Sindbad~EG File Manager
"""
Expose public exceptions & warnings
"""
from __future__ import annotations
import ctypes
from pandas._config.config import OptionError
from pandas._libs.tslibs import (
OutOfBoundsDatetime,
OutOfBoundsTimedelta,
)
from pandas.util.version import InvalidVersion
class IntCastingNaNError(ValueError):
"""
Exception raised when converting (``astype``) an array with NaN to an integer type.
Examples
--------
>>> pd.DataFrame(np.array([[1, np.nan], [2, 3]]), dtype="i8")
Traceback (most recent call last):
IntCastingNaNError: Cannot convert non-finite values (NA or inf) to integer
"""
class NullFrequencyError(ValueError):
"""
Exception raised when a ``freq`` cannot be null.
Particularly ``DatetimeIndex.shift``, ``TimedeltaIndex.shift``,
``PeriodIndex.shift``.
Examples
--------
>>> df = pd.DatetimeIndex(["2011-01-01 10:00", "2011-01-01"], freq=None)
>>> df.shift(2)
Traceback (most recent call last):
NullFrequencyError: Cannot shift with no freq
"""
class PerformanceWarning(Warning):
"""
Warning raised when there is a possible performance impact.
Examples
--------
>>> df = pd.DataFrame({"jim": [0, 0, 1, 1],
... "joe": ["x", "x", "z", "y"],
... "jolie": [1, 2, 3, 4]})
>>> df = df.set_index(["jim", "joe"])
>>> df
jolie
jim joe
0 x 1
x 2
1 z 3
y 4
>>> df.loc[(1, 'z')] # doctest: +SKIP
# PerformanceWarning: indexing past lexsort depth may impact performance.
df.loc[(1, 'z')]
jolie
jim joe
1 z 3
"""
class UnsupportedFunctionCall(ValueError):
"""
Exception raised when attempting to call a unsupported numpy function.
For example, ``np.cumsum(groupby_object)``.
Examples
--------
>>> df = pd.DataFrame({"A": [0, 0, 1, 1],
... "B": ["x", "x", "z", "y"],
... "C": [1, 2, 3, 4]}
... )
>>> np.cumsum(df.groupby(["A"]))
Traceback (most recent call last):
UnsupportedFunctionCall: numpy operations are not valid with groupby.
Use .groupby(...).cumsum() instead
"""
class UnsortedIndexError(KeyError):
"""
Error raised when slicing a MultiIndex which has not been lexsorted.
Subclass of `KeyError`.
Examples
--------
>>> df = pd.DataFrame({"cat": [0, 0, 1, 1],
... "color": ["white", "white", "brown", "black"],
... "lives": [4, 4, 3, 7]},
... )
>>> df = df.set_index(["cat", "color"])
>>> df
lives
cat color
0 white 4
white 4
1 brown 3
black 7
>>> df.loc[(0, "black"):(1, "white")]
Traceback (most recent call last):
UnsortedIndexError: 'Key length (2) was greater
than MultiIndex lexsort depth (1)'
"""
class ParserError(ValueError):
"""
Exception that is raised by an error encountered in parsing file contents.
This is a generic error raised for errors encountered when functions like
`read_csv` or `read_html` are parsing contents of a file.
See Also
--------
read_csv : Read CSV (comma-separated) file into a DataFrame.
read_html : Read HTML table into a DataFrame.
Examples
--------
>>> data = '''a,b,c
... cat,foo,bar
... dog,foo,"baz'''
>>> from io import StringIO
>>> pd.read_csv(StringIO(data), skipfooter=1, engine='python')
Traceback (most recent call last):
ParserError: ',' expected after '"'. Error could possibly be due
to parsing errors in the skipped footer rows
"""
class DtypeWarning(Warning):
"""
Warning raised when reading different dtypes in a column from a file.
Raised for a dtype incompatibility. This can happen whenever `read_csv`
or `read_table` encounter non-uniform dtypes in a column(s) of a given
CSV file.
See Also
--------
read_csv : Read CSV (comma-separated) file into a DataFrame.
read_table : Read general delimited file into a DataFrame.
Notes
-----
This warning is issued when dealing with larger files because the dtype
checking happens per chunk read.
Despite the warning, the CSV file is read with mixed types in a single
column which will be an object type. See the examples below to better
understand this issue.
Examples
--------
This example creates and reads a large CSV file with a column that contains
`int` and `str`.
