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train | DatetimeLikeArrayMixin.repeat | Repeat elements of an array.
See Also
--------
numpy.ndarray.repeat | pandas/core/arrays/datetimelike.py | 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) | def repeat(self, repeats, *args, **kwargs):
"""
Repeat elements of an array.
See Also
--------
numpy.ndarray.repeat
"""
nv.validate_repeat(args, kwargs)
values = self._data.repeat(repeats)
return type(self)(values.view('i8'), dtype=self.dtype) | [
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train | DatetimeLikeArrayMixin.value_counts | Return a Series containing counts of unique values.
Parameters
----------
dropna : boolean, default True
Don't include counts of NaT values.
Returns
-------
Series | pandas/core/arrays/datetimelike.py | 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 impo... | def value_counts(self, dropna=False):
"""
Return a Series containing counts of unique values.
Parameters
----------
dropna : boolean, default True
Don't include counts of NaT values.
Returns
-------
Series
"""
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train | DatetimeLikeArrayMixin._maybe_mask_results | 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
N... | pandas/core/arrays/datetimelike.py | 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_va... | 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
-------
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train | DatetimeLikeArrayMixin._validate_frequency | 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
... | pandas/core/arrays/datetimelike.py | 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 deter... | def _validate_frequency(cls, index, freq, **kwargs):
"""
Validate that a frequency is compatible with the values of a given
Datetime Array/Index or Timedelta Array/Index
Parameters
----------
index : DatetimeIndex or TimedeltaIndex
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train | DatetimeLikeArrayMixin._add_delta | 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]
... | pandas/core/arrays/datetimelike.py | 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
... | def _add_delta(self, other):
"""
Add a timedelta-like, Tick or TimedeltaIndex-like object
to self, yielding an int64 numpy array
Parameters
----------
delta : {timedelta, np.timedelta64, Tick,
TimedeltaIndex, ndarray[timedelta64]}
Returns
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train | DatetimeLikeArrayMixin._add_timedeltalike_scalar | Add a delta of a timedeltalike
return the i8 result view | pandas/core/arrays/datetimelike.py | def _add_timedeltalike_scalar(self, other):
"""
Add a delta of a timedeltalike
return the i8 result view
"""
if isna(other):
# i.e np.timedelta64("NaT"), not recognized by delta_to_nanoseconds
new_values = np.empty(len(self), dtype='i8')
new_va... | def _add_timedeltalike_scalar(self, other):
"""
Add a delta of a timedeltalike
return the i8 result view
"""
if isna(other):
# i.e np.timedelta64("NaT"), not recognized by delta_to_nanoseconds
new_values = np.empty(len(self), dtype='i8')
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train | DatetimeLikeArrayMixin._add_delta_tdi | Add a delta of a TimedeltaIndex
return the i8 result view | pandas/core/arrays/datetimelike.py | 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 ... | 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):
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train | DatetimeLikeArrayMixin._add_nat | Add pd.NaT to self | pandas/core/arrays/datetimelike.py | def _add_nat(self):
"""
Add pd.NaT to self
"""
if is_period_dtype(self):
raise TypeError('Cannot add {cls} and {typ}'
.format(cls=type(self).__name__,
typ=type(NaT).__name__))
# GH#19124 pd.NaT is treate... | def _add_nat(self):
"""
Add pd.NaT to self
"""
if is_period_dtype(self):
raise TypeError('Cannot add {cls} and {typ}'
.format(cls=type(self).__name__,
typ=type(NaT).__name__))
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train | DatetimeLikeArrayMixin._sub_nat | Subtract pd.NaT from self | pandas/core/arrays/datetimelike.py | 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
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# For datetime64 dtypes by convention we treat NaT as a datetime,... | def _sub_nat(self):
"""
Subtract pd.NaT from self
"""
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train | DatetimeLikeArrayMixin._sub_period_array | 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.ndarra... | pandas/core/arrays/datetimelike.py | 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
... | def _sub_period_array(self, other):
"""
Subtract a Period Array/Index from self. This is only valid if self
is itself a Period Array/Index, raises otherwise. Both objects must
have the same frequency.
