Overview

Dataset statistics

Number of variables5
Number of observations54
Missing cells43
Missing cells (%)15.9%
Duplicate rows1
Duplicate rows (%)1.9%
Total size in memory2.2 KiB
Average record size in memory42.4 B

Variable types

Text4
Unsupported1

Alerts

Dataset has 1 (1.9%) duplicate rowsDuplicates
습지보호지역 지정현황(‘17.06월 기준) has 6 (11.1%) missing valuesMissing
Unnamed: 1 has 8 (14.8%) missing valuesMissing
Unnamed: 2 has 10 (18.5%) missing valuesMissing
Unnamed: 3 has 9 (16.7%) missing valuesMissing
Unnamed: 4 has 10 (18.5%) missing valuesMissing
Unnamed: 2 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2024-03-14 02:51:39.104703
Analysis finished2024-03-14 02:51:39.702248
Duration0.6 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

습지보호지역 지정현황(‘17.06월 기준)
Text

MISSING 

Distinct48
Distinct (%)100.0%
Missing6
Missing (%)11.1%
Memory size564.0 B
2024-03-14T11:51:39.880256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length41
Median length23
Mean length7.9166667
Min length2

Characters and Unicode

Total characters380
Distinct characters126
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks5 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique48 ?
Unique (%)100.0%

Sample

1st row지정현황 : 43개 지역, 364.726㎢(개선지역 및 주변관리지역 포함)
2nd row지역명
3rd row환경부 지정 : 23개소, 126.772㎢
4th row낙동강하구
5th row대암산용늪
ValueCountFrequency (%)
제주 4
 
4.7%
갯벌 4
 
4.7%
3
 
3.5%
지정 2
 
2.4%
동천하구 1
 
1.2%
섬진강 1
 
1.2%
침실습지 1
 
1.2%
문경 1
 
1.2%
돌리네 1
 
1.2%
해양수산부 1
 
1.2%
Other values (66) 66
77.6%
2024-03-14T11:51:40.220244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
37
 
9.7%
지 26
 
6.8%
벌 15
 
3.9%
습 14
 
3.7%
갯 14
 
3.7%
도 10
 
2.6%
산 8
 
2.1%
2 7
 
1.8%
주 7
 
1.8%
천 6
 
1.6%
Other values (116) 236
62.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 287
75.5%
Space Separator 37
 
9.7%
Decimal Number 33
 
8.7%
Other Punctuation 15
 
3.9%
Other Symbol 4
 
1.1%
Close Punctuation 2
 
0.5%
Open Punctuation 2
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
지 26
 
9.1%
벌 15
 
5.2%
습 14
 
4.9%
갯 14
 
4.9%
도 10
 
3.5%
산 8
 
2.8%
주 7
 
2.4%
천 6
 
2.1%
제 6
 
2.1%
고 5
 
1.7%
Other values (97) 176
61.3%
Decimal Number
ValueCountFrequency (%)
2 7
21.2%
7 5
15.2%
1 4
12.1%
3 4
12.1%
0 4
12.1%
6 3
9.1%
4 3
9.1%
9 1
 
3.0%
5 1
 
3.0%
8 1
 
3.0%
Other Punctuation
ValueCountFrequency (%)
, 4
26.7%
. 4
26.7%
: 3
20.0%
․ 2
13.3%
· 2
13.3%
Space Separator
ValueCountFrequency (%)
37
100.0%
Other Symbol
ValueCountFrequency (%)
㎢ 4
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 287
75.5%
Common 93
 
24.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
지 26
 
9.1%
벌 15
 
5.2%
습 14
 
4.9%
갯 14
 
4.9%
도 10
 
3.5%
산 8
 
2.8%
주 7
 
2.4%
천 6
 
2.1%
제 6
 
2.1%
고 5
 
1.7%
Other values (97) 176
61.3%
Common
ValueCountFrequency (%)
37
39.8%
2 7
 
7.5%
7 5
 
5.4%
1 4
 
4.3%
, 4
 
4.3%
3 4
 
4.3%
0 4
 
4.3%
㎢ 4
 
4.3%
. 4
 
4.3%
6 3
 
3.2%
Other values (9) 17
18.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 287
75.5%
ASCII 85
 
