Ordering should be kept when OrderedDict records are passed

[Zaharid]

When passing a list OrderedDict objects to pd.DataFrame, the ordering of the columns is not kept:

od =  OrderedDict([('Z', 5), ('xx', 8), ('uno', 2), ('etc', 1), ('j', 76), ('e', 7)])
l = [od]*4
pd.DataFrame(l).columns
#Out[23]: Index(['Z', 'e', 'etc', 'j', 'uno', 'xx'], dtype='object')

[jorisvandenbossche]

Can you be a bit more specific? Passed to what? (DataFrame?)
Maybe give a small example.

[Zaharid]

Sorry, I pressed submit by mistake. Hope it's clear now.

[jreback]

It does work, but you are passing a list of dicts, which is quite different.

In [20]: od
Out[20]: OrderedDict([('Z', [5]), ('xx', [8]), ('uno', [2]), ('etc', [1]), ('j', [76]), ('e', [7])])

In [21]: DataFrame(od)
Out[21]: 
   Z  xx  uno  etc   j  e
0  5   8    2    1  76  7

I am +0 about supporting a nested version of this. I don't see the utility.

[Zaharid]

My use case is:

def results_table(results):
    records = [OrderedDict([
                ('Observable'       , result.obs),
                ('PDF'              , result.pdf),
                ('Collaboration'    , result.pdf.collaboration),
                ('alpha_sMref'      , result.pdf.AlphaS_MZ),
                ('PDF_OrderQCD'     , result.pdf.oqcd_str),
                ('NumFlavors'       , result.pdf.NumFlavors),
                ('CV'               , result.central_value),
                ('Up68'             , result.error68['max']),
                ('Down68'           , result.error68['min']),
                ('Remarks'          , [],  )
               ]) for result in results]
    return pd.DataFrame(records)

That is, I have a list of objects and I would like to turn them into a DataFrame to be able to display and aggregate them. Doing a list comprehension for each column doesn't look very nice.

[hayd]

This seems similar to passing namedtuples. Where I think it's clearer to be more explicit:

In [11]: pd.DataFrame(l, columns=od.keys()).columns
Out[11]: Index(['Z', 'xx', 'uno', 'etc', 'j', 'e'], dtype='object')

In [12]: pd.DataFrame(l, columns=l[0].keys()).columns
Out[12]: Index(['Z', 'xx', 'uno', 'etc', 'j', 'e'], dtype='object')

[bashtage]

One workaround:

data=((1,2),(3,4))
records = [pd.Series(OrderedDict([('a',d[0]),('b',d[1])])) for d in data]
records =  pd.DataFrame(records)

[jason-curtis]

Since ordering is now guaranteed on dicts in Python 3.7+, it seems reasonable to expect this behavior with regular dicts in addition to OrderedDicts. This should work in cpython 3.6 as well though ordering is not "guaranteed" as strongly as it is in 3.7.

Expected:

In[2]: DataFrame([{'First': 1, 'Second': 2, 'Third': 3, 'Fourth': 4}])                                      
Out[2]: 
   First  Second  Third  Fourth
0      1       2       3      4

Actual (pandas 0.24.1 on Python 3.6.7) (sometimes - the output ordering is inconsistent):

In[2]: DataFrame([{'First': 1, 'Second': 2, 'Third': 3, 'Fourth': 4}])                                      
Out[2]: 
   First  Fourth  Second  Third
0      1       4       2      3

[bashtage]

I was never sure if ordering was guaranteed with literals. It is if you use dict(a=a, b=b) or insert line by line.

[jason-curtis]

The thread I just linked to is entitled "Guarantee ordered dict literals in v3.7?" so I would say yes. A quick test in 3.6 vs 2.7 seemed to corroborate this too.

[jason-curtis]

to be clear, the quick test I did was based on

list({1:2, 3:4, 5:6, 7:8, 'nine': 10, 'eleven': 12}.items())                                                 
# in Python 3.6 but not 2.7, always produces
[(1, 2), (3, 4), (5, 6), (7, 8), ('nine', 10), ('eleven', 12)]

Hard to pin down these issues though, as often it'll look ordered for a bunch of examples and then you'll change something and the order will change.

Is this related to #22708 ?