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pandas-dev/pandas | pandas/core/arrays/categorical.py | Categorical.take_nd | def take_nd(self, indexer, allow_fill=None, fill_value=None):
"""
Take elements from the Categorical.
Parameters
----------
indexer : sequence of int
The indices in `self` to take. The meaning of negative values in
`indexer` depends on the value of `allow... | python | def take_nd(self, indexer, allow_fill=None, fill_value=None):
"""
Take elements from the Categorical.
Parameters
----------
indexer : sequence of int
The indices in `self` to take. The meaning of negative values in
`indexer` depends on the value of `allow... | [
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pandas-dev/pandas | pandas/core/arrays/categorical.py | Categorical._slice | def _slice(self, slicer):
"""
Return a slice of myself.
For internal compatibility with numpy arrays.
"""
# only allow 1 dimensional slicing, but can
# in a 2-d case be passd (slice(None),....)
if isinstance(slicer, tuple) and len(slicer) == 2:
if no... | python | def _slice(self, slicer):
"""
Return a slice of myself.
For internal compatibility with numpy arrays.
"""
# only allow 1 dimensional slicing, but can
# in a 2-d case be passd (slice(None),....)
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pandas-dev/pandas | pandas/core/arrays/categorical.py | Categorical._tidy_repr | def _tidy_repr(self, max_vals=10, footer=True):
""" a short repr displaying only max_vals and an optional (but default
footer)
"""
num = max_vals // 2
head = self[:num]._get_repr(length=False, footer=False)
tail = self[-(max_vals - num):]._get_repr(length=False, footer=Fa... | python | def _tidy_repr(self, max_vals=10, footer=True):
""" a short repr displaying only max_vals and an optional (but default
footer)
"""
num = max_vals // 2
head = self[:num]._get_repr(length=False, footer=False)
tail = self[-(max_vals - num):]._get_repr(length=False, footer=Fa... | [
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pandas-dev/pandas | pandas/core/arrays/categorical.py | Categorical._repr_categories | def _repr_categories(self):
"""
return the base repr for the categories
"""
max_categories = (10 if get_option("display.max_categories") == 0 else
get_option("display.max_categories"))
from pandas.io.formats import format as fmt
if len(self.categ... | python | def _repr_categories(self):
"""
return the base repr for the categories
"""
max_categories = (10 if get_option("display.max_categories") == 0 else
get_option("display.max_categories"))
from pandas.io.formats import format as fmt
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pandas-dev/pandas | pandas/core/arrays/categorical.py | Categorical._repr_categories_info | def _repr_categories_info(self):
"""
Returns a string representation of the footer.
"""
category_strs = self._repr_categories()
dtype = getattr(self.categories, 'dtype_str',
str(self.categories.dtype))
levheader = "Categories ({length}, {dtype}):... | python | def _repr_categories_info(self):
"""
Returns a string representation of the footer.
"""
category_strs = self._repr_categories()
dtype = getattr(self.categories, 'dtype_str',
str(self.categories.dtype))
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pandas-dev/pandas | pandas/core/arrays/categorical.py | Categorical._maybe_coerce_indexer | def _maybe_coerce_indexer(self, indexer):
"""
return an indexer coerced to the codes dtype
"""
if isinstance(indexer, np.ndarray) and indexer.dtype.kind == 'i':
indexer = indexer.astype(self._codes.dtype)
return indexer | python | def _maybe_coerce_indexer(self, indexer):
"""
return an indexer coerced to the codes dtype
"""
if isinstance(indexer, np.ndarray) and indexer.dtype.kind == 'i':
indexer = indexer.astype(self._codes.dtype)
return indexer | [
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pandas-dev/pandas | pandas/core/arrays/categorical.py | Categorical._reverse_indexer | def _reverse_indexer(self):
"""
Compute the inverse of a categorical, returning
a dict of categories -> indexers.
*This is an internal function*
Returns
-------
dict of categories -> indexers
Example
-------
In [1]: c = pd.Categorical(li... | python | def _reverse_indexer(self):
"""
Compute the inverse of a categorical, returning
a dict of categories -> indexers.
*This is an internal function*
Returns
-------
dict of categories -> indexers
Example
-------
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In [2]: c
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pandas-dev/pandas | pandas/core/arrays/categorical.py | Categorical.min | def min(self, numeric_only=None, **kwargs):
"""
The minimum value of the object.
Only ordered `Categoricals` have a minimum!
Raises
------
TypeError
If the `Categorical` is not `ordered`.
Returns
-------
min : the minimum of this `Ca... | python | def min(self, numeric_only=None, **kwargs):
"""
The minimum value of the object.
Only ordered `Categoricals` have a minimum!
Raises
------
TypeError
If the `Categorical` is not `ordered`.
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-------
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pandas-dev/pandas | pandas/core/arrays/categorical.py | Categorical.mode | def mode(self, dropna=True):
"""
Returns the mode(s) of the Categorical.
Always returns `Categorical` even if only one value.
Parameters
----------
dropna : bool, default True
Don't consider counts of NaN/NaT.
.. versionadded:: 0.24.0
R... | python | def mode(self, dropna=True):
"""
Returns the mode(s) of the Categorical.
