partition stringclasses 3
values | func_name stringlengths 1 134 | docstring stringlengths 1 46.9k | path stringlengths 4 223 | original_string stringlengths 75 104k | code stringlengths 75 104k | docstring_tokens listlengths 1 1.97k | repo stringlengths 7 55 | language stringclasses 1
value | url stringlengths 87 315 | code_tokens listlengths 19 28.4k | sha stringlengths 40 40 |
|---|---|---|---|---|---|---|---|---|---|---|---|
train | BusinessHourMixin.rollback | Roll provided date backward to next offset only if not on offset. | pandas/tseries/offsets.py | def rollback(self, dt):
"""
Roll provided date backward to next offset only if not on offset.
"""
if not self.onOffset(dt):
businesshours = self._get_business_hours_by_sec
if self.n >= 0:
dt = self._prev_opening_time(
dt) + time... | def rollback(self, dt):
"""
Roll provided date backward to next offset only if not on offset.
"""
if not self.onOffset(dt):
businesshours = self._get_business_hours_by_sec
if self.n >= 0:
dt = self._prev_opening_time(
dt) + time... | [
"Roll",
"provided",
"date",
"backward",
"to",
"next",
"offset",
"only",
"if",
"not",
"on",
"offset",
"."
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/tseries/offsets.py#L654-L666 | [
"def",
"rollback",
"(",
"self",
",",
"dt",
")",
":",
"if",
"not",
"self",
".",
"onOffset",
"(",
"dt",
")",
":",
"businesshours",
"=",
"self",
".",
"_get_business_hours_by_sec",
"if",
"self",
".",
"n",
">=",
"0",
":",
"dt",
"=",
"self",
".",
"_prev_op... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | BusinessHourMixin.rollforward | Roll provided date forward to next offset only if not on offset. | pandas/tseries/offsets.py | def rollforward(self, dt):
"""
Roll provided date forward to next offset only if not on offset.
"""
if not self.onOffset(dt):
if self.n >= 0:
return self._next_opening_time(dt)
else:
return self._prev_opening_time(dt)
return... | def rollforward(self, dt):
"""
Roll provided date forward to next offset only if not on offset.
"""
if not self.onOffset(dt):
if self.n >= 0:
return self._next_opening_time(dt)
else:
return self._prev_opening_time(dt)
return... | [
"Roll",
"provided",
"date",
"forward",
"to",
"next",
"offset",
"only",
"if",
"not",
"on",
"offset",
"."
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/tseries/offsets.py#L669-L678 | [
"def",
"rollforward",
"(",
"self",
",",
"dt",
")",
":",
"if",
"not",
"self",
".",
"onOffset",
"(",
"dt",
")",
":",
"if",
"self",
".",
"n",
">=",
"0",
":",
"return",
"self",
".",
"_next_opening_time",
"(",
"dt",
")",
"else",
":",
"return",
"self",
... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | BusinessHourMixin._onOffset | Slight speedups using calculated values. | pandas/tseries/offsets.py | def _onOffset(self, dt, businesshours):
"""
Slight speedups using calculated values.
"""
# if self.normalize and not _is_normalized(dt):
# return False
# Valid BH can be on the different BusinessDay during midnight
# Distinguish by the time spent from previous... | def _onOffset(self, dt, businesshours):
"""
Slight speedups using calculated values.
"""
# if self.normalize and not _is_normalized(dt):
# return False
# Valid BH can be on the different BusinessDay during midnight
# Distinguish by the time spent from previous... | [
"Slight",
"speedups",
"using",
"calculated",
"values",
"."
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/tseries/offsets.py#L767-L783 | [
"def",
"_onOffset",
"(",
"self",
",",
"dt",
",",
"businesshours",
")",
":",
"# if self.normalize and not _is_normalized(dt):",
"# return False",
"# Valid BH can be on the different BusinessDay during midnight",
"# Distinguish by the time spent from previous opening time",
"if",
"se... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | _CustomBusinessMonth.cbday_roll | Define default roll function to be called in apply method. | pandas/tseries/offsets.py | def cbday_roll(self):
"""
Define default roll function to be called in apply method.
"""
cbday = CustomBusinessDay(n=self.n, normalize=False, **self.kwds)
if self._prefix.endswith('S'):
# MonthBegin
roll_func = cbday.rollforward
else:
... | def cbday_roll(self):
"""
Define default roll function to be called in apply method.
"""
cbday = CustomBusinessDay(n=self.n, normalize=False, **self.kwds)
if self._prefix.endswith('S'):
# MonthBegin
roll_func = cbday.rollforward
else:
... | [
"Define",
"default",
"roll",
"function",
"to",
"be",
"called",
"in",
"apply",
"method",
"."
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/tseries/offsets.py#L1007-L1019 | [
"def",
"cbday_roll",
"(",
"self",
")",
":",
"cbday",
"=",
"CustomBusinessDay",
"(",
"n",
"=",
"self",
".",
"n",
",",
"normalize",
"=",
"False",
",",
"*",
"*",
"self",
".",
"kwds",
")",
"if",
"self",
".",
"_prefix",
".",
"endswith",
"(",
"'S'",
")",... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | _CustomBusinessMonth.month_roll | Define default roll function to be called in apply method. | pandas/tseries/offsets.py | def month_roll(self):
"""
Define default roll function to be called in apply method.
"""
if self._prefix.endswith('S'):
# MonthBegin
roll_func = self.m_offset.rollback
else:
# MonthEnd
roll_func = self.m_offset.rollforward
r... | def month_roll(self):
"""
Define default roll function to be called in apply method.
"""
if self._prefix.endswith('S'):
# MonthBegin
roll_func = self.m_offset.rollback
else:
# MonthEnd
roll_func = self.m_offset.rollforward
r... | [
"Define",
"default",
"roll",
"function",
"to",
"be",
"called",
"in",
"apply",
"method",
"."
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/tseries/offsets.py#L1032-L1042 | [
"def",
"month_roll",
"(",
"self",
")",
":",
"if",
"self",
".",
"_prefix",
".",
"endswith",
"(",
"'S'",
")",
":",
"# MonthBegin",
"roll_func",
"=",
"self",
".",
"m_offset",
".",
"rollback",
"else",
":",
"# MonthEnd",
"roll_func",
"=",
"self",
".",
"m_offs... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | SemiMonthBegin._apply_index_days | Add days portion of offset to DatetimeIndex i.
Parameters
----------
i : DatetimeIndex
roll : ndarray[int64_t]
Returns
-------
result : DatetimeIndex | pandas/tseries/offsets.py | def _apply_index_days(self, i, roll):
"""
Add days portion of offset to DatetimeIndex i.
Parameters
----------
i : DatetimeIndex
roll : ndarray[int64_t]
Returns
-------
result : DatetimeIndex
"""
nanos = (roll % 2) * Timedelta(day... | def _apply_index_days(self, i, roll):
"""
Add days portion of offset to DatetimeIndex i.
Parameters
----------
i : DatetimeIndex
roll : ndarray[int64_t]
Returns
-------
result : DatetimeIndex
"""
nanos = (roll % 2) * Timedelta(day... | [
"Add",
"days",
"portion",
"of",
"offset",
"to",
"DatetimeIndex",
"i",
"."
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/tseries/offsets.py#L1279-L1293 | [
"def",
"_apply_index_days",
"(",
"self",
",",
"i",
",",
"roll",
")",
":",
"nanos",
"=",
"(",
"roll",
"%",
"2",
")",
"*",
"Timedelta",
"(",
"days",
"=",
"self",
".",
"day_of_month",
"-",
"1",
")",
".",
"value",
"return",
"i",
"+",
"nanos",
".",
"a... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | Week._end_apply_index | Add self to the given DatetimeIndex, specialized for case where
self.weekday is non-null.
Parameters
----------
dtindex : DatetimeIndex
Returns
-------
result : DatetimeIndex | pandas/tseries/offsets.py | def _end_apply_index(self, dtindex):
"""
Add self to the given DatetimeIndex, specialized for case where
self.weekday is non-null.
Parameters
----------
dtindex : DatetimeIndex
Returns
-------
result : DatetimeIndex
"""
off = dtin... | def _end_apply_index(self, dtindex):
"""
Add self to the given DatetimeIndex, specialized for case where
self.weekday is non-null.
Parameters
----------
dtindex : DatetimeIndex
Returns
-------
result : DatetimeIndex
"""
off = dtin... | [
"Add",
"self",
"to",
"the",
"given",
"DatetimeIndex",
"specialized",
"for",
"case",
"where",
"self",
".",
"weekday",
"is",
"non",
"-",
"null",
"."
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/tseries/offsets.py#L1354-L1390 | [
"def",
"_end_apply_index",
"(",
"self",
",",
"dtindex",
")",
":",
"off",
"=",
"dtindex",
".",
"to_perioddelta",
"(",
"'D'",
")",
"base",
",",
"mult",
"=",
"libfrequencies",
".",
"get_freq_code",
"(",
"self",
".",
"freqstr",
")",
"base_period",
"=",
"dtinde... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | WeekOfMonth._get_offset_day | Find the day in the same month as other that has the same
weekday as self.weekday and is the self.week'th such day in the month.
Parameters
----------
other : datetime
Returns
-------
day : int | pandas/tseries/offsets.py | def _get_offset_day(self, other):
"""
Find the day in the same month as other that has the same
weekday as self.weekday and is the self.week'th such day in the month.
Parameters
----------
other : datetime
Returns
-------
day : int
"""
... | def _get_offset_day(self, other):
"""
Find the day in the same month as other that has the same
weekday as self.weekday and is the self.week'th such day in the month.
Parameters
----------
other : datetime
Returns
-------
day : int
"""
... | [
"Find",
"the",
"day",
"in",
"the",
"same",
"month",
"as",
"other",
"that",
"has",
"the",
"same",
"weekday",
"as",
"self",
".",
"weekday",
"and",
"is",
"the",
"self",
".",
"week",
"th",
"such",
"day",
"in",
"the",
"month",
"."
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/tseries/offsets.py#L1474-L1490 | [
"def",
"_get_offset_day",
"(",
"self",
",",
"other",
")",
":",
"mstart",
"=",
"datetime",
"(",
"other",
".",
"year",
",",
"other",
".",
"month",
",",
"1",
")",
"wday",
"=",
"mstart",
".",
"weekday",
"(",
")",
"shift_days",
"=",
"(",
"self",
".",
"w... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | LastWeekOfMonth._get_offset_day | Find the day in the same month as other that has the same
weekday as self.weekday and is the last such day in the month.
Parameters
----------
other: datetime
Returns
-------
day: int | pandas/tseries/offsets.py | def _get_offset_day(self, other):
"""
Find the day in the same month as other that has the same
weekday as self.weekday and is the last such day in the month.
Parameters
----------
other: datetime
Returns
-------
day: int
"""
dim ... | def _get_offset_day(self, other):
"""
Find the day in the same month as other that has the same
weekday as self.weekday and is the last such day in the month.
Parameters
----------
other: datetime
Returns
-------
day: int
"""
dim ... | [
"Find",
"the",
"day",
"in",
"the",
"same",
"month",
"as",
"other",
"that",
"has",
"the",
"same",
"weekday",
"as",
"self",
".",
"weekday",
"and",
"is",
"the",
"last",
"such",
"day",
"in",
"the",
"month",
"."
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/tseries/offsets.py#L1543-L1560 | [
"def",
"_get_offset_day",
"(",
"self",
",",
"other",
")",
":",
"dim",
"=",
"ccalendar",
".",
"get_days_in_month",
"(",
"other",
".",
"year",
",",
"other",
".",
"month",
")",
"mend",
"=",
"datetime",
"(",
"other",
".",
"year",
",",
"other",
".",
"month"... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | FY5253Quarter._rollback_to_year | Roll `other` back to the most recent date that was on a fiscal year
end.
Return the date of that year-end, the number of full quarters
elapsed between that year-end and other, and the remaining Timedelta
since the most recent quarter-end.
Parameters
----------
o... | pandas/tseries/offsets.py | def _rollback_to_year(self, other):
"""
Roll `other` back to the most recent date that was on a fiscal year
end.
Return the date of that year-end, the number of full quarters
elapsed between that year-end and other, and the remaining Timedelta
since the most recent quart... | def _rollback_to_year(self, other):
"""
Roll `other` back to the most recent date that was on a fiscal year
end.
Return the date of that year-end, the number of full quarters
elapsed between that year-end and other, and the remaining Timedelta
since the most recent quart... | [
"Roll",
"other",
"back",
"to",
"the",
"most",
"recent",
"date",
"that",
"was",
"on",
"a",
"fiscal",
"year",
"end",
"."
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/tseries/offsets.py#L2054-L2099 | [
"def",
"_rollback_to_year",
"(",
"self",
",",
"other",
")",
":",
"num_qtrs",
"=",
"0",
"norm",
"=",
"Timestamp",
"(",
"other",
")",
".",
"tz_localize",
"(",
"None",
")",
"start",
"=",
"self",
".",
"_offset",
".",
"rollback",
"(",
"norm",
")",
"# Note: ... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | concat | Concatenate pandas objects along a particular axis with optional set logic
along the other axes.
Can also add a layer of hierarchical indexing on the concatenation axis,
which may be useful if the labels are the same (or overlapping) on
the passed axis number.
Parameters
----------
objs : ... | pandas/core/reshape/concat.py | def concat(objs, axis=0, join='outer', join_axes=None, ignore_index=False,
keys=None, levels=None, names=None, verify_integrity=False,
sort=None, copy=True):
"""
Concatenate pandas objects along a particular axis with optional set logic
along the other axes.
Can also add a layer o... | def concat(objs, axis=0, join='outer', join_axes=None, ignore_index=False,
keys=None, levels=None, names=None, verify_integrity=False,
sort=None, copy=True):
"""
Concatenate pandas objects along a particular axis with optional set logic
along the other axes.
Can also add a layer o... | [
"Concatenate",
"pandas",
"objects",
"along",
"a",
"particular",
"axis",
"with",
"optional",
"set",
"logic",
"along",
"the",
"other",
"axes",
"."
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/core/reshape/concat.py#L24-L229 | [
"def",
"concat",
"(",
"objs",
",",
"axis",
"=",
"0",
",",
"join",
"=",
"'outer'",
",",
"join_axes",
"=",
"None",
",",
"ignore_index",
"=",
"False",
",",
"keys",
"=",
"None",
",",
"levels",
"=",
"None",
",",
"names",
"=",
"None",
",",
"verify_integrit... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | _Concatenator._get_concat_axis | Return index to be used along concatenation axis. | pandas/core/reshape/concat.py | def _get_concat_axis(self):
"""
Return index to be used along concatenation axis.
"""
if self._is_series:
if self.axis == 0:
indexes = [x.index for x in self.objs]
elif self.ignore_index:
idx = ibase.default_index(len(self.objs))
... | def _get_concat_axis(self):
"""
Return index to be used along concatenation axis.
"""
if self._is_series:
if self.axis == 0:
indexes = [x.index for x in self.objs]
elif self.ignore_index:
idx = ibase.default_index(len(self.objs))
... | [
"Return",
"index",
"to",
"be",
"used",
"along",
"concatenation",
"axis",
"."
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/core/reshape/concat.py#L475-L521 | [
"def",
"_get_concat_axis",
"(",
"self",
")",
":",
"if",
"self",
".",
"_is_series",
":",
"if",
"self",
".",
"axis",
"==",
"0",
":",
"indexes",
"=",
"[",
"x",
".",
"index",
"for",
"x",
"in",
"self",
".",
"objs",
"]",
"elif",
"self",
".",
"ignore_inde... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | _in | Compute the vectorized membership of ``x in y`` if possible, otherwise
use Python. | pandas/core/computation/ops.py | def _in(x, y):
"""Compute the vectorized membership of ``x in y`` if possible, otherwise
use Python.
"""
try:
return x.isin(y)
except AttributeError:
if is_list_like(x):
try:
return y.isin(x)
except AttributeError:
pass
... | def _in(x, y):
"""Compute the vectorized membership of ``x in y`` if possible, otherwise
use Python.
