INSTRUCTION stringlengths 1 46.3k | RESPONSE stringlengths 75 80.2k |
|---|---|
Get result for Cythonized functions.
Parameters
----------
how : str, Cythonized function name to be called
grouper : Grouper object containing pertinent group info
aggregate : bool, default False
Whether the result should be aggregated to match the number of
... | def _get_cythonized_result(self, how, grouper, aggregate=False,
cython_dtype=None, needs_values=False,
needs_mask=False, needs_ngroups=False,
result_is_index=False,
pre_processing=None, post_proce... |
Shift each group by periods observations.
Parameters
----------
periods : integer, default 1
number of periods to shift
freq : frequency string
axis : axis to shift, default 0
fill_value : optional
.. versionadded:: 0.24.0 | def shift(self, periods=1, freq=None, axis=0, fill_value=None):
"""
Shift each group by periods observations.
Parameters
----------
periods : integer, default 1
number of periods to shift
freq : frequency string
axis : axis to shift, default 0
... |
Return first n rows of each group.
Essentially equivalent to ``.apply(lambda x: x.head(n))``,
except ignores as_index flag.
%(see_also)s
Examples
--------
>>> df = pd.DataFrame([[1, 2], [1, 4], [5, 6]],
columns=['A', 'B'])
>>> df.gr... | def head(self, n=5):
"""
Return first n rows of each group.
Essentially equivalent to ``.apply(lambda x: x.head(n))``,
except ignores as_index flag.
%(see_also)s
Examples
--------
>>> df = pd.DataFrame([[1, 2], [1, 4], [5, 6]],
... |
Return last n rows of each group.
Essentially equivalent to ``.apply(lambda x: x.tail(n))``,
except ignores as_index flag.
%(see_also)s
Examples
--------
>>> df = pd.DataFrame([['a', 1], ['a', 2], ['b', 1], ['b', 2]],
columns=['A', 'B'])
... | def tail(self, n=5):
"""
Return last n rows of each group.
Essentially equivalent to ``.apply(lambda x: x.tail(n))``,
except ignores as_index flag.
%(see_also)s
Examples
--------
>>> df = pd.DataFrame([['a', 1], ['a', 2], ['b', 1], ['b', 2]],
... |
If holiday falls on Saturday, use following Monday instead;
if holiday falls on Sunday, use Monday instead | def next_monday(dt):
"""
If holiday falls on Saturday, use following Monday instead;
if holiday falls on Sunday, use Monday instead
"""
if dt.weekday() == 5:
return dt + timedelta(2)
elif dt.weekday() == 6:
return dt + timedelta(1)
return dt |
For second holiday of two adjacent ones!
If holiday falls on Saturday, use following Monday instead;
if holiday falls on Sunday or Monday, use following Tuesday instead
(because Monday is already taken by adjacent holiday on the day before) | def next_monday_or_tuesday(dt):
"""
For second holiday of two adjacent ones!
If holiday falls on Saturday, use following Monday instead;
if holiday falls on Sunday or Monday, use following Tuesday instead
(because Monday is already taken by adjacent holiday on the day before)
"""
dow = dt.we... |
If holiday falls on Saturday or Sunday, use previous Friday instead. | def previous_friday(dt):
"""
If holiday falls on Saturday or Sunday, use previous Friday instead.
"""
if dt.weekday() == 5:
return dt - timedelta(1)
elif dt.weekday() == 6:
return dt - timedelta(2)
return dt |
If holiday falls on Sunday or Saturday,
use day thereafter (Monday) instead.
Needed for holidays such as Christmas observation in Europe | def weekend_to_monday(dt):
"""
If holiday falls on Sunday or Saturday,
use day thereafter (Monday) instead.
Needed for holidays such as Christmas observation in Europe
"""
if dt.weekday() == 6:
return dt + timedelta(1)
elif dt.weekday() == 5:
return dt + timedelta(2)
retu... |
If holiday falls on Saturday, use day before (Friday) instead;
if holiday falls on Sunday, use day thereafter (Monday) instead. | def nearest_workday(dt):
"""
If holiday falls on Saturday, use day before (Friday) instead;
if holiday falls on Sunday, use day thereafter (Monday) instead.
"""
if dt.weekday() == 5:
return dt - timedelta(1)
elif dt.weekday() == 6:
return dt + timedelta(1)
return dt |
returns next weekday used for observances | def next_workday(dt):
"""
returns next weekday used for observances
"""
dt += timedelta(days=1)
while dt.weekday() > 4:
# Mon-Fri are 0-4
dt += timedelta(days=1)
return dt |
returns previous weekday used for observances | def previous_workday(dt):
"""
returns previous weekday used for observances
"""
dt -= timedelta(days=1)
while dt.weekday() > 4:
# Mon-Fri are 0-4
dt -= timedelta(days=1)
return dt |
Calculate holidays observed between start date and end date
Parameters
----------
start_date : starting date, datetime-like, optional
end_date : ending date, datetime-like, optional
return_name : bool, optional, default=False
If True, return a series that has dates a... | def dates(self, start_date, end_date, return_name=False):
"""
Calculate holidays observed between start date and end date
Parameters
----------
start_date : starting date, datetime-like, optional
end_date : ending date, datetime-like, optional
return_name : bool,... |
Get reference dates for the holiday.
