def test_to_object_array_tuples(self): r = (5,6) values = [r] result = lib.to_object_array_tuples(values) try: # make sure record array works from collections import namedtuple record = namedtuple('record', 'x y') r = record(5, 6) values = [r] result = lib.to_object_array_tuples(values) except ImportError: pass
def test_to_object_array_tuples(self): r = (5, 6) values = [r] result = lib.to_object_array_tuples(values) try: # make sure record array works from collections import namedtuple record = namedtuple('record', 'x y') r = record(5, 6) values = [r] result = lib.to_object_array_tuples(values) except ImportError: pass
def from_tuples(cls, tuples, sortorder=None, names=None): """ Convert list of tuples to MultiIndex Parameters ---------- tuples : array-like sortorder : int or None Level of sortedness (must be lexicographically sorted by that level) Returns ------- index : MultiIndex """ if len(tuples) == 0: raise Exception('Cannot infer number of levels from empty list') if isinstance(tuples, np.ndarray): arrays = list(lib.tuples_to_object_array(tuples).T) elif isinstance(tuples, list): arrays = list(lib.to_object_array_tuples(tuples).T) else: arrays = zip(*tuples) return MultiIndex.from_arrays(arrays, sortorder=sortorder, names=names)
def from_tuples(cls, tuples, sortorder=None, names=None): """ Convert list of tuples to MultiIndex Parameters ---------- tuples : array-like sortorder : int or None Level of sortedness (must be lexicographically sorted by that level) Returns ------- index : MultiIndex """ if len(tuples) == 0: raise Exception("Cannot infer number of levels from empty list") if isinstance(tuples, np.ndarray): arrays = list(lib.tuples_to_object_array(tuples).T) elif isinstance(tuples, list): arrays = list(lib.to_object_array_tuples(tuples).T) else: arrays = zip(*tuples) return MultiIndex.from_arrays(arrays, sortorder=sortorder, names=names)
def list_to_dataframe(rows, names): """ Turns a rows of data into a dataframe and gives them the column names specified :params rows: the data you want to put in the dataframe :params names: the column names for the dataframe """ from pandas import DataFrame col_convert_func = None try: import pandas._tseries as lib col_convert_func = lib.convert_sql_column except ImportError: import pandas.lib as lib try: col_convert_func = lib.convert_sql_column except: col_convert_func = partial(lib.maybe_convert_objects, try_float=1) if isinstance(rows, tuple): rows = list(rows) columns = dict(zip(names, lib.to_object_array_tuples(rows).T)) if col_convert_func is not None: for k, v in columns.iteritems(): columns[k] = col_convert_func(v) return DataFrame(columns, columns=names)
def list_to_dataframe(rows, names): import pandas._tseries as lib from pandas import DataFrame if isinstance(rows, tuple): rows = list(rows) columns = dict(zip(names, lib.to_object_array_tuples(rows).T)) for k, v in columns.iteritems(): columns[k] = lib.convert_sql_column(v) return DataFrame(columns, columns=names)
def list_to_dataframe(rows, names): """ Turns a rows of data into a dataframe and gives them the column names specified :params rows: the data you want to put in the dataframe :params names: the column names for the dataframe """ from pandas import DataFrame try: import pandas._tseries as lib except ImportError: import pandas.lib as lib if isinstance(rows, tuple): rows = list(rows) columns = dict(zip(names, lib.to_object_array_tuples(rows).T)) for k, v in columns.iteritems(): columns[k] = lib.convert_sql_column(v) return DataFrame(columns, columns=names)