Overview

Dataset statistics

Number of variables12
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.1 MiB
Average record size in memory114.0 B

Variable types

Numeric9
Categorical3

Dataset

Description파일 다운로드
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-2221/S/1/datasetView.do

Alerts

측정소명 is highly overall correlated with 측정소코드 and 2 other fieldsHigh correlation
권역명 is highly overall correlated with 측정소코드 and 2 other fieldsHigh correlation
권역코드 is highly overall correlated with 측정소코드 and 2 other fieldsHigh correlation
측정소코드 is highly overall correlated with 권역코드 and 2 other fieldsHigh correlation
미세먼지 1시간(㎍/㎥) is highly overall correlated with 미세먼지 24시간(㎍/㎥) and 1 other fieldsHigh correlation
미세먼지 24시간(㎍/㎥) is highly overall correlated with 미세먼지 1시간(㎍/㎥) and 1 other fieldsHigh correlation
초미세먼지(㎍/㎥) is highly overall correlated with 미세먼지 1시간(㎍/㎥) and 2 other fieldsHigh correlation
오존(ppm) is highly overall correlated with 이산화질소농도(ppm)High correlation
이산화질소농도(ppm) is highly overall correlated with 오존(ppm) and 1 other fieldsHigh correlation
일산화탄소농도(ppm) is highly overall correlated with 초미세먼지(㎍/㎥) and 1 other fieldsHigh correlation
미세먼지 1시간(㎍/㎥) has 197 (2.0%) zerosZeros
초미세먼지(㎍/㎥) has 115 (1.1%) zerosZeros
오존(ppm) has 115 (1.1%) zerosZeros

Reproduction

Analysis started2024-05-11 05:10:42.053760
Analysis finished2024-05-11 05:11:21.441839
Duration39.39 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

측정일시
Real number (ℝ)

Distinct672
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0190215 × 1011
Minimum2.0190201 × 1011
Maximum2.0190228 × 1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T05:11:21.592919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.0190201 × 1011
5-th percentile2.0190202 × 1011
Q12.0190207 × 1011
median2.0190214 × 1011
Q32.0190221 × 1011
95-th percentile2.0190227 × 1011
Maximum2.0190228 × 1011
Range272300
Interquartile range (IQR)139900

Descriptive statistics

Standard deviation80618.002
Coefficient of variation (CV)3.9929245 × 10-7
Kurtosis-1.1940122
Mean2.0190215 × 1011
Median Absolute Deviation (MAD)70000
Skewness0.015197933
Sum2.0190215 × 1015
Variance6.4992623 × 109
MonotonicityNot monotonic
2024-05-11T05:11:22.142207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
201902140700 22
 
0.2%
201902231000 21
 
0.2%
201902141600 21
 
0.2%
201902282000 20
 
0.2%
201902272200 20
 
0.2%
201902010200 20
 
0.2%
201902232200 20
 
0.2%
201902171200 20
 
0.2%
201902182000 20
 
0.2%
201902050400 20
 
0.2%
Other values (662) 9796
98.0%
ValueCountFrequency (%)
201902010000 12
0.1%
201902010100 12
0.1%
201902010200 20
0.2%
201902010300 17
0.2%
201902010400 16
0.2%
201902010500 17
0.2%
201902010600 15
0.1%
201902010700 16
0.2%
201902010800 14
0.1%
201902010900 11
0.1%
ValueCountFrequency (%)
201902282300 12
0.1%
201902282200 15
0.1%
201902282100 16
0.2%
201902282000 20
0.2%
201902281900 15
0.1%
201902281800 15
0.1%
201902281700 16
0.2%
201902281600 15
0.1%
201902281500 15
0.1%
201902281400 15
0.1%

권역코드
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
102
3235 
103
2748 
104
1570 
100
1240 
101
1207 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row102
2nd row104
3rd row100
4th row103
5th row100

Common Values

ValueCountFrequency (%)
102 3235
32.4%
103 2748
27.5%
104 1570
15.7%
100 1240
 
12.4%
101 1207
 
12.1%

Length

2024-05-11T05:11:22.721132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T05:11:23.044672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
102 3235
32.4%
103 2748
27.5%
104 1570
15.7%
100 1240
 
12.4%
101 1207
 
12.1%

권역명
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
동북권
3235 
서남권
2748 
동남권
1570 
도심권
1240 
서북권
1207 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row동북권
2nd row동남권
3rd row도심권
4th row서남권
5th row도심권