Always returns `Categorical` even if only one value.
Parameters
----------
dropna : bool, default True
Don't consider counts of NaN/NaT.
.. versionadded:: 0.24.0
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pandas-dev/pandas | pandas/core/arrays/categorical.py | Categorical.unique | def unique(self):
"""
Return the ``Categorical`` which ``categories`` and ``codes`` are
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- unordered category: values and categories are sorted by appearance
order.
- ordered category: values are sorted by appearance order, ca... | python | def unique(self):
"""
Return the ``Categorical`` which ``categories`` and ``codes`` are
unique. Unused categories are NOT returned.
- unordered category: values and categories are sorted by appearance
order.
- ordered category: values are sorted by appearance order, ca... | [
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pandas-dev/pandas | pandas/core/arrays/categorical.py | Categorical.equals | def equals(self, other):
"""
Returns True if categorical arrays are equal.
Parameters
----------
other : `Categorical`
Returns
-------
bool
"""
if self.is_dtype_equal(other):
if self.categories.equals(other.categories):
... | python | def equals(self, other):
"""
Returns True if categorical arrays are equal.
Parameters
----------
other : `Categorical`
Returns
-------
bool
"""
if self.is_dtype_equal(other):
if self.categories.equals(other.categories):
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pandas-dev/pandas | pandas/core/arrays/categorical.py | Categorical.is_dtype_equal | def is_dtype_equal(self, other):
"""
Returns True if categoricals are the same dtype
same categories, and same ordered
Parameters
----------
other : Categorical
Returns
-------
bool
"""
try:
return hash(self.dtype) ... | python | def is_dtype_equal(self, other):
"""
Returns True if categoricals are the same dtype
same categories, and same ordered
Parameters
----------
other : Categorical
Returns
-------
bool
"""
try:
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pandas-dev/pandas | pandas/core/arrays/categorical.py | Categorical.describe | def describe(self):
"""
Describes this Categorical
Returns
-------
description: `DataFrame`
A dataframe with frequency and counts by category.
"""
counts = self.value_counts(dropna=False)
freqs = counts / float(counts.sum())
from pand... | python | def describe(self):
"""
Describes this Categorical
Returns
-------
description: `DataFrame`
A dataframe with frequency and counts by category.
"""
counts = self.value_counts(dropna=False)
freqs = counts / float(counts.sum())
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pandas-dev/pandas | pandas/core/arrays/categorical.py | Categorical.isin | def isin(self, values):
"""
Check whether `values` are contained in Categorical.
Return a boolean NumPy Array showing whether each element in
the Categorical matches an element in the passed sequence of
`values` exactly.
Parameters
----------
values : se... | python | def isin(self, values):
"""
Check whether `values` are contained in Categorical.
Return a boolean NumPy Array showing whether each element in
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Parameters
----------
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pandas-dev/pandas | pandas/core/tools/timedeltas.py | to_timedelta | def to_timedelta(arg, unit='ns', box=True, errors='raise'):
"""
Convert argument to timedelta.
Timedeltas are absolute differences in times, expressed in difference
units (e.g. days, hours, minutes, seconds). This method converts
an argument from a recognized timedelta format / value into
a Tim... | python | def to_timedelta(arg, unit='ns', box=True, errors='raise'):
"""
Convert argument to timedelta.
Timedeltas are absolute differences in times, expressed in difference
units (e.g. days, hours, minutes, seconds). This method converts
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pandas-dev/pandas | pandas/core/tools/timedeltas.py | _coerce_scalar_to_timedelta_type | def _coerce_scalar_to_timedelta_type(r, unit='ns', box=True, errors='raise'):
"""Convert string 'r' to a timedelta object."""
try:
result = Timedelta(r, unit)
if not box:
# explicitly view as timedelta64 for case when result is pd.NaT
result = result.asm8.view('timedelta... | python | def _coerce_scalar_to_timedelta_type(r, unit='ns', box=True, errors='raise'):
"""Convert string 'r' to a timedelta object."""
try:
result = Timedelta(r, unit)
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pandas-dev/pandas | pandas/tseries/offsets.py | generate_range | def generate_range(start=None, end=None, periods=None, offset=BDay()):
"""
Generates a sequence of dates corresponding to the specified time
offset. Similar to dateutil.rrule except uses pandas DateOffset
objects to represent time increments.
Parameters
----------
start : datetime (default ... | python | def generate_range(start=None, end=None, periods=None, offset=BDay()):
"""
Generates a sequence of dates corresponding to the specified time
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pandas-dev/pandas | pandas/tseries/offsets.py | DateOffset.apply_index | def apply_index(self, i):
"""
Vectorized apply of DateOffset to DatetimeIndex,
raises NotImplentedError for offsets without a
vectorized implementation.
Parameters
----------
i : DatetimeIndex
Returns
-------
y : DatetimeIndex
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"""
Vectorized apply of DateOffset to DatetimeIndex,
raises NotImplentedError for offsets without a
vectorized implementation.
