Sindbad~EG File Manager
�
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Expose public exceptions & warnings
� )� annotationsN)� OptionError)�OutOfBoundsDatetime�OutOfBoundsTimedelta)�InvalidVersionc � � e Zd ZdZy)�IntCastingNaNErrora1
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
N��__name__�
__module__� __qualname__�__doc__� � �A/usr/local/lib/python3.12/site-packages/pandas/errors/__init__.pyr r � � �r r c � � e Zd ZdZy)�NullFrequencyErrorao
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
Nr
r r r r r � � � r r c � � e Zd ZdZy)�PerformanceWarningaT
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
Nr
r r r r r . s � �r r c � � e Zd ZdZy)�UnsupportedFunctionCalla
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
Nr
r r r r r H � � �r r c � � e Zd ZdZy)�UnsortedIndexErrora�
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)'
Nr
r r r r r [ s � �r r c � � e Zd ZdZy)� ParserErrora�
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
Nr
r r r r r v s � �r r c � � e Zd ZdZy)� DtypeWarninga
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.
Nr
r r r r r � s � �3r r c � � e Zd ZdZy)�EmptyDataErrora!
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
Nr
r r r r" r" � � � �
r r" c � � e Zd ZdZy)�
ParserWarninga9
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')
Nr
r r r r% r% � s � �%r r% c � � e Zd ZdZy)�
MergeErrora~
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
Nr
r r r r' r' � � � �r r' c �" � e Zd ZdZddd�Zdd�Zy)�AbstractMethodErrora�
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
c �T � h d�}||vrt d|� d|� d�� �|| _ || _ y )N> �method�property� classmethod� staticmethodzmethodtype must be one of z, got z instead.)�
ValueError�
methodtype�class_instance)�selfr2 r1 �typess r �__init__z AbstractMethodError.__init__& s? � �E�� �U� "� �,�Z�L��u�g�Y�O��
� %��� ,��r c � � | j dk( r| j j }n t | j � j }d| j � d|� �S )Nr. zThis z' must be defined in the concrete class )r1 r2 r �type)r3 �names r �__str__zAbstractMethodError.__str__/ sN � � �?�?�m� +��&�&�/�/�D���+�+�,�5�5�D��t���'�'N�t�f�U�Ur N)r, )r1 �str�return�None)r; r: )r r r
r r5 r9 r r r r* r* s � ��(-�Vr r* c � � e Zd ZdZy)�NumbaUtilErrora�
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']
Nr
r r r r> r> 7 s � �
r r> c � � e Zd ZdZy)�DuplicateLabelErrora�
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]
Nr
r r r r@ r@ H r r r@ c � � e Zd ZdZy)�InvalidIndexErrora�
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)
Nr
r r r rB rB [ r( r rB c � � e Zd ZdZy)� DataErrorah
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
Nr
r r r rD rD o r r rD c � � e Zd ZdZy)�SpecificationErrora�
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
Nr
r r r rF rF s � �r rF c � � e Zd ZdZy)�SettingWithCopyErrora�
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...
Nr
r r r rH rH � � � �r rH c � � e Zd ZdZy)�SettingWithCopyWarninga�
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...
Nr
r r r rK rK � rI r rK c � � e Zd ZdZy)�ChainedAssignmentErrora
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
Nr
r r r rM rM � s � �r rM a A value is trying to be set on a copy of a DataFrame or Series through chained assignment.
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.
Try using '.loc[row_indexer, col_indexer] = value' instead, to perform the assignment in a single step.
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copya� A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.
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.
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.
a ChainedAssignmentError: behaviour will change in pandas 3.0!
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.
A typical example is when you are setting values in a column of a DataFrame, like:
df["col"][row_indexer] = value
Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
a� A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.
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.
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.
c �� � t | d� r\ | j d � }|�yt |d� r9| j d |j v r | |j | j d u S y)N�_cacher� F� _item_cacher )�hasattrrO rQ )�obj�parents r �
_check_cacherrU sk � �
�s�I� � ����Q� �!�� �>�� �6�=� )��{�{�1�~��!3�!3�3� �f� 0� 0����Q� � @�@�@� r c � � e Zd ZdZy)�NumExprClobberingErrora�
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...
