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 | ParserBase._extract_multi_indexer_columns | extract and return the names, index_names, col_names
header is a list-of-lists returned from the parsers | pandas/io/parsers.py | def _extract_multi_indexer_columns(self, header, index_names, col_names,
passed_names=False):
""" extract and return the names, index_names, col_names
header is a list-of-lists returned from the parsers """
if len(header) < 2:
return header[... | def _extract_multi_indexer_columns(self, header, index_names, col_names,
passed_names=False):
""" extract and return the names, index_names, col_names
header is a list-of-lists returned from the parsers """
if len(header) < 2:
return header[... | [
"extract",
"and",
"return",
"the",
"names",
"index_names",
"col_names",
"header",
"is",
"a",
"list",
"-",
"of",
"-",
"lists",
"returned",
"from",
"the",
"parsers"
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/io/parsers.py#L1451-L1504 | [
"def",
"_extract_multi_indexer_columns",
"(",
"self",
",",
"header",
",",
"index_names",
",",
"col_names",
",",
"passed_names",
"=",
"False",
")",
":",
"if",
"len",
"(",
"header",
")",
"<",
"2",
":",
"return",
"header",
"[",
"0",
"]",
",",
"index_names",
... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | ParserBase._infer_types | Infer types of values, possibly casting
Parameters
----------
values : ndarray
na_values : set
try_num_bool : bool, default try
try to cast values to numeric (first preference) or boolean
Returns:
--------
converted : ndarray
na_count ... | pandas/io/parsers.py | def _infer_types(self, values, na_values, try_num_bool=True):
"""
Infer types of values, possibly casting
Parameters
----------
values : ndarray
na_values : set
try_num_bool : bool, default try
try to cast values to numeric (first preference) or boolea... | def _infer_types(self, values, na_values, try_num_bool=True):
"""
Infer types of values, possibly casting
Parameters
----------
values : ndarray
na_values : set
try_num_bool : bool, default try
try to cast values to numeric (first preference) or boolea... | [
"Infer",
"types",
"of",
"values",
"possibly",
"casting"
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/io/parsers.py#L1719-L1764 | [
"def",
"_infer_types",
"(",
"self",
",",
"values",
",",
"na_values",
",",
"try_num_bool",
"=",
"True",
")",
":",
"na_count",
"=",
"0",
"if",
"issubclass",
"(",
"values",
".",
"dtype",
".",
"type",
",",
"(",
"np",
".",
"number",
",",
"np",
".",
"bool_... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | ParserBase._cast_types | Cast values to specified type
Parameters
----------
values : ndarray
cast_type : string or np.dtype
dtype to cast values to
column : string
column name - used only for error reporting
Returns
-------
converted : ndarray | pandas/io/parsers.py | def _cast_types(self, values, cast_type, column):
"""
Cast values to specified type
Parameters
----------
values : ndarray
cast_type : string or np.dtype
dtype to cast values to
column : string
column name - used only for error reporting
... | def _cast_types(self, values, cast_type, column):
"""
Cast values to specified type
Parameters
----------
values : ndarray
cast_type : string or np.dtype
dtype to cast values to
column : string
column name - used only for error reporting
... | [
"Cast",
"values",
"to",
"specified",
"type"
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/io/parsers.py#L1766-L1821 | [
"def",
"_cast_types",
"(",
"self",
",",
"values",
",",
"cast_type",
",",
"column",
")",
":",
"if",
"is_categorical_dtype",
"(",
"cast_type",
")",
":",
"known_cats",
"=",
"(",
"isinstance",
"(",
"cast_type",
",",
"CategoricalDtype",
")",
"and",
"cast_type",
"... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | CParserWrapper._set_noconvert_columns | Set the columns that should not undergo dtype conversions.
Currently, any column that is involved with date parsing will not
undergo such conversions. | pandas/io/parsers.py | def _set_noconvert_columns(self):
"""
Set the columns that should not undergo dtype conversions.
Currently, any column that is involved with date parsing will not
undergo such conversions.
"""
names = self.orig_names
if self.usecols_dtype == 'integer':
... | def _set_noconvert_columns(self):
"""
Set the columns that should not undergo dtype conversions.
Currently, any column that is involved with date parsing will not
undergo such conversions.
"""
names = self.orig_names
if self.usecols_dtype == 'integer':
... | [
"Set",
"the",
"columns",
"that",
"should",
"not",
"undergo",
"dtype",
"conversions",
"."
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/io/parsers.py#L1951-L2003 | [
"def",
"_set_noconvert_columns",
"(",
"self",
")",
":",
"names",
"=",
"self",
".",
"orig_names",
"if",
"self",
".",
"usecols_dtype",
"==",
"'integer'",
":",
"# A set of integers will be converted to a list in",
"# the correct order every single time.",
"usecols",
"=",
"li... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | PythonParser._handle_usecols | Sets self._col_indices
usecols_key is used if there are string usecols. | pandas/io/parsers.py | def _handle_usecols(self, columns, usecols_key):
"""
Sets self._col_indices
usecols_key is used if there are string usecols.
"""
if self.usecols is not None:
if callable(self.usecols):
col_indices = _evaluate_usecols(self.usecols, usecols_key)
... | def _handle_usecols(self, columns, usecols_key):
"""
Sets self._col_indices
usecols_key is used if there are string usecols.
"""
if self.usecols is not None:
if callable(self.usecols):
col_indices = _evaluate_usecols(self.usecols, usecols_key)
... | [
"Sets",
"self",
".",
"_col_indices"
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/io/parsers.py#L2678-L2707 | [
"def",
"_handle_usecols",
"(",
"self",
",",
"columns",
",",
"usecols_key",
")",
":",
"if",
"self",
".",
"usecols",
"is",
"not",
"None",
":",
"if",
"callable",
"(",
"self",
".",
"usecols",
")",
":",
"col_indices",
"=",
"_evaluate_usecols",
"(",
"self",
".... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | PythonParser._check_for_bom | Checks whether the file begins with the BOM character.
If it does, remove it. In addition, if there is quoting
in the field subsequent to the BOM, remove it as well
because it technically takes place at the beginning of
the name, not the middle of it. | pandas/io/parsers.py | def _check_for_bom(self, first_row):
"""
Checks whether the file begins with the BOM character.
If it does, remove it. In addition, if there is quoting
in the field subsequent to the BOM, remove it as well
because it technically takes place at the beginning of
the name, n... | def _check_for_bom(self, first_row):
"""
Checks whether the file begins with the BOM character.
If it does, remove it. In addition, if there is quoting
in the field subsequent to the BOM, remove it as well
because it technically takes place at the beginning of
the name, n... | [
"Checks",
"whether",
"the",
"file",
"begins",
"with",
"the",
"BOM",
"character",
".",
"If",
"it",
"does",
"remove",
"it",
".",
"In",
"addition",
"if",
"there",
"is",
"quoting",
"in",
"the",
"field",
"subsequent",
"to",
"the",
"BOM",
"remove",
"it",
"as",... | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/io/parsers.py#L2718-L2768 | [
"def",
"_check_for_bom",
"(",
"self",
",",
"first_row",
")",
":",
"# first_row will be a list, so we need to check",
"# that that list is not empty before proceeding.",
"if",
"not",
"first_row",
":",
"return",
"first_row",
"# The first element of this row is the one that could have t... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | PythonParser._alert_malformed | Alert a user about a malformed row.
If `self.error_bad_lines` is True, the alert will be `ParserError`.
If `self.warn_bad_lines` is True, the alert will be printed out.
Parameters
----------
msg : The error message to display.
row_num : The row number where the parsing ... | pandas/io/parsers.py | def _alert_malformed(self, msg, row_num):
"""
Alert a user about a malformed row.
If `self.error_bad_lines` is True, the alert will be `ParserError`.
If `self.warn_bad_lines` is True, the alert will be printed out.
Parameters
----------
msg : The error message t... | def _alert_malformed(self, msg, row_num):
"""
Alert a user about a malformed row.
If `self.error_bad_lines` is True, the alert will be `ParserError`.
If `self.warn_bad_lines` is True, the alert will be printed out.
Parameters
----------
msg : The error message t... | [
"Alert",
"a",
"user",
"about",
"a",
"malformed",
"row",
"."
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/io/parsers.py#L2837-L2856 | [
"def",
"_alert_malformed",
"(",
"self",
",",
"msg",
",",
"row_num",
")",
":",
"if",
"self",
".",
"error_bad_lines",
":",
"raise",
"ParserError",
"(",
"msg",
")",
"elif",
"self",
".",
"warn_bad_lines",
":",
"base",
"=",
"'Skipping line {row_num}: '",
".",
"fo... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | PythonParser._next_iter_line | Wrapper around iterating through `self.data` (CSV source).
When a CSV error is raised, we check for specific
error messages that allow us to customize the
error message displayed to the user.
Parameters
----------
row_num : The row number of the line being parsed. | pandas/io/parsers.py | def _next_iter_line(self, row_num):
"""
Wrapper around iterating through `self.data` (CSV source).
When a CSV error is raised, we check for specific
error messages that allow us to customize the
error message displayed to the user.
Parameters
----------
... | def _next_iter_line(self, row_num):
"""
Wrapper around iterating through `self.data` (CSV source).
When a CSV error is raised, we check for specific
error messages that allow us to customize the
error message displayed to the user.
Parameters
----------
... | [
"Wrapper",
"around",
"iterating",
"through",
"self",
".",
"data",
"(",
"CSV",
"source",
")",
"."
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/io/parsers.py#L2858-L2892 | [
"def",
"_next_iter_line",
"(",
"self",
",",
"row_num",
")",
":",
"try",
":",
"return",
"next",
"(",
"self",
".",
"data",
")",
"except",
"csv",
".",
"Error",
"as",
"e",
":",
"if",
"self",
".",
"warn_bad_lines",
"or",
"self",
".",
"error_bad_lines",
":",... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | PythonParser._remove_empty_lines | Iterate through the lines and remove any that are
either empty or contain only one whitespace value
Parameters
----------
lines : array-like
The array of lines that we are to filter.
Returns
-------
filtered_lines : array-like
The same ar... | pandas/io/parsers.py | def _remove_empty_lines(self, lines):
"""
Iterate through the lines and remove any that are
either empty or contain only one whitespace value
Parameters
----------
lines : array-like
The array of lines that we are to filter.
Returns
-------
... | def _remove_empty_lines(self, lines):
"""
Iterate through the lines and remove any that are
either empty or contain only one whitespace value
Parameters
----------
lines : array-like
The array of lines that we are to filter.
Returns
-------
... | [
"Iterate",
"through",
"the",
"lines",
"and",
"remove",
"any",
"that",
"are",
"either",
"empty",
"or",
"contain",
"only",
"one",
"whitespace",
"value"
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/io/parsers.py#L2912-L2934 | [
"def",
"_remove_empty_lines",
"(",
"self",
",",
"lines",
")",
":",
"ret",
"=",
"[",
"]",
"for",
"l",
"in",
"lines",
":",
"# Remove empty lines and lines with only one whitespace value",
"if",
"(",
"len",
"(",
"l",
")",
">",
"1",
"or",
"len",
"(",
"l",
")",... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | PythonParser._get_index_name | Try several cases to get lines:
0) There are headers on row 0 and row 1 and their
total summed lengths equals the length of the next line.
Treat row 0 as columns and row 1 as indices
1) Look for implicit index: there are more columns
on row 1 than row 0. If this is true, assume ... | pandas/io/parsers.py | def _get_index_name(self, columns):
"""
Try several cases to get lines:
0) There are headers on row 0 and row 1 and their
total summed lengths equals the length of the next line.
Treat row 0 as columns and row 1 as indices
1) Look for implicit index: there are more colum... | def _get_index_name(self, columns):
"""
Try several cases to get lines:
0) There are headers on row 0 and row 1 and their
total summed lengths equals the length of the next line.
Treat row 0 as columns and row 1 as indices
1) Look for implicit index: there are more colum... | [
"Try",
"several",
"cases",
"to",
"get",
"lines",
":"
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/io/parsers.py#L2973-L3035 | [
"def",
"_get_index_name",
"(",
"self",
",",
"columns",
")",
":",
"orig_names",
"=",
"list",
"(",
"columns",
")",
"columns",
"=",
"list",
"(",
"columns",
")",
"try",
":",
"line",
"=",
"self",
".",
"_next_line",
"(",
")",
"except",
"StopIteration",
":",
... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | FixedWidthReader.get_rows | Read rows from self.f, skipping as specified.
We distinguish buffer_rows (the first <= infer_nrows
lines) from the rows returned to detect_colspecs
because it's simpler to leave the other locations
with skiprows logic alone than to modify them to
deal with the fact we skipped so... | pandas/io/parsers.py | def get_rows(self, infer_nrows, skiprows=None):
"""
Read rows from self.f, skipping as specified.
We distinguish buffer_rows (the first <= infer_nrows
lines) from the rows returned to detect_colspecs
because it's simpler to leave the other locations
with skiprows logic a... | def get_rows(self, infer_nrows, skiprows=None):
"""
Read rows from self.f, skipping as specified.
We distinguish buffer_rows (the first <= infer_nrows
lines) from the rows returned to detect_colspecs
because it's simpler to leave the other locations
with skiprows logic a... | [
"Read",
"rows",
"from",
"self",
".",
"f",
"skipping",
"as",
"specified",
"."
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/io/parsers.py#L3535-L3571 | [
"def",
"get_rows",
"(",
"self",
",",
"infer_nrows",
",",
"skiprows",
"=",
"None",
")",
":",
"if",
"skiprows",
"is",
"None",
":",
"skiprows",
"=",
"set",
"(",
")",
"buffer_rows",
"=",
"[",
"]",
"detect_rows",
"=",
"[",
"]",
"for",
"i",
",",
"row",
"... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | linkcode_resolve | Determine the URL corresponding to Python object | doc/source/conf.py | def linkcode_resolve(domain, info):
"""
Determine the URL corresponding to Python object
"""
if domain != 'py':
return None
modname = info['module']
fullname = info['fullname']
submod = sys.modules.get(modname)
if submod is None:
return None
obj = submod
for pa... | def linkcode_resolve(domain, info):
"""
Determine the URL corresponding to Python object
"""
if domain != 'py':
return None
modname = info['module']
fullname = info['fullname']
submod = sys.modules.get(modname)
if submod is None:
return None
obj = submod
for pa... | [
"Determine",
"the",
"URL",
"corresponding",
"to",
"Python",
"object"
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/doc/source/conf.py#L629-L678 | [
"def",
"linkcode_resolve",
"(",
"domain",
",",
"info",
")",
":",
"if",
"domain",
"!=",
"'py'",
":",
"return",
"None",
"modname",
"=",
"info",
"[",
"'module'",
"]",
"fullname",
"=",
"info",
"[",
"'fullname'",
"]",
"submod",
"=",
"sys",
".",
"modules",
"... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | process_class_docstrings | For those classes for which we use ::
:template: autosummary/class_without_autosummary.rst
the documented attributes/methods have to be listed in the class
docstring. However, if one of those lists is empty, we use 'None',
which then generates warnings in sphinx / ugly html output.
This "autodoc-p... | doc/source/conf.py | def process_class_docstrings(app, what, name, obj, options, lines):
"""
For those classes for which we use ::
:template: autosummary/class_without_autosummary.rst
the documented attributes/methods have to be listed in the class
docstring. However, if one of those lists is empty, we use 'None',
... | def process_class_docstrings(app, what, name, obj, options, lines):
"""
For those classes for which we use ::
:template: autosummary/class_without_autosummary.rst
the documented attributes/methods have to be listed in the class
docstring. However, if one of those lists is empty, we use 'None',
... | [
"For",
"those",
"classes",
"for",
"which",
"we",
"use",
"::"
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/doc/source/conf.py#L688-L724 | [
"def",
"process_class_docstrings",
"(",
"app",
",",
"what",
",",
"name",
",",
"obj",
",",
"options",
",",
"lines",
")",
":",
"if",
"what",
"==",
"\"class\"",
":",
"joined",
"=",
"'\\n'",
".",
"join",
"(",
"lines",
")",
"templates",
"=",
"[",
"\"\"\".. ... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | pack | Pack object `o` and write it to `stream`
See :class:`Packer` for options. | pandas/io/msgpack/__init__.py | def pack(o, stream, **kwargs):
"""
Pack object `o` and write it to `stream`
See :class:`Packer` for options.
"""
packer = Packer(**kwargs)
stream.write(packer.pack(o)) | def pack(o, stream, **kwargs):
"""
Pack object `o` and write it to `stream`
See :class:`Packer` for options.
