Overview

Dataset statistics

Number of variables9
Number of observations9150
Missing cells335
Missing cells (%)0.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory706.0 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 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 started2024-05-11 01:53:51.423763
Analysis finished2024-05-11 01:54:10.934213
Duration19.51 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

측정일시
Real number (ℝ)

Distinct366
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20200667
Minimum20200101
Maximum20201231
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size80.5 KiB
2024-05-11T01:54:11.547904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20200101
5-th percentile20200119
Q120200401
median20200702
Q320201001
95-th percentile20201213
Maximum20201231
Range1130
Interquartile range (IQR)600

Descriptive statistics

Standard deviation345.31148
Coefficient of variation (CV)1.7094063 × 10-5
Kurtosis-1.2096343
Mean20200667
Median Absolute Deviation (MAD)300
Skewness-0.0058377455
Sum1.848361 × 1011
Variance119240.02
MonotonicityIncreasing
2024-05-11T01:54:12.098964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20200101 25
 
0.3%
20200909 25
 
0.3%
20200907 25
 
0.3%
20200906 25
 
0.3%
20200905 25
 
0.3%
20200904 25
 
0.3%
20200903 25
 
0.3%
20200902 25
 
0.3%
20200901 25
 
0.3%
20200831 25
 
0.3%
Other values (356) 8900
97.3%
ValueCountFrequency (%)
20200101 25
0.3%
20200102 25
0.3%
20200103 25
0.3%
20200104 25
0.3%
20200105 25
0.3%
20200106 25
0.3%
20200107 25
0.3%
20200108 25
0.3%
20200109 25
0.3%
20200110 25
0.3%
ValueCountFrequency (%)
20201231 25
0.3%
20201230 25
0.3%
20201229 25
0.3%
20201228 25
0.3%
20201227 25
0.3%
20201226 25
0.3%
20201225 25
0.3%
20201224 25
0.3%
20201223 25
0.3%
20201222 25
0.3%

권역명
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size71.6 KiB
동북권
2928 
서남권
2562 
동남권
1464 
서북권
1098 
도심권
1098 

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 2928
32.0%
서남권 2562
28.0%
동남권 1464
16.0%
서북권 1098
 
12.0%
도심권 1098
 
12.0%

Length

2024-05-11T01:54:12.720694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T01:54:13.043266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
동북권 2928
32.0%
서남권 2562
28.0%
동남권 1464
16.0%
서북권 1098
 
12.0%
도심권 1098
 
12.0%

측정소명
Categorical

HIGH CORRELATION 

Distinct25
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size71.6 KiB
강남구
 
366
강동구
 
366
강북구
 
366
강서구
 
366
관악구
 
366
Other values (20)
7320 

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 366
 
4.0%
강동구 366
 
4.0%
강북구 366
 
4.0%
강서구 366
 
4.0%
관악구 366
 
4.0%
광진구 366
 
4.0%
구로구 366
 
4.0%
금천구 366
 
4.0%
노원구 366
 
4.0%
도봉구 366
 
4.0%
Other values (15) 5490
60.0%

Length

2024-05-11T01:54:13.476727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
강남구 366
 
4.0%
서대문구 366
 
4.0%
중구 366
 
4.0%
종로구 366
 
4.0%
은평구 366
 
4.0%
용산구 366
 
4.0%
영등포구 366
 
4.0%
양천구 366
 
4.0%
송파구 366
 
4.0%
성북구 366
 
4.0%
Other values (15) 5490
60.0%

이산화질소농도(ppm)
Real number (ℝ)

HIGH CORRELATION 

Distinct74
Distinct (%)0.8%
Missing68
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean0.023825809
Minimum0.001
Maximum0.082
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size80.5 KiB
2024-05-11T01:54:14.135844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.001
5-th percentile0.009
Q10.014
median0.021
Q30.031
95-th percentile0.048
Maximum0.082
Range0.081
Interquartile range (IQR)0.017