Parameters
----------
i : DatetimeIndex
Returns
-------
y : DatetimeIndex
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pandas-dev/pandas | pandas/tseries/offsets.py | DateOffset.rollback | def rollback(self, dt):
"""
Roll provided date backward to next offset only if not on offset.
"""
dt = as_timestamp(dt)
if not self.onOffset(dt):
dt = dt - self.__class__(1, normalize=self.normalize, **self.kwds)
return dt | python | def rollback(self, dt):
"""
Roll provided date backward to next offset only if not on offset.
"""
dt = as_timestamp(dt)
if not self.onOffset(dt):
dt = dt - self.__class__(1, normalize=self.normalize, **self.kwds)
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pandas-dev/pandas | pandas/tseries/offsets.py | DateOffset.rollforward | def rollforward(self, dt):
"""
Roll provided date forward to next offset only if not on offset.
"""
dt = as_timestamp(dt)
if not self.onOffset(dt):
dt = dt + self.__class__(1, normalize=self.normalize, **self.kwds)
return dt | python | def rollforward(self, dt):
"""
Roll provided date forward to next offset only if not on offset.
"""
dt = as_timestamp(dt)
if not self.onOffset(dt):
dt = dt + self.__class__(1, normalize=self.normalize, **self.kwds)
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pandas-dev/pandas | pandas/tseries/offsets.py | BusinessHourMixin.next_bday | def next_bday(self):
"""
Used for moving to next business day.
"""
if self.n >= 0:
nb_offset = 1
else:
nb_offset = -1
if self._prefix.startswith('C'):
# CustomBusinessHour
return CustomBusinessDay(n=nb_offset,
... | python | def next_bday(self):
"""
Used for moving to next business day.
"""
if self.n >= 0:
nb_offset = 1
else:
nb_offset = -1
if self._prefix.startswith('C'):
# CustomBusinessHour
return CustomBusinessDay(n=nb_offset,
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pandas-dev/pandas | pandas/tseries/offsets.py | BusinessHourMixin._next_opening_time | def _next_opening_time(self, other):
"""
If n is positive, return tomorrow's business day opening time.
Otherwise yesterday's business day's opening time.
Opening time always locates on BusinessDay.
Otherwise, closing time may not if business hour extends over midnight.
... | python | def _next_opening_time(self, other):
"""
If n is positive, return tomorrow's business day opening time.
Otherwise yesterday's business day's opening time.
Opening time always locates on BusinessDay.
Otherwise, closing time may not if business hour extends over midnight.
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pandas-dev/pandas | pandas/tseries/offsets.py | BusinessHourMixin._get_business_hours_by_sec | def _get_business_hours_by_sec(self):
"""
Return business hours in a day by seconds.
"""
if self._get_daytime_flag:
# create dummy datetime to calculate businesshours in a day
dtstart = datetime(2014, 4, 1, self.start.hour, self.start.minute)
until = d... | python | def _get_business_hours_by_sec(self):
"""
Return business hours in a day by seconds.
"""
if self._get_daytime_flag:
# create dummy datetime to calculate businesshours in a day
dtstart = datetime(2014, 4, 1, self.start.hour, self.start.minute)
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pandas-dev/pandas | pandas/tseries/offsets.py | BusinessHourMixin.rollback | def rollback(self, dt):
"""
Roll provided date backward to next offset only if not on offset.
"""
if not self.onOffset(dt):
businesshours = self._get_business_hours_by_sec
if self.n >= 0:
dt = self._prev_opening_time(
dt) + time... | python | def rollback(self, dt):
"""
Roll provided date backward to next offset only if not on offset.
"""
if not self.onOffset(dt):
businesshours = self._get_business_hours_by_sec
if self.n >= 0:
dt = self._prev_opening_time(
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pandas-dev/pandas | pandas/tseries/offsets.py | BusinessHourMixin.rollforward | def rollforward(self, dt):
"""
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"""
if not self.onOffset(dt):
if self.n >= 0:
return self._next_opening_time(dt)
else:
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"""
Roll provided date forward to next offset only if not on offset.
"""
if not self.onOffset(dt):
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pandas-dev/pandas | pandas/tseries/offsets.py | BusinessHourMixin._onOffset | def _onOffset(self, dt, businesshours):
"""
Slight speedups using calculated values.
"""
# if self.normalize and not _is_normalized(dt):
# return False
# Valid BH can be on the different BusinessDay during midnight
# Distinguish by the time spent from previous... | python | def _onOffset(self, dt, businesshours):
"""
Slight speedups using calculated values.
"""
# if self.normalize and not _is_normalized(dt):
# return False
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pandas-dev/pandas | pandas/tseries/offsets.py | _CustomBusinessMonth.cbday_roll | def cbday_roll(self):
"""
Define default roll function to be called in apply method.
"""
cbday = CustomBusinessDay(n=self.n, normalize=False, **self.kwds)
if self._prefix.endswith('S'):
# MonthBegin
roll_func = cbday.rollforward
else:
... | python | def cbday_roll(self):
"""
Define default roll function to be called in apply method.
"""
cbday = CustomBusinessDay(n=self.n, normalize=False, **self.kwds)
if self._prefix.endswith('S'):
# MonthBegin
roll_func = cbday.rollforward
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pandas-dev/pandas | pandas/tseries/offsets.py | _CustomBusinessMonth.month_roll | def month_roll(self):
"""
Define default roll function to be called in apply method.
