Overview

Dataset statistics

Number of variables13
Number of observations10000
Missing cells4150
Missing cells (%)3.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.2 MiB
Average record size in memory122.0 B

Variable types

Numeric9
Categorical4

Dataset

Description경기도 대기환경정보 일평균자료
Author경기도
URLhttps://data.gg.go.kr/portal/data/service/selectServicePage.do?&infId=XB5S3V2IJ8WNLWEX13QR33613659&infSeq=1

Alerts

도시명 is highly overall correlated with 도시코드 and 2 other fieldsHigh correlation
측정망코드 is highly overall correlated with 측정소명 and 1 other fieldsHigh correlation
측정소명 is highly overall correlated with 도시코드 and 4 other fieldsHigh correlation
측정망구분 is highly overall correlated with 측정소명 and 1 other fieldsHigh correlation
도시코드 is highly overall correlated with 측정소코드 and 2 other fieldsHigh correlation
측정소코드 is highly overall correlated with 도시코드 and 2 other fieldsHigh correlation
측정날짜 is highly overall correlated with SO2(ppm)High correlation
SO2(ppm) is highly overall correlated with 측정날짜 and 1 other fieldsHigh correlation
NO2(ppm) is highly overall correlated with SO2(ppm) and 3 other fieldsHigh correlation
CO(ppm) is highly overall correlated with NO2(ppm) and 2 other fieldsHigh correlation
PM10(㎍/㎥) is highly overall correlated with NO2(ppm) and 2 other fieldsHigh correlation
PM25(㎍/㎥) is highly overall correlated with NO2(ppm) and 2 other fieldsHigh correlation
측정망코드 is highly imbalanced (66.0%)Imbalance
측정망구분 is highly imbalanced (66.0%)Imbalance
SO2(ppm) has 285 (2.9%) missing valuesMissing
NO2(ppm) has 349 (3.5%) missing valuesMissing
CO(ppm) has 277 (2.8%) missing valuesMissing
O3(ppm) has 264 (2.6%) missing valuesMissing
PM10(㎍/㎥) has 553 (5.5%) missing valuesMissing
PM25(㎍/㎥) has 2422 (24.2%) missing valuesMissing

Reproduction

Analysis started2023-12-10 22:06:03.964903
Analysis finished2023-12-10 22:06:13.811344
Duration9.85 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

도시코드
Real number (ℝ)

HIGH CORRELATION 

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.0855
Minimum6
Maximum30
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T07:06:13.850895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile6
Q110
median11
Q316
95-th percentile30
Maximum30
Range24
Interquartile range (IQR)6

Descriptive statistics

Standard deviation7.4976004
Coefficient of variation (CV)0.5322921
Kurtosis-0.21937658
Mean14.0855
Median Absolute Deviation (MAD)1
Skewness1.0966405
Sum140855
Variance56.214011
MonotonicityNot monotonic
2023-12-11T07:06:13.939938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
10 2985
29.8%
11 1424
14.2%
6 1385
13.9%
12 1366
13.7%
25 1277
12.8%
30 979
 
9.8%
16 584
 
5.8%
ValueCountFrequency (%)
6 1385
13.9%
10 2985
29.8%
11 1424
14.2%
12 1366
13.7%
16 584
 
5.8%
25 1277
12.8%
30 979
 
9.8%
ValueCountFrequency (%)
30 979
 
9.8%
25 1277
12.8%
16 584
 
5.8%
12 1366
13.7%
11 1424
14.2%
10 2985
29.8%
6 1385
13.9%

도시명
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
고양시
2985 
과천시
1424 
광명시
1385 
구리시
1366 
광주시
1277 
Other values (2)
1563 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row과천시
2nd row구리시
3rd row가평군
4th row군포시
5th row광주시

Common Values

ValueCountFrequency (%)
고양시 2985
29.8%
과천시 1424
14.2%
광명시 1385
13.9%
구리시 1366
13.7%
광주시 1277
12.8%
가평군 979
 