Descriptive statistics

Standard deviation0.012268158
Coefficient of variation (CV)0.51491044
Kurtosis0.28868144
Mean0.023825809
Median Absolute Deviation (MAD)0.008
Skewness0.9015987
Sum216.386
Variance0.0001505077
MonotonicityNot monotonic
2024-05-11T01:54:14.713415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.014 384
 
4.2%
0.016 372
 
4.1%
0.017 370
 
4.0%
0.015 368
 
4.0%
0.012 366
 
4.0%
0.013 366
 
4.0%
0.019 353
 
3.9%
0.02 336
 
3.7%
0.018 329
 
3.6%
0.011 314
 
3.4%
Other values (64) 5524
60.4%
ValueCountFrequency (%)
0.001 2
 
< 0.1%
0.002 3
 
< 0.1%
0.003 4
 
< 0.1%
0.004 21
 
0.2%
0.005 51
 
0.6%
0.006 55
 
0.6%
0.007 91
 
1.0%
0.008 176
1.9%
0.009 207
2.3%
0.01 252
2.8%
ValueCountFrequency (%)
0.082 1
 
< 0.1%
0.073 1
 
< 0.1%
0.072 1
 
< 0.1%
0.071 2
 
< 0.1%
0.07 3
< 0.1%
0.069 3
< 0.1%
0.068 2
 
< 0.1%
0.067 1
 
< 0.1%
0.066 1
 
< 0.1%
0.065 5
0.1%

오존농도(ppm)
Real number (ℝ)

HIGH CORRELATION 

Distinct85
Distinct (%)0.9%
Missing30
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean0.024617654
Minimum0.002
Maximum0.097
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size80.5 KiB
2024-05-11T01:54:15.474185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.002
5-th percentile0.007
Q10.015
median0.023
Q30.032
95-th percentile0.047
Maximum0.097
Range0.095
Interquartile range (IQR)0.017

Descriptive statistics

Standard deviation0.01248672
Coefficient of variation (CV)0.50722623
Kurtosis0.78925904
Mean0.024617654
Median Absolute Deviation (MAD)0.008
Skewness0.70114364
Sum224.513
Variance0.00015591817
MonotonicityNot monotonic
2024-05-11T01:54:16.153996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.022 333
 
3.6%
0.02 331
 
3.6%
0.023 308
 
3.4%
0.021 303
 
3.3%
0.018 300
 
3.3%
0.019 283
 
3.1%
0.024 280
 
3.1%
0.017 274
 
3.0%
0.026 268
 
2.9%
0.025 264
 
2.9%
Other values (75) 6176
67.5%
ValueCountFrequency (%)
0.002 35
 
0.4%
0.003 48
 
0.5%
0.004 77
 
0.8%
0.005 110
1.2%
0.006 152
1.7%
0.007 175
1.9%
0.008 218
2.4%
0.009 169
1.8%
0.01 209
2.3%
0.011 209
2.3%
ValueCountFrequency (%)
0.097 1
< 0.1%
0.093 1
< 0.1%
0.092 1
< 0.1%
0.09 1
< 0.1%
0.087 1
< 0.1%
0.085 1
< 0.1%
0.083 1
< 0.1%
0.082 1
< 0.1%
0.08 2
< 0.1%
0.079 2
< 0.1%

일산화탄소농도(ppm)
Real number (ℝ)

HIGH CORRELATION 

Distinct15
Distinct (%)0.2%
Missing77
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean0.4754326
Minimum0.1
Maximum1.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size80.5 KiB
2024-05-11T01:54:16.669262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.2
Q10.4
median0.4
Q30.6
95-th percentile0.8
Maximum1.5
Range1.4
Interquartile range (IQR)0.2

