2070 lines
170 KiB
Python
2070 lines
170 KiB
Python
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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"OECD": "https://stats.oecd.org/sdmx-json/data/DP_LIVE/"
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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 Leading_Indicators_OECD(startdate = "1950-01", enddate = "2021-05"):
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# CLI
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tmp_url = url["OECD"] + "EA19.CLI.AMPLITUD.LTRENDIDX.M/OECD"
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ua = UserAgent(verify_ssl=False)
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request_params = {
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"contentType": "csv",
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"detail": "code",
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"separator": "comma",
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"csv-lang": "en",
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"startPeriod": "{}".format(startdate),
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"endPeriod": "{}".format(enddate)
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}
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request_header = {"User-Agent": ua.random}
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r = requests.get(tmp_url, params = request_params, headers=request_header)
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data_text = r.content
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df_cli = pd.read_csv(io.StringIO(data_text.decode('utf-8')))[["TIME", "Value"]]
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df_cli.columns = ["Date", "EU_OECD_CLI"]
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df_cli["Date"] = pd.to_datetime(df_cli["Date"], format = "%Y-%m")
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df_cli["EU_OECD_CLI"] = df_cli["EU_OECD_CLI"].astype(float)
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#BCI
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tmp_url = url["OECD"] + "EA19.BCI.AMPLITUD.LTRENDIDX.M/OECD"
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ua = UserAgent(verify_ssl=False)
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request_params = {
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"contentType": "csv",
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"detail": "code",
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"separator": "comma",
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"csv-lang": "en",
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"startPeriod": "{}".format(startdate),
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"endPeriod": "{}".format(enddate)
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}
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request_header = {"User-Agent": ua.random}
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r = requests.get(tmp_url, params = request_params, headers=request_header)
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data_text = r.content
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df_bci = pd.read_csv(io.StringIO(data_text.decode('utf-8')))[["TIME", "Value"]]
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df_bci.columns = ["Date", "EU_OECD_BCI"]
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df_bci["Date"] = pd.to_datetime(df_bci["Date"], format = "%Y-%m")
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df_bci["EU_OECD_BCI"] = df_bci["EU_OECD_BCI"].astype(float)
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# CCI
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tmp_url = url["OECD"] + "EA19.CCI.AMPLITUD.LTRENDIDX.M/OECD"
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ua = UserAgent(verify_ssl=False)
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request_params = {
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"contentType": "csv",
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"detail": "code",
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"separator": "comma",
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"csv-lang": "en",
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"startPeriod": "{}".format(startdate),
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"endPeriod": "{}".format(enddate)
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}
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request_header = {"User-Agent": ua.random}
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r = requests.get(tmp_url, params = request_params, headers=request_header)
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data_text = r.content
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df_cci = pd.read_csv(io.StringIO(data_text.decode('utf-8')))[["TIME", "Value"]]
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df_cci.columns = ["Date", "EU_OECD_CCI"]
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df_cci["Date"] = pd.to_datetime(df_cci["Date"], format = "%Y-%m")
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df_cci["EU_OECD_CCI"] = df_cci["EU_OECD_CCI"].astype(float)
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df = pd.merge_asof(df_cli, df_bci, on = "Date")
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df = pd.merge_asof(df, df_cci, on = "Date")
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return df
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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)
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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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'CCEUSP02EZQ655N': "National Currency to Euro Spot Exchange Rate for the Euro Area",
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'CCUSMA02EZQ618N': "National Currency to US Dollar Exchange Rate: Average of Daily Rates for the Euro Area",
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'CCUSSP01EZQ650N': "US Dollar to National Currency Spot Exchange Rate for the Euro Area",
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'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_Compensation_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_Compensation_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 Consumer_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
|
|
|
|
def EU_EPU_Monthly():
|
|
df = pd.read_excel("https://www.policyuncertainty.com/media/Europe_Policy_Uncertainty_Data.xlsx")[:-1]
|
|
df['Date']=pd.to_datetime(df['Year'].apply(str).str.cat(df['Month'].apply(int).apply(str),sep='-'), format='%Y-%m')
|
|
df = df[["Date", "European_News_Index", "Germany_News_Index", "Italy_News_Index", "UK_News_Index", "France_News_Index"]]
|
|
return df
|
|
|
|
class ecb_data(object):
|
|
def __init__(self, url=url["ecb"]):
|
|
self.url = url
|
|
|
|
def codebook(self):
|
|
return "please follow the ECB's codebook: https://sdw.ecb.europa.eu/browse.do?node=9691101"
|
|
|
|
def get_data(self,
|
|
datacode="ICP",
|
|
key="M.U2.N.000000.4.ANR",
|
|
startdate="2000-01-01",
|
|
enddate="2020-01-01"):
|
|
"""
|
|
"""
|
|
tmp_url = self.url + "{}/".format(datacode) + "{}".format(key)
|
|
ua = UserAgent(verify_ssl=False)
|
|
request_header = {"User-Agent": ua.random, 'Accept': 'text/csv'}
|
|
request_params = {
|
|
"startPeriod": "{}".format(startdate),
|
|
"endPeriod": "{}".format(enddate)
|
|
}
|
|
r = requests.get(
|
|
tmp_url,
|
|
params=request_params,
|
|
headers=request_header)
|
|
data_text = r.content
|
|
df = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
|
|
return df
|
|
|
|
|
|
class eurostat_data(object):
|
|
def __init__(self, url=url["eurostat"]):
|
|
self.url = url
|
|
|
|
def codebook(self):
|
|
return "please follow the EuroStat's codebook: \nhttps://ec.europa.eu/eurostat/estat-navtree-portlet-prod/BulkDownloadListing?sort=1&dir=dic"
|
|
|
|
def get_data(self,
|
|
datasetcode="nama_10_gdp",
|
|
precision="1",
|
|
unit="CP_MEUR",
|
|
na_item="B1GQ",
|
|
time="2020"):
|
|
"""
|
|
"""
|
|
tmp_url = self.url + "{}".format(datasetcode)
|
|
ua = UserAgent(verify_ssl=False)
|
|
request_header = {"User-Agent": ua.random, 'Accept': 'text/csv'}
|
|
request_params = {
|
|
"precision": "{}".format(precision),
|
|
"unit": "{}".format(unit),
|
|
"na_item": "{}".format(na_item),
|
|
"time": "{}".format(time)
|
|
}
|
|
r = requests.get(
|
|
tmp_url,
|
|
params=request_params,
|
|
headers=request_header)
|
|
data_text = r.text
|
|
data_json = demjson.decode(data_text)
|
|
value = data_json['value']
|
|
abb = data_json['dimension']['geo']['category']['index']
|
|
abb = {abb[k]: k for k in abb}
|
|
