Overview

Dataset statistics

Number of variables5
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.0 KiB
Average record size in memory41.3 B

Variable types

Text4
Categorical1

Alerts

습지명 has unique valuesUnique
위도 has unique valuesUnique
경도 has unique valuesUnique

Reproduction

Analysis started2023-12-10 11:42:12.850479
Analysis finished2023-12-10 11:42:15.283887
Duration2.43 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

습지명
Text

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T20:42:15.604163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length9
Mean length4.59
Min length2

Characters and Unicode

Total characters459
Distinct characters134
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique100 ?
Unique (%)100.0%

Sample

1st row(구)봉포습지
2nd row괘릉
3rd row군물
4th row낫늪
5th row뜬늪
ValueCountFrequency (%)
웃못 3
 
2.2%
갈대습지 3
 
2.2%
정읍 2
 
1.5%
익산 2
 
1.5%
하도습지 2
 
1.5%
구하도습지 2
 
1.5%
고성 2
 
1.5%
김제 2
 
1.5%
화봉리습지 2
 
1.5%
경천호 1
 
0.7%
Other values (114) 114
84.4%
2023-12-10T20:42:16.303010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
지 42
 
9.2%
35
 
7.6%
습 32
 
7.0%
늪 28
 
6.1%
천 15
 
3.3%
못 15
 
3.3%
대 14
 
3.1%
호 9
 
2.0%
물 8
 
1.7%
도 8
 
1.7%
Other values (124) 253
55.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 412
89.8%
Space Separator 35
 
7.6%
Open Punctuation 6
 
1.3%
Close Punctuation 6
 
1.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
지 42
 
10.2%
습 32
 
7.8%
늪 28
 
6.8%
천 15
 
3.6%
못 15
 
3.6%
대 14
 
3.4%
호 9
 
2.2%
물 8
 
1.9%
도 8
 
1.9%
구 7
 
1.7%
Other values (121) 234
56.8%
Space Separator
ValueCountFrequency (%)
35
100.0%
Open Punctuation
ValueCountFrequency (%)
( 6
100.0%
Close Punctuation
ValueCountFrequency (%)
) 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 412
89.8%
Common 47
 
10.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
지 42
 
10.2%
습 32
 
7.8%
늪 28
 
6.8%
천 15
 
3.6%
못 15
 
3.6%
대 14
 
3.4%
호 9
 
2.2%
물 8
 
1.9%
도 8
 
1.9%
구 7
 
1.7%
Other values (121) 234
56.8%
Common
ValueCountFrequency (%)
35
74.5%
( 6
 
12.8%
) 6
 
12.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 412
89.8%
ASCII 47
 
10.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
지 42
 
10.2%
습 32
 
7.8%
늪 28
 
6.8%
천 15
 
3.6%
못 15
 
3.6%
대 14
 
3.4%
호 9
 
2.2%
물 8
 
1.9%
도 8
 
1.9%
구 7
 
1.7%
Other values (121) 234
56.8%
ASCII
ValueCountFrequency (%)
35
74.5%
( 6
 
12.8%
) 6
 
12.8%

주소
Text

Distinct93
Distinct (%)93.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T20:42:16.857943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length16
Mean length16.59
Min length15

Characters and Unicode

Total characters1659
Distinct characters162
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique88 ?
Unique (%)88.0%

Sample

1st row강원도 고성군 토성면 봉포리
2nd row경상북도 경주시 외동읍 활성리
3rd row제주특별자치도 서귀포시 안덕면 사계리
4th row대구광역시 달성군 논공읍 상리
5th row경상남도 함안군 군북면 월촌리
ValueCountFrequency (%)
경상남도 26
 
6.5%
제주특별자치도 21
 
5.2%
경상북도 16
 
4.0%
제주시 12
 
3.0%
전라남도 11
 
2.8%
강원도 11
 
2.8%
서귀포시 9
 
2.2%
함안군 9
 
2.2%
전라북도 8
 
2.0%
경주시 7
 
1.8%
Other values (207) 270
67.5%
2023-12-10T20:42:17.592988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
300
 