Common Values

ValueCountFrequency (%)
동북권 3235
32.4%
서남권 2748
27.5%
동남권 1570
15.7%
도심권 1240
 
12.4%
서북권 1207
 
12.1%

Length

2024-05-11T05:11:23.458422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T05:11:23.782438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
동북권 3235
32.4%
서남권 2748
27.5%
동남권 1570
15.7%
도심권 1240
 
12.4%
서북권 1207
 
12.1%

측정소코드
Real number (ℝ)

HIGH CORRELATION 

Distinct25
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean111210.33
Minimum111121
Maximum111311
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T05:11:24.145338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum111121
5-th percentile111123
Q1111152
median111212
Q3111262
95-th percentile111301
Maximum111311
Range190
Interquartile range (IQR)110

Descriptive statistics

Standard deviation60.200013
Coefficient of variation (CV)0.00054131675
Kurtosis-1.3782576
Mean111210.33
Median Absolute Deviation (MAD)60
Skewness0.050972202
Sum1.1121033 × 109
Variance3624.0416
MonotonicityNot monotonic
2024-05-11T05:11:24.566757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
111123 430
 
4.3%
111151 421
 
4.2%
111141 419
 
4.2%
111131 416
 
4.2%
111261 416
 
4.2%
111281 414
 
4.1%
111191 410
 
4.1%
111241 409
 
4.1%
111311 408
 
4.1%
111171 405
 
4.0%
Other values (15) 5852
58.5%
ValueCountFrequency (%)
111121 394
3.9%
111123 430
4.3%
111131 416
4.2%
111141 419
4.2%
111142 402
4.0%
111151 421
4.2%
111152 386
3.9%
111161 395
4.0%
111171 405
4.0%
111181 400
4.0%
ValueCountFrequency (%)
111311 408
4.1%
111301 390
3.9%
111291 399
4.0%
111281 414
4.1%
111274 371
3.7%
111273 385
3.9%
111262 398
4.0%
111261 416
4.2%
111251 385
3.9%
111241 409
4.1%

측정소명
Categorical

HIGH CORRELATION 

Distinct25
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
종로구
 
430
중랑구
 
421
광진구
 
419
용산구
 
416
강남구
 
416
Other values (20)
7898 

Length

Max length4
Median length3
Mean length3.08
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row광진구
2nd row강동구
3rd row중구
4th row동작구
5th row중구

Common Values

ValueCountFrequency (%)
종로구 430
 
4.3%
중랑구 421
 
4.2%
광진구 419
 
4.2%
용산구 416
 
4.2%
강남구 416
 
4.2%
금천구 414
 
4.1%
서대문구 410
 
4.1%
동작구 409
 
4.1%
노원구 408
 
4.1%
도봉구 405
 
4.0%
Other values (15) 5852
58.5%

Length

2024-05-11T05:11:24.833263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
종로구 430
 
4.3%
중랑구 421
 
4.2%
광진구 419
 
4.2%
용산구 416
 
4.2%
강남구 416
 
4.2%
금천구 414
 
4.1%
서대문구 410
 
4.1%
동작구 409
 
4.1%
노원구 408
 
4.1%
도봉구 405
 
4.0%
Other values (15) 5852
58.5%

미세먼지 1시간(㎍/㎥)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct149
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean55.8354
Minimum0
Maximum176
Zeros197
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T05:11:25.092299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile24
Q137
median49
Q371
95-th percentile108
Maximum176
Range176
Interquartile range (IQR)34

Descriptive statistics

Standard deviation26.836273
Coefficient of variation (CV)0.48063187
Kurtosis0.65638493
Mean55.8354
Median Absolute Deviation (MAD)15
Skewness0.8300726
Sum558354
Variance720.18553
MonotonicityNot monotonic
2024-05-11T05:11:25.374593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
38 232
 
2.3%
39 223
 
2.2%
40 220
 
2.2%
43 218
 
2.2%
41 218
 
2.2%
34 210
 
2.1%
37 209
 
2.1%
36 206
 
2.1%
33 203
 
2.0%
42 199
 
2.0%
Other values (139) 7862
78.6%
ValueCountFrequency (%)
0 197
2.0%
8 1
 
< 0.1%
11 2
 
< 0.1%
12 3
 
< 0.1%
13 4
 
< 0.1%
14 4
 
< 0.1%
15 8
 
0.1%
16 8
 
0.1%
17 15
 
0.1%
18 18
 
0.2%
ValueCountFrequency (%)
176 1
 
< 0.1%
175 1
 
< 0.1%
159 1
 
< 0.1%
155 1
 
< 0.1%
153 3
< 0.1%
152 1
 
< 0.1%
151 1
 
< 0.1%
150 4
< 0.1%
149 3
< 0.1%
148 4
< 0.1%

미세먼지 24시간(㎍/㎥)
Real number (ℝ)

