U _{fZK)@sdZddlmZddlZddlmZddlmZmZddl m Z Gddde Z Gd d d e Z Gd d d eZGd dde ZGdddeZGddde ZGdddeZGddde ZGdddeZGddde ZGdddeZGdddeZGdd d eZGd!d"d"e ZGd#d$d$eZGd%d&d&eZGd'd(d(eZ Gd)d*d*e Z!Gd+d,d,eZ"Gd-d.d.eZ#d/Z$Gd0d1d1e%Z&Gd2d3d3e%Z'Gd4d5d5eZ(Gd6d7d7e)Z*Gd8d9d9e*Z+Gd:d;d;e,Z-Gdd?d?eZ/Gd@dAdAeZ0GdBdCdCeZ1GdDdEdEe2Z3GdFdGdGeZ4GdHdIdIeZ5GdJdKdKeZ6GdLdMdMeZ7GdNdOdOeZ8GdPdQdQeZ9GdRdSdSeZ:dddCdMd?d;dEd&dd"ddAddKdSd$dTd5dOddQd d d1dUdVdWddd d=dGd7d9d*d,d(d3dddIg)Z;dS)Xz% Expose public exceptions & warnings ) annotationsN) OptionError)OutOfBoundsDatetimeOutOfBoundsTimedelta)InvalidVersionc@seZdZdZdS)IntCastingNaNErrorz] Exception raised when converting (``astype``) an array with NaN to an integer type. N__name__ __module__ __qualname____doc__r r U/var/www/html/Darija-Ai-API/env/lib/python3.8/site-packages/pandas/errors/__init__.pyrsrc@seZdZdZdS)NullFrequencyErrorz Exception raised when a ``freq`` cannot be null. Particularly ``DatetimeIndex.shift``, ``TimedeltaIndex.shift``, ``PeriodIndex.shift``. Nrr r r rrsrc@seZdZdZdS)PerformanceWarningzE Warning raised when there is a possible performance impact. Nrr r r rr!src@seZdZdZdS)UnsupportedFunctionCallz Exception raised when attempting to call a unsupported numpy function. For example, ``np.cumsum(groupby_object)``. Nrr r r rr'src@seZdZdZdS)UnsortedIndexErrorzk Error raised when slicing a MultiIndex which has not been lexsorted. Subclass of `KeyError`. Nrr r r rr/src@seZdZdZdS) 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. Nrr r r rr7src@seZdZdZdS) 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 >>> df2.iloc[262150, 0] # doctest: +SKIP 1 >>> type(df2.iloc[262150, 0]) # doctest: +SKIP 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. Nrr r r rrEsrc@seZdZdZdS)EmptyDataErrorzW Exception raised in ``pd.read_csv`` when empty data or header is encountered. Nrr r r rr|src@seZdZdZdS) 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') Nrr r r rrsrc@seZdZdZdS) MergeErrorzN Exception raised when merging data. Subclass of ``ValueError``. Nrr r r rrsrc@seZdZdZdS)AccessorRegistrationWarningzC Warning for attribute conflicts in accessor registration. Nrr r r rrsrc@s0eZdZdZd dddddZddd d Zd S) AbstractMethodErrorzO Raise this error instead of NotImplementedError for abstract methods. methodstrNone) methodtypereturncCs:ddddh}||kr*td|d|d||_||_dS)Nr classmethod staticmethodpropertyzmethodtype must be one of z, got z instead.) ValueErrorrclass_instance)selfr#rtypesr r r__init__s zAbstractMethodError.__init__)rcCs2|jdkr|jj}n t|jj}d|jd|S)NrzThis z' must be defined in the concrete class )rr#r type)r$namer r r__str__s   zAbstractMethodError.__str__N)r)r r r r r&r)r r r rrs rc@seZdZdZdS)NumbaUtilErrorz= Error raised for unsupported Numba engine routines. Nrr r r rr*sr*c@seZdZdZdS)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] Nrr r r rr+sr+c@seZdZdZdS)InvalidIndexErrorzd Exception raised when attempting to use an invalid index key. .. versionadded:: 1.1.0 Nrr r r rr,sr,c@seZdZdZdS) 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. Nrr r r rr-sr-c@seZdZdZdS)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 Nrr r r rr.sr.c@seZdZdZdS)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`. For more information on view vs. copy, see :ref:`the user guide`. 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... Nrr r r rr/sr/c@seZdZdZdS)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`. For more information on view vs. copy, see :ref:`the user guide`. 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... Nrr r r rr0-sr0c@seZdZdZdS)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`. 