Python pandas.core.frame.DataFrame.sort_index() Examples
The following are 8
code examples of pandas.core.frame.DataFrame.sort_index().
You can vote up the ones you like or vote down the ones you don't like,
and go to the original project or source file by following the links above each example.
You may also want to check out all available functions/classes of the module
pandas.core.frame.DataFrame
, or try the search function
.
.
Example #1
| Source File: series.py From vnpy_crypto with MIT License | 5 votes |
|
def sortlevel(self, level=0, ascending=True, sort_remaining=True):
"""Sort Series with MultiIndex by chosen level. Data will be
lexicographically sorted by the chosen level followed by the other
levels (in order),
.. deprecated:: 0.20.0
Use :meth:`Series.sort_index`
Parameters
----------
level : int or level name, default None
ascending : bool, default True
Returns
-------
sorted : Series
See Also
--------
Series.sort_index(level=...)
"""
warnings.warn("sortlevel is deprecated, use sort_index(level=...)",
FutureWarning, stacklevel=2)
return self.sort_index(level=level, ascending=ascending,
sort_remaining=sort_remaining)
Example #2
| Source File: series.py From Splunking-Crime with GNU Affero General Public License v3.0 | 5 votes |
|
def sortlevel(self, level=0, ascending=True, sort_remaining=True):
"""
DEPRECATED: use :meth:`Series.sort_index`
Sort Series with MultiIndex by chosen level. Data will be
lexicographically sorted by the chosen level followed by the other
levels (in order)
Parameters
----------
level : int or level name, default None
ascending : bool, default True
Returns
-------
sorted : Series
See Also
--------
Series.sort_index(level=...)
"""
warnings.warn("sortlevel is deprecated, use sort_index(level=...)",
FutureWarning, stacklevel=2)
return self.sort_index(level=level, ascending=ascending,
sort_remaining=sort_remaining)
Example #3
| Source File: series.py From elasticintel with GNU General Public License v3.0 | 5 votes |
|
def sortlevel(self, level=0, ascending=True, sort_remaining=True):
"""
DEPRECATED: use :meth:`Series.sort_index`
Sort Series with MultiIndex by chosen level. Data will be
lexicographically sorted by the chosen level followed by the other
levels (in order)
Parameters
----------
level : int or level name, default None
ascending : bool, default True
Returns
-------
sorted : Series
See Also
--------
Series.sort_index(level=...)
"""
warnings.warn("sortlevel is deprecated, use sort_index(level=...)",
FutureWarning, stacklevel=2)
return self.sort_index(level=level, ascending=ascending,
sort_remaining=sort_remaining)
Example #4
| Source File: series.py From recruit with Apache License 2.0 | 4 votes |
|
def _init_dict(self, data, index=None, dtype=None):
"""
Derive the "_data" and "index" attributes of a new Series from a
dictionary input.
Parameters
----------
data : dict or dict-like
Data used to populate the new Series
index : Index or index-like, default None
index for the new Series: if None, use dict keys
dtype : dtype, default None
dtype for the new Series: if None, infer from data
Returns
-------
_data : BlockManager for the new Series
index : index for the new Series
"""
# Looking for NaN in dict doesn't work ({np.nan : 1}[float('nan')]
# raises KeyError), so we iterate the entire dict, and align
if data:
keys, values = zip(*compat.iteritems(data))
values = list(values)
elif index is not None:
# fastpath for Series(data=None). Just use broadcasting a scalar
# instead of reindexing.
values = na_value_for_dtype(dtype)
keys = index
else:
keys, values = [], []
# Input is now list-like, so rely on "standard" construction:
s = Series(values, index=keys, dtype=dtype)
# Now we just make sure the order is respected, if any
if data and index is not None:
s = s.reindex(index, copy=False)
elif not PY36 and not isinstance(data, OrderedDict) and data:
# Need the `and data` to avoid sorting Series(None, index=[...])
# since that isn't really dict-like
try:
s = s.sort_index()
except TypeError:
pass
return s._data, s.index
Example #5
| Source File: series.py From vnpy_crypto with MIT License | 4 votes |
|
def _init_dict(self, data, index=None, dtype=None):
"""
Derive the "_data" and "index" attributes of a new Series from a
dictionary input.
