id int32 0 252k | repo stringlengths 7 55 | path stringlengths 4 127 | func_name stringlengths 1 88 | original_string stringlengths 75 19.8k | language stringclasses 1
value | code stringlengths 75 19.8k | code_tokens list | docstring stringlengths 3 17.3k | docstring_tokens list | sha stringlengths 40 40 | url stringlengths 87 242 |
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19,600 | pandas-dev/pandas | pandas/core/internals/managers.py | BlockManager.make_empty | def make_empty(self, axes=None):
""" return an empty BlockManager with the items axis of len 0 """
if axes is None:
axes = [ensure_index([])] + [ensure_index(a)
for a in self.axes[1:]]
# preserve dtype if possible
if self.ndim == 1:
... | python | def make_empty(self, axes=None):
""" return an empty BlockManager with the items axis of len 0 """
if axes is None:
axes = [ensure_index([])] + [ensure_index(a)
for a in self.axes[1:]]
# preserve dtype if possible
if self.ndim == 1:
... | [
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19,601 | pandas-dev/pandas | pandas/core/internals/managers.py | BlockManager.rename_axis | def rename_axis(self, mapper, axis, copy=True, level=None):
"""
Rename one of axes.
Parameters
----------
mapper : unary callable
axis : int
copy : boolean, default True
level : int, default None
"""
obj = self.copy(deep=copy)
obj.... | python | def rename_axis(self, mapper, axis, copy=True, level=None):
"""
Rename one of axes.
Parameters
----------
mapper : unary callable
axis : int
copy : boolean, default True
level : int, default None
"""
obj = self.copy(deep=copy)
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19,602 | pandas-dev/pandas | pandas/core/internals/managers.py | BlockManager._get_counts | def _get_counts(self, f):
""" return a dict of the counts of the function in BlockManager """
self._consolidate_inplace()
counts = dict()
for b in self.blocks:
v = f(b)
counts[v] = counts.get(v, 0) + b.shape[0]
return counts | python | def _get_counts(self, f):
""" return a dict of the counts of the function in BlockManager """
self._consolidate_inplace()
counts = dict()
for b in self.blocks:
v = f(b)
counts[v] = counts.get(v, 0) + b.shape[0]
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19,603 | pandas-dev/pandas | pandas/core/internals/managers.py | BlockManager.apply | def apply(self, f, axes=None, filter=None, do_integrity_check=False,
consolidate=True, **kwargs):
"""
iterate over the blocks, collect and create a new block manager
Parameters
----------
f : the callable or function name to operate on at the block level
ax... | python | def apply(self, f, axes=None, filter=None, do_integrity_check=False,
consolidate=True, **kwargs):
"""
iterate over the blocks, collect and create a new block manager
Parameters
----------
f : the callable or function name to operate on at the block level
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19,604 | pandas-dev/pandas | pandas/core/internals/managers.py | BlockManager.quantile | def quantile(self, axis=0, consolidate=True, transposed=False,
interpolation='linear', qs=None, numeric_only=None):
"""
Iterate over blocks applying quantile reduction.
This routine is intended for reduction type operations and
will do inference on the generated blocks.
... | python | def quantile(self, axis=0, consolidate=True, transposed=False,
interpolation='linear', qs=None, numeric_only=None):
"""
Iterate over blocks applying quantile reduction.
This routine is intended for reduction type operations and
will do inference on the generated blocks.
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19,605 | pandas-dev/pandas | pandas/core/internals/managers.py | BlockManager.replace_list | def replace_list(self, src_list, dest_list, inplace=False, regex=False):
""" do a list replace """
inplace = validate_bool_kwarg(inplace, 'inplace')
# figure out our mask a-priori to avoid repeated replacements
values = self.as_array()
def comp(s, regex=False):
"""... | python | def replace_list(self, src_list, dest_list, inplace=False, regex=False):
""" do a list replace """
inplace = validate_bool_kwarg(inplace, 'inplace')
# figure out our mask a-priori to avoid repeated replacements
values = self.as_array()
def comp(s, regex=False):
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19,606 | pandas-dev/pandas | pandas/core/internals/managers.py | BlockManager.combine | def combine(self, blocks, copy=True):
""" return a new manager with the blocks """
if len(blocks) == 0:
return self.make_empty()
# FIXME: optimization potential
indexer = np.sort(np.concatenate([b.mgr_locs.as_array
for b in blocks]))... | python | def combine(self, blocks, copy=True):
""" return a new manager with the blocks """
if len(blocks) == 0:
return self.make_empty()
# FIXME: optimization potential
indexer = np.sort(np.concatenate([b.mgr_locs.as_array
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19,607 | pandas-dev/pandas | pandas/core/internals/managers.py | BlockManager.copy | 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
... | python | 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
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19,608 | pandas-dev/pandas | pandas/core/internals/managers.py | BlockManager.as_array | def as_array(self, transpose=False, items=None):
"""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 w... | python | def as_array(self, transpose=False, items=None):
"""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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19,609 | pandas-dev/pandas | pandas/core/internals/managers.py | BlockManager._interleave | 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... | python | 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... | [
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19,610 | pandas-dev/pandas | pandas/core/internals/managers.py | BlockManager.fast_xs | def fast_xs(self, loc):
"""
get a cross sectional for a given location in the
items ; handle dups
return the result, is *could* be a view in the case of a
single block
"""
if len(self.blocks) == 1:
return self.blocks[0].iget((slice(None), loc))
... | python | def fast_xs(self, loc):
"""
get a cross sectional for a given location in the
items ; handle dups
return the result, is *could* be a view in the case of a
single block
"""
if len(self.blocks) == 1:
return self.blocks[0].iget((slice(None), loc))
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19,611 | pandas-dev/pandas | pandas/core/internals/managers.py | BlockManager.consolidate | def consolidate(self):
"""
Join together blocks having same dtype
Returns
-------
y : BlockManager
"""
if self.is_consolidated():
return self
bm = self.__class__(self.blocks, self.axes)
bm._is_consolidated = False
bm._consolid... | python | def consolidate(self):
"""
Join together blocks having same dtype
Returns
-------
y : BlockManager
"""
if self.is_consolidated():
return self
bm = self.__class__(self.blocks, self.axes)
bm._is_consolidated = False
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19,612 | pandas-dev/pandas | pandas/core/internals/managers.py | BlockManager.iget | 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... | python | 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])
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19,613 | pandas-dev/pandas | pandas/core/internals/managers.py | BlockManager.insert | def insert(self, loc, item, value, allow_duplicates=False):
"""
Insert item at selected position.
