Overview

Dataset statistics

Number of variables8
Number of observations10000
Missing cells337
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 4 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
아황산가스농도(ppm) is highly overall correlated with 이산화질소농도(ppm)High 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

Reproduction

Analysis started2024-05-04 06:13:13.977574
Analysis finished2024-05-04 06:13:34.445804
Duration20.47 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%
Mean20220669
Minimum20220101
Maximum20221231
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T06:13:34.678890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20220101
5-th percentile20220119
Q120220403
median20220703
Q320221001
95-th percentile20221213
Maximum20221231
Range1130
Interquartile range (IQR)598

Descriptive statistics

Standard deviation344.07737
Coefficient of variation (CV)1.7016122 × 10-5
Kurtosis-1.1956801
Mean20220669
Median Absolute Deviation (MAD)299
Skewness-0.015028032
Sum2.0220669 × 1011
Variance118389.24
MonotonicityNot monotonic
2024-05-04T06:13:35.128451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20220727 36
 
0.4%
20220521 36
 
0.4%
20220501 35
 
0.4%
20220304 35
 
0.4%
20221007 35
 
0.4%
20220812 34
 
0.3%
20220731 34
 
0.3%
20220810 34
 
0.3%
20220816 34
 
0.3%
20220101 34
 
0.3%
Other values (355) 9653
96.5%
ValueCountFrequency (%)
20220101 34
0.3%
20220102 28
0.3%
20220103 23
0.2%
20220104 29
0.3%
20220105 29
0.3%
20220106 27
0.3%
20220107 28
0.3%
20220108 23
0.2%
20220109 20
0.2%
20220110 25
0.2%
ValueCountFrequency (%)
20221231 24
0.2%
20221230 28
0.3%
20221229 28
0.3%
20221228 26
0.3%
20221227 30
0.3%
20221226 30
0.3%
20221225 27
0.3%
20221224 31
0.3%
20221223 27
0.3%
20221222 27
0.3%

측정소명
Categorical

Distinct50
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
광진구
 
220
시흥대로
 
218
마포아트센터
 
218
도산대로
 
213
관악구
 
213
Other values (45)
8918 

Length

Max length6
Median length3
Mean length3.3065
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row동작구
2nd row종로
3rd row청계천로
4th row도봉구
5th row공항대로

Common Values

ValueCountFrequency (%)
광진구 220
 
2.2%
시흥대로 218
 
2.2%
마포아트센터 218
 
2.2%
도산대로 213
 
2.1%
관악구 213
 
2.1%
종로 209
 
2.1%
강변북로 208
 
2.1%
정릉로 208
 
2.1%
남산 208
 
2.1%
강남구 207
 
2.1%
Other values (40) 7878
78.8%

Length

2024-05-04T06:13:35.569723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
광진구 220
 
2.2%
시흥대로 218
 
2.2%
마포아트센터 218
 
2.2%
도산대로 213
 
2.1%
관악구 213
 
2.1%
종로 209
 
2.1%
강변북로 208
 
2.1%
정릉로 208
 
2.1%
남산 208
 
2.1%
강남구 207
 
2.1%
Other values (40) 7878
78.8%

이산화질소농도(ppm)
Real number (ℝ)

HIGH CORRELATION 

Distinct72
Distinct (%)0.7%
Missing47
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean0.022977494
Minimum0
Maximum0.073
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T06:13:35.959432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.007
Q10.013
median0.021
Q30.031
95-th percentile0.046
Maximum0.073
Range0.073
Interquartile range (IQR)0.018

Descriptive statistics

Standard deviation0.01195244
Coefficient of variation (CV)0.52018032
Kurtosis-0.058375248
Mean0.022977494
Median Absolute Deviation (MAD)0.008
Skewness0.70422943
Sum228.695
Variance0.00014286083
MonotonicityNot monotonic
2024-05-04T06:13:36.400381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.015 401
 
4.0%
0.017 383
 
3.8%
0.013 382
 
3.8%
0.014 379
 
3.8%
0.011 377
 
3.8%
0.012 366
 
3.7%
0.016 347
 
3.5%
0.01 339
 
3.4%
0.018 316
 
3.2%
0.023 313
 
3.1%
Other values (62) 6350
63.5%
ValueCountFrequency (%)
0.0 1
 
< 0.1%
0.001 1
 
< 0.1%
0.002 4
 
< 0.1%
0.003 27
 
0.3%
0.004 51
 
0.5%
0.005 96
 
1.0%
0.006 141
1.4%
0.007 192
1.9%
0.008 243
2.4%
0.009 282
2.8%
ValueCountFrequency (%)
0.073 1
 