"""
if self._prefix.endswith('S'):
# MonthBegin
roll_func = self.m_offset.rollback
else:
# MonthEnd
roll_func = self.m_offset.rollforward
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"""
Define default roll function to be called in apply method.
"""
if self._prefix.endswith('S'):
# MonthBegin
roll_func = self.m_offset.rollback
else:
# MonthEnd
roll_func = self.m_offset.rollforward
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pandas-dev/pandas | pandas/tseries/offsets.py | SemiMonthBegin._apply_index_days | def _apply_index_days(self, i, roll):
"""
Add days portion of offset to DatetimeIndex i.
Parameters
----------
i : DatetimeIndex
roll : ndarray[int64_t]
Returns
-------
result : DatetimeIndex
"""
nanos = (roll % 2) * Timedelta(day... | python | def _apply_index_days(self, i, roll):
"""
Add days portion of offset to DatetimeIndex i.
Parameters
----------
i : DatetimeIndex
roll : ndarray[int64_t]
Returns
-------
result : DatetimeIndex
"""
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pandas-dev/pandas | pandas/tseries/offsets.py | Week._end_apply_index | def _end_apply_index(self, dtindex):
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Add self to the given DatetimeIndex, specialized for case where
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Parameters
----------
dtindex : DatetimeIndex
Returns
-------
result : DatetimeIndex
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"""
Add self to the given DatetimeIndex, specialized for case where
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result : DatetimeIndex
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Find the day in the same month as other that has the same
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pandas-dev/pandas | pandas/tseries/offsets.py | LastWeekOfMonth._get_offset_day | def _get_offset_day(self, other):
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Find the day in the same month as other that has the same
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Find the day in the same month as other that has the same
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pandas-dev/pandas | pandas/tseries/offsets.py | FY5253Quarter._rollback_to_year | def _rollback_to_year(self, other):
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Roll `other` back to the most recent date that was on a fiscal year
end.
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"""
Roll `other` back to the most recent date that was on a fiscal year
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pandas-dev/pandas | pandas/core/reshape/concat.py | concat | def concat(objs, axis=0, join='outer', join_axes=None, ignore_index=False,
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sort=None, copy=True):
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pandas-dev/pandas | pandas/core/reshape/concat.py | _Concatenator._get_concat_axis | def _get_concat_axis(self):
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pandas-dev/pandas | pandas/core/computation/ops.py | _in | def _in(x, y):
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except AttributeError:
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pandas-dev/pandas | pandas/core/computation/ops.py | _cast_inplace | def _cast_inplace(terms, acceptable_dtypes, dtype):
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Parameters
----------
terms : Op
The expression that should cast.
acceptable_dtypes : list of acceptable numpy.dtype
Will not cast if term's dtype in this list.
.. versionadded:: 0.19.0
... | python | def _cast_inplace(terms, acceptable_dtypes, dtype):
"""Cast an expression inplace.
Parameters
----------
terms : Op
The expression that should cast.
acceptable_dtypes : list of acceptable numpy.dtype
Will not cast if term's dtype in this list.
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pandas-dev/pandas | pandas/core/computation/ops.py | Term.update | def update(self, value):
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pandas-dev/pandas | pandas/core/computation/ops.py | BinOp.evaluate | def evaluate(self, env, engine, parser, term_type, eval_in_python):
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Parameters
----------
env : Scope
engine : str
parser : str
term_type : type
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... | python | def evaluate(self, env, engine, parser, term_type, eval_in_python):
"""Evaluate a binary operation *before* being passed to the engine.
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env : Scope
engine : str
parser : str
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pandas-dev/pandas | pandas/core/computation/ops.py | BinOp.convert_values | def convert_values(self):
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"""
def stringify(value):
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encoder = partial(pprint_thing_encoded,
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"""Convert datetimes to a comparable value in an expression.
"""
def stringify(value):
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pandas-dev/pandas | pandas/core/reshape/pivot.py | crosstab | def crosstab(index, columns, values=None, rownames=None, colnames=None,
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normalize=False):
"""
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pandas-dev/pandas | pandas/util/_doctools.py | TablePlotter._shape | def _shape(self, df):
"""
Calculate table chape considering index levels.
"""
row, col = df.shape
return row + df.columns.nlevels, col + df.index.nlevels | python | def _shape(self, df):
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row, col = df.shape
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pandas-dev/pandas | pandas/util/_doctools.py | TablePlotter._get_cells | def _get_cells(self, left, right, vertical):
"""
Calculate appropriate figure size based on left and right data.
"""
if vertical:
# calculate required number of cells
vcells = max(sum(self._shape(l)[0] for l in left),
self._shape(right)[0... | python | def _get_cells(self, left, right, vertical):
"""
Calculate appropriate figure size based on left and right data.
"""
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Plot left / right DataFrames in specified layout.
