Overview

Dataset statistics

Number of variables11
Number of observations10000
Missing cells9542
Missing cells (%)8.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1015.6 KiB
Average record size in memory104.0 B

Variable types

Categorical3
Numeric8

Dataset

Description에어코리아_최종확정 측정자료 (지역, 망, 측정소코드, 측정소명, 측정일시, SO2, CO, O3, NO2, PM10, PM25) 정보 제공
Author한국환경공단
URLhttps://www.data.go.kr/data/15122830/fileData.do

Alerts

측정소명 is highly overall correlated with 측정소코드 and 2 other fieldsHigh correlation
지역 is highly overall correlated with 측정소코드 and 2 other fieldsHigh correlation
주소 is highly overall correlated with 측정소코드 and 2 other fieldsHigh correlation
측정소코드 is highly overall correlated with 지역 and 2 other fieldsHigh correlation
SO2 is highly overall correlated with CO and 1 other fieldsHigh correlation
CO is highly overall correlated with SO2 and 3 other fieldsHigh correlation
O3 is highly overall correlated with CO and 1 other fieldsHigh correlation
NO2 is highly overall correlated with SO2 and 2 other fieldsHigh correlation
PM10 is highly overall correlated with PM25High correlation
PM25 is highly overall correlated with CO and 1 other fieldsHigh correlation
SO2 has 216 (2.2%) missing valuesMissing
CO has 344 (3.4%) missing valuesMissing
O3 has 414 (4.1%) missing valuesMissing
NO2 has 247 (2.5%) missing valuesMissing
PM10 has 313 (3.1%) missing valuesMissing
PM25 has 8008 (80.1%) missing valuesMissing

Reproduction

Analysis started2024-03-14 21:20:48.398142
Analysis finished2024-03-14 21:21:06.095907
Duration17.7 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

지역
Categorical

HIGH CORRELATION 

Distinct22
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
경기 성남시
1721 
경기 부천시
1089 
경기 수원시
725 
경기 김포시
645 
경기 고양시
629 
Other values (17)
5191 

Length

Max length7
Median length6
Mean length6.0698
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row경기 수원시
2nd row경기 부천시
3rd row경기 성남시
4th row경기 남양주시
5th row경기 성남시

Common Values

ValueCountFrequency (%)
경기 성남시 1721
17.2%
경기 부천시 1089
 
10.9%
경기 수원시 725
 
7.2%
경기 김포시 645
 
6.5%
경기 고양시 629
 
6.3%
강원 춘천시 469
 
4.7%
경기 남양주시 455
 
4.5%
경기 광명시 449
 
4.5%
경기 과천시 444
 
4.4%
강원 원주시 424
 
4.2%
Other values (12) 2950
29.5%

Length

2024-03-15T06:21:06.555375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기 7630
38.1%
강원 2370
 
11.8%
성남시 1721
 
8.6%
부천시 1089
 
5.4%
수원시 725
 
3.6%
김포시 645
 
3.2%
고양시 629
 
3.1%
춘천시 469
 
2.3%
남양주시 455
 
2.3%
광명시 449
 
2.2%
Other values (14) 3818
19.1%

측정소코드
Real number (ℝ)

HIGH CORRELATION 

Distinct47
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean293313.16
Minimum131111
Maximum831155
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-15T06:21:07.098874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum131111
5-th percentile131113
Q1131161
median131392
Q3632122
95-th percentile831153
Maximum831155
Range700044
Interquartile range (IQR)500961

Descriptive statistics

Standard deviation264686.1
Coefficient of variation (CV)0.90240102
Kurtosis-0.58142865
Mean293313.16
Median Absolute Deviation (MAD)268
Skewness1.1085234
Sum2.9331316 × 109
Variance7.005873 × 1010
MonotonicityNot monotonic
2024-03-15T06:21:07.604059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
132113 243
 
