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value | url stringlengths 87 315 | code_tokens listlengths 19 28.4k | sha stringlengths 40 40 |
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train | Block._replace_coerce | Replace value corresponding to the given boolean array with another
value.
Parameters
----------
to_replace : object or pattern
Scalar to replace or regular expression to match.
value : object
Replacement object.
inplace : bool, default False
... | pandas/core/internals/blocks.py | def _replace_coerce(self, to_replace, value, inplace=True, regex=False,
convert=False, mask=None):
"""
Replace value corresponding to the given boolean array with another
value.
Parameters
----------
to_replace : object or pattern
Scal... | def _replace_coerce(self, to_replace, value, inplace=True, regex=False,
convert=False, mask=None):
"""
Replace value corresponding to the given boolean array with another
value.
Parameters
----------
to_replace : object or pattern
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train | NonConsolidatableMixIn.putmask | putmask the data to the block; we must be a single block and not
generate other blocks
return the resulting block
Parameters
----------
mask : the condition to respect
new : a ndarray/object
align : boolean, perform alignment on other/cond, default is True
... | pandas/core/internals/blocks.py | def putmask(self, mask, new, align=True, inplace=False, axis=0,
transpose=False):
"""
putmask the data to the block; we must be a single block and not
generate other blocks
return the resulting block
Parameters
----------
mask : the condition to... | def putmask(self, mask, new, align=True, inplace=False, axis=0,
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putmask the data to the block; we must be a single block and not
generate other blocks
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train | NonConsolidatableMixIn._get_unstack_items | Get the placement, values, and mask for a Block unstack.
This is shared between ObjectBlock and ExtensionBlock. They
differ in that ObjectBlock passes the values, while ExtensionBlock
passes the dummy ndarray of positions to be used by a take
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This is shared between ObjectBlock and ExtensionBlock. They
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Get the placement, values, and mask for a Block unstack.
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train | ExtensionBlock._maybe_coerce_values | Unbox to an extension array.
This will unbox an ExtensionArray stored in an Index or Series.
ExtensionArrays pass through. No dtype coercion is done.
Parameters
----------
values : Index, Series, ExtensionArray
Returns
-------
ExtensionArray | pandas/core/internals/blocks.py | def _maybe_coerce_values(self, values):
"""Unbox to an extension array.
This will unbox an ExtensionArray stored in an Index or Series.
ExtensionArrays pass through. No dtype coercion is done.
Parameters
----------
values : Index, Series, ExtensionArray
Returns... | def _maybe_coerce_values(self, values):
"""Unbox to an extension array.
This will unbox an ExtensionArray stored in an Index or Series.
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----------
values : Index, Series, ExtensionArray
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train | ExtensionBlock.setitem | Set the value inplace, returning a same-typed block.
This differs from Block.setitem by not allowing setitem to change
the dtype of the Block.
Parameters
----------
indexer : tuple, list-like, array-like, slice
The subset of self.values to set
value : object... | pandas/core/internals/blocks.py | def setitem(self, indexer, value):
"""Set the value inplace, returning a same-typed block.
This differs from Block.setitem by not allowing setitem to change
the dtype of the Block.
Parameters
----------
indexer : tuple, list-like, array-like, slice
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"""Set the value inplace, returning a same-typed block.
This differs from Block.setitem by not allowing setitem to change
the dtype of the Block.
Parameters
----------
indexer : tuple, list-like, array-like, slice
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train | ExtensionBlock.take_nd | Take values according to indexer and return them as a block. | pandas/core/internals/blocks.py | def take_nd(self, indexer, axis=0, new_mgr_locs=None, fill_tuple=None):
"""
Take values according to indexer and return them as a block.
"""
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fill_value = None
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train | ExtensionBlock._slice | return a slice of my values | pandas/core/internals/blocks.py | def _slice(self, slicer):
""" return a slice of my values """
# slice the category
# return same dims as we currently have
if isinstance(slicer, tuple) and len(slicer) == 2:
if not com.is_null_slice(slicer[0]):
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""" return a slice of my values """
# slice the category
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train | ExtensionBlock.concat_same_type | Concatenate list of single blocks of the same type. | pandas/core/internals/blocks.py | def concat_same_type(self, to_concat, placement=None):
"""
Concatenate list of single blocks of the same type.
