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pandas-dev/pandas | pandas/core/ops.py | _get_method_wrappers | def _get_method_wrappers(cls):
"""
Find the appropriate operation-wrappers to use when defining flex/special
arithmetic, boolean, and comparison operations with the given class.
Parameters
----------
cls : class
Returns
-------
arith_flex : function or None
comp_flex : function... | python | def _get_method_wrappers(cls):
"""
Find the appropriate operation-wrappers to use when defining flex/special
arithmetic, boolean, and comparison operations with the given class.
Parameters
----------
cls : class
Returns
-------
arith_flex : function or None
comp_flex : function... | [
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pandas-dev/pandas | pandas/core/ops.py | add_special_arithmetic_methods | def add_special_arithmetic_methods(cls):
"""
Adds the full suite of special arithmetic methods (``__add__``,
``__sub__``, etc.) to the class.
Parameters
----------
cls : class
special methods will be defined and pinned to this class
"""
_, _, arith_method, comp_method, bool_meth... | python | def add_special_arithmetic_methods(cls):
"""
Adds the full suite of special arithmetic methods (``__add__``,
``__sub__``, etc.) to the class.
Parameters
----------
cls : class
special methods will be defined and pinned to this class
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pandas-dev/pandas | pandas/core/ops.py | add_flex_arithmetic_methods | def add_flex_arithmetic_methods(cls):
"""
Adds the full suite of flex arithmetic methods (``pow``, ``mul``, ``add``)
to the class.
Parameters
----------
cls : class
flex methods will be defined and pinned to this class
"""
flex_arith_method, flex_comp_method, _, _, _ = _get_meth... | python | def add_flex_arithmetic_methods(cls):
"""
Adds the full suite of flex arithmetic methods (``pow``, ``mul``, ``add``)
to the class.
Parameters
----------
cls : class
flex methods will be defined and pinned to this class
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pandas-dev/pandas | pandas/core/ops.py | _align_method_SERIES | def _align_method_SERIES(left, right, align_asobject=False):
""" align lhs and rhs Series """
# ToDo: Different from _align_method_FRAME, list, tuple and ndarray
# are not coerced here
# because Series has inconsistencies described in #13637
if isinstance(right, ABCSeries):
# avoid repeate... | python | def _align_method_SERIES(left, right, align_asobject=False):
""" align lhs and rhs Series """
# ToDo: Different from _align_method_FRAME, list, tuple and ndarray
# are not coerced here
# because Series has inconsistencies described in #13637
if isinstance(right, ABCSeries):
# avoid repeate... | [
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pandas-dev/pandas | pandas/core/ops.py | _construct_result | def _construct_result(left, result, index, name, dtype=None):
"""
If the raw op result has a non-None name (e.g. it is an Index object) and
the name argument is None, then passing name to the constructor will
not be enough; we still need to override the name attribute.
"""
out = left._constructo... | python | def _construct_result(left, result, index, name, dtype=None):
"""
If the raw op result has a non-None name (e.g. it is an Index object) and
the name argument is None, then passing name to the constructor will
not be enough; we still need to override the name attribute.
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pandas-dev/pandas | pandas/core/ops.py | _construct_divmod_result | def _construct_divmod_result(left, result, index, name, dtype=None):
"""divmod returns a tuple of like indexed series instead of a single series.
"""
return (
_construct_result(left, result[0], index=index, name=name,
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_construct_result(left, result[1],... | python | def _construct_divmod_result(left, result, index, name, dtype=None):
"""divmod returns a tuple of like indexed series instead of a single series.
"""
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pandas-dev/pandas | pandas/core/ops.py | _arith_method_SERIES | def _arith_method_SERIES(cls, op, special):
"""
Wrapper function for Series arithmetic operations, to avoid
code duplication.
"""
str_rep = _get_opstr(op, cls)
op_name = _get_op_name(op, special)
eval_kwargs = _gen_eval_kwargs(op_name)
fill_zeros = _gen_fill_zeros(op_name)
construct_... | python | def _arith_method_SERIES(cls, op, special):
"""
Wrapper function for Series arithmetic operations, to avoid
code duplication.
"""
str_rep = _get_opstr(op, cls)
op_name = _get_op_name(op, special)
eval_kwargs = _gen_eval_kwargs(op_name)
fill_zeros = _gen_fill_zeros(op_name)
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pandas-dev/pandas | pandas/core/ops.py | _comp_method_SERIES | def _comp_method_SERIES(cls, op, special):
"""
Wrapper function for Series arithmetic operations, to avoid
code duplication.
"""
op_name = _get_op_name(op, special)
masker = _gen_eval_kwargs(op_name).get('masker', False)
def na_op(x, y):
# TODO:
# should have guarantess on w... | python | def _comp_method_SERIES(cls, op, special):
"""
Wrapper function for Series arithmetic operations, to avoid
code duplication.
"""
op_name = _get_op_name(op, special)
masker = _gen_eval_kwargs(op_name).get('masker', False)
def na_op(x, y):
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pandas-dev/pandas | pandas/core/ops.py | _bool_method_SERIES | def _bool_method_SERIES(cls, op, special):
"""
Wrapper function for Series arithmetic operations, to avoid
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"""
op_name = _get_op_name(op, special)
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"""
Wrapper function for Series arithmetic operations, to avoid
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"""
op_name = _get_op_name(op, special)
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pandas-dev/pandas | pandas/core/ops.py | _combine_series_frame | def _combine_series_frame(self, other, func, fill_value=None, axis=None,
level=None):
"""
Apply binary operator `func` to self, other using alignment and fill
conventions determined by the fill_value, axis, and level kwargs.
