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Wrapper function for Series arithmetic operations, to avoid
code duplication. | def _bool_method_SERIES(cls, op, special):
"""
Wrapper function for Series arithmetic operations, to avoid
code duplication.
"""
op_name = _get_op_name(op, special)
def na_op(x, y):
try:
result = op(x, y)
except TypeError:
assert not isinstance(y, (list, ABCSeries, ABCIndexClass))
if isinstance(y, np.ndarray):
# bool-bool dtype operations should be OK, should not get here
assert not (is_bool_dtype(x) and is_bool_dtype(y))
x = ensure_object(x)
y = ensure_object(y)
result = libops.vec_binop(x, y, op)
else:
# let null fall thru
assert lib.is_scalar(y)
if not isna(y):
y = bool(y)
try:
result = libops.scalar_binop(x, y, op)
except (TypeError, ValueError, AttributeError,
OverflowError, NotImplementedError):
raise TypeError("cannot compare a dtyped [{dtype}] array "
"with a scalar of type [{typ}]"
.format(dtype=x.dtype,
typ=type(y).__name__))
return result
fill_int = lambda x: x.fillna(0)
fill_bool = lambda x: x.fillna(False).astype(bool)
def wrapper(self, other):
is_self_int_dtype = is_integer_dtype(self.dtype)
self, other = _align_method_SERIES(self, other, align_asobject=True)
res_name = get_op_result_name(self, other)
if isinstance(other, ABCDataFrame):
# Defer to DataFrame implementation; fail early
return NotImplemented
elif isinstance(other, (ABCSeries, ABCIndexClass)):
is_other_int_dtype = is_integer_dtype(other.dtype)
other = fill_int(other) if is_other_int_dtype else fill_bool(other)
ovalues = other.values
finalizer = lambda x: x
else:
# scalars, list, tuple, np.array
is_other_int_dtype = is_integer_dtype(np.asarray(other))
if is_list_like(other) and not isinstance(other, np.ndarray):
# TODO: Can we do this before the is_integer_dtype check?
# could the is_integer_dtype check be checking the wrong
# thing? e.g. other = [[0, 1], [2, 3], [4, 5]]?
other = construct_1d_object_array_from_listlike(other)
ovalues = other
finalizer = lambda x: x.__finalize__(self)
# For int vs int `^`, `|`, `&` are bitwise operators and return
# integer dtypes. Otherwise these are boolean ops
filler = (fill_int if is_self_int_dtype and is_other_int_dtype
else fill_bool)
res_values = na_op(self.values, ovalues)
unfilled = self._constructor(res_values,
index=self.index, name=res_name)
filled = filler(unfilled)
return finalizer(filled)
wrapper.__name__ = op_name
return wrapper |
Apply binary operator `func` to self, other using alignment and fill
conventions determined by the fill_value, axis, and level kwargs.
Parameters
----------
self : DataFrame
other : Series
func : binary operator
fill_value : object, default None
axis : {0, 1, 'columns', 'index', None}, default None
level : int or None, default None
Returns
-------
result : DataFrame | 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
other : Series
func : binary operator
fill_value : object, default None
axis : {0, 1, 'columns', 'index', None}, default None
level : int or None, default None
Returns
-------
result : DataFrame
"""
if fill_value is not None:
raise NotImplementedError("fill_value {fill} not supported."
.format(fill=fill_value))
if axis is not None:
axis = self._get_axis_number(axis)
if axis == 0:
return self._combine_match_index(other, func, level=level)
else:
return self._combine_match_columns(other, func, level=level)
else:
if not len(other):
return self * np.nan
if not len(self):
# Ambiguous case, use _series so works with DataFrame
return self._constructor(data=self._series, index=self.index,
columns=self.columns)
# default axis is columns
return self._combine_match_columns(other, func, level=level) |
convert rhs to meet lhs dims if input is list, tuple or np.ndarray | 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}: '
'given {given_len}')
if axis is not None and left._get_axis_name(axis) == 'index':
if len(left.index) != len(right):
raise ValueError(msg.format(req_len=len(left.index),
given_len=len(right)))
right = left._constructor_sliced(right, index=left.index)
else:
if len(left.columns) != len(right):
raise ValueError(msg.format(req_len=len(left.columns),
given_len=len(right)))
right = left._constructor_sliced(right, index=left.columns)
return right
if isinstance(right, np.ndarray):
if right.ndim == 1:
right = to_series(right)
elif right.ndim == 2:
if right.shape == left.shape:
right = left._constructor(right, index=left.index,
columns=left.columns)
elif right.shape[0] == left.shape[0] and right.shape[1] == 1:
# Broadcast across columns
right = np.broadcast_to(right, left.shape)
right = left._constructor(right,
index=left.index,
columns=left.columns)
elif right.shape[1] == left.shape[1] and right.shape[0] == 1:
# Broadcast along rows
right = to_series(right[0, :])
else:
raise ValueError("Unable to coerce to DataFrame, shape "
"must be {req_shape}: given {given_shape}"
.format(req_shape=left.shape,
given_shape=right.shape))
elif right.ndim > 2:
raise ValueError('Unable to coerce to Series/DataFrame, dim '
'must be <= 2: {dim}'.format(dim=right.shape))
elif (is_list_like(right) and
not isinstance(right, (ABCSeries, ABCDataFrame))):
# GH17901
right = to_series(right)
return right |
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 : SparseArray | 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 : SparseArray
"""
from pandas.core.sparse.api import SparseDtype
opname = opname.strip('_')
# TODO: This should be moved to the array?
if is_integer_dtype(left) and is_integer_dtype(right):
# series coerces to float64 if result should have NaN/inf
if opname in ('floordiv', 'mod') and (right.values == 0).any():
left = left.astype(SparseDtype(np.float64, left.fill_value))
right = right.astype(SparseDtype(np.float64, right.fill_value))
elif opname in ('rfloordiv', 'rmod') and (left.values == 0).any():
left = left.astype(SparseDtype(np.float64, left.fill_value))
right = right.astype(SparseDtype(np.float64, right.fill_value))
return left, right |
Wrapper function for Series arithmetic operations, to avoid
code duplication. | 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 isinstance(other, ABCSeries):
if not isinstance(other, ABCSparseSeries):
other = other.to_sparse(fill_value=self.fill_value)
return _sparse_series_op(self, other, op, op_name)
elif is_scalar(other):
with np.errstate(all='ignore'):
new_values = op(self.values, other)
return self._constructor(new_values,
index=self.index,
name=self.name)
else: # pragma: no cover
raise TypeError('operation with {other} not supported'
.format(other=type(other)))
wrapper.__name__ = op_name
return wrapper |
Wrapper function for Series arithmetic operations, to avoid
code duplication. | 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 (
SparseArray, _sparse_array_op, _wrap_result, _get_fill)
if isinstance(other, np.ndarray):
if len(self) != len(other):
raise AssertionError("length mismatch: {self} vs. {other}"
.format(self=len(self), other=len(other)))
if not isinstance(other, SparseArray):
dtype = getattr(other, 'dtype', None)
other = SparseArray(other, fill_value=self.fill_value,
dtype=dtype)
return _sparse_array_op(self, other, op, op_name)
elif is_scalar(other):
with np.errstate(all='ignore'):
fill = op(_get_fill(self), np.asarray(other))
result = op(self.sp_values, other)
return _wrap_result(op_name, result, self.sp_index, fill)
else: # pragma: no cover
raise TypeError('operation with {other} not supported'
.format(other=type(other)))
wrapper.__name__ = op_name
return wrapper |
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 periods is None, float, or int | 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 periods is None, float, or int
"""
if periods is not None:
if lib.is_float(periods):
periods = int(periods)
elif not lib.is_integer(periods):
raise TypeError('periods must be a number, got {periods}'
.format(periods=periods))
return periods |
Check that the `closed` argument is among [None, "left", "right"]
Parameters
----------
closed : {None, "left", "right"}
Returns
-------
left_closed : bool
right_closed : bool
Raises
------
ValueError : if argument is not among valid values | def validate_endpoints(closed):
"""
Check that the `closed` argument is among [None, "left", "right"]
Parameters
----------
closed : {None, "left", "right"}
Returns
-------
left_closed : bool
right_closed : bool
Raises
------
ValueError : if argument is not among valid values
"""
left_closed = False
right_closed = False
if closed is None:
left_closed = True
right_closed = True
elif closed == "left":
left_closed = True
elif closed == "right":
right_closed = True
else:
raise ValueError("Closed has to be either 'left', 'right' or None")
return left_closed, right_closed |
If the user passes a freq and another freq is inferred from passed data,
require that they match.
Parameters
----------
freq : DateOffset or None
inferred_freq : DateOffset or None
freq_infer : bool
Returns
-------
freq : DateOffset or None
freq_infer : bool
Notes
-----
We assume at this point that `maybe_infer_freq` has been called, so
`freq` is either a DateOffset object or None. | def validate_inferred_freq(freq, inferred_freq, freq_infer):
"""
If the user passes a freq and another freq is inferred from passed data,
require that they match.
Parameters
----------
freq : DateOffset or None
inferred_freq : DateOffset or None
freq_infer : bool
Returns
-------
freq : DateOffset or None
freq_infer : bool
Notes
-----
We assume at this point that `maybe_infer_freq` has been called, so
`freq` is either a DateOffset object or None.
"""
if inferred_freq is not None:
if freq is not None and freq != inferred_freq:
raise ValueError('Inferred frequency {inferred} from passed '
'values does not conform to passed frequency '
'{passed}'
.format(inferred=inferred_freq,
passed=freq.freqstr))
elif freq is None:
freq = inferred_freq
freq_infer = False
return freq, freq_infer |
Comparing a DateOffset to the string "infer" raises, so we need to
be careful about comparisons. Make a dummy variable `freq_infer` to
signify the case where the given freq is "infer" and set freq to None
to avoid comparison trouble later on.
Parameters
----------
freq : {DateOffset, None, str}
Returns
-------
freq : {DateOffset, None}
freq_infer : bool | def maybe_infer_freq(freq):
"""
Comparing a DateOffset to the string "infer" raises, so we need to
be careful about comparisons. Make a dummy variable `freq_infer` to
signify the case where the given freq is "infer" and set freq to None
to avoid comparison trouble later on.
Parameters
----------
freq : {DateOffset, None, str}
Returns
-------
freq : {DateOffset, None}
freq_infer : bool
"""
freq_infer = False
if not isinstance(freq, DateOffset):
# if a passed freq is None, don't infer automatically
if freq != 'infer':
freq = frequencies.to_offset(freq)
else:
freq_infer = True
freq = None
return freq, freq_infer |
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 directly.
Returns
-------
i8 1d array | 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 directly.
Returns
-------
i8 1d array
"""
from pandas import Index
from pandas.core.arrays import PeriodArray
if lib.is_scalar(other) and isna(other):
return iNaT
elif isinstance(other, (PeriodArray, ABCIndexClass,
DatetimeLikeArrayMixin)):
# convert tz if needed
if getattr(other, 'tz', None) is not None:
if to_utc:
other = other.tz_convert('UTC')
else:
other = other.tz_localize(None)
else:
try:
return np.array(other, copy=False).view('i8')
except TypeError:
# period array cannot be coerced to int
other = Index(other)
return other.asi8 |
Construct a scalar type from a string.
Parameters
----------
value : str
Returns
-------
Period, Timestamp, or Timedelta, or NaT
Whatever the type of ``self._scalar_type`` is.
Notes
-----
This should call ``self._check_compatible_with`` before
unboxing the result. | 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
Whatever the type of ``self._scalar_type`` is.
Notes
-----
This should call ``self._check_compatible_with`` before
unboxing the result.
"""
raise AbstractMethodError(self) |
Unbox the integer value of a scalar `value`.
Parameters
----------
value : Union[Period, Timestamp, Timedelta]
Returns
-------
int
Examples
--------
>>> self._unbox_scalar(Timedelta('10s')) # DOCTEST: +SKIP
10000000000 | 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
Examples
--------
>>> self._unbox_scalar(Timedelta('10s')) # DOCTEST: +SKIP
10000000000
"""
raise AbstractMethodError(self) |
Verify that `self` and `other` are compatible.
* DatetimeArray verifies that the timezones (if any) match
* PeriodArray verifies that the freq matches
* Timedelta has no verification
In each case, NaT is considered compatible.
Parameters
----------
other
Raises
------
Exception | 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
* Timedelta has no verification
In each case, NaT is considered compatible.
Parameters
----------
other
Raises
------
Exception
"""
raise AbstractMethodError(self) |
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 format
doc <%(URL)s>`__.
Parameters
----------
date_format : str
Date format string (e.g. "%%Y-%%m-%%d").
Returns
-------
Index
Index of formatted strings.
See Also
--------
to_datetime : Convert the given argument to datetime.
DatetimeIndex.normalize : Return DatetimeIndex with times to midnight.
DatetimeIndex.round : Round the DatetimeIndex to the specified freq.
DatetimeIndex.floor : Floor the DatetimeIndex to the specified freq.
