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19,800 | pandas-dev/pandas | pandas/core/arrays/categorical.py | Categorical.astype | def astype(self, dtype, copy=True):
"""
Coerce this type to another dtype
Parameters
----------
dtype : numpy dtype or pandas type
copy : bool, default True
By default, astype always returns a newly allocated object.
If copy is set to False and dt... | python | def astype(self, dtype, copy=True):
"""
Coerce this type to another dtype
Parameters
----------
dtype : numpy dtype or pandas type
copy : bool, default True
By default, astype always returns a newly allocated object.
If copy is set to False and dt... | [
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19,801 | pandas-dev/pandas | pandas/core/arrays/categorical.py | Categorical._from_inferred_categories | def _from_inferred_categories(cls, inferred_categories, inferred_codes,
dtype, true_values=None):
"""
Construct a Categorical from inferred values.
For inferred categories (`dtype` is None) the categories are sorted.
For explicit `dtype`, the `inferred_... | python | def _from_inferred_categories(cls, inferred_categories, inferred_codes,
dtype, true_values=None):
"""
Construct a Categorical from inferred values.
For inferred categories (`dtype` is None) the categories are sorted.
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19,802 | pandas-dev/pandas | pandas/core/arrays/categorical.py | Categorical.from_codes | def from_codes(cls, codes, categories=None, ordered=None, dtype=None):
"""
Make a Categorical type from codes and categories or dtype.
This constructor is useful if you already have codes and
categories/dtype and so do not need the (computation intensive)
factorization step, whi... | python | def from_codes(cls, codes, categories=None, ordered=None, dtype=None):
"""
Make a Categorical type from codes and categories or dtype.
This constructor is useful if you already have codes and
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19,803 | pandas-dev/pandas | pandas/core/arrays/categorical.py | Categorical._get_codes | def _get_codes(self):
"""
Get the codes.
Returns
-------
codes : integer array view
A non writable view of the `codes` array.
"""
v = self._codes.view()
v.flags.writeable = False
return v | python | def _get_codes(self):
"""
Get the codes.
Returns
-------
codes : integer array view
A non writable view of the `codes` array.
"""
v = self._codes.view()
v.flags.writeable = False
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19,804 | pandas-dev/pandas | pandas/core/arrays/categorical.py | Categorical._set_categories | def _set_categories(self, categories, fastpath=False):
"""
Sets new categories inplace
Parameters
----------
fastpath : bool, default False
Don't perform validation of the categories for uniqueness or nulls
Examples
--------
>>> c = pd.Categor... | python | def _set_categories(self, categories, fastpath=False):
"""
Sets new categories inplace
Parameters
----------
fastpath : bool, default False
Don't perform validation of the categories for uniqueness or nulls
Examples
--------
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19,805 | pandas-dev/pandas | pandas/core/arrays/categorical.py | Categorical._set_dtype | def _set_dtype(self, dtype):
"""
Internal method for directly updating the CategoricalDtype
Parameters
----------
dtype : CategoricalDtype
Notes
-----
We don't do any validation here. It's assumed that the dtype is
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"""
Internal method for directly updating the CategoricalDtype
Parameters
----------
dtype : CategoricalDtype
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-----
We don't do any validation here. It's assumed that the dtype is
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19,806 | pandas-dev/pandas | pandas/core/arrays/categorical.py | Categorical.set_ordered | def set_ordered(self, value, inplace=False):
"""
Set the ordered attribute to the boolean value.
Parameters
----------
value : bool
Set whether this categorical is ordered (True) or not (False).
inplace : bool, default False
Whether or not to set th... | python | def set_ordered(self, value, inplace=False):
"""
Set the ordered attribute to the boolean value.
Parameters
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value : bool
Set whether this categorical is ordered (True) or not (False).
inplace : bool, default False
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19,807 | pandas-dev/pandas | pandas/core/arrays/categorical.py | Categorical.as_ordered | def as_ordered(self, inplace=False):
"""
Set the Categorical to be ordered.
Parameters
----------
inplace : bool, default False
Whether or not to set the ordered attribute in-place or return
a copy of this categorical with ordered set to True.
"""
... | python | def as_ordered(self, inplace=False):
"""
Set the Categorical to be ordered.