Parameters
----------
loc : int
item : hashable
value : array_like
allow_duplicates: bool
If False, trying to insert non-unique item will raise
... | python | def insert(self, loc, item, value, allow_duplicates=False):
"""
Insert item at selected position.
Parameters
----------
loc : int
item : hashable
value : array_like
allow_duplicates: bool
If False, trying to insert non-unique item will raise
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19,614 | pandas-dev/pandas | pandas/core/internals/managers.py | BlockManager.reindex_axis | def reindex_axis(self, new_index, axis, method=None, limit=None,
fill_value=None, copy=True):
"""
Conform block manager to new index.
"""
new_index = ensure_index(new_index)
new_index, indexer = self.axes[axis].reindex(new_index, method=method,
... | python | def reindex_axis(self, new_index, axis, method=None, limit=None,
fill_value=None, copy=True):
"""
Conform block manager to new index.
"""
new_index = ensure_index(new_index)
new_index, indexer = self.axes[axis].reindex(new_index, method=method,
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19,615 | pandas-dev/pandas | pandas/core/internals/managers.py | BlockManager.take | def take(self, indexer, axis=1, verify=True, convert=True):
"""
Take items along any axis.
"""
self._consolidate_inplace()
indexer = (np.arange(indexer.start, indexer.stop, indexer.step,
dtype='int64')
if isinstance(indexer, slice)
... | python | def take(self, indexer, axis=1, verify=True, convert=True):
"""
Take items along any axis.
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19,616 | pandas-dev/pandas | pandas/core/internals/managers.py | BlockManager.unstack | def unstack(self, unstacker_func, fill_value):
"""Return a blockmanager with all blocks unstacked.
Parameters
----------
unstacker_func : callable
A (partially-applied) ``pd.core.reshape._Unstacker`` class.
fill_value : Any
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19,617 | pandas-dev/pandas | pandas/core/internals/managers.py | SingleBlockManager.delete | def delete(self, item):
"""
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Ensures that self.blocks doesn't become empty.
"""
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self._block.delete(loc)
self.axes[0] = self.axes[0].delete(loc) | python | def delete(self, item):
"""
Delete single item from SingleBlockManager.
Ensures that self.blocks doesn't become empty.
"""
loc = self.items.get_loc(item)
self._block.delete(loc)
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19,618 | pandas-dev/pandas | pandas/core/internals/managers.py | SingleBlockManager.concat | def concat(self, to_concat, new_axis):
"""
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SingleBlockManager.
Used for pd.concat of Series objects with axis=0.
Parameters
----------
to_concat : list of SingleBlockManagers
new_axis : Index of the... | python | def concat(self, to_concat, new_axis):
"""
Concatenate a list of SingleBlockManagers into a single
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Used for pd.concat of Series objects with axis=0.
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19,619 | pandas-dev/pandas | pandas/core/sparse/series.py | SparseSeries.from_array | def from_array(cls, arr, index=None, name=None, copy=False,
fill_value=None, fastpath=False):
"""Construct SparseSeries from array.
.. deprecated:: 0.23.0
Use the pd.SparseSeries(..) constructor instead.
"""
warnings.warn("'from_array' is deprecated and wi... | python | def from_array(cls, arr, index=None, name=None, copy=False,
fill_value=None, fastpath=False):
"""Construct SparseSeries from array.
.. deprecated:: 0.23.0
Use the pd.SparseSeries(..) constructor instead.
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19,620 | pandas-dev/pandas | pandas/core/sparse/series.py | SparseSeries.as_sparse_array | def as_sparse_array(self, kind=None, fill_value=None, copy=False):
""" return my self as a sparse array, do not copy by default """
if fill_value is None:
fill_value = self.fill_value
if kind is None:
kind = self.kind
return SparseArray(self.values, sparse_index=... | python | def as_sparse_array(self, kind=None, fill_value=None, copy=False):
""" return my self as a sparse array, do not copy by default """
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fill_value = self.fill_value
if kind is None:
kind = self.kind
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19,621 | pandas-dev/pandas | pandas/core/sparse/series.py | SparseSeries._reduce | def _reduce(self, op, name, axis=0, skipna=True, numeric_only=None,
filter_type=None, **kwds):
""" perform a reduction operation """
return op(self.get_values(), skipna=skipna, **kwds) | python | def _reduce(self, op, name, axis=0, skipna=True, numeric_only=None,
filter_type=None, **kwds):
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19,622 | pandas-dev/pandas | pandas/core/sparse/series.py | SparseSeries._ixs | def _ixs(self, i, axis=0):
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----------
i : int, slice, or sequence of integers
Returns
-------
value : scalar (int) or Series (slice, sequence)
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19,623 | pandas-dev/pandas | pandas/core/sparse/series.py | SparseSeries.abs | 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).__... | python | def abs(self):
"""
Return an object with absolute value taken. Only applicable to objects
that are all numeric
Returns
-------
abs: same type as caller
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19,624 | pandas-dev/pandas | pandas/core/sparse/series.py | SparseSeries.get | 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
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"""
Returns value occupying requested label, default to specified
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label : object
Label value looking for
default : object, optional
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19,625 | pandas-dev/pandas | pandas/core/sparse/series.py | SparseSeries.get_value | def get_value(self, label, takeable=False):
"""
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
... | python | def get_value(self, label, takeable=False):
"""
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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19,626 | pandas-dev/pandas | pandas/core/sparse/series.py | SparseSeries.set_value | def set_value(self, label, value, takeable=False):
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new object is created with the label placed at the end of the result
index
.. deprecated:: 0.21.0
Please use .at[] or .iat[] accessors.
