/usr/lib/python3/dist-packages/pandas/core/internals.py is in python3-pandas 0.13.1-2ubuntu2.
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import re
from datetime import datetime, timedelta
import copy
from collections import defaultdict
import numpy as np
from pandas.core.base import PandasObject
from pandas.core.common import (_possibly_downcast_to_dtype, isnull, notnull,
_NS_DTYPE, _TD_DTYPE, ABCSeries, is_list_like,
ABCSparseSeries, _infer_dtype_from_scalar,
_values_from_object, _is_null_datelike_scalar)
from pandas.core.index import (Index, MultiIndex, _ensure_index,
_handle_legacy_indexes)
from pandas.core.indexing import (_check_slice_bounds, _maybe_convert_indices,
_length_of_indexer)
import pandas.core.common as com
from pandas.sparse.array import _maybe_to_sparse, SparseArray
import pandas.lib as lib
import pandas.tslib as tslib
import pandas.computation.expressions as expressions
from pandas.tslib import Timestamp
from pandas import compat
from pandas.compat import range, lrange, lmap, callable, map, zip, u
from pandas.tseries.timedeltas import _coerce_scalar_to_timedelta_type
class Block(PandasObject):
"""
Canonical n-dimensional unit of homogeneous dtype contained in a pandas
data structure
Index-ignorant; let the container take care of that
"""
__slots__ = ['items', 'ref_items', '_ref_locs', 'values', 'ndim']
is_numeric = False
is_float = False
is_integer = False
is_complex = False
is_datetime = False
is_timedelta = False
is_bool = False
is_object = False
is_sparse = False
_can_hold_na = False
_downcast_dtype = None
_can_consolidate = True
_verify_integrity = True
_ftype = 'dense'
def __init__(self, values, items, ref_items, ndim=None, fastpath=False,
placement=None):
if ndim is None:
ndim = values.ndim
if values.ndim != ndim:
raise ValueError('Wrong number of dimensions')
if len(items) != len(values):
raise ValueError('Wrong number of items passed %d, indices imply '
'%d' % (len(items), len(values)))
self.set_ref_locs(placement)
self.values = values
self.ndim = ndim
if fastpath:
self.items = items
self.ref_items = ref_items
else:
self.items = _ensure_index(items)
self.ref_items = _ensure_index(ref_items)
@property
def _consolidate_key(self):
return (self._can_consolidate, self.dtype.name)
@property
def _is_single_block(self):
return self.ndim == 1
@property
def is_datelike(self):
""" return True if I am a non-datelike """
return self.is_datetime or self.is_timedelta
@property
def fill_value(self):
return np.nan
@property
def ref_locs(self):
if self._ref_locs is None:
# we have a single block, maybe have duplicates
# but indexer is easy
# also if we are not really reindexing, just numbering
if self._is_single_block or self.ref_items.equals(self.items):
indexer = np.arange(len(self.items))
else:
indexer = self.ref_items.get_indexer(self.items)
indexer = com._ensure_platform_int(indexer)
if (indexer == -1).any():
# this means that we have nan's in our block
try:
indexer[indexer == -1] = np.arange(
len(self.items))[isnull(self.items)]
except:
raise AssertionError('Some block items were not in '
'block ref_items')
self._ref_locs = indexer
return self._ref_locs
def take_ref_locs(self, indexer):
"""
need to preserve the ref_locs and just shift them
return None if ref_locs is None
see GH6509
"""
ref_locs = self._ref_locs
if ref_locs is None:
return None
tindexer = np.ones(len(ref_locs),dtype=bool)
tindexer[indexer] = False
tindexer = tindexer.astype(int).cumsum()[indexer]
ref_locs = ref_locs[indexer]
ref_locs -= tindexer
return ref_locs
def reset_ref_locs(self):
""" reset the block ref_locs """
self._ref_locs = np.empty(len(self.items), dtype='int64')
def set_ref_locs(self, placement):
""" explicity set the ref_locs indexer, only necessary for duplicate
indicies
"""
if placement is None:
self._ref_locs = None
else:
self._ref_locs = np.array(placement, dtype='int64', copy=True)
def set_ref_items(self, ref_items, maybe_rename=True):
"""
If maybe_rename=True, need to set the items for this guy
"""
if not isinstance(ref_items, Index):
raise AssertionError('block ref_items must be an Index')
if maybe_rename == 'clear':
self._ref_locs = None
elif maybe_rename:
self.items = ref_items.take(self.ref_locs)
self.ref_items = ref_items
def __unicode__(self):
# don't want to print out all of the items here
name = com.pprint_thing(self.__class__.__name__)
if self._is_single_block:
result = '%s: %s dtype: %s' % (
name, len(self), self.dtype)
else:
shape = ' x '.join([com.pprint_thing(s) for s in self.shape])
result = '%s: %s, %s, dtype: %s' % (
name, com.pprint_thing(self.items), shape, self.dtype)
return result
def __contains__(self, item):
return item in self.items
def __len__(self):
return len(self.values)
def __getstate__(self):
# should not pickle generally (want to share ref_items), but here for
# completeness
return (self.items, self.ref_items, self.values)
def __setstate__(self, state):
items, ref_items, values = state
self.items = _ensure_index(items)
self.ref_items = _ensure_index(ref_items)
self.values = values
self.ndim = values.ndim
def _slice(self, slicer):
""" return a slice of my values """
return self.values[slicer]
@property
def shape(self):
return self.values.shape
@property
def itemsize(self):
return self.values.itemsize
@property
def dtype(self):
return self.values.dtype
@property
def ftype(self):
return "%s:%s" % (self.dtype, self._ftype)
def merge(self, other):
if not self.ref_items.equals(other.ref_items):
raise AssertionError('Merge operands must have same ref_items')
# Not sure whether to allow this or not
# if not union_ref.equals(other.ref_items):
# union_ref = self.ref_items + other.ref_items
return _merge_blocks([self, other], self.ref_items)
def reindex_axis(self, indexer, method=None, axis=1, fill_value=None,
limit=None, mask_info=None):
"""
Reindex using pre-computed indexer information
"""
if axis < 1:
raise AssertionError('axis must be at least 1, got %d' % axis)
if fill_value is None:
fill_value = self.fill_value
new_values = com.take_nd(self.values, indexer, axis,
fill_value=fill_value, mask_info=mask_info)
return make_block(new_values, self.items, self.ref_items,
ndim=self.ndim, fastpath=True,
placement=self._ref_locs)
def reindex_items_from(self, new_ref_items, indexer=None, method=None,
fill_value=None, limit=None, copy=True):
"""
Reindex to only those items contained in the input set of items
E.g. if you have ['a', 'b'], and the input items is ['b', 'c', 'd'],
then the resulting items will be ['b']
Returns
-------
reindexed : Block
"""
if indexer is None:
new_ref_items, indexer = self.items.reindex(new_ref_items,
limit=limit)
needs_fill = method is not None and limit is None
if fill_value is None:
fill_value = self.fill_value
new_items = new_ref_items
if indexer is None:
new_values = self.values.copy() if copy else self.values
else:
# single block reindex
if self.ndim == 1:
new_values = com.take_1d(self.values, indexer,
fill_value=fill_value)
else:
masked_idx = indexer[indexer != -1]
new_items = self.items.take(masked_idx)
new_values = com.take_nd(self.values, masked_idx, axis=0,
allow_fill=False)
# fill if needed
if needs_fill:
new_values = com.interpolate_2d(new_values, method=method,
limit=limit, fill_value=fill_value)
block = make_block(new_values, new_items, new_ref_items,
ndim=self.ndim, fastpath=True)
# down cast if needed
if not self.is_float and (needs_fill or notnull(fill_value)):
block = block.downcast()
return block
def get(self, item):
loc = self.items.get_loc(item)
return self.values[loc]
def iget(self, i):
return self.values[i]
def set(self, item, value, check=False):
"""
Modify Block in-place with new item value
Returns
-------
None
"""
loc = self.items.get_loc(item)
self.values[loc] = value
def delete(self, item):
"""
Returns
-------
y : Block (new object)
"""
loc = self.items.get_loc(item)
new_items = self.items.delete(loc)
new_values = np.delete(self.values, loc, 0)
return make_block(new_values, new_items, self.ref_items,
ndim=self.ndim, klass=self.__class__, fastpath=True)
def split_block_at(self, item):
"""
Split block into zero or more blocks around columns with given label,
for "deleting" a column without having to copy data by returning views
on the original array.
Returns
-------
generator of Block
"""
loc = self.items.get_loc(item)
if type(loc) == slice or type(loc) == int:
mask = [True] * len(self)
mask[loc] = False
else: # already a mask, inverted
mask = -loc
for s, e in com.split_ranges(mask):
yield make_block(self.values[s:e],
self.items[s:e].copy(),
self.ref_items,
ndim=self.ndim,
klass=self.__class__,
fastpath=True)
def fillna(self, value, inplace=False, downcast=None):
if not self._can_hold_na:
if inplace:
return [self]
else:
return [self.copy()]
mask = com.isnull(self.values)
value = self._try_fill(value)
blocks = self.putmask(mask, value, inplace=inplace)
return self._maybe_downcast(blocks, downcast)
def _maybe_downcast(self, blocks, downcast=None):
# no need to downcast our float
# unless indicated
if downcast is None and self.is_float:
return blocks
elif downcast is None and (self.is_timedelta or self.is_datetime):
return blocks
result_blocks = []
for b in blocks:
result_blocks.extend(b.downcast(downcast))
return result_blocks
def downcast(self, dtypes=None):
""" try to downcast each item to the dict of dtypes if present """
# turn it off completely
if dtypes is False:
return [self]
values = self.values
# single block handling
if self._is_single_block:
# try to cast all non-floats here
if dtypes is None:
dtypes = 'infer'
nv = _possibly_downcast_to_dtype(values, dtypes)
return [make_block(nv, self.items, self.ref_items, ndim=self.ndim,
fastpath=True)]
# ndim > 1
if dtypes is None:
return [self]
if not (dtypes == 'infer' or isinstance(dtypes, dict)):
raise ValueError("downcast must have a dictionary or 'infer' as "
"its argument")
# item-by-item
# this is expensive as it splits the blocks items-by-item
blocks = []
for i, item in enumerate(self.items):
if dtypes == 'infer':
dtype = 'infer'
else:
dtype = dtypes.get(item, self._downcast_dtype)
if dtype is None:
nv = _block_shape(values[i], ndim=self.ndim)
else:
nv = _possibly_downcast_to_dtype(values[i], dtype)
nv = _block_shape(nv, ndim=self.ndim)
blocks.append(make_block(nv, Index([item]), self.ref_items,
ndim=self.ndim, fastpath=True))
return blocks
def astype(self, dtype, copy=False, raise_on_error=True, values=None):
return self._astype(dtype, copy=copy, raise_on_error=raise_on_error,
values=values)
def _astype(self, dtype, copy=False, raise_on_error=True, values=None,
klass=None):
"""
Coerce to the new type (if copy=True, return a new copy)
raise on an except if raise == True
"""
dtype = np.dtype(dtype)
if self.dtype == dtype:
if copy:
return self.copy()
return self
try:
# force the copy here
if values is None:
values = com._astype_nansafe(self.values, dtype, copy=True)
newb = make_block(values, self.items, self.ref_items,
ndim=self.ndim, placement=self._ref_locs,
fastpath=True, dtype=dtype, klass=klass)
except:
if raise_on_error is True:
raise
newb = self.copy() if copy else self
if newb.is_numeric and self.is_numeric:
if newb.shape != self.shape:
raise TypeError("cannot set astype for copy = [%s] for dtype "
"(%s [%s]) with smaller itemsize that current "
"(%s [%s])" % (copy, self.dtype.name,
self.itemsize, newb.dtype.name,
newb.itemsize))
return [newb]
def convert(self, copy=True, **kwargs):
""" attempt to coerce any object types to better types
return a copy of the block (if copy = True)
by definition we are not an ObjectBlock here! """
return [self.copy()] if copy else [self]
def prepare_for_merge(self, **kwargs):
""" a regular block is ok to merge as is """
return self
def post_merge(self, items, **kwargs):
""" we are non-sparse block, try to convert to a sparse block(s) """
overlap = set(items.keys()) & set(self.items)
if len(overlap):
overlap = _ensure_index(overlap)
new_blocks = []
for item in overlap:
dtypes = set(items[item])
# this is a safe bet with multiple dtypes
dtype = list(dtypes)[0] if len(dtypes) == 1 else np.float64
b = make_block(SparseArray(self.get(item), dtype=dtype),
[item], self.ref_items)
new_blocks.append(b)
return new_blocks
return self
def _can_hold_element(self, value):
raise NotImplementedError()
def _try_cast(self, value):
raise NotImplementedError()
def _try_cast_result(self, result, dtype=None):
""" try to cast the result to our original type,
we may have roundtripped thru object in the mean-time """
if dtype is None:
dtype = self.dtype
if self.is_integer or self.is_bool or self.is_datetime:
pass
elif self.is_float and result.dtype == self.dtype:
# protect against a bool/object showing up here
if isinstance(dtype, compat.string_types) and dtype == 'infer':
return result
if not isinstance(dtype, type):
dtype = dtype.type
if issubclass(dtype, (np.bool_, np.object_)):
if issubclass(dtype, np.bool_):
if isnull(result).all():
return result.astype(np.bool_)
else:
result = result.astype(np.object_)
result[result == 1] = True
result[result == 0] = False
return result
else:
return result.astype(np.object_)
return result
# may need to change the dtype here
return _possibly_downcast_to_dtype(result, dtype)
def _try_operate(self, values):
""" return a version to operate on as the input """
return values
def _try_coerce_args(self, values, other):
""" provide coercion to our input arguments """
return values, other
def _try_coerce_result(self, result):
""" reverse of try_coerce_args """
return result
def _try_fill(self, value):
return value
def to_native_types(self, slicer=None, na_rep='', **kwargs):
""" convert to our native types format, slicing if desired """
values = self.values
if slicer is not None:
values = values[:, slicer]
values = np.array(values, dtype=object)
mask = isnull(values)
values[mask] = na_rep
return values.tolist()
# block actions ####
def copy(self, deep=True, ref_items=None):
values = self.values
if deep:
values = values.copy()
if ref_items is None:
ref_items = self.ref_items
return make_block(values, self.items, ref_items, ndim=self.ndim,
klass=self.__class__, fastpath=True,
placement=self._ref_locs)
def replace(self, to_replace, value, inplace=False, filter=None,
regex=False):
""" replace the to_replace value with value, possible to create new
blocks here this is just a call to putmask. regex is not used here.
