Overview

Dataset statistics

Number of variables14
Number of observations438
Missing cells876
Missing cells (%)14.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory50.2 KiB
Average record size in memory117.3 B

Variable types

Numeric2
Categorical8
Text2
Unsupported2

Alerts

last_load_dttm has constant value ""Constant
kind is highly overall correlated with rsh_loc and 2 other fieldsHigh correlation
data_day is highly overall correlated with skey and 2 other fieldsHigh correlation
rsh_year is highly overall correlated with skey and 1 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_year and 1 other fieldsHigh correlation
rsh_month is highly overall correlated with data_dayHigh correlation
instt_code has 438 (100.0%) missing valuesMissing
apr_at has 438 (100.0%) missing valuesMissing
skey has unique valuesUnique
instt_code is an unsupported type, check if it needs cleaning or further analysisUnsupported
apr_at is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2024-04-17 13:25:30.736870
Analysis finished2024-04-17 13:25:31.931821
Duration1.19 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

skey
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct438
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5128.5
Minimum4910
Maximum5347
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2024-04-17T22:25:32.003249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4910
5-th percentile4931.85
Q15019.25
median5128.5
Q35237.75
95-th percentile5325.15
Maximum5347
Range437
Interquartile range (IQR)218.5

Descriptive statistics

Standard deviation126.58396
Coefficient of variation (CV)0.024682454
Kurtosis-1.2
Mean5128.5
Median Absolute Deviation (MAD)109.5
Skewness0
Sum2246283
Variance16023.5
MonotonicityNot monotonic
2024-04-17T22:25:32.125048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5213 1
 
0.2%
5012 1
 
0.2%
5023 1
 
0.2%
5022 1
 
0.2%
5021 1
 
0.2%
5020 1
 
0.2%
5019 1
 
0.2%
5018 1
 
0.2%
5017 1
 
0.2%
5016 1
 
0.2%
Other values (428) 428
97.7%
ValueCountFrequency (%)
4910 1
0.2%
4911 1
0.2%
4912 1
0.2%
4913 1
0.2%
4914 1
0.2%
4915 1
0.2%
4916 1
0.2%
4917 1
0.2%
4918 1
0.2%
4919 1
0.2%
ValueCountFrequency (%)
5347 1
0.2%
5346 1
0.2%
5345 1
0.2%
5344 1
0.2%
5343 1
0.2%
5342 1
0.2%
5341 1
0.2%
5340 1
0.2%
5339 1
0.2%
5338 1
0.2%

rsh_year
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
2018
231 
2019
207 

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 231
52.7%
2019 207
47.3%

Length

2024-04-17T22:25:32.230014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T22:25:32.316014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2018 231
52.7%
2019 207
47.3%

rsh_month
Real number (ℝ)

HIGH CORRELATION 

Distinct12
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.4109589
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2024-04-17T22:25:32.404086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q15
median8
Q310
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.1434169
Coefficient of variation (CV)0.42415791
Kurtosis-1.0646838
Mean7.4109589
Median Absolute Deviation (MAD)3
Skewness-0.28612879
Sum3246
Variance9.8810696
MonotonicityNot monotonic
2024-04-17T22:25:32.500067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
10 53
12.1%
11 50
11.4%
8 48
11.0%
9 46
10.5%
3 40
9.1%
7 38
8.7%
12 38
8.7%
4 38
8.7%
6 32
7.3%
5 28
6.4%
Other values (2) 27
6.2%
ValueCountFrequency (%)
1 12
 
2.7%
2 15
 
3.4%
3 40
9.1%
4 38
8.7%
5 28
6.4%
6 32
7.3%
7 38
8.7%
8 48
11.0%
9 46
10.5%
10 53
12.1%
ValueCountFrequency (%)
12 38
8.7%
11 50
11.4%
10 53
12.1%
9 46
10.5%
8 48
11.0%
7 38
8.7%
6 32
7.3%
5 28
6.4%
4 38
8.7%
3 40
9.1%