"""
packer = Packer(**kwargs)
stream.write(packer.pack(o)) | [
"Pack",
"object",
"o",
"and",
"write",
"it",
"to",
"stream"
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/io/msgpack/__init__.py#L26-L33 | [
"def",
"pack",
"(",
"o",
",",
"stream",
",",
"*",
"*",
"kwargs",
")",
":",
"packer",
"=",
"Packer",
"(",
"*",
"*",
"kwargs",
")",
"stream",
".",
"write",
"(",
"packer",
".",
"pack",
"(",
"o",
")",
")"
] | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | get_mgr_concatenation_plan | Construct concatenation plan for given block manager and indexers.
Parameters
----------
mgr : BlockManager
indexers : dict of {axis: indexer}
Returns
-------
plan : list of (BlockPlacement, JoinUnit) tuples | pandas/core/internals/concat.py | def get_mgr_concatenation_plan(mgr, indexers):
"""
Construct concatenation plan for given block manager and indexers.
Parameters
----------
mgr : BlockManager
indexers : dict of {axis: indexer}
Returns
-------
plan : list of (BlockPlacement, JoinUnit) tuples
"""
# Calculat... | def get_mgr_concatenation_plan(mgr, indexers):
"""
Construct concatenation plan for given block manager and indexers.
Parameters
----------
mgr : BlockManager
indexers : dict of {axis: indexer}
Returns
-------
plan : list of (BlockPlacement, JoinUnit) tuples
"""
# Calculat... | [
"Construct",
"concatenation",
"plan",
"for",
"given",
"block",
"manager",
"and",
"indexers",
"."
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/core/internals/concat.py#L21-L98 | [
"def",
"get_mgr_concatenation_plan",
"(",
"mgr",
",",
"indexers",
")",
":",
"# Calculate post-reindex shape , save for item axis which will be separate",
"# for each block anyway.",
"mgr_shape",
"=",
"list",
"(",
"mgr",
".",
"shape",
")",
"for",
"ax",
",",
"indexer",
"in"... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | concatenate_join_units | Concatenate values from several join units along selected axis. | pandas/core/internals/concat.py | def concatenate_join_units(join_units, concat_axis, copy):
"""
Concatenate values from several join units along selected axis.
"""
if concat_axis == 0 and len(join_units) > 1:
# Concatenating join units along ax0 is handled in _merge_blocks.
raise AssertionError("Concatenating join units... | def concatenate_join_units(join_units, concat_axis, copy):
"""
Concatenate values from several join units along selected axis.
"""
if concat_axis == 0 and len(join_units) > 1:
# Concatenating join units along ax0 is handled in _merge_blocks.
raise AssertionError("Concatenating join units... | [
"Concatenate",
"values",
"from",
"several",
"join",
"units",
"along",
"selected",
"axis",
"."
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/core/internals/concat.py#L229-L257 | [
"def",
"concatenate_join_units",
"(",
"join_units",
",",
"concat_axis",
",",
"copy",
")",
":",
"if",
"concat_axis",
"==",
"0",
"and",
"len",
"(",
"join_units",
")",
">",
"1",
":",
"# Concatenating join units along ax0 is handled in _merge_blocks.",
"raise",
"Assertion... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | get_empty_dtype_and_na | Return dtype and N/A values to use when concatenating specified units.
Returned N/A value may be None which means there was no casting involved.
Returns
-------
dtype
na | pandas/core/internals/concat.py | def get_empty_dtype_and_na(join_units):
"""
Return dtype and N/A values to use when concatenating specified units.
Returned N/A value may be None which means there was no casting involved.
Returns
-------
dtype
na
"""
if len(join_units) == 1:
blk = join_units[0].block
... | def get_empty_dtype_and_na(join_units):
"""
Return dtype and N/A values to use when concatenating specified units.
Returned N/A value may be None which means there was no casting involved.
Returns
-------
dtype
na
"""
if len(join_units) == 1:
blk = join_units[0].block
... | [
"Return",
"dtype",
"and",
"N",
"/",
"A",
"values",
"to",
"use",
"when",
"concatenating",
"specified",
"units",
"."
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/core/internals/concat.py#L260-L363 | [
"def",
"get_empty_dtype_and_na",
"(",
"join_units",
")",
":",
"if",
"len",
"(",
"join_units",
")",
"==",
"1",
":",
"blk",
"=",
"join_units",
"[",
"0",
"]",
".",
"block",
"if",
"blk",
"is",
"None",
":",
"return",
"np",
".",
"float64",
",",
"np",
".",
... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | is_uniform_join_units | Check if the join units consist of blocks of uniform type that can
be concatenated using Block.concat_same_type instead of the generic
concatenate_join_units (which uses `_concat._concat_compat`). | pandas/core/internals/concat.py | def is_uniform_join_units(join_units):
"""
Check if the join units consist of blocks of uniform type that can
be concatenated using Block.concat_same_type instead of the generic
concatenate_join_units (which uses `_concat._concat_compat`).
"""
return (
# all blocks need to have the same... | def is_uniform_join_units(join_units):
"""
Check if the join units consist of blocks of uniform type that can
be concatenated using Block.concat_same_type instead of the generic
concatenate_join_units (which uses `_concat._concat_compat`).
"""
return (
# all blocks need to have the same... | [
"Check",
"if",
"the",
"join",
"units",
"consist",
"of",
"blocks",
"of",
"uniform",
"type",
"that",
"can",
"be",
"concatenated",
"using",
"Block",
".",
"concat_same_type",
"instead",
"of",
"the",
"generic",
"concatenate_join_units",
"(",
"which",
"uses",
"_concat... | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/core/internals/concat.py#L366-L384 | [
"def",
"is_uniform_join_units",
"(",
"join_units",
")",
":",
"return",
"(",
"# all blocks need to have the same type",
"all",
"(",
"type",
"(",
"ju",
".",
"block",
")",
"is",
"type",
"(",
"join_units",
"[",
"0",
"]",
".",
"block",
")",
"for",
"ju",
"in",
"... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | trim_join_unit | Reduce join_unit's shape along item axis to length.
Extra items that didn't fit are returned as a separate block. | pandas/core/internals/concat.py | def trim_join_unit(join_unit, length):
"""
Reduce join_unit's shape along item axis to length.
Extra items that didn't fit are returned as a separate block.
"""
if 0 not in join_unit.indexers:
extra_indexers = join_unit.indexers
if join_unit.block is None:
extra_block ... | def trim_join_unit(join_unit, length):
"""
Reduce join_unit's shape along item axis to length.
Extra items that didn't fit are returned as a separate block.
"""
if 0 not in join_unit.indexers:
extra_indexers = join_unit.indexers
if join_unit.block is None:
extra_block ... | [
"Reduce",
"join_unit",
"s",
"shape",
"along",
"item",
"axis",
"to",
"length",
"."
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/core/internals/concat.py#L395-L421 | [
"def",
"trim_join_unit",
"(",
"join_unit",
",",
"length",
")",
":",
"if",
"0",
"not",
"in",
"join_unit",
".",
"indexers",
":",
"extra_indexers",
"=",
"join_unit",
".",
"indexers",
"if",
"join_unit",
".",
"block",
"is",
"None",
":",
"extra_block",
"=",
"Non... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | combine_concat_plans | Combine multiple concatenation plans into one.
existing_plan is updated in-place. | pandas/core/internals/concat.py | def combine_concat_plans(plans, concat_axis):
"""
Combine multiple concatenation plans into one.
existing_plan is updated in-place.
"""
if len(plans) == 1:
for p in plans[0]:
yield p[0], [p[1]]
elif concat_axis == 0:
offset = 0
for plan in plans:
... | def combine_concat_plans(plans, concat_axis):
"""
Combine multiple concatenation plans into one.
existing_plan is updated in-place.
"""
if len(plans) == 1:
for p in plans[0]:
yield p[0], [p[1]]
elif concat_axis == 0:
offset = 0
for plan in plans:
... | [
"Combine",
"multiple",
"concatenation",
"plans",
"into",
"one",
"."
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/core/internals/concat.py#L424-L484 | [
"def",
"combine_concat_plans",
"(",
"plans",
",",
"concat_axis",
")",
":",
"if",
"len",
"(",
"plans",
")",
"==",
"1",
":",
"for",
"p",
"in",
"plans",
"[",
"0",
"]",
":",
"yield",
"p",
"[",
"0",
"]",
",",
"[",
"p",
"[",
"1",
"]",
"]",
"elif",
... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | _Options.use | Temporarily set a parameter value using the with statement.
Aliasing allowed. | pandas/plotting/_style.py | def use(self, key, value):
"""
Temporarily set a parameter value using the with statement.
Aliasing allowed.
"""
old_value = self[key]
try:
self[key] = value
yield self
finally:
self[key] = old_value | def use(self, key, value):
"""
Temporarily set a parameter value using the with statement.
Aliasing allowed.
"""
old_value = self[key]
try:
self[key] = value
yield self
finally:
self[key] = old_value | [
"Temporarily",
"set",
"a",
"parameter",
"value",
"using",
"the",
"with",
"statement",
".",
"Aliasing",
"allowed",
"."
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/plotting/_style.py#L151-L161 | [
"def",
"use",
"(",
"self",
",",
"key",
",",
"value",
")",
":",
"old_value",
"=",
"self",
"[",
"key",
"]",
"try",
":",
"self",
"[",
"key",
"]",
"=",
"value",
"yield",
"self",
"finally",
":",
"self",
"[",
"key",
"]",
"=",
"old_value"
] | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | _stata_elapsed_date_to_datetime_vec | Convert from SIF to datetime. http://www.stata.com/help.cgi?datetime
Parameters
----------
dates : Series
The Stata Internal Format date to convert to datetime according to fmt
fmt : str
The format to convert to. Can be, tc, td, tw, tm, tq, th, ty
Returns
Returns
------... | pandas/io/stata.py | def _stata_elapsed_date_to_datetime_vec(dates, fmt):
"""
Convert from SIF to datetime. http://www.stata.com/help.cgi?datetime
Parameters
----------
dates : Series
The Stata Internal Format date to convert to datetime according to fmt
fmt : str
The format to convert to. Can be, t... | def _stata_elapsed_date_to_datetime_vec(dates, fmt):
"""
Convert from SIF to datetime. http://www.stata.com/help.cgi?datetime
Parameters
----------
dates : Series
The Stata Internal Format date to convert to datetime according to fmt
fmt : str
The format to convert to. Can be, t... | [
"Convert",
"from",
"SIF",
"to",
"datetime",
".",
"http",
":",
"//",
"www",
".",
"stata",
".",
"com",
"/",
"help",
".",
"cgi?datetime"
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/io/stata.py#L203-L364 | [
"def",
"_stata_elapsed_date_to_datetime_vec",
"(",
"dates",
",",
"fmt",
")",
":",
"MIN_YEAR",
",",
"MAX_YEAR",
"=",
"Timestamp",
".",
"min",
".",
"year",
",",
"Timestamp",
".",
"max",
".",
"year",
"MAX_DAY_DELTA",
"=",
"(",
"Timestamp",
".",
"max",
"-",
"d... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | _datetime_to_stata_elapsed_vec | Convert from datetime to SIF. http://www.stata.com/help.cgi?datetime
Parameters
----------
dates : Series
Series or array containing datetime.datetime or datetime64[ns] to
convert to the Stata Internal Format given by fmt
fmt : str
The format to convert to. Can be, tc, td, tw, t... | pandas/io/stata.py | def _datetime_to_stata_elapsed_vec(dates, fmt):
"""
Convert from datetime to SIF. http://www.stata.com/help.cgi?datetime
Parameters
----------
dates : Series
Series or array containing datetime.datetime or datetime64[ns] to
convert to the Stata Internal Format given by fmt
fmt :... | def _datetime_to_stata_elapsed_vec(dates, fmt):
"""
Convert from datetime to SIF. http://www.stata.com/help.cgi?datetime
Parameters
----------
dates : Series
Series or array containing datetime.datetime or datetime64[ns] to
convert to the Stata Internal Format given by fmt
fmt :... | [
"Convert",
"from",
"datetime",
"to",
"SIF",
".",
"http",
":",
"//",
"www",
".",
"stata",
".",
"com",
"/",
"help",
".",
"cgi?datetime"
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/io/stata.py#L367-L461 | [
"def",
"_datetime_to_stata_elapsed_vec",
"(",
"dates",
",",
"fmt",
")",
":",
"index",
"=",
"dates",
".",
"index",
"NS_PER_DAY",
"=",
"24",
"*",
"3600",
"*",
"1000",
"*",
"1000",
"*",
"1000",
"US_PER_DAY",
"=",
"NS_PER_DAY",
"/",
"1000",
"def",
"parse_dates... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | _cast_to_stata_types | Checks the dtypes of the columns of a pandas DataFrame for
compatibility with the data types and ranges supported by Stata, and
converts if necessary.
Parameters
----------
data : DataFrame
The DataFrame to check and convert
Notes
-----
Numeric columns in Stata must be one of i... | pandas/io/stata.py | def _cast_to_stata_types(data):
"""Checks the dtypes of the columns of a pandas DataFrame for
compatibility with the data types and ranges supported by Stata, and
converts if necessary.
Parameters
----------
data : DataFrame
The DataFrame to check and convert
Notes
-----
Nu... | def _cast_to_stata_types(data):
"""Checks the dtypes of the columns of a pandas DataFrame for
compatibility with the data types and ranges supported by Stata, and
converts if necessary.
Parameters
----------
data : DataFrame
The DataFrame to check and convert
Notes
-----
Nu... | [
"Checks",
"the",
"dtypes",
"of",
"the",
"columns",
"of",
"a",
"pandas",
"DataFrame",
"for",
"compatibility",
"with",
"the",
"data",
"types",
"and",
"ranges",
"supported",
"by",
"Stata",
"and",
"converts",
"if",
"necessary",
"."
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/io/stata.py#L508-L592 | [
"def",
"_cast_to_stata_types",
"(",
"data",
")",
":",
"ws",
"=",
"''",
"# original, if small, if large",
"conversion_data",
"=",
"(",
"(",
"np",
".",
"bool",
",",
"np",
".",
"int8",
",",
"np",
".",
"int8",
")",
",",
"(",
"np",
".",
"uint8"... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | _dtype_to_stata_type | Convert dtype types to stata types. Returns the byte of the given ordinal.
See TYPE_MAP and comments for an explanation. This is also explained in
the dta spec.
1 - 244 are strings of this length
Pandas Stata
251 - for int8 byte
252 - for int16 int
253 - for ... | pandas/io/stata.py | def _dtype_to_stata_type(dtype, column):
"""
Convert dtype types to stata types. Returns the byte of the given ordinal.
See TYPE_MAP and comments for an explanation. This is also explained in
the dta spec.
1 - 244 are strings of this length
Pandas Stata
251 - for int8... | def _dtype_to_stata_type(dtype, column):
"""
Convert dtype types to stata types. Returns the byte of the given ordinal.
See TYPE_MAP and comments for an explanation. This is also explained in
the dta spec.
1 - 244 are strings of this length
Pandas Stata
251 - for int8... | [
"Convert",
"dtype",
"types",
"to",
"stata",
"types",
".",
"Returns",
"the",
"byte",
"of",
"the",
"given",
"ordinal",
".",
"See",
"TYPE_MAP",
"and",
"comments",
"for",
"an",
"explanation",
".",
"This",
"is",
"also",
"explained",
"in",
"the",
"dta",
"spec",
... | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/io/stata.py#L1832-L1866 | [
"def",
"_dtype_to_stata_type",
"(",
"dtype",
",",
"column",
")",
":",
"# TODO: expand to handle datetime to integer conversion",
"if",
"dtype",
".",
"type",
"==",
"np",
".",
"object_",
":",
"# try to coerce it to the biggest string",
"# not memory efficient, what else could we"... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | _dtype_to_default_stata_fmt | Map numpy dtype to stata's default format for this type. Not terribly
important since users can change this in Stata. Semantics are
object -> "%DDs" where DD is the length of the string. If not a string,
raise ValueError
float64 -> "%10.0g"
float32 -> "%9.0g"
int64 -> "%9.0g"
... | pandas/io/stata.py | def _dtype_to_default_stata_fmt(dtype, column, dta_version=114,
force_strl=False):
"""
Map numpy dtype to stata's default format for this type. Not terribly
important since users can change this in Stata. Semantics are
object -> "%DDs" where DD is the length of the stri... | def _dtype_to_default_stata_fmt(dtype, column, dta_version=114,
force_strl=False):
"""
Map numpy dtype to stata's default format for this type. Not terribly
important since users can change this in Stata. Semantics are
object -> "%DDs" where DD is the length of the stri... | [
"Map",
"numpy",
"dtype",
"to",
"stata",
"s",
"default",
"format",
"for",
"this",
"type",
".",
"Not",
"terribly",
"important",
"since",
"users",
"can",
"change",
"this",
"in",
"Stata",
".",
"Semantics",
"are"
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/io/stata.py#L1869-L1922 | [
"def",
"_dtype_to_default_stata_fmt",
"(",
"dtype",
",",
"column",
",",
"dta_version",
"=",
"114",
",",
"force_strl",
"=",
"False",
")",
":",
"# TODO: Refactor to combine type with format",
"# TODO: expand this to handle a default datetime format?",
"if",
"dta_version",
"<",
... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | _pad_bytes_new | Takes a bytes instance and pads it with null bytes until it's length chars. | pandas/io/stata.py | def _pad_bytes_new(name, length):
"""
Takes a bytes instance and pads it with null bytes until it's length chars.
