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20,400 | pandas-dev/pandas | pandas/core/arrays/datetimes.py | DatetimeArray._add_delta | def _add_delta(self, delta):
"""
Add a timedelta-like, Tick, or TimedeltaIndex-like object
to self, yielding a new DatetimeArray
Parameters
----------
other : {timedelta, np.timedelta64, Tick,
TimedeltaIndex, ndarray[timedelta64]}
Returns
... | python | def _add_delta(self, delta):
"""
Add a timedelta-like, Tick, or TimedeltaIndex-like object
to self, yielding a new DatetimeArray
Parameters
----------
other : {timedelta, np.timedelta64, Tick,
TimedeltaIndex, ndarray[timedelta64]}
Returns
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20,401 | pandas-dev/pandas | pandas/core/arrays/datetimes.py | DatetimeArray.normalize | def normalize(self):
"""
Convert times to midnight.
The time component of the date-time is converted to midnight i.e.
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Length is unaltered. The timezones are unaffected.
This method is available on Series ... | python | def normalize(self):
"""
Convert times to midnight.
The time component of the date-time is converted to midnight i.e.
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Length is unaltered. The timezones are unaffected.
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20,402 | pandas-dev/pandas | pandas/core/arrays/datetimes.py | DatetimeArray.to_perioddelta | def to_perioddelta(self, freq):
"""
Calculate TimedeltaArray of difference between index
values and index converted to PeriodArray at specified
freq. Used for vectorized offsets
Parameters
----------
freq : Period frequency
Returns
-------
... | python | def to_perioddelta(self, freq):
"""
Calculate TimedeltaArray of difference between index
values and index converted to PeriodArray at specified
freq. Used for vectorized offsets
Parameters
----------
freq : Period frequency
Returns
-------
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20,403 | pandas-dev/pandas | pandas/core/arrays/datetimes.py | DatetimeArray.month_name | def month_name(self, locale=None):
"""
Return the month names of the DateTimeIndex with specified locale.
.. versionadded:: 0.23.0
Parameters
----------
locale : str, optional
Locale determining the language in which to return the month name.
Def... | python | def month_name(self, locale=None):
"""
Return the month names of the DateTimeIndex with specified locale.
.. versionadded:: 0.23.0
Parameters
----------
locale : str, optional
Locale determining the language in which to return the month name.
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20,404 | pandas-dev/pandas | pandas/core/arrays/datetimes.py | DatetimeArray.time | def time(self):
"""
Returns numpy array of datetime.time. The time part of the Timestamps.
"""
# If the Timestamps have a timezone that is not UTC,
# convert them into their i8 representation while
# keeping their timezone and not using UTC
if self.tz is not None ... | python | def time(self):
"""
Returns numpy array of datetime.time. The time part of the Timestamps.
"""
# If the Timestamps have a timezone that is not UTC,
# convert them into their i8 representation while
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20,405 | pandas-dev/pandas | scripts/validate_docstrings.py | get_api_items | def get_api_items(api_doc_fd):
"""
Yield information about all public API items.
Parse api.rst file from the documentation, and extract all the functions,
methods, classes, attributes... This should include all pandas public API.
Parameters
----------
api_doc_fd : file descriptor
A... | python | def get_api_items(api_doc_fd):
"""
Yield information about all public API items.
Parse api.rst file from the documentation, and extract all the functions,
methods, classes, attributes... This should include all pandas public API.
Parameters
----------
api_doc_fd : file descriptor
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20,406 | pandas-dev/pandas | scripts/validate_docstrings.py | validate_one | def validate_one(func_name):
"""
Validate the docstring for the given func_name
Parameters
----------
func_name : function
Function whose docstring will be evaluated (e.g. pandas.read_csv).
Returns
-------
dict
A dictionary containing all the information obtained from v... | python | def validate_one(func_name):
"""
Validate the docstring for the given func_name
Parameters
----------
func_name : function
Function whose docstring will be evaluated (e.g. pandas.read_csv).
Returns
-------
dict
A dictionary containing all the information obtained from v... | [
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20,407 | pandas-dev/pandas | scripts/validate_docstrings.py | validate_all | def validate_all(prefix, ignore_deprecated=False):
"""
Execute the validation of all docstrings, and return a dict with the
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Parameters
----------
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If provided, only the docstrings that start with this pattern will be
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Execute the validation of all docstrings, and return a dict with the
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20,408 | pandas-dev/pandas | scripts/validate_docstrings.py | Docstring._load_obj | def _load_obj(name):
"""
Import Python object from its name as string.
Parameters
----------
name : str
Object name to import (e.g. pandas.Series.str.upper)
Returns
-------
object
Python object that can be a class, method, functio... | python | def _load_obj(name):
"""
Import Python object from its name as string.
Parameters
----------
name : str
Object name to import (e.g. pandas.Series.str.upper)
Returns
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20,409 | pandas-dev/pandas | scripts/validate_docstrings.py | Docstring._to_original_callable | def _to_original_callable(obj):
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Find the Python object that contains the source code of the object.
This is useful to find the place in the source code (file and line
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Find the Python object that contains the source code of the object.
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20,410 | pandas-dev/pandas | scripts/validate_docstrings.py | Docstring.method_returns_something | def method_returns_something(self):
'''
Check if the docstrings method can return something.
Bare returns, returns valued None and returns from nested functions are
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Returns
-------
bool
Whether the docstrings method can return somethin... | python | def method_returns_something(self):
'''
Check if the docstrings method can return something.
Bare returns, returns valued None and returns from nested functions are
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Returns
-------
bool
Whether the docstrings method can return somethin... | [
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20,411 | pandas-dev/pandas | pandas/io/excel/_base.py | ExcelWriter._value_with_fmt | def _value_with_fmt(self, val):
"""Convert numpy types to Python types for the Excel writers.
