update eu
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import pandas as pd
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import numpy as np
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import io
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import os
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import demjson
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import requests
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import numpy as np
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import pandas as pd
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from fake_useragent import UserAgent
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from pandas.core.frame import DataFrame
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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):
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data = pd.merge_asof(data_1, data_2, on=col_name)
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return data
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def National_Account():
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tmp_url = url["fred_econ"] + "bgcolor=%23e1e9f0&chart_type=line&drp=0&fo=open%20sans&graph_bgcolor=%23ffffff&height=450&mode=fred&recession_bars=off&txtcolor=%23444444&ts=12&tts=12&width=1168&nt=0&thu=0&trc=0&show_legend=yes&show_axis_titles=yes&show_tooltip=yes&id=NAEXCP04EZQ189S,NAEXCP02EZQ189S,NAEXCP01EZQ189S,NAEXCP06EZQ189S,NAEXCP07EZQ189S,NAEXCP03EZQ189S,NAGIGP01EZQ661S,NAEXKP06EZQ659S,NAEXKP04EZQ659S,NAEXKP01EZQ652S,NAEXKP07EZQ652S,NAEXKP03EZQ659S&scale=left,left,left,left,left,left,left,left,left,left,left,left&cosd=1995-01-01,1995-01-01,1995-01-01,1995-01-01,1995-01-01,1995-01-01,1995-01-01,1996-01-01,1996-01-01,1995-01-01,1995-01-01,1996-01-01&coed=2020-10-01,2020-10-01,2020-10-01,2020-10-01,2020-10-01,2020-10-01,2020-10-01,2020-10-01,2020-10-01,2021-01-01,2020-10-01,2020-10-01&line_color=%234572a7,%23aa4643,%2389a54e,%2380699b,%233d96ae,%23db843d,%2392a8cd,%23a47d7c,%23b5ca92,%2391e8e1,%238d4653,%238085e8&link_values=false,false,false,false,false,false,false,false,false,false,false,false&line_style=solid,solid,solid,solid,solid,solid,solid,solid,solid,solid,solid,solid&mark_type=none,none,none,none,none,none,none,none,none,none,none,none&mw=3,3,3,3,3,3,3,3,3,3,3,3&lw=2,2,2,2,2,2,2,2,2,2,2,2&ost=-99999,-99999,-99999,-99999,-99999,-99999,-99999,-99999,-99999,-99999,-99999,-99999&oet=99999,99999,99999,99999,99999,99999,99999,99999,99999,99999,99999,99999&mma=0,0,0,0,0,0,0,0,0,0,0,0&fml=a,a,a,a,a,a,a,a,a,a,a,a&fq=Quarterly,Quarterly,Quarterly,Quarterly,Quarterly,Quarterly,Quarterly,Quarterly,Quarterly,Quarterly,Quarterly,Quarterly&fam=avg,avg,avg,avg,avg,avg,avg,avg,avg,avg,avg,avg&fgst=lin,lin,lin,lin,lin,lin,lin,lin,lin,lin,lin,lin&fgsnd=2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01&line_index=1,2,3,4,5,6,7,8,9,10,11,12&transformation=lin,lin,lin,lin,lin,lin,lin,lin,lin,lin,lin,lin&vintage_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07&revision_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07&nd=1995-01-01,1995-01-01,1995-01-01,1995-01-01,1995-01-01,1995-01-01,1995-01-01,1996-01-01,1996-01-01,1995-01-01,1995-01-01,1996-01-01"
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ua = UserAgent(verify_ssl=False)
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request_header = {"User-Agent": ua.random}
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r = requests.get(tmp_url, headers=request_header)
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data_text = r.content
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df = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
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df["DATE"] = pd.to_datetime(df["DATE"], format="%Y-%m-%d")
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#df = df[list(df.columns[1:])].replace(".", np.nan).astype(float)
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name_list = {
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'NAEXCP04EZQ189S': "Gross Domestic Product by Expenditure in Current Prices: Gross Fixed Capital Formation for the Euro Area",
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'NAEXCP02EZQ189S': "Gross Domestic Product by Expenditure in Current Prices: Private Final Consumption Expenditure for the Euro Area",
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'NAEXCP01EZQ189S': "Gross Domestic Product by Expenditure in Current Prices: Total Gross Domestic Product for the Euro Area",
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'NAEXCP06EZQ189S': "Gross Domestic Product by Expenditure in Current Prices: Exports of Goods and Services for the Euro Area",
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'NAEXCP07EZQ189S': "Gross Domestic Product by Expenditure in Current Prices: Less Imports of Goods and Services for the Euro Area",
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'NAEXCP03EZQ189S': "Gross Domestic Product by Expenditure in Current Prices: Government Final Consumption Expenditure for the Euro Area",
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'NAGIGP01EZQ661S': "Gross Domestic Product Deflator for the Euro Area",
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'NAEXKP06EZQ659S': "Gross Domestic Product by Expenditure in Constant Prices: Exports of Goods and Services for the Euro Area",
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'NAEXKP04EZQ659S': "Gross Domestic Product by Expenditure in Constant Prices: Gross Fixed Capital Formation for the Euro Area",
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'NAEXKP01EZQ652S': "Gross Domestic Product by Expenditure in Constant Prices: Total Gross Domestic Product for the Euro Area",
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'NAEXKP07EZQ652S': "Gross Domestic Product by Expenditure in Constant Prices: Less: Imports of Goods and Services for the Euro Area",
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'NAEXKP03EZQ659S': "Gross Domestic Product by Expenditure in Constant Prices: Government Final Consumption Expenditure for the Euro Area"}
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description = "National Accounts, Quarterly, Seasonally, Adjusted"
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return df, name_list, description
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def International_Trade():
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tmp_url = url["fred_econ"] + "bgcolor=%23e1e9f0&chart_type=line&drp=0&fo=open%20sans&graph_bgcolor=%23ffffff&height=450&mode=fred&recession_bars=off&txtcolor=%23444444&ts=12&tts=12&width=1168&nt=0&thu=0&trc=0&show_legend=yes&show_axis_titles=yes&show_tooltip=yes&id=XTEXVA01EZQ188S,XTIMVA01EZQ188S,EA19XTNTVA01STSAQ&scale=left,left,left&cosd=1995-01-01,1995-01-01,1995-01-01&coed=2020-10-01,2020-10-01,2017-04-01&line_color=%234572a7,%23aa4643,%2389a54e&link_values=false,false,false&line_style=solid,solid,solid&mark_type=none,none,none&mw=3,3,3&lw=2,2,2&ost=-99999,-99999,-99999&oet=99999,99999,99999&mma=0,0,0&fml=a,a,a&fq=Quarterly,Quarterly,Quarterly&fam=avg,avg,avg&fgst=lin,lin,lin&fgsnd=2020-02-01,2020-02-01,2017-04-01&line_index=1,2,3&transformation=lin,lin,lin&vintage_date=2021-06-07,2021-06-07,2021-06-07&revision_date=2021-06-07,2021-06-07,2021-06-07&nd=1995-01-01,1995-01-01,1995-01-01"
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ua = UserAgent(verify_ssl=False)
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request_header = {"User-Agent": ua.random}
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r = requests.get(tmp_url, headers=request_header)
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data_text = r.content
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df = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
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df["DATE"] = pd.to_datetime(df["DATE"], format="%Y-%m-%d")
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#df = df[list(df.columns[1:])].replace(".", np.nan).astype(float)
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name_list = {
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'XTEXVA01EZQ188S': "Exports: Value Goods for the Euro Area",
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'XTIMVA01EZQ188SS': "Imports: Value Goods for the Euro Area",
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'EA19XTNTVA01STSAQ': "International Trade: Net trade: Value (goods): Total for the Euro Area"}
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description = "International Trade, Quarterly, Seasonally Adjusted"
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return df, name_list, description
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def Balance_of_Payments_BPM6():
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tmp_url = url["fred_econ"] + "bgcolor=%23e1e9f0&chart_type=line&drp=0&fo=open%20sans&graph_bgcolor=%23ffffff&height=450&mode=fred&recession_bars=off&txtcolor=%23444444&ts=12&tts=12&width=1168&nt=0&thu=0&trc=0&show_legend=yes&show_axis_titles=yes&show_tooltip=yes&id=EA19B6BLTT02STSAQ,EA19B6DBSE02STSAQ,EA19B6DBSE03STSAQ,EA19B6CRSE03STSAQ,EA19B6CRSE02STSAQ&scale=left,left,left,left,left&cosd=1999-01-01,1999-01-01,1999-01-01,1999-01-01,1999-01-01&coed=2020-10-01,2020-10-01,2020-10-01,2020-10-01,2020-10-01&line_color=%234572a7,%23aa4643,%2389a54e,%2380699b,%233d96ae&link_values=false,false,false,false,false&line_style=solid,solid,solid,solid,solid&mark_type=none,none,none,none,none&mw=3,3,3,3,3&lw=2,2,2,2,2&ost=-99999,-99999,-99999,-99999,-99999&oet=99999,99999,99999,99999,99999&mma=0,0,0,0,0&fml=a,a,a,a,a&fq=Quarterly,Quarterly,Quarterly,Quarterly,Quarterly&fam=avg,avg,avg,avg,avg&fgst=lin,lin,lin,lin,lin&fgsnd=2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01&line_index=1,2,3,4,5&transformation=lin,lin,lin,lin,lin&vintage_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07&revision_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07&nd=1999-01-01,1999-01-01,1999-01-01,1999-01-01,1999-01-01"
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ua = UserAgent(verify_ssl=False)
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request_header = {"User-Agent": ua.random}
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r = requests.get(tmp_url, headers=request_header)
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data_text = r.content
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df = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
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df["DATE"] = pd.to_datetime(df["DATE"], format="%Y-%m-%d")
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#df = df[list(df.columns[1:])].replace(".", np.nan).astype(float)
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name_list = {
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'EA19B6BLTT02STSAQ': "Balance of payments BPM6: Current account Debits: Services: Total Debits as % of Current account for the Euro Area",
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'EA19B6DBSE02STSAQ': "Balance of payments BPM6: Current account Debits: Services: Total Debits as % of Current account for the Euro Area",
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'EA19B6DBSE03STSAQ': "Balance of payments BPM6: Current account Debits: Services: Total Debits as % of Goods and Services for the Euro Area",
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'EA19B6CRSE03STSAQ': "Balance of payments BPM6: Current account Credits: Services: Total Credits as % of Goods and Services for Euro Area",
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'EA19B6CRSE02STSAQ': "Balance of payments BPM6: Current account Credits: Services: Total Credits as % of Current account for Euro Area"}
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description = "Balanced of payments BPM6, Quarterly, Seasonally Adjusted"
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return df, name_list, description
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def Learning_Indicators_OECD():
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tmp_url = url["fred_econ"] + "bgcolor=%23e1e9f0&chart_type=line&drp=0&fo=open%20sans&graph_bgcolor=%23ffffff&height=450&mode=fred&recession_bars=off&txtcolor=%23444444&ts=12&tts=12&width=1168&nt=0&thu=0&trc=0&show_legend=yes&show_axis_titles=yes&show_tooltip=yes&id=EA19LORSGPNOSTSAM,EA19LOLITOTRGYSAM,EA19LOLITONOSTSAM,EA19LOLITOAASTSAM,EA19LORSGPORIXOBSAM,EA19LORSGPRTSTSAM,EA19LORSGPTDSTSAM&scale=left,left,left,left,left,left,left&cosd=1960-03-01,1966-12-01,1965-12-01,1965-12-01,1960-03-01,1960-03-01,1960-03-01&coed=2020-11-01,2020-11-01,2021-03-01,2021-03-01,2020-11-01,2020-11-01,2020-11-01&line_color=%234572a7,%23aa4643,%2389a54e,%2380699b,%233d96ae,%23db843d,%2392a8cd&link_values=false,false,false,false,false,false,false&line_style=solid,solid,solid,solid,solid,solid,solid&mark_type=none,none,none,none,none,none,none&mw=3,3,3,3,3,3,3&lw=2,2,2,2,2,2,2&ost=-99999,-99999,-99999,-99999,-99999,-99999,-99999&oet=99999,99999,99999,99999,99999,99999,99999&mma=0,0,0,0,0,0,0&fml=a,a,a,a,a,a,a&fq=Monthly,Monthly,Monthly,Monthly,Monthly,Monthly,Monthly&fam=avg,avg,avg,avg,avg,avg,avg&fgst=lin,lin,lin,lin,lin,lin,lin&fgsnd=2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01&line_index=1,2,3,4,5,6,7&transformation=lin,lin,lin,lin,lin,lin,lin&vintage_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07&revision_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07&nd=1960-03-01,1966-12-01,1965-12-01,1965-12-01,1960-03-01,1960-03-01,1960-03-01"
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ua = UserAgent(verify_ssl=False)
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request_header = {"User-Agent": ua.random}
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r = requests.get(tmp_url, headers=request_header)
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data_text = r.content
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df = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
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df["DATE"] = pd.to_datetime(df["DATE"], format="%Y-%m-%d")
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#df = df[list(df.columns[1:])].replace(".", np.nan).astype(float)
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name_list = {
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'EA19CPALTT01GYQ': "Leading Indicators OECD: Reference series: Gross Domestic Product (GDP): Normalised for the Euro Area",
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'EA19LOLITOTRGYSAM': "Leading Indicators OECD: Leading indicators: CLI: Trend restored for the Euro Area",
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'EA19LOLITONOSTSAM': "Leading Indicators OECD: Leading indicators: CLI: Normalised for the Euro Area",
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'EA19LOLITOAASTSAM': "Leading Indicators OECD: Leading indicators: CLI: Amplitude adjusted for the Euro Area",
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'EA19LORSGPORIXOBSAM': "Leading Indicators OECD: Reference series: Gross Domestic Product (GDP): Original series for the Euro Area",
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'EA19LORSGPRTSTSAM': "Leading Indicators OECD: Reference series: Gross Domestic Product (GDP): Ratio to trend for the Euro Area",
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'EA19LORSGPTDSTSAM': "Leading Indicators OECD: Reference series: Gross Domestic Product (GDP): Trend for the Euro Area"}
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description = "Leading Indicators OECD, Monthly, Seasonally Adjusted"
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return df, name_list, description
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def Monetary_Aggregates_Monthly_Adj():
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tmp_url = url["fred_econ"] + "bgcolor=%23e1e9f0&chart_type=line&drp=0&fo=open%20sans&graph_bgcolor=%23ffffff&height=450&mode=fred&recession_bars=off&txtcolor=%23444444&ts=12&tts=12&width=1168&nt=0&thu=0&trc=0&show_legend=yes&show_axis_titles=yes&show_tooltip=yes&id=EA19MABMM301GYSAM,EA19MANMM101IXOBSAM&scale=left,left&cosd=1971-01-01,1970-01-01&coed=2021-03-01,2021-03-01&line_color=%234572a7,%23aa4643&link_values=false,false&line_style=solid,solid&mark_type=none,none&mw=3,3&lw=2,2&ost=-99999,-99999&oet=99999,99999&mma=0,0&fml=a,a&fq=Monthly,Monthly&fam=avg,avg&fgst=lin,lin&fgsnd=2020-02-01,2020-02-01&line_index=1,2&transformation=lin,lin&vintage_date=2021-06-07,2021-06-07&revision_date=2021-06-07,2021-06-07&nd=1971-01-01,1970-01-01"
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ua = UserAgent(verify_ssl=False)
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request_header = {"User-Agent": ua.random}
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r = requests.get(tmp_url, headers=request_header)
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data_text = r.content
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df = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
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df["DATE"] = pd.to_datetime(df["DATE"], format="%Y-%m-%d")
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#df = df[list(df.columns[1:])].replace(".", np.nan).astype(float)
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name_list = {'EA19MABMM301GYSAM': "Monetary aggregates and their components: Broad money and components: M3: M3 for the Euro Area",
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'EA19MANMM101IXOBSAM': "Monetary aggregates and their components: Narrow money and components: M1 and components: M1 for the Euro Area"}
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description = "Monetary aggregates and their components, Monthly, Seasonally Adjusted"
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return df, name_list, description
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def Monetary_Aggregates_Quarterly_Adj():
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tmp_url = url["fred_econ"] + "bgcolor=%23e1e9f0&chart_type=line&drp=0&fo=open%20sans&graph_bgcolor=%23ffffff&height=450&mode=fred&recession_bars=off&txtcolor=%23444444&ts=12&tts=12&width=1168&nt=0&thu=0&trc=0&show_legend=yes&show_axis_titles=yes&show_tooltip=yes&id=MABMM301EZQ189S,MANMM101EZQ189S&scale=left,left&cosd=1970-01-01,1970-01-01&coed=2021-01-01,2021-01-01&line_color=%234572a7,%23aa4643&link_values=false,false&line_style=solid,solid&mark_type=none,none&mw=3,3&lw=2,2&ost=-99999,-99999&oet=99999,99999&mma=0,0&fml=a,a&fq=Quarterly,Quarterly&fam=avg,avg&fgst=lin,lin&fgsnd=2020-02-01,2020-02-01&line_index=1,2&transformation=lin,lin&vintage_date=2021-06-07,2021-06-07&revision_date=2021-06-07,2021-06-07&nd=1970-01-01,1970-01-01"
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ua = UserAgent(verify_ssl=False)
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request_header = {"User-Agent": ua.random}
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r = requests.get(tmp_url, headers=request_header)
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data_text = r.content
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df = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
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df["DATE"] = pd.to_datetime(df["DATE"], format="%Y-%m-%d")
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#df = df[list(df.columns[1:])].replace(".", np.nan).astype(float)
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name_list = {
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'MABMM301EZQ189S': "M3 for the Euro Area",
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'MANMM101EZQ189S': "M1 for the Euro Area"
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}
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description = "Monetary aggregates and their components, Quarterly, Seasonally Adjusted"
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return df, name_list, description
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def Currency_Conversion_Quarterly():
