DataFrame.apply in python pandas alters both original and duplicate DataFrames

Question

I'm having a bit of trouble altering a duplicated pandas DataFrame and not having the edits apply to both the duplicate and the original DataFrame. Here's an example. Say I create an arbitrary DataFrame from a list of dictionaries: ``` In [67]: d = [{'a':3, 'b':5}, {'a':1, 'b':1}] In [68]: d = DataFrame(d) In [69]: d Out[69]: a b 0 3 5 1 1 1 ``` Then I assign the 'd' dataframe to variable 'e' and apply some arbitrary math to column 'a' using apply: ``` In [70]: e = d In [71]: e['a'] = e['a'].apply(lambda x: x + 1) ``` The problem arises in that the apply function apparently applies to both the duplicate DataFrame 'e' and original DataFrame 'd', which I cannot for the life of me figure out: ``` In [72]: e # duplicate DataFrame Out[72]: a b 0 4 5 1 2 1 In [73]: d # original DataFrame, notice the alterations to frame 'e' were also applied Out[73]: a b 0 4 5 1 2 1 ``` I've searched both the pandas documentation and Google for a reason why this would be so, but to no avail. I can't underst…

Accepted Answer

This is not a pandas-specific issue. In Python, assignment never copies anything:

```
>>> a = [1,2,3]
>>> b = a
>>> b[0] = 'WHOA!'
>>> a
['WHOA!', 2, 3]
```

If you want a new DataFrame, make a copy with `e = d.copy()`.

Edit: I should clarify that assignment to a bare name never copies anything. Assignment to an item or attribute (e.g., `a[1] = x` or `a.foo = bar`) is converted into method calls under the hood and may do copying depending on what kind of object `a` is.

DataFrame.apply in python pandas alters both original and duplicate DataFrames

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