Skip to main content

Data import/export and EViews function calls from Python

Project description

The purpose of the pyeviews package is to make it easier for EViews and Python to talk to each other, so Python programmers can use the econometric engine of EViews directly from Python. We’ve written a Python package, pyeviews, that uses COM to transfer data between Python and EViews. (For more information on COM and EViews, take a look at our whitepaper on the subject.)

Here’s a simple example going from Python to EViews. We’re going to use the popular Chow-Lin interpolation routine in EViews using data created in Python. Chow-Lin interpolation is a regression-based technique to transform low-frequency data (in our example, annual) into higher-frequency data (in our example, quarterly). It has the ability to use a higher-frequency series as a pattern for the interpolated series to follow. The quarterly interpolated series is chosen to match the annual benchmark series in one of four ways: first (the first quarter value of the interpolated series matches the annual series), last (same, but for the fourth quarter value), sum (the sum of the first through fourth quarters matches the annual series), and average (the average of the first through fourth quarters matches the annual series).

We’re going to create two series in Python using the time series functionality of the pandas package, transfer it to EViews, perform Chow-Lin interpolation on our series, and bring it back into Python. The data are taken from [BLO2001] in an example originally meant for Denton interpolation.

  1. If you don’t have Python, we recommend the Anaconda distribution, which will include most of the packages we’ll need. Then head over to the Python Package Index and get the pyeviews module by opening a Windows command line program (e.g., Command Prompt or PowerShell) and using the command:

:: python

$ pip install package

or by downloading the package, navigating to your installation directory, and using the command

$ python install
  1. Create two time series using pandas. We’ll call the annual series “benchmark” and the quarterly series “indicator”:

>>> import numpy as np
>>> import pandas as pa
>>> dtsa = pa.date_range('1998', periods = 3, freq = 'A')
>>> benchmark = pa.Series([4000.,4161.4,np.nan], index=dtsa, name = 'benchmark')
>>> dtsq = pa.date_range('1998q1', periods = 12, freq = 'Q')
>>> indicator = pa.Series([98.2, 100.8, 102.2, 100.8, 99., 101.6, 102.7, 101.5, 100.5, 103., 103.5, 101.5], index = dtsq, name = 'indicator')`
  1. Load the pyeviews package and create a custom COM application object so we can customize our settings. Set “showwindow” (which displays the EViews window) to True. Then call the “PutPythonAsWF” function to create pages for the benchmark and indicator series:

>>> import pyeviews as evp
>>> eviewsapp = evp.GetEViewsApp(instance='new', showwindow=True)
>>> evp.PutPythonAsWF(benchmark, app=eviewsapp)
>>> evp.PutPythonAsWF(indicator, app=eviewsapp, newwf=False)
  1. Name the pages of the workfile:

>>> evp.Run('pageselect Untitled', app=eviewsapp)
>>> evp.Run('pagerename Untitled annual', app=eviewsapp)
>>> evp.Run('pageselect Untitled1', app=eviewsapp)
>>> evp.Run('pagerename Untitled1 quarterly', app=eviewsapp)
  1. Use the EViews copy command to copy the benchmark series in the annual page to the quarterly page, using the indicator series in the quarterly page as the high-frequency indicator and matching the sum of the benchmarked series for each year (four quarters) with the matching annual value of the benchmark series:

>>> evp.Run('copy(rho=.7, c=chowlins, overwrite) annual\\benchmark quarterly\\benchmarked @indicator indicator', app=eviewsapp)
  1. Bring the new series back into Python:

>>> benchmarked = evp.GetWFAsPython(app=eviewsapp, pagename= 'quarterly', namefilter= 'benchmarked ')
>>> print benchmarked
1998-03-01          NaN
1998-06-01          NaN
1998-09-01          NaN
1998-12-01   991.746591
1999-03-01   925.719318
1999-06-01  1021.092045
1999-09-01  1061.442045
1999-12-01  1017.423864
2000-03-01   980.742045
2000-06-01  1072.446591
2000-09-01  1090.787500
2000-12-01  1017.423864
  1. Release the memory allocated to the COM process (this does not happen automatically in interactive mode):

>>> eviewsapp.Hide()
>>> eviewsapp = None
>>> evp.Cleanup()

Note that if you choose not to create a custom COM application object (the GetEViewsApp function), you won’t need to use the first two lines in the last step. You only need to call Cleanup(). If you create a custom object but choose not to show it, you won’t need to use the first line (the Hide() function).



Bloem, A.M, Dippelsman, R.J. and Maehle, N.O. 2001 Quarterly National Accounts Manual–Concepts, Data Sources, and Compilation. IMF.

Project details

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution (25.7 kB view hashes)

Uploaded Source

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page