Code
stringlengths 103
85.9k
| Summary
sequencelengths 0
94
|
|---|---|
Please provide a description of the function:def apply(self, func, **kwargs):
with np.errstate(all='ignore'):
result = func(self.values, **kwargs)
if not isinstance(result, Block):
result = self.make_block(values=_block_shape(result,
ndim=self.ndim))
return result | [
" apply the function to my values; return a block if we are not\n one\n "
] |
Please provide a description of the function:def fillna(self, value, limit=None, inplace=False, downcast=None):
inplace = validate_bool_kwarg(inplace, 'inplace')
if not self._can_hold_na:
if inplace:
return self
else:
return self.copy()
mask = isna(self.values)
if limit is not None:
if not is_integer(limit):
raise ValueError('Limit must be an integer')
if limit < 1:
raise ValueError('Limit must be greater than 0')
if self.ndim > 2:
raise NotImplementedError("number of dimensions for 'fillna' "
"is currently limited to 2")
mask[mask.cumsum(self.ndim - 1) > limit] = False
# fillna, but if we cannot coerce, then try again as an ObjectBlock
try:
values, _ = self._try_coerce_args(self.values, value)
blocks = self.putmask(mask, value, inplace=inplace)
blocks = [b.make_block(values=self._try_coerce_result(b.values))
for b in blocks]
return self._maybe_downcast(blocks, downcast)
except (TypeError, ValueError):
# we can't process the value, but nothing to do
if not mask.any():
return self if inplace else self.copy()
# operate column-by-column
def f(m, v, i):
block = self.coerce_to_target_dtype(value)
# slice out our block
if i is not None:
block = block.getitem_block(slice(i, i + 1))
return block.fillna(value,
limit=limit,
inplace=inplace,
downcast=None)
return self.split_and_operate(mask, f, inplace) | [
" fillna on the block with the value. If we fail, then convert to\n ObjectBlock and try again\n "
] |
Please provide a description of the function:def split_and_operate(self, mask, f, inplace):
if mask is None:
mask = np.ones(self.shape, dtype=bool)
new_values = self.values
def make_a_block(nv, ref_loc):
if isinstance(nv, Block):
block = nv
elif isinstance(nv, list):
block = nv[0]
else:
# Put back the dimension that was taken from it and make
# a block out of the result.
try:
nv = _block_shape(nv, ndim=self.ndim)
except (AttributeError, NotImplementedError):
pass
block = self.make_block(values=nv,
placement=ref_loc)
return block
# ndim == 1
if self.ndim == 1:
if mask.any():
nv = f(mask, new_values, None)
else:
nv = new_values if inplace else new_values.copy()
block = make_a_block(nv, self.mgr_locs)
return [block]
# ndim > 1
new_blocks = []
for i, ref_loc in enumerate(self.mgr_locs):
m = mask[i]
v = new_values[i]
# need a new block
if m.any():
nv = f(m, v, i)
else:
nv = v if inplace else v.copy()
block = make_a_block(nv, [ref_loc])
new_blocks.append(block)
return new_blocks | [
"\n split the block per-column, and apply the callable f\n per-column, return a new block for each. Handle\n masking which will not change a block unless needed.\n\n Parameters\n ----------\n mask : 2-d boolean mask\n f : callable accepting (1d-mask, 1d values, indexer)\n inplace : boolean\n\n Returns\n -------\n list of blocks\n "
] |
Please provide a description of the function:def downcast(self, dtypes=None):
# turn it off completely
if dtypes is False:
return self
values = self.values
# single block handling
if self._is_single_block:
# try to cast all non-floats here
if dtypes is None:
dtypes = 'infer'
nv = maybe_downcast_to_dtype(values, dtypes)
return self.make_block(nv)
# ndim > 1
if dtypes is None:
return self
if not (dtypes == 'infer' or isinstance(dtypes, dict)):
raise ValueError("downcast must have a dictionary or 'infer' as "
"its argument")
# operate column-by-column
# this is expensive as it splits the blocks items-by-item
def f(m, v, i):
if dtypes == 'infer':
dtype = 'infer'
else:
raise AssertionError("dtypes as dict is not supported yet")
if dtype is not None:
v = maybe_downcast_to_dtype(v, dtype)
return v
return self.split_and_operate(None, f, False) | [
" try to downcast each item to the dict of dtypes if present "
] |
Please provide a description of the function:def _astype(self, dtype, copy=False, errors='raise', values=None,
**kwargs):
errors_legal_values = ('raise', 'ignore')
if errors not in errors_legal_values:
invalid_arg = ("Expected value of kwarg 'errors' to be one of {}. "
"Supplied value is '{}'".format(
list(errors_legal_values), errors))
raise ValueError(invalid_arg)
if (inspect.isclass(dtype) and
issubclass(dtype, (PandasExtensionDtype, ExtensionDtype))):
msg = ("Expected an instance of {}, but got the class instead. "
"Try instantiating 'dtype'.".format(dtype.__name__))
raise TypeError(msg)
# may need to convert to categorical
if self.is_categorical_astype(dtype):
# deprecated 17636
if ('categories' in kwargs or 'ordered' in kwargs):
if isinstance(dtype, CategoricalDtype):
raise TypeError(
"Cannot specify a CategoricalDtype and also "
"`categories` or `ordered`. Use "
"`dtype=CategoricalDtype(categories, ordered)`"
" instead.")
warnings.warn("specifying 'categories' or 'ordered' in "
".astype() is deprecated; pass a "
"CategoricalDtype instead",
FutureWarning, stacklevel=7)
categories = kwargs.get('categories', None)
ordered = kwargs.get('ordered', None)
if com._any_not_none(categories, ordered):
dtype = CategoricalDtype(categories, ordered)
if is_categorical_dtype(self.values):
# GH 10696/18593: update an existing categorical efficiently
return self.make_block(self.values.astype(dtype, copy=copy))
return self.make_block(Categorical(self.values, dtype=dtype))
dtype = pandas_dtype(dtype)
# astype processing
if is_dtype_equal(self.dtype, dtype):
if copy:
return self.copy()
return self
try:
# force the copy here
if values is None:
if self.is_extension:
values = self.values.astype(dtype)
else:
if issubclass(dtype.type, str):
# use native type formatting for datetime/tz/timedelta
if self.is_datelike:
values = self.to_native_types()
# astype formatting
else:
values = self.get_values()
else:
values = self.get_values(dtype=dtype)
# _astype_nansafe works fine with 1-d only
values = astype_nansafe(values.ravel(), dtype, copy=True)
# TODO(extension)
# should we make this attribute?
try:
values = values.reshape(self.shape)
except AttributeError:
pass
newb = make_block(values, placement=self.mgr_locs,
ndim=self.ndim)
except Exception: # noqa: E722
if errors == 'raise':
raise
newb = self.copy() if copy else self
if newb.is_numeric and self.is_numeric:
if newb.shape != self.shape:
raise TypeError(
"cannot set astype for copy = [{copy}] for dtype "
"({dtype} [{shape}]) to different shape "
"({newb_dtype} [{newb_shape}])".format(
copy=copy, dtype=self.dtype.name,
shape=self.shape, newb_dtype=newb.dtype.name,
newb_shape=newb.shape))
return newb | [
"Coerce to the new type\n\n Parameters\n ----------\n dtype : str, dtype convertible\n copy : boolean, default False\n copy if indicated\n errors : str, {'raise', 'ignore'}, default 'ignore'\n - ``raise`` : allow exceptions to be raised\n - ``ignore`` : suppress exceptions. On error return original object\n\n Returns\n -------\n Block\n "
] |
Please provide a description of the function:def _can_hold_element(self, element):
dtype = self.values.dtype.type
tipo = maybe_infer_dtype_type(element)
if tipo is not None:
return issubclass(tipo.type, dtype)
return isinstance(element, dtype) | [
" require the same dtype as ourselves "
] |
Please provide a description of the function:def _try_cast_result(self, result, dtype=None):
if dtype is None:
dtype = self.dtype
if self.is_integer or self.is_bool or self.is_datetime:
pass
elif self.is_float and result.dtype == self.dtype:
# protect against a bool/object showing up here
if isinstance(dtype, str) and dtype == 'infer':
return result
if not isinstance(dtype, type):
dtype = dtype.type
if issubclass(dtype, (np.bool_, np.object_)):
if issubclass(dtype, np.bool_):
if isna(result).all():
return result.astype(np.bool_)
else:
result = result.astype(np.object_)
result[result == 1] = True
result[result == 0] = False
return result
else:
return result.astype(np.object_)
return result
# may need to change the dtype here
return maybe_downcast_to_dtype(result, dtype) | [
" try to cast the result to our original type, we may have\n roundtripped thru object in the mean-time\n "
] |
Please provide a description of the function:def _try_coerce_args(self, values, other):
if np.any(notna(other)) and not self._can_hold_element(other):
# coercion issues
# let higher levels handle
raise TypeError("cannot convert {} to an {}".format(
type(other).__name__,
type(self).__name__.lower().replace('Block', '')))
return values, other | [
" provide coercion to our input arguments "
] |
Please provide a description of the function:def to_native_types(self, slicer=None, na_rep='nan', quoting=None,
**kwargs):
values = self.get_values()
if slicer is not None:
values = values[:, slicer]
mask = isna(values)
if not self.is_object and not quoting:
values = values.astype(str)
else:
values = np.array(values, dtype='object')
values[mask] = na_rep
return values | [
" convert to our native types format, slicing if desired "
] |
Please provide a description of the function:def copy(self, deep=True):
values = self.values
if deep:
values = values.copy()
return self.make_block_same_class(values, ndim=self.ndim) | [
" copy constructor "
] |
Please provide a description of the function:def replace(self, to_replace, value, inplace=False, filter=None,
regex=False, convert=True):
inplace = validate_bool_kwarg(inplace, 'inplace')
original_to_replace = to_replace
# try to replace, if we raise an error, convert to ObjectBlock and
# retry
try:
values, to_replace = self._try_coerce_args(self.values,
to_replace)
mask = missing.mask_missing(values, to_replace)
if filter is not None:
filtered_out = ~self.mgr_locs.isin(filter)
mask[filtered_out.nonzero()[0]] = False
blocks = self.putmask(mask, value, inplace=inplace)
if convert:
blocks = [b.convert(by_item=True, numeric=False,
copy=not inplace) for b in blocks]
return blocks
except (TypeError, ValueError):
