Overview

Dataset statistics

Number of variables5
Number of observations120
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.3 KiB
Average record size in memory45.1 B

Variable types

Numeric4
Categorical1

Dataset

DescriptionSample
Author㈜해양정보기술
URLhttps://www.bigdata-coast.kr/gdsInfo/gdsInfoDetail.do?gdsCd=CT04MIT004

Alerts

SPOT_NM has constant value ""Constant
WTCH_TDLV is highly overall correlated with PRDN_TDLVHigh correlation
PRDN_TDLV is highly overall correlated with WTCH_TDLVHigh correlation
WTCH_YMDHMS has unique valuesUnique

Reproduction

Analysis started2024-03-13 12:44:35.647236
Analysis finished2024-03-13 12:44:38.578806
Duration2.93 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

WTCH_YMDHMS
Real number (ℝ)

UNIQUE 

Distinct120
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0000744 × 1013
Minimum2.0000728 × 1013
Maximum2.0000801 × 1013
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2024-03-13T21:44:38.684465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.0000728 × 1013
5-th percentile2.0000728 × 1013
Q12.0000729 × 1013
median2.000073 × 1013
Q32.0000731 × 1013
95-th percentile2.0000801 × 1013
Maximum2.0000801 × 1013
Range73230000
Interquartile range (IQR)2115000

Descriptive statistics

Standard deviation28737551
Coefficient of variation (CV)1.4368241 × 10-6
Kurtosis0.30609012
Mean2.0000744 × 1013
Median Absolute Deviation (MAD)1060000
Skewness1.514406
Sum2.4000893 × 1015
Variance8.2584685 × 1014
MonotonicityStrictly increasing
2024-03-13T21:44:38.894773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20000728000000 1
 
0.8%
20000730130000 1
 
0.8%
20000731170000 1
 
0.8%
20000731160000 1
 
0.8%
20000731150000 1
 
0.8%
20000731140000 1
 
0.8%
20000731130000 1
 
0.8%
20000731120000 1
 
0.8%
20000731110000 1
 
0.8%
20000731100000 1
 
0.8%
Other values (110) 110
91.7%
ValueCountFrequency (%)
20000728000000 1
0.8%
20000728010000 1
0.8%
20000728020000 1
0.8%
20000728030000 1
0.8%
20000728040000 1
0.8%
20000728050000 1
0.8%
20000728060000 1
0.8%
20000728070000 1
0.8%
20000728080000 1
0.8%
20000728090000 1
0.8%
ValueCountFrequency (%)
20000801230000 1
0.8%
20000801220000 1
0.8%
20000801210000 1
0.8%
20000801200000 1
0.8%
20000801190000 1
0.8%
20000801180000 1
0.8%
20000801170000 1
0.8%
20000801160000 1
0.8%
20000801150000 1
0.8%
20000801140000 1
0.8%

WTCH_TDLV
Real number (ℝ)

HIGH CORRELATION 

Distinct99
Distinct (%)82.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean113.55833
Minimum16
Maximum219
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2024-03-13T21:44:39.116021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum16
5-th percentile36.9
Q165.5
median112.5
Q3160.5
95-th percentile200.05
Maximum219
Range203
Interquartile range (IQR)95

Descriptive statistics

Standard deviation54.941496
Coefficient of variation (CV)0.48381739
Kurtosis-1.1870715
Mean113.55833
Median Absolute Deviation (MAD)48
Skewness0.085241678
Sum13627
Variance3018.568
MonotonicityNot monotonic
2024-03-13T21:44:39.327689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37 3
 
2.5%
50 3
 
2.5%
66 2
 
1.7%
73 2
 
1.7%
136 2
 
1.7%
169 2
 
1.7%
62 2
 
1.7%
48 2
 
1.7%
147 2
 
1.7%
180 2
 
1.7%
Other values (89) 98
81.7%
ValueCountFrequency (%)
16 1
 
0.8%
19 1
 
0.8%
28 1
 
0.8%
30 1
 
0.8%
31 1
 
0.8%
35 1
 
0.8%
37 3
2.5%
38 1
 
0.8%
41 1
 
0.8%
42 1
 
0.8%
ValueCountFrequency (%)
219 1
0.8%
214 1
0.8%
213 1
0.8%
211 1
0.8%
210 1
0.8%
201 1
0.8%
200 2
1.7%
199 1
0.8%
193 1
0.8%
190 1
0.8%

PRDN_TDLV
Real number (ℝ)

HIGH CORRELATION 

Distinct118
Distinct (%)98.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean109.35958
Minimum17.39
Maximum213.07
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2024-03-13T21:44:39.990780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum17.39
5-th percentile30.5305
Q158.8975
median111.7
Q3157.2625
95-th percentile199.6415
Maximum213.07
Range195.68
Interquartile range (IQR)98.365

Descriptive statistics

Standard deviation54.648164
Coefficient of variation (CV)0.4997108
Kurtosis-1.208877
Mean109.35958
Median Absolute Deviation (MAD)47.535
Skewness0.050266258
Sum13123.15
Variance2986.4219
MonotonicityNot monotonic
2024-03-13T21:44:40.316114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
118.89 2
 