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tmp_url = url["fred_econ"] + "bgcolor=%23e1e9f0&chart_type=line&drp=0&fo=open%20sans&graph_bgcolor=%23ffffff&height=450&mode=fred&recession_bars=off&txtcolor=%23444444&ts=12&tts=12&width=1168&nt=0&thu=0&trc=0&show_legend=yes&show_axis_titles=yes&show_tooltip=yes&id=CCEUSP02EZQ655N,CCUSMA02EZQ618N,CCUSSP01EZQ650N,CCRETT02EZQ661N,CCRETT01EZQ661N&scale=left,left,left,left,left&cosd=1999-01-01,1979-01-01,1999-01-01,1970-01-01,1970-01-01&coed=2021-01-01,2021-01-01,2021-01-01,2021-01-01,2021-01-01&line_color=%234572a7,%23aa4643,%2389a54e,%2380699b,%233d96ae&link_values=false,false,false,false,false&line_style=solid,solid,solid,solid,solid&mark_type=none,none,none,none,none&mw=3,3,3,3,3&lw=2,2,2,2,2&ost=-99999,-99999,-99999,-99999,-99999&oet=99999,99999,99999,99999,99999&mma=0,0,0,0,0&fml=a,a,a,a,a&fq=Quarterly,Quarterly,Quarterly,Quarterly,Quarterly&fam=avg,avg,avg,avg,avg&fgst=lin,lin,lin,lin,lin&fgsnd=2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01&line_index=1,2,3,4,5&transformation=lin,lin,lin,lin,lin&vintage_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07&revision_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07&nd=1999-01-01,1979-01-01,1999-01-01,1970-01-01,1970-01-01"
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ua = UserAgent(verify_ssl=False)
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request_header = {"User-Agent": ua.random}
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r = requests.get(tmp_url, headers=request_header)
|
||||
data_text = r.content
|
||||
df = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
|
||||
df["DATE"] = pd.to_datetime(df["DATE"], format="%Y-%m-%d")
|
||||
#df = df[list(df.columns[1:])].replace(".", np.nan).astype(float)
|
||||
name_list = {
|
||||
'CCEUSP02EZQ655N': "National Currency to Euro Spot Exchange Rate for the Euro Area",
|
||||
'CCUSMA02EZQ618N': "National Currency to US Dollar Exchange Rate: Average of Daily Rates for the Euro Area",
|
||||
'CCUSSP01EZQ650N': "US Dollar to National Currency Spot Exchange Rate for the Euro Area",
|
||||
'CCRETT02EZQ661N': "Real Effective Exchange Rates Based on Manufacturing Unit Labor Cost for the Euro Area",
|
||||
'CCRETT01EZQ661N': "Real Effective Exchange Rates Based on Manufacturing Consumer Price Index for the Euro Area"}
|
||||
description = "Currency Conversions, Quarterly, Not Seasonally Adjusted"
|
||||
return df, name_list, description
|
||||
|
||||
|
||||
def Currency_Conversion_Monthly():
|
||||
tmp_url = url["fred_econ"] + "bgcolor=%23e1e9f0&chart_type=line&drp=0&fo=open%20sans&graph_bgcolor=%23ffffff&height=450&mode=fred&recession_bars=off&txtcolor=%23444444&ts=12&tts=12&width=1168&nt=0&thu=0&trc=0&show_legend=yes&show_axis_titles=yes&show_tooltip=yes&id=CCRETT01EZM661N,CCUSMA02EZM659N,CCUSSP01EZM650N,CCEUSP02EZM655N&scale=left,left,left,left&cosd=1970-01-01,1991-01-01,1999-01-01,1999-01-01&coed=2021-04-01,2021-04-01,2021-03-01,2021-03-01&line_color=%234572a7,%23aa4643,%2389a54e,%2380699b&link_values=false,false,false,false&line_style=solid,solid,solid,solid&mark_type=none,none,none,none&mw=3,3,3,3&lw=2,2,2,2&ost=-99999,-99999,-99999,-99999&oet=99999,99999,99999,99999&mma=0,0,0,0&fml=a,a,a,a&fq=Monthly,Monthly,Monthly,Monthly&fam=avg,avg,avg,avg&fgst=lin,lin,lin,lin&fgsnd=2020-02-01,2020-02-01,2020-02-01,2020-02-01&line_index=1,2,3,4&transformation=lin,lin,lin,lin&vintage_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07&revision_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07&nd=1970-01-01,1991-01-01,1999-01-01,1999-01-01"
|
||||
ua = UserAgent(verify_ssl=False)
|
||||
request_header = {"User-Agent": ua.random}
|
||||
r = requests.get(tmp_url, headers=request_header)
|
||||
data_text = r.content
|
||||
df = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
|
||||
df["DATE"] = pd.to_datetime(df["DATE"], format="%Y-%m-%d")
|
||||
#df = df[list(df.columns[1:])].replace(".", np.nan).astype(float)
|
||||
name_list = {
|
||||
'CCRETT01EZM661N': "Real Effective Exchange Rates Based on Manufacturing Consumer Price Index for the Euro Area",
|
||||
'CCUSMA02EZM659N': "National Currency to US Dollar Exchange Rate: Average of Daily Rates for the Euro Area",
|
||||
'CCUSSP01EZM650N': "US Dollar to National Currency Spot Exchange Rate for the Euro Area",
|
||||
'CCEUSP02EZM655N': "National Currency to Euro Spot Exchange Rate for the Euro Area"}
|
||||
description = "Currency Conversions, Monthly, Not Seasonally Adjusted"
|
||||
return df, name_list, description
|
||||
|
||||
|
||||
def Interest_Rates_Quarterly():
|
||||
tmp_url = url["fred_econ"] + "bgcolor=%23e1e9f0&chart_type=line&drp=0&fo=open%20sans&graph_bgcolor=%23ffffff&height=450&mode=fred&recession_bars=off&txtcolor=%23444444&ts=12&tts=12&width=1168&nt=0&thu=0&trc=0&show_legend=yes&show_axis_titles=yes&show_tooltip=yes&id=IRLTLT01EZQ156N,IR3TIB01EZQ156N,IRSTCI01EZQ156N&scale=left,left,left&cosd=1970-01-01,1994-01-01,1994-01-01&coed=2021-01-01,2021-01-01,2021-01-01&line_color=%234572a7,%23aa4643,%2389a54e&link_values=false,false,false&line_style=solid,solid,solid&mark_type=none,none,none&mw=3,3,3&lw=2,2,2&ost=-99999,-99999,-99999&oet=99999,99999,99999&mma=0,0,0&fml=a,a,a&fq=Quarterly,Quarterly,Quarterly&fam=avg,avg,avg&fgst=lin,lin,lin&fgsnd=2020-02-01,2020-02-01,2020-02-01&line_index=1,2,3&transformation=lin,lin,lin&vintage_date=2021-06-07,2021-06-07,2021-06-07&revision_date=2021-06-07,2021-06-07,2021-06-07&nd=1970-01-01,1994-01-01,1994-01-01"
|
||||
ua = UserAgent(verify_ssl=False)
|
||||
request_header = {"User-Agent": ua.random}
|
||||
r = requests.get(tmp_url, headers=request_header)
|
||||
data_text = r.content
|
||||
df = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
|
||||
df["DATE"] = pd.to_datetime(df["DATE"], format="%Y-%m-%d")
|
||||
#df = df[list(df.columns[1:])].replace(".", np.nan).astype(float)
|
||||
name_list = {
|
||||
'IRLTLT01EZQ156N': "Long-Term Government Bond Yields: 10-year: Main (Including Benchmark) for the Euro Area",
|
||||
'IR3TIB01EZQ156N': "3-Month or 90-day Rates and Yields: Interbank Rates for the Euro Area",
|
||||
'IRSTCI01EZQ156N': "Immediate Rates: Less than 24 Hours: Call Money/Interbank Rate for the Euro Area"}
|
||||
description = "Interest Rates, Quarterly, Not Seasonally Adjusted"
|
||||
return df, name_list, description
|
||||
|
||||
|
||||
def Interest_Rates_Monthly():
|
||||
tmp_url = url["fred_econ"] + "bgcolor=%23e1e9f0&chart_type=line&drp=0&fo=open%20sans&graph_bgcolor=%23ffffff&height=450&mode=fred&recession_bars=off&txtcolor=%23444444&ts=12&tts=12&width=1168&nt=0&thu=0&trc=0&show_legend=yes&show_axis_titles=yes&show_tooltip=yes&id=IRLTLT01EZM156N,IR3TIB01EZM156N,IRSTCI01EZM156N&scale=left,left,left&cosd=1970-01-01,1994-01-01,1994-01-01&coed=2021-04-01,2021-04-01,2021-04-01&line_color=%234572a7,%23aa4643,%2389a54e&link_values=false,false,false&line_style=solid,solid,solid&mark_type=none,none,none&mw=3,3,3&lw=2,2,2&ost=-99999,-99999,-99999&oet=99999,99999,99999&mma=0,0,0&fml=a,a,a&fq=Monthly,Monthly,Monthly&fam=avg,avg,avg&fgst=lin,lin,lin&fgsnd=2020-02-01,2020-02-01,2020-02-01&line_index=1,2,3&transformation=lin,lin,lin&vintage_date=2021-06-07,2021-06-07,2021-06-07&revision_date=2021-06-07,2021-06-07,2021-06-07&nd=1970-01-01,1994-01-01,1994-01-01"
|
||||
ua = UserAgent(verify_ssl=False)
|
||||
request_header = {"User-Agent": ua.random}
|
||||
r = requests.get(tmp_url, headers=request_header)
|
||||
data_text = r.content
|
||||
df = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
|
||||
df["DATE"] = pd.to_datetime(df["DATE"], format="%Y-%m-%d")
|
||||
#df = df[list(df.columns[1:])].replace(".", np.nan).astype(float)
|
||||
name_list = {
|
||||
'IRLTLT01EZM156N': "Long-Term Government Bond Yields: 10-year: Main (Including Benchmark) for the Euro Area",
|
||||
'IR3TIB01EZM156N': "3-Month or 90-day Rates and Yields: Interbank Rates for the Euro Area",
|
||||
'IRSTCI01EZM156N': "Immediate Rates: Less than 24 Hours: Call Money/Interbank Rate for the Euro Area"}
|
||||
description = "Interest Rates, Monthly, Not Seasonally Adjusted"
|
||||
return df, name_list, description
|
||||
|
||||
|
||||
def Share_Prices_Quarterly():
|
||||
tmp_url = url["fred_econ"] + "bgcolor=%23e1e9f0&chart_type=line&drp=0&fo=open%20sans&graph_bgcolor=%23ffffff&height=450&mode=fred&recession_bars=off&txtcolor=%23444444&ts=12&tts=12&width=1168&nt=0&thu=0&trc=0&show_legend=yes&show_axis_titles=yes&show_tooltip=yes&id=SPASTT01EZQ661N&scale=left&cosd=1987-01-01&coed=2021-01-01&line_color=%234572a7&link_values=false&line_style=solid&mark_type=none&mw=3&lw=2&ost=-99999&oet=99999&mma=0&fml=a&fq=Quarterly&fam=avg&fgst=lin&fgsnd=2020-02-01&line_index=1&transformation=lin&vintage_date=2021-06-07&revision_date=2021-06-07&nd=1987-01-01"
|
||||
ua = UserAgent(verify_ssl=False)
|
||||
request_header = {"User-Agent": ua.random}
|
||||
r = requests.get(tmp_url, headers=request_header)
|
||||
data_text = r.content
|
||||
df = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
|
||||
df["DATE"] = pd.to_datetime(df["DATE"], format="%Y-%m-%d")
|
||||
#df = df[list(df.columns[1:])].replace(".", np.nan).astype(float)
|
||||
name_list = {
|
||||
'SPASTT01EZQ661N': "Total Share Prices for All Shares for the Euro Area"}
|
||||
description = "Share Prices, Quarterly, Not Seasonally Adjusted"
|
||||
return df, name_list, description
|
||||
|
||||
|
||||
def Share_Prices_Monthly():
|
||||
tmp_url = url["fred_econ"] + "bgcolor=%23e1e9f0&chart_type=line&drp=0&fo=open%20sans&graph_bgcolor=%23ffffff&height=450&mode=fred&recession_bars=off&txtcolor=%23444444&ts=12&tts=12&width=1168&nt=0&thu=0&trc=0&show_legend=yes&show_axis_titles=yes&show_tooltip=yes&id=SPASTT01EZM661N&scale=left&cosd=1986-12-01&coed=2021-04-01&line_color=%234572a7&link_values=false&line_style=solid&mark_type=none&mw=3&lw=2&ost=-99999&oet=99999&mma=0&fml=a&fq=Monthly&fam=avg&fgst=lin&fgsnd=2020-02-01&line_index=1&transformation=lin&vintage_date=2021-06-07&revision_date=2021-06-07&nd=1986-12-01"
|
||||
ua = UserAgent(verify_ssl=False)
|
||||
request_header = {"User-Agent": ua.random}
|
||||
r = requests.get(tmp_url, headers=request_header)
|
||||
data_text = r.content
|
||||
df = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
|
||||
df["DATE"] = pd.to_datetime(df["DATE"], format="%Y-%m-%d")
|
||||
#df = df[list(df.columns[1:])].replace(".", np.nan).astype(float)
|
||||
name_list = {
|
||||
'SPASTT01EZM661N': "Total Share Prices for All Shares for the Euro Area"}
|
||||
description = "Share Prices, Monthly, Not Seasonally Adjusted"
|
||||
return df, name_list, description
|
||||
|
||||
|
||||
def CPI_Monthly(startdate="1970-01-01", enddate="2021-01-01"):
|
||||
"""
|
||||
"""
|
||||
tmp_url = url["fred_econ"] + "bgcolor=%23e1e9f0&chart_type=line&drp=0&fo=open%20sans&graph_bgcolor=%23ffffff&height=450&mode=fred&recession_bars=off&txtcolor=%23444444&ts=12&tts=12&width=748&nt=0&thu=0&trc=0&show_legend=yes&show_axis_titles=yes&show_tooltip=yes&id=CPHPTT01EZM661N,EA19CPHP0401IXOBM,EA19CPHP0403IXOBM,EA19CPHP0404IXOBM,EA19CPHP0405IXOBM,EA19CPHP0500IXOBM,EA19CPHP0600IXOBM,EA19CPHP0700IXOBM,EA19CPHP0702IXOBM,EA19CPHP0800IXOBM,EA19CPHP0900IXOBM,CPHPEN01EZM661N&scale=left,left,left,left,left,left,left,left,left,left,left,left&cosd=1990-01-01,1996-01-01,1996-01-01,1996-01-01,1996-01-01,1996-01-01,1996-01-01,1996-01-01,1996-01-01,1996-01-01,1996-01-01,1996-01-01&coed=2021-03-01,2021-03-01,2021-03-01,2021-03-01,2021-03-01,2021-03-01,2021-03-01,2021-03-01,2021-03-01,2021-03-01,2021-03-01,2021-03-01&line_color=%234572a7,%23aa4643,%2389a54e,%2380699b,%233d96ae,%23db843d,%2392a8cd,%23a47d7c,%23b5ca92,%2391e8e1,%238d4653,%238085e8&link_values=false,false,false,false,false,false,false,false,false,false,false,false&line_style=solid,solid,solid,solid,solid,solid,solid,solid,solid,solid,solid,solid&mark_type=none,none,none,none,none,none,none,none,none,none,none,none&mw=3,3,3,3,3,3,3,3,3,3,3,3&lw=2,2,2,2,2,2,2,2,2,2,2,2&ost=-99999,-99999,-99999,-99999,-99999,-99999,-99999,-99999,-99999,-99999,-99999,-99999&oet=99999,99999,99999,99999,99999,99999,99999,99999,99999,99999,99999,99999&mma=0,0,0,0,0,0,0,0,0,0,0,0&fml=a,a,a,a,a,a,a,a,a,a,a,a&fq=Monthly,Monthly,Monthly,Monthly,Monthly,Monthly,Monthly,Monthly,Monthly,Monthly,Monthly,Monthly&fam=avg,avg,avg,avg,avg,avg,avg,avg,avg,avg,avg,avg&fgst=lin,lin,lin,lin,lin,lin,lin,lin,lin,lin,lin,lin&fgsnd=2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01&line_index=1,2,3,4,5,6,7,8,9,10,11,12&transformation=lin,lin,lin,lin,lin,lin,lin,lin,lin,lin,lin,lin&vintage_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07&revision_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07&nd=1990-01-01,1996-01-01,1996-01-01,1996-01-01,1996-01-01,1996-01-01,1996-01-01,1996-01-01,1996-01-01,1996-01-01,1996-01-01,1996-01-01"
|
||||
ua = UserAgent(verify_ssl=False)
|
||||
request_header = {"User-Agent": ua.random}
|
||||
r = requests.get(tmp_url, headers=request_header)
|
||||
data_text = r.content
|
||||
df = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
|
||||
df["DATE"] = pd.to_datetime(df["DATE"], format="%Y-%m-%d")
|
||||
#df = df[list(df.columns[1:])].replace(".", np.nan).astype(float)
|
||||
name_list = {
|
||||
"CPHPTT01EZM661N": "CPI:Harmonized Prices: Total All Items for the Euro Area",
|
||||
"EA19CPHP0401IXOBM": "CPI:Harmonised_Price:Housing, water, electricity, gas and other fuels (COICOP 04): Actual rentals for housing for the Euro Area",
|
||||
"EA19CPHP0403IXOBM": "CPI:Harmonised_Price:Housing, water, electricity, gas and other fuels (COICOP 04): Maintenance & repairs of the dwellings for the Euro Area",
|
||||
"EA19CPHP0404IXOBM": "CPI:Harmonised_Price:Housing, water, electricity, gas and other fuels (COICOP 04): Water supply and miscellaneous services relating to the dwelling for the Euro Area",
|
||||
"EA19CPHP0405IXOBM": "CPI:Harmonised_Price:Housing, water, electricity, gas and other fuels (COICOP 04): Electricity, gas and other fuels for the Euro Area",
|
||||
"EA19CPHP0500IXOBM": "CPI:Harmonised_Price:Furnishings, household equip. and routine household maintenance (COICOP 05): Total for the Euro Area ",
|
||||
"EA19CPHP0600IXOBM": "CPI:Harmonised_Price:Health (COICOP 06): Total for the Euro Area",
|
||||
"EA19CPHP0700IXOBM": "CPI:Harmonised_Price:Transport (COICOP 07): Total for the Euro Area",
|
||||
"EA19CPHP0702IXOBM": "CPI:Harmonised_Price:Transport (COICOP 07): Fuels and lubricants for personal transport equipment for the Euro Area",
|
||||
"EA19CPHP0800IXOBM": "CPI:Harmonised_Price:Communication (COICOP 08): Total for the Euro Area",
|
||||
"EA19CPHP0900IXOBM": "CPI:Harmonised_Price:Recreation and culture (COICOP 09): Total for the Euro Area",
|
||||
"CPHPEN01EZM661N": "CPI:Harmonized Prices: Total Energy for the Euro Area"}
|
||||
description = "Consumer Price Index, Monthly, Not Seasonally Adjusted"
|
||||
return df, name_list, description
|
||||
|
||||
|
||||
def CPI_Quarterly():
|
||||
"""
|
||||
"""
|
||||
tmp_url = url["fred_econ"] + "bgcolor=%23e1e9f0&chart_type=line&drp=0&fo=open%20sans&graph_bgcolor=%23ffffff&height=450&mode=fred&recession_bars=off&txtcolor=%23444444&ts=12&tts=12&width=748&nt=0&thu=0&trc=0&show_legend=yes&show_axis_titles=yes&show_tooltip=yes&id=EA19CPALTT01GYQ,EA19CPGRLE01GYQ,EA19CPGREN01GYQ,EA19CPHP0401IXOBQ&scale=left,left,left,left&cosd=1991-01-01,1997-01-01,1997-01-01,1996-01-01&coed=2021-01-01,2021-01-01,2021-01-01,2021-01-01&line_color=%234572a7,%23aa4643,%2389a54e,%2380699b&link_values=false,false,false,false&line_style=solid,solid,solid,solid&mark_type=none,none,none,none&mw=3,3,3,3&lw=2,2,2,2&ost=-99999,-99999,-99999,-99999&oet=99999,99999,99999,99999&mma=0,0,0,0&fml=a,a,a,a&fq=Quarterly,Quarterly,Quarterly,Quarterly&fam=avg,avg,avg,avg&fgst=lin,lin,lin,lin&fgsnd=2020-02-01,2020-02-01,2020-02-01,2020-02-01&line_index=1,2,3,4&transformation=lin,lin,lin,lin&vintage_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07&revision_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07&nd=1991-01-01,1997-01-01,1997-01-01,1996-01-01"
|
||||
ua = UserAgent(verify_ssl=False)
|
||||
request_header = {"User-Agent": ua.random}
|
||||
r = requests.get(tmp_url, headers=request_header)
|
||||
data_text = r.content
|
||||
df = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
|
||||
df["DATE"] = pd.to_datetime(df["DATE"], format="%Y-%m-%d")
|
||||
#df = df[list(df.columns[1:])].replace(".", np.nan).astype(float)
|
||||
name_list = {
|
||||
'EA19CPALTT01GYQ': "CPI:All items:Total:Total for the Euro Area",
|
||||
'EA19CPGRLE01GYQ': "CPI:OECD Groups:All items non-food non-energy:Total for the Euro Area",
|
||||
'EA19CPGREN01GYQ': "CPI:OECD Groups:Energy (Fuel, electricity & gasoline):Total for the Euro Area",
|
||||
'EA19CPHP0401IXOBQ': "CPI:Harmonised prices:Housing, water, electricity, gas and other fuels (COICOP 04):Actual rentals for housing for the Euro Area"}
|
||||
description = "Consumer Price Index, Quarterly, Not Seasonally Adjusted"
|
||||
return df, name_list, description
|
||||
|
||||
|
||||
def PPI_Monthly():
|
||||
tmp_url = url["fred_econ"] + "bgcolor=%23e1e9f0&chart_type=line&drp=0&fo=open%20sans&graph_bgcolor=%23ffffff&height=450&mode=fred&recession_bars=off&txtcolor=%23444444&ts=12&tts=12&width=748&nt=0&thu=0&trc=0&show_legend=yes&show_axis_titles=yes&show_tooltip=yes&id=PIEAMP02EZM659N,PIEAMP01EZM661N,PIEATI01EZM661N,PIEATI02EZM661N,PITGND02EZM661N,PITGND01EZM661N,PITGIG01EZM661N,PITGIG02EZM661N,PIEAFD02EZM661N,PITGCG02EZM661N,PITGCG01EZM661N,PITGCD01EZM661N&scale=left,left,left,left,left,left,left,left,left,left,left,left&cosd=1996-01-01,2000-01-01,2000-01-01,2000-01-01,1995-01-01,2000-01-01,2000-01-01,1995-01-01,1995-01-01,1995-01-01,2000-01-01,2000-01-01&coed=2021-03-01,2021-02-01,2021-02-01,2021-03-01,2021-03-01,2021-02-01,2021-02-01,2021-03-01,2021-03-01,2021-03-01,2021-02-01,2021-02-01&line_color=%234572a7,%23aa4643,%2389a54e,%2380699b,%233d96ae,%23db843d,%2392a8cd,%23a47d7c,%23b5ca92,%2391e8e1,%238d4653,%238085e8&link_values=false,false,false,false,false,false,false,false,false,false,false,false&line_style=solid,solid,solid,solid,solid,solid,solid,solid,solid,solid,solid,solid&mark_type=none,none,none,none,none,none,none,none,none,none,none,none&mw=3,3,3,3,3,3,3,3,3,3,3,3&lw=2,2,2,2,2,2,2,2,2,2,2,2&ost=-99999,-99999,-99999,-99999,-99999,-99999,-99999,-99999,-99999,-99999,-99999,-99999&oet=99999,99999,99999,99999,99999,99999,99999,99999,99999,99999,99999,99999&mma=0,0,0,0,0,0,0,0,0,0,0,0&fml=a,a,a,a,a,a,a,a,a,a,a,a&fq=Monthly,Monthly,Monthly,Monthly,Monthly,Monthly,Monthly,Monthly,Monthly,Monthly,Monthly,Monthly&fam=avg,avg,avg,avg,avg,avg,avg,avg,avg,avg,avg,avg&fgst=lin,lin,lin,lin,lin,lin,lin,lin,lin,lin,lin,lin&fgsnd=2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01&line_index=1,2,3,4,5,6,7,8,9,10,11,12&transformation=lin,lin,lin,lin,lin,lin,lin,lin,lin,lin,lin,lin&vintage_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07&revision_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07&nd=1996-01-01,2000-01-01,2000-01-01,2000-01-01,1995-01-01,2000-01-01,2000-01-01,1995-01-01,1995-01-01,1995-01-01,2000-01-01,2000-01-01"
|
||||
ua = UserAgent(verify_ssl=False)
|
||||
request_header = {"User-Agent": ua.random}
|
||||
r = requests.get(tmp_url, headers=request_header)
|
||||
data_text = r.content
|
||||
df = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
|
||||
df["DATE"] = pd.to_datetime(df["DATE"], format="%Y-%m-%d")
|
||||
#df = df[list(df.columns[1:])].replace(".", np.nan).astype(float)
|
||||
name_list = {
|
||||
'PIEAMP02EZM659N': "Producer Prices Index: Economic Activities: Domestic Manufacturing for the Euro Area",
|
||||
"PIEAMP01EZM661N": "Producer Prices Index: Economic Activities: Total Manufacturing for the Euro Area",
|
||||
"PIEATI01EZM661N": "Producer Prices Index: Economic Activities: Total Industrial Activities for the Euro Area",
|
||||
"PIEATI02EZM661N": "Producer Prices Index: Economic Activities: Domestic Industrial Activities for the Euro Area",
|
||||
"PITGND02EZM661N": "Producer Prices Index: Domestic Nondurable Consumer Goods for the Euro Area",
|
||||
"PITGND01EZM661N": "Producer Prices Index: Total Nondurable Consumer Goods for the Euro Area",
|
||||
"PITGIG01EZM661N": "Producer Prices Index: Total Intermediate Goods for the Euro Area",
|
||||
"PITGIG02EZM661N": "Producer Prices Index: Domestic Intermediate Goods for the Euro Area",
|
||||
"PIEAFD02EZM661N": "Producer Prices Index: Economic Activities: Domestic Manufacture of Food Products for the Euro Area",
