/usr/share/pyshared/pandas/core/reshape.py is in python-pandas 0.7.0-1.
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# pylint: disable=W0703,W0622,W0613,W0201
import itertools
import numpy as np
from pandas.core.series import Series
from pandas.core.frame import DataFrame
from pandas.core.panel import Panel
from pandas.core.common import notnull
from pandas.core.groupby import get_group_index
from pandas.core.index import MultiIndex
class ReshapeError(Exception):
pass
class _Unstacker(object):
"""
Helper class to unstack data / pivot with multi-level index
Parameters
----------
level : int or str, default last level
Level to "unstack". Accepts a name for the level.
Examples
--------
>>> s
one a 1.
one b 2.
two a 3.
two b 4.
>>> s.unstack(level=-1)
a b
one 1. 2.
two 3. 4.
>>> s.unstack(level=0)
one two
a 1. 2.
b 3. 4.
Returns
-------
unstacked : DataFrame
"""
def __init__(self, values, index, level=-1, value_columns=None):
if values.ndim == 1:
values = values[:, np.newaxis]
self.values = values
self.value_columns = value_columns
if value_columns is None and values.shape[1] != 1: # pragma: no cover
raise ValueError('must pass column labels for multi-column data')
self.index = index
self.level = self.index._get_level_number(level)
self.new_index_levels = list(index.levels)
self.new_index_names = list(index.names)
self.removed_name = self.new_index_names.pop(self.level)
self.removed_level = self.new_index_levels.pop(self.level)
v = self.level
lshape = self.index.levshape
self.full_shape = np.prod(lshape[:v] + lshape[v+1:]), lshape[v]
self._make_sorted_values_labels()
self._make_selectors()
def _make_sorted_values_labels(self):
v = self.level
labs = self.index.labels
to_sort = labs[:v] + labs[v+1:] + [labs[v]]
indexer = np.lexsort(to_sort[::-1])
self.sorted_values = self.values.take(indexer, axis=0)
self.sorted_labels = [l.take(indexer) for l in to_sort]
def _make_selectors(self):
new_levels = self.new_index_levels
# make the mask
group_index = get_group_index(self.sorted_labels,
[len(x) for x in new_levels])
group_mask = np.zeros(self.full_shape[0], dtype=bool)
group_mask.put(group_index, True)
stride = self.index.levshape[self.level]
selector = self.sorted_labels[-1] + stride * group_index
mask = np.zeros(np.prod(self.full_shape), dtype=bool)
mask.put(selector, True)
# compress labels
unique_groups = np.arange(self.full_shape[0])[group_mask]
compressor = group_index.searchsorted(unique_groups)
if mask.sum() < len(self.index):
raise ReshapeError('Index contains duplicate entries, '
'cannot reshape')
self.group_mask = group_mask
self.group_index = group_index
self.mask = mask
self.unique_groups = unique_groups
self.compressor = compressor
def get_result(self):
# TODO: find a better way than this masking business
values, value_mask = self.get_new_values()
columns = self.get_new_columns()
index = self.get_new_index()
# filter out missing levels
if values.shape[1] > 0:
mask = value_mask.sum(0) > 0
values = values[:, mask]
columns = columns[mask]
return DataFrame(values, index=index, columns=columns)
def get_new_values(self):
return self._reshape_values(self.values)
def _reshape_values(self, values):
values = self.values
# place the values
length, width = self.full_shape
stride = values.shape[1]
result_width = width * stride
new_values = np.empty((length, result_width), dtype=values.dtype)
new_mask = np.zeros((length, result_width), dtype=bool)
if issubclass(values.dtype.type, np.integer):
new_values = new_values.astype(float)
new_values.fill(np.nan)
