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)