Parameters
----------
other : PeriodIndex or PeriodArray
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train | DatetimeLikeArrayMixin._addsub_int_array | Add or subtract array-like of integers equivalent to applying
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Parameters
----------
other : Index, ExtensionArray, np.ndarray
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op : {operator.add, operator.sub}
Returns
-------
result : same class as self | pandas/core/arrays/datetimelike.py | def _addsub_int_array(self, other, op):
"""
Add or subtract array-like of integers equivalent to applying
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Parameters
----------
other : Index, ExtensionArray, np.ndarray
integer-dtype
op : {operator.add, operator.sub}
... | def _addsub_int_array(self, other, op):
"""
Add or subtract array-like of integers equivalent to applying
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Parameters
----------
other : Index, ExtensionArray, np.ndarray
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train | DatetimeLikeArrayMixin._addsub_offset_array | 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 | pandas/core/arrays/datetimelike.py | 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
-------
... | def _addsub_offset_array(self, other, op):
"""
Add or subtract array-like of DateOffset objects
Parameters
----------
other : Index, np.ndarray
object-dtype containing pd.DateOffset objects
op : {operator.add, operator.sub}
Returns
-------
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train | DatetimeLikeArrayMixin._time_shift | 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.DateOf... | pandas/core/arrays/datetimelike.py | 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
... | def _time_shift(self, periods, freq=None):
"""
Shift each value by `periods`.
Note this is different from ExtensionArray.shift, which
shifts the *position* of each element, padding the end with
missing values.
Parameters
----------
periods : int
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train | DatetimeLikeArrayMixin._ensure_localized | 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, ... | pandas/core/arrays/datetimelike.py | 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
... | 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
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See Also
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"""
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.
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Return the minimum value of the Array or minimum along
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train | _period_array_cmp | Wrap comparison operations to convert Period-like to PeriodDtype | pandas/core/arrays/period.py | def _period_array_cmp(cls, op):
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Wrap comparison operations to convert Period-like to PeriodDtype
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Wrap comparison operations to convert Period-like to PeriodDtype
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"""
Helper function to render a consistent error message when raising
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Parameters
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left : PeriodArray
right : DateOffset, Period, ndarray, or timedelta-like
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dtype : dtype
freq : DateOffset or None
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freq : DateOffset
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Parameters
----------
dtype : dtype
freq : DateOffset or None
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Parameters
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dtype : dtype
freq : DateOffset or None
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train | PeriodArray._from_datetime64 | Construct a PeriodArray from a datetime64 array
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data : ndarray[datetime64[ns], datetime64[ns, tz]]
freq : str or Tick
tz : tzinfo, optional
Returns
-------
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Construct a PeriodArray from a datetime64 array
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----------
data : ndarray[datetime64[ns], datetime64[ns, tz]]
freq : str or Tick
tz : tzinfo, optional
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Construct a PeriodArray from a datetime64 array
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data : ndarray[datetime64[ns], datetime64[ns, tz]]
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Target frequency. The default is 'D' for week or longer,
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Cast to DatetimeArray/Index.
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freq : string or DateOffset, optional
Target frequency. The default is 'D' for week or longer,
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Shift each value by `periods`.
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"""
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train | PeriodArray._add_timedeltalike_scalar | Parameters
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other : timedelta, Tick, np.timedelta64
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-------
result : ndarray[int64] | pandas/core/arrays/period.py | def _add_timedeltalike_scalar(self, other):
"""
Parameters
----------
other : timedelta, Tick, np.timedelta64
Returns
-------
result : ndarray[int64]
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other : timedelta, Tick, np.timedelta64
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result : ndarray[int64]
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train | PeriodArray._add_delta_tdi | Parameters
----------
other : TimedeltaArray or ndarray[timedelta64]
Returns
-------
result : ndarray[int64] | pandas/core/arrays/period.py | def _add_delta_tdi(self, other):
"""
Parameters
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other : TimedeltaArray or ndarray[timedelta64]
Returns
-------
result : ndarray[int64]
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train | PeriodArray._add_delta | Add a timedelta-like, Tick, or TimedeltaIndex-like object
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Parameters
----------
other : {timedelta, np.timedelta64, Tick,
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Returns
-------
result : PeriodArray | pandas/core/arrays/period.py | def _add_delta(self, other):
"""
Add a timedelta-like, Tick, or TimedeltaIndex-like object
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other : {timedelta, np.timedelta64, Tick,
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"""
Add a timedelta-like, Tick, or TimedeltaIndex-like object
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other : {timedelta, np.timedelta64, Tick,
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train | _isna_old | Detect missing values. Treat None, NaN, INF, -INF as null.