"""
try:
return x.isin(y)
except AttributeError:
if is_list_like(x):
try:
return y.isin(x)
except AttributeError:
pass
... | [
"Compute",
"the",
"vectorized",
"membership",
"of",
"x",
"in",
"y",
"if",
"possible",
"otherwise",
"use",
"Python",
"."
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/core/computation/ops.py#L234-L246 | [
"def",
"_in",
"(",
"x",
",",
"y",
")",
":",
"try",
":",
"return",
"x",
".",
"isin",
"(",
"y",
")",
"except",
"AttributeError",
":",
"if",
"is_list_like",
"(",
"x",
")",
":",
"try",
":",
"return",
"y",
".",
"isin",
"(",
"x",
")",
"except",
"Attr... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | _not_in | Compute the vectorized membership of ``x not in y`` if possible,
otherwise use Python. | pandas/core/computation/ops.py | def _not_in(x, y):
"""Compute the vectorized membership of ``x not in y`` if possible,
otherwise use Python.
"""
try:
return ~x.isin(y)
except AttributeError:
if is_list_like(x):
try:
return ~y.isin(x)
except AttributeError:
pas... | def _not_in(x, y):
"""Compute the vectorized membership of ``x not in y`` if possible,
otherwise use Python.
"""
try:
return ~x.isin(y)
except AttributeError:
if is_list_like(x):
try:
return ~y.isin(x)
except AttributeError:
pas... | [
"Compute",
"the",
"vectorized",
"membership",
"of",
"x",
"not",
"in",
"y",
"if",
"possible",
"otherwise",
"use",
"Python",
"."
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/core/computation/ops.py#L249-L261 | [
"def",
"_not_in",
"(",
"x",
",",
"y",
")",
":",
"try",
":",
"return",
"~",
"x",
".",
"isin",
"(",
"y",
")",
"except",
"AttributeError",
":",
"if",
"is_list_like",
"(",
"x",
")",
":",
"try",
":",
"return",
"~",
"y",
".",
"isin",
"(",
"x",
")",
... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | _cast_inplace | Cast an expression inplace.
Parameters
----------
terms : Op
The expression that should cast.
acceptable_dtypes : list of acceptable numpy.dtype
Will not cast if term's dtype in this list.
.. versionadded:: 0.19.0
dtype : str or numpy.dtype
The dtype to cast to. | pandas/core/computation/ops.py | def _cast_inplace(terms, acceptable_dtypes, dtype):
"""Cast an expression inplace.
Parameters
----------
terms : Op
The expression that should cast.
acceptable_dtypes : list of acceptable numpy.dtype
Will not cast if term's dtype in this list.
.. versionadded:: 0.19.0
... | def _cast_inplace(terms, acceptable_dtypes, dtype):
"""Cast an expression inplace.
Parameters
----------
terms : Op
The expression that should cast.
acceptable_dtypes : list of acceptable numpy.dtype
Will not cast if term's dtype in this list.
.. versionadded:: 0.19.0
... | [
"Cast",
"an",
"expression",
"inplace",
"."
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/core/computation/ops.py#L288-L312 | [
"def",
"_cast_inplace",
"(",
"terms",
",",
"acceptable_dtypes",
",",
"dtype",
")",
":",
"dt",
"=",
"np",
".",
"dtype",
"(",
"dtype",
")",
"for",
"term",
"in",
"terms",
":",
"if",
"term",
".",
"type",
"in",
"acceptable_dtypes",
":",
"continue",
"try",
"... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | Term.update | search order for local (i.e., @variable) variables:
scope, key_variable
[('locals', 'local_name'),
('globals', 'local_name'),
('locals', 'key'),
('globals', 'key')] | pandas/core/computation/ops.py | def update(self, value):
"""
search order for local (i.e., @variable) variables:
scope, key_variable
[('locals', 'local_name'),
('globals', 'local_name'),
('locals', 'key'),
('globals', 'key')]
"""
key = self.name
# if it's a variable ... | def update(self, value):
"""
search order for local (i.e., @variable) variables:
scope, key_variable
[('locals', 'local_name'),
('globals', 'local_name'),
('locals', 'key'),
('globals', 'key')]
"""
key = self.name
# if it's a variable ... | [
"search",
"order",
"for",
"local",
"(",
"i",
".",
"e",
".",
"@variable",
")",
"variables",
":"
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/core/computation/ops.py#L88-L104 | [
"def",
"update",
"(",
"self",
",",
"value",
")",
":",
"key",
"=",
"self",
".",
"name",
"# if it's a variable name (otherwise a constant)",
"if",
"isinstance",
"(",
"key",
",",
"str",
")",
":",
"self",
".",
"env",
".",
"swapkey",
"(",
"self",
".",
"local_na... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | BinOp.evaluate | Evaluate a binary operation *before* being passed to the engine.
Parameters
----------
env : Scope
engine : str
parser : str
term_type : type
eval_in_python : list
Returns
-------
term_type
The "pre-evaluated" expression as an... | pandas/core/computation/ops.py | def evaluate(self, env, engine, parser, term_type, eval_in_python):
"""Evaluate a binary operation *before* being passed to the engine.
Parameters
----------
env : Scope
engine : str
parser : str
term_type : type
eval_in_python : list
Returns
... | def evaluate(self, env, engine, parser, term_type, eval_in_python):
"""Evaluate a binary operation *before* being passed to the engine.
Parameters
----------
env : Scope
engine : str
parser : str
term_type : type
eval_in_python : list
Returns
... | [
"Evaluate",
"a",
"binary",
"operation",
"*",
"before",
"*",
"being",
"passed",
"to",
"the",
"engine",
"."
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/core/computation/ops.py#L369-L405 | [
"def",
"evaluate",
"(",
"self",
",",
"env",
",",
"engine",
",",
"parser",
",",
"term_type",
",",
"eval_in_python",
")",
":",
"if",
"engine",
"==",
"'python'",
":",
"res",
"=",
"self",
"(",
"env",
")",
"else",
":",
"# recurse over the left/right nodes",
"le... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | BinOp.convert_values | Convert datetimes to a comparable value in an expression. | pandas/core/computation/ops.py | def convert_values(self):
"""Convert datetimes to a comparable value in an expression.
"""
def stringify(value):
if self.encoding is not None:
encoder = partial(pprint_thing_encoded,
encoding=self.encoding)
else:
... | def convert_values(self):
"""Convert datetimes to a comparable value in an expression.
"""
def stringify(value):
if self.encoding is not None:
encoder = partial(pprint_thing_encoded,
encoding=self.encoding)
else:
... | [
"Convert",
"datetimes",
"to",
"a",
"comparable",
"value",
"in",
"an",
"expression",
"."
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/core/computation/ops.py#L407-L436 | [
"def",
"convert_values",
"(",
"self",
")",
":",
"def",
"stringify",
"(",
"value",
")",
":",
"if",
"self",
".",
"encoding",
"is",
"not",
"None",
":",
"encoder",
"=",
"partial",
"(",
"pprint_thing_encoded",
",",
"encoding",
"=",
"self",
".",
"encoding",
")... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | crosstab | Compute a simple cross tabulation of two (or more) factors. By default
computes a frequency table of the factors unless an array of values and an
aggregation function are passed.
Parameters
----------
index : array-like, Series, or list of arrays/Series
Values to group by in the rows.
c... | pandas/core/reshape/pivot.py | def crosstab(index, columns, values=None, rownames=None, colnames=None,
aggfunc=None, margins=False, margins_name='All', dropna=True,
normalize=False):
"""
Compute a simple cross tabulation of two (or more) factors. By default
computes a frequency table of the factors unless an arr... | def crosstab(index, columns, values=None, rownames=None, colnames=None,
aggfunc=None, margins=False, margins_name='All', dropna=True,
normalize=False):
"""
Compute a simple cross tabulation of two (or more) factors. By default
computes a frequency table of the factors unless an arr... | [
"Compute",
"a",
"simple",
"cross",
"tabulation",
"of",
"two",
"(",
"or",
"more",
")",
"factors",
".",
"By",
"default",
"computes",
"a",
"frequency",
"table",
"of",
"the",
"factors",
"unless",
"an",
"array",
"of",
"values",
"and",
"an",
"aggregation",
"func... | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/core/reshape/pivot.py#L391-L528 | [
"def",
"crosstab",
"(",
"index",
",",
"columns",
",",
"values",
"=",
"None",
",",
"rownames",
"=",
"None",
",",
"colnames",
"=",
"None",
",",
"aggfunc",
"=",
"None",
",",
"margins",
"=",
"False",
",",
"margins_name",
"=",
"'All'",
",",
"dropna",
"=",
... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | TablePlotter._shape | Calculate table chape considering index levels. | pandas/util/_doctools.py | def _shape(self, df):
"""
Calculate table chape considering index levels.
"""
row, col = df.shape
return row + df.columns.nlevels, col + df.index.nlevels | def _shape(self, df):
"""
Calculate table chape considering index levels.
"""
row, col = df.shape
return row + df.columns.nlevels, col + df.index.nlevels | [
"Calculate",
"table",
"chape",
"considering",
"index",
"levels",
"."
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/util/_doctools.py#L17-L23 | [
"def",
"_shape",
"(",
"self",
",",
"df",
")",
":",
"row",
",",
"col",
"=",
"df",
".",
"shape",
"return",
"row",
"+",
"df",
".",
"columns",
".",
"nlevels",
",",
"col",
"+",
"df",
".",
"index",
".",
"nlevels"
] | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | TablePlotter._get_cells | Calculate appropriate figure size based on left and right data. | pandas/util/_doctools.py | def _get_cells(self, left, right, vertical):
"""
Calculate appropriate figure size based on left and right data.
"""
if vertical:
# calculate required number of cells
vcells = max(sum(self._shape(l)[0] for l in left),
self._shape(right)[0... | def _get_cells(self, left, right, vertical):
"""
Calculate appropriate figure size based on left and right data.
"""
if vertical:
# calculate required number of cells
vcells = max(sum(self._shape(l)[0] for l in left),
self._shape(right)[0... | [
"Calculate",
"appropriate",
"figure",
"size",
"based",
"on",
"left",
"and",
"right",
"data",
"."
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/util/_doctools.py#L25-L41 | [
"def",
"_get_cells",
"(",
"self",
",",
"left",
",",
"right",
",",
"vertical",
")",
":",
"if",
"vertical",
":",
"# calculate required number of cells",
"vcells",
"=",
"max",
"(",
"sum",
"(",
"self",
".",
"_shape",
"(",
"l",
")",
"[",
"0",
"]",
"for",
"l... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | TablePlotter.plot | Plot left / right DataFrames in specified layout.
Parameters
----------
left : list of DataFrames before operation is applied
right : DataFrame of operation result
labels : list of str to be drawn as titles of left DataFrames
vertical : bool
If True, use vert... | pandas/util/_doctools.py | def plot(self, left, right, labels=None, vertical=True):
"""
Plot left / right DataFrames in specified layout.
Parameters
----------
left : list of DataFrames before operation is applied
right : DataFrame of operation result
labels : list of str to be drawn as ti... | def plot(self, left, right, labels=None, vertical=True):
"""
Plot left / right DataFrames in specified layout.
Parameters
----------
left : list of DataFrames before operation is applied
right : DataFrame of operation result
labels : list of str to be drawn as ti... | [
"Plot",
"left",
"/",
"right",
"DataFrames",
"in",
"specified",
"layout",
"."
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/util/_doctools.py#L43-L101 | [
"def",
"plot",
"(",
"self",
",",
"left",
",",
"right",
",",
"labels",
"=",
"None",
",",
"vertical",
"=",
"True",
")",
":",
"import",
"matplotlib",
".",
"pyplot",
"as",
"plt",
"import",
"matplotlib",
".",
"gridspec",
"as",
"gridspec",
"if",
"not",
"isin... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | TablePlotter._conv | Convert each input to appropriate for table outplot | pandas/util/_doctools.py | def _conv(self, data):
"""Convert each input to appropriate for table outplot"""
if isinstance(data, pd.Series):
if data.name is None:
data = data.to_frame(name='')
else:
data = data.to_frame()
data = data.fillna('NaN')
return data | def _conv(self, data):
"""Convert each input to appropriate for table outplot"""
if isinstance(data, pd.Series):
if data.name is None:
data = data.to_frame(name='')
else:
data = data.to_frame()
data = data.fillna('NaN')
return data | [
"Convert",
"each",
"input",
"to",
"appropriate",
"for",
"table",
"outplot"
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/util/_doctools.py#L103-L111 | [
"def",
"_conv",
"(",
"self",
",",
"data",
")",
":",
"if",
"isinstance",
"(",
"data",
",",
"pd",
".",
"Series",
")",
":",
"if",
"data",
".",
"name",
"is",
"None",
":",
"data",
"=",
"data",
".",
"to_frame",
"(",
"name",
"=",
"''",
")",
"else",
":... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | cut | Bin values into discrete intervals.
Use `cut` when you need to segment and sort data values into bins. This
function is also useful for going from a continuous variable to a
categorical variable. For example, `cut` could convert ages to groups of
age ranges. Supports binning into an equal number of bin... | pandas/core/reshape/tile.py | def cut(x, bins, right=True, labels=None, retbins=False, precision=3,
include_lowest=False, duplicates='raise'):
"""
Bin values into discrete intervals.
Use `cut` when you need to segment and sort data values into bins. This
function is also useful for going from a continuous variable to a
... | def cut(x, bins, right=True, labels=None, retbins=False, precision=3,
include_lowest=False, duplicates='raise'):
"""
Bin values into discrete intervals.
Use `cut` when you need to segment and sort data values into bins. This
function is also useful for going from a continuous variable to a
... | [
"Bin",
"values",
"into",
"discrete",
"intervals",
"."
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/core/reshape/tile.py#L23-L245 | [
"def",
"cut",
"(",
"x",
",",
"bins",
",",
"right",
"=",
"True",
",",
"labels",
"=",
"None",
",",
"retbins",
"=",
"False",
",",
"precision",
"=",
"3",
",",
"include_lowest",
"=",
"False",
",",
"duplicates",
"=",
"'raise'",
")",
":",
"# NOTE: this binnin... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | qcut | Quantile-based discretization function. Discretize variable into
equal-sized buckets based on rank or based on sample quantiles. For example
1000 values for 10 quantiles would produce a Categorical object indicating
quantile membership for each data point.