rsh_loc
Categorical

HIGH CORRELATION 

Distinct44
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
부산광역시청
 
29
고리원전인근
 
27
덕산_원수
 
27
덕산_정수
 
27
화명_원수
 
27
Other values (39)
301 

Length

Max length12
Median length11
Mean length6.6849315
Min length3

Unique

Unique5 ?
Unique (%)1.1%

Sample

1st row기장초등학교
2nd row부산환경공단기장사업소
3rd row장안읍사무소
4th row고리원전인근
5th row기장군 청광농장

Common Values

ValueCountFrequency (%)
부산광역시청 29
 
6.6%
고리원전인근 27
 
6.2%
덕산_원수 27
 
6.2%
덕산_정수 27
 
6.2%
화명_원수 27
 
6.2%
화명_정수 27
 
6.2%
명장_원수 27
 
6.2%
명장_정수 27
 
6.2%
기장초등학교 25
 
5.7%
범어사_정수 22
 
5.0%
Other values (34) 173
39.5%

Length

2024-04-17T22:25:32.609768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
기장군 31
 
5.6%
부산광역시청 29
 
5.2%
덕산_원수 27
 
4.9%
화명_원수 27
 
4.9%
화명_정수 27
 
4.9%
명장_원수 27
 
4.9%
명장_정수 27
 
4.9%
고리원전인근 27
 
4.9%
덕산_정수 27
 
4.9%
기장초등학교 25
 
4.5%
Other values (44) 281
50.6%

kind
Categorical

HIGH CORRELATION 

Distinct9
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
상수
206 
대기
56 
연안해수
47 
강수
37 
토양
28 
Other values (4)
64 

Length

Max length4
Median length2
Mean length2.3607306
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
상수 206
47.0%
대기 56
 
12.8%
연안해수 47
 
10.7%
강수 37
 
8.4%
토양 28
 
6.4%
지하수 25
 
5.7%
수돗물 18
 
4.1%
하천수 13
 
3.0%
먹는물 8
 
1.8%

Length

2024-04-17T22:25:32.724086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T22:25:32.843005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
상수 206
47.0%
대기 56
 
12.8%
연안해수 47
 
10.7%
강수 37
 
8.4%
토양 28
 
6.4%
지하수 25
 
5.7%
수돗물 18
 
4.1%
하천수 13
 
3.0%
먹는물 8
 
1.8%

collec_day
Text

Distinct187
Distinct (%)42.7%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
2024-04-17T22:25:33.045202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length10
Mean length10.906393
Min length5

Characters and Unicode

Total characters4777
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

Unique35 ?
Unique (%)8.0%

Sample

1st row2019-05-01~05-09
2nd row2019-05-01~05-23
3rd row2019-05-01~05-23
4th row2019-05-07
5th row2019-05-07
ValueCountFrequency (%)
2018-10-12 9
 
2.1%
2018-08-28 8
 
1.8%
2018-09-04 8
 
1.8%
2018-08-08 8
 
1.8%
2019-03-19 6
 
1.4%
2018-09-17 6
 
1.4%
2018-12-19 6
 
1.4%
2018-08-17 4
 
0.9%
2018-12-12 4
 
0.9%
2018-12-05 4
 
0.9%
Other values (177) 375
85.6%
2024-04-17T22:25:33.354422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1029
21.5%
- 933
19.5%
1 919
19.2%
2 659
13.8%
8 350
 
7.3%
9 305
 
6.4%
3 129
 
2.7%
4 103
 
2.2%
7 101
 
2.1%
~ 95
 
2.0%
Other values (2) 154
 
3.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3749
78.5%
Dash Punctuation 933
 
19.5%
Math Symbol 95
 
2.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1029
27.4%
1 919
24.5%
2 659
17.6%
8 350
 
9.3%
9 305
 
8.1%
3 129
 
3.4%
4 103
 
2.7%
7 101
 
2.7%
5 79
 
2.1%
6 75
 
2.0%
Dash Punctuation
ValueCountFrequency (%)
- 933
100.0%
Math Symbol
ValueCountFrequency (%)
~ 95
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4777
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1029
21.5%
- 933
19.5%
1 919
19.2%
2 659
13.8%
8 350
 
7.3%
9 305
 
6.4%
3 129
 
2.7%
4 103
 
2.2%
7 101
 
2.1%
~ 95
 
2.0%
Other values (2) 154
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4777
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1029
21.5%
- 933
19.5%
1 919
19.2%
2 659
13.8%
8 350
 
7.3%
9 305
 
6.4%
3 129
 
2.7%
4 103
 
2.2%
7 101
 
2.1%
~ 95
 
2.0%
Other values (2) 154
 
3.2%

iodine_131
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
불검출
210 
적합
206 
비대상
 
