Ë
|
nñúh j ã ó d Z ddlmZ ddlZddlmZ ddlmZmZ ddl m
|
Z
|
G d de « Z G d d
|
e « Z G d d e« Z G d de « Z G d de« Z G d de « Z G d de« Z G d de « Z G d de« Z G d de « Z G d d e« Z G d d e« Z G d d e « Z G d! d"e« Z G d# d$e« Z G d% d&e« Z G d' d(e « Z G d) d*e« Z! G d+ d,e« Z"d-Z#d.Z$d/Z%d0Z&d1 Z' G d2 d3e(« Z) G d4 d5e(« Z* G d6 d7e« Z+ G d8 d9e,« Z- G d: d;e-« Z. G d< d=e/« Z0 G d> d?e« Z1 G d@ dAe« Z2 G dB dCe« Z3 G dD dEe« Z4 G dF dGe5« Z6 G dH dIe« Z7 G dJ dKe« Z8 G dL dMe« Z9 G dN dOe« Z: G dP dQe« Z; G dR dSe« Z< G dT dUe« Z=g dV¢Z>y)Wz%
|
Expose public exceptions & warnings
|
é )Ú annotationsN)Ú OptionError)ÚOutOfBoundsDatetimeÚOutOfBoundsTimedelta)ÚInvalidVersionc ó e Zd ZdZy)ÚIntCastingNaNErrora1
|
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©Ú__name__Ú
|
__module__Ú __qualname__Ú__doc__© ó úIH:\Change_password\venv_build\Lib\site-packages\pandas/errors/__init__.pyr r ó òr r c ó e Zd ZdZy)ÚNullFrequencyErrorao
|
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
|
r r r r r ó ò r r c ó e Zd ZdZy)ÚPerformanceWarningaT
|
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
|
r r r r r . s òr r c ó e Zd ZdZy)ÚUnsupportedFunctionCalla
|
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
|
r r r r r H ó òr r c ó e Zd ZdZy)ÚUnsortedIndexErrora§
|
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
|
r r r r r [ s òr r c ó e Zd ZdZy)Ú ParserErroraÎ
|
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
|
r r r r r v s òr r c ó e Zd ZdZy)Ú 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
|
<class 'str'>
|
>>> df2.iloc[262150, 0] # doctest: +SKIP
|
1
|
>>> type(df2.iloc[262150, 0]) # doctest: +SKIP
|
<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}) # doctest: +SKIP
|
|
No warning was issued.
|
Nr
|
r r r r r s ò3r r c ó e Zd ZdZy)ÚEmptyDataErrora!
|
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
|
r r r r" r" Æ ó ò
|
r r" c ó e Zd ZdZy)Ú 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
|
r r r r% r% Ô s ò%r r% c ó e Zd ZdZy)Ú
|
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
|
r r r r' r' ý ó òr r' c ó" e Zd ZdZdddZddZy)ÚAbstractMethodErrora
|
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
|
c óT h d£}||vrt d| d| d« || _ || _ y )N> ÚmethodÚpropertyÚ classmethodÚ staticmethodzmethodtype must be one of z, got z instead.)Ú
|
ValueErrorÚ
|
methodtypeÚclass_instance)Úselfr2 r1 Útypess r Ú__init__z AbstractMethodError.__init__& s? ÚEØ UÑ "Ü Ø,¨Z¨L¸¸u¸gÀYÐOóð ð %Ø ,Õr c ó° | j dk( r| j j }n t | j « j }d| j d| S )Nr. zThis z' must be defined in the concrete class )r1 r2 r Útype)r3 Únames r Ú__str__zAbstractMethodError.__str__/ sN Ø ??mÒ +Ø×&Ñ&×/Ñ/Dä×+Ñ+Ó,×5Ñ5DØtÐ'Ð'NÈtÈfÐUÐUr N)r, )r1 ÚstrÚreturnÚNone)r; r: )r r r r r5 r9 r r r r* r* s ñô(-ôVr r* c ó e Zd ZdZy)ÚNumbaUtilErroraæ
|
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
|
r r r r> r> 7 s ò r r> c ó e Zd ZdZy)ÚDuplicateLabelErrora
|
Error raised when an operation would introduce duplicate labels.