9.8%
군포시 584
 
5.8%

Length

2023-12-11T07:06:14.050423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T07:06:14.138016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
고양시 2985
29.8%
과천시 1424
14.2%
광명시 1385
13.9%
구리시 1366
13.7%
광주시 1277
12.8%
가평군 979
 
9.8%
군포시 584
 
5.8%

측정소코드
Real number (ℝ)

HIGH CORRELATION 

Distinct18
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean131333.57
Minimum131161
Maximum131612
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T07:06:14.233320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum131161
5-th percentile131161
Q1131202
median131382
Q3131392
95-th percentile131611
Maximum131612
Range451
Interquartile range (IQR)190

Descriptive statistics

Standard deviation137.72069
Coefficient of variation (CV)0.0010486328
Kurtosis-0.70418205
Mean131333.57
Median Absolute Deviation (MAD)170
Skewness0.443597
Sum1.3133357 × 109
Variance18966.988
MonotonicityNot monotonic
2023-12-11T07:06:14.321790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
131392 757
 
7.6%
131381 727
 
7.3%
131201 718
 
7.2%
131611 711
 
7.1%
131161 711
 
7.1%
131202 706
 
7.1%
131212 693
 
6.9%
131382 692
 
6.9%
131163 674
 
6.7%
131211 673
 
6.7%
Other values (8) 2938
29.4%
ValueCountFrequency (%)
131161 711
7.1%
131163 674
6.7%
131201 718
7.2%
131202 706
7.1%
131211 673
6.7%
131212 693
6.9%
131381 727
7.3%
131382 692
6.9%
131383 633
6.3%
131384 514
5.1%
ValueCountFrequency (%)
131612 268
 
2.7%
131611 711
7.1%
131502 213
 
2.1%
131501 371
3.7%
131395 230
 
2.3%
131394 290
 
2.9%
131392 757
7.6%
131385 419
4.2%
131384 514
5.1%
131383 633
6.3%

측정소명
Categorical

HIGH CORRELATION 

Distinct18
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
경안동
757 
행신동
727 
별양동
718 
가평
711 
철산동
711 
Other values (13)
6376 

Length

Max length4
Median length3
Mean length2.9208
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row과천동
2nd row교문동
3rd row가평
4th row산본동
5th row오포1동

Common Values

ValueCountFrequency (%)
경안동 757
 
7.6%
행신동 727
 
7.3%
별양동 718
 
7.2%
가평 711
 
7.1%
철산동 711
 
7.1%
과천동 706
 
7.1%
동구동 693
 
6.9%
식사동 692
 
6.9%
소하동 674
 
6.7%
교문동 673
 
6.7%
Other values (8) 2938
29.4%

Length

2023-12-11T07:06:14.425747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경안동 757
 
7.6%
행신동 727
 
7.3%
별양동 718
 
7.2%
가평 711
 
7.1%
철산동 711
 
7.1%
과천동 706
 
7.1%
동구동 693
 
6.9%
식사동 692
 
6.9%
소하동 674
 
6.7%
교문동 673
 
6.7%
Other values (8) 2938
29.4%

측정망코드
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
9367 
2
 
633

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 9367
93.7%
2 633
 
6.3%

Length

2023-12-11T07:06:14.527185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T07:06:14.867555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 9367
93.7%
2 633
 
6.3%

측정망구분
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
도시대기
9367 
도로변
 
633

Length

Max length4
Median length4
Mean length3.9367
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row도시대기
2nd row도시대기
3rd row도시대기
4th row도시대기
5th row도시대기

Common Values

ValueCountFrequency (%)
도시대기 9367
93.7%
도로변 633
 
6.3%

Length

2023-12-11T07:06:14.951193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T07:06:15.028454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
도시대기 9367
93.7%
도로변 633
 
6.3%

측정날짜
Real number (ℝ)

HIGH CORRELATION 

Distinct3079
Distinct (%)30.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20192332
Minimum20150101
Maximum20230912
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T07:06:15.144194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20150101
5-th percentile20150712
Q120170714
median20191117
Q320211015
95-th percentile20230419
Maximum20230912
Range80811
Interquartile range (IQR)40301.25

