Question

This is my first time trying Pandas. I think I have a reasonable use case, but I am stumbling. I want to load a tab delimited file into a Pandas Dataframe, then group it by Symbol and plot it with the x.axis indexed by the TimeStamp column. Here is a subset of the data:

Symbol,Price,M1,M2,Volume,TimeStamp
TBET,2.19,3,8.05,1124179,9:59:14 AM
FUEL,3.949,9,1.15,109674,9:59:11 AM
SUNH,4.37,6,0.09,24394,9:59:09 AM
FUEL,3.9099,8,1.11,105265,9:59:09 AM
TBET,2.18,2,8.03,1121629,9:59:05 AM
ORBC,3.4,2,0.22,10509,9:59:02 AM
FUEL,3.8599,7,1.07,102116,9:58:47 AM
FUEL,3.8544,6,1.05,100116,9:58:40 AM
GBR,3.83,4,0.46,64251,9:58:24 AM
GBR,3.8,3,0.45,63211,9:58:20 AM
XRA,3.6167,3,0.12,42310,9:58:08 AM
GBR,3.75,2,0.34,47521,9:57:52 AM
MPET,1.42,3,0.26,44600,9:57:52 AM

Note two things about the TimeStamp column;

1. it has duplicate values and
2. the intervals are irregular.

I thought I could do something like this…

from pandas import *
import pylab as plt

df = read_csv('data.txt',index_col=5)
df.sort(ascending=False)

df.plot()
plt.show()

But the read_csv method raises an exception “Tried columns 1-X as index but found duplicates”. Is there an option that will allow me to specify an index column with duplicate values?

I would also be interested in aligning my irregular timestamp intervals to one second resolution, I would still wish to plot multiple events for a given second, but maybe I could introduce a unique index, then align my prices to it?

Answer 1

I created several issues just now to address some features / conveniences that I think would be nice to have: GH-856, GH-857, GH-858

We’re currently working on a revamp of the time series capabilities and doing alignment to secondly resolution is possible now (though not with duplicates, so would need to write some functions for that). I also want to support duplicate timestamps in a better way. However, this is really panel (3D) data, so one way that you might alter things is the following:

In [29]: df.pivot('Symbol', 'TimeStamp').stack()
Out[29]: 
                   M1    M2   Price   Volume
Symbol TimeStamp                            
FUEL   9:58:40 AM   6  1.05  3.8544   100116
       9:58:47 AM   7  1.07  3.8599   102116
       9:59:09 AM   8  1.11  3.9099   105265
       9:59:11 AM   9  1.15  3.9490   109674
GBR    9:57:52 AM   2  0.34  3.7500    47521
       9:58:20 AM   3  0.45  3.8000    63211
       9:58:24 AM   4  0.46  3.8300    64251
MPET   9:57:52 AM   3  0.26  1.4200    44600
ORBC   9:59:02 AM   2  0.22  3.4000    10509
SUNH   9:59:09 AM   6  0.09  4.3700    24394
TBET   9:59:05 AM   2  8.03  2.1800  1121629
       9:59:14 AM   3  8.05  2.1900  1124179
XRA    9:58:08 AM   3  0.12  3.6167    42310

note that this created a MultiIndex. Another way I could have gotten this:

In [32]: df.set_index(['Symbol', 'TimeStamp'])
Out[32]: 
                    Price  M1    M2   Volume
Symbol TimeStamp                            
TBET   9:59:14 AM  2.1900   3  8.05  1124179
FUEL   9:59:11 AM  3.9490   9  1.15   109674
SUNH   9:59:09 AM  4.3700   6  0.09    24394
FUEL   9:59:09 AM  3.9099   8  1.11   105265
TBET   9:59:05 AM  2.1800   2  8.03  1121629
ORBC   9:59:02 AM  3.4000   2  0.22    10509
FUEL   9:58:47 AM  3.8599   7  1.07   102116
       9:58:40 AM  3.8544   6  1.05   100116
GBR    9:58:24 AM  3.8300   4  0.46    64251
       9:58:20 AM  3.8000   3  0.45    63211
XRA    9:58:08 AM  3.6167   3  0.12    42310
GBR    9:57:52 AM  3.7500   2  0.34    47521
MPET   9:57:52 AM  1.4200   3  0.26    44600

In [33]: df.set_index(['Symbol', 'TimeStamp']).sortlevel(0)
Out[33]: 
                    Price  M1    M2   Volume
Symbol TimeStamp                            
FUEL   9:58:40 AM  3.8544   6  1.05   100116
       9:58:47 AM  3.8599   7  1.07   102116
       9:59:09 AM  3.9099   8  1.11   105265
       9:59:11 AM  3.9490   9  1.15   109674
GBR    9:57:52 AM  3.7500   2  0.34    47521
       9:58:20 AM  3.8000   3  0.45    63211
       9:58:24 AM  3.8300   4  0.46    64251
MPET   9:57:52 AM  1.4200   3  0.26    44600
ORBC   9:59:02 AM  3.4000   2  0.22    10509
SUNH   9:59:09 AM  4.3700   6  0.09    24394
TBET   9:59:05 AM  2.1800   2  8.03  1121629
       9:59:14 AM  2.1900   3  8.05  1124179
XRA    9:58:08 AM  3.6167   3  0.12    42310

you can get this data in a true panel format like so:

In [35]: df.set_index(['TimeStamp', 'Symbol']).sortlevel(0).to_panel()
Out[35]: 
<class 'pandas.core.panel.Panel'>
Dimensions: 4 (items) x 11 (major) x 7 (minor)
Items: Price to Volume
Major axis: 9:57:52 AM to 9:59:14 AM
Minor axis: FUEL to XRA

In [36]: panel = df.set_index(['TimeStamp', 'Symbol']).sortlevel(0).to_panel()

In [37]: panel['Price']
Out[37]: 
Symbol        FUEL   GBR  MPET  ORBC  SUNH  TBET     XRA
TimeStamp                                               
9:57:52 AM     NaN  3.75  1.42   NaN   NaN   NaN     NaN
9:58:08 AM     NaN   NaN   NaN   NaN   NaN   NaN  3.6167
9:58:20 AM     NaN  3.80   NaN   NaN   NaN   NaN     NaN
9:58:24 AM     NaN  3.83   NaN   NaN   NaN   NaN     NaN
9:58:40 AM  3.8544   NaN   NaN   NaN   NaN   NaN     NaN
9:58:47 AM  3.8599   NaN   NaN   NaN   NaN   NaN     NaN
9:59:02 AM     NaN   NaN   NaN   3.4   NaN   NaN     NaN
9:59:05 AM     NaN   NaN   NaN   NaN   NaN  2.18     NaN
9:59:09 AM  3.9099   NaN   NaN   NaN  4.37   NaN     NaN
9:59:11 AM  3.9490   NaN   NaN   NaN   NaN   NaN     NaN
9:59:14 AM     NaN   NaN   NaN   NaN   NaN  2.19     NaN

you can then generate some plots from that data.

note here that the timestamps are still as strings– I guess they could be converted to Python datetime.time objects and things might be a bit easier to work with. I don’t have many plans to provide a lot of support for raw times vs. timestamps (date + time) but if enough people need it I suppose I can be convinced 🙂

If you have multiple observations on a second for a single symbol then some of the above methods will not work. But I want to build in better support for that in upcoming releases of pandas, so knowing your use cases will be helpful to me– consider joining the mailing list (pystatsmodels)