pandas DataFrame groupby/freq with value range

Question

Is there any method in python pandas to slice price range a DataFrame, not only time range, like groupby('D')? I need price range from this DataFrame, when price range is 5, and I want to other columns start/end/min/max of prices.

Time,Price,Volume
2022-04-10 00:01:00,100,5
2022-04-10 00:02:00,102,5
2022-04-10 00:03:00,104,5
2022-04-10 00:04:00,102,5
2022-04-10 00:05:00,105,5
2022-04-10 00:06:00,105,5
2022-04-10 00:07:00,106,5
2022-04-10 00:08:00,104,5
2022-04-10 00:09:00,103,5
2022-04-10 00:10:00,106,5
2022-04-10 00:11:00,108,5
2022-04-10 00:12:00,107,5
2022-04-10 00:13:00,105,5
2022-04-10 00:14:00,108,5
2022-04-10 00:15:00,104,5
2022-04-10 00:16:00,103,5

I'd like to get this:

Time,Start_price,End_price,Min_price,Max_price,Sum_volume
2022-04-10 00:01:00,100,105,100,105,25
2022-04-10 00:06:00,105,108,103,108,30
2022-04-10 00:12:00,107,103,103,108,25

Answer 1

Try this for making groupby col:

df['Price'] = df['Price'].astype(int)
r = df['Price'].iloc[0]
out = []
g = 0
for price in df['Price']:
    if abs(price-r)>=5:
        out.append(g)
        g+=1
        r = price
    else:
        if price < r:
            r = price
        out.append(g)

Output:

[0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2]

The code iterates on the Price column and creates groups based on the absolute difference of the observed max and mins.