2.4%
131571 243
 
2.4%
131129 237
 
2.4%
131241 235
 
2.4%
131163 232
 
2.3%
131502 228
 
2.3%
831152 228
 
2.3%
131201 227
 
2.3%
632431 226
 
2.3%
132112 226
 
2.3%
Other values (37) 7675
76.8%
ValueCountFrequency (%)
131111 220
2.2%
131112 213
2.1%
131113 217
2.2%
131114 75
 
0.8%
131120 223
2.2%
131121 208
2.1%
131123 204
2.0%
131124 216
2.2%
131125 217
2.2%
131126 205
2.1%
ValueCountFrequency (%)
831155 223
2.2%
831154 218
2.2%
831153 218
2.2%
831152 228
2.3%
831151 202
2.0%
632431 226
2.3%
632421 220
2.2%
632371 213
2.1%
632161 202
2.0%
632151 201
2.0%

측정소명
Categorical

HIGH CORRELATION 

Distinct47
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
석사동
 
243
보산동
 
243
상대원동
 
237
금곡동
 
235
소하동
 
232
Other values (42)
8810 

Length

Max length7
Median length3
Mean length3.1091
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row신풍동
2nd row오정동
3rd row운중동
4th row금곡동
5th row백현동

Common Values

ValueCountFrequency (%)
석사동 243
 
2.4%
보산동 243
 
2.4%
상대원동 237
 
2.4%
금곡동 235
 
2.4%
소하동 232
 
2.3%
내동 228
 
2.3%
산본동 228
 
2.3%
별양동 227
 
2.3%
치악산 226
 
2.3%
고촌읍 226
 
2.3%
Other values (37) 7675
76.8%

Length

2024-03-15T06:21:08.089049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
석사동 243
 
2.4%
보산동 243
 
2.4%
상대원동 237
 
2.4%
금곡동 235
 
2.4%
소하동 232
 
2.3%
내동 228
 
2.3%
산본동 228
 
2.3%
별양동 227
 
2.3%
고촌읍 226
 
2.3%
중앙로 226
 
2.3%
Other values (37) 7675
76.8%

측정일시
Real number (ℝ)

Distinct2147
Distinct (%)21.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0150215 × 109
Minimum2.0150101 × 109
Maximum2.0150331 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-15T06:21:08.526247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.0150101 × 109
5-th percentile2.0150105 × 109
Q12.0150122 × 109
median2.0150214 × 109
Q32.0150309 × 109
95-th percentile2.0150327 × 109
Maximum2.0150331 × 109
Range23023
Interquartile range (IQR)18688

Descriptive statistics

Standard deviation8363.909
Coefficient of variation (CV)4.1507791 × 10-6
Kurtosis-1.5172124
Mean2.0150215 × 109
Median Absolute Deviation (MAD)9304.5
Skewness0.034755078
Sum2.0150215 × 1013
Variance69954974
MonotonicityNot monotonic
2024-03-15T06:21:09.009781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2015030201 14
 
0.1%
2015032220 13
 
0.1%
2015030614 12
 
0.1%
2015011815 12
 
0.1%
2015013103 11
 
0.1%
2015011205 11
 
0.1%
2015010317 11
 
0.1%
2015021419 11
 
0.1%
2015012604 11
 
0.1%
2015010304 11
 
0.1%
Other values (2137) 9883
98.8%
ValueCountFrequency (%)
2015010101 4
< 0.1%
2015010102 3
< 0.1%
2015010103 2
 
< 0.1%
2015010104 3
< 0.1%
2015010105 4
< 0.1%
2015010106 4
< 0.1%
2015010107 6
0.1%
2015010108 3
< 0.1%
2015010109 3
< 0.1%
2015010110 7
0.1%
ValueCountFrequency (%)
2015033124 6
0.1%
2015033123 2
 
< 0.1%
2015033122 3
< 0.1%
2015033121 3
< 0.1%
2015033120 2
 
< 0.1%
2015033119 4
< 0.1%
2015033118 4
< 0.1%
2015033117 7
0.1%
2015033116 5
0.1%
2015033115 3
< 0.1%

