Find the highest and lowest value locations within an interval on a column? - pandas
Given this pandas dataframe with two columns, 'Values' and 'Intervals'. How do I get a third column 'MinMax' indicating whether the value is a maximum or a minimum within that interval? The challenge for me is that the interval length and the distance between intervals are not fixed, therefore I post the question.
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 the third column below you can see where the max and min is for each interval.
+-------+----------+-----------+---------+
| 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 | |
+-------+----------+-----------+---------+
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]
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.
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SQL Performance multiple exclusion from the same table
I have a table where I have a list of people, lets say i have 100 people listed in that table I need to filter out the people using different criteria's and put them in groups, problem is when i start excluding on the 4th-5th level, performance issues come up and it becomes slow with lst_tous_movements as ( select t1.refid_eClinibase t1.[dthrfinmouvement] t1.[unite_service_id] t1.[unite_service_suiv_id] from sometable t1 ) ,lst_patients_hospitalisés as ( select distinct t1.refid_eClinibase from lst_tous_movements t1 where t1.[dthrfinmouvement] = '4000-01-01' ) ,lst_patients_admisUIB_transferes as ( select distinct t1.refid_eClinibase from lst_tous_movements t1 left join lst_patients_hospitalisés t2 on t1.refid_eClinibase = t2.refid_eClinibase where t1.[unite_service_id] = 4 and t1.[unite_service_suiv_id] <> 0 and t2.refid_eClinibase is null ) ,lst_patients_admisUIB_nonTransferes as ( select distinct t1.refid_eClinibase from lst_tous_movements t1 left join lst_patients_admisUIB_transferes t2 on t1.refid_eClinibase = t2.refid_eClinibase left join lst_patients_hospitalisés t3 on t1.refid_eClinibase = t3.refid_eClinibase where t1.[unite_service_id] = 4 and t1.[unite_service_suiv_id] = 0 and t2.refid_eClinibase is null and t3.refid_eClinibase is null ) ,lst_patients_autres as ( select distinct t1.refid_eClinibase from lst_patients t1 left join lst_patients_admisUIB_transferes t2 on t1.refid_eClinibase = t2.refid_eClinibase left join lst_patients_hospitalisés t3 on t1.refid_eClinibase = t3.refid_eClinibase left join lst_patients_admisUIB_nonTransferes t4 on t1.refid_eClinibase = t4.refid_eClinibase where t2.refid_eClinibase is null and t3.refid_eClinibase is null and t4.refid_eClinibase is null ) as you can see i have a multi level filtering out going on here... 1st i get the people where t1.[dthrfinmouvement] = '4000-01-01' 2nd i get the people with another criteria EXCLUDING the 1st group 3rd i get the people with yet another criteria EXCLUDING the 1st and the 2nd group etc.. when i get to the 4th level, my query takes 6 - 10 seconds to complete is there any way to speed this up ? this is my dataset i'm working with: +------------------+-------------------------------+------------------+------------------+-----------------------+ | refid_eClinibase | nodossierpermanent_eClinibase | dthrfinmouvement | unite_service_id | unite_service_suiv_id | +------------------+-------------------------------+------------------+------------------+-----------------------+ | 25611 | P0017379 | 2013-04-27 | 58 | 0 | | 25611 | P0017379 | 2013-05-02 | 4 | 2 | | 25611 | P0017379 | 2013-05-18 | 2 | 0 | | 85886 | P0077918 | 2013-04-10 | 58 | 0 | | 85886 | P0077918 | 2013-05-06 | 6 | 12 | | 85886 | P0077918 | 4000-01-01 | 12 | 0 | | 91312 | P0083352 | 2013-07-24 | 3 | 14 | | 91312 | P0083352 | 2013-07-24 | 14 | 3 | | 91312 | P0083352 | 2013-07-30 | 3 | 8 | | 91312 | P0083352 | 4000-01-01 | 8 | 0 | | 93835 | P0085879 | 2013-04-30 | 58 | 0 | | 93835 | P0085879 | 2013-05-07 | 4 | 2 | | 93835 | P0085879 | 2013-05-16 | 2 | 0 | | 93835 | P0085879 | 2013-05-22 | 58 | 0 | | 93835 | P0085879 | 2013-05-24 | 4 | 0 | | 93835 | P0085879 | 2013-05-31 | 58 | 0 | | 93836 | P0085880 | 2013-05-20 | 58 | 0 | | 93836 | P0085880 | 2013-05-22 | 4 | 2 | | 93836 | P0085880 | 2013-05-31 | 2 | 0 | | 97509 | P0089576 | 2013-04-09 | 58 | 0 | | 97509 | P0089576 | 2013-04-11 | 4 | 0 | | 102787 | P0094886 | 2013-04-08 | 58 | 0 | | 102787 | P0094886 | 2013-04-11 | 4 | 2 | | 102787 | P0094886 | 2013-05-21 | 2 | 0 | | 103029 | P0095128 | 2013-04-04 | 58 | 0 | | 103029 | P0095128 | 2013-04-10 | 4 | 1 | | 103029 | P0095128 | 2013-05-03 | 1 | 0 | | 103813 | P0095922 | 2013-07-02 | 58 | 0 | | 103813 | P0095922 | 2013-07-03 | 4 | 6 | | 103813 | P0095922 | 2013-08-14 | 6 | 0 | | 105106 | P0097215 | 2013-08-09 | 58 | 0 | | 105106 | P0097215 | 2013-08-13 | 4 | 0 | | 105106 | P0097215 | 2013-08-14 | 58 | 0 | | 105106 | P0097215 | 4000-01-01 | 4 | 0 | | 106223 | P0098332 | 2013-06-11 | 1 | 0 | | 106223 | P0098332 | 2013-08-01 | 58 | 0 | | 106223 | P0098332 | 4000-01-01 | 1 | 0 | | 106245 | P0098354 | 2013-04-02 | 58 | 0 | | 106245 | P0098354 | 2013-05-24 | 58 | 0 | | 106245 | P0098354 | 2013-05-29 | 4 | 1 | | 106245 | P0098354 | 2013-07-12 | 1 | 0 | | 106280 | P0098389 | 2013-04-07 | 58 | 0 | | 106280 | P0098389 | 2013-04-09 | 4 | 0 | | 106416 | P0098525 | 2013-04-19 | 58 | 0 | | 106416 | P0098525 | 2013-04-23 | 4 | 0 | | 106444 | P0098553 | 2013-04-22 | 58 | 0 | | 106444 | P0098553 | 2013-04-25 | 4 | 0 | | 106609 | P0098718 | 2013-05-08 | 58 | 0 | | 106609 | P0098718 | 2013-05-10 | 4 | 11 | | 106609 | P0098718 | 2013-07-24 | 11 | 12 | | 106609 | P0098718 | 4000-01-01 | 12 | 0 | | 106616 | P0098725 | 2013-05-09 | 58 | 0 | | 106616 | P0098725 | 2013-05-09 | 4 | 1 | | 106616 | P0098725 | 2013-07-27 | 1 | 0 | | 106698 | P0098807 | 2013-05-16 | 58 | 0 | | 106698 | P0098807 | 2013-05-22 | 4 | 6 | | 106698 | P0098807 | 2013-06-14 | 6 | 1 | | 106698 | P0098807 | 2013-06-28 | 1 | 0 | | 106714 | P0098823 | 2013-05-20 | 58 | 0 | | 106714 | P0098823 | 2013-05-21 | 58 | 0 | | 106714 | P0098823 | 2013-05-24 | 58 | 0 | | 106729 | P0098838 | 2013-05-21 | 58 | 0 | | 106729 | P0098838 | 2013-05-23 | 4 | 1 | | 106729 | P0098838 | 2013-06-03 | 1 | 0 | | 107038 | P0099147 | 2013-06-25 | 58 | 0 | | 107038 | P0099147 | 2013-06-28 | 4 | 1 | | 107038 | P0099147 | 2013-07-04 | 1 | 0 | | 107038 | P0099147 | 2013-08-13 | 58 | 0 | | 107038 | P0099147 | 2013-08-15 | 4 | 6 | | 107038 | P0099147 | 4000-01-01 | 6 | 0 | | 107082 | P0099191 | 2013-06-29 | 58 | 0 | | 107082 | P0099191 | 2013-07-04 | 4 | 6 | | 107082 | P0099191 | 2013-07-19 | 6 | 0 | | 107157 | P0099267 | 4000-01-01 | 13 | 0 | | 107336 | P0099446 | 4000-01-01 | 6 | 0 | +------------------+-------------------------------+------------------+------------------+-----------------------+ thanks.
It is hard to understand exactly what all your rules are from the question, but the general approach should be to add a "Grouping" column to a singl query that uses a CASE statement to categorize the people. The conditions in a CASE are evaluated in order, so that if the first criteria is met, then the subsequent criteria are not even evaluated for that row. Here is some code to get you started.... select t1.refid_eClinibase ,t1.[dthrfinmouvement] ,t1.[unite_service_id] ,t1.[unite_service_suiv_id] CASE WHEN [dthrfinmouvement] = '4000-01-01' THEN 'Group1 Label' WHEN condition2 = something THEN 'Group2 Label' .... WHEN conditionN = something THEN 'GroupN Label' ELSE 'Catch All Label' END as person_category from sometable t1