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train | Index.slice_indexer | For an ordered or unique index, compute the slice indexer for input
labels and step.
Parameters
----------
start : label, default None
If None, defaults to the beginning
end : label, default None
If None, defaults to the end
step : int, default No... | pandas/core/indexes/base.py | 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.
Parameters
----------
start : label, default None
If None, defaults to the beginning
end : la... | 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.
Parameters
----------
start : label, default None
If None, defaults to the beginning
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train | Index._maybe_cast_indexer | If we have a float key and are not a floating index, then try to cast
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"""
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"""
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"""
If we have a float key and are not a floating index, then try to cast
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"""
if is_float(key) and not self.is_floating():
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ckey = int(key)
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train | Index._validate_indexer | If we are positional indexer, validate that we have appropriate
typed bounds must be an integer. | pandas/core/indexes/base.py | def _validate_indexer(self, form, key, kind):
"""
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"""
assert kind in ['ix', 'loc', 'getitem', 'iloc']
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"""
If we are positional indexer, validate that we have appropriate
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train | Index.get_slice_bound | Calculate slice bound that corresponds to given label.
Returns leftmost (one-past-the-rightmost if ``side=='right'``) position
of given label.
Parameters
----------
label : object
side : {'left', 'right'}
kind : {'ix', 'loc', 'getitem'} | pandas/core/indexes/base.py | def get_slice_bound(self, label, side, kind):
"""
Calculate slice bound that corresponds to given label.
Returns leftmost (one-past-the-rightmost if ``side=='right'``) position
of given label.
Parameters
----------
label : object
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Calculate slice bound that corresponds to given label.
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train | Index.slice_locs | Compute slice locations for input labels.
Parameters
----------
start : label, default None
If None, defaults to the beginning
end : label, default None
If None, defaults to the end
step : int, defaults None
If None, defaults to 1
kind... | pandas/core/indexes/base.py | 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, defaults to the beginning
end : label, default None
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"""
Compute slice locations for input labels.
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----------
start : label, default None
If None, defaults to the beginning
end : label, default None
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train | Index.delete | Make new Index with passed location(-s) deleted.
Returns
-------
new_index : Index | pandas/core/indexes/base.py | def delete(self, loc):
"""
Make new Index with passed location(-s) deleted.
Returns
-------
new_index : Index
"""
return self._shallow_copy(np.delete(self._data, loc)) | def delete(self, loc):
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new_index : Index
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train | Index.insert | Make new Index inserting new item at location.
Follows Python list.append semantics for negative values.
Parameters
----------
loc : int
item : object
Returns
-------
new_index : Index | pandas/core/indexes/base.py | def insert(self, loc, item):
"""
Make new Index inserting new item at location.
Follows Python list.append semantics for negative values.
Parameters
----------
loc : int
item : object
Returns
-------
new_index : Index
"""
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"""
Make new Index inserting new item at location.
Follows Python list.append semantics for negative values.
Parameters
----------
loc : int
item : object
Returns
-------
new_index : Index
"""
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train | Index.drop | Make new Index with passed list of labels deleted.
Parameters
----------
labels : array-like
errors : {'ignore', 'raise'}, default 'raise'
If 'ignore', suppress error and existing labels are dropped.
Returns
-------
dropped : Index
Raises
... | pandas/core/indexes/base.py | def drop(self, labels, errors='raise'):
"""
Make new Index with passed list of labels deleted.
Parameters
----------
labels : array-like
errors : {'ignore', 'raise'}, default 'raise'
If 'ignore', suppress error and existing labels are dropped.
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"""
Make new Index with passed list of labels deleted.
Parameters
----------
labels : array-like
errors : {'ignore', 'raise'}, default 'raise'
If 'ignore', suppress error and existing labels are dropped.
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train | Index._add_comparison_methods | Add in comparison methods. | pandas/core/indexes/base.py | 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)
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Add in comparison methods.
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train | Index._add_numeric_methods_add_sub_disabled | Add in the numeric add/sub methods to disable. | pandas/core/indexes/base.py | def _add_numeric_methods_add_sub_disabled(cls):
"""
Add in the numeric add/sub methods to disable.
"""
cls.__add__ = make_invalid_op('__add__')
cls.__radd__ = make_invalid_op('__radd__')
cls.__iadd__ = make_invalid_op('__iadd__')
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"""
Add in the numeric add/sub methods to disable.