Descriptive statistics

Standard deviation24815.985
Coefficient of variation (CV)0.0012289806
Kurtosis-1.1853288
Mean20192332
Median Absolute Deviation (MAD)20112.5
Skewness-0.14226338
Sum2.0192332 × 1011
Variance6.1583309 × 108
MonotonicityNot monotonic
2023-12-11T07:06:15.279930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20210903 9
 
0.1%
20210202 9
 
0.1%
20201229 9
 
0.1%
20170702 8
 
0.1%
20220922 8
 
0.1%
20220712 8
 
0.1%
20211106 8
 
0.1%
20170308 8
 
0.1%
20221014 8
 
0.1%
20221020 8
 
0.1%
Other values (3069) 9917
99.2%
ValueCountFrequency (%)
20150101 2
< 0.1%
20150102 2
< 0.1%
20150103 3
< 0.1%
20150104 4
< 0.1%
20150105 2
< 0.1%
20150106 2
< 0.1%
20150107 3
< 0.1%
20150108 4
< 0.1%
20150109 3
< 0.1%
20150110 1
 
< 0.1%
ValueCountFrequency (%)
20230912 3
< 0.1%
20230911 2
< 0.1%
20230910 3
< 0.1%
20230909 1
 
< 0.1%
20230908 1
 
< 0.1%
20230907 3
< 0.1%
20230906 4
< 0.1%
20230905 1
 
< 0.1%
20230904 4
< 0.1%
20230903 3
< 0.1%

SO2(ppm)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct61
Distinct (%)0.6%
Missing285
Missing (%)2.9%
Infinite0
Infinite (%)0.0%
Mean0.003348317
Minimum0
Maximum0.02
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T07:06:15.455481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0012
Q10.002
median0.003
Q30.004
95-th percentile0.006
Maximum0.02
Range0.02
Interquartile range (IQR)0.002

Descriptive statistics

Standard deviation0.0016570194
Coefficient of variation (CV)0.49488128
Kurtosis6.1569216
Mean0.003348317
Median Absolute Deviation (MAD)0.001
Skewness1.7165669
Sum32.5289
Variance2.7457134 × 10-6
MonotonicityNot monotonic
2023-12-11T07:06:15.589606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.003 2659
26.6%
0.002 2418
24.2%
0.004 1786
17.9%
0.005 911
 
9.1%
0.006 446
 
4.5%
0.001 389
 
3.9%
0.007 221
 
2.2%
0.008 110
 
1.1%
0.0012 52
 
0.5%
0.009 50
 
0.5%
Other values (51) 673
 
6.7%
(Missing) 285
 
2.9%
ValueCountFrequency (%)
0.0 1
 
< 0.1%
0.0001 1
 
< 0.1%
0.0002 1
 
< 0.1%
0.0004 1
 
< 0.1%
0.0005 1
 
< 0.1%
0.0006 2
 
< 0.1%
0.0007 11
 
0.1%
0.0008 14
 
0.1%
0.0009 24
 
0.2%
0.001 389
3.9%
ValueCountFrequency (%)
0.02 1
 
< 0.1%
0.017 1
 
< 0.1%
0.015 3
 
< 0.1%
0.014 6
 
0.1%
0.013 11
 
0.1%
0.012 10
 
0.1%
0.011 11
 
0.1%
0.01 20
 
0.2%
0.009 50
0.5%
0.008 110
1.1%

NO2(ppm)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct362
Distinct (%)3.8%
Missing349
Missing (%)3.5%
Infinite0
Infinite (%)0.0%
Mean0.022744617
Minimum0.001
Maximum0.097
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T07:06:15.730366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.001
5-th percentile0.0067
Q10.013
median0.02
Q30.031
95-th percentile0.047
Maximum0.097
Range0.096
Interquartile range (IQR)0.018

Descriptive statistics

Standard deviation0.012848494
Coefficient of variation (CV)0.56490265
Kurtosis0.69392013
Mean0.022744617
Median Absolute Deviation (MAD)0.0086
Skewness0.90016615
Sum219.5083
Variance0.00016508381
MonotonicityNot monotonic
2023-12-11T07:06:15.869246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.014 359
 