Parameters
----------
self : DataFrame
o... | python | def _combine_series_frame(self, other, func, fill_value=None, axis=None,
level=None):
"""
Apply binary operator `func` to self, other using alignment and fill
conventions determined by the fill_value, axis, and level kwargs.
Parameters
----------
self : DataFrame
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pandas-dev/pandas | pandas/core/ops.py | _align_method_FRAME | def _align_method_FRAME(left, right, axis):
""" convert rhs to meet lhs dims if input is list, tuple or np.ndarray """
def to_series(right):
msg = ('Unable to coerce to Series, length must be {req_len}: '
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if axis is not None and left._get_axis_name(axis) == '... | python | def _align_method_FRAME(left, right, axis):
""" convert rhs to meet lhs dims if input is list, tuple or np.ndarray """
def to_series(right):
msg = ('Unable to coerce to Series, length must be {req_len}: '
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pandas-dev/pandas | pandas/core/ops.py | _cast_sparse_series_op | def _cast_sparse_series_op(left, right, opname):
"""
For SparseSeries operation, coerce to float64 if the result is expected
to have NaN or inf values
Parameters
----------
left : SparseArray
right : SparseArray
opname : str
Returns
-------
left : SparseArray
right : Sp... | python | def _cast_sparse_series_op(left, right, opname):
"""
For SparseSeries operation, coerce to float64 if the result is expected
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Parameters
----------
left : SparseArray
right : SparseArray
opname : str
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left : SparseArray
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pandas-dev/pandas | pandas/core/ops.py | _arith_method_SPARSE_SERIES | def _arith_method_SPARSE_SERIES(cls, op, special):
"""
Wrapper function for Series arithmetic operations, to avoid
code duplication.
"""
op_name = _get_op_name(op, special)
def wrapper(self, other):
if isinstance(other, ABCDataFrame):
return NotImplemented
elif isins... | python | def _arith_method_SPARSE_SERIES(cls, op, special):
"""
Wrapper function for Series arithmetic operations, to avoid
code duplication.
"""
op_name = _get_op_name(op, special)
def wrapper(self, other):
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pandas-dev/pandas | pandas/core/ops.py | _arith_method_SPARSE_ARRAY | def _arith_method_SPARSE_ARRAY(cls, op, special):
"""
Wrapper function for Series arithmetic operations, to avoid
code duplication.
"""
op_name = _get_op_name(op, special)
def wrapper(self, other):
from pandas.core.arrays.sparse.array import (
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"""
Wrapper function for Series arithmetic operations, to avoid
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"""
op_name = _get_op_name(op, special)
def wrapper(self, other):
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pandas-dev/pandas | pandas/core/arrays/datetimelike.py | validate_periods | def validate_periods(periods):
"""
If a `periods` argument is passed to the Datetime/Timedelta Array/Index
constructor, cast it to an integer.
Parameters
----------
periods : None, float, int
Returns
-------
periods : None or int
Raises
------
TypeError
if peri... | python | def validate_periods(periods):
"""
If a `periods` argument is passed to the Datetime/Timedelta Array/Index
constructor, cast it to an integer.
Parameters
----------
periods : None, float, int
Returns
-------
periods : None or int
Raises
------
TypeError
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pandas-dev/pandas | pandas/core/arrays/datetimelike.py | validate_endpoints | def validate_endpoints(closed):
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Check that the `closed` argument is among [None, "left", "right"]
Parameters
----------
closed : {None, "left", "right"}
Returns
-------
left_closed : bool
right_closed : bool
Raises
------
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"""
Check that the `closed` argument is among [None, "left", "right"]
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----------
closed : {None, "left", "right"}
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left_closed : bool
right_closed : bool
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pandas-dev/pandas | pandas/core/arrays/datetimelike.py | validate_inferred_freq | def validate_inferred_freq(freq, inferred_freq, freq_infer):
"""
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Parameters
----------
freq : DateOffset or None
inferred_freq : DateOffset or None
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"""
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pandas-dev/pandas | pandas/core/arrays/datetimelike.py | maybe_infer_freq | def maybe_infer_freq(freq):
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"""
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pandas-dev/pandas | pandas/core/arrays/datetimelike.py | _ensure_datetimelike_to_i8 | def _ensure_datetimelike_to_i8(other, to_utc=False):
"""
Helper for coercing an input scalar or array to i8.
Parameters
----------
other : 1d array
to_utc : bool, default False
If True, convert the values to UTC before extracting the i8 values
If False, extract the i8 values dir... | python | def _ensure_datetimelike_to_i8(other, to_utc=False):
"""
Helper for coercing an input scalar or array to i8.
Parameters
----------
other : 1d array
to_utc : bool, default False
If True, convert the values to UTC before extracting the i8 values
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pandas-dev/pandas | pandas/core/arrays/datetimelike.py | AttributesMixin._scalar_from_string | def _scalar_from_string(
self,
value: str,
) -> Union[Period, Timestamp, Timedelta, NaTType]:
"""
Construct a scalar type from a string.
Parameters
----------
value : str
Returns
-------
Period, Timestamp, or Timedelta, or NaT... | python | def _scalar_from_string(
self,
value: str,
) -> Union[Period, Timestamp, Timedelta, NaTType]:
"""
Construct a scalar type from a string.
Parameters
----------
value : str
Returns
-------
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pandas-dev/pandas | pandas/core/arrays/datetimelike.py | AttributesMixin._unbox_scalar | def _unbox_scalar(
self,
value: Union[Period, Timestamp, Timedelta, NaTType],
) -> int:
"""
Unbox the integer value of a scalar `value`.
