Overview

Dataset statistics

Number of variables8
Number of observations10000
Missing cells1849
Missing cells (%)2.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory771.5 KiB
Average record size in memory79.0 B

Variable types

Numeric7
Categorical1

Dataset

Description파일 다운로드
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-2218/S/1/datasetView.do

Alerts

이산화질소농도(ppm) is highly overall correlated with 오존농도(ppm) and 3 other fieldsHigh correlation
오존농도(ppm) is highly overall correlated with 이산화질소농도(ppm)High correlation
일산화탄소농도(ppm) is highly overall correlated with 이산화질소농도(ppm) and 2 other fieldsHigh correlation
미세먼지농도(㎍/㎥) is highly overall correlated with 이산화질소농도(ppm) and 2 other fieldsHigh correlation
초미세먼지농도(㎍/㎥) is highly overall correlated with 이산화질소농도(ppm) and 2 other fieldsHigh correlation
이산화질소농도(ppm) has 301 (3.0%) missing valuesMissing
오존농도(ppm) has 242 (2.4%) missing valuesMissing
일산화탄소농도(ppm) has 283 (2.8%) missing valuesMissing
아황산가스농도(ppm) has 278 (2.8%) missing valuesMissing
미세먼지농도(㎍/㎥) has 376 (3.8%) missing valuesMissing
초미세먼지농도(㎍/㎥) has 369 (3.7%) missing valuesMissing

Reproduction

Analysis started2024-05-11 06:24:12.273789
Analysis finished2024-05-11 06:24:26.946788
Duration14.67 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

측정일시
Real number (ℝ)

Distinct366
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20200670
Minimum20200101
Maximum20201231
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T15:24:27.110868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20200101
5-th percentile20200119
Q120200402
median20200702
Q320201002
95-th percentile20201213
Maximum20201231
Range1130
Interquartile range (IQR)600

Descriptive statistics

Standard deviation344.47854
Coefficient of variation (CV)1.7052827 × 10-5
Kurtosis-1.2043407
Mean20200670
Median Absolute Deviation (MAD)300
Skewness-0.014674894
Sum2.020067 × 1011
Variance118665.46
MonotonicityNot monotonic
2024-05-11T15:24:27.346713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20201006 39
 
0.4%
20200717 38
 
0.4%
20201018 38
 
0.4%
20200829 37
 
0.4%
20201117 36
 
0.4%
20201222 36
 
0.4%
20201107 36
 
0.4%
20201126 36
 
0.4%
20200519 35
 
0.4%
20201008 35
 
0.4%
Other values (356) 9634
96.3%
ValueCountFrequency (%)
20200101 30
0.3%
20200102 25
0.2%
20200103 25
0.2%
20200104 29
0.3%
20200105 28
0.3%
20200106 22
0.2%
20200107 16
0.2%
20200108 28
0.3%
20200109 28
0.3%
20200110 28
0.3%
ValueCountFrequency (%)
20201231 27
0.3%
20201230 22
0.2%
20201229 29
0.3%
20201228 30
0.3%
20201227 31
0.3%
20201226 25
0.2%
20201225 27
0.3%
20201224 33
0.3%
20201223 27
0.3%
20201222 36
0.4%

측정소명
Categorical

Distinct50
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
화랑로
 
213
은평구
 
213
강서구
 
213
자연사박물관
 
213
올림픽공원
 
212
Other values (45)
8936 

Length

Max length6
Median length3
Mean length3.3016
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row동대문구
2nd row강동구
3rd row강북구
4th row금천구
5th row종로구

Common Values

ValueCountFrequency (%)
화랑로 213
 
2.1%
은평구 213
 
2.1%
강서구 213
 
2.1%
자연사박물관 213
 
2.1%
올림픽공원 212
 
2.1%
강남대로 211
 
2.1%
용산구 210
 
2.1%
동작구 208
 
2.1%
동작대로 208
 
2.1%
세곡 207
 
2.1%
Other values (40) 7892
78.9%

Length

2024-05-11T15:24:27.545536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
화랑로 213
 
