Overview

Dataset statistics

Number of variables8
Number of observations10000
Missing cells344
Missing cells (%)0.4%
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) and 1 other fieldsHigh correlation
일산화탄소농도(ppm) is highly overall correlated with 이산화질소농도(ppm) and 3 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
일산화탄소농도(ppm) has 109 (1.1%) missing valuesMissing

Reproduction

Analysis started2024-05-04 06:03:55.611912
Analysis finished2024-05-04 06:04:18.872822
Duration23.26 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

측정일시
Real number (ℝ)

Distinct365
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20210667
Minimum20210101
Maximum20211231
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T06:04:19.244438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20210101
5-th percentile20210118
Q120210331
median20210701
Q320211002
95-th percentile20211214
Maximum20211231
Range1130
Interquartile range (IQR)671

Descriptive statistics

Standard deviation346.74047
Coefficient of variation (CV)1.715631 × 10-5
Kurtosis-1.2233058
Mean20210667
Median Absolute Deviation (MAD)301
Skewness-0.0012491866
Sum2.0210667 × 1011
Variance120228.96
MonotonicityNot monotonic
2024-05-04T06:04:19.832690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20210106 37
 
0.4%
20211222 37
 
0.4%
20210530 37
 
0.4%
20211120 36
 
0.4%
20210131 36
 
0.4%
20210201 35
 
0.4%
20211001 35
 
0.4%
20210313 35
 
0.4%
20210104 35
 
0.4%
20210514 34
 
0.3%
Other values (355) 9643
96.4%
ValueCountFrequency (%)
20210101 30
0.3%
20210102 25
0.2%
20210103 24
0.2%
20210104 35
0.4%
20210105 28
0.3%
20210106 37
0.4%
20210107 28
0.3%
20210108 26
0.3%
20210109 32
0.3%
20210110 29
0.3%
ValueCountFrequency (%)
20211231 25
0.2%
20211230 28
0.3%
20211229 25
0.2%
20211228 26
0.3%
20211227 28
0.3%
20211226 26
0.3%
20211225 30
0.3%
20211224 30
0.3%
20211223 27
0.3%
20211222 37
0.4%

측정소명
Categorical

Distinct50
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
공항대로
 
219
강북구
 
218
은평구
 
218
영등포로
 
217
강변북로
 
216
Other values (45)
8912 

Length

Max length6
Median length3
Mean length3.3004
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row자연사박물관
2nd row은평구
3rd row중구
4th row올림픽공원
5th row관악구

Common Values

ValueCountFrequency (%)
공항대로 219
 
2.2%
강북구 218
 
2.2%
은평구 218
 
2.2%
영등포로 217
 
2.2%
강변북로 216
 
2.2%
북한산 215
 
2.1%
금천구 213
 
2.1%
광진구 213
 
2.1%
도산대로 213
 
2.1%
구로구 212
 
2.1%
Other values (40) 7846
78.5%

Length

2024-05-04T06:04:20.412110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
공항대로 219
 
2.2%
은평구 218
 
2.2%
강북구 218
 
2.2%
영등포로 217
 
2.2%
강변북로 216
 
2.2%
북한산 215
 
2.1%
금천구 213
 
2.1%
광진구 213
 
2.1%
도산대로 213
 
2.1%
구로구 212
 
2.1%
Other values (40) 7846
78.5%

이산화질소농도(ppm)
Real number (ℝ)

HIGH CORRELATION 

Distinct78
Distinct (%)0.8%
Missing65
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean0.025952693
Minimum0.002
Maximum0.092
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T06:04:21.068265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.002
5-th percentile0.008
Q10.015
median0.024
Q30.035
95-th percentile0.05
Maximum0.092
Range0.09
Interquartile range (IQR)0.02

Descriptive statistics

Standard deviation0.013276526
Coefficient of variation (CV)0.51156641
Kurtosis-0.12587434
Mean0.025952693
Median Absolute Deviation (MAD)0.01
Skewness0.6303638
Sum257.84
Variance0.00017626613
MonotonicityNot monotonic
2024-05-04T06:04:22.093563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.014 332
 
3.3%
0.016 329
 
3.3%
0.015 324
 
3.2%
0.017 317
 
3.2%
0.013 317
 
3.2%
0.019 293
 
2.9%
0.02 291
 
2.9%
0.012 287
 
2.9%
0.021 284
 
2.8%
0.018 275
 
2.8%
Other values (68) 6886
68.9%
ValueCountFrequency (%)
0.002 5
 
0.1%
0.003 10
 
0.1%
0.004 39
 
0.4%
0.005 76
 
0.8%
0.006 103
1.0%
0.007 149
1.5%
0.008 181
1.8%
0.009 240
2.4%
0.01 243
2.4%
0.011 253
2.5%
ValueCountFrequency (%)
0.092 1
 