"""
values = self._holder._concat_same_type(
[blk.values for blk in to_concat])
placement = placement or slice(0, len(values), 1)
return self.make_bl... | def concat_same_type(self, to_concat, placement=None):
"""
Concatenate list of single blocks of the same type.
"""
values = self._holder._concat_same_type(
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train | ExtensionBlock.shift | Shift the block by `periods`.
Dispatches to underlying ExtensionArray and re-boxes in an
ExtensionBlock. | pandas/core/internals/blocks.py | def shift(self,
periods: int,
axis: libinternals.BlockPlacement = 0,
fill_value: Any = None) -> List['ExtensionBlock']:
"""
Shift the block by `periods`.
Dispatches to underlying ExtensionArray and re-boxes in an
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axis: libinternals.BlockPlacement = 0,
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Shift the block by `periods`.
Dispatches to underlying ExtensionArray and re-boxes in an
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train | FloatBlock.to_native_types | convert to our native types format, slicing if desired | pandas/core/internals/blocks.py | def to_native_types(self, slicer=None, na_rep='', float_format=None,
decimal='.', quoting=None, **kwargs):
""" convert to our native types format, slicing if desired """
values = self.values
if slicer is not None:
values = values[:, slicer]
# see gh-... | def to_native_types(self, slicer=None, na_rep='', float_format=None,
decimal='.', quoting=None, **kwargs):
""" convert to our native types format, slicing if desired """
values = self.values
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train | DatetimeLikeBlockMixin.get_values | return object dtype as boxed values, such as Timestamps/Timedelta | pandas/core/internals/blocks.py | def get_values(self, dtype=None):
"""
return object dtype as boxed values, such as Timestamps/Timedelta
"""
if is_object_dtype(dtype):
values = self.values.ravel()
result = self._holder(values).astype(object)
return result.reshape(self.values.shape)
... | def get_values(self, dtype=None):
"""
return object dtype as boxed values, such as Timestamps/Timedelta
"""
if is_object_dtype(dtype):
values = self.values.ravel()
result = self._holder(values).astype(object)
return result.reshape(self.values.shape)
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train | DatetimeBlock._maybe_coerce_values | Input validation for values passed to __init__. Ensure that
we have datetime64ns, coercing if necessary.
Parameters
----------
values : array-like
Must be convertible to datetime64
Returns
-------
values : ndarray[datetime64ns]
Overridden by... | pandas/core/internals/blocks.py | def _maybe_coerce_values(self, values):
"""Input validation for values passed to __init__. Ensure that
we have datetime64ns, coercing if necessary.
Parameters
----------
values : array-like
Must be convertible to datetime64
Returns
-------
va... | def _maybe_coerce_values(self, values):
"""Input validation for values passed to __init__. Ensure that
we have datetime64ns, coercing if necessary.
Parameters
----------
values : array-like
Must be convertible to datetime64
Returns
-------
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train | DatetimeBlock._astype | these automatically copy, so copy=True has no effect
raise on an except if raise == True | pandas/core/internals/blocks.py | def _astype(self, dtype, **kwargs):
"""
these automatically copy, so copy=True has no effect
raise on an except if raise == True
"""
dtype = pandas_dtype(dtype)
# if we are passed a datetime64[ns, tz]
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train | DatetimeBlock._try_coerce_args | Coerce values and other to dtype 'i8'. NaN and NaT convert to
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back in _try_coerce_result. values is always ndarray-like, other
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Parameters
----------
values : ndarray-like
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"""
Coerce values and other to dtype 'i8'. NaN and NaT convert to
the smallest i8, and will correctly round-trip to NaT if converted
back in _try_coerce_result. values is always ndarray-like, other
may not be
Parameters
... | def _try_coerce_args(self, values, other):
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Coerce values and other to dtype 'i8'. NaN and NaT convert to
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train | DatetimeBlock._try_coerce_result | reverse of try_coerce_args | pandas/core/internals/blocks.py | def _try_coerce_result(self, result):
""" reverse of try_coerce_args """
if isinstance(result, np.ndarray):
if result.dtype.kind in ['i', 'f']:
result = result.astype('M8[ns]')
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train | DatetimeTZBlock._try_coerce_args | localize and return i8 for the values
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values : ndarray-like
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axis : int, axis to diff upon. default 0
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Note
----
The arguments here are mimicking shift so they are called... | pandas/core/internals/blocks.py | def diff(self, n, axis=0):
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----------
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axis : int, axis to diff upon. default 0
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A list with a new TimeDeltaBlock.
