Overview

Dataset statistics

Number of variables9
Number of observations9125
Missing cells457
Missing cells (%)0.6%
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 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
이산화질소농도(ppm) has 99 (1.1%) missing valuesMissing
일산화탄소농도(ppm) has 159 (1.7%) missing valuesMissing

Reproduction

Analysis started2024-05-03 21:50:18.730598
Analysis finished2024-05-03 21:50:43.015892
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%
Mean20210668
Minimum20210101
Maximum20211231
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size80.3 KiB
2024-05-03T21:50:43.317799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20210101
5-th percentile20210119
Q120210402
median20210702
Q320211001
95-th percentile20211213
Maximum20211231
Range1130
Interquartile range (IQR)599

Descriptive statistics

Standard deviation345.02079
Coefficient of variation (CV)1.7071221 × 10-5
Kurtosis-1.2057171
Mean20210668
Median Absolute Deviation (MAD)300
Skewness-0.010696166
Sum1.8442235 × 1011
Variance119039.35
MonotonicityIncreasing
2024-05-03T21:50:43.789849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20210101 25
 
0.3%
20210909 25
 
0.3%
20210907 25
 
0.3%
20210906 25
 
0.3%
20210905 25
 
0.3%
20210904 25
 
0.3%
20210903 25
 
0.3%
20210902 25
 
0.3%
20210901 25
 
0.3%
20210831 25
 
0.3%
Other values (355) 8875
97.3%
ValueCountFrequency (%)
20210101 25
0.3%
20210102 25
0.3%
20210103 25
0.3%
20210104 25
0.3%
20210105 25
0.3%
20210106 25
0.3%
20210107 25
0.3%
20210108 25
0.3%
20210109 25
0.3%
20210110 25
0.3%
ValueCountFrequency (%)
20211231 25
0.3%
20211230 25
0.3%
20211229 25
0.3%
20211228 25
0.3%
20211227 25
0.3%
20211226 25
0.3%
20211225 25
0.3%
20211224 25
0.3%
20211223 25
0.3%
20211222 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

2024-05-03T21:50:44.380380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-03T21:50:44.751276image/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

2024-05-03T21:50:45.564202image/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  MISSING 

Distinct71
Distinct (%)0.8%
Missing99
Missing (%)1.1%
Infinite0
Infinite (%)0.0%
Mean0.023506647
Minimum0.002
Maximum0.081
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size80.3 KiB
2024-05-03T21:50:46.003218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.002
5-th percentile0.009
Q10.014
median0.021
Q30.031
95-th percentile0.047
Maximum0.081
Range0.079
Interquartile range (IQR)0.017

Descriptive statistics

Standard deviation0.012104055
Coefficient of variation (CV)0.51492053
Kurtosis0.21374002
Mean0.023506647
Median Absolute Deviation (MAD)0.008
Skewness0.8651156
Sum212.171
Variance0.00014650816
MonotonicityNot monotonic
2024-05-03T21:50:46.677080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.014 404
 
4.4%
0.015 385
 
4.2%
0.013 382
 
4.2%
0.016 378
 
4.1%
0.012 370
 
4.1%
0.017 366
 
4.0%
0.019 337
 
3.7%
0.018 322
 
3.5%
0.011 314
 
3.4%
0.02 306
 
3.4%
Other values (61) 5462
59.9%
ValueCountFrequency (%)
0.002 2
 
< 0.1%
0.003 5
 
0.1%
0.004 18
 
0.2%
0.005 33
 
0.4%
0.006 70
 
0.8%
0.007 119
 
1.3%
0.008 170
1.9%
0.009 258
2.8%
0.01 271
3.0%
0.011 314
3.4%
ValueCountFrequency (%)
0.081 1
 
< 0.1%
0.073 1
 
< 0.1%
0.071 1
 
< 0.1%
0.069 1
 
< 0.1%
0.068 1
 
< 0.1%
0.067 2
 
< 0.1%
0.066 2
 
< 0.1%
0.065 3
 
< 0.1%
0.064 5
0.1%
0.063 11
0.1%

오존농도(ppm)
Real number (ℝ)

HIGH CORRELATION 

Distinct79
Distinct (%)0.9%
Missing42
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean0.02764351
Minimum0.001
Maximum0.08
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size80.3 KiB
2024-05-03T21:50:47.336978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.001
5-th percentile0.007
Q10.018
median0.027
Q30.037
95-th percentile0.048
Maximum0.08
Range0.079
Interquartile range (IQR)0.019