Parameters
----------
inplace : bool, default False
Whether or not to set the ordered attribute in-place or return
a copy of this categorical with ordered set to True.
"""
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19,808 | pandas-dev/pandas | pandas/core/arrays/categorical.py | Categorical.as_unordered | def as_unordered(self, inplace=False):
"""
Set the Categorical to be unordered.
Parameters
----------
inplace : bool, default False
Whether or not to set the ordered attribute in-place or return
a copy of this categorical with ordered set to False.
... | python | def as_unordered(self, inplace=False):
"""
Set the Categorical to be unordered.
Parameters
----------
inplace : bool, default False
Whether or not to set the ordered attribute in-place or return
a copy of this categorical with ordered set to False.
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19,809 | pandas-dev/pandas | pandas/core/arrays/categorical.py | Categorical.set_categories | def set_categories(self, new_categories, ordered=None, rename=False,
inplace=False):
"""
Set the categories to the specified new_categories.
`new_categories` can include new categories (which will result in
unused categories) or remove old categories (which result... | python | def set_categories(self, new_categories, ordered=None, rename=False,
inplace=False):
"""
Set the categories to the specified new_categories.
`new_categories` can include new categories (which will result in
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19,810 | pandas-dev/pandas | pandas/core/arrays/categorical.py | Categorical.rename_categories | def rename_categories(self, new_categories, inplace=False):
"""
Rename categories.
Parameters
----------
new_categories : list-like, dict-like or callable
* list-like: all items must be unique and the number of items in
the new categories must match the ... | python | def rename_categories(self, new_categories, inplace=False):
"""
Rename categories.
Parameters
----------
new_categories : list-like, dict-like or callable
* list-like: all items must be unique and the number of items in
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19,811 | pandas-dev/pandas | pandas/core/arrays/categorical.py | Categorical.reorder_categories | def reorder_categories(self, new_categories, ordered=None, inplace=False):
"""
Reorder categories as specified in new_categories.
`new_categories` need to include all old categories and no new category
items.
Parameters
----------
new_categories : Index-like
... | python | def reorder_categories(self, new_categories, ordered=None, inplace=False):
"""
Reorder categories as specified in new_categories.
`new_categories` need to include all old categories and no new category
items.
Parameters
----------
new_categories : Index-like
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19,812 | pandas-dev/pandas | pandas/core/arrays/categorical.py | Categorical.add_categories | def add_categories(self, new_categories, inplace=False):
"""
Add new categories.
`new_categories` will be included at the last/highest place in the
categories and will be unused directly after this call.
Parameters
----------
new_categories : category or list-li... | python | def add_categories(self, new_categories, inplace=False):
"""
Add new categories.
`new_categories` will be included at the last/highest place in the
categories and will be unused directly after this call.
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19,813 | pandas-dev/pandas | pandas/core/arrays/categorical.py | Categorical.remove_categories | def remove_categories(self, removals, inplace=False):
"""
Remove the specified categories.
`removals` must be included in the old categories. Values which were in
the removed categories will be set to NaN
Parameters
----------
removals : category or list of cate... | python | def remove_categories(self, removals, inplace=False):
"""
Remove the specified categories.
`removals` must be included in the old categories. Values which were in
the removed categories will be set to NaN
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19,814 | pandas-dev/pandas | pandas/core/arrays/categorical.py | Categorical.remove_unused_categories | def remove_unused_categories(self, inplace=False):
"""
Remove categories which are not used.
Parameters
----------
inplace : bool, default False
Whether or not to drop unused categories inplace or return a copy of
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"""
Remove categories which are not used.
Parameters
----------
inplace : bool, default False
Whether or not to drop unused categories inplace or return a copy of
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19,815 | pandas-dev/pandas | pandas/core/arrays/categorical.py | Categorical.shift | def shift(self, periods, fill_value=None):
"""
Shift Categorical by desired number of periods.
Parameters
----------
periods : int
Number of periods to move, can be positive or negative
fill_value : object, optional
The scalar value to use for new... | python | def shift(self, periods, fill_value=None):
"""
Shift Categorical by desired number of periods.