Return reference dates for the holiday also returning the year
prior to the start_date and year following the end_date. This ensures
that any offsets to be applied will yield the holidays within
the passed in dates. | def _reference_dates(self, start_date, end_date):
"""
Get reference dates for the holiday.
Return reference dates for the holiday also returning the year
prior to the start_date and year following the end_date. This ensures
that any offsets to be applied will yield the holidays... |
Apply the given offset/observance to a DatetimeIndex of dates.
Parameters
----------
dates : DatetimeIndex
Dates to apply the given offset/observance rule
Returns
-------
Dates with rules applied | def _apply_rule(self, dates):
"""
Apply the given offset/observance to a DatetimeIndex of dates.
Parameters
----------
dates : DatetimeIndex
Dates to apply the given offset/observance rule
Returns
-------
Dates with rules applied
"""
... |
Returns a curve with holidays between start_date and end_date
Parameters
----------
start : starting date, datetime-like, optional
end : ending date, datetime-like, optional
return_name : bool, optional
If True, return a series that has dates and holiday names.
... | def holidays(self, start=None, end=None, return_name=False):
"""
Returns a curve with holidays between start_date and end_date
Parameters
----------
start : starting date, datetime-like, optional
end : ending date, datetime-like, optional
return_name : bool, opti... |
Merge holiday calendars together. The base calendar
will take precedence to other. The merge will be done
based on each holiday's name.
Parameters
----------
base : AbstractHolidayCalendar
instance/subclass or array of Holiday objects
other : AbstractHolidayCal... | def merge_class(base, other):
"""
Merge holiday calendars together. The base calendar
will take precedence to other. The merge will be done
based on each holiday's name.
Parameters
----------
base : AbstractHolidayCalendar
instance/subclass or array of ... |
Merge holiday calendars together. The caller's class
rules take precedence. The merge will be done
based on each holiday's name.
Parameters
----------
other : holiday calendar
inplace : bool (default=False)
If True set rule_table to holidays, else return ar... | def merge(self, other, inplace=False):
"""
Merge holiday calendars together. The caller's class
rules take precedence. The merge will be done
based on each holiday's name.
Parameters
----------
other : holiday calendar
inplace : bool (default=False)
... |
Register an option in the package-wide pandas config object
Parameters
----------
key - a fully-qualified key, e.g. "x.y.option - z".
defval - the default value of the option
doc - a string description of the option
validator - a function of a single argument, should raise `Value... | def register_option(key, defval, doc='', validator=None, cb=None):
"""Register an option in the package-wide pandas config object
Parameters
----------
key - a fully-qualified key, e.g. "x.y.option - z".
defval - the default value of the option
doc - a string description of the o... |
Mark option `key` as deprecated, if code attempts to access this option,
a warning will be produced, using `msg` if given, or a default message
if not.
if `rkey` is given, any access to the key will be re-routed to `rkey`.
Neither the existence of `key` nor that if `rkey` is checked. If they
do not... | def deprecate_option(key, msg=None, rkey=None, removal_ver=None):
"""
Mark option `key` as deprecated, if code attempts to access this option,
a warning will be produced, using `msg` if given, or a default message
if not.
if `rkey` is given, any access to the key will be re-routed to `rkey`.
Ne... |
if key id deprecated and a replacement key defined, will return the
replacement key, otherwise returns `key` as - is | def _translate_key(key):
"""
if key id deprecated and a replacement key defined, will return the
replacement key, otherwise returns `key` as - is
"""
d = _get_deprecated_option(key)
if d:
return d.rkey or key
else:
return key |
returns a list of keys matching `pat`
if pat=="all", returns all registered options | def _select_options(pat):
"""returns a list of keys matching `pat`
if pat=="all", returns all registered options
"""
# short-circuit for exact key
if pat in _registered_options:
return [pat]
# else look through all of them
keys = sorted(_registered_options.keys())
if pat == 'a... |
Builds a formatted description of a registered option and prints it | def _build_option_description(k):
""" Builds a formatted description of a registered option and prints it """
o = _get_registered_option(k)
d = _get_deprecated_option(k)
s = '{k} '.format(k=k)
if o.doc:
s += '\n'.join(o.doc.strip().split('\n'))
else:
s += 'No description avail... |
contextmanager for multiple invocations of API with a common prefix
supported API functions: (register / get / set )__option
Warning: This is not thread - safe, and won't work properly if you import
the API functions into your module using the "from x import y" construct.
Example:
import pandas.... | def config_prefix(prefix):
"""contextmanager for multiple invocations of API with a common prefix
supported API functions: (register / get / set )__option
Warning: This is not thread - safe, and won't work properly if you import
the API functions into your module using the "from x import y" construct.... |
Generates (prop, value) pairs from declarations
In a future version may generate parsed tokens from tinycss/tinycss2 | def parse(self, declarations_str):
"""Generates (prop, value) pairs from declarations
In a future version may generate parsed tokens from tinycss/tinycss2
"""
for decl in declarations_str.split(';'):
if not decl.strip():
continue
prop, sep, val = ... |
Create an array.