"""
if isinstance(name, str):
name = bytes(name, 'utf-8')
return name + b'\x00' * (length - len(name)) | def _pad_bytes_new(name, length):
"""
Takes a bytes instance and pads it with null bytes until it's length chars.
"""
if isinstance(name, str):
name = bytes(name, 'utf-8')
return name + b'\x00' * (length - len(name)) | [
"Takes",
"a",
"bytes",
"instance",
"and",
"pads",
"it",
"with",
"null",
"bytes",
"until",
"it",
"s",
"length",
"chars",
"."
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/io/stata.py#L2477-L2483 | [
"def",
"_pad_bytes_new",
"(",
"name",
",",
"length",
")",
":",
"if",
"isinstance",
"(",
"name",
",",
"str",
")",
":",
"name",
"=",
"bytes",
"(",
"name",
",",
"'utf-8'",
")",
"return",
"name",
"+",
"b'\\x00'",
"*",
"(",
"length",
"-",
"len",
"(",
"n... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | StataValueLabel.generate_value_label | Parameters
----------
byteorder : str
Byte order of the output
encoding : str
File encoding
Returns
-------
value_label : bytes
Bytes containing the formatted value label | pandas/io/stata.py | def generate_value_label(self, byteorder, encoding):
"""
Parameters
----------
byteorder : str
Byte order of the output
encoding : str
File encoding
Returns
-------
value_label : bytes
Bytes containing the formatted val... | def generate_value_label(self, byteorder, encoding):
"""
Parameters
----------
byteorder : str
Byte order of the output
encoding : str
File encoding
Returns
-------
value_label : bytes
Bytes containing the formatted val... | [
"Parameters",
"----------",
"byteorder",
":",
"str",
"Byte",
"order",
"of",
"the",
"output",
"encoding",
":",
"str",
"File",
"encoding"
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/io/stata.py#L662-L713 | [
"def",
"generate_value_label",
"(",
"self",
",",
"byteorder",
",",
"encoding",
")",
":",
"self",
".",
"_encoding",
"=",
"encoding",
"bio",
"=",
"BytesIO",
"(",
")",
"null_string",
"=",
"'\\x00'",
"null_byte",
"=",
"b'\\x00'",
"# len",
"bio",
".",
"write",
... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | StataReader._setup_dtype | Map between numpy and state dtypes | pandas/io/stata.py | def _setup_dtype(self):
"""Map between numpy and state dtypes"""
if self._dtype is not None:
return self._dtype
dtype = [] # Convert struct data types to numpy data type
for i, typ in enumerate(self.typlist):
if typ in self.NUMPY_TYPE_MAP:
dtype.... | def _setup_dtype(self):
"""Map between numpy and state dtypes"""
if self._dtype is not None:
return self._dtype
dtype = [] # Convert struct data types to numpy data type
for i, typ in enumerate(self.typlist):
if typ in self.NUMPY_TYPE_MAP:
dtype.... | [
"Map",
"between",
"numpy",
"and",
"state",
"dtypes"
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/io/stata.py#L1307-L1322 | [
"def",
"_setup_dtype",
"(",
"self",
")",
":",
"if",
"self",
".",
"_dtype",
"is",
"not",
"None",
":",
"return",
"self",
".",
"_dtype",
"dtype",
"=",
"[",
"]",
"# Convert struct data types to numpy data type",
"for",
"i",
",",
"typ",
"in",
"enumerate",
"(",
... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | StataReader._do_convert_categoricals | Converts categorical columns to Categorical type. | pandas/io/stata.py | def _do_convert_categoricals(self, data, value_label_dict, lbllist,
order_categoricals):
"""
Converts categorical columns to Categorical type.
"""
value_labels = list(value_label_dict.keys())
cat_converted_data = []
for col, label in zip(d... | def _do_convert_categoricals(self, data, value_label_dict, lbllist,
order_categoricals):
"""
Converts categorical columns to Categorical type.
"""
value_labels = list(value_label_dict.keys())
cat_converted_data = []
for col, label in zip(d... | [
"Converts",
"categorical",
"columns",
"to",
"Categorical",
"type",
"."
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/io/stata.py#L1698-L1740 | [
"def",
"_do_convert_categoricals",
"(",
"self",
",",
"data",
",",
"value_label_dict",
",",
"lbllist",
",",
"order_categoricals",
")",
":",
"value_labels",
"=",
"list",
"(",
"value_label_dict",
".",
"keys",
"(",
")",
")",
"cat_converted_data",
"=",
"[",
"]",
"f... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | StataWriter._write | Helper to call encode before writing to file for Python 3 compat. | pandas/io/stata.py | def _write(self, to_write):
"""
Helper to call encode before writing to file for Python 3 compat.
"""
self._file.write(to_write.encode(self._encoding or
self._default_encoding)) | def _write(self, to_write):
"""
Helper to call encode before writing to file for Python 3 compat.
"""
self._file.write(to_write.encode(self._encoding or
self._default_encoding)) | [
"Helper",
"to",
"call",
"encode",
"before",
"writing",
"to",
"file",
"for",
"Python",
"3",
"compat",
"."
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/io/stata.py#L2018-L2023 | [
"def",
"_write",
"(",
"self",
",",
"to_write",
")",
":",
"self",
".",
"_file",
".",
"write",
"(",
"to_write",
".",
"encode",
"(",
"self",
".",
"_encoding",
"or",
"self",
".",
"_default_encoding",
")",
")"
] | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | StataWriter._prepare_categoricals | Check for categorical columns, retain categorical information for
Stata file and convert categorical data to int | pandas/io/stata.py | def _prepare_categoricals(self, data):
"""Check for categorical columns, retain categorical information for
Stata file and convert categorical data to int"""
is_cat = [is_categorical_dtype(data[col]) for col in data]
self._is_col_cat = is_cat
self._value_labels = []
if n... | def _prepare_categoricals(self, data):
"""Check for categorical columns, retain categorical information for
Stata file and convert categorical data to int"""
is_cat = [is_categorical_dtype(data[col]) for col in data]
self._is_col_cat = is_cat
self._value_labels = []
if n... | [
"Check",
"for",
"categorical",
"columns",
"retain",
"categorical",
"information",
"for",
"Stata",
"file",
"and",
"convert",
"categorical",
"data",
"to",
"int"
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/io/stata.py#L2025-L2061 | [
"def",
"_prepare_categoricals",
"(",
"self",
",",
"data",
")",
":",
"is_cat",
"=",
"[",
"is_categorical_dtype",
"(",
"data",
"[",
"col",
"]",
")",
"for",
"col",
"in",
"data",
"]",
"self",
".",
"_is_col_cat",
"=",
"is_cat",
"self",
".",
"_value_labels",
"... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | StataWriter._replace_nans | Checks floating point data columns for nans, and replaces these with
the generic Stata for missing value (.) | pandas/io/stata.py | def _replace_nans(self, data):
# return data
"""Checks floating point data columns for nans, and replaces these with
the generic Stata for missing value (.)"""
for c in data:
dtype = data[c].dtype
if dtype in (np.float32, np.float64):
if dtype == n... | def _replace_nans(self, data):
# return data
"""Checks floating point data columns for nans, and replaces these with
the generic Stata for missing value (.)"""
for c in data:
dtype = data[c].dtype
if dtype in (np.float32, np.float64):
if dtype == n... | [
"Checks",
"floating",
"point",
"data",
"columns",
"for",
"nans",
"and",
"replaces",
"these",
"with",
"the",
"generic",
"Stata",
"for",
"missing",
"value",
"(",
".",
")"
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/io/stata.py#L2063-L2076 | [
"def",
"_replace_nans",
"(",
"self",
",",
"data",
")",
":",
"# return data",
"for",
"c",
"in",
"data",
":",
"dtype",
"=",
"data",
"[",
"c",
"]",
".",
"dtype",
"if",
"dtype",
"in",
"(",
"np",
".",
"float32",
",",
"np",
".",
"float64",
")",
":",
"i... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | StataWriter._check_column_names | Checks column names to ensure that they are valid Stata column names.
This includes checks for:
* Non-string names
* Stata keywords
* Variables that start with numbers
* Variables with names that are too long
When an illegal variable name is detected, it ... | pandas/io/stata.py | def _check_column_names(self, data):
"""
Checks column names to ensure that they are valid Stata column names.
This includes checks for:
* Non-string names
* Stata keywords
* Variables that start with numbers
* Variables with names that are too lon... | def _check_column_names(self, data):
"""
Checks column names to ensure that they are valid Stata column names.
This includes checks for:
* Non-string names
* Stata keywords
* Variables that start with numbers
* Variables with names that are too lon... | [
"Checks",
"column",
"names",
"to",
"ensure",
"that",
"they",
"are",
"valid",
"Stata",
"column",
"names",
".",
"This",
"includes",
"checks",
"for",
":",
"*",
"Non",
"-",
"string",
"names",
"*",
"Stata",
"keywords",
"*",
"Variables",
"that",
"start",
"with",... | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/io/stata.py#L2082-L2157 | [
"def",
"_check_column_names",
"(",
"self",
",",
"data",
")",
":",
"converted_names",
"=",
"{",
"}",
"columns",
"=",
"list",
"(",
"data",
".",
"columns",
")",
"original_columns",
"=",
"columns",
"[",
":",
"]",
"duplicate_var_id",
"=",
"0",
"for",
"j",
","... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | StataWriter._close | Close the file if it was created by the writer.
If a buffer or file-like object was passed in, for example a GzipFile,
then leave this file open for the caller to close. In either case,
attempt to flush the file contents to ensure they are written to disk
(if supported) | pandas/io/stata.py | def _close(self):
"""
Close the file if it was created by the writer.
If a buffer or file-like object was passed in, for example a GzipFile,
then leave this file open for the caller to close. In either case,
attempt to flush the file contents to ensure they are written to disk
... | def _close(self):
"""
Close the file if it was created by the writer.
If a buffer or file-like object was passed in, for example a GzipFile,
then leave this file open for the caller to close. In either case,
attempt to flush the file contents to ensure they are written to disk
... | [
"Close",
"the",
"file",
"if",
"it",
"was",
"created",
"by",
"the",
"writer",
"."
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/io/stata.py#L2249-L2264 | [
"def",
"_close",
"(",
"self",
")",
":",
"# Some file-like objects might not support flush",
"try",
":",
"self",
".",
"_file",
".",
"flush",
"(",
")",
"except",
"AttributeError",
":",
"pass",
"if",
"self",
".",
"_own_file",
":",
"self",
".",
"_file",
".",
"cl... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | StataStrLWriter.generate_table | Generates the GSO lookup table for the DataFRame
Returns
-------
gso_table : OrderedDict
Ordered dictionary using the string found as keys
and their lookup position (v,o) as values
gso_df : DataFrame
DataFrame where strl columns have been converted to... | pandas/io/stata.py | def generate_table(self):
"""
Generates the GSO lookup table for the DataFRame
Returns
-------
gso_table : OrderedDict
Ordered dictionary using the string found as keys
and their lookup position (v,o) as values
gso_df : DataFrame
DataF... | def generate_table(self):
"""
Generates the GSO lookup table for the DataFRame
Returns
-------
gso_table : OrderedDict
Ordered dictionary using the string found as keys
and their lookup position (v,o) as values
gso_df : DataFrame
DataF... | [
"Generates",
"the",
"GSO",
"lookup",
"table",
"for",
"the",
"DataFRame"
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/io/stata.py#L2546-L2596 | [
"def",
"generate_table",
"(",
"self",
")",
":",
"gso_table",
"=",
"self",
".",
"_gso_table",
"gso_df",
"=",
"self",
".",
"df",
"columns",
"=",
"list",
"(",
"gso_df",
".",
"columns",
")",
"selected",
"=",
"gso_df",
"[",
"self",
".",
"columns",
"]",
"col... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | StataStrLWriter.generate_blob | Generates the binary blob of GSOs that is written to the dta file.
Parameters
----------
gso_table : OrderedDict
Ordered dictionary (str, vo)
Returns
-------
gso : bytes
Binary content of dta file to be placed between strl tags
Notes
... | pandas/io/stata.py | def generate_blob(self, gso_table):
"""
Generates the binary blob of GSOs that is written to the dta file.
Parameters
----------
gso_table : OrderedDict
Ordered dictionary (str, vo)
Returns
-------
gso : bytes
Binary content of dt... | def generate_blob(self, gso_table):
"""
Generates the binary blob of GSOs that is written to the dta file.
Parameters
----------
gso_table : OrderedDict
Ordered dictionary (str, vo)
Returns
-------
gso : bytes
Binary content of dt... | [
"Generates",
"the",
"binary",
"blob",
"of",
"GSOs",
"that",
"is",
"written",
"to",
"the",
"dta",
"file",
"."
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/io/stata.py#L2604-L2666 | [
"def",
"generate_blob",
"(",
"self",
",",
"gso_table",
")",
":",
"# Format information",
"# Length includes null term",
"# 117",
"# GSOvvvvooootllllxxxxxxxxxxxxxxx...x",
"# 3 u4 u4 u1 u4 string + null term",
"#",
"# 118, 119",
"# GSOvvvvooooooootllllxxxxxxxxxxxxxxx...x",
"# 3 u... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | StataWriter117._tag | Surround val with <tag></tag> | pandas/io/stata.py | def _tag(val, tag):
"""Surround val with <tag></tag>"""
if isinstance(val, str):
val = bytes(val, 'utf-8')
return (bytes('<' + tag + '>', 'utf-8') + val +
bytes('</' + tag + '>', 'utf-8')) | def _tag(val, tag):
"""Surround val with <tag></tag>"""
if isinstance(val, str):
val = bytes(val, 'utf-8')
return (bytes('<' + tag + '>', 'utf-8') + val +
bytes('</' + tag + '>', 'utf-8')) | [
"Surround",
"val",
"with",
"<tag",
">",
"<",
"/",
"tag",
">"
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/io/stata.py#L2761-L2766 | [
"def",
"_tag",
"(",
"val",
",",
"tag",
")",
":",
"if",
"isinstance",
"(",
"val",
",",
"str",
")",
":",
"val",
"=",
"bytes",
"(",
"val",
",",
"'utf-8'",
")",
"return",
"(",
"bytes",
"(",
"'<'",
"+",
"tag",
"+",
"'>'",
",",
"'utf-8'",
")",
"+",
... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | StataWriter117._write_header | Write the file header | pandas/io/stata.py | def _write_header(self, data_label=None, time_stamp=None):
"""Write the file header"""
byteorder = self._byteorder
self._file.write(bytes('<stata_dta>', 'utf-8'))
bio = BytesIO()
# ds_format - 117
bio.write(self._tag(bytes('117', 'utf-8'), 'release'))
# byteorder
... | def _write_header(self, data_label=None, time_stamp=None):
"""Write the file header"""
byteorder = self._byteorder
self._file.write(bytes('<stata_dta>', 'utf-8'))
bio = BytesIO()
# ds_format - 117
bio.write(self._tag(bytes('117', 'utf-8'), 'release'))
# byteorder
... | [
"Write",
"the",
"file",
"header"
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/io/stata.py#L2772-L2808 | [
"def",
"_write_header",
"(",
"self",
",",
"data_label",
"=",
"None",
",",
"time_stamp",
"=",
"None",
")",
":",
"byteorder",
"=",
"self",
".",
"_byteorder",
"self",
".",
"_file",
".",
"write",
"(",
"bytes",
"(",
"'<stata_dta>'",
",",
"'utf-8'",
")",
")",
... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | StataWriter117._write_map | Called twice during file write. The first populates the values in
the map with 0s. The second call writes the final map locations when
all blocks have been written. | pandas/io/stata.py | def _write_map(self):
"""Called twice during file write. The first populates the values in
the map with 0s. The second call writes the final map locations when
all blocks have been written."""