Parameters
----------
value : Union[Period, Timestamp, Timedelta]
Returns
-------
int
... | python | def _unbox_scalar(
self,
value: Union[Period, Timestamp, Timedelta, NaTType],
) -> int:
"""
Unbox the integer value of a scalar `value`.
Parameters
----------
value : Union[Period, Timestamp, Timedelta]
Returns
-------
int
... | [
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Examples
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pandas-dev/pandas | pandas/core/arrays/datetimelike.py | AttributesMixin._check_compatible_with | def _check_compatible_with(
self,
other: Union[Period, Timestamp, Timedelta, NaTType],
) -> None:
"""
Verify that `self` and `other` are compatible.
* DatetimeArray verifies that the timezones (if any) match
* PeriodArray verifies that the freq matches
... | python | def _check_compatible_with(
self,
other: Union[Period, Timestamp, Timedelta, NaTType],
) -> None:
"""
Verify that `self` and `other` are compatible.
* DatetimeArray verifies that the timezones (if any) match
* PeriodArray verifies that the freq matches
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pandas-dev/pandas | pandas/core/arrays/datetimelike.py | DatelikeOps.strftime | def strftime(self, date_format):
"""
Convert to Index using specified date_format.
Return an Index of formatted strings specified by date_format, which
supports the same string format as the python standard library. Details
of the string format can be found in `python string for... | python | def strftime(self, date_format):
"""
Convert to Index using specified date_format.
Return an Index of formatted strings specified by date_format, which
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pandas-dev/pandas | pandas/core/arrays/datetimelike.py | DatetimeLikeArrayMixin.searchsorted | def searchsorted(self, value, side='left', sorter=None):
"""
Find indices where elements should be inserted to maintain order.
Find the indices into a sorted array `self` such that, if the
corresponding elements in `value` were inserted before the indices,
the order of `self` wo... | python | def searchsorted(self, value, side='left', sorter=None):
"""
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pandas-dev/pandas | pandas/core/arrays/datetimelike.py | DatetimeLikeArrayMixin.repeat | def repeat(self, repeats, *args, **kwargs):
"""
Repeat elements of an array.
See Also
--------
numpy.ndarray.repeat
"""
nv.validate_repeat(args, kwargs)
values = self._data.repeat(repeats)
return type(self)(values.view('i8'), dtype=self.dtype) | python | def repeat(self, repeats, *args, **kwargs):
"""
Repeat elements of an array.
See Also
--------
numpy.ndarray.repeat
"""
nv.validate_repeat(args, kwargs)
values = self._data.repeat(repeats)
return type(self)(values.view('i8'), dtype=self.dtype) | [
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pandas-dev/pandas | pandas/core/arrays/datetimelike.py | DatetimeLikeArrayMixin.value_counts | def value_counts(self, dropna=False):
"""
Return a Series containing counts of unique values.
Parameters
----------
dropna : boolean, default True
Don't include counts of NaT values.
Returns
-------
Series
"""
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Return a Series containing counts of unique values.
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dropna : boolean, default True
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pandas-dev/pandas | pandas/core/arrays/datetimelike.py | DatetimeLikeArrayMixin._maybe_mask_results | def _maybe_mask_results(self, result, fill_value=iNaT, convert=None):
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result : a ndarray
fill_value : object, default iNaT
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pandas-dev/pandas | pandas/core/arrays/datetimelike.py | DatetimeLikeArrayMixin._validate_frequency | def _validate_frequency(cls, index, freq, **kwargs):
"""
Validate that a frequency is compatible with the values of a given
Datetime Array/Index or Timedelta Array/Index
Parameters
----------
index : DatetimeIndex or TimedeltaIndex
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pandas-dev/pandas | pandas/core/arrays/datetimelike.py | DatetimeLikeArrayMixin._add_delta | def _add_delta(self, other):
"""
Add a timedelta-like, Tick or TimedeltaIndex-like object
to self, yielding an int64 numpy array
Parameters
----------
delta : {timedelta, np.timedelta64, Tick,
TimedeltaIndex, ndarray[timedelta64]}
Returns
... | python | def _add_delta(self, other):
"""
Add a timedelta-like, Tick or TimedeltaIndex-like object
to self, yielding an int64 numpy array
Parameters
----------
delta : {timedelta, np.timedelta64, Tick,
TimedeltaIndex, ndarray[timedelta64]}
Returns
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pandas-dev/pandas | pandas/core/arrays/datetimelike.py | DatetimeLikeArrayMixin._add_timedeltalike_scalar | def _add_timedeltalike_scalar(self, other):
"""
Add a delta of a timedeltalike
return the i8 result view
"""
if isna(other):
# i.e np.timedelta64("NaT"), not recognized by delta_to_nanoseconds
new_values = np.empty(len(self), dtype='i8')
new_va... | python | def _add_timedeltalike_scalar(self, other):
"""
Add a delta of a timedeltalike
return the i8 result view
"""
if isna(other):
# i.e np.timedelta64("NaT"), not recognized by delta_to_nanoseconds
new_values = np.empty(len(self), dtype='i8')
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pandas-dev/pandas | pandas/core/arrays/datetimelike.py | DatetimeLikeArrayMixin._add_delta_tdi | def _add_delta_tdi(self, other):
"""
Add a delta of a TimedeltaIndex
return the i8 result view
"""
if len(self) != len(other):
raise ValueError("cannot add indices of unequal length")
if isinstance(other, np.ndarray):
# ndarray[timedelta64]; wrap ... | python | def _add_delta_tdi(self, other):
"""
Add a delta of a TimedeltaIndex
return the i8 result view
"""
if len(self) != len(other):
raise ValueError("cannot add indices of unequal length")
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# ndarray[timedelta64]; wrap ... | [
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pandas-dev/pandas | pandas/core/arrays/datetimelike.py | DatetimeLikeArrayMixin._add_nat | def _add_nat(self):
"""
Add pd.NaT to self
"""
if is_period_dtype(self):
raise TypeError('Cannot add {cls} and {typ}'
.format(cls=type(self).__name__,
typ=type(NaT).__name__))
# GH#19124 pd.NaT is treate... | python | def _add_nat(self):
"""
Add pd.NaT to self
"""
if is_period_dtype(self):
raise TypeError('Cannot add {cls} and {typ}'
.format(cls=type(self).__name__,
typ=type(NaT).__name__))
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pandas-dev/pandas | pandas/core/arrays/datetimelike.py | DatetimeLikeArrayMixin._sub_nat | def _sub_nat(self):
"""
Subtract pd.NaT from self
"""
