885 lines
32 KiB
Python
885 lines
32 KiB
Python
import pandas as pd
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import numpy as np
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import requests
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from fake_useragent import UserAgent
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import io
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import os
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import demjson
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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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"philfed": "https://www.philadelphiafed.org/surveys-and-data/real-time-data-research/",
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"chicagofed": "https://www.chicagofed.org/~/media/publications/"}
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def gdp_quarterly(startdate="1947-01-01", enddate="2021-01-01"):
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"""
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Full Name: Gross Domestic Product
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Description: Billions of Dollars, Quarterly, Seasonally Adjusted Annual Rate
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Return: pd.DataFrame
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"""
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tmp_url = url["fred_econ"]
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ua = UserAgent(verify_ssl=False)
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request_header = {"User-Agent": ua.random}
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request_params = {
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"id": "GDP",
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"cosd": "{}".format(startdate),
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"coed": "{}".format(enddate)
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}
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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 = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
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return df
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def gdpc1_quarterly(startdate="1947-01-01", enddate="2021-01-01"):
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"""
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Full Name: Real Gross Domestic Product
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Description: Billions of Chained 2012 Dollars, Quarterly, Seasonally Adjusted Annual Rate
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Return: pd.DataFrame
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"""
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tmp_url = url["fred_econ"]
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ua = UserAgent(verify_ssl=False)
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request_header = {"User-Agent": ua.random}
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request_params = {
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"id": "GDPC1",
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"cosd": "{}".format(startdate),
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"coed": "{}".format(enddate)
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}
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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 = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
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return df
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def oecd_gdp_monthly(startdate="1947-01-01", enddate="2021-01-01"):
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"""
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Full Name: Real Gross Domestic Product
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Description: Billions of Chained 2012 Dollars, Quarterly, Seasonally Adjusted Annual Rate
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Return: pd.DataFrame
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"""
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tmp_url = url["fred_econ"]
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ua = UserAgent(verify_ssl=False)
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request_header = {"User-Agent": ua.random}
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request_params = {
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"id": "USALORSGPNOSTSAM",
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"cosd": "{}".format(startdate),
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"coed": "{}".format(enddate)
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}
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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 = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
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return df
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def payems_monthly(startdate="1939-01-01", enddate="2021-01-01"):
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"""
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Full Name: All Employees, Total Nonfarm
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Description: Thousands of Persons,Seasonally Adjusted, Monthly
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Return: pd.DataFrame
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"""
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tmp_url = url["fred_econ"]
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ua = UserAgent(verify_ssl=False)
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request_header = {"User-Agent": ua.random}
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request_params = {
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"id": "PAYEMS",
