How to add dataframe column data to a range of indexes in another dataframe?

Question

I have a dataframe called df1:

        Long_ID     IndexBegin         IndexEnd
0       10000001        0                  3
1       10000002        3                  6
2       10000003        6                 10            

I have a second dataframe called df2, which can be up to 1 million rows long:

      Short_ID
0        1
1        2
2        3
3        10
4        20
5        30
6        100
7        101
8        102
9        103

I want to link Long_ID to Short_ID in such a way that if (IndexBegin:IndexEnd) is (0:3), then Long_ID gets inserted into df2 at indexes 0 through 2 (IndexEnd - 1). The starting index and ending index are determined using the last two columns of df1.

So that ultimately, my final dataframe looks like this: df3:

      Short_ID       Long_ID
0        1          10000001
1        2          10000001
2        3          10000001
3        10         10000002
4        20         10000002
5        30         10000002
6        100        10000003
7        101        10000003
8        102        10000003
9        103        10000003

First, I tried storing the index of df2 as a key and Short_ID as a value in a dictionary, then iterating row by row, but that was too slow. This led me to learn about vectorization.

Then, I tried using where(), but I got "ValueError: Can only compare identically-labeled Series objects."

df2 = df2.reset_index()
df2['Long_ID'] = df1['Long_ID'] [ (df2['index'] < df1['IndexEnd']) & (df2['index'] >= df1['IndexBegin']) ]

I am relatively new to programming, and I appreciate if anyone can give a better approach to solving this problem. I have reproduced the code below:

df1_data = [(10000001, 0, 3), (10000002, 3, 6), (10000003, 6, 10)]
df1 = pd.DataFrame(df1_data, columns = ['Long_ID', 'IndexBegin', 'IndexEnd'])

df2_data = [1, 2, 3, 10, 20, 30, 100, 101, 102, 103]
df2 = pd.DataFrame(df2_data, columns = ['Short_ID'])

Answer 1

df2 does not need "IndexEnd" as long as the ranges are contiguous. You may use pd.merge_asof:

(pd.merge_asof(df2.reset_index(), df1, left_on='index', right_on='IndexBegin')
   .reindex(['Short_ID', 'Long_ID'], axis=1))

   Short_ID   Long_ID
0         1  10000001
1         2  10000001
2         3  10000001
3        10  10000002
4        20  10000002
5        30  10000002
6       100  10000003
7       101  10000003
8       102  10000003
9       103  10000003