Suchen Sie die Orte mit dem höchsten und niedrigsten Wert innerhalb eines Intervalls in einer Spalte?

Question

Gegeben dieser Pandas Datenrahmen mit zwei Spalten, "Werte" und "Intervalle". Wie bekomme ich eine dritte Spalte "MinMax", die angibt, ob der Wert innerhalb dieses Intervalls ein Maximum oder ein Minimum ist? Die Herausforderung für mich ist, dass die Intervalllänge und der Abstand zwischen den Intervallen nicht festgelegt sind, daher poste ich die Frage.

import pandas as pd 
import numpy as np 


data = pd.DataFrame([ 
     [1879.289,np.nan],[1879.281,np.nan],[1879.292,1],[1879.295,1],[1879.481,1],[1879.294,1],[1879.268,1], 
     [1879.293,1],[1879.277,1],[1879.285,1],[1879.464,1],[1879.475,1],[1879.971,1],[1879.779,1], 
     [1879.986,1],[1880.791,1],[1880.29,1],[1879.253,np.nan],[1878.268,np.nan],[1875.73,1],[1876.792,1], 
     [1875.977,1],[1876.408,1],[1877.159,1],[1877.187,1],[1883.164,1],[1883.171,1],[1883.495,1], 
     [1883.962,1],[1885.158,1],[1885.974,1],[1886.479,np.nan],[1885.969,np.nan],[1884.693,1],[1884.977,1], 
     [1884.967,1],[1884.691,1],[1886.171,1],[1886.166,np.nan],[1884.476,np.nan],[1884.66,1],[1882.962,1], 
     [1881.496,1],[1871.163,1],[1874.985,1],[1874.979,1],[1871.173,np.nan],[1871.973,np.nan],[1871.682,np.nan], 
     [1872.476,np.nan],[1882.361,1],[1880.869,1],[1882.165,1],[1881.857,1],[1880.375,1],[1880.66,1], 
     [1880.891,1],[1880.377,1],[1881.663,1],[1881.66,1],[1877.888,1],[1875.69,1],[1875.161,1], 
     [1876.697,np.nan],[1876.671,np.nan],[1879.666,np.nan],[1877.182,np.nan],[1878.898,1],[1878.668,1],[1878.871,1], 
     [1878.882,1],[1879.173,1],[1878.887,1],[1878.68,1],[1878.872,1],[1878.677,1],[1877.877,1], 
     [1877.669,1],[1877.69,1],[1877.684,1],[1877.68,1],[1877.885,1],[1877.863,1],[1877.674,1], 
     [1877.676,1],[1877.687,1],[1878.367,1],[1878.179,1],[1877.696,1],[1877.665,1],[1877.667,np.nan], 
     [1878.678,np.nan],[1878.661,1],[1878.171,1],[1877.371,1],[1877.359,1],[1878.381,1],[1875.185,1], 
     [1875.367,np.nan],[1865.492,np.nan],[1865.495,1],[1866.995,1],[1866.672,1],[1867.465,1],[1867.663,1], 
     [1867.186,1],[1867.687,1],[1867.459,1],[1867.168,1],[1869.689,1],[1869.693,1],[1871.676,1], 
     [1873.174,1],[1873.691,np.nan],[1873.685,np.nan] 
    ]) 

In der dritten Spalte unten können Sie sehen, wo die Max und Min für jedes Intervall ist.

