Overview

Dataset statistics

Number of variables11
Number of observations80
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.3 KiB
Average record size in memory93.6 B

Variable types

Numeric4
Text7

Dataset

Description샘플 데이터
Author한국해양과학기술원
URLhttps://www.bigdata-environment.kr/user/data_market/detail.do?id=939e7ce0-5ee2-11ec-ace8-ed82a0cc1285

Alerts

데이터_건수 is highly overall correlated with 분급도 and 1 other fieldsHigh correlation
분급도 is highly overall correlated with 데이터_건수 and 2 other fieldsHigh correlation
왜도 is highly overall correlated with 분급도 and 1 other fieldsHigh correlation
첨도 is highly overall correlated with 데이터_건수 and 2 other fieldsHigh correlation
데이터_건수 has unique valuesUnique
관리번호 has unique valuesUnique

Reproduction

Analysis started2024-04-21 03:19:20.300066
Analysis finished2024-04-21 03:19:28.478102
Duration8.18 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

데이터_건수
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct80
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.5
Minimum1
Maximum80
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size848.0 B
2024-04-21T12:19:28.677868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4.95
Q120.75
median40.5
Q360.25
95-th percentile76.05
Maximum80
Range79
Interquartile range (IQR)39.5

Descriptive statistics

Standard deviation23.2379
Coefficient of variation (CV)0.57377531
Kurtosis-1.2
Mean40.5
Median Absolute Deviation (MAD)20
Skewness0
Sum3240
Variance540
MonotonicityNot monotonic
2024-04-21T12:19:29.128507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.2%
47 1
 
1.2%
63 1
 
1.2%
62 1
 
1.2%
61 1
 
1.2%
60 1
 
1.2%
6 1
 
1.2%
59 1
 
1.2%
58 1
 
1.2%
57 1
 
1.2%
Other values (70) 70
87.5%
ValueCountFrequency (%)
1 1
1.2%
2 1
1.2%
3 1
1.2%
4 1
1.2%
5 1
1.2%
6 1
1.2%
7 1
1.2%
8 1
1.2%
9 1
1.2%
10 1
1.2%
ValueCountFrequency (%)
80 1
1.2%
79 1
1.2%
78 1
1.2%
77 1
1.2%
76 1
1.2%
75 1
1.2%
74 1
1.2%
73 1
1.2%
72 1
1.2%
71 1
1.2%

관리번호
Text

UNIQUE 

Distinct80
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size768.0 B
2024-04-21T12:19:30.229800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.9125
Min length2

Characters and Unicode

Total characters233
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

Unique80 ?
Unique (%)100.0%

Sample

1st rowK1
2nd rowK12
3rd rowK13
4th rowK15
5th rowK16
ValueCountFrequency (%)
k1 1
 
1.2%
k12 1
 
1.2%
k65 1
 
1.2%
k64 1
 
1.2%
k63 1
 
1.2%
k7 1
 
1.2%
k62 1
 
1.2%
k61 1
 
1.2%
k60 1
 
1.2%
k66 1
 
1.2%
Other values (70) 70
87.5%
2024-04-21T12:19:31.727177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
K 80
34.3%
2 19
 
8.2%
1 18
 
7.7%
3 18
 
7.7%
5 18
 
7.7%
6 18
 
7.7%
7 18
 
7.7%
4 17
 
7.3%
8 11
 
4.7%
9 8
 
3.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 153
65.7%
Uppercase Letter 80
34.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 19
12.4%
1 18
11.8%
3 18
11.8%
5 18
11.8%
6 18
11.8%
7 18
11.8%
4 17
11.1%
8 11
7.2%
9 8
5.2%
0 8
5.2%
Uppercase Letter
ValueCountFrequency (%)
K 80
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 153
65.7%
Latin 80
34.3%

Most frequent character per script

Common
ValueCountFrequency (%)
2 19
12.4%
1 18
11.8%
3 18
11.8%
5 18
11.8%
6 18
11.8%
7 18
11.8%
4 17
11.1%
8 11
7.2%
9 8
5.2%
0 8
5.2%
Latin
ValueCountFrequency (%)
K 80
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 233
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
K 80
34.3%
2 19
 