22.4%
CJK Compat 4
 
1.1%
Punctuation 2
 
0.5%
None 2
 
0.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
37
43.5%
2 7
 
8.2%
7 5
 
5.9%
1 4
 
4.7%
, 4
 
4.7%
3 4
 
4.7%
0 4
 
4.7%
. 4
 
4.7%
6 3
 
3.5%
4 3
 
3.5%
Other values (6) 10
 
11.8%
Hangul
ValueCountFrequency (%)
지 26
 
9.1%
벌 15
 
5.2%
습 14
 
4.9%
갯 14
 
4.9%
도 10
 
3.5%
산 8
 
2.8%
주 7
 
2.4%
천 6
 
2.1%
제 6
 
2.1%
고 5
 
1.7%
Other values (97) 176
61.3%
CJK Compat
ValueCountFrequency (%)
㎢ 4
100.0%
Punctuation
ValueCountFrequency (%)
․ 2
100.0%
None
ValueCountFrequency (%)
· 2
100.0%

Unnamed: 1
Text

MISSING 

Distinct46
Distinct (%)100.0%
Missing8
Missing (%)14.8%
Memory size564.0 B
2024-03-14T11:51:40.499739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length43
Median length33
Mean length19.108696
Min length3

Characters and Unicode

Total characters879
Distinct characters156
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique46 ?
Unique (%)100.0%

Sample

1st row위 치
2nd row부산 사하구 신평, 장림, 다대동 일원 해면 및 강서구 명지동 하단 해면
3rd row강원 인제군 서화면 대암산의 큰용늪과 작은용늪 일원
4th row경남 창녕군 대합면, 이방면, 유어면, 대지면 일원
5th row울산 울주군 삼동면 조일리 일원
ValueCountFrequency (%)
일원 17
 
7.3%
전남 10
 
4.3%
경남 5
 
2.2%
전북 5
 
2.2%
일대 5
 
2.2%
제주 5
 
2.2%
강원 5
 
2.2%
제주시 4
 
1.7%
신안군 3
 
1.3%
충남 2
 
0.9%
Other values (155) 171
73.7%
2024-03-14T11:51:41.012781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
189
21.5%
면 45
 
5.1%
원 36
 
4.1%
일 27
 
3.1%
, 27
 
3.1%
동 25
 
2.8%
군 24
 
2.7%
리 23
 
2.6%
시 22
 
2.5%
남 20
 
2.3%
Other values (146) 441
50.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 642
73.0%
Space Separator 189
 
21.5%
Other Punctuation 30
 
3.4%
Decimal Number 6
 
0.7%
Open Punctuation 4
 
0.5%
Close Punctuation 4
 
0.5%
Letter Number 2
 
0.2%
Math Symbol 1
 
0.1%
Dash Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
면 45
 
7.0%
원 36
 
5.6%
일 27
 
4.2%
동 25
 
3.9%
군 24
 
3.7%
리 23
 
3.6%
시 22
 
3.4%
남 20
 
3.1%
대 17
 
2.6%
전 17
 
2.6%
Other values (133) 386
60.1%
Other Punctuation
ValueCountFrequency (%)
, 27
90.0%
‧ 2
 
6.7%
․ 1
 
3.3%
Decimal Number
ValueCountFrequency (%)
9 2
33.3%
1 2
33.3%
2 2
33.3%
Letter Number
ValueCountFrequency (%)
Ⅰ 1
50.0%
Ⅱ 1
50.0%
Space Separator
ValueCountFrequency (%)
189
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 642
73.0%
Common 235
 
26.7%
Latin 2
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
면 45
 
7.0%
원 36
 
5.6%
일 27
 
4.2%
동 25
 
3.9%
군 24
 
3.7%
리 23
 
3.6%
시 22
 
3.4%
남 20
 
3.1%
대 17
 
2.6%
전 17
 
2.6%
Other values (133) 386
60.1%
Common
ValueCountFrequency (%)
189
80.4%
, 27
 
11.5%
( 4
 
1.7%
) 4
 
1.7%
9 2
 
0.9%
1 2
 
0.9%
‧ 2
 
0.9%
2 2
 
0.9%
․ 1
 
0.4%
~ 1
 
0.4%
Latin
ValueCountFrequency (%)
Ⅰ 1
50.0%
Ⅱ 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 642
73.0%
ASCII 232
 
26.4%
Punctuation 3
 
0.3%
Number Forms 2
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
189
81.5%
, 27
 
