Overview

Dataset statistics

Number of variables14
Number of observations618
Missing cells618
Missing cells (%)7.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory70.7 KiB
Average record size in memory117.2 B

Variable types

Numeric2
Categorical6
Text2
Unsupported1
DateTime2
Boolean1

Alerts

instt_code has constant value ""Constant
apr_at has constant value ""Constant
last_load_dttm has constant value ""Constant
kind is highly overall correlated with rsh_loc and 2 other fieldsHigh correlation
iodine_131 is highly overall correlated with rsh_loc and 2 other fieldsHigh correlation
cesium_134 is highly overall correlated with rsh_loc and 2 other fieldsHigh correlation
rsh_loc is highly overall correlated with kind and 2 other fieldsHigh correlation
skey is highly overall correlated with rsh_yearHigh correlation
rsh_year is highly overall correlated with skeyHigh correlation
h3 has 618 (100.0%) missing valuesMissing
skey has unique valuesUnique
h3 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2024-04-17 13:27:39.132776
Analysis finished2024-04-17 13:27:40.294749
Duration1.16 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

skey
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct618
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5656.5
Minimum5348
Maximum5965
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2024-04-17T22:27:40.359382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5348
5-th percentile5378.85
Q15502.25
median5656.5
Q35810.75
95-th percentile5934.15
Maximum5965
Range617
Interquartile range (IQR)308.5

Descriptive statistics

Standard deviation178.54551
Coefficient of variation (CV)0.031564662
Kurtosis-1.2
Mean5656.5
Median Absolute Deviation (MAD)154.5
Skewness0
Sum3495717
Variance31878.5
MonotonicityNot monotonic
2024-04-17T22:27:40.482003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5644 1
 
0.2%
5932 1
 
0.2%
5925 1
 
0.2%
5926 1
 
0.2%
5927 1
 
0.2%
5928 1
 
0.2%
5929 1
 
0.2%
5930 1
 
0.2%
5931 1
 
0.2%
5933 1
 
0.2%
Other values (608) 608
98.4%
ValueCountFrequency (%)
5348 1
0.2%
5349 1
0.2%
5350 1
0.2%
5351 1
0.2%
5352 1
0.2%
5353 1
0.2%
5354 1
0.2%
5355 1
0.2%
5356 1
0.2%
5357 1
0.2%
ValueCountFrequency (%)
5965 1
0.2%
5964 1
0.2%
5963 1
0.2%
5962 1
0.2%
5961 1
0.2%
5960 1
0.2%
5959 1
0.2%
5958 1
0.2%
5957 1
0.2%
5956 1
0.2%

rsh_year
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
2018
228 
2019
207 
2020
183 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2019
2nd row2019
3rd row2019
4th row2019
5th row2019

Common Values

ValueCountFrequency (%)
2018 228
36.9%
2019 207
33.5%
2020 183
29.6%

Length

2024-04-17T22:27:40.598092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T22:27:40.689445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2018 228
36.9%
2019 207
33.5%
2020 183
29.6%

rsh_month
Real number (ℝ)

Distinct12
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.907767
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2024-04-17T22:27:40.776103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q14
median7
Q310
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.1968742
Coefficient of variation (CV)0.46279415
Kurtosis-1.1361692
Mean6.907767
Median Absolute Deviation (MAD)3
Skewness-0.14500872
Sum4269
Variance10.220004
MonotonicityNot monotonic
2024-04-17T22:27:40.875704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
8 66
10.7%
10 63
10.2%
9 62
10.0%
11 62
10.0%
3 61
9.9%
4 60
9.7%
7 59
9.5%
6 47
7.6%
5 41
6.6%
12 38
6.1%
Other values (2) 59
9.5%
ValueCountFrequency (%)
1 26
 
