def nanmax(values, axis=None, skipna=True):
values, mask, dtype, dtype_max = _get_values(values,
skipna,
fill_value_typ='-inf')
# numpy 1.6.1 workaround in Python 3.x
if is_object_dtype(values) and compat.PY3:
if values.ndim > 1:
apply_ax = axis if axis is not None else 0
result = np.apply_along_axis(builtins.max, apply_ax, values)
else:
try:
result = builtins.max(values)
except:
result = np.nan
else:
if ((axis is not None and values.shape[axis] == 0)
or values.size == 0):
try:
result = ensure_float(values.sum(axis, dtype=dtype_max))
result.fill(np.nan)
except:
result = np.nan
else:
result = values.max(axis)
result = _wrap_results(result, dtype)
return _maybe_null_out(result, axis, mask)
def nanmax(values, axis=None, skipna=True):
values, mask, dtype = _get_values(values, skipna, fill_value_typ='-inf')
# numpy 1.6.1 workaround in Python 3.x
if (values.dtype == np.object_ and compat.PY3):
if values.ndim > 1:
apply_ax = axis if axis is not None else 0
result = np.apply_along_axis(builtins.max, apply_ax, values)
else:
try:
result = builtins.max(values)
except:
result = np.nan
else:
if ((axis is not None and values.shape[axis] == 0)
or values.size == 0):
try:
result = com.ensure_float(values.sum(axis))
result.fill(np.nan)
except:
result = np.nan
else:
result = values.max(axis)
result = _wrap_results(result, dtype)
return _maybe_null_out(result, axis, mask)
def _nanmax(values, axis=None, skipna=True):
mask = isnull(values)
dtype = values.dtype
if skipna and _na_ok_dtype(dtype):
values = values.copy()
np.putmask(values, mask, -np.inf)
values = _view_if_needed(values)
# numpy 1.6.1 workaround in Python 3.x
if (values.dtype == np.object_
and sys.version_info[0] >= 3): # pragma: no cover
import __builtin__
if values.ndim > 1:
apply_ax = axis if axis is not None else 0
result = np.apply_along_axis(__builtin__.max, apply_ax, values)
else:
result = __builtin__.max(values)
else:
if ((axis is not None and values.shape[axis] == 0)
or values.size == 0):
result = com.ensure_float(values.sum(axis))
result.fill(np.nan)
else:
result = values.max(axis)
result = _wrap_results(result,dtype)
return _maybe_null_out(result, axis, mask)
def shift(self, periods, offset=None, timeRule=None):
"""
Shift the underlying series of the DataMatrix and Series objects within
by given number (positive or negative) of periods.
Parameters
----------
periods : int (+ or -)
Number of periods to move
offset : DateOffset, optional
Increment to use from datetools module
timeRule : string
Time rule to use by name
Returns
-------
DataMatrix
"""
if periods == 0:
return self
if timeRule is not None and offset is None:
offset = datetools.getOffset(timeRule)
if offset is None:
indexer = self._shift_indexer(periods)
new_values = self.values.take(indexer, axis=0)
new_index = self.index
new_values = common.ensure_float(new_values)
if periods > 0:
new_values[:periods] = NaN
else:
new_values[periods:] = NaN
else:
new_index = self.index.shift(periods, offset)
new_values = self.values.copy()
if self.objects is not None:
shifted_objects = self.objects.shift(periods,
offset=offset,
timeRule=timeRule)
shifted_objects.index = new_index
else:
shifted_objects = None
return DataMatrix(data=new_values,
index=new_index,
columns=self.columns,
objects=shifted_objects)
def shift(self, periods, offset=None, timeRule=None):
"""
Shift the underlying series of the DataMatrix and Series objects within
by given number (positive or negative) of periods.