Parameters
----------
x : 1d ndarray o... | pandas/core/reshape/tile.py | def qcut(x, q, labels=None, retbins=False, precision=3, duplicates='raise'):
"""
Quantile-based discretization function. Discretize variable into
equal-sized buckets based on rank or based on sample quantiles. For example
1000 values for 10 quantiles would produce a Categorical object indicating
qua... | def qcut(x, q, labels=None, retbins=False, precision=3, duplicates='raise'):
"""
Quantile-based discretization function. Discretize variable into
equal-sized buckets based on rank or based on sample quantiles. For example
1000 values for 10 quantiles would produce a Categorical object indicating
qua... | [
"Quantile",
"-",
"based",
"discretization",
"function",
".",
"Discretize",
"variable",
"into",
"equal",
"-",
"sized",
"buckets",
"based",
"on",
"rank",
"or",
"based",
"on",
"sample",
"quantiles",
".",
"For",
"example",
"1000",
"values",
"for",
"10",
"quantiles... | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/core/reshape/tile.py#L248-L317 | [
"def",
"qcut",
"(",
"x",
",",
"q",
",",
"labels",
"=",
"None",
",",
"retbins",
"=",
"False",
",",
"precision",
"=",
"3",
",",
"duplicates",
"=",
"'raise'",
")",
":",
"x_is_series",
",",
"series_index",
",",
"name",
",",
"x",
"=",
"_preprocess_for_cut",... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | _coerce_to_type | if the passed data is of datetime/timedelta type,
this method converts it to numeric so that cut method can
handle it | pandas/core/reshape/tile.py | def _coerce_to_type(x):
"""
if the passed data is of datetime/timedelta type,
this method converts it to numeric so that cut method can
handle it
"""
dtype = None
if is_datetime64tz_dtype(x):
dtype = x.dtype
elif is_datetime64_dtype(x):
x = to_datetime(x)
dtype =... | def _coerce_to_type(x):
"""
if the passed data is of datetime/timedelta type,
this method converts it to numeric so that cut method can
handle it
"""
dtype = None
if is_datetime64tz_dtype(x):
dtype = x.dtype
elif is_datetime64_dtype(x):
x = to_datetime(x)
dtype =... | [
"if",
"the",
"passed",
"data",
"is",
"of",
"datetime",
"/",
"timedelta",
"type",
"this",
"method",
"converts",
"it",
"to",
"numeric",
"so",
"that",
"cut",
"method",
"can",
"handle",
"it"
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/core/reshape/tile.py#L377-L398 | [
"def",
"_coerce_to_type",
"(",
"x",
")",
":",
"dtype",
"=",
"None",
"if",
"is_datetime64tz_dtype",
"(",
"x",
")",
":",
"dtype",
"=",
"x",
".",
"dtype",
"elif",
"is_datetime64_dtype",
"(",
"x",
")",
":",
"x",
"=",
"to_datetime",
"(",
"x",
")",
"dtype",
... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | _convert_bin_to_numeric_type | if the passed bin is of datetime/timedelta type,
this method converts it to integer
Parameters
----------
bins : list-like of bins
dtype : dtype of data
Raises
------
ValueError if bins are not of a compat dtype to dtype | pandas/core/reshape/tile.py | def _convert_bin_to_numeric_type(bins, dtype):
"""
if the passed bin is of datetime/timedelta type,
this method converts it to integer
Parameters
----------
bins : list-like of bins
dtype : dtype of data
Raises
------
ValueError if bins are not of a compat dtype to dtype
""... | def _convert_bin_to_numeric_type(bins, dtype):
"""
if the passed bin is of datetime/timedelta type,
this method converts it to integer
Parameters
----------
bins : list-like of bins
dtype : dtype of data
Raises
------
ValueError if bins are not of a compat dtype to dtype
""... | [
"if",
"the",
"passed",
"bin",
"is",
"of",
"datetime",
"/",
"timedelta",
"type",
"this",
"method",
"converts",
"it",
"to",
"integer"
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/core/reshape/tile.py#L401-L427 | [
"def",
"_convert_bin_to_numeric_type",
"(",
"bins",
",",
"dtype",
")",
":",
"bins_dtype",
"=",
"infer_dtype",
"(",
"bins",
",",
"skipna",
"=",
"False",
")",
"if",
"is_timedelta64_dtype",
"(",
"dtype",
")",
":",
"if",
"bins_dtype",
"in",
"[",
"'timedelta'",
"... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | _convert_bin_to_datelike_type | Convert bins to a DatetimeIndex or TimedeltaIndex if the orginal dtype is
datelike
Parameters
----------
bins : list-like of bins
dtype : dtype of data
Returns
-------
bins : Array-like of bins, DatetimeIndex or TimedeltaIndex if dtype is
datelike | pandas/core/reshape/tile.py | def _convert_bin_to_datelike_type(bins, dtype):
"""
Convert bins to a DatetimeIndex or TimedeltaIndex if the orginal dtype is
datelike
Parameters
----------
bins : list-like of bins
dtype : dtype of data
Returns
-------
bins : Array-like of bins, DatetimeIndex or TimedeltaIndex... | def _convert_bin_to_datelike_type(bins, dtype):
"""
Convert bins to a DatetimeIndex or TimedeltaIndex if the orginal dtype is
datelike
Parameters
----------
bins : list-like of bins
dtype : dtype of data
Returns
-------
bins : Array-like of bins, DatetimeIndex or TimedeltaIndex... | [
"Convert",
"bins",
"to",
"a",
"DatetimeIndex",
"or",
"TimedeltaIndex",
"if",
"the",
"orginal",
"dtype",
"is",
"datelike"
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/core/reshape/tile.py#L430-L450 | [
"def",
"_convert_bin_to_datelike_type",
"(",
"bins",
",",
"dtype",
")",
":",
"if",
"is_datetime64tz_dtype",
"(",
"dtype",
")",
":",
"bins",
"=",
"to_datetime",
"(",
"bins",
".",
"astype",
"(",
"np",
".",
"int64",
")",
",",
"utc",
"=",
"True",
")",
".",
... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | _format_labels | based on the dtype, return our labels | pandas/core/reshape/tile.py | def _format_labels(bins, precision, right=True,
include_lowest=False, dtype=None):
""" based on the dtype, return our labels """
closed = 'right' if right else 'left'
if is_datetime64tz_dtype(dtype):
formatter = partial(Timestamp, tz=dtype.tz)
adjust = lambda x: x - Time... | def _format_labels(bins, precision, right=True,
include_lowest=False, dtype=None):
""" based on the dtype, return our labels """
closed = 'right' if right else 'left'
if is_datetime64tz_dtype(dtype):
formatter = partial(Timestamp, tz=dtype.tz)
adjust = lambda x: x - Time... | [
"based",
"on",
"the",
"dtype",
"return",
"our",
"labels"
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/core/reshape/tile.py#L453-L484 | [
"def",
"_format_labels",
"(",
"bins",
",",
"precision",
",",
"right",
"=",
"True",
",",
"include_lowest",
"=",
"False",
",",
"dtype",
"=",
"None",
")",
":",
"closed",
"=",
"'right'",
"if",
"right",
"else",
"'left'",
"if",
"is_datetime64tz_dtype",
"(",
"dty... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | _preprocess_for_cut | handles preprocessing for cut where we convert passed
input to array, strip the index information and store it
separately | pandas/core/reshape/tile.py | def _preprocess_for_cut(x):
"""
handles preprocessing for cut where we convert passed
input to array, strip the index information and store it
separately
"""
x_is_series = isinstance(x, Series)
series_index = None
name = None
if x_is_series:
series_index = x.index
na... | def _preprocess_for_cut(x):
"""
handles preprocessing for cut where we convert passed
input to array, strip the index information and store it
separately
"""
x_is_series = isinstance(x, Series)
series_index = None
name = None
if x_is_series:
series_index = x.index
na... | [
"handles",
"preprocessing",
"for",
"cut",
"where",
"we",
"convert",
"passed",
"input",
"to",
"array",
"strip",
"the",
"index",
"information",
"and",
"store",
"it",
"separately"
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/core/reshape/tile.py#L487-L509 | [
"def",
"_preprocess_for_cut",
"(",
"x",
")",
":",
"x_is_series",
"=",
"isinstance",
"(",
"x",
",",
"Series",
")",
"series_index",
"=",
"None",
"name",
"=",
"None",
"if",
"x_is_series",
":",
"series_index",
"=",
"x",
".",
"index",
"name",
"=",
"x",
".",
... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | _postprocess_for_cut | handles post processing for the cut method where
we combine the index information if the originally passed
datatype was a series | pandas/core/reshape/tile.py | def _postprocess_for_cut(fac, bins, retbins, x_is_series,
series_index, name, dtype):
"""
handles post processing for the cut method where
we combine the index information if the originally passed
datatype was a series
"""
if x_is_series:
fac = Series(fac, index=... | def _postprocess_for_cut(fac, bins, retbins, x_is_series,
series_index, name, dtype):
"""
handles post processing for the cut method where
we combine the index information if the originally passed
datatype was a series
"""
if x_is_series:
fac = Series(fac, index=... | [
"handles",
"post",
"processing",
"for",
"the",
"cut",
"method",
"where",
"we",
"combine",
"the",
"index",
"information",
"if",
"the",
"originally",
"passed",
"datatype",
"was",
"a",
"series"
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/core/reshape/tile.py#L512-L527 | [
"def",
"_postprocess_for_cut",
"(",
"fac",
",",
"bins",
",",
"retbins",
",",
"x_is_series",
",",
"series_index",
",",
"name",
",",
"dtype",
")",
":",
"if",
"x_is_series",
":",
"fac",
"=",
"Series",
"(",
"fac",
",",
"index",
"=",
"series_index",
",",
"nam... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | _round_frac | Round the fractional part of the given number | pandas/core/reshape/tile.py | def _round_frac(x, precision):
"""
Round the fractional part of the given number
"""
if not np.isfinite(x) or x == 0:
return x
else:
frac, whole = np.modf(x)
if whole == 0:
digits = -int(np.floor(np.log10(abs(frac)))) - 1 + precision
else:
digi... | def _round_frac(x, precision):
"""
Round the fractional part of the given number
"""
if not np.isfinite(x) or x == 0:
return x
else:
frac, whole = np.modf(x)
if whole == 0:
digits = -int(np.floor(np.log10(abs(frac)))) - 1 + precision
else:
digi... | [
"Round",
"the",
"fractional",
"part",
"of",
"the",
"given",
"number"
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/core/reshape/tile.py#L530-L542 | [
"def",
"_round_frac",
"(",
"x",
",",
"precision",
")",
":",
"if",
"not",
"np",
".",
"isfinite",
"(",
"x",
")",
"or",
"x",
"==",
"0",
":",
"return",
"x",
"else",
":",
"frac",
",",
"whole",
"=",
"np",
".",
"modf",
"(",
"x",
")",
"if",
"whole",
... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | _infer_precision | Infer an appropriate precision for _round_frac | pandas/core/reshape/tile.py | def _infer_precision(base_precision, bins):
"""Infer an appropriate precision for _round_frac
"""
for precision in range(base_precision, 20):
levels = [_round_frac(b, precision) for b in bins]
if algos.unique(levels).size == bins.size:
return precision
return base_precision | def _infer_precision(base_precision, bins):
"""Infer an appropriate precision for _round_frac
"""
for precision in range(base_precision, 20):
levels = [_round_frac(b, precision) for b in bins]
if algos.unique(levels).size == bins.size:
return precision
return base_precision | [
"Infer",
"an",
"appropriate",
"precision",
"for",
"_round_frac"
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/core/reshape/tile.py#L545-L552 | [
"def",
"_infer_precision",
"(",
"base_precision",
",",
"bins",
")",
":",
"for",
"precision",
"in",
"range",
"(",
"base_precision",
",",
"20",
")",
":",
"levels",
"=",
"[",
"_round_frac",
"(",
"b",
",",
"precision",
")",
"for",
"b",
"in",
"bins",
"]",
"... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | detect_console_encoding | Try to find the most capable encoding supported by the console.
slightly modified from the way IPython handles the same issue. | pandas/_config/display.py | def detect_console_encoding():
"""
Try to find the most capable encoding supported by the console.
slightly modified from the way IPython handles the same issue.
"""
global _initial_defencoding
encoding = None
try:
encoding = sys.stdout.encoding or sys.stdin.encoding
except (Att... | def detect_console_encoding():
"""
Try to find the most capable encoding supported by the console.
slightly modified from the way IPython handles the same issue.
"""
global _initial_defencoding
encoding = None
try:
encoding = sys.stdout.encoding or sys.stdin.encoding
except (Att... | [
"Try",
"to",
"find",
"the",
"most",
"capable",
"encoding",
"supported",
"by",
"the",
"console",
".",
"slightly",
"modified",
"from",
"the",
"way",
"IPython",
"handles",
"the",
"same",
"issue",
"."
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/_config/display.py#L14-L43 | [
"def",
"detect_console_encoding",
"(",
")",
":",
"global",
"_initial_defencoding",
"encoding",
"=",
"None",
"try",
":",
"encoding",
"=",
"sys",
".",
"stdout",
".",
"encoding",
"or",
"sys",
".",
"stdin",
".",
"encoding",
"except",
"(",
"AttributeError",
",",
... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | _check_arg_length | Checks whether 'args' has length of at most 'compat_args'. Raises
a TypeError if that is not the case, similar to in Python when a
function is called with too many arguments. | pandas/util/_validators.py | def _check_arg_length(fname, args, max_fname_arg_count, compat_args):
"""
Checks whether 'args' has length of at most 'compat_args'. Raises
a TypeError if that is not the case, similar to in Python when a
function is called with too many arguments.
"""
if max_fname_arg_count < 0:
raise ... | def _check_arg_length(fname, args, max_fname_arg_count, compat_args):
"""
Checks whether 'args' has length of at most 'compat_args'. Raises
a TypeError if that is not the case, similar to in Python when a
function is called with too many arguments.
"""
if max_fname_arg_count < 0:
raise ... | [
"Checks",
"whether",
"args",
"has",
"length",
"of",
"at",
"most",
"compat_args",
".",
"Raises",
"a",
"TypeError",
"if",
"that",
"is",
"not",
"the",
"case",
"similar",
"to",
"in",
"Python",
"when",
"a",
"function",
"is",
"called",
"with",
"too",
"many",
"... | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/util/_validators.py#L10-L29 | [
"def",
"_check_arg_length",
"(",
"fname",
",",
"args",
",",
"max_fname_arg_count",
",",
"compat_args",
")",
":",
"if",
"max_fname_arg_count",
"<",
"0",
":",
"raise",
"ValueError",
"(",
"\"'max_fname_arg_count' must be non-negative\"",
")",
"if",
"len",
"(",
"args",
... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | _check_for_default_values | Check that the keys in `arg_val_dict` are mapped to their
default values as specified in `compat_args`.
Note that this function is to be called only when it has been
checked that arg_val_dict.keys() is a subset of compat_args | pandas/util/_validators.py | def _check_for_default_values(fname, arg_val_dict, compat_args):
"""
Check that the keys in `arg_val_dict` are mapped to their
default values as specified in `compat_args`.
Note that this function is to be called only when it has been
checked that arg_val_dict.keys() is a subset of compat_args
... | def _check_for_default_values(fname, arg_val_dict, compat_args):
"""
Check that the keys in `arg_val_dict` are mapped to their
default values as specified in `compat_args`.
Note that this function is to be called only when it has been
checked that arg_val_dict.keys() is a subset of compat_args
... | [
"Check",
"that",
"the",
"keys",
"in",
"arg_val_dict",
"are",
"mapped",
"to",
"their",
"default",
"values",
"as",
"specified",
"in",
"compat_args",
"."
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/util/_validators.py#L32-L69 | [
"def",
"_check_for_default_values",
"(",
"fname",
",",
"arg_val_dict",
",",
"compat_args",
")",
":",
"for",
"key",
"in",
"arg_val_dict",
":",
"# try checking equality directly with '=' operator,",
"# as comparison may have been overridden for the left",
"# hand object",
"try",
... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | validate_args | Checks whether the length of the `*args` argument passed into a function
has at most `len(compat_args)` arguments and whether or not all of these
elements in `args` are set to their default values.
fname: str
The name of the function being passed the `*args` parameter
args: tuple
The `... | pandas/util/_validators.py | def validate_args(fname, args, max_fname_arg_count, compat_args):
"""
Checks whether the length of the `*args` argument passed into a function
has at most `len(compat_args)` arguments and whether or not all of these
elements in `args` are set to their default values.
fname: str
The name of ... | def validate_args(fname, args, max_fname_arg_count, compat_args):
"""
Checks whether the length of the `*args` argument passed into a function
has at most `len(compat_args)` arguments and whether or not all of these
elements in `args` are set to their default values.
fname: str
The name of ... | [
"Checks",
"whether",
"the",
"length",
"of",
"the",
"*",
"args",
"argument",
"passed",
"into",
"a",
"function",
"has",
"at",
"most",
"len",
"(",
"compat_args",
")",
"arguments",
"and",
"whether",
"or",
"not",
"all",
"of",
"these",
"elements",
"in",
"args",
... | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/util/_validators.py#L72-L111 | [
"def",
"validate_args",
"(",
"fname",
",",
"args",
",",
"max_fname_arg_count",
",",
"compat_args",
")",
":",
"_check_arg_length",
"(",
"fname",
",",
"args",
",",
"max_fname_arg_count",
",",
"compat_args",
")",
"# We do this so that we can provide a more informative",
"#... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | _check_for_invalid_keys | Checks whether 'kwargs' contains any keys that are not
in 'compat_args' and raises a TypeError if there is one. | pandas/util/_validators.py | def _check_for_invalid_keys(fname, kwargs, compat_args):
"""
Checks whether 'kwargs' contains any keys that are not
in 'compat_args' and raises a TypeError if there is one.