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left : list of DataFrames before operation is applied
right : DataFrame of operation result
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pandas-dev/pandas | pandas/util/_doctools.py | TablePlotter._conv | def _conv(self, data):
"""Convert each input to appropriate for table outplot"""
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data = data.to_frame()
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"""Convert each input to appropriate for table outplot"""
if isinstance(data, pd.Series):
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data = data.to_frame(name='')
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Bin values into discrete intervals.
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Bin values into discrete intervals.
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pandas-dev/pandas | pandas/core/reshape/tile.py | qcut | def qcut(x, q, labels=None, retbins=False, precision=3, duplicates='raise'):
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pandas-dev/pandas | pandas/core/reshape/tile.py | _coerce_to_type | def _coerce_to_type(x):
"""
if the passed data is of datetime/timedelta type,
this method converts it to numeric so that cut method can
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"""
dtype = None
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"""
if the passed data is of datetime/timedelta type,
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"""
dtype = None
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pandas-dev/pandas | pandas/core/reshape/tile.py | _convert_bin_to_numeric_type | def _convert_bin_to_numeric_type(bins, dtype):
"""
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this method converts it to integer
Parameters
----------
bins : list-like of bins
dtype : dtype of data
Raises
------
ValueError if bins are not of a compat dtype to dtype
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bins : list-like of bins
dtype : dtype of data
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pandas-dev/pandas | pandas/core/reshape/tile.py | _convert_bin_to_datelike_type | def _convert_bin_to_datelike_type(bins, dtype):
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pandas-dev/pandas | pandas/core/reshape/tile.py | _format_labels | def _format_labels(bins, precision, right=True,
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""" based on the dtype, return our labels """
closed = 'right' if right else 'left'
if is_datetime64tz_dtype(dtype):
formatter = partial(Timestamp, tz=dtype.tz)
adjust = lambda x: x - Time... | python | def _format_labels(bins, precision, right=True,
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""" based on the dtype, return our labels """
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pandas-dev/pandas | pandas/core/reshape/tile.py | _preprocess_for_cut | def _preprocess_for_cut(x):
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pandas-dev/pandas | pandas/core/reshape/tile.py | _postprocess_for_cut | def _postprocess_for_cut(fac, bins, retbins, x_is_series,
series_index, name, dtype):
"""
handles post processing for the cut method where
we combine the index information if the originally passed
datatype was a series
"""
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fac = Series(fac, index=... | python | def _postprocess_for_cut(fac, bins, retbins, x_is_series,
series_index, name, dtype):
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pandas-dev/pandas | pandas/core/reshape/tile.py | _round_frac | def _round_frac(x, precision):
"""
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"""
if not np.isfinite(x) or x == 0:
return x
else:
frac, whole = np.modf(x)
if whole == 0:
digits = -int(np.floor(np.log10(abs(frac)))) - 1 + precision
else:
digi... | python | def _round_frac(x, precision):
"""
Round the fractional part of the given number
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if not np.isfinite(x) or x == 0:
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pandas-dev/pandas | pandas/core/reshape/tile.py | _infer_precision | def _infer_precision(base_precision, bins):
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levels = [_round_frac(b, precision) for b in bins]
if algos.unique(levels).size == bins.size:
return precision
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pandas-dev/pandas | pandas/_config/display.py | detect_console_encoding | def detect_console_encoding():
"""
Try to find the most capable encoding supported by the console.
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"""
global _initial_defencoding
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"""
Try to find the most capable encoding supported by the console.
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pandas-dev/pandas | pandas/util/_validators.py | _check_arg_length | def _check_arg_length(fname, args, max_fname_arg_count, compat_args):
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Checks whether 'args' has length of at most 'compat_args'. Raises
a TypeError if that is not the case, similar to in Python when a
function is called with too many arguments.
"""
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"""
Checks whether 'args' has length of at most 'compat_args'. Raises
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pandas-dev/pandas | pandas/util/_validators.py | validate_args | def validate_args(fname, args, max_fname_arg_count, compat_args):
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Checks whether the length of the `*args` argument passed into a function
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pandas-dev/pandas | pandas/util/_validators.py | validate_kwargs | def validate_kwargs(fname, kwargs, compat_args):
"""
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pandas-dev/pandas | pandas/util/_validators.py | validate_args_and_kwargs | def validate_args_and_kwargs(fname, args, kwargs,
max_fname_arg_count,
compat_args):
"""
Checks whether parameters passed to the *args and **kwargs argument in a
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compat_args):
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Checks whether parameters passed to the *args and **kwargs argument in a
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pandas-dev/pandas | pandas/util/_validators.py | validate_bool_kwarg | def validate_bool_kwarg(value, arg_name):
""" Ensures that argument passed in arg_name is of type bool. """
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raise ValueError('For argument "{arg}" expected type bool, received '
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... | python | def validate_bool_kwarg(value, arg_name):
""" Ensures that argument passed in arg_name is of type bool. """
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raise ValueError('For argument "{arg}" expected type bool, received '
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pandas-dev/pandas | pandas/util/_validators.py | validate_axis_style_args | def validate_axis_style_args(data, args, kwargs, arg_name, method_name):
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pandas-dev/pandas | pandas/util/_validators.py | validate_fillna_kwargs | def validate_fillna_kwargs(value, method, validate_scalar_dict_value=True):
"""Validate the keyword arguments to 'fillna'.
