o ҷhNa@sdZddlmZddlZddlmZddlmZmZddl m Z Gddde Z Gd d d e Z Gd d d eZGd dde ZGdddeZGddde ZGdddeZGddde ZGdddeZGddde ZGdddeZGdddeZGdd d e ZGd!d"d"eZGd#d$d$eZGd%d&d&eZGd'd(d(e Z Gd)d*d*eZ!Gd+d,d,eZ"d-Z#d.Z$Gd/d0d0e%Z&Gd1d2d2e%Z'Gd3d4d4eZ(Gd5d6d6e)Z*Gd7d8d8e*Z+Gd9d:d:e,Z-Gd;d<dd>eZ/Gd?d@d@eZ0GdAdBdBeZ1GdCdDdDe2Z3GdEdFdFeZ4GdGdHdHeZ5GdIdJdJeZ6GdKdLdLeZ7GdMdNdNeZ8GdOdPdPeZ9GdQdRdReZ:gdSZ;dS)Tz% Expose public exceptions & warnings ) annotationsN) OptionError)OutOfBoundsDatetimeOutOfBoundsTimedelta)InvalidVersionc@eZdZdZdS)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__rrI/var/www/html/venv/lib/python3.10/site-packages/pandas/errors/__init__.pyrrc@r)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 rrrrrrrc@r)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 rrrrr.rrc@r)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 rrrrrHrrc@r)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 rrrrr[rrc@r) 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 rrrrrvrrc@r) 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. Nr rrrrrrrc@r)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 rrrrrrrc@r) 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 rrrrrrrc@r) 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 rrrrrrrc@s&eZdZdZd d ddZdd d Zd S)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 method methodtypestrreturnNonecCs6hd}||vrtd|d|d||_||_dS)N>rproperty classmethod staticmethodzmethodtype must be one of z, got z instead.) ValueErrorrclass_instance)selfr$rtypesrrr__init__&s zAbstractMethodError.__init__cCs2|jdkr |jj}nt|jj}d|jd|S)Nr!zThis z' must be defined in the concrete class )rr$r type)r%namerrr__str__/s   zAbstractMethodError.__str__N)r)rrrr)rr)r r r r r'r*rrrrrs  rc@r)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 rrrrr+7rr+c@r)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 rrrrr,Hrr,c@r)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 rrrrr-]rr-c@r) 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 rrrrr.qrr.c@r)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 rrrrr/rr/c@r)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... Nr rrrrr0rr0c@r)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... Nr rrrrr1rr1c@r)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 Nr rrrrr2rr2aA 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-copyaA 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. c@r)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 rrrrr3rr3cs$eZdZdZd d fd d 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 Nr)ris_local bool | Nonerrcs8t|d}|rd|}nd|}t|dS)Nz is not definedzlocal variable zname )reprsuperr')r%r)r5base_msgmsg __class__rrr's   zUndefinedVariableError.__init__)N)r)rr5r6rrr r r r r' __classcell__rrr;rr4 sr4c@r) 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 rrrrr?'rr?c@r)PyperclipExceptionz Exception raised when clipboard functionality is unsupported. Raised by ``to_clipboard()`` and ``read_clipboard()``. Nr rrrrr@<rr@cs"eZdZdZdfdd 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. messagerrrcs$|dtd7}t|dS)Nz ())ctypesWinErrorr8r')r%rBr;rrr'Lsz"PyperclipWindowsException.__init__)rBrrrr=rrr;rrADsrAc@r) 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 rrrrrFRrrFc@r)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 rrrrrGgrrGc@r)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 rrrrrHsrrHc@r)IncompatibilityWarningzX Warning raised when trying to use where criteria on an incompatible HDF5 file. Nr rrrrrIrrIc@r)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 rrrrrJrrJc@r) 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 rrrrrKrrKc@r)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 rrrrrLrrLc@r)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 rrrrrMrrMc@r)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 rrrrrNrrNc@r)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 rrrrrOrrOc@r)LossySetitemErrorz Raised when trying to do a __setitem__ on an np.ndarray that is not lossless. Notes ----- This is an internal error. Nr rrrrrPrrPc@r)NoBufferPresentz^ Exception is raised in _get_data_buffer to signal that there is no requested buffer. Nr rrrrrQrrQc@r)InvalidComparisonz Exception is raised by _validate_comparison_value to indicate an invalid comparison. Notes ----- This is an internal error. Nr rrrrrRrrR)(rrJrOrHrFrKr.rr,rrIrrNrRr-rr?rPrrQrr+r3rrrrrrrGrLr@rAr0r1r/r4rrrM)s`    7)&