Re-assignment in Pandas: Copy or view?

Question

Say we have the following dataframe:

df = pd.DataFrame({'A' : ['foo', 'bar', 'foo', 'bar',
                          'foo', 'bar', 'foo', 'foo'],  
                   'B' : ['one', 'one', 'two', 'three',
                          'two', 'two', 'one', 'three'],
                   'C' : randn(8), 'D' : randn(8)})

shown below:

> df
     A      B         C         D
0  foo    one  0.846192  0.478651
1  bar    one  2.352421  0.141416
2  foo    two -1.413699 -0.577435
3  bar  three  0.569572 -0.508984
4  foo    two -1.384092  0.659098
5  bar    two  0.845167 -0.381740
6  foo    one  3.355336 -0.791471
7  foo  three  0.303303  0.452966

And then I do the following:

df2 = df
df  = df[df['C']>0]

If you now look at df and df2 you will see that df2 holds the original data, whereas df was updated to only keep the values where C was greater than 0.

I thought Pandas wasn't supposed to make a copy in an assignment like df2 = df and that it would only make copies with either:

1. df2 = df.copy(deep=True)
2. df2 = copy.deepcopy(df)

What happened above then? Did df2 = df make a copy? I presume that the answer is no, so it must have been df = df[df['C']>0] that made a copy, and I presume that, if I didn't have df2=df above, there would have been a copy without any reference to it floating in memory. Is that correct?

Note: I read through Returning a view versus a copy and I wonder if the following:

Whenever an array of labels or a boolean vector are involved in the indexing operation, the result will be a copy.

explains this behavior.

Answer 1

It's not that df2 is making the copy, it's that the df = df[df['C'] > 0] is returning a copy.

Just print out the ids and you'll see:

print id(df)
df2 = df
print id(df2)
df = df[df['C'] > 0]
print id(df)