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"cosd": "{}".format(startdate),
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"coed": "{}".format(enddate)
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}
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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 = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
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return df
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def unrate(startdate="1948-01-01", enddate="2021-01-01"):
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"""
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Full Name: Unemployment Rate: Aged 15-64: All Persons for the United States
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Description: Percent, Seasonally Adjusted, Monthly, Quarterly and Annually
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Return: pd.DataFrame
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"""
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tmp_url = url["fred_econ"]
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ua = UserAgent(verify_ssl=False)
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request_header = {"User-Agent": ua.random}
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request_params = {
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"id": "LRUN64TTUSM156S",
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"cosd": "{}".format(startdate),
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"coed": "{}".format(enddate)
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}
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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_monthly = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
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df_monthly["DATE"] = pd.to_datetime(df_monthly["DATE"], format="%Y-%m-%d")
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ua = UserAgent(verify_ssl=False)
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request_header = {"User-Agent": ua.random}
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request_params = {
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"id": "LRUN64TTUSQ156S",
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"cosd": "{}".format(startdate),
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"coed": "{}".format(enddate)
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}
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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_quarterly = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
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df_quarterly["DATE"] = pd.to_datetime(
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df_quarterly["DATE"], format="%Y-%m-%d")
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ua = UserAgent(verify_ssl=False)
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request_header = {"User-Agent": ua.random}
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request_params = {
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"id": "LRUN64TTUSA156S",
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"cosd": "{}".format(startdate),
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"coed": "{}".format(enddate)
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}
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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_annually = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
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df_annually["DATE"] = pd.to_datetime(
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df_annually["DATE"], format="%Y-%m-%d")
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df = pd.merge_asof(
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df_monthly,
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df_quarterly,
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on="DATE",
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direction="backward")
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df = pd.merge_asof(df, df_annually, on="DATE", direction="backward")
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df.columns = ["Date", "UR_Monthly", "UR_Quarterly", "UR_Annually"]
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return df
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def erate(startdate="1955-01-01", enddate="2021-01-01"):
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"""
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Full Name: Employment Rate: Aged 25-54: All Persons for the United States
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Description: Percent,Seasonally Adjusted, Monthly, Quarterly and Annually
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Return: pd.DataFrame
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"""
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tmp_url = url["fred_econ"]
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ua = UserAgent(verify_ssl=False)
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request_header = {"User-Agent": ua.random}
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request_params = {
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"id": "LREM25TTUSM156S",
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"cosd": "{}".format(startdate),
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"coed": "{}".format(enddate)
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}
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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_monthly = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
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df_monthly["DATE"] = pd.to_datetime(df_monthly["DATE"], format="%Y-%m-%d")
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ua = UserAgent(verify_ssl=False)