2.1%
은평구 213
 
2.1%
강서구 213
 
2.1%
자연사박물관 213
 
2.1%
올림픽공원 212
 
2.1%
강남대로 211
 
2.1%
용산구 210
 
2.1%
동작구 208
 
2.1%
동작대로 208
 
2.1%
세곡 207
 
2.1%
Other values (40) 7892
78.9%

이산화질소농도(ppm)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct82
Distinct (%)0.8%
Missing301
Missing (%)3.0%
Infinite0
Infinite (%)0.0%
Mean0.025863697
Minimum0.001
Maximum0.123
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T15:24:27.736208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.001
5-th percentile0.009
Q10.015
median0.023
Q30.034
95-th percentile0.051
Maximum0.123
Range0.122
Interquartile range (IQR)0.019

Descriptive statistics

Standard deviation0.013354471
Coefficient of variation (CV)0.51634038
Kurtosis0.41498711
Mean0.025863697
Median Absolute Deviation (MAD)0.009
Skewness0.80828397
Sum250.852
Variance0.00017834191
MonotonicityNot monotonic
2024-05-11T15:24:28.039766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.013 340
 
3.4%
0.014 337
 
3.4%
0.016 331
 
3.3%
0.012 326
 
3.3%
0.019 323
 
3.2%
0.018 320
 
3.2%
0.017 320
 
3.2%
0.015 319
 
3.2%
0.02 311
 
3.1%
0.011 307
 
3.1%
Other values (72) 6465
64.6%
(Missing) 301
 
3.0%
ValueCountFrequency (%)
0.001 2
 
< 0.1%
0.002 1
 
< 0.1%
0.003 5
 
0.1%
0.004 13
 
0.1%
0.005 54
 
0.5%
0.006 66
 
0.7%
0.007 101
1.0%
0.008 176
1.8%
0.009 205
2.1%
0.01 240
2.4%
ValueCountFrequency (%)
0.123 1
< 0.1%
0.095 1
< 0.1%
0.091 1
< 0.1%
0.087 1
< 0.1%
0.082 1
< 0.1%
0.081 2
< 0.1%
0.08 1
< 0.1%
0.078 1
< 0.1%
0.077 1
< 0.1%
0.076 1
< 0.1%

오존농도(ppm)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct79
Distinct (%)0.8%
Missing242
Missing (%)2.4%
Infinite0
Infinite (%)0.0%
Mean0.02351076
Minimum0.002
Maximum0.093
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T15:24:28.337431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.002
5-th percentile0.006
Q10.014
median0.022
Q30.031
95-th percentile0.046
Maximum0.093
Range0.091
Interquartile range (IQR)0.017

Descriptive statistics

Standard deviation0.012365577
Coefficient of variation (CV)0.52595393
Kurtosis0.55154892
Mean0.02351076
Median Absolute Deviation (MAD)0.008
Skewness0.69327991
Sum229.418
Variance0.00015290749
MonotonicityNot monotonic
2024-05-11T15:24:28.569585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.021 350
 
3.5%
0.018 322
 
3.2%
0.023 316
 
3.2%
0.022 313
 
3.1%
0.025 305
 
3.0%
0.02 305
 
3.0%
0.017 295
 
2.9%
0.024 293
 
2.9%
0.016 290
 
2.9%
0.019 290
 
2.9%
Other values (69) 6679
66.8%
ValueCountFrequency (%)
0.002 36
 
0.4%
0.003 85
 
0.9%
0.004 139
1.4%
0.005 152
1.5%
0.006 184
1.8%
0.007 198
2.0%
0.008 217
2.2%
0.009 243
2.4%
0.01 248
2.5%
0.011 246
2.5%
ValueCountFrequency (%)
0.093 1
< 0.1%
0.092 1
< 0.1%
0.082 1
< 0.1%
0.08 1
< 0.1%
0.079 1
< 0.1%
0.078 1
< 0.1%
0.076 2
< 0.1%
0.075 1
< 0.1%
0.072 1
< 0.1%
0.071 1
< 0.1%