18.1%
리 100
 
6.0%
도 99
 
6.0%
면 72
 
4.3%
경 58
 
3.5%
시 57
 
3.4%
남 49
 
3.0%
주 47
 
2.8%
상 46
 
2.8%
군 46
 
2.8%
Other values (152) 785
47.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1359
81.9%
Space Separator 300
 
18.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
리 100
 
7.4%
도 99
 
7.3%
면 72
 
5.3%
경 58
 
4.3%
시 57
 
4.2%
남 49
 
3.6%
주 47
 
3.5%
상 46
 
3.4%
군 46
 
3.4%
제 37
 
2.7%
Other values (151) 748
55.0%
Space Separator
ValueCountFrequency (%)
300
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1359
81.9%
Common 300
 
18.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
리 100
 
7.4%
도 99
 
7.3%
면 72
 
5.3%
경 58
 
4.3%
시 57
 
4.2%
남 49
 
3.6%
주 47
 
3.5%
상 46
 
3.4%
군 46
 
3.4%
제 37
 
2.7%
Other values (151) 748
55.0%
Common
ValueCountFrequency (%)
300
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1359
81.9%
ASCII 300
 
18.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
300
100.0%
Hangul
ValueCountFrequency (%)
리 100
 
7.4%
도 99
 
7.3%
면 72
 
5.3%
경 58
 
4.3%
시 57
 
4.2%
남 49
 
3.6%
주 47
 
3.5%
상 46
 
3.4%
군 46
 
3.4%
제 37
 
2.7%
Other values (151) 748
55.0%

위도
Text

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T20:42:18.063824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length13
Mean length13
Min length13

Characters and Unicode

Total characters1300
Distinct characters15
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique100 ?
Unique (%)100.0%

Sample

1st rowN38°14’54.36"
2nd rowN35°45’30.97"
3rd rowN33°14’59.67"
4th rowN35°45’01.64"
5th rowN35°18’18.49"
ValueCountFrequency (%)
n38°14’54.36 1
 
1.0%
n37°47’49.87 1
 
1.0%
n33°22’14.80 1
 
1.0%
n33°14’48.12 1
 
1.0%
n35°39’59.10 1
 
1.0%
n35°21’42.07 1
 
1.0%
n35°22’52.61 1
 
1.0%
n35°20’19.40 1
 
1.0%
n35°19’25.09 1
 
1.0%
n38°14’30.23 1
 
1.0%
Other values (90) 90
90.0%
2023-12-10T20:42:18.632058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3 179
13.8%
5 122
9.4%
N 100
7.7%
° 100
7.7%
’ 100
7.7%
. 100
7.7%
" 100
7.7%
1 96
7.4%
4 86
 
6.6%
2 85
 
6.5%
Other values (5) 232
17.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 800
61.5%
Other Punctuation 200
 
15.4%
Uppercase Letter 100
 
7.7%
Other Symbol 100
 
7.7%
Final Punctuation 100
 
7.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 179
22.4%
5 122
15.2%
1 96
12.0%
4 86
10.8%
2 85
10.6%
0 69
 
8.6%
6 44
 
5.5%
7 41
 
5.1%
8 39
 
4.9%
9 39
 
4.9%
Other Punctuation
ValueCountFrequency (%)
. 100
50.0%
" 100
50.0%
Uppercase Letter
ValueCountFrequency (%)
N 100
100.0%
Other Symbol
ValueCountFrequency (%)
° 100
100.0%
Final Punctuation
ValueCountFrequency (%)
’ 100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1200
92.3%
Latin 100
 
7.7%

Most frequent character per script

Common
ValueCountFrequency (%)
3 179
14.9%
5 122
10.2%
° 100
8.3%
’ 100
8.3%
. 100
8.3%
" 100
8.3%
1 96
8.0%
4 86
7.2%
2 85
7.1%
0 69
 