geo = data_json['dimension']['geo']['category']['label']
|
|
geo_list = [abb[int(k)] for k in list(value.keys())]
|
|
geo = [geo[k] for k in geo_list]
|
|
df = pd.DataFrame(
|
|
{"Geo": geo, "{}".format(na_item): list(value.values())})
|
|
return df
|
|
|
|
def QtoM(data:pd.Series):
|
|
date = pd.PeriodIndex(data.str.replace(r'(Q\d)_(\d+)', r'20\2-\1'), freq='Q').strftime('%Y-%m-%d')
|
|
return date
|
|
|
|
# EU - Main Economic Indicator
|
|
ecb = ecb_data()
|
|
eurostat = eurostat_data()
|
|
eu_columns_list = {
|
|
"Gross Domestic Product", "Private Finance Consumption", "Government final consumption",
|
|
"Gross fixed capital formation", "Changes in inventories and acquisition less disposals of valuables",
|
|
"Exports of goods and services", "Imports of goods and services"
|
|
}
|
|
|
|
# https://www.ecb.europa.eu/stats/ecb_statistics/key_euro_area_indicators/html/index.en.html
|
|
|
|
startdate, enddate = "2010-01-01", "2021-01-01"
|
|
daterange = pd.DataFrame({"Date": pd.date_range(start=startdate, end=enddate, freq="MS")})
|
|
|
|
class real_sector():
|
|
def __init__(self, startdate=startdate, enddate=enddate, daterange=daterange):
|
|
self.startdate = startdate
|
|
self.enddate = enddate
|
|
self.daterange = daterange
|
|
|
|
class current_price_gdp_by_expenditure_category(real_sector):
|
|
## National Account (current price)
|
|
def __init__(self):
|
|
super(current_price_gdp_by_expenditure_category, self).__init__()
|
|
pass
|
|
|
|
def gdp(self):
|
|
"""
|
|
* Title: Gross domestic product at market prices
|
|
* URL: https://sdw.ecb.europa.eu/quickview.do?SERIES_KEY=MNA.Q.Y.I8.W2.S1.S1.B.B1GQ._Z._Z._Z.EUR.V.N
|
|
* Reference area: Euro area 19 (fixed composition) as of 1 January 2015 (I8)
|
|
* Frequency: Quarterly
|
|
"""
|
|
eu_gdp = ecb.get_data(datacode="MNA", key="Q.Y.I8.W2.S1.S1.B.B1GQ._Z._Z._Z.EUR.V.N", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]]
|
|
eu_gdp.columns = ["Date", "EU_GDP"]
|
|
eu_gdp["Date"] = pd.to_datetime(QtoM(eu_gdp["Date"]), format="%Y-%m-%d")
|
|
eu_gdp = pd.merge_asof(self.daterange, eu_gdp, on="Date", direction="nearest")
|
|
return eu_gdp
|
|
|
|
def pfc(self):
|
|
"""
|
|
* Title: Private final consumption
|
|
* URL: http://sdw.ecb.europa.eu/quickview.do?SERIES_KEY=MNA.Q.Y.I8.W0.S1M.S1.D.P31._Z._Z._T.EUR.V.N
|
|
* Reference area: Euro area 19 (fixed composition) as of 1 January 2015 (I8)
|
|
* Frequency: Quarterly
|
|
"""
|
|
eu_pfc = ecb.get_data(datacode="MNA", key="Q.Y.I8.W0.S1M.S1.D.P31._Z._Z._T.EUR.V.N", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]]
|
|
eu_pfc.columns = ["Date", "EU_PFC"]
|
|
eu_pfc["Date"] = pd.to_datetime(QtoM(eu_pfc["Date"]), format="%Y-%m-%d")
|
|
eu_pfc = pd.merge_asof(self.daterange, eu_pfc, on="Date", direction="nearest")
|
|
return eu_pfc
|
|
|
|
def gfc(self):
|
|
"""
|
|
* Title: Government final consumption
|
|
* URL: https://sdw.ecb.europa.eu/quickview.do?SERIES_KEY=MNA.Q.Y.I8.W0.S13.S1.D.P3._Z._Z._T.EUR.V.N
|
|
* Reference area: Euro area 19 (fixed composition) as of 1 January 2015 (I8)
|
|
* Frequency: Quarterly
|
|
"""
|
|
eu_gfc = ecb.get_data(datacode="MNA", key="Q.Y.I8.W0.S13.S1.D.P3._Z._Z._T.EUR.V.N", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]]
|
|
eu_gfc.columns = ["Date", "EU_GFC"]
|
|
eu_gfc["Date"] = pd.to_datetime(QtoM(eu_gfc["Date"]), format="%Y-%m-%d")
|
|
eu_gfc = pd.merge_asof(daterange, self.eu_gfc, on="Date", direction="nearest")
|
|
return eu_gfc
|
|
|
|
def gfcf(self):
|
|
"""
|
|
* Title: Gross fixed capital formation
|
|
* URL: https://sdw.ecb.europa.eu/quickview.do?SERIES_KEY=MNA.Q.Y.I8.W0.S1.S1.D.P51G.N11G._T._Z.EUR.V.N
|
|
* Reference area: Euro area 19 (fixed composition) as of 1 January 2015 (I8)
|
|
* Frequency: Quarterly
|
|
"""
|
|
eu_gfcf = ecb.get_data(datacode="MNA", key="Q.Y.I8.W0.S1.S1.D.P51G.N11G._T._Z.EUR.V.N", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]]
|
|
eu_gfcf.columns = ["Date", "EU_GFCF"]
|
|
eu_gfcf["Date"] = pd.to_datetime(QtoM(eu_gfcf["Date"]), format="%Y-%m-%d")
|
|
eu_gfcf = pd.merge_asof(self.daterange, eu_gfcf, on="Date", direction="nearest")
|
|
return eu_gfcf
|
|
|
|
def cia(self):
|
|
"""
|
|
* Title: Changes in inventories and acquisition less disposals of valuables
|
|
* URL: https://sdw.ecb.europa.eu/quickview.do?SERIES_KEY=MNA.Q.Y.I8.W0.S1.S1.D.P5M.N1MG._T._Z.EUR.V.N
|
|
* Reference area: Euro area 19 (fixed composition) as of 1 January 2015 (I8)
|
|
* Frequency: Quarterly
|
|
"""
|
|
eu_cia = ecb.get_data(datacode="MNA", key="Q.Y.I8.W0.S1.S1.D.P5M.N1MG._T._Z.EUR.V.N", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]]
|
|
eu_cia.columns = ["Date", "EU_CIA"]
|
|
eu_cia["Date"] = pd.to_datetime(QtoM(eu_cia["Date"]), format="%Y-%m-%d")
|
|
eu_cia = pd.merge_asof(self.daterange, eu_cia, on="Date", direction="nearest")
|
|
return eu_cia
|
|
|
|
def export(self):
|
|
"""
|
|
* Title: Exports of goods and services
|
|
* URL: https://sdw.ecb.europa.eu/quickview.do?SERIES_KEY=Q.Y.I8.W1.S1.S1.D.P6._Z._Z._Z.EUR.V.NN
|
|
* Reference area: Euro area 19 (fixed composition) as of 1 January 2015 (I8)
|
|
* Frequency: Quarterly
|
|
"""
|
|
eu_export = ecb.get_data(datacode="MNA", key="Q.Y.I8.W1.S1.S1.D.P6._Z._Z._Z.EUR.V.N", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]]
|
|
eu_export.columns = ["Date", "EU_EXPORT"]
|
|
eu_export["Date"] = pd.to_datetime(QtoM(eu_export["Date"]), format="%Y-%m-%d")
|
|
eu_export = pd.merge_asof(self.daterange, eu_export, on="Date", direction="nearest")
|
|
|
|
def import_(self):
|
|
"""
|
|
* Title: Imports of goods and services
|
|
* URL: https://sdw.ecb.europa.eu/quickview.do?SERIES_KEY=Q.Y.I8.W1.S1.S1.C.P7._Z._Z._Z.EUR.V.N
|
|
* Reference area: Euro area 19 (fixed composition) as of 1 January 2015 (I8)
|
|
* Frequency: Quarterly
|
|
"""
|
|
eu_import = ecb.get_data(datacode="MNA", key="Q.Y.I8.W1.S1.S1.C.P7._Z._Z._Z.EUR.V.N", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]]
|
|
eu_import.columns = ["Date", "EU_IMPORT"]
|
|
eu_import["Date"] = pd.to_datetime(QtoM(eu_import["Date"]), format="%Y-%m-%d")
|
|
eu_import = pd.merge_asof(self.daterange, eu_import, on="Date", direction="nearest")
|
|
return eu_import
|
|
|
|
class volume_gdp_by_expenditure_category_in_previous_year_price(real_sector):
|
|
## National Account (volume in previous year price)
|
|
## National Account (current price)
|
|
def __init__(self):
|
|
super(volume_gdp_by_expenditure_category_in_previous_year_price, self).__init__()
|
|
pass
|
|
|
|
def gdp(self):
|
|
"""
|
|
* Title: Gross domestic product at market prices
|
|
* URL: https://sdw.ecb.europa.eu/quickview.do?SERIES_KEY=MNA.Q.Y.I8.W2.S1.S1.B.B1GQ._Z._Z._Z.IX.LR.N
|
|
* Reference area: Euro area 19 (fixed composition) as of 1 January 2015 (I8)
|
|
* Frequency: Quarterly
|
|
"""
|
|
eu_gdp = ecb.get_data(datacode="MNA", key="Q.Y.I8.W2.S1.S1.B.B1GQ._Z._Z._Z.IX.LR.N", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]]
|
|
eu_gdp.columns = ["Date", "EU_GDP"]
|
|
eu_gdp["Date"] = pd.to_datetime(QtoM(eu_gdp["Date"]), format="%Y-%m") + pd.tseries.offsets.MonthBegin(-1)
|
|
eu_gdp = pd.merge_asof(self.daterange, eu_gdp, on="Date", direction="nearest")
|
|
return eu_gdp
|
|
|
|
def pfc(self):
|
|
"""
|
|
* Title: Private final consumption
|
|
* URL: http://sdw.ecb.europa.eu/quickview.do?SERIES_KEY=MNA.Q.Y.I8.W0.S1M.S1.D.P31._Z._Z._T.IX.LR.N
|
|
* Reference area: Euro area 19 (fixed composition) as of 1 January 2015 (I8)
|
|
* Frequency: Quarterly
|
|
"""
|
|
eu_pfc = ecb.get_data(datacode="MNA", key="Q.Y.I8.W0.S1M.S1.D.P31._Z._Z._T.IX.LR.N", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]]
|
|
eu_pfc.columns = ["Date", "EU_PFC"]
|
|
eu_pfc["Date"] = pd.to_datetime(QtoM(eu_pfc["Date"]), format="%Y-%m") + pd.tseries.offsets.MonthBegin(-1)
|
|
eu_pfc = pd.merge_asof(self.daterange, eu_pfc, on="Date", direction="nearest")
|
|
return eu_pfc
|
|
|
|
def gfc(self):
|
|
"""
|
|
* Title: Government final consumption
|
|
* URL: https://sdw.ecb.europa.eu/quickview.do?SERIES_KEY=MNA.Q.Y.I8.W0.S13.S1.D.P3._Z._Z._T.IX.LR.N
|
|
* Reference area: Euro area 19 (fixed composition) as of 1 January 2015 (I8)
|
|
* Frequency: Quarterly
|
|
"""
|
|
eu_gfc = ecb.get_data(datacode="MNA", key="Q.Y.I8.W0.S13.S1.D.P3._Z._Z._T.IX.LR.N", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]]
|
|
eu_gfc.columns = ["Date", "EU_GFC"]
|
|
eu_gfc["Date"] = pd.to_datetime(QtoM(eu_gfc["Date"]), format="%Y-%m") + pd.tseries.offsets.MonthBegin(-1)
|
|
eu_gfc = pd.merge_asof(self.daterange, eu_gfc, on="Date", direction="nearest")
|
|
return eu_gfc
|
|
|
|
def gfcf(self):
|
|
"""
|
|
* Title: Gross fixed capital formation
|
|
* URL: https://sdw.ecb.europa.eu/quickview.do?SERIES_KEY=MNA.Q.Y.I8.W0.S1.S1.D.P51G.N11G._T._Z.IX.LR.N
|
|
* Reference area: Euro area 19 (fixed composition) as of 1 January 2015 (I8)