It is used in ObjectBlocks. It is here for API
compatibility."""
mask = com.mask_missing(self.values, to_replace)
if filter is not None:
for i, item in enumerate(self.items):
if item not in filter:
mask[i] = False
if not mask.any():
if inplace:
return [self]
return [self.copy()]
return self.putmask(mask, value, inplace=inplace)
def setitem(self, indexer, value):
""" set the value inplace; return a new block (of a possibly different
dtype)
indexer is a direct slice/positional indexer; value must be a
compatible shape
"""
# coerce args
values, value = self._try_coerce_args(self.values, value)
arr_value = np.array(value)
# cast the values to a type that can hold nan (if necessary)
if not self._can_hold_element(value):
dtype, _ = com._maybe_promote(arr_value.dtype)
values = values.astype(dtype)
transf = (lambda x: x.T) if self.ndim == 2 else (lambda x: x)
values = transf(values)
l = len(values)
# length checking
# boolean with truth values == len of the value is ok too
if isinstance(indexer, (np.ndarray, list)):
if is_list_like(value) and len(indexer) != len(value):
if not (isinstance(indexer, np.ndarray) and
indexer.dtype == np.bool_ and
len(indexer[indexer]) == len(value)):
raise ValueError("cannot set using a list-like indexer "
"with a different length than the value")
# slice
elif isinstance(indexer, slice):
if is_list_like(value) and l:
if len(value) != _length_of_indexer(indexer, values):
raise ValueError("cannot set using a slice indexer with a "
"different length than the value")
try:
# setting a single element for each dim and with a rhs that could be say a list
# GH 6043
if arr_value.ndim == 1 and (
np.isscalar(indexer) or (isinstance(indexer, tuple) and all([ np.isscalar(idx) for idx in indexer ]))):
values[indexer] = value
# if we are an exact match (ex-broadcasting),
# then use the resultant dtype
elif len(arr_value.shape) and arr_value.shape[0] == values.shape[0] and np.prod(arr_value.shape) == np.prod(values.shape):
values[indexer] = value
values = values.astype(arr_value.dtype)
# set
else:
values[indexer] = value
# coerce and try to infer the dtypes of the result
if np.isscalar(value):
dtype, _ = _infer_dtype_from_scalar(value)
else:
dtype = 'infer'
values = self._try_coerce_result(values)
values = self._try_cast_result(values, dtype)
return [make_block(transf(values), self.items, self.ref_items,
ndim=self.ndim, fastpath=True)]
except (ValueError, TypeError) as detail:
raise
except Exception as detail:
pass
return [self]
def putmask(self, mask, new, align=True, inplace=False):
""" putmask the data to the block; it is possible that we may create a
new dtype of block
return the resulting block(s)
Parameters
----------
mask : the condition to respect
new : a ndarray/object
align : boolean, perform alignment on other/cond, default is True
inplace : perform inplace modification, default is False
Returns
-------
a new block(s), the result of the putmask
"""
new_values = self.values if inplace else self.values.copy()
# may need to align the new
if hasattr(new, 'reindex_axis'):
if align:
axis = getattr(new, '_info_axis_number', 0)
new = new.reindex_axis(self.items, axis=axis,
copy=False).values.T
else:
new = new.values.T
# may need to align the mask
if hasattr(mask, 'reindex_axis'):
if align:
axis = getattr(mask, '_info_axis_number', 0)
mask = mask.reindex_axis(
self.items, axis=axis, copy=False).values.T
else:
mask = mask.values.T
# if we are passed a scalar None, convert it here
if not is_list_like(new) and isnull(new):
new = self.fill_value
if self._can_hold_element(new):
new = self._try_cast(new)
# pseudo-broadcast
if isinstance(new, np.ndarray) and new.ndim == self.ndim - 1:
new = np.repeat(new, self.shape[-1]).reshape(self.shape)
np.putmask(new_values, mask, new)
# maybe upcast me
elif mask.any():
# need to go column by column
new_blocks = []
def create_block(v, m, n, item, reshape=True):
""" return a new block, try to preserve dtype if possible """
# n should the length of the mask or a scalar here
if not is_list_like(n):
n = np.array([n] * len(m))
# see if we are only masking values that if putted
# will work in the current dtype
nv = None
try:
nn = n[m]
nn_at = nn.astype(self.dtype)
if (nn == nn_at).all():
nv = v.copy()
nv[mask] = nn_at
except:
pass
# change the dtype
if nv is None:
dtype, _ = com._maybe_promote(n.dtype)
nv = v.astype(dtype)
try:
nv[m] = n
except:
np.putmask(nv, m, n)
if reshape:
nv = _block_shape(nv)
return make_block(nv, [item], self.ref_items)
else:
return make_block(nv, item, self.ref_items)
if self.ndim > 1:
for i, item in enumerate(self.items):
m = mask[i]
v = new_values[i]
# need a new block
if m.any():
n = new[i] if isinstance(
new, np.ndarray) else np.array(new)
# type of the new block
dtype, _ = com._maybe_promote(n.dtype)
# we need to exiplicty astype here to make a copy
n = n.astype(dtype)
block = create_block(v, m, n, item)
else:
nv = v if inplace else v.copy()
nv = _block_shape(nv)
block = make_block(
nv, Index([item]), self.ref_items, fastpath=True)
new_blocks.append(block)
else:
new_blocks.append(create_block(new_values, mask, new,
self.items, reshape=False))
return new_blocks
if inplace:
return [self]
return [make_block(new_values, self.items, self.ref_items,
placement=self._ref_locs, fastpath=True)]
def interpolate(self, method='pad', axis=0, index=None,
values=None, inplace=False, limit=None,
fill_value=None, coerce=False, downcast=None, **kwargs):
# a fill na type method
try:
m = com._clean_fill_method(method)
except:
m = None
if m is not None:
return self._interpolate_with_fill(method=m,
axis=axis,
inplace=inplace,
limit=limit,
fill_value=fill_value,
coerce=coerce,
downcast=downcast)
# try an interp method
try:
m = com._clean_interp_method(method, **kwargs)
except:
m = None
if m is not None:
return self._interpolate(method=m,
index=index,
values=values,
axis=axis,
limit=limit,
fill_value=fill_value,
inplace=inplace,
downcast=downcast,
**kwargs)
raise ValueError("invalid method '{0}' to interpolate.".format(method))
def _interpolate_with_fill(self, method='pad', axis=0, inplace=False,
limit=None, fill_value=None, coerce=False,
downcast=None):
""" fillna but using the interpolate machinery """
# if we are coercing, then don't force the conversion
# if the block can't hold the type
if coerce:
if not self._can_hold_na:
if inplace:
return [self]
else:
return [self.copy()]
fill_value = self._try_fill(fill_value)
values = self.values if inplace else self.values.copy()
values = self._try_operate(values)
values = com.interpolate_2d(values, method, axis, limit, fill_value)
values = self._try_coerce_result(values)
blocks = [make_block(values, self.items, self.ref_items,
ndim=self.ndim, klass=self.__class__,
fastpath=True)]
return self._maybe_downcast(blocks, downcast)
def _interpolate(self, method=None, index=None, values=None,
fill_value=None, axis=0, limit=None,
inplace=False, downcast=None, **kwargs):
""" interpolate using scipy wrappers """
data = self.values if inplace else self.values.copy()
# only deal with floats
if not self.is_float:
if not self.is_integer:
return self
data = data.astype(np.float64)
if fill_value is None:
fill_value = self.fill_value
if method in ('krogh', 'piecewise_polynomial', 'pchip'):
if not index.is_monotonic:
raise ValueError("{0} interpolation requires that the "
"index be monotonic.".format(method))
# process 1-d slices in the axis direction
def func(x):
# process a 1-d slice, returning it
# should the axis argument be handled below in apply_along_axis?
# i.e. not an arg to com.interpolate_1d
return com.interpolate_1d(index, x, method=method, limit=limit,
fill_value=fill_value,
bounds_error=False, **kwargs)
# interp each column independently
interp_values = np.apply_along_axis(func, axis, data)
blocks = [make_block(interp_values, self.items, self.ref_items,
ndim=self.ndim, klass=self.__class__, fastpath=True)]
return self._maybe_downcast(blocks, downcast)
def take(self, indexer, ref_items, new_axis, axis=1):
if axis < 1:
raise AssertionError('axis must be at least 1, got %d' % axis)
new_values = com.take_nd(self.values, indexer, axis=axis,
allow_fill=False)
# need to preserve the ref_locs and just shift them
# GH6121
ref_locs = None
if not new_axis.is_unique:
ref_locs = self._ref_locs
return [make_block(new_values, self.items, ref_items, ndim=self.ndim,
klass=self.__class__, placement=ref_locs, fastpath=True)]
def get_values(self, dtype=None):
return self.values
def get_merge_length(self):
return len(self.values)
def diff(self, n):
""" return block for the diff of the values """
new_values = com.diff(self.values, n, axis=1)
return [make_block(new_values, self.items, self.ref_items,
ndim=self.ndim, fastpath=True)]
def shift(self, indexer, periods, axis=0):
""" shift the block by periods, possibly upcast """
new_values = self.values.take(indexer, axis=axis)
# convert integer to float if necessary. need to do a lot more than
# that, handle boolean etc also
new_values, fill_value = com._maybe_upcast(new_values)
# 1-d
if self.ndim == 1:
if periods > 0:
new_values[:periods] = fill_value
else:
new_values[periods:] = fill_value
# 2-d
else:
if periods > 0:
new_values[:, :periods] = fill_value
else:
new_values[:, periods:] = fill_value
return [make_block(new_values, self.items, self.ref_items,
ndim=self.ndim, fastpath=True)]
def eval(self, func, other, raise_on_error=True, try_cast=False):
"""
evaluate the block; return result block from the result
Parameters
----------
func : how to combine self, other
other : a ndarray/object
raise_on_error : if True, raise when I can't perform the function,
False by default (and just return the data that we had coming in)
Returns
-------
a new block, the result of the func
"""
values = self.values
# see if we can align other
if hasattr(other, 'reindex_axis'):
axis = getattr(other, '_info_axis_number', 0)
other = other.reindex_axis(
self.items, axis=axis, copy=False).values
# make sure that we can broadcast
is_transposed = False
if hasattr(other, 'ndim') and hasattr(values, 'ndim'):
if values.ndim != other.ndim:
is_transposed = True
else:
if values.shape == other.shape[::-1]:
is_transposed = True
elif values.shape[0] == other.shape[-1]:
is_transposed = True
else:
# this is a broadcast error heree
raise ValueError("cannot broadcast shape [%s] with block "
"values [%s]" % (values.T.shape,
other.shape))
transf = (lambda x: x.T) if is_transposed else (lambda x: x)
# coerce/transpose the args if needed
values, other = self._try_coerce_args(transf(values), other)
# get the result, may need to transpose the other
def get_result(other):
return self._try_coerce_result(func(values, other))
# error handler if we have an issue operating with the function
def handle_error():
if raise_on_error:
raise TypeError('Could not operate %s with block values %s'
% (repr(other), str(detail)))
else:
# return the values
result = np.empty(values.shape, dtype='O')
result.fill(np.nan)
return result
# get the result
try:
result = get_result(other)
# if we have an invalid shape/broadcast error
# GH4576, so raise instead of allowing to pass through
except ValueError as detail:
raise
except Exception as detail:
result = handle_error()
# technically a broadcast error in numpy can 'work' by returning a
# boolean False
if not isinstance(result, np.ndarray):
if not isinstance(result, np.ndarray):
# differentiate between an invalid ndarray-ndarray comparison
# and an invalid type comparison
if isinstance(values, np.ndarray) and is_list_like(other):
raise ValueError('Invalid broadcasting comparison [%s] '
'with block values' % repr(other))
raise TypeError('Could not compare [%s] with block values'
% repr(other))
# transpose if needed
result = transf(result)
# try to cast if requested
if try_cast:
result = self._try_cast_result(result)
return [make_block(result, self.items, self.ref_items, ndim=self.ndim,
fastpath=True)]
def where(self, other, cond, align=True, raise_on_error=True,
try_cast=False):
"""
evaluate the block; return result block(s) from the result
Parameters
----------
other : a ndarray/object
cond : the condition to respect
align : boolean, perform alignment on other/cond
raise_on_error : if True, raise when I can't perform the function,
False by default (and just return the data that we had coming in)
Returns
-------
a new block(s), the result of the func
"""
values = self.values
# see if we can align other
if hasattr(other, 'reindex_axis'):
if align:
axis = getattr(other, '_info_axis_number', 0)
other = other.reindex_axis(self.items, axis=axis,
copy=True).values
else:
other = other.values
# make sure that we can broadcast
is_transposed = False
if hasattr(other, 'ndim') and hasattr(values, 'ndim'):
if values.ndim != other.ndim or values.shape == other.shape[::-1]:
# pseodo broadcast (its a 2d vs 1d say and where needs it in a
# specific direction)
if (other.ndim >= 1 and values.ndim - 1 == other.ndim and
values.shape[0] != other.shape[0]):
other = _block_shape(other).T
else:
values = values.T
is_transposed = True
# see if we can align cond
if not hasattr(cond, 'shape'):
raise ValueError(
"where must have a condition that is ndarray like")
if align and hasattr(cond, 'reindex_axis'):
axis = getattr(cond, '_info_axis_number', 0)
cond = cond.reindex_axis(self.items, axis=axis, copy=True).values
else:
cond = cond.values
# may need to undo transpose of values
if hasattr(values, 'ndim'):
if values.ndim != cond.ndim or values.shape == cond.shape[::-1]:
values = values.T
is_transposed = not is_transposed
# our where function
def func(c, v, o):
if c.ravel().all():
return v
v, o = self._try_coerce_args(v, o)
try:
return self._try_coerce_result(
expressions.where(c, v, o, raise_on_error=True)