"""
cls.__add__ = make_invalid_op('__add__')
cls.__radd__ = make_invalid_op('__radd__')
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train | Index._add_numeric_methods_disabled | Add in numeric methods to disable other than add/sub. | pandas/core/indexes/base.py | def _add_numeric_methods_disabled(cls):
"""
Add in numeric methods to disable other than add/sub.
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"""
Add in numeric methods to disable other than add/sub.
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train | Index._validate_for_numeric_unaryop | Validate if we can perform a numeric unary operation. | pandas/core/indexes/base.py | 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 "
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"""
Validate if we can perform a numeric unary operation.
"""
if not self._is_numeric_dtype:
raise TypeError("cannot evaluate a numeric op "
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train | Index._validate_for_numeric_binop | Return valid other; evaluate or raise TypeError if we are not of
the appropriate type.
Notes
-----
This is an internal method called by ops. | pandas/core/indexes/base.py | 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 an internal method called by ops.
"""
opstr = '__{opname}__'.format(opname=op.__name__)
... | def _validate_for_numeric_binop(self, other, op):
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Return valid other; evaluate or raise TypeError if we are not of
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-----
This is an internal method called by ops.
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opstr = '__{opname}__'.format(opname=op.__name__)
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train | Index._add_numeric_methods_binary | Add in numeric methods. | pandas/core/indexes/base.py | 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... | 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)
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train | Index._add_numeric_methods_unary | Add in numeric unary methods. | pandas/core/indexes/base.py | 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()
... | 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()
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train | Index._add_logical_methods | Add in logical methods. | pandas/core/indexes/base.py | 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.%(outnam... | def _add_logical_methods(cls):
"""
Add in logical methods.
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These parameters will be passed to numpy.%(outname)s.
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train | _get_grouper | create and return a BaseGrouper, which is an internal
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create and return a BaseGrouper, which is an internal
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train | Grouper._get_grouper | Parameters
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obj : the subject object
validate : boolean, default True
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a tuple of binner, grouper, obj (possibly sorted)
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train | to_pickle | Pickle (serialize) object to file.
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obj : any object
Any python object.
path : str
File path where the pickled object will be stored.
compression : {'infer', 'gzip', 'bz2', 'zip', 'xz', None}, default 'infer'
A string representing the compression to use ... | pandas/io/pickle.py | def to_pickle(obj, path, compression='infer',
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"""
Pickle (serialize) object to file.
Parameters
----------
obj : any object
Any python object.
path : str
File path where the pickled object will be stored.
compression : {'infer... | def to_pickle(obj, path, compression='infer',
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"""
Pickle (serialize) object to file.
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obj : any object
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train | read_pickle | Load pickled pandas object (or any object) from file.
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Loading pickled data received from untrusted sources can be
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----------
path : str
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Load pickled pandas object (or any object) from file.
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Loading pickled data received from untrusted sources can be
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train | mask_missing | Return a masking array of same size/shape as arr
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"""
Return a masking array of same size/shape as arr
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"""
dtype, values_to_mask = infer_dtype_from_array(values_to_mask)
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train | interpolate_1d | Logic for the 1-d interpolation. The result should be 1-d, inputs
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Logic for the 1-d interpolation. The result should be 1-d, inputs
xvalues and yvalues will each... | def interpolate_1d(xvalues, yvalues, method='linear', limit=None,
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Passed off to scipy.interpolate.interp1d. method is scipy's kind.
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train | _from_derivatives | Convenience function for interpolate.BPoly.from_derivatives.
Construct a piecewise polynomial in the Bernstein basis, compatible
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Parameters
----------
xi : array_like
sorted 1D array of x-coordinates
yi : array_like or list of a... | pandas/core/missing.py | def _from_derivatives(xi, yi, x, order=None, der=0, extrapolate=False):
"""
Convenience function for interpolate.BPoly.from_derivatives.
Construct a piecewise polynomial in the Bernstein basis, compatible
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----------
xi : ... | def _from_derivatives(xi, yi, x, order=None, der=0, extrapolate=False):
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Convenience function for interpolate.BPoly.from_derivatives.
Construct a piecewise polynomial in the Bernstein basis, compatible
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train | _akima_interpolate | Convenience function for akima interpolation.