|
|
* Frequency: Quarterly
|
|
"""
|
|
eu_gfcf = ecb.get_data(datacode="MNA", key="Q.Y.I8.W0.S1.S1.D.P51G.N11G._T._Z.IX.LR.N", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]]
|
|
eu_gfcf.columns = ["Date", "EU_GFCF"]
|
|
eu_gfcf["Date"] = pd.to_datetime(QtoM(eu_gfcf["Date"]), format="%Y-%m") + pd.tseries.offsets.MonthBegin(-1)
|
|
eu_gfcf = pd.merge_asof(self.daterange, eu_gfcf, on="Date", direction="nearest")
|
|
return eu_gfcf
|
|
|
|
def export(self):
|
|
"""
|
|
* Title: Exports of goods and services
|
|
* URL: https://sdw.ecb.europa.eu/quickview.do?SERIES_KEY=Q.Y.I8.W1.S1.S1.D.P6._Z._Z._Z.IX.LR.N
|
|
* Reference area: Euro area 19 (fixed composition) as of 1 January 2015 (I8)
|
|
* Frequency: Quarterly
|
|
"""
|
|
eu_export = ecb.get_data(datacode="MNA", key="Q.Y.I8.W1.S1.S1.D.P6._Z._Z._Z.IX.LR.N", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]]
|
|
eu_export.columns = ["Date", "EU_EXPORT"]
|
|
eu_export["Date"] = pd.to_datetime(QtoM(eu_export["Date"]), format="%Y-%m") + pd.tseries.offsets.MonthBegin(-1)
|
|
eu_export = pd.merge_asof(self.daterange, eu_export, on="Date", direction="nearest")
|
|
return eu_gfcf
|
|
|
|
def import_(self):
|
|
"""
|
|
* Title: Imports of goods and services
|
|
* URL: https://sdw.ecb.europa.eu/quickview.do?SERIES_KEY=Q.Y.I8.W1.S1.S1.C.P7._Z._Z._Z.IX.LR.N
|
|
* Reference area: Euro area 19 (fixed composition) as of 1 January 2015 (I8)
|
|
* Frequency: Quarterly
|
|
"""
|
|
eu_import = ecb.get_data(datacode="MNA", key="Q.Y.I8.W1.S1.S1.C.P7._Z._Z._Z.IX.LR.N", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]]
|
|
eu_import.columns = ["Date", "EU_IMPORT"]
|
|
eu_import["Date"] = pd.to_datetime(QtoM(eu_import["Date"]), format="%Y-%m") + pd.tseries.offsets.MonthBegin(-1)
|
|
eu_import = pd.merge_asof(self.daterange, eu_import, on="Date", direction="nearest")
|
|
return eu_import
|
|
|
|
def industrial_production(self):
|
|
"""
|
|
* Title: Industrial production for the euro area
|
|
* URL: https://sdw.ecb.europa.eu/quickview.do?SERIES_KEY=132.STS.M.I8.Y.PROD.NS0020.4.000
|
|
* Reference area: Euro area 19 (fixed composition) as of 1 January 2015 (I8)
|
|
* Frequency: Quarterly
|
|
"""
|
|
eu_industrial_production = ecb.get_data(datacode="STS", key="M.I8.Y.PROD.NS0020.4.000", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]]
|
|
eu_industrial_production.columns = ["Date", "EU_INDUSTRIAL_PRODUCTION"]
|
|
eu_industrial_production["Date"] = pd.to_datetime(QtoM(eu_industrial_production["Date"]), format="%Y-%m") + pd.tseries.offsets.MonthBegin(-1)
|
|
eu_industrial_production = pd.merge_asof(self.daterange, eu_industrial_production, on="Date", direction="nearest")
|
|
return eu_industrial_production
|
|
|
|
def employment(self):
|
|
"""
|
|
* Title: Employment (in thousands of persons)
|
|
* URL: https://sdw.ecb.europa.eu/quickview.do?SERIES_KEY=ENA.Q.Y.I8.W2.S1.S1._Z.EMP._Z._T._Z.PS._Z.N
|
|
* Reference area: Euro area 19 (fixed composition) as of 1 January 2015 (I8)
|
|
* Frequency: Quarterly
|
|
"""
|
|
eu_employment = ecb.get_data(datacode="ENA", key="Q.Y.I8.W2.S1.S1._Z.EMP._Z._T._Z.PS._Z.N", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]]
|
|
eu_employment.columns = ["Date", "EU_EMPLOYMENT"]
|
|
eu_employment["Date"] = pd.to_datetime(QtoM(eu_employment["Date"]), format="%Y-%m") + pd.tseries.offsets.MonthBegin(-1)
|
|
eu_employment = pd.merge_asof(self.daterange, eu_employment, on="Date", direction="nearest")
|
|
return eu_employment
|
|
|
|
def unemployment(self):
|
|
"""
|
|
* Title: Unemployment (in thousands of persons)
|
|
* URL: https://sdw.ecb.europa.eu/quickview.do?SERIES_KEY=LFSI.M.I8.S.UNEMPL.TOTAL0.15_74.T
|
|
* Reference area: Euro area 19 (fixed composition) as of 1 January 2015 (I8)
|
|
* Frequency: Monthly
|
|
"""
|
|
eu_unemployment = ecb.get_data(datacode="LFSI", key="M.I8.S.UNEMPL.TOTAL0.15_74.T", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]]
|
|
eu_unemployment.columns = ["Date", "EU_UNEMPLOYMENT"]
|
|
eu_unemployment["Date"] = pd.to_datetime(eu_unemployment["Date"], format="%Y-%m") + pd.tseries.offsets.MonthBegin(-1)
|
|
return eu_unemployment
|
|
|
|
def unemployment_rate(self):
|
|
"""
|
|
* Title: Unemployment Rate
|
|
* URL: https://sdw.ecb.europa.eu/quickview.do?SERIES_KEY=LFSI.M.I8.S.UNEHRT.TOTAL0.15_74.T
|
|
* Reference area: Euro area 19 (fixed composition) as of 1 January 2015 (I8)
|
|
* Frequency: Monthly
|
|
"""
|
|
eu_unemployment_rate = ecb.get_data(datacode="LFSI", key="M.I8.S.UNEHRT.TOTAL0.15_74.T", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]]
|
|
eu_unemployment_rate.columns = ["Date", "EU_UNEMPLOYMENT_RATE"]
|
|
eu_unemployment_rate["Date"] = pd.to_datetime(eu_unemployment_rate["Date"], format="%Y-%m-%d")
|
|
return eu_unemployment_rate
|
|
|
|
def labour_cost_index(self):
|
|
"""
|
|
* Title: Labour cost index
|
|
* URL: https://sdw.ecb.europa.eu/quickview.do?SERIES_KEY=LCI.Q.I8.Y.LCI_T.BTN
|
|
* Reference area: Euro area 19 (fixed composition) as of 1 January 2015 (I8)
|
|
* Frequency: Quarterly
|
|
"""
|
|
eu_labour_cost_index = ecb.get_data(datacode="LCI", key="Q.I8.Y.LCI_T.BTN", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]]
|
|
eu_labour_cost_index.columns = ["Date", "EU_LABOUR_COST_INDEX"]
|
|
eu_labour_cost_index["Date"] = pd.to_datetime(QtoM(eu_labour_cost_index["Date"]), format="%Y-%m") + pd.tseries.offsets.MonthBegin(-1)
|
|
eu_labour_cost_index = pd.merge_asof(self.daterange, eu_labour_cost_index, on="Date", direction="nearest")
|
|
return eu_labour_cost_index
|
|
|
|
def hicp(self):
|
|
"""
|
|
* Title: HICP - Overall index
|
|
* URL: https://sdw.ecb.europa.eu/quickview.do?SERIES_KEY=ICP.M.U2.N.000000.4.INX
|
|
* Reference area: Euro area 19 (fixed composition) as of 1 January 2015 (I8)
|
|
* Frequency: Monthly
|
|
"""
|
|
eu_hicp = ecb.get_data(datacode="ICP", key="M.U2.N.000000.4.INX", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]]
|
|
eu_hicp.columns = ["Date", "EU_HICP"]
|
|
eu_hicp["Date"] = pd.to_datetime(eu_hicp["Date"], format="%Y-%m-%d")
|
|
return eu_hicp
|
|
|
|
def ppi(self):
|
|
"""
|
|
* Title:Industrial producer prices (excl. construction) for the euro area [PPI]
|
|
* URL: https://sdw.ecb.europa.eu/quickview.do?SERIES_KEY=STS.M.I8.N.PRIN.NS0020.4.000
|
|
* Reference area: Euro area 19 (fixed composition) as of 1 January 2015 (I8)
|
|
* Frequency: Monthly
|
|
"""
|
|
eu_ppi = ecb.get_data(datacode="STS", key="M.I8.N.PRIN.NS0020.4.000", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]]
|
|
eu_ppi.columns = ["Date", "EU_PPI"]
|
|
eu_ppi["Date"] = pd.to_datetime(eu_ppi["Date"], format="%Y-%m-%d")
|
|
return eu_ppi
|
|
|
|
class fiscal_sector():
|
|
def __init__(self, startdate=startdate, enddate=enddate, daterange=daterange):
|
|
self.startdate = startdate
|
|
self.enddate = enddate
|
|
self.daterange = daterange
|
|
|
|
class general_government_operation(fiscal_sector):
|
|
## National Account (current price)
|
|
def __init__(self):
|
|
super(general_government_operation, self).__init__()
|
|
pass
|
|
|
|
def revenue(self):
|
|
"""
|
|
* Title: Government total revenue (as % of GDP)
|
|
* URL: https://sdw.ecb.europa.eu/quickview.do?SERIES_KEY=325.GFS.Q.N.I8.W0.S13.S1.P.C.OTR._Z._Z._Z.XDC_R_B1GQ_CY._Z.S.V.CY._T
|
|
* Reference area: Euro area 19 (fixed composition) as of 1 January 2015 (I8)
|
|
* Frequency: Quarterly
|
|
"""
|
|
eu_gtr = ecb.get_data(datacode="GFS", key="Q.N.I8.W0.S13.S1.P.C.OTR._Z._Z._Z.XDC_R_B1GQ_CY._Z.S.V.CY._T", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]]
|
|
eu_gtr.columns = ["Date", "EU_GOVERNMENT_TOTAL_REVENUE"]
|
|
eu_gtr["Date"] = pd.to_datetime(QtoM(eu_gtr["Date"]), format="%Y-%m") + pd.tseries.offsets.MonthBegin(-1)
|
|
eu_gtr = pd.merge_asof(self.daterange, eu_gtr, on="Date", direction="nearest")
|
|
return eu_gtr
|
|
|
|
def expenditure(self):
|
|
"""
|
|
* Title: Government total expenditure (as % of GDP)
|
|
* URL: https://sdw.ecb.europa.eu/quickview.do?SERIES_KEY=325.GFS.Q.N.I8.W0.S13.S1.P.D.OTE._Z._Z._T.XDC_R_B1GQ_CY._Z.S.V.CY._T
|
|
* Reference area: Euro area 19 (fixed composition) as of 1 January 2015 (I8)
|
|
* Frequency: Quarterly
|
|
"""
|
|
eu_gte = ecb.get_data(datacode="GFS", key="Q.N.I8.W0.S13.S1.P.D.OTE._Z._Z._T.XDC_R_B1GQ_CY._Z.S.V.CY._T", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]]
|
|
eu_gte.columns = ["Date", "EU_GOVERNMENT_TOTAL_EXPENDITURE"]
|
|
eu_gte["Date"] = pd.to_datetime(QtoM(eu_gte["Date"]), format="%Y-%m") + pd.tseries.offsets.MonthBegin(-1)
|
|
eu_gte = pd.merge_asof(self.daterange, eu_gte, on="Date", direction="nearest")
|
|
return eu_gte
|
|
|
|
def interest_expenditure(self):
|
|
"""
|
|
* Title: Government interest expenditure (as % of GDP)
|
|
