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Merged
merged 7 commits into from
Nov 25, 2017
Merged

Cross off a few tslibs-TODOs #18443

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7f96594
remove unused func
jbrockmendel Nov 23, 2017
596383a
remove unused Period._comparables
jbrockmendel Nov 23, 2017
6ef9227
fix c warnings, whitespace; remove unused func
jbrockmendel Nov 23, 2017
c08e4ce
de-privatize _checknull_with_nat
jbrockmendel Nov 23, 2017
c5f79d1
typo fixup, indentation
jbrockmendel Nov 23, 2017
438662d
Merge branch 'master' of https://github.com/pandas-dev/pandas into ts…
jbrockmendel Nov 24, 2017
5dd4469
Merge branch 'master' of https://github.com/pandas-dev/pandas into ts…
jbrockmendel Nov 24, 2017
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13 changes: 0 additions & 13 deletions pandas/_libs/lib.pyx
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Expand Up @@ -929,19 +929,6 @@ def write_csv_rows(list data, ndarray data_index,
# ------------------------------------------------------------------------------
# Groupby-related functions

@cython.boundscheck(False)
def arrmap(ndarray[object] index, object func):
cdef int length = index.shape[0]
cdef int i = 0

cdef ndarray[object] result = np.empty(length, dtype=np.object_)

for i from 0 <= i < length:
result[i] = func(index[i])

return result


@cython.wraparound(False)
@cython.boundscheck(False)
def is_lexsorted(list list_of_arrays):
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1 change: 0 additions & 1 deletion pandas/_libs/period.pyx
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Expand Up @@ -559,7 +559,6 @@ cdef class _Period(object):
int64_t ordinal
object freq

_comparables = ['name', 'freqstr']
_typ = 'period'

def __cinit__(self, ordinal, freq):
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1 change: 0 additions & 1 deletion pandas/_libs/src/datetime.pxd
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Expand Up @@ -54,7 +54,6 @@ cdef extern from "datetime/np_datetime.h":

int dayofweek(int y, int m, int d) nogil
int is_leapyear(int64_t year) nogil
PANDAS_DATETIMEUNIT get_datetime64_unit(object o)

cdef extern from "datetime/np_datetime_strings.h":

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64 changes: 30 additions & 34 deletions pandas/_libs/src/datetime/np_datetime.c
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Expand Up @@ -564,18 +564,15 @@ void pandas_datetime_to_datetimestruct(npy_datetime val, PANDAS_DATETIMEUNIT fr,

void pandas_timedelta_to_timedeltastruct(npy_timedelta val,
PANDAS_DATETIMEUNIT fr,
pandas_timedeltastruct *result) {
pandas_timedeltastruct *result) {
pandas_datetime_metadata meta;

meta.base = fr;
meta.num - 1;
meta.num = 1;
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Thanks for fixing this typo. Do we even need the assignment at all though? Worth considering removal?

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I'm advocating moving the timedelta_struct stuff out of src/ entirely. The src/ directory is a PITA and we can put it in cython directly without taking a performance hit.


convert_timedelta_to_timedeltastruct(&meta, val, result);
}

PANDAS_DATETIMEUNIT get_datetime64_unit(PyObject *obj) {
return (PANDAS_DATETIMEUNIT)((PyDatetimeScalarObject *)obj)->obmeta.base;
}

/*
* Converts a datetime from a datetimestruct to a datetime based
Expand Down Expand Up @@ -1001,7 +998,6 @@ int convert_datetime_to_datetimestruct(pandas_datetime_metadata *meta,
int convert_timedelta_to_timedeltastruct(pandas_timedelta_metadata *meta,
npy_timedelta td,
pandas_timedeltastruct *out) {
npy_int64 perday;
npy_int64 frac;
npy_int64 sfrac;
npy_int64 ifrac;
Expand All @@ -1016,11 +1012,11 @@ int convert_timedelta_to_timedeltastruct(pandas_timedelta_metadata *meta,

// put frac in seconds
if (td < 0 && td % (1000LL * 1000LL * 1000LL) != 0)
frac = td / (1000LL * 1000LL * 1000LL) - 1;
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I think we already lint this file (cpplint). is this not picked up? rather have the proper config settings than manually changing things.

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No argument here, but cpplint is well outside my domain of competence. I only noticed this indentation when trying to move this func into cython.

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@WillAyd any experience with cpplint? want to give this a shot, e.g. making sure we are linting the *.c (more/better)?

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@WillAyd WillAyd Nov 23, 2017 •
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Can't claim experience with cpplint but looking at the bash script it appears to already hit all of the files in the datetime folder. I ran both versions of this file through cpplint locally and neither caused any issue with the highest level of verbosity. Surprisingly I couldn't get an error when changing the line to a 1 or 3 space indent either, so I think there might be some bug with cpplint itself.