3.6%
0.018 312
 
3.1%
0.01 304
 
3.0%
0.016 303
 
3.0%
0.013 299
 
3.0%
0.012 295
 
2.9%
0.011 295
 
2.9%
0.019 287
 
2.9%
0.017 278
 
2.8%
0.023 278
 
2.8%
Other values (352) 6641
66.4%
(Missing) 349
 
3.5%
ValueCountFrequency (%)
0.001 1
 
< 0.1%
0.0013 1
 
< 0.1%
0.0019 1
 
< 0.1%
0.002 7
 
0.1%
0.0021 2
 
< 0.1%
0.0024 1
 
< 0.1%
0.0027 2
 
< 0.1%
0.0029 2
 
< 0.1%
0.003 24
0.2%
0.0032 2
 
< 0.1%
ValueCountFrequency (%)
0.097 1
< 0.1%
0.088 1
< 0.1%
0.084 1
< 0.1%
0.083 1
< 0.1%
0.082 1
< 0.1%
0.08 1
< 0.1%
0.079 1
< 0.1%
0.078 2
< 0.1%
0.077 1
< 0.1%
0.076 2
< 0.1%

CO(ppm)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct87
Distinct (%)0.9%
Missing277
Missing (%)2.8%
Infinite0
Infinite (%)0.0%
Mean0.47447496
Minimum0.1
Maximum2.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T07:06:16.004021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.2
Q10.36
median0.4
Q30.6
95-th percentile0.8
Maximum2.1
Range2
Interquartile range (IQR)0.24

Descriptive statistics

Standard deviation0.18442201
Coefficient of variation (CV)0.3886865
Kurtosis3.0299609
Mean0.47447496
Median Absolute Deviation (MAD)0.1
Skewness1.2247388
Sum4613.32
Variance0.034011479
MonotonicityNot monotonic
2023-12-11T07:06:16.132083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.4 2407
24.1%
0.5 1866
18.7%
0.3 1586
15.9%
0.6 1197
12.0%
0.7 625
 
6.2%
0.2 547
 
5.5%
0.8 345
 
3.5%
0.9 186
 
1.9%
1.0 104
 
1.0%
1.1 52
 
0.5%
Other values (77) 808
 
8.1%
(Missing) 277
 
2.8%
ValueCountFrequency (%)
0.1 18
 
0.2%
0.13 2
 
< 0.1%
0.14 2
 
< 0.1%
0.15 3
 
< 0.1%
0.16 1
 
< 0.1%
0.17 3
 
< 0.1%
0.18 6
 
0.1%
0.19 9
 
0.1%
0.2 547
5.5%
0.21 10
 
0.1%
ValueCountFrequency (%)
2.1 1
 
< 0.1%
1.8 2
 
< 0.1%
1.6 2
 
< 0.1%
1.4 4
 
< 0.1%
1.3 15
 
0.1%
1.2 27
 
0.3%
1.1 52
0.5%
1.06 1
 
< 0.1%
1.0 104
1.0%
0.99 1
 
< 0.1%

O3(ppm)
Real number (ℝ)

MISSING 

Distinct455
Distinct (%)4.7%
Missing264
Missing (%)2.6%
Infinite0
Infinite (%)0.0%
Mean0.026457683
Minimum0.001
Maximum0.087
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T07:06:16.263068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.001
5-th percentile0.007
Q10.016
median0.025
Q30.035
95-th percentile0.05
Maximum0.087
Range0.086
Interquartile range (IQR)0.019

Descriptive statistics

Standard deviation0.013302989
Coefficient of variation (CV)0.5028025
Kurtosis0.10433532
Mean0.026457683
Median Absolute Deviation (MAD)0.009
Skewness0.53180514
Sum257.592
Variance0.00017696952
MonotonicityNot monotonic
2023-12-11T07:06:16.384671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.024 304
 
