Overview

Dataset statistics

Number of variables15
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.3 MiB
Average record size in memory140.0 B

Variable types

Categorical2
DateTime1
Numeric12

Dataset

Description청주시 무인악취시스템 수집데이터로 복합악취, 황화수소, 암모니아, 휘발성 유기물, 에탄올, 트리메틸아민, 메틸 메르탑탄, 염소, 미세먼지, 황산화물, 질소산화물에 대한 데이터를 포함합니다.
Author충청북도 청주시
URLhttps://www.data.go.kr/data/15040173/fileData.do

Alerts

HQL has constant value ""Constant
황화수소 is highly overall correlated with 센서명High correlation
암모니아 is highly overall correlated with 황산화물 and 1 other fieldsHigh correlation
휘발성유기물 is highly overall correlated with 센서명High correlation
미세먼지2_5 is highly overall correlated with 미세먼지10High correlation
미세먼지10 is highly overall correlated with 미세먼지2_5High correlation
황산화물 is highly overall correlated with 암모니아 and 1 other fieldsHigh correlation
질소산화물 is highly overall correlated with 센서명High correlation
센서명 is highly overall correlated with 황화수소 and 4 other fieldsHigh correlation
에탄올 has 920 (9.2%) zerosZeros
미세먼지2_5 has 1572 (15.7%) zerosZeros
미세먼지10 has 1429 (14.3%) zerosZeros

Reproduction

Analysis started2023-12-12 21:55:52.957399
Analysis finished2023-12-12 21:56:13.924850
Duration20.97 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

HQL
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
고정식
10000 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row고정식
2nd row고정식
3rd row고정식
4th row고정식
5th row고정식

Common Values

ValueCountFrequency (%)
고정식 10000
100.0%

Length

2023-12-13T06:56:13.985320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T06:56:14.097947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
고정식 10000
100.0%

센서명
Categorical

HIGH CORRELATION 

Distinct16
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
스탬코 입구 앞
874 
삼성 SDI 맞은편 언덕
872 
기아모터스
859 
사거리 백산린텍스 앞
847 
SK 바이오 랜드입구
846 
Other values (11)
5702 

Length

Max length13
Median length11
Mean length9.1193
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSK 바이오 랜드입구
2nd row스탬코 입구 앞
3rd row오창대 교회 맞은편
4th row주택삼거리(원앤씨)
5th row스탬코 입구 앞

Common Values

ValueCountFrequency (%)
스탬코 입구 앞 874
 
8.7%
삼성 SDI 맞은편 언덕 872
 
8.7%
기아모터스 859
 
8.6%
사거리 백산린텍스 앞 847
 
8.5%
SK 바이오 랜드입구 846
 
8.5%
미래나노텍 입구 맞은편 837
 
8.4%
오창 청원구 보건소 792
 
7.9%
오창 수변옆 대로 길 588
 
5.9%
오창읍사무소 483
 
4.8%
풍천장어 맞은편 478
 
4.8%
Other values (6) 2524
25.2%

Length

2023-12-13T06:56:14.206290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
맞은편 2599
 
10.4%
입구 2179
 
8.7%
1721
 
6.9%
오창 1380
 
5.5%
스탬코 874
 
3.5%
삼성 872
 
3.5%
sdi 872
 
3.5%
언덕 872
 
3.5%
기아모터스 859
 
3.4%
사거리 847
 
3.4%
Other values (20) 11897
47.6%
Distinct5708
Distinct (%)57.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2022-01-01 00:00:00
Maximum2022-02-28 23:20:00
2023-12-13T06:56:14.324956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:56:14.484864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

복합악취
Real number (ℝ)

Distinct19
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2191
Minimum1
Maximum30
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T06:56:14.615304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile7
Maximum30
Range29
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.7967242
Coefficient of variation (CV)0.5581449
Kurtosis9.4715538
Mean3.2191
Median Absolute Deviation (MAD)1
Skewness2.0237185
Sum32191
Variance3.228218
MonotonicityNot monotonic
2023-12-13T06:56:14.748187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
2 4212
42.1%
3 1968
19.7%
4 1494
 