8.2%
1 18
 
7.7%
3 18
 
7.7%
5 18
 
7.7%
6 18
 
7.7%
7 18
 
7.7%
4 17
 
7.3%
8 11
 
4.7%
9 8
 
3.4%

위치(좌표계WGS84)위도_도
Text

Distinct77
Distinct (%)96.2%
Missing0
Missing (%)0.0%
Memory size768.0 B
2024-04-21T12:19:32.552481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length11
Mean length10.9875
Min length10

Characters and Unicode

Total characters879
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

Unique74 ?
Unique (%)92.5%

Sample

1st rowN 37.60995°
2nd rowN 37.56177°
3rd rowN 37.59547°
4th rowN 37.57725°
5th rowN 37.57187°
ValueCountFrequency (%)
n 80
50.0%
37.57725° 2
 
1.2%
37.61392° 2
 
1.2%
37.58747° 2
 
1.2%
37.58853° 1
 
0.6%
37.61480° 1
 
0.6%
37.57212° 1
 
0.6%
37.57855° 1
 
0.6%
37.59647° 1
 
0.6%
37.60638° 1
 
0.6%
Other values (68) 68
42.5%
2024-04-21T12:19:33.756680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
7 140
15.9%
3 108
12.3%
5 107
12.2%
N 80
9.1%
80
9.1%
. 80
9.1%
° 80
9.1%
2 39
 
4.4%
6 37
 
4.2%
8 34
 
3.9%
Other values (4) 94
10.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 559
63.6%
Uppercase Letter 80
 
9.1%
Space Separator 80
 
9.1%
Other Punctuation 80
 
9.1%
Other Symbol 80
 
9.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
7 140
25.0%
3 108
19.3%
5 107
19.1%
2 39
 
7.0%
6 37
 
6.6%
8 34
 
6.1%
0 32
 
5.7%
9 26
 
4.7%
1 22
 
3.9%
4 14
 
2.5%
Uppercase Letter
ValueCountFrequency (%)
N 80
100.0%
Space Separator
ValueCountFrequency (%)
80
100.0%
Other Punctuation
ValueCountFrequency (%)
. 80
100.0%
Other Symbol
ValueCountFrequency (%)
° 80
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 799
90.9%
Latin 80
 
9.1%

Most frequent character per script

Common
ValueCountFrequency (%)
7 140
17.5%
3 108
13.5%
5 107
13.4%
80
10.0%
. 80
10.0%
° 80
10.0%
2 39
 
4.9%
6 37
 
4.6%
8 34
 
4.3%
0 32
 
4.0%
Other values (3) 62
7.8%
Latin
ValueCountFrequency (%)
N 80
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 799
90.9%
None 80
 
9.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
7 140
17.5%
3 108
13.5%
5 107
13.4%
N 80
10.0%
80
10.0%
. 80
10.0%
2 39
 
4.9%
6 37
 
4.6%
8 34
 
4.3%
0 32
 
4.0%
Other values (3) 62
7.8%
None
ValueCountFrequency (%)
° 80
100.0%

위치(좌표계WGS84)경도_도
Text

Distinct75
Distinct (%)93.8%
Missing0
Missing (%)0.0%
Memory size768.0 B
2024-04-21T12:19:34.583123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length11
Mean length10.9125
Min length9

Characters and Unicode

Total characters873
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

Unique70 ?
Unique (%)87.5%

Sample

1st rowE 126.5433°
2nd rowE 126.4851°
3rd rowE 126.4831°
4th rowE 126.4803°
5th rowE 126.4833°
ValueCountFrequency (%)
e 80
50.0%
126.3487° 2
 
1.2%
126.3667° 2
 
1.2%
126.3668° 2
 
1.2%
126.4324° 2
 
1.2%
126.3495° 2
 
1.2%
126.3491° 1
 
0.6%
126.5433° 1
 
0.6%
126.3654° 1
 
0.6%
126.5217° 1
 
0.6%
Other values (66) 66
41.2%
2024-04-21T12:19:35.843276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
6 115
13.2%
1 102
11.7%
2 99
11.3%
E 80
9.2%
80
9.2%
. 80
9.2%
° 80
9.2%
3 71
8.1%
4 62
7.1%
5 29
 
