"""American National Election Survey 1996"""
__docformat__ = 'restructuredtext'
COPYRIGHT = """This is public domain."""
TITLE = __doc__
SOURCE = """
http://www.electionstudies.org/
The American National Election Studies.
"""
DESCRSHORT = """This data is a subset of the American National Election Studies of 1996."""
DESCRLONG = DESCRSHORT
NOTE = """::
Number of observations - 944
Number of variables - 10
Variables name definitions::
popul - Census place population in 1000s
TVnews - Number of times per week that respondent watches TV news.
PID - Party identification of respondent.
0 - Strong Democrat
1 - Weak Democrat
2 - Independent-Democrat
3 - Independent-Indpendent
4 - Independent-Republican
5 - Weak Republican
6 - Strong Republican
age : Age of respondent.
educ - Education level of respondent
1 - 1-8 grades
2 - Some high school
3 - High school graduate
4 - Some college
5 - College degree
6 - Master's degree
7 - PhD
income - Income of household
1 - None or less than $2,999
2 - $3,000-$4,999
3 - $5,000-$6,999
4 - $7,000-$8,999
5 - $9,000-$9,999
6 - $10,000-$10,999
7 - $11,000-$11,999
8 - $12,000-$12,999
9 - $13,000-$13,999
10 - $14,000-$14.999
11 - $15,000-$16,999
12 - $17,000-$19,999
13 - $20,000-$21,999
14 - $22,000-$24,999
15 - $25,000-$29,999
16 - $30,000-$34,999
17 - $35,000-$39,999
18 - $40,000-$44,999
19 - $45,000-$49,999
20 - $50,000-$59,999
21 - $60,000-$74,999
22 - $75,000-89,999
23 - $90,000-$104,999
24 - $105,000 and over
vote - Expected vote
0 - Clinton
1 - Dole
The following 3 variables all take the values:
1 - Extremely liberal
2 - Liberal
3 - Slightly liberal
4 - Moderate
5 - Slightly conservative
6 - Conservative
7 - Extremely Conservative
selfLR - Respondent's self-reported political leanings from "Left"
to "Right".
ClinLR - Respondents impression of Bill Clinton's political
leanings from "Left" to "Right".
DoleLR - Respondents impression of Bob Dole's political leanings
from "Left" to "Right".
logpopul - log(popul + .1)
"""
from numpy import recfromtxt, column_stack, array, log
import numpy.lib.recfunctions as nprf
from statsmodels.datasets import utils as du
from os.path import dirname, abspath
[docs]def load():
"""Load the anes96 data and returns a Dataset class.
Returns
-------
Dataset instance:
See DATASET_PROPOSAL.txt for more information.
"""
data = _get_data()
return du.process_recarray(data, endog_idx=5, exog_idx=[10,2,6,7,8],
dtype=float)
[docs]def load_pandas():
"""Load the anes96 data and returns a Dataset class.
Returns
-------
Dataset instance:
See DATASET_PROPOSAL.txt for more information.
"""
data = _get_data()
return du.process_recarray_pandas(data, endog_idx=5, exog_idx=[10,2,6,7,8],
dtype=float)
def _get_data():
filepath = dirname(abspath(__file__))
data = recfromtxt(open(filepath + '/anes96.csv',"rb"), delimiter="\t",
names = True, dtype=float)
logpopul = log(data['popul'] + .1)
data = nprf.append_fields(data, 'logpopul', logpopul, usemask=False,
asrecarray=True)
return data