# is there a simpler / faster way of doing this?
for i in xrange(self.values.shape[1]):
chunk = new_values[:, i * width : (i + 1) * width]
mask_chunk = new_mask[:, i * width : (i + 1) * width]
chunk.flat[self.mask] = self.sorted_values[:, i]
mask_chunk.flat[self.mask] = True
new_values = new_values.take(self.unique_groups, axis=0)
return new_values, new_mask
def get_new_columns(self):
if self.value_columns is None:
return self.removed_level
stride = len(self.removed_level)
width = len(self.value_columns)
propagator = np.repeat(np.arange(width), stride)
if isinstance(self.value_columns, MultiIndex):
new_levels = self.value_columns.levels + [self.removed_level]
new_names = self.value_columns.names + [self.removed_name]
new_labels = [lab.take(propagator)
for lab in self.value_columns.labels]
new_labels.append(np.tile(np.arange(stride), width))
else:
new_levels = [self.value_columns, self.removed_level]
new_names = [self.value_columns.name, self.removed_name]
new_labels = []
new_labels.append(propagator)
new_labels.append(np.tile(np.arange(stride), width))
return MultiIndex(levels=new_levels, labels=new_labels,
names=new_names)
def get_new_index(self):
result_labels = []
for cur in self.sorted_labels[:-1]:
result_labels.append(cur.take(self.compressor))
# construct the new index
if len(self.new_index_levels) == 1:
new_index = self.new_index_levels[0].take(self.unique_groups)
new_index.name = self.new_index_names[0]
else:
new_index = MultiIndex(levels=self.new_index_levels,
labels=result_labels,
names=self.new_index_names)
return new_index
def pivot(self, index=None, columns=None, values=None):
"""
See DataFrame.pivot
"""
index_vals = self[index]
column_vals = self[columns]
mindex = MultiIndex.from_arrays([index_vals, column_vals],
names=[index, columns])
if values is None:
items = self.columns - [index, columns]
mat = self.reindex(columns=items).values
else:
items = [values]
mat = np.atleast_2d(self[values].values).T
stacked = DataFrame(mat, index=mindex, columns=items)
if not mindex.is_lexsorted():
stacked = stacked.sortlevel(level=0)
unstacked = stacked.unstack()
if values is not None:
unstacked.columns = unstacked.columns.droplevel(0)
return unstacked
def pivot_simple(index, columns, values):
"""
Produce 'pivot' table based on 3 columns of this DataFrame.
Uses unique values from index / columns and fills with values.
Parameters
----------
index : ndarray
Labels to use to make new frame's index
columns : ndarray
Labels to use to make new frame's columns
values : ndarray
Values to use for populating new frame's values
Note
----
Obviously, all 3 of the input arguments must have the same length
Returns
-------
DataFrame
"""
assert(len(index) == len(columns) == len(values))
if len(index) == 0:
return DataFrame(index=[])
hindex = MultiIndex.from_arrays([index, columns])
series = Series(values.ravel(), index=hindex)
series = series.sortlevel(0)
return series.unstack()
def _slow_pivot(index, columns, values):
"""
Produce 'pivot' table based on 3 columns of this DataFrame.
Uses unique values from index / columns and fills with values.
Parameters
----------
index : string or object
Column name to use to make new frame's index
columns : string or object
Column name to use to make new frame's columns
values : string or object
Column name to use for populating new frame's values
Could benefit from some Cython here.
