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1"""
2Expose public exceptions & warnings
3"""
4from __future__ import annotations
6import ctypes
8from pandas._config.config import OptionError
10from pandas._libs.tslibs import (
11 OutOfBoundsDatetime,
12 OutOfBoundsTimedelta,
13)
15from pandas.util.version import InvalidVersion
18class IntCastingNaNError(ValueError):
19 """
20 Exception raised when converting (``astype``) an array with NaN to an integer type.
22 Examples
23 --------
24 >>> pd.DataFrame(np.array([[1, np.nan], [2, 3]]), dtype="i8")
25 Traceback (most recent call last):
26 IntCastingNaNError: Cannot convert non-finite values (NA or inf) to integer
27 """
30class NullFrequencyError(ValueError):
31 """
32 Exception raised when a ``freq`` cannot be null.
34 Particularly ``DatetimeIndex.shift``, ``TimedeltaIndex.shift``,
35 ``PeriodIndex.shift``.
37 Examples
38 --------
39 >>> df = pd.DatetimeIndex(["2011-01-01 10:00", "2011-01-01"], freq=None)
40 >>> df.shift(2)
41 Traceback (most recent call last):
42 NullFrequencyError: Cannot shift with no freq
43 """
46class PerformanceWarning(Warning):
47 """
48 Warning raised when there is a possible performance impact.
50 Examples
51 --------
52 >>> df = pd.DataFrame({"jim": [0, 0, 1, 1],
53 ... "joe": ["x", "x", "z", "y"],
54 ... "jolie": [1, 2, 3, 4]})
55 >>> df = df.set_index(["jim", "joe"])
56 >>> df
57 jolie
58 jim joe
59 0 x 1
60 x 2
61 1 z 3
62 y 4
63 >>> df.loc[(1, 'z')] # doctest: +SKIP
64 # PerformanceWarning: indexing past lexsort depth may impact performance.
65 df.loc[(1, 'z')]
66 jolie
67 jim joe
68 1 z 3
69 """
72class UnsupportedFunctionCall(ValueError):
73 """
74 Exception raised when attempting to call a unsupported numpy function.
76 For example, ``np.cumsum(groupby_object)``.
78 Examples
79 --------
80 >>> df = pd.DataFrame({"A": [0, 0, 1, 1],
81 ... "B": ["x", "x", "z", "y"],
82 ... "C": [1, 2, 3, 4]}
83 ... )
84 >>> np.cumsum(df.groupby(["A"]))
85 Traceback (most recent call last):
86 UnsupportedFunctionCall: numpy operations are not valid with groupby.
87 Use .groupby(...).cumsum() instead
88 """
91class UnsortedIndexError(KeyError):
92 """
93 Error raised when slicing a MultiIndex which has not been lexsorted.
95 Subclass of `KeyError`.
97 Examples
98 --------
99 >>> df = pd.DataFrame({"cat": [0, 0, 1, 1],
100 ... "color": ["white", "white", "brown", "black"],
101 ... "lives": [4, 4, 3, 7]},
102 ... )
103 >>> df = df.set_index(["cat", "color"])
104 >>> df
105 lives
106 cat color
107 0 white 4
108 white 4
109 1 brown 3
110 black 7
111 >>> df.loc[(0, "black"):(1, "white")]
112 Traceback (most recent call last):
113 UnsortedIndexError: 'Key length (2) was greater
114 than MultiIndex lexsort depth (1)'
115 """
118class ParserError(ValueError):
119 """
120 Exception that is raised by an error encountered in parsing file contents.
122 This is a generic error raised for errors encountered when functions like
123 `read_csv` or `read_html` are parsing contents of a file.
125 See Also
126 --------
127 read_csv : Read CSV (comma-separated) file into a DataFrame.
128 read_html : Read HTML table into a DataFrame.
