Overview

Dataset statistics

Number of variables6
Number of observations88
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.5 KiB
Average record size in memory52.5 B

Variable types

Categorical3
Numeric2
Text1

Dataset

Description국내 해역(동해, 서해, 남해)의 각 세부 지점의 해수 방사능(세슘, 삼중수소)의 농도를 측정한 데이터입니다.해수 방사능 관련 연구나 정책 입안에 사용할 수 있습니다.※ 기준연도 : 2019-2022년[ 단위 ]※ 세슘의 농도(mBq/kg), 삼중수소의 농도(Bq/L)
Author원자력안전위원회
URLhttps://www.data.go.kr/data/15123566/fileData.do

Alerts

구분 is highly overall correlated with 조사정점High correlation
조사정점 is highly overall correlated with 구분High correlation

Reproduction

Analysis started2023-12-12 10:44:51.392234
Analysis finished2023-12-12 10:44:52.388031
Duration1 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연도
Categorical

Distinct4
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Memory size836.0 B
2022
23 
2020
22 
2019
22 
2021
21 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022
2nd row2022
3rd row2022
4th row2022
5th row2022

Common Values

ValueCountFrequency (%)
2022 23
26.1%
2020 22
25.0%
2019 22
25.0%
2021 21
23.9%

Length

2023-12-12T19:44:52.461500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T19:44:52.589975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022 23
26.1%
2020 22
25.0%
2019 22
25.0%
2021 21
23.9%

구분
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Memory size836.0 B
동해
36 
남해
28 
서해
24 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
동해 36
40.9%
남해 28
31.8%
서해 24
27.3%

Length

2023-12-12T19:44:52.714570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T19:44:52.868700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
동해 36
40.9%
남해 28
31.8%
서해 24
27.3%

조사정점
Categorical

HIGH CORRELATION 

Distinct23
Distinct (%)26.1%
Missing0
Missing (%)0.0%
Memory size836.0 B
107-07
 
4
205-05
 
4
307-09
 
4
106-05
 
4
104-04
 
4
Other values (18)
68 

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique1 ?
Unique (%)1.1%

Sample

1st row107-07
2nd row106-02
3rd row106-05
4th row105-11
5th row104-11

Common Values

ValueCountFrequency (%)
107-07 4
 
4.5%
205-05 4
 
4.5%
307-09 4
 
4.5%
106-05 4
 
4.5%
104-04 4
 
4.5%
103-11 4
 
4.5%
102-04 4
 
4.5%
209-08 4
 
4.5%
208-01 4
 
4.5%
207-03 4
 
4.5%
Other values (13) 48
54.5%

Length

2023-12-12T19:44:52.995995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
107-07 4
 
4.5%
205-05 4
 
4.5%
308-01 4
 
4.5%
309-09 4
 
4.5%
310-03 4
 
4.5%
311-09 4
 
4.5%
312-02 4
 
4.5%
313-09 4
 
4.5%
314-01 4
 
4.5%
203-03 4
 
4.5%
Other values (13) 48
54.5%

세슘(최소)
Real number (ℝ)

Distinct52
Distinct (%)59.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2499659
Minimum0.828
Maximum1.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size924.0 B
2023-12-12T19:44:53.140384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.828
5-th percentile1.02
Q11.12
median1.25
Q31.37
95-th percentile1.54
Maximum1.8
Range0.972
Interquartile range (IQR)0.25

Descriptive statistics

Standard deviation0.18207908
Coefficient of variation (CV)0.14566724
Kurtosis-0.011574954
Mean1.2499659
Median Absolute Deviation (MAD)0.13
Skewness0.25708231
Sum109.997
Variance0.033152792
MonotonicityNot monotonic
2023-12-12T19:44:53.335918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.12 4
 
4.5%
1.08 4
 
4.5%
1.34 3
 
3.4%
1.32 3
 
3.4%
1.27 3
 
3.4%
1.02 3
 
3.4%
1.14 3
 
3.4%
1.26 3
 
3.4%
1.17 3
 
3.4%
1.23 3
 
3.4%
Other values (42) 56
63.6%
ValueCountFrequency (%)
0.828 1
 
1.1%
0.892 1
 
1.1%
0.908 1
 
1.1%
0.909 1
 
1.1%
1.02 3
3.4%
1.03 2
2.3%
1.04 1
 
1.1%
1.05 1
 
1.1%
1.06 1
 
1.1%
1.07 2
2.3%
ValueCountFrequency (%)
1.8 1
1.1%
1.58 1
1.1%
1.57 1
1.1%
1.56 1
1.1%
1.54 2
2.3%
1.53 1
1.1%
1.52 2
2.3%
1.51 1
1.1%
1.49 1
1.1%
1.48 1
1.1%

세슘(최대)
Real number (ℝ)

Distinct56
Distinct (%)63.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.59625
Minimum1.01
Maximum2.26
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size924.0 B
2023-12-12T19:44:53.495210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.01
5-th percentile1.294
Q11.4575
median1.615
Q31.71
95-th percentile1.9165
Maximum2.26
Range1.25
Interquartile range (IQR)0.2525

Descriptive statistics

Standard deviation0.2012222
Coefficient of variation (CV)0.12605933
Kurtosis1.0290234
Mean1.59625
Median Absolute Deviation (MAD)0.13
Skewness0.17718008
Sum140.47
Variance0.040490374
MonotonicityNot monotonic
2023-12-12T19:44:54.012189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.68 6
 
