577 行
17 KiB
Python
577 行
17 KiB
Python
"""
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Expose public exceptions & warnings
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"""
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from __future__ import annotations
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import ctypes
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from pandas._config.config import OptionError
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from pandas._libs.tslibs import (
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OutOfBoundsDatetime,
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OutOfBoundsTimedelta,
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)
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class IntCastingNaNError(ValueError):
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"""
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Exception raised when converting (``astype``) an array with NaN to an integer type.
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"""
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class NullFrequencyError(ValueError):
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"""
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Exception raised when a ``freq`` cannot be null.
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Particularly ``DatetimeIndex.shift``, ``TimedeltaIndex.shift``,
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``PeriodIndex.shift``.
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"""
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class PerformanceWarning(Warning):
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"""
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Warning raised when there is a possible performance impact.
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"""
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class UnsupportedFunctionCall(ValueError):
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"""
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Exception raised when attempting to call a unsupported numpy function.
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For example, ``np.cumsum(groupby_object)``.
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"""
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class UnsortedIndexError(KeyError):
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"""
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Error raised when slicing a MultiIndex which has not been lexsorted.
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Subclass of `KeyError`.
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"""
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class ParserError(ValueError):
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"""
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Exception that is raised by an error encountered in parsing file contents.
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This is a generic error raised for errors encountered when functions like
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`read_csv` or `read_html` are parsing contents of a file.
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See Also
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--------
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read_csv : Read CSV (comma-separated) file into a DataFrame.
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read_html : Read HTML table into a DataFrame.
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"""
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class DtypeWarning(Warning):
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"""
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Warning raised when reading different dtypes in a column from a file.
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Raised for a dtype incompatibility. This can happen whenever `read_csv`
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or `read_table` encounter non-uniform dtypes in a column(s) of a given
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CSV file.
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See Also
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--------
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read_csv : Read CSV (comma-separated) file into a DataFrame.
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read_table : Read general delimited file into a DataFrame.
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Notes
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-----
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This warning is issued when dealing with larger files because the dtype
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checking happens per chunk read.
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Despite the warning, the CSV file is read with mixed types in a single
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column which will be an object type. See the examples below to better
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understand this issue.
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Examples
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--------
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This example creates and reads a large CSV file with a column that contains
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`int` and `str`.
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>>> df = pd.DataFrame({'a': (['1'] * 100000 + ['X'] * 100000 +
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... ['1'] * 100000),
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... 'b': ['b'] * 300000}) # doctest: +SKIP
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>>> df.to_csv('test.csv', index=False) # doctest: +SKIP
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>>> df2 = pd.read_csv('test.csv') # doctest: +SKIP
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... # DtypeWarning: Columns (0) have mixed types
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Important to notice that ``df2`` will contain both `str` and `int` for the
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same input, '1'.
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>>> df2.iloc[262140, 0] # doctest: +SKIP
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'1'
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>>> type(df2.iloc[262140, 0]) # doctest: +SKIP
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<class 'str'>
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>>> df2.iloc[262150, 0] # doctest: +SKIP
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1
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>>> type(df2.iloc[262150, 0]) # doctest: +SKIP
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<class 'int'>
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One way to solve this issue is using the `dtype` parameter in the
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`read_csv` and `read_table` functions to explicit the conversion:
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>>> df2 = pd.read_csv('test.csv', sep=',', dtype={'a': str}) # doctest: +SKIP
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No warning was issued.
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"""
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class EmptyDataError(ValueError):
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"""
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Exception raised in ``pd.read_csv`` when empty data or header is encountered.
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"""
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class ParserWarning(Warning):
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"""
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Warning raised when reading a file that doesn't use the default 'c' parser.
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Raised by `pd.read_csv` and `pd.read_table` when it is necessary to change
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parsers, generally from the default 'c' parser to 'python'.
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It happens due to a lack of support or functionality for parsing a
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particular attribute of a CSV file with the requested engine.
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Currently, 'c' unsupported options include the following parameters:
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1. `sep` other than a single character (e.g. regex separators)
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2. `skipfooter` higher than 0
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3. `sep=None` with `delim_whitespace=False`
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The warning can be avoided by adding `engine='python'` as a parameter in
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`pd.read_csv` and `pd.read_table` methods.
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See Also
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--------
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pd.read_csv : Read CSV (comma-separated) file into DataFrame.
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pd.read_table : Read general delimited file into DataFrame.