This checks that exactly one of 'value' and 'method' is specified.
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----------
value, method :... | python | def validate_fillna_kwargs(value, method, validate_scalar_dict_value=True):
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pandas-dev/pandas | pandas/core/resample.py | _maybe_process_deprecations | def _maybe_process_deprecations(r, how=None, fill_method=None, limit=None):
"""
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"""
if how is not None:
# .resample(..., how='sum')
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"""
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pandas-dev/pandas | pandas/core/resample.py | resample | def resample(obj, kind=None, **kwds):
"""
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"""
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"""
Create a TimeGrouper and return our resampler.
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tg = TimeGrouper(**kwds)
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pandas-dev/pandas | pandas/core/resample.py | get_resampler_for_grouping | def get_resampler_for_grouping(groupby, rule, how=None, fill_method=None,
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"""
Return our appropriate resampler when grouping as well.
"""
# .resample uses 'on' similar to how .groupby uses 'key'
kwargs['key'] = kwargs.pop('on', None)
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# .resample uses 'on' similar to how .groupby uses 'key'
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pandas-dev/pandas | pandas/core/resample.py | _get_timestamp_range_edges | def _get_timestamp_range_edges(first, last, offset, closed='left', base=0):
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pandas-dev/pandas | pandas/core/resample.py | _get_period_range_edges | def _get_period_range_edges(first, last, offset, closed='left', base=0):
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pandas-dev/pandas | pandas/core/resample.py | asfreq | def asfreq(obj, freq, method=None, how=None, normalize=False, fill_value=None):
"""
Utility frequency conversion method for Series/DataFrame.
"""
if isinstance(obj.index, PeriodIndex):
if method is not None:
raise NotImplementedError("'method' argument is not supported")
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"""
Utility frequency conversion method for Series/DataFrame.
"""
if isinstance(obj.index, PeriodIndex):
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pandas-dev/pandas | pandas/core/resample.py | Resampler._from_selection | def _from_selection(self):
"""
Is the resampling from a DataFrame column or MultiIndex level.
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pandas-dev/pandas | pandas/core/resample.py | Resampler._set_binner | def _set_binner(self):
"""
Setup our binners.
Cache these as we are an immutable object
"""
if self.binner is None:
self.binner, self.grouper = self._get_binner() | python | def _set_binner(self):
"""
Setup our binners.
Cache these as we are an immutable object
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pandas-dev/pandas | pandas/core/resample.py | Resampler._get_binner | def _get_binner(self):
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Create the BinGrouper, assume that self.set_grouper(obj)
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"""
binner, bins, binlabels = self._get_binner_for_time()
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return binner, bin_group... | python | def _get_binner(self):
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Create the BinGrouper, assume that self.set_grouper(obj)
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"""
binner, bins, binlabels = self._get_binner_for_time()
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pandas-dev/pandas | pandas/core/resample.py | Resampler.transform | def transform(self, arg, *args, **kwargs):
"""
Call function producing a like-indexed Series on each group and return
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Parameters
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arg : function
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Call function producing a like-indexed Series on each group and return
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pandas-dev/pandas | pandas/core/resample.py | Resampler._gotitem | def _gotitem(self, key, ndim, subset=None):
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Sub-classes to define. Return a sliced object.
Parameters
----------
key : string / list of selections
ndim : 1,2
requested ndim of result
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subset to act on
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key : string / list of selections
ndim : 1,2
requested ndim of result
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pandas-dev/pandas | pandas/core/resample.py | Resampler._groupby_and_aggregate | def _groupby_and_aggregate(self, how, grouper=None, *args, **kwargs):
"""
Re-evaluate the obj with a groupby aggregation.
"""
if grouper is None:
self._set_binner()
grouper = self.grouper
obj = self._selected_obj
grouped = groupby(obj, by=None, ... | python | def _groupby_and_aggregate(self, how, grouper=None, *args, **kwargs):
"""
Re-evaluate the obj with a groupby aggregation.
"""
if grouper is None:
self._set_binner()
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pandas-dev/pandas | pandas/core/resample.py | Resampler._apply_loffset | def _apply_loffset(self, result):
"""
If loffset is set, offset the result index.
This is NOT an idempotent routine, it will be applied
exactly once to the result.
Parameters
----------
result : Series or DataFrame
the result of resample
"""
... | python | def _apply_loffset(self, result):
"""
If loffset is set, offset the result index.
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Parameters
----------
result : Series or DataFrame
the result of resample
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pandas-dev/pandas | pandas/core/resample.py | Resampler._get_resampler_for_grouping | def _get_resampler_for_grouping(self, groupby, **kwargs):
"""
Return the correct class for resampling with groupby.
"""
return self._resampler_for_grouping(self, groupby=groupby, **kwargs) | python | def _get_resampler_for_grouping(self, groupby, **kwargs):
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Return the correct class for resampling with groupby.
"""
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pandas-dev/pandas | pandas/core/resample.py | Resampler._wrap_result | def _wrap_result(self, result):
"""
Potentially wrap any results.