6.8%
1.62 4
 
4.5%
1.71 4
 
4.5%
1.53 4
 
4.5%
1.79 4
 
4.5%
1.63 3
 
3.4%
1.36 3
 
3.4%
1.64 2
 
2.3%
1.37 2
 
2.3%
1.46 2
 
2.3%
Other values (46) 54
61.4%
ValueCountFrequency (%)
1.01 1
 
1.1%
1.17 1
 
1.1%
1.25 1
 
1.1%
1.27 1
 
1.1%
1.28 1
 
1.1%
1.32 1
 
1.1%
1.35 1
 
1.1%
1.36 3
3.4%
1.37 2
2.3%
1.38 1
 
1.1%
ValueCountFrequency (%)
2.26 1
1.1%
2.09 1
1.1%
1.94 1
1.1%
1.93 1
1.1%
1.92 1
1.1%
1.91 1
1.1%
1.88 1
1.1%
1.83 1
1.1%
1.82 1
1.1%
1.81 1
1.1%

삼중수소
Text

Distinct72
Distinct (%)81.8%
Missing0
Missing (%)0.0%
Memory size836.0 B
2023-12-12T19:44:54.306379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12.5
Mean length8.4772727
Min length5

Characters and Unicode

Total characters746
Distinct characters14
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique60 ?
Unique (%)68.2%

Sample

1st row<0.129~0.242
2nd row<0.134~0.286
3rd row<0.133~0.258
4th row0.110~0.291
5th row0.113~0.295
ValueCountFrequency (%)
0.113 4
 
4.5%
0.186 3
 
3.4%
0.199 3
 
3.4%
0.187 2
 
2.3%
0.114 2
 
2.3%
0.135 2
 
2.3%
0.125 2
 
2.3%
0.185 2
 
2.3%
0.132 2
 
2.3%
0.142 2
 
2.3%
Other values (62) 64
72.7%
2023-12-12T19:44:54.783884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 162
21.7%
. 127
17.0%
1 113
15.1%
< 65
8.7%
2 44
 
5.9%
3 42
 
5.6%
4 33
 
4.4%
6 28
 
3.8%
5 25
 
3.4%
7 24
 
3.2%
Other values (4) 83
11.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 515
69.0%
Other Punctuation 127
 
17.0%
Math Symbol 104
 
13.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 162
31.5%
1 113
21.9%
2 44
 
8.5%
3 42
 
8.2%
4 33
 
6.4%
6 28
 
5.4%
5 25
 
4.9%
7 24
 
4.7%
8 23
 
4.5%
9 21
 
4.1%
Math Symbol
ValueCountFrequency (%)
< 65
62.5%
~ 23
 
22.1%
± 16
 
15.4%
Other Punctuation
ValueCountFrequency (%)
. 127
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 746
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 162
21.7%
. 127
17.0%
1 113
15.1%
< 65
8.7%
2 44
 
5.9%
3 42
 
5.6%
4 33
 
4.4%
6 28
 
3.8%
5 25
 
3.4%
7 24
 
3.2%
Other values (4) 83
11.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 730
97.9%
None 16
 
2.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 162
22.2%
. 127
17.4%
1 113
15.5%
< 65
8.9%
2 44
 
6.0%
3 42
 
5.8%
4 33
 
4.5%
6 28
 
3.8%
5 25
 
3.4%
7 24
 
3.3%
Other values (3) 67
9.2%
None
ValueCountFrequency (%)
± 16
100.0%

Interactions

2023-12-12T19:44:51.952183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:51.739655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:52.063780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:51.854777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T19:44:54.921684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도구분조사정점세슘(최소)세슘(최대)삼중수소
연도1.0000.0000.0000.1770.0000.964
구분0.0001.0001.0000.5000.5040.978
조사정점0.0001.0001.0000.2750.7140.737
세슘(최소)0.1770.5000.2751.0000.1850.000
세슘(최대)0.0000.5040.7140.1851.0000.563
삼중수소0.9640.9780.7370.0000.5631.000
2023-12-12T19:44:55.054942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
조사정점구분연도
조사정점1.0000.8740.000
구분0.8741.0000.000
연도0.0000.0001.000
2023-12-12T19:44:55.183134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
세슘(최소)세슘(최대)연도구분조사정점
세슘(최소)1.0000.4020.1060.2430.082
세슘(최대)0.4021.0000.0000.3340.323
연도0.1060.0001.0000.0000.000
구분0.2430.3340.0001.0000.874
조사정점0.0820.3230.0000.8741.000

Missing values

2023-12-12T19:44:52.202123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T19:44:52.329670image/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

연도구분조사정점세슘(최소)세슘(최대)삼중수소
02022동해107-071.341.68<0.129~0.242
12022동해106-021.121.63<0.134~0.286
22022동해106-051.211.5<0.133~0.258
32022동해105-111.171.830.110~0.291
42022동해104-111.262.260.113~0.295
52022동해104-041.131.69<0.127~0.306
62022동해103-111.251.68<0.131~0.277
72022동해102-041.351.940.153~0.261
82022동해209-081.211.71<0.131~0.298
92022동해208-011.161.53<0.166~0.451
연도구분조사정점세슘(최소)세슘(최대)삼중수소
782019남해204-021.021.54<0.183
792019남해203-031.231.61<0.187
802019남해314-010.8921.68<0.191
812019남해313-091.321.37<0.199
822019서해312-021.321.460.0695±0.0203
832019서해311-091.311.42<0.0608
842019서해310-031.131.170.0716±0.0202
852019서해309-091.141.37<0.0585
862019서해308-011.281.36<0.0577
872019서해307-091.071.270.0890±0.0203