Rewritten with what I think is hopefully an easier question to understand.
What I think the problem is, is not being able to append more than one item to a list at a time.
Imagine using df.iloc[3:6], But instead it is: myList.append(Start:Finish)
# looping through an index and adding something to it when true
for (x)each index in the index:
if condition is true:
myList.append(x)
# looping through an index and adding something to it when true
for (x)each index in the index:
if condition is true:
myList.append(x),myList.append(x+1),myList.append(x+2)
# looping through an index and adding something to it when true
for (x)each index in the index:
if condition is true:
myList.append(x -> x+2) #myList.append(x+1),myList.append(x+2)
# so I want: myList.append(x to x+1 to x+2)
# but without having to keep writing x+this , x+that ...
myList.append(x), myList.append(x+1), myList.append(x_2) (etc)
myList.append(THIS POSITION -> numbers inbetween AND -> FINAL POSITION)
If it is not clear enough I am sorry and I will write spaghetti code until I figure it out
IIUC:
a = []
g = lambda x,y: list(range(x,x+y))
a.append(g(7,4))
a.append(g(11,4))
a:
[[7, 8, 9, 10], [11, 12, 13, 14]]
If I understand properly your condition n
consecutive rows where the value is increasing (x1<x2<x3<x4)
This solution will help:
https://stackoverflow.com/a/65090526/6660373
You need to modify as per your requirement.
Borrowing the code from that answer:
# Is the current "Close" increasing or same (compared with previous row)
df['incr'] = df.Close >= df.Close.shift(fill_value=0)
# Generate the result column
df['DaysDecr'] = df.groupby(df.incr.cumsum()).apply(
lambda grp: (~grp.incr).cumsum()).reset_index(level=0, drop=True)
df.drop(columns='incr', inplace=True)
N=3
idx = df.loc[(df['DaysDecr'].rolling(window=N , min_periods=N)\
.apply(lambda x: (x==0).all()).eq(1))].index
f = lambda x: list(range(x-N+1,x+1))
for i in idx:
print(f(i)) # <--------- here is your indices where the condition is satisfied
[0, 1, 2]
df:
Date Close incr DaysDecr
0 2015-11-27 105.449997 True 0
1 2015-11-30 106.239998 True 0
2 2015-12-01 107.120003 True 0
3 2015-12-02 106.070000 False 1
4 2015-12-03 104.379997 False 2
5 2020-11-18 271.970001 True 0
6 2020-11-19 272.940002 True 0
7 2020-11-20 269.700012 False 1
8 2020-11-23 268.429993 False 2
9 2020-11-24 276.920013 True 0
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