# GH#19124 Timedelta - datetime is not in general well-defined.
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"""
Subtract pd.NaT from self
"""
# GH#19124 Timedelta - datetime is not in general well-defined.
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pandas-dev/pandas | pandas/core/arrays/datetimelike.py | DatetimeLikeArrayMixin._sub_period_array | def _sub_period_array(self, other):
"""
Subtract a Period Array/Index from self. This is only valid if self
is itself a Period Array/Index, raises otherwise. Both objects must
have the same frequency.
Parameters
----------
other : PeriodIndex or PeriodArray
... | python | def _sub_period_array(self, other):
"""
Subtract a Period Array/Index from self. This is only valid if self
is itself a Period Array/Index, raises otherwise. Both objects must
have the same frequency.
Parameters
----------
other : PeriodIndex or PeriodArray
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pandas-dev/pandas | pandas/core/arrays/datetimelike.py | DatetimeLikeArrayMixin._addsub_int_array | def _addsub_int_array(self, other, op):
"""
Add or subtract array-like of integers equivalent to applying
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Parameters
----------
other : Index, ExtensionArray, np.ndarray
integer-dtype
op : {operator.add, operator.sub}
... | python | def _addsub_int_array(self, other, op):
"""
Add or subtract array-like of integers equivalent to applying
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Parameters
----------
other : Index, ExtensionArray, np.ndarray
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pandas-dev/pandas | pandas/core/arrays/datetimelike.py | DatetimeLikeArrayMixin._addsub_offset_array | def _addsub_offset_array(self, other, op):
"""
Add or subtract array-like of DateOffset objects
Parameters
----------
other : Index, np.ndarray
object-dtype containing pd.DateOffset objects
op : {operator.add, operator.sub}
Returns
-------
... | python | def _addsub_offset_array(self, other, op):
"""
Add or subtract array-like of DateOffset objects
Parameters
----------
other : Index, np.ndarray
object-dtype containing pd.DateOffset objects
op : {operator.add, operator.sub}
Returns
-------
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pandas-dev/pandas | pandas/core/arrays/datetimelike.py | DatetimeLikeArrayMixin._time_shift | def _time_shift(self, periods, freq=None):
"""
Shift each value by `periods`.
Note this is different from ExtensionArray.shift, which
shifts the *position* of each element, padding the end with
missing values.
Parameters
----------
periods : int
... | python | def _time_shift(self, periods, freq=None):
"""
Shift each value by `periods`.
Note this is different from ExtensionArray.shift, which
shifts the *position* of each element, padding the end with
missing values.
Parameters
----------
periods : int
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pandas-dev/pandas | pandas/core/arrays/datetimelike.py | DatetimeLikeArrayMixin._ensure_localized | def _ensure_localized(self, arg, ambiguous='raise', nonexistent='raise',
from_utc=False):
"""
Ensure that we are re-localized.
This is for compat as we can then call this on all datetimelike
arrays generally (ignored for Period/Timedelta)
Parameters
... | python | def _ensure_localized(self, arg, ambiguous='raise', nonexistent='raise',
from_utc=False):
"""
Ensure that we are re-localized.
This is for compat as we can then call this on all datetimelike
arrays generally (ignored for Period/Timedelta)
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pandas-dev/pandas | pandas/core/arrays/datetimelike.py | DatetimeLikeArrayMixin.min | def min(self, axis=None, skipna=True, *args, **kwargs):
"""
Return the minimum value of the Array or minimum along
an axis.
See Also
--------
numpy.ndarray.min
Index.min : Return the minimum value in an Index.
Series.min : Return the minimum value in a Se... | python | def min(self, axis=None, skipna=True, *args, **kwargs):
"""
Return the minimum value of the Array or minimum along
an axis.
See Also
--------
numpy.ndarray.min
Index.min : Return the minimum value in an Index.
Series.min : Return the minimum value in a Se... | [
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pandas-dev/pandas | pandas/core/arrays/datetimelike.py | DatetimeLikeArrayMixin.max | def max(self, axis=None, skipna=True, *args, **kwargs):
"""
Return the maximum value of the Array or maximum along
an axis.
See Also
--------
numpy.ndarray.max
Index.max : Return the maximum value in an Index.
Series.max : Return the maximum value in a Se... | python | def max(self, axis=None, skipna=True, *args, **kwargs):
"""
Return the maximum value of the Array or maximum along
an axis.
See Also
--------
numpy.ndarray.max
Index.max : Return the maximum value in an Index.
