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value | url stringlengths 87 315 | code_tokens listlengths 19 28.4k | sha stringlengths 40 40 |
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train | Docstring.method_returns_something | Check if the docstrings method can return something.
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bool
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'''
Check if the docstrings method can return something.
Bare returns, returns valued None and returns from nested functions are
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-------
bool
Whether the docstrings method can return somethin... | def method_returns_something(self):
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train | ExcelWriter.check_extension | checks that path's extension against the Writer's supported
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The type may either be String, Expr, or list-like of Exprs.
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----------
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train | maybe_expression | loose checking if s is a pytables-acceptable expression | pandas/core/computation/pytables.py | def maybe_expression(s):
""" loose checking if s is a pytables-acceptable expression """
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train | BinOp.conform | inplace conform rhs | pandas/core/computation/pytables.py | def conform(self, rhs):
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train | BinOp.generate | create and return the op string for this TermValue | pandas/core/computation/pytables.py | def generate(self, v):
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train | BinOp.convert_value | convert the expression that is in the term to something that is
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train | FilterBinOp.invert | invert the filter | pandas/core/computation/pytables.py | def invert(self):
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train | Expr.evaluate | create and return the numexpr condition and filter | pandas/core/computation/pytables.py | def evaluate(self):
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train | TermValue.tostring | quote the string if not encoded
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train | _ensure_decoded | if we have bytes, decode them to unicode | pandas/core/computation/common.py | def _ensure_decoded(s):
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if isinstance(s, (np.bytes_, bytes)):
s = s.decode(pd.get_option('display.encoding'))
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train | _result_type_many | wrapper around numpy.result_type which overcomes the NPY_MAXARGS (32)
argument limit | pandas/core/computation/common.py | def _result_type_many(*arrays_and_dtypes):
""" wrapper around numpy.result_type which overcomes the NPY_MAXARGS (32)
argument limit """
try:
return np.result_type(*arrays_and_dtypes)
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try:
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train | validate_argmin_with_skipna | If 'Series.argmin' is called via the 'numpy' library,
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train | validate_argmax_with_skipna | If 'Series.argmax' is called via the 'numpy' library,
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"""
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
'skipna' parameter is either an instance of ndarray or
is None, since '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
'skipna' parameter is either an instance of ndarray or
is None, since 'skipna' ... | [
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train | validate_argsort_with_ascending | 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 'ascending' itself should
be a boolean | pandas/compat/numpy/function.py | 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... | 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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train | validate_clip_with_axis | 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
'axis' itself should either be an integer or None | pandas/compat/numpy/function.py | def validate_clip_with_axis(axis, args, kwargs):
"""
If 'NDFrame.clip' is called via the numpy library, the third
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"... | def validate_clip_with_axis(axis, args, kwargs):
"""
If 'NDFrame.clip' is called via the numpy library, the third
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train | validate_cum_func_with_skipna | 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 | pandas/compat/numpy/function.py | 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... | 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
"""
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train | validate_take_with_convert | 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
an instance of ndarray or is None | pandas/compat/numpy/function.py | 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
an instance of ndarray or is None
"""
if isin... | 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
an instance of ndarray or is None
"""
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train | validate_groupby_func | 'args' and 'kwargs' should be empty, except for allowed
kwargs because all of
their necessary parameters are explicitly listed in
the function signature | pandas/compat/numpy/function.py | 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... | 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:
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train | validate_resampler_func | 'args' and 'kwargs' should be empty because all of
their necessary parameters are explicitly listed in
the function signature | pandas/compat/numpy/function.py | def validate_resampler_func(method, args, kwargs):
"""
'args' and 'kwargs' should be empty because all of
their necessary parameters are explicitly listed in
the function signature
"""
if len(args) + len(kwargs) > 0:
if method in RESAMPLER_NUMPY_OPS:
raise UnsupportedFunction... | def validate_resampler_func(method, args, kwargs):
"""
'args' and 'kwargs' should be empty because all of
their necessary parameters are explicitly listed in
the function signature
"""
if len(args) + len(kwargs) > 0:
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train | validate_minmax_axis | Ensure that the axis argument passed to min, max, argmin, or argmax is
zero or None, as otherwise it will be incorrectly ignored.
