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 2 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 3 other fieldsHigh correlation
이산화질소농도(ppm) is highly overall correlated with 초미세먼지(㎍/㎥) and 1 other fieldsHigh correlation
일산화탄소농도(ppm) is highly overall correlated with 미세먼지 1시간(㎍/㎥) and 2 other fieldsHigh correlation
미세먼지 1시간(㎍/㎥) has 247 (2.5%) zerosZeros
초미세먼지(㎍/㎥) has 132 (1.3%) zerosZeros

Reproduction

Analysis started2024-05-11 05:07:44.749427
Analysis finished2024-05-11 05:08:26.664606
Duration41.92 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

측정일시
Real number (ℝ)

Distinct744
Distinct (%)7.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0190516 × 1011
Minimum2.0190501 × 1011
Maximum2.0190531 × 1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T05:08:26.954974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.0190501 × 1011
5-th percentile2.0190502 × 1011
Q12.0190508 × 1011
median2.0190516 × 1011
Q32.0190524 × 1011
95-th percentile2.019053 × 1011
Maximum2.0190531 × 1011
Range302300
Interquartile range (IQR)159100

Descriptive statistics

Standard deviation89615.199
Coefficient of variation (CV)4.4384798 × 10-7
Kurtosis-1.2148007
Mean2.0190516 × 1011
Median Absolute Deviation (MAD)79500
Skewness-0.009642388
Sum2.0190516 × 1015
Variance8.0308839 × 109
MonotonicityNot monotonic
2024-05-11T05:08:27.378380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
201905181000 21
 
0.2%
201905180000 21
 
0.2%
201905110000 21
 
0.2%
201905131400 21
 
0.2%
201905301300 21
 
0.2%
201905241000 20
 
0.2%
201905170200 20
 
0.2%
201905270400 20
 
0.2%
201905150400 19
 
0.2%
201905191900 19
 
0.2%
Other values (734) 9797
98.0%
ValueCountFrequency (%)
201905010000 13
0.1%
201905010100 11
0.1%
201905010200 12
0.1%
201905010300 15
0.1%
201905010400 15
0.1%
201905010500 12
0.1%
201905010600 17
0.2%
201905010700 12
0.1%
201905010800 16
0.2%
201905010900 14
0.1%
ValueCountFrequency (%)
201905312300 8
0.1%
201905312200 12
0.1%
201905312100 15
0.1%
201905312000 13
0.1%
201905311900 13
0.1%
201905311800 12
0.1%
201905311700 13
0.1%
201905311600 15
0.1%
201905311500 13
0.1%
201905311400 14
0.1%

권역코드
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
102
3160 
103
2793 
104
1628 
101
1245 
100
1174 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row104
2nd row101
3rd row102
4th row102
5th row103

Common Values

ValueCountFrequency (%)
102 3160
31.6%
103 2793
27.9%
104 1628
16.3%
101 1245
 
12.4%
100 1174
 
11.7%

Length

2024-05-11T05:08:27.791793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T05:08:28.139021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
102 3160
31.6%
103 2793
27.9%
104 1628
16.3%
101 1245
 
12.4%
100 1174
 
11.7%

권역명
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
동북권
3160 
서남권
2793 
동남권
1628 
서북권
1245 
도심권
1174 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
동북권 3160
31.6%
서남권 2793
27.9%
동남권 1628
16.3%
서북권 1245
 
12.4%
도심권 1174
 
11.7%

Length

2024-05-11T05:08:28.483125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T05:08:28.776962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
동북권 3160
31.6%
서남권 2793
27.9%
동남권 1628
16.3%
서북권 1245
 
12.4%
도심권 1174
 
11.7%

측정소코드
Real number (ℝ)

HIGH CORRELATION 

Distinct25
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean111211.67
Minimum111121
Maximum111311
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T05:08:29.587616image/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 deviation59.855751
Coefficient of variation (CV)0.00053821465
Kurtosis-1.3666538
Mean111211.67
Median Absolute Deviation (MAD)60
Skewness0.02025017
Sum1.1121167 × 109
Variance3582.7109
MonotonicityNot monotonic
2024-05-11T05:08:30.111434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
111274 429
 