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Examples
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--------
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Using a `sep` in `pd.read_csv` other than a single character:
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>>> import io
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>>> csv = '''a;b;c
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... 1;1,8
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... 1;2,1'''
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>>> df = pd.read_csv(io.StringIO(csv), sep='[;,]') # doctest: +SKIP
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... # ParserWarning: Falling back to the 'python' engine...
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Adding `engine='python'` to `pd.read_csv` removes the Warning:
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>>> df = pd.read_csv(io.StringIO(csv), sep='[;,]', engine='python')
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"""
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class MergeError(ValueError):
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"""
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Exception raised when merging data.
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Subclass of ``ValueError``.
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"""
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class AccessorRegistrationWarning(Warning):
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"""
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Warning for attribute conflicts in accessor registration.
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"""
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class AbstractMethodError(NotImplementedError):
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"""
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Raise this error instead of NotImplementedError for abstract methods.
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"""
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def __init__(self, class_instance, methodtype: str = "method") -> None:
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types = {"method", "classmethod", "staticmethod", "property"}
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if methodtype not in types:
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raise ValueError(
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f"methodtype must be one of {methodtype}, got {types} instead."
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)
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self.methodtype = methodtype
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self.class_instance = class_instance
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def __str__(self) -> str:
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if self.methodtype == "classmethod":
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name = self.class_instance.__name__
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else:
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name = type(self.class_instance).__name__
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return f"This {self.methodtype} must be defined in the concrete class {name}"
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class NumbaUtilError(Exception):
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"""
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Error raised for unsupported Numba engine routines.
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"""
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class DuplicateLabelError(ValueError):
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"""
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Error raised when an operation would introduce duplicate labels.
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.. versionadded:: 1.2.0
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Examples
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--------
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>>> s = pd.Series([0, 1, 2], index=['a', 'b', 'c']).set_flags(
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... allows_duplicate_labels=False
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... )
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>>> s.reindex(['a', 'a', 'b'])
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Traceback (most recent call last):
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...
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DuplicateLabelError: Index has duplicates.
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positions
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label
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a [0, 1]
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"""
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class InvalidIndexError(Exception):
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"""
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Exception raised when attempting to use an invalid index key.
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.. versionadded:: 1.1.0
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"""
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class DataError(Exception):
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"""
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Exceptionn raised when performing an operation on non-numerical data.
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For example, calling ``ohlc`` on a non-numerical column or a function
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on a rolling window.
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"""
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class SpecificationError(Exception):
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"""
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Exception raised by ``agg`` when the functions are ill-specified.
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The exception raised in two scenarios.
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The first way is calling ``agg`` on a
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Dataframe or Series using a nested renamer (dict-of-dict).
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The second way is calling ``agg`` on a Dataframe with duplicated functions
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names without assigning column name.
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Examples
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--------
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>>> df = pd.DataFrame({'A': [1, 1, 1, 2, 2],
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... 'B': range(5),
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... 'C': range(5)})
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>>> df.groupby('A').B.agg({'foo': 'count'}) # doctest: +SKIP
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... # SpecificationError: nested renamer is not supported
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>>> df.groupby('A').agg({'B': {'foo': ['sum', 'max']}}) # doctest: +SKIP
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... # SpecificationError: nested renamer is not supported
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>>> df.groupby('A').agg(['min', 'min']) # doctest: +SKIP
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... # SpecificationError: nested renamer is not supported
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"""
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class SettingWithCopyError(ValueError):
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"""
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Exception raised when trying to set on a copied slice from a ``DataFrame``.
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The ``mode.chained_assignment`` needs to be set to set to 'raise.' This can
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happen unintentionally when chained indexing.
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For more information on eveluation order,
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see :ref:`the user guide<indexing.evaluation_order>`.
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For more information on view vs. copy,
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see :ref:`the user guide<indexing.view_versus_copy>`.
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Examples
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--------
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>>> pd.options.mode.chained_assignment = 'raise'
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>>> df = pd.DataFrame({'A': [1, 1, 1, 2, 2]}, columns=['A'])
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>>> df.loc[0:3]['A'] = 'a' # doctest: +SKIP
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... # SettingWithCopyError: A value is trying to be set on a copy of a...
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"""
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class SettingWithCopyWarning(Warning):
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"""
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Warning raised when trying to set on a copied slice from a ``DataFrame``.
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The ``mode.chained_assignment`` needs to be set to set to 'warn.'
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'Warn' is the default option. This can happen unintentionally when
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chained indexing.
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For more information on eveluation order,
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see :ref:`the user guide<indexing.evaluation_order>`.