< 0.1%
0.071 2
< 0.1%
0.069 1
 
< 0.1%
0.068 1
 
< 0.1%
0.067 2
< 0.1%
0.066 2
< 0.1%
0.065 1
 
< 0.1%
0.064 2
< 0.1%
0.063 1
 
< 0.1%
0.062 3
< 0.1%

오존농도(ppm)
Real number (ℝ)

HIGH CORRELATION 

Distinct89
Distinct (%)0.9%
Missing45
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean0.028313209
Minimum0.001
Maximum0.179
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T06:13:36.920513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.001
5-th percentile0.008
Q10.018
median0.027
Q30.037
95-th percentile0.054
Maximum0.179
Range0.178
Interquartile range (IQR)0.019

Descriptive statistics

Standard deviation0.014116523
Coefficient of variation (CV)0.49858433
Kurtosis1.6523066
Mean0.028313209
Median Absolute Deviation (MAD)0.01
Skewness0.77162031
Sum281.858
Variance0.00019927621
MonotonicityNot monotonic
2024-05-04T06:13:37.365544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.021 300
 
3.0%
0.02 297
 
3.0%
0.022 286
 
2.9%
0.024 281
 
2.8%
0.023 279
 
2.8%
0.028 277
 
2.8%
0.016 277
 
2.8%
0.026 271
 
2.7%
0.017 271
 
2.7%
0.027 267
 
2.7%
Other values (79) 7149
71.5%
ValueCountFrequency (%)
0.001 1
 
< 0.1%
0.002 7
 
0.1%
0.003 30
 
0.3%
0.004 58
 
0.6%
0.005 89
0.9%
0.006 105
1.1%
0.007 105
1.1%
0.008 124
1.2%
0.009 159
1.6%
0.01 158
1.6%
ValueCountFrequency (%)
0.179 1
 
< 0.1%
0.096 1
 
< 0.1%
0.095 1
 
< 0.1%
0.09 1
 
< 0.1%
0.088 3
< 0.1%
0.085 2
< 0.1%
0.084 4
< 0.1%
0.082 2
< 0.1%
0.081 3
< 0.1%
0.08 4
< 0.1%

일산화탄소농도(ppm)
Real number (ℝ)

HIGH CORRELATION 

Distinct16
Distinct (%)0.2%
Missing46
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean0.47263412
Minimum0.1
Maximum1.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T06:13:38.032079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.2
Q10.3
median0.4
Q30.6
95-th percentile0.8
Maximum1.6
Range1.5
Interquartile range (IQR)0.3

Descriptive statistics

Standard deviation0.18778255
Coefficient of variation (CV)0.39731061
Kurtosis2.3764941
Mean0.47263412
Median Absolute Deviation (MAD)0.1
Skewness1.1963439
Sum4704.6
Variance0.035262286
MonotonicityNot monotonic
2024-05-04T06:13:38.437960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0.4 2663
26.6%
0.5 2026
20.3%
0.3 2003
20.0%
0.6 1173
11.7%
0.7 671
 
6.7%
0.2 541
 
5.4%
0.8 378
 
3.8%
0.9 198
 
2.0%
1.0 110
 
1.1%
0.1 68
 
0.7%
Other values (6) 123
 
1.2%
ValueCountFrequency (%)
0.1 68
 
0.7%
0.2 541
 
5.4%
0.3 2003
20.0%
0.4 2663
26.6%
0.5 2026
20.3%
0.6 1173
11.7%
0.7 671
 
6.7%
0.8 378
 
3.8%
0.9 198
 
2.0%
1.0 110
 
1.1%
ValueCountFrequency (%)
1.6 1
 
< 0.1%
1.5 4
 
< 0.1%
1.4 11
 
0.1%
1.3 14
 
0.1%
1.2 29
 
0.3%
1.1 64
 
0.6%
1.0 110
 
1.1%
0.9 198
 
2.0%
0.8 378
3.8%
0.7 671
6.7%

아황산가스농도(ppm)
Real number (ℝ)

HIGH CORRELATION 

Distinct9
Distinct (%)0.1%
Missing46
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean0.0029531846
Minimum0.001
Maximum0.009
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T06:13:39.064282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.001
5-th percentile0.002
Q10.002
median0.003
Q30.003
95-th percentile0.004
Maximum0.009
Range0.008
Interquartile range (IQR)0.001

