8
-1
-41
-71
-97
-124
-126
-117
-107
-78
-26
10
46
63
94
100
88
87
105
109
81
39
7
-12

-126
109

1
10
11
1

a = [8, -1, -41, -71, -97, -124, -126, -117, -107, -78, -26, 10, 46, 63, 94, 100, 88, 87, 105, 109, 81, 39, 7, -12]


def search_min(a):
    el_min = a[0]
    for elm in a[1:]:
        if elm < el_min:
            el_min = elm
    print("The minimum value in the list is: " + str(el_min))


def search_max(a):
    el_max = a[0]
    for elm in a[1:]:
        if elm > el_max:
            el_max = elm
    print("The maximum value in the list is: " + str(el_max))

search_min(a)
search_max(a)

import numpy as np
import pandas as pd

fn = r'C:Temp.data.txt'
s = pd.read_csv(fn, header=None, squeeze=True)

grp = s.groupby((np.sign(s).diff().fillna(0).ne(0)).cumsum())

extremums = grp.apply(lambda x: x.abs().max() * np.sign(x[x.abs().idxmax()]))
sizes = grp.count()

In [103]: grp.agg([lambda x: x.abs().max() * np.sign(x[x.abs().idxmax()]), 'count'])
             .rename(columns={'<lambda>':'extremum'})
Out[103]:
    extremum  count
0
0         46      2
1       -316     29
2        148     31
3       -126     10
4        109     12
5       -168     33
6         41      5
7       -333     45
8         85     18
9       -123     17
10        70     14
11       -11      1
12        35     14
13       -97     18
14       190     75
15       -73     13

import numpy as np
import pandas as pd

s = pd.Series([1,2,1,-4,-7,-2,-5,4,3,1,2,-1,-2,-3])

extremums = s.groupby((np.sign(s).diff().fillna(0).ne(0)).cumsum())
             .apply(lambda x: x.abs().max() * np.sign(x[x.abs().idxmax()]))

In [74]: print(extremums)
0    2
1   -7
2    4
3   -3
dtype: int64

In [75]: print(extremums.values)
[ 2 -7  4 -3]