.. versionadded:: 0.24.0
Parameters
----------
data : Sequence of objects
The scalars inside `data` should be instances of the
scalar type for `dtype`. It's expected that `data`
represents a 1-dimensional array of data.
When `data` is an Index or Serie... | def array(data: Sequence[object],
dtype: Optional[Union[str, np.dtype, ExtensionDtype]] = None,
copy: bool = True,
) -> ABCExtensionArray:
"""
Create an array.
.. versionadded:: 0.24.0
Parameters
----------
data : Sequence of objects
The scalars inside `da... |
Try to do platform conversion, with special casing for IntervalArray.
Wrapper around maybe_convert_platform that alters the default return
dtype in certain cases to be compatible with IntervalArray. For example,
empty lists return with integer dtype instead of object dtype, which is
prohibited for Inte... | def maybe_convert_platform_interval(values):
"""
Try to do platform conversion, with special casing for IntervalArray.
Wrapper around maybe_convert_platform that alters the default return
dtype in certain cases to be compatible with IntervalArray. For example,
empty lists return with integer dtype ... |
Check if the object is a file-like object.
For objects to be considered file-like, they must
be an iterator AND have either a `read` and/or `write`
method as an attribute.
Note: file-like objects must be iterable, but
iterable objects need not be file-like.
.. versionadded:: 0.20.0
Param... | def is_file_like(obj):
"""
Check if the object is a file-like object.
For objects to be considered file-like, they must
be an iterator AND have either a `read` and/or `write`
method as an attribute.
Note: file-like objects must be iterable, but
iterable objects need not be file-like.
... |
Check if the object is list-like.
Objects that are considered list-like are for example Python
lists, tuples, sets, NumPy arrays, and Pandas Series.
Strings and datetime objects, however, are not considered list-like.
Parameters
----------
obj : The object to check
allow_sets : boolean, d... | def is_list_like(obj, allow_sets=True):
"""
Check if the object is list-like.
Objects that are considered list-like are for example Python
lists, tuples, sets, NumPy arrays, and Pandas Series.
Strings and datetime objects, however, are not considered list-like.
Parameters
----------
o... |
Check if the object is list-like, and that all of its elements
are also list-like.
.. versionadded:: 0.20.0
Parameters
----------
obj : The object to check
Returns
-------
is_list_like : bool
Whether `obj` has list-like properties.
Examples
--------
>>> is_nested_... | def is_nested_list_like(obj):
"""
Check if the object is list-like, and that all of its elements
are also list-like.
.. versionadded:: 0.20.0
Parameters
----------
obj : The object to check
Returns
-------
is_list_like : bool
Whether `obj` has list-like properties.
... |
Check if the object is dict-like.
Parameters
----------
obj : The object to check
Returns
-------
is_dict_like : bool
Whether `obj` has dict-like properties.
Examples
--------
>>> is_dict_like({1: 2})
True
>>> is_dict_like([1, 2, 3])
False
>>> is_dict_like(... | def is_dict_like(obj):
"""
Check if the object is dict-like.
Parameters
----------
obj : The object to check
Returns
-------
is_dict_like : bool
Whether `obj` has dict-like properties.
Examples
--------
>>> is_dict_like({1: 2})
True
>>> is_dict_like([1, 2, ... |
Check if the object is a sequence of objects.
String types are not included as sequences here.
Parameters
----------
obj : The object to check
Returns
-------
is_sequence : bool
Whether `obj` is a sequence of objects.
Examples
--------
>>> l = [1, 2, 3]
>>>
>>>... | def is_sequence(obj):
"""
Check if the object is a sequence of objects.
String types are not included as sequences here.
Parameters
----------
obj : The object to check
Returns
-------
is_sequence : bool
Whether `obj` is a sequence of objects.
Examples
--------
... |
This is called upon unpickling, rather than the default which doesn't
have arguments and breaks __new__ | def _new_DatetimeIndex(cls, d):
""" This is called upon unpickling, rather than the default which doesn't
have arguments and breaks __new__ """
if "data" in d and not isinstance(d["data"], DatetimeIndex):
# Avoid need to verify integrity by calling simple_new directly
data = d.pop("data")
... |
Return a fixed frequency DatetimeIndex.
Parameters
----------
start : str or datetime-like, optional
Left bound for generating dates.
end : str or datetime-like, optional
Right bound for generating dates.
periods : integer, optional
Number of periods to generate.
freq : ... | def date_range(start=None, end=None, periods=None, freq=None, tz=None,
normalize=False, name=None, closed=None, **kwargs):
"""
Return a fixed frequency DatetimeIndex.
Parameters
----------
start : str or datetime-like, optional
Left bound for generating dates.
end : str o... |
Return a fixed frequency DatetimeIndex, with business day as the default
frequency
Parameters
----------
start : string or datetime-like, default None
Left bound for generating dates.
end : string or datetime-like, default None
Right bound for generating dates.
periods : integer... | def bdate_range(start=None, end=None, periods=None, freq='B', tz=None,
normalize=True, name=None, weekmask=None, holidays=None,
closed=None, **kwargs):
"""
Return a fixed frequency DatetimeIndex, with business day as the default
frequency
Parameters
----------
st... |
Split data into blocks & return conformed data. | def _create_blocks(self):
"""
Split data into blocks & return conformed data.
