text_prompt stringlengths 157 13.1k | code_prompt stringlengths 7 19.8k ⌀ |
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def _isnan(self):
""" Return if each value is NaN. """ |
if self._can_hold_na:
return isna(self)
else:
# shouldn't reach to this condition by checking hasnans beforehand
values = np.empty(len(self), dtype=np.bool_)
values.fill(False)
return values |
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def get_duplicates(self):
""" Extract duplicated index elements. .. deprecated:: 0.23.0 Use idx[idx.duplicated()].unique() instead Returns a sorted list of index... |
warnings.warn("'get_duplicates' is deprecated and will be removed in "
"a future release. You can use "
"idx[idx.duplicated()].unique() instead",
FutureWarning, stacklevel=2)
return self[self.duplicated()].unique() |
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def _get_unique_index(self, dropna=False):
""" Returns an index containing unique values. Parameters dropna : bool If True, NaN values are dropped. Returns -----... |
if self.is_unique and not dropna:
return self
values = self.values
if not self.is_unique:
values = self.unique()
if dropna:
try:
if self.hasnans:
values = values[~isna(values)]
except NotImplementedEr... |
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def _get_reconciled_name_object(self, other):
""" If the result of a set operation will be self, return self, unless the name changes, in which case make a shall... |
name = get_op_result_name(self, other)
if self.name != name:
return self._shallow_copy(name=name)
return self |
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def union(self, other, sort=None):
""" Form the union of two Index objects. Parameters other : Index or array-like sort : bool or None, default None Whether to s... |
self._validate_sort_keyword(sort)
self._assert_can_do_setop(other)
other = ensure_index(other)
if len(other) == 0 or self.equals(other):
return self._get_reconciled_name_object(other)
if len(self) == 0:
return other._get_reconciled_name_object(self)
... |
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def difference(self, other, sort=None):
""" Return a new Index with elements from the index that are not in `other`. This is the set difference of two Index obje... |
self._validate_sort_keyword(sort)
self._assert_can_do_setop(other)
if self.equals(other):
# pass an empty np.ndarray with the appropriate dtype
return self._shallow_copy(self._data[:0])
other, result_name = self._convert_can_do_setop(other)
this = self... |
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def symmetric_difference(self, other, result_name=None, sort=None):
""" Compute the symmetric difference of two Index objects. Parameters other : Index or array-... |
self._validate_sort_keyword(sort)
self._assert_can_do_setop(other)
other, result_name_update = self._convert_can_do_setop(other)
if result_name is None:
result_name = result_name_update
this = self._get_unique_index()
other = other._get_unique_index()
... |
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def _invalid_indexer(self, form, key):
""" Consistent invalid indexer message. """ |
raise TypeError("cannot do {form} indexing on {klass} with these "
"indexers [{key}] of {kind}".format(
form=form, klass=type(self), key=key,
kind=type(key))) |
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def _try_convert_to_int_index(cls, data, copy, name, dtype):
""" Attempt to convert an array of data into an integer index. Parameters data : The data to convert... |
from .numeric import Int64Index, UInt64Index
if not is_unsigned_integer_dtype(dtype):
# skip int64 conversion attempt if uint-like dtype is passed, as
# this could return Int64Index when UInt64Index is what's desrired
try:
res = data.astype('i8', cop... |
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def _coerce_to_ndarray(cls, data):
""" Coerces data to ndarray. Converts other iterables to list first and then to array. Does not touch ndarrays. Raises ------ ... |
if not isinstance(data, (np.ndarray, Index)):
if data is None or is_scalar(data):
cls._scalar_data_error(data)
# other iterable of some kind
if not isinstance(data, (ABCSeries, list, tuple)):
data = list(data)
data = np.asarray(d... |
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def _coerce_scalar_to_index(self, item):
""" We need to coerce a scalar to a compat for our index type. Parameters item : scalar item to coerce """ |
dtype = self.dtype
if self._is_numeric_dtype and isna(item):
