/
common.py
533 lines (444 loc) · 14.4 KB
/
common.py
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"""
Misc tools for implementing data structures
"""
try:
from io import BytesIO
except ImportError: # Python < 2.6
from cStringIO import StringIO as BytesIO
import itertools
from numpy.lib.format import read_array, write_array
import numpy as np
import pandas._tseries as lib
# XXX: HACK for NumPy 1.5.1 to suppress warnings
try:
np.seterr(all='ignore')
except Exception: # pragma: no cover
pass
class PandasError(Exception):
pass
def isnull(obj):
'''
Replacement for numpy.isnan / -numpy.isfinite which is suitable
for use on object arrays.
Parameters
----------
arr: ndarray or object value
Returns
-------
boolean ndarray or boolean
'''
if np.isscalar(obj) or obj is None:
return lib.checknull(obj)
from pandas.core.generic import PandasObject
from pandas import Series
if isinstance(obj, np.ndarray):
if obj.dtype.kind in ('O', 'S'):
# Working around NumPy ticket 1542
shape = obj.shape
result = np.empty(shape, dtype=bool)
vec = lib.isnullobj(obj.ravel())
result[:] = vec.reshape(shape)
if isinstance(obj, Series):
result = Series(result, index=obj.index, copy=False)
else:
result = -np.isfinite(obj)
return result
elif isinstance(obj, PandasObject):
# TODO: optimize for DataFrame, etc.
return obj.apply(isnull)
else:
return obj is None
def notnull(obj):
'''
Replacement for numpy.isfinite / -numpy.isnan which is suitable
for use on object arrays.
Parameters
----------
arr: ndarray or object value
Returns
-------
boolean ndarray or boolean
'''
res = isnull(obj)
if np.isscalar(res):
return not res
return -res
def _pickle_array(arr):
arr = arr.view(np.ndarray)
buf = BytesIO()
write_array(buf, arr)
return buf.getvalue()
def _unpickle_array(bytes):
arr = read_array(BytesIO(bytes))
return arr
def _take_1d_bool(arr, indexer, out):
view = arr.view(np.uint8)
outview = out.view(np.uint8)
lib.take_1d_bool(view, indexer, outview)
def _take_2d_axis0_bool(arr, indexer, out):
view = arr.view(np.uint8)
outview = out.view(np.uint8)
lib.take_2d_axis0_bool(view, indexer, outview)
def _take_2d_axis1_bool(arr, indexer, out):
view = arr.view(np.uint8)
outview = out.view(np.uint8)
lib.take_2d_axis1_bool(view, indexer, outview)
_take1d_dict = {
'float64' : lib.take_1d_float64,
'int32' : lib.take_1d_int32,
'int64' : lib.take_1d_int64,
'object' : lib.take_1d_object,
'bool' : _take_1d_bool
}
_take2d_axis0_dict = {
'float64' : lib.take_2d_axis0_float64,
'int32' : lib.take_2d_axis0_int32,
'int64' : lib.take_2d_axis0_int64,
'object' : lib.take_2d_axis0_object,
'bool' : _take_2d_axis0_bool
}
_take2d_axis1_dict = {
'float64' : lib.take_2d_axis1_float64,
'int32' : lib.take_2d_axis1_int32,
'int64' : lib.take_2d_axis1_int64,
'object' : lib.take_2d_axis1_object,
'bool' : _take_2d_axis1_bool
}
def _get_take2d_function(dtype_str, axis=0):
if axis == 0:
return _take2d_axis0_dict[dtype_str]
else:
return _take2d_axis1_dict[dtype_str]
def take_1d(arr, indexer, out=None):
"""
Specialized Cython take which sets NaN values in one pass
"""
dtype_str = arr.dtype.name
n = len(indexer)
if not isinstance(indexer, np.ndarray):
# Cython methods expects 32-bit integers
indexer = np.array(indexer, dtype=np.int32)
out_passed = out is not None
if dtype_str in ('int32', 'int64', 'bool'):
try:
if out is None:
out = np.empty(n, dtype=arr.dtype)
take_f = _take1d_dict[dtype_str]
take_f(arr, indexer, out=out)
except ValueError:
mask = indexer == -1
out = arr.take(indexer, out=out)
if mask.any():
if out_passed:
raise Exception('out with dtype %s does not support NA' %
out.dtype)
out = _maybe_upcast(out)
np.putmask(out, mask, np.nan)
elif dtype_str in ('float64', 'object'):
if out is None:
out = np.empty(n, dtype=arr.dtype)
take_f = _take1d_dict[dtype_str]
take_f(arr, indexer, out=out)
else:
out = arr.take(indexer, out=out)
mask = indexer == -1
if mask.any():
if out_passed:
raise Exception('out with dtype %s does not support NA' %
out.dtype)
out = _maybe_upcast(out)
np.putmask(out, mask, np.nan)
