"""
Contains data structures designed for manipulating panel (3-dimensional) data
"""
# pylint: disable=E1103,W0231,W0212,W0621
from __future__ import division
import numpy as np
import warnings
from pandas.core.dtypes.cast import (
infer_dtype_from_scalar,
maybe_cast_item)
from pandas.core.dtypes.common import (
is_integer, is_list_like,
is_string_like, is_scalar)
from pandas.core.dtypes.missing import notnull
import pandas.core.computation.expressions as expressions
import pandas.core.common as com
import pandas.core.ops as ops
import pandas.core.missing as missing
from pandas import compat
from pandas.compat import (map, zip, range, u, OrderedDict, OrderedDefaultdict)
from pandas.compat.numpy import function as nv
from pandas.core.common import _try_sort, _default_index
from pandas.core.frame import DataFrame
from pandas.core.generic import NDFrame, _shared_docs
from pandas.core.index import (Index, MultiIndex, _ensure_index,
_get_combined_index)
from pandas.io.formats.printing import pprint_thing
from pandas.core.indexing import maybe_droplevels
from pandas.core.internals import (BlockManager,
create_block_manager_from_arrays,
create_block_manager_from_blocks)
from pandas.core.ops import _op_descriptions
from pandas.core.series import Series
from pandas.core.reshape.util import cartesian_product
from pandas.util._decorators import (deprecate, Appender)
_shared_doc_kwargs = dict(
axes='items, major_axis, minor_axis',
klass="Panel",
axes_single_arg="{0, 1, 2, 'items', 'major_axis', 'minor_axis'}")
_shared_doc_kwargs['args_transpose'] = ("three positional arguments: each one"
"of\n%s" %
_shared_doc_kwargs['axes_single_arg'])
def _ensure_like_indices(time, panels):
"""
Makes sure that time and panels are conformable
"""
n_time = len(time)
n_panel = len(panels)
u_panels = np.unique(panels) # this sorts!
u_time = np.unique(time)
if len(u_time) == n_time:
time = np.tile(u_time, len(u_panels))
if len(u_panels) == n_panel:
panels = np.repeat(u_panels, len(u_time))
return time, panels
def panel_index(time, panels, names=None):
"""
Returns a multi-index suitable for a panel-like DataFrame
Parameters
----------
time : array-like
Time index, does not have to repeat
panels : array-like
Panel index, does not have to repeat
names : list, optional
List containing the names of the indices
Returns
-------
multi_index : MultiIndex
Time index is the first level, the panels are the second level.
Examples
--------
>>> years = range(1960,1963)
>>> panels = ['A', 'B', 'C']
>>> panel_idx = panel_index(years, panels)
>>> panel_idx
MultiIndex([(1960, 'A'), (1961, 'A'), (1962, 'A'), (1960, 'B'),
(1961, 'B'), (1962, 'B'), (1960, 'C'), (1961, 'C'),
(1962, 'C')], dtype=object)
or
>>> import numpy as np
>>> years = np.repeat(range(1960,1963), 3)
>>> panels = np.tile(['A', 'B', 'C'], 3)
>>> panel_idx = panel_index(years, panels)
>>> panel_idx
MultiIndex([(1960, 'A'), (1960, 'B'), (1960, 'C'), (1961, 'A'),
(1961, 'B'), (1961, 'C'), (1962, 'A'), (1962, 'B'),
(1962, 'C')], dtype=object)
"""
if names is None:
names = ['time', 'panel']
time, panels = _ensure_like_indices(time, panels)
return MultiIndex.from_arrays([time, panels], sortorder=None, names=names)
class Panel(NDFrame):
"""
Represents wide format panel data, stored as 3-dimensional array
Parameters
----------
data : ndarray (items x major x minor), or dict of DataFrames
items : Index or array-like
axis=0
major_axis : Index or array-like
axis=1
minor_axis : Index or array-like
axis=2
dtype : dtype, default None
Data type to force, otherwise infer
copy : boolean, default False
Copy data from inputs. Only affects DataFrame / 2d ndarray input
"""
@property
def _constructor(self):
return type(self)
_constructor_sliced = DataFrame
def __init__(self, data=None, items=None, major_axis=None, minor_axis=None,
copy=False, dtype=None):
# deprecation GH13563
warnings.warn("\nPanel is deprecated and will be removed in a "
"future version.\nThe recommended way to represent "
"these types of 3-dimensional data are with a "
"MultiIndex on a DataFrame, via the "
"Panel.to_frame() method\n"
"Alternatively, you can use the xarray package "
"http://xarray.pydata.org/en/stable/.\n"
"Pandas provides a `.to_xarray()` method to help "
"automate this conversion.\n",
DeprecationWarning, stacklevel=3)
self._init_data(data=data, items=items, major_axis=major_axis,
minor_axis=minor_axis, copy=copy, dtype=dtype)
def _init_data(self, data, copy, dtype, **kwargs):
"""
Generate ND initialization; axes are passed
as required objects to __init__
"""
if data is None:
data = {}
if dtype is not None:
dtype = self._validate_dtype(dtype)
passed_axes = [kwargs.pop(a, None) for a in self._AXIS_ORDERS]
if kwargs:
raise TypeError('_init_data() got an unexpected keyword '
'argument "{0}"'.format(list(kwargs.keys())[0]))
axes = None
if isinstance(data, BlockManager):
if any(x is not None for x in passed_axes):
axes = [x if x is not None else y
for x, y in zip(passed_axes, data.axes)]
mgr = data
elif isinstance(data, dict):
mgr = self._init_dict(data, passed_axes, dtype=dtype)
copy = False
dtype = None
elif isinstance(data, (np.ndarray, list)):
mgr = self._init_matrix(data, passed_axes, dtype=dtype, copy=copy)
copy = False
dtype = None
elif is_scalar(data) and all(x is not None for x in passed_axes):
if dtype is None:
dtype, data = infer_dtype_from_scalar(data)
values = np.empty([len(x) for x in passed_axes], dtype=dtype)
values.fill(data)
mgr = self._init_matrix(values, passed_axes, dtype=dtype,
copy=False)
copy = False
else: # pragma: no cover
raise ValueError('Panel constructor not properly called!')
