Overview

Dataset statistics

Number of variables14
Number of observations2588
Missing cells6924
Missing cells (%)19.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory313.5 KiB
Average record size in memory124.1 B

Variable types

DateTime1
Categorical2
Numeric11

Dataset

Description경기도 중금속 측정결과 현황
Author경기도
URLhttps://data.gg.go.kr/portal/data/service/selectServicePage.do?&infId=U296AUQAC7S3BZNYM66P11856499&infSeq=1

Alerts

납측정값(μg/m³) is highly overall correlated with 카드뮴측정값(μg/m³) and 9 other fieldsHigh correlation
카드뮴측정값(μg/m³) is highly overall correlated with 납측정값(μg/m³) and 8 other fieldsHigh correlation
크롬측정값(μg/m³) is highly overall correlated with 납측정값(μg/m³) and 9 other fieldsHigh correlation
구리측정값(μg/m³) is highly overall correlated with 납측정값(μg/m³) and 8 other fieldsHigh correlation
망간측정값(μg/m³) is highly overall correlated with 납측정값(μg/m³) and 8 other fieldsHigh correlation
철측정값(μg/m³) is highly overall correlated with 납측정값(μg/m³) and 8 other fieldsHigh correlation
니켈측정값(μg/m³) is highly overall correlated with 납측정값(μg/m³) and 7 other fieldsHigh correlation
비소측정값(μg/m³) is highly overall correlated with 납측정값(μg/m³) and 4 other fieldsHigh correlation
알루미늄측정값(μg/m³) is highly overall correlated with 납측정값(μg/m³) and 10 other fieldsHigh correlation
칼슘측정값(μg/m³) is highly overall correlated with 납측정값(μg/m³) and 10 other fieldsHigh correlation
마그네슘측정값(μg/m³) is highly overall correlated with 납측정값(μg/m³) and 10 other fieldsHigh correlation
베릴륨측정값(μg/m³) is highly overall correlated with 크롬측정값(μg/m³) and 3 other fieldsHigh correlation
베릴륨측정값(μg/m³) is highly imbalanced (99.1%)Imbalance
알루미늄측정값(μg/m³) has 2308 (89.2%) missing valuesMissing
칼슘측정값(μg/m³) has 2308 (89.2%) missing valuesMissing
마그네슘측정값(μg/m³) has 2308 (89.2%) missing valuesMissing
카드뮴측정값(μg/m³) is highly skewed (γ1 = 32.23436433)Skewed
카드뮴측정값(μg/m³) has 879 (34.0%) zerosZeros
크롬측정값(μg/m³) has 153 (5.9%) zerosZeros
니켈측정값(μg/m³) has 186 (7.2%) zerosZeros
비소측정값(μg/m³) has 61 (2.4%) zerosZeros

Reproduction

Analysis started2024-04-29 13:31:38.378879
Analysis finished2024-04-29 13:31:52.633060
Duration14.25 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Distinct555
Distinct (%)21.4%
Missing0
Missing (%)0.0%
Memory size20.3 KiB
Minimum2016-06-06 00:00:00
Maximum2024-03-15 00:00:00
2024-04-29T22:31:52.699710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:31:52.831516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

시군명
Categorical

Distinct8
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size20.3 KiB
의정부시
469 
평택시
462 
안산시
449 
수원시
425 
안성시
254 
Other values (3)
529 

Length

Max length4
Median length3
Mean length3.181221
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row가평군
2nd row수원시
3rd row안산시
4th row안성시
5th row의정부시

Common Values

ValueCountFrequency (%)
의정부시 469
18.1%
평택시 462
17.9%
안산시 449
17.3%
수원시 425
16.4%
안성시 254
9.8%
포천시 250
9.7%
가평군 239
9.2%
의왕시 40
 
1.5%

Length

2024-04-29T22:31:52.950643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-29T22:31:53.057087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
의정부시 469
18.1%
평택시 462
17.9%
안산시 449
17.3%
수원시 425
16.4%
안성시 254
9.8%
포천시 250
9.7%
가평군 239
9.2%
의왕시 40
 
1.5%

납측정값(μg/m³)
Real number (ℝ)

HIGH CORRELATION 

Distinct137
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.026932767
Minimum0
Maximum0.779
Zeros6
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size22.9 KiB
2024-04-29T22:31:53.190956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.002
Q10.01
median0.02
Q30.034
95-th percentile0.07165
Maximum0.779
Range0.779
Interquartile range (IQR)0.024

