Python stata.StataReader类代码示例

    def test_read_dta18(self):
        parsed_118 = self.read_dta(self.dta22_118)
        parsed_118["Bytes"] = parsed_118["Bytes"].astype('O')
        expected = DataFrame.from_records(
            [['Cat', 'Bogota', u'Bogotá', 1, 1.0, u'option b Ünicode', 1.0],
             ['Dog', 'Boston', u'Uzunköprü', np.nan, np.nan, np.nan, np.nan],
             ['Plane', 'Rome', u'Tromsø', 0, 0.0, 'option a', 0.0],
             ['Potato', 'Tokyo', u'Elâzığ', -4, 4.0, 4, 4],
             ['', '', '', 0, 0.3332999, 'option a', 1/3.]
             ],
            columns=['Things', 'Cities', 'Unicode_Cities_Strl', 'Ints', 'Floats', 'Bytes', 'Longs'])
        expected["Floats"] = expected["Floats"].astype(np.float32)
        for col in parsed_118.columns:
            tm.assert_almost_equal(parsed_118[col], expected[col])

        rdr = StataReader(self.dta22_118)
        vl = rdr.variable_labels()
        vl_expected = {u'Unicode_Cities_Strl': u'Here are some strls with Ünicode chars',
                       u'Longs': u'long data',
                       u'Things': u'Here are some things',
                       u'Bytes': u'byte data',
                       u'Ints': u'int data',
                       u'Cities': u'Here are some cities',
                       u'Floats': u'float data'}
        tm.assert_dict_equal(vl, vl_expected)

        self.assertEqual(rdr.data_label, u'This is a  Ünicode data label')

	def read_stata(self, *args, **kwargs):
		reader = StataReader(*args, **kwargs)
		self.df = reader.data()
		self.variable_labels = reader.variable_labels()
		self._initialize_variable_labels()
		self.value_labels = reader.value_labels()
		# self.data_label = reader.data_label()
		return self.df

    def test_data_method(self):
        # Minimal testing of legacy data method
        reader_114 = StataReader(self.dta1_114)
        with warnings.catch_warnings(record=True) as w:
            parsed_114_data = reader_114.data()

        reader_114 = StataReader(self.dta1_114)
        parsed_114_read = reader_114.read()
        tm.assert_frame_equal(parsed_114_data, parsed_114_read)

    def test_read_dta1(self):
        reader = StataReader(self.dta1)
        parsed = reader.data()
        # Pandas uses np.nan as missing value. Thus, all columns will be of type float, regardless of their name.
        expected = DataFrame([(np.nan, np.nan, np.nan, np.nan, np.nan)],
                             columns=['float_miss', 'double_miss', 'byte_miss', 'int_miss', 'long_miss'])

        for i, col in enumerate(parsed.columns):
            np.testing.assert_almost_equal(
                parsed[col],
                expected[expected.columns[i]]
            )

    def test_read_dta1(self):
        reader = StataReader(self.dta1)
        parsed = reader.data()
        # Pandas uses np.nan as missing value.
        # Thus, all columns will be of type float, regardless of their name.
        expected = DataFrame([(np.nan, np.nan, np.nan, np.nan, np.nan)],
                             columns=['float_miss', 'double_miss', 'byte_miss',
                                      'int_miss', 'long_miss'])

        # this is an oddity as really the nan should be float64, but
        # the casting doesn't fail so need to match stata here
        expected['float_miss'] = expected['float_miss'].astype(np.float32)
        tm.assert_frame_equal(parsed, expected)

def _retrieve_data(dtafile):
	'''retrieve data dictionary from STATA .dta file'''

	datafile = os.path.basename(dtafile).split('.')
	if len(datafile) != 2:
		raise ValueError('dtafile must look like "file.dta"')
	if datafile[1] != 'dta':
		raise ValueError('dtafile must have ".dta" extension')