"""
# set(dict) --> set of the dictionary's keys
diff = set(kwargs) - set(compat_args)
if diff:
bad_arg = lis... | def _check_for_invalid_keys(fname, kwargs, compat_args):
"""
Checks whether 'kwargs' contains any keys that are not
in 'compat_args' and raises a TypeError if there is one.
"""
# set(dict) --> set of the dictionary's keys
diff = set(kwargs) - set(compat_args)
if diff:
bad_arg = lis... | [
"Checks",
"whether",
"kwargs",
"contains",
"any",
"keys",
"that",
"are",
"not",
"in",
"compat_args",
"and",
"raises",
"a",
"TypeError",
"if",
"there",
"is",
"one",
"."
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/util/_validators.py#L114-L127 | [
"def",
"_check_for_invalid_keys",
"(",
"fname",
",",
"kwargs",
",",
"compat_args",
")",
":",
"# set(dict) --> set of the dictionary's keys",
"diff",
"=",
"set",
"(",
"kwargs",
")",
"-",
"set",
"(",
"compat_args",
")",
"if",
"diff",
":",
"bad_arg",
"=",
"list",
... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | validate_kwargs | Checks whether parameters passed to the **kwargs argument in a
function `fname` are valid parameters as specified in `*compat_args`
and whether or not they are set to their default values.
Parameters
----------
fname: str
The name of the function being passed the `**kwargs` parameter
k... | pandas/util/_validators.py | def validate_kwargs(fname, kwargs, compat_args):
"""
Checks whether parameters passed to the **kwargs argument in a
function `fname` are valid parameters as specified in `*compat_args`
and whether or not they are set to their default values.
Parameters
----------
fname: str
The name... | def validate_kwargs(fname, kwargs, compat_args):
"""
Checks whether parameters passed to the **kwargs argument in a
function `fname` are valid parameters as specified in `*compat_args`
and whether or not they are set to their default values.
Parameters
----------
fname: str
The name... | [
"Checks",
"whether",
"parameters",
"passed",
"to",
"the",
"**",
"kwargs",
"argument",
"in",
"a",
"function",
"fname",
"are",
"valid",
"parameters",
"as",
"specified",
"in",
"*",
"compat_args",
"and",
"whether",
"or",
"not",
"they",
"are",
"set",
"to",
"their... | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/util/_validators.py#L130-L157 | [
"def",
"validate_kwargs",
"(",
"fname",
",",
"kwargs",
",",
"compat_args",
")",
":",
"kwds",
"=",
"kwargs",
".",
"copy",
"(",
")",
"_check_for_invalid_keys",
"(",
"fname",
",",
"kwargs",
",",
"compat_args",
")",
"_check_for_default_values",
"(",
"fname",
",",
... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | validate_args_and_kwargs | Checks whether parameters passed to the *args and **kwargs argument in a
function `fname` are valid parameters as specified in `*compat_args`
and whether or not they are set to their default values.
Parameters
----------
fname: str
The name of the function being passed the `**kwargs` parame... | pandas/util/_validators.py | def validate_args_and_kwargs(fname, args, kwargs,
max_fname_arg_count,
compat_args):
"""
Checks whether parameters passed to the *args and **kwargs argument in a
function `fname` are valid parameters as specified in `*compat_args`
and whether or ... | def validate_args_and_kwargs(fname, args, kwargs,
max_fname_arg_count,
compat_args):
"""
Checks whether parameters passed to the *args and **kwargs argument in a
function `fname` are valid parameters as specified in `*compat_args`
and whether or ... | [
"Checks",
"whether",
"parameters",
"passed",
"to",
"the",
"*",
"args",
"and",
"**",
"kwargs",
"argument",
"in",
"a",
"function",
"fname",
"are",
"valid",
"parameters",
"as",
"specified",
"in",
"*",
"compat_args",
"and",
"whether",
"or",
"not",
"they",
"are",... | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/util/_validators.py#L160-L218 | [
"def",
"validate_args_and_kwargs",
"(",
"fname",
",",
"args",
",",
"kwargs",
",",
"max_fname_arg_count",
",",
"compat_args",
")",
":",
"# Check that the total number of arguments passed in (i.e.",
"# args and kwargs) does not exceed the length of compat_args",
"_check_arg_length",
... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | validate_bool_kwarg | Ensures that argument passed in arg_name is of type bool. | pandas/util/_validators.py | def validate_bool_kwarg(value, arg_name):
""" Ensures that argument passed in arg_name is of type bool. """
if not (is_bool(value) or value is None):
raise ValueError('For argument "{arg}" expected type bool, received '
'type {typ}.'.format(arg=arg_name,
... | def validate_bool_kwarg(value, arg_name):
""" Ensures that argument passed in arg_name is of type bool. """
if not (is_bool(value) or value is None):
raise ValueError('For argument "{arg}" expected type bool, received '
'type {typ}.'.format(arg=arg_name,
... | [
"Ensures",
"that",
"argument",
"passed",
"in",
"arg_name",
"is",
"of",
"type",
"bool",
"."
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/util/_validators.py#L221-L227 | [
"def",
"validate_bool_kwarg",
"(",
"value",
",",
"arg_name",
")",
":",
"if",
"not",
"(",
"is_bool",
"(",
"value",
")",
"or",
"value",
"is",
"None",
")",
":",
"raise",
"ValueError",
"(",
"'For argument \"{arg}\" expected type bool, received '",
"'type {typ}.'",
"."... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | validate_axis_style_args | Argument handler for mixed index, columns / axis functions
In an attempt to handle both `.method(index, columns)`, and
`.method(arg, axis=.)`, we have to do some bad things to argument
parsing. This translates all arguments to `{index=., columns=.}` style.
Parameters
----------
data : DataFram... | pandas/util/_validators.py | def validate_axis_style_args(data, args, kwargs, arg_name, method_name):
"""Argument handler for mixed index, columns / axis functions
In an attempt to handle both `.method(index, columns)`, and
`.method(arg, axis=.)`, we have to do some bad things to argument
parsing. This translates all arguments to ... | def validate_axis_style_args(data, args, kwargs, arg_name, method_name):
"""Argument handler for mixed index, columns / axis functions
In an attempt to handle both `.method(index, columns)`, and
`.method(arg, axis=.)`, we have to do some bad things to argument
parsing. This translates all arguments to ... | [
"Argument",
"handler",
"for",
"mixed",
"index",
"columns",
"/",
"axis",
"functions"
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/util/_validators.py#L230-L322 | [
"def",
"validate_axis_style_args",
"(",
"data",
",",
"args",
",",
"kwargs",
",",
"arg_name",
",",
"method_name",
")",
":",
"# TODO: Change to keyword-only args and remove all this",
"out",
"=",
"{",
"}",
"# Goal: fill 'out' with index/columns-style arguments",
"# like out = {... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | validate_fillna_kwargs | Validate the keyword arguments to 'fillna'.
This checks that exactly one of 'value' and 'method' is specified.
If 'method' is specified, this validates that it's a valid method.
Parameters
----------
value, method : object
The 'value' and 'method' keyword arguments for 'fillna'.
valida... | pandas/util/_validators.py | def validate_fillna_kwargs(value, method, validate_scalar_dict_value=True):
"""Validate the keyword arguments to 'fillna'.
This checks that exactly one of 'value' and 'method' is specified.
If 'method' is specified, this validates that it's a valid method.
Parameters
----------
value, method :... | def validate_fillna_kwargs(value, method, validate_scalar_dict_value=True):
"""Validate the keyword arguments to 'fillna'.
This checks that exactly one of 'value' and 'method' is specified.
If 'method' is specified, this validates that it's a valid method.
Parameters
----------
value, method :... | [
"Validate",
"the",
"keyword",
"arguments",
"to",
"fillna",
"."
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/util/_validators.py#L325-L358 | [
"def",
"validate_fillna_kwargs",
"(",
"value",
",",
"method",
",",
"validate_scalar_dict_value",
"=",
"True",
")",
":",
"from",
"pandas",
".",
"core",
".",
"missing",
"import",
"clean_fill_method",
"if",
"value",
"is",
"None",
"and",
"method",
"is",
"None",
":... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | _maybe_process_deprecations | Potentially we might have a deprecation warning, show it
but call the appropriate methods anyhow. | pandas/core/resample.py | def _maybe_process_deprecations(r, how=None, fill_method=None, limit=None):
"""
Potentially we might have a deprecation warning, show it
but call the appropriate methods anyhow.
"""
if how is not None:
# .resample(..., how='sum')
if isinstance(how, str):
method = "{0}()... | def _maybe_process_deprecations(r, how=None, fill_method=None, limit=None):
"""
Potentially we might have a deprecation warning, show it
but call the appropriate methods anyhow.
"""
if how is not None:
# .resample(..., how='sum')
if isinstance(how, str):
method = "{0}()... | [
"Potentially",
"we",
"might",
"have",
"a",
"deprecation",
"warning",
"show",
"it",
"but",
"call",
"the",
"appropriate",
"methods",
"anyhow",
"."
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/core/resample.py#L877-L922 | [
"def",
"_maybe_process_deprecations",
"(",
"r",
",",
"how",
"=",
"None",
",",
"fill_method",
"=",
"None",
",",
"limit",
"=",
"None",
")",
":",
"if",
"how",
"is",
"not",
"None",
":",
"# .resample(..., how='sum')",
"if",
"isinstance",
"(",
"how",
",",
"str",... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | resample | Create a TimeGrouper and return our resampler. | pandas/core/resample.py | def resample(obj, kind=None, **kwds):
"""
Create a TimeGrouper and return our resampler.
"""
tg = TimeGrouper(**kwds)
return tg._get_resampler(obj, kind=kind) | def resample(obj, kind=None, **kwds):
"""
Create a TimeGrouper and return our resampler.
"""
tg = TimeGrouper(**kwds)
return tg._get_resampler(obj, kind=kind) | [
"Create",
"a",
"TimeGrouper",
"and",
"return",
"our",
"resampler",
"."
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/core/resample.py#L1238-L1243 | [
"def",
"resample",
"(",
"obj",
",",
"kind",
"=",
"None",
",",
"*",
"*",
"kwds",
")",
":",
"tg",
"=",
"TimeGrouper",
"(",
"*",
"*",
"kwds",
")",
"return",
"tg",
".",
"_get_resampler",
"(",
"obj",
",",
"kind",
"=",
"kind",
")"
] | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | get_resampler_for_grouping | Return our appropriate resampler when grouping as well. | pandas/core/resample.py | def get_resampler_for_grouping(groupby, rule, how=None, fill_method=None,
limit=None, kind=None, **kwargs):
"""
Return our appropriate resampler when grouping as well.
"""
# .resample uses 'on' similar to how .groupby uses 'key'
kwargs['key'] = kwargs.pop('on', None)
... | def get_resampler_for_grouping(groupby, rule, how=None, fill_method=None,
limit=None, kind=None, **kwargs):
"""
Return our appropriate resampler when grouping as well.
"""
# .resample uses 'on' similar to how .groupby uses 'key'
kwargs['key'] = kwargs.pop('on', None)
... | [
"Return",
"our",
"appropriate",
"resampler",
"when",
"grouping",
"as",
"well",
"."
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/core/resample.py#L1249-L1264 | [
"def",
"get_resampler_for_grouping",
"(",
"groupby",
",",
"rule",
",",
"how",
"=",
"None",
",",
"fill_method",
"=",
"None",
",",
"limit",
"=",
"None",
",",
"kind",
"=",
"None",
",",
"*",
"*",
"kwargs",
")",
":",
"# .resample uses 'on' similar to how .groupby u... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | _get_timestamp_range_edges | Adjust the `first` Timestamp to the preceeding Timestamp that resides on
the provided offset. Adjust the `last` Timestamp to the following
Timestamp that resides on the provided offset. Input Timestamps that
already reside on the offset will be adjusted depending on the type of
offset and the `closed` p... | pandas/core/resample.py | def _get_timestamp_range_edges(first, last, offset, closed='left', base=0):
"""
Adjust the `first` Timestamp to the preceeding Timestamp that resides on
the provided offset. Adjust the `last` Timestamp to the following
Timestamp that resides on the provided offset. Input Timestamps that
already resi... | def _get_timestamp_range_edges(first, last, offset, closed='left', base=0):
"""
Adjust the `first` Timestamp to the preceeding Timestamp that resides on
the provided offset. Adjust the `last` Timestamp to the following
Timestamp that resides on the provided offset. Input Timestamps that
already resi... | [
"Adjust",
"the",
"first",
"Timestamp",
"to",
"the",
"preceeding",
"Timestamp",
"that",
"resides",
"on",
"the",
"provided",
"offset",
".",
"Adjust",
"the",
"last",
"Timestamp",
"to",
"the",
"following",
"Timestamp",
"that",
"resides",
"on",
"the",
"provided",
"... | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/core/resample.py#L1582-L1634 | [
"def",
"_get_timestamp_range_edges",
"(",
"first",
",",
"last",
",",
"offset",
",",
"closed",
"=",
"'left'",
",",
"base",
"=",
"0",
")",
":",
"if",
"isinstance",
"(",
"offset",
",",
"Tick",
")",
":",
"if",
"isinstance",
"(",
"offset",
",",
"Day",
")",
... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | _get_period_range_edges | Adjust the provided `first` and `last` Periods to the respective Period of
the given offset that encompasses them.
Parameters
----------
first : pd.Period
The beginning Period of the range to be adjusted.
last : pd.Period
The ending Period of the range to be adjusted.
offset : p... | pandas/core/resample.py | def _get_period_range_edges(first, last, offset, closed='left', base=0):
"""
Adjust the provided `first` and `last` Periods to the respective Period of
the given offset that encompasses them.
Parameters
----------
first : pd.Period
The beginning Period of the range to be adjusted.
l... | def _get_period_range_edges(first, last, offset, closed='left', base=0):
"""
Adjust the provided `first` and `last` Periods to the respective Period of
the given offset that encompasses them.
Parameters
----------
first : pd.Period
The beginning Period of the range to be adjusted.
l... | [
"Adjust",
"the",
"provided",
"first",
"and",
"last",
"Periods",
"to",
"the",
"respective",
"Period",
"of",
"the",
"given",
"offset",
"that",
"encompasses",
"them",
"."
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/core/resample.py#L1637-L1673 | [
"def",
"_get_period_range_edges",
"(",
"first",
",",
"last",
",",
"offset",
",",
"closed",
"=",
"'left'",
",",
"base",
"=",
"0",
")",
":",
"if",
"not",
"all",
"(",
"isinstance",
"(",
"obj",
",",
"pd",
".",
"Period",
")",
"for",
"obj",
"in",
"[",
"f... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | asfreq | Utility frequency conversion method for Series/DataFrame. | pandas/core/resample.py | def asfreq(obj, freq, method=None, how=None, normalize=False, fill_value=None):
"""
Utility frequency conversion method for Series/DataFrame.
"""
if isinstance(obj.index, PeriodIndex):
if method is not None:
raise NotImplementedError("'method' argument is not supported")
if ... | def asfreq(obj, freq, method=None, how=None, normalize=False, fill_value=None):
"""
Utility frequency conversion method for Series/DataFrame.
"""
if isinstance(obj.index, PeriodIndex):
if method is not None:
raise NotImplementedError("'method' argument is not supported")
if ... | [
"Utility",
"frequency",
"conversion",
"method",
"for",
"Series",
"/",
"DataFrame",
"."
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/core/resample.py#L1734-L1759 | [
"def",
"asfreq",
"(",
"obj",
",",
"freq",
",",
"method",
"=",
"None",
",",
"how",
"=",
"None",
",",
"normalize",
"=",
"False",
",",
"fill_value",
"=",
"None",
")",
":",
"if",
"isinstance",
"(",
"obj",
".",
"index",
",",
"PeriodIndex",
")",
":",
"if... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | Resampler._from_selection | Is the resampling from a DataFrame column or MultiIndex level. | pandas/core/resample.py | def _from_selection(self):
"""
Is the resampling from a DataFrame column or MultiIndex level.
