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 1 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 이산화질소농도(ppm)High correlation
미세먼지 1시간(㎍/㎥) has 236 (2.4%) zerosZeros
초미세먼지(㎍/㎥) has 185 (1.8%) zerosZeros

Reproduction

Analysis started2024-05-11 05:08:47.428238
Analysis finished2024-05-11 05:09:25.542730
Duration38.11 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

측정일시
Real number (ℝ)

Distinct720
Distinct (%)7.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0190416 × 1011
Minimum2.0190401 × 1011
Maximum2.019043 × 1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T05:09:25.890412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.0190401 × 1011
5-th percentile2.0190402 × 1011
Q12.0190408 × 1011
median2.0190415 × 1011
Q32.0190423 × 1011
95-th percentile2.0190429 × 1011
Maximum2.019043 × 1011
Range292300
Interquartile range (IQR)150100

Descriptive statistics

Standard deviation86665.219
Coefficient of variation (CV)4.292394 × 10-7
Kurtosis-1.2029241
Mean2.0190416 × 1011
Median Absolute Deviation (MAD)77800
Skewness0.0017577307
Sum2.0190416 × 1015
Variance7.5108602 × 109
MonotonicityNot monotonic
2024-05-11T05:09:26.481560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
201904081700 24
 
0.2%
201904222000 20
 
0.2%
201904030000 20
 
0.2%
201904271500 20
 
0.2%
201904010600 20
 
0.2%
201904100300 20
 
0.2%
201904151700 20
 
0.2%
201904010900 20
 
0.2%
201904060800 19
 
0.2%
201904060600 19
 
0.2%
Other values (710) 9798
98.0%
ValueCountFrequency (%)
201904010000 16
0.2%
201904010100 18
0.2%
201904010200 17
0.2%
201904010300 16
0.2%
201904010400 14
0.1%
201904010500 11
0.1%
201904010600 20
0.2%
201904010700 14
0.1%
201904010800 13
0.1%
201904010900 20
0.2%
ValueCountFrequency (%)
201904302300 14
0.1%
201904302200 15
0.1%
201904302100 13
0.1%
201904302000 10
0.1%
201904301900 17
0.2%
201904301800 8
0.1%
201904301700 13
0.1%
201904301600 15
0.1%
201904301500 14
0.1%
201904301400 14
0.1%

권역코드
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
102
3177 
103
2803 
104
1579 
100
1228 
101
1213 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
102 3177
31.8%
103 2803
28.0%
104 1579
15.8%
100 1228
 
12.3%
101 1213
 
12.1%

Length

2024-05-11T05:09:27.020330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T05:09:27.427133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
102 3177
31.8%
103 2803
28.0%
104 1579
15.8%
100 1228
 
12.3%
101 1213
 
12.1%

권역명
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
동북권
3177 
서남권
2803 
동남권
1579 
도심권
1228 
서북권
1213 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
동북권 3177
31.8%
서남권 2803
28.0%
동남권 1579
15.8%
도심권 1228
 
12.3%
서북권 1213
 
12.1%

Length

2024-05-11T05:09:27.961246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T05:09:28.346128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
동북권 3177
31.8%
서남권 2803
28.0%
동남권 1579
15.8%
도심권 1228
 
12.3%
서북권 1213
 
12.1%

측정소코드
Real number (ℝ)

HIGH CORRELATION 

Distinct25
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean111210.97
Minimum111121
Maximum111311
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T05:09:28.934243image/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 deviation60.000765
Coefficient of variation (CV)0.000539522
Kurtosis-1.3692355
Mean111210.97
Median Absolute Deviation (MAD)60
Skewness0.029444742
Sum1.1121097 × 109
Variance3600.0918
MonotonicityNot monotonic
2024-05-11T05:09:29.344227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
111251 427
 