* URL: https://sdw.ecb.europa.eu/quickview.do?SERIES_KEY=325.GFS.Q.N.I8.W0.S13.S1.C.D.D41._Z._Z._T.XDC_R_B1GQ_CY._Z.S.V.CY._T
|
|
* Reference area: Euro area 19 (fixed composition) as of 1 January 2015 (I8)
|
|
* Frequency: Quarterly
|
|
"""
|
|
eu_gie = ecb.get_data(datacode="GFS", key="Q.N.I8.W0.S13.S1.C.D.D41._Z._Z._T.XDC_R_B1GQ_CY._Z.S.V.CY._T", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]]
|
|
eu_gie.columns = ["Date", "EU_GOVERNMENT_INTEREST_EXPENDITURE"]
|
|
eu_gie["Date"] = pd.to_datetime(QtoM(eu_gie["Date"]), format="%Y-%m") + pd.tseries.offsets.MonthBegin(-1)
|
|
eu_gie = pd.merge_asof(self.daterange, eu_gie, on="Date", direction="nearest")
|
|
return eu_gie
|
|
|
|
def investment_expenditure(self):
|
|
"""
|
|
* Title: Government investment expenditure (as % of GDP)
|
|
* URL: https://sdw.ecb.europa.eu/quickview.do?SERIES_KEY=325.GFS.Q.N.I8.W0.S13.S1.N.D.P51G._Z._Z._T.XDC_R_B1GQ_CY._Z.S.V.CY._T
|
|
* Reference area: Euro area 19 (fixed composition) as of 1 January 2015 (I8)
|
|
* Frequency: Quarterly
|
|
"""
|
|
eu_gie = ecb.get_data(datacode="GFS", key="Q.N.I8.W0.S13.S1.N.D.P51G._Z._Z._T.XDC_R_B1GQ_CY._Z.S.V.CY._T", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]]
|
|
eu_gie.columns = ["Date", "EU_GOVERNMENT_INVESTMENT_EXPENDITURE"]
|
|
eu_gie["Date"] = pd.to_datetime(QtoM(eu_gie["Date"]), format="%Y-%m") + pd.tseries.offsets.MonthBegin(-1)
|
|
eu_gie = pd.merge_asof(self.daterange, eu_gie, on="Date", direction="nearest")
|
|
return eu_gie
|
|
|
|
def balance(self):
|
|
"""
|
|
* Title: Government deficit(-) or surplus(+) (as % of GDP)
|
|
* URL: https://sdw.ecb.europa.eu/quickview.do?SERIES_KEY=325.GFS.Q.N.I8.W0.S13.S1._Z.B.B9._Z._Z._Z.XDC_R_B1GQ_CY._Z.S.V.CY._T
|
|
* Reference area: Euro area 19 (fixed composition) as of 1 January 2015 (I8)
|
|
* Frequency: Quarterly
|
|
"""
|
|
eu_balance = ecb.get_data(datacode="GFS", key="Q.N.I8.W0.S13.S1._Z.B.B9._Z._Z._Z.XDC_R_B1GQ_CY._Z.S.V.CY._T", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]]
|
|
eu_balance.columns = ["Date", "EU_GOVERNMENT_BALANCE"]
|
|
eu_balance["Date"] = pd.to_datetime(QtoM(eu_balance["Date"]), format="%Y-%m") + pd.tseries.offsets.MonthBegin(-1)
|
|
eu_balance = pd.merge_asof(self.daterange, eu_balance, on="Date", direction="nearest")
|
|
return eu_balance
|
|
|
|
class general_government_debt(fiscal_sector):
|
|
## National Account (current price)
|
|
def __init__(self):
|
|
super(general_government_debt, self).__init__()
|
|
pass
|
|
|
|
def gross_outstanding_debt_total(self):
|
|
"""
|
|
* Title: Government debt (consolidated) (as % of GDP)
|
|
* URL: https://sdw.ecb.europa.eu/quickview.do?SERIES_KEY=325.GFS.A.N.I8.W0.S13.S1.C.L.LE.GD.T._Z.XDC_R_B1GQ._T.F.V.N._T
|
|
* Reference area: Euro area 19 (fixed composition) as of 1 January 2015 (I8)
|
|
* Frequency: Yearly
|
|
"""
|
|
eu_od = ecb.get_data(datacode="GFS", key="A.N.I8.W0.S13.S1.C.L.LE.GD.T._Z.XDC_R_B1GQ._T.F.V.N._T", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]]
|
|
eu_od.columns = ["Date", "EU_GOVERNMENT_OUT_STANDING_DEBT_TOTAL"]
|
|
eu_od["Date"] = pd.to_datetime(eu_od["Date"], format="%Y") + pd.tseries.offsets.MonthBegin(-1)
|
|
eu_od = pd.merge_asof(self.daterange, eu_od, on="Date", direction="nearest")
|
|
return eu_od
|
|
|
|
def gross_outstanding_debt_in_euro(self):
|
|
"""
|
|
* Title: Government debt denominated in national currency and euro (as % of GDP)
|
|
* URL: https://sdw.ecb.europa.eu/quickview.do?SERIES_KEY=325.GFS.A.N.I8.W0.S13.S1.C.L.LE.GD.T._Z.XDC_R_B1GQ.EUR.F.V.N._T
|
|
* Reference area: Euro area 19 (fixed composition) as of 1 January 2015 (I8)
|
|
* Frequency: Yearly
|
|
"""
|
|
eu_od = ecb.get_data(datacode="GFS", key="A.N.I8.W0.S13.S1.C.L.LE.GD.T._Z.XDC_R_B1GQ.EUR.F.V.N._T", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]]
|
|
eu_od.columns = ["Date", "EU_GOVERNMENT_OUT_STANDING_DEBT_IN_EURO"]
|
|
eu_od["Date"] = pd.to_datetime(eu_od["Date"], format="%Y") + pd.tseries.offsets.MonthBegin(-1)
|
|
eu_od = pd.merge_asof(self.daterange, eu_od, on="Date", direction="nearest")
|
|
return eu_od
|
|
|
|
def gross_outstanding_debt_no_euro(self):
|
|
"""
|
|
* Title: Government debt denominated in currencies other than national currency and euro (as % of GDP)
|
|
* URL: https://sdw.ecb.europa.eu/quickview.do?SERIES_KEY=325.GFS.A.N.I8.W0.S13.S1.C.L.LE.GD.T._Z.XDC_R_B1GQ.XNC.F.V.N._T
|
|
* Reference area: Euro area 19 (fixed composition) as of 1 January 2015 (I8)
|
|
* Frequency: Yearly
|
|
"""
|
|
eu_od = ecb.get_data(datacode="GFS", key="A.N.I8.W0.S13.S1.C.L.LE.GD.T._Z.XDC_R_B1GQ.XNC.F.V.N._T", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]]
|
|
eu_od.columns = ["Date", "EU_GOVERNMENT_OUT_STANDING_DEBT_NO_EURO"]
|
|
eu_od["Date"] = pd.to_datetime(eu_od["Date"], format="%Y") + pd.tseries.offsets.MonthBegin(-1)
|
|
eu_od = pd.merge_asof(self.daterange, eu_od, on="Date", direction="nearest")
|
|
return eu_od
|
|
|
|
def gross_outstanding_debt_resident(self):
|
|
"""
|
|
* Title: Government debt held by residents (as % of GDP)
|
|
* URL: https://sdw.ecb.europa.eu/quickview.do?SERIES_KEY=325.GFS.A.N.I8.W2.S13.S1.C.L.LE.GD.T._Z.XDC_R_B1GQ._T.F.V.N._T
|
|
* Reference area: Euro area 19 (fixed composition) as of 1 January 2015 (I8)
|
|
* Frequency: Yearly
|
|
"""
|
|
eu_od = ecb.get_data(datacode="GFS", key="A.N.I8.W2.S13.S1.C.L.LE.GD.T._Z.XDC_R_B1GQ._T.F.V.N._T", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]]
|
|
eu_od.columns = ["Date", "EU_GOVERNMENT_OUT_STANDING_DEBT_RESIDENTS"]
|
|
eu_od["Date"] = pd.to_datetime(eu_od["Date"], format="%Y") + pd.tseries.offsets.MonthBegin(-1)
|
|
eu_od = pd.merge_asof(self.daterange, eu_od, on="Date", direction="nearest")
|
|
return eu_od
|
|
|
|
def gross_outstanding_debt_mfi(self):
|
|
"""
|
|
* Title: Government debt held by monetary financial institutions (as % of GDP)
|
|
* URL: https://sdw.ecb.europa.eu/quickview.do?SERIES_KEY=325.GFS.A.N.I8.W2.S13.S12K.C.L.LE.GD.T._Z.XDC_R_B1GQ._T.F.V.N._T
|
|
* Reference area: Euro area 19 (fixed composition) as of 1 January 2015 (I8)
|
|
* Frequency: Yearly
|
|
"""
|
|
eu_od = ecb.get_data(datacode="GFS", key="A.N.I8.W2.S13.S12K.C.L.LE.GD.T._Z.XDC_R_B1GQ._T.F.V.N._T", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]]
|
|
eu_od.columns = ["Date", "EU_GOVERNMENT_OUT_STANDING_DEBT_MFI"]
|
|
eu_od["Date"] = pd.to_datetime(eu_od["Date"], format="%Y") + pd.tseries.offsets.MonthBegin(-1)
|
|
eu_od = pd.merge_asof(self.daterange, eu_od, on="Date", direction="nearest")
|
|
return eu_od
|
|
|
|
def gross_outstanding_debt_non_mfi(self):
|
|
"""
|
|
* Title: Government debt held by financial institutions other than monetary financial institutions (as % of GDP)
|
|
* URL: https://sdw.ecb.europa.eu/quickview.do?SERIES_KEY=325.GFS.A.N.I8.W2.S13.S12P.C.L.LE.GD.T._Z.XDC_R_B1GQ._T.F.V.N._T
|
|
* Reference area: Euro area 19 (fixed composition) as of 1 January 2015 (I8)
|
|
* Frequency: Yearly
|
|
"""
|
|
eu_od = ecb.get_data(datacode="GFS", key="A.N.I8.W2.S13.S12P.C.L.LE.GD.T._Z.XDC_R_B1GQ._T.F.V.N._T", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]]
|
|
eu_od.columns = ["Date", "EU_GOVERNMENT_OUT_STANDING_DEBT_NON_MFI"]
|
|
eu_od["Date"] = pd.to_datetime(eu_od["Date"], format="%Y") + pd.tseries.offsets.MonthBegin(-1)
|
|
eu_od = pd.merge_asof(self.daterange, eu_od, on="Date", direction="nearest")
|
|
return eu_od
|
|
|
|
def gross_outstanding_debt_non_fin_sector(self):
|
|
"""
|
|
* Title: Government debt held by the non-financial sectors (as % of GDP)
|
|
* URL: https://sdw.ecb.europa.eu/quickview.do?SERIES_KEY=325.GFS.A.N.I8.W2.S13.S1U.C.L.LE.GD.T._Z.XDC_R_B1GQ._T.F.V.N._T
|
|
* Reference area: Euro area 19 (fixed composition) as of 1 January 2015 (I8)
|
|
* Frequency: Yearly
|
|
"""
|
|
eu_od = ecb.get_data(datacode="GFS", key="A.N.I8.W2.S13.S1U.C.L.LE.GD.T._Z.XDC_R_B1GQ._T.F.V.N._T", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]]
|
|
eu_od.columns = ["Date", "EU_GOVERNMENT_OUT_STANDING_DEBT_NON_FIN_SECTOR"]
|
|
eu_od["Date"] = pd.to_datetime(eu_od["Date"], format="%Y") + pd.tseries.offsets.MonthBegin(-1)
|
|
eu_od = pd.merge_asof(self.daterange, eu_od, on="Date", direction="nearest")
|
|
return eu_od
|
|
|
|
def gross_outstanding_debt_non_resident(self):
|
|
"""
|
|
* Title: Government debt held by non-residents (as % of GDP)
|
|
* URL: https://sdw.ecb.europa.eu/quickview.do?SERIES_KEY=325.GFS.A.N.I8.W1.S13.S1.C.L.LE.GD.T._Z.XDC_R_B1GQ._T.F.V.N._T
|
|
* Reference area: Euro area 19 (fixed composition) as of 1 January 2015 (I8)
|
|
* Frequency: Yearly
|
|
"""
|
|
eu_od = ecb.get_data(datacode="GFS", key="A.N.I8.W1.S13.S1.C.L.LE.GD.T._Z.XDC_R_B1GQ._T.F.V.N._T", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]]
|
|
eu_od.columns = ["Date", "EU_GOVERNMENT_OUT_STANDING_DEBT_NON_RESIDENT"]
|
|
