python - 如何将这两个数据框连接在一起以使它们重叠并适合日期？

Question

我希望它们均匀地组合在一起，而不是相互堆叠。此外，日期也没有完全对齐，我很困惑如何解决这个问题。如果可能的话，12/31 的股本为 0 会很棒。谢谢

gos_dataset = pd.DataFrame({'Date':gosd, 'Outstanding Shares':gos}, columns = ['Date', 'Outstanding Shares'])
gcs_dataset = pd.DataFrame({'Date':gcsd, 'Capital Stock':gcs}, columns = ['Date', 'Capital Stock'])
print(pd.concat([gcs_dataset, gos_dataset]))

         Date  Capital Stock         Outstanding Shares
0  2020-01-02        7251.39                        NaN
1  2020-01-03       47200.86                        NaN
2  2020-01-06      119020.28                        NaN
3  2020-01-07    11751250.39                        NaN
4  2020-01-08     4790267.25                        NaN
5  2020-01-09      -54597.18                        NaN
6  2020-01-10      -46410.80                        NaN
7  2020-01-13       78669.05                        NaN
8  2020-01-14      150819.02                        NaN
9  2020-01-15      -23295.45                        NaN
10 2020-01-16       87836.67                        NaN
11 2020-01-17        6346.19                        NaN
12 2020-01-21       10304.31                        NaN
13 2020-01-22     -335114.92                        NaN
14 2020-01-23       94276.75                        NaN
15 2020-01-24      -38526.78                        NaN
16 2020-01-27        9998.97                        NaN
17 2020-01-28      357659.16                        NaN
18 2020-01-29        5487.23                        NaN
19 2020-01-30      143213.17                        NaN
20 2020-01-31      -25900.72                        NaN
0  2019-12-31            NaN                3693737.147
1  2020-01-02            NaN                    706.570
2  2020-01-03            NaN                   4718.445
3  2020-01-06            NaN                  11964.175
4  2020-01-07            NaN                1179829.280
5  2020-01-08            NaN                 481078.653
6  2020-01-09            NaN                  -5471.248
7  2020-01-10            NaN                  -4629.751
8  2020-01-13            NaN                   7812.787
9  2020-01-14            NaN                  15096.288
10 2020-01-15            NaN                  -2314.353
11 2020-01-16            NaN                   8753.650
12 2020-01-17            NaN                    683.555
13 2020-01-21            NaN                   1023.227
14 2020-01-22            NaN                 -33172.984
15 2020-01-23            NaN                   8838.869
16 2020-01-24            NaN                  -3351.471
17 2020-01-27            NaN                   1001.065
18 2020-01-28            NaN                  35921.377
19 2020-01-29            NaN                    549.450
20 2020-01-30            NaN                  14307.865
21 2020-01-31            NaN                  -2585.328

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