130 Examples
131 --------
132 >>> data = '''a,b,c
133 ... cat,foo,bar
134 ... dog,foo,"baz'''
135 >>> from io import StringIO
136 >>> pd.read_csv(StringIO(data), skipfooter=1, engine='python')
137 Traceback (most recent call last):
138 ParserError: ',' expected after '"'. Error could possibly be due
139 to parsing errors in the skipped footer rows
140 """
143class DtypeWarning(Warning):
144 """
145 Warning raised when reading different dtypes in a column from a file.
147 Raised for a dtype incompatibility. This can happen whenever `read_csv`
148 or `read_table` encounter non-uniform dtypes in a column(s) of a given
149 CSV file.
151 See Also
152 --------
153 read_csv : Read CSV (comma-separated) file into a DataFrame.
154 read_table : Read general delimited file into a DataFrame.
156 Notes
157 -----
158 This warning is issued when dealing with larger files because the dtype
159 checking happens per chunk read.
161 Despite the warning, the CSV file is read with mixed types in a single
162 column which will be an object type. See the examples below to better
163 understand this issue.
165 Examples
166 --------
167 This example creates and reads a large CSV file with a column that contains
168 `int` and `str`.
170 >>> df = pd.DataFrame({'a': (['1'] * 100000 + ['X'] * 100000 +
171 ... ['1'] * 100000),
172 ... 'b': ['b'] * 300000}) # doctest: +SKIP
173 >>> df.to_csv('test.csv', index=False) # doctest: +SKIP
174 >>> df2 = pd.read_csv('test.csv') # doctest: +SKIP
175 ... # DtypeWarning: Columns (0) have mixed types
177 Important to notice that ``df2`` will contain both `str` and `int` for the
178 same input, '1'.
180 >>> df2.iloc[262140, 0] # doctest: +SKIP
181 '1'
182 >>> type(df2.iloc[262140, 0]) # doctest: +SKIP
183 <class 'str'>
184 >>> df2.iloc[262150, 0] # doctest: +SKIP
185 1
186 >>> type(df2.iloc[262150, 0]) # doctest: +SKIP
187 <class 'int'>
189 One way to solve this issue is using the `dtype` parameter in the
190 `read_csv` and `read_table` functions to explicit the conversion:
192 >>> df2 = pd.read_csv('test.csv', sep=',', dtype={'a': str}) # doctest: +SKIP
194 No warning was issued.
195 """
198class EmptyDataError(ValueError):
199 """
200 Exception raised in ``pd.read_csv`` when empty data or header is encountered.
202 Examples
203 --------
204 >>> from io import StringIO
205 >>> empty = StringIO()
206 >>> pd.read_csv(empty)
207 Traceback (most recent call last):
208 EmptyDataError: No columns to parse from file
209 """
212class ParserWarning(Warning):
213 """
214 Warning raised when reading a file that doesn't use the default 'c' parser.
216 Raised by `pd.read_csv` and `pd.read_table` when it is necessary to change
217 parsers, generally from the default 'c' parser to 'python'.
219 It happens due to a lack of support or functionality for parsing a
220 particular attribute of a CSV file with the requested engine.
222 Currently, 'c' unsupported options include the following parameters:
224 1. `sep` other than a single character (e.g. regex separators)
225 2. `skipfooter` higher than 0
226 3. `sep=None` with `delim_whitespace=False`
228 The warning can be avoided by adding `engine='python'` as a parameter in
229 `pd.read_csv` and `pd.read_table` methods.
231 See Also
232 --------
233 pd.read_csv : Read CSV (comma-separated) file into DataFrame.
234 pd.read_table : Read general delimited file into DataFrame.
236 Examples
237 --------
238 Using a `sep` in `pd.read_csv` other than a single character:
240 >>> import io
241 >>> csv = '''a;b;c
242 ... 1;1,8
243 ... 1;2,1'''
244 >>> df = pd.read_csv(io.StringIO(csv), sep='[;,]') # doctest: +SKIP
245 ... # ParserWarning: Falling back to the 'python' engine...