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request_header = {"User-Agent": ua.random}
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request_params = {
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"id": "LREM25TTUSQ156S",
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"cosd": "{}".format(startdate),
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"coed": "{}".format(enddate)
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}
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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_quarterly = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
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df_quarterly["DATE"] = pd.to_datetime(
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df_quarterly["DATE"], format="%Y-%m-%d")
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ua = UserAgent(verify_ssl=False)
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request_header = {"User-Agent": ua.random}
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request_params = {
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"id": "LREM25TTUSA156S",
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"cosd": "{}".format(startdate),
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"coed": "{}".format(enddate)
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}
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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_annually = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
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df_annually["DATE"] = pd.to_datetime(
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df_annually["DATE"], format="%Y-%m-%d")
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df = pd.merge_asof(
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df_monthly,
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df_quarterly,
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on="DATE",
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direction="backward")
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df = pd.merge_asof(df, df_annually, on="DATE", direction="backward")
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df.columns = ["Date", "ER_Monthly", "ER_Quarterly", "ER_Annually"]
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def pce_monthly(startdate="1959-01-01", enddate="2021-01-01"):
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"""
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Full Name: PCE
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Description: Percent, Monthly, Seasonally Adjusted
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Return: pd.DataFrame
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"""
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tmp_url = url["fred_econ"]
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ua = UserAgent(verify_ssl=False)
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request_header = {"User-Agent": ua.random}
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request_params = {
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"id": "PCE",
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"cosd": "{}".format(startdate),
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"coed": "{}".format(enddate)
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}
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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 = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
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return df
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def cpi(startdate="1960-01-01", enddate="2021-01-01"):
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"""
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Full Name: Consumer Price Index: Total All Items for the United States
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Description: Percent, Monthly, Quarterly and Annually, Seasonally Adjusted
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Return: pd.DataFrame
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"""
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tmp_url = url["fred_econ"]
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ua = UserAgent(verify_ssl=False)
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request_header = {"User-Agent": ua.random}
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request_params = {
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"id": "CPALTT01USM661S",
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"cosd": "{}".format(startdate),
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"coed": "{}".format(enddate)
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}
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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_monthly = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
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df_monthly["DATE"] = pd.to_datetime(df_monthly["DATE"], format="%Y-%m-%d")
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ua = UserAgent(verify_ssl=False)
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request_header = {"User-Agent": ua.random}
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request_params = {
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"id": "CPALTT01USQ661S",
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"cosd": "{}".format(startdate),
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"coed": "{}".format(enddate)
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}
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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_quarterly = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
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df_quarterly["DATE"] = pd.to_datetime(