Parameters
----------
axis : int or None
Raises
------
ValueError | pandas/compat/numpy/function.py | def validate_minmax_axis(axis):
"""
Ensure that the axis argument passed to min, max, argmin, or argmax is
zero or None, as otherwise it will be incorrectly ignored.
Parameters
----------
axis : int or None
Raises
------
ValueError
"""
ndim = 1 # hard-coded for Index
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"""
Ensure that the axis argument passed to min, max, argmin, or argmax is
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Parameters
----------
axis : int or None
Raises
------
ValueError
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train | to_msgpack | msgpack (serialize) object to input file path
THIS IS AN EXPERIMENTAL LIBRARY and the storage format
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Parameters
----------
path_or_buf : string File path, buffer-like, or None
if None, return generated string
args : an object or objec... | pandas/io/packers.py | def to_msgpack(path_or_buf, *args, **kwargs):
"""
msgpack (serialize) object to input file path
THIS IS AN EXPERIMENTAL LIBRARY and the storage format
may not be stable until a future release.
Parameters
----------
path_or_buf : string File path, buffer-like, or None
if N... | def to_msgpack(path_or_buf, *args, **kwargs):
"""
msgpack (serialize) object to input file path
THIS IS AN EXPERIMENTAL LIBRARY and the storage format
may not be stable until a future release.
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train | read_msgpack | Load msgpack pandas object from the specified
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THIS IS AN EXPERIMENTAL LIBRARY and the storage format
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Parameters
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path_or_buf : string File path, BytesIO like or string
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"""
Load msgpack pandas object from the specified
file path
THIS IS AN EXPERIMENTAL LIBRARY and the storage format
may not be stable until a future release.
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----------
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"""
Load msgpack pandas object from the specified
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THIS IS AN EXPERIMENTAL LIBRARY and the storage format
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train | dtype_for | return my dtype mapping, whether number or name | pandas/io/packers.py | def dtype_for(t):
""" return my dtype mapping, whether number or name """
if t in dtype_dict:
return dtype_dict[t]
return np.typeDict.get(t, t) | def dtype_for(t):
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train | c2f | Convert strings to complex number instance with specified numpy type. | pandas/io/packers.py | def c2f(r, i, ctype_name):
"""
Convert strings to complex number instance with specified numpy type.
"""
ftype = c2f_dict[ctype_name]
return np.typeDict[ctype_name](ftype(r) + 1j * ftype(i)) | def c2f(r, i, ctype_name):
"""
Convert strings to complex number instance with specified numpy type.
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ftype = c2f_dict[ctype_name]
return np.typeDict[ctype_name](ftype(r) + 1j * ftype(i)) | [
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train | convert | convert the numpy values to a list | pandas/io/packers.py | 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()
if needs_i8_conversion(dtype):
values = values.view('i8')
v = values.ravel()... | 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()
if needs_i8_conversion(dtype):
values = values.view('i8')
v = values.ravel()... | [
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train | encode | Data encoder | pandas/io/packers.py | def encode(obj):
"""
Data encoder
"""
tobj = type(obj)
if isinstance(obj, Index):
if isinstance(obj, RangeIndex):
return {'typ': 'range_index',
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"""
Data encoder
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Decoder for deserializing numpy data types.
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return obj
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train | read_json | Convert a JSON string to pandas object.
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train | JsonReader._preprocess_data | At this point, the data either has a `read` attribute (e.g. a file
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train | JsonReader._combine_lines | Combines a list of JSON objects into one JSON object. | pandas/io/json/json.py | def _combine_lines(self, lines):
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lines = filter(None, map(lambda x: x.strip(), lines))
return '[' + ','.join(lines) + ']' | def _combine_lines(self, lines):
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train | JsonReader.read | Read the whole JSON input into a pandas object. | pandas/io/json/json.py | def read(self):
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train | JsonReader._get_object_parser | Parses a json document into a pandas object. | pandas/io/json/json.py | def _get_object_parser(self, json):
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Parses a json document into a pandas object.
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dtype = self.dtype
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train | Parser.check_keys_split | Checks that dict has only the appropriate keys for orient='split'. | pandas/io/json/json.py | 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))
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train | Parser._convert_axes | Try to convert axes. | pandas/io/json/json.py | def _convert_axes(self):
"""
Try to convert axes.