if self._map is None:
self._map = OrderedDict((('stata_data', 0),
... | def _write_map(self):
"""Called twice during file write. The first populates the values in
the map with 0s. The second call writes the final map locations when
all blocks have been written."""
if self._map is None:
self._map = OrderedDict((('stata_data', 0),
... | [
"Called",
"twice",
"during",
"file",
"write",
".",
"The",
"first",
"populates",
"the",
"values",
"in",
"the",
"map",
"with",
"0s",
".",
"The",
"second",
"call",
"writes",
"the",
"final",
"map",
"locations",
"when",
"all",
"blocks",
"have",
"been",
"written... | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/io/stata.py#L2810-L2835 | [
"def",
"_write_map",
"(",
"self",
")",
":",
"if",
"self",
".",
"_map",
"is",
"None",
":",
"self",
".",
"_map",
"=",
"OrderedDict",
"(",
"(",
"(",
"'stata_data'",
",",
"0",
")",
",",
"(",
"'map'",
",",
"self",
".",
"_file",
".",
"tell",
"(",
")",
... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | StataWriter117._update_strl_names | Update column names for conversion to strl if they might have been
changed to comply with Stata naming rules | pandas/io/stata.py | def _update_strl_names(self):
"""Update column names for conversion to strl if they might have been
changed to comply with Stata naming rules"""
# Update convert_strl if names changed
for orig, new in self._converted_names.items():
if orig in self._convert_strl:
... | def _update_strl_names(self):
"""Update column names for conversion to strl if they might have been
changed to comply with Stata naming rules"""
# Update convert_strl if names changed
for orig, new in self._converted_names.items():
if orig in self._convert_strl:
... | [
"Update",
"column",
"names",
"for",
"conversion",
"to",
"strl",
"if",
"they",
"might",
"have",
"been",
"changed",
"to",
"comply",
"with",
"Stata",
"naming",
"rules"
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/io/stata.py#L2948-L2955 | [
"def",
"_update_strl_names",
"(",
"self",
")",
":",
"# Update convert_strl if names changed",
"for",
"orig",
",",
"new",
"in",
"self",
".",
"_converted_names",
".",
"items",
"(",
")",
":",
"if",
"orig",
"in",
"self",
".",
"_convert_strl",
":",
"idx",
"=",
"s... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | StataWriter117._convert_strls | Convert columns to StrLs if either very large or in the
convert_strl variable | pandas/io/stata.py | def _convert_strls(self, data):
"""Convert columns to StrLs if either very large or in the
convert_strl variable"""
convert_cols = [
col for i, col in enumerate(data)
if self.typlist[i] == 32768 or col in self._convert_strl]
if convert_cols:
ssw = Sta... | def _convert_strls(self, data):
"""Convert columns to StrLs if either very large or in the
convert_strl variable"""
convert_cols = [
col for i, col in enumerate(data)
if self.typlist[i] == 32768 or col in self._convert_strl]
if convert_cols:
ssw = Sta... | [
"Convert",
"columns",
"to",
"StrLs",
"if",
"either",
"very",
"large",
"or",
"in",
"the",
"convert_strl",
"variable"
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/io/stata.py#L2957-L2969 | [
"def",
"_convert_strls",
"(",
"self",
",",
"data",
")",
":",
"convert_cols",
"=",
"[",
"col",
"for",
"i",
",",
"col",
"in",
"enumerate",
"(",
"data",
")",
"if",
"self",
".",
"typlist",
"[",
"i",
"]",
"==",
"32768",
"or",
"col",
"in",
"self",
".",
... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | register | Register Pandas Formatters and Converters with matplotlib
This function modifies the global ``matplotlib.units.registry``
dictionary. Pandas adds custom converters for
* pd.Timestamp
* pd.Period
* np.datetime64
* datetime.datetime
* datetime.date
* datetime.time
See Also
-----... | pandas/plotting/_converter.py | def register(explicit=True):
"""
Register Pandas Formatters and Converters with matplotlib
This function modifies the global ``matplotlib.units.registry``
dictionary. Pandas adds custom converters for
* pd.Timestamp
* pd.Period
* np.datetime64
* datetime.datetime
* datetime.date
... | def register(explicit=True):
"""
Register Pandas Formatters and Converters with matplotlib
This function modifies the global ``matplotlib.units.registry``
dictionary. Pandas adds custom converters for
* pd.Timestamp
* pd.Period
* np.datetime64
* datetime.datetime
* datetime.date
... | [
"Register",
"Pandas",
"Formatters",
"and",
"Converters",
"with",
"matplotlib"
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/plotting/_converter.py#L54-L84 | [
"def",
"register",
"(",
"explicit",
"=",
"True",
")",
":",
"# Renamed in pandas.plotting.__init__",
"global",
"_WARN",
"if",
"explicit",
":",
"_WARN",
"=",
"False",
"pairs",
"=",
"get_pairs",
"(",
")",
"for",
"type_",
",",
"cls",
"in",
"pairs",
":",
"convert... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | deregister | Remove pandas' formatters and converters
Removes the custom converters added by :func:`register`. This
attempts to set the state of the registry back to the state before
pandas registered its own units. Converters for pandas' own types like
Timestamp and Period are removed completely. Converters for ty... | pandas/plotting/_converter.py | def deregister():
"""
Remove pandas' formatters and converters
Removes the custom converters added by :func:`register`. This
attempts to set the state of the registry back to the state before
pandas registered its own units. Converters for pandas' own types like
Timestamp and Period are removed... | def deregister():
"""
Remove pandas' formatters and converters
Removes the custom converters added by :func:`register`. This
attempts to set the state of the registry back to the state before
pandas registered its own units. Converters for pandas' own types like
Timestamp and Period are removed... | [
"Remove",
"pandas",
"formatters",
"and",
"converters"
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/plotting/_converter.py#L87-L113 | [
"def",
"deregister",
"(",
")",
":",
"# Renamed in pandas.plotting.__init__",
"for",
"type_",
",",
"cls",
"in",
"get_pairs",
"(",
")",
":",
"# We use type to catch our classes directly, no inheritance",
"if",
"type",
"(",
"units",
".",
"registry",
".",
"get",
"(",
"t... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | _dt_to_float_ordinal | Convert :mod:`datetime` to the Gregorian date as UTC float days,
preserving hours, minutes, seconds and microseconds. Return value
is a :func:`float`. | pandas/plotting/_converter.py | def _dt_to_float_ordinal(dt):
"""
Convert :mod:`datetime` to the Gregorian date as UTC float days,
preserving hours, minutes, seconds and microseconds. Return value
is a :func:`float`.
"""
if (isinstance(dt, (np.ndarray, Index, ABCSeries)
) and is_datetime64_ns_dtype(dt)):
... | def _dt_to_float_ordinal(dt):
"""
Convert :mod:`datetime` to the Gregorian date as UTC float days,
preserving hours, minutes, seconds and microseconds. Return value
is a :func:`float`.
"""
if (isinstance(dt, (np.ndarray, Index, ABCSeries)
) and is_datetime64_ns_dtype(dt)):
... | [
"Convert",
":",
"mod",
":",
"datetime",
"to",
"the",
"Gregorian",
"date",
"as",
"UTC",
"float",
"days",
"preserving",
"hours",
"minutes",
"seconds",
"and",
"microseconds",
".",
"Return",
"value",
"is",
"a",
":",
"func",
":",
"float",
"."
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/plotting/_converter.py#L269-L280 | [
"def",
"_dt_to_float_ordinal",
"(",
"dt",
")",
":",
"if",
"(",
"isinstance",
"(",
"dt",
",",
"(",
"np",
".",
"ndarray",
",",
"Index",
",",
"ABCSeries",
")",
")",
"and",
"is_datetime64_ns_dtype",
"(",
"dt",
")",
")",
":",
"base",
"=",
"dates",
".",
"e... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | _get_default_annual_spacing | Returns a default spacing between consecutive ticks for annual data. | pandas/plotting/_converter.py | def _get_default_annual_spacing(nyears):
"""
Returns a default spacing between consecutive ticks for annual data.
"""
if nyears < 11:
(min_spacing, maj_spacing) = (1, 1)
elif nyears < 20:
(min_spacing, maj_spacing) = (1, 2)
elif nyears < 50:
(min_spacing, maj_spacing) = (... | def _get_default_annual_spacing(nyears):
"""
Returns a default spacing between consecutive ticks for annual data.
"""
if nyears < 11:
(min_spacing, maj_spacing) = (1, 1)
elif nyears < 20:
(min_spacing, maj_spacing) = (1, 2)
elif nyears < 50:
(min_spacing, maj_spacing) = (... | [
"Returns",
"a",
"default",
"spacing",
"between",
"consecutive",
"ticks",
"for",
"annual",
"data",
"."
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/plotting/_converter.py#L537-L556 | [
"def",
"_get_default_annual_spacing",
"(",
"nyears",
")",
":",
"if",
"nyears",
"<",
"11",
":",
"(",
"min_spacing",
",",
"maj_spacing",
")",
"=",
"(",
"1",
",",
"1",
")",
"elif",
"nyears",
"<",
"20",
":",
"(",
"min_spacing",
",",
"maj_spacing",
")",
"="... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | period_break | Returns the indices where the given period changes.
Parameters
----------
dates : PeriodIndex
Array of intervals to monitor.
period : string
Name of the period to monitor. | pandas/plotting/_converter.py | def period_break(dates, period):
"""
Returns the indices where the given period changes.
Parameters
----------
dates : PeriodIndex
Array of intervals to monitor.
period : string
Name of the period to monitor.
"""
current = getattr(dates, period)
previous = getattr(da... | def period_break(dates, period):
"""
Returns the indices where the given period changes.
Parameters
----------
dates : PeriodIndex
Array of intervals to monitor.
period : string
Name of the period to monitor.
"""
current = getattr(dates, period)
previous = getattr(da... | [
"Returns",
"the",
"indices",
"where",
"the",
"given",
"period",
"changes",
"."
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/plotting/_converter.py#L559-L572 | [
"def",
"period_break",
"(",
"dates",
",",
"period",
")",
":",
"current",
"=",
"getattr",
"(",
"dates",
",",
"period",
")",
"previous",
"=",
"getattr",
"(",
"dates",
"-",
"1",
"*",
"dates",
".",
"freq",
",",
"period",
")",
"return",
"np",
".",
"nonzer... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | has_level_label | Returns true if the ``label_flags`` indicate there is at least one label
for this level.
if the minimum view limit is not an exact integer, then the first tick
label won't be shown, so we must adjust for that. | pandas/plotting/_converter.py | def has_level_label(label_flags, vmin):
"""
Returns true if the ``label_flags`` indicate there is at least one label
for this level.
if the minimum view limit is not an exact integer, then the first tick
label won't be shown, so we must adjust for that.
"""
if label_flags.size == 0 or (labe... | def has_level_label(label_flags, vmin):
"""
Returns true if the ``label_flags`` indicate there is at least one label
for this level.
if the minimum view limit is not an exact integer, then the first tick
label won't be shown, so we must adjust for that.
"""
if label_flags.size == 0 or (labe... | [
"Returns",
"true",
"if",
"the",
"label_flags",
"indicate",
"there",
"is",
"at",
"least",
"one",
"label",
"for",
"this",
"level",
"."
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/plotting/_converter.py#L575-L588 | [
"def",
"has_level_label",
"(",
"label_flags",
",",
"vmin",
")",
":",
"if",
"label_flags",
".",
"size",
"==",
"0",
"or",
"(",
"label_flags",
".",
"size",
"==",
"1",
"and",
"label_flags",
"[",
"0",
"]",
"==",
"0",
"and",
"vmin",
"%",
"1",
">",
"0.0",
... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | DatetimeConverter.axisinfo | Return the :class:`~matplotlib.units.AxisInfo` for *unit*.
*unit* is a tzinfo instance or None.
The *axis* argument is required but not used. | pandas/plotting/_converter.py | def axisinfo(unit, axis):
"""
Return the :class:`~matplotlib.units.AxisInfo` for *unit*.
*unit* is a tzinfo instance or None.
The *axis* argument is required but not used.
"""
tz = unit
majloc = PandasAutoDateLocator(tz=tz)
majfmt = PandasAutoDateFormatt... | def axisinfo(unit, axis):
"""
Return the :class:`~matplotlib.units.AxisInfo` for *unit*.
*unit* is a tzinfo instance or None.
The *axis* argument is required but not used.
"""
tz = unit
majloc = PandasAutoDateLocator(tz=tz)
majfmt = PandasAutoDateFormatt... | [
"Return",
"the",
":",
"class",
":",
"~matplotlib",
".",
"units",
".",
"AxisInfo",
"for",
"*",
"unit",
"*",
"."
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/plotting/_converter.py#L340-L355 | [
"def",
"axisinfo",
"(",
"unit",
",",
"axis",
")",
":",
"tz",
"=",
"unit",
"majloc",
"=",
"PandasAutoDateLocator",
"(",
"tz",
"=",
"tz",
")",
"majfmt",
"=",
"PandasAutoDateFormatter",
"(",
"majloc",
",",
"tz",
"=",
"tz",
")",
"datemin",
"=",
"pydt",
"."... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | PandasAutoDateLocator.get_locator | Pick the best locator based on a distance. | pandas/plotting/_converter.py | def get_locator(self, dmin, dmax):
'Pick the best locator based on a distance.'
_check_implicitly_registered()
delta = relativedelta(dmax, dmin)
num_days = (delta.years * 12.0 + delta.months) * 31.0 + delta.days
num_sec = (delta.hours * 60.0 + delta.minutes) * 60.0 + delta.secon... | def get_locator(self, dmin, dmax):
'Pick the best locator based on a distance.'
_check_implicitly_registered()
delta = relativedelta(dmax, dmin)
num_days = (delta.years * 12.0 + delta.months) * 31.0 + delta.days
num_sec = (delta.hours * 60.0 + delta.minutes) * 60.0 + delta.secon... | [
"Pick",
"the",
"best",
"locator",
"based",
"on",
"a",
"distance",
"."
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/plotting/_converter.py#L369-L387 | [
"def",
"get_locator",
"(",
"self",
",",
"dmin",
",",
"dmax",
")",
":",
"_check_implicitly_registered",
"(",
")",
"delta",
"=",
"relativedelta",
"(",
"dmax",
",",
"dmin",
")",
"num_days",
"=",
"(",
"delta",
".",
"years",
"*",
"12.0",
"+",
"delta",
".",
... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | MilliSecondLocator.autoscale | Set the view limits to include the data range. | pandas/plotting/_converter.py | def autoscale(self):
"""
Set the view limits to include the data range.
"""
dmin, dmax = self.datalim_to_dt()
if dmin > dmax:
dmax, dmin = dmin, dmax
# We need to cap at the endpoints of valid datetime
# TODO(wesm): unused?
# delta = relativ... | def autoscale(self):
"""
Set the view limits to include the data range.
"""
dmin, dmax = self.datalim_to_dt()
if dmin > dmax:
dmax, dmin = dmin, dmax
# We need to cap at the endpoints of valid datetime
# TODO(wesm): unused?
# delta = relativ... | [
"Set",
"the",
"view",
"limits",
"to",
"include",
"the",
"data",
"range",
"."
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/plotting/_converter.py#L478-L507 | [
"def",
"autoscale",
"(",
"self",
")",
":",
"dmin",
",",
"dmax",
"=",
"self",
".",
"datalim_to_dt",
"(",
")",
"if",
"dmin",
">",
"dmax",
":",
"dmax",
",",
"dmin",
"=",
"dmin",
",",
"dmax",
"# We need to cap at the endpoints of valid datetime",
"# TODO(wesm): un... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | TimeSeries_DateLocator._get_default_locs | Returns the default locations of ticks. | pandas/plotting/_converter.py | def _get_default_locs(self, vmin, vmax):
"Returns the default locations of ticks."
if self.plot_obj.date_axis_info is None:
self.plot_obj.date_axis_info = self.finder(vmin, vmax, self.freq)
locator = self.plot_obj.date_axis_info
if self.isminor:
return np.compr... | def _get_default_locs(self, vmin, vmax):
"Returns the default locations of ticks."
if self.plot_obj.date_axis_info is None:
self.plot_obj.date_axis_info = self.finder(vmin, vmax, self.freq)
locator = self.plot_obj.date_axis_info
if self.isminor:
return np.compr... | [
"Returns",
"the",
"default",
"locations",
"of",
"ticks",
"."