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Modify Block in-place with new item value
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train | ObjectBlock._try_coerce_args | provide coercion to our input arguments | pandas/core/internals/blocks.py | def _try_coerce_args(self, values, other):
""" provide coercion to our input arguments """
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train | ObjectBlock._replace_single | Replace elements by the given value.
Parameters
----------
to_replace : object or pattern
Scalar to replace or regular expression to match.
value : object
Replacement object.
inplace : bool, default False
Perform inplace modification.
... | pandas/core/internals/blocks.py | def _replace_single(self, to_replace, value, inplace=False, filter=None,
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"""
Replace elements by the given value.
Parameters
----------
to_replace : object or pattern
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"""
Replace elements by the given value.
Parameters
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Scalar to replace or regular expression to match.
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train | CategoricalBlock.to_native_types | convert to our native types format, slicing if desired | pandas/core/internals/blocks.py | def to_native_types(self, slicer=None, na_rep='', quoting=None, **kwargs):
""" convert to our native types format, slicing if desired """
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train | _XlwtWriter._style_to_xlwt | helper which recursively generate an xlwt easy style string
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"bottom": "thin",
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num_format_str : optional number format string
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train | _XlsxStyler.convert | converts a style_dict to an xlsxwriter format dict
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style_dict : style dictionary to convert
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converts a style_dict to an xlsxwriter format dict
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style_dict : style dictionary to convert
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series : Series
A Series with an ExtensionArray for values
level : Any
The level name or number.
fill_value : Any
The user-level (not physical storage) fill value to use for
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Unstack an ExtensionArray-backed Series.
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series : Series
A Series with an ExtensionArray for values
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Unstack an ExtensionArray-backed Series.
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train | stack | Convert DataFrame to Series with multi-level Index. Columns become the
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train | get_dummies | Convert categorical variable into dummy/indicator variables.
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prefix : str, list of str, or dict of str, default None
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train | make_axis_dummies | Construct 1-0 dummy variables corresponding to designated axis
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frame : DataFrame
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transform : function, default None
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Construct 1-0 dummy variables corresponding to designated axis
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frame : DataFrame
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train | _reorder_for_extension_array_stack | Re-orders the values when stacking multiple extension-arrays.
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Fixed-length string to split
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train | _parse_float_vec | Parse a vector of float values representing IBM 8 byte floats into
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xport2 = vec1['f1']
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train | XportReader._record_count | Get number of records in file.
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Reads lines from Xport file and returns as dataframe
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train | construction_error | raise a helpful message about our construction | pandas/core/internals/managers.py | def construction_error(tot_items, block_shape, axes, e=None):
""" raise a helpful message about our construction """
passed = tuple(map(int, [tot_items] + list(block_shape)))
# Correcting the user facing error message during dataframe construction
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train | _sparse_blockify | return an array of blocks that potentially have different dtypes (and
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train | _interleaved_dtype | Find the common dtype for `blocks`.
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blocks : List[Block]
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dtype : Optional[Union[np.dtype, ExtensionDtype]]
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blocks : List[Block]
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dtype : Optional[Union[np.dtype, ExtensionDtype]]
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blocks : List[Block]
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train | _consolidate | Merge blocks having same dtype, exclude non-consolidating blocks | pandas/core/internals/managers.py | def _consolidate(blocks):
"""
Merge blocks having same dtype, exclude non-consolidating blocks
"""
# sort by _can_consolidate, dtype
gkey = lambda x: x._consolidate_key
grouper = itertools.groupby(sorted(blocks, key=gkey), gkey)
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"""
Merge blocks having same dtype, exclude non-consolidating blocks
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gkey = lambda x: x._consolidate_key
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train | _compare_or_regex_search | Compare two array_like inputs of the same shape or two scalar values
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----------
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Compare two array_like inputs of the same shape or two scalar values
Calls operator.eq or re.search, depending on regex argument. If regex is
True, perform an element-wise regex matching.
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----------
a : array_like or scalar
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Compare two array_like inputs of the same shape or two scalar values
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train | items_overlap_with_suffix | If two indices overlap, add suffixes to overlapping entries.
If corresponding suffix is empty, the entry is simply converted to string. | pandas/core/internals/managers.py | def items_overlap_with_suffix(left, lsuffix, right, rsuffix):
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train | _transform_index | Apply function to all values found in index.