"""
if isinstance(result, ABCSeries) and self._selection is not None:
result.name = self._selection
if isinstance(result, ABCSeries) and result.empty:
obj = self.obj
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"""
Potentially wrap any results.
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pandas-dev/pandas | pandas/core/resample.py | Resampler.interpolate | def interpolate(self, method='linear', axis=0, limit=None, inplace=False,
limit_direction='forward', limit_area=None,
downcast=None, **kwargs):
"""
Interpolate values according to different methods.
.. versionadded:: 0.18.1
"""
result = se... | python | def interpolate(self, method='linear', axis=0, limit=None, inplace=False,
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"""
Interpolate values according to different methods.
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pandas-dev/pandas | pandas/core/resample.py | Resampler.std | def std(self, ddof=1, *args, **kwargs):
"""
Compute standard deviation of groups, excluding missing values.
Parameters
----------
ddof : integer, default 1
Degrees of freedom.
"""
nv.validate_resampler_func('std', args, kwargs)
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"""
Compute standard deviation of groups, excluding missing values.
Parameters
----------
ddof : integer, default 1
Degrees of freedom.
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nv.validate_resampler_func('std', args, kwargs)
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pandas-dev/pandas | pandas/core/resample.py | Resampler.var | def var(self, ddof=1, *args, **kwargs):
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Compute variance of groups, excluding missing values.
Parameters
----------
ddof : integer, default 1
degrees of freedom
"""
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"""
Compute variance of groups, excluding missing values.
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----------
ddof : integer, default 1
degrees of freedom
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pandas-dev/pandas | pandas/core/resample.py | _GroupByMixin._apply | def _apply(self, f, grouper=None, *args, **kwargs):
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"""
def func(x):
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pandas-dev/pandas | pandas/core/resample.py | DatetimeIndexResampler._downsample | def _downsample(self, how, **kwargs):
"""
Downsample the cython defined function.
Parameters
----------
how : string / cython mapped function
**kwargs : kw args passed to how function
"""
self._set_binner()
how = self._is_cython_func(how) or how
... | python | def _downsample(self, how, **kwargs):
"""
Downsample the cython defined function.
Parameters
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how : string / cython mapped function
**kwargs : kw args passed to how function
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pandas-dev/pandas | pandas/core/resample.py | DatetimeIndexResampler._adjust_binner_for_upsample | def _adjust_binner_for_upsample(self, binner):
"""
Adjust our binner when upsampling.
The range of a new index should not be outside specified range
"""
if self.closed == 'right':
binner = binner[1:]
else:
binner = binner[:-1]
return binne... | python | def _adjust_binner_for_upsample(self, binner):
"""
Adjust our binner when upsampling.
The range of a new index should not be outside specified range
"""
if self.closed == 'right':
binner = binner[1:]
else:
binner = binner[:-1]
return binne... | [
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pandas-dev/pandas | pandas/core/resample.py | DatetimeIndexResampler._upsample | def _upsample(self, method, limit=None, fill_value=None):
"""
Parameters
----------
method : string {'backfill', 'bfill', 'pad',
'ffill', 'asfreq'} method for upsampling
limit : int, default None
Maximum size gap to fill when reindexing
fill_value ... | python | def _upsample(self, method, limit=None, fill_value=None):
"""
Parameters
----------
method : string {'backfill', 'bfill', 'pad',
'ffill', 'asfreq'} method for upsampling
limit : int, default None
Maximum size gap to fill when reindexing
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pandas-dev/pandas | pandas/core/resample.py | PeriodIndexResampler._downsample | def _downsample(self, how, **kwargs):
"""
Downsample the cython defined function.
Parameters
----------
how : string / cython mapped function
**kwargs : kw args passed to how function
"""
# we may need to actually resample as if we are timestamps
... | python | def _downsample(self, how, **kwargs):
"""
Downsample the cython defined function.
Parameters
----------
how : string / cython mapped function
**kwargs : kw args passed to how function
"""
# we may need to actually resample as if we are timestamps
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pandas-dev/pandas | pandas/core/resample.py | PeriodIndexResampler._upsample | def _upsample(self, method, limit=None, fill_value=None):
"""
Parameters
----------
method : string {'backfill', 'bfill', 'pad', 'ffill'}
method for upsampling
limit : int, default None
Maximum size gap to fill when reindexing
fill_value : scalar, ... | python | def _upsample(self, method, limit=None, fill_value=None):
"""
Parameters
----------
method : string {'backfill', 'bfill', 'pad', 'ffill'}
method for upsampling
limit : int, default None
Maximum size gap to fill when reindexing
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Maximum size gap to fill when reindexing
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pandas-dev/pandas | pandas/core/resample.py | TimeGrouper._get_resampler | def _get_resampler(self, obj, kind=None):
"""
Return my resampler or raise if we have an invalid axis.
Parameters
----------
obj : input object
kind : string, optional
'period','timestamp','timedelta' are valid
Returns
-------
a Resam... | python | def _get_resampler(self, obj, kind=None):
"""
Return my resampler or raise if we have an invalid axis.
