Overview

Dataset statistics

Number of variables12
Number of observations8760
Missing cells39
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory915.5 KiB
Average record size in memory107.0 B

Variable types

DateTime1
Numeric11

Dataset

Description한국원자력연구원_환경방사선 감시 현황 데이터 입니다. 데이터 칼럼 리스트는 측정시간, 본관동편(ERM_2) mR/h, 기상탑(ERM_3) mR/h, 독신료(ERM_4) mR/h, 하나로서쪽(ERM_6) mR/h, 구즉도서관(ERM_7) mR/h, 제6연구동(ERM_8) mR/h, 구룡지(ERM_9) mR/h, 연산비교지점(ERM_R1) mR/h, 서울MW서쪽(ERM_S1) mR/h, 서울MW동쪽(ERM_S2) mR/h, 서울kW서쪽(ERM_S3) mR/h 입니다.원자력안전법 104조 (환경보전)에 따라, 원자력시설로부터의 방사선 비상을 조기에 탐지하기 위하여 한국원자력연구원 부지 내에 환경방사선을 감시하여 비교지점(논산시 연산면 소재)과 실시간으로 모니터링.측정된 방사선량률 수치는 시간당 밀리-렌트겐(mR/h) 또는 시간당 마이크로-렌트겐(uR/h)으로 표시
Author한국원자력연구원
URLhttps://www.data.go.kr/data/15012708/fileData.do

Alerts

본관동편(ERM_2) is highly overall correlated with 기상탑(ERM_3) and 6 other fieldsHigh correlation
기상탑(ERM_3) is highly overall correlated with 본관동편(ERM_2) and 5 other fieldsHigh correlation
독신료(ERM_4) is highly overall correlated with 본관동편(ERM_2) and 1 other fieldsHigh correlation
하나로서쪽(ERM_6) is highly overall correlated with 본관동편(ERM_2) and 5 other fieldsHigh correlation
구즉도서관(ERM_7) is highly overall correlated with 본관동편(ERM_2) and 4 other fieldsHigh correlation
제6연구동(ERM_8) is highly overall correlated with 본관동편(ERM_2) and 7 other fieldsHigh correlation
구룡지(ERM_9) is highly overall correlated with 본관동편(ERM_2) and 5 other fieldsHigh correlation
연산비교지점(ERM_R1) is highly overall correlated with 본관동편(ERM_2) and 4 other fieldsHigh correlation
서울2호기서쪽(ERM_S1) is highly overall correlated with 서울2호기동쪽(ERM_S2) and 1 other fieldsHigh correlation
서울2호기동쪽(ERM_S2) is highly overall correlated with 서울2호기서쪽(ERM_S1) and 1 other fieldsHigh correlation
서울1호기서쪽(ERM_S3) is highly overall correlated with 제6연구동(ERM_8) and 2 other fieldsHigh correlation
측정시간 has unique valuesUnique

Reproduction

Analysis started2023-12-12 08:13:19.559620
Analysis finished2023-12-12 08:13:39.180139
Duration19.62 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

측정시간
Date

UNIQUE 

Distinct8760
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size68.6 KiB
Minimum2021-01-01 00:00:00
Maximum2021-12-31 23:00:00
2023-12-12T17:13:39.278342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:13:39.499978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

본관동편(ERM_2)
Real number (ℝ)

HIGH CORRELATION 

Distinct55
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.011880811
Minimum0.0112
Maximum0.0175
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size77.1 KiB
2023-12-12T17:13:39.688527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.0112
5-th percentile0.0115
Q10.0116
median0.0118
Q30.012
95-th percentile0.0124
Maximum0.0175
Range0.0063
Interquartile range (IQR)0.0004

Descriptive statistics

Standard deviation0.00045381817
Coefficient of variation (CV)0.038197577
Kurtosis31.360415
Mean0.011880811
Median Absolute Deviation (MAD)0.0002
Skewness4.4821593
Sum104.0759
Variance2.0595094 × 10-7
MonotonicityNot monotonic
2023-12-12T17:13:39.875665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0117 1555
17.8%
0.0118 1352
15.4%
0.0116 1350
15.4%
0.0119 1082
12.4%
0.012 832
9.5%
0.0115 745
8.5%
0.0121 529
 
