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def _get_index(self, index=None):
""" Return index as ndarrays. Returns ------- tuple of (index, index_as_ndarray) """ |
if self.is_freq_type:
if index is None:
index = self._on
return index, index.asi8
return index, index |
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def _wrap_result(self, result, block=None, obj=None):
""" Wrap a single result. """ |
if obj is None:
obj = self._selected_obj
index = obj.index
if isinstance(result, np.ndarray):
# coerce if necessary
if block is not None:
if is_timedelta64_dtype(block.values.dtype):
from pandas import to_timedelta
... |
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def _wrap_results(self, results, blocks, obj):
""" Wrap the results. Parameters results : list of ndarrays blocks : list of blocks obj : conformed data (may be r... |
from pandas import Series, concat
from pandas.core.index import ensure_index
final = []
for result, block in zip(results, blocks):
result = self._wrap_result(result, block=block, obj=obj)
if result.ndim == 1:
return result
final.app... |
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def _center_window(self, result, window):
""" Center the result in the window. """ |
if self.axis > result.ndim - 1:
raise ValueError("Requested axis is larger then no. of argument "
"dimensions")
offset = _offset(window, True)
if offset > 0:
if isinstance(result, (ABCSeries, ABCDataFrame)):
result = result.s... |
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def _prep_window(self, **kwargs):
""" Provide validation for our window type, return the window we have already been validated. """ |
window = self._get_window()
if isinstance(window, (list, tuple, np.ndarray)):
return com.asarray_tuplesafe(window).astype(float)
elif is_integer(window):
import scipy.signal as sig
# the below may pop from kwargs
def _validate_win_type(win_type,... |
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def _apply_window(self, mean=True, **kwargs):
""" Applies a moving window of type ``window_type`` on the data. Parameters mean : bool, default True If True compu... |
window = self._prep_window(**kwargs)
center = self.center
blocks, obj, index = self._create_blocks()
results = []
for b in blocks:
try:
values = self._prep_values(b.values)
except TypeError:
results.append(b.values.copy())... |
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def _apply(self, func, name, window=None, center=None, check_minp=None, **kwargs):
""" Dispatch to apply; we are stripping all of the _apply kwargs and performin... |
def f(x, name=name, *args):
x = self._shallow_copy(x)
if isinstance(name, str):
return getattr(x, name)(*args, **kwargs)
return x.apply(name, *args, **kwargs)
return self._groupby.apply(f) |
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def _apply(self, func, name=None, window=None, center=None, check_minp=None, **kwargs):
""" Rolling statistical measure using supplied function. Designed to be u... |
if center is None:
center = self.center
if window is None:
window = self._get_window()
if check_minp is None:
check_minp = _use_window
blocks, obj, index = self._create_blocks()
index, indexi = self._get_index(index=index)
results = ... |
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def _validate_monotonic(self):
""" Validate on is_monotonic. """ |
if not self._on.is_monotonic:
formatted = self.on or 'index'
raise ValueError("{0} must be "
"monotonic".format(formatted)) |
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def _validate_freq(self):
""" Validate & return window frequency. """ |
from pandas.tseries.frequencies import to_offset
try:
return to_offset(self.window)
except (TypeError, ValueError):
raise ValueError("passed window {0} is not "
"compatible with a datetimelike "
"index".format(sel... |
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def _get_window(self, other=None):
""" Get the window length over which to perform some operation. Parameters other : object, default None The other object that ... |
axis = self.obj._get_axis(self.axis)
length = len(axis) + (other is not None) * len(axis)
other = self.min_periods or -1
return max(length, other) |
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def _apply(self, func, **kwargs):
""" Rolling statistical measure using supplied function. Designed to be used with passed-in Cython array-based functions. Param... |
blocks, obj, index = self._create_blocks()
results = []
for b in blocks:
try:
values = self._prep_values(b.values)
except TypeError:
results.append(b.values.copy())
continue
if values.size == 0:
... |
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def mean(self, *args, **kwargs):
""" Exponential weighted moving average. Parameters *args, **kwargs Arguments and keyword arguments to be passed into func. """ |
nv.validate_window_func('mean', args, kwargs)
return self._apply('ewma', **kwargs) |
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def std(self, bias=False, *args, **kwargs):
""" Exponential weighted moving stddev. """ |
nv.validate_window_func('std', args, kwargs)
return _zsqrt(self.var(bias=bias, **kwargs)) |
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def var(self, bias=False, *args, **kwargs):
""" Exponential weighted moving variance. """ |
nv.validate_window_func('var', args, kwargs)
def f(arg):
return libwindow.ewmcov(arg, arg, self.com, int(self.adjust),
int(self.ignore_na), int(self.min_periods),
int(bias))
return self._apply(f, **kwargs) |
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def cov(self, other=None, pairwise=None, bias=False, **kwargs):
""" Exponential weighted sample covariance. """ |
if other is None:
other = self._selected_obj
# only default unset
pairwise = True if pairwise is None else pairwise
other = self._shallow_copy(other)
def _get_cov(X, Y):
X = self._shallow_copy(X)
Y = self._shallow_copy(Y)
... |
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def corr(self, other=None, pairwise=None, **kwargs):
""" Exponential weighted sample correlation. """ |
if other is None:
other = self._selected_obj
# only default unset
pairwise = True if pairwise is None else pairwise
other = self._shallow_copy(other)
def _get_corr(X, Y):
X = self._shallow_copy(X)
Y = self._shallow_copy(Y)
... |
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def _ensure_like_indices(time, panels):
""" Makes sure that time and panels are conformable. """ |
n_time = len(time)
n_panel = len(panels)
u_panels = np.unique(panels) # this sorts!
u_time = np.unique(time)
if len(u_time) == n_time:
time = np.tile(u_time, len(u_panels))
if len(u_panels) == n_panel:
panels = np.repeat(u_panels, len(u_time))
return time, panels |
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def panel_index(time, panels, names=None):
""" Returns a multi-index suitable for a panel-like DataFrame. Parameters time : array-like Time index, does not have ... |
if names is None:
names = ['time', 'panel']
time, panels = _ensure_like_indices(time, panels)
return MultiIndex.from_arrays([time, panels], sortorder=None, names=names) |
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def from_dict(cls, data, intersect=False, orient='items', dtype=None):
""" Construct Panel from dict of DataFrame objects. Parameters data : dict {field : DataFr... |
from collections import defaultdict
orient = orient.lower()
if orient == 'minor':
new_data = defaultdict(OrderedDict)
for col, df in data.items():
for item, s in df.items():
new_data[item][col] = s
data = new_data
... |
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def to_excel(self, path, na_rep='', engine=None, **kwargs):
""" Write each DataFrame in Panel to a separate excel sheet. Parameters path : string or ExcelWriter ... |
from pandas.io.excel import ExcelWriter
if isinstance(path, str):
writer = ExcelWriter(path, engine=engine)
else:
writer = path
kwargs['na_rep'] = na_rep
for item, df in self.iteritems():
name = str(item)
df.to_excel(writer, name... |
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def _unpickle_panel_compat(self, state):
# pragma: no cover """ Unpickle the panel. """ |
from pandas.io.pickle import _unpickle_array
_unpickle = _unpickle_array
vals, items, major, minor = state
items = _unpickle(items)
major = _unpickle(major)
minor = _unpickle(minor)
values = _unpickle(vals)
wp = Panel(values, items, major, minor)
... |
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def conform(self, frame, axis='items'):
""" Conform input DataFrame to align with chosen axis pair. Parameters frame : DataFrame axis : {'items', 'major', 'minor... |
axes = self._get_plane_axes(axis)
return frame.reindex(**self._extract_axes_for_slice(self, axes)) |
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def round(self, decimals=0, *args, **kwargs):
""" Round each value in Panel to a specified number of decimal places. .. versionadded:: 0.18.0 Parameters decimals... |
nv.validate_round(args, kwargs)
