Overview

Dataset statistics

Number of variables8
Number of observations10000
Missing cells2126
Missing cells (%)2.7%
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 일산화탄소농도(ppm) and 2 other fieldsHigh 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 333 (3.3%) missing valuesMissing
오존농도(ppm) has 274 (2.7%) missing valuesMissing
일산화탄소농도(ppm) has 407 (4.1%) missing valuesMissing
아황산가스농도(ppm) has 319 (3.2%) missing valuesMissing
미세먼지농도(㎍/㎥) has 406 (4.1%) missing valuesMissing
초미세먼지농도(㎍/㎥) has 387 (3.9%) missing valuesMissing

Reproduction

Analysis started2024-05-04 06:05:07.376999
Analysis finished2024-05-04 06:05:33.064506
Duration25.69 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%
Mean20190675
Minimum20190101
Maximum20191231
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T06:05:33.330186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20190101
5-th percentile20190118
Q120190406
median20190704
Q320191002
95-th percentile20191214
Maximum20191231
Range1130
Interquartile range (IQR)596

Descriptive statistics

Standard deviation344.41059
Coefficient of variation (CV)1.7057904 × 10-5
Kurtosis-1.1829068
Mean20190675
Median Absolute Deviation (MAD)298
Skewness-0.035387834
Sum2.0190675 × 1011
Variance118618.66
MonotonicityNot monotonic
2024-05-04T06:05:33.855725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20190526 37
 
0.4%
20190629 37
 
0.4%
20191225 36
 
0.4%
20190710 36
 
0.4%
20190826 35
 
0.4%
20191005 35
 
0.4%
20190703 35
 
0.4%
20191219 35
 
0.4%
20191122 34
 
0.3%
20191013 34
 
0.3%
Other values (355) 9646
96.5%
ValueCountFrequency (%)
20190101 29
0.3%
20190102 31
0.3%
20190103 24
0.2%
20190104 33
0.3%
20190105 29
0.3%
20190106 24
0.2%
20190107 27
0.3%
20190108 28
0.3%
20190109 26
0.3%
20190110 30
0.3%
ValueCountFrequency (%)
20191231 30
0.3%
20191230 28
0.3%
20191229 27
0.3%
20191228 17
0.2%
20191227 27
0.3%
20191226 31
0.3%
20191225 36
0.4%
20191224 31
0.3%
20191223 32
0.3%
20191222 29
0.3%

측정소명
Categorical

Distinct50
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
강변북로
 
223
영등포구
 
222
강북구
 
219
강서구
 
218
도봉구
 
218
Other values (45)
8900 

Length

Max length6
Median length3
Mean length3.2626
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row강변북로
2nd row강북구
3rd row동작대로
4th row노원구
5th row중구

Common Values

ValueCountFrequency (%)
강변북로 223
 
2.2%
영등포구 222
 
2.2%
강북구 219
 
2.2%
강서구 218
 
2.2%
도봉구 218
 
2.2%
서대문구 217
 
2.2%
성북구 216
 
2.2%
공항대로 215
 
2.1%
은평구 215
 
2.1%
한강대로 215
 
2.1%
Other values (40) 7822
78.2%

Length

2024-05-04T06:05:34.550980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
강변북로 223
 
2.2%
영등포구 222
 
2.2%
강북구 219
 
2.2%
강서구 218
 
2.2%
도봉구 218
 
2.2%
서대문구 217
 
2.2%
성북구 216
 
2.2%
공항대로 215
 
2.1%
은평구 215
 
2.1%
한강대로 215
 
2.1%
Other values (40) 7822
78.2%

이산화질소농도(ppm)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct99
Distinct (%)1.0%
Missing333
Missing (%)3.3%
Infinite0
Infinite (%)0.0%
Mean0.031161167
Minimum0.001
Maximum0.137
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T06:05:35.270250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.001
5-th percentile0.009
Q10.019
median0.029
Q30.041
95-th percentile0.058
Maximum0.137
Range0.136
Interquartile range (IQR)0.022

Descriptive statistics

Standard deviation0.015267831
Coefficient of variation (CV)0.48996338
Kurtosis0.67716608
Mean0.031161167
Median Absolute Deviation (MAD)0.011
Skewness0.67824331
Sum301.235
Variance0.00023310665
MonotonicityNot monotonic
2024-05-04T06:05:36.133606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.022 270
 