< 0.1%
0.081 1
 
< 0.1%
0.077 1
 
< 0.1%
0.076 2
 
< 0.1%
0.075 1
 
< 0.1%
0.074 1
 
< 0.1%
0.073 3
< 0.1%
0.072 5
0.1%
0.071 1
 
< 0.1%
0.07 2
 
< 0.1%

오존농도(ppm)
Real number (ℝ)

HIGH CORRELATION 

Distinct88
Distinct (%)0.9%
Missing37
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean0.02646482
Minimum0.001
Maximum0.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T06:04:22.533036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.001
5-th percentile0.007
Q10.017
median0.025
Q30.035
95-th percentile0.049
Maximum0.1
Range0.099
Interquartile range (IQR)0.018

Descriptive statistics

Standard deviation0.013222147
Coefficient of variation (CV)0.49961221
Kurtosis0.52627913
Mean0.02646482
Median Absolute Deviation (MAD)0.009
Skewness0.55904327
Sum263.669
Variance0.00017482518
MonotonicityNot monotonic
2024-05-04T06:04:23.096292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.025 313
 
3.1%
0.021 307
 
3.1%
0.028 298
 
3.0%
0.026 291
 
2.9%
0.022 287
 
2.9%
0.027 284
 
2.8%
0.02 283
 
2.8%
0.024 281
 
2.8%
0.019 280
 
2.8%
0.017 279
 
2.8%
Other values (78) 7060
70.6%
ValueCountFrequency (%)
0.001 2
 
< 0.1%
0.002 71
 
0.7%
0.003 88
0.9%
0.004 85
0.9%
0.005 105
1.1%
0.006 139
1.4%
0.007 155
1.6%
0.008 141
1.4%
0.009 150
1.5%
0.01 185
1.8%
ValueCountFrequency (%)
0.1 1
 
< 0.1%
0.098 1
 
< 0.1%
0.093 1
 
< 0.1%
0.09 1
 
< 0.1%
0.087 1
 
< 0.1%
0.086 1
 
< 0.1%
0.082 2
< 0.1%
0.081 1
 
< 0.1%
0.08 1
 
< 0.1%
0.079 3
< 0.1%

일산화탄소농도(ppm)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct16
Distinct (%)0.2%
Missing109
Missing (%)1.1%
Infinite0
Infinite (%)0.0%
Mean0.50213325
Minimum0.1
Maximum1.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T06:04:23.470139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.3
Q10.4
median0.5
Q30.6
95-th percentile0.9
Maximum1.6
Range1.5
Interquartile range (IQR)0.2

Descriptive statistics

Standard deviation0.19247339
Coefficient of variation (CV)0.38331138
Kurtosis1.5507027
Mean0.50213325
Median Absolute Deviation (MAD)0.1
Skewness1.0582515
Sum4966.6
Variance0.037046005
MonotonicityNot monotonic
2024-05-04T06:04:23.981528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0.4 2529
25.3%
0.5 2152
21.5%
0.3 1636
16.4%
0.6 1282
12.8%
0.7 834
 
8.3%
0.8 460
 
4.6%
0.2 382
 
3.8%
0.9 305
 
3.0%
1.0 156
 
1.6%
1.1 79
 
0.8%
Other values (6) 76
 
0.8%
(Missing) 109
 
1.1%
ValueCountFrequency (%)
0.1 20
 
0.2%
0.2 382
 
3.8%
0.3 1636
16.4%
0.4 2529
25.3%
0.5 2152
21.5%
0.6 1282
12.8%
0.7 834
 
8.3%
0.8 460
 
4.6%
0.9 305
 
3.0%
1.0 156
 
1.6%
ValueCountFrequency (%)
1.6 1
 
< 0.1%
1.5 6
 
0.1%
1.4 4
 
< 0.1%
1.3 12
 
0.1%
1.2 33
 
0.3%
1.1 79
 
0.8%
1.0 156
 
1.6%
0.9 305
 
3.0%
0.8 460
4.6%
0.7 834
8.3%

아황산가스농도(ppm)
Real number (ℝ)

Distinct10
Distinct (%)0.1%
Missing55
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean0.0031054801
Minimum0.001
Maximum0.011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T06:04:24.492826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.001
5-th percentile0.002
Q10.003
median0.003
Q30.004
95-th percentile0.004
Maximum0.011
Range0.01
Interquartile range (IQR)0.001

