============ py_apsrtable

Overview

py_apsrtable aims to be the python implemenation of the R package apsrtable (http://cran.r-project.org/web/packages/apsrtable/index.html).

py_asprtable takes as its input a model, or set of models, generated by the python packages statsmodels (http://statsmodels.sourceforge.net/) and pandas (http://pandas.pydata.org/), and outputs a LaTeX table formatted in a manner consistent with common political science formatting. The formatting is also nice for other applications.

Options for the models includes standard errors, two-tailed p values, and one-tailed p values. The variable and model names can be changed, and the stars (*) can be placed next to the values in parentheses depending on the significance level defined by the user.

Updates

#####01.14.13

• Added N, AIC, and BIC to the bottom of the model.

#####01.07.13

• Added the ability to print the table to the Python shell instead of writing to a file.

#####01.02.13

• Simplified the execution down to one function.
• Added the ability to change the signficance level.
• Refactored the code some to be cleaner.

Future

Eventually I would like to add an option to add a different number of stars for different p-values and the option to change which statistics are reported at the bottom of the table, e.g., R^2, adjusted R^2.

Example

Usage for a single model::

#Imports
import py_apsrtable
import statsmodels.api as sm
import pandas as pd

#Generate some data to use
data = sm.datasets.longley.load()
df = pd.DataFrame(data.exog, columns=data.exog_name)
y = data.endog
df['intercept'] = 1.

#Generate the OLS output and store it in olsresult
olsresult = sm.OLS(y, df).fit()

#Add the results to a list. The functions require the results to be in a list.
models = [olsresult]

#Check the order of the variable names
print sorted(olsresult.params.iteritems())

#Define the names to replace the variables
replaceNames = ['Armed', 'Gross National Product', 'GNPDEFL', 'Population', 
'Unemployment', 'Year', 'intercept']

#Assign the generateTable class with the initial values
#Print is an alternate option for the output argument
a = py_apsrtable.generateTable('/path/to/file/table.tex', models, center = 'True', parens= 'se', sig_level=0.05, var_names = replaceNames)

#Create the table
a.create_table(caption='OLS Results Table', label='tab:ols', model_name = None, stars=True)

Usage for multiple models::

#Imports
import py_apsrtable
import statsmodels.api as sm
import pandas as pd

#Generate some data to use
data = sm.datasets.longley.load()
df = pd.DataFrame(data.exog, columns=data.exog_name)
y = data.endog
df['intercept'] = 1.

#Generate three separate models, each with different numbers of variables. 
olsresult = sm.OLS(y, df).fit()
olsresult2 = sm.OLS(y, df[['GNP', 'UNEMP', 'ARMED']]).fit()
olsresult3 = sm.OLS(y, df[['GNP', 'POP', 'ARMED', 'YEAR']]).fit()

#Put the models into a list
models = [olsresult, olsresult2, olsresult3]

#Check the order of the variable names
print sorted(olsresult.params.iteritems())

#Define the names to replace the variables
replaceNames = ['Armed', 'Gross National Product', 'GNPDEFL', 'Population', 
'Unemployment', 'Year', 'intercept']

#Assign the generateTable class with the initial values
a = py_apsrtable.generateTable('print', models, center = 'True', parens= 'se', sig_level=0.10, var_names = replaceNames)

#Create the table
a.create_table(caption='OLS Results Table', label='tab:ols', model_name = None, stars=True)

An example of the final PDF output generated by the above code can be seen here.