)
except Exception as detail:
if raise_on_error:
raise TypeError('Could not operate [%s] with block values '
'[%s]' % (repr(o), str(detail)))
else:
# return the values
result = np.empty(v.shape, dtype='float64')
result.fill(np.nan)
return result
# see if we can operate on the entire block, or need item-by-item
# or if we are a single block (ndim == 1)
result = func(cond, values, other)
if self._can_hold_na or self.ndim == 1:
if not isinstance(result, np.ndarray):
raise TypeError('Could not compare [%s] with block values'
% repr(other))
if is_transposed:
result = result.T
# try to cast if requested
if try_cast:
result = self._try_cast_result(result)
return make_block(result, self.items, self.ref_items,
ndim=self.ndim)
# might need to separate out blocks
axis = cond.ndim - 1
cond = cond.swapaxes(axis, 0)
mask = np.array([cond[i].all() for i in range(cond.shape[0])],
dtype=bool)
result_blocks = []
for m in [mask, ~mask]:
if m.any():
items = self.items[m]
slices = [slice(None)] * cond.ndim
slices[axis] = self.items.get_indexer(items)
r = self._try_cast_result(result[slices])
result_blocks.append(make_block(r.T, items, self.ref_items))
return result_blocks
def equals(self, other):
if self.dtype != other.dtype or self.shape != other.shape: return False
return np.array_equal(self.values, other.values)
class NumericBlock(Block):
is_numeric = True
_can_hold_na = True
class FloatOrComplexBlock(NumericBlock):
def equals(self, other):
if self.dtype != other.dtype or self.shape != other.shape: return False
left, right = self.values, other.values
return ((left == right) | (np.isnan(left) & np.isnan(right))).all()
class FloatBlock(FloatOrComplexBlock):
is_float = True
_downcast_dtype = 'int64'
def _can_hold_element(self, element):
if is_list_like(element):
element = np.array(element)
return issubclass(element.dtype.type, (np.floating, np.integer))
return (isinstance(element, (float, int, np.float_, np.int_)) and
not isinstance(bool, np.bool_))
def _try_cast(self, element):
try:
return float(element)
except: # pragma: no cover
return element
def to_native_types(self, slicer=None, na_rep='', float_format=None,
**kwargs):
""" convert to our native types format, slicing if desired """
values = self.values
if slicer is not None:
values = values[:, slicer]
values = np.array(values, dtype=object)
mask = isnull(values)
values[mask] = na_rep
if float_format:
imask = (-mask).ravel()
values.flat[imask] = np.array(
[float_format % val for val in values.ravel()[imask]])
return values.tolist()
def should_store(self, value):
# when inserting a column should not coerce integers to floats
# unnecessarily
return (issubclass(value.dtype.type, np.floating) and
value.dtype == self.dtype)
class ComplexBlock(FloatOrComplexBlock):
is_complex = True
def _can_hold_element(self, element):
return isinstance(element, complex)
def _try_cast(self, element):
try:
return complex(element)
except: # pragma: no cover
return element
def should_store(self, value):
return issubclass(value.dtype.type, np.complexfloating)
class IntBlock(NumericBlock):
is_integer = True
_can_hold_na = False
def _can_hold_element(self, element):
if is_list_like(element):
element = np.array(element)
return issubclass(element.dtype.type, np.integer)
return com.is_integer(element)
def _try_cast(self, element):
try:
return int(element)
except: # pragma: no cover
return element
def should_store(self, value):
return com.is_integer_dtype(value) and value.dtype == self.dtype
class TimeDeltaBlock(IntBlock):
is_timedelta = True
_can_hold_na = True
is_numeric = False
@property
def fill_value(self):
return tslib.iNaT
def _try_fill(self, value):
""" if we are a NaT, return the actual fill value """
if isinstance(value, type(tslib.NaT)) or isnull(value):
value = tslib.iNaT
elif isinstance(value, np.timedelta64):
pass
elif com.is_integer(value):
# coerce to seconds of timedelta
value = np.timedelta64(int(value * 1e9))
elif isinstance(value, timedelta):
value = np.timedelta64(value)
return value
def _try_coerce_args(self, values, other):
""" provide coercion to our input arguments
we are going to compare vs i8, so coerce to floats
repring NaT with np.nan so nans propagate
values is always ndarray like, other may not be """
def masker(v):
mask = isnull(v)
v = v.view('i8').astype('float64')
v[mask] = np.nan
return v
values = masker(values)
if _is_null_datelike_scalar(other):
other = np.nan
elif isinstance(other, np.timedelta64):
other = _coerce_scalar_to_timedelta_type(other, unit='s').item()
if other == tslib.iNaT:
other = np.nan
else:
other = masker(other)
return values, other
def _try_operate(self, values):
""" return a version to operate on """
return values.view('i8')
def _try_coerce_result(self, result):
""" reverse of try_coerce_args / try_operate """
if isinstance(result, np.ndarray):
mask = isnull(result)
if result.dtype.kind in ['i', 'f', 'O']:
result = result.astype('m8[ns]')
result[mask] = tslib.iNaT
elif isinstance(result, np.integer):
result = np.timedelta64(result)
return result
def should_store(self, value):
return issubclass(value.dtype.type, np.timedelta64)
def to_native_types(self, slicer=None, na_rep=None, **kwargs):
""" convert to our native types format, slicing if desired """
values = self.values
if slicer is not None:
values = values[:, slicer]
mask = isnull(values)
rvalues = np.empty(values.shape, dtype=object)
if na_rep is None:
na_rep = 'NaT'
rvalues[mask] = na_rep
imask = (-mask).ravel()
rvalues.flat[imask] = np.array([lib.repr_timedelta64(val)
for val in values.ravel()[imask]],
dtype=object)
return rvalues.tolist()
class BoolBlock(NumericBlock):
is_bool = True
_can_hold_na = False
def _can_hold_element(self, element):
if is_list_like(element):
element = np.array(element)
return issubclass(element.dtype.type, np.integer)
return isinstance(element, (int, bool))
def _try_cast(self, element):
try:
return bool(element)
except: # pragma: no cover
return element
def should_store(self, value):
return issubclass(value.dtype.type, np.bool_)
class ObjectBlock(Block):
is_object = True
_can_hold_na = True
def __init__(self, values, items, ref_items, ndim=2, fastpath=False,
placement=None):
if issubclass(values.dtype.type, compat.string_types):
values = np.array(values, dtype=object)
super(ObjectBlock, self).__init__(values, items, ref_items, ndim=ndim,
fastpath=fastpath,
placement=placement)
@property
def is_bool(self):
""" we can be a bool if we have only bool values but are of type
object
"""
return lib.is_bool_array(self.values.ravel())
def convert(self, convert_dates=True, convert_numeric=True, convert_timedeltas=True,
copy=True, by_item=True):
""" attempt to coerce any object types to better types
return a copy of the block (if copy = True)
by definition we ARE an ObjectBlock!!!!!
can return multiple blocks!
"""
# attempt to create new type blocks
is_unique = self.items.is_unique
blocks = []
if by_item and not self._is_single_block:
for i, c in enumerate(self.items):
values = self.iget(i)
values = com._possibly_convert_objects(
values.ravel(), convert_dates=convert_dates,
convert_numeric=convert_numeric,
convert_timedeltas=convert_timedeltas,
).reshape(values.shape)
values = _block_shape(values, ndim=self.ndim)
items = self.items.take([i])
placement = None if is_unique else [i]
newb = make_block(values, items, self.ref_items,
ndim=self.ndim, placement=placement)
blocks.append(newb)
else:
values = com._possibly_convert_objects(
self.values.ravel(), convert_dates=convert_dates,
convert_numeric=convert_numeric
).reshape(self.values.shape)
blocks.append(make_block(values, self.items, self.ref_items,
ndim=self.ndim))
return blocks
def set(self, item, value, check=False):
"""
Modify Block in-place with new item value
Returns
-------
None
"""
loc = self.items.get_loc(item)
# GH6026
if check:
try:
if (self.values[loc] == value).all():
return
except:
pass
try:
self.values[loc] = value
except (ValueError):
# broadcasting error
# see GH6171
new_shape = list(value.shape)
new_shape[0] = len(self.items)
self.values = np.empty(tuple(new_shape),dtype=self.dtype)
self.values.fill(np.nan)
self.values[loc] = value
def _maybe_downcast(self, blocks, downcast=None):
if downcast is not None:
return blocks
# split and convert the blocks
result_blocks = []
for blk in blocks:
result_blocks.extend(blk.convert(convert_dates=True,
convert_numeric=False))
return result_blocks
def _can_hold_element(self, element):
return True
def _try_cast(self, element):
return element
def should_store(self, value):
return not issubclass(value.dtype.type,
(np.integer, np.floating, np.complexfloating,
np.datetime64, np.bool_))
def replace(self, to_replace, value, inplace=False, filter=None,
regex=False):
blk = [self]
to_rep_is_list = com.is_list_like(to_replace)
value_is_list = com.is_list_like(value)
both_lists = to_rep_is_list and value_is_list
either_list = to_rep_is_list or value_is_list
if not either_list and com.is_re(to_replace):
blk[0], = blk[0]._replace_single(to_replace, value,
inplace=inplace, filter=filter,
regex=True)
elif not (either_list or regex):
blk = super(ObjectBlock, self).replace(to_replace, value,
inplace=inplace,
filter=filter, regex=regex)
elif both_lists:
for to_rep, v in zip(to_replace, value):
blk[0], = blk[0]._replace_single(to_rep, v, inplace=inplace,
filter=filter, regex=regex)
elif to_rep_is_list and regex:
for to_rep in to_replace:
blk[0], = blk[0]._replace_single(to_rep, value,
inplace=inplace,
filter=filter, regex=regex)
else:
blk[0], = blk[0]._replace_single(to_replace, value,
inplace=inplace, filter=filter,
regex=regex)
return blk
def _replace_single(self, to_replace, value, inplace=False, filter=None,
regex=False):
# to_replace is regex compilable
to_rep_re = com.is_re_compilable(to_replace)
# regex is regex compilable
regex_re = com.is_re_compilable(regex)
# only one will survive
if to_rep_re and regex_re:
raise AssertionError('only one of to_replace and regex can be '
'regex compilable')
# if regex was passed as something that can be a regex (rather than a
# boolean)
if regex_re:
to_replace = regex
regex = regex_re or to_rep_re
# try to get the pattern attribute (compiled re) or it's a string
try:
pattern = to_replace.pattern
except AttributeError:
pattern = to_replace
# if the pattern is not empty and to_replace is either a string or a
# regex
if regex and pattern:
rx = re.compile(to_replace)
else:
# if the thing to replace is not a string or compiled regex call
# the superclass method -> to_replace is some kind of object
result = super(ObjectBlock, self).replace(to_replace, value,
inplace=inplace,
filter=filter,
regex=regex)
if not isinstance(result, list):
result = [result]
return result
new_values = self.values if inplace else self.values.copy()
# deal with replacing values with objects (strings) that match but
# whose replacement is not a string (numeric, nan, object)
if isnull(value) or not isinstance(value, compat.string_types):
def re_replacer(s):
try:
return value if rx.search(s) is not None else s
except TypeError:
return s
else:
# value is guaranteed to be a string here, s can be either a string
# or null if it's null it gets returned
def re_replacer(s):
try:
return rx.sub(value, s)
except TypeError:
return s
f = np.vectorize(re_replacer, otypes=[self.dtype])
try:
filt = lmap(self.items.get_loc, filter)
except TypeError:
filt = slice(None)
new_values[filt] = f(new_values[filt])
return [self if inplace else make_block(new_values, self.items,
self.ref_items, fastpath=True)]
class DatetimeBlock(Block):
is_datetime = True
_can_hold_na = True
def __init__(self, values, items, ref_items, fastpath=False,
placement=None, **kwargs):
if values.dtype != _NS_DTYPE:
values = tslib.cast_to_nanoseconds(values)
super(DatetimeBlock, self).__init__(values, items, ref_items,
fastpath=True, placement=placement,
**kwargs)
def _can_hold_element(self, element):
if is_list_like(element):
element = np.array(element)
return element.dtype == _NS_DTYPE or element.dtype == np.int64
return (com.is_integer(element) or
isinstance(element, datetime) or
isnull(element))
def _try_cast(self, element):
try:
return int(element)
except:
return element
def _try_operate(self, values):
""" return a version to operate on """
return values.view('i8')
def _try_coerce_args(self, values, other):
""" provide coercion to our input arguments
we are going to compare vs i8, so coerce to integer
values is always ndarra like, other may not be """
values = values.view('i8')
if _is_null_datelike_scalar(other):
other = tslib.iNaT
elif isinstance(other, datetime):
other = lib.Timestamp(other).asm8.view('i8')
else:
other = other.view('i8')
return values, other
def _try_coerce_result(self, result):
""" reverse of try_coerce_args """
if isinstance(result, np.ndarray):
if result.dtype == 'i8':
result = tslib.array_to_datetime(
result.astype(object).ravel()).reshape(result.shape)
elif result.dtype.kind in ['i', 'f', 'O']:
result = result.astype('M8[ns]')
elif isinstance(result, (np.integer, np.datetime64)):
result = lib.Timestamp(result)
return result
@property
def fill_value(self):
return tslib.iNaT
def _try_fill(self, value):
""" if we are a NaT, return the actual fill value """
if isinstance(value, type(tslib.NaT)) or isnull(value):
value = tslib.iNaT
return value
def fillna(self, value, inplace=False, downcast=None):
# straight putmask here
values = self.values if inplace else self.values.copy()
mask = com.isnull(self.values)
value = self._try_fill(value)
np.putmask(values, mask, value)
return [self if inplace else
make_block(values, self.items, self.ref_items, fastpath=True)]
def to_native_types(self, slicer=None, na_rep=None, date_format=None,
