Overview

Dataset statistics

Number of variables11
Number of observations90
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory8.2 KiB
Average record size in memory93.5 B

Variable types

Numeric4
Text4
Categorical3

Dataset

Description샘플 데이터
Author한국해양과학기술원
URLhttps://www.bigdata-environment.kr/user/data_market/detail.do?id=2bdbb660-5e40-11ec-ace8-ed82a0cc1285

Alerts

위치(좌표계WGS84)위도_도 is highly overall correlated with 데이터_건수 and 2 other fieldsHigh correlation
위치(좌표계WGS84)위도_도분 is highly overall correlated with 데이터_건수 and 2 other fieldsHigh correlation
위치(좌표계WGS84)위도_도분초 is highly overall correlated with 데이터_건수 and 2 other fieldsHigh correlation
데이터_건수 is highly overall correlated with 분급도 and 3 other fieldsHigh correlation
분급도 is highly overall correlated with 데이터_건수 and 1 other fieldsHigh correlation
첨도 is highly overall correlated with 분급도High correlation
데이터_건수 has unique valuesUnique
관리번호 has unique valuesUnique

Reproduction

Analysis started2023-12-10 13:21:20.188984
Analysis finished2023-12-10 13:21:24.955613
Duration4.77 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

데이터_건수
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct90
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean160.5
Minimum116
Maximum205
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size942.0 B
2023-12-10T22:21:25.069608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum116
5-th percentile120.45
Q1138.25
median160.5
Q3182.75
95-th percentile200.55
Maximum205
Range89
Interquartile range (IQR)44.5

Descriptive statistics

Standard deviation26.124701
Coefficient of variation (CV)0.16277072
Kurtosis-1.2
Mean160.5
Median Absolute Deviation (MAD)22.5
Skewness0
Sum14445
Variance682.5
MonotonicityStrictly increasing
2023-12-10T22:21:25.358805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
116 1
 
1.1%
184 1
 
1.1%
182 1
 
1.1%
181 1
 
1.1%
180 1
 
1.1%
179 1
 
1.1%
178 1
 
1.1%
177 1
 
1.1%
176 1
 
1.1%
175 1
 
1.1%
Other values (80) 80
88.9%
ValueCountFrequency (%)
116 1
1.1%
117 1
1.1%
118 1
1.1%
119 1
1.1%
120 1
1.1%
121 1
1.1%
122 1
1.1%
123 1
1.1%
124 1
1.1%
125 1
1.1%
ValueCountFrequency (%)
205 1
1.1%
204 1
1.1%
203 1
1.1%
202 1
1.1%
201 1
1.1%
200 1
1.1%
199 1
1.1%
198 1
1.1%
197 1
1.1%
196 1
1.1%

관리번호
Text

UNIQUE 

Distinct90
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size852.0 B
2023-12-10T22:21:25.928607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.9
Min length2

Characters and Unicode

Total characters261
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique90 ?
Unique (%)100.0%

Sample

1st rowN1
2nd rowN2
3rd rowN3
4th rowN4
5th rowN5
ValueCountFrequency (%)
n1 1
 
1.1%
n67 1
 
1.1%
n65 1
 
1.1%
n64 1
 
1.1%
n63 1
 
1.1%
n62 1
 
1.1%
n61 1
 
1.1%
n60 1
 
1.1%
n59 1
 
1.1%
n58 1
 
1.1%
Other values (80) 80
88.9%
2023-12-10T22:21:26.600485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
N 90
34.5%
1 19
 
7.3%
2 19
 
7.3%
4 19
 
7.3%
5 19
 
7.3%
6 19
 
7.3%
7 19
 
7.3%
8 19
 
7.3%
3 19
 
7.3%
9 10
 
3.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 171
65.5%
Uppercase Letter 90
34.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 19
11.1%
2 19
11.1%
4 19
11.1%
5 19
11.1%
6 19
11.1%
7 19
11.1%
8 19
11.1%
3 19
11.1%
9 10
5.8%
0 9
5.3%
Uppercase Letter
ValueCountFrequency (%)
N 90
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 171
65.5%
Latin 90
34.5%

Most frequent character per script

Common
ValueCountFrequency (%)
1 19
11.1%
2 19
11.1%
4 19
11.1%
5 19
11.1%
6 19
11.1%
7 19
11.1%
8 19
11.1%
3 19
11.1%
9 10
5.8%
0 9
5.3%
Latin
ValueCountFrequency (%)
N 90
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 261
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 90
34.5%
1 19
 