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See `Akima1DInterpolator` for details.
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"""
Convenience function for akima interpolation.
xi and yi are arrays of values used to approximate some function f,
with ``yi = f(xi)``.
See `Akima1DInterpolator` for details.
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----------
xi : array_like
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Convenience function for akima interpolation.
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train | interpolate_2d | Perform an actual interpolation of values, values will be make 2-d if
needed fills inplace, returns the result. | pandas/core/missing.py | def interpolate_2d(values, method='pad', axis=0, limit=None, fill_value=None,
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"""
Perform an actual interpolation of values, values will be make 2-d if
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Perform an actual interpolation of values, values will be make 2-d if
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train | _cast_values_for_fillna | Cast values to a dtype that algos.pad and algos.backfill can handle. | pandas/core/missing.py | 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_dty... | def _cast_values_for_fillna(values, dtype):
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Cast values to a dtype that algos.pad and algos.backfill can handle.
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# TODO: for int-dtypes we make a copy, but for everything else this
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train | fill_zeros | If this is a reversed op, then flip x,y
If we have an integer value (or array in y)
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return the result.
Mask the nan's from x. | pandas/core/missing.py | 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 the fill,
return the result.
Mask the nan's from x.
"""
if fill is None or is_float_dtype(result):
return result
... | 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 the fill,
return the result.
Mask the nan's from x.
"""
if fill is None or is_float_dtype(result):
return result
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train | mask_zero_div_zero | Set results of 0 / 0 or 0 // 0 to np.nan, regardless of the dtypes
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x : ndarray
y : ndarray
result : ndarray
copy : bool (default False)
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"""
Set results of 0 / 0 or 0 // 0 to np.nan, regardless of the dtypes
of the numerator or the denominator.
Parameters
----------
x : ndarray
y : ndarray
result : ndarray
copy : bool (default False)
Whether to always create a... | def mask_zero_div_zero(x, y, result, copy=False):
"""
Set results of 0 / 0 or 0 // 0 to np.nan, regardless of the dtypes
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x : ndarray
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train | dispatch_missing | Fill nulls caused by division by zero, casting to a diffferent dtype
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Parameters
----------
op : function (operator.add, operator.div, ...)
left : object (Index for non-reversed ops)
right : object (Index fof reversed ops)
result : ndarray
Returns
-------
result : ... | pandas/core/missing.py | def dispatch_missing(op, left, right, result):
"""
Fill nulls caused by division by zero, casting to a diffferent dtype
if necessary.
Parameters
----------
op : function (operator.add, operator.div, ...)
left : object (Index for non-reversed ops)
right : object (Index fof reversed ops)
... | def dispatch_missing(op, left, right, result):
"""
Fill nulls caused by division by zero, casting to a diffferent dtype
if necessary.
Parameters
----------
op : function (operator.add, operator.div, ...)
left : object (Index for non-reversed ops)
right : object (Index fof reversed ops)
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train | _interp_limit | Get indexers of values that won't be filled
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Parameters
----------
invalid : boolean ndarray
fw_limit : int or None
forward limit to index
bw_limit : int or None
backward limit to index
Returns
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set of indexers
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--... | pandas/core/missing.py | 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 ndarray
fw_limit : int or None
forward limit to index
bw_limit : int or None
backward limit to index
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"""
Get indexers of values that won't be filled
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Parameters
----------
invalid : boolean ndarray
fw_limit : int or None
forward limit to index
bw_limit : int or None
backward limit to index
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train | _rolling_window | [True, True, False, True, False], 2 ->
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"""
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[False, True],
[True, False],
]
"""
# https://stackoverflow.com/a/6811241
shape = a.shape[:-1] + (a.shape[-1] - window + 1, window)
strides = a.stri... | def _rolling_window(a, window):
"""
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train | get_console_size | Return console size as tuple = (width, height).
Returns (None,None) in non-interactive session. | pandas/io/formats/console.py | def get_console_size():
"""Return console size as tuple = (width, height).
Returns (None,None) in non-interactive session.
"""
from pandas import get_option
display_width = get_option('display.width')
# deprecated.
display_height = get_option('display.max_rows')
# Consider
# inter... | def get_console_size():
"""Return console size as tuple = (width, height).
Returns (None,None) in non-interactive session.
