update other fed data
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@ -4,7 +4,6 @@ import re
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import demjson
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import demjson
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import requests
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import requests
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from fake_useragent import UserAgent
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from fake_useragent import UserAgent
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from config import config
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# TODO need add comments
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# TODO need add comments
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@ -3,10 +3,14 @@ import numpy as np
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import requests
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import requests
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from fake_useragent import UserAgent
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from fake_useragent import UserAgent
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import io
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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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# Main Economic Indicators: https://alfred.stlouisfed.org/release?rid=205
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url = {
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url = {
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"fred_econ": "https://fred.stlouisfed.org/graph/fredgraph.csv?"
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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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}
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}
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def gdp_quarterly(startdate="1947-01-01", enddate="2021-01-01"):
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def gdp_quarterly(startdate="1947-01-01", enddate="2021-01-01"):
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@ -682,3 +686,94 @@ def bir(startdate="2003-01-01", enddate="2021-01-01"):
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df = pd.merge_asof(df_5y, df_10y, on = "DATE", direction = "backward")
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df = pd.merge_asof(df_5y, df_10y, on = "DATE", direction = "backward")
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df.columns = ["Date", "BIR_5y", "BIR_10y"]
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df.columns = ["Date", "BIR_5y", "BIR_10y"]
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return df
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return df
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def adsbci():
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"""
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An index designed to track real business conditions at high observation frequency
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"""
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ua = UserAgent()
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request_header = {"User-Agent": ua.random}
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tmp_url = url["philfed"] + "ads"
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r = requests.get(tmp_url, headers = request_header)
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file = open("ads_temp.xls", "wb")
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file.write(r.content)
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file.close()
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df = pd.read_excel("ads_temp.xls")
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df.columns = ["Date", "ADS_Index"]
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df['Date'] = pd.to_datetime(df["Date"], format="%Y:%m:%d")
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os.remove("ads_temp.xls")
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return df
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def pci():
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"""
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Tracks the degree of political disagreement among U.S. politicians at the federal level, Monthly
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"""
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df = pd.read_excel("https://www.philadelphiafed.org/-/media/frbp/assets/data-visualizations/partisan-conflict.xlsx")
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df["Date"] = df["Year"].astype(str) + df["Month"]
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df["Date"] = pd.to_datetime(df["Date"], format = "%Y%B")
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df = df.drop(["Year", "Month"], axis=1)
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df = df[["Date", "Partisan Conflict"]]
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return df
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def inflation_noewcasting():
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"""
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"""
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ua = UserAgent()
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request_header = {"User-Agent": ua.random}
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tmp_url = "https://www.clevelandfed.org/~/media/files/charting/%20nowcast_quarter.json"
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r = requests.get(tmp_url, headers = request_header)
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tmp_df = pd.DataFrame(demjson.decode(r.text))
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df = pd.DataFrame()
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for i in range(0, len(tmp_df)):
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date = tmp_df['chart'][i]['subcaption'][:4] + "/" + \
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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] + "/" + \
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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]
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CPI_I = pd.DataFrame((pd.DataFrame(tmp_df["dataset"][i])['data'])[0])["value"]
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C_CPI_I = pd.DataFrame((pd.DataFrame(tmp_df["dataset"][i])['data'])[1])["value"]
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PCE_I = pd.DataFrame((pd.DataFrame(tmp_df["dataset"][i])['data'])[2])["value"]
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C_PCE_I = pd.DataFrame((pd.DataFrame(tmp_df["dataset"][i])['data'])[3])["value"]
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A_CPI_I = pd.DataFrame((pd.DataFrame(tmp_df["dataset"][i])['data'])[4])["value"]
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A_C_CPI_I = pd.DataFrame((pd.DataFrame(tmp_df["dataset"][i])['data'])[5])["value"]
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A_PCE_I = pd.DataFrame((pd.DataFrame(tmp_df["dataset"][i])['data'])[6])["value"]
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A_C_PCE_I = pd.DataFrame((pd.DataFrame(tmp_df["dataset"][i])['data'])[7])["value"]
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tmp_df2 = pd.DataFrame({"date": date,
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"CPI_I": CPI_I,
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"C_CPI_I": C_CPI_I,
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"PCE_I": PCE_I,
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"C_PCE_I": C_PCE_I,
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"A_CPI_I": A_CPI_I,
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"A_C_CPI_I": A_C_CPI_I,
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"A_PCE_I": A_PCE_I,
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"A_C_PCE_I": A_C_PCE_I})
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df = pd.concat([df,tmp_df2], axis=0)
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df.reset_index(drop=True, inplace=True)
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df.replace('', np.nan, inplace = True)
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return df
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def bbki():
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tmp_url = url["chicagofed"] + "bbki/bbki-monthly-data-series-csv.csv"
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df = pd.read_csv(tmp_url)
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return df
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def cfnai():
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tmp_url = url["chicagofed"] + "cfnai/cfnai-data-series-csv.csv"
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df = pd.read_csv(tmp_url)
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return df
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def cfsbc():
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tmp_url = url["chicagofed"] + "cfsbc-activity-index-csv.csv"
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df = pd.read_csv(tmp_url)
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return df
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def nfci():
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tmp_url = url["chicagofed"] + "nfci/decomposition-nfci-csv.csv"
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df = pd.read_csv(tmp_url)
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return df
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def nfci():
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tmp_url = url["chicagofed"] + "nfci/decomposition-anfci-csv.csv"
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df = pd.read_csv(tmp_url)
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return df
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@ -22,11 +22,12 @@ Via the `pypi`:
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python -m pip install CEDApy
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python -m pip install CEDApy
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```
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```
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## Acknowledgement
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## Acknowledgement
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* Thanks [akshare](https://github.com/jindaxiang/akshare/)
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* Thanks [akshare](https://github.com/jindaxiang/akshare/)
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* Thanks [EastMoney](https://www.eastmoney.com)
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* Thanks [EastMoney](https://www.eastmoney.com)
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* Thanks [St.Louis Fred Reserve Bank](https://fred.stlouisfed.org/)
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* Thanks [St.Louis Federal Reserve Bank](https://fred.stlouisfed.org/)
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* Thanks [Chicago Federal Reserve Bank](https://www.chicagofed.org/)
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* Thanks [Philadelphia Federal Reserve Bank](https://www.philadelphiafed.org/)
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* Thanks [eurostat Economic Indicators](https://ec.europa.eu/eurostat/cache/infographs/economy/desktop/index.html)
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* Thanks [eurostat Economic Indicators](https://ec.europa.eu/eurostat/cache/infographs/economy/desktop/index.html)
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* Thanks [Europen Central Bank](https://www.ecb.europa.eu)
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* Thanks [Europen Central Bank](https://www.ecb.europa.eu)
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