7.3%
2 19
 
7.3%
4 19
 
7.3%
5 19
 
7.3%
6 19
 
7.3%
7 19
 
7.3%
8 19
 
7.3%
3 19
 
7.3%
9 10
 
3.8%

위치(좌표계WGS84)위도_도
Categorical

HIGH CORRELATION 

Distinct35
Distinct (%)38.9%
Missing0
Missing (%)0.0%
Memory size852.0 B
N 35.036667°
12 
N 35.075000°
11 
N 35.046667°
11 
N 35.058333°
8 
N 35.065000°
6 
Other values (30)
42 

Length

Max length12
Median length12
Mean length11.988889
Min length11

Unique

Unique23 ?
Unique (%)25.6%

Sample

1st rowN 35.135167°
2nd rowN 35.135167°
3rd rowN 35.125000°
4th rowN 35.116667°
5th rowN 35.116667°

Common Values

ValueCountFrequency (%)
N 35.036667° 12
13.3%
N 35.075000° 11
12.2%
N 35.046667° 11
12.2%
N 35.058333° 8
 
8.9%
N 35.065000° 6
 
6.7%
N 35.080000° 5
 
5.6%
N 35.080833° 3
 
3.3%
N 35.088333° 3
 
3.3%
N 35.116667° 2
 
2.2%
N 35.111667° 2
 
2.2%
Other values (25) 27
30.0%

Length

2023-12-10T22:21:26.855595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
n 90
50.0%
35.036667° 12
 
6.7%
35.075000° 11
 
6.1%
35.046667° 11
 
6.1%
35.058333° 8
 
4.4%
35.065000° 6
 
3.3%
35.080000° 5
 
2.8%
35.080833° 3
 
1.7%
35.088333° 3
 
1.7%
35.095000° 2
 
1.1%
Other values (26) 29
 
16.1%

위치(좌표계WGS84)경도_도
Text

Distinct52
Distinct (%)57.8%
Missing0
Missing (%)0.0%
Memory size852.0 B
2023-12-10T22:21:27.238219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length13
Mean length12.988889
Min length12

Characters and Unicode

Total characters1169
Distinct characters14
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique33 ?
Unique (%)36.7%

Sample

1st rowE 128.957217°
2nd rowE 128.961033°
3rd rowE 128.953333°
4th rowE 128.941667°
5th rowE 128.954167°
ValueCountFrequency (%)
e 90
50.0%
128.891667° 7
 
3.9%
128.861667° 5
 
2.8%
128.950000° 5
 
2.8%
128.901667° 4
 
2.2%
128.873333° 4
 
2.2%
128.913333° 3
 
1.7%
128.925000° 3
 
1.7%
128.850000° 3
 
1.7%
128.883333° 3
 
1.7%
Other values (43) 53
29.4%
2023-12-10T22:21:27.781353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
8 149
12.7%
1 134
11.5%
2 103
8.8%
3 96
8.2%
E 90
7.7%
90
7.7%
. 90
7.7%
° 89
7.6%
0 86
7.4%
6 80
6.8%
Other values (4) 162
13.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 810
69.3%
Uppercase Letter 90
 
7.7%
Space Separator 90
 
7.7%
Other Punctuation 90
 
7.7%
Other Symbol 89
 
7.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 149
18.4%
1 134
16.5%
2 103
12.7%
3 96
11.9%
0 86
10.6%
6 80
9.9%
9 70
8.6%
7 47
 
5.8%
5 31
 
3.8%
4 14
 
1.7%
Uppercase Letter
ValueCountFrequency (%)
E 90
100.0%
Space Separator
ValueCountFrequency (%)
90
100.0%
Other Punctuation
ValueCountFrequency (%)
. 90
100.0%
Other Symbol
ValueCountFrequency (%)
° 89
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1079
92.3%
Latin 90
 
7.7%

Most frequent character per script

Common
ValueCountFrequency (%)
8 149
13.8%
1 134
12.4%
2 103
9.5%
3 96
8.9%
90
8.3%
. 90
8.3%
° 89
8.2%
0 86
8.0%
6 80
7.4%
9 70
6.5%
Other values (3) 92
8.5%
Latin
ValueCountFrequency (%)
E 90
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1080
92.4%
None 89
 
