Data import/export and EViews function calls from Python
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. This package 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.
$ pip install pyeviews
Or, download the package, navigate to your installation directory, and use:
$ python setup.py install
For more details on installation, see our whitepaper.
Start python and 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')
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)
Behind the scenes, pyeviews will detect if the DatetimeIndex of your pandas object (if you have one) needs to be adjusted to match EViews’ dating customs. Since EViews assigns dates to be the beginning of a given period depending on the frequency, this can lead to misalignment issues and unexpected results when calculations are performed. For example, a DatetimeIndex with an annual ‘A’ frequency and a date of 2000-12-31 will be assigned an internal EViews date of 2000-12-01. In this case, pyeviews will adjust the date to 2000-01-01 before pushing the data to EViews.
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)
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) annual\\benchmark quarterly\\benchmarked @indicator indicator', app=eviewsapp)
Bring the new series back into Python:
>>> benchmarked = evp.GetWFAsPython(app=eviewsapp, pagename= 'quarterly', namefilter= 'benchmarked') >>> print(benchmarked) BENCHMARKED 1998-01-01 867.421429 1998-04-01 1017.292857 1998-07-01 1097.992857 1998-10-01 1017.292857 1999-01-01 913.535714 1999-04-01 1063.407143 1999-07-01 1126.814286 1999-10-01 1057.642857 2000-01-01 1000.000000 2000-04-01 1144.107143 2000-07-01 1172.928571 2000-10-01 1057.642857
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).
If you want, plot everything to see how the interpolated series follows the indicator series:
>>> # load the matplotlib package to plot import matplotlib.pyplot as plt >>> # reindex the benchmarked series to the end of the quarter so the dates match those of the indicator series benchmarked_reindexed = pa.Series(benchmarked.values.flatten(), index = benchmarked.index + pa.DateOffset(months = 3, days = -1)) >>> # plot fig, ax1 = plt.subplots() plt.xticks(rotation=70) ax1.plot(benchmarked_reindexed, 'b-', label='benchmarked') # multiply the indicator series by 10 to put it on the same axis as the benchmarked series ax1.plot(indicator*10, 'b--', label='indicator*10') ax1.set_xlabel('dates') ax1.set_ylabel('indicator & interpolated values', color='b') ax1.xaxis.grid(True) for tl in ax1.get_yticklabels(): tl.set_color('b') plt.legend(loc='lower right') ax2 = ax1.twinx() ax2.set_ylim([3975, 4180]) ax2.plot(benchmark, 'ro', label='benchmark') ax2.set_ylabel('benchmark', color='r') for tl in ax2.get_yticklabels(): tl.set_color('r') plt.legend(loc='upper left') plt.title("Chow-Lin interpolation: \nannual sum of benchmarked = benchmark", fontsize=14) plt.show()
For more information on the pyeviews package, including a list of functions, please take a look at our whitepaper on the subject.
Bloem, A.M, Dippelsman, R.J. and Maehle, N.O. 2001 Quarterly National Accounts Manual - Concepts, Data Sources, and Compilation. IMF. http://www.imf.org/external/pubs/ft/qna/2000/Textbook/index.htm
EViews, of course
comtypes, numpy, and pandas
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