4.3%
111151 420
 
4.2%
111181 419
 
4.2%
111201 417
 
4.2%
111221 414
 
4.1%
111311 410
 
4.1%
111191 409
 
4.1%
111141 408
 
4.1%
111251 408
 
4.1%
111301 406
 
4.1%
Other values (15) 5860
58.6%
ValueCountFrequency (%)
111121 398
4.0%
111123 380
3.8%
111131 396
4.0%
111141 408
4.1%
111142 377
3.8%
111151 420
4.2%
111152 386
3.9%
111161 378
3.8%
111171 389
3.9%
111181 419
4.2%
ValueCountFrequency (%)
111311 410
4.1%
111301 406
4.1%
111291 392
3.9%
111281 396
4.0%
111274 429
4.3%
111273 397
4.0%
111262 402
4.0%
111261 400
4.0%
111251 408
4.1%
111241 404
4.0%

측정소명
Categorical

HIGH CORRELATION 

Distinct25
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
강동구
 
429
중랑구
 
420
은평구
 
419
마포구
 
417
구로구
 
414
Other values (20)
7901 

Length

Max length4
Median length3
Mean length3.079
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row송파구
2nd row마포구
3rd row동대문구
4th row성북구
5th row구로구

Common Values

ValueCountFrequency (%)
강동구 429
 
4.3%
중랑구 420
 
4.2%
은평구 419
 
4.2%
마포구 417
 
4.2%
구로구 414
 
4.1%
노원구 410
 
4.1%
서대문구 409
 
4.1%
광진구 408
 
4.1%
관악구 408
 
4.1%
양천구 406
 
4.1%
Other values (15) 5860
58.6%

Length

2024-05-11T05:08:30.749998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
강동구 429
 
4.3%
중랑구 420
 
4.2%
은평구 419
 
4.2%
마포구 417
 
4.2%
구로구 414
 
4.1%
노원구 410
 
4.1%
서대문구 409
 
4.1%
광진구 408
 
4.1%
관악구 408
 
4.1%
양천구 406
 
4.1%
Other values (15) 5860
58.6%

미세먼지 1시간(㎍/㎥)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct158
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51.334
Minimum0
Maximum178
Zeros247
Zeros (%)2.5%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T05:08:31.381417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile12
Q133
median48
Q367
95-th percentile103.05
Maximum178
Range178
Interquartile range (IQR)34

Descriptive statistics

Standard deviation27.460529
Coefficient of variation (CV)0.53493842
Kurtosis0.60495317
Mean51.334
Median Absolute Deviation (MAD)17
Skewness0.69045968
Sum513340
Variance754.08065
MonotonicityNot monotonic
2024-05-11T05:08:31.980688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 247
 
2.5%
42 192
 
1.9%
37 184
 
1.8%
41 182
 
1.8%
38 181
 
1.8%
36 173
 
1.7%
40 173
 
1.7%
44 169
 
1.7%
35 168
 
1.7%
46 165
 
1.7%
Other values (148) 8166
81.7%
ValueCountFrequency (%)
0 247
2.5%
3 1
 
< 0.1%
4 3
 
< 0.1%
5 9
 
0.1%
6 14
 
0.1%
7 31
 
0.3%
8 21
 
0.2%
9 52
 
0.5%
10 42
 
0.4%
11 57
 
0.6%
ValueCountFrequency (%)
178 1
 
< 0.1%
169 1
 
< 0.1%
168 1
 
< 0.1%
162 1
 
< 0.1%
160 1
 
< 0.1%
158 1
 
< 0.1%
157 2
< 0.1%
155 3
< 0.1%
154 2
< 0.1%
153 4
< 0.1%

미세먼지 24시간(㎍/㎥)
Real number (ℝ)

HIGH CORRELATION 

Distinct129
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.9207
Minimum0
Maximum138
Zeros45
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T05:08:32.529014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile16
Q136
median49
Q362
95-th percentile87.05
Maximum138
Range138
Interquartile range (IQR)26

Descriptive statistics

Standard deviation20.993915
Coefficient of variation (CV)0.42054529
Kurtosis0.47542807
Mean49.9207
Median Absolute Deviation (MAD)13
Skewness0.42760245
Sum499207
Variance440.74449
MonotonicityNot monotonic
2024-05-11T05:08:33.303371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
54 213
 