Descriptive statistics

Standard deviation0.029867406
Coefficient of variation (CV)1.1089617
Kurtosis164.66946
Mean0.026932767
Median Absolute Deviation (MAD)0.011
Skewness8.2344932
Sum69.702
Variance0.00089206196
MonotonicityNot monotonic
2024-04-29T22:31:53.344159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.004 97
 
3.7%
0.002 90
 
3.5%
0.018 73
 
2.8%
0.02 69
 
2.7%
0.017 69
 
2.7%
0.024 67
 
2.6%
0.015 66
 
2.6%
0.016 66
 
2.6%
0.009 66
 
2.6%
0.011 65
 
2.5%
Other values (127) 1860
71.9%
ValueCountFrequency (%)
0.0 6
 
0.2%
0.001 59
2.3%
0.002 90
3.5%
0.003 59
2.3%
0.004 97
3.7%
0.005 52
2.0%
0.006 50
1.9%
0.007 63
2.4%
0.008 65
2.5%
0.009 66
2.6%
ValueCountFrequency (%)
0.779 1
< 0.1%
0.286 1
< 0.1%
0.284 1
< 0.1%
0.277 1
< 0.1%
0.23 1
< 0.1%
0.198 1
< 0.1%
0.191 1
< 0.1%
0.175 1
< 0.1%
0.173 1
< 0.1%
0.172 1
< 0.1%

카드뮴측정값(μg/m³)
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct31
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0014196291
Minimum0
Maximum0.384
Zeros879
Zeros (%)34.0%
Negative0
Negative (%)0.0%
Memory size22.9 KiB
2024-04-29T22:31:53.465358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.001
Q30.001
95-th percentile0.003
Maximum0.384
Range0.384
Interquartile range (IQR)0.001

Descriptive statistics

Standard deviation0.0093296847
Coefficient of variation (CV)6.5719172
Kurtosis1203.9557
Mean0.0014196291
Median Absolute Deviation (MAD)0
Skewness32.234364
Sum3.674
Variance8.7043016 × 10-5
MonotonicityNot monotonic
2024-04-29T22:31:53.585260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
0.001 1310
50.6%
0.0 879
34.0%
0.002 238
 
9.2%
0.003 55
 
2.1%
0.004 36
 
1.4%
0.005 14
 
0.5%
0.006 7
 
0.3%
0.007 5
 
0.2%
0.011 5
 
0.2%
0.012 4
 
0.2%
Other values (21) 35
 
1.4%
ValueCountFrequency (%)
0.0 879
34.0%
0.001 1310
50.6%
0.002 238
 
9.2%
0.003 55
 
2.1%
0.004 36
 
1.4%
0.005 14
 
0.5%
0.006 7
 
0.3%
0.007 5
 
0.2%
0.008 4
 
0.2%
0.009 3
 
0.1%
ValueCountFrequency (%)
0.384 1
< 0.1%
0.212 1
< 0.1%
0.103 1
< 0.1%
0.099 1
< 0.1%
0.06 1
< 0.1%
0.031 1
< 0.1%
0.027 1
< 0.1%
0.025 1
< 0.1%
0.024 1
< 0.1%
0.023 2
0.1%

크롬측정값(μg/m³)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct36
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0042824575
Minimum0
Maximum0.067
Zeros153
Zeros (%)5.9%
Negative0
Negative (%)0.0%
Memory size22.9 KiB
2024-04-29T22:31:53.884907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.002
median0.003
Q30.005
95-th percentile0.013
Maximum0.067
Range0.067
Interquartile range (IQR)0.003

Descriptive statistics

Standard deviation0.0046753316
Coefficient of variation (CV)1.0917403
Kurtosis21.437599
Mean0.0042824575
Median Absolute Deviation (MAD)0.002
Skewness3.4179543
Sum11.083
Variance2.1858726 × 10-5
MonotonicityNot monotonic
2024-04-29T22:31:54.006347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
0.002 531
20.5%
0.001 449
17.3%
0.003 379
14.6%
0.004 291
11.2%
0.005 197
 