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df_quarterly["DATE"], format="%Y-%m-%d")
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ua = UserAgent(verify_ssl=False)
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request_header = {"User-Agent": ua.random}
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request_params = {
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"id": "CPALTT01USA661S",
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"cosd": "{}".format(startdate),
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"coed": "{}".format(enddate)
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}
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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_annually = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
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df_annually["DATE"] = pd.to_datetime(
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df_annually["DATE"], format="%Y-%m-%d")
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df = pd.merge_asof(
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df_monthly,
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df_quarterly,
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on="DATE",
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direction="backward")
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df = pd.merge_asof(df, df_annually, on="DATE", direction="backward")
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df.columns = ["Date", "CPI_Monthly", "CPI_Quarterly", "CPI_Annually"]
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return df
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def m1(startdate="1960-01-01", enddate="2021-01-01"):
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"""
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Full Name: Consumer Price Index: M3 for the United States
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Description: Growth Rate Previous Period, Monthly, Quarterly and Annually, Seasonally Adjusted
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Return: pd.DataFrame
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"""
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tmp_url = url["fred_econ"]
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ua = UserAgent(verify_ssl=False)
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request_header = {"User-Agent": ua.random}
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request_params = {
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"id": "WM1NS",
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"cosd": "{}".format(startdate),
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"coed": "{}".format(enddate)
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}
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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_weekly = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
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df_weekly["DATE"] = pd.to_datetime(df_weekly["DATE"], format="%Y-%m-%d")
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ua = UserAgent(verify_ssl=False)
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request_header = {"User-Agent": ua.random}
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request_params = {
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"id": "MANMM101USM657S",
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"cosd": "{}".format(startdate),
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"coed": "{}".format(enddate)
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}
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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_monthly = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
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df_monthly["DATE"] = pd.to_datetime(df_monthly["DATE"], format="%Y-%m-%d")
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ua = UserAgent(verify_ssl=False)
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request_header = {"User-Agent": ua.random}
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request_params = {
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"id": "MANMM101USQ657S",
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"cosd": "{}".format(startdate),
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"coed": "{}".format(enddate)
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}
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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_quarterly = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
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df_quarterly["DATE"] = pd.to_datetime(
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df_quarterly["DATE"], format="%Y-%m-%d")
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ua = UserAgent(verify_ssl=False)
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request_header = {"User-Agent": ua.random}
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request_params = {
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"id": "MANMM101USA657S",
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"cosd": "{}".format(startdate),
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"coed": "{}".format(enddate)
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}
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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_annually = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
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df_annually["DATE"] = pd.to_datetime(
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df_annually["DATE"], format="%Y-%m-%d")
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df = pd.merge_asof(df_weekly, df_monthly, on="DATE", direction="backward")
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df = pd.merge_asof(df, df_quarterly, on="DATE", direction="backward")