Parameters
----------
periods : int (+ or -)
Number of periods to move
offset : DateOffset, optional
Increment to use from datetools module
timeRule : string
Time rule to use by name
Returns
-------
DataMatrix
"""
if periods == 0:
return self
if timeRule is not None and offset is None:
offset = datetools.getOffset(timeRule)
if offset is None:
indexer = self._shift_indexer(periods)
new_values = self.values.take(indexer, axis=0)
new_index = self.index
new_values = common.ensure_float(new_values)
if periods > 0:
new_values[:periods] = NaN
else:
new_values[periods:] = NaN
else:
new_index = self.index.shift(periods, offset)
new_values = self.values.copy()
if self.objects is not None:
shifted_objects = self.objects.shift(periods, offset=offset,
timeRule=timeRule)
shifted_objects.index = new_index
else:
shifted_objects = None
return DataMatrix(data=new_values, index=new_index,
columns=self.columns, objects=shifted_objects)
def nanmin(values, axis=None, skipna=True):
values, mask, dtype = _get_values(values, skipna, fill_value_typ = '+inf')
# numpy 1.6.1 workaround in Python 3.x
if (values.dtype == np.object_ and compat.PY3):
if values.ndim > 1:
apply_ax = axis if axis is not None else 0
result = np.apply_along_axis(builtins.min, apply_ax, values)
else:
result = builtins.min(values)
else:
if ((axis is not None and values.shape[axis] == 0)
or values.size == 0):
result = com.ensure_float(values.sum(axis))
result.fill(np.nan)
else:
result = values.min(axis)
result = _wrap_results(result,dtype)
return _maybe_null_out(result, axis, mask)
def nanmin(values, axis=None, skipna=True):
values, mask, dtype = _get_values(values, skipna, fill_value_typ = '+inf')
# numpy 1.6.1 workaround in Python 3.x
if (values.dtype == np.object_
and sys.version_info[0] >= 3): # pragma: no cover
if values.ndim > 1:
apply_ax = axis if axis is not None else 0
result = np.apply_along_axis(builtins.min, apply_ax, values)
else:
result = builtins.min(values)
else:
if ((axis is not None and values.shape[axis] == 0)
or values.size == 0):
result = com.ensure_float(values.sum(axis))
result.fill(np.nan)
else:
result = values.min(axis)
result = _wrap_results(result,dtype)
return _maybe_null_out(result, axis, mask)
def _nanmax(values, axis=None, skipna=True):
values, mask, dtype = _get_values(values, skipna, fill_value_typ ='-inf')
# numpy 1.6.1 workaround in Python 3.x
if (values.dtype == np.object_
and sys.version_info[0] >= 3): # pragma: no cover
import __builtin__
if values.ndim > 1:
apply_ax = axis if axis is not None else 0
result = np.apply_along_axis(__builtin__.max, apply_ax, values)
else:
result = __builtin__.max(values)
else:
if ((axis is not None and values.shape[axis] == 0)
or values.size == 0):
result = com.ensure_float(values.sum(axis))
result.fill(np.nan)
else:
result = values.max(axis)
result = _wrap_results(result,dtype)
return _maybe_null_out(result, axis, mask)
def _nanmax(values, axis=None, skipna=True):
mask = isnull(values)
dtype = values.dtype
if skipna and not issubclass(dtype.type, (np.integer, np.datetime64)):
values = values.copy()
np.putmask(values, mask, -np.inf)
if issubclass(dtype.type, np.datetime64):
values = values.view(np.int64)
# numpy 1.6.1 workaround in Python 3.x
if (values.dtype == np.object_
and sys.version_info[0] >= 3): # pragma: no cover
import __builtin__
if values.ndim > 1:
apply_ax = axis if axis is not None else 0
result = np.apply_along_axis(__builtin__.max, apply_ax, values)
else:
result = __builtin__.max(values)
else:
if ((axis is not None and values.shape[axis] == 0)
or values.size == 0):
result = com.ensure_float(values.sum(axis))
result.fill(np.nan)
else:
result = values.max(axis)
if issubclass(dtype.type, np.datetime64):
if not isinstance(result, np.ndarray):
result = lib.Timestamp(result)
else:
result = result.view(dtype)
return _maybe_null_out(result, axis, mask)
def _nanmax(values, axis=None, skipna=True):
mask = isnull(values)
dtype = values.dtype
if skipna and not issubclass(dtype.type, (np.integer, np.datetime64)):
values = values.copy()
np.putmask(values, mask, -np.inf)
if issubclass(dtype.type, np.datetime64):
values = values.view(np.int64)
# numpy 1.6.1 workaround in Python 3.x
if (values.dtype == np.object_
and sys.version_info[0] >= 3): # pragma: no cover
import __builtin__
if values.ndim > 1:
apply_ax = axis if axis is not None else 0
result = np.apply_along_axis(__builtin__.max, apply_ax, values)
else:
result = __builtin__.max(values)
else:
if ((axis is not None and values.shape[axis] == 0)
or values.size == 0):
result = com.ensure_float(values.sum(axis))
result.fill(np.nan)
else:
result = values.max(axis)
if issubclass(dtype.type, np.datetime64):
if not isinstance(result, np.ndarray):
result = lib.Timestamp(result)
else:
result = result.view(dtype)
return _maybe_null_out(result, axis, mask)