4.3%
111261 421
 
4.2%
111121 417
 
4.2%
111201 413
 
4.1%
111131 412
 
4.1%
111291 411
 
4.1%
111141 407
 
4.1%
111311 404
 
4.0%
111281 404
 
4.0%
111221 404
 
4.0%
Other values (15) 5880
58.8%
ValueCountFrequency (%)
111121 417
4.2%
111123 399
4.0%
111131 412
4.1%
111141 407
4.1%
111142 395
4.0%
111151 378
3.8%
111152 401
4.0%
111161 395
4.0%
111171 386
3.9%
111181 398
4.0%
ValueCountFrequency (%)
111311 404
4.0%
111301 387
3.9%
111291 411
4.1%
111281 404
4.0%
111274 374
3.7%
111273 398
4.0%
111262 386
3.9%
111261 421
4.2%
111251 427
4.3%
111241 401
4.0%

측정소명
Categorical

HIGH CORRELATION 

Distinct25
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
관악구
 
427
강남구
 
421
중구
 
417
마포구
 
413
용산구
 
412
Other values (20)
7910 

Length

Max length4
Median length3
Mean length3.0775
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row성북구
2nd row종로구
3rd row노원구
4th row송파구
5th row성동구

Common Values

ValueCountFrequency (%)
관악구 427
 
4.3%
강남구 421
 
4.2%
중구 417
 
4.2%
마포구 413
 
4.1%
용산구 412
 
4.1%
강북구 411
 
4.1%
광진구 407
 
4.1%
구로구 404
 
4.0%
노원구 404
 
4.0%
금천구 404
 
4.0%
Other values (15) 5880
58.8%

Length

2024-05-11T05:09:29.856166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
관악구 427
 
4.3%
강남구 421
 
4.2%
중구 417
 
4.2%
마포구 413
 
4.1%
용산구 412
 
4.1%
강북구 411
 
4.1%
광진구 407
 
4.1%
구로구 404
 
4.0%
노원구 404
 
4.0%
금천구 404
 
4.0%
Other values (15) 5880
58.8%

미세먼지 1시간(㎍/㎥)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct202
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.2262
Minimum0
Maximum288
Zeros236
Zeros (%)2.4%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T05:09:30.313478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q124
median35
Q350
95-th percentile87
Maximum288
Range288
Interquartile range (IQR)26

Descriptive statistics

Standard deviation27.940353
Coefficient of variation (CV)0.69458096
Kurtosis7.6616602
Mean40.2262
Median Absolute Deviation (MAD)13
Skewness2.1408406
Sum402262
Variance780.6633
MonotonicityNot monotonic
2024-05-11T05:09:30.867578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
32 257
 
2.6%
29 247
 
2.5%
30 245
 
2.5%
0 236
 
2.4%
38 233
 
2.3%
33 233
 
2.3%
34 230
 
2.3%
35 229
 
2.3%
28 229
 
2.3%
36 223
 
2.2%
Other values (192) 7638
76.4%
ValueCountFrequency (%)
0 236
2.4%
3 184
1.8%
4 62
 
0.6%
5 52
 
0.5%
6 62
 
0.6%
7 54
 
0.5%
8 76
 
0.8%
9 76
 
0.8%
10 79
 
0.8%
11 69
 
0.7%
ValueCountFrequency (%)
288 1
< 0.1%
232 1
< 0.1%
225 1
< 0.1%
224 1
< 0.1%
223 1
< 0.1%
221 1
< 0.1%
208 1
< 0.1%
206 1
< 0.1%
204 1
< 0.1%
202 2
< 0.1%

미세먼지 24시간(㎍/㎥)
Real number (ℝ)

HIGH CORRELATION 

Distinct160
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.0021
Minimum0
Maximum172
Zeros22
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T05:09:31.336836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile9
Q126
median37
Q349
95-th percentile81
Maximum172
Range172
Interquartile range (IQR)23

Descriptive statistics

Standard deviation22.928159
Coefficient of variation (CV)0.57317388
Kurtosis5.0094155
Mean40.0021
Median Absolute Deviation (MAD)11
Skewness1.6894013
Sum400021
Variance525.70047
MonotonicityNot monotonic
2024-05-11T05:09:31.853494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37 288
 