14.9%
5 728
 
7.3%
1 528
 
5.3%
6 452
 
4.5%
7 284
 
2.8%
8 169
 
1.7%
9 83
 
0.8%
10 46
 
0.5%
Other values (9) 36
 
0.4%
ValueCountFrequency (%)
1 528
 
5.3%
2 4212
42.1%
3 1968
19.7%
4 1494
 
14.9%
5 728
 
7.3%
6 452
 
4.5%
7 284
 
2.8%
8 169
 
1.7%
9 83
 
0.8%
10 46
 
0.5%
ValueCountFrequency (%)
30 1
 
< 0.1%
22 1
 
< 0.1%
18 1
 
< 0.1%
17 1
 
< 0.1%
15 2
 
< 0.1%
14 1
 
< 0.1%
13 2
 
< 0.1%
12 6
 
0.1%
11 21
0.2%
10 46
0.5%

황화수소
Real number (ℝ)

HIGH CORRELATION 

Distinct54
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0975778
Minimum0
Maximum0.12
Zeros48
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T06:56:14.870413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.081
Q10.088
median0.101
Q30.106
95-th percentile0.112
Maximum0.12
Range0.12
Interquartile range (IQR)0.018

Descriptive statistics

Standard deviation0.012273378
Coefficient of variation (CV)0.12578044
Kurtosis17.439802
Mean0.0975778
Median Absolute Deviation (MAD)0.008
Skewness-2.5619721
Sum975.778
Variance0.00015063581
MonotonicityNot monotonic
2023-12-13T06:56:15.018013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.105 684
 
6.8%
0.101 575
 
5.8%
0.102 573
 
5.7%
0.106 526
 
5.3%
0.088 486
 
4.9%
0.103 466
 
4.7%
0.104 464
 
4.6%
0.086 429
 
4.3%
0.087 426
 
4.3%
0.089 417
 
4.2%
Other values (44) 4954
49.5%
ValueCountFrequency (%)
0.0 48
0.5%
0.006 1
 
< 0.1%
0.036 1
 
< 0.1%
0.069 1
 
< 0.1%
0.071 1
 
< 0.1%
0.072 5
 
0.1%
0.073 11
 
0.1%
0.074 15
 
0.1%
0.075 8
 
0.1%
0.076 18
 
0.2%
ValueCountFrequency (%)
0.12 4
 
< 0.1%
0.119 14
 
0.1%
0.118 18
 
0.2%
0.117 16
 
0.2%
0.116 56
 
0.6%
0.115 88
0.9%
0.114 111
1.1%
0.113 172
1.7%
0.112 161
1.6%
0.111 208
2.1%

암모니아
Real number (ℝ)

HIGH CORRELATION 

Distinct71
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1074338
Minimum0
Maximum0.208
Zeros48
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T06:56:15.183300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.089
Q10.096
median0.104
Q30.111
95-th percentile0.123
Maximum0.208
Range0.208
Interquartile range (IQR)0.015

Descriptive statistics

Standard deviation0.023340161
Coefficient of variation (CV)0.21725156
Kurtosis10.716589
Mean0.1074338
Median Absolute Deviation (MAD)0.007
Skewness2.3536759
Sum1074.338
Variance0.00054476309
MonotonicityNot monotonic
2023-12-13T06:56:15.330668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.111 486
 
4.9%
0.109 453
 
4.5%
0.11 398
 
4.0%
0.112 387
 
3.9%
0.096 386
 
3.9%
0.094 366
 
3.7%
0.108 359
 
3.6%
0.095 358
 
3.6%
0.103 355
 
3.5%
0.105 354
 
3.5%
Other values (61) 6098
61.0%
ValueCountFrequency (%)
0.0 48
0.5%
0.079 2
 
< 0.1%
0.08 7
 
0.1%
0.081 3
 
< 0.1%
0.082 8
 
0.1%
0.083 7
 
0.1%
0.084 22
 
0.2%
0.085 48
0.5%
0.086 75
0.8%
0.087 111
1.1%
ValueCountFrequency (%)
0.208 1
 
< 0.1%
0.207 2
 
< 0.1%
0.206 6
 
0.1%
0.205 7
 
0.1%
0.204 7
 
0.1%
0.203 20
 
0.2%
0.202 63
0.6%
0.201 79
0.8%
0.2 83
0.8%
0.199 79
0.8%

휘발성유기물
Real number (ℝ)