NDFrame.__init__(self, mgr, axes=axes, copy=copy, dtype=dtype)
def _init_dict(self, data, axes, dtype=None):
haxis = axes.pop(self._info_axis_number)
# prefilter if haxis passed
if haxis is not None:
haxis = _ensure_index(haxis)
data = OrderedDict((k, v)
for k, v in compat.iteritems(data)
if k in haxis)
else:
ks = list(data.keys())
if not isinstance(data, OrderedDict):
ks = _try_sort(ks)
haxis = Index(ks)
for k, v in compat.iteritems(data):
if isinstance(v, dict):
data[k] = self._constructor_sliced(v)
# extract axis for remaining axes & create the slicemap
raxes = [self._extract_axis(self, data, axis=i) if a is None else a
for i, a in enumerate(axes)]
raxes_sm = self._extract_axes_for_slice(self, raxes)
# shallow copy
arrays = []
haxis_shape = [len(a) for a in raxes]
for h in haxis:
v = values = data.get(h)
if v is None:
values = np.empty(haxis_shape, dtype=dtype)
values.fill(np.nan)
elif isinstance(v, self._constructor_sliced):
d = raxes_sm.copy()
d['copy'] = False
v = v.reindex(**d)
if dtype is not None:
v = v.astype(dtype)
values = v.values
arrays.append(values)
return self._init_arrays(arrays, haxis, [haxis] + raxes)
def _init_arrays(self, arrays, arr_names, axes):
return create_block_manager_from_arrays(arrays, arr_names, axes)
@classmethod
def from_dict(cls, data, intersect=False, orient='items', dtype=None):
"""
Construct Panel from dict of DataFrame objects
Parameters
----------
data : dict
{field : DataFrame}
intersect : boolean
Intersect indexes of input DataFrames
orient : {'items', 'minor'}, default 'items'
The "orientation" of the data. If the keys of the passed dict
should be the items of the result panel, pass 'items'
(default). Otherwise if the columns of the values of the passed
DataFrame objects should be the items (which in the case of
mixed-dtype data you should do), instead pass 'minor'
dtype : dtype, default None
Data type to force, otherwise infer
Returns
-------
Panel
"""
orient = orient.lower()
if orient == 'minor':
new_data = OrderedDefaultdict(dict)
for col, df in compat.iteritems(data):
for item, s in compat.iteritems(df):
new_data[item][col] = s
data = new_data
elif orient != 'items': # pragma: no cover
raise ValueError('Orientation must be one of {items, minor}.')
d = cls._homogenize_dict(cls, data, intersect=intersect, dtype=dtype)
ks = list(d['data'].keys())
if not isinstance(d['data'], OrderedDict):
ks = list(sorted(ks))
d[cls._info_axis_name] = Index(ks)
return cls(**d)
def __getitem__(self, key):
key = com._apply_if_callable(key, self)
if isinstance(self._info_axis, MultiIndex):
return self._getitem_multilevel(key)
if not (is_list_like(key) or isinstance(key, slice)):
return super(Panel, self).__getitem__(key)
return self.loc[key]
def _getitem_multilevel(self, key):
info = self._info_axis
loc = info.get_loc(key)
if isinstance(loc, (slice, np.ndarray)):
new_index = info[loc]
result_index = maybe_droplevels(new_index, key)
slices = [loc] + [slice(None) for x in range(self._AXIS_LEN - 1)]
new_values = self.values[slices]
d = self._construct_axes_dict(self._AXIS_ORDERS[1:])
d[self._info_axis_name] = result_index
result = self._constructor(new_values, **d)
return result
else:
return self._get_item_cache(key)
def _init_matrix(self, data, axes, dtype=None, copy=False):
values = self._prep_ndarray(self, data, copy=copy)
if dtype is not None:
try:
values = values.astype(dtype)
except Exception:
raise ValueError('failed to cast to %s' % dtype)
shape = values.shape
fixed_axes = []
for i, ax in enumerate(axes):
if ax is None:
ax = _default_index(shape[i])
else:
ax = _ensure_index(ax)
fixed_axes.append(ax)
return create_block_manager_from_blocks([values], fixed_axes)
# ----------------------------------------------------------------------
# Comparison methods
def _compare_constructor(self, other, func):
if not self._indexed_same(other):
raise Exception('Can only compare identically-labeled '
'same type objects')
new_data = {}
for col in self._info_axis:
new_data[col] = func(self[col], other[col])
d = self._construct_axes_dict(copy=False)
return self._constructor(data=new_data, **d)
# ----------------------------------------------------------------------
# Magic methods
def __unicode__(self):
"""
Return a string representation for a particular Panel
Invoked by unicode(df) in py2 only.
Yields a Unicode String in both py2/py3.
