I have 2 dfs:
ipos_df:
|index|Date|Symbol|Company Name|IPO Price|Current|Return||---|---|---|---|---|---|---||33|2024-02-27 00:00:00|SMXT|SolarMax Technology, Inc.|$4.00|$10.42|160.50%||34|2024-02-22 00:00:00|VHAI|Vocodia Holdings Corp|$4.25|$0.16|-96.29%||35|2024-02-21 00:00:00|DYCQ|DT Cloud Acquisition Corporation|$10.00|$10.35|3.50%||36|2024-02-16 00:00:00|CHRO|Chromocell Therapeutics Corp|$6.00|$1.77|-70.50%||37|2024-02-14 00:00:00|UMAC|Unusual Machines, Inc.|$4.00|$1.30|-67.50%|and history_data:
| index | Open | High | Low | Close | Volume | Dividends | Stock Splits | Symbol | Adj Close | Date |
|---|---|---|---|---|---|---|---|---|---|---|
| 2024-02-27 | 3.5 | 9.350000381469727 | 3.5 | 8.0 | 2840400.0 | 0.0 | 0.0 | SMXT | NaN | 2024-02-27 00:00:00 |
| 2024-02-28 | 6.420000076293945 | 7.239999771118164 | 5.599999904632568 | 5.860000133514404 | 243200.0 | 0.0 | 0.0 | SMXT | NaN | 2024-02-28 00:00:00 |
| 2024-02-29 | 5.739999771118164 | 5.840000152587891 | 4.300000190734863 | 4.889999866485596 | 130000.0 | 0.0 | 0.0 | SMXT | NaN | 2024-02-29 00:00:00 |
| 2024-03-01 | 3.940000057220459 | 5.150000095367432 | 3.799999952316284 | 4.880000114440918 | 485000.0 | 0.0 | 0.0 | SMXT | NaN | 2024-03-01 00:00:00 |
| 2024-03-04 | 4.730000019073486 | 5.099999904632568 | 4.257999897003174 | 4.659999847412109 | 114300.0 | 0.0 | 0.0 | SMXT | NaN | 2024-03-04 00:00:00 |
And I need to create 30 columns (for 30 days) in ipos_df with close price (from history_data df) n days after IPO.Trying this:for i in range(1,30):ipos_df['growth_'+str(i)+'d'] = history_data.loc[(history_data['Symbol'].equals(ipos_df['Symbol'])) & ((history_data['Date'] - timedelta(days=i)).equals(ipos_df['Date'])), history_data['Close']].dropna().values[0][0]
Getting this:KeyError: 'False: boolean label can not be used without a boolean index'