# We can't coerce to the numeric dtype of "self" (unless
# it's float) if there are NaN values in our output.
dtype = None
return Index([item], dtype=dtype, **self._get_attributes_dict()) |
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def _assert_can_do_op(self, value):
""" Check value is valid for scalar op. """ |
if not is_scalar(value):
msg = "'value' must be a scalar, passed: {0}"
raise TypeError(msg.format(type(value).__name__)) |
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def append(self, other):
""" Append a collection of Index options together. Parameters other : Index or list/tuple of indices Returns ------- appended : Index ""... |
to_concat = [self]
if isinstance(other, (list, tuple)):
to_concat = to_concat + list(other)
else:
to_concat.append(other)
for obj in to_concat:
if not isinstance(obj, Index):
raise TypeError('all inputs must be Index')
name... |
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def putmask(self, mask, value):
""" Return a new Index of the values set with the mask. See Also -------- numpy.ndarray.putmask """ |
values = self.values.copy()
try:
np.putmask(values, mask, self._convert_for_op(value))
return self._shallow_copy(values)
except (ValueError, TypeError) as err:
if is_object_dtype(self):
raise err
# coerces to object
re... |
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def equals(self, other):
""" Determine if two Index objects contain the same elements. """ |
if self.is_(other):
return True
if not isinstance(other, Index):
return False
if is_object_dtype(self) and not is_object_dtype(other):
# if other is not object, use other's logic for coercion
return other.equals(self)
try:
r... |
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def identical(self, other):
""" Similar to equals, but check that other comparable attributes are also equal. """ |
return (self.equals(other) and
all((getattr(self, c, None) == getattr(other, c, None)
for c in self._comparables)) and
type(self) == type(other)) |
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def asof(self, label):
""" Return the label from the index, or, if not present, the previous one. Assuming that the index is sorted, return the passed index labe... |
try:
loc = self.get_loc(label, method='pad')
except KeyError:
return self._na_value
else:
if isinstance(loc, slice):
loc = loc.indices(len(self))[-1]
return self[loc] |
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def sort_values(self, return_indexer=False, ascending=True):
""" Return a sorted copy of the index. Return a sorted copy of the index, and optionally return the ... |
_as = self.argsort()
if not ascending:
_as = _as[::-1]
sorted_index = self.take(_as)
if return_indexer:
return sorted_index, _as
else:
return sorted_index |
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def argsort(self, *args, **kwargs):
""" Return the integer indices that would sort the index. Parameters *args Passed to `numpy.ndarray.argsort`. **kwargs Passed... |
result = self.asi8
if result is None:
result = np.array(self)
return result.argsort(*args, **kwargs) |
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def get_value(self, series, key):
""" Fast lookup of value from 1-dimensional ndarray. Only use this if you know what you're doing. """ |
# if we have something that is Index-like, then
# use this, e.g. DatetimeIndex
# Things like `Series._get_value` (via .at) pass the EA directly here.
s = getattr(series, '_values', series)
if isinstance(s, (ExtensionArray, Index)) and is_scalar(key):
# GH 20882, 212... |
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def set_value(self, arr, key, value):
""" Fast lookup of value from 1-dimensional ndarray. Notes ----- Only use this if you know what you're doing. """ |
self._engine.set_value(com.values_from_object(arr),
com.values_from_object(key), value) |
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def get_indexer_for(self, target, **kwargs):
""" Guaranteed return of an indexer even when non-unique. This dispatches to get_indexer or get_indexer_nonunique as... |
if self.is_unique:
return self.get_indexer(target, **kwargs)
indexer, _ = self.get_indexer_non_unique(target, **kwargs)
return indexer |
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def groupby(self, values):
""" Group the index labels by a given array of values. Parameters values : array Values used to determine the groups. Returns ------- ... |
# TODO: if we are a MultiIndex, we can do better
# that converting to tuples
if isinstance(values, ABCMultiIndex):
values = values.values
values = ensure_categorical(values)
result = values._reverse_indexer()
# map to the label
result = {k: self.tak... |
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def isin(self, values, level=None):
""" Return a boolean array where the index values are in `values`. Compute boolean array of whether each index value is found... |
if level is not None:
self._validate_index_level(level)
return algos.isin(self, values) |
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def slice_indexer(self, start=None, end=None, step=None, kind=None):
""" For an ordered or unique index, compute the slice indexer for input labels and step. Par... |
start_slice, end_slice = self.slice_locs(start, end, step=step,
kind=kind)
# return a slice
if not is_scalar(start_slice):
raise AssertionError("Start slice bound is non-scalar")
if not is_scalar(end_slice):
raise... |
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def _maybe_cast_indexer(self, key):
""" If we have a float key and are not a floating index, then try to cast to an int if equivalent. """ |
if is_float(key) and not self.is_floating():
try:
ckey = int(key)
if ckey == key:
key = ckey
except (OverflowError, ValueError, TypeError):
pass
return key |
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def _validate_indexer(self, form, key, kind):
""" If we are positional indexer, validate that we have appropriate typed bounds must be an integer. """ |
assert kind in ['ix', 'loc', 'getitem', 'iloc']
if key is None:
pass
elif is_integer(key):
pass
elif kind in ['iloc', 'getitem']:
self._invalid_indexer(form, key)
return key |
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def get_slice_bound(self, label, side, kind):
""" Calculate slice bound that corresponds to given label. Returns leftmost (one-past-the-rightmost if ``side=='rig... |
assert kind in ['ix', 'loc', 'getitem', None]
if side not in ('left', 'right'):
raise ValueError("Invalid value for side kwarg,"
" must be either 'left' or 'right': %s" %
(side, ))
original_label = label
# For date... |
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def slice_locs(self, start=None, end=None, step=None, kind=None):
""" Compute slice locations for input labels. Parameters start : label, default None If None, d... |
inc = (step is None or step >= 0)
if not inc:
# If it's a reverse slice, temporarily swap bounds.
start, end = end, start
# GH 16785: If start and end happen to be date strings with UTC offsets
# attempt to parse and check that the offsets are the same
... |
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def insert(self, loc, item):
""" Make new Index inserting new item at location. Follows Python list.append semantics for negative values. Parameters loc : int it... |
_self = np.asarray(self)
item = self._coerce_scalar_to_index(item)._ndarray_values
idx = np.concatenate((_self[:loc], item, _self[loc:]))
return self._shallow_copy_with_infer(idx) |
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def drop(self, labels, errors='raise'):
""" Make new Index with passed list of labels deleted. Parameters labels : array-like errors : {'ignore', 'raise'}, defau... |
arr_dtype = 'object' if self.dtype == 'object' else None
labels = com.index_labels_to_array(labels, dtype=arr_dtype)
indexer = self.get_indexer(labels)
mask = indexer == -1
if mask.any():
if errors != 'ignore':
raise KeyError(
'{} ... |
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def _add_comparison_methods(cls):
""" Add in comparison methods. """ |
cls.__eq__ = _make_comparison_op(operator.eq, cls)
cls.__ne__ = _make_comparison_op(operator.ne, cls)
cls.__lt__ = _make_comparison_op(operator.lt, cls)
cls.__gt__ = _make_comparison_op(operator.gt, cls)
cls.__le__ = _make_comparison_op(operator.le, cls)
cls.__ge__ = _ma... |
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def _validate_for_numeric_unaryop(self, op, opstr):
""" Validate if we can perform a numeric unary operation. """ |
if not self._is_numeric_dtype:
raise TypeError("cannot evaluate a numeric op "
"{opstr} for type: {typ}"
.format(opstr=opstr, typ=type(self).__name__)) |
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def _validate_for_numeric_binop(self, other, op):
""" Return valid other; evaluate or raise TypeError if we are not of the appropriate type. Notes ----- This is ... |
opstr = '__{opname}__'.format(opname=op.__name__)
# if we are an inheritor of numeric,
# but not actually numeric (e.g. DatetimeIndex/PeriodIndex)
if not self._is_numeric_dtype:
raise TypeError("cannot evaluate a numeric op {opstr} "
"for type: {t... |
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def _add_numeric_methods_binary(cls):
""" Add in numeric methods. """ |
cls.__add__ = _make_arithmetic_op(operator.add, cls)
cls.__radd__ = _make_arithmetic_op(ops.radd, cls)
cls.__sub__ = _make_arithmetic_op(operator.sub, cls)
cls.__rsub__ = _make_arithmetic_op(ops.rsub, cls)
cls.__rpow__ = _make_arithmetic_op(ops.rpow, cls)
cls.__pow__ = _... |
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def _add_numeric_methods_unary(cls):
""" Add in numeric unary methods. """ |
def _make_evaluate_unary(op, opstr):
def _evaluate_numeric_unary(self):
self._validate_for_numeric_unaryop(op, opstr)
attrs = self._get_attributes_dict()
attrs = self._maybe_update_attributes(attrs)
return Index(op(self.values), **at... |
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def _add_logical_methods(cls):
""" Add in logical methods. """ |
_doc = """
%(desc)s
Parameters
----------
*args
These parameters will be passed to numpy.%(outname)s.
**kwargs
These parameters will be passed to numpy.%(outname)s.
Returns
-------
%(outname)s : bool or array_like (if axi... |
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def _set_grouper(self, obj, sort=False):
""" given an object and the specifications, setup the internal grouper for this particular specification Parameters obj ... |
if self.key is not None and self.level is not None:
raise ValueError(
"The Grouper cannot specify both a key and a level!")