"""
class_name = str(self.__class__)
shape = self.shape
dims = u('Dimensions: %s') % ' x '.join(
["%d (%s)" % (s, a) for a, s in zip(self._AXIS_ORDERS, shape)])
def axis_pretty(a):
v = getattr(self, a)
if len(v) > 0:
return u('%s axis: %s to %s') % (a.capitalize(),
pprint_thing(v[0]),
pprint_thing(v[-1]))
else:
return u('%s axis: None') % a.capitalize()
output = '\n'.join(
[class_name, dims] + [axis_pretty(a) for a in self._AXIS_ORDERS])
return output
def _get_plane_axes_index(self, axis):
"""
Get my plane axes indexes: these are already
(as compared with higher level planes),
as we are returning a DataFrame axes indexes
"""
axis_name = self._get_axis_name(axis)
if axis_name == 'major_axis':
index = 'minor_axis'
columns = 'items'
if axis_name == 'minor_axis':
index = 'major_axis'
columns = 'items'
elif axis_name == 'items':
index = 'major_axis'
columns = 'minor_axis'
return index, columns
def _get_plane_axes(self, axis):
"""
Get my plane axes indexes: these are already
(as compared with higher level planes),
as we are returning a DataFrame axes
"""
return [self._get_axis(axi)
for axi in self._get_plane_axes_index(axis)]
fromDict = from_dict
def to_sparse(self, *args, **kwargs):
"""
NOT IMPLEMENTED: do not call this method, as sparsifying is not
supported for Panel objects and will raise an error.
Convert to SparsePanel
"""
raise NotImplementedError("sparsifying is not supported "
"for Panel objects")
def to_excel(self, path, na_rep='', engine=None, **kwargs):
"""
Write each DataFrame in Panel to a separate excel sheet
Parameters
----------
path : string or ExcelWriter object
File path or existing ExcelWriter
na_rep : string, default ''
Missing data representation
engine : string, default None
write engine to use - you can also set this via the options
``io.excel.xlsx.writer``, ``io.excel.xls.writer``, and
``io.excel.xlsm.writer``.
Other Parameters
----------------
float_format : string, default None
Format string for floating point numbers
cols : sequence, optional
Columns to write
header : boolean or list of string, default True
Write out column names. If a list of string is given it is
assumed to be aliases for the column names
index : boolean, default True
Write row names (index)
index_label : string or sequence, default None
Column label for index column(s) if desired. If None is given, and
`header` and `index` are True, then the index names are used. A
sequence should be given if the DataFrame uses MultiIndex.
startrow : upper left cell row to dump data frame
startcol : upper left cell column to dump data frame
Notes
-----
Keyword arguments (and na_rep) are passed to the ``to_excel`` method
for each DataFrame written.
"""
from pandas.io.excel import ExcelWriter
if isinstance(path, compat.string_types):
writer = ExcelWriter(path, engine=engine)
else:
writer = path
kwargs['na_rep'] = na_rep
for item, df in self.iteritems():
name = str(item)
df.to_excel(writer, name, **kwargs)
writer.save()
def as_matrix(self):
self._consolidate_inplace()
return self._data.as_matrix()
# ----------------------------------------------------------------------
# Getting and setting elements
def get_value(self, *args, **kwargs):
"""
Quickly retrieve single value at (item, major, minor) location
Parameters
----------
item : item label (panel item)
major : major axis label (panel item row)
minor : minor axis label (panel item column)
takeable : interpret the passed labels as indexers, default False
Returns
-------
value : scalar value
"""
nargs = len(args)
nreq = self._AXIS_LEN
# require an arg for each axis
if nargs != nreq:
raise TypeError('There must be an argument for each axis, you gave'
' {0} args, but {1} are required'.format(nargs,
nreq))
takeable = kwargs.pop('takeable', None)
if kwargs:
raise TypeError('get_value() got an unexpected keyword '
'argument "{0}"'.format(list(kwargs.keys())[0]))
if takeable is True:
lower = self._iget_item_cache(args[0])
else:
lower = self._get_item_cache(args[0])
return lower.get_value(*args[1:], takeable=takeable)
def set_value(self, *args, **kwargs):
"""
Quickly set single value at (item, major, minor) location
Parameters
----------
item : item label (panel item)
major : major axis label (panel item row)
minor : minor axis label (panel item column)
value : scalar
takeable : interpret the passed labels as indexers, default False
Returns
-------
panel : Panel
If label combo is contained, will be reference to calling Panel,
otherwise a new object
"""
# require an arg for each axis and the value
nargs = len(args)
nreq = self._AXIS_LEN + 1
if nargs != nreq:
raise TypeError('There must be an argument for each axis plus the '
'value provided, you gave {0} args, but {1} are '
'required'.format(nargs, nreq))
takeable = kwargs.pop('takeable', None)
if kwargs:
raise TypeError('set_value() got an unexpected keyword '
'argument "{0}"'.format(list(kwargs.keys())[0]))
try:
if takeable is True:
lower = self._iget_item_cache(args[0])
else:
lower = self._get_item_cache(args[0])
lower.set_value(*args[1:], takeable=takeable)
return self
except KeyError:
axes = self._expand_axes(args)
d = self._construct_axes_dict_from(self, axes, copy=False)
result = self.reindex(**d)
args = list(args)
likely_dtype, args[-1] = infer_dtype_from_scalar(args[-1])
made_bigger = not np.array_equal(axes[0], self._info_axis)