HIGH CORRELATION 

Distinct115
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54.0187
Minimum0
Maximum129
Zeros12
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T05:11:25.813086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile29
Q139
median50
Q365
95-th percentile91
Maximum129
Range129
Interquartile range (IQR)26

Descriptive statistics

Standard deviation19.369994
Coefficient of variation (CV)0.35857942
Kurtosis0.27626528
Mean54.0187
Median Absolute Deviation (MAD)13
Skewness0.75834889
Sum540187
Variance375.19667
MonotonicityNot monotonic
2024-05-11T05:11:26.116973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
39 253
 
2.5%
38 252
 
2.5%
43 252
 
2.5%
36 251
 
2.5%
40 250
 
2.5%
41 243
 
2.4%
37 238
 
2.4%
44 238
 
2.4%
42 237
 
2.4%
46 226
 
2.3%
Other values (105) 7560
75.6%
ValueCountFrequency (%)
0 12
 
0.1%
15 1
 
< 0.1%
17 5
 
0.1%
18 8
 
0.1%
19 10
 
0.1%
20 17
 
0.2%
21 11
 
0.1%
22 28
0.3%
23 25
0.2%
24 47
0.5%
ValueCountFrequency (%)
129 1
 
< 0.1%
128 1
 
< 0.1%
127 2
 
< 0.1%
126 6
0.1%
125 2
 
< 0.1%
124 5
0.1%
123 1
 
< 0.1%
122 4
< 0.1%
121 3
< 0.1%
120 4
< 0.1%

초미세먼지(㎍/㎥)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct106
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.9477
Minimum0
Maximum116
Zeros115
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T05:11:26.496518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile12
Q121
median30
Q346
95-th percentile72
Maximum116
Range116
Interquartile range (IQR)25

Descriptive statistics

Standard deviation18.825536
Coefficient of variation (CV)0.53867739
Kurtosis0.36401888
Mean34.9477
Median Absolute Deviation (MAD)11
Skewness0.8689736
Sum349477
Variance354.4008
MonotonicityNot monotonic
2024-05-11T05:11:26.937224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25 316
 
3.2%
23 311
 
3.1%
27 300
 
3.0%
20 299
 
3.0%
17 290
 
2.9%
26 289
 
2.9%
22 289
 
2.9%
19 288
 
2.9%
21 275
 
2.8%
24 265
 
2.6%
Other values (96) 7078
70.8%
ValueCountFrequency (%)
0 115
1.1%
2 6
 
0.1%
3 4
 
< 0.1%
4 9
 
0.1%
5 6
 
0.1%
6 23
 
0.2%
7 28
 
0.3%
8 33
 
0.3%
9 61
0.6%
10 70
0.7%
ValueCountFrequency (%)
116 2
 
< 0.1%
114 1
 
< 0.1%
107 3
< 0.1%
103 1
 
< 0.1%
102 3
< 0.1%
101 2
 
< 0.1%
100 4
< 0.1%
99 4
< 0.1%
98 5
0.1%
97 5
0.1%

오존(ppm)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct67
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.017661
Minimum0
Maximum0.072
Zeros115
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T05:11:27.371568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.002
Q10.006
median0.017
Q30.027
95-th percentile0.039
Maximum0.072
Range0.072
Interquartile range (IQR)0.021

Descriptive statistics

Standard deviation0.012613786
Coefficient of variation (CV)0.71421696
Kurtosis-0.34460226
Mean0.017661
Median Absolute Deviation (MAD)0.01
Skewness0.53739228
Sum176.61
Variance0.00015910759
MonotonicityNot monotonic
2024-05-11T05:11:27.818606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.002 929
 