Descriptive statistics

Standard deviation0.18437473
Coefficient of variation (CV)0.38780413
Kurtosis1.3558632
Mean0.4754326
Median Absolute Deviation (MAD)0.1
Skewness1.0017473
Sum4313.6
Variance0.03399404
MonotonicityNot monotonic
2024-05-11T01:54:17.060768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0.4 2605
28.5%
0.5 1771
19.4%
0.3 1694
18.5%
0.6 1043
11.4%
0.7 586
 
6.4%
0.2 466
 
5.1%
0.8 432
 
4.7%
0.9 244
 
2.7%
1.0 88
 
1.0%
0.1 67
 
0.7%
Other values (5) 77
 
0.8%
(Missing) 77
 
0.8%
ValueCountFrequency (%)
0.1 67
 
0.7%
0.2 466
 
5.1%
0.3 1694
18.5%
0.4 2605
28.5%
0.5 1771
19.4%
0.6 1043
11.4%
0.7 586
 
6.4%
0.8 432
 
4.7%
0.9 244
 
2.7%
1.0 88
 
1.0%
ValueCountFrequency (%)
1.5 3
 
< 0.1%
1.4 2
 
< 0.1%
1.3 4
 
< 0.1%
1.2 15
 
0.2%
1.1 53
 
0.6%
1.0 88
 
1.0%
0.9 244
 
2.7%
0.8 432
4.7%
0.7 586
6.4%
0.6 1043
11.4%

아황산가스농도(ppm)
Real number (ℝ)

HIGH CORRELATION 

Distinct9
Distinct (%)0.1%
Missing85
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean0.0030681743
Minimum0.001
Maximum0.01
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size80.5 KiB
2024-05-11T01:54:17.588660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.001
5-th percentile0.002
Q10.002
median0.003
Q30.004
95-th percentile0.005
Maximum0.01
Range0.009
Interquartile range (IQR)0.002

Descriptive statistics

Standard deviation0.0008795427
Coefficient of variation (CV)0.28666647
Kurtosis1.9384299
Mean0.0030681743
Median Absolute Deviation (MAD)0.001
Skewness0.89379666
Sum27.813
Variance7.7359535 × 10-7
MonotonicityNot monotonic
2024-05-11T01:54:18.067913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0.003 4372
47.8%
0.002 2312
25.3%
0.004 1871
20.4%
0.005 374
 
4.1%
0.006 78
 
0.9%
0.001 27
 
0.3%
0.007 26
 
0.3%
0.008 4
 
< 0.1%
0.01 1
 
< 0.1%
(Missing) 85
 
0.9%
ValueCountFrequency (%)
0.001 27
 
0.3%
0.002 2312
25.3%
0.003 4372
47.8%
0.004 1871
20.4%
0.005 374
 
4.1%
0.006 78
 
0.9%
0.007 26
 
0.3%
0.008 4
 
< 0.1%
0.01 1
 
< 0.1%
ValueCountFrequency (%)
0.01 1
 
< 0.1%
0.008 4
 
< 0.1%
0.007 26
 
0.3%
0.006 78
 
0.9%
0.005 374
 
4.1%
0.004 1871
20.4%
0.003 4372
47.8%
0.002 2312
25.3%
0.001 27
 
0.3%

미세먼지농도(㎍/㎥)
Real number (ℝ)

HIGH CORRELATION 

Distinct117
Distinct (%)1.3%
Missing45
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean35.119824
Minimum3
Maximum146
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size80.5 KiB
2024-05-11T01:54:18.596046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile10
Q122
median33
Q346
95-th percentile68
Maximum146
Range143
Interquartile range (IQR)24

Descriptive statistics

Standard deviation17.962304
Coefficient of variation (CV)0.51145768
Kurtosis1.1808324
Mean35.119824
Median Absolute Deviation (MAD)12
Skewness0.83818483
Sum319766
Variance322.64436
MonotonicityNot monotonic
2024-05-11T01:54:19.131428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25 276
 