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 Nrr r r rr1Csr1aA 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-copyc@seZdZdZdS)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... Nrr r r rr2hsr2cs,eZdZdZd ddddfdd ZZS) 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 Nrz bool | Noner)r(is_localrcs8t|d}|rd|}n d|}t|dS)Nz is not definedzlocal variable zname )reprsuperr&)r$r(r4Zbase_msgmsg __class__r rr&s   zUndefinedVariableError.__init__)Nr r r r r& __classcell__r r r8rr3{sr3c@seZdZdZdS) IndexingErrora Exception is raised when trying to index and there is a mismatch in dimensions. Examples -------- >>> df = pd.DataFrame({'A': [1, 1, 1]}) >>> df.loc[..., ..., 'A'] # doctest: +SKIP ... # IndexingError: indexer may only contain one '...' entry >>> df = pd.DataFrame({'A': [1, 1, 1]}) >>> df.loc[1, ..., ...] # doctest: +SKIP ... # IndexingError: Too many indexers >>> df[pd.Series([True], dtype=bool)] # doctest: +SKIP ... # IndexingError: Unalignable boolean Series provided as indexer... >>> s = pd.Series(range(2), ... index = pd.MultiIndex.from_product([["a", "b"], ["c"]])) >>> s.loc["a", "c", "d"] # doctest: +SKIP ... # IndexingError: Too many indexers Nrr r r rr<sr<c@seZdZdZdS)PyperclipExceptionz Exception raised when clipboard functionality is unsupported. Raised by ``to_clipboard()`` and ``read_clipboard()``. Nrr r r rr=sr=cs(eZdZdZdddfdd ZZS)PyperclipWindowsExceptionz Exception raised when clipboard functionality is unsupported by Windows. Access to the clipboard handle would be denied due to some other window process is accessing it. rr)messagercs$|dtd7}t|dS)Nz ())ctypesZWinErrorr6r&)r$r?r8r rr&sz"PyperclipWindowsException.__init__r:r r r8rr>sr>c@seZdZdZdS) CSSWarninga Warning is raised when converting css styling fails. This can be due to the styling not having an equivalent value or because the styling isn't properly formatted. Examples -------- >>> df = pd.DataFrame({'A': [1, 1, 1]}) >>> (df.style.applymap(lambda x: 'background-color: blueGreenRed;') ... .to_excel('styled.xlsx')) # doctest: +SKIP ... # CSSWarning: Unhandled color format: 'blueGreenRed' >>> (df.style.applymap(lambda x: 'border: 1px solid red red;') ... .to_excel('styled.xlsx')) # doctest: +SKIP ... # CSSWarning: Too many tokens provided to "border" (expected 1-3) Nrr r r rrBsrBc@seZdZdZdS)PossibleDataLossErrora& Exception raised when trying to open a HDFStore file when already opened. Examples -------- >>> store = pd.HDFStore('my-store', 'a') # doctest: +SKIP >>> store.open("w") # doctest: +SKIP ... # PossibleDataLossError: Re-opening the file [my-store] with mode [a]... Nrr r r rrCsrCc@seZdZdZdS)ClosedFileErrora8 Exception is raised when trying to perform an operation on a closed HDFStore file. Examples -------- >>> store = pd.HDFStore('my-store', 'a') # doctest: +SKIP >>> store.close() # doctest: +SKIP >>> store.keys() # doctest: +SKIP ... # ClosedFileError: my-store file is not open! Nrr r r rrDsrDc@seZdZdZdS)IncompatibilityWarningzX Warning raised when trying to use where criteria on an incompatible HDF5 file. Nrr r r rrEsrEc@seZdZdZdS)AttributeConflictWarninga$ 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. Nrr r r rrFsrFc@seZdZdZdS) 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 Nrr r r rrGsrGc@seZdZdZdS)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... Nrr r r rrH srHc@seZdZdZdS)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... Nrr r r rrIsrIc@seZdZdZdS)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... Nrr r r rrJ$srJc@seZdZdZdS)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... Nrr r r rrK3srKc@seZdZdZdS)LossySetitemErrorzW Raised when trying to do a __setitem__ on an np.ndarray that is not lossless. Nrr r r rrLAsrLc@seZdZdZdS)NoBufferPresentz^ Exception is raised in _get_data_buffer to signal that there is no requested buffer. Nrr r r rrMGsrMc@seZdZdZdS)InvalidComparisonz^ Exception is raised by _validate_comparison_value to indicate an invalid comparison. Nrr r r rrNMsrNrrrr) UserWarningrBrCrDrErFOSErrorrGrHrIrJrKrLrMrN__all__r r r rs    7)