In looking at Google's style guide they suggest a 2 space indent. To match that and keep consistent with the rest of the file I would suggest removing the two spaces added here and remove the two from the indented block after the else statement instead

https://google.github.io/styleguide/cppguide.html#Conditionals

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see https://github.com/apache/arrow?files=1

for some hints in how to lint / enforce this as well

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Are you saying that within arrow cpplint is calling out those whitespace issues? I tried replicating the filters I saw in that project via:

cpplint --verbose=2 --filter=-whitespace/comments,-readability/todo,-build/header_guard,-build/c++11,-runtime/references,-build/include_order pandas/_libs/src/datetime/np_datetime.c

And then tried an even more verbose version without any filter

cpplint --verbose=1 pandas/_libs/src/datetime/np_datetime.c

And neither made any mention of those lines, whether I set them at 2, 4 or even 3 space indents.

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no just a guess that they have more options set for linting

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Looking at the cpplint source, I don't see anything that checks for proper 2-space indentation of conditional statements, nor was there any documentation hinting that it could. The whitespace/indentat check offered only seems to make sure that you don't start with 1 or 3 spaces, but doesn't do much beyond that. Unfortunately I don't see any programmatic options to continually validate this with that tool as is

frac = td / (1000LL * 1000LL * 1000LL) - 1;
else
frac = td / (1000LL * 1000LL * 1000LL);

if (frac < 0) {
if (frac < 0) {
sign = -1;

// even fraction
Expand All @@ -1030,66 +1026,66 @@ int convert_timedelta_to_timedeltastruct(pandas_timedelta_metadata *meta,
} else {
frac = -frac;
}
} else {
} else {
sign = 1;
out->days = 0;
}
}

if (frac >= 86400) {
if (frac >= 86400) {
out->days += frac / 86400LL;
frac -= out->days * 86400LL;
}
}

if (frac >= 3600) {
if (frac >= 3600) {
out->hrs = frac / 3600LL;
frac -= out->hrs * 3600LL;
} else {
} else {
out->hrs = 0;
}
}

if (frac >= 60) {
if (frac >= 60) {
out->min = frac / 60LL;
frac -= out->min * 60LL;
} else {
} else {
out->min = 0;
}
}

if (frac >= 0) {
if (frac >= 0) {
out->sec = frac;
frac -= out->sec;
} else {
} else {
out->sec = 0;
}
}

sfrac = (out->hrs * 3600LL + out->min * 60LL
+ out->sec) * (1000LL * 1000LL * 1000LL);
sfrac = (out->hrs * 3600LL + out->min * 60LL
+ out->sec) * (1000LL * 1000LL * 1000LL);

if (sign < 0)
if (sign < 0)
out->days = -out->days;

ifrac = td - (out->days * DAY_NS + sfrac);
ifrac = td - (out->days * DAY_NS + sfrac);

if (ifrac != 0) {
if (ifrac != 0) {
out->ms = ifrac / (1000LL * 1000LL);
ifrac -= out->ms * 1000LL * 1000LL;
out->us = ifrac / 1000LL;
ifrac -= out->us * 1000LL;
out->ns = ifrac;
} else {
} else {
out->ms = 0;
out->us = 0;
out->ns = 0;
}
}

out->seconds = out->hrs * 3600 + out->min * 60 + out->sec;
out->microseconds = out->ms * 1000 + out->us;
out->nanoseconds = out->ns;
break;
out->seconds = out->hrs * 3600 + out->min * 60 + out->sec;
out->microseconds = out->ms * 1000 + out->us;
out->nanoseconds = out->ns;
break;

default:
PyErr_SetString(PyExc_RuntimeError,
"NumPy datetime metadata is corrupted with invalid "
"base unit");
"NumPy timedelta metadata is corrupted with "
"invalid base unit");
return -1;
}

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3 changes: 0 additions & 3 deletions pandas/_libs/src/datetime/np_datetime.h
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Expand Up @@ -148,7 +148,4 @@ convert_timedelta_to_timedeltastruct(pandas_timedelta_metadata *meta,
pandas_timedeltastruct *out);


PANDAS_DATETIMEUNIT get_datetime64_unit(PyObject *obj);


#endif // PANDAS__LIBS_SRC_DATETIME_NP_DATETIME_H_
12 changes: 6 additions & 6 deletions pandas/_libs/tslib.pyx
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Expand Up @@ -60,7 +60,7 @@ from tslibs.conversion cimport (tz_convert_single, _TSObject,
from tslibs.conversion import tz_convert_single

from tslibs.nattype import NaT, nat_strings, iNaT
from tslibs.nattype cimport _checknull_with_nat, NPY_NAT
from tslibs.nattype cimport checknull_with_nat, NPY_NAT

from tslibs.timestamps cimport (create_timestamp_from_ts,
_NS_UPPER_BOUND, _NS_LOWER_BOUND)
Expand Down Expand Up @@ -409,7 +409,7 @@ cpdef array_with_unit_to_datetime(ndarray values, unit, errors='coerce'):
for i in range(n):
val = values[i]
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these array_* should be moved to conversion I think (obviously new PR), then tslib.pyx is moot?

