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CEDApy/CEDA/MacroEcon/cn.py

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import pandas as pd
import numpy as np
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import re
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import demjson
import requests
from fake_useragent import UserAgent
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# TODO need add comments
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url = {
"eastmoney": "http://datainterface.eastmoney.com/EM_DataCenter/JS.aspx"
}
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def gdp_quarterly():
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"""
ABS: absolute value (per 100 million CNY)
YoY: year on year growth
"""
ua = UserAgent()
request_header = {"User-Agent": ua.random}
tmp_url = url["eastmoney"]
request_params = {
"cb": "datatable7519513",
"type": "GJZB",
"sty": "ZGZB",
"js": "({data:[(x)],pages:(pc)})",
"p": "1",
"ps": "200",
"mkt": "20",
"_": "1622020352668"
}
r = requests.get(tmp_url, params = request_params, headers = request_header)
data_text = r.text
data_json = demjson.decode(data_text[data_text.find("{") : -1])
df = pd.DataFrame([item.split(",") for item in data_json["data"]])
df.columns = [
"Date",
"Absolute_Value",
"YoY",
"Primary_Industry_ABS",
"Primary_Industry_YoY",
"Secondary_Industry_ABS",
"Secondary_Industry_YoY",
"Tertiary_Industry_ABS",
"Tertiary_Industry_YoY",
]
#df[(df['Date'] >= startdate) & (df['Date'] <= enddate)]
return df
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def ppi_monthly():
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"""
ABS: absolute value (per 100 million CNY)
YoY: year on year growth
"""
ua = UserAgent()
request_header = {"User-Agent": ua.random}
tmp_url = url["eastmoney"]
request_params = {
"cb": "datatable9051497",
"type": "GJZB",
"sty": "ZGZB",
"js": "({data:[(x)],pages:(pc)})",
"p": "1",
"ps": "200",
"mkt": "22",
"_": "1622047940401"
}
r = requests.get(tmp_url, params = request_params, headers = request_header)
data_text = r.text
data_json = demjson.decode(data_text[data_text.find("{") : -1])
df = pd.DataFrame([item.split(",") for item in data_json["data"]])
df.columns = [
"Date",
"Current_Month",
"Current_Month_YoY",
"Current_Month_Accum"
]
#df[(df['Date'] >= startdate) & (df['Date'] <= enddate)]
return df
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def cpi_monthly():
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"""
Accum: Accumulation
YoY: year on year growth
MoM: month on month growth
"""
tmp_url = url["eastmoney"]
ua = UserAgent()
request_header = {"User-Agent": ua.random}
tmp_url = url["eastmoney"]
request_params = {
"cb": "datatable2790750",
"type": "GJZB",
"sty": "ZGZB",
"js": "({data:[(x)],pages:(pc)})",
"p": "1",
"ps": "200",
"mkt": "19",
"_": "1622020352668"
}
r = requests.get(tmp_url, params = request_params, headers = request_header)
data_text = r.text
data_json = demjson.decode(data_text[data_text.find("{") : -1])
df = pd.DataFrame([item.split(",") for item in data_json["data"]])
df.columns = [
"Date",
"Notion_Monthly",
"Notion_YoY",
"Notion_MoM",
"Notion_Accum",
"Urban_Monthly",
"Urban_YoY",
"Urban_MoM",
"Urban_Accum",
"Rural_Monthly",
"Rural_YoY",
"Rural_MoM",
"Rural_Accum",
]
return df
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def pmi_monthly():
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"""
Man: manufacturing
Non-Man: Non-manufacturing
"""
tmp_url = url["eastmoney"]
ua = UserAgent()
request_header = {"User-Agent": ua.random}
tmp_url = url["eastmoney"]
request_params = {
"cb": "datatable4515395",
"type": "GJZB",
"sty": "ZGZB",
"js": "({data:[(x)],pages:(pc)})",
"p": "2",
"ps": "200",
"mkt": "21",
"_": "162202151821"
}
r = requests.get(tmp_url, params = request_params, headers = request_header)