"""
obj, index = self._convert_freq()
if index is not None:
index = self._on
# filter out the on from the object
if self.on is not None:
if obj.ndim == 2:
... |
Return a fixed frequency DatetimeIndex, with CustomBusinessDay as the
default frequency
.. deprecated:: 0.21.0
Parameters
----------
start : string or datetime-like, default None
Left bound for generating dates
end : string or datetime-like, default None
Right bound for generat... | def cdate_range(start=None, end=None, periods=None, freq='C', tz=None,
normalize=True, name=None, closed=None, **kwargs):
"""
Return a fixed frequency DatetimeIndex, with CustomBusinessDay as the
default frequency
.. deprecated:: 0.21.0
Parameters
----------
start : string ... |
Sub-classes to define. Return a sliced object.
Parameters
----------
key : str / 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 : str / list of selections
ndim : 1,2
requested ndim of result
subset : object, default None
subset to act on
... |
Return index as ndarrays.
Returns
-------
tuple of (index, index_as_ndarray) | def _get_index(self, index=None):
"""
Return index as ndarrays.
Returns
-------
tuple of (index, index_as_ndarray)
"""
if self.is_freq_type:
if index is None:
index = self._on
return index, index.asi8
return index,... |
Wrap a single result. | def _wrap_result(self, result, block=None, obj=None):
"""
Wrap a single result.
"""
if obj is None:
obj = self._selected_obj
index = obj.index
if isinstance(result, np.ndarray):
# coerce if necessary
if block is not None:
... |
Wrap the results.
Parameters
----------
results : list of ndarrays
blocks : list of blocks
obj : conformed data (may be resampled) | def _wrap_results(self, results, blocks, obj):
"""
Wrap the results.
Parameters
----------
results : list of ndarrays
blocks : list of blocks
obj : conformed data (may be resampled)
"""
from pandas import Series, concat
from pandas.core.i... |
Center the result in the window. | def _center_window(self, result, window):
"""
Center the result in the window.
"""
if self.axis > result.ndim - 1:
raise ValueError("Requested axis is larger then no. of argument "
"dimensions")
offset = _offset(window, True)
if o... |
Provide validation for our window type, return the window
we have already been validated. | def _prep_window(self, **kwargs):
"""
Provide validation for our window type, return the window
we have already been validated.
"""
window = self._get_window()
if isinstance(window, (list, tuple, np.ndarray)):
return com.asarray_tuplesafe(window).astype(float... |
Applies a moving window of type ``window_type`` on the data.
Parameters
----------
mean : bool, default True
If True computes weighted mean, else weighted sum
Returns
-------
y : same type as input argument | def _apply_window(self, mean=True, **kwargs):
"""
Applies a moving window of type ``window_type`` on the data.
Parameters
----------
mean : bool, default True
If True computes weighted mean, else weighted sum
Returns
-------
y : same type as ... |
Dispatch to apply; we are stripping all of the _apply kwargs and
performing the original function call on the grouped object. | def _apply(self, func, name, window=None, center=None,
check_minp=None, **kwargs):
"""
Dispatch to apply; we are stripping all of the _apply kwargs and
performing the original function call on the grouped object.
"""
def f(x, name=name, *args):
x = sel... |
Rolling statistical measure using supplied function.
Designed to be used with passed-in Cython array-based functions.
Parameters
----------
func : str/callable to apply
name : str, optional
name of this function
window : int/array, default to _get_window()
... | def _apply(self, func, name=None, window=None, center=None,
check_minp=None, **kwargs):
"""
Rolling statistical measure using supplied function.
Designed to be used with passed-in Cython array-based functions.
Parameters
----------
func : str/callable to ... |
Validate on is_monotonic. | def _validate_monotonic(self):
"""
Validate on is_monotonic.
"""
if not self._on.is_monotonic:
formatted = self.on or 'index'
raise ValueError("{0} must be "
"monotonic".format(formatted)) |
Validate & return window frequency. | def _validate_freq(self):
"""
Validate & return window frequency.
"""
from pandas.tseries.frequencies import to_offset
try:
return to_offset(self.window)
except (TypeError, ValueError):
raise ValueError("passed window {0} is not "
... |
Get the window length over which to perform some operation.
Parameters
----------
other : object, default None
The other object that is involved in the operation.
Such an object is involved for operations like covariance.
Returns
-------
window :... | def _get_window(self, other=None):
"""
Get the window length over which to perform some operation.
Parameters
----------
other : object, default None
The other object that is involved in the operation.
Such an object is involved for operations like covari... |
Rolling statistical measure using supplied function. Designed to be
used with passed-in Cython array-based functions.
Parameters
----------
func : str/callable to apply
Returns
-------
y : same type as input argument | def _apply(self, func, **kwargs):
"""
Rolling statistical measure using supplied function. Designed to be
used with passed-in Cython array-based functions.
Parameters
----------
func : str/callable to apply
Returns
-------
y : same type as input ... |
Exponential weighted moving average.
Parameters
----------
*args, **kwargs
Arguments and keyword arguments to be passed into func. | def mean(self, *args, **kwargs):
"""
Exponential weighted moving average.
