Overview

Dataset statistics

Number of variables9
Number of observations9125
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory704.1 KiB
Average record size in memory79.0 B

Variable types

Numeric7
Categorical2

Dataset

Description파일 다운로드
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-2220/S/1/datasetView.do

Alerts

측정소명 is highly overall correlated with 권역명High correlation
권역명 is highly overall correlated with 측정소명High correlation
이산화질소농도(ppm) is highly overall correlated with 일산화탄소농도(ppm) and 3 other fieldsHigh 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
미세먼지(㎍/㎥) is highly overall correlated with 이산화질소농도(ppm) and 2 other fieldsHigh correlation
초미세먼지(㎍/㎥) is highly overall correlated with 이산화질소농도(ppm) and 2 other fieldsHigh correlation

Reproduction

Analysis started2024-05-03 21:50:55.081257
Analysis finished2024-05-03 21:51:19.372702
Duration24.29 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

측정일시
Real number (ℝ)

Distinct365
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20220668
Minimum20220101
Maximum20221231
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size80.3 KiB
2024-05-03T21:51:19.759892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20220101
5-th percentile20220119
Q120220402
median20220702
Q320221001
95-th percentile20221213
Maximum20221231
Range1130
Interquartile range (IQR)599

Descriptive statistics

Standard deviation345.02079
Coefficient of variation (CV)1.7062779 × 10-5
Kurtosis-1.2057171
Mean20220668
Median Absolute Deviation (MAD)300
Skewness-0.010696166
Sum1.845136 × 1011
Variance119039.35
MonotonicityIncreasing
2024-05-03T21:51:20.346762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20220101 25
 
0.3%
20220909 25
 
0.3%
20220907 25
 
0.3%
20220906 25
 
0.3%
20220905 25
 
0.3%
20220904 25
 
0.3%
20220903 25
 
0.3%
20220902 25
 
0.3%
20220901 25
 
0.3%
20220831 25
 
0.3%
Other values (355) 8875
97.3%
ValueCountFrequency (%)
20220101 25
0.3%
20220102 25
0.3%
20220103 25
0.3%
20220104 25
0.3%
20220105 25
0.3%
20220106 25
0.3%
20220107 25
0.3%
20220108 25
0.3%
20220109 25
0.3%
20220110 25
0.3%
ValueCountFrequency (%)
20221231 25
0.3%
20221230 25
0.3%
20221229 25
0.3%
20221228 25
0.3%
20221227 25
0.3%
20221226 25
0.3%
20221225 25
0.3%
20221224 25
0.3%
20221223 25
0.3%
20221222 25
0.3%

권역명
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size71.4 KiB
동북권
2920 
서남권
2555 
동남권
1460 
서북권
1095 
도심권
1095 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row동남권
2nd row동남권
3rd row동북권
4th row서남권
5th row서남권

Common Values

ValueCountFrequency (%)
동북권 2920
32.0%
서남권 2555
28.0%
동남권 1460
16.0%
서북권 1095
 
12.0%
도심권 1095
 
12.0%

Length

2024-05-03T21:51:20.952326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-03T21:51:21.372249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
동북권 2920
32.0%
서남권 2555
28.0%
동남권 1460
16.0%
서북권 1095
 
12.0%
도심권 1095
 
12.0%

측정소명
Categorical

HIGH CORRELATION 

Distinct25
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size71.4 KiB
강남구
 
365
강동구
 
365
강북구
 
365
강서구
 
365
관악구
 
365
Other values (20)
7300 

Length

Max length4
Median length3
Mean length3.08
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row강남구
2nd row강동구
3rd row강북구
4th row강서구
5th row관악구

Common Values

ValueCountFrequency (%)
강남구 365
 
4.0%
강동구 365
 
4.0%
강북구 365
 
4.0%
강서구 365
 
4.0%
관악구 365
 
4.0%
광진구 365
 
4.0%
구로구 365
 
4.0%
금천구 365
 
4.0%
노원구 365
 
4.0%
도봉구 365
 
4.0%
Other values (15) 5475
60.0%

Length

2024-05-03T21:51:21.850872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
강남구 365
 