6.0%
0.0122 349
 
4.0%
0.0123 234
 
2.7%
0.0114 176
 
2.0%
Other values (45) 556
 
6.3%
ValueCountFrequency (%)
0.0112 1
 
< 0.1%
0.0113 13
 
0.1%
0.0114 176
 
2.0%
0.0115 745
8.5%
0.0116 1350
15.4%
0.0117 1555
17.8%
0.0118 1352
15.4%
0.0119 1082
12.4%
0.012 832
9.5%
0.0121 529
 
6.0%
ValueCountFrequency (%)
0.0175 1
 
< 0.1%
0.0171 3
< 0.1%
0.017 1
 
< 0.1%
0.0168 1
 
< 0.1%
0.0164 1
 
< 0.1%
0.0163 1
 
< 0.1%
0.0161 2
< 0.1%
0.016 1
 
< 0.1%
0.0158 2
< 0.1%
0.0157 3
< 0.1%

기상탑(ERM_3)
Real number (ℝ)

HIGH CORRELATION 

Distinct53
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.010467032
Minimum0.0095
Maximum0.0153
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size77.1 KiB
2023-12-12T17:13:40.053833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.0095
5-th percentile0.0101
Q10.0102
median0.0104
Q30.0106
95-th percentile0.011
Maximum0.0153
Range0.0058
Interquartile range (IQR)0.0004

Descriptive statistics

Standard deviation0.00041281267
Coefficient of variation (CV)0.039439324
Kurtosis26.483148
Mean0.010467032
Median Absolute Deviation (MAD)0.0002
Skewness3.9620692
Sum91.6912
Variance1.704143 × 10-7
MonotonicityNot monotonic
2023-12-12T17:13:40.246566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0104 1454
16.6%
0.0103 1417
16.2%
0.0102 1171
13.4%
0.0105 1163
13.3%
0.0106 855
9.8%
0.0101 804
9.2%
0.0107 512
 
5.8%
0.0108 346
 
3.9%
0.01 277
 
3.2%
0.0109 179
 
2.0%
Other values (43) 582
6.6%
ValueCountFrequency (%)
0.0095 1
 
< 0.1%
0.0098 1
 
< 0.1%
0.0099 37
 
0.4%
0.01 277
 
3.2%
0.0101 804
9.2%
0.0102 1171
13.4%
0.0103 1417
16.2%
0.0104 1454
16.6%
0.0105 1163
13.3%
0.0106 855
9.8%
ValueCountFrequency (%)
0.0153 1
< 0.1%
0.0151 2
< 0.1%
0.015 1
< 0.1%
0.0149 1
< 0.1%
0.0146 1
< 0.1%
0.0144 2
< 0.1%
0.0143 1
< 0.1%
0.0142 1
< 0.1%
0.0141 1
< 0.1%
0.014 2
< 0.1%

독신료(ERM_4)
Real number (ℝ)

HIGH CORRELATION 

Distinct52
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.014741221
Minimum0.0136
Maximum0.0192
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size77.1 KiB
2023-12-12T17:13:40.421477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.0136
5-th percentile0.014
Q10.0145
median0.0147
Q30.015
95-th percentile0.0154
Maximum0.0192
Range0.0056
Interquartile range (IQR)0.0005

Descriptive statistics

Standard deviation0.00048088534
Coefficient of variation (CV)0.032621811
Kurtosis10.207806
Mean0.014741221
Median Absolute Deviation (MAD)0.0002
Skewness1.8176285
Sum129.1331
Variance2.3125071 × 10-7
MonotonicityNot monotonic
2023-12-12T17:13:40.639207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0147 1049
12.0%
0.0146 998
11.4%
0.0149 968
11.1%
0.0148 954
10.9%
0.015 764
8.7%
0.0145 763
8.7%
0.0151 494
 
5.6%
0.0144 447
 
5.1%
0.0143 306
 
3.5%
0.0152 301
 
3.4%
Other values (42) 1716
19.6%
ValueCountFrequency (%)
0.0136 13
 
0.1%
0.0137 42
 
0.5%
0.0138 117
 
1.3%
0.0139 143
 
1.6%
0.014 244
 
2.8%
0.0141 259
 
3.0%
0.0142 244
 
2.8%
0.0143 306
3.5%
0.0144 447
5.1%
0.0145 763
8.7%
ValueCountFrequency (%)
0.0192 1
 