**kwargs):
""" convert to our native types format, slicing if desired """
values = self.values
if slicer is not None:
values = values[:, slicer]
mask = isnull(values)
rvalues = np.empty(values.shape, dtype=object)
if na_rep is None:
na_rep = 'NaT'
rvalues[mask] = na_rep
imask = (-mask).ravel()
if date_format is None:
date_formatter = lambda x: Timestamp(x)._repr_base
else:
date_formatter = lambda x: Timestamp(x).strftime(date_format)
rvalues.flat[imask] = np.array([date_formatter(val) for val in
values.ravel()[imask]], dtype=object)
return rvalues.tolist()
def should_store(self, value):
return issubclass(value.dtype.type, np.datetime64)
def astype(self, dtype, copy=False, raise_on_error=True):
"""
handle convert to object as a special case
"""
klass = None
if np.dtype(dtype).type == np.object_:
klass = ObjectBlock
return self._astype(dtype, copy=copy, raise_on_error=raise_on_error,
klass=klass)
def set(self, item, value, check=False):
"""
Modify Block in-place with new item value
Returns
-------
None
"""
loc = self.items.get_loc(item)
if value.dtype != _NS_DTYPE:
value = tslib.cast_to_nanoseconds(value)
self.values[loc] = value
def get_values(self, dtype=None):
# return object dtype as Timestamps
if dtype == object:
return lib.map_infer(self.values.ravel(), lib.Timestamp)\
.reshape(self.values.shape)
return self.values
class SparseBlock(Block):
""" implement as a list of sparse arrays of the same dtype """
__slots__ = ['items', 'ref_items', '_ref_locs', 'ndim', 'values']
is_sparse = True
is_numeric = True
_can_hold_na = True
_can_consolidate = False
_verify_integrity = False
_ftype = 'sparse'
def __init__(self, values, items, ref_items, ndim=None, fastpath=False,
placement=None):
# kludgetastic
if ndim is not None:
if ndim == 1:
ndim = 1
elif ndim > 2:
ndim = ndim
else:
if len(items) != 1:
ndim = 1
else:
ndim = 2
self.ndim = ndim
self._ref_locs = None
self.values = values
if fastpath:
self.items = items
self.ref_items = ref_items
else:
self.items = _ensure_index(items)
self.ref_items = _ensure_index(ref_items)
@property
def shape(self):
return (len(self.items), self.sp_index.length)
@property
def itemsize(self):
return self.dtype.itemsize
@property
def fill_value(self):
return self.values.fill_value
@fill_value.setter
def fill_value(self, v):
# we may need to upcast our fill to match our dtype
if issubclass(self.dtype.type, np.floating):
v = float(v)
self.values.fill_value = v
@property
def sp_values(self):
return self.values.sp_values
@sp_values.setter
def sp_values(self, v):
# reset the sparse values
self.values = SparseArray(v, sparse_index=self.sp_index,
kind=self.kind, dtype=v.dtype,
fill_value=self.fill_value, copy=False)
@property
def sp_index(self):
return self.values.sp_index
@property
def kind(self):
return self.values.kind
def __len__(self):
try:
return self.sp_index.length
except:
return 0
def should_store(self, value):
return isinstance(value, SparseArray)
def prepare_for_merge(self, **kwargs):
""" create a dense block """
return make_block(self.get_values(), self.items, self.ref_items)
def post_merge(self, items, **kwargs):
return self
def set(self, item, value, check=False):
self.values = value
def get(self, item):
if self.ndim == 1:
loc = self.items.get_loc(item)
return self.values[loc]
else:
return self.values
def _slice(self, slicer):
""" return a slice of my values (but densify first) """
return self.get_values()[slicer]
def get_values(self, dtype=None):
""" need to to_dense myself (and always return a ndim sized object) """
values = self.values.to_dense()
if values.ndim == self.ndim - 1:
values = values.reshape((1,) + values.shape)
return values
def get_merge_length(self):
return 1
def make_block(self, values, items=None, ref_items=None, sparse_index=None,
kind=None, dtype=None, fill_value=None, copy=False,
fastpath=True):
""" return a new block """
if dtype is None:
dtype = self.dtype
if fill_value is None:
fill_value = self.fill_value
if items is None:
items = self.items
if ref_items is None:
ref_items = self.ref_items
new_values = SparseArray(values, sparse_index=sparse_index,
kind=kind or self.kind, dtype=dtype,
fill_value=fill_value, copy=copy)
return make_block(new_values, items, ref_items, ndim=self.ndim,
fastpath=fastpath)
def interpolate(self, method='pad', axis=0, inplace=False,
limit=None, fill_value=None, **kwargs):
values = com.interpolate_2d(
self.values.to_dense(), method, axis, limit, fill_value)
return self.make_block(values, self.items, self.ref_items)
def fillna(self, value, inplace=False, downcast=None):
# we may need to upcast our fill to match our dtype
if issubclass(self.dtype.type, np.floating):
value = float(value)
values = self.values if inplace else self.values.copy()
return [self.make_block(values.get_values(value), fill_value=value)]
def shift(self, indexer, periods, axis=0):
""" shift the block by periods """
new_values = self.values.to_dense().take(indexer)
# convert integer to float if necessary. need to do a lot more than
# that, handle boolean etc also
new_values, fill_value = com._maybe_upcast(new_values)
if periods > 0:
new_values[:periods] = fill_value
else:
new_values[periods:] = fill_value
return [self.make_block(new_values)]
def take(self, indexer, ref_items, new_axis, axis=1):
""" going to take our items
along the long dimension"""
if axis < 1:
raise AssertionError('axis must be at least 1, got %d' % axis)
return [self.make_block(self.values.take(indexer))]
def reindex_axis(self, indexer, method=None, axis=1, fill_value=None,
limit=None, mask_info=None):
"""
Reindex using pre-computed indexer information
"""
if axis < 1:
raise AssertionError('axis must be at least 1, got %d' % axis)
# taking on the 0th axis always here
if fill_value is None:
fill_value = self.fill_value
return self.make_block(self.values.take(indexer), items=self.items,
fill_value=fill_value)
def reindex_items_from(self, new_ref_items, indexer=None, method=None,
fill_value=None, limit=None, copy=True):
"""
Reindex to only those items contained in the input set of items
E.g. if you have ['a', 'b'], and the input items is ['b', 'c', 'd'],
then the resulting items will be ['b']
Returns
-------
reindexed : Block
"""
# 1-d always
if indexer is None:
new_ref_items, indexer = self.items.reindex(new_ref_items,
limit=limit)
if indexer is None:
indexer = np.arange(len(self.items))
# single block
if self.ndim == 1:
new_items = new_ref_items
new_values = com.take_1d(self.values.values, indexer)
else:
# if we don't overlap at all, then don't include this block
new_items = self.items & new_ref_items
if not len(new_items):
return None
new_values = self.values.values
# fill if needed
if method is not None or limit is not None:
if fill_value is None:
fill_value = self.fill_value
new_values = com.interpolate_2d(new_values, method=method,
limit=limit, fill_value=fill_value)
return self.make_block(new_values, items=new_items,
ref_items=new_ref_items, copy=copy)
def sparse_reindex(self, new_index):
""" sparse reindex and return a new block
current reindex only works for float64 dtype! """
values = self.values
values = values.sp_index.to_int_index().reindex(
values.sp_values.astype('float64'), values.fill_value, new_index)
return self.make_block(values, sparse_index=new_index)
def split_block_at(self, item):
if len(self.items) == 1 and item == self.items[0]:
return []
return super(SparseBlock, self).split_block_at(self, item)
def _try_cast_result(self, result, dtype=None):
return result
def make_block(values, items, ref_items, klass=None, ndim=None, dtype=None,
fastpath=False, placement=None):
if klass is None:
dtype = dtype or values.dtype
vtype = dtype.type
if isinstance(values, SparseArray):
klass = SparseBlock
elif issubclass(vtype, np.floating):
klass = FloatBlock
elif (issubclass(vtype, np.integer) and
issubclass(vtype, np.timedelta64)):
klass = TimeDeltaBlock
elif (issubclass(vtype, np.integer) and
not issubclass(vtype, np.datetime64)):
klass = IntBlock
elif dtype == np.bool_:
klass = BoolBlock
elif issubclass(vtype, np.datetime64):
klass = DatetimeBlock
elif issubclass(vtype, np.complexfloating):
klass = ComplexBlock
# try to infer a DatetimeBlock, or set to an ObjectBlock
else:
if np.prod(values.shape):
flat = values.ravel()
# try with just the first element; we just need to see if
# this is a datetime or not
inferred_type = lib.infer_dtype(flat[0:1])
if inferred_type in ['datetime', 'datetime64']:
# we have an object array that has been inferred as
# datetime, so convert it
try:
values = tslib.array_to_datetime(
flat).reshape(values.shape)
if issubclass(values.dtype.type, np.datetime64):
klass = DatetimeBlock
except: # it already object, so leave it
pass
if klass is None:
klass = ObjectBlock
return klass(values, items, ref_items, ndim=ndim, fastpath=fastpath,
placement=placement)
# TODO: flexible with index=None and/or items=None
class BlockManager(PandasObject):
"""
Core internal data structure to implement DataFrame
Manage a bunch of labeled 2D mixed-type ndarrays. Essentially it's a
lightweight blocked set of labeled data to be manipulated by the DataFrame
public API class
Parameters
----------
Notes
-----
This is *not* a public API class
"""
__slots__ = ['axes', 'blocks', '_ndim', '_shape', '_known_consolidated',
'_is_consolidated', '_has_sparse', '_ref_locs', '_items_map']
def __init__(self, blocks, axes, do_integrity_check=True, fastpath=True):
self.axes = [_ensure_index(ax) for ax in axes]
self.blocks = blocks
ndim = self.ndim
for block in blocks:
if not block.is_sparse and ndim != block.ndim:
raise AssertionError(('Number of Block dimensions (%d) must '
'equal number of axes (%d)')
% (block.ndim, ndim))
if do_integrity_check:
self._verify_integrity()
self._has_sparse = False
self._consolidate_check()
# we have a duplicate items index, setup the block maps
if not self.items.is_unique:
self._set_ref_locs(do_refs=True)
def make_empty(self, axes=None):
""" return an empty BlockManager with the items axis of len 0 """
if axes is None:
axes = [_ensure_index([])] + [
_ensure_index(a) for a in self.axes[1:]
]
# preserve dtype if possible
if self.ndim == 1:
blocks = np.array([], dtype=self.dtype)
else:
blocks = []
return self.__class__(blocks, axes)
def __nonzero__(self):
return True
# Python3 compat
__bool__ = __nonzero__
@property
def shape(self):
if getattr(self, '_shape', None) is None:
self._shape = tuple(len(ax) for ax in self.axes)
return self._shape
@property
def ndim(self):
if getattr(self, '_ndim', None) is None:
self._ndim = len(self.axes)
return self._ndim
def _set_axis(self, axis, value, check_axis=True):
cur_axis = self.axes[axis]
value = _ensure_index(value)
if check_axis and len(value) != len(cur_axis):
raise ValueError('Length mismatch: Expected axis has %d elements, '
'new values have %d elements' % (len(cur_axis),
len(value)))
self.axes[axis] = value
self._shape = None
return cur_axis, value
def set_axis(self, axis, value, maybe_rename=True, check_axis=True):
cur_axis, value = self._set_axis(axis, value, check_axis)
if axis == 0:
# set/reset ref_locs based on the current index
# and map the new index if needed
self._set_ref_locs(labels=cur_axis)
# take via ref_locs
for block in self.blocks:
block.set_ref_items(self.items, maybe_rename=maybe_rename)
# set/reset ref_locs based on the new index
self._set_ref_locs(labels=value, do_refs=True)
def _reset_ref_locs(self):
""" take the current _ref_locs and reset ref_locs on the blocks
to correctly map, ignoring Nones;
reset both _items_map and _ref_locs """
# let's reset the ref_locs in individual blocks
if self.items.is_unique:
for b in self.blocks:
b._ref_locs = None
else:
for b in self.blocks:
b.reset_ref_locs()
self._rebuild_ref_locs()
self._ref_locs = None
self._items_map = None
def _rebuild_ref_locs(self):
"""Take _ref_locs and set the individual block ref_locs, skipping Nones
no effect on a unique index
"""
if getattr(self, '_ref_locs', None) is not None:
item_count = 0
for v in self._ref_locs:
if v is not None:
block, item_loc = v
if block._ref_locs is None:
block.reset_ref_locs()
block._ref_locs[item_loc] = item_count
item_count += 1
def _set_ref_locs(self, labels=None, do_refs=False):
"""
if we have a non-unique index on this axis, set the indexers
we need to set an absolute indexer for the blocks
return the indexer if we are not unique
labels : the (new) labels for this manager
ref : boolean, whether to set the labels (one a 1-1 mapping)
"""
if labels is None:
labels = self.items
# we are unique, and coming from a unique
is_unique = labels.is_unique
if is_unique and not do_refs:
if not self.items.is_unique:
# reset our ref locs
self._ref_locs = None
for b in self.blocks:
b._ref_locs = None
return None
# we are going to a non-unique index
# we have ref_locs on the block at this point
if (not is_unique and do_refs) or do_refs == 'force':
# create the items map
im = getattr(self, '_items_map', None)
if im is None:
im = dict()
for block in self.blocks:
# if we have a duplicate index but
# _ref_locs have not been set
try:
rl = block.ref_locs
except:
raise AssertionError(
'Cannot create BlockManager._ref_locs because '
'block [%s] with duplicate items [%s] does not '
'have _ref_locs set' % (block, labels))
m = maybe_create_block_in_items_map(im, block)
for i, item in enumerate(block.items):
m[i] = rl[i]
self._items_map = im
# create the _ref_loc map here
rl = [None] * len(labels)
for block, items in im.items():
for i, loc in enumerate(items):
rl[loc] = (block, i)
self._ref_locs = rl
return rl
elif do_refs:
self._reset_ref_locs()
# return our cached _ref_locs (or will compute again
# when we recreate the block manager if needed
return getattr(self, '_ref_locs', None)