Examples
--------
>>> rng = pd.date_range(pd.Timestamp("2018-03-10 09:00"),
... periods=3, freq='s')
>>> rng.strftime('%%B %%d, %%Y, %%r')
Index(['March 10, 2018, 09:00:00 AM', 'March 10, 2018, 09:00:01 AM',
'March 10, 2018, 09:00:02 AM'],
dtype='object') | 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 format
doc <%(URL)s>`__.
Parameters
----------
date_format : str
Date format string (e.g. "%%Y-%%m-%%d").
Returns
-------
Index
Index of formatted strings.
See Also
--------
to_datetime : Convert the given argument to datetime.
DatetimeIndex.normalize : Return DatetimeIndex with times to midnight.
DatetimeIndex.round : Round the DatetimeIndex to the specified freq.
DatetimeIndex.floor : Floor the DatetimeIndex to the specified freq.
Examples
--------
>>> rng = pd.date_range(pd.Timestamp("2018-03-10 09:00"),
... periods=3, freq='s')
>>> rng.strftime('%%B %%d, %%Y, %%r')
Index(['March 10, 2018, 09:00:00 AM', 'March 10, 2018, 09:00:01 AM',
'March 10, 2018, 09:00:02 AM'],
dtype='object')
"""
from pandas import Index
return Index(self._format_native_types(date_format=date_format)) |
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` would be preserved.
Parameters
----------
value : array_like
Values to insert into `self`.
side : {'left', 'right'}, optional
If 'left', the index of the first suitable location found is given.
If 'right', return the last such index. If there is no suitable
index, return either 0 or N (where N is the length of `self`).
sorter : 1-D array_like, optional
Optional array of integer indices that sort `self` into ascending
order. They are typically the result of ``np.argsort``.
Returns
-------
indices : array of ints
Array of insertion points with the same shape as `value`. | 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` would be preserved.
Parameters
----------
value : array_like
Values to insert into `self`.
side : {'left', 'right'}, optional
If 'left', the index of the first suitable location found is given.
If 'right', return the last such index. If there is no suitable
index, return either 0 or N (where N is the length of `self`).
sorter : 1-D array_like, optional
Optional array of integer indices that sort `self` into ascending
order. They are typically the result of ``np.argsort``.
Returns
-------
indices : array of ints
Array of insertion points with the same shape as `value`.
"""
if isinstance(value, str):
value = self._scalar_from_string(value)
if not (isinstance(value, (self._scalar_type, type(self)))
or isna(value)):
raise ValueError("Unexpected type for 'value': {valtype}"
.format(valtype=type(value)))
self._check_compatible_with(value)
if isinstance(value, type(self)):
value = value.asi8
else:
value = self._unbox_scalar(value)
return self.asi8.searchsorted(value, side=side, sorter=sorter) |
Repeat elements of an array.
See Also
--------
numpy.ndarray.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) |
Return a Series containing counts of unique values.
Parameters
----------
dropna : boolean, default True
Don't include counts of NaT values.
Returns
-------
Series | 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
"""
from pandas import Series, Index
if dropna:
values = self[~self.isna()]._data
else:
values = self._data
cls = type(self)
result = value_counts(values, sort=False, dropna=dropna)
index = Index(cls(result.index.view('i8'), dtype=self.dtype),
name=result.index.name)
return Series(result.values, index=index, name=result.name) |
Parameters
----------
result : a ndarray
fill_value : object, default iNaT
convert : string/dtype or None
Returns
-------
result : ndarray with values replace by the fill_value
mask the result if needed, convert to the provided dtype if its not
None
This is an internal routine. | def _maybe_mask_results(self, result, fill_value=iNaT, convert=None):
"""
Parameters
----------
result : a ndarray
fill_value : object, default iNaT
convert : string/dtype or None
Returns
-------
result : ndarray with values replace by the fill_value
mask the result if needed, convert to the provided dtype if its not
None
This is an internal routine.
"""
if self._hasnans:
if convert:
result = result.astype(convert)
if fill_value is None:
fill_value = np.nan
result[self._isnan] = fill_value
return result |
Validate that a frequency is compatible with the values of a given
Datetime Array/Index or Timedelta Array/Index
Parameters
----------
index : DatetimeIndex or TimedeltaIndex
The index on which to determine if the given frequency is valid
freq : DateOffset
The frequency to validate | 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
The index on which to determine if the given frequency is valid
freq : DateOffset
The frequency to validate
"""
if is_period_dtype(cls):
# Frequency validation is not meaningful for Period Array/Index
return None
inferred = index.inferred_freq
if index.size == 0 or inferred == freq.freqstr:
return None
try:
on_freq = cls._generate_range(start=index[0], end=None,
periods=len(index), freq=freq,
**kwargs)
if not np.array_equal(index.asi8, on_freq.asi8):
raise ValueError
except ValueError as e:
if "non-fixed" in str(e):
# non-fixed frequencies are not meaningful for timedelta64;
# we retain that error message
raise e
# GH#11587 the main way this is reached is if the `np.array_equal`
# check above is False. This can also be reached if index[0]
# is `NaT`, in which case the call to `cls._generate_range` will
# raise a ValueError, which we re-raise with a more targeted
# message.
raise ValueError('Inferred frequency {infer} from passed values '
'does not conform to passed frequency {passed}'
.format(infer=inferred, passed=freq.freqstr)) |
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
-------
result : ndarray[int64]
Notes
-----
The result's name is set outside of _add_delta by the calling
method (__add__ or __sub__), if necessary (i.e. for Indexes). | 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
-------
result : ndarray[int64]
Notes
-----
The result's name is set outside of _add_delta by the calling
method (__add__ or __sub__), if necessary (i.e. for Indexes).
"""
if isinstance(other, (Tick, timedelta, np.timedelta64)):
new_values = self._add_timedeltalike_scalar(other)
elif is_timedelta64_dtype(other):
# ndarray[timedelta64] or TimedeltaArray/index
new_values = self._add_delta_tdi(other)
return new_values |
Add a delta of a timedeltalike
return the i8 result view | 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_values[:] = iNaT
return new_values
inc = delta_to_nanoseconds(other)
new_values = checked_add_with_arr(self.asi8, inc,
arr_mask=self._isnan).view('i8')
new_values = self._maybe_mask_results(new_values)
return new_values.view('i8') |
Add a delta of a TimedeltaIndex
return the i8 result view | 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 in TimedeltaIndex for op
from pandas import TimedeltaIndex
other = TimedeltaIndex(other)
self_i8 = self.asi8
other_i8 = other.asi8
new_values = checked_add_with_arr(self_i8, other_i8,
arr_mask=self._isnan,
b_mask=other._isnan)
if self._hasnans or other._hasnans:
mask = (self._isnan) | (other._isnan)
new_values[mask] = iNaT
return new_values.view('i8') |
Add pd.NaT to self | 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 treated like a timedelta for both timedelta
# and datetime dtypes
result = np.zeros(len(self), dtype=np.int64)
result.fill(iNaT)
return type(self)(result, dtype=self.dtype, freq=None) |
Subtract pd.NaT from self | def _sub_nat(self):
"""
Subtract pd.NaT from self
"""
# GH#19124 Timedelta - datetime is not in general well-defined.
# We make an exception for pd.NaT, which in this case quacks
# like a timedelta.
# For datetime64 dtypes by convention we treat NaT as a datetime, so
# this subtraction returns a timedelta64 dtype.
# For period dtype, timedelta64 is a close-enough return dtype.
result = np.zeros(len(self), dtype=np.int64)
result.fill(iNaT)
return result.view('timedelta64[ns]') |
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
Returns
-------
result : np.ndarray[object]
Array of DateOffset objects; nulls represented by NaT. | 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
Returns
-------
result : np.ndarray[object]
Array of DateOffset objects; nulls represented by NaT.
"""
if not is_period_dtype(self):
raise TypeError("cannot subtract {dtype}-dtype from {cls}"
.format(dtype=other.dtype,
cls=type(self).__name__))
if len(self) != len(other):
raise ValueError("cannot subtract arrays/indices of "
"unequal length")
if self.freq != other.freq:
msg = DIFFERENT_FREQ.format(cls=type(self).__name__,
own_freq=self.freqstr,
other_freq=other.freqstr)
raise IncompatibleFrequency(msg)
new_values = checked_add_with_arr(self.asi8, -other.asi8,
arr_mask=self._isnan,
b_mask=other._isnan)
new_values = np.array([self.freq.base * x for x in new_values])
if self._hasnans or other._hasnans:
mask = (self._isnan) | (other._isnan)
new_values[mask] = NaT
return new_values |
Add or subtract array-like of integers equivalent to applying
`_time_shift` pointwise.
Parameters
----------
other : Index, ExtensionArray, np.ndarray
integer-dtype
op : {operator.add, operator.sub}
Returns
-------
result : same class as self | def _addsub_int_array(self, other, op):
"""
Add or subtract array-like of integers equivalent to applying
`_time_shift` pointwise.
Parameters
----------
other : Index, ExtensionArray, np.ndarray
integer-dtype
op : {operator.add, operator.sub}
Returns
-------
result : same class as self
"""
# _addsub_int_array is overriden by PeriodArray
assert not is_period_dtype(self)
assert op in [operator.add, operator.sub]
if self.freq is None:
# GH#19123
raise NullFrequencyError("Cannot shift with no freq")
elif isinstance(self.freq, Tick):
# easy case where we can convert to timedelta64 operation
td = Timedelta(self.freq)
return op(self, td * other)
# We should only get here with DatetimeIndex; dispatch
# to _addsub_offset_array
assert not is_timedelta64_dtype(self)
return op(self, np.array(other) * self.freq) |
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
-------
result : same class as self | 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
-------
result : same class as self
"""
assert op in [operator.add, operator.sub]
if len(other) == 1:
return op(self, other[0])
warnings.warn("Adding/subtracting array of DateOffsets to "
"{cls} not vectorized"
.format(cls=type(self).__name__), PerformanceWarning)
# For EA self.astype('O') returns a numpy array, not an Index
left = lib.values_from_object(self.astype('O'))
res_values = op(left, np.array(other))
kwargs = {}
if not is_period_dtype(self):
kwargs['freq'] = 'infer'
return self._from_sequence(res_values, **kwargs) |
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
Number of periods to shift by.
freq : pandas.DateOffset, pandas.Timedelta, or string
Frequency increment to shift by. | 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
Number of periods to shift by.
freq : pandas.DateOffset, pandas.Timedelta, or string
Frequency increment to shift by.
"""
if freq is not None and freq != self.freq:
if isinstance(freq, str):
freq = frequencies.to_offset(freq)
offset = periods * freq
result = self + offset
return result
if periods == 0:
# immutable so OK
return self.copy()
if self.freq is None:
raise NullFrequencyError("Cannot shift with no freq")
start = self[0] + periods * self.freq
end = self[-1] + periods * self.freq
# Note: in the DatetimeTZ case, _generate_range will infer the
# appropriate timezone from `start` and `end`, so tz does not need
# to be passed explicitly.
return self._generate_range(start=start, end=end, periods=None,
freq=self.freq) |
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
----------
arg : Union[DatetimeLikeArray, DatetimeIndexOpsMixin, ndarray]
ambiguous : str, bool, or bool-ndarray, default 'raise'
nonexistent : str, default 'raise'
from_utc : bool, default False
If True, localize the i8 ndarray to UTC first before converting to
the appropriate tz. If False, localize directly to the tz.
Returns
-------
localized array | 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
----------
arg : Union[DatetimeLikeArray, DatetimeIndexOpsMixin, ndarray]
ambiguous : str, bool, or bool-ndarray, default 'raise'
nonexistent : str, default 'raise'
from_utc : bool, default False
If True, localize the i8 ndarray to UTC first before converting to
the appropriate tz. If False, localize directly to the tz.
Returns
-------
localized array
"""
# reconvert to local tz
tz = getattr(self, 'tz', None)
if tz is not None:
if not isinstance(arg, type(self)):
arg = self._simple_new(arg)
if from_utc:
arg = arg.tz_localize('UTC').tz_convert(self.tz)
else:
arg = arg.tz_localize(
self.tz, ambiguous=ambiguous, nonexistent=nonexistent
)
return arg |
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 Series. | 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 Series.
"""
nv.validate_min(args, kwargs)
nv.validate_minmax_axis(axis)
result = nanops.nanmin(self.asi8, skipna=skipna, mask=self.isna())
if isna(result):
# Period._from_ordinal does not handle np.nan gracefully
return NaT
return self._box_func(result) |
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 Series. | 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 Series.
"""
# TODO: skipna is broken with max.
# See https://github.com/pandas-dev/pandas/issues/24265
nv.validate_max(args, kwargs)
nv.validate_minmax_axis(axis)
mask = self.isna()
if skipna:
values = self[~mask].asi8
elif mask.any():
return NaT
else:
values = self.asi8
if not len(values):
# short-circut for empty max / min
return NaT
result = nanops.nanmax(values, skipna=skipna)
# Don't have to worry about NA `result`, since no NA went in.
return self._box_func(result) |
Helper function to render a consistent error message when raising
IncompatibleFrequency.