# GH 22083, TypeError or ValueError occurred within error handling
# causes infinite loop. Cast and retry only if not objectblock.
if is_object_dtype(self):
raise
# try again with a compatible block
block = self.astype(object)
return block.replace(to_replace=original_to_replace,
value=value,
inplace=inplace,
filter=filter,
regex=regex,
convert=convert) | [
"replace the to_replace value with value, possible to create new\n blocks here this is just a call to putmask. regex is not used here.\n It is used in ObjectBlocks. It is here for API compatibility.\n "
] |
Please provide a description of the function:def setitem(self, indexer, value):
# coerce None values, if appropriate
if value is None:
if self.is_numeric:
value = np.nan
# coerce if block dtype can store value
values = self.values
try:
values, value = self._try_coerce_args(values, value)
# can keep its own dtype
if hasattr(value, 'dtype') and is_dtype_equal(values.dtype,
value.dtype):
dtype = self.dtype
else:
dtype = 'infer'
except (TypeError, ValueError):
# current dtype cannot store value, coerce to common dtype
find_dtype = False
if hasattr(value, 'dtype'):
dtype = value.dtype
find_dtype = True
elif lib.is_scalar(value):
if isna(value):
# NaN promotion is handled in latter path
dtype = False
else:
dtype, _ = infer_dtype_from_scalar(value,
pandas_dtype=True)
find_dtype = True
else:
dtype = 'infer'
if find_dtype:
dtype = find_common_type([values.dtype, dtype])
if not is_dtype_equal(self.dtype, dtype):
b = self.astype(dtype)
return b.setitem(indexer, value)
# value must be storeable at this moment
arr_value = np.array(value)
# cast the values to a type that can hold nan (if necessary)
if not self._can_hold_element(value):
dtype, _ = maybe_promote(arr_value.dtype)
values = values.astype(dtype)
transf = (lambda x: x.T) if self.ndim == 2 else (lambda x: x)
values = transf(values)
# length checking
check_setitem_lengths(indexer, value, values)
def _is_scalar_indexer(indexer):
# return True if we are all scalar indexers
if arr_value.ndim == 1:
if not isinstance(indexer, tuple):
indexer = tuple([indexer])
return any(isinstance(idx, np.ndarray) and len(idx) == 0
for idx in indexer)
return False
def _is_empty_indexer(indexer):
# return a boolean if we have an empty indexer
if is_list_like(indexer) and not len(indexer):
return True
if arr_value.ndim == 1:
if not isinstance(indexer, tuple):
indexer = tuple([indexer])
return any(isinstance(idx, np.ndarray) and len(idx) == 0
for idx in indexer)
return False
# empty indexers
# 8669 (empty)
if _is_empty_indexer(indexer):
pass
# setting a single element for each dim and with a rhs that could
# be say a list
# GH 6043
elif _is_scalar_indexer(indexer):
values[indexer] = value
# if we are an exact match (ex-broadcasting),
# then use the resultant dtype
elif (len(arr_value.shape) and
arr_value.shape[0] == values.shape[0] and
np.prod(arr_value.shape) == np.prod(values.shape)):
values[indexer] = value
try:
values = values.astype(arr_value.dtype)
except ValueError:
pass
# set
else:
values[indexer] = value
# coerce and try to infer the dtypes of the result
values = self._try_coerce_and_cast_result(values, dtype)
block = self.make_block(transf(values))
return block | [
"Set the value inplace, returning a a maybe different typed block.\n\n Parameters\n ----------\n indexer : tuple, list-like, array-like, slice\n The subset of self.values to set\n value : object\n The value being set\n\n Returns\n -------\n Block\n\n Notes\n -----\n `indexer` is a direct slice/positional indexer. `value` must\n be a compatible shape.\n "
] |
Please provide a description of the function:def putmask(self, mask, new, align=True, inplace=False, axis=0,
transpose=False):
new_values = self.values if inplace else self.values.copy()
new = getattr(new, 'values', new)
mask = getattr(mask, 'values', mask)
# if we are passed a scalar None, convert it here
if not is_list_like(new) and isna(new) and not self.is_object:
new = self.fill_value
if self._can_hold_element(new):
_, new = self._try_coerce_args(new_values, new)
if transpose:
new_values = new_values.T
# If the default repeat behavior in np.putmask would go in the
# wrong direction, then explicitly repeat and reshape new instead
if getattr(new, 'ndim', 0) >= 1:
if self.ndim - 1 == new.ndim and axis == 1:
new = np.repeat(
new, new_values.shape[-1]).reshape(self.shape)
new = new.astype(new_values.dtype)
# we require exact matches between the len of the
# values we are setting (or is compat). np.putmask
# doesn't check this and will simply truncate / pad
# the output, but we want sane error messages
#
# TODO: this prob needs some better checking
# for 2D cases
if ((is_list_like(new) and
np.any(mask[mask]) and
getattr(new, 'ndim', 1) == 1)):
if not (mask.shape[-1] == len(new) or
mask[mask].shape[-1] == len(new) or
len(new) == 1):
raise ValueError("cannot assign mismatch "
"length to masked array")
np.putmask(new_values, mask, new)
# maybe upcast me
elif mask.any():
if transpose:
mask = mask.T
if isinstance(new, np.ndarray):
new = new.T
axis = new_values.ndim - axis - 1
# Pseudo-broadcast
if getattr(new, 'ndim', 0) >= 1:
if self.ndim - 1 == new.ndim:
new_shape = list(new.shape)
new_shape.insert(axis, 1)
new = new.reshape(tuple(new_shape))
# operate column-by-column
def f(m, v, i):
if i is None:
# ndim==1 case.
n = new
else:
if isinstance(new, np.ndarray):
n = np.squeeze(new[i % new.shape[0]])
else:
n = np.array(new)
# type of the new block
dtype, _ = maybe_promote(n.dtype)
# we need to explicitly astype here to make a copy
n = n.astype(dtype)
nv = _putmask_smart(v, m, n)
return nv
new_blocks = self.split_and_operate(mask, f, inplace)
return new_blocks
if inplace:
return [self]
if transpose:
new_values = new_values.T
return [self.make_block(new_values)] | [
" putmask the data to the block; it is possible that we may create a\n new dtype of block\n\n return the resulting block(s)\n\n Parameters\n ----------\n mask : the condition to respect\n new : a ndarray/object\n align : boolean, perform alignment on other/cond, default is True\n inplace : perform inplace modification, default is False\n axis : int\n transpose : boolean\n Set to True if self is stored with axes reversed\n\n Returns\n -------\n a list of new blocks, the result of the putmask\n "
] |
Please provide a description of the function:def coerce_to_target_dtype(self, other):
# if we cannot then coerce to object
dtype, _ = infer_dtype_from(other, pandas_dtype=True)
if is_dtype_equal(self.dtype, dtype):
return self
if self.is_bool or is_object_dtype(dtype) or is_bool_dtype(dtype):
# we don't upcast to bool
return self.astype(object)
elif ((self.is_float or self.is_complex) and
(is_integer_dtype(dtype) or is_float_dtype(dtype))):
# don't coerce float/complex to int
return self
elif (self.is_datetime or
is_datetime64_dtype(dtype) or
is_datetime64tz_dtype(dtype)):
# not a datetime
if not ((is_datetime64_dtype(dtype) or
is_datetime64tz_dtype(dtype)) and self.is_datetime):
return self.astype(object)
# don't upcast timezone with different timezone or no timezone
mytz = getattr(self.dtype, 'tz', None)
othertz = getattr(dtype, 'tz', None)
if str(mytz) != str(othertz):
return self.astype(object)
raise AssertionError("possible recursion in "
"coerce_to_target_dtype: {} {}".format(
self, other))
elif (self.is_timedelta or is_timedelta64_dtype(dtype)):
# not a timedelta
if not (is_timedelta64_dtype(dtype) and self.is_timedelta):
return self.astype(object)
raise AssertionError("possible recursion in "
"coerce_to_target_dtype: {} {}".format(
self, other))
try:
return self.astype(dtype)
except (ValueError, TypeError, OverflowError):
pass
return self.astype(object) | [
"\n coerce the current block to a dtype compat for other\n we will return a block, possibly object, and not raise\n\n we can also safely try to coerce to the same dtype\n and will receive the same block\n "
] |
Please provide a description of the function:def _interpolate_with_fill(self, method='pad', axis=0, inplace=False,
limit=None, fill_value=None, coerce=False,
downcast=None):
inplace = validate_bool_kwarg(inplace, 'inplace')
# if we are coercing, then don't force the conversion
# if the block can't hold the type
if coerce:
if not self._can_hold_na:
if inplace:
return [self]
else:
return [self.copy()]
values = self.values if inplace else self.values.copy()
values, fill_value = self._try_coerce_args(values, fill_value)
values = missing.interpolate_2d(values, method=method, axis=axis,
limit=limit, fill_value=fill_value,
dtype=self.dtype)
values = self._try_coerce_result(values)
blocks = [self.make_block_same_class(values, ndim=self.ndim)]
return self._maybe_downcast(blocks, downcast) | [
" fillna but using the interpolate machinery "
] |
Please provide a description of the function:def _interpolate(self, method=None, index=None, values=None,
fill_value=None, axis=0, limit=None,
limit_direction='forward', limit_area=None,
inplace=False, downcast=None, **kwargs):
inplace = validate_bool_kwarg(inplace, 'inplace')
data = self.values if inplace else self.values.copy()
# only deal with floats
if not self.is_float:
if not self.is_integer:
return self
data = data.astype(np.float64)
if fill_value is None:
fill_value = self.fill_value
if method in ('krogh', 'piecewise_polynomial', 'pchip'):
if not index.is_monotonic:
raise ValueError("{0} interpolation requires that the "
"index be monotonic.".format(method))
# process 1-d slices in the axis direction
def func(x):
# process a 1-d slice, returning it
# should the axis argument be handled below in apply_along_axis?
# i.e. not an arg to missing.interpolate_1d
return missing.interpolate_1d(index, x, method=method, limit=limit,
limit_direction=limit_direction,
limit_area=limit_area,
fill_value=fill_value,
bounds_error=False, **kwargs)
# interp each column independently
interp_values = np.apply_along_axis(func, axis, data)
blocks = [self.make_block_same_class(interp_values)]
return self._maybe_downcast(blocks, downcast) | [
" interpolate using scipy wrappers "
] |
Please provide a description of the function:def take_nd(self, indexer, axis, new_mgr_locs=None, fill_tuple=None):
# algos.take_nd dispatches for DatetimeTZBlock, CategoricalBlock
# so need to preserve types
# sparse is treated like an ndarray, but needs .get_values() shaping
values = self.values
if self.is_sparse:
values = self.get_values()
if fill_tuple is None:
fill_value = self.fill_value
new_values = algos.take_nd(values, indexer, axis=axis,
allow_fill=False, fill_value=fill_value)
else:
fill_value = fill_tuple[0]
new_values = algos.take_nd(values, indexer, axis=axis,
allow_fill=True, fill_value=fill_value)
if new_mgr_locs is None:
if axis == 0:
slc = libinternals.indexer_as_slice(indexer)
if slc is not None:
new_mgr_locs = self.mgr_locs[slc]
else:
new_mgr_locs = self.mgr_locs[indexer]
else:
new_mgr_locs = self.mgr_locs
if not is_dtype_equal(new_values.dtype, self.dtype):
return self.make_block(new_values, new_mgr_locs)
else:
return self.make_block_same_class(new_values, new_mgr_locs) | [
"\n Take values according to indexer and return them as a block.bb\n\n "
] |
Please provide a description of the function:def diff(self, n, axis=1):
new_values = algos.diff(self.values, n, axis=axis)
return [self.make_block(values=new_values)] | [
" return block for the diff of the values "
] |
Please provide a description of the function:def shift(self, periods, axis=0, fill_value=None):
# convert integer to float if necessary. need to do a lot more than
# that, handle boolean etc also
new_values, fill_value = maybe_upcast(self.values, fill_value)
# make sure array sent to np.roll is c_contiguous
f_ordered = new_values.flags.f_contiguous
if f_ordered:
new_values = new_values.T
axis = new_values.ndim - axis - 1
if np.prod(new_values.shape):
new_values = np.roll(new_values, ensure_platform_int(periods),
axis=axis)
axis_indexer = [slice(None)] * self.ndim
if periods > 0:
axis_indexer[axis] = slice(None, periods)
else:
axis_indexer[axis] = slice(periods, None)
new_values[tuple(axis_indexer)] = fill_value
# restore original order
if f_ordered:
new_values = new_values.T
return [self.make_block(new_values)] | [
" shift the block by periods, possibly upcast "
] |
Please provide a description of the function:def where(self, other, cond, align=True, errors='raise',
try_cast=False, axis=0, transpose=False):
import pandas.core.computation.expressions as expressions
assert errors in ['raise', 'ignore']
values = self.values
orig_other = other
if transpose:
values = values.T
other = getattr(other, '_values', getattr(other, 'values', other))
cond = getattr(cond, 'values', cond)
# If the default broadcasting would go in the wrong direction, then
# explicitly reshape other instead
if getattr(other, 'ndim', 0) >= 1:
if values.ndim - 1 == other.ndim and axis == 1:
other = other.reshape(tuple(other.shape + (1, )))
elif transpose and values.ndim == self.ndim - 1:
cond = cond.T
if not hasattr(cond, 'shape'):
raise ValueError("where must have a condition that is ndarray "
"like")
# our where function
def func(cond, values, other):
if cond.ravel().all():
return values
values, other = self._try_coerce_args(values, other)
try:
return self._try_coerce_result(expressions.where(
cond, values, other))
except Exception as detail:
if errors == 'raise':
raise TypeError(
'Could not operate [{other!r}] with block values '
'[{detail!s}]'.format(other=other, detail=detail))
else:
# return the values
result = np.empty(values.shape, dtype='float64')
result.fill(np.nan)
return result
# see if we can operate on the entire block, or need item-by-item
# or if we are a single block (ndim == 1)
try:
result = func(cond, values, other)
except TypeError:
# we cannot coerce, return a compat dtype
# we are explicitly ignoring errors
block = self.coerce_to_target_dtype(other)
blocks = block.where(orig_other, cond, align=align,
errors=errors,
try_cast=try_cast, axis=axis,
transpose=transpose)
return self._maybe_downcast(blocks, 'infer')
if self._can_hold_na or self.ndim == 1:
if transpose:
result = result.T
# try to cast if requested
if try_cast:
result = self._try_cast_result(result)
return self.make_block(result)
# might need to separate out blocks
axis = cond.ndim - 1
cond = cond.swapaxes(axis, 0)
mask = np.array([cond[i].all() for i in range(cond.shape[0])],
dtype=bool)
result_blocks = []
for m in [mask, ~mask]:
if m.any():
r = self._try_cast_result(result.take(m.nonzero()[0],
axis=axis))
result_blocks.append(
self.make_block(r.T, placement=self.mgr_locs[m]))
return result_blocks | [
"\n evaluate the block; return result block(s) from the result\n\n Parameters\n ----------\n other : a ndarray/object\n cond : the condition to respect\n align : boolean, perform alignment on other/cond\n errors : str, {'raise', 'ignore'}, default 'raise'\n - ``raise`` : allow exceptions to be raised\n - ``ignore`` : suppress exceptions. On error return original object\n\n axis : int\n transpose : boolean\n Set to True if self is stored with axes reversed\n\n Returns\n -------\n a new block(s), the result of the func\n "
] |
Please provide a description of the function:def _unstack(self, unstacker_func, new_columns, n_rows, fill_value):
unstacker = unstacker_func(self.values.T)
new_items = unstacker.get_new_columns()
new_placement = new_columns.get_indexer(new_items)
new_values, mask = unstacker.get_new_values()
mask = mask.any(0)
new_values = new_values.T[mask]
new_placement = new_placement[mask]
blocks = [make_block(new_values, placement=new_placement)]
return blocks, mask | [
"Return a list of unstacked blocks of self\n\n Parameters\n ----------\n unstacker_func : callable\n Partially applied unstacker.\n new_columns : Index\n All columns of the unstacked BlockManager.\n n_rows : int\n Only used in ExtensionBlock.unstack\n fill_value : int\n Only used in ExtensionBlock.unstack\n\n Returns\n -------\n blocks : list of Block\n New blocks of unstacked values.\n mask : array_like of bool\n The mask of columns of `blocks` we should keep.\n "
] |
Please provide a description of the function:def quantile(self, qs, interpolation='linear', axis=0):
if self.is_datetimetz:
# TODO: cleanup this special case.
# We need to operate on i8 values for datetimetz
# but `Block.get_values()` returns an ndarray of objects
# right now. We need an API for "values to do numeric-like ops on"
values = self.values.asi8
# TODO: NonConsolidatableMixin shape
# Usual shape inconsistencies for ExtensionBlocks
if self.ndim > 1:
values = values[None, :]
else:
values = self.get_values()
values, _ = self._try_coerce_args(values, values)
is_empty = values.shape[axis] == 0
orig_scalar = not is_list_like(qs)
if orig_scalar:
# make list-like, unpack later
qs = [qs]
if is_empty:
if self.ndim == 1:
result = self._na_value
else:
# create the array of na_values
# 2d len(values) * len(qs)
result = np.repeat(np.array([self.fill_value] * len(qs)),
len(values)).reshape(len(values),
len(qs))
else:
# asarray needed for Sparse, see GH#24600
# TODO: Why self.values and not values?
mask = np.asarray(isna(self.values))
result = nanpercentile(values, np.array(qs) * 100,
axis=axis, na_value=self.fill_value,
mask=mask, ndim=self.ndim,
interpolation=interpolation)
result = np.array(result, copy=False)
if self.ndim > 1:
result = result.T
if orig_scalar and not lib.is_scalar(result):
# result could be scalar in case with is_empty and self.ndim == 1
assert result.shape[-1] == 1, result.shape
result = result[..., 0]
result = lib.item_from_zerodim(result)
ndim = getattr(result, 'ndim', None) or 0
result = self._try_coerce_result(result)
return make_block(result,
placement=np.arange(len(result)),
ndim=ndim) | [
"\n compute the quantiles of the\n\n Parameters\n ----------\n qs: a scalar or list of the quantiles to be computed\n interpolation: type of interpolation, default 'linear'\n axis: axis to compute, default 0\n\n Returns\n -------\n Block\n "
] |
Please provide a description of the function:def _replace_coerce(self, to_replace, value, inplace=True, regex=False,
convert=False, mask=None):
if mask.any():
if not regex:
self = self.coerce_to_target_dtype(value)
return self.putmask(mask, value, inplace=inplace)
else:
return self._replace_single(to_replace, value, inplace=inplace,
regex=regex,
convert=convert,
mask=mask)
return self | [
"\n Replace value corresponding to the given boolean array with another\n value.\n\n Parameters\n ----------\n to_replace : object or pattern\n Scalar to replace or regular expression to match.\n value : object\n Replacement object.\n inplace : bool, default False\n Perform inplace modification.\n regex : bool, default False\n If true, perform regular expression substitution.\n convert : bool, default True\n If true, try to coerce any object types to better types.\n mask : array-like of bool, optional\n True indicate corresponding element is ignored.\n\n Returns\n -------\n A new block if there is anything to replace or the original block.\n "
] |
Please provide a description of the function:def putmask(self, mask, new, align=True, inplace=False, axis=0,
transpose=False):
inplace = validate_bool_kwarg(inplace, 'inplace')
# use block's copy logic.
# .values may be an Index which does shallow copy by default
new_values = self.values if inplace else self.copy().values
new_values, new = self._try_coerce_args(new_values, new)
if isinstance(new, np.ndarray) and len(new) == len(mask):
new = new[mask]
mask = _safe_reshape(mask, new_values.shape)
new_values[mask] = new
new_values = self._try_coerce_result(new_values)
return [self.make_block(values=new_values)] | [
"\n putmask the data to the block; we must be a single block and not\n generate other blocks\n\n return the resulting block\n\n Parameters\n ----------\n mask : the condition to respect\n new : a ndarray/object\n align : boolean, perform alignment on other/cond, default is True\n inplace : perform inplace modification, default is False\n\n Returns\n -------\n a new block, the result of the putmask\n "
] |
Please provide a description of the function:def _get_unstack_items(self, unstacker, new_columns):
# shared with ExtensionBlock
new_items = unstacker.get_new_columns()
new_placement = new_columns.get_indexer(new_items)
new_values, mask = unstacker.get_new_values()
mask = mask.any(0)
return new_placement, new_values, mask | [
"\n Get the placement, values, and mask for a Block unstack.\n\n This is shared between ObjectBlock and ExtensionBlock. They\n differ in that ObjectBlock passes the values, while ExtensionBlock\n passes the dummy ndarray of positions to be used by a take\n later.\n\n Parameters\n ----------\n unstacker : pandas.core.reshape.reshape._Unstacker\n new_columns : Index\n All columns of the unstacked BlockManager.\n\n Returns\n -------\n new_placement : ndarray[int]\n The placement of the new columns in `new_columns`.\n new_values : Union[ndarray, ExtensionArray]\n The first return value from _Unstacker.get_new_values.\n mask : ndarray[bool]\n The second return value from _Unstacker.get_new_values.\n "
] |
Please provide a description of the function:def _maybe_coerce_values(self, values):
if isinstance(values, (ABCIndexClass, ABCSeries)):
values = values._values
return values | [
"Unbox to an extension array.\n\n This will unbox an ExtensionArray stored in an Index or Series.\n ExtensionArrays pass through. No dtype coercion is done.\n\n Parameters\n ----------\n values : Index, Series, ExtensionArray\n\n Returns\n -------\n ExtensionArray\n "
] |
Please provide a description of the function:def setitem(self, indexer, value):
if isinstance(indexer, tuple):
# we are always 1-D
indexer = indexer[0]
check_setitem_lengths(indexer, value, self.values)
self.values[indexer] = value
return self | [
"Set the value inplace, returning a same-typed block.\n\n This differs from Block.setitem by not allowing setitem to change\n the dtype of the Block.\n\n Parameters\n ----------\n indexer : tuple, list-like, array-like, slice\n The subset of self.values to set\n value : object\n The value being set\n\n Returns\n -------\n Block\n\n Notes\n -----\n `indexer` is a direct slice/positional indexer. `value` must\n be a compatible shape.\n "
] |
Please provide a description of the function:def take_nd(self, indexer, axis=0, new_mgr_locs=None, fill_tuple=None):
if fill_tuple is None:
fill_value = None
else:
fill_value = fill_tuple[0]
# axis doesn't matter; we are really a single-dim object
# but are passed the axis depending on the calling routing
# if its REALLY axis 0, then this will be a reindex and not a take
new_values = self.values.take(indexer, fill_value=fill_value,
allow_fill=True)
if self.ndim == 1 and new_mgr_locs is None:
new_mgr_locs = [0]
else:
if new_mgr_locs is None:
new_mgr_locs = self.mgr_locs
return self.make_block_same_class(new_values, new_mgr_locs) | [
"\n Take values according to indexer and return them as a block.\n "
] |
Please provide a description of the function:def _slice(self, slicer):
# slice the category
# return same dims as we currently have
if isinstance(slicer, tuple) and len(slicer) == 2:
if not com.is_null_slice(slicer[0]):
raise AssertionError("invalid slicing for a 1-ndim "
"categorical")
slicer = slicer[1]
return self.values[slicer] | [
" return a slice of my values "
] |
Please provide a description of the function:def concat_same_type(self, to_concat, placement=None):
values = self._holder._concat_same_type(
[blk.values for blk in to_concat])
placement = placement or slice(0, len(values), 1)
return self.make_block_same_class(values, ndim=self.ndim,
placement=placement) | [
"\n Concatenate list of single blocks of the same type.\n "
] |
Please provide a description of the function:def shift(self,
periods: int,
axis: libinternals.BlockPlacement = 0,
fill_value: Any = None) -> List['ExtensionBlock']:
return [
self.make_block_same_class(
self.values.shift(periods=periods, fill_value=fill_value),
placement=self.mgr_locs, ndim=self.ndim)
] | [
"\n Shift the block by `periods`.\n\n Dispatches to underlying ExtensionArray and re-boxes in an\n ExtensionBlock.\n "
] |
Please provide a description of the function:def to_native_types(self, slicer=None, na_rep='', float_format=None,
decimal='.', quoting=None, **kwargs):
values = self.values
if slicer is not None:
values = values[:, slicer]
# see gh-13418: no special formatting is desired at the
# output (important for appropriate 'quoting' behaviour),
# so do not pass it through the FloatArrayFormatter
if float_format is None and decimal == '.':
mask = isna(values)
if not quoting:
values = values.astype(str)
else:
values = np.array(values, dtype='object')
values[mask] = na_rep
return values
from pandas.io.formats.format import FloatArrayFormatter
formatter = FloatArrayFormatter(values, na_rep=na_rep,
float_format=float_format,
decimal=decimal, quoting=quoting,
fixed_width=False)
return formatter.get_result_as_array() | [
" convert to our native types format, slicing if desired "
] |
Please provide a description of the function:def get_values(self, dtype=None):
if is_object_dtype(dtype):
values = self.values.ravel()
result = self._holder(values).astype(object)
return result.reshape(self.values.shape)
return self.values | [
"\n return object dtype as boxed values, such as Timestamps/Timedelta\n "
] |
Please provide a description of the function:def _maybe_coerce_values(self, values):
if values.dtype != _NS_DTYPE:
values = conversion.ensure_datetime64ns(values)
if isinstance(values, DatetimeArray):
values = values._data
assert isinstance(values, np.ndarray), type(values)
return values | [
"Input validation for values passed to __init__. Ensure that\n we have datetime64ns, coercing if necessary.\n\n Parameters\n ----------\n values : array-like\n Must be convertible to datetime64\n\n Returns\n -------\n values : ndarray[datetime64ns]\n\n Overridden by DatetimeTZBlock.\n "
] |
Please provide a description of the function:def _astype(self, dtype, **kwargs):
dtype = pandas_dtype(dtype)
# if we are passed a datetime64[ns, tz]
if is_datetime64tz_dtype(dtype):
values = self.values
if getattr(values, 'tz', None) is None:
values = DatetimeIndex(values).tz_localize('UTC')
values = values.tz_convert(dtype.tz)
return self.make_block(values)
# delegate
return super()._astype(dtype=dtype, **kwargs) | [
"\n these automatically copy, so copy=True has no effect\n raise on an except if raise == True\n "
] |
Please provide a description of the function:def _try_coerce_args(self, values, other):
values = values.view('i8')
if isinstance(other, bool):
raise TypeError
elif is_null_datetimelike(other):
other = tslibs.iNaT
elif isinstance(other, (datetime, np.datetime64, date)):
other = self._box_func(other)
if getattr(other, 'tz') is not None:
raise TypeError("cannot coerce a Timestamp with a tz on a "
"naive Block")
other = other.asm8.view('i8')
elif hasattr(other, 'dtype') and is_datetime64_dtype(other):
other = other.astype('i8', copy=False).view('i8')
else:
# coercion issues
# let higher levels handle
raise TypeError(other)
return values, other | [
"\n Coerce values and other to dtype 'i8'. NaN and NaT convert to\n the smallest i8, and will correctly round-trip to NaT if converted\n back in _try_coerce_result. values is always ndarray-like, other\n may not be\n\n Parameters\n ----------\n values : ndarray-like\n other : ndarray-like or scalar\n\n Returns\n -------\n base-type values, base-type other\n "
] |
Please provide a description of the function:def _try_coerce_result(self, result):
if isinstance(result, np.ndarray):
if result.dtype.kind in ['i', 'f']:
result = result.astype('M8[ns]')
elif isinstance(result, (np.integer, np.float, np.datetime64)):
result = self._box_func(result)
return result | [
" reverse of try_coerce_args "
] |
Please provide a description of the function:def to_native_types(self, slicer=None, na_rep=None, date_format=None,
quoting=None, **kwargs):
values = self.values
i8values = self.values.view('i8')
if slicer is not None:
values = values[..., slicer]
i8values = i8values[..., slicer]
from pandas.io.formats.format import _get_format_datetime64_from_values
fmt = _get_format_datetime64_from_values(values, date_format)
result = tslib.format_array_from_datetime(
i8values.ravel(), tz=getattr(self.values, 'tz', None),
format=fmt, na_rep=na_rep).reshape(i8values.shape)
return np.atleast_2d(result) | [
" convert to our native types format, slicing if desired "
] |
Please provide a description of the function:def set(self, locs, values):
values = conversion.ensure_datetime64ns(values, copy=False)
self.values[locs] = values | [
"\n Modify Block in-place with new item value\n\n Returns\n -------\n None\n "
] |
Please provide a description of the function:def _maybe_coerce_values(self, values):
if not isinstance(values, self._holder):
values = self._holder(values)
if values.tz is None:
raise ValueError("cannot create a DatetimeTZBlock without a tz")
return values | [
"Input validation for values passed to __init__. Ensure that\n we have datetime64TZ, coercing if necessary.\n\n Parametetrs\n -----------\n values : array-like\n Must be convertible to datetime64\n\n Returns\n -------\n values : DatetimeArray\n "
] |
Please provide a description of the function:def get_values(self, dtype=None):
values = self.values
if is_object_dtype(dtype):
values = values._box_values(values._data)
values = np.asarray(values)
if self.ndim == 2:
# Ensure that our shape is correct for DataFrame.
# ExtensionArrays are always 1-D, even in a DataFrame when
# the analogous NumPy-backed column would be a 2-D ndarray.
values = values.reshape(1, -1)
return values | [
"\n Returns an ndarray of values.\n\n Parameters\n ----------\n dtype : np.dtype\n Only `object`-like dtypes are respected here (not sure\n why).\n\n Returns\n -------\n values : ndarray\n When ``dtype=object``, then and object-dtype ndarray of\n boxed values is returned. Otherwise, an M8[ns] ndarray\n is returned.\n\n DatetimeArray is always 1-d. ``get_values`` will reshape\n the return value to be the same dimensionality as the\n block.\n "
] |
Please provide a description of the function:def _slice(self, slicer):
if isinstance(slicer, tuple):
col, loc = slicer
if not com.is_null_slice(col) and col != 0:
raise IndexError("{0} only contains one item".format(self))
return self.values[loc]
return self.values[slicer] | [
" return a slice of my values "
] |
Please provide a description of the function:def _try_coerce_args(self, values, other):
# asi8 is a view, needs copy
values = _block_shape(values.view("i8"), ndim=self.ndim)
if isinstance(other, ABCSeries):
other = self._holder(other)
if isinstance(other, bool):
raise TypeError
elif is_datetime64_dtype(other):
# add the tz back
other = self._holder(other, dtype=self.dtype)
elif is_null_datetimelike(other):
other = tslibs.iNaT
elif isinstance(other, self._holder):
if other.tz != self.values.tz:
raise ValueError("incompatible or non tz-aware value")
other = _block_shape(other.asi8, ndim=self.ndim)
elif isinstance(other, (np.datetime64, datetime, date)):
other = tslibs.Timestamp(other)
tz = getattr(other, 'tz', None)
# test we can have an equal time zone
if tz is None or str(tz) != str(self.values.tz):
raise ValueError("incompatible or non tz-aware value")
other = other.value
else:
raise TypeError(other)
return values, other | [
"\n localize and return i8 for the values\n\n Parameters\n ----------\n values : ndarray-like\n other : ndarray-like or scalar\n\n Returns\n -------\n base-type values, base-type other\n "
] |
Please provide a description of the function:def _try_coerce_result(self, result):
if isinstance(result, np.ndarray):
if result.dtype.kind in ['i', 'f']:
result = result.astype('M8[ns]')
elif isinstance(result, (np.integer, np.float, np.datetime64)):
result = self._box_func(result)
if isinstance(result, np.ndarray):
# allow passing of > 1dim if its trivial
if result.ndim > 1:
result = result.reshape(np.prod(result.shape))
# GH#24096 new values invalidates a frequency
result = self._holder._simple_new(result, freq=None,
dtype=self.values.dtype)
return result | [
" reverse of try_coerce_args "
] |
Please provide a description of the function:def diff(self, n, axis=0):
if axis == 0:
# Cannot currently calculate diff across multiple blocks since this
# function is invoked via apply
raise NotImplementedError
new_values = (self.values - self.shift(n, axis=axis)[0].values).asi8
# Reshape the new_values like how algos.diff does for timedelta data
new_values = new_values.reshape(1, len(new_values))
new_values = new_values.astype('timedelta64[ns]')
return [TimeDeltaBlock(new_values, placement=self.mgr_locs.indexer)] | [
"1st discrete difference\n\n Parameters\n ----------\n n : int, number of periods to diff\n axis : int, axis to diff upon. default 0\n\n Return\n ------\n A list with a new TimeDeltaBlock.\n\n Note\n ----\n The arguments here are mimicking shift so they are called correctly\n by apply.\n "
] |
Please provide a description of the function:def _try_coerce_args(self, values, other):
values = values.view('i8')
if isinstance(other, bool):
raise TypeError
elif is_null_datetimelike(other):
other = tslibs.iNaT
elif isinstance(other, (timedelta, np.timedelta64)):
other = Timedelta(other).value
elif hasattr(other, 'dtype') and is_timedelta64_dtype(other):
other = other.astype('i8', copy=False).view('i8')
else:
# coercion issues
# let higher levels handle
raise TypeError(other)
return values, other | [
"\n Coerce values and other to int64, with null values converted to\n iNaT. values is always ndarray-like, other may not be\n\n Parameters\n ----------\n values : ndarray-like\n other : ndarray-like or scalar\n\n Returns\n -------\n base-type values, base-type other\n "
] |
Please provide a description of the function:def _try_coerce_result(self, result):
if isinstance(result, np.ndarray):
mask = isna(result)
if result.dtype.kind in ['i', 'f']:
result = result.astype('m8[ns]')
result[mask] = tslibs.iNaT
elif isinstance(result, (np.integer, np.float)):
result = self._box_func(result)
return result | [
" reverse of try_coerce_args / try_operate "
] |
Please provide a description of the function:def to_native_types(self, slicer=None, na_rep=None, quoting=None,
**kwargs):
values = self.values
if slicer is not None:
values = values[:, slicer]
mask = isna(values)
rvalues = np.empty(values.shape, dtype=object)
if na_rep is None:
na_rep = 'NaT'
rvalues[mask] = na_rep
imask = (~mask).ravel()
# FIXME:
# should use the formats.format.Timedelta64Formatter here
# to figure what format to pass to the Timedelta
# e.g. to not show the decimals say
rvalues.flat[imask] = np.array([Timedelta(val)._repr_base(format='all')
for val in values.ravel()[imask]],
dtype=object)
return rvalues | [
" convert to our native types format, slicing if desired "
] |
Please provide a description of the function:def convert(self, *args, **kwargs):
if args:
raise NotImplementedError
by_item = kwargs.get('by_item', True)
new_inputs = ['coerce', 'datetime', 'numeric', 'timedelta']
new_style = False
for kw in new_inputs:
new_style |= kw in kwargs
if new_style:
fn = soft_convert_objects
fn_inputs = new_inputs
else:
fn = maybe_convert_objects
fn_inputs = ['convert_dates', 'convert_numeric',
'convert_timedeltas']
fn_inputs += ['copy']
fn_kwargs = {key: kwargs[key] for key in fn_inputs if key in kwargs}
# operate column-by-column
def f(m, v, i):
shape = v.shape
values = fn(v.ravel(), **fn_kwargs)
try:
values = values.reshape(shape)
values = _block_shape(values, ndim=self.ndim)
except (AttributeError, NotImplementedError):
pass
return values
if by_item and not self._is_single_block:
blocks = self.split_and_operate(None, f, False)
else:
values = f(None, self.values.ravel(), None)
blocks = [make_block(values, ndim=self.ndim,
placement=self.mgr_locs)]
return blocks | [
" attempt to coerce any object types to better types return a copy of\n the block (if copy = True) by definition we ARE an ObjectBlock!!!!!\n\n can return multiple blocks!\n "
] |
Please provide a description of the function:def set(self, locs, values):
try:
self.values[locs] = values
except (ValueError):
# broadcasting error
# see GH6171
new_shape = list(values.shape)
new_shape[0] = len(self.items)
self.values = np.empty(tuple(new_shape), dtype=self.dtype)
self.values.fill(np.nan)
self.values[locs] = values | [
"\n Modify Block in-place with new item value\n\n Returns\n -------\n None\n "
] |
Please provide a description of the function:def _try_coerce_args(self, values, other):
if isinstance(other, ABCDatetimeIndex):