7.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 149
13.8%
1 134
12.4%
2 103
9.5%
3 96
8.9%
E 90
8.3%
90
8.3%
. 90
8.3%
0 86
8.0%
6 80
7.4%
9 70
6.5%
Other values (3) 92
8.5%
None
ValueCountFrequency (%)
° 89
100.0%

위치(좌표계WGS84)위도_도분
Categorical

HIGH CORRELATION 

Distinct35
Distinct (%)38.9%
Missing0
Missing (%)0.0%
Memory size852.0 B
N 35° 2.200'
12 
N 35° 4.500'
11 
N 35° 2.800'
11 
N 35° 3.500'
8 
N 35° 3.900'
6 
Other values (30)
42 

Length

Max length12
Median length12
Mean length12
Min length12

Unique

Unique23 ?
Unique (%)25.6%

Sample

1st rowN 35° 8.110'
2nd rowN 35° 8.110'
3rd rowN 35° 7.500'
4th rowN 35° 7.000'
5th rowN 35° 7.000'

Common Values

ValueCountFrequency (%)
N 35° 2.200' 12
13.3%
N 35° 4.500' 11
12.2%
N 35° 2.800' 11
12.2%
N 35° 3.500' 8
 
8.9%
N 35° 3.900' 6
 
6.7%
N 35° 4.800' 5
 
5.6%
N 35° 4.850' 3
 
3.3%
N 35° 5.300' 3
 
3.3%
N 35° 7.000' 2
 
2.2%
N 35° 6.700' 2
 
2.2%
Other values (25) 27
30.0%

Length

2023-12-10T22:21:27.998552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
n 90
33.3%
35° 90
33.3%
2.200 12
 
4.4%
4.500 11
 
4.1%
2.800 11
 
4.1%
3.500 8
 
3.0%
3.900 6
 
2.2%
4.800 5
 
1.9%
4.850 3
 
1.1%
5.300 3
 
1.1%
Other values (27) 31
 
11.5%

위치(좌표계WGS84)경도_도분
Text

Distinct51
Distinct (%)56.7%
Missing0
Missing (%)0.0%
Memory size852.0 B
2023-12-10T22:21:28.281380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length14
Mean length14
Min length14

Characters and Unicode

Total characters1260
Distinct characters15
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique31 ?
Unique (%)34.4%

Sample

1st rowE 128° 57.433'
2nd rowE 128° 57.662'
3rd rowE 128° 57.200'
4th rowE 128° 56.500'
5th rowE 128° 57.250'
ValueCountFrequency (%)
e 90
33.3%
128° 90
33.3%
53.500 7
 
2.6%
51.700 5
 
1.9%
57.000 5
 
1.9%
54.100 4
 
1.5%
52.400 4
 
1.5%
53.000 3
 
1.1%
55.500 3
 
1.1%
54.800 3
 
1.1%
Other values (43) 56
20.7%
2023-12-10T22:21:28.899940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
180
14.3%
0 159
12.6%
5 123
9.8%
1 114
9.0%
2 105
8.3%
8 98
7.8%
E 90
7.1%
° 90
7.1%
. 90
7.1%
' 90
7.1%
Other values (5) 121
9.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 720
57.1%
Space Separator 180
 
14.3%
Other Punctuation 180
 
14.3%
Uppercase Letter 90
 
7.1%
Other Symbol 90
 
7.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 159
22.1%
5 123
17.1%
1 114
15.8%
2 105
14.6%
8 98
13.6%
3 31
 
4.3%
7 29
 
4.0%
4 26
 
3.6%
6 23
 
3.2%
9 12
 
1.7%
Other Punctuation
ValueCountFrequency (%)
. 90
50.0%
' 90
50.0%
Space Separator
ValueCountFrequency (%)
180
100.0%
Uppercase Letter
ValueCountFrequency (%)
E 90
100.0%
Other Symbol
ValueCountFrequency (%)
° 90
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1170
92.9%
Latin 90
 
7.1%

Most frequent character per script

Common
ValueCountFrequency (%)
180
15.4%
0 159
13.6%
5 123
10.5%
1 114
9.7%
2 105
9.0%
8 98
8.4%
° 90
7.7%
. 90
7.7%
' 90
7.7%
3 31
 
2.6%
Other values (4) 90
7.7%
Latin
ValueCountFrequency (%)
E 90
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1170
92.9%
None 90
 