if is_integer(decimals):
result = np.apply_along_axis(np.round, 0, self.values)
return self._wrap_result(result, axis=0)
raise TypeError("decimals must be an integer") |
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def dropna(self, axis=0, how='any', inplace=False):
""" Drop 2D from panel, holding passed axis constant. Parameters axis : int, default 0 Axis to hold constant.... |
axis = self._get_axis_number(axis)
values = self.values
mask = notna(values)
for ax in reversed(sorted(set(range(self._AXIS_LEN)) - {axis})):
mask = mask.sum(ax)
per_slice = np.prod(values.shape[:axis] + values.shape[axis + 1:])
if how == 'all':
... |
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def xs(self, key, axis=1):
""" Return slice of panel along selected axis. Parameters key : object Label axis : {'items', 'major', 'minor}, default 1/'major' Retu... |
axis = self._get_axis_number(axis)
if axis == 0:
return self[key]
self._consolidate_inplace()
axis_number = self._get_axis_number(axis)
new_data = self._data.xs(key, axis=axis_number, copy=False)
result = self._construct_return_type(new_data)
copy = ... |
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def _apply_2d(self, func, axis):
""" Handle 2-d slices, equiv to iterating over the other axis. """ |
ndim = self.ndim
axis = [self._get_axis_number(a) for a in axis]
# construct slabs, in 2-d this is a DataFrame result
indexer_axis = list(range(ndim))
for a in axis:
indexer_axis.remove(a)
indexer_axis = indexer_axis[0]
slicer = [slice(None, None)] ... |
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def _construct_return_type(self, result, axes=None):
""" Return the type for the ndim of the result. """ |
ndim = getattr(result, 'ndim', None)
# need to assume they are the same
if ndim is None:
if isinstance(result, dict):
ndim = getattr(list(result.values())[0], 'ndim', 0)
# have a dict, so top-level is +1 dim
if ndim != 0:
... |
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def count(self, axis='major'):
""" Return number of observations over requested axis. Parameters axis : {'items', 'major', 'minor'} or {0, 1, 2} Returns ------- ... |
i = self._get_axis_number(axis)
values = self.values
mask = np.isfinite(values)
result = mask.sum(axis=i, dtype='int64')
return self._wrap_result(result, axis) |
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def shift(self, periods=1, freq=None, axis='major'):
""" Shift index by desired number of periods with an optional time freq. The shifted data will not include t... |
if freq:
return self.tshift(periods, freq, axis=axis)
return super().slice_shift(periods, axis=axis) |
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def join(self, other, how='left', lsuffix='', rsuffix=''):
""" Join items with other Panel either on major and minor axes column. Parameters other : Panel or lis... |
from pandas.core.reshape.concat import concat
if isinstance(other, Panel):
join_major, join_minor = self._get_join_index(other, how)
this = self.reindex(major=join_major, minor=join_minor)
other = other.reindex(major=join_major, minor=join_minor)
merged_... |
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def update(self, other, join='left', overwrite=True, filter_func=None, errors='ignore'):
""" Modify Panel in place using non-NA values from other Panel. May also... |
if not isinstance(other, self._constructor):
other = self._constructor(other)
axis_name = self._info_axis_name
axis_values = self._info_axis
other = other.reindex(**{axis_name: axis_values})
for frame in axis_values:
self[frame].update(other[frame], jo... |
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def _extract_axes(self, data, axes, **kwargs):
""" Return a list of the axis indices. """ |
return [self._extract_axis(self, data, axis=i, **kwargs)
for i, a in enumerate(axes)] |
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def _extract_axes_for_slice(self, axes):
""" Return the slice dictionary for these axes. """ |
return {self._AXIS_SLICEMAP[i]: a for i, a in
zip(self._AXIS_ORDERS[self._AXIS_LEN - len(axes):], axes)} |
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def decons_obs_group_ids(comp_ids, obs_ids, shape, labels, xnull):
""" reconstruct labels from observed group ids Parameters xnull: boolean, if nulls are exclude... |
if not xnull:
lift = np.fromiter(((a == -1).any() for a in labels), dtype='i8')
shape = np.asarray(shape, dtype='i8') + lift
if not is_int64_overflow_possible(shape):