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arr: ndarray or object value
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arr: ndarray or object value
Returns
-------
boolean ndarray or boolean
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arr: ndarray or object value
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boolean ndarray or boolean
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train | _use_inf_as_na | Option change callback for na/inf behaviour
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flag: bool
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train | _isna_compat | Parameters
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arr: a numpy array
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-------
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arr: a numpy array
fill_value: fill value, default to np.nan
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arr: a numpy array
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train | array_equivalent | True if two arrays, left and right, have equal non-NaN elements, and NaNs
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"""
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train | _infer_fill_value | infer the fill value for the nan/NaT from the provided
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train | _maybe_fill | if we have a compatible fill_value and arr dtype, then fill | pandas/core/dtypes/missing.py | 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)
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train | na_value_for_dtype | 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)
... | pandas/core/dtypes/missing.py | 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
>... | 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
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train | remove_na_arraylike | Return array-like containing only true/non-NaN values, possibly empty. | pandas/core/dtypes/missing.py | 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)]
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Return array-like containing only true/non-NaN values, possibly empty.
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ax : Matplotlib axes object
data : DataFrame or Series
data for table contents
kwargs : keywords, optional
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----------
ax : Matplotlib axes object
data : DataFrame or Series
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ax : Matplotlib axes object
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train | _subplots | Create a figure with a set of subplots already made.
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Keyword arguments:
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Number of required axes. Exceeded axes are set invisible. Default is
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train | maybe_cythonize | Render tempita templates before calling cythonize | setup.py | def maybe_cythonize(extensions, *args, **kwargs):
"""
Render tempita templates before calling cythonize
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if len(sys.argv) > 1 and 'clean' in sys.argv:
# Avoid running cythonize on `python setup.py clean`
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Render tempita templates before calling cythonize
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train | NDFrameGroupBy._transform_fast | Fast transform path for aggregations | pandas/core/groupby/generic.py | 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
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Fast transform path for aggregations
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train | NDFrameGroupBy.filter | Return a copy of a DataFrame excluding elements from groups that
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Return a copy of a DataFrame excluding elements from groups that
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return DataFrame(output, index=index, columns=names)
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train | SeriesGroupBy.count | Compute count of group, excluding missing values | pandas/core/groupby/generic.py | def count(self):
""" Compute count of group, excluding missing values """
ids, _, ngroups = self.grouper.group_info
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train | SeriesGroupBy.pct_change | Calcuate pct_change of each value to previous entry in group | pandas/core/groupby/generic.py | 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,
... | def pct_change(self, periods=1, fill_method='pad', limit=None, freq=None):
"""Calcuate pct_change of each value to previous entry in group"""
# TODO: Remove this conditional when #23918 is fixed
if freq:
return self.apply(lambda x: x.pct_change(periods=periods,
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train | DataFrameGroupBy._gotitem | sub-classes to define
return a sliced object
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----------
key : string / list of selections
ndim : 1,2
requested ndim of result
subset : object, default None
subset to act on | pandas/core/groupby/generic.py | def _gotitem(self, key, ndim, subset=None):
"""
sub-classes to define
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----------
key : string / list of selections
ndim : 1,2
requested ndim of result
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requested ndim of result
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train | DataFrameGroupBy._reindex_output | 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 | pandas/core/groupby/generic.py | def _reindex_output(self, result):
"""
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we have a fully reindex-output to the levels. These may have not
participated in the groupings (e.g. may have all been
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train | DataFrameGroupBy._fill | Overridden method to join grouped columns in output | pandas/core/groupby/generic.py | 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((sel... | def _fill(self, direction, limit=None):
"""Overridden method to join grouped columns in output"""
res = super()._fill(direction, limit=limit)
output = OrderedDict(
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train | DataFrameGroupBy.count | Compute count of group, excluding missing values | pandas/core/groupby/generic.py | 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
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""" Compute count of group, excluding missing values """
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data, _ = self._get_data_to_aggregate()
ids, _, ngroups = self.grouper.group_info
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train | DataFrameGroupBy.nunique | Return DataFrame with number of distinct observations per group for
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.. versionadded:: 0.20.0
Parameters
----------
dropna : boolean, default True
Don't include NaN in the counts.