2.9%
33 267
 
2.7%
34 261
 
2.6%
30 257
 
2.6%
40 256
 
2.6%
31 254
 
2.5%
35 249
 
2.5%
38 249
 
2.5%
32 247
 
2.5%
36 243
 
2.4%
Other values (150) 7429
74.3%
ValueCountFrequency (%)
0 22
 
0.2%
3 56
0.6%
4 77
0.8%
5 80
0.8%
6 62
0.6%
7 79
0.8%
8 99
1.0%
9 73
0.7%
10 73
0.7%
11 79
0.8%
ValueCountFrequency (%)
172 1
 
< 0.1%
169 1
 
< 0.1%
162 5
0.1%
160 3
< 0.1%
159 2
 
< 0.1%
158 2
 
< 0.1%
157 1
 
< 0.1%
156 2
 
< 0.1%
155 2
 
< 0.1%
154 3
< 0.1%

초미세먼지(㎍/㎥)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct86
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.2264
Minimum0
Maximum89
Zeros185
Zeros (%)1.8%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T05:09:32.411887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q112
median18
Q325.25
95-th percentile46
Maximum89
Range89
Interquartile range (IQR)13.25

Descriptive statistics

Standard deviation12.735665
Coefficient of variation (CV)0.62965555
Kurtosis2.7389179
Mean20.2264
Median Absolute Deviation (MAD)7
Skewness1.3752162
Sum202264
Variance162.19716
MonotonicityNot monotonic
2024-05-11T05:09:32.873730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17 469
 
4.7%
15 459
 
4.6%
16 457
 
4.6%
14 442
 
4.4%
11 424
 
4.2%
12 422
 
4.2%
18 421
 
4.2%
13 419
 
4.2%
19 397
 
4.0%
10 371
 
3.7%
Other values (76) 5719
57.2%
ValueCountFrequency (%)
0 185
1.8%
1 138
1.4%
2 61
 
0.6%
3 98
 
1.0%
4 103
 
1.0%
5 110
 
1.1%
6 136
1.4%
7 182
1.8%
8 215
2.1%
9 288
2.9%
ValueCountFrequency (%)
89 1
 
< 0.1%
86 1
 
< 0.1%
83 1
 
< 0.1%
82 3
< 0.1%
81 1
 
< 0.1%
80 4
< 0.1%
79 1
 
< 0.1%
78 3
< 0.1%
77 5
0.1%
76 4
< 0.1%

오존(ppm)
Real number (ℝ)

HIGH CORRELATION 

Distinct91
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0290348
Minimum0
Maximum0.097
Zeros62
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T05:09:33.342266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.004
Q10.016
median0.029
Q30.041
95-th percentile0.057
Maximum0.097
Range0.097
Interquartile range (IQR)0.025

Descriptive statistics

Standard deviation0.01648979
Coefficient of variation (CV)0.56793194
Kurtosis-0.47283625
Mean0.0290348
Median Absolute Deviation (MAD)0.012
Skewness0.24702062
Sum290.348
Variance0.00027191318
MonotonicityNot monotonic
2024-05-11T05:09:33.865682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.031 247
 
2.5%
0.028 238
 
2.4%
0.033 225
 
2.2%
0.036 221
 
2.2%
0.035 221
 
2.2%
0.034 221
 
2.2%
0.025 220
 
2.2%
0.03 218
 
2.2%
0.024 213
 
2.1%
0.004 212
 
2.1%
Other values (81) 7764
77.6%
ValueCountFrequency (%)
0.0 62
 
0.6%
0.001 33
 
0.3%
0.002 210
2.1%
0.003 193
1.9%
0.004 212
2.1%
0.005 198
2.0%
0.006 166
1.7%
0.007 177
1.8%
0.008 147
1.5%
0.009 116
1.2%
ValueCountFrequency (%)
0.097 2
< 0.1%
0.095 1
< 0.1%
0.092 1
< 0.1%
0.091 1
< 0.1%
0.087 1
< 0.1%
0.086 1
< 0.1%
0.085 1
< 0.1%
0.084 2
< 0.1%
0.083 1
< 0.1%
0.082 1
< 0.1%

이산화질소농도(ppm)
Real number (ℝ)

