Overview

Dataset statistics

Number of variables8
Number of observations582
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory40.5 KiB
Average record size in memory71.2 B

Variable types

Numeric6
Categorical2

Dataset

Description제주도 해양환경측정망 CTD를 통해 관측된 해양환경정보(수온, 염분)를 제공합니다. 출처는 해양환경공단입니다. (데이터 미집계로 인하여 일부 데이터값에 공란이 존재할 수 있습니다.)
Author제주특별자치도
URLhttps://www.data.go.kr/data/15110789/fileData.do

Alerts

위도 is highly overall correlated with 정점명High correlation
경도 is highly overall correlated with 정점명High correlation
수온 is highly overall correlated with 염분 and 1 other fieldsHigh correlation
염분 is highly overall correlated with 수온 and 1 other fieldsHigh correlation
측정 월 is highly overall correlated with 수온 and 1 other fieldsHigh correlation
정점명 is highly overall correlated with 위도 and 1 other fieldsHigh correlation

Reproduction

Analysis started2023-12-12 15:31:25.238761
Analysis finished2023-12-12 15:31:29.843918
Duration4.61 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

측정 연도
Real number (ℝ)

Distinct12
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2016.5258
Minimum2011
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.2 KiB
2023-12-13T00:31:29.902987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2011
5-th percentile2011
Q12014
median2016.5
Q32019
95-th percentile2021
Maximum2022
Range11
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.1516773
Coefficient of variation (CV)0.0015629244
Kurtosis-1.0895284
Mean2016.5258
Median Absolute Deviation (MAD)2.5
Skewness0.0062075909
Sum1173618
Variance9.9330695
MonotonicityIncreasing
2023-12-13T00:31:30.009586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
2015 62
10.7%
2014 57
9.8%
2017 57
9.8%
2013 55
9.5%
2019 55
9.5%
2021 52
8.9%
2016 51
8.8%
2018 51
8.8%
2020 49
8.4%
2012 35
6.0%
Other values (2) 58
10.0%
ValueCountFrequency (%)
2011 31
5.3%
2012 35
6.0%
2013 55
9.5%
2014 57
9.8%
2015 62
10.7%
2016 51
8.8%
2017 57
9.8%
2018 51
8.8%
2019 55
9.5%
2020 49
8.4%
ValueCountFrequency (%)
2022 27
4.6%
2021 52
8.9%
2020 49
8.4%
2019 55
9.5%
2018 51
8.8%
2017 57
9.8%
2016 51
8.8%
2015 62
10.7%
2014 57
9.8%
2013 55
9.5%

측정 월
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size4.7 KiB
2
311 
8
271 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8
2nd row8
3rd row8
4th row8
5th row8

Common Values

ValueCountFrequency (%)
2 311
53.4%
8 271
46.6%

Length

2023-12-13T00:31:30.123342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T00:31:30.211984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 311
53.4%
8 271
46.6%

정점명
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size4.7 KiB
한림항 H01
109 
제주항 H01
96 
서귀포항 H01
94 
제주항 H02
78 
성산포항 H01
76 
Other values (2)
129 

Length

Max length8
Median length7
Mean length7.3986254
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row서귀포항 H01
2nd row서귀포항 H01
3rd row서귀포항 H01
4th row서귀포항 H01
5th row성산포항 H01

Common Values

ValueCountFrequency (%)
한림항 H01 109
18.7%
제주항 H01 96
16.5%
서귀포항 H01 94
16.2%
제주항 H02 78
13.4%
성산포항 H01 76
13.1%
한림항 H02 67
11.5%
서귀포항 H02 62
10.7%

Length

2023-12-13T00:31:30.311061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T00:31:30.424214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
h01 375
32.2%
h02 207
17.8%
한림항 176
15.1%
제주항 174
14.9%
서귀포항 156
13.4%
성산포항 76
 
6.5%

위도
Real number (ℝ)

HIGH CORRELATION 

Distinct7
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.408654
Minimum33.236389
Maximum33.527222
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.2 KiB
2023-12-13T00:31:30.530163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum33.236389
5-th percentile33.236389
Q133.240278
median33.418056
Q333.526111
95-th percentile33.527222
Maximum33.527222
Range0.290833
Interquartile range (IQR)0.285833

Descriptive statistics

Standard deviation0.11188521
Coefficient of variation (CV)0.003348989
Kurtosis-1.1574772
Mean33.408654
Median Absolute Deviation (MAD)0.108055
Skewness-0.58269147
Sum19443.836
Variance0.012518301
MonotonicityNot monotonic
2023-12-13T00:31:30.622685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
33.415556 109
18.7%
33.526111 96
16.5%
33.236389 94
16.2%
33.527222 78
13.4%
33.470833 76
13.1%
33.418056 67
11.5%
33.240278 62
10.7%
ValueCountFrequency (%)
33.236389 94
16.2%
33.240278 62
10.7%
33.415556 109
18.7%
33.418056 67
11.5%
33.470833 76
13.1%
33.526111 96
16.5%
33.527222 78
13.4%
ValueCountFrequency (%)
33.527222 78
13.4%
33.526111 96
16.5%
33.470833 76
13.1%
33.418056 67
11.5%
33.415556 109
18.7%
33.240278 62
10.7%
33.236389 94
16.2%

