diff --git a/CEDA/MacroEcon/__init__.py b/CEDA/MacroEcon/__init__.py deleted file mode 100644 index 8ad896a..0000000 --- a/CEDA/MacroEcon/__init__.py +++ /dev/null @@ -1,4 +0,0 @@ -# -*- coding: utf-8 -*- -# time: 05/25/2021 UTC+8 -# author: terencelau -# email: t_lau@uicstat.com diff --git a/CEDA/MacroEcon/cn.py b/CEDA/MacroEcon/cn.py deleted file mode 100644 index 8a8884a..0000000 --- a/CEDA/MacroEcon/cn.py +++ /dev/null @@ -1,1217 +0,0 @@ -import pandas as pd -import numpy as np -import re -import demjson -import requests -from fake_useragent import UserAgent - -# TODO need add comments - -url = { - "eastmoney": "http://datainterface.eastmoney.com/EM_DataCenter/JS.aspx" -} - - -def gdp_quarterly(): - """ - ABS: absolute value (per 100 million CNY) - YoY: year on year growth - Data source: http://data.eastmoney.com/cjsj/gdp.html - """ - ua = UserAgent(verify_ssl=False) - 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_Rate", - "Primary_Industry_ABS", - "Primary_Industry_YoY_Rate", - "Secondary_Industry_ABS", - "Secondary_Industry_YoY_Rate", - "Tertiary_Industry_ABS", - "Tertiary_Industry_YoY_Rate", - ] - df["Date"] = pd.to_datetime(df["Date"], format="%Y-%m-%d") - df["Absolute_Value"] = df["Absolute_Value"].astype(float) - df["Secondary_Industry_ABS"] = df["Secondary_Industry_ABS"].astype(float) - df["Tertiary_Industry_ABS"] = df["Tertiary_Industry_ABS"].astype(float) - df["Absolute_Value"] = df["Absolute_Value"].astype(float) - df["YoY_Rate"] = df["YoY_Rate"].astype(float) / 100 - df["Secondary_Industry_YoY_Rate"] = df["Secondary_Industry_YoY_Rate"].astype( - float) / 100 - df["Tertiary_Industry_YoY_Rate"] = df["Tertiary_Industry_YoY_Rate"].astype( - float) / 100 - return df - - -def ppi_monthly(): - """ - ABS: absolute value (per 100 million CNY) - YoY: year on year growth - Accum: Accumulation - Data source: http://data.eastmoney.com/cjsj/ppi.html - """ - ua = UserAgent(verify_ssl=False) - 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_Rate", - "Current_Month_Accum" - ] - df["Date"] = pd.to_datetime(df["Date"], format="%Y-%m-%d") - df["Current_Month"] = df["Current_Month"].astype(float) - df["Current_Month_YoY_Rate"] = df["Current_Month_YoY_Rate"].astype( - float) / 100 - df["Current_Month_Accum"] = df["Current_Month_Accum"].astype(float) - return df - - -def cpi_monthly(): - """ - Accum: Accumulation - YoY: year on year growth - MoM: month on month growth - Data source: http://data.eastmoney.com/cjsj/cpi.html - """ - tmp_url = url["eastmoney"] - ua = UserAgent(verify_ssl=False) - 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_Rate", - "Notion_MoM_Rate", - "Notion_Accum", - "Urban_Monthly", - "Urban_YoY_Rate", - "Urban_MoM_Rate", - "Urban_Accum", - "Rural_Monthly", - "Rural_YoY_Rate", - "Rural_MoM_Rate", - "Rural_Accum", - ] - df["Date"] = pd.to_datetime(df["Date"], format="%Y-%m-%d") - df[["Notion_Monthly", - "Notion_Accum", - "Urban_Monthly", - "Urban_Accum", - "Rural_Monthly", - "Rural_Accum"]] = df[["Notion_Monthly", - "Notion_Accum", - "Urban_Monthly", - "Urban_Accum", - "Rural_Monthly", - "Rural_Accum"]].astype(float) - df[["Notion_YoY_Rate", - "Notion_MoM_Rate", - "Urban_YoY_Rate", - "Urban_MoM_Rate", - "Rural_YoY_Rate", - "Rural_MoM_Rate"]] = df[["Notion_YoY_Rate", - "Notion_MoM_Rate", - "Urban_YoY_Rate", - "Urban_MoM_Rate", - "Rural_YoY_Rate", - "Rural_MoM_Rate"]].astype(float) / 100 - return df - - -def pmi_monthly(): - """ - Man: manufacturing - Non-Man: Non-manufacturing - Data Source: http://data.eastmoney.com/cjsj/pmi.html - """ - tmp_url = url["eastmoney"] - ua = UserAgent(verify_ssl=False) - 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]) - df = pd.DataFrame([item.split(",") for item in data_json["data"]]) - df.columns = [ - "Date", - "Man_Industry_Index", - "Man_Index_YoY_Rate", - "Non-Man_Industry_Index", - "Non-Man_Index_YoY_Rate", - ] - df["Date"] = pd.to_datetime(df["Date"], format="%Y-%m-%d") - df[["Man_Industry_Index", "Non-Man_Industry_Index"]] = \ - df[["Man_Industry_Index", "Non-Man_Industry_Index"]].astype(float) - df[["Man_Index_YoY_Rate", "Non-Man_Index_YoY_Rate"]] = \ - df[["Man_Index_YoY_Rate", "Non-Man_Index_YoY_Rate"]].astype(float) / 100 - return df - - -def fai_monthly(): # fix asset investment - """ - Man: manufacturing - Non-Man: Non-manufacturing - Data Source: http://data.eastmoney.com/cjsj/gdzctz.html - """ - tmp_url = url["eastmoney"] - ua = UserAgent(verify_ssl=False) - 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_Rate", - "MoM_Rate", - "Current_Year_Accum" - ] - df["Date"] = pd.to_datetime(df["Date"], format="%Y-%m-%d") - df[["Current_Month", "Current_Year_Accum"]] = \ - df[["Current_Month", "Current_Year_Accum"]].astype(float) - df[["YoY_Rate", "MoM_Rate"]] = \ - df[["YoY_Rate", "MoM_Rate"]].astype(float) / 100 - return df - - -def hi_old_monthly(): # house index old version (2008-2010) - """ - Man: manufacturing - Non-Man: Non-manufacturing - Data Source: http://data.eastmoney.com/cjsj/house.html - """ - tmp_url = url["eastmoney"] - ua = UserAgent(verify_ssl=False) - 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_Rate", - "Land_Development_Area_Index", - "LDAI_YoY_Rate", - "Sales_Price_Index", - "SPI_YoY_Rate" - ] - df["Date"] = pd.to_datetime(df["Date"], format="%Y-%m-%d") - df[["Housing_Prosperity_Index", - "Land_Development_Area_Index", - "Sales_Price_Index"]] = df[["Housing_Prosperity_Index", - "Land_Development_Area_Index", - "Sales_Price_Index"]].astype(float) - df[["HPI_YoY_Rate", "LDAI_YoY_Rate", "SPI_YoY_Rate"]] = \ - df[["HPI_YoY_Rate", "LDAI_YoY_Rate", "SPI_YoY_Rate"]].astype(float) / 100 - return df - -# mkt=1&stat=2&city1=%E5%B9%BF%E5%B7%9E&city2=%E4%B8%8A%E6%B5%B7 - - -# newly built commercial housing & second-hand commercial housing -def hi_new_monthly(city1: str, city2: str): - """ - Man: manufacturing - Non-Man: Non-manufacturing - Data Source: http://data.eastmoney.com/cjsj/newhouse.html - """ - tmp_url = "http://data.eastmoney.com/dataapi/cjsj/getnewhousechartdata?" - ua = UserAgent(verify_ssl=False) - request_header = {"User-Agent": ua.random} - request_params_nbch_MoM = { - "mkt": "1", - "stat": "2", - "city1": "{}".format(city1), - "city2": "{}".format(city2) - } - request_params_shch_MoM = { - "mkt": "1", - "stat": "3", - "city1": "{}".format(city1), - "city2": "{}".format(city2) - } - r_nbch_MoM = requests.get( - tmp_url, - params=request_params_nbch_MoM, - headers=request_header) - r_shch_MoM = requests.get( - tmp_url, - params=request_params_shch_MoM, - headers=request_header) - data_text_nbch_MoM = r_nbch_MoM.text - data_text_shch_MoM = r_shch_MoM.text - data_json_nbch_MoM = demjson.decode(data_text_nbch_MoM) - data_json_shch_MoM = demjson.decode(data_text_shch_MoM) - date_nbch = data_json_nbch_MoM['chart']['series']['value'] - data1_nbch_MoM = data_json_nbch_MoM['chart']['graphs']['graph'][0]['value'] - data2_nbch_MoM = data_json_nbch_MoM['chart']['graphs']['graph'][1]['value'] - data1_shch_MoM = data_json_shch_MoM['chart']['graphs']['graph'][0]['value'] - data2_shch_MoM = data_json_shch_MoM['chart']['graphs']['graph'][1]['value'] - df_MoM = pd.DataFrame({"Date": date_nbch, - "City1_nbch_MoM": data1_nbch_MoM, - "City1_shch_MoM": data1_shch_MoM, - "City2_nbch_MoM": data2_nbch_MoM, - "City2_shch_MoM": data2_shch_MoM}) - df_MoM["Date"] = pd.to_datetime(df_MoM["Date"], format="%m/%d/%Y") - - request_params_nbch_YoY = { - "mkt": "2", - "stat": "2", - "city1": "{}".format(city1), - "city2": "{}".format(city2) - } - request_params_shch_YoY = { - "mkt": "2", - "stat": "3", - "city1": "{}".format(city1), - "city2": "{}".format(city2) - } - r_nbch_YoY = requests.get( - tmp_url, - params=request_params_nbch_YoY, - headers=request_header) - r_shch_YoY = requests.get( - tmp_url, - params=request_params_shch_YoY, - headers=request_header) - data_text_nbch_YoY = r_nbch_YoY.text - data_text_shch_YoY = r_shch_YoY.text - data_json_nbch_YoY = demjson.decode(data_text_nbch_YoY) - data_json_shch_YoY = demjson.decode(data_text_shch_YoY) - date_nbch = data_json_nbch_YoY['chart']['series']['value'] - data1_nbch_YoY = data_json_nbch_YoY['chart']['graphs']['graph'][0]['value'] - data2_nbch_YoY = data_json_nbch_YoY['chart']['graphs']['graph'][1]['value'] - data1_shch_YoY = data_json_shch_YoY['chart']['graphs']['graph'][0]['value'] - data2_shch_YoY = data_json_shch_YoY['chart']['graphs']['graph'][1]['value'] - df_YoY = pd.DataFrame({"Date": date_nbch, - "City1_nbch_YoY": data1_nbch_YoY, - "City1_shch_YoY": data1_shch_YoY, - "City2_nbch_YoY": data2_nbch_YoY, - "City2_shch_YoY": data2_shch_YoY}) - df_YoY["Date"] = pd.to_datetime(df_YoY["Date"], format="%m/%d/%Y") - df = df_YoY.merge(df_MoM, on="Date") - return df - - -def ci_eei_monthly(): # Climate Index & Entrepreneur Expectation Index - """ - Man: manufacturing - Non-Man: Non-manufacturing - Data Source: http://data.eastmoney.com/cjsj/qyjqzs.html - """ - tmp_url = url["eastmoney"] - ua = UserAgent(verify_ssl=False) - request_header = {"User-Agent": ua.random} - tmp_url = url["eastmoney"] - request_params = { - "cb": "datatable7709842", - "type": "GJZB", - "sty": "ZGZB", - "js": "({data:[(x)],pages:(pc)})", - "p": "1", - "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_Rate", - "CI_MoM_Rate", - "Entrepreneur_Expectation_Index", - "EEI_YoY_Rate", - "EEI_MoM_Rate" - ] - df.replace('', np.nan, inplace=True) - df["Date"] = pd.to_datetime(df["Date"], format="%Y-%m-%d") - df[["Climate_Index", "Entrepreneur_Expectation_Index"]] = \ - df[["Climate_Index", "Entrepreneur_Expectation_Index"]].astype(float) - df[["CI_YoY_Rate", "CI_MoM_Rate", "EEI_YoY_Rate", "EEI_MoM_Rate"]] = df[[ - "CI_YoY_Rate", "CI_MoM_Rate", "EEI_YoY_Rate", "EEI_MoM_Rate"]].astype(float) / 100 - return df - - -def ig_monthly(): # Industry Growth - """ - Man: manufacturing - Non-Man: Non-manufacturing - Data Source: http://data.eastmoney.com/cjsj/gyzjz.html - """ - tmp_url = url["eastmoney"] - ua = UserAgent(verify_ssl=False) - 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" - } - 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", - "IG_YoY_Rate", - "IG_Accum_Rate", - ] - df["Date"] = pd.to_datetime(df["Date"], format="%Y-%m-%d") - df[["IG_YoY_Rate", "IG_Accum_Rate"]] = \ - df[["IG_YoY_Rate", "IG_Accum_Rate"]].astype(float) / 100 - return df - - -def cgpi_monthly(): # Corporate Goods Price Index - """ - Man: manufacturing - Non-Man: Non-manufacturing - Data Source: http://data.eastmoney.com/cjsj/qyspjg.html - """ - tmp_url = url["eastmoney"] - ua = UserAgent(verify_ssl=False) - 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_Rate", - "Total_Index_MoM_Rate", - "Agricultural_Product", - "Agricultural_Product_YoY_Rate", - "Agricultural_Product_MoM_Rate", - "Mineral_Product", - "Mineral_Product_YoY_Rate", - "Mineral_Product_MoM_Rate", - "Coal_Oil_Electricity", - "Coal_Oil_Electricity_YoY_Rate", - "Coal_Oil_Electricity_MoM_Rate" - ] - df["Date"] = pd.to_datetime(df["Date"], format="%Y-%m-%d") - df[["General_Index", - "Agricultural_Product", - "Mineral_Product", - "Coal_Oil_Electricity"]] = df[["General_Index", - "Agricultural_Product", - "Mineral_Product", - "Coal_Oil_Electricity"]].astype(float) - df[["General_Index_YoY_Rate", - "Total_Index_MoM_Rate", - "Agricultural_Product_YoY_Rate", - "Agricultural_Product_MoM_Rate", - "Coal_Oil_Electricity_YoY_Rate", - "Coal_Oil_Electricity_MoM_Rate"]] = df[["General_Index_YoY_Rate", - "Total_Index_MoM_Rate", - "Agricultural_Product_YoY_Rate", - "Agricultural_Product_MoM_Rate", - "Coal_Oil_Electricity_YoY_Rate", - "Coal_Oil_Electricity_MoM_Rate"]].astype(float) / 100 - return df - - -def cci_csi_cei_monthly(): # Consumer Confidence Index & Consumer Satisfaction Index & Consumer Expectation Index - """ - Man: manufacturing - Non-Man: Non-manufacturing - Data Source: http://data.eastmoney.com/cjsj/xfzxx.html - """ - tmp_url = url["eastmoney"] - ua = UserAgent(verify_ssl=False) - 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_Rate", - "CCI_MoM_Rate", - "CSI", - "CSI_YoY_Rate", - "CSI_MoM_Rate", - "CEI", - "CEI_YoY_Rate", - "CEI_MoM_Rate" - ] - df["Date"] = pd.to_datetime(df["Date"], format="%Y-%m-%d") - df[["CCI", "CSI", "CEI"]] = \ - df[["CCI", "CSI", "CEI"]].astype(float) - df[["CCI_YoY_Rate", "CCI_MoM_Rate", - "CSI_YoY_Rate", "CSI_MoM_Rate", - "CEI_YoY_Rate", "CEI_MoM_Rate"]] = \ - df[["CCI_YoY_Rate", "CCI_MoM_Rate", - "CSI_YoY_Rate", "CSI_MoM_Rate", - "CEI_YoY_Rate", "CEI_MoM_Rate"]].astype(float) / 100 - return df - - -def trscg_monthly(): # Total Retail Sales of Consumer Goods - """ - Man: manufacturing - Non-Man: Non-manufacturing - Data Source: http://data.eastmoney.com/cjsj/xfp.html - """ - tmp_url = url["eastmoney"] - ua = UserAgent(verify_ssl=False) - 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_Rate", - "TRSCG_MoM_Rate", - "TRSCG_Accum", - "TRSCG_Accum_YoY_Rate" - ] - df.replace("", np.nan, inplace=True) - df["Date"] = pd.to_datetime(df["Date"], format="%Y-%m-%d") - df[["Current_Month", "TRSCG_Accum"]] = \ - df[["Current_Month", "TRSCG_Accum"]].astype(float) - df[["TRSCG_YoY_Rate", "TRSCG_MoM_Rate", "TRSCG_Accum_YoY_Rate"]] = df[[ - "TRSCG_YoY_Rate", "TRSCG_MoM_Rate", "TRSCG_Accum_YoY_Rate"]].astype(float) / 100 - return df - - -def ms_monthly(): # monetary Supply - """ - Man: manufacturing - Non-Man: Non-manufacturing - Data Source: http://data.eastmoney.com/cjsj/hbgyl.html - """ - tmp_url = url["eastmoney"] - ua = UserAgent(verify_ssl=False) - 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_Rate", - "M2_MoM_Rate", - "M1", - "M1_YoY_Rate", - "M1_MoM_Rate", - "M0", - "M0_YoY_Rate", - "M0_MoM_Rate" - ] - df["Date"] = pd.to_datetime(df["Date"], format="%Y-%m-%d") - df[["M0", "M1", "M2"]] = \ - df[["M0", "M1", "M2"]].astype(float) - df[["M0_YoY_Rate", "M1_YoY_Rate", "M2_YoY_Rate", - "M0_MoM_Rate", "M1_MoM_Rate", "M2_MoM_Rate"]] = \ - df[["M0_YoY_Rate", "M1_YoY_Rate", "M2_YoY_Rate", - "M0_MoM_Rate", "M1_MoM_Rate", "M2_MoM_Rate"]].astype(float) / 100 - return df - - -def ie_monthly(): # Import & Export - """ - Data Source: http://data.eastmoney.com/cjsj/hgjck.html - """ - tmp_url = url["eastmoney"] - ua = UserAgent(verify_ssl=False) - 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_Rate", - "Current_Month_Export_MoM_Rate", - "Current_Month_Import", - "Current_Month_Import_YoY_Rate", - "Current_Month_Import_MoM_Rate", - "Accumulation_Export", - "Accumulation_Export_YoY_Rate", - "Accumulation_Import", - "Accumulation_Import_YoY_Rate" - ] - df["Date"] = pd.to_datetime(df["Date"], format="%Y-%m-%d") - df[["Current_Month_Export", "Current_Month_Import", - "Accumulation_Export", "Accumulation_Import"]] = \ - df[["Current_Month_Export", "Current_Month_Import", - "Accumulation_Export", "Accumulation_Import"]].astype(float) - df[["Current_Month_Export_YoY_Rate", - "Current_Month_Export_MoM_Rate", - "Current_Month_Import_YoY_Rate", - "Current_Month_Import_MoM_Rate", - "Accumulation_Export_YoY_Rate", - "Accumulation_Export_MoM_Rate"]] = df[["Current_Month_Export_YoY_Rate", - "Current_Month_Export_MoM_Rate", - "Current_Month_Import_YoY_Rate", - "Current_Month_Import_MoM_Rate", - "Accumulation_Export_YoY_Rate", - "Accumulation_Export_MoM_Rate"]].astype(float) / 100 - return df - - -def stock_monthly(): # Import & Export - """ - Data Source: http://data.eastmoney.com/cjsj/gpjytj.html - """ - tmp_url = url["eastmoney"] - ua = UserAgent(verify_ssl=False) - request_header = {"User-Agent": ua.random} - tmp_url = url["eastmoney"] - request_params = { - "cb": "jQuery112308659690274138041_1622084599455", - "type": "GJZB", - "sty": "ZGZB", - "js": "({data:[(x)],pages:(pc)})", - "p": "1", - "ps": "200", - "mkt": "2", - "_": "1622084599456" - } - 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:-1]) - df = pd.DataFrame([item.split(",") for item in data_json["data"]]) - df.columns = [ - "Date", - "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" - ] - df.replace("", np.nan, inplace=True) - df["Date"] = pd.to_datetime(df["Date"], format="%Y-%m-%d") - df[list(df.columns[1:])] = df[list(df.columns[1:])].astype(float) - return df - - -def fgr_monthly(): # Forex and Gold Reserve - """ - Data Source: http://data.eastmoney.com/cjsj/gpjytj.html - """ - tmp_url = url["eastmoney"] - ua = UserAgent(verify_ssl=False) - request_header = {"User-Agent": ua.random} - tmp_url = url["eastmoney"] - request_params = { - "cb": "tatable6260802", - "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_Rate", - "Forex_MoM_Rate", - "Gold", - "Gold_YoY_Rate", - "Gold_MoM_Rate" - ] - df.replace("", np.nan, inplace=True) - df["Date"] = pd.to_datetime(df["Date"], format="%Y-%m-%d") - df[["Forex", "Gold"]] = \ - df["Forex", "Gold"].astype(float) - df[["Forex_YoY_Rate", "Gold_YoY_Rate", - "Forex_MoM_Rate", "Gold_MoM_Rate"]] = \ - df["Forex_YoY_Rate", "Gold_YoY_Rate", - "Forex_MoM_Rate", "Gold_MoM_Rate"].astype(float) / 100 - return df -# TODO: SPECIAL CASE - - -def ctsf_monthly(): # Client Transaction Settlement Funds - """ - http://data.eastmoney.com/cjsj/banktransfer.html - """ - tmp_url = "http://data.eastmoney.com/dataapi/cjsj/getbanktransferdata?" - ua = UserAgent(verify_ssl=False) - 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) - df.replace("", np.nan, inplace=True) - df["StartDate"] = pd.to_datetime(df["StartDate"], format="%Y-%m-%d") - df["EndDate"] = pd.to_datetime(df["EndDate"], format="%Y-%m-%d") - df[list(df.columns)[2:]] = df[list(df.columns)[2:]].astype(float) - return df - -# TODO: SPECIAL CASE - - -def sao_monthly(): # Stock Account Overview - """ - http://data.eastmoney.com/cjsj/gpkhsj.html - """ - tmp_url = "http://dcfm.eastmoney.com/em_mutisvcexpandinterface/api/js/get?" - ua = UserAgent(verify_ssl=False) - request_header = {"User-Agent": ua.random} - request_params = { - "callback": "datatable4006236", - "type": "GPKHData", - "js": "({data:[(x)],pages:(pc)})", - "st": "SDATE", - "sr": "-1", - "token": "894050c76af8597a853f5b408b759f5d", - "p": "1", - "ps": "2000", - "_": "1622079339035" - } - r = requests.get(tmp_url, params=request_params, headers=request_header) - data_text = r.text - data_json = demjson.decode(data_text[data_text.find("{") + 6: -14]) - df = pd.DataFrame(data_json[0]) - df.columns = [ - "Date", - "New_Investor", - "New_Investor_MoM_Rate", - "New_Investor_YoY_Rate", - "Active_Investor", - "Active_Investor_A_Share", - "Active_Investor_B_share", - "SHIndex_Close", - "SHIndex_Rate", - "SHSZ_Market_Capitalization", - "SHSZ_Average_Capitalization" - ] - df.replace("-", np.nan, inplace=True) - df.Date = pd.to_datetime(df.Date, format="%Y年%m月") - df[list(df.columns[~df.columns.isin(["Date", "New_Investor_MoM_Rate", "New_Investor_YoY_Rate"])])] = df[list( - df.columns[~df.columns.isin(["Date", "New_Investor_MoM_Rate", "New_Investor_YoY_Rate"])])].astype(float) - df[["New_Investor_MoM_Rate", "New_Investor_YoY_Rate"]] = \ - df[["New_Investor_MoM_Rate", "New_Investor_YoY_Rate"]].astype(float) / 100 - return df - - -def fdi_monthly(): # Foreign Direct Investment - """ - http://data.eastmoney.com/cjsj/fdi.html - """ - tmp_url = url["eastmoney"] - ua = UserAgent(verify_ssl=False) - 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_Rate", - "MoM_Rate", - "Accumulation", - "Accum_YoY_Rate" - ] - df.replace("", np.nan, inplace=True) - df["Date"] = pd.to_datetime(df["Date"], format="%Y-%m-%d") - df[["Current_Month", "Accumulation"]] = \ - df[["Current_Month", "Accumulation"]].astype(float) - df[["YoY_Rate", "MoM_Rate", "Accum_YoY_Rate"]] = \ - df[["YoY_Rate", "MoM_Rate", "Accum_YoY_Rate"]].astype(float) / 100 - return df - - -def gr_monthly(): # Government Revenue - """ - http://data.eastmoney.com/cjsj/czsr.html - """ - tmp_url = url["eastmoney"] - ua = UserAgent(verify_ssl=False) - 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_Rate", - "MoM_Rate", - "Accumulation", - "Accum_YoY_Rate" - ] - df["Date"] = pd.to_datetime(df["Date"], format="%Y-%m-%d") - df[["Current_Month", "Accumulation"]] = \ - df[["Current_Month", "Accumulation"]].astype(float) - df[["YoY_Rate", "MoM_rate", "Accum_YoY_Rate"]] = \ - df[["YoY_Rate", "MoM_rate", "Accum_YoY_Rate"]].astype(float) / 100 - return df - - -def ti_monthly(): # Tax Income - """ - http://data.eastmoney.com/cjsj/qgsssr.html - """ - tmp_url = url["eastmoney"] - ua = UserAgent(verify_ssl=False) - 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_Rate", - "MoM_Rate" - ] - df["Date"] = pd.to_datetime(df["Date"], format="%Y-%m-%d") - df[["Current_Month"]] = \ - df[["Current_Month"]].astype(float) - df[["YoY_Rate", "MoM_rate"]] = \ - df[["YoY_Rate", "MoM_rate"]].astype(float) / 100 - return df - - -def nl_monthly(): # New Loan - """ - http://data.eastmoney.com/cjsj/xzxd.html - """ - tmp_url = url["eastmoney"] - ua = UserAgent(verify_ssl=False) - 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_Rate", - "MoM_Rate", - "Accumulation", - "Accum_YoY_Rate" - ] - df["Date"] = pd.to_datetime(df["Date"], format="%Y-%m-%d") - df[["Current_Month", "Accumulation"]] = \ - df[["Current_Month", "Accumulation"]].astype(float) - df[["YoY_Rate", "MoM_Rate", "Accum_YoY_Rate"]] =\ - df[["YoY_Rate", "MoM_Rate", "Accum_YoY_Rate"]].astype(float) / 100 - return df - - -def dfclc_monthly(): # Deposit of Foreign Currency and Local Currency - """ - http://data.eastmoney.com/cjsj/wbck.html - """ - tmp_url = url["eastmoney"] - ua = UserAgent(verify_ssl=False) - 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_Rate", - "MoM_Rate", - "Accumulation" - ] - df["Date"] = pd.to_datetime(df["Date"], format="%Y-%m-%d") - df[["Current_Month", "Accumulation"]] = \ - df[["Current_Month", "Accumulation"]].astype(float) - df[["YoY_Rate", "MoM_Rate"]] = \ - df[["YoY_Rate", "MoM_Rate"]].astype(float) / 100 - return df - - -def fl_monthly(): # Forex Loan - """ - http://data.eastmoney.com/cjsj/whxd.html - """ - tmp_url = url["eastmoney"] - ua = UserAgent(verify_ssl=False) - 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" - ] - df["Date"] = pd.to_datetime(df["Date"], format="%Y-%m-%d") - df[["Current_Month", "Accumulation"]] = \ - df[["Current_Month", "Accumulation"]].astype(float) - df[["YoY_Rate", "MoM_Rate"]] = \ - df[["YoY_Rate", "MoM_Rate"]].astype(float) / 100 - return df - - -def drr_monthly(): # Deposit Reserve Ratio - """ - http://data.eastmoney.com/cjsj/ckzbj.html - """ - tmp_url = url["eastmoney"] - ua = UserAgent(verify_ssl=False) - 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" - ] - df["Announcement Date"] = pd.to_datetime( - df["Announcement Date"], format="%Y-%m-%d") - df["Effective Date"] = pd.to_datetime( - df["Effective Date"], format="%Y-%m-%d") - df[["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"]] = df[["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"]].astype(float) / 100 - return df - - -def interest_monthly(): # Interest - """ - http://data.eastmoney.com/cjsj/yhll.html - """ - tmp_url = url["eastmoney"] - ua = UserAgent(verify_ssl=False) - 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" - ]] - df[list(df.columns)] = df[list(df.columns)].astype(float) / 100 - return df - -# TODO: SPECIAL CASE - - -def gdc_daily(): # gasoline, Diesel and Crude Oil - """ - http://data.eastmoney.com/cjsj/oil_default.html - """ - tmp_url = "http://datacenter-web.eastmoney.com/api/data/get?" - ua = UserAgent(verify_ssl=False) - 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"]] - df = pd.to_datetime(df["Date"], format="%Y-%m-%d") - return df - - -""" -if __name__ == "__main__": -""" diff --git a/CEDA/MacroEcon/eu.py b/CEDA/MacroEcon/eu.py deleted file mode 100644 index 1b90988..0000000 --- a/CEDA/MacroEcon/eu.py +++ /dev/null @@ -1,908 +0,0 @@ -import io -import os -import demjson -import requests -import numpy as np -import pandas as pd -from fake_useragent import UserAgent -from pandas.core.frame import DataFrame -from pandas.core.reshape.merge import merge - -# Main Economic Indicators: https://alfred.stlouisfed.org/release?rid=205 -url = { - "fred_econ": "https://fred.stlouisfed.org/graph/fredgraph.csv?", - "eurostat": "http://ec.europa.eu/eurostat/wdds/rest/data/v2.1/json/en/", - "ecb": "https://sdw-wsrest.ecb.europa.eu/service/data/" -} - - -def merge_data(data_1: pd.DataFrame, data_2: pd.DataFrame, col_name: str): - data = pd.merge_asof(data_1, data_2, on=col_name) - return data - - -def National_Account(): - tmp_url = url["fred_econ"] + "bgcolor=%23e1e9f0&chart_type=line&drp=0&fo=open%20sans&graph_bgcolor=%23ffffff&height=450&mode=fred&recession_bars=off&txtcolor=%23444444&ts=12&tts=12&width=1168&nt=0&thu=0&trc=0&show_legend=yes&show_axis_titles=yes&show_tooltip=yes&id=NAEXCP04EZQ189S,NAEXCP02EZQ189S,NAEXCP01EZQ189S,NAEXCP06EZQ189S,NAEXCP07EZQ189S,NAEXCP03EZQ189S,NAGIGP01EZQ661S,NAEXKP06EZQ659S,NAEXKP04EZQ659S,NAEXKP01EZQ652S,NAEXKP07EZQ652S,NAEXKP03EZQ659S&scale=left,left,left,left,left,left,left,left,left,left,left,left&cosd=1995-01-01,1995-01-01,1995-01-01,1995-01-01,1995-01-01,1995-01-01,1995-01-01,1996-01-01,1996-01-01,1995-01-01,1995-01-01,1996-01-01&coed=2020-10-01,2020-10-01,2020-10-01,2020-10-01,2020-10-01,2020-10-01,2020-10-01,2020-10-01,2020-10-01,2021-01-01,2020-10-01,2020-10-01&line_color=%234572a7,%23aa4643,%2389a54e,%2380699b,%233d96ae,%23db843d,%2392a8cd,%23a47d7c,%23b5ca92,%2391e8e1,%238d4653,%238085e8&link_values=false,false,false,false,false,false,false,false,false,false,false,false&line_style=solid,solid,solid,solid,solid,solid,solid,solid,solid,solid,solid,solid&mark_type=none,none,none,none,none,none,none,none,none,none,none,none&mw=3,3,3,3,3,3,3,3,3,3,3,3&lw=2,2,2,2,2,2,2,2,2,2,2,2&ost=-99999,-99999,-99999,-99999,-99999,-99999,-99999,-99999,-99999,-99999,-99999,-99999&oet=99999,99999,99999,99999,99999,99999,99999,99999,99999,99999,99999,99999&mma=0,0,0,0,0,0,0,0,0,0,0,0&fml=a,a,a,a,a,a,a,a,a,a,a,a&fq=Quarterly,Quarterly,Quarterly,Quarterly,Quarterly,Quarterly,Quarterly,Quarterly,Quarterly,Quarterly,Quarterly,Quarterly&fam=avg,avg,avg,avg,avg,avg,avg,avg,avg,avg,avg,avg&fgst=lin,lin,lin,lin,lin,lin,lin,lin,lin,lin,lin,lin&fgsnd=2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01&line_index=1,2,3,4,5,6,7,8,9,10,11,12&transformation=lin,lin,lin,lin,lin,lin,lin,lin,lin,lin,lin,lin&vintage_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07&revision_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07&nd=1995-01-01,1995-01-01,1995-01-01,1995-01-01,1995-01-01,1995-01-01,1995-01-01,1996-01-01,1996-01-01,1995-01-01,1995-01-01,1996-01-01" - ua = UserAgent(verify_ssl=False) - request_header = {"User-Agent": ua.random} - r = requests.get(tmp_url, headers=request_header) - data_text = r.content - df = pd.read_csv(io.StringIO(data_text.decode('utf-8'))) - df["DATE"] = pd.to_datetime(df["DATE"], format="%Y-%m-%d") - #df = df[list(df.columns[1:])].replace(".", np.nan).astype(float) - name_list = { - 'NAEXCP04EZQ189S': "Gross Domestic Product by Expenditure in Current Prices: Gross Fixed Capital Formation for the Euro Area", - 'NAEXCP02EZQ189S': "Gross Domestic Product by Expenditure in Current Prices: Private Final Consumption Expenditure for the Euro Area", - 'NAEXCP01EZQ189S': "Gross Domestic Product by Expenditure in Current Prices: Total Gross Domestic Product for the Euro Area", - 'NAEXCP06EZQ189S': "Gross Domestic Product by Expenditure in Current Prices: Exports of Goods and Services for the Euro Area", - 'NAEXCP07EZQ189S': "Gross Domestic Product by Expenditure in Current Prices: Less Imports of Goods and Services for the Euro Area", - 'NAEXCP03EZQ189S': "Gross Domestic Product by Expenditure in Current Prices: Government Final Consumption Expenditure for the Euro Area", - 'NAGIGP01EZQ661S': "Gross Domestic Product Deflator for the Euro Area", - 'NAEXKP06EZQ659S': "Gross Domestic Product by Expenditure in Constant Prices: Exports of Goods and Services for the Euro Area", - 'NAEXKP04EZQ659S': "Gross Domestic Product by Expenditure in Constant Prices: Gross Fixed Capital Formation for the Euro Area", - 'NAEXKP01EZQ652S': "Gross Domestic Product by Expenditure in Constant Prices: Total Gross Domestic Product for the Euro Area", - 'NAEXKP07EZQ652S': "Gross Domestic Product by Expenditure in Constant Prices: Less: Imports of Goods and Services for the Euro Area", - 'NAEXKP03EZQ659S': "Gross Domestic Product by Expenditure in Constant Prices: Government Final Consumption Expenditure for the Euro Area"} - description = "National Accounts, Quarterly, Seasonally, Adjusted" - return df, name_list, description - - -def International_Trade(): - tmp_url = url["fred_econ"] + "bgcolor=%23e1e9f0&chart_type=line&drp=0&fo=open%20sans&graph_bgcolor=%23ffffff&height=450&mode=fred&recession_bars=off&txtcolor=%23444444&ts=12&tts=12&width=1168&nt=0&thu=0&trc=0&show_legend=yes&show_axis_titles=yes&show_tooltip=yes&id=XTEXVA01EZQ188S,XTIMVA01EZQ188S,EA19XTNTVA01STSAQ&scale=left,left,left&cosd=1995-01-01,1995-01-01,1995-01-01&coed=2020-10-01,2020-10-01,2017-04-01&line_color=%234572a7,%23aa4643,%2389a54e&link_values=false,false,false&line_style=solid,solid,solid&mark_type=none,none,none&mw=3,3,3&lw=2,2,2&ost=-99999,-99999,-99999&oet=99999,99999,99999&mma=0,0,0&fml=a,a,a&fq=Quarterly,Quarterly,Quarterly&fam=avg,avg,avg&fgst=lin,lin,lin&fgsnd=2020-02-01,2020-02-01,2017-04-01&line_index=1,2,3&transformation=lin,lin,lin&vintage_date=2021-06-07,2021-06-07,2021-06-07&revision_date=2021-06-07,2021-06-07,2021-06-07&nd=1995-01-01,1995-01-01,1995-01-01" - ua = UserAgent(verify_ssl=False) - request_header = {"User-Agent": ua.random} - r = requests.get(tmp_url, headers=request_header) - data_text = r.content - df = pd.read_csv(io.StringIO(data_text.decode('utf-8'))) - df["DATE"] = pd.to_datetime(df["DATE"], format="%Y-%m-%d") - #df = df[list(df.columns[1:])].replace(".", np.nan).astype(float) - name_list = { - 'XTEXVA01EZQ188S': "Exports: Value Goods for the Euro Area", - 'XTIMVA01EZQ188SS': "Imports: Value Goods for the Euro Area", - 'EA19XTNTVA01STSAQ': "International Trade: Net trade: Value (goods): Total for the Euro Area"} - description = "International Trade, Quarterly, Seasonally Adjusted" - return df, name_list, description - - -def Balance_of_Payments_BPM6(): - tmp_url = url["fred_econ"] + "bgcolor=%23e1e9f0&chart_type=line&drp=0&fo=open%20sans&graph_bgcolor=%23ffffff&height=450&mode=fred&recession_bars=off&txtcolor=%23444444&ts=12&tts=12&width=1168&nt=0&thu=0&trc=0&show_legend=yes&show_axis_titles=yes&show_tooltip=yes&id=EA19B6BLTT02STSAQ,EA19B6DBSE02STSAQ,EA19B6DBSE03STSAQ,EA19B6CRSE03STSAQ,EA19B6CRSE02STSAQ&scale=left,left,left,left,left&cosd=1999-01-01,1999-01-01,1999-01-01,1999-01-01,1999-01-01&coed=2020-10-01,2020-10-01,2020-10-01,2020-10-01,2020-10-01&line_color=%234572a7,%23aa4643,%2389a54e,%2380699b,%233d96ae&link_values=false,false,false,false,false&line_style=solid,solid,solid,solid,solid&mark_type=none,none,none,none,none&mw=3,3,3,3,3&lw=2,2,2,2,2&ost=-99999,-99999,-99999,-99999,-99999&oet=99999,99999,99999,99999,99999&mma=0,0,0,0,0&fml=a,a,a,a,a&fq=Quarterly,Quarterly,Quarterly,Quarterly,Quarterly&fam=avg,avg,avg,avg,avg&fgst=lin,lin,lin,lin,lin&fgsnd=2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01&line_index=1,2,3,4,5&transformation=lin,lin,lin,lin,lin&vintage_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07&revision_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07&nd=1999-01-01,1999-01-01,1999-01-01,1999-01-01,1999-01-01" - ua = UserAgent(verify_ssl=False) - request_header = {"User-Agent": ua.random} - r = requests.get(tmp_url, headers=request_header) - data_text = r.content - df = pd.read_csv(io.StringIO(data_text.decode('utf-8'))) - df["DATE"] = pd.to_datetime(df["DATE"], format="%Y-%m-%d") - #df = df[list(df.columns[1:])].replace(".", np.nan).astype(float) - name_list = { - 'EA19B6BLTT02STSAQ': "Balance of payments BPM6: Current account Debits: Services: Total Debits as % of Current account for the Euro Area", - 'EA19B6DBSE02STSAQ': "Balance of payments BPM6: Current account Debits: Services: Total Debits as % of Current account for the Euro Area", - 'EA19B6DBSE03STSAQ': "Balance of payments BPM6: Current account Debits: Services: Total Debits as % of Goods and Services for the Euro Area", - 'EA19B6CRSE03STSAQ': "Balance of payments BPM6: Current account Credits: Services: Total Credits as % of Goods and Services for Euro Area", - 'EA19B6CRSE02STSAQ': "Balance of payments BPM6: Current account Credits: Services: Total Credits as % of Current account for Euro Area"} - description = "Balanced of payments BPM6, Quarterly, Seasonally Adjusted" - return df, name_list, description - - -def Learning_Indicators_OECD(): - tmp_url = url["fred_econ"] + "bgcolor=%23e1e9f0&chart_type=line&drp=0&fo=open%20sans&graph_bgcolor=%23ffffff&height=450&mode=fred&recession_bars=off&txtcolor=%23444444&ts=12&tts=12&width=1168&nt=0&thu=0&trc=0&show_legend=yes&show_axis_titles=yes&show_tooltip=yes&id=EA19LORSGPNOSTSAM,EA19LOLITOTRGYSAM,EA19LOLITONOSTSAM,EA19LOLITOAASTSAM,EA19LORSGPORIXOBSAM,EA19LORSGPRTSTSAM,EA19LORSGPTDSTSAM&scale=left,left,left,left,left,left,left&cosd=1960-03-01,1966-12-01,1965-12-01,1965-12-01,1960-03-01,1960-03-01,1960-03-01&coed=2020-11-01,2020-11-01,2021-03-01,2021-03-01,2020-11-01,2020-11-01,2020-11-01&line_color=%234572a7,%23aa4643,%2389a54e,%2380699b,%233d96ae,%23db843d,%2392a8cd&link_values=false,false,false,false,false,false,false&line_style=solid,solid,solid,solid,solid,solid,solid&mark_type=none,none,none,none,none,none,none&mw=3,3,3,3,3,3,3&lw=2,2,2,2,2,2,2&ost=-99999,-99999,-99999,-99999,-99999,-99999,-99999&oet=99999,99999,99999,99999,99999,99999,99999&mma=0,0,0,0,0,0,0&fml=a,a,a,a,a,a,a&fq=Monthly,Monthly,Monthly,Monthly,Monthly,Monthly,Monthly&fam=avg,avg,avg,avg,avg,avg,avg&fgst=lin,lin,lin,lin,lin,lin,lin&fgsnd=2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01&line_index=1,2,3,4,5,6,7&transformation=lin,lin,lin,lin,lin,lin,lin&vintage_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07&revision_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07&nd=1960-03-01,1966-12-01,1965-12-01,1965-12-01,1960-03-01,1960-03-01,1960-03-01" - ua = UserAgent(verify_ssl=False) - request_header = {"User-Agent": ua.random} - r = requests.get(tmp_url, headers=request_header) - data_text = r.content - df = pd.read_csv(io.StringIO(data_text.decode('utf-8'))) - df["DATE"] = pd.to_datetime(df["DATE"], format="%Y-%m-%d") - #df = df[list(df.columns[1:])].replace(".", np.nan).astype(float) - name_list = { - 'EA19CPALTT01GYQ': "Leading Indicators OECD: Reference series: Gross Domestic Product (GDP): Normalised for the Euro Area", - 'EA19LOLITOTRGYSAM': "Leading Indicators OECD: Leading indicators: CLI: Trend restored for the Euro Area", - 'EA19LOLITONOSTSAM': "Leading Indicators OECD: Leading indicators: CLI: Normalised for the Euro Area", - 'EA19LOLITOAASTSAM': "Leading Indicators OECD: Leading indicators: CLI: Amplitude adjusted for the Euro Area", - 'EA19LORSGPORIXOBSAM': "Leading Indicators OECD: Reference series: Gross Domestic Product (GDP): Original series for the Euro Area", - 'EA19LORSGPRTSTSAM': "Leading Indicators OECD: Reference series: Gross Domestic Product (GDP): Ratio to trend for the Euro Area", - 'EA19LORSGPTDSTSAM': "Leading Indicators OECD: Reference series: Gross Domestic Product (GDP): Trend for the Euro Area"} - description = "Leading Indicators OECD, Monthly, Seasonally Adjusted" - return df, name_list, description - - -def Monetary_Aggregates_Monthly_Adj(): - tmp_url = url["fred_econ"] + "bgcolor=%23e1e9f0&chart_type=line&drp=0&fo=open%20sans&graph_bgcolor=%23ffffff&height=450&mode=fred&recession_bars=off&txtcolor=%23444444&ts=12&tts=12&width=1168&nt=0&thu=0&trc=0&show_legend=yes&show_axis_titles=yes&show_tooltip=yes&id=EA19MABMM301GYSAM,EA19MANMM101IXOBSAM&scale=left,left&cosd=1971-01-01,1970-01-01&coed=2021-03-01,2021-03-01&line_color=%234572a7,%23aa4643&link_values=false,false&line_style=solid,solid&mark_type=none,none&mw=3,3&lw=2,2&ost=-99999,-99999&oet=99999,99999&mma=0,0&fml=a,a&fq=Monthly,Monthly&fam=avg,avg&fgst=lin,lin&fgsnd=2020-02-01,2020-02-01&line_index=1,2&transformation=lin,lin&vintage_date=2021-06-07,2021-06-07&revision_date=2021-06-07,2021-06-07&nd=1971-01-01,1970-01-01" - ua = UserAgent(verify_ssl=False) - request_header = {"User-Agent": ua.random} - r = requests.get(tmp_url, headers=request_header) - data_text = r.content - df = pd.read_csv(io.StringIO(data_text.decode('utf-8'))) - df["DATE"] = pd.to_datetime(df["DATE"], format="%Y-%m-%d") - #df = df[list(df.columns[1:])].replace(".", np.nan).astype(float) - name_list = {'EA19MABMM301GYSAM': "Monetary aggregates and their components: Broad money and components: M3: M3 for the Euro Area", - 'EA19MANMM101IXOBSAM': "Monetary aggregates and their components: Narrow money and components: M1 and components: M1 for the Euro Area"} - description = "Monetary aggregates and their components, Monthly, Seasonally Adjusted" - return df, name_list, description - - -def Monetary_Aggregates_Quarterly_Adj(): - tmp_url = url["fred_econ"] + "bgcolor=%23e1e9f0&chart_type=line&drp=0&fo=open%20sans&graph_bgcolor=%23ffffff&height=450&mode=fred&recession_bars=off&txtcolor=%23444444&ts=12&tts=12&width=1168&nt=0&thu=0&trc=0&show_legend=yes&show_axis_titles=yes&show_tooltip=yes&id=MABMM301EZQ189S,MANMM101EZQ189S&scale=left,left&cosd=1970-01-01,1970-01-01&coed=2021-01-01,2021-01-01&line_color=%234572a7,%23aa4643&link_values=false,false&line_style=solid,solid&mark_type=none,none&mw=3,3&lw=2,2&ost=-99999,-99999&oet=99999,99999&mma=0,0&fml=a,a&fq=Quarterly,Quarterly&fam=avg,avg&fgst=lin,lin&fgsnd=2020-02-01,2020-02-01&line_index=1,2&transformation=lin,lin&vintage_date=2021-06-07,2021-06-07&revision_date=2021-06-07,2021-06-07&nd=1970-01-01,1970-01-01" - ua = UserAgent(verify_ssl=False) - request_header = {"User-Agent": ua.random} - r = requests.get(tmp_url, headers=request_header) - data_text = r.content - df = pd.read_csv(io.StringIO(data_text.decode('utf-8'))) - df["DATE"] = pd.to_datetime(df["DATE"], format="%Y-%m-%d") - #df = df[list(df.columns[1:])].replace(".", np.nan).astype(float) - name_list = { - 'MABMM301EZQ189S': "M3 for the Euro Area", - 'MANMM101EZQ189S': "M1 for the Euro Area" - } - description = "Monetary aggregates and their components, Quarterly, Seasonally Adjusted" - return df, name_list, description - - -def Currency_Conversion_Quarterly(): - tmp_url = url["fred_econ"] + "bgcolor=%23e1e9f0&chart_type=line&drp=0&fo=open%20sans&graph_bgcolor=%23ffffff&height=450&mode=fred&recession_bars=off&txtcolor=%23444444&ts=12&tts=12&width=1168&nt=0&thu=0&trc=0&show_legend=yes&show_axis_titles=yes&show_tooltip=yes&id=CCEUSP02EZQ655N,CCUSMA02EZQ618N,CCUSSP01EZQ650N,CCRETT02EZQ661N,CCRETT01EZQ661N&scale=left,left,left,left,left&cosd=1999-01-01,1979-01-01,1999-01-01,1970-01-01,1970-01-01&coed=2021-01-01,2021-01-01,2021-01-01,2021-01-01,2021-01-01&line_color=%234572a7,%23aa4643,%2389a54e,%2380699b,%233d96ae&link_values=false,false,false,false,false&line_style=solid,solid,solid,solid,solid&mark_type=none,none,none,none,none&mw=3,3,3,3,3&lw=2,2,2,2,2&ost=-99999,-99999,-99999,-99999,-99999&oet=99999,99999,99999,99999,99999&mma=0,0,0,0,0&fml=a,a,a,a,a&fq=Quarterly,Quarterly,Quarterly,Quarterly,Quarterly&fam=avg,avg,avg,avg,avg&fgst=lin,lin,lin,lin,lin&fgsnd=2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01&line_index=1,2,3,4,5&transformation=lin,lin,lin,lin,lin&vintage_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07&revision_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07&nd=1999-01-01,1979-01-01,1999-01-01,1970-01-01,1970-01-01" - ua = UserAgent(verify_ssl=False) - request_header = {"User-Agent": ua.random} - r = requests.get(tmp_url, headers=request_header) - data_text = r.content - df = pd.read_csv(io.StringIO(data_text.decode('utf-8'))) - df["DATE"] = pd.to_datetime(df["DATE"], format="%Y-%m-%d") - #df = df[list(df.columns[1:])].replace(".", np.nan).astype(float) - name_list = { - 'CCEUSP02EZQ655N': "National Currency to Euro Spot Exchange Rate for the Euro Area", - 'CCUSMA02EZQ618N': "National Currency to US Dollar Exchange Rate: Average of Daily Rates for the Euro Area", - 'CCUSSP01EZQ650N': "US Dollar to National Currency Spot Exchange Rate for the Euro Area", - 'CCRETT02EZQ661N': "Real Effective Exchange Rates Based on Manufacturing Unit Labor Cost for the Euro Area", - 'CCRETT01EZQ661N': "Real Effective Exchange Rates Based on Manufacturing Consumer Price Index for the Euro Area"} - description = "Currency Conversions, Quarterly, Not Seasonally Adjusted" - return df, name_list, description - - -def Currency_Conversion_Monthly(): - tmp_url = url["fred_econ"] + "bgcolor=%23e1e9f0&chart_type=line&drp=0&fo=open%20sans&graph_bgcolor=%23ffffff&height=450&mode=fred&recession_bars=off&txtcolor=%23444444&ts=12&tts=12&width=1168&nt=0&thu=0&trc=0&show_legend=yes&show_axis_titles=yes&show_tooltip=yes&id=CCRETT01EZM661N,CCUSMA02EZM659N,CCUSSP01EZM650N,CCEUSP02EZM655N&scale=left,left,left,left&cosd=1970-01-01,1991-01-01,1999-01-01,1999-01-01&coed=2021-04-01,2021-04-01,2021-03-01,2021-03-01&line_color=%234572a7,%23aa4643,%2389a54e,%2380699b&link_values=false,false,false,false&line_style=solid,solid,solid,solid&mark_type=none,none,none,none&mw=3,3,3,3&lw=2,2,2,2&ost=-99999,-99999,-99999,-99999&oet=99999,99999,99999,99999&mma=0,0,0,0&fml=a,a,a,a&fq=Monthly,Monthly,Monthly,Monthly&fam=avg,avg,avg,avg&fgst=lin,lin,lin,lin&fgsnd=2020-02-01,2020-02-01,2020-02-01,2020-02-01&line_index=1,2,3,4&transformation=lin,lin,lin,lin&vintage_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07&revision_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07&nd=1970-01-01,1991-01-01,1999-01-01,1999-01-01" - ua = UserAgent(verify_ssl=False) - request_header = {"User-Agent": ua.random} - r = requests.get(tmp_url, headers=request_header) - data_text = r.content - df = pd.read_csv(io.StringIO(data_text.decode('utf-8'))) - df["DATE"] = pd.to_datetime(df["DATE"], format="%Y-%m-%d") - #df = df[list(df.columns[1:])].replace(".", np.nan).astype(float) - name_list = { - 'CCRETT01EZM661N': "Real Effective Exchange Rates Based on Manufacturing Consumer Price Index for the Euro Area", - 'CCUSMA02EZM659N': "National Currency to US Dollar Exchange Rate: Average of Daily Rates for the Euro Area", - 'CCUSSP01EZM650N': "US Dollar to National Currency Spot Exchange Rate for the Euro Area", - 'CCEUSP02EZM655N': "National Currency to Euro Spot Exchange Rate for the Euro Area"} - description = "Currency Conversions, Monthly, Not Seasonally Adjusted" - return df, name_list, description - - -def Interest_Rates_Quarterly(): - tmp_url = url["fred_econ"] + "bgcolor=%23e1e9f0&chart_type=line&drp=0&fo=open%20sans&graph_bgcolor=%23ffffff&height=450&mode=fred&recession_bars=off&txtcolor=%23444444&ts=12&tts=12&width=1168&nt=0&thu=0&trc=0&show_legend=yes&show_axis_titles=yes&show_tooltip=yes&id=IRLTLT01EZQ156N,IR3TIB01EZQ156N,IRSTCI01EZQ156N&scale=left,left,left&cosd=1970-01-01,1994-01-01,1994-01-01&coed=2021-01-01,2021-01-01,2021-01-01&line_color=%234572a7,%23aa4643,%2389a54e&link_values=false,false,false&line_style=solid,solid,solid&mark_type=none,none,none&mw=3,3,3&lw=2,2,2&ost=-99999,-99999,-99999&oet=99999,99999,99999&mma=0,0,0&fml=a,a,a&fq=Quarterly,Quarterly,Quarterly&fam=avg,avg,avg&fgst=lin,lin,lin&fgsnd=2020-02-01,2020-02-01,2020-02-01&line_index=1,2,3&transformation=lin,lin,lin&vintage_date=2021-06-07,2021-06-07,2021-06-07&revision_date=2021-06-07,2021-06-07,2021-06-07&nd=1970-01-01,1994-01-01,1994-01-01" - ua = UserAgent(verify_ssl=False) - request_header = {"User-Agent": ua.random} - r = requests.get(tmp_url, headers=request_header) - data_text = r.content - df = pd.read_csv(io.StringIO(data_text.decode('utf-8'))) - df["DATE"] = pd.to_datetime(df["DATE"], format="%Y-%m-%d") - #df = df[list(df.columns[1:])].replace(".", np.nan).astype(float) - name_list = { - 'IRLTLT01EZQ156N': "Long-Term Government Bond Yields: 10-year: Main (Including Benchmark) for the Euro Area", - 'IR3TIB01EZQ156N': "3-Month or 90-day Rates and Yields: Interbank Rates for the Euro Area", - 'IRSTCI01EZQ156N': "Immediate Rates: Less than 24 Hours: Call Money/Interbank Rate for the Euro Area"} - description = "Interest Rates, Quarterly, Not Seasonally Adjusted" - return df, name_list, description - - -def Interest_Rates_Monthly(): - tmp_url = url["fred_econ"] + "bgcolor=%23e1e9f0&chart_type=line&drp=0&fo=open%20sans&graph_bgcolor=%23ffffff&height=450&mode=fred&recession_bars=off&txtcolor=%23444444&ts=12&tts=12&width=1168&nt=0&thu=0&trc=0&show_legend=yes&show_axis_titles=yes&show_tooltip=yes&id=IRLTLT01EZM156N,IR3TIB01EZM156N,IRSTCI01EZM156N&scale=left,left,left&cosd=1970-01-01,1994-01-01,1994-01-01&coed=2021-04-01,2021-04-01,2021-04-01&line_color=%234572a7,%23aa4643,%2389a54e&link_values=false,false,false&line_style=solid,solid,solid&mark_type=none,none,none&mw=3,3,3&lw=2,2,2&ost=-99999,-99999,-99999&oet=99999,99999,99999&mma=0,0,0&fml=a,a,a&fq=Monthly,Monthly,Monthly&fam=avg,avg,avg&fgst=lin,lin,lin&fgsnd=2020-02-01,2020-02-01,2020-02-01&line_index=1,2,3&transformation=lin,lin,lin&vintage_date=2021-06-07,2021-06-07,2021-06-07&revision_date=2021-06-07,2021-06-07,2021-06-07&nd=1970-01-01,1994-01-01,1994-01-01" - ua = UserAgent(verify_ssl=False) - request_header = {"User-Agent": ua.random} - r = requests.get(tmp_url, headers=request_header) - data_text = r.content - df = pd.read_csv(io.StringIO(data_text.decode('utf-8'))) - df["DATE"] = pd.to_datetime(df["DATE"], format="%Y-%m-%d") - #df = df[list(df.columns[1:])].replace(".", np.nan).astype(float) - name_list = { - 'IRLTLT01EZM156N': "Long-Term Government Bond Yields: 10-year: Main (Including Benchmark) for the Euro Area", - 'IR3TIB01EZM156N': "3-Month or 90-day Rates and Yields: Interbank Rates for the Euro Area", - 'IRSTCI01EZM156N': "Immediate Rates: Less than 24 Hours: Call Money/Interbank Rate for the Euro Area"} - description = "Interest Rates, Monthly, Not Seasonally Adjusted" - return df, name_list, description - - -def Share_Prices_Quarterly(): - tmp_url = url["fred_econ"] + "bgcolor=%23e1e9f0&chart_type=line&drp=0&fo=open%20sans&graph_bgcolor=%23ffffff&height=450&mode=fred&recession_bars=off&txtcolor=%23444444&ts=12&tts=12&width=1168&nt=0&thu=0&trc=0&show_legend=yes&show_axis_titles=yes&show_tooltip=yes&id=SPASTT01EZQ661N&scale=left&cosd=1987-01-01&coed=2021-01-01&line_color=%234572a7&link_values=false&line_style=solid&mark_type=none&mw=3&lw=2&ost=-99999&oet=99999&mma=0&fml=a&fq=Quarterly&fam=avg&fgst=lin&fgsnd=2020-02-01&line_index=1&transformation=lin&vintage_date=2021-06-07&revision_date=2021-06-07&nd=1987-01-01" - ua = UserAgent(verify_ssl=False) - request_header = {"User-Agent": ua.random} - r = requests.get(tmp_url, headers=request_header) - data_text = r.content - df = pd.read_csv(io.StringIO(data_text.decode('utf-8'))) - df["DATE"] = pd.to_datetime(df["DATE"], format="%Y-%m-%d") - #df = df[list(df.columns[1:])].replace(".", np.nan).astype(float) - name_list = { - 'SPASTT01EZQ661N': "Total Share Prices for All Shares for the Euro Area"} - description = "Share Prices, Quarterly, Not Seasonally Adjusted" - return df, name_list, description - - -def Share_Prices_Monthly(): - tmp_url = url["fred_econ"] + "bgcolor=%23e1e9f0&chart_type=line&drp=0&fo=open%20sans&graph_bgcolor=%23ffffff&height=450&mode=fred&recession_bars=off&txtcolor=%23444444&ts=12&tts=12&width=1168&nt=0&thu=0&trc=0&show_legend=yes&show_axis_titles=yes&show_tooltip=yes&id=SPASTT01EZM661N&scale=left&cosd=1986-12-01&coed=2021-04-01&line_color=%234572a7&link_values=false&line_style=solid&mark_type=none&mw=3&lw=2&ost=-99999&oet=99999&mma=0&fml=a&fq=Monthly&fam=avg&fgst=lin&fgsnd=2020-02-01&line_index=1&transformation=lin&vintage_date=2021-06-07&revision_date=2021-06-07&nd=1986-12-01" - ua = UserAgent(verify_ssl=False) - request_header = {"User-Agent": ua.random} - r = requests.get(tmp_url, headers=request_header) - data_text = r.content - df = pd.read_csv(io.StringIO(data_text.decode('utf-8'))) - df["DATE"] = pd.to_datetime(df["DATE"], format="%Y-%m-%d") - #df = df[list(df.columns[1:])].replace(".", np.nan).astype(float) - name_list = { - 'SPASTT01EZM661N': "Total Share Prices for All Shares for the Euro Area"} - description = "Share Prices, Monthly, Not Seasonally Adjusted" - return df, name_list, description - - -def CPI_Monthly(startdate="1970-01-01", enddate="2021-01-01"): - """ - """ - tmp_url = url["fred_econ"] + "bgcolor=%23e1e9f0&chart_type=line&drp=0&fo=open%20sans&graph_bgcolor=%23ffffff&height=450&mode=fred&recession_bars=off&txtcolor=%23444444&ts=12&tts=12&width=748&nt=0&thu=0&trc=0&show_legend=yes&show_axis_titles=yes&show_tooltip=yes&id=CPHPTT01EZM661N,EA19CPHP0401IXOBM,EA19CPHP0403IXOBM,EA19CPHP0404IXOBM,EA19CPHP0405IXOBM,EA19CPHP0500IXOBM,EA19CPHP0600IXOBM,EA19CPHP0700IXOBM,EA19CPHP0702IXOBM,EA19CPHP0800IXOBM,EA19CPHP0900IXOBM,CPHPEN01EZM661N&scale=left,left,left,left,left,left,left,left,left,left,left,left&cosd=1990-01-01,1996-01-01,1996-01-01,1996-01-01,1996-01-01,1996-01-01,1996-01-01,1996-01-01,1996-01-01,1996-01-01,1996-01-01,1996-01-01&coed=2021-03-01,2021-03-01,2021-03-01,2021-03-01,2021-03-01,2021-03-01,2021-03-01,2021-03-01,2021-03-01,2021-03-01,2021-03-01,2021-03-01&line_color=%234572a7,%23aa4643,%2389a54e,%2380699b,%233d96ae,%23db843d,%2392a8cd,%23a47d7c,%23b5ca92,%2391e8e1,%238d4653,%238085e8&link_values=false,false,false,false,false,false,false,false,false,false,false,false&line_style=solid,solid,solid,solid,solid,solid,solid,solid,solid,solid,solid,solid&mark_type=none,none,none,none,none,none,none,none,none,none,none,none&mw=3,3,3,3,3,3,3,3,3,3,3,3&lw=2,2,2,2,2,2,2,2,2,2,2,2&ost=-99999,-99999,-99999,-99999,-99999,-99999,-99999,-99999,-99999,-99999,-99999,-99999&oet=99999,99999,99999,99999,99999,99999,99999,99999,99999,99999,99999,99999&mma=0,0,0,0,0,0,0,0,0,0,0,0&fml=a,a,a,a,a,a,a,a,a,a,a,a&fq=Monthly,Monthly,Monthly,Monthly,Monthly,Monthly,Monthly,Monthly,Monthly,Monthly,Monthly,Monthly&fam=avg,avg,avg,avg,avg,avg,avg,avg,avg,avg,avg,avg&fgst=lin,lin,lin,lin,lin,lin,lin,lin,lin,lin,lin,lin&fgsnd=2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01&line_index=1,2,3,4,5,6,7,8,9,10,11,12&transformation=lin,lin,lin,lin,lin,lin,lin,lin,lin,lin,lin,lin&vintage_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07&revision_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07&nd=1990-01-01,1996-01-01,1996-01-01,1996-01-01,1996-01-01,1996-01-01,1996-01-01,1996-01-01,1996-01-01,1996-01-01,1996-01-01,1996-01-01" - ua = UserAgent(verify_ssl=False) - request_header = {"User-Agent": ua.random} - r = requests.get(tmp_url, headers=request_header) - data_text = r.content - df = pd.read_csv(io.StringIO(data_text.decode('utf-8'))) - df["DATE"] = pd.to_datetime(df["DATE"], format="%Y-%m-%d") - #df = df[list(df.columns[1:])].replace(".", np.nan).astype(float) - name_list = { - "CPHPTT01EZM661N": "CPI:Harmonized Prices: Total All Items for the Euro Area", - "EA19CPHP0401IXOBM": "CPI:Harmonised_Price:Housing, water, electricity, gas and other fuels (COICOP 04): Actual rentals for housing for the Euro Area", - "EA19CPHP0403IXOBM": "CPI:Harmonised_Price:Housing, water, electricity, gas and other fuels (COICOP 04): Maintenance & repairs of the dwellings for the Euro Area", - "EA19CPHP0404IXOBM": "CPI:Harmonised_Price:Housing, water, electricity, gas and other fuels (COICOP 04): Water supply and miscellaneous services relating to the dwelling for the Euro Area", - "EA19CPHP0405IXOBM": "CPI:Harmonised_Price:Housing, water, electricity, gas and other fuels (COICOP 04): Electricity, gas and other fuels for the Euro Area", - "EA19CPHP0500IXOBM": "CPI:Harmonised_Price:Furnishings, household equip. and routine household maintenance (COICOP 05): Total for the Euro Area ", - "EA19CPHP0600IXOBM": "CPI:Harmonised_Price:Health (COICOP 06): Total for the Euro Area", - "EA19CPHP0700IXOBM": "CPI:Harmonised_Price:Transport (COICOP 07): Total for the Euro Area", - "EA19CPHP0702IXOBM": "CPI:Harmonised_Price:Transport (COICOP 07): Fuels and lubricants for personal transport equipment for the Euro Area", - "EA19CPHP0800IXOBM": "CPI:Harmonised_Price:Communication (COICOP 08): Total for the Euro Area", - "EA19CPHP0900IXOBM": "CPI:Harmonised_Price:Recreation and culture (COICOP 09): Total for the Euro Area", - "CPHPEN01EZM661N": "CPI:Harmonized Prices: Total Energy for the Euro Area"} - description = "Consumer Price Index, Monthly, Not Seasonally Adjusted" - return df, name_list, description - - -def CPI_Quarterly(): - """ - """ - tmp_url = url["fred_econ"] + "bgcolor=%23e1e9f0&chart_type=line&drp=0&fo=open%20sans&graph_bgcolor=%23ffffff&height=450&mode=fred&recession_bars=off&txtcolor=%23444444&ts=12&tts=12&width=748&nt=0&thu=0&trc=0&show_legend=yes&show_axis_titles=yes&show_tooltip=yes&id=EA19CPALTT01GYQ,EA19CPGRLE01GYQ,EA19CPGREN01GYQ,EA19CPHP0401IXOBQ&scale=left,left,left,left&cosd=1991-01-01,1997-01-01,1997-01-01,1996-01-01&coed=2021-01-01,2021-01-01,2021-01-01,2021-01-01&line_color=%234572a7,%23aa4643,%2389a54e,%2380699b&link_values=false,false,false,false&line_style=solid,solid,solid,solid&mark_type=none,none,none,none&mw=3,3,3,3&lw=2,2,2,2&ost=-99999,-99999,-99999,-99999&oet=99999,99999,99999,99999&mma=0,0,0,0&fml=a,a,a,a&fq=Quarterly,Quarterly,Quarterly,Quarterly&fam=avg,avg,avg,avg&fgst=lin,lin,lin,lin&fgsnd=2020-02-01,2020-02-01,2020-02-01,2020-02-01&line_index=1,2,3,4&transformation=lin,lin,lin,lin&vintage_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07&revision_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07&nd=1991-01-01,1997-01-01,1997-01-01,1996-01-01" - ua = UserAgent(verify_ssl=False) - request_header = {"User-Agent": ua.random} - r = requests.get(tmp_url, headers=request_header) - data_text = r.content - df = pd.read_csv(io.StringIO(data_text.decode('utf-8'))) - df["DATE"] = pd.to_datetime(df["DATE"], format="%Y-%m-%d") - #df = df[list(df.columns[1:])].replace(".", np.nan).astype(float) - name_list = { - 'EA19CPALTT01GYQ': "CPI:All items:Total:Total for the Euro Area", - 'EA19CPGRLE01GYQ': "CPI:OECD Groups:All items non-food non-energy:Total for the Euro Area", - 'EA19CPGREN01GYQ': "CPI:OECD Groups:Energy (Fuel, electricity & gasoline):Total for the Euro Area", - 'EA19CPHP0401IXOBQ': "CPI:Harmonised prices:Housing, water, electricity, gas and other fuels (COICOP 04):Actual rentals for housing for the Euro Area"} - description = "Consumer Price Index, Quarterly, Not Seasonally Adjusted" - return df, name_list, description - - -def PPI_Monthly(): - tmp_url = url["fred_econ"] + "bgcolor=%23e1e9f0&chart_type=line&drp=0&fo=open%20sans&graph_bgcolor=%23ffffff&height=450&mode=fred&recession_bars=off&txtcolor=%23444444&ts=12&tts=12&width=748&nt=0&thu=0&trc=0&show_legend=yes&show_axis_titles=yes&show_tooltip=yes&id=PIEAMP02EZM659N,PIEAMP01EZM661N,PIEATI01EZM661N,PIEATI02EZM661N,PITGND02EZM661N,PITGND01EZM661N,PITGIG01EZM661N,PITGIG02EZM661N,PIEAFD02EZM661N,PITGCG02EZM661N,PITGCG01EZM661N,PITGCD01EZM661N&scale=left,left,left,left,left,left,left,left,left,left,left,left&cosd=1996-01-01,2000-01-01,2000-01-01,2000-01-01,1995-01-01,2000-01-01,2000-01-01,1995-01-01,1995-01-01,1995-01-01,2000-01-01,2000-01-01&coed=2021-03-01,2021-02-01,2021-02-01,2021-03-01,2021-03-01,2021-02-01,2021-02-01,2021-03-01,2021-03-01,2021-03-01,2021-02-01,2021-02-01&line_color=%234572a7,%23aa4643,%2389a54e,%2380699b,%233d96ae,%23db843d,%2392a8cd,%23a47d7c,%23b5ca92,%2391e8e1,%238d4653,%238085e8&link_values=false,false,false,false,false,false,false,false,false,false,false,false&line_style=solid,solid,solid,solid,solid,solid,solid,solid,solid,solid,solid,solid&mark_type=none,none,none,none,none,none,none,none,none,none,none,none&mw=3,3,3,3,3,3,3,3,3,3,3,3&lw=2,2,2,2,2,2,2,2,2,2,2,2&ost=-99999,-99999,-99999,-99999,-99999,-99999,-99999,-99999,-99999,-99999,-99999,-99999&oet=99999,99999,99999,99999,99999,99999,99999,99999,99999,99999,99999,99999&mma=0,0,0,0,0,0,0,0,0,0,0,0&fml=a,a,a,a,a,a,a,a,a,a,a,a&fq=Monthly,Monthly,Monthly,Monthly,Monthly,Monthly,Monthly,Monthly,Monthly,Monthly,Monthly,Monthly&fam=avg,avg,avg,avg,avg,avg,avg,avg,avg,avg,avg,avg&fgst=lin,lin,lin,lin,lin,lin,lin,lin,lin,lin,lin,lin&fgsnd=2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01&line_index=1,2,3,4,5,6,7,8,9,10,11,12&transformation=lin,lin,lin,lin,lin,lin,lin,lin,lin,lin,lin,lin&vintage_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07&revision_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07&nd=1996-01-01,2000-01-01,2000-01-01,2000-01-01,1995-01-01,2000-01-01,2000-01-01,1995-01-01,1995-01-01,1995-01-01,2000-01-01,2000-01-01" - ua = UserAgent(verify_ssl=False) - request_header = {"User-Agent": ua.random} - r = requests.get(tmp_url, headers=request_header) - data_text = r.content - df = pd.read_csv(io.StringIO(data_text.decode('utf-8'))) - df["DATE"] = pd.to_datetime(df["DATE"], format="%Y-%m-%d") - #df = df[list(df.columns[1:])].replace(".", np.nan).astype(float) - name_list = { - 'PIEAMP02EZM659N': "Producer Prices Index: Economic Activities: Domestic Manufacturing for the Euro Area", - "PIEAMP01EZM661N": "Producer Prices Index: Economic Activities: Total Manufacturing for the Euro Area", - "PIEATI01EZM661N": "Producer Prices Index: Economic Activities: Total Industrial Activities for the Euro Area", - "PIEATI02EZM661N": "Producer Prices Index: Economic Activities: Domestic Industrial Activities for the Euro Area", - "PITGND02EZM661N": "Producer Prices Index: Domestic Nondurable Consumer Goods for the Euro Area", - "PITGND01EZM661N": "Producer Prices Index: Total Nondurable Consumer Goods for the Euro Area", - "PITGIG01EZM661N": "Producer Prices Index: Total Intermediate Goods for the Euro Area", - "PITGIG02EZM661N": "Producer Prices Index: Domestic Intermediate Goods for the Euro Area", - "PIEAFD02EZM661N": "Producer Prices Index: Economic Activities: Domestic Manufacture of Food Products for the Euro Area", - "PITGCG02EZM661N": "Producer Prices Index: Domestic Consumer Goods for the Euro Area", - "PITGCG01EZM661N": "Producer Prices Index: Total Consumer Goods for the Euro Area", - "PITGCD01EZM661N": "Producer Prices Index: Total Durable Consumer Goods for the Euro Area"} - description = "Producer Prices Index, Monthly, Not Seasonally Adjusted" - return df, name_list, description - - -def PPI_Quarterly(): - tmp_url = url["fred_econ"] + "bgcolor=%23e1e9f0&chart_type=line&drp=0&fo=open%20sans&graph_bgcolor=%23ffffff&height=450&mode=fred&recession_bars=off&txtcolor=%23444444&ts=12&tts=12&width=748&nt=0&thu=0&trc=0&show_legend=yes&show_axis_titles=yes&show_tooltip=yes&id=PIEAFD01EZQ661N,PIEAEN02EZQ661N,PIEAEN01EZQ661N,PITGND02EZQ661N,PITGND01EZQ661N,PITGIG01EZQ661N,PITGIG02EZQ661N,PIEAFD02EZQ661N,PITGCD02EZQ661N,PITGCD01EZQ661N,PITGVG01EZQ661N,PITGVG02EZQ661N&scale=left,left,left,left,left,left,left,left,left,left,left,left&cosd=2000-01-01,2000-01-01,2000-01-01,1995-01-01,2000-01-01,2000-01-01,1995-01-01,1995-01-01,2000-01-01,2000-01-01,2000-01-01,1995-01-01&coed=2020-10-01,2021-01-01,2020-10-01,2021-01-01,2020-10-01,2020-10-01,2021-01-01,2021-01-01,2021-01-01,2020-10-01,2020-10-01,2021-01-01&line_color=%234572a7,%23aa4643,%2389a54e,%2380699b,%233d96ae,%23db843d,%2392a8cd,%23a47d7c,%23b5ca92,%2391e8e1,%238d4653,%238085e8&link_values=false,false,false,false,false,false,false,false,false,false,false,false&line_style=solid,solid,solid,solid,solid,solid,solid,solid,solid,solid,solid,solid&mark_type=none,none,none,none,none,none,none,none,none,none,none,none&mw=3,3,3,3,3,3,3,3,3,3,3,3&lw=2,2,2,2,2,2,2,2,2,2,2,2&ost=-99999,-99999,-99999,-99999,-99999,-99999,-99999,-99999,-99999,-99999,-99999,-99999&oet=99999,99999,99999,99999,99999,99999,99999,99999,99999,99999,99999,99999&mma=0,0,0,0,0,0,0,0,0,0,0,0&fml=a,a,a,a,a,a,a,a,a,a,a,a&fq=Quarterly,Quarterly,Quarterly,Quarterly,Quarterly,Quarterly,Quarterly,Quarterly,Quarterly,Quarterly,Quarterly,Quarterly&fam=avg,avg,avg,avg,avg,avg,avg,avg,avg,avg,avg,avg&fgst=lin,lin,lin,lin,lin,lin,lin,lin,lin,lin,lin,lin&fgsnd=2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01&line_index=1,2,3,4,5,6,7,8,9,10,11,12&transformation=lin,lin,lin,lin,lin,lin,lin,lin,lin,lin,lin,lin&vintage_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07&revision_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07&nd=2000-01-01,2000-01-01,2000-01-01,1995-01-01,2000-01-01,2000-01-01,1995-01-01,1995-01-01,2000-01-01,2000-01-01,2000-01-01,1995-01-01" - ua = UserAgent(verify_ssl=False) - request_header = {"User-Agent": ua.random} - r = requests.get(tmp_url, headers=request_header) - data_text = r.content - df = pd.read_csv(io.StringIO(data_text.decode('utf-8'))) - df["DATE"] = pd.to_datetime(df["DATE"], format="%Y-%m-%d") - #df = df[list(df.columns[1:])].replace(".", np.nan).astype(float) - name_list = { - 'PIEAFD01EZQ661N': "Producer Prices Index: Economic Activities: Total Manufacture of Food Products for the Euro Area", - "PIEAEN02EZQ661N": "Producer Prices Index: Economic Activities: Domestic Energy for the Euro Area", - "PIEAEN01EZQ661N": "Producer Prices Index: Economic Activities: Total Energy for the Euro Area", - "PITGND02EZQ661N": "Producer Prices Index: Domestic Nondurable Consumer Goods for the Euro Area", - "PITGND01EZQ661N": "Producer Prices Index: Total Nondurable Consumer Goods for the Euro Area", - "PITGIG01EZQ661N": "Producer Prices Index: Total Intermediate Goods for the Euro Area", - "PITGIG02EZQ661N": "Producer Prices Index: Domestic Intermediate Goods for the Euro Area", - "PIEAFD02EZQ661N": "Producer Prices Index: Economic Activities: Domestic Manufacture of Food Products for the Euro Area", - "PITGCD02EZQ661N": "Producer Prices Index: Domestic Durable Consumer Goods for the Euro Area", - "PITGCD01EZQ661N": "Producer Prices Index: Total Durable Consumer Goods for the Euro Area", - "PITGVG01EZQ661N": "Producer Prices Index: Investments Goods: Total for the Euro Area", - "PITGVG02EZQ661N": "Producer Prices Index: Domestic Investments Goods for the Euro Area"} - description = "Producer Prices Index, Quarterly, Not Seasonally Adjusted" - return df, name_list, description - - -def Business_Tendency_Surveys_Construction(): - tmp_url = url["fred_econ"] + "bgcolor=%23e1e9f0&chart_type=line&drp=0&fo=open%20sans&graph_bgcolor=%23ffffff&height=450&mode=fred&recession_bars=off&txtcolor=%23444444&ts=12&tts=12&width=748&nt=0&thu=0&trc=0&show_legend=yes&show_axis_titles=yes&show_tooltip=yes&id=EA19BCBUTE02STSAM,BCOBLV02EZM460S,BCEMFT02EZM460S,BCCICP02EZM460S,BCSPFT02EZM460S&scale=left,left,left,left,left&cosd=1985-01-01,1985-01-01,1985-01-01,1985-01-01,1985-01-01&coed=2021-04-01,2021-04-01,2021-04-01,2021-04-01,2021-04-01&line_color=%234572a7,%23aa4643,%2389a54e,%2380699b,%233d96ae&link_values=false,false,false,false,false&line_style=solid,solid,solid,solid,solid&mark_type=none,none,none,none,none&mw=3,3,3,3,3&lw=2,2,2,2,2&ost=-99999,-99999,-99999,-99999,-99999&oet=99999,99999,99999,99999,99999&mma=0,0,0,0,0&fml=a,a,a,a,a&fq=Monthly,Monthly,Monthly,Monthly,Monthly&fam=avg,avg,avg,avg,avg&fgst=lin,lin,lin,lin,lin&fgsnd=2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01&line_index=1,2,3,4,5&transformation=lin,lin,lin,lin,lin&vintage_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07&revision_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07&nd=1985-01-01,1985-01-01,1985-01-01,1985-01-01,1985-01-01" - ua = UserAgent(verify_ssl=False) - request_header = {"User-Agent": ua.random} - r = requests.get(tmp_url, headers=request_header) - data_text = r.content - df = pd.read_csv(io.StringIO(data_text.decode('utf-8'))) - df["DATE"] = pd.to_datetime(df["DATE"], format="%Y-%m-%d") - #df = df[list(df.columns[1:])].replace(".", np.nan).astype(float) - name_list = { - 'EA19BCBUTE02STSAM': "Business tendency surveys (construction): Business situation - Activity: Tendency: National indicator for the Euro Area", - 'BCOBLV02EZM460S': "Business Tendency Surveys for Construction: Order Books: Level: European Commission Indicator for the Euro Area", - 'BCEMFT02EZM460S': "Business Tendency Surveys for Construction: Employment: Future Tendency: European Commission and National Indicators for the Euro Area", - 'BCCICP02EZM460S': "Business Tendency Surveys for Construction: Confidence Indicators: Composite Indicators: European Commission and National Indicators for the Euro Area", - 'BCSPFT02EZM460S': "Business Tendency Surveys for Construction: Selling Prices: Future Tendency: European Commission Indicator for the Euro Area"} - description = "Business tendency surveys (construction), Monthly, Seasonally Adjusted" - return df, name_list, description - - -def Business_Tendency_Surveys_Services(): - tmp_url = url["fred_econ"] + "bgcolor=%23e1e9f0&chart_type=line&drp=0&fo=open%20sans&graph_bgcolor=%23ffffff&height=450&mode=fred&recession_bars=off&txtcolor=%23444444&ts=12&tts=12&width=748&nt=0&thu=0&trc=0&show_legend=yes&show_axis_titles=yes&show_tooltip=yes&id=EA19BVBUTE02STSAM,BVCICP02EZM460S,BVEMTE02EZM460S,BVEMFT02EZM460S,BVDEFT02EZM460S,BVDETE02EZM460S&scale=left,left,left,left,left,left&cosd=1995-04-01,1995-04-01,1995-04-01,1996-10-01,1995-04-01,1995-04-01&coed=2021-04-01,2021-04-01,2021-04-01,2021-04-01,2021-04-01,2021-04-01&line_color=%234572a7,%23aa4643,%2389a54e,%2380699b,%233d96ae,%23db843d&link_values=false,false,false,false,false,false&line_style=solid,solid,solid,solid,solid,solid&mark_type=none,none,none,none,none,none&mw=3,3,3,3,3,3&lw=2,2,2,2,2,2&ost=-99999,-99999,-99999,-99999,-99999,-99999&oet=99999,99999,99999,99999,99999,99999&mma=0,0,0,0,0,0&fml=a,a,a,a,a,a&fq=Monthly,Monthly,Monthly,Monthly,Monthly,Monthly&fam=avg,avg,avg,avg,avg,avg&fgst=lin,lin,lin,lin,lin,lin&fgsnd=2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01&line_index=1,2,3,4,5,6&transformation=lin,lin,lin,lin,lin,lin&vintage_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07&revision_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07&nd=1995-04-01,1995-04-01,1995-04-01,1996-10-01,1995-04-01,1995-04-01" - ua = UserAgent(verify_ssl=False) - request_header = {"User-Agent": ua.random} - r = requests.get(tmp_url, headers=request_header) - data_text = r.content - df = pd.read_csv(io.StringIO(data_text.decode('utf-8'))) - df["DATE"] = pd.to_datetime(df["DATE"], format="%Y-%m-%d") - #df = df[list(df.columns[1:])].replace(".", np.nan).astype(float) - name_list = { - 'EA19BVBUTE02STSAM': "Business tendency surveys (services): Business situation - Activity: Tendency: National indicator for Euro Area", - 'BVCICP02EZM460S': "Business Tendency Surveys for Services: Confidence Indicators: Composite Indicators: European Commission and National Indicators for the Euro Area", - 'BVEMTE02EZM460S': "Business Tendency Surveys for Services: Employment: Tendency: European Commission Indicator for the Euro Area", - 'BVEMFT02EZM460S': "Business Tendency Surveys for Services: Employment: Future Tendency: European Commission and National Indicators for the Euro Area", - 'BVDEFT02EZM460S': "Business Tendency Surveys for Services: Demand Evolution: Future Tendency: European Commission Indicator for the Euro Area", - 'BVDETE02EZM460S': "Business Tendency Surveys for Services: Demand Evolution: Tendency: European Commission Indicator for the Euro Area"} - description = "Business tendency surveys (services), Monthly, Seasonally Adjusted" - return df, name_list, description - - -def Business_Tendency_Surveys_Manufacturing_Quarterly(): - tmp_url = url["fred_econ"] + "bgcolor=%23e1e9f0&chart_type=line&drp=0&fo=open%20sans&graph_bgcolor=%23ffffff&height=450&mode=fred&recession_bars=off&txtcolor=%23444444&ts=12&tts=12&width=748&nt=0&thu=0&trc=0&show_legend=yes&show_axis_titles=yes&show_tooltip=yes&id=BSCURT02EZQ160S,BSOITE02EZQ460S&scale=left,left&cosd=1985-01-01,1985-01-01&coed=2021-04-01,2021-04-01&line_color=%234572a7,%23aa4643&link_values=false,false&line_style=solid,solid&mark_type=none,none&mw=3,3&lw=2,2&ost=-99999,-99999&oet=99999,99999&mma=0,0&fml=a,a&fq=Quarterly,Quarterly&fam=avg,avg&fgst=lin,lin&fgsnd=2020-02-01,2020-02-01&line_index=1,2&transformation=lin,lin&vintage_date=2021-06-07,2021-06-07&revision_date=2021-06-07,2021-06-07&nd=1985-01-01,1985-01-01" - ua = UserAgent(verify_ssl=False) - request_header = {"User-Agent": ua.random} - r = requests.get(tmp_url, headers=request_header) - data_text = r.content - df = pd.read_csv(io.StringIO(data_text.decode('utf-8'))) - df["DATE"] = pd.to_datetime(df["DATE"], format="%Y-%m-%d") - #df = df[list(df.columns[1:])].replace(".", np.nan).astype(float) - name_list = { - 'BSCURT02EZQ160S': "Business Tendency Surveys for Manufacturing: Capacity Utilization: Rate of Capacity Utilization: European Commission and National Indicators for the Euro Area", - 'BSOITE02EZQ460S': "Business Tendency Surveys for Manufacturing: Orders Inflow: Tendency: European Commission Indicator for the Euro Area"} - description = "Business tendency surveys (manufacturing), Quarterly, Seasonally Adjusted" - return df, name_list, description - - -def Business_Tendency_Surveys_Manufacturing_Monthly(): - tmp_url = url["fred_econ"] + "bgcolor=%23e1e9f0&chart_type=line&drp=0&fo=open%20sans&graph_bgcolor=%23ffffff&height=450&mode=fred&recession_bars=off&txtcolor=%23444444&ts=12&tts=12&width=748&nt=0&thu=0&trc=0&show_legend=yes&show_axis_titles=yes&show_tooltip=yes&id=BSSPFT02EZM460S,BSOBLV02EZM460S,BSEMFT02EZM460S,BSFGLV02EZM460S,BSXRLV02EZM086S,BSCICP02EZM460S,BSPRTE02EZM460S,BSPRFT02EZM460S&scale=left,left,left,left,left,left,left,left&cosd=1985-01-01,1985-01-01,1985-01-01,1985-01-01,1985-01-01,1985-01-01,1985-01-01,1985-01-01&coed=2021-04-01,2021-04-01,2021-04-01,2021-04-01,2021-04-01,2021-04-01,2021-04-01,2021-04-01&line_color=%234572a7,%23aa4643,%2389a54e,%2380699b,%233d96ae,%23db843d,%2392a8cd,%23a47d7c&link_values=false,false,false,false,false,false,false,false&line_style=solid,solid,solid,solid,solid,solid,solid,solid&mark_type=none,none,none,none,none,none,none,none&mw=3,3,3,3,3,3,3,3&lw=2,2,2,2,2,2,2,2&ost=-99999,-99999,-99999,-99999,-99999,-99999,-99999,-99999&oet=99999,99999,99999,99999,99999,99999,99999,99999&mma=0,0,0,0,0,0,0,0&fml=a,a,a,a,a,a,a,a&fq=Monthly,Monthly,Monthly,Monthly,Monthly,Monthly,Monthly,Monthly&fam=avg,avg,avg,avg,avg,avg,avg,avg&fgst=lin,lin,lin,lin,lin,lin,lin,lin&fgsnd=2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01&line_index=1,2,3,4,5,6,7,8&transformation=lin,lin,lin,lin,lin,lin,lin,lin&vintage_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07&revision_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07&nd=1985-01-01,1985-01-01,1985-01-01,1985-01-01,1985-01-01,1985-01-01,1985-01-01,1985-01-01" - ua = UserAgent(verify_ssl=False) - request_header = {"User-Agent": ua.random} - r = requests.get(tmp_url, headers=request_header) - data_text = r.content - df = pd.read_csv(io.StringIO(data_text.decode('utf-8'))) - df["DATE"] = pd.to_datetime(df["DATE"], format="%Y-%m-%d") - #df = df[list(df.columns[1:])].replace(".", np.nan).astype(float) - name_list = { - 'BSSPFT02EZM460S': "Business Tendency Surveys for Manufacturing: Selling Prices: Future Tendency: European Commission Indicator for the Euro Area", - 'BSOBLV02EZM460S': "Business Tendency Surveys for Manufacturing: Order Books: Level: European Commission and National Indicators for the Euro Area", - 'BSEMFT02EZM460S': "Business Tendency Surveys for Manufacturing: Employment: Future Tendency: European Commission and National Indicators for the Euro Area", - 'BSFGLV02EZM460S': "Business Tendency Surveys for Manufacturing: Finished Goods Stocks: Level: European Commission and National Indicators for the Euro Area", - 'BSXRLV02EZM086S': "Business Tendency Surveys for Manufacturing: Export Order Books or Demand: Level: European Commission Indicator for the Euro Area", - 'BSCICP02EZM460S': "Business Tendency Surveys for Manufacturing: Confidence Indicators: Composite Indicators: European Commission and National Indicators for the Euro Area", - 'BSPRTE02EZM460S': "Business Tendency Surveys for Manufacturing: Production: Tendency: European Commission and National Indicators for the Euro Area", - 'BSPRFT02EZM460S': "Business Tendency Surveys for Manufacturing: Production: Future Tendency: European Commission and National Indicators for the Euro Area"} - description = "Business tendency surveys (manufacturing), Monthly, Seasonally Adjusted" - return df, name_list, description - - -def Business_Tendency_Surveys_Retail_Trade(): - tmp_url = url["fred_econ"] + "bgcolor=%23e1e9f0&chart_type=line&drp=0&fo=open%20sans&graph_bgcolor=%23ffffff&height=450&mode=fred&recession_bars=off&txtcolor=%23444444&ts=12&tts=12&width=748&nt=0&thu=0&trc=0&show_legend=yes&show_axis_titles=yes&show_tooltip=yes&id=EA19BREMFT02STSAM,EA19BRODFT02STSAM,EA19BRVSLV02STSAM,EA19BRCICP02STSAM,EA19BRBUFT02STSAM,EA19BRBUTE02STSAM&scale=left,left,left,left,left,left&cosd=1985-04-01,1985-02-01,1985-01-01,1985-01-01,1985-01-01,1985-01-01&coed=2021-04-01,2021-04-01,2021-04-01,2021-04-01,2021-04-01,2021-04-01&line_color=%234572a7,%23aa4643,%2389a54e,%2380699b,%233d96ae,%23db843d&link_values=false,false,false,false,false,false&line_style=solid,solid,solid,solid,solid,solid&mark_type=none,none,none,none,none,none&mw=3,3,3,3,3,3&lw=2,2,2,2,2,2&ost=-99999,-99999,-99999,-99999,-99999,-99999&oet=99999,99999,99999,99999,99999,99999&mma=0,0,0,0,0,0&fml=a,a,a,a,a,a&fq=Monthly,Monthly,Monthly,Monthly,Monthly,Monthly&fam=avg,avg,avg,avg,avg,avg&fgst=lin,lin,lin,lin,lin,lin&fgsnd=2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01&line_index=1,2,3,4,5,6&transformation=lin,lin,lin,lin,lin,lin&vintage_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07&revision_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07&nd=1985-04-01,1985-02-01,1985-01-01,1985-01-01,1985-01-01,1985-01-01" - ua = UserAgent(verify_ssl=False) - request_header = {"User-Agent": ua.random} - r = requests.get(tmp_url, headers=request_header) - data_text = r.content - df = pd.read_csv(io.StringIO(data_text.decode('utf-8'))) - df["DATE"] = pd.to_datetime(df["DATE"], format="%Y-%m-%d") - #df = df[list(df.columns[1:])].replace(".", np.nan).astype(float) - name_list = { - 'EA19BREMFT02STSAM': "Business tendency surveys (retail trade): Employment: Future tendency: National indicator for the Euro Area", - 'EA19BRODFT02STSAM': "Business tendency surveys (retail trade): Order intentions or Demand: Future tendency: National indicator for the Euro Area", - 'EA19BRVSLV02STSAM': "Business tendency surveys (retail trade): Volume of stocks: Level: National indicator for the Euro Area", - 'EA19BRCICP02STSAM': "Business tendency surveys (retail trade): Confidence indicators: Composite indicators: National indicator for the Euro Area", - 'EA19BRBUFT02STSAM': "Business tendency surveys (retail trade): Business situation - Activity: Future tendency: National indicator for Euro Area", - 'EA19BRBUTE02STSAM': "Business tendency surveys (retail trade): Business situation - Activity: Tendency: National indicator for Euro Area"} - description = "Business tendency surveys (retail trade), Monthly, Seasonally Adjusted" - return df, name_list, description - - -def Labor_Compoenstiveion_Quarterly_Adj(): - tmp_url = url["fred_econ"] + "bgcolor=%23e1e9f0&chart_type=line&drp=0&fo=open%20sans&graph_bgcolor=%23ffffff&height=450&mode=fred&recession_bars=off&txtcolor=%23444444&ts=12&tts=12&width=748&nt=0&thu=0&trc=0&show_legend=yes&show_axis_titles=yes&show_tooltip=yes&id=LCEAMN01EZQ661S,LCEAPR01EZQ661S&scale=left,left&cosd=1971-01-01,1996-01-01&coed=2020-10-01,2020-10-01&line_color=%234572a7,%23aa4643&link_values=false,false&line_style=solid,solid&mark_type=none,none&mw=3,3&lw=2,2&ost=-99999,-99999&oet=99999,99999&mma=0,0&fml=a,a&fq=Quarterly,Quarterly&fam=avg,avg&fgst=lin,lin&fgsnd=2020-02-01,2020-02-01&line_index=1,2&transformation=lin,lin&vintage_date=2021-06-07,2021-06-07&revision_date=2021-06-07,2021-06-07&nd=1971-01-01,1996-01-01" - ua = UserAgent(verify_ssl=False) - request_header = {"User-Agent": ua.random} - r = requests.get(tmp_url, headers=request_header) - data_text = r.content - df = pd.read_csv(io.StringIO(data_text.decode('utf-8'))) - df["DATE"] = pd.to_datetime(df["DATE"], format="%Y-%m-%d") - #df = df[list(df.columns[1:])].replace(".", np.nan).astype(float) - name_list = { - 'LCEAMN01EZQ661S': "Hourly Earnings: Manufacturing for the Euro Area", - 'LCEAPR01EZQ661S': "Hourly Earnings: Private Sector for the Euro Area" - } - description = "Labor Compensation, Quarterly, Seasonally Adjusted" - return df, name_list, description - - -def Labor_Compoenstiveion_Quarterly_NAdj(): - tmp_url = url["fred_econ"] + "bgcolor=%23e1e9f0&chart_type=line&drp=0&fo=open%20sans&graph_bgcolor=%23ffffff&height=450&mode=fred&recession_bars=off&txtcolor=%23444444&ts=12&tts=12&width=748&nt=0&thu=0&trc=0&show_legend=yes&show_axis_titles=yes&show_tooltip=yes&id=LCEAMN01EZQ661S,LCEAPR01EZQ661S&scale=left,left&cosd=1971-01-01,1996-01-01&coed=2020-10-01,2020-10-01&line_color=%234572a7,%23aa4643&link_values=false,false&line_style=solid,solid&mark_type=none,none&mw=3,3&lw=2,2&ost=-99999,-99999&oet=99999,99999&mma=0,0&fml=a,a&fq=Quarterly,Quarterly&fam=avg,avg&fgst=lin,lin&fgsnd=2020-02-01,2020-02-01&line_index=1,2&transformation=lin,lin&vintage_date=2021-06-07,2021-06-07&revision_date=2021-06-07,2021-06-07&nd=1971-01-01,1996-01-01" - ua = UserAgent(verify_ssl=False) - request_header = {"User-Agent": ua.random} - r = requests.get(tmp_url, headers=request_header) - data_text = r.content - df = pd.read_csv(io.StringIO(data_text.decode('utf-8'))) - df["DATE"] = pd.to_datetime(df["DATE"], format="%Y-%m-%d") - #df = df[list(df.columns[1:])].replace(".", np.nan).astype(float) - name_list = { - 'LCEAMN01EZQ661N': "Hourly Earnings: Manufacturing for the Euro Area", - 'LCEAPR01EZQ661N': "Hourly Earnings: Private Sector for the Euro Area" - } - description = "Labor Compensation, Quarterly, Not Seasonally Adjusted" - return df, name_list, description - - -def Unit_Labor_costs(): - tmp_url = url["fred_econ"] + "bgcolor=%23e1e9f0&chart_type=line&drp=0&fo=open%20sans&graph_bgcolor=%23ffffff&height=450&mode=fred&recession_bars=off&txtcolor=%23444444&ts=12&tts=12&width=748&nt=0&thu=0&trc=0&show_legend=yes&show_axis_titles=yes&show_tooltip=yes&id=ULQECU01EZQ661S,ULQEUL01EZQ659S,ULQELP01EZQ661S&scale=left,left,left&cosd=1995-01-01,1996-01-01,1995-01-01&coed=2020-10-01,2020-10-01,2020-10-01&line_color=%234572a7,%23aa4643,%2389a54e&link_values=false,false,false&line_style=solid,solid,solid&mark_type=none,none,none&mw=3,3,3&lw=2,2,2&ost=-99999,-99999,-99999&oet=99999,99999,99999&mma=0,0,0&fml=a,a,a&fq=Quarterly,Quarterly,Quarterly&fam=avg,avg,avg&fgst=lin,lin,lin&fgsnd=2020-02-01,2020-02-01,2020-02-01&line_index=1,2,3&transformation=lin,lin,lin&vintage_date=2021-06-07,2021-06-07,2021-06-07&revision_date=2021-06-07,2021-06-07,2021-06-07&nd=1995-01-01,1996-01-01,1995-01-01" - ua = UserAgent(verify_ssl=False) - request_header = {"User-Agent": ua.random} - r = requests.get(tmp_url, headers=request_header) - data_text = r.content - df = pd.read_csv(io.StringIO(data_text.decode('utf-8'))) - df["DATE"] = pd.to_datetime(df["DATE"], format="%Y-%m-%d") - #df = df[list(df.columns[1:])].replace(".", np.nan).astype(float) - name_list = { - 'ULQECU01EZQ661S': "Early Estimate of Quarterly ULC Indicators: Total Labor Compensation per Unit of Labor Input for the Euro Area", - 'ULQEUL01EZQ659S': "Early Estimate of Quarterly ULC Indicators: Total for the Euro Area", - 'ULQELP01EZQ661S': "Early Estimate of Quarterly ULC Indicators: Total Labor Productivity for the Euro Area"} - description = "Unit Labor Costs, Quarterly, Seasonally Adjusted" - return df, name_list, description - - -def Labor_Force_Survey_Rates_Quarterly_NAdj(): - tmp_url = url["fred_econ"] + "bgcolor=%23e1e9f0&chart_type=line&drp=0&fo=open%20sans&graph_bgcolor=%23ffffff&height=450&mode=fred&recession_bars=off&txtcolor=%23444444&ts=12&tts=12&width=748&nt=0&thu=0&trc=0&show_legend=yes&show_axis_titles=yes&show_tooltip=yes&id=LRHU24TTEZQ156N,LRHU24FEEZQ156N,LRHU24MAEZQ156N,LRHUADMAEZQ156N,LRHUADTTEZQ156N,LRHUADFEEZQ156N,LRHUTTFEEZQ156N,LRHUTTTTEZQ156N,LRHUTTMAEZQ156N&scale=left,left,left,left,left,left,left,left,left&cosd=1995-01-01,1995-01-01,1995-01-01,1995-01-01,1995-01-01,1995-01-01,1993-01-01,1993-01-01,1993-01-01&coed=2021-01-01,2021-01-01,2021-01-01,2021-01-01,2021-01-01,2021-01-01,2021-01-01,2021-01-01,2021-01-01&line_color=%234572a7,%23aa4643,%2389a54e,%2380699b,%233d96ae,%23db843d,%2392a8cd,%23a47d7c,%23b5ca92&link_values=false,false,false,false,false,false,false,false,false&line_style=solid,solid,solid,solid,solid,solid,solid,solid,solid&mark_type=none,none,none,none,none,none,none,none,none&mw=3,3,3,3,3,3,3,3,3&lw=2,2,2,2,2,2,2,2,2&ost=-99999,-99999,-99999,-99999,-99999,-99999,-99999,-99999,-99999&oet=99999,99999,99999,99999,99999,99999,99999,99999,99999&mma=0,0,0,0,0,0,0,0,0&fml=a,a,a,a,a,a,a,a,a&fq=Quarterly,Quarterly,Quarterly,Quarterly,Quarterly,Quarterly,Quarterly,Quarterly,Quarterly&fam=avg,avg,avg,avg,avg,avg,avg,avg,avg&fgst=lin,lin,lin,lin,lin,lin,lin,lin,lin&fgsnd=2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01&line_index=1,2,3,4,5,6,7,8,9&transformation=lin,lin,lin,lin,lin,lin,lin,lin,lin&vintage_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07&revision_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07&nd=1995-01-01,1995-01-01,1995-01-01,1995-01-01,1995-01-01,1995-01-01,1993-01-01,1993-01-01,1993-01-01" - ua = UserAgent(verify_ssl=False) - request_header = {"User-Agent": ua.random} - r = requests.get(tmp_url, headers=request_header) - data_text = r.content - df = pd.read_csv(io.StringIO(data_text.decode('utf-8'))) - df["DATE"] = pd.to_datetime(df["DATE"], format="%Y-%m-%d") - #df = df[list(df.columns[1:])].replace(".", np.nan).astype(float) - name_list = { - 'LRHU24TTEZQ156N': "Harmonized Unemployment: Aged 15-24: All Persons for the Euro Area", - 'LRHU24FEEZQ156N': "Harmonized Unemployment: Aged 15-24: Females for the Euro Area", - 'LRHU24MAEZQ156N': "Harmonized Unemployment: Aged 15-24: Males for the Euro Area", - 'LRHUADMAEZQ156N': "Harmonized Unemployment: Aged 25 and Over: Males for the Euro Area", - 'LRHUADTTEZQ156N': "Harmonized Unemployment: Aged 25 and Over: All Persons for the Euro Area", - 'LRHUADFEEZQ156N': "Harmonized Unemployment: Aged 25 and Over: Females for the Euro Area", - 'LRHUTTFEEZQ156N': "Harmonized Unemployment: Total: Females for the Euro Area", - 'LRHUTTTTEZQ156N': "Harmonized Unemployment Rate: Total: All Persons for the Euro Area", - 'LRHUTTMAEZQ156N': "Harmonized Unemployment: Total: Males for the Euro Area"} - description = "Labor Force Survey - quarterly rates, Quarterly, Not Seasonally Adjusted" - return df, name_list, description - - -def Labor_Force_Survey_Rates_Quarterly_Adj(): - tmp_url = url["fred_econ"] + "bgcolor=%23e1e9f0&chart_type=line&drp=0&fo=open%20sans&graph_bgcolor=%23ffffff&height=450&mode=fred&recession_bars=off&txtcolor=%23444444&ts=12&tts=12&width=748&nt=0&thu=0&trc=0&show_legend=yes&show_axis_titles=yes&show_tooltip=yes&id=LRHU24MAEZQ156S,LRHU24TTEZQ156S,LRHU24FEEZQ156S,LRHUADFEEZQ156S,LRHUADMAEZQ156S,LRHUADTTEZQ156S,LRHUTTTTEZQ156S,LRHUTTMAEZQ156S,LRHUTTFEEZQ156S&scale=left,left,left,left,left,left,left,left,left&cosd=1995-01-01,1995-01-01,1995-01-01,1995-01-01,1995-01-01,1995-01-01,1990-07-01,1990-07-01,1990-07-01&coed=2021-01-01,2021-01-01,2021-01-01,2021-01-01,2021-01-01,2021-01-01,2021-01-01,2021-01-01,2021-01-01&line_color=%234572a7,%23aa4643,%2389a54e,%2380699b,%233d96ae,%23db843d,%2392a8cd,%23a47d7c,%23b5ca92&link_values=false,false,false,false,false,false,false,false,false&line_style=solid,solid,solid,solid,solid,solid,solid,solid,solid&mark_type=none,none,none,none,none,none,none,none,none&mw=3,3,3,3,3,3,3,3,3&lw=2,2,2,2,2,2,2,2,2&ost=-99999,-99999,-99999,-99999,-99999,-99999,-99999,-99999,-99999&oet=99999,99999,99999,99999,99999,99999,99999,99999,99999&mma=0,0,0,0,0,0,0,0,0&fml=a,a,a,a,a,a,a,a,a&fq=Quarterly,Quarterly,Quarterly,Quarterly,Quarterly,Quarterly,Quarterly,Quarterly,Quarterly&fam=avg,avg,avg,avg,avg,avg,avg,avg,avg&fgst=lin,lin,lin,lin,lin,lin,lin,lin,lin&fgsnd=2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01&line_index=1,2,3,4,5,6,7,8,9&transformation=lin,lin,lin,lin,lin,lin,lin,lin,lin&vintage_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07&revision_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07&nd=1995-01-01,1995-01-01,1995-01-01,1995-01-01,1995-01-01,1995-01-01,1990-07-01,1990-07-01,1990-07-01" - ua = UserAgent(verify_ssl=False) - request_header = {"User-Agent": ua.random} - r = requests.get(tmp_url, headers=request_header) - data_text = r.content - df = pd.read_csv(io.StringIO(data_text.decode('utf-8'))) - df["DATE"] = pd.to_datetime(df["DATE"], format="%Y-%m-%d") - #df = df[list(df.columns[1:])].replace(".", np.nan).astype(float) - name_list = { - 'LRHU24MAEZQ156S': "Harmonized Unemployment: Aged 15-24: Males for the Euro Area", - 'LRHU24TTEZQ156S': "Harmonized Unemployment: Aged 15-24: All Persons for the Euro Area", - 'LRHU24FEEZQ156S': "Harmonized Unemployment: Aged 15-24: Females for the Euro Area", - 'LRHUADFEEZQ156S': "Harmonized Unemployment: Aged 25 and Over: Females for the Euro Area", - 'LRHUADMAEZQ156S': "Harmonized Unemployment: Aged 25 and Over: Males for the Euro Area", - 'LRHUADTTEZQ156S': "Harmonized Unemployment: Aged 25 and Over: All Persons for the Euro Area", - 'LRHUTTTTEZQ156S': "Harmonized Unemployment Rate: Total: All Persons for the Euro Area", - 'LRHUTTMAEZQ156S': "Harmonized Unemployment: Total: Males for the Euro Area", - 'LRHUTTFEEZQ156S': "Harmonized Unemployment: Total: Females for the Euro Area"} - description = "Labor Force Survey - quarterly rates, Quarterly, Seasonally Adjusted" - return df, name_list, description - - -def Labor_Force_Survey_Rates_Monthly_NAdj(): - tmp_url = url["fred_econ"] + "bgcolor=%23e1e9f0&chart_type=line&drp=0&fo=open%20sans&graph_bgcolor=%23ffffff&height=450&mode=fred&recession_bars=off&txtcolor=%23444444&ts=12&tts=12&width=748&nt=0&thu=0&trc=0&show_legend=yes&show_axis_titles=yes&show_tooltip=yes&id=LRHUTTFEEZM156N,LRHUTTMAEZM156N,LRHUTTTTEZM156N,LRHUADTTEZM156N,LRHUADMAEZM156N,LRHUADFEEZM156N,LRHU24FEEZM156N,LRHU24MAEZM156N,LRHU24TTEZM156N&scale=left,left,left,left,left,left,left,left,left&cosd=1993-01-01,1993-01-01,1993-01-01,1995-01-01,1995-01-01,1995-01-01,1995-01-01,1995-01-01,1995-01-01&coed=2021-03-01,2021-03-01,2021-03-01,2021-03-01,2021-03-01,2021-03-01,2021-03-01,2021-03-01,2021-03-01&line_color=%234572a7,%23aa4643,%2389a54e,%2380699b,%233d96ae,%23db843d,%2392a8cd,%23a47d7c,%23b5ca92&link_values=false,false,false,false,false,false,false,false,false&line_style=solid,solid,solid,solid,solid,solid,solid,solid,solid&mark_type=none,none,none,none,none,none,none,none,none&mw=3,3,3,3,3,3,3,3,3&lw=2,2,2,2,2,2,2,2,2&ost=-99999,-99999,-99999,-99999,-99999,-99999,-99999,-99999,-99999&oet=99999,99999,99999,99999,99999,99999,99999,99999,99999&mma=0,0,0,0,0,0,0,0,0&fml=a,a,a,a,a,a,a,a,a&fq=Monthly,Monthly,Monthly,Monthly,Monthly,Monthly,Monthly,Monthly,Monthly&fam=avg,avg,avg,avg,avg,avg,avg,avg,avg&fgst=lin,lin,lin,lin,lin,lin,lin,lin,lin&fgsnd=2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01&line_index=1,2,3,4,5,6,7,8,9&transformation=lin,lin,lin,lin,lin,lin,lin,lin,lin&vintage_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07&revision_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07&nd=1993-01-01,1993-01-01,1993-01-01,1995-01-01,1995-01-01,1995-01-01,1995-01-01,1995-01-01,1995-01-01" - ua = UserAgent(verify_ssl=False) - request_header = {"User-Agent": ua.random} - r = requests.get(tmp_url, headers=request_header) - data_text = r.content - df = pd.read_csv(io.StringIO(data_text.decode('utf-8'))) - df["DATE"] = pd.to_datetime(df["DATE"], format="%Y-%m-%d") - #df = df[list(df.columns[1:])].replace(".", np.nan).astype(float) - name_list = { - 'LRHUTTFEEZM156N': "Harmonized Unemployment: Total: Females for the Euro Area", - 'LRHUTTMAEZM156N': "Harmonized Unemployment: Total: Males for the Euro Area", - 'LRHUTTTTEZM156N': "Harmonized Unemployment Rate: Total: All Persons for the Euro Area", - 'LRHUADTTEZM156N': "Harmonized Unemployment: Aged 25 and Over: All Persons for the Euro Area", - 'LRHUADMAEZM156N': "Harmonized Unemployment: Aged 25 and Over: Males for the Euro Area", - 'LRHUADFEEZM156N': "Harmonized Unemployment: Aged 25 and Over: Females for the Euro Area", - 'LRHU24FEEZM156N': "Harmonized Unemployment: Aged 15-24: Females for the Euro Area", - 'LRHU24MAEZM156N': "Harmonized Unemployment: Aged 15-24: Males for the Euro Area", - 'LRHU24TTEZM156N': "Harmonized Unemployment: Aged 15-24: All Persons for the Euro Area"} - description = "Labor Force Survey - quarterly rates, Monthly, Seasonally Adjusted" - return df, name_list, description - - -def Labor_Force_Survey_Level_Quarterly_NAdj(): - tmp_url = url["fred_econ"] + "bgcolor=%23e1e9f0&chart_type=line&drp=0&fo=open%20sans&graph_bgcolor=%23ffffff&height=450&mode=fred&recession_bars=off&txtcolor=%23444444&ts=12&tts=12&width=748&nt=0&thu=0&trc=0&show_legend=yes&show_axis_titles=yes&show_tooltip=yes&id=LRHUTTFEEZM156N,LRHUTTMAEZM156N,LRHUTTTTEZM156N,LRHUADTTEZM156N,LRHUADMAEZM156N,LRHUADFEEZM156N,LRHU24FEEZM156N,LRHU24MAEZM156N,LRHU24TTEZM156N&scale=left,left,left,left,left,left,left,left,left&cosd=1993-01-01,1993-01-01,1993-01-01,1995-01-01,1995-01-01,1995-01-01,1995-01-01,1995-01-01,1995-01-01&coed=2021-03-01,2021-03-01,2021-03-01,2021-03-01,2021-03-01,2021-03-01,2021-03-01,2021-03-01,2021-03-01&line_color=%234572a7,%23aa4643,%2389a54e,%2380699b,%233d96ae,%23db843d,%2392a8cd,%23a47d7c,%23b5ca92&link_values=false,false,false,false,false,false,false,false,false&line_style=solid,solid,solid,solid,solid,solid,solid,solid,solid&mark_type=none,none,none,none,none,none,none,none,none&mw=3,3,3,3,3,3,3,3,3&lw=2,2,2,2,2,2,2,2,2&ost=-99999,-99999,-99999,-99999,-99999,-99999,-99999,-99999,-99999&oet=99999,99999,99999,99999,99999,99999,99999,99999,99999&mma=0,0,0,0,0,0,0,0,0&fml=a,a,a,a,a,a,a,a,a&fq=Monthly,Monthly,Monthly,Monthly,Monthly,Monthly,Monthly,Monthly,Monthly&fam=avg,avg,avg,avg,avg,avg,avg,avg,avg&fgst=lin,lin,lin,lin,lin,lin,lin,lin,lin&fgsnd=2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01&line_index=1,2,3,4,5,6,7,8,9&transformation=lin,lin,lin,lin,lin,lin,lin,lin,lin&vintage_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07&revision_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07&nd=1993-01-01,1993-01-01,1993-01-01,1995-01-01,1995-01-01,1995-01-01,1995-01-01,1995-01-01,1995-01-01" - ua = UserAgent(verify_ssl=False) - request_header = {"User-Agent": ua.random} - r = requests.get(tmp_url, headers=request_header) - data_text = r.content - df = pd.read_csv(io.StringIO(data_text.decode('utf-8'))) - df["DATE"] = pd.to_datetime(df["DATE"], format="%Y-%m-%d") - #df = df[list(df.columns[1:])].replace(".", np.nan).astype(float) - name_list = { - 'LFHU24FEEZQ647N': "Harmonized Unemployment: Aged 15-24: Females for the Euro Area", - 'LFHU24TTEZQ647N': "Harmonized Unemployment: Aged 15-24: All Persons for the Euro Area", - 'LFHU24MAEZQ647N': "Harmonized Unemployment: Aged 15-24: Males for the Euro Area", - 'LFHUADTTEZQ647N': "Harmonized Unemployment: Aged 25 and Over: All Persons for the Euro Area", - 'LFHUADMAEZQ647N': "Harmonized Unemployment: Aged 25 and Over: Males for the Euro Area", - 'LFHUADFEEZQ647N': "Harmonized Unemployment: Aged 25 and Over: Females for the Euro Area", - 'LFHUTTMAEZQ647N': "Total Harmonized Unemployment: Males for the Euro Area", - 'LFHUTTFEEZQ647N': "Total Harmonized Unemployment: Females for the Euro Area", - 'LFHUTTTTEZQ647N': "Total Harmonized Unemployment: All Persons for the Euro Area"} - description = "Labor Force Survey - quarterly levels, Quarterly, Not Seasonally Adjusted" - return df, name_list, description - - -def Labor_Force_Survey_Level_Quarterly_Adj(): - tmp_url = url["fred_econ"] + "bgcolor=%23e1e9f0&chart_type=line&drp=0&fo=open%20sans&graph_bgcolor=%23ffffff&height=450&mode=fred&recession_bars=off&txtcolor=%23444444&ts=12&tts=12&width=748&nt=0&thu=0&trc=0&show_legend=yes&show_axis_titles=yes&show_tooltip=yes&id=LRHUTTFEEZM156N,LRHUTTMAEZM156N,LRHUTTTTEZM156N,LRHUADTTEZM156N,LRHUADMAEZM156N,LRHUADFEEZM156N,LRHU24FEEZM156N,LRHU24MAEZM156N,LRHU24TTEZM156N&scale=left,left,left,left,left,left,left,left,left&cosd=1993-01-01,1993-01-01,1993-01-01,1995-01-01,1995-01-01,1995-01-01,1995-01-01,1995-01-01,1995-01-01&coed=2021-03-01,2021-03-01,2021-03-01,2021-03-01,2021-03-01,2021-03-01,2021-03-01,2021-03-01,2021-03-01&line_color=%234572a7,%23aa4643,%2389a54e,%2380699b,%233d96ae,%23db843d,%2392a8cd,%23a47d7c,%23b5ca92&link_values=false,false,false,false,false,false,false,false,false&line_style=solid,solid,solid,solid,solid,solid,solid,solid,solid&mark_type=none,none,none,none,none,none,none,none,none&mw=3,3,3,3,3,3,3,3,3&lw=2,2,2,2,2,2,2,2,2&ost=-99999,-99999,-99999,-99999,-99999,-99999,-99999,-99999,-99999&oet=99999,99999,99999,99999,99999,99999,99999,99999,99999&mma=0,0,0,0,0,0,0,0,0&fml=a,a,a,a,a,a,a,a,a&fq=Monthly,Monthly,Monthly,Monthly,Monthly,Monthly,Monthly,Monthly,Monthly&fam=avg,avg,avg,avg,avg,avg,avg,avg,avg&fgst=lin,lin,lin,lin,lin,lin,lin,lin,lin&fgsnd=2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01&line_index=1,2,3,4,5,6,7,8,9&transformation=lin,lin,lin,lin,lin,lin,lin,lin,lin&vintage_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07&revision_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07&nd=1993-01-01,1993-01-01,1993-01-01,1995-01-01,1995-01-01,1995-01-01,1995-01-01,1995-01-01,1995-01-01" - ua = UserAgent(verify_ssl=False) - request_header = {"User-Agent": ua.random} - r = requests.get(tmp_url, headers=request_header) - data_text = r.content - df = pd.read_csv(io.StringIO(data_text.decode('utf-8'))) - df["DATE"] = pd.to_datetime(df["DATE"], format="%Y-%m-%d") - #df = df[list(df.columns[1:])].replace(".", np.nan).astype(float) - name_list = { - 'LFHU24TTEZQ647S': "Harmonized Unemployment: Aged 15-24: All Persons for the Euro Area", - 'LFHU24MAEZQ647S': "Harmonized Unemployment: Aged 15-24: Males for the Euro Area", - 'LFHU24FEEZQ647S': "Harmonized Unemployment: Aged 15-24: Females for the Euro Area", - 'LFHUTTFEEZQ647S': "Total Harmonized Unemployment: Females for the Euro Area", - 'LFHUTTTTEZQ647S': "Total Harmonized Unemployment: All Persons for the Euro Area", - 'LFHUTTMAEZQ647S': "Total Harmonized Unemployment: Males for the Euro Area", - 'LFHUADMAEZQ647S': "Harmonized Unemployment: Aged 25 and Over: Males for the Euro Area", - 'LFHUADFEEZQ647S': "Harmonized Unemployment: Aged 25 and Over: Females for the Euro Area", - 'LFHUADTTEZQ647S': "Harmonized Unemployment: Aged 25 and Over: All Persons for the Euro Area"} - description = "Labor Force Survey - quarterly levels, Quarterly, Seasonally Adjusted" - return df, name_list, description - - -def Labor_Force_Survey_Level_Monthly_Adj(): - tmp_url = url["fred_econ"] + "bgcolor=%23e1e9f0&chart_type=line&drp=0&fo=open%20sans&graph_bgcolor=%23ffffff&height=450&mode=fred&recession_bars=off&txtcolor=%23444444&ts=12&tts=12&width=748&nt=0&thu=0&trc=0&show_legend=yes&show_axis_titles=yes&show_tooltip=yes&id=LFHU24FEEZM647S,LFHU24TTEZM647S,LFHU24MAEZM647S,LFHUADFEEZM647S,LFHUADTTEZM647S,LFHUADMAEZM647S,LFHUTTTTEZM647S,LFHUTTMAEZM647S,LFHUTTFEEZM647S&scale=left,left,left,left,left,left,left,left,left&cosd=1995-01-01,1995-01-01,1995-01-01,1995-01-01,1995-01-01,1995-01-01,1995-01-01,1995-01-01,1995-01-01&coed=2021-03-01,2021-03-01,2021-03-01,2021-03-01,2021-03-01,2021-03-01,2021-03-01,2021-03-01,2021-03-01&line_color=%234572a7,%23aa4643,%2389a54e,%2380699b,%233d96ae,%23db843d,%2392a8cd,%23a47d7c,%23b5ca92&link_values=false,false,false,false,false,false,false,false,false&line_style=solid,solid,solid,solid,solid,solid,solid,solid,solid&mark_type=none,none,none,none,none,none,none,none,none&mw=3,3,3,3,3,3,3,3,3&lw=2,2,2,2,2,2,2,2,2&ost=-99999,-99999,-99999,-99999,-99999,-99999,-99999,-99999,-99999&oet=99999,99999,99999,99999,99999,99999,99999,99999,99999&mma=0,0,0,0,0,0,0,0,0&fml=a,a,a,a,a,a,a,a,a&fq=Monthly,Monthly,Monthly,Monthly,Monthly,Monthly,Monthly,Monthly,Monthly&fam=avg,avg,avg,avg,avg,avg,avg,avg,avg&fgst=lin,lin,lin,lin,lin,lin,lin,lin,lin&fgsnd=2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01&line_index=1,2,3,4,5,6,7,8,9&transformation=lin,lin,lin,lin,lin,lin,lin,lin,lin&vintage_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07&revision_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07&nd=1995-01-01,1995-01-01,1995-01-01,1995-01-01,1995-01-01,1995-01-01,1995-01-01,1995-01-01,1995-01-01" - ua = UserAgent(verify_ssl=False) - request_header = {"User-Agent": ua.random} - r = requests.get(tmp_url, headers=request_header) - data_text = r.content - df = pd.read_csv(io.StringIO(data_text.decode('utf-8'))) - df["DATE"] = pd.to_datetime(df["DATE"], format="%Y-%m-%d") - #df = df[list(df.columns[1:])].replace(".", np.nan).astype(float) - name_list = { - 'LFHU24FEEZM647S': "Harmonized Unemployment: Aged 15-24: Females for the Euro Area", - 'LFHU24TTEZM647S': "Harmonized Unemployment: Aged 15-24: All Persons for the Euro Area", - 'LFHU24MAEZM647S': "Harmonized Unemployment: Aged 15-24: Males for the Euro Area", - 'LFHUADFEEZM647S': "Harmonized Unemployment: Aged 25 and Over: Females for the Euro Area", - 'LFHUADTTEZM647S': "Harmonized Unemployment: Aged 25 and Over: All Persons for the Euro Area", - 'LFHUADMAEZM647S': "Harmonized Unemployment: Aged 25 and Over: Males for the Euro Area", - 'LFHUTTTTEZM647S': "Total Harmonized Unemployment: All Persons for the Euro Area", - 'LFHUTTMAEZM647S': "Total Harmonized Unemployment: Males for the Euro Area", - 'LFHUTTFEEZM647S': "Total Harmonized Unemployment: Females for the Euro Area"} - description = "Labor Force Survey - quarterly levels, Monthly, Seasonally Adjusted" - return df, name_list, description - - -def Labor_Force_Survey_Level_Monthly_NAdj(): - tmp_url = url["fred_econ"] + "bgcolor=%23e1e9f0&chart_type=line&drp=0&fo=open%20sans&graph_bgcolor=%23ffffff&height=450&mode=fred&recession_bars=off&txtcolor=%23444444&ts=12&tts=12&width=748&nt=0&thu=0&trc=0&show_legend=yes&show_axis_titles=yes&show_tooltip=yes&id=LFHU24MAEZM647N,LFHU24FEEZM647N,LFHU24TTEZM647N,LFHUADMAEZM647N,LFHUADFEEZM647N,LFHUADTTEZM647N,LFHUTTFEEZM647N,LFHUTTTTEZM647N,LFHUTTMAEZM647N&scale=left,left,left,left,left,left,left,left,left&cosd=1995-01-01,1995-01-01,1995-01-01,1995-01-01,1995-01-01,1995-01-01,1995-01-01,1995-01-01,1995-01-01&coed=2021-03-01,2021-03-01,2021-03-01,2021-03-01,2021-03-01,2021-03-01,2021-03-01,2021-03-01,2021-03-01&line_color=%234572a7,%23aa4643,%2389a54e,%2380699b,%233d96ae,%23db843d,%2392a8cd,%23a47d7c,%23b5ca92&link_values=false,false,false,false,false,false,false,false,false&line_style=solid,solid,solid,solid,solid,solid,solid,solid,solid&mark_type=none,none,none,none,none,none,none,none,none&mw=3,3,3,3,3,3,3,3,3&lw=2,2,2,2,2,2,2,2,2&ost=-99999,-99999,-99999,-99999,-99999,-99999,-99999,-99999,-99999&oet=99999,99999,99999,99999,99999,99999,99999,99999,99999&mma=0,0,0,0,0,0,0,0,0&fml=a,a,a,a,a,a,a,a,a&fq=Monthly,Monthly,Monthly,Monthly,Monthly,Monthly,Monthly,Monthly,Monthly&fam=avg,avg,avg,avg,avg,avg,avg,avg,avg&fgst=lin,lin,lin,lin,lin,lin,lin,lin,lin&fgsnd=2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01&line_index=1,2,3,4,5,6,7,8,9&transformation=lin,lin,lin,lin,lin,lin,lin,lin,lin&vintage_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07&revision_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07&nd=1995-01-01,1995-01-01,1995-01-01,1995-01-01,1995-01-01,1995-01-01,1995-01-01,1995-01-01,1995-01-01" - ua = UserAgent(verify_ssl=False) - request_header = {"User-Agent": ua.random} - r = requests.get(tmp_url, headers=request_header) - data_text = r.content - df = pd.read_csv(io.StringIO(data_text.decode('utf-8'))) - df["DATE"] = pd.to_datetime(df["DATE"], format="%Y-%m-%d") - #df = df[list(df.columns[1:])].replace(".", np.nan).astype(float) - name_list = { - 'LFHU24MAEZM647N': "Harmonized Unemployment: Aged 15-24: Males for the Euro Area", - 'LFHU24FEEZM647N': "Harmonized Unemployment: Aged 15-24: Females for the Euro Area", - 'LFHU24TTEZM647N': "Harmonized Unemployment: Aged 15-24: All Persons for the Euro Area", - 'LFHUADMAEZM647N': "Harmonized Unemployment: Aged 25 and Over: Males for the Euro Area", - 'LFHUADFEEZM647N': "Harmonized Unemployment: Aged 25 and Over: Females for the Euro Area", - 'LFHUADTTEZM647N': "Harmonized Unemployment: Aged 25 and Over: All Persons for the Euro Area", - 'LFHUTTFEEZM647N': "Total Harmonized Unemployment: Females for the Euro Area", - 'LFHUTTTTEZM647N': "Total Harmonized Unemployment: All Persons for the Euro Area", - 'LFHUTTMAEZM647N': "Total Harmonized Unemployment: Males for the Euro Area"} - description = "Labor Force Survey - quarterly levels, Monthly, Not Seasonally Adjusted" - return df, name_list, description - - -def Production_Monthly_Adj(): - tmp_url = url["fred_econ"] + "bgcolor=%23e1e9f0&chart_type=line&drp=0&fo=open%20sans&graph_bgcolor=%23ffffff&height=450&mode=fred&recession_bars=off&txtcolor=%23444444&ts=12&tts=12&width=748&nt=0&thu=0&trc=0&show_legend=yes&show_axis_titles=yes&show_tooltip=yes&id=EA19PRINTO01GYSAM,EA19PRMNCG03IXOBSAM,EA19PRMNCG02IXOBSAM,EA19PRMNVG01IXOBSAM,EA19PRMNTO01IXOBSAM,EA19PRMNIG01IXOBSAM,EA19PRCNTO01IXOBSAM&scale=left,left,left,left,left,left,left&cosd=1976-07-01,1985-01-01,1990-01-01,1985-01-01,1980-01-01,1985-01-01,1985-01-01&coed=2021-02-01,2017-12-01,2018-12-01,2018-12-01,2021-02-01,2018-12-01,2021-02-01&line_color=%234572a7,%23aa4643,%2389a54e,%2380699b,%233d96ae,%23db843d,%2392a8cd&link_values=false,false,false,false,false,false,false&line_style=solid,solid,solid,solid,solid,solid,solid&mark_type=none,none,none,none,none,none,none&mw=3,3,3,3,3,3,3&lw=2,2,2,2,2,2,2&ost=-99999,-99999,-99999,-99999,-99999,-99999,-99999&oet=99999,99999,99999,99999,99999,99999,99999&mma=0,0,0,0,0,0,0&fml=a,a,a,a,a,a,a&fq=Monthly,Monthly,Monthly,Monthly,Monthly,Monthly,Monthly&fam=avg,avg,avg,avg,avg,avg,avg&fgst=lin,lin,lin,lin,lin,lin,lin&fgsnd=2020-02-01,2017-12-01,2018-12-01,2018-12-01,2020-02-01,2018-12-01,2020-02-01&line_index=1,2,3,4,5,6,7&transformation=lin,lin,lin,lin,lin,lin,lin&vintage_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07&revision_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07&nd=1976-07-01,1985-01-01,1990-01-01,1985-01-01,1980-01-01,1985-01-01,1985-01-01" - ua = UserAgent(verify_ssl=False) - request_header = {"User-Agent": ua.random} - r = requests.get(tmp_url, headers=request_header) - data_text = r.content - df = pd.read_csv(io.StringIO(data_text.decode('utf-8'))) - df["DATE"] = pd.to_datetime(df["DATE"], format="%Y-%m-%d") - #df = df[list(df.columns[1:])].replace(".", np.nan).astype(float) - name_list = { - 'EA19PRINTO01GYSAM': "Production: Industry: Total industry: Total industry excluding construction for the Euro Area", - 'EA19PRMNCG03IXOBSAM': "Production: Manufacturing: Consumer goods: Non durable goods for the Euro Area", - 'EA19PRMNCG02IXOBSAM': "Production: Manufacturing: Consumer goods: Durable goods for the Euro Area", - 'EA19PRMNVG01IXOBSAM': "Production: Manufacturing: Investment goods: Total for the Euro Area", - 'EA19PRMNTO01IXOBSAM': "Production: Manufacturing: Total manufacturing: Total manufacturing for the Euro Area", - 'EA19PRMNIG01IXOBSAM': "Production: Manufacturing: Intermediate goods: Total for the Euro Area", - 'EA19PRCNTO01IXOBSAM': "Production: Construction: Total construction: Total for the Euro Area"} - description = "Production, Monthly, Seasonally Adjusted" - return df, name_list, description - - -def Production_Quarterly_Adj(): - tmp_url = url["fred_econ"] + "bgcolor=%23e1e9f0&chart_type=line&drp=0&fo=open%20sans&graph_bgcolor=%23ffffff&height=450&mode=fred&recession_bars=off&txtcolor=%23444444&ts=12&tts=12&width=748&nt=0&thu=0&trc=0&show_legend=yes&show_axis_titles=yes&show_tooltip=yes&id=PRINTO01EZQ659S,PRMNVG01EZQ661S,PRMNCG02EZQ661S,PRMNCG03EZQ661S,PRMNTO01EZQ661S,PRMNIG01EZQ661S,PRCNTO01EZQ661S&scale=left,left,left,left,left,left,left&cosd=1976-07-01,1985-01-01,1990-01-01,1985-01-01,1980-01-01,1985-01-01,1985-01-01&coed=2020-10-01,2018-10-01,2018-10-01,2017-10-01,2020-10-01,2018-10-01,2020-10-01&line_color=%234572a7,%23aa4643,%2389a54e,%2380699b,%233d96ae,%23db843d,%2392a8cd&link_values=false,false,false,false,false,false,false&line_style=solid,solid,solid,solid,solid,solid,solid&mark_type=none,none,none,none,none,none,none&mw=3,3,3,3,3,3,3&lw=2,2,2,2,2,2,2&ost=-99999,-99999,-99999,-99999,-99999,-99999,-99999&oet=99999,99999,99999,99999,99999,99999,99999&mma=0,0,0,0,0,0,0&fml=a,a,a,a,a,a,a&fq=Quarterly,Quarterly,Quarterly,Quarterly,Quarterly,Quarterly,Quarterly&fam=avg,avg,avg,avg,avg,avg,avg&fgst=lin,lin,lin,lin,lin,lin,lin&fgsnd=2020-02-01,2018-10-01,2018-10-01,2017-10-01,2020-02-01,2018-10-01,2020-02-01&line_index=1,2,3,4,5,6,7&transformation=lin,lin,lin,lin,lin,lin,lin&vintage_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07&revision_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07&nd=1976-07-01,1985-01-01,1990-01-01,1985-01-01,1980-01-01,1985-01-01,1985-01-01" - ua = UserAgent(verify_ssl=False) - request_header = {"User-Agent": ua.random} - r = requests.get(tmp_url, headers=request_header) - data_text = r.content - df = pd.read_csv(io.StringIO(data_text.decode('utf-8'))) - df["DATE"] = pd.to_datetime(df["DATE"], format="%Y-%m-%d") - #df = df[list(df.columns[1:])].replace(".", np.nan).astype(float) - name_list = { - 'PRINTO01EZQ659S': "Total Industry Production Excluding Construction for the Euro Area", - 'PRMNVG01EZQ661S': "Total Production of Investment Goods for Manufacturing for the Euro Area", - 'PRMNCG02EZQ661S': "Production of Durable Consumer Goods for Manufacturing for the Euro Area", - 'PRMNCG03EZQ661S': "Production of Nondurable Consumer Goods for Manufacturing for the Euro Area", - 'PRMNTO01EZQ661S': "Total Manufacturing Production for the Euro Area", - 'PRMNIG01EZQ661S': "Total Production of Intermediate Goods for Manufacturing for the Euro Area", - 'PRCNTO01EZQ661S': "Total Construction for the Euro Area"} - description = "Production, Monthly, Not Seasonally Adjusted" - return df, name_list, description - - -def Production_Monthly_NAdj(): - tmp_url = url["fred_econ"] + "bgcolor=%23e1e9f0&chart_type=line&drp=0&fo=open%20sans&graph_bgcolor=%23ffffff&height=450&mode=fred&recession_bars=off&txtcolor=%23444444&ts=12&tts=12&width=748&nt=0&thu=0&trc=0&show_legend=yes&show_axis_titles=yes&show_tooltip=yes&id=EA19PRMNIG01IXOBM,EA19PRMNTO01IXOBM,EA19PRMNCG02IXOBM,EA19PRMNCG03IXOBM,EA19PRMNVG01IXOBM,EA19PRCNTO01IXOBM,EA19PRINTO01IXOBM&scale=left,left,left,left,left,left,left&cosd=1985-01-01,1980-01-01,1990-01-01,1985-01-01,1985-01-01,1985-01-01,1980-01-01&coed=2018-12-01,2021-02-01,2018-12-01,2018-12-01,2018-12-01,2021-02-01,2021-02-01&line_color=%234572a7,%23aa4643,%2389a54e,%2380699b,%233d96ae,%23db843d,%2392a8cd&link_values=false,false,false,false,false,false,false&line_style=solid,solid,solid,solid,solid,solid,solid&mark_type=none,none,none,none,none,none,none&mw=3,3,3,3,3,3,3&lw=2,2,2,2,2,2,2&ost=-99999,-99999,-99999,-99999,-99999,-99999,-99999&oet=99999,99999,99999,99999,99999,99999,99999&mma=0,0,0,0,0,0,0&fml=a,a,a,a,a,a,a&fq=Monthly,Monthly,Monthly,Monthly,Monthly,Monthly,Monthly&fam=avg,avg,avg,avg,avg,avg,avg&fgst=lin,lin,lin,lin,lin,lin,lin&fgsnd=2018-12-01,2020-02-01,2018-12-01,2018-12-01,2018-12-01,2020-02-01,2020-02-01&line_index=1,2,3,4,5,6,7&transformation=lin,lin,lin,lin,lin,lin,lin&vintage_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07&revision_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07&nd=1985-01-01,1980-01-01,1990-01-01,1985-01-01,1985-01-01,1985-01-01,1980-01-01" - ua = UserAgent(verify_ssl=False) - request_header = {"User-Agent": ua.random} - r = requests.get(tmp_url, headers=request_header) - data_text = r.content - df = pd.read_csv(io.StringIO(data_text.decode('utf-8'))) - df["DATE"] = pd.to_datetime(df["DATE"], format="%Y-%m-%d") - #df = df[list(df.columns[1:])].replace(".", np.nan).astype(float) - name_list = { - 'EA19PRMNIG01IXOBM': "Production: Manufacturing: Intermediate goods: Total for the Euro Area", - 'EA19PRMNTO01IXOBM': "Production: Manufacturing: Total manufacturing: Total manufacturing for the Euro Area", - 'EA19PRMNCG02IXOBM': "Production: Manufacturing: Consumer goods: Durable goods for the Euro Area", - 'EA19PRMNCG03IXOBM': "Production: Manufacturing: Consumer goods: Non durable goods for the Euro Area", - 'EA19PRMNVG01IXOBM': "Production: Manufacturing: Investment goods: Total for the Euro Area", - 'EA19PRCNTO01IXOBM': "Production: Construction: Total construction: Total for the Euro Area", - 'EA19PRINTO01IXOBM': "Production: Industry: Total industry: Total industry excluding construction for the Euro Area"} - description = "Production, Monthly, Not Seasonally Adjusted" - return df, name_list, description - - -def Production_Quarterly_NAdj(): - tmp_url = url["fred_econ"] + "bgcolor=%23e1e9f0&chart_type=line&drp=0&fo=open%20sans&graph_bgcolor=%23ffffff&height=450&mode=fred&recession_bars=off&txtcolor=%23444444&ts=12&tts=12&width=748&nt=0&thu=0&trc=0&show_legend=yes&show_axis_titles=yes&show_tooltip=yes&id=PRMNCG03EZQ661N,PRMNCG02EZQ661N,PRMNVG01EZQ661N,PRMNIG01EZQ661N,PRMNTO01EZQ661N,PRINTO01EZQ661N,PRCNTO01EZQ661N&scale=left,left,left,left,left,left,left&cosd=1985-01-01,1990-01-01,1985-01-01,1985-01-01,1980-01-01,1980-01-01,1985-01-01&coed=2018-10-01,2018-10-01,2018-10-01,2018-10-01,2020-10-01,2020-10-01,2020-10-01&line_color=%234572a7,%23aa4643,%2389a54e,%2380699b,%233d96ae,%23db843d,%2392a8cd&link_values=false,false,false,false,false,false,false&line_style=solid,solid,solid,solid,solid,solid,solid&mark_type=none,none,none,none,none,none,none&mw=3,3,3,3,3,3,3&lw=2,2,2,2,2,2,2&ost=-99999,-99999,-99999,-99999,-99999,-99999,-99999&oet=99999,99999,99999,99999,99999,99999,99999&mma=0,0,0,0,0,0,0&fml=a,a,a,a,a,a,a&fq=Quarterly,Quarterly,Quarterly,Quarterly,Quarterly,Quarterly,Quarterly&fam=avg,avg,avg,avg,avg,avg,avg&fgst=lin,lin,lin,lin,lin,lin,lin&fgsnd=2018-10-01,2018-10-01,2018-10-01,2018-10-01,2020-02-01,2020-02-01,2020-02-01&line_index=1,2,3,4,5,6,7&transformation=lin,lin,lin,lin,lin,lin,lin&vintage_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07&revision_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07&nd=1985-01-01,1990-01-01,1985-01-01,1985-01-01,1980-01-01,1980-01-01,1985-01-01" - ua = UserAgent(verify_ssl=False) - request_header = {"User-Agent": ua.random} - r = requests.get(tmp_url, headers=request_header) - data_text = r.content - df = pd.read_csv(io.StringIO(data_text.decode('utf-8'))) - df["DATE"] = pd.to_datetime(df["DATE"], format="%Y-%m-%d") - #df = df[list(df.columns[1:])].replace(".", np.nan).astype(float) - name_list = { - 'PRMNCG03EZQ661N': "Production of Nondurable Consumer Goods for Manufacturing for the Euro Area", - 'PRMNCG02EZQ661N': "Production of Durable Consumer Goods for Manufacturing for the Euro Area", - 'PRMNVG01EZQ661N': "Total Production of Investment Goods for Manufacturing for the Euro Area", - 'PRMNIG01EZQ661N': "Total Production of Intermediate Goods for Manufacturing for the Euro Area", - 'PRMNTO01EZQ661N': "Total Manufacturing Production for the Euro Area", - 'PRINTO01EZQ661N': "Total Industry Production Excluding Construction for the Euro Area", - 'PRCNTO01EZQ661N': "Total Construction for the Euro Area"} - description = "Production, Quarterly, Not Seasonally Adjusted" - return df, name_list, description - - -def Sales_Monthly_Adj(): - tmp_url = url["fred_econ"] + "bgcolor=%23e1e9f0&chart_type=line&drp=0&fo=open%20sans&graph_bgcolor=%23ffffff&height=450&mode=fred&recession_bars=off&txtcolor=%23444444&ts=12&tts=12&width=748&nt=0&thu=0&trc=0&show_legend=yes&show_axis_titles=yes&show_tooltip=yes&id=EA19SLMNTO02IXOBSAM,EA19SLMNIG02IXOBSAM,EA19SLMNCD02IXOBSAM,EA19SLMNCN02IXOBSAM,EA19SLMNVG02IXOBSAM,EA19SLRTTO01IXOBSAM,EA19SLRTTO02IXOBSAM,EA19SLRTCR03IXOBSAM&scale=left,left,left,left,left,left,left,left&cosd=1980-01-01,1990-01-01,1993-01-01,1995-01-01,1980-01-01,1995-01-01,1995-01-01,1970-01-01&coed=2021-02-01,2018-12-01,2018-12-01,2018-12-01,2018-12-01,2021-02-01,2021-02-01,2018-12-01&line_color=%234572a7,%23aa4643,%2389a54e,%2380699b,%233d96ae,%23db843d,%2392a8cd,%23a47d7c&link_values=false,false,false,false,false,false,false,false&line_style=solid,solid,solid,solid,solid,solid,solid,solid&mark_type=none,none,none,none,none,none,none,none&mw=3,3,3,3,3,3,3,3&lw=2,2,2,2,2,2,2,2&ost=-99999,-99999,-99999,-99999,-99999,-99999,-99999,-99999&oet=99999,99999,99999,99999,99999,99999,99999,99999&mma=0,0,0,0,0,0,0,0&fml=a,a,a,a,a,a,a,a&fq=Monthly,Monthly,Monthly,Monthly,Monthly,Monthly,Monthly,Monthly&fam=avg,avg,avg,avg,avg,avg,avg,avg&fgst=lin,lin,lin,lin,lin,lin,lin,lin&fgsnd=2020-02-01,2018-12-01,2018-12-01,2018-12-01,2018-12-01,2020-02-01,2020-02-01,2018-12-01&line_index=1,2,3,4,5,6,7,8&transformation=lin,lin,lin,lin,lin,lin,lin,lin&vintage_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07&revision_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07&nd=1980-01-01,1990-01-01,1993-01-01,1995-01-01,1980-01-01,1995-01-01,1995-01-01,1970-01-01" - ua = UserAgent(verify_ssl=False) - request_header = {"User-Agent": ua.random} - r = requests.get(tmp_url, headers=request_header) - data_text = r.content - df = pd.read_csv(io.StringIO(data_text.decode('utf-8'))) - df["DATE"] = pd.to_datetime(df["DATE"], format="%Y-%m-%d") - #df = df[list(df.columns[1:])].replace(".", np.nan).astype(float) - name_list = { - 'EA19SLMNTO02IXOBSAM': "Sales: Manufacturing: Total manufacturing: Value for the Euro Area", - 'EA19SLMNIG02IXOBSAM': "Sales: Manufacturing: Intermediate goods: Value for the Euro Area", - 'EA19SLMNCD02IXOBSAM': "Sales: Manufacturing: Consumer goods durable: Value for the Euro Area", - 'EA19SLMNCN02IXOBSAM': "Sales: Manufacturing: Consumer goods non durable: Value for the Euro Area", - 'EA19SLMNVG02IXOBSAM': "Sales: Manufacturing: Investment goods: Value for the Euro Area", - 'EA19SLRTTO01IXOBSAM': "Sales: Retail trade: Total retail trade: Volume for the Euro Area", - 'EA19SLRTTO02IXOBSAM': "Sales: Retail trade: Total retail trade: Value for the Euro Area", - 'EA19SLRTCR03IXOBSAM': "Sales: Retail trade: Car registration: Passenger cars for the Euro Area"} - description = "Sales, Monthly, Seasonally Adjusted" - return df, name_list, description - - -def Sales_Quarterly_Adj(): - tmp_url = url["fred_econ"] + "bgcolor=%23e1e9f0&chart_type=line&drp=0&fo=open%20sans&graph_bgcolor=%23ffffff&height=450&mode=fred&recession_bars=off&txtcolor=%23444444&ts=12&tts=12&width=748&nt=0&thu=0&trc=0&show_legend=yes&show_axis_titles=yes&show_tooltip=yes&id=SLMNTO02EZQ661S,SLMNVG02EZQ661S,SLMNCD02EZQ661S,SLMNCN02EZQ661S,SLMNIG02EZQ661S,SLRTTO02EZQ661S,SLRTTO01EZQ659S,SLRTCR03EZQ661S&scale=left,left,left,left,left,left,left,left&cosd=1980-01-01,1980-01-01,1993-01-01,1995-01-01,1990-01-01,1995-01-01,1996-01-01,1970-01-01&coed=2020-10-01,2018-10-01,2018-10-01,2018-10-01,2018-10-01,2020-10-01,2020-10-01,2018-10-01&line_color=%234572a7,%23aa4643,%2389a54e,%2380699b,%233d96ae,%23db843d,%2392a8cd,%23a47d7c&link_values=false,false,false,false,false,false,false,false&line_style=solid,solid,solid,solid,solid,solid,solid,solid&mark_type=none,none,none,none,none,none,none,none&mw=3,3,3,3,3,3,3,3&lw=2,2,2,2,2,2,2,2&ost=-99999,-99999,-99999,-99999,-99999,-99999,-99999,-99999&oet=99999,99999,99999,99999,99999,99999,99999,99999&mma=0,0,0,0,0,0,0,0&fml=a,a,a,a,a,a,a,a&fq=Quarterly,Quarterly,Quarterly,Quarterly,Quarterly,Quarterly,Quarterly,Quarterly&fam=avg,avg,avg,avg,avg,avg,avg,avg&fgst=lin,lin,lin,lin,lin,lin,lin,lin&fgsnd=2020-02-01,2018-10-01,2018-10-01,2018-10-01,2018-10-01,2020-02-01,2020-02-01,2018-10-01&line_index=1,2,3,4,5,6,7,8&transformation=lin,lin,lin,lin,lin,lin,lin,lin&vintage_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07&revision_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07&nd=1980-01-01,1980-01-01,1993-01-01,1995-01-01,1990-01-01,1995-01-01,1996-01-01,1970-01-01" - ua = UserAgent(verify_ssl=False) - request_header = {"User-Agent": ua.random} - r = requests.get(tmp_url, headers=request_header) - data_text = r.content - df = pd.read_csv(io.StringIO(data_text.decode('utf-8'))) - df["DATE"] = pd.to_datetime(df["DATE"], format="%Y-%m-%d") - #df = df[list(df.columns[1:])].replace(".", np.nan).astype(float) - name_list = { - 'SLMNTO02EZQ661S': "Sales Value of Total Manufactured Goods for the Euro Area", - 'SLMNVG02EZQ661S': "Sales Value of Manufactured Investment Goods for the Euro Area", - 'SLMNCD02EZQ661S': "Sales Value of Manufactured Durable Consumer Goods for the Euro Area", - 'SLMNCN02EZQ661S': "Sales Value of Manufactured Nondurable Consumer Goods for the Euro Area", - 'SLMNIG02EZQ661S': "Sales Value of Manufactured Intermediate Goods for the Euro Area", - 'SLRTTO02EZQ661S': "Value of Total Retail Trade sales for the Euro Areaa", - 'SLRTTO01EZQ659S': "Volume of Total Retail Trade sales for the Euro Area", - 'SLRTCR03EZQ661S': "Retail Trade Sales: Passenger Car Registrations for the Euro Area"} - description = "Sales, Quarterly, Seasonally Adjusted" - return df, name_list, description - - -def Sales_Monthly_NAdj(): - tmp_url = url["fred_econ"] + "bgcolor=%23e1e9f0&chart_type=line&drp=0&fo=open%20sans&graph_bgcolor=%23ffffff&height=450&mode=fred&recession_bars=off&txtcolor=%23444444&ts=12&tts=12&width=748&nt=0&thu=0&trc=0&show_legend=yes&show_axis_titles=yes&show_tooltip=yes&id=EA19SLMNIG02IXOBM,EA19SLRTTO02IXOBM,EA19SLMNCD02IXOBM,EA19SLMNCN02IXOBM,EA19SLMNTO02IXOBM,EA19SLRTCR03IXOBM,EA19SLRTTO01IXOBM&scale=left,left,left,left,left,left,left&cosd=1990-01-01,1995-01-01,1993-01-01,1995-01-01,1980-01-01,1985-01-01,1995-01-01&coed=2018-12-01,2021-02-01,2018-12-01,2018-12-01,2021-02-01,2021-03-01,2021-02-01&line_color=%234572a7,%23aa4643,%2389a54e,%2380699b,%233d96ae,%23db843d,%2392a8cd&link_values=false,false,false,false,false,false,false&line_style=solid,solid,solid,solid,solid,solid,solid&mark_type=none,none,none,none,none,none,none&mw=3,3,3,3,3,3,3&lw=2,2,2,2,2,2,2&ost=-99999,-99999,-99999,-99999,-99999,-99999,-99999&oet=99999,99999,99999,99999,99999,99999,99999&mma=0,0,0,0,0,0,0&fml=a,a,a,a,a,a,a&fq=Monthly,Monthly,Monthly,Monthly,Monthly,Monthly,Monthly&fam=avg,avg,avg,avg,avg,avg,avg&fgst=lin,lin,lin,lin,lin,lin,lin&fgsnd=2018-12-01,2020-02-01,2018-12-01,2018-12-01,2020-02-01,2020-02-01,2020-02-01&line_index=1,2,3,4,5,6,7&transformation=lin,lin,lin,lin,lin,lin,lin&vintage_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07&revision_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07&nd=1990-01-01,1995-01-01,1993-01-01,1995-01-01,1980-01-01,1985-01-01,1995-01-01" - ua = UserAgent(verify_ssl=False) - request_header = {"User-Agent": ua.random} - r = requests.get(tmp_url, headers=request_header) - data_text = r.content - df = pd.read_csv(io.StringIO(data_text.decode('utf-8'))) - df["DATE"] = pd.to_datetime(df["DATE"], format="%Y-%m-%d") - #df = df[list(df.columns[1:])].replace(".", np.nan).astype(float) - name_list = { - 'EA19SLMNIG02IXOBM': "Sales: Manufacturing: Intermediate goods: Value for the Euro Area", - 'EA19SLRTTO02IXOBM': "Sales: Retail trade: Total retail trade: Value for the Euro Area", - 'EA19SLMNCD02IXOBM': "Sales: Manufacturing: Consumer goods durable: Value for the Euro Area", - 'EA19SLMNCN02IXOBM': "Sales: Manufacturing: Consumer goods non durable: Value for the Euro Area", - 'EA19SLMNTO02IXOBM': "Sales: Manufacturing: Total manufacturing: Value for the Euro Area", - 'EA19SLRTCR03IXOBM': "Sales: Retail trade: Car registration: Passenger cars for the Euro Area", - 'EA19SLRTTO01IXOBM': "Sales: Retail trade: Total retail trade: Volume for the Euro Area"} - description = "Sales, Monthly, Not Seasonally Adjusted" - return df, name_list, description - - -def Sales_Quarterly_NAdj(): - tmp_url = url["fred_econ"] + "bgcolor=%23e1e9f0&chart_type=line&drp=0&fo=open%20sans&graph_bgcolor=%23ffffff&height=450&mode=fred&recession_bars=off&txtcolor=%23444444&ts=12&tts=12&width=748&nt=0&thu=0&trc=0&show_legend=yes&show_axis_titles=yes&show_tooltip=yes&id=SLMNIG02EZQ661N,SLMNTO02EZQ661N,SLMNCD02EZQ661N,SLMNCN02EZQ661N,SLRTTO01EZQ661N,SLRTTO02EZQ661N,SLRTCR03EZQ661N&scale=left,left,left,left,left,left,left&cosd=1990-01-01,1980-01-01,1993-01-01,1995-01-01,1995-01-01,1995-01-01,1985-01-01&coed=2018-10-01,2020-10-01,2018-10-01,2018-10-01,2020-10-01,2020-10-01,2021-01-01&line_color=%234572a7,%23aa4643,%2389a54e,%2380699b,%233d96ae,%23db843d,%2392a8cd&link_values=false,false,false,false,false,false,false&line_style=solid,solid,solid,solid,solid,solid,solid&mark_type=none,none,none,none,none,none,none&mw=3,3,3,3,3,3,3&lw=2,2,2,2,2,2,2&ost=-99999,-99999,-99999,-99999,-99999,-99999,-99999&oet=99999,99999,99999,99999,99999,99999,99999&mma=0,0,0,0,0,0,0&fml=a,a,a,a,a,a,a&fq=Quarterly,Quarterly,Quarterly,Quarterly,Quarterly,Quarterly,Quarterly&fam=avg,avg,avg,avg,avg,avg,avg&fgst=lin,lin,lin,lin,lin,lin,lin&fgsnd=2018-10-01,2020-02-01,2018-10-01,2018-10-01,2020-02-01,2020-02-01,2020-02-01&line_index=1,2,3,4,5,6,7&transformation=lin,lin,lin,lin,lin,lin,lin&vintage_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07&revision_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07&nd=1990-01-01,1980-01-01,1993-01-01,1995-01-01,1995-01-01,1995-01-01,1985-01-01" - ua = UserAgent(verify_ssl=False) - request_header = {"User-Agent": ua.random} - r = requests.get(tmp_url, headers=request_header) - data_text = r.content - df = pd.read_csv(io.StringIO(data_text.decode('utf-8'))) - df["DATE"] = pd.to_datetime(df["DATE"], format="%Y-%m-%d") - #df = df[list(df.columns[1:])].replace(".", np.nan).astype(float) - name_list = { - 'SLMNIG02EZQ661N': "Sales Value of Manufactured Intermediate Goods for the Euro Area", - 'SLMNTO02EZQ661N': "Sales Value of Total Manufactured Goods for the Euro Area", - 'SLMNCD02EZQ661N': "Sales Value of Manufactured Durable Consumer Goods for the Euro Area", - 'SLMNCN02EZQ661N': "Sales Value of Manufactured Nondurable Consumer Goods for the Euro Area", - 'SLRTTO01EZQ661N': "Volume of Total Retail Trade sales for the Euro Area", - 'SLRTTO02EZQ661N': "Value of Total Retail Trade sales for the Euro Area", - 'SLRTCR03EZQ661N': "Retail Trade Sales: Passenger Car Registrations for the Euro Area"} - description = "Sales, Quarterly, Not Seasonally Adjusted" - return df, name_list, description - - -def Cumsumer_Opinion_Survey(): - tmp_url = url["fred_econ"] + "bgcolor=%23e1e9f0&chart_type=line&drp=0&fo=open%20sans&graph_bgcolor=%23ffffff&height=450&mode=fred&recession_bars=off&txtcolor=%23444444&ts=12&tts=12&width=748&nt=0&thu=0&trc=0&show_legend=yes&show_axis_titles=yes&show_tooltip=yes&id=CSCICP02EZM460S,CSESFT02EZM460S,CSINFT02EZM460S&scale=left,left,left&cosd=1973-01-01,1985-01-01,1985-01-01&coed=2021-04-01,2021-04-01,2021-04-01&line_color=%234572a7,%23aa4643,%2389a54e&link_values=false,false,false&line_style=solid,solid,solid&mark_type=none,none,none&mw=3,3,3&lw=2,2,2&ost=-99999,-99999,-99999&oet=99999,99999,99999&mma=0,0,0&fml=a,a,a&fq=Monthly,Monthly,Monthly&fam=avg,avg,avg&fgst=lin,lin,lin&fgsnd=2020-02-01,2020-02-01,2020-02-01&line_index=1,2,3&transformation=lin,lin,lin&vintage_date=2021-06-07,2021-06-07,2021-06-07&revision_date=2021-06-07,2021-06-07,2021-06-07&nd=1973-01-01,1985-01-01,1985-01-01" - ua = UserAgent(verify_ssl=False) - request_header = {"User-Agent": ua.random} - r = requests.get(tmp_url, headers=request_header) - data_text = r.content - df = pd.read_csv(io.StringIO(data_text.decode('utf-8'))) - df["DATE"] = pd.to_datetime(df["DATE"], format="%Y-%m-%d") - #df = df[list(df.columns[1:])].replace(".", np.nan).astype(float) - name_list = { - 'CSCICP02EZM460S': "Consumer Opinion Surveys: Confidence Indicators: Composite Indicators: European Commission and National Indicators for the Euro Area", - 'CSESFT02EZM460S': "Consumer Opinion Surveys: Economic Situation: Future Tendency: European Commission Indicator for the Euro Area", - 'CSINFT02EZM460S': "Consumer Opinion Surveys: Consumer Prices: Future Tendency of Inflation: European Commission and National Indicators for the Euro Area"} - description = "Consumer opinion surveys, Monthly, Seasonally Adjusted" - return df, name_list, description - - -class ecb_data(object): - def __init__(self, url=url["ecb"]): - self.url = url - - def codebook(self): - return "please follow the ECB's codebook: https://sdw.ecb.europa.eu/browse.do?node=9691101" - - def get_data(self, - datacode="ICP", - key="M.U2.N.000000.4.ANR", - startdate="2000-01-01", - enddate="2020-01-01"): - """ - """ - tmp_url = self.url + "{}/".format(datacode) + "{}".format(key) - ua = UserAgent(verify_ssl=False) - request_header = {"User-Agent": ua.random, 'Accept': 'text/csv'} - request_params = { - "startPeriod": "{}".format(startdate), - "endPeriod": "{}".format(enddate) - } - r = requests.get( - tmp_url, - params=request_params, - headers=request_header) - data_text = r.content - df = pd.read_csv(io.StringIO(data_text.decode('utf-8'))) - return df - - -class eurostat_data(object): - def __init__(self, url=url["eurostat"]): - self.url = url - - def codebook(self): - return "please follow the EuroStat's codebook: \nhttps://ec.europa.eu/eurostat/estat-navtree-portlet-prod/BulkDownloadListing?sort=1&dir=dic" - - def get_data(self, - datasetcode="nama_10_gdp", - precision="1", - unit="CP_MEUR", - na_item="B1GQ", - time="2020"): - """ - """ - tmp_url = self.url + "{}".format(datasetcode) - ua = UserAgent(verify_ssl=False) - request_header = {"User-Agent": ua.random, 'Accept': 'text/csv'} - request_params = { - "precision": "{}".format(precision), - "unit": "{}".format(unit), - "na_item": "{}".format(na_item), - "time": "{}".format(time) - } - r = requests.get( - tmp_url, - params=request_params, - headers=request_header) - data_text = r.text - data_json = demjson.decode(data_text) - value = data_json['value'] - abb = data_json['dimension']['geo']['category']['index'] - abb = {abb[k]: k for k in abb} - geo = data_json['dimension']['geo']['category']['label'] - geo_list = [abb[int(k)] for k in list(value.keys())] - geo = [geo[k] for k in geo_list] - df = pd.DataFrame( - {"Geo": geo, "{}".format(na_item): list(value.values())}) - return df - - -if __name__ == "__main__": - data, name_list = CPI_monthly() diff --git a/CEDA/MacroEcon/us.py b/CEDA/MacroEcon/us.py deleted file mode 100644 index 96cbb6d..0000000 --- a/CEDA/MacroEcon/us.py +++ /dev/null @@ -1,884 +0,0 @@ -import pandas as pd -import numpy as np -import requests -from fake_useragent import UserAgent -import io -import os -import demjson - -# Main Economic Indicators: https://alfred.stlouisfed.org/release?rid=205 -url = { - "fred_econ": "https://fred.stlouisfed.org/graph/fredgraph.csv?", - "philfed": "https://www.philadelphiafed.org/surveys-and-data/real-time-data-research/", - "chicagofed": "https://www.chicagofed.org/~/media/publications/"} - - -def gdp_quarterly(startdate="1947-01-01", enddate="2021-01-01"): - """ - Full Name: Gross Domestic Product - Description: Billions of Dollars, Quarterly, Seasonally Adjusted Annual Rate - Return: pd.DataFrame - """ - tmp_url = url["fred_econ"] - ua = UserAgent(verify_ssl=False) - request_header = {"User-Agent": ua.random} - request_params = { - "id": "GDP", - "cosd": "{}".format(startdate), - "coed": "{}".format(enddate) - } - r = requests.get(tmp_url, params=request_params, headers=request_header) - data_text = r.content - df = pd.read_csv(io.StringIO(data_text.decode('utf-8'))) - return df - - -def gdpc1_quarterly(startdate="1947-01-01", enddate="2021-01-01"): - """ - Full Name: Real Gross Domestic Product - Description: Billions of Chained 2012 Dollars, Quarterly, Seasonally Adjusted Annual Rate - Return: pd.DataFrame - """ - tmp_url = url["fred_econ"] - ua = UserAgent(verify_ssl=False) - request_header = {"User-Agent": ua.random} - request_params = { - "id": "GDPC1", - "cosd": "{}".format(startdate), - "coed": "{}".format(enddate) - } - r = requests.get(tmp_url, params=request_params, headers=request_header) - data_text = r.content - df = pd.read_csv(io.StringIO(data_text.decode('utf-8'))) - return df - - -def oecd_gdp_monthly(startdate="1947-01-01", enddate="2021-01-01"): - """ - Full Name: Real Gross Domestic Product - Description: Billions of Chained 2012 Dollars, Quarterly, Seasonally Adjusted Annual Rate - Return: pd.DataFrame - """ - tmp_url = url["fred_econ"] - ua = UserAgent(verify_ssl=False) - request_header = {"User-Agent": ua.random} - request_params = { - "id": "USALORSGPNOSTSAM", - "cosd": "{}".format(startdate), - "coed": "{}".format(enddate) - } - r = requests.get(tmp_url, params=request_params, headers=request_header) - data_text = r.content - df = pd.read_csv(io.StringIO(data_text.decode('utf-8'))) - return df - - -def payems_monthly(startdate="1939-01-01", enddate="2021-01-01"): - """ - Full Name: All Employees, Total Nonfarm - Description: Thousands of Persons,Seasonally Adjusted, Monthly - Return: pd.DataFrame - """ - tmp_url = url["fred_econ"] - ua = UserAgent(verify_ssl=False) - request_header = {"User-Agent": ua.random} - request_params = { - "id": "PAYEMS", - "cosd": "{}".format(startdate), - "coed": "{}".format(enddate) - } - r = requests.get(tmp_url, params=request_params, headers=request_header) - data_text = r.content - df = pd.read_csv(io.StringIO(data_text.decode('utf-8'))) - return df - - -def unrate(startdate="1948-01-01", enddate="2021-01-01"): - """ - Full Name: Unemployment Rate: Aged 15-64: All Persons for the United States - Description: Percent, Seasonally Adjusted, Monthly, Quarterly and Annually - Return: pd.DataFrame - """ - tmp_url = url["fred_econ"] - ua = UserAgent(verify_ssl=False) - request_header = {"User-Agent": ua.random} - request_params = { - "id": "LRUN64TTUSM156S", - "cosd": "{}".format(startdate), - "coed": "{}".format(enddate) - } - r = requests.get(tmp_url, params=request_params, headers=request_header) - data_text = r.content - df_monthly = pd.read_csv(io.StringIO(data_text.decode('utf-8'))) - df_monthly["DATE"] = pd.to_datetime(df_monthly["DATE"], format="%Y-%m-%d") - ua = UserAgent(verify_ssl=False) - request_header = {"User-Agent": ua.random} - request_params = { - "id": "LRUN64TTUSQ156S", - "cosd": "{}".format(startdate), - "coed": "{}".format(enddate) - } - r = requests.get(tmp_url, params=request_params, headers=request_header) - data_text = r.content - df_quarterly = pd.read_csv(io.StringIO(data_text.decode('utf-8'))) - df_quarterly["DATE"] = pd.to_datetime( - df_quarterly["DATE"], format="%Y-%m-%d") - ua = UserAgent(verify_ssl=False) - request_header = {"User-Agent": ua.random} - request_params = { - "id": "LRUN64TTUSA156S", - "cosd": "{}".format(startdate), - "coed": "{}".format(enddate) - } - r = requests.get(tmp_url, params=request_params, headers=request_header) - data_text = r.content - df_annually = pd.read_csv(io.StringIO(data_text.decode('utf-8'))) - df_annually["DATE"] = pd.to_datetime( - df_annually["DATE"], format="%Y-%m-%d") - df = pd.merge_asof( - df_monthly, - df_quarterly, - on="DATE", - direction="backward") - df = pd.merge_asof(df, df_annually, on="DATE", direction="backward") - df.columns = ["Date", "UR_Monthly", "UR_Quarterly", "UR_Annually"] - return df - - -def erate(startdate="1955-01-01", enddate="2021-01-01"): - """ - Full Name: Employment Rate: Aged 25-54: All Persons for the United States - Description: Percent,Seasonally Adjusted, Monthly, Quarterly and Annually - Return: pd.DataFrame - """ - tmp_url = url["fred_econ"] - ua = UserAgent(verify_ssl=False) - request_header = {"User-Agent": ua.random} - request_params = { - "id": "LREM25TTUSM156S", - "cosd": "{}".format(startdate), - "coed": "{}".format(enddate) - } - r = requests.get(tmp_url, params=request_params, headers=request_header) - data_text = r.content - df_monthly = pd.read_csv(io.StringIO(data_text.decode('utf-8'))) - df_monthly["DATE"] = pd.to_datetime(df_monthly["DATE"], format="%Y-%m-%d") - ua = UserAgent(verify_ssl=False) - request_header = {"User-Agent": ua.random} - request_params = { - "id": "LREM25TTUSQ156S", - "cosd": "{}".format(startdate), - "coed": "{}".format(enddate) - } - r = requests.get(tmp_url, params=request_params, headers=request_header) - data_text = r.content - df_quarterly = pd.read_csv(io.StringIO(data_text.decode('utf-8'))) - df_quarterly["DATE"] = pd.to_datetime( - df_quarterly["DATE"], format="%Y-%m-%d") - ua = UserAgent(verify_ssl=False) - request_header = {"User-Agent": ua.random} - request_params = { - "id": "LREM25TTUSA156S", - "cosd": "{}".format(startdate), - "coed": "{}".format(enddate) - } - r = requests.get(tmp_url, params=request_params, headers=request_header) - data_text = r.content - df_annually = pd.read_csv(io.StringIO(data_text.decode('utf-8'))) - df_annually["DATE"] = pd.to_datetime( - df_annually["DATE"], format="%Y-%m-%d") - df = pd.merge_asof( - df_monthly, - df_quarterly, - on="DATE", - direction="backward") - df = pd.merge_asof(df, df_annually, on="DATE", direction="backward") - df.columns = ["Date", "ER_Monthly", "ER_Quarterly", "ER_Annually"] - - -def pce_monthly(startdate="1959-01-01", enddate="2021-01-01"): - """ - Full Name: PCE - Description: Percent, Monthly, Seasonally Adjusted - Return: pd.DataFrame - """ - tmp_url = url["fred_econ"] - ua = UserAgent(verify_ssl=False) - request_header = {"User-Agent": ua.random} - request_params = { - "id": "PCE", - "cosd": "{}".format(startdate), - "coed": "{}".format(enddate) - } - r = requests.get(tmp_url, params=request_params, headers=request_header) - data_text = r.content - df = pd.read_csv(io.StringIO(data_text.decode('utf-8'))) - return df - - -def cpi(startdate="1960-01-01", enddate="2021-01-01"): - """ - Full Name: Consumer Price Index: Total All Items for the United States - Description: Percent, Monthly, Quarterly and Annually, Seasonally Adjusted - Return: pd.DataFrame - """ - tmp_url = url["fred_econ"] - ua = UserAgent(verify_ssl=False) - request_header = {"User-Agent": ua.random} - request_params = { - "id": "CPALTT01USM661S", - "cosd": "{}".format(startdate), - "coed": "{}".format(enddate) - } - r = requests.get(tmp_url, params=request_params, headers=request_header) - data_text = r.content - df_monthly = pd.read_csv(io.StringIO(data_text.decode('utf-8'))) - df_monthly["DATE"] = pd.to_datetime(df_monthly["DATE"], format="%Y-%m-%d") - ua = UserAgent(verify_ssl=False) - request_header = {"User-Agent": ua.random} - request_params = { - "id": "CPALTT01USQ661S", - "cosd": "{}".format(startdate), - "coed": "{}".format(enddate) - } - r = requests.get(tmp_url, params=request_params, headers=request_header) - data_text = r.content - df_quarterly = pd.read_csv(io.StringIO(data_text.decode('utf-8'))) - df_quarterly["DATE"] = pd.to_datetime( - df_quarterly["DATE"], format="%Y-%m-%d") - ua = UserAgent(verify_ssl=False) - request_header = {"User-Agent": ua.random} - request_params = { - "id": "CPALTT01USA661S", - "cosd": "{}".format(startdate), - "coed": "{}".format(enddate) - } - r = requests.get(tmp_url, params=request_params, headers=request_header) - data_text = r.content - df_annually = pd.read_csv(io.StringIO(data_text.decode('utf-8'))) - df_annually["DATE"] = pd.to_datetime( - df_annually["DATE"], format="%Y-%m-%d") - df = pd.merge_asof( - df_monthly, - df_quarterly, - on="DATE", - direction="backward") - df = pd.merge_asof(df, df_annually, on="DATE", direction="backward") - df.columns = ["Date", "CPI_Monthly", "CPI_Quarterly", "CPI_Annually"] - return df - - -def m1(startdate="1960-01-01", enddate="2021-01-01"): - """ - Full Name: Consumer Price Index: M3 for the United States - Description: Growth Rate Previous Period, Monthly, Quarterly and Annually, Seasonally Adjusted - Return: pd.DataFrame - """ - tmp_url = url["fred_econ"] - ua = UserAgent(verify_ssl=False) - request_header = {"User-Agent": ua.random} - request_params = { - "id": "WM1NS", - "cosd": "{}".format(startdate), - "coed": "{}".format(enddate) - } - r = requests.get(tmp_url, params=request_params, headers=request_header) - data_text = r.content - df_weekly = pd.read_csv(io.StringIO(data_text.decode('utf-8'))) - df_weekly["DATE"] = pd.to_datetime(df_weekly["DATE"], format="%Y-%m-%d") - ua = UserAgent(verify_ssl=False) - request_header = {"User-Agent": ua.random} - request_params = { - "id": "MANMM101USM657S", - "cosd": "{}".format(startdate), - "coed": "{}".format(enddate) - } - r = requests.get(tmp_url, params=request_params, headers=request_header) - data_text = r.content - df_monthly = pd.read_csv(io.StringIO(data_text.decode('utf-8'))) - df_monthly["DATE"] = pd.to_datetime(df_monthly["DATE"], format="%Y-%m-%d") - ua = UserAgent(verify_ssl=False) - request_header = {"User-Agent": ua.random} - request_params = { - "id": "MANMM101USQ657S", - "cosd": "{}".format(startdate), - "coed": "{}".format(enddate) - } - r = requests.get(tmp_url, params=request_params, headers=request_header) - data_text = r.content - df_quarterly = pd.read_csv(io.StringIO(data_text.decode('utf-8'))) - df_quarterly["DATE"] = pd.to_datetime( - df_quarterly["DATE"], format="%Y-%m-%d") - ua = UserAgent(verify_ssl=False) - request_header = {"User-Agent": ua.random} - request_params = { - "id": "MANMM101USA657S", - "cosd": "{}".format(startdate), - "coed": "{}".format(enddate) - } - r = requests.get(tmp_url, params=request_params, headers=request_header) - data_text = r.content - df_annually = pd.read_csv(io.StringIO(data_text.decode('utf-8'))) - df_annually["DATE"] = pd.to_datetime( - df_annually["DATE"], format="%Y-%m-%d") - df = pd.merge_asof(df_weekly, df_monthly, on="DATE", direction="backward") - df = pd.merge_asof(df, df_quarterly, on="DATE", direction="backward") - df = pd.merge_asof(df, df_annually, on="DATE", direction="backward") - df.columns = [ - "Date", - "M1_weekly", - "M1_Monthly", - "M1_Quarterly", - "M1_Annually"] - return df - - -def m2(startdate="1960-01-01", enddate="2021-01-01"): - """ - Full Name: M2 Money Stock - Description: Seasonally Adjusted, Weekly, Monthly, Quarterly and Annually, Seasonally Adjusted - Return: pd.DataFrame - """ - tmp_url = url["fred_econ"] - ua = UserAgent(verify_ssl=False) - request_header = {"User-Agent": ua.random} - request_params = { - "id": "WM2NS", - "cosd": "{}".format(startdate), - "coed": "{}".format(enddate) - } - r = requests.get(tmp_url, params=request_params, headers=request_header) - data_text = r.content - df_weekly = pd.read_csv(io.StringIO(data_text.decode('utf-8'))) - df_weekly["DATE"] = pd.to_datetime(df_weekly["DATE"], format="%Y-%m-%d") - ua = UserAgent(verify_ssl=False) - request_header = {"User-Agent": ua.random} - request_params = { - "id": "M2SL", - "cosd": "{}".format(startdate), - "coed": "{}".format(enddate) - } - r = requests.get(tmp_url, params=request_params, headers=request_header) - data_text = r.content - df_monthly = pd.read_csv(io.StringIO(data_text.decode('utf-8'))) - df_monthly["DATE"] = pd.to_datetime(df_monthly["DATE"], format="%Y-%m-%d") - df = pd.merge_asof(df_weekly, df_monthly, on="DATE", direction="backward") - df.columns = ["Date", "M2_Weekly", "M2_Monthly"] - - -def m3(startdate="1960-01-01", enddate="2021-01-01"): - """ - Full Name: Consumer Price Index: M3 for the United States - Description: Growth Rate Previous Period, Monthly, Quarterly and Annually, Seasonally Adjusted - Return: pd.DataFrame - """ - tmp_url = url["fred_econ"] - ua = UserAgent(verify_ssl=False) - request_header = {"User-Agent": ua.random} - request_params = { - "id": "MABMM301USM657S", - "cosd": "{}".format(startdate), - "coed": "{}".format(enddate) - } - r = requests.get(tmp_url, params=request_params, headers=request_header) - data_text = r.content - df_monthly = pd.read_csv(io.StringIO(data_text.decode('utf-8'))) - df_monthly["DATE"] = pd.to_datetime(df_monthly["DATE"], format="%Y-%m-%d") - ua = UserAgent(verify_ssl=False) - request_header = {"User-Agent": ua.random} - request_params = { - "id": "MABMM301USQ657S", - "cosd": "{}".format(startdate), - "coed": "{}".format(enddate) - } - r = requests.get(tmp_url, params=request_params, headers=request_header) - data_text = r.content - df_quarterly = pd.read_csv(io.StringIO(data_text.decode('utf-8'))) - df_quarterly["DATE"] = pd.to_datetime( - df_quarterly["DATE"], format="%Y-%m-%d") - ua = UserAgent(verify_ssl=False) - request_header = {"User-Agent": ua.random} - request_params = { - "id": "MABMM301USA657S", - "cosd": "{}".format(startdate), - "coed": "{}".format(enddate) - } - r = requests.get(tmp_url, params=request_params, headers=request_header) - data_text = r.content - df_annually = pd.read_csv(io.StringIO(data_text.decode('utf-8'))) - df_annually["DATE"] = pd.to_datetime( - df_annually["DATE"], format="%Y-%m-%d") - df = pd.merge_asof( - df_monthly, - df_quarterly, - on="DATE", - direction="backward") - df = pd.merge_asof(df, df_annually, on="DATE", direction="backward") - df.columns = ["Date", "M3_Monthly", "M3_Quarterly", "M3_Annually"] - return df - - -def ltgby_10(startdate="1955-01-01", enddate="2021-01-01"): - """ - Full Name: Long-Term Government Bond Yields: 10-year: Main (Including Benchmark) for the United States - Description: Percent,Not Seasonally Adjusted, Monthly, Quarterly and Annually - Return: pd.DataFrame - """ - tmp_url = url["fred_econ"] - ua = UserAgent(verify_ssl=False) - request_header = {"User-Agent": ua.random} - request_params = { - "id": "IRLTLT01USM156N", - "cosd": "{}".format(startdate), - "coed": "{}".format(enddate) - } - r = requests.get(tmp_url, params=request_params, headers=request_header) - data_text = r.content - df_monthly = pd.read_csv(io.StringIO(data_text.decode('utf-8'))) - df_monthly["DATE"] = pd.to_datetime(df_monthly["DATE"], format="%Y-%m-%d") - ua = UserAgent(verify_ssl=False) - request_header = {"User-Agent": ua.random} - request_params = { - "id": "IRLTLT01USQ156N", - "cosd": "{}".format(startdate), - "coed": "{}".format(enddate) - } - r = requests.get(tmp_url, params=request_params, headers=request_header) - data_text = r.content - df_quarterly = pd.read_csv(io.StringIO(data_text.decode('utf-8'))) - df_quarterly["DATE"] = pd.to_datetime( - df_quarterly["DATE"], format="%Y-%m-%d") - ua = UserAgent(verify_ssl=False) - request_header = {"User-Agent": ua.random} - request_params = { - "id": "IRLTLT01USA156N", - "cosd": "{}".format(startdate), - "coed": "{}".format(enddate) - } - r = requests.get(tmp_url, params=request_params, headers=request_header) - data_text = r.content - df_annually = pd.read_csv(io.StringIO(data_text.decode('utf-8'))) - df_annually["DATE"] = pd.to_datetime( - df_annually["DATE"], format="%Y-%m-%d") - df = pd.merge_asof( - df_monthly, - df_quarterly, - on="DATE", - direction="backward") - df = pd.merge_asof(df, df_annually, on="DATE", direction="backward") - df.columns = ["Date", "ltgby_Monthly", "ltgby_Quarterly", "ltgby_Annually"] - return df - - -def gdp_ipd(startdate="1955-01-01", enddate="2021-01-01"): - """ - Full Name: Long-Term Government Bond Yields: 10-year: Main (Including Benchmark) for the United States - Description: Percent,Not Seasonally Adjusted, Monthly, Quarterly and Annually - Return: pd.DataFrame - """ - tmp_url = url["fred_econ"] - ua = UserAgent(verify_ssl=False) - request_header = {"User-Agent": ua.random} - request_params = { - "id": "USAGDPDEFQISMEI", - "cosd": "{}".format(startdate), - "coed": "{}".format(enddate) - } - r = requests.get(tmp_url, params=request_params, headers=request_header) - data_text = r.content - df_quarterly = pd.read_csv(io.StringIO(data_text.decode('utf-8'))) - df_quarterly["DATE"] = pd.to_datetime( - df_quarterly["DATE"], format="%Y-%m-%d") - ua = UserAgent(verify_ssl=False) - request_header = {"User-Agent": ua.random} - request_params = { - "id": "USAGDPDEFAISMEI", - "cosd": "{}".format(startdate), - "coed": "{}".format(enddate) - } - r = requests.get(tmp_url, params=request_params, headers=request_header) - data_text = r.content - df_annually = pd.read_csv(io.StringIO(data_text.decode('utf-8'))) - df_annually["DATE"] = pd.to_datetime( - df_annually["DATE"], format="%Y-%m-%d") - df = pd.merge_asof( - df_quarterly, - df_annually, - on="DATE", - direction="backward") - df.columns = ["Date", "gdp_ipd_Quarterly", "gdp_ipd_Annually"] - return df - - -def cci(startdate="1955-01-01", enddate="2021-01-01"): - """ - Full Name: Consumer Opinion Surveys: Confidence Indicators: Composite Indicators: OECD Indicator for the United States - Description: Normalised (Normal=100), Seasonally Adjusted, Monthly - Return: pd.DataFrame - """ - tmp_url = url["fred_econ"] - ua = UserAgent(verify_ssl=False) - request_header = {"User-Agent": ua.random} - request_params = { - "id": "CSCICP03USM665S", - "cosd": "{}".format(startdate), - "coed": "{}".format(enddate) - } - r = requests.get(tmp_url, params=request_params, headers=request_header) - data_text = r.content - df = pd.read_csv(io.StringIO(data_text.decode('utf-8'))) - df.columns = ["Date", "CCI_Monthly"] - return df - - -def bci(startdate="1955-01-01", enddate="2021-01-01"): - """ - Full Name: Consumer Opinion Surveys: Confidence Indicators: Composite Indicators: OECD Indicator for the United States - Description: Normalised (Normal=100), Seasonally Adjusted, Monthly - Return: pd.DataFrame - """ - tmp_url = url["fred_econ"] - ua = UserAgent(verify_ssl=False) - request_header = {"User-Agent": ua.random} - request_params = { - "id": "BSCICP03USM665S", - "cosd": "{}".format(startdate), - "coed": "{}".format(enddate) - } - r = requests.get(tmp_url, params=request_params, headers=request_header) - data_text = r.content - df = pd.read_csv(io.StringIO(data_text.decode('utf-8'))) - df.columns = ["Date", "BCI_Annually"] - return df - - -def ibr_3(startdate="1965-01-01", enddate="2021-01-01"): - """ - Full Name: 3-Month or 90-day Rates and Yields: Interbank Rates for the United States - Description: Percent, Not Seasonally Adjusted, Monthly and Quarterly - """ - tmp_url = url["fred_econ"] - ua = UserAgent(verify_ssl=False) - request_header = {"User-Agent": ua.random} - request_params = { - "id": "IR3TIB01USM156N", - "cosd": "{}".format(startdate), - "coed": "{}".format(enddate) - } - r = requests.get(tmp_url, params=request_params, headers=request_header) - data_text = r.content - df_monthly = pd.read_csv(io.StringIO(data_text.decode('utf-8'))) - df_monthly["DATE"] = pd.to_datetime(df_monthly["DATE"], format="%Y-%m-%d") - ua = UserAgent(verify_ssl=False) - request_header = {"User-Agent": ua.random} - request_params = { - "id": "IR3TIB01USQ156N", - "cosd": "{}".format(startdate), - "coed": "{}".format(enddate) - } - r = requests.get(tmp_url, params=request_params, headers=request_header) - data_text = r.content - df_quarterly = pd.read_csv(io.StringIO(data_text.decode('utf-8'))) - df_quarterly["DATE"] = pd.to_datetime( - df_quarterly["DATE"], format="%Y-%m-%d") - df = pd.merge_asof( - df_quarterly, - df_quarterly, - on="DATE", - direction="backward") - df.columns = ["Date", "ibr3_monthly", "ibr3_Quarterly"] - - -def gfcf_3(startdate="1965-01-01", enddate="2021-01-01"): - """ - Full Name: Gross Fixed Capital Formation in United States - Description: United States Dollars,Not Seasonally Adjusted, Quarterly and Annually - """ - tmp_url = url["fred_econ"] - ua = UserAgent(verify_ssl=False) - request_header = {"User-Agent": ua.random} - request_params = { - "id": "USAGFCFQDSMEI", - "cosd": "{}".format(startdate), - "coed": "{}".format(enddate) - } - r = requests.get(tmp_url, params=request_params, headers=request_header) - data_text = r.content - df_quarterly = pd.read_csv(io.StringIO(data_text.decode('utf-8'))) - df_quarterly["DATE"] = pd.to_datetime( - df_quarterly["DATE"], format="%Y-%m-%d") - ua = UserAgent(verify_ssl=False) - request_header = {"User-Agent": ua.random} - request_params = { - "id": "USAGFCFADSMEI", - "cosd": "{}".format(startdate), - "coed": "{}".format(enddate) - } - r = requests.get(tmp_url, params=request_params, headers=request_header) - data_text = r.content - df_annually = pd.read_csv(io.StringIO(data_text.decode('utf-8'))) - df_annually["DATE"] = pd.to_datetime( - df_annually["DATE"], format="%Y-%m-%d") - df = pd.merge_asof( - df_quarterly, - df_quarterly, - on="DATE", - direction="backward") - df.columns = ["Date", "ibr3_monthly", "ibr3_Annually"] - - -def pfce(startdate="1955-01-01", enddate="2021-01-01"): - """ - Full Name: Employment Rate: Aged 25-54: All Persons for the United States - Description: Percent,Seasonally Adjusted, Monthly, Quarterly and Annually - Return: pd.DataFrame - """ - tmp_url = url["fred_econ"] - ua = UserAgent(verify_ssl=False) - request_header = {"User-Agent": ua.random} - request_params = { - "id": "USAPFCEQDSMEI", - "cosd": "{}".format(startdate), - "coed": "{}".format(enddate) - } - r = requests.get(tmp_url, params=request_params, headers=request_header) - data_text = r.content - df_quarterly = pd.read_csv(io.StringIO(data_text.decode('utf-8'))) - df_quarterly["DATE"] = pd.to_datetime( - df_quarterly["DATE"], format="%Y-%m-%d") - ua = UserAgent(verify_ssl=False) - request_header = {"User-Agent": ua.random} - request_params = { - "id": "USAPFCEADSMEI", - "cosd": "{}".format(startdate), - "coed": "{}".format(enddate) - } - r = requests.get(tmp_url, params=request_params, headers=request_header) - data_text = r.content - df_annually = pd.read_csv(io.StringIO(data_text.decode('utf-8'))) - df_annually["DATE"] = pd.to_datetime( - df_annually["DATE"], format="%Y-%m-%d") - df = pd.merge_asof( - df_quarterly, - df_annually, - on="DATE", - direction="backward") - df.columns = ["Date", "PFCE_Quarterly", "PFCE_Annually"] - - -def tlp(startdate="1955-01-01", enddate="2021-01-01"): - """ - Full Name: Early Estimate of Quarterly ULC Indicators: Total Labor Productivity for the United States - Description: Growth Rate Previous Period,Seasonally Adjusted, Quarterly and YoY - Return: pd.DataFrame - """ - tmp_url = url["fred_econ"] - ua = UserAgent(verify_ssl=False) - request_header = {"User-Agent": ua.random} - request_params = { - "id": "ULQELP01USQ657S", - "cosd": "{}".format(startdate), - "coed": "{}".format(enddate) - } - r = requests.get(tmp_url, params=request_params, headers=request_header) - data_text = r.content - df_quarterly = pd.read_csv(io.StringIO(data_text.decode('utf-8'))) - df_quarterly["DATE"] = pd.to_datetime( - df_quarterly["DATE"], format="%Y-%m-%d") - ua = UserAgent(verify_ssl=False) - request_header = {"User-Agent": ua.random} - request_params = { - "id": "ULQELP01USQ659S", - "cosd": "{}".format(startdate), - "coed": "{}".format(enddate) - } - r = requests.get(tmp_url, params=request_params, headers=request_header) - data_text = r.content - df_annually = pd.read_csv(io.StringIO(data_text.decode('utf-8'))) - df_annually["DATE"] = pd.to_datetime( - df_annually["DATE"], format="%Y-%m-%d") - df = pd.merge_asof( - df_quarterly, - df_annually, - on="DATE", - direction="backward") - df.columns = ["Date", "PFCE_Quarterly", "PFCE_Quarterly_YoY"] - - -def rt(startdate="1955-01-01", enddate="2021-01-01"): - """ - Full Name:Total Retail Trade in United States - Description: Monthly and Anually - Return: pd.DataFrame - """ - tmp_url = url["fred_econ"] - ua = UserAgent(verify_ssl=False) - request_header = {"User-Agent": ua.random} - request_params = { - "id": "USASARTMISMEI", - "cosd": "{}".format(startdate), - "coed": "{}".format(enddate) - } - r = requests.get(tmp_url, params=request_params, headers=request_header) - data_text = r.content - df_monthly = pd.read_csv(io.StringIO(data_text.decode('utf-8'))) - df_monthly["DATE"] = pd.to_datetime(df_monthly["DATE"], format="%Y-%m-%d") - request_header = {"User-Agent": ua.random} - request_params = { - "id": "USASARTAISMEI", - "cosd": "{}".format(startdate), - "coed": "{}".format(enddate) - } - r = requests.get(tmp_url, params=request_params, headers=request_header) - data_text = r.content - df_annually = pd.read_csv(io.StringIO(data_text.decode('utf-8'))) - df_annually["DATE"] = pd.to_datetime( - df_annually["DATE"], format="%Y-%m-%d") - df = pd.merge_asof( - df_monthly, - df_annually, - on="DATE", - direction="backward") - df.columns = ["Date", "RT_Quarterly", "RT_Annually"] - return df - - -def bir(startdate="2003-01-01", enddate="2021-01-01"): - """ - Full Name:Total Retail Trade in United States - Description: Monthly and Anually - Return: pd.DataFrame - """ - tmp_url = url["fred_econ"] - ua = UserAgent(verify_ssl=False) - request_header = {"User-Agent": ua.random} - request_params = { - "id": "T5YIE", - "cosd": "{}".format(startdate), - "coed": "{}".format(enddate) - } - r = requests.get(tmp_url, params=request_params, headers=request_header) - data_text = r.content - df_5y = pd.read_csv(io.StringIO(data_text.decode('utf-8'))) - df_5y["DATE"] = pd.to_datetime(df_5y["DATE"], format="%Y-%m-%d") - request_header = {"User-Agent": ua.random} - request_params = { - "id": "T10YIE", - "cosd": "{}".format(startdate), - "coed": "{}".format(enddate) - } - r = requests.get(tmp_url, params=request_params, headers=request_header) - data_text = r.content - df_10y = pd.read_csv(io.StringIO(data_text.decode('utf-8'))) - df_10y["DATE"] = pd.to_datetime(df_10y["DATE"], format="%Y-%m-%d") - df = pd.merge_asof(df_5y, df_10y, on="DATE", direction="backward") - df.columns = ["Date", "BIR_5y", "BIR_10y"] - return df - - -def adsbci(): - """ - An index designed to track real business conditions at high observation frequency - """ - ua = UserAgent(verify_ssl=False) - request_header = {"User-Agent": ua.random} - tmp_url = url["philfed"] + "ads" - r = requests.get(tmp_url, headers=request_header) - file = open("ads_temp.xls", "wb") - file.write(r.content) - file.close() - df = pd.read_excel("ads_temp.xls") - df.columns = ["Date", "ADS_Index"] - df['Date'] = pd.to_datetime(df["Date"], format="%Y:%m:%d") - os.remove("ads_temp.xls") - return df - - -def pci(): - """ - Tracks the degree of political disagreement among U.S. politicians at the federal level, Monthly - """ - df = pd.read_excel( - "https://www.philadelphiafed.org/-/media/frbp/assets/data-visualizations/partisan-conflict.xlsx") - df["Date"] = df["Year"].astype(str) + df["Month"] - df["Date"] = pd.to_datetime(df["Date"], format="%Y%B") - df = df.drop(["Year", "Month"], axis=1) - df = df[["Date", "Partisan Conflict"]] - return df - - -def inflation_noewcasting(): - """ - - """ - ua = UserAgent(verify_ssl=False) - request_header = {"User-Agent": ua.random} - tmp_url = "https://www.clevelandfed.org/~/media/files/charting/%20nowcast_quarter.json" - - r = requests.get(tmp_url, headers=request_header) - tmp_df = pd.DataFrame(demjson.decode(r.text)) - df = pd.DataFrame() - for i in range(0, len(tmp_df)): - date = tmp_df['chart'][i]['subcaption'][:4] + "/" + \ - pd.DataFrame(tmp_df["dataset"][i][0]['data'])['tooltext'].str.extract(r"\b(0?[1-9]|1[0-2])/(0?[1-9]|[12][0-9]|3[01])\b")[0] + "/" + \ - pd.DataFrame(tmp_df["dataset"][i][0]['data'])['tooltext'].str.extract(r"\b(0?[1-9]|1[0-2])/(0?[1-9]|[12][0-9]|3[01])\b")[1] - CPI_I = pd.DataFrame( - (pd.DataFrame(tmp_df["dataset"][i])['data'])[0])["value"] - C_CPI_I = pd.DataFrame( - (pd.DataFrame(tmp_df["dataset"][i])['data'])[1])["value"] - PCE_I = pd.DataFrame( - (pd.DataFrame(tmp_df["dataset"][i])['data'])[2])["value"] - C_PCE_I = pd.DataFrame( - (pd.DataFrame(tmp_df["dataset"][i])['data'])[3])["value"] - A_CPI_I = pd.DataFrame( - (pd.DataFrame(tmp_df["dataset"][i])['data'])[4])["value"] - A_C_CPI_I = pd.DataFrame( - (pd.DataFrame(tmp_df["dataset"][i])['data'])[5])["value"] - A_PCE_I = pd.DataFrame( - (pd.DataFrame(tmp_df["dataset"][i])['data'])[6])["value"] - A_C_PCE_I = pd.DataFrame( - (pd.DataFrame(tmp_df["dataset"][i])['data'])[7])["value"] - tmp_df2 = pd.DataFrame({"date": date, - "CPI_I": CPI_I, - "C_CPI_I": C_CPI_I, - "PCE_I": PCE_I, - "C_PCE_I": C_PCE_I, - "A_CPI_I": A_CPI_I, - "A_C_CPI_I": A_C_CPI_I, - "A_PCE_I": A_PCE_I, - "A_C_PCE_I": A_C_PCE_I}) - df = pd.concat([df, tmp_df2], axis=0) - df.reset_index(drop=True, inplace=True) - - df.replace('', np.nan, inplace=True) - return df - - -def bbki(): - tmp_url = url["chicagofed"] + "bbki/bbki-monthly-data-series-csv.csv" - df = pd.read_csv(tmp_url) - return df - - -def cfnai(): - tmp_url = url["chicagofed"] + "cfnai/cfnai-data-series-csv.csv" - df = pd.read_csv(tmp_url) - return df - - -def cfsbc(): - tmp_url = url["chicagofed"] + "cfsbc-activity-index-csv.csv" - df = pd.read_csv(tmp_url) - return df - - -def nfci(): - tmp_url = url["chicagofed"] + "nfci/decomposition-nfci-csv.csv" - df = pd.read_csv(tmp_url) - return df - - -def nfci(): - tmp_url = url["chicagofed"] + "nfci/decomposition-anfci-csv.csv" - df = pd.read_csv(tmp_url) - return df