>>> df = pd.DataFrame({'a': (['1'] * 100000 + ['X'] * 100000 +
... ['1'] * 100000),
... 'b': ['b'] * 300000}) # doctest: +SKIP
>>> df.to_csv('test.csv', index=False) # doctest: +SKIP
>>> df2 = pd.read_csv('test.csv') # doctest: +SKIP
... # DtypeWarning: Columns (0) have mixed types
Important to notice that ``df2`` will contain both `str` and `int` for the
same input, '1'.
>>> df2.iloc[262140, 0] # doctest: +SKIP
'1'
>>> type(df2.iloc[262140, 0]) # doctest: +SKIP
<class 'str'>
>>> df2.iloc[262150, 0] # doctest: +SKIP
1
>>> type(df2.iloc[262150, 0]) # doctest: +SKIP
<class 'int'>
One way to solve this issue is using the `dtype` parameter in the
`read_csv` and `read_table` functions to explicit the conversion:
>>> df2 = pd.read_csv('test.csv', sep=',', dtype={'a': str}) # doctest: +SKIP
No warning was issued.
"""
class EmptyDataError(ValueError):
"""
Exception raised in ``pd.read_csv`` when empty data or header is encountered.
Examples
--------
>>> from io import StringIO
>>> empty = StringIO()
>>> pd.read_csv(empty)
Traceback (most recent call last):
EmptyDataError: No columns to parse from file
"""
class ParserWarning(Warning):
"""
Warning raised when reading a file that doesn't use the default 'c' parser.
Raised by `pd.read_csv` and `pd.read_table` when it is necessary to change
parsers, generally from the default 'c' parser to 'python'.
It happens due to a lack of support or functionality for parsing a
particular attribute of a CSV file with the requested engine.
Currently, 'c' unsupported options include the following parameters:
1. `sep` other than a single character (e.g. regex separators)
2. `skipfooter` higher than 0
3. `sep=None` with `delim_whitespace=False`
The warning can be avoided by adding `engine='python'` as a parameter in
`pd.read_csv` and `pd.read_table` methods.
See Also
--------
pd.read_csv : Read CSV (comma-separated) file into DataFrame.
pd.read_table : Read general delimited file into DataFrame.
Examples
--------
Using a `sep` in `pd.read_csv` other than a single character:
>>> import io
>>> csv = '''a;b;c
... 1;1,8
... 1;2,1'''
>>> df = pd.read_csv(io.StringIO(csv), sep='[;,]') # doctest: +SKIP
... # ParserWarning: Falling back to the 'python' engine...
Adding `engine='python'` to `pd.read_csv` removes the Warning:
>>> df = pd.read_csv(io.StringIO(csv), sep='[;,]', engine='python')
"""
class MergeError(ValueError):
"""
Exception raised when merging data.
Subclass of ``ValueError``.
Examples
--------
>>> left = pd.DataFrame({"a": ["a", "b", "b", "d"],
... "b": ["cat", "dog", "weasel", "horse"]},
... index=range(4))
>>> right = pd.DataFrame({"a": ["a", "b", "c", "d"],
... "c": ["meow", "bark", "chirp", "nay"]},
... index=range(4)).set_index("a")
>>> left.join(right, on="a", validate="one_to_one",)
Traceback (most recent call last):
MergeError: Merge keys are not unique in left dataset; not a one-to-one merge
"""
class AbstractMethodError(NotImplementedError):
"""
Raise this error instead of NotImplementedError for abstract methods.
Examples
--------
>>> class Foo:
... @classmethod
... def classmethod(cls):
... raise pd.errors.AbstractMethodError(cls, methodtype="classmethod")
... def method(self):
... raise pd.errors.AbstractMethodError(self)
>>> test = Foo.classmethod()
Traceback (most recent call last):
AbstractMethodError: This classmethod must be defined in the concrete class Foo
>>> test2 = Foo().method()
Traceback (most recent call last):
AbstractMethodError: This classmethod must be defined in the concrete class Foo
"""
def __init__(self, class_instance, methodtype: str = "method") -> None:
types = {"method", "classmethod", "staticmethod", "property"}
if methodtype not in types:
raise ValueError(
f"methodtype must be one of {methodtype}, got {types} instead."