Descriptive statistics

Standard deviation0.00081260876
Coefficient of variation (CV)0.26166928
Kurtosis2.9207219
Mean0.0031054801
Median Absolute Deviation (MAD)0
Skewness0.87116076
Sum30.884
Variance6.60333 × 10-7
MonotonicityNot monotonic
2024-05-04T06:04:25.009488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0.003 5384
53.8%
0.004 2084
 
20.8%
0.002 1992
 
19.9%
0.005 375
 
3.8%
0.006 60
 
0.6%
0.001 31
 
0.3%
0.007 11
 
0.1%
0.008 5
 
0.1%
0.009 2
 
< 0.1%
0.011 1
 
< 0.1%
(Missing) 55
 
0.5%
ValueCountFrequency (%)
0.001 31
 
0.3%
0.002 1992
 
19.9%
0.003 5384
53.8%
0.004 2084
 
20.8%
0.005 375
 
3.8%
0.006 60
 
0.6%
0.007 11
 
0.1%
0.008 5
 
0.1%
0.009 2
 
< 0.1%
0.011 1
 
< 0.1%
ValueCountFrequency (%)
0.011 1
 
< 0.1%
0.009 2
 
< 0.1%
0.008 5
 
0.1%
0.007 11
 
0.1%
0.006 60
 
0.6%
0.005 375
 
3.8%
0.004 2084
 
20.8%
0.003 5384
53.8%
0.002 1992
 
19.9%
0.001 31
 
0.3%

미세먼지농도(㎍/㎥)
Real number (ℝ)

HIGH CORRELATION 

Distinct219
Distinct (%)2.2%
Missing42
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean38.588672
Minimum3
Maximum505
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T06:04:25.431743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile10
Q121
median31
Q346
95-th percentile86.15
Maximum505
Range502
Interquartile range (IQR)25

Descriptive statistics

Standard deviation36.596331
Coefficient of variation (CV)0.9483698
Kurtosis56.028626
Mean38.588672
Median Absolute Deviation (MAD)12
Skewness6.1142757
Sum384266
Variance1339.2915
MonotonicityNot monotonic
2024-05-04T06:04:25.860999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22 268
 
2.7%
26 258
 
2.6%
25 257
 
2.6%
28 254
 
2.5%
23 252
 
2.5%
27 248
 
2.5%
21 241
 
2.4%
32 228
 
2.3%
29 228
 
2.3%
30 228
 
2.3%
Other values (209) 7496
75.0%
ValueCountFrequency (%)
3 11
 
0.1%
4 25
 
0.2%
5 35
 
0.4%
6 58
 
0.6%
7 64
 
0.6%
8 102
1.0%
9 113
1.1%
10 154
1.5%
11 155
1.6%
12 180
1.8%
ValueCountFrequency (%)
505 1
< 0.1%
503 1
< 0.1%
482 1
< 0.1%
478 1
< 0.1%
476 1
< 0.1%
466 1
< 0.1%
465 1
< 0.1%
464 1
< 0.1%
463 1
< 0.1%
456 2
< 0.1%

초미세먼지농도(㎍/㎥)
Real number (ℝ)

HIGH CORRELATION 

Distinct110
Distinct (%)1.1%
Missing36
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean19.919109
Minimum1
Maximum124
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T06:04:26.269394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q110
median16
Q325
95-th percentile48
Maximum124
Range123
Interquartile range (IQR)15

Descriptive statistics

Standard deviation15.086464
Coefficient of variation (CV)0.75738651
Kurtosis7.1323044
Mean19.919109
Median Absolute Deviation (MAD)7
Skewness2.2448499
Sum198474
Variance227.60141
MonotonicityNot monotonic
2024-05-04T06:04:26.714486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14 443
 
4.4%
12 429
 
4.3%
15 420
 
4.2%
13 419
 
4.2%
11 403
 
4.0%
17 403
 
4.0%
18 370
 
3.7%
16 365
 
3.6%
10 362
 
3.6%
8 361
 
3.6%
Other values (100) 5989
59.9%
ValueCountFrequency (%)
1 33
 
0.3%
2 98
 
1.0%
3 196
2.0%
4 235
2.4%
5 294
2.9%
6 276
2.8%
7 322
3.2%
8 361
3.6%
9 352
3.5%
10 362
3.6%
ValueCountFrequency (%)
124 1
 
