Python StataReader.value_labels方法代码示例

# 需要导入模块: from pandas.io.stata import StataReader [as 别名]
# 或者: from pandas.io.stata.StataReader import value_labels [as 别名]
	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

# 需要导入模块: from pandas.io.stata import StataReader [as 别名]
# 或者: from pandas.io.stata.StataReader import value_labels [as 别名]
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}

# 需要导入模块: from pandas.io.stata import StataReader [as 别名]
# 或者: from pandas.io.stata.StataReader import value_labels [as 别名]
    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