# obs ids are deconstructable! take the fast route!
out = decons_group_index(obs_ids, shape)
return ou... |
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def _check_ne_builtin_clash(expr):
"""Attempt to prevent foot-shooting in a helpful way. Parameters terms : Term Terms can contain """ |
names = expr.names
overlap = names & _ne_builtins
if overlap:
s = ', '.join(map(repr, overlap))
raise NumExprClobberingError('Variables in expression "{expr}" '
'overlap with builtins: ({s})'
.format(expr=expr, s=s)) |
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def evaluate(self):
"""Run the engine on the expression This method performs alignment which is necessary no matter what engine is being used, thus its implement... |
if not self._is_aligned:
self.result_type, self.aligned_axes = _align(self.expr.terms)
# make sure no names in resolvers and locals/globals clash
res = self._evaluate()
return _reconstruct_object(self.result_type, res, self.aligned_axes,
s... |
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def get_block_type(values, dtype=None):
""" Find the appropriate Block subclass to use for the given values and dtype. Parameters values : ndarray-like dtype : n... |
dtype = dtype or values.dtype
vtype = dtype.type
if is_sparse(dtype):
# Need this first(ish) so that Sparse[datetime] is sparse
cls = ExtensionBlock
elif is_categorical(values):
cls = CategoricalBlock
elif issubclass(vtype, np.datetime64):
assert not is_datetime64tz... |
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def _extend_blocks(result, blocks=None):
""" return a new extended blocks, givin the result """ |
from pandas.core.internals import BlockManager
if blocks is None:
blocks = []
if isinstance(result, list):
for r in result:
if isinstance(r, list):
blocks.extend(r)
else:
blocks.append(r)
elif isinstance(result, BlockManager):
... |
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def _block_shape(values, ndim=1, shape=None):
""" guarantee the shape of the values to be at least 1 d """ |
if values.ndim < ndim:
if shape is None:
shape = values.shape
if not is_extension_array_dtype(values):
# TODO: https://github.com/pandas-dev/pandas/issues/23023
# block.shape is incorrect for "2D" ExtensionArrays
# We can't, and don't need to, reshape... |
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def _putmask_smart(v, m, n):
""" Return a new ndarray, try to preserve dtype if possible. Parameters v : `values`, updated in-place (array like) m : `mask`, appl... |
# we cannot use np.asarray() here as we cannot have conversions
# that numpy does when numeric are mixed with strings
# n should be the length of the mask or a scalar here
if not is_list_like(n):
n = np.repeat(n, len(m))
elif isinstance(n, np.ndarray) and n.ndim == 0: # numpy scalar
... |
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def _check_ndim(self, values, ndim):
""" ndim inference and validation. Infers ndim from 'values' if not provided to __init__. Validates that values.ndim and ndi... |
if ndim is None:
ndim = values.ndim
if self._validate_ndim and values.ndim != ndim:
msg = ("Wrong number of dimensions. values.ndim != ndim "
"[{} != {}]")
raise ValueError(msg.format(values.ndim, ndim))
return ndim |
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def is_categorical_astype(self, dtype):
""" validate that we have a astypeable to categorical, returns a boolean if we are a categorical """ |
if dtype is Categorical or dtype is CategoricalDtype:
# this is a pd.Categorical, but is not
# a valid type for astypeing
raise TypeError("invalid type {0} for astype".format(dtype))
elif is_categorical_dtype(dtype):
return True
return False |
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def get_values(self, dtype=None):
""" return an internal format, currently just the ndarray this is often overridden to handle to_dense like operations """ |
if is_object_dtype(dtype):
return self.values.astype(object)
return self.values |
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def make_block(self, values, placement=None, ndim=None):
""" Create a new block, with type inference propagate any values that are not specified """ |
if placement is None:
placement = self.mgr_locs
if ndim is None:
ndim = self.ndim
return make_block(values, placement=placement, ndim=ndim) |
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def make_block_same_class(self, values, placement=None, ndim=None, dtype=None):
""" Wrap given values in a block of same type as self. """ |
if dtype is not None:
# issue 19431 fastparquet is passing this
warnings.warn("dtype argument is deprecated, will be removed "
"in a future release.", DeprecationWarning)
if placement is None:
placement = self.mgr_locs
return make_bl... |
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def apply(self, func, **kwargs):
""" apply the function to my values; return a block if we are not one """ |
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 |
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def fillna(self, value, limit=None, inplace=False, downcast=None):
""" fillna on the block with the value. If we fail, then convert to ObjectBlock and try again ... |
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):
rai... |
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def split_and_operate(self, mask, f, inplace):
""" split the block per-column, and apply the callable f per-column, return a new block for each. Handle masking w... |
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:
#... |
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def downcast(self, dtypes=None):
""" try to downcast each item to the dict of dtypes if present """ |
# 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_d... |
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def _can_hold_element(self, element):
""" require the same dtype as ourselves """ |
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) |
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def _try_cast_result(self, result, dtype=None):
""" try to cast the result to our original type, we may have roundtripped thru object in the mean-time """ |
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':
... |
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def replace(self, to_replace, value, inplace=False, filter=None, regex=False, convert=True):
"""replace the to_replace value with value, possible to create new b... |
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,