7.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
180
15.4%
0 159
13.6%
5 123
10.5%
1 114
9.7%
2 105
9.0%
8 98
8.4%
E 90
7.7%
. 90
7.7%
' 90
7.7%
3 31
 
2.6%
Other values (4) 90
7.7%
None
ValueCountFrequency (%)
° 90
100.0%

위치(좌표계WGS84)위도_도분초
Categorical

HIGH CORRELATION 

Distinct36
Distinct (%)40.0%
Missing0
Missing (%)0.0%
Memory size852.0 B
N 35° 2' 48.000"
11 
N 35° 2' 12.000"
11 
N 35° 4' 30.000"
11 
N 35° 3' 30.000"
8 
N 35° 3' 54.000"
6 
Other values (31)
43 

Length

Max length17
Median length16
Mean length15.922222
Min length15

Unique

Unique24 ?
Unique (%)26.7%

Sample

1st rowN 35° 8' 6.600"
2nd rowN 35° 8' 6.600"
3rd rowN 35° 7' 30.000"
4th rowN 35° 7' 0.000"
5th rowN 35° 7' 0.000"

Common Values

ValueCountFrequency (%)
N 35° 2' 48.000" 11
 
12.2%
N 35° 2' 12.000" 11
 
12.2%
N 35° 4' 30.000" 11
 
12.2%
N 35° 3' 30.000" 8
 
8.9%
N 35° 3' 54.000" 6
 
6.7%
N 35° 4' 48.000" 5
 
5.6%
N 35° 4' 51.000" 3
 
3.3%
N 35° 5' 18.000" 3
 
3.3%
N 35° 6' 42.000" 2
 
2.2%
N 35° 7' 0.000" 2
 
2.2%
Other values (26) 28
31.1%

Length

2023-12-10T22:21:29.151489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
n 90
25.0%
35° 90
25.0%
2 25
 
6.9%
4 22
 
6.1%
30.000 21
 
5.8%
3 21
 
5.8%
48.000 18
 
5.0%
12.000 12
 
3.3%
5 11
 
3.1%
54.000 6
 
1.7%
Other values (24) 44
12.2%

위치(좌표계WGS84)경도_도분초
Text

Distinct53
Distinct (%)58.9%
Missing0
Missing (%)0.0%
Memory size852.0 B
2023-12-10T22:21:29.421035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length18
Mean length17.777778
Min length16

Characters and Unicode

Total characters1600
Distinct characters16
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique34 ?
Unique (%)37.8%

Sample

1st rowE 128° 57' 25.980"
2nd rowE 128° 57' 39.720"
3rd rowE 128° 57' 12.000"
4th rowE 128° 56' 30.000"
5th rowE 128° 57' 15.000"
ValueCountFrequency (%)
128° 90
25.1%
e 89
24.8%
53 17
 
4.7%
56 15
 
4.2%
57 14
 
3.9%
54 13
 
3.6%
0.000 13
 
3.6%
55 11
 
3.1%
51 11
 
3.1%
30.000 11
 
3.1%
Other values (30) 75
20.9%
2023-12-10T22:21:29.930808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
271
16.9%
0 264
16.5%
2 126
7.9%
5 121
7.6%
1 119
7.4%
8 106
 
6.6%
° 90
 
5.6%
' 90
 
5.6%
. 90
 
5.6%
" 90
 
5.6%
Other values (6) 233
14.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 880
55.0%
Space Separator 271
 
16.9%
Other Punctuation 270
 
16.9%
Other Symbol 90
 
5.6%
Uppercase Letter 89
 
5.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 264
30.0%
2 126
14.3%
5 121
13.8%
1 119
13.5%
8 106
12.0%
4 54
 
6.1%
3 36
 
4.1%
6 30
 
3.4%
7 16
 
1.8%
9 8
 
0.9%
Other Punctuation
ValueCountFrequency (%)
' 90
33.3%
. 90
33.3%
" 90
33.3%
Space Separator
ValueCountFrequency (%)
271
100.0%
Other Symbol
ValueCountFrequency (%)
° 90
100.0%
Uppercase Letter
ValueCountFrequency (%)
E 89
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1511
94.4%
Latin 89
 
5.6%

Most frequent character per script

Common
ValueCountFrequency (%)
271
17.9%
0 264
17.5%
2 126
8.3%
5 121
8.0%
1 119
7.9%
8 106
 
