U
    sVcKC                  %   @  s  d Z ddlmZ ddlZddlmZ ddlmZmZ G dd de	Z
G dd	 d	e	ZG d
d deZG dd de	ZG dd deZG dd de	ZG dd deZG dd de	ZG dd deZG dd de	ZG dd deZG dd deZG dd deZG d d! d!e	ZG d"d# d#eZG d$d% d%eZG d&d' d'eZG d(d) d)e	ZG d*d+ d+eZ G d,d- d-e!Z"G d.d/ d/e!Z#G d0d1 d1eZ$G d2d3 d3e%Z&G d4d5 d5e&Z'G d6d7 d7e(Z)G d8d9 d9eZ*G d:d; d;eZ+G d<d= d=eZ,G d>d? d?eZ-G d@dA dAe.Z/G dBdC dCeZ0G dDdE dEeZ1G dFdG dGeZ2G dHdI dIeZ3ddd?dId;d7dAd%dd!dd=ddGd#d1dd	dd-dJdKdLdddd9dCd3d5d)d+d'd/dddEg%Z4dS )Mz%
Expose public exceptions & warnings
    )annotationsN)OptionError)OutOfBoundsDatetimeOutOfBoundsTimedeltac                   @  s   e Zd ZdZdS )IntCastingNaNErrorz]
    Exception raised when converting (``astype``) an array with NaN to an integer type.
    N__name__
__module____qualname____doc__ r   r   :/tmp/pip-unpacked-wheel-xj8nt62q/pandas/errors/__init__.pyr      s   r   c                   @  s   e Zd ZdZdS )NullFrequencyErrorz
    Exception raised when a ``freq`` cannot be null.

    Particularly ``DatetimeIndex.shift``, ``TimedeltaIndex.shift``,
    ``PeriodIndex.shift``.
    Nr   r   r   r   r   r      s   r   c                   @  s   e Zd ZdZdS )PerformanceWarningzE
    Warning raised when there is a possible performance impact.
    Nr   r   r   r   r   r      s   r   c                   @  s   e Zd ZdZdS )UnsupportedFunctionCallz
    Exception raised when attempting to call a unsupported numpy function.

    For example, ``np.cumsum(groupby_object)``.
    Nr   r   r   r   r   r   %   s   r   c                   @  s   e Zd ZdZdS )UnsortedIndexErrorzk
    Error raised when slicing a MultiIndex which has not been lexsorted.

    Subclass of `KeyError`.
    Nr   r   r   r   r   r   -   s   r   c                   @  s   e Zd ZdZdS )ParserErrorao  
    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.
    Nr   r   r   r   r   r   5   s   r   c                   @  s   e Zd ZdZdS )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   C   s   r   c                   @  s   e Zd ZdZdS )EmptyDataErrorzW
    Exception raised in ``pd.read_csv`` when empty data or header is encountered.
    Nr   r   r   r   r   r   z   s   r   c                   @  s   e Zd ZdZdS )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   c                   @  s   e Zd ZdZdS )
MergeErrorzN
    Exception raised when merging data.

    Subclass of ``ValueError``.
    Nr   r   r   r   r   r      s   r   c                   @  s   e Zd ZdZdS )AccessorRegistrationWarningzC
    Warning for attribute conflicts in accessor registration.
    Nr   r   r   r   r   r      s   r   c                   @  s0   e Zd ZdZddddddZddd	d
ZdS )AbstractMethodErrorzO
    Raise this error instead of NotImplementedError for abstract methods.
    methodstrNone)
methodtypereturnc                 C  s:   ddddh}||kr*t d| d| d|| _|| _d S )Nr   classmethodstaticmethodpropertyzmethodtype must be one of z, got z	 instead.)
ValueErrorr   class_instance)selfr"   r   typesr   r   r   __init__   s    zAbstractMethodError.__init__)r   c                 C  s2   | j dkr| jj}nt| jj}d| j  d| S )Nr   zThis z' must be defined in the concrete class )r   r"   r   type)r#   namer   r   r   __str__   s    

zAbstractMethodError.__str__N)r   )r   r	   r
   r   r%   r(   r   r   r   r   r      s   	r   c                   @  s   e Zd ZdZdS )NumbaUtilErrorz=
    Error raised for unsupported Numba engine routines.
    Nr   r   r   r   r   r)      s   r)   c                   @  s   e Zd ZdZdS )DuplicateLabelErrora  
    Error raised when an operation would introduce duplicate labels.