eu_od["Date"] = pd.to_datetime(eu_od["Date"], format="%Y") + pd.tseries.offsets.MonthBegin(-1)
|
|
eu_od = pd.merge_asof(self.daterange, eu_od, on="Date", direction="nearest")
|
|
return eu_od
|
|
|
|
class financial_sector():
|
|
def __init__(self, startdate=startdate, enddate=enddate, daterange=daterange):
|
|
self.startdate = startdate
|
|
self.enddate = enddate
|
|
self.daterange = daterange
|
|
|
|
class analytical_accounts_of_the_banking_sector(financial_sector):
|
|
## National Account (current price)
|
|
def __init__(self):
|
|
super(analytical_accounts_of_the_banking_sector, self).__init__()
|
|
pass
|
|
|
|
def monetary_aggrate_m1(self):
|
|
"""
|
|
* Title: Monetary aggregate M1 vis-a-vis euro area non-MFI excl. central gov. reported by MFI & central gov. & post office giro Inst. in the euro area (stock)
|
|
* URL: https://sdw.ecb.europa.eu/quickview.do?SERIES_KEY=BSI.M.U2.Y.V.M10.X.1.U2.2300.Z01.E
|
|
* Reference area: Euro area 19 (fixed composition) as of 1 January 2015 (I8)
|
|
* Frequency: Monthly
|
|
"""
|
|
eu_m1 = ecb.get_data(datacode="BSI", key="M.U2.Y.V.M30.X.1.U2.2300.Z01.E", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]]
|
|
eu_m1.columns = ["Date", "EU_MONETARY_AGGRATE_M3"]
|
|
eu_m1["Date"] = pd.to_datetime(eu_m1["Date"], format="%Y-%m-%d")
|
|
return eu_m1
|
|
|
|
def monetary_aggrate_m2(self):
|
|
"""
|
|
* Title: Monetary aggregate M2 vis-a-vis euro area non-MFI excl. central gov. reported by MFI & central gov. & post office giro Inst. in the euro area (stock)
|
|
* URL: https://sdw.ecb.europa.eu/quickview.do?SERIES_KEY=BSI.M.U2.Y.V.M10.X.1.U2.2300.Z01.E
|
|
* Reference area: Euro area 19 (fixed composition) as of 1 January 2015 (I8)
|
|
* Frequency: Monthly
|
|
"""
|
|
eu_m2 = ecb.get_data(datacode="BSI", key="M.U2.Y.V.M20.X.1.U2.2300.Z01.E", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]]
|
|
eu_m2.columns = ["Date", "EU_MONETARY_AGGRATE_M3"]
|
|
eu_m2["Date"] = pd.to_datetime(eu_m2["Date"], format="%Y-%m-%d")
|
|
return eu_m2
|
|
|
|
def monetary_aggrate_m3(self):
|
|
"""
|
|
* Title: Monetary aggregate M3 vis-a-vis euro area non-MFI excl. central gov. reported by MFI & central gov. & post office giro Inst. in the euro area (stock)
|
|
* URL: https://sdw.ecb.europa.eu/quickview.do?SERIES_KEY=BSI.M.U2.Y.V.M30.X.1.U2.2300.Z01.E
|
|
* Reference area: Euro area 19 (fixed composition) as of 1 January 2015 (I8)
|
|
* Frequency: Monthly
|
|
"""
|
|
eu_m3 = ecb.get_data(datacode="BSI", key="M.U2.Y.V.M30.X.1.U2.2300.Z01.E", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]]
|
|
eu_m3.columns = ["Date", "EU_MONETARY_AGGRATE_M3"]
|
|
eu_m3["Date"] = pd.to_datetime(eu_m3["Date"], format="%Y-%m-%d")
|
|
return eu_m3
|
|
|
|
def domestic_credit(self):
|
|
"""
|
|
* Title: Total loans and securities vis-a-vis euro area non-MFI reported by MFI in the euro area (stock)
|
|
* URL: https://sdw.ecb.europa.eu/quickview.do?SERIES_KEY=BSI.M.U2.Y.U.AT2.A.1.U2.2000.Z01.E
|
|
* Reference area: Euro area 19 (fixed composition) as of 1 January 2015 (I8)
|
|
* Frequency: Monthly
|
|
"""
|
|
eu_dc = ecb.get_data(datacode="BSI", key="M.U2.Y.U.AT2.A.1.U2.2000.Z01.E", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]]
|
|
eu_dc.columns = ["Date", "EU_DOMESTIC_CREDIT"]
|
|
eu_dc["Date"] = pd.to_datetime(eu_dc["Date"], format="%Y-%m-%d")
|
|
return eu_dc
|
|
|
|
def credit_general_government(self):
|
|
"""
|
|
* Title: Total loans and securities vis-a-vis euro area General Government reported by MFI in the euro area (stock)
|
|
* URL: https://sdw.ecb.europa.eu/quickview.do?SERIES_KEY=BSI.M.U2.Y.U.AT2.A.1.U2.2100.Z01.E
|
|
* Reference area: Euro area 19 (fixed composition) as of 1 January 2015 (I8)
|
|
* Frequency: Monthly
|
|
"""
|
|
eu_dc = ecb.get_data(datacode="BSI", key="M.U2.Y.U.AT2.A.1.U2.2100.Z01.E", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]]
|
|
eu_dc.columns = ["Date", "EU_GOVERNMENT_CREDIT"]
|
|
eu_dc["Date"] = pd.to_datetime(eu_dc["Date"], format="%Y-%m-%d")
|
|
return eu_dc
|
|
|
|
def credit_general_other_resident(self):
|
|
"""
|
|
* Title: Total loans and securities vis-a-vis euro area non-MFI excl. general gov. reported by MFI in the euro area (stock)
|
|
* URL: https://sdw.ecb.europa.eu/quickview.do?SERIES_KEY=BSI.M.U2.Y.U.AT2.A.1.U2.2200.Z01.E
|
|
* Reference area: Euro area 19 (fixed composition) as of 1 January 2015 (I8)
|
|
* Frequency: Monthly
|
|
"""
|
|
eu_dc = ecb.get_data(datacode="BSI", key="M.U2.Y.U.AT2.A.1.U2.2200.Z01.E", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]]
|
|
eu_dc.columns = ["Date", "EU_OTHER_RESIDENT_CREDIT"]
|
|
eu_dc["Date"] = pd.to_datetime(eu_dc["Date"], format="%Y-%m-%d")
|
|
return eu_dc
|
|
|
|
def external_assets(self):
|
|
"""
|
|
* Title: External assets reported by MFI in the euro area (stock)
|
|
* URL: https://sdw.ecb.europa.eu/quickview.do?SERIES_KEY=BSI.M.U2.Y.U.AXG.A.1.U4.0000.Z01.E
|
|
* Reference area: Euro area 19 (fixed composition) as of 1 January 2015 (I8)
|
|
* Frequency: Monthly
|
|
"""
|
|
eu_ea = ecb.get_data(datacode="BSI", key="M.U2.Y.U.AXG.A.1.U4.0000.Z01.E", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]]
|
|
eu_ea.columns = ["Date", "EU_EXTERNAL_ASSETS"]
|
|
eu_ea["Date"] = pd.to_datetime(eu_ea["Date"], format="%Y-%m-%d")
|
|
return eu_ea
|
|
|
|
def external_liabilities(self):
|
|
"""
|
|
* Title: External liabilities reported by MFI in the euro area (stock)
|
|
* URL: https://sdw.ecb.europa.eu/quickview.do?SERIES_KEY=BSI.M.U2.Y.U.LXG.A.1.U4.0000.Z01.E
|
|
* Reference area: Euro area 19 (fixed composition) as of 1 January 2015 (I8)
|
|
* Frequency: Monthly
|
|
"""
|
|
eu_el = ecb.get_data(datacode="BSI", key="M.U2.Y.U.LXG.A.1.U4.0000.Z01.E", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]]
|
|
eu_el.columns = ["Date", "EU_EXTERNAL_LIABILITIES"]
|
|
eu_el["Date"] = pd.to_datetime(eu_el["Date"], format="%Y-%m-%d")
|
|
return eu_el
|
|
|
|
class analytical_accounts_of_the_central_banks(financial_sector):
|
|
## National Account (current price)
|
|
def __init__(self):
|
|
super(analytical_accounts_of_the_banking_sector, self).__init__()
|
|
pass
|
|
|
|
def currency_in_circulation(self):
|
|
"""
|
|
* Title: Currency in circulation reported by Eurosystem in the euro area (stock)
|
|
* URL:https://sdw.ecb.europa.eu/quickview.do?SERIES_KEY=117.BSI.M.U2.N.C.L10.X.1.Z5.0000.Z01.E
|
|
* Reference area: Euro area 19 (fixed composition) as of 1 January 2015 (I8)
|
|
* Frequency: Monthly
|
|
"""
|
|
eu_cc = ecb.get_data(datacode="BSI", key="M.U2.N.C.L10.X.1.Z5.0000.Z01.E", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]]
|
|
eu_cc.columns = ["Date", "EU_CURRENCY_IN_CIRCULATION"]
|
|
eu_cc["Date"] = pd.to_datetime(eu_cc["Date"], format="%Y-%m-%d")
|
|
return eu_cc
|
|
|
|
def deposits_at_eurosystem_mfi(self):
|
|
"""
|
|
* Title: Deposit liabilities vis-a-vis euro area MFI reported by Eurosystem in the euro area (stock)
|
|
* URL:https://sdw.ecb.europa.eu/quickview.do?SERIES_KEY=117.BSI.M.U2.N.C.L20.A.1.U2.1000.Z01.E
|
|
* Reference area: Euro area 19 (fixed composition) as of 1 January 2015 (I8)
|
|
* Frequency: Monthly
|
|
"""
|
|
eu_cc = ecb.get_data(datacode="BSI", key="M.U2.N.C.L20.A.1.U2.1000.Z01.E", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]]
|
|
eu_cc.columns = ["Date", "EU_DEPOSITS_AT_EUROSYSTEM_MFI"]
|
|
eu_cc["Date"] = pd.to_datetime(eu_cc["Date"], format="%Y-%m-%d")
|
|
return eu_cc
|
|
|
|
def credit(self):
|
|
"""
|
|
* Title: Total loans and securities vis-a-vis euro area non-MFI reported by Eurosystem in the euro area (stock)
|
|
* URL:https://sdw.ecb.europa.eu/quickview.do?SERIES_KEY=117.BSI.M.U2.N.C.AT2.A.1.U2.2000.Z01.E
|
|
* Reference area: Euro area 19 (fixed composition) as of 1 January 2015 (I8)
|
|
* Frequency: Monthly
|
|
"""
|
|
eu_cc = ecb.get_data(datacode="BSI", key="M.U2.N.C.AT2.A.1.U2.2000.Z01.E", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]]
|
|
eu_cc.columns = ["Date", "EU_CREDIT"]
|
|
eu_cc["Date"] = pd.to_datetime(eu_cc["Date"], format="%Y-%m-%d")
|
|
return eu_cc
|
|
|
|
def credit_to_general_governemnt(self):
|
|
"""
|
|
* Title: Total loans and securities vis-a-vis euro area non-MFI reported by Eurosystem in the euro area (stock)
|
|
* URL:https://sdw.ecb.europa.eu/quickview.do?SERIES_KEY=117.BSI.M.U2.N.C.AT2.A.1.U2.2100.Z01.E
|
|
* Reference area: Euro area 19 (fixed composition) as of 1 January 2015 (I8)
|
|
* Frequency: Monthly
|
|
"""
|
|
eu_cc = ecb.get_data(datacode="BSI", key="M.U2.N.C.AT2.A.1.U2.2100.Z01.E", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]]
|
|
eu_cc.columns = ["Date", "EU_CREDIT_TO_GENERAL_GOVERNMENT"]
|
|
eu_cc["Date"] = pd.to_datetime(eu_cc["Date"], format="%Y-%m-%d")
|
|
return eu_cc
|
|
|
|
def credit_to_other_resident_sector(self):
|
|
"""
|
|