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train | _fast_count_smallints | Faster version of set(arr) for sequences of small numbers. | pandas/core/internals/managers.py | def _fast_count_smallints(arr):
"""Faster version of set(arr) for sequences of small numbers."""
counts = np.bincount(arr.astype(np.int_))
nz = counts.nonzero()[0]
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counts = np.bincount(arr.astype(np.int_))
nz = counts.nonzero()[0]
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train | concatenate_block_managers | Concatenate block managers into one.
Parameters
----------
mgrs_indexers : list of (BlockManager, {axis: indexer,...}) tuples
axes : list of Index
concat_axis : int
copy : bool | pandas/core/internals/managers.py | def concatenate_block_managers(mgrs_indexers, axes, concat_axis, copy):
"""
Concatenate block managers into one.
Parameters
----------
mgrs_indexers : list of (BlockManager, {axis: indexer,...}) tuples
axes : list of Index
concat_axis : int
copy : bool
"""
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Concatenate block managers into one.
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mgrs_indexers : list of (BlockManager, {axis: indexer,...}) tuples
axes : list of Index
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copy : bool
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train | BlockManager.make_empty | return an empty BlockManager with the items axis of len 0 | pandas/core/internals/managers.py | def make_empty(self, axes=None):
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train | BlockManager.rename_axis | Rename one of axes.
Parameters
----------
mapper : unary callable
axis : int
copy : boolean, default True
level : int, default None | pandas/core/internals/managers.py | def rename_axis(self, mapper, axis, copy=True, level=None):
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Rename one of axes.
Parameters
----------
mapper : unary callable
axis : int
copy : boolean, default True
level : int, default None
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"""
Rename one of axes.
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mapper : unary callable
axis : int
copy : boolean, default True
level : int, default None
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train | BlockManager._rebuild_blknos_and_blklocs | Update mgr._blknos / mgr._blklocs. | pandas/core/internals/managers.py | def _rebuild_blknos_and_blklocs(self):
"""
Update mgr._blknos / mgr._blklocs.
"""
new_blknos = np.empty(self.shape[0], dtype=np.int64)
new_blklocs = np.empty(self.shape[0], dtype=np.int64)
new_blknos.fill(-1)
new_blklocs.fill(-1)
for blkno, blk in enumera... | def _rebuild_blknos_and_blklocs(self):
"""
Update mgr._blknos / mgr._blklocs.
"""
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new_blklocs = np.empty(self.shape[0], dtype=np.int64)
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train | BlockManager._get_counts | return a dict of the counts of the function in BlockManager | pandas/core/internals/managers.py | def _get_counts(self, f):
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f : the callable or function name to operate on at the block level
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iterate over the blocks, collect and create a new block manager
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train | BlockManager.quantile | Iterate over blocks applying quantile reduction.
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Parameters
----------
axis: reduction axis, default 0
consolidate: boolean, default True. Join together blocks having sa... | pandas/core/internals/managers.py | def quantile(self, axis=0, consolidate=True, transposed=False,
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Iterate over blocks applying quantile reduction.
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Iterate over blocks applying quantile reduction.
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train | BlockManager.get_bool_data | Parameters
----------
copy : boolean, default False
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"""
Parameters
----------
copy : boolean, default False
Whether to copy the blocks
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train | BlockManager.get_numeric_data | Parameters
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copy : boolean, default False
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"""
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----------
copy : boolean, default False
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train | BlockManager.combine | return a new manager with the blocks | pandas/core/internals/managers.py | def combine(self, blocks, copy=True):
""" return a new manager with the blocks """
if len(blocks) == 0:
return self.make_empty()
# FIXME: optimization potential
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train | BlockManager.copy | Make deep or shallow copy of BlockManager
Parameters
----------
deep : boolean o rstring, default True
If False, return shallow copy (do not copy data)
If 'all', copy data and a deep copy of the index
Returns
-------
copy : BlockManager | pandas/core/internals/managers.py | def copy(self, deep=True):
"""
Make deep or shallow copy of BlockManager
Parameters
----------
deep : boolean o rstring, default True
If False, return shallow copy (do not copy data)
If 'all', copy data and a deep copy of the index
Returns
... | def copy(self, deep=True):
"""
Make deep or shallow copy of BlockManager
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----------
deep : boolean o rstring, default True
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If 'all', copy data and a deep copy of the index
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train | BlockManager.as_array | Convert the blockmanager data into an numpy array.