일산화탄소농도(ppm)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct19
Distinct (%)0.2%
Missing283
Missing (%)2.8%
Infinite0
Infinite (%)0.0%
Mean0.50743028
Minimum0.1
Maximum1.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T15:24:28.751337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.2
Q10.4
median0.5
Q30.6
95-th percentile0.9
Maximum1.9
Range1.8
Interquartile range (IQR)0.2

Descriptive statistics

Standard deviation0.21001665
Coefficient of variation (CV)0.41388277
Kurtosis1.5936943
Mean0.50743028
Median Absolute Deviation (MAD)0.1
Skewness0.99476201
Sum4930.7
Variance0.044106992
MonotonicityNot monotonic
2024-05-11T15:24:28.995468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0.4 2351
23.5%
0.5 1873
18.7%
0.3 1509
15.1%
0.6 1306
13.1%
0.7 812
 
8.1%
0.2 530
 
5.3%
0.8 529
 
5.3%
0.9 354
 
3.5%
1.0 192
 
1.9%
0.1 94
 
0.9%
Other values (9) 167
 
1.7%
(Missing) 283
 
2.8%
ValueCountFrequency (%)
0.1 94
 
0.9%
0.2 530
 
5.3%
0.3 1509
15.1%
0.4 2351
23.5%
0.5 1873
18.7%
0.6 1306
13.1%
0.7 812
 
8.1%
0.8 529
 
5.3%
0.9 354
 
3.5%
1.0 192
 
1.9%
ValueCountFrequency (%)
1.9 1
 
< 0.1%
1.8 1
 
< 0.1%
1.7 2
 
< 0.1%
1.6 2
 
< 0.1%
1.5 5
 
0.1%
1.4 9
 
0.1%
1.3 19
 
0.2%
1.2 40
 
0.4%
1.1 88
0.9%
1.0 192
1.9%

아황산가스농도(ppm)
Real number (ℝ)

MISSING 

Distinct9
Distinct (%)0.1%
Missing278
Missing (%)2.8%
Infinite0
Infinite (%)0.0%
Mean0.0032907838
Minimum0.001
Maximum0.01
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T15:24:29.239658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.001
5-th percentile0.002
Q10.003
median0.003
Q30.004
95-th percentile0.005
Maximum0.01
Range0.009
Interquartile range (IQR)0.001

Descriptive statistics

Standard deviation0.001029198
Coefficient of variation (CV)0.31275162
Kurtosis1.4760936
Mean0.0032907838
Median Absolute Deviation (MAD)0.001
Skewness0.85465198
Sum31.993
Variance1.0592485 × 10-6
MonotonicityNot monotonic
2024-05-11T15:24:29.424504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0.003 4298
43.0%
0.004 2420
24.2%
0.002 1884
18.8%
0.005 711
 
7.1%
0.006 230
 
2.3%
0.001 92
 
0.9%
0.007 74
 
0.7%
0.008 12
 
0.1%
0.01 1
 
< 0.1%
(Missing) 278
 
2.8%
ValueCountFrequency (%)
0.001 92
 
0.9%
0.002 1884
18.8%
0.003 4298
43.0%
0.004 2420
24.2%
0.005 711
 
7.1%
0.006 230
 
2.3%
0.007 74
 
0.7%
0.008 12
 
0.1%
0.01 1
 
< 0.1%
ValueCountFrequency (%)
0.01 1
 
< 0.1%
0.008 12
 
0.1%
0.007 74
 
0.7%
0.006 230
 
2.3%
0.005 711
 
7.1%
0.004 2420
24.2%
0.003 4298
43.0%
0.002 1884
18.8%
0.001 92
 
0.9%

미세먼지농도(㎍/㎥)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct113
Distinct (%)1.2%
Missing376
Missing (%)3.8%
Infinite0
Infinite (%)0.0%
Mean36.030445
Minimum3
Maximum158
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T15:24:29.628413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile10
Q123
median33
Q347
95-th percentile69
Maximum158
Range155
Interquartile range (IQR)24