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Let's hold off on that. I'd like to do auditing before moving, and the array_* funcs may need some work/thought (#17697)


if _checknull_with_nat(val):
if checknull_with_nat(val):
iresult[i] = NPY_NAT

elif is_integer_object(val) or is_float_object(val):
Expand Down Expand Up @@ -475,7 +475,7 @@ cpdef array_with_unit_to_datetime(ndarray values, unit, errors='coerce'):
for i in range(n):
val = values[i]

if _checknull_with_nat(val):
if checknull_with_nat(val):
oresult[i] = NaT
elif is_integer_object(val) or is_float_object(val):

Expand Down Expand Up @@ -526,7 +526,7 @@ cpdef array_to_datetime(ndarray[object] values, errors='raise',
for i in range(n):
val = values[i]

if _checknull_with_nat(val):
if checknull_with_nat(val):
iresult[i] = NPY_NAT

elif PyDateTime_Check(val):
Expand Down Expand Up @@ -686,7 +686,7 @@ cpdef array_to_datetime(ndarray[object] values, errors='raise',
val = values[i]

# set as nan except if its a NaT
if _checknull_with_nat(val):
if checknull_with_nat(val):
if PyFloat_Check(val):
oresult[i] = np.nan
else:
Expand All @@ -704,7 +704,7 @@ cpdef array_to_datetime(ndarray[object] values, errors='raise',

for i in range(n):
val = values[i]
if _checknull_with_nat(val):
if checknull_with_nat(val):
oresult[i] = val
elif is_string_object(val):

Expand Down
4 changes: 2 additions & 2 deletions pandas/_libs/tslibs/conversion.pyx
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Expand Up @@ -40,7 +40,7 @@ from timezones cimport (
from parsing import parse_datetime_string

from nattype import nat_strings, NaT
from nattype cimport NPY_NAT, _checknull_with_nat
from nattype cimport NPY_NAT, checknull_with_nat

# ----------------------------------------------------------------------
# Constants
Expand Down Expand Up @@ -140,7 +140,7 @@ def datetime_to_datetime64(ndarray[object] values):
iresult = result.view('i8')
for i in range(n):
val = values[i]
if _checknull_with_nat(val):
if checknull_with_nat(val):
iresult[i] = NPY_NAT
elif PyDateTime_Check(val):
if val.tzinfo is not None:
Expand Down
2 changes: 1 addition & 1 deletion pandas/_libs/tslibs/nattype.pxd
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Expand Up @@ -6,4 +6,4 @@ cdef int64_t NPY_NAT

cdef bint _nat_scalar_rules[6]

cdef bint _checknull_with_nat(object val)
cdef bint checknull_with_nat(object val)
2 changes: 1 addition & 1 deletion pandas/_libs/tslibs/nattype.pyx
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Expand Up @@ -572,7 +572,7 @@ NaT = NaTType()

# ----------------------------------------------------------------------

cdef inline bint _checknull_with_nat(object val):
cdef inline bint checknull_with_nat(object val):
""" utility to check if a value is a nat or not """
return val is None or (
PyFloat_Check(val) and val != val) or val is NaT
4 changes: 2 additions & 2 deletions pandas/_libs/tslibs/strptime.pyx
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Expand Up @@ -38,7 +38,7 @@ from np_datetime cimport (check_dts_bounds,

from util cimport is_string_object

from nattype cimport _checknull_with_nat, NPY_NAT
from nattype cimport checknull_with_nat, NPY_NAT
from nattype import nat_strings


Expand Down Expand Up @@ -142,7 +142,7 @@ def array_strptime(ndarray[object] values, object fmt,
iresult[i] = NPY_NAT
continue
else:
if _checknull_with_nat(val):
if checknull_with_nat(val):
iresult[i] = NPY_NAT
continue
else:
Expand Down
8 changes: 4 additions & 4 deletions pandas/_libs/tslibs/timedeltas.pyx
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Expand Up @@ -30,7 +30,7 @@ from np_datetime cimport (cmp_scalar, reverse_ops, td64_to_tdstruct,
pandas_timedeltastruct)

from nattype import nat_strings, NaT
from nattype cimport _checknull_with_nat, NPY_NAT
from nattype cimport checknull_with_nat, NPY_NAT

# ----------------------------------------------------------------------
# Constants
Expand Down Expand Up @@ -111,7 +111,7 @@ cpdef convert_to_timedelta64(object ts, object unit):
# kludgy here until we have a timedelta scalar
# handle the numpy < 1.7 case
"""
if _checknull_with_nat(ts):
if checknull_with_nat(ts):
return np.timedelta64(NPY_NAT)
elif isinstance(ts, Timedelta):
# already in the proper format
Expand Down Expand Up @@ -443,7 +443,7 @@ cdef inline timedelta_from_spec(object number, object frac, object unit):

cdef bint _validate_ops_compat(other):
# return True if we are compat with operating
if _checknull_with_nat(other):
if checknull_with_nat(other):
return True
elif PyDelta_Check(other) or is_timedelta64_object(other):
return True
Expand Down Expand Up @@ -837,7 +837,7 @@ class Timedelta(_Timedelta):
elif is_integer_object(value) or is_float_object(value):
# unit=None is de-facto 'ns'
value = convert_to_timedelta64(value, unit)
elif _checknull_with_nat(value):
elif checknull_with_nat(value):
return NaT
else:
raise ValueError(
Expand Down