Parameters
----------
periods : int
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19,816 | pandas-dev/pandas | pandas/core/arrays/categorical.py | Categorical.memory_usage | def memory_usage(self, deep=False):
"""
Memory usage of my values
Parameters
----------
deep : bool
Introspect the data deeply, interrogate
`object` dtypes for system-level memory consumption
Returns
-------
bytes used
No... | python | def memory_usage(self, deep=False):
"""
Memory usage of my values
Parameters
----------
deep : bool
Introspect the data deeply, interrogate
`object` dtypes for system-level memory consumption
Returns
-------
bytes used
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19,817 | pandas-dev/pandas | pandas/core/arrays/categorical.py | Categorical.value_counts | def value_counts(self, dropna=True):
"""
Return a Series containing counts of each category.
Every category will have an entry, even those with a count of 0.
Parameters
----------
dropna : bool, default True
Don't include counts of NaN.
Returns
... | python | def value_counts(self, dropna=True):
"""
Return a Series containing counts of each category.
Every category will have an entry, even those with a count of 0.
Parameters
----------
dropna : bool, default True
Don't include counts of NaN.
Returns
... | [
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19,818 | pandas-dev/pandas | pandas/core/arrays/categorical.py | Categorical.get_values | def get_values(self):
"""
Return the values.
For internal compatibility with pandas formatting.
Returns
-------
numpy.array
A numpy array of the same dtype as categorical.categories.dtype or
Index if datetime / periods.
"""
# if w... | python | def get_values(self):
"""
Return the values.
For internal compatibility with pandas formatting.
Returns
-------
numpy.array
A numpy array of the same dtype as categorical.categories.dtype or
Index if datetime / periods.
"""
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19,819 | pandas-dev/pandas | pandas/core/arrays/categorical.py | Categorical.sort_values | def sort_values(self, inplace=False, ascending=True, na_position='last'):
"""
Sort the Categorical by category value returning a new
Categorical by default.
While an ordering is applied to the category values, sorting in this
context refers more to organizing and grouping togeth... | python | def sort_values(self, inplace=False, ascending=True, na_position='last'):
"""
Sort the Categorical by category value returning a new
Categorical by default.
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19,820 | 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
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19,821 | 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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19,822 | 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
if len(self.categ... | [
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19,823 | 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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19,824 | 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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19,825 | 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
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-------
dict of categories -> indexers
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19,826 | 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`.
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-------
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!
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------
TypeError
If the `Categorical` is not `ordered`.
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19,827 | pandas-dev/pandas | pandas/core/arrays/categorical.py | Categorical.unique | 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... | python | def unique(self):
"""
Return the ``Categorical`` which ``categories`` and ``codes`` are
unique. Unused categories are NOT returned.
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order.
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19,828 | 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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19,829 | 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
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Parameters
----------
other : Categorical
Returns
-------
bool
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19,830 | 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)
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19,831 | 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
the Categorical matches an element in the passed sequence of
`values` exactly.
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----------
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19,832 | 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
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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
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19,833 | 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'):
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result = Timedelta(r, unit)
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# explicitly view as timedelta64 for case when result is pd.NaT
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"""Convert string 'r' to a timedelta object."""
try:
result = Timedelta(r, unit)
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19,834 | pandas-dev/pandas | pandas/core/tools/timedeltas.py | _convert_listlike | def _convert_listlike(arg, unit='ns', box=True, errors='raise', name=None):
"""Convert a list of objects to a timedelta index object."""
if isinstance(arg, (list, tuple)) or not hasattr(arg, 'dtype'):
# This is needed only to ensure that in the case where we end up
# returning arg (errors == "... | python | def _convert_listlike(arg, unit='ns', box=True, errors='raise', name=None):
"""Convert a list of objects to a timedelta index object."""
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19,835 | 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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19,836 | 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
"""... | python | 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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19,837 | 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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19,838 | 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.
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"""
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Otherwise yesterday's business day's opening time.
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Otherwise, closing time may not if business hour extends over midnight.
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19,839 | 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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19,840 | 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.
"""
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# return False
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19,841 | 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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19,842 | pandas-dev/pandas | pandas/tseries/offsets.py | Week._end_apply_index | def _end_apply_index(self, dtindex):
"""
Add self to the given DatetimeIndex, specialized for case where
self.weekday is non-null.
