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from CEDA.economic.macro import (
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cn_gdp_quarter
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)
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# -*- coding: utf-8 -*-
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# time: 05/25/2021 UTC+8
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# author: terencelau
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# email: t_lau@uicstat.com
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import pandas as pd
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import numpy as np
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import re
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import demjson
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import requests
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from fake_useragent import UserAgent
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url = {
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"eastmoney": "http://datainterface.eastmoney.com/EM_DataCenter/JS.aspx"
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}
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def cn_gdp_quarter():
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"""
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ABS: absolute value (per 100 million CNY)
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YoY: year on year growth
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"""
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ua = UserAgent()
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request_header = {"User-Agent": ua.random}
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tmp_url = url["eastmoney"]
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request_params = {
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"cb": "datatable7519513",
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"type": "GJZB",
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"sty": "ZGZB",
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"js": "({data:[(x)],pages:(pc)})",
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"p": "1",
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"ps": "200",
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"mkt": "20",
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"_": "1622020352668"
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}
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r = requests.get(tmp_url, params = request_params, headers = request_header)
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data_text = r.text
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data_json = demjson.decode(data_text[data_text.find("{") : -1])
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df = pd.DataFrame([item.split(",") for item in data_json["data"]])
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df.columns = [
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"Date",
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"Absolute_Value",
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"YoY",
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"Primary_Industry_ABS",
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"Primary_Industry_YoY",
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"Secondary_Industry_ABS",
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"Secondary_Industry_YoY",
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"Tertiary_Industry_ABS",
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"Tertiary_Industry_YoY",
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]
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#df[(df['Date'] >= startdate) & (df['Date'] <= enddate)]
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return df
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def cn_cpi_monthly():
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"""
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Accum: Accumulation
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YoY: year on year growth
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MoM: month on month growth
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"""
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tmp_url = url["eastmoney"]
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ua = UserAgent()
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request_header = {"User-Agent": ua.random}
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tmp_url = url["eastmoney"]
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request_params = {
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"cb": "datatable2790750",
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"type": "GJZB",
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"sty": "ZGZB",
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"js": "({data:[(x)],pages:(pc)})",
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"p": "1",
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"ps": "200",
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"mkt": "19",
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"_": "1622020352668"
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}
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r = requests.get(tmp_url, params = request_params, headers = request_header)
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data_text = r.text
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data_json = demjson.decode(data_text[data_text.find("{") : -1])
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df = pd.DataFrame([item.split(",") for item in data_json["data"]])
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df.columns = [
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"Date",
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"Notion_Monthly",
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"Notion_YoY",
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"Notion_MoM",
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"Notion_Accum",
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"Urban_Monthly",
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"Urban_YoY",
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"Urban_MoM",
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"Urban_Accum",
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"Rural_Monthly",
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"Rural_YoY",
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"Rural_MoM",
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"Rural_Accum",
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]
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return df
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def cn_pmi_monthly():
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"""
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Man: manufacturing
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Non-Man: Non-manufacturing
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"""
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tmp_url = url["eastmoney"]
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ua = UserAgent()
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request_header = {"User-Agent": ua.random}
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tmp_url = url["eastmoney"]
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request_params = {
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"cb": "datatable4515395",
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"type": "GJZB",
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"sty": "ZGZB",
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"js": "({data:[(x)],pages:(pc)})",
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"p": "2",
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"ps": "200",
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"mkt": "21",
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"_": "162202151821"
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}
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r = requests.get(tmp_url, params = request_params, headers = request_header)
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data_text = r.text
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data_json = demjson.decode(data_text[data_text.find("{") : -1])
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temp_df = pd.DataFrame([item.split(",") for item in data_json["data"]])
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temp_df.columns = [
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"Date",
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"Man_Industry_Index",
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"Man_Index_YoY",
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"Non-Man_Industry_Index",
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"Non-Man_Index_YoY",
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]
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return temp_df
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def cn_fai_monthly(): # fix asset investment
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"""
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Man: manufacturing
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Non-Man: Non-manufacturing
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"""
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tmp_url = url["eastmoney"]
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ua = UserAgent()
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request_header = {"User-Agent": ua.random}
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tmp_url = url["eastmoney"]
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request_params = {
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"cb": "datatable607120",
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"type": "GJZB",
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"sty": "ZGZB",
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"js": "({data:[(x)],pages:(pc)})",
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"p": "1",
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"ps": "200",
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"mkt": "12",
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"_": "1622021790947"
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}
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r = requests.get(tmp_url, params = request_params, headers = request_header)
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data_text = r.text
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data_json = demjson.decode(data_text[data_text.find("{") : -1])
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df = pd.DataFrame([item.split(",") for item in data_json["data"]])
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df.columns = [
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"Date",
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"Current_Month",
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"YoY",
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"MoM",
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"Current_Year_Accum"
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]
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return df
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def cn_hi_old_monthly(): # house index old version (2008-2010)
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"""
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Man: manufacturing
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Non-Man: Non-manufacturing
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"""
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tmp_url = url["eastmoney"]
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ua = UserAgent()
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request_header = {"User-Agent": ua.random}
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tmp_url = url["eastmoney"]
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request_params = {
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"cb": "datatable1895714",
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"type": "GJZB",
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"sty": "ZGZB",
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"js": "({data:[(x)],pages:(pc)})",
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"p": "1",
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"ps": "200",
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"mkt": "10",
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"_": "1622022794457"
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}
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r = requests.get(tmp_url, params = request_params, headers = request_header)
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data_text = r.text
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data_json = demjson.decode(data_text[data_text.find("{") : -1])
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df = pd.DataFrame([item.split(",") for item in data_json["data"]])
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df.columns = [
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"Date",
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"Housing_Prosperity_Index",
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"HPI_YoY",
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"Land_Development_Area_Index",
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"LDAI_YoY",
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"Sales_Price_Index",
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"SPI_YoY"
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]
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return df
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def cn_hi_mew_monthly(): # house index old version (2008-2010)
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"""
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Man: manufacturing
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Non-Man: Non-manufacturing
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http://data.eastmoney.com/dataapi/cjsj/getnewhousechartdata?mkt=1&stat=1&city1=%E5%8C%97%E4%BA%AC&city2=%E9%95%BF%E6%98%A5
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"""
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tmp_url = url["eastmoney"]
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ua = UserAgent()
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request_header = {"User-Agent": ua.random}
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request_params = {
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"cb": "datatable6451982",
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"type": "GJZB",
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"sty": "XFJLB",
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"js": "({data:[(x)],pages:(pc)})",
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"p": "1",
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"ps": "2000",
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"mkt": "19",
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"pageNo": "1",
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"pageNum": "1",
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"_": "1603023435552",
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}
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r = requests.get(tmp_url, params = request_params, headers = request_header)
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data_text = r.text
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data_json = demjson.decode(data_text[data_text.find("{") : -1])
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data = pd.DataFrame([item.split(",") for item in data_json["data"]])
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df.columns = [
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"Date",
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"Housing_Prosperity_Index",
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"HPI_YoY",
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"Land_Development_Area_Index",
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"LDAI_YoY",
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"Sales_Price_Index",
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"SPI_YoY"
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]
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return df
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"""
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if __name__ == "__main__":
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"""
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