data_text = r.text
data_json = demjson.decode(data_text[data_text.find("{") : -1])
temp_df = pd.DataFrame([item.split(",") for item in data_json["data"]])
temp_df.columns = [
"Date",
"Man_Industry_Index",
"Man_Index_YoY",
"Non-Man_Industry_Index",
"Non-Man_Index_YoY",
]
return temp_df
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def fai_monthly(): # fix asset investment
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"""
Man: manufacturing
Non-Man: Non-manufacturing
"""
tmp_url = url["eastmoney"]
ua = UserAgent()
request_header = {"User-Agent": ua.random}
tmp_url = url["eastmoney"]
request_params = {
"cb": "datatable607120",
"type": "GJZB",
"sty": "ZGZB",
"js": "({data:[(x)],pages:(pc)})",
"p": "1",
"ps": "200",
"mkt": "12",
"_": "1622021790947"
}
r = requests.get(tmp_url, params = request_params, headers = request_header)
data_text = r.text
data_json = demjson.decode(data_text[data_text.find("{") : -1])
df = pd.DataFrame([item.split(",") for item in data_json["data"]])
df.columns = [
"Date",
"Current_Month",
"YoY",
"MoM",
"Current_Year_Accum"
]
return df
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def hi_old_monthly(): # house index old version (2008-2010)
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"""
Man: manufacturing
Non-Man: Non-manufacturing
"""
tmp_url = url["eastmoney"]
ua = UserAgent()
request_header = {"User-Agent": ua.random}
tmp_url = url["eastmoney"]
request_params = {
"cb": "datatable1895714",
"type": "GJZB",
"sty": "ZGZB",
"js": "({data:[(x)],pages:(pc)})",
"p": "1",
"ps": "200",
"mkt": "10",
"_": "1622022794457"
}
r = requests.get(tmp_url, params = request_params, headers = request_header)
data_text = r.text
data_json = demjson.decode(data_text[data_text.find("{") : -1])
df = pd.DataFrame([item.split(",") for item in data_json["data"]])
df.columns = [
"Date",
"Housing_Prosperity_Index",
"HPI_YoY",
"Land_Development_Area_Index",
"LDAI_YoY",
"Sales_Price_Index",
"SPI_YoY"
]
return df
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# mkt=1&stat=2&city1=%E5%B9%BF%E5%B7%9E&city2=%E4%B8%8A%E6%B5%B7
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def hi_new_monthly(city1:str, city2:str): # newly built commercial housing & second-hand commercial housing
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"""
Man: manufacturing
Non-Man: Non-manufacturing
"""
tmp_url = "http://data.eastmoney.com/dataapi/cjsj/getnewhousechartdata?"
ua = UserAgent()
request_header = {"User-Agent": ua.random}
request_params_nbch = {
"mkt": "1",
"stat": "2",
"city1": "{}".format(city1),
"city2": "{}".format(city2)
}
request_params_shch = {
"mkt": "1",
"stat": "3",
"city1": "{}".format(city1),
"city2": "{}".format(city2)
}
r_nbch = requests.get(tmp_url, params = request_params_nbch, headers = request_header)
r_shch = requests.get(tmp_url, params = request_params_shch, headers = request_header)
data_text_nbch = r_nbch.text
data_text_shch = r_shch.text
data_json_nbch = demjson.decode(data_text_nbch)
data_json_shch = demjson.decode(data_text_shch)
date_nbch = data_json_nbch['chart']['series']['value']
data1_nbch = data_json_nbch['chart']['graphs']['graph'][0]['value']
data2_nbch = data_json_nbch['chart']['graphs']['graph'][1]['value']
data1_shch = data_json_shch['chart']['graphs']['graph'][0]['value']
data2_shch = data_json_shch['chart']['graphs']['graph'][1]['value']
df = pd.DataFrame({"Date": date_nbch,
"City1":data1_nbch,
"City2":data2_nbch,
"City1":data1_shch,
"City2":data2_shch})
return df
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def ci_eei_monthly(): # Climate Index & Entrepreneur Expectation Index
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"""
Man: manufacturing
Non-Man: Non-manufacturing
"""
tmp_url = url["eastmoney"]
ua = UserAgent()