247 Adding `engine='python'` to `pd.read_csv` removes the Warning:
249 >>> df = pd.read_csv(io.StringIO(csv), sep='[;,]', engine='python')
250 """
253class MergeError(ValueError):
254 """
255 Exception raised when merging data.
257 Subclass of ``ValueError``.
259 Examples
260 --------
261 >>> left = pd.DataFrame({"a": ["a", "b", "b", "d"],
262 ... "b": ["cat", "dog", "weasel", "horse"]},
263 ... index=range(4))
264 >>> right = pd.DataFrame({"a": ["a", "b", "c", "d"],
265 ... "c": ["meow", "bark", "chirp", "nay"]},
266 ... index=range(4)).set_index("a")
267 >>> left.join(right, on="a", validate="one_to_one",)
268 Traceback (most recent call last):
269 MergeError: Merge keys are not unique in left dataset; not a one-to-one merge
270 """
273class AbstractMethodError(NotImplementedError):
274 """
275 Raise this error instead of NotImplementedError for abstract methods.
277 Examples
278 --------
279 >>> class Foo:
280 ... @classmethod
281 ... def classmethod(cls):
282 ... raise pd.errors.AbstractMethodError(cls, methodtype="classmethod")
283 ... def method(self):
284 ... raise pd.errors.AbstractMethodError(self)
285 >>> test = Foo.classmethod()
286 Traceback (most recent call last):
287 AbstractMethodError: This classmethod must be defined in the concrete class Foo
289 >>> test2 = Foo().method()
290 Traceback (most recent call last):
291 AbstractMethodError: This classmethod must be defined in the concrete class Foo
292 """
294 def __init__(self, class_instance, methodtype: str = "method") -> None:
295 types = {"method", "classmethod", "staticmethod", "property"}
296 if methodtype not in types:
297 raise ValueError(
298 f"methodtype must be one of {methodtype}, got {types} instead."
299 )
300 self.methodtype = methodtype
301 self.class_instance = class_instance
303 def __str__(self) -> str:
304 if self.methodtype == "classmethod":
305 name = self.class_instance.__name__
306 else:
307 name = type(self.class_instance).__name__
308 return f"This {self.methodtype} must be defined in the concrete class {name}"
311class NumbaUtilError(Exception):
312 """
313 Error raised for unsupported Numba engine routines.
315 Examples
316 --------
317 >>> df = pd.DataFrame({"key": ["a", "a", "b", "b"], "data": [1, 2, 3, 4]},
318 ... columns=["key", "data"])
319 >>> def incorrect_function(x):
320 ... return sum(x) * 2.7
321 >>> df.groupby("key").agg(incorrect_function, engine="numba")
322 Traceback (most recent call last):
323 NumbaUtilError: The first 2 arguments to incorrect_function
324 must be ['values', 'index']
325 """
328class DuplicateLabelError(ValueError):
329 """
330 Error raised when an operation would introduce duplicate labels.
332 Examples
333 --------
334 >>> s = pd.Series([0, 1, 2], index=['a', 'b', 'c']).set_flags(
335 ... allows_duplicate_labels=False
336 ... )
337 >>> s.reindex(['a', 'a', 'b'])
338 Traceback (most recent call last):
339 ...
340 DuplicateLabelError: Index has duplicates.
341 positions
342 label
343 a [0, 1]
344 """
347class InvalidIndexError(Exception):
348 """
349 Exception raised when attempting to use an invalid index key.
351 Examples
352 --------
353 >>> idx = pd.MultiIndex.from_product([["x", "y"], [0, 1]])
354 >>> df = pd.DataFrame([[1, 1, 2, 2],
355 ... [3, 3, 4, 4]], columns=idx)
356 >>> df
357 x y
358 0 1 0 1
359 0 1 1 2 2
360 1 3 3 4 4
361 >>> df[:, 0]
362 Traceback (most recent call last):
363 InvalidIndexError: (slice(None, None, None), 0)
364 """
367class DataError(Exception):
368 """
369 Exceptionn raised when performing an operation on non-numerical data.
371 For example, calling ``ohlc`` on a non-numerical column or a function
372 on a rolling window.
374 Examples
375 --------
376 >>> ser = pd.Series(['a', 'b', 'c'])
377 >>> ser.rolling(2).sum()
378 Traceback (most recent call last):
379 DataError: No numeric types to aggregate
380 """
383class SpecificationError(Exception):
384 """
385 Exception raised by ``agg`` when the functions are ill-specified.
387 The exception raised in two scenarios.
389 The first way is calling ``agg`` on a
390 Dataframe or Series using a nested renamer (dict-of-dict).
392 The second way is calling ``agg`` on a Dataframe with duplicated functions
393 names without assigning column name.