"""
# upsampling and PeriodIndex resampling do not work
# with selection, this state used to catch and raise an error
return (self.groupby is not None and
(self.g... | def _from_selection(self):
"""
Is the resampling from a DataFrame column or MultiIndex level.
"""
# upsampling and PeriodIndex resampling do not work
# with selection, this state used to catch and raise an error
return (self.groupby is not None and
(self.g... | [
"Is",
"the",
"resampling",
"from",
"a",
"DataFrame",
"column",
"or",
"MultiIndex",
"level",
"."
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/core/resample.py#L135-L143 | [
"def",
"_from_selection",
"(",
"self",
")",
":",
"# upsampling and PeriodIndex resampling do not work",
"# with selection, this state used to catch and raise an error",
"return",
"(",
"self",
".",
"groupby",
"is",
"not",
"None",
"and",
"(",
"self",
".",
"groupby",
".",
"k... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | Resampler._set_binner | Setup our binners.
Cache these as we are an immutable object | pandas/core/resample.py | def _set_binner(self):
"""
Setup our binners.
Cache these as we are an immutable object
"""
if self.binner is None:
self.binner, self.grouper = self._get_binner() | def _set_binner(self):
"""
Setup our binners.
Cache these as we are an immutable object
"""
if self.binner is None:
self.binner, self.grouper = self._get_binner() | [
"Setup",
"our",
"binners",
"."
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/core/resample.py#L163-L170 | [
"def",
"_set_binner",
"(",
"self",
")",
":",
"if",
"self",
".",
"binner",
"is",
"None",
":",
"self",
".",
"binner",
",",
"self",
".",
"grouper",
"=",
"self",
".",
"_get_binner",
"(",
")"
] | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | Resampler._get_binner | Create the BinGrouper, assume that self.set_grouper(obj)
has already been called. | pandas/core/resample.py | def _get_binner(self):
"""
Create the BinGrouper, assume that self.set_grouper(obj)
has already been called.
"""
binner, bins, binlabels = self._get_binner_for_time()
bin_grouper = BinGrouper(bins, binlabels, indexer=self.groupby.indexer)
return binner, bin_group... | def _get_binner(self):
"""
Create the BinGrouper, assume that self.set_grouper(obj)
has already been called.
"""
binner, bins, binlabels = self._get_binner_for_time()
bin_grouper = BinGrouper(bins, binlabels, indexer=self.groupby.indexer)
return binner, bin_group... | [
"Create",
"the",
"BinGrouper",
"assume",
"that",
"self",
".",
"set_grouper",
"(",
"obj",
")",
"has",
"already",
"been",
"called",
"."
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/core/resample.py#L172-L180 | [
"def",
"_get_binner",
"(",
"self",
")",
":",
"binner",
",",
"bins",
",",
"binlabels",
"=",
"self",
".",
"_get_binner_for_time",
"(",
")",
"bin_grouper",
"=",
"BinGrouper",
"(",
"bins",
",",
"binlabels",
",",
"indexer",
"=",
"self",
".",
"groupby",
".",
"... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | Resampler.transform | Call function producing a like-indexed Series on each group and return
a Series with the transformed values.
Parameters
----------
arg : function
To apply to each group. Should return a Series with the same index.
Returns
-------
transformed : Series... | pandas/core/resample.py | def transform(self, arg, *args, **kwargs):
"""
Call function producing a like-indexed Series on each group and return
a Series with the transformed values.
Parameters
----------
arg : function
To apply to each group. Should return a Series with the same index... | def transform(self, arg, *args, **kwargs):
"""
Call function producing a like-indexed Series on each group and return
a Series with the transformed values.
Parameters
----------
arg : function
To apply to each group. Should return a Series with the same index... | [
"Call",
"function",
"producing",
"a",
"like",
"-",
"indexed",
"Series",
"on",
"each",
"group",
"and",
"return",
"a",
"Series",
"with",
"the",
"transformed",
"values",
"."
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/core/resample.py#L280-L299 | [
"def",
"transform",
"(",
"self",
",",
"arg",
",",
"*",
"args",
",",
"*",
"*",
"kwargs",
")",
":",
"return",
"self",
".",
"_selected_obj",
".",
"groupby",
"(",
"self",
".",
"groupby",
")",
".",
"transform",
"(",
"arg",
",",
"*",
"args",
",",
"*",
... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | Resampler._gotitem | Sub-classes to define. Return a sliced object.
Parameters
----------
key : string / list of selections
ndim : 1,2
requested ndim of result
subset : object, default None
subset to act on | pandas/core/resample.py | def _gotitem(self, key, ndim, subset=None):
"""
Sub-classes to define. Return a sliced object.
Parameters
----------
key : string / list of selections
ndim : 1,2
requested ndim of result
subset : object, default None
subset to act on
... | def _gotitem(self, key, ndim, subset=None):
"""
Sub-classes to define. Return a sliced object.
Parameters
----------
key : string / list of selections
ndim : 1,2
requested ndim of result
subset : object, default None
subset to act on
... | [
"Sub",
"-",
"classes",
"to",
"define",
".",
"Return",
"a",
"sliced",
"object",
"."
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/core/resample.py#L307-L329 | [
"def",
"_gotitem",
"(",
"self",
",",
"key",
",",
"ndim",
",",
"subset",
"=",
"None",
")",
":",
"self",
".",
"_set_binner",
"(",
")",
"grouper",
"=",
"self",
".",
"grouper",
"if",
"subset",
"is",
"None",
":",
"subset",
"=",
"self",
".",
"obj",
"grou... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | Resampler._groupby_and_aggregate | Re-evaluate the obj with a groupby aggregation. | pandas/core/resample.py | def _groupby_and_aggregate(self, how, grouper=None, *args, **kwargs):
"""
Re-evaluate the obj with a groupby aggregation.
"""
if grouper is None:
self._set_binner()
grouper = self.grouper
obj = self._selected_obj
grouped = groupby(obj, by=None, ... | def _groupby_and_aggregate(self, how, grouper=None, *args, **kwargs):
"""
Re-evaluate the obj with a groupby aggregation.
"""
if grouper is None:
self._set_binner()
grouper = self.grouper
obj = self._selected_obj
grouped = groupby(obj, by=None, ... | [
"Re",
"-",
"evaluate",
"the",
"obj",
"with",
"a",
"groupby",
"aggregation",
"."
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/core/resample.py#L331-L357 | [
"def",
"_groupby_and_aggregate",
"(",
"self",
",",
"how",
",",
"grouper",
"=",
"None",
",",
"*",
"args",
",",
"*",
"*",
"kwargs",
")",
":",
"if",
"grouper",
"is",
"None",
":",
"self",
".",
"_set_binner",
"(",
")",
"grouper",
"=",
"self",
".",
"groupe... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | Resampler._apply_loffset | If loffset is set, offset the result index.
This is NOT an idempotent routine, it will be applied
exactly once to the result.
Parameters
----------
result : Series or DataFrame
the result of resample | pandas/core/resample.py | def _apply_loffset(self, result):
"""
If loffset is set, offset the result index.
This is NOT an idempotent routine, it will be applied
exactly once to the result.
Parameters
----------
result : Series or DataFrame
the result of resample
"""
... | def _apply_loffset(self, result):
"""
If loffset is set, offset the result index.
This is NOT an idempotent routine, it will be applied
exactly once to the result.
Parameters
----------
result : Series or DataFrame
the result of resample
"""
... | [
"If",
"loffset",
"is",
"set",
"offset",
"the",
"result",
"index",
"."
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/core/resample.py#L359-L383 | [
"def",
"_apply_loffset",
"(",
"self",
",",
"result",
")",
":",
"needs_offset",
"=",
"(",
"isinstance",
"(",
"self",
".",
"loffset",
",",
"(",
"DateOffset",
",",
"timedelta",
",",
"np",
".",
"timedelta64",
")",
")",
"and",
"isinstance",
"(",
"result",
"."... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | Resampler._get_resampler_for_grouping | Return the correct class for resampling with groupby. | pandas/core/resample.py | def _get_resampler_for_grouping(self, groupby, **kwargs):
"""
Return the correct class for resampling with groupby.
"""
return self._resampler_for_grouping(self, groupby=groupby, **kwargs) | def _get_resampler_for_grouping(self, groupby, **kwargs):
"""
Return the correct class for resampling with groupby.
"""
return self._resampler_for_grouping(self, groupby=groupby, **kwargs) | [
"Return",
"the",
"correct",
"class",
"for",
"resampling",
"with",
"groupby",
"."
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/core/resample.py#L385-L389 | [
"def",
"_get_resampler_for_grouping",
"(",
"self",
",",
"groupby",
",",
"*",
"*",
"kwargs",
")",
":",
"return",
"self",
".",
"_resampler_for_grouping",
"(",
"self",
",",
"groupby",
"=",
"groupby",
",",
"*",
"*",
"kwargs",
")"
] | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | Resampler._wrap_result | Potentially wrap any results. | pandas/core/resample.py | def _wrap_result(self, result):
"""
Potentially wrap any results.
"""
if isinstance(result, ABCSeries) and self._selection is not None:
result.name = self._selection
if isinstance(result, ABCSeries) and result.empty:
obj = self.obj
if isinstan... | def _wrap_result(self, result):
"""
Potentially wrap any results.
"""
if isinstance(result, ABCSeries) and self._selection is not None:
result.name = self._selection
if isinstance(result, ABCSeries) and result.empty:
obj = self.obj
if isinstan... | [
"Potentially",
"wrap",
"any",
"results",
"."
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/core/resample.py#L391-L406 | [
"def",
"_wrap_result",
"(",
"self",
",",
"result",
")",
":",
"if",
"isinstance",
"(",
"result",
",",
"ABCSeries",
")",
"and",
"self",
".",
"_selection",
"is",
"not",
"None",
":",
"result",
".",
"name",
"=",
"self",
".",
"_selection",
"if",
"isinstance",
... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | Resampler.interpolate | Interpolate values according to different methods.
.. versionadded:: 0.18.1 | pandas/core/resample.py | def interpolate(self, method='linear', axis=0, limit=None, inplace=False,
limit_direction='forward', limit_area=None,
downcast=None, **kwargs):
"""
Interpolate values according to different methods.
.. versionadded:: 0.18.1
"""
result = se... | def interpolate(self, method='linear', axis=0, limit=None, inplace=False,
limit_direction='forward', limit_area=None,
downcast=None, **kwargs):
"""
Interpolate values according to different methods.
.. versionadded:: 0.18.1
"""
result = se... | [
"Interpolate",
"values",
"according",
"to",
"different",
"methods",
"."
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/core/resample.py#L756-L769 | [
"def",
"interpolate",
"(",
"self",
",",
"method",
"=",
"'linear'",
",",
"axis",
"=",
"0",
",",
"limit",
"=",
"None",
",",
"inplace",
"=",
"False",
",",
"limit_direction",
"=",
"'forward'",
",",
"limit_area",
"=",
"None",
",",
"downcast",
"=",
"None",
"... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | Resampler.std | Compute standard deviation of groups, excluding missing values.
Parameters
----------
ddof : integer, default 1
Degrees of freedom. | pandas/core/resample.py | def std(self, ddof=1, *args, **kwargs):
"""
Compute standard deviation of groups, excluding missing values.
Parameters
----------
ddof : integer, default 1
Degrees of freedom.
"""
nv.validate_resampler_func('std', args, kwargs)
return self._do... | def std(self, ddof=1, *args, **kwargs):
"""
Compute standard deviation of groups, excluding missing values.
Parameters
----------
ddof : integer, default 1
Degrees of freedom.
"""
nv.validate_resampler_func('std', args, kwargs)
return self._do... | [
"Compute",
"standard",
"deviation",
"of",
"groups",
"excluding",
"missing",
"values",
"."
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/core/resample.py#L790-L800 | [
"def",
"std",
"(",
"self",
",",
"ddof",
"=",
"1",
",",
"*",
"args",
",",
"*",
"*",
"kwargs",
")",
":",
"nv",
".",
"validate_resampler_func",
"(",
"'std'",
",",
"args",
",",
"kwargs",
")",
"return",
"self",
".",
"_downsample",
"(",
"'std'",
",",
"dd... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | Resampler.var | Compute variance of groups, excluding missing values.
Parameters
----------
ddof : integer, default 1
degrees of freedom | pandas/core/resample.py | def var(self, ddof=1, *args, **kwargs):
"""
Compute variance of groups, excluding missing values.
Parameters
----------
ddof : integer, default 1
degrees of freedom
"""
nv.validate_resampler_func('var', args, kwargs)
return self._downsample('v... | def var(self, ddof=1, *args, **kwargs):
"""
Compute variance of groups, excluding missing values.
Parameters
----------
ddof : integer, default 1
degrees of freedom
"""
nv.validate_resampler_func('var', args, kwargs)
return self._downsample('v... | [
"Compute",
"variance",
"of",
"groups",
"excluding",
"missing",
"values",
"."
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/core/resample.py#L802-L812 | [
"def",
"var",
"(",
"self",
",",
"ddof",
"=",
"1",
",",
"*",
"args",
",",
"*",
"*",
"kwargs",
")",
":",
"nv",
".",
"validate_resampler_func",
"(",
"'var'",
",",
"args",
",",
"kwargs",
")",
"return",
"self",
".",
"_downsample",
"(",
"'var'",
",",
"dd... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | _GroupByMixin._apply | Dispatch to _upsample; we are stripping all of the _upsample kwargs and
performing the original function call on the grouped object. | pandas/core/resample.py | def _apply(self, f, grouper=None, *args, **kwargs):
"""
Dispatch to _upsample; we are stripping all of the _upsample kwargs and
performing the original function call on the grouped object.
"""
def func(x):
x = self._shallow_copy(x, groupby=self.groupby)
... | def _apply(self, f, grouper=None, *args, **kwargs):
"""
Dispatch to _upsample; we are stripping all of the _upsample kwargs and
performing the original function call on the grouped object.
"""
def func(x):
x = self._shallow_copy(x, groupby=self.groupby)
... | [
"Dispatch",
"to",
"_upsample",
";",
"we",
"are",
"stripping",
"all",
"of",
"the",
"_upsample",
"kwargs",
"and",
"performing",
"the",
"original",
"function",
"call",
"on",
"the",
"grouped",
"object",
"."
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/core/resample.py#L947-L962 | [
"def",
"_apply",
"(",
"self",
",",
"f",
",",
"grouper",
"=",
"None",
",",
"*",
"args",
",",
"*",
"*",
"kwargs",
")",
":",
"def",
"func",
"(",
"x",
")",
":",
"x",
"=",
"self",
".",
"_shallow_copy",
"(",
"x",
",",
"groupby",
"=",
"self",
".",
"... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | DatetimeIndexResampler._downsample | Downsample the cython defined function.
Parameters
----------
how : string / cython mapped function
**kwargs : kw args passed to how function | pandas/core/resample.py | def _downsample(self, how, **kwargs):
"""
Downsample the cython defined function.
Parameters
----------
how : string / cython mapped function
**kwargs : kw args passed to how function
"""
self._set_binner()
how = self._is_cython_func(how) or how
... | def _downsample(self, how, **kwargs):
"""
Downsample the cython defined function.
Parameters
----------
how : string / cython mapped function
**kwargs : kw args passed to how function
"""
self._set_binner()
how = self._is_cython_func(how) or how
... | [
"Downsample",
"the",
"cython",
"defined",
"function",
"."
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/core/resample.py#L982-L1016 | [
"def",
"_downsample",
"(",
"self",
",",
"how",
",",
"*",
"*",
"kwargs",
")",
":",
"self",
".",
"_set_binner",
"(",
")",
"how",
"=",
"self",
".",
"_is_cython_func",
"(",
"how",
")",
"or",
"how",
"ax",
"=",
"self",
".",
"ax",
"obj",
"=",
"self",
".... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | DatetimeIndexResampler._adjust_binner_for_upsample | Adjust our binner when upsampling.
The range of a new index should not be outside specified range | pandas/core/resample.py | def _adjust_binner_for_upsample(self, binner):
"""
Adjust our binner when upsampling.