3.0%
0.022 301
 
3.0%
0.025 274
 
2.7%
0.019 265
 
2.6%
0.017 254
 
2.5%
0.021 245
 
2.5%
0.026 245
 
2.5%
0.016 245
 
2.5%
0.028 243
 
2.4%
0.02 242
 
2.4%
Other values (445) 7118
71.2%
(Missing) 264
 
2.6%
ValueCountFrequency (%)
0.001 2
 
< 0.1%
0.0016 1
 
< 0.1%
0.002 14
 
0.1%
0.0021 1
 
< 0.1%
0.0025 1
 
< 0.1%
0.0029 1
 
< 0.1%
0.003 71
0.7%
0.0035 2
 
< 0.1%
0.004 96
1.0%
0.0042 1
 
< 0.1%
ValueCountFrequency (%)
0.087 1
 
< 0.1%
0.0832 1
 
< 0.1%
0.083 1
 
< 0.1%
0.0813 1
 
< 0.1%
0.08 2
 
< 0.1%
0.079 2
 
< 0.1%
0.078 1
 
< 0.1%
0.077 2
 
< 0.1%
0.074 5
0.1%
0.073 1
 
< 0.1%

PM10(㎍/㎥)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct191
Distinct (%)2.0%
Missing553
Missing (%)5.5%
Infinite0
Infinite (%)0.0%
Mean44.349741
Minimum0
Maximum652
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T07:06:16.498956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile13
Q127
median39
Q356
95-th percentile90
Maximum652
Range652
Interquartile range (IQR)29

Descriptive statistics

Standard deviation29.731836
Coefficient of variation (CV)0.6703948
Kurtosis80.324095
Mean44.349741
Median Absolute Deviation (MAD)14
Skewness5.5893641
Sum418972
Variance883.98205
MonotonicityNot monotonic
2023-12-11T07:06:16.623521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29 209
 
2.1%
36 208
 
2.1%
30 208
 
2.1%
27 208
 
2.1%
37 206
 
2.1%
32 204
 
2.0%
34 203
 
2.0%
26 188
 
1.9%
40 182
 
1.8%
31 182
 
1.8%
Other values (181) 7449
74.5%
(Missing) 553
 
5.5%
ValueCountFrequency (%)
0 2
 
< 0.1%
2 2
 
< 0.1%
3 5
 
0.1%
4 14
 
0.1%
5 14
 
0.1%
6 17
 
0.2%
7 39
0.4%
8 32
0.3%
9 54
0.5%
10 68
0.7%
ValueCountFrequency (%)
652 1
< 0.1%
624 1
< 0.1%
577 1
< 0.1%
567 1
< 0.1%
544 1
< 0.1%
510 1
< 0.1%
465 1
< 0.1%
355 1
< 0.1%
353 1
< 0.1%
344 1
< 0.1%

PM25(㎍/㎥)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct119
Distinct (%)1.6%
Missing2422
Missing (%)24.2%
Infinite0
Infinite (%)0.0%
Mean22.220243
Minimum0
Maximum152
Zeros6
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T07:06:16.740938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q112
median19
Q329
95-th percentile51
Maximum152
Range152
Interquartile range (IQR)17

Descriptive statistics

Standard deviation15.701121
Coefficient of variation (CV)0.70661338
Kurtosis6.9108373
Mean22.220243
Median Absolute Deviation (MAD)8
Skewness1.977182
Sum168385
Variance246.5252
MonotonicityNot monotonic
2023-12-11T07:06:16.865783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14 292
 
2.9%
16 290
 
2.9%
12 285
 
2.9%
13 283
 
2.8%
15 275
 
2.8%
11 274
 
2.7%
19 262
 
2.6%
17 258
 
2.6%
20 256
 
2.6%
18 253
 
2.5%
Other values (109) 4850
48.5%
(Missing) 2422
24.2%
ValueCountFrequency (%)
0 6
 
