Overview

Dataset statistics

Number of variables8
Number of observations10000
Missing cells635
Missing cells (%)0.8%
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
일산화탄소농도(ppm) has 275 (2.8%) missing valuesMissing
아황산가스농도(ppm) has 130 (1.3%) missing valuesMissing

Reproduction

Analysis started2024-05-11 06:29:39.921151
Analysis finished2024-05-11 06:29:54.305018
Duration14.38 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%
Mean20230667
Minimum20230101
Maximum20231231
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T15:29:54.406355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20230101
5-th percentile20230119
Q120230401
median20230701
Q320231001
95-th percentile20231213
Maximum20231231
Range1130
Interquartile range (IQR)600

Descriptive statistics

Standard deviation344.06598
Coefficient of variation (CV)1.700715 × 10-5
Kurtosis-1.2082289
Mean20230667
Median Absolute Deviation (MAD)300
Skewness-0.0089762241
Sum2.0230667 × 1011
Variance118381.4
MonotonicityNot monotonic
2024-05-11T15:29:54.604267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20230205 38
 
0.4%
20231101 38
 
0.4%
20230811 37
 
0.4%
20230513 37
 
0.4%
20230425 36
 
0.4%
20230210 36
 
0.4%
20231119 35
 
0.4%
20231025 35
 
0.4%
20230207 35
 
0.4%
20230429 34
 
0.3%
Other values (355) 9639
96.4%
ValueCountFrequency (%)
20230101 24
0.2%
20230102 30
0.3%
20230103 22
0.2%
20230104 28
0.3%
20230105 26
0.3%
20230106 26
0.3%
20230107 24
0.2%
20230108 27
0.3%
20230109 31
0.3%
20230110 22
0.2%
ValueCountFrequency (%)
20231231 22
0.2%
20231230 27
0.3%
20231229 23
0.2%
20231228 31
0.3%
20231227 21
0.2%
20231226 32
0.3%
20231225 27
0.3%
20231224 21
0.2%
20231223 28
0.3%
20231222 28
0.3%

측정소명
Categorical

Distinct50
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
도봉구
 
220
중랑구
 
218
강동구
 
217
올림픽공원
 
217
세곡
 
215
Other values (45)
8913 

Length

Max length6
Median length3
Mean length3.2986
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row강동구
2nd row중랑구
3rd row구로구
4th row마포구
5th row양천구

Common Values

ValueCountFrequency (%)
도봉구 220
 
2.2%
중랑구 218
 
2.2%
강동구 217
 
2.2%
올림픽공원 217
 
2.2%
세곡 215
 
2.1%
화랑로 213
 
2.1%
금천구 210
 
2.1%
송파구 209
 
2.1%
신촌로 208
 
2.1%
성북구 208
 
2.1%
Other values (40) 7865
78.6%

Length

2024-05-11T15:29:54.822173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
도봉구 220
 
2.2%
중랑구 218
 
2.2%
강동구 217
 
2.2%
올림픽공원 217
 
2.2%
세곡 215
 
2.1%
화랑로 213
 
2.1%
금천구 210
 
2.1%
송파구 209
 
2.1%
신촌로 208
 
2.1%
성북구 208
 
2.1%
Other values (40) 7865
78.6%

이산화질소농도(ppm)
Real number (ℝ)

HIGH CORRELATION 

Distinct590
Distinct (%)5.9%
Missing53
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean0.022249553
Minimum0.0013
Maximum0.0761
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T15:29:55.022194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.0013
5-th percentile0.00723
Q10.0131
median0.0198
Q30.0293
95-th percentile0.04547
Maximum0.0761
Range0.0748
Interquartile range (IQR)0.0162

Descriptive statistics

Standard deviation0.011865411
Coefficient of variation (CV)0.53328762
Kurtosis0.44982707
Mean0.022249553
Median Absolute Deviation (MAD)0.0077
Skewness0.88227673
Sum221.3163
Variance0.00014078798
MonotonicityNot monotonic
2024-05-11T15:29:55.270786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0155 54
 
0.5%
0.0153 50
 
0.5%
0.0123 49
 
0.5%
0.0143 48
 
0.5%
0.016 47
 
0.5%
0.0163 47
 
0.5%
0.019 46
 
0.5%
0.0171 46
 
0.5%
0.0138 46
 
0.5%
0.015 46
 
0.5%
Other values (580) 9468
94.7%
(Missing) 53
 
0.5%
ValueCountFrequency (%)
0.0013 1
 
< 0.1%
0.0017 1
 
< 0.1%
0.0019 1
 
< 0.1%
0.0024 1
 
< 0.1%
0.0027 1
 
< 0.1%
0.0028 1
 
< 0.1%
0.003 1
 
< 0.1%
0.0031 3
< 0.1%
0.0032 2
 
< 0.1%
0.0033 5
0.1%
ValueCountFrequency (%)
0.0761 1
 
< 0.1%
0.0729 1
 
< 0.1%
0.0701 1
 
< 0.1%
0.0695 1
 
< 0.1%
0.0692 1
 
< 0.1%
0.0684 1
 
< 0.1%
0.0681 1
 
< 0.1%
0.0675 1
 
< 0.1%
0.0672 3
< 0.1%
0.067 1
 
< 0.1%

오존농도(ppm)
Real number (ℝ)