3.3%
Other values (4) 75
8.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 553
63.3%
Uppercase Letter 80
 
9.2%
Space Separator 80
 
9.2%
Other Punctuation 80
 
9.2%
Other Symbol 80
 
9.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
6 115
20.8%
1 102
18.4%
2 99
17.9%
3 71
12.8%
4 62
11.2%
5 29
 
5.2%
8 27
 
4.9%
9 25
 
4.5%
7 12
 
2.2%
0 11
 
2.0%
Uppercase Letter
ValueCountFrequency (%)
E 80
100.0%
Space Separator
ValueCountFrequency (%)
80
100.0%
Other Punctuation
ValueCountFrequency (%)
. 80
100.0%
Other Symbol
ValueCountFrequency (%)
° 80
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 793
90.8%
Latin 80
 
9.2%

Most frequent character per script

Common
ValueCountFrequency (%)
6 115
14.5%
1 102
12.9%
2 99
12.5%
80
10.1%
. 80
10.1%
° 80
10.1%
3 71
9.0%
4 62
7.8%
5 29
 
3.7%
8 27
 
3.4%
Other values (3) 48
6.1%
Latin
ValueCountFrequency (%)
E 80
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 793
90.8%
None 80
 
9.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
6 115
14.5%
1 102
12.9%
2 99
12.5%
E 80
10.1%
80
10.1%
. 80
10.1%
3 71
9.0%
4 62
7.8%
5 29
 
3.7%
8 27
 
3.4%
Other values (3) 48
6.1%
None
ValueCountFrequency (%)
° 80
100.0%

위치(좌표계WGS84)위도_도분
Text

Distinct73
Distinct (%)91.2%
Missing0
Missing (%)0.0%
Memory size768.0 B
2024-04-21T12:19:36.836466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length14
Mean length14
Min length14

Characters and Unicode

Total characters1120
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

Unique66 ?
Unique (%)82.5%

Sample

1st rowN 37° 36.597'
2nd rowN 37° 33.779'
3rd rowN 37° 35.617'
4th rowN 37° 34.635'
5th rowN 37° 34.312'
ValueCountFrequency (%)
n 80
33.3%
37° 80
33.3%
33.779 2
 
0.8%
33.451 2
 
0.8%
33.182 2
 
0.8%
35.302 2
 
0.8%
35.248 2
 
0.8%
35.788 2
 
0.8%
34.635 2
 
0.8%
36.888 1
 
0.4%
Other values (65) 65
27.1%
2024-04-21T12:19:38.187280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
240
21.4%
3 215
19.2%
7 119
10.6%
N 80
 
7.1%
° 80
 
7.1%
. 80
 
7.1%
' 80
 
7.1%
4 48
 
4.3%
5 33
 
2.9%
2 31
 
2.8%
Other values (5) 114
10.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 560
50.0%
Space Separator 240
21.4%
Other Punctuation 160
 
14.3%
Uppercase Letter 80
 
7.1%
Other Symbol 80
 
7.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 215
38.4%
7 119
21.2%
4 48
 
8.6%
5 33
 
5.9%
2 31
 
5.5%
8 27
 
4.8%
6 26
 
4.6%
1 25
 
4.5%
0 23
 
4.1%
9 13
 
2.3%
Other Punctuation
ValueCountFrequency (%)
. 80
50.0%
' 80
50.0%
Space Separator
ValueCountFrequency (%)
240
100.0%
Uppercase Letter
ValueCountFrequency (%)
N 80
100.0%
Other Symbol
ValueCountFrequency (%)
° 80
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1040
92.9%
Latin 80
 