4.0%
서대문구 365
 
4.0%
중구 365
 
4.0%
종로구 365
 
4.0%
은평구 365
 
4.0%
용산구 365
 
4.0%
영등포구 365
 
4.0%
양천구 365
 
4.0%
송파구 365
 
4.0%
성북구 365
 
4.0%
Other values (15) 5475
60.0%

이산화질소농도(ppm)
Real number (ℝ)

HIGH CORRELATION 

Distinct62
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.020599233
Minimum0
Maximum0.067
Zeros32
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size80.3 KiB
2024-05-03T21:51:22.246014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.008
Q10.012
median0.018
Q30.027
95-th percentile0.042
Maximum0.067
Range0.067
Interquartile range (IQR)0.015

Descriptive statistics

Standard deviation0.010814867
Coefficient of variation (CV)0.52501311
Kurtosis0.37873147
Mean0.020599233
Median Absolute Deviation (MAD)0.007
Skewness0.91727502
Sum187.968
Variance0.00011696135
MonotonicityNot monotonic
2024-05-03T21:51:22.702376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.012 472
 
5.2%
0.014 466
 
5.1%
0.013 455
 
5.0%
0.011 453
 
5.0%
0.015 447
 
4.9%
0.017 420
 
4.6%
0.01 387
 
4.2%
0.016 372
 
4.1%
0.018 340
 
3.7%
0.009 336
 
3.7%
Other values (52) 4977
54.5%
ValueCountFrequency (%)
0.0 32
 
0.4%
0.001 2
 
< 0.1%
0.003 5
 
0.1%
0.004 23
 
0.3%
0.005 67
 
0.7%
0.006 107
 
1.2%
0.007 183
2.0%
0.008 285
3.1%
0.009 336
3.7%
0.01 387
4.2%
ValueCountFrequency (%)
0.067 2
 
< 0.1%
0.064 1
 
< 0.1%
0.06 3
 
< 0.1%
0.059 8
0.1%
0.058 5
 
0.1%
0.057 5
 
0.1%
0.056 10
0.1%
0.055 15
0.2%
0.054 9
0.1%
0.053 17
0.2%

오존농도(ppm)
Real number (ℝ)

Distinct88
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.029598466
Minimum0
Maximum0.09
Zeros27
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size80.3 KiB
2024-05-03T21:51:23.196761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.009
Q10.019
median0.028
Q30.039
95-th percentile0.055
Maximum0.09
Range0.09
Interquartile range (IQR)0.02

Descriptive statistics

Standard deviation0.014002093
Coefficient of variation (CV)0.47306821
Kurtosis0.36310005
Mean0.029598466
Median Absolute Deviation (MAD)0.009
Skewness0.62859478
Sum270.086
Variance0.00019605861
MonotonicityNot monotonic
2024-05-03T21:51:23.865472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.024 288
 
3.2%
0.02 284
 
3.1%
0.023 280
 
3.1%
0.025 271
 
3.0%
0.021 271
 
3.0%
0.022 268
 
2.9%
0.029 256
 
2.8%
0.026 251
 
2.8%
0.031 251
 
2.8%
0.028 248
 
2.7%
Other values (78) 6457
70.8%
ValueCountFrequency (%)
0.0 27
 
0.3%
0.001 1
 
< 0.1%
0.002 2
 
< 0.1%
0.003 14
 
0.2%
0.004 25
 
0.3%
0.005 58
0.6%
0.006 74
0.8%
0.007 77
0.8%
0.008 90
1.0%
0.009 110
1.2%
ValueCountFrequency (%)
0.09 2
 