)
self.methodtype = methodtype
self.class_instance = class_instance
def __str__(self) -> str:
if self.methodtype == "classmethod":
name = self.class_instance.__name__
else:
name = type(self.class_instance).__name__
return f"This {self.methodtype} must be defined in the concrete class {name}"
class NumbaUtilError(Exception):
"""
Error raised for unsupported Numba engine routines.
Examples
--------
>>> df = pd.DataFrame({"key": ["a", "a", "b", "b"], "data": [1, 2, 3, 4]},
... columns=["key", "data"])
>>> def incorrect_function(x):
... return sum(x) * 2.7
>>> df.groupby("key").agg(incorrect_function, engine="numba")
Traceback (most recent call last):
NumbaUtilError: The first 2 arguments to incorrect_function
must be ['values', 'index']
"""
class DuplicateLabelError(ValueError):
"""
Error raised when an operation would introduce duplicate labels.
Examples
--------
>>> s = pd.Series([0, 1, 2], index=['a', 'b', 'c']).set_flags(
... allows_duplicate_labels=False
... )
>>> s.reindex(['a', 'a', 'b'])
Traceback (most recent call last):
...
DuplicateLabelError: Index has duplicates.
positions
label
a [0, 1]
"""
class InvalidIndexError(Exception):
"""
Exception raised when attempting to use an invalid index key.
Examples
--------
>>> idx = pd.MultiIndex.from_product([["x", "y"], [0, 1]])
>>> df = pd.DataFrame([[1, 1, 2, 2],
... [3, 3, 4, 4]], columns=idx)
>>> df
x y
0 1 0 1
0 1 1 2 2
1 3 3 4 4
>>> df[:, 0]
Traceback (most recent call last):
InvalidIndexError: (slice(None, None, None), 0)
"""
class DataError(Exception):
"""
Exceptionn raised when performing an operation on non-numerical data.
For example, calling ``ohlc`` on a non-numerical column or a function
on a rolling window.
Examples
--------
>>> ser = pd.Series(['a', 'b', 'c'])
>>> ser.rolling(2).sum()
Traceback (most recent call last):
DataError: No numeric types to aggregate
"""
class SpecificationError(Exception):
"""
Exception raised by ``agg`` when the functions are ill-specified.
The exception raised in two scenarios.
The first way is calling ``agg`` on a
Dataframe or Series using a nested renamer (dict-of-dict).
The second way is calling ``agg`` on a Dataframe with duplicated functions
names without assigning column name.
Examples
--------
>>> df = pd.DataFrame({'A': [1, 1, 1, 2, 2],
... 'B': range(5),
... 'C': range(5)})
>>> df.groupby('A').B.agg({'foo': 'count'}) # doctest: +SKIP
... # SpecificationError: nested renamer is not supported
>>> df.groupby('A').agg({'B': {'foo': ['sum', 'max']}}) # doctest: +SKIP
... # SpecificationError: nested renamer is not supported
>>> df.groupby('A').agg(['min', 'min']) # doctest: +SKIP
... # SpecificationError: nested renamer is not supported
"""
class SettingWithCopyError(ValueError):
"""
Exception raised when trying to set on a copied slice from a ``DataFrame``.
The ``mode.chained_assignment`` needs to be set to set to 'raise.' This can
happen unintentionally when chained indexing.
For more information on evaluation order,
see :ref:`the user guide<indexing.evaluation_order>`.
For more information on view vs. copy,
see :ref:`the user guide<indexing.view_versus_copy>`.
Examples
--------
>>> pd.options.mode.chained_assignment = 'raise'
>>> df = pd.DataFrame({'A': [1, 1, 1, 2, 2]}, columns=['A'])
>>> df.loc[0:3]['A'] = 'a' # doctest: +SKIP
... # SettingWithCopyError: A value is trying to be set on a copy of a...
"""
class SettingWithCopyWarning(Warning):
"""
Warning raised when trying to set on a copied slice from a ``DataFrame``.
The ``mode.chained_assignment`` needs to be set to set to 'warn.'
'Warn' is the default option. This can happen unintentionally when
chained indexing.
For more information on evaluation order,
see :ref:`the user guide<indexing.evaluation_order>`.
For more information on view vs. copy,
see :ref:`the user guide<indexing.view_versus_copy>`.
Examples
--------
>>> df = pd.DataFrame({'A': [1, 1, 1, 2, 2]}, columns=['A'])
>>> df.loc[0:3]['A'] = 'a' # doctest: +SKIP
... # SettingWithCopyWarning: A value is trying to be set on a copy of a...
