Merge (numpy) Arrays Based On Date

I have N arrays each structured as the following Array 1: [['2014-01-01', '2014-01-03' ...], [1.1, 0.5, ...]] Array 2: [['2014-01-01', '2014-01-02' ...], [1.4, 0.9, ...]] Array 3:

Solution 1:

This should solve it, using merge function and outer method

>>> import pandas as pd
>>> import numpy as np
>>> d1 = pd.DataFrame(np.array([['2014-01-01', '2014-01-03'], [1.1, 0.5]])).T
>>> d2 = pd.DataFrame(np.array([['2014-01-01', '2014-01-02'], [1.4, 0.9]])).T
>>> d3 = pd.DataFrame(np.array([['2014-01-02', '2014-01-04'], [0.8, 1.5]])).T
>>> d1.columns = d2.columns = d3.columns = ['t','v']
>>> pd.DataFrame(np.array(d1.merge(d2, on='t', how='outer').
...                          merge(d3, on='t', how='outer').
...                          sort('t')),
...                          columns=['date','1-data','2-data','3-data'])
... 
         date 1-data 2-data 3-data
0  2014-01-01    1.1    1.4    NaN
1  2014-01-02    NaN    0.9    0.8
2  2014-01-03    0.5    NaN    NaN
3  2014-01-04    NaN    NaN    1.5

[4 rows x 4 columns]