< 0.1%
0.086 1
 
< 0.1%
0.085 2
 
< 0.1%
0.084 1
 
< 0.1%
0.083 3
< 0.1%
0.082 1
 
< 0.1%
0.081 2
 
< 0.1%
0.08 4
< 0.1%
0.079 5
0.1%
0.078 4
< 0.1%

일산화탄소농도(ppm)
Real number (ℝ)

HIGH CORRELATION 

Distinct16
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.43499178
Minimum0
Maximum1.5
Zeros28
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size80.3 KiB
2024-05-03T21:51:24.318927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.2
Q10.3
median0.4
Q30.5
95-th percentile0.7
Maximum1.5
Range1.5
Interquartile range (IQR)0.2

Descriptive statistics

Standard deviation0.16246847
Coefficient of variation (CV)0.37349778
Kurtosis2.8017527
Mean0.43499178
Median Absolute Deviation (MAD)0.1
Skewness1.1770512
Sum3969.3
Variance0.026396002
MonotonicityNot monotonic
2024-05-03T21:51:25.153355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0.4 2651
29.1%
0.3 2420
26.5%
0.5 1716
18.8%
0.6 842
 
9.2%
0.2 536
 
5.9%
0.7 487
 
5.3%
0.8 222
 
2.4%
0.9 118
 
1.3%
1.0 38
 
0.4%
0.0 28
 
0.3%
Other values (6) 67
 
0.7%
ValueCountFrequency (%)
0.0 28
 
0.3%
0.1 28
 
0.3%
0.2 536
 
5.9%
0.3 2420
26.5%
0.4 2651
29.1%
0.5 1716
18.8%
0.6 842
 
9.2%
0.7 487
 
5.3%
0.8 222
 
2.4%
0.9 118
 
1.3%
ValueCountFrequency (%)
1.5 5
 
0.1%
1.4 1
 
< 0.1%
1.3 4
 
< 0.1%
1.2 10
 
0.1%
1.1 19
 
0.2%
1.0 38
 
0.4%
0.9 118
 
1.3%
0.8 222
 
2.4%
0.7 487
5.3%
0.6 842
9.2%

아황산가스농도(ppm)
Real number (ℝ)

HIGH CORRELATION 

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0028883288
Minimum0
Maximum0.009
Zeros27
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size80.3 KiB
2024-05-03T21:51:25.554271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

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

Descriptive statistics

Standard deviation0.00077649087
Coefficient of variation (CV)0.26883743
Kurtosis2.5926925
Mean0.0028883288
Median Absolute Deviation (MAD)0
Skewness0.5134256
Sum26.356
Variance6.0293808 × 10-7
MonotonicityNot monotonic
2024-05-03T21:51:26.040050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0.003 4952
54.3%
0.002 2498
27.4%
0.004 1356
 
14.9%
0.005 141
 
1.5%
0.001 110
 
1.2%
0.006 27
 
0.3%
0.0 27
 
0.3%
0.007 10
 
0.1%
0.008 3
 
< 0.1%
0.009 1
 
< 0.1%
ValueCountFrequency (%)
0.0 27
 
0.3%
0.001 110
 
1.2%
0.002 2498
27.4%
0.003 4952
54.3%
0.004 1356
 
14.9%
0.005 141
 
1.5%
0.006 27
 
0.3%
0.007 10
 
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 10
 
0.1%
0.006 27
 
0.3%
0.005 141
 
1.5%
0.004 1356
 
14.9%
0.003 4952
54.3%
0.002 2498
27.4%
0.001 110
 
1.2%
0.0 27
 
0.3%

미세먼지(㎍/㎥)
Real number (ℝ)

HIGH CORRELATION 

Distinct159
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.884493
Minimum0
Maximum404
Zeros28
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size80.3 KiB
2024-05-03T21:51:26.455611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile10
Q121
median29
Q340
95-th percentile67
Maximum404
Range404
Interquartile range (IQR)19