Parameters
----------
val : object
Value to be written into cells
Returns
-------
Tuple with the first element being the converted value and the second
... | python | def _value_with_fmt(self, val):
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Parameters
----------
val : object
Value to be written into cells
Returns
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20,412 | pandas-dev/pandas | pandas/io/excel/_base.py | ExcelWriter.check_extension | def check_extension(cls, ext):
"""checks that path's extension against the Writer's supported
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if ext.startswith('.'):
ext = ext[1:]
if not any(ext in extension for extension in cls.supported_extensions):
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"""checks that path's extension against the Writer's supported
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if ext.startswith('.'):
ext = ext[1:]
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20,413 | pandas-dev/pandas | pandas/core/computation/pytables.py | _validate_where | def _validate_where(w):
"""
Validate that the where statement is of the right type.
The type may either be String, Expr, or list-like of Exprs.
Parameters
----------
w : String term expression, Expr, or list-like of Exprs.
Returns
-------
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"""
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The type may either be String, Expr, or list-like of Exprs.
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w : String term expression, Expr, or list-like of Exprs.
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20,414 | pandas-dev/pandas | pandas/core/computation/pytables.py | maybe_expression | def maybe_expression(s):
""" loose checking if s is a pytables-acceptable expression """
if not isinstance(s, str):
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ops = ExprVisitor.binary_ops + ExprVisitor.unary_ops + ('=',)
# make sure we have an op at least
return any(op in s for op in ops) | python | def maybe_expression(s):
""" loose checking if s is a pytables-acceptable expression """
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20,415 | pandas-dev/pandas | pandas/core/computation/pytables.py | BinOp.conform | def conform(self, rhs):
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20,416 | pandas-dev/pandas | pandas/core/computation/pytables.py | BinOp.generate | def generate(self, v):
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20,418 | pandas-dev/pandas | pandas/core/computation/pytables.py | FilterBinOp.invert | def invert(self):
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20,419 | pandas-dev/pandas | pandas/core/computation/pytables.py | Expr.evaluate | def evaluate(self):
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20,421 | pandas-dev/pandas | pandas/compat/numpy/function.py | validate_argmin_with_skipna | def validate_argmin_with_skipna(skipna, args, kwargs):
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"""
If 'Series.argmin' is called via the 'numpy' library,
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20,422 | pandas-dev/pandas | pandas/compat/numpy/function.py | validate_argmax_with_skipna | def validate_argmax_with_skipna(skipna, args, kwargs):
"""
If 'Series.argmax' is called via the 'numpy' library,
the third parameter in its signature is 'out', which
takes either an ndarray or 'None', so check if the
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"""
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20,423 | pandas-dev/pandas | pandas/compat/numpy/function.py | validate_argsort_with_ascending | def validate_argsort_with_ascending(ascending, args, kwargs):
"""
If 'Categorical.argsort' is called via the 'numpy' library, the
first parameter in its signature is 'axis', which takes either
an integer or 'None', so check if the 'ascending' parameter has
either integer type or is None, since 'asce... | python | def validate_argsort_with_ascending(ascending, args, kwargs):
"""
If 'Categorical.argsort' is called via the 'numpy' library, the
first parameter in its signature is 'axis', which takes either
an integer or 'None', so check if the 'ascending' parameter has
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20,424 | pandas-dev/pandas | pandas/compat/numpy/function.py | validate_clip_with_axis | def validate_clip_with_axis(axis, args, kwargs):
"""
If 'NDFrame.clip' is called via the numpy library, the third
parameter in its signature is 'out', which can takes an ndarray,
so check if the 'axis' parameter is an instance of ndarray, since
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"... | python | def validate_clip_with_axis(axis, args, kwargs):
"""
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20,425 | pandas-dev/pandas | pandas/compat/numpy/function.py | validate_cum_func_with_skipna | def validate_cum_func_with_skipna(skipna, args, kwargs, name):
"""
If this function is called via the 'numpy' library, the third
parameter in its signature is 'dtype', which takes either a
'numpy' dtype or 'None', so check if the 'skipna' parameter is
a boolean or not
"""
if not is_bool(skip... | python | def validate_cum_func_with_skipna(skipna, args, kwargs, name):
"""
If this function is called via the 'numpy' library, the third
parameter in its signature is 'dtype', which takes either a
'numpy' dtype or 'None', so check if the 'skipna' parameter is
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20,426 | pandas-dev/pandas | pandas/compat/numpy/function.py | validate_take_with_convert | def validate_take_with_convert(convert, args, kwargs):
"""
If this function is called via the 'numpy' library, the third
parameter in its signature is 'axis', which takes either an
ndarray or 'None', so check if the 'convert' parameter is either
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"""
if isin... | python | def validate_take_with_convert(convert, args, kwargs):
"""
If this function is called via the 'numpy' library, the third
parameter in its signature is 'axis', which takes either an
ndarray or 'None', so check if the 'convert' parameter is either
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20,427 | pandas-dev/pandas | pandas/compat/numpy/function.py | validate_groupby_func | def validate_groupby_func(name, args, kwargs, allowed=None):
"""
'args' and 'kwargs' should be empty, except for allowed
kwargs because all of
their necessary parameters are explicitly listed in
the function signature
"""
if allowed is None:
allowed = []
kwargs = set(kwargs) - s... | python | def validate_groupby_func(name, args, kwargs, allowed=None):
"""
'args' and 'kwargs' should be empty, except for allowed
kwargs because all of
their necessary parameters are explicitly listed in
the function signature
"""
if allowed is None:
allowed = []
kwargs = set(kwargs) - s... | [
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20,428 | pandas-dev/pandas | pandas/compat/numpy/function.py | validate_resampler_func | def validate_resampler_func(method, args, kwargs):
"""
'args' and 'kwargs' should be empty because all of
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the function signature
"""
if len(args) + len(kwargs) > 0:
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20,429 | pandas-dev/pandas | pandas/compat/numpy/function.py | validate_minmax_axis | def validate_minmax_axis(axis):
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20,430 | pandas-dev/pandas | pandas/io/packers.py | read_msgpack | def read_msgpack(path_or_buf, encoding='utf-8', iterator=False, **kwargs):
"""
Load msgpack pandas object from the specified
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THIS IS AN EXPERIMENTAL LIBRARY and the storage format
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"""
Load msgpack pandas object from the specified
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20,431 | pandas-dev/pandas | pandas/io/packers.py | dtype_for | def dtype_for(t):
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20,432 | pandas-dev/pandas | pandas/io/packers.py | c2f | def c2f(r, i, ctype_name):
"""
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ftype = c2f_dict[ctype_name]
return np.typeDict[ctype_name](ftype(r) + 1j * ftype(i)) | python | def c2f(r, i, ctype_name):
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20,433 | pandas-dev/pandas | pandas/io/packers.py | convert | def convert(values):
""" convert the numpy values to a list """
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if is_categorical_dtype(values):
return values
elif is_object_dtype(dtype):
return values.ravel().tolist()
if needs_i8_conversion(dtype):
values = values.view('i8')
v = values.ravel()... | python | def convert(values):
""" convert the numpy values to a list """
dtype = values.dtype
if is_categorical_dtype(values):
return values
elif is_object_dtype(dtype):
return values.ravel().tolist()
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values = values.view('i8')
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20,434 | pandas-dev/pandas | pandas/io/packers.py | pack | def pack(o, default=encode,
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"""
Pack an object and return the packed bytes.