9.3%
0.003 453
 
4.5%
0.005 322
 
3.2%
0.004 296
 
3.0%
0.007 276
 
2.8%
0.022 265
 
2.6%
0.023 265
 
2.6%
0.024 260
 
2.6%
0.02 259
 
2.6%
0.018 257
 
2.6%
Other values (57) 6418
64.2%
ValueCountFrequency (%)
0.0 115
 
1.1%
0.001 221
 
2.2%
0.002 929
9.3%
0.003 453
4.5%
0.004 296
 
3.0%
0.005 322
 
3.2%
0.006 252
 
2.5%
0.007 276
 
2.8%
0.008 249
 
2.5%
0.009 235
 
2.4%
ValueCountFrequency (%)
0.072 1
 
< 0.1%
0.067 3
 
< 0.1%
0.066 1
 
< 0.1%
0.063 3
 
< 0.1%
0.062 2
 
< 0.1%
0.061 4
< 0.1%
0.06 3
 
< 0.1%
0.059 2
 
< 0.1%
0.058 8
0.1%
0.057 4
< 0.1%

이산화질소농도(ppm)
Real number (ℝ)

HIGH CORRELATION 

Distinct98
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0357404
Minimum0
Maximum0.111
Zeros85
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T05:11:28.250634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.011
Q10.021
median0.034
Q30.048
95-th percentile0.067
Maximum0.111
Range0.111
Interquartile range (IQR)0.027

Descriptive statistics

Standard deviation0.017689531
Coefficient of variation (CV)0.49494496
Kurtosis-0.38767631
Mean0.0357404
Median Absolute Deviation (MAD)0.013
Skewness0.42035372
Sum357.404
Variance0.0003129195
MonotonicityNot monotonic
2024-05-11T05:11:28.664476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.018 230
 
2.3%
0.017 230
 
2.3%
0.016 226
 
2.3%
0.02 217
 
2.2%
0.033 215
 
2.1%
0.042 213
 
2.1%
0.014 212
 
2.1%
0.021 211
 
2.1%
0.022 211
 
2.1%
0.019 208
 
2.1%
Other values (88) 7827
78.3%
ValueCountFrequency (%)
0.0 85
0.9%
0.002 1
 
< 0.1%
0.003 2
 
< 0.1%
0.004 5
 
0.1%
0.005 18
 
0.2%
0.006 34
 
0.3%
0.007 50
0.5%
0.008 55
0.5%
0.009 71
0.7%
0.01 85
0.9%
ValueCountFrequency (%)
0.111 1
 
< 0.1%
0.103 1
 
< 0.1%
0.1 2
 
< 0.1%
0.098 2
 
< 0.1%
0.097 1
 
< 0.1%
0.094 1
 
< 0.1%
0.093 1
 
< 0.1%
0.091 2
 
< 0.1%
0.09 3
< 0.1%
0.089 5
0.1%

일산화탄소농도(ppm)
Real number (ℝ)

HIGH CORRELATION 

Distinct27
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.68128
Minimum0
Maximum3.2
Zeros64
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T05:11:29.031699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.3
Q10.5
median0.6
Q30.8
95-th percentile1.2
Maximum3.2
Range3.2
Interquartile range (IQR)0.3

Descriptive statistics

Standard deviation0.27304032
Coefficient of variation (CV)0.40077548
Kurtosis3.7239185
Mean0.68128
Median Absolute Deviation (MAD)0.1
Skewness1.2485487
Sum6812.8
Variance0.074551017
MonotonicityNot monotonic
2024-05-11T05:11:29.395850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0.5 1781
17.8%
0.6 1738
17.4%
0.7 1523
15.2%
0.8 1192
11.9%
0.4 1123
11.2%
0.9 719
7.2%
1.0 481
 
4.8%
0.3 410
 
4.1%
1.1 329
 
3.3%
1.2 191
 
1.9%
Other values (17) 513
 
5.1%
ValueCountFrequency (%)
0.0 64
 
0.6%
0.1 3
 
< 0.1%
0.2 78
 
0.8%
0.3 410
 
4.1%
0.4 1123
11.2%
0.5 1781
17.8%
0.6 1738
17.4%
0.7 1523
15.2%
0.8 1192
11.9%
0.9 719
7.2%
ValueCountFrequency (%)
3.2 1
 
< 0.1%
2.7 1
 
< 0.1%
2.5 1
 
< 0.1%
2.4 3
 
< 0.1%
2.2 2
 
< 0.1%
2.1 4
 
< 0.1%
2.0 5
 
0.1%
1.9 9
 
0.1%
1.8 17
0.2%
1.7 26
0.3%
Distinct17
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0048544
Minimum0
Maximum0.018
Zeros72
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T05:11:29.661374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.003
Q10.004
median0.005
Q30.006
95-th percentile0.008
Maximum0.018
Range0.018
Interquartile range (IQR)0.002