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For more information on view vs. copy,
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see :ref:`the user guide<indexing.view_versus_copy>`.
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Examples
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--------
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>>> df = pd.DataFrame({'A': [1, 1, 1, 2, 2]}, columns=['A'])
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>>> df.loc[0:3]['A'] = 'a' # doctest: +SKIP
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... # SettingWithCopyWarning: A value is trying to be set on a copy of a...
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"""
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class NumExprClobberingError(NameError):
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"""
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Exception raised when trying to use a built-in numexpr name as a variable name.
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``eval`` or ``query`` will throw the error if the engine is set
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to 'numexpr'. 'numexpr' is the default engine value for these methods if the
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numexpr package is installed.
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Examples
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--------
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>>> df = pd.DataFrame({'abs': [1, 1, 1]})
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>>> df.query("abs > 2") # doctest: +SKIP
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... # NumExprClobberingError: Variables in expression "(abs) > (2)" overlap...
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>>> sin, a = 1, 2
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>>> pd.eval("sin + a", engine='numexpr') # doctest: +SKIP
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... # NumExprClobberingError: Variables in expression "(sin) + (a)" overlap...
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"""
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class UndefinedVariableError(NameError):
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"""
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Exception raised by ``query`` or ``eval`` when using an undefined variable name.
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It will also specify whether the undefined variable is local or not.
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Examples
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--------
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>>> df = pd.DataFrame({'A': [1, 1, 1]})
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>>> df.query("A > x") # doctest: +SKIP
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... # UndefinedVariableError: name 'x' is not defined
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>>> df.query("A > @y") # doctest: +SKIP
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... # UndefinedVariableError: local variable 'y' is not defined
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>>> pd.eval('x + 1') # doctest: +SKIP
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... # UndefinedVariableError: name 'x' is not defined
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"""
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def __init__(self, name: str, is_local: bool | None = None) -> None:
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base_msg = f"{repr(name)} is not defined"
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if is_local:
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msg = f"local variable {base_msg}"
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else:
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msg = f"name {base_msg}"
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super().__init__(msg)
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class IndexingError(Exception):
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"""
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Exception is raised when trying to index and there is a mismatch in dimensions.
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Examples
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--------
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>>> df = pd.DataFrame({'A': [1, 1, 1]})
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>>> df.loc[..., ..., 'A'] # doctest: +SKIP
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... # IndexingError: indexer may only contain one '...' entry
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>>> df = pd.DataFrame({'A': [1, 1, 1]})
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>>> df.loc[1, ..., ...] # doctest: +SKIP
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... # IndexingError: Too many indexers
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>>> df[pd.Series([True], dtype=bool)] # doctest: +SKIP
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... # IndexingError: Unalignable boolean Series provided as indexer...
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>>> s = pd.Series(range(2),
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... index = pd.MultiIndex.from_product([["a", "b"], ["c"]]))
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>>> s.loc["a", "c", "d"] # doctest: +SKIP
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... # IndexingError: Too many indexers
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"""
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class PyperclipException(RuntimeError):
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"""
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Exception raised when clipboard functionality is unsupported.
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Raised by ``to_clipboard()`` and ``read_clipboard()``.
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"""
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class PyperclipWindowsException(PyperclipException):
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"""
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Exception raised when clipboard functionality is unsupported by Windows.
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Access to the clipboard handle would be denied due to some other
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window process is accessing it.
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"""
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def __init__(self, message: str) -> None:
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# attr only exists on Windows, so typing fails on other platforms
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message += f" ({ctypes.WinError()})" # type: ignore[attr-defined]
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super().__init__(message)
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class CSSWarning(UserWarning):
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"""
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Warning is raised when converting css styling fails.
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This can be due to the styling not having an equivalent value or because the
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styling isn't properly formatted.
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Examples
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--------
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>>> df = pd.DataFrame({'A': [1, 1, 1]})
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>>> df.style.applymap(lambda x: 'background-color: blueGreenRed;')
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... .to_excel('styled.xlsx') # doctest: +SKIP
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... # CSSWarning: Unhandled color format: 'blueGreenRed'
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>>> df.style.applymap(lambda x: 'border: 1px solid red red;')
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... .to_excel('styled.xlsx') # doctest: +SKIP
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... # CSSWarning: Too many tokens provided to "border" (expected 1-3)
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"""
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class PossibleDataLossError(Exception):
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"""
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Exception raised when trying to open a HDFStore file when already opened.