# how to make this logic simpler?
if made_bigger:
maybe_cast_item(result, args[0], likely_dtype)
return result.set_value(*args)
def _box_item_values(self, key, values):
if self.ndim == values.ndim:
result = self._constructor(values)
# a dup selection will yield a full ndim
if result._get_axis(0).is_unique:
result = result[key]
return result
d = self._construct_axes_dict_for_slice(self._AXIS_ORDERS[1:])
return self._constructor_sliced(values, **d)
def __setitem__(self, key, value):
key = com._apply_if_callable(key, self)
shape = tuple(self.shape)
if isinstance(value, self._constructor_sliced):
value = value.reindex(
**self._construct_axes_dict_for_slice(self._AXIS_ORDERS[1:]))
mat = value.values
elif isinstance(value, np.ndarray):
if value.shape != shape[1:]:
raise ValueError('shape of value must be {0}, shape of given '
'object was {1}'.format(
shape[1:], tuple(map(int, value.shape))))
mat = np.asarray(value)
elif is_scalar(value):
dtype, value = infer_dtype_from_scalar(value)
mat = np.empty(shape[1:], dtype=dtype)
mat.fill(value)
else:
raise TypeError('Cannot set item of type: %s' % str(type(value)))
mat = mat.reshape(tuple([1]) + shape[1:])
NDFrame._set_item(self, key, mat)
def _unpickle_panel_compat(self, state): # pragma: no cover
"Unpickle the panel"
_unpickle = com._unpickle_array
vals, items, major, minor = state
items = _unpickle(items)
major = _unpickle(major)
minor = _unpickle(minor)
values = _unpickle(vals)
wp = Panel(values, items, major, minor)
self._data = wp._data
def conform(self, frame, axis='items'):
"""
Conform input DataFrame to align with chosen axis pair.
Parameters
----------
frame : DataFrame
axis : {'items', 'major', 'minor'}
Axis the input corresponds to. E.g., if axis='major', then
the frame's columns would be items, and the index would be
values of the minor axis
Returns
-------
DataFrame
"""
axes = self._get_plane_axes(axis)
return frame.reindex(**self._extract_axes_for_slice(self, axes))
def head(self, n=5):
raise NotImplementedError
def tail(self, n=5):
raise NotImplementedError
def round(self, decimals=0, *args, **kwargs):
"""
Round each value in Panel to a specified number of decimal places.
.. versionadded:: 0.18.0
Parameters
----------
decimals : int
Number of decimal places to round to (default: 0).
If decimals is negative, it specifies the number of
positions to the left of the decimal point.
Returns
-------
Panel object
See Also
--------
numpy.around
"""
nv.validate_round(args, kwargs)
if is_integer(decimals):
result = np.apply_along_axis(np.round, 0, self.values)
return self._wrap_result(result, axis=0)
raise TypeError("decimals must be an integer")
def _needs_reindex_multi(self, axes, method, level):
""" don't allow a multi reindex on Panel or above ndim """
return False
def align(self, other, **kwargs):
raise NotImplementedError
def dropna(self, axis=0, how='any', inplace=False):
"""
Drop 2D from panel, holding passed axis constant
Parameters
----------
axis : int, default 0
Axis to hold constant. E.g. axis=1 will drop major_axis entries
having a certain amount of NA data
how : {'all', 'any'}, default 'any'
'any': one or more values are NA in the DataFrame along the
axis. For 'all' they all must be.
inplace : bool, default False
If True, do operation inplace and return None.
Returns
-------
dropped : Panel
"""
axis = self._get_axis_number(axis)
values = self.values
mask = notnull(values)
for ax in reversed(sorted(set(range(self._AXIS_LEN)) - set([axis]))):
mask = mask.sum(ax)
per_slice = np.prod(values.shape[:axis] + values.shape[axis + 1:])
if how == 'all':
cond = mask > 0
else:
cond = mask == per_slice
new_ax = self._get_axis(axis)[cond]
result = self.reindex_axis(new_ax, axis=axis)
if inplace:
self._update_inplace(result)
else:
return result
def _combine(self, other, func, axis=0):
if isinstance(other, Panel):
return self._combine_panel(other, func)
elif isinstance(other, DataFrame):
return self._combine_frame(other, func, axis=axis)
elif is_scalar(other):
return self._combine_const(other, func)
else:
raise NotImplementedError("%s is not supported in combine "
"operation with %s" %
(str(type(other)), str(type(self))))
def _combine_const(self, other, func):
with np.errstate(all='ignore'):
new_values = func(self.values, other)
d = self._construct_axes_dict()
return self._constructor(new_values, **d)
def _combine_frame(self, other, func, axis=0):
index, columns = self._get_plane_axes(axis)
axis = self._get_axis_number(axis)
other = other.reindex(index=index, columns=columns)
with np.errstate(all='ignore'):
if axis == 0:
new_values = func(self.values, other.values)
elif axis == 1:
new_values = func(self.values.swapaxes(0, 1), other.values.T)
new_values = new_values.swapaxes(0, 1)
elif axis == 2:
new_values = func(self.values.swapaxes(0, 2), other.values)
new_values = new_values.swapaxes(0, 2)
return self._constructor(new_values, self.items, self.major_axis,
self.minor_axis)
def _combine_panel(self, other, func):
items = self.items.union(other.items)
major = self.major_axis.union(other.major_axis)
minor = self.minor_axis.union(other.minor_axis)
# could check that everything's the same size, but forget it
this = self.reindex(items=items, major=major, minor=minor)
other = other.reindex(items=items, major=major, minor=minor)
with np.errstate(all='ignore'):
result_values = func(this.values, other.values)
return self._constructor(result_values, items, major, minor)
def major_xs(self, key):
"""
Return slice of panel along major axis
Parameters
----------
key : object
Major axis label
Returns
-------
y : DataFrame
index -> minor axis, columns -> items
Notes
-----
major_xs is only for getting, not setting values.
MultiIndex Slicers is a generic way to get/set values on any level or
levels and is a superset of major_xs functionality, see
:ref:`MultiIndex Slicers <advanced.mi_slicers>`
"""
return self.xs(key, axis=self._AXIS_LEN - 2)
def minor_xs(self, key):
"""
Return slice of panel along minor axis
Parameters
----------
key : object
Minor axis label
Returns
-------
y : DataFrame
index -> major axis, columns -> items
Notes
-----
minor_xs is only for getting, not setting values.