|
||||
"PITGCG02EZM661N": "Producer Prices Index: Domestic Consumer Goods for the Euro Area",
|
||||
"PITGCG01EZM661N": "Producer Prices Index: Total Consumer Goods for the Euro Area",
|
||||
"PITGCD01EZM661N": "Producer Prices Index: Total Durable Consumer Goods for the Euro Area"}
|
||||
description = "Producer Prices Index, Monthly, Not Seasonally Adjusted"
|
||||
return df, name_list, description
|
||||
|
||||
|
||||
def PPI_Quarterly():
|
||||
tmp_url = url["fred_econ"] + "bgcolor=%23e1e9f0&chart_type=line&drp=0&fo=open%20sans&graph_bgcolor=%23ffffff&height=450&mode=fred&recession_bars=off&txtcolor=%23444444&ts=12&tts=12&width=748&nt=0&thu=0&trc=0&show_legend=yes&show_axis_titles=yes&show_tooltip=yes&id=PIEAFD01EZQ661N,PIEAEN02EZQ661N,PIEAEN01EZQ661N,PITGND02EZQ661N,PITGND01EZQ661N,PITGIG01EZQ661N,PITGIG02EZQ661N,PIEAFD02EZQ661N,PITGCD02EZQ661N,PITGCD01EZQ661N,PITGVG01EZQ661N,PITGVG02EZQ661N&scale=left,left,left,left,left,left,left,left,left,left,left,left&cosd=2000-01-01,2000-01-01,2000-01-01,1995-01-01,2000-01-01,2000-01-01,1995-01-01,1995-01-01,2000-01-01,2000-01-01,2000-01-01,1995-01-01&coed=2020-10-01,2021-01-01,2020-10-01,2021-01-01,2020-10-01,2020-10-01,2021-01-01,2021-01-01,2021-01-01,2020-10-01,2020-10-01,2021-01-01&line_color=%234572a7,%23aa4643,%2389a54e,%2380699b,%233d96ae,%23db843d,%2392a8cd,%23a47d7c,%23b5ca92,%2391e8e1,%238d4653,%238085e8&link_values=false,false,false,false,false,false,false,false,false,false,false,false&line_style=solid,solid,solid,solid,solid,solid,solid,solid,solid,solid,solid,solid&mark_type=none,none,none,none,none,none,none,none,none,none,none,none&mw=3,3,3,3,3,3,3,3,3,3,3,3&lw=2,2,2,2,2,2,2,2,2,2,2,2&ost=-99999,-99999,-99999,-99999,-99999,-99999,-99999,-99999,-99999,-99999,-99999,-99999&oet=99999,99999,99999,99999,99999,99999,99999,99999,99999,99999,99999,99999&mma=0,0,0,0,0,0,0,0,0,0,0,0&fml=a,a,a,a,a,a,a,a,a,a,a,a&fq=Quarterly,Quarterly,Quarterly,Quarterly,Quarterly,Quarterly,Quarterly,Quarterly,Quarterly,Quarterly,Quarterly,Quarterly&fam=avg,avg,avg,avg,avg,avg,avg,avg,avg,avg,avg,avg&fgst=lin,lin,lin,lin,lin,lin,lin,lin,lin,lin,lin,lin&fgsnd=2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01&line_index=1,2,3,4,5,6,7,8,9,10,11,12&transformation=lin,lin,lin,lin,lin,lin,lin,lin,lin,lin,lin,lin&vintage_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07&revision_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07&nd=2000-01-01,2000-01-01,2000-01-01,1995-01-01,2000-01-01,2000-01-01,1995-01-01,1995-01-01,2000-01-01,2000-01-01,2000-01-01,1995-01-01"
|
||||
ua = UserAgent(verify_ssl=False)
|
||||
request_header = {"User-Agent": ua.random}
|
||||
r = requests.get(tmp_url, headers=request_header)
|
||||
data_text = r.content
|
||||
df = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
|
||||
df["DATE"] = pd.to_datetime(df["DATE"], format="%Y-%m-%d")
|
||||
#df = df[list(df.columns[1:])].replace(".", np.nan).astype(float)
|
||||
name_list = {
|
||||
'PIEAFD01EZQ661N': "Producer Prices Index: Economic Activities: Total Manufacture of Food Products for the Euro Area",
|
||||
"PIEAEN02EZQ661N": "Producer Prices Index: Economic Activities: Domestic Energy for the Euro Area",
|
||||
"PIEAEN01EZQ661N": "Producer Prices Index: Economic Activities: Total Energy for the Euro Area",
|
||||
"PITGND02EZQ661N": "Producer Prices Index: Domestic Nondurable Consumer Goods for the Euro Area",
|
||||
"PITGND01EZQ661N": "Producer Prices Index: Total Nondurable Consumer Goods for the Euro Area",
|
||||
"PITGIG01EZQ661N": "Producer Prices Index: Total Intermediate Goods for the Euro Area",
|
||||
"PITGIG02EZQ661N": "Producer Prices Index: Domestic Intermediate Goods for the Euro Area",
|
||||
"PIEAFD02EZQ661N": "Producer Prices Index: Economic Activities: Domestic Manufacture of Food Products for the Euro Area",
|
||||
"PITGCD02EZQ661N": "Producer Prices Index: Domestic Durable Consumer Goods for the Euro Area",
|
||||
"PITGCD01EZQ661N": "Producer Prices Index: Total Durable Consumer Goods for the Euro Area",
|
||||
"PITGVG01EZQ661N": "Producer Prices Index: Investments Goods: Total for the Euro Area",
|
||||
"PITGVG02EZQ661N": "Producer Prices Index: Domestic Investments Goods for the Euro Area"}
|
||||
description = "Producer Prices Index, Quarterly, Not Seasonally Adjusted"
|
||||
return df, name_list, description
|
||||
|
||||
|
||||
def Business_Tendency_Surveys_Construction():
|
||||
tmp_url = url["fred_econ"] + "bgcolor=%23e1e9f0&chart_type=line&drp=0&fo=open%20sans&graph_bgcolor=%23ffffff&height=450&mode=fred&recession_bars=off&txtcolor=%23444444&ts=12&tts=12&width=748&nt=0&thu=0&trc=0&show_legend=yes&show_axis_titles=yes&show_tooltip=yes&id=EA19BCBUTE02STSAM,BCOBLV02EZM460S,BCEMFT02EZM460S,BCCICP02EZM460S,BCSPFT02EZM460S&scale=left,left,left,left,left&cosd=1985-01-01,1985-01-01,1985-01-01,1985-01-01,1985-01-01&coed=2021-04-01,2021-04-01,2021-04-01,2021-04-01,2021-04-01&line_color=%234572a7,%23aa4643,%2389a54e,%2380699b,%233d96ae&link_values=false,false,false,false,false&line_style=solid,solid,solid,solid,solid&mark_type=none,none,none,none,none&mw=3,3,3,3,3&lw=2,2,2,2,2&ost=-99999,-99999,-99999,-99999,-99999&oet=99999,99999,99999,99999,99999&mma=0,0,0,0,0&fml=a,a,a,a,a&fq=Monthly,Monthly,Monthly,Monthly,Monthly&fam=avg,avg,avg,avg,avg&fgst=lin,lin,lin,lin,lin&fgsnd=2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01&line_index=1,2,3,4,5&transformation=lin,lin,lin,lin,lin&vintage_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07&revision_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07&nd=1985-01-01,1985-01-01,1985-01-01,1985-01-01,1985-01-01"
|
||||
ua = UserAgent(verify_ssl=False)
|
||||
request_header = {"User-Agent": ua.random}
|
||||
r = requests.get(tmp_url, headers=request_header)
|
||||
data_text = r.content
|
||||
df = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
|
||||
df["DATE"] = pd.to_datetime(df["DATE"], format="%Y-%m-%d")
|
||||
#df = df[list(df.columns[1:])].replace(".", np.nan).astype(float)
|
||||
name_list = {
|
||||
'EA19BCBUTE02STSAM': "Business tendency surveys (construction): Business situation - Activity: Tendency: National indicator for the Euro Area",
|
||||
'BCOBLV02EZM460S': "Business Tendency Surveys for Construction: Order Books: Level: European Commission Indicator for the Euro Area",
|
||||
'BCEMFT02EZM460S': "Business Tendency Surveys for Construction: Employment: Future Tendency: European Commission and National Indicators for the Euro Area",
|
||||
'BCCICP02EZM460S': "Business Tendency Surveys for Construction: Confidence Indicators: Composite Indicators: European Commission and National Indicators for the Euro Area",
|
||||
'BCSPFT02EZM460S': "Business Tendency Surveys for Construction: Selling Prices: Future Tendency: European Commission Indicator for the Euro Area"}
|
||||
description = "Business tendency surveys (construction), Monthly, Seasonally Adjusted"
|
||||
return df, name_list, description
|
||||
|
||||
|
||||
def Business_Tendency_Surveys_Services():
|
||||
tmp_url = url["fred_econ"] + "bgcolor=%23e1e9f0&chart_type=line&drp=0&fo=open%20sans&graph_bgcolor=%23ffffff&height=450&mode=fred&recession_bars=off&txtcolor=%23444444&ts=12&tts=12&width=748&nt=0&thu=0&trc=0&show_legend=yes&show_axis_titles=yes&show_tooltip=yes&id=EA19BVBUTE02STSAM,BVCICP02EZM460S,BVEMTE02EZM460S,BVEMFT02EZM460S,BVDEFT02EZM460S,BVDETE02EZM460S&scale=left,left,left,left,left,left&cosd=1995-04-01,1995-04-01,1995-04-01,1996-10-01,1995-04-01,1995-04-01&coed=2021-04-01,2021-04-01,2021-04-01,2021-04-01,2021-04-01,2021-04-01&line_color=%234572a7,%23aa4643,%2389a54e,%2380699b,%233d96ae,%23db843d&link_values=false,false,false,false,false,false&line_style=solid,solid,solid,solid,solid,solid&mark_type=none,none,none,none,none,none&mw=3,3,3,3,3,3&lw=2,2,2,2,2,2&ost=-99999,-99999,-99999,-99999,-99999,-99999&oet=99999,99999,99999,99999,99999,99999&mma=0,0,0,0,0,0&fml=a,a,a,a,a,a&fq=Monthly,Monthly,Monthly,Monthly,Monthly,Monthly&fam=avg,avg,avg,avg,avg,avg&fgst=lin,lin,lin,lin,lin,lin&fgsnd=2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01&line_index=1,2,3,4,5,6&transformation=lin,lin,lin,lin,lin,lin&vintage_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07&revision_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07&nd=1995-04-01,1995-04-01,1995-04-01,1996-10-01,1995-04-01,1995-04-01"
|
||||
ua = UserAgent(verify_ssl=False)
|
||||
request_header = {"User-Agent": ua.random}
|
||||
r = requests.get(tmp_url, headers=request_header)
|
||||
data_text = r.content
|
||||
df = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
|
||||
df["DATE"] = pd.to_datetime(df["DATE"], format="%Y-%m-%d")
|
||||
#df = df[list(df.columns[1:])].replace(".", np.nan).astype(float)
|
||||
name_list = {
|
||||
'EA19BVBUTE02STSAM': "Business tendency surveys (services): Business situation - Activity: Tendency: National indicator for Euro Area",
|
||||
'BVCICP02EZM460S': "Business Tendency Surveys for Services: Confidence Indicators: Composite Indicators: European Commission and National Indicators for the Euro Area",
|
||||
'BVEMTE02EZM460S': "Business Tendency Surveys for Services: Employment: Tendency: European Commission Indicator for the Euro Area",
|
||||
'BVEMFT02EZM460S': "Business Tendency Surveys for Services: Employment: Future Tendency: European Commission and National Indicators for the Euro Area",
|
||||
'BVDEFT02EZM460S': "Business Tendency Surveys for Services: Demand Evolution: Future Tendency: European Commission Indicator for the Euro Area",
|
||||
'BVDETE02EZM460S': "Business Tendency Surveys for Services: Demand Evolution: Tendency: European Commission Indicator for the Euro Area"}
|
||||
description = "Business tendency surveys (services), Monthly, Seasonally Adjusted"
|
||||
return df, name_list, description
|
||||
|
||||
|
||||
def Business_Tendency_Surveys_Manufacturing_Quarterly():
|
||||
tmp_url = url["fred_econ"] + "bgcolor=%23e1e9f0&chart_type=line&drp=0&fo=open%20sans&graph_bgcolor=%23ffffff&height=450&mode=fred&recession_bars=off&txtcolor=%23444444&ts=12&tts=12&width=748&nt=0&thu=0&trc=0&show_legend=yes&show_axis_titles=yes&show_tooltip=yes&id=BSCURT02EZQ160S,BSOITE02EZQ460S&scale=left,left&cosd=1985-01-01,1985-01-01&coed=2021-04-01,2021-04-01&line_color=%234572a7,%23aa4643&link_values=false,false&line_style=solid,solid&mark_type=none,none&mw=3,3&lw=2,2&ost=-99999,-99999&oet=99999,99999&mma=0,0&fml=a,a&fq=Quarterly,Quarterly&fam=avg,avg&fgst=lin,lin&fgsnd=2020-02-01,2020-02-01&line_index=1,2&transformation=lin,lin&vintage_date=2021-06-07,2021-06-07&revision_date=2021-06-07,2021-06-07&nd=1985-01-01,1985-01-01"
|
||||
ua = UserAgent(verify_ssl=False)
|
||||
request_header = {"User-Agent": ua.random}
|
||||
r = requests.get(tmp_url, headers=request_header)
|
||||
data_text = r.content
|
||||
df = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
|
||||
df["DATE"] = pd.to_datetime(df["DATE"], format="%Y-%m-%d")
|
||||
#df = df[list(df.columns[1:])].replace(".", np.nan).astype(float)
|
||||
name_list = {
|
||||
'BSCURT02EZQ160S': "Business Tendency Surveys for Manufacturing: Capacity Utilization: Rate of Capacity Utilization: European Commission and National Indicators for the Euro Area",
|
||||
'BSOITE02EZQ460S': "Business Tendency Surveys for Manufacturing: Orders Inflow: Tendency: European Commission Indicator for the Euro Area"}
|
||||
description = "Business tendency surveys (manufacturing), Quarterly, Seasonally Adjusted"
|
||||
return df, name_list, description
|
||||
|
||||
|
||||
def Business_Tendency_Surveys_Manufacturing_Monthly():
|
||||
tmp_url = url["fred_econ"] + "bgcolor=%23e1e9f0&chart_type=line&drp=0&fo=open%20sans&graph_bgcolor=%23ffffff&height=450&mode=fred&recession_bars=off&txtcolor=%23444444&ts=12&tts=12&width=748&nt=0&thu=0&trc=0&show_legend=yes&show_axis_titles=yes&show_tooltip=yes&id=BSSPFT02EZM460S,BSOBLV02EZM460S,BSEMFT02EZM460S,BSFGLV02EZM460S,BSXRLV02EZM086S,BSCICP02EZM460S,BSPRTE02EZM460S,BSPRFT02EZM460S&scale=left,left,left,left,left,left,left,left&cosd=1985-01-01,1985-01-01,1985-01-01,1985-01-01,1985-01-01,1985-01-01,1985-01-01,1985-01-01&coed=2021-04-01,2021-04-01,2021-04-01,2021-04-01,2021-04-01,2021-04-01,2021-04-01,2021-04-01&line_color=%234572a7,%23aa4643,%2389a54e,%2380699b,%233d96ae,%23db843d,%2392a8cd,%23a47d7c&link_values=false,false,false,false,false,false,false,false&line_style=solid,solid,solid,solid,solid,solid,solid,solid&mark_type=none,none,none,none,none,none,none,none&mw=3,3,3,3,3,3,3,3&lw=2,2,2,2,2,2,2,2&ost=-99999,-99999,-99999,-99999,-99999,-99999,-99999,-99999&oet=99999,99999,99999,99999,99999,99999,99999,99999&mma=0,0,0,0,0,0,0,0&fml=a,a,a,a,a,a,a,a&fq=Monthly,Monthly,Monthly,Monthly,Monthly,Monthly,Monthly,Monthly&fam=avg,avg,avg,avg,avg,avg,avg,avg&fgst=lin,lin,lin,lin,lin,lin,lin,lin&fgsnd=2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01&line_index=1,2,3,4,5,6,7,8&transformation=lin,lin,lin,lin,lin,lin,lin,lin&vintage_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07&revision_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07&nd=1985-01-01,1985-01-01,1985-01-01,1985-01-01,1985-01-01,1985-01-01,1985-01-01,1985-01-01"
|
||||
ua = UserAgent(verify_ssl=False)
|
||||
request_header = {"User-Agent": ua.random}
|
||||
r = requests.get(tmp_url, headers=request_header)
|
||||
data_text = r.content
|
||||
df = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
|
||||
df["DATE"] = pd.to_datetime(df["DATE"], format="%Y-%m-%d")
|
||||
#df = df[list(df.columns[1:])].replace(".", np.nan).astype(float)
|
||||
name_list = {
|
||||
'BSSPFT02EZM460S': "Business Tendency Surveys for Manufacturing: Selling Prices: Future Tendency: European Commission Indicator for the Euro Area",
|
||||
'BSOBLV02EZM460S': "Business Tendency Surveys for Manufacturing: Order Books: Level: European Commission and National Indicators for the Euro Area",
|
||||
'BSEMFT02EZM460S': "Business Tendency Surveys for Manufacturing: Employment: Future Tendency: European Commission and National Indicators for the Euro Area",
|
||||
'BSFGLV02EZM460S': "Business Tendency Surveys for Manufacturing: Finished Goods Stocks: Level: European Commission and National Indicators for the Euro Area",
|
||||
'BSXRLV02EZM086S': "Business Tendency Surveys for Manufacturing: Export Order Books or Demand: Level: European Commission Indicator for the Euro Area",
|
||||
'BSCICP02EZM460S': "Business Tendency Surveys for Manufacturing: Confidence Indicators: Composite Indicators: European Commission and National Indicators for the Euro Area",
|
||||
'BSPRTE02EZM460S': "Business Tendency Surveys for Manufacturing: Production: Tendency: European Commission and National Indicators for the Euro Area",
|
||||
'BSPRFT02EZM460S': "Business Tendency Surveys for Manufacturing: Production: Future Tendency: European Commission and National Indicators for the Euro Area"}
|
||||
description = "Business tendency surveys (manufacturing), Monthly, Seasonally Adjusted"
|
||||
return df, name_list, description
|
||||
|
||||
|
||||
def Business_Tendency_Surveys_Retail_Trade():
|
||||
tmp_url = url["fred_econ"] + "bgcolor=%23e1e9f0&chart_type=line&drp=0&fo=open%20sans&graph_bgcolor=%23ffffff&height=450&mode=fred&recession_bars=off&txtcolor=%23444444&ts=12&tts=12&width=748&nt=0&thu=0&trc=0&show_legend=yes&show_axis_titles=yes&show_tooltip=yes&id=EA19BREMFT02STSAM,EA19BRODFT02STSAM,EA19BRVSLV02STSAM,EA19BRCICP02STSAM,EA19BRBUFT02STSAM,EA19BRBUTE02STSAM&scale=left,left,left,left,left,left&cosd=1985-04-01,1985-02-01,1985-01-01,1985-01-01,1985-01-01,1985-01-01&coed=2021-04-01,2021-04-01,2021-04-01,2021-04-01,2021-04-01,2021-04-01&line_color=%234572a7,%23aa4643,%2389a54e,%2380699b,%233d96ae,%23db843d&link_values=false,false,false,false,false,false&line_style=solid,solid,solid,solid,solid,solid&mark_type=none,none,none,none,none,none&mw=3,3,3,3,3,3&lw=2,2,2,2,2,2&ost=-99999,-99999,-99999,-99999,-99999,-99999&oet=99999,99999,99999,99999,99999,99999&mma=0,0,0,0,0,0&fml=a,a,a,a,a,a&fq=Monthly,Monthly,Monthly,Monthly,Monthly,Monthly&fam=avg,avg,avg,avg,avg,avg&fgst=lin,lin,lin,lin,lin,lin&fgsnd=2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01&line_index=1,2,3,4,5,6&transformation=lin,lin,lin,lin,lin,lin&vintage_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07&revision_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07&nd=1985-04-01,1985-02-01,1985-01-01,1985-01-01,1985-01-01,1985-01-01"
|
||||
ua = UserAgent(verify_ssl=False)
|
||||
request_header = {"User-Agent": ua.random}
|
||||
r = requests.get(tmp_url, headers=request_header)
|
||||
data_text = r.content
|
||||
df = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
|
||||
df["DATE"] = pd.to_datetime(df["DATE"], format="%Y-%m-%d")
|
||||
#df = df[list(df.columns[1:])].replace(".", np.nan).astype(float)
|
||||
name_list = {
|
||||
'EA19BREMFT02STSAM': "Business tendency surveys (retail trade): Employment: Future tendency: National indicator for the Euro Area",
|
||||
'EA19BRODFT02STSAM': "Business tendency surveys (retail trade): Order intentions or Demand: Future tendency: National indicator for the Euro Area",
|
||||
'EA19BRVSLV02STSAM': "Business tendency surveys (retail trade): Volume of stocks: Level: National indicator for the Euro Area",
|
||||
'EA19BRCICP02STSAM': "Business tendency surveys (retail trade): Confidence indicators: Composite indicators: National indicator for the Euro Area",
|
||||
'EA19BRBUFT02STSAM': "Business tendency surveys (retail trade): Business situation - Activity: Future tendency: National indicator for Euro Area",
|
||||
'EA19BRBUTE02STSAM': "Business tendency surveys (retail trade): Business situation - Activity: Tendency: National indicator for Euro Area"}
|
||||
description = "Business tendency surveys (retail trade), Monthly, Seasonally Adjusted"
|
||||
return df, name_list, description
|
||||
|
||||
|
||||
def Labor_Compoenstiveion_Quarterly_Adj():
|
||||
tmp_url = url["fred_econ"] + "bgcolor=%23e1e9f0&chart_type=line&drp=0&fo=open%20sans&graph_bgcolor=%23ffffff&height=450&mode=fred&recession_bars=off&txtcolor=%23444444&ts=12&tts=12&width=748&nt=0&thu=0&trc=0&show_legend=yes&show_axis_titles=yes&show_tooltip=yes&id=LCEAMN01EZQ661S,LCEAPR01EZQ661S&scale=left,left&cosd=1971-01-01,1996-01-01&coed=2020-10-01,2020-10-01&line_color=%234572a7,%23aa4643&link_values=false,false&line_style=solid,solid&mark_type=none,none&mw=3,3&lw=2,2&ost=-99999,-99999&oet=99999,99999&mma=0,0&fml=a,a&fq=Quarterly,Quarterly&fam=avg,avg&fgst=lin,lin&fgsnd=2020-02-01,2020-02-01&line_index=1,2&transformation=lin,lin&vintage_date=2021-06-07,2021-06-07&revision_date=2021-06-07,2021-06-07&nd=1971-01-01,1996-01-01"
|
||||
ua = UserAgent(verify_ssl=False)
|
||||
request_header = {"User-Agent": ua.random}
|
||||
r = requests.get(tmp_url, headers=request_header)
|
||||
data_text = r.content
|
||||
df = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
|
||||
df["DATE"] = pd.to_datetime(df["DATE"], format="%Y-%m-%d")
|
||||
#df = df[list(df.columns[1:])].replace(".", np.nan).astype(float)
|
||||
name_list = {
|
||||
'LCEAMN01EZQ661S': "Hourly Earnings: Manufacturing for the Euro Area",
|
||||
'LCEAPR01EZQ661S': "Hourly Earnings: Private Sector for the Euro Area"
|
||||
}
|
||||
description = "Labor Compensation, Quarterly, Seasonally Adjusted"
|
||||
return df, name_list, description
|
||||
|
||||
|
||||
def Labor_Compoenstiveion_Quarterly_NAdj():
|
||||
tmp_url = url["fred_econ"] + "bgcolor=%23e1e9f0&chart_type=line&drp=0&fo=open%20sans&graph_bgcolor=%23ffffff&height=450&mode=fred&recession_bars=off&txtcolor=%23444444&ts=12&tts=12&width=748&nt=0&thu=0&trc=0&show_legend=yes&show_axis_titles=yes&show_tooltip=yes&id=LCEAMN01EZQ661S,LCEAPR01EZQ661S&scale=left,left&cosd=1971-01-01,1996-01-01&coed=2020-10-01,2020-10-01&line_color=%234572a7,%23aa4643&link_values=false,false&line_style=solid,solid&mark_type=none,none&mw=3,3&lw=2,2&ost=-99999,-99999&oet=99999,99999&mma=0,0&fml=a,a&fq=Quarterly,Quarterly&fam=avg,avg&fgst=lin,lin&fgsnd=2020-02-01,2020-02-01&line_index=1,2&transformation=lin,lin&vintage_date=2021-06-07,2021-06-07&revision_date=2021-06-07,2021-06-07&nd=1971-01-01,1996-01-01"
|
||||
ua = UserAgent(verify_ssl=False)
|
||||
request_header = {"User-Agent": ua.random}
|
||||
r = requests.get(tmp_url, headers=request_header)
|
||||
data_text = r.content
|
||||
df = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
|
||||
df["DATE"] = pd.to_datetime(df["DATE"], format="%Y-%m-%d")
|
||||
#df = df[list(df.columns[1:])].replace(".", np.nan).astype(float)
|
||||
name_list = {
|
||||
'LCEAMN01EZQ661N': "Hourly Earnings: Manufacturing for the Euro Area",
|
||||
'LCEAPR01EZQ661N': "Hourly Earnings: Private Sector for the Euro Area"
|
||||
}
|
||||
description = "Labor Compensation, Quarterly, Not Seasonally Adjusted"
|
||||
return df, name_list, description
|
||||
|
||||
|
||||
def Unit_Labor_costs():
|
||||
tmp_url = url["fred_econ"] + "bgcolor=%23e1e9f0&chart_type=line&drp=0&fo=open%20sans&graph_bgcolor=%23ffffff&height=450&mode=fred&recession_bars=off&txtcolor=%23444444&ts=12&tts=12&width=748&nt=0&thu=0&trc=0&show_legend=yes&show_axis_titles=yes&show_tooltip=yes&id=ULQECU01EZQ661S,ULQEUL01EZQ659S,ULQELP01EZQ661S&scale=left,left,left&cosd=1995-01-01,1996-01-01,1995-01-01&coed=2020-10-01,2020-10-01,2020-10-01&line_color=%234572a7,%23aa4643,%2389a54e&link_values=false,false,false&line_style=solid,solid,solid&mark_type=none,none,none&mw=3,3,3&lw=2,2,2&ost=-99999,-99999,-99999&oet=99999,99999,99999&mma=0,0,0&fml=a,a,a&fq=Quarterly,Quarterly,Quarterly&fam=avg,avg,avg&fgst=lin,lin,lin&fgsnd=2020-02-01,2020-02-01,2020-02-01&line_index=1,2,3&transformation=lin,lin,lin&vintage_date=2021-06-07,2021-06-07,2021-06-07&revision_date=2021-06-07,2021-06-07,2021-06-07&nd=1995-01-01,1996-01-01,1995-01-01"
|
||||
ua = UserAgent(verify_ssl=False)
|
||||
request_header = {"User-Agent": ua.random}
|
||||
r = requests.get(tmp_url, headers=request_header)
|
||||
data_text = r.content
|
||||
df = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
|
||||
df["DATE"] = pd.to_datetime(df["DATE"], format="%Y-%m-%d")
|
||||
#df = df[list(df.columns[1:])].replace(".", np.nan).astype(float)
|
||||
name_list = {
|
||||
'ULQECU01EZQ661S': "Early Estimate of Quarterly ULC Indicators: Total Labor Compensation per Unit of Labor Input for the Euro Area",
|
||||
'ULQEUL01EZQ659S': "Early Estimate of Quarterly ULC Indicators: Total for the Euro Area",
|
||||
'ULQELP01EZQ661S': "Early Estimate of Quarterly ULC Indicators: Total Labor Productivity for the Euro Area"}
|
||||
description = "Unit Labor Costs, Quarterly, Seasonally Adjusted"
|
||||
return df, name_list, description
|
||||
|
||||
|
||||
def Labor_Force_Survey_Rates_Quarterly_NAdj():
|
||||
tmp_url = url["fred_econ"] + "bgcolor=%23e1e9f0&chart_type=line&drp=0&fo=open%20sans&graph_bgcolor=%23ffffff&height=450&mode=fred&recession_bars=off&txtcolor=%23444444&ts=12&tts=12&width=748&nt=0&thu=0&trc=0&show_legend=yes&show_axis_titles=yes&show_tooltip=yes&id=LRHU24TTEZQ156N,LRHU24FEEZQ156N,LRHU24MAEZQ156N,LRHUADMAEZQ156N,LRHUADTTEZQ156N,LRHUADFEEZQ156N,LRHUTTFEEZQ156N,LRHUTTTTEZQ156N,LRHUTTMAEZQ156N&scale=left,left,left,left,left,left,left,left,left&cosd=1995-01-01,1995-01-01,1995-01-01,1995-01-01,1995-01-01,1995-01-01,1993-01-01,1993-01-01,1993-01-01&coed=2021-01-01,2021-01-01,2021-01-01,2021-01-01,2021-01-01,2021-01-01,2021-01-01,2021-01-01,2021-01-01&line_color=%234572a7,%23aa4643,%2389a54e,%2380699b,%233d96ae,%23db843d,%2392a8cd,%23a47d7c,%23b5ca92&link_values=false,false,false,false,false,false,false,false,false&line_style=solid,solid,solid,solid,solid,solid,solid,solid,solid&mark_type=none,none,none,none,none,none,none,none,none&mw=3,3,3,3,3,3,3,3,3&lw=2,2,2,2,2,2,2,2,2&ost=-99999,-99999,-99999,-99999,-99999,-99999,-99999,-99999,-99999&oet=99999,99999,99999,99999,99999,99999,99999,99999,99999&mma=0,0,0,0,0,0,0,0,0&fml=a,a,a,a,a,a,a,a,a&fq=Quarterly,Quarterly,Quarterly,Quarterly,Quarterly,Quarterly,Quarterly,Quarterly,Quarterly&fam=avg,avg,avg,avg,avg,avg,avg,avg,avg&fgst=lin,lin,lin,lin,lin,lin,lin,lin,lin&fgsnd=2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01&line_index=1,2,3,4,5,6,7,8,9&transformation=lin,lin,lin,lin,lin,lin,lin,lin,lin&vintage_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07&revision_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07&nd=1995-01-01,1995-01-01,1995-01-01,1995-01-01,1995-01-01,1995-01-01,1993-01-01,1993-01-01,1993-01-01"
|
||||
ua = UserAgent(verify_ssl=False)
|
||||
request_header = {"User-Agent": ua.random}
|
||||
r = requests.get(tmp_url, headers=request_header)
|
||||
data_text = r.content
|
||||
df = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
|
||||
df["DATE"] = pd.to_datetime(df["DATE"], format="%Y-%m-%d")
|
||||
#df = df[list(df.columns[1:])].replace(".", np.nan).astype(float)
|
||||
name_list = {
|
||||
'LRHU24TTEZQ156N': "Harmonized Unemployment: Aged 15-24: All Persons for the Euro Area",
|
||||
'LRHU24FEEZQ156N': "Harmonized Unemployment: Aged 15-24: Females for the Euro Area",
|
||||
'LRHU24MAEZQ156N': "Harmonized Unemployment: Aged 15-24: Males for the Euro Area",
|
||||
'LRHUADMAEZQ156N': "Harmonized Unemployment: Aged 25 and Over: Males for the Euro Area",
|
||||
'LRHUADTTEZQ156N': "Harmonized Unemployment: Aged 25 and Over: All Persons for the Euro Area",
|
||||
'LRHUADFEEZQ156N': "Harmonized Unemployment: Aged 25 and Over: Females for the Euro Area",
|
||||
'LRHUTTFEEZQ156N': "Harmonized Unemployment: Total: Females for the Euro Area",
|
||||
'LRHUTTTTEZQ156N': "Harmonized Unemployment Rate: Total: All Persons for the Euro Area",
|
||||
'LRHUTTMAEZQ156N': "Harmonized Unemployment: Total: Males for the Euro Area"}
|
||||
description = "Labor Force Survey - quarterly rates, Quarterly, Not Seasonally Adjusted"
|
||||
return df, name_list, description
|
||||
|
||||
|
||||
def Labor_Force_Survey_Rates_Quarterly_Adj():
|
||||
tmp_url = url["fred_econ"] + "bgcolor=%23e1e9f0&chart_type=line&drp=0&fo=open%20sans&graph_bgcolor=%23ffffff&height=450&mode=fred&recession_bars=off&txtcolor=%23444444&ts=12&tts=12&width=748&nt=0&thu=0&trc=0&show_legend=yes&show_axis_titles=yes&show_tooltip=yes&id=LRHU24MAEZQ156S,LRHU24TTEZQ156S,LRHU24FEEZQ156S,LRHUADFEEZQ156S,LRHUADMAEZQ156S,LRHUADTTEZQ156S,LRHUTTTTEZQ156S,LRHUTTMAEZQ156S,LRHUTTFEEZQ156S&scale=left,left,left,left,left,left,left,left,left&cosd=1995-01-01,1995-01-01,1995-01-01,1995-01-01,1995-01-01,1995-01-01,1990-07-01,1990-07-01,1990-07-01&coed=2021-01-01,2021-01-01,2021-01-01,2021-01-01,2021-01-01,2021-01-01,2021-01-01,2021-01-01,2021-01-01&line_color=%234572a7,%23aa4643,%2389a54e,%2380699b,%233d96ae,%23db843d,%2392a8cd,%23a47d7c,%23b5ca92&link_values=false,false,false,false,false,false,false,false,false&line_style=solid,solid,solid,solid,solid,solid,solid,solid,solid&mark_type=none,none,none,none,none,none,none,none,none&mw=3,3,3,3,3,3,3,3,3&lw=2,2,2,2,2,2,2,2,2&ost=-99999,-99999,-99999,-99999,-99999,-99999,-99999,-99999,-99999&oet=99999,99999,99999,99999,99999,99999,99999,99999,99999&mma=0,0,0,0,0,0,0,0,0&fml=a,a,a,a,a,a,a,a,a&fq=Quarterly,Quarterly,Quarterly,Quarterly,Quarterly,Quarterly,Quarterly,Quarterly,Quarterly&fam=avg,avg,avg,avg,avg,avg,avg,avg,avg&fgst=lin,lin,lin,lin,lin,lin,lin,lin,lin&fgsnd=2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01&line_index=1,2,3,4,5,6,7,8,9&transformation=lin,lin,lin,lin,lin,lin,lin,lin,lin&vintage_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07&revision_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07&nd=1995-01-01,1995-01-01,1995-01-01,1995-01-01,1995-01-01,1995-01-01,1990-07-01,1990-07-01,1990-07-01"
|
||||
ua = UserAgent(verify_ssl=False)
|
||||
request_header = {"User-Agent": ua.random}
|
||||
r = requests.get(tmp_url, headers=request_header)
|
||||
data_text = r.content
|
||||
df = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
|
||||
df["DATE"] = pd.to_datetime(df["DATE"], format="%Y-%m-%d")
|
||||
#df = df[list(df.columns[1:])].replace(".", np.nan).astype(float)
|
||||
name_list = {
|
||||
'LRHU24MAEZQ156S': "Harmonized Unemployment: Aged 15-24: Males for the Euro Area",
|
||||
'LRHU24TTEZQ156S': "Harmonized Unemployment: Aged 15-24: All Persons for the Euro Area",
|
||||
'LRHU24FEEZQ156S': "Harmonized Unemployment: Aged 15-24: Females for the Euro Area",
|
||||
'LRHUADFEEZQ156S': "Harmonized Unemployment: Aged 25 and Over: Females for the Euro Area",
|
||||
'LRHUADMAEZQ156S': "Harmonized Unemployment: Aged 25 and Over: Males for the Euro Area",
|
||||
'LRHUADTTEZQ156S': "Harmonized Unemployment: Aged 25 and Over: All Persons for the Euro Area",
|
||||
'LRHUTTTTEZQ156S': "Harmonized Unemployment Rate: Total: All Persons for the Euro Area",
|
||||
'LRHUTTMAEZQ156S': "Harmonized Unemployment: Total: Males for the Euro Area",
|
||||
'LRHUTTFEEZQ156S': "Harmonized Unemployment: Total: Females for the Euro Area"}
|
||||
description = "Labor Force Survey - quarterly rates, Quarterly, Seasonally Adjusted"
|
||||
return df, name_list, description
|
||||
|
||||
|
||||
def Labor_Force_Survey_Rates_Monthly_NAdj():
|
||||
tmp_url = url["fred_econ"] + "bgcolor=%23e1e9f0&chart_type=line&drp=0&fo=open%20sans&graph_bgcolor=%23ffffff&height=450&mode=fred&recession_bars=off&txtcolor=%23444444&ts=12&tts=12&width=748&nt=0&thu=0&trc=0&show_legend=yes&show_axis_titles=yes&show_tooltip=yes&id=LRHUTTFEEZM156N,LRHUTTMAEZM156N,LRHUTTTTEZM156N,LRHUADTTEZM156N,LRHUADMAEZM156N,LRHUADFEEZM156N,LRHU24FEEZM156N,LRHU24MAEZM156N,LRHU24TTEZM156N&scale=left,left,left,left,left,left,left,left,left&cosd=1993-01-01,1993-01-01,1993-01-01,1995-01-01,1995-01-01,1995-01-01,1995-01-01,1995-01-01,1995-01-01&coed=2021-03-01,2021-03-01,2021-03-01,2021-03-01,2021-03-01,2021-03-01,2021-03-01,2021-03-01,2021-03-01&line_color=%234572a7,%23aa4643,%2389a54e,%2380699b,%233d96ae,%23db843d,%2392a8cd,%23a47d7c,%23b5ca92&link_values=false,false,false,false,false,false,false,false,false&line_style=solid,solid,solid,solid,solid,solid,solid,solid,solid&mark_type=none,none,none,none,none,none,none,none,none&mw=3,3,3,3,3,3,3,3,3&lw=2,2,2,2,2,2,2,2,2&ost=-99999,-99999,-99999,-99999,-99999,-99999,-99999,-99999,-99999&oet=99999,99999,99999,99999,99999,99999,99999,99999,99999&mma=0,0,0,0,0,0,0,0,0&fml=a,a,a,a,a,a,a,a,a&fq=Monthly,Monthly,Monthly,Monthly,Monthly,Monthly,Monthly,Monthly,Monthly&fam=avg,avg,avg,avg,avg,avg,avg,avg,avg&fgst=lin,lin,lin,lin,lin,lin,lin,lin,lin&fgsnd=2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01&line_index=1,2,3,4,5,6,7,8,9&transformation=lin,lin,lin,lin,lin,lin,lin,lin,lin&vintage_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07&revision_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07&nd=1993-01-01,1993-01-01,1993-01-01,1995-01-01,1995-01-01,1995-01-01,1995-01-01,1995-01-01,1995-01-01"
|
||||
ua = UserAgent(verify_ssl=False)
|
||||
request_header = {"User-Agent": ua.random}
|
||||
r = requests.get(tmp_url, headers=request_header)
|
||||
data_text = r.content
|
||||
df = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
|
||||
df["DATE"] = pd.to_datetime(df["DATE"], format="%Y-%m-%d")
|
||||
#df = df[list(df.columns[1:])].replace(".", np.nan).astype(float)
|
||||
name_list = {
|
||||
'LRHUTTFEEZM156N': "Harmonized Unemployment: Total: Females for the Euro Area",
|
||||
'LRHUTTMAEZM156N': "Harmonized Unemployment: Total: Males for the Euro Area",
|
||||
'LRHUTTTTEZM156N': "Harmonized Unemployment Rate: Total: All Persons for the Euro Area",
|
||||
'LRHUADTTEZM156N': "Harmonized Unemployment: Aged 25 and Over: All Persons for the Euro Area",
|
||||
'LRHUADMAEZM156N': "Harmonized Unemployment: Aged 25 and Over: Males for the Euro Area",
|
||||
'LRHUADFEEZM156N': "Harmonized Unemployment: Aged 25 and Over: Females for the Euro Area",
|
||||
'LRHU24FEEZM156N': "Harmonized Unemployment: Aged 15-24: Females for the Euro Area",
|
||||
'LRHU24MAEZM156N': "Harmonized Unemployment: Aged 15-24: Males for the Euro Area",
|
||||
'LRHU24TTEZM156N': "Harmonized Unemployment: Aged 15-24: All Persons for the Euro Area"}
|
||||
description = "Labor Force Survey - quarterly rates, Monthly, Seasonally Adjusted"
|
||||
return df, name_list, description
|
||||
|
||||
|
||||
def Labor_Force_Survey_Level_Quarterly_NAdj():
|
||||
tmp_url = url["fred_econ"] + "bgcolor=%23e1e9f0&chart_type=line&drp=0&fo=open%20sans&graph_bgcolor=%23ffffff&height=450&mode=fred&recession_bars=off&txtcolor=%23444444&ts=12&tts=12&width=748&nt=0&thu=0&trc=0&show_legend=yes&show_axis_titles=yes&show_tooltip=yes&id=LRHUTTFEEZM156N,LRHUTTMAEZM156N,LRHUTTTTEZM156N,LRHUADTTEZM156N,LRHUADMAEZM156N,LRHUADFEEZM156N,LRHU24FEEZM156N,LRHU24MAEZM156N,LRHU24TTEZM156N&scale=left,left,left,left,left,left,left,left,left&cosd=1993-01-01,1993-01-01,1993-01-01,1995-01-01,1995-01-01,1995-01-01,1995-01-01,1995-01-01,1995-01-01&coed=2021-03-01,2021-03-01,2021-03-01,2021-03-01,2021-03-01,2021-03-01,2021-03-01,2021-03-01,2021-03-01&line_color=%234572a7,%23aa4643,%2389a54e,%2380699b,%233d96ae,%23db843d,%2392a8cd,%23a47d7c,%23b5ca92&link_values=false,false,false,false,false,false,false,false,false&line_style=solid,solid,solid,solid,solid,solid,solid,solid,solid&mark_type=none,none,none,none,none,none,none,none,none&mw=3,3,3,3,3,3,3,3,3&lw=2,2,2,2,2,2,2,2,2&ost=-99999,-99999,-99999,-99999,-99999,-99999,-99999,-99999,-99999&oet=99999,99999,99999,99999,99999,99999,99999,99999,99999&mma=0,0,0,0,0,0,0,0,0&fml=a,a,a,a,a,a,a,a,a&fq=Monthly,Monthly,Monthly,Monthly,Monthly,Monthly,Monthly,Monthly,Monthly&fam=avg,avg,avg,avg,avg,avg,avg,avg,avg&fgst=lin,lin,lin,lin,lin,lin,lin,lin,lin&fgsnd=2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01&line_index=1,2,3,4,5,6,7,8,9&transformation=lin,lin,lin,lin,lin,lin,lin,lin,lin&vintage_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07&revision_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07&nd=1993-01-01,1993-01-01,1993-01-01,1995-01-01,1995-01-01,1995-01-01,1995-01-01,1995-01-01,1995-01-01"
|
||||
ua = UserAgent(verify_ssl=False)
|
||||
request_header = {"User-Agent": ua.random}
|
||||
r = requests.get(tmp_url, headers=request_header)
|
||||
data_text = r.content
|
||||
df = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
|
||||
df["DATE"] = pd.to_datetime(df["DATE"], format="%Y-%m-%d")
|
||||
#df = df[list(df.columns[1:])].replace(".", np.nan).astype(float)
|
||||
name_list = {
|
||||
'LFHU24FEEZQ647N': "Harmonized Unemployment: Aged 15-24: Females for the Euro Area",
|
||||
'LFHU24TTEZQ647N': "Harmonized Unemployment: Aged 15-24: All Persons for the Euro Area",
|
||||
'LFHU24MAEZQ647N': "Harmonized Unemployment: Aged 15-24: Males for the Euro Area",
|
||||
'LFHUADTTEZQ647N': "Harmonized Unemployment: Aged 25 and Over: All Persons for the Euro Area",
|
||||
'LFHUADMAEZQ647N': "Harmonized Unemployment: Aged 25 and Over: Males for the Euro Area",
|
||||
'LFHUADFEEZQ647N': "Harmonized Unemployment: Aged 25 and Over: Females for the Euro Area",
|
||||
'LFHUTTMAEZQ647N': "Total Harmonized Unemployment: Males for the Euro Area",
|
||||
'LFHUTTFEEZQ647N': "Total Harmonized Unemployment: Females for the Euro Area",
|
||||
'LFHUTTTTEZQ647N': "Total Harmonized Unemployment: All Persons for the Euro Area"}
|
||||
description = "Labor Force Survey - quarterly levels, Quarterly, Not Seasonally Adjusted"
|
||||
return df, name_list, description
|
||||
|
||||
|
||||
def Labor_Force_Survey_Level_Quarterly_Adj():
|
||||
tmp_url = url["fred_econ"] + "bgcolor=%23e1e9f0&chart_type=line&drp=0&fo=open%20sans&graph_bgcolor=%23ffffff&height=450&mode=fred&recession_bars=off&txtcolor=%23444444&ts=12&tts=12&width=748&nt=0&thu=0&trc=0&show_legend=yes&show_axis_titles=yes&show_tooltip=yes&id=LRHUTTFEEZM156N,LRHUTTMAEZM156N,LRHUTTTTEZM156N,LRHUADTTEZM156N,LRHUADMAEZM156N,LRHUADFEEZM156N,LRHU24FEEZM156N,LRHU24MAEZM156N,LRHU24TTEZM156N&scale=left,left,left,left,left,left,left,left,left&cosd=1993-01-01,1993-01-01,1993-01-01,1995-01-01,1995-01-01,1995-01-01,1995-01-01,1995-01-01,1995-01-01&coed=2021-03-01,2021-03-01,2021-03-01,2021-03-01,2021-03-01,2021-03-01,2021-03-01,2021-03-01,2021-03-01&line_color=%234572a7,%23aa4643,%2389a54e,%2380699b,%233d96ae,%23db843d,%2392a8cd,%23a47d7c,%23b5ca92&link_values=false,false,false,false,false,false,false,false,false&line_style=solid,solid,solid,solid,solid,solid,solid,solid,solid&mark_type=none,none,none,none,none,none,none,none,none&mw=3,3,3,3,3,3,3,3,3&lw=2,2,2,2,2,2,2,2,2&ost=-99999,-99999,-99999,-99999,-99999,-99999,-99999,-99999,-99999&oet=99999,99999,99999,99999,99999,99999,99999,99999,99999&mma=0,0,0,0,0,0,0,0,0&fml=a,a,a,a,a,a,a,a,a&fq=Monthly,Monthly,Monthly,Monthly,Monthly,Monthly,Monthly,Monthly,Monthly&fam=avg,avg,avg,avg,avg,avg,avg,avg,avg&fgst=lin,lin,lin,lin,lin,lin,lin,lin,lin&fgsnd=2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01&line_index=1,2,3,4,5,6,7,8,9&transformation=lin,lin,lin,lin,lin,lin,lin,lin,lin&vintage_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07&revision_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07&nd=1993-01-01,1993-01-01,1993-01-01,1995-01-01,1995-01-01,1995-01-01,1995-01-01,1995-01-01,1995-01-01"
|
||||
ua = UserAgent(verify_ssl=False)
|
||||
request_header = {"User-Agent": ua.random}
|
||||
r = requests.get(tmp_url, headers=request_header)
|
||||
data_text = r.content
|
||||
df = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
|
||||
df["DATE"] = pd.to_datetime(df["DATE"], format="%Y-%m-%d")
|
||||
#df = df[list(df.columns[1:])].replace(".", np.nan).astype(float)
|
||||
name_list = {
|
||||
'LFHU24TTEZQ647S': "Harmonized Unemployment: Aged 15-24: All Persons for the Euro Area",
|
||||
'LFHU24MAEZQ647S': "Harmonized Unemployment: Aged 15-24: Males for the Euro Area",
|
||||
'LFHU24FEEZQ647S': "Harmonized Unemployment: Aged 15-24: Females for the Euro Area",
|
||||
'LFHUTTFEEZQ647S': "Total Harmonized Unemployment: Females for the Euro Area",
|
||||
'LFHUTTTTEZQ647S': "Total Harmonized Unemployment: All Persons for the Euro Area",
|
||||
'LFHUTTMAEZQ647S': "Total Harmonized Unemployment: Males for the Euro Area",
|
||||
'LFHUADMAEZQ647S': "Harmonized Unemployment: Aged 25 and Over: Males for the Euro Area",
|
||||
'LFHUADFEEZQ647S': "Harmonized Unemployment: Aged 25 and Over: Females for the Euro Area",
|
||||
'LFHUADTTEZQ647S': "Harmonized Unemployment: Aged 25 and Over: All Persons for the Euro Area"}
|
||||
description = "Labor Force Survey - quarterly levels, Quarterly, Seasonally Adjusted"
|
||||
return df, name_list, description
|
||||
|
||||
|
||||
def Labor_Force_Survey_Level_Monthly_Adj():
|
||||
tmp_url = url["fred_econ"] + "bgcolor=%23e1e9f0&chart_type=line&drp=0&fo=open%20sans&graph_bgcolor=%23ffffff&height=450&mode=fred&recession_bars=off&txtcolor=%23444444&ts=12&tts=12&width=748&nt=0&thu=0&trc=0&show_legend=yes&show_axis_titles=yes&show_tooltip=yes&id=LFHU24FEEZM647S,LFHU24TTEZM647S,LFHU24MAEZM647S,LFHUADFEEZM647S,LFHUADTTEZM647S,LFHUADMAEZM647S,LFHUTTTTEZM647S,LFHUTTMAEZM647S,LFHUTTFEEZM647S&scale=left,left,left,left,left,left,left,left,left&cosd=1995-01-01,1995-01-01,1995-01-01,1995-01-01,1995-01-01,1995-01-01,1995-01-01,1995-01-01,1995-01-01&coed=2021-03-01,2021-03-01,2021-03-01,2021-03-01,2021-03-01,2021-03-01,2021-03-01,2021-03-01,2021-03-01&line_color=%234572a7,%23aa4643,%2389a54e,%2380699b,%233d96ae,%23db843d,%2392a8cd,%23a47d7c,%23b5ca92&link_values=false,false,false,false,false,false,false,false,false&line_style=solid,solid,solid,solid,solid,solid,solid,solid,solid&mark_type=none,none,none,none,none,none,none,none,none&mw=3,3,3,3,3,3,3,3,3&lw=2,2,2,2,2,2,2,2,2&ost=-99999,-99999,-99999,-99999,-99999,-99999,-99999,-99999,-99999&oet=99999,99999,99999,99999,99999,99999,99999,99999,99999&mma=0,0,0,0,0,0,0,0,0&fml=a,a,a,a,a,a,a,a,a&fq=Monthly,Monthly,Monthly,Monthly,Monthly,Monthly,Monthly,Monthly,Monthly&fam=avg,avg,avg,avg,avg,avg,avg,avg,avg&fgst=lin,lin,lin,lin,lin,lin,lin,lin,lin&fgsnd=2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01&line_index=1,2,3,4,5,6,7,8,9&transformation=lin,lin,lin,lin,lin,lin,lin,lin,lin&vintage_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07&revision_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07&nd=1995-01-01,1995-01-01,1995-01-01,1995-01-01,1995-01-01,1995-01-01,1995-01-01,1995-01-01,1995-01-01"
|
||||
ua = UserAgent(verify_ssl=False)
|
||||
request_header = {"User-Agent": ua.random}
|
||||
r = requests.get(tmp_url, headers=request_header)
|
||||
data_text = r.content
|
||||
df = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
|
||||
df["DATE"] = pd.to_datetime(df["DATE"], format="%Y-%m-%d")
|
||||
#df = df[list(df.columns[1:])].replace(".", np.nan).astype(float)
|
||||
name_list = {
|
||||
'LFHU24FEEZM647S': "Harmonized Unemployment: Aged 15-24: Females for the Euro Area",
|
||||