Descriptive statistics

Standard deviation0.00080502441
Coefficient of variation (CV)0.27259535
Kurtosis1.7057707
Mean0.0029531846
Median Absolute Deviation (MAD)0
Skewness0.60847861
Sum29.396
Variance6.4806431 × 10-7
MonotonicityNot monotonic
2024-05-04T06:13:39.422088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0.003 5150
51.5%
0.002 2611
26.1%
0.004 1785
 
17.8%
0.005 210
 
2.1%
0.001 134
 
1.3%
0.006 53
 
0.5%
0.007 7
 
0.1%
0.008 3
 
< 0.1%
0.009 1
 
< 0.1%
(Missing) 46
 
0.5%
ValueCountFrequency (%)
0.001 134
 
1.3%
0.002 2611
26.1%
0.003 5150
51.5%
0.004 1785
 
17.8%
0.005 210
 
2.1%
0.006 53
 
0.5%
0.007 7
 
0.1%
0.008 3
 
< 0.1%
0.009 1
 
< 0.1%
ValueCountFrequency (%)
0.009 1
 
< 0.1%
0.008 3
 
< 0.1%
0.007 7
 
0.1%
0.006 53
 
0.5%
0.005 210
 
2.1%
0.004 1785
 
17.8%
0.003 5150
51.5%
0.002 2611
26.1%
0.001 134
 
1.3%

미세먼지농도(㎍/㎥)
Real number (ℝ)

HIGH CORRELATION 

Distinct160
Distinct (%)1.6%
Missing79
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean33.597621
Minimum3
Maximum404
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T06:13:39.852591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile11
Q121
median29
Q341
95-th percentile68
Maximum404
Range401
Interquartile range (IQR)20

Descriptive statistics

Standard deviation20.49057
Coefficient of variation (CV)0.60988158
Kurtosis26.374967
Mean33.597621
Median Absolute Deviation (MAD)9
Skewness3.28436
Sum333322
Variance419.86348
MonotonicityNot monotonic
2024-05-04T06:13:40.442942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
28 328
 
3.3%
27 321
 
3.2%
29 320
 
3.2%
26 316
 
3.2%
23 307
 
3.1%
24 304
 
3.0%
32 294
 
2.9%
25 276
 
2.8%
21 268
 
2.7%
22 267
 
2.7%
Other values (150) 6920
69.2%
ValueCountFrequency (%)
3 15
 
0.1%
4 27
 
0.3%
5 44
 
0.4%
6 50
 
0.5%
7 75
0.8%
8 71
0.7%
9 91
0.9%
10 109
1.1%
11 129
1.3%
12 123
1.2%
ValueCountFrequency (%)
404 1
< 0.1%
229 1
< 0.1%
226 1
< 0.1%
223 2
< 0.1%
217 1
< 0.1%
215 1
< 0.1%
213 1
< 0.1%
211 2
< 0.1%
210 1
< 0.1%
206 1
< 0.1%

초미세먼지농도(㎍/㎥)
Real number (ℝ)

HIGH CORRELATION 

Distinct93
Distinct (%)0.9%
Missing74
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean18.103264
Minimum1
Maximum105
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T06:13:41.019137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q110
median15
Q323
95-th percentile41
Maximum105
Range104
Interquartile range (IQR)13

Descriptive statistics

Standard deviation12.084852
Coefficient of variation (CV)0.66755098
Kurtosis4.789516
Mean18.103264
Median Absolute Deviation (MAD)6
Skewness1.6802246
Sum179693
Variance146.04364
MonotonicityNot monotonic
2024-05-04T06:13:41.596466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9 465
 
4.7%
14 465
 
4.7%
13 456
 
4.6%
12 441
 
4.4%
15 438
 
4.4%
11 418
 
4.2%
10 413
 
4.1%
8 408
 
4.1%
16 397
 
4.0%
17 389
 
3.9%
Other values (83) 5636
56.4%
ValueCountFrequency (%)
1 70
 
0.7%
2 110
 
1.1%
3 197
2.0%
4 237
2.4%
5 241
2.4%
6 314
3.1%
7 351
3.5%
8 408
4.1%
9 465
4.7%
10 413
4.1%
ValueCountFrequency (%)
105 1
< 0.1%
104 1
< 0.1%
103 2
< 0.1%
102 1
< 0.1%
100 1
< 0.1%
97 1
< 0.1%
96 1
< 0.1%
93 1
< 0.1%
92 2
< 0.1%
90 1
< 0.1%

