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20,000 | pandas-dev/pandas | setup.py | maybe_cythonize | def maybe_cythonize(extensions, *args, **kwargs):
"""
Render tempita templates before calling cythonize
"""
if len(sys.argv) > 1 and 'clean' in sys.argv:
# Avoid running cythonize on `python setup.py clean`
# See https://github.com/cython/cython/issues/1495
return extensions
... | python | def maybe_cythonize(extensions, *args, **kwargs):
"""
Render tempita templates before calling cythonize
"""
if len(sys.argv) > 1 and 'clean' in sys.argv:
# Avoid running cythonize on `python setup.py clean`
# See https://github.com/cython/cython/issues/1495
return extensions
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20,001 | pandas-dev/pandas | pandas/core/groupby/generic.py | NDFrameGroupBy._transform_fast | def _transform_fast(self, result, obj, func_nm):
"""
Fast transform path for aggregations
"""
# if there were groups with no observations (Categorical only?)
# try casting data to original dtype
cast = self._transform_should_cast(func_nm)
# for each col, reshape ... | python | def _transform_fast(self, result, obj, func_nm):
"""
Fast transform path for aggregations
"""
# if there were groups with no observations (Categorical only?)
# try casting data to original dtype
cast = self._transform_should_cast(func_nm)
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20,002 | pandas-dev/pandas | pandas/core/groupby/generic.py | NDFrameGroupBy.filter | def filter(self, func, dropna=True, *args, **kwargs): # noqa
"""
Return a copy of a DataFrame excluding elements from groups that
do not satisfy the boolean criterion specified by func.
Parameters
----------
f : function
Function to apply to each subframe. S... | python | def filter(self, func, dropna=True, *args, **kwargs): # noqa
"""
Return a copy of a DataFrame excluding elements from groups that
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20,003 | pandas-dev/pandas | pandas/core/groupby/generic.py | SeriesGroupBy.filter | def filter(self, func, dropna=True, *args, **kwargs): # noqa
"""
Return a copy of a Series excluding elements from groups that
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Parameters
----------
func : function
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20,004 | pandas-dev/pandas | pandas/core/groupby/generic.py | SeriesGroupBy.nunique | def nunique(self, dropna=True):
"""
Return number of unique elements in the group.
"""
ids, _, _ = self.grouper.group_info
val = self.obj.get_values()
try:
sorter = np.lexsort((val, ids))
except TypeError: # catches object dtypes
msg = '... | python | def nunique(self, dropna=True):
"""
Return number of unique elements in the group.
"""
ids, _, _ = self.grouper.group_info
val = self.obj.get_values()
try:
sorter = np.lexsort((val, ids))
except TypeError: # catches object dtypes
msg = '... | [
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20,005 | pandas-dev/pandas | pandas/core/groupby/generic.py | SeriesGroupBy.pct_change | def pct_change(self, periods=1, fill_method='pad', limit=None, freq=None):
"""Calcuate pct_change of each value to previous entry in group"""
# TODO: Remove this conditional when #23918 is fixed
if freq:
return self.apply(lambda x: x.pct_change(periods=periods,
... | python | def pct_change(self, periods=1, fill_method='pad', limit=None, freq=None):
"""Calcuate pct_change of each value to previous entry in group"""
# TODO: Remove this conditional when #23918 is fixed
if freq:
return self.apply(lambda x: x.pct_change(periods=periods,
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20,006 | pandas-dev/pandas | pandas/core/groupby/generic.py | DataFrameGroupBy._gotitem | def _gotitem(self, key, ndim, subset=None):
"""
sub-classes to define
return a sliced object
Parameters
----------
key : string / list of selections
ndim : 1,2
requested ndim of result
subset : object, default None
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"""
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requested ndim of result
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20,007 | pandas-dev/pandas | pandas/core/groupby/generic.py | DataFrameGroupBy._fill | def _fill(self, direction, limit=None):
"""Overridden method to join grouped columns in output"""
res = super()._fill(direction, limit=limit)
output = OrderedDict(
(grp.name, grp.grouper) for grp in self.grouper.groupings)
from pandas import concat
return concat((sel... | python | def _fill(self, direction, limit=None):
"""Overridden method to join grouped columns in output"""
res = super()._fill(direction, limit=limit)
output = OrderedDict(
(grp.name, grp.grouper) for grp in self.grouper.groupings)
from pandas import concat
return concat((sel... | [
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20,008 | pandas-dev/pandas | pandas/core/groupby/generic.py | DataFrameGroupBy.nunique | def nunique(self, dropna=True):
"""
Return DataFrame with number of distinct observations per group for
each column.
.. versionadded:: 0.20.0
Parameters
----------
dropna : boolean, default True
Don't include NaN in the counts.
Returns
... | python | def nunique(self, dropna=True):
"""
Return DataFrame with number of distinct observations per group for
each column.
.. versionadded:: 0.20.0
Parameters
----------
dropna : boolean, default True
Don't include NaN in the counts.
Returns
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20,009 | pandas-dev/pandas | pandas/core/internals/arrays.py | extract_array | def extract_array(obj, extract_numpy=False):
"""
Extract the ndarray or ExtensionArray from a Series or Index.
For all other types, `obj` is just returned as is.
Parameters
----------
obj : object
For Series / Index, the underlying ExtensionArray is unboxed.
For Numpy-backed Ex... | python | def extract_array(obj, extract_numpy=False):
"""
Extract the ndarray or ExtensionArray from a Series or Index.
For all other types, `obj` is just returned as is.
Parameters
----------
obj : object
For Series / Index, the underlying ExtensionArray is unboxed.
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20,010 | pandas-dev/pandas | pandas/core/common.py | flatten | def flatten(l):
"""
Flatten an arbitrarily nested sequence.
Parameters
----------
l : sequence
The non string sequence to flatten
Notes
-----
This doesn't consider strings sequences.
Returns
-------
flattened : generator
"""
for el in l:
if _iterabl... | python | def flatten(l):
"""
Flatten an arbitrarily nested sequence.
Parameters
----------
l : sequence
The non string sequence to flatten
Notes
-----
This doesn't consider strings sequences.
Returns
-------
flattened : generator
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20,011 | pandas-dev/pandas | pandas/core/common.py | is_bool_indexer | def is_bool_indexer(key: Any) -> bool:
"""
Check whether `key` is a valid boolean indexer.
Parameters
----------
key : Any
Only list-likes may be considered boolean indexers.
All other types are not considered a boolean indexer.
For array-like input, boolean ndarrays or Exte... | python | def is_bool_indexer(key: Any) -> bool:
"""
Check whether `key` is a valid boolean indexer.
Parameters
----------
key : Any
Only list-likes may be considered boolean indexers.
All other types are not considered a boolean indexer.