Parameters
----------
*args, **kwargs
Arguments and keyword arguments to be passed into func.
"""
nv.validate_window_func('mean', args, kwargs)
return self._apply('ewma'... |
Exponential weighted moving stddev. | def std(self, bias=False, *args, **kwargs):
"""
Exponential weighted moving stddev.
"""
nv.validate_window_func('std', args, kwargs)
return _zsqrt(self.var(bias=bias, **kwargs)) |
Exponential weighted moving variance. | def var(self, bias=False, *args, **kwargs):
"""
Exponential weighted moving variance.
"""
nv.validate_window_func('var', args, kwargs)
def f(arg):
return libwindow.ewmcov(arg, arg, self.com, int(self.adjust),
int(self.ignore_na), i... |
Exponential weighted sample covariance. | def cov(self, other=None, pairwise=None, bias=False, **kwargs):
"""
Exponential weighted sample covariance.
"""
if other is None:
other = self._selected_obj
# only default unset
pairwise = True if pairwise is None else pairwise
other = self._sh... |
Exponential weighted sample correlation. | def corr(self, other=None, pairwise=None, **kwargs):
"""
Exponential weighted sample correlation.
"""
if other is None:
other = self._selected_obj
# only default unset
pairwise = True if pairwise is None else pairwise
other = self._shallow_copy... |
Makes sure that time and panels are conformable. | def _ensure_like_indices(time, panels):
"""
Makes sure that time and panels are conformable.
"""
n_time = len(time)
n_panel = len(panels)
u_panels = np.unique(panels) # this sorts!
u_time = np.unique(time)
if len(u_time) == n_time:
time = np.tile(u_time, len(u_panels))
if le... |
Returns a multi-index suitable for a panel-like DataFrame.
Parameters
----------
time : array-like
Time index, does not have to repeat
panels : array-like
Panel index, does not have to repeat
names : list, optional
List containing the names of the indices
Returns
--... | def panel_index(time, panels, names=None):
"""
Returns a multi-index suitable for a panel-like DataFrame.
Parameters
----------
time : array-like
Time index, does not have to repeat
panels : array-like
Panel index, does not have to repeat
names : list, optional
List ... |
Generate ND initialization; axes are passed
as required objects to __init__. | def _init_data(self, data, copy, dtype, **kwargs):
"""
Generate ND initialization; axes are passed
as required objects to __init__.
"""
if data is None:
data = {}
if dtype is not None:
dtype = self._validate_dtype(dtype)
passed_axes = [kwa... |
Construct Panel from dict of DataFrame objects.
Parameters
----------
data : dict
{field : DataFrame}
intersect : boolean
Intersect indexes of input DataFrames
orient : {'items', 'minor'}, default 'items'
The "orientation" of the data. If the ... | def from_dict(cls, data, intersect=False, orient='items', dtype=None):
"""
Construct Panel from dict of DataFrame objects.
Parameters
----------
data : dict
{field : DataFrame}
intersect : boolean
Intersect indexes of input DataFrames
orie... |
Get my plane axes indexes: these are already
(as compared with higher level planes),
as we are returning a DataFrame axes indexes. | def _get_plane_axes_index(self, axis):
"""
Get my plane axes indexes: these are already
(as compared with higher level planes),
as we are returning a DataFrame axes indexes.
"""
axis_name = self._get_axis_name(axis)
if axis_name == 'major_axis':
index... |
Get my plane axes indexes: these are already
(as compared with higher level planes),
as we are returning a DataFrame axes. | def _get_plane_axes(self, axis):
"""
Get my plane axes indexes: these are already
(as compared with higher level planes),
as we are returning a DataFrame axes.
"""
return [self._get_axis(axi)
for axi in self._get_plane_axes_index(axis)] |
Write each DataFrame in Panel to a separate excel sheet.
Parameters
----------
path : string or ExcelWriter object
File path or existing ExcelWriter
na_rep : string, default ''
Missing data representation
engine : string, default None
write en... | def to_excel(self, path, na_rep='', engine=None, **kwargs):
"""
Write each DataFrame in Panel to a separate excel sheet.
Parameters
----------
path : string or ExcelWriter object
File path or existing ExcelWriter
na_rep : string, default ''
Missin... |
Quickly retrieve single value at (item, major, minor) location.
.. deprecated:: 0.21.0
Please use .at[] or .iat[] accessors.
Parameters
----------
item : item label (panel item)
major : major axis label (panel item row)
minor : minor axis label (panel item colu... | def get_value(self, *args, **kwargs):
"""
Quickly retrieve single value at (item, major, minor) location.
.. deprecated:: 0.21.0
Please use .at[] or .iat[] accessors.
Parameters
----------
item : item label (panel item)
major : major axis label (panel i... |
Quickly set single value at (item, major, minor) location.
.. deprecated:: 0.21.0
Please use .at[] or .iat[] accessors.
Parameters
----------
item : item label (panel item)
major : major axis label (panel item row)
minor : minor axis label (panel item column)
... | def set_value(self, *args, **kwargs):
"""
Quickly set single value at (item, major, minor) location.
.. deprecated:: 0.21.0
Please use .at[] or .iat[] accessors.