4.2%
2 33
5.3%
3 61
9.9%
4 60
9.7%
5 41
6.6%
6 47
7.6%
7 59
9.5%
8 66
10.7%
9 62
10.0%
10 63
10.2%
ValueCountFrequency (%)
12 38
6.1%
11 62
10.0%
10 63
10.2%
9 62
10.0%
8 66
10.7%
7 59
9.5%
6 47
7.6%
5 41
6.6%
4 60
9.7%
3 61
9.9%

rsh_loc
Categorical

HIGH CORRELATION 

Distinct44
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
부산광역시청
 
40
고리원전인근
 
38
덕산_원수
 
36
명장_정수
 
36
덕산_정수
 
36
Other values (39)
432 

Length

Max length12
Median length11
Mean length6.7394822
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row수영구 광안리해수욕장
2nd row고리원전인근
3rd row장안읍사무소
4th row부산환경공단기장사업소
5th row기장초등학교

Common Values

ValueCountFrequency (%)
부산광역시청 40
 
6.5%
고리원전인근 38
 
6.1%
덕산_원수 36
 
5.8%
명장_정수 36
 
5.8%
덕산_정수 36
 
5.8%
화명_원수 36
 
5.8%
화명_정수 36
 
5.8%
명장_원수 36
 
5.8%
기장초등학교 36
 
5.8%
범어사_정수 31
 
5.0%
Other values (34) 257
41.6%

Length

2024-04-17T22:27:40.989126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
기장군 50
 
6.3%
부산광역시청 40
 
5.1%
고리원전인근 38
 
4.8%
덕산_원수 36
 
4.6%
덕산_정수 36
 
4.6%
화명_원수 36
 
4.6%
화명_정수 36
 
4.6%
명장_원수 36
 
4.6%
기장초등학교 36
 
4.6%
명장_정수 36
 
4.6%
Other values (44) 410
51.9%

kind
Categorical

HIGH CORRELATION 

Distinct9
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
상수
278 
대기
78 
연안해수
66 
강수
59 
지하수
43 
Other values (4)
94 

Length

Max length4
Median length2
Mean length2.3705502
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row연안해수
2nd row연안해수
3rd row강수
4th row강수
5th row대기

Common Values

ValueCountFrequency (%)
상수 278
45.0%
대기 78
 
12.6%
연안해수 66
 
10.7%
강수 59
 
9.5%
지하수 43
 
7.0%
토양 40
 
6.5%
수돗물 22
 
3.6%
하천수 21
 
3.4%
먹는물 11
 
1.8%

Length

2024-04-17T22:27:41.105430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T22:27:41.222694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
상수 278
45.0%
대기 78
 
12.6%
연안해수 66
 
10.7%
강수 59
 
9.5%
지하수 43
 
7.0%
토양 40
 
6.5%
수돗물 22
 
3.6%
하천수 21
 
3.4%
먹는물 11
 
1.8%

collec_day
Text

Distinct273
Distinct (%)44.2%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
2024-04-17T22:27:41.434511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length10
Mean length11.296117
Min length5

Characters and Unicode

Total characters6981
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique44 ?
Unique (%)7.1%

Sample

1st row2019-06-17
2nd row2019-06-04
3rd row2019-06-03~06-24
4th row2019-06-03~06-24
5th row2019-06-04~06-11
ValueCountFrequency (%)
2018-10-12 9
 
1.5%
2018-08-28 8
 
1.3%
2018-09-04 8
 
1.3%
2018-08-08 8
 
1.3%
2018-12-19 6
 
1.0%
2018-09-17 6
 
1.0%
2019-03-19 6
 
1.0%
2020-07-09 6
 
1.0%
2020-09-16 5
 
0.8%
2018-09-27 4
 
0.6%
Other values (263) 552
89.3%
2024-04-17T22:27:41.772873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1757
25.2%
- 1365
19.6%
2 1186
17.0%
1 1076
15.4%
8 398
 
5.7%
9 357
 
5.1%
3 188
 
2.7%
7 145
 
2.1%
4 141
 
2.0%
~ 139
 
2.0%
Other values (2) 229
 
3.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5477
78.5%
Dash Punctuation 1365
 