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20,012 | pandas-dev/pandas | pandas/core/common.py | cast_scalar_indexer | def cast_scalar_indexer(val):
"""
To avoid numpy DeprecationWarnings, cast float to integer where valid.
Parameters
----------
val : scalar
Returns
-------
outval : scalar
"""
# assumes lib.is_scalar(val)
if lib.is_float(val) and val == int(val):
return int(val)
... | python | def cast_scalar_indexer(val):
"""
To avoid numpy DeprecationWarnings, cast float to integer where valid.
Parameters
----------
val : scalar
Returns
-------
outval : scalar
"""
# assumes lib.is_scalar(val)
if lib.is_float(val) and val == int(val):
return int(val)
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20,013 | pandas-dev/pandas | pandas/core/common.py | index_labels_to_array | def index_labels_to_array(labels, dtype=None):
"""
Transform label or iterable of labels to array, for use in Index.
Parameters
----------
dtype : dtype
If specified, use as dtype of the resulting array, otherwise infer.
Returns
-------
array
"""
if isinstance(labels, (... | python | def index_labels_to_array(labels, dtype=None):
"""
Transform label or iterable of labels to array, for use in Index.
Parameters
----------
dtype : dtype
If specified, use as dtype of the resulting array, otherwise infer.
Returns
-------
array
"""
if isinstance(labels, (... | [
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20,014 | pandas-dev/pandas | pandas/core/common.py | is_null_slice | def is_null_slice(obj):
"""
We have a null slice.
"""
return (isinstance(obj, slice) and obj.start is None and
obj.stop is None and obj.step is None) | python | def is_null_slice(obj):
"""
We have a null slice.
"""
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20,015 | pandas-dev/pandas | pandas/core/common.py | is_full_slice | def is_full_slice(obj, l):
"""
We have a full length slice.
"""
return (isinstance(obj, slice) and obj.start == 0 and obj.stop == l and
obj.step is None) | python | def is_full_slice(obj, l):
"""
We have a full length slice.
"""
return (isinstance(obj, slice) and obj.start == 0 and obj.stop == l and
obj.step is None) | [
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20,016 | pandas-dev/pandas | pandas/core/common.py | apply_if_callable | def apply_if_callable(maybe_callable, obj, **kwargs):
"""
Evaluate possibly callable input using obj and kwargs if it is callable,
otherwise return as it is.
Parameters
----------
maybe_callable : possibly a callable
obj : NDFrame
**kwargs
"""
if callable(maybe_callable):
... | python | def apply_if_callable(maybe_callable, obj, **kwargs):
"""
Evaluate possibly callable input using obj and kwargs if it is callable,
otherwise return as it is.
Parameters
----------
maybe_callable : possibly a callable
obj : NDFrame
**kwargs
"""
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20,017 | pandas-dev/pandas | pandas/core/common.py | standardize_mapping | def standardize_mapping(into):
"""
Helper function to standardize a supplied mapping.
.. versionadded:: 0.21.0
Parameters
----------
into : instance or subclass of collections.abc.Mapping
Must be a class, an initialized collections.defaultdict,
or an instance of a collections.a... | python | def standardize_mapping(into):
"""
Helper function to standardize a supplied mapping.
.. versionadded:: 0.21.0
Parameters
----------
into : instance or subclass of collections.abc.Mapping
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20,018 | pandas-dev/pandas | pandas/core/common.py | random_state | def random_state(state=None):
"""
Helper function for processing random_state arguments.
Parameters
----------
state : int, np.random.RandomState, None.
If receives an int, passes to np.random.RandomState() as seed.
If receives an np.random.RandomState object, just returns object.
... | python | def random_state(state=None):
"""
Helper function for processing random_state arguments.
Parameters
----------
state : int, np.random.RandomState, None.
If receives an int, passes to np.random.RandomState() as seed.
If receives an np.random.RandomState object, just returns object.
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20,019 | pandas-dev/pandas | pandas/core/common.py | _pipe | def _pipe(obj, func, *args, **kwargs):
"""
Apply a function ``func`` to object ``obj`` either by passing obj as the
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"""
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20,020 | pandas-dev/pandas | pandas/core/nanops.py | _get_fill_value | def _get_fill_value(dtype, fill_value=None, fill_value_typ=None):
""" return the correct fill value for the dtype of the values """
if fill_value is not None:
return fill_value
if _na_ok_dtype(dtype):
if fill_value_typ is None:
return np.nan
else:
if fill_valu... | python | def _get_fill_value(dtype, fill_value=None, fill_value_typ=None):
""" return the correct fill value for the dtype of the values """
if fill_value is not None:
return fill_value
if _na_ok_dtype(dtype):
if fill_value_typ is None:
return np.nan
else:
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20,021 | pandas-dev/pandas | pandas/core/nanops.py | _get_values | def _get_values(values, skipna, fill_value=None, fill_value_typ=None,
isfinite=False, copy=True, mask=None):
""" utility to get the values view, mask, dtype
if necessary copy and mask using the specified fill_value
copy = True will force the copy
"""
if is_datetime64tz_dtype(values)... | python | def _get_values(values, skipna, fill_value=None, fill_value_typ=None,
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""" utility to get the values view, mask, dtype
if necessary copy and mask using the specified fill_value
copy = True will force the copy
"""
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20,022 | pandas-dev/pandas | pandas/core/nanops.py | _wrap_results | def _wrap_results(result, dtype, fill_value=None):
""" wrap our results if needed """
if is_datetime64_dtype(dtype) or is_datetime64tz_dtype(dtype):
if fill_value is None:
# GH#24293
fill_value = iNaT
if not isinstance(result, np.ndarray):
tz = getattr(dtype,... | python | def _wrap_results(result, dtype, fill_value=None):
""" wrap our results if needed """
if is_datetime64_dtype(dtype) or is_datetime64tz_dtype(dtype):
if fill_value is None:
# GH#24293
fill_value = iNaT
if not isinstance(result, np.ndarray):
tz = getattr(dtype,... | [
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20,023 | pandas-dev/pandas | pandas/core/nanops.py | _na_for_min_count | def _na_for_min_count(values, axis):
"""Return the missing value for `values`
Parameters
----------
values : ndarray
axis : int or None
axis for the reduction
Returns
-------
result : scalar or ndarray
For 1-D values, returns a scalar of the correct missing type.
... | python | def _na_for_min_count(values, axis):
"""Return the missing value for `values`
Parameters
----------
values : ndarray
axis : int or None
axis for the reduction
Returns
-------
result : scalar or ndarray
For 1-D values, returns a scalar of the correct missing type.
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20,024 | pandas-dev/pandas | pandas/core/nanops.py | nanany | def nanany(values, axis=None, skipna=True, mask=None):
"""
Check if any elements along an axis evaluate to True.