Descriptive statistics

Standard deviation0.0017366353
Coefficient of variation (CV)0.35774459
Kurtosis2.4609152
Mean0.0048544
Median Absolute Deviation (MAD)0.001
Skewness0.95877536
Sum48.544
Variance3.0159022 × 10-6
MonotonicityNot monotonic
2024-05-11T05:11:29.882163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
0.004 2792
27.9%
0.005 2348
23.5%
0.003 1752
17.5%
0.006 1294
12.9%
0.007 769
 
7.7%
0.008 400
 
4.0%
0.009 223
 
2.2%
0.002 185
 
1.8%
0.01 96
 
1.0%
0.0 72
 
0.7%
Other values (7) 69
 
0.7%
ValueCountFrequency (%)
0.0 72
 
0.7%
0.001 12
 
0.1%
0.002 185
 
1.8%
0.003 1752
17.5%
0.004 2792
27.9%
0.005 2348
23.5%
0.006 1294
12.9%
0.007 769
 
7.7%
0.008 400
 
4.0%
0.009 223
 
2.2%
ValueCountFrequency (%)
0.018 2
 
< 0.1%
0.017 2
 
< 0.1%
0.016 1
 
< 0.1%
0.013 8
 
0.1%
0.012 10
 
0.1%
0.011 34
 
0.3%
0.01 96
 
1.0%
0.009 223
 
2.2%
0.008 400
4.0%
0.007 769
7.7%

Interactions

2024-05-11T05:11:18.705070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:10:50.582513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:10:53.715891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:10:57.043168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:11:00.667439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:11:04.264659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:11:08.584040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:11:13.271221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:11:16.439263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:11:18.990415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:10:51.016325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:10:54.020823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:10:57.445822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:11:01.052846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:11:04.664775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:11:09.112033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:11:13.718568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:11:16.727187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:11:19.257504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:10:51.419385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:10:54.342658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:10:57.784665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:11:01.428547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:11:05.053352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:11:09.471871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:11:14.129947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:11:17.012360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:11:19.466091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:10:51.737894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:10:54.668857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:10:58.179440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:11:01.738666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:11:05.571823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:11:10.280047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:11:14.452496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:11:17.254719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:11:19.664005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:10:52.040107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:10:54.963251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:10:58.605747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:11:02.130220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:11:05.986796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:11:10.778797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:11:14.861940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:11:17.541835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:11:19.858639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:10:52.409018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:10:55.460307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:10:59.013129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:11:02.522651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:11:06.358971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:11:11.196855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:11:15.199411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:11:17.775109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:11:20.106554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:10:52.763324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:10:55.894094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:10:59.415908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:11:03.072757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:11:06.925980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:11:11.532445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:11:15.562074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:11:17.999768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:11:20.367671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:10:53.057879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:10:56.294363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:10:59.876974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:11:03.452442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:11:07.440897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:11:12.123022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:11:15.866086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:11:18.212477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:11:20.569981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:10:53.365914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:10:56.628938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:11:00.275142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:11:03.842667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:11:08.106705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:11:12.682288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:11:16.150770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:11:18.479616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-11T05:11:30.074089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
측정일시권역코드권역명측정소코드측정소명미세먼지 1시간(㎍/㎥)미세먼지 24시간(㎍/㎥)초미세먼지(㎍/㎥)오존(ppm)이산화질소농도(ppm)일산화탄소농도(ppm)아황산가스농도(ppm)
측정일시1.0000.0000.0000.0000.0000.6690.7210.6450.4740.5440.4630.350
권역코드0.0001.0001.0000.9961.0000.1510.1920.1480.1590.2300.3450.331
권역명0.0001.0001.0000.9961.0000.1510.1920.1480.1590.2300.3450.331
측정소코드0.0000.9960.9961.0001.0000.1950.2350.2200.2020.2460.4480.473
측정소명0.0001.0001.0001.0001.0000.2230.2680.2810.2630.3430.5220.658
미세먼지 1시간(㎍/㎥)0.6690.1510.1510.1950.2231.0000.8360.7880.2990.4410.4360.381
미세먼지 24시간(㎍/㎥)0.7210.1920.1920.2350.2680.8361.0000.6860.2820.4890.3850.232
초미세먼지(㎍/㎥)0.6450.1480.1480.2200.2810.7880.6861.0000.3030.4520.5390.318
오존(ppm)0.4740.1590.1590.2020.2630.2990.2820.3031.0000.7180.4260.132
이산화질소농도(ppm)0.5440.2300.2300.2460.3430.4410.4890.4520.7181.0000.5590.391
일산화탄소농도(ppm)0.4630.3450.3450.4480.5220.4360.3850.5390.4260.5591.0000.311
아황산가스농도(ppm)0.3500.3310.3310.4730.6580.3810.2320.3180.1320.3910.3111.000
2024-05-11T05:11:30.444453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
측정소명권역명권역코드
측정소명1.0000.9990.999
권역명0.9991.0001.000
권역코드0.9991.0001.000
2024-05-11T05:11:30.681539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
측정일시측정소코드미세먼지 1시간(㎍/㎥)미세먼지 24시간(㎍/㎥)초미세먼지(㎍/㎥)오존(ppm)이산화질소농도(ppm)일산화탄소농도(ppm)아황산가스농도(ppm)권역코드권역명측정소명
측정일시1.0000.0030.2440.2400.443-0.0180.3520.2930.2570.0000.0000.000
측정소코드0.0031.0000.0970.1000.064-0.0040.005-0.1580.0310.9080.9080.999
미세먼지 1시간(㎍/㎥)0.2440.0971.0000.8610.800-0.0310.3310.4150.2560.0640.0640.080
미세먼지 24시간(㎍/㎥)0.2400.1000.8611.0000.7000.0400.2370.3000.1880.0810.0810.098
초미세먼지(㎍/㎥)0.4430.0640.8000.7001.000-0.0590.3650.5120.2530.0620.0620.103
오존(ppm)-0.018-0.004-0.0310.040-0.0591.000-0.744-0.426-0.0290.0660.0660.096
이산화질소농도(ppm)0.3520.0050.3310.2370.365-0.7441.0000.5360.2140.0970.0970.127
일산화탄소농도(ppm)0.293-0.1580.4150.3000.512-0.4260.5361.0000.1660.1500.1500.211
아황산가스농도(ppm)0.2570.0310.2560.1880.253-0.0290.2140.1661.0000.1430.1430.294
권역코드0.0000.9080.0640.0810.0620.0660.0970.1500.1431.0001.0000.999
권역명0.0000.9080.0640.0810.0620.0660.0970.1500.1431.0001.0000.999
측정소명0.0000.9990.0800.0980.1030.0960.1270.2110.2940.9990.9991.000