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/plotting/_converter.py#L1002-L1012 | [
"def",
"_get_default_locs",
"(",
"self",
",",
"vmin",
",",
"vmax",
")",
":",
"if",
"self",
".",
"plot_obj",
".",
"date_axis_info",
"is",
"None",
":",
"self",
".",
"plot_obj",
".",
"date_axis_info",
"=",
"self",
".",
"finder",
"(",
"vmin",
",",
"vmax",
... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | TimeSeries_DateLocator.autoscale | Sets the view limits to the nearest multiples of base that contain the
data. | pandas/plotting/_converter.py | def autoscale(self):
"""
Sets the view limits to the nearest multiples of base that contain the
data.
"""
# requires matplotlib >= 0.98.0
(vmin, vmax) = self.axis.get_data_interval()
locs = self._get_default_locs(vmin, vmax)
(vmin, vmax) = locs[[0, -1]]
... | def autoscale(self):
"""
Sets the view limits to the nearest multiples of base that contain the
data.
"""
# requires matplotlib >= 0.98.0
(vmin, vmax) = self.axis.get_data_interval()
locs = self._get_default_locs(vmin, vmax)
(vmin, vmax) = locs[[0, -1]]
... | [
"Sets",
"the",
"view",
"limits",
"to",
"the",
"nearest",
"multiples",
"of",
"base",
"that",
"contain",
"the",
"data",
"."
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/plotting/_converter.py#L1035-L1048 | [
"def",
"autoscale",
"(",
"self",
")",
":",
"# requires matplotlib >= 0.98.0",
"(",
"vmin",
",",
"vmax",
")",
"=",
"self",
".",
"axis",
".",
"get_data_interval",
"(",
")",
"locs",
"=",
"self",
".",
"_get_default_locs",
"(",
"vmin",
",",
"vmax",
")",
"(",
... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | TimeSeries_DateFormatter._set_default_format | Returns the default ticks spacing. | pandas/plotting/_converter.py | def _set_default_format(self, vmin, vmax):
"Returns the default ticks spacing."
if self.plot_obj.date_axis_info is None:
self.plot_obj.date_axis_info = self.finder(vmin, vmax, self.freq)
info = self.plot_obj.date_axis_info
if self.isminor:
format = np.compress(i... | def _set_default_format(self, vmin, vmax):
"Returns the default ticks spacing."
if self.plot_obj.date_axis_info is None:
self.plot_obj.date_axis_info = self.finder(vmin, vmax, self.freq)
info = self.plot_obj.date_axis_info
if self.isminor:
format = np.compress(i... | [
"Returns",
"the",
"default",
"ticks",
"spacing",
"."
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/plotting/_converter.py#L1084-L1097 | [
"def",
"_set_default_format",
"(",
"self",
",",
"vmin",
",",
"vmax",
")",
":",
"if",
"self",
".",
"plot_obj",
".",
"date_axis_info",
"is",
"None",
":",
"self",
".",
"plot_obj",
".",
"date_axis_info",
"=",
"self",
".",
"finder",
"(",
"vmin",
",",
"vmax",
... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | TimeSeries_DateFormatter.set_locs | Sets the locations of the ticks | pandas/plotting/_converter.py | def set_locs(self, locs):
'Sets the locations of the ticks'
# don't actually use the locs. This is just needed to work with
# matplotlib. Force to use vmin, vmax
_check_implicitly_registered()
self.locs = locs
(vmin, vmax) = vi = tuple(self.axis.get_view_interval())
... | def set_locs(self, locs):
'Sets the locations of the ticks'
# don't actually use the locs. This is just needed to work with
# matplotlib. Force to use vmin, vmax
_check_implicitly_registered()
self.locs = locs
(vmin, vmax) = vi = tuple(self.axis.get_view_interval())
... | [
"Sets",
"the",
"locations",
"of",
"the",
"ticks"
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/plotting/_converter.py#L1099-L1113 | [
"def",
"set_locs",
"(",
"self",
",",
"locs",
")",
":",
"# don't actually use the locs. This is just needed to work with",
"# matplotlib. Force to use vmin, vmax",
"_check_implicitly_registered",
"(",
")",
"self",
".",
"locs",
"=",
"locs",
"(",
"vmin",
",",
"vmax",
")",
... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | set_default_names | Sets index names to 'index' for regular, or 'level_x' for Multi | pandas/io/json/table_schema.py | def set_default_names(data):
"""Sets index names to 'index' for regular, or 'level_x' for Multi"""
if com._all_not_none(*data.index.names):
nms = data.index.names
if len(nms) == 1 and data.index.name == 'index':
warnings.warn("Index name of 'index' is not round-trippable")
el... | def set_default_names(data):
"""Sets index names to 'index' for regular, or 'level_x' for Multi"""
if com._all_not_none(*data.index.names):
nms = data.index.names
if len(nms) == 1 and data.index.name == 'index':
warnings.warn("Index name of 'index' is not round-trippable")
el... | [
"Sets",
"index",
"names",
"to",
"index",
"for",
"regular",
"or",
"level_x",
"for",
"Multi"
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/io/json/table_schema.py#L71-L89 | [
"def",
"set_default_names",
"(",
"data",
")",
":",
"if",
"com",
".",
"_all_not_none",
"(",
"*",
"data",
".",
"index",
".",
"names",
")",
":",
"nms",
"=",
"data",
".",
"index",
".",
"names",
"if",
"len",
"(",
"nms",
")",
"==",
"1",
"and",
"data",
... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | convert_json_field_to_pandas_type | Converts a JSON field descriptor into its corresponding NumPy / pandas type
Parameters
----------
field
A JSON field descriptor
Returns
-------
dtype
Raises
-----
ValueError
If the type of the provided field is unknown or currently unsupported
Examples
---... | pandas/io/json/table_schema.py | def convert_json_field_to_pandas_type(field):
"""
Converts a JSON field descriptor into its corresponding NumPy / pandas type
Parameters
----------
field
A JSON field descriptor
Returns
-------
dtype
Raises
-----
ValueError
If the type of the provided field... | def convert_json_field_to_pandas_type(field):
"""
Converts a JSON field descriptor into its corresponding NumPy / pandas type
Parameters
----------
field
A JSON field descriptor
Returns
-------
dtype
Raises
-----
ValueError
If the type of the provided field... | [
"Converts",
"a",
"JSON",
"field",
"descriptor",
"into",
"its",
"corresponding",
"NumPy",
"/",
"pandas",
"type"
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/io/json/table_schema.py#L120-L180 | [
"def",
"convert_json_field_to_pandas_type",
"(",
"field",
")",
":",
"typ",
"=",
"field",
"[",
"'type'",
"]",
"if",
"typ",
"==",
"'string'",
":",
"return",
"'object'",
"elif",
"typ",
"==",
"'integer'",
":",
"return",
"'int64'",
"elif",
"typ",
"==",
"'number'"... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | build_table_schema | Create a Table schema from ``data``.
Parameters
----------
data : Series, DataFrame
index : bool, default True
Whether to include ``data.index`` in the schema.
primary_key : bool or None, default True
column names to designate as the primary key.
The default `None` will set ... | pandas/io/json/table_schema.py | def build_table_schema(data, index=True, primary_key=None, version=True):
"""
Create a Table schema from ``data``.
Parameters
----------
data : Series, DataFrame
index : bool, default True
Whether to include ``data.index`` in the schema.
primary_key : bool or None, default True
... | def build_table_schema(data, index=True, primary_key=None, version=True):
"""
Create a Table schema from ``data``.
Parameters
----------
data : Series, DataFrame
index : bool, default True
Whether to include ``data.index`` in the schema.
primary_key : bool or None, default True
... | [
"Create",
"a",
"Table",
"schema",
"from",
"data",
"."
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/io/json/table_schema.py#L183-L259 | [
"def",
"build_table_schema",
"(",
"data",
",",
"index",
"=",
"True",
",",
"primary_key",
"=",
"None",
",",
"version",
"=",
"True",
")",
":",
"if",
"index",
"is",
"True",
":",
"data",
"=",
"set_default_names",
"(",
"data",
")",
"schema",
"=",
"{",
"}",
... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | parse_table_schema | Builds a DataFrame from a given schema
Parameters
----------
json :
A JSON table schema
precise_float : boolean
Flag controlling precision when decoding string to double values, as
dictated by ``read_json``
Returns
-------
df : DataFrame
Raises
------
N... | pandas/io/json/table_schema.py | def parse_table_schema(json, precise_float):
"""
Builds a DataFrame from a given schema
Parameters
----------
json :
A JSON table schema
precise_float : boolean
Flag controlling precision when decoding string to double values, as
dictated by ``read_json``
Returns
... | def parse_table_schema(json, precise_float):
"""
Builds a DataFrame from a given schema
Parameters
----------
json :
A JSON table schema
precise_float : boolean
Flag controlling precision when decoding string to double values, as
dictated by ``read_json``
Returns
... | [
"Builds",
"a",
"DataFrame",
"from",
"a",
"given",
"schema"
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/io/json/table_schema.py#L262-L326 | [
"def",
"parse_table_schema",
"(",
"json",
",",
"precise_float",
")",
":",
"table",
"=",
"loads",
"(",
"json",
",",
"precise_float",
"=",
"precise_float",
")",
"col_order",
"=",
"[",
"field",
"[",
"'name'",
"]",
"for",
"field",
"in",
"table",
"[",
"'schema'... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | get_op_result_name | Find the appropriate name to pin to an operation result. This result
should always be either an Index or a Series.
Parameters
----------
left : {Series, Index}
right : object
Returns
-------
name : object
Usually a string | pandas/core/ops.py | def get_op_result_name(left, right):
"""
Find the appropriate name to pin to an operation result. This result
should always be either an Index or a Series.
Parameters
----------
left : {Series, Index}
right : object
Returns
-------
name : object
Usually a string
""... | def get_op_result_name(left, right):
"""
Find the appropriate name to pin to an operation result. This result
should always be either an Index or a Series.
Parameters
----------
left : {Series, Index}
right : object
Returns
-------
name : object
Usually a string
""... | [
"Find",
"the",
"appropriate",
"name",
"to",
"pin",
"to",
"an",
"operation",
"result",
".",
"This",
"result",
"should",
"always",
"be",
"either",
"an",
"Index",
"or",
"a",
"Series",
"."
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/core/ops.py#L38-L58 | [
"def",
"get_op_result_name",
"(",
"left",
",",
"right",
")",
":",
"# `left` is always a pd.Series when called from within ops",
"if",
"isinstance",
"(",
"right",
",",
"(",
"ABCSeries",
",",
"pd",
".",
"Index",
")",
")",
":",
"name",
"=",
"_maybe_match_name",
"(",
... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | _maybe_match_name | Try to find a name to attach to the result of an operation between
a and b. If only one of these has a `name` attribute, return that
name. Otherwise return a consensus name if they match of None if
they have different names.
Parameters
----------
a : object
b : object
Returns
---... | pandas/core/ops.py | def _maybe_match_name(a, b):
"""
Try to find a name to attach to the result of an operation between
a and b. If only one of these has a `name` attribute, return that
name. Otherwise return a consensus name if they match of None if
they have different names.
Parameters
----------
a : o... | def _maybe_match_name(a, b):
"""
Try to find a name to attach to the result of an operation between
a and b. If only one of these has a `name` attribute, return that
name. Otherwise return a consensus name if they match of None if
they have different names.
Parameters
----------
a : o... | [
"Try",
"to",
"find",
"a",
"name",
"to",
"attach",
"to",
"the",
"result",
"of",
"an",
"operation",
"between",
"a",
"and",
"b",
".",
"If",
"only",
"one",
"of",
"these",
"has",
"a",
"name",
"attribute",
"return",
"that",
"name",
".",
"Otherwise",
"return"... | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/core/ops.py#L61-L93 | [
"def",
"_maybe_match_name",
"(",
"a",
",",
"b",
")",
":",
"a_has",
"=",
"hasattr",
"(",
"a",
",",
"'name'",
")",
"b_has",
"=",
"hasattr",
"(",
"b",
",",
"'name'",
")",
"if",
"a_has",
"and",
"b_has",
":",
"if",
"a",
".",
"name",
"==",
"b",
".",
... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | maybe_upcast_for_op | Cast non-pandas objects to pandas types to unify behavior of arithmetic
and comparison operations.
Parameters
----------
obj: object
Returns
-------
out : object
Notes
-----
Be careful to call this *after* determining the `name` attribute to be
attached to the result of th... | pandas/core/ops.py | def maybe_upcast_for_op(obj):
"""
Cast non-pandas objects to pandas types to unify behavior of arithmetic
and comparison operations.
Parameters
----------
obj: object
Returns
-------
out : object
Notes
-----
Be careful to call this *after* determining the `name` attrib... | def maybe_upcast_for_op(obj):
"""
Cast non-pandas objects to pandas types to unify behavior of arithmetic
and comparison operations.
Parameters
----------
obj: object
Returns
-------
out : object
Notes
-----
Be careful to call this *after* determining the `name` attrib... | [
"Cast",
"non",
"-",
"pandas",
"objects",
"to",
"pandas",
"types",
"to",
"unify",
"behavior",
"of",
"arithmetic",
"and",
"comparison",
"operations",
"."
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/core/ops.py#L96-L131 | [
"def",
"maybe_upcast_for_op",
"(",
"obj",
")",
":",
"if",
"type",
"(",
"obj",
")",
"is",
"datetime",
".",
"timedelta",
":",
"# GH#22390 cast up to Timedelta to rely on Timedelta",
"# implementation; otherwise operation against numeric-dtype",
"# raises TypeError",
"return",
... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | make_invalid_op | Return a binary method that always raises a TypeError.
Parameters
----------
name : str
Returns
-------
invalid_op : function | pandas/core/ops.py | def make_invalid_op(name):
"""
Return a binary method that always raises a TypeError.
Parameters
----------
name : str
Returns
-------
invalid_op : function
"""
def invalid_op(self, other=None):
raise TypeError("cannot perform {name} with this index type: "
... | def make_invalid_op(name):
"""
Return a binary method that always raises a TypeError.
Parameters
----------
name : str
Returns
-------
invalid_op : function
"""
def invalid_op(self, other=None):
raise TypeError("cannot perform {name} with this index type: "
... | [
"Return",
"a",
"binary",
"method",
"that",
"always",
"raises",
"a",
"TypeError",
"."
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/core/ops.py#L195-L212 | [
"def",
"make_invalid_op",
"(",
"name",
")",
":",
"def",
"invalid_op",
"(",
"self",
",",
"other",
"=",
"None",
")",
":",
"raise",
"TypeError",
"(",
"\"cannot perform {name} with this index type: \"",
"\"{typ}\"",
".",
"format",
"(",
"name",
"=",
"name",
",",
"t... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | _gen_eval_kwargs | Find the keyword arguments to pass to numexpr for the given operation.
Parameters
----------
name : str
Returns
-------
eval_kwargs : dict
Examples
--------
>>> _gen_eval_kwargs("__add__")
{}
>>> _gen_eval_kwargs("rtruediv")
{'reversed': True, 'truediv': True} | pandas/core/ops.py | def _gen_eval_kwargs(name):
"""
Find the keyword arguments to pass to numexpr for the given operation.
Parameters
----------
name : str
Returns
-------
eval_kwargs : dict
Examples
--------
>>> _gen_eval_kwargs("__add__")
{}
>>> _gen_eval_kwargs("rtruediv")
{'r... | def _gen_eval_kwargs(name):
"""
Find the keyword arguments to pass to numexpr for the given operation.
Parameters
----------
name : str
Returns
-------
eval_kwargs : dict
Examples
--------
>>> _gen_eval_kwargs("__add__")
{}
>>> _gen_eval_kwargs("rtruediv")
{'r... | [
"Find",
"the",
"keyword",
"arguments",
"to",
"pass",
"to",
"numexpr",
"for",
"the",
"given",
"operation",
"."
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/core/ops.py#L215-L253 | [
"def",
"_gen_eval_kwargs",
"(",
"name",
")",
":",
"kwargs",
"=",
"{",
"}",
"# Series and Panel appear to only pass __add__, __radd__, ...",
"# but DataFrame gets both these dunder names _and_ non-dunder names",
"# add, radd, ...",
"name",
"=",
"name",
".",
"replace",
"(",
"'__'... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | _gen_fill_zeros | Find the appropriate fill value to use when filling in undefined values
in the results of the given operation caused by operating on
(generally dividing by) zero.