"""
tree = {}
for i, (idx, col) in enumerate(itertools.izip(index, columns)):
if col not in tree:
tree[col] = {}
branch = tree[col]
branch[idx] = values[i]
return DataFrame(tree)
def unstack(obj, level):
if isinstance(obj, DataFrame):
if isinstance(obj.index, MultiIndex):
return _unstack_frame(obj, level)
else:
return obj.T.stack(dropna=False)
else:
unstacker = _Unstacker(obj.values, obj.index, level=level)
return unstacker.get_result()
def _unstack_frame(obj, level):
from pandas.core.internals import BlockManager, make_block
if obj._is_mixed_type:
unstacker = _Unstacker(np.empty(obj.shape, dtype=bool), # dummy
obj.index, level=level,
value_columns=obj.columns)
new_columns = unstacker.get_new_columns()
new_index = unstacker.get_new_index()
new_axes = [new_columns, new_index]
new_blocks = []
mask_blocks = []
for blk in obj._data.blocks:
bunstacker = _Unstacker(blk.values.T, obj.index, level=level,
value_columns=blk.items)
new_items = bunstacker.get_new_columns()
new_values, mask = bunstacker.get_new_values()
mblk = make_block(mask.T, new_items, new_columns)
mask_blocks.append(mblk)
newb = make_block(new_values.T, new_items, new_columns)
new_blocks.append(newb)
result = DataFrame(BlockManager(new_blocks, new_axes))
mask_frame = DataFrame(BlockManager(mask_blocks, new_axes))
return result.ix[:, mask_frame.sum(0) > 0]
else:
unstacker = _Unstacker(obj.values, obj.index, level=level,
value_columns=obj.columns)
return unstacker.get_result()
def stack(frame, level=-1, dropna=True):
"""
Convert DataFrame to Series with multi-level Index. Columns become the
second level of the resulting hierarchical index
Returns
-------
stacked : Series
"""
N, K = frame.shape
if isinstance(level, int) and level < 0:
level += frame.columns.nlevels
level = frame.columns._get_level_number(level)
if isinstance(frame.columns, MultiIndex):
return _stack_multi_columns(frame, level=level, dropna=True)
elif isinstance(frame.index, MultiIndex):
new_levels = list(frame.index.levels)
new_levels.append(frame.columns)
new_labels = [lab.repeat(K) for lab in frame.index.labels]
new_labels.append(np.tile(np.arange(K), N).ravel())
new_names = list(frame.index.names)
new_names.append(frame.columns.name)
new_index = MultiIndex(levels=new_levels, labels=new_labels,
names=new_names)
else:
ilabels = np.arange(N).repeat(K)
clabels = np.tile(np.arange(K), N).ravel()
new_index = MultiIndex(levels=[frame.index, frame.columns],
labels=[ilabels, clabels],
names=[frame.index.name, frame.columns.name])
new_values = frame.values.ravel()
if dropna:
mask = notnull(new_values)
new_values = new_values[mask]
new_index = new_index[mask]
return Series(new_values, index=new_index)
def _stack_multi_columns(frame, level=-1, dropna=True):
this = frame.copy()
# this makes life much simpler
if level != frame.columns.nlevels - 1:
# roll levels to put selected level at end
roll_columns = this.columns
for i in range(level, frame.columns.nlevels - 1):
roll_columns = roll_columns.swaplevel(i, i + 1)
this.columns = roll_columns
if not this.columns.is_lexsorted():
this = this.sortlevel(0, axis=1)
# tuple list excluding level for grouping columns
if len(frame.columns.levels) > 2:
tuples = zip(*[lev.values.take(lab)
for lev, lab in zip(this.columns.levels[:-1],
this.columns.labels[:-1])])
unique_groups = [key for key, _ in itertools.groupby(tuples)]
new_names = this.columns.names[:-1]
new_columns = MultiIndex.from_tuples(unique_groups, names=new_names)
else:
new_columns = unique_groups = this.columns.levels[0]
# time to ravel the values
new_data = {}
level_vals = this.columns.levels[-1]
levsize = len(level_vals)
for key in unique_groups:
loc = this.columns.get_loc(key)
# can make more efficient?
if loc.stop - loc.start != levsize:
chunk = this.ix[:, this.columns[loc]]
chunk.columns = level_vals.take(chunk.columns.labels[-1])
value_slice = chunk.reindex(columns=level_vals).values
else:
if frame._is_mixed_type:
value_slice = this.ix[:, this.columns[loc]].values
else:
value_slice = this.values[:, loc]
new_data[key] = value_slice.ravel()
N = len(this)
if isinstance(this.index, MultiIndex):
new_levels = list(this.index.levels)
new_names = list(this.index.names)
new_labels = [lab.repeat(levsize) for lab in this.index.labels]
else:
new_levels = [this.index]
new_labels = [np.arange(N).repeat(levsize)]
new_names = [this.index.name] # something better?