19.6%
Math Symbol 139
 
2.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1757
32.1%
2 1186
21.7%
1 1076
19.6%
8 398
 
7.3%
9 357
 
6.5%
3 188
 
3.4%
7 145
 
2.6%
4 141
 
2.6%
5 118
 
2.2%
6 111
 
2.0%
Dash Punctuation
ValueCountFrequency (%)
- 1365
100.0%
Math Symbol
ValueCountFrequency (%)
~ 139
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 6981
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1757
25.2%
- 1365
19.6%
2 1186
17.0%
1 1076
15.4%
8 398
 
5.7%
9 357
 
5.1%
3 188
 
2.7%
7 145
 
2.1%
4 141
 
2.0%
~ 139
 
2.0%
Other values (2) 229
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6981
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1757
25.2%
- 1365
19.6%
2 1186
17.0%
1 1076
15.4%
8 398
 
5.7%
9 357
 
5.1%
3 188
 
2.7%
7 145
 
2.1%
4 141
 
2.0%
~ 139
 
2.0%
Other values (2) 229
 
3.3%

iodine_131
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
불검출
299 
적합
278 
비대상
39 
미측정
 
2

Length

Max length3
Median length3
Mean length2.5501618
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row불검출
2nd row불검출
3rd row비대상
4th row불검출
5th row불검출

Common Values

ValueCountFrequency (%)
불검출 299
48.4%
적합 278
45.0%
비대상 39
 
6.3%
미측정 2
 
0.3%

Length

2024-04-17T22:27:41.901921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T22:27:41.994190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
불검출 299
48.4%
적합 278
45.0%
비대상 39
 
6.3%
미측정 2
 
0.3%

cesium_134
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
불검출
299 
적합
278 
비대상
39 
미측정
 
2

Length

Max length3
Median length3
Mean length2.5501618
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row불검출
2nd row불검출
3rd row비대상
4th row불검출
5th row불검출

Common Values

ValueCountFrequency (%)
불검출 299
48.4%
적합 278
45.0%
비대상 39
 
6.3%
미측정 2
 
0.3%

Length

2024-04-17T22:27:42.097264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T22:27:42.189975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
불검출 299
48.4%
적합 278
45.0%
비대상 39
 
6.3%
미측정 2
 
0.3%

cesium_137
Text

Distinct90
Distinct (%)14.6%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
2024-04-17T22:27:42.403668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length16
Mean length4.1213592
Min length2

Characters and Unicode

Total characters2547
Distinct characters24
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique81 ?
Unique (%)13.1%

Sample

1st row0.00169±0.00026
2nd row0.00149± 0.00024
3rd row비대상
4th row불검출
5th row불검출
ValueCountFrequency (%)
적합 278
43.0%
불검출 208
32.1%
비대상 39
 
6.0%
0.00023 7
 
1.1%
0.00024 7
 
1.1%
0.00022 5
 
0.8%
0.00173± 3
 
0.5%
0.00191± 3
 
0.5%
미측정 2
 
0.3%
1.21±0.06 2
 
0.3%
Other values (86) 93
 
14.4%
2024-04-17T22:27:42.770869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 535
21.0%
적 278
10.9%
합 278
10.9%
불 208
 
8.2%
검 208
 
8.2%
출 208
 
8.2%
. 182
 
7.1%
2 98
 
3.8%
1 98
 
3.8%
± 91
 
3.6%
Other values (14) 363
14.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1303
51.2%
Decimal Number 942
37.0%
Other Punctuation 182
 
7.1%
Math Symbol 91
 
3.6%
Space Separator 29
 
1.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
적 278
21.3%
합 278
21.3%
불 208
16.0%
검 208
16.0%
출 208
16.0%
상 39
 