HIGH CORRELATION 

Distinct171
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1257621
Minimum0
Maximum0.341
Zeros48
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T06:56:15.458419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.057
Q10.059
median0.065
Q30.254
95-th percentile0.315
Maximum0.341
Range0.341
Interquartile range (IQR)0.195

Descriptive statistics

Standard deviation0.1046286
Coefficient of variation (CV)0.83195651
Kurtosis-0.77731179
Mean0.1257621
Median Absolute Deviation (MAD)0.007
Skewness1.0720594
Sum1257.621
Variance0.010947143
MonotonicityNot monotonic
2023-12-13T06:56:15.596735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.057 925
 
9.2%
0.058 849
 
8.5%
0.063 729
 
7.3%
0.059 705
 
7.0%
0.062 564
 
5.6%
0.064 559
 
5.6%
0.068 294
 
2.9%
0.069 289
 
2.9%
0.071 235
 
2.4%
0.065 223
 
2.2%
Other values (161) 4628
46.3%
ValueCountFrequency (%)
0.0 48
 
0.5%
0.052 1
 
< 0.1%
0.053 4
 
< 0.1%
0.054 7
 
0.1%
0.055 23
 
0.2%
0.056 106
 
1.1%
0.057 925
9.2%
0.058 849
8.5%
0.059 705
7.0%
0.06 193
 
1.9%
ValueCountFrequency (%)
0.341 1
 
< 0.1%
0.335 1
 
< 0.1%
0.333 1
 
< 0.1%
0.332 1
 
< 0.1%
0.331 2
 
< 0.1%
0.33 1
 
< 0.1%
0.329 14
 
0.1%
0.328 11
 
0.1%
0.327 29
0.3%
0.326 47
0.5%

에탄올
Real number (ℝ)

ZEROS 

Distinct15
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0052173
Minimum0
Maximum0.017
Zeros920
Zeros (%)9.2%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T06:56:15.727324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.005
median0.006
Q30.006
95-th percentile0.007
Maximum0.017
Range0.017
Interquartile range (IQR)0.001

Descriptive statistics

Standard deviation0.0018890309
Coefficient of variation (CV)0.36207058
Kurtosis3.1689699
Mean0.0052173
Median Absolute Deviation (MAD)0.001
Skewness-1.5994073
Sum52.173
Variance3.5684376 × 10-6
MonotonicityNot monotonic
2023-12-13T06:56:15.834344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0.006 3650
36.5%
0.005 3301
33.0%
0.007 1347
 
13.5%
0.0 920
 
9.2%
0.004 498
 
5.0%
0.008 232
 
2.3%
0.009 23
 
0.2%
0.003 7
 
0.1%
0.012 6
 
0.1%
0.011 5
 
0.1%
Other values (5) 11
 
0.1%
ValueCountFrequency (%)
0.0 920
 
9.2%
0.003 7
 
0.1%
0.004 498
 
5.0%
0.005 3301
33.0%
0.006 3650
36.5%
0.007 1347
 
13.5%
0.008 232
 
2.3%
0.009 23
 
0.2%
0.01 5
 
0.1%
0.011 5
 
0.1%
ValueCountFrequency (%)
0.017 1
 
< 0.1%
0.015 1
 
< 0.1%
0.014 2
 
< 0.1%
0.013 2
 
< 0.1%
0.012 6
 
0.1%
0.011 5
 
0.1%
0.01 5
 
0.1%
0.009 23
 
0.2%
0.008 232
 
2.3%
0.007 1347
13.5%

트리메틸아민
Real number (ℝ)

Distinct76
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.026668
Minimum0
Maximum0.075
Zeros48
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T06:56:15.986435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.004
Q10.015
median0.026
Q30.036
95-th percentile0.053
Maximum0.075
Range0.075
Interquartile range (IQR)0.021

Descriptive statistics

Standard deviation0.015009687
Coefficient of variation (CV)0.56283512
Kurtosis-0.14175315
Mean0.026668
Median Absolute Deviation (MAD)0.01
Skewness0.40317083
Sum266.68
Variance0.00022529071
MonotonicityNot monotonic
2023-12-13T06:56:16.140901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.024 295
 
