Why is Pandas Concatenation (pandas.concat) so Memory Inefficient?

Question

I have about 30 GB of data (in a list of about 900 dataframes) that I am attempting to concatenate together. The machine I am working with is a moderately powerful Linux Box with about 256 GB of ram. However, when I try to concatenate my files I quickly run out of available ram. I have tried all sorts of workarounds to fix this (concatenating in smaller batches with for loops, etc.) but I still cannot get these to concatenate. Two questions spring to mind:

1. Has anyone else dealt with this and found an effective workaround? I cannot use a straight append because I need the 'column merging' (for lack of a better word) functionality of the join='outer' argument in pd.concat().

2. Why is Pandas concatenation (which I know is just calling numpy.concatenate) so inefficient with its use of memory?

I should also note that I do not think the problem is an explosion of columns as concatenating 100 of the dataframes together gives about 3000 columns whereas the base dataframe has about 1000.

Edit:

The data I am working with is financial data about 1000 columns wide and about 50,000 rows deep for each of my 900 dataframes. The types of data going across left to right are:

1. date in string format,
2. string
3. np.float
4. int

... and so on repeating. I am concatenating on column name with an outer join which means that any columns in df2 that are not in df1 will not be discarded but shunted off to the side.

---

Example:

 #example code
 data=pd.concat(datalist4, join="outer", axis=0, ignore_index=True)
 #two example dataframes (about 90% of the column names should be in common
 #between the two dataframes, the unnamed columns, etc are not a significant
 #number of the columns)

print datalist4[0].head()
                800_1     800_2   800_3  800_4               900_1     900_2  0 2014-08-06 09:00:00  BEST_BID  1117.1    103 2014-08-06 09:00:00  BEST_BID   
1 2014-08-06 09:00:00  BEST_ASK  1120.0    103 2014-08-06 09:00:00  BEST_ASK   
2 2014-08-06 09:00:00  BEST_BID  1106.9     11 2014-08-06 09:00:00  BEST_BID   
3 2014-08-06 09:00:00  BEST_ASK  1125.8     62 2014-08-06 09:00:00  BEST_ASK   
4 2014-08-06 09:00:00  BEST_BID  1117.1    103 2014-08-06 09:00:00  BEST_BID   

    900_3  900_4              1000_1    1000_2    ...     2400_4  0  1017.2    103 2014-08-06 09:00:00  BEST_BID    ...        NaN   
1  1020.1    103 2014-08-06 09:00:00  BEST_ASK    ...        NaN   
2  1004.3     11 2014-08-06 09:00:00  BEST_BID    ...        NaN   
3  1022.9     11 2014-08-06 09:00:00  BEST_ASK    ...        NaN   
4  1006.7     10 2014-08-06 09:00:00  BEST_BID    ...        NaN   

                      _1  _2  _3  _4                   _1.1 _2.1 _3.1  _4.1  0  #N/A Invalid Security NaN NaN NaN  #N/A Invalid Security  NaN  NaN   NaN   
1                    NaN NaN NaN NaN                    NaN  NaN  NaN   NaN   
2                    NaN NaN NaN NaN                    NaN  NaN  NaN   NaN   
3                    NaN NaN NaN NaN                    NaN  NaN  NaN   NaN   
4                    NaN NaN NaN NaN                    NaN  NaN  NaN   NaN   

      dater  
0  2014.8.6  
1  2014.8.6  
2  2014.8.6  
3  2014.8.6  
4  2014.8.6  

[5 rows x 777 columns]

print datalist4[1].head()
                150_1     150_2   150_3  150_4               200_1     200_2  0 2013-12-04 09:00:00  BEST_BID  1639.6     30 2013-12-04 09:00:00  BEST_ASK   
1 2013-12-04 09:00:00  BEST_ASK  1641.8    133 2013-12-04 09:00:08  BEST_BID   
2 2013-12-04 09:00:01  BEST_BID  1639.5     30 2013-12-04 09:00:08  BEST_ASK   
3 2013-12-04 09:00:05  BEST_BID  1639.4     30 2013-12-04 09:00:08  BEST_ASK   
4 2013-12-04 09:00:08  BEST_BID  1639.3    133 2013-12-04 09:00:08  BEST_BID   

    200_3  200_4               250_1     250_2    ...                 2500_1  0  1591.9    133 2013-12-04 09:00:00  BEST_BID    ...    2013-12-04 10:29:41   
1  1589.4     30 2013-12-04 09:00:00  BEST_ASK    ...    2013-12-04 11:59:22   
2  1591.6    103 2013-12-04 09:00:01  BEST_BID    ...    2013-12-04 11:59:23   
3  1591.6    133 2013-12-04 09:00:04  BEST_BID    ...    2013-12-04 11:59:26   
4  1589.4    133 2013-12-04 09:00:07  BEST_BID    ...    2013-12-04 11:59:29   

     2500_2 2500_3 2500_4         Unnamed: 844_1  Unnamed: 844_2  0  BEST_ASK   0.35     50  #N/A Invalid Security             NaN   
1  BEST_ASK   0.35     11                    NaN             NaN   
2  BEST_ASK   0.40     11                    NaN             NaN   
3  BEST_ASK   0.45     11                    NaN             NaN   
4  BEST_ASK   0.50     21                    NaN             NaN   

  Unnamed: 844_3 Unnamed: 844_4         Unnamed: 848_1      dater  
0            NaN            NaN  #N/A Invalid Security  2013.12.4  
1            NaN            NaN                    NaN  2013.12.4  
2            NaN            NaN                    NaN  2013.12.4  
3            NaN            NaN                    NaN  2013.12.4  
4            NaN            NaN                    NaN  2013.12.4  

[5 rows x 850 columns]

Answer 1

I've had performance issues concatenating a large number of DataFrames to a 'growing' DataFrame. My workaround was appending all sub DataFrames to a list, and then concatenating the list of DataFrames once processing of the sub DataFrames has been completed.