7.1%

Most frequent character per script

Common
ValueCountFrequency (%)
240
23.1%
3 215
20.7%
7 119
11.4%
° 80
 
7.7%
. 80
 
7.7%
' 80
 
7.7%
4 48
 
4.6%
5 33
 
3.2%
2 31
 
3.0%
8 27
 
2.6%
Other values (4) 87
 
8.4%
Latin
ValueCountFrequency (%)
N 80
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1040
92.9%
None 80
 
7.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
240
23.1%
3 215
20.7%
7 119
11.4%
N 80
 
7.7%
. 80
 
7.7%
' 80
 
7.7%
4 48
 
4.6%
5 33
 
3.2%
2 31
 
3.0%
8 27
 
2.6%
Other values (4) 87
 
8.4%
None
ValueCountFrequency (%)
° 80
100.0%

위치(좌표계WGS84)경도_도분
Text

Distinct74
Distinct (%)92.5%
Missing0
Missing (%)0.0%
Memory size768.0 B
2024-04-21T12:19:39.185712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length15
Mean length15
Min length15

Characters and Unicode

Total characters1200
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

Unique68 ?
Unique (%)85.0%

Sample

1st rowE 126° 32.600'
2nd rowE 126° 29.785'
3rd rowE 126° 28.988'
4th rowE 126° 28.817'
5th rowE 126° 28.996'
ValueCountFrequency (%)
e 80
33.3%
126° 80
33.3%
29.785 2
 
0.8%
20.968 2
 
0.8%
25.943 2
 
0.8%
22.003 2
 
0.8%
22.007 2
 
0.8%
20.011 2
 
0.8%
21.972 1
 
0.4%
22.948 1
 
0.4%
Other values (66) 66
27.5%
2024-04-21T12:19:40.551772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
240
20.0%
2 187
15.6%
1 118
9.8%
6 99
8.2%
E 80
 
6.7%
° 80
 
6.7%
. 80
 
6.7%
' 80
 
6.7%
0 58
 
4.8%
9 54
 
4.5%
Other values (5) 124
10.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 640
53.3%
Space Separator 240
 
20.0%
Other Punctuation 160
 
13.3%
Uppercase Letter 80
 
6.7%
Other Symbol 80
 
6.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 187
29.2%
1 118
18.4%
6 99
15.5%
0 58
 
9.1%
9 54
 
8.4%
8 28
 
4.4%
3 28
 
4.4%
7 25
 
3.9%
5 23
 
3.6%
4 20
 
3.1%
Other Punctuation
ValueCountFrequency (%)
. 80
50.0%
' 80
50.0%
Space Separator
ValueCountFrequency (%)
240
100.0%
Uppercase Letter
ValueCountFrequency (%)
E 80
100.0%
Other Symbol
ValueCountFrequency (%)
° 80
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1120
93.3%
Latin 80
 
6.7%

Most frequent character per script

Common
ValueCountFrequency (%)
240
21.4%
2 187
16.7%
1 118
10.5%
6 99
8.8%
° 80
 
7.1%
. 80
 
7.1%
' 80
 
7.1%
0 58
 
5.2%
9 54
 
4.8%
8 28
 
2.5%
Other values (4) 96
 
8.6%
Latin
ValueCountFrequency (%)
E 80
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1120
93.3%
None 80
 
6.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
240
21.4%
2 187
16.7%
1 118
10.5%
6 99
8.8%
E 80
 
7.1%
. 80
 
7.1%
' 80
 
7.1%
0 58
 
5.2%
9 54
 
4.8%
8 28
 
2.5%
Other values (4) 96
 
8.6%
None
ValueCountFrequency (%)
° 80
100.0%

위치(좌표계WGS84)위도_도분초
Text

Distinct75
Distinct (%)93.8%
Missing0
Missing (%)0.0%
Memory size768.0 B
2024-04-21T12:19:41.530763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length18
Mean length17.9875
Min length17

Characters and Unicode

Total characters1439
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

Unique71 ?
Unique (%)88.8%

Sample

1st rowN 37° 36' 35.8199"
2nd rowN 37° 33' 42.3720"
3rd rowN 37° 35' 43.6919"
4th rowN 37° 34' 38.0999"
5th rowN 37° 34' 38.0999"
ValueCountFrequency (%)
n 80
25.0%
37° 80
25.0%
34 29
 