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": "datatable7709842",
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"type": "GJZB",
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"sty": "ZGZB",
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"js": "({data:[(x)],pages:(pc)})",
"p": "1",
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"ps": "200",
"mkt": "8",
"_": "1622041485306"
}
r = requests.get(tmp_url, params = request_params, headers = request_header)
data_text = r.text
data_json = demjson.decode(data_text[data_text.find("{") : -1])
df = pd.DataFrame([item.split(",") for item in data_json["data"]])
df.columns = [
"Date",
"Climate_Index",
"CI_YoY",
"CI_MoM",
"Entrepreneur_Expectation_Index",
"EEI_YoY",
"EEI_MoM"
]
return df
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def ig_monthly(): # Industry Growth
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"""
Man: manufacturing
Non-Man: Non-manufacturing
"""
tmp_url = url["eastmoney"]
ua = UserAgent()
request_header = {"User-Agent": ua.random}
tmp_url = url["eastmoney"]
request_params = {
"cb": "datatable4577327",
"type": "GJZB",
"sty": "ZGZB",
"js": "({data:[(x)],pages:(pc)})",
"p": "1",
"ps": "200",
"mkt": "0",
"_": "1622042259898"
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}
r = requests.get(tmp_url, params = request_params, headers = request_header)
data_text = r.text
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 = [
"Date",
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"IG_YoY",
"IG_Accum",
]
return df
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def cgpi_monthly(): # Corporate Goods Price Index
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"""
Man: manufacturing
Non-Man: Non-manufacturing
"""
tmp_url = url["eastmoney"]
ua = UserAgent()
request_header = {"User-Agent": ua.random}
tmp_url = url["eastmoney"]
request_params = {
"cb": "datatable7184534",
"type": "GJZB",
"sty": "ZGZB",
"js": "({data:[(x)],pages:(pc)})",
"p": "1",
"ps": "200",
"mkt": "9",
"_": "1622042652353"
}
r = requests.get(tmp_url, params = request_params, headers = request_header)
data_text = r.text
data_json = demjson.decode(data_text[data_text.find("{") : -1])
df = pd.DataFrame([item.split(",") for item in data_json["data"]])
df.columns = [
"Date",
"General_Index",
"General_Index_YoY",
"Total_Index_MoM",
"Agricultural_Product",
"Agricultural_Product_YoY",
"Agricultural_PRoduct_MoM",
"Mineral_Product",
"Mineral_Product_YoY",
"Mineral_Product_MoM",
"Coal_Oil_Electricity",
"Coal_Oil_Electricity_YoY",
"Coal_Oil_Electricity_MoM"
]
return df
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def cci_csi_cei_monthly(): # Consumer Confidence Index & Consumer Satisfaction Index & Consumer Expectation Index
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"""
Man: manufacturing
Non-Man: Non-manufacturing
"""
tmp_url = url["eastmoney"]
ua = UserAgent()
request_header = {"User-Agent": ua.random}
tmp_url = url["eastmoney"]
request_params = {
"cb": "datatable1243218",
"type": "GJZB",
"sty": "ZGZB",
"js": "({data:[(x)],pages:(pc)})",
"p": "1",
"ps": "200",
"mkt": "4",
"_": "1622043704818"
}
r = requests.get(tmp_url, params = request_params, headers = request_header)
data_text = r.text
data_json = demjson.decode(data_text[data_text.find("{") : -1])
df = pd.DataFrame([item.split(",") for item in data_json["data"]])
df.columns = [
"Date",
"CCI",
"CCI_YoY",
"CCI_MoM",
"CSI",
"CSI_YoY",
"CSI_MoM",
"CEI",
"CEI_YoY",
"CEI_MoM"
]
return df
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def trscg_monthly(): # Total Retail Sales of Consumer Goods
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"""
Man: manufacturing
Non-Man: Non-manufacturing
"""
tmp_url = url["eastmoney"]
ua = UserAgent()
request_header = {"User-Agent": ua.random}
tmp_url = url["eastmoney"]