# May get a DatetimeIndex here. Unbox it.
other = other.array
if isinstance(other, DatetimeArray):
# hit in pandas/tests/indexing/test_coercion.py
# ::TestWhereCoercion::test_where_series_datetime64[datetime64tz]
# when falling back to ObjectBlock.where
other = other.astype(object)
return values, other | [
" provide coercion to our input arguments "
] |
Please provide a description of the function:def _replace_single(self, to_replace, value, inplace=False, filter=None,
regex=False, convert=True, mask=None):
inplace = validate_bool_kwarg(inplace, 'inplace')
# to_replace is regex compilable
to_rep_re = regex and is_re_compilable(to_replace)
# regex is regex compilable
regex_re = is_re_compilable(regex)
# only one will survive
if to_rep_re and regex_re:
raise AssertionError('only one of to_replace and regex can be '
'regex compilable')
# if regex was passed as something that can be a regex (rather than a
# boolean)
if regex_re:
to_replace = regex
regex = regex_re or to_rep_re
# try to get the pattern attribute (compiled re) or it's a string
try:
pattern = to_replace.pattern
except AttributeError:
pattern = to_replace
# if the pattern is not empty and to_replace is either a string or a
# regex
if regex and pattern:
rx = re.compile(to_replace)
else:
# if the thing to replace is not a string or compiled regex call
# the superclass method -> to_replace is some kind of object
return super().replace(to_replace, value, inplace=inplace,
filter=filter, regex=regex)
new_values = self.values if inplace else self.values.copy()
# deal with replacing values with objects (strings) that match but
# whose replacement is not a string (numeric, nan, object)
if isna(value) or not isinstance(value, str):
def re_replacer(s):
try:
return value if rx.search(s) is not None else s
except TypeError:
return s
else:
# value is guaranteed to be a string here, s can be either a string
# or null if it's null it gets returned
def re_replacer(s):
try:
return rx.sub(value, s)
except TypeError:
return s
f = np.vectorize(re_replacer, otypes=[self.dtype])
if filter is None:
filt = slice(None)
else:
filt = self.mgr_locs.isin(filter).nonzero()[0]
if mask is None:
new_values[filt] = f(new_values[filt])
else:
new_values[filt][mask] = f(new_values[filt][mask])
# convert
block = self.make_block(new_values)
if convert:
block = block.convert(by_item=True, numeric=False)
return block | [
"\n Replace elements by the given value.\n\n Parameters\n ----------\n to_replace : object or pattern\n Scalar to replace or regular expression to match.\n value : object\n Replacement object.\n inplace : bool, default False\n Perform inplace modification.\n filter : list, optional\n regex : bool, default False\n If true, perform regular expression substitution.\n convert : bool, default True\n If true, try to coerce any object types to better types.\n mask : array-like of bool, optional\n True indicate corresponding element is ignored.\n\n Returns\n -------\n a new block, the result after replacing\n "
] |
Please provide a description of the function:def _replace_coerce(self, to_replace, value, inplace=True, regex=False,
convert=False, mask=None):
if mask.any():
block = super()._replace_coerce(
to_replace=to_replace, value=value, inplace=inplace,
regex=regex, convert=convert, mask=mask)
if convert:
block = [b.convert(by_item=True, numeric=False, copy=True)
for b in block]
return block
return self | [
"\n Replace value corresponding to the given boolean array with another\n value.\n\n Parameters\n ----------\n to_replace : object or pattern\n Scalar to replace or regular expression to match.\n value : object\n Replacement object.\n inplace : bool, default False\n Perform inplace modification.\n regex : bool, default False\n If true, perform regular expression substitution.\n convert : bool, default True\n If true, try to coerce any object types to better types.\n mask : array-like of bool, optional\n True indicate corresponding element is ignored.\n\n Returns\n -------\n A new block if there is anything to replace or the original block.\n "
] |
Please provide a description of the function:def _try_coerce_result(self, result):
# GH12564: CategoricalBlock is 1-dim only
# while returned results could be any dim
if ((not is_categorical_dtype(result)) and
isinstance(result, np.ndarray)):
result = _block_shape(result, ndim=self.ndim)
return result | [
" reverse of try_coerce_args "
] |
Please provide a description of the function:def to_native_types(self, slicer=None, na_rep='', quoting=None, **kwargs):
values = self.values
if slicer is not None:
# Categorical is always one dimension
values = values[slicer]
mask = isna(values)
values = np.array(values, dtype='object')
values[mask] = na_rep
# we are expected to return a 2-d ndarray
return values.reshape(1, len(values)) | [
" convert to our native types format, slicing if desired "
] |
Please provide a description of the function:def _style_to_xlwt(cls, item, firstlevel=True, field_sep=',',
line_sep=';'):
if hasattr(item, 'items'):
if firstlevel:
it = ["{key}: {val}"
.format(key=key, val=cls._style_to_xlwt(value, False))
for key, value in item.items()]
out = "{sep} ".format(sep=(line_sep).join(it))
return out
else:
it = ["{key} {val}"
.format(key=key, val=cls._style_to_xlwt(value, False))
for key, value in item.items()]
out = "{sep} ".format(sep=(field_sep).join(it))
return out
else:
item = "{item}".format(item=item)
item = item.replace("True", "on")
item = item.replace("False", "off")
return item | [
"helper which recursively generate an xlwt easy style string\n for example:\n\n hstyle = {\"font\": {\"bold\": True},\n \"border\": {\"top\": \"thin\",\n \"right\": \"thin\",\n \"bottom\": \"thin\",\n \"left\": \"thin\"},\n \"align\": {\"horiz\": \"center\"}}\n will be converted to\n font: bold on; \\\n border: top thin, right thin, bottom thin, left thin; \\\n align: horiz center;\n "
] |
Please provide a description of the function:def _convert_to_style(cls, style_dict, num_format_str=None):
import xlwt
if style_dict:
xlwt_stylestr = cls._style_to_xlwt(style_dict)
style = xlwt.easyxf(xlwt_stylestr, field_sep=',', line_sep=';')
else:
style = xlwt.XFStyle()
if num_format_str is not None:
style.num_format_str = num_format_str
return style | [
"\n converts a style_dict to an xlwt style object\n Parameters\n ----------\n style_dict : style dictionary to convert\n num_format_str : optional number format string\n "
] |
Please provide a description of the function:def convert(cls, style_dict, num_format_str=None):
# Create a XlsxWriter format object.
props = {}
if num_format_str is not None:
props['num_format'] = num_format_str
if style_dict is None:
return props
if 'borders' in style_dict:
style_dict = style_dict.copy()
style_dict['border'] = style_dict.pop('borders')
for style_group_key, style_group in style_dict.items():
for src, dst in cls.STYLE_MAPPING.get(style_group_key, []):
# src is a sequence of keys into a nested dict
# dst is a flat key
if dst in props:
continue
v = style_group
for k in src:
try:
v = v[k]
except (KeyError, TypeError):
break
else:
props[dst] = v
if isinstance(props.get('pattern'), str):
# TODO: support other fill patterns
props['pattern'] = 0 if props['pattern'] == 'none' else 1
for k in ['border', 'top', 'right', 'bottom', 'left']:
if isinstance(props.get(k), str):
try:
props[k] = ['none', 'thin', 'medium', 'dashed', 'dotted',
'thick', 'double', 'hair', 'mediumDashed',
'dashDot', 'mediumDashDot', 'dashDotDot',
'mediumDashDotDot',
'slantDashDot'].index(props[k])
except ValueError:
props[k] = 2
if isinstance(props.get('font_script'), str):
props['font_script'] = ['baseline', 'superscript',
'subscript'].index(props['font_script'])
if isinstance(props.get('underline'), str):
props['underline'] = {'none': 0, 'single': 1, 'double': 2,
'singleAccounting': 33,
'doubleAccounting': 34}[props['underline']]
return props | [
"\n converts a style_dict to an xlsxwriter format dict\n\n Parameters\n ----------\n style_dict : style dictionary to convert\n num_format_str : optional number format string\n "
] |
Please provide a description of the function:def _unstack_extension_series(series, level, fill_value):
# Implementation note: the basic idea is to
# 1. Do a regular unstack on a dummy array of integers
# 2. Followup with a columnwise take.
# We use the dummy take to discover newly-created missing values
# introduced by the reshape.
from pandas.core.reshape.concat import concat
dummy_arr = np.arange(len(series))
# fill_value=-1, since we will do a series.values.take later
result = _Unstacker(dummy_arr, series.index,
level=level, fill_value=-1).get_result()
out = []
values = extract_array(series, extract_numpy=False)
for col, indices in result.iteritems():
out.append(Series(values.take(indices.values,
allow_fill=True,
fill_value=fill_value),
name=col, index=result.index))
return concat(out, axis='columns', copy=False, keys=result.columns) | [
"\n Unstack an ExtensionArray-backed Series.\n\n The ExtensionDtype is preserved.\n\n Parameters\n ----------\n series : Series\n A Series with an ExtensionArray for values\n level : Any\n The level name or number.\n fill_value : Any\n The user-level (not physical storage) fill value to use for\n missing values introduced by the reshape. Passed to\n ``series.values.take``.\n\n Returns\n -------\n DataFrame\n Each column of the DataFrame will have the same dtype as\n the input Series.\n "
] |
Please provide a description of the function:def stack(frame, level=-1, dropna=True):
def factorize(index):
if index.is_unique:
return index, np.arange(len(index))
codes, categories = _factorize_from_iterable(index)
return categories, codes
N, K = frame.shape
# Will also convert negative level numbers and check if out of bounds.
level_num = frame.columns._get_level_number(level)
if isinstance(frame.columns, MultiIndex):
return _stack_multi_columns(frame, level_num=level_num, dropna=dropna)
elif isinstance(frame.index, MultiIndex):
new_levels = list(frame.index.levels)
new_codes = [lab.repeat(K) for lab in frame.index.codes]
clev, clab = factorize(frame.columns)
new_levels.append(clev)
new_codes.append(np.tile(clab, N).ravel())
new_names = list(frame.index.names)
new_names.append(frame.columns.name)
new_index = MultiIndex(levels=new_levels, codes=new_codes,
names=new_names, verify_integrity=False)
else:
levels, (ilab, clab) = zip(*map(factorize, (frame.index,
frame.columns)))
codes = ilab.repeat(K), np.tile(clab, N).ravel()
new_index = MultiIndex(levels=levels, codes=codes,
names=[frame.index.name, frame.columns.name],
verify_integrity=False)
if frame._is_homogeneous_type:
# For homogeneous EAs, frame.values will coerce to object. So
# we concatenate instead.
dtypes = list(frame.dtypes.values)
dtype = dtypes[0]
if is_extension_array_dtype(dtype):
arr = dtype.construct_array_type()
new_values = arr._concat_same_type([
col._values for _, col in frame.iteritems()
])
new_values = _reorder_for_extension_array_stack(new_values, N, K)
else:
# homogeneous, non-EA
new_values = frame.values.ravel()
else:
# non-homogeneous
new_values = frame.values.ravel()
if dropna:
mask = notna(new_values)
new_values = new_values[mask]
new_index = new_index[mask]
return frame._constructor_sliced(new_values, index=new_index) | [
"\n Convert DataFrame to Series with multi-level Index. Columns become the\n second level of the resulting hierarchical index\n\n Returns\n -------\n stacked : Series\n "
] |
Please provide a description of the function:def get_dummies(data, prefix=None, prefix_sep='_', dummy_na=False,
columns=None, sparse=False, drop_first=False, dtype=None):
from pandas.core.reshape.concat import concat
from itertools import cycle
dtypes_to_encode = ['object', 'category']
if isinstance(data, DataFrame):
# determine columns being encoded
if columns is None:
data_to_encode = data.select_dtypes(
include=dtypes_to_encode)
else:
data_to_encode = data[columns]
# validate prefixes and separator to avoid silently dropping cols
def check_len(item, name):
len_msg = ("Length of '{name}' ({len_item}) did not match the "
"length of the columns being encoded ({len_enc}).")
if is_list_like(item):
if not len(item) == data_to_encode.shape[1]:
len_msg = len_msg.format(name=name, len_item=len(item),
len_enc=data_to_encode.shape[1])
raise ValueError(len_msg)
check_len(prefix, 'prefix')
check_len(prefix_sep, 'prefix_sep')
if isinstance(prefix, str):
prefix = cycle([prefix])
if isinstance(prefix, dict):
prefix = [prefix[col] for col in data_to_encode.columns]
if prefix is None:
prefix = data_to_encode.columns
# validate separators
if isinstance(prefix_sep, str):
prefix_sep = cycle([prefix_sep])
elif isinstance(prefix_sep, dict):
prefix_sep = [prefix_sep[col] for col in data_to_encode.columns]
if data_to_encode.shape == data.shape:
# Encoding the entire df, do not prepend any dropped columns
with_dummies = []
elif columns is not None:
# Encoding only cols specified in columns. Get all cols not in
# columns to prepend to result.
with_dummies = [data.drop(columns, axis=1)]
else:
# Encoding only object and category dtype columns. Get remaining
# columns to prepend to result.
with_dummies = [data.select_dtypes(exclude=dtypes_to_encode)]
for (col, pre, sep) in zip(data_to_encode.iteritems(), prefix,
prefix_sep):
# col is (column_name, column), use just column data here
dummy = _get_dummies_1d(col[1], prefix=pre, prefix_sep=sep,
dummy_na=dummy_na, sparse=sparse,
drop_first=drop_first, dtype=dtype)
with_dummies.append(dummy)
result = concat(with_dummies, axis=1)
else:
result = _get_dummies_1d(data, prefix, prefix_sep, dummy_na,
sparse=sparse,
drop_first=drop_first,
dtype=dtype)
return result | [
"\n Convert categorical variable into dummy/indicator variables.\n\n Parameters\n ----------\n data : array-like, Series, or DataFrame\n Data of which to get dummy indicators.\n prefix : str, list of str, or dict of str, default None\n String to append DataFrame column names.\n Pass a list with length equal to the number of columns\n when calling get_dummies on a DataFrame. Alternatively, `prefix`\n can be a dictionary mapping column names to prefixes.\n prefix_sep : str, default '_'\n If appending prefix, separator/delimiter to use. Or pass a\n list or dictionary as with `prefix`.\n dummy_na : bool, default False\n Add a column to indicate NaNs, if False NaNs are ignored.\n columns : list-like, default None\n Column names in the DataFrame to be encoded.\n If `columns` is None then all the columns with\n `object` or `category` dtype will be converted.\n sparse : bool, default False\n Whether the dummy-encoded columns should be backed by\n a :class:`SparseArray` (True) or a regular NumPy array (False).\n drop_first : bool, default False\n Whether to get k-1 dummies out of k categorical levels by removing the\n first level.\n\n .. versionadded:: 0.18.0\n\n dtype : dtype, default np.uint8\n Data type for new columns. Only a single dtype is allowed.\n\n .. versionadded:: 0.23.0\n\n Returns\n -------\n DataFrame\n Dummy-coded data.\n\n See Also\n --------\n Series.str.get_dummies : Convert Series to dummy codes.\n\n Examples\n --------\n >>> s = pd.Series(list('abca'))\n\n >>> pd.get_dummies(s)\n a b c\n 0 1 0 0\n 1 0 1 0\n 2 0 0 1\n 3 1 0 0\n\n >>> s1 = ['a', 'b', np.nan]\n\n >>> pd.get_dummies(s1)\n a b\n 0 1 0\n 1 0 1\n 2 0 0\n\n >>> pd.get_dummies(s1, dummy_na=True)\n a b NaN\n 0 1 0 0\n 1 0 1 0\n 2 0 0 1\n\n >>> df = pd.DataFrame({'A': ['a', 'b', 'a'], 'B': ['b', 'a', 'c'],\n ... 'C': [1, 2, 3]})\n\n >>> pd.get_dummies(df, prefix=['col1', 'col2'])\n C col1_a col1_b col2_a col2_b col2_c\n 0 1 1 0 0 1 0\n 1 2 0 1 1 0 0\n 2 3 1 0 0 0 1\n\n >>> pd.get_dummies(pd.Series(list('abcaa')))\n a b c\n 0 1 0 0\n 1 0 1 0\n 2 0 0 1\n 3 1 0 0\n 4 1 0 0\n\n >>> pd.get_dummies(pd.Series(list('abcaa')), drop_first=True)\n b c\n 0 0 0\n 1 1 0\n 2 0 1\n 3 0 0\n 4 0 0\n\n >>> pd.get_dummies(pd.Series(list('abc')), dtype=float)\n a b c\n 0 1.0 0.0 0.0\n 1 0.0 1.0 0.0\n 2 0.0 0.0 1.0\n "
] |
Please provide a description of the function:def make_axis_dummies(frame, axis='minor', transform=None):
numbers = {'major': 0, 'minor': 1}
num = numbers.get(axis, axis)
items = frame.index.levels[num]
codes = frame.index.codes[num]
if transform is not None:
mapped_items = items.map(transform)
codes, items = _factorize_from_iterable(mapped_items.take(codes))
values = np.eye(len(items), dtype=float)
values = values.take(codes, axis=0)
return DataFrame(values, columns=items, index=frame.index) | [
"\n Construct 1-0 dummy variables corresponding to designated axis\n labels\n\n Parameters\n ----------\n frame : DataFrame\n axis : {'major', 'minor'}, default 'minor'\n transform : function, default None\n Function to apply to axis labels first. For example, to\n get \"day of week\" dummies in a time series regression\n you might call::\n\n make_axis_dummies(panel, axis='major',\n transform=lambda d: d.weekday())\n Returns\n -------\n dummies : DataFrame\n Column names taken from chosen axis\n "
] |
Please provide a description of the function:def _reorder_for_extension_array_stack(arr, n_rows, n_columns):
# final take to get the order correct.
# idx is an indexer like
# [c0r0, c1r0, c2r0, ...,
# c0r1, c1r1, c2r1, ...]
idx = np.arange(n_rows * n_columns).reshape(n_columns, n_rows).T.ravel()
return arr.take(idx) | [
"\n Re-orders the values when stacking multiple extension-arrays.\n\n The indirect stacking method used for EAs requires a followup\n take to get the order correct.\n\n Parameters\n ----------\n arr : ExtensionArray\n n_rows, n_columns : int\n The number of rows and columns in the original DataFrame.\n\n Returns\n -------\n taken : ExtensionArray\n The original `arr` with elements re-ordered appropriately\n\n Examples\n --------\n >>> arr = np.array(['a', 'b', 'c', 'd', 'e', 'f'])\n >>> _reorder_for_extension_array_stack(arr, 2, 3)\n array(['a', 'c', 'e', 'b', 'd', 'f'], dtype='<U1')\n\n >>> _reorder_for_extension_array_stack(arr, 3, 2)\n array(['a', 'd', 'b', 'e', 'c', 'f'], dtype='<U1')\n "
] |
Please provide a description of the function:def _split_line(s, parts):
out = {}
start = 0
for name, length in parts:
out[name] = s[start:start + length].strip()
start += length
del out['_']
return out | [
"\n Parameters\n ----------\n s: string\n Fixed-length string to split\n parts: list of (name, length) pairs\n Used to break up string, name '_' will be filtered from output.\n\n Returns\n -------\n Dict of name:contents of string at given location.\n "
] |
Please provide a description of the function:def _parse_float_vec(vec):
dtype = np.dtype('>u4,>u4')
vec1 = vec.view(dtype=dtype)
xport1 = vec1['f0']
xport2 = vec1['f1']
# Start by setting first half of ieee number to first half of IBM
# number sans exponent
ieee1 = xport1 & 0x00ffffff
# The fraction bit to the left of the binary point in the ieee
# format was set and the number was shifted 0, 1, 2, or 3
# places. This will tell us how to adjust the ibm exponent to be a
# power of 2 ieee exponent and how to shift the fraction bits to
# restore the correct magnitude.
shift = np.zeros(len(vec), dtype=np.uint8)
shift[np.where(xport1 & 0x00200000)] = 1
shift[np.where(xport1 & 0x00400000)] = 2
shift[np.where(xport1 & 0x00800000)] = 3
# shift the ieee number down the correct number of places then
# set the second half of the ieee number to be the second half
# of the ibm number shifted appropriately, ored with the bits
# from the first half that would have been shifted in if we
# could shift a double. All we are worried about are the low
# order 3 bits of the first half since we're only shifting by
# 1, 2, or 3.
ieee1 >>= shift
ieee2 = (xport2 >> shift) | ((xport1 & 0x00000007) << (29 + (3 - shift)))
# clear the 1 bit to the left of the binary point
ieee1 &= 0xffefffff
# set the exponent of the ieee number to be the actual exponent
# plus the shift count + 1023. Or this into the first half of the
# ieee number. The ibm exponent is excess 64 but is adjusted by 65
# since during conversion to ibm format the exponent is
# incremented by 1 and the fraction bits left 4 positions to the
# right of the radix point. (had to add >> 24 because C treats &
# 0x7f as 0x7f000000 and Python doesn't)
ieee1 |= ((((((xport1 >> 24) & 0x7f) - 65) << 2) +
shift + 1023) << 20) | (xport1 & 0x80000000)
ieee = np.empty((len(ieee1),), dtype='>u4,>u4')
ieee['f0'] = ieee1
ieee['f1'] = ieee2
ieee = ieee.view(dtype='>f8')
ieee = ieee.astype('f8')
return ieee | [
"\n Parse a vector of float values representing IBM 8 byte floats into\n native 8 byte floats.\n "
] |
Please provide a description of the function:def _record_count(self):
self.filepath_or_buffer.seek(0, 2)
total_records_length = (self.filepath_or_buffer.tell() -
self.record_start)
if total_records_length % 80 != 0:
warnings.warn("xport file may be corrupted")
if self.record_length > 80:
self.filepath_or_buffer.seek(self.record_start)
return total_records_length // self.record_length
self.filepath_or_buffer.seek(-80, 2)
last_card = self.filepath_or_buffer.read(80)
last_card = np.frombuffer(last_card, dtype=np.uint64)
# 8 byte blank
ix = np.flatnonzero(last_card == 2314885530818453536)
if len(ix) == 0:
tail_pad = 0
else:
tail_pad = 8 * len(ix)
self.filepath_or_buffer.seek(self.record_start)
return (total_records_length - tail_pad) // self.record_length | [
"\n Get number of records in file.\n\n This is maybe suboptimal because we have to seek to the end of\n the file.\n\n Side effect: returns file position to record_start.\n "
] |
Please provide a description of the function:def get_chunk(self, size=None):
if size is None:
size = self._chunksize
return self.read(nrows=size) | [
"\n Reads lines from Xport file and returns as dataframe\n\n Parameters\n ----------\n size : int, defaults to None\n Number of lines to read. If None, reads whole file.\n\n Returns\n -------\n DataFrame\n "
] |
Please provide a description of the function:def construction_error(tot_items, block_shape, axes, e=None):
passed = tuple(map(int, [tot_items] + list(block_shape)))
# Correcting the user facing error message during dataframe construction
if len(passed) <= 2:
passed = passed[::-1]
implied = tuple(len(ax) for ax in axes)
# Correcting the user facing error message during dataframe construction
if len(implied) <= 2:
implied = implied[::-1]
if passed == implied and e is not None:
raise e
if block_shape[0] == 0:
raise ValueError("Empty data passed with indices specified.")
raise ValueError("Shape of passed values is {0}, indices imply {1}".format(
passed, implied)) | [
" raise a helpful message about our construction "
] |
Please provide a description of the function:def _simple_blockify(tuples, dtype):
values, placement = _stack_arrays(tuples, dtype)
# CHECK DTYPE?
if dtype is not None and values.dtype != dtype: # pragma: no cover
values = values.astype(dtype)
block = make_block(values, placement=placement)
return [block] | [
" return a single array of a block that has a single dtype; if dtype is\n not None, coerce to this dtype\n "
] |
Please provide a description of the function:def _multi_blockify(tuples, dtype=None):
# group by dtype
grouper = itertools.groupby(tuples, lambda x: x[2].dtype)
new_blocks = []
for dtype, tup_block in grouper:
values, placement = _stack_arrays(list(tup_block), dtype)
block = make_block(values, placement=placement)
new_blocks.append(block)
return new_blocks | [
" return an array of blocks that potentially have different dtypes "
] |
Please provide a description of the function:def _sparse_blockify(tuples, dtype=None):
new_blocks = []
for i, names, array in tuples:
array = _maybe_to_sparse(array)
block = make_block(array, placement=[i])
new_blocks.append(block)
return new_blocks | [
" return an array of blocks that potentially have different dtypes (and\n are sparse)\n "
] |
Please provide a description of the function:def _interleaved_dtype(
blocks: List[Block]
) -> Optional[Union[np.dtype, ExtensionDtype]]:
if not len(blocks):
return None
return find_common_type([b.dtype for b in blocks]) | [
"Find the common dtype for `blocks`.\n\n Parameters\n ----------\n blocks : List[Block]\n\n Returns\n -------\n dtype : Optional[Union[np.dtype, ExtensionDtype]]\n None is returned when `blocks` is empty.\n "
] |
Please provide a description of the function:def _consolidate(blocks):
# sort by _can_consolidate, dtype
gkey = lambda x: x._consolidate_key
grouper = itertools.groupby(sorted(blocks, key=gkey), gkey)
new_blocks = []
for (_can_consolidate, dtype), group_blocks in grouper:
merged_blocks = _merge_blocks(list(group_blocks), dtype=dtype,
_can_consolidate=_can_consolidate)
new_blocks = _extend_blocks(merged_blocks, new_blocks)
return new_blocks | [
"\n Merge blocks having same dtype, exclude non-consolidating blocks\n "
] |
Please provide a description of the function:def _compare_or_regex_search(a, b, regex=False):
if not regex:
op = lambda x: operator.eq(x, b)
else:
op = np.vectorize(lambda x: bool(re.search(b, x)) if isinstance(x, str)
else False)
is_a_array = isinstance(a, np.ndarray)
is_b_array = isinstance(b, np.ndarray)
# numpy deprecation warning to have i8 vs integer comparisons
if is_datetimelike_v_numeric(a, b):
result = False
# numpy deprecation warning if comparing numeric vs string-like
elif is_numeric_v_string_like(a, b):
result = False
else:
result = op(a)
if is_scalar(result) and (is_a_array or is_b_array):
type_names = [type(a).__name__, type(b).__name__]
if is_a_array:
type_names[0] = 'ndarray(dtype={dtype})'.format(dtype=a.dtype)
if is_b_array:
type_names[1] = 'ndarray(dtype={dtype})'.format(dtype=b.dtype)
raise TypeError(
"Cannot compare types {a!r} and {b!r}".format(a=type_names[0],
b=type_names[1]))
return result | [
"\n Compare two array_like inputs of the same shape or two scalar values\n\n Calls operator.eq or re.search, depending on regex argument. If regex is\n True, perform an element-wise regex matching.\n\n Parameters\n ----------\n a : array_like or scalar\n b : array_like or scalar\n regex : bool, default False\n\n Returns\n -------\n mask : array_like of bool\n "
] |
Please provide a description of the function:def items_overlap_with_suffix(left, lsuffix, right, rsuffix):
to_rename = left.intersection(right)
if len(to_rename) == 0:
return left, right
else:
if not lsuffix and not rsuffix:
raise ValueError('columns overlap but no suffix specified: '
'{rename}'.format(rename=to_rename))
def renamer(x, suffix):
if x in to_rename and suffix is not None:
return '{x}{suffix}'.format(x=x, suffix=suffix)
return x
lrenamer = partial(renamer, suffix=lsuffix)
rrenamer = partial(renamer, suffix=rsuffix)
return (_transform_index(left, lrenamer),
_transform_index(right, rrenamer)) | [
"\n If two indices overlap, add suffixes to overlapping entries.\n\n If corresponding suffix is empty, the entry is simply converted to string.\n\n ",
"Rename the left and right indices.\n\n If there is overlap, and suffix is not None, add\n suffix, otherwise, leave it as-is.\n\n Parameters\n ----------\n x : original column name\n suffix : str or None\n\n Returns\n -------\n x : renamed column name\n "
] |
Please provide a description of the function:def _transform_index(index, func, level=None):
if isinstance(index, MultiIndex):
if level is not None:
items = [tuple(func(y) if i == level else y
for i, y in enumerate(x)) for x in index]
else:
items = [tuple(func(y) for y in x) for x in index]
return MultiIndex.from_tuples(items, names=index.names)
else:
items = [func(x) for x in index]
return Index(items, name=index.name, tupleize_cols=False) | [
"\n Apply function to all values found in index.\n\n This includes transforming multiindex entries separately.\n Only apply function to one level of the MultiIndex if level is specified.\n\n "
] |
Please provide a description of the function:def _fast_count_smallints(arr):
counts = np.bincount(arr.astype(np.int_))
nz = counts.nonzero()[0]
return np.c_[nz, counts[nz]] | [
"Faster version of set(arr) for sequences of small numbers."