Descriptive statistics

Standard deviation20.516683
Coefficient of variation (CV)0.62390144
Kurtosis29.400449
Mean32.884493
Median Absolute Deviation (MAD)9
Skewness3.5249075
Sum300071
Variance420.93427
MonotonicityNot monotonic
2024-05-03T21:51:27.121876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
26 319
 
3.5%
24 309
 
3.4%
23 294
 
3.2%
27 292
 
3.2%
29 292
 
3.2%
28 288
 
3.2%
25 284
 
3.1%
22 265
 
2.9%
32 256
 
2.8%
30 252
 
2.8%
Other values (149) 6274
68.8%
ValueCountFrequency (%)
0 28
 
0.3%
3 13
 
0.1%
4 26
 
0.3%
5 38
 
0.4%
6 45
 
0.5%
7 66
0.7%
8 82
0.9%
9 76
0.8%
10 108
1.2%
11 132
1.4%
ValueCountFrequency (%)
404 1
< 0.1%
255 1
< 0.1%
232 1
< 0.1%
229 1
< 0.1%
226 2
< 0.1%
224 1
< 0.1%
215 1
< 0.1%
213 1
< 0.1%
211 1
< 0.1%
210 1
< 0.1%

초미세먼지(㎍/㎥)
Real number (ℝ)

HIGH CORRELATION 

Distinct97
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.265096
Minimum0
Maximum105
Zeros33
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size80.3 KiB
2024-05-03T21:51:27.578878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q110
median16
Q323
95-th percentile41
Maximum105
Range105
Interquartile range (IQR)13

Descriptive statistics

Standard deviation12.179525
Coefficient of variation (CV)0.66681966
Kurtosis5.8346804
Mean18.265096
Median Absolute Deviation (MAD)6
Skewness1.7963612
Sum166669
Variance148.34083
MonotonicityNot monotonic
2024-05-03T21:51:28.158726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14 439
 
4.8%
13 433
 
4.7%
15 404
 
4.4%
11 402
 
4.4%
12 398
 
4.4%
16 390
 
4.3%
9 381
 
4.2%
17 367
 
4.0%
8 355
 
3.9%
10 355
 
3.9%
Other values (87) 5201
57.0%
ValueCountFrequency (%)
0 33
 
0.4%
1 47
 
0.5%
2 115
 
1.3%
3 155
1.7%
4 188
2.1%
5 214
2.3%
6 279
3.1%
7 333
3.6%
8 355
3.9%
9 381
4.2%
ValueCountFrequency (%)
105 2
< 0.1%
104 1
< 0.1%
103 1
< 0.1%
102 1
< 0.1%
101 1
< 0.1%
100 2
< 0.1%
98 2
< 0.1%
97 1
< 0.1%
93 1
< 0.1%
92 2
< 0.1%