... |
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def setitem(self, indexer, value):
"""Set the value inplace, returning a a maybe different typed block. Parameters indexer : tuple, list-like, array-like, slice ... |
# 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 ow... |
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def putmask(self, mask, new, align=True, inplace=False, axis=0, transpose=False):
""" putmask the data to the block; it is possible that we may create a new dtyp... |
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_valu... |
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def coerce_to_target_dtype(self, other):
""" coerce the current block to a dtype compat for other we will return a block, possibly object, and not raise we can a... |
# 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.as... |
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def _interpolate_with_fill(self, method='pad', axis=0, inplace=False, limit=None, fill_value=None, coerce=False, downcast=None):
""" fillna but using the interpo... |
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:
... |
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def _interpolate(self, method=None, index=None, values=None, fill_value=None, axis=0, limit=None, limit_direction='forward', limit_area=None, inplace=False, downc... |
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 No... |
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def take_nd(self, indexer, axis, new_mgr_locs=None, fill_tuple=None):
""" Take values according to indexer and return them as a block.bb """ |
# 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:
... |
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def diff(self, n, axis=1):
""" return block for the diff of the values """ |
new_values = algos.diff(self.values, n, axis=axis)
return [self.make_block(values=new_values)] |
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def shift(self, periods, axis=0, fill_value=None):
""" shift the block by periods, possibly upcast """ |
# 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:
... |
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def _unstack(self, unstacker_func, new_columns, n_rows, fill_value):
"""Return a list of unstacked blocks of self Parameters unstacker_func : callable Partially ... |
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[ma... |
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def quantile(self, qs, interpolation='linear', axis=0):
""" compute the quantiles of the Parameters qs: a scalar or list of the quantiles to be computed interpol... |
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... |
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def putmask(self, mask, new, align=True, inplace=False, axis=0, transpose=False):
""" putmask the data to the block; we must be a single block and not generate o... |
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(n... |
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def _get_unstack_items(self, unstacker, new_columns):
""" Get the placement, values, and mask for a Block unstack. This is shared between ObjectBlock and Extensi... |
# 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 |
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def _maybe_coerce_values(self, values):
"""Unbox to an extension array. This will unbox an ExtensionArray stored in an Index or Series. ExtensionArrays pass thro... |
if isinstance(values, (ABCIndexClass, ABCSeries)):
values = values._values
return values |
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def setitem(self, indexer, value):
"""Set the value inplace, returning a same-typed block. This differs from Block.setitem by not allowing setitem to change the ... |
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 |
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def take_nd(self, indexer, axis=0, new_mgr_locs=None, fill_tuple=None):
""" Take values according to indexer and return them as a block. """ |
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 ta... |
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def shift(self, periods: int, axis: libinternals.BlockPlacement = 0, fill_value: Any = None) -> List['ExtensionBlock']: """ Shift the block by `periods`. Dispatch... |
return [
self.make_block_same_class(
self.values.shift(periods=periods, fill_value=fill_value),
placement=self.mgr_locs, ndim=self.ndim)
] |
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def _astype(self, dtype, **kwargs):
""" these automatically copy, so copy=True has no effect raise on an except if raise == True """ |
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(dty... |
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def _try_coerce_args(self, values, other):
""" Coerce values and other to dtype 'i8'. NaN and NaT convert to the smallest i8, and will correctly round-trip to Na... |
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') i... |
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def get_values(self, dtype=None):
""" Returns an ndarray of values. Parameters dtype : np.dtype Only `object`-like dtypes are respected here (not sure why). Retu... |
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
... |
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def _try_coerce_args(self, values, other):
""" localize and return i8 for the values Parameters values : ndarray-like other : ndarray-like or scalar Returns ----... |
# 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... |
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def diff(self, n, axis=0):
"""1st discrete difference Parameters n : int, number of periods to diff axis : int, axis to diff upon. default 0 Return ------ A list... |
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... |
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def _try_coerce_args(self, values, other):
""" Coerce values and other to int64, with null values converted to iNaT. values is always ndarray-like, other may not... |
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 ... |
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def _replace_single(self, to_replace, value, inplace=False, filter=None, regex=False, convert=True, mask=None):
""" Replace elements by the given value. Paramete... |
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:
... |
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def convert(cls, style_dict, num_format_str=None):