2.9%
0.008 295
 
2.9%
0.023 292
 
2.9%
0.025 284
 
2.8%
0.029 283
 
2.8%
0.026 280
 
2.8%
0.028 275
 
2.8%
0.021 265
 
2.6%
0.03 262
 
2.6%
0.027 260
 
2.6%
Other values (66) 7209
72.1%
ValueCountFrequency (%)
0.0 48
 
0.5%
0.001 158
1.6%
0.002 82
 
0.8%
0.003 84
 
0.8%
0.004 140
1.4%
0.005 191
1.9%
0.006 213
2.1%
0.007 244
2.4%
0.008 295
2.9%
0.009 255
2.5%
ValueCountFrequency (%)
0.075 2
 
< 0.1%
0.074 3
 
< 0.1%
0.073 3
 
< 0.1%
0.072 9
 
0.1%
0.071 14
0.1%
0.07 13
0.1%
0.069 16
0.2%
0.068 19
0.2%
0.067 25
0.2%
0.066 27
0.3%

메틸메르캅탄
Real number (ℝ)

Distinct43
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0096551
Minimum0
Maximum0.06
Zeros48
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T06:56:16.613523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.001
Q10.005
median0.008
Q30.013
95-th percentile0.025
Maximum0.06
Range0.06
Interquartile range (IQR)0.008

Descriptive statistics

Standard deviation0.0069028624
Coefficient of variation (CV)0.71494468
Kurtosis2.0638322
Mean0.0096551
Median Absolute Deviation (MAD)0.004
Skewness1.2841615
Sum96.551
Variance4.7649509 × 10-5
MonotonicityNot monotonic
2023-12-13T06:56:16.748477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
0.007 757
 
7.6%
0.008 754
 
7.5%
0.001 750
 
7.5%
0.006 717
 
7.2%
0.009 709
 
7.1%
0.01 631
 
6.3%
0.005 555
 
5.5%
0.011 545
 
5.5%
0.002 526
 
5.3%
0.004 500
 
5.0%
Other values (33) 3556
35.6%
ValueCountFrequency (%)
0.0 48
 
0.5%
0.001 750
7.5%
0.002 526
5.3%
0.003 481
4.8%
0.004 500
5.0%
0.005 555
5.5%
0.006 717
7.2%
0.007 757
7.6%
0.008 754
7.5%
0.009 709
7.1%
ValueCountFrequency (%)
0.06 1
 
< 0.1%
0.053 1
 
< 0.1%
0.042 2
 
< 0.1%
0.04 2
 
< 0.1%
0.038 4
 
< 0.1%
0.037 6
 
0.1%
0.036 5
 
0.1%
0.035 6
 
0.1%
0.034 24
0.2%
0.033 26
0.3%

염소
Real number (ℝ)

Distinct35
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.008563
Minimum0
Maximum0.046
Zeros48
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T06:56:16.908839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.004
Q10.006
median0.008
Q30.01
95-th percentile0.015
Maximum0.046
Range0.046
Interquartile range (IQR)0.004

Descriptive statistics

Standard deviation0.0032956816
Coefficient of variation (CV)0.38487465
Kurtosis6.6021399
Mean0.008563
Median Absolute Deviation (MAD)0.002
Skewness1.3981827
Sum85.63
Variance1.0861517 × 10-5
MonotonicityNot monotonic
2023-12-13T06:56:17.030221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
0.006 1516
15.2%
0.007 1482
14.8%
0.008 1416
14.2%
0.009 1290
12.9%
0.01 936
9.4%
0.011 695
7.0%
0.005 551
 
5.5%
0.012 466
 
4.7%
0.003 326
 
3.3%
0.013 321
 
3.2%
Other values (25) 1001
10.0%
ValueCountFrequency (%)
0.0 48
 
0.5%
0.003 326
 
3.3%
0.004 231
 
2.3%
0.005 551
 
5.5%
0.006 1516
15.2%
0.007 1482
14.8%
0.008 1416
14.2%
0.009 1290
12.9%
0.01 936
9.4%
0.011 695
7.0%
ValueCountFrequency (%)
0.046 1
< 0.1%
0.04 1
< 0.1%
0.039 1
< 0.1%
0.038 1
< 0.1%
0.036 1
< 0.1%
0.035 1
< 0.1%
0.031 1
< 0.1%
0.029 1
< 0.1%
0.028 2
< 0.1%
0.027 2
< 0.1%