"""
from pandas import get_option
display_width = get_option('display.width')
# deprecated.
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train | in_interactive_session | check if we're running in an interactive shell
returns True if running under python/ipython interactive shell | pandas/io/formats/console.py | 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:
retur... | 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
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train | recode_for_groupby | Code the categories to ensure we can groupby for categoricals.
If observed=True, we return a new Categorical with the observed
categories only.
If sort=False, return a copy of self, coded with categories as
returned by .unique(), followed by any categories not appearing in
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"""
Code the categories to ensure we can groupby for categoricals.
If observed=True, we return a new Categorical with the observed
categories only.
If sort=False, return a copy of self, coded with categories as
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Code the categories to ensure we can groupby for categoricals.
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train | recode_from_groupby | Reverse the codes_to_groupby to account for sort / observed.
Parameters
----------
c : Categorical
sort : boolean
The value of the sort parameter groupby was called with.
ci : CategoricalIndex
The codes / categories to recode
Returns
-------
CategoricalIndex | pandas/core/groupby/categorical.py | def recode_from_groupby(c, sort, ci):
"""
Reverse the codes_to_groupby to account for sort / observed.
Parameters
----------
c : Categorical
sort : boolean
The value of the sort parameter groupby was called with.
ci : CategoricalIndex
The codes / categories to recode
Re... | def recode_from_groupby(c, sort, ci):
"""
Reverse the codes_to_groupby to account for sort / observed.
Parameters
----------
c : Categorical
sort : boolean
The value of the sort parameter groupby was called with.
ci : CategoricalIndex
The codes / categories to recode
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train | get_engine | return our implementation | pandas/io/parquet.py | 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:
retu... | def get_engine(engine):
""" return our implementation """
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train | to_parquet | Write a DataFrame to the parquet format.
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----------
path : str
File path or Root Directory path. Will be used as Root Directory path
while writing a partitioned dataset.
.. versionchanged:: 0.24.0
engine : {'auto', 'pyarrow', 'fastparquet'}, default 'auto'
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"""
Write a DataFrame to the parquet format.
Parameters
----------
path : str
File path or Root Directory path. Will be used as Root Directory path
while writing ... | def to_parquet(df, path, engine='auto', compression='snappy', index=None,
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"""
Write a DataFrame to the parquet format.
Parameters
----------
path : str
File path or Root Directory path. Will be used as Root Directory path
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path : string
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path : string
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train | generate_bins_generic | Generate bin edge offsets and bin labels for one array using another array
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Parameters
----------
values : array of values
binner : a comparable array of values representing bins into which to bin
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"""
Generate bin edge offsets and bin labels for one array using another array
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Parameters
----------
values : array of values
binner : a comparable array of values representing bins int... | def generate_bins_generic(values, binner, closed):
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----------
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train | BaseGrouper.get_iterator | Groupby iterator
Returns
-------
Generator yielding sequence of (name, subsetted object)
for each group | pandas/core/groupby/ops.py | def get_iterator(self, data, axis=0):
"""
Groupby iterator
Returns
-------
Generator yielding sequence of (name, subsetted object)
for each group
"""
splitter = self._get_splitter(data, axis=axis)
keys = self._get_group_keys()
for key, (i,... | def get_iterator(self, data, axis=0):
"""
Groupby iterator
Returns
-------
Generator yielding sequence of (name, subsetted object)
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splitter = self._get_splitter(data, axis=axis)
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train | BaseGrouper.indices | dict {group name -> group indices} | pandas/core/groupby/ops.py | def indices(self):
""" dict {group name -> group indices} """
if len(self.groupings) == 1:
return self.groupings[0].indices
else:
label_list = [ping.labels for ping in self.groupings]
keys = [com.values_from_object(ping.group_index)
for pin... | def indices(self):
""" dict {group name -> group indices} """
if len(self.groupings) == 1:
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train | BaseGrouper.size | Compute group sizes | pandas/core/groupby/ops.py | def size(self):
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train | BinGrouper.groups | dict {group name -> group labels} | pandas/core/groupby/ops.py | def groups(self):
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data : DataFrame
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Safe get multiple indices, translate keys for
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train | _GroupBy._set_group_selection | Create group based selection.
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Create group based selection.
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train | GroupBy.mean | Compute mean of groups, excluding missing values.
