Overview

Dataset statistics

Number of variables3
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.8 KiB
Average record size in memory28.3 B

Variable types

Numeric2
Categorical1

Dataset

Description샘플 데이터
Author한국기상산업기술원
URLhttps://www.bigdata-environment.kr/user/data_market/detail.do?id=8e9af870-32bd-11ea-ba04-23e33148e18d

Alerts

위도 has constant value ""Constant
경도 is highly overall correlated with 염도High correlation
염도 is highly overall correlated with 경도High correlation
경도 has unique valuesUnique
염도 has unique valuesUnique

Reproduction

Analysis started2023-12-10 10:20:52.069941
Analysis finished2023-12-10 10:20:52.944565
Duration0.87 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

경도
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean119.125
Minimum115
Maximum123.25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:20:53.095982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum115
5-th percentile115.41253
Q1117.06247
median119.125
Q3121.18753
95-th percentile122.83747
Maximum123.25
Range8.25
Interquartile range (IQR)4.12505

Descriptive statistics

Standard deviation2.4176247
Coefficient of variation (CV)0.020294856
Kurtosis-1.1999992
Mean119.125
Median Absolute Deviation (MAD)2.08335
Skewness4.1462192 × 10-17
Sum11912.5
Variance5.8449093
MonotonicityStrictly increasing
2023-12-10T19:20:53.331163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
115.0 1
 
1.0%
120.3333 1
 
1.0%
121.1667 1
 
1.0%
121.0833 1
 
1.0%
121.0 1
 
1.0%
120.9167 1
 
1.0%
120.8333 1
 
1.0%
120.75 1
 
1.0%
120.6667 1
 
1.0%
120.5833 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
115.0 1
1.0%
115.0833 1
1.0%
115.1667 1
1.0%
115.25 1
1.0%
115.3333 1
1.0%
115.4167 1
1.0%
115.5 1
1.0%
115.5833 1
1.0%
115.6667 1
1.0%
115.75 1
1.0%
ValueCountFrequency (%)
123.25 1
1.0%
123.1667 1
1.0%
123.0833 1
1.0%
123.0 1
1.0%
122.9167 1
1.0%
122.8333 1
1.0%
122.75 1
1.0%
122.6667 1
1.0%
122.5833 1
1.0%
122.5 1
1.0%

위도
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
20
100 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20
2nd row20
3rd row20
4th row20
5th row20

Common Values

ValueCountFrequency (%)
20 100
100.0%

Length

2023-12-10T19:20:53.544076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:20:53.685475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20 100
100.0%

염도
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.56935
Minimum34.345825
Maximum34.68357
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:20:54.298059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum34.345825
5-th percentile34.387608
Q134.507248
median34.608356
Q334.619074
95-th percentile34.67883
Maximum34.68357
Range0.337745
Interquartile range (IQR)0.11182575

Descriptive statistics

Standard deviation0.090346558
Coefficient of variation (CV)0.0026134873
Kurtosis0.030056678
Mean34.56935
Median Absolute Deviation (MAD)0.023951
Skewness-1.0856109
Sum3456.935
Variance0.0081625005
MonotonicityNot monotonic
2023-12-10T19:20:54.578356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
34.391705 1
 
1.0%
34.633614 1
 
1.0%
34.60355 1
 
1.0%
34.599823 1
 
1.0%
34.597992 1
 
1.0%
34.600792 1
 
1.0%
34.597534 1
 
1.0%
34.596485 1
 
1.0%
34.605125 1
 
1.0%
34.61444 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
34.345825 1
1.0%
34.346592 1
1.0%
34.359653 1
1.0%
34.36002 1
1.0%
34.377613 1
1.0%
34.388134 1
1.0%
34.391705 1
1.0%
34.394394 1
1.0%
34.396217 1
1.0%
34.400284 1
1.0%
ValueCountFrequency (%)
34.68357 1
1.0%
34.683197 1
1.0%
34.6818 1
1.0%
34.679512 1
1.0%
34.679367 1
1.0%
34.678802 1
1.0%
34.674717 1
1.0%
34.665436 1
1.0%
34.663857 1
1.0%
34.66247 1
1.0%

Interactions

2023-12-10T19:20:52.436375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:20:52.181883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:20:52.563308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:20:52.289973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T19:20:54.719828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
경도염도
경도1.0000.924
염도0.9241.000
2023-12-10T19:20:54.852214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
경도염도
경도1.0000.772
염도0.7721.000

Missing values

2023-12-10T19:20:52.773443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T19:20:52.896080image/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

경도위도염도
0115.02034.391705
1115.08332034.388134
2115.16672034.359653
3115.252034.345825
4115.33332034.346592
5115.41672034.36002
6115.52034.377613
7115.58332034.400284
8115.66672034.423798
9115.752034.447193
경도위도염도
90122.52034.66247
91122.58332034.663857
92122.66672034.665436
93122.752034.674717
94122.83332034.678802
95122.91672034.6818
96123.02034.679512
97123.08332034.679367
98123.16672034.683197
99123.252034.68357