미세먼지2_5
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct333
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.44581
Minimum0
Maximum106
Zeros1572
Zeros (%)15.7%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T06:56:17.162174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median7.375
Q316
95-th percentile42.05
Maximum106
Range106
Interquartile range (IQR)14

Descriptive statistics

Standard deviation14.908914
Coefficient of variation (CV)1.1979063
Kurtosis6.1129894
Mean12.44581
Median Absolute Deviation (MAD)6.375
Skewness2.2293858
Sum124458.1
Variance222.27571
MonotonicityNot monotonic
2023-12-13T06:56:17.350192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 1572
 
15.7%
4.0 580
 
5.8%
6.0 483
 
4.8%
2.0 477
 
4.8%
5.0 464
 
4.6%
3.0 355
 
3.5%
8.0 342
 
3.4%
1.0 339
 
3.4%
7.0 325
 
3.2%
10.0 277
 
2.8%
Other values (323) 4786
47.9%
ValueCountFrequency (%)
0.0 1572
15.7%
0.1 5
 
0.1%
0.2 4
 
< 0.1%
0.3 4
 
< 0.1%
0.4 7
 
0.1%
0.5 4
 
< 0.1%
0.6 7
 
0.1%
0.7 9
 
0.1%
0.8 7
 
0.1%
0.9 10
 
0.1%
ValueCountFrequency (%)
106.0 1
 
< 0.1%
102.0 2
< 0.1%
101.0 1
 
< 0.1%
96.0 1
 
< 0.1%
94.0 4
< 0.1%
93.0 1
 
< 0.1%
92.0 3
< 0.1%
91.0 2
< 0.1%
90.0 3
< 0.1%
89.0 1
 
< 0.1%

미세먼지10
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct371
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.04398
Minimum0
Maximum166
Zeros1429
Zeros (%)14.3%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T06:56:17.499156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14.5
median12
Q328
95-th percentile65
Maximum166
Range166
Interquartile range (IQR)23.5

Descriptive statistics

Standard deviation23.38756
Coefficient of variation (CV)1.1668122
Kurtosis6.5493195
Mean20.04398
Median Absolute Deviation (MAD)10
Skewness2.2779982
Sum200439.8
Variance546.97799
MonotonicityNot monotonic
2023-12-13T06:56:17.627527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 1429
 
14.3%
8.0 420
 
4.2%
6.0 337
 
3.4%
10.0 321
 
3.2%
9.0 290
 
2.9%
4.0 284
 
2.8%
7.0 276
 
2.8%
5.0 257
 
2.6%
11.0 249
 
2.5%
2.0 236
 
2.4%
Other values (361) 5901
59.0%
ValueCountFrequency (%)
0.0 1429
14.3%
0.1 5
 
0.1%
0.2 4
 
< 0.1%
0.3 2
 
< 0.1%
0.4 1
 
< 0.1%
0.5 2
 
< 0.1%
0.6 5
 
0.1%
0.7 2
 
< 0.1%
0.8 6
 
0.1%
0.9 6
 
0.1%
ValueCountFrequency (%)
166.0 1
 
< 0.1%
153.0 2
< 0.1%
152.0 2
< 0.1%
149.0 2
< 0.1%
148.0 1
 
< 0.1%
146.0 3
< 0.1%
145.0 1
 
< 0.1%
144.0 1
 
< 0.1%
143.0 1
 
< 0.1%
142.0 1
 
< 0.1%

황산화물
Real number (ℝ)

HIGH CORRELATION 

Distinct71
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1075819
Minimum0
Maximum0.209
Zeros48
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T06:56:17.757747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.09
Q10.098
median0.103
Q30.112
95-th percentile0.123
Maximum0.209
Range0.209
Interquartile range (IQR)0.014

Descriptive statistics

Standard deviation0.023033501
Coefficient of variation (CV)0.21410201
Kurtosis10.972123
Mean0.1075819
Median Absolute Deviation (MAD)0.006
Skewness2.3617999
Sum1075.819
Variance0.00053054215
MonotonicityNot monotonic
2023-12-13T06:56:17.895791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1 576
 