Returns
-------
pandas.Series or pandas.DataFrame
%(see_also)s
Examples
--------
>>> df = pd.DataFrame({'A': [1, 1, 2, 1, 2],
... 'B': [np.nan, 2, 3, 4, 5],
... ... | pandas/core/groupby/groupby.py | def mean(self, *args, **kwargs):
"""
Compute mean of groups, excluding missing values.
Returns
-------
pandas.Series or pandas.DataFrame
%(see_also)s
Examples
--------
>>> df = pd.DataFrame({'A': [1, 1, 2, 1, 2],
... 'B'... | def mean(self, *args, **kwargs):
"""
Compute mean of groups, excluding missing values.
Returns
-------
pandas.Series or pandas.DataFrame
%(see_also)s
Examples
--------
>>> df = pd.DataFrame({'A': [1, 1, 2, 1, 2],
... 'B'... | [
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train | GroupBy.median | Compute median of groups, excluding missing values.
For multiple groupings, the result index will be a MultiIndex | pandas/core/groupby/groupby.py | def median(self, **kwargs):
"""
Compute median of groups, excluding missing values.
For multiple groupings, the result index will be a MultiIndex
"""
try:
return self._cython_agg_general('median', **kwargs)
except GroupByError:
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excep... | def median(self, **kwargs):
"""
Compute median of groups, excluding missing values.
For multiple groupings, the result index will be a MultiIndex
"""
try:
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train | GroupBy.std | Compute standard deviation of groups, excluding missing values.
For multiple groupings, the result index will be a MultiIndex.
Parameters
----------
ddof : integer, default 1
degrees of freedom | pandas/core/groupby/groupby.py | def std(self, ddof=1, *args, **kwargs):
"""
Compute standard deviation of groups, excluding missing values.
For multiple groupings, the result index will be a MultiIndex.
Parameters
----------
ddof : integer, default 1
degrees of freedom
"""
... | def std(self, ddof=1, *args, **kwargs):
"""
Compute standard deviation of groups, excluding missing values.
For multiple groupings, the result index will be a MultiIndex.
Parameters
----------
ddof : integer, default 1
degrees of freedom
"""
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train | GroupBy.var | Compute variance of groups, excluding missing values.
For multiple groupings, the result index will be a MultiIndex.
Parameters
----------
ddof : integer, default 1
degrees of freedom | pandas/core/groupby/groupby.py | def var(self, ddof=1, *args, **kwargs):
"""
Compute variance of groups, excluding missing values.
For multiple groupings, the result index will be a MultiIndex.
Parameters
----------
ddof : integer, default 1
degrees of freedom
"""
nv.validat... | def var(self, ddof=1, *args, **kwargs):
"""
Compute variance of groups, excluding missing values.
For multiple groupings, the result index will be a MultiIndex.
Parameters
----------
ddof : integer, default 1
degrees of freedom
"""
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train | GroupBy.sem | Compute standard error of the mean of groups, excluding missing values.
For multiple groupings, the result index will be a MultiIndex.
Parameters
----------
ddof : integer, default 1
degrees of freedom | pandas/core/groupby/groupby.py | 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.
Parameters
----------
ddof : integer, default 1
degrees of freedom
"""
return s... | 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.
Parameters
----------
ddof : integer, default 1
degrees of freedom
"""
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train | GroupBy.size | Compute group sizes. | pandas/core/groupby/groupby.py | def size(self):
"""
Compute group sizes.
"""
result = self.grouper.size()
if isinstance(self.obj, Series):
result.name = getattr(self.obj, 'name', None)
return result | 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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train | GroupBy._add_numeric_operations | Add numeric operations to the GroupBy generically. | pandas/core/groupby/groupby.py | 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)... | 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):
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train | GroupBy.resample | Provide resampling when using a TimeGrouper.
Given a grouper, the function resamples it according to a string
"string" -> "frequency".
See the :ref:`frequency aliases <timeseries.offset_aliases>`
documentation for more details.
Parameters
----------
rule : str ... | pandas/core/groupby/groupby.py | def resample(self, rule, *args, **kwargs):
"""
Provide resampling when using a TimeGrouper.
Given a grouper, the function resamples it according to a string
"string" -> "frequency".
See the :ref:`frequency aliases <timeseries.offset_aliases>`
documentation for more deta... | def resample(self, rule, *args, **kwargs):
"""
Provide resampling when using a TimeGrouper.