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pandas-dev/pandas | pandas/core/arrays/period.py | _period_array_cmp | def _period_array_cmp(cls, op):
"""
Wrap comparison operations to convert Period-like to PeriodDtype
"""
opname = '__{name}__'.format(name=op.__name__)
nat_result = opname == '__ne__'
def wrapper(self, other):
op = getattr(self.asi8, opname)
if isinstance(other, (ABCDataFrame, ... | python | def _period_array_cmp(cls, op):
"""
Wrap comparison operations to convert Period-like to PeriodDtype
"""
opname = '__{name}__'.format(name=op.__name__)
nat_result = opname == '__ne__'
def wrapper(self, other):
op = getattr(self.asi8, opname)
if isinstance(other, (ABCDataFrame, ... | [
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pandas-dev/pandas | pandas/core/arrays/period.py | _raise_on_incompatible | def _raise_on_incompatible(left, right):
"""
Helper function to render a consistent error message when raising
IncompatibleFrequency.
Parameters
----------
left : PeriodArray
right : DateOffset, Period, ndarray, or timedelta-like
Raises
------
IncompatibleFrequency
"""
... | python | def _raise_on_incompatible(left, right):
"""
Helper function to render a consistent error message when raising
IncompatibleFrequency.
Parameters
----------
left : PeriodArray
right : DateOffset, Period, ndarray, or timedelta-like
Raises
------
IncompatibleFrequency
"""
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pandas-dev/pandas | pandas/core/arrays/period.py | period_array | def period_array(
data: Sequence[Optional[Period]],
freq: Optional[Tick] = None,
copy: bool = False,
) -> PeriodArray:
"""
Construct a new PeriodArray from a sequence of Period scalars.
Parameters
----------
data : Sequence of Period objects
A sequence of Period obje... | python | def period_array(
data: Sequence[Optional[Period]],
freq: Optional[Tick] = None,
copy: bool = False,
) -> PeriodArray:
"""
Construct a new PeriodArray from a sequence of Period scalars.
Parameters
----------
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pandas-dev/pandas | pandas/core/arrays/period.py | validate_dtype_freq | def validate_dtype_freq(dtype, freq):
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Parameters
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dtype : dtype
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"""
If both a dtype and a freq are available, ensure they match. If only
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Parameters
----------
dtype : dtype
freq : DateOffset or None
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freq : DateOffset
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pandas-dev/pandas | pandas/core/arrays/period.py | dt64arr_to_periodarr | def dt64arr_to_periodarr(data, freq, tz=None):
"""
Convert an datetime-like array to values Period ordinals.
Parameters
----------
data : Union[Series[datetime64[ns]], DatetimeIndex, ndarray[datetime64ns]]
freq : Optional[Union[str, Tick]]
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"""
Convert an datetime-like array to values Period ordinals.
Parameters
----------
data : Union[Series[datetime64[ns]], DatetimeIndex, ndarray[datetime64ns]]
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pandas-dev/pandas | pandas/core/arrays/period.py | PeriodArray._from_datetime64 | def _from_datetime64(cls, data, freq, tz=None):
"""
Construct a PeriodArray from a datetime64 array
Parameters
----------
data : ndarray[datetime64[ns], datetime64[ns, tz]]
freq : str or Tick
tz : tzinfo, optional
Returns
-------
PeriodAr... | python | def _from_datetime64(cls, data, freq, tz=None):
"""
Construct a PeriodArray from a datetime64 array
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----------
data : ndarray[datetime64[ns], datetime64[ns, tz]]
freq : str or Tick
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pandas-dev/pandas | pandas/core/arrays/period.py | PeriodArray.to_timestamp | def to_timestamp(self, freq=None, how='start'):
"""
Cast to DatetimeArray/Index.
Parameters
----------
freq : string or DateOffset, optional
Target frequency. The default is 'D' for week or longer,
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"""
Cast to DatetimeArray/Index.
Parameters
----------
freq : string or DateOffset, optional
Target frequency. The default is 'D' for week or longer,
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pandas-dev/pandas | pandas/core/arrays/period.py | PeriodArray._time_shift | def _time_shift(self, periods, freq=None):
"""
Shift each value by `periods`.
Note this is different from ExtensionArray.shift, which
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Parameters
----------
periods : int
... | python | def _time_shift(self, periods, freq=None):
"""
Shift each value by `periods`.
Note this is different from ExtensionArray.shift, which
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Parameters
----------
periods : int
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pandas-dev/pandas | pandas/core/arrays/period.py | PeriodArray.asfreq | def asfreq(self, freq=None, how='E'):
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----------
freq : str
a frequency
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"""
Convert the Period Array/Index to the specified frequency `freq`.
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freq : str
a frequency
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pandas-dev/pandas | pandas/core/arrays/period.py | PeriodArray._format_native_types | def _format_native_types(self, na_rep='NaT', date_format=None, **kwargs):
"""
actually format my specific types
"""
values = self.astype(object)
if date_format:
formatter = lambda dt: dt.strftime(date_format)
else:
formatter = lambda dt: '%s' % dt... | python | def _format_native_types(self, na_rep='NaT', date_format=None, **kwargs):
"""
actually format my specific types
"""
values = self.astype(object)
if date_format:
formatter = lambda dt: dt.strftime(date_format)
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pandas-dev/pandas | pandas/core/arrays/period.py | PeriodArray._add_timedeltalike_scalar | def _add_timedeltalike_scalar(self, other):
"""
Parameters
----------
other : timedelta, Tick, np.timedelta64
Returns
-------
result : ndarray[int64]
"""
assert isinstance(self.freq, Tick) # checked by calling function
assert isinstance(o... | python | def _add_timedeltalike_scalar(self, other):
"""
Parameters
----------
other : timedelta, Tick, np.timedelta64
Returns
-------
result : ndarray[int64]
"""
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pandas-dev/pandas | pandas/core/arrays/period.py | PeriodArray._add_delta_tdi | def _add_delta_tdi(self, other):
"""
Parameters
----------
other : TimedeltaArray or ndarray[timedelta64]
Returns
-------
result : ndarray[int64]
"""
assert isinstance(self.freq, Tick) # checked by calling function
delta = self._check_ti... | python | def _add_delta_tdi(self, other):
"""
Parameters
----------
other : TimedeltaArray or ndarray[timedelta64]
Returns
-------
result : ndarray[int64]
"""
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pandas-dev/pandas | pandas/core/arrays/period.py | PeriodArray._add_delta | def _add_delta(self, other):
"""
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Parameters
----------
other : {timedelta, np.timedelta64, Tick,
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Returns
... | python | def _add_delta(self, other):
"""
Add a timedelta-like, Tick, or TimedeltaIndex-like object
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----------
other : {timedelta, np.timedelta64, Tick,
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pandas-dev/pandas | pandas/core/arrays/period.py | PeriodArray._check_timedeltalike_freq_compat | def _check_timedeltalike_freq_compat(self, other):
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pandas-dev/pandas | pandas/core/dtypes/missing.py | _isna_old | def _isna_old(obj):
"""Detect missing values. Treat None, NaN, INF, -INF as null.