5.8%
0.098 558
 
5.6%
0.099 544
 
5.4%
0.101 466
 
4.7%
0.116 459
 
4.6%
0.107 436
 
4.4%
0.106 408
 
4.1%
0.097 369
 
3.7%
0.115 338
 
3.4%
0.103 332
 
3.3%
Other values (61) 5514
55.1%
ValueCountFrequency (%)
0.0 48
0.5%
0.08 1
 
< 0.1%
0.081 1
 
< 0.1%
0.082 8
 
0.1%
0.083 29
0.3%
0.084 35
0.4%
0.085 34
0.3%
0.086 41
0.4%
0.087 69
0.7%
0.088 70
0.7%
ValueCountFrequency (%)
0.209 3
 
< 0.1%
0.208 3
 
< 0.1%
0.207 6
 
0.1%
0.206 6
 
0.1%
0.205 10
 
0.1%
0.204 19
 
0.2%
0.203 23
0.2%
0.202 38
0.4%
0.201 43
0.4%
0.2 53
0.5%

질소산화물
Real number (ℝ)

HIGH CORRELATION 

Distinct62
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0988318
Minimum0
Maximum0.219
Zeros48
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T06:56:18.041316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.005
Q10.092
median0.097
Q30.112
95-th percentile0.134
Maximum0.219
Range0.219
Interquartile range (IQR)0.02

Descriptive statistics

Standard deviation0.039900756
Coefficient of variation (CV)0.40372386
Kurtosis3.4332984
Mean0.0988318
Median Absolute Deviation (MAD)0.01
Skewness0.033884579
Sum988.318
Variance0.0015920703
MonotonicityNot monotonic
2023-12-13T06:56:18.185744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.005 871
 
