How to use 'pandas filter rows by condition' in Python

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def filter(self, func):
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    """
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    Return a copy of a DataFrame excluding elements from groups that
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    do not satisfy the boolean criterion specified by func.
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    Parameters
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    ----------
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    f : function
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        Function to apply to each subframe. Should return True or False.
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    dropna : Drop groups that do not pass the filter. True by default;
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        if False, groups that evaluate False are filled with NaNs.
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    Returns
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    -------
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    filtered : DataFrame
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    Notes
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    -----
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    Each subframe is endowed the attribute 'name' in case you need to know
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    which group you are working on.
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    Examples
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    --------
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    >>> df = ks.DataFrame({'A' : ['foo', 'bar', 'foo', 'bar',
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    ...                           'foo', 'bar'],
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    ...                    'B' : [1, 2, 3, 4, 5, 6],
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    ...                    'C' : [2.0, 5., 8., 1., 2., 9.]}, columns=['A', 'B', 'C'])
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    >>> grouped = df.groupby('A')
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    >>> grouped.filter(lambda x: x['B'].mean() > 3.)
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         A  B    C
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    1  bar  2  5.0
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    3  bar  4  1.0
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    5  bar  6  9.0
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    """
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    if not isinstance(func, Callable):
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        raise TypeError("%s object is not callable" % type(func))
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    data_schema = self._kdf._sdf.schema
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    groupby_names = [s.name for s in self._groupkeys]
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    def pandas_filter(pdf):
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        return pdf.groupby(groupby_names).filter(func)
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    sdf = self._spark_group_map_apply(
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        pandas_filter, data_schema, retain_index=True)
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    return DataFrame(self._kdf._internal.copy(
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        sdf=sdf,
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        column_scols=[scol_for(sdf, col) for col in self._kdf._internal.data_columns]))