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Examples
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--------
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>>> store = pd.HDFStore('my-store', 'a') # doctest: +SKIP
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>>> store.open("w") # doctest: +SKIP
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... # PossibleDataLossError: Re-opening the file [my-store] with mode [a]...
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"""
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class ClosedFileError(Exception):
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"""
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Exception is raised when trying to perform an operation on a closed HDFStore file.
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Examples
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--------
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>>> store = pd.HDFStore('my-store', 'a') # doctest: +SKIP
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>>> store.close() # doctest: +SKIP
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>>> store.keys() # doctest: +SKIP
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... # ClosedFileError: my-store file is not open!
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"""
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class IncompatibilityWarning(Warning):
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"""
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Warning raised when trying to use where criteria on an incompatible HDF5 file.
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"""
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class AttributeConflictWarning(Warning):
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"""
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Warning raised when index attributes conflict when using HDFStore.
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Occurs when attempting to append an index with a different
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name than the existing index on an HDFStore or attempting to append an index with a
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different frequency than the existing index on an HDFStore.
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"""
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class DatabaseError(OSError):
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"""
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Error is raised when executing sql with bad syntax or sql that throws an error.
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Examples
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--------
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>>> from sqlite3 import connect
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>>> conn = connect(':memory:')
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>>> pd.read_sql('select * test', conn) # doctest: +SKIP
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... # DatabaseError: Execution failed on sql 'test': near "test": syntax error
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"""
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class PossiblePrecisionLoss(Warning):
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"""
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Warning raised by to_stata on a column with a value outside or equal to int64.
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When the column value is outside or equal to the int64 value the column is
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converted to a float64 dtype.
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Examples
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--------
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>>> df = pd.DataFrame({"s": pd.Series([1, 2**53], dtype=np.int64)})
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>>> df.to_stata('test') # doctest: +SKIP
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... # PossiblePrecisionLoss: Column converted from int64 to float64...
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"""
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class ValueLabelTypeMismatch(Warning):
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"""
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Warning raised by to_stata on a category column that contains non-string values.
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Examples
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--------
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>>> df = pd.DataFrame({"categories": pd.Series(["a", 2], dtype="category")})
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>>> df.to_stata('test') # doctest: +SKIP
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... # ValueLabelTypeMismatch: Stata value labels (pandas categories) must be str...
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"""
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class InvalidColumnName(Warning):
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"""
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Warning raised by to_stata the column contains a non-valid stata name.
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Because the column name is an invalid Stata variable, the name needs to be
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converted.
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Examples
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--------
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>>> df = pd.DataFrame({"0categories": pd.Series([2, 2])})
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>>> df.to_stata('test') # doctest: +SKIP
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... # InvalidColumnName: Not all pandas column names were valid Stata variable...
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"""
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class CategoricalConversionWarning(Warning):
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"""
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Warning is raised when reading a partial labeled Stata file using a iterator.
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Examples
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--------
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>>> from pandas.io.stata import StataReader
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>>> with StataReader('dta_file', chunksize=2) as reader: # doctest: +SKIP
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... for i, block in enumerate(reader):
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... print(i, block))
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... # CategoricalConversionWarning: One or more series with value labels...
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"""
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__all__ = [
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"AbstractMethodError",
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"AccessorRegistrationWarning",
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"AttributeConflictWarning",
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"CategoricalConversionWarning",
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"ClosedFileError",
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"CSSWarning",
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"DatabaseError",
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"DataError",
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"DtypeWarning",
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"DuplicateLabelError",
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"EmptyDataError",
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"IncompatibilityWarning",
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"IntCastingNaNError",
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"InvalidColumnName",
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"InvalidIndexError",
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"IndexingError",
|
|
"MergeError",
|
|
"NullFrequencyError",
|
|
"NumbaUtilError",
|
|
"NumExprClobberingError",
|
|
"OptionError",
|
|
"OutOfBoundsDatetime",
|
|
"OutOfBoundsTimedelta",
|
|
"ParserError",
|
|
"ParserWarning",
|
|
"PerformanceWarning",
|
|
"PossibleDataLossError",
|
|
"PossiblePrecisionLoss",
|
|
"PyperclipException",
|
|
"PyperclipWindowsException",
|
|
"SettingWithCopyError",
|
|
"SettingWithCopyWarning",
|
|
"SpecificationError",
|
|
"UndefinedVariableError",
|
|
"UnsortedIndexError",
|
|
"UnsupportedFunctionCall",
|
|
"ValueLabelTypeMismatch",
|
|
]
|