Copy argument vs Series.Copy()

问题

y = pd.Series(x, copy=True,dtype=float)
z = pd.Series(x, copy=True)
a = pd.Series(x)
f = pd.Series.copy(x)

All the above expressions give the same output of x value and even after updating the x value the change is not reflecting. So I need to know what is the use of copy as argument and the series.copy() and also how to copy x series to some other series such that any changes made in x is reflected back in the new series also.

If any thing is wrong or not possible please forgive me... I'm a newbie to Python and I'm asking these questions out of my curiosity...

Thanks a lot in advance for the help!

回答1:

If we look at the source code of pandas Series we can see the following,

def __init__(self, data=None, index=None, dtype=None, name=None,
             copy=False, fastpath=False):

    if not isinstance(data, SingleBlockManager):
        data = SingleBlockManager(data, index, fastpath=True)
    if copy:
        data = data.copy()

So you have passed x for the data argument, therefore, the line data = data.copy() makes a copy of x. This should be equivalent to doing,

g = pd.Series(x.copy())

This is subtly different to pd.Series.copy(x) which now makes a copy of the Series object itself, not the underlying x. Therefore, the underlying x might still be referenced by the new Series.

The idea of making a copy is that you be confident that any operations you perform on the Series do not change the original object x.

来源：https://stackoverflow.com/questions/48185809/copy-argument-vs-series-copy

标签

python

pandas