'LFHU24TTEZM647S': "Harmonized Unemployment: Aged 15-24: All Persons for the Euro Area",
|
||||
'LFHU24MAEZM647S': "Harmonized Unemployment: Aged 15-24: Males for the Euro Area",
|
||||
'LFHUADFEEZM647S': "Harmonized Unemployment: Aged 25 and Over: Females for the Euro Area",
|
||||
'LFHUADTTEZM647S': "Harmonized Unemployment: Aged 25 and Over: All Persons for the Euro Area",
|
||||
'LFHUADMAEZM647S': "Harmonized Unemployment: Aged 25 and Over: Males for the Euro Area",
|
||||
'LFHUTTTTEZM647S': "Total Harmonized Unemployment: All Persons for the Euro Area",
|
||||
'LFHUTTMAEZM647S': "Total Harmonized Unemployment: Males for the Euro Area",
|
||||
'LFHUTTFEEZM647S': "Total Harmonized Unemployment: Females for the Euro Area"}
|
||||
description = "Labor Force Survey - quarterly levels, Monthly, Seasonally Adjusted"
|
||||
return df, name_list, description
|
||||
|
||||
|
||||
def Labor_Force_Survey_Level_Monthly_NAdj():
|
||||
tmp_url = url["fred_econ"] + "bgcolor=%23e1e9f0&chart_type=line&drp=0&fo=open%20sans&graph_bgcolor=%23ffffff&height=450&mode=fred&recession_bars=off&txtcolor=%23444444&ts=12&tts=12&width=748&nt=0&thu=0&trc=0&show_legend=yes&show_axis_titles=yes&show_tooltip=yes&id=LFHU24MAEZM647N,LFHU24FEEZM647N,LFHU24TTEZM647N,LFHUADMAEZM647N,LFHUADFEEZM647N,LFHUADTTEZM647N,LFHUTTFEEZM647N,LFHUTTTTEZM647N,LFHUTTMAEZM647N&scale=left,left,left,left,left,left,left,left,left&cosd=1995-01-01,1995-01-01,1995-01-01,1995-01-01,1995-01-01,1995-01-01,1995-01-01,1995-01-01,1995-01-01&coed=2021-03-01,2021-03-01,2021-03-01,2021-03-01,2021-03-01,2021-03-01,2021-03-01,2021-03-01,2021-03-01&line_color=%234572a7,%23aa4643,%2389a54e,%2380699b,%233d96ae,%23db843d,%2392a8cd,%23a47d7c,%23b5ca92&link_values=false,false,false,false,false,false,false,false,false&line_style=solid,solid,solid,solid,solid,solid,solid,solid,solid&mark_type=none,none,none,none,none,none,none,none,none&mw=3,3,3,3,3,3,3,3,3&lw=2,2,2,2,2,2,2,2,2&ost=-99999,-99999,-99999,-99999,-99999,-99999,-99999,-99999,-99999&oet=99999,99999,99999,99999,99999,99999,99999,99999,99999&mma=0,0,0,0,0,0,0,0,0&fml=a,a,a,a,a,a,a,a,a&fq=Monthly,Monthly,Monthly,Monthly,Monthly,Monthly,Monthly,Monthly,Monthly&fam=avg,avg,avg,avg,avg,avg,avg,avg,avg&fgst=lin,lin,lin,lin,lin,lin,lin,lin,lin&fgsnd=2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01&line_index=1,2,3,4,5,6,7,8,9&transformation=lin,lin,lin,lin,lin,lin,lin,lin,lin&vintage_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07&revision_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07&nd=1995-01-01,1995-01-01,1995-01-01,1995-01-01,1995-01-01,1995-01-01,1995-01-01,1995-01-01,1995-01-01"
|
||||
ua = UserAgent(verify_ssl=False)
|
||||
request_header = {"User-Agent": ua.random}
|
||||
r = requests.get(tmp_url, headers=request_header)
|
||||
data_text = r.content
|
||||
df = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
|
||||
df["DATE"] = pd.to_datetime(df["DATE"], format="%Y-%m-%d")
|
||||
#df = df[list(df.columns[1:])].replace(".", np.nan).astype(float)
|
||||
name_list = {
|
||||
'LFHU24MAEZM647N': "Harmonized Unemployment: Aged 15-24: Males for the Euro Area",
|
||||
'LFHU24FEEZM647N': "Harmonized Unemployment: Aged 15-24: Females for the Euro Area",
|
||||
'LFHU24TTEZM647N': "Harmonized Unemployment: Aged 15-24: All Persons for the Euro Area",
|
||||
'LFHUADMAEZM647N': "Harmonized Unemployment: Aged 25 and Over: Males for the Euro Area",
|
||||
'LFHUADFEEZM647N': "Harmonized Unemployment: Aged 25 and Over: Females for the Euro Area",
|
||||
'LFHUADTTEZM647N': "Harmonized Unemployment: Aged 25 and Over: All Persons for the Euro Area",
|
||||
'LFHUTTFEEZM647N': "Total Harmonized Unemployment: Females for the Euro Area",
|
||||
'LFHUTTTTEZM647N': "Total Harmonized Unemployment: All Persons for the Euro Area",
|
||||
'LFHUTTMAEZM647N': "Total Harmonized Unemployment: Males for the Euro Area"}
|
||||
description = "Labor Force Survey - quarterly levels, Monthly, Not Seasonally Adjusted"
|
||||
return df, name_list, description
|
||||
|
||||
|
||||
def Production_Monthly_Adj():
|
||||
tmp_url = url["fred_econ"] + "bgcolor=%23e1e9f0&chart_type=line&drp=0&fo=open%20sans&graph_bgcolor=%23ffffff&height=450&mode=fred&recession_bars=off&txtcolor=%23444444&ts=12&tts=12&width=748&nt=0&thu=0&trc=0&show_legend=yes&show_axis_titles=yes&show_tooltip=yes&id=EA19PRINTO01GYSAM,EA19PRMNCG03IXOBSAM,EA19PRMNCG02IXOBSAM,EA19PRMNVG01IXOBSAM,EA19PRMNTO01IXOBSAM,EA19PRMNIG01IXOBSAM,EA19PRCNTO01IXOBSAM&scale=left,left,left,left,left,left,left&cosd=1976-07-01,1985-01-01,1990-01-01,1985-01-01,1980-01-01,1985-01-01,1985-01-01&coed=2021-02-01,2017-12-01,2018-12-01,2018-12-01,2021-02-01,2018-12-01,2021-02-01&line_color=%234572a7,%23aa4643,%2389a54e,%2380699b,%233d96ae,%23db843d,%2392a8cd&link_values=false,false,false,false,false,false,false&line_style=solid,solid,solid,solid,solid,solid,solid&mark_type=none,none,none,none,none,none,none&mw=3,3,3,3,3,3,3&lw=2,2,2,2,2,2,2&ost=-99999,-99999,-99999,-99999,-99999,-99999,-99999&oet=99999,99999,99999,99999,99999,99999,99999&mma=0,0,0,0,0,0,0&fml=a,a,a,a,a,a,a&fq=Monthly,Monthly,Monthly,Monthly,Monthly,Monthly,Monthly&fam=avg,avg,avg,avg,avg,avg,avg&fgst=lin,lin,lin,lin,lin,lin,lin&fgsnd=2020-02-01,2017-12-01,2018-12-01,2018-12-01,2020-02-01,2018-12-01,2020-02-01&line_index=1,2,3,4,5,6,7&transformation=lin,lin,lin,lin,lin,lin,lin&vintage_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07&revision_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07&nd=1976-07-01,1985-01-01,1990-01-01,1985-01-01,1980-01-01,1985-01-01,1985-01-01"
|
||||
ua = UserAgent(verify_ssl=False)
|
||||
request_header = {"User-Agent": ua.random}
|
||||
r = requests.get(tmp_url, headers=request_header)
|
||||
data_text = r.content
|
||||
df = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
|
||||
df["DATE"] = pd.to_datetime(df["DATE"], format="%Y-%m-%d")
|
||||
#df = df[list(df.columns[1:])].replace(".", np.nan).astype(float)
|
||||
name_list = {
|
||||
'EA19PRINTO01GYSAM': "Production: Industry: Total industry: Total industry excluding construction for the Euro Area",
|
||||
'EA19PRMNCG03IXOBSAM': "Production: Manufacturing: Consumer goods: Non durable goods for the Euro Area",
|
||||
'EA19PRMNCG02IXOBSAM': "Production: Manufacturing: Consumer goods: Durable goods for the Euro Area",
|
||||
'EA19PRMNVG01IXOBSAM': "Production: Manufacturing: Investment goods: Total for the Euro Area",
|
||||
'EA19PRMNTO01IXOBSAM': "Production: Manufacturing: Total manufacturing: Total manufacturing for the Euro Area",
|
||||
'EA19PRMNIG01IXOBSAM': "Production: Manufacturing: Intermediate goods: Total for the Euro Area",
|
||||
'EA19PRCNTO01IXOBSAM': "Production: Construction: Total construction: Total for the Euro Area"}
|
||||
description = "Production, Monthly, Seasonally Adjusted"
|
||||
return df, name_list, description
|
||||
|
||||
|
||||
def Production_Quarterly_Adj():
|
||||
tmp_url = url["fred_econ"] + "bgcolor=%23e1e9f0&chart_type=line&drp=0&fo=open%20sans&graph_bgcolor=%23ffffff&height=450&mode=fred&recession_bars=off&txtcolor=%23444444&ts=12&tts=12&width=748&nt=0&thu=0&trc=0&show_legend=yes&show_axis_titles=yes&show_tooltip=yes&id=PRINTO01EZQ659S,PRMNVG01EZQ661S,PRMNCG02EZQ661S,PRMNCG03EZQ661S,PRMNTO01EZQ661S,PRMNIG01EZQ661S,PRCNTO01EZQ661S&scale=left,left,left,left,left,left,left&cosd=1976-07-01,1985-01-01,1990-01-01,1985-01-01,1980-01-01,1985-01-01,1985-01-01&coed=2020-10-01,2018-10-01,2018-10-01,2017-10-01,2020-10-01,2018-10-01,2020-10-01&line_color=%234572a7,%23aa4643,%2389a54e,%2380699b,%233d96ae,%23db843d,%2392a8cd&link_values=false,false,false,false,false,false,false&line_style=solid,solid,solid,solid,solid,solid,solid&mark_type=none,none,none,none,none,none,none&mw=3,3,3,3,3,3,3&lw=2,2,2,2,2,2,2&ost=-99999,-99999,-99999,-99999,-99999,-99999,-99999&oet=99999,99999,99999,99999,99999,99999,99999&mma=0,0,0,0,0,0,0&fml=a,a,a,a,a,a,a&fq=Quarterly,Quarterly,Quarterly,Quarterly,Quarterly,Quarterly,Quarterly&fam=avg,avg,avg,avg,avg,avg,avg&fgst=lin,lin,lin,lin,lin,lin,lin&fgsnd=2020-02-01,2018-10-01,2018-10-01,2017-10-01,2020-02-01,2018-10-01,2020-02-01&line_index=1,2,3,4,5,6,7&transformation=lin,lin,lin,lin,lin,lin,lin&vintage_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07&revision_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07&nd=1976-07-01,1985-01-01,1990-01-01,1985-01-01,1980-01-01,1985-01-01,1985-01-01"
|
||||
ua = UserAgent(verify_ssl=False)
|
||||
request_header = {"User-Agent": ua.random}
|
||||
r = requests.get(tmp_url, headers=request_header)
|
||||
data_text = r.content
|
||||
df = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
|
||||
df["DATE"] = pd.to_datetime(df["DATE"], format="%Y-%m-%d")
|
||||
#df = df[list(df.columns[1:])].replace(".", np.nan).astype(float)
|
||||
name_list = {
|
||||
'PRINTO01EZQ659S': "Total Industry Production Excluding Construction for the Euro Area",
|
||||
'PRMNVG01EZQ661S': "Total Production of Investment Goods for Manufacturing for the Euro Area",
|
||||
'PRMNCG02EZQ661S': "Production of Durable Consumer Goods for Manufacturing for the Euro Area",
|
||||
'PRMNCG03EZQ661S': "Production of Nondurable Consumer Goods for Manufacturing for the Euro Area",
|
||||
'PRMNTO01EZQ661S': "Total Manufacturing Production for the Euro Area",
|
||||
'PRMNIG01EZQ661S': "Total Production of Intermediate Goods for Manufacturing for the Euro Area",
|
||||
'PRCNTO01EZQ661S': "Total Construction for the Euro Area"}
|
||||
description = "Production, Monthly, Not Seasonally Adjusted"
|
||||
return df, name_list, description
|
||||
|
||||
|
||||
def Production_Monthly_NAdj():
|
||||
tmp_url = url["fred_econ"] + "bgcolor=%23e1e9f0&chart_type=line&drp=0&fo=open%20sans&graph_bgcolor=%23ffffff&height=450&mode=fred&recession_bars=off&txtcolor=%23444444&ts=12&tts=12&width=748&nt=0&thu=0&trc=0&show_legend=yes&show_axis_titles=yes&show_tooltip=yes&id=EA19PRMNIG01IXOBM,EA19PRMNTO01IXOBM,EA19PRMNCG02IXOBM,EA19PRMNCG03IXOBM,EA19PRMNVG01IXOBM,EA19PRCNTO01IXOBM,EA19PRINTO01IXOBM&scale=left,left,left,left,left,left,left&cosd=1985-01-01,1980-01-01,1990-01-01,1985-01-01,1985-01-01,1985-01-01,1980-01-01&coed=2018-12-01,2021-02-01,2018-12-01,2018-12-01,2018-12-01,2021-02-01,2021-02-01&line_color=%234572a7,%23aa4643,%2389a54e,%2380699b,%233d96ae,%23db843d,%2392a8cd&link_values=false,false,false,false,false,false,false&line_style=solid,solid,solid,solid,solid,solid,solid&mark_type=none,none,none,none,none,none,none&mw=3,3,3,3,3,3,3&lw=2,2,2,2,2,2,2&ost=-99999,-99999,-99999,-99999,-99999,-99999,-99999&oet=99999,99999,99999,99999,99999,99999,99999&mma=0,0,0,0,0,0,0&fml=a,a,a,a,a,a,a&fq=Monthly,Monthly,Monthly,Monthly,Monthly,Monthly,Monthly&fam=avg,avg,avg,avg,avg,avg,avg&fgst=lin,lin,lin,lin,lin,lin,lin&fgsnd=2018-12-01,2020-02-01,2018-12-01,2018-12-01,2018-12-01,2020-02-01,2020-02-01&line_index=1,2,3,4,5,6,7&transformation=lin,lin,lin,lin,lin,lin,lin&vintage_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07&revision_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07&nd=1985-01-01,1980-01-01,1990-01-01,1985-01-01,1985-01-01,1985-01-01,1980-01-01"
|
||||
ua = UserAgent(verify_ssl=False)
|
||||
request_header = {"User-Agent": ua.random}
|
||||
r = requests.get(tmp_url, headers=request_header)
|
||||
data_text = r.content
|
||||
df = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
|
||||
df["DATE"] = pd.to_datetime(df["DATE"], format="%Y-%m-%d")
|
||||
#df = df[list(df.columns[1:])].replace(".", np.nan).astype(float)
|
||||
name_list = {
|
||||
'EA19PRMNIG01IXOBM': "Production: Manufacturing: Intermediate goods: Total for the Euro Area",
|
||||
'EA19PRMNTO01IXOBM': "Production: Manufacturing: Total manufacturing: Total manufacturing for the Euro Area",
|
||||
'EA19PRMNCG02IXOBM': "Production: Manufacturing: Consumer goods: Durable goods for the Euro Area",
|
||||
'EA19PRMNCG03IXOBM': "Production: Manufacturing: Consumer goods: Non durable goods for the Euro Area",
|
||||
'EA19PRMNVG01IXOBM': "Production: Manufacturing: Investment goods: Total for the Euro Area",
|
||||
'EA19PRCNTO01IXOBM': "Production: Construction: Total construction: Total for the Euro Area",
|
||||
'EA19PRINTO01IXOBM': "Production: Industry: Total industry: Total industry excluding construction for the Euro Area"}
|
||||
description = "Production, Monthly, Not Seasonally Adjusted"
|
||||
return df, name_list, description
|
||||
|
||||
|
||||
def Production_Quarterly_NAdj():
|
||||
tmp_url = url["fred_econ"] + "bgcolor=%23e1e9f0&chart_type=line&drp=0&fo=open%20sans&graph_bgcolor=%23ffffff&height=450&mode=fred&recession_bars=off&txtcolor=%23444444&ts=12&tts=12&width=748&nt=0&thu=0&trc=0&show_legend=yes&show_axis_titles=yes&show_tooltip=yes&id=PRMNCG03EZQ661N,PRMNCG02EZQ661N,PRMNVG01EZQ661N,PRMNIG01EZQ661N,PRMNTO01EZQ661N,PRINTO01EZQ661N,PRCNTO01EZQ661N&scale=left,left,left,left,left,left,left&cosd=1985-01-01,1990-01-01,1985-01-01,1985-01-01,1980-01-01,1980-01-01,1985-01-01&coed=2018-10-01,2018-10-01,2018-10-01,2018-10-01,2020-10-01,2020-10-01,2020-10-01&line_color=%234572a7,%23aa4643,%2389a54e,%2380699b,%233d96ae,%23db843d,%2392a8cd&link_values=false,false,false,false,false,false,false&line_style=solid,solid,solid,solid,solid,solid,solid&mark_type=none,none,none,none,none,none,none&mw=3,3,3,3,3,3,3&lw=2,2,2,2,2,2,2&ost=-99999,-99999,-99999,-99999,-99999,-99999,-99999&oet=99999,99999,99999,99999,99999,99999,99999&mma=0,0,0,0,0,0,0&fml=a,a,a,a,a,a,a&fq=Quarterly,Quarterly,Quarterly,Quarterly,Quarterly,Quarterly,Quarterly&fam=avg,avg,avg,avg,avg,avg,avg&fgst=lin,lin,lin,lin,lin,lin,lin&fgsnd=2018-10-01,2018-10-01,2018-10-01,2018-10-01,2020-02-01,2020-02-01,2020-02-01&line_index=1,2,3,4,5,6,7&transformation=lin,lin,lin,lin,lin,lin,lin&vintage_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07&revision_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07&nd=1985-01-01,1990-01-01,1985-01-01,1985-01-01,1980-01-01,1980-01-01,1985-01-01"
|
||||
ua = UserAgent(verify_ssl=False)
|
||||
request_header = {"User-Agent": ua.random}
|
||||
r = requests.get(tmp_url, headers=request_header)
|
||||
data_text = r.content
|
||||
df = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
|
||||
df["DATE"] = pd.to_datetime(df["DATE"], format="%Y-%m-%d")
|
||||
#df = df[list(df.columns[1:])].replace(".", np.nan).astype(float)
|
||||
name_list = {
|
||||
'PRMNCG03EZQ661N': "Production of Nondurable Consumer Goods for Manufacturing for the Euro Area",
|
||||
'PRMNCG02EZQ661N': "Production of Durable Consumer Goods for Manufacturing for the Euro Area",
|
||||
'PRMNVG01EZQ661N': "Total Production of Investment Goods for Manufacturing for the Euro Area",
|
||||
'PRMNIG01EZQ661N': "Total Production of Intermediate Goods for Manufacturing for the Euro Area",
|
||||
'PRMNTO01EZQ661N': "Total Manufacturing Production for the Euro Area",
|
||||
'PRINTO01EZQ661N': "Total Industry Production Excluding Construction for the Euro Area",
|
||||
'PRCNTO01EZQ661N': "Total Construction for the Euro Area"}
|
||||
description = "Production, Quarterly, Not Seasonally Adjusted"
|
||||
return df, name_list, description
|
||||
|
||||
|
||||
def Sales_Monthly_Adj():
|
||||
tmp_url = url["fred_econ"] + "bgcolor=%23e1e9f0&chart_type=line&drp=0&fo=open%20sans&graph_bgcolor=%23ffffff&height=450&mode=fred&recession_bars=off&txtcolor=%23444444&ts=12&tts=12&width=748&nt=0&thu=0&trc=0&show_legend=yes&show_axis_titles=yes&show_tooltip=yes&id=EA19SLMNTO02IXOBSAM,EA19SLMNIG02IXOBSAM,EA19SLMNCD02IXOBSAM,EA19SLMNCN02IXOBSAM,EA19SLMNVG02IXOBSAM,EA19SLRTTO01IXOBSAM,EA19SLRTTO02IXOBSAM,EA19SLRTCR03IXOBSAM&scale=left,left,left,left,left,left,left,left&cosd=1980-01-01,1990-01-01,1993-01-01,1995-01-01,1980-01-01,1995-01-01,1995-01-01,1970-01-01&coed=2021-02-01,2018-12-01,2018-12-01,2018-12-01,2018-12-01,2021-02-01,2021-02-01,2018-12-01&line_color=%234572a7,%23aa4643,%2389a54e,%2380699b,%233d96ae,%23db843d,%2392a8cd,%23a47d7c&link_values=false,false,false,false,false,false,false,false&line_style=solid,solid,solid,solid,solid,solid,solid,solid&mark_type=none,none,none,none,none,none,none,none&mw=3,3,3,3,3,3,3,3&lw=2,2,2,2,2,2,2,2&ost=-99999,-99999,-99999,-99999,-99999,-99999,-99999,-99999&oet=99999,99999,99999,99999,99999,99999,99999,99999&mma=0,0,0,0,0,0,0,0&fml=a,a,a,a,a,a,a,a&fq=Monthly,Monthly,Monthly,Monthly,Monthly,Monthly,Monthly,Monthly&fam=avg,avg,avg,avg,avg,avg,avg,avg&fgst=lin,lin,lin,lin,lin,lin,lin,lin&fgsnd=2020-02-01,2018-12-01,2018-12-01,2018-12-01,2018-12-01,2020-02-01,2020-02-01,2018-12-01&line_index=1,2,3,4,5,6,7,8&transformation=lin,lin,lin,lin,lin,lin,lin,lin&vintage_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07&revision_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07&nd=1980-01-01,1990-01-01,1993-01-01,1995-01-01,1980-01-01,1995-01-01,1995-01-01,1970-01-01"
|
||||
ua = UserAgent(verify_ssl=False)
|
||||
request_header = {"User-Agent": ua.random}
|
||||
r = requests.get(tmp_url, headers=request_header)
|
||||
data_text = r.content
|
||||
df = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
|
||||
df["DATE"] = pd.to_datetime(df["DATE"], format="%Y-%m-%d")
|
||||
#df = df[list(df.columns[1:])].replace(".", np.nan).astype(float)
|
||||
name_list = {
|
||||
'EA19SLMNTO02IXOBSAM': "Sales: Manufacturing: Total manufacturing: Value for the Euro Area",
|
||||
'EA19SLMNIG02IXOBSAM': "Sales: Manufacturing: Intermediate goods: Value for the Euro Area",
|
||||
'EA19SLMNCD02IXOBSAM': "Sales: Manufacturing: Consumer goods durable: Value for the Euro Area",
|
||||
'EA19SLMNCN02IXOBSAM': "Sales: Manufacturing: Consumer goods non durable: Value for the Euro Area",
|
||||
'EA19SLMNVG02IXOBSAM': "Sales: Manufacturing: Investment goods: Value for the Euro Area",
|
||||
'EA19SLRTTO01IXOBSAM': "Sales: Retail trade: Total retail trade: Volume for the Euro Area",
|
||||
'EA19SLRTTO02IXOBSAM': "Sales: Retail trade: Total retail trade: Value for the Euro Area",
|
||||
'EA19SLRTCR03IXOBSAM': "Sales: Retail trade: Car registration: Passenger cars for the Euro Area"}
|
||||
description = "Sales, Monthly, Seasonally Adjusted"
|
||||
return df, name_list, description
|
||||
|
||||
|
||||
def Sales_Quarterly_Adj():
|
||||
tmp_url = url["fred_econ"] + "bgcolor=%23e1e9f0&chart_type=line&drp=0&fo=open%20sans&graph_bgcolor=%23ffffff&height=450&mode=fred&recession_bars=off&txtcolor=%23444444&ts=12&tts=12&width=748&nt=0&thu=0&trc=0&show_legend=yes&show_axis_titles=yes&show_tooltip=yes&id=SLMNTO02EZQ661S,SLMNVG02EZQ661S,SLMNCD02EZQ661S,SLMNCN02EZQ661S,SLMNIG02EZQ661S,SLRTTO02EZQ661S,SLRTTO01EZQ659S,SLRTCR03EZQ661S&scale=left,left,left,left,left,left,left,left&cosd=1980-01-01,1980-01-01,1993-01-01,1995-01-01,1990-01-01,1995-01-01,1996-01-01,1970-01-01&coed=2020-10-01,2018-10-01,2018-10-01,2018-10-01,2018-10-01,2020-10-01,2020-10-01,2018-10-01&line_color=%234572a7,%23aa4643,%2389a54e,%2380699b,%233d96ae,%23db843d,%2392a8cd,%23a47d7c&link_values=false,false,false,false,false,false,false,false&line_style=solid,solid,solid,solid,solid,solid,solid,solid&mark_type=none,none,none,none,none,none,none,none&mw=3,3,3,3,3,3,3,3&lw=2,2,2,2,2,2,2,2&ost=-99999,-99999,-99999,-99999,-99999,-99999,-99999,-99999&oet=99999,99999,99999,99999,99999,99999,99999,99999&mma=0,0,0,0,0,0,0,0&fml=a,a,a,a,a,a,a,a&fq=Quarterly,Quarterly,Quarterly,Quarterly,Quarterly,Quarterly,Quarterly,Quarterly&fam=avg,avg,avg,avg,avg,avg,avg,avg&fgst=lin,lin,lin,lin,lin,lin,lin,lin&fgsnd=2020-02-01,2018-10-01,2018-10-01,2018-10-01,2018-10-01,2020-02-01,2020-02-01,2018-10-01&line_index=1,2,3,4,5,6,7,8&transformation=lin,lin,lin,lin,lin,lin,lin,lin&vintage_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07&revision_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07&nd=1980-01-01,1980-01-01,1993-01-01,1995-01-01,1990-01-01,1995-01-01,1996-01-01,1970-01-01"
|
||||
ua = UserAgent(verify_ssl=False)
|
||||
request_header = {"User-Agent": ua.random}
|
||||
r = requests.get(tmp_url, headers=request_header)
|
||||
data_text = r.content
|
||||
df = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
|
||||
df["DATE"] = pd.to_datetime(df["DATE"], format="%Y-%m-%d")
|
||||
#df = df[list(df.columns[1:])].replace(".", np.nan).astype(float)
|
||||
name_list = {
|
||||
'SLMNTO02EZQ661S': "Sales Value of Total Manufactured Goods for the Euro Area",
|
||||
'SLMNVG02EZQ661S': "Sales Value of Manufactured Investment Goods for the Euro Area",
|
||||
'SLMNCD02EZQ661S': "Sales Value of Manufactured Durable Consumer Goods for the Euro Area",