7.6%
0.0 153
 
5.9%
0.006 122
 
4.7%
0.007 97
 
3.7%
0.008 59
 
2.3%
0.01 57
 
2.2%
Other values (26) 253
9.8%
ValueCountFrequency (%)
0.0 153
 
5.9%
0.001 449
17.3%
0.002 531
20.5%
0.003 379
14.6%
0.004 291
11.2%
0.005 197
 
7.6%
0.006 122
 
4.7%
0.007 97
 
3.7%
0.008 59
 
2.3%
0.009 50
 
1.9%
ValueCountFrequency (%)
0.067 1
 
< 0.1%
0.04 1
 
< 0.1%
0.034 1
 
< 0.1%
0.033 3
0.1%
0.032 2
0.1%
0.031 3
0.1%
0.03 1
 
< 0.1%
0.029 2
0.1%
0.028 3
0.1%
0.026 2
0.1%

구리측정값(μg/m³)
Real number (ℝ)

HIGH CORRELATION 

Distinct218
Distinct (%)8.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.033171561
Minimum0
Maximum0.825
Zeros4
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size22.9 KiB
2024-04-29T22:31:54.140553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.003
Q10.009
median0.017
Q30.032
95-th percentile0.131
Maximum0.825
Range0.825
Interquartile range (IQR)0.023

Descriptive statistics

Standard deviation0.052633286
Coefficient of variation (CV)1.5866991
Kurtosis40.711814
Mean0.033171561
Median Absolute Deviation (MAD)0.01
Skewness4.9689418
Sum85.848
Variance0.0027702628
MonotonicityNot monotonic
2024-04-29T22:31:54.268584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.005 102
 
3.9%
0.006 100
 
3.9%
0.013 96
 
3.7%
0.007 95
 
3.7%
0.011 94
 
3.6%
0.009 91
 
3.5%
0.008 84
 
3.2%
0.012 84
 
3.2%
0.014 83
 
3.2%
0.01 82
 
3.2%
Other values (208) 1677
64.8%
ValueCountFrequency (%)
0.0 4
 
0.2%
0.001 26
 
1.0%
0.002 54
2.1%
0.003 57
2.2%
0.004 81
3.1%
0.005 102
3.9%
0.006 100
3.9%
0.007 95
3.7%
0.008 84
3.2%
0.009 91
3.5%
ValueCountFrequency (%)
0.825 1
< 0.1%
0.671 1
< 0.1%
0.462 1
< 0.1%
0.448 1
< 0.1%
0.44 1
< 0.1%
0.389 1
< 0.1%
0.387 1
< 0.1%
0.373 1
< 0.1%
0.357 1
< 0.1%
0.356 1
< 0.1%

망간측정값(μg/m³)
Real number (ℝ)

HIGH CORRELATION 

Distinct126
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.025104328
Minimum0
Maximum0.207
Zeros6
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size22.9 KiB
2024-04-29T22:31:54.396702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.004
Q10.011
median0.019
Q30.031
95-th percentile0.067
Maximum0.207
Range0.207
Interquartile range (IQR)0.02

Descriptive statistics

Standard deviation0.022864552
Coefficient of variation (CV)0.91078128
Kurtosis12.421689
Mean0.025104328
Median Absolute Deviation (MAD)0.01
Skewness2.8038746
Sum64.97
Variance0.00052278772
MonotonicityNot monotonic
2024-04-29T22:31:54.523382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.011 94
 
3.6%
0.014 91
 
3.5%
0.008 89
 
3.4%
0.01 83
 
3.2%
0.009 82
 
3.2%
0.013 81
 
3.1%
0.012 79
 
3.1%
0.021 75
 
2.9%
0.015 74
 
2.9%
0.016 74
 
2.9%
Other values (116) 1766
68.2%
ValueCountFrequency (%)
0.0 6
 
0.2%
0.001 22
 
0.9%
0.002 49
1.9%
0.003 48
1.9%
0.004 62
2.4%
0.005 46
1.8%
0.006 67
2.6%
0.007 70
2.7%
0.008 89
3.4%
0.009 82
3.2%
ValueCountFrequency (%)
0.207 1
< 0.1%
0.205 1
< 0.1%
0.19 1
< 0.1%
0.188 1
< 0.1%
0.185 1
< 0.1%
0.182 1
< 0.1%
0.172 1
< 0.1%
0.167 2
0.1%
0.164 1
< 0.1%
0.162 1
< 0.1%

철측정값(μg/m³)
Real number (ℝ)