MultiIndex Slicers is a generic way to get/set values on any level or
levels and is a superset of minor_xs functionality, see
:ref:`MultiIndex Slicers <advanced.mi_slicers>`
"""
return self.xs(key, axis=self._AXIS_LEN - 1)
def xs(self, key, axis=1):
"""
Return slice of panel along selected axis
Parameters
----------
key : object
Label
axis : {'items', 'major', 'minor}, default 1/'major'
Returns
-------
y : ndim(self)-1
Notes
-----
xs is only for getting, not setting values.
MultiIndex Slicers is a generic way to get/set values on any level or
levels and is a superset of xs functionality, see
:ref:`MultiIndex Slicers <advanced.mi_slicers>`
"""
axis = self._get_axis_number(axis)
if axis == 0:
return self[key]
self._consolidate_inplace()
axis_number = self._get_axis_number(axis)
new_data = self._data.xs(key, axis=axis_number, copy=False)
result = self._construct_return_type(new_data)
copy = new_data.is_mixed_type
result._set_is_copy(self, copy=copy)
return result
_xs = xs
def _ixs(self, i, axis=0):
"""
i : int, slice, or sequence of integers
axis : int
"""
ax = self._get_axis(axis)
key = ax[i]
# xs cannot handle a non-scalar key, so just reindex here
# if we have a multi-index and a single tuple, then its a reduction
# (GH 7516)
if not (isinstance(ax, MultiIndex) and isinstance(key, tuple)):
if is_list_like(key):
indexer = {self._get_axis_name(axis): key}
return self.reindex(**indexer)
# a reduction
if axis == 0:
values = self._data.iget(i)
return self._box_item_values(key, values)
# xs by position
self._consolidate_inplace()
new_data = self._data.xs(i, axis=axis, copy=True, takeable=True)
return self._construct_return_type(new_data)
def groupby(self, function, axis='major'):
"""
Group data on given axis, returning GroupBy object
Parameters
----------
function : callable
Mapping function for chosen access
axis : {'major', 'minor', 'items'}, default 'major'
Returns
-------
grouped : PanelGroupBy
"""
from pandas.core.groupby import PanelGroupBy
axis = self._get_axis_number(axis)
return PanelGroupBy(self, function, axis=axis)
def to_frame(self, filter_observations=True):
"""
Transform wide format into long (stacked) format as DataFrame whose
columns are the Panel's items and whose index is a MultiIndex formed
of the Panel's major and minor axes.
Parameters
----------
filter_observations : boolean, default True
Drop (major, minor) pairs without a complete set of observations
across all the items
Returns
-------
y : DataFrame
"""
_, N, K = self.shape
if filter_observations:
# shaped like the return DataFrame
mask = notnull(self.values).all(axis=0)
# size = mask.sum()
selector = mask.ravel()
else:
# size = N * K
selector = slice(None, None)
data = {}
for item in self.items:
data[item] = self[item].values.ravel()[selector]
def construct_multi_parts(idx, n_repeat, n_shuffle=1):
axis_idx = idx.to_hierarchical(n_repeat, n_shuffle)
labels = [x[selector] for x in axis_idx.labels]
levels = axis_idx.levels
names = axis_idx.names
return labels, levels, names
def construct_index_parts(idx, major=True):
levels = [idx]
if major:
labels = [np.arange(N).repeat(K)[selector]]
names = idx.name or 'major'
else:
labels = np.arange(K).reshape(1, K)[np.zeros(N, dtype=int)]
labels = [labels.ravel()[selector]]
names = idx.name or 'minor'
names = [names]
return labels, levels, names
if isinstance(self.major_axis, MultiIndex):
major_labels, major_levels, major_names = construct_multi_parts(
self.major_axis, n_repeat=K)
else:
major_labels, major_levels, major_names = construct_index_parts(
self.major_axis)
if isinstance(self.minor_axis, MultiIndex):
minor_labels, minor_levels, minor_names = construct_multi_parts(
self.minor_axis, n_repeat=N, n_shuffle=K)
else:
minor_labels, minor_levels, minor_names = construct_index_parts(
self.minor_axis, major=False)
levels = major_levels + minor_levels
labels = major_labels + minor_labels
names = major_names + minor_names
index = MultiIndex(levels=levels, labels=labels, names=names,
verify_integrity=False)
return DataFrame(data, index=index, columns=self.items)
to_long = deprecate('to_long', to_frame)
toLong = deprecate('toLong', to_frame)
def apply(self, func, axis='major', **kwargs):
"""
Applies function along axis (or axes) of the Panel
Parameters
----------
func : function
Function to apply to each combination of 'other' axes
e.g. if axis = 'items', the combination of major_axis/minor_axis
will each be passed as a Series; if axis = ('items', 'major'),
DataFrames of items & major axis will be passed
axis : {'items', 'minor', 'major'}, or {0, 1, 2}, or a tuple with two
axes
Additional keyword arguments will be passed as keywords to the function
Examples
--------
Returns a Panel with the square root of each element
>>> p = pd.Panel(np.random.rand(4,3,2))
>>> p.apply(np.sqrt)
Equivalent to p.sum(1), returning a DataFrame
>>> p.apply(lambda x: x.sum(), axis=1)
Equivalent to previous:
>>> p.apply(lambda x: x.sum(), axis='minor')
Return the shapes of each DataFrame over axis 2 (i.e the shapes of
items x major), as a Series
>>> p.apply(lambda x: x.shape, axis=(0,1))
Returns
-------
result : Panel, DataFrame, or Series
"""
if kwargs and not isinstance(func, np.ufunc):
f = lambda x: func(x, **kwargs)
else:
f = func
# 2d-slabs
if isinstance(axis, (tuple, list)) and len(axis) == 2:
return self._apply_2d(f, axis=axis)
axis = self._get_axis_number(axis)
# try ufunc like
if isinstance(f, np.ufunc):
try:
with np.errstate(all='ignore'):
result = np.apply_along_axis(func, axis, self.values)
return self._wrap_result(result, axis=axis)
except (AttributeError):
pass
# 1d
return self._apply_1d(f, axis=axis)
def _apply_1d(self, func, axis):
axis_name = self._get_axis_name(axis)