HIGH CORRELATION 

Distinct699
Distinct (%)7.0%
Missing53
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean0.028966874
Minimum0.0011
Maximum0.104
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T15:29:55.527434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.0011
5-th percentile0.0086
Q10.0191
median0.0276
Q30.0374
95-th percentile0.05327
Maximum0.104
Range0.1029
Interquartile range (IQR)0.0183

Descriptive statistics

Standard deviation0.013652032
Coefficient of variation (CV)0.47129807
Kurtosis0.44167329
Mean0.028966874
Median Absolute Deviation (MAD)0.0091
Skewness0.59798399
Sum288.1335
Variance0.00018637798
MonotonicityNot monotonic
2024-05-11T15:29:55.723221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0243 49
 
0.5%
0.0283 44
 
0.4%
0.032 41
 
0.4%
0.0281 41
 
0.4%
0.026 40
 
0.4%
0.0204 40
 
0.4%
0.0218 40
 
0.4%
0.0316 39
 
0.4%
0.0231 39
 
0.4%
0.0263 38
 
0.4%
Other values (689) 9536
95.4%
(Missing) 53
 
0.5%
ValueCountFrequency (%)
0.0011 1
 
< 0.1%
0.0022 1
 
< 0.1%
0.0023 1
 
< 0.1%
0.0025 3
< 0.1%
0.0027 2
 
< 0.1%
0.0028 2
 
< 0.1%
0.0029 3
< 0.1%
0.003 5
0.1%
0.0031 1
 
< 0.1%
0.0032 3
< 0.1%
ValueCountFrequency (%)
0.104 1
< 0.1%
0.1005 1
< 0.1%
0.092 1
< 0.1%
0.0887 1
< 0.1%
0.0876 1
< 0.1%
0.087 1
< 0.1%
0.086 1
< 0.1%
0.0856 1
< 0.1%
0.0838 2
< 0.1%
0.0817 1
< 0.1%

일산화탄소농도(ppm)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct123
Distinct (%)1.3%
Missing275
Missing (%)2.8%
Infinite0
Infinite (%)0.0%
Mean0.47792494
Minimum0.03
Maximum1.41
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T15:29:55.928504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.03
5-th percentile0.26
Q10.36
median0.45
Q30.56
95-th percentile0.81
Maximum1.41
Range1.38
Interquartile range (IQR)0.2

Descriptive statistics

Standard deviation0.16967821
Coefficient of variation (CV)0.35503109
Kurtosis1.9258782
Mean0.47792494
Median Absolute Deviation (MAD)0.1
Skewness1.1019107
Sum4647.82
Variance0.028790696
MonotonicityNot monotonic
2024-05-11T15:29:56.100332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.4 315
 
3.1%
0.39 313
 
3.1%
0.35 309
 
3.1%
0.45 301
 
3.0%
0.38 298
 
3.0%
0.37 296
 
3.0%
0.43 295
 
2.9%
0.36 294
 
2.9%
0.42 291
 
2.9%
0.41 282
 
2.8%
Other values (113) 6731
67.3%
(Missing) 275
 
2.8%
ValueCountFrequency (%)
0.03 2
 
< 0.1%
0.08 2
 
< 0.1%
0.09 4
 
< 0.1%
0.1 4
 
< 0.1%
0.11 6
0.1%
0.12 1
 
< 0.1%
0.13 8
0.1%
0.14 10
0.1%
0.15 10
0.1%
0.16 9
0.1%
ValueCountFrequency (%)
1.41 1
 
< 0.1%
1.39 1
 
< 0.1%
1.37 1
 
< 0.1%
1.34 4
< 0.1%
1.3 1
 
< 0.1%
1.29 1
 
< 0.1%
1.28 2
< 0.1%
1.25 2
< 0.1%
1.24 1
 
< 0.1%
1.21 3
< 0.1%

아황산가스농도(ppm)
Real number (ℝ)