"""
class ChainedAssignmentError(Warning):
"""
Warning raised when trying to set using chained assignment.
When the ``mode.copy_on_write`` option is enabled, chained assignment can
never work. In such a situation, we are always setting into a temporary
object that is the result of an indexing operation (getitem), which under
Copy-on-Write always behaves as a copy. Thus, assigning through a chain
can never update the original Series or DataFrame.
For more information on view vs. copy,
see :ref:`the user guide<indexing.view_versus_copy>`.
Examples
--------
>>> pd.options.mode.copy_on_write = True
>>> df = pd.DataFrame({'A': [1, 1, 1, 2, 2]}, columns=['A'])
>>> df["A"][0:3] = 10 # doctest: +SKIP
... # ChainedAssignmentError: ...
>>> pd.options.mode.copy_on_write = False
"""
_chained_assignment_msg = (
"A value is trying to be set on a copy of a DataFrame or Series "
"through chained assignment.\n"
"When using the Copy-on-Write mode, such chained assignment never works "
"to update the original DataFrame or Series, because the intermediate "
"object on which we are setting values always behaves as a copy.\n\n"
"Try using '.loc[row_indexer, col_indexer] = value' instead, to perform "
"the assignment in a single step.\n\n"
"See the caveats in the documentation: "
"https://pandas.pydata.org/pandas-docs/stable/user_guide/"
"indexing.html#returning-a-view-versus-a-copy"
)
_chained_assignment_method_msg = (
"A value is trying to be set on a copy of a DataFrame or Series "
"through chained assignment using an inplace method.\n"
"When using the Copy-on-Write mode, such inplace method never works "
"to update the original DataFrame or Series, because the intermediate "
"object on which we are setting values always behaves as a copy.\n\n"
"For example, when doing 'df[col].method(value, inplace=True)', try "
"using 'df.method({col: value}, inplace=True)' instead, to perform "
"the operation inplace on the original object.\n\n"
)
_chained_assignment_warning_msg = (
"ChainedAssignmentError: behaviour will change in pandas 3.0!\n"
"You are setting values through chained assignment. Currently this works "
"in certain cases, but when using Copy-on-Write (which will become the "
"default behaviour in pandas 3.0) this will never work to update the "
"original DataFrame or Series, because the intermediate object on which "
"we are setting values will behave as a copy.\n"
"A typical example is when you are setting values in a column of a "
"DataFrame, like:\n\n"
'df["col"][row_indexer] = value\n\n'
'Use `df.loc[row_indexer, "col"] = values` instead, to perform the '
"assignment in a single step and ensure this keeps updating the original `df`.\n\n"
"See the caveats in the documentation: "
"https://pandas.pydata.org/pandas-docs/stable/user_guide/"
"indexing.html#returning-a-view-versus-a-copy\n"
)
_chained_assignment_warning_method_msg = (
"A value is trying to be set on a copy of a DataFrame or Series "
"through chained assignment using an inplace method.\n"
"The behavior will change in pandas 3.0. This inplace method will "
"never work because the intermediate object on which we are setting "
"values always behaves as a copy.\n\n"
"For example, when doing 'df[col].method(value, inplace=True)', try "
"using 'df.method({col: value}, inplace=True)' or "
"df[col] = df[col].method(value) instead, to perform "
"the operation inplace on the original object.\n\n"
)
def _check_cacher(obj):
# This is a mess, selection paths that return a view set the _cacher attribute
# on the Series; most of them also set _item_cache which adds 1 to our relevant
# reference count, but iloc does not, so we have to check if we are actually
# in the item cache
if hasattr(obj, "_cacher"):
parent = obj._cacher[1]()
# parent could be dead
if parent is None:
return False
if hasattr(parent, "_item_cache"):
if obj._cacher[0] in parent._item_cache:
# Check if we are actually the item from item_cache, iloc creates a
# new object
return obj is parent._item_cache[obj._cacher[0]]
return False
class NumExprClobberingError(NameError):
"""
Exception raised when trying to use a built-in numexpr name as a variable name.
``eval`` or ``query`` will throw the error if the engine is set
to 'numexpr'. 'numexpr' is the default engine value for these methods if the
numexpr package is installed.