Parameters
----------
dtindex : DatetimeIndex
Returns
-------
result : DatetimeIndex
"""
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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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19,843 | pandas-dev/pandas | pandas/tseries/offsets.py | WeekOfMonth._get_offset_day | def _get_offset_day(self, other):
"""
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weekday as self.weekday and is the self.week'th such day in the month.
Parameters
----------
other : datetime
Returns
-------
day : int
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"""
Find the day in the same month as other that has the same
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other : datetime
Returns
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day : int
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19,844 | pandas-dev/pandas | pandas/tseries/offsets.py | LastWeekOfMonth._get_offset_day | def _get_offset_day(self, other):
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other: datetime
Returns
-------
day: int
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other: datetime
Returns
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day: int
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19,845 | pandas-dev/pandas | pandas/tseries/offsets.py | FY5253Quarter._rollback_to_year | def _rollback_to_year(self, other):
"""
Roll `other` back to the most recent date that was on a fiscal year
end.
Return the date of that year-end, the number of full quarters
elapsed between that year-end and other, and the remaining Timedelta
since the most recent quart... | python | def _rollback_to_year(self, other):
"""
Roll `other` back to the most recent date that was on a fiscal year
end.
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19,846 | 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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19,847 | pandas-dev/pandas | pandas/core/reshape/concat.py | _Concatenator._get_concat_axis | def _get_concat_axis(self):
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19,848 | pandas-dev/pandas | pandas/core/computation/ops.py | _in | def _in(x, y):
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19,850 | pandas-dev/pandas | pandas/core/computation/ops.py | _cast_inplace | 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.
.. 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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19,851 | pandas-dev/pandas | pandas/core/computation/ops.py | BinOp.convert_values | def convert_values(self):
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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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19,852 | pandas-dev/pandas | pandas/util/_doctools.py | TablePlotter._shape | def _shape(self, df):
"""
Calculate table chape considering index levels.
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19,853 | pandas-dev/pandas | pandas/util/_doctools.py | TablePlotter._get_cells | def _get_cells(self, left, right, vertical):
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19,854 | pandas-dev/pandas | pandas/util/_doctools.py | TablePlotter._conv | def _conv(self, data):
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data = data.to_frame(name='')
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data = data.to_frame()
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19,855 | pandas-dev/pandas | pandas/core/reshape/tile.py | cut | def cut(x, bins, right=True, labels=None, retbins=False, precision=3,
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Bin values into discrete intervals.
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19,856 | 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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19,857 | 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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bins : list-like of bins
dtype : dtype of data
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19,858 | pandas-dev/pandas | pandas/core/reshape/tile.py | _format_labels | def _format_labels(bins, precision, right=True,
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closed = 'right' if right else 'left'
if is_datetime64tz_dtype(dtype):
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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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19,859 | pandas-dev/pandas | pandas/core/reshape/tile.py | _preprocess_for_cut | def _preprocess_for_cut(x):
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"""
x_is_series = isinstance(x, Series)
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series_index = x.index
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19,860 | 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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handles post processing for the cut method where
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19,861 | 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:
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else:
digi... | python | def _round_frac(x, precision):
"""
Round the fractional part of the given number
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19,862 | pandas-dev/pandas | pandas/core/reshape/tile.py | _infer_precision | def _infer_precision(base_precision, bins):
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return precision
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19,863 | 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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encoding = sys.stdout.encoding or sys.stdin.encoding
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Try to find the most capable encoding supported by the console.
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19,864 | pandas-dev/pandas | pandas/util/_validators.py | _check_arg_length | def _check_arg_length(fname, args, max_fname_arg_count, compat_args):
"""
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
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"""
if max_fname_arg_count < 0:
raise ... | python | def _check_arg_length(fname, args, max_fname_arg_count, compat_args):
"""
Checks whether 'args' has length of at most 'compat_args'. Raises
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19,865 | pandas-dev/pandas | pandas/util/_validators.py | _check_for_default_values | def _check_for_default_values(fname, arg_val_dict, compat_args):
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Check that the keys in `arg_val_dict` are mapped to their
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19,867 | 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. """
if not (is_bool(value) or value is None):
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. """
if not (is_bool(value) or value is None):
raise ValueError('For argument "{arg}" expected type bool, received '
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19,868 | 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.
