How to remove duplicies within time interval

I have a two pandas dataframes, let's say:

df1 = pd.DataFrame({
    'IN': ['2023-01-01', '2023-02-01', '2023-03-01', '2023-04-01'],
    'OUT': ['2023-01-10', '2023-02-10', '2023-03-10', '2023-04-10'],
    'Ticker': ['AAPL', 'AAPL', 'GOOG', 'GOOG']
})

df2 = pd.DataFrame({
    'IN': ['2023-01-05', '2023-05-01', '2023-02-05', '2023-05-01'],
    'OUT': ['2023-01-15', '2023-05-15', '2023-02-15', '2023-05-15'],
    'Ticker': ['AAPL', 'GOOG', 'MSFT', 'XXXX']
})

The question is how to remove (or copy index for later drop) from df2 such records which are already in df1 (let's say like open trades) between interval IN-OUT.

E.g. the first trade/row in df1 is AAPL from 2023-01-01 to 2023-01-10, therefore the first trade in df2 must be removed because its interval is 2023-01-05 to 2023-01-15. But the second trade/row must be kept.

Does exists a way how to do it simply without iterations?

I have tried something like:

df1 = df1[~((df1['Ticker'].isin(df2['Ticker'])) & (df1['IN'].between(df2['OUT'], df2['OUT'])))]

but did not get right result and besides, it does not work if number of rows of dataframes are different.

You can use merge to match tickers between dataframes then use query to keep rows you want to drop:

idx_to_drop = (df2.reset_index().merge(df1, on='Ticker')
                  .query('(IN_y > IN_x)')['index'].tolist())
out = df2.drop(idx_to_drop)

Output:

>>> out
          IN        OUT Ticker
1 2023-05-01 2023-05-15   GOOG
2 2023-02-05 2023-02-15   MSFT
3 2023-05-01 2023-05-15   XXXX

Intermediate step:

>>> df2.reset_index().merge(df1, on='Ticker')
   index       IN_x      OUT_x Ticker       IN_y      OUT_y
0      0 2023-01-05 2023-01-15   AAPL 2023-01-01 2023-01-10
1      0 2023-01-05 2023-01-15   AAPL 2023-02-01 2023-02-10
2      1 2023-05-01 2023-05-15   GOOG 2023-03-01 2023-03-10
3      1 2023-05-01 2023-05-15   GOOG 2023-04-01 2023-04-10