python - add a 'now' timestamp column to a pandas df -

i have following code:

s1 = pd.dataframe(np.random.uniform(-1,1,size=10)) s2 = pd.dataframe(np.random.normal(-1,1, size=10)) s3 = pd.concat([s1, s2], axis=1) s3.columns = ['s1','s2'] 

which generates df looks this:

    s1          s2 0   -0.841204   -1.857014 1    0.961539   -1.417853 2    0.382173   -1.332674 3   -0.535656   -2.226776 4   -0.854898   -0.644856 5   -0.538241   -2.178466 6   -0.761268   -0.662137 7    0.935139    0.475334 8   -0.622293   -0.612169 9    0.872111   -0.880220 

how can add column (or replace index 0-9), timestamp time? np array not have size 10

you can use datetime's now method create time stamp , either assign new column like: s3['new_col'] = dt.datetime.now() or assign direct index:

in [9]:  import datetime dt s3.index = pd.series([dt.datetime.now()] * len(s3)) s3 out[9]:                                   s1        s2 2014-08-17 23:59:35.766968  0.916588 -1.868320 2014-08-17 23:59:35.766968  0.139161 -0.939818 2014-08-17 23:59:35.766968 -0.486001 -2.524608 2014-08-17 23:59:35.766968  0.739789 -0.609835 2014-08-17 23:59:35.766968 -0.822114 -0.304406 2014-08-17 23:59:35.766968 -0.050685 -1.295435 2014-08-17 23:59:35.766968 -0.196441 -1.715921 2014-08-17 23:59:35.766968 -0.421514 -1.618596 2014-08-17 23:59:35.766968 -0.695084 -1.241447 2014-08-17 23:59:35.766968 -0.541561 -0.997481 

note going lot of duplicate values in index due resolution , speed of assignment, not sure how useful is, better have separate column in opinion.