HIGH CORRELATION 

Distinct1175
Distinct (%)45.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.63784042
Minimum0.013
Maximum5.85
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.9 KiB
2024-04-29T22:31:54.652902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.013
5-th percentile0.111
Q10.337
median0.561
Q30.84625
95-th percentile1.41
Maximum5.85
Range5.837
Interquartile range (IQR)0.50925

Descriptive statistics

Standard deviation0.44925525
Coefficient of variation (CV)0.704338
Kurtosis21.31735
Mean0.63784042
Median Absolute Deviation (MAD)0.249
Skewness2.7559638
Sum1650.731
Variance0.20183028
MonotonicityNot monotonic
2024-04-29T22:31:54.797768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.378 9
 
0.3%
0.491 7
 
0.3%
0.358 7
 
0.3%
0.606 7
 
0.3%
0.381 7
 
0.3%
0.623 7
 
0.3%
0.559 7
 
0.3%
0.506 7
 
0.3%
0.243 6
 
0.2%
0.151 6
 
0.2%
Other values (1165) 2518
97.3%
ValueCountFrequency (%)
0.013 1
< 0.1%
0.015 1
< 0.1%
0.016 1
< 0.1%
0.017 1
< 0.1%
0.02 1
< 0.1%
0.022 1
< 0.1%
0.023 1
< 0.1%
0.026 2
0.1%
0.027 1
< 0.1%
0.028 1
< 0.1%
ValueCountFrequency (%)
5.85 1
< 0.1%
5.64 1
< 0.1%
5.567 1
< 0.1%
4.471 1
< 0.1%
3.473 1
< 0.1%
2.658 1
< 0.1%
2.633 1
< 0.1%
2.5 1
< 0.1%
2.454 1
< 0.1%
2.315 1
< 0.1%

니켈측정값(μg/m³)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct37
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0040714838
Minimum0
Maximum0.08
Zeros186
Zeros (%)7.2%
Negative0
Negative (%)0.0%
Memory size22.9 KiB
2024-04-29T22:31:54.935842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.001
median0.003
Q30.005
95-th percentile0.013
Maximum0.08
Range0.08
Interquartile range (IQR)0.004

Descriptive statistics

Standard deviation0.0046420281
Coefficient of variation (CV)1.1401318
Kurtosis35.753295
Mean0.0040714838
Median Absolute Deviation (MAD)0.002
Skewness3.9566057
Sum10.537
Variance2.1548425 × 10-5
MonotonicityNot monotonic
2024-04-29T22:31:55.064042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
0.001 564
21.8%
0.002 518
20.0%
0.003 346
13.4%
0.004 212
 
8.2%
0.0 186
 
7.2%
0.005 153
 
5.9%
0.006 131
 
5.1%
0.007 100
 
3.9%
0.008 74
 
2.9%
0.009 52
 
2.0%
Other values (27) 252
9.7%
ValueCountFrequency (%)
0.0 186
 
7.2%
0.001 564
21.8%
0.002 518
20.0%
0.003 346
13.4%
0.004 212
 
8.2%
0.005 153
 
5.9%
0.006 131
 
5.1%
0.007 100
 
3.9%
0.008 74
 
2.9%
0.009 52
 
2.0%
ValueCountFrequency (%)
0.08 1
< 0.1%
0.039 1
< 0.1%
0.037 1
< 0.1%
0.036 1
< 0.1%
0.035 2
0.1%
0.033 1
< 0.1%
0.031 1
< 0.1%
0.03 1
< 0.1%
0.029 1
< 0.1%
0.028 1
< 0.1%

비소측정값(μg/m³)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct68
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0075799845
Minimum0
Maximum0.368
Zeros61
Zeros (%)2.4%
Negative0
Negative (%)0.0%
Memory size22.9 KiB
2024-04-29T22:31:55.205271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.001
Q10.002
median0.004
Q30.007
95-th percentile0.019
Maximum0.368
Range0.368
Interquartile range (IQR)0.005

Descriptive statistics

Standard deviation0.02111425
Coefficient of variation (CV)2.7855267
Kurtosis176.9877
Mean0.0075799845
Median Absolute Deviation (MAD)0.002
Skewness12.462635
Sum19.617
Variance0.00044581154
MonotonicityNot monotonic
2024-04-29T22:31:55.343873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.002 346
13.4%
0.003 337
13.0%
0.001 320
12.4%
0.004 309
11.9%
0.005 275
10.6%
0.006 188
7.3%
0.007 141
 