2.7%
0.019 257
 
2.6%
0.025 255
 
2.5%
0.024 253
 
2.5%
0.029 250
 
2.5%
0.027 249
 
2.5%
0.023 245
 
2.5%
0.028 245
 
2.5%
0.021 242
 
2.4%
0.016 241
 
2.4%
Other values (89) 7160
71.6%
(Missing) 333
 
3.3%
ValueCountFrequency (%)
0.001 2
 
< 0.1%
0.002 3
 
< 0.1%
0.003 11
 
0.1%
0.004 20
 
0.2%
0.005 34
 
0.3%
0.006 63
0.6%
0.007 97
1.0%
0.008 114
1.1%
0.009 147
1.5%
0.01 143
1.4%
ValueCountFrequency (%)
0.137 1
< 0.1%
0.133 1
< 0.1%
0.116 1
< 0.1%
0.115 1
< 0.1%
0.101 2
< 0.1%
0.099 1
< 0.1%
0.097 2
< 0.1%
0.096 1
< 0.1%
0.094 1
< 0.1%
0.091 1
< 0.1%

오존농도(ppm)
Real number (ℝ)

MISSING 

Distinct79
Distinct (%)0.8%
Missing274
Missing (%)2.7%
Infinite0
Infinite (%)0.0%
Mean0.022882686
Minimum0.001
Maximum0.102
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T06:05:36.781219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.001
5-th percentile0.005
Q10.012
median0.021
Q30.031
95-th percentile0.046
Maximum0.102
Range0.101
Interquartile range (IQR)0.019

Descriptive statistics

Standard deviation0.012922825
Coefficient of variation (CV)0.56474248
Kurtosis0.20821606
Mean0.022882686
Median Absolute Deviation (MAD)0.009
Skewness0.65125327
Sum222.557
Variance0.0001669994
MonotonicityNot monotonic
2024-05-04T06:05:37.457568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.012 299
 
3.0%
0.021 293
 
2.9%
0.014 293
 
2.9%
0.018 290
 
2.9%
0.023 290
 
2.9%
0.01 288
 
2.9%
0.019 286
 
2.9%
0.011 278
 
2.8%
0.009 277
 
2.8%
0.024 275
 
2.8%
Other values (69) 6857
68.6%
(Missing) 274
 
2.7%
ValueCountFrequency (%)
0.001 3
 
< 0.1%
0.002 38
 
0.4%
0.003 137
1.4%
0.004 200
2.0%
0.005 212
2.1%
0.006 228
2.3%
0.007 250
2.5%
0.008 234
2.3%
0.009 277
2.8%
0.01 288
2.9%
ValueCountFrequency (%)
0.102 1
 
< 0.1%
0.087 1
 
< 0.1%
0.085 1
 
< 0.1%
0.079 2
< 0.1%
0.078 1
 
< 0.1%
0.077 2
< 0.1%
0.074 1
 
< 0.1%
0.073 1
 
< 0.1%
0.072 2
< 0.1%
0.071 3
< 0.1%

일산화탄소농도(ppm)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct23
Distinct (%)0.2%
Missing407
Missing (%)4.1%
Infinite0
Infinite (%)0.0%
Mean0.56846659
Minimum0.1
Maximum3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T06:05:37.998147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.3
Q10.4
median0.5
Q30.7
95-th percentile1
Maximum3
Range2.9
Interquartile range (IQR)0.3

Descriptive statistics

Standard deviation0.24340148
Coefficient of variation (CV)0.42817201
Kurtosis4.6461152
Mean0.56846659
Median Absolute Deviation (MAD)0.1
Skewness1.4031781
Sum5453.3
Variance0.059244281
MonotonicityNot monotonic
2024-05-04T06:05:38.610794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0.4 1927
19.3%
0.5 1859
18.6%
0.6 1523
15.2%
0.7 1063
10.6%
0.3 1058
10.6%
0.8 683
 
6.8%
0.9 432
 
4.3%
0.2 327
 
3.3%
1.0 252
 
2.5%
1.1 157
 
1.6%
Other values (13) 312
 
3.1%
(Missing) 407
 
4.1%
ValueCountFrequency (%)
0.1 58
 
0.6%
0.2 327
 
3.3%
0.3 1058
10.6%
0.4 1927
19.3%
0.5 1859
18.6%
0.6 1523
15.2%
0.7 1063
10.6%
0.8 683
 
6.8%
0.9 432
 
4.3%
1.0 252
 
2.5%
ValueCountFrequency (%)
3.0 1
 
< 0.1%
2.8 1
 
< 0.1%
2.6 1
 
< 0.1%
2.2 3
 
< 0.1%
1.9 1
 
< 0.1%
1.8 3
 
< 0.1%
1.7 13
 
0.1%
1.6 16
0.2%
1.5 24
0.2%
1.4 33
0.3%

아황산가스농도(ppm)
Real number (ℝ)

MISSING 

Distinct13
Distinct (%)0.1%
Missing319
Missing (%)3.2%
Infinite0
Infinite (%)0.0%
Mean0.0040327446
Minimum0.001
Maximum0.016
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T06:05:39.145655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.001
5-th percentile0.002
Q10.003
median0.004
Q30.005
95-th percentile0.006
Maximum0.016
Range0.015
Interquartile range (IQR)0.002