"""
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Pack an object and return the packed bytes.
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20,435 | pandas-dev/pandas | pandas/io/json/json.py | read_json | def read_json(path_or_buf=None, orient=None, typ='frame', dtype=None,
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numpy=False, precise_float=False, date_unit=None, encoding=None,
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Convert a JSON s... | python | def read_json(path_or_buf=None, orient=None, typ='frame', dtype=None,
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numpy=False, precise_float=False, date_unit=None, encoding=None,
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20,436 | pandas-dev/pandas | pandas/io/json/json.py | FrameWriter._format_axes | def _format_axes(self):
"""
Try to format axes if they are datelike.
"""
if not self.obj.index.is_unique and self.orient in (
'index', 'columns'):
raise ValueError("DataFrame index must be unique for orient="
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"""
Try to format axes if they are datelike.
"""
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20,437 | pandas-dev/pandas | pandas/io/json/json.py | JsonReader._combine_lines | def _combine_lines(self, lines):
"""
Combines a list of JSON objects into one JSON object.
"""
lines = filter(None, map(lambda x: x.strip(), lines))
return '[' + ','.join(lines) + ']' | python | def _combine_lines(self, lines):
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Combines a list of JSON objects into one JSON object.
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20,438 | pandas-dev/pandas | pandas/io/json/json.py | JsonReader.read | def read(self):
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Read the whole JSON input into a pandas object.
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data = to_str(self.data)
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"""
Read the whole JSON input into a pandas object.
"""
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20,439 | pandas-dev/pandas | pandas/io/json/json.py | JsonReader._get_object_parser | def _get_object_parser(self, json):
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Parses a json document into a pandas object.
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"""
Parses a json document into a pandas object.
"""
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dtype = self.dtype
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20,440 | pandas-dev/pandas | pandas/io/json/json.py | Parser.check_keys_split | def check_keys_split(self, decoded):
"""
Checks that dict has only the appropriate keys for orient='split'.
"""
bad_keys = set(decoded.keys()).difference(set(self._split_keys))
if bad_keys:
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raise ValueError("JSON data had une... | python | def check_keys_split(self, decoded):
"""
Checks that dict has only the appropriate keys for orient='split'.
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20,441 | pandas-dev/pandas | pandas/io/json/json.py | Parser._convert_axes | def _convert_axes(self):
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"""
Try to convert axes.
"""
for axis in self.obj._AXIS_NUMBERS.keys():
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20,442 | pandas-dev/pandas | pandas/io/json/json.py | FrameParser._process_converter | def _process_converter(self, f, filt=None):
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20,443 | pandas-dev/pandas | pandas/io/formats/format.py | format_array | def format_array(values, formatter, float_format=None, na_rep='NaN',
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leading_space=None):
"""
Format an array for printing.
Parameters
----------
values
formatter
float_format
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digits
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"""
Format an array for printing.
Parameters
----------
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20,444 | pandas-dev/pandas | pandas/io/formats/format.py | format_percentiles | def format_percentiles(percentiles):
"""
Outputs rounded and formatted percentiles.
Parameters
----------
percentiles : list-like, containing floats from interval [0,1]
Returns
-------
formatted : list of strings
Notes
-----
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"""
Outputs rounded and formatted percentiles.
Parameters
----------
percentiles : list-like, containing floats from interval [0,1]
Returns
-------
formatted : list of strings
Notes
-----
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20,445 | pandas-dev/pandas | pandas/io/formats/format.py | _get_format_timedelta64 | def _get_format_timedelta64(values, nat_rep='NaT', box=False):
"""
Return a formatter function for a range of timedeltas.
These will all have the same format argument
If box, then show the return in quotes
"""
values_int = values.astype(np.int64)
consider_values = values_int != iNaT
... | python | def _get_format_timedelta64(values, nat_rep='NaT', box=False):
"""
Return a formatter function for a range of timedeltas.
These will all have the same format argument
If box, then show the return in quotes
"""
values_int = values.astype(np.int64)
consider_values = values_int != iNaT
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20,446 | pandas-dev/pandas | pandas/io/formats/format.py | _trim_zeros_complex | def _trim_zeros_complex(str_complexes, na_rep='NaN'):
"""
Separates the real and imaginary parts from the complex number, and
executes the _trim_zeros_float method on each of those.