The range of a new index should not be outside specified range
"""
if self.closed == 'right':
binner = binner[1:]
else:
binner = binner[:-1]
return binne... | def _adjust_binner_for_upsample(self, binner):
"""
Adjust our binner when upsampling.
The range of a new index should not be outside specified range
"""
if self.closed == 'right':
binner = binner[1:]
else:
binner = binner[:-1]
return binne... | [
"Adjust",
"our",
"binner",
"when",
"upsampling",
"."
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/core/resample.py#L1018-L1028 | [
"def",
"_adjust_binner_for_upsample",
"(",
"self",
",",
"binner",
")",
":",
"if",
"self",
".",
"closed",
"==",
"'right'",
":",
"binner",
"=",
"binner",
"[",
"1",
":",
"]",
"else",
":",
"binner",
"=",
"binner",
"[",
":",
"-",
"1",
"]",
"return",
"binn... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | DatetimeIndexResampler._upsample | Parameters
----------
method : string {'backfill', 'bfill', 'pad',
'ffill', 'asfreq'} method for upsampling
limit : int, default None
Maximum size gap to fill when reindexing
fill_value : scalar, default None
Value to use for missing values
Se... | pandas/core/resample.py | def _upsample(self, method, limit=None, fill_value=None):
"""
Parameters
----------
method : string {'backfill', 'bfill', 'pad',
'ffill', 'asfreq'} method for upsampling
limit : int, default None
Maximum size gap to fill when reindexing
fill_value ... | def _upsample(self, method, limit=None, fill_value=None):
"""
Parameters
----------
method : string {'backfill', 'bfill', 'pad',
'ffill', 'asfreq'} method for upsampling
limit : int, default None
Maximum size gap to fill when reindexing
fill_value ... | [
"Parameters",
"----------",
"method",
":",
"string",
"{",
"backfill",
"bfill",
"pad",
"ffill",
"asfreq",
"}",
"method",
"for",
"upsampling",
"limit",
":",
"int",
"default",
"None",
"Maximum",
"size",
"gap",
"to",
"fill",
"when",
"reindexing",
"fill_value",
":"... | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/core/resample.py#L1030-L1069 | [
"def",
"_upsample",
"(",
"self",
",",
"method",
",",
"limit",
"=",
"None",
",",
"fill_value",
"=",
"None",
")",
":",
"self",
".",
"_set_binner",
"(",
")",
"if",
"self",
".",
"axis",
":",
"raise",
"AssertionError",
"(",
"'axis must be 0'",
")",
"if",
"s... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | PeriodIndexResampler._downsample | Downsample the cython defined function.
Parameters
----------
how : string / cython mapped function
**kwargs : kw args passed to how function | pandas/core/resample.py | def _downsample(self, how, **kwargs):
"""
Downsample the cython defined function.
Parameters
----------
how : string / cython mapped function
**kwargs : kw args passed to how function
"""
# we may need to actually resample as if we are timestamps
... | def _downsample(self, how, **kwargs):
"""
Downsample the cython defined function.
Parameters
----------
how : string / cython mapped function
**kwargs : kw args passed to how function
"""
# we may need to actually resample as if we are timestamps
... | [
"Downsample",
"the",
"cython",
"defined",
"function",
"."
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/core/resample.py#L1124-L1159 | [
"def",
"_downsample",
"(",
"self",
",",
"how",
",",
"*",
"*",
"kwargs",
")",
":",
"# we may need to actually resample as if we are timestamps",
"if",
"self",
".",
"kind",
"==",
"'timestamp'",
":",
"return",
"super",
"(",
")",
".",
"_downsample",
"(",
"how",
",... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | PeriodIndexResampler._upsample | Parameters
----------
method : string {'backfill', 'bfill', 'pad', 'ffill'}
method for upsampling
limit : int, default None
Maximum size gap to fill when reindexing
fill_value : scalar, default None
Value to use for missing values
See Also
... | pandas/core/resample.py | def _upsample(self, method, limit=None, fill_value=None):
"""
Parameters
----------
method : string {'backfill', 'bfill', 'pad', 'ffill'}
method for upsampling
limit : int, default None
Maximum size gap to fill when reindexing
fill_value : scalar, ... | def _upsample(self, method, limit=None, fill_value=None):
"""
Parameters
----------
method : string {'backfill', 'bfill', 'pad', 'ffill'}
method for upsampling
limit : int, default None
Maximum size gap to fill when reindexing
fill_value : scalar, ... | [
"Parameters",
"----------",
"method",
":",
"string",
"{",
"backfill",
"bfill",
"pad",
"ffill",
"}",
"method",
"for",
"upsampling",
"limit",
":",
"int",
"default",
"None",
"Maximum",
"size",
"gap",
"to",
"fill",
"when",
"reindexing",
"fill_value",
":",
"scalar"... | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/core/resample.py#L1161-L1194 | [
"def",
"_upsample",
"(",
"self",
",",
"method",
",",
"limit",
"=",
"None",
",",
"fill_value",
"=",
"None",
")",
":",
"# we may need to actually resample as if we are timestamps",
"if",
"self",
".",
"kind",
"==",
"'timestamp'",
":",
"return",
"super",
"(",
")",
... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | TimeGrouper._get_resampler | Return my resampler or raise if we have an invalid axis.
Parameters
----------
obj : input object
kind : string, optional
'period','timestamp','timedelta' are valid
Returns
-------
a Resampler
Raises
------
TypeError if incom... | pandas/core/resample.py | def _get_resampler(self, obj, kind=None):
"""
Return my resampler or raise if we have an invalid axis.
Parameters
----------
obj : input object
kind : string, optional
'period','timestamp','timedelta' are valid
Returns
-------
a Resam... | def _get_resampler(self, obj, kind=None):
"""
Return my resampler or raise if we have an invalid axis.
Parameters
----------
obj : input object
kind : string, optional
'period','timestamp','timedelta' are valid
Returns
-------
a Resam... | [
"Return",
"my",
"resampler",
"or",
"raise",
"if",
"we",
"have",
"an",
"invalid",
"axis",
"."
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/core/resample.py#L1334-L1373 | [
"def",
"_get_resampler",
"(",
"self",
",",
"obj",
",",
"kind",
"=",
"None",
")",
":",
"self",
".",
"_set_grouper",
"(",
"obj",
")",
"ax",
"=",
"self",
".",
"ax",
"if",
"isinstance",
"(",
"ax",
",",
"DatetimeIndex",
")",
":",
"return",
"DatetimeIndexRes... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | _combine_hash_arrays | Parameters
----------
arrays : generator
num_items : int
Should be the same as CPython's tupleobject.c | pandas/core/util/hashing.py | def _combine_hash_arrays(arrays, num_items):
"""
Parameters
----------
arrays : generator
num_items : int
Should be the same as CPython's tupleobject.c
"""
try:
first = next(arrays)
except StopIteration:
return np.array([], dtype=np.uint64)
arrays = itertools.ch... | def _combine_hash_arrays(arrays, num_items):
"""
Parameters
----------
arrays : generator
num_items : int
Should be the same as CPython's tupleobject.c
"""
try:
first = next(arrays)
except StopIteration:
return np.array([], dtype=np.uint64)
arrays = itertools.ch... | [
"Parameters",
"----------",
"arrays",
":",
"generator",
"num_items",
":",
"int"
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/core/util/hashing.py#L21-L46 | [
"def",
"_combine_hash_arrays",
"(",
"arrays",
",",
"num_items",
")",
":",
"try",
":",
"first",
"=",
"next",
"(",
"arrays",
")",
"except",
"StopIteration",
":",
"return",
"np",
".",
"array",
"(",
"[",
"]",
",",
"dtype",
"=",
"np",
".",
"uint64",
")",
... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | hash_pandas_object | Return a data hash of the Index/Series/DataFrame
.. versionadded:: 0.19.2
Parameters
----------
index : boolean, default True
include the index in the hash (if Series/DataFrame)
encoding : string, default 'utf8'
encoding for data & key when strings
hash_key : string key to enco... | pandas/core/util/hashing.py | def hash_pandas_object(obj, index=True, encoding='utf8', hash_key=None,
categorize=True):
"""
Return a data hash of the Index/Series/DataFrame
.. versionadded:: 0.19.2
Parameters
----------
index : boolean, default True
include the index in the hash (if Series/Da... | def hash_pandas_object(obj, index=True, encoding='utf8', hash_key=None,
categorize=True):
"""
Return a data hash of the Index/Series/DataFrame
.. versionadded:: 0.19.2
Parameters
----------
index : boolean, default True
include the index in the hash (if Series/Da... | [
"Return",
"a",
"data",
"hash",
"of",
"the",
"Index",
"/",
"Series",
"/",
"DataFrame"
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/core/util/hashing.py#L49-L117 | [
"def",
"hash_pandas_object",
"(",
"obj",
",",
"index",
"=",
"True",
",",
"encoding",
"=",
"'utf8'",
",",
"hash_key",
"=",
"None",
",",
"categorize",
"=",
"True",
")",
":",
"from",
"pandas",
"import",
"Series",
"if",
"hash_key",
"is",
"None",
":",
"hash_k... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | hash_tuples | Hash an MultiIndex / list-of-tuples efficiently
.. versionadded:: 0.20.0
Parameters
----------
vals : MultiIndex, list-of-tuples, or single tuple
encoding : string, default 'utf8'
hash_key : string key to encode, default to _default_hash_key
Returns
-------
ndarray of hashed value... | pandas/core/util/hashing.py | def hash_tuples(vals, encoding='utf8', hash_key=None):
"""
Hash an MultiIndex / list-of-tuples efficiently
.. versionadded:: 0.20.0
Parameters
----------
vals : MultiIndex, list-of-tuples, or single tuple
encoding : string, default 'utf8'
hash_key : string key to encode, default to _de... | def hash_tuples(vals, encoding='utf8', hash_key=None):
"""
Hash an MultiIndex / list-of-tuples efficiently
.. versionadded:: 0.20.0
Parameters
----------
vals : MultiIndex, list-of-tuples, or single tuple
encoding : string, default 'utf8'
hash_key : string key to encode, default to _de... | [
"Hash",
"an",
"MultiIndex",
"/",
"list",
"-",
"of",
"-",
"tuples",
"efficiently"
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/core/util/hashing.py#L120-L164 | [
"def",
"hash_tuples",
"(",
"vals",
",",
"encoding",
"=",
"'utf8'",
",",
"hash_key",
"=",
"None",
")",
":",
"is_tuple",
"=",
"False",
"if",
"isinstance",
"(",
"vals",
",",
"tuple",
")",
":",
"vals",
"=",
"[",
"vals",
"]",
"is_tuple",
"=",
"True",
"eli... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | hash_tuple | Hash a single tuple efficiently
Parameters
----------
val : single tuple
encoding : string, default 'utf8'
hash_key : string key to encode, default to _default_hash_key
Returns
-------
hash | pandas/core/util/hashing.py | def hash_tuple(val, encoding='utf8', hash_key=None):
"""
Hash a single tuple efficiently
Parameters
----------
val : single tuple
encoding : string, default 'utf8'
hash_key : string key to encode, default to _default_hash_key
Returns
-------
hash
"""
hashes = (_hash_sc... | def hash_tuple(val, encoding='utf8', hash_key=None):
"""
Hash a single tuple efficiently
Parameters
----------
val : single tuple
encoding : string, default 'utf8'
hash_key : string key to encode, default to _default_hash_key
Returns
-------
hash
"""
hashes = (_hash_sc... | [
"Hash",
"a",
"single",
"tuple",
"efficiently"
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/core/util/hashing.py#L167-L187 | [
"def",
"hash_tuple",
"(",
"val",
",",
"encoding",
"=",
"'utf8'",
",",
"hash_key",
"=",
"None",
")",
":",
"hashes",
"=",
"(",
"_hash_scalar",
"(",
"v",
",",
"encoding",
"=",
"encoding",
",",
"hash_key",
"=",
"hash_key",
")",
"for",
"v",
"in",
"val",
"... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | _hash_categorical | Hash a Categorical by hashing its categories, and then mapping the codes
to the hashes
Parameters
----------
c : Categorical
encoding : string, default 'utf8'
hash_key : string key to encode, default to _default_hash_key
Returns
-------
ndarray of hashed values array, same size as ... | pandas/core/util/hashing.py | def _hash_categorical(c, encoding, hash_key):
"""
Hash a Categorical by hashing its categories, and then mapping the codes
to the hashes
Parameters
----------
c : Categorical
encoding : string, default 'utf8'
hash_key : string key to encode, default to _default_hash_key
Returns
... | def _hash_categorical(c, encoding, hash_key):
"""
Hash a Categorical by hashing its categories, and then mapping the codes
to the hashes
Parameters
----------
c : Categorical
encoding : string, default 'utf8'
hash_key : string key to encode, default to _default_hash_key
Returns
... | [
"Hash",
"a",
"Categorical",
"by",
"hashing",
"its",
"categories",
"and",
"then",
"mapping",
"the",
"codes",
"to",
"the",
"hashes"
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/core/util/hashing.py#L190-L226 | [
"def",
"_hash_categorical",
"(",
"c",
",",
"encoding",
",",
"hash_key",
")",
":",
"# Convert ExtensionArrays to ndarrays",
"values",
"=",
"np",
".",
"asarray",
"(",
"c",
".",
"categories",
".",
"values",
")",
"hashed",
"=",
"hash_array",
"(",
"values",
",",
... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | hash_array | Given a 1d array, return an array of deterministic integers.
.. versionadded:: 0.19.2
Parameters
----------
vals : ndarray, Categorical
encoding : string, default 'utf8'
encoding for data & key when strings
hash_key : string key to encode, default to _default_hash_key
categorize : ... | pandas/core/util/hashing.py | def hash_array(vals, encoding='utf8', hash_key=None, categorize=True):
"""
Given a 1d array, return an array of deterministic integers.
.. versionadded:: 0.19.2
Parameters
----------
vals : ndarray, Categorical
encoding : string, default 'utf8'
encoding for data & key when strings
... | def hash_array(vals, encoding='utf8', hash_key=None, categorize=True):
"""
Given a 1d array, return an array of deterministic integers.
.. versionadded:: 0.19.2
Parameters
----------
vals : ndarray, Categorical
encoding : string, default 'utf8'
encoding for data & key when strings
... | [
"Given",
"a",
"1d",
"array",
"return",
"an",
"array",
"of",
"deterministic",
"integers",
"."
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/core/util/hashing.py#L229-L305 | [
"def",
"hash_array",
"(",
"vals",
",",
"encoding",
"=",
"'utf8'",
",",
"hash_key",
"=",
"None",
",",
"categorize",
"=",
"True",
")",
":",
"if",
"not",
"hasattr",
"(",
"vals",
",",
"'dtype'",
")",
":",
"raise",
"TypeError",
"(",
"\"must pass a ndarray-like\... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | _hash_scalar | Hash scalar value
Returns
-------
1d uint64 numpy array of hash value, of length 1 | pandas/core/util/hashing.py | def _hash_scalar(val, encoding='utf8', hash_key=None):
"""
Hash scalar value
Returns
-------
1d uint64 numpy array of hash value, of length 1
"""
if isna(val):
# this is to be consistent with the _hash_categorical implementation
return np.array([np.iinfo(np.uint64).max], dt... | def _hash_scalar(val, encoding='utf8', hash_key=None):
"""
Hash scalar value
Returns
-------
1d uint64 numpy array of hash value, of length 1
"""
if isna(val):
# this is to be consistent with the _hash_categorical implementation
return np.array([np.iinfo(np.uint64).max], dt... | [
"Hash",
"scalar",
"value"
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/core/util/hashing.py#L308-L333 | [
"def",
"_hash_scalar",
"(",
"val",
",",
"encoding",
"=",
"'utf8'",
",",
"hash_key",
"=",
"None",
")",
":",
"if",
"isna",
"(",
"val",
")",
":",
"# this is to be consistent with the _hash_categorical implementation",
"return",
"np",
".",
"array",
"(",
"[",
"np",
... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | DocBuilder._process_single_doc | Make sure the provided value for --single is a path to an existing
.rst/.ipynb file, or a pandas object that can be imported.