11.6%
( 4
 
1.7%
) 4
 
1.7%
9 2
 
0.9%
1 2
 
0.9%
2 2
 
0.9%
~ 1
 
0.4%
- 1
 
0.4%
Hangul
ValueCountFrequency (%)
면 45
 
7.0%
원 36
 
5.6%
일 27
 
4.2%
동 25
 
3.9%
군 24
 
3.7%
리 23
 
3.6%
시 22
 
3.4%
남 20
 
3.1%
대 17
 
2.6%
전 17
 
2.6%
Other values (133) 386
60.1%
Punctuation
ValueCountFrequency (%)
‧ 2
66.7%
․ 1
33.3%
Number Forms
ValueCountFrequency (%)
Ⅰ 1
50.0%
Ⅱ 1
50.0%

Unnamed: 2
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10
Missing (%)18.5%
Memory size564.0 B

Unnamed: 3
Text

MISSING 

Distinct44
Distinct (%)97.8%
Missing9
Missing (%)16.7%
Memory size564.0 B
2024-03-14T11:51:41.239262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length49
Median length31
Mean length23.822222
Min length3

Characters and Unicode

Total characters1072
Distinct characters182
Distinct categories7 ?
Distinct scripts4 ?
Distinct blocks6 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique43 ?
Unique (%)95.6%

Sample

1st row특 징
2nd row철새도래지
3rd row우리나라 유일의 고층습원
4th row우리나라 최고(最古)의 원시 자연늪
5th row산지습지
ValueCountFrequency (%)
서식 14
 
6.1%
등 10
 
4.3%
생물다양성 9
 
3.9%
및 8
 
3.5%
풍부 7
 
3.0%
멸종위기종 6
 
2.6%
생물다양성이 6
 
2.6%
산지습지 4
 
1.7%
철새도래지 4
 
1.7%
발달 3
 
1.3%
Other values (128) 159
69.1%
2024-03-14T11:51:41.565325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
188
 
17.5%
식 35
 
3.3%
, 34
 
3.2%
물 33
 
3.1%
생 30
 
2.8%
지 28
 
2.6%
종 26
 
2.4%
서 25
 
2.3%
이 20
 
1.9%
다 20
 
1.9%
Other values (172) 633
59.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 833
77.7%
Space Separator 188
 
17.5%
Other Punctuation 44
 
4.1%
Close Punctuation 2
 
0.2%
Open Punctuation 2
 
0.2%
Decimal Number 2
 
0.2%
Letter Number 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
식 35
 
4.2%
물 33
 
4.0%
생 30
 
3.6%
지 28
 
3.4%
종 26
 
3.1%
서 25
 
3.0%
이 20
 
2.4%
다 20
 
2.4%
기 18
 
2.2%
성 18
 
2.2%
Other values (163) 580
69.6%
Other Punctuation
ValueCountFrequency (%)
, 34
77.3%
․ 9
 
20.5%
· 1
 
2.3%
Decimal Number
ValueCountFrequency (%)
8 1
50.0%
6 1
50.0%
Space Separator
ValueCountFrequency (%)
188
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Letter Number
ValueCountFrequency (%)
Ⅱ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 831
77.5%
Common 238
 
22.2%
Han 2
 
0.2%
Latin 1
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
식 35
 
4.2%
물 33
 
4.0%
생 30
 
3.6%
지 28
 
3.4%
종 26
 
3.1%
서 25
 
3.0%
이 20
 
2.4%
다 20
 
2.4%
기 18
 
2.2%
성 18
 
2.2%
Other values (161) 578
69.6%
Common
ValueCountFrequency (%)
188
79.0%
, 34
 
14.3%
․ 9
 
3.8%
) 2
 
0.8%
( 2
 
0.8%
8 1
 
0.4%
6 1
 
0.4%
· 1
 
0.4%
Han
ValueCountFrequency (%)
古 1
50.0%
最 1
50.0%
Latin
ValueCountFrequency (%)
Ⅱ 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 831
77.5%
ASCII 228
 
21.3%
Punctuation 9
 
0.8%
CJK 2
 
0.2%
Number Forms 1
 
0.1%
None 1
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
188
82.5%
, 34
 
14.9%
) 2
 
0.9%
( 2
 
0.9%
8 1
 
0.4%
6 1
 
0.4%
Hangul
ValueCountFrequency (%)
식 35
 
4.2%
물 33
 
4.0%
생 30
 
3.6%
지 28
 
3.4%
종 26
 
3.1%
서 25
 
3.0%
이 20
 
2.4%
다 20
 
2.4%
기 18
 
2.2%
성 18
 
2.2%
Other values (161) 578
69.6%
Punctuation
ValueCountFrequency (%)
․ 9
100.0%
Number Forms
ValueCountFrequency (%)
Ⅱ 1
100.0%
CJK
ValueCountFrequency (%)
古 1
50.0%
最 1
50.0%
None
ValueCountFrequency (%)
· 1
100.0%