"""
def separate_and_trim(str_complex, na_rep):
num_arr = str_complex.split('+')
return (_trim_zeros_f... | python | def _trim_zeros_complex(str_complexes, na_rep='NaN'):
"""
Separates the real and imaginary parts from the complex number, and
executes the _trim_zeros_float method on each of those.
"""
def separate_and_trim(str_complex, na_rep):
num_arr = str_complex.split('+')
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20,447 | pandas-dev/pandas | pandas/io/formats/format.py | _trim_zeros_float | def _trim_zeros_float(str_floats, na_rep='NaN'):
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Trims zeros, leaving just one before the decimal points if need be.
"""
trimmed = str_floats
def _is_number(x):
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def _cond(values):
finite = [x for x in values if _is_number(x)]
... | python | def _trim_zeros_float(str_floats, na_rep='NaN'):
"""
Trims zeros, leaving just one before the decimal points if need be.
"""
trimmed = str_floats
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20,448 | pandas-dev/pandas | pandas/io/formats/format.py | set_eng_float_format | def set_eng_float_format(accuracy=3, use_eng_prefix=False):
"""
Alter default behavior on how float is formatted in DataFrame.
Format float in engineering format. By accuracy, we mean the number of
decimal digits after the floating point.
See also EngFormatter.
"""
set_option("display.floa... | python | def set_eng_float_format(accuracy=3, use_eng_prefix=False):
"""
Alter default behavior on how float is formatted in DataFrame.
Format float in engineering format. By accuracy, we mean the number of
decimal digits after the floating point.
See also EngFormatter.
"""
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20,449 | pandas-dev/pandas | pandas/io/formats/format.py | get_level_lengths | def get_level_lengths(levels, sentinel=''):
"""For each index in each level the function returns lengths of indexes.
Parameters
----------
levels : list of lists
List of values on for level.
sentinel : string, optional
Value which states that no new index starts on there.
Retur... | python | def get_level_lengths(levels, sentinel=''):
"""For each index in each level the function returns lengths of indexes.
Parameters
----------
levels : list of lists
List of values on for level.
sentinel : string, optional
Value which states that no new index starts on there.
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20,450 | pandas-dev/pandas | pandas/io/formats/format.py | buffer_put_lines | def buffer_put_lines(buf, lines):
"""
Appends lines to a buffer.
Parameters
----------
buf
The buffer to write to
lines
The lines to append.
"""
if any(isinstance(x, str) for x in lines):
lines = [str(x) for x in lines]
buf.write('\n'.join(lines)) | python | def buffer_put_lines(buf, lines):
"""
Appends lines to a buffer.
Parameters
----------
buf
The buffer to write to
lines
The lines to append.
"""
if any(isinstance(x, str) for x in lines):
lines = [str(x) for x in lines]
buf.write('\n'.join(lines)) | [
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20,451 | pandas-dev/pandas | pandas/io/formats/format.py | EastAsianTextAdjustment.len | def len(self, text):
"""
Calculate display width considering unicode East Asian Width
"""
if not isinstance(text, str):
return len(text)
return sum(self._EAW_MAP.get(east_asian_width(c), self.ambiguous_width)
for c in text) | python | def len(self, text):
"""
Calculate display width considering unicode East Asian Width
"""
if not isinstance(text, str):
return len(text)
return sum(self._EAW_MAP.get(east_asian_width(c), self.ambiguous_width)
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20,452 | pandas-dev/pandas | pandas/io/formats/format.py | FloatArrayFormatter._value_formatter | def _value_formatter(self, float_format=None, threshold=None):
"""Returns a function to be applied on each value to format it
"""
# the float_format parameter supersedes self.float_format
if float_format is None:
float_format = self.float_format
# we are going to co... | python | def _value_formatter(self, float_format=None, threshold=None):
"""Returns a function to be applied on each value to format it
"""
# the float_format parameter supersedes self.float_format
if float_format is None:
float_format = self.float_format
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20,453 | pandas-dev/pandas | pandas/io/formats/format.py | FloatArrayFormatter.get_result_as_array | def get_result_as_array(self):
"""
Returns the float values converted into strings using
the parameters given at initialisation, as a numpy array
"""
if self.formatter is not None:
return np.array([self.formatter(x) for x in self.values])
if self.fixed_width... | python | def get_result_as_array(self):
"""
Returns the float values converted into strings using
the parameters given at initialisation, as a numpy array
"""
if self.formatter is not None:
return np.array([self.formatter(x) for x in self.values])
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20,454 | pandas-dev/pandas | pandas/io/formats/format.py | Datetime64TZFormatter._format_strings | def _format_strings(self):
""" we by definition have a TZ """
values = self.values.astype(object)
is_dates_only = _is_dates_only(values)
formatter = (self.formatter or
_get_format_datetime64(is_dates_only,
date_format=self... | python | def _format_strings(self):
""" we by definition have a TZ """
values = self.values.astype(object)
is_dates_only = _is_dates_only(values)
formatter = (self.formatter or
_get_format_datetime64(is_dates_only,
date_format=self... | [
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20,455 | pandas-dev/pandas | pandas/core/indexes/interval.py | _get_interval_closed_bounds | def _get_interval_closed_bounds(interval):
"""
Given an Interval or IntervalIndex, return the corresponding interval with
closed bounds.
"""
left, right = interval.left, interval.right
if interval.open_left:
left = _get_next_label(left)
if interval.open_right:
right = _get_pr... | python | def _get_interval_closed_bounds(interval):
"""
Given an Interval or IntervalIndex, return the corresponding interval with
closed bounds.