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df = pd.merge_asof(df, df_annually, on="DATE", direction="backward")
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df.columns = [
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"Date",
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"M1_weekly",
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"M1_Monthly",
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"M1_Quarterly",
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"M1_Annually"]
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return df
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def m2(startdate="1960-01-01", enddate="2021-01-01"):
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"""
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Full Name: M2 Money Stock
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Description: Seasonally Adjusted, Weekly, Monthly, Quarterly and Annually, Seasonally Adjusted
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Return: pd.DataFrame
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"""
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tmp_url = url["fred_econ"]
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ua = UserAgent(verify_ssl=False)
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request_header = {"User-Agent": ua.random}
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request_params = {
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"id": "WM2NS",
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"cosd": "{}".format(startdate),
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"coed": "{}".format(enddate)
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}
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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_weekly = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
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df_weekly["DATE"] = pd.to_datetime(df_weekly["DATE"], format="%Y-%m-%d")
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ua = UserAgent(verify_ssl=False)
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request_header = {"User-Agent": ua.random}
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request_params = {
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"id": "M2SL",
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"cosd": "{}".format(startdate),
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"coed": "{}".format(enddate)
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}
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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_monthly = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
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df_monthly["DATE"] = pd.to_datetime(df_monthly["DATE"], format="%Y-%m-%d")
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df = pd.merge_asof(df_weekly, df_monthly, on="DATE", direction="backward")
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df.columns = ["Date", "M2_Weekly", "M2_Monthly"]
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def m3(startdate="1960-01-01", enddate="2021-01-01"):
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"""
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Full Name: Consumer Price Index: M3 for the United States
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Description: Growth Rate Previous Period, Monthly, Quarterly and Annually, Seasonally Adjusted
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Return: pd.DataFrame
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"""
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tmp_url = url["fred_econ"]
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ua = UserAgent(verify_ssl=False)
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request_header = {"User-Agent": ua.random}
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request_params = {
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"id": "MABMM301USM657S",
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"cosd": "{}".format(startdate),
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"coed": "{}".format(enddate)
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}
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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_monthly = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
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df_monthly["DATE"] = pd.to_datetime(df_monthly["DATE"], format="%Y-%m-%d")
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ua = UserAgent(verify_ssl=False)
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request_header = {"User-Agent": ua.random}
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request_params = {
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"id": "MABMM301USQ657S",
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"cosd": "{}".format(startdate),
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"coed": "{}".format(enddate)
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}
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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_quarterly = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
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df_quarterly["DATE"] = pd.to_datetime(
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df_quarterly["DATE"], format="%Y-%m-%d")
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ua = UserAgent(verify_ssl=False)
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request_header = {"User-Agent": ua.random}
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request_params = {
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"id": "MABMM301USA657S",
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"cosd": "{}".format(startdate),