|
||||
'SLMNCN02EZQ661S': "Sales Value of Manufactured Nondurable Consumer Goods for the Euro Area",
|
||||
'SLMNIG02EZQ661S': "Sales Value of Manufactured Intermediate Goods for the Euro Area",
|
||||
'SLRTTO02EZQ661S': "Value of Total Retail Trade sales for the Euro Areaa",
|
||||
'SLRTTO01EZQ659S': "Volume of Total Retail Trade sales for the Euro Area",
|
||||
'SLRTCR03EZQ661S': "Retail Trade Sales: Passenger Car Registrations for the Euro Area"}
|
||||
description = "Sales, Quarterly, Seasonally Adjusted"
|
||||
return df, name_list, description
|
||||
|
||||
|
||||
def Sales_Monthly_NAdj():
|
||||
tmp_url = url["fred_econ"] + "bgcolor=%23e1e9f0&chart_type=line&drp=0&fo=open%20sans&graph_bgcolor=%23ffffff&height=450&mode=fred&recession_bars=off&txtcolor=%23444444&ts=12&tts=12&width=748&nt=0&thu=0&trc=0&show_legend=yes&show_axis_titles=yes&show_tooltip=yes&id=EA19SLMNIG02IXOBM,EA19SLRTTO02IXOBM,EA19SLMNCD02IXOBM,EA19SLMNCN02IXOBM,EA19SLMNTO02IXOBM,EA19SLRTCR03IXOBM,EA19SLRTTO01IXOBM&scale=left,left,left,left,left,left,left&cosd=1990-01-01,1995-01-01,1993-01-01,1995-01-01,1980-01-01,1985-01-01,1995-01-01&coed=2018-12-01,2021-02-01,2018-12-01,2018-12-01,2021-02-01,2021-03-01,2021-02-01&line_color=%234572a7,%23aa4643,%2389a54e,%2380699b,%233d96ae,%23db843d,%2392a8cd&link_values=false,false,false,false,false,false,false&line_style=solid,solid,solid,solid,solid,solid,solid&mark_type=none,none,none,none,none,none,none&mw=3,3,3,3,3,3,3&lw=2,2,2,2,2,2,2&ost=-99999,-99999,-99999,-99999,-99999,-99999,-99999&oet=99999,99999,99999,99999,99999,99999,99999&mma=0,0,0,0,0,0,0&fml=a,a,a,a,a,a,a&fq=Monthly,Monthly,Monthly,Monthly,Monthly,Monthly,Monthly&fam=avg,avg,avg,avg,avg,avg,avg&fgst=lin,lin,lin,lin,lin,lin,lin&fgsnd=2018-12-01,2020-02-01,2018-12-01,2018-12-01,2020-02-01,2020-02-01,2020-02-01&line_index=1,2,3,4,5,6,7&transformation=lin,lin,lin,lin,lin,lin,lin&vintage_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07&revision_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07&nd=1990-01-01,1995-01-01,1993-01-01,1995-01-01,1980-01-01,1985-01-01,1995-01-01"
|
||||
ua = UserAgent(verify_ssl=False)
|
||||
request_header = {"User-Agent": ua.random}
|
||||
r = requests.get(tmp_url, headers=request_header)
|
||||
data_text = r.content
|
||||
df = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
|
||||
df["DATE"] = pd.to_datetime(df["DATE"], format="%Y-%m-%d")
|
||||
#df = df[list(df.columns[1:])].replace(".", np.nan).astype(float)
|
||||
name_list = {
|
||||
'EA19SLMNIG02IXOBM': "Sales: Manufacturing: Intermediate goods: Value for the Euro Area",
|
||||
'EA19SLRTTO02IXOBM': "Sales: Retail trade: Total retail trade: Value for the Euro Area",
|
||||
'EA19SLMNCD02IXOBM': "Sales: Manufacturing: Consumer goods durable: Value for the Euro Area",
|
||||
'EA19SLMNCN02IXOBM': "Sales: Manufacturing: Consumer goods non durable: Value for the Euro Area",
|
||||
'EA19SLMNTO02IXOBM': "Sales: Manufacturing: Total manufacturing: Value for the Euro Area",
|
||||
'EA19SLRTCR03IXOBM': "Sales: Retail trade: Car registration: Passenger cars for the Euro Area",
|
||||
'EA19SLRTTO01IXOBM': "Sales: Retail trade: Total retail trade: Volume for the Euro Area"}
|
||||
description = "Sales, Monthly, Not Seasonally Adjusted"
|
||||
return df, name_list, description
|
||||
|
||||
|
||||
def Sales_Quarterly_NAdj():
|
||||
tmp_url = url["fred_econ"] + "bgcolor=%23e1e9f0&chart_type=line&drp=0&fo=open%20sans&graph_bgcolor=%23ffffff&height=450&mode=fred&recession_bars=off&txtcolor=%23444444&ts=12&tts=12&width=748&nt=0&thu=0&trc=0&show_legend=yes&show_axis_titles=yes&show_tooltip=yes&id=SLMNIG02EZQ661N,SLMNTO02EZQ661N,SLMNCD02EZQ661N,SLMNCN02EZQ661N,SLRTTO01EZQ661N,SLRTTO02EZQ661N,SLRTCR03EZQ661N&scale=left,left,left,left,left,left,left&cosd=1990-01-01,1980-01-01,1993-01-01,1995-01-01,1995-01-01,1995-01-01,1985-01-01&coed=2018-10-01,2020-10-01,2018-10-01,2018-10-01,2020-10-01,2020-10-01,2021-01-01&line_color=%234572a7,%23aa4643,%2389a54e,%2380699b,%233d96ae,%23db843d,%2392a8cd&link_values=false,false,false,false,false,false,false&line_style=solid,solid,solid,solid,solid,solid,solid&mark_type=none,none,none,none,none,none,none&mw=3,3,3,3,3,3,3&lw=2,2,2,2,2,2,2&ost=-99999,-99999,-99999,-99999,-99999,-99999,-99999&oet=99999,99999,99999,99999,99999,99999,99999&mma=0,0,0,0,0,0,0&fml=a,a,a,a,a,a,a&fq=Quarterly,Quarterly,Quarterly,Quarterly,Quarterly,Quarterly,Quarterly&fam=avg,avg,avg,avg,avg,avg,avg&fgst=lin,lin,lin,lin,lin,lin,lin&fgsnd=2018-10-01,2020-02-01,2018-10-01,2018-10-01,2020-02-01,2020-02-01,2020-02-01&line_index=1,2,3,4,5,6,7&transformation=lin,lin,lin,lin,lin,lin,lin&vintage_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07&revision_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07&nd=1990-01-01,1980-01-01,1993-01-01,1995-01-01,1995-01-01,1995-01-01,1985-01-01"
|
||||
ua = UserAgent(verify_ssl=False)
|
||||
request_header = {"User-Agent": ua.random}
|
||||
r = requests.get(tmp_url, headers=request_header)
|
||||
data_text = r.content
|
||||
df = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
|
||||
df["DATE"] = pd.to_datetime(df["DATE"], format="%Y-%m-%d")
|
||||
#df = df[list(df.columns[1:])].replace(".", np.nan).astype(float)
|
||||
name_list = {
|
||||
'SLMNIG02EZQ661N': "Sales Value of Manufactured Intermediate Goods for the Euro Area",
|
||||
'SLMNTO02EZQ661N': "Sales Value of Total Manufactured Goods for the Euro Area",
|
||||
'SLMNCD02EZQ661N': "Sales Value of Manufactured Durable Consumer Goods for the Euro Area",
|
||||
'SLMNCN02EZQ661N': "Sales Value of Manufactured Nondurable Consumer Goods for the Euro Area",
|
||||
'SLRTTO01EZQ661N': "Volume of Total Retail Trade sales for the Euro Area",
|
||||
'SLRTTO02EZQ661N': "Value of Total Retail Trade sales for the Euro Area",
|
||||
'SLRTCR03EZQ661N': "Retail Trade Sales: Passenger Car Registrations for the Euro Area"}
|
||||
description = "Sales, Quarterly, Not Seasonally Adjusted"
|
||||
return df, name_list, description
|
||||
|
||||
|
||||
def Cumsumer_Opinion_Survey():
|
||||
tmp_url = url["fred_econ"] + "bgcolor=%23e1e9f0&chart_type=line&drp=0&fo=open%20sans&graph_bgcolor=%23ffffff&height=450&mode=fred&recession_bars=off&txtcolor=%23444444&ts=12&tts=12&width=748&nt=0&thu=0&trc=0&show_legend=yes&show_axis_titles=yes&show_tooltip=yes&id=CSCICP02EZM460S,CSESFT02EZM460S,CSINFT02EZM460S&scale=left,left,left&cosd=1973-01-01,1985-01-01,1985-01-01&coed=2021-04-01,2021-04-01,2021-04-01&line_color=%234572a7,%23aa4643,%2389a54e&link_values=false,false,false&line_style=solid,solid,solid&mark_type=none,none,none&mw=3,3,3&lw=2,2,2&ost=-99999,-99999,-99999&oet=99999,99999,99999&mma=0,0,0&fml=a,a,a&fq=Monthly,Monthly,Monthly&fam=avg,avg,avg&fgst=lin,lin,lin&fgsnd=2020-02-01,2020-02-01,2020-02-01&line_index=1,2,3&transformation=lin,lin,lin&vintage_date=2021-06-07,2021-06-07,2021-06-07&revision_date=2021-06-07,2021-06-07,2021-06-07&nd=1973-01-01,1985-01-01,1985-01-01"
|
||||
ua = UserAgent(verify_ssl=False)
|
||||
request_header = {"User-Agent": ua.random}
|
||||
r = requests.get(tmp_url, headers=request_header)
|
||||
data_text = r.content
|
||||
df = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
|
||||
df["DATE"] = pd.to_datetime(df["DATE"], format="%Y-%m-%d")
|
||||
#df = df[list(df.columns[1:])].replace(".", np.nan).astype(float)
|
||||
name_list = {
|
||||
'CSCICP02EZM460S': "Consumer Opinion Surveys: Confidence Indicators: Composite Indicators: European Commission and National Indicators for the Euro Area",
|
||||
'CSESFT02EZM460S': "Consumer Opinion Surveys: Economic Situation: Future Tendency: European Commission Indicator for the Euro Area",
|
||||
'CSINFT02EZM460S': "Consumer Opinion Surveys: Consumer Prices: Future Tendency of Inflation: European Commission and National Indicators for the Euro Area"}
|
||||
description = "Consumer opinion surveys, Monthly, Seasonally Adjusted"
|
||||
return df, name_list, description
|
||||
|
||||
|
||||
class ecb_data(object):
|
||||
def __init__(self, url=url["ecb"]):
|
||||
self.url = url
|
||||
|
|
@ -18,9 +841,9 @@ class ecb_data(object):
|
|||
return "please follow the ECB's codebook: https://sdw.ecb.europa.eu/browse.do?node=9691101"
|
||||
|
||||
def get_data(self,
|
||||
datacode="ICP",
|
||||
key="M.U2.N.000000.4.ANR",
|
||||
startdate="2000-01-01",
|
||||
datacode="ICP",
|
||||
key="M.U2.N.000000.4.ANR",
|
||||
startdate="2000-01-01",
|
||||
enddate="2020-01-01"):
|
||||
"""
|
||||
"""
|
||||
|
|
@ -31,20 +854,24 @@ class ecb_data(object):
|
|||
"startPeriod": "{}".format(startdate),
|
||||
"endPeriod": "{}".format(enddate)
|
||||
}
|
||||
r = requests.get(tmp_url, params = request_params, headers = request_header)
|
||||
r = requests.get(
|
||||
tmp_url,
|
||||
params=request_params,
|
||||
headers=request_header)
|
||||
data_text = r.content
|
||||
df = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
|
||||
return df
|
||||
|
||||
|
||||
class eurostat_data(object):
|
||||
def __init__(self, url=url["eurostat"]):
|
||||
self.url = url
|
||||
|
||||
def codebook(self):
|
||||
return "please follow the EuroStat's codebook: \nhttps://ec.europa.eu/eurostat/estat-navtree-portlet-prod/BulkDownloadListing?sort=1&dir=dic"
|
||||
|
||||
|
||||
def get_data(self,
|
||||
datasetcode="nama_10_gdp",
|
||||
datasetcode="nama_10_gdp",
|
||||
precision="1",
|
||||
unit="CP_MEUR",
|
||||
na_item="B1GQ",
|
||||
|
|
@ -60,14 +887,22 @@ class eurostat_data(object):
|
|||
"na_item": "{}".format(na_item),
|
||||
"time": "{}".format(time)
|
||||
}
|
||||
r = requests.get(tmp_url, params = request_params, headers = request_header)
|
||||
r = requests.get(
|
||||
tmp_url,
|
||||
params=request_params,
|
||||
headers=request_header)
|
||||
data_text = r.text
|
||||
data_json = demjson.decode(data_text)
|
||||
value = data_json['value']
|
||||
abb = data_json['dimension']['geo']['category']['index']
|
||||
abb = {abb[k]:k for k in abb}
|
||||
abb = {abb[k]: k for k in abb}
|
||||
geo = data_json['dimension']['geo']['category']['label']
|
||||
geo_list = [abb[int(k)] for k in list(value.keys())]
|
||||
geo = [geo[k] for k in geo_list]
|
||||
df = pd.DataFrame({"Geo":geo, "{}".format(na_item): list(value.values())})
|
||||
return df
|
||||
df = pd.DataFrame(
|
||||
{"Geo": geo, "{}".format(na_item): list(value.values())})
|
||||
return df
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
data, name_list = CPI_monthly()
|
||||
|
|
|
|||
|
|
@ -3,15 +3,15 @@ import numpy as np
|
|||
import requests
|
||||
from fake_useragent import UserAgent
|
||||
import io
|
||||
import os
|
||||
import os
|
||||
import demjson
|
||||
|
||||
# Main Economic Indicators: https://alfred.stlouisfed.org/release?rid=205
|
||||
url = {
|
||||
"fred_econ": "https://fred.stlouisfed.org/graph/fredgraph.csv?",
|
||||
"philfed": "https://www.philadelphiafed.org/surveys-and-data/real-time-data-research/",
|
||||
"chicagofed": "https://www.chicagofed.org/~/media/publications/"
|
||||
}
|
||||
"chicagofed": "https://www.chicagofed.org/~/media/publications/"}
|
||||
|
||||
|
||||
def gdp_quarterly(startdate="1947-01-01", enddate="2021-01-01"):
|
||||
"""
|
||||
|
|
@ -27,11 +27,12 @@ def gdp_quarterly(startdate="1947-01-01", enddate="2021-01-01"):
|
|||
"cosd": "{}".format(startdate),
|
||||
"coed": "{}".format(enddate)
|
||||
}
|
||||
r = requests.get(tmp_url, params = request_params, headers = request_header)
|
||||
r = requests.get(tmp_url, params=request_params, headers=request_header)
|
||||
data_text = r.content
|
||||
df = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
|
||||
return df
|
||||
|
||||
|
||||
def gdpc1_quarterly(startdate="1947-01-01", enddate="2021-01-01"):
|
||||
"""
|
||||
Full Name: Real Gross Domestic Product
|
||||
|
|
@ -46,11 +47,12 @@ def gdpc1_quarterly(startdate="1947-01-01", enddate="2021-01-01"):
|
|||
"cosd": "{}".format(startdate),
|
||||
"coed": "{}".format(enddate)
|
||||
}
|
||||
r = requests.get(tmp_url, params = request_params, headers = request_header)
|
||||
r = requests.get(tmp_url, params=request_params, headers=request_header)
|
||||
data_text = r.content
|
||||
df = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
|
||||
return df
|
||||
|
||||
|
||||
def oecd_gdp_monthly(startdate="1947-01-01", enddate="2021-01-01"):
|
||||
"""
|
||||
Full Name: Real Gross Domestic Product
|
||||
|
|
@ -65,11 +67,12 @@ def oecd_gdp_monthly(startdate="1947-01-01", enddate="2021-01-01"):
|
|||
"cosd": "{}".format(startdate),
|
||||
"coed": "{}".format(enddate)
|
||||
}
|
||||
r = requests.get(tmp_url, params = request_params, headers = request_header)
|
||||
r = requests.get(tmp_url, params=request_params, headers=request_header)
|
||||
data_text = r.content
|
||||
df = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
|
||||
return df
|
||||
|
||||
|
||||
def payems_monthly(startdate="1939-01-01", enddate="2021-01-01"):
|
||||
"""
|
||||
Full Name: All Employees, Total Nonfarm
|
||||
|
|
@ -84,11 +87,12 @@ def payems_monthly(startdate="1939-01-01", enddate="2021-01-01"):
|
|||
"cosd": "{}".format(startdate),
|
||||
"coed": "{}".format(enddate)
|
||||
}
|
||||
r = requests.get(tmp_url, params = request_params, headers = request_header)
|
||||
r = requests.get(tmp_url, params=request_params, headers=request_header)
|
||||
data_text = r.content
|
||||
df = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
|
||||
return df
|
||||
|
||||
|
||||
def unrate(startdate="1948-01-01", enddate="2021-01-01"):
|
||||
"""
|
||||
Full Name: Unemployment Rate: Aged 15-64: All Persons for the United States
|
||||
|
|
@ -103,7 +107,7 @@ def unrate(startdate="1948-01-01", enddate="2021-01-01"):
|
|||
"cosd": "{}".format(startdate),
|
||||
"coed": "{}".format(enddate)
|
||||
}
|
||||
r = requests.get(tmp_url, params = request_params, headers = request_header)
|
||||
r = requests.get(tmp_url, params=request_params, headers=request_header)
|
||||
data_text = r.content
|
||||
df_monthly = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
|
||||
df_monthly["DATE"] = pd.to_datetime(df_monthly["DATE"], format="%Y-%m-%d")
|
||||
|
|
@ -114,10 +118,11 @@ def unrate(startdate="1948-01-01", enddate="2021-01-01"):
|
|||
"cosd": "{}".format(startdate),
|
||||
"coed": "{}".format(enddate)
|
||||
}
|
||||
r = requests.get(tmp_url, params = request_params, headers = request_header)
|
||||
r = requests.get(tmp_url, params=request_params, headers=request_header)
|
||||
data_text = r.content
|
||||
df_quarterly = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
|
||||
df_quarterly["DATE"] = pd.to_datetime(df_quarterly["DATE"], format="%Y-%m-%d")
|
||||
df_quarterly["DATE"] = pd.to_datetime(
|
||||
df_quarterly["DATE"], format="%Y-%m-%d")
|
||||
ua = UserAgent(verify_ssl=False)
|
||||
request_header = {"User-Agent": ua.random}
|
||||
request_params = {
|
||||
|
|
@ -125,15 +130,21 @@ def unrate(startdate="1948-01-01", enddate="2021-01-01"):
|
|||
"cosd": "{}".format(startdate),
|
||||
"coed": "{}".format(enddate)
|
||||
}
|
||||
r = requests.get(tmp_url, params = request_params, headers = request_header)
|
||||
r = requests.get(tmp_url, params=request_params, headers=request_header)
|
||||
data_text = r.content
|
||||
df_annually = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
|
||||
df_annually["DATE"] = pd.to_datetime(df_annually["DATE"], format="%Y-%m-%d")
|
||||
df = pd.merge_asof(df_monthly, df_quarterly, on = "DATE", direction = "backward")
|
||||
df = pd.merge_asof(df, df_annually, on = "DATE", direction = "backward")
|
||||
df_annually["DATE"] = pd.to_datetime(
|
||||
df_annually["DATE"], format="%Y-%m-%d")
|
||||
df = pd.merge_asof(
|
||||
df_monthly,
|
||||
df_quarterly,
|
||||
on="DATE",
|
||||
direction="backward")
|
||||
df = pd.merge_asof(df, df_annually, on="DATE", direction="backward")
|
||||
df.columns = ["Date", "UR_Monthly", "UR_Quarterly", "UR_Annually"]
|
||||
return df
|
||||
|
||||
|
||||
def erate(startdate="1955-01-01", enddate="2021-01-01"):
|
||||
"""
|
||||
Full Name: Employment Rate: Aged 25-54: All Persons for the United States
|
||||
|
|
@ -148,7 +159,7 @@ def erate(startdate="1955-01-01", enddate="2021-01-01"):
|
|||
"cosd": "{}".format(startdate),
|
||||
"coed": "{}".format(enddate)
|
||||
}
|
||||
r = requests.get(tmp_url, params = request_params, headers = request_header)
|
||||
r = requests.get(tmp_url, params=request_params, headers=request_header)
|
||||
data_text = r.content
|
||||
df_monthly = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
|
||||
df_monthly["DATE"] = pd.to_datetime(df_monthly["DATE"], format="%Y-%m-%d")
|
||||
|
|
@ -159,10 +170,11 @@ def erate(startdate="1955-01-01", enddate="2021-01-01"):
|
|||
"cosd": "{}".format(startdate),
|
||||
"coed": "{}".format(enddate)
|
||||
}
|
||||
r = requests.get(tmp_url, params = request_params, headers = request_header)
|
||||
r = requests.get(tmp_url, params=request_params, headers=request_header)
|
||||
data_text = r.content
|
||||
df_quarterly = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
|
||||
df_quarterly["DATE"] = pd.to_datetime(df_quarterly["DATE"], format="%Y-%m-%d")
|
||||
df_quarterly["DATE"] = pd.to_datetime(
|
||||
df_quarterly["DATE"], format="%Y-%m-%d")
|
||||
ua = UserAgent(verify_ssl=False)
|
||||
request_header = {"User-Agent": ua.random}
|
||||
request_params = {
|
||||
|
|
@ -170,14 +182,20 @@ def erate(startdate="1955-01-01", enddate="2021-01-01"):
|
|||
"cosd": "{}".format(startdate),
|
||||
"coed": "{}".format(enddate)
|
||||
}
|
||||
r = requests.get(tmp_url, params = request_params, headers = request_header)
|
||||
r = requests.get(tmp_url, params=request_params, headers=request_header)
|
||||
data_text = r.content
|
||||
df_annually = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
|
||||
df_annually["DATE"] = pd.to_datetime(df_annually["DATE"], format="%Y-%m-%d")
|
||||
df = pd.merge_asof(df_monthly, df_quarterly, on = "DATE", direction = "backward")
|
||||
df = pd.merge_asof(df, df_annually, on = "DATE", direction = "backward")
|
||||
df_annually["DATE"] = pd.to_datetime(
|
||||
df_annually["DATE"], format="%Y-%m-%d")
|
||||
df = pd.merge_asof(
|
||||
df_monthly,
|
||||
df_quarterly,
|
||||
on="DATE",
|
||||
direction="backward")
|
||||
df = pd.merge_asof(df, df_annually, on="DATE", direction="backward")
|
||||
df.columns = ["Date", "ER_Monthly", "ER_Quarterly", "ER_Annually"]
|
||||
|
||||
|
||||
def pce_monthly(startdate="1959-01-01", enddate="2021-01-01"):
|
||||
"""
|
||||
Full Name: PCE
|
||||
|
|
@ -192,11 +210,12 @@ def pce_monthly(startdate="1959-01-01", enddate="2021-01-01"):
|
|||
"cosd": "{}".format(startdate),
|
||||
"coed": "{}".format(enddate)
|
||||
}
|
||||
r = requests.get(tmp_url, params = request_params, headers = request_header)
|
||||
r = requests.get(tmp_url, params=request_params, headers=request_header)
|
||||
data_text = r.content
|
||||
df = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
|
||||
return df
|
||||
|
||||
|
||||
def cpi(startdate="1960-01-01", enddate="2021-01-01"):
|
||||
"""
|
||||
Full Name: Consumer Price Index: Total All Items for the United States
|
||||
|
|
@ -211,7 +230,7 @@ def cpi(startdate="1960-01-01", enddate="2021-01-01"):
|
|||
"cosd": "{}".format(startdate),
|
||||
"coed": "{}".format(enddate)
|
||||
}
|
||||
r = requests.get(tmp_url, params = request_params, headers = request_header)
|
||||
r = requests.get(tmp_url, params=request_params, headers=request_header)
|
||||
data_text = r.content
|
||||
df_monthly = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
|
||||
df_monthly["DATE"] = pd.to_datetime(df_monthly["DATE"], format="%Y-%m-%d")
|
||||
|
|
@ -222,10 +241,11 @@ def cpi(startdate="1960-01-01", enddate="2021-01-01"):
|
|||
"cosd": "{}".format(startdate),
|
||||
"coed": "{}".format(enddate)
|
||||
}
|
||||
r = requests.get(tmp_url, params = request_params, headers = request_header)
|
||||
r = requests.get(tmp_url, params=request_params, headers=request_header)
|
||||
data_text = r.content
|
||||
df_quarterly = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
|
||||
df_quarterly["DATE"] = pd.to_datetime(df_quarterly["DATE"], format="%Y-%m-%d")
|
||||
df_quarterly["DATE"] = pd.to_datetime(
|
||||
df_quarterly["DATE"], format="%Y-%m-%d")
|
||||
ua = UserAgent(verify_ssl=False)
|
||||
request_header = {"User-Agent": ua.random}
|
||||
request_params = {
|
||||
|
|