"""
left, right = interval.left, interval.right
if interval.open_left:
left = _get_next_label(left)
if interval.open_right:
right = _get_pr... | [
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20,456 | pandas-dev/pandas | pandas/core/indexes/interval.py | interval_range | def interval_range(start=None, end=None, periods=None, freq=None,
name=None, closed='right'):
"""
Return a fixed frequency IntervalIndex
Parameters
----------
start : numeric or datetime-like, default None
Left bound for generating intervals
end : numeric or datetime-... | python | def interval_range(start=None, end=None, periods=None, freq=None,
name=None, closed='right'):
"""
Return a fixed frequency IntervalIndex
Parameters
----------
start : numeric or datetime-like, default None
Left bound for generating intervals
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20,457 | pandas-dev/pandas | pandas/io/formats/csvs.py | CSVFormatter.save | def save(self):
"""
Create the writer & save
"""
# GH21227 internal compression is not used when file-like passed.
if self.compression and hasattr(self.path_or_buf, 'write'):
msg = ("compression has no effect when passing file-like "
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"""
Create the writer & save
"""
# GH21227 internal compression is not used when file-like passed.
if self.compression and hasattr(self.path_or_buf, 'write'):
msg = ("compression has no effect when passing file-like "
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20,458 | pandas-dev/pandas | pandas/core/accessor.py | delegate_names | def delegate_names(delegate, accessors, typ, overwrite=False):
"""
Add delegated names to a class using a class decorator. This provides
an alternative usage to directly calling `_add_delegate_accessors`
below a class definition.
Parameters
----------
delegate : object
the class to... | python | def delegate_names(delegate, accessors, typ, overwrite=False):
"""
Add delegated names to a class using a class decorator. This provides
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20,459 | pandas-dev/pandas | pandas/core/accessor.py | PandasDelegate._add_delegate_accessors | def _add_delegate_accessors(cls, delegate, accessors, typ,
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20,460 | pandas-dev/pandas | pandas/core/computation/expressions.py | _can_use_numexpr | def _can_use_numexpr(op, op_str, a, b, dtype_check):
""" return a boolean if we WILL be using numexpr """
if op_str is not None:
# required min elements (otherwise we are adding overhead)
if np.prod(a.shape) > _MIN_ELEMENTS:
# check for dtype compatibility
dtypes = set(... | python | def _can_use_numexpr(op, op_str, a, b, dtype_check):
""" return a boolean if we WILL be using numexpr """
if op_str is not None:
# required min elements (otherwise we are adding overhead)
if np.prod(a.shape) > _MIN_ELEMENTS:
# check for dtype compatibility
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20,461 | pandas-dev/pandas | pandas/core/computation/expressions.py | evaluate | def evaluate(op, op_str, a, b, use_numexpr=True,
**eval_kwargs):
""" evaluate and return the expression of the op on a and b
Parameters
----------
op : the actual operand
op_str: the string version of the op
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""" evaluate and return the expression of the op on a and b
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20,462 | pandas-dev/pandas | pandas/core/computation/expressions.py | where | def where(cond, a, b, use_numexpr=True):
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cond : a boolean array
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20,463 | pandas-dev/pandas | pandas/io/feather_format.py | to_feather | def to_feather(df, path):
"""
Write a DataFrame to the feather-format
Parameters
----------
df : DataFrame
path : string file path, or file-like object
"""
path = _stringify_path(path)
if not isinstance(df, DataFrame):
raise ValueError("feather only support IO with DataFram... | python | def to_feather(df, path):
"""
Write a DataFrame to the feather-format
Parameters
----------
df : DataFrame
path : string file path, or file-like object
"""
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20,464 | pandas-dev/pandas | pandas/io/feather_format.py | read_feather | def read_feather(path, columns=None, use_threads=True):
"""
Load a feather-format object from the file path
.. versionadded 0.20.0
Parameters
----------
path : string file path, or file-like object
columns : sequence, default None
If not provided, all columns are read
.. v... | python | def read_feather(path, columns=None, use_threads=True):
"""
Load a feather-format object from the file path
.. versionadded 0.20.0
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path : string file path, or file-like object
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20,465 | pandas-dev/pandas | pandas/core/arrays/_ranges.py | generate_regular_range | def generate_regular_range(start, end, periods, freq):
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Generate a range of dates with the spans between dates described by
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Parameters
----------
start : Timestamp or None
first point of produced date range
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"""
Generate a range of dates with the spans between dates described by
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start : Timestamp or None
first point of produced date range
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20,466 | pandas-dev/pandas | pandas/core/arrays/_ranges.py | _generate_range_overflow_safe | def _generate_range_overflow_safe(endpoint, periods, stride, side='start'):
"""
Calculate the second endpoint for passing to np.arange, checking
to avoid an integer overflow. Catch OverflowError and re-raise
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endpoint : int
nanosecond ti... | python | def _generate_range_overflow_safe(endpoint, periods, stride, side='start'):
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20,467 | pandas-dev/pandas | pandas/_config/localization.py | set_locale | def set_locale(new_locale, lc_var=locale.LC_ALL):
"""
Context manager for temporarily setting a locale.
Parameters
----------
new_locale : str or tuple
A string of the form <language_country>.<encoding>. For example to set
the current locale to US English with a UTF8 encoding, you w... | python | def set_locale(new_locale, lc_var=locale.LC_ALL):
"""
Context manager for temporarily setting a locale.
Parameters
----------
new_locale : str or tuple
A string of the form <language_country>.<encoding>. For example to set
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20,468 | pandas-dev/pandas | pandas/_config/localization.py | can_set_locale | def can_set_locale(lc, lc_var=locale.LC_ALL):
"""
Check to see if we can set a locale, and subsequently get the locale,
without raising an Exception.
Parameters
----------
lc : str
The locale to attempt to set.
lc_var : int, default `locale.LC_ALL`
The category of the locale... | python | def can_set_locale(lc, lc_var=locale.LC_ALL):
"""
Check to see if we can set a locale, and subsequently get the locale,
without raising an Exception.
Parameters
----------
lc : str
The locale to attempt to set.