9.1%
33 22
 
6.9%
35 15
 
4.7%
36 10
 
3.1%
38.0999 3
 
0.9%
37 3
 
0.9%
14.8920 2
 
0.6%
47.0279 2
 
0.6%
Other values (73) 74
23.1%
2024-04-21T12:19:42.881245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
240
16.7%
3 214
14.9%
7 119
 
8.3%
9 90
 
6.3%
N 80
 
5.6%
° 80
 
5.6%
' 80
 
5.6%
. 80
 
5.6%
" 80
 
5.6%
4 75
 
5.2%
Other values (6) 301
20.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 799
55.5%
Space Separator 240
 
16.7%
Other Punctuation 240
 
16.7%
Uppercase Letter 80
 
5.6%
Other Symbol 80
 
5.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 214
26.8%
7 119
14.9%
9 90
11.3%
4 75
 
9.4%
0 71
 
8.9%
2 63
 
7.9%
1 62
 
7.8%
5 46
 
5.8%
8 34
 
4.3%
6 25
 
3.1%
Other Punctuation
ValueCountFrequency (%)
' 80
33.3%
. 80
33.3%
" 80
33.3%
Space Separator
ValueCountFrequency (%)
240
100.0%
Uppercase Letter
ValueCountFrequency (%)
N 80
100.0%
Other Symbol
ValueCountFrequency (%)
° 80
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1359
94.4%
Latin 80
 
5.6%

Most frequent character per script

Common
ValueCountFrequency (%)
240
17.7%
3 214
15.7%
7 119
8.8%
9 90
 
6.6%
° 80
 
5.9%
' 80
 
5.9%
. 80
 
5.9%
" 80
 
5.9%
4 75
 
5.5%
0 71
 
5.2%
Other values (5) 230
16.9%
Latin
ValueCountFrequency (%)
N 80
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1359
94.4%
None 80
 
5.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
240
17.7%
3 214
15.7%
7 119
8.8%
9 90
 
6.6%
N 80
 
5.9%
' 80
 
5.9%
. 80
 
5.9%
" 80
 
5.9%
4 75
 
5.5%
0 71
 
5.2%
Other values (5) 230
16.9%
None
ValueCountFrequency (%)
° 80
100.0%

위치(좌표계WGS84)경도_도분초
Text

Distinct74
Distinct (%)92.5%
Missing0
Missing (%)0.0%
Memory size768.0 B
2024-04-21T12:19:43.839813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length19
Mean length18.6125
Min length18

Characters and Unicode

Total characters1489
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

Unique68 ?
Unique (%)85.0%

Sample

1st rowE 126° 32' 35.8800"
2nd rowE 126° 29' 6.3600"
3rd rowE 126° 28' 59.1599"
4th rowE 126° 28' 49.0799"
5th rowE 126° 28' 49.0799"
ValueCountFrequency (%)
e 80
25.0%
126° 80
25.0%
20 10
 
3.1%
22 9
 
2.8%
23 9
 
2.8%
27 7
 
2.2%
19 7
 
2.2%
25 7
 
2.2%
21 6
 
1.9%
26 5
 
1.6%
Other values (72) 100
31.2%
2024-04-21T12:19:45.182897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
240
16.1%
2 182
12.2%
0 132
8.9%
1 127
8.5%
6 110
 
7.4%
9 99
 
6.6%
E 80
 
5.4%
° 80
 
5.4%
' 80
 
5.4%
. 80
 
5.4%
Other values (6) 279
18.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 849
57.0%
Space Separator 240
 
16.1%
Other Punctuation 240
 
16.1%
Uppercase Letter 80
 
5.4%
Other Symbol 80
 
5.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 182
21.4%
0 132
15.5%
1 127
15.0%
6 110
13.0%
9 99
11.7%
5 63
 