Given a grouper, the function resamples it according to a string
"string" -> "frequency".
See the :ref:`frequency aliases <timeseries.offset_aliases>`
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train | GroupBy.rolling | Return a rolling grouper, providing rolling functionality per group. | pandas/core/groupby/groupby.py | 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) | def rolling(self, *args, **kwargs):
"""
Return a rolling grouper, providing rolling functionality per group.
"""
from pandas.core.window import RollingGroupby
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train | GroupBy.expanding | Return an expanding grouper, providing expanding
functionality per group. | pandas/core/groupby/groupby.py | 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) | 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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train | GroupBy._fill | Shared function for `pad` and `backfill` to call Cython method.
Parameters
----------
direction : {'ffill', 'bfill'}
Direction passed to underlying Cython function. `bfill` will cause
values to be filled backwards. `ffill` and any other values will
default to... | pandas/core/groupby/groupby.py | def _fill(self, direction, limit=None):
"""
Shared function for `pad` and `backfill` to call Cython method.
Parameters
----------
direction : {'ffill', 'bfill'}
Direction passed to underlying Cython function. `bfill` will cause
values to be filled backwar... | def _fill(self, direction, limit=None):
"""
Shared function for `pad` and `backfill` to call Cython method.
Parameters
----------
direction : {'ffill', 'bfill'}
Direction passed to underlying Cython function. `bfill` will cause
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train | GroupBy.nth | Take the nth row from each group if n is an int, or a subset of rows
if n is a list of ints.
If dropna, will take the nth non-null row, dropna is either
Truthy (if a Series) or 'all', 'any' (if a DataFrame);
this is equivalent to calling dropna(how=dropna) before the
groupby.
... | pandas/core/groupby/groupby.py | def nth(self, n, dropna=None):
"""
Take the nth row from each group if n is an int, or a subset of rows
if n is a list of ints.
If dropna, will take the nth non-null row, dropna is either
Truthy (if a Series) or 'all', 'any' (if a DataFrame);
this is equivalent to callin... | def nth(self, n, dropna=None):
"""
Take the nth row from each group if n is an int, or a subset of rows
if n is a list of ints.
If dropna, will take the nth non-null row, dropna is either
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train | GroupBy.quantile | Return group values at the given quantile, a la numpy.percentile.
Parameters
----------
q : float or array-like, default 0.5 (50% quantile)
Value(s) between 0 and 1 providing the quantile(s) to compute.
interpolation : {'linear', 'lower', 'higher', 'midpoint', 'nearest'}
... | pandas/core/groupby/groupby.py | 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, default 0.5 (50% quantile)
Value(s) between 0 and 1 providing the quantile(s) to compute.
i... | 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, default 0.5 (50% quantile)
Value(s) between 0 and 1 providing the quantile(s) to compute.
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train | GroupBy.ngroup | Number each group from 0 to the number of groups - 1.
This is the enumerative complement of cumcount. Note that the
numbers given to the groups match the order in which the groups
would be seen when iterating over the groupby object, not the
order they are first observed.
.. v... | pandas/core/groupby/groupby.py | 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 given to the groups match the order in which the groups
would be seen when iterating over the groupby object, not th... | def ngroup(self, ascending=True):
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Number each group from 0 to the number of groups - 1.
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train | GroupBy.cumcount | Number each item in each group from 0 to the length of that group - 1.
Essentially this is equivalent to
>>> self.apply(lambda x: pd.Series(np.arange(len(x)), x.index))
Parameters
----------
ascending : bool, default True
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"""
Number each item in each group from 0 to the length of that group - 1.
Essentially this is equivalent to
>>> self.apply(lambda x: pd.Series(np.arange(len(x)), x.index))
Parameters
----------
ascending : bool, default True... | def cumcount(self, ascending=True):
"""
Number each item in each group from 0 to the length of that group - 1.
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>>> self.apply(lambda x: pd.Series(np.arange(len(x)), x.index))
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----------
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train | GroupBy.rank | Provide the rank of values within each group.
Parameters
----------
method : {'average', 'min', 'max', 'first', 'dense'}, default 'average'
* average: average rank of group
* min: lowest rank in group
* max: highest rank in group
* first: ranks as... | pandas/core/groupby/groupby.py | def rank(self, method='average', ascending=True, na_option='keep',
pct=False, axis=0):
"""
Provide the rank of values within each group.