Descriptive statistics

Standard deviation0.0012910459
Coefficient of variation (CV)0.32014077
Kurtosis2.7714664
Mean0.0040327446
Median Absolute Deviation (MAD)0.001
Skewness0.98662578
Sum39.041
Variance1.6667996 × 10-6
MonotonicityNot monotonic
2024-05-04T06:05:39.581211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0.004 3258
32.6%
0.003 2816
28.2%
0.005 1737
17.4%
0.002 709
 
7.1%
0.006 683
 
6.8%
0.007 300
 
3.0%
0.008 112
 
1.1%
0.001 32
 
0.3%
0.009 26
 
0.3%
0.01 4
 
< 0.1%
Other values (3) 4
 
< 0.1%
(Missing) 319
 
3.2%
ValueCountFrequency (%)
0.001 32
 
0.3%
0.002 709
 
7.1%
0.003 2816
28.2%
0.004 3258
32.6%
0.005 1737
17.4%
0.006 683
 
6.8%
0.007 300
 
3.0%
0.008 112
 
1.1%
0.009 26
 
0.3%
0.01 4
 
< 0.1%
ValueCountFrequency (%)
0.016 1
 
< 0.1%
0.015 2
 
< 0.1%
0.012 1
 
< 0.1%
0.01 4
 
< 0.1%
0.009 26
 
0.3%
0.008 112
 
1.1%
0.007 300
 
3.0%
0.006 683
 
6.8%
0.005 1737
17.4%
0.004 3258
32.6%

미세먼지농도(㎍/㎥)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct187
Distinct (%)1.9%
Missing406
Missing (%)4.1%
Infinite0
Infinite (%)0.0%
Mean42.268188
Minimum3
Maximum228
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T06:05:40.006876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile12
Q125
median36
Q352
95-th percentile91
Maximum228
Range225
Interquartile range (IQR)27

Descriptive statistics

Standard deviation26.21216
Coefficient of variation (CV)0.62013919
Kurtosis5.4875585
Mean42.268188
Median Absolute Deviation (MAD)13
Skewness1.8549549
Sum405521
Variance687.07734
MonotonicityNot monotonic
2024-05-04T06:05:40.620086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35 228
 
2.3%
25 227
 
2.3%
33 224
 
2.2%
30 223
 
2.2%
27 220
 
2.2%
36 209
 
2.1%
28 208
 
2.1%
32 208
 
2.1%
24 205
 
2.1%
29 203
 
2.0%
Other values (177) 7439
74.4%
(Missing) 406
 
4.1%
ValueCountFrequency (%)
3 3
 
< 0.1%
4 11
 
0.1%
5 23
 
0.2%
6 40
 
0.4%
7 43
 
0.4%
8 55
0.5%
9 62
0.6%
10 83
0.8%
11 85
0.9%
12 108
1.1%
ValueCountFrequency (%)
228 1
< 0.1%
212 1
< 0.1%
199 1
< 0.1%
198 1
< 0.1%
196 1
< 0.1%
193 2
< 0.1%
192 1
< 0.1%
191 2
< 0.1%
189 1
< 0.1%
188 1
< 0.1%

초미세먼지농도(㎍/㎥)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct141
Distinct (%)1.5%
Missing387
Missing (%)3.9%
Infinite0
Infinite (%)0.0%
Mean24.6434
Minimum1
Maximum153
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T06:05:41.212127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q114
median20
Q330
95-th percentile57
Maximum153
Range152
Interquartile range (IQR)16

Descriptive statistics

Standard deviation18.0497
Coefficient of variation (CV)0.73243548
Kurtosis9.9309009
Mean24.6434
Median Absolute Deviation (MAD)8
Skewness2.5379147
Sum236897
Variance325.79167
MonotonicityNot monotonic
2024-05-04T06:05:41.794453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20 394
 
3.9%
19 360
 
3.6%
21 356
 
3.6%
15 348
 
3.5%
16 347
 
3.5%
22 347
 
3.5%
18 345
 
3.5%
17 334
 
3.3%
14 330
 
3.3%
13 313
 
3.1%
Other values (131) 6139
61.4%
(Missing) 387
 
3.9%
ValueCountFrequency (%)
1 9
 
0.1%
2 27
 
0.3%
3 75
 
0.8%
4 90
 
0.9%
5 180
1.8%
6 182
1.8%
7 196
2.0%
8 199
2.0%
9 232
2.3%
10 277
2.8%
ValueCountFrequency (%)
153 1
 
