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    >>> p2 = p.append(4)
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        * Multiple updates are done to the same vector and the intermediate results are of no
          interest. In this case using an evolver may be a more efficient and easier to work with.
        * You need to pass a vector into a legacy function or a function that you have no control
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        The following example illustrates a typical workflow when working with evolvers. It also
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        Create the evolver and perform various mutating updates to it:

        >>> v1 = v(1, 2, 3, 4, 5)
        >>> e = v1.evolver()
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        >>> _ = e.append(6)
        >>> _ = e.extend([7, 8, 9])
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        9

        The underlying pvector remains the same:

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        The changes are kept in the evolver. An updated pvector can be created using the
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        >>> v2 = e.persistent()
        >>> v2
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        >>> news_paper = freeze({'articles': [{'author': 'Sara', 'content': 'A short article'},
        ...                                   {'author': 'Steve', 'content': 'A slightly longer article'}],
        ...                      'weather': {'temperature': '11C', 'wind': '5m/s'}})
        >>> short_news = news_paper.transform(['articles', ny, 'content'], lambda c: c[:25] + '...' if len(c) > 25 else c)
        >>> very_short_news = news_paper.transform(['articles', ny, 'content'], lambda c: c[:15] + '...' if len(c) > 15 else c)
        >>> very_short_news.articles[0].content
        'A short article'
        >>> very_short_news.articles[1].content
        'A slightly long...'

        When nothing has been transformed the original data structure is kept

        >>> short_news is news_paper
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