SO2
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct97
Distinct (%)1.0%
Missing216
Missing (%)2.2%
Infinite0
Infinite (%)0.0%
Mean0.00621852
Minimum0.0007
Maximum0.054
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-15T06:21:09.380197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.0007
5-th percentile0.0022
Q10.004
median0.006
Q30.008
95-th percentile0.012
Maximum0.054
Range0.0533
Interquartile range (IQR)0.004

Descriptive statistics

Standard deviation0.003200879
Coefficient of variation (CV)0.51473325
Kurtosis12.318443
Mean0.00621852
Median Absolute Deviation (MAD)0.002
Skewness2.1740562
Sum60.842
Variance1.0245627 × 10-5
MonotonicityNot monotonic
2024-03-15T06:21:09.844888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.005 1624
16.2%
0.004 1531
15.3%
0.006 1480
14.8%
0.007 1112
11.1%
0.008 790
7.9%
0.003 668
6.7%
0.009 539
 
5.4%
0.01 348
 
3.5%
0.011 213
 
2.1%
0.012 165
 
1.7%
Other values (87) 1314
13.1%
(Missing) 216
 
2.2%
ValueCountFrequency (%)
0.0007 2
 
< 0.1%
0.0008 2
 
< 0.1%
0.0009 1
 
< 0.1%
0.001 11
 
0.1%
0.0011 13
 
0.1%
0.0012 10
 
0.1%
0.0013 21
0.2%
0.0014 34
0.3%
0.0015 46
0.5%
0.0016 43
0.4%
ValueCountFrequency (%)
0.054 1
 
< 0.1%
0.038 2
 
< 0.1%
0.031 3
< 0.1%
0.03 1
 
< 0.1%
0.028 2
 
< 0.1%
0.027 2
 
< 0.1%
0.026 5
0.1%
0.025 4
< 0.1%
0.024 2
 
< 0.1%
0.023 3
< 0.1%

CO
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct35
Distinct (%)0.4%
Missing344
Missing (%)3.4%
Infinite0
Infinite (%)0.0%
Mean0.72101284
Minimum0.1
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-15T06:21:10.260519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.3
Q10.5
median0.7
Q30.9
95-th percentile1.4
Maximum5
Range4.9
Interquartile range (IQR)0.4

Descriptive statistics

Standard deviation0.35080827
Coefficient of variation (CV)0.48654927
Kurtosis7.6577533
Mean0.72101284
Median Absolute Deviation (MAD)0.2
Skewness1.7957606
Sum6962.1
Variance0.12306644
MonotonicityNot monotonic
2024-03-15T06:21:10.681910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
0.6 1411
14.1%
0.5 1401
14.0%
0.7 1218
12.2%
0.4 1183
11.8%
0.8 982
9.8%
0.9 699
7.0%
0.3 684
6.8%
1.0 554
 
5.5%
1.1 367
 
3.7%
1.2 282
 
2.8%
Other values (25) 875
8.8%
(Missing) 344
 
3.4%
ValueCountFrequency (%)
0.1 17
 
0.2%
0.2 130
 
1.3%
0.3 684
6.8%
0.4 1183
11.8%
0.5 1401
14.0%
0.6 1411
14.1%
0.7 1218
12.2%
0.8 982
9.8%
0.9 699
7.0%
1.0 554
 
5.5%
ValueCountFrequency (%)
5.0 1
 
< 0.1%
4.4 1
 
< 0.1%
3.9 1
 
< 0.1%
3.4 1
 
< 0.1%
3.3 1
 
< 0.1%
3.1 1
 
< 0.1%
2.9 2
 
< 0.1%
2.8 1
 
< 0.1%
2.7 2
 
< 0.1%
2.6 5
0.1%

O3
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct89
Distinct (%)0.9%
Missing414
Missing (%)4.1%
Infinite0
Infinite (%)0.0%
Mean0.01946255
Minimum0.001
Maximum0.109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-15T06:21:11.112013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.001
5-th percentile0.002
Q10.006
median0.018
Q30.029
95-th percentile0.046
Maximum0.109
Range0.108
Interquartile range (IQR)0.023