8.7%
0.093 760
 
7.6%
0.094 695
 
7.0%
0.092 606
 
6.1%
0.095 517
 
5.2%
0.097 438
 
4.4%
0.096 415
 
4.2%
0.091 357
 
3.6%
0.11 310
 
3.1%
0.111 310
 
3.1%
Other values (52) 4721
47.2%
ValueCountFrequency (%)
0.0 48
 
0.5%
0.005 871
8.7%
0.006 1
 
< 0.1%
0.081 1
 
< 0.1%
0.082 3
 
< 0.1%
0.083 2
 
< 0.1%
0.084 11
 
0.1%
0.085 21
 
0.2%
0.086 92
 
0.9%
0.087 148
 
1.5%
ValueCountFrequency (%)
0.219 12
 
0.1%
0.218 82
0.8%
0.217 173
1.7%
0.216 143
1.4%
0.215 37
 
0.4%
0.214 19
 
0.2%
0.213 2
 
< 0.1%
0.136 3
 
< 0.1%
0.135 24
 
0.2%
0.134 21
 
0.2%

Interactions

2023-12-13T06:56:12.248872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:55:57.686072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:55:58.962814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:56:00.159926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:56:01.216979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:56:02.534942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:56:04.419532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:56:05.860298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:56:07.035905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:56:08.221300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:56:09.471686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:56:11.024978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:56:12.360237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:55:57.833330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:55:59.063003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:56:00.240917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:56:01.314933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:56:02.710732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:56:04.542500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:56:05.968006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:56:07.141140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:56:08.337832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:56:09.578454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:56:11.133057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:56:12.493559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:55:57.935955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:55:59.149718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:56:00.316003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:56:01.422215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:56:02.841062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:56:04.656151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:56:06.063912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:56:07.235547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:56:08.455343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:56:09.704197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:56:11.236250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:56:12.601673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:55:58.046776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:55:59.251791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:56:00.393223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:56:01.532762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:56:02.942055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:56:04.781564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:56:06.148756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:56:07.322518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:56:08.546475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:56:09.813408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:56:11.329947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:56:12.726363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:55:58.179806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:55:59.374135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:56:00.478189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:56:01.636669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:56:03.093062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:56:04.912580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:56:06.246512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:56:07.418720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:56:08.651365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:56:09.910587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:56:11.440483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:56:12.855861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:55:58.310018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:55:59.476521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:56:00.565984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:56:01.755131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:56:03.228636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:56:05.045337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:56:06.343927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:56:07.515112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:56:08.753421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:56:10.009554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:56:11.538574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:56:12.956051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:55:58.411435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:55:59.567585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:56:00.660640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:56:01.856617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:56:03.352185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:56:05.178647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:56:06.465551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:56:07.615580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:56:08.874156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:56:10.112087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:56:11.671017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:56:13.059669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:55:58.512427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:55:59.656322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:56:00.741002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:56:01.986962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:56:03.463915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:56:05.302047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:56:06.562027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:56:07.726059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:56:09.007673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:56:10.225636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:56:11.787240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:56:13.156303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:55:58.600732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:55:59.737056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:56:00.818764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:56:02.104341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:56:03.870683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:56:05.411318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:56:06.645660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:56:07.836991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:56:09.102589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:56:10.335265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:56:11.890090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:56:13.254501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:55:58.699255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:55:59.847084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:56:00.950819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:56:02.201326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:56:04.022435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:56:05.531575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:56:06.757468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:56:07.924525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:56:09.196672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:56:10.434801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:56:11.983449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:56:13.350665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:55:58.784325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:55:59.944800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:56:01.039261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:56:02.294615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:56:04.154701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:56:05.645911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:56:06.860445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:56:08.016219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:56:09.290602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:56:10.536121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:56:12.084450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:56:13.445171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:55:58.877043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:56:00.056005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:56:01.136281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:56:02.411104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:56:04.283864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:56:05.741081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:56:06.947495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:56:08.106934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:56:09.383321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:56:10.621544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:56:12.166317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T06:56:18.274743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
센서명복합악취황화수소암모니아휘발성유기물에탄올트리메틸아민메틸메르캅탄염소미세먼지2_5미세먼지10황산화물질소산화물
센서명1.0000.5170.8300.8220.9050.6910.7570.7180.6630.3860.3750.8220.960
복합악취0.5171.0000.1340.1770.3260.2770.2820.3480.3110.2380.2380.1710.166
황화수소0.8300.1341.0000.6980.7300.5030.5040.3960.4030.2500.2510.8580.637
암모니아0.8220.1770.6981.0000.7000.3290.3900.3210.3830.2060.1960.8290.866
휘발성유기물0.9050.3260.7300.7001.0000.5440.4940.4890.4240.1220.1320.7000.608
에탄올0.6910.2770.5030.3290.5441.0000.6720.8870.8530.4700.4560.2880.686
트리메틸아민0.7570.2820.5040.3900.4940.6721.0000.6590.3430.2150.2360.2600.635
메틸메르캅탄0.7180.3480.3960.3210.4890.8870.6591.0000.8560.2780.2600.3780.495
염소0.6630.3110.4030.3830.4240.8530.3430.8561.0000.1600.1870.4080.362
미세먼지2_50.3860.2380.2500.2060.1220.4700.2150.2780.1601.0000.9710.1740.131
미세먼지100.3750.2380.2510.1960.1320.4560.2360.2600.1870.9711.0000.1540.120
황산화물0.8220.1710.8580.8290.7000.2880.2600.3780.4080.1740.1541.0000.694
질소산화물0.9600.1660.6370.8660.6080.6860.6350.4950.3620.1310.1200.6941.000
2023-12-13T06:56:18.410559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
복합악취황화수소암모니아휘발성유기물에탄올트리메틸아민메틸메르캅탄염소미세먼지2_5미세먼지10황산화물질소산화물센서명
복합악취1.000-0.0070.1250.1910.3210.3310.4820.4370.2050.2120.0990.1060.206
황화수소-0.0071.0000.2320.0370.3030.1220.456-0.2630.3580.3320.3450.4960.572
암모니아0.1250.2321.0000.079-0.0950.4730.0680.1070.3120.3060.6770.2060.580
휘발성유기물0.1910.0370.0791.0000.0690.2240.006-0.1860.1820.207-0.0710.2190.567
에탄올0.3210.303-0.0950.0691.000-0.0420.2810.1720.3800.3510.1370.2470.356
트리메틸아민0.3310.1220.4730.224-0.0421.0000.3320.2800.1930.213-0.104-0.1190.421
메틸메르캅탄0.4820.4560.0680.0060.2810.3321.0000.2320.1870.1950.1390.4590.397
염소0.437-0.2630.107-0.1860.1720.2800.2321.000-0.014-0.017-0.008-0.0140.332
미세먼지2_50.2050.3580.3120.1820.3800.1930.187-0.0141.0000.9730.3360.1050.162
미세먼지100.2120.3320.3060.2070.3510.2130.195-0.0170.9731.0000.3120.1010.156
황산화물0.0990.3450.677-0.0710.137-0.1040.139-0.0080.3360.3121.0000.5000.563
질소산화물0.1060.4960.2060.2190.247-0.1190.459-0.0140.1050.1010.5001.0000.867
센서명0.2060.5720.5800.5670.3560.4210.3970.3320.1620.1560.5630.8671.000