20
미측정
 
2

Length

Max length3
Median length3
Mean length2.5296804
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
불검출 210
47.9%
적합 206
47.0%
비대상 20
 
4.6%
미측정 2
 
0.5%

Length

2024-04-17T22:25:33.476255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T22:25:33.571681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
불검출 210
47.9%
적합 206
47.0%
비대상 20
 
4.6%
미측정 2
 
0.5%

cesium_134
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
불검출
210 
적합
206 
비대상
 
20
미측정
 
2

Length

Max length3
Median length3
Mean length2.5296804
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
불검출 210
47.9%
적합 206
47.0%
비대상 20
 
4.6%
미측정 2
 
0.5%

Length

2024-04-17T22:25:33.672654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T22:25:33.775171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
불검출 210
47.9%
적합 206
47.0%
비대상 20
 
4.6%
미측정 2
 
0.5%

cesium_137
Text

Distinct64
Distinct (%)14.6%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
2024-04-17T22:25:33.965211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length16
Mean length4.1164384
Min length2

Characters and Unicode

Total characters1803
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

Unique56 ?
Unique (%)12.8%

Sample

1st row불검출
2nd row불검출
3rd row비대상
4th row0.00185± 0.00024
5th row비대상
ValueCountFrequency (%)
적합 206
44.1%
불검출 146
31.3%
비대상 20
 
4.3%
0.00023 7
 
1.5%
0.00024 7
 
1.5%
0.00022 5
 
1.1%
0.00191± 3
 
0.6%
0.00173± 3
 
0.6%
0.00026 2
 
0.4%
0.00021 2
 
0.4%
Other values (60) 66
 
14.1%
2024-04-17T22:25:34.305162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 380
21.1%
적 206
11.4%
합 206
11.4%
불 146
 
8.1%
검 146
 
8.1%
출 146
 
8.1%
. 128
 
7.1%
2 72
 
4.0%
1 66
 
3.7%
± 64
 
3.5%
Other values (14) 243
13.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 916
50.8%
Decimal Number 666
36.9%
Other Punctuation 128
 
7.1%
Math Symbol 64
 
3.5%
Space Separator 29
 
1.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
적 206
22.5%
합 206
22.5%
불 146
15.9%
검 146
15.9%
출 146
15.9%
상 20
 
2.2%
대 20
 
2.2%
비 20
 
2.2%
미 2
 
0.2%
측 2
 
0.2%
Decimal Number
ValueCountFrequency (%)
0 380
57.1%
2 72
 
10.8%
1 66
 
9.9%
3 36
 
5.4%
5 22
 
3.3%
9 22
 
3.3%
8 18
 
2.7%
4 18
 
2.7%
6 18
 
2.7%
7 14
 
2.1%
Other Punctuation
ValueCountFrequency (%)
. 128
100.0%
Math Symbol
ValueCountFrequency (%)
± 64
100.0%
Space Separator
ValueCountFrequency (%)
29
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 916
50.8%
Common 887
49.2%

Most frequent character per script

Common
ValueCountFrequency (%)
0 380
42.8%
. 128
 
14.4%
2 72
 
8.1%
1 66
 
7.4%
± 64
 
7.2%
3 36
 
4.1%
29
 
3.3%
5 22
 
2.5%
9 22
 
2.5%
8 18
 
2.0%
Other values (3) 50
 
5.6%
Hangul
ValueCountFrequency (%)
적 206
22.5%
합 206
22.5%
불 146
15.9%
검 146
15.9%
출 146
15.9%
상 20
 
2.2%
대 20
 
2.2%
비 20
 
2.2%
미 2
 
0.2%
측 2
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 916
50.8%
ASCII 823
45.6%
None 64
 
3.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 380
46.2%
. 128
 
15.6%
2 72
 
8.7%
1 66
 
8.0%
3 36
 
4.4%
29
 
3.5%
5 22
 
2.7%
9 22
 
2.7%
8 18
 
2.2%
4 18
 
2.2%
Other values (2) 32
 
3.9%
Hangul
ValueCountFrequency (%)
적 206
22.5%
합 206
22.5%
불 146
15.9%
검 146
15.9%
출 146
15.9%
상 20
 
2.2%
대 20
 
2.2%
비 20
 
2.2%
미 2
 
0.2%
측 2
 
0.2%
None
ValueCountFrequency (%)
± 64
100.0%

h3
Categorical

Distinct10
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
불검출
160 
적합
136 
비대상
109 
<NA>
24 
미측정
 