0.1%
1 26
 
0.3%
2 63
 
0.6%
3 101
1.0%
4 134
1.3%
5 151
1.5%
6 183
1.8%
7 217
2.2%
8 230
2.3%
9 236
2.4%
ValueCountFrequency (%)
152 2
< 0.1%
145 1
< 0.1%
143 1
< 0.1%
131 1
< 0.1%
127 1
< 0.1%
125 1
< 0.1%
124 1
< 0.1%
119 1
< 0.1%
118 1
< 0.1%
117 1
< 0.1%

Interactions

2023-12-11T07:06:12.569017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:06:06.103227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:06:06.913446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:06:07.711620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:06:08.618391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:06:09.438072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:06:10.337982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:06:11.066533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:06:11.755747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:06:12.670196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:06:06.183285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:06:06.993464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:06:07.819504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:06:08.708170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:06:09.531019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:06:10.412314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:06:11.138841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:06:11.833641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:06:12.750674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:06:06.259862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:06:07.075420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:06:07.920979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:06:08.812757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:06:09.609497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:06:10.494581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:06:11.205659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:06:11.924379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:06:12.842629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:06:06.353206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:06:07.162253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:06:08.039418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:06:08.935617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:06:09.692899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:06:10.590096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:06:11.288491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:06:12.044054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:06:12.934577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:06:06.437984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:06:07.271623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:06:08.149527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:06:09.030057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:06:09.771306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:06:10.675665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:06:11.367272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:06:12.139261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:06:13.043234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:06:06.514457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:06:07.352458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:06:08.255703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:06:09.109408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:06:09.843369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:06:10.750855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:06:11.438484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:06:12.222418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:06:13.151749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:06:06.601296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:06:07.457425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:06:08.363710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:06:09.196988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:06:09.922369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:06:10.833654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:06:11.529673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:06:12.310977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:06:13.238926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:06:06.723875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:06:07.531642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:06:08.444565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:06:09.271108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:06:09.989662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:06:10.910088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:06:11.597806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:06:12.387767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:06:13.319487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:06:06.831732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:06:07.606412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:06:08.529376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:06:09.355160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:06:10.257184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:06:10.989073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:06:11.675688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:06:12.487938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T07:06:16.975217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
도시코드도시명측정소코드측정소명측정망코드측정망구분측정날짜SO2(ppm)NO2(ppm)CO(ppm)O3(ppm)PM10(㎍/㎥)PM25(㎍/㎥)
도시코드1.0001.0000.9971.0000.5500.5500.2010.2640.3440.1610.0780.0440.083
도시명1.0001.0001.0001.0000.3720.3720.1910.2620.3330.2490.0730.0540.101
측정소코드0.9971.0001.0001.0000.5420.5420.2060.2840.3460.2540.0740.0620.105
측정소명1.0001.0001.0001.0001.0001.0000.3680.3550.4080.3150.1520.0870.133
측정망코드0.5500.3720.5421.0001.0001.0000.0400.1360.2170.1370.0940.0440.054
측정망구분0.5500.3720.5421.0001.0001.0000.0400.1360.2170.1370.0940.0440.054
측정날짜0.2010.1910.2060.3680.0400.0401.0000.6360.3680.3460.2420.1730.312
SO2(ppm)0.2640.2620.2840.3550.1360.1360.6361.0000.5430.4890.2990.2530.351
NO2(ppm)0.3440.3330.3460.4080.2170.2170.3680.5431.0000.7120.5540.2970.675
CO(ppm)0.1610.2490.2540.3150.1370.1370.3460.4890.7121.0000.5320.3240.653
O3(ppm)0.0780.0730.0740.1520.0940.0940.2420.2990.5540.5321.0000.1090.341
PM10(㎍/㎥)0.0440.0540.0620.0870.0440.0440.1730.2530.2970.3240.1091.0000.614
PM25(㎍/㎥)0.0830.1010.1050.1330.0540.0540.3120.3510.6750.6530.3410.6141.000
2023-12-11T07:06:17.107129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
도시명측정망코드측정소명측정망구분
도시명1.0000.3980.9990.398
측정망코드0.3981.0000.9990.999
측정소명0.9990.9991.0000.999
측정망구분0.3980.9990.9991.000
2023-12-11T07:06:17.227071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
도시코드측정소코드측정날짜SO2(ppm)NO2(ppm)CO(ppm)O3(ppm)PM10(㎍/㎥)PM25(㎍/㎥)도시명측정소명측정망코드측정망구분
도시코드1.0000.6800.062-0.136-0.208-0.106-0.028-0.076-0.0281.0000.9990.3980.398
측정소코드0.6801.0000.118-0.023-0.249-0.1080.010-0.050-0.0201.0000.9990.3980.398
측정날짜0.0620.1181.000-0.637-0.355-0.2230.152-0.286-0.1950.0980.1500.0300.030
SO2(ppm)-0.136-0.023-0.6371.0000.5060.412-0.2240.4900.4040.1540.1620.1230.123
NO2(ppm)-0.208-0.249-0.3550.5061.0000.679-0.4820.6100.6260.1740.1680.1670.167
CO(ppm)-0.106-0.108-0.2230.4120.6791.000-0.3840.5630.6110.1310.1240.0970.097
O3(ppm)-0.0280.0100.152-0.224-0.482-0.3841.000-0.097-0.1220.0370.0580.0710.071
PM10(㎍/㎥)-0.076-0.050-0.2860.4900.6100.563-0.0971.0000.8690.0280.0290.0440.044
PM25(㎍/㎥)-0.028-0.020-0.1950.4040.6260.611-0.1220.8691.0000.0510.0510.0420.042
도시명1.0001.0000.0980.1540.1740.1310.0370.0280.0511.0000.9990.3980.398
측정소명0.9990.9990.1500.1620.1680.1240.0580.0290.0510.9991.0000.9990.999
측정망코드0.3980.3980.0300.1230.1670.0970.0710.0440.0420.3980.9991.0000.999
측정망구분0.3980.3980.0300.1230.1670.0970.0710.0440.0420.3980.9990.9991.000