Descriptive statistics

Standard deviation0.014795166
Coefficient of variation (CV)0.76018641
Kurtosis0.82731254
Mean0.01946255
Median Absolute Deviation (MAD)0.012
Skewness0.89086417
Sum186.568
Variance0.00021889693
MonotonicityNot monotonic
2024-03-15T06:21:11.603640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.003 745
 
7.4%
0.004 635
 
6.3%
0.002 563
 
5.6%
0.005 334
 
3.3%
0.006 277
 
2.8%
0.007 230
 
2.3%
0.03 222
 
2.2%
0.009 219
 
2.2%
0.029 218
 
2.2%
0.024 217
 
2.2%
Other values (79) 5926
59.3%
(Missing) 414
 
4.1%
ValueCountFrequency (%)
0.001 42
 
0.4%
0.002 563
5.6%
0.003 745
7.4%
0.004 635
6.3%
0.005 334
3.3%
0.006 277
 
2.8%
0.007 230
 
2.3%
0.008 215
 
2.1%
0.009 219
 
2.2%
0.01 194
 
1.9%
ValueCountFrequency (%)
0.109 1
< 0.1%
0.104 1
< 0.1%
0.094 1
< 0.1%
0.091 1
< 0.1%
0.089 1
< 0.1%
0.087 1
< 0.1%
0.086 1
< 0.1%
0.083 1
< 0.1%
0.082 1
< 0.1%
0.081 1
< 0.1%

NO2
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct272
Distinct (%)2.8%
Missing247
Missing (%)2.5%
Infinite0
Infinite (%)0.0%
Mean0.032087696
Minimum0.0015
Maximum0.128
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-15T06:21:11.986902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.0015
5-th percentile0.006
Q10.016
median0.03
Q30.046
95-th percentile0.068
Maximum0.128
Range0.1265
Interquartile range (IQR)0.03

Descriptive statistics

Standard deviation0.019836245
Coefficient of variation (CV)0.61818852
Kurtosis0.063052955
Mean0.032087696
Median Absolute Deviation (MAD)0.015
Skewness0.66673949
Sum312.9513
Variance0.00039347663
MonotonicityNot monotonic
2024-03-15T06:21:12.457964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.01 202
 
2.0%
0.016 195
 
1.9%
0.015 195
 
1.9%
0.018 194
 
1.9%
0.033 190
 
1.9%
0.022 187
 
1.9%
0.028 183
 
1.8%
0.024 180
 
1.8%
0.027 178
 
1.8%
0.02 177
 
1.8%
Other values (262) 7872
78.7%
(Missing) 247
 
2.5%
ValueCountFrequency (%)
0.0015 2
 
< 0.1%
0.0017 3
 
< 0.1%
0.0018 3
 
< 0.1%
0.0019 3
 
< 0.1%
0.002 8
0.1%
0.0021 1
 
< 0.1%
0.0022 3
 
< 0.1%
0.0023 4
< 0.1%
0.0024 6
0.1%
0.0025 8
0.1%
ValueCountFrequency (%)
0.128 1
< 0.1%
0.121 1
< 0.1%
0.116 2
< 0.1%
0.113 1
< 0.1%
0.111 1
< 0.1%
0.11 1
< 0.1%
0.108 1
< 0.1%
0.107 1
< 0.1%
0.106 1
< 0.1%
0.105 1
< 0.1%

PM10
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct363
Distinct (%)3.7%
Missing313
Missing (%)3.1%
Infinite0
Infinite (%)0.0%
Mean71.954475
Minimum3
Maximum1037
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-15T06:21:12.898438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile25
Q140
median56
Q380
95-th percentile154
Maximum1037
Range1034
Interquartile range (IQR)40

Descriptive statistics

Standard deviation73.871124
Coefficient of variation (CV)1.026637
Kurtosis61.027199
Mean71.954475
Median Absolute Deviation (MAD)18
Skewness6.7835041
Sum697023
Variance5456.9429
MonotonicityNot monotonic
2024-03-15T06:21:13.262764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
44 204
 