+-------+----------+-----------+---------+ 
| index | Value | Intervals | Min/Max | 
+-------+----------+-----------+---------+ 
|  0 | 1879.289 | np.nan |   | 
|  1 | 1879.281 | np.nan |   | 
|  2 | 1879.292 | 1   |   | 
|  3 | 1879.295 | 1   |   | 
|  4 | 1879.481 | 1   |   | 
|  5 | 1879.294 | 1   |   | 
|  6 | 1879.268 | 1   | min  | 
|  7 | 1879.293 | 1   |   | 
|  8 | 1879.277 | 1   |   | 
|  9 | 1879.285 | 1   |   | 
| 10 | 1879.464 | 1   |   | 
| 11 | 1879.475 | 1   |   | 
| 12 | 1879.971 | 1   |   | 
| 13 | 1879.779 | 1   |   | 
| 17 | 1879.986 | 1   |   | 
| 18 | 1880.791 | 1   | max  | 
| 19 | 1880.29 | 1   |   | 
| 55 | 1879.253 | np.nan |   | 
| 56 | 1878.268 | np.nan |   | 
| 57 | 1875.73 | 1   |   | 
| 58 | 1876.792 | 1   |   | 
| 59 | 1875.977 | 1   | min  | 
| 60 | 1876.408 | 1   |   | 
| 61 | 1877.159 | 1   |   | 
| 62 | 1877.187 | 1   |   | 
| 63 | 1883.164 | 1   |   | 
| 64 | 1883.171 | 1   |   | 
| 65 | 1883.495 | 1   |   | 
| 66 | 1883.962 | 1   |   | 
| 67 | 1885.158 | 1   |   | 
| 68 | 1885.974 | 1   | max  | 
| 69 | 1886.479 | np.nan |   | 
| 70 | 1885.969 | np.nan |   | 
| 71 | 1884.693 | 1   |   | 
| 72 | 1884.977 | 1   |   | 
| 73 | 1884.967 | 1   |   | 
| 74 | 1884.691 | 1   | min  | 
| 75 | 1886.171 | 1   | max  | 
| 76 | 1886.166 | np.nan |   | 
| 77 | 1884.476 | np.nan |   | 
| 78 | 1884.66 | 1   | max  | 
| 79 | 1882.962 | 1   |   | 
| 80 | 1881.496 | 1   |   | 
| 81 | 1871.163 | 1   | min  | 
| 82 | 1874.985 | 1   |   | 
| 83 | 1874.979 | 1   |   | 
| 84 | 1871.173 | np.nan |   | 
| 85 | 1871.973 | np.nan |   | 
| 86 | 1871.682 | np.nan |   | 
| 87 | 1872.476 | np.nan |   | 
| 88 | 1882.361 | 1   | max  | 
| 89 | 1880.869 | 1   |   | 
| 90 | 1882.165 | 1   |   | 
| 91 | 1881.857 | 1   |   | 
| 92 | 1880.375 | 1   |   | 
| 93 | 1880.66 | 1   |   | 
| 94 | 1880.891 | 1   |   | 
| 95 | 1880.377 | 1   |   | 
| 96 | 1881.663 | 1   |   | 
| 97 | 1881.66 | 1   |   | 
| 98 | 1877.888 | 1   |   | 
| 99 | 1875.69 | 1   |   | 
| 100 | 1875.161 | 1   | min  | 
| 101 | 1876.697 | np.nan |   | 
| 102 | 1876.671 | np.nan |   | 
| 103 | 1879.666 | np.nan |   | 
| 111 | 1877.182 | np.nan |   | 
| 112 | 1878.898 | 1   |   | 
| 113 | 1878.668 | 1   |   | 
| 114 | 1878.871 | 1   |   | 
| 115 | 1878.882 | 1   |   | 
| 116 | 1879.173 | 1   | max  | 
| 117 | 1878.887 | 1   |   | 
| 118 | 1878.68 | 1   |   | 
| 119 | 1878.872 | 1   |   | 
| 120 | 1878.677 | 1   |   | 
| 121 | 1877.877 | 1   |   | 
| 122 | 1877.669 | 1   |   | 
| 123 | 1877.69 | 1   |   | 
| 124 | 1877.684 | 1   |   | 
| 125 | 1877.68 | 1   |   | 
| 126 | 1877.885 | 1   |   | 
| 127 | 1877.863 | 1   |   | 
| 128 | 1877.674 | 1   |   | 
| 129 | 1877.676 | 1   |   | 
| 130 | 1877.687 | 1   |   | 
| 131 | 1878.367 | 1   |   | 
| 132 | 1878.179 | 1   |   | 
| 133 | 1877.696 | 1   |   | 
| 134 | 1877.665 | 1   | min  | 
| 135 | 1877.667 | np.nan |   | 
| 136 | 1878.678 | np.nan |   | 
| 137 | 1878.661 | 1   | max  | 
| 138 | 1878.171 | 1   |   | 
| 139 | 1877.371 | 1   |   | 
| 140 | 1877.359 | 1   |   | 
| 141 | 1878.381 | 1   |   | 
| 142 | 1875.185 | 1   | min  | 
| 143 | 1875.367 | np.nan |   | 
| 144 | 1865.492 | np.nan |   | 
| 145 | 1865.495 | 1   | max  | 
| 146 | 1866.995 | 1   |   | 
| 147 | 1866.672 | 1   |   | 
| 148 | 1867.465 | 1   |   | 
| 149 | 1867.663 | 1   |   | 
| 150 | 1867.186 | 1   |   | 
| 151 | 1867.687 | 1   |   | 
| 152 | 1867.459 | 1   |   | 
| 153 | 1867.168 | 1   |   | 
| 154 | 1869.689 | 1   |   | 
| 155 | 1869.693 | 1   |   | 
| 156 | 1871.676 | 1   |   | 
| 157 | 1873.174 | 1   | min  | 
| 158 | 1873.691 | np.nan |   | 
| 159 | 1873.685 | np.nan |   | 
+-------+----------+-----------+---------+ 