""" converts a style_dict to an xlsxwriter format dict Parameters style_dict : style dictionary to convert num... |
# 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['bord... |
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def _unstack_extension_series(series, level, fill_value):
""" Unstack an ExtensionArray-backed Series. The ExtensionDtype is preserved. Parameters series : Serie... |
# 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 = n... |
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def stack(frame, level=-1, dropna=True):
""" Convert DataFrame to Series with multi-level Index. Columns become the second level of the resulting hierarchical in... |
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.co... |
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def make_axis_dummies(frame, axis='minor', transform=None):
""" Construct 1-0 dummy variables corresponding to designated axis labels Parameters frame : DataFram... |
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(i... |
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def _reorder_for_extension_array_stack(arr, n_rows, n_columns):
""" Re-orders the values when stacking multiple extension-arrays. The indirect stacking method us... |
# 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) |
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def _parse_float_vec(vec):
""" Parse a vector of float values representing IBM 8 byte floats into native 8 byte floats. """ |
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
# f... |
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def _record_count(self):
""" Get number of records in file. This is maybe suboptimal because we have to seek to the end of the file. Side effect: returns file po... |
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.file... |
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def get_chunk(self, size=None):
""" Reads lines from Xport file and returns as dataframe Parameters size : int, defaults to None Number of lines to read. If None... |
if size is None:
size = self._chunksize
return self.read(nrows=size) |
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def construction_error(tot_items, block_shape, axes, e=None):
""" raise a helpful message about our construction """ |
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
... |
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def _simple_blockify(tuples, dtype):
""" return a single array of a block that has a single dtype; if dtype is not None, coerce to this 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] |
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def _multi_blockify(tuples, dtype=None):
""" return an array of blocks that potentially have different dtypes """ |
# 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_bloc... |
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def _interleaved_dtype( blocks: List[Block] ) -> Optional[Union[np.dtype, ExtensionDtype]]: """Find the common dtype for `blocks`. Parameters blocks : List[Block]... |
if not len(blocks):
return None
return find_common_type([b.dtype for b in blocks]) |
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def _consolidate(blocks):
""" Merge blocks having same dtype, exclude non-consolidating 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,
... |
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def _compare_or_regex_search(a, b, regex=False):
""" Compare two array_like inputs of the same shape or two scalar values Calls operator.eq or re.search, dependi... |
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 v... |
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def items_overlap_with_suffix(left, lsuffix, right, rsuffix):
""" If two indices overlap, add suffixes to overlapping entries. If corresponding suffix is empty, ... |
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):
... |
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def _transform_index(index, func, level=None):
""" Apply function to all values found in index. This includes transforming multiindex entries separately. Only ap... |
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, name... |
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def concatenate_block_managers(mgrs_indexers, axes, concat_axis, copy):
""" Concatenate block managers into one. Parameters axes : list of Index concat_axis : in... |
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:
... |
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def make_empty(self, axes=None):
""" return an empty BlockManager with the items axis of len 0 """ |
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 = []
ret... |
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def rename_axis(self, mapper, axis, copy=True, level=None):
""" Rename one of axes. Parameters mapper : unary callable axis : int copy : boolean, default True le... |
obj = self.copy(deep=copy)
obj.set_axis(axis, _transform_index(self.axes[axis], mapper, level))
return obj |
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def _get_counts(self, f):
""" return a dict of the counts of the function in BlockManager """ |
self._consolidate_inplace()
counts = dict()
for b in self.blocks:
v = f(b)
counts[v] = counts.get(v, 0) + b.shape[0]
return counts |
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def apply(self, f, axes=None, filter=None, do_integrity_check=False, consolidate=True, **kwargs):
""" iterate over the blocks, collect and create a new block man... |
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
... |
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def quantile(self, axis=0, consolidate=True, transposed=False, interpolation='linear', qs=None, numeric_only=None):
""" Iterate over blocks applying quantile red... |
# 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
... |
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def replace_list(self, src_list, dest_list, inplace=False, regex=False):
""" do a list replace """ |
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):
"""
Generate a bool array by perform an equality check, or perform
an element-wise re... |
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def combine(self, blocks, copy=True):
""" return a new manager with the blocks """ |
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... |
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