5.8%
Other values (4) 163
13.6%
Latin
ValueCountFrequency (%)
N 100
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1100
84.6%
None 100
 
7.7%
Punctuation 100
 
7.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 179
16.3%
5 122
11.1%
N 100
9.1%
. 100
9.1%
" 100
9.1%
1 96
8.7%
4 86
7.8%
2 85
7.7%
0 69
 
6.3%
6 44
 
4.0%
Other values (3) 119
10.8%
None
ValueCountFrequency (%)
° 100
100.0%
Punctuation
ValueCountFrequency (%)
’ 100
100.0%

경도
Text

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T20:42:19.058388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length14
Mean length14
Min length14

Characters and Unicode

Total characters1400
Distinct characters15
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique100 ?
Unique (%)100.0%

Sample

1st rowE128°33’51.29"
2nd rowE129°19’13.90"
3rd rowE126°19’04.03"
4th rowE128°24’06.29"
5th rowE128°18’08.87"
ValueCountFrequency (%)
e128°33’51.29 1
 
1.0%
e128°54’14.31 1
 
1.0%
e126°47’31.94 1
 
1.0%
e126°16’34.77 1
 
1.0%
e129°22’27.22 1
 
1.0%
e128°25’37.55 1
 
1.0%
e128°30’23.66 1
 
1.0%
e128°18’16.42 1
 
1.0%
e128°17’47.79 1
 
1.0%
e128°33’58.91 1
 
1.0%
Other values (90) 90
90.0%
2023-12-10T20:42:19.971380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 180
12.9%
1 170
12.1%
E 100
 
7.1%
° 100
 
7.1%
’ 100
 
7.1%
. 100
 
7.1%
" 100
 
7.1%
0 77
 
5.5%
8 76
 
5.4%
3 74
 
5.3%
Other values (5) 323
23.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 900
64.3%
Other Punctuation 200
 
14.3%
Uppercase Letter 100
 
7.1%
Other Symbol 100
 
7.1%
Final Punctuation 100
 
7.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 180
20.0%
1 170
18.9%
0 77
8.6%
8 76
8.4%
3 74
8.2%
6 69
 
7.7%
7 66
 
7.3%
4 64
 
7.1%
5 63
 
7.0%
9 61
 
6.8%
Other Punctuation
ValueCountFrequency (%)
. 100
50.0%
" 100
50.0%
Uppercase Letter
ValueCountFrequency (%)
E 100
100.0%
Other Symbol
ValueCountFrequency (%)
° 100
100.0%
Final Punctuation
ValueCountFrequency (%)
’ 100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1300
92.9%
Latin 100
 
7.1%

Most frequent character per script

Common
ValueCountFrequency (%)
2 180
13.8%
1 170
13.1%
° 100
 
7.7%
’ 100
 
7.7%
. 100
 
7.7%
" 100
 
7.7%
0 77
 
5.9%
8 76
 
5.8%
3 74
 
5.7%
6 69
 
5.3%
Other values (4) 254
19.5%
Latin
ValueCountFrequency (%)
E 100
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1200
85.7%
None 100
 
7.1%
Punctuation 100
 
7.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 180
15.0%
1 170
14.2%
E 100
8.3%
. 100
8.3%
" 100
8.3%
0 77
6.4%
8 76
6.3%
3 74
 
6.2%
6 69
 
5.8%
7 66
 
5.5%
Other values (3) 188
15.7%
None
ValueCountFrequency (%)
° 100
100.0%
Punctuation
ValueCountFrequency (%)
’ 100
100.0%

타입국명
Categorical

Distinct14
Distinct (%)14.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
담수호습지
42 
하도습지
14 
인공호습지
10 
석호습지
6 
저층습원
6 
Other values (9)
22 