7.0%
° 90
 
6.0%
' 90
 
6.0%
. 90
 
6.0%
" 90
 
6.0%
Other values (5) 144
9.5%
Latin
ValueCountFrequency (%)
E 89
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1510
94.4%
None 90
 
5.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
271
17.9%
0 264
17.5%
2 126
8.3%
5 121
8.0%
1 119
7.9%
8 106
 
7.0%
' 90
 
6.0%
. 90
 
6.0%
" 90
 
6.0%
E 89
 
5.9%
Other values (5) 144
9.5%
None
ValueCountFrequency (%)
° 90
100.0%

분급도
Real number (ℝ)

HIGH CORRELATION 

Distinct78
Distinct (%)86.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.8653333
Minimum0.46
Maximum3.69
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size942.0 B
2023-12-10T22:21:30.243316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.46
5-th percentile0.48
Q11.08
median1.885
Q32.75
95-th percentile3.4155
Maximum3.69
Range3.23
Interquartile range (IQR)1.67

Descriptive statistics

Standard deviation0.98594756
Coefficient of variation (CV)0.52856374
Kurtosis-1.2519141
Mean1.8653333
Median Absolute Deviation (MAD)0.845
Skewness0.17030003
Sum167.88
Variance0.97209258
MonotonicityNot monotonic
2023-12-10T22:21:30.512655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.48 3
 
3.3%
1.97 3
 
3.3%
2.0 2
 
2.2%
1.08 2
 
2.2%
1.09 2
 
2.2%
1.15 2
 
2.2%
2.37 2
 
2.2%
0.5 2
 
2.2%
0.47 2
 
2.2%
1.01 2
 
2.2%
Other values (68) 68
75.6%
ValueCountFrequency (%)
0.46 1
 
1.1%
0.47 2
2.2%
0.48 3
3.3%
0.49 1
 
1.1%
0.5 2
2.2%
0.51 1
 
1.1%
0.52 1
 
1.1%
0.54 1
 
1.1%
0.64 1
 
1.1%
0.65 1
 
1.1%
ValueCountFrequency (%)
3.69 1
1.1%
3.65 1
1.1%
3.64 1
1.1%
3.54 1
1.1%
3.42 1
1.1%
3.41 1
1.1%
3.4 1
1.1%
3.3 1
1.1%
3.29 1
1.1%
3.17 1
1.1%

왜도
Real number (ℝ)

Distinct63
Distinct (%)70.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.38922222
Minimum-0.44
Maximum0.86
Zeros0
Zeros (%)0.0%
Negative15
Negative (%)16.7%
Memory size942.0 B
2023-12-10T22:21:30.870327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-0.44
5-th percentile-0.171
Q10.2
median0.415
Q30.6675
95-th percentile0.8155
Maximum0.86
Range1.3
Interquartile range (IQR)0.4675

Descriptive statistics

Standard deviation0.32699595
Coefficient of variation (CV)0.84012663
Kurtosis-0.44880059
Mean0.38922222
Median Absolute Deviation (MAD)0.235
Skewness-0.59270894
Sum35.03
Variance0.10692635
MonotonicityNot monotonic
2023-12-10T22:21:31.290689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.39 4
 
4.4%
0.76 3
 
3.3%
0.47 3
 
3.3%
0.2 3
 
3.3%
0.74 3
 
3.3%
0.78 2
 
2.2%
0.57 2
 
2.2%
0.77 2
 
2.2%
0.24 2
 
2.2%
0.25 2
 
2.2%
Other values (53) 64
71.1%
ValueCountFrequency (%)
-0.44 1
1.1%
-0.35 2
2.2%
-0.29 1
1.1%
-0.18 1
1.1%
-0.16 1
1.1%
-0.14 1
1.1%
-0.12 1
1.1%
-0.11 1
1.1%
-0.09 2
2.2%
-0.08 2
2.2%
ValueCountFrequency (%)
0.86 1
 
1.1%
0.84 1
 
1.1%
0.83 2
2.2%
0.82 1
 
1.1%
0.81 1
 
1.1%
0.78 2
2.2%
0.77 2
2.2%
0.76 3
3.3%
0.75 1
 
1.1%
0.74 3
3.3%

첨도
Real number (ℝ)