3.0%
24 265
 
2.9%
23 244
 
2.7%
22 244
 
2.7%
27 234
 
2.6%
26 224
 
2.4%
28 212
 
2.3%
21 211
 
2.3%
34 208
 
2.3%
32 201
 
2.2%
Other values (107) 6786
74.2%
ValueCountFrequency (%)
3 20
 
0.2%
4 34
 
0.4%
5 54
0.6%
6 63
0.7%
7 73
0.8%
8 83
0.9%
9 74
0.8%
10 95
1.0%
11 100
1.1%
12 117
1.3%
ValueCountFrequency (%)
146 1
 
< 0.1%
145 1
 
< 0.1%
140 1
 
< 0.1%
131 1
 
< 0.1%
124 1
 
< 0.1%
122 2
< 0.1%
119 3
< 0.1%
118 1
 
< 0.1%
116 1
 
< 0.1%
114 1
 
< 0.1%

초미세먼지농도(㎍/㎥)
Real number (ℝ)

HIGH CORRELATION 

Distinct80
Distinct (%)0.9%
Missing30
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean20.698246
Minimum1
Maximum91
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size80.5 KiB
2024-05-11T01:54:19.707042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q112
median18
Q327
95-th percentile45
Maximum91
Range90
Interquartile range (IQR)15

Descriptive statistics

Standard deviation12.371154
Coefficient of variation (CV)0.59769097
Kurtosis1.2712495
Mean20.698246
Median Absolute Deviation (MAD)8
Skewness1.0315298
Sum188768
Variance153.04546
MonotonicityNot monotonic
2024-05-11T01:54:20.165045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15 353
 
3.9%
11 352
 
3.8%
16 349
 
3.8%
18 345
 
3.8%
13 339
 
3.7%
17 339
 
3.7%
14 335
 
3.7%
12 331
 
3.6%
10 329
 
3.6%
9 324
 
3.5%
Other values (70) 5724
62.6%
ValueCountFrequency (%)
1 45
 
0.5%
2 91
 
1.0%
3 132
1.4%
4 146
1.6%
5 156
1.7%
6 196
2.1%
7 232
2.5%
8 268
2.9%
9 324
3.5%
10 329
3.6%
ValueCountFrequency (%)
91 1
 
