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videollama2/lib/python3.10/site-packages/pandas/errors/__init__.py

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+"""
+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",
+]