Parameters
----------
left : PeriodArray
right : DateOffset, Period, ndarray, or timedelta-like
Raises
------
IncompatibleFrequency | 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
"""
# GH#24283 error message format depends on whether right is scalar
if isinstance(right, np.ndarray):
other_freq = None
elif isinstance(right, (ABCPeriodIndex, PeriodArray, Period, DateOffset)):
other_freq = right.freqstr
else:
other_freq = _delta_to_tick(Timedelta(right)).freqstr
msg = DIFFERENT_FREQ.format(cls=type(left).__name__,
own_freq=left.freqstr,
other_freq=other_freq)
raise IncompatibleFrequency(msg) |
Wrap comparison operations to convert Period-like to PeriodDtype | 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, ABCSeries, ABCIndexClass)):
return NotImplemented
if is_list_like(other) and len(other) != len(self):
raise ValueError("Lengths must match")
if isinstance(other, Period):
self._check_compatible_with(other)
result = op(other.ordinal)
elif isinstance(other, cls):
self._check_compatible_with(other)
result = op(other.asi8)
mask = self._isnan | other._isnan
if mask.any():
result[mask] = nat_result
return result
elif other is NaT:
result = np.empty(len(self.asi8), dtype=bool)
result.fill(nat_result)
else:
other = Period(other, freq=self.freq)
result = op(other.ordinal)
if self._hasnans:
result[self._isnan] = nat_result
return result
return compat.set_function_name(wrapper, opname, cls) |
Construct a new PeriodArray from a sequence of Period scalars.
Parameters
----------
data : Sequence of Period objects
A sequence of Period objects. These are required to all have
the same ``freq.`` Missing values can be indicated by ``None``
or ``pandas.NaT``.
freq : str, Tick, or Offset
The frequency of every element of the array. This can be specified
to avoid inferring the `freq` from `data`.
copy : bool, default False
Whether to ensure a copy of the data is made.
Returns
-------
PeriodArray
See Also
--------
PeriodArray
pandas.PeriodIndex
Examples
--------
>>> period_array([pd.Period('2017', freq='A'),
... pd.Period('2018', freq='A')])
<PeriodArray>
['2017', '2018']
Length: 2, dtype: period[A-DEC]
>>> period_array([pd.Period('2017', freq='A'),
... pd.Period('2018', freq='A'),
... pd.NaT])
<PeriodArray>
['2017', '2018', 'NaT']
Length: 3, dtype: period[A-DEC]
Integers that look like years are handled
>>> period_array([2000, 2001, 2002], freq='D')
['2000-01-01', '2001-01-01', '2002-01-01']
Length: 3, dtype: period[D]
Datetime-like strings may also be passed
>>> period_array(['2000-Q1', '2000-Q2', '2000-Q3', '2000-Q4'], freq='Q')
<PeriodArray>
['2000Q1', '2000Q2', '2000Q3', '2000Q4']
Length: 4, dtype: period[Q-DEC] | 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 objects. These are required to all have
the same ``freq.`` Missing values can be indicated by ``None``
or ``pandas.NaT``.
freq : str, Tick, or Offset
The frequency of every element of the array. This can be specified
to avoid inferring the `freq` from `data`.
copy : bool, default False
Whether to ensure a copy of the data is made.
Returns
-------
PeriodArray
See Also
--------
PeriodArray
pandas.PeriodIndex
Examples
--------
>>> period_array([pd.Period('2017', freq='A'),
... pd.Period('2018', freq='A')])
<PeriodArray>
['2017', '2018']
Length: 2, dtype: period[A-DEC]
>>> period_array([pd.Period('2017', freq='A'),
... pd.Period('2018', freq='A'),
... pd.NaT])
<PeriodArray>
['2017', '2018', 'NaT']
Length: 3, dtype: period[A-DEC]
Integers that look like years are handled
>>> period_array([2000, 2001, 2002], freq='D')
['2000-01-01', '2001-01-01', '2002-01-01']
Length: 3, dtype: period[D]
Datetime-like strings may also be passed
>>> period_array(['2000-Q1', '2000-Q2', '2000-Q3', '2000-Q4'], freq='Q')
<PeriodArray>
['2000Q1', '2000Q2', '2000Q3', '2000Q4']
Length: 4, dtype: period[Q-DEC]
"""
if is_datetime64_dtype(data):
return PeriodArray._from_datetime64(data, freq)
if isinstance(data, (ABCPeriodIndex, ABCSeries, PeriodArray)):
return PeriodArray(data, freq)
# other iterable of some kind
if not isinstance(data, (np.ndarray, list, tuple)):
data = list(data)
data = np.asarray(data)
if freq:
dtype = PeriodDtype(freq)
else:
dtype = None
if is_float_dtype(data) and len(data) > 0:
raise TypeError("PeriodIndex does not allow "
"floating point in construction")
data = ensure_object(data)
return PeriodArray._from_sequence(data, dtype=dtype) |
If both a dtype and a freq are available, ensure they match. If only
dtype is available, extract the implied freq.
Parameters
----------
dtype : dtype
freq : DateOffset or None
Returns
-------
freq : DateOffset
Raises
------
ValueError : non-period dtype
IncompatibleFrequency : mismatch between dtype and freq | def validate_dtype_freq(dtype, freq):
"""
If both a dtype and a freq are available, ensure they match. If only
dtype is available, extract the implied freq.
Parameters
----------
dtype : dtype
freq : DateOffset or None
Returns
-------
freq : DateOffset
Raises
------
ValueError : non-period dtype
IncompatibleFrequency : mismatch between dtype and freq
"""
if freq is not None:
freq = frequencies.to_offset(freq)
if dtype is not None:
dtype = pandas_dtype(dtype)
if not is_period_dtype(dtype):
raise ValueError('dtype must be PeriodDtype')
if freq is None:
freq = dtype.freq
elif freq != dtype.freq:
raise IncompatibleFrequency('specified freq and dtype '
'are different')
return freq |
Convert an datetime-like array to values Period ordinals.
Parameters
----------
data : Union[Series[datetime64[ns]], DatetimeIndex, ndarray[datetime64ns]]
freq : Optional[Union[str, Tick]]
Must match the `freq` on the `data` if `data` is a DatetimeIndex
or Series.
tz : Optional[tzinfo]
Returns
-------
ordinals : ndarray[int]
freq : Tick
The frequencey extracted from the Series or DatetimeIndex if that's
used. | 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]]
Must match the `freq` on the `data` if `data` is a DatetimeIndex
or Series.
tz : Optional[tzinfo]
Returns
-------
ordinals : ndarray[int]
freq : Tick
The frequencey extracted from the Series or DatetimeIndex if that's
used.
"""
if data.dtype != np.dtype('M8[ns]'):
raise ValueError('Wrong dtype: {dtype}'.format(dtype=data.dtype))
if freq is None:
if isinstance(data, ABCIndexClass):
data, freq = data._values, data.freq
elif isinstance(data, ABCSeries):
data, freq = data._values, data.dt.freq
freq = Period._maybe_convert_freq(freq)
if isinstance(data, (ABCIndexClass, ABCSeries)):
data = data._values
base, mult = libfrequencies.get_freq_code(freq)
return libperiod.dt64arr_to_periodarr(data.view('i8'), base, tz), freq |
Construct a PeriodArray from a datetime64 array
Parameters
----------
data : ndarray[datetime64[ns], datetime64[ns, tz]]
freq : str or Tick
tz : tzinfo, optional
Returns
-------
PeriodArray[freq] | 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
-------
PeriodArray[freq]
"""
data, freq = dt64arr_to_periodarr(data, freq, tz)
return cls(data, freq=freq) |
Cast to DatetimeArray/Index.
Parameters
----------
freq : string or DateOffset, optional
Target frequency. The default is 'D' for week or longer,
'S' otherwise
how : {'s', 'e', 'start', 'end'}
Returns
-------
DatetimeArray/Index | 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,
'S' otherwise
how : {'s', 'e', 'start', 'end'}
Returns
-------
DatetimeArray/Index
"""
from pandas.core.arrays import DatetimeArray
how = libperiod._validate_end_alias(how)
end = how == 'E'
if end:
if freq == 'B':
# roll forward to ensure we land on B date
adjust = Timedelta(1, 'D') - Timedelta(1, 'ns')
return self.to_timestamp(how='start') + adjust
else:
adjust = Timedelta(1, 'ns')
return (self + self.freq).to_timestamp(how='start') - adjust
if freq is None:
base, mult = libfrequencies.get_freq_code(self.freq)
freq = libfrequencies.get_to_timestamp_base(base)
else:
freq = Period._maybe_convert_freq(freq)
base, mult = libfrequencies.get_freq_code(freq)
new_data = self.asfreq(freq, how=how)
new_data = libperiod.periodarr_to_dt64arr(new_data.asi8, base)
return DatetimeArray._from_sequence(new_data, freq='infer') |
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
Number of periods to shift by.
freq : pandas.DateOffset, pandas.Timedelta, or string
Frequency increment to shift by. | 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
Number of periods to shift by.
freq : pandas.DateOffset, pandas.Timedelta, or string
Frequency increment to shift by.
"""
if freq is not None:
raise TypeError("`freq` argument is not supported for "
"{cls}._time_shift"
.format(cls=type(self).__name__))
values = self.asi8 + periods * self.freq.n
if self._hasnans:
values[self._isnan] = iNaT
return type(self)(values, freq=self.freq) |
Convert the Period Array/Index to the specified frequency `freq`.
Parameters
----------
freq : str
a frequency
how : str {'E', 'S'}
'E', 'END', or 'FINISH' for end,
'S', 'START', or 'BEGIN' for start.
Whether the elements should be aligned to the end
or start within pa period. January 31st ('END') vs.
January 1st ('START') for example.
Returns
-------
new : Period Array/Index with the new frequency
Examples
--------
>>> pidx = pd.period_range('2010-01-01', '2015-01-01', freq='A')
>>> pidx
PeriodIndex(['2010', '2011', '2012', '2013', '2014', '2015'],
dtype='period[A-DEC]', freq='A-DEC')
>>> pidx.asfreq('M')
PeriodIndex(['2010-12', '2011-12', '2012-12', '2013-12', '2014-12',
'2015-12'], dtype='period[M]', freq='M')
>>> pidx.asfreq('M', how='S')
PeriodIndex(['2010-01', '2011-01', '2012-01', '2013-01', '2014-01',
'2015-01'], dtype='period[M]', freq='M') | def asfreq(self, freq=None, how='E'):
"""
Convert the Period Array/Index to the specified frequency `freq`.
Parameters
----------
freq : str
a frequency
how : str {'E', 'S'}
'E', 'END', or 'FINISH' for end,
'S', 'START', or 'BEGIN' for start.
Whether the elements should be aligned to the end
or start within pa period. January 31st ('END') vs.
January 1st ('START') for example.
Returns
-------
new : Period Array/Index with the new frequency
Examples
--------
>>> pidx = pd.period_range('2010-01-01', '2015-01-01', freq='A')
>>> pidx
PeriodIndex(['2010', '2011', '2012', '2013', '2014', '2015'],
dtype='period[A-DEC]', freq='A-DEC')
>>> pidx.asfreq('M')
PeriodIndex(['2010-12', '2011-12', '2012-12', '2013-12', '2014-12',
'2015-12'], dtype='period[M]', freq='M')
>>> pidx.asfreq('M', how='S')
PeriodIndex(['2010-01', '2011-01', '2012-01', '2013-01', '2014-01',
'2015-01'], dtype='period[M]', freq='M')
"""
how = libperiod._validate_end_alias(how)
freq = Period._maybe_convert_freq(freq)
base1, mult1 = libfrequencies.get_freq_code(self.freq)
base2, mult2 = libfrequencies.get_freq_code(freq)
asi8 = self.asi8
# mult1 can't be negative or 0
end = how == 'E'
if end:
ordinal = asi8 + mult1 - 1
else:
ordinal = asi8
new_data = period_asfreq_arr(ordinal, base1, base2, end)
if self._hasnans:
new_data[self._isnan] = iNaT
return type(self)(new_data, freq=freq) |
actually format my specific 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
if self._hasnans:
mask = self._isnan
values[mask] = na_rep
imask = ~mask
values[imask] = np.array([formatter(dt) for dt
in values[imask]])
else:
values = np.array([formatter(dt) for dt in values])
return values |
Parameters
----------
other : timedelta, Tick, np.timedelta64
Returns
-------
result : ndarray[int64] | 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(other, (timedelta, np.timedelta64, Tick))
if notna(other):
# special handling for np.timedelta64("NaT"), avoid calling
# _check_timedeltalike_freq_compat as that would raise TypeError
other = self._check_timedeltalike_freq_compat(other)
# Note: when calling parent class's _add_timedeltalike_scalar,
# it will call delta_to_nanoseconds(delta). Because delta here
# is an integer, delta_to_nanoseconds will return it unchanged.
ordinals = super()._add_timedeltalike_scalar(other)
return ordinals |
Parameters
----------
other : TimedeltaArray or ndarray[timedelta64]
Returns
-------
result : ndarray[int64] | 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_timedeltalike_freq_compat(other)
return self._addsub_int_array(delta, operator.add).asi8 |
Add a timedelta-like, Tick, or TimedeltaIndex-like object
to self, yielding a new PeriodArray
Parameters
----------
other : {timedelta, np.timedelta64, Tick,
TimedeltaIndex, ndarray[timedelta64]}
Returns
-------
result : PeriodArray | def _add_delta(self, other):
"""
Add a timedelta-like, Tick, or TimedeltaIndex-like object
to self, yielding a new PeriodArray
Parameters
----------
other : {timedelta, np.timedelta64, Tick,
TimedeltaIndex, ndarray[timedelta64]}
Returns
-------
result : PeriodArray
"""
if not isinstance(self.freq, Tick):
# We cannot add timedelta-like to non-tick PeriodArray
_raise_on_incompatible(self, other)
new_ordinals = super()._add_delta(other)
return type(self)(new_ordinals, freq=self.freq) |
Arithmetic operations with timedelta-like scalars or array `other`
are only valid if `other` is an integer multiple of `self.freq`.