new_levels.append(frame.columns.levels[level])
new_labels.append(np.tile(np.arange(levsize), N))
new_names.append(frame.columns.names[level])
new_index = MultiIndex(levels=new_levels, labels=new_labels,
names=new_names)
result = DataFrame(new_data, index=new_index, columns=new_columns)
# more efficient way to go about this? can do the whole masking biz but
# will only save a small amount of time...
if dropna:
result = result.dropna(axis=0, how='all')
return result
def melt(frame, id_vars=None, value_vars=None):
"""
"Unpivots" a DataFrame from wide format to long format, optionally leaving
id variables set
Parameters
----------
frame : DataFrame
id_vars :
value_vars :
Examples
--------
>>> df
A B C
a 1 2
b 3 4
c 5 6
>>> melt(df, id_vars=['A'])
A variable value
a B 1
b B 3
c B 5
a C 2
b C 4
c C 6
"""
# TODO: what about the existing index?
N, K = frame.shape
mdata = {}
if id_vars is not None:
id_vars = list(id_vars)
frame = frame.copy()
K -= len(id_vars)
for col in id_vars:
mdata[col] = np.tile(frame.pop(col).values, K)
else:
id_vars = []
mcolumns = id_vars + ['variable', 'value']
mdata['value'] = frame.values.ravel('F')
mdata['variable'] = np.asarray(frame.columns).repeat(N)
return DataFrame(mdata, columns=mcolumns)
def convert_dummies(data, cat_variables, prefix_sep='_'):
"""
Compute DataFrame with specified columns converted to dummy variables (0 /
1). Result columns will be prefixed with the column name, then the level
name, e.g. 'A_foo' for column A and level foo
Parameters
----------
data : DataFrame
cat_variables : list-like
Must be column names in the DataFrame
prefix_sep : string, default '_'
String to use to separate column name from dummy level
Returns
-------
dummies : DataFrame
"""
result = data.drop(cat_variables, axis=1)
for variable in cat_variables:
dummies = make_column_dummies(data, variable, prefix=True,
prefix_sep=prefix_sep)
result = result.join(dummies)
return result
def make_column_dummies(data, column, prefix=False, prefix_sep='_'):
from pandas import Factor
factor = Factor(data[column].values)
dummy_mat = np.eye(len(factor.levels)).take(factor.labels, axis=0)
if prefix:
dummy_cols = ['%s%s%s' % (column, prefix_sep, str(v))
for v in factor.levels]
else:
dummy_cols = factor.levels
dummies = DataFrame(dummy_mat, index=data.index, columns=dummy_cols)
return dummies
def make_axis_dummies(frame, axis='minor', transform=None):
"""
Construct 1-0 dummy variables corresponding to designated axis
labels
Parameters
----------
axis : {'major', 'minor'}, default 'minor'
transform : function, default None
Function to apply to axis labels first. For example, to
get "day of week" dummies in a time series regression you might
call:
make_axis_dummies(panel, axis='major',
transform=lambda d: d.weekday())
Returns
-------
dummies : DataFrame
Column names taken from chosen axis
"""
from pandas import Factor
numbers = {
'major' : 0,
'minor' : 1
}
num = numbers.get(axis, axis)
items = frame.index.levels[num]
labels = frame.index.labels[num]
if transform is not None:
mapped_items = items.map(transform)
factor = Factor(mapped_items.take(labels))
labels = factor.labels
items = factor.levels
values = np.eye(len(items), dtype=float)
values = values.take(labels, axis=0)
return DataFrame(values, columns=items, index=frame.index)
def block2d_to_block3d(values, items, shape, major_labels, minor_labels,
ref_items=None):
"""
Developer method for pivoting DataFrame -> Panel. Used in HDFStore and
DataFrame.to_panel
"""
from pandas.core.internals import make_block
panel_shape = (len(items),) + shape
# TODO: lexsort depth needs to be 2!!
# Create observation selection vector using major and minor
# labels, for converting to panel format.
selector = minor_labels + shape[1] * major_labels
mask = np.zeros(np.prod(shape), dtype=bool)
mask.put(selector, True)
pvalues = np.empty(panel_shape, dtype=values.dtype)
if not issubclass(pvalues.dtype.type, np.integer):
pvalues.fill(np.nan)
values = values
for i in xrange(len(items)):
pvalues[i].flat[mask] = values[:, i]
if ref_items is None:
ref_items = items
return make_block(pvalues, items, ref_items)
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