] |
Please provide a description of the function:def concatenate_block_managers(mgrs_indexers, axes, concat_axis, copy):
concat_plans = [get_mgr_concatenation_plan(mgr, indexers)
for mgr, indexers in mgrs_indexers]
concat_plan = combine_concat_plans(concat_plans, concat_axis)
blocks = []
for placement, join_units in concat_plan:
if len(join_units) == 1 and not join_units[0].indexers:
b = join_units[0].block
values = b.values
if copy:
values = values.copy()
elif not copy:
values = values.view()
b = b.make_block_same_class(values, placement=placement)
elif is_uniform_join_units(join_units):
b = join_units[0].block.concat_same_type(
[ju.block for ju in join_units], placement=placement)
else:
b = make_block(
concatenate_join_units(join_units, concat_axis, copy=copy),
placement=placement)
blocks.append(b)
return BlockManager(blocks, axes) | [
"\n Concatenate block managers into one.\n\n Parameters\n ----------\n mgrs_indexers : list of (BlockManager, {axis: indexer,...}) tuples\n axes : list of Index\n concat_axis : int\n copy : bool\n\n "
] |
Please provide a description of the function:def make_empty(self, axes=None):
if axes is None:
axes = [ensure_index([])] + [ensure_index(a)
for a in self.axes[1:]]
# preserve dtype if possible
if self.ndim == 1:
blocks = np.array([], dtype=self.array_dtype)
else:
blocks = []
return self.__class__(blocks, axes) | [
" return an empty BlockManager with the items axis of len 0 "
] |
Please provide a description of the function:def rename_axis(self, mapper, axis, copy=True, level=None):
obj = self.copy(deep=copy)
obj.set_axis(axis, _transform_index(self.axes[axis], mapper, level))
return obj | [
"\n Rename one of axes.\n\n Parameters\n ----------\n mapper : unary callable\n axis : int\n copy : boolean, default True\n level : int, default None\n "
] |
Please provide a description of the function:def _rebuild_blknos_and_blklocs(self):
new_blknos = np.empty(self.shape[0], dtype=np.int64)
new_blklocs = np.empty(self.shape[0], dtype=np.int64)
new_blknos.fill(-1)
new_blklocs.fill(-1)
for blkno, blk in enumerate(self.blocks):
rl = blk.mgr_locs
new_blknos[rl.indexer] = blkno
new_blklocs[rl.indexer] = np.arange(len(rl))
if (new_blknos == -1).any():
raise AssertionError("Gaps in blk ref_locs")
self._blknos = new_blknos
self._blklocs = new_blklocs | [
"\n Update mgr._blknos / mgr._blklocs.\n "
] |
Please provide a description of the function:def _get_counts(self, f):
self._consolidate_inplace()
counts = dict()
for b in self.blocks:
v = f(b)
counts[v] = counts.get(v, 0) + b.shape[0]
return counts | [
" return a dict of the counts of the function in BlockManager "
] |
Please provide a description of the function:def apply(self, f, axes=None, filter=None, do_integrity_check=False,
consolidate=True, **kwargs):
result_blocks = []
# filter kwarg is used in replace-* family of methods
if filter is not None:
filter_locs = set(self.items.get_indexer_for(filter))
if len(filter_locs) == len(self.items):
# All items are included, as if there were no filtering
filter = None
else:
kwargs['filter'] = filter_locs
if consolidate:
self._consolidate_inplace()
if f == 'where':
align_copy = True
if kwargs.get('align', True):
align_keys = ['other', 'cond']
else:
align_keys = ['cond']
elif f == 'putmask':
align_copy = False
if kwargs.get('align', True):
align_keys = ['new', 'mask']
else:
align_keys = ['mask']
elif f == 'fillna':
# fillna internally does putmask, maybe it's better to do this
# at mgr, not block level?
align_copy = False
align_keys = ['value']
else:
align_keys = []
# TODO(EA): may interfere with ExtensionBlock.setitem for blocks
# with a .values attribute.
aligned_args = {k: kwargs[k]
for k in align_keys
if hasattr(kwargs[k], 'values') and
not isinstance(kwargs[k], ABCExtensionArray)}
for b in self.blocks:
if filter is not None:
if not b.mgr_locs.isin(filter_locs).any():
result_blocks.append(b)
continue
if aligned_args:
b_items = self.items[b.mgr_locs.indexer]
for k, obj in aligned_args.items():
axis = getattr(obj, '_info_axis_number', 0)
kwargs[k] = obj.reindex(b_items, axis=axis,
copy=align_copy)
applied = getattr(b, f)(**kwargs)
result_blocks = _extend_blocks(applied, result_blocks)
if len(result_blocks) == 0:
return self.make_empty(axes or self.axes)
bm = self.__class__(result_blocks, axes or self.axes,
do_integrity_check=do_integrity_check)
bm._consolidate_inplace()
return bm | [
"\n iterate over the blocks, collect and create a new block manager\n\n Parameters\n ----------\n f : the callable or function name to operate on at the block level\n axes : optional (if not supplied, use self.axes)\n filter : list, if supplied, only call the block if the filter is in\n the block\n do_integrity_check : boolean, default False. Do the block manager\n integrity check\n consolidate: boolean, default True. Join together blocks having same\n dtype\n\n Returns\n -------\n Block Manager (new object)\n\n "
] |
Please provide a description of the function:def quantile(self, axis=0, consolidate=True, transposed=False,
interpolation='linear', qs=None, numeric_only=None):
# Series dispatches to DataFrame for quantile, which allows us to
# simplify some of the code here and in the blocks
assert self.ndim >= 2
if consolidate:
self._consolidate_inplace()
def get_axe(block, qs, axes):
from pandas import Float64Index
if is_list_like(qs):
ax = Float64Index(qs)
elif block.ndim == 1:
ax = Float64Index([qs])
else:
ax = axes[0]
return ax
axes, blocks = [], []
for b in self.blocks:
block = b.quantile(axis=axis, qs=qs, interpolation=interpolation)
axe = get_axe(b, qs, axes=self.axes)
axes.append(axe)
blocks.append(block)
# note that some DatetimeTZ, Categorical are always ndim==1
ndim = {b.ndim for b in blocks}
assert 0 not in ndim, ndim
if 2 in ndim:
new_axes = list(self.axes)
# multiple blocks that are reduced
if len(blocks) > 1:
new_axes[1] = axes[0]
# reset the placement to the original
for b, sb in zip(blocks, self.blocks):
b.mgr_locs = sb.mgr_locs
else:
new_axes[axis] = Index(np.concatenate(
[ax.values for ax in axes]))
if transposed:
new_axes = new_axes[::-1]
blocks = [b.make_block(b.values.T,
placement=np.arange(b.shape[1])
) for b in blocks]
return self.__class__(blocks, new_axes)
# single block, i.e. ndim == {1}
values = _concat._concat_compat([b.values for b in blocks])
# compute the orderings of our original data
if len(self.blocks) > 1:
indexer = np.empty(len(self.axes[0]), dtype=np.intp)
i = 0
for b in self.blocks:
for j in b.mgr_locs:
indexer[j] = i
i = i + 1
values = values.take(indexer)
return SingleBlockManager(
[make_block(values,
ndim=1,
placement=np.arange(len(values)))],
axes[0]) | [
"\n Iterate over blocks applying quantile reduction.\n This routine is intended for reduction type operations and\n will do inference on the generated blocks.\n\n Parameters\n ----------\n axis: reduction axis, default 0\n consolidate: boolean, default True. Join together blocks having same\n dtype\n transposed: boolean, default False\n we are holding transposed data\n interpolation : type of interpolation, default 'linear'\n qs : a scalar or list of the quantiles to be computed\n numeric_only : ignored\n\n Returns\n -------\n Block Manager (new object)\n "
] |
Please provide a description of the function:def replace_list(self, src_list, dest_list, inplace=False, regex=False):
inplace = validate_bool_kwarg(inplace, 'inplace')
# figure out our mask a-priori to avoid repeated replacements
values = self.as_array()
def comp(s, regex=False):
if isna(s):
return isna(values)
if hasattr(s, 'asm8'):
return _compare_or_regex_search(maybe_convert_objects(values),
getattr(s, 'asm8'), regex)
return _compare_or_regex_search(values, s, regex)
masks = [comp(s, regex) for i, s in enumerate(src_list)]
result_blocks = []
src_len = len(src_list) - 1
for blk in self.blocks:
# its possible to get multiple result blocks here
# replace ALWAYS will return a list
rb = [blk if inplace else blk.copy()]
for i, (s, d) in enumerate(zip(src_list, dest_list)):
new_rb = []
for b in rb:
m = masks[i][b.mgr_locs.indexer]
convert = i == src_len
result = b._replace_coerce(mask=m, to_replace=s, value=d,
inplace=inplace,
convert=convert, regex=regex)
if m.any():
new_rb = _extend_blocks(result, new_rb)
else:
new_rb.append(b)
rb = new_rb
result_blocks.extend(rb)
bm = self.__class__(result_blocks, self.axes)
bm._consolidate_inplace()
return bm | [
" do a list replace ",
"\n Generate a bool array by perform an equality check, or perform\n an element-wise regular expression matching\n "
] |
Please provide a description of the function:def get_bool_data(self, copy=False):
self._consolidate_inplace()
return self.combine([b for b in self.blocks if b.is_bool], copy) | [
"\n Parameters\n ----------\n copy : boolean, default False\n Whether to copy the blocks\n "
] |
Please provide a description of the function:def get_numeric_data(self, copy=False):
self._consolidate_inplace()
return self.combine([b for b in self.blocks if b.is_numeric], copy) | [
"\n Parameters\n ----------\n copy : boolean, default False\n Whether to copy the blocks\n "
] |
Please provide a description of the function:def combine(self, blocks, copy=True):
if len(blocks) == 0:
return self.make_empty()
# FIXME: optimization potential
indexer = np.sort(np.concatenate([b.mgr_locs.as_array
for b in blocks]))
inv_indexer = lib.get_reverse_indexer(indexer, self.shape[0])
new_blocks = []
for b in blocks:
b = b.copy(deep=copy)
b.mgr_locs = algos.take_1d(inv_indexer, b.mgr_locs.as_array,
axis=0, allow_fill=False)
new_blocks.append(b)
axes = list(self.axes)
axes[0] = self.items.take(indexer)
return self.__class__(new_blocks, axes, do_integrity_check=False) | [
" return a new manager with the blocks "
] |
Please provide a description of the function:def copy(self, deep=True):
# this preserves the notion of view copying of axes
if deep:
if deep == 'all':
copy = lambda ax: ax.copy(deep=True)
else:
copy = lambda ax: ax.view()
new_axes = [copy(ax) for ax in self.axes]
else:
new_axes = list(self.axes)
return self.apply('copy', axes=new_axes, deep=deep,
do_integrity_check=False) | [
"\n Make deep or shallow copy of BlockManager\n\n Parameters\n ----------\n deep : boolean o rstring, default True\n If False, return shallow copy (do not copy data)\n If 'all', copy data and a deep copy of the index\n\n Returns\n -------\n copy : BlockManager\n "
] |
Please provide a description of the function:def as_array(self, transpose=False, items=None):
if len(self.blocks) == 0:
arr = np.empty(self.shape, dtype=float)
return arr.transpose() if transpose else arr
if items is not None:
mgr = self.reindex_axis(items, axis=0)
else:
mgr = self
if self._is_single_block and mgr.blocks[0].is_datetimetz:
# TODO(Block.get_values): Make DatetimeTZBlock.get_values
# always be object dtype. Some callers seem to want the
# DatetimeArray (previously DTI)
arr = mgr.blocks[0].get_values(dtype=object)
elif self._is_single_block or not self.is_mixed_type:
arr = np.asarray(mgr.blocks[0].get_values())
else:
arr = mgr._interleave()
return arr.transpose() if transpose else arr | [
"Convert the blockmanager data into an numpy array.\n\n Parameters\n ----------\n transpose : boolean, default False\n If True, transpose the return array\n items : list of strings or None\n Names of block items that will be included in the returned\n array. ``None`` means that all block items will be used\n\n Returns\n -------\n arr : ndarray\n "
] |
Please provide a description of the function:def _interleave(self):
from pandas.core.dtypes.common import is_sparse
dtype = _interleaved_dtype(self.blocks)
