I have the following dataframe:

```
df = pd.DataFrame({"A":['a','b','c','d','e','f','g','h','i','j','k'],
                    "B":[1,3,4,5,6,7,6,5,8,5,5]})
df

```

displayed as:

```
    A   B
0   a   1
1   b   3
2   c   4
3   d   5
4   e   6
5   f   7
6   g   6
7   h   5
8   i   8
9   j   5
10  k   5

```

I first want to find the letter in column "A" that corresponds to the first occurrence of a value in column "B" that is >= 6. Looking at this, we see that this would be row index 4, corresponding to a value of 6 and "e" in column "A".

I can identify the column "A" value we just got with this code:

```
#Find first occurrence >= threshold
threshold = 6
array = df.values
array[np.where(array[:,1] >= threshold)][0,0]

```

This code returns `'e'`, which is what I want.

This code is referenced from this Stack Overflow source: [Python find first occurrence in Pandas dataframe column 2 below threshold and return column 1 value same row using NumPy](https://stackoverflow.com/questions/73746488/python-find-first-occurrence-in-pandas-dataframe-column-2-below-threshold-and-re)

What I am having trouble figuring out is how to modify this code to find the last occurrence meeting my criteria of being >= the threshold of 6. And so looking at my code above, I want to produce `'i'`, because looking at the above data frame, the row containing "i" in column "A" correspond to a value of 8 in column "B", which is the last occurrence of a value >= the threshold of 6. I want to preserve the order of the rows as alphabetical referencing column "A". I am guessing this might have to do with somehow modifying the indexing in my code, specifically the `array[:,1]` component or the `[0,0]` component, but I am not sure how to specifically call for the last occurrence meeting my criteria. How can I modify my code to find the value in column "A" corresponding to the last occurrence of a value >= the threshold of 6 in column "B"?