@ -233,15 +253,21 @@ def cpi(startdate="1960-01-01", enddate="2021-01-01"):
|
|||
"cosd": "{}".format(startdate),
|
||||
"coed": "{}".format(enddate)
|
||||
}
|
||||
r = requests.get(tmp_url, params = request_params, headers = request_header)
|
||||
r = requests.get(tmp_url, params=request_params, headers=request_header)
|
||||
data_text = r.content
|
||||
df_annually = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
|
||||
df_annually["DATE"] = pd.to_datetime(df_annually["DATE"], format="%Y-%m-%d")
|
||||
df = pd.merge_asof(df_monthly, df_quarterly, on = "DATE", direction = "backward")
|
||||
df = pd.merge_asof(df, df_annually, on = "DATE", direction = "backward")
|
||||
df_annually["DATE"] = pd.to_datetime(
|
||||
df_annually["DATE"], format="%Y-%m-%d")
|
||||
df = pd.merge_asof(
|
||||
df_monthly,
|
||||
df_quarterly,
|
||||
on="DATE",
|
||||
direction="backward")
|
||||
df = pd.merge_asof(df, df_annually, on="DATE", direction="backward")
|
||||
df.columns = ["Date", "CPI_Monthly", "CPI_Quarterly", "CPI_Annually"]
|
||||
return df
|
||||
|
||||
|
||||
def m1(startdate="1960-01-01", enddate="2021-01-01"):
|
||||
"""
|
||||
Full Name: Consumer Price Index: M3 for the United States
|
||||
|
|
@ -256,7 +282,7 @@ def m1(startdate="1960-01-01", enddate="2021-01-01"):
|
|||
"cosd": "{}".format(startdate),
|
||||
"coed": "{}".format(enddate)
|
||||
}
|
||||
r = requests.get(tmp_url, params = request_params, headers = request_header)
|
||||
r = requests.get(tmp_url, params=request_params, headers=request_header)
|
||||
data_text = r.content
|
||||
df_weekly = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
|
||||
df_weekly["DATE"] = pd.to_datetime(df_weekly["DATE"], format="%Y-%m-%d")
|
||||
|
|
@ -267,7 +293,7 @@ def m1(startdate="1960-01-01", enddate="2021-01-01"):
|
|||
"cosd": "{}".format(startdate),
|
||||
"coed": "{}".format(enddate)
|
||||
}
|
||||
r = requests.get(tmp_url, params = request_params, headers = request_header)
|
||||
r = requests.get(tmp_url, params=request_params, headers=request_header)
|
||||
data_text = r.content
|
||||
df_monthly = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
|
||||
df_monthly["DATE"] = pd.to_datetime(df_monthly["DATE"], format="%Y-%m-%d")
|
||||
|
|
@ -278,10 +304,11 @@ def m1(startdate="1960-01-01", enddate="2021-01-01"):
|
|||
"cosd": "{}".format(startdate),
|
||||
"coed": "{}".format(enddate)
|
||||
}
|
||||
r = requests.get(tmp_url, params = request_params, headers = request_header)
|
||||
r = requests.get(tmp_url, params=request_params, headers=request_header)
|
||||
data_text = r.content
|
||||
df_quarterly = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
|
||||
df_quarterly["DATE"] = pd.to_datetime(df_quarterly["DATE"], format="%Y-%m-%d")
|
||||
df_quarterly["DATE"] = pd.to_datetime(
|
||||
df_quarterly["DATE"], format="%Y-%m-%d")
|
||||
ua = UserAgent(verify_ssl=False)
|
||||
request_header = {"User-Agent": ua.random}
|
||||
request_params = {
|
||||
|
|
@ -289,16 +316,23 @@ def m1(startdate="1960-01-01", enddate="2021-01-01"):
|
|||
"cosd": "{}".format(startdate),
|
||||
"coed": "{}".format(enddate)
|
||||
}
|
||||
r = requests.get(tmp_url, params = request_params, headers = request_header)
|
||||
r = requests.get(tmp_url, params=request_params, headers=request_header)
|
||||
data_text = r.content
|
||||
df_annually = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
|
||||
df_annually["DATE"] = pd.to_datetime(df_annually["DATE"], format="%Y-%m-%d")
|
||||
df = pd.merge_asof(df_weekly, df_monthly, on = "DATE", direction = "backward")
|
||||
df = pd.merge_asof(df, df_quarterly, on = "DATE", direction = "backward")
|
||||
df = pd.merge_asof(df, df_annually, on = "DATE", direction = "backward")
|
||||
df.columns = ["Date", "M1_weekly", "M1_Monthly", "M1_Quarterly", "M1_Annually"]
|
||||
df_annually["DATE"] = pd.to_datetime(
|
||||
df_annually["DATE"], format="%Y-%m-%d")
|
||||
df = pd.merge_asof(df_weekly, df_monthly, on="DATE", direction="backward")
|
||||
df = pd.merge_asof(df, df_quarterly, on="DATE", direction="backward")
|
||||
df = pd.merge_asof(df, df_annually, on="DATE", direction="backward")
|
||||
df.columns = [
|
||||
"Date",
|
||||
"M1_weekly",
|
||||
"M1_Monthly",
|
||||
"M1_Quarterly",
|
||||
"M1_Annually"]
|
||||
return df
|
||||
|
||||
|
||||
def m2(startdate="1960-01-01", enddate="2021-01-01"):
|
||||
"""
|
||||
Full Name: M2 Money Stock
|
||||
|
|
@ -313,7 +347,7 @@ def m2(startdate="1960-01-01", enddate="2021-01-01"):
|
|||
"cosd": "{}".format(startdate),
|
||||
"coed": "{}".format(enddate)
|
||||
}
|
||||
r = requests.get(tmp_url, params = request_params, headers = request_header)
|
||||
r = requests.get(tmp_url, params=request_params, headers=request_header)
|
||||
data_text = r.content
|
||||
df_weekly = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
|
||||
df_weekly["DATE"] = pd.to_datetime(df_weekly["DATE"], format="%Y-%m-%d")
|
||||
|
|
@ -324,13 +358,14 @@ def m2(startdate="1960-01-01", enddate="2021-01-01"):
|
|||
"cosd": "{}".format(startdate),
|
||||
"coed": "{}".format(enddate)
|
||||
}
|
||||
r = requests.get(tmp_url, params = request_params, headers = request_header)
|
||||
r = requests.get(tmp_url, params=request_params, headers=request_header)
|
||||
data_text = r.content
|
||||
df_monthly = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
|
||||
df_monthly["DATE"] = pd.to_datetime(df_monthly["DATE"], format="%Y-%m-%d")
|
||||
df = pd.merge_asof(df_weekly, df_monthly, on = "DATE", direction = "backward")
|
||||
df = pd.merge_asof(df_weekly, df_monthly, on="DATE", direction="backward")
|
||||
df.columns = ["Date", "M2_Weekly", "M2_Monthly"]
|
||||
|
||||
|
||||
def m3(startdate="1960-01-01", enddate="2021-01-01"):
|
||||
"""
|
||||
Full Name: Consumer Price Index: M3 for the United States
|
||||
|
|
@ -345,7 +380,7 @@ def m3(startdate="1960-01-01", enddate="2021-01-01"):
|
|||
"cosd": "{}".format(startdate),
|
||||
"coed": "{}".format(enddate)
|
||||
}
|
||||
r = requests.get(tmp_url, params = request_params, headers = request_header)
|
||||
r = requests.get(tmp_url, params=request_params, headers=request_header)
|
||||
data_text = r.content
|
||||
df_monthly = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
|
||||
df_monthly["DATE"] = pd.to_datetime(df_monthly["DATE"], format="%Y-%m-%d")
|
||||
|
|
@ -356,10 +391,11 @@ def m3(startdate="1960-01-01", enddate="2021-01-01"):
|
|||
"cosd": "{}".format(startdate),
|
||||
"coed": "{}".format(enddate)
|
||||
}
|
||||
r = requests.get(tmp_url, params = request_params, headers = request_header)
|
||||
r = requests.get(tmp_url, params=request_params, headers=request_header)
|
||||
data_text = r.content
|
||||
df_quarterly = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
|
||||
df_quarterly["DATE"] = pd.to_datetime(df_quarterly["DATE"], format="%Y-%m-%d")
|
||||
df_quarterly["DATE"] = pd.to_datetime(
|
||||
df_quarterly["DATE"], format="%Y-%m-%d")
|
||||
ua = UserAgent(verify_ssl=False)
|
||||
request_header = {"User-Agent": ua.random}
|
||||
request_params = {
|
||||
|
|
@ -367,16 +403,22 @@ def m3(startdate="1960-01-01", enddate="2021-01-01"):
|
|||
"cosd": "{}".format(startdate),
|
||||
"coed": "{}".format(enddate)
|
||||
}
|
||||
r = requests.get(tmp_url, params = request_params, headers = request_header)
|
||||
r = requests.get(tmp_url, params=request_params, headers=request_header)
|
||||
data_text = r.content
|
||||
df_annually = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
|
||||
df_annually["DATE"] = pd.to_datetime(df_annually["DATE"], format="%Y-%m-%d")
|
||||
df = pd.merge_asof(df_monthly, df_quarterly, on = "DATE", direction = "backward")
|
||||
df = pd.merge_asof(df, df_annually, on = "DATE", direction = "backward")
|
||||
df_annually["DATE"] = pd.to_datetime(
|
||||
df_annually["DATE"], format="%Y-%m-%d")
|
||||
df = pd.merge_asof(
|
||||
df_monthly,
|
||||
df_quarterly,
|
||||
on="DATE",
|
||||
direction="backward")
|
||||
df = pd.merge_asof(df, df_annually, on="DATE", direction="backward")
|
||||
df.columns = ["Date", "M3_Monthly", "M3_Quarterly", "M3_Annually"]
|
||||
return df
|
||||
|
||||
def ltgby(startdate="1955-01-01", enddate="2021-01-01"):
|
||||
|
||||
def ltgby_10(startdate="1955-01-01", enddate="2021-01-01"):
|
||||
"""
|
||||
Full Name: Long-Term Government Bond Yields: 10-year: Main (Including Benchmark) for the United States
|
||||
Description: Percent,Not Seasonally Adjusted, Monthly, Quarterly and Annually
|
||||
|
|
@ -390,7 +432,7 @@ def ltgby(startdate="1955-01-01", enddate="2021-01-01"):
|
|||
"cosd": "{}".format(startdate),
|
||||
"coed": "{}".format(enddate)
|
||||
}
|
||||
r = requests.get(tmp_url, params = request_params, headers = request_header)
|
||||
r = requests.get(tmp_url, params=request_params, headers=request_header)
|
||||
data_text = r.content
|
||||
df_monthly = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
|
||||
df_monthly["DATE"] = pd.to_datetime(df_monthly["DATE"], format="%Y-%m-%d")
|
||||
|
|
@ -401,10 +443,11 @@ def ltgby(startdate="1955-01-01", enddate="2021-01-01"):
|
|||
"cosd": "{}".format(startdate),
|
||||
"coed": "{}".format(enddate)
|
||||
}
|
||||
r = requests.get(tmp_url, params = request_params, headers = request_header)
|
||||
r = requests.get(tmp_url, params=request_params, headers=request_header)
|
||||
data_text = r.content
|
||||
df_quarterly = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
|
||||
df_quarterly["DATE"] = pd.to_datetime(df_quarterly["DATE"], format="%Y-%m-%d")
|
||||
df_quarterly["DATE"] = pd.to_datetime(
|
||||
df_quarterly["DATE"], format="%Y-%m-%d")
|
||||
ua = UserAgent(verify_ssl=False)
|
||||
request_header = {"User-Agent": ua.random}
|
||||
request_params = {
|
||||
|
|
@ -412,15 +455,21 @@ def ltgby(startdate="1955-01-01", enddate="2021-01-01"):
|
|||
"cosd": "{}".format(startdate),
|
||||
"coed": "{}".format(enddate)
|
||||
}
|
||||
r = requests.get(tmp_url, params = request_params, headers = request_header)
|
||||
r = requests.get(tmp_url, params=request_params, headers=request_header)
|
||||
data_text = r.content
|
||||
df_annually = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
|
||||
df_annually["DATE"] = pd.to_datetime(df_annually["DATE"], format="%Y-%m-%d")
|
||||
df = pd.merge_asof(df_monthly, df_quarterly, on = "DATE", direction = "backward")
|
||||
df = pd.merge_asof(df, df_annually, on = "DATE", direction = "backward")
|
||||
df_annually["DATE"] = pd.to_datetime(
|
||||
df_annually["DATE"], format="%Y-%m-%d")
|
||||
df = pd.merge_asof(
|
||||
df_monthly,
|
||||
df_quarterly,
|
||||
on="DATE",
|
||||
direction="backward")
|
||||
df = pd.merge_asof(df, df_annually, on="DATE", direction="backward")
|
||||
df.columns = ["Date", "ltgby_Monthly", "ltgby_Quarterly", "ltgby_Annually"]
|
||||
return df
|
||||
|
||||
|
||||
def gdp_ipd(startdate="1955-01-01", enddate="2021-01-01"):
|
||||
"""
|
||||
Full Name: Long-Term Government Bond Yields: 10-year: Main (Including Benchmark) for the United States
|
||||
|
|
@ -435,10 +484,11 @@ def gdp_ipd(startdate="1955-01-01", enddate="2021-01-01"):
|
|||
"cosd": "{}".format(startdate),
|
||||
"coed": "{}".format(enddate)
|
||||
}
|
||||
r = requests.get(tmp_url, params = request_params, headers = request_header)
|
||||
r = requests.get(tmp_url, params=request_params, headers=request_header)
|
||||
data_text = r.content
|
||||
df_quarterly = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
|
||||
df_quarterly["DATE"] = pd.to_datetime(df_quarterly["DATE"], format="%Y-%m-%d")
|
||||
df_quarterly["DATE"] = pd.to_datetime(
|
||||
df_quarterly["DATE"], format="%Y-%m-%d")
|
||||
ua = UserAgent(verify_ssl=False)
|
||||
request_header = {"User-Agent": ua.random}
|
||||
request_params = {
|
||||
|
|
@ -446,14 +496,20 @@ def gdp_ipd(startdate="1955-01-01", enddate="2021-01-01"):
|
|||
"cosd": "{}".format(startdate),
|
||||
"coed": "{}".format(enddate)
|
||||
}
|
||||
r = requests.get(tmp_url, params = request_params, headers = request_header)
|
||||
r = requests.get(tmp_url, params=request_params, headers=request_header)
|
||||
data_text = r.content
|
||||
df_annually = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
|
||||
df_annually["DATE"] = pd.to_datetime(df_annually["DATE"], format="%Y-%m-%d")
|
||||
df = pd.merge_asof(df_quarterly, df_annually, on = "DATE", direction = "backward")
|
||||
df_annually["DATE"] = pd.to_datetime(
|
||||
df_annually["DATE"], format="%Y-%m-%d")
|
||||
df = pd.merge_asof(
|
||||
df_quarterly,
|
||||
df_annually,
|
||||
on="DATE",
|
||||
direction="backward")
|
||||
df.columns = ["Date", "gdp_ipd_Quarterly", "gdp_ipd_Annually"]
|
||||
return df
|
||||
|
||||
|
||||
def cci(startdate="1955-01-01", enddate="2021-01-01"):
|
||||
"""
|
||||
Full Name: Consumer Opinion Surveys: Confidence Indicators: Composite Indicators: OECD Indicator for the United States
|
||||
|
|
@ -468,12 +524,13 @@ def cci(startdate="1955-01-01", enddate="2021-01-01"):
|
|||
"cosd": "{}".format(startdate),
|
||||
"coed": "{}".format(enddate)
|
||||
}
|
||||
r = requests.get(tmp_url, params = request_params, headers = request_header)
|
||||
r = requests.get(tmp_url, params=request_params, headers=request_header)
|
||||
data_text = r.content
|
||||
df = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
|
||||
df.columns = ["Date", "CCI_Monthly"]
|
||||
return df
|
||||
|
||||
|
||||
def bci(startdate="1955-01-01", enddate="2021-01-01"):
|
||||
"""
|
||||
Full Name: Consumer Opinion Surveys: Confidence Indicators: Composite Indicators: OECD Indicator for the United States
|
||||
|
|
@ -488,14 +545,13 @@ def bci(startdate="1955-01-01", enddate="2021-01-01"):
|
|||
"cosd": "{}".format(startdate),
|
||||
"coed": "{}".format(enddate)
|
||||
}
|
||||
r = requests.get(tmp_url, params = request_params, headers = request_header)
|
||||
r = requests.get(tmp_url, params=request_params, headers=request_header)
|
||||
data_text = r.content
|
||||
df = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
|
||||
df.columns = ["Date", "BCI_Annually"]
|
||||
return df
|
||||
|
||||
|
||||
|
||||
def ibr_3(startdate="1965-01-01", enddate="2021-01-01"):
|
||||
"""
|
||||
Full Name: 3-Month or 90-day Rates and Yields: Interbank Rates for the United States
|
||||
|
|
@ -509,7 +565,7 @@ def ibr_3(startdate="1965-01-01", enddate="2021-01-01"):
|
|||
"cosd": "{}".format(startdate),
|
||||
"coed": "{}".format(enddate)
|
||||
}
|
||||
r = requests.get(tmp_url, params = request_params, headers = request_header)
|
||||
r = requests.get(tmp_url, params=request_params, headers=request_header)
|
||||
data_text = r.content
|
||||
df_monthly = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
|
||||
df_monthly["DATE"] = pd.to_datetime(df_monthly["DATE"], format="%Y-%m-%d")
|
||||
|
|
@ -520,13 +576,19 @@ def ibr_3(startdate="1965-01-01", enddate="2021-01-01"):
|
|||
"cosd": "{}".format(startdate),
|
||||
"coed": "{}".format(enddate)
|
||||
}
|
||||
r = requests.get(tmp_url, params = request_params, headers = request_header)
|
||||
r = requests.get(tmp_url, params=request_params, headers=request_header)
|
||||
data_text = r.content
|
||||
df_quarterly = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
|
||||
df_quarterly["DATE"] = pd.to_datetime(df_quarterly["DATE"], format="%Y-%m-%d")
|
||||
df = pd.merge_asof(df_quarterly, df_quarterly, on = "DATE", direction = "backward")
|
||||
df_quarterly["DATE"] = pd.to_datetime(
|
||||
df_quarterly["DATE"], format="%Y-%m-%d")
|
||||
df = pd.merge_asof(
|
||||
df_quarterly,
|
||||
df_quarterly,
|
||||
on="DATE",
|
||||
direction="backward")
|
||||
df.columns = ["Date", "ibr3_monthly", "ibr3_Quarterly"]
|
||||
|
||||
|
||||
def gfcf_3(startdate="1965-01-01", enddate="2021-01-01"):
|
||||
"""
|
||||
Full Name: Gross Fixed Capital Formation in United States
|
||||
|
|
@ -540,10 +602,11 @@ def gfcf_3(startdate="1965-01-01", enddate="2021-01-01"):
|
|||
"cosd": "{}".format(startdate),
|
||||
"coed": "{}".format(enddate)
|
||||
}
|
||||
r = requests.get(tmp_url, params = request_params, headers = request_header)
|
||||
r = requests.get(tmp_url, params=request_params, headers=request_header)
|
||||
data_text = r.content
|
||||
df_quarterly = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
|
||||
df_quarterly["DATE"] = pd.to_datetime(df_quarterly["DATE"], format="%Y-%m-%d")
|
||||
df_quarterly["DATE"] = pd.to_datetime(
|
||||
df_quarterly["DATE"], format="%Y-%m-%d")
|
||||
ua = UserAgent(verify_ssl=False)
|
||||
request_header = {"User-Agent": ua.random}
|
||||
request_params = {
|
||||
|
|
@ -551,11 +614,16 @@ def gfcf_3(startdate="1965-01-01", enddate="2021-01-01"):
|
|||
"cosd": "{}".format(startdate),
|
||||
"coed": "{}".format(enddate)
|
||||
}
|
||||
r = requests.get(tmp_url, params = request_params, headers = request_header)
|
||||
r = requests.get(tmp_url, params=request_params, headers=request_header)
|
||||
data_text = r.content
|
||||
df_annually = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
|
||||
df_annually["DATE"] = pd.to_datetime(df_annually["DATE"], format="%Y-%m-%d")
|
||||
df = pd.merge_asof(df_quarterly, df_quarterly, on = "DATE", direction = "backward")
|
||||
df_annually["DATE"] = pd.to_datetime(
|
||||
df_annually["DATE"], format="%Y-%m-%d")
|
||||
df = pd.merge_asof(
|
||||
df_quarterly,
|
||||
df_quarterly,
|
||||
on="DATE",
|
||||
direction="backward")
|
||||
df.columns = ["Date", "ibr3_monthly", "ibr3_Annually"]
|
||||
|
||||
|
||||
|
|
@ -573,10 +641,11 @@ def pfce(startdate="1955-01-01", enddate="2021-01-01"):
|
|||
"cosd": "{}".format(startdate),
|
||||
"coed": "{}".format(enddate)
|
||||
}
|
||||
r = requests.get(tmp_url, params = request_params, headers = request_header)
|
||||
r = requests.get(tmp_url, params=request_params, headers=request_header)
|
||||
data_text = r.content
|
||||
df_quarterly = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
|
||||
df_quarterly["DATE"] = pd.to_datetime(df_quarterly["DATE"], format="%Y-%m-%d")
|
||||
df_quarterly["DATE"] = pd.to_datetime(
|
||||
df_quarterly["DATE"], format="%Y-%m-%d")
|
||||
ua = UserAgent(verify_ssl=False)
|
||||
request_header = {"User-Agent": ua.random}
|
||||
request_params = {
|
||||
|
|
@ -584,13 +653,19 @@ def pfce(startdate="1955-01-01", enddate="2021-01-01"):
|
|||
"cosd": "{}".format(startdate),
|
||||
"coed": "{}".format(enddate)
|
||||
}
|
||||
r = requests.get(tmp_url, params = request_params, headers = request_header)
|
||||
r = requests.get(tmp_url, params=request_params, headers=request_header)
|
||||
data_text = r.content
|
||||
df_annually = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
|
||||
df_annually["DATE"] = pd.to_datetime(df_annually["DATE"], format="%Y-%m-%d")
|
||||
df = pd.merge_asof(df_quarterly, df_annually, on = "DATE", direction = "backward")
|
||||
df_annually["DATE"] = pd.to_datetime(
|
||||
df_annually["DATE"], format="%Y-%m-%d")
|
||||
df = pd.merge_asof(
|
||||
df_quarterly,
|
||||
df_annually,
|
||||
on="DATE",
|
||||
direction="backward")
|
||||
df.columns = ["Date", "PFCE_Quarterly", "PFCE_Annually"]
|
||||
|
||||
|
||||
def tlp(startdate="1955-01-01", enddate="2021-01-01"):
|
||||
"""
|
||||
Full Name: Early Estimate of Quarterly ULC Indicators: Total Labor Productivity for the United States
|
||||
|
|
@ -605,10 +680,11 @@ def tlp(startdate="1955-01-01", enddate="2021-01-01"):
|
|||
"cosd": "{}".format(startdate),
|
||||
"coed": "{}".format(enddate)
|
||||
}
|
||||
r = requests.get(tmp_url, params = request_params, headers = request_header)
|
||||
r = requests.get(tmp_url, params=request_params, headers=request_header)
|
||||
data_text = r.content
|
||||
df_quarterly = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
|
||||
df_quarterly["DATE"] = pd.to_datetime(df_quarterly["DATE"], format="%Y-%m-%d")
|
||||
df_quarterly["DATE"] = pd.to_datetime(
|
||||
df_quarterly["DATE"], format="%Y-%m-%d")
|
||||
ua = UserAgent(verify_ssl=False)
|
||||
request_header = {"User-Agent": ua.random}
|
||||
request_params = {
|
||||
|
|
@ -616,13 +692,19 @@ def tlp(startdate="1955-01-01", enddate="2021-01-01"):
|
|||
"cosd": "{}".format(startdate),
|
||||
"coed": "{}".format(enddate)
|
||||
}
|
||||
r = requests.get(tmp_url, params = request_params, headers = request_header)
|
||||
r = requests.get(tmp_url, params=request_params, headers=request_header)
|
||||
data_text = r.content
|
||||
df_annually = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
