/usr/share/pyshared/pandas/core/nanops.py is in python-pandas 0.7.0-1.
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import numpy as np
from pandas.core.common import isnull, notnull
import pandas.core.common as com
import pandas._tseries as lib
try:
import bottleneck as bn
_USE_BOTTLENECK = True
except ImportError: # pragma: no cover
_USE_BOTTLENECK = False
def _bottleneck_switch(bn_name, alt, **kwargs):
try:
bn_func = getattr(bn, bn_name)
except (AttributeError, NameError): # pragma: no cover
bn_func = None
def f(values, axis=None, skipna=True):
try:
if _USE_BOTTLENECK and skipna and values.dtype != np.object_:
result = bn_func(values, axis=axis, **kwargs)
# prefer to treat inf/-inf as NA
if _has_infs(result):
result = alt(values, axis=axis, skipna=skipna, **kwargs)
else:
result = alt(values, axis=axis, skipna=skipna, **kwargs)
except Exception:
result = alt(values, axis=axis, skipna=skipna, **kwargs)
return result
return f
def _has_infs(result):
if isinstance(result, np.ndarray):
if result.dtype == 'f8':
return lib.has_infs_f8(result)
elif result.dtype == 'f4':
return lib.has_infs_f4(result)
else: # pragma: no cover
raise TypeError('Only suppose float32/64 here')
else:
return np.isinf(result) or np.isneginf(result)
def _nansum(values, axis=None, skipna=True):
mask = isnull(values)
if skipna and not issubclass(values.dtype.type, np.integer):
values = values.copy()
np.putmask(values, mask, 0)
the_sum = values.sum(axis)
the_sum = _maybe_null_out(the_sum, axis, mask)
return the_sum
def _nanmean(values, axis=None, skipna=True):
mask = isnull(values)
if skipna and not issubclass(values.dtype.type, np.integer):
values = values.copy()
np.putmask(values, mask, 0)
the_sum = _ensure_numeric(values.sum(axis))
count = _get_counts(mask, axis)
if axis is not None:
the_mean = the_sum / count
ct_mask = count == 0
if ct_mask.any():
the_mean[ct_mask] = np.nan
else:
the_mean = the_sum / count if count > 0 else np.nan
return the_mean
def _nanmedian(values, axis=None, skipna=True):
def get_median(x):
mask = notnull(x)
if not skipna and not mask.all():
return np.nan
return lib.median(x[mask])
if values.dtype != np.float64:
values = values.astype('f8')
if values.ndim > 1:
return np.apply_along_axis(get_median, axis, values)
else:
return get_median(values)
def _nanvar(values, axis=None, skipna=True, ddof=1):
mask = isnull(values)
if axis is not None:
count = (values.shape[axis] - mask.sum(axis)).astype(float)
else:
count = float(values.size - mask.sum())
if skipna:
values = values.copy()
np.putmask(values, mask, 0)
X = _ensure_numeric(values.sum(axis))
XX = _ensure_numeric((values ** 2).sum(axis))
return (XX - X ** 2 / count) / (count - ddof)
def _nanmin(values, axis=None, skipna=True):
mask = isnull(values)
if skipna and not issubclass(values.dtype.type, np.integer):
values = values.copy()
np.putmask(values, mask, np.inf)
# numpy 1.6.1 workaround in Python 3.x
if (values.dtype == np.object_
and sys.version_info[0] >= 3): # pragma: no cover
import __builtin__
if values.ndim > 1:
apply_ax = axis if axis is not None else 0
result = np.apply_along_axis(__builtin__.min, apply_ax, values)
else:
result = __builtin__.min(values)
else:
result = values.min(axis)
return _maybe_null_out(result, axis, mask)
def _nanmax(values, axis=None, skipna=True):
mask = isnull(values)
if skipna and not issubclass(values.dtype.type, np.integer):
values = values.copy()
np.putmask(values, mask, -np.inf)
# numpy 1.6.1 workaround in Python 3.x
if (values.dtype == np.object_
and sys.version_info[0] >= 3): # pragma: no cover
import __builtin__
if values.ndim > 1:
apply_ax = axis if axis is not None else 0
result = np.apply_along_axis(__builtin__.max, apply_ax, values)
else:
result = __builtin__.max(values)
else:
result = values.max(axis)
return _maybe_null_out(result, axis, mask)
def nanargmax(values, axis=None, skipna=True):
"""
Returns -1 in the NA case
"""
mask = -np.isfinite(values)
if not issubclass(values.dtype.type, np.integer):
values = values.copy()
np.putmask(values, mask, -np.inf)
result = values.argmax(axis)
result = _maybe_arg_null_out(result, axis, mask, skipna)
return result
def nanargmin(values, axis=None, skipna=True):
"""
Returns -1 in the NA case
"""
mask = -np.isfinite(values)
if not issubclass(values.dtype.type, np.integer):
values = values.copy()
np.putmask(values, mask, np.inf)
result = values.argmin(axis)
result = _maybe_arg_null_out(result, axis, mask, skipna)
return result
