title: How do I concat these two data frames together to get them to overlap and fit with the date?
body: <p>I want them to fit evenly together instead of stacking on eachother. Also the date isn't aligned perfectly and I'm quite stuck on how to work around that. If possible a 0 on the 12/31 of capital stock would be great. thanks</p>
  <pre><code>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]))
  </code></pre>
  <pre><code>         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
  </code></pre>