|
||||
df_annually["DATE"] = pd.to_datetime(df_annually["DATE"], format="%Y-%m-%d")
|
||||
df = pd.merge_asof(df_quarterly, df_annually, on = "DATE", direction = "backward")
|
||||
df_annually["DATE"] = pd.to_datetime(
|
||||
df_annually["DATE"], format="%Y-%m-%d")
|
||||
df = pd.merge_asof(
|
||||
df_quarterly,
|
||||
df_annually,
|
||||
on="DATE",
|
||||
direction="backward")
|
||||
df.columns = ["Date", "PFCE_Quarterly", "PFCE_Quarterly_YoY"]
|
||||
|
||||
|
||||
def rt(startdate="1955-01-01", enddate="2021-01-01"):
|
||||
"""
|
||||
Full Name:Total Retail Trade in United States
|
||||
|
|
@ -637,7 +719,7 @@ def rt(startdate="1955-01-01", enddate="2021-01-01"):
|
|||
"cosd": "{}".format(startdate),
|
||||
"coed": "{}".format(enddate)
|
||||
}
|
||||
r = requests.get(tmp_url, params = request_params, headers = request_header)
|
||||
r = requests.get(tmp_url, params=request_params, headers=request_header)
|
||||
data_text = r.content
|
||||
df_monthly = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
|
||||
df_monthly["DATE"] = pd.to_datetime(df_monthly["DATE"], format="%Y-%m-%d")
|
||||
|
|
@ -647,14 +729,20 @@ def rt(startdate="1955-01-01", enddate="2021-01-01"):
|
|||
"cosd": "{}".format(startdate),
|
||||
"coed": "{}".format(enddate)
|
||||
}
|
||||
r = requests.get(tmp_url, params = request_params, headers = request_header)
|
||||
r = requests.get(tmp_url, params=request_params, headers=request_header)
|
||||
data_text = r.content
|
||||
df_annually = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
|
||||
df_annually["DATE"] = pd.to_datetime(df_annually["DATE"], format="%Y-%m-%d")
|
||||
df = pd.merge_asof(df_monthly, df_annually, on = "DATE", direction = "backward")
|
||||
df_annually["DATE"] = pd.to_datetime(
|
||||
df_annually["DATE"], format="%Y-%m-%d")
|
||||
df = pd.merge_asof(
|
||||
df_monthly,
|
||||
df_annually,
|
||||
on="DATE",
|
||||
direction="backward")
|
||||
df.columns = ["Date", "RT_Quarterly", "RT_Annually"]
|
||||
return df
|
||||
|
||||
|
||||
def bir(startdate="2003-01-01", enddate="2021-01-01"):
|
||||
"""
|
||||
Full Name:Total Retail Trade in United States
|
||||
|
|
@ -669,7 +757,7 @@ def bir(startdate="2003-01-01", enddate="2021-01-01"):
|
|||
"cosd": "{}".format(startdate),
|
||||
"coed": "{}".format(enddate)
|
||||
}
|
||||
r = requests.get(tmp_url, params = request_params, headers = request_header)
|
||||
r = requests.get(tmp_url, params=request_params, headers=request_header)
|
||||
data_text = r.content
|
||||
df_5y = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
|
||||
df_5y["DATE"] = pd.to_datetime(df_5y["DATE"], format="%Y-%m-%d")
|
||||
|
|
@ -679,14 +767,15 @@ def bir(startdate="2003-01-01", enddate="2021-01-01"):
|
|||
"cosd": "{}".format(startdate),
|
||||
"coed": "{}".format(enddate)
|
||||
}
|
||||
r = requests.get(tmp_url, params = request_params, headers = request_header)
|
||||
r = requests.get(tmp_url, params=request_params, headers=request_header)
|
||||
data_text = r.content
|
||||
df_10y = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
|
||||
df_10y["DATE"] = pd.to_datetime(df_10y["DATE"], format="%Y-%m-%d")
|
||||
df = pd.merge_asof(df_5y, df_10y, on = "DATE", direction = "backward")
|
||||
df = pd.merge_asof(df_5y, df_10y, on="DATE", direction="backward")
|
||||
df.columns = ["Date", "BIR_5y", "BIR_10y"]
|
||||
return df
|
||||
|
||||
|
||||
def adsbci():
|
||||
"""
|
||||
An index designed to track real business conditions at high observation frequency
|
||||
|
|
@ -694,7 +783,7 @@ def adsbci():
|
|||
ua = UserAgent(verify_ssl=False)
|
||||
request_header = {"User-Agent": ua.random}
|
||||
tmp_url = url["philfed"] + "ads"
|
||||
r = requests.get(tmp_url, headers = request_header)
|
||||
r = requests.get(tmp_url, headers=request_header)
|
||||
file = open("ads_temp.xls", "wb")
|
||||
file.write(r.content)
|
||||
file.close()
|
||||
|
|
@ -704,40 +793,51 @@ def adsbci():
|
|||
os.remove("ads_temp.xls")
|
||||
return df
|
||||
|
||||
|
||||
def pci():
|
||||
"""
|
||||
Tracks the degree of political disagreement among U.S. politicians at the federal level, Monthly
|
||||
"""
|
||||
df = pd.read_excel("https://www.philadelphiafed.org/-/media/frbp/assets/data-visualizations/partisan-conflict.xlsx")
|
||||
df = pd.read_excel(
|
||||
"https://www.philadelphiafed.org/-/media/frbp/assets/data-visualizations/partisan-conflict.xlsx")
|
||||
df["Date"] = df["Year"].astype(str) + df["Month"]
|
||||
df["Date"] = pd.to_datetime(df["Date"], format = "%Y%B")
|
||||
df["Date"] = pd.to_datetime(df["Date"], format="%Y%B")
|
||||
df = df.drop(["Year", "Month"], axis=1)
|
||||
df = df[["Date", "Partisan Conflict"]]
|
||||
return df
|
||||
|
||||
|
||||
def inflation_noewcasting():
|
||||
"""
|
||||
|
||||
|
||||
"""
|
||||
ua = UserAgent(verify_ssl=False)
|
||||
request_header = {"User-Agent": ua.random}
|
||||
tmp_url = "https://www.clevelandfed.org/~/media/files/charting/%20nowcast_quarter.json"
|
||||
|
||||
r = requests.get(tmp_url, headers = request_header)
|
||||
r = requests.get(tmp_url, headers=request_header)
|
||||
tmp_df = pd.DataFrame(demjson.decode(r.text))
|
||||
df = pd.DataFrame()
|
||||
for i in range(0, len(tmp_df)):
|
||||
date = tmp_df['chart'][i]['subcaption'][:4] + "/" + \
|
||||
pd.DataFrame(tmp_df["dataset"][i][0]['data'])['tooltext'].str.extract(r"\b(0?[1-9]|1[0-2])/(0?[1-9]|[12][0-9]|3[01])\b")[0] + "/" + \
|
||||
date = tmp_df['chart'][i]['subcaption'][:4] + "/" + \
|
||||
pd.DataFrame(tmp_df["dataset"][i][0]['data'])['tooltext'].str.extract(r"\b(0?[1-9]|1[0-2])/(0?[1-9]|[12][0-9]|3[01])\b")[0] + "/" + \
|
||||
pd.DataFrame(tmp_df["dataset"][i][0]['data'])['tooltext'].str.extract(r"\b(0?[1-9]|1[0-2])/(0?[1-9]|[12][0-9]|3[01])\b")[1]
|
||||
CPI_I = pd.DataFrame((pd.DataFrame(tmp_df["dataset"][i])['data'])[0])["value"]
|
||||
C_CPI_I = pd.DataFrame((pd.DataFrame(tmp_df["dataset"][i])['data'])[1])["value"]
|
||||
PCE_I = pd.DataFrame((pd.DataFrame(tmp_df["dataset"][i])['data'])[2])["value"]
|
||||
C_PCE_I = pd.DataFrame((pd.DataFrame(tmp_df["dataset"][i])['data'])[3])["value"]
|
||||
A_CPI_I = pd.DataFrame((pd.DataFrame(tmp_df["dataset"][i])['data'])[4])["value"]
|
||||
A_C_CPI_I = pd.DataFrame((pd.DataFrame(tmp_df["dataset"][i])['data'])[5])["value"]
|
||||
A_PCE_I = pd.DataFrame((pd.DataFrame(tmp_df["dataset"][i])['data'])[6])["value"]
|
||||
A_C_PCE_I = pd.DataFrame((pd.DataFrame(tmp_df["dataset"][i])['data'])[7])["value"]
|
||||
CPI_I = pd.DataFrame(
|
||||
(pd.DataFrame(tmp_df["dataset"][i])['data'])[0])["value"]
|
||||
C_CPI_I = pd.DataFrame(
|
||||
(pd.DataFrame(tmp_df["dataset"][i])['data'])[1])["value"]
|
||||
PCE_I = pd.DataFrame(
|
||||
(pd.DataFrame(tmp_df["dataset"][i])['data'])[2])["value"]
|
||||
C_PCE_I = pd.DataFrame(
|
||||
(pd.DataFrame(tmp_df["dataset"][i])['data'])[3])["value"]
|
||||
A_CPI_I = pd.DataFrame(
|
||||
(pd.DataFrame(tmp_df["dataset"][i])['data'])[4])["value"]
|
||||
A_C_CPI_I = pd.DataFrame(
|
||||
(pd.DataFrame(tmp_df["dataset"][i])['data'])[5])["value"]
|
||||
A_PCE_I = pd.DataFrame(
|
||||
(pd.DataFrame(tmp_df["dataset"][i])['data'])[6])["value"]
|
||||
A_C_PCE_I = pd.DataFrame(
|
||||
(pd.DataFrame(tmp_df["dataset"][i])['data'])[7])["value"]
|
||||
tmp_df2 = pd.DataFrame({"date": date,
|
||||
"CPI_I": CPI_I,
|
||||
"C_CPI_I": C_CPI_I,
|
||||
|
|
@ -747,32 +847,37 @@ def inflation_noewcasting():
|
|||
"A_C_CPI_I": A_C_CPI_I,
|
||||
"A_PCE_I": A_PCE_I,
|
||||
"A_C_PCE_I": A_C_PCE_I})
|
||||
df = pd.concat([df,tmp_df2], axis=0)
|
||||
df = pd.concat([df, tmp_df2], axis=0)
|
||||
df.reset_index(drop=True, inplace=True)
|
||||
|
||||
df.replace('', np.nan, inplace = True)
|
||||
|
||||
df.replace('', np.nan, inplace=True)
|
||||
return df
|
||||
|
||||
|
||||
def bbki():
|
||||
tmp_url = url["chicagofed"] + "bbki/bbki-monthly-data-series-csv.csv"
|
||||
df = pd.read_csv(tmp_url)
|
||||
return df
|
||||
|
||||
|
||||
def cfnai():
|
||||
tmp_url = url["chicagofed"] + "cfnai/cfnai-data-series-csv.csv"
|
||||
df = pd.read_csv(tmp_url)
|
||||
return df
|
||||
|
||||
|
||||
def cfsbc():
|
||||
tmp_url = url["chicagofed"] + "cfsbc-activity-index-csv.csv"
|
||||
df = pd.read_csv(tmp_url)
|
||||
return df
|
||||
|
||||
|
||||
def nfci():
|
||||
tmp_url = url["chicagofed"] + "nfci/decomposition-nfci-csv.csv"
|
||||
df = pd.read_csv(tmp_url)
|
||||
return df
|
||||
|
||||
|
||||
def nfci():
|
||||
tmp_url = url["chicagofed"] + "nfci/decomposition-anfci-csv.csv"
|
||||
df = pd.read_csv(tmp_url)
|
||||
|
|
|
|||
|
|
@ -12,6 +12,7 @@ url = {
|
|||
"dukascopy": "http://data.uicstat.com/api_1.0"
|
||||
}
|
||||
|
||||
|
||||
def dukascopy(
|
||||
instrument: str,
|
||||
startdate: str,
|
||||
|
|
@ -46,4 +47,4 @@ def dukascopy(
|
|||
"Close",
|
||||
"Volume"
|
||||
]
|
||||
return df
|
||||
return df
|
||||
|
|
|
|||
|
|
@ -12,16 +12,18 @@ url = {
|
|||
"moneywatch": "https://www.marketwatch.com/investing/"
|
||||
}
|
||||
|
||||
def forex(instrument = "eurusd", startdate = "2019-01-01", enddate = "2021-01-01"):
|
||||
|
||||
def forex(instrument="eurusd", startdate="2019-01-01", enddate="2021-01-01"):
|
||||
startdate = datetime.strptime(startdate, "%Y-%m-%d").strftime("%m/%d/%y")
|
||||
enddate = datetime.strptime(enddate, "%Y-%m-%d").strftime("%m/%d/%y")
|
||||
df = pd.DataFrame()
|
||||
|
||||
def _FX(instrument = "eurusd", startdate = "01/01/2020", enddate = "01/01/2021"):
|
||||
def _FX(instrument="eurusd", startdate="01/01/2020", enddate="01/01/2021"):
|
||||
"""
|
||||
https://www.marketwatch.com/investing/
|
||||
"""
|
||||
tmp_url = url["moneywatch"] + "currency/{}/downloaddatapartial".format(instrument)
|
||||
tmp_url = url["moneywatch"] + \
|
||||
"currency/{}/downloaddatapartial".format(instrument)
|
||||
ua = UserAgent(verify_ssl=False)
|
||||
request_header = {"User-Agent": ua.random}
|
||||
request_params = urlencode({
|
||||
|
|
@ -31,14 +33,15 @@ def forex(instrument = "eurusd", startdate = "2019-01-01", enddate = "2021-01-01
|
|||
"frequency": "p1d",
|
||||
"csvdownload": "true",
|
||||
"downloadpartial": "false",
|
||||
"newdates": "false"}, quote_via= quote)
|
||||
r = requests.get(tmp_url, params=request_params.replace("%2F", "/").replace("%20", " ").replace("%3A", ":"), headers=request_header)
|
||||
"newdates": "false"}, quote_via=quote)
|
||||
r = requests.get(tmp_url, params=request_params.replace(
|
||||
"%2F", "/").replace("%20", " ").replace("%3A", ":"), headers=request_header)
|
||||
data_text = r.content
|
||||
df = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
|
||||
Date = []
|
||||
for i in range(0, len(df)):
|
||||
Date.append(datetime.strptime(df["Date"][i], "%m/%d/%Y"))
|
||||
|
||||
|
||||
df["Date"] = Date
|
||||
return df
|
||||
|
||||
|
|
@ -47,30 +50,35 @@ def forex(instrument = "eurusd", startdate = "2019-01-01", enddate = "2021-01-01
|
|||
tmp_startdate = startdate
|
||||
else:
|
||||
tmp_startdate = "01/01/" + str(i) + " 00:00:00"
|
||||
if (i+1) == int(enddate[6:10]):
|
||||
if (i + 1) == int(enddate[6:10]):
|
||||
tmp_enddate = enddate
|
||||
else:
|
||||
tmp_enddate = "01/01/" + str(i+1) + " 00:00:00"
|
||||
|
||||
tmp_df = _FX(instrument=instrument, startdate = tmp_startdate, enddate = tmp_enddate)
|
||||
else:
|
||||
tmp_enddate = "01/01/" + str(i + 1) + " 00:00:00"
|
||||
|
||||
tmp_df = _FX(
|
||||
instrument=instrument,
|
||||
startdate=tmp_startdate,
|
||||
enddate=tmp_enddate)
|
||||
if i == int(startdate[6:10]):
|
||||
df = tmp_df
|
||||
else:
|
||||
df = pd.concat([tmp_df, df], axis=0)
|
||||
|
||||
df = df.reset_index(drop = True)
|
||||
|
||||
df = df.reset_index(drop=True)
|
||||
return df
|
||||
|
||||
def index(instrument = "vix", startdate = "2019-01-01", enddate = "2021-01-01"):
|
||||
|
||||
def index(instrument="vix", startdate="2019-01-01", enddate="2021-01-01"):
|
||||
startdate = datetime.strptime(startdate, "%Y-%m-%d").strftime("%m/%d/%y")
|
||||
enddate = datetime.strptime(enddate, "%Y-%m-%d").strftime("%m/%d/%y")
|
||||
df = pd.DataFrame()
|
||||
|
||||
def _index(instrument = "vix", startdate = "01/01/2020", enddate = "01/01/2021"):
|
||||
def _index(instrument="vix", startdate="01/01/2020", enddate="01/01/2021"):
|
||||
"""
|
||||
https://www.marketwatch.com/investing/
|
||||
"""
|
||||
tmp_url = url["moneywatch"] + "index/{}/downloaddatapartial".format(instrument)
|
||||
tmp_url = url["moneywatch"] + \
|
||||
"index/{}/downloaddatapartial".format(instrument)
|
||||
ua = UserAgent(verify_ssl=False)
|
||||
request_header = {"User-Agent": ua.random}
|
||||
request_params = urlencode({
|
||||
|
|
@ -80,14 +88,15 @@ def index(instrument = "vix", startdate = "2019-01-01", enddate = "2021-01-01"):
|
|||
"frequency": "p1d",
|
||||
"csvdownload": "true",
|
||||
"downloadpartial": "false",
|
||||
"newdates": "false"}, quote_via= quote)
|
||||
r = requests.get(tmp_url, params=request_params.replace("%2F", "/").replace("%20", " ").replace("%3A", ":"), headers=request_header)
|
||||
"newdates": "false"}, quote_via=quote)
|
||||
r = requests.get(tmp_url, params=request_params.replace(
|
||||
"%2F", "/").replace("%20", " ").replace("%3A", ":"), headers=request_header)
|
||||
data_text = r.content
|
||||
df = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
|
||||
Date = []
|
||||
for i in range(0, len(df)):
|
||||
Date.append(datetime.strptime(df["Date"][i], "%m/%d/%Y"))
|
||||
|
||||
|
||||
df["Date"] = Date
|
||||
return df
|
||||
|
||||
|
|
@ -96,30 +105,38 @@ def index(instrument = "vix", startdate = "2019-01-01", enddate = "2021-01-01"):
|
|||
tmp_startdate = startdate
|
||||
else:
|
||||
tmp_startdate = "01/01/" + str(i) + " 00:00:00"
|
||||
if (i+1) == int(enddate[6:10]):
|
||||
if (i + 1) == int(enddate[6:10]):
|
||||
tmp_enddate = enddate
|
||||
else:
|
||||
tmp_enddate = "01/01/" + str(i+1) + " 00:00:00"
|
||||
|
||||
tmp_df = _index(instrument=instrument, startdate = tmp_startdate, enddate = tmp_enddate)
|
||||
else:
|
||||
tmp_enddate = "01/01/" + str(i + 1) + " 00:00:00"
|
||||
|
||||
tmp_df = _index(
|
||||
instrument=instrument,
|
||||
startdate=tmp_startdate,
|
||||
enddate=tmp_enddate)
|
||||
if i == int(startdate[6:10]):
|
||||
df = tmp_df
|
||||
else:
|
||||
df = pd.concat([tmp_df, df], axis=0)
|
||||
|
||||
df = df.reset_index(drop = True)
|
||||
|
||||
df = df.reset_index(drop=True)
|
||||
return df
|
||||
|
||||
def crypto(instrument = "btcusd", startdate = "2019-01-01", enddate = "2021-01-01"):
|
||||
|
||||
def crypto(instrument="btcusd", startdate="2019-01-01", enddate="2021-01-01"):
|
||||
startdate = datetime.strptime(startdate, "%Y-%m-%d").strftime("%m/%d/%y")
|
||||
enddate = datetime.strptime(enddate, "%Y-%m-%d").strftime("%m/%d/%y")
|
||||
df = pd.DataFrame()
|
||||
|
||||
def _crypto(instrument = "btcusd", startdate = "01/01/2020", enddate = "01/01/2021"):
|
||||
def _crypto(
|
||||
instrument="btcusd",
|
||||
startdate="01/01/2020",
|
||||
enddate="01/01/2021"):
|
||||
"""
|
||||
https://www.marketwatch.com/investing/
|
||||
"""
|
||||
tmp_url = url["moneywatch"] + "cryptocurrency/{}/downloaddatapartial".format(instrument)
|
||||
tmp_url = url["moneywatch"] + \
|
||||
"cryptocurrency/{}/downloaddatapartial".format(instrument)
|
||||
ua = UserAgent(verify_ssl=False)
|
||||
request_header = {"User-Agent": ua.random}
|
||||
request_params = urlencode({
|
||||
|
|
@ -129,14 +146,15 @@ def crypto(instrument = "btcusd", startdate = "2019-01-01", enddate = "2021-01-0
|
|||
"frequency": "p1d",
|
||||
"csvdownload": "true",
|
||||
"downloadpartial": "false",
|
||||
"newdates": "false"}, quote_via= quote)
|
||||
r = requests.get(tmp_url, params=request_params.replace("%2F", "/").replace("%20", " ").replace("%3A", ":"), headers=request_header)
|
||||
"newdates": "false"}, quote_via=quote)
|
||||
r = requests.get(tmp_url, params=request_params.replace(
|
||||
"%2F", "/").replace("%20", " ").replace("%3A", ":"), headers=request_header)
|
||||
data_text = r.content
|
||||
df = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
|
||||
Date = []
|
||||
for i in range(0, len(df)):
|
||||
Date.append(datetime.strptime(df["Date"][i], "%m/%d/%Y"))
|
||||
|
||||
|
||||
df["Date"] = Date
|
||||
return df
|
||||
|
||||
|
|
@ -145,30 +163,43 @@ def crypto(instrument = "btcusd", startdate = "2019-01-01", enddate = "2021-01-0
|
|||
tmp_startdate = startdate
|
||||
else:
|
||||
tmp_startdate = "01/01/" + str(i) + " 00:00:00"
|
||||
if (i+1) == int(enddate[6:10]):
|
||||
if (i + 1) == int(enddate[6:10]):
|
||||
tmp_enddate = enddate
|
||||
else:
|
||||
tmp_enddate = "01/01/" + str(i+1) + " 00:00:00"
|
||||
|
||||
tmp_df = _crypto(instrument=instrument, startdate = tmp_startdate, enddate = tmp_enddate)
|
||||
else:
|
||||
tmp_enddate = "01/01/" + str(i + 1) + " 00:00:00"
|
||||
|
||||
tmp_df = _crypto(
|
||||
instrument=instrument,
|
||||
startdate=tmp_startdate,
|
||||
enddate=tmp_enddate)
|
||||
if i == int(startdate[6:10]):
|
||||
df = tmp_df
|
||||
else:
|
||||
df = pd.concat([tmp_df, df], axis=0)
|
||||
|
||||
df = df.reset_index(drop = True)
|
||||
|
||||
df = df.reset_index(drop=True)
|
||||
return df
|
||||
|
||||
def stock(countrycode = "cn", instrument = "601988", startdate = "2019-01-01", enddate = "2021-01-01"):
|
||||
|
||||
def stock(
|
||||
countrycode="cn",
|
||||
instrument="601988",
|
||||
startdate="2019-01-01",
|
||||
enddate="2021-01-01"):
|
||||
startdate = datetime.strptime(startdate, "%Y-%m-%d").strftime("%m/%d/%y")
|
||||
enddate = datetime.strptime(enddate, "%Y-%m-%d").strftime("%m/%d/%y")
|
||||
df = pd.DataFrame()
|
||||
|
||||
def _stock(countrycode = "cn", instrument = "601988", startdate = "01/01/2020", enddate = "01/01/2021"):
|
||||
def _stock(
|
||||
countrycode="cn",
|
||||
instrument="601988",
|
||||
startdate="01/01/2020",
|
||||
enddate="01/01/2021"):
|
||||
"""
|
||||
https://www.marketwatch.com/investing/
|
||||
"""
|
||||
tmp_url = url["moneywatch"] + "stock/{}/downloaddatapartial".format(instrument)
|
||||
tmp_url = url["moneywatch"] + \
|
||||
"stock/{}/downloaddatapartial".format(instrument)
|
||||
ua = UserAgent(verify_ssl=False)
|
||||
request_header = {"User-Agent": ua.random}
|
||||
request_params = urlencode({
|
||||
|
|
@ -179,14 +210,15 @@ def stock(countrycode = "cn", instrument = "601988", startdate = "2019-01-01", e
|
|||
"csvdownload": "true",
|
||||
"downloadpartial": "false",
|
||||
"newdates": "false",
|
||||
"countrycode": "{}".format(countrycode)}, quote_via= quote)
|
||||
r = requests.get(tmp_url, params=request_params.replace("%2F", "/").replace("%20", " ").replace("%3A", ":"), headers=request_header)
|
||||
"countrycode": "{}".format(countrycode)}, quote_via=quote)
|
||||
r = requests.get(tmp_url, params=request_params.replace(
|
||||
"%2F", "/").replace("%20", " ").replace("%3A", ":"), headers=request_header)
|
||||
data_text = r.content
|
||||
df = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
|
||||
Date = []
|
||||
for i in range(0, len(df)):
|
||||
Date.append(datetime.strptime(df["Date"][i], "%m/%d/%Y"))
|
||||
|
||||
|
||||
df["Date"] = Date
|
||||
return df
|
||||
|
||||
|
|
@ -195,24 +227,26 @@ def stock(countrycode = "cn", instrument = "601988", startdate = "2019-01-01", e
|
|||
tmp_startdate = startdate
|
||||
else:
|
||||
tmp_startdate = "01/01/" + str(i) + " 00:00:00"
|
||||
if (i+1) == int(enddate[6:10]):
|
||||
if (i + 1) == int(enddate[6:10]):
|
||||
tmp_enddate = enddate
|
||||
else:
|
||||
tmp_enddate = "01/01/" + str(i+1) + " 00:00:00"
|
||||
|
||||
else:
|
||||
tmp_enddate = "01/01/" + str(i + 1) + " 00:00:00"
|
||||
|
||||
if countrycode == "us":
|
||||
countrycode = ""
|
||||
tmp_df = _stock(countrycode = countrycode, instrument=instrument, startdate = tmp_startdate, enddate = tmp_enddate)
|
||||
tmp_df = _stock(
|
||||
countrycode=countrycode,
|
||||
instrument=instrument,
|
||||
startdate=tmp_startdate,
|
||||
enddate=tmp_enddate)
|
||||
if i == int(startdate[6:10]):
|
||||
df = tmp_df
|
||||
else:
|
||||
df = pd.concat([tmp_df, df], axis=0)
|
||||
|
||||
df = df.reset_index(drop = True)
|
||||
|
||||
df = df.reset_index(drop=True)
|
||||
return df
|
||||
|
||||
#https://www.marketwatch.com/investing/stock/googl/downloaddatapartial?startdate=05/05/2021 00:00:00&enddate=06/04/2021 23:59:59&daterange=d30&frequency=p1d&csvdownload=true&downloadpartial=false&newdates=false
|
||||
|
||||
if __name__ == "__main__":
|
||||
data = FX()
|
||||
|
||||
|
|
|
|||
Loading…
Reference in New Issue