Descending Filtering For Dataframe

I have a dataframe such as Index Results Price 0 Buy 10 1 Sell 11 2 Buy 12 3 Neutral 13 4 Buy 14 5 Sell 15 I would l

Solution 1:

You can use a manual loop with a couple of Boolean flags. Here I use numba to add some element of optimisation:

from numba import njit

@njit
def get_diffs(results, prices):
    res = np.full(prices.shape, np.nan)
    prev_one, prev_zero = True, False
    for i in range(len(results)):
        if prev_one and (results[i] == 0):
            price_start = prices[i]
            prev_zero, prev_one = True, False
        elif prev_zero and (results[i] == 1):
            res[i] = prices[i] - price_start
            prev_zero, prev_one = False, True
    return res

results = df['Results'].map({'Buy': 0, 'Sell': 1})

df['Difference'] = get_diffs(results.values, df['Price'].values)

print(df)

   Index  Results  Price  Difference
0      0      Buy     10         NaN
1      1     Sell     11         1.0
2      2      Buy     12         NaN
3      3  Neutral     13         NaN
4      4      Buy     14         NaN
5      5     Sell     15         3.0