If 'method' is specified, this validates that it's a valid method.
Parameters
----------
value, method :... | python | 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.
If 'method' is specified, this validates that it's a valid method.
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19,869 | pandas-dev/pandas | pandas/core/resample.py | _maybe_process_deprecations | def _maybe_process_deprecations(r, how=None, fill_method=None, limit=None):
"""
Potentially we might have a deprecation warning, show it
but call the appropriate methods anyhow.
"""
if how is not None:
# .resample(..., how='sum')
if isinstance(how, str):
method = "{0}()... | python | def _maybe_process_deprecations(r, how=None, fill_method=None, limit=None):
"""
Potentially we might have a deprecation warning, show it
but call the appropriate methods anyhow.
"""
if how is not None:
# .resample(..., how='sum')
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19,870 | pandas-dev/pandas | pandas/core/resample.py | resample | def resample(obj, kind=None, **kwds):
"""
Create a TimeGrouper and return our resampler.
"""
tg = TimeGrouper(**kwds)
return tg._get_resampler(obj, kind=kind) | python | def resample(obj, kind=None, **kwds):
"""
Create a TimeGrouper and return our resampler.
"""
tg = TimeGrouper(**kwds)
return tg._get_resampler(obj, kind=kind) | [
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19,871 | 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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Return our appropriate resampler when grouping as well.
"""
# .resample uses 'on' similar to how .groupby uses 'key'
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19,872 | pandas-dev/pandas | pandas/core/resample.py | _get_timestamp_range_edges | def _get_timestamp_range_edges(first, last, offset, closed='left', base=0):
"""
Adjust the `first` Timestamp to the preceeding Timestamp that resides on
the provided offset. Adjust the `last` Timestamp to the following
Timestamp that resides on the provided offset. Input Timestamps that
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"""
Adjust the `first` Timestamp to the preceeding Timestamp that resides on
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19,873 | pandas-dev/pandas | pandas/core/resample.py | _get_period_range_edges | def _get_period_range_edges(first, last, offset, closed='left', base=0):
"""
Adjust the provided `first` and `last` Periods to the respective Period of
the given offset that encompasses them.
Parameters
----------
first : pd.Period
The beginning Period of the range to be adjusted.
l... | python | def _get_period_range_edges(first, last, offset, closed='left', base=0):
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first : pd.Period
The beginning Period of the range to be adjusted.
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first : pd.Period
The beginning Period of the range to be adjusted.
last : pd.Period
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19,874 | pandas-dev/pandas | pandas/core/resample.py | Resampler._from_selection | def _from_selection(self):
"""
Is the resampling from a DataFrame column or MultiIndex level.
"""
# upsampling and PeriodIndex resampling do not work
# with selection, this state used to catch and raise an error
return (self.groupby is not None and
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"""
Is the resampling from a DataFrame column or MultiIndex level.
"""
# upsampling and PeriodIndex resampling do not work
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19,875 | 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
"""
if self.binner is None:
self.binner, self.grouper = self._get_binner() | [
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19,876 | 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
a Series with the transformed values.
Parameters
----------
arg : function
To apply to each group. Should return a Series with the same index... | python | 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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19,877 | 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()
grouper = self.grouper
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19,878 | 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.
This is NOT an idempotent routine, it will be applied
exactly once to the result.
Parameters
----------
result : Series or DataFrame
the result of resample
"""
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19,879 | 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):
"""
Return the correct class for resampling with groupby.
"""
return self._resampler_for_grouping(self, groupby=groupby, **kwargs) | [
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19,880 | 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
if isinstan... | python | 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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19,881 | pandas-dev/pandas | pandas/core/resample.py | _GroupByMixin._apply | def _apply(self, f, grouper=None, *args, **kwargs):
"""
Dispatch to _upsample; we are stripping all of the _upsample kwargs and
performing the original function call on the grouped object.