395 Examples
396 --------
397 >>> df = pd.DataFrame({'A': [1, 1, 1, 2, 2],
398 ... 'B': range(5),
399 ... 'C': range(5)})
400 >>> df.groupby('A').B.agg({'foo': 'count'}) # doctest: +SKIP
401 ... # SpecificationError: nested renamer is not supported
403 >>> df.groupby('A').agg({'B': {'foo': ['sum', 'max']}}) # doctest: +SKIP
404 ... # SpecificationError: nested renamer is not supported
406 >>> df.groupby('A').agg(['min', 'min']) # doctest: +SKIP
407 ... # SpecificationError: nested renamer is not supported
408 """
411class SettingWithCopyError(ValueError):
412 """
413 Exception raised when trying to set on a copied slice from a ``DataFrame``.
415 The ``mode.chained_assignment`` needs to be set to set to 'raise.' This can
416 happen unintentionally when chained indexing.
418 For more information on evaluation order,
419 see :ref:`the user guide<indexing.evaluation_order>`.
421 For more information on view vs. copy,
422 see :ref:`the user guide<indexing.view_versus_copy>`.
424 Examples
425 --------
426 >>> pd.options.mode.chained_assignment = 'raise'
427 >>> df = pd.DataFrame({'A': [1, 1, 1, 2, 2]}, columns=['A'])
428 >>> df.loc[0:3]['A'] = 'a' # doctest: +SKIP
429 ... # SettingWithCopyError: A value is trying to be set on a copy of a...
430 """
433class SettingWithCopyWarning(Warning):
434 """
435 Warning raised when trying to set on a copied slice from a ``DataFrame``.
437 The ``mode.chained_assignment`` needs to be set to set to 'warn.'
438 'Warn' is the default option. This can happen unintentionally when
439 chained indexing.
441 For more information on evaluation order,
442 see :ref:`the user guide<indexing.evaluation_order>`.
444 For more information on view vs. copy,
445 see :ref:`the user guide<indexing.view_versus_copy>`.
447 Examples
448 --------
449 >>> df = pd.DataFrame({'A': [1, 1, 1, 2, 2]}, columns=['A'])
450 >>> df.loc[0:3]['A'] = 'a' # doctest: +SKIP
451 ... # SettingWithCopyWarning: A value is trying to be set on a copy of a...
452 """
455class ChainedAssignmentError(Warning):
456 """
457 Warning raised when trying to set using chained assignment.
459 When the ``mode.copy_on_write`` option is enabled, chained assignment can
460 never work. In such a situation, we are always setting into a temporary
461 object that is the result of an indexing operation (getitem), which under
462 Copy-on-Write always behaves as a copy. Thus, assigning through a chain
463 can never update the original Series or DataFrame.
465 For more information on view vs. copy,
466 see :ref:`the user guide<indexing.view_versus_copy>`.
468 Examples
469 --------
470 >>> pd.options.mode.copy_on_write = True
471 >>> df = pd.DataFrame({'A': [1, 1, 1, 2, 2]}, columns=['A'])
472 >>> df["A"][0:3] = 10 # doctest: +SKIP
473 ... # ChainedAssignmentError: ...