If the operation is valid, find that integer multiple. Otherwise,
raise because the operation is invalid.
Parameters
----------
other : timedelta, np.timedelta64, Tick,
ndarray[timedelta64], TimedeltaArray, TimedeltaIndex
Returns
-------
multiple : int or ndarray[int64]
Raises
------
IncompatibleFrequency | def _check_timedeltalike_freq_compat(self, other):
"""
Arithmetic operations with timedelta-like scalars or array `other`
are only valid if `other` is an integer multiple of `self.freq`.
If the operation is valid, find that integer multiple. Otherwise,
raise because the operation is invalid.
Parameters
----------
other : timedelta, np.timedelta64, Tick,
ndarray[timedelta64], TimedeltaArray, TimedeltaIndex
Returns
-------
multiple : int or ndarray[int64]
Raises
------
IncompatibleFrequency
"""
assert isinstance(self.freq, Tick) # checked by calling function
own_offset = frequencies.to_offset(self.freq.rule_code)
base_nanos = delta_to_nanoseconds(own_offset)
if isinstance(other, (timedelta, np.timedelta64, Tick)):
nanos = delta_to_nanoseconds(other)
elif isinstance(other, np.ndarray):
# numpy timedelta64 array; all entries must be compatible
assert other.dtype.kind == 'm'
if other.dtype != _TD_DTYPE:
# i.e. non-nano unit
# TODO: disallow unit-less timedelta64
other = other.astype(_TD_DTYPE)
nanos = other.view('i8')
else:
# TimedeltaArray/Index
nanos = other.asi8
if np.all(nanos % base_nanos == 0):
# nanos being added is an integer multiple of the
# base-frequency to self.freq
delta = nanos // base_nanos
# delta is the integer (or integer-array) number of periods
# by which will be added to self.
return delta
_raise_on_incompatible(self, other) |
Detect missing values. Treat None, NaN, INF, -INF as null.
Parameters
----------
arr: ndarray or object value
Returns
-------
boolean ndarray or boolean | 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):
return libmissing.checknull_old(obj)
# hack (for now) because MI registers as ndarray
elif isinstance(obj, ABCMultiIndex):
raise NotImplementedError("isna is not defined for MultiIndex")
elif isinstance(obj, (ABCSeries, np.ndarray, ABCIndexClass)):
return _isna_ndarraylike_old(obj)
elif isinstance(obj, ABCGeneric):
return obj._constructor(obj._data.isna(func=_isna_old))
elif isinstance(obj, list):
return _isna_ndarraylike_old(np.asarray(obj, dtype=object))
elif hasattr(obj, '__array__'):
return _isna_ndarraylike_old(np.asarray(obj))
else:
return obj is None |
Option change callback for na/inf behaviour
Choose which replacement for numpy.isnan / -numpy.isfinite is used.
Parameters
----------
flag: bool
True means treat None, NaN, INF, -INF as null (old way),
False means None and NaN are null, but INF, -INF are not null
(new way).
Notes
-----
This approach to setting global module values is discussed and
approved here:
* http://stackoverflow.com/questions/4859217/
programmatically-creating-variables-in-python/4859312#4859312 | def _use_inf_as_na(key):
"""Option change callback for na/inf behaviour
Choose which replacement for numpy.isnan / -numpy.isfinite is used.
Parameters
----------
flag: bool
True means treat None, NaN, INF, -INF as null (old way),
False means None and NaN are null, but INF, -INF are not null
(new way).
Notes
-----
This approach to setting global module values is discussed and
approved here:
* http://stackoverflow.com/questions/4859217/
programmatically-creating-variables-in-python/4859312#4859312
"""
from pandas._config import get_option
flag = get_option(key)
if flag:
globals()['_isna'] = _isna_old
else:
globals()['_isna'] = _isna_new |
Parameters
----------
arr: a numpy array
fill_value: fill value, default to np.nan
Returns
-------
True if we can fill using this fill_value | 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
is_integer_dtype(dtype))
return True |
True if two arrays, left and right, have equal non-NaN elements, and NaNs
in corresponding locations. False otherwise. It is assumed that left and
right are NumPy arrays of the same dtype. The behavior of this function
(particularly with respect to NaNs) is not defined if the dtypes are
different.
Parameters
----------
left, right : ndarrays
strict_nan : bool, default False
If True, consider NaN and None to be different.
Returns
-------
b : bool
Returns True if the arrays are equivalent.
Examples
--------
>>> array_equivalent(
... np.array([1, 2, np.nan]),
... np.array([1, 2, np.nan]))
True
>>> array_equivalent(
... np.array([1, np.nan, 2]),
... np.array([1, 2, np.nan]))
False | def array_equivalent(left, right, strict_nan=False):
"""
True if two arrays, left and right, have equal non-NaN elements, and NaNs
in corresponding locations. False otherwise. It is assumed that left and
right are NumPy arrays of the same dtype. The behavior of this function
(particularly with respect to NaNs) is not defined if the dtypes are
different.
Parameters
----------
left, right : ndarrays
strict_nan : bool, default False
If True, consider NaN and None to be different.
Returns
-------
b : bool
Returns True if the arrays are equivalent.
Examples
--------
>>> array_equivalent(
... np.array([1, 2, np.nan]),
... np.array([1, 2, np.nan]))
True
>>> array_equivalent(
... np.array([1, np.nan, 2]),
... np.array([1, 2, np.nan]))
False
"""
left, right = np.asarray(left), np.asarray(right)
# shape compat
if left.shape != right.shape:
return False
# Object arrays can contain None, NaN and NaT.
# string dtypes must be come to this path for NumPy 1.7.1 compat
if is_string_dtype(left) or is_string_dtype(right):
if not strict_nan:
# isna considers NaN and None to be equivalent.
return lib.array_equivalent_object(
ensure_object(left.ravel()), ensure_object(right.ravel()))
for left_value, right_value in zip(left, right):
if left_value is NaT and right_value is not NaT:
return False
elif isinstance(left_value, float) and np.isnan(left_value):
if (not isinstance(right_value, float) or
not np.isnan(right_value)):
return False
else:
if left_value != right_value:
return False
return True
# NaNs can occur in float and complex arrays.
if is_float_dtype(left) or is_complex_dtype(left):
# empty
if not (np.prod(left.shape) and np.prod(right.shape)):
return True
return ((left == right) | (isna(left) & isna(right))).all()
# numpy will will not allow this type of datetimelike vs integer comparison
elif is_datetimelike_v_numeric(left, right):
return False
# M8/m8
elif needs_i8_conversion(left) and needs_i8_conversion(right):
if not is_dtype_equal(left.dtype, right.dtype):
return False
left = left.view('i8')
right = right.view('i8')
# if we have structured dtypes, compare first
if (left.dtype.type is np.void or
right.dtype.type is np.void):
if left.dtype != right.dtype:
return False
return np.array_equal(left, right) |
infer the fill value for the nan/NaT from the provided
scalar/ndarray/list-like if we are a NaT, return the correct dtyped
element to provide proper block construction | def _infer_fill_value(val):
"""
infer the fill value for the nan/NaT from the provided
scalar/ndarray/list-like if we are a NaT, return the correct dtyped
element to provide proper block construction
"""
if not is_list_like(val):
val = [val]
val = np.array(val, copy=False)
if is_datetimelike(val):
return np.array('NaT', dtype=val.dtype)
elif is_object_dtype(val.dtype):
dtype = lib.infer_dtype(ensure_object(val), skipna=False)
if dtype in ['datetime', 'datetime64']:
return np.array('NaT', dtype=_NS_DTYPE)
elif dtype in ['timedelta', 'timedelta64']:
return np.array('NaT', dtype=_TD_DTYPE)
return np.nan |
if we have a compatible fill_value and arr dtype, then fill | def _maybe_fill(arr, fill_value=np.nan):
"""
if we have a compatible fill_value and arr dtype, then fill
"""
if _isna_compat(arr, fill_value):
arr.fill(fill_value)
return arr |
Return a dtype compat na value
Parameters
----------
dtype : string / dtype
compat : boolean, default True
Returns
-------
np.dtype or a pandas dtype
Examples
--------
>>> na_value_for_dtype(np.dtype('int64'))
0
>>> na_value_for_dtype(np.dtype('int64'), compat=False)
nan
>>> na_value_for_dtype(np.dtype('float64'))
nan
>>> na_value_for_dtype(np.dtype('bool'))
False
>>> na_value_for_dtype(np.dtype('datetime64[ns]'))
NaT | def na_value_for_dtype(dtype, compat=True):
"""
Return a dtype compat na value
Parameters
----------
dtype : string / dtype
compat : boolean, default True
Returns
-------
np.dtype or a pandas dtype
Examples
--------
>>> na_value_for_dtype(np.dtype('int64'))
0
>>> na_value_for_dtype(np.dtype('int64'), compat=False)
nan
>>> na_value_for_dtype(np.dtype('float64'))
nan
>>> na_value_for_dtype(np.dtype('bool'))
False
>>> na_value_for_dtype(np.dtype('datetime64[ns]'))
NaT
"""
dtype = pandas_dtype(dtype)
if is_extension_array_dtype(dtype):
return dtype.na_value
if (is_datetime64_dtype(dtype) or is_datetime64tz_dtype(dtype) or
is_timedelta64_dtype(dtype) or is_period_dtype(dtype)):
return NaT
elif is_float_dtype(dtype):
return np.nan
elif is_integer_dtype(dtype):
if compat:
return 0
return np.nan
elif is_bool_dtype(dtype):
return False
return np.nan |
Return array-like containing only true/non-NaN values, possibly empty. | def remove_na_arraylike(arr):
"""
Return array-like containing only true/non-NaN values, possibly empty.
"""
if is_extension_array_dtype(arr):
return arr[notna(arr)]
else:
return arr[notna(lib.values_from_object(arr))] |
Helper function to convert DataFrame and Series to matplotlib.table
Parameters
----------
ax : Matplotlib axes object
data : DataFrame or Series
data for table contents
kwargs : keywords, optional
keyword arguments which passed to matplotlib.table.table.
If `rowLabels` or `colLabels` is not specified, data index or column
name will be used.
Returns
-------
matplotlib table object | def table(ax, data, rowLabels=None, colLabels=None, **kwargs):
"""
Helper function to convert DataFrame and Series to matplotlib.table
Parameters
----------
ax : Matplotlib axes object
data : DataFrame or Series
data for table contents
kwargs : keywords, optional
keyword arguments which passed to matplotlib.table.table.
If `rowLabels` or `colLabels` is not specified, data index or column
name will be used.
Returns
-------
matplotlib table object
"""
if isinstance(data, ABCSeries):
data = data.to_frame()
elif isinstance(data, ABCDataFrame):
pass
else:
raise ValueError('Input data must be DataFrame or Series')
if rowLabels is None:
rowLabels = data.index
if colLabels is None:
colLabels = data.columns
cellText = data.values
import matplotlib.table
table = matplotlib.table.table(ax, cellText=cellText,
rowLabels=rowLabels,
colLabels=colLabels, **kwargs)
return table |
Create a figure with a set of subplots already made.
This utility wrapper makes it convenient to create common layouts of
subplots, including the enclosing figure object, in a single call.
Keyword arguments:
naxes : int
Number of required axes. Exceeded axes are set invisible. Default is
nrows * ncols.
sharex : bool
If True, the X axis will be shared amongst all subplots.
sharey : bool
If True, the Y axis will be shared amongst all subplots.
squeeze : bool
If True, extra dimensions are squeezed out from the returned axis object:
- if only one subplot is constructed (nrows=ncols=1), the resulting
single Axis object is returned as a scalar.
- for Nx1 or 1xN subplots, the returned object is a 1-d numpy object
array of Axis objects are returned as numpy 1-d arrays.
- for NxM subplots with N>1 and M>1 are returned as a 2d array.
If False, no squeezing is done: the returned axis object is always
a 2-d array containing Axis instances, even if it ends up being 1x1.
subplot_kw : dict
Dict with keywords passed to the add_subplot() call used to create each
subplots.
ax : Matplotlib axis object, optional
layout : tuple
Number of rows and columns of the subplot grid.
If not specified, calculated from naxes and layout_type
layout_type : {'box', 'horziontal', 'vertical'}, default 'box'
Specify how to layout the subplot grid.
fig_kw : Other keyword arguments to be passed to the figure() call.
Note that all keywords not recognized above will be
automatically included here.