* Title: Total loans and securities vis-a-vis euro area non-MFI reported by Eurosystem in the euro area (stock)
|
|
* URL:https://sdw.ecb.europa.eu/quickview.do?SERIES_KEY=117.BSI.M.U2.N.C.AT2.A.1.U2.2200.Z01.E
|
|
* Reference area: Euro area 19 (fixed composition) as of 1 January 2015 (I8)
|
|
* Frequency: Monthly
|
|
"""
|
|
eu_cc = ecb.get_data(datacode="BSI", key="M.U2.N.C.AT2.A.1.U2.2200.Z01.E", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]]
|
|
eu_cc.columns = ["Date", "EU_CREDIT_TO_OTHER_RESIDENT"]
|
|
eu_cc["Date"] = pd.to_datetime(eu_cc["Date"], format="%Y-%m-%d")
|
|
return eu_cc
|
|
|
|
def external_assets(self):
|
|
"""
|
|
* Title: External assets reported by Eurosystem in the euro area (stock)
|
|
* URL: https://sdw.ecb.europa.eu/quickview.do?SERIES_KEY=BSI.M.U2.N.C.AXG.A.1.U4.0000.Z01.E
|
|
* Reference area: Euro area 19 (fixed composition) as of 1 January 2015 (I8)
|
|
* Frequency: Monthly
|
|
"""
|
|
eu_ea = ecb.get_data(datacode="BSI", key="M.U2.N.C.AXG.A.1.U4.0000.Z01.E", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]]
|
|
eu_ea.columns = ["Date", "EU_EXTERNAL_ASSETS"]
|
|
eu_ea["Date"] = pd.to_datetime(eu_ea["Date"], format="%Y-%m-%d")
|
|
return eu_ea
|
|
|
|
def external_liabilities(self):
|
|
"""
|
|
* Title: External liabilities reported by Eurosystem in the euro area (stock)
|
|
* URL: https://sdw.ecb.europa.eu/quickview.do?SERIES_KEY=BSI.M.U2.N.C.LXG.A.1.U4.0000.Z01.E
|
|
* Reference area: Euro area 19 (fixed composition) as of 1 January 2015 (I8)
|
|
* Frequency: Monthly
|
|
"""
|
|
eu_el = ecb.get_data(datacode="BSI", key="M.U2.N.C.LXG.A.1.U4.0000.Z01.E", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]]
|
|
eu_el.columns = ["Date", "EU_EXTERNAL_LIABILITIES"]
|
|
eu_el["Date"] = pd.to_datetime(eu_el["Date"], format="%Y-%m-%d")
|
|
return eu_el
|
|
|
|
class interest_rate(financial_sector):
|
|
## National Account (current price)
|
|
def __init__(self):
|
|
super(interest_rate, self).__init__()
|
|
pass
|
|
|
|
def one_year_interbank(self):
|
|
"""
|
|
* Title: Euribor 1-year - Historical close, average of observations through period
|
|
* URL: https://sdw.ecb.europa.eu/quickview.do?SERIES_KEY=143.FM.M.U2.EUR.RT.MM.EURIBOR1YD_.HSTA
|
|
* Reference area: Euro area 19 (fixed composition) as of 1 January 2015 (I8)
|
|
* Frequency: Monthly
|
|
"""
|
|
eu_el = ecb.get_data(datacode="FM", key="M.U2.EUR.RT.MM.EURIBOR1YD_.HSTA", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]]
|
|
eu_el.columns = ["Date", "EU_ONE_YEAR_INTERBANK_RATE"]
|
|
eu_el["Date"] = pd.to_datetime(eu_el["Date"], format="%Y-%m-%d")
|
|
return eu_el
|
|
|
|
def ten_year_government_banchmar_bond_yild(self):
|
|
"""
|
|
* Title: Euro area 10-year Government Benchmark bond yield - Yield
|
|
* URL: https://sdw.ecb.europa.eu/quickview.do?SERIES_KEY=143.FM.M.U2.EUR.4F.BB.U2_10Y.YLD
|
|
* Reference area: Euro area 19 (fixed composition) as of 1 January 2015 (I8)
|
|
* Frequency: Monthly
|
|
"""
|
|
eu_el = ecb.get_data(datacode="FM", key="M.U2.EUR.4F.BB.U2_10Y.YLD", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]]
|
|
eu_el.columns = ["Date", "EU_TEN_YEART_BOND_RATE"]
|
|
eu_el["Date"] = pd.to_datetime(eu_el["Date"], format="%Y-%m-%d")
|
|
return eu_el
|
|
|
|
class external_sector():
|
|
def __init__(self, startdate=startdate, enddate=enddate, daterange=daterange):
|
|
self.startdate = startdate
|
|
self.enddate = enddate
|
|
self.daterange = daterange
|
|
|
|
class balance_of_payments(external_sector):
|
|
## National Account (current price)
|
|
def __init__(self):
|
|
super(balance_of_payments, self).__init__()
|
|
pass
|
|
|
|
def net_current_account(self):
|
|
"""
|
|
* Title: Current account
|
|
* URL: https://sdw.ecb.europa.eu/quickview.do?SERIES_KEY=338.BP6.M.Y.I8.W1.S1.S1.T.B.CA._Z._Z._Z.EUR._T._X.N
|
|
* Reference area: Euro area 19 (fixed composition) as of 1 January 2015 (I8)
|
|
* Frequency: Monthly
|
|
"""
|
|
eu_ca = ecb.get_data(datacode="BP6", key="M.Y.I8.W1.S1.S1.T.B.CA._Z._Z._Z.EUR._T._X.N", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]]
|
|
eu_ca.columns = ["Date", "EU_CURRENT_ACCOUNT"]
|
|
eu_ca["Date"] = pd.to_datetime(eu_ca["Date"], format="%Y-%m-%d")
|
|
return eu_ca
|
|
|
|
def exports_goods(self):
|
|
"""
|
|
* Title: Goods
|
|
* URL: https://sdw.ecb.europa.eu/quickview.do?SERIES_KEY=338.BP6.M.Y.I8.W1.S1.S1.T.C.G._Z._Z._Z.EUR._T._X.N
|
|
* Reference area: Euro area 19 (fixed composition) as of 1 January 2015 (I8)
|
|
* Frequency: Monthly
|
|
"""
|
|
eu_eg = ecb.get_data(datacode="BP6", key="M.Y.I8.W1.S1.S1.T.C.G._Z._Z._Z.EUR._T._X.N", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]]
|
|
eu_eg.columns = ["Date", "EU_EXPORT_GOODS"]
|
|
eu_eg["Date"] = pd.to_datetime(eu_eg["Date"], format="%Y-%m-%d")
|
|
return eu_ca
|
|
|
|
def exports_services(self):
|
|
"""
|
|
* Title: Services
|
|
* URL: https://sdw.ecb.europa.eu/quickview.do?SERIES_KEY=338.BP6.M.Y.I8.W1.S1.S1.T.C.S._Z._Z._Z.EUR._T._X.N
|
|
* Reference area: Euro area 19 (fixed composition) as of 1 January 2015 (I8)
|
|
* Frequency: Monthly
|
|
"""
|
|
eu_eg = ecb.get_data(datacode="BP6", key="M.Y.I8.W1.S1.S1.T.C.S._Z._Z._Z.EUR._T._X.N", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]]
|
|
eu_es.columns = ["Date", "EU_EXPORT_SERVICES"]
|
|
eu_es["Date"] = pd.to_datetime(eu_es["Date"], format="%Y-%m-%d")
|
|
return eu_es
|
|
|
|
def imports_goods(self):
|
|
"""
|
|
* Title: Goods
|
|
* URL: https://sdw.ecb.europa.eu/quickview.do?SERIES_KEY=338.BP6.M.Y.I8.W1.S1.S1.T.D.G._Z._Z._Z.EUR._T._X.N
|
|
* Reference area: Euro area 19 (fixed composition) as of 1 January 2015 (I8)
|
|
* Frequency: Monthly
|
|
"""
|
|
eu_ig = ecb.get_data(datacode="BP6", key="M.Y.I8.W1.S1.S1.T.D.G._Z._Z._Z.EUR._T._X.N", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]]
|
|
eu_ig.columns = ["Date", "EU_IMPORT_GOODS"]
|
|
eu_ig["Date"] = pd.to_datetime(eu_ig["Date"], format="%Y-%m-%d")
|
|
return eu_ca
|
|
|
|
def import_services(self):
|
|
"""
|
|
* Title: Services
|
|
* URL: https://sdw.ecb.europa.eu/quickview.do?SERIES_KEY=338.BP6.M.Y.I8.W1.S1.S1.T.D.S._Z._Z._Z.EUR._T._X.N
|
|
* Reference area: Euro area 19 (fixed composition) as of 1 January 2015 (I8)
|
|
* Frequency: Monthly
|
|
"""
|
|
eu_is = ecb.get_data(datacode="BP6", key="M.Y.I8.W1.S1.S1.T.D.S._Z._Z._Z.EUR._T._X.N", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]]
|
|
eu_is.columns = ["Date", "EU_IMPORT_SERVICES"]
|
|
eu_is["Date"] = pd.to_datetime(eu_is["Date"], format="%Y-%m-%d")
|
|
return eu_is
|
|
|
|
def net_primary_income(self):
|
|
"""
|
|
* Title: Primary income
|
|
* URL: https://sdw.ecb.europa.eu/quickview.do?SERIES_KEY=338.BP6.M.Y.I8.W1.S1.S1.T.B.IN1._Z._Z._Z.EUR._T._X.N
|
|
* Reference area: Euro area 19 (fixed composition) as of 1 January 2015 (I8)
|
|
* Frequency: Monthly
|
|
"""
|
|
eu_npi = ecb.get_data(datacode="BP6", key="M.Y.I8.W1.S1.S1.T.B.IN1._Z._Z._Z.EUR._T._X.N", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]]
|
|
eu_npi.columns = ["Date", "EU_NET_PRIMARY_INCOME"]
|
|
eu_npi["Date"] = pd.to_datetime(eu_npi["Date"], format="%Y-%m-%d")
|
|
return eu_npi
|
|
|
|
def net_secondary_income(self):
|
|
"""
|
|
* Title: Secondary income
|
|
* URL: https://sdw.ecb.europa.eu/quickview.do?SERIES_KEY=338.BP6.M.Y.I8.W1.S1.S1.T.B.IN2._Z._Z._Z.EUR._T._X.N
|
|
* Reference area: Euro area 19 (fixed composition) as of 1 January 2015 (I8)
|
|
* Frequency: Monthly
|
|
"""
|
|
eu_nsi = ecb.get_data(datacode="BP6", key="M.Y.I8.W1.S1.S1.T.B.IN2._Z._Z._Z.EUR._T._X.N", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]]
|
|
eu_nsi.columns = ["Date", "EU_NET_SECONDARY_INCOME"]
|
|
eu_nsi["Date"] = pd.to_datetime(eu_nsi["Date"], format="%Y-%m-%d")
|
|
return eu_nsi
|
|
|
|
def net_capital_account(self):
|
|
"""
|
|
* Title: Capitial account
|
|
* URL: https://sdw.ecb.europa.eu/quickview.do?SERIES_KEY=338.BP6.M.N.I8.W1.S1.S1.T.B.KA._Z._Z._Z.EUR._T._X.N
|
|
* Reference area: Euro area 19 (fixed composition) as of 1 January 2015 (I8)
|
|
* Frequency: Monthly
|
|
"""
|
|
eu_nca = ecb.get_data(datacode="BP6", key="M.N.I8.W1.S1.S1.T.B.KA._Z._Z._Z.EUR._T._X.N", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]]
|
|
eu_nca.columns = ["Date", "EU_NET_CAPTIAL_ACCOUNT"]
|
|
eu_nca["Date"] = pd.to_datetime(eu_nca["Date"], format="%Y-%m-%d")
|
|
return eu_nca
|
|
|
|
def net_financial_account(self):
|
|
"""
|
|
* Title: Total financial assets/liabilities
|
|
* URL: https://sdw.ecb.europa.eu/quickview.do?SERIES_KEY=338.BP6.M.N.I8.W1.S1.S1.T.N.FA._T.F._Z.EUR._T._X.N
|
|
* Reference area: Euro area 19 (fixed composition) as of 1 January 2015 (I8)
|
|
* Frequency: Monthly
|
|
"""
|
|
eu_nfa = ecb.get_data(datacode="BP6", key="M.N.I8.W1.S1.S1.T.N.FA._T.F._Z.EUR._T._X.N", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]]
|
|
eu_nfa.columns = ["Date", "EU_NET_FINANCIAL_ACCOUNT"]
