Classe « DataFrame »

Méthode pandas.DataFrame.query

Signature de la méthode query

def query(self, expr, inplace=False, **kwargs) 

Description

query.__doc__

        Query the columns of a DataFrame with a boolean expression.

        Parameters
        ----------
        expr : str
            The query string to evaluate.

            You can refer to variables
            in the environment by prefixing them with an '@' character like
            ``@a + b``.

            You can refer to column names that are not valid Python variable names
            by surrounding them in backticks. Thus, column names containing spaces
            or punctuations (besides underscores) or starting with digits must be
            surrounded by backticks. (For example, a column named "Area (cm^2) would
            be referenced as `Area (cm^2)`). Column names which are Python keywords
            (like "list", "for", "import", etc) cannot be used.

            For example, if one of your columns is called ``a a`` and you want
            to sum it with ``b``, your query should be ```a a` + b``.

            .. versionadded:: 0.25.0
                Backtick quoting introduced.

            .. versionadded:: 1.0.0
                Expanding functionality of backtick quoting for more than only spaces.

        inplace : bool
            Whether the query should modify the data in place or return
            a modified copy.
        **kwargs
            See the documentation for :func:`eval` for complete details
            on the keyword arguments accepted by :meth:`DataFrame.query`.

        Returns
        -------
        DataFrame or None
            DataFrame resulting from the provided query expression or
            None if ``inplace=True``.

        See Also
        --------
        eval : Evaluate a string describing operations on
            DataFrame columns.
        DataFrame.eval : Evaluate a string describing operations on
            DataFrame columns.

        Notes
        -----
        The result of the evaluation of this expression is first passed to
        :attr:`DataFrame.loc` and if that fails because of a
        multidimensional key (e.g., a DataFrame) then the result will be passed
        to :meth:`DataFrame.__getitem__`.

        This method uses the top-level :func:`eval` function to
        evaluate the passed query.

        The :meth:`~pandas.DataFrame.query` method uses a slightly
        modified Python syntax by default. For example, the ``&`` and ``|``
        (bitwise) operators have the precedence of their boolean cousins,
        :keyword:`and` and :keyword:`or`. This *is* syntactically valid Python,
        however the semantics are different.

        You can change the semantics of the expression by passing the keyword
        argument ``parser='python'``. This enforces the same semantics as
        evaluation in Python space. Likewise, you can pass ``engine='python'``
        to evaluate an expression using Python itself as a backend. This is not
        recommended as it is inefficient compared to using ``numexpr`` as the
        engine.

        The :attr:`DataFrame.index` and
        :attr:`DataFrame.columns` attributes of the
        :class:`~pandas.DataFrame` instance are placed in the query namespace
        by default, which allows you to treat both the index and columns of the
        frame as a column in the frame.
        The identifier ``index`` is used for the frame index; you can also
        use the name of the index to identify it in a query. Please note that
        Python keywords may not be used as identifiers.

        For further details and examples see the ``query`` documentation in
        :ref:`indexing <indexing.query>`.

        *Backtick quoted variables*

        Backtick quoted variables are parsed as literal Python code and
        are converted internally to a Python valid identifier.
        This can lead to the following problems.

        During parsing a number of disallowed characters inside the backtick
        quoted string are replaced by strings that are allowed as a Python identifier.
        These characters include all operators in Python, the space character, the
        question mark, the exclamation mark, the dollar sign, and the euro sign.
        For other characters that fall outside the ASCII range (U+0001..U+007F)
        and those that are not further specified in PEP 3131,
        the query parser will raise an error.
        This excludes whitespace different than the space character,
        but also the hashtag (as it is used for comments) and the backtick
        itself (backtick can also not be escaped).

        In a special case, quotes that make a pair around a backtick can
        confuse the parser.
        For example, ```it's` > `that's``` will raise an error,
        as it forms a quoted string (``'s > `that'``) with a backtick inside.

        See also the Python documentation about lexical analysis
        (https://docs.python.org/3/reference/lexical_analysis.html)
        in combination with the source code in :mod:`pandas.core.computation.parsing`.

        Examples
        --------
        >>> df = pd.DataFrame({'A': range(1, 6),
        ...                    'B': range(10, 0, -2),
        ...                    'C C': range(10, 5, -1)})
        >>> df
           A   B  C C
        0  1  10   10
        1  2   8    9
        2  3   6    8
        3  4   4    7
        4  5   2    6
        >>> df.query('A > B')
           A  B  C C
        4  5  2    6

        The previous expression is equivalent to

        >>> df[df.A > df.B]
           A  B  C C
        4  5  2    6

        For columns with spaces in their name, you can use backtick quoting.

        >>> df.query('B == `C C`')
           A   B  C C
        0  1  10   10

        The previous expression is equivalent to

        >>> df[df.B == df['C C']]
           A   B  C C
        0  1  10   10