3.0%
대 39
 
3.0%
비 39
 
3.0%
미 2
 
0.2%
측 2
 
0.2%
Decimal Number
ValueCountFrequency (%)
0 535
56.8%
2 98
 
10.4%
1 98
 
10.4%
3 44
 
4.7%
5 32
 
3.4%
4 30
 
3.2%
9 28
 
3.0%
6 28
 
3.0%
8 25
 
2.7%
7 24
 
2.5%
Other Punctuation
ValueCountFrequency (%)
. 182
100.0%
Math Symbol
ValueCountFrequency (%)
± 91
100.0%
Space Separator
ValueCountFrequency (%)
29
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1303
51.2%
Common 1244
48.8%

Most frequent character per script

Common
ValueCountFrequency (%)
0 535
43.0%
. 182
 
14.6%
2 98
 
7.9%
1 98
 
7.9%
± 91
 
7.3%
3 44
 
3.5%
5 32
 
2.6%
4 30
 
2.4%
29
 
2.3%
9 28
 
2.3%
Other values (3) 77
 
6.2%
Hangul
ValueCountFrequency (%)
적 278
21.3%
합 278
21.3%
불 208
16.0%
검 208
16.0%
출 208
16.0%
상 39
 
3.0%
대 39
 
3.0%
비 39
 
3.0%
미 2
 
0.2%
측 2
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1303
51.2%
ASCII 1153
45.3%
None 91
 
3.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 535
46.4%
. 182
 
15.8%
2 98
 
8.5%
1 98
 
8.5%
3 44
 
3.8%
5 32
 
2.8%
4 30
 
2.6%
29
 
2.5%
9 28
 
2.4%
6 28
 
2.4%
Other values (2) 49
 
4.2%
Hangul
ValueCountFrequency (%)
적 278
21.3%
합 278
21.3%
불 208
16.0%
검 208
16.0%
출 208
16.0%
상 39
 
3.0%
대 39
 
3.0%
비 39
 
3.0%
미 2
 
0.2%
측 2
 
0.2%
None
ValueCountFrequency (%)
± 91
100.0%

h3
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing618
Missing (%)100.0%
Memory size5.6 KiB

data_day
Date

Distinct32
Distinct (%)5.2%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
Minimum2018-04-23 00:00:00
Maximum2020-12-29 00:00:00
2024-04-17T22:27:42.889670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T22:27:42.986898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)

instt_code
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
6261610
618 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row6261610
2nd row6261610
3rd row6261610
4th row6261610
5th row6261610

Common Values

ValueCountFrequency (%)
6261610 618
100.0%

Length

2024-04-17T22:27:43.092107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T22:27:43.171686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
6261610 618
100.0%

apr_at
Boolean

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size750.0 B
False
618 
ValueCountFrequency (%)
False 618
100.0%
2024-04-17T22:27:43.238548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

last_load_dttm
Date

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
Minimum2021-03-01 05:54:03
Maximum2021-03-01 05:54:03
2024-04-17T22:27:43.315560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T22:27:43.391191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2024-04-17T22:27:39.849124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T22:27:39.687809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T22:27:39.960035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T22:27:39.769259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-17T22:27:43.464731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
skeyrsh_yearrsh_monthrsh_lockindiodine_131cesium_134cesium_137data_day
skey1.0000.9610.8940.0000.2920.2080.2080.3410.991
rsh_year0.9611.0000.3420.1300.2570.1440.1440.3270.995
rsh_month0.8940.3421.0000.4500.5150.0730.0730.2270.998
rsh_loc0.0000.1300.4501.0000.9960.9810.9810.9280.000
kind0.2920.2570.5150.9961.0000.7980.7980.8640.616
iodine_1310.2080.1440.0730.9810.7981.0001.0001.0000.302
cesium_1340.2080.1440.0730.9810.7981.0001.0001.0000.302
cesium_1370.3410.3270.2270.9280.8641.0001.0001.0000.557
data_day0.9910.9950.9980.0000.6160.3020.3020.5571.000
2024-04-17T22:27:43.583957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
kindrsh_yeariodine_131cesium_134rsh_loc
kind1.0000.1160.6560.6560.928
rsh_year0.1161.0000.1360.1360.060
iodine_1310.6560.1361.0001.0000.823
cesium_1340.6560.1361.0001.0000.823
rsh_loc0.9280.0600.8230.8231.000
2024-04-17T22:27:43.679708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
skeyrsh_monthrsh_yearrsh_lockindiodine_131cesium_134
skey1.000-0.0050.9560.0000.1340.1250.125
rsh_month-0.0051.0000.2170.1660.2640.0430.043
rsh_year0.9560.2171.0000.0600.1160.1360.136
rsh_loc0.0000.1660.0601.0000.9280.8230.823
kind0.1340.2640.1160.9281.0000.6560.656
iodine_1310.1250.0430.1360.8230.6561.0001.000
cesium_1340.1250.0430.1360.8230.6561.0001.000

