def nanskew(values, axis=None, skipna=True):
mask = isnull(values)
if not is_floating_dtype(values):
values = values.astype('f8')
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 nankurt(values, axis=None, skipna=True):
mask = isnull(values)
if not is_floating_dtype(values):
values = values.astype('f8')
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
D = (values**4).sum(axis) / count - A**4 - 6 * B * A * A - 4 * C * A
B = _zero_out_fperr(B)
D = _zero_out_fperr(D)
if not isinstance(B, np.ndarray):
# if B is a scalar, check these corner cases first before doing division
if count < 4:
return np.nan
if B == 0:
return 0
result = (((count * count - 1.) * D / (B * B) - 3 * ((count - 1.)**2)) /
((count - 2.) * (count - 3.)))
if isinstance(result, np.ndarray):
result = np.where(B == 0, 0, result)
result[count < 4] = np.nan
return result
def nankurt(values, axis=None, skipna=True):
mask = isnull(values)
if not is_floating_dtype(values):
values = values.astype('f8')
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
D = (values ** 4).sum(axis) / count - A ** 4 - 6 * B * A * A - 4 * C * A
B = _zero_out_fperr(B)
D = _zero_out_fperr(D)
if not isinstance(B, np.ndarray):
# if B is a scalar, check these corner cases first before doing division
if count < 4:
return np.nan
if B == 0:
return 0
result = (((count * count - 1.) * D / (B * B) - 3 * ((count - 1.) ** 2)) /
((count - 2.) * (count - 3.)))
if isinstance(result, np.ndarray):
result = np.where(B == 0, 0, result)
result[count < 4] = np.nan
return result
def nanskew(values, axis=None, skipna=True):
mask = isnull(values)
if not is_floating_dtype(values):
values = values.astype('f8')
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 nansem(values, axis=None, skipna=True, ddof=1):
var = nanvar(values, axis, skipna, ddof=ddof)
mask = isnull(values)
if not is_floating_dtype(values):
values = values.astype('f8')
count, _ = _get_counts_nanvar(mask, axis, ddof)
return np.sqrt(var) / np.sqrt(count)
def nansem(values, axis=None, skipna=True, ddof=1):
var = nanvar(values, axis, skipna, ddof=ddof)
mask = isnull(values)
if not is_floating_dtype(values):
values = values.astype('f8')
count, _ = _get_counts_nanvar(mask, axis, ddof)
return np.sqrt(var)/np.sqrt(count)
def _nanvar(values, axis=None, skipna=True, ddof=1):
# private nanvar calculator
mask = isnull(values)
if not is_floating_dtype(values):
values = values.astype('f8')
count, d = _get_counts_nanvar(mask, axis, ddof)
if skipna:
values = values.copy()
np.putmask(values, mask, 0)
X = _ensure_numeric(values.sum(axis))
XX = _ensure_numeric((values ** 2).sum(axis))
return np.fabs((XX - X ** 2 / count) / d)