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20,469 | pandas-dev/pandas | pandas/_config/localization.py | _valid_locales | def _valid_locales(locales, normalize):
"""
Return a list of normalized locales that do not throw an ``Exception``
when set.
Parameters
----------
locales : str
A string where each locale is separated by a newline.
normalize : bool
Whether to call ``locale.normalize`` on eac... | python | def _valid_locales(locales, normalize):
"""
Return a list of normalized locales that do not throw an ``Exception``
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A string where each locale is separated by a newline.
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20,470 | pandas-dev/pandas | pandas/_config/localization.py | get_locales | def get_locales(prefix=None, normalize=True,
locale_getter=_default_locale_getter):
"""
Get all the locales that are available on the system.
Parameters
----------
prefix : str
If not ``None`` then return only those locales with the prefix
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locale_getter=_default_locale_getter):
"""
Get all the locales that are available on the system.
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20,471 | pandas-dev/pandas | pandas/core/dtypes/common.py | ensure_float | def ensure_float(arr):
"""
Ensure that an array object has a float dtype if possible.
Parameters
----------
arr : array-like
The array whose data type we want to enforce as float.
Returns
-------
float_arr : The original array cast to the float dtype if
possible... | python | def ensure_float(arr):
"""
Ensure that an array object has a float dtype if possible.
Parameters
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arr : array-like
The array whose data type we want to enforce as float.
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20,472 | pandas-dev/pandas | pandas/core/dtypes/common.py | ensure_int64_or_float64 | def ensure_int64_or_float64(arr, copy=False):
"""
Ensure that an dtype array of some integer dtype
has an int64 dtype if possible
If it's not possible, potentially because of overflow,
convert the array to float64 instead.
Parameters
----------
arr : array-like
The array whose... | python | def ensure_int64_or_float64(arr, copy=False):
"""
Ensure that an dtype array of some integer dtype
has an int64 dtype if possible
If it's not possible, potentially because of overflow,
convert the array to float64 instead.
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----------
arr : array-like
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20,473 | pandas-dev/pandas | pandas/core/dtypes/common.py | classes_and_not_datetimelike | def classes_and_not_datetimelike(*klasses):
"""
evaluate if the tipo is a subclass of the klasses
and not a datetimelike
"""
return lambda tipo: (issubclass(tipo, klasses) and
not issubclass(tipo, (np.datetime64, np.timedelta64))) | python | def classes_and_not_datetimelike(*klasses):
"""
evaluate if the tipo is a subclass of the klasses
and not a datetimelike
"""
return lambda tipo: (issubclass(tipo, klasses) and
not issubclass(tipo, (np.datetime64, np.timedelta64))) | [
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20,474 | pandas-dev/pandas | pandas/core/dtypes/common.py | is_sparse | def is_sparse(arr):
"""
Check whether an array-like is a 1-D pandas sparse array.
Check that the one-dimensional array-like is a pandas sparse array.
Returns True if it is a pandas sparse array, not another type of
sparse array.
Parameters
----------
arr : array-like
Array-like... | python | def is_sparse(arr):
"""
Check whether an array-like is a 1-D pandas sparse array.
Check that the one-dimensional array-like is a pandas sparse array.
Returns True if it is a pandas sparse array, not another type of
sparse array.
Parameters
----------
arr : array-like
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20,475 | pandas-dev/pandas | pandas/core/dtypes/common.py | is_scipy_sparse | def is_scipy_sparse(arr):
"""
Check whether an array-like is a scipy.sparse.spmatrix instance.
Parameters
----------
arr : array-like
The array-like to check.
Returns
-------
boolean
Whether or not the array-like is a scipy.sparse.spmatrix instance.
Notes
-----... | python | def is_scipy_sparse(arr):
"""
Check whether an array-like is a scipy.sparse.spmatrix instance.
Parameters
----------
arr : array-like
The array-like to check.
Returns
-------
boolean
Whether or not the array-like is a scipy.sparse.spmatrix instance.
Notes
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20,476 | pandas-dev/pandas | pandas/core/dtypes/common.py | is_offsetlike | def is_offsetlike(arr_or_obj):
"""
Check if obj or all elements of list-like is DateOffset
Parameters
----------
arr_or_obj : object
Returns
-------
boolean
Whether the object is a DateOffset or listlike of DatetOffsets
Examples
--------
>>> is_offsetlike(pd.DateOf... | python | def is_offsetlike(arr_or_obj):
"""
Check if obj or all elements of list-like is DateOffset
Parameters
----------
arr_or_obj : object
Returns
-------
boolean
Whether the object is a DateOffset or listlike of DatetOffsets
Examples
--------
>>> is_offsetlike(pd.DateOf... | [
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>>> is_offsetlike(pd.DateOffset(days=1))
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20,477 | pandas-dev/pandas | pandas/core/dtypes/common.py | is_period | def is_period(arr):
"""
Check whether an array-like is a periodical index.
.. deprecated:: 0.24.0
Parameters
----------
arr : array-like
The array-like to check.
Returns
-------
boolean
Whether or not the array-like is a periodical index.
Examples
--------... | python | def is_period(arr):
"""
Check whether an array-like is a periodical index.
.. deprecated:: 0.24.0
Parameters
----------
arr : array-like
The array-like to check.
Returns
-------
boolean
Whether or not the array-like is a periodical index.
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20,478 | pandas-dev/pandas | pandas/core/dtypes/common.py | is_string_dtype | def is_string_dtype(arr_or_dtype):
"""
Check whether the provided array or dtype is of the string dtype.
Parameters
----------
arr_or_dtype : array-like
The array or dtype to check.
Returns
-------
boolean
Whether or not the array or dtype is of the string dtype.
E... | python | def is_string_dtype(arr_or_dtype):
"""
Check whether the provided array or dtype is of the string dtype.
Parameters
----------
arr_or_dtype : array-like
The array or dtype to check.