Missing values

2024-04-17T22:27:40.076203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-17T22:27:40.233171image/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.

Sample

skeyrsh_yearrsh_monthrsh_lockindcollec_dayiodine_131cesium_134cesium_137h3data_dayinstt_codeapr_atlast_load_dttm
0564420196수영구 광안리해수욕장연안해수2019-06-17불검출불검출0.00169±0.00026<NA>2019-07-256261610N2021-03-01 05:54:03
1564520196고리원전인근연안해수2019-06-04불검출불검출0.00149± 0.00024<NA>2019-07-256261610N2021-03-01 05:54:03
2564620196장안읍사무소강수2019-06-03~06-24비대상비대상비대상<NA>2019-07-256261610N2021-03-01 05:54:03
3564720196부산환경공단기장사업소강수2019-06-03~06-24불검출불검출불검출<NA>2019-07-256261610N2021-03-01 05:54:03
4564820196기장초등학교대기2019-06-04~06-11불검출불검출불검출<NA>2019-07-256261610N2021-03-01 05:54:03
5564920196부산광역시청대기2019-06-04~06-11불검출불검출불검출<NA>2019-07-256261610N2021-03-01 05:54:03
6565020195범어사_정수상수2019-05-28적합적합적합<NA>2019-06-286261610N2021-03-01 05:54:03
7565120195범어사_원수상수2019-05-28적합적합적합<NA>2019-06-286261610N2021-03-01 05:54:03
8565220195명장_정수상수2019-05-21적합적합적합<NA>2019-06-286261610N2021-03-01 05:54:03
9565320195명장_원수상수2019-05-21적합적합적합<NA>2019-06-286261610N2021-03-01 05:54:03
skeyrsh_yearrsh_monthrsh_lockindcollec_dayiodine_131cesium_134cesium_137h3data_dayinstt_codeapr_atlast_load_dttm
6085797201810덕산_정수상수2018-10-05적합적합적합<NA>2018-11-276261610N2021-03-01 05:54:03
6095798201810덕산_원수상수2018-10-05적합적합적합<NA>2018-11-276261610N2021-03-01 05:54:03
6105799201810남구 대연중앙교회수돗물2018-10-24불검출불검출불검출<NA>2018-11-276261610N2021-03-01 05:54:03
6115800201810북구 그린코아아파트수돗물2018-10-17불검출불검출불검출<NA>2018-11-276261610N2021-03-01 05:54:03
6125801201810강서구 지사공원수돗물2018-10-17불검출불검출불검출<NA>2018-11-276261610N2021-03-01 05:54:03
6135802201810사하구 샛별공원수돗물2018-10-12불검출불검출불검출<NA>2018-11-276261610N2021-03-01 05:54:03
6145803201810기장군 선재영농법인수돗물2018-10-12불검출불검출불검출<NA>2018-11-276261610N2021-03-01 05:54:03
6155804201810고리원전인근연안해수2018-10-12불검출불검출0.00173± 0.00023<NA>2018-11-276261610N2021-03-01 05:54:03
6165805201810기장읍(하수처리장내)강수2018-10-01~10-31불검출불검출불검출<NA>2018-11-276261610N2021-03-01 05:54:03
6175806201810기장초등학교대기2018-10-01~10-08불검출불검출불검출<NA>2018-11-276261610N2021-03-01 05:54:03