def get_items_map(self, use_cached=True):
"""
return an inverted ref_loc map for an item index
block -> item (in that block) location -> column location
use_cached : boolean, use the cached items map, or recreate
"""
# cache check
if use_cached:
im = getattr(self, '_items_map', None)
if im is not None:
return im
im = dict()
rl = self._set_ref_locs()
# we have a non-duplicative index
if rl is None:
axis = self.axes[0]
for block in self.blocks:
m = maybe_create_block_in_items_map(im, block)
for i, item in enumerate(block.items):
m[i] = axis.get_loc(item)
# use the ref_locs to construct the map
else:
for i, (block, idx) in enumerate(rl):
m = maybe_create_block_in_items_map(im, block)
m[idx] = i
self._items_map = im
return im
# make items read only for now
def _get_items(self):
return self.axes[0]
items = property(fget=_get_items)
def _get_counts(self, f):
""" return a dict of the counts of the function in BlockManager """
self._consolidate_inplace()
counts = dict()
for b in self.blocks:
v = f(b)
counts[v] = counts.get(v, 0) + b.shape[0]
return counts
def _get_types(self, f):
""" return a list of the f per item """
self._consolidate_inplace()
# unique
if self.items.is_unique:
l = [ None ] * len(self.items)
for b in self.blocks:
v = f(b)
for rl in b.ref_locs:
l[rl] = v
return l
# non-unique
ref_locs = self._set_ref_locs()
return [ f(ref_locs[i][0]) for i, item in enumerate(self.items) ]
def get_dtype_counts(self):
return self._get_counts(lambda b: b.dtype.name)
def get_ftype_counts(self):
return self._get_counts(lambda b: b.ftype)
def get_dtypes(self):
return self._get_types(lambda b: b.dtype)
def get_ftypes(self):
return self._get_types(lambda b: b.ftype)
def __getstate__(self):
block_values = [b.values for b in self.blocks]
block_items = [b.items for b in self.blocks]
axes_array = [ax for ax in self.axes]
return axes_array, block_values, block_items
def __setstate__(self, state):
# discard anything after 3rd, support beta pickling format for a little
# while longer
ax_arrays, bvalues, bitems = state[:3]
self.axes = [_ensure_index(ax) for ax in ax_arrays]
self.axes = _handle_legacy_indexes(self.axes)
blocks = []
for values, items in zip(bvalues, bitems):
# numpy < 1.7 pickle compat
if values.dtype == 'M8[us]':
values = values.astype('M8[ns]')
blk = make_block(values, items, self.axes[0])
blocks.append(blk)
self.blocks = blocks
self._post_setstate()
def _post_setstate(self):
self._is_consolidated = False
self._known_consolidated = False
self._set_has_sparse()
def __len__(self):
return len(self.items)
def __unicode__(self):
output = com.pprint_thing(self.__class__.__name__)
for i, ax in enumerate(self.axes):
if i == 0:
output += '\nItems: %s' % ax
else:
output += '\nAxis %d: %s' % (i, ax)
for block in self.blocks:
output += '\n%s' % com.pprint_thing(block)
return output
def _verify_integrity(self):
mgr_shape = self.shape
tot_items = sum(len(x.items) for x in self.blocks)
for block in self.blocks:
if block.ref_items is not self.items:
raise AssertionError("Block ref_items must be BlockManager "
"items")
if not block.is_sparse and block.values.shape[1:] != mgr_shape[1:]:
construction_error(
tot_items, block.values.shape[1:], self.axes)
if len(self.items) != tot_items:
raise AssertionError('Number of manager items must equal union of '
'block items\n# manager items: {0}, # '
'tot_items: {1}'.format(len(self.items),
tot_items))
def apply(self, f, *args, **kwargs):
""" iterate over the blocks, collect and create a new block manager
Parameters
----------
f : the callable or function name to operate on at the block level
axes : optional (if not supplied, use self.axes)
filter : list, if supplied, only call the block if the filter is in
the block
"""
axes = kwargs.pop('axes', None)
filter = kwargs.get('filter')
do_integrity_check = kwargs.pop('do_integrity_check', False)
result_blocks = []
for blk in self.blocks:
if filter is not None:
kwargs['filter'] = set(kwargs['filter'])
if not blk.items.isin(filter).any():
result_blocks.append(blk)
continue
if callable(f):
applied = f(blk, *args, **kwargs)
# if we are no a block, try to coerce
if not isinstance(applied, Block):
applied = make_block(applied,
blk.items,
blk.ref_items)
else:
applied = getattr(blk, f)(*args, **kwargs)
if isinstance(applied, list):
result_blocks.extend(applied)
else:
result_blocks.append(applied)
if len(result_blocks) == 0:
return self.make_empty(axes or self.axes)
bm = self.__class__(result_blocks, axes or self.axes,
do_integrity_check=do_integrity_check)
bm._consolidate_inplace()
return bm
def where(self, *args, **kwargs):
return self.apply('where', *args, **kwargs)
def eval(self, *args, **kwargs):
return self.apply('eval', *args, **kwargs)
def setitem(self, *args, **kwargs):
return self.apply('setitem', *args, **kwargs)
def putmask(self, *args, **kwargs):
return self.apply('putmask', *args, **kwargs)
def diff(self, *args, **kwargs):
return self.apply('diff', *args, **kwargs)
def interpolate(self, *args, **kwargs):
return self.apply('interpolate', *args, **kwargs)
def shift(self, *args, **kwargs):
return self.apply('shift', *args, **kwargs)
def fillna(self, *args, **kwargs):
return self.apply('fillna', *args, **kwargs)
def downcast(self, *args, **kwargs):
return self.apply('downcast', *args, **kwargs)
def astype(self, *args, **kwargs):
return self.apply('astype', *args, **kwargs)
def convert(self, *args, **kwargs):
return self.apply('convert', *args, **kwargs)
def replace(self, *args, **kwargs):
return self.apply('replace', *args, **kwargs)
def replace_list(self, src_lst, dest_lst, inplace=False, regex=False):
""" do a list replace """
# figure out our mask a-priori to avoid repeated replacements
values = self.as_matrix()
def comp(s):
if isnull(s):
return isnull(values)
return values == getattr(s, 'asm8', s)
masks = [comp(s) for i, s in enumerate(src_lst)]
result_blocks = []
for blk in self.blocks:
# its possible to get multiple result blocks here
# replace ALWAYS will return a list
rb = [blk if inplace else blk.copy()]
for i, (s, d) in enumerate(zip(src_lst, dest_lst)):
new_rb = []
for b in rb:
if b.dtype == np.object_:
result = b.replace(s, d, inplace=inplace,
regex=regex)
if isinstance(result, list):
new_rb.extend(result)
else:
new_rb.append(result)
else:
# get our mask for this element, sized to this
# particular block
m = masks[i][b.ref_locs]
if m.any():
new_rb.extend(b.putmask(m, d, inplace=True))
else:
new_rb.append(b)
rb = new_rb
result_blocks.extend(rb)
bm = self.__class__(result_blocks, self.axes)
bm._consolidate_inplace()
return bm
def prepare_for_merge(self, *args, **kwargs):
""" prepare for merging, return a new block manager with
Sparse -> Dense
"""
self._consolidate_inplace()
if self._has_sparse:
return self.apply('prepare_for_merge', *args, **kwargs)
return self
def post_merge(self, objs, **kwargs):
""" try to sparsify items that were previously sparse """
is_sparse = defaultdict(list)
for o in objs:
for blk in o._data.blocks:
if blk.is_sparse:
# record the dtype of each item
for i in blk.items:
is_sparse[i].append(blk.dtype)
if len(is_sparse):
return self.apply('post_merge', items=is_sparse)
return self
def is_consolidated(self):
"""
Return True if more than one block with the same dtype
"""
if not self._known_consolidated:
self._consolidate_check()
return self._is_consolidated
def _consolidate_check(self):
ftypes = [blk.ftype for blk in self.blocks]
self._is_consolidated = len(ftypes) == len(set(ftypes))
self._known_consolidated = True
self._set_has_sparse()
def _set_has_sparse(self):
self._has_sparse = any((blk.is_sparse for blk in self.blocks))
@property
def is_mixed_type(self):
# Warning, consolidation needs to get checked upstairs
self._consolidate_inplace()
return len(self.blocks) > 1
@property
def is_numeric_mixed_type(self):
# Warning, consolidation needs to get checked upstairs
self._consolidate_inplace()
return all([block.is_numeric for block in self.blocks])
@property
def is_datelike_mixed_type(self):
# Warning, consolidation needs to get checked upstairs
self._consolidate_inplace()
return any([block.is_datelike for block in self.blocks])
def get_block_map(self, copy=False, typ=None, columns=None,
is_numeric=False, is_bool=False):
""" return a dictionary mapping the ftype -> block list
Parameters
----------
typ : return a list/dict
copy : copy if indicated
columns : a column filter list
filter if the type is indicated """
# short circuit - mainly for merging
if (typ == 'dict' and columns is None and not is_numeric and
not is_bool and not copy):
bm = defaultdict(list)
for b in self.blocks:
bm[str(b.ftype)].append(b)
return bm
self._consolidate_inplace()
if is_numeric:
filter_blocks = lambda block: block.is_numeric
elif is_bool:
filter_blocks = lambda block: block.is_bool
else:
filter_blocks = lambda block: True
def filter_columns(b):
if columns:
if not columns in b.items:
return None
b = b.reindex_items_from(columns)
return b
maybe_copy = lambda b: b.copy() if copy else b
def maybe_copy(b):
if copy:
b = b.copy()
return b
if typ == 'list':
bm = []
for b in self.blocks:
if filter_blocks(b):
b = filter_columns(b)
if b is not None:
bm.append(maybe_copy(b))
else:
if typ == 'dtype':
key = lambda b: b.dtype
else:
key = lambda b: b.ftype
bm = defaultdict(list)
for b in self.blocks:
if filter_blocks(b):
b = filter_columns(b)
if b is not None:
bm[str(key(b))].append(maybe_copy(b))
return bm
def get_bool_data(self, **kwargs):
kwargs['is_bool'] = True
return self.get_data(**kwargs)
def get_numeric_data(self, **kwargs):
kwargs['is_numeric'] = True
return self.get_data(**kwargs)
def get_data(self, copy=False, columns=None, **kwargs):
"""
Parameters
----------
copy : boolean, default False
Whether to copy the blocks
"""
blocks = self.get_block_map(
typ='list', copy=copy, columns=columns, **kwargs)
if len(blocks) == 0:
return self.make_empty()
return self.combine(blocks)
def combine(self, blocks):
""" return a new manager with the blocks """
indexer = np.sort(np.concatenate([b.ref_locs for b in blocks]))
new_items = self.items.take(indexer)
new_blocks = []
for b in blocks:
b = b.copy(deep=False)
b.ref_items = new_items
new_blocks.append(b)
new_axes = list(self.axes)
new_axes[0] = new_items
return self.__class__(new_blocks, new_axes, do_integrity_check=False)
def get_slice(self, slobj, axis=0, raise_on_error=False):
new_axes = list(self.axes)
if raise_on_error:
_check_slice_bounds(slobj, new_axes[axis])
new_axes[axis] = new_axes[axis][slobj]
if axis == 0:
new_items = new_axes[0]
# we want to preserver the view of a single-block
if len(self.blocks) == 1:
blk = self.blocks[0]
ref_locs = blk.take_ref_locs(slobj)
newb = make_block(blk._slice(slobj), new_items, new_items,
klass=blk.__class__, fastpath=True,
placement=ref_locs)
new_blocks = [newb]
else:
return self.reindex_items(
new_items, indexer=np.arange(len(self.items))[slobj])
else:
new_blocks = self._slice_blocks(slobj, axis)
bm = self.__class__(new_blocks, new_axes, do_integrity_check=False)
bm._consolidate_inplace()
return bm
def _slice_blocks(self, slobj, axis):
new_blocks = []
slicer = [slice(None, None) for _ in range(self.ndim)]
slicer[axis] = slobj
slicer = tuple(slicer)
for block in self.blocks:
newb = make_block(block._slice(slicer),
block.items,
block.ref_items,
klass=block.__class__,
fastpath=True,
placement=block._ref_locs)
newb.set_ref_locs(block._ref_locs)
new_blocks.append(newb)
return new_blocks
def get_series_dict(self):
# For DataFrame
return _blocks_to_series_dict(self.blocks, self.axes[1])
def __contains__(self, item):
return item in self.items
@property
def nblocks(self):
return len(self.blocks)
def copy(self, deep=True):
"""
Make deep or shallow copy of BlockManager
Parameters
----------
deep : boolean, default True
If False, return shallow copy (do not copy data)
Returns
-------
copy : BlockManager
"""
if deep:
new_axes = [ax.view() for ax in self.axes]
else:
new_axes = list(self.axes)
return self.apply('copy', axes=new_axes, deep=deep,
ref_items=new_axes[0], do_integrity_check=False)
def as_matrix(self, items=None):
if len(self.blocks) == 0:
mat = np.empty(self.shape, dtype=float)
elif len(self.blocks) == 1:
blk = self.blocks[0]
if items is None or blk.items.equals(items):
# if not, then just call interleave per below
mat = blk.get_values()
else:
mat = self.reindex_items(items).as_matrix()
else:
if items is None:
mat = self._interleave(self.items)
else:
mat = self.reindex_items(items).as_matrix()
return mat
def _interleave(self, items):
"""
Return ndarray from blocks with specified item order
Items must be contained in the blocks
"""
dtype = _interleaved_dtype(self.blocks)
items = _ensure_index(items)
result = np.empty(self.shape, dtype=dtype)
itemmask = np.zeros(len(items), dtype=bool)
# By construction, all of the item should be covered by one of the
# blocks
if items.is_unique:
for block in self.blocks:
indexer = items.get_indexer(block.items)
if (indexer == -1).any():
raise AssertionError('Items must contain all block items')
result[indexer] = block.get_values(dtype)
itemmask[indexer] = 1
else:
# non-unique, must use ref_locs
rl = self._set_ref_locs()
for i, (block, idx) in enumerate(rl):
result[i] = block.get_values(dtype)[idx]
itemmask[i] = 1
if not itemmask.all():
raise AssertionError('Some items were not contained in blocks')
return result
def xs(self, key, axis=1, copy=True, takeable=False):
if axis < 1:
raise AssertionError('Can only take xs across axis >= 1, got %d'
% axis)
# take by position
if takeable:
loc = key
else:
loc = self.axes[axis].get_loc(key)
slicer = [slice(None, None) for _ in range(self.ndim)]
slicer[axis] = loc
slicer = tuple(slicer)
new_axes = list(self.axes)