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train | FrameParser._process_converter | Take a conversion function and possibly recreate the frame. | pandas/io/json/json.py | def _process_converter(self, f, filt=None):
"""
Take a conversion function and possibly recreate the frame.
"""
if filt is None:
filt = lambda col, c: True
needs_new_obj = False
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train | format_array | Format an array for printing.
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----------
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digits
space
justify
decimal
leading_space : bool, optional
Whether the array should be formatted with a leading space.
When an array as a column of a Series or DataFrame... | pandas/io/formats/format.py | def format_array(values, formatter, float_format=None, na_rep='NaN',
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leading_space=None):
"""
Format an array for printing.
Parameters
----------
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Format an array for printing.
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train | format_percentiles | Outputs rounded and formatted percentiles.
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percentiles : list-like, containing floats from interval [0,1]
Returns
-------
formatted : list of strings
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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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Rounding precision is chosen so that: (1) if any... | def format_percentiles(percentiles):
"""
Outputs rounded and formatted percentiles.
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percentiles : list-like, containing floats from interval [0,1]
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-------
formatted : list of strings
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-----
Rounding precision is chosen so that: (1) if any... | [
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train | _get_format_timedelta64 | Return a formatter function for a range of timedeltas.
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Return a formatter function for a range of timedeltas.
These will all have the same format argument
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train | _trim_zeros_complex | Separates the real and imaginary parts from the complex number, and
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"""
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):
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Separates the real and imaginary parts from the complex number, and
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train | set_eng_float_format | Alter default behavior on how float is formatted in DataFrame.
Format float in engineering format. By accuracy, we mean the number of
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See also EngFormatter. | pandas/io/formats/format.py | def set_eng_float_format(accuracy=3, use_eng_prefix=False):
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Alter default behavior on how float is formatted in DataFrame.
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train | get_level_lengths | For each index in each level the function returns lengths of indexes.
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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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----------
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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----------
levels : list of lists
List of values on for level.
sentinel : string, optional
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train | buffer_put_lines | Appends lines to a buffer.
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buf
The buffer to write to
lines
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"""
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):
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Appends lines to a buffer.
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----------
buf
The buffer to write to
lines
The lines to append.
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train | EastAsianTextAdjustment.len | Calculate display width considering unicode East Asian Width | pandas/io/formats/format.py | 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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"""
Calculate display width considering unicode East Asian Width
"""
if not isinstance(text, str):
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train | DataFrameFormatter._to_str_columns | Render a DataFrame to a list of columns (as lists of strings). | pandas/io/formats/format.py | def _to_str_columns(self):
"""
Render a DataFrame to a list of columns (as lists of strings).
"""
frame = self.tr_frame
# may include levels names also
str_index = self._get_formatted_index(frame)
if not is_list_like(self.header) and not self.header:
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"""
Render a DataFrame to a list of columns (as lists of strings).
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frame = self.tr_frame
# may include levels names also
str_index = self._get_formatted_index(frame)
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train | DataFrameFormatter.to_string | Render a DataFrame to a console-friendly tabular output. | pandas/io/formats/format.py | def to_string(self):
"""
Render a DataFrame to a console-friendly tabular output.
"""
from pandas import Series
frame = self.frame
if len(frame.columns) == 0 or len(frame.index) == 0:
info_line = ('Empty {name}\nColumns: {col}\nIndex: {idx}'
... | def to_string(self):
"""
Render a DataFrame to a console-friendly tabular output.
"""
from pandas import Series
frame = self.frame
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info_line = ('Empty {name}\nColumns: {col}\nIndex: {idx}'
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train | DataFrameFormatter.to_latex | Render a DataFrame to a LaTeX tabular/longtable environment output. | pandas/io/formats/format.py | def to_latex(self, column_format=None, longtable=False, encoding=None,
multicolumn=False, multicolumn_format=None, multirow=False):
"""
Render a DataFrame to a LaTeX tabular/longtable environment output.
"""
from pandas.io.formats.latex import LatexFormatter
lat... | def to_latex(self, column_format=None, longtable=False, encoding=None,
multicolumn=False, multicolumn_format=None, multirow=False):
"""
Render a DataFrame to a LaTeX tabular/longtable environment output.