Parameters
----------
name : str
Returns
-------
fill_value : {None, np.nan, np.inf} | pandas/core/ops.py | def _gen_fill_zeros(name):
"""
Find the appropriate fill value to use when filling in undefined values
in the results of the given operation caused by operating on
(generally dividing by) zero.
Parameters
----------
name : str
Returns
-------
fill_value : {None, np.nan, np.inf}... | def _gen_fill_zeros(name):
"""
Find the appropriate fill value to use when filling in undefined values
in the results of the given operation caused by operating on
(generally dividing by) zero.
Parameters
----------
name : str
Returns
-------
fill_value : {None, np.nan, np.inf}... | [
"Find",
"the",
"appropriate",
"fill",
"value",
"to",
"use",
"when",
"filling",
"in",
"undefined",
"values",
"in",
"the",
"results",
"of",
"the",
"given",
"operation",
"caused",
"by",
"operating",
"on",
"(",
"generally",
"dividing",
"by",
")",
"zero",
"."
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/core/ops.py#L256-L279 | [
"def",
"_gen_fill_zeros",
"(",
"name",
")",
":",
"name",
"=",
"name",
".",
"strip",
"(",
"'__'",
")",
"if",
"'div'",
"in",
"name",
":",
"# truediv, floordiv, div, and reversed variants",
"fill_value",
"=",
"np",
".",
"inf",
"elif",
"'mod'",
"in",
"name",
":"... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | _get_opstr | Find the operation string, if any, to pass to numexpr for this
operation.
Parameters
----------
op : binary operator
cls : class
Returns
-------
op_str : string or None | pandas/core/ops.py | def _get_opstr(op, cls):
"""
Find the operation string, if any, to pass to numexpr for this
operation.
Parameters
----------
op : binary operator
cls : class
Returns
-------
op_str : string or None
"""
# numexpr is available for non-sparse classes
subtyp = getattr(c... | def _get_opstr(op, cls):
"""
Find the operation string, if any, to pass to numexpr for this
operation.
Parameters
----------
op : binary operator
cls : class
Returns
-------
op_str : string or None
"""
# numexpr is available for non-sparse classes
subtyp = getattr(c... | [
"Find",
"the",
"operation",
"string",
"if",
"any",
"to",
"pass",
"to",
"numexpr",
"for",
"this",
"operation",
"."
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/core/ops.py#L307-L356 | [
"def",
"_get_opstr",
"(",
"op",
",",
"cls",
")",
":",
"# numexpr is available for non-sparse classes",
"subtyp",
"=",
"getattr",
"(",
"cls",
",",
"'_subtyp'",
",",
"''",
")",
"use_numexpr",
"=",
"'sparse'",
"not",
"in",
"subtyp",
"if",
"not",
"use_numexpr",
":... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | _get_op_name | Find the name to attach to this method according to conventions
for special and non-special methods.
Parameters
----------
op : binary operator
special : bool
Returns
-------
op_name : str | pandas/core/ops.py | def _get_op_name(op, special):
"""
Find the name to attach to this method according to conventions
for special and non-special methods.
Parameters
----------
op : binary operator
special : bool
Returns
-------
op_name : str
"""
opname = op.__name__.strip('_')
if spe... | def _get_op_name(op, special):
"""
Find the name to attach to this method according to conventions
for special and non-special methods.
Parameters
----------
op : binary operator
special : bool
Returns
-------
op_name : str
"""
opname = op.__name__.strip('_')
if spe... | [
"Find",
"the",
"name",
"to",
"attach",
"to",
"this",
"method",
"according",
"to",
"conventions",
"for",
"special",
"and",
"non",
"-",
"special",
"methods",
"."
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/core/ops.py#L359-L376 | [
"def",
"_get_op_name",
"(",
"op",
",",
"special",
")",
":",
"opname",
"=",
"op",
".",
"__name__",
".",
"strip",
"(",
"'_'",
")",
"if",
"special",
":",
"opname",
"=",
"'__{opname}__'",
".",
"format",
"(",
"opname",
"=",
"opname",
")",
"return",
"opname"... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | _make_flex_doc | Make the appropriate substitutions for the given operation and class-typ
into either _flex_doc_SERIES or _flex_doc_FRAME to return the docstring
to attach to a generated method.
Parameters
----------
op_name : str {'__add__', '__sub__', ... '__eq__', '__ne__', ...}
typ : str {series, 'dataframe... | pandas/core/ops.py | def _make_flex_doc(op_name, typ):
"""
Make the appropriate substitutions for the given operation and class-typ
into either _flex_doc_SERIES or _flex_doc_FRAME to return the docstring
to attach to a generated method.
Parameters
----------
op_name : str {'__add__', '__sub__', ... '__eq__', '_... | def _make_flex_doc(op_name, typ):
"""
Make the appropriate substitutions for the given operation and class-typ
into either _flex_doc_SERIES or _flex_doc_FRAME to return the docstring
to attach to a generated method.
Parameters
----------
op_name : str {'__add__', '__sub__', ... '__eq__', '_... | [
"Make",
"the",
"appropriate",
"substitutions",
"for",
"the",
"given",
"operation",
"and",
"class",
"-",
"typ",
"into",
"either",
"_flex_doc_SERIES",
"or",
"_flex_doc_FRAME",
"to",
"return",
"the",
"docstring",
"to",
"attach",
"to",
"a",
"generated",
"method",
".... | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/core/ops.py#L1029-L1082 | [
"def",
"_make_flex_doc",
"(",
"op_name",
",",
"typ",
")",
":",
"op_name",
"=",
"op_name",
".",
"replace",
"(",
"'__'",
",",
"''",
")",
"op_desc",
"=",
"_op_descriptions",
"[",
"op_name",
"]",
"if",
"op_desc",
"[",
"'reversed'",
"]",
":",
"equiv",
"=",
... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | fill_binop | If a non-None fill_value is given, replace null entries in left and right
with this value, but only in positions where _one_ of left/right is null,
not both.
Parameters
----------
left : array-like
right : array-like
fill_value : object
Returns
-------
left : array-like
rig... | pandas/core/ops.py | def fill_binop(left, right, fill_value):
"""
If a non-None fill_value is given, replace null entries in left and right
with this value, but only in positions where _one_ of left/right is null,
not both.
Parameters
----------
left : array-like
right : array-like
fill_value : object
... | def fill_binop(left, right, fill_value):
"""
If a non-None fill_value is given, replace null entries in left and right
with this value, but only in positions where _one_ of left/right is null,
not both.
Parameters
----------
left : array-like
right : array-like
fill_value : object
... | [
"If",
"a",
"non",
"-",
"None",
"fill_value",
"is",
"given",
"replace",
"null",
"entries",
"in",
"left",
"and",
"right",
"with",
"this",
"value",
"but",
"only",
"in",
"positions",
"where",
"_one_",
"of",
"left",
"/",
"right",
"is",
"null",
"not",
"both",
... | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/core/ops.py#L1088-L1120 | [
"def",
"fill_binop",
"(",
"left",
",",
"right",
",",
"fill_value",
")",
":",
"# TODO: can we make a no-copy implementation?",
"if",
"fill_value",
"is",
"not",
"None",
":",
"left_mask",
"=",
"isna",
"(",
"left",
")",
"right_mask",
"=",
"isna",
"(",
"right",
")"... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | mask_cmp_op | Apply the function `op` to only non-null points in x and y.
Parameters
----------
x : array-like
y : array-like
op : binary operation
allowed_types : class or tuple of classes
Returns
-------
result : ndarray[bool] | pandas/core/ops.py | def mask_cmp_op(x, y, op, allowed_types):
"""
Apply the function `op` to only non-null points in x and y.
Parameters
----------
x : array-like
y : array-like
op : binary operation
allowed_types : class or tuple of classes
Returns
-------
result : ndarray[bool]
"""
#... | def mask_cmp_op(x, y, op, allowed_types):
"""
Apply the function `op` to only non-null points in x and y.
Parameters
----------
x : array-like
y : array-like
op : binary operation
allowed_types : class or tuple of classes
Returns
-------
result : ndarray[bool]
"""
#... | [
"Apply",
"the",
"function",
"op",
"to",
"only",
"non",
"-",
"null",
"points",
"in",
"x",
"and",
"y",
"."
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/core/ops.py#L1123-L1155 | [
"def",
"mask_cmp_op",
"(",
"x",
",",
"y",
",",
"op",
",",
"allowed_types",
")",
":",
"# TODO: Can we make the allowed_types arg unnecessary?",
"xrav",
"=",
"x",
".",
"ravel",
"(",
")",
"result",
"=",
"np",
".",
"empty",
"(",
"x",
".",
"size",
",",
"dtype",... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | masked_arith_op | If the given arithmetic operation fails, attempt it again on
only the non-null elements of the input array(s).
Parameters
----------
x : np.ndarray
y : np.ndarray, Series, Index
op : binary operator | pandas/core/ops.py | def masked_arith_op(x, y, op):
"""
If the given arithmetic operation fails, attempt it again on
only the non-null elements of the input array(s).
Parameters
----------
x : np.ndarray
y : np.ndarray, Series, Index
op : binary operator
"""
# For Series `x` is 1D so ravel() is a no... | def masked_arith_op(x, y, op):
"""
If the given arithmetic operation fails, attempt it again on
only the non-null elements of the input array(s).
Parameters
----------
x : np.ndarray
y : np.ndarray, Series, Index
op : binary operator
"""
# For Series `x` is 1D so ravel() is a no... | [
"If",
"the",
"given",
"arithmetic",
"operation",
"fails",
"attempt",
"it",
"again",
"on",
"only",
"the",
"non",
"-",
"null",
"elements",
"of",
"the",
"input",
"array",
"(",
"s",
")",
"."
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/core/ops.py#L1158-L1213 | [
"def",
"masked_arith_op",
"(",
"x",
",",
"y",
",",
"op",
")",
":",
"# For Series `x` is 1D so ravel() is a no-op; calling it anyway makes",
"# the logic valid for both Series and DataFrame ops.",
"xrav",
"=",
"x",
".",
"ravel",
"(",
")",
"assert",
"isinstance",
"(",
"x",
... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | invalid_comparison | If a comparison has mismatched types and is not necessarily meaningful,
follow python3 conventions by:
- returning all-False for equality
- returning all-True for inequality
- raising TypeError otherwise
Parameters
----------
left : array-like
right : scalar, array-like
... | pandas/core/ops.py | def invalid_comparison(left, right, op):
"""
If a comparison has mismatched types and is not necessarily meaningful,
follow python3 conventions by:
- returning all-False for equality
- returning all-True for inequality
- raising TypeError otherwise
Parameters
----------
... | def invalid_comparison(left, right, op):
"""
If a comparison has mismatched types and is not necessarily meaningful,
follow python3 conventions by:
- returning all-False for equality
- returning all-True for inequality
- raising TypeError otherwise
Parameters
----------
... | [
"If",
"a",
"comparison",
"has",
"mismatched",
"types",
"and",
"is",
"not",
"necessarily",
"meaningful",
"follow",
"python3",
"conventions",
"by",
":"
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/core/ops.py#L1216-L1242 | [
"def",
"invalid_comparison",
"(",
"left",
",",
"right",
",",
"op",
")",
":",
"if",
"op",
"is",
"operator",
".",
"eq",
":",
"res_values",
"=",
"np",
".",
"zeros",
"(",
"left",
".",
"shape",
",",
"dtype",
"=",
"bool",
")",
"elif",
"op",
"is",
"operat... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | should_series_dispatch | Identify cases where a DataFrame operation should dispatch to its
Series counterpart.
Parameters
----------
left : DataFrame
right : DataFrame
op : binary operator
Returns
-------
override : bool | pandas/core/ops.py | def should_series_dispatch(left, right, op):
"""
Identify cases where a DataFrame operation should dispatch to its
Series counterpart.
Parameters
----------
left : DataFrame
right : DataFrame
op : binary operator
Returns
-------
override : bool
"""
if left._is_mixed... | def should_series_dispatch(left, right, op):
"""
Identify cases where a DataFrame operation should dispatch to its
Series counterpart.
Parameters
----------
left : DataFrame
right : DataFrame
op : binary operator
Returns
-------
override : bool
"""
if left._is_mixed... | [
"Identify",
"cases",
"where",
"a",
"DataFrame",
"operation",
"should",
"dispatch",
"to",
"its",
"Series",
"counterpart",
"."
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/core/ops.py#L1248-L1282 | [
"def",
"should_series_dispatch",
"(",
"left",
",",
"right",
",",
"op",
")",
":",
"if",
"left",
".",
"_is_mixed_type",
"or",
"right",
".",
"_is_mixed_type",
":",
"return",
"True",
"if",
"not",
"len",
"(",
"left",
".",
"columns",
")",
"or",
"not",
"len",
... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | dispatch_to_series | Evaluate the frame operation func(left, right) by evaluating
column-by-column, dispatching to the Series implementation.
Parameters
----------
left : DataFrame
right : scalar or DataFrame
func : arithmetic or comparison operator
str_rep : str or None, default None
axis : {None, 0, 1, "i... | pandas/core/ops.py | def dispatch_to_series(left, right, func, str_rep=None, axis=None):
"""
Evaluate the frame operation func(left, right) by evaluating
column-by-column, dispatching to the Series implementation.
Parameters
----------
left : DataFrame
right : scalar or DataFrame
func : arithmetic or compar... | def dispatch_to_series(left, right, func, str_rep=None, axis=None):
"""
Evaluate the frame operation func(left, right) by evaluating
column-by-column, dispatching to the Series implementation.
Parameters
----------
left : DataFrame
right : scalar or DataFrame
func : arithmetic or compar... | [
"Evaluate",
"the",
"frame",
"operation",
"func",
"(",
"left",
"right",
")",
"by",
"evaluating",
"column",
"-",
"by",
"-",
"column",
"dispatching",
"to",
"the",
"Series",
"implementation",
"."
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/core/ops.py#L1285-L1346 | [
"def",
"dispatch_to_series",
"(",
"left",
",",
"right",
",",
"func",
",",
"str_rep",
"=",
"None",
",",
"axis",
"=",
"None",
")",
":",
"# Note: we use iloc to access columns for compat with cases",
"# with non-unique columns.",
"import",
"pandas",
".",
"core",
".... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | dispatch_to_index_op | Wrap Series left in the given index_class to delegate the operation op
to the index implementation. DatetimeIndex and TimedeltaIndex perform
type checking, timezone handling, overflow checks, etc.
Parameters
----------
op : binary operator (operator.add, operator.sub, ...)
left : Series
ri... | pandas/core/ops.py | def dispatch_to_index_op(op, left, right, index_class):
"""
Wrap Series left in the given index_class to delegate the operation op
to the index implementation. DatetimeIndex and TimedeltaIndex perform
type checking, timezone handling, overflow checks, etc.
Parameters
----------
op : binary... | def dispatch_to_index_op(op, left, right, index_class):
"""
Wrap Series left in the given index_class to delegate the operation op
to the index implementation. DatetimeIndex and TimedeltaIndex perform
type checking, timezone handling, overflow checks, etc.
Parameters
----------
op : binary... | [
"Wrap",
"Series",
"left",
"in",
"the",
"given",
"index_class",
"to",
"delegate",
"the",
"operation",
"op",
"to",
"the",
"index",
"implementation",
".",
"DatetimeIndex",
"and",
"TimedeltaIndex",
"perform",
"type",
"checking",
"timezone",
"handling",
"overflow",
"ch... | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/core/ops.py#L1349-L1380 | [
"def",
"dispatch_to_index_op",
"(",
"op",
",",
"left",
",",
"right",
",",
"index_class",
")",
":",
"left_idx",
"=",
"index_class",
"(",
"left",
")",
"# avoid accidentally allowing integer add/sub. For datetime64[tz] dtypes,",
"# left_idx may inherit a freq from a cached Dateti... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | dispatch_to_extension_op | Assume that left or right is a Series backed by an ExtensionArray,
apply the operator defined by op. | pandas/core/ops.py | def dispatch_to_extension_op(op, left, right):
"""
Assume that left or right is a Series backed by an ExtensionArray,
apply the operator defined by op.
"""
# The op calls will raise TypeError if the op is not defined
# on the ExtensionArray
# unbox Series and Index to arrays
if isinsta... | def dispatch_to_extension_op(op, left, right):
"""
Assume that left or right is a Series backed by an ExtensionArray,
apply the operator defined by op.