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19,627 | pandas-dev/pandas | pandas/core/sparse/series.py | SparseSeries.to_dense | def to_dense(self):
"""
Convert SparseSeries to a Series.
Returns
-------
s : Series
"""
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name=self.name) | python | def to_dense(self):
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Convert SparseSeries to a Series.
Returns
-------
s : Series
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19,628 | pandas-dev/pandas | pandas/core/sparse/series.py | SparseSeries.copy | 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)
... | python | def copy(self, deep=True):
"""
Make a copy of the SparseSeries. Only the actual sparse values need to
be copied
"""
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19,629 | pandas-dev/pandas | pandas/core/sparse/series.py | SparseSeries.sparse_reindex | def sparse_reindex(self, new_index):
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----------
new_index : {BlockIndex, IntIndex}
Returns
-------
reindexed : SparseSeries
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"""
Conform sparse values to new SparseIndex
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----------
new_index : {BlockIndex, IntIndex}
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-------
reindexed : SparseSeries
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19,630 | pandas-dev/pandas | pandas/core/sparse/series.py | SparseSeries.dropna | 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:... | python | 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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19,631 | pandas-dev/pandas | pandas/core/sparse/series.py | SparseSeries.combine_first | 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
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y : Series
"""
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"""
Combine Series values, choosing the calling Series's values
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other : Series
Returns
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y : Series
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19,632 | pandas-dev/pandas | pandas/core/tools/datetimes.py | _maybe_cache | 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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"""
Create a cache of unique dates from an array of dates
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----------
arg : integer, float, string, datetime, list, tuple, 1-d array, Series
format : string
Strftime format to parse time
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19,633 | pandas-dev/pandas | pandas/core/tools/datetimes.py | _convert_and_box_cache | 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... | python | 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
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19,634 | pandas-dev/pandas | pandas/core/tools/datetimes.py | _return_parsed_timezone_results | def _return_parsed_timezone_results(result, timezones, box, tz, name):
"""
Return results from array_strptime if a %z or %Z directive was passed.
Parameters
----------
result : ndarray
int64 date representations of the dates
timezones : ndarray
pytz timezone objects
box : bo... | python | def _return_parsed_timezone_results(result, timezones, box, tz, name):
"""
Return results from array_strptime if a %z or %Z directive was passed.
Parameters
----------
result : ndarray
int64 date representations of the dates
timezones : ndarray
pytz timezone objects
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19,635 | pandas-dev/pandas | pandas/core/tools/datetimes.py | _adjust_to_origin | def _adjust_to_origin(arg, origin, unit):
"""
Helper function for to_datetime.
Adjust input argument to the specified origin
Parameters
----------
arg : list, tuple, ndarray, Series, Index
date to be adjusted
origin : 'julian' or Timestamp
origin offset for the arg
unit ... | python | def _adjust_to_origin(arg, origin, unit):
"""
Helper function for to_datetime.
Adjust input argument to the specified origin
Parameters
----------
arg : list, tuple, ndarray, Series, Index
date to be adjusted
origin : 'julian' or Timestamp
origin offset for the arg
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19,636 | pandas-dev/pandas | pandas/core/tools/datetimes.py | to_datetime | def to_datetime(arg, errors='raise', dayfirst=False, yearfirst=False,
utc=None, box=True, format=None, exact=True,
unit=None, infer_datetime_format=False, origin='unix',
cache=False):
"""
Convert argument to datetime.
Parameters
----------
arg : integ... | python | def to_datetime(arg, errors='raise', dayfirst=False, yearfirst=False,
utc=None, box=True, format=None, exact=True,
unit=None, infer_datetime_format=False, origin='unix',
cache=False):
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Convert argument to datetime.
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19,637 | pandas-dev/pandas | pandas/util/_decorators.py | deprecate | def deprecate(name, alternative, version, alt_name=None,
klass=None, stacklevel=2, msg=None):
"""
Return a new function that emits a deprecation warning on use.
To use this method for a deprecated function, another function
`alternative` with the same signature must exist. The deprecated
... | python | def deprecate(name, alternative, version, alt_name=None,
klass=None, stacklevel=2, msg=None):
"""
Return a new function that emits a deprecation warning on use.
To use this method for a deprecated function, another function
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19,638 | pandas-dev/pandas | pandas/util/_decorators.py | deprecate_kwarg | def deprecate_kwarg(old_arg_name, new_arg_name, mapping=None, stacklevel=2):
"""
Decorator to deprecate a keyword argument of a function.
Parameters
----------
old_arg_name : str
Name of argument in function to deprecate
new_arg_name : str or None
Name of preferred argument in f... | python | def deprecate_kwarg(old_arg_name, new_arg_name, mapping=None, stacklevel=2):
"""
Decorator to deprecate a keyword argument of a function.
Parameters
----------
old_arg_name : str
Name of argument in function to deprecate
new_arg_name : str or None
Name of preferred argument in f... | [
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19,639 | pandas-dev/pandas | pandas/util/_decorators.py | make_signature | def make_signature(func):
"""
Returns a tuple containing the paramenter list with defaults
and parameter list.
Examples
--------
>>> def f(a, b, c=2):
>>> return a * b * c
>>> print(make_signature(f))
(['a', 'b', 'c=2'], ['a', 'b', 'c'])
"""
spec = inspect.getfullargspe... | python | def make_signature(func):
"""
Returns a tuple containing the paramenter list with defaults
and parameter list.
