from pandas_datareader import get_data_fred
df = get_data_fred('GDP')
df.head()| GDP | |
|---|---|
| DATE | |
| 2019-10-01 | 21902.390 |
| 2020-01-01 | 21706.513 |
| 2020-04-01 | 19913.143 |
| 2020-07-01 | 21647.640 |
| 2020-10-01 | 22024.502 |
The pandas-datareader Pacakge
FRED Data
FRED (Federal Reserve Economic Data) provides a wide range of economic datasets covering various aspects such as national accounts, labor markets, prices, interest rates, exchange rates, and more. Below is a list of some key datasets that you can access from FRED, along with their dataset IDs, which you can use directly in pandas_datareader or through the FRED API.
- National Accounts (GDP, Income, and Expenditure)
- Gross Domestic Product (GDP):
GDP(Quarterly) - Real GDP:
GDPC1(Quarterly, Inflation-adjusted) - Personal Consumption Expenditures (PCE):
PCE(Monthly) - Government Consumption Expenditures & Investment:
GCEC1(Quarterly)
- Gross Domestic Product (GDP):
- Employment and Unemployment
- Civilian Unemployment Rate:
UNRATE(Monthly) - Civilian Employment-Population Ratio:
EMRATIO(Monthly) - Nonfarm Payroll Employment:
PAYEMS(Monthly) - Initial Claims for Unemployment Insurance:
ICSA(Weekly)
- Civilian Unemployment Rate:
- Prices and Inflation
- Consumer Price Index for All Urban Consumers (CPI-U):
CPIAUCSL(Monthly) - Producer Price Index (PPI):
PPIACO(Monthly) - Core CPI (Excluding Food and Energy):
CPILFESL(Monthly) - Personal Consumption Expenditures Price Index:
PCEPI(Monthly)
- Consumer Price Index for All Urban Consumers (CPI-U):
- Interest Rates
- Effective Federal Funds Rate:
FEDFUNDS(Daily/Monthly) - 10-Year Treasury Constant Maturity Rate:
DGS10(Daily/Monthly) - 30-Year Fixed-Rate Mortgage Average:
MORTGAGE30US(Weekly) - Prime Bank Loan Rate:
MPRIME(Monthly)
- Effective Federal Funds Rate:
- Money, Banking, and Finance
- M1 Money Stock:
M1SL(Monthly) - M2 Money Stock:
M2SL(Monthly) - Commercial Bank Loans:
LOANS(Monthly) - Bank Prime Loan Rate:
MPRIME(Monthly)
- M1 Money Stock:
- Exchange Rates
- US Dollar to Euro Exchange Rate:
DEXUSEU(Daily/Monthly) - US Dollar to Japanese Yen Exchange Rate:
DEXJPUS(Daily/Monthly) - US Dollar Index:
DTWEXM(Monthly)
- US Dollar to Euro Exchange Rate:
- Population and Demographics
- Total Population (Monthly):
POPTHM - Total Population (Annual):
POP - Civilian Noninstitutional Population (16 Years and Over):
CNP16OV(Monthly)
- Total Population (Monthly):
- Housing and Real Estate
- House Price Index (HPI):
CSUSHPINSA(Monthly) - New Privately-Owned Housing Units Started:
HOUST(Monthly) - New One-Family Houses Sold:
HSN1F(Monthly)
- House Price Index (HPI):
- Trade and International Transactions
- U.S. International Trade in Goods and Services:
BOPGSTB(Monthly) - Current Account Balance:
IEABC(Quarterly)
- U.S. International Trade in Goods and Services:
- Manufacturing and Industrial Output
- Industrial Production Index:
INDPRO(Monthly) - Manufacturing Output:
IPMAN(Monthly) - Capacity Utilization:
TCU(Monthly)
- Business and Consumer Sentiment
- University of Michigan Consumer Sentiment Index:
UMCSENT(Monthly) - Consumer Confidence Index:
CONCCONF(Monthly)
- Corporate Profits
- Corporate Profits after Tax:
CP(Quarterly) - Corporate Profits before Tax:
A445RC1Q027SBEA(Quarterly)
You can access these datasets directly in Python using the pandas_datareader library, as follows:
Specifying start and end dates, as desired:
from datetime import datetime
from pandas_datareader import get_data_fred
start = datetime(1900, 1, 1)
end = datetime(2023, 1, 1)
df = get_data_fred('GDP', start, end)
df.head()| GDP | |
|---|---|
| DATE | |
| 1947-01-01 | 243.164 |
| 1947-04-01 | 245.968 |
| 1947-07-01 | 249.585 |
| 1947-10-01 | 259.745 |
| 1948-01-01 | 265.742 |