ndim = self.ndim
values = self.values
# iter thru the axes
slice_axis = self._get_axis(axis)
slice_indexer = [0] * (ndim - 1)
indexer = np.zeros(ndim, 'O')
indlist = list(range(ndim))
indlist.remove(axis)
indexer[axis] = slice(None, None)
indexer.put(indlist, slice_indexer)
planes = [self._get_axis(axi) for axi in indlist]
shape = np.array(self.shape).take(indlist)
# all the iteration points
points = cartesian_product(planes)
results = []
for i in range(np.prod(shape)):
# construct the object
pts = tuple([p[i] for p in points])
indexer.put(indlist, slice_indexer)
obj = Series(values[tuple(indexer)], index=slice_axis, name=pts)
result = func(obj)
results.append(result)
# increment the indexer
slice_indexer[-1] += 1
n = -1
while (slice_indexer[n] >= shape[n]) and (n > (1 - ndim)):
slice_indexer[n - 1] += 1
slice_indexer[n] = 0
n -= 1
# empty object
if not len(results):
return self._constructor(**self._construct_axes_dict())
# same ndim as current
if isinstance(results[0], Series):
arr = np.vstack([r.values for r in results])
arr = arr.T.reshape(tuple([len(slice_axis)] + list(shape)))
tranp = np.array([axis] + indlist).argsort()
arr = arr.transpose(tuple(list(tranp)))
return self._constructor(arr, **self._construct_axes_dict())
# ndim-1 shape
results = np.array(results).reshape(shape)
if results.ndim == 2 and axis_name != self._info_axis_name:
results = results.T
planes = planes[::-1]
return self._construct_return_type(results, planes)
def _apply_2d(self, func, axis):
""" handle 2-d slices, equiv to iterating over the other axis """
ndim = self.ndim
axis = [self._get_axis_number(a) for a in axis]
# construct slabs, in 2-d this is a DataFrame result
indexer_axis = list(range(ndim))
for a in axis:
indexer_axis.remove(a)
indexer_axis = indexer_axis[0]
slicer = [slice(None, None)] * ndim
ax = self._get_axis(indexer_axis)
results = []
for i, e in enumerate(ax):
slicer[indexer_axis] = i
sliced = self.iloc[tuple(slicer)]
obj = func(sliced)
results.append((e, obj))
return self._construct_return_type(dict(results))
def _reduce(self, op, name, axis=0, skipna=True, numeric_only=None,
filter_type=None, **kwds):
if numeric_only:
raise NotImplementedError('Panel.{0} does not implement '
'numeric_only.'.format(name))
axis_name = self._get_axis_name(axis)
axis_number = self._get_axis_number(axis_name)
f = lambda x: op(x, axis=axis_number, skipna=skipna, **kwds)
with np.errstate(all='ignore'):
result = f(self.values)
axes = self._get_plane_axes(axis_name)
if result.ndim == 2 and axis_name != self._info_axis_name:
result = result.T
return self._construct_return_type(result, axes)
def _construct_return_type(self, result, axes=None):
""" return the type for the ndim of the result """
ndim = getattr(result, 'ndim', None)
# need to assume they are the same
if ndim is None:
if isinstance(result, dict):
ndim = getattr(list(compat.itervalues(result))[0], 'ndim', 0)
# have a dict, so top-level is +1 dim
if ndim != 0:
ndim += 1
# scalar
if ndim == 0:
return Series(result)
# same as self
elif self.ndim == ndim:
# return the construction dictionary for these axes
if axes is None:
return self._constructor(result)
return self._constructor(result, **self._construct_axes_dict())
# sliced
elif self.ndim == ndim + 1:
if axes is None:
return self._constructor_sliced(result)
return self._constructor_sliced(
result, **self._extract_axes_for_slice(self, axes))
raise ValueError('invalid _construct_return_type [self->%s] '
'[result->%s]' % (self, result))
def _wrap_result(self, result, axis):
axis = self._get_axis_name(axis)
axes = self._get_plane_axes(axis)
if result.ndim == 2 and axis != self._info_axis_name:
result = result.T
return self._construct_return_type(result, axes)
@Appender(_shared_docs['reindex'] % _shared_doc_kwargs)
def reindex(self, items=None, major_axis=None, minor_axis=None, **kwargs):
major_axis = (major_axis if major_axis is not None else
kwargs.pop('major', None))
minor_axis = (minor_axis if minor_axis is not None else
kwargs.pop('minor', None))
return super(Panel, self).reindex(items=items, major_axis=major_axis,
minor_axis=minor_axis, **kwargs)
@Appender(_shared_docs['rename'] % _shared_doc_kwargs)
def rename(self, items=None, major_axis=None, minor_axis=None, **kwargs):
major_axis = (major_axis if major_axis is not None else
kwargs.pop('major', None))
minor_axis = (minor_axis if minor_axis is not None else
kwargs.pop('minor', None))
return super(Panel, self).rename(items=items, major_axis=major_axis,
minor_axis=minor_axis, **kwargs)
@Appender(_shared_docs['reindex_axis'] % _shared_doc_kwargs)
def reindex_axis(self, labels, axis=0, method=None, level=None, copy=True,
limit=None, fill_value=np.nan):
return super(Panel, self).reindex_axis(labels=labels, axis=axis,
method=method, level=level,
copy=copy, limit=limit,
fill_value=fill_value)
@Appender(_shared_docs['transpose'] % _shared_doc_kwargs)
def transpose(self, *args, **kwargs):
# check if a list of axes was passed in instead as a
# single *args element
if (len(args) == 1 and hasattr(args[0], '__iter__') and
not is_string_like(args[0])):
axes = args[0]
else:
axes = args
if 'axes' in kwargs and axes:
raise TypeError("transpose() got multiple values for "
"keyword argument 'axes'")
elif not axes:
axes = kwargs.pop('axes', ())
return super(Panel, self).transpose(*axes, **kwargs)
@Appender(_shared_docs['fillna'] % _shared_doc_kwargs)
def fillna(self, value=None, method=None, axis=None, inplace=False,
limit=None, downcast=None, **kwargs):
return super(Panel, self).fillna(value=value, method=method, axis=axis,
inplace=inplace, limit=limit,
downcast=downcast, **kwargs)
def count(self, axis='major'):
"""
Return number of observations over requested axis.