MISSING 

Distinct52
Distinct (%)0.5%
Missing130
Missing (%)1.3%
Infinite0
Infinite (%)0.0%
Mean0.0028813779
Minimum0.0012
Maximum0.0074
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T15:29:56.292650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.0012
5-th percentile0.002
Q10.0025
median0.0028
Q30.0032
95-th percentile0.0039
Maximum0.0074
Range0.0062
Interquartile range (IQR)0.0007

Descriptive statistics

Standard deviation0.00058421473
Coefficient of variation (CV)0.20275533
Kurtosis1.9139872
Mean0.0028813779
Median Absolute Deviation (MAD)0.0004
Skewness0.78080309
Sum28.4392
Variance3.4130685 × 10-7
MonotonicityNot monotonic
2024-05-11T15:29:56.465649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.003 761
 
7.6%
0.0026 701
 
7.0%
0.0029 697
 
7.0%
0.0027 695
 
7.0%
0.0028 646
 
6.5%
0.0024 611
 
6.1%
0.0025 603
 
6.0%
0.0031 591
 
5.9%
0.0023 520
 
5.2%
0.0032 502
 
5.0%
Other values (42) 3543
35.4%
ValueCountFrequency (%)
0.0012 1
 
< 0.1%
0.0013 3
 
< 0.1%
0.0014 6
 
0.1%
0.0015 4
 
< 0.1%
0.0016 23
 
0.2%
0.0017 39
 
0.4%
0.0018 61
 
0.6%
0.0019 128
1.3%
0.002 294
2.9%
0.0021 290
2.9%
ValueCountFrequency (%)
0.0074 1
 
< 0.1%
0.0072 1
 
< 0.1%
0.0068 1
 
< 0.1%
0.0065 1
 
< 0.1%
0.0063 1
 
< 0.1%
0.0061 2
 
< 0.1%
0.006 2
 
< 0.1%
0.0057 1
 
< 0.1%
0.0056 1
 
< 0.1%
0.0054 7
0.1%

미세먼지농도(㎍/㎥)
Real number (ℝ)

HIGH CORRELATION 

Distinct200
Distinct (%)2.0%
Missing60
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean38.41841
Minimum3
Maximum334
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T15:29:56.646645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile11
Q122
median32
Q346
95-th percentile88
Maximum334
Range331
Interquartile range (IQR)24

Descriptive statistics

Standard deviation27.164142
Coefficient of variation (CV)0.70706054
Kurtosis17.588399
Mean38.41841
Median Absolute Deviation (MAD)12
Skewness3.0256593
Sum381879
Variance737.89062
MonotonicityNot monotonic
2024-05-11T15:29:56.843222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21 267
 
2.7%
29 263
 
2.6%
23 249
 
2.5%
28 246
 
2.5%
22 241
 
2.4%
24 240
 
2.4%
26 234
 
2.3%
30 234
 
2.3%
31 233
 
2.3%
34 231
 
2.3%
Other values (190) 7502
75.0%
ValueCountFrequency (%)
3 12
 
0.1%
4 41
 
0.4%
5 35
 
0.4%
6 65
0.7%
7 70
0.7%
8 87
0.9%
9 74
0.7%
10 110
1.1%
11 111
1.1%
12 127
1.3%
ValueCountFrequency (%)
334 1
< 0.1%
321 1
< 0.1%
309 1
< 0.1%
306 1
< 0.1%
299 1
< 0.1%
295 1
< 0.1%
291 1
< 0.1%
288 1
< 0.1%
270 1
< 0.1%
268 1
< 0.1%

초미세먼지농도(㎍/㎥)
Real number (ℝ)

HIGH CORRELATION 

Distinct90
Distinct (%)0.9%
Missing64
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean19.574477
Minimum1
Maximum116
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T15:29:57.067503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q111
median17
Q325
95-th percentile47
Maximum116
Range115
Interquartile range (IQR)14

Descriptive statistics

Standard deviation13.040299
Coefficient of variation (CV)0.66618892
Kurtosis3.7872308
Mean19.574477
Median Absolute Deviation (MAD)7
Skewness1.6209045
Sum194492
Variance170.04941
MonotonicityNot monotonic
2024-05-11T15:29:57.237940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14 485
 
4.9%
16 450
 
4.5%
15 425
 
4.2%
13 425
 
4.2%
12 418
 
4.2%
17 411
 
4.1%
10 401
 
4.0%
18 397
 
4.0%
11 390
 
3.9%
19 385
 
3.9%
Other values (80) 5749
57.5%
ValueCountFrequency (%)
1 72
 
0.7%
2 102
 
1.0%
3 154
 
1.5%
4 189
1.9%
5 226
2.3%
6 242
2.4%
7 285
2.9%
8 303
3.0%
9 364
3.6%
10 401
4.0%
ValueCountFrequency (%)
116 1
 