# Keep self.grouper value before overriding
if self._grouper is None:
self._grouper = self.grouper
# the key must be a v... |
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def _interpolate_scipy_wrapper(x, y, new_x, method, fill_value=None, bounds_error=False, order=None, **kwargs):
""" Passed off to scipy.interpolate.interp1d. met... |
try:
from scipy import interpolate
# TODO: Why is DatetimeIndex being imported here?
from pandas import DatetimeIndex # noqa
except ImportError:
raise ImportError('{method} interpolation requires SciPy'
.format(method=method))
new_x = np.asarray(n... |
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def _from_derivatives(xi, yi, x, order=None, der=0, extrapolate=False):
""" Convenience function for interpolate.BPoly.from_derivatives. Construct a piecewise po... |
from scipy import interpolate
# return the method for compat with scipy version & backwards compat
method = interpolate.BPoly.from_derivatives
m = method(xi, yi.reshape(-1, 1),
orders=order, extrapolate=extrapolate)
return m(x) |
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def interpolate_2d(values, method='pad', axis=0, limit=None, fill_value=None, dtype=None):
""" Perform an actual interpolation of values, values will be make 2-d... |
transf = (lambda x: x) if axis == 0 else (lambda x: x.T)
# reshape a 1 dim if needed
ndim = values.ndim
if values.ndim == 1:
if axis != 0: # pragma: no cover
raise AssertionError("cannot interpolate on a ndim == 1 with "
"axis != 0")
value... |
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def _cast_values_for_fillna(values, dtype):
""" Cast values to a dtype that algos.pad and algos.backfill can handle. """ |
# TODO: for int-dtypes we make a copy, but for everything else this
# alters the values in-place. Is this intentional?
if (is_datetime64_dtype(dtype) or is_datetime64tz_dtype(dtype) or
is_timedelta64_dtype(dtype)):
values = values.view(np.int64)
elif is_integer_dtype(values):
... |
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def fill_zeros(result, x, y, name, fill):
""" If this is a reversed op, then flip x,y If we have an integer value (or array in y) and we have 0's, fill them with... |
if fill is None or is_float_dtype(result):
return result
if name.startswith(('r', '__r')):
x, y = y, x
is_variable_type = (hasattr(y, 'dtype') or hasattr(y, 'type'))
is_scalar_type = is_scalar(y)
if not is_variable_type and not is_scalar_type:
return result
if is_sca... |
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def dispatch_missing(op, left, right, result):
""" Fill nulls caused by division by zero, casting to a diffferent dtype if necessary. Parameters left : object (I... |
opstr = '__{opname}__'.format(opname=op.__name__).replace('____', '__')
if op in [operator.truediv, operator.floordiv,
getattr(operator, 'div', None)]:
result = mask_zero_div_zero(left, right, result)
elif op is operator.mod:
result = fill_zeros(result, left, right, opstr, np.... |
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def _interp_limit(invalid, fw_limit, bw_limit):
""" Get indexers of values that won't be filled because they exceed the limits. Parameters invalid : boolean ndar... |
# handle forward first; the backward direction is the same except
# 1. operate on the reversed array
# 2. subtract the returned indices from N - 1
N = len(invalid)
f_idx = set()
b_idx = set()
def inner(invalid, limit):
limit = min(limit, N)
windowed = _rolling_window(invali... |
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def in_interactive_session():
""" check if we're running in an interactive shell returns True if running under python/ipython interactive shell """ |
from pandas import get_option
def check_main():
try:
import __main__ as main
except ModuleNotFoundError:
return get_option('mode.sim_interactive')
return (not hasattr(main, '__file__') or
get_option('mode.sim_interactive'))
try:
retu... |
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def recode_for_groupby(c, sort, observed):
""" Code the categories to ensure we can groupby for categoricals. If observed=True, we return a new Categorical with ... |
# we only care about observed values
if observed:
unique_codes = unique1d(c.codes)
take_codes = unique_codes[unique_codes != -1]