Parameters
----------
values : ndarray
axis : int, optional
skipna : bool, default True
mask : ndarray[bool], optional
nan-mask if known
Returns
-------
... | python | def nanany(values, axis=None, skipna=True, mask=None):
"""
Check if any elements along an axis evaluate to True.
Parameters
----------
values : ndarray
axis : int, optional
skipna : bool, default True
mask : ndarray[bool], optional
nan-mask if known
Returns
-------
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20,025 | pandas-dev/pandas | pandas/core/nanops.py | nanall | def nanall(values, axis=None, skipna=True, mask=None):
"""
Check if all elements along an axis evaluate to True.
Parameters
----------
values : ndarray
axis: int, optional
skipna : bool, default True
mask : ndarray[bool], optional
nan-mask if known
Returns
-------
r... | python | def nanall(values, axis=None, skipna=True, mask=None):
"""
Check if all elements along an axis evaluate to True.
Parameters
----------
values : ndarray
axis: int, optional
skipna : bool, default True
mask : ndarray[bool], optional
nan-mask if known
Returns
-------
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20,026 | pandas-dev/pandas | pandas/core/nanops.py | nansum | def nansum(values, axis=None, skipna=True, min_count=0, mask=None):
"""
Sum the elements along an axis ignoring NaNs
Parameters
----------
values : ndarray[dtype]
axis: int, optional
skipna : bool, default True
min_count: int, default 0
mask : ndarray[bool], optional
nan-mas... | python | def nansum(values, axis=None, skipna=True, min_count=0, mask=None):
"""
Sum the elements along an axis ignoring NaNs
Parameters
----------
values : ndarray[dtype]
axis: int, optional
skipna : bool, default True
min_count: int, default 0
mask : ndarray[bool], optional
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20,027 | pandas-dev/pandas | pandas/core/nanops.py | nanmean | def nanmean(values, axis=None, skipna=True, mask=None):
"""
Compute the mean of the element along an axis ignoring NaNs
Parameters
----------
values : ndarray
axis: int, optional
skipna : bool, default True
mask : ndarray[bool], optional
nan-mask if known
Returns
------... | python | def nanmean(values, axis=None, skipna=True, mask=None):
"""
Compute the mean of the element along an axis ignoring NaNs
Parameters
----------
values : ndarray
axis: int, optional
skipna : bool, default True
mask : ndarray[bool], optional
nan-mask if known
Returns
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20,028 | pandas-dev/pandas | pandas/core/nanops.py | nanstd | def nanstd(values, axis=None, skipna=True, ddof=1, mask=None):
"""
Compute the standard deviation along given axis while ignoring NaNs
Parameters
----------
values : ndarray
axis: int, optional
skipna : bool, default True
ddof : int, default 1
Delta Degrees of Freedom. The divis... | python | def nanstd(values, axis=None, skipna=True, ddof=1, mask=None):
"""
Compute the standard deviation along given axis while ignoring NaNs
Parameters
----------
values : ndarray
axis: int, optional
skipna : bool, default True
ddof : int, default 1
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20,029 | pandas-dev/pandas | pandas/core/nanops.py | nanvar | def nanvar(values, axis=None, skipna=True, ddof=1, mask=None):
"""
Compute the variance along given axis while ignoring NaNs
Parameters
----------
values : ndarray
axis: int, optional
skipna : bool, default True
ddof : int, default 1
Delta Degrees of Freedom. The divisor used in... | python | def nanvar(values, axis=None, skipna=True, ddof=1, mask=None):
"""
Compute the variance along given axis while ignoring NaNs
Parameters
----------
values : ndarray
axis: int, optional
skipna : bool, default True
ddof : int, default 1
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20,030 | pandas-dev/pandas | pandas/core/nanops.py | nansem | def nansem(values, axis=None, skipna=True, ddof=1, mask=None):
"""
Compute the standard error in the mean along given axis while ignoring NaNs
Parameters
----------
values : ndarray
axis: int, optional
skipna : bool, default True
ddof : int, default 1
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"""
Compute the standard error in the mean along given axis while ignoring NaNs
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values : ndarray
axis: int, optional
skipna : bool, default True
ddof : int, default 1
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20,031 | pandas-dev/pandas | pandas/core/nanops.py | nanskew | def nanskew(values, axis=None, skipna=True, mask=None):
""" Compute the sample skewness.
The statistic computed here is the adjusted Fisher-Pearson standardized
moment coefficient G1. The algorithm computes this coefficient directly
from the second and third central moment.
Parameters
--------... | python | def nanskew(values, axis=None, skipna=True, mask=None):
""" Compute the sample skewness.
The statistic computed here is the adjusted Fisher-Pearson standardized
moment coefficient G1. The algorithm computes this coefficient directly
from the second and third central moment.
Parameters
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20,032 | pandas-dev/pandas | pandas/core/nanops.py | nankurt | def nankurt(values, axis=None, skipna=True, mask=None):
"""
Compute the sample excess kurtosis
The statistic computed here is the adjusted Fisher-Pearson standardized
moment coefficient G2, computed directly from the second and fourth
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Parameters
----------
values : ndar... | python | def nankurt(values, axis=None, skipna=True, mask=None):
"""
Compute the sample excess kurtosis
The statistic computed here is the adjusted Fisher-Pearson standardized
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20,033 | pandas-dev/pandas | pandas/core/nanops.py | _nanpercentile_1d | def _nanpercentile_1d(values, mask, q, na_value, interpolation):
"""
Wraper for np.percentile that skips missing values, specialized to
1-dimensional case.
Parameters
----------
values : array over which to find quantiles
mask : ndarray[bool]
locations in values that should be consi... | python | def _nanpercentile_1d(values, mask, q, na_value, interpolation):
"""
Wraper for np.percentile that skips missing values, specialized to
1-dimensional case.
Parameters
----------
values : array over which to find quantiles
mask : ndarray[bool]
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20,034 | pandas-dev/pandas | pandas/core/nanops.py | nanpercentile | def nanpercentile(values, q, axis, na_value, mask, ndim, interpolation):
"""
Wraper for np.percentile that skips missing values.
Parameters
----------
values : array over which to find quantiles
q : scalar or array of quantile indices to find
axis : {0, 1}
na_value : scalar
valu... | python | def nanpercentile(values, q, axis, na_value, mask, ndim, interpolation):
"""
Wraper for np.percentile that skips missing values.