< 0.1%
0.019 1
 
< 0.1%
0.0189 1
 
< 0.1%
0.0187 1
 
< 0.1%
0.0185 2
< 0.1%
0.0184 1
 
< 0.1%
0.0182 2
< 0.1%
0.0181 2
< 0.1%
0.018 2
< 0.1%
0.0178 3
< 0.1%

하나로서쪽(ERM_6)
Real number (ℝ)

HIGH CORRELATION 

Distinct57
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.012931199
Minimum0.0119
Maximum0.0192
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size77.1 KiB
2023-12-12T17:13:40.836889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.0119
5-th percentile0.0125
Q10.0127
median0.0129
Q30.0131
95-th percentile0.0136
Maximum0.0192
Range0.0073
Interquartile range (IQR)0.0004

Descriptive statistics

Standard deviation0.00048066431
Coefficient of variation (CV)0.037170902
Kurtosis24.679789
Mean0.012931199
Median Absolute Deviation (MAD)0.0002
Skewness3.6935524
Sum113.2773
Variance2.3103818 × 10-7
MonotonicityNot monotonic
2023-12-12T17:13:41.298332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0128 1167
13.3%
0.0129 1097
12.5%
0.0127 1012
11.6%
0.013 962
11.0%
0.0126 948
10.8%
0.0125 722
8.2%
0.0131 717
8.2%
0.0132 585
6.7%
0.0133 368
 
4.2%
0.0124 288
 
3.3%
Other values (47) 894
10.2%
ValueCountFrequency (%)
0.0119 3
 
< 0.1%
0.012 12
 
0.1%
0.0121 7
 
0.1%
0.0122 33
 
0.4%
0.0123 91
 
1.0%
0.0124 288
 
3.3%
0.0125 722
8.2%
0.0126 948
10.8%
0.0127 1012
11.6%
0.0128 1167
13.3%
ValueCountFrequency (%)
0.0192 1
< 0.1%
0.0183 1
< 0.1%
0.0179 2
< 0.1%
0.0178 1
< 0.1%
0.0177 2
< 0.1%
0.0175 1
< 0.1%
0.0173 2
< 0.1%
0.017 2
< 0.1%
0.0169 1
< 0.1%
0.0167 1
< 0.1%

구즉도서관(ERM_7)
Real number (ℝ)

HIGH CORRELATION 

Distinct45
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.012757158
Minimum0.0117
Maximum0.0175
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size77.1 KiB
2023-12-12T17:13:41.482293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.0117
5-th percentile0.0122
Q10.0125
median0.0127
Q30.0129
95-th percentile0.0133
Maximum0.0175
Range0.0058
Interquartile range (IQR)0.0004

Descriptive statistics

Standard deviation0.00039730673
Coefficient of variation (CV)0.031143828
Kurtosis12.320968
Mean0.012757158
Median Absolute Deviation (MAD)0.0002
Skewness1.916973
Sum111.7527
Variance1.5785263 × 10-7
MonotonicityNot monotonic
2023-12-12T17:13:41.679361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
0.0127 1228
14.0%
0.0128 1159
13.2%
0.0126 1004
11.5%
0.0129 972
11.1%
0.013 787
9.0%
0.0125 773
8.8%
0.0124 540
6.2%
0.0131 478
 
5.5%
0.0123 311
 
3.6%
0.0122 287
 
3.3%
Other values (35) 1221
13.9%
ValueCountFrequency (%)
0.0117 4
 
< 0.1%
0.0118 26
 
0.3%
0.0119 55
 
0.6%
0.012 95
 
1.1%
0.0121 198
 
2.3%
0.0122 287
 
3.3%
0.0123 311
 
3.6%
0.0124 540
6.2%
0.0125 773
8.8%
0.0126 1004
11.5%
ValueCountFrequency (%)
0.0175 1
 