HIGH CORRELATION 

Distinct76
Distinct (%)84.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.4225556
Minimum0.53
Maximum6.26
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size942.0 B
2023-12-10T22:21:31.546391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.53
5-th percentile0.6145
Q10.8475
median1.81
Q33.8425
95-th percentile4.729
Maximum6.26
Range5.73
Interquartile range (IQR)2.995

Descriptive statistics

Standard deviation1.6266099
Coefficient of variation (CV)0.67144379
Kurtosis-1.2897234
Mean2.4225556
Median Absolute Deviation (MAD)1.17
Skewness0.38971531
Sum218.03
Variance2.6458597
MonotonicityNot monotonic
2023-12-10T22:21:31.853741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.66 3
 
3.3%
0.7 2
 
2.2%
3.71 2
 
2.2%
1.32 2
 
2.2%
3.6 2
 
2.2%
0.84 2
 
2.2%
4.16 2
 
2.2%
0.78 2
 
2.2%
0.6 2
 
2.2%
0.97 2
 
2.2%
Other values (66) 69
76.7%
ValueCountFrequency (%)
0.53 1
1.1%
0.59 1
1.1%
0.6 2
2.2%
0.61 1
1.1%
0.62 1
1.1%
0.63 2
2.2%
0.65 1
1.1%
0.66 1
1.1%
0.7 2
2.2%
0.71 1
1.1%
ValueCountFrequency (%)
6.26 1
1.1%
5.71 1
1.1%
5.39 1
1.1%
5.35 1
1.1%
4.81 1
1.1%
4.63 2
2.2%
4.59 1
1.1%
4.55 1
1.1%
4.52 1
1.1%
4.48 1
1.1%

Interactions

2023-12-10T22:21:23.965171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:21.435953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:22.251034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:22.857470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:24.088350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:21.663052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:22.399192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:23.006257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:24.272676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:21.857051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:22.557137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:23.263528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:24.397942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:22.124477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:22.704355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:23.468155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T22:21:32.047676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
데이터_건수관리번호위치(좌표계WGS84)위도_도위치(좌표계WGS84)경도_도위치(좌표계WGS84)위도_도분위치(좌표계WGS84)경도_도분위치(좌표계WGS84)위도_도분초위치(좌표계WGS84)경도_도분초분급도왜도첨도
데이터_건수1.0001.0000.9600.0000.9600.0000.9560.0000.4220.2290.328
관리번호1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
위치(좌표계WGS84)위도_도0.9601.0001.0000.9781.0000.9781.0000.9810.5860.7100.000
위치(좌표계WGS84)경도_도0.0001.0000.9781.0000.9781.0000.9751.0000.6440.7630.000
위치(좌표계WGS84)위도_도분0.9601.0001.0000.9781.0000.9781.0000.9810.5860.7100.000
위치(좌표계WGS84)경도_도분0.0001.0000.9781.0000.9781.0000.9761.0000.6420.7710.000
위치(좌표계WGS84)위도_도분초0.9561.0001.0000.9751.0000.9761.0000.9780.6240.6880.000
위치(좌표계WGS84)경도_도분초0.0001.0000.9811.0000.9811.0000.9781.0000.6030.7670.000
분급도0.4221.0000.5860.6440.5860.6420.6240.6031.0000.7770.728
왜도0.2291.0000.7100.7630.7100.7710.6880.7670.7771.0000.445
첨도0.3281.0000.0000.0000.0000.0000.0000.0000.7280.4451.000
2023-12-10T22:21:32.306589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
위치(좌표계WGS84)위도_도위치(좌표계WGS84)위도_도분위치(좌표계WGS84)위도_도분초
위치(좌표계WGS84)위도_도1.0001.0000.991
위치(좌표계WGS84)위도_도분1.0001.0000.991
위치(좌표계WGS84)위도_도분초0.9910.9911.000
2023-12-10T22:21:32.490794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
데이터_건수분급도왜도첨도위치(좌표계WGS84)위도_도위치(좌표계WGS84)위도_도분위치(좌표계WGS84)위도_도분초
데이터_건수1.000-0.5130.0520.3960.6250.6250.618
분급도-0.5131.0000.206-0.5590.1890.1890.213
왜도0.0520.2061.0000.3770.2480.2480.230
첨도0.396-0.5590.3771.0000.0000.0000.000
위치(좌표계WGS84)위도_도0.6250.1890.2480.0001.0001.0000.991
위치(좌표계WGS84)위도_도분0.6250.1890.2480.0001.0001.0000.991
위치(좌표계WGS84)위도_도분초0.6180.2130.2300.0000.9910.9911.000