HIGH CORRELATION 

Distinct90
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0289302
Minimum0
Maximum0.093
Zeros46
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T05:09:34.419565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.01
Q10.018
median0.026
Q30.038
95-th percentile0.058
Maximum0.093
Range0.093
Interquartile range (IQR)0.02

Descriptive statistics

Standard deviation0.014917164
Coefficient of variation (CV)0.51562602
Kurtosis0.55248897
Mean0.0289302
Median Absolute Deviation (MAD)0.01
Skewness0.8417152
Sum289.302
Variance0.00022252178
MonotonicityNot monotonic
2024-05-11T05:09:34.993877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.02 323
 
3.2%
0.021 307
 
3.1%
0.019 302
 
3.0%
0.022 293
 
2.9%
0.017 291
 
2.9%
0.015 291
 
2.9%
0.018 290
 
2.9%
0.023 286
 
2.9%
0.026 273
 
2.7%
0.014 269
 
2.7%
Other values (80) 7075
70.8%
ValueCountFrequency (%)
0.0 46
 
0.5%
0.002 1
 
< 0.1%
0.003 1
 
< 0.1%
0.004 15
 
0.1%
0.005 23
 
0.2%
0.006 60
 
0.6%
0.007 86
0.9%
0.008 111
1.1%
0.009 145
1.5%
0.01 163
1.6%
ValueCountFrequency (%)
0.093 2
 
< 0.1%
0.091 4
< 0.1%
0.09 1
 
< 0.1%
0.089 1
 
< 0.1%
0.087 2
 
< 0.1%
0.085 3
< 0.1%
0.084 4
< 0.1%
0.083 6
0.1%
0.082 5
0.1%
0.081 4
< 0.1%

일산화탄소농도(ppm)
Real number (ℝ)

HIGH CORRELATION 

Distinct17
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.45469
Minimum0
Maximum1.6
Zeros51
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T05:09:35.365068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.2
Q10.3
median0.4
Q30.5
95-th percentile0.8
Maximum1.6
Range1.6
Interquartile range (IQR)0.2

Descriptive statistics

Standard deviation0.18103945
Coefficient of variation (CV)0.39816017
Kurtosis2.2026461
Mean0.45469
Median Absolute Deviation (MAD)0.1
Skewness1.1092378
Sum4546.9
Variance0.032775281
MonotonicityNot monotonic
2024-05-11T05:09:35.766716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
0.4 2819
28.2%
0.3 2308
23.1%
0.5 1750
17.5%
0.6 1099
 
11.0%
0.7 636
 
6.4%
0.2 631
 
6.3%
0.8 345
 
3.5%
0.9 184
 
1.8%
1.0 95
 
0.9%
0.0 51
 
0.5%
Other values (7) 82
 
0.8%
ValueCountFrequency (%)
0.0 51
 
0.5%
0.1 13
 
0.1%
0.2 631
 
6.3%
0.3 2308
23.1%
0.4 2819
28.2%
0.5 1750
17.5%
0.6 1099
 
11.0%
0.7 636
 
6.4%
0.8 345
 
3.5%
0.9 184
 
1.8%
ValueCountFrequency (%)
1.6 1
 
< 0.1%
1.5 3
 
< 0.1%
1.4 6
 
0.1%
1.3 10
 
0.1%
1.2 18
 
0.2%
1.1 31
 
0.3%
1.0 95
 
0.9%
0.9 184
 
1.8%
0.8 345
3.5%
0.7 636
6.4%
Distinct15
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0037934
Minimum0
Maximum0.014
Zeros92
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T05:09:36.213775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.002
Q10.003
median0.004
Q30.004
95-th percentile0.006
Maximum0.014
Range0.014
Interquartile range (IQR)0.001

Descriptive statistics

Standard deviation0.0012853333
Coefficient of variation (CV)0.3388341
Kurtosis2.1450635
Mean0.0037934
Median Absolute Deviation (MAD)0.001
Skewness0.67177801
Sum37.934
Variance1.6520816 × 10-6
MonotonicityNot monotonic
2024-05-11T05:09:36.754226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0.004 3255
32.6%
0.003 3121
31.2%
0.005 1485
14.8%
0.002 1146
 