7.4%
3 40
 
4.7%
4 37
 
4.4%
7 31
 
3.7%
8 28
 
3.3%
Other Punctuation
ValueCountFrequency (%)
' 80
33.3%
. 80
33.3%
" 80
33.3%
Space Separator
ValueCountFrequency (%)
240
100.0%
Uppercase Letter
ValueCountFrequency (%)
E 80
100.0%
Other Symbol
ValueCountFrequency (%)
° 80
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1409
94.6%
Latin 80
 
5.4%

Most frequent character per script

Common
ValueCountFrequency (%)
240
17.0%
2 182
12.9%
0 132
9.4%
1 127
9.0%
6 110
7.8%
9 99
7.0%
° 80
 
5.7%
' 80
 
5.7%
. 80
 
5.7%
" 80
 
5.7%
Other values (5) 199
14.1%
Latin
ValueCountFrequency (%)
E 80
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1409
94.6%
None 80
 
5.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
240
17.0%
2 182
12.9%
0 132
9.4%
1 127
9.0%
6 110
7.8%
9 99
7.0%
E 80
 
5.7%
' 80
 
5.7%
. 80
 
5.7%
" 80
 
5.7%
Other values (5) 199
14.1%
None
ValueCountFrequency (%)
° 80
100.0%

분급도
Real number (ℝ)

HIGH CORRELATION 

Distinct61
Distinct (%)76.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.22575
Minimum0.43
Maximum3.55
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size848.0 B
2024-04-21T12:19:45.583539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.43
5-th percentile0.575
Q12.1275
median2.36
Q32.6575
95-th percentile2.861
Maximum3.55
Range3.12
Interquartile range (IQR)0.53

Descriptive statistics

Standard deviation0.64174362
Coefficient of variation (CV)0.28832691
Kurtosis2.0768864
Mean2.22575
Median Absolute Deviation (MAD)0.255
Skewness-1.4510867
Sum178.06
Variance0.41183487
MonotonicityNot monotonic
2024-04-21T12:19:46.011146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.37 5
 
6.2%
2.36 3
 
3.8%
2.68 3
 
3.8%
2.27 3
 
3.8%
2.09 2
 
2.5%
2.19 2
 
2.5%
2.4 2
 
2.5%
2.42 2
 
2.5%
2.41 2
 
2.5%
0.46 2
 
2.5%
Other values (51) 54
67.5%
ValueCountFrequency (%)
0.43 1
1.2%
0.46 2
2.5%
0.48 1
1.2%
0.58 1
1.2%
0.68 1
1.2%
0.77 1
1.2%
1.24 1
1.2%
1.32 1
1.2%
1.46 1
1.2%
1.61 1
1.2%
ValueCountFrequency (%)
3.55 1
1.2%
3.1 1
1.2%
2.91 1
1.2%
2.88 1
1.2%
2.86 1
1.2%
2.85 1
1.2%
2.84 1
1.2%
2.82 1
1.2%
2.81 2
2.5%
2.78 1
1.2%

왜도
Real number (ℝ)

HIGH CORRELATION 

Distinct69
Distinct (%)86.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.424125
Minimum-1.01
Maximum3.86
Zeros0
Zeros (%)0.0%
Negative2
Negative (%)2.5%
Memory size848.0 B
2024-04-21T12:19:46.431043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-1.01
5-th percentile0.3455
Q10.8375
median1.415
Q31.875
95-th percentile2.8415
Maximum3.86
Range4.87
Interquartile range (IQR)1.0375

Descriptive statistics

Standard deviation0.83102605
Coefficient of variation (CV)0.58353448
Kurtosis1.3007753
Mean1.424125
Median Absolute Deviation (MAD)0.495
Skewness0.1664485
Sum113.93
Variance0.69060429
MonotonicityNot monotonic
2024-04-21T12:19:46.862016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.79 3
 