nansum = _bottleneck_switch('nansum', _nansum)
nanmean = _bottleneck_switch('nanmean', _nanmean)
nanmedian = _bottleneck_switch('nanmedian', _nanmedian)
nanvar = _bottleneck_switch('nanvar', _nanvar, ddof=1)
nanmin = _bottleneck_switch('nanmin', _nanmin)
nanmax = _bottleneck_switch('nanmax', _nanmax)
def nanskew(values, axis=None, skipna=True):
if not isinstance(values.dtype.type, np.floating):
values = values.astype('f8')
mask = isnull(values)
count = _get_counts(mask, axis)
if skipna:
values = values.copy()
np.putmask(values, mask, 0)
A = values.sum(axis) / count
B = (values ** 2).sum(axis) / count - A ** 2
C = (values ** 3).sum(axis) / count - A ** 3 - 3 * A * B
# floating point error
B = _zero_out_fperr(B)
C = _zero_out_fperr(C)
result = ((np.sqrt((count ** 2 - count)) * C) /
((count - 2) * np.sqrt(B) ** 3))
if isinstance(result, np.ndarray):
result = np.where(B == 0, 0, result)
result[count < 3] = np.nan
return result
else:
result = 0 if B == 0 else result
if count < 3:
return np.nan
return result
def nanprod(values, axis=None, skipna=True):
mask = isnull(values)
if skipna and not issubclass(values.dtype.type, np.integer):
values = values.copy()
values[mask] = 1
result = values.prod(axis)
return _maybe_null_out(result, axis, mask)
def _maybe_arg_null_out(result, axis, mask, skipna):
# helper function for nanargmin/nanargmax
if axis is None:
if skipna:
if mask.all():
result = -1
else:
if mask.any():
result = -1
else:
if skipna:
na_mask = mask.all(axis)
else:
na_mask = mask.any(axis)
if na_mask.any():
result[na_mask] = -1
return result
def _get_counts(mask, axis):
if axis is not None:
count = (mask.shape[axis] - mask.sum(axis)).astype(float)
else:
count = float(mask.size - mask.sum())
return count
def _maybe_null_out(result, axis, mask):
if axis is not None:
null_mask = (mask.shape[axis] - mask.sum(axis)) == 0
if null_mask.any():
result = result.astype('f8')
result[null_mask] = np.nan
else:
null_mask = mask.size - mask.sum()
if null_mask == 0:
result = np.nan
return result
def _zero_out_fperr(arg):
if isinstance(arg, np.ndarray):
return np.where(np.abs(arg) < 1e-14, 0, arg)
else:
return 0 if np.abs(arg) < 1e-14 else arg
def nancorr(a, b, method='pearson'):
"""
a, b: ndarrays
"""
assert(len(a) == len(b))
valid = notnull(a) & notnull(b)
if not valid.all():
a = a[valid]
b = b[valid]
if len(a) == 0:
return np.nan
f = get_corr_func(method)
return f(a, b)
def get_corr_func(method):
if method in ['kendall', 'spearman']:
from scipy.stats import kendalltau, spearmanr
def _pearson(a, b):
return np.corrcoef(a, b)[0, 1]
def _kendall(a, b):
return kendalltau(a, b)[0]
def _spearman(a, b):
return spearmanr(a, b)[0]
_cor_methods = {
'pearson' : _pearson,
'kendall' : _kendall,
'spearman' : _spearman
}
return _cor_methods[method]
def nancov(a, b):
assert(len(a) == len(b))
valid = notnull(a) & notnull(b)
if not valid.all():
a = a[valid]
b = b[valid]
if len(a) == 0:
return np.nan
return np.cov(a, b)[0, 1]
def _ensure_numeric(x):
if isinstance(x, np.ndarray):
if x.dtype == np.object_:
x = x.astype(np.float64)
elif not (com.is_float(x) or com.is_integer(x)):
try:
x = float(x)
except Exception:
raise TypeError('Could not convert %s to numeric' % str(x))
return x
# NA-friendly array comparisons
import operator
def make_nancomp(op):
def f(x, y):
xmask = isnull(x)
ymask = isnull(y)
mask = xmask | ymask
result = op(x, y)
if mask.any():
if result.dtype == np.bool_:
result = result.astype('O')
np.putmask(result, mask, np.nan)
return result
return f
nangt = make_nancomp(operator.gt)
nange = make_nancomp(operator.ge)
nanlt = make_nancomp(operator.lt)
nanle = make_nancomp(operator.le)
naneq = make_nancomp(operator.eq)
nanne = make_nancomp(operator.ne)
def unique1d(values):
"""
Hash table-based unique
"""
if issubclass(values.dtype.type, np.floating):
if values.dtype != np.float64:
values = values.astype(np.float64)
table = lib.Float64HashTable(len(values))
uniques = np.array(table.unique(values), dtype=np.float64)
elif issubclass(values.dtype.type, np.integer):
if values.dtype != np.int64:
values = values.astype(np.int64)
table = lib.Int64HashTable(len(values))
uniques = np.array(table.unique(values), dtype=np.int64)
else:
if not values.dtype == np.object_:
values = values.astype(np.object_)
table = lib.PyObjectHashTable(len(values))
uniques = lib.list_to_object_array(table.unique(values))
uniques = lib.maybe_convert_objects(uniques)
return uniques
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