Parameters
----------
obj : input object
kind : string, optional
'period','timestamp','timedelta' are valid
Returns
-------
a Resam... | [
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pandas-dev/pandas | pandas/core/util/hashing.py | _combine_hash_arrays | def _combine_hash_arrays(arrays, num_items):
"""
Parameters
----------
arrays : generator
num_items : int
Should be the same as CPython's tupleobject.c
"""
try:
first = next(arrays)
except StopIteration:
return np.array([], dtype=np.uint64)
arrays = itertools.ch... | python | def _combine_hash_arrays(arrays, num_items):
"""
Parameters
----------
arrays : generator
num_items : int
Should be the same as CPython's tupleobject.c
"""
try:
first = next(arrays)
except StopIteration:
return np.array([], dtype=np.uint64)
arrays = itertools.ch... | [
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pandas-dev/pandas | pandas/core/util/hashing.py | hash_pandas_object | def hash_pandas_object(obj, index=True, encoding='utf8', hash_key=None,
categorize=True):
"""
Return a data hash of the Index/Series/DataFrame
.. versionadded:: 0.19.2
Parameters
----------
index : boolean, default True
include the index in the hash (if Series/Da... | python | def hash_pandas_object(obj, index=True, encoding='utf8', hash_key=None,
categorize=True):
"""
Return a data hash of the Index/Series/DataFrame
.. versionadded:: 0.19.2
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----------
index : boolean, default True
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pandas-dev/pandas | pandas/core/util/hashing.py | hash_tuples | def hash_tuples(vals, encoding='utf8', hash_key=None):
"""
Hash an MultiIndex / list-of-tuples efficiently
.. versionadded:: 0.20.0
Parameters
----------
vals : MultiIndex, list-of-tuples, or single tuple
encoding : string, default 'utf8'
hash_key : string key to encode, default to _de... | python | def hash_tuples(vals, encoding='utf8', hash_key=None):
"""
Hash an MultiIndex / list-of-tuples efficiently
.. versionadded:: 0.20.0
Parameters
----------
vals : MultiIndex, list-of-tuples, or single tuple
encoding : string, default 'utf8'
hash_key : string key to encode, default to _de... | [
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pandas-dev/pandas | pandas/core/util/hashing.py | hash_tuple | def hash_tuple(val, encoding='utf8', hash_key=None):
"""
Hash a single tuple efficiently
Parameters
----------
val : single tuple
encoding : string, default 'utf8'
hash_key : string key to encode, default to _default_hash_key
Returns
-------
hash
"""
hashes = (_hash_sc... | python | def hash_tuple(val, encoding='utf8', hash_key=None):
"""
Hash a single tuple efficiently
Parameters
----------
val : single tuple
encoding : string, default 'utf8'
hash_key : string key to encode, default to _default_hash_key
Returns
-------
hash
"""
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pandas-dev/pandas | pandas/core/util/hashing.py | _hash_categorical | def _hash_categorical(c, encoding, hash_key):
"""
Hash a Categorical by hashing its categories, and then mapping the codes
to the hashes
Parameters
----------
c : Categorical
encoding : string, default 'utf8'
hash_key : string key to encode, default to _default_hash_key
Returns
... | python | def _hash_categorical(c, encoding, hash_key):
"""
Hash a Categorical by hashing its categories, and then mapping the codes
to the hashes
Parameters
----------
c : Categorical
encoding : string, default 'utf8'
hash_key : string key to encode, default to _default_hash_key
Returns
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pandas-dev/pandas | pandas/core/util/hashing.py | hash_array | def hash_array(vals, encoding='utf8', hash_key=None, categorize=True):
"""
Given a 1d array, return an array of deterministic integers.
.. versionadded:: 0.19.2
Parameters
----------
vals : ndarray, Categorical
encoding : string, default 'utf8'
encoding for data & key when strings
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"""
Given a 1d array, return an array of deterministic integers.
.. versionadded:: 0.19.2
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vals : ndarray, Categorical
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encoding for data & key when strings
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pandas-dev/pandas | pandas/core/util/hashing.py | _hash_scalar | def _hash_scalar(val, encoding='utf8', hash_key=None):
"""
Hash scalar value
Returns
-------
1d uint64 numpy array of hash value, of length 1
"""
if isna(val):
# this is to be consistent with the _hash_categorical implementation
return np.array([np.iinfo(np.uint64).max], dt... | python | def _hash_scalar(val, encoding='utf8', hash_key=None):
"""
Hash scalar value
Returns
-------
1d uint64 numpy array of hash value, of length 1
"""
if isna(val):
# this is to be consistent with the _hash_categorical implementation
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pandas-dev/pandas | doc/make.py | DocBuilder._process_single_doc | def _process_single_doc(self, single_doc):
"""
Make sure the provided value for --single is a path to an existing
.rst/.ipynb file, or a pandas object that can be imported.
For example, categorial.rst or pandas.DataFrame.head. For the latter,
return the corresponding file path
... | python | def _process_single_doc(self, single_doc):
"""
Make sure the provided value for --single is a path to an existing
.rst/.ipynb file, or a pandas object that can be imported.
For example, categorial.rst or pandas.DataFrame.head. For the latter,
return the corresponding file path
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