Is there an efficient way to compute column values in Pandas using values from previous rows based on conditional values from other columns?

Consider looping through my DataFrame:

import pandas as pd

df = pd.DataFrame({
    'Price': [1000, 1000, 1000, 2000, 2000, 2000, 2000, 1400, 1400],
    'Count': [0, 0, 0, 0, 0, 0, 0, 0, 0]
})

for idx in df.index:
    if df['Price'].iloc[idx] > 1500:
        if idx > 0:
            df['Count'].iloc[idx] = df['Count'].iloc[idx - 1] + 1

Resulting in:

Is there a more efficient way to do this?

>Solution :

Create pseudo-groups using Series.cumsum, then use groupby.cumcount to generate the within-group counts:

groups = df.Price.le(1500).cumsum()
df['Count'] = df.Price.gt(1500).groupby(groups).cumcount()

#    Price  Count
# 0   1000      0
# 1   1000      0
# 2   1000      0
# 3   2000      1
# 4   2000      2
# 5   2000      3
# 6   2000      4
# 7   1400      0
# 8   1400      0