경도
Real number (ℝ)

HIGH CORRELATION 

Distinct7
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.50996
Minimum126.25528
Maximum126.9275
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.2 KiB
2023-12-13T00:31:30.736728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.25528
5-th percentile126.25528
Q1126.25972
median126.54
Q3126.56528
95-th percentile126.9275
Maximum126.9275
Range0.672222
Interquartile range (IQR)0.305556

Descriptive statistics

Standard deviation0.20775814
Coefficient of variation (CV)0.0016422276
Kurtosis-0.18482867
Mean126.50996
Median Absolute Deviation (MAD)0.025278
Skewness0.52171592
Sum73628.796
Variance0.043163446
MonotonicityNot monotonic
2023-12-13T00:31:30.835469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
126.255278 109
18.7%
126.54 96
16.5%
126.565278 94
16.2%
126.530556 78
13.4%
126.9275 76
13.1%
126.259722 67
11.5%
126.56 62
10.7%
ValueCountFrequency (%)
126.255278 109
18.7%
126.259722 67
11.5%
126.530556 78
13.4%
126.54 96
16.5%
126.56 62
10.7%
126.565278 94
16.2%
126.9275 76
13.1%
ValueCountFrequency (%)
126.9275 76
13.1%
126.565278 94
16.2%
126.56 62
10.7%
126.54 96
16.5%
126.530556 78
13.4%
126.259722 67
11.5%
126.255278 109
18.7%

수심
Real number (ℝ)

Distinct19
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.1494845
Minimum1
Maximum21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.2 KiB
2023-12-13T00:31:30.957769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median4
Q35
95-th percentile12.95
Maximum21
Range20
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.5649843
Coefficient of variation (CV)0.85913908
Kurtosis3.1142072
Mean4.1494845
Median Absolute Deviation (MAD)2
Skewness1.7026285
Sum2415
Variance12.709113
MonotonicityNot monotonic
2023-12-13T00:31:31.113147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
1 148
25.4%
2 119
20.4%
4 106
18.2%
5 63
10.8%
6 42
 
7.2%
10 29
 
5.0%
3 13
 
2.2%
8 13
 
2.2%
7 11
 
1.9%
15 10
 
1.7%
Other values (9) 28
 
4.8%
ValueCountFrequency (%)
1 148
25.4%
2 119
20.4%
3 13
 
2.2%
4 106
18.2%
5 63
10.8%
6 42
 
7.2%
7 11
 
1.9%
8 13
 
2.2%
9 4
 
0.7%
10 29
 
5.0%
ValueCountFrequency (%)
21 1
 
0.2%
20 1
 
0.2%
18 2
 
0.3%
17 1
 
0.2%
15 10
 
1.7%
14 8
 
1.4%
13 7
 
1.2%
12 1
 
0.2%
11 3
 
0.5%
10 29
5.0%

수온
Real number (ℝ)

HIGH CORRELATION 

Distinct430
Distinct (%)73.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.845412
Minimum9.53
Maximum30.16
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.2 KiB
2023-12-13T00:31:31.321123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9.53
5-th percentile11.1115
Q113.2975
median15.77
Q324.735
95-th percentile27.7735
Maximum30.16
Range20.63
Interquartile range (IQR)11.4375

Descriptive statistics

Standard deviation6.0770707
Coefficient of variation (CV)0.3224695
Kurtosis-1.5570868
Mean18.845412
Median Absolute Deviation (MAD)4.75
Skewness0.22722262
Sum10968.03
Variance36.930788
MonotonicityNot monotonic
2023-12-13T00:31:31.481647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
26.91 5
 
0.9%
12.64 5
 
0.9%
13.7 4
 
0.7%
14.89 4
 
0.7%
14.99 4
 
0.7%
14.95 4
 
0.7%
13.23 4
 
0.7%
14.53 4
 
0.7%
26.82 4
 
0.7%
12.23 3
 
0.5%
Other values (420) 541
93.0%
ValueCountFrequency (%)
9.53 1
0.2%
9.56 2
0.3%
9.57 1
0.2%
9.59 1
0.2%
9.71 1
0.2%
9.77 1
0.2%
9.78 2
0.3%
9.79 1
0.2%
10.17 1
0.2%
10.22 1
0.2%
ValueCountFrequency (%)
30.16 1
 
0.2%
30.13 1
 
0.2%
30.08 1
 
0.2%
29.94 1
 
0.2%
29.66 1
 
0.2%
28.96 1
 
0.2%
28.94 3
0.5%
28.93 1
 
0.2%
28.91 1
 
0.2%
28.9 1
 
0.2%

염분
Real number (ℝ)

HIGH CORRELATION 

Distinct301
Distinct (%)51.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.685739
Minimum25.79
Maximum34.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.2 KiB
2023-12-13T00:31:31.626744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum25.79
5-th percentile29.612
Q131.49
median33.42
Q334.0775
95-th percentile34.38
Maximum34.6
Range8.81
Interquartile range (IQR)2.5875