"""
# The op calls will raise TypeError if the op is not defined
# on the ExtensionArray
# unbox Series and Index to arrays
if isinsta... | [
"Assume",
"that",
"left",
"or",
"right",
"is",
"a",
"Series",
"backed",
"by",
"an",
"ExtensionArray",
"apply",
"the",
"operator",
"defined",
"by",
"op",
"."
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/core/ops.py#L1383-L1410 | [
"def",
"dispatch_to_extension_op",
"(",
"op",
",",
"left",
",",
"right",
")",
":",
"# The op calls will raise TypeError if the op is not defined",
"# on the ExtensionArray",
"# unbox Series and Index to arrays",
"if",
"isinstance",
"(",
"left",
",",
"(",
"ABCSeries",
",",
"... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | _get_method_wrappers | Find the appropriate operation-wrappers to use when defining flex/special
arithmetic, boolean, and comparison operations with the given class.
Parameters
----------
cls : class
Returns
-------
arith_flex : function or None
comp_flex : function or None
arith_special : function
c... | pandas/core/ops.py | def _get_method_wrappers(cls):
"""
Find the appropriate operation-wrappers to use when defining flex/special
arithmetic, boolean, and comparison operations with the given class.
Parameters
----------
cls : class
Returns
-------
arith_flex : function or None
comp_flex : function... | def _get_method_wrappers(cls):
"""
Find the appropriate operation-wrappers to use when defining flex/special
arithmetic, boolean, and comparison operations with the given class.
Parameters
----------
cls : class
Returns
-------
arith_flex : function or None
comp_flex : function... | [
"Find",
"the",
"appropriate",
"operation",
"-",
"wrappers",
"to",
"use",
"when",
"defining",
"flex",
"/",
"special",
"arithmetic",
"boolean",
"and",
"comparison",
"operations",
"with",
"the",
"given",
"class",
"."
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/core/ops.py#L1417-L1473 | [
"def",
"_get_method_wrappers",
"(",
"cls",
")",
":",
"if",
"issubclass",
"(",
"cls",
",",
"ABCSparseSeries",
")",
":",
"# Be sure to catch this before ABCSeries and ABCSparseArray,",
"# as they will both come see SparseSeries as a subclass",
"arith_flex",
"=",
"_flex_method_SERIE... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | add_special_arithmetic_methods | Adds the full suite of special arithmetic methods (``__add__``,
``__sub__``, etc.) to the class.
Parameters
----------
cls : class
special methods will be defined and pinned to this class | pandas/core/ops.py | def add_special_arithmetic_methods(cls):
"""
Adds the full suite of special arithmetic methods (``__add__``,
``__sub__``, etc.) to the class.
Parameters
----------
cls : class
special methods will be defined and pinned to this class
"""
_, _, arith_method, comp_method, bool_meth... | def add_special_arithmetic_methods(cls):
"""
Adds the full suite of special arithmetic methods (``__add__``,
``__sub__``, etc.) to the class.
Parameters
----------
cls : class
special methods will be defined and pinned to this class
"""
_, _, arith_method, comp_method, bool_meth... | [
"Adds",
"the",
"full",
"suite",
"of",
"special",
"arithmetic",
"methods",
"(",
"__add__",
"__sub__",
"etc",
".",
")",
"to",
"the",
"class",
"."
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/core/ops.py#L1551-L1599 | [
"def",
"add_special_arithmetic_methods",
"(",
"cls",
")",
":",
"_",
",",
"_",
",",
"arith_method",
",",
"comp_method",
",",
"bool_method",
"=",
"_get_method_wrappers",
"(",
"cls",
")",
"new_methods",
"=",
"_create_methods",
"(",
"cls",
",",
"arith_method",
",",
... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | add_flex_arithmetic_methods | Adds the full suite of flex arithmetic methods (``pow``, ``mul``, ``add``)
to the class.
Parameters
----------
cls : class
flex methods will be defined and pinned to this class | pandas/core/ops.py | def add_flex_arithmetic_methods(cls):
"""
Adds the full suite of flex arithmetic methods (``pow``, ``mul``, ``add``)
to the class.
Parameters
----------
cls : class
flex methods will be defined and pinned to this class
"""
flex_arith_method, flex_comp_method, _, _, _ = _get_meth... | def add_flex_arithmetic_methods(cls):
"""
Adds the full suite of flex arithmetic methods (``pow``, ``mul``, ``add``)
to the class.
Parameters
----------
cls : class
flex methods will be defined and pinned to this class
"""
flex_arith_method, flex_comp_method, _, _, _ = _get_meth... | [
"Adds",
"the",
"full",
"suite",
"of",
"flex",
"arithmetic",
"methods",
"(",
"pow",
"mul",
"add",
")",
"to",
"the",
"class",
"."
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/core/ops.py#L1602-L1622 | [
"def",
"add_flex_arithmetic_methods",
"(",
"cls",
")",
":",
"flex_arith_method",
",",
"flex_comp_method",
",",
"_",
",",
"_",
",",
"_",
"=",
"_get_method_wrappers",
"(",
"cls",
")",
"new_methods",
"=",
"_create_methods",
"(",
"cls",
",",
"flex_arith_method",
","... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | _align_method_SERIES | align lhs and rhs Series | pandas/core/ops.py | def _align_method_SERIES(left, right, align_asobject=False):
""" align lhs and rhs Series """
# ToDo: Different from _align_method_FRAME, list, tuple and ndarray
# are not coerced here
# because Series has inconsistencies described in #13637
if isinstance(right, ABCSeries):
# avoid repeate... | def _align_method_SERIES(left, right, align_asobject=False):
""" align lhs and rhs Series """
# ToDo: Different from _align_method_FRAME, list, tuple and ndarray
# are not coerced here
# because Series has inconsistencies described in #13637
if isinstance(right, ABCSeries):
# avoid repeate... | [
"align",
"lhs",
"and",
"rhs",
"Series"
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/core/ops.py#L1628-L1646 | [
"def",
"_align_method_SERIES",
"(",
"left",
",",
"right",
",",
"align_asobject",
"=",
"False",
")",
":",
"# ToDo: Different from _align_method_FRAME, list, tuple and ndarray",
"# are not coerced here",
"# because Series has inconsistencies described in #13637",
"if",
"isinstance",
... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | _construct_result | If the raw op result has a non-None name (e.g. it is an Index object) and
the name argument is None, then passing name to the constructor will
not be enough; we still need to override the name attribute. | pandas/core/ops.py | def _construct_result(left, result, index, name, dtype=None):
"""
If the raw op result has a non-None name (e.g. it is an Index object) and
the name argument is None, then passing name to the constructor will
not be enough; we still need to override the name attribute.
"""
out = left._constructo... | def _construct_result(left, result, index, name, dtype=None):
"""
If the raw op result has a non-None name (e.g. it is an Index object) and
the name argument is None, then passing name to the constructor will
not be enough; we still need to override the name attribute.
"""
out = left._constructo... | [
"If",
"the",
"raw",
"op",
"result",
"has",
"a",
"non",
"-",
"None",
"name",
"(",
"e",
".",
"g",
".",
"it",
"is",
"an",
"Index",
"object",
")",
"and",
"the",
"name",
"argument",
"is",
"None",
"then",
"passing",
"name",
"to",
"the",
"constructor",
"w... | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/core/ops.py#L1649-L1658 | [
"def",
"_construct_result",
"(",
"left",
",",
"result",
",",
"index",
",",
"name",
",",
"dtype",
"=",
"None",
")",
":",
"out",
"=",
"left",
".",
"_constructor",
"(",
"result",
",",
"index",
"=",
"index",
",",
"dtype",
"=",
"dtype",
")",
"out",
"=",
... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | _construct_divmod_result | divmod returns a tuple of like indexed series instead of a single series. | pandas/core/ops.py | def _construct_divmod_result(left, result, index, name, dtype=None):
"""divmod returns a tuple of like indexed series instead of a single series.
"""
return (
_construct_result(left, result[0], index=index, name=name,
dtype=dtype),
_construct_result(left, result[1],... | def _construct_divmod_result(left, result, index, name, dtype=None):
"""divmod returns a tuple of like indexed series instead of a single series.
"""
return (
_construct_result(left, result[0], index=index, name=name,
dtype=dtype),
_construct_result(left, result[1],... | [
"divmod",
"returns",
"a",
"tuple",
"of",
"like",
"indexed",
"series",
"instead",
"of",
"a",
"single",
"series",
"."
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/core/ops.py#L1661-L1669 | [
"def",
"_construct_divmod_result",
"(",
"left",
",",
"result",
",",
"index",
",",
"name",
",",
"dtype",
"=",
"None",
")",
":",
"return",
"(",
"_construct_result",
"(",
"left",
",",
"result",
"[",
"0",
"]",
",",
"index",
"=",
"index",
",",
"name",
"=",
... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | _arith_method_SERIES | Wrapper function for Series arithmetic operations, to avoid
code duplication. | pandas/core/ops.py | def _arith_method_SERIES(cls, op, special):
"""
Wrapper function for Series arithmetic operations, to avoid
code duplication.
"""
str_rep = _get_opstr(op, cls)
op_name = _get_op_name(op, special)
eval_kwargs = _gen_eval_kwargs(op_name)
fill_zeros = _gen_fill_zeros(op_name)
construct_... | def _arith_method_SERIES(cls, op, special):
"""
Wrapper function for Series arithmetic operations, to avoid
code duplication.
"""
str_rep = _get_opstr(op, cls)
op_name = _get_op_name(op, special)
eval_kwargs = _gen_eval_kwargs(op_name)
fill_zeros = _gen_fill_zeros(op_name)
construct_... | [
"Wrapper",
"function",
"for",
"Series",
"arithmetic",
"operations",
"to",
"avoid",
"code",
"duplication",
"."
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/core/ops.py#L1672-L1770 | [
"def",
"_arith_method_SERIES",
"(",
"cls",
",",
"op",
",",
"special",
")",
":",
"str_rep",
"=",
"_get_opstr",
"(",
"op",
",",
"cls",
")",
"op_name",
"=",
"_get_op_name",
"(",
"op",
",",
"special",
")",
"eval_kwargs",
"=",
"_gen_eval_kwargs",
"(",
"op_name"... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | _comp_method_SERIES | Wrapper function for Series arithmetic operations, to avoid
code duplication. | pandas/core/ops.py | def _comp_method_SERIES(cls, op, special):
"""
Wrapper function for Series arithmetic operations, to avoid
code duplication.
"""
op_name = _get_op_name(op, special)
masker = _gen_eval_kwargs(op_name).get('masker', False)
def na_op(x, y):
# TODO:
# should have guarantess on w... | def _comp_method_SERIES(cls, op, special):
"""
Wrapper function for Series arithmetic operations, to avoid
code duplication.
"""
op_name = _get_op_name(op, special)
masker = _gen_eval_kwargs(op_name).get('masker', False)
def na_op(x, y):
# TODO:
# should have guarantess on w... | [
"Wrapper",
"function",
"for",
"Series",
"arithmetic",
"operations",
"to",
"avoid",
"code",
"duplication",
"."
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/core/ops.py#L1789-L1959 | [
"def",
"_comp_method_SERIES",
"(",
"cls",
",",
"op",
",",
"special",
")",
":",
"op_name",
"=",
"_get_op_name",
"(",
"op",
",",
"special",
")",
"masker",
"=",
"_gen_eval_kwargs",
"(",
"op_name",
")",
".",
"get",
"(",
"'masker'",
",",
"False",
")",
"def",
... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | _bool_method_SERIES | Wrapper function for Series arithmetic operations, to avoid
code duplication. | pandas/core/ops.py | def _bool_method_SERIES(cls, op, special):
"""
Wrapper function for Series arithmetic operations, to avoid
code duplication.
"""
op_name = _get_op_name(op, special)
def na_op(x, y):
try:
result = op(x, y)
except TypeError:
assert not isinstance(y, (list, ... | def _bool_method_SERIES(cls, op, special):
"""
Wrapper function for Series arithmetic operations, to avoid
code duplication.
"""
op_name = _get_op_name(op, special)
def na_op(x, y):
try:
result = op(x, y)
except TypeError:
assert not isinstance(y, (list, ... | [
"Wrapper",
"function",
"for",
"Series",
"arithmetic",
"operations",
"to",
"avoid",
"code",
"duplication",
"."
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/core/ops.py#L1962-L2039 | [
"def",
"_bool_method_SERIES",
"(",
"cls",
",",
"op",
",",
"special",
")",
":",
"op_name",
"=",
"_get_op_name",
"(",
"op",
",",
"special",
")",
"def",
"na_op",
"(",
"x",
",",
"y",
")",
":",
"try",
":",
"result",
"=",
"op",
"(",
"x",
",",
"y",
")",... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | _combine_series_frame | Apply binary operator `func` to self, other using alignment and fill
conventions determined by the fill_value, axis, and level kwargs.
Parameters
----------
self : DataFrame
other : Series
func : binary operator
fill_value : object, default None
axis : {0, 1, 'columns', 'index', None}, ... | pandas/core/ops.py | def _combine_series_frame(self, other, func, fill_value=None, axis=None,
level=None):
"""
Apply binary operator `func` to self, other using alignment and fill
conventions determined by the fill_value, axis, and level kwargs.
Parameters
----------
self : DataFrame
o... | def _combine_series_frame(self, other, func, fill_value=None, axis=None,
level=None):
"""
Apply binary operator `func` to self, other using alignment and fill
conventions determined by the fill_value, axis, and level kwargs.
Parameters
----------
self : DataFrame
o... | [
"Apply",
"binary",
"operator",
"func",
"to",
"self",
"other",
"using",
"alignment",
"and",
"fill",
"conventions",
"determined",
"by",
"the",
"fill_value",
"axis",
"and",
"level",
"kwargs",
"."
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/core/ops.py#L2073-L2112 | [
"def",
"_combine_series_frame",
"(",
"self",
",",
"other",
",",
"func",
",",
"fill_value",
"=",
"None",
",",
"axis",
"=",
"None",
",",
"level",
"=",
"None",
")",
":",
"if",
"fill_value",
"is",
"not",
"None",
":",
"raise",
"NotImplementedError",
"(",
"\"f... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | _align_method_FRAME | convert rhs to meet lhs dims if input is list, tuple or np.ndarray | pandas/core/ops.py | def _align_method_FRAME(left, right, axis):
""" convert rhs to meet lhs dims if input is list, tuple or np.ndarray """
def to_series(right):
msg = ('Unable to coerce to Series, length must be {req_len}: '
'given {given_len}')
if axis is not None and left._get_axis_name(axis) == '... | def _align_method_FRAME(left, right, axis):
""" convert rhs to meet lhs dims if input is list, tuple or np.ndarray """
def to_series(right):
msg = ('Unable to coerce to Series, length must be {req_len}: '
'given {given_len}')
if axis is not None and left._get_axis_name(axis) == '... | [
"convert",
"rhs",
"to",
"meet",
"lhs",
"dims",
"if",
"input",
"is",
"list",
"tuple",
"or",
"np",
".",
"ndarray"
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/core/ops.py#L2115-L2169 | [
"def",
"_align_method_FRAME",
"(",
"left",
",",
"right",
",",
"axis",
")",
":",
"def",
"to_series",
"(",
"right",
")",
":",
"msg",
"=",
"(",
"'Unable to coerce to Series, length must be {req_len}: '",
"'given {given_len}'",
")",
"if",
"axis",
"is",
"not",
"None",
... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | _cast_sparse_series_op | For SparseSeries operation, coerce to float64 if the result is expected
to have NaN or inf values
Parameters
----------
left : SparseArray
right : SparseArray
opname : str
Returns
-------
left : SparseArray
right : SparseArray | pandas/core/ops.py | def _cast_sparse_series_op(left, right, opname):
"""
For SparseSeries operation, coerce to float64 if the result is expected
to have NaN or inf values
Parameters
----------
left : SparseArray
right : SparseArray
opname : str
Returns
-------
left : SparseArray
right : Sp... | def _cast_sparse_series_op(left, right, opname):
"""
For SparseSeries operation, coerce to float64 if the result is expected
to have NaN or inf values
Parameters
----------
left : SparseArray
right : SparseArray
opname : str
Returns
-------
left : SparseArray
right : Sp... | [
"For",
"SparseSeries",
"operation",
"coerce",
"to",
"float64",
"if",
"the",
"result",
"is",
"expected",
"to",
"have",
"NaN",
"or",
"inf",
"values"
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/core/ops.py#L2389-L2419 | [
"def",
"_cast_sparse_series_op",
"(",
"left",
",",
"right",
",",
"opname",
")",
":",
"from",
"pandas",
".",
"core",
".",
"sparse",
".",
"api",
"import",
"SparseDtype",
"opname",
"=",
"opname",
".",
"strip",
"(",
"'_'",
")",
"# TODO: This should be moved to the... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | _arith_method_SPARSE_SERIES | Wrapper function for Series arithmetic operations, to avoid
code duplication. | pandas/core/ops.py | def _arith_method_SPARSE_SERIES(cls, op, special):
"""
Wrapper function for Series arithmetic operations, to avoid
code duplication.