Parameters
----------
transpose : boolean, default False
If True, transpose the return array
items : list of strings or None
Names of block items that will be included in the returned
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"""Convert the blockmanager data into an numpy array.
Parameters
----------
transpose : boolean, default False
If True, transpose the return array
items : list of strings or None
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train | BlockManager._interleave | Return ndarray from blocks with specified item order
Items must be contained in the blocks | pandas/core/internals/managers.py | def _interleave(self):
"""
Return ndarray from blocks with specified item order
Items must be contained in the blocks
"""
from pandas.core.dtypes.common import is_sparse
dtype = _interleaved_dtype(self.blocks)
# TODO: https://github.com/pandas-dev/pandas/issues/2... | def _interleave(self):
"""
Return ndarray from blocks with specified item order
Items must be contained in the blocks
"""
from pandas.core.dtypes.common import is_sparse
dtype = _interleaved_dtype(self.blocks)
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Parameters
----------
copy : boolean, default True
Returns
-------
values : a dict of dtype -> BlockManager
Notes
-----
This consolidates based on str(dtype) | pandas/core/internals/managers.py | def to_dict(self, copy=True):
"""
Return a dict of str(dtype) -> BlockManager
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----------
copy : boolean, default True
Returns
-------
values : a dict of dtype -> BlockManager
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values : a dict of dtype -> BlockManager
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train | BlockManager.consolidate | Join together blocks having same dtype
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y : BlockManager | pandas/core/internals/managers.py | def consolidate(self):
"""
Join together blocks having same dtype
Returns
-------
y : BlockManager
"""
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return self
bm = self.__class__(self.blocks, self.axes)
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Join together blocks having same dtype
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y : BlockManager
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train | BlockManager.get | Return values for selected item (ndarray or BlockManager). | pandas/core/internals/managers.py | def get(self, item, fastpath=True):
"""
Return values for selected item (ndarray or BlockManager).
"""
if self.items.is_unique:
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loc = self.items.get_loc(item)
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train | BlockManager.iget | Return the data as a SingleBlockManager if fastpath=True and possible
Otherwise return as a ndarray | pandas/core/internals/managers.py | def iget(self, i, fastpath=True):
"""
Return the data as a SingleBlockManager if fastpath=True and possible
Otherwise return as a ndarray
"""
block = self.blocks[self._blknos[i]]
values = block.iget(self._blklocs[i])
if not fastpath or not block._box_to_block_val... | def iget(self, i, fastpath=True):
"""
Return the data as a SingleBlockManager if fastpath=True and possible
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train | BlockManager.delete | Delete selected item (items if non-unique) in-place. | pandas/core/internals/managers.py | def delete(self, item):
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train | BlockManager.set | Set new item in-place. Does not consolidate. Adds new Block if not
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Set new item in-place. Does not consolidate. Adds new Block if not
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train | BlockManager.insert | Insert item at selected position.
Parameters
----------
loc : int
item : hashable
value : array_like
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"""
Insert item at selected position.
Parameters
----------
loc : int
item : hashable
value : array_like
allow_duplicates: bool
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Insert item at selected position.
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train | BlockManager.reindex_axis | Conform block manager to new index. | pandas/core/internals/managers.py | def reindex_axis(self, new_index, axis, method=None, limit=None,
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"""
Conform block manager to new index.
"""
new_index = ensure_index(new_index)
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Conform block manager to new index.
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train | BlockManager.reindex_indexer | Parameters
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new_axis : Index
indexer : ndarray of int64 or None
axis : int
fill_value : object
allow_dups : bool
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new_axis : Index
indexer : ndarray of int64 or None
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-------
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train | BlockManager.take | Take items along any axis. | pandas/core/internals/managers.py | def take(self, indexer, axis=1, verify=True, convert=True):
"""
Take items along any axis.
"""
self._consolidate_inplace()
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unstacker_func : callable
A (partially-applied) ``pd.core.reshape._Unstacker`` class.
fill_value : Any
fill_value for newly introduced missing values.
Returns
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... | pandas/core/internals/managers.py | def unstack(self, unstacker_func, fill_value):
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Parameters
----------
unstacker_func : callable
A (partially-applied) ``pd.core.reshape._Unstacker`` class.
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train | SingleBlockManager.delete | Delete single item from SingleBlockManager.
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Delete single item from SingleBlockManager.
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Used for pd.concat of Series objects with axis=0.