For example, categorial.rst or pandas.DataFrame.head. For the latter,
return the corresponding file path
(e.g. reference/api/pandas.DataFrame.head.rst). | doc/make.py | def _process_single_doc(self, single_doc):
"""
Make sure the provided value for --single is a path to an existing
.rst/.ipynb file, or a pandas object that can be imported.
For example, categorial.rst or pandas.DataFrame.head. For the latter,
return the corresponding file path
... | def _process_single_doc(self, single_doc):
"""
Make sure the provided value for --single is a path to an existing
.rst/.ipynb file, or a pandas object that can be imported.
For example, categorial.rst or pandas.DataFrame.head. For the latter,
return the corresponding file path
... | [
"Make",
"sure",
"the",
"provided",
"value",
"for",
"--",
"single",
"is",
"a",
"path",
"to",
"an",
"existing",
".",
"rst",
"/",
".",
"ipynb",
"file",
"or",
"a",
"pandas",
"object",
"that",
"can",
"be",
"imported",
"."
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/doc/make.py#L59-L88 | [
"def",
"_process_single_doc",
"(",
"self",
",",
"single_doc",
")",
":",
"base_name",
",",
"extension",
"=",
"os",
".",
"path",
".",
"splitext",
"(",
"single_doc",
")",
"if",
"extension",
"in",
"(",
"'.rst'",
",",
"'.ipynb'",
")",
":",
"if",
"os",
".",
... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | DocBuilder._run_os | Execute a command as a OS terminal.
Parameters
----------
*args : list of str
Command and parameters to be executed
Examples
--------
>>> DocBuilder()._run_os('python', '--version') | doc/make.py | def _run_os(*args):
"""
Execute a command as a OS terminal.
Parameters
----------
*args : list of str
Command and parameters to be executed
Examples
--------
>>> DocBuilder()._run_os('python', '--version')
"""
subprocess.check... | def _run_os(*args):
"""
Execute a command as a OS terminal.
Parameters
----------
*args : list of str
Command and parameters to be executed
Examples
--------
>>> DocBuilder()._run_os('python', '--version')
"""
subprocess.check... | [
"Execute",
"a",
"command",
"as",
"a",
"OS",
"terminal",
"."
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/doc/make.py#L91-L104 | [
"def",
"_run_os",
"(",
"*",
"args",
")",
":",
"subprocess",
".",
"check_call",
"(",
"args",
",",
"stdout",
"=",
"sys",
".",
"stdout",
",",
"stderr",
"=",
"sys",
".",
"stderr",
")"
] | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | DocBuilder._sphinx_build | Call sphinx to build documentation.
Attribute `num_jobs` from the class is used.
Parameters
----------
kind : {'html', 'latex'}
Examples
--------
>>> DocBuilder(num_jobs=4)._sphinx_build('html') | doc/make.py | def _sphinx_build(self, kind):
"""
Call sphinx to build documentation.
Attribute `num_jobs` from the class is used.
Parameters
----------
kind : {'html', 'latex'}
Examples
--------
>>> DocBuilder(num_jobs=4)._sphinx_build('html')
"""
... | def _sphinx_build(self, kind):
"""
Call sphinx to build documentation.
Attribute `num_jobs` from the class is used.
Parameters
----------
kind : {'html', 'latex'}
Examples
--------
>>> DocBuilder(num_jobs=4)._sphinx_build('html')
"""
... | [
"Call",
"sphinx",
"to",
"build",
"documentation",
"."
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/doc/make.py#L106-L133 | [
"def",
"_sphinx_build",
"(",
"self",
",",
"kind",
")",
":",
"if",
"kind",
"not",
"in",
"(",
"'html'",
",",
"'latex'",
")",
":",
"raise",
"ValueError",
"(",
"'kind must be html or latex, '",
"'not {}'",
".",
"format",
"(",
"kind",
")",
")",
"cmd",
"=",
"[... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | DocBuilder._open_browser | Open a browser tab showing single | doc/make.py | def _open_browser(self, single_doc_html):
"""
Open a browser tab showing single
"""
url = os.path.join('file://', DOC_PATH, 'build', 'html',
single_doc_html)
webbrowser.open(url, new=2) | def _open_browser(self, single_doc_html):
"""
Open a browser tab showing single
"""
url = os.path.join('file://', DOC_PATH, 'build', 'html',
single_doc_html)
webbrowser.open(url, new=2) | [
"Open",
"a",
"browser",
"tab",
"showing",
"single"
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/doc/make.py#L135-L141 | [
"def",
"_open_browser",
"(",
"self",
",",
"single_doc_html",
")",
":",
"url",
"=",
"os",
".",
"path",
".",
"join",
"(",
"'file://'",
",",
"DOC_PATH",
",",
"'build'",
",",
"'html'",
",",
"single_doc_html",
")",
"webbrowser",
".",
"open",
"(",
"url",
",",
... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | DocBuilder._get_page_title | Open the rst file `page` and extract its title. | doc/make.py | def _get_page_title(self, page):
"""
Open the rst file `page` and extract its title.
"""
fname = os.path.join(SOURCE_PATH, '{}.rst'.format(page))
option_parser = docutils.frontend.OptionParser(
components=(docutils.parsers.rst.Parser,))
doc = docutils.utils.ne... | def _get_page_title(self, page):
"""
Open the rst file `page` and extract its title.
"""
fname = os.path.join(SOURCE_PATH, '{}.rst'.format(page))
option_parser = docutils.frontend.OptionParser(
components=(docutils.parsers.rst.Parser,))
doc = docutils.utils.ne... | [
"Open",
"the",
"rst",
"file",
"page",
"and",
"extract",
"its",
"title",
"."
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/doc/make.py#L143-L167 | [
"def",
"_get_page_title",
"(",
"self",
",",
"page",
")",
":",
"fname",
"=",
"os",
".",
"path",
".",
"join",
"(",
"SOURCE_PATH",
",",
"'{}.rst'",
".",
"format",
"(",
"page",
")",
")",
"option_parser",
"=",
"docutils",
".",
"frontend",
".",
"OptionParser",... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | DocBuilder._add_redirects | Create in the build directory an html file with a redirect,
for every row in REDIRECTS_FILE. | doc/make.py | def _add_redirects(self):
"""
Create in the build directory an html file with a redirect,
for every row in REDIRECTS_FILE.
"""
html = '''
<html>
<head>
<meta http-equiv="refresh" content="0;URL={url}"/>
</head>
<body>
... | def _add_redirects(self):
"""
Create in the build directory an html file with a redirect,
for every row in REDIRECTS_FILE.
"""
html = '''
<html>
<head>
<meta http-equiv="refresh" content="0;URL={url}"/>
</head>
<body>
... | [
"Create",
"in",
"the",
"build",
"directory",
"an",
"html",
"file",
"with",
"a",
"redirect",
"for",
"every",
"row",
"in",
"REDIRECTS_FILE",
"."
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/doc/make.py#L169-L212 | [
"def",
"_add_redirects",
"(",
"self",
")",
":",
"html",
"=",
"'''\n <html>\n <head>\n <meta http-equiv=\"refresh\" content=\"0;URL={url}\"/>\n </head>\n <body>\n <p>\n The page has been moved to <a href=\"{url}\... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | DocBuilder.html | Build HTML documentation. | doc/make.py | def html(self):
"""
Build HTML documentation.
"""
ret_code = self._sphinx_build('html')
zip_fname = os.path.join(BUILD_PATH, 'html', 'pandas.zip')
if os.path.exists(zip_fname):
os.remove(zip_fname)
if self.single_doc_html is not None:
self... | def html(self):
"""
Build HTML documentation.
"""
ret_code = self._sphinx_build('html')
zip_fname = os.path.join(BUILD_PATH, 'html', 'pandas.zip')
if os.path.exists(zip_fname):
os.remove(zip_fname)
if self.single_doc_html is not None:
self... | [
"Build",
"HTML",
"documentation",
"."
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/doc/make.py#L214-L227 | [
"def",
"html",
"(",
"self",
")",
":",
"ret_code",
"=",
"self",
".",
"_sphinx_build",
"(",
"'html'",
")",
"zip_fname",
"=",
"os",
".",
"path",
".",
"join",
"(",
"BUILD_PATH",
",",
"'html'",
",",
"'pandas.zip'",
")",
"if",
"os",
".",
"path",
".",
"exis... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | DocBuilder.latex | Build PDF documentation. | doc/make.py | def latex(self, force=False):
"""
Build PDF documentation.
"""
if sys.platform == 'win32':
sys.stderr.write('latex build has not been tested on windows\n')
else:
ret_code = self._sphinx_build('latex')
os.chdir(os.path.join(BUILD_PATH, 'latex'))... | def latex(self, force=False):
"""
Build PDF documentation.
"""
if sys.platform == 'win32':
sys.stderr.write('latex build has not been tested on windows\n')
else:
ret_code = self._sphinx_build('latex')
os.chdir(os.path.join(BUILD_PATH, 'latex'))... | [
"Build",
"PDF",
"documentation",
"."
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/doc/make.py#L229-L247 | [
"def",
"latex",
"(",
"self",
",",
"force",
"=",
"False",
")",
":",
"if",
"sys",
".",
"platform",
"==",
"'win32'",
":",
"sys",
".",
"stderr",
".",
"write",
"(",
"'latex build has not been tested on windows\\n'",
")",
"else",
":",
"ret_code",
"=",
"self",
".... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | DocBuilder.clean | Clean documentation generated files. | doc/make.py | def clean():
"""
Clean documentation generated files.
"""
shutil.rmtree(BUILD_PATH, ignore_errors=True)
shutil.rmtree(os.path.join(SOURCE_PATH, 'reference', 'api'),
ignore_errors=True) | def clean():
"""
Clean documentation generated files.
"""
shutil.rmtree(BUILD_PATH, ignore_errors=True)
shutil.rmtree(os.path.join(SOURCE_PATH, 'reference', 'api'),
ignore_errors=True) | [
"Clean",
"documentation",
"generated",
"files",
"."
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/doc/make.py#L256-L262 | [
"def",
"clean",
"(",
")",
":",
"shutil",
".",
"rmtree",
"(",
"BUILD_PATH",
",",
"ignore_errors",
"=",
"True",
")",
"shutil",
".",
"rmtree",
"(",
"os",
".",
"path",
".",
"join",
"(",
"SOURCE_PATH",
",",
"'reference'",
",",
"'api'",
")",
",",
"ignore_err... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | DocBuilder.zip_html | Compress HTML documentation into a zip file. | doc/make.py | def zip_html(self):
"""
Compress HTML documentation into a zip file.
"""
zip_fname = os.path.join(BUILD_PATH, 'html', 'pandas.zip')
if os.path.exists(zip_fname):
os.remove(zip_fname)
dirname = os.path.join(BUILD_PATH, 'html')
fnames = os.listdir(dirnam... | def zip_html(self):
"""
Compress HTML documentation into a zip file.
"""
zip_fname = os.path.join(BUILD_PATH, 'html', 'pandas.zip')
if os.path.exists(zip_fname):
os.remove(zip_fname)
dirname = os.path.join(BUILD_PATH, 'html')
fnames = os.listdir(dirnam... | [
"Compress",
"HTML",
"documentation",
"into",
"a",
"zip",
"file",
"."
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/doc/make.py#L264-L278 | [
"def",
"zip_html",
"(",
"self",
")",
":",
"zip_fname",
"=",
"os",
".",
"path",
".",
"join",
"(",
"BUILD_PATH",
",",
"'html'",
",",
"'pandas.zip'",
")",
"if",
"os",
".",
"path",
".",
"exists",
"(",
"zip_fname",
")",
":",
"os",
".",
"remove",
"(",
"z... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | LatexFormatter.write_result | Render a DataFrame to a LaTeX tabular/longtable environment output. | pandas/io/formats/latex.py | def write_result(self, buf):
"""
Render a DataFrame to a LaTeX tabular/longtable environment output.
"""
# string representation of the columns
if len(self.frame.columns) == 0 or len(self.frame.index) == 0:
info_line = ('Empty {name}\nColumns: {col}\nIndex: {idx}'
... | def write_result(self, buf):
"""
Render a DataFrame to a LaTeX tabular/longtable environment output.
"""
# string representation of the columns
if len(self.frame.columns) == 0 or len(self.frame.index) == 0:
info_line = ('Empty {name}\nColumns: {col}\nIndex: {idx}'
... | [
"Render",
"a",
"DataFrame",
"to",
"a",
"LaTeX",
"tabular",
"/",
"longtable",
"environment",
"output",
"."
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/io/formats/latex.py#L40-L163 | [
"def",
"write_result",
"(",
"self",
",",
"buf",
")",
":",
"# string representation of the columns",
"if",
"len",
"(",
"self",
".",
"frame",
".",
"columns",
")",
"==",
"0",
"or",
"len",
"(",
"self",
".",
"frame",
".",
"index",
")",
"==",
"0",
":",
"info... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | LatexFormatter._format_multicolumn | r"""
Combine columns belonging to a group to a single multicolumn entry
according to self.multicolumn_format
e.g.:
a & & & b & c &
will become
\multicolumn{3}{l}{a} & b & \multicolumn{2}{l}{c} | pandas/io/formats/latex.py | def _format_multicolumn(self, row, ilevels):
r"""
Combine columns belonging to a group to a single multicolumn entry
according to self.multicolumn_format
e.g.:
a & & & b & c &
will become
\multicolumn{3}{l}{a} & b & \multicolumn{2}{l}{c}
"""
row... | def _format_multicolumn(self, row, ilevels):
r"""
Combine columns belonging to a group to a single multicolumn entry
according to self.multicolumn_format
e.g.:
a & & & b & c &
will become
\multicolumn{3}{l}{a} & b & \multicolumn{2}{l}{c}
"""
row... | [
"r",
"Combine",
"columns",
"belonging",
"to",
"a",
"group",
"to",
"a",
"single",
"multicolumn",
"entry",
"according",
"to",
"self",
".",
"multicolumn_format"
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/io/formats/latex.py#L165-L201 | [
"def",
"_format_multicolumn",
"(",
"self",
",",
"row",
",",
"ilevels",
")",
":",
"row2",
"=",
"list",
"(",
"row",
"[",
":",
"ilevels",
"]",
")",
"ncol",
"=",
"1",
"coltext",
"=",
"''",
"def",
"append_col",
"(",
")",
":",
"# write multicolumn if needed",
... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | LatexFormatter._format_multirow | r"""
Check following rows, whether row should be a multirow
e.g.: becomes:
a & 0 & \multirow{2}{*}{a} & 0 &
& 1 & & 1 &
b & 0 & \cline{1-2}
b & 0 & | pandas/io/formats/latex.py | def _format_multirow(self, row, ilevels, i, rows):
r"""
Check following rows, whether row should be a multirow
e.g.: becomes:
a & 0 & \multirow{2}{*}{a} & 0 &
& 1 & & 1 &
b & 0 & \cline{1-2}
b & 0 &
"""
for j in range(ileve... | def _format_multirow(self, row, ilevels, i, rows):
r"""
Check following rows, whether row should be a multirow
e.g.: becomes:
a & 0 & \multirow{2}{*}{a} & 0 &
& 1 & & 1 &
b & 0 & \cline{1-2}
b & 0 &
"""
for j in range(ileve... | [
"r",
"Check",
"following",
"rows",
"whether",
"row",
"should",
"be",
"a",
"multirow"
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/io/formats/latex.py#L203-L227 | [
"def",
"_format_multirow",
"(",
"self",
",",
"row",
",",
"ilevels",
",",
"i",
",",
"rows",
")",
":",
"for",
"j",
"in",
"range",
"(",
"ilevels",
")",
":",
"if",
"row",
"[",
"j",
"]",
".",
"strip",
"(",
")",
":",
"nrow",
"=",
"1",
"for",
"r",
"... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | LatexFormatter._print_cline | Print clines after multirow-blocks are finished | pandas/io/formats/latex.py | def _print_cline(self, buf, i, icol):
"""
Print clines after multirow-blocks are finished
"""
for cl in self.clinebuf:
if cl[0] == i:
buf.write('\\cline{{{cl:d}-{icol:d}}}\n'
.format(cl=cl[1], icol=icol))
# remove entries that... | def _print_cline(self, buf, i, icol):
"""
Print clines after multirow-blocks are finished
"""
for cl in self.clinebuf:
if cl[0] == i:
buf.write('\\cline{{{cl:d}-{icol:d}}}\n'
.format(cl=cl[1], icol=icol))
# remove entries that... | [
"Print",
"clines",
"after",
"multirow",
"-",
"blocks",
"are",
"finished"
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/io/formats/latex.py#L229-L238 | [
"def",
"_print_cline",
"(",
"self",
",",
"buf",
",",
"i",
",",
"icol",
")",
":",
"for",
"cl",
"in",
"self",
".",
"clinebuf",
":",
"if",
"cl",
"[",
"0",
"]",
"==",
"i",
":",
"buf",
".",
"write",
"(",
"'\\\\cline{{{cl:d}-{icol:d}}}\\n'",
".",
"format",... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | _validate_integer | Checks whether the 'name' parameter for parsing is either
an integer OR float that can SAFELY be cast to an integer
without losing accuracy. Raises a ValueError if that is
not the case.