< 0.1%
0.0168 1
 
< 0.1%
0.0165 1
 
< 0.1%
0.0163 2
 
< 0.1%
0.0162 1
 
< 0.1%
0.016 1
 
< 0.1%
0.0155 1
 
< 0.1%
0.0154 4
< 0.1%
0.0153 6
0.1%
0.0152 4
< 0.1%

제6연구동(ERM_8)
Real number (ℝ)

HIGH CORRELATION 

Distinct54
Distinct (%)0.6%
Missing4
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean0.010790612
Minimum0.0102
Maximum0.0165
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size77.1 KiB
2023-12-12T17:13:41.852413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.0102
5-th percentile0.0104
Q10.0106
median0.0107
Q30.0109
95-th percentile0.0114
Maximum0.0165
Range0.0063
Interquartile range (IQR)0.0003

Descriptive statistics

Standard deviation0.00046894517
Coefficient of variation (CV)0.043458625
Kurtosis31.545691
Mean0.010790612
Median Absolute Deviation (MAD)0.0002
Skewness4.5671823
Sum94.4826
Variance2.1990957 × 10-7
MonotonicityNot monotonic
2023-12-12T17:13:42.010412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0106 1772
20.2%
0.0107 1375
15.7%
0.0105 1310
15.0%
0.0108 1033
11.8%
0.0109 816
9.3%
0.011 605
 
6.9%
0.0104 562
 
6.4%
0.0111 328
 
3.7%
0.0112 194
 
2.2%
0.0103 159
 
1.8%
Other values (44) 602
 
6.9%
ValueCountFrequency (%)
0.0102 29
 
0.3%
0.0103 159
 
1.8%
0.0104 562
 
6.4%
0.0105 1310
15.0%
0.0106 1772
20.2%
0.0107 1375
15.7%
0.0108 1033
11.8%
0.0109 816
9.3%
0.011 605
 
6.9%
0.0111 328
 
3.7%
ValueCountFrequency (%)
0.0165 2
< 0.1%
0.0161 1
< 0.1%
0.016 2
< 0.1%
0.0159 1
< 0.1%
0.0153 2
< 0.1%
0.0152 1
< 0.1%
0.0151 1
< 0.1%
0.015 1
< 0.1%
0.0149 2
< 0.1%
0.0148 2
< 0.1%

구룡지(ERM_9)
Real number (ℝ)

HIGH CORRELATION 

Distinct50
Distinct (%)0.6%
Missing25
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean0.011502977
Minimum0.0108
Maximum0.0175
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size77.1 KiB
2023-12-12T17:13:42.163897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.0108
5-th percentile0.0111
Q10.0113
median0.0114
Q30.0116
95-th percentile0.0122
Maximum0.0175
Range0.0067
Interquartile range (IQR)0.0003

Descriptive statistics

Standard deviation0.0004395392
Coefficient of variation (CV)0.03821091
Kurtosis21.753003
Mean0.011502977
Median Absolute Deviation (MAD)0.0002
Skewness3.4555755
Sum100.4785
Variance1.9319471 × 10-7
MonotonicityNot monotonic
2023-12-12T17:13:42.307353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0113 1698
19.4%
0.0114 1317
15.0%
0.0112 1245
14.2%
0.0115 882
10.1%
0.0116 659
 
7.5%
0.0111 636
 
7.3%
0.0117 479
 
5.5%
0.0118 366
 
4.2%
0.0119 339
 
3.9%
0.012 233
 
2.7%
Other values (40) 881
10.1%
ValueCountFrequency (%)
0.0108 9
 
0.1%
0.0109 51
 
0.6%
0.011 176
 
2.0%
0.0111 636
 
7.3%
0.0112 1245
14.2%
0.0113 1698
19.4%
0.0114 1317
15.0%
0.0115 882
10.1%
0.0116 659
 
7.5%
0.0117 479
 
5.5%
ValueCountFrequency (%)
0.0175 1
 
< 0.1%
0.0165 1
 
< 0.1%
0.016 1
 
< 0.1%
0.0159 2
< 0.1%
0.0156 1
 
< 0.1%
0.0153 1
 
< 0.1%
0.0152 2
< 0.1%
0.0151 1
 
< 0.1%
0.0149 3
< 0.1%
0.0148 2
< 0.1%

연산비교지점(ERM_R1)
Real number (ℝ)