2.0%
40 192
 
1.9%
38 184
 
1.8%
47 175
 
1.8%
43 173
 
1.7%
50 168
 
1.7%
46 167
 
1.7%
42 163
 
1.6%
52 160
 
1.6%
36 157
 
1.6%
Other values (353) 7944
79.4%
(Missing) 313
 
3.1%
ValueCountFrequency (%)
3 2
 
< 0.1%
5 4
 
< 0.1%
6 4
 
< 0.1%
7 4
 
< 0.1%
8 11
0.1%
9 8
 
0.1%
10 11
0.1%
11 9
 
0.1%
12 12
0.1%
13 26
0.3%
ValueCountFrequency (%)
1037 1
< 0.1%
979 1
< 0.1%
955 1
< 0.1%
945 1
< 0.1%
937 1
< 0.1%
935 1
< 0.1%
925 1
< 0.1%
910 1
< 0.1%
908 1
< 0.1%
902 1
< 0.1%

PM25
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct112
Distinct (%)5.6%
Missing8008
Missing (%)80.1%
Infinite0
Infinite (%)0.0%
Mean35.073795
Minimum1
Maximum168
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-15T06:21:13.675809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8
Q119
median31
Q347
95-th percentile75
Maximum168
Range167
Interquartile range (IQR)28

Descriptive statistics

Standard deviation21.534504
Coefficient of variation (CV)0.61397703
Kurtosis2.8246869
Mean35.073795
Median Absolute Deviation (MAD)14
Skewness1.2475095
Sum69867
Variance463.73488
MonotonicityNot monotonic
2024-03-15T06:21:14.126573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16 52
 
0.5%
20 45
 
0.4%
17 44
 
0.4%
36 44
 
0.4%
26 44
 
0.4%
14 43
 
0.4%
44 43
 
0.4%
28 43
 
0.4%
19 43
 
0.4%
23 41
 
0.4%
Other values (102) 1550
 
15.5%
(Missing) 8008
80.1%
ValueCountFrequency (%)
1 5
 
0.1%
2 5
 
0.1%
3 7
 
0.1%
4 11
 
0.1%
5 11
 
0.1%
6 29
0.3%
7 18
0.2%
8 19
0.2%
9 26
0.3%
10 25
0.2%
ValueCountFrequency (%)
168 1
 
< 0.1%
154 1
 
< 0.1%
153 1
 
< 0.1%
148 1
 
< 0.1%
144 1
 
< 0.1%
138 1
 
< 0.1%
137 1
 
< 0.1%
114 1
 
< 0.1%
113 1
 
< 0.1%
111 4
< 0.1%

주소
Categorical

HIGH CORRELATION 

Distinct47
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
강원 춘천시 석사동 322-1(석사119안전센터 2층 옥상)
 
243
경기 동두천시 싸리말로 28(보산동 314-5)
 
243
경기 성남시 중원구 상대원동 1451
 
237
경기 남양주시 금곡동 185-10(남양주시청)
 
235
경기 광명시 소하동 1331
 
232
Other values (42)
8810 

Length

Max length36
Median length27
Mean length23.4152
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row경기 수원시 팔달구 신풍동 123-69(선경도서관)
2nd row경기 부천시 오정구 성오로 172 (오정어울마당 오정아트홀)
3rd row경기 성남시 분당구 운중동 935
4th row경기 남양주시 금곡동 185-10(남양주시청)
5th row경기 성남시 분당구 백현동