Parameters
----------
data : dict or dict-like
Data used to populate the new Series
index : Index or index-like, default None
index for the new Series: if None, use dict keys
dtype : dtype, default None
dtype for the new Series: if None, infer from data
Returns
-------
_data : BlockManager for the new Series
index : index for the new Series
"""
# Looking for NaN in dict doesn't work ({np.nan : 1}[float('nan')]
# raises KeyError), so we iterate the entire dict, and align
if data:
keys, values = zip(*compat.iteritems(data))
values = list(values)
elif index is not None:
# fastpath for Series(data=None). Just use broadcasting a scalar
# instead of reindexing.
values = na_value_for_dtype(dtype)
keys = index
else:
keys, values = [], []
# Input is now list-like, so rely on "standard" construction:
s = Series(values, index=keys, dtype=dtype)
# Now we just make sure the order is respected, if any
if data and index is not None:
s = s.reindex(index, copy=False)
elif not PY36 and not isinstance(data, OrderedDict) and data:
# Need the `and data` to avoid sorting Series(None, index=[...])
# since that isn't really dict-like
try:
s = s.sort_index()
except TypeError:
pass
return s._data, s.index
Example #6
| Source File: series.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 4 votes |
|
def _init_dict(self, data, index=None, dtype=None):
"""
Derive the "_data" and "index" attributes of a new Series from a
dictionary input.
Parameters
----------
data : dict or dict-like
Data used to populate the new Series
index : Index or index-like, default None
index for the new Series: if None, use dict keys
dtype : dtype, default None
dtype for the new Series: if None, infer from data
Returns
-------
_data : BlockManager for the new Series
index : index for the new Series
"""
# Looking for NaN in dict doesn't work ({np.nan : 1}[float('nan')]
# raises KeyError), so we iterate the entire dict, and align
if data:
keys, values = zip(*compat.iteritems(data))
values = list(values)
elif index is not None:
# fastpath for Series(data=None). Just use broadcasting a scalar
# instead of reindexing.
values = na_value_for_dtype(dtype)
keys = index
else:
keys, values = [], []
# Input is now list-like, so rely on "standard" construction:
s = Series(values, index=keys, dtype=dtype)
# Now we just make sure the order is respected, if any
if data and index is not None:
s = s.reindex(index, copy=False)
elif not PY36 and not isinstance(data, OrderedDict) and data:
# Need the `and data` to avoid sorting Series(None, index=[...])
# since that isn't really dict-like
try:
s = s.sort_index()
except TypeError:
pass
return s._data, s.index
Example #7
| Source File: series.py From Splunking-Crime with GNU Affero General Public License v3.0 | 4 votes |
|
def sort_index(self, axis=0, level=None, ascending=True, inplace=False,
kind='quicksort', na_position='last', sort_remaining=True):
# TODO: this can be combined with DataFrame.sort_index impl as
# almost identical
inplace = validate_bool_kwarg(inplace, 'inplace')
axis = self._get_axis_number(axis)
index = self.index
if level:
new_index, indexer = index.sortlevel(level, ascending=ascending,
sort_remaining=sort_remaining)
elif isinstance(index, MultiIndex):
from pandas.core.sorting import lexsort_indexer
labels = index._sort_levels_monotonic()
indexer = lexsort_indexer(labels._get_labels_for_sorting(),
orders=ascending,
na_position=na_position)
else:
from pandas.core.sorting import nargsort
# Check monotonic-ness before sort an index
# GH11080
if ((ascending and index.is_monotonic_increasing) or
(not ascending and index.is_monotonic_decreasing)):
if inplace:
return
else:
return self.copy()
indexer = nargsort(index, kind=kind, ascending=ascending,
na_position=na_position)
indexer = _ensure_platform_int(indexer)
new_index = index.take(indexer)
new_index = new_index._sort_levels_monotonic()
new_values = self._values.take(indexer)
result = self._constructor(new_values, index=new_index)
if inplace:
self._update_inplace(result)
else:
return result.__finalize__(self)
Example #8
| Source File: series.py From elasticintel with GNU General Public License v3.0 | 4 votes |
|
def sort_index(self, axis=0, level=None, ascending=True, inplace=False,
kind='quicksort', na_position='last', sort_remaining=True):
# TODO: this can be combined with DataFrame.sort_index impl as
# almost identical
inplace = validate_bool_kwarg(inplace, 'inplace')
axis = self._get_axis_number(axis)
index = self.index
if level:
new_index, indexer = index.sortlevel(level, ascending=ascending,
sort_remaining=sort_remaining)
elif isinstance(index, MultiIndex):
from pandas.core.sorting import lexsort_indexer
labels = index._sort_levels_monotonic()
indexer = lexsort_indexer(labels._get_labels_for_sorting(),
orders=ascending,
na_position=na_position)
else:
from pandas.core.sorting import nargsort
# Check monotonic-ness before sort an index
# GH11080
if ((ascending and index.is_monotonic_increasing) or
(not ascending and index.is_monotonic_decreasing)):
if inplace:
return
else:
return self.copy()
indexer = nargsort(index, kind=kind, ascending=ascending,
na_position=na_position)
indexer = _ensure_platform_int(indexer)
new_index = index.take(indexer)
new_index = new_index._sort_levels_monotonic()
new_values = self._values.take(indexer)
result = self._constructor(new_values, index=new_index)
if inplace:
self._update_inplace(result)
else:
return result.__finalize__(self)
