def get_china_yearly_cx_pmi():
"""
获取中国年度财新PMI数据, 数据区间从20120120-至今
:return: pandas.Series
2012-01-20 48.8
2012-02-22 49.6
2012-03-22 48.3
2012-04-23 49.1
2012-05-02 49.3
...
2019-07-01 49.4
2019-08-01 49.9
2019-09-02 50.4
2019-09-30 51.4
2019-11-01 0
"""
t = time.time()
res = requests.get(
JS_CHINA_CX_PMI_YEARLY_URL.format(
str(int(round(t * 1000))), str(int(round(t * 1000)) + 90)
)
)
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)
value_df.columns = json_data["kinds"]
value_df.index = pd.to_datetime(date_list)
temp_df = value_df["今值"]
temp_df.name = "cx_pmi"
return temp_df
def macro_china_cx_pmi_yearly():
"""
中国年度财新PMI数据, 数据区间从20120120-至今
https://datacenter.jin10.com/reportType/dc_chinese_caixin_manufacturing_pmi
https://cdn.jin10.com/dc/reports/dc_chinese_caixin_manufacturing_pmi_all.js?v=1578818009
:return: pandas.Series
"""
t = time.time()
res = requests.get(
JS_CHINA_CX_PMI_YEARLY_URL.format(
str(int(round(t * 1000))), str(int(round(t * 1000)) + 90)
)
)
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)
value_df.columns = json_data["kinds"]
value_df.index = pd.to_datetime(date_list)
temp_df = value_df["今值"]
temp_df.name = "cx_pmi"
return temp_df