request_params = {
"cb": "datatable3665821",
"type": "GJZB",
"sty": "ZGZB",
"js": "({data:[(x)],pages:(pc)})",
"p": "1",
"ps": "200",
"mkt": "5",
"_": "1622044011316"
}
r = requests.get(tmp_url, params = request_params, headers = request_header)
data_text = r.text
data_json = demjson.decode(data_text[data_text.find("{") : -1])
df = pd.DataFrame([item.split(",") for item in data_json["data"]])
df.columns = [
"Date",
"Current_Month",
"TRSCG_YoY",
"TRSCG_MoM",
"TRSCG_Accum",
"TRSCG_Accum_YoY"
]
return df
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def ms_monthly(): # monetary Supply
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"""
Man: manufacturing
Non-Man: Non-manufacturing
"""
tmp_url = url["eastmoney"]
ua = UserAgent()
request_header = {"User-Agent": ua.random}
tmp_url = url["eastmoney"]
request_params = {
"cb": "datatable3818891",
"type": "GJZB",
"sty": "ZGZB",
"js": "({data:[(x)],pages:(pc)})",
"p": "1",
"ps": "200",
"mkt": "11",
"_": "1622044292103"
}
r = requests.get(tmp_url, params = request_params, headers = request_header)
data_text = r.text
data_json = demjson.decode(data_text[data_text.find("{") : -1])
df = pd.DataFrame([item.split(",") for item in data_json["data"]])
df.columns = [
"Date",
"M2",
"M2_YoY",
"M2_MoM",
"M1",
"M1_YoY",
"M1_MoM",
"M0",
"M0_YoY",
"M0_MoM"
]
return df
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def ie_monthly(): # Import & Export
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"""
"""
tmp_url = url["eastmoney"]
ua = UserAgent()
request_header = {"User-Agent": ua.random}
tmp_url = url["eastmoney"]
request_params = {
"cb": "datatable3818891",
"type": "GJZB",
"sty": "ZGZB",
"js": "({data:[(x)],pages:(pc)})",
"p": "1",
"ps": "200",
"mkt": "1",
"_": "1622044292103"
}
r = requests.get(tmp_url, params = request_params, headers = request_header)
data_text = r.text
data_json = demjson.decode(data_text[data_text.find("{") : -1])
df = pd.DataFrame([item.split(",") for item in data_json["data"]])
df.columns = [
"Date",
"Current_Month_Export",
"Current_Month_Export_YoY",
"Current_Month_Export_MoM",
"Current_Month_Import",
"Current_Month_Import_YoY",
"Current_Month_Import_MoM",
"Accumulation_Export",
"Accumulation_Export_YoY",
"Accumulation_Import",
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"Accumulation_Import_YoY"
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]
return df
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def stock_monthly(): # Import & Export
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"""
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&type=GJZB&sty=ZGZB&js=(%5B(x)%5D)&p=1&ps=200&mkt=2&_=1622084599456
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"""
tmp_url = url["eastmoney"]
ua = UserAgent()
request_header = {"User-Agent": ua.random}
tmp_url = url["eastmoney"]
request_params = {
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"cb": "jQuery112308659690274138041_1622084599455",
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"type": "GJZB",
"sty": "ZGZB",
"js": "({data:[(x)],pages:(pc)})",
"p": "1",
"ps": "200",
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"mkt": "2",
"_": "1622084599456"
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}
r = requests.get(tmp_url, params = request_params, headers = request_header)
data_text = r.text
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data_json = demjson.decode(data_text[data_text.find("(")+1:-1])
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df = pd.DataFrame([item.split(",") for item in data_json["data"]])
df.columns = [
"Date",
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"SH_Total_Stock_issue",
"SZ_Total_Stock_Issue",
"SH_Total_Market_Capitalization",
"SZ_Total_Market_Capitalization",
"SH_Turnover",
"SZ_Turnover",
"SH_Volume",
"SZ_Volume",
