Merge pandas dataframes and transform in SQL-CodePudding

I have few dataframes:

DF_1

|    | TYPE | SYMBOL | DESCRIPTION | OPOL | FIRST_INTEREST_DATE |
|----|------|--------|-------------|------|---------------------|
|  0 | BOND |      1 | FIRST       |   10 |            20220531 |
|  1 | BOND |      2 | SECOND      |   20 |            20220515 |
|  2 | BOND |      3 | THIRD       |   30 |            20220630 |
|  3 | BOND |      4 | FOURTH      |   40 |            20210815 |

DF_2

|    | TYPE  | SYMBOL | DESCRIPTION | OPOL | ISIN         |
|----|-------|--------|-------------|------|--------------|
|  0 | STOCK |      1 | FIRST       |  101 | ABCDEFGHIKLM |
|  1 | STOCK |      7 | SEVENTH     |  202 | MLKIHGFEDCBA |
|  2 | STOCK |      9 | NINETH      |  303 | OPQRSTUVWXYZ |
|  3 | STOCK |     13 | THIRTEENTH  |  404 | ZYXWVUTSRQPO |
|  4 | STOCK |     17 | SEVENTEENTH |  505 | ABCDEFFEDCBA |

How can i get dataframe DF_3 like table in bottom? And is it possible to transorm DF_3 in sql format?

|    | TYPE  | SYMBOL | DESCRIPTION   |   OPOL |FIRST_INTEREST_DATE| ISIN         |
|----|-------|--------|---------------|--------|-------------------|--------------|
|  0 | BOND  |      1 | FIRST         |     10 |          20220531 |              |
|  1 | BOND  |      2 | SECOND        |     20 |          20220515 |              |
|  2 | BOND  |      3 | THIRD         |     30 |          20220630 |              |
|  3 | BOND  |      4 | FOURTH        |     40 |          20210815 |              |
|  4 | STOCK |      1 | FIRST         |    101 |                   | ABCDEFGHIKLM |
|  5 | STOCK |      7 | SEVENTH       |    202 |                   | MLKIHGFEDCBA |
|  6 | STOCK |      9 | NINETH        |    303 |                   | OPQRSTUVWXYZ |
|  7 | STOCK |     13 | THIRTEENTH    |    404 |                   | ZYXWVUTSRQPO |
|  8 | STOCK |     17 | SEVENTEENTH   |    505 |                   | ABCDEFFEDCBA |

CodePudding user response：

you need to upload code or show a sample of your data.

Anyways here's a solution based on a limited information that you've provided:

df_3 = pd.concat([df_1, df_2], axis=0)

then you can use

from sqlalchemy import create_engine
engine = create_engine('sqlite://', echo=False)
df_3.to_sql('your_df_name', con=engine)