474 >>> pd.options.mode.copy_on_write = False
475 """
478_chained_assignment_msg = (
479 "A value is trying to be set on a copy of a DataFrame or Series "
480 "through chained assignment.\n"
481 "When using the Copy-on-Write mode, such chained assignment never works "
482 "to update the original DataFrame or Series, because the intermediate "
483 "object on which we are setting values always behaves as a copy.\n\n"
484 "Try using '.loc[row_indexer, col_indexer] = value' instead, to perform "
485 "the assignment in a single step.\n\n"
486 "See the caveats in the documentation: "
487 "https://pandas.pydata.org/pandas-docs/stable/user_guide/"
488 "indexing.html#returning-a-view-versus-a-copy"
489)
492_chained_assignment_method_msg = (
493 "A value is trying to be set on a copy of a DataFrame or Series "
494 "through chained assignment using an inplace method.\n"
495 "When using the Copy-on-Write mode, such inplace method never works "
496 "to update the original DataFrame or Series, because the intermediate "
497 "object on which we are setting values always behaves as a copy.\n\n"
498 "For example, when doing 'df[col].method(value, inplace=True)', try "
499 "using 'df.method({col: value}, inplace=True)' instead, to perform "
500 "the operation inplace on the original object.\n\n"
501)
504_chained_assignment_warning_msg = (
505 "ChainedAssignmentError: behaviour will change in pandas 3.0!\n"
506 "You are setting values through chained assignment. Currently this works "
507 "in certain cases, but when using Copy-on-Write (which will become the "
508 "default behaviour in pandas 3.0) this will never work to update the "
509 "original DataFrame or Series, because the intermediate object on which "
510 "we are setting values will behave as a copy.\n"
511 "A typical example is when you are setting values in a column of a "
512 "DataFrame, like:\n\n"
513 'df["col"][row_indexer] = value\n\n'
514 'Use `df.loc[row_indexer, "col"] = values` instead, to perform the '
515 "assignment in a single step and ensure this keeps updating the original `df`.\n\n"
516 "See the caveats in the documentation: "
517 "https://pandas.pydata.org/pandas-docs/stable/user_guide/"
518 "indexing.html#returning-a-view-versus-a-copy\n"
519)
522_chained_assignment_warning_method_msg = (
523 "A value is trying to be set on a copy of a DataFrame or Series "
524 "through chained assignment using an inplace method.\n"
525 "The behavior will change in pandas 3.0. This inplace method will "
526 "never work because the intermediate object on which we are setting "
527 "values always behaves as a copy.\n\n"
528 "For example, when doing 'df[col].method(value, inplace=True)', try "
529 "using 'df.method({col: value}, inplace=True)' or "
530 "df[col] = df[col].method(value) instead, to perform "
531 "the operation inplace on the original object.\n\n"
532)
535def _check_cacher(obj):
536 # This is a mess, selection paths that return a view set the _cacher attribute
537 # on the Series; most of them also set _item_cache which adds 1 to our relevant
538 # reference count, but iloc does not, so we have to check if we are actually
539 # in the item cache
540 if hasattr(obj, "_cacher"):
541 parent = obj._cacher[1]()
542 # parent could be dead
543 if parent is None:
544 return False
545 if hasattr(parent, "_item_cache"):
546 if obj._cacher[0] in parent._item_cache:
547 # Check if we are actually the item from item_cache, iloc creates a
548 # new object
549 return obj is parent._item_cache[obj._cacher[0]]
550 return False
553class NumExprClobberingError(NameError):
554 """
555 Exception raised when trying to use a built-in numexpr name as a variable name.
557 ``eval`` or ``query`` will throw the error if the engine is set
558 to 'numexpr'. 'numexpr' is the default engine value for these methods if the
559 numexpr package is installed.
561 Examples
562 --------
563 >>> df = pd.DataFrame({'abs': [1, 1, 1]})
564 >>> df.query("abs > 2") # doctest: +SKIP
565 ... # NumExprClobberingError: Variables in expression "(abs) > (2)" overlap...
566 >>> sin, a = 1, 2
567 >>> pd.eval("sin + a", engine='numexpr') # doctest: +SKIP
568 ... # NumExprClobberingError: Variables in expression "(sin) + (a)" overlap...
569 """
572class UndefinedVariableError(NameError):
573 """
574 Exception raised by ``query`` or ``eval`` when using an undefined variable name.
576 It will also specify whether the undefined variable is local or not.
578 Examples
579 --------
580 >>> df = pd.DataFrame({'A': [1, 1, 1]})
581 >>> df.query("A > x") # doctest: +SKIP
582 ... # UndefinedVariableError: name 'x' is not defined
583 >>> df.query("A > @y") # doctest: +SKIP
584 ... # UndefinedVariableError: local variable 'y' is not defined
585 >>> pd.eval('x + 1') # doctest: +SKIP
586 ... # UndefinedVariableError: name 'x' is not defined
587 """
589 def __init__(self, name: str, is_local: bool | None = None) -> None:
590 base_msg = f"{repr(name)} is not defined"
591 if is_local:
592 msg = f"local variable {base_msg}"
593 else:
594 msg = f"name {base_msg}"
595 super().__init__(msg)
598class IndexingError(Exception):
599 """