"SH_Highest",
"SZ_Highest",
"SH_lowest",
"SZ_lowest"
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]
return df
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def fgr_monthly(): # Forex and Gold Reserve
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"""
"""
tmp_url = url["eastmoney"]
ua = UserAgent()
request_header = {"User-Agent": ua.random}
tmp_url = url["eastmoney"]
request_params = {
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"cb": "tatable6260802",
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"type": "GJZB",
"sty": "ZGZB",
"js": "({data:[(x)],pages:(pc)})",
"p": "1",
"ps": "200",
"mkt": "16",
"_": "1622044863548"
}
r = requests.get(tmp_url, params = request_params, headers = request_header)
data_text = r.text
data_json = demjson.decode(data_text[data_text.find("{") : -1])
df = pd.DataFrame([item.split(",") for item in data_json["data"]])
df.columns = [
"Date",
"Forex",
"Forex_YoY",
"Forex_MoM",
"Gold",
"Gold_YoY",
"Gold_MoM"
]
return df
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#TODO: SPECIAL CASE
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def ctsf_monthly(): # Client Transaction Settlement Funds
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"""
"""
tmp_url = "http://data.eastmoney.com/dataapi/cjsj/getbanktransferdata?"
ua = UserAgent()
request_header = {"User-Agent": ua.random}
request_params = {
"p": "1",
"ps": "200"
}
r = requests.get(tmp_url, params = request_params, headers = request_header)
data_text = r.text
data_json = demjson.decode(data_text[data_text.find("["):-11])
df = pd.DataFrame(data_json)
return df
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# TODO: SPECIAL CASE
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def sao_monthly(): # Stock Account Overview
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"""
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"""
tmp_url = "http://dcfm.eastmoney.com/em_mutisvcexpandinterface/api/js/get?"
ua = UserAgent()
request_header = {"User-Agent": ua.random}
request_params = {
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"callback": "datatable4006236",
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"type": "GPKHData",
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"js" : "({data:[(x)],pages:(pc)})",
"st": "SDATE",
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"sr": "-1",
"token": "894050c76af8597a853f5b408b759f5d",
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"p": "1",
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"ps": "2000",
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"_": "1622079339035"
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}
r = requests.get(tmp_url, params = request_params, headers = request_header)
data_text = r.text
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data_json = demjson.decode(data_text[data_text.find("{")+6 : -14])
df = pd.DataFrame(data_json[0])
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df.columns = [
"Date",
"New_Investor",
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"New_Investor_MoM",
"New_Investor_YoY",
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"Active_Investor",
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"Active_Investor_A_Share",
"Active_Investor_B_share",
"SHIndex_Close",
"SHIndex_Rate",
"SHSZ_Market_Capitalization",
"SHSZ_Average_Capitalization"
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]
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df.Date = pd.to_datetime(df.Date, format = "%Y年%m月")
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return df
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def fdi_monthly(): # Foreign Direct Investment
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"""
"""
tmp_url = url["eastmoney"]