HIGH CORRELATION 

Distinct56
Distinct (%)0.6%
Missing10
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean0.011792937
Minimum0.0112
Maximum0.02
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size77.1 KiB
2023-12-12T17:13:42.457828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.0112
5-th percentile0.0114
Q10.0116
median0.0117
Q30.0119
95-th percentile0.0124
Maximum0.02
Range0.0088
Interquartile range (IQR)0.0003

Descriptive statistics

Standard deviation0.00046961188
Coefficient of variation (CV)0.039821452
Kurtosis44.833951
Mean0.011792937
Median Absolute Deviation (MAD)0.0001
Skewness5.0929606
Sum103.1882
Variance2.2053532 × 10-7
MonotonicityNot monotonic
2023-12-12T17:13:42.609154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0117 1647
18.8%
0.0116 1646
18.8%
0.0115 1197
13.7%
0.0118 1110
12.7%
0.0119 626
 
7.1%
0.0114 583
 
6.7%
0.012 464
 
5.3%
0.0121 326
 
3.7%
0.0122 267
 
3.0%
0.0113 185
 
2.1%
Other values (46) 699
8.0%
ValueCountFrequency (%)
0.0112 27
 
0.3%
0.0113 185
 
2.1%
0.0114 583
 
6.7%
0.0115 1197
13.7%
0.0116 1646
18.8%
0.0117 1647
18.8%
0.0118 1110
12.7%
0.0119 626
 
7.1%
0.012 464
 
5.3%
0.0121 326
 
3.7%
ValueCountFrequency (%)
0.02 1
 
< 0.1%
0.0186 1
 
< 0.1%
0.0179 1
 
< 0.1%
0.0172 3
< 0.1%
0.0169 1
 
< 0.1%
0.0165 1
 
< 0.1%
0.0163 3
< 0.1%
0.0161 2
< 0.1%
0.016 1
 
< 0.1%
0.0159 1
 
< 0.1%

서울2호기서쪽(ERM_S1)
Real number (ℝ)

HIGH CORRELATION 

Distinct318
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.765523
Minimum12.65
Maximum18.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size77.1 KiB
2023-12-12T17:13:42.772838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum12.65
5-th percentile13.28
Q113.52
median13.7
Q313.9
95-th percentile14.37
Maximum18.8
Range6.15
Interquartile range (IQR)0.38

Descriptive statistics

Standard deviation0.45493426
Coefficient of variation (CV)0.033048818
Kurtosis20.652916
Mean13.765523
Median Absolute Deviation (MAD)0.19
Skewness3.475368
Sum120585.98
Variance0.20696518
MonotonicityNot monotonic
2023-12-12T17:13:42.957855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13.7 162
 
1.8%
13.68 150
 
1.7%
13.66 147
 
1.7%
13.73 144
 
1.6%
13.74 143
 
1.6%
13.62 142
 
1.6%
13.78 131
 
1.5%
13.69 130
 
1.5%
13.65 130
 
1.5%
13.6 128
 
1.5%
Other values (308) 7353
83.9%
ValueCountFrequency (%)
12.65 1
 
< 0.1%
12.96 2
 
< 0.1%
12.99 2
 
< 0.1%
13.01 4
< 0.1%
13.02 1
 
< 0.1%
13.03 4
< 0.1%
13.04 7
0.1%
13.05 2
 
< 0.1%
13.06 2
 
< 0.1%
13.07 3
< 0.1%
ValueCountFrequency (%)
18.8 1
< 0.1%
18.37 1
< 0.1%
18.19 1
< 0.1%
17.99 1
< 0.1%
17.93 1
< 0.1%
17.9 1
< 0.1%
17.85 1
< 0.1%
17.68 1
< 0.1%
17.63 1
< 0.1%
17.48 1
< 0.1%

서울2호기동쪽(ERM_S2)
Real number (ℝ)

HIGH CORRELATION 

Distinct339
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.5869
Minimum12.78
Maximum19.79
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size77.1 KiB
2023-12-12T17:13:43.144470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum12.78
5-th percentile13.13
Q113.36
median13.51
Q313.7
95-th percentile14.13
Maximum19.79
Range7.01
Interquartile range (IQR)0.34

