@@ -2063,18 +2063,77 @@ def __delitem__(self, key):
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def take (self , indices , axis = 0 , convert = True , is_copy = True , ** kwargs ):
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"""
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- Analogous to ndarray.take
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+ Return the elements in the given *positional* indices along an axis.
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+
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+ This means that we are not indexing according to actual values in
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+ the index attribute of the object. We are indexing according to the
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+ actual position of the element in the object.
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Parameters
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----------
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- indices : list / array of ints
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+ indices : array-like
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+ An array of ints indicating which positions to take.
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axis : int, default 0
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- convert : translate neg to pos indices (default)
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- is_copy : mark the returned frame as a copy
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+ The axis on which to select elements. "0" means that we are
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+ selecting rows, "1" means that we are selecting columns, etc.
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+ convert : bool, default True
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+ Whether to convert negative indices to positive ones, just as with
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+ indexing into Python lists. For example, if `-1` was passed in,
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+ this index would be converted ``n - 1``.
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+ is_copy : bool, default True
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+ Whether to return a copy of the original object or not.
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+
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+ Examples
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+ --------
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+ >>> df = pd.DataFrame([('falcon', 'bird', 389.0),
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+ ('parrot', 'bird', 24.0),
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+ ('lion', 'mammal', 80.5),
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+ ('monkey', 'mammal', np.nan)],
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+ columns=('name', 'class', 'max_speed'),
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+ index=[0, 2, 3, 1])
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+ >>> df
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+ name class max_speed
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+ 0 falcon bird 389.0
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+ 2 parrot bird 24.0
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+ 3 lion mammal 80.5
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+ 1 monkey mammal NaN
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+
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+ Take elements at positions 0 and 3 along the axis 0 (default).
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+
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+ Note how the actual indices selected (0 and 1) do not correspond to
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+ our selected indices 0 and 3. That's because we are selecting the 0th
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+ and 3rd rows, not rows whose indices equal 0 and 3.
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+
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+ >>> df.take([0, 3])
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+ 0 falcon bird 389.0
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+ 1 monkey mammal NaN
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+
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+ Take elements at indices 1 and 2 along the axis 1 (column selection).
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+
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+ >>> df.take([1, 2], axis=1)
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+ class max_speed
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+ 0 bird 389.0
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+ 2 bird 24.0
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+ 3 mammal 80.5
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+ 1 mammal NaN
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+
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+ We may take elements using negative integers for positive indices,
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+ starting from the end of the object, just like with Python lists.
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+
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+ >>> df.take([-1, -2])
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+ name class max_speed
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+ 1 monkey mammal NaN
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+ 3 lion mammal 80.5
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Returns
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-------
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taken : type of caller
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+ An array-like containing the elements taken from the object.
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+
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+ See Also
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+ --------
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+ numpy.ndarray.take
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+ numpy.take
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"""
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nv .validate_take (tuple (), kwargs )
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self ._consolidate_inplace ()
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