return out
def take_2d(arr, indexer, out=None, mask=None, needs_masking=None, axis=0):
"""
Specialized Cython take which sets NaN values in one pass
"""
dtype_str = arr.dtype.name
out_shape = list(arr.shape)
out_shape[axis] = len(indexer)
out_shape = tuple(out_shape)
if not isinstance(indexer, np.ndarray):
# Cython methods expects 32-bit integers
indexer = np.array(indexer, dtype=np.int32)
if dtype_str in ('int32', 'int64', 'bool'):
if mask is None:
mask = indexer == -1
needs_masking = mask.any()
if needs_masking:
# upcasting may be required
result = arr.take(indexer, axis=axis, out=out)
result = _maybe_mask(result, mask, needs_masking, axis=axis,
out_passed=out is not None)
return result
else:
if out is None:
out = np.empty(out_shape, dtype=arr.dtype)
take_f = _get_take2d_function(dtype_str, axis=axis)
take_f(arr, indexer, out=out)
return out
elif dtype_str in ('float64', 'object'):
if out is None:
out = np.empty(out_shape, dtype=arr.dtype)
take_f = _get_take2d_function(dtype_str, axis=axis)
take_f(arr, indexer, out=out)
return out
else:
if mask is None:
mask = indexer == -1
needs_masking = mask.any()
result = arr.take(indexer, axis=axis, out=out)
result = _maybe_mask(result, mask, needs_masking, axis=axis,
out_passed=out is not None)
return result
def null_out_axis(arr, mask, axis):
indexer = [slice(None)] * arr.ndim
indexer[axis] = mask
arr[tuple(indexer)] = np.NaN
def take_fast(arr, indexer, mask, needs_masking, axis=0, out=None):
if arr.ndim == 2:
return take_2d(arr, indexer, out=out, mask=mask,
needs_masking=needs_masking,
axis=axis)
result = arr.take(indexer, axis=axis, out=out)
result = _maybe_mask(result, mask, needs_masking, axis=axis,
out_passed=out is not None)
return result
def _maybe_mask(result, mask, needs_masking, axis=0, out_passed=False):
if needs_masking:
if out_passed and _need_upcast(result):
raise Exception('incompatible type for NAs')
else:
# a bit spaghettified
result = _maybe_upcast(result)
null_out_axis(result, mask, axis)
return result
def _maybe_upcast(values):
if issubclass(values.dtype.type, np.integer):
values = values.astype(float)
elif issubclass(values.dtype.type, np.bool_):
values = values.astype(object)
return values
def _need_upcast(values):
if issubclass(values.dtype.type, (np.integer, np.bool_)):
return True
return False
#-------------------------------------------------------------------------------
# Lots of little utilities
def _infer_dtype(value):
if isinstance(value, (float, np.floating)):
return float
elif isinstance(value, (bool, np.bool_)):
return bool
elif isinstance(value, (int, np.integer)):
return int
else:
return object
def _is_bool_indexer(key):
if isinstance(key, np.ndarray) and key.dtype == np.object_:
mask = isnull(key)
if mask.any():
raise ValueError('cannot index with vector containing '
'NA / NaN values')
return set([True, False]).issubset(set(key))
elif isinstance(key, np.ndarray) and key.dtype == np.bool_:
return True
elif isinstance(key, list):
try:
return np.asarray(key).dtype == np.bool_
except TypeError: # pragma: no cover
return False
return False
def _default_index(n):
from pandas.core.index import NULL_INDEX
if n == 0:
return NULL_INDEX
else:
return np.arange(n)
def ensure_float(arr):
if issubclass(arr.dtype.type, np.integer):
arr = arr.astype(float)
return arr
def _mut_exclusive(arg1, arg2):
if arg1 is not None and arg2 is not None:
raise Exception('mutually exclusive arguments')
elif arg1 is not None:
return arg1
else:
return arg2
def _any_none(*args):
for arg in args:
if arg is None:
return True
return False
def _all_not_none(*args):
for arg in args:
if arg is None:
return False
return True
def _try_sort(iterable):
listed = list(iterable)
try:
return sorted(listed)
except Exception:
return listed
def set_printoptions(precision=None, column_space=None):
"""
Alter default behavior of DataFrame.toString
precision : int
Floating point output precision
column_space : int
Default space for DataFrame columns, defaults to 12
"""
global _float_format, _column_space
