I have two dataframes. One has the workdays and the stockprice for the Apple-stock. The other one, holds quarterly data on the EPS. However, the list of dates differ, but are in cronological order. I want add the cronological values of the eps frame to the existing price dataframe.
    date    close
0   2020-07-06  373.85
1   2020-07-02  364.11
2   2020-07-01  364.11
3   2020-06-30  364.80
4   2020-06-29  361.78
...     ...     ...
9969    1980-12-18  0.48
9970    1980-12-17  0.46
9971    1980-12-16  0.45
9972    1980-12-15  0.49
9973    1980-12-12  0.51
EPS:
    date        eps
0   2020-03-28  2.59
1   2019-12-28  5.04
2   2019-09-28  3.05
3   2019-06-29  2.22
4   2019-03-30  2.48
...     ...     ...
71  2002-06-29  0.09
72  2002-03-30  0.11
73  2001-12-29  0.11
74  2001-09-29  -0.11
75  2001-06-30  0.17
So my result should look something like this:
            close   eps
date
...         
2020-04-01  240.91  NaN
2020-03-31  254.29  NaN
2020-03-30  254.81  NaN
2020-03-28     NaN  2.59
2020-03-27  247.74  NaN
2020-03-26  258.44  NaN
Notice the value "2020-03-28", which previously only existed in the eps frame, and sits now neatly were it belongs.
However, I can't get it to work. First i thought there must be a simple join, merge or concat that has this function and fits the data right were it belongs, in cronological order, but so far, I couldn't find it.
My failed attempts:
- pd.concat([df, eps], axis=0, sort=True)- does simply append the two dataframes
- pd.merge_ordered(df, eps, fill_method="ffill", left_by="date")- Simply ignores the eps dates
The goal is to plot this Dataframe with two graphs - One the stock price, and the other one with the eps data.
 
    