3.8%
1.45 3
 
3.8%
1.84 2
 
2.5%
1.58 2
 
2.5%
1.85 2
 
2.5%
1.19 2
 
2.5%
0.64 2
 
2.5%
2.27 2
 
2.5%
1.37 2
 
2.5%
1.92 1
 
1.2%
Other values (59) 59
73.8%
ValueCountFrequency (%)
-1.01 1
1.2%
-0.76 1
1.2%
0.19 1
1.2%
0.26 1
1.2%
0.35 1
1.2%
0.37 1
1.2%
0.38 1
1.2%
0.48 1
1.2%
0.54 1
1.2%
0.57 1
1.2%
ValueCountFrequency (%)
3.86 1
1.2%
3.46 1
1.2%
3.29 1
1.2%
3.06 1
1.2%
2.83 1
1.2%
2.64 1
1.2%
2.62 1
1.2%
2.51 1
1.2%
2.27 2
2.5%
2.21 1
1.2%

첨도
Real number (ℝ)

HIGH CORRELATION 

Distinct76
Distinct (%)95.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.523375
Minimum1.54
Maximum28.87
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size848.0 B
2024-04-21T12:19:47.254059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.54
5-th percentile1.879
Q13.025
median4.245
Q36.305
95-th percentile13.9455
Maximum28.87
Range27.33
Interquartile range (IQR)3.28

Descriptive statistics

Standard deviation4.2698931
Coefficient of variation (CV)0.7730587
Kurtosis11.647543
Mean5.523375
Median Absolute Deviation (MAD)1.66
Skewness2.9079488
Sum441.87
Variance18.231987
MonotonicityNot monotonic
2024-04-21T12:19:47.671649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.99 2
 
2.5%
3.82 2
 
2.5%
2.19 2
 
2.5%
6.23 2
 
2.5%
10.0 1
 
1.2%
3.25 1
 
1.2%
5.84 1
 
1.2%
7.7 1
 
1.2%
7.04 1
 
1.2%
3.06 1
 
1.2%
Other values (66) 66
82.5%
ValueCountFrequency (%)
1.54 1
1.2%
1.61 1
1.2%
1.75 1
1.2%
1.86 1
1.2%
1.88 1
1.2%
1.96 1
1.2%
2.0 1
1.2%
2.03 1
1.2%
2.19 2
2.5%
2.2 1
1.2%
ValueCountFrequency (%)
28.87 1
1.2%
18.12 1
1.2%
17.52 1
1.2%
14.24 1
1.2%
13.93 1
1.2%
11.28 1
1.2%
10.0 1
1.2%
9.93 1
1.2%
9.63 1
1.2%
9.5 1
1.2%

Interactions

2024-04-21T12:19:26.746818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T12:19:23.782206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T12:19:24.807216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T12:19:25.808503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T12:19:26.937949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T12:19:24.050247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T12:19:25.070996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T12:19:26.278720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T12:19:27.180785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T12:19:24.307815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T12:19:25.320230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T12:19:26.474897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T12:19:27.420157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T12:19:24.558945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T12:19:25.566420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T12:19:26.610139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-21T12:19:48.155019image/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_건수1.0001.0000.7960.9740.7960.9140.9220.9790.5060.4200.435
관리번호1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
위치(좌표계WGS84)위도_도0.7961.0001.0000.9950.9990.9941.0000.9960.9380.6840.000
위치(좌표계WGS84)경도_도0.9741.0000.9951.0000.9640.9980.9941.0000.0000.0000.000
위치(좌표계WGS84)위도_도분0.7961.0000.9990.9641.0000.9740.9950.9640.9100.8250.750
위치(좌표계WGS84)경도_도분0.9141.0000.9940.9980.9741.0000.9840.9990.9230.5070.537
위치(좌표계WGS84)위도_도분초0.9221.0001.0000.9940.9950.9841.0000.9940.9450.8880.746
위치(좌표계WGS84)경도_도분초0.9791.0000.9961.0000.9640.9990.9941.0000.0000.7330.000
분급도0.5061.0000.9380.0000.9100.9230.9450.0001.0000.7800.776
왜도0.4201.0000.6840.0000.8250.5070.8880.7330.7801.0000.847
첨도0.4351.0000.0000.0000.7500.5370.7460.0000.7760.8471.000
2024-04-21T12:19:48.477670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
데이터_건수분급도왜도첨도
데이터_건수1.000-0.5240.4140.606
분급도-0.5241.000-0.620-0.832
왜도0.414-0.6201.0000.863
첨도0.606-0.8320.8631.000