Returns
-------
boolean
Whether or not the array or dtype is of the string dtype.
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20,479 | pandas-dev/pandas | pandas/core/dtypes/common.py | is_period_arraylike | def is_period_arraylike(arr):
"""
Check whether an array-like is a periodical array-like or PeriodIndex.
Parameters
----------
arr : array-like
The array-like to check.
Returns
-------
boolean
Whether or not the array-like is a periodical array-like or
PeriodInd... | python | def is_period_arraylike(arr):
"""
Check whether an array-like is a periodical array-like or PeriodIndex.
Parameters
----------
arr : array-like
The array-like to check.
Returns
-------
boolean
Whether or not the array-like is a periodical array-like or
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20,480 | pandas-dev/pandas | pandas/core/dtypes/common.py | is_datetime_arraylike | def is_datetime_arraylike(arr):
"""
Check whether an array-like is a datetime array-like or DatetimeIndex.
Parameters
----------
arr : array-like
The array-like to check.
Returns
-------
boolean
Whether or not the array-like is a datetime array-like or
DatetimeI... | python | def is_datetime_arraylike(arr):
"""
Check whether an array-like is a datetime array-like or DatetimeIndex.
Parameters
----------
arr : array-like
The array-like to check.
Returns
-------
boolean
Whether or not the array-like is a datetime array-like or
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20,481 | pandas-dev/pandas | pandas/core/dtypes/common.py | is_datetimelike | def is_datetimelike(arr):
"""
Check whether an array-like is a datetime-like array-like.
Acceptable datetime-like objects are (but not limited to) datetime
indices, periodic indices, and timedelta indices.
Parameters
----------
arr : array-like
The array-like to check.
Returns... | python | def is_datetimelike(arr):
"""
Check whether an array-like is a datetime-like array-like.
Acceptable datetime-like objects are (but not limited to) datetime
indices, periodic indices, and timedelta indices.
Parameters
----------
arr : array-like
The array-like to check.
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20,482 | pandas-dev/pandas | pandas/core/dtypes/common.py | is_dtype_equal | def is_dtype_equal(source, target):
"""
Check if two dtypes are equal.
Parameters
----------
source : The first dtype to compare
target : The second dtype to compare
Returns
----------
boolean
Whether or not the two dtypes are equal.
Examples
--------
>>> is_dt... | python | def is_dtype_equal(source, target):
"""
Check if two dtypes are equal.
Parameters
----------
source : The first dtype to compare
target : The second dtype to compare
Returns
----------
boolean
Whether or not the two dtypes are equal.
Examples
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20,483 | pandas-dev/pandas | pandas/core/dtypes/common.py | is_dtype_union_equal | def is_dtype_union_equal(source, target):
"""
Check whether two arrays have compatible dtypes to do a union.
numpy types are checked with ``is_dtype_equal``. Extension types are
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Parameters
----------
source : The first dtype to compare
target : The second dtype to co... | python | def is_dtype_union_equal(source, target):
"""
Check whether two arrays have compatible dtypes to do a union.
numpy types are checked with ``is_dtype_equal``. Extension types are
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20,484 | pandas-dev/pandas | pandas/core/dtypes/common.py | is_numeric_v_string_like | def is_numeric_v_string_like(a, b):
"""
Check if we are comparing a string-like object to a numeric ndarray.
NumPy doesn't like to compare such objects, especially numeric arrays
and scalar string-likes.
Parameters
----------
a : array-like, scalar
The first object to check.
b ... | python | def is_numeric_v_string_like(a, b):
"""
Check if we are comparing a string-like object to a numeric ndarray.
NumPy doesn't like to compare such objects, especially numeric arrays
and scalar string-likes.
Parameters
----------
a : array-like, scalar
The first object to check.
b ... | [
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20,485 | pandas-dev/pandas | pandas/core/dtypes/common.py | is_datetimelike_v_numeric | def is_datetimelike_v_numeric(a, b):
"""
Check if we are comparing a datetime-like object to a numeric object.
By "numeric," we mean an object that is either of an int or float dtype.
Parameters
----------
a : array-like, scalar
The first object to check.
b : array-like, scalar
... | python | def is_datetimelike_v_numeric(a, b):
"""
Check if we are comparing a datetime-like object to a numeric object.
By "numeric," we mean an object that is either of an int or float dtype.
Parameters
----------
a : array-like, scalar
The first object to check.
b : array-like, scalar
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20,486 | pandas-dev/pandas | pandas/core/dtypes/common.py | is_datetimelike_v_object | def is_datetimelike_v_object(a, b):
"""
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Parameters
----------
a : array-like, scalar
The first object to check.
b : array-like, scalar
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"""
Check if we are comparing a datetime-like object to an object instance.
Parameters
----------
a : array-like, scalar
The first object to check.
b : array-like, scalar
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20,487 | pandas-dev/pandas | pandas/core/dtypes/common.py | needs_i8_conversion | def needs_i8_conversion(arr_or_dtype):
"""
Check whether the array or dtype should be converted to int64.
An array-like or dtype "needs" such a conversion if the array-like
or dtype is of a datetime-like dtype
Parameters
----------
arr_or_dtype : array-like
The array or dtype to ch... | python | def needs_i8_conversion(arr_or_dtype):
"""
Check whether the array or dtype should be converted to int64.
An array-like or dtype "needs" such a conversion if the array-like
or dtype is of a datetime-like dtype
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----------
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The array or dtype to ch... | [
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20,488 | pandas-dev/pandas | pandas/core/dtypes/common.py | is_bool_dtype | def is_bool_dtype(arr_or_dtype):
"""
Check whether the provided array or dtype is of a boolean dtype.
Parameters
----------
arr_or_dtype : array-like
The array or dtype to check.
Returns
-------
boolean
Whether or not the array or dtype is of a boolean dtype.