11.5%
0.006 587
 
5.9%
0.007 226
 
2.3%
0.0 92
 
0.9%
0.008 62
 
0.6%
0.009 11
 
0.1%
0.01 6
 
0.1%
Other values (5) 9
 
0.1%
ValueCountFrequency (%)
0.0 92
 
0.9%
0.001 3
 
< 0.1%
0.002 1146
 
11.5%
0.003 3121
31.2%
0.004 3255
32.6%
0.005 1485
14.8%
0.006 587
 
5.9%
0.007 226
 
2.3%
0.008 62
 
0.6%
0.009 11
 
0.1%
ValueCountFrequency (%)
0.014 1
 
< 0.1%
0.013 1
 
< 0.1%
0.012 1
 
< 0.1%
0.011 3
 
< 0.1%
0.01 6
 
0.1%
0.009 11
 
0.1%
0.008 62
 
0.6%
0.007 226
 
2.3%
0.006 587
 
5.9%
0.005 1485
14.8%

Interactions

2024-05-11T05:09:20.681300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:08:57.377962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:09:01.015473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:09:04.564188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:09:07.295597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:09:09.908097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:09:12.321667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:09:15.076741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:09:17.766896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:09:20.993982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:08:57.928514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:09:01.493558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:09:04.913918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:09:07.593811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:09:10.194069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:09:12.595262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:09:15.365300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:09:18.075274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:09:21.323839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:08:58.347294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:09:01.907480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:09:05.210053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:09:07.892242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:09:10.465782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:09:12.910843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:09:15.648499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:09:18.385725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:09:21.643031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:08:58.722102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:09:02.300853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:09:05.494307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:09:08.180494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:09:10.732186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:09:13.169524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:09:15.939963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:09:18.694879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:09:22.216445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:08:59.053494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:09:02.663081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:09:05.781569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:09:08.466991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:09:10.993637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:09:13.474270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:09:16.224928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:09:19.035608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:09:22.523048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:08:59.392887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:09:02.936457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:09:06.054906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:09:08.736029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:09:11.244966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:09:13.836082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:09:16.497949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:09:19.333884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:09:22.843452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:08:59.810956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:09:03.211788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:09:06.333356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:09:09.039792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:09:11.534818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:09:14.198907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:09:16.776449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:09:19.650169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:09:23.348232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:09:00.183301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:09:03.914667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:09:06.605879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:09:09.328933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:09:11.770978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:09:14.474884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:09:17.040477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:09:19.964953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:09:23.855082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:09:00.592362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:09:04.257083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:09:06.903222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:09:09.629132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:09:12.046713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:09:14.779930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:09:17.344564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:09:20.321222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-11T05:09:37.149112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
측정일시권역코드권역명측정소코드측정소명미세먼지 1시간(㎍/㎥)미세먼지 24시간(㎍/㎥)초미세먼지(㎍/㎥)오존(ppm)이산화질소농도(ppm)일산화탄소농도(ppm)아황산가스농도(ppm)
측정일시1.0000.0000.0000.0000.0000.6390.7330.6720.3410.3940.3600.355
권역코드0.0001.0001.0000.9961.0000.1790.2300.1810.1610.2580.3910.290
권역명0.0001.0001.0000.9961.0000.1790.2300.1810.1610.2580.3910.290
측정소코드0.0000.9960.9961.0001.0000.1820.2250.2470.2070.2300.4790.404
측정소명0.0001.0001.0001.0001.0000.2210.2750.2890.2710.3510.5630.630
미세먼지 1시간(㎍/㎥)0.6390.1790.1790.1820.2211.0000.8600.7230.2340.4350.3520.266
미세먼지 24시간(㎍/㎥)0.7330.2300.2300.2250.2750.8601.0000.7700.2460.4010.3470.294
초미세먼지(㎍/㎥)0.6720.1810.1810.2470.2890.7230.7701.0000.2420.4050.4110.292
오존(ppm)0.3410.1610.1610.2070.2710.2340.2460.2421.0000.6550.4760.194
이산화질소농도(ppm)0.3940.2580.2580.2300.3510.4350.4010.4050.6551.0000.6470.328
일산화탄소농도(ppm)0.3600.3910.3910.4790.5630.3520.3470.4110.4760.6471.0000.577
아황산가스농도(ppm)0.3550.2900.2900.4040.6300.2660.2940.2920.1940.3280.5771.000
2024-05-11T05:09:37.723586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
측정소명권역명권역코드
측정소명1.0000.9990.999
권역명0.9991.0001.000
권역코드0.9991.0001.000
2024-05-11T05:09:38.155668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
측정일시측정소코드미세먼지 1시간(㎍/㎥)미세먼지 24시간(㎍/㎥)초미세먼지(㎍/㎥)오존(ppm)이산화질소농도(ppm)일산화탄소농도(ppm)아황산가스농도(ppm)권역코드권역명측정소명
측정일시1.0000.005-0.068-0.0590.097-0.0730.0340.018-0.0700.0000.0000.000
측정소코드0.0051.0000.0700.0790.072-0.062-0.019-0.198-0.0020.9060.9060.999
미세먼지 1시간(㎍/㎥)-0.0680.0701.0000.8860.726-0.0380.3550.3450.2640.0750.0750.079
미세먼지 24시간(㎍/㎥)-0.0590.0790.8861.0000.6500.0080.2630.2710.2430.0970.0970.100
초미세먼지(㎍/㎥)0.0970.0720.7260.6501.000-0.0840.3780.3710.2570.0760.0760.106
오존(ppm)-0.073-0.062-0.0380.008-0.0841.000-0.666-0.4100.0650.0670.0670.098
이산화질소농도(ppm)0.034-0.0190.3550.2630.378-0.6661.0000.6140.1280.1100.1100.131
일산화탄소농도(ppm)0.018-0.1980.3450.2710.371-0.4100.6141.0000.1070.1730.1730.232
아황산가스농도(ppm)-0.070-0.0020.2640.2430.2570.0650.1280.1071.0000.1240.1240.275
권역코드0.0000.9060.0750.0970.0760.0670.1100.1730.1241.0001.0000.999
권역명0.0000.9060.0750.0970.0760.0670.1100.1730.1241.0001.0000.999
측정소명0.0000.9990.0790.1000.1060.0980.1310.2320.2750.9990.9991.000