Unnamed: 4
Text

MISSING 

Distinct41
Distinct (%)93.2%
Missing10
Missing (%)18.5%
Memory size564.0 B
2024-03-14T11:51:41.743949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length21.5
Mean length14.931818
Min length9

Characters and Unicode

Total characters657
Distinct characters23
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique40 ?
Unique (%)90.9%

Sample

1st row지정일자(람사르등록)
2nd row1999.08.09
3rd row1999.08.09(`97.03.28)
4th row1999.08.09(`98.03.02)
5th row1999.8.9(`07.12.20)
ValueCountFrequency (%)
2016.11.15 4
 
9.1%
2009.12.31.(`14.07.10 1
 
2.3%
1999.08.09 1
 
2.3%
1999.08.09(`97.03.28 1
 
2.3%
2017.06.15 1
 
2.3%
2001.12.28(`08.01.14 1
 
2.3%
2002.12.28 1
 
2.3%
2003.12.31(`06.1.20 1
 
2.3%
2003.12.31(`06.01.20 1
 
2.3%
2003.12.31 1
 
2.3%
Other values (31) 31
70.5%
2024-03-14T11:51:42.084095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 135
20.5%
. 128
19.5%
1 113
17.2%
2 89
13.5%
9 26
 
4.0%
3 21
 
3.2%
) 21
 
3.2%
( 21
 
3.2%
` 20
 
3.0%
5 17
 
2.6%
Other values (13) 66
10.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 458
69.7%
Other Punctuation 128
 
19.5%
Close Punctuation 21
 
3.2%
Open Punctuation 21
 
3.2%
Modifier Symbol 20
 
3.0%
Other Letter 9
 
1.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 135
29.5%
1 113
24.7%
2 89
19.4%
9 26
 
5.7%
3 21
 
4.6%
5 17
 
3.7%
7 15
 
3.3%
8 15
 
3.3%
6 15
 
3.3%
4 12
 
2.6%
Other Letter
ValueCountFrequency (%)
등 1
11.1%
록 1
11.1%
람 1
11.1%
르 1
11.1%
사 1
11.1%
자 1
11.1%
정 1
11.1%
지 1
11.1%
일 1
11.1%
Other Punctuation
ValueCountFrequency (%)
. 128
100.0%
Close Punctuation
ValueCountFrequency (%)
) 21
100.0%
Open Punctuation
ValueCountFrequency (%)
( 21
100.0%
Modifier Symbol
ValueCountFrequency (%)
` 20
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 648
98.6%
Hangul 9
 
1.4%

Most frequent character per script

Common
ValueCountFrequency (%)
0 135
20.8%
. 128
19.8%
1 113
17.4%
2 89
13.7%
9 26
 
4.0%
3 21
 
3.2%
) 21
 
3.2%
( 21
 
3.2%
` 20
 
3.1%
5 17
 
2.6%
Other values (4) 57
8.8%
Hangul
ValueCountFrequency (%)
등 1
11.1%
록 1
11.1%
람 1
11.1%
르 1
11.1%
사 1
11.1%
자 1
11.1%
정 1
11.1%
지 1
11.1%
일 1
11.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 648
98.6%
Hangul 9
 
1.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 135
20.8%
. 128
19.8%
1 113
17.4%
2 89
13.7%
9 26
 
4.0%
3 21
 
3.2%
) 21
 
3.2%
( 21
 
3.2%
` 20
 
3.1%
5 17
 
2.6%
Other values (4) 57
8.8%
Hangul
ValueCountFrequency (%)
등 1
11.1%
록 1
11.1%
람 1
11.1%
르 1
11.1%
사 1
11.1%
자 1
11.1%
정 1
11.1%
지 1
11.1%
일 1
11.1%

Correlations

2024-03-14T11:51:42.162269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
습지보호지역 지정현황(‘17.06월 기준)Unnamed: 1Unnamed: 3Unnamed: 4
습지보호지역 지정현황(‘17.06월 기준)1.0001.0001.0001.000
Unnamed: 11.0001.0001.0001.000
Unnamed: 31.0001.0001.0000.984
Unnamed: 41.0001.0000.9841.000

Missing values

2024-03-14T11:51:39.442709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T11:51:39.515217image/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.
2024-03-14T11:51:39.612489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

습지보호지역 지정현황(‘17.06월 기