if c.ordered:
take_codes = np.sort(take_codes)
# we recode according to the uniques
categories = c.categories.take(take_codes)
... |
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Description:
def get_engine(engine):
""" return our implementation """ |
if engine == 'auto':
engine = get_option('io.parquet.engine')
if engine == 'auto':
# try engines in this order
try:
return PyArrowImpl()
except ImportError:
pass
try:
return FastParquetImpl()
except ImportError:
... |
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def to_parquet(df, path, engine='auto', compression='snappy', index=None, partition_cols=None, **kwargs):
""" Write a DataFrame to the parquet format. Parameters... |
impl = get_engine(engine)
return impl.write(df, path, compression=compression, index=index,
partition_cols=partition_cols, **kwargs) |
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def generate_bins_generic(values, binner, closed):
""" Generate bin edge offsets and bin labels for one array using another array which has bin edge values. Both... |
lenidx = len(values)
lenbin = len(binner)
if lenidx <= 0 or lenbin <= 0:
raise ValueError("Invalid length for values or for binner")
# check binner fits data
if values[0] < binner[0]:
raise ValueError("Values falls before first bin")
if values[lenidx - 1] > binner[lenbin - 1]... |
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def size(self):
""" Compute group sizes """ |
ids, _, ngroup = self.group_info
ids = ensure_platform_int(ids)
if ngroup:
out = np.bincount(ids[ids != -1], minlength=ngroup)
else:
out = []
return Series(out,
index=self.result_index,
dtype='int64') |
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def lreshape(data, groups, dropna=True, label=None):
""" Reshape long-format data to wide. Generalized inverse of DataFrame.pivot Parameters data : DataFrame gro... |
if isinstance(groups, dict):
keys = list(groups.keys())
values = list(groups.values())
else:
keys, values = zip(*groups)
all_cols = list(set.union(*[set(x) for x in values]))
id_cols = list(data.columns.difference(all_cols))
K = len(values[0])
for seq in values:
... |
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Description:
def wide_to_long(df, stubnames, i, j, sep="", suffix=r'\d+'):
r""" Wide panel to long format. Less flexible but more user-friendly than melt. With stubnames ['A'... |
def get_var_names(df, stub, sep, suffix):
regex = r'^{stub}{sep}{suffix}$'.format(
stub=re.escape(stub), sep=re.escape(sep), suffix=suffix)
pattern = re.compile(regex)
return [col for col in df.columns if pattern.match(col)]
def melt_stub(df, stub, i, j, value_vars, sep):
... |
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def _get_indices(self, names):
""" Safe get multiple indices, translate keys for datelike to underlying repr. """ |
def get_converter(s):
# possibly convert to the actual key types
# in the indices, could be a Timestamp or a np.datetime64
if isinstance(s, (Timestamp, datetime.datetime)):
return lambda key: Timestamp(key)
elif isinstance(s, np.datetime64):
... |
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def _set_group_selection(self):
""" Create group based selection. Used when selection is not passed directly but instead via a grouper. NOTE: this should be pair... |
grp = self.grouper
if not (self.as_index and
getattr(grp, 'groupings', None) is not None and
self.obj.ndim > 1 and
self._group_selection is None):
return
ax = self.obj._info_axis
groupers = [g.name for g in grp.groupings
... |
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def get_group(self, name, obj=None):
""" Construct NDFrame from group with provided name. Parameters name : object the name of the group to get as a DataFrame ob... |
if obj is None:
obj = self._selected_obj
inds = self._get_index(name)
if not len(inds):
raise KeyError(name)
return obj._take(inds, axis=self.axis) |
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def _try_cast(self, result, obj, numeric_only=False):
""" Try to cast the result to our obj original type, we may have roundtripped through object in the mean-ti... |
if obj.ndim > 1:
dtype = obj._values.dtype
else:
dtype = obj.dtype
if not is_scalar(result):
if is_datetime64tz_dtype(dtype):