2.1%
36 211
 
2.1%
60 208
 
2.1%
61 207
 
2.1%
58 206
 
2.1%
38 203
 
2.0%
37 201
 
2.0%
43 199
 
2.0%
56 197
 
2.0%
44 196
 
2.0%
Other values (119) 7959
79.6%
ValueCountFrequency (%)
0 45
0.4%
6 1
 
< 0.1%
7 10
 
0.1%
8 20
 
0.2%
9 22
 
0.2%
10 37
0.4%
11 41
0.4%
12 58
0.6%
13 65
0.7%
14 87
0.9%
ValueCountFrequency (%)
138 1
 
< 0.1%
135 2
< 0.1%
134 1
 
< 0.1%
131 1
 
< 0.1%
130 3
< 0.1%
129 1
 
< 0.1%
128 4
< 0.1%
126 1
 
< 0.1%
125 1
 
< 0.1%
124 4
< 0.1%

초미세먼지(㎍/㎥)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct116
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.7929
Minimum0
Maximum147
Zeros132
Zeros (%)1.3%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T05:08:33.925936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6
Q115
median25
Q339
95-th percentile64
Maximum147
Range147
Interquartile range (IQR)24

Descriptive statistics

Standard deviation18.294258
Coefficient of variation (CV)0.63537393
Kurtosis1.6112864
Mean28.7929
Median Absolute Deviation (MAD)11
Skewness1.1036137
Sum287929
Variance334.67988
MonotonicityNot monotonic
2024-05-11T05:08:34.585352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17 297
 
3.0%
16 273
 
2.7%
18 257
 
2.6%
14 252
 
2.5%
22 249
 
2.5%
19 249
 
2.5%
12 245
 
2.5%
23 241
 
2.4%
13 240
 
2.4%
15 238
 
2.4%
Other values (106) 7459
74.6%
ValueCountFrequency (%)
0 132
1.3%
1 7
 
0.1%
2 18
 
0.2%
3 45
 
0.4%
4 83
 
0.8%
5 116
1.2%
6 146
1.5%
7 173
1.7%
8 194
1.9%
9 222
2.2%
ValueCountFrequency (%)
147 1
 
< 0.1%
125 2
< 0.1%
122 1
 
< 0.1%
118 1
 
< 0.1%
117 2
< 0.1%
115 1
 
< 0.1%
112 1
 
< 0.1%
110 3
< 0.1%
109 1
 
< 0.1%
108 3
< 0.1%

오존(ppm)
Real number (ℝ)

Distinct156
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0421538
Minimum0
Maximum0.186
Zeros61
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T05:08:35.032501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.008
Q10.024
median0.04
Q30.057
95-th percentile0.083
Maximum0.186
Range0.186
Interquartile range (IQR)0.033

Descriptive statistics

Standard deviation0.023998257
Coefficient of variation (CV)0.56930234
Kurtosis1.7053003
Mean0.0421538
Median Absolute Deviation (MAD)0.016
Skewness0.87146307
Sum421.538
Variance0.00057591634
MonotonicityNot monotonic
2024-05-11T05:08:35.683577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.034 187
 
1.9%
0.037 182
 
1.8%
0.042 177
 
1.8%
0.038 173
 
1.7%
0.033 173
 
1.7%
0.019 170
 
1.7%
0.04 169
 
1.7%
0.035 168
 
1.7%
0.032 167
 
1.7%
0.039 164
 
1.6%
Other values (146) 8270
82.7%
ValueCountFrequency (%)
0.0 61
0.6%
0.001 4
 
< 0.1%
0.002 28
 
0.3%
0.003 60
0.6%
0.004 63
0.6%
0.005 77
0.8%
0.006 77
0.8%
0.007 74
0.7%
0.008 65
0.7%
0.009 100
1.0%
ValueCountFrequency (%)
0.186 1
 
< 0.1%
0.182 1
 
< 0.1%
0.179 1
 
< 0.1%
0.175 1
 
< 0.1%
0.17 1
 
< 0.1%
0.169 1
 
< 0.1%
0.166 1
 
< 0.1%
0.165 1
 
< 0.1%
0.163 3
< 0.1%
0.162 2
< 0.1%

이산화질소농도(ppm)
Real number (ℝ)