Descriptive statistics

Standard deviation0.46711922
Coefficient of variation (CV)0.034380119
Kurtosis27.815931
Mean13.5869
Median Absolute Deviation (MAD)0.17
Skewness4.1786561
Sum119021.24
Variance0.21820036
MonotonicityNot monotonic
2023-12-12T17:13:43.344195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13.38 166
 
1.9%
13.51 160
 
1.8%
13.44 157
 
1.8%
13.48 155
 
1.8%
13.41 151
 
1.7%
13.46 150
 
1.7%
13.54 147
 
1.7%
13.55 145
 
1.7%
13.45 142
 
1.6%
13.57 141
 
1.6%
Other values (329) 7246
82.7%
ValueCountFrequency (%)
12.78 2
 
< 0.1%
12.83 3
< 0.1%
12.84 2
 
< 0.1%
12.85 4
< 0.1%
12.87 2
 
< 0.1%
12.88 3
< 0.1%
12.89 5
0.1%
12.9 4
< 0.1%
12.91 3
< 0.1%
12.92 6
0.1%
ValueCountFrequency (%)
19.79 1
< 0.1%
18.99 1
< 0.1%
18.54 1
< 0.1%
18.26 1
< 0.1%
18.09 1
< 0.1%
18.07 1
< 0.1%
17.72 1
< 0.1%
17.71 1
< 0.1%
17.67 1
< 0.1%
17.64 1
< 0.1%

서울1호기서쪽(ERM_S3)
Real number (ℝ)

HIGH CORRELATION 

Distinct330
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.950475
Minimum11.15
Maximum17.19
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size77.1 KiB
2023-12-12T17:13:43.558455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11.15
5-th percentile11.59
Q111.76
median11.87
Q312.01
95-th percentile12.45
Maximum17.19
Range6.04
Interquartile range (IQR)0.25

Descriptive statistics

Standard deviation0.44036459
Coefficient of variation (CV)0.036849129
Kurtosis30.1375
Mean11.950475
Median Absolute Deviation (MAD)0.12
Skewness4.6623223
Sum104686.16
Variance0.19392098
MonotonicityNot monotonic
2023-12-12T17:13:43.731292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11.89 233
 
2.7%
11.8 220
 
2.5%
11.85 213
 
2.4%
11.79 195
 
2.2%
11.75 193
 
2.2%
11.84 193
 
2.2%
11.88 191
 
2.2%
11.86 190
 
2.2%
11.87 186
 
2.1%
11.83 186
 
2.1%
Other values (320) 6760
77.2%
ValueCountFrequency (%)
11.15 1
 
< 0.1%
11.16 1
 
< 0.1%
11.19 1
 
< 0.1%
11.24 1
 
< 0.1%
11.26 1
 
< 0.1%
11.27 4
< 0.1%
11.28 1
 
< 0.1%
11.29 2
< 0.1%
11.3 3
< 0.1%
11.31 2
< 0.1%
ValueCountFrequency (%)
17.19 1
< 0.1%
16.78 1
< 0.1%
16.65 1
< 0.1%
16.58 1
< 0.1%
16.23 1
< 0.1%
16.13 1
< 0.1%
16.11 1
< 0.1%
15.89 1
< 0.1%
15.86 1
< 0.1%
15.8 1
< 0.1%