Examples
--------
>>> df = pd.DataFrame({'abs': [1, 1, 1]})
>>> df.query("abs > 2") # doctest: +SKIP
... # NumExprClobberingError: Variables in expression "(abs) > (2)" overlap...
>>> sin, a = 1, 2
>>> pd.eval("sin + a", engine='numexpr') # doctest: +SKIP
... # NumExprClobberingError: Variables in expression "(sin) + (a)" overlap...
"""
class UndefinedVariableError(NameError):
"""
Exception raised by ``query`` or ``eval`` when using an undefined variable name.
It will also specify whether the undefined variable is local or not.
Examples
--------
>>> df = pd.DataFrame({'A': [1, 1, 1]})
>>> df.query("A > x") # doctest: +SKIP
... # UndefinedVariableError: name 'x' is not defined
>>> df.query("A > @y") # doctest: +SKIP
... # UndefinedVariableError: local variable 'y' is not defined
>>> pd.eval('x + 1') # doctest: +SKIP
... # UndefinedVariableError: name 'x' is not defined
"""
def __init__(self, name: str, is_local: bool | None = None) -> None:
base_msg = f"{repr(name)} is not defined"
if is_local:
msg = f"local variable {base_msg}"
else:
msg = f"name {base_msg}"
super().__init__(msg)
class IndexingError(Exception):
"""
Exception is raised when trying to index and there is a mismatch in dimensions.
Examples
--------
>>> df = pd.DataFrame({'A': [1, 1, 1]})
>>> df.loc[..., ..., 'A'] # doctest: +SKIP
... # IndexingError: indexer may only contain one '...' entry
>>> df = pd.DataFrame({'A': [1, 1, 1]})
>>> df.loc[1, ..., ...] # doctest: +SKIP
... # IndexingError: Too many indexers
>>> df[pd.Series([True], dtype=bool)] # doctest: +SKIP
... # IndexingError: Unalignable boolean Series provided as indexer...
>>> s = pd.Series(range(2),
... index = pd.MultiIndex.from_product([["a", "b"], ["c"]]))
>>> s.loc["a", "c", "d"] # doctest: +SKIP
... # IndexingError: Too many indexers
"""
class PyperclipException(RuntimeError):
"""
Exception raised when clipboard functionality is unsupported.
Raised by ``to_clipboard()`` and ``read_clipboard()``.
"""
class PyperclipWindowsException(PyperclipException):
"""
Exception raised when clipboard functionality is unsupported by Windows.
Access to the clipboard handle would be denied due to some other
window process is accessing it.
"""
def __init__(self, message: str) -> None:
# attr only exists on Windows, so typing fails on other platforms
message += f" ({ctypes.WinError()})" # type: ignore[attr-defined]
super().__init__(message)
class CSSWarning(UserWarning):
"""
Warning is raised when converting css styling fails.
This can be due to the styling not having an equivalent value or because the
styling isn't properly formatted.
Examples
--------
>>> df = pd.DataFrame({'A': [1, 1, 1]})
>>> df.style.applymap(
... lambda x: 'background-color: blueGreenRed;'
... ).to_excel('styled.xlsx') # doctest: +SKIP
CSSWarning: Unhandled color format: 'blueGreenRed'
>>> df.style.applymap(
... lambda x: 'border: 1px solid red red;'
... ).to_excel('styled.xlsx') # doctest: +SKIP
CSSWarning: Unhandled color format: 'blueGreenRed'
"""
class PossibleDataLossError(Exception):
"""
Exception raised when trying to open a HDFStore file when already opened.
Examples
--------
>>> store = pd.HDFStore('my-store', 'a') # doctest: +SKIP
>>> store.open("w") # doctest: +SKIP
... # PossibleDataLossError: Re-opening the file [my-store] with mode [a]...
"""
class ClosedFileError(Exception):
"""
Exception is raised when trying to perform an operation on a closed HDFStore file.
Examples
--------
>>> store = pd.HDFStore('my-store', 'a') # doctest: +SKIP
>>> store.close() # doctest: +SKIP
>>> store.keys() # doctest: +SKIP
... # ClosedFileError: my-store file is not open!
"""
class IncompatibilityWarning(Warning):
"""
Warning raised when trying to use where criteria on an incompatible HDF5 file.
"""
class AttributeConflictWarning(Warning):
"""
Warning raised when index attributes conflict when using HDFStore.
Occurs when attempting to append an index with a different
name than the existing index on an HDFStore or attempting to append an index with a
different frequency than the existing index on an HDFStore.