|
|
eu_nfa["Date"] = pd.to_datetime(eu_nfa["Date"], format="%Y-%m-%d")
|
|
return eu_nfa
|
|
|
|
def direct_investment(self):
|
|
"""
|
|
* Title: Direct Investment, Total financial assets/liabilities
|
|
* URL: https://sdw.ecb.europa.eu/quickview.do?SERIES_KEY=338.BP6.M.N.I8.W1.S1.S1.T.N.FA.D.F._Z.EUR._T._X.N
|
|
* Reference area: Euro area 19 (fixed composition) as of 1 January 2015 (I8)
|
|
* Frequency: Monthly
|
|
"""
|
|
eu_di = ecb.get_data(datacode="BP6", key="M.N.I8.W1.S1.S1.T.N.FA.D.F._Z.EUR._T._X.N", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]]
|
|
eu_dia = ecb.get_data(datacode="BP6", key="M.N.I8.W1.S1.S1.T.A.FA.D.F._Z.EUR._T._X.N", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]]
|
|
eu_dil = ecb.get_data(datacode="BP6", key="M.N.I8.W1.S1.S1.T.L.FA.D.F._Z.EUR._T._X.N", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]]
|
|
eu_di.columns = ["Date", "EU_DIRECT_INVESTMENT"]
|
|
eu_di["Date"] = pd.to_datetime(eu_di["Date"], format="%Y-%m-%d")
|
|
eu_di["EU_DIRECT_INVESTMENT_ASSETS"], eu_di["EU_DIRECT_INVESTMENT_LIABILITIES"] = eu_dia["OBS_VALUE"], eu_dil["OBS_VALUE"]
|
|
return eu_di
|
|
|
|
def porfolio_investment(self):
|
|
"""
|
|
* Title: Portfolio Investment, Total financial assets/liabilities
|
|
* URL: https://sdw.ecb.europa.eu/quickview.do?SERIES_KEY=338.BP6.M.N.I8.W1.S1.S1.T.N.FA.P.F._Z.EUR._T.M.N
|
|
* Reference area: Euro area 19 (fixed composition) as of 1 January 2015 (I8)
|
|
* Frequency: Monthly
|
|
"""
|
|
eu_pi = ecb.get_data(datacode="BP6", key="M.N.I8.W1.S1.S1.T.N.FA.P.F._Z.EUR._T.M.N", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]]
|
|
eu_pia = ecb.get_data(datacode="BP6", key="M.N.I8.W1.S1.S1.T.A.FA.P.F._Z.EUR._T.M.N", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]]
|
|
eu_pil = ecb.get_data(datacode="BP6", key="M.N.I8.W1.S1.S1.T.L.FA.P.F._Z.EUR._T._X.N", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]]
|
|
eu_pi.columns = ["Date", "EU_PORFOLIO_INVESTMENT"]
|
|
eu_pi["Date"] = pd.to_datetime(eu_pi["Date"], format="%Y-%m-%d")
|
|
eu_pi["EU_PORFOLIO_INVESTMENT_ASSETS"], eu_pi["EU_PORFOLIO_INVESTMENT_LIABILITIES"] = eu_pia["OBS_VALUE"], eu_pil["OBS_VALUE"]
|
|
return eu_pi
|
|
|
|
def other_investment(self):
|
|
"""
|
|
* Title: Other Investment, Total financial assets/liabilities
|
|
* URL: https://sdw.ecb.europa.eu/quickview.do?SERIES_KEY=338.BP6.M.N.I8.W1.S1.S1.T.N.FA.O.F._Z.EUR._T._X.N
|
|
* Reference area: Euro area 19 (fixed composition) as of 1 January 2015 (I8)
|
|
* Frequency: Monthly
|
|
"""
|
|
eu_pi = ecb.get_data(datacode="BP6", key="M.N.I8.W1.S1.S1.T.N.FA.O.F._Z.EUR._T._X.N", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]]
|
|
eu_pia = ecb.get_data(datacode="BP6", key="M.N.I8.W1.S1.S1.T.A.FA.O.F._Z.EUR._T._X.N", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]]
|
|
eu_pil = ecb.get_data(datacode="BP6", key="M.N.I8.W1.S1.S1.T.L.FA.O.F._Z.EUR._T._X.N", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]]
|
|
eu_pi.columns = ["Date", "EU_OTHER_INVESTMENT"]
|
|
eu_pi["Date"] = pd.to_datetime(eu_pi["Date"], format="%Y-%m-%d")
|
|
eu_pi["EU_OTHER_INVESTMENT_ASSETS"], eu_pi["EU_OTHER_INVESTMENT_LIABILITIES"] = eu_pia["OBS_VALUE"], eu_pil["OBS_VALUE"]
|
|
return eu_pi
|
|
|
|
def financial_derivatives(self):
|
|
"""
|
|
* Title: Financial Derivatives and Employee Stock Options, Financial derivatives and employee stock options
|
|
* URL: https://sdw.ecb.europa.eu/quickview.do?SERIES_KEY=338.BP6.M.N.I8.W1.S1.S1.T.N.FA.F.F7.T.EUR._T.T.N
|
|
* Reference area: Euro area 19 (fixed composition) as of 1 January 2015 (I8)
|
|
* Frequency: Monthly
|
|
"""
|
|
eu_nfa = ecb.get_data(datacode="BP6", key="M.N.I8.W1.S1.S1.T.N.FA.F.F7.T.EUR._T.T.N", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]]
|
|
eu_nfa.columns = ["Date", "EU_FINANCIAL_DERIVATIVES"]
|
|
eu_nfa["Date"] = pd.to_datetime(eu_nfa["Date"], format="%Y-%m-%d")
|
|
return eu_nfa
|
|
|
|
def reserve_assets(self):
|
|
"""
|
|
* Title: Reserve Assets, Total financial assets/liabilities
|
|
* URL: https://sdw.ecb.europa.eu/quickview.do?SERIES_KEY=338.BP6.M.N.I8.W1.S121.S1.T.A.FA.R.F._Z.EUR.X1._X.N
|
|
* Reference area: Euro area 19 (fixed composition) as of 1 January 2015 (I8)
|
|
* Frequency: Monthly
|
|
"""
|
|
eu_nfa = ecb.get_data(datacode="BP6", key="M.N.I8.W1.S121.S1.T.A.FA.R.F._Z.EUR.X1._X.N", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]]
|
|
eu_nfa.columns = ["Date", "EU_RESERVE_ASSETS"]
|
|
eu_nfa["Date"] = pd.to_datetime(eu_nfa["Date"], format="%Y-%m-%d")
|
|
return eu_nfa
|
|
|
|
class international_reserves_and_foreign_currency_liquidity(external_sector):
|
|
## National Account (current price)
|
|
def __init__(self):
|
|
super(balance_of_payments, self).__init__()
|
|
pass
|
|
|
|
def official_reserve_assets(self):
|
|
"""
|
|
* Title: Official reserve assets
|
|
* URL: https://sdw.ecb.europa.eu/quickview.do?SERIES_KEY=340.RA6.M.N.U2.W1.S121.S1.LE.A.FA.R.F._Z.EUR.X1._X.N
|
|
* Reference area: Euro area 19 (fixed composition) as of 1 January 2015 (I8)
|
|
* Frequency: Monthly
|
|
"""
|
|
eu_ora = ecb.get_data(datacode="RA6", key="M.N.U2.W1.S121.S1.LE.A.FA.R.F._Z.EUR.X1._X.N", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]]
|
|
eu_ora.columns = ["Date", "EU_OFFICIAL_RESERVE_ASSETS"]
|
|
eu_ora["Date"] = pd.to_datetime(eu_ora["Date"], format="%Y-%m-%d")
|
|
return eu_ora
|
|
|
|
def monetary_gold(self):
|
|
"""
|
|
* Title: Monetary gold
|
|
* URL: https://sdw.ecb.europa.eu/quickview.do?SERIES_KEY=340.RA6.M.N.U2.W1.S121.S1.LE.A.FA.R.F11._Z.EUR.XAU.M.N
|
|
* Reference area: Euro area 19 (fixed composition) as of 1 January 2015 (I8)
|
|
* Frequency: Monthly
|
|
"""
|
|
eu_mg = ecb.get_data(datacode="RA6", key="M.N.U2.W1.S121.S1.LE.A.FA.R.F11._Z.EUR.XAU.M.N", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]]
|
|
eu_mg.columns = ["Date", "EU_MONETARY_GOLD"]
|
|
eu_mg["Date"] = pd.to_datetime(eu_mg["Date"], format="%Y-%m-%d")
|
|
return eu_mg
|
|
|
|
def imf_reserve_position(self):
|
|
"""
|
|
* Title: Reserve position in the IMF
|
|
* URL: https://sdw.ecb.europa.eu/quickview.do?SERIES_KEY=340.RA6.M.N.U2.1C.S121.S121.LE.A.FA.R.FK._Z.EUR.XDR.M.N
|
|
* Reference area: Euro area 19 (fixed composition) as of 1 January 2015 (I8)
|
|
* Frequency: Monthly
|
|
"""
|
|
eu_img_rp = ecb.get_data(datacode="RA6", key="M.N.U2.1C.S121.S121.LE.A.FA.R.FK._Z.EUR.XDR.M.N", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]]
|
|
eu_img_rp.columns = ["Date", "EU_IMF_RESERVE_POSITION"]
|
|
eu_img_rp["Date"] = pd.to_datetime(eu_img_rp["Date"], format="%Y-%m-%d")
|
|
return eu_img_rp
|
|
|
|
def sdr(self):
|
|
"""
|
|
* Title: Reserve position in the IMF
|
|
* URL: https://sdw.ecb.europa.eu/quickview.do?SERIES_KEY=340.RA6.M.N.U2.W1.S121.S1N.LE.A.FA.R.F12.T.EUR.XDR.M.N
|
|
* Reference area: Euro area 19 (fixed composition) as of 1 January 2015 (I8)
|
|
* Frequency: Monthly
|
|
"""
|
|
eu_img_rp = ecb.get_data(datacode="RA6", key="M.N.U2.W1.S121.S1N.LE.A.FA.R.F12.T.EUR.XDR.M.N", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]]
|
|
eu_img_rp.columns = ["Date", "EU_SDR"]
|
|
eu_img_rp["Date"] = pd.to_datetime(eu_img_rp["Date"], format="%Y-%m-%d")
|
|
return eu_img_rp
|
|
|
|
def other_reserve_assets(self):
|
|
"""
|
|
* Title: Other reserve assets
|
|
* URL: https://sdw.ecb.europa.eu/quickview.do?SERIES_KEY=340.RA6.M.N.U2.W1.S121.S1.LE.A.FA.R.FR2._Z.EUR.X1._X.N
|
|
* Reference area: Euro area 19 (fixed composition) as of 1 January 2015 (I8)
|
|
* Frequency: Monthly
|
|
"""
|
|
eu_img_rp = ecb.get_data(datacode="RA6", key="M.N.U2.W1.S121.S1.LE.A.FA.R.FR2._Z.EUR.X1._X.N", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]]
|
|
eu_img_rp.columns = ["Date", "EU_OTHER_RESERVE_ASSETS"]
|
|
eu_img_rp["Date"] = pd.to_datetime(eu_img_rp["Date"], format="%Y-%m-%d")
|
|
return eu_img_rp
|
|
|
|
def other_foreign_currency_assets(self):
|
|
"""
|
|
* Title: Other foreign currency assets (not included in reserve assets)
|
|
* URL: http://sdw.ecb.europa.eu/quickview.do?SERIES_KEY=340.RA6.M.N.U2.W0.S121.S1.LE.A.FA.RT.F._Z.EUR.X1._X.N
|
|
* Reference area: Euro area 19 (fixed composition) as of 1 January 2015 (I8)
|
|
* Frequency: Monthly
|
|
"""
|
|
eu_img_rp = ecb.get_data(datacode="RA6", key="M.N.U2.W0.S121.S1.LE.A.FA.RT.F._Z.EUR.X1._X.N", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]]
|
|
eu_img_rp.columns = ["Date", "EU_FOREIGN_CURRENCY_ASSETS"]
|
|
eu_img_rp["Date"] = pd.to_datetime(eu_img_rp["Date"], format="%Y-%m-%d")
|
|
return eu_img_rp
|
|
|
|
def predeterminated_short_term_net_drains_on_foreign_currency_assets(self):
|
|
"""
|
|
* Title: Not applicable, Total financial assets/liabilities
|
|
* URL: http://sdw.ecb.europa.eu/quickview.do?SERIES_KEY=340.RA6.M.N.U2.W0.S121.S1.FP.FN._Z.RT.F.TS.EUR.X1.N.N
|
|