4
Other values (5)
 
5

Length

Max length9
Median length3
Mean length2.8127854
Min length2

Unique

Unique5 ?
Unique (%)1.1%

Sample

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

Common Values

ValueCountFrequency (%)
불검출 160
36.5%
적합 136
31.1%
비대상 109
24.9%
<NA> 24
 
5.5%
미측정 4
 
0.9%
1.22�0.07 1
 
0.2%
0.84�0.21 1
 
0.2%
1.01�0.06 1
 
0.2%
0.62�0.06 1
 
0.2%
2.02�0.16 1
 
0.2%

Length

2024-04-17T22:25:34.432084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T22:25:34.556945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
불검출 160
36.5%
적합 136
31.1%
비대상 109
24.9%
na 24
 
5.5%
미측정 4
 
0.9%
1.22�0.07 1
 
0.2%
0.84�0.21 1
 
0.2%
1.01�0.06 1
 
0.2%
0.62�0.06 1
 
0.2%
2.02�0.16 1
 
0.2%

data_day
Categorical

HIGH CORRELATION 

Distinct21
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
2018-11-27
34 
2019-12-05
33 
2018-10-15
32 
2018-12-24
32 
2018-09-28
30 
Other values (16)
277 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2019-06-28
2nd row2019-06-28
3rd row2019-06-28
4th row2019-06-28
5th row2019-06-28

Common Values

ValueCountFrequency (%)
2018-11-27 34
 
7.8%
2019-12-05 33
 
7.5%
2018-10-15 32
 
7.3%
2018-12-24 32
 
7.3%
2018-09-28 30
 
6.8%
2019-01-09 24
 
5.5%
2019-05-30 23
 
5.3%
2019-04-26 21
 
4.8%
2019-09-02 21
 
4.8%
2018-04-23 19
 
4.3%
Other values (11) 169
38.6%

Length

2024-04-17T22:25:34.679116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2018-11-27 34
 
7.8%
2019-12-05 33
 
7.5%
2018-10-15 32
 
7.3%
2018-12-24 32
 
7.3%
2018-09-28 30
 
6.8%
2019-01-09 24
 
5.5%
2019-05-30 23
 
5.3%
2019-04-26 21
 
4.8%
2019-09-02 21
 
4.8%
2018-04-23 19
 
4.3%
Other values (11) 169
38.6%

instt_code
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing438
Missing (%)100.0%
Memory size4.0 KiB

apr_at
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing438
Missing (%)100.0%
Memory size4.0 KiB

last_load_dttm
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
2020-12-21 15:18:37
438 

Length

Max length19
Median length19
Mean length19
Min length19

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020-12-21 15:18:37
2nd row2020-12-21 15:18:37
3rd row2020-12-21 15:18:37
4th row2020-12-21 15:18:37
5th row2020-12-21 15:18:37

Common Values

ValueCountFrequency (%)
2020-12-21 15:18:37 438
100.0%

Length

2024-04-17T22:25:34.783221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T22:25:34.864365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020-12-21 438
50.0%
15:18:37 438
50.0%