# TODO: https://github.com/pandas-dev/pandas/issues/22791
# Give EAs some input on what happens here. Sparse needs this.
if is_sparse(dtype):
dtype = dtype.subtype
elif is_extension_array_dtype(dtype):
dtype = 'object'
result = np.empty(self.shape, dtype=dtype)
itemmask = np.zeros(self.shape[0])
for blk in self.blocks:
rl = blk.mgr_locs
result[rl.indexer] = blk.get_values(dtype)
itemmask[rl.indexer] = 1
if not itemmask.all():
raise AssertionError('Some items were not contained in blocks')
return result | [
"\n Return ndarray from blocks with specified item order\n Items must be contained in the blocks\n "
] |
Please provide a description of the function:def to_dict(self, copy=True):
self._consolidate_inplace()
bd = {}
for b in self.blocks:
bd.setdefault(str(b.dtype), []).append(b)
return {dtype: self.combine(blocks, copy=copy)
for dtype, blocks in bd.items()} | [
"\n Return a dict of str(dtype) -> BlockManager\n\n Parameters\n ----------\n copy : boolean, default True\n\n Returns\n -------\n values : a dict of dtype -> BlockManager\n\n Notes\n -----\n This consolidates based on str(dtype)\n "
] |
Please provide a description of the function:def fast_xs(self, loc):
if len(self.blocks) == 1:
return self.blocks[0].iget((slice(None), loc))
items = self.items
# non-unique (GH4726)
if not items.is_unique:
result = self._interleave()
if self.ndim == 2:
result = result.T
return result[loc]
# unique
dtype = _interleaved_dtype(self.blocks)
n = len(items)
if is_extension_array_dtype(dtype):
# we'll eventually construct an ExtensionArray.
result = np.empty(n, dtype=object)
else:
result = np.empty(n, dtype=dtype)
for blk in self.blocks:
# Such assignment may incorrectly coerce NaT to None
# result[blk.mgr_locs] = blk._slice((slice(None), loc))
for i, rl in enumerate(blk.mgr_locs):
result[rl] = blk._try_coerce_result(blk.iget((i, loc)))
if is_extension_array_dtype(dtype):
result = dtype.construct_array_type()._from_sequence(
result, dtype=dtype
)
return result | [
"\n get a cross sectional for a given location in the\n items ; handle dups\n\n return the result, is *could* be a view in the case of a\n single block\n "
] |
Please provide a description of the function:def consolidate(self):
if self.is_consolidated():
return self
bm = self.__class__(self.blocks, self.axes)
bm._is_consolidated = False
bm._consolidate_inplace()
return bm | [
"\n Join together blocks having same dtype\n\n Returns\n -------\n y : BlockManager\n "
] |
Please provide a description of the function:def get(self, item, fastpath=True):
if self.items.is_unique:
if not isna(item):
loc = self.items.get_loc(item)
else:
indexer = np.arange(len(self.items))[isna(self.items)]
# allow a single nan location indexer
if not is_scalar(indexer):
if len(indexer) == 1:
loc = indexer.item()
else:
raise ValueError("cannot label index with a null key")
return self.iget(loc, fastpath=fastpath)
else:
if isna(item):
raise TypeError("cannot label index with a null key")
indexer = self.items.get_indexer_for([item])
return self.reindex_indexer(new_axis=self.items[indexer],
indexer=indexer, axis=0,
allow_dups=True) | [
"\n Return values for selected item (ndarray or BlockManager).\n "
] |
Please provide a description of the function:def iget(self, i, fastpath=True):
block = self.blocks[self._blknos[i]]
values = block.iget(self._blklocs[i])
if not fastpath or not block._box_to_block_values or values.ndim != 1:
return values
# fastpath shortcut for select a single-dim from a 2-dim BM
return SingleBlockManager(
[block.make_block_same_class(values,
placement=slice(0, len(values)),
ndim=1)],
self.axes[1]) | [
"\n Return the data as a SingleBlockManager if fastpath=True and possible\n\n Otherwise return as a ndarray\n "
] |
Please provide a description of the function:def delete(self, item):
indexer = self.items.get_loc(item)
is_deleted = np.zeros(self.shape[0], dtype=np.bool_)
is_deleted[indexer] = True
ref_loc_offset = -is_deleted.cumsum()
is_blk_deleted = [False] * len(self.blocks)
if isinstance(indexer, int):
affected_start = indexer
else:
affected_start = is_deleted.nonzero()[0][0]
for blkno, _ in _fast_count_smallints(self._blknos[affected_start:]):
blk = self.blocks[blkno]
bml = blk.mgr_locs
blk_del = is_deleted[bml.indexer].nonzero()[0]
if len(blk_del) == len(bml):
is_blk_deleted[blkno] = True
continue
elif len(blk_del) != 0:
blk.delete(blk_del)
bml = blk.mgr_locs
blk.mgr_locs = bml.add(ref_loc_offset[bml.indexer])
# FIXME: use Index.delete as soon as it uses fastpath=True
self.axes[0] = self.items[~is_deleted]
self.blocks = tuple(b for blkno, b in enumerate(self.blocks)
if not is_blk_deleted[blkno])
self._shape = None
self._rebuild_blknos_and_blklocs() | [
"\n Delete selected item (items if non-unique) in-place.\n "
] |
Please provide a description of the function:def set(self, item, value):
# FIXME: refactor, clearly separate broadcasting & zip-like assignment
# can prob also fix the various if tests for sparse/categorical
# TODO(EA): Remove an is_extension_ when all extension types satisfy
# the interface
value_is_extension_type = (is_extension_type(value) or
is_extension_array_dtype(value))
# categorical/spares/datetimetz
if value_is_extension_type:
def value_getitem(placement):
return value
else:
if value.ndim == self.ndim - 1:
value = _safe_reshape(value, (1,) + value.shape)
def value_getitem(placement):
return value
else:
def value_getitem(placement):
return value[placement.indexer]
if value.shape[1:] != self.shape[1:]:
raise AssertionError('Shape of new values must be compatible '
'with manager shape')
try:
loc = self.items.get_loc(item)
except KeyError:
# This item wasn't present, just insert at end
self.insert(len(self.items), item, value)
return
if isinstance(loc, int):
loc = [loc]
blknos = self._blknos[loc]
blklocs = self._blklocs[loc].copy()
unfit_mgr_locs = []
unfit_val_locs = []
removed_blknos = []
for blkno, val_locs in libinternals.get_blkno_placements(blknos,
self.nblocks,
group=True):
blk = self.blocks[blkno]
blk_locs = blklocs[val_locs.indexer]
if blk.should_store(value):
blk.set(blk_locs, value_getitem(val_locs))
else:
unfit_mgr_locs.append(blk.mgr_locs.as_array[blk_locs])
unfit_val_locs.append(val_locs)
# If all block items are unfit, schedule the block for removal.
if len(val_locs) == len(blk.mgr_locs):
removed_blknos.append(blkno)
else:
self._blklocs[blk.mgr_locs.indexer] = -1
blk.delete(blk_locs)
self._blklocs[blk.mgr_locs.indexer] = np.arange(len(blk))
if len(removed_blknos):
# Remove blocks & update blknos accordingly
is_deleted = np.zeros(self.nblocks, dtype=np.bool_)
is_deleted[removed_blknos] = True
new_blknos = np.empty(self.nblocks, dtype=np.int64)
new_blknos.fill(-1)
new_blknos[~is_deleted] = np.arange(self.nblocks -
len(removed_blknos))
self._blknos = algos.take_1d(new_blknos, self._blknos, axis=0,
allow_fill=False)
self.blocks = tuple(blk for i, blk in enumerate(self.blocks)
if i not in set(removed_blknos))
if unfit_val_locs:
unfit_mgr_locs = np.concatenate(unfit_mgr_locs)
unfit_count = len(unfit_mgr_locs)
new_blocks = []
if value_is_extension_type:
# This code (ab-)uses the fact that sparse blocks contain only
# one item.
new_blocks.extend(
make_block(values=value.copy(), ndim=self.ndim,
placement=slice(mgr_loc, mgr_loc + 1))
for mgr_loc in unfit_mgr_locs)
self._blknos[unfit_mgr_locs] = (np.arange(unfit_count) +
len(self.blocks))
self._blklocs[unfit_mgr_locs] = 0
else:
# unfit_val_locs contains BlockPlacement objects
unfit_val_items = unfit_val_locs[0].append(unfit_val_locs[1:])
new_blocks.append(
make_block(values=value_getitem(unfit_val_items),
ndim=self.ndim, placement=unfit_mgr_locs))
self._blknos[unfit_mgr_locs] = len(self.blocks)
self._blklocs[unfit_mgr_locs] = np.arange(unfit_count)
self.blocks += tuple(new_blocks)
# Newly created block's dtype may already be present.
self._known_consolidated = False | [
"\n Set new item in-place. Does not consolidate. Adds new Block if not\n contained in the current set of items\n "
] |
Please provide a description of the function:def insert(self, loc, item, value, allow_duplicates=False):
if not allow_duplicates and item in self.items:
# Should this be a different kind of error??
raise ValueError('cannot insert {}, already exists'.format(item))
if not isinstance(loc, int):
raise TypeError("loc must be int")
# insert to the axis; this could possibly raise a TypeError
new_axis = self.items.insert(loc, item)
block = make_block(values=value, ndim=self.ndim,
placement=slice(loc, loc + 1))
for blkno, count in _fast_count_smallints(self._blknos[loc:]):
blk = self.blocks[blkno]
if count == len(blk.mgr_locs):
blk.mgr_locs = blk.mgr_locs.add(1)
else:
new_mgr_locs = blk.mgr_locs.as_array.copy()
new_mgr_locs[new_mgr_locs >= loc] += 1
blk.mgr_locs = new_mgr_locs
if loc == self._blklocs.shape[0]:
# np.append is a lot faster, let's use it if we can.
self._blklocs = np.append(self._blklocs, 0)
self._blknos = np.append(self._blknos, len(self.blocks))
else:
self._blklocs = np.insert(self._blklocs, loc, 0)
self._blknos = np.insert(self._blknos, loc, len(self.blocks))
self.axes[0] = new_axis
self.blocks += (block,)
self._shape = None
self._known_consolidated = False
if len(self.blocks) > 100:
self._consolidate_inplace() | [
"\n Insert item at selected position.\n\n Parameters\n ----------\n loc : int\n item : hashable\n value : array_like\n allow_duplicates: bool\n If False, trying to insert non-unique item will raise\n\n "
] |
Please provide a description of the function:def reindex_axis(self, new_index, axis, method=None, limit=None,
fill_value=None, copy=True):
new_index = ensure_index(new_index)
new_index, indexer = self.axes[axis].reindex(new_index, method=method,
limit=limit)
return self.reindex_indexer(new_index, indexer, axis=axis,
fill_value=fill_value, copy=copy) | [
"\n Conform block manager to new index.\n "
] |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.