5.4%
0.008 89
 
3.4%
0.009 72
 
2.8%
0.01 66
 
2.6%
Other values (58) 445
17.2%
ValueCountFrequency (%)
0.0 61
 
2.4%
0.001 320
12.4%
0.002 346
13.4%
0.003 337
13.0%
0.004 309
11.9%
0.005 275
10.6%
0.006 188
7.3%
0.007 141
5.4%
0.008 89
 
3.4%
0.009 72
 
2.8%
ValueCountFrequency (%)
0.368 1
< 0.1%
0.358 1
< 0.1%
0.356 1
< 0.1%
0.329 1
< 0.1%
0.313 1
< 0.1%
0.295 1
< 0.1%
0.285 1
< 0.1%
0.261 1
< 0.1%
0.255 1
< 0.1%
0.219 1
< 0.1%

베릴륨측정값(μg/m³)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size20.3 KiB
0.0
2586 
0.001
 
2

Length

Max length5
Median length3
Mean length3.0015456
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 2586
99.9%
0.001 2
 
0.1%

Length

2024-04-29T22:31:55.470612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-29T22:31:55.565441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 2586
99.9%
0.001 2
 
0.1%

알루미늄측정값(μg/m³)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct228
Distinct (%)81.4%
Missing2308
Missing (%)89.2%
Infinite0
Infinite (%)0.0%
Mean0.27535357
Minimum0.009
Maximum0.822
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.9 KiB
2024-04-29T22:31:55.671629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.009
5-th percentile0.027
Q10.114
median0.2385
Q30.39525
95-th percentile0.64645
Maximum0.822
Range0.813
Interquartile range (IQR)0.28125

Descriptive statistics

Standard deviation0.19464588
Coefficient of variation (CV)0.70689433
Kurtosis-0.42275507
Mean0.27535357
Median Absolute Deviation (MAD)0.1375
Skewness0.67165341
Sum77.099
Variance0.037887018
MonotonicityNot monotonic
2024-04-29T22:31:55.820024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.224 4
 
0.2%
0.027 3
 
0.1%
0.241 3
 
0.1%
0.376 3
 
0.1%
0.206 3
 
0.1%
0.069 3
 
0.1%
0.132 3
 
0.1%
0.28 2
 
0.1%
0.215 2
 
0.1%
0.237 2
 
0.1%
Other values (218) 252
 
9.7%
(Missing) 2308
89.2%
ValueCountFrequency (%)
0.009 1
< 0.1%
0.011 1
< 0.1%
0.012 2
0.1%
0.017 1
< 0.1%
0.018 1
< 0.1%
0.019 1
< 0.1%
0.02 1
< 0.1%
0.021 1
< 0.1%
0.023 1
< 0.1%
0.024 1
< 0.1%
ValueCountFrequency (%)
0.822 1
< 0.1%
0.798 1
< 0.1%
0.76 1
< 0.1%
0.721 1
< 0.1%
0.719 1
< 0.1%
0.708 1
< 0.1%
0.706 1
< 0.1%
0.7 1
< 0.1%
0.691 1
< 0.1%
0.69 1
< 0.1%

칼슘측정값(μg/m³)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct230
Distinct (%)82.1%
Missing2308
Missing (%)89.2%
Infinite0
Infinite (%)0.0%
Mean0.3103
Minimum0.007
Maximum1.125
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.9 KiB
2024-04-29T22:31:55.969987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.007
5-th percentile0.03795
Q10.13075
median0.2545
Q30.442
95-th percentile0.74415
Maximum1.125
Range1.118
Interquartile range (IQR)0.31125

Descriptive statistics

Standard deviation0.22157029
Coefficient of variation (CV)0.71405185
Kurtosis0.45902856
Mean0.3103
Median Absolute Deviation (MAD)0.1505
Skewness0.92528993
Sum86.884
Variance0.049093394
MonotonicityNot monotonic
2024-04-29T22:31:56.272233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.254 3
 