Parameters
----------
to_concat : list of SingleBlockManagers
new_axis : Index of the result
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----------
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train | SparseSeries.from_array | Construct SparseSeries from array.
.. deprecated:: 0.23.0
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.. deprecated:: 0.23.0
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train | SparseSeries.as_sparse_array | return my self as a sparse array, do not copy by default | pandas/core/sparse/series.py | def as_sparse_array(self, kind=None, fill_value=None, copy=False):
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train | SparseSeries._reduce | perform a reduction operation | pandas/core/sparse/series.py | def _reduce(self, op, name, axis=0, skipna=True, numeric_only=None,
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""" perform a reduction operation """
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train | SparseSeries._ixs | Return the i-th value or values in the SparseSeries by location
Parameters
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i : int, slice, or sequence of integers
Returns
-------
value : scalar (int) or Series (slice, sequence) | pandas/core/sparse/series.py | def _ixs(self, i, axis=0):
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Return the i-th value or values in the SparseSeries by location
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i : int, slice, or sequence of integers
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value : scalar (int) or Series (slice, sequence)
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Return the i-th value or values in the SparseSeries by location
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train | SparseSeries.abs | Return an object with absolute value taken. Only applicable to objects
that are all numeric
Returns
-------
abs: same type as caller | pandas/core/sparse/series.py | def abs(self):
"""
Return an object with absolute value taken. Only applicable to objects
that are all numeric
Returns
-------
abs: same type as caller
"""
return self._constructor(np.abs(self.values),
index=self.index).__... | def abs(self):
"""
Return an object with absolute value taken. Only applicable to objects
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Returns
-------
abs: same type as caller
"""
return self._constructor(np.abs(self.values),
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train | SparseSeries.get | Returns value occupying requested label, default to specified
missing value if not present. Analogous to dict.get
Parameters
----------
label : object
Label value looking for
default : object, optional
Value to return if label not in index
Return... | pandas/core/sparse/series.py | def get(self, label, default=None):
"""
Returns value occupying requested label, default to specified
missing value if not present. Analogous to dict.get
Parameters
----------
label : object
Label value looking for
default : object, optional
... | def get(self, label, default=None):
"""
Returns value occupying requested label, default to specified
missing value if not present. Analogous to dict.get
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----------
label : object
Label value looking for
default : object, optional
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train | SparseSeries.get_value | Retrieve single value at passed index label
.. deprecated:: 0.21.0
Please use .at[] or .iat[] accessors.
Parameters
----------
index : label
takeable : interpret the index as indexers, default False
Returns
-------
value : scalar value | pandas/core/sparse/series.py | def get_value(self, label, takeable=False):
"""
Retrieve single value at passed index label
.. deprecated:: 0.21.0
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Parameters
----------
index : label
takeable : interpret the index as indexers, default False
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"""
Retrieve single value at passed index label
.. deprecated:: 0.21.0
Please use .at[] or .iat[] accessors.
Parameters
----------
index : label
takeable : interpret the index as indexers, default False
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train | SparseSeries.set_value | Quickly set single value at passed label. If label is not contained, a
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index
.. deprecated:: 0.21.0
Please use .at[] or .iat[] accessors.
Parameters
----------
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"""
Quickly set single value at passed label. If label is not contained, a
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index
.. deprecated:: 0.21.0
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Quickly set single value at passed label. If label is not contained, a
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.. deprecated:: 0.21.0
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train | SparseSeries.to_dense | Convert SparseSeries to a Series.
Returns
-------
s : Series | pandas/core/sparse/series.py | def to_dense(self):
"""
Convert SparseSeries to a Series.
Returns
-------
s : Series
"""
return Series(self.values.to_dense(), index=self.index,
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Convert SparseSeries to a Series.
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s : Series
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train | SparseSeries.copy | Make a copy of the SparseSeries. Only the actual sparse values need to
be copied | pandas/core/sparse/series.py | def copy(self, deep=True):
"""
Make a copy of the SparseSeries. Only the actual sparse values need to
be copied
"""
# TODO: https://github.com/pandas-dev/pandas/issues/22314
# We skip the block manager till that is resolved.
new_data = self.values.copy(deep=deep)
... | def copy(self, deep=True):
"""
Make a copy of the SparseSeries. Only the actual sparse values need to
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"""
