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import io
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import os
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import demjson
import requests
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import numpy as np
import pandas as pd
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from fake_useragent import UserAgent
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from pandas . core . frame import DataFrame
from pandas . core . reshape . merge import merge
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# Main Economic Indicators: https://alfred.stlouisfed.org/release?rid=205
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url = {
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" fred_econ " : " https://fred.stlouisfed.org/graph/fredgraph.csv? " ,
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" eurostat " : " http://ec.europa.eu/eurostat/wdds/rest/data/v2.1/json/en/ " ,
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" ecb " : " https://sdw-wsrest.ecb.europa.eu/service/data/ "
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}
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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= %23e 1e9f0&chart_type=line&drp=0&fo=open %20s ans&graph_bgcolor= %23f fffff&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= %234572a 7, %23a a4643, %2389a 54e, % 2380699b, %233d 96ae, %23d b843d, %2392a 8cd, %23a 47d7c, % 23b5ca92, %2391e 8e1, %238d 4653, %238085e 8&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= %23e 1e9f0&chart_type=line&drp=0&fo=open %20s ans&graph_bgcolor= %23f fffff&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= %234572a 7, %23a a4643, %2389a 54e&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= %23e 1e9f0&chart_type=line&drp=0&fo=open %20s ans&graph_bgcolor= %23f fffff&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= %234572a 7, %23a a4643, %2389a 54e, % 2380699b, %233d 96ae&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 % o f Current account for the Euro Area " ,
' EA19B6DBSE02STSAQ ' : " Balance of payments BPM6: Current account Debits: Services: Total Debits as % o f Current account for the Euro Area " ,
' EA19B6DBSE03STSAQ ' : " Balance of payments BPM6: Current account Debits: Services: Total Debits as % o f Goods and Services for the Euro Area " ,
' EA19B6CRSE03STSAQ ' : " Balance of payments BPM6: Current account Credits: Services: Total Credits as % o f Goods and Services for Euro Area " ,
' EA19B6CRSE02STSAQ ' : " Balance of payments BPM6: Current account Credits: Services: Total Credits as % o f 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= %23e 1e9f0&chart_type=line&drp=0&fo=open %20s ans&graph_bgcolor= %23f fffff&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= %234572a 7, %23a a4643, %2389a 54e, % 2380699b, %233d 96ae, %23d b843d, %2392a 8cd&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= %23e 1e9f0&chart_type=line&drp=0&fo=open %20s ans&graph_bgcolor= %23f fffff&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= %234572a 7, %23a a4643&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= %23e 1e9f0&chart_type=line&drp=0&fo=open %20s ans&graph_bgcolor= %23f fffff&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= %234572a 7, %23a a4643&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= %23e 1e9f0&chart_type=line&drp=0&fo=open %20s ans&graph_bgcolor= %23f fffff&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= %234572a 7, %23a a4643, %2389a 54e, % 2380699b, %233d 96ae&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= %23e 1e9f0&chart_type=line&drp=0&fo=open %20s ans&graph_bgcolor= %23f fffff&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= %234572a 7, %23a a4643, %2389a 54e, % 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= %23e 1e9f0&chart_type=line&drp=0&fo=open %20s ans&graph_bgcolor= %23f fffff&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= %234572a 7, %23a a4643, %2389a 54e&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= %23e 1e9f0&chart_type=line&drp=0&fo=open %20s ans&graph_bgcolor= %23f fffff&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= %234572a 7, %23a a4643, %2389a 54e&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= %23e 1e9f0&chart_type=line&drp=0&fo=open %20s ans&graph_bgcolor= %23f fffff&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= %234572a 7&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= %23e 1e9f0&chart_type=line&drp=0&fo=open %20s ans&graph_bgcolor= %23f fffff&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= %234572a 7&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= %23e 1e9f0&chart_type=line&drp=0&fo=open %20s ans&graph_bgcolor= %23f fffff&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= %234572a 7, %23a a4643, %2389a 54e, % 2380699b, %233d 96ae, %23d b843d, %2392a 8cd, %23a 47d7c, % 23b5ca92, %2391e 8e1, %238d 4653, %238085e 8&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= %23e 