	base    = datafile[0]
	hdf     = os.path.join('.data_cache', '{}.h5'.format(base))
	lPickle = os.path.join('.data_cache', '{}_labels.pickle'.format(base))
	vPickle = os.path.join('.data_cache', '{}_vlabels.pickle'.format(base))
	dTime   = os.path.join('.data_cache', '{}_dtime.pickle'.format(base))

	if all([os.path.isfile(d) for d in [hdf, lPickle, vPickle, dTime]]):

		if os.path.getmtime(dtafile) == cPickle.load(open(dTime, 'rb')):
			from pandas import read_hdf

			data = read_hdf(hdf, 'data')
			labels = cPickle.load(open(lPickle, 'rb'))
			vlabels = cPickle.load(open(vPickle, 'rb'))

	elif not os.path.isdir('.data_cache'):
		os.makedirs('.data_cache')

	try:
		data
	except:
		from pandas.io.stata import StataReader
		from pandas import HDFStore

		print "Data is changed or no cached data found"
		print "Creating data objects from {}".format(dtafile)

		reader = StataReader(dtafile)
		data = reader.data(convert_dates=False,convert_categoricals=False)
		labels = reader.variable_labels()
		vlabels = reader.value_labels()

		store = HDFStore(hdf)
		store['data'] = data
		cPickle.dump(labels, open(lPickle, 'wb'))
		cPickle.dump(vlabels, open(vPickle, 'wb'))
		cPickle.dump(os.path.getmtime(dtafile), open(dTime, 'wb'))

		store.close()

	return {'data':data, 'labels':labels, 'vlabels':vlabels}

    def test_read_dta1(self):
        reader_114 = StataReader(self.dta1_114)
        parsed_114 = reader_114.data()
        reader_117 = StataReader(self.dta1_117)
        parsed_117 = reader_117.data()
        # Pandas uses np.nan as missing value.
        # Thus, all columns will be of type float, regardless of their name.
        expected = DataFrame(
            [(np.nan, np.nan, np.nan, np.nan, np.nan)],
            columns=["float_miss", "double_miss", "byte_miss", "int_miss", "long_miss"],
        )

        # this is an oddity as really the nan should be float64, but
        # the casting doesn't fail so need to match stata here
        expected["float_miss"] = expected["float_miss"].astype(np.float32)

        tm.assert_frame_equal(parsed_114, expected)
        tm.assert_frame_equal(parsed_117, expected)

from setup_prediction_lag import predict_abc
from load_data import extrap, abcd

from paths import paths

#----------------------------------------------------------------

seed = 1234
aux_draw = 99

#----------------------------------------------------------------


# bring in file with indexes for extrapolation bootstrap
reader = StataReader(paths.psid_bsid)
psid = reader.data(convert_dates=False, convert_categoricals=False)
psid = psid.iloc[:,0:aux_draw] # limit PSID to the number of repetitions you need
nlsy = pd.read_csv(paths.nlsy_bsid)

# set up extrapolation indexes (there are multiple data sets)
extrap_index = pd.concat([psid, nlsy], axis=0, keys=('psid', 'nlsy'), names=('dataset','id'))
extrap_source= ['psid' for j in range(0, psid.shape[0])] + ['nlsy' for k in range(0, nlsy.shape[0])]

#----------------------------------------------------------------

def boot_predict_aux(extrap, adraw):

	# prepare indexes of extrapolation data for bootstrap
	extrap_draw = extrap_index.loc[:, 'draw{}'.format(adraw)]
	extrap_tuples = list(zip(*[extrap_source,extrap_draw]))

if not os.path.exists(paths.data):
	os.mkdir(paths.data)

'''Load and Cache Datasets
   -----------------------

Notes:
- Ensures no overlap in id
- Trims observations with any labor income over $300,000 (U.S., 2014)
'''