Returns
-------
nunique: DataFrame
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"""
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
... | def nunique(self, dropna=True):
"""
Return DataFrame with number of distinct observations per group for
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.. versionadded:: 0.20.0
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----------
dropna : boolean, default True
Don't include NaN in the counts.
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obj : object
For Series / Index, the underlying ExtensionArray is unboxed.
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"""
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For all other types, `obj` is just returned as is.
Parameters
----------
obj : object
For Series / Index, the underlying ExtensionArray is unboxed.
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l : sequence
The non string sequence to flatten
Notes
-----
This doesn't consider strings sequences.
Returns
-------
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"""
Flatten an arbitrarily nested sequence.
Parameters
----------
l : sequence
The non string sequence to flatten
Notes
-----
This doesn't consider strings sequences.
Returns
-------
flattened : generator
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Flatten an arbitrarily nested sequence.
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l : sequence
The non string sequence to flatten
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This doesn't consider strings sequences.
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-------
flattened : generator
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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
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"""
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.
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"""
Check whether `key` is a valid boolean indexer.
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----------
key : Any
Only list-likes may be considered boolean indexers.
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train | cast_scalar_indexer | To avoid numpy DeprecationWarnings, cast float to integer where valid.
Parameters
----------
val : scalar
Returns
-------
outval : scalar | pandas/core/common.py | 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)
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"""
To avoid numpy DeprecationWarnings, cast float to integer where valid.
Parameters
----------
val : scalar
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-------
outval : scalar
"""
# assumes lib.is_scalar(val)
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train | index_labels_to_array | Transform label or iterable of labels to array, for use in Index.
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dtype : dtype
If specified, use as dtype of the resulting array, otherwise infer.
Returns
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"""
Transform label or iterable of labels to array, for use in Index.
Parameters
----------
dtype : dtype
If specified, use as dtype of the resulting array, otherwise infer.
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train | apply_if_callable | Evaluate possibly callable input using obj and kwargs if it is callable,
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maybe_callable : possibly a callable
obj : NDFrame
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"""
Evaluate possibly callable input using obj and kwargs if it is callable,
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Parameters
----------
maybe_callable : possibly a callable
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**kwargs
"""
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maybe_callable : possibly a callable
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into : instance or subclass of collections.abc.Mapping
Must be a class, an initialized collections.defaultdict,
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"""
Helper function to standardize a supplied mapping.
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Parameters
----------
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"""
Helper function to standardize a supplied mapping.
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train | random_state | Helper function for processing random_state arguments.
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----------
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If receives an int, passes to np.random.RandomState() as seed.
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----------
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train | _get_fill_value | return the correct fill value for the dtype of the values | pandas/core/nanops.py | def _get_fill_value(dtype, fill_value=None, fill_value_typ=None):
""" return the correct fill value for the dtype of the values """
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return fill_value
if _na_ok_dtype(dtype):
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train | _get_values | utility to get the values view, mask, dtype
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copy = True will force the copy | pandas/core/nanops.py | def _get_values(values, skipna, fill_value=None, fill_value_typ=None,
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train | _wrap_results | wrap our results if needed | pandas/core/nanops.py | def _wrap_results(result, dtype, fill_value=None):
""" wrap our results if needed """
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# GH#24293
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train | _na_for_min_count | Return the missing value for `values`
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----------
values : ndarray
axis : int or None
axis for the reduction
Returns
-------
result : scalar or ndarray
For 1-D values, returns a scalar of the correct missing type.
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"""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.
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----------
values : ndarray
axis : int or None
axis for the reduction
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-------
result : scalar or ndarray
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train | nanany | Check if any elements along an axis evaluate to True.
