8th day of python challenges 111-117

venv/lib/python3.6/site-packages/pandas/errors/__init__.py

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+# flake8: noqa
+
+"""
+Expose public exceptions & warnings
+"""
+
+from pandas._libs.tslibs import OutOfBoundsDatetime
+
+
+class PerformanceWarning(Warning):
+    """
+    Warning raised when there is a possible performance impact.
+    """
+
+
+class UnsupportedFunctionCall(ValueError):
+    """
+    Exception raised when attempting to call a numpy function
+    on a pandas object, but that function is not supported by
+    the object e.g. ``np.cumsum(groupby_object)``.
+    """
+
+
+class UnsortedIndexError(KeyError):
+    """
+    Error raised when attempting to get a slice of a MultiIndex,
+    and the index has not been lexsorted. Subclass of `KeyError`.
+
+    .. versionadded:: 0.20.0
+    """
+
+
+class ParserError(ValueError):
+    """
+    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.
+    """
+
+
+class DtypeWarning(Warning):
+    """
+    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})
+    >>> df.to_csv('test.csv', index=False)
+    >>> df2 = pd.read_csv('test.csv')
+    ... # 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]
+    '1'
+    >>> type(df2.iloc[262140, 0])
+    <class 'str'>
+    >>> df2.iloc[262150, 0]
+    1
+    >>> type(df2.iloc[262150, 0])
+    <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})
+
+    No warning was issued.
+
+    >>> import os
+    >>> os.remove('test.csv')
+    """
+
+
+class EmptyDataError(ValueError):
+    """
+    Exception that is thrown in `pd.read_csv` (by both the C and
+    Python engines) when empty data or header is encountered.
+    """
+
+
+class ParserWarning(Warning):
+    """
+    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')
+    """
+
+
+class MergeError(ValueError):
+    """
+    Error raised when problems arise during merging due to problems
+    with input data. Subclass of `ValueError`.
+    """
+
+
+class NullFrequencyError(ValueError):
+    """
+    Error raised when a null `freq` attribute is used in an operation
+    that needs a non-null frequency, particularly `DatetimeIndex.shift`,
+    `TimedeltaIndex.shift`, `PeriodIndex.shift`.
+    """
+
+
+class AccessorRegistrationWarning(Warning):
+    """Warning for attribute conflicts in accessor registration."""
+
+
+class AbstractMethodError(NotImplementedError):
+    """Raise this error instead of NotImplementedError for abstract methods
+    while keeping compatibility with Python 2 and Python 3.
+    """
+
+    def __init__(self, class_instance, methodtype="method"):
+        types = {"method", "classmethod", "staticmethod", "property"}
+        if methodtype not in types:
+            msg = "methodtype must be one of {}, got {} instead.".format(
+                methodtype, types
+            )
+            raise ValueError(msg)
+        self.methodtype = methodtype
+        self.class_instance = class_instance
+
+    def __str__(self):
+        if self.methodtype == "classmethod":
+            name = self.class_instance.__name__
+        else:
+            name = self.class_instance.__class__.__name__
+        msg = "This {methodtype} must be defined in the concrete class {name}"
+        return msg.format(methodtype=self.methodtype, name=name)