Interactions

2023-12-12T17:13:37.224664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:13:22.893356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:13:24.366540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:13:25.655142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:13:27.361169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:13:28.777780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:13:30.134310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:13:31.669294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:13:32.844352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:13:34.373725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:13:35.777138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:13:37.361534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:13:23.029727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:13:24.521523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:13:25.828425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:13:27.472636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:13:28.895733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:13:30.258435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:13:31.777132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:13:32.941625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:13:34.493407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:13:35.918915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:13:37.499140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:13:23.162736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:13:24.640017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:13:25.932164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:13:27.603296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:13:29.034524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:13:30.375889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:13:31.885293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:13:33.056779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:13:34.617702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:13:36.039803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:13:37.619906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:13:23.287758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:13:24.742861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:13:26.028131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:13:27.731904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:13:29.136903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:13:30.492877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:13:31.984959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:13:33.155161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:13:34.749533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:13:36.174583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:13:37.760022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:13:23.433307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:13:24.862727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:13:26.150970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:13:27.881283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:13:29.252353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:13:30.642510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:13:32.094695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:13:33.272325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:13:34.878709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:13:36.315964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:13:37.910882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:13:23.575031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:13:24.981225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:13:26.577153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:13:28.005970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:13:29.360121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:13:30.754048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:13:32.190120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:13:33.400722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:13:34.999776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:13:36.431567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:13:38.072834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:13:23.709471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:13:25.107467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:13:26.701236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:13:28.131393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:13:29.485333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:13:30.906944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:13:32.304295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:13:33.516110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:13:35.136894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:13:36.546370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:13:38.216111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:13:23.828934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:13:25.222873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:13:26.842098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:13:28.286662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:13:29.623106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:13:31.064551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:13:32.405923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:13:33.649664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:13:35.254156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:13:36.690127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:13:38.321136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:13:23.948256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:13:25.325917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:13:26.961923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:13:28.405188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:13:29.752275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:13:31.200275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:13:32.499166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:13:33.749912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:13:35.381197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:13:36.802520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:13:38.427778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:13:24.089448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:13:25.424133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:13:27.071187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:13:28.531498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:13:29.875719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:13:31.350237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:13:32.602832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:13:33.848897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:13:35.516932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:13:36.923539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:13:38.558886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:13:24.217827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:13:25.530775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:13:27.219455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:13:28.649862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:13:29.998213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:13:31.531894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:13:32.738833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:13:33.952779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:13:35.647860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:13:37.066878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T17:13:43.854125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
본관동편(ERM_2)기상탑(ERM_3)독신료(ERM_4)하나로서쪽(ERM_6)구즉도서관(ERM_7)제6연구동(ERM_8)구룡지(ERM_9)연산비교지점(ERM_R1)서울2호기서쪽(ERM_S1)서울2호기동쪽(ERM_S2)서울1호기서쪽(ERM_S3)
본관동편(ERM_2)1.0000.9380.9470.9470.9190.9770.9240.8400.6250.6400.642
기상탑(ERM_3)0.9381.0000.9190.9330.9160.9600.9070.8150.6530.6230.670
독신료(ERM_4)0.9470.9191.0000.9130.8820.9420.8990.7980.6060.6230.628
하나로서쪽(ERM_6)0.9470.9330.9131.0000.9520.9460.9440.8950.6050.6020.666
구즉도서관(ERM_7)0.9190.9160.8820.9521.0000.9250.9540.9040.6300.6230.637
제6연구동(ERM_8)0.9770.9600.9420.9460.9251.0000.9150.8550.6230.6290.650
구룡지(ERM_9)0.9240.9070.8990.9440.9540.9151.0000.9050.6080.5710.592
연산비교지점(ERM_R1)0.8400.8150.7980.8950.9040.8550.9051.0000.5940.6180.614
서울2호기서쪽(ERM_S1)0.6250.6530.6060.6050.6300.6230.6080.5941.0000.9470.948
서울2호기동쪽(ERM_S2)0.6400.6230.6230.6020.6230.6290.5710.6180.9471.0000.965
서울1호기서쪽(ERM_S3)0.6420.6700.6280.6660.6370.6500.5920.6140.9480.9651.000
2023-12-12T17:13:44.057829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
본관동편(ERM_2)기상탑(ERM_3)독신료(ERM_4)하나로서쪽(ERM_6)구즉도서관(ERM_7)제6연구동(ERM_8)구룡지(ERM_9)연산비교지점(ERM_R1)서울2호기서쪽(ERM_S1)서울2호기동쪽(ERM_S2)서울1호기서쪽(ERM_S3)
본관동편(ERM_2)1.0000.7600.6910.7360.7030.8860.7150.6360.3520.4450.465
기상탑(ERM_3)0.7601.0000.4810.5980.6980.8600.7050.5850.4440.4400.479
독신료(ERM_4)0.6910.4811.0000.4360.3360.6110.4920.3960.2840.2520.256
하나로서쪽(ERM_6)0.7360.5980.4361.0000.6060.7300.7150.6580.0670.2900.423
구즉도서관(ERM_7)0.7030.6980.3360.6061.0000.7170.6160.4500.3870.4220.452
제6연구동(ERM_8)0.8860.8600.6110.7300.7171.0000.7100.6360.3320.4610.519
구룡지(ERM_9)0.7150.7050.4920.7150.6160.7101.0000.5740.4400.2980.383
연산비교지점(ERM_R1)0.6360.5850.3960.6580.4500.6360.5741.0000.1560.4170.459
서울2호기서쪽(ERM_S1)0.3520.4440.2840.0670.3870.3320.4400.1561.0000.5980.507
서울2호기동쪽(ERM_S2)0.4450.4400.2520.2900.4220.4610.2980.4170.5981.0000.830
서울1호기서쪽(ERM_S3)0.4650.4790.2560.4230.4520.5190.3830.4590.5070.8301.000