Returns:
fig, ax : tuple
- fig is the Matplotlib Figure object
- ax can be either a single axis object or an array of axis objects if
more than one subplot was created. The dimensions of the resulting array
can be controlled with the squeeze keyword, see above.
**Examples:**
x = np.linspace(0, 2*np.pi, 400)
y = np.sin(x**2)
# Just a figure and one subplot
f, ax = plt.subplots()
ax.plot(x, y)
ax.set_title('Simple plot')
# Two subplots, unpack the output array immediately
f, (ax1, ax2) = plt.subplots(1, 2, sharey=True)
ax1.plot(x, y)
ax1.set_title('Sharing Y axis')
ax2.scatter(x, y)
# Four polar axes
plt.subplots(2, 2, subplot_kw=dict(polar=True)) | def _subplots(naxes=None, sharex=False, sharey=False, squeeze=True,
subplot_kw=None, ax=None, layout=None, layout_type='box',
**fig_kw):
"""Create a figure with a set of subplots already made.
This utility wrapper makes it convenient to create common layouts of
subplots, including the enclosing figure object, in a single call.
Keyword arguments:
naxes : int
Number of required axes. Exceeded axes are set invisible. Default is
nrows * ncols.
sharex : bool
If True, the X axis will be shared amongst all subplots.
sharey : bool
If True, the Y axis will be shared amongst all subplots.
squeeze : bool
If True, extra dimensions are squeezed out from the returned axis object:
- if only one subplot is constructed (nrows=ncols=1), the resulting
single Axis object is returned as a scalar.
- for Nx1 or 1xN subplots, the returned object is a 1-d numpy object
array of Axis objects are returned as numpy 1-d arrays.
- for NxM subplots with N>1 and M>1 are returned as a 2d array.
If False, no squeezing is done: the returned axis object is always
a 2-d array containing Axis instances, even if it ends up being 1x1.
subplot_kw : dict
Dict with keywords passed to the add_subplot() call used to create each
subplots.
ax : Matplotlib axis object, optional
layout : tuple
Number of rows and columns of the subplot grid.
If not specified, calculated from naxes and layout_type
layout_type : {'box', 'horziontal', 'vertical'}, default 'box'
Specify how to layout the subplot grid.
fig_kw : Other keyword arguments to be passed to the figure() call.
Note that all keywords not recognized above will be
automatically included here.
Returns:
fig, ax : tuple
- fig is the Matplotlib Figure object
- ax can be either a single axis object or an array of axis objects if
more than one subplot was created. The dimensions of the resulting array
can be controlled with the squeeze keyword, see above.
**Examples:**
x = np.linspace(0, 2*np.pi, 400)
y = np.sin(x**2)
# Just a figure and one subplot
f, ax = plt.subplots()
ax.plot(x, y)
ax.set_title('Simple plot')
# Two subplots, unpack the output array immediately
f, (ax1, ax2) = plt.subplots(1, 2, sharey=True)
ax1.plot(x, y)
ax1.set_title('Sharing Y axis')
ax2.scatter(x, y)
# Four polar axes
plt.subplots(2, 2, subplot_kw=dict(polar=True))
"""
import matplotlib.pyplot as plt
if subplot_kw is None:
subplot_kw = {}
if ax is None:
fig = plt.figure(**fig_kw)
else:
if is_list_like(ax):
ax = _flatten(ax)
if layout is not None:
warnings.warn("When passing multiple axes, layout keyword is "
"ignored", UserWarning)
if sharex or sharey:
warnings.warn("When passing multiple axes, sharex and sharey "
"are ignored. These settings must be specified "
"when creating axes", UserWarning,
stacklevel=4)
if len(ax) == naxes:
fig = ax[0].get_figure()
return fig, ax
else:
raise ValueError("The number of passed axes must be {0}, the "
"same as the output plot".format(naxes))
fig = ax.get_figure()
# if ax is passed and a number of subplots is 1, return ax as it is
if naxes == 1:
if squeeze:
return fig, ax
else:
return fig, _flatten(ax)
else:
warnings.warn("To output multiple subplots, the figure containing "
"the passed axes is being cleared", UserWarning,
stacklevel=4)
fig.clear()
nrows, ncols = _get_layout(naxes, layout=layout, layout_type=layout_type)
nplots = nrows * ncols
# Create empty object array to hold all axes. It's easiest to make it 1-d
# so we can just append subplots upon creation, and then
axarr = np.empty(nplots, dtype=object)
# Create first subplot separately, so we can share it if requested
ax0 = fig.add_subplot(nrows, ncols, 1, **subplot_kw)
if sharex:
subplot_kw['sharex'] = ax0
if sharey:
subplot_kw['sharey'] = ax0
axarr[0] = ax0
# Note off-by-one counting because add_subplot uses the MATLAB 1-based
# convention.
for i in range(1, nplots):
kwds = subplot_kw.copy()
# Set sharex and sharey to None for blank/dummy axes, these can
# interfere with proper axis limits on the visible axes if
# they share axes e.g. issue #7528
if i >= naxes:
kwds['sharex'] = None
kwds['sharey'] = None
ax = fig.add_subplot(nrows, ncols, i + 1, **kwds)
axarr[i] = ax
if naxes != nplots:
for ax in axarr[naxes:]:
ax.set_visible(False)
_handle_shared_axes(axarr, nplots, naxes, nrows, ncols, sharex, sharey)
if squeeze:
# Reshape the array to have the final desired dimension (nrow,ncol),
# though discarding unneeded dimensions that equal 1. If we only have
# one subplot, just return it instead of a 1-element array.
if nplots == 1:
axes = axarr[0]
else:
axes = axarr.reshape(nrows, ncols).squeeze()
else:
# returned axis array will be always 2-d, even if nrows=ncols=1
axes = axarr.reshape(nrows, ncols)
return fig, axes |
Render tempita templates before calling 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
if not cython:
# Avoid trying to look up numpy when installing from sdist
# https://github.com/pandas-dev/pandas/issues/25193
# TODO: See if this can be removed after pyproject.toml added.
return extensions
numpy_incl = pkg_resources.resource_filename('numpy', 'core/include')
# TODO: Is this really necessary here?
for ext in extensions:
if (hasattr(ext, 'include_dirs') and
numpy_incl not in ext.include_dirs):
ext.include_dirs.append(numpy_incl)
build_ext.render_templates(_pxifiles)
return cythonize(extensions, *args, **kwargs) |
Fast transform path for aggregations | 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)
# for each col, reshape to to size of original frame
# by take operation
ids, _, ngroup = self.grouper.group_info
output = []
for i, _ in enumerate(result.columns):
res = algorithms.take_1d(result.iloc[:, i].values, ids)
if cast:
res = self._try_cast(res, obj.iloc[:, i])
output.append(res)
return DataFrame._from_arrays(output, columns=result.columns,
index=obj.index) |
Return a copy of a DataFrame excluding elements from groups that
do not satisfy the boolean criterion specified by func.
Parameters
----------
f : function
Function to apply to each subframe. Should return True or False.
dropna : Drop groups that do not pass the filter. True by default;
if False, groups that evaluate False are filled with NaNs.
Returns
-------
filtered : DataFrame
Notes
-----
Each subframe is endowed the attribute 'name' in case you need to know
which group you are working on.
Examples
--------
>>> df = pd.DataFrame({'A' : ['foo', 'bar', 'foo', 'bar',
... 'foo', 'bar'],
... 'B' : [1, 2, 3, 4, 5, 6],
... 'C' : [2.0, 5., 8., 1., 2., 9.]})
>>> grouped = df.groupby('A')
>>> grouped.filter(lambda x: x['B'].mean() > 3.)
A B C
1 bar 2 5.0
3 bar 4 1.0
5 bar 6 9.0 | def filter(self, func, dropna=True, *args, **kwargs): # noqa
"""
Return a copy of a DataFrame excluding elements from groups that
do not satisfy the boolean criterion specified by func.
Parameters
----------
f : function
Function to apply to each subframe. Should return True or False.
dropna : Drop groups that do not pass the filter. True by default;
if False, groups that evaluate False are filled with NaNs.
Returns
-------
filtered : DataFrame
Notes
-----
Each subframe is endowed the attribute 'name' in case you need to know
which group you are working on.
Examples
--------
>>> df = pd.DataFrame({'A' : ['foo', 'bar', 'foo', 'bar',
... 'foo', 'bar'],
... 'B' : [1, 2, 3, 4, 5, 6],
... 'C' : [2.0, 5., 8., 1., 2., 9.]})
>>> grouped = df.groupby('A')
>>> grouped.filter(lambda x: x['B'].mean() > 3.)
A B C
1 bar 2 5.0
3 bar 4 1.0
5 bar 6 9.0
"""
indices = []
obj = self._selected_obj
gen = self.grouper.get_iterator(obj, axis=self.axis)
for name, group in gen:
object.__setattr__(group, 'name', name)
res = func(group, *args, **kwargs)
try:
res = res.squeeze()
except AttributeError: # allow e.g., scalars and frames to pass
pass
# interpret the result of the filter
if is_bool(res) or (is_scalar(res) and isna(res)):
if res and notna(res):
indices.append(self._get_index(name))
else:
# non scalars aren't allowed
raise TypeError("filter function returned a %s, "
"but expected a scalar bool" %
type(res).__name__)
return self._apply_filter(indices, dropna) |
common agg/transform wrapping logic | 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
if name is None:
name = self._selected_obj.name
return Series(output, index=index, name=name) |
fast version of transform, only applicable to
builtin/cythonizable functions | def _transform_fast(self, func, func_nm):
"""
fast version of transform, only applicable to
builtin/cythonizable functions
"""
if isinstance(func, str):
func = getattr(self, func)
ids, _, ngroup = self.grouper.group_info
cast = self._transform_should_cast(func_nm)
out = algorithms.take_1d(func()._values, ids)
if cast:
out = self._try_cast(out, self.obj)
return Series(out, index=self.obj.index, name=self.obj.name) |
Return a copy of a Series excluding elements from groups that
do not satisfy the boolean criterion specified by func.
Parameters
----------
func : function
To apply to each group. Should return True or False.
dropna : Drop groups that do not pass the filter. True by default;
if False, groups that evaluate False are filled with NaNs.
Examples
--------
>>> df = pd.DataFrame({'A' : ['foo', 'bar', 'foo', 'bar',
... 'foo', 'bar'],
... 'B' : [1, 2, 3, 4, 5, 6],
... 'C' : [2.0, 5., 8., 1., 2., 9.]})
>>> grouped = df.groupby('A')
>>> df.groupby('A').B.filter(lambda x: x.mean() > 3.)
1 2
3 4
5 6
Name: B, dtype: int64
Returns
-------
filtered : Series | def filter(self, func, dropna=True, *args, **kwargs): # noqa
"""
Return a copy of a Series excluding elements from groups that
do not satisfy the boolean criterion specified by func.
Parameters
----------
func : function
To apply to each group. Should return True or False.
dropna : Drop groups that do not pass the filter. True by default;
if False, groups that evaluate False are filled with NaNs.
Examples
--------
>>> df = pd.DataFrame({'A' : ['foo', 'bar', 'foo', 'bar',
... 'foo', 'bar'],
... 'B' : [1, 2, 3, 4, 5, 6],
... 'C' : [2.0, 5., 8., 1., 2., 9.]})
>>> grouped = df.groupby('A')
>>> df.groupby('A').B.filter(lambda x: x.mean() > 3.)