# GH 23683
# Prior results _may_ have been generated in UTC.
# Ensure we localize to... |
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def sem(self, ddof=1):
""" Compute standard error of the mean of groups, excluding missing values. For multiple groupings, the result index will be a MultiIndex.... |
return self.std(ddof=ddof) / np.sqrt(self.count()) |
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def size(self):
""" Compute group sizes. """ |
result = self.grouper.size()
if isinstance(self.obj, Series):
result.name = getattr(self.obj, 'name', None)
return result |
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def _add_numeric_operations(cls):
""" Add numeric operations to the GroupBy generically. """ |
def groupby_function(name, alias, npfunc,
numeric_only=True, _convert=False,
min_count=-1):
_local_template = "Compute %(f)s of group values"
@Substitution(name='groupby', f=name)
@Appender(_common_see_also)
... |
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def resample(self, rule, *args, **kwargs):
""" Provide resampling when using a TimeGrouper. Given a grouper, the function resamples it according to a string "str... |
from pandas.core.resample import get_resampler_for_grouping
return get_resampler_for_grouping(self, rule, *args, **kwargs) |
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def rolling(self, *args, **kwargs):
""" Return a rolling grouper, providing rolling functionality per group. """ |
from pandas.core.window import RollingGroupby
return RollingGroupby(self, *args, **kwargs) |
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def expanding(self, *args, **kwargs):
""" Return an expanding grouper, providing expanding functionality per group. """ |
from pandas.core.window import ExpandingGroupby
return ExpandingGroupby(self, *args, **kwargs) |
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def _fill(self, direction, limit=None):
""" Shared function for `pad` and `backfill` to call Cython method. Parameters direction : {'ffill', 'bfill'} Direction p... |
# Need int value for Cython
if limit is None:
limit = -1
return self._get_cythonized_result('group_fillna_indexer',
self.grouper, needs_mask=True,
cython_dtype=np.int64,
... |
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def quantile(self, q=0.5, interpolation='linear'):
""" Return group values at the given quantile, a la numpy.percentile. Parameters q : float or array-like, defa... |
def pre_processor(
vals: np.ndarray
) -> Tuple[np.ndarray, Optional[Type]]:
if is_object_dtype(vals):
raise TypeError("'quantile' cannot be performed against "
"'object' dtypes!")
inference = None
if i... |
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def ngroup(self, ascending=True):
""" Number each group from 0 to the number of groups - 1. This is the enumerative complement of cumcount. Note that the numbers... |
with _group_selection_context(self):
index = self._selected_obj.index
result = Series(self.grouper.group_info[0], index)
if not ascending:
result = self.ngroups - 1 - result
return result |
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def cumcount(self, ascending=True):
""" Number each item in each group from 0 to the length of that group - 1. Essentially this is equivalent to Parameters ascen... |
with _group_selection_context(self):
index = self._selected_obj.index
cumcounts = self._cumcount_array(ascending=ascending)
return Series(cumcounts, index) |
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def rank(self, method='average', ascending=True, na_option='keep', pct=False, axis=0):
""" Provide the rank of values within each group. Parameters method : {'av... |
if na_option not in {'keep', 'top', 'bottom'}:
msg = "na_option must be one of 'keep', 'top', or 'bottom'"
raise ValueError(msg)
return self._cython_transform('rank', numeric_only=False,
ties_method=method, ascending=ascending,
... |
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def cumprod(self, axis=0, *args, **kwargs):
""" Cumulative product for each group. """ |
nv.validate_groupby_func('cumprod', args, kwargs,
['numeric_only', 'skipna'])
if axis != 0:
return self.apply(lambda x: x.cumprod(axis=axis, **kwargs))
return self._cython_transform('cumprod', **kwargs) |
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def cummin(self, axis=0, **kwargs):
""" Cumulative min for each group. """ |
if axis != 0:
return self.apply(lambda x: np.minimum.accumulate(x, axis))
return self._cython_transform('cummin', numeric_only=False) |
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def cummax(self, axis=0, **kwargs):
""" Cumulative max for each group. """ |
if axis != 0:
return self.apply(lambda x: np.maximum.accumulate(x, axis))
return self._cython_transform('cummax', numeric_only=False) |
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def _get_cythonized_result(self, how, grouper, aggregate=False, cython_dtype=None, needs_values=False, needs_mask=False, needs_ngroups=False, result_is_index=Fals... |
if result_is_index and aggregate:
raise ValueError("'result_is_index' and 'aggregate' cannot both "
"be True!")
if post_processing:
if not callable(pre_processing):
raise ValueError("'post_processing' must be a callable!")