ua = UserAgent()
request_header = {"User-Agent": ua.random}
tmp_url = url["eastmoney"]
request_params = {
"cb": "datatable1477466",
"type": "GJZB",
"sty": "ZGZB",
"js": "({data:[(x)],pages:(pc)})",
"p": "1",
"ps": "200",
"mkt": "15",
"_": "1622044863548"
}
r = requests.get(tmp_url, params = request_params, headers = request_header)
data_text = r.text
data_json = demjson.decode(data_text[data_text.find("{") : -1])
df = pd.DataFrame([item.split(",") for item in data_json["data"]])
df.columns = [
"Date",
"Current_Month",
"YoY",
"MoM",
"Accumulation",
"Accum_YoY"
]
return df
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def gr_monthly(): # Government Revenue
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"""
"""
tmp_url = url["eastmoney"]
ua = UserAgent()
request_header = {"User-Agent": ua.random}
tmp_url = url["eastmoney"]
request_params = {
"cb": "datatable7840652",
"type": "GJZB",
"sty": "ZGZB",
"js": "({data:[(x)],pages:(pc)})",
"p": "1",
"ps": "200",
"mkt": "14",
"_": "1622080618625"
}
r = requests.get(tmp_url, params = request_params, headers = request_header)
data_text = r.text
data_json = demjson.decode(data_text[data_text.find("{") : -1])
df = pd.DataFrame([item.split(",") for item in data_json["data"]])
df.columns = [
"Date",
"Current_Month",
"YoY",
"MoM",
"Accumulation",
"Accum_YoY"
]
return df
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def ti_monthly(): # Tax Income
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"""
"""
tmp_url = url["eastmoney"]
ua = UserAgent()
request_header = {"User-Agent": ua.random}
tmp_url = url["eastmoney"]
request_params = {
"cb": "datatable8280567",
"type": "GJZB",
"sty": "ZGZB",
"js": "({data:[(x)],pages:(pc)})",
"p": "1",
"ps": "200",
"mkt": "3",
"_": "1622080669713"
}
r = requests.get(tmp_url, params = request_params, headers = request_header)
data_text = r.text
data_json = demjson.decode(data_text[data_text.find("{") : -1])
df = pd.DataFrame([item.split(",") for item in data_json["data"]])
df.columns = [
"Date",
"Current_Month",
"YoY",
"MoM"
]
return df
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def nl_monthly(): # New Loan
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"""
"""
tmp_url = url["eastmoney"]
ua = UserAgent()
request_header = {"User-Agent": ua.random}
tmp_url = url["eastmoney"]
request_params = {
"cb": "datatable2533707",
"type": "GJZB",
"sty": "ZGZB",
"js": "({data:[(x)],pages:(pc)})",
"p": "1",
"ps": "200",
"mkt": "7",
"_": "1622080800162"
}
r = requests.get(tmp_url, params = request_params, headers = request_header)
data_text = r.text
data_json = demjson.decode(data_text[data_text.find("{") : -1])
df = pd.DataFrame([item.split(",") for item in data_json["data"]])
df.columns = [
"Date",
"Current_Month",
"YoY",
"MoM",
"Accumulation",
"Accum_YoY"
]
return df
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def dfclc_monthly(): # Deposit of Foreign Currency and Local Currency
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"""
"""
tmp_url = url["eastmoney"]
ua = UserAgent()
request_header = {"User-Agent": ua.random}
tmp_url = url["eastmoney"]
request_params = {
"cb": "datatable2899877",
"type": "GJZB",
"sty": "ZGZB",
"js": "({data:[(x)],pages:(pc)})",
"p": "1",
"ps": "200",
"mkt": "18",
"_": "1622081057370"
}
r = requests.get(tmp_url, params = request_params, headers = request_header)
data_text = r.text
data_json = demjson.decode(data_text[data_text.find("{") : -1])
df = pd.DataFrame([item.split(",") for item in data_json["data"]])
df.columns = [
"Date",
"Current_Month",
"YoY",
"MoM",
"Accumulation"
]
return df
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def fl_monthly(): # Forex Loan
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"""
"""
tmp_url = url["eastmoney"]
ua = UserAgent()
request_header = {"User-Agent": ua.random}