Descriptive statistics

Standard deviation0.01278252
Coefficient of variation (CV)0.46240584
Kurtosis-0.019403376
Mean0.02764351
Median Absolute Deviation (MAD)0.009
Skewness0.29219225
Sum251.086
Variance0.00016339283
MonotonicityNot monotonic
2024-05-03T21:50:47.830421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.025 291
 
3.2%
0.027 285
 
3.1%
0.033 269
 
2.9%
0.021 261
 
2.9%
0.03 258
 
2.8%
0.028 257
 
2.8%
0.024 254
 
2.8%
0.023 254
 
2.8%
0.026 253
 
2.8%
0.038 251
 
2.8%
Other values (69) 6450
70.7%
ValueCountFrequency (%)
0.001 8
 
0.1%
0.002 58
 
0.6%
0.003 84
0.9%
0.004 64
0.7%
0.005 87
1.0%
0.006 92
1.0%
0.007 104
1.1%
0.008 110
1.2%
0.009 111
1.2%
0.01 151
1.7%
ValueCountFrequency (%)
0.08 1
 
< 0.1%
0.078 3
 
< 0.1%
0.077 2
 
< 0.1%
0.076 2
 
< 0.1%
0.075 2
 
< 0.1%
0.074 1
 
< 0.1%
0.073 1
 
< 0.1%
0.072 2
 
< 0.1%
0.071 2
 
< 0.1%
0.07 8
0.1%

일산화탄소농도(ppm)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct14
Distinct (%)0.2%
Missing159
Missing (%)1.7%
Infinite0
Infinite (%)0.0%
Mean0.46309391
Minimum0.1
Maximum1.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size80.3 KiB
2024-05-03T21:50:48.195213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.3
Q10.3
median0.4
Q30.5
95-th percentile0.8
Maximum1.5
Range1.4
Interquartile range (IQR)0.2

Descriptive statistics

Standard deviation0.16824867
Coefficient of variation (CV)0.36331437
Kurtosis1.2769007
Mean0.46309391
Median Absolute Deviation (MAD)0.1
Skewness1.0639354
Sum4152.1
Variance0.028307616
MonotonicityNot monotonic
2024-05-03T21:50:48.563830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0.4 2634
28.9%
0.3 2032
22.3%
0.5 1772
19.4%
0.6 968
 
10.6%
0.7 571
 
6.3%
0.2 356
 
3.9%
0.8 322
 
3.5%
0.9 200
 
2.2%
1.0 77
 
0.8%
1.1 25
 
0.3%
Other values (4) 9
 
0.1%
(Missing) 159
 
1.7%
ValueCountFrequency (%)
0.1 2
 
< 0.1%
0.2 356
 
3.9%
0.3 2032
22.3%
0.4 2634
28.9%
0.5 1772
19.4%
0.6 968
 
10.6%
0.7 571
 
6.3%
0.8 322
 
3.5%
0.9 200
 
2.2%
1.0 77
 
0.8%
ValueCountFrequency (%)
1.5 1
 
< 0.1%
1.4 1
 
< 0.1%
1.2 5
 
0.1%
1.1 25
 
0.3%
1.0 77
 
0.8%
0.9 200
 
2.2%
0.8 322
 
3.5%
0.7 571
 
6.3%
0.6 968
10.6%
0.5 1772
19.4%
Distinct9
Distinct (%)0.1%
Missing67
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean0.0030895341
Minimum0.001
Maximum0.009
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size80.3 KiB
2024-05-03T21:50:48.870116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.001
5-th percentile0.002
Q10.003
median0.003
Q30.003
95-th percentile0.004
Maximum0.009
Range0.008
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.00078424279
Coefficient of variation (CV)0.25383853
Kurtosis2.4182165
Mean0.0030895341
Median Absolute Deviation (MAD)0
Skewness0.84172158
Sum27.985
Variance6.1503675 × 10-7
MonotonicityNot monotonic
2024-05-03T21:50:49.189734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0.003 4995
54.7%
0.004 1881
 