if precision is not None:
float_format = '%.' + '%d' % precision + 'g'
_float_format = lambda x: float_format % x
if column_space is not None:
_column_space = column_space
_float_format = lambda x: '%.4g' % x
_column_space = 12
def _pfixed(s, space, nanRep=None, float_format=None):
if isinstance(s, float):
if nanRep is not None and isnull(s):
if np.isnan(s):
s = nanRep
return (' %s' % s).ljust(space)
if float_format:
formatted = float_format(s)
else:
is_neg = s < 0
formatted = _float_format(np.abs(s))
if is_neg:
formatted = '-' + formatted
else:
formatted = ' ' + formatted
return formatted.ljust(space)
else:
stringified = _stringify(s)
return (' %s' % stringified)[:space].ljust(space)
def _stringify(col):
# unicode workaround
if isinstance(col, tuple):
return str(col)
else:
return '%s' % col
def _format(s, nanRep=None, float_format=None):
if isinstance(s, float):
if nanRep is not None and isnull(s):
if np.isnan(s):
s = nanRep
return ' %s' % s
if float_format:
formatted = float_format(s)
else:
is_neg = s < 0
formatted = _float_format(np.abs(s))
if is_neg:
formatted = '-' + formatted
else:
formatted = ' ' + formatted
return formatted
else:
return ' %s' % _stringify(s)
#-------------------------------------------------------------------------------
# miscellaneous python tools
def rands(n):
"""Generates a random alphanumeric string of length *n*"""
from random import Random
import string
return ''.join(Random().sample(string.ascii_letters+string.digits, n))
def adjoin(space, *lists):
"""
Glues together two sets of strings using the amount of space requested.
The idea is to prettify.
"""
outLines = []
newLists = []
lengths = [max(map(len, x)) + space for x in lists[:-1]]
# not the last one
lengths.append(max(map(len, lists[-1])))
maxLen = max(map(len, lists))
for i, lst in enumerate(lists):
nl = [x.ljust(lengths[i]) for x in lst]
nl.extend([' ' * lengths[i]] * (maxLen - len(lst)))
newLists.append(nl)
toJoin = zip(*newLists)
for lines in toJoin:
outLines.append(''.join(lines))
return '\n'.join(outLines)
def iterpairs(seq):
"""
Parameters
----------
seq: sequence
Returns
-------
iterator returning overlapping pairs of elements
Example
-------
>>> iterpairs([1, 2, 3, 4])
[(1, 2), (2, 3), (3, 4)
"""
# input may not be sliceable
seq_it = iter(seq)
seq_it_next = iter(seq)
_ = seq_it_next.next()
return itertools.izip(seq_it, seq_it_next)
def indent(string, spaces=4):
dent = ' ' * spaces
return '\n'.join([dent + x for x in string.split('\n')])
def banner(message):
"""
Return 80-char width message declaration with = bars on top and bottom.
"""
bar = '=' * 80
return '%s\n%s\n%s' % (bar, message, bar)
class groupby(dict):
"""
A simple groupby different from the one in itertools.
Does not require the sequence elements to be sorted by keys,
however it is slower.
"""
def __init__(self, seq, key=lambda x:x):
for value in seq:
k = key(value)
self.setdefault(k, []).append(value)
try:
__iter__ = dict.iteritems
except AttributeError: # Python 3
def __iter__(self):
return iter(dict.items(self))
def map_indices_py(arr):
"""
Returns a dictionary with (element, index) pairs for each element in the
given array/list
"""
return dict([(x, i) for i, x in enumerate(arr)])
def union(*seqs):
result = set([])
for seq in seqs:
if not isinstance(seq, set):
seq = set(seq)
result |= seq
return type(seqs[0])(list(result))
def difference(a, b):
return type(a)(list(set(a) - set(b)))
def intersection(*seqs):
result = set(seqs[0])
for seq in seqs:
if not isinstance(seq, set):
seq = set(seq)
result &= seq
return type(seqs[0])(list(result))
def _asarray_tuplesafe(values, dtype=None):
if not isinstance(values, (list, tuple, np.ndarray)):
values = list(values)
if isinstance(values, list) and dtype == np.object_:
return lib.list_to_object_array(values)
result = np.asarray(values, dtype=dtype)
if issubclass(result.dtype.type, basestring):
result = np.asarray(values, dtype=object)
if result.ndim == 2:
result = np.empty(len(values), dtype=object)
result[:] = values
return result