1.7%
168.46 2
 
1.7%
30.79 1
 
0.8%
40.06 1
 
0.8%
20.03 1
 
0.8%
21.88 1
 
0.8%
45.07 1
 
0.8%
83.3 1
 
0.8%
125.93 1
 
0.8%
160.3 1
 
0.8%
Other values (108) 108
90.0%
ValueCountFrequency (%)
17.39 1
0.8%
20.03 1
0.8%
20.69 1
0.8%
21.88 1
0.8%
23.06 1
0.8%
25.6 1
0.8%
30.79 1
0.8%
31.32 1
0.8%
31.35 1
0.8%
33.37 1
0.8%
ValueCountFrequency (%)
213.07 1
0.8%
209.98 1
0.8%
204.2 1
0.8%
200.89 1
0.8%
200.07 1
0.8%
199.67 1
0.8%
199.64 1
0.8%
192.7 1
0.8%
188.58 1
0.8%
186.49 1
0.8%

SRG_HGH
Real number (ℝ)

Distinct119
Distinct (%)99.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.1986667
Minimum-5.3
Maximum18.68
Zeros0
Zeros (%)0.0%
Negative31
Negative (%)25.8%
Memory size1.2 KiB
2024-03-13T21:44:40.576909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-5.3
5-th percentile-2.8875
Q1-0.12
median3.435
Q37.8125
95-th percentile13.1755
Maximum18.68
Range23.98
Interquartile range (IQR)7.9325

Descriptive statistics

Standard deviation5.2242029
Coefficient of variation (CV)1.2442528
Kurtosis-0.11301821
Mean4.1986667
Median Absolute Deviation (MAD)4.085
Skewness0.55501909
Sum503.84
Variance27.292296
MonotonicityNot monotonic
2024-03-13T21:44:40.839329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1.3 2
 
1.7%
0.13 1
 
0.8%
17.6 1
 
0.8%
6.94 1
 
0.8%
9.97 1
 
0.8%
13.12 1
 
0.8%
14.93 1
 
0.8%
9.7 1
 
0.8%
10.07 1
 
0.8%
6.7 1
 
0.8%
Other values (109) 109
90.8%
ValueCountFrequency (%)
-5.3 1
0.8%
-4.63 1
0.8%
-3.59 1
0.8%
-3.13 1
0.8%
-3.08 1
0.8%
-3.03 1
0.8%
-2.88 1
0.8%
-2.68 1
0.8%
-2.57 1
0.8%
-2.38 1
0.8%
ValueCountFrequency (%)
18.68 1
0.8%
17.63 1
0.8%
17.6 1
0.8%
17.06 1
0.8%
14.93 1
0.8%
14.23 1
0.8%
13.12 1
0.8%
12.85 1
0.8%
11.33 1
0.8%
11.21 1
0.8%

SPOT_NM
Categorical

CONSTANT 

Distinct1
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
가덕도
120 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
가덕도 120
100.0%

Length

2024-03-13T21:44:41.009937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T21:44:41.139236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
가덕도 120
100.0%

Interactions

2024-03-13T21:44:37.751179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:44:35.917772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:44:36.608114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:44:37.234267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:44:37.938060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:44:36.067324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:44:36.782276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:44:37.368794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:44:38.107985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:44:36.239858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:44:36.944452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:44:37.499151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:44:38.241376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:44:36.424792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:44:37.100140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:44:37.616202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-13T21:44:41.223524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
WTCH_YMDHMSWTCH_TDLVPRDN_TDLVSRG_HGH
WTCH_YMDHMS1.0000.0000.1790.418
WTCH_TDLV0.0001.0000.9820.285
PRDN_TDLV0.1790.9821.0000.022
SRG_HGH0.4180.2850.0221.000
2024-03-13T21:44:41.372931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
WTCH_YMDHMSWTCH_TDLVPRDN_TDLVSRG_HGH
WTCH_YMDHMS1.0000.0770.0520.295
WTCH_TDLV0.0771.0000.9950.150
PRDN_TDLV0.0520.9951.0000.065
SRG_HGH0.2950.1500.0651.000

Missing values

2024-03-13T21:44:38.372054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-13T21:44:38.506192image/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

WTCH_YMDHMSWTCH_TDLVPRDN_TDLVSRG_HGHSPOT_NM
0200007280000006665.870.13가덕도
1200007280100007575.76-0.76가덕도
2200007280200009393.27-0.27가덕도
320000728030000112113.28-1.28가덕도
420000728040000130131.3-1.3가덕도
520000728050000143143.79-0.79가덕도
620000728060000148147.440.56가덕도
720000728070000140139.830.17가덕도
820000728080000123121.21.8가덕도
9200007280900009695.570.43가덕도
WTCH_YMDHMSWTCH_TDLVPRDN_TDLVSRG_HGHSPOT_NM
110200008011400003736.950.05가덕도
111200008011500001617.39-1.39가덕도
112200008011600001920.69-1.69가덕도
113200008011700004445.92-1.92가덕도
114200008011800008385.57-2.57가덕도
11520000801190000128129.67-1.67가덕도
11620000801200000169170.3-1.3가덕도
11720000801210000200200.89-0.89가덕도
11820000801220000213213.07-0.07가덕도
11920000801230000200199.640.36가덕도