Well that's not very helpful. Should be improved at some point

Am I right in saying these give better errors in dev?

pd.concat([s1,df,s2])
ValueError: arrays must have same number of dimensions

pd.concat([s1,df,s2], axis=1)
ValueError: Shape of passed values is (3,), indices imply (3, 100)

I like the first one, prob should catch the error in concat and put up a better message for the 2nd one though....

Agreed.

ok...let's move to 0.12 then...

It's not the incorrect error which is the problem, but the fact that an error is thrown at all. This makes it difficult/tedious to write generic code that works whether or not the input is a DataFrame or a TimeSeries.

In this case the TimeSeries should be treated as a DataFrame with one column where the column label is taken from the TimeSeries name. It the axis parameter was 0 then the TimeSeries should be treated as a DataFrame with one row where the row label is taken from the TimeSeries name.

@dhirschfeld I think we will have a detailed look at this, but nothing is stopping you from pre-conditining, e.g.

 pd.concat([ pd.DataFrame(x) for x in [s1,df,s2] ], axis=1).shape

The DataFrame constructor does exactly what you want for a series with axis=1, and passed thru a DataFrame
(axis=0 you would need to pass the orient so it transposes I think)

I also stumbled on this, but I saw there is already this issue for it.

I think in current master it is still very confusing. An example (with the series being a column from a dataframe, full example: http://nbviewer.ipython.org/5868420/bug_pandas-concat-series.ipynb ):

• concat with [df1, df2[col]]: IndexError: list index out of range
• concat with [df1[col], df2]: AttributeError: 'DataFrame' object has no attribute 'name'

I think at the moment it is not clear from the docs that this is not allowed (mixing dataframes with series/columns), and when you do it, you don't get a very informative error message.

So should work like this:

In [1]: df = pd.DataFrame([[1, 2], [3, 4]])

In [2]: s = pd.Series([5, 6], name=3)

In [3]: pd.concat([df, pd.DataFrame(s).T])  # but with [df, s]
Out[3]:
   0  1
0  1  2
1  3  4
3  5  6

In [4]: pd.concat([df, pd.DataFrame(s)], axis=1)  # but with [df. s]
Out[4]:
   0  1  3
0  1  2  5
1  3  4  6

# also both with [s, df]

series must have a name FYI (otherwise needs to raise)

atm it's a bit sketchy with name:

In [5]: s1 = pd.Series([5, 6])

In [6]: pd.concat([s, s1], axis=1)   # loses name of s
Out[6]:
   0  1
0  5  5
1  6  6

Not sure should always raise w/o name.

@hayd that's the problem, it "works" but really should not (as its assigned as if the column index doesn't exist). I would just be careful about it, maybe only allowing it if as_index=False or series has a name

not exactly the same situation, but append raises if the appended series doesn't have a name and axis=0, so certainly precedent for it.

Was suggestion above that these should be the same (not quite there in master): #2385 (comment)

df = pd.DataFrame([[1, 2], [3, 4]])
s = pd.Series([5, 6], name=3)

In [6]: pd.concat([df, s])
Out[6]:
   0   1
0  1   2
1  3   4
0  5 NaN
1  6 NaN

In [7]: pd.concat([df, pd.DataFrame(s).T])
Out[7]:
   0  1
0  1  2
1  3  4
3  5  6

open as sep issue?

Definitely looking good though!

I'll add them as tests

@hayd It seems that your above example should work, but how does concat know to transpose the input? (e.g. should the series be aligned column-wise (as is the default), or row-wise as you suggest?)

I could put a note that all alignment would be column-wise. (also maybe could add a keywork, align to deal with this, e.g. you could specify align='index' if you want row-wise alignment), but seems kind of over-kill