if ... |
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def shift(self, periods=1, freq=None, axis=0, fill_value=None):
""" Shift each group by periods observations. Parameters periods : integer, default 1 number of p... |
if freq is not None or axis != 0 or not isna(fill_value):
return self.apply(lambda x: x.shift(periods, freq,
axis, fill_value))
return self._get_cythonized_result('group_shift_indexer',
self.grouper... |
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def head(self, n=5):
""" Return first n rows of each group. Essentially equivalent to ``.apply(lambda x: x.head(n))``, except ignores as_index flag. %(see_also)s... |
self._reset_group_selection()
mask = self._cumcount_array() < n
return self._selected_obj[mask] |
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def tail(self, n=5):
""" Return last n rows of each group. Essentially equivalent to ``.apply(lambda x: x.tail(n))``, except ignores as_index flag. %(see_also)s ... |
self._reset_group_selection()
mask = self._cumcount_array(ascending=False) < n
return self._selected_obj[mask] |
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def next_monday(dt):
""" If holiday falls on Saturday, use following Monday instead; if holiday falls on Sunday, use Monday instead """ |
if dt.weekday() == 5:
return dt + timedelta(2)
elif dt.weekday() == 6:
return dt + timedelta(1)
return dt |
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def previous_friday(dt):
""" If holiday falls on Saturday or Sunday, use previous Friday instead. """ |
if dt.weekday() == 5:
return dt - timedelta(1)
elif dt.weekday() == 6:
return dt - timedelta(2)
return dt |
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def next_workday(dt):
""" returns next weekday used for observances """ |
dt += timedelta(days=1)
while dt.weekday() > 4:
# Mon-Fri are 0-4
dt += timedelta(days=1)
return dt |
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def previous_workday(dt):
""" returns previous weekday used for observances """ |
dt -= timedelta(days=1)
while dt.weekday() > 4:
# Mon-Fri are 0-4
dt -= timedelta(days=1)
return dt |
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def dates(self, start_date, end_date, return_name=False):
""" Calculate holidays observed between start date and end date Parameters start_date : starting date, ... |
start_date = Timestamp(start_date)
end_date = Timestamp(end_date)
filter_start_date = start_date
filter_end_date = end_date
if self.year is not None:
dt = Timestamp(datetime(self.year, self.month, self.day))
if return_name:
return Series... |
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def _reference_dates(self, start_date, end_date):
""" Get reference dates for the holiday. Return reference dates for the holiday also returning the year prior t... |
if self.start_date is not None:
start_date = self.start_date.tz_localize(start_date.tz)
if self.end_date is not None:
end_date = self.end_date.tz_localize(start_date.tz)
year_offset = DateOffset(years=1)
reference_start_date = Timestamp(
datetime(st... |
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def holidays(self, start=None, end=None, return_name=False):
""" Returns a curve with holidays between start_date and end_date Parameters start : starting date, ... |
if self.rules is None:
raise Exception('Holiday Calendar {name} does not have any '
'rules specified'.format(name=self.name))
if start is None:
start = AbstractHolidayCalendar.start_date
if end is None:
end = AbstractHolidayCalen... |
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def merge_class(base, other):
""" Merge holiday calendars together. The base calendar will take precedence to other. The merge will be done based on each holiday... |
try:
other = other.rules
except AttributeError:
pass
if not isinstance(other, list):
other = [other]
other_holidays = {holiday.name: holiday for holiday in other}
try:
base = base.rules
except AttributeError:
... |
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def merge(self, other, inplace=False):
""" Merge holiday calendars together. The caller's class rules take precedence. The merge will be done based on each holid... |
holidays = self.merge_class(self, other)
if inplace:
self.rules = holidays
else:
return holidays |
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def register_option(key, defval, doc='', validator=None, cb=None):
"""Register an option in the package-wide pandas config object Parameters key - a fully-qualif... |
import tokenize
import keyword
key = key.lower()
if key in _registered_options:
msg = "Option '{key}' has already been registered"
raise OptionError(msg.format(key=key))
if key in _reserved_keys:
msg = "Option '{key}' is a reserved key"
raise OptionError(msg.format(... |
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def deprecate_option(key, msg=None, rkey=None, removal_ver=None):
""" Mark option `key` as deprecated, if code attempts to access this option, a warning will be ... |
key = key.lower()
if key in _deprecated_options:
msg = "Option '{key}' has already been defined as deprecated."
raise OptionError(msg.format(key=key))
_deprecated_options[key] = DeprecatedOption(key, msg, rkey, removal_ver) |
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def _select_options(pat):
"""returns a list of keys matching `pat` if pat=="all", returns all registered options """ |
# short-circuit for exact key
if pat in _registered_options:
return [pat]
# else look through all of them
keys = sorted(_registered_options.keys())
if pat == 'all': # reserved key
return keys
return [k for k in keys if re.search(pat, k, re.I)] |
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def _translate_key(key):
""" if key id deprecated and a replacement key defined, will return the replacement key, otherwise returns `key` as - is """ |
d = _get_deprecated_option(key)
if d:
return d.rkey or key
else:
return key |
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def _build_option_description(k):
""" Builds a formatted description of a registered option and prints it """ |
o = _get_registered_option(k)
d = _get_deprecated_option(k)
s = '{k} '.format(k=k)
if o.doc:
s += '\n'.join(o.doc.strip().split('\n'))
else:
s += 'No description available.'