#--------------------------------------------------------------------

print "Loading PSID"
reader = StataReader(paths.psid)
psid = reader.read(convert_dates=False, convert_categoricals=False)
psid = psid.dropna(subset=['id']).set_index('id')

# Trimming
inc = psid.filter(regex='^inc_labor[0-9][0-9]')
psid = psid.loc[psid.male == 0]
psid = psid.loc[psid.black == 1]
psid = psid.loc[((inc < inc.quantile(0.90)) | (inc.isnull())).all(axis=1)]

# Interpolating
plong = pd.wide_to_long(psid[inc.columns].reset_index(), 
    ['inc_labor'], i='id', j='age').sort_index()
plong = plong.interpolate(limit=5)
pwide = plong.unstack()
pwide.columns = pwide.columns.droplevel(0)

    def meta_labels(self):
        """Read the labels for the variables and code values for the variables, using the 
        Stata reader. """
        import re
        import os
        import struct
        import pandas as pd

        from pandas.io.stata import StataReader
   
        var_labels = None
        val_labels = None

        if not os.path.exists(self.filesystem.path('meta','variable_labels.yaml')):

            for name, fn in self.sources():
   
                if name.endswith('l'):

                    self.log("Getting labels for {}  from {} (This is really slow)".format(name, fn))
   
                    reader = StataReader(fn)

                    df = reader.data() # Can't get labels before reading data
            
                    var_labels = reader.variable_labels()
                    val_labels = reader.value_labels()
                    
                    break
                    
                    
            self.filesystem.write_yaml(var_labels, 'meta','variable_labels.yaml')
            self.filesystem.write_yaml(val_labels, 'meta','value_labels.yaml')
            
        else:
            self.log("Skipping extracts; already exist")

        # The value codes include both the value codes and the imputation codes. The imputation codes
        # are extracted  as positive integers, when they really should be negative. 
        table_values = {}
        imputation_values = {}
        
        if not val_labels:
            val_labels = self.filesystem.read_yaml('meta','value_labels.yaml')
            
        for k,v in val_labels.items():
            table_values[k] = {}
            imputation_values[k] = { -10:  'NO IMPUTATION' }
        
            for code, code_val in v.items():
                
                signed_code = struct.unpack('i',struct.pack('I',int(code)))[0] # Convert the unsigned to signed
                
                if signed_code < 0:
                    imputation_values[k][signed_code] = code_val
                else:
                    table_values[k][code] = code_val

        self.filesystem.write_yaml(table_values, 'meta','table_codes.yaml')
        self.filesystem.write_yaml(imputation_values, 'meta','imputation_codes.yaml')
            
        self.log("{} table variables".format(len(table_values)))
        self.log("{} imputation variables".format(len(imputation_values)))

        return True

Desc:   This code selects the IPW variables for specific ABC outcomes.
        We only do this for the pooled sample to increase power. We use
        a linear probiaility model for this. We select the 3 variables
        that minimize the BIC.
"""
import pandas as pd
from pandas.io.stata import StataReader
import numpy as np
import statsmodels.api as sm
from patsy import dmatrices
import itertools
from paths import paths

# import data
reader = StataReader(paths.abccare)
data = reader.read(convert_dates=False, convert_categoricals=False)
data = data.set_index('id')
data = data.sort_index()
data.drop(data.loc[(data.RV==1) & (data.R==0)].index, inplace=True)

# bring in outcomes files, and find the ABC-only/CARE-only ones
outcomes = pd.read_csv(paths.outcomes, index_col='variable')
only_abc = outcomes.loc[outcomes.only_abc == 1].index
only_care = outcomes.loc[outcomes.only_care == 1].index

bank = pd.read_csv(paths.controls)
ipwvars = np.unique(outcomes.loc[~outcomes.ipw_var.isnull(),'ipw_var'].get_values())

# generate the list of all possible models
models = itertools.chain.from_iterable([itertools.combinations(bank.loc[:, 'variable'], 3)])