"""
op_name = _get_op_name(op, special)
def wrapper(self, other):
if isinstance(other, ABCDataFrame):
return NotImplemented
elif isins... | def _arith_method_SPARSE_SERIES(cls, op, special):
"""
Wrapper function for Series arithmetic operations, to avoid
code duplication.
"""
op_name = _get_op_name(op, special)
def wrapper(self, other):
if isinstance(other, ABCDataFrame):
return NotImplemented
elif isins... | [
"Wrapper",
"function",
"for",
"Series",
"arithmetic",
"operations",
"to",
"avoid",
"code",
"duplication",
"."
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/core/ops.py#L2422-L2447 | [
"def",
"_arith_method_SPARSE_SERIES",
"(",
"cls",
",",
"op",
",",
"special",
")",
":",
"op_name",
"=",
"_get_op_name",
"(",
"op",
",",
"special",
")",
"def",
"wrapper",
"(",
"self",
",",
"other",
")",
":",
"if",
"isinstance",
"(",
"other",
",",
"ABCDataF... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | _arith_method_SPARSE_ARRAY | Wrapper function for Series arithmetic operations, to avoid
code duplication. | pandas/core/ops.py | def _arith_method_SPARSE_ARRAY(cls, op, special):
"""
Wrapper function for Series arithmetic operations, to avoid
code duplication.
"""
op_name = _get_op_name(op, special)
def wrapper(self, other):
from pandas.core.arrays.sparse.array import (
SparseArray, _sparse_array_op, ... | def _arith_method_SPARSE_ARRAY(cls, op, special):
"""
Wrapper function for Series arithmetic operations, to avoid
code duplication.
"""
op_name = _get_op_name(op, special)
def wrapper(self, other):
from pandas.core.arrays.sparse.array import (
SparseArray, _sparse_array_op, ... | [
"Wrapper",
"function",
"for",
"Series",
"arithmetic",
"operations",
"to",
"avoid",
"code",
"duplication",
"."
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/core/ops.py#L2461-L2491 | [
"def",
"_arith_method_SPARSE_ARRAY",
"(",
"cls",
",",
"op",
",",
"special",
")",
":",
"op_name",
"=",
"_get_op_name",
"(",
"op",
",",
"special",
")",
"def",
"wrapper",
"(",
"self",
",",
"other",
")",
":",
"from",
"pandas",
".",
"core",
".",
"arrays",
"... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | validate_periods | If a `periods` argument is passed to the Datetime/Timedelta Array/Index
constructor, cast it to an integer.
Parameters
----------
periods : None, float, int
Returns
-------
periods : None or int
Raises
------
TypeError
if periods is None, float, or int | pandas/core/arrays/datetimelike.py | def validate_periods(periods):
"""
If a `periods` argument is passed to the Datetime/Timedelta Array/Index
constructor, cast it to an integer.
Parameters
----------
periods : None, float, int
Returns
-------
periods : None or int
Raises
------
TypeError
if peri... | def validate_periods(periods):
"""
If a `periods` argument is passed to the Datetime/Timedelta Array/Index
constructor, cast it to an integer.
Parameters
----------
periods : None, float, int
Returns
-------
periods : None or int
Raises
------
TypeError
if peri... | [
"If",
"a",
"periods",
"argument",
"is",
"passed",
"to",
"the",
"Datetime",
"/",
"Timedelta",
"Array",
"/",
"Index",
"constructor",
"cast",
"it",
"to",
"an",
"integer",
"."
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/core/arrays/datetimelike.py#L1441-L1465 | [
"def",
"validate_periods",
"(",
"periods",
")",
":",
"if",
"periods",
"is",
"not",
"None",
":",
"if",
"lib",
".",
"is_float",
"(",
"periods",
")",
":",
"periods",
"=",
"int",
"(",
"periods",
")",
"elif",
"not",
"lib",
".",
"is_integer",
"(",
"periods",... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | validate_endpoints | Check that the `closed` argument is among [None, "left", "right"]
Parameters
----------
closed : {None, "left", "right"}
Returns
-------
left_closed : bool
right_closed : bool
Raises
------
ValueError : if argument is not among valid values | pandas/core/arrays/datetimelike.py | def validate_endpoints(closed):
"""
Check that the `closed` argument is among [None, "left", "right"]
Parameters
----------
closed : {None, "left", "right"}
Returns
-------
left_closed : bool
right_closed : bool
Raises
------
ValueError : if argument is not among valid... | def validate_endpoints(closed):
"""
Check that the `closed` argument is among [None, "left", "right"]
Parameters
----------
closed : {None, "left", "right"}
Returns
-------
left_closed : bool
right_closed : bool
Raises
------
ValueError : if argument is not among valid... | [
"Check",
"that",
"the",
"closed",
"argument",
"is",
"among",
"[",
"None",
"left",
"right",
"]"
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/core/arrays/datetimelike.py#L1468-L1498 | [
"def",
"validate_endpoints",
"(",
"closed",
")",
":",
"left_closed",
"=",
"False",
"right_closed",
"=",
"False",
"if",
"closed",
"is",
"None",
":",
"left_closed",
"=",
"True",
"right_closed",
"=",
"True",
"elif",
"closed",
"==",
"\"left\"",
":",
"left_closed",... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | validate_inferred_freq | If the user passes a freq and another freq is inferred from passed data,
require that they match.
Parameters
----------
freq : DateOffset or None
inferred_freq : DateOffset or None
freq_infer : bool
Returns
-------
freq : DateOffset or None
freq_infer : bool
Notes
----... | pandas/core/arrays/datetimelike.py | def validate_inferred_freq(freq, inferred_freq, freq_infer):
"""
If the user passes a freq and another freq is inferred from passed data,
require that they match.
Parameters
----------
freq : DateOffset or None
inferred_freq : DateOffset or None
freq_infer : bool
Returns
------... | def validate_inferred_freq(freq, inferred_freq, freq_infer):
"""
If the user passes a freq and another freq is inferred from passed data,
require that they match.
Parameters
----------
freq : DateOffset or None
inferred_freq : DateOffset or None
freq_infer : bool
Returns
------... | [
"If",
"the",
"user",
"passes",
"a",
"freq",
"and",
"another",
"freq",
"is",
"inferred",
"from",
"passed",
"data",
"require",
"that",
"they",
"match",
"."
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/core/arrays/datetimelike.py#L1501-L1533 | [
"def",
"validate_inferred_freq",
"(",
"freq",
",",
"inferred_freq",
",",
"freq_infer",
")",
":",
"if",
"inferred_freq",
"is",
"not",
"None",
":",
"if",
"freq",
"is",
"not",
"None",
"and",
"freq",
"!=",
"inferred_freq",
":",
"raise",
"ValueError",
"(",
"'Infe... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | maybe_infer_freq | Comparing a DateOffset to the string "infer" raises, so we need to
be careful about comparisons. Make a dummy variable `freq_infer` to
signify the case where the given freq is "infer" and set freq to None
to avoid comparison trouble later on.
Parameters
----------
freq : {DateOffset, None, str... | pandas/core/arrays/datetimelike.py | def maybe_infer_freq(freq):
"""
Comparing a DateOffset to the string "infer" raises, so we need to
be careful about comparisons. Make a dummy variable `freq_infer` to
signify the case where the given freq is "infer" and set freq to None
to avoid comparison trouble later on.
Parameters
----... | def maybe_infer_freq(freq):
"""
Comparing a DateOffset to the string "infer" raises, so we need to
be careful about comparisons. Make a dummy variable `freq_infer` to
signify the case where the given freq is "infer" and set freq to None
to avoid comparison trouble later on.
Parameters
----... | [
"Comparing",
"a",
"DateOffset",
"to",
"the",
"string",
"infer",
"raises",
"so",
"we",
"need",
"to",
"be",
"careful",
"about",
"comparisons",
".",
"Make",
"a",
"dummy",
"variable",
"freq_infer",
"to",
"signify",
"the",
"case",
"where",
"the",
"given",
"freq",... | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/core/arrays/datetimelike.py#L1536-L1560 | [
"def",
"maybe_infer_freq",
"(",
"freq",
")",
":",
"freq_infer",
"=",
"False",
"if",
"not",
"isinstance",
"(",
"freq",
",",
"DateOffset",
")",
":",
"# if a passed freq is None, don't infer automatically",
"if",
"freq",
"!=",
"'infer'",
":",
"freq",
"=",
"frequencie... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | _ensure_datetimelike_to_i8 | Helper for coercing an input scalar or array to i8.
Parameters
----------
other : 1d array
to_utc : bool, default False
If True, convert the values to UTC before extracting the i8 values
If False, extract the i8 values directly.
Returns
-------
i8 1d array | pandas/core/arrays/datetimelike.py | def _ensure_datetimelike_to_i8(other, to_utc=False):
"""
Helper for coercing an input scalar or array to i8.
Parameters
----------
other : 1d array
to_utc : bool, default False
If True, convert the values to UTC before extracting the i8 values
If False, extract the i8 values dir... | def _ensure_datetimelike_to_i8(other, to_utc=False):
"""
Helper for coercing an input scalar or array to i8.
Parameters
----------
other : 1d array
to_utc : bool, default False
If True, convert the values to UTC before extracting the i8 values
If False, extract the i8 values dir... | [
"Helper",
"for",
"coercing",
"an",
"input",
"scalar",
"or",
"array",
"to",
"i8",
"."
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/core/arrays/datetimelike.py#L1563-L1597 | [
"def",
"_ensure_datetimelike_to_i8",
"(",
"other",
",",
"to_utc",
"=",
"False",
")",
":",
"from",
"pandas",
"import",
"Index",
"from",
"pandas",
".",
"core",
".",
"arrays",
"import",
"PeriodArray",
"if",
"lib",
".",
"is_scalar",
"(",
"other",
")",
"and",
"... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | AttributesMixin._scalar_from_string | Construct a scalar type from a string.
Parameters
----------
value : str
Returns
-------
Period, Timestamp, or Timedelta, or NaT
Whatever the type of ``self._scalar_type`` is.
Notes
-----
This should call ``self._check_compatible_wit... | pandas/core/arrays/datetimelike.py | def _scalar_from_string(
self,
value: str,
) -> Union[Period, Timestamp, Timedelta, NaTType]:
"""
Construct a scalar type from a string.
Parameters
----------
value : str
Returns
-------
Period, Timestamp, or Timedelta, or NaT... | def _scalar_from_string(
self,
value: str,
) -> Union[Period, Timestamp, Timedelta, NaTType]:
"""
Construct a scalar type from a string.
Parameters
----------
value : str
Returns
-------
Period, Timestamp, or Timedelta, or NaT... | [
"Construct",
"a",
"scalar",
"type",
"from",
"a",
"string",
"."
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/core/arrays/datetimelike.py#L68-L89 | [
"def",
"_scalar_from_string",
"(",
"self",
",",
"value",
":",
"str",
",",
")",
"->",
"Union",
"[",
"Period",
",",
"Timestamp",
",",
"Timedelta",
",",
"NaTType",
"]",
":",
"raise",
"AbstractMethodError",
"(",
"self",
")"
] | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | AttributesMixin._unbox_scalar | Unbox the integer value of a scalar `value`.
Parameters
----------
value : Union[Period, Timestamp, Timedelta]
Returns
-------
int
Examples
--------
>>> self._unbox_scalar(Timedelta('10s')) # DOCTEST: +SKIP
10000000000 | pandas/core/arrays/datetimelike.py | def _unbox_scalar(
self,
value: Union[Period, Timestamp, Timedelta, NaTType],
) -> int:
"""
Unbox the integer value of a scalar `value`.
Parameters
----------
value : Union[Period, Timestamp, Timedelta]
Returns
-------
int
... | def _unbox_scalar(
self,
value: Union[Period, Timestamp, Timedelta, NaTType],
) -> int:
"""
Unbox the integer value of a scalar `value`.
Parameters
----------
value : Union[Period, Timestamp, Timedelta]
Returns
-------
int
... | [
"Unbox",
"the",
"integer",
"value",
"of",
"a",
"scalar",
"value",
"."
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/core/arrays/datetimelike.py#L91-L111 | [
"def",
"_unbox_scalar",
"(",
"self",
",",
"value",
":",
"Union",
"[",
"Period",
",",
"Timestamp",
",",
"Timedelta",
",",
"NaTType",
"]",
",",
")",
"->",
"int",
":",
"raise",
"AbstractMethodError",
"(",
"self",
")"
] | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | AttributesMixin._check_compatible_with | Verify that `self` and `other` are compatible.
* DatetimeArray verifies that the timezones (if any) match
* PeriodArray verifies that the freq matches
* Timedelta has no verification
In each case, NaT is considered compatible.
Parameters
----------
other
... | pandas/core/arrays/datetimelike.py | def _check_compatible_with(
self,
other: Union[Period, Timestamp, Timedelta, NaTType],
) -> None:
"""
Verify that `self` and `other` are compatible.
* DatetimeArray verifies that the timezones (if any) match
* PeriodArray verifies that the freq matches
... | def _check_compatible_with(
self,
other: Union[Period, Timestamp, Timedelta, NaTType],
) -> None:
"""
Verify that `self` and `other` are compatible.
* DatetimeArray verifies that the timezones (if any) match
* PeriodArray verifies that the freq matches
... | [
"Verify",
"that",
"self",
"and",
"other",
"are",
"compatible",
"."
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/core/arrays/datetimelike.py#L113-L134 | [
"def",
"_check_compatible_with",
"(",
"self",
",",
"other",
":",
"Union",
"[",
"Period",
",",
"Timestamp",
",",
"Timedelta",
",",
"NaTType",
"]",
",",
")",
"->",
"None",
":",
"raise",
"AbstractMethodError",
"(",
"self",
")"
] | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | DatelikeOps.strftime | Convert to Index using specified date_format.
Return an Index of formatted strings specified by date_format, which
supports the same string format as the python standard library. Details
of the string format can be found in `python string format
doc <%(URL)s>`__.
Parameters
... | pandas/core/arrays/datetimelike.py | def strftime(self, date_format):
"""
Convert to Index using specified date_format.
Return an Index of formatted strings specified by date_format, which
supports the same string format as the python standard library. Details
of the string format can be found in `python string for... | def strftime(self, date_format):
"""
Convert to Index using specified date_format.
Return an Index of formatted strings specified by date_format, which
supports the same string format as the python standard library. Details
of the string format can be found in `python string for... | [
"Convert",
"to",
"Index",
"using",
"specified",
"date_format",
"."
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/core/arrays/datetimelike.py#L144-L180 | [
"def",
"strftime",
"(",
"self",
",",
"date_format",
")",
":",
"from",
"pandas",
"import",
"Index",
"return",
"Index",
"(",
"self",
".",
"_format_native_types",
"(",
"date_format",
"=",
"date_format",
")",
")"
] | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
train | DatetimeLikeArrayMixin.searchsorted | Find indices where elements should be inserted to maintain order.
Find the indices into a sorted array `self` such that, if the
corresponding elements in `value` were inserted before the indices,
the order of `self` would be preserved.
Parameters
----------
value : arra... | pandas/core/arrays/datetimelike.py | def searchsorted(self, value, side='left', sorter=None):
"""
Find indices where elements should be inserted to maintain order.
Find the indices into a sorted array `self` such that, if the
corresponding elements in `value` were inserted before the indices,
the order of `self` wo... | def searchsorted(self, value, side='left', sorter=None):
"""
Find indices where elements should be inserted to maintain order.
Find the indices into a sorted array `self` such that, if the
corresponding elements in `value` were inserted before the indices,
the order of `self` wo... | [
"Find",
"indices",
"where",
"elements",
"should",
"be",
"inserted",
"to",
"maintain",
"order",
"."
] | pandas-dev/pandas | python | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/core/arrays/datetimelike.py#L627-L666 | [
"def",
"searchsorted",
"(",
"self",
",",
"value",
",",
"side",
"=",
"'left'",
",",
"sorter",
"=",
"None",
")",
":",
"if",
"isinstance",
"(",
"value",
",",
"str",
")",
":",
"value",
"=",
"self",
".",
"_scalar_from_string",
"(",
"value",
")",
"if",
"no... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.