Resampling, grouping, pivoting a pandas dataframe

Question

I have a log file with timestamps and two columns. I would now like to resample and "pivot" the dateframe created from the logfile.

Example orig dataframe/log file:

timestamp            colA  colB

2015-01-01 00:10:01   a     x
2014-01-01 00:10:01   b     y
2015-01-01 00:10:03   a     x
2015-01-01 00:10:03   a     x
2015-01-01 00:10:03   a     y
2015-01-01 00:10:04   b     x
2014-01-01 00:10:04   b     y
2014-01-01 00:10:04   b     y
2014-01-01 00:10:04   a     x
2014-01-01 00:10:05   a     x
2014-01-01 00:10:05   a     x
2014-01-01 00:10:07   a     y
2014-01-01 00:10:08   a     x

Example result for resampling by seconds:

                      a           b
timestamp             x     y     x     y

2015-01-01 00:10:01   1     0     0     1
2015-01-01 00:10:02   0     0     0     0
2015-01-01 00:10:03   2     1     0     0
2015-01-01 00:10:04   1     0     1     2
2014-01-01 00:10:05   2     0     0     0
2014-01-01 00:10:06   0     0     0     0
2014-01-01 00:10:07   0     1     0     0
2014-01-01 00:10:08   1     0     0     0

How would I achieve this? Resample first, then groupby/pivot? Or the other way around? To be more specific I the cells should contain the count for colA/colB combinations for each specific resample timeintervall. In the example seconds, but it could be minutes, hours, etc.

I am not fixed on this format, I could also think of getting a result that is resampled and groupby timestamp/colA like

                             colB
timestamp              colA   x     y

2015-01-01 00:10:01     a     1     0
                        b     0     1
2015-01-01 00:10:02     a     0     0
                        b     0     0
2015-01-01 00:10:03     a     2     1
                        b     0     0
2015-01-01 00:10:04     a     1     0
                        b     1     2
2014-01-01 00:10:05     a     2     0
                        b     0     0
2014-01-01 00:10:06     a     0     0
                        b     0     0
2014-01-01 00:10:07     a     0     1
                        b     0     0
2014-01-01 00:10:08     a     1     0
                        b     0     0

The final usage would be to plot the different count values

THX.

Answer 1

You could use pd.crosstab :

import numpy as np
import pandas as pd

df = pd.read_table('data', sep='\s{2,}', parse_dates=[0])
table = pd.crosstab(index=[df['timestamp']], columns=[df['colA'], df['colB']])

yields

colA                 a     b   
colB                 x  y  x  y
timestamp                      
2014-01-01 00:10:01  0  0  0  1
2014-01-01 00:10:04  1  0  0  2
2014-01-01 00:10:05  2  0  0  0
2014-01-01 00:10:07  0  1  0  0
2014-01-01 00:10:08  1  0  0  0
2015-01-01 00:10:01  1  0  0  0
2015-01-01 00:10:03  2  1  0  0
2015-01-01 00:10:04  0  0  1  0