600 Exception is raised when trying to index and there is a mismatch in dimensions.
602 Examples
603 --------
604 >>> df = pd.DataFrame({'A': [1, 1, 1]})
605 >>> df.loc[..., ..., 'A'] # doctest: +SKIP
606 ... # IndexingError: indexer may only contain one '...' entry
607 >>> df = pd.DataFrame({'A': [1, 1, 1]})
608 >>> df.loc[1, ..., ...] # doctest: +SKIP
609 ... # IndexingError: Too many indexers
610 >>> df[pd.Series([True], dtype=bool)] # doctest: +SKIP
611 ... # IndexingError: Unalignable boolean Series provided as indexer...
612 >>> s = pd.Series(range(2),
613 ... index = pd.MultiIndex.from_product([["a", "b"], ["c"]]))
614 >>> s.loc["a", "c", "d"] # doctest: +SKIP
615 ... # IndexingError: Too many indexers
616 """
619class PyperclipException(RuntimeError):
620 """
621 Exception raised when clipboard functionality is unsupported.
623 Raised by ``to_clipboard()`` and ``read_clipboard()``.
624 """
627class PyperclipWindowsException(PyperclipException):
628 """
629 Exception raised when clipboard functionality is unsupported by Windows.
631 Access to the clipboard handle would be denied due to some other
632 window process is accessing it.
633 """
635 def __init__(self, message: str) -> None:
636 # attr only exists on Windows, so typing fails on other platforms
637 message += f" ({ctypes.WinError()})" # type: ignore[attr-defined]
638 super().__init__(message)
641class CSSWarning(UserWarning):
642 """
643 Warning is raised when converting css styling fails.
645 This can be due to the styling not having an equivalent value or because the
646 styling isn't properly formatted.
648 Examples
649 --------
650 >>> df = pd.DataFrame({'A': [1, 1, 1]})
651 >>> df.style.applymap(
652 ... lambda x: 'background-color: blueGreenRed;'
653 ... ).to_excel('styled.xlsx') # doctest: +SKIP
654 CSSWarning: Unhandled color format: 'blueGreenRed'
655 >>> df.style.applymap(
656 ... lambda x: 'border: 1px solid red red;'
657 ... ).to_excel('styled.xlsx') # doctest: +SKIP
658 CSSWarning: Unhandled color format: 'blueGreenRed'
659 """
662class PossibleDataLossError(Exception):
663 """
664 Exception raised when trying to open a HDFStore file when already opened.
666 Examples
667 --------
668 >>> store = pd.HDFStore('my-store', 'a') # doctest: +SKIP
669 >>> store.open("w") # doctest: +SKIP
670 ... # PossibleDataLossError: Re-opening the file [my-store] with mode [a]...
671 """
674class ClosedFileError(Exception):
675 """
676 Exception is raised when trying to perform an operation on a closed HDFStore file.
678 Examples
679 --------
680 >>> store = pd.HDFStore('my-store', 'a') # doctest: +SKIP
681 >>> store.close() # doctest: +SKIP
682 >>> store.keys() # doctest: +SKIP
683 ... # ClosedFileError: my-store file is not open!
684 """
687class IncompatibilityWarning(Warning):
688 """
689 Warning raised when trying to use where criteria on an incompatible HDF5 file.
690 """
693class AttributeConflictWarning(Warning):
694 """
695 Warning raised when index attributes conflict when using HDFStore.
697 Occurs when attempting to append an index with a different
698 name than the existing index on an HDFStore or attempting to append an index with a
699 different frequency than the existing index on an HDFStore.
701 Examples
702 --------
703 >>> idx1 = pd.Index(['a', 'b'], name='name1')
704 >>> df1 = pd.DataFrame([[1, 2], [3, 4]], index=idx1)
705 >>> df1.to_hdf('file', 'data', 'w', append=True) # doctest: +SKIP
706 >>> idx2 = pd.Index(['c', 'd'], name='name2')
707 >>> df2 = pd.DataFrame([[5, 6], [7, 8]], index=idx2)
708 >>> df2.to_hdf('file', 'data', 'a', append=True) # doctest: +SKIP
709 AttributeConflictWarning: the [index_name] attribute of the existing index is
710 [name1] which conflicts with the new [name2]...