Missing values

2023-12-13T06:56:13.597670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T06:56:13.800471image/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.

Sample

HQL센서명수신일시복합악취황화수소암모니아휘발성유기물에탄올트리메틸아민메틸메르캅탄염소미세먼지2_5미세먼지10황산화물질소산화물
69086고정식SK 바이오 랜드입구2022-02-10 05:2050.0880.0960.0580.0060.0170.0070.0160.00.00.0990.094
88694고정식스탬코 입구 앞2022-02-06 11:1010.10.1010.3060.0050.0130.0020.0034.08.00.0990.096
37377고정식오창대 교회 맞은편2022-01-21 20:4060.0860.0870.0690.0070.0110.0140.00917.027.00.1030.086
48030고정식주택삼거리(원앤씨)2022-01-11 14:1020.1010.10.0570.0050.0110.0060.0064.08.00.1070.095
90869고정식스탬코 입구 앞2022-02-21 13:4010.10.1030.2960.0050.0190.0020.0037.012.00.10.097
59666고정식풍천장어 맞은편2022-01-04 11:0030.0810.0980.0620.0050.0070.010.0070.00.00.110.112
31019고정식오창 수변옆 대로 길2022-01-07 11:2030.0870.0930.0560.0050.0290.010.0120.00.00.0960.09
16142고정식사거리 백산린텍스 앞2022-01-23 13:4040.0990.090.2290.0060.0310.020.0088.013.00.0870.107
23500고정식스탬코 입구 앞2022-01-14 23:2040.1020.1020.3030.0050.020.0070.00414.024.00.1010.097
23241고정식스탬코 입구 앞2022-01-13 04:0020.1030.1030.3030.0050.020.0040.0049.015.00.1010.098
HQL센서명수신일시복합악취황화수소암모니아휘발성유기물에탄올트리메틸아민메틸메르캅탄염소미세먼지2_5미세먼지10황산화물질소산화물
85531고정식삼성 SDI 맞은편 언덕2022-02-12 12:0020.0740.1070.0750.00.0290.0040.0132.04.00.0910.005
71008고정식SK 바이오 랜드입구2022-02-23 13:4020.0880.0910.0580.0040.0020.0010.010.00.00.0960.094
35684고정식오창대 교회 맞은편2022-01-10 00:3040.0880.0870.0690.0060.0090.0110.00854.094.00.1040.087
92571고정식오창 청원구 보건소2022-02-05 09:2020.1120.1140.0640.0040.0250.0120.0083.07.00.1180.121
54052고정식지구대 옥상2022-01-25 04:4020.1020.120.0640.0060.0390.0060.0071.03.00.110.091
25869고정식오창 청원구 보건소2022-01-01 13:3020.1090.1110.0630.0040.020.0090.0064.08.00.1150.119
8090고정식기아모터스2022-01-27 08:2040.1070.1090.0580.0070.0330.0140.00932.050.00.1060.093
37820고정식오창대 교회 맞은편2022-01-24 22:4020.0860.0880.0680.0060.0040.0030.0052.05.00.1030.087
20458고정식삼성 SDI 맞은편 언덕2022-01-23 17:0020.0770.110.0730.00.0370.0040.0094.06.00.0960.005
16051고정식사거리 백산린텍스 앞2022-01-22 22:3050.1010.0940.2870.0070.0390.0270.01211.018.00.0890.108