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"coed": "{}".format(enddate)
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}
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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_annually = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
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df_annually["DATE"] = pd.to_datetime(
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df_annually["DATE"], format="%Y-%m-%d")
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df = pd.merge_asof(
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df_monthly,
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df_quarterly,
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on="DATE",
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direction="backward")
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df = pd.merge_asof(df, df_annually, on="DATE", direction="backward")
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df.columns = ["Date", "M3_Monthly", "M3_Quarterly", "M3_Annually"]
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return df
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def ltgby_10(startdate="1955-01-01", enddate="2021-01-01"):
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"""
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Full Name: Long-Term Government Bond Yields: 10-year: Main (Including Benchmark) for the United States
|
|
Description: Percent,Not Seasonally Adjusted, Monthly, Quarterly and Annually
|
|
Return: pd.DataFrame
|
|
"""
|
|
tmp_url = url["fred_econ"]
|
|
ua = UserAgent(verify_ssl=False)
|
|
request_header = {"User-Agent": ua.random}
|
|
request_params = {
|
|
"id": "IRLTLT01USM156N",
|
|
"cosd": "{}".format(startdate),
|
|
"coed": "{}".format(enddate)
|
|
}
|
|
r = requests.get(tmp_url, params=request_params, headers=request_header)
|
|
data_text = r.content
|
|
df_monthly = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
|
|
df_monthly["DATE"] = pd.to_datetime(df_monthly["DATE"], format="%Y-%m-%d")
|
|
ua = UserAgent(verify_ssl=False)
|
|
request_header = {"User-Agent": ua.random}
|
|
request_params = {
|
|
"id": "IRLTLT01USQ156N",
|
|
"cosd": "{}".format(startdate),
|
|
"coed": "{}".format(enddate)
|
|
}
|
|
r = requests.get(tmp_url, params=request_params, headers=request_header)
|
|
data_text = r.content
|
|
df_quarterly = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
|
|
df_quarterly["DATE"] = pd.to_datetime(
|
|
df_quarterly["DATE"], format="%Y-%m-%d")
|
|
ua = UserAgent(verify_ssl=False)
|
|
request_header = {"User-Agent": ua.random}
|
|
request_params = {
|
|
"id": "IRLTLT01USA156N",
|
|
"cosd": "{}".format(startdate),
|
|
"coed": "{}".format(enddate)
|
|
}
|
|
r = requests.get(tmp_url, params=request_params, headers=request_header)
|
|
data_text = r.content
|
|
df_annually = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
|
|
df_annually["DATE"] = pd.to_datetime(
|
|
df_annually["DATE"], format="%Y-%m-%d")
|
|
df = pd.merge_asof(
|
|
df_monthly,
|
|
df_quarterly,
|
|
on="DATE",
|
|
direction="backward")
|
|
df = pd.merge_asof(df, df_annually, on="DATE", direction="backward")
|
|
df.columns = ["Date", "ltgby_Monthly", "ltgby_Quarterly", "ltgby_Annually"]
|
|
return df
|
|
|
|
|
|
def gdp_ipd(startdate="1955-01-01", enddate="2021-01-01"):
|
|
"""
|
|
Full Name: Long-Term Government Bond Yields: 10-year: Main (Including Benchmark) for the United States
|
|
Description: Percent,Not Seasonally Adjusted, Monthly, Quarterly and Annually
|
|
Return: pd.DataFrame
|
|
"""
|
|
tmp_url = url["fred_econ"]
|
|
ua = UserAgent(verify_ssl=False)
|
|
request_header = {"User-Agent": ua.random}
|
|
request_params = {
|
|
"id": "USAGDPDEFQISMEI",
|
|
"cosd": "{}".format(startdate),
|
|
"coed": "{}".format(enddate)
|
|
}
|
|
r = requests.get(tmp_url, params=request_params, headers=request_header)
|
|
data_text = r.content
|
|
df_quarterly = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
|
|
df_quarterly["DATE"] = pd.to_datetime(
|
|
df_quarterly["DATE"], format="%Y-%m-%d")
|
|
ua = UserAgent(verify_ssl=False)
|
|
request_header = {"User-Agent": ua.random}
|
|
request_params = {
|
|
"id": "USAGDPDEFAISMEI",
|
|
"cosd": "{}".format(startdate),
|
|
"coed": "{}".format(enddate)
|
|
}
|
|
r = requests.get(tmp_url, params=request_params, headers=request_header)
|
|
data_text = r.content
|
|
df_annually = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
|
|
df_annually["DATE"] = pd.to_datetime(
|
|
df_annually["DATE"], format="%Y-%m-%d")
|
|
df = pd.merge_asof(
|
|
df_quarterly,
|
|
df_annually,
|
|
on="DATE",
|
|
direction="backward")
|
|
df.columns = ["Date", "gdp_ipd_Quarterly", "gdp_ipd_Annually"]
|
|
return df
|
|
|
|
|
|
def cci(startdate="1955-01-01", enddate="2021-01-01"):
|
|
"""
|
|
Full Name: Consumer Opinion Surveys: Confidence Indicators: Composite Indicators: OECD Indicator for the United States
|
|
Description: Normalised (Normal=100), Seasonally Adjusted, Monthly
|
|
Return: pd.DataFrame
|
|
"""
|
|
tmp_url = url["fred_econ"]
|
|
ua = UserAgent(verify_ssl=False)
|
|
request_header = {"User-Agent": ua.random}
|
|
request_params = {
|
|
"id": "CSCICP03USM665S",
|
|
"cosd": "{}".format(startdate),
|
|
"coed": "{}".format(enddate)
|
|
}
|
|
r = requests.get(tmp_url, params=request_params, headers=request_header)
|
|
data_text = r.content
|
|
df = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
|
|
df.columns = ["Date", "CCI_Monthly"]
|
|
return df
|
|
|
|
|
|
def bci(startdate="1955-01-01", enddate="2021-01-01"):
|
|
"""
|
|
Full Name: Consumer Opinion Surveys: Confidence Indicators: Composite Indicators: OECD Indicator for the United States
|
|
Description: Normalised (Normal=100), Seasonally Adjusted, Monthly
|
|
Return: pd.DataFrame
|
|
"""
|
|
tmp_url = url["fred_econ"]
|
|
ua = UserAgent(verify_ssl=False)
|
|
request_header = {"User-Agent": ua.random}
|
|
request_params = {
|
|
"id": "BSCICP03USM665S",