Descriptive statistics

Standard deviation18.204064
Coefficient of variation (CV)0.50524116
Kurtosis0.73575698
Mean36.030445
Median Absolute Deviation (MAD)12
Skewness0.75029373
Sum346757
Variance331.38793
MonotonicityNot monotonic
2024-05-11T15:24:29.865325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
27 256
 
2.6%
25 249
 
2.5%
24 248
 
2.5%
23 234
 
2.3%
30 232
 
2.3%
28 231
 
2.3%
22 225
 
2.2%
29 223
 
2.2%
26 215
 
2.1%
21 213
 
2.1%
Other values (103) 7298
73.0%
(Missing) 376
 
3.8%
ValueCountFrequency (%)
3 16
 
0.2%
4 26
 
0.3%
5 38
 
0.4%
6 72
0.7%
7 59
0.6%
8 85
0.9%
9 94
0.9%
10 105
1.1%
11 103
1.0%
12 119
1.2%
ValueCountFrequency (%)
158 1
 
< 0.1%
122 1
 
< 0.1%
119 3
< 0.1%
118 1
 
< 0.1%
114 1
 
< 0.1%
113 2
< 0.1%
112 1
 
< 0.1%
111 4
< 0.1%
110 4
< 0.1%
109 3
< 0.1%

초미세먼지농도(㎍/㎥)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct81
Distinct (%)0.8%
Missing369
Missing (%)3.7%
Infinite0
Infinite (%)0.0%
Mean20.644897
Minimum1
Maximum91
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T15:24:30.131187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q111
median18
Q327
95-th percentile45
Maximum91
Range90
Interquartile range (IQR)16

Descriptive statistics

Standard deviation12.460069
Coefficient of variation (CV)0.60354234
Kurtosis1.2361776
Mean20.644897
Median Absolute Deviation (MAD)8
Skewness1.0183705
Sum198831
Variance155.25333
MonotonicityNot monotonic
2024-05-11T15:24:30.382723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14 366
 
3.7%
18 359
 
3.6%
17 354
 
3.5%
13 353
 
3.5%
16 352
 
3.5%
11 349
 
3.5%
10 344
 
3.4%
12 343
 
3.4%
15 341
 
3.4%
9 341
 
3.4%
Other values (71) 6129
61.3%
(Missing) 369
 
3.7%
ValueCountFrequency (%)
1 57
 
0.6%
2 94
 
0.9%
3 158
1.6%
4 158
1.6%
5 188
1.9%
6 198
2.0%
7 271
2.7%
8 295
2.9%
9 341
3.4%
10 344
3.4%
ValueCountFrequency (%)
91 1
 