Parameters
----------
name : string
Parameter name (used for error reporting)
val : int or float
... | pandas/io/parsers.py | def _validate_integer(name, val, min_val=0):
"""
Checks whether the 'name' parameter for parsing is either
an integer OR float that can SAFELY be cast to an integer
without losing accuracy. Raises a ValueError if that is
not the case.
Parameters
----------
name : string
Paramete... | def _validate_integer(name, val, min_val=0):
"""
Checks whether the 'name' parameter for parsing is either
an integer OR float that can SAFELY be cast to an integer
without losing accuracy. Raises a ValueError if that is
not the case.
Parameters
----------
name : string
Paramete... | [
"Checks",
"whether",
"the",
"name",
"parameter",
"for",
"parsing",
"is",
"either",
"an",
"integer",
"OR",
"float",
"that",
"can",
"SAFELY",
"be",
"cast",
"to",
"an",
"integer",
"without",
"losing",
"accuracy",
".",
"Raises",
"a",
"ValueError",
"if",
"that",
... | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/io/parsers.py#L349-L376 | [
"def",
"_validate_integer",
"(",
"name",
",",
"val",
",",
"min_val",
"=",
"0",
")",
":",
"msg",
"=",
"\"'{name:s}' must be an integer >={min_val:d}\"",
".",
"format",
"(",
"name",
"=",
"name",
",",
"min_val",
"=",
"min_val",
")",
"if",
"val",
"is",
"not",
... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | _validate_names | Check if the `names` parameter contains duplicates.
If duplicates are found, we issue a warning before returning.
Parameters
----------
names : array-like or None
An array containing a list of the names used for the output DataFrame.
Returns
-------
names : array-like or None
... | pandas/io/parsers.py | def _validate_names(names):
"""
Check if the `names` parameter contains duplicates.
If duplicates are found, we issue a warning before returning.
Parameters
----------
names : array-like or None
An array containing a list of the names used for the output DataFrame.
Returns
---... | def _validate_names(names):
"""
Check if the `names` parameter contains duplicates.
If duplicates are found, we issue a warning before returning.
Parameters
----------
names : array-like or None
An array containing a list of the names used for the output DataFrame.
Returns
---... | [
"Check",
"if",
"the",
"names",
"parameter",
"contains",
"duplicates",
"."
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/io/parsers.py#L379-L402 | [
"def",
"_validate_names",
"(",
"names",
")",
":",
"if",
"names",
"is",
"not",
"None",
":",
"if",
"len",
"(",
"names",
")",
"!=",
"len",
"(",
"set",
"(",
"names",
")",
")",
":",
"msg",
"=",
"(",
"\"Duplicate names specified. This \"",
"\"will raise an error... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | _read | Generic reader of line files. | pandas/io/parsers.py | def _read(filepath_or_buffer: FilePathOrBuffer, kwds):
"""Generic reader of line files."""
encoding = kwds.get('encoding', None)
if encoding is not None:
encoding = re.sub('_', '-', encoding).lower()
kwds['encoding'] = encoding
compression = kwds.get('compression', 'infer')
compress... | def _read(filepath_or_buffer: FilePathOrBuffer, kwds):
"""Generic reader of line files."""
encoding = kwds.get('encoding', None)
if encoding is not None:
encoding = re.sub('_', '-', encoding).lower()
kwds['encoding'] = encoding
compression = kwds.get('compression', 'infer')
compress... | [
"Generic",
"reader",
"of",
"line",
"files",
"."
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/io/parsers.py#L405-L452 | [
"def",
"_read",
"(",
"filepath_or_buffer",
":",
"FilePathOrBuffer",
",",
"kwds",
")",
":",
"encoding",
"=",
"kwds",
".",
"get",
"(",
"'encoding'",
",",
"None",
")",
"if",
"encoding",
"is",
"not",
"None",
":",
"encoding",
"=",
"re",
".",
"sub",
"(",
"'_... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | read_fwf | r"""
Read a table of fixed-width formatted lines into DataFrame.
Also supports optionally iterating or breaking of the file
into chunks.
Additional help can be found in the `online docs for IO Tools
<http://pandas.pydata.org/pandas-docs/stable/io.html>`_.
Parameters
----------
filepat... | pandas/io/parsers.py | def read_fwf(filepath_or_buffer: FilePathOrBuffer,
colspecs='infer',
widths=None,
infer_nrows=100,
**kwds):
r"""
Read a table of fixed-width formatted lines into DataFrame.
Also supports optionally iterating or breaking of the file
into chunks.
... | def read_fwf(filepath_or_buffer: FilePathOrBuffer,
colspecs='infer',
widths=None,
infer_nrows=100,
**kwds):
r"""
Read a table of fixed-width formatted lines into DataFrame.
Also supports optionally iterating or breaking of the file
into chunks.
... | [
"r",
"Read",
"a",
"table",
"of",
"fixed",
"-",
"width",
"formatted",
"lines",
"into",
"DataFrame",
"."
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/io/parsers.py#L735-L813 | [
"def",
"read_fwf",
"(",
"filepath_or_buffer",
":",
"FilePathOrBuffer",
",",
"colspecs",
"=",
"'infer'",
",",
"widths",
"=",
"None",
",",
"infer_nrows",
"=",
"100",
",",
"*",
"*",
"kwds",
")",
":",
"# Check input arguments.",
"if",
"colspecs",
"is",
"None",
"... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | _is_potential_multi_index | Check whether or not the `columns` parameter
could be converted into a MultiIndex.
Parameters
----------
columns : array-like
Object which may or may not be convertible into a MultiIndex
Returns
-------
boolean : Whether or not columns could become a MultiIndex | pandas/io/parsers.py | def _is_potential_multi_index(columns):
"""
Check whether or not the `columns` parameter
could be converted into a MultiIndex.
Parameters
----------
columns : array-like
Object which may or may not be convertible into a MultiIndex
Returns
-------
boolean : Whether or not co... | def _is_potential_multi_index(columns):
"""
Check whether or not the `columns` parameter
could be converted into a MultiIndex.
Parameters
----------
columns : array-like
Object which may or may not be convertible into a MultiIndex
Returns
-------
boolean : Whether or not co... | [
"Check",
"whether",
"or",
"not",
"the",
"columns",
"parameter",
"could",
"be",
"converted",
"into",
"a",
"MultiIndex",
"."
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/io/parsers.py#L1190-L1205 | [
"def",
"_is_potential_multi_index",
"(",
"columns",
")",
":",
"return",
"(",
"len",
"(",
"columns",
")",
"and",
"not",
"isinstance",
"(",
"columns",
",",
"MultiIndex",
")",
"and",
"all",
"(",
"isinstance",
"(",
"c",
",",
"tuple",
")",
"for",
"c",
"in",
... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | _evaluate_usecols | Check whether or not the 'usecols' parameter
is a callable. If so, enumerates the 'names'
parameter and returns a set of indices for
each entry in 'names' that evaluates to True.
If not a callable, returns 'usecols'. | pandas/io/parsers.py | def _evaluate_usecols(usecols, names):
"""
Check whether or not the 'usecols' parameter
is a callable. If so, enumerates the 'names'
parameter and returns a set of indices for
each entry in 'names' that evaluates to True.
If not a callable, returns 'usecols'.
"""
if callable(usecols):
... | def _evaluate_usecols(usecols, names):
"""
Check whether or not the 'usecols' parameter
is a callable. If so, enumerates the 'names'
parameter and returns a set of indices for
each entry in 'names' that evaluates to True.
If not a callable, returns 'usecols'.
"""
if callable(usecols):
... | [
"Check",
"whether",
"or",
"not",
"the",
"usecols",
"parameter",
"is",
"a",
"callable",
".",
"If",
"so",
"enumerates",
"the",
"names",
"parameter",
"and",
"returns",
"a",
"set",
"of",
"indices",
"for",
"each",
"entry",
"in",
"names",
"that",
"evaluates",
"t... | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/io/parsers.py#L1208-L1218 | [
"def",
"_evaluate_usecols",
"(",
"usecols",
",",
"names",
")",
":",
"if",
"callable",
"(",
"usecols",
")",
":",
"return",
"{",
"i",
"for",
"i",
",",
"name",
"in",
"enumerate",
"(",
"names",
")",
"if",
"usecols",
"(",
"name",
")",
"}",
"return",
"usec... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | _validate_usecols_names | Validates that all usecols are present in a given
list of names. If not, raise a ValueError that
shows what usecols are missing.
Parameters
----------
usecols : iterable of usecols
The columns to validate are present in names.
names : iterable of names
The column names to check ... | pandas/io/parsers.py | def _validate_usecols_names(usecols, names):
"""
Validates that all usecols are present in a given
list of names. If not, raise a ValueError that
shows what usecols are missing.
Parameters
----------
usecols : iterable of usecols
The columns to validate are present in names.
nam... | def _validate_usecols_names(usecols, names):
"""
Validates that all usecols are present in a given
list of names. If not, raise a ValueError that
shows what usecols are missing.
Parameters
----------
usecols : iterable of usecols
The columns to validate are present in names.
nam... | [
"Validates",
"that",
"all",
"usecols",
"are",
"present",
"in",
"a",
"given",
"list",
"of",
"names",
".",
"If",
"not",
"raise",
"a",
"ValueError",
"that",
"shows",
"what",
"usecols",
"are",
"missing",
"."
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/io/parsers.py#L1221-L1250 | [
"def",
"_validate_usecols_names",
"(",
"usecols",
",",
"names",
")",
":",
"missing",
"=",
"[",
"c",
"for",
"c",
"in",
"usecols",
"if",
"c",
"not",
"in",
"names",
"]",
"if",
"len",
"(",
"missing",
")",
">",
"0",
":",
"raise",
"ValueError",
"(",
"\"Use... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | _validate_usecols_arg | Validate the 'usecols' parameter.
Checks whether or not the 'usecols' parameter contains all integers
(column selection by index), strings (column by name) or is a callable.
Raises a ValueError if that is not the case.
Parameters
----------
usecols : list-like, callable, or None
List o... | pandas/io/parsers.py | def _validate_usecols_arg(usecols):
"""
Validate the 'usecols' parameter.
Checks whether or not the 'usecols' parameter contains all integers
(column selection by index), strings (column by name) or is a callable.
Raises a ValueError if that is not the case.
Parameters
----------
useco... | def _validate_usecols_arg(usecols):
"""
Validate the 'usecols' parameter.
Checks whether or not the 'usecols' parameter contains all integers
(column selection by index), strings (column by name) or is a callable.
Raises a ValueError if that is not the case.
Parameters
----------
useco... | [
"Validate",
"the",
"usecols",
"parameter",
"."
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/io/parsers.py#L1284-L1330 | [
"def",
"_validate_usecols_arg",
"(",
"usecols",
")",
":",
"msg",
"=",
"(",
"\"'usecols' must either be list-like of all strings, all unicode, \"",
"\"all integers or a callable.\"",
")",
"if",
"usecols",
"is",
"not",
"None",
":",
"if",
"callable",
"(",
"usecols",
")",
"... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | _validate_parse_dates_arg | Check whether or not the 'parse_dates' parameter
is a non-boolean scalar. Raises a ValueError if
that is the case. | pandas/io/parsers.py | def _validate_parse_dates_arg(parse_dates):
"""
Check whether or not the 'parse_dates' parameter
is a non-boolean scalar. Raises a ValueError if
that is the case.
"""
msg = ("Only booleans, lists, and "
"dictionaries are accepted "
"for the 'parse_dates' parameter")
if... | def _validate_parse_dates_arg(parse_dates):
"""
Check whether or not the 'parse_dates' parameter
is a non-boolean scalar. Raises a ValueError if
that is the case.
"""
msg = ("Only booleans, lists, and "
"dictionaries are accepted "
"for the 'parse_dates' parameter")
if... | [
"Check",
"whether",
"or",
"not",
"the",
"parse_dates",
"parameter",
"is",
"a",
"non",
"-",
"boolean",
"scalar",
".",
"Raises",
"a",
"ValueError",
"if",
"that",
"is",
"the",
"case",
"."
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/io/parsers.py#L1333-L1351 | [
"def",
"_validate_parse_dates_arg",
"(",
"parse_dates",
")",
":",
"msg",
"=",
"(",
"\"Only booleans, lists, and \"",
"\"dictionaries are accepted \"",
"\"for the 'parse_dates' parameter\"",
")",
"if",
"parse_dates",
"is",
"not",
"None",
":",
"if",
"is_scalar",
"(",
"parse... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | _stringify_na_values | return a stringified and numeric for these values | pandas/io/parsers.py | def _stringify_na_values(na_values):
""" return a stringified and numeric for these values """
result = []
for x in na_values:
result.append(str(x))
result.append(x)
try:
v = float(x)
# we are like 999 here
if v == int(v):
v = int(... | def _stringify_na_values(na_values):
""" return a stringified and numeric for these values """
result = []
for x in na_values:
result.append(str(x))
result.append(x)
try:
v = float(x)
# we are like 999 here
if v == int(v):
v = int(... | [
"return",
"a",
"stringified",
"and",
"numeric",
"for",
"these",
"values"
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/io/parsers.py#L3425-L3447 | [
"def",
"_stringify_na_values",
"(",
"na_values",
")",
":",
"result",
"=",
"[",
"]",
"for",
"x",
"in",
"na_values",
":",
"result",
".",
"append",
"(",
"str",
"(",
"x",
")",
")",
"result",
".",
"append",
"(",
"x",
")",
"try",
":",
"v",
"=",
"float",
... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | _get_na_values | Get the NaN values for a given column.
Parameters
----------
col : str
The name of the column.
na_values : array-like, dict
The object listing the NaN values as strings.
na_fvalues : array-like, dict
The object listing the NaN values as floats.
keep_default_na : bool
... | pandas/io/parsers.py | def _get_na_values(col, na_values, na_fvalues, keep_default_na):
"""
Get the NaN values for a given column.
Parameters
----------
col : str
The name of the column.
na_values : array-like, dict
The object listing the NaN values as strings.
na_fvalues : array-like, dict
... | def _get_na_values(col, na_values, na_fvalues, keep_default_na):
"""
Get the NaN values for a given column.
Parameters
----------
col : str
The name of the column.
na_values : array-like, dict
The object listing the NaN values as strings.
na_fvalues : array-like, dict
... | [
"Get",
"the",
"NaN",
"values",
"for",
"a",
"given",
"column",
"."
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/io/parsers.py#L3450-L3483 | [
"def",
"_get_na_values",
"(",
"col",
",",
"na_values",
",",
"na_fvalues",
",",
"keep_default_na",
")",
":",
"if",
"isinstance",
"(",
"na_values",
",",
"dict",
")",
":",
"if",
"col",
"in",
"na_values",
":",
"return",
"na_values",
"[",
"col",
"]",
",",
"na... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.