Parameters
----------
axis : {'items', 'major', 'minor'} or {0, 1, 2}
Returns
-------
count : DataFrame
"""
i = self._get_axis_number(axis)
values = self.values
mask = np.isfinite(values)
result = mask.sum(axis=i, dtype='int64')
return self._wrap_result(result, axis)
def shift(self, periods=1, freq=None, axis='major'):
"""
Shift index by desired number of periods with an optional time freq.
The shifted data will not include the dropped periods and the
shifted axis will be smaller than the original. This is different
from the behavior of DataFrame.shift()
Parameters
----------
periods : int
Number of periods to move, can be positive or negative
freq : DateOffset, timedelta, or time rule string, optional
axis : {'items', 'major', 'minor'} or {0, 1, 2}
Returns
-------
shifted : Panel
"""
if freq:
return self.tshift(periods, freq, axis=axis)
return super(Panel, self).slice_shift(periods, axis=axis)
def tshift(self, periods=1, freq=None, axis='major'):
return super(Panel, self).tshift(periods, freq, axis)
def join(self, other, how='left', lsuffix='', rsuffix=''):
"""
Join items with other Panel either on major and minor axes column
Parameters
----------
other : Panel or list of Panels
Index should be similar to one of the columns in this one
how : {'left', 'right', 'outer', 'inner'}
How to handle indexes of the two objects. Default: 'left'
for joining on index, None otherwise
* left: use calling frame's index
* right: use input frame's index
* outer: form union of indexes
* inner: use intersection of indexes
lsuffix : string
Suffix to use from left frame's overlapping columns
rsuffix : string
Suffix to use from right frame's overlapping columns
Returns
-------
joined : Panel
"""
from pandas.core.reshape.concat import concat
if isinstance(other, Panel):
join_major, join_minor = self._get_join_index(other, how)
this = self.reindex(major=join_major, minor=join_minor)
other = other.reindex(major=join_major, minor=join_minor)
merged_data = this._data.merge(other._data, lsuffix, rsuffix)
return self._constructor(merged_data)
else:
if lsuffix or rsuffix:
raise ValueError('Suffixes not supported when passing '
'multiple panels')
if how == 'left':
how = 'outer'
join_axes = [self.major_axis, self.minor_axis]
elif how == 'right':
raise ValueError('Right join not supported with multiple '
'panels')
else:
join_axes = None
return concat([self] + list(other), axis=0, join=how,
join_axes=join_axes, verify_integrity=True)
def update(self, other, join='left', overwrite=True, filter_func=None,
raise_conflict=False):
"""
Modify Panel in place using non-NA values from passed
Panel, or object coercible to Panel. Aligns on items
Parameters
----------
other : Panel, or object coercible to Panel
join : How to join individual DataFrames
{'left', 'right', 'outer', 'inner'}, default 'left'
overwrite : boolean, default True
If True then overwrite values for common keys in the calling panel
filter_func : callable(1d-array) -> 1d-array<boolean>, default None
Can choose to replace values other than NA. Return True for values
that should be updated
raise_conflict : bool
If True, will raise an error if a DataFrame and other both
contain data in the same place.
"""
if not isinstance(other, self._constructor):
other = self._constructor(other)
axis_name = self._info_axis_name
axis_values = self._info_axis
other = other.reindex(**{axis_name: axis_values})
for frame in axis_values:
self[frame].update(other[frame], join, overwrite, filter_func,
raise_conflict)
def _get_join_index(self, other, how):
if how == 'left':
join_major, join_minor = self.major_axis, self.minor_axis
elif how == 'right':
join_major, join_minor = other.major_axis, other.minor_axis
elif how == 'inner':
join_major = self.major_axis.intersection(other.major_axis)
join_minor = self.minor_axis.intersection(other.minor_axis)
elif how == 'outer':
join_major = self.major_axis.union(other.major_axis)
join_minor = self.minor_axis.union(other.minor_axis)
return join_major, join_minor
# miscellaneous data creation
@staticmethod
def _extract_axes(self, data, axes, **kwargs):
""" return a list of the axis indicies """
return [self._extract_axis(self, data, axis=i, **kwargs)
for i, a in enumerate(axes)]
@staticmethod
def _extract_axes_for_slice(self, axes):
""" return the slice dictionary for these axes """
return dict([(self._AXIS_SLICEMAP[i], a)
for i, a in zip(
self._AXIS_ORDERS[self._AXIS_LEN - len(axes):],
axes)])
@staticmethod
def _prep_ndarray(self, values, copy=True):
if not isinstance(values, np.ndarray):
values = np.asarray(values)
# NumPy strings are a pain, convert to object
if issubclass(values.dtype.type, compat.string_types):
values = np.array(values, dtype=object, copy=True)
else:
if copy:
values = values.copy()
if values.ndim != self._AXIS_LEN:
raise ValueError("The number of dimensions required is {0}, "
"but the number of dimensions of the "
"ndarray given was {1}".format(self._AXIS_LEN,
values.ndim))
return values
@staticmethod
def _homogenize_dict(self, frames, intersect=True, dtype=None):
"""
Conform set of _constructor_sliced-like objects to either
an intersection of indices / columns or a union.