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values : ndarray
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skipna : bool, default True
mask : ndarray[bool], optional
nan-mask if known
Returns
-------
result : bool
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----------
values : ndarray
axis : int, optional
skipna : bool, default True
mask : ndarray[bool], optional
nan-mask if known
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-------
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values : ndarray
axis : int, optional
skipna : bool, default True
mask : ndarray[bool], optional
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train | nanall | Check if all elements along an axis evaluate to True.
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skipna : bool, default True
mask : ndarray[bool], optional
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values : ndarray
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r... | 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
-------
r... | [
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train | nansum | 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
-------
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Examples
-------... | pandas/core/nanops.py | 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-mas... | 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
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train | nanmean | 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
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"""
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
------... | 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
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train | nanmedian | 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
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Exa... | pandas/core/nanops.py | 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
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axis: int, optional
skipna : bool, default True
mask : ndarray[bool], optional
nan-mask if known
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train | nanstd | 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 ... | pandas/core/nanops.py | def nanstd(values, axis=None, skipna=True, ddof=1, mask=None):
"""
Compute the standard deviation along given axis while ignoring NaNs
Parameters
----------
values : ndarray
axis: int, optional
skipna : bool, default True
ddof : int, default 1
Delta Degrees of Freedom. The divis... | def nanstd(values, axis=None, skipna=True, ddof=1, mask=None):
"""
Compute the standard deviation along given axis while ignoring NaNs
Parameters
----------
values : ndarray
axis: int, optional
skipna : bool, default True
ddof : int, default 1
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train | nanvar | 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,
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"""
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... | 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
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train | nansem | 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... | pandas/core/nanops.py | 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
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"""
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
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train | nanargmax | 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 p... | pandas/core/nanops.py | 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... | 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
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--------
result : int
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train | nanargmin | 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 p... | pandas/core/nanops.py | 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... | 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
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train | nanskew | 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 : boo... | pandas/core/nanops.py | 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
--------... | 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
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train | nankurt | 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... | pandas/core/nanops.py | 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 : ndar... | 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 : ndar... | [
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train | nanprod | 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
... | pandas/core/nanops.py | 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 : ... | 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
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-------
result : ... | [
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train | nancorr | a, b: ndarrays | pandas/core/nanops.py | 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]
... | def nancorr(a, b, method='pearson', min_periods=None):
"""
a, b: ndarrays
"""
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raise AssertionError('Operands to nancorr must have same size')
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min_periods = 1
valid = notna(a) & notna(b)
if not valid.all():
a = a[valid]
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train | _nanpercentile_1d | Wraper for np.percentile that skips missing values, specialized to
1-dimensional case.
Parameters
----------
values : array over which to find quantiles
mask : ndarray[bool]
locations in values that should be considered missing
q : scalar or array of quantile indices to find
na_valu... | pandas/core/nanops.py | def _nanpercentile_1d(values, mask, q, na_value, interpolation):
"""
Wraper for np.percentile that skips missing values, specialized to
1-dimensional case.
Parameters
----------
values : array over which to find quantiles
mask : ndarray[bool]
locations in values that should be consi... | def _nanpercentile_1d(values, mask, q, na_value, interpolation):
"""
Wraper for np.percentile that skips missing values, specialized to
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Parameters
----------
values : array over which to find quantiles
mask : ndarray[bool]
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train | _HtmlFrameParser._handle_hidden_tables | Return list of tables, potentially removing hidden elements
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train | get_dtype_kinds | Parameters
----------
l : list of arrays
Returns
-------
a set of kinds that exist in this list of arrays | pandas/core/dtypes/concat.py | def get_dtype_kinds(l):
"""
Parameters
----------
l : list of arrays
Returns
-------
a set of kinds that exist in this list of arrays
"""
typs = set()
for arr in l:
dtype = arr.dtype
if is_categorical_dtype(dtype):
typ = 'category'
elif is_s... | def get_dtype_kinds(l):
"""
Parameters
----------
l : list of arrays
Returns
-------
a set of kinds that exist in this list of arrays
"""
typs = set()
for arr in l:
dtype = arr.dtype
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typ = 'category'
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