if o:
s += ('\n [default: {default}] [currently: {current}]'
.format(default... |
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def config_prefix(prefix):
"""contextmanager for multiple invocations of API with a common prefix supported API functions: (register / get / set )__option Warnin... |
# Note: reset_option relies on set_option, and on key directly
# it does not fit in to this monkey-patching scheme
global register_option, get_option, set_option, reset_option
def wrap(func):
def inner(key, *args, **kwds):
pkey = '{prefix}.{key}'.format(prefix=prefix, key=key)
... |
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def maybe_convert_platform_interval(values):
""" Try to do platform conversion, with special casing for IntervalArray. Wrapper around maybe_convert_platform that... |
if isinstance(values, (list, tuple)) and len(values) == 0:
# GH 19016
# empty lists/tuples get object dtype by default, but this is not
# prohibited for IntervalArray, so coerce to integer instead
return np.array([], dtype=np.int64)
elif is_categorical_dtype(values):
val... |
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def is_file_like(obj):
""" Check if the object is a file-like object. For objects to be considered file-like, they must be an iterator AND have either a `read` a... |
if not (hasattr(obj, 'read') or hasattr(obj, 'write')):
return False
if not hasattr(obj, "__iter__"):
return False
return True |
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def is_list_like(obj, allow_sets=True):
""" Check if the object is list-like. Objects that are considered list-like are for example Python lists, tuples, sets, N... |
return (isinstance(obj, abc.Iterable) and
# we do not count strings/unicode/bytes as list-like
not isinstance(obj, (str, bytes)) and
# exclude zero-dimensional numpy arrays, effectively scalars
not (isinstance(obj, np.ndarray) and obj.ndim == 0) and
# ... |
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def is_nested_list_like(obj):
""" Check if the object is list-like, and that all of its elements are also list-like. .. versionadded:: 0.20.0 Parameters obj : Th... |
return (is_list_like(obj) and hasattr(obj, '__len__') and
len(obj) > 0 and all(is_list_like(item) for item in obj)) |
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def is_dict_like(obj):
""" Check if the object is dict-like. Parameters obj : The object to check Returns ------- is_dict_like : bool Whether `obj` has dict-like... |
dict_like_attrs = ("__getitem__", "keys", "__contains__")
return (all(hasattr(obj, attr) for attr in dict_like_attrs)
# [GH 25196] exclude classes
and not isinstance(obj, type)) |
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def is_sequence(obj):
""" Check if the object is a sequence of objects. String types are not included as sequences here. Parameters obj : The object to check Ret... |
try:
iter(obj) # Can iterate over it.
len(obj) # Has a length associated with it.
return not isinstance(obj, (str, bytes))
except (TypeError, AttributeError):
return False |
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def date_range(start=None, end=None, periods=None, freq=None, tz=None, normalize=False, name=None, closed=None, **kwargs):
""" Return a fixed frequency DatetimeI... |
if freq is None and com._any_none(periods, start, end):
freq = 'D'
dtarr = DatetimeArray._generate_range(
start=start, end=end, periods=periods,
freq=freq, tz=tz, normalize=normalize,
closed=closed, **kwargs)
return DatetimeIndex._simple_new(
dtarr, tz=dtarr.tz, fr... |
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def bdate_range(start=None, end=None, periods=None, freq='B', tz=None, normalize=True, name=None, weekmask=None, holidays=None, closed=None, **kwargs):
""" Retur... |
if freq is None:
msg = 'freq must be specified for bdate_range; use date_range instead'
raise TypeError(msg)
if is_string_like(freq) and freq.startswith('C'):
try:
weekmask = weekmask or 'Mon Tue Wed Thu Fri'
freq = prefix_mapping[freq](holidays=holidays, weekma... |
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Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
def cdate_range(start=None, end=None, periods=None, freq='C', tz=None, normalize=True, name=None, closed=None, **kwargs):
""" Return a fixed frequency DatetimeIn... |
warnings.warn("cdate_range is deprecated and will be removed in a future "
"version, instead use pd.bdate_range(..., freq='{freq}')"
.format(freq=freq), FutureWarning, stacklevel=2)
if freq == 'C':
holidays = kwargs.pop('holidays', [])
weekmask = kwargs.pop(... |
<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
def _create_blocks(self):
""" Split data into blocks & return conformed data. """ |
obj, index = self._convert_freq()
if index is not None:
index = self._on
# filter out the on from the object
if self.on is not None:
if obj.ndim == 2:
obj = obj.reindex(columns=obj.columns.difference([self.on]),
... |
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