711 """
714class DatabaseError(OSError):
715 """
716 Error is raised when executing sql with bad syntax or sql that throws an error.
718 Examples
719 --------
720 >>> from sqlite3 import connect
721 >>> conn = connect(':memory:')
722 >>> pd.read_sql('select * test', conn) # doctest: +SKIP
723 ... # DatabaseError: Execution failed on sql 'test': near "test": syntax error
724 """
727class PossiblePrecisionLoss(Warning):
728 """
729 Warning raised by to_stata on a column with a value outside or equal to int64.
731 When the column value is outside or equal to the int64 value the column is
732 converted to a float64 dtype.
734 Examples
735 --------
736 >>> df = pd.DataFrame({"s": pd.Series([1, 2**53], dtype=np.int64)})
737 >>> df.to_stata('test') # doctest: +SKIP
738 ... # PossiblePrecisionLoss: Column converted from int64 to float64...
739 """
742class ValueLabelTypeMismatch(Warning):
743 """
744 Warning raised by to_stata on a category column that contains non-string values.
746 Examples
747 --------
748 >>> df = pd.DataFrame({"categories": pd.Series(["a", 2], dtype="category")})
749 >>> df.to_stata('test') # doctest: +SKIP
750 ... # ValueLabelTypeMismatch: Stata value labels (pandas categories) must be str...
751 """
754class InvalidColumnName(Warning):
755 """
756 Warning raised by to_stata the column contains a non-valid stata name.
758 Because the column name is an invalid Stata variable, the name needs to be
759 converted.
761 Examples
762 --------
763 >>> df = pd.DataFrame({"0categories": pd.Series([2, 2])})
764 >>> df.to_stata('test') # doctest: +SKIP
765 ... # InvalidColumnName: Not all pandas column names were valid Stata variable...
766 """
769class CategoricalConversionWarning(Warning):
770 """
771 Warning is raised when reading a partial labeled Stata file using a iterator.
773 Examples
774 --------
775 >>> from pandas.io.stata import StataReader
776 >>> with StataReader('dta_file', chunksize=2) as reader: # doctest: +SKIP
777 ... for i, block in enumerate(reader):
778 ... print(i, block)
779 ... # CategoricalConversionWarning: One or more series with value labels...
780 """
783class LossySetitemError(Exception):
784 """
785 Raised when trying to do a __setitem__ on an np.ndarray that is not lossless.
787 Notes
788 -----
789 This is an internal error.
790 """
793class NoBufferPresent(Exception):
794 """
795 Exception is raised in _get_data_buffer to signal that there is no requested buffer.
796 """
799class InvalidComparison(Exception):
800 """
801 Exception is raised by _validate_comparison_value to indicate an invalid comparison.
803 Notes
804 -----
805 This is an internal error.
806 """
809__all__ = [
810 "AbstractMethodError",
811 "AttributeConflictWarning",
812 "CategoricalConversionWarning",
813 "ClosedFileError",
814 "CSSWarning",
815 "DatabaseError",
816 "DataError",
817 "DtypeWarning",
818 "DuplicateLabelError",
819 "EmptyDataError",
820 "IncompatibilityWarning",
821 "IntCastingNaNError",
822 "InvalidColumnName",
823 "InvalidComparison",
824 "InvalidIndexError",
825 "InvalidVersion",
826 "IndexingError",
827 "LossySetitemError",
828 "MergeError",
829 "NoBufferPresent",
830 "NullFrequencyError",
831 "NumbaUtilError",
832 "NumExprClobberingError",
833 "OptionError",
834 "OutOfBoundsDatetime",
835 "OutOfBoundsTimedelta",
836 "ParserError",
837 "ParserWarning",
838 "PerformanceWarning",
839 "PossibleDataLossError",
840 "PossiblePrecisionLoss",
841 "PyperclipException",
842 "PyperclipWindowsException",
843 "SettingWithCopyError",
844 "SettingWithCopyWarning",
845 "SpecificationError",
846 "UndefinedVariableError",
847 "UnsortedIndexError",
848 "UnsupportedFunctionCall",
849 "ValueLabelTypeMismatch",
850]