Parameters
----------
method : {'average', 'min', 'max', 'first', 'dense'}, default 'average'
* average: average rank... | def rank(self, method='average', ascending=True, na_option='keep',
pct=False, axis=0):
"""
Provide the rank of values within each group.
Parameters
----------
method : {'average', 'min', 'max', 'first', 'dense'}, default 'average'
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train | GroupBy.cumprod | Cumulative product for each group. | pandas/core/groupby/groupby.py | def cumprod(self, axis=0, *args, **kwargs):
"""
Cumulative product for each group.
"""
nv.validate_groupby_func('cumprod', args, kwargs,
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train | GroupBy.cummin | Cumulative min for each group. | pandas/core/groupby/groupby.py | 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) | def cummin(self, axis=0, **kwargs):
"""
Cumulative min for each group.
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train | GroupBy.cummax | Cumulative max for each group. | pandas/core/groupby/groupby.py | def cummax(self, axis=0, **kwargs):
"""
Cumulative max for each group.
"""
if axis != 0:
return self.apply(lambda x: np.maximum.accumulate(x, axis))
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"""
Cumulative max for each group.
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train | GroupBy._get_cythonized_result | Get result for Cythonized functions.
Parameters
----------
how : str, Cythonized function name to be called
grouper : Grouper object containing pertinent group info
aggregate : bool, default False
Whether the result should be aggregated to match the number of
... | pandas/core/groupby/groupby.py | def _get_cythonized_result(self, how, grouper, aggregate=False,
cython_dtype=None, needs_values=False,
needs_mask=False, needs_ngroups=False,
result_is_index=False,
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train | GroupBy.shift | Shift each group by periods observations.
Parameters
----------
periods : integer, default 1
number of periods to shift
freq : frequency string
axis : axis to shift, default 0
fill_value : optional
.. versionadded:: 0.24.0 | pandas/core/groupby/groupby.py | 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 periods to shift
freq : frequency string
axis : axis to shift, default 0
... | 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 periods to shift
freq : frequency string
axis : axis to shift, default 0
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train | GroupBy.head | Return first n rows of each group.
Essentially equivalent to ``.apply(lambda x: x.head(n))``,
except ignores as_index flag.
%(see_also)s
Examples
--------
>>> df = pd.DataFrame([[1, 2], [1, 4], [5, 6]],
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>>> df.gr... | pandas/core/groupby/groupby.py | 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
Examples
--------
>>> df = pd.DataFrame([[1, 2], [1, 4], [5, 6]],
... | 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
Examples
--------
>>> df = pd.DataFrame([[1, 2], [1, 4], [5, 6]],
... | [
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train | GroupBy.tail | Return last n rows of each group.
Essentially equivalent to ``.apply(lambda x: x.tail(n))``,
except ignores as_index flag.
%(see_also)s
Examples
--------
>>> df = pd.DataFrame([['a', 1], ['a', 2], ['b', 1], ['b', 2]],
columns=['A', 'B'])
... | pandas/core/groupby/groupby.py | 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
Examples
--------
>>> df = pd.DataFrame([['a', 1], ['a', 2], ['b', 1], ['b', 2]],
... | 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
Examples
--------
>>> df = pd.DataFrame([['a', 1], ['a', 2], ['b', 1], ['b', 2]],
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train | next_monday | If holiday falls on Saturday, use following Monday instead;
if holiday falls on Sunday, use Monday instead | pandas/tseries/holiday.py | 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 | 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)
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train | next_monday_or_tuesday | For second holiday of two adjacent ones!
If holiday falls on Saturday, use following Monday instead;
if holiday falls on Sunday or Monday, use following Tuesday instead
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"""
For second holiday of two adjacent ones!
If holiday falls on Saturday, use following Monday instead;
if holiday falls on Sunday or Monday, use following Tuesday instead
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"""
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For second holiday of two adjacent ones!
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train | previous_friday | If holiday falls on Saturday or Sunday, use previous Friday instead. | pandas/tseries/holiday.py | 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)
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If holiday falls on Saturday or Sunday, use previous Friday instead.
"""
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train | weekend_to_monday | If holiday falls on Sunday or Saturday,
use day thereafter (Monday) instead.
