Return a list of Column objects from dictionary keys.
422499
def DictKeyColumns(d):
    ''
    return [ColumnItem(k, k, type=deduceType(d[k])) for k in d.keys()]
738879
def _fieldnames_to_colnames(model_cls, fieldnames):
        """"""
        get_field = model_cls._meta.get_field
        fields = map(get_field, fieldnames)
        return {f.column for f in fields}
213717
def _return_rows(self, table, cols, values, return_type):
        """"""
        if return_type is dict:
            # Pack each row into a dictionary
            cols = self.get_columns(table) if cols is '*' else cols
            if len(values) > 0 and isinstance(values[0], (set, list, tuple)):
                return [dict(zip(cols, row)) for row in values]
            else:
                return dict(zip(cols, values))
        elif return_type is tuple:
            return [tuple(row) for row in values]
        else:
            return values
1371770
def dicts_from_table(table, keys=None):
    """"""
    lod = []
    for row in table:
        if not keys:
            keys = row
            if not all((k not in (None, '') or bool(k.strip())) for k in keys):
                keys = None
            continue
        lod += [OrderedDict((k, v) for k, v in zip(keys, row))]
    return lod
1195445
def to_dict_list(df, use_ordered_dict=True):
    
    if use_ordered_dict:
        dict = OrderedDict

    columns = df.columns
    data = list()
    for tp in itertuple(df):
        data.append(dict(zip(columns, tp)))
    return data
689393
def keys(self):
        " "
        if self.ndims == 1:
            return [k[0] for k in self.data.keys()]
        else:
            return list(self.data.keys())
903845
def extract_columns(data, *cols):
    
    out = []
    try:
        # for python 2.2:
        # return [ [r[c] for c in cols] for r in data]
        for r in data:
            col = []
            for c in cols:
                col.append(r[c])
            out.append(col)
    except IndexError:
        raise IndexError("data=%s col=%s" % (data, col))
    return out
416144
def getFields(cls) :
		""""""
		s = set(cls.columns.keys())
		s.remove('json')
		s.remove('raba_id')
		return s
704900
def get_column_labels(obj):
    
    if not isinstance(obj, (list, tuple, pd.np.ndarray)):
        try:
            labels = [f.name for f in obj.model._meta.fields]
        except:
            try:
                labels = obj.keys()
            except:
                try:
                    labels = dir(obj)
                except:
                    labels = None
    elif all(isinstance(heading, basestring) for heading in obj[0]):
        labels = list(obj[0])
        # if obj isn't a reference to a mutable (dict, DataFrame, list, etc), this won't work
        del obj[0]
    return labels
1320416
def fetch_column_names(self, table: str) -> List[str]:
        """"""
        return self.flavour.fetch_column_names(self, table)
167370
def as_dictlist(self):
        
        data = []
        for row_i, row in enumerate(self.row_index):
            for col_i, col in enumerate(self.col_index):
                value = self.values_by_row[row_i][col_i]
                data.append({
                    "row": row,
                    "col": col,
                    "value": value,
                    })
        return data
320413
def _convert_to_internal(self, data):
        """'"""
        for _dict in data:
            keys = list(_dict.keys())[:]
            for key in keys:
                if key not in self._disp_cols:
                    raise Exception(key + ' not a column in current List.')
                _dict[self._disp_cols[key]['name']] = self._sp_type(key, _dict.pop(key))
384036
def PyobjColumns(obj):
    ''
    return [ColumnAttr(k, type(getattr(obj, k))) for k in getPublicAttrs(obj)]
1131047
def _parse_columns(self):
    
        '''  '''
        
    # construct column list
        column_map = {}
        for key, value in self.model.keyMap.items():
            record_key = key[1:]
            if record_key:
                if self.item_key.findall(record_key):
                    pass
                else:
                    if value['value_datatype'] == 'map':
                        continue
                    datatype = value['value_datatype']
                    if value['value_datatype'] == 'number':
                        datatype = 'float'
                        if 'integer_data' in value.keys():
                            if value['integer_data']:
                                datatype = 'integer'
                    replace_key = ''
                    if 'field_metadata' in value.keys():
                        if 'replace_key' in value['field_metadata'].keys():
                            if isinstance(value['field_metadata']['replace_key'], str):
                                replace_key = value['field_metadata']['replace_key']
                    max_length = None
                    if 'max_length' in value.keys():
                        max_length = value['max_length']
                    column_map[record_key] = (record_key, datatype, replace_key, max_length)
        
        return column_map
53585
def calculate_columns(sequence):
    
    columns = {}

    for row in sequence:
        for key in row.keys():
            if key not in columns:
                columns[key] = len(key)

            value_length = len(str(row[key]))
            if value_length > columns[key]:
                columns[key] = value_length

    return columns
1339424
def _dict_factory(cursor, row):
        ''' 
        '''
        d = {}
        for idx, col in enumerate(cursor.description):
            if col[0] == 'rowid':
                d['_id'] = row[idx]
            else:
                d[col[0]] = row[idx]
        return d
894412
def _get_column_names(self):
        
        column_names = set()
        for column in self.df:
            column_names.add(column)
        for column in self.unmaterialized_cols:
            column_names.add(column)
        return list(column_names)