< 0.1%
87 1
 
< 0.1%
85 2
< 0.1%
83 2
< 0.1%
82 1
 
< 0.1%
81 2
< 0.1%
78 1
 
< 0.1%
76 2
< 0.1%
75 2
< 0.1%
74 3
< 0.1%

Interactions

2024-05-11T15:24:24.920652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:24:15.135086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:24:16.787405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:24:18.469904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:24:20.035687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:24:21.548614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:24:23.341300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:24:25.113690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:24:15.319816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:24:17.020787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:24:18.673199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:24:20.280130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:24:21.752886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:24:23.576242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:24:25.306051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:24:15.592046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:24:17.359612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:24:18.922647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:24:20.529915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:24:22.024963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:24:23.779899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:24:25.474592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:24:15.852840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:24:17.583185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:24:19.093864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:24:20.730309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:24:22.262304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:24:24.000882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:24:25.629959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:24:16.112845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:24:17.840582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:24:19.387943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:24:20.955511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:24:22.431202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:24:24.236378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:24:25.797871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:24:16.362907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:24:18.076690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:24:19.642145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:24:21.208593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:24:23.004167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:24:24.457269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:24:25.997886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:24:16.589910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:24:18.284164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:24:19.862539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:24:21.373540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:24:23.185067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:24:24.738165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-11T15:24:30.557799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
측정일시측정소명이산화질소농도(ppm)오존농도(ppm)일산화탄소농도(ppm)아황산가스농도(ppm)미세먼지농도(㎍/㎥)초미세먼지농도(㎍/㎥)
측정일시1.0000.0000.3530.6390.5560.3120.4250.541
측정소명0.0001.0000.4870.2990.5110.6300.1570.154
이산화질소농도(ppm)0.3530.4871.0000.4270.5210.4790.5640.436
오존농도(ppm)0.6390.2990.4271.0000.5030.2070.2400.404
일산화탄소농도(ppm)0.5560.5110.5210.5031.0000.3740.4370.630
아황산가스농도(ppm)0.3120.6300.4790.2070.3741.0000.4490.318
미세먼지농도(㎍/㎥)0.4250.1570.5640.2400.4370.4491.0000.767
초미세먼지농도(㎍/㎥)0.5410.1540.4360.4040.6300.3180.7671.000
2024-05-11T15:24:31.394555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
측정일시이산화질소농도(ppm)오존농도(ppm)일산화탄소농도(ppm)아황산가스농도(ppm)미세먼지농도(㎍/㎥)초미세먼지농도(㎍/㎥)측정소명
측정일시1.000-0.086-0.160-0.079-0.091-0.234-0.2110.000
이산화질소농도(ppm)-0.0861.000-0.5120.6880.4160.5570.5620.197
오존농도(ppm)-0.160-0.5121.000-0.438-0.225-0.078-0.1350.099
일산화탄소농도(ppm)-0.0790.688-0.4381.0000.3840.5450.5910.187
아황산가스농도(ppm)-0.0910.416-0.2250.3841.0000.4180.4050.281
미세먼지농도(㎍/㎥)-0.2340.557-0.0780.5450.4181.0000.8900.058
초미세먼지농도(㎍/㎥)-0.2110.562-0.1350.5910.4050.8901.0000.051
측정소명0.0000.1970.0990.1870.2810.0580.0511.000

Missing values

2024-05-11T15:24:26.215936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-11T15:24:26.495829image/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.
2024-05-11T15:24:26.771071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

측정일시측정소명이산화질소농도(ppm)오존농도(ppm)일산화탄소농도(ppm)아황산가스농도(ppm)미세먼지농도(㎍/㎥)초미세먼지농도(㎍/㎥)
1056720200730동대문구0.020.0250.50.0021912
1170220200822강동구0.0190.0120.30.002159
5420200102강북구0.0430.0070.80.0036243
931220200705금천구0.0130.0420.40.0033025
874120200623종로구0.0350.0450.50.0054125
139720200128행주0.0150.0260.70.003157
1546320201105남산0.0410.0110.40.0043924
1447120201016마포아트센터<NA><NA><NA><NA><NA><NA>
1372820201001세곡0.0150.0210.50.0031811
997420200718서울숲0.0290.0290.50.0044025
측정일시측정소명이산화질소농도(ppm)오존농도(ppm)일산화탄소농도(ppm)아황산가스농도(ppm)미세먼지농도(㎍/㎥)초미세먼지농도(㎍/㎥)
416520200324도봉구0.0240.0360.60.0035030
1213320200830영등포구0.0090.0180.40.0031811
1466320201020남산<NA><NA><NA><NA><NA><NA>
1023620200723용산구0.0160.0170.40.0032111
429620200326한강대로0.0450.0220.60.0045634
821620200613도산대로0.0420.0390.70.0035025
255520200221강서구0.0430.0180.70.0046139
1643520201124올림픽공원0.0360.0050.60.0032718
1651220201126금천구0.0480.0080.90.0046442
1159320200819중랑구0.0160.0180.30.0034632