Skip to content

nanops pep8 fixes #7461

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
merged 1 commit into from
Jun 17, 2014
Merged

nanops pep8 fixes #7461

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
42 changes: 21 additions & 21 deletions pandas/core/nanops.py
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -1,26 +1,21 @@
from pandas import compat
import sys
import itertools
import functools

import numpy as np

from pandas.core.common import isnull, notnull, _values_from_object, is_float
import pandas.core.common as com
import pandas.lib as lib
import pandas.algos as algos
import pandas.hashtable as _hash
import pandas.tslib as tslib

from pandas.compat import builtins


try:
import bottleneck as bn
_USE_BOTTLENECK = True
except ImportError: # pragma: no cover
_USE_BOTTLENECK = False

import pandas.core.common as com
import pandas.hashtable as _hash
from pandas import compat, lib, algos, tslib
from pandas.compat import builtins
from pandas.core.common import isnull, notnull, _values_from_object, is_float


class disallow(object):

Expand Down Expand Up @@ -75,7 +70,8 @@ def f(values, axis=None, skipna=True, **kwds):
result.fill(0)
return result

if _USE_BOTTLENECK and skipna and _bn_ok_dtype(values.dtype, bn_name):
if _USE_BOTTLENECK and skipna and _bn_ok_dtype(values.dtype,
bn_name):
result = bn_func(values, axis=axis, **kwds)

# prefer to treat inf/-inf as NA, but must compute the func
Expand All @@ -94,7 +90,8 @@ def f(values, axis=None, skipna=True, **kwds):

def _bn_ok_dtype(dt, name):
# Bottleneck chokes on datetime64
if dt != np.object_ and not issubclass(dt.type, (np.datetime64, np.timedelta64)):
if dt != np.object_ and not issubclass(dt.type, (np.datetime64,
np.timedelta64)):

# bottleneck does not properly upcast during the sum
# so can overflow
Expand Down Expand Up @@ -179,8 +176,9 @@ def _get_values(values, skipna, fill_value=None, fill_value_typ=None,

# return a platform independent precision dtype
dtype_max = dtype
if dtype.kind == 'i' and not issubclass(
dtype.type, (np.bool, np.datetime64, np.timedelta64)):
if dtype.kind == 'i' and not issubclass(dtype.type, (np.bool,
np.datetime64,
Copy link
Contributor

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

u can use com.is_integer_dtype here (if u can replace anything else with the com.is_ functions would be gr8)

Copy link
Contributor Author

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

I can do a separate code cleanup in another pull request, but for triaging purposes I think it would be best to restrict this pull request to only stylistic (non-functional) changes. Ideally changes like you suggested should not have an affect, but they still alter the code paths to some degree so I think they should be kept separate.

np.timedelta64)):
dtype_max = np.int64
elif dtype.kind in ['b'] or issubclass(dtype.type, np.bool):
dtype_max = np.int64
Expand Down Expand Up @@ -251,7 +249,7 @@ def nanall(values, axis=None, skipna=True):
@bottleneck_switch(zero_value=0)
def nansum(values, axis=None, skipna=True):
values, mask, dtype, dtype_max = _get_values(values, skipna, 0)
the_sum = values.sum(axis,dtype=dtype_max)
the_sum = values.sum(axis, dtype=dtype_max)
the_sum = _maybe_null_out(the_sum, axis, mask)

return _wrap_results(the_sum, dtype)
Expand Down Expand Up @@ -365,7 +363,8 @@ def nansem(values, axis=None, skipna=True, ddof=1):

@bottleneck_switch()
def nanmin(values, axis=None, skipna=True):
values, mask, dtype, dtype_max = _get_values(values, skipna, fill_value_typ='+inf')
values, mask, dtype, dtype_max = _get_values(values, skipna,
fill_value_typ='+inf')

# numpy 1.6.1 workaround in Python 3.x
if (values.dtype == np.object_ and compat.PY3):
Expand All @@ -381,7 +380,7 @@ def nanmin(values, axis=None, skipna=True):
if ((axis is not None and values.shape[axis] == 0)
or values.size == 0):
try:
result = com.ensure_float(values.sum(axis,dtype=dtype_max))
result = com.ensure_float(values.sum(axis, dtype=dtype_max))
result.fill(np.nan)
except:
result = np.nan
Expand All @@ -394,7 +393,8 @@ def nanmin(values, axis=None, skipna=True):

@bottleneck_switch()
def nanmax(values, axis=None, skipna=True):
values, mask, dtype, dtype_max = _get_values(values, skipna, fill_value_typ='-inf')
values, mask, dtype, dtype_max = _get_values(values, skipna,
fill_value_typ='-inf')

# numpy 1.6.1 workaround in Python 3.x
if (values.dtype == np.object_ and compat.PY3):
Expand Down Expand Up @@ -427,7 +427,7 @@ def nanargmax(values, axis=None, skipna=True):
Returns -1 in the NA case
"""
values, mask, dtype, _ = _get_values(values, skipna, fill_value_typ='-inf',
isfinite=True)
isfinite=True)
result = values.argmax(axis)
result = _maybe_arg_null_out(result, axis, mask, skipna)
return result
Expand All @@ -438,7 +438,7 @@ def nanargmin(values, axis=None, skipna=True):
Returns -1 in the NA case
"""
values, mask, dtype, _ = _get_values(values, skipna, fill_value_typ='+inf',
isfinite=True)
isfinite=True)
result = values.argmin(axis)
result = _maybe_arg_null_out(result, axis, mask, skipna)
return result
Expand Down