tmp_url = url["eastmoney"]
request_params = {
"cb": "datatable636844",
"type": "GJZB",
"sty": "ZGZB",
"js": "({data:[(x)],pages:(pc)})",
"p": "1",
"ps": "200",
"mkt": "17",
"_": "1622081336038"
}
r = requests.get(tmp_url, params = request_params, headers = request_header)
data_text = r.text
data_json = demjson.decode(data_text[data_text.find("{") : -1])
df = pd.DataFrame([item.split(",") for item in data_json["data"]])
df.columns = [
"Date",
"Current_Month",
"YoY",
"MoM",
"Accumulation"
]
return df
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def drr_monthly(): # Deposit Reserve Ratio
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"""
"""
tmp_url = url["eastmoney"]
ua = UserAgent()
request_header = {"User-Agent": ua.random}
tmp_url = url["eastmoney"]
request_params = {
"cb": "datatable4285562",
"type": "GJZB",
"sty": "ZGZB",
"js": "({data:[(x)],pages:(pc)})",
"p": "1",
"ps": "200",
"mkt": "23",
"_": "1622081448882"
}
r = requests.get(tmp_url, params = request_params, headers = request_header)
data_text = r.text
data_json = demjson.decode(data_text[data_text.find("{") : -1])
df = pd.DataFrame([item.split(",") for item in data_json["data"]])
df.columns = [
"Announcement Date",
"Effective Date",
"Large_Financial_institution_Before",
"Large_Financial_institution_After",
"Large_Financial_institution_Adj_Rate",
"S&M_Financial_institution_Before",
"S&M_Financial_institution_After",
"S&M_Financial_institution_Adj_Rate",
"Comment",
"SHIndex_Rate",
"SZIndex_Rate"
]
return df
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def interest_monthly(): # Interest
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"""
"""
tmp_url = url["eastmoney"]
ua = UserAgent()
request_header = {"User-Agent": ua.random}
tmp_url = url["eastmoney"]
request_params = {
"cb": "datatable7591685",
"type": "GJZB",
"sty": "ZGZB",
"js": "({data:[(x)],pages:(pc)})",
"p": "1",
"ps": "200",
"mkt": "13",
"_": "1622081956464"
}
r = requests.get(tmp_url, params = request_params, headers = request_header)
data_text = r.text
data_json = demjson.decode(data_text[data_text.find("{") : -1])
df = pd.DataFrame([item.split(",") for item in data_json["data"]])
df.columns = [
"Announcement Date",
"Deposit_Benchmark_Interest_Rate_Before",
"Deposit_Benchmark_Interest_Rate_After",
"Deposit_Benchmark_Interest_Rate_Adj_Rate",
"Loan_Benchmark_Interest_Rate_Before",
"Loan_Benchmark_Interest_Rate_After",
"Loan_Benchmark_Interest_Rate_Adj_Rate",
"SHIndex_Rate",
"SZIndex_Rate",
"Effective Date"
]
df = df[[
"Announcement Date",
"Effective Date",
"Deposit_Benchmark_Interest_Rate_Before",
"Deposit_Benchmark_Interest_Rate_After",
"Deposit_Benchmark_Interest_Rate_Adj_Rate",
"Loan_Benchmark_Interest_Rate_Before",
"Loan_Benchmark_Interest_Rate_After",
"Loan_Benchmark_Interest_Rate_Adj_Rate",
"SHIndex_Rate",
"SZIndex_Rate"
]]
return df
#TODO: SPECIAL CASE
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def gdc_daily(): # gasoline, Diesel and Crude Oil
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"""
"""
tmp_url = "http://datacenter-web.eastmoney.com/api/data/get?"
ua = UserAgent()
request_header = {"User-Agent": ua.random}
request_params = {
"callback": "jQuery112302601302322321093_1622082348721",
"type": "RPTA_WEB_JY_HQ",
"sty": "ALL",
"st": "date",
"sr": "-1",
"token": "894050c76af8597a853f5b408b759f5d",
"p": "1",
"ps": "50000",
"source": "WEB",
"_":"1622082348722"
}
r = requests.get(tmp_url, params = request_params, headers = request_header)
data_text = r.text
data_json = demjson.decode(data_text[data_text.find("{") : -2])
df = pd.DataFrame(data_json["result"]["data"])
df.columns = ["Crude_Oil", "Date", "Gasoline", "Diesel"]
df = df[["Date", "Gasoline", "Diesel", "Crude_Oil"]]
return df
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"""
if __name__ == "__main__":
"""