Parameters
----------
arr: ndarray or object value
Returns
-------
boolean ndarray or boolean
"""
if is_scalar(obj):
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# hack (for now) because MI regist... | python | def _isna_old(obj):
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Parameters
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arr: ndarray or object value
Returns
-------
boolean ndarray or boolean
"""
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pandas-dev/pandas | pandas/core/dtypes/missing.py | _use_inf_as_na | def _use_inf_as_na(key):
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Parameters
----------
flag: bool
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pandas-dev/pandas | pandas/core/dtypes/missing.py | _isna_compat | def _isna_compat(arr, fill_value=np.nan):
"""
Parameters
----------
arr: a numpy array
fill_value: fill value, default to np.nan
Returns
-------
True if we can fill using this fill_value
"""
dtype = arr.dtype
if isna(fill_value):
return not (is_bool_dtype(dtype) or
... | python | def _isna_compat(arr, fill_value=np.nan):
"""
Parameters
----------
arr: a numpy array
fill_value: fill value, default to np.nan
Returns
-------
True if we can fill using this fill_value
"""
dtype = arr.dtype
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pandas-dev/pandas | pandas/core/dtypes/missing.py | _maybe_fill | def _maybe_fill(arr, fill_value=np.nan):
"""
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if _isna_compat(arr, fill_value):
arr.fill(fill_value)
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pandas-dev/pandas | pandas/core/dtypes/missing.py | na_value_for_dtype | def na_value_for_dtype(dtype, compat=True):
"""
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Parameters
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dtype : string / dtype
compat : boolean, default True
Returns
-------
np.dtype or a pandas dtype
Examples
--------
>>> na_value_for_dtype(np.dtype('int64'))
0
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"""
Return a dtype compat na value
Parameters
----------
dtype : string / dtype
compat : boolean, default True
Returns
-------
np.dtype or a pandas dtype
Examples
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>>> na_value_for_dtype(np.dtype('int64'))
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pandas-dev/pandas | pandas/core/dtypes/missing.py | remove_na_arraylike | def remove_na_arraylike(arr):
"""
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return arr[notna(lib.values_from_object(arr))] | python | def remove_na_arraylike(arr):
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Return array-like containing only true/non-NaN values, possibly empty.
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pandas-dev/pandas | pandas/plotting/_tools.py | table | def table(ax, data, rowLabels=None, colLabels=None, **kwargs):
"""
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----------
ax : Matplotlib axes object
data : DataFrame or Series
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"""
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ax : Matplotlib axes object
data : DataFrame or Series
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pandas-dev/pandas | pandas/plotting/_tools.py | _subplots | def _subplots(naxes=None, sharex=False, sharey=False, squeeze=True,
subplot_kw=None, ax=None, layout=None, layout_type='box',
**fig_kw):
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subplots, includi... | python | def _subplots(naxes=None, sharex=False, sharey=False, squeeze=True,
subplot_kw=None, ax=None, layout=None, layout_type='box',
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pandas-dev/pandas | setup.py | maybe_cythonize | def maybe_cythonize(extensions, *args, **kwargs):
"""
Render tempita templates before calling cythonize
"""
if len(sys.argv) > 1 and 'clean' in sys.argv:
# Avoid running cythonize on `python setup.py clean`
# See https://github.com/cython/cython/issues/1495
return extensions
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"""
Render tempita templates before calling cythonize
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pandas-dev/pandas | pandas/core/groupby/generic.py | NDFrameGroupBy._transform_fast | def _transform_fast(self, result, obj, func_nm):
"""
Fast transform path for aggregations
"""
# if there were groups with no observations (Categorical only?)
# try casting data to original dtype
cast = self._transform_should_cast(func_nm)
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"""
Fast transform path for aggregations
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# if there were groups with no observations (Categorical only?)
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pandas-dev/pandas | pandas/core/groupby/generic.py | SeriesGroupBy._wrap_output | def _wrap_output(self, output, index, names=None):
""" common agg/transform wrapping logic """
output = output[self._selection_name]
if names is not None:
return DataFrame(output, index=index, columns=names)
else:
name = self._selection_name
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""" common agg/transform wrapping logic """
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pandas-dev/pandas | pandas/core/groupby/generic.py | SeriesGroupBy._transform_fast | def _transform_fast(self, func, func_nm):
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pandas-dev/pandas | pandas/core/groupby/generic.py | SeriesGroupBy.nunique | def nunique(self, dropna=True):
"""
Return number of unique elements in the group.