"""
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train | DataFrameFormatter.to_html | Render a DataFrame to a html table.
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----------
classes : str or list-like
classes to include in the `class` attribute of the opening
``<table>`` tag, in addition to the default "dataframe".
notebook : {True, False}, optional, default False
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"""
Render a DataFrame to a html table.
Parameters
----------
classes : str or list-like
classes to include in the `class` attribute of the opening
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Render a DataFrame to a html table.
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----------
classes : str or list-like
classes to include in the `class` attribute of the opening
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train | FloatArrayFormatter._value_formatter | Returns a function to be applied on each value to format it | pandas/io/formats/format.py | 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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train | FloatArrayFormatter.get_result_as_array | Returns the float values converted into strings using
the parameters given at initialisation, as a numpy array | pandas/io/formats/format.py | 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... | def get_result_as_array(self):
"""
Returns the float values converted into strings using
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"""
if self.formatter is not None:
return np.array([self.formatter(x) for x in self.values])
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train | Datetime64Formatter._format_strings | we by definition have DO NOT have a TZ | pandas/io/formats/format.py | def _format_strings(self):
""" we by definition have DO NOT have a TZ """
values = self.values
if not isinstance(values, DatetimeIndex):
values = DatetimeIndex(values)
if self.formatter is not None and callable(self.formatter):
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""" we by definition have DO NOT have a TZ """
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values = DatetimeIndex(values)
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end : numeric or datetime-like, default None
Right bound for generating intervals
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train | _can_use_numexpr | return a boolean if we WILL be using numexpr | pandas/core/computation/expressions.py | def _can_use_numexpr(op, op_str, a, b, dtype_check):
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b : right operand
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Name of sheet which will contain DataFrame
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train | to_feather | Write a DataFrame to the feather-format
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train | read_feather | Load a feather-format object from the file path
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path : string file path, or file-like object
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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
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Load a feather-format object from the file path
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path : string file path, or file-like object
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train | generate_regular_range | 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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last 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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first point of produced date range
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train | _generate_range_overflow_safe | Calculate the second endpoint for passing to np.arange, checking
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endpoint : int
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train | _generate_range_overflow_safe_signed | A special case for _generate_range_overflow_safe where `periods * stride`
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train | set_locale | Context manager for temporarily setting a locale.
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A string of the form <language_country>.<encoding>. For example to set
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Context manager for temporarily setting a locale.
Parameters
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new_locale : str or tuple
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train | can_set_locale | Check to see if we can set a locale, and subsequently get the locale,
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lc : str
The locale to attempt to set.
lc_var : int, default `locale.LC_ALL`
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train | _valid_locales | Return a list of normalized locales that do not throw an ``Exception``
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locales : str
A string where each locale is separated by a newline.
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Whether to call ``locale.normalize`` on each locale.
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locales : str
A string where each locale is separated by a newline.
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train | get_locales | Get all the locales that are available on the system.
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Get all the locales that are available on the system.
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train | ensure_float | Ensure that an array object has a float dtype if possible.
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The array whose data type we want to enforce as float.
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arr : array-like
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Ensure that an array object has a float dtype if possible.
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train | ensure_categorical | Ensure that an array-like object is a Categorical (if not already).
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arr : array-like
The array that we want to convert into a Categorical.
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cat_arr : The original array cast as a Categorical. If it already
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arr : array-like
The array that we want to convert into a Categorical.
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Ensure that an array-like object is a Categorical (if not already).
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The array that we want to convert into a Categorical.
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train | ensure_int64_or_float64 | Ensure that an dtype array of some integer dtype
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If it's not possible, potentially because of overflow,
convert the array to float64 instead.
Parameters
----------
arr : array-like
The array whose data type we want to enforce.
copy: boolean
... | pandas/core/dtypes/common.py | def ensure_int64_or_float64(arr, copy=False):
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Ensure that an dtype array of some integer dtype
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If it's not possible, potentially because of overflow,
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----------
arr : array-like
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"""
Ensure that an dtype array of some integer dtype
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train | classes_and_not_datetimelike | evaluate if the tipo is a subclass of the klasses
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"""
evaluate if the tipo is a subclass of the klasses
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"""
return lambda tipo: (issubclass(tipo, klasses) and
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train | is_sparse | 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
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arr : array-like
Array-like to check.