    .. versionadded:: 1.2.0

    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*      s   r*   c                   @  s   e Zd ZdZdS )InvalidIndexErrorzd
    Exception raised when attempting to use an invalid index key.

    .. versionadded:: 1.1.0
    Nr   r   r   r   r   r+      s   r+   c                   @  s   e Zd ZdZdS )	DataErrorz
    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.
    Nr   r   r   r   r   r,      s   r,   c                   @  s   e Zd ZdZdS )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   r   r-      s   r-   c                   @  s   e Zd ZdZdS )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 eveluation 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   r   r.     s   r.   c                   @  s   e Zd ZdZdS )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 eveluation 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   r   r/   +  s   r/   c                   @  s   e Zd ZdZdS )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   r   r0   A  s   r0   c                      s,   e Zd ZdZd	dddd fddZ  ZS )
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
    Nr   zbool | Noner   )r'   is_localr   c                   s8   t | d}|rd| }n
d| }t | d S )Nz is not definedzlocal variable zname )reprsuperr%   )r#   r'   r2   Zbase_msgmsg	__class__r   r   r%   e  s
    
zUndefinedVariableError.__init__)Nr   r	   r
   r   r%   __classcell__r   r   r6   r   r1   T  s   r1   c                   @  s   e Zd ZdZdS )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   r   r:   n  s   r:   c                   @  s   e Zd ZdZdS )PyperclipExceptionz
    Exception raised when clipboard functionality is unsupported.

    Raised by ``to_clipboard()`` and ``read_clipboard()``.
    Nr   r   r   r   r   r;     s   r;   c                      s(   e Zd ZdZddd fddZ  ZS )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.
    r   r   )messager   c                   s$   |dt   d7 }t | d S )Nz ())ctypesZWinErrorr4   r%   )r#   r=   r6   r   r   r%     s    z"PyperclipWindowsException.__init__r8   r   r   r6   r   r<     s   r<   c                   @  s   e Zd ZdZdS )
CSSWarninga  
    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: Too many tokens provided to "border" (expected 1-3)
    Nr   r   r   r   r   r@     s   r@   c                   @  s   e Zd ZdZdS )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   r   rA     s   rA   c                   @  s   e Zd ZdZdS )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   r   rB     s   rB   c                   @  s   e Zd ZdZdS )IncompatibilityWarningzX
    Warning raised when trying to use where criteria on an incompatible HDF5 file.
    Nr   r   r   r   r   rC     s   rC   c                   @  s   e Zd ZdZdS )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.
    Nr   r   r   r   r   rD     s   rD   c                   @  s   e Zd ZdZdS )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   rE     s   rE   c                   @  s   e Zd ZdZdS )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   rF     s   rF   c                   @  s   e Zd ZdZdS )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   rG     s   rG   c                   @  s   e Zd ZdZdS )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   rH     s   rH   c                   @  s   e Zd ZdZdS )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   rI     s   rI   r   r   r   )5r   
__future__r   r?   Zpandas._config.configr   Zpandas._libs.tslibsr   r   r!   r   r   Warningr   r   KeyErrorr   r   r   r   r   r   r   NotImplementedErrorr   	Exceptionr)   r*   r+   r,   r-   r.   r/   	NameErrorr0   r1   r:   RuntimeErrorr;   r<   UserWarningr@   rA   rB   rC   rD   OSErrorrE   rF   rG   rH   rI   __all__r   r   r   r   <module>   s   	7)	