Examples
--------
>>> idx1 = pd.Index(['a', 'b'], name='name1')
>>> df1 = pd.DataFrame([[1, 2], [3, 4]], index=idx1)
>>> df1.to_hdf('file', 'data', 'w', append=True) # doctest: +SKIP
>>> idx2 = pd.Index(['c', 'd'], name='name2')
>>> df2 = pd.DataFrame([[5, 6], [7, 8]], index=idx2)
>>> df2.to_hdf('file', 'data', 'a', append=True) # doctest: +SKIP
AttributeConflictWarning: the [index_name] attribute of the existing index is
[name1] which conflicts with the new [name2]...
"""
class DatabaseError(OSError):
"""
Error is raised when executing sql with bad syntax or sql that throws an error.
Examples
--------
>>> from sqlite3 import connect
>>> conn = connect(':memory:')
>>> pd.read_sql('select * test', conn) # doctest: +SKIP
... # DatabaseError: Execution failed on sql 'test': near "test": syntax error
"""
class PossiblePrecisionLoss(Warning):
"""
Warning raised by to_stata on a column with a value outside or equal to int64.
When the column value is outside or equal to the int64 value the column is
converted to a float64 dtype.
Examples
--------
>>> df = pd.DataFrame({"s": pd.Series([1, 2**53], dtype=np.int64)})
>>> df.to_stata('test') # doctest: +SKIP
... # PossiblePrecisionLoss: Column converted from int64 to float64...
"""
class ValueLabelTypeMismatch(Warning):
"""
Warning raised by to_stata on a category column that contains non-string values.
Examples
--------
>>> df = pd.DataFrame({"categories": pd.Series(["a", 2], dtype="category")})
>>> df.to_stata('test') # doctest: +SKIP
... # ValueLabelTypeMismatch: Stata value labels (pandas categories) must be str...
"""
class InvalidColumnName(Warning):
"""
Warning raised by to_stata the column contains a non-valid stata name.
Because the column name is an invalid Stata variable, the name needs to be
converted.
Examples
--------
>>> df = pd.DataFrame({"0categories": pd.Series([2, 2])})
>>> df.to_stata('test') # doctest: +SKIP
... # InvalidColumnName: Not all pandas column names were valid Stata variable...
"""
class CategoricalConversionWarning(Warning):
"""
Warning is raised when reading a partial labeled Stata file using a iterator.
Examples
--------
>>> from pandas.io.stata import StataReader
>>> with StataReader('dta_file', chunksize=2) as reader: # doctest: +SKIP
... for i, block in enumerate(reader):
... print(i, block)
... # CategoricalConversionWarning: One or more series with value labels...
"""
class LossySetitemError(Exception):
"""
Raised when trying to do a __setitem__ on an np.ndarray that is not lossless.
Notes
-----
This is an internal error.
"""
class NoBufferPresent(Exception):
"""
Exception is raised in _get_data_buffer to signal that there is no requested buffer.
"""
class InvalidComparison(Exception):
"""
Exception is raised by _validate_comparison_value to indicate an invalid comparison.
Notes
-----
This is an internal error.
"""
__all__ = [
"AbstractMethodError",
"AttributeConflictWarning",
"CategoricalConversionWarning",
"ClosedFileError",
"CSSWarning",
"DatabaseError",
"DataError",
"DtypeWarning",
"DuplicateLabelError",
"EmptyDataError",
"IncompatibilityWarning",
"IntCastingNaNError",
"InvalidColumnName",
"InvalidComparison",
"InvalidIndexError",
"InvalidVersion",
"IndexingError",
"LossySetitemError",
"MergeError",
"NoBufferPresent",
"NullFrequencyError",
"NumbaUtilError",
"NumExprClobberingError",
"OptionError",
"OutOfBoundsDatetime",
"OutOfBoundsTimedelta",
"ParserError",
"ParserWarning",
"PerformanceWarning",
"PossibleDataLossError",
"PossiblePrecisionLoss",
"PyperclipException",
"PyperclipWindowsException",
"SettingWithCopyError",
"SettingWithCopyWarning",
"SpecificationError",
"UndefinedVariableError",
"UnsortedIndexError",
"UnsupportedFunctionCall",
"ValueLabelTypeMismatch",
]
Sindbad File Manager Version 1.0, Coded By Sindbad EG ~ The Terrorists