Common Values

ValueCountFrequency (%)
강원 춘천시 석사동 322-1(석사119안전센터 2층 옥상) 243
 
2.4%
경기 동두천시 싸리말로 28(보산동 314-5) 243
 
2.4%
경기 성남시 중원구 상대원동 1451 237
 
2.4%
경기 남양주시 금곡동 185-10(남양주시청) 235
 
2.4%
경기 광명시 소하동 1331 232
 
2.3%
경기 부천시 오정구 삼작로 114 (신흥동주민센터) 228
 
2.3%
경기 군포시 금정동 844(여성회관) 228
 
2.3%
경기 과천시 별양동 16번지 (문원초등학교) 227
 
2.3%
강원 횡성군 강림면 강림리(치악산) 226
 
2.3%
경기 김포시 고촌면 신곡리 530-1(고촌면사무소) 226
 
2.3%
Other values (37) 7675
76.8%

Length

2024-03-15T06:21:14.600930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기 7630
 
15.2%
강원 2370
 
4.7%
성남시 1721
 
3.4%
부천시 1089
 
2.2%
옥상 887
 
1.8%
분당구 854
 
1.7%
수원시 725
 
1.4%
김포시 645
 
1.3%
수정구 630
 
1.3%
고양시 629
 
1.3%
Other values (144) 32986
65.8%

Interactions

2024-03-15T06:21:02.591137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:20:49.801007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:20:51.720952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:20:53.514220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:20:55.622854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:20:57.486174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:20:58.995272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:21:00.782489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:21:02.806452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:20:50.088468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:20:51.901457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:20:53.792389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:20:55.892531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:20:57.661864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:20:59.236922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:21:01.005087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:21:03.077894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:20:50.332816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:20:52.118021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:20:54.050529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:20:56.155926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:20:57.843277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:20:59.479052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:21:01.208403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:21:03.355070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:20:50.504826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:20:52.381447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:20:54.320061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:20:56.420881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:20:58.008724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:20:59.644641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:21:01.533780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:21:03.615843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:20:50.695920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:20:52.644734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:20:54.583941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:20:56.692268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:20:58.252469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:20:59.836500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:21:01.807058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:21:03.900908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:20:50.869093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:20:52.858633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:20:55.073627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:20:56.914979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:20:58.412691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:21:00.111722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:21:01.973448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:21:04.173548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:20:51.126972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:20:53.096151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:20:55.291693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:20:57.158911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:20:58.578020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:21:00.339876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:21:02.165523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:21:04.446826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:20:51.437018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:20:53.252618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:20:55.449586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:20:57.313073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:20:58.736116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:21:00.584114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:21:02.420891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-15T06:21:14.940547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지역측정소코드측정소명측정일시SO2COO3NO2PM10PM25주소
지역1.0001.0001.0000.0260.3610.3650.4260.5270.1130.2681.000
측정소코드1.0001.0001.0000.0000.1880.1320.2500.4060.0400.1871.000
측정소명1.0001.0001.0000.1270.4400.4720.4670.5820.1390.2721.000
측정일시0.0260.0000.1271.0000.1690.1870.3180.1610.3050.1920.127
SO20.3610.1880.4400.1691.0000.8090.1700.3210.2030.3590.440
CO0.3650.1320.4720.1870.8091.0000.5360.5960.1720.4220.472
O30.4260.2500.4670.3180.1700.5361.0000.6530.2220.2530.467
NO20.5270.4060.5820.1610.3210.5960.6531.0000.1870.3540.582
PM100.1130.0400.1390.3050.2030.1720.2220.1871.0000.6950.139
PM250.2680.1870.2720.1920.3590.4220.2530.3540.6951.0000.272
주소1.0001.0001.0000.1270.4400.4720.4670.5820.1390.2721.000
2024-03-15T06:21:15.164338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
측정소명지역주소
측정소명1.0000.9991.000
지역0.9991.0000.999
주소1.0000.9991.000
2024-03-15T06:21:15.327936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
측정소코드측정일시SO2COO3NO2PM10PM25지역측정소명주소
측정소코드1.0000.019-0.101-0.1370.212-0.355-0.0140.0570.9990.9980.998
측정일시0.0191.000-0.142-0.2040.330-0.0210.1780.0430.0120.0540.054
SO2-0.101-0.1421.0000.520-0.2770.5040.3570.4220.1550.1810.181
CO-0.137-0.2040.5201.000-0.6280.6460.3830.5430.1440.1810.181
O30.2120.330-0.277-0.6281.000-0.737-0.109-0.2920.1720.1780.178
NO2-0.355-0.0210.5040.646-0.7371.0000.2790.4370.2250.2390.239
PM10-0.0140.1780.3570.383-0.1090.2791.0000.7620.0420.0480.048
PM250.0570.0430.4220.543-0.2920.4370.7621.0000.1340.1270.127
지역0.9990.0120.1550.1440.1720.2250.0420.1341.0000.9990.999
측정소명0.9980.0540.1810.1810.1780.2390.0480.1270.9991.0001.000
주소0.9980.0540.1810.1810.1780.2390.0480.1270.9991.0001.000