Needed for holidays such as Christmas observation in Europe | pandas/tseries/holiday.py | def weekend_to_monday(dt):
"""
If holiday falls on Sunday or Saturday,
use day thereafter (Monday) instead.
Needed for holidays such as Christmas observation in Europe
"""
if dt.weekday() == 6:
return dt + timedelta(1)
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retu... | def weekend_to_monday(dt):
"""
If holiday falls on Sunday or Saturday,
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"""
if dt.weekday() == 6:
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train | nearest_workday | If holiday falls on Saturday, use day before (Friday) instead;
if holiday falls on Sunday, use day thereafter (Monday) instead. | pandas/tseries/holiday.py | def nearest_workday(dt):
"""
If holiday falls on Saturday, use day before (Friday) instead;
if holiday falls on Sunday, use day thereafter (Monday) instead.
"""
if dt.weekday() == 5:
return dt - timedelta(1)
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return dt + timedelta(1)
return dt | def nearest_workday(dt):
"""
If holiday falls on Saturday, use day before (Friday) instead;
if holiday falls on Sunday, use day thereafter (Monday) instead.
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if dt.weekday() == 5:
return dt - timedelta(1)
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train | next_workday | returns next weekday used for observances | pandas/tseries/holiday.py | 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 | 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)
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train | previous_workday | returns previous weekday used for observances | pandas/tseries/holiday.py | 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 | def previous_workday(dt):
"""
returns previous weekday used for observances
"""
dt -= timedelta(days=1)
while dt.weekday() > 4:
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start_date : starting date, datetime-like, optional
end_date : ending date, datetime-like, optional
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"""
Calculate holidays observed between start date and end date
Parameters
----------
start_date : starting date, datetime-like, optional
end_date : ending date, datetime-like, optional
return_name : bool,... | def dates(self, start_date, end_date, return_name=False):
"""
Calculate holidays observed between start date and end date
Parameters
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start_date : starting date, datetime-like, optional
end_date : ending date, datetime-like, optional
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Get reference dates for the holiday.
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"""
Apply the given offset/observance to a DatetimeIndex of dates.
Parameters
----------
dates : DatetimeIndex
Dates to apply the given offset/observance rule
Returns
-------
Dates with rules applied
"""
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Apply the given offset/observance to a DatetimeIndex of dates.
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dates : DatetimeIndex
Dates to apply the given offset/observance rule
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Dates with rules applied
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train | AbstractHolidayCalendar.holidays | Returns a curve with holidays between start_date and end_date
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start : starting date, datetime-like, optional
end : ending date, datetime-like, optional
return_name : bool, optional
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"""
Returns a curve with holidays between start_date and end_date
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----------
start : starting date, datetime-like, optional
end : ending date, datetime-like, optional
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"""
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base : AbstractHolidayCalendar
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"""
Merge holiday calendars together. The base calendar
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base : AbstractHolidayCalendar
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"""
Merge holiday calendars together. The base calendar
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base : AbstractHolidayCalendar
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"""
Merge holiday calendars together. The caller's class
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Merge holiday calendars together. The caller's class
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train | register_option | Register an option in the package-wide pandas config object
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key - a fully-qualified key, e.g. "x.y.option - z".
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Mark option `key` as deprecated, if code attempts to access this option,
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Mark option `key` as deprecated, if code attempts to access this option,
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train | _select_options | returns a list of keys matching `pat`
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"""
# short-circuit for exact key
if pat in _registered_options:
return [pat]
# else look through all of them
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train | _translate_key | if key id deprecated and a replacement key defined, will return the
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train | _build_option_description | Builds a formatted description of a registered option and prints it | pandas/_config/config.py | def _build_option_description(k):
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s = '{k} '.format(k=k)
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o = _get_registered_option(k)
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train | config_prefix | contextmanager for multiple invocations of API with a common prefix
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Warning: This is not thread - safe, and won't work properly if you import
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Example:
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supported API functions: (register / get / set )__option
Warning: This is not thread - safe, and won't work properly if you import
the API functions into your module using the "from x import y" construct.... | def config_prefix(prefix):
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train | CSSResolver.parse | Generates (prop, value) pairs from declarations
In a future version may generate parsed tokens from tinycss/tinycss2 | pandas/io/formats/css.py | def parse(self, declarations_str):
"""Generates (prop, value) pairs from declarations
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"""
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