# could be an array indexer!
if isinstance(loc, (slice, np.ndarray)):
new_axes[axis] = new_axes[axis][loc]
else:
new_axes.pop(axis)
new_blocks = []
if len(self.blocks) > 1:
if not copy:
raise Exception('cannot get view of mixed-type or '
'non-consolidated DataFrame')
for blk in self.blocks:
newb = make_block(blk.values[slicer],
blk.items,
blk.ref_items,
klass=blk.__class__,
fastpath=True)
new_blocks.append(newb)
elif len(self.blocks) == 1:
block = self.blocks[0]
vals = block.values[slicer]
if copy:
vals = vals.copy()
new_blocks = [make_block(vals,
self.items,
self.items,
klass=block.__class__,
fastpath=True)]
return self.__class__(new_blocks, new_axes)
def fast_2d_xs(self, loc, copy=False):
"""
get a cross sectional for a given location in the
items ; handle dups
return the result and a flag if a copy was actually made
"""
if len(self.blocks) == 1:
result = self.blocks[0].values[:, loc]
if copy:
result = result.copy()
return result, copy
items = self.items
# non-unique (GH4726)
if not items.is_unique:
return self._interleave(items).ravel(), True
# unique
dtype = _interleaved_dtype(self.blocks)
n = len(items)
result = np.empty(n, dtype=dtype)
for blk in self.blocks:
for j, item in enumerate(blk.items):
i = items.get_loc(item)
result[i] = blk._try_coerce_result(blk.iget((j, loc)))
return result, True
def consolidate(self):
"""
Join together blocks having same dtype
Returns
-------
y : BlockManager
"""
if self.is_consolidated():
return self
bm = self.__class__(self.blocks, self.axes)
bm._consolidate_inplace()
return bm
def _consolidate_inplace(self):
if not self.is_consolidated():
self.blocks = _consolidate(self.blocks, self.items)
# reset our mappings
if not self.items.is_unique:
self._ref_locs = None
self._items_map = None
self._set_ref_locs(do_refs=True)
self._is_consolidated = True
self._known_consolidated = True
self._set_has_sparse()
def get(self, item):
if self.items.is_unique:
if isnull(item):
indexer = np.arange(len(self.items))[isnull(self.items)]
return self.get_for_nan_indexer(indexer)
_, block = self._find_block(item)
return block.get(item)
else:
if isnull(item):
raise ValueError("cannot label index with a null key")
indexer = self.items.get_loc(item)
ref_locs = np.array(self._set_ref_locs())
# duplicate index but only a single result
if com.is_integer(indexer):
b, loc = ref_locs[indexer]
values = [b.iget(loc)]
index = Index([self.items[indexer]])
# we have a multiple result, potentially across blocks
else:
values = [block.iget(i) for block, i in ref_locs[indexer]]
index = self.items[indexer]
# create and return a new block manager
axes = [index] + self.axes[1:]
blocks = form_blocks(values, index, axes)
mgr = BlockManager(blocks, axes)
mgr._consolidate_inplace()
return mgr
def iget(self, i):
item = self.items[i]
# unique
if self.items.is_unique:
if notnull(item):
return self.get(item)
return self.get_for_nan_indexer(i)
ref_locs = self._set_ref_locs()
b, loc = ref_locs[i]
return b.iget(loc)
def get_for_nan_indexer(self, indexer):
# allow a single nan location indexer
if not np.isscalar(indexer):
if len(indexer) == 1:
indexer = indexer.item()
else:
raise ValueError("cannot label index with a null key")
# take a nan indexer and return the values
ref_locs = self._set_ref_locs(do_refs='force')
b, loc = ref_locs[indexer]
return b.iget(loc)
def get_scalar(self, tup):
"""
Retrieve single item
"""
item = tup[0]
_, blk = self._find_block(item)
# this could obviously be seriously sped up in cython
item_loc = blk.items.get_loc(item),
full_loc = item_loc + tuple(ax.get_loc(x)
for ax, x in zip(self.axes[1:], tup[1:]))
return blk.values[full_loc]
def delete(self, item):
is_unique = self.items.is_unique
loc = self.items.get_loc(item)
# dupe keys may return mask
loc = _possibly_convert_to_indexer(loc)
self._delete_from_all_blocks(loc, item)
# _ref_locs, and _items_map are good here
new_items = self.items.delete(loc)
self.set_items_norename(new_items)
self._known_consolidated = False
if not is_unique:
self._consolidate_inplace()
def set(self, item, value, check=False):
"""
Set new item in-place. Does not consolidate. Adds new Block if not
contained in the current set of items
if check, then validate that we are not setting the same data in-place
"""
if not isinstance(value, SparseArray):
if value.ndim == self.ndim - 1:
value = value.reshape((1,) + value.shape)
if value.shape[1:] != self.shape[1:]:
raise AssertionError('Shape of new values must be compatible '
'with manager shape')
def _set_item(item, arr):
i, block = self._find_block(item)
if not block.should_store(value):
# delete from block, create and append new block
self._delete_from_block(i, item)
self._add_new_block(item, arr, loc=None)
else:
block.set(item, arr, check=check)
try:
loc = self.items.get_loc(item)
if isinstance(loc, int):
_set_item(self.items[loc], value)
else:
subset = self.items[loc]
if len(value) != len(subset):
raise AssertionError(
'Number of items to set did not match')
# we are inserting multiple non-unique items as replacements
# we are inserting one by one, so the index can go from unique
# to non-unique during the loop, need to have _ref_locs defined
# at all times
if np.isscalar(item) and (com.is_list_like(loc) or isinstance(loc, slice)):
# first delete from all blocks
self.delete(item)
loc = _possibly_convert_to_indexer(loc)
for i, (l, k, arr) in enumerate(zip(loc, subset, value)):
# insert the item
self.insert(
l, k, arr[None, :], allow_duplicates=True)
# reset the _ref_locs on indiviual blocks
# rebuild ref_locs
if self.items.is_unique:
self._reset_ref_locs()
self._set_ref_locs(do_refs='force')
self._rebuild_ref_locs()
else:
for i, (item, arr) in enumerate(zip(subset, value)):
_set_item(item, arr[None, :])
except KeyError:
# insert at end
self.insert(len(self.items), item, value)
self._known_consolidated = False
def insert(self, loc, item, value, allow_duplicates=False):
if not allow_duplicates and item in self.items:
# Should this be a different kind of error??
raise ValueError('cannot insert %s, already exists' % item)
try:
new_items = self.items.insert(loc, item)
self.set_items_norename(new_items)
# new block
self._add_new_block(item, value, loc=loc)
except:
# so our insertion operation failed, so back out of the new items
# GH 3010
new_items = self.items.delete(loc)
self.set_items_norename(new_items)
# re-raise
raise
if len(self.blocks) > 100:
self._consolidate_inplace()
self._known_consolidated = False
# clear the internal ref_loc mappings if necessary
if loc != len(self.items) - 1 and new_items.is_unique:
self.set_items_clear(new_items)
def set_items_norename(self, value):
self.set_axis(0, value, maybe_rename=False, check_axis=False)
self._shape = None
def set_items_clear(self, value):
""" clear the ref_locs on all blocks """
self.set_axis(0, value, maybe_rename='clear', check_axis=False)
def _delete_from_all_blocks(self, loc, item):
""" delete from the items loc the item
the item could be in multiple blocks which could
change each iteration (as we split blocks) """
# possibily convert to an indexer
loc = _possibly_convert_to_indexer(loc)
if isinstance(loc, (list, tuple, np.ndarray)):
for l in loc:
for i, b in enumerate(self.blocks):
if item in b.items:
self._delete_from_block(i, item)
else:
i, _ = self._find_block(item)
self._delete_from_block(i, item)
def _delete_from_block(self, i, item):
"""
Delete and maybe remove the whole block
Remap the split blocks to there old ranges,
so after this function, _ref_locs and _items_map (if used)
are correct for the items, None fills holes in _ref_locs
"""
block = self.blocks.pop(i)
ref_locs = self._set_ref_locs()
prev_items_map = self._items_map.pop(
block) if ref_locs is not None else None
# if we can't consolidate, then we are removing this block in its
# entirey
if block._can_consolidate:
# compute the split mask
loc = block.items.get_loc(item)
if type(loc) == slice or com.is_integer(loc):
mask = np.array([True] * len(block))
mask[loc] = False
else: # already a mask, inverted
mask = -loc
# split the block
counter = 0
for s, e in com.split_ranges(mask):
sblock = make_block(block.values[s:e],
block.items[s:e].copy(),
block.ref_items,
klass=block.__class__,
fastpath=True)
self.blocks.append(sblock)
# update the _ref_locs/_items_map
if ref_locs is not None:
# fill the item_map out for this sub-block
m = maybe_create_block_in_items_map(
self._items_map, sblock)
for j, itm in enumerate(sblock.items):
# is this item masked (e.g. was deleted)?
while (True):
if counter > len(mask) or mask[counter]:
break
else:
counter += 1
# find my mapping location
m[j] = prev_items_map[counter]
counter += 1
# set the ref_locs in this block
sblock.set_ref_locs(m)
# reset the ref_locs to the new structure
if ref_locs is not None:
# items_map is now good, with the original locations
self._set_ref_locs(do_refs=True)
# reset the ref_locs based on the now good block._ref_locs
self._reset_ref_locs()
def _add_new_block(self, item, value, loc=None):