"""
ids, _, _ = self.grouper.group_info
val = self.obj.get_values()
try:
sorter = np.lexsort((val, ids))
except TypeError: # catches object dtypes
msg = '... | python | def nunique(self, dropna=True):
"""
Return number of unique elements in the group.
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ids, _, _ = self.grouper.group_info
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pandas-dev/pandas | pandas/core/groupby/generic.py | SeriesGroupBy.count | def count(self):
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pandas-dev/pandas | pandas/core/groupby/generic.py | SeriesGroupBy.pct_change | def pct_change(self, periods=1, fill_method='pad', limit=None, freq=None):
"""Calcuate pct_change of each value to previous entry in group"""
# TODO: Remove this conditional when #23918 is fixed
if freq:
return self.apply(lambda x: x.pct_change(periods=periods,
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"""Calcuate pct_change of each value to previous entry in group"""
# TODO: Remove this conditional when #23918 is fixed
if freq:
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pandas-dev/pandas | pandas/core/groupby/generic.py | DataFrameGroupBy._gotitem | def _gotitem(self, key, ndim, subset=None):
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requested ndim of result
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pandas-dev/pandas | pandas/core/groupby/generic.py | DataFrameGroupBy._reindex_output | def _reindex_output(self, result):
"""
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"""
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pandas-dev/pandas | pandas/core/groupby/generic.py | DataFrameGroupBy._fill | def _fill(self, direction, limit=None):
"""Overridden method to join grouped columns in output"""
res = super()._fill(direction, limit=limit)
output = OrderedDict(
(grp.name, grp.grouper) for grp in self.grouper.groupings)
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return concat((sel... | python | def _fill(self, direction, limit=None):
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pandas-dev/pandas | pandas/core/groupby/generic.py | DataFrameGroupBy.count | def count(self):
""" Compute count of group, excluding missing values """
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""" Compute count of group, excluding missing values """
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ids, _, ngroups = self.grouper.group_info
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pandas-dev/pandas | pandas/core/groupby/generic.py | DataFrameGroupBy.nunique | def nunique(self, dropna=True):
"""
Return DataFrame with number of distinct observations per group for
each column.
.. versionadded:: 0.20.0
Parameters
----------
dropna : boolean, default True
Don't include NaN in the counts.
Returns
... | python | def nunique(self, dropna=True):
"""
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dropna : boolean, default True
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pandas-dev/pandas | pandas/core/internals/arrays.py | extract_array | def extract_array(obj, extract_numpy=False):
"""
Extract the ndarray or ExtensionArray from a Series or Index.
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----------
obj : object
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pandas-dev/pandas | pandas/core/common.py | flatten | def flatten(l):
"""
Flatten an arbitrarily nested sequence.
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----------
l : sequence
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-----
This doesn't consider strings sequences.
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-------
flattened : generator
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pandas-dev/pandas | pandas/core/common.py | is_bool_indexer | def is_bool_indexer(key: Any) -> bool:
"""
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----------
key : Any
Only list-likes may be considered boolean indexers.
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pandas-dev/pandas | pandas/core/common.py | cast_scalar_indexer | def cast_scalar_indexer(val):
"""
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----------
val : scalar
Returns
-------
outval : scalar
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# assumes lib.is_scalar(val)
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return int(val)
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"""
To avoid numpy DeprecationWarnings, cast float to integer where valid.
Parameters
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val : scalar
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-------
outval : scalar
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pandas-dev/pandas | pandas/core/common.py | index_labels_to_array | def index_labels_to_array(labels, dtype=None):
"""
Transform label or iterable of labels to array, for use in Index.
Parameters
----------
dtype : dtype
If specified, use as dtype of the resulting array, otherwise infer.
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-------
array
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"""
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dtype : dtype
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array
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pandas-dev/pandas | pandas/core/common.py | is_null_slice | def is_null_slice(obj):
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pandas-dev/pandas | pandas/core/common.py | apply_if_callable | def apply_if_callable(maybe_callable, obj, **kwargs):
"""
Evaluate possibly callable input using obj and kwargs if it is callable,
otherwise return as it is.
Parameters
----------
maybe_callable : possibly a callable
obj : NDFrame
**kwargs
"""
if callable(maybe_callable):
... | python | def apply_if_callable(maybe_callable, obj, **kwargs):
"""
Evaluate possibly callable input using obj and kwargs if it is callable,
otherwise return as it is.
Parameters
----------
maybe_callable : possibly a callable
obj : NDFrame
**kwargs
"""
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pandas-dev/pandas | pandas/core/common.py | standardize_mapping | def standardize_mapping(into):
"""
Helper function to standardize a supplied mapping.
.. versionadded:: 0.21.0
Parameters
----------
into : instance or subclass of collections.abc.Mapping
Must be a class, an initialized collections.defaultdict,
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"""
Helper function to standardize a supplied mapping.
.. versionadded:: 0.21.0
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----------
into : instance or subclass of collections.abc.Mapping
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pandas-dev/pandas | pandas/core/common.py | random_state | def random_state(state=None):
"""
Helper function for processing random_state arguments.
Parameters
----------
state : int, np.random.RandomState, None.
If receives an int, passes to np.random.RandomState() as seed.
If receives an np.random.RandomState object, just returns object.
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"""
Helper function for processing random_state arguments.
Parameters
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state : int, np.random.RandomState, None.
If receives an int, passes to np.random.RandomState() as seed.
If receives an np.random.RandomState object, just returns object.