Parameters
----------
frames : dict
intersect : boolean, default True
Returns
-------
dict of aligned results & indicies
"""
result = dict()
# caller differs dict/ODict, presered type
if isinstance(frames, OrderedDict):
result = OrderedDict()
adj_frames = OrderedDict()
for k, v in compat.iteritems(frames):
if isinstance(v, dict):
adj_frames[k] = self._constructor_sliced(v)
else:
adj_frames[k] = v
axes = self._AXIS_ORDERS[1:]
axes_dict = dict([(a, ax) for a, ax in zip(axes, self._extract_axes(
self, adj_frames, axes, intersect=intersect))])
reindex_dict = dict(
[(self._AXIS_SLICEMAP[a], axes_dict[a]) for a in axes])
reindex_dict['copy'] = False
for key, frame in compat.iteritems(adj_frames):
if frame is not None:
result[key] = frame.reindex(**reindex_dict)
else:
result[key] = None
axes_dict['data'] = result
axes_dict['dtype'] = dtype
return axes_dict
@staticmethod
def _extract_axis(self, data, axis=0, intersect=False):
index = None
if len(data) == 0:
index = Index([])
elif len(data) > 0:
raw_lengths = []
indexes = []
have_raw_arrays = False
have_frames = False
for v in data.values():
if isinstance(v, self._constructor_sliced):
have_frames = True
indexes.append(v._get_axis(axis))
elif v is not None:
have_raw_arrays = True
raw_lengths.append(v.shape[axis])
if have_frames:
index = _get_combined_index(indexes, intersect=intersect)
if have_raw_arrays:
lengths = list(set(raw_lengths))
if len(lengths) > 1:
raise ValueError('ndarrays must match shape on axis %d' % axis)
if have_frames:
if lengths[0] != len(index):
raise AssertionError('Length of data and index must match')
else:
index = Index(np.arange(lengths[0]))
if index is None:
index = Index([])
return _ensure_index(index)
@classmethod
def _add_aggregate_operations(cls, use_numexpr=True):
""" add the operations to the cls; evaluate the doc strings again """
# doc strings substitors
_agg_doc = """
Wrapper method for %%s
Parameters
----------
other : %s or %s""" % (cls._constructor_sliced.__name__, cls.__name__) + """
axis : {""" + ', '.join(cls._AXIS_ORDERS) + "}" + """
Axis to broadcast over
Returns
-------
""" + cls.__name__ + "\n"
def _panel_arith_method(op, name, str_rep=None, default_axis=None,
fill_zeros=None, **eval_kwargs):
def na_op(x, y):
try:
result = expressions.evaluate(op, str_rep, x, y,
raise_on_error=True,
**eval_kwargs)
except TypeError:
result = op(x, y)
# handles discrepancy between numpy and numexpr on division/mod
# by 0 though, given that these are generally (always?)
# non-scalars, I'm not sure whether it's worth it at the moment
result = missing.fill_zeros(result, x, y, name, fill_zeros)
return result
if name in _op_descriptions:
op_name = name.replace('__', '')
op_desc = _op_descriptions[op_name]
if op_desc['reversed']:
equiv = 'other ' + op_desc['op'] + ' panel'
else:
equiv = 'panel ' + op_desc['op'] + ' other'
_op_doc = """
%%s of series and other, element-wise (binary operator `%%s`).
Equivalent to ``%%s``.
Parameters
----------
other : %s or %s""" % (cls._constructor_sliced.__name__,
cls.__name__) + """
axis : {""" + ', '.join(cls._AXIS_ORDERS) + "}" + """
Axis to broadcast over
Returns
-------
""" + cls.__name__ + """
See also
--------
""" + cls.__name__ + ".%s\n"
doc = _op_doc % (op_desc['desc'], op_name, equiv,
op_desc['reverse'])
else:
doc = _agg_doc % name
@Appender(doc)
def f(self, other, axis=0):
return self._combine(other, na_op, axis=axis)
f.__name__ = name
return f
# add `div`, `mul`, `pow`, etc..
ops.add_flex_arithmetic_methods(
cls, _panel_arith_method, use_numexpr=use_numexpr,
flex_comp_method=ops._comp_method_PANEL)
Panel._setup_axes(axes=['items', 'major_axis', 'minor_axis'], info_axis=0,
stat_axis=1, aliases={'major': 'major_axis',
'minor': 'minor_axis'},
slicers={'major_axis': 'index',
'minor_axis': 'columns'})
ops.add_special_arithmetic_methods(Panel, **ops.panel_special_funcs)
Panel._add_aggregate_operations()
Panel._add_numeric_operations()
# legacy
class WidePanel(Panel):
def __init__(self, *args, **kwargs):
# deprecation, #10892
warnings.warn("WidePanel is deprecated. Please use Panel",
FutureWarning, stacklevel=2)
super(WidePanel, self).__init__(*args, **kwargs)
class LongPanel(DataFrame):
def __init__(self, *args, **kwargs):
# deprecation, #10892
warnings.warn("LongPanel is deprecated. Please use DataFrame",
FutureWarning, stacklevel=2)
super(LongPanel, self).__init__(*args, **kwargs)