Nr
r r r rW rW ) r r rW c �& � � e Zd ZdZdd� fd�
Z� xZS )�UndefinedVariableErrora$
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
c �Z �� t |� � d�}|rd|� �}nd|� �}t �| � |� y )Nz is not definedzlocal variable zname )�repr�superr5 )r3 r8 �is_local�base_msg�msg� __class__s �r r5 z UndefinedVariableError.__init__M s; �� ��4�j�\� �1�� �#�H�:�.�C��(��$�C�
���� r )N)r8 r: r] z bool | Noner; r< �r r r
r r5 �
__classcell__�r` s @r rY rY < s �� �� � r rY c � � e Zd ZdZy)�
IndexingErrora�
Exception is raised when trying to index and there is a mismatch in dimensions.
Examples
--------
>>> df = pd.DataFrame({'A': [1, 1, 1]})
>>> df.loc[..., ..., 'A'] # doctest: +SKIP
... # IndexingError: indexer may only contain one '...' entry
>>> df = pd.DataFrame({'A': [1, 1, 1]})
>>> df.loc[1, ..., ...] # doctest: +SKIP
... # IndexingError: Too many indexers
>>> df[pd.Series([True], dtype=bool)] # doctest: +SKIP
... # IndexingError: Unalignable boolean Series provided as indexer...
>>> s = pd.Series(range(2),
... index = pd.MultiIndex.from_product([["a", "b"], ["c"]]))
>>> s.loc["a", "c", "d"] # doctest: +SKIP
... # IndexingError: Too many indexers
Nr
r r r re re V � � �r re c � � e Zd ZdZy)�PyperclipExceptionz�
Exception raised when clipboard functionality is unsupported.
Raised by ``to_clipboard()`` and ``read_clipboard()``.
Nr
r r r rh rh k s � �r rh c �$ � � e Zd ZdZd� fd� Z� xZS )�PyperclipWindowsExceptionz�
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.
c �Z �� |dt j � � d�z
}t �| �
|� y )Nz (�))�ctypes�WinErrorr\ r5 )r3 �messager` s �r r5 z"PyperclipWindowsException.__init__{ s+ �� ��R����)�*�!�,�,��
����!r )ro r: r; r< ra rc s @r rj rj s s �� ��"� "r rj c � � e Zd ZdZy)�
CSSWarningat
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'
Nr
r r r rq rq � rf r rq c � � e Zd ZdZy)�PossibleDataLossErrora&
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]...
Nr
r r r rs rs � r r rs c � � e Zd ZdZy)�ClosedFileErrora8
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!
Nr
r r r ru ru � � � � r ru c � � e Zd ZdZy)�IncompatibilityWarningzX
Warning raised when trying to use where criteria on an incompatible HDF5 file.
Nr
r r r rx rx � � � �r rx c � � e Zd ZdZy)�AttributeConflictWarninga)
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]...
Nr
r r r r{ r{ � rf r r{ c � � e Zd ZdZy)�
DatabaseErroraJ
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
Nr
r r r r} r} � rv r r} c � � e Zd ZdZy)�PossiblePrecisionLossa�
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...
Nr
r r r r r � � � � r r c � � e Zd ZdZy)�ValueLabelTypeMismatchaK
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...
Nr
r r r r� r� � r r r� c � � e Zd ZdZy)�InvalidColumnNamea�
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...
Nr
r r r r� r� � r� r r� c � � e Zd ZdZy)� CategoricalConversionWarninga�
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...
Nr
r r r r� r� r# r r� c � � e Zd ZdZy)�LossySetitemErrorz�
Raised when trying to do a __setitem__ on an np.ndarray that is not lossless.
Notes
-----
This is an internal error.
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r r r r� r� � � �r r� c � � e Zd ZdZy)�NoBufferPresentz^
Exception is raised in _get_data_buffer to signal that there is no requested buffer.
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r r r r� r� ry r r� c � � e Zd ZdZy)�InvalidComparisonz�
Exception is raised by _validate_comparison_value to indicate an invalid comparison.
Notes
-----
This is an internal error.
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