False): params.update({"from": i * 31}) r = requests.get(url, params=params) data_text = r.text - data_json = demjson.decode(data_text[data_text.find("{") : -3]) + data_json = demjson.decode(data_text[data_text.find("{"): -3]) temp_df = pd.DataFrame(data_json["data"]["非累计"]) big_df = pd.concat([big_df, temp_df], ignore_index=True) big_df.columns = [item[1] for item in data_json["config"]["all"]] @@ -3888,14 +3899,14 @@ def macro_china_postal_telecommunicational() -> pd.DataFrame: } r = requests.get(url, params=params) data_text = r.text - data_json = demjson.decode(data_text[data_text.find("{") : -3]) + data_json = demjson.decode(data_text[data_text.find("{"): -3]) page_num = math.ceil(int(data_json["count"]) / 31) big_df = pd.DataFrame(data_json["data"]["非累计"]) for i in tqdm(range(1, page_num)): params.update({"from": i * 31}) r = requests.get(url, params=params) data_text = r.text - data_json = demjson.decode(data_text[data_text.find("{") : -3]) + data_json = demjson.decode(data_text[data_text.find("{"): -3]) temp_df = pd.DataFrame(data_json["data"]["非累计"]) big_df = pd.concat([big_df, temp_df], ignore_index=True) big_df.columns = [item[1] for item in data_json["config"]["all"]] @@ -3922,14 +3933,14 @@ def macro_china_international_tourism_fx() -> pd.DataFrame: } r = requests.get(url, params=params) data_text = r.text - data_json = demjson.decode(data_text[data_text.find("{") : -3]) + data_json = demjson.decode(data_text[data_text.find("{"): -3]) page_num = math.ceil(int(data_json["count"]) / 31) big_df = pd.DataFrame(data_json["data"]) for i in tqdm(range(1, page_num)): params.update({"from": i * 31}) r = requests.get(url, params=params) data_text = r.text - data_json = demjson.decode(data_text[data_text.find("{") : -3]) + data_json = demjson.decode(data_text[data_text.find("{"): -3]) temp_df = pd.DataFrame(data_json["data"]) big_df = pd.concat([big_df, temp_df], ignore_index=True) big_df.columns = [item[1] for item in data_json["config"]["all"]] @@ -3954,14 +3965,14 @@ def macro_china_passenger_load_factor() -> pd.DataFrame: } r = requests.get(url, params=params) data_text = r.text - data_json = demjson.decode(data_text[data_text.find("{") : -3]) + data_json = demjson.decode(data_text[data_text.find("{"): -3]) page_num = math.ceil(int(data_json["count"]) / 31) big_df = pd.DataFrame(data_json["data"]) for i in tqdm(range(1, page_num)): params.update({"from": i * 31}) r = requests.get(url, params=params) data_text = r.text - data_json = demjson.decode(data_text[data_text.find("{") : -3]) + data_json = demjson.decode(data_text[data_text.find("{"): -3]) temp_df = pd.DataFrame(data_json["data"]) big_df = pd.concat([big_df, temp_df], ignore_index=True) big_df.columns = [item[1] for item in data_json["config"]["all"]] @@ -3986,14 +3997,14 @@ def _macro_china_freight_index() -> pd.DataFrame: } r = requests.get(url, params=params) data_text = r.text - data_json = demjson.decode(data_text[data_text.find("{") : -3]) + data_json = demjson.decode(data_text[data_text.find("{"): -3]) page_num = math.ceil(int(data_json["count"]) / 31) big_df = pd.DataFrame(data_json["data"]) for i in tqdm(range(1, page_num)): params.update({"from": i * 31}) r = requests.get(url, params=params) data_text = r.text - data_json = demjson.decode(data_text[data_text.find("{") : -3]) + data_json = demjson.decode(data_text[data_text.find("{"): -3]) temp_df = pd.DataFrame(data_json["data"]) big_df = big_df.append(temp_df, ignore_index=True) big_df.columns = [item[1] for item in data_json["config"]["all"]] @@ -4053,14 +4064,14 @@ def macro_china_central_bank_balance() -> pd.DataFrame: } r = requests.get(url, params=params) data_text = r.text - data_json = demjson.decode(data_text[data_text.find("{") : -3]) + data_json = demjson.decode(data_text[data_text.find("{"): -3]) page_num = math.ceil(int(data_json["count"]) / 31) big_df = pd.DataFrame(data_json["data"]) for i in tqdm(range(1, page_num)): params.update({"from": i * 31}) r = requests.get(url, params=params) data_text = r.text - data_json = demjson.decode(data_text[data_text.find("{") : -3]) + data_json = demjson.decode(data_text[data_text.find("{"): -3]) temp_df = pd.DataFrame(data_json["data"]) big_df = pd.concat([big_df, temp_df], ignore_index=True) big_df.columns = [item[1] for item in data_json["config"]["all"]] @@ -4085,14 +4096,14 @@ def macro_china_insurance() -> pd.DataFrame: } r = requests.get(url, params=params) data_text = r.text - data_json = demjson.decode(data_text[data_text.find("{") : -3]) + data_json = demjson.decode(data_text[data_text.find("{"): -3]) page_num = math.ceil(int(data_json["count"]) / 31) big_df = pd.DataFrame(data_json["data"]) for