Missing values

2023-12-10T22:21:24.577281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T22:21:24.853397image/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

데이터_건수관리번호위치(좌표계WGS84)위도_도위치(좌표계WGS84)경도_도위치(좌표계WGS84)위도_도분위치(좌표계WGS84)경도_도분위치(좌표계WGS84)위도_도분초위치(좌표계WGS84)경도_도분초분급도왜도첨도
0116N1N 35.135167°E 128.957217°N 35° 8.110'E 128° 57.433'N 35° 8' 6.600"E 128° 57' 25.980"2.00.433.46
1117N2N 35.135167°E 128.961033°N 35° 8.110'E 128° 57.662'N 35° 8' 6.600"E 128° 57' 39.720"2.660.751.93
2118N3N 35.125000°E 128.953333°N 35° 7.500'E 128° 57.200'N 35° 7' 30.000"E 128° 57' 12.000"2.310.692.95
3119N4N 35.116667°E 128.941667°N 35° 7.000'E 128° 56.500'N 35° 7' 0.000"E 128° 56' 30.000"2.38-0.110.89
4120N5N 35.116667°E 128.954167°N 35° 7.000'E 128° 57.250'N 35° 7' 0.000"E 128° 57' 15.000"0.480.11.4
5121N6N 35.111667°E 128.893333°N 35° 6.700'E 128° 53.600'N 35° 6' 42.000"E 128° 53' 36.000"3.120.810.84
6122N7N 35.111667°E 128.898333°N 35° 6.700'E 128° 53.900'N 35° 6' 42.000"E 128° 53' 54.000"2.37-0.160.82
7123N8N 35.102300°E 128.895833°N 35° 6.138'E 128° 53.750'N 35° 6' 8.280"E 128° 53' 45.000"3.54-0.090.62
8124N9N 35.095000°E 128.891667°N 35° 5.700'E 128° 53.500'N 35° 5' 42.000"E 128° 53' 30.000"1.340.544.32
9125N10N 35.095000°E 128.898333°N 35° 5.700'E 128° 53.900'N 35° 5' 42.000"E 128° 53' 54.000"3.40.180.6
데이터_건수관리번호위치(좌표계WGS84)위도_도위치(좌표계WGS84)경도_도위치(좌표계WGS84)위도_도분위치(좌표계WGS84)경도_도분위치(좌표계WGS84)위도_도분초위치(좌표계WGS84)경도_도분초분급도왜도첨도
80196N81N 35.036667°E 128.881667°N 35° 2.200'E 128° 52.900'N 35° 2' 12.000"E 128° 52' 54.000"1.10.654.81
81197N82N 35.036667°E 128.891667°N 35° 2.200'E 128° 53.500'N 35° 2' 12.000"E 128° 53' 30.000"2.030.833.04
82198N83N 35.036667°E 128.901667°N 35° 2.200'E 128° 54.100'N 35° 2' 12.000"E 128° 54' 6.000"3.080.410.66
83199N84N 35.036667°E 128.911667°N 35° 2.200'E 128° 54.700'N 35° 2' 12.000"E 128° 54' 42.000"1.220.43.67
84200N85N 35.036667°E 128.921667°N 35° 2.200'E 128° 55.300'N 35° 2' 12.000"E 128° 55' 18.000"0.480.241.33
85201N86N 35.036667°E 128.931667°N 35° 2.200'E 128° 55.900'N 35° 2' 12.000"E 128° 55' 54.000"1.020.575.35
86202N87N 35.036667°E 128.940000°N 35° 2.200'E 128° 56.400'N 35° 2' 12.000"E 128° 56' 24.000"1.310.394.55
87203N88N 35.036667°E 128.950000°N 35° 2.200'E 128° 57.000'N 35° 2' 12.000"E 128° 57' 0.000"1.090.473.73
88204N89N 35.036667°E 128.960000°N 35° 2.200'E 128° 57.600'N 35° 2' 12.000"E 128° 57' 36.000"1.20.484.35
89205N90N 35.040900°E 128.963650°N 35° 2.454'E 128° 57.819'N 35° 2' 27.240"E 128° 57' 49.140"0.490.21.3