0.1%
0.095 3
 
0.1%
0.633 3
 
0.1%
0.414 3
 
0.1%
0.185 3
 
0.1%
0.073 3
 
0.1%
0.104 2
 
0.1%
0.366 2
 
0.1%
0.385 2
 
0.1%
0.363 2
 
0.1%
Other values (220) 254
 
9.8%
(Missing) 2308
89.2%
ValueCountFrequency (%)
0.007 1
< 0.1%
0.016 1
< 0.1%
0.017 1
< 0.1%
0.021 2
0.1%
0.025 1
< 0.1%
0.027 1
< 0.1%
0.029 1
< 0.1%
0.03 1
< 0.1%
0.031 1
< 0.1%
0.034 2
0.1%
ValueCountFrequency (%)
1.125 1
< 0.1%
0.962 1
< 0.1%
0.956 1
< 0.1%
0.944 1
< 0.1%
0.895 1
< 0.1%
0.884 1
< 0.1%
0.845 1
< 0.1%
0.825 1
< 0.1%
0.821 1
< 0.1%
0.797 1
< 0.1%

마그네슘측정값(μg/m³)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct165
Distinct (%)58.9%
Missing2308
Missing (%)89.2%
Infinite0
Infinite (%)0.0%
Mean0.099975
Minimum0.004
Maximum0.418
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.9 KiB
2024-04-29T22:31:56.396748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.004
5-th percentile0.013
Q10.041
median0.083
Q30.13325
95-th percentile0.26
Maximum0.418
Range0.414
Interquartile range (IQR)0.09225

Descriptive statistics

Standard deviation0.076995481
Coefficient of variation (CV)0.77014734
Kurtosis1.3945452
Mean0.099975
Median Absolute Deviation (MAD)0.044
Skewness1.2327766
Sum27.993
Variance0.005928304
MonotonicityNot monotonic
2024-04-29T22:31:56.524061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.12 5
 
0.2%
0.032 5
 
0.2%
0.02 5
 
0.2%
0.041 5
 
0.2%
0.204 4
 
0.2%
0.069 4
 
0.2%
0.036 4
 
0.2%
0.103 4
 
0.2%
0.021 3
 
0.1%
0.212 3
 
0.1%
Other values (155) 238
 
9.2%
(Missing) 2308
89.2%
ValueCountFrequency (%)
0.004 2
0.1%
0.005 3
0.1%
0.006 2
0.1%
0.007 1
 
< 0.1%
0.008 1
 
< 0.1%
0.01 1
 
< 0.1%
0.011 1
 
< 0.1%
0.012 2
0.1%
0.013 2
0.1%
0.014 3
0.1%
ValueCountFrequency (%)
0.418 1
< 0.1%
0.379 1
< 0.1%
0.321 1
< 0.1%
0.315 1
< 0.1%
0.313 1
< 0.1%
0.305 1
< 0.1%
0.294 1
< 0.1%
0.283 1
< 0.1%
0.28 1
< 0.1%
0.276 1
< 0.1%