20.6%
0.002 1833
 
20.1%
0.005 273
 
3.0%
0.006 53
 
0.6%
0.007 10
 
0.1%
0.001 7
 
0.1%
0.008 4
 
< 0.1%
0.009 2
 
< 0.1%
(Missing) 67
 
0.7%
ValueCountFrequency (%)
0.001 7
 
0.1%
0.002 1833
 
20.1%
0.003 4995
54.7%
0.004 1881
 
20.6%
0.005 273
 
3.0%
0.006 53
 
0.6%
0.007 10
 
0.1%
0.008 4
 
< 0.1%
0.009 2
 
< 0.1%
ValueCountFrequency (%)
0.009 2
 
< 0.1%
0.008 4
 
< 0.1%
0.007 10
 
0.1%
0.006 53
 
0.6%
0.005 273
 
3.0%
0.004 1881
 
20.6%
0.003 4995
54.7%
0.002 1833
 
20.1%
0.001 7
 
0.1%

미세먼지농도(㎍/㎥)
Real number (ℝ)

HIGH CORRELATION 

Distinct217
Distinct (%)2.4%
Missing47
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean37.899647
Minimum3
Maximum503
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size80.3 KiB
2024-05-03T21:50:49.617928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile10
Q120
median30
Q344
95-th percentile86
Maximum503
Range500
Interquartile range (IQR)24

Descriptive statistics

Standard deviation37.411182
Coefficient of variation (CV)0.9871116
Kurtosis55.73103
Mean37.899647
Median Absolute Deviation (MAD)11
Skewness6.2089401
Sum344053
Variance1399.5965
MonotonicityNot monotonic
2024-05-03T21:50:50.010779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22 272
 
3.0%
23 251
 
2.8%
28 244
 
2.7%
21 240
 
2.6%
25 235
 
2.6%
27 226
 
2.5%
26 224
 
2.5%
30 215
 
2.4%
19 215
 
2.4%
29 215
 
2.4%
Other values (207) 6741
73.9%
ValueCountFrequency (%)
3 8
 
0.1%
4 24
 
0.3%
5 35
 
0.4%
6 43
 
0.5%
7 64
 
0.7%
8 92
1.0%
9 120
1.3%
10 156
1.7%
11 146
1.6%
12 162
1.8%
ValueCountFrequency (%)
503 1
< 0.1%
497 1
< 0.1%
478 1
< 0.1%
473 1
< 0.1%
471 1
< 0.1%
466 1
< 0.1%
465 1
< 0.1%
463 1
< 0.1%
460 1
< 0.1%
458 1
< 0.1%

초미세먼지농도(㎍/㎥)
Real number (ℝ)

HIGH CORRELATION 

Distinct112
Distinct (%)1.2%
Missing43
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean19.774499
Minimum1
Maximum123
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size80.3 KiB
2024-05-03T21:50:50.391625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q110
median16
Q324
95-th percentile48
Maximum123
Range122
Interquartile range (IQR)14

Descriptive statistics

Standard deviation15.017943
Coefficient of variation (CV)0.75946011
Kurtosis7.296018
Mean19.774499
Median Absolute Deviation (MAD)7
Skewness2.2729945
Sum179592
Variance225.53862
MonotonicityNot monotonic
2024-05-03T21:50:50.810401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12 428
 
4.7%
13 408
 
4.5%
14 404
 
4.4%
11 378
 
4.1%
15 374
 
4.1%
16 371
 
4.1%
17 371
 
4.1%
10 344
 
3.8%
18 330
 
3.6%
9 317
 
3.5%
Other values (102) 5357
58.7%
ValueCountFrequency (%)
1 29
 
0.3%
2 104
 
1.1%
3 153
1.7%
4 238
2.6%
5 263
2.9%
6 240
2.6%
7 288
3.2%
8 313
3.4%
9 317
3.5%
10 344
3.8%
ValueCountFrequency (%)
123 1
 