1e9f0&chart_type=line&drp=0&fo=open %20s ans&graph_bgcolor= %23f fffff&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= %234572a 7, %23a a4643, %2389a 54e, % 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= %23e 1e9f0&chart_type=line&drp=0&fo=open %20s ans&graph_bgcolor= %23f fffff&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= %234572a 7, %23a a4643, %2389a 54e, % 2380699b, %233d 96ae, %23d b843d, %2392a 8cd, %23a 47d7c, % 23b5ca92, %2391e 8e1, %238d 4653, %238085e 8&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= %23e 1e9f0&chart_type=line&drp=0&fo=open %20s ans&graph_bgcolor= %23f fffff&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= %234572a 7, %23a a4643, %2389a 54e, % 2380699b, %233d 96ae, %23d b843d, %2392a 8cd, %23a 47d7c, % 23b5ca92, %2391e 8e1, %238d 4653, %238085e 8&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= %23e 1e9f0&chart_type=line&drp=0&fo=open %20s ans&graph_bgcolor= %23f fffff&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= %234572a 7, %23a a4643, %2389a 54e, % 2380699b, %233d 96ae&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= %23e 1e9f0&chart_type=line&drp=0&fo=open %20s ans&graph_bgcolor= %23f fffff&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= %234572a 7, %23a a4643, %2389a 54e, % 2380699b, %233d 96ae, %23d b843d&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= %23e 1e9f0&chart_type=line&drp=0&fo=open %20s ans&graph_bgcolor= %23f fffff&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= %234572a 7, %23a a4643&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= %23e 1e9f0&chart_type=line&drp=0&fo=open %20s ans&graph_bgcolor= %23f fffff&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= %234572a 7, %23a a4643, %2389a 54e, % 2380699b, %233d 96ae, %23d b843d, %2392a 8cd, %23a 47d7c&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= %23e 1e9f0&chart_type=line&drp=0&fo=open %20s ans&graph_bgcolor= %23f fffff&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= %234572a 7, %23a a4643, %2389a 54e, % 2380699b, %233d 96ae, %23d b843d&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= %23e 1e9f0&chart_type=line&drp=0&fo=open %20s ans&graph_bgcolor= %23f fffff&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= %234572a 7, %23a a4643&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= %23e 1e9f0&chart_type=line&drp=0&fo=open %20s ans&graph_bgcolor= %23f fffff&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= %234572a 7, %23a a4643&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= %23e 1e9f0&chart_type=line&drp=0&fo=open %20s ans&graph_bgcolor= %23f fffff&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= %234572a 7, %23a a4643, %2389a 54e&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= %23e 1e9f0&chart_type=line&drp=0&fo=open %20s ans&graph_bgcolor= %23f fffff&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= %234572a 7, %23a a4643, %2389a 54e, % 2380699b, %233d 96ae, %23d b843d, %2392a 8cd, %23a 47d7c, % 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= %23e 1e9f0&chart_type=line&drp=0&fo=open %20s ans&graph_bgcolor= %23f fffff&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= %234572a 7, %23a a4643, %2389a 54e, % 2380699b, %233d 96ae, %23d b843d, %2392a 8cd, %23a 47d7c, % 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= %23e 1e9f0&chart_type=line&drp=0&fo=open %20s ans&graph_bgcolor= %23f fffff&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= %234572a 7, %23a a4643, %2389a 54e, % 2380699b, %233d 96ae, %23d b843d, %2392a 8cd, %23a 47d7c, % 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= %23e 1e9f0&chart_type=line&drp=0&fo=open %20s ans&graph_bgcolor= %23f fffff&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= %234572a 7, %23a a4643, %2389a 54e, % 2380699b, %233d 96ae, %23d b843d, %2392a 8cd, %23a 47d7c, % 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= %23e 1e9f0&chart_type=line&drp=0&fo=open %20s ans&graph_bgcolor= %23f fffff&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= %234572a 7, %23a a4643, %2389a 54e, % 2380699b, %233d 96ae, %23d b843d, %2392a 8cd, %23a 47d7c, % 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= %23e 1e9f0&chart_type=line&drp=0&fo=open %20s ans&graph_bgcolor= %23f fffff&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= %234572a 7, %23a a4643, %2389a 54e, % 2380699b, %233d 96ae, %23d b843d, %2392a 8cd, %23a 47d7c, % 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= %23e 1e9f0&chart_type=line&drp=0&fo=open %20s ans&graph_bgcolor= %23f fffff&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= %234572a 7, %23a a4643, %2389a 54e, % 2380699b, %233d 96ae, %23d b843d, %2392a 8cd, %23a 47d7c, % 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= %23e 1e9f0&chart_type=line&drp=0&fo=open %20s ans&graph_bgcolor= %23f fffff&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= %234572a 7, %23a a4643, %2389a 54e, % 2380699b, %233d 96ae, %23d b843d, %2392a 8cd&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= %23e 1e9f0&chart_type=line&drp=0&fo=open %20s ans&graph_bgcolor= %23f fffff&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= %234572a 7, %23a a4643, %2389a 54e, % 2380699b, %233d 96ae, %23d b843d, %2392a 8cd&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= %23e 1e9f0&chart_type=line&drp=0&fo=open %20s ans&graph_bgcolor= %23f fffff&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= %234572a 7, %23a a4643, %2389a 54e, % 2380699b, %233d 96ae, %23d b843d, %2392a 8cd&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= %23e 1e9f0&chart_type=line&drp=0&fo=open %20s ans&graph_bgcolor= %23f fffff&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= %234572a 7, %23a a4643, %2389a 54e, % 2380699b, %233d 96ae, %23d b843d, %2392a 8cd&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= %23e 1e9f0&chart_type=line&drp=0&fo=open %20s ans&graph_bgcolor= %23f fffff&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= %234572a 7, %23a a4643, %2389a 54e, % 2380699b, %233d 96ae, %23d b843d, %2392a 8cd, %23a 47d7c&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= %23e 1e9f0&chart_type=line&drp=0&fo=open %20s ans&graph_bgcolor= %23f fffff&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= %234572a 7, %23a a4643, %2389a 54e, % 2380699b, %233d 96ae, %23d b843d, %2392a 8cd, %23a 47d7c&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= %23e 1e9f0&chart_type=line&drp=0&fo=open %20s ans&graph_bgcolor= %23f fffff&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= %234572a 7, %23a a4643, %2389a 54e, % 2380699b, %233d 96ae, %23d b843d, %2392a 8cd&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= %23e 1e9f0&chart_type=line&drp=0&fo=open %20s ans&graph_bgcolor= %23f fffff&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= %234572a 7, %23a a4643, %2389a 54e, % 2380699b, %233d 96ae, %23d b843d, %2392a 8cd&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= %23e 1e9f0&chart_type=line&drp=0&fo=open %20s ans&graph_bgcolor= %23f fffff&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= %234572a 7, %23a a4643, %2389a 54e&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
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class ecb_data ( object ) :
def __init__ ( self , url = url [ " ecb " ] ) :
self . url = url
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def codebook ( self ) :
return " please follow the ECB ' s codebook: https://sdw.ecb.europa.eu/browse.do?node=9691101 "
def get_data ( self ,
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datacode = " ICP " ,
key = " M.U2.N.000000.4.ANR " ,
startdate = " 2000-01-01 " ,
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enddate = " 2020-01-01 " ) :
"""
"""
tmp_url = self . url + " {} / " . format ( datacode ) + " {} " . format ( key )
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ua = UserAgent ( verify_ssl = False )
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request_header = { " User-Agent " : ua . random , ' Accept ' : ' text/csv ' }
request_params = {
" startPeriod " : " {} " . format ( startdate ) ,
" endPeriod " : " {} " . format ( enddate )
}
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r = requests . get (
tmp_url ,
params = request_params ,
headers = request_header )
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data_text = r . content
df = pd . read_csv ( io . StringIO ( data_text . decode ( ' utf-8 ' ) ) )
return df
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class eurostat_data ( object ) :
def __init__ ( self , url = url [ " eurostat " ] ) :
self . url = url
def codebook ( self ) :
return " please follow the EuroStat ' s codebook: \n https://ec.europa.eu/eurostat/estat-navtree-portlet-prod/BulkDownloadListing?sort=1&dir=dic "
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def get_data ( self ,
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datasetcode = " nama_10_gdp " ,
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precision = " 1 " ,
unit = " CP_MEUR " ,
na_item = " B1GQ " ,
time = " 2020 " ) :
"""
"""
tmp_url = self . url + " {} " . format ( datasetcode )
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ua = UserAgent ( verify_ssl = False )
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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 )
}
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r = requests . get (
tmp_url ,
params = request_params ,
headers = request_header )
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data_text = r . text
data_json = demjson . decode ( data_text )
value = data_json [ ' value ' ]
abb = data_json [ ' dimension ' ] [ ' geo ' ] [ ' category ' ] [ ' index ' ]
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abb = { abb [ k ] : k for k in abb }
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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 ]
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df = pd . DataFrame (
{ " Geo " : geo , " {} " . format ( na_item ) : list ( value . values ( ) ) } )
return df
if __name__ == " __main__ " :
data , name_list = CPI_monthly ( )