Missing values

2024-03-15T06:21:04.832568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-15T06:21:05.460614image/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.
2024-03-15T06:21:05.879740image/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

지역측정소코드측정소명측정일시SO2COO3NO2PM10PM25주소
94836경기 수원시131111신풍동20150323130.0050.50.0410.01259<NA>경기 수원시 팔달구 신풍동 123-69(선경도서관)
71682경기 부천시831154오정동20150117190.0050.40.015<NA>3212경기 부천시 오정구 성오로 172 (오정어울마당 오정아트홀)
88678경기 성남시131128운중동20150105230.0081.20.0020.0688044경기 성남시 분당구 운중동 935
58408경기 남양주시131241금곡동20150104170.0060.60.0130.035<NA><NA>경기 남양주시 금곡동 185-10(남양주시청)
77132경기 성남시131120백현동20150305210.0111.1<NA>0.07371<NA>경기 성남시 분당구 백현동
70727경기 부천시831153중2동20150308240.010.90.010.079143<NA>경기 부천시 원미구 심중로 121 (책마루도서관)
49159경기 군포시131501당동20150310080.0030.30.0230.01442<NA>경기 군포시 당동 752-10 (군포도서관)
43008경기 광주시131392경안동20150324010.0040.70.0060.04554<NA>경기 광주시 경안동 53-4
86948경기 성남시131126복정동20150123210.0050.50.0120.04725<NA>경기 성남시 수정구 복정동 515번지 (상수도사업소)
33628경기 과천시131201별양동20150221050.0040.90.0050.04945<NA>경기 과천시 별양동 16번지 (문원초등학교)
지역측정소코드측정소명측정일시SO2COO3NO2PM10PM25주소
2006강원 강릉시632132옥천동20150325150.0050.40.0290.0234913강원 강릉시 옥천동 327-2(옥천동주민센터)
58516경기 남양주시131241금곡동20150109050.0040.70.0020.03649<NA>경기 남양주시 금곡동 185-10(남양주시청)
20019강원 춘천시132113석사동20150125040.0061.50.0040.0399859강원 춘천시 석사동 322-1(석사119안전센터 2층 옥상)
88358경기 성남시131126복정동2015032315<NA><NA><NA><NA><NA><NA>경기 성남시 수정구 복정동 515번지 (상수도사업소)
96322경기 수원시131112인계동20150223110.0050.60.0160.024673168경기 수원시 팔달구 인계동 1111(수원시청)
21266강원 춘천시132113석사동20150318030.0061.30.0070.0319755강원 춘천시 석사동 322-1(석사119안전센터 2층 옥상)
86609경기 성남시131126복정동20150109180.0080.60.0080.05663<NA>경기 성남시 수정구 복정동 515번지 (상수도사업소)
27600경기 고양시131381행신동20150312010.0040.40.0330.01327<NA>경기 고양시 덕양구 행신동 59-3 행신배수지
66733경기 부천시831151소사본동20150322140.0070.70.0560.019175<NA>경기 부천시 소사구 경인옛로 72 (소사어울림마당 소향관)
21508강원 춘천시132113석사동20150328050.0040.70.0210.0255727강원 춘천시 석사동 322-1(석사119안전센터 2층 옥상)