< 0.1%
114 1
 
< 0.1%
113 1
 
< 0.1%
111 1
 
< 0.1%
110 3
< 0.1%
109 1
 
< 0.1%
108 1
 
< 0.1%
107 2
< 0.1%
106 2
< 0.1%
104 3
< 0.1%

Interactions

2024-05-03T21:50:38.585210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:50:22.604224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:50:25.730185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:50:28.572587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:50:30.986857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:50:33.327911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:50:36.262208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:50:38.940276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:50:23.074817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:50:26.318662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:50:28.919251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:50:31.271816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:50:33.631827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:50:36.645461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:50:39.303854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:50:23.620198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:50:26.700252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:50:29.246570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:50:31.647589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:50:34.048987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:50:36.950588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:50:39.631571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:50:24.097074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:50:27.083359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:50:29.609223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:50:31.983700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:50:34.549656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:50:37.211516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:50:39.931362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:50:24.625965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:50:27.427829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:50:29.978798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:50:32.423927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:50:34.964560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:50:37.573334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:50:40.368955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:50:25.008785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:50:27.785723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:50:30.328731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:50:32.755220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:50:35.388817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:50:38.020158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:50:40.647895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:50:25.352826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:50:28.176263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:50:30.633125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:50:33.041533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:50:35.786565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:50:38.298982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-03T21:50:51.044437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
측정일시권역명측정소명이산화질소농도(ppm)오존농도(ppm)일산화탄소농도(ppm)아황산가스농도(ppm)미세먼지농도(㎍/㎥)초미세먼지농도(㎍/㎥)
측정일시1.0000.0000.0000.5740.6990.4530.3070.4630.480
권역명0.0001.0001.0000.2180.0640.0990.2220.0410.066
측정소명0.0001.0001.0000.3260.1190.2850.4510.0000.070
이산화질소농도(ppm)0.5740.2180.3261.0000.6580.7710.4030.4660.637
오존농도(ppm)0.6990.0640.1190.6581.0000.4710.2480.2260.425
일산화탄소농도(ppm)0.4530.0990.2850.7710.4711.0000.4150.3690.536
아황산가스농도(ppm)0.3070.2220.4510.4030.2480.4151.0000.2380.353
미세먼지농도(㎍/㎥)0.4630.0410.0000.4660.2260.3690.2381.0000.805
초미세먼지농도(㎍/㎥)0.4800.0660.0700.6370.4250.5360.3530.8051.000
2024-05-03T21:50:51.271330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
측정소명권역명
측정소명1.0000.999
권역명0.9991.000
2024-05-03T21:50:51.443155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
측정일시이산화질소농도(ppm)오존농도(ppm)일산화탄소농도(ppm)아황산가스농도(ppm)미세먼지농도(㎍/㎥)초미세먼지농도(㎍/㎥)권역명측정소명
측정일시1.000-0.116-0.108-0.097-0.140-0.237-0.2130.0000.000
이산화질소농도(ppm)-0.1161.000-0.5840.7540.4940.6030.6320.0920.120
오존농도(ppm)-0.108-0.5841.000-0.491-0.203-0.120-0.1310.0260.042
일산화탄소농도(ppm)-0.0970.754-0.4911.0000.4850.6180.6830.0570.113
아황산가스농도(ppm)-0.1400.494-0.2030.4851.0000.4370.4550.1290.190
미세먼지농도(㎍/㎥)-0.2370.603-0.1200.6180.4371.0000.9000.0170.000
초미세먼지농도(㎍/㎥)-0.2130.632-0.1310.6830.4550.9001.0000.0270.025
권역명0.0000.0920.0260.0570.1290.0170.0271.0000.999
측정소명0.0000.1200.0420.1130.1900.0000.0250.9991.000

Missing values

2024-05-03T21:50:41.472640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-03T21:50:42.108224image/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.
2024-05-03T21:50:42.702620image/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(㎍/㎥)
020210101동남권강남구0.0220.0170.50.0042214
120210101동남권강동구0.0290.0140.50.0033020
220210101동북권강북구0.0250.0130.60.0033319
320210101서남권강서구0.0270.0180.30.0052514
420210101서남권관악구0.0330.0130.60.0042112
520210101동북권광진구0.0250.0110.6<NA>2516
620210101서남권구로구0.0290.0160.40.0042414
720210101서남권금천구0.030.0150.50.0032818
820210101동북권노원구0.0290.0150.60.0042921
920210101동북권도봉구0.0230.0170.40.0022217
측정일시권역명측정소명이산화질소농도(ppm)오존농도(ppm)일산화탄소농도(ppm)아황산가스농도(ppm)미세먼지농도(㎍/㎥)초미세먼지농도(㎍/㎥)
911520211231동북권성동구0.0140.0310.30.003215
911620211231동북권성북구0.0140.030.40.002174
911720211231동남권송파구0.020.0250.40.003237
911820211231서남권양천구0.0170.0240.40.002228
911920211231서남권영등포구0.0150.0260.30.002226
912020211231도심권용산구0.0120.0280.30.003258
912120211231서북권은평구0.0140.027<NA>0.004268
912220211231도심권종로구0.0120.0320.30.003236
912320211231도심권중구0.0130.0330.30.003207
912420211231동북권중랑구0.0170.0280.30.003226