Notes... | python | def is_bool_dtype(arr_or_dtype):
"""
Check whether the provided array or dtype is of a boolean dtype.
Parameters
----------
arr_or_dtype : array-like
The array or dtype to check.
Returns
-------
boolean
Whether or not the array or dtype is of a boolean dtype.
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20,489 | pandas-dev/pandas | pandas/core/dtypes/common.py | is_extension_type | def is_extension_type(arr):
"""
Check whether an array-like is of a pandas extension class instance.
Extension classes include categoricals, pandas sparse objects (i.e.
classes represented within the pandas library and not ones external
to it like scipy sparse matrices), and datetime-like arrays.
... | python | def is_extension_type(arr):
"""
Check whether an array-like is of a pandas extension class instance.
Extension classes include categoricals, pandas sparse objects (i.e.
classes represented within the pandas library and not ones external
to it like scipy sparse matrices), and datetime-like arrays.
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20,490 | pandas-dev/pandas | pandas/core/dtypes/common.py | is_extension_array_dtype | def is_extension_array_dtype(arr_or_dtype):
"""
Check if an object is a pandas extension array type.
See the :ref:`Use Guide <extending.extension-types>` for more.
Parameters
----------
arr_or_dtype : object
For array-like input, the ``.dtype`` attribute will
be extracted.
... | python | def is_extension_array_dtype(arr_or_dtype):
"""
Check if an object is a pandas extension array type.
See the :ref:`Use Guide <extending.extension-types>` for more.
Parameters
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arr_or_dtype : object
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20,491 | pandas-dev/pandas | pandas/core/dtypes/common.py | _get_dtype | def _get_dtype(arr_or_dtype):
"""
Get the dtype instance associated with an array
or dtype object.
Parameters
----------
arr_or_dtype : array-like
The array-like or dtype object whose dtype we want to extract.
Returns
-------
obj_dtype : The extract dtype instance from the
... | python | def _get_dtype(arr_or_dtype):
"""
Get the dtype instance associated with an array
or dtype object.
Parameters
----------
arr_or_dtype : array-like
The array-like or dtype object whose dtype we want to extract.
Returns
-------
obj_dtype : The extract dtype instance from the
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20,492 | pandas-dev/pandas | pandas/core/dtypes/common.py | infer_dtype_from_object | def infer_dtype_from_object(dtype):
"""
Get a numpy dtype.type-style object for a dtype object.
This methods also includes handling of the datetime64[ns] and
datetime64[ns, TZ] objects.
If no dtype can be found, we return ``object``.
Parameters
----------
dtype : dtype, type
T... | python | def infer_dtype_from_object(dtype):
"""
Get a numpy dtype.type-style object for a dtype object.
This methods also includes handling of the datetime64[ns] and
datetime64[ns, TZ] objects.
If no dtype can be found, we return ``object``.
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----------
dtype : dtype, type
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20,493 | pandas-dev/pandas | pandas/core/dtypes/common.py | _validate_date_like_dtype | def _validate_date_like_dtype(dtype):
"""
Check whether the dtype is a date-like dtype. Raises an error if invalid.
Parameters
----------
dtype : dtype, type
The dtype to check.
Raises
------
TypeError : The dtype could not be casted to a date-like dtype.
ValueError : The d... | python | def _validate_date_like_dtype(dtype):
"""
Check whether the dtype is a date-like dtype. Raises an error if invalid.
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dtype : dtype, type
The dtype to check.
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20,494 | pandas-dev/pandas | pandas/core/dtypes/common.py | pandas_dtype | def pandas_dtype(dtype):
"""
Convert input into a pandas only dtype object or a numpy dtype object.
Parameters
----------
dtype : object to be converted
Returns
-------
np.dtype or a pandas dtype
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# short-circuit
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"""
Convert input into a pandas only dtype object or a numpy dtype object.
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dtype : object to be converted
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-------
np.dtype or a pandas dtype
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20,495 | pandas-dev/pandas | pandas/core/reshape/merge.py | _groupby_and_merge | def _groupby_and_merge(by, on, left, right, _merge_pieces,
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"""
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20,496 | pandas-dev/pandas | pandas/core/reshape/merge.py | merge_asof | def merge_asof(left, right, on=None,
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20,497 | pandas-dev/pandas | pandas/core/reshape/merge.py | _MergeOperation._maybe_restore_index_levels | def _maybe_restore_index_levels(self, result):
"""
Restore index levels specified as `on` parameters
Here we check for cases where `self.left_on` and `self.right_on` pairs
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20,498 | pandas-dev/pandas | pandas/core/reshape/merge.py | _MergeOperation._create_join_index | def _create_join_index(self, index, other_index, indexer,
other_indexer, how='left'):
"""
Create a join index by rearranging one index to match another
Parameters
----------
index: Index being rearranged
other_index: Index used to supply values... | python | def _create_join_index(self, index, other_index, indexer,
other_indexer, how='left'):
"""
Create a join index by rearranging one index to match another
Parameters
----------
index: Index being rearranged
other_index: Index used to supply values... | [
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Parameters
----------
index: Index being rearranged
other_index: Index used to supply values not found in index
indexer: how to rearrange index
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20,499 | pandas-dev/pandas | pandas/core/dtypes/base.py | _DtypeOpsMixin.is_dtype | def is_dtype(cls, dtype):
"""Check if we match 'dtype'.
Parameters
----------
dtype : object
The object to check.
Returns
-------
is_dtype : bool
Notes
-----
The default implementation is True if
1. ``cls.construct_f... | python | def is_dtype(cls, dtype):
"""Check if we match 'dtype'.
Parameters
----------
dtype : object
The object to check.
Returns
-------
is_dtype : bool
Notes
-----
The default implementation is True if
1. ``cls.construct_f... | [
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Parameters
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dtype : object
The object to check.
Returns
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is_dtype : bool
Notes
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The default implementation is True if
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