HIGH CORRELATION 

Distinct96
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0276833
Minimum0
Maximum0.112
Zeros58
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T05:08:36.205405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.008
Q10.016
median0.025
Q30.037
95-th percentile0.055
Maximum0.112
Range0.112
Interquartile range (IQR)0.021

Descriptive statistics

Standard deviation0.014927427
Coefficient of variation (CV)0.53922138
Kurtosis0.74975775
Mean0.0276833
Median Absolute Deviation (MAD)0.01
Skewness0.84675215
Sum276.833
Variance0.00022282808
MonotonicityNot monotonic
2024-05-11T05:08:36.782294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.017 325
 
3.2%
0.021 316
 
3.2%
0.016 315
 
3.1%
0.015 301
 
3.0%
0.014 294
 
2.9%
0.019 292
 
2.9%
0.02 289
 
2.9%
0.018 287
 
2.9%
0.023 281
 
2.8%
0.013 281
 
2.8%
Other values (86) 7019
70.2%
ValueCountFrequency (%)
0.0 58
 
0.6%
0.001 4
 
< 0.1%
0.002 2
 
< 0.1%
0.003 13
 
0.1%
0.004 25
 
0.2%
0.005 58
 
0.6%
0.006 81
0.8%
0.007 144
1.4%
0.008 153
1.5%
0.009 165
1.7%
ValueCountFrequency (%)
0.112 1
 
< 0.1%
0.11 1
 
< 0.1%
0.107 1
 
< 0.1%
0.106 1
 
< 0.1%
0.105 1
 
< 0.1%
0.102 1
 
< 0.1%
0.1 3
< 0.1%
0.093 1
 
< 0.1%
0.092 1
 
< 0.1%
0.09 1
 
< 0.1%

일산화탄소농도(ppm)
Real number (ℝ)

HIGH CORRELATION 

Distinct15
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.47463
Minimum0
Maximum1.5
Zeros55
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T05:08:37.164056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.2
Q10.3
median0.4
Q30.6
95-th percentile0.8
Maximum1.5
Range1.5
Interquartile range (IQR)0.3

Descriptive statistics

Standard deviation0.18961265
Coefficient of variation (CV)0.39949572
Kurtosis0.77677577
Mean0.47463
Median Absolute Deviation (MAD)0.1
Skewness0.6905462
Sum4746.3
Variance0.035952958
MonotonicityNot monotonic
2024-05-11T05:08:37.606174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0.4 2217
22.2%
0.3 1973
19.7%
0.5 1948
19.5%
0.6 1374
13.7%
0.7 834
 
8.3%
0.2 688
 
6.9%
0.8 450
 
4.5%
0.9 227
 
2.3%
1.0 97
 
1.0%
0.1 68
 
0.7%
Other values (5) 124
 
1.2%
ValueCountFrequency (%)
0.0 55
 
0.5%
0.1 68
 
0.7%
0.2 688
 
6.9%
0.3 1973
19.7%
0.4 2217
22.2%
0.5 1948
19.5%
0.6 1374
13.7%
0.7 834
 
8.3%
0.8 450
 
4.5%
0.9 227
 
2.3%
ValueCountFrequency (%)
1.5 2
 
< 0.1%
1.3 5
 
0.1%
1.2 20
 
0.2%
1.1 42
 
0.4%
1.0 97
 
1.0%
0.9 227
 
2.3%
0.8 450
 
4.5%
0.7 834
8.3%
0.6 1374
13.7%
0.5 1948
19.5%
Distinct17
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0045533
Minimum0
Maximum0.018
Zeros67
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T05:08:38.196164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.002
Q10.004
median0.004
Q30.005
95-th percentile0.007
Maximum0.018
Range0.018
Interquartile range (IQR)0.001

Descriptive statistics

Standard deviation0.0015906641
Coefficient of variation (CV)0.34934313
Kurtosis2.9743947
Mean0.0045533
Median Absolute Deviation (MAD)0.001
Skewness1.0019195
Sum45.533
Variance2.5302121 × 10-6
MonotonicityNot monotonic
2024-05-11T05:08:38.593055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
0.004 3407
34.1%
0.005 2135
21.3%
0.003 1746
17.5%
0.006 1145
 