1 2
3 4
5 6
Name: B, dtype: int64
Returns
-------
filtered : Series
"""
if isinstance(func, str):
wrapper = lambda x: getattr(x, func)(*args, **kwargs)
else:
wrapper = lambda x: func(x, *args, **kwargs)
# Interpret np.nan as False.
def true_and_notna(x, *args, **kwargs):
b = wrapper(x, *args, **kwargs)
return b and notna(b)
try:
indices = [self._get_index(name) for name, group in self
if true_and_notna(group)]
except ValueError:
raise TypeError("the filter must return a boolean result")
except TypeError:
raise TypeError("the filter must return a boolean result")
filtered = self._apply_filter(indices, dropna)
return filtered |
Return number of unique elements in the group. | 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 = 'val.dtype must be object, got {}'.format(val.dtype)
assert val.dtype == object, msg
val, _ = algorithms.factorize(val, sort=False)
sorter = np.lexsort((val, ids))
_isna = lambda a: a == -1
else:
_isna = isna
ids, val = ids[sorter], val[sorter]
# group boundaries are where group ids change
# unique observations are where sorted values change
idx = np.r_[0, 1 + np.nonzero(ids[1:] != ids[:-1])[0]]
inc = np.r_[1, val[1:] != val[:-1]]
# 1st item of each group is a new unique observation
mask = _isna(val)
if dropna:
inc[idx] = 1
inc[mask] = 0
else:
inc[mask & np.r_[False, mask[:-1]]] = 0
inc[idx] = 1
out = np.add.reduceat(inc, idx).astype('int64', copy=False)
if len(ids):
# NaN/NaT group exists if the head of ids is -1,
# so remove it from res and exclude its index from idx
if ids[0] == -1:
res = out[1:]
idx = idx[np.flatnonzero(idx)]
else:
res = out
else:
res = out[1:]
ri = self.grouper.result_index
# we might have duplications among the bins
if len(res) != len(ri):
res, out = np.zeros(len(ri), dtype=out.dtype), res
res[ids[idx]] = out
return Series(res,
index=ri,
name=self._selection_name) |
Compute count of group, excluding missing values | def count(self):
""" Compute count of group, excluding missing values """
ids, _, ngroups = self.grouper.group_info
val = self.obj.get_values()
mask = (ids != -1) & ~isna(val)
ids = ensure_platform_int(ids)
minlength = ngroups or 0
out = np.bincount(ids[mask], minlength=minlength)
return Series(out,
index=self.grouper.result_index,
name=self._selection_name,
dtype='int64') |
Calcuate pct_change of each value to previous entry in group | 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,
fill_method=fill_method,
limit=limit, freq=freq))
filled = getattr(self, fill_method)(limit=limit)
fill_grp = filled.groupby(self.grouper.labels)
shifted = fill_grp.shift(periods=periods, freq=freq)
return (filled / shifted) - 1 |
sub-classes to define
return a sliced object
Parameters
----------
key : string / list of selections
ndim : 1,2
requested ndim of result
subset : object, default None
subset to act on | def _gotitem(self, key, ndim, subset=None):
"""
sub-classes to define
return a sliced object
Parameters
----------
key : string / list of selections
ndim : 1,2
requested ndim of result
subset : object, default None
subset to act on
"""
if ndim == 2:
if subset is None:
subset = self.obj
return DataFrameGroupBy(subset, self.grouper, selection=key,
grouper=self.grouper,
exclusions=self.exclusions,
as_index=self.as_index,
observed=self.observed)
elif ndim == 1:
if subset is None:
subset = self.obj[key]
return SeriesGroupBy(subset, selection=key,
grouper=self.grouper)
raise AssertionError("invalid ndim for _gotitem") |
If we have categorical groupers, then we want to make sure that
we have a fully reindex-output to the levels. These may have not
participated in the groupings (e.g. may have all been
nan groups);
This can re-expand the output space | def _reindex_output(self, result):
"""
If we have categorical groupers, then we want to make sure that
we have a fully reindex-output to the levels. These may have not
participated in the groupings (e.g. may have all been
nan groups);
This can re-expand the output space
"""
# we need to re-expand the output space to accomodate all values
# whether observed or not in the cartesian product of our groupes
groupings = self.grouper.groupings
if groupings is None:
return result
elif len(groupings) == 1:
return result
# if we only care about the observed values
# we are done
elif self.observed:
return result
# reindexing only applies to a Categorical grouper
elif not any(isinstance(ping.grouper, (Categorical, CategoricalIndex))
for ping in groupings):
return result
levels_list = [ping.group_index for ping in groupings]
index, _ = MultiIndex.from_product(
levels_list, names=self.grouper.names).sortlevel()
if self.as_index:
d = {self.obj._get_axis_name(self.axis): index, 'copy': False}
return result.reindex(**d)
# GH 13204
# Here, the categorical in-axis groupers, which need to be fully
# expanded, are columns in `result`. An idea is to do:
# result = result.set_index(self.grouper.names)
# .reindex(index).reset_index()
# but special care has to be taken because of possible not-in-axis
# groupers.
# So, we manually select and drop the in-axis grouper columns,
# reindex `result`, and then reset the in-axis grouper columns.
# Select in-axis groupers
in_axis_grps = ((i, ping.name) for (i, ping)
in enumerate(groupings) if ping.in_axis)
g_nums, g_names = zip(*in_axis_grps)
result = result.drop(labels=list(g_names), axis=1)
# Set a temp index and reindex (possibly expanding)
result = result.set_index(self.grouper.result_index
).reindex(index, copy=False)
# Reset in-axis grouper columns
# (using level numbers `g_nums` because level names may not be unique)
result = result.reset_index(level=g_nums)
return result.reset_index(drop=True) |
Overridden method to join grouped columns in output | 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)
from pandas import concat
return concat((self._wrap_transformed_output(output), res), axis=1) |
Compute count of group, excluding missing values | def count(self):
""" Compute count of group, excluding missing values """
from pandas.core.dtypes.missing import _isna_ndarraylike as _isna
data, _ = self._get_data_to_aggregate()
ids, _, ngroups = self.grouper.group_info
mask = ids != -1
val = ((mask & ~_isna(np.atleast_2d(blk.get_values())))
for blk in data.blocks)
loc = (blk.mgr_locs for blk in data.blocks)
counter = partial(
lib.count_level_2d, labels=ids, max_bin=ngroups, axis=1)
blk = map(make_block, map(counter, val), loc)
return self._wrap_agged_blocks(data.items, list(blk)) |
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
-------
nunique: DataFrame
Examples
--------
>>> df = pd.DataFrame({'id': ['spam', 'egg', 'egg', 'spam',
... 'ham', 'ham'],
... 'value1': [1, 5, 5, 2, 5, 5],
... 'value2': list('abbaxy')})
>>> df
id value1 value2
0 spam 1 a
1 egg 5 b
2 egg 5 b
3 spam 2 a
4 ham 5 x
5 ham 5 y
>>> df.groupby('id').nunique()
id value1 value2
id
egg 1 1 1
ham 1 1 2
spam 1 2 1
Check for rows with the same id but conflicting values:
>>> df.groupby('id').filter(lambda g: (g.nunique() > 1).any())
id value1 value2
0 spam 1 a
3 spam 2 a
4 ham 5 x
5 ham 5 y | 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
-------
nunique: DataFrame
Examples
--------
>>> df = pd.DataFrame({'id': ['spam', 'egg', 'egg', 'spam',
... 'ham', 'ham'],
... 'value1': [1, 5, 5, 2, 5, 5],
... 'value2': list('abbaxy')})
>>> df
id value1 value2
0 spam 1 a
1 egg 5 b
2 egg 5 b
3 spam 2 a
4 ham 5 x
5 ham 5 y
>>> df.groupby('id').nunique()
id value1 value2
id
egg 1 1 1
ham 1 1 2
spam 1 2 1
Check for rows with the same id but conflicting values:
>>> df.groupby('id').filter(lambda g: (g.nunique() > 1).any())
id value1 value2
0 spam 1 a
3 spam 2 a
4 ham 5 x
5 ham 5 y
"""
obj = self._selected_obj
def groupby_series(obj, col=None):
return SeriesGroupBy(obj,
selection=col,
grouper=self.grouper).nunique(dropna=dropna)
if isinstance(obj, Series):
results = groupby_series(obj)
else:
from pandas.core.reshape.concat import concat
results = [groupby_series(obj[col], col) for col in obj.columns]
results = concat(results, axis=1)
results.columns.names = obj.columns.names
if not self.as_index:
results.index = ibase.default_index(len(results))
return results |
Extract the ndarray or ExtensionArray from a Series or Index.
For all other types, `obj` is just returned as is.
Parameters
----------
obj : object
For Series / Index, the underlying ExtensionArray is unboxed.
For Numpy-backed ExtensionArrays, the ndarray is extracted.
extract_numpy : bool, default False
Whether to extract the ndarray from a PandasArray
Returns
-------
arr : object
Examples
--------
>>> extract_array(pd.Series(['a', 'b', 'c'], dtype='category'))
[a, b, c]
Categories (3, object): [a, b, c]
Other objects like lists, arrays, and DataFrames are just passed through.
>>> extract_array([1, 2, 3])
[1, 2, 3]
For an ndarray-backed Series / Index a PandasArray is returned.
>>> extract_array(pd.Series([1, 2, 3]))
<PandasArray>
[1, 2, 3]
Length: 3, dtype: int64
To extract all the way down to the ndarray, pass ``extract_numpy=True``.
>>> extract_array(pd.Series([1, 2, 3]), extract_numpy=True)
array([1, 2, 3]) | def extract_array(obj, extract_numpy=False):
"""
Extract the ndarray or ExtensionArray from a Series or Index.
For all other types, `obj` is just returned as is.
Parameters
----------
obj : object
For Series / Index, the underlying ExtensionArray is unboxed.
For Numpy-backed ExtensionArrays, the ndarray is extracted.
extract_numpy : bool, default False
Whether to extract the ndarray from a PandasArray
Returns
-------
arr : object
Examples
--------
>>> extract_array(pd.Series(['a', 'b', 'c'], dtype='category'))
[a, b, c]
Categories (3, object): [a, b, c]
Other objects like lists, arrays, and DataFrames are just passed through.
>>> extract_array([1, 2, 3])
[1, 2, 3]
For an ndarray-backed Series / Index a PandasArray is returned.
>>> extract_array(pd.Series([1, 2, 3]))
<PandasArray>
[1, 2, 3]
Length: 3, dtype: int64
To extract all the way down to the ndarray, pass ``extract_numpy=True``.
>>> extract_array(pd.Series([1, 2, 3]), extract_numpy=True)
array([1, 2, 3])
"""
if isinstance(obj, (ABCIndexClass, ABCSeries)):
obj = obj.array
if extract_numpy and isinstance(obj, ABCPandasArray):
obj = obj.to_numpy()
return obj |
Flatten an arbitrarily nested sequence.
Parameters
----------
l : sequence
The non string sequence to flatten
Notes
-----
This doesn't consider strings sequences.
Returns
-------
flattened : generator | def flatten(l):
"""
Flatten an arbitrarily nested sequence.
Parameters
----------
l : sequence
The non string sequence to flatten
Notes
-----
This doesn't consider strings sequences.
Returns
-------
flattened : generator
"""
for el in l:
if _iterable_not_string(el):
for s in flatten(el):
yield s
else:
yield el |
Check whether `key` is a valid boolean indexer.
Parameters
----------
key : Any
Only list-likes may be considered boolean indexers.
All other types are not considered a boolean indexer.
For array-like input, boolean ndarrays or ExtensionArrays
with ``_is_boolean`` set are considered boolean indexers.
Returns
-------
bool
Raises
------
ValueError
When the array is an object-dtype ndarray or ExtensionArray
and contains missing values. | def is_bool_indexer(key: Any) -> bool:
"""
Check whether `key` is a valid boolean indexer.
Parameters
----------
key : Any
Only list-likes may be considered boolean indexers.
All other types are not considered a boolean indexer.
For array-like input, boolean ndarrays or ExtensionArrays
with ``_is_boolean`` set are considered boolean indexers.
Returns
-------
bool
Raises
------
ValueError
When the array is an object-dtype ndarray or ExtensionArray
and contains missing values.
"""
na_msg = 'cannot index with vector containing NA / NaN values'
if (isinstance(key, (ABCSeries, np.ndarray, ABCIndex)) or
(is_array_like(key) and is_extension_array_dtype(key.dtype))):
if key.dtype == np.object_:
key = np.asarray(values_from_object(key))
if not lib.is_bool_array(key):
if isna(key).any():
raise ValueError(na_msg)
return False
return True
elif is_bool_dtype(key.dtype):
# an ndarray with bool-dtype by definition has no missing values.
# So we only need to check for NAs in ExtensionArrays
if is_extension_array_dtype(key.dtype):
if np.any(key.isna()):
raise ValueError(na_msg)
return True
elif isinstance(key, list):
try:
arr = np.asarray(key)
return arr.dtype == np.bool_ and len(arr) == len(key)
except TypeError: # pragma: no cover
return False
return False |
To avoid numpy DeprecationWarnings, cast float to integer where valid.
Parameters
----------
val : scalar
Returns
-------
outval : scalar | def cast_scalar_indexer(val):
"""
To avoid numpy DeprecationWarnings, cast float to integer where valid.
Parameters
----------
val : scalar
Returns
-------
outval : scalar
"""
# assumes lib.is_scalar(val)
if lib.is_float(val) and val == int(val):
return int(val)
return val |
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.
Returns
-------
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.
Returns
-------
array
"""
if isinstance(labels, (str, tuple)):
labels = [labels]
if not isinstance(labels, (list, np.ndarray)):
try:
labels = list(labels)
except TypeError: # non-iterable
labels = [labels]
labels = asarray_tuplesafe(labels, dtype=dtype)
return labels |
We have a null slice. | def is_null_slice(obj):
"""
We have a null slice.
"""
return (isinstance(obj, slice) and obj.start is None and
obj.stop is None and obj.step is None) |
We have a full length slice. | def is_full_slice(obj, l):
"""
We have a full length slice.
"""
return (isinstance(obj, slice) and obj.start == 0 and obj.stop == l and
obj.step is None) |
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 | 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):
return maybe_callable(obj, **kwargs)
return maybe_callable |
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,
or an instance of a collections.abc.Mapping subclass.
Returns
-------
mapping : a collections.abc.Mapping subclass or other constructor
a callable object that can accept an iterator to create
the desired Mapping.
See Also
--------
DataFrame.to_dict
Series.to_dict | 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,
or an instance of a collections.abc.Mapping subclass.
Returns
-------
mapping : a collections.abc.Mapping subclass or other constructor
a callable object that can accept an iterator to create
the desired Mapping.
See Also
--------
DataFrame.to_dict
Series.to_dict
"""
if not inspect.isclass(into):
if isinstance(into, collections.defaultdict):
return partial(
collections.defaultdict, into.default_factory)
into = type(into)
if not issubclass(into, abc.Mapping):
raise TypeError('unsupported type: {into}'.format(into=into))
elif into == collections.defaultdict:
raise TypeError(
'to_dict() only accepts initialized defaultdicts')
return into |
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.