Interactions

2024-04-29T22:31:51.258217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:31:40.310218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:31:41.565922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:31:42.614832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:31:43.660966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:31:44.792170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:31:45.819652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:31:46.838941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:31:48.073594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:31:49.115565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:31:50.160423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:31:51.347023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:31:40.608237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:31:41.678776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:31:42.728655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:31:43.906741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:31:44.893883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:31:45.918620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:31:46.951172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:31:48.180064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:31:49.209008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:31:50.250723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:31:51.440789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:31:40.702840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:31:41.783534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:31:42.826522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:31:44.008585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:31:44.990416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:31:46.012837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:31:47.050865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:31:48.291552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:31:49.304572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:31:50.350096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:31:51.524672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:31:40.797920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:31:41.881732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:31:42.936463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:31:44.118755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:31:45.086790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:31:46.110426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:31:47.146154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:31:48.387388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:31:49.397717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:31:50.433421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:31:51.602061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:31:40.891747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:31:41.969455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:31:43.022810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:31:44.201024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:31:45.175385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:31:46.196744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:31:47.233621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:31:48.472051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:31:49.484346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:31:50.505473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:31:51.686356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:31:40.984521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:31:42.056853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:31:43.111288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:31:44.280151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:31:45.265839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:31:46.280464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:31:47.502009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:31:48.549384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:31:49.585222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:31:50.593043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:31:51.775420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:31:41.088445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:31:42.153550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:31:43.209917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:31:44.369861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:31:45.382606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:31:46.371110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:31:47.604102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:31:48.642404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:31:49.676654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:31:50.679453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:31:51.860532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:31:41.194020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:31:42.251390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:31:43.309919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:31:44.461008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:31:45.475458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:31:46.480229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:31:47.713501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:31:48.737701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:31:49.778258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:31:50.922567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:31:51.946594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:31:41.292962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:31:42.347091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:31:43.401669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:31:44.550839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:31:45.562680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:31:46.578619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:31:47.816778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:31:48.838978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:31:49.877167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:31:51.009363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:31:52.035987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:31:41.390506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:31:42.440713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:31:43.492344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:31:44.636309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:31:45.655593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:31:46.670314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:31:47.907202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:31:48.947697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:31:49.974467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:31:51.099840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:31:52.115781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:31:41.477262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:31:42.528265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:31:43.575567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:31:44.715036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:31:45.740574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:31:46.755905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:31:47.995837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:31:49.034465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:31:50.068282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:31:51.181964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-29T22:31:56.618729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군명납측정값(μg/m³)카드뮴측정값(μg/m³)크롬측정값(μg/m³)구리측정값(μg/m³)망간측정값(μg/m³)철측정값(μg/m³)니켈측정값(μg/m³)비소측정값(μg/m³)베릴륨측정값(μg/m³)알루미늄측정값(μg/m³)칼슘측정값(μg/m³)마그네슘측정값(μg/m³)
시군명1.0000.2610.0610.4200.5670.4370.4050.3980.0000.0000.1960.2100.202
납측정값(μg/m³)0.2611.0000.8970.2560.3550.2730.1220.2500.1600.0000.2980.1320.165
카드뮴측정값(μg/m³)0.0610.8971.0000.0000.0000.0000.0000.0000.0000.0000.2360.1690.000
크롬측정값(μg/m³)0.4200.2560.0001.0000.3460.5280.3450.4870.0000.6540.2810.2380.232
구리측정값(μg/m³)0.5670.3550.0000.3461.0000.3970.3050.2730.0820.0000.4110.5520.561
망간측정값(μg/m³)0.4370.2730.0000.5280.3971.0000.6750.5250.0000.0000.3680.4040.364
철측정값(μg/m³)0.4050.1220.0000.3450.3050.6751.0000.3740.0000.0000.6730.7630.813
니켈측정값(μg/m³)0.3980.2500.0000.4870.2730.5250.3741.0000.0000.0000.2140.1490.441
비소측정값(μg/m³)0.0000.1600.0000.0000.0820.0000.0000.0001.0000.0000.1960.1840.084
베릴륨측정값(μg/m³)0.0000.0000.0000.6540.0000.0000.0000.0000.0001.000NaNNaNNaN
알루미늄측정값(μg/m³)0.1960.2980.2360.2810.4110.3680.6730.2140.196NaN1.0000.9340.744
칼슘측정값(μg/m³)0.2100.1320.1690.2380.5520.4040.7630.1490.184NaN0.9341.0000.796
마그네슘측정값(μg/m³)0.2020.1650.0000.2320.5610.3640.8130.4410.084NaN0.7440.7961.000
2024-04-29T22:31:56.765737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군명베릴륨측정값(μg/m³)
시군명1.0000.000
베릴륨측정값(μg/m³)0.0001.000
2024-04-29T22:31:56.853021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
납측정값(μg/m³)카드뮴측정값(μg/m³)크롬측정값(μg/m³)구리측정값(μg/m³)망간측정값(μg/m³)철측정값(μg/m³)니켈측정값(μg/m³)비소측정값(μg/m³)알루미늄측정값(μg/m³)칼슘측정값(μg/m³)마그네슘측정값(μg/m³)시군명베릴륨측정값(μg/m³)
납측정값(μg/m³)1.0000.7930.5310.6630.5720.6620.5300.7130.6190.5860.5630.1630.000
카드뮴측정값(μg/m³)0.7931.0000.5040.5950.5390.6200.4960.5430.5740.5460.5460.0370.000
크롬측정값(μg/m³)0.5310.5041.0000.7530.8150.6930.8050.3380.6680.7090.6620.2400.705
구리측정값(μg/m³)0.6630.5950.7531.0000.7160.7100.7490.4020.6460.6960.6350.2200.000
망간측정값(μg/m³)0.5720.5390.8150.7161.0000.8250.8120.3830.6500.7020.6600.2250.000
철측정값(μg/m³)0.6620.6200.6930.7100.8251.0000.7120.4880.8530.8880.8300.1450.000
니켈측정값(μg/m³)0.5300.4960.8050.7490.8120.7121.0000.2910.6890.7210.7050.2060.000
비소측정값(μg/m³)0.7130.5430.3380.4020.3830.4880.2911.0000.5700.5380.5050.0000.000
알루미늄측정값(μg/m³)0.6190.5740.6680.6460.6500.8530.6890.5701.0000.9630.9080.0991.000
칼슘측정값(μg/m³)0.5860.5460.7090.6960.7020.8880.7210.5380.9631.0000.9040.1071.000
마그네슘측정값(μg/m³)0.5630.5460.6620.6350.6600.8300.7050.5050.9080.9041.0000.1061.000
시군명0.1630.0370.2400.2200.2250.1450.2060.0000.0990.1070.1061.0000.000
베릴륨측정값(μg/m³)0.0000.0000.7050.0000.0000.0000.0000.0001.0001.0001.0000.0001.000

