April 5, 2017 Update: I’ve put together a newer version of the code/guide located here.
The following tutorial shows how to use python’s requests package to get data directly from the International Monetary Fund (IMF). The IMF’s application programming interface (API) provides access to economic and financial data of more than 180 countries over more than 60 years.
More information on the IMF Data API
The IMF offers guidance on using their data services. The API is not stable, so check the IMF data services news if you receive error messages.
More information on Python
This example works with the Anaconda distribution of Python 2.7.
A short example: Loading IMF data into pandas
Below is a short working example of loading the Australian export price index time-series from International Financial Statistics (IFS) into a pandas dataframe directly from the IMF API.
In [1]:
# Example: loading IMF data into pandas
# Import libraries
import requests
import pandas as pd
# URL for the IMF JSON Restful Web Service,
# IFS database, and Australian export prices series
url = 'http://dataservices.imf.org/REST/SDMX_JSON.svc/CompactData/IFS/Q.AU.PXP_IX.?startPeriod=1957&endPeriod=2016'
# Get data from the above URL using the requests package
data = requests.get(url).json()
# Load data into a pandas dataframe
auxp = pd.DataFrame(data['CompactData']['DataSet']['Series']['Obs'])
# Show the last five observiations
auxp.tail()
Out [1]:
|
@BASE_YEAR |
@OBS_VALUE |
@TIME_PERIOD |
| 232 |
2010 |
94.6044171093095 |
2015-Q1 |
| 233 |
2010 |
90.4668716801789 |
2015-Q2 |
| 234 |
2010 |
90.4668716801789 |
2015-Q3 |
| 235 |
2010 |
85.5465473860777 |
2015-Q4 |
| 236 |
2010 |
81.5208275090858 |
2016-Q1 |
Breaking down the URL
The ‘key’ in our request is the URL, which contains instructions about which data we want.
The URL has three parts,
- http://dataservices.imf.org/REST/SDMX_JSON.svc/CompactData/ The base for requests using the JSON restful data service;
- IFS/ the database ID, IFS, for International Financial Statistics;
- Q.AU.PXP_IX.?startPeriod=1957&endPeriod=2016 the data dimension and time period information.
The third part, data dimension and time period information, is broken down on the IMF Web Service knowledge base as:
{item1 from dimension1}+{item2 from dimension1}{item N from dimension1}.{item1 from dimension2} +{item2 from dimension2}+{item M from dimension2}? startPeriod={start date}&endPeriod={end date}
For guidance on finding dimension information and building your request, see my previous example of using the IMF API to retrieve Direction of Trade Statistics (DOTS) data.
Cleaning up the dataframe
Let’s add more meaningful headers and set the date as our index
In [2]:
# Rename columns
auxp.columns = ['baseyear','auxp','date']
# Set the price index series as a float (rather than string)
auxp.auxp = auxp.auxp.astype(float)
# Read the dates as quarters and set as the dataframe index
rng = pd.date_range(pd.to_datetime(auxp.date[0]), periods=len(auxp.index), freq='QS')
auxp = auxp.set_index(pd.DatetimeIndex(rng))
del auxp['date']
# Show last five rows
auxp.tail()
Out [2]:
|
baseyear |
auxp |
| 2015-01-01 |
2010 |
94.604417 |
| 2015-04-01 |
2010 |
90.466872 |
| 2015-07-01 |
2010 |
90.466872 |
| 2015-10-01 |
2010 |
85.546547 |
| 2016-01-01 |
2010 |
81.520828 |
Plot the data
Now we can use matplotlib to create a line plot showing the history of the Australian export price index.
In [3]:
# import matplotlib and pyplot
import matplotlib as mpl
import matplotlib.pyplot as plt
%matplotlib inline
# Create line plot and add labels and title
auxp.auxp.plot(grid=True, figsize=(9, 5), color="orange", linewidth=2,)
plt.ylabel('Index')
plt.xlabel('Year')
plt.title('Australia: Export Price Index (baseyear=' + str(auxp.baseyear[0]) + ')');
Out [3]:
Save as a csv file
To save the data for use in another program, you can easily create a csv file.
In [4]:
auxp.to_csv('auxp.csv')