Missing values

2023-12-11T07:06:13.458705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T07:06:13.616008image/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.
2023-12-11T07:06:13.746719image/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

도시코드도시명측정소코드측정소명측정망코드측정망구분측정날짜SO2(ppm)NO2(ppm)CO(ppm)O3(ppm)PM10(㎍/㎥)PM25(㎍/㎥)
1687211과천시131202과천동1도시대기201911040.0020.0290.50.01205
3791612구리시131211교문동1도시대기201701300.0060.0140.40.0296248
273830가평군131611가평1도시대기202301070.00210.02211.060.01589275
4450816군포시131502산본동1도시대기201702150.0080.0550.60.01159<NA>
3455825광주시131394오포1동1도시대기202210270.0020.0280.60.0153818
418730가평군131612설악면1도시대기202112240.0030.0170.60.0124936
1026710고양시131382식사동1도시대기202205130.0030.0090.30.046399
1873310고양시131381행신동1도시대기202204170.0030.0220.50.0385432
3651825광주시131394오포1동1도시대기202012110.0030.0370.90.01310771
1023410고양시131382식사동1도시대기202204100.0030.0120.30.0573715
도시코드도시명측정소코드측정소명측정망코드측정망구분측정날짜SO2(ppm)NO2(ppm)CO(ppm)O3(ppm)PM10(㎍/㎥)PM25(㎍/㎥)
3973812구리시131212동구동1도시대기201610250.0020.0210.40.012438
230330가평군131611가평1도시대기202104240.0030.0080.20.0372010
1726610고양시131381행신동1도시대기201708090.0030.0110.40.0344421
1412910고양시131385주엽동1도시대기202004030.0040.0210.50.0375833
297686광명시131161철산동1도시대기201612310.0060.0391.00.0045931
3932312구리시131211교문동1도시대기202108280.0030.0130.40.0413417
3889312구리시131211교문동1도시대기201709110.0030.0140.30.0332615
263176광명시131163소하동1도시대기201910010.0030.0460.60.0246437
3586725광주시131394오포1동1도시대기202309110.00150.01450.480.03443521
3424925광주시131394오포1동1도시대기202208270.0010.0070.30.0332210