Missing values

2024-04-21T12:19:27.761860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-21T12:19:28.272449image/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
01K1N 37.60995°E 126.5433°N 37° 36.597'E 126° 32.600'N 37° 36' 35.8199"E 126° 32' 35.8800"2.560.792.21
110K12N 37.56177°E 126.4851°N 37° 33.779'E 126° 29.785'N 37° 33' 42.3720"E 126° 29' 6.3600"2.911.233.39
211K13N 37.59547°E 126.4831°N 37° 35.617'E 126° 28.988'N 37° 35' 43.6919"E 126° 28' 59.1599"2.810.381.54
312K15N 37.57725°E 126.4803°N 37° 34.635'E 126° 28.817'N 37° 34' 38.0999"E 126° 28' 49.0799"2.181.454.34
413K16N 37.57187°E 126.4833°N 37° 34.312'E 126° 28.996'N 37° 34' 38.0999"E 126° 28' 49.0799"2.351.54.25
514K17N 37.56298°E 126.4819°N 37° 33.779'E 126° 29.105'N 37° 33' 46.7280"E 126° 28' 54.8399"2.331.284.09
615K18N 37.55907°E 126.4842°N 37° 33.182'E 126° 29.050'N 37° 33' 32.6519"E 126° 29' 3.1200"2.521.052.88
716K19N 37.59005°E 126.4669°N 37° 35.403'E 126° 28.013'N 37° 35' 24.1799"E 126° 28' 0.8399"2.371.343.64
817K20N 37.58012°E 126.466°N 37° 34.807'E 126° 27.961'N 37° 34' 48.4320"E 126° 27' 57.5999"2.850.632.19
918K21N 37.57320°E 126.4664°N 37° 34.392'E 126° 27.984'N 37° 34' 23.5199"E 126° 27' 59.0399"2.361.193.15
데이터_건수관리번호위치(좌표계WGS84)위도_도위치(좌표계WGS84)경도_도위치(좌표계WGS84)위도_도분위치(좌표계WGS84)경도_도분위치(좌표계WGS84)위도_도분초위치(좌표계WGS84)경도_도분초분급도왜도첨도
7073K76N 37.58127°E 126.3382°N 37° 35.302'E 126° 20.011'N 37° 34' 52.5720"E 126° 20' 1.5200"2.481.855.62
7174K77N 37.57725°E 126.3304°N 37° 34.635'E 126° 19.826'N 37° 34' 38.0999"E 126° 19' 49.4399"2.192.629.93
7275K78N 37.57025°E 126.3292°N 37° 34.215'E 126° 19.752'N 37° 34' 12.9000"E 126° 19' 45.1200"1.833.0613.93
7376K79N 37.56295°E 126.3353°N 37° 33.777'E 126° 20.120'N 37° 33' 46.6200"E 126° 20' 7.0800"2.262.277.73
7477K80N 37.58747°E 126.3204°N 37° 35.248'E 126° 19.222'N 37° 35' 14.8920"E 126° 19' 13.4400"0.580.854.04
7578K81N 37.57865°E 126.3194°N 37° 34.719'E 126° 19.162'N 37° 34' 43.1400"E 126° 19' 9.8400"0.431.276.1
7679K82N 37.57203°E 126.3187°N 37° 34.322'E 126° 19.124'N 37° 34' 19.3079"E 126° 19' 7.3200"0.461.749.43
778K10N 37.57748°E 126.4954°N 37° 34.649'E 126° 29.726'N 37° 34' 38.9280"E 126° 29' 43.4400"2.710.82.19
7880K83N 37.56270°E 126.3192°N 37° 33.762'E 126° 19.154'N 37° 33' 45.7199"E 126° 19' 9.1199"0.461.9911.28
799K11N 37.57218°E 126.4964°N 37° 34.331'E 126° 29.785'N 37° 34' 19.8480"E 126° 29' 47.0399"2.880.642.0