< 0.1%
104 1
 
< 0.1%
98 2
< 0.1%
96 2
< 0.1%
95 3
< 0.1%
94 2
< 0.1%
93 2
< 0.1%
92 1
 
< 0.1%
91 2
< 0.1%
90 1
 
< 0.1%

Interactions

2024-05-11T15:29:52.439113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:29:42.900335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:29:44.600421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:29:46.010455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:29:47.992178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:29:49.519066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:29:50.781660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:29:52.629925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:29:43.102143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:29:44.786852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:29:46.479475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:29:48.191838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:29:49.748290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:29:50.961980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:29:52.805042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:29:43.346218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:29:44.986614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:29:46.713753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:29:48.413518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:29:49.905801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:29:51.173034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:29:52.965259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:29:43.560986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:29:45.175247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:29:46.971798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:29:48.644728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:29:50.068527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:29:51.362469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:29:53.168315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:29:43.837540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:29:45.398993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:29:47.212728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:29:48.887564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:29:50.299532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:29:51.554438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:29:53.374145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:29:44.070879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:29:45.606451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:29:47.469407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:29:49.154366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:29:50.472150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:29:51.734895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:29:53.576565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:29:44.355227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:29:45.811716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:29:47.695425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:29:49.332737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:29:50.627331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:29:52.247314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-11T15:29:57.359201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
측정일시측정소명이산화질소농도(ppm)오존농도(ppm)일산화탄소농도(ppm)아황산가스농도(ppm)미세먼지농도(㎍/㎥)초미세먼지농도(㎍/㎥)
측정일시1.0000.0000.4870.6320.5080.3770.5700.507
측정소명0.0001.0000.5550.3490.4780.4770.0750.112
이산화질소농도(ppm)0.4870.5551.0000.5840.7560.5540.5020.609
오존농도(ppm)0.6320.3490.5841.0000.5020.2020.1980.354
일산화탄소농도(ppm)0.5080.4780.7560.5021.0000.6060.5130.675
아황산가스농도(ppm)0.3770.4770.5540.2020.6061.0000.4310.355
미세먼지농도(㎍/㎥)0.5700.0750.5020.1980.5130.4311.0000.757
초미세먼지농도(㎍/㎥)0.5070.1120.6090.3540.6750.3550.7571.000
2024-05-11T15:29:57.486702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
측정일시이산화질소농도(ppm)오존농도(ppm)일산화탄소농도(ppm)아황산가스농도(ppm)미세먼지농도(㎍/㎥)초미세먼지농도(㎍/㎥)측정소명
측정일시1.000-0.161-0.102-0.102-0.272-0.422-0.2980.000
이산화질소농도(ppm)-0.1611.000-0.5020.7130.4800.5180.5580.209
오존농도(ppm)-0.102-0.5021.000-0.376-0.1020.0630.0230.118
일산화탄소농도(ppm)-0.1020.713-0.3761.0000.4440.5180.6160.172
아황산가스농도(ppm)-0.2720.480-0.1020.4441.0000.4880.4610.171
미세먼지농도(㎍/㎥)-0.4220.5180.0630.5180.4881.0000.8540.024
초미세먼지농도(㎍/㎥)-0.2980.5580.0230.6160.4610.8541.0000.037
측정소명0.0000.2090.1180.1720.1710.0240.0371.000

Missing values

2024-05-11T15:29:53.794557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-11T15:29:54.001352image/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.
2024-05-11T15:29:54.190771image/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(㎍/㎥)
1355120230929강동구<NA><NA><NA><NA><NA><NA>
827420230615중랑구0.01590.03740.260.0022215
990620230718구로구0.01260.01180.240.00295
396220230321마포구0.04720.02860.640.00365741
1326820230923양천구0.01970.02660.430.00282110
676120230516동작구0.01460.08110.430.00325229
861120230622동작구0.01050.0330.290.00252110
370820230316노원구0.01090.03640.340.00265911
514420230413항동0.01720.02570.370.003813325
962820230712도산대로0.02330.02810.650.00222414
측정일시측정소명이산화질소농도(ppm)오존농도(ppm)일산화탄소농도(ppm)아황산가스농도(ppm)미세먼지농도(㎍/㎥)초미세먼지농도(㎍/㎥)
1405620231009구로구0.00930.02850.240.001994
1741720231215송파구0.01730.02520.340.001942
916020230703동대문구0.01340.05420.410.00314022
722420230525중랑구0.01490.0540.310.00283314
81620230117성북구0.03490.01010.630.0033317
95020230120강남구0.0140.02760.340.00286216
1012420230722중랑구0.01090.03670.30.00273022
1691320231205서대문구0.03210.02020.590.00543624
1223520230902천호대로0.02540.02250.450.00253015
899020230629관악산0.00440.05620.330.0018116