|
|
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
|
r r r r@ r@ H r r r@ c ó e Zd ZdZy)ÚInvalidIndexErroraË
|
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
|
r r r rB rB [ r( r rB c ó e Zd ZdZy)Ú 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
|
r r r rD rD o r r rD c ó e Zd ZdZy)ÚSpecificationErrora
|
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
|
r r r rF rF s òr rF c ó e Zd ZdZy)ÚSettingWithCopyErrora¶
|
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<indexing.evaluation_order>`.
|
|
For more information on view vs. copy,
|
see :ref:`the user guide<indexing.view_versus_copy>`.
|
|
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
|
r r r rH rH ó òr rH c ó e Zd ZdZy)ÚSettingWithCopyWarninga¢
|
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<indexing.evaluation_order>`.
|
|
For more information on view vs. copy,
|
see :ref:`the user guide<indexing.view_versus_copy>`.
|
|
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
|
r r r rK rK ± rI r rK c ó e Zd ZdZy)ÚChainedAssignmentErrora
|
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<indexing.view_versus_copy>`.
|
|
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
|
r r r rM rM Ç s òr rM a A 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-copyað A 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.
|
|
a ChainedAssignmentError: behaviour will change in pandas 3.0!
|
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
|
A typical example is when you are setting values in a column of a DataFrame, like:
|
|
df["col"][row_indexer] = value
|
|
Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
|
|
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
|
að A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.
|
The behavior will change in pandas 3.0. This inplace method will never work 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)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.
|
|
c óÔ t | d« r\ | j d « }|yt |d« r9| j d |j v r | |j | j d u S y)NÚ_cacheré FÚ _item_cacher )ÚhasattrrO rQ )ÚobjÚparents r Ú _check_cacherrU sk ô
|
sIÔ Ø Q Ó!à >ØÜ 6=Ô )Ø{{1~ ×!3Ñ!3Ñ3ð f× 0Ñ 0°·±¸Q± Ñ @Ð@Ð@Ø r c ó e Zd ZdZy)ÚNumExprClobberingErrora
|
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
|
r r r rW rW ) r r rW c ó& e Zd ZdZdd fd Z xZS )ÚUndefinedVariableErrora$
|
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
|
c óZ t |« d}|rd| }nd| }t | |« y )Nz is not definedzlocal variable zname )ÚreprÚsuperr5 )r3 r8 Úis_localÚbase_msgÚmsgÚ __class__s r r5 z UndefinedVariableError.__init__M s; ø Ü4j\ Ð1Ù Ø# H :Ð.Cà(Ð$CÜ ÑÕ r )N)r8 r: r] z bool | Noner; r< ©r r r r r5 Ú __classcell__©r` s @r rY rY < s ø ñ÷ ò r rY c ó e Zd ZdZy)Ú IndexingErroraã
|
Exception is raised when trying to index and there is a mismatch in dimensions.
|
|
Examples
|
--------
|
>>> df = pd.DataFrame({'A': [1, 1, 1]})
|
>>> df.loc[..., ..., 'A'] # doctest: +SKIP
|
... # IndexingError: indexer may only contain one '...' entry
|
>>> df = pd.DataFrame({'A': [1, 1, 1]})
|
>>> df.loc[1, ..., ...] # doctest: +SKIP
|
... # IndexingError: Too many indexers
|
>>> df[pd.Series([True], dtype=bool)] # doctest: +SKIP
|
... # IndexingError: Unalignable boolean Series provided as indexer...
|
>>> s = pd.Series(range(2),
|
... index = pd.MultiIndex.from_product([["a", "b"], ["c"]]))
|
>>> s.loc["a", "c", "d"] # doctest: +SKIP
|
... # IndexingError: Too many indexers
|
Nr
|
r r r re re V ó òr re c ó e Zd ZdZy)ÚPyperclipExceptionz
|
Exception raised when clipboard functionality is unsupported.