Interactions

2024-04-17T22:25:31.502121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T22:25:31.353593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T22:25:31.576531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T22:25:31.424483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-17T22:25:34.925048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
skeyrsh_yearrsh_monthrsh_lockindiodine_131cesium_134cesium_137h3data_day
skey1.0000.9980.9480.0000.4000.2420.2420.3340.4520.982
rsh_year0.9981.0000.4000.2890.2000.3400.3400.3210.4921.000
rsh_month0.9480.4001.0000.3740.5410.0930.0930.2560.2420.997
rsh_loc0.0000.2890.3741.0000.9950.9820.9820.9210.8420.000
kind0.4000.2000.5410.9951.0000.7920.7920.8470.8170.617
iodine_1310.2420.3400.0930.9820.7921.0001.0001.0000.6640.275
cesium_1340.2420.3400.0930.9820.7921.0001.0001.0000.6640.275
cesium_1370.3340.3210.2560.9210.8471.0001.0001.0000.0000.516
h30.4520.4920.2420.8420.8170.6640.6640.0001.0000.477
data_day0.9821.0000.9970.0000.6170.2750.2750.5160.4771.000
2024-04-17T22:25:35.036922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
kinddata_dayrsh_yeariodine_131cesium_134rsh_loch3
kind1.0000.2880.1980.6460.6460.9070.393
data_day0.2881.0000.9780.1480.1480.0000.202
rsh_year0.1980.9781.0000.2270.2270.2180.489
iodine_1310.6460.1480.2271.0001.0000.8120.492
cesium_1340.6460.1480.2271.0001.0000.8120.492
rsh_loc0.9070.0000.2180.8120.8121.0000.473
h30.3930.2020.4890.4920.4920.4731.000
2024-04-17T22:25:35.138289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
skeyrsh_monthrsh_yearrsh_lockindiodine_131cesium_134h3data_day
skey1.0000.2530.9490.0000.1930.1450.1450.2230.877
rsh_month0.2531.0000.3040.1310.2820.0550.0550.1120.968
rsh_year0.9490.3041.0000.2180.1980.2270.2270.4890.978
rsh_loc0.0000.1310.2181.0000.9070.8120.8120.4730.000
kind0.1930.2820.1980.9071.0000.6460.6460.3930.288
iodine_1310.1450.0550.2270.8120.6461.0001.0000.4920.148
cesium_1340.1450.0550.2270.8120.6461.0001.0000.4920.148
h30.2230.1120.4890.4730.3930.4920.4921.0000.202
data_day0.8770.9680.9780.0000.2880.1480.1480.2021.000

Missing values

2024-04-17T22:25:31.693639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-17T22:25:31.871090image/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
0521320195기장초등학교대기2019-05-01~05-09불검출불검출불검출불검출2019-06-28<NA><NA>2020-12-21 15:18:37
1521420195부산환경공단기장사업소강수2019-05-01~05-23불검출불검출불검출불검출2019-06-28<NA><NA>2020-12-21 15:18:37
2521520195장안읍사무소강수2019-05-01~05-23비대상비대상비대상불검출2019-06-28<NA><NA>2020-12-21 15:18:37
3521620195고리원전인근연안해수2019-05-07불검출불검출0.00185± 0.00024불검출2019-06-28<NA><NA>2020-12-21 15:18:37
4521720195기장군 청광농장지하수2019-05-07비대상비대상비대상불검출2019-06-28<NA><NA>2020-12-21 15:18:37
5521820195덕산_원수상수2019-05-08적합적합적합적합2019-06-28<NA><NA>2020-12-21 15:18:37
6521920195덕산_정수상수2019-05-08적합적합적합적합2019-06-28<NA><NA>2020-12-21 15:18:37
7522020195화명_원수상수2019-05-16적합적합적합적합2019-06-28<NA><NA>2020-12-21 15:18:37
8522120195화명_정수상수2019-05-16적합적합적합적합2019-06-28<NA><NA>2020-12-21 15:18:37
9522220195명장_원수상수2019-05-21적합적합적합적합2019-06-28<NA><NA>2020-12-21 15:18:37
skeyrsh_yearrsh_monthrsh_lockindcollec_dayiodine_131cesium_134cesium_137h3data_dayinstt_codeapr_atlast_load_dttm
4285338201912고리원전인근연안해수2019-12-10불검출불검출0.00176±0.00025불검출2020-01-30<NA><NA>2020-12-21 15:18:37
4295339201912기장군 청광농장지하수2019-12-10비대상비대상비대상불검출2020-01-30<NA><NA>2020-12-21 15:18:37
4305340201912덕산_원수상수2019-12-03적합적합적합적합2020-01-30<NA><NA>2020-12-21 15:18:37
4315341201912덕산_정수상수2019-12-03적합적합적합적합2020-01-30<NA><NA>2020-12-21 15:18:37
4325342201912화명_원수상수2019-12-11적합적합적합적합2020-01-30<NA><NA>2020-12-21 15:18:37
4335343201912화명_정수상수2019-12-11적합적합적합적합2020-01-30<NA><NA>2020-12-21 15:18:37
4345344201912명장_원수상수2019-12-24적합적합적합적합2020-01-30<NA><NA>2020-12-21 15:18:37
4355345201912명장_정수상수2019-12-24적합적합적합적합2020-01-30<NA><NA>2020-12-21 15:18:37
4365346201912범어사_원수상수2019-12-17적합적합적합적합2020-01-30<NA><NA>2020-12-21 15:18:37
4375347201912범어사_정수상수2019-12-17적합적합적합적합2020-01-30<NA><NA>2020-12-21 15:18:37