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pandas-dev/pandas | pandas/core/common.py | _get_rename_function | def _get_rename_function(mapper):
"""
Returns a function that will map names/labels, dependent if mapper
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"""
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"""
Returns a function that will map names/labels, dependent if mapper
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"""
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def f(x):
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pandas-dev/pandas | pandas/core/nanops.py | _get_fill_value | def _get_fill_value(dtype, fill_value=None, fill_value_typ=None):
""" return the correct fill value for the dtype of the values """
if fill_value is not None:
return fill_value
if _na_ok_dtype(dtype):
if fill_value_typ is None:
return np.nan
else:
if fill_valu... | python | def _get_fill_value(dtype, fill_value=None, fill_value_typ=None):
""" return the correct fill value for the dtype of the values """
if fill_value is not None:
return fill_value
if _na_ok_dtype(dtype):
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return np.nan
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pandas-dev/pandas | pandas/core/nanops.py | _get_values | def _get_values(values, skipna, fill_value=None, fill_value_typ=None,
isfinite=False, copy=True, mask=None):
""" utility to get the values view, mask, dtype
if necessary copy and mask using the specified fill_value
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"""
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""" utility to get the values view, mask, dtype
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pandas-dev/pandas | pandas/core/nanops.py | _wrap_results | def _wrap_results(result, dtype, fill_value=None):
""" wrap our results if needed """
if is_datetime64_dtype(dtype) or is_datetime64tz_dtype(dtype):
if fill_value is None:
# GH#24293
fill_value = iNaT
if not isinstance(result, np.ndarray):
tz = getattr(dtype,... | python | def _wrap_results(result, dtype, fill_value=None):
""" wrap our results if needed """
if is_datetime64_dtype(dtype) or is_datetime64tz_dtype(dtype):
if fill_value is None:
# GH#24293
fill_value = iNaT
if not isinstance(result, np.ndarray):
tz = getattr(dtype,... | [
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pandas-dev/pandas | pandas/core/nanops.py | _na_for_min_count | def _na_for_min_count(values, axis):
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Parameters
----------
values : ndarray
axis : int or None
axis for the reduction
Returns
-------
result : scalar or ndarray
For 1-D values, returns a scalar of the correct missing type.
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Parameters
----------
values : ndarray
axis : int or None
axis for the reduction
Returns
-------
result : scalar or ndarray
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pandas-dev/pandas | pandas/core/nanops.py | nanany | def nanany(values, axis=None, skipna=True, mask=None):
"""
Check if any elements along an axis evaluate to True.
Parameters
----------
values : ndarray
axis : int, optional
skipna : bool, default True
mask : ndarray[bool], optional
nan-mask if known
Returns
-------
... | python | def nanany(values, axis=None, skipna=True, mask=None):
"""
Check if any elements along an axis evaluate to True.
Parameters
----------
values : ndarray
axis : int, optional
skipna : bool, default True
mask : ndarray[bool], optional
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pandas-dev/pandas | pandas/core/nanops.py | nanall | def nanall(values, axis=None, skipna=True, mask=None):
"""
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Parameters
----------
values : ndarray
axis: int, optional
skipna : bool, default True
mask : ndarray[bool], optional
nan-mask if known
Returns
-------
r... | python | def nanall(values, axis=None, skipna=True, mask=None):
"""
Check if all elements along an axis evaluate to True.
Parameters
----------
values : ndarray
axis: int, optional
skipna : bool, default True
mask : ndarray[bool], optional
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pandas-dev/pandas | pandas/core/nanops.py | nansum | def nansum(values, axis=None, skipna=True, min_count=0, mask=None):
"""
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Parameters
----------
values : ndarray[dtype]
axis: int, optional
skipna : bool, default True
min_count: int, default 0
mask : ndarray[bool], optional
nan-mas... | python | def nansum(values, axis=None, skipna=True, min_count=0, mask=None):
"""
Sum the elements along an axis ignoring NaNs
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----------
values : ndarray[dtype]
axis: int, optional
skipna : bool, default True
min_count: int, default 0
mask : ndarray[bool], optional
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pandas-dev/pandas | pandas/core/nanops.py | nanmean | def nanmean(values, axis=None, skipna=True, mask=None):
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Parameters
----------
values : ndarray
axis: int, optional
skipna : bool, default True
mask : ndarray[bool], optional
nan-mask if known
Returns
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"""
Compute the mean of the element along an axis ignoring NaNs
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----------
values : ndarray
axis: int, optional
skipna : bool, default True
mask : ndarray[bool], optional
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pandas-dev/pandas | pandas/core/nanops.py | nanmedian | def nanmedian(values, axis=None, skipna=True, mask=None):
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----------
values : ndarray
axis: int, optional
skipna : bool, default True
mask : ndarray[bool], optional
nan-mask if known
Returns
-------
result : float
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"""
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values : ndarray
axis: int, optional
skipna : bool, default True
mask : ndarray[bool], optional
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result : float
Unless input is a float array, in which case use the same
precision as the input array.
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pandas-dev/pandas | pandas/core/nanops.py | nanstd | def nanstd(values, axis=None, skipna=True, ddof=1, mask=None):
"""
Compute the standard deviation along given axis while ignoring NaNs
Parameters
----------
values : ndarray
axis: int, optional
skipna : bool, default True
ddof : int, default 1
Delta Degrees of Freedom. The divis... | python | def nanstd(values, axis=None, skipna=True, ddof=1, mask=None):
"""
Compute the standard deviation along given axis while ignoring NaNs
Parameters
----------
values : ndarray
axis: int, optional
skipna : bool, default True
ddof : int, default 1
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Parameters
----------
values : ndarray
axis: int, optional
skipna : bool, default True
ddof : int, default 1
Delta Degrees of Freedom. The divisor used in calculations is N - ddof,
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