master looks ok on these (no warnings) .......

IIRC 0.13.1 did have some false positives

I don't get on 0.13.1

I am on numpy 1.7.1....that could be a factor....

Strange, I get these on

In [7]: pd.__version__
Out[7]: '0.13.1-496-gd0aebea'

In [8]: np.__version__
Out[8]: '1.7.1'

In [1]: pd.__version__
Out[1]: '0.13.1-533-g2ada054'

In [2]: np.__version__
Out[2]: '1.7.1'

hmm...do you get them ALWAYS, even on a fresh ipython?

Strange, first it seemed that I couldn't replicate this with a fresh ipython, but now it seems that I only see it if I first print the frame:

If I run this on a fresh ipython, I get the warnings:

df = pd.DataFrame(np.arange(40).reshape(10, 4), columns=list('ABCD'))
df
df = df[df['A']>0]
df.loc[df['B']==17, 'C'] = 1000
df['D'] = df['D'].replace({7:2000})

and with this not:

df = pd.DataFrame(np.arange(40).reshape(10, 4), columns=list('ABCD'))
df = df[df['A']>0]
df.loc[df['B']==17, 'C'] = 1000
df['D'] = df['D'].replace({7:2000})

Although with the original code, this was certainly not the case (that it was first printed).

And above was now with latest master ('0.13.1-543-g4bd1e6a')

hmm...ok I can replicate that

It is legit because of : df = df[df['A']>0]

which is a sliced version of the original
then when you set with loc you could misinterpret this as setting the original frame (that's the intent of the warning).

The problem is that you are reassigning df to a version of itself; if you use a different variable name then you don't get this. This might be a case where this is not detectable. Let me see.

In [10]: df = pd.DataFrame(np.arange(40).reshape(10, 4), columns=list('ABCD'))

In [11]: x = df[df['A']>0]

In [12]: df.is_copy

In [13]: x.is_copy
Out[13]: <weakref at 0x4746d08; to 'DataFrame' at 0x4759610>

In [14]: x.loc[df['B']==17, 'C'] = 1000

In [15]: x['D'] = df['D'].replace({7:2000})

I also get the warning when assigning to a different variable (and then also without printing the frame first!):

In [1]: df = pd.DataFrame(np.arange(40).reshape(10, 4), columns=list('ABCD'))

In [2]: x = df[df['A']>0]

In [3]: x.loc[df['B']==17, 'C'] = 1000
C:\Anaconda\envs\devel\Scripts\ipython-script.py:1: SettingWithCopyWarning: A va
lue is trying to be set on a copy of a slice from a DataFrame
  #!C:\Anaconda\envs\devel\python.exe

But I suppose here the warning is legitimate, as it is indeed not changed in the original.

that is legit

ah, yes, was just updating my comment saying that.

of course with the caveat that its not wrong per se, just a warning (mainly for new users).

I get this when I do inplace sort_index and fillna:

portfolio_analytics\attribution\Hierarchies.py:212: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame
data_frame.sort_index(inplace=True)

C:\Python27\lib\site-packages\pandas\core\generic.py:2174: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame
obj.fillna(v, inplace=True)

Another one:

C:\Python27\lib\site-packages\pandas\core\indexing.py:346: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_index,col_indexer] = value instead
self.obj[item] = s

Wow! This thing is everywhere:

# Recalculate portfolio betas
portfolio_data_returns[field_beta_by_portfolio_weight] = portfolio_data_returns[field_beta] * portfolio_data_returns[field_portfolio_weight]
portfolio_betas = portfolio_data_returns[field_beta_by_portfolio_weight].groupby(level=field_date).sum()

-c:2: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_index,col_indexer] = value instead

Read this:

http://stackoverflow.com/questions/21463589/pandas-chained-assignments/21463854#21463854
http://stackoverflow.com/questions/22532302/pandas-peculiar-performance-drop-for-inplace-rename-after-dropna/22533110#22533110

once it happens, it will repeat in general (until you create a copy)

don't do inplace ops

Ok. In my last example, what am I doing inplace?

it prob was already not a copy
the assignment does the check

you need to look at the first time it happens
and can always check is_copy property

It's a copy, yes. I wanted it explicitly just to make sure the original DataFrame is left intact.

portfolio_data_returns = portfolio_data_all.loc[return_date_first:return_date_last].copy()

and is that a series?

DataFrame

you need to use loc just as it says then

I'm getting similar warnings for an operation like this, where I am just trying to truncate a variable at a particular value:

lab_subset.YEAR_AGE[lab_subset.YEAR_AGE > 75] = 75

So, I go in and try to use .loc:

lab_subset.YEAR_AGE.loc[lab_subset.YEAR_AGE > 75] = 75

But get the same error. The object lab_subset is also the result of an indexing operation:

lab_subset = measles_data[(CONFIRMED | DISCARDED) & measles_data.YEAR_AGE.notnull() & measles_data.COUNTY.notnull()]

So, I tried to use .loc on that as well, but the warning persists.

Its not clear to me what is going on here. Running '0.14.1-486-g1d65bc8' on Python 2.7.6 and OS X 10.9.5.

no, you are still chaining, you need to use .loc on the DataFrame

lab_subset.loc[lab_subset.YEAR_AGE > 75,'YEAR_AGE'] = 75

OK, I see now. Man, that's going to be a tough one for new users to digest.

that's why its a warning! That's the reason for it though. It sometimes does work. (and has been around since 0.13.0), just getting better / less spurious though the versions.

So, I still get the warning despite the syntax change. lab_subset isn't a view on measles_data, is it?

you should put .copy() at the end of the first expression. Otherwise you end up changing data in the measles_dataset! (if its a view, which if its a single dtype ti will be), otherwise it would make a copy.

But that's the rub, you don't want to have to care/know its a view (and if its a view, you certainly don't want to propogate back to the original, except explicity).

closing as stale. pls reopen if still an issue.