If receives `None`, returns np.random.
If receives anything else, raises an informative ValueError.
Default None.
Returns
-------
np.random.RandomState | 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.
If receives `None`, returns np.random.
If receives anything else, raises an informative ValueError.
Default None.
Returns
-------
np.random.RandomState
"""
if is_integer(state):
return np.random.RandomState(state)
elif isinstance(state, np.random.RandomState):
return state
elif state is None:
return np.random
else:
raise ValueError("random_state must be an integer, a numpy "
"RandomState, or None") |
Apply a function ``func`` to object ``obj`` either by passing obj as the
first argument to the function or, in the case that the func is a tuple,
interpret the first element of the tuple as a function and pass the obj to
that function as a keyword argument whose key is the value of the second
element of the tuple.
Parameters
----------
func : callable or tuple of (callable, string)
Function to apply to this object or, alternatively, a
``(callable, data_keyword)`` tuple where ``data_keyword`` is a
string indicating the keyword of `callable`` that expects the
object.
args : iterable, optional
positional arguments passed into ``func``.
kwargs : dict, optional
a dictionary of keyword arguments passed into ``func``.
Returns
-------
object : the return type of ``func``. | def _pipe(obj, func, *args, **kwargs):
"""
Apply a function ``func`` to object ``obj`` either by passing obj as the
first argument to the function or, in the case that the func is a tuple,
interpret the first element of the tuple as a function and pass the obj to
that function as a keyword argument whose key is the value of the second
element of the tuple.
Parameters
----------
func : callable or tuple of (callable, string)
Function to apply to this object or, alternatively, a
``(callable, data_keyword)`` tuple where ``data_keyword`` is a
string indicating the keyword of `callable`` that expects the
object.
args : iterable, optional
positional arguments passed into ``func``.
kwargs : dict, optional
a dictionary of keyword arguments passed into ``func``.
Returns
-------
object : the return type of ``func``.
"""
if isinstance(func, tuple):
func, target = func
if target in kwargs:
msg = '%s is both the pipe target and a keyword argument' % target
raise ValueError(msg)
kwargs[target] = obj
return func(*args, **kwargs)
else:
return func(obj, *args, **kwargs) |
Returns a function that will map names/labels, dependent if mapper
is a dict, Series or just a function. | def _get_rename_function(mapper):
"""
Returns a function that will map names/labels, dependent if mapper
is a dict, Series or just a function.
"""
if isinstance(mapper, (abc.Mapping, ABCSeries)):
def f(x):
if x in mapper:
return mapper[x]
else:
return x
else:
f = mapper
return f |
return the correct fill value for the dtype of the values | 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_value_typ == '+inf':
return np.inf
else:
return -np.inf
else:
if fill_value_typ is None:
return tslibs.iNaT
else:
if fill_value_typ == '+inf':
# need the max int here
return _int64_max
else:
return tslibs.iNaT |
utility to get the values view, mask, dtype
if necessary copy and mask using the specified fill_value
copy = True will force the copy | 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
copy = True will force the copy
"""
if is_datetime64tz_dtype(values):
# com.values_from_object returns M8[ns] dtype instead of tz-aware,
# so this case must be handled separately from the rest
dtype = values.dtype
values = getattr(values, "_values", values)
else:
values = com.values_from_object(values)
dtype = values.dtype
if mask is None:
if isfinite:
mask = _isfinite(values)
else:
mask = isna(values)
if is_datetime_or_timedelta_dtype(values) or is_datetime64tz_dtype(values):
# changing timedelta64/datetime64 to int64 needs to happen after
# finding `mask` above
values = getattr(values, "asi8", values)
values = values.view(np.int64)
dtype_ok = _na_ok_dtype(dtype)
# get our fill value (in case we need to provide an alternative
# dtype for it)
fill_value = _get_fill_value(dtype, fill_value=fill_value,
fill_value_typ=fill_value_typ)
if skipna:
if copy:
values = values.copy()
if dtype_ok:
np.putmask(values, mask, fill_value)
# promote if needed
else:
values, changed = maybe_upcast_putmask(values, mask, fill_value)
elif copy:
values = values.copy()
# return a platform independent precision dtype
dtype_max = dtype
if is_integer_dtype(dtype) or is_bool_dtype(dtype):
dtype_max = np.int64
elif is_float_dtype(dtype):
dtype_max = np.float64
return values, mask, dtype, dtype_max, fill_value |
wrap our results if needed | 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, 'tz', None)
assert not isna(fill_value), "Expected non-null fill_value"
if result == fill_value:
result = np.nan
result = tslibs.Timestamp(result, tz=tz)
else:
result = result.view(dtype)
elif is_timedelta64_dtype(dtype):
if not isinstance(result, np.ndarray):
if result == fill_value:
result = np.nan
# raise if we have a timedelta64[ns] which is too large
if np.fabs(result) > _int64_max:
raise ValueError("overflow in timedelta operation")
result = tslibs.Timedelta(result, unit='ns')
else:
result = result.astype('i8').view(dtype)
return result |
Return the missing value for `values`
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.
For 2-D values, returns a 1-D array where each element is missing. | def _na_for_min_count(values, axis):
"""Return the missing value for `values`
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.
For 2-D values, returns a 1-D array where each element is missing.
"""
# we either return np.nan or pd.NaT
if is_numeric_dtype(values):
values = values.astype('float64')
fill_value = na_value_for_dtype(values.dtype)
if values.ndim == 1:
return fill_value
else:
result_shape = (values.shape[:axis] +
values.shape[axis + 1:])
result = np.empty(result_shape, dtype=values.dtype)
result.fill(fill_value)
return result |
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
-------
result : bool
Examples
--------
>>> import pandas.core.nanops as nanops
>>> s = pd.Series([1, 2])
>>> nanops.nanany(s)
True
>>> import pandas.core.nanops as nanops
>>> s = pd.Series([np.nan])
>>> nanops.nanany(s)
False | 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
-------
result : bool
Examples
--------
>>> import pandas.core.nanops as nanops
>>> s = pd.Series([1, 2])
>>> nanops.nanany(s)
True
>>> import pandas.core.nanops as nanops
>>> s = pd.Series([np.nan])
>>> nanops.nanany(s)
False
"""
values, mask, dtype, _, _ = _get_values(values, skipna, False, copy=skipna,
mask=mask)
return values.any(axis) |
Check if all 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
-------
result : bool
Examples
--------
>>> import pandas.core.nanops as nanops
>>> s = pd.Series([1, 2, np.nan])
>>> nanops.nanall(s)
True
>>> import pandas.core.nanops as nanops
>>> s = pd.Series([1, 0])
>>> nanops.nanall(s)
False | 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
nan-mask if known
Returns
-------
result : bool
Examples
--------
>>> import pandas.core.nanops as nanops
>>> s = pd.Series([1, 2, np.nan])
>>> nanops.nanall(s)
True
>>> import pandas.core.nanops as nanops
>>> s = pd.Series([1, 0])
>>> nanops.nanall(s)
False
"""
values, mask, dtype, _, _ = _get_values(values, skipna, True, copy=skipna,
mask=mask)
return values.all(axis) |
Sum the elements along an axis ignoring NaNs
Parameters
----------
values : ndarray[dtype]
axis: int, optional
skipna : bool, default True
min_count: int, default 0
mask : ndarray[bool], optional
nan-mask if known
Returns
-------
result : dtype
Examples
--------
>>> import pandas.core.nanops as nanops
>>> s = pd.Series([1, 2, np.nan])
>>> nanops.nansum(s)
3.0 | def nansum(values, axis=None, skipna=True, min_count=0, mask=None):
"""
Sum the elements along an axis ignoring NaNs
Parameters
----------
values : ndarray[dtype]
axis: int, optional
skipna : bool, default True
min_count: int, default 0
mask : ndarray[bool], optional
nan-mask if known
Returns
-------
result : dtype
Examples
--------
>>> import pandas.core.nanops as nanops
>>> s = pd.Series([1, 2, np.nan])
>>> nanops.nansum(s)
3.0
"""
values, mask, dtype, dtype_max, _ = _get_values(values,
skipna, 0, mask=mask)
dtype_sum = dtype_max
if is_float_dtype(dtype):
dtype_sum = dtype
elif is_timedelta64_dtype(dtype):
dtype_sum = np.float64
the_sum = values.sum(axis, dtype=dtype_sum)
the_sum = _maybe_null_out(the_sum, axis, mask, min_count=min_count)
return _wrap_results(the_sum, dtype) |
Compute the mean of the element along an axis ignoring NaNs
Parameters
----------
values : ndarray
axis: int, optional
skipna : bool, default True
mask : ndarray[bool], optional
nan-mask if known
Returns
-------
result : float
Unless input is a float array, in which case use the same
precision as the input array.
Examples
--------
>>> import pandas.core.nanops as nanops
>>> s = pd.Series([1, 2, np.nan])
>>> nanops.nanmean(s)
1.5 | def nanmean(values, axis=None, skipna=True, mask=None):
"""
Compute the mean of the element along an axis ignoring NaNs
Parameters
----------
values : ndarray
axis: int, optional
skipna : bool, default True
mask : ndarray[bool], optional
nan-mask if known
Returns
-------
result : float
Unless input is a float array, in which case use the same
precision as the input array.
Examples
--------
>>> import pandas.core.nanops as nanops
>>> s = pd.Series([1, 2, np.nan])
>>> nanops.nanmean(s)
1.5
"""
values, mask, dtype, dtype_max, _ = _get_values(
values, skipna, 0, mask=mask)
dtype_sum = dtype_max
dtype_count = np.float64
if (is_integer_dtype(dtype) or is_timedelta64_dtype(dtype) or
is_datetime64_dtype(dtype) or is_datetime64tz_dtype(dtype)):
dtype_sum = np.float64
elif is_float_dtype(dtype):
dtype_sum = dtype
dtype_count = dtype
count = _get_counts(mask, axis, dtype=dtype_count)
the_sum = _ensure_numeric(values.sum(axis, dtype=dtype_sum))
if axis is not None and getattr(the_sum, 'ndim', False):
with np.errstate(all="ignore"):
# suppress division by zero warnings
the_mean = the_sum / count
ct_mask = count == 0
if ct_mask.any():
the_mean[ct_mask] = np.nan
else:
the_mean = the_sum / count if count > 0 else np.nan
return _wrap_results(the_mean, dtype) |
Parameters
----------
values : ndarray
axis: int, optional
skipna : bool, default True
mask : ndarray[bool], optional
nan-mask if known
Returns
-------
result : float
Unless input is a float array, in which case use the same
precision as the input array.
Examples
--------
>>> import pandas.core.nanops as nanops
>>> s = pd.Series([1, np.nan, 2, 2])
>>> nanops.nanmedian(s)
2.0 | def nanmedian(values, axis=None, skipna=True, mask=None):
"""
Parameters
----------
values : ndarray
axis: int, optional
skipna : bool, default True
mask : ndarray[bool], optional
nan-mask if known
Returns
-------
result : float
Unless input is a float array, in which case use the same
precision as the input array.
Examples
--------
>>> import pandas.core.nanops as nanops
>>> s = pd.Series([1, np.nan, 2, 2])
>>> nanops.nanmedian(s)
2.0
"""
def get_median(x):
mask = notna(x)
if not skipna and not mask.all():
return np.nan
return np.nanmedian(x[mask])
values, mask, dtype, dtype_max, _ = _get_values(values, skipna, mask=mask)
if not is_float_dtype(values):
values = values.astype('f8')
values[mask] = np.nan
if axis is None:
values = values.ravel()
notempty = values.size
# an array from a frame
if values.ndim > 1:
# there's a non-empty array to apply over otherwise numpy raises
if notempty:
if not skipna:
return _wrap_results(
np.apply_along_axis(get_median, axis, values), dtype)
# fastpath for the skipna case
return _wrap_results(np.nanmedian(values, axis), dtype)
# must return the correct shape, but median is not defined for the
# empty set so return nans of shape "everything but the passed axis"
# since "axis" is where the reduction would occur if we had a nonempty
# array
shp = np.array(values.shape)
dims = np.arange(values.ndim)
ret = np.empty(shp[dims != axis])
ret.fill(np.nan)
return _wrap_results(ret, dtype)
# otherwise return a scalar value
return _wrap_results(get_median(values) if notempty else np.nan, dtype) |
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 divisor used in calculations is N - ddof,
where N represents the number of elements.
mask : ndarray[bool], optional
nan-mask if known
Returns
-------
result : float
Unless input is a float array, in which case use the same
precision as the input array.
Examples
--------
>>> import pandas.core.nanops as nanops
>>> s = pd.Series([1, np.nan, 2, 3])
>>> nanops.nanstd(s)
1.0 | 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 divisor used in calculations is N - ddof,
where N represents the number of elements.
mask : ndarray[bool], optional
nan-mask if known
Returns
-------
result : float
Unless input is a float array, in which case use the same
precision as the input array.