Examples
--------
>>> def f(a, b, c=2):
>>> return a * b * c
>>> print(make_signature(f))
(['a', 'b', 'c=2'], ['a', 'b', 'c'])
"""
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19,640 | pandas-dev/pandas | pandas/core/indexes/range.py | RangeIndex.from_range | def from_range(cls, data, name=None, dtype=None, **kwargs):
""" Create RangeIndex from a range object. """
if not isinstance(data, range):
raise TypeError(
'{0}(...) must be called with object coercible to a '
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""" Create RangeIndex from a range object. """
if not isinstance(data, range):
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19,641 | pandas-dev/pandas | pandas/core/indexes/range.py | RangeIndex.min | def min(self, axis=None, skipna=True):
"""The minimum value of the RangeIndex"""
nv.validate_minmax_axis(axis)
return self._minmax('min') | python | def min(self, axis=None, skipna=True):
"""The minimum value of the RangeIndex"""
nv.validate_minmax_axis(axis)
return self._minmax('min') | [
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19,642 | pandas-dev/pandas | pandas/core/indexes/range.py | RangeIndex.max | def max(self, axis=None, skipna=True):
"""The maximum value of the RangeIndex"""
nv.validate_minmax_axis(axis)
return self._minmax('max') | python | def max(self, axis=None, skipna=True):
"""The maximum value of the RangeIndex"""
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19,643 | pandas-dev/pandas | pandas/core/indexes/range.py | RangeIndex.argsort | def argsort(self, *args, **kwargs):
"""
Returns the indices that would sort the index and its
underlying data.
Returns
-------
argsorted : numpy array
See Also
--------
numpy.ndarray.argsort
"""
nv.validate_argsort(args, kwargs)
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"""
Returns the indices that would sort the index and its
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Returns
-------
argsorted : numpy array
See Also
--------
numpy.ndarray.argsort
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19,644 | pandas-dev/pandas | pandas/core/indexes/range.py | RangeIndex._min_fitting_element | def _min_fitting_element(self, lower_limit):
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19,645 | pandas-dev/pandas | pandas/core/indexes/range.py | RangeIndex._max_fitting_element | def _max_fitting_element(self, upper_limit):
"""Returns the largest element smaller than or equal to the limit"""
no_steps = (upper_limit - self._start) // abs(self._step)
return self._start + abs(self._step) * no_steps | python | def _max_fitting_element(self, upper_limit):
"""Returns the largest element smaller than or equal to the limit"""
no_steps = (upper_limit - self._start) // abs(self._step)
return self._start + abs(self._step) * no_steps | [
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19,646 | pandas-dev/pandas | pandas/core/indexes/range.py | RangeIndex.union | def union(self, other, sort=None):
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Form the union of two Index objects and sorts if possible
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other : Index or array-like
sort : False or None, default None
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"""
Form the union of two Index objects and sorts if possible
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19,647 | pandas-dev/pandas | pandas/core/indexes/range.py | RangeIndex._add_numeric_methods_binary | def _add_numeric_methods_binary(cls):
""" add in numeric methods, specialized to RangeIndex """
def _make_evaluate_binop(op, step=False):
"""
Parameters
----------
op : callable that accepts 2 parms
perform the binary op
step :... | python | def _add_numeric_methods_binary(cls):
""" add in numeric methods, specialized to RangeIndex """
def _make_evaluate_binop(op, step=False):
"""
Parameters
----------
op : callable that accepts 2 parms
perform the binary op
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19,648 | pandas-dev/pandas | pandas/io/formats/printing.py | adjoin | def adjoin(space, *lists, **kwargs):
"""
Glues together two sets of strings using the amount of space requested.
The idea is to prettify.
----------
space : int
number of spaces for padding
lists : str
list of str which being joined
strlen : callable
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Glues together two sets of strings using the amount of space requested.
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number of spaces for padding
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19,649 | pandas-dev/pandas | pandas/io/gbq.py | read_gbq | def read_gbq(query, project_id=None, index_col=None, col_order=None,
reauth=False, auth_local_webserver=False, dialect=None,
location=None, configuration=None, credentials=None,
use_bqstorage_api=None, private_key=None, verbose=None):
"""
Load data from Google BigQuery.
... | python | def read_gbq(query, project_id=None, index_col=None, col_order=None,
reauth=False, auth_local_webserver=False, dialect=None,
location=None, configuration=None, credentials=None,
use_bqstorage_api=None, private_key=None, verbose=None):
"""
Load data from Google BigQuery.
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19,650 | pandas-dev/pandas | pandas/plotting/_misc.py | andrews_curves | def andrews_curves(frame, class_column, ax=None, samples=200, color=None,
colormap=None, **kwds):
"""
Generate a matplotlib plot of Andrews curves, for visualising clusters of
multivariate data.
Andrews curves have the functional form:
f(t) = x_1/sqrt(2) + x_2 sin(t) + x_3 cos(t... | python | def andrews_curves(frame, class_column, ax=None, samples=200, color=None,
colormap=None, **kwds):
"""
Generate a matplotlib plot of Andrews curves, for visualising clusters of
multivariate data.
Andrews curves have the functional form:
f(t) = x_1/sqrt(2) + x_2 sin(t) + x_3 cos(t... | [
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19,651 | pandas-dev/pandas | pandas/plotting/_misc.py | bootstrap_plot | def bootstrap_plot(series, fig=None, size=50, samples=500, **kwds):
"""
Bootstrap plot on mean, median and mid-range statistics.
The bootstrap plot is used to estimate the uncertainty of a statistic
by relaying on random sampling with replacement [1]_. This function will
generate bootstrapping plot... | python | def bootstrap_plot(series, fig=None, size=50, samples=500, **kwds):
"""
Bootstrap plot on mean, median and mid-range statistics.
The bootstrap plot is used to estimate the uncertainty of a statistic
by relaying on random sampling with replacement [1]_. This function will
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19,652 | pandas-dev/pandas | pandas/plotting/_misc.py | autocorrelation_plot | def autocorrelation_plot(series, ax=None, **kwds):
"""
Autocorrelation plot for time series.
Parameters:
-----------
series: Time series
ax: Matplotlib axis object, optional
kwds : keywords
Options to pass to matplotlib plotting method
Returns:
-----------
class:`matplo... | python | def autocorrelation_plot(series, ax=None, **kwds):
"""
Autocorrelation plot for time series.
Parameters:
-----------
series: Time series
ax: Matplotlib axis object, optional
kwds : keywords
Options to pass to matplotlib plotting method
Returns:
-----------
class:`matplo... | [
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19,653 | pandas-dev/pandas | pandas/core/computation/align.py | _any_pandas_objects | def _any_pandas_objects(terms):
"""Check a sequence of terms for instances of PandasObject."""
return any(isinstance(term.value, pd.core.generic.PandasObject)
for term in terms) | python | def _any_pandas_objects(terms):
"""Check a sequence of terms for instances of PandasObject."""
return any(isinstance(term.value, pd.core.generic.PandasObject)
for term in terms) | [
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19,654 | pandas-dev/pandas | pandas/core/computation/align.py | _align | def _align(terms):
"""Align a set of terms"""
try:
# flatten the parse tree (a nested list, really)
terms = list(com.flatten(terms))
except TypeError:
# can't iterate so it must just be a constant or single variable
if isinstance(terms.value, pd.core.generic.NDFrame):
... | python | def _align(terms):
"""Align a set of terms"""
try:
# flatten the parse tree (a nested list, really)
terms = list(com.flatten(terms))
except TypeError:
# can't iterate so it must just be a constant or single variable
if isinstance(terms.value, pd.core.generic.NDFrame):
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19,655 | pandas-dev/pandas | pandas/plotting/_timeseries.py | tsplot | def tsplot(series, plotf, ax=None, **kwargs):
import warnings
"""
Plots a Series on the given Matplotlib axes or the current axes
Parameters
----------
axes : Axes
series : Series
Notes
_____
Supports same kwargs as Axes.plot
.. deprecated:: 0.23.0
Use Series.plot(... | python | def tsplot(series, plotf, ax=None, **kwargs):
import warnings
"""
Plots a Series on the given Matplotlib axes or the current axes
Parameters
----------
axes : Axes
series : Series
Notes
_____
Supports same kwargs as Axes.plot
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19,656 | pandas-dev/pandas | pandas/plotting/_timeseries.py | _decorate_axes | def _decorate_axes(ax, freq, kwargs):
"""Initialize axes for time-series plotting"""
if not hasattr(ax, '_plot_data'):
ax._plot_data = []
ax.freq = freq
xaxis = ax.get_xaxis()
xaxis.freq = freq
if not hasattr(ax, 'legendlabels'):
ax.legendlabels = [kwargs.get('label', None)]
... | python | def _decorate_axes(ax, freq, kwargs):
"""Initialize axes for time-series plotting"""
if not hasattr(ax, '_plot_data'):
ax._plot_data = []
ax.freq = freq
xaxis = ax.get_xaxis()
xaxis.freq = freq
if not hasattr(ax, 'legendlabels'):
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19,657 | pandas-dev/pandas | pandas/core/frame.py | DataFrame._is_homogeneous_type | def _is_homogeneous_type(self):
"""
Whether all the columns in a DataFrame have the same type.
Returns
-------
bool
Examples
--------
>>> DataFrame({"A": [1, 2], "B": [3, 4]})._is_homogeneous_type
True
>>> DataFrame({"A": [1, 2], "B": [3.... | python | def _is_homogeneous_type(self):
"""
Whether all the columns in a DataFrame have the same type.
Returns
-------
bool
Examples
--------
>>> DataFrame({"A": [1, 2], "B": [3, 4]})._is_homogeneous_type
True
>>> DataFrame({"A": [1, 2], "B": [3.... | [
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19,658 | pandas-dev/pandas | pandas/core/frame.py | DataFrame._repr_html_ | def _repr_html_(self):
"""
Return a html representation for a particular DataFrame.
Mainly for IPython notebook.
"""
if self._info_repr():
buf = StringIO("")
self.info(buf=buf)
# need to escape the <class>, should be the first line.
... | python | def _repr_html_(self):
"""
Return a html representation for a particular DataFrame.
Mainly for IPython notebook.
"""
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buf = StringIO("")
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19,659 | pandas-dev/pandas | pandas/core/frame.py | DataFrame.itertuples | def itertuples(self, index=True, name="Pandas"):
"""
Iterate over DataFrame rows as namedtuples.
Parameters
----------
index : bool, default True
If True, return the index as the first element of the tuple.
name : str or None, default "Pandas"
The... | python | def itertuples(self, index=True, name="Pandas"):
"""
Iterate over DataFrame rows as namedtuples.
Parameters
----------
index : bool, default True
If True, return the index as the first element of the tuple.
name : str or None, default "Pandas"
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name : str or None, default "Pandas"
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19,660 | pandas-dev/pandas | pandas/core/frame.py | DataFrame.dot | def dot(self, other):
"""
Compute the matrix mutiplication between the DataFrame and other.
This method computes the matrix product between the DataFrame and the
values of an other Series, DataFrame or a numpy array.
It can also be called using ``self @ other`` in Python >= 3.5... | python | def dot(self, other):
"""
Compute the matrix mutiplication between the DataFrame and other.
This method computes the matrix product between the DataFrame and the
values of an other Series, DataFrame or a numpy array.
It can also be called using ``self @ other`` in Python >= 3.5... | [
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19,661 | pandas-dev/pandas | pandas/core/frame.py | DataFrame.from_dict | def from_dict(cls, data, orient='columns', dtype=None, columns=None):
"""
Construct DataFrame from dict of array-like or dicts.
Creates DataFrame object from dictionary by columns or by index
allowing dtype specification.
Parameters
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data : dict
... | python | def from_dict(cls, data, orient='columns', dtype=None, columns=None):
"""
Construct DataFrame from dict of array-like or dicts.
Creates DataFrame object from dictionary by columns or by index
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19,662 | pandas-dev/pandas | pandas/core/frame.py | DataFrame.to_numpy | def to_numpy(self, dtype=None, copy=False):
"""
Convert the DataFrame to a NumPy array.
.. versionadded:: 0.24.0
By default, the dtype of the returned array will be the common NumPy
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``float16`` and ``fl... | python | def to_numpy(self, dtype=None, copy=False):
"""
Convert the DataFrame to a NumPy array.
.. versionadded:: 0.24.0
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19,663 | pandas-dev/pandas | pandas/core/frame.py | DataFrame.to_dict | def to_dict(self, orient='dict', into=dict):
"""
Convert the DataFrame to a dictionary.
The type of the key-value pairs can be customized with the parameters
(see below).
Parameters
----------
orient : str {'dict', 'list', 'series', 'split', 'records', 'index'}
... | python | def to_dict(self, orient='dict', into=dict):
"""
Convert the DataFrame to a dictionary.
The type of the key-value pairs can be customized with the parameters
(see below).
Parameters
----------
orient : str {'dict', 'list', 'series', 'split', 'records', 'index'}
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19,664 | pandas-dev/pandas | pandas/core/frame.py | DataFrame.to_records | def to_records(self, index=True, convert_datetime64=None,
column_dtypes=None, index_dtypes=None):
"""
Convert DataFrame to a NumPy record array.
Index will be included as the first field of the record array if
requested.
Parameters
----------
... | python | def to_records(self, index=True, convert_datetime64=None,
column_dtypes=None, index_dtypes=None):
"""
Convert DataFrame to a NumPy record array.
Index will be included as the first field of the record array if
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Parameters
----------
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19,665 | pandas-dev/pandas | pandas/core/frame.py | DataFrame.from_items | def from_items(cls, items, columns=None, orient='columns'):
"""
Construct a DataFrame from a list of tuples.
.. deprecated:: 0.23.0
`from_items` is deprecated and will be removed in a future version.
Use :meth:`DataFrame.from_dict(dict(items)) <DataFrame.from_dict>`
... | python | def from_items(cls, items, columns=None, orient='columns'):
"""
Construct a DataFrame from a list of tuples.
.. deprecated:: 0.23.0
`from_items` is deprecated and will be removed in a future version.
Use :meth:`DataFrame.from_dict(dict(items)) <DataFrame.from_dict>`
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19,666 | pandas-dev/pandas | pandas/core/frame.py | DataFrame.to_sparse | def to_sparse(self, fill_value=None, kind='block'):
"""
Convert to SparseDataFrame.
Implement the sparse version of the DataFrame meaning that any data
matching a specific value it's omitted in the representation.
The sparse DataFrame allows for a more efficient storage.
... | python | def to_sparse(self, fill_value=None, kind='block'):
"""
Convert to SparseDataFrame.
Implement the sparse version of the DataFrame meaning that any data
matching a specific value it's omitted in the representation.
The sparse DataFrame allows for a more efficient storage.
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19,667 | pandas-dev/pandas | pandas/core/frame.py | DataFrame.to_stata | def to_stata(self, fname, convert_dates=None, write_index=True,
encoding="latin-1", byteorder=None, time_stamp=None,
data_label=None, variable_labels=None, version=114,
convert_strl=None):
"""
Export DataFrame object to Stata dta format.
Writes... | python | def to_stata(self, fname, convert_dates=None, write_index=True,
encoding="latin-1", byteorder=None, time_stamp=None,
data_label=None, variable_labels=None, version=114,
convert_strl=None):
"""
Export DataFrame object to Stata dta format.
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19,668 | pandas-dev/pandas | pandas/core/frame.py | DataFrame.to_feather | def to_feather(self, fname):
"""
Write out the binary feather-format for DataFrames.
.. versionadded:: 0.20.0
Parameters
----------
fname : str
string file path
"""
from pandas.io.feather_format import to_feather
to_feather(self, fnam... | python | def to_feather(self, fname):
"""
Write out the binary feather-format for DataFrames.
.. versionadded:: 0.20.0
Parameters
----------
fname : str
string file path
"""
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19,669 | pandas-dev/pandas | pandas/core/frame.py | DataFrame.to_parquet | def to_parquet(self, fname, engine='auto', compression='snappy',
index=None, partition_cols=None, **kwargs):
"""
Write a DataFrame to the binary parquet format.
.. versionadded:: 0.21.0
This function writes the dataframe as a `parquet file
<https://parquet.ap... | python | def to_parquet(self, fname, engine='auto', compression='snappy',
index=None, partition_cols=None, **kwargs):
"""
Write a DataFrame to the binary parquet format.
.. versionadded:: 0.21.0
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19,670 | pandas-dev/pandas | pandas/core/frame.py | DataFrame.memory_usage | def memory_usage(self, index=True, deep=False):
"""
Return the memory usage of each column in bytes.
The memory usage can optionally include the contribution of
the index and elements of `object` dtype.
This value is displayed in `DataFrame.info` by default. This can be
... | python | def memory_usage(self, index=True, deep=False):
"""
Return the memory usage of each column in bytes.
The memory usage can optionally include the contribution of
the index and elements of `object` dtype.
This value is displayed in `DataFrame.info` by default. This can be
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19,671 | pandas-dev/pandas | pandas/core/frame.py | DataFrame.transpose | def transpose(self, *args, **kwargs):
"""
Transpose index and columns.
Reflect the DataFrame over its main diagonal by writing rows as columns
and vice-versa. The property :attr:`.T` is an accessor to the method
:meth:`transpose`.
Parameters
----------
c... | python | def transpose(self, *args, **kwargs):
"""
Transpose index and columns.
Reflect the DataFrame over its main diagonal by writing rows as columns
and vice-versa. The property :attr:`.T` is an accessor to the method
:meth:`transpose`.
Parameters
----------
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19,672 | pandas-dev/pandas | pandas/core/frame.py | DataFrame.get_value | def get_value(self, index, col, takeable=False):
"""
Quickly retrieve single value at passed column and index.
.. deprecated:: 0.21.0
Use .at[] or .iat[] accessors instead.
Parameters
----------
index : row label
col : column label
takeable :... | python | def get_value(self, index, col, takeable=False):
"""
Quickly retrieve single value at passed column and index.
.. deprecated:: 0.21.0
Use .at[] or .iat[] accessors instead.
Parameters
----------
index : row label
col : column label
takeable :... | [
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19,673 | pandas-dev/pandas | pandas/core/frame.py | DataFrame.set_value | def set_value(self, index, col, value, takeable=False):
"""
Put single value at passed column and index.
.. deprecated:: 0.21.0
Use .at[] or .iat[] accessors instead.
Parameters
----------
index : row label
col : column label
value : scalar
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"""
Put single value at passed column and index.
.. deprecated:: 0.21.0
Use .at[] or .iat[] accessors instead.
Parameters
----------
index : row label
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value : scalar
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19,674 | pandas-dev/pandas | pandas/core/frame.py | DataFrame.query | def query(self, expr, inplace=False, **kwargs):
"""
Query the columns of a DataFrame with a boolean expression.
Parameters
----------
expr : str
The query string to evaluate. You can refer to variables
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"""
Query the columns of a DataFrame with a boolean expression.
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The query string to evaluate. You can refer to variables
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19,675 | pandas-dev/pandas | pandas/core/frame.py | DataFrame.eval | def eval(self, expr, inplace=False, **kwargs):
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19,676 | pandas-dev/pandas | pandas/core/frame.py | DataFrame.select_dtypes | def select_dtypes(self, include=None, exclude=None):
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19,677 | pandas-dev/pandas | pandas/core/frame.py | DataFrame._box_col_values | def _box_col_values(self, values, items):
"""
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klass = self._constructor_sliced
return klass(values, index=self.index, name=items, fastpath=True) | python | def _box_col_values(self, values, items):
"""
Provide boxed values for a column.
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19,678 | pandas-dev/pandas | pandas/core/frame.py | DataFrame._ensure_valid_index | def _ensure_valid_index(self, value):
"""
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# GH5632, make sure that we are a Series convertible
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19,679 | pandas-dev/pandas | pandas/core/frame.py | DataFrame._set_item | def _set_item(self, key, value):
"""
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If series is a numpy-array (not a Series/TimeSeries), it must be the
same length as the DataFrames index or an error will be thrown.
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"""
Add series to DataFrame in specified column.
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19,680 | pandas-dev/pandas | pandas/core/frame.py | DataFrame.insert | def insert(self, loc, column, value, allow_duplicates=False):
"""
Insert column into DataFrame at specified location.
Raises a ValueError if `column` is already contained in the DataFrame,
unless `allow_duplicates` is set to True.
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loc : int... | python | def insert(self, loc, column, value, allow_duplicates=False):
"""
Insert column into DataFrame at specified location.
Raises a ValueError if `column` is already contained in the DataFrame,
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19,681 | pandas-dev/pandas | pandas/core/frame.py | DataFrame.assign | def assign(self, **kwargs):
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Assign new columns to a DataFrame.
Returns a new object with all original columns in addition to new ones.
Existing columns that are re-assigned will be overwritten.
Parameters
----------
**kwargs : dict of {str: callable or Seri... | python | def assign(self, **kwargs):
r"""
Assign new columns to a DataFrame.
Returns a new object with all original columns in addition to new ones.
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19,682 | pandas-dev/pandas | pandas/core/frame.py | DataFrame.lookup | def lookup(self, row_labels, col_labels):
"""
Label-based "fancy indexing" function for DataFrame.
Given equal-length arrays of row and column labels, return an
array of the values corresponding to each (row, col) pair.
Parameters
----------
row_labels : sequenc... | python | def lookup(self, row_labels, col_labels):
"""
Label-based "fancy indexing" function for DataFrame.
Given equal-length arrays of row and column labels, return an
array of the values corresponding to each (row, col) pair.
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19,683 | pandas-dev/pandas | pandas/core/frame.py | DataFrame._reindex_multi | def _reindex_multi(self, axes, copy, fill_value):
"""
We are guaranteed non-Nones in the axes.
"""
new_index, row_indexer = self.index.reindex(axes['index'])
new_columns, col_indexer = self.columns.reindex(axes['columns'])
if row_indexer is not None and col_indexer is n... | python | def _reindex_multi(self, axes, copy, fill_value):
"""
We are guaranteed non-Nones in the axes.
"""
new_index, row_indexer = self.index.reindex(axes['index'])
new_columns, col_indexer = self.columns.reindex(axes['columns'])
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19,684 | pandas-dev/pandas | pandas/core/frame.py | DataFrame.drop | def drop(self, labels=None, axis=0, index=None, columns=None,
level=None, inplace=False, errors='raise'):
"""
Drop specified labels from rows or columns.
Remove rows or columns by specifying label names and corresponding
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19,685 | pandas-dev/pandas | pandas/core/frame.py | DataFrame.rename | def rename(self, *args, **kwargs):
"""
Alter axes labels.
Function / dict values must be unique (1-to-1). Labels not contained in
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"""
Alter axes labels.
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19,686 | pandas-dev/pandas | pandas/core/frame.py | DataFrame.dropna | def dropna(self, axis=0, how='any', thresh=None, subset=None,
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Remove missing values.
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19,687 | pandas-dev/pandas | pandas/core/frame.py | DataFrame.drop_duplicates | def drop_duplicates(self, subset=None, keep='first', inplace=False):
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----------
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19,688 | pandas-dev/pandas | pandas/core/frame.py | DataFrame.duplicated | def duplicated(self, subset=None, keep='first'):
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subset : column label or sequence of labels, optional
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] | 9feb3ad92cc0397a04b665803a49299ee7aa1037 | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/core/frame.py#L4681-L4732 |
19,689 | pandas-dev/pandas | pandas/core/frame.py | DataFrame.nlargest | def nlargest(self, n, columns, keep='first'):
"""
Return the first `n` rows ordered by `columns` in descending order.
Return the first `n` rows with the largest values in `columns`, in
descending order. The columns that are not specified are returned as
well, but not used for or... | python | def nlargest(self, n, columns, keep='first'):
"""
Return the first `n` rows ordered by `columns` in descending order.
Return the first `n` rows with the largest values in `columns`, in
descending order. The columns that are not specified are returned as
well, but not used for or... | [
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19,690 | pandas-dev/pandas | pandas/core/frame.py | DataFrame.nsmallest | def nsmallest(self, n, columns, keep='first'):
"""
Return the first `n` rows ordered by `columns` in ascending order.
Return the first `n` rows with the smallest values in `columns`, in
ascending order. The columns that are not specified are returned as
well, but not used for or... | python | def nsmallest(self, n, columns, keep='first'):
"""
Return the first `n` rows ordered by `columns` in ascending order.
Return the first `n` rows with the smallest values in `columns`, in
ascending order. The columns that are not specified are returned as
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19,691 | pandas-dev/pandas | pandas/core/frame.py | DataFrame.swaplevel | def swaplevel(self, i=-2, j=-1, axis=0):
"""
Swap levels i and j in a MultiIndex on a particular axis.
Parameters
----------
i, j : int, string (can be mixed)
Level of index to be swapped. Can pass level name as string.
Returns
-------
DataFr... | python | def swaplevel(self, i=-2, j=-1, axis=0):
"""
Swap levels i and j in a MultiIndex on a particular axis.
Parameters
----------
i, j : int, string (can be mixed)
Level of index to be swapped. Can pass level name as string.
Returns
-------
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19,692 | pandas-dev/pandas | pandas/core/frame.py | DataFrame.reorder_levels | def reorder_levels(self, order, axis=0):
"""
Rearrange index levels using input order. May not drop or
duplicate levels.
Parameters
----------
order : list of int or list of str
List representing new level order. Reference level by number
(positio... | python | def reorder_levels(self, order, axis=0):
"""
Rearrange index levels using input order. May not drop or
duplicate levels.
Parameters
----------
order : list of int or list of str
List representing new level order. Reference level by number
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19,693 | pandas-dev/pandas | pandas/core/frame.py | DataFrame.combine | def combine(self, other, func, fill_value=None, overwrite=True):
"""
Perform column-wise combine with another DataFrame.
Combines a DataFrame with `other` DataFrame using `func`
to element-wise combine columns. The row and column indexes of the
resulting DataFrame will be the un... | python | def combine(self, other, func, fill_value=None, overwrite=True):
"""
Perform column-wise combine with another DataFrame.
Combines a DataFrame with `other` DataFrame using `func`
to element-wise combine columns. The row and column indexes of the
resulting DataFrame will be the un... | [
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Combines a DataFrame with `other` DataFrame using `func`
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19,694 | pandas-dev/pandas | pandas/core/frame.py | DataFrame.combine_first | def combine_first(self, other):
"""
Update null elements with value in the same location in `other`.
Combine two DataFrame objects by filling null values in one DataFrame
with non-null values from other DataFrame. The row and column indexes
of the resulting DataFrame will be the... | python | def combine_first(self, other):
"""
Update null elements with value in the same location in `other`.
Combine two DataFrame objects by filling null values in one DataFrame
with non-null values from other DataFrame. The row and column indexes
of the resulting DataFrame will be the... | [
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19,695 | pandas-dev/pandas | pandas/core/frame.py | DataFrame.update | def update(self, other, join='left', overwrite=True, filter_func=None,
errors='ignore'):
"""
Modify in place using non-NA values from another DataFrame.
Aligns on indices. There is no return value.
Parameters
----------
other : DataFrame, or object coerci... | python | def update(self, other, join='left', overwrite=True, filter_func=None,
errors='ignore'):
"""
Modify in place using non-NA values from another DataFrame.
Aligns on indices. There is no return value.
Parameters
----------
other : DataFrame, or object coerci... | [
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Aligns on indices. There is no return value.
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19,696 | pandas-dev/pandas | pandas/core/frame.py | DataFrame.apply | def apply(self, func, axis=0, broadcast=None, raw=False, reduce=None,
result_type=None, args=(), **kwds):
"""
Apply a function along an axis of the DataFrame.
Objects passed to the function are Series objects whose index is
either the DataFrame's index (``axis=0``) or the ... | python | def apply(self, func, axis=0, broadcast=None, raw=False, reduce=None,
result_type=None, args=(), **kwds):
"""
Apply a function along an axis of the DataFrame.
Objects passed to the function are Series objects whose index is
either the DataFrame's index (``axis=0``) or the ... | [
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19,697 | pandas-dev/pandas | pandas/core/frame.py | DataFrame.applymap | def applymap(self, func):
"""
Apply a function to a Dataframe elementwise.
This method applies a function that accepts and returns a scalar
to every element of a DataFrame.
Parameters
----------
func : callable
Python function, returns a single value... | python | def applymap(self, func):
"""
Apply a function to a Dataframe elementwise.
This method applies a function that accepts and returns a scalar
to every element of a DataFrame.
Parameters
----------
func : callable
Python function, returns a single value... | [
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19,698 | pandas-dev/pandas | pandas/core/frame.py | DataFrame.append | def append(self, other, ignore_index=False,
verify_integrity=False, sort=None):
"""
Append rows of `other` to the end of caller, returning a new object.
Columns in `other` that are not in the caller are added as new columns.
Parameters
----------
other : ... | python | def append(self, other, ignore_index=False,
verify_integrity=False, sort=None):
"""
Append rows of `other` to the end of caller, returning a new object.
Columns in `other` that are not in the caller are added as new columns.
Parameters
----------
other : ... | [
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other : DataFrame or Series/dict-like object, or list of these
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19,699 | pandas-dev/pandas | pandas/core/frame.py | DataFrame.join | def join(self, other, on=None, how='left', lsuffix='', rsuffix='',
sort=False):
"""
Join columns of another DataFrame.
Join columns with `other` DataFrame either on index or on a key
column. Efficiently join multiple DataFrame objects by index at once by
passing a l... | python | def join(self, other, on=None, how='left', lsuffix='', rsuffix='',
sort=False):
"""
Join columns of another DataFrame.
Join columns with `other` DataFrame either on index or on a key
column. Efficiently join multiple DataFrame objects by index at once by
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