Missing values

2023-12-12T17:13:38.756874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T17:13:38.941265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-12-12T17:13:39.086173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

측정시간본관동편(ERM_2)기상탑(ERM_3)독신료(ERM_4)하나로서쪽(ERM_6)구즉도서관(ERM_7)제6연구동(ERM_8)구룡지(ERM_9)연산비교지점(ERM_R1)서울2호기서쪽(ERM_S1)서울2호기동쪽(ERM_S2)서울1호기서쪽(ERM_S3)
02021-01-01 00:00:000.01170.01060.01450.01280.01280.01080.01140.011813.7613.6711.98
12021-01-01 01:00:000.01170.01060.01440.01280.01290.01090.01150.011813.813.7212.03
22021-01-01 02:00:000.01180.01070.01450.01290.01290.01080.01150.011713.813.6312.05
32021-01-01 03:00:000.01180.01070.01450.01290.01290.01090.01140.011813.7713.7212.13
42021-01-01 04:00:000.01180.01080.01450.01290.01290.01090.01140.011813.8613.8312.13
52021-01-01 05:00:000.01190.01080.01460.0130.0130.0110.01150.011813.8313.7812.12
62021-01-01 06:00:000.01190.01080.01460.0130.0130.0110.01150.011913.9413.8212.19
72021-01-01 07:00:000.01190.01080.01470.0130.0130.0110.01140.011913.9713.812.26
82021-01-01 08:00:000.01190.01080.01480.0130.0130.0110.01150.011914.0413.8312.28
92021-01-01 09:00:000.0120.01090.01470.0130.01310.01110.01170.011913.9413.8912.28
측정시간본관동편(ERM_2)기상탑(ERM_3)독신료(ERM_4)하나로서쪽(ERM_6)구즉도서관(ERM_7)제6연구동(ERM_8)구룡지(ERM_9)연산비교지점(ERM_R1)서울2호기서쪽(ERM_S1)서울2호기동쪽(ERM_S2)서울1호기서쪽(ERM_S3)
87502021-12-31 14:00:000.01140.01030.01460.01230.01230.01040.01110.011513.6413.3111.61
87512021-12-31 15:00:000.01140.01020.01460.01230.01210.01040.01110.011513.6513.3311.61
87522021-12-31 16:00:000.01140.01020.01460.01230.01240.01040.01110.011413.6613.2411.55
87532021-12-31 17:00:000.01140.01030.01440.01230.01240.01040.01110.011413.6313.2711.58
87542021-12-31 18:00:000.01140.01030.01450.01240.01240.01050.01120.011513.6313.2711.58
87552021-12-31 19:00:000.01130.01030.01460.01240.01250.01050.01110.011413.613.2411.64
87562021-12-31 20:00:000.01140.01030.01450.01240.01250.01050.01120.011513.6913.2711.59
87572021-12-31 21:00:000.01150.01040.01450.01240.01250.01050.01120.011513.713.311.67
87582021-12-31 22:00:000.01140.01030.01450.01250.01250.01060.01120.011513.6713.311.68
87592021-12-31 23:00:000.01150.01040.01450.01240.01250.01060.01120.011613.7413.3911.66