add a 'now' timestamp column to a pandas df

Question

I have the 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 a DF that looks like 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 I add a column (or replace the index 0-9), by a timestamp with the now time? The np array will not always have size 10

Answer 1

You can use datetime's now method to create the time stamp and either assign this to a new column like: s3['new_col'] = dt.datetime.now() or assign direct to the index:

In [9]:

import datetime as 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 that you are going to get a lot of duplicate values in your index due to the resolution and speed of the assignment, not sure how useful this is, better to have it as a separate column in my opinion.