11.5%
0.007 565
 
5.7%
0.002 449
 
4.5%
0.008 273
 
2.7%
0.009 120
 
1.2%
0.0 67
 
0.7%
0.01 46
 
0.5%
Other values (7) 47
 
0.5%
ValueCountFrequency (%)
0.0 67
 
0.7%
0.001 1
 
< 0.1%
0.002 449
 
4.5%
0.003 1746
17.5%
0.004 3407
34.1%
0.005 2135
21.3%
0.006 1145
 
11.5%
0.007 565
 
5.7%
0.008 273
 
2.7%
0.009 120
 
1.2%
ValueCountFrequency (%)
0.018 1
 
< 0.1%
0.016 2
 
< 0.1%
0.014 1
 
< 0.1%
0.013 1
 
< 0.1%
0.012 16
 
0.2%
0.011 25
 
0.2%
0.01 46
 
0.5%
0.009 120
 
1.2%
0.008 273
2.7%
0.007 565
5.7%

Interactions

2024-05-11T05:08:22.226489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:07:54.155595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:07:57.160463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:07:59.715094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:08:03.365452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:08:07.205044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:08:10.991612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:08:15.122002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:08:19.169121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:08:22.597586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:07:54.594464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:07:57.446465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:07:59.955459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:08:03.835503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:08:07.507660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:08:11.414128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:08:15.699154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:08:19.466392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:08:23.063278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:07:54.952943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:07:57.741139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:08:00.268123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:08:04.479534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:08:07.893977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:08:11.868798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:08:16.272059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:08:19.767474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:08:23.440040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:07:55.258358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:07:57.992950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:08:00.539187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:08:04.898810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:08:08.318233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:08:12.277157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:08:16.702695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:08:20.069062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:08:23.772029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:07:55.546652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:07:58.255813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:08:00.807586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:08:05.256958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:08:09.026727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:08:12.743659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:08:17.175410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:08:20.362597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:08:24.115801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:07:55.977480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:07:58.538237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:08:01.100966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:08:05.583542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:08:09.383464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:08:13.228925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:08:17.633417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:08:20.744374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:08:24.512679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:07:56.276229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:07:58.810674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:08:01.486674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:08:06.065549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:08:09.710517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:08:13.587413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:08:18.076816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:08:21.096703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:08:24.822523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:07:56.558592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:07:59.105155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:08:02.126920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:08:06.368936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:08:10.129479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:08:14.102116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:08:18.490047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:08:21.459311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:08:25.155539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:07:56.857084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:07:59.409787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:08:02.732759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:08:06.747118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:08:10.530585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:08:14.590625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:08:18.883468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:08:21.826980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-11T05:08:38.882544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
측정일시권역코드권역명측정소코드측정소명미세먼지 1시간(㎍/㎥)미세먼지 24시간(㎍/㎥)초미세먼지(㎍/㎥)오존(ppm)이산화질소농도(ppm)일산화탄소농도(ppm)아황산가스농도(ppm)
측정일시1.0000.0000.0000.0000.0000.5940.6460.6290.4570.5080.4390.340
권역코드0.0001.0001.0000.9961.0000.2040.2570.1540.0940.1890.3350.215
권역명0.0001.0001.0000.9961.0000.2040.2570.1540.0940.1890.3350.215
측정소코드0.0000.9960.9961.0001.0000.2140.2620.1870.1340.1880.4310.339
측정소명0.0001.0001.0001.0001.0000.2570.3140.2050.1960.2980.5390.527
미세먼지 1시간(㎍/㎥)0.5940.2040.2040.2140.2571.0000.8880.9040.4030.4690.5690.423
미세먼지 24시간(㎍/㎥)0.6460.2570.2570.2620.3140.8881.0000.8420.3610.4270.5350.376
초미세먼지(㎍/㎥)0.6290.1540.1540.1870.2050.9040.8421.0000.3920.5390.6020.435
오존(ppm)0.4570.0940.0940.1340.1960.4030.3610.3921.0000.4960.2850.318
이산화질소농도(ppm)0.5080.1890.1890.1880.2980.4690.4270.5390.4961.0000.6270.342
일산화탄소농도(ppm)0.4390.3350.3350.4310.5390.5690.5350.6020.2850.6271.0000.583
아황산가스농도(ppm)0.3400.2150.2150.3390.5270.4230.3760.4350.3180.3420.5831.000
2024-05-11T05:08:39.362329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
측정소명권역명권역코드
측정소명1.0000.9990.999
권역명0.9991.0001.000
권역코드0.9991.0001.000
2024-05-11T05:08:39.771246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
측정일시측정소코드미세먼지 1시간(㎍/㎥)미세먼지 24시간(㎍/㎥)초미세먼지(㎍/㎥)오존(ppm)이산화질소농도(ppm)일산화탄소농도(ppm)아황산가스농도(ppm)권역코드권역명측정소명
측정일시1.0000.004-0.257-0.266-0.1440.146-0.199-0.125-0.0070.0000.0000.000
측정소코드0.0041.0000.0610.0690.048-0.058-0.091-0.159-0.1200.9060.9060.999
미세먼지 1시간(㎍/㎥)-0.2570.0611.0000.8720.8330.2390.4350.5540.3630.0860.0860.093
미세먼지 24시간(㎍/㎥)-0.2660.0690.8721.0000.7710.2440.3920.4970.3070.1090.1090.115
초미세먼지(㎍/㎥)-0.1440.0480.8330.7711.0000.1620.5260.5970.4070.0640.0640.073
오존(ppm)0.146-0.0580.2390.2440.1621.000-0.339-0.0950.2400.0390.0390.070
이산화질소농도(ppm)-0.199-0.0910.4350.3920.526-0.3391.0000.6580.3200.0800.0800.109
일산화탄소농도(ppm)-0.125-0.1590.5540.4970.597-0.0950.6581.0000.3880.1380.1380.210
아황산가스농도(ppm)-0.007-0.1200.3630.3070.4070.2400.3200.3881.0000.0900.0900.214
권역코드0.0000.9060.0860.1090.0640.0390.0800.1380.0901.0001.0000.999
권역명0.0000.9060.0860.1090.0640.0390.0800.1380.0901.0001.0000.999
측정소명0.0000.9990.0930.1150.0730.0700.1090.2100.2140.9990.9991.000

