def set_value(self, item, major, minor, value):
"""
Quickly set single value at (item, major, minor) location
Parameters
----------
item : item label (panel item)
major : major axis label (panel item row)
minor : minor axis label (panel item column)
value : scalar
Returns
-------
panel : Panel
If label combo is contained, will be reference to calling Panel,
otherwise a new object
"""
try:
frame = self._get_item_cache(item)
frame.set_value(major, minor, value)
return self
except KeyError:
ax1, ax2, ax3 = self._expand_axes((item, major, minor))
result = self.reindex(items=ax1, major=ax2, minor=ax3, copy=False)
likely_dtype = com._infer_dtype(value)
made_bigger = not np.array_equal(ax1, self.items)
# how to make this logic simpler?
if made_bigger:
com._possibly_cast_item(result, item, likely_dtype)
return result.set_value(item, major, minor, value)
def set_value(self, item, major, minor, value):
"""
Quickly set single value at (item, major, minor) location
Parameters
----------
item : item label (panel item)
major : major axis label (panel item row)
minor : minor axis label (panel item column)
value : scalar
Returns
-------
panel : Panel
If label combo is contained, will be reference to calling Panel,
otherwise a new object
"""
try:
frame = self._get_item_cache(item)
frame.set_value(major, minor, value)
return self
except KeyError:
ax1, ax2, ax3 = self._expand_axes((item, major, minor))
result = self.reindex(items=ax1, major=ax2, minor=ax3, copy=False)
likely_dtype = com._infer_dtype(value)
made_bigger = not np.array_equal(ax1, self.items)
# how to make this logic simpler?
if made_bigger:
com._possibly_cast_item(result, item, likely_dtype)
return result.set_value(item, major, minor, value)
def _make_na_block(self, items, ref_items, fill_value=np.nan):
# TODO: infer dtypes other than float64 from fill_value
block_shape = list(self.shape)
block_shape[0] = len(items)
dtype = com._infer_dtype(fill_value)
block_values = np.empty(block_shape, dtype=dtype)
block_values.fill(fill_value)
na_block = make_block(block_values, items, ref_items)
return na_block
def _make_na_block(self, items, ref_items, fill_value=np.nan):
# TODO: infer dtypes other than float64 from fill_value
block_shape = list(self.shape)
block_shape[0] = len(items)
dtype = com._infer_dtype(fill_value)
block_values = np.empty(block_shape, dtype=dtype)
block_values.fill(fill_value)
na_block = make_block(block_values, items, ref_items, do_integrity_check=True)
return na_block
def __setitem__(self, key, value):
_, N, K = self.shape
if isinstance(value, DataFrame):
value = value.reindex(index=self.major_axis, columns=self.minor_axis)
mat = value.values
elif isinstance(value, np.ndarray):
assert value.shape == (N, K)
mat = np.asarray(value)
elif np.isscalar(value):
dtype = _infer_dtype(value)
mat = np.empty((N, K), dtype=dtype)
mat.fill(value)
mat = mat.reshape((1, N, K))
NDFrame._set_item(self, key, mat)
def __setitem__(self, key, value):
_, N, K = self.shape
if isinstance(value, DataFrame):
value = value.reindex(index=self.major_axis,
columns=self.minor_axis)
mat = value.values
elif isinstance(value, np.ndarray):
assert(value.shape == (N, K))
mat = np.asarray(value)
elif np.isscalar(value):
dtype = _infer_dtype(value)
mat = np.empty((N, K), dtype=dtype)
mat.fill(value)
mat = mat.reshape((1, N, K))
NDFrame._set_item(self, key, mat)
def __setitem__(self, key, value):
_, N, K = self.shape
if isinstance(value, DataFrame):
value = value.reindex(index=self.major_axis, columns=self.minor_axis)
mat = value.values
elif isinstance(value, np.ndarray):
if value.shape != (N, K):
raise AssertionError(("Shape of values must be (%d, %d), " "not (%d, %d)") % ((N, K) + values.shape))
mat = np.asarray(value)
elif np.isscalar(value):
dtype = _infer_dtype(value)
mat = np.empty((N, K), dtype=dtype)
mat.fill(value)
else:
raise TypeError("Cannot set item of type: %s" % str(type(value)))
mat = mat.reshape((1, N, K))
NDFrame._set_item(self, key, mat)
def __setitem__(self, key, value):
_, N, K = self.shape
if isinstance(value, DataFrame):
value = value.reindex(index=self.major_axis,
columns=self.minor_axis)
mat = value.values
elif isinstance(value, np.ndarray):
if value.shape != (N, K):
raise AssertionError(
('Shape of values must be (%d, %d), '
'not (%d, %d)') % ((N, K) + values.shape))
mat = np.asarray(value)
elif np.isscalar(value):
dtype = _infer_dtype(value)
mat = np.empty((N, K), dtype=dtype)
mat.fill(value)
else:
raise TypeError('Cannot set item of type: %s' % str(type(value)))
mat = mat.reshape((1, N, K))
NDFrame._set_item(self, key, mat)
def __setitem__(self, key, value):
_, N, K = self.shape
# XXX
if isinstance(value, LongPanel):
if len(value.items) != 1:
raise ValueError("Input panel must have only one item!")
value = value.to_wide()[value.items[0]]
if isinstance(value, DataFrame):
value = value.reindex(index=self.major_axis, columns=self.minor_axis)
mat = value.values
elif np.isscalar(value):
dtype = _infer_dtype(value)
mat = np.empty((N, K), dtype=dtype)
mat.fill(value)
mat = mat.reshape((1, N, K))
self._data.set(key, mat)
def __setitem__(self, key, value):
_, N, K = self.shape
# XXX
if isinstance(value, LongPanel):
if len(value.items) != 1:
raise ValueError('Input panel must have only one item!')
value = value.to_wide()[value.items[0]]
if isinstance(value, DataFrame):
value = value.reindex(index=self.major_axis,
columns=self.minor_axis)
mat = value.values
elif isinstance(value, np.ndarray):
assert(value.shape == (N, K))
mat = np.asarray(value)
elif np.isscalar(value):
dtype = _infer_dtype(value)
mat = np.empty((N, K), dtype=dtype)
mat.fill(value)
mat = mat.reshape((1, N, K))
NDFrame._set_item(self, key, mat)