|
|
Raised by ``to_clipboard()`` and ``read_clipboard()``.
|
Nr
|
r r r rh rh k s òr rh c ó$ e Zd ZdZd fd Z xZS )ÚPyperclipWindowsExceptionz¼
|
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.
|
c óZ |dt j « dz }t | |« y )Nz (ú))ÚctypesÚWinErrorr\ r5 )r3 Úmessager` s r r5 z"PyperclipWindowsException.__init__{ s+ ø àRÓ)Ð*¨!Ð,Ñ,Ü ÑÕ!r )ro r: r; r< ra rc s @r rj rj s s ø ñ÷"ñ "r rj c ó e Zd ZdZy)Ú
|
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
|
r r r rq rq rf r rq c ó e Zd ZdZy)ÚPossibleDataLossErrora&
|
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
|
r r r rs rs r r rs c ó e Zd ZdZy)ÚClosedFileErrora8
|
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
|
r r r ru ru ¢ ó ò r ru c ó e Zd ZdZy)ÚIncompatibilityWarningzX
|
Warning raised when trying to use where criteria on an incompatible HDF5 file.
|
Nr
|
r r r rx rx ¯ ó òr rx c ó e Zd ZdZy)ÚAttributeConflictWarninga)
|
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
|
r r r r{ r{ µ rf r r{ c ó e Zd ZdZy)Ú 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
|
r r r r} r} Ê rv r r} c ó e Zd ZdZy)ÚPossiblePrecisionLossa¥
|
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
|
r r r r r × ó ò r r c ó e Zd ZdZy)ÚValueLabelTypeMismatchaK
|
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
|
r r r r r æ r r r c ó e Zd ZdZy)ÚInvalidColumnNamea
|
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
|
r r r r r ò r r r c ó e Zd ZdZy)Ú 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
|
r r r r r r# r r c ó e Zd ZdZy)ÚLossySetitemErrorz
|
Raised when trying to do a __setitem__ on an np.ndarray that is not lossless.
|
|
Notes
|
-----
|
This is an internal error.
|
Nr
|
r r r r r ó òr r c ó e Zd ZdZy)ÚNoBufferPresentz^
|
Exception is raised in _get_data_buffer to signal that there is no requested buffer.
|
Nr
|
r r r r r ry r r c ó e Zd ZdZy)ÚInvalidComparisonz
|
Exception is raised by _validate_comparison_value to indicate an invalid comparison.
|
|
Notes
|
-----
|
This is an internal error.
|
Nr
|
r r r r r r r r )(r* r{ r ru rq r} rD r r@ r" rx r r r rB r re r r' r r r> rW r r r r r% r rs r rh rj rH rK rF rY r r r )?r Ú
|
__future__r rm Úpandas._config.configr Úpandas._libs.tslibsr r Úpandas.util.versionr r0 r r ÚWarningr r ÚKeyErrorr r r r" r% r' ÚNotImplementedErrorr* Ú Exceptionr> r@ rB rD rF rH rK rM Ú_chained_assignment_msgÚ _chained_assignment_method_msgÚ _chained_assignment_warning_msgÚ&_chained_assignment_warning_method_msgrU Ú NameErrorrW rY re Ú RuntimeErrorrh rj Ú UserWarningrq rs ru rx r{ ÚOSErrorr} r r r r r r r Ú__all__r r r ú<module>r s ðñõ #ã å -÷õ
|
/ô ô ô ô ô ô ô4jô ô&ô ô6*ô ô247ô 4ôn Zô ô &Gô &ôRô ô(#VÐ-ô #VôLYô ô"*ô ô& ô ô( ô ô ô ô8:ô ô,Wô ô,Wô ð0 3ð ð 8ð ð 5ð ð&8ð 'òô$Yô ô& Yô ô4Iô ô* ô ô "Ð 2ô "ô ô ô* Iô ô
|
iô
|
ôWô ô wô ô*
|
Gô
|
ô Gô ô Wô ô ô ô 7ô ô ô ôiô ô ô ò) r
|