< 0.1%
151 1
 
< 0.1%
150 1
 
< 0.1%
148 1
 
< 0.1%
147 2
< 0.1%
146 1
 
< 0.1%
145 2
< 0.1%
142 1
 
< 0.1%
141 1
 
< 0.1%
139 4
< 0.1%

Interactions

2024-05-04T06:05:28.255585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:05:11.512023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:05:14.352257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:05:17.141825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:05:19.432817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:05:22.302037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:05:25.413585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:05:28.686631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:05:11.971709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:05:14.693796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:05:17.475695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:05:19.731361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:05:22.702180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:05:25.749054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:05:29.252980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:05:12.261413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:05:15.116256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:05:17.849146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:05:20.139947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:05:23.144960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:05:26.150001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:05:29.676911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:05:12.549682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:05:15.541791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:05:18.213106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:05:20.520691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:05:23.607556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:05:26.629813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:05:30.132376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:05:12.914258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:05:15.867255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:05:18.530702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:05:20.879826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:05:23.989510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:05:26.921527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:05:30.563534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:05:13.290709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:05:16.334074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:05:18.838389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:05:21.299026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:05:24.485157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:05:27.295027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:05:30.974581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:05:13.934291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:05:16.720639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:05:19.118369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:05:21.777101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:05:24.893579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:05:27.706557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-04T06:05:42.234081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
측정일시측정소명이산화질소농도(ppm)오존농도(ppm)일산화탄소농도(ppm)아황산가스농도(ppm)미세먼지농도(㎍/㎥)초미세먼지농도(㎍/㎥)
측정일시1.0000.0560.4510.6500.4930.3680.5930.565
측정소명0.0561.0000.5820.3300.5330.5540.1340.111
이산화질소농도(ppm)0.4510.5821.0000.4460.5500.3010.5370.522
오존농도(ppm)0.6500.3300.4461.0000.3560.1470.2010.232
일산화탄소농도(ppm)0.4930.5330.5500.3561.0000.2890.5900.616
아황산가스농도(ppm)0.3680.5540.3010.1470.2891.0000.3530.344
미세먼지농도(㎍/㎥)0.5930.1340.5370.2010.5900.3531.0000.930
초미세먼지농도(㎍/㎥)0.5650.1110.5220.2320.6160.3440.9301.000
2024-05-04T06:05:42.782006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
측정일시이산화질소농도(ppm)오존농도(ppm)일산화탄소농도(ppm)아황산가스농도(ppm)미세먼지농도(㎍/㎥)초미세먼지농도(㎍/㎥)측정소명
측정일시1.000-0.187-0.177-0.161-0.413-0.424-0.3570.019
이산화질소농도(ppm)-0.1871.000-0.4500.5600.3860.5500.5290.237
오존농도(ppm)-0.177-0.4501.000-0.352-0.048-0.072-0.0480.111
일산화탄소농도(ppm)-0.1610.560-0.3521.0000.3480.5690.5620.198
아황산가스농도(ppm)-0.4130.386-0.0480.3481.0000.4730.4400.244
미세먼지농도(㎍/㎥)-0.4240.550-0.0720.5690.4731.0000.8840.042
초미세먼지농도(㎍/㎥)-0.3570.529-0.0480.5620.4400.8841.0000.035
측정소명0.0190.2370.1110.1980.2440.0420.0351.000

Missing values

2024-05-04T06:05:31.713502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-04T06:05:32.315584image/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.
2024-05-04T06:05:32.720600image/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(㎍/㎥)
997320190729강변북로0.0310.0060.20.004135
87820190120강북구0.0110.0320.50.0046832
1691420191215동작대로0.0540.0050.90.0054430
507420190421노원구0.020.030.40.0032920
496020190418중구0.0340.0270.40.0043418
1391320191016동작구0.0350.0160.50.0033116
1660120191209공항대로0.0510.0040.70.005<NA><NA>
330620190313천호대로0.0330.0240.40.0034512
752220190610강동구0.0090.0240.30.00394
359820190320구로구0.050.0140.50.0069672
측정일시측정소명이산화질소농도(ppm)오존농도(ppm)일산화탄소농도(ppm)아황산가스농도(ppm)미세먼지농도(㎍/㎥)초미세먼지농도(㎍/㎥)
997820190729관악산0.0060.0120.50.0021510
1205120190908화랑로0.0250.0350.30.0034021
1466020191031도봉구0.0260.030.60.0037926
1093520190817도봉구0.0070.0620.50.0042815
518220190423양천구0.0480.0280.70.0069045
308120190308화랑로0.0460.0130.40.0054930
212620190216구로구0.0160.030.50.0055237
1649420191206화랑로0.0310.010.40.0043919
244720190223광진구0.0460.0130.90.0055639
1764120191229한강대로0.0410.0060.70.0044924