Parameters
----------
values : array over which to find quantiles
q : scalar or array of quantile indices to find
axis : {0, 1}
na_value : scalar
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20,035 | pandas-dev/pandas | pandas/io/clipboards.py | read_clipboard | def read_clipboard(sep=r'\s+', **kwargs): # pragma: no cover
r"""
Read text from clipboard and pass to read_csv. See read_csv for the
full argument list
Parameters
----------
sep : str, default '\s+'
A string or regex delimiter. The default of '\s+' denotes
one or more whitespa... | python | def read_clipboard(sep=r'\s+', **kwargs): # pragma: no cover
r"""
Read text from clipboard and pass to read_csv. See read_csv for the
full argument list
Parameters
----------
sep : str, default '\s+'
A string or regex delimiter. The default of '\s+' denotes
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20,036 | pandas-dev/pandas | pandas/io/clipboards.py | to_clipboard | def to_clipboard(obj, excel=True, sep=None, **kwargs): # pragma: no cover
"""
Attempt to write text representation of object to the system clipboard
The clipboard can be then pasted into Excel for example.
Parameters
----------
obj : the object to write to the clipboard
excel : boolean, de... | python | def to_clipboard(obj, excel=True, sep=None, **kwargs): # pragma: no cover
"""
Attempt to write text representation of object to the system clipboard
The clipboard can be then pasted into Excel for example.
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----------
obj : the object to write to the clipboard
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20,037 | pandas-dev/pandas | pandas/io/html.py | _get_skiprows | def _get_skiprows(skiprows):
"""Get an iterator given an integer, slice or container.
Parameters
----------
skiprows : int, slice, container
The iterator to use to skip rows; can also be a slice.
Raises
------
TypeError
* If `skiprows` is not a slice, integer, or Container
... | python | def _get_skiprows(skiprows):
"""Get an iterator given an integer, slice or container.
Parameters
----------
skiprows : int, slice, container
The iterator to use to skip rows; can also be a slice.
Raises
------
TypeError
* If `skiprows` is not a slice, integer, or Container
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20,038 | pandas-dev/pandas | pandas/io/html.py | _read | def _read(obj):
"""Try to read from a url, file or string.
Parameters
----------
obj : str, unicode, or file-like
Returns
-------
raw_text : str
"""
if _is_url(obj):
with urlopen(obj) as url:
text = url.read()
elif hasattr(obj, 'read'):
text = obj.re... | python | def _read(obj):
"""Try to read from a url, file or string.
Parameters
----------
obj : str, unicode, or file-like
Returns
-------
raw_text : str
"""
if _is_url(obj):
with urlopen(obj) as url:
text = url.read()
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text = obj.re... | [
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20,039 | pandas-dev/pandas | pandas/io/html.py | _build_xpath_expr | def _build_xpath_expr(attrs):
"""Build an xpath expression to simulate bs4's ability to pass in kwargs to
search for attributes when using the lxml parser.
Parameters
----------
attrs : dict
A dict of HTML attributes. These are NOT checked for validity.
Returns
-------
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"""Build an xpath expression to simulate bs4's ability to pass in kwargs to
search for attributes when using the lxml parser.
Parameters
----------
attrs : dict
A dict of HTML attributes. These are NOT checked for validity.
Returns
-------
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20,040 | pandas-dev/pandas | pandas/io/html.py | _parser_dispatch | def _parser_dispatch(flavor):
"""Choose the parser based on the input flavor.
Parameters
----------
flavor : str
The type of parser to use. This must be a valid backend.
Returns
-------
cls : _HtmlFrameParser subclass
The parser class based on the requested input flavor.
... | python | def _parser_dispatch(flavor):
"""Choose the parser based on the input flavor.
Parameters
----------
flavor : str
The type of parser to use. This must be a valid backend.
Returns
-------
cls : _HtmlFrameParser subclass
The parser class based on the requested input flavor.
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20,041 | pandas-dev/pandas | pandas/io/html.py | read_html | def read_html(io, match='.+', flavor=None, header=None, index_col=None,
skiprows=None, attrs=None, parse_dates=False,
tupleize_cols=None, thousands=',', encoding=None,
decimal='.', converters=None, na_values=None,
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20,042 | pandas-dev/pandas | pandas/io/html.py | _HtmlFrameParser.parse_tables | def parse_tables(self):
"""
Parse and return all tables from the DOM.
Returns
-------
list of parsed (header, body, footer) tuples from tables.
"""
tables = self._parse_tables(self._build_doc(), self.match, self.attrs)
return (self._parse_thead_tbody_tfoo... | python | def parse_tables(self):
"""
Parse and return all tables from the DOM.
Returns
-------
list of parsed (header, body, footer) tuples from tables.
"""
tables = self._parse_tables(self._build_doc(), self.match, self.attrs)
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20,043 | pandas-dev/pandas | pandas/io/html.py | _HtmlFrameParser._parse_thead_tbody_tfoot | def _parse_thead_tbody_tfoot(self, table_html):
"""
Given a table, return parsed header, body, and foot.
Parameters
----------
table_html : node-like
Returns
-------
tuple of (header, body, footer), each a list of list-of-text rows.
Notes
... | python | def _parse_thead_tbody_tfoot(self, table_html):
"""
Given a table, return parsed header, body, and foot.
Parameters
----------
table_html : node-like
Returns
-------
tuple of (header, body, footer), each a list of list-of-text rows.
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20,044 | pandas-dev/pandas | pandas/io/html.py | _HtmlFrameParser._handle_hidden_tables | def _handle_hidden_tables(self, tbl_list, attr_name):
"""
Return list of tables, potentially removing hidden elements
Parameters
----------
tbl_list : list of node-like
Type of list elements will vary depending upon parser used
attr_name : str
Nam... | python | def _handle_hidden_tables(self, tbl_list, attr_name):
"""
Return list of tables, potentially removing hidden elements
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----------
tbl_list : list of node-like
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20,045 | pandas-dev/pandas | pandas/core/dtypes/concat.py | _get_series_result_type | def _get_series_result_type(result, objs=None):
"""
return appropriate class of Series concat
input is either dict or array-like
"""
from pandas import SparseSeries, SparseDataFrame, DataFrame
# concat Series with axis 1
if isinstance(result, dict):
# concat Series with axis 1
... | python | def _get_series_result_type(result, objs=None):
"""
return appropriate class of Series concat
input is either dict or array-like
"""
from pandas import SparseSeries, SparseDataFrame, DataFrame
# concat Series with axis 1
if isinstance(result, dict):
# concat Series with axis 1
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20,046 | pandas-dev/pandas | pandas/core/dtypes/concat.py | _get_frame_result_type | def _get_frame_result_type(result, objs):
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otherwise, return 1st obj
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20,047 | pandas-dev/pandas | pandas/core/dtypes/concat.py | union_categoricals | def union_categoricals(to_union, sort_categories=False, ignore_order=False):
"""
Combine list-like of Categorical-like, unioning categories. All
categories must have the same dtype.
.. versionadded:: 0.19.0
Parameters
----------
to_union : list-like of Categorical, CategoricalIndex,
... | python | def union_categoricals(to_union, sort_categories=False, ignore_order=False):
"""
Combine list-like of Categorical-like, unioning categories. All
categories must have the same dtype.