Parameters
----------
item : item label (panel item)
major : major axis label (panel item r... |
Unpickle the panel. | def _unpickle_panel_compat(self, state): # pragma: no cover
"""
Unpickle the panel.
"""
from pandas.io.pickle import _unpickle_array
_unpickle = _unpickle_array
vals, items, major, minor = state
items = _unpickle(items)
major = _unpickle(major)
... |
Conform input DataFrame to align with chosen axis pair.
Parameters
----------
frame : DataFrame
axis : {'items', 'major', 'minor'}
Axis the input corresponds to. E.g., if axis='major', then
the frame's columns would be items, and the index would be
v... | def conform(self, frame, axis='items'):
"""
Conform input DataFrame to align with chosen axis pair.
Parameters
----------
frame : DataFrame
axis : {'items', 'major', 'minor'}
Axis the input corresponds to. E.g., if axis='major', then
the frame's ... |
Round each value in Panel to a specified number of decimal places.
.. versionadded:: 0.18.0
Parameters
----------
decimals : int
Number of decimal places to round to (default: 0).
If decimals is negative, it specifies the number of
positions to the l... | def round(self, decimals=0, *args, **kwargs):
"""
Round each value in Panel to a specified number of decimal places.
.. versionadded:: 0.18.0
Parameters
----------
decimals : int
Number of decimal places to round to (default: 0).
If decimals is n... |
Drop 2D from panel, holding passed axis constant.
Parameters
----------
axis : int, default 0
Axis to hold constant. E.g. axis=1 will drop major_axis entries
having a certain amount of NA data
how : {'all', 'any'}, default 'any'
'any': one or more val... | def dropna(self, axis=0, how='any', inplace=False):
"""
Drop 2D from panel, holding passed axis constant.
Parameters
----------
axis : int, default 0
Axis to hold constant. E.g. axis=1 will drop major_axis entries
having a certain amount of NA data
... |
Return slice of panel along selected axis.
Parameters
----------
key : object
Label
axis : {'items', 'major', 'minor}, default 1/'major'
Returns
-------
y : ndim(self)-1
Notes
-----
xs is only for getting, not setting values.... | def xs(self, key, axis=1):
"""
Return slice of panel along selected axis.
Parameters
----------
key : object
Label
axis : {'items', 'major', 'minor}, default 1/'major'
Returns
-------
y : ndim(self)-1
Notes
-----
... |
Parameters
----------
i : int, slice, or sequence of integers
axis : int | def _ixs(self, i, axis=0):
"""
Parameters
----------
i : int, slice, or sequence of integers
axis : int
"""
ax = self._get_axis(axis)
key = ax[i]
# xs cannot handle a non-scalar key, so just reindex here
# if we have a multi-index and a s... |
Transform wide format into long (stacked) format as DataFrame whose
columns are the Panel's items and whose index is a MultiIndex formed
of the Panel's major and minor axes.
Parameters
----------
filter_observations : boolean, default True
Drop (major, minor) pairs w... | def to_frame(self, filter_observations=True):
"""
Transform wide format into long (stacked) format as DataFrame whose
columns are the Panel's items and whose index is a MultiIndex formed
of the Panel's major and minor axes.
Parameters
----------
filter_observatio... |
Apply function along axis (or axes) of the Panel.
Parameters
----------
func : function
Function to apply to each combination of 'other' axes
e.g. if axis = 'items', the combination of major_axis/minor_axis
will each be passed as a Series; if axis = ('items',... | def apply(self, func, axis='major', **kwargs):
"""
Apply function along axis (or axes) of the Panel.
Parameters
----------
func : function
Function to apply to each combination of 'other' axes
e.g. if axis = 'items', the combination of major_axis/minor_ax... |
Handle 2-d slices, equiv to iterating over the other axis. | def _apply_2d(self, func, axis):
"""
Handle 2-d slices, equiv to iterating over the other axis.
"""
ndim = self.ndim
axis = [self._get_axis_number(a) for a in axis]
# construct slabs, in 2-d this is a DataFrame result
indexer_axis = list(range(ndim))
for ... |
Return the type for the ndim of the result. | def _construct_return_type(self, result, axes=None):
"""
Return the type for the ndim of the result.
"""
ndim = getattr(result, 'ndim', None)
# need to assume they are the same
if ndim is None:
if isinstance(result, dict):
ndim = getattr(list(... |
Return number of observations over requested axis.
Parameters
----------
axis : {'items', 'major', 'minor'} or {0, 1, 2}
Returns
-------
count : DataFrame | def count(self, axis='major'):
"""
Return number of observations over requested axis.
Parameters
----------
axis : {'items', 'major', 'minor'} or {0, 1, 2}
Returns
-------
count : DataFrame
"""
i = self._get_axis_number(axis)
val... |
Shift index by desired number of periods with an optional time freq.
The shifted data will not include the dropped periods and the
shifted axis will be smaller than the original. This is different
from the behavior of DataFrame.shift()
Parameters
----------
periods : in... | def shift(self, periods=1, freq=None, axis='major'):
"""
Shift index by desired number of periods with an optional time freq.
The shifted data will not include the dropped periods and the
shifted axis will be smaller than the original. This is different
from the behavior of Data... |
Join items with other Panel either on major and minor axes column.