* Reference area: Euro area 19 (fixed composition) as of 1 January 2015 (I8)
|
|
* Frequency: Monthly
|
|
"""
|
|
eu_img_rp = ecb.get_data(datacode="RA6", key="M.N.U2.W0.S121.S1.FP.FN._Z.RT.F.TS.EUR.X1.N.N", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]]
|
|
eu_img_rp.columns = ["Date", "EU_TOTAL_FINANCIAL_ASSETS"]
|
|
eu_img_rp["Date"] = pd.to_datetime(eu_img_rp["Date"], format="%Y-%m-%d")
|
|
return eu_img_rp
|
|
|
|
class merchandise_trade(external_sector):
|
|
## National Account (current price)
|
|
def __init__(self):
|
|
super(merchandise_trade, self).__init__()
|
|
pass
|
|
|
|
def merchandise_trade(self):
|
|
"""
|
|
* Title: Total trade, Value (Community concept) (Export/Import)
|
|
* URL1: https://sdw.ecb.europa.eu/quickview.do?SERIES_KEY=133.TRD.M.I8.Y.X.TTT.J8.4.VAL
|
|
* URL2: https://sdw.ecb.europa.eu/quickview.do?SERIES_KEY=133.TRD.M.I8.Y.M.TTT.J8.4.VAL
|
|
* Reference area: Euro area 19 (fixed composition) as of 1 January 2015 (I8)
|
|
* Frequency: Monthly
|
|
"""
|
|
eu_mte = ecb.get_data(datacode="TRD", key="M.I8.Y.X.TTT.J8.4.VAL", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]]
|
|
eu_mti = ecb.get_data(datacode="TRD", key="M.I8.Y.M.TTT.J8.4.VAL", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]]
|
|
eu_mte.columns = ["Date", "EU_MERCHANDISE_TRADE_EXPORT"]
|
|
eu_mte["Date"] = pd.to_datetime(eu_mte["Date"], format="%Y-%m-%d")
|
|
eu_mte["EU_MERCHANDISE_TRADE_IMPORT"] = eu_mti["OBS_VALUE"]
|
|
return eu_mte
|
|
|
|
class international_investment_position(external_sector):
|
|
## National Account (current price)
|
|
def __init__(self):
|
|
super(international_investment_position, self).__init__()
|
|
pass
|
|
|
|
def total_net_international_investment_position(self):
|
|
"""
|
|
* Title: Total financial assets/liabilities
|
|
* URL: https://sdw.ecb.europa.eu/quickview.do?SERIES_KEY=338.BP6.Q.N.I8.W1.S1.S1.LE.N.FA._T.F._Z.EUR._T._X.N
|
|
* Reference area: Euro area 19 (fixed composition) as of 1 January 2015 (I8)
|
|
* Frequency: Quarterly
|
|
"""
|
|
eu_tniip = ecb.get_data(datacode="BP6", key="Q.N.I8.W1.S1.S1.LE.N.FA._T.F._Z.EUR._T._X.N", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]]
|
|
eu_tniip.columns = ["Date", "EU_TOTAL_FINANCIAL_ASSETS"]
|
|
eu_tniip["Date"] = pd.to_datetime(QtoM(eu_tniip["Date"]), format="%Y-%m") + pd.tseries.offsets.MonthBegin(-1)
|
|
return eu_tniip
|
|
|
|
def direct_investment(self):
|
|
"""
|
|
* Title: Direct Investment, Total financial assets/liabilities
|
|
* URL: https://sdw.ecb.europa.eu/quickview.do?SERIES_KEY=338.BP6.Q.N.I8.W1.S1.S1.LE.N.FA.D.F._Z.EUR._T._X.N
|
|
* Reference area: Euro area 19 (fixed composition) as of 1 January 2015 (I8)
|
|
* Frequency: Quarterly
|
|
"""
|
|
eu_di = ecb.get_data(datacode="BP6", key="Q.N.I8.W1.S1.S1.LE.N.FA.D.F._Z.EUR._T._X.N", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]]
|
|
eu_dia = ecb.get_data(datacode="BP6", key="Q.N.I8.W1.S1.S1.LE.A.FA.D.F._Z.EUR._T._X.N", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]]
|
|
eu_dil = ecb.get_data(datacode="BP6", key="Q.N.I8.W1.S1.S1.LE.L.FA.D.F._Z.EUR._T._X.N", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]]
|
|
eu_di.columns = ["Date", "EU_DIRECT_INVESTMENT"]
|
|
eu_dia.columns = ["Date", "EU_DIRECT_INVESTMENT_ASSETS"]
|
|
eu_dil.columns = ["Date", "EU_DIRECT_INVESTMENT_LIABILITIES"]
|
|
eu_di["Date"] = pd.to_datetime(QtoM(eu_di["Date"]), format="%Y-%m") + pd.tseries.offsets.MonthBegin(-1)
|
|
eu_dia["Date"] = pd.to_datetime(QtoM(eu_dia["Date"]), format="%Y-%m") + pd.tseries.offsets.MonthBegin(-1)
|
|
eu_dil["Date"] = pd.to_datetime(QtoM(eu_dil["Date"]), format="%Y-%m") + pd.tseries.offsets.MonthBegin(-1)
|
|
eu_di = pd.merge_asof(eu_di, eu_dia, on="Date")
|
|
eu_di = pd.merge_asof(eu_di, eu_dil, on="Date")
|
|
return eu_di
|
|
|
|
def portfolio_investment(self):
|
|
"""
|
|
* Title: Direct Investment, Total financial assets/liabilities
|
|
* URL: https://sdw.ecb.europa.eu/quickview.do?SERIES_KEY=338.BP6.Q.N.I8.W1.S1.S1.LE.N.FA.P.F._Z.EUR._T.M.N
|
|
* Reference area: Euro area 19 (fixed composition) as of 1 January 2015 (I8)
|
|
* Frequency: Quarterly
|
|
"""
|
|
eu_pi = ecb.get_data(datacode="BP6", key="Q.N.I8.W1.S1.S1.LE.N.FA.P.F._Z.EUR._T.M.N", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]]
|
|
eu_pia = ecb.get_data(datacode="BP6", key="Q.N.I8.W1.S1.S1.LE.A.FA.P.F5._Z.EUR._T.M.N", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]]
|
|
eu_pil = ecb.get_data(datacode="BP6", key="Q.N.I8.W1.S1.S1.LE.L.FA.P.F5._Z.EUR._T.M.N", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]]
|
|
eu_pida = ecb.get_data(datacode="BP6", key="Q.N.I8.W1.S1.S1.LE.A.FA.P.F3.T.EUR._T.M.N", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]]
|
|
eu_pidl = ecb.get_data(datacode="BP6", key="Q.N.I8.W1.S1.S1.LE.L.FA.P.F3.T.EUR._T.M.N", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]]
|
|
eu_pi.columns = ["Date", "EU_PORTFOLIO_INVESTMENT"]
|
|
eu_pia.columns = ["Date", "EU_EQUITY_AND_INVESTMENT_FUND_ASSETS"]
|
|
eu_pil.columns = ["Date", "EU_EQUITY_AND_INVESTMENT_FUND_LIABILITIES"]
|
|
eu_pida.columns = ["Date", "EU_DEBT_SECURITIES_aSSETS"]
|
|
eu_pidl.columns = ["Date", "EU_DEBT_SECURITIES_LIABILITIES"]
|
|
eu_pi["Date"] = pd.to_datetime(QtoM(eu_pi["Date"]), format="%Y-%m") + pd.tseries.offsets.MonthBegin(-1)
|
|
eu_pia["Date"] = pd.to_datetime(QtoM(eu_pia["Date"]), format="%Y-%m") + pd.tseries.offsets.MonthBegin(-1)
|
|
eu_pil["Date"] = pd.to_datetime(QtoM(eu_pil["Date"]), format="%Y-%m") + pd.tseries.offsets.MonthBegin(-1)
|
|
eu_pida["Date"] = pd.to_datetime(QtoM(eu_pil["Date"]), format="%Y-%m") + pd.tseries.offsets.MonthBegin(-1)
|
|
eu_pidl["Date"] = pd.to_datetime(QtoM(eu_pil["Date"]), format="%Y-%m") + pd.tseries.offsets.MonthBegin(-1)
|
|
eu_pi = pd.merge_asof(eu_pi, eu_pia, on="Date")
|
|
eu_pi = pd.merge_asof(eu_pi, eu_pil, on="Date")
|
|
eu_pi = pd.merge_asof(eu_pi, eu_pida, on="Date")
|
|
eu_pi = pd.merge_asof(eu_pi, eu_pidl, on="Date")
|
|
return eu_pi
|
|
|
|
def other_investment(self):
|
|
"""
|
|
* Title: Other Investment, Total financial assets/liabilities
|
|
* URL: https://sdw.ecb.europa.eu/quickview.do?SERIES_KEY=338.BP6.Q.N.I8.W1.S1.S1.LE.N.FA.O.F._Z.EUR._T._X.N
|
|
* Reference area: Euro area 19 (fixed composition) as of 1 January 2015 (I8)
|
|
* Frequency: Quarterly
|
|
"""
|
|
eu_oi = ecb.get_data(datacode="BP6", key="Q.N.I8.W1.S1.S1.LE.N.FA.O.F._Z.EUR._T._X.N", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]]
|
|
eu_oia = ecb.get_data(datacode="BP6", key="Q.N.I8.W1.S1.S1.LE.A.FA.O.F._Z.EUR._T._X.N", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]]
|
|
eu_oil = ecb.get_data(datacode="BP6", key="Q.N.I8.W1.S1.S1.LE.L.FA.O.F._Z.EUR._T._X.N", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]]
|
|
eu_oi.columns = ["Date", "EU_DIRECT_INVESTMENT"]
|
|
eu_oia.columns = ["Date", "EU_DIRECT_INVESTMENT_ASSETS"]
|
|
eu_oil.columns = ["Date", "EU_DIRECT_INVESTMENT_LIABILITIES"]
|
|
eu_oi["Date"] = pd.to_datetime(QtoM(eu_oi["Date"]), format="%Y-%m") + pd.tseries.offsets.MonthBegin(-1)
|
|
eu_oia["Date"] = pd.to_datetime(QtoM(eu_oia["Date"]), format="%Y-%m") + pd.tseries.offsets.MonthBegin(-1)
|
|
eu_oil["Date"] = pd.to_datetime(QtoM(eu_oil["Date"]), format="%Y-%m") + pd.tseries.offsets.MonthBegin(-1)
|
|
eu_oi = pd.merge_asof(eu_oi, eu_oia, on="Date")
|
|
eu_oi = pd.merge_asof(eu_oi, eu_oil, on="Date")
|
|
return eu_di
|
|
|
|
def reserve_assetsc(self):
|
|
"""
|
|
* Title: Reserve Assets, Total financial assets/liabilities
|
|
* URL: https://sdw.ecb.europa.eu/quickview.do?SERIES_KEY=338.BP6.Q.N.I8.W1.S121.S1.LE.A.FA.R.F._Z.EUR.X1._X.N
|
|
* Reference area: Euro area 19 (fixed composition) as of 1 January 2015 (I8)
|
|
* Frequency: Quarterly
|
|
"""
|
|
eu_ra = ecb.get_data(datacode="BP6", key="Q.N.I8.W1.S121.S1.LE.A.FA.R.F._Z.EUR.X1._X.N", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]]
|
|
eu_ra.columns = ["Date", "EU_OTHER_INVESTMENT_ASSETS"]
|
|
eu_ra["Date"] = pd.to_datetime(QtoM(eu_ra["Date"]), format="%Y-%m") + pd.tseries.offsets.MonthBegin(-1)
|
|
return eu_ra
|
|
|
|
def gross_external_debt(self):
|
|
"""
|
|
* Title: Gross external debt
|
|
* URL: https://sdw.ecb.europa.eu/quickview.do?SERIES_KEY=338.BP6.Q.N.I8.W1.S121.S1.LE.A.FA.R.F._Z.EUR.X1._X.N
|
|
* Reference area: Euro area 19 (fixed composition) as of 1 January 2015 (I8)
|
|
* Frequency: Quarterly
|
|
"""
|
|
eu_ged = ecb.get_data(datacode="BP6", key="Q.N.I8.W1.S1.S1.LE.L.FA._T.FGED._Z.EUR._T._X.N", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]]
|
|
eu_ged.columns = ["Date", "EU_GROSS_EXTERNAL_DEBT"]
|
|
eu_ged["Date"] = pd.to_datetime(QtoM(eu_ged["Date"]), format="%Y-%m") + pd.tseries.offsets.MonthBegin(-1)
|
|
return eu_ged
|
|
|
|
if __name__ == "__main__":
|
|
data, name_list = CPI_monthly()
|