Answer 1

isnull = data.iloc[:, 1].isnull() 
minmax = data.groupby(isnull.cumsum()[~isnull])[0].agg(['idxmax', 'idxmin']) 
data.loc[minmax['idxmax'], 'MinMax'] = 'max' 
data.loc[minmax['idxmin'], 'MinMax'] = 'min' 
data.MinMax = data.MinMax.fillna('') 
print(data) 

      0 1 MinMax 
0 1879.289 NaN  
1 1879.281 NaN  
2 1879.292 1.0  
3 1879.295 1.0  
4 1879.481 1.0  
5 1879.294 1.0  
6 1879.268 1.0 min 
7 1879.293 1.0  
8 1879.277 1.0  
9 1879.285 1.0  
10 1879.464 1.0  
11 1879.475 1.0  
12 1879.971 1.0  
13 1879.779 1.0  
14 1879.986 1.0  
15 1880.791 1.0 max 
16 1880.290 1.0  
17 1879.253 NaN  
18 1878.268 NaN  
19 1875.730 1.0 min 
20 1876.792 1.0  
21 1875.977 1.0  
22 1876.408 1.0  
23 1877.159 1.0  
24 1877.187 1.0  
25 1883.164 1.0  
26 1883.171 1.0  
27 1883.495 1.0  
28 1883.962 1.0  
29 1885.158 1.0  
..  ... ... ... 
85 1877.687 1.0  
86 1878.367 1.0  
87 1878.179 1.0  
88 1877.696 1.0  
89 1877.665 1.0 min 
90 1877.667 NaN  
91 1878.678 NaN  
92 1878.661 1.0 max 
93 1878.171 1.0  
94 1877.371 1.0  
95 1877.359 1.0  
96 1878.381 1.0  
97 1875.185 1.0 min 
98 1875.367 NaN  
99 1865.492 NaN  
100 1865.495 1.0 min 
101 1866.995 1.0  
102 1866.672 1.0  
103 1867.465 1.0  
104 1867.663 1.0  
105 1867.186 1.0  
106 1867.687 1.0  
107 1867.459 1.0  
108 1867.168 1.0  
109 1869.689 1.0  
110 1869.693 1.0  
111 1871.676 1.0  
112 1873.174 1.0 max 
113 1873.691 NaN  
114 1873.685 NaN  

[115 rows x 3 columns] 

Answer 2

data.columns=['Value','Interval'] 

data['Ingroup'] = (data['Interval'].notnull() + 0) 

Use data['Interval'].notnull() to separate the groups... 
Use cumsum() to number them with `groupno`... 
Use groupby(groupno).. 

Finally you want something using apply/idxmax/idxmin to label the max/min 

But of course a for-loop as you suggested is the non-Pythonic but possibly simpler hack.