Parameters
----------
other : Panel or list of Panels
Index should be similar to one of the columns in this one
how : {'left', 'right', 'outer', 'inner'}
How to handle indexes of the two objects. ... | def join(self, other, how='left', lsuffix='', rsuffix=''):
"""
Join items with other Panel either on major and minor axes column.
Parameters
----------
other : Panel or list of Panels
Index should be similar to one of the columns in this one
how : {'left', 'r... |
Modify Panel in place using non-NA values from other Panel.
May also use object coercible to Panel. Will align on items.
Parameters
----------
other : Panel, or object coercible to Panel
The object from which the caller will be udpated.
join : {'left', 'right', 'out... | def update(self, other, join='left', overwrite=True, filter_func=None,
errors='ignore'):
"""
Modify Panel in place using non-NA values from other Panel.
May also use object coercible to Panel. Will align on items.
Parameters
----------
other : Panel, or o... |
Return a list of the axis indices. | def _extract_axes(self, data, axes, **kwargs):
"""
Return a list of the axis indices.
"""
return [self._extract_axis(self, data, axis=i, **kwargs)
for i, a in enumerate(axes)] |
Return the slice dictionary for these axes. | def _extract_axes_for_slice(self, axes):
"""
Return the slice dictionary for these axes.
"""
return {self._AXIS_SLICEMAP[i]: a for i, a in
zip(self._AXIS_ORDERS[self._AXIS_LEN - len(axes):], axes)} |
Conform set of _constructor_sliced-like objects to either
an intersection of indices / columns or a union.
Parameters
----------
frames : dict
intersect : boolean, default True
Returns
-------
dict of aligned results & indices | def _homogenize_dict(self, frames, intersect=True, dtype=None):
"""
Conform set of _constructor_sliced-like objects to either
an intersection of indices / columns or a union.
Parameters
----------
frames : dict
intersect : boolean, default True
Returns
... |
For the particular label_list, gets the offsets into the hypothetical list
representing the totally ordered cartesian product of all possible label
combinations, *as long as* this space fits within int64 bounds;
otherwise, though group indices identify unique combinations of
labels, they cannot be decon... | def get_group_index(labels, shape, sort, xnull):
"""
For the particular label_list, gets the offsets into the hypothetical list
representing the totally ordered cartesian product of all possible label
combinations, *as long as* this space fits within int64 bounds;
otherwise, though group indices ide... |
reconstruct labels from observed group ids
Parameters
----------
xnull: boolean,
if nulls are excluded; i.e. -1 labels are passed through | def decons_obs_group_ids(comp_ids, obs_ids, shape, labels, xnull):
"""
reconstruct labels from observed group ids
Parameters
----------
xnull: boolean,
if nulls are excluded; i.e. -1 labels are passed through
"""
if not xnull:
lift = np.fromiter(((a == -1).any() for a in la... |
This is intended to be a drop-in replacement for np.argsort which
handles NaNs. It adds ascending and na_position parameters.
GH #6399, #5231 | def nargsort(items, kind='quicksort', ascending=True, na_position='last'):
"""
This is intended to be a drop-in replacement for np.argsort which
handles NaNs. It adds ascending and na_position parameters.
GH #6399, #5231
"""
# specially handle Categorical
if is_categorical_dtype(items):
... |
return a diction of {labels} -> {indexers} | def get_indexer_dict(label_list, keys):
""" return a diction of {labels} -> {indexers} """
shape = list(map(len, keys))
group_index = get_group_index(label_list, shape, sort=True, xnull=True)
ngroups = ((group_index.size and group_index.max()) + 1) \
if is_int64_overflow_possible(shape) \
... |
algos.groupsort_indexer implements `counting sort` and it is at least
O(ngroups), where
ngroups = prod(shape)
shape = map(len, keys)
that is, linear in the number of combinations (cartesian product) of unique
values of groupby keys. This can be huge when doing multi-key groupby.
np.argso... | def get_group_index_sorter(group_index, ngroups):
"""
algos.groupsort_indexer implements `counting sort` and it is at least
O(ngroups), where
ngroups = prod(shape)
shape = map(len, keys)
that is, linear in the number of combinations (cartesian product) of unique
values of groupby key... |
Group_index is offsets into cartesian product of all possible labels. This
space can be huge, so this function compresses it, by computing offsets
(comp_ids) into the list of unique labels (obs_group_ids). | def compress_group_index(group_index, sort=True):
"""
Group_index is offsets into cartesian product of all possible labels. This
space can be huge, so this function compresses it, by computing offsets
(comp_ids) into the list of unique labels (obs_group_ids).
"""
size_hint = min(len(group_index... |
Sort ``values`` and reorder corresponding ``labels``.
``values`` should be unique if ``labels`` is not None.
Safe for use with mixed types (int, str), orders ints before strs.
.. versionadded:: 0.19.0
Parameters
----------
values : list-like
Sequence; must be unique if ``labels`` is no... | def safe_sort(values, labels=None, na_sentinel=-1, assume_unique=False):
"""
Sort ``values`` and reorder corresponding ``labels``.
``values`` should be unique if ``labels`` is not None.
Safe for use with mixed types (int, str), orders ints before strs.