|
|
"cosd": "{}".format(startdate),
|
|
"coed": "{}".format(enddate)
|
|
}
|
|
r = requests.get(tmp_url, params=request_params, headers=request_header)
|
|
data_text = r.content
|
|
df = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
|
|
df.columns = ["Date", "BCI_Annually"]
|
|
return df
|
|
|
|
|
|
def ibr_3(startdate="1965-01-01", enddate="2021-01-01"):
|
|
"""
|
|
Full Name: 3-Month or 90-day Rates and Yields: Interbank Rates for the United States
|
|
Description: Percent, Not Seasonally Adjusted, Monthly and Quarterly
|
|
"""
|
|
tmp_url = url["fred_econ"]
|
|
ua = UserAgent(verify_ssl=False)
|
|
request_header = {"User-Agent": ua.random}
|
|
request_params = {
|
|
"id": "IR3TIB01USM156N",
|
|
"cosd": "{}".format(startdate),
|
|
"coed": "{}".format(enddate)
|
|
}
|
|
r = requests.get(tmp_url, params=request_params, headers=request_header)
|
|
data_text = r.content
|
|
df_monthly = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
|
|
df_monthly["DATE"] = pd.to_datetime(df_monthly["DATE"], format="%Y-%m-%d")
|
|
ua = UserAgent(verify_ssl=False)
|
|
request_header = {"User-Agent": ua.random}
|
|
request_params = {
|
|
"id": "IR3TIB01USQ156N",
|
|
"cosd": "{}".format(startdate),
|
|
"coed": "{}".format(enddate)
|
|
}
|
|
r = requests.get(tmp_url, params=request_params, headers=request_header)
|
|
data_text = r.content
|
|
df_quarterly = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
|
|
df_quarterly["DATE"] = pd.to_datetime(
|
|
df_quarterly["DATE"], format="%Y-%m-%d")
|
|
df = pd.merge_asof(
|
|
df_quarterly,
|
|
df_quarterly,
|
|
on="DATE",
|
|
direction="backward")
|
|
df.columns = ["Date", "ibr3_monthly", "ibr3_Quarterly"]
|
|
|
|
|
|
def gfcf_3(startdate="1965-01-01", enddate="2021-01-01"):
|
|
"""
|
|
Full Name: Gross Fixed Capital Formation in United States
|
|
Description: United States Dollars,Not Seasonally Adjusted, Quarterly and Annually
|
|
"""
|
|
tmp_url = url["fred_econ"]
|
|
ua = UserAgent(verify_ssl=False)
|
|
request_header = {"User-Agent": ua.random}
|
|
request_params = {
|
|
"id": "USAGFCFQDSMEI",
|
|
"cosd": "{}".format(startdate),
|
|
"coed": "{}".format(enddate)
|
|
}
|
|
r = requests.get(tmp_url, params=request_params, headers=request_header)
|
|
data_text = r.content
|
|
df_quarterly = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
|
|
df_quarterly["DATE"] = pd.to_datetime(
|
|
df_quarterly["DATE"], format="%Y-%m-%d")
|
|
ua = UserAgent(verify_ssl=False)
|
|
request_header = {"User-Agent": ua.random}
|
|
request_params = {
|
|
"id": "USAGFCFADSMEI",
|
|
"cosd": "{}".format(startdate),
|
|
"coed": "{}".format(enddate)
|
|
}
|
|
r = requests.get(tmp_url, params=request_params, headers=request_header)
|
|
data_text = r.content
|
|
df_annually = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
|
|
df_annually["DATE"] = pd.to_datetime(
|
|
df_annually["DATE"], format="%Y-%m-%d")
|
|
df = pd.merge_asof(
|
|
df_quarterly,
|
|
df_quarterly,
|
|
on="DATE",
|
|
direction="backward")
|
|
df.columns = ["Date", "ibr3_monthly", "ibr3_Annually"]
|
|
|
|
|
|
def pfce(startdate="1955-01-01", enddate="2021-01-01"):
|
|
"""
|
|
Full Name: Employment Rate: Aged 25-54: All Persons for the United States
|
|
Description: Percent,Seasonally Adjusted, Monthly, Quarterly and Annually
|
|
Return: pd.DataFrame
|
|
"""
|
|
tmp_url = url["fred_econ"]
|
|
ua = UserAgent(verify_ssl=False)
|
|
request_header = {"User-Agent": ua.random}
|
|
request_params = {
|
|
"id": "USAPFCEQDSMEI",
|
|
"cosd": "{}".format(startdate),
|
|
"coed": "{}".format(enddate)
|
|
}
|
|
r = requests.get(tmp_url, params=request_params, headers=request_header)
|
|
data_text = r.content
|
|
df_quarterly = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
|
|
df_quarterly["DATE"] = pd.to_datetime(
|
|
df_quarterly["DATE"], format="%Y-%m-%d")
|
|
ua = UserAgent(verify_ssl=False)
|
|
request_header = {"User-Agent": ua.random}
|
|
request_params = {
|
|
"id": "USAPFCEADSMEI",
|
|
"cosd": "{}".format(startdate),
|
|
"coed": "{}".format(enddate)
|
|
}
|
|
r = requests.get(tmp_url, params=request_params, headers=request_header)
|
|
data_text = r.content
|
|
df_annually = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
|
|
df_annually["DATE"] = pd.to_datetime(
|
|
df_annually["DATE"], format="%Y-%m-%d")
|
|
df = pd.merge_asof(
|
|
df_quarterly,
|
|
df_annually,
|
|
on="DATE",
|
|
direction="backward")
|
|
df.columns = ["Date", "PFCE_Quarterly", "PFCE_Annually"]
|
|
|
|
|
|
def tlp(startdate="1955-01-01", enddate="2021-01-01"):
|
|
"""
|
|
Full Name: Early Estimate of Quarterly ULC Indicators: Total Labor Productivity for the United States
|
|
Description: Growth Rate Previous Period,Seasonally Adjusted, Quarterly and YoY
|
|
Return: pd.DataFrame
|
|
"""
|
|
tmp_url = url["fred_econ"]
|
|
ua = UserAgent(verify_ssl=False)
|
|
request_header = {"User-Agent": ua.random}
|
|
request_params = {
|
|
"id": "ULQELP01USQ657S",
|
|
"cosd": "{}".format(startdate),
|
|
"coed": "{}".format(enddate)
|
|
}
|
|
r = requests.get(tmp_url, params=request_params, headers=request_header)
|
|
data_text = r.content
|
|
df_quarterly = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
|
|
df_quarterly["DATE"] = pd.to_datetime(
|
|
df_quarterly["DATE"], format="%Y-%m-%d")
|
|
ua = UserAgent(verify_ssl=False)
|
|
request_header = {"User-Agent": ua.random}
|
|
request_params = {
|
|
"id": "ULQELP01USQ659S",
|
|
"cosd": "{}".format(startdate),
|
|
"coed": "{}".format(enddate)
|
|
}
|
|
r = requests.get(tmp_url, params=request_params, headers=request_header)
|
|
data_text = r.content
|
|
df_annually = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
|
|
df_annually["DATE"] = pd.to_datetime(
|
|
df_annually["DATE"], format="%Y-%m-%d")
|
|
df = pd.merge_asof(
|
|
df_quarterly,
|
|
df_annually,
|
|
on="DATE",
|
|
direction="backward")
|
|
df.columns = ["Date", "PFCE_Quarterly", "PFCE_Quarterly_YoY"]
|
|
|
|
|
|
def rt(startdate="1955-01-01", enddate="2021-01-01"):
|
|
"""
|
|
