Pandas Percent increase/decrease when row switch from True/False

Question

My df look like this:

      close  in_uptrend  count_uptrend
83  2        True              1
84  3        True              2
85  4        True              3
86  1        True              4
87  3        True              5
88  5        True              6
89  7        True              7
90  6.6      True              8
91  8        True              9
92  8.9      True             10
93  9        True             11
94  11       True             12
95  10       False             1
96  9        False             2
97  8        False             3
98  5       False              4

I want to create a new column named 'percent_trend' that calculate the increase of the 'close' column value, when a switch happens between 'in_trend' = 'True' to 'False' and percent decrease when a switch happens between 'in_trend' = 'False' to 'True'

Note that the column 'count_uptrend' is equal 1 when there is a switch between True and False.

Note sure if I explain it clearly, but the excepted result should help:

Expected result:

      close  in_uptrend  count_uptrend percent_trend
83  2        True              1       
84  3        True              2
85  4        True              3
86  1        True              4
87  3        True              5
88  5        True              6
89  7        True              7
90  6.6      True              8
91  8        True              9
92  8.9      True             10
93  9        True             11
94  11       True             12         450
95  10       False             1
96  9        False             2
97  8        False             3
98  5        False             4         -50

Answer 1

Annotated code

# identify blocks of continuous True/False values
b = df['in_uptrend'].diff().ne(0).cumsum()

# group the 'close' column by blocks
g = df['close'].groupby(b)

# Broadcast the first and last value per group
last, first = g.transform('last'), g.transform('first')

# calculate percent change
df['pct_trend'] = (last - first) / first * 100

# Mask the duplicate values
df['pct_trend'] = df['pct_trend'].mask(b.duplicated(keep='last'))

---

    close  in_uptrend  count_uptrend  pct_trend
83    2.0        True              1        NaN
84    3.0        True              2        NaN
85    4.0        True              3        NaN
86    1.0        True              4        NaN
87    3.0        True              5        NaN
88    5.0        True              6        NaN
89    7.0        True              7        NaN
90    6.6        True              8        NaN
91    8.0        True              9        NaN
92    8.9        True             10        NaN
93    9.0        True             11        NaN
94   11.0        True             12      450.0
95   10.0       False              1        NaN
96    9.0       False              2        NaN
97    8.0       False              3        NaN
98    5.0       False              4      -50.0