< 0.1%
114 1
 
< 0.1%
110 2
 
< 0.1%
109 1
 
< 0.1%
108 1
 
< 0.1%
107 2
 
< 0.1%
106 5
0.1%
105 3
< 0.1%
104 4
< 0.1%
102 3
< 0.1%

Interactions

2024-05-04T06:04:14.481822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:04:00.957318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:04:03.788939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:04:06.373435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:04:08.542876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:04:10.786924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:04:12.573758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:04:14.780903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:04:01.343664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:04:04.181846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:04:06.660542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:04:08.828476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:04:11.128850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:04:12.817759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:04:15.036241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:04:01.759568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:04:04.522587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:04:06.918675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:04:09.190599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:04:11.396439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:04:13.092355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:04:15.354717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:04:02.219841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:04:04.825602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:04:07.197638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:04:09.535094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:04:11.671520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:04:13.361081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:04:15.749331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:04:02.610770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:04:05.241259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:04:07.558446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:04:09.913545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:04:11.944165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:04:13.644126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:04:16.162021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:04:03.016529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:04:05.617542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:04:07.943270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:04:10.216262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:04:12.161487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:04:13.923484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:04:16.525553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:04:03.413583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:04:05.965252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:04:08.270956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:04:10.500084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:04:12.366982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:04:14.195951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-04T06:04:27.082117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
측정일시측정소명이산화질소농도(ppm)오존농도(ppm)일산화탄소농도(ppm)아황산가스농도(ppm)미세먼지농도(㎍/㎥)초미세먼지농도(㎍/㎥)
측정일시1.0000.0000.4550.6330.5090.3990.4630.482
측정소명0.0001.0000.5590.4180.5200.5230.0000.079
이산화질소농도(ppm)0.4550.5591.0000.6210.7150.5170.4490.605
오존농도(ppm)0.6330.4180.6211.0000.5650.3610.1810.370
일산화탄소농도(ppm)0.5090.5200.7150.5651.0000.5470.4540.623
아황산가스농도(ppm)0.3990.5230.5170.3610.5471.0000.3020.435
미세먼지농도(㎍/㎥)0.4630.0000.4490.1810.4540.3021.0000.806
초미세먼지농도(㎍/㎥)0.4820.0790.6050.3700.6230.4350.8061.000
2024-05-04T06:04:27.614485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
측정일시이산화질소농도(ppm)오존농도(ppm)일산화탄소농도(ppm)아황산가스농도(ppm)미세먼지농도(㎍/㎥)초미세먼지농도(㎍/㎥)측정소명
측정일시1.000-0.085-0.120-0.053-0.069-0.240-0.2100.000
이산화질소농도(ppm)-0.0851.000-0.6220.7280.4680.5860.5980.209
오존농도(ppm)-0.120-0.6221.000-0.519-0.272-0.142-0.1470.146
일산화탄소농도(ppm)-0.0530.728-0.5191.0000.4730.5690.6240.185
아황산가스농도(ppm)-0.0690.468-0.2720.4731.0000.4100.4300.197
미세먼지농도(㎍/㎥)-0.2400.586-0.1420.5690.4101.0000.9000.000
초미세먼지농도(㎍/㎥)-0.2100.598-0.1470.6240.4300.9001.0000.025
측정소명0.0000.2090.1460.1850.1970.0000.0251.000

Missing values

2024-05-04T06:04:17.100657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-04T06:04:17.826538image/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.
2024-05-04T06:04:18.488551image/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(㎍/㎥)
488820210408자연사박물관0.0180.0410.50.0033211
918720210703은평구0.0060.0350.20.00275
1669220211130중구0.0340.0120.50.0042518
293520210228올림픽공원0.0170.020.50.0021311
1680720211203관악구0.0220.0160.40.0022310
323820210306자연사박물관0.020.0280.50.0042216
319820210305홍릉로0.0470.0110.70.0044024
1578720211112은평구0.0150.0170.40.003189
940920210708광진구0.0160.0330.50.0032314
299020210301종로0.0310.0180.40.002159
측정일시측정소명이산화질소농도(ppm)오존농도(ppm)일산화탄소농도(ppm)아황산가스농도(ppm)미세먼지농도(㎍/㎥)초미세먼지농도(㎍/㎥)
32020210107마포구0.0110.0190.40.002339
795020210609강남구0.0290.060.50.0044127
1140120210817강남대로0.0230.0230.50.003135
940120210708강남대로0.0260.0260.60.0021811
689420210518천호대로0.0330.0210.50.0033512
1235120210905강남대로0.0180.0250.40.00383
693420210519영등포로0.0430.0350.60.0043519
350020210312강남구0.0420.0240.70.0046449
947720210709성북구0.0180.0360.40.002187
621720210505동대문구0.0130.0390.40.0024210