Descriptive statistics

Standard deviation1.7007368
Coefficient of variation (CV)0.052032993
Kurtosis1.0982941
Mean32.685739
Median Absolute Deviation (MAD)0.87
Skewness-1.1437046
Sum19023.1
Variance2.8925057
MonotonicityNot monotonic
2023-12-13T00:31:31.770487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33.92 9
 
1.5%
34.14 9
 
1.5%
34.2 9
 
1.5%
34.16 7
 
1.2%
33.87 7
 
1.2%
34.54 6
 
1.0%
34.23 6
 
1.0%
34.21 6
 
1.0%
34.15 6
 
1.0%
34.22 6
 
1.0%
Other values (291) 511
87.8%
ValueCountFrequency (%)
25.79 1
0.2%
25.9 1
0.2%
26.2 1
0.2%
26.37 1
0.2%
26.89 1
0.2%
27.23 1
0.2%
27.43 1
0.2%
27.48 1
0.2%
27.73 1
0.2%
28.08 1
0.2%
ValueCountFrequency (%)
34.6 1
 
0.2%
34.57 2
 
0.3%
34.56 2
 
0.3%
34.55 2
 
0.3%
34.54 6
1.0%
34.49 2
 
0.3%
34.48 2
 
0.3%
34.45 1
 
0.2%
34.44 2
 
0.3%
34.43 2
 
0.3%

Interactions

2023-12-13T00:31:29.096853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:31:25.655869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:31:26.365601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:31:27.023026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:31:27.594563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:31:28.244057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:31:29.186568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:31:25.782707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:31:26.491603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:31:27.120471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:31:27.723520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:31:28.324697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:31:29.289832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:31:25.898834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:31:26.605792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:31:27.219568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:31:27.832594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:31:28.682284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:31:29.391393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:31:25.999458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:31:26.719824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:31:27.309636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:31:27.944304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:31:28.797977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:31:29.486660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:31:26.109910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:31:26.833618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:31:27.409466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:31:28.036309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:31:28.926729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:31:29.564911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:31:26.252341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:31:26.923043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:31:27.492583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:31:28.157234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:31:29.005291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T00:31:31.863803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
측정 연도측정 월정점명위도경도수심수온염분
측정 연도1.0000.0880.0000.0000.0000.0000.6790.642
측정 월0.0881.0000.0000.0000.0000.0001.0000.997
정점명0.0000.0001.0001.0001.0000.4390.4890.174
위도0.0000.0001.0001.0001.0000.4140.5780.161
경도0.0000.0001.0001.0001.0000.3460.5370.113
수심0.0000.0000.4390.4140.3461.0000.3210.000
수온0.6791.0000.4890.5780.5370.3211.0000.855
염분0.6420.9970.1740.1610.1130.0000.8551.000
2023-12-13T00:31:31.969164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
측정 월정점명
측정 월1.0000.000
정점명0.0001.000
2023-12-13T00:31:32.065526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
측정 연도위도경도수심수온염분측정 월정점명
측정 연도1.0000.050-0.050-0.0550.0110.0690.0650.000
위도0.0501.000-0.1610.096-0.0970.0020.0000.997
경도-0.050-0.1611.000-0.0780.070-0.0240.0000.997
수심-0.0550.096-0.0781.000-0.0500.1780.1040.238
수온0.011-0.0970.070-0.0501.000-0.7650.9930.273
염분0.0690.002-0.0240.178-0.7651.0000.9460.088
측정 월0.0650.0000.0000.1040.9930.9461.0000.000
정점명0.0000.9970.9970.2380.2730.0880.0001.000

Missing values

2023-12-13T00:31:29.684680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T00:31:29.797775image/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

측정 연도측정 월정점명위도경도수심수온염분
020118서귀포항 H0133.236389126.565278125.629.71
120118서귀포항 H0133.236389126.565278225.5630.16
220118서귀포항 H0133.236389126.565278425.4830.79
320118서귀포항 H0133.236389126.565278525.530.77
420118성산포항 H0133.470833126.9275124.8530.1
520118성산포항 H0133.470833126.9275224.4330.47
620118성산포항 H0133.470833126.9275424.1831.02
720118제주항 H0133.526111126.54124.2830.92
820118제주항 H0133.526111126.54524.0230.99
920118제주항 H0133.526111126.541023.9531.0
측정 연도측정 월정점명위도경도수심수온염분
57220222제주항 H0233.527222126.530556413.0234.04
57320222제주항 H0233.527222126.530556512.9934.06
57420222한림항 H0133.415556126.255278111.6934.23
57520222한림항 H0133.415556126.255278211.734.23
57620222한림항 H0133.415556126.255278411.6934.23
57720222한림항 H0133.415556126.255278611.6834.23
57820222한림항 H0233.418056126.259722110.3833.7
57920222한림항 H0233.418056126.259722210.4533.77
58020222한림항 H0233.418056126.259722410.2233.93
58120222한림항 H0233.418056126.259722610.1733.94