Examples
--------
>>> import pandas.core.nanops as nanops
>>> s = pd.Series([1, np.nan, 2, 3])
>>> nanops.nanstd(s)
1.0
"""
result = np.sqrt(nanvar(values, axis=axis, skipna=skipna, ddof=ddof,
mask=mask))
return _wrap_results(result, values.dtype) |
Compute the variance 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 divisor used in calculations is N - ddof,
where N represents the number of elements.
mask : ndarray[bool], optional
nan-mask if known
Returns
-------
result : float
Unless input is a float array, in which case use the same
precision as the input array.
Examples
--------
>>> import pandas.core.nanops as nanops
>>> s = pd.Series([1, np.nan, 2, 3])
>>> nanops.nanvar(s)
1.0 | def nanvar(values, axis=None, skipna=True, ddof=1, mask=None):
"""
Compute the variance 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 divisor used in calculations is N - ddof,
where N represents the number of elements.
mask : ndarray[bool], optional
nan-mask if known
Returns
-------
result : float
Unless input is a float array, in which case use the same
precision as the input array.
Examples
--------
>>> import pandas.core.nanops as nanops
>>> s = pd.Series([1, np.nan, 2, 3])
>>> nanops.nanvar(s)
1.0
"""
values = com.values_from_object(values)
dtype = values.dtype
if mask is None:
mask = isna(values)
if is_any_int_dtype(values):
values = values.astype('f8')
values[mask] = np.nan
if is_float_dtype(values):
count, d = _get_counts_nanvar(mask, axis, ddof, values.dtype)
else:
count, d = _get_counts_nanvar(mask, axis, ddof)
if skipna:
values = values.copy()
np.putmask(values, mask, 0)
# xref GH10242
# Compute variance via two-pass algorithm, which is stable against
# cancellation errors and relatively accurate for small numbers of
# observations.
#
# See https://en.wikipedia.org/wiki/Algorithms_for_calculating_variance
avg = _ensure_numeric(values.sum(axis=axis, dtype=np.float64)) / count
if axis is not None:
avg = np.expand_dims(avg, axis)
sqr = _ensure_numeric((avg - values) ** 2)
np.putmask(sqr, mask, 0)
result = sqr.sum(axis=axis, dtype=np.float64) / d
# Return variance as np.float64 (the datatype used in the accumulator),
# unless we were dealing with a float array, in which case use the same
# precision as the original values array.
if is_float_dtype(dtype):
result = result.astype(dtype)
return _wrap_results(result, values.dtype) |
Compute the standard error in the mean 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 divisor used in calculations is N - ddof,
where N represents the number of elements.
mask : ndarray[bool], optional
nan-mask if known
Returns
-------
result : float64
Unless input is a float array, in which case use the same
precision as the input array.
Examples
--------
>>> import pandas.core.nanops as nanops
>>> s = pd.Series([1, np.nan, 2, 3])
>>> nanops.nansem(s)
0.5773502691896258 | def nansem(values, axis=None, skipna=True, ddof=1, mask=None):
"""
Compute the standard error in the mean 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 divisor used in calculations is N - ddof,
where N represents the number of elements.
mask : ndarray[bool], optional
nan-mask if known
Returns
-------
result : float64
Unless input is a float array, in which case use the same
precision as the input array.
Examples
--------
>>> import pandas.core.nanops as nanops
>>> s = pd.Series([1, np.nan, 2, 3])
>>> nanops.nansem(s)
0.5773502691896258
"""
# This checks if non-numeric-like data is passed with numeric_only=False
# and raises a TypeError otherwise
nanvar(values, axis, skipna, ddof=ddof, mask=mask)
if mask is None:
mask = isna(values)
if not is_float_dtype(values.dtype):
values = values.astype('f8')
count, _ = _get_counts_nanvar(mask, axis, ddof, values.dtype)
var = nanvar(values, axis, skipna, ddof=ddof)
return np.sqrt(var) / np.sqrt(count) |
Parameters
----------
values : ndarray
axis: int, optional
skipna : bool, default True
mask : ndarray[bool], optional
nan-mask if known
Returns
--------
result : int
The index of max value in specified axis or -1 in the NA case
Examples
--------
>>> import pandas.core.nanops as nanops
>>> s = pd.Series([1, 2, 3, np.nan, 4])
>>> nanops.nanargmax(s)
4 | def nanargmax(values, axis=None, skipna=True, mask=None):
"""
Parameters
----------
values : ndarray
axis: int, optional
skipna : bool, default True
mask : ndarray[bool], optional
nan-mask if known
Returns
--------
result : int
The index of max value in specified axis or -1 in the NA case
Examples
--------
>>> import pandas.core.nanops as nanops
>>> s = pd.Series([1, 2, 3, np.nan, 4])
>>> nanops.nanargmax(s)
4
"""
values, mask, dtype, _, _ = _get_values(
values, skipna, fill_value_typ='-inf', mask=mask)
result = values.argmax(axis)
result = _maybe_arg_null_out(result, axis, mask, skipna)
return result |
Parameters
----------
values : ndarray
axis: int, optional
skipna : bool, default True
mask : ndarray[bool], optional
nan-mask if known
Returns
--------
result : int
The index of min value in specified axis or -1 in the NA case
Examples
--------
>>> import pandas.core.nanops as nanops
>>> s = pd.Series([1, 2, 3, np.nan, 4])
>>> nanops.nanargmin(s)
0 | def nanargmin(values, axis=None, skipna=True, mask=None):
"""
Parameters
----------
values : ndarray
axis: int, optional
skipna : bool, default True
mask : ndarray[bool], optional
nan-mask if known
Returns
--------
result : int
The index of min value in specified axis or -1 in the NA case
Examples
--------
>>> import pandas.core.nanops as nanops
>>> s = pd.Series([1, 2, 3, np.nan, 4])
>>> nanops.nanargmin(s)
0
"""
values, mask, dtype, _, _ = _get_values(
values, skipna, fill_value_typ='+inf', mask=mask)
result = values.argmin(axis)
result = _maybe_arg_null_out(result, axis, mask, skipna)
return result |
Compute the sample skewness.
The statistic computed here is the adjusted Fisher-Pearson standardized
moment coefficient G1. The algorithm computes this coefficient directly
from the second and third central moment.
Parameters
----------
values : ndarray
axis: int, optional
skipna : bool, default True
mask : ndarray[bool], optional
nan-mask if known
Returns
-------
result : float64
Unless input is a float array, in which case use the same
precision as the input array.
Examples
--------
>>> import pandas.core.nanops as nanops
>>> s = pd.Series([1,np.nan, 1, 2])
>>> nanops.nanskew(s)
1.7320508075688787 | def nanskew(values, axis=None, skipna=True, mask=None):
""" Compute the sample skewness.
The statistic computed here is the adjusted Fisher-Pearson standardized
moment coefficient G1. The algorithm computes this coefficient directly
from the second and third central moment.
Parameters
----------
values : ndarray
axis: int, optional
skipna : bool, default True
mask : ndarray[bool], optional
nan-mask if known
Returns
-------
result : float64
Unless input is a float array, in which case use the same
precision as the input array.
Examples
--------
>>> import pandas.core.nanops as nanops
>>> s = pd.Series([1,np.nan, 1, 2])
>>> nanops.nanskew(s)
1.7320508075688787
"""
values = com.values_from_object(values)
if mask is None:
mask = isna(values)
if not is_float_dtype(values.dtype):
values = values.astype('f8')
count = _get_counts(mask, axis)
else:
count = _get_counts(mask, axis, dtype=values.dtype)
if skipna:
values = values.copy()
np.putmask(values, mask, 0)
mean = values.sum(axis, dtype=np.float64) / count
if axis is not None:
mean = np.expand_dims(mean, axis)
adjusted = values - mean
if skipna:
np.putmask(adjusted, mask, 0)
adjusted2 = adjusted ** 2
adjusted3 = adjusted2 * adjusted
m2 = adjusted2.sum(axis, dtype=np.float64)
m3 = adjusted3.sum(axis, dtype=np.float64)
# floating point error
#
# #18044 in _libs/windows.pyx calc_skew follow this behavior
# to fix the fperr to treat m2 <1e-14 as zero
m2 = _zero_out_fperr(m2)
m3 = _zero_out_fperr(m3)
with np.errstate(invalid='ignore', divide='ignore'):
result = (count * (count - 1) ** 0.5 / (count - 2)) * (m3 / m2 ** 1.5)
dtype = values.dtype
if is_float_dtype(dtype):
result = result.astype(dtype)
if isinstance(result, np.ndarray):
result = np.where(m2 == 0, 0, result)
result[count < 3] = np.nan
return result
else:
result = 0 if m2 == 0 else result
if count < 3:
return np.nan
return result |
Compute the sample excess kurtosis
The statistic computed here is the adjusted Fisher-Pearson standardized
moment coefficient G2, computed directly from the second and fourth
central moment.
Parameters
----------
values : ndarray
axis: int, optional
skipna : bool, default True
mask : ndarray[bool], optional
nan-mask if known
Returns
-------
result : float64
Unless input is a float array, in which case use the same
precision as the input array.
Examples
--------
>>> import pandas.core.nanops as nanops
>>> s = pd.Series([1,np.nan, 1, 3, 2])
>>> nanops.nankurt(s)
-1.2892561983471076 | def nankurt(values, axis=None, skipna=True, mask=None):
"""
Compute the sample excess kurtosis
The statistic computed here is the adjusted Fisher-Pearson standardized
moment coefficient G2, computed directly from the second and fourth
central moment.
Parameters
----------
values : ndarray
axis: int, optional
skipna : bool, default True
mask : ndarray[bool], optional
nan-mask if known
Returns
-------
result : float64
Unless input is a float array, in which case use the same
precision as the input array.
Examples
--------
>>> import pandas.core.nanops as nanops
>>> s = pd.Series([1,np.nan, 1, 3, 2])
>>> nanops.nankurt(s)
-1.2892561983471076
"""
values = com.values_from_object(values)
if mask is None:
mask = isna(values)
if not is_float_dtype(values.dtype):
values = values.astype('f8')
count = _get_counts(mask, axis)
else:
count = _get_counts(mask, axis, dtype=values.dtype)
if skipna:
values = values.copy()
np.putmask(values, mask, 0)
mean = values.sum(axis, dtype=np.float64) / count
if axis is not None:
mean = np.expand_dims(mean, axis)
adjusted = values - mean
if skipna:
np.putmask(adjusted, mask, 0)
adjusted2 = adjusted ** 2
adjusted4 = adjusted2 ** 2
m2 = adjusted2.sum(axis, dtype=np.float64)
m4 = adjusted4.sum(axis, dtype=np.float64)
with np.errstate(invalid='ignore', divide='ignore'):
adj = 3 * (count - 1) ** 2 / ((count - 2) * (count - 3))
numer = count * (count + 1) * (count - 1) * m4
denom = (count - 2) * (count - 3) * m2 ** 2
# floating point error
#
# #18044 in _libs/windows.pyx calc_kurt follow this behavior
# to fix the fperr to treat denom <1e-14 as zero
numer = _zero_out_fperr(numer)
denom = _zero_out_fperr(denom)
if not isinstance(denom, np.ndarray):
# if ``denom`` is a scalar, check these corner cases first before
# doing division
if count < 4:
return np.nan
if denom == 0:
return 0
with np.errstate(invalid='ignore', divide='ignore'):
result = numer / denom - adj
dtype = values.dtype
if is_float_dtype(dtype):
result = result.astype(dtype)
if isinstance(result, np.ndarray):
result = np.where(denom == 0, 0, result)
result[count < 4] = np.nan
return result |
Parameters
----------
values : ndarray[dtype]
axis: int, optional
skipna : bool, default True
min_count: int, default 0
mask : ndarray[bool], optional
nan-mask if known
Returns
-------
result : dtype
Examples
--------
>>> import pandas.core.nanops as nanops
>>> s = pd.Series([1, 2, 3, np.nan])
>>> nanops.nanprod(s)
6.0
Returns
--------
The product of all elements on a given axis. ( NaNs are treated as 1) | def nanprod(values, axis=None, skipna=True, min_count=0, mask=None):
"""
Parameters
----------
values : ndarray[dtype]
axis: int, optional
skipna : bool, default True
min_count: int, default 0
mask : ndarray[bool], optional
nan-mask if known
Returns
-------
result : dtype
Examples
--------
>>> import pandas.core.nanops as nanops
>>> s = pd.Series([1, 2, 3, np.nan])
>>> nanops.nanprod(s)
6.0
Returns
--------
The product of all elements on a given axis. ( NaNs are treated as 1)
"""
if mask is None:
mask = isna(values)
if skipna and not is_any_int_dtype(values):
values = values.copy()
values[mask] = 1
result = values.prod(axis)
return _maybe_null_out(result, axis, mask, min_count=min_count) |
a, b: ndarrays | def nancorr(a, b, method='pearson', min_periods=None):
"""
a, b: ndarrays
"""
if len(a) != len(b):
raise AssertionError('Operands to nancorr must have same size')
if min_periods is None:
min_periods = 1
valid = notna(a) & notna(b)
if not valid.all():
a = a[valid]
b = b[valid]
if len(a) < min_periods:
return np.nan
f = get_corr_func(method)
return f(a, b) |
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