< 0.1%
87 1
 
< 0.1%
85 2
< 0.1%
83 1
 
< 0.1%
81 2
< 0.1%
80 1
 
< 0.1%
76 1
 
< 0.1%
75 3
< 0.1%
74 3
< 0.1%
73 3
< 0.1%

Interactions

2024-05-11T01:54:06.936376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:53:54.467016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:53:56.447226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:53:58.447048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:54:00.356279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:54:02.537081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:54:04.622660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:54:07.216986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:53:54.729151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:53:56.728039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:53:58.709151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:54:00.614020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:54:02.830264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:54:04.956107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:54:07.530775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:53:55.031116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:53:57.055691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:53:58.992952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:54:00.942708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:54:03.167199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:54:05.289696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:54:07.810937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:53:55.251106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:53:57.343560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:53:59.267145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:54:01.290447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:54:03.462235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:54:05.673082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:54:08.215804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:53:55.589898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:53:57.618943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:53:59.544869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:54:01.559911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:54:03.776723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:54:06.104443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:54:08.551793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:53:55.864406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:53:57.895207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:53:59.810804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:54:01.844070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:54:04.040274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:54:06.386644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:54:08.997632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:53:56.162054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:53:58.190764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:54:00.116698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:54:02.145016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:54:04.353712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:54:06.679870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-11T01:54:20.514998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
측정일시권역명측정소명이산화질소농도(ppm)오존농도(ppm)일산화탄소농도(ppm)아황산가스농도(ppm)미세먼지농도(㎍/㎥)초미세먼지농도(㎍/㎥)
측정일시1.0000.0000.0000.5730.6810.6220.3730.5660.552
권역명0.0001.0001.0000.1990.0850.1730.2010.0790.073
측정소명0.0001.0001.0000.2850.1830.3810.4450.1110.094
이산화질소농도(ppm)0.5730.1990.2851.0000.6010.7650.4040.5910.617
오존농도(ppm)0.6810.0850.1830.6011.0000.5330.2260.3650.441
일산화탄소농도(ppm)0.6220.1730.3810.7650.5331.0000.5240.6330.696
아황산가스농도(ppm)0.3730.2010.4450.4040.2260.5241.0000.3520.344
미세먼지농도(㎍/㎥)0.5660.0790.1110.5910.3650.6330.3521.0000.892
초미세먼지농도(㎍/㎥)0.5520.0730.0940.6170.4410.6960.3440.8921.000
2024-05-11T01:54:20.891714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
측정소명권역명
측정소명1.0000.999
권역명0.9991.000
2024-05-11T01:54:21.142049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
측정일시이산화질소농도(ppm)오존농도(ppm)일산화탄소농도(ppm)아황산가스농도(ppm)미세먼지농도(㎍/㎥)초미세먼지농도(㎍/㎥)권역명측정소명
측정일시1.000-0.124-0.161-0.1210.017-0.212-0.2120.0000.000
이산화질소농도(ppm)-0.1241.000-0.5070.7370.5080.5710.6000.0840.104
오존농도(ppm)-0.161-0.5071.000-0.417-0.252-0.064-0.1220.0340.065
일산화탄소농도(ppm)-0.1210.737-0.4171.0000.4610.5980.6600.0730.143
아황산가스농도(ppm)0.0170.508-0.2520.4611.0000.4470.4390.1170.187
미세먼지농도(㎍/㎥)-0.2120.571-0.0640.5980.4471.0000.8920.0330.039
초미세먼지농도(㎍/㎥)-0.2120.600-0.1220.6600.4390.8921.0000.0280.033
권역명0.0000.0840.0340.0730.1170.0330.0281.0000.999
측정소명0.0000.1040.0650.1430.1870.0390.0330.9991.000

Missing values

2024-05-11T01:54:09.481480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-11T01:54:10.096427image/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.
2024-05-11T01:54:10.641523image/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

측정일시권역명측정소명이산화질소농도(ppm)오존농도(ppm)일산화탄소농도(ppm)아황산가스농도(ppm)미세먼지농도(㎍/㎥)초미세먼지농도(㎍/㎥)
020200101동남권강남구0.0370.0030.70.0033123
120200101동남권강동구0.0360.0040.60.0033728
220200101동북권강북구0.0450.0040.70.0034127
320200101서남권강서구0.0410.0040.60.0053923
420200101서남권관악구0.0430.0080.70.0033522
520200101동북권광진구0.0320.0030.60.0023319
620200101서남권구로구0.0350.0080.50.0043223
720200101서남권금천구0.0380.0020.50.0032017
820200101동북권노원구0.0370.0050.60.0042824
920200101동북권도봉구0.0310.0050.70.0023120
측정일시권역명측정소명이산화질소농도(ppm)오존농도(ppm)일산화탄소농도(ppm)아황산가스농도(ppm)미세먼지농도(㎍/㎥)초미세먼지농도(㎍/㎥)
914020201231동북권성동구0.0270.0210.40.0032212
914120201231동북권성북구0.0230.016<NA>0.004239
914220201231동남권송파구0.0270.0170.50.0042413
914320201231서남권양천구0.0240.0150.50.0033013
914420201231서남권영등포구0.0220.0150.50.0032311
914520201231도심권용산구0.0190.0150.40.0033510
914620201231서북권은평구0.0160.0220.50.0042610
914720201231도심권종로구0.0160.0220.50.0032813
914820201231도심권중구0.0170.020.40.0032913
914920201231동북권중랑구0.0260.0150.50.0042410