i in tqdm(range(1, page_num)): params.update({"from": i * 31}) r = requests.get(url, params=params) data_text = r.text - data_json = demjson.decode(data_text[data_text.find("{") : -3]) + data_json = demjson.decode(data_text[data_text.find("{"): -3]) temp_df = pd.DataFrame(data_json["data"]) big_df = pd.concat([big_df, temp_df], ignore_index=True) big_df.columns = [item[1] for item in data_json["config"]["all"]] @@ -4117,14 +4128,14 @@ def macro_china_supply_of_money() -> pd.DataFrame: } r = requests.get(url, params=params) data_text = r.text - data_json = demjson.decode(data_text[data_text.find("{") : -3]) + data_json = demjson.decode(data_text[data_text.find("{"): -3]) page_num = math.ceil(int(data_json["count"]) / 31) big_df = pd.DataFrame(data_json["data"]) for i in tqdm(range(1, page_num)): params.update({"from": i * 31}) r = requests.get(url, params=params) data_text = r.text - data_json = demjson.decode(data_text[data_text.find("{") : -3]) + data_json = demjson.decode(data_text[data_text.find("{"): -3]) temp_df = pd.DataFrame(data_json["data"]) big_df = pd.concat([big_df, temp_df], ignore_index=True) big_df.columns = [item[1] for item in data_json["config"]["all"]] @@ -4132,7 +4143,7 @@ def macro_china_supply_of_money() -> pd.DataFrame: def macro_china_swap_rate( - start_date: str = "20221027", end_date: str = "20221127" + start_date: str = "20221027", end_date: str = "20221127" ) -> pd.DataFrame: """ FR007利率互换曲线历史数据; 只能获取近一年的数据 @@ -4267,14 +4278,14 @@ def macro_china_foreign_exchange_gold() -> pd.DataFrame: } r = requests.get(url, params=params) data_text = r.text - data_json = demjson.decode(data_text[data_text.find("{") : -3]) + data_json = demjson.decode(data_text[data_text.find("{"): -3]) page_num = math.ceil(int(data_json["count"]) / 31) big_df = pd.DataFrame(data_json["data"]) for i in tqdm(range(1, page_num)): params.update({"from": i * 31}) r = requests.get(url, params=params) data_text = r.text - data_json = demjson.decode(data_text[data_text.find("{") : -3]) + data_json = demjson.decode(data_text[data_text.find("{"): -3]) temp_df = pd.DataFrame(data_json["data"]) big_df = pd.concat([big_df, temp_df], ignore_index=True) big_df.columns = [item[1] for item in data_json["config"]["all"]] @@ -4299,14 +4310,14 @@ def macro_china_retail_price_index() -> pd.DataFrame: } r = requests.get(url, params=params) data_text = r.text - data_json = demjson.decode(data_text[data_text.find("{") : -3]) + data_json = demjson.decode(data_text[data_text.find("{"): -3]) page_num = math.ceil(int(data_json["count"]) / 31) big_df = pd.DataFrame(data_json["data"]) for i in tqdm(range(1, page_num)): params.update({"from": i * 31}) r = requests.get(url, params=params) data_text = r.text - data_json = demjson.decode(data_text[data_text.find("{") : -3]) + data_json = demjson.decode(data_text[data_text.find("{"): -3]) temp_df = pd.DataFrame(data_json["data"]) big_df = pd.concat([big_df, temp_df], ignore_index=True) big_df.columns = [item[1] for item in data_json["config"]["all"]] diff --git a/docs/changelog.md b/docs/changelog.md index 1ce71242b74..51d98475d1b 100644 --- a/docs/changelog.md +++ b/docs/changelog.md @@ -63,6 +63,10 @@ ## 更新说明详情 +1.10.80 fix: fix macro_china_gyzjz interface + + 1. 修复 macro_china_gyzjz 接口 + 1.10.79 add: add stock_hold_management_detail_em interface 1. 新增 stock_hold_management_detail_em 接口 @@ -2741,6 +2745,8 @@ ## 版本更新说明 +1.10.80 fix: fix macro_china_gyzjz interface + 1.10.79 add: add stock_hold_management_detail_em interface 1.10.78 fix: fix get_cffex_rank_table interface diff --git a/docs/data/macro/macro.md b/docs/data/macro/macro.md index fa83ec388ab..6b657067173 100644 --- a/docs/data/macro/macro.md +++ b/docs/data/macro/macro.md @@ -788,6 +788,7 @@ macro_china_trade_balance_df: | 月份 | object | - | | 同比增长 | float64 | 注意单位: % | | 累计增长 | float64 | 注意单位: % | +| 发布时间 | object | - | 接口示例 @@ -801,18 +802,19 @@ print(macro_china_gyzjz_df) 数据示例 ``` - 月份 同比增长 累计增长 -0 2021-05-01 8.8 17.8 -1 2021-04-01 9.8 20.3 -2 2021-03-01 14.1 24.5 -3 2021-02-01 0.0 35.1 -4 2020-12-01 7.3 2.8 -.. ... ... ... -142 2008-06-01 16.0 16.3 -143 2008-05-01 16.0 16.3 -144 2008-04-01 15.7 16.3 -145 2008-03-01 17.8 16.4 -146 2008-02-01 15.4 15.4 + 月份 同比增长 累计增长 发布时间 +0 2008年02月份 15.4 15.4 2008-02-01 +1 2008年03月份 17.8 16.4 2008-03-01 +2 2008年04月份 15.7 16.3 2008-04-01 +3 2008年05月份 16.0 16.3 2008-05-01 +4 2008年06月份 16.0 16.3 2008-06-01 +.. ... ... ... ... +165 2023年02月份 NaN 2.4 2023-02-01 +166 2023年03月份 3.9 3.0 2023-03-01 +167 2023年04月份 5.6 3.6 2023-04-01 +168 2023年05月份 3.5 3.6 2023-05-01 +169 2023年06月份 4.4 3.8 2023-06-01 +[170 rows x 4 columns] ``` ##### 规模以上工业增加值年率