Returns
----... | pandas/core/dtypes/common.py | 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.
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----------
arr : array-like
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Check whether an array-like is a 1-D pandas sparse array.
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train | is_scipy_sparse | Check whether an array-like is a scipy.sparse.spmatrix instance.
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arr : array-like
The array-like to check.
Returns
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boolean
Whether or not the array-like is a scipy.sparse.spmatrix instance.
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Check whether an array-like is a scipy.sparse.spmatrix instance.
Parameters
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arr : array-like
The array-like to check.
Returns
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boolean
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"""
Check whether an array-like is a scipy.sparse.spmatrix instance.
Parameters
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arr : array-like
The array-like to check.
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train | is_offsetlike | 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.DateOffset(days=1))
True
>>> is_offsetlik... | pandas/core/dtypes/common.py | 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... | 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
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--------
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train | is_period | 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
--------
>>> is_period([1, 2, 3])
... | pandas/core/dtypes/common.py | 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
--------... | 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
-------
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Whether or not the array-like is a periodical index.
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train | is_string_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
-------
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Whether or not the array or dtype is of the string dtype.
Examples
--------
>>> is_string_dtype(st... | pandas/core/dtypes/common.py | 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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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
-------
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Whether or not the array or dtype is of the string dtype.
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train | is_period_arraylike | 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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Examples
--------
... | pandas/core/dtypes/common.py | 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... | 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.
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-------
boolean
Whether or not the array-like is a periodical array-like or
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train | is_datetime_arraylike | 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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Examples
--------
>>> is_... | pandas/core/dtypes/common.py | 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... | 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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train | is_datetimelike | 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
-------
boolean
Wheth... | pandas/core/dtypes/common.py | 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... | 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
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----------
arr : array-like
The array-like to check.
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train | is_dtype_equal | 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_dtype_equal(int, float)
False
>>> is_dtype... | pandas/core/dtypes/common.py | 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... | 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... | [
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train | is_dtype_union_equal | Check whether two arrays have compatible dtypes to do a union.
numpy types are checked with ``is_dtype_equal``. Extension types are
checked separately.
Parameters
----------
source : The first dtype to compare
target : The second dtype to compare
Returns
----------
boolean
... | pandas/core/dtypes/common.py | 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... | def is_dtype_union_equal(source, target):
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Check whether two arrays have compatible dtypes to do a union.
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----------
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train | is_datetime64_ns_dtype | Check whether the provided array or dtype is of the datetime64[ns] 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 datetime64[ns] dtype.
Examples
--------
>>> is... | pandas/core/dtypes/common.py | def is_datetime64_ns_dtype(arr_or_dtype):
"""
Check whether the provided array or dtype is of the datetime64[ns] 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 datet... | def is_datetime64_ns_dtype(arr_or_dtype):
"""
Check whether the provided array or dtype is of the datetime64[ns] dtype.
Parameters
----------
arr_or_dtype : array-like
The array or dtype to check.
Returns
-------
boolean
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train | is_numeric_v_string_like | 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 : array-like, scalar
The second object t... | pandas/core/dtypes/common.py | 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 ... | 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.
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train | is_datetimelike_v_numeric | 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
The second object to check.
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... | pandas/core/dtypes/common.py | 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
... | 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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train | is_datetimelike_v_object | Check if we are comparing a datetime-like object to an object instance.
Parameters
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a : array-like, scalar
The first object to check.
b : array-like, scalar
The second object to check.
Returns
-------
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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
The second object to check.
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-------
boolean
... | def is_datetimelike_v_object(a, b):
"""
Check if we are comparing a datetime-like object to an object instance.
Parameters
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a : array-like, scalar
The first object to check.
b : array-like, scalar
The second object to check.
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-------
boolean
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train | needs_i8_conversion | 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 check.
Returns
-------
boolean
W... | pandas/core/dtypes/common.py | 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... | 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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"None",
":",
"return",
"False",
"return",
"(",
"is_datetime_or_timedelta_dtype",
"(",
"arr_or_dtype",
")",
"or",
"is_datetime64tz_dtype",
"(",
"arr_or_dtype",
")",
"or",
"is_per... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 |
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