# Do we care about dtype at the moment?
# hm, elaborate hack?
if loc is None:
loc = self.items.get_loc(item)
new_block = make_block(value, self.items[loc:loc + 1].copy(),
self.items, fastpath=True)
self.blocks.append(new_block)
# set ref_locs based on the this new block
# and add to the ref/items maps
if not self.items.is_unique:
# insert into the ref_locs at the appropriate location
# _ref_locs is already long enough,
# but may need to shift elements
new_block.set_ref_locs([0])
# need to shift elements to the right
if self._ref_locs[loc] is not None:
for i in reversed(lrange(loc + 1, len(self._ref_locs))):
self._ref_locs[i] = self._ref_locs[i - 1]
self._ref_locs[loc] = (new_block, 0)
# and reset
self._reset_ref_locs()
self._set_ref_locs(do_refs=True)
def _find_block(self, item):
self._check_have(item)
for i, block in enumerate(self.blocks):
if item in block:
return i, block
def _check_have(self, item):
if item not in self.items:
raise KeyError('no item named %s' % com.pprint_thing(item))
def reindex_axis(self, new_axis, indexer=None, method=None, axis=0,
fill_value=None, limit=None, copy=True):
new_axis = _ensure_index(new_axis)
cur_axis = self.axes[axis]
if new_axis.equals(cur_axis):
if copy:
result = self.copy(deep=True)
result.axes[axis] = new_axis
result._shape = None
if axis == 0:
# patch ref_items, #1823
for blk in result.blocks:
blk.ref_items = new_axis
return result
else:
return self
if axis == 0:
if method is not None or limit is not None:
return self.reindex_axis0_with_method(
new_axis, indexer=indexer, method=method,
fill_value=fill_value, limit=limit, copy=copy
)
return self.reindex_items(new_axis, indexer=indexer, copy=copy,
fill_value=fill_value)
new_axis, indexer = cur_axis.reindex(
new_axis, method, copy_if_needed=True)
return self.reindex_indexer(new_axis, indexer, axis=axis,
fill_value=fill_value)
def reindex_axis0_with_method(self, new_axis, indexer=None, method=None,
fill_value=None, limit=None, copy=True):
raise AssertionError('method argument not supported for '
'axis == 0')
def reindex_indexer(self, new_axis, indexer, axis=1, fill_value=None,
allow_dups=False):
"""
pandas-indexer with -1's only.
"""
# trying to reindex on an axis with duplicates
if not allow_dups and not self.axes[axis].is_unique:
raise ValueError("cannot reindex from a duplicate axis")
if not self.is_consolidated():
self = self.consolidate()
if axis == 0:
return self._reindex_indexer_items(new_axis, indexer, fill_value)
new_blocks = []
for block in self.blocks:
newb = block.reindex_axis(
indexer, axis=axis, fill_value=fill_value)
new_blocks.append(newb)
new_axes = list(self.axes)
new_axes[axis] = new_axis
return self.__class__(new_blocks, new_axes)
def _reindex_indexer_items(self, new_items, indexer, fill_value):
# TODO: less efficient than I'd like
item_order = com.take_1d(self.items.values, indexer)
new_axes = [new_items] + self.axes[1:]
new_blocks = []
is_unique = new_items.is_unique
# we have duplicates in the items and what we are reindexing
if not is_unique and not self.items.is_unique:
rl = self._set_ref_locs(do_refs='force')
for i, idx in enumerate(indexer):
item = new_items.take([i])
if idx >= 0:
blk, lidx = rl[idx]
blk = make_block(_block_shape(blk.iget(lidx)), item,
new_items, ndim=self.ndim, fastpath=True,
placement=[i])
# a missing value
else:
blk = self._make_na_block(item,
new_items,
placement=[i],
fill_value=fill_value)
new_blocks.append(blk)
new_blocks = _consolidate(new_blocks, new_items)
# keep track of what items aren't found anywhere
else:
l = np.arange(len(item_order))
mask = np.zeros(len(item_order), dtype=bool)
for blk in self.blocks:
blk_indexer = blk.items.get_indexer(item_order)
selector = blk_indexer != -1
# update with observed items
mask |= selector
if not selector.any():
continue
new_block_items = new_items.take(selector.nonzero()[0])
new_values = com.take_nd(blk.values, blk_indexer[selector], axis=0,
allow_fill=False)
placement = l[selector] if not is_unique else None
new_blocks.append(make_block(new_values,
new_block_items,
new_items,
placement=placement,
fastpath=True))
if not mask.all():
na_items = new_items[-mask]
placement = l[-mask] if not is_unique else None
na_block = self._make_na_block(na_items,
new_items,
placement=placement,
fill_value=fill_value)
new_blocks.append(na_block)
new_blocks = _consolidate(new_blocks, new_items)
return self.__class__(new_blocks, new_axes)
def reindex_items(self, new_items, indexer=None, copy=True,
fill_value=None):
"""
"""
new_items = _ensure_index(new_items)
data = self
if not data.is_consolidated():
data = data.consolidate()
return data.reindex_items(new_items, copy=copy,
fill_value=fill_value)
if indexer is None:
new_items, indexer = self.items.reindex(new_items,
copy_if_needed=True)
new_axes = [new_items] + self.axes[1:]
# could have so me pathological (MultiIndex) issues here
new_blocks = []
if indexer is None:
for blk in self.blocks:
if copy:
blk = blk.reindex_items_from(new_items)
else:
blk.ref_items = new_items
new_blocks.extend(_valid_blocks(blk))
else:
# unique
if self.axes[0].is_unique and new_items.is_unique:
for block in self.blocks:
blk = block.reindex_items_from(new_items, copy=copy)
new_blocks.extend(_valid_blocks(blk))
# non-unique
else:
rl = self._set_ref_locs(do_refs='force')
for i, idx in enumerate(indexer):
blk, lidx = rl[idx]
item = new_items.take([i])
blk = make_block(_block_shape(blk.iget(lidx)), item,
new_items, ndim=self.ndim, fastpath=True,
placement=[i])
new_blocks.append(blk)
# add a na block if we are missing items
mask = indexer == -1
if mask.any():
extra_items = new_items[mask]
na_block = self._make_na_block(extra_items, new_items,
fill_value=fill_value)
new_blocks.append(na_block)
new_blocks = _consolidate(new_blocks, new_items)
return self.__class__(new_blocks, new_axes)
def _make_na_block(self, items, ref_items, placement=None,
fill_value=None):
# TODO: infer dtypes other than float64 from fill_value
if fill_value is None:
fill_value = np.nan
block_shape = list(self.shape)
block_shape[0] = len(items)
dtype, fill_value = com._infer_dtype_from_scalar(fill_value)
block_values = np.empty(block_shape, dtype=dtype)
block_values.fill(fill_value)
return make_block(block_values, items, ref_items, placement=placement)
def take(self, indexer, new_index=None, axis=1, verify=True):
if axis < 1:
raise AssertionError('axis must be at least 1, got %d' % axis)
self._consolidate_inplace()
if isinstance(indexer, list):
indexer = np.array(indexer)
indexer = com._ensure_platform_int(indexer)
n = len(self.axes[axis])
if verify:
indexer = _maybe_convert_indices(indexer, n)
if ((indexer == -1) | (indexer >= n)).any():
raise Exception('Indices must be nonzero and less than '
'the axis length')
new_axes = list(self.axes)
if new_index is None:
new_index = self.axes[axis].take(indexer)
new_axes[axis] = new_index
return self.apply('take',
axes=new_axes,
indexer=indexer,
ref_items=new_axes[0],
new_axis=new_axes[axis],
axis=axis)
def merge(self, other, lsuffix=None, rsuffix=None):
if not self._is_indexed_like(other):
raise AssertionError('Must have same axes to merge managers')
this, other = self._maybe_rename_join(other, lsuffix, rsuffix)
cons_items = this.items + other.items
new_axes = list(this.axes)
new_axes[0] = cons_items
consolidated = _consolidate(this.blocks + other.blocks, cons_items)
return self.__class__(consolidated, new_axes)
def _maybe_rename_join(self, other, lsuffix, rsuffix, copydata=True):
to_rename = self.items.intersection(other.items)
if len(to_rename) > 0:
if not lsuffix and not rsuffix:
raise ValueError('columns overlap but no suffix specified: %s'
% to_rename)
def lrenamer(x):
if x in to_rename:
return '%s%s' % (x, lsuffix)
return x
def rrenamer(x):
if x in to_rename:
return '%s%s' % (x, rsuffix)
return x
this = self.rename_items(lrenamer, copy=copydata)
other = other.rename_items(rrenamer, copy=copydata)
else:
this = self
return this, other
def _is_indexed_like(self, other):
"""
Check all axes except items
"""
if self.ndim != other.ndim:
raise AssertionError(('Number of dimensions must agree '
'got %d and %d') % (self.ndim, other.ndim))
for ax, oax in zip(self.axes[1:], other.axes[1:]):
if not ax.equals(oax):
return False
return True
def rename(self, mapper, axis, copy=False):
""" generic rename """
if axis == 0:
return self.rename_items(mapper, copy=copy)
return self.rename_axis(mapper, axis=axis)
def rename_axis(self, mapper, axis=1):
index = self.axes[axis]
if isinstance(index, MultiIndex):
new_axis = MultiIndex.from_tuples(
[tuple(mapper(y) for y in x) for x in index],
names=index.names)
else:
new_axis = Index([mapper(x) for x in index], name=index.name)
if not new_axis.is_unique:
raise AssertionError('New axis must be unique to rename')
new_axes = list(self.axes)
new_axes[axis] = new_axis
return self.__class__(self.blocks, new_axes)
def rename_items(self, mapper, copy=True):
if isinstance(self.items, MultiIndex):
items = [tuple(mapper(y) for y in x) for x in self.items]
new_items = MultiIndex.from_tuples(items, names=self.items.names)
else:
items = [mapper(x) for x in self.items]
new_items = Index(items, name=self.items.name)
new_blocks = []
for block in self.blocks:
newb = block.copy(deep=copy)
newb.set_ref_items(new_items, maybe_rename=True)
new_blocks.append(newb)
new_axes = list(self.axes)
new_axes[0] = new_items
return self.__class__(new_blocks, new_axes)
def add_prefix(self, prefix):
f = (('%s' % prefix) + '%s').__mod__
return self.rename_items(f)
def add_suffix(self, suffix):
f = ('%s' + ('%s' % suffix)).__mod__
return self.rename_items(f)
@property
def block_id_vector(self):
# TODO
result = np.empty(len(self.items), dtype=int)
result.fill(-1)
for i, blk in enumerate(self.blocks):
indexer = self.items.get_indexer(blk.items)
if (indexer == -1).any():
raise AssertionError('Block items must be in manager items')
result.put(indexer, i)
if (result < 0).any():
raise AssertionError('Some items were not in any block')
return result
@property
def item_dtypes(self):
result = np.empty(len(self.items), dtype='O')
mask = np.zeros(len(self.items), dtype=bool)
for i, blk in enumerate(self.blocks):
indexer = self.items.get_indexer(blk.items)
result.put(indexer, blk.dtype.name)
mask.put(indexer, 1)
if not (mask.all()):
raise AssertionError('Some items were not in any block')
return result
def equals(self, other):
self_axes, other_axes = self.axes, other.axes
if len(self_axes) != len(other_axes):
return False
if not all (ax1.equals(ax2) for ax1, ax2 in zip(self_axes, other_axes)):
return False
self._consolidate_inplace()
other._consolidate_inplace()
return all(block.equals(oblock) for block, oblock in
zip(self.blocks, other.blocks))
class SingleBlockManager(BlockManager):
""" manage a single block with """
ndim = 1
_is_consolidated = True
_known_consolidated = True
__slots__ = ['axes', 'blocks', '_block',
'_values', '_shape', '_has_sparse']
def __init__(self, block, axis, do_integrity_check=False, fastpath=True):
if isinstance(axis, list):
if len(axis) != 1:
raise ValueError(
"cannot create SingleBlockManager with more than 1 axis")
axis = axis[0]
# passed from constructor, single block, single axis
if fastpath:
self.axes = [axis]
if isinstance(block, list):
# empty block
if len(block) == 0:
block = [np.array([])]
elif len(block) != 1:
raise ValueError('Cannot create SingleBlockManager with '
'more than 1 block')
block = block[0]
if not isinstance(block, Block):
block = make_block(block, axis, axis, ndim=1, fastpath=True)
else:
self.axes = [_ensure_index(axis)]
# create the block here
if isinstance(block, list):
# provide consolidation to the interleaved_dtype
if len(block) > 1:
dtype = _interleaved_dtype(block)
block = [b.astype(dtype) for b in block]
block = _consolidate(block, axis)
if len(block) != 1:
raise ValueError('Cannot create SingleBlockManager with '
'more than 1 block')
block = block[0]
if not isinstance(block, Block):
block = make_block(block, axis, axis, ndim=1, fastpath=True)
self.blocks = [block]
self._block = self.blocks[0]
self._values = self._block.values
self._has_sparse = self._block.is_sparse
def _post_setstate(self):
self._block = self.blocks[0]
self._values = self._block.values
def _get_counts(self, f):
return { f(self._block) : 1 }
@property
def shape(self):
if getattr(self, '_shape', None) is None:
self._shape = tuple([len(self.axes[0])])
return self._shape
def reindex(self, new_axis, indexer=None, method=None, fill_value=None,
limit=None, copy=True):
# if we are the same and don't copy, just return
if not copy and self.index.equals(new_axis):
return self
block = self._block.reindex_items_from(new_axis, indexer=indexer,
method=method,
fill_value=fill_value,
limit=limit, copy=copy)
mgr = SingleBlockManager(block, new_axis)
mgr._consolidate_inplace()
return mgr
def _reindex_indexer_items(self, new_items, indexer, fill_value):
# equiv to a reindex
return self.reindex(new_items, indexer=indexer, fill_value=fill_value,
copy=False)
def reindex_axis0_with_method(self, new_axis, indexer=None, method=None,
fill_value=None, limit=None, copy=True):
if method is None:
indexer = None
return self.reindex(new_axis, indexer=indexer, method=method,
fill_value=fill_value, limit=limit, copy=copy)
def _delete_from_block(self, i, item):
super(SingleBlockManager, self)._delete_from_block(i, item)
# reset our state
self._block = (
self.blocks[0] if len(self.blocks) else
make_block(np.array([], dtype=self._block.dtype), [], [])
)
self._values = self._block.values
def get_slice(self, slobj, raise_on_error=False):
if raise_on_error:
_check_slice_bounds(slobj, self.index)
return self.__class__(self._block._slice(slobj),
self.index._getitem_slice(slobj), fastpath=True)
def set_axis(self, axis, value, maybe_rename=True, check_axis=True):
cur_axis, value = self._set_axis(axis, value, check_axis)
self._block.set_ref_items(self.items, maybe_rename=maybe_rename)
def set_ref_items(self, ref_items, maybe_rename=True):
""" we can optimize and our ref_locs are always equal to ref_items """
if maybe_rename:
self.items = ref_items
self.ref_items = ref_items
@property
def index(self):
return self.axes[0]
def convert(self, *args, **kwargs):
""" convert the whole block as one """
kwargs['by_item'] = False
return self.apply('convert', *args, **kwargs)
@property
def dtype(self):
return self._block.dtype
@property
def ftype(self):
return self._block.ftype
@property
def values(self):
return self._values.view()
@property
def itemsize(self):
return self._block.itemsize
@property
def _can_hold_na(self):
return self._block._can_hold_na
def is_consolidated(self):
return True
def _consolidate_check(self):
pass
def _consolidate_inplace(self):
pass
def construction_error(tot_items, block_shape, axes, e=None):
""" raise a helpful message about our construction """
passed = tuple(map(int, [tot_items] + list(block_shape)))
implied = tuple(map(int, [len(ax) for ax in axes]))
if passed == implied and e is not None:
raise e
raise ValueError("Shape of passed values is {0}, indices imply {1}".format(
passed,implied))
def create_block_manager_from_blocks(blocks, axes):
try:
# if we are passed values, make the blocks
if len(blocks) == 1 and not isinstance(blocks[0], Block):
placement = None if axes[0].is_unique else np.arange(len(axes[0]))
blocks = [
make_block(blocks[0], axes[0], axes[0], placement=placement)]
mgr = BlockManager(blocks, axes)
mgr._consolidate_inplace()
return mgr
except (ValueError) as e:
blocks = [getattr(b, 'values', b) for b in blocks]
tot_items = sum(b.shape[0] for b in blocks)
construction_error(tot_items, blocks[0].shape[1:], axes, e)
def create_block_manager_from_arrays(arrays, names, axes):
try:
blocks = form_blocks(arrays, names, axes)
mgr = BlockManager(blocks, axes)
mgr._consolidate_inplace()
return mgr
except (ValueError) as e:
construction_error(len(arrays), arrays[0].shape[1:], axes, e)
def maybe_create_block_in_items_map(im, block):
""" create/return the block in an items_map """
try:
return im[block]
except:
im[block] = l = [None] * len(block.items)
return l
def form_blocks(arrays, names, axes):
# pre-filter out items if we passed it
items = axes[0]
if len(arrays) < len(items):
nn = set(names)
extra_items = Index([i for i in items if i not in nn])
else:
extra_items = []