Missing values

2024-05-11T05:08:25.619078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-11T05:08:26.200623image/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)
10099201905150400104동남권111273송파구5753550.0040.0490.50.005
405201905310700101서북권111201마포구2028130.0460.0150.20.004
9008201905162300102동북권111152동대문구3939290.0410.0270.40.004
5238201905230600102동북권111161성북구7361440.0170.0450.90.004
11489201905122000103서남권111221구로구7674450.0810.0250.40.004
4738201905240200102동북권111151중랑구6357450.0190.0370.70.007
11060201905131300102동북권111171도봉구5761350.0550.0180.50.005
4771201905240100104동남권111273송파구6561370.0190.0620.70.005
960201905300900102동북권111291강북구4733180.0150.0460.60.004
5275201905230400100도심권111123종로구5853330.0710.0160.60.006
측정일시권역코드권역명측정소코드측정소명미세먼지 1시간(㎍/㎥)미세먼지 24시간(㎍/㎥)초미세먼지(㎍/㎥)오존(ppm)이산화질소농도(ppm)일산화탄소농도(ppm)아황산가스농도(ppm)
15093201905062000103서남권111231영등포구824800.0320.0130.30.004
14883201905070400102동북권111142성동구3741100.0170.0360.40.003
7597201905190800104동남권111262서초구1813140.0210.0190.20.003
13311201905091900102동북권111311노원구3740200.0470.0260.40.004
1383201905291600102동북권111311노원구4243180.0560.0140.40.004
16283201905042000102동북권111161성북구6388290.0620.0330.60.005
15795201905051600103서남권111241동작구4554200.0820.0140.30.004
12158201905111700102동북권111152동대문구7062340.0840.0240.40.005
14946201905070200104동남권111261강남구3234110.0250.0160.30.005
15575201905060000100도심권111121중구4749150.0490.0110.30.003