.. versionadded:: 0.19.0
Parameters
-... |
Attempt to prevent foot-shooting in a helpful way.
Parameters
----------
terms : Term
Terms can contain | def _check_ne_builtin_clash(expr):
"""Attempt to prevent foot-shooting in a helpful way.
Parameters
----------
terms : Term
Terms can contain
"""
names = expr.names
overlap = names & _ne_builtins
if overlap:
s = ', '.join(map(repr, overlap))
raise NumExprClobber... |
Run the engine on the expression
This method performs alignment which is necessary no matter what engine
is being used, thus its implementation is in the base class.
Returns
-------
obj : object
The result of the passed expression. | def evaluate(self):
"""Run the engine on the expression
This method performs alignment which is necessary no matter what engine
is being used, thus its implementation is in the base class.
Returns
-------
obj : object
The result of the passed expression.
... |
Find the appropriate Block subclass to use for the given values and dtype.
Parameters
----------
values : ndarray-like
dtype : numpy or pandas dtype
Returns
-------
cls : class, subclass of Block | def get_block_type(values, dtype=None):
"""
Find the appropriate Block subclass to use for the given values and dtype.
Parameters
----------
values : ndarray-like
dtype : numpy or pandas dtype
Returns
-------
cls : class, subclass of Block
"""
dtype = dtype or values.dtype
... |
return a new extended blocks, givin the result | def _extend_blocks(result, blocks=None):
""" return a new extended blocks, givin the result """
from pandas.core.internals import BlockManager
if blocks is None:
blocks = []
if isinstance(result, list):
for r in result:
if isinstance(r, list):
blocks.extend(r)... |
guarantee the shape of the values to be at least 1 d | def _block_shape(values, ndim=1, shape=None):
""" guarantee the shape of the values to be at least 1 d """
if values.ndim < ndim:
if shape is None:
shape = values.shape
if not is_extension_array_dtype(values):
# TODO: https://github.com/pandas-dev/pandas/issues/23023
... |
If possible, reshape `arr` to have shape `new_shape`,
with a couple of exceptions (see gh-13012):
1) If `arr` is a ExtensionArray or Index, `arr` will be
returned as is.
2) If `arr` is a Series, the `_values` attribute will
be reshaped and returned.
Parameters
----------
arr : ar... | def _safe_reshape(arr, new_shape):
"""
If possible, reshape `arr` to have shape `new_shape`,
with a couple of exceptions (see gh-13012):
1) If `arr` is a ExtensionArray or Index, `arr` will be
returned as is.
2) If `arr` is a Series, the `_values` attribute will
be reshaped and return... |
Return a new ndarray, try to preserve dtype if possible.
Parameters
----------
v : `values`, updated in-place (array like)
m : `mask`, applies to both sides (array like)
n : `new values` either scalar or an array like aligned with `values`
Returns
-------
values : ndarray with updated ... | def _putmask_smart(v, m, n):
"""
Return a new ndarray, try to preserve dtype if possible.
Parameters
----------
v : `values`, updated in-place (array like)
m : `mask`, applies to both sides (array like)
n : `new values` either scalar or an array like aligned with `values`
Returns
-... |
ndim inference and validation.
Infers ndim from 'values' if not provided to __init__.
Validates that values.ndim and ndim are consistent if and only if
the class variable '_validate_ndim' is True.
Parameters
----------
values : array-like
ndim : int or None
... | def _check_ndim(self, values, ndim):
"""
ndim inference and validation.
Infers ndim from 'values' if not provided to __init__.
Validates that values.ndim and ndim are consistent if and only if
the class variable '_validate_ndim' is True.
Parameters
----------
... |
validate that we have a astypeable to categorical,
returns a boolean if we are a categorical | def is_categorical_astype(self, dtype):
"""
validate that we have a astypeable to categorical,
returns a boolean if we are a categorical
"""
if dtype is Categorical or dtype is CategoricalDtype:
# this is a pd.Categorical, but is not
# a valid type for ast... |
return an internal format, currently just the ndarray
this is often overridden to handle to_dense like operations | def get_values(self, dtype=None):
"""
return an internal format, currently just the ndarray
this is often overridden to handle to_dense like operations
"""
if is_object_dtype(dtype):
return self.values.astype(object)
return self.values |
Create a new block, with type inference propagate any values that are
not specified | def make_block(self, values, placement=None, ndim=None):
"""
Create a new block, with type inference propagate any values that are
not specified
"""
if placement is None:
placement = self.mgr_locs
if ndim is None:
ndim = self.ndim
return m... |
Wrap given values in a block of same type as self. | def make_block_same_class(self, values, placement=None, ndim=None,
dtype=None):
""" Wrap given values in a block of same type as self. """
if dtype is not None:
# issue 19431 fastparquet is passing this
warnings.warn("dtype argument is deprecated, wi... |
Perform __getitem__-like, return result as block.
As of now, only supports slices that preserve dimensionality. | def getitem_block(self, slicer, new_mgr_locs=None):
"""
Perform __getitem__-like, return result as block.
As of now, only supports slices that preserve dimensionality.
"""
if new_mgr_locs is None:
if isinstance(slicer, tuple):
axis0_slicer = slicer[0]... |
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