Missing values

2024-05-11T05:09:24.513665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-11T05:09:25.227181image/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)
3981201904240800102동북권111161성북구4266270.0140.0360.90.003
16251201904032100100도심권111123종로구3437190.0410.0260.40.004
4986201904221600102동북권111311노원구9969240.0560.0170.40.003
9373201904150900104동남권111273송파구5345160.0080.050.60.004
8282201904170400102동북권111142성동구4341240.0020.0660.70.004
7580201904180800101서북권111181은평구5030280.0190.0370.70.008
12350201904100900100도심권111121중구4410.030.0310.40.002
16138201904040200102동북권111151중랑구3029180.0220.0290.30.005
11304201904120300101서북권111201마포구6050430.0120.0370.50.004
15329201904051000101서북권111201마포구11266540.030.0310.50.004
측정일시권역코드권역명측정소코드측정소명미세먼지 1시간(㎍/㎥)미세먼지 24시간(㎍/㎥)초미세먼지(㎍/㎥)오존(ppm)이산화질소농도(ppm)일산화탄소농도(ppm)아황산가스농도(ppm)
368201904300900103서남권111281금천구3928230.0050.0580.80.004
16399201904031600104동남권111261강남구2936180.0420.0140.30.005
7421201904181500104동남권111261강남구222190.0360.0160.30.007
11214201904120700103서남권111212강서구6755410.0050.050.50.004
2798201904260800104동남권111273송파구0990.0190.030.30.002
10699201904130400104동남권111274강동구5250350.0030.0620.60.004
14453201904062100101서북권111201마포구5074230.0280.0190.40.004
7033201904190600102동북권111171도봉구5458360.0270.010.40.003
1039201904290600103서남권111251관악구2017120.0060.050.40.005
17893201904010400103서남권111231영등포구2227120.0260.010.20.003