"""
def func(x):
x = self._shallow_copy(x, groupby=self.groupby)
... | python | def _apply(self, f, grouper=None, *args, **kwargs):
"""
Dispatch to _upsample; we are stripping all of the _upsample kwargs and
performing the original function call on the grouped object.
"""
def func(x):
x = self._shallow_copy(x, groupby=self.groupby)
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19,882 | 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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19,883 | 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
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-------
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19,884 | 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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19,885 | 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
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19,886 | 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.
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vals : ndarray, Categorical
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19,887 | pandas-dev/pandas | pandas/core/util/hashing.py | _hash_scalar | def _hash_scalar(val, encoding='utf8', hash_key=None):
"""
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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
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if isna(val):
# this is to be consistent with the _hash_categorical implementation
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19,888 | pandas-dev/pandas | doc/make.py | DocBuilder._run_os | def _run_os(*args):
"""
Execute a command as a OS terminal.
Parameters
----------
*args : list of str
Command and parameters to be executed
Examples
--------
>>> DocBuilder()._run_os('python', '--version')
"""
subprocess.check... | python | def _run_os(*args):
"""
Execute a command as a OS terminal.
Parameters
----------
*args : list of str
Command and parameters to be executed
Examples
--------
>>> DocBuilder()._run_os('python', '--version')
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19,889 | pandas-dev/pandas | doc/make.py | DocBuilder._sphinx_build | def _sphinx_build(self, kind):
"""
Call sphinx to build documentation.
Attribute `num_jobs` from the class is used.
Parameters
----------
kind : {'html', 'latex'}
Examples
--------
>>> DocBuilder(num_jobs=4)._sphinx_build('html')
"""
... | python | def _sphinx_build(self, kind):
"""
Call sphinx to build documentation.
Attribute `num_jobs` from the class is used.
Parameters
----------
kind : {'html', 'latex'}
Examples
--------
>>> DocBuilder(num_jobs=4)._sphinx_build('html')
"""
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19,890 | pandas-dev/pandas | doc/make.py | DocBuilder._open_browser | def _open_browser(self, single_doc_html):
"""
Open a browser tab showing single
"""
url = os.path.join('file://', DOC_PATH, 'build', 'html',
single_doc_html)
webbrowser.open(url, new=2) | python | def _open_browser(self, single_doc_html):
"""
Open a browser tab showing single
"""
url = os.path.join('file://', DOC_PATH, 'build', 'html',
single_doc_html)
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19,891 | pandas-dev/pandas | doc/make.py | DocBuilder._get_page_title | def _get_page_title(self, page):
"""
Open the rst file `page` and extract its title.
"""
fname = os.path.join(SOURCE_PATH, '{}.rst'.format(page))
option_parser = docutils.frontend.OptionParser(
components=(docutils.parsers.rst.Parser,))
doc = docutils.utils.ne... | python | def _get_page_title(self, page):
"""
Open the rst file `page` and extract its title.
"""
fname = os.path.join(SOURCE_PATH, '{}.rst'.format(page))
option_parser = docutils.frontend.OptionParser(
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19,892 | pandas-dev/pandas | doc/make.py | DocBuilder.html | def html(self):
"""
Build HTML documentation.
"""
ret_code = self._sphinx_build('html')
zip_fname = os.path.join(BUILD_PATH, 'html', 'pandas.zip')
if os.path.exists(zip_fname):
os.remove(zip_fname)
if self.single_doc_html is not None:
self... | python | def html(self):
"""
Build HTML documentation.
"""
ret_code = self._sphinx_build('html')
zip_fname = os.path.join(BUILD_PATH, 'html', 'pandas.zip')
if os.path.exists(zip_fname):
os.remove(zip_fname)
if self.single_doc_html is not None:
self... | [
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19,893 | pandas-dev/pandas | doc/make.py | DocBuilder.latex | def latex(self, force=False):
"""
Build PDF documentation.
"""
if sys.platform == 'win32':
sys.stderr.write('latex build has not been tested on windows\n')
else:
ret_code = self._sphinx_build('latex')
os.chdir(os.path.join(BUILD_PATH, 'latex'))... | python | def latex(self, force=False):
"""
Build PDF documentation.