Missing values

2024-04-29T22:31:52.229341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-29T22:31:52.413170image/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.
2024-04-29T22:31:52.576794image/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

집계일자시군명납측정값(μg/m³)카드뮴측정값(μg/m³)크롬측정값(μg/m³)구리측정값(μg/m³)망간측정값(μg/m³)철측정값(μg/m³)니켈측정값(μg/m³)비소측정값(μg/m³)베릴륨측정값(μg/m³)알루미늄측정값(μg/m³)칼슘측정값(μg/m³)마그네슘측정값(μg/m³)
02024-03-15가평군0.0110.0010.0030.010.0170.6240.0040.0040.00.6460.7340.209
12024-03-15수원시0.010.00.0020.010.0180.5230.0040.0030.00.4220.5090.178
22024-03-15안산시0.0340.0090.0050.0580.0260.5740.0070.0030.00.3520.4560.176
32024-03-15안성시0.0120.0010.0030.0140.0210.7470.0040.0030.00.6380.7470.212
42024-03-15의정부시0.0150.0010.0040.0170.0230.7980.0060.0040.00.5770.8950.237
52024-03-15평택시0.0140.00.0030.0190.0310.7720.0080.0030.00.5420.6330.213
62024-03-15포천시0.0140.0010.0040.0140.0220.710.0050.0040.00.6910.7970.237
72024-03-14가평군0.020.0010.0030.010.0170.5990.0030.0050.00.6110.6260.212
82024-03-14수원시0.0180.0010.0030.0160.0230.7290.0040.0040.00.5810.7260.262
92024-03-14안산시0.0740.0170.0060.1010.0320.7710.0060.0040.00.5820.6960.268
집계일자시군명납측정값(μg/m³)카드뮴측정값(μg/m³)크롬측정값(μg/m³)구리측정값(μg/m³)망간측정값(μg/m³)철측정값(μg/m³)니켈측정값(μg/m³)비소측정값(μg/m³)베릴륨측정값(μg/m³)알루미늄측정값(μg/m³)칼슘측정값(μg/m³)마그네슘측정값(μg/m³)
25782016-06-08의정부시0.0150.0010.00.0130.0070.5210.0040.0030.0<NA><NA><NA>
25792016-06-08평택시0.010.00.00.0030.0060.6230.0120.0020.0<NA><NA><NA>
25802016-06-07안산시0.0630.0130.00.1440.0190.6890.0080.0050.0<NA><NA><NA>
25812016-06-07의왕시0.0240.0010.00.020.0060.6470.0030.0040.0<NA><NA><NA>
25822016-06-07의정부시0.0110.00.00.0080.00.3950.00.0030.0<NA><NA><NA>
25832016-06-07평택시0.0180.0010.00.0090.0130.8550.0170.0060.0<NA><NA><NA>
25842016-06-06안산시0.0160.0010.00.010.00.4830.0020.0030.0<NA><NA><NA>
25852016-06-06의왕시0.0090.00.00.0130.00.4370.00.0020.0<NA><NA><NA>
25862016-06-06의정부시0.010.00.00.0110.00.5250.00.0030.0<NA><NA><NA>
25872016-06-06평택시0.0070.00.00.0040.00.4420.00.0020.0<NA><NA><NA>