Interactions

2024-05-04T06:13:31.171479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:13:16.768313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:13:19.277607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:13:21.777874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:13:24.150694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:13:26.574685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:13:29.090949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:13:31.433767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:13:17.039871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:13:19.624769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:13:22.105351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:13:24.499091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:13:26.949808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:13:29.360610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:13:31.800026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:13:17.343906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:13:19.945426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:13:22.439743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:13:25.041970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:13:27.247962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:13:29.653533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:13:32.146566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:13:17.626895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:13:20.243841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:13:22.771477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:13:25.410757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:13:27.771715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:13:29.943191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:13:32.428502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:13:17.902561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:13:20.525239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:13:23.106781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:13:25.689024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:13:28.077257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:13:30.280966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:13:32.726471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:13:18.252658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:13:20.974929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:13:23.453480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:13:25.997800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:13:28.389869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:13:30.591992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:13:33.018728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:13:18.909095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:13:21.341444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:13:23.846434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:13:26.289664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:13:28.795215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:13:30.889310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-04T06:13:42.024677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
측정일시측정소명이산화질소농도(ppm)오존농도(ppm)일산화탄소농도(ppm)아황산가스농도(ppm)미세먼지농도(㎍/㎥)초미세먼지농도(㎍/㎥)
측정일시1.0000.0000.4910.5110.5280.3790.3430.501
측정소명0.0001.0000.5650.3300.4480.4540.0700.127
이산화질소농도(ppm)0.4910.5651.0000.4070.7340.4310.3550.588
오존농도(ppm)0.5110.3300.4071.0000.3310.2010.2180.209
일산화탄소농도(ppm)0.5280.4480.7340.3311.0000.4730.3860.649
아황산가스농도(ppm)0.3790.4540.4310.2010.4731.0000.2560.330
미세먼지농도(㎍/㎥)0.3430.0700.3550.2180.3860.2561.0000.662
초미세먼지농도(㎍/㎥)0.5010.1270.5880.2090.6490.3300.6621.000
2024-05-04T06:13:42.736567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
측정일시이산화질소농도(ppm)오존농도(ppm)일산화탄소농도(ppm)아황산가스농도(ppm)미세먼지농도(㎍/㎥)초미세먼지농도(㎍/㎥)측정소명
측정일시1.000-0.077-0.220-0.106-0.156-0.245-0.2400.000
이산화질소농도(ppm)-0.0771.000-0.5230.7110.5160.5780.5800.214
오존농도(ppm)-0.220-0.5231.000-0.399-0.237-0.031-0.0440.137
일산화탄소농도(ppm)-0.1060.711-0.3991.0000.4920.5830.6160.154
아황산가스농도(ppm)-0.1560.516-0.2370.4921.0000.4680.4430.180
미세먼지농도(㎍/㎥)-0.2450.578-0.0310.5830.4681.0000.8920.028
초미세먼지농도(㎍/㎥)-0.2400.580-0.0440.6160.4430.8921.0000.041
측정소명0.0000.2140.1370.1540.1800.0280.0411.000

Missing values

2024-05-04T06:13:33.400878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-04T06:13:33.866879image/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.
2024-05-04T06:13:34.230814image/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(㎍/㎥)
1753220221217동작구0.020.0190.40.0032616
426120220327종로0.0150.0330.40.0033111
1055620220731청계천로0.0110.0110.30.004147
1208520220830도봉구0.010.0160.30.00352
1049420220729공항대로0.0260.0290.40.0033119
1708920221208도봉구0.0280.0150.40.003229
675720220516천호대로0.0240.0460.40.0033414
1717720221210서울숲0.0420.0070.80.0044422
801420220610은평구0.0140.0280.40.0022920
416320220325자연사박물관0.030.0330.60.0037050
측정일시측정소명이산화질소농도(ppm)오존농도(ppm)일산화탄소농도(ppm)아황산가스농도(ppm)미세먼지농도(㎍/㎥)초미세먼지농도(㎍/㎥)
57520220112성동구0.0170.0190.40.0032912
1277320220913세곡0.0180.010.60.002208
76620220116올림픽공원0.0220.0280.70.0044328
490220220409홍릉로0.0440.0290.70.0046429
159720220201강서구0.0160.0310.60.0034931
298520220301도봉구0.0220.0280.50.0033727
1533020221103서대문구0.0220.0160.70.0053111
247320220219세곡0.030.0210.60.0054932
1620520221121한강대로0.0450.0130.80.0044622
1768020221220마포아트센터0.0410.0090.50.0042918