Interactions

2024-05-03T21:51:15.271843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:50:59.713829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:51:02.203445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:51:05.097290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:51:07.373160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:51:09.964125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:51:12.362664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:51:15.647347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:51:00.017629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:51:02.657944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:51:05.444632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:51:07.727929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:51:10.352393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:51:12.813711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:51:15.958102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:51:00.393322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:51:03.072216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:51:05.785697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:51:08.139205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:51:10.785973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:51:13.237089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:51:16.247250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:51:00.835600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:51:03.373200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:51:06.050892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:51:08.489260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:51:11.120410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:51:13.630858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:51:16.620425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:51:01.298468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:51:04.014953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:51:06.357873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:51:08.830798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:51:11.475137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:51:14.074103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:51:17.081457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:51:01.550019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:51:04.319292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:51:06.720214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:51:09.170211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:51:11.758729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:51:14.406315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:51:17.520962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:51:01.903150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:51:04.734633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:51:07.091178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:51:09.580793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:51:12.078263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:51:14.843822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-03T21:51:28.539790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
측정일시권역명측정소명이산화질소농도(ppm)오존농도(ppm)일산화탄소농도(ppm)아황산가스농도(ppm)미세먼지(㎍/㎥)초미세먼지(㎍/㎥)
측정일시1.0000.0000.0000.5930.7120.5790.4490.3540.495
권역명0.0001.0001.0000.1730.0670.2060.2900.0000.011
측정소명0.0001.0001.0000.2670.1070.3180.4520.0000.062
이산화질소농도(ppm)0.5930.1730.2671.0000.6290.7700.5950.3640.605
오존농도(ppm)0.7120.0670.1070.6291.0000.5410.3880.2230.379
일산화탄소농도(ppm)0.5790.2060.3180.7700.5411.0000.7610.4200.684
아황산가스농도(ppm)0.4490.2900.4520.5950.3880.7611.0000.2690.444
미세먼지(㎍/㎥)0.3540.0000.0000.3640.2230.4200.2691.0000.684
초미세먼지(㎍/㎥)0.4950.0110.0620.6050.3790.6840.4440.6841.000
2024-05-03T21:51:28.888354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
측정소명권역명
측정소명1.0000.999
권역명0.9991.000
2024-05-03T21:51:29.136994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
측정일시이산화질소농도(ppm)오존농도(ppm)일산화탄소농도(ppm)아황산가스농도(ppm)미세먼지(㎍/㎥)초미세먼지(㎍/㎥)권역명측정소명
측정일시1.000-0.094-0.212-0.113-0.179-0.247-0.2310.0000.000
이산화질소농도(ppm)-0.0941.000-0.4640.7320.5220.5850.6040.0730.097
오존농도(ppm)-0.212-0.4641.000-0.347-0.119-0.003-0.0230.0250.036
일산화탄소농도(ppm)-0.1130.732-0.3471.0000.4930.6190.6810.0800.111
아황산가스농도(ppm)-0.1790.522-0.1190.4931.0000.4640.4470.1250.176
미세먼지(㎍/㎥)-0.2470.585-0.0030.6190.4641.0000.8950.0000.000
초미세먼지(㎍/㎥)-0.2310.604-0.0230.6810.4470.8951.0000.0000.019
권역명0.0000.0730.0250.0800.1250.0000.0001.0000.999
측정소명0.0000.0970.0360.1110.1760.0000.0190.9991.000

Missing values

2024-05-03T21:51:18.192965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-03T21:51:18.989009image/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.

Sample

측정일시권역명측정소명이산화질소농도(ppm)오존농도(ppm)일산화탄소농도(ppm)아황산가스농도(ppm)미세먼지(㎍/㎥)초미세먼지(㎍/㎥)
020220101동남권강남구0.0290.0140.50.0032512
120220101동남권강동구0.0320.010.50.0033215
220220101동북권강북구0.030.0120.60.0033216
320220101서남권강서구0.0310.0170.60.0043012
420220101서남권관악구0.0370.0110.60.0032413
520220101동북권광진구0.0330.0130.80.0012513
620220101서남권구로구0.0310.0190.40.0032710
720220101서남권금천구0.0430.010.60.0042616
820220101동북권노원구0.0350.0110.70.0043219
920220101동북권도봉구0.0340.0110.80.0023216
측정일시권역명측정소명이산화질소농도(ppm)오존농도(ppm)일산화탄소농도(ppm)아황산가스농도(ppm)미세먼지(㎍/㎥)초미세먼지(㎍/㎥)
911520221231동북권성동구0.0520.0050.80.0034730
911620221231동북권성북구0.0510.0060.90.0044535
911720221231동남권송파구0.050.0050.80.0034635
911820221231서남권양천구0.0370.010.60.0034332
911920221231서남권영등포구0.0420.010.60.0044027
912020221231도심권용산구0.0420.0060.80.0044334
912120221231서북권은평구0.0330.0110.80.0043725
912220221231도심권종로구0.0490.0070.90.0044840
912320221231도심권중구0.0510.0120.90.0044843
912420221231동북권중랑구0.0450.0070.70.0034535