# put "leftover" items in float bucket, where else?
# generalize?
float_items = []
complex_items = []
int_items = []
bool_items = []
object_items = []
sparse_items = []
datetime_items = []
for i, (k, v) in enumerate(zip(names, arrays)):
if isinstance(v, (SparseArray, ABCSparseSeries)):
sparse_items.append((i, k, v))
elif issubclass(v.dtype.type, np.floating):
float_items.append((i, k, v))
elif issubclass(v.dtype.type, np.complexfloating):
complex_items.append((i, k, v))
elif issubclass(v.dtype.type, np.datetime64):
if v.dtype != _NS_DTYPE:
v = tslib.cast_to_nanoseconds(v)
if hasattr(v, 'tz') and v.tz is not None:
object_items.append((i, k, v))
else:
datetime_items.append((i, k, v))
elif issubclass(v.dtype.type, np.integer):
if v.dtype == np.uint64:
# HACK #2355 definite overflow
if (v > 2 ** 63 - 1).any():
object_items.append((i, k, v))
continue
int_items.append((i, k, v))
elif v.dtype == np.bool_:
bool_items.append((i, k, v))
else:
object_items.append((i, k, v))
is_unique = items.is_unique
blocks = []
if len(float_items):
float_blocks = _multi_blockify(float_items, items, is_unique=is_unique)
blocks.extend(float_blocks)
if len(complex_items):
complex_blocks = _simple_blockify(
complex_items, items, np.complex128, is_unique=is_unique)
blocks.extend(complex_blocks)
if len(int_items):
int_blocks = _multi_blockify(int_items, items, is_unique=is_unique)
blocks.extend(int_blocks)
if len(datetime_items):
datetime_blocks = _simple_blockify(
datetime_items, items, _NS_DTYPE, is_unique=is_unique)
blocks.extend(datetime_blocks)
if len(bool_items):
bool_blocks = _simple_blockify(
bool_items, items, np.bool_, is_unique=is_unique)
blocks.extend(bool_blocks)
if len(object_items) > 0:
object_blocks = _simple_blockify(
object_items, items, np.object_, is_unique=is_unique)
blocks.extend(object_blocks)
if len(sparse_items) > 0:
sparse_blocks = _sparse_blockify(sparse_items, items)
blocks.extend(sparse_blocks)
if len(extra_items):
shape = (len(extra_items),) + tuple(len(x) for x in axes[1:])
# empty items -> dtype object
block_values = np.empty(shape, dtype=object)
block_values.fill(np.nan)
placement = None if is_unique else np.arange(len(extra_items))
na_block = make_block(
block_values, extra_items, items, placement=placement)
blocks.append(na_block)
return blocks
def _simple_blockify(tuples, ref_items, dtype, is_unique=True):
""" return a single array of a block that has a single dtype; if dtype is
not None, coerce to this dtype
"""
block_items, values, placement = _stack_arrays(tuples, ref_items, dtype)
# CHECK DTYPE?
if dtype is not None and values.dtype != dtype: # pragma: no cover
values = values.astype(dtype)
if is_unique:
placement = None
block = make_block(values, block_items, ref_items, placement=placement)
return [block]
def _multi_blockify(tuples, ref_items, dtype=None, is_unique=True):
""" return an array of blocks that potentially have different dtypes """
# group by dtype
grouper = itertools.groupby(tuples, lambda x: x[2].dtype)
new_blocks = []
for dtype, tup_block in grouper:
block_items, values, placement = _stack_arrays(
list(tup_block), ref_items, dtype)
if is_unique:
placement = None
block = make_block(values, block_items, ref_items, placement=placement)
new_blocks.append(block)
return new_blocks
def _sparse_blockify(tuples, ref_items, dtype=None):
""" return an array of blocks that potentially have different dtypes (and
are sparse)
"""
new_blocks = []
for i, names, array in tuples:
if not isinstance(names, (list, tuple)):
names = [names]
items = ref_items[ref_items.isin(names)]
array = _maybe_to_sparse(array)
block = make_block(
array, items, ref_items, klass=SparseBlock, fastpath=True)
new_blocks.append(block)
return new_blocks
def _stack_arrays(tuples, ref_items, dtype):
# fml
def _asarray_compat(x):
if isinstance(x, ABCSeries):
return x.values
else:
return np.asarray(x)
def _shape_compat(x):
if isinstance(x, ABCSeries):
return len(x),
else:
return x.shape
placement, names, arrays = zip(*tuples)
first = arrays[0]
shape = (len(arrays),) + _shape_compat(first)
stacked = np.empty(shape, dtype=dtype)
for i, arr in enumerate(arrays):
stacked[i] = _asarray_compat(arr)
# index may box values
if ref_items.is_unique:
items = ref_items[ref_items.isin(names)]
else:
# a mi
if isinstance(ref_items, MultiIndex):
names = MultiIndex.from_tuples(names)
items = ref_items[ref_items.isin(names)]
# plain old dups
else:
items = _ensure_index([n for n in names if n in ref_items])
if len(items) != len(stacked):
raise ValueError("invalid names passed _stack_arrays")
return items, stacked, placement
def _blocks_to_series_dict(blocks, index=None):
from pandas.core.series import Series
series_dict = {}
for block in blocks:
for item, vec in zip(block.items, block.values):
series_dict[item] = Series(vec, index=index, name=item)
return series_dict
def _interleaved_dtype(blocks):
if not len(blocks):
return None
counts = defaultdict(lambda: [])
for x in blocks:
counts[type(x)].append(x)
def _lcd_dtype(l):
""" find the lowest dtype that can accomodate the given types """
m = l[0].dtype
for x in l[1:]:
if x.dtype.itemsize > m.itemsize:
m = x.dtype
return m
have_int = len(counts[IntBlock]) > 0
have_bool = len(counts[BoolBlock]) > 0
have_object = len(counts[ObjectBlock]) > 0
have_float = len(counts[FloatBlock]) > 0
have_complex = len(counts[ComplexBlock]) > 0
have_dt64 = len(counts[DatetimeBlock]) > 0
have_td64 = len(counts[TimeDeltaBlock]) > 0
have_sparse = len(counts[SparseBlock]) > 0
have_numeric = have_float or have_complex or have_int
if (have_object or
(have_bool and have_numeric) or
(have_numeric and (have_dt64 or have_td64))):
return np.dtype(object)
elif have_bool:
return np.dtype(bool)
elif have_int and not have_float and not have_complex:
# if we are mixing unsigned and signed, then return
# the next biggest int type (if we can)
lcd = _lcd_dtype(counts[IntBlock])
kinds = set([i.dtype.kind for i in counts[IntBlock]])
if len(kinds) == 1:
return lcd
if lcd == 'uint64' or lcd == 'int64':
return np.dtype('int64')
# return 1 bigger on the itemsize if unsinged
if lcd.kind == 'u':
return np.dtype('int%s' % (lcd.itemsize * 8 * 2))
return lcd
elif have_dt64 and not have_float and not have_complex:
return np.dtype('M8[ns]')
elif have_td64 and not have_float and not have_complex:
return np.dtype('m8[ns]')
elif have_complex:
return np.dtype('c16')
else:
return _lcd_dtype(counts[FloatBlock] + counts[SparseBlock])
def _consolidate(blocks, items):
"""
Merge blocks having same dtype, exclude non-consolidating blocks
"""
# sort by _can_consolidate, dtype
gkey = lambda x: x._consolidate_key
grouper = itertools.groupby(sorted(blocks, key=gkey), gkey)
new_blocks = []
for (_can_consolidate, dtype), group_blocks in grouper:
merged_blocks = _merge_blocks(list(group_blocks), items, dtype=dtype,
_can_consolidate=_can_consolidate)
if isinstance(merged_blocks, list):
new_blocks.extend(merged_blocks)
else:
new_blocks.append(merged_blocks)
return new_blocks
def _valid_blocks(newb):
if newb is None:
return []
if not isinstance(newb, list):
newb = [ newb ]
return [ b for b in newb if len(b.items) > 0 ]
def _merge_blocks(blocks, items, dtype=None, _can_consolidate=True):
if len(blocks) == 1:
return blocks[0]
if _can_consolidate:
if dtype is None:
if len(set([b.dtype for b in blocks])) != 1:
raise AssertionError("_merge_blocks are invalid!")
dtype = blocks[0].dtype
if not items.is_unique:
blocks = sorted(blocks, key=lambda b: b.ref_locs.tolist())
new_values = _vstack([b.values for b in blocks], dtype)
new_items = blocks[0].items.append([b.items for b in blocks[1:]])
new_block = make_block(new_values, new_items, items)
# unique, can reindex
if items.is_unique:
return new_block.reindex_items_from(items)
# merge the ref_locs
new_ref_locs = [b._ref_locs for b in blocks]
if all([x is not None for x in new_ref_locs]):
new_block.set_ref_locs(np.concatenate(new_ref_locs))
return new_block
# no merge
return blocks
def _block_shape(values, ndim=1, shape=None):
""" guarantee the shape of the values to be at least 1 d """
if values.ndim <= ndim:
if shape is None:
shape = values.shape
values = values.reshape(tuple((1,) + shape))
return values
def _vstack(to_stack, dtype):
# work around NumPy 1.6 bug
if dtype == _NS_DTYPE or dtype == _TD_DTYPE:
new_values = np.vstack([x.view('i8') for x in to_stack])
return new_values.view(dtype)
else:
return np.vstack(to_stack)
def _possibly_convert_to_indexer(loc):
if com._is_bool_indexer(loc):
loc = [i for i, v in enumerate(loc) if v]
elif isinstance(loc, slice):
loc = lrange(loc.start, loc.stop)
return loc
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