"""
if sys.platform == 'win32':
sys.stderr.write('latex build has not been tested on windows\n')
else:
ret_code = self._sphinx_build('latex')
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19,894 | pandas-dev/pandas | doc/make.py | DocBuilder.clean | def clean():
"""
Clean documentation generated files.
"""
shutil.rmtree(BUILD_PATH, ignore_errors=True)
shutil.rmtree(os.path.join(SOURCE_PATH, 'reference', 'api'),
ignore_errors=True) | python | def clean():
"""
Clean documentation generated files.
"""
shutil.rmtree(BUILD_PATH, ignore_errors=True)
shutil.rmtree(os.path.join(SOURCE_PATH, 'reference', 'api'),
ignore_errors=True) | [
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19,895 | pandas-dev/pandas | doc/make.py | DocBuilder.zip_html | def zip_html(self):
"""
Compress HTML documentation into a zip file.
"""
zip_fname = os.path.join(BUILD_PATH, 'html', 'pandas.zip')
if os.path.exists(zip_fname):
os.remove(zip_fname)
dirname = os.path.join(BUILD_PATH, 'html')
fnames = os.listdir(dirnam... | python | def zip_html(self):
"""
Compress HTML documentation into a zip file.
"""
zip_fname = os.path.join(BUILD_PATH, 'html', 'pandas.zip')
if os.path.exists(zip_fname):
os.remove(zip_fname)
dirname = os.path.join(BUILD_PATH, 'html')
fnames = os.listdir(dirnam... | [
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19,896 | pandas-dev/pandas | pandas/io/formats/latex.py | LatexFormatter._format_multicolumn | def _format_multicolumn(self, row, ilevels):
r"""
Combine columns belonging to a group to a single multicolumn entry
according to self.multicolumn_format
e.g.:
a & & & b & c &
will become
\multicolumn{3}{l}{a} & b & \multicolumn{2}{l}{c}
"""
row... | python | def _format_multicolumn(self, row, ilevels):
r"""
Combine columns belonging to a group to a single multicolumn entry
according to self.multicolumn_format
e.g.:
a & & & b & c &
will become
\multicolumn{3}{l}{a} & b & \multicolumn{2}{l}{c}
"""
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19,897 | pandas-dev/pandas | pandas/io/formats/latex.py | LatexFormatter._format_multirow | def _format_multirow(self, row, ilevels, i, rows):
r"""
Check following rows, whether row should be a multirow
e.g.: becomes:
a & 0 & \multirow{2}{*}{a} & 0 &
& 1 & & 1 &
b & 0 & \cline{1-2}
b & 0 &
"""
for j in range(ileve... | python | def _format_multirow(self, row, ilevels, i, rows):
r"""
Check following rows, whether row should be a multirow
e.g.: becomes:
a & 0 & \multirow{2}{*}{a} & 0 &
& 1 & & 1 &
b & 0 & \cline{1-2}
b & 0 &
"""
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19,898 | pandas-dev/pandas | pandas/io/formats/latex.py | LatexFormatter._print_cline | def _print_cline(self, buf, i, icol):
"""
Print clines after multirow-blocks are finished
"""
for cl in self.clinebuf:
if cl[0] == i:
buf.write('\\cline{{{cl:d}-{icol:d}}}\n'
.format(cl=cl[1], icol=icol))
# remove entries that... | python | def _print_cline(self, buf, i, icol):
"""
Print clines after multirow-blocks are finished
"""
for cl in self.clinebuf:
if cl[0] == i:
buf.write('\\cline{{{cl:d}-{icol:d}}}\n'
.format(cl=cl[1], icol=icol))
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19,899 | pandas-dev/pandas | pandas/io/parsers.py | _validate_integer | def _validate_integer(name, val, min_val=0):
"""
Checks whether the 'name' parameter for parsing is either
an integer OR float that can SAFELY be cast to an integer
without losing accuracy. Raises a ValueError if that is
not the case.
Parameters
----------
name : string
Paramete... | python | def _validate_integer(name, val, min_val=0):
"""
Checks whether the 'name' parameter for parsing is either
an integer OR float that can SAFELY be cast to an integer
without losing accuracy. Raises a ValueError if that is
not the case.
Parameters
----------
name : string
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