Full Name:Total Retail Trade in United States
|
|
Description: Monthly and Anually
|
|
Return: pd.DataFrame
|
|
"""
|
|
tmp_url = url["fred_econ"]
|
|
ua = UserAgent(verify_ssl=False)
|
|
request_header = {"User-Agent": ua.random}
|
|
request_params = {
|
|
"id": "USASARTMISMEI",
|
|
"cosd": "{}".format(startdate),
|
|
"coed": "{}".format(enddate)
|
|
}
|
|
r = requests.get(tmp_url, params=request_params, headers=request_header)
|
|
data_text = r.content
|
|
df_monthly = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
|
|
df_monthly["DATE"] = pd.to_datetime(df_monthly["DATE"], format="%Y-%m-%d")
|
|
request_header = {"User-Agent": ua.random}
|
|
request_params = {
|
|
"id": "USASARTAISMEI",
|
|
"cosd": "{}".format(startdate),
|
|
"coed": "{}".format(enddate)
|
|
}
|
|
r = requests.get(tmp_url, params=request_params, headers=request_header)
|
|
data_text = r.content
|
|
df_annually = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
|
|
df_annually["DATE"] = pd.to_datetime(
|
|
df_annually["DATE"], format="%Y-%m-%d")
|
|
df = pd.merge_asof(
|
|
df_monthly,
|
|
df_annually,
|
|
on="DATE",
|
|
direction="backward")
|
|
df.columns = ["Date", "RT_Quarterly", "RT_Annually"]
|
|
return df
|
|
|
|
|
|
def bir(startdate="2003-01-01", enddate="2021-01-01"):
|
|
"""
|
|
Full Name:Total Retail Trade in United States
|
|
Description: Monthly and Anually
|
|
Return: pd.DataFrame
|
|
"""
|
|
tmp_url = url["fred_econ"]
|
|
ua = UserAgent(verify_ssl=False)
|
|
request_header = {"User-Agent": ua.random}
|
|
request_params = {
|
|
"id": "T5YIE",
|
|
"cosd": "{}".format(startdate),
|
|
"coed": "{}".format(enddate)
|
|
}
|
|
r = requests.get(tmp_url, params=request_params, headers=request_header)
|
|
data_text = r.content
|
|
df_5y = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
|
|
df_5y["DATE"] = pd.to_datetime(df_5y["DATE"], format="%Y-%m-%d")
|
|
request_header = {"User-Agent": ua.random}
|
|
request_params = {
|
|
"id": "T10YIE",
|
|
"cosd": "{}".format(startdate),
|
|
"coed": "{}".format(enddate)
|
|
}
|
|
r = requests.get(tmp_url, params=request_params, headers=request_header)
|
|
data_text = r.content
|
|
df_10y = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
|
|
df_10y["DATE"] = pd.to_datetime(df_10y["DATE"], format="%Y-%m-%d")
|
|
df = pd.merge_asof(df_5y, df_10y, on="DATE", direction="backward")
|
|
df.columns = ["Date", "BIR_5y", "BIR_10y"]
|
|
return df
|
|
|
|
|
|
def adsbci():
|
|
"""
|
|
An index designed to track real business conditions at high observation frequency
|
|
"""
|
|
ua = UserAgent(verify_ssl=False)
|
|
request_header = {"User-Agent": ua.random}
|
|
tmp_url = url["philfed"] + "ads"
|
|
r = requests.get(tmp_url, headers=request_header)
|
|
file = open("ads_temp.xls", "wb")
|
|
file.write(r.content)
|
|
file.close()
|
|
df = pd.read_excel("ads_temp.xls")
|
|
df.columns = ["Date", "ADS_Index"]
|
|
df['Date'] = pd.to_datetime(df["Date"], format="%Y:%m:%d")
|
|
os.remove("ads_temp.xls")
|
|
return df
|
|
|
|
|
|
def pci():
|
|
"""
|
|
Tracks the degree of political disagreement among U.S. politicians at the federal level, Monthly
|
|
"""
|
|
df = pd.read_excel(
|
|
"https://www.philadelphiafed.org/-/media/frbp/assets/data-visualizations/partisan-conflict.xlsx")
|
|
df["Date"] = df["Year"].astype(str) + df["Month"]
|
|
df["Date"] = pd.to_datetime(df["Date"], format="%Y%B")
|
|
df = df.drop(["Year", "Month"], axis=1)
|
|
df = df[["Date", "Partisan Conflict"]]
|
|
return df
|
|
|
|
|
|
def inflation_noewcasting():
|
|
"""
|
|
|
|
"""
|
|
ua = UserAgent(verify_ssl=False)
|
|
request_header = {"User-Agent": ua.random}
|
|
tmp_url = "https://www.clevelandfed.org/~/media/files/charting/%20nowcast_quarter.json"
|
|
|
|
r = requests.get(tmp_url, headers=request_header)
|
|
tmp_df = pd.DataFrame(demjson.decode(r.text))
|
|
df = pd.DataFrame()
|
|
for i in range(0, len(tmp_df)):
|
|
date = tmp_df['chart'][i]['subcaption'][:4] + "/" + \
|
|
pd.DataFrame(tmp_df["dataset"][i][0]['data'])['tooltext'].str.extract(r"\b(0?[1-9]|1[0-2])/(0?[1-9]|[12][0-9]|3[01])\b")[0] + "/" + \
|
|
pd.DataFrame(tmp_df["dataset"][i][0]['data'])['tooltext'].str.extract(r"\b(0?[1-9]|1[0-2])/(0?[1-9]|[12][0-9]|3[01])\b")[1]
|
|
CPI_I = pd.DataFrame(
|
|
(pd.DataFrame(tmp_df["dataset"][i])['data'])[0])["value"]
|
|
C_CPI_I = pd.DataFrame(
|
|
(pd.DataFrame(tmp_df["dataset"][i])['data'])[1])["value"]
|
|
PCE_I = pd.DataFrame(
|
|
(pd.DataFrame(tmp_df["dataset"][i])['data'])[2])["value"]
|
|
C_PCE_I = pd.DataFrame(
|
|
(pd.DataFrame(tmp_df["dataset"][i])['data'])[3])["value"]
|
|
A_CPI_I = pd.DataFrame(
|
|
(pd.DataFrame(tmp_df["dataset"][i])['data'])[4])["value"]
|
|
A_C_CPI_I = pd.DataFrame(
|
|
(pd.DataFrame(tmp_df["dataset"][i])['data'])[5])["value"]
|
|
A_PCE_I = pd.DataFrame(
|
|
(pd.DataFrame(tmp_df["dataset"][i])['data'])[6])["value"]
|
|
A_C_PCE_I = pd.DataFrame(
|
|
(pd.DataFrame(tmp_df["dataset"][i])['data'])[7])["value"]
|
|
tmp_df2 = pd.DataFrame({"date": date,
|
|
"CPI_I": CPI_I,
|
|
"C_CPI_I": C_CPI_I,
|
|
"PCE_I": PCE_I,
|
|
"C_PCE_I": C_PCE_I,
|
|
"A_CPI_I": A_CPI_I,
|
|
"A_C_CPI_I": A_C_CPI_I,
|
|
"A_PCE_I": A_PCE_I,
|
|
"A_C_PCE_I": A_C_PCE_I})
|
|
df = pd.concat([df, tmp_df2], axis=0)
|
|
df.reset_index(drop=True, inplace=True)
|
|
|
|
df.replace('', np.nan, inplace=True)
|
|
return df
|
|
|
|
|
|
def bbki():
|
|
tmp_url = url["chicagofed"] + "bbki/bbki-monthly-data-series-csv.csv"
|
|
df = pd.read_csv(tmp_url)
|
|
return df
|
|
|
|
|
|
def cfnai():
|
|
tmp_url = url["chicagofed"] + "cfnai/cfnai-data-series-csv.csv"
|
|
df = pd.read_csv(tmp_url)
|
|
return df
|
|
|
|
|
|
def cfsbc():
|
|
tmp_url = url["chicagofed"] + "cfsbc-activity-index-csv.csv"
|
|
df = pd.read_csv(tmp_url)
|
|
return df
|
|
|
|
|
|
def nfci():
|
|
tmp_url = url["chicagofed"] + "nfci/decomposition-nfci-csv.csv"
|
|
df = pd.read_csv(tmp_url)
|
|
return df
|
|
|
|
|
|
def nfci():
|
|
tmp_url = url["chicagofed"] + "nfci/decomposition-anfci-csv.csv"
|
|
df = pd.read_csv(tmp_url)
|
|
return df
|