# TODO: https://github.com/pandas-dev/pandas/issues/22314
# We skip the block manager till that is resolved.
new_data = self.values.copy(deep=deep)
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train | SparseSeries.sparse_reindex | Conform sparse values to new SparseIndex
Parameters
----------
new_index : {BlockIndex, IntIndex}
Returns
-------
reindexed : SparseSeries | pandas/core/sparse/series.py | def sparse_reindex(self, new_index):
"""
Conform sparse values to new SparseIndex
Parameters
----------
new_index : {BlockIndex, IntIndex}
Returns
-------
reindexed : SparseSeries
"""
if not isinstance(new_index, splib.SparseIndex):
... | def sparse_reindex(self, new_index):
"""
Conform sparse values to new SparseIndex
Parameters
----------
new_index : {BlockIndex, IntIndex}
Returns
-------
reindexed : SparseSeries
"""
if not isinstance(new_index, splib.SparseIndex):
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train | SparseSeries.cumsum | Cumulative sum of non-NA/null values.
When performing the cumulative summation, any non-NA/null values will
be skipped. The resulting SparseSeries will preserve the locations of
NaN values, but the fill value will be `np.nan` regardless.
Parameters
----------
axis : {0}... | pandas/core/sparse/series.py | def cumsum(self, axis=0, *args, **kwargs):
"""
Cumulative sum of non-NA/null values.
When performing the cumulative summation, any non-NA/null values will
be skipped. The resulting SparseSeries will preserve the locations of
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"""
Cumulative sum of non-NA/null values.
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train | SparseSeries.dropna | Analogous to Series.dropna. If fill_value=NaN, returns a dense Series | pandas/core/sparse/series.py | def dropna(self, axis=0, inplace=False, **kwargs):
"""
Analogous to Series.dropna. If fill_value=NaN, returns a dense Series
"""
# TODO: make more efficient
# Validate axis
self._get_axis_number(axis or 0)
dense_valid = self.to_dense().dropna()
if inplace:... | def dropna(self, axis=0, inplace=False, **kwargs):
"""
Analogous to Series.dropna. If fill_value=NaN, returns a dense Series
"""
# TODO: make more efficient
# Validate axis
self._get_axis_number(axis or 0)
dense_valid = self.to_dense().dropna()
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train | SparseSeries.combine_first | Combine Series values, choosing the calling Series's values
first. Result index will be the union of the two indexes
Parameters
----------
other : Series
Returns
-------
y : Series | pandas/core/sparse/series.py | def combine_first(self, other):
"""
Combine Series values, choosing the calling Series's values
first. Result index will be the union of the two indexes
Parameters
----------
other : Series
Returns
-------
y : Series
"""
if isinst... | def combine_first(self, other):
"""
Combine Series values, choosing the calling Series's values
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----------
other : Series
Returns
-------
y : Series
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train | _maybe_cache | Create a cache of unique dates from an array of dates
Parameters
----------
arg : integer, float, string, datetime, list, tuple, 1-d array, Series
format : string
Strftime format to parse time
cache : boolean
True attempts to create a cache of converted values
convert_listlike :... | pandas/core/tools/datetimes.py | def _maybe_cache(arg, format, cache, convert_listlike):
"""
Create a cache of unique dates from an array of dates
Parameters
----------
arg : integer, float, string, datetime, list, tuple, 1-d array, Series
format : string
Strftime format to parse time
cache : boolean
True a... | def _maybe_cache(arg, format, cache, convert_listlike):
"""
Create a cache of unique dates from an array of dates
Parameters
----------
arg : integer, float, string, datetime, list, tuple, 1-d array, Series
format : string
Strftime format to parse time
cache : boolean
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train | _convert_and_box_cache | Convert array of dates with a cache and box the result
Parameters
----------
arg : integer, float, string, datetime, list, tuple, 1-d array, Series
cache_array : Series
Cache of converted, unique dates
box : boolean
True boxes result as an Index-like, False returns an ndarray
er... | pandas/core/tools/datetimes.py | def _convert_and_box_cache(arg, cache_array, box, errors, name=None):
"""
Convert array of dates with a cache and box the result
Parameters
----------
arg : integer, float, string, datetime, list, tuple, 1-d array, Series
cache_array : Series
Cache of converted, unique dates
box : b... | def _convert_and_box_cache(arg, cache_array, box, errors, name=None):
"""
Convert array of dates with a cache and box the result
Parameters
----------
arg : integer, float, string, datetime, list, tuple, 1-d array, Series
cache_array : Series
Cache of converted, unique dates
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