Length

Max length6
Median length5
Mean length4.53
Min length3

Unique

Unique4 ?
Unique (%)4.0%

Sample

1st row석호습지
2nd row하도습지
3rd row담수호습지
4th row우각호습지
5th row생태수변공원

Common Values

ValueCountFrequency (%)
담수호습지 42
42.0%
하도습지 14
 
14.0%
인공호습지 10
 
10.0%
석호습지 6
 
6.0%
저층습원 6
 
6.0%
저습지 5
 
5.0%
배후습지 5
 
5.0%
우각호습지 4
 
4.0%
보습지 2
 
2.0%
소택지 2
 
2.0%
Other values (4) 4
 
4.0%

Length

2023-12-10T20:42:20.141297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
담수호습지 42
42.0%
하도습지 14
 
14.0%
인공호습지 10
 
10.0%
석호습지 6
 
6.0%
저층습원 6
 
6.0%
저습지 5
 
5.0%
배후습지 5
 
5.0%
우각호습지 4
 
4.0%
보습지 2
 
2.0%
소택지 2
 
2.0%
Other values (4) 4
 
4.0%

Correlations

2023-12-10T20:42:20.260441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
습지명주소위도경도타입국명
습지명1.0001.0001.0001.0001.000
주소1.0001.0001.0001.0000.996
위도1.0001.0001.0001.0001.000
경도1.0001.0001.0001.0001.000
타입국명1.0000.9961.0001.0001.000

Missing values

2023-12-10T20:42:15.075190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T20:42:15.234143image/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

습지명주소위도경도타입국명
0(구)봉포습지강원도 고성군 토성면 봉포리N38°14’54.36"E128°33’51.29"석호습지
1괘릉경상북도 경주시 외동읍 활성리N35°45’30.97"E129°19’13.90"하도습지
2군물제주특별자치도 서귀포시 안덕면 사계리N33°14’59.67"E126°19’04.03"담수호습지
3낫늪대구광역시 달성군 논공읍 상리N35°45’01.64"E128°24’06.29"우각호습지
4뜬늪경상남도 함안군 군북면 월촌리N35°18’18.49"E128°18’08.87"생태수변공원
5매호강원도 양양군 현남면 광진리N37°57’00.89"E128°46’17.97"우각호습지
6먼못제주특별자치도 제주시 한경면 두모리N33°21’11.47"E126°11’08.58"담수호습지
7반못제주특별자치도 제주시 조천읍 선흘리N33°30’31.11"E126°43’02.65"담수호습지
8쌍호강원도 양양군 손양면 오산리N38°05’06.77"E128°39’44.63"석호습지
9장못경기도 연천군 미산면 우정리N38°02’49.87"E127°01’08.20"담수호습지
습지명주소위도경도타입국명
90돈대늪경상남도 의령군 용덕면 소상리N35°20’18.01"E128°18’47.50"하도습지
91돔배물제주특별자치도 제주시 애월읍 어음리N33°24’14.01"E126°20’20.85"담수호습지
92돗곳물제주특별자치도 제주시 한경면 조수리N33°20’13.90"E126°13’17.43"담수호습지
93뒤방죽경기도 안성시 서운면 양촌리N36°57’12.95"E127°15’37.40"담수호습지
94뒤실지충청북도 제천시 송학면 포전리N37°12’11.22"E128°14’30.96"담수호습지
95떼기늪경상북도 경주시 양남면 신대리N35°39’55.16"E129°22’13.00"저습지
96마천늪경상남도 창녕군 부곡면 청암리N35°23’46.42"E128°35’53.20"하도습지
97만디늪경상북도 경주시 양남면 신대리N35°40’02.37"E129°22’29.95"저습지
98매미못제주특별자치도 서귀포시 성산읍 삼달리N33°22’35.94"E126°50’37.05"담수호습지
99목포늪경상남도 창녕군 이방면 안리N35°33’46.75"E128°24’23.87"배후습지