Missing values

2024-05-11T05:11:20.882678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-11T05:11:21.298440image/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

측정일시권역코드권역명측정소코드측정소명미세먼지 1시간(㎍/㎥)미세먼지 24시간(㎍/㎥)초미세먼지(㎍/㎥)오존(ppm)이산화질소농도(ppm)일산화탄소농도(ppm)아황산가스농도(ppm)
4134201902220200102동북권111141광진구7563590.0010.0631.70.004
9648201902122200104동남권111274강동구5059310.0220.0320.60.005
777201902271600100도심권111121중구9362650.0330.0630.70.008
11867201902090500103서남권111241동작구3641180.0270.0140.30.003
7050201902170500100도심권111121중구3947270.0280.020.60.003
7219201902162300103서남권111221구로구3252210.0320.0170.30.005
2118201902251100103서남권111251관악구9983800.0110.0520.70.005
11314201902100300103서남권111231영등포구3940210.0130.0270.70.005
3241201902231400103서남권111251관악구3459260.0330.0230.30.004
5899201902190400104동남권111262서초구4545290.0020.0580.50.004
측정일시권역코드권역명측정소코드측정소명미세먼지 1시간(㎍/㎥)미세먼지 24시간(㎍/㎥)초미세먼지(㎍/㎥)오존(ppm)이산화질소농도(ppm)일산화탄소농도(ppm)아황산가스농도(ppm)
11715201902091100103서남권111281금천구3936180.0270.0160.50.005
15683201902022000102동북권111311노원구6560430.0070.0510.80.006
11927201902090200100도심권111121중구4138230.0180.0210.60.004
2728201902241000101서북권111201마포구6860380.0090.0530.90.005
9245201902131400103서남권111281금천구4040240.0320.020.50.005
5226201902200600100도심권111131용산구030210.0240.0210.40.003
12791201902071600103서남권111301양천구3252160.0360.0150.30.003
11991201902090000103서남권111241동작구4543270.0030.0450.60.003
13743201902060200103서남권111251관악구139127600.0020.0861.30.006
8954201902140100101서북권111181은평구4742190.0220.020.60.006