查找列的区间内的最高值和最低值的位置?(Find the highest and lowest va

2019-09-30 14:58发布

鉴于这种大熊猫据帧有两列,“价值”和“间隔”。 如何获得第三列“最小最大”指示值是否为最大或区间内的最小? 对于我的挑战是,间隔长度和间隔之间的距离不是固定的,因此,我发布问题。

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]
    ])

在第三列中可以看到,其中最大和最小为每一个间隔。

+-------+----------+-----------+---------+
| 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.


文章来源: Find the highest and lowest value locations within an interval on a column?