.. versionadded:: 0.19.0
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----------
to_union : list-like of Categorical, CategoricalIndex,
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20,048 | pandas-dev/pandas | pandas/core/dtypes/concat.py | _concat_datetimetz | def _concat_datetimetz(to_concat, name=None):
"""
concat DatetimeIndex with the same tz
all inputs must be DatetimeIndex
it is used in DatetimeIndex.append also
"""
# Right now, internals will pass a List[DatetimeArray] here
# for reductions like quantile. I would like to disentangle
# a... | python | def _concat_datetimetz(to_concat, name=None):
"""
concat DatetimeIndex with the same tz
all inputs must be DatetimeIndex
it is used in DatetimeIndex.append also
"""
# Right now, internals will pass a List[DatetimeArray] here
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20,049 | pandas-dev/pandas | pandas/core/dtypes/concat.py | _concat_index_asobject | def _concat_index_asobject(to_concat, name=None):
"""
concat all inputs as object. DatetimeIndex, TimedeltaIndex and
PeriodIndex are converted to object dtype before concatenation
"""
from pandas import Index
from pandas.core.arrays import ExtensionArray
klasses = (ABCDatetimeIndex, ABCTime... | python | def _concat_index_asobject(to_concat, name=None):
"""
concat all inputs as object. DatetimeIndex, TimedeltaIndex and
PeriodIndex are converted to object dtype before concatenation
"""
from pandas import Index
from pandas.core.arrays import ExtensionArray
klasses = (ABCDatetimeIndex, ABCTime... | [
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20,050 | pandas-dev/pandas | pandas/util/_exceptions.py | rewrite_exception | def rewrite_exception(old_name, new_name):
"""Rewrite the message of an exception."""
try:
yield
except Exception as e:
msg = e.args[0]
msg = msg.replace(old_name, new_name)
args = (msg,)
if len(e.args) > 1:
args = args + e.args[1:]
e.args = args
... | python | def rewrite_exception(old_name, new_name):
"""Rewrite the message of an exception."""
try:
yield
except Exception as e:
msg = e.args[0]
msg = msg.replace(old_name, new_name)
args = (msg,)
if len(e.args) > 1:
args = args + e.args[1:]
e.args = args
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20,051 | pandas-dev/pandas | pandas/io/formats/style.py | _get_level_lengths | def _get_level_lengths(index, hidden_elements=None):
"""
Given an index, find the level length for each element.
Optional argument is a list of index positions which
should not be visible.
Result is a dictionary of (level, inital_position): span
"""
sentinel = object()
levels = index.f... | python | def _get_level_lengths(index, hidden_elements=None):
"""
Given an index, find the level length for each element.
Optional argument is a list of index positions which
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Result is a dictionary of (level, inital_position): span
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20,052 | pandas-dev/pandas | pandas/io/formats/style.py | Styler.format | def format(self, formatter, subset=None):
"""
Format the text display value of cells.
.. versionadded:: 0.18.0
Parameters
----------
formatter : str, callable, or dict
subset : IndexSlice
An argument to ``DataFrame.loc`` that restricts which elements... | python | def format(self, formatter, subset=None):
"""
Format the text display value of cells.
.. versionadded:: 0.18.0
Parameters
----------
formatter : str, callable, or dict
subset : IndexSlice
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20,053 | pandas-dev/pandas | pandas/io/formats/style.py | Styler.render | def render(self, **kwargs):
"""
Render the built up styles to HTML.
Parameters
----------
**kwargs
Any additional keyword arguments are passed
through to ``self.template.render``.
This is useful when you need to provide
additional ... | python | def render(self, **kwargs):
"""
Render the built up styles to HTML.
Parameters
----------
**kwargs
Any additional keyword arguments are passed
through to ``self.template.render``.
This is useful when you need to provide
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20,054 | pandas-dev/pandas | pandas/io/formats/style.py | Styler._update_ctx | def _update_ctx(self, attrs):
"""
Update the state of the Styler.
Collects a mapping of {index_label: ['<property>: <value>']}.
attrs : Series or DataFrame
should contain strings of '<property>: <value>;<prop2>: <val2>'
Whitespace shouldn't matter and the final trailing... | python | def _update_ctx(self, attrs):
"""
Update the state of the Styler.
Collects a mapping of {index_label: ['<property>: <value>']}.
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20,055 | pandas-dev/pandas | pandas/io/formats/style.py | Styler._compute | def _compute(self):
"""
Execute the style functions built up in `self._todo`.
Relies on the conventions that all style functions go through
.apply or .applymap. The append styles to apply as tuples of
(application method, *args, **kwargs)
"""
r = self
fo... | python | def _compute(self):
"""
Execute the style functions built up in `self._todo`.
Relies on the conventions that all style functions go through
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20,056 | pandas-dev/pandas | pandas/io/formats/style.py | Styler.apply | def apply(self, func, axis=0, subset=None, **kwargs):
"""
Apply a function column-wise, row-wise, or table-wise,
updating the HTML representation with the result.
Parameters
----------
func : function
``func`` should take a Series or DataFrame (depending
... | python | def apply(self, func, axis=0, subset=None, **kwargs):
"""
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Parameters
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20,057 | pandas-dev/pandas | pandas/io/formats/style.py | Styler.applymap | def applymap(self, func, subset=None, **kwargs):
"""
Apply a function elementwise, updating the HTML
representation with the result.
Parameters
----------
func : function
``func`` should take a scalar and return a scalar
subset : IndexSlice
... | python | def applymap(self, func, subset=None, **kwargs):
"""
Apply a function elementwise, updating the HTML
representation with the result.
Parameters
----------
func : function
``func`` should take a scalar and return a scalar
subset : IndexSlice
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20,058 | pandas-dev/pandas | pandas/io/formats/style.py | Styler.where | def where(self, cond, value, other=None, subset=None, **kwargs):
"""
Apply a function elementwise, updating the HTML
representation with a style which is selected in
accordance with the return value of a function.
.. versionadded:: 0.21.0
Parameters
----------
... | python | def where(self, cond, value, other=None, subset=None, **kwargs):
"""
Apply a function elementwise, updating the HTML
representation with a style which is selected in
accordance with the return value of a function.
.. versionadded:: 0.21.0
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20,059 | pandas-dev/pandas | pandas/io/formats/style.py | Styler.hide_columns | def hide_columns(self, subset):
"""
Hide columns from rendering.
.. versionadded:: 0.23.0
Parameters
----------
subset : IndexSlice
An argument to ``DataFrame.loc`` that identifies which columns
are hidden.
Returns
-------
... | python | def hide_columns(self, subset):
"""
Hide columns from rendering.
.. versionadded:: 0.23.0
Parameters
----------
subset : IndexSlice
An argument to ``DataFrame.loc`` that identifies which columns
are hidden.
Returns
-------
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20,060 | pandas-dev/pandas | pandas/io/formats/style.py | Styler.highlight_null | def highlight_null(self, null_color='red'):
"""
Shade the background ``null_color`` for missing values.
Parameters
----------
null_color : str
Returns
-------
self : Styler
"""
self.applymap(self._highlight_null, null_color=null_color)
... | python | def highlight_null(self, null_color='red'):
"""
Shade the background ``null_color`` for missing values.
Parameters
----------
null_color : str
Returns
-------
self : Styler
"""
self.applymap(self._highlight_null, null_color=null_color)
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20,061 | pandas-dev/pandas | pandas/io/formats/style.py | Styler._background_gradient | def _background_gradient(s, cmap='PuBu', low=0, high=0,
text_color_threshold=0.408):
"""
Color background in a range according to the data.
"""
if (not isinstance(text_color_threshold, (float, int)) or
not 0 <= text_color_threshold <= 1):
... | python | def _background_gradient(s, cmap='PuBu', low=0, high=0,
text_color_threshold=0.408):
"""
Color background in a range according to the data.
"""
if (not isinstance(text_color_threshold, (float, int)) or
not 0 <= text_color_threshold <= 1):
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20,062 | pandas-dev/pandas | pandas/io/formats/style.py | Styler.set_properties | def set_properties(self, subset=None, **kwargs):
"""
Convenience method for setting one or more non-data dependent
properties or each cell.
Parameters
----------
subset : IndexSlice
a valid slice for ``data`` to limit the style application to
kwargs :... | python | def set_properties(self, subset=None, **kwargs):
"""
Convenience method for setting one or more non-data dependent
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Parameters
----------
subset : IndexSlice
a valid slice for ``data`` to limit the style application to
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20,063 | pandas-dev/pandas | pandas/io/formats/style.py | Styler._bar | def _bar(s, align, colors, width=100, vmin=None, vmax=None):
"""
Draw bar chart in dataframe cells.
"""
# Get input value range.
smin = s.min() if vmin is None else vmin
if isinstance(smin, ABCSeries):
smin = smin.min()
smax = s.max() if vmax is None e... | python | def _bar(s, align, colors, width=100, vmin=None, vmax=None):
"""
Draw bar chart in dataframe cells.
"""
# Get input value range.
smin = s.min() if vmin is None else vmin
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20,064 | pandas-dev/pandas | pandas/io/formats/style.py | Styler.bar | def bar(self, subset=None, axis=0, color='#d65f5f', width=100,
align='left', vmin=None, vmax=None):
"""
Draw bar chart in the cell backgrounds.
Parameters
----------
subset : IndexSlice, optional
A valid slice for `data` to limit the style application to.... | python | def bar(self, subset=None, axis=0, color='#d65f5f', width=100,
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Draw bar chart in the cell backgrounds.
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20,065 | pandas-dev/pandas | pandas/io/formats/style.py | Styler.highlight_max | def highlight_max(self, subset=None, color='yellow', axis=0):
"""
Highlight the maximum by shading the background.
Parameters
----------
subset : IndexSlice, default None
a valid slice for ``data`` to limit the style application to.
color : str, default 'yell... | python | def highlight_max(self, subset=None, color='yellow', axis=0):
"""
Highlight the maximum by shading the background.
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----------
subset : IndexSlice, default None
a valid slice for ``data`` to limit the style application to.
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20,066 | pandas-dev/pandas | pandas/io/formats/style.py | Styler.highlight_min | def highlight_min(self, subset=None, color='yellow', axis=0):
"""
Highlight the minimum by shading the background.
Parameters
----------
subset : IndexSlice, default None
a valid slice for ``data`` to limit the style application to.
color : str, default 'yell... | python | def highlight_min(self, subset=None, color='yellow', axis=0):
"""
Highlight the minimum by shading the background.
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----------
subset : IndexSlice, default None
a valid slice for ``data`` to limit the style application to.
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20,067 | pandas-dev/pandas | pandas/io/formats/style.py | Styler._highlight_extrema | def _highlight_extrema(data, color='yellow', max_=True):
"""
Highlight the min or max in a Series or DataFrame.
"""
attr = 'background-color: {0}'.format(color)
if data.ndim == 1: # Series from .apply
if max_:
extrema = data == data.max()
... | python | def _highlight_extrema(data, color='yellow', max_=True):
"""
Highlight the min or max in a Series or DataFrame.
"""
attr = 'background-color: {0}'.format(color)
if data.ndim == 1: # Series from .apply
if max_:
extrema = data == data.max()
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20,068 | pandas-dev/pandas | pandas/io/formats/style.py | Styler.from_custom_template | def from_custom_template(cls, searchpath, name):
"""
Factory function for creating a subclass of ``Styler``
with a custom template and Jinja environment.
Parameters
----------
searchpath : str or list
Path or paths of directories containing the templates
... | python | def from_custom_template(cls, searchpath, name):
"""
Factory function for creating a subclass of ``Styler``
with a custom template and Jinja environment.
Parameters
----------
searchpath : str or list
Path or paths of directories containing the templates
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20,069 | pandas-dev/pandas | pandas/core/indexes/numeric.py | Int64Index._assert_safe_casting | def _assert_safe_casting(cls, data, subarr):
"""
Ensure incoming data can be represented as ints.
"""
if not issubclass(data.dtype.type, np.signedinteger):
if not np.array_equal(data, subarr):
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"""
Ensure incoming data can be represented as ints.
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20,070 | pandas-dev/pandas | pandas/core/indexes/numeric.py | Float64Index.get_value | def get_value(self, series, key):
""" we always want to get an index value, never a value """
if not is_scalar(key):
raise InvalidIndexError
k = com.values_from_object(key)
loc = self.get_loc(k)
new_values = com.values_from_object(series)[loc]
return new_val... | python | def get_value(self, series, key):
""" we always want to get an index value, never a value """
if not is_scalar(key):
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loc = self.get_loc(k)
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20,071 | pandas-dev/pandas | pandas/io/pytables.py | to_hdf | def to_hdf(path_or_buf, key, value, mode=None, complevel=None, complib=None,
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""" store this object, close it if we opened it """
if append:
f = lambda store: store.append(key, value, **kwargs)
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""" store this object, close it if we opened it """
if append:
f = lambda store: store.append(key, value, **kwargs)
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20,072 | pandas-dev/pandas | pandas/io/pytables.py | read_hdf | def read_hdf(path_or_buf, key=None, mode='r', **kwargs):
"""
Read from the store, close it if we opened it.
Retrieve pandas object stored in file, optionally based on where
criteria
Parameters
----------
path_or_buf : string, buffer or path object
Path to the file to open, or an op... | python | def read_hdf(path_or_buf, key=None, mode='r', **kwargs):
"""
Read from the store, close it if we opened it.
Retrieve pandas object stored in file, optionally based on where
criteria
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----------
path_or_buf : string, buffer or path object
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20,073 | pandas-dev/pandas | pandas/io/pytables.py | _is_metadata_of | def _is_metadata_of(group, parent_group):
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"""Check if a given group is a metadata group for a given parent_group."""
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return False
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20,074 | pandas-dev/pandas | pandas/io/pytables.py | _get_tz | def _get_tz(tz):
""" for a tz-aware type, return an encoded zone """
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if zone is None:
zone = tz.utcoffset().total_seconds()
return zone | python | def _get_tz(tz):
""" for a tz-aware type, return an encoded zone """
zone = timezones.get_timezone(tz)
if zone is None:
zone = tz.utcoffset().total_seconds()
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20,075 | pandas-dev/pandas | pandas/io/pytables.py | _set_tz | def _set_tz(values, tz, preserve_UTC=False, coerce=False):
"""
coerce the values to a DatetimeIndex if tz is set
preserve the input shape if possible
Parameters
----------
values : ndarray
tz : string/pickled tz object
preserve_UTC : boolean,
preserve the UTC of the result
c... | python | def _set_tz(values, tz, preserve_UTC=False, coerce=False):
"""
coerce the values to a DatetimeIndex if tz is set
preserve the input shape if possible
Parameters
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values : ndarray
tz : string/pickled tz object
preserve_UTC : boolean,
preserve the UTC of the result
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20,076 | pandas-dev/pandas | pandas/io/pytables.py | _convert_string_array | def _convert_string_array(data, encoding, errors, itemsize=None):
"""
we take a string-like that is object dtype and coerce to a fixed size
string type
Parameters
----------
data : a numpy array of object dtype
encoding : None or string-encoding
errors : handler for encoding errors
... | python | def _convert_string_array(data, encoding, errors, itemsize=None):
"""
we take a string-like that is object dtype and coerce to a fixed size
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Parameters
----------
data : a numpy array of object dtype
encoding : None or string-encoding
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20,077 | pandas-dev/pandas | pandas/io/pytables.py | _unconvert_string_array | def _unconvert_string_array(data, nan_rep=None, encoding=None,
errors='strict'):
"""
inverse of _convert_string_array
Parameters
----------
data : fixed length string dtyped array
nan_rep : the storage repr of NaN, optional
encoding : the encoding of the data, op... | python | def _unconvert_string_array(data, nan_rep=None, encoding=None,
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"""
inverse of _convert_string_array
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data : fixed length string dtyped array
nan_rep : the storage repr of NaN, optional
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20,078 | pandas-dev/pandas | pandas/io/pytables.py | HDFStore.open | def open(self, mode='a', **kwargs):
"""
Open the file in the specified mode
Parameters
----------
mode : {'a', 'w', 'r', 'r+'}, default 'a'
See HDFStore docstring or tables.open_file for info about modes
"""
tables = _tables()
if self._mode !... | python | def open(self, mode='a', **kwargs):
"""
Open the file in the specified mode
Parameters
----------
mode : {'a', 'w', 'r', 'r+'}, default 'a'
See HDFStore docstring or tables.open_file for info about modes
"""
tables = _tables()
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20,079 | pandas-dev/pandas | pandas/io/pytables.py | HDFStore.flush | def flush(self, fsync=False):
"""
Force all buffered modifications to be written to disk.
Parameters
----------
fsync : bool (default False)
call ``os.fsync()`` on the file handle to force writing to disk.
Notes
-----
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"""
Force all buffered modifications to be written to disk.
Parameters
----------
fsync : bool (default False)
call ``os.fsync()`` on the file handle to force writing to disk.
Notes
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20,080 | pandas-dev/pandas | pandas/io/pytables.py | HDFStore.get | def get(self, key):
"""
Retrieve pandas object stored in file
Parameters
----------
key : object
Returns
-------
obj : same type as object stored in file
"""
group = self.get_node(key)
if group is None:
raise KeyError(... | python | def get(self, key):
"""
Retrieve pandas object stored in file
Parameters
----------
key : object
Returns
-------
obj : same type as object stored in file
"""
group = self.get_node(key)
if group is None:
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20,081 | pandas-dev/pandas | pandas/io/pytables.py | HDFStore.select | def select(self, key, where=None, start=None, stop=None, columns=None,
iterator=False, chunksize=None, auto_close=False, **kwargs):
"""
Retrieve pandas object stored in file, optionally based on where
criteria
Parameters
----------
key : object
whe... | python | def select(self, key, where=None, start=None, stop=None, columns=None,
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Retrieve pandas object stored in file, optionally based on where
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20,082 | pandas-dev/pandas | pandas/io/pytables.py | HDFStore.select_as_coordinates | def select_as_coordinates(
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return the selection as an Index
Parameters
----------
key : object
where : list of Term (or convertible) objects, optional
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20,083 | pandas-dev/pandas | pandas/io/pytables.py | HDFStore.select_column | def select_column(self, key, column, **kwargs):
"""
return a single column from the table. This is generally only useful to
select an indexable
Parameters
----------
key : object
column: the column of interest
Exceptions
----------
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"""
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----------
key : object
column: the column of interest
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----------
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20,084 | pandas-dev/pandas | pandas/io/pytables.py | HDFStore.select_as_multiple | def select_as_multiple(self, keys, where=None, selector=None, columns=None,
start=None, stop=None, iterator=False,
chunksize=None, auto_close=False, **kwargs):
""" Retrieve pandas objects from multiple tables
Parameters
----------
ke... | python | def select_as_multiple(self, keys, where=None, selector=None, columns=None,
start=None, stop=None, iterator=False,
chunksize=None, auto_close=False, **kwargs):
""" Retrieve pandas objects from multiple tables
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----------
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20,085 | pandas-dev/pandas | pandas/io/pytables.py | HDFStore.put | def put(self, key, value, format=None, append=False, **kwargs):
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Store object in HDFStore
Parameters
----------
key : object
value : {Series, DataFrame}
format : 'fixed(f)|table(t)', default is 'fixed'
fixed(f) : Fixed format
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Store object in HDFStore
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key : object
value : {Series, DataFrame}
format : 'fixed(f)|table(t)', default is 'fixed'
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20,086 | pandas-dev/pandas | pandas/io/pytables.py | HDFStore.remove | def remove(self, key, where=None, start=None, stop=None):
"""
Remove pandas object partially by specifying the where condition
Parameters
----------
key : string
Node to remove or delete rows from
where : list of Term (or convertible) objects, optional
... | python | def remove(self, key, where=None, start=None, stop=None):
"""
Remove pandas object partially by specifying the where condition
Parameters
----------
key : string
Node to remove or delete rows from
where : list of Term (or convertible) objects, optional
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Parameters
----------
key : string
Node to remove or delete rows from
where : list of Term (or convertible) objects, optional
start : integer (defaults to None), row number to start selection
st... | [
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20,087 | pandas-dev/pandas | pandas/io/pytables.py | HDFStore.append | def append(self, key, value, format=None, append=True, columns=None,
dropna=None, **kwargs):
"""
Append to Table in file. Node must already exist and be Table
format.
Parameters
----------
key : object
value : {Series, DataFrame}
format : '... | python | def append(self, key, value, format=None, append=True, columns=None,
dropna=None, **kwargs):
"""
Append to Table in file. Node must already exist and be Table
format.
Parameters
----------
key : object
value : {Series, DataFrame}
format : '... | [
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20,088 | pandas-dev/pandas | pandas/io/pytables.py | HDFStore.append_to_multiple | def append_to_multiple(self, d, value, selector, data_columns=None,
axes=None, dropna=False, **kwargs):
"""
Append to multiple tables
Parameters
----------
d : a dict of table_name to table_columns, None is acceptable as the
values of one n... | python | def append_to_multiple(self, d, value, selector, data_columns=None,
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"""
Append to multiple tables
Parameters
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20,089 | pandas-dev/pandas | pandas/io/pytables.py | HDFStore.walk | def walk(self, where="/"):
""" Walk the pytables group hierarchy for pandas objects
This generator will yield the group path, subgroups and pandas object
names for each group.
Any non-pandas PyTables objects that are not a group will be ignored.
The `where` group itself is list... | python | def walk(self, where="/"):
""" Walk the pytables group hierarchy for pandas objects
This generator will yield the group path, subgroups and pandas object
names for each group.
Any non-pandas PyTables objects that are not a group will be ignored.
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20,090 | pandas-dev/pandas | pandas/io/pytables.py | HDFStore.get_node | def get_node(self, key):
""" return the node with the key or None if it does not exist """
self._check_if_open()
try:
if not key.startswith('/'):
key = '/' + key
return self._handle.get_node(self.root, key)
except _table_mod.exceptions.NoSuchNodeEr... | python | def get_node(self, key):
""" return the node with the key or None if it does not exist """
self._check_if_open()
try:
if not key.startswith('/'):
key = '/' + key
return self._handle.get_node(self.root, key)
except _table_mod.exceptions.NoSuchNodeEr... | [
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20,091 | pandas-dev/pandas | pandas/io/pytables.py | HDFStore.get_storer | def get_storer(self, key):
""" return the storer object for a key, raise if not in the file """
group = self.get_node(key)
if group is None:
raise KeyError('No object named {key} in the file'.format(key=key))
s = self._create_storer(group)
s.infer_axes()
retu... | python | def get_storer(self, key):
""" return the storer object for a key, raise if not in the file """
group = self.get_node(key)
if group is None:
raise KeyError('No object named {key} in the file'.format(key=key))
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20,092 | pandas-dev/pandas | pandas/io/pytables.py | HDFStore.copy | def copy(self, file, mode='w', propindexes=True, keys=None, complib=None,
complevel=None, fletcher32=False, overwrite=True):
""" copy the existing store to a new file, upgrading in place
Parameters
----------
propindexes: restore indexes in copied file (defaults... | python | def copy(self, file, mode='w', propindexes=True, keys=None, complib=None,
complevel=None, fletcher32=False, overwrite=True):
""" copy the existing store to a new file, upgrading in place
Parameters
----------
propindexes: restore indexes in copied file (defaults... | [
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20,093 | pandas-dev/pandas | pandas/io/pytables.py | HDFStore.info | def info(self):
"""
Print detailed information on the store.
.. versionadded:: 0.21.0
"""
output = '{type}\nFile path: {path}\n'.format(
type=type(self), path=pprint_thing(self._path))
if self.is_open:
lkeys = sorted(list(self.keys()))
... | python | def info(self):
"""
Print detailed information on the store.
.. versionadded:: 0.21.0
"""
output = '{type}\nFile path: {path}\n'.format(
type=type(self), path=pprint_thing(self._path))
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20,094 | pandas-dev/pandas | pandas/io/pytables.py | HDFStore._create_storer | def _create_storer(self, group, format=None, value=None, append=False,
**kwargs):
""" return a suitable class to operate """
def error(t):
raise TypeError(
"cannot properly create the storer for: [{t}] [group->"
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""" return a suitable class to operate """
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raise TypeError(
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20,095 | pandas-dev/pandas | pandas/io/pytables.py | IndexCol.set_name | def set_name(self, name, kind_attr=None):
""" set the name of this indexer """
self.name = name
self.kind_attr = kind_attr or "{name}_kind".format(name=name)
if self.cname is None:
self.cname = name
return self | python | def set_name(self, name, kind_attr=None):
""" set the name of this indexer """
self.name = name
self.kind_attr = kind_attr or "{name}_kind".format(name=name)
if self.cname is None:
self.cname = name
return self | [
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20,096 | pandas-dev/pandas | pandas/io/pytables.py | IndexCol.set_pos | def set_pos(self, pos):
""" set the position of this column in the Table """
self.pos = pos
if pos is not None and self.typ is not None:
self.typ._v_pos = pos
return self | python | def set_pos(self, pos):
""" set the position of this column in the Table """
self.pos = pos
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self.typ._v_pos = pos
return self | [
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20,097 | pandas-dev/pandas | pandas/io/pytables.py | IndexCol.is_indexed | def is_indexed(self):
""" return whether I am an indexed column """
try:
return getattr(self.table.cols, self.cname).is_indexed
except AttributeError:
False | python | def is_indexed(self):
""" return whether I am an indexed column """
try:
return getattr(self.table.cols, self.cname).is_indexed
except AttributeError:
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20,098 | pandas-dev/pandas | pandas/io/pytables.py | IndexCol.set_info | def set_info(self, info):
""" set my state from the passed info """
idx = info.get(self.name)
if idx is not None:
self.__dict__.update(idx) | python | def set_info(self, info):
""" set my state from the passed info """
idx = info.get(self.name)
if idx is not None:
self.__dict__.update(idx) | [
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20,099 | pandas-dev/pandas | pandas/io/pytables.py | IndexCol.validate_metadata | def validate_metadata(self, handler):
""" validate that kind=category does not change the categories """
if self.meta == 'category':
new_metadata = self.metadata
cur_metadata = handler.read_metadata(self.cname)
if (new_metadata is not None and cur_metadata is not None... | python | def validate_metadata(self, handler):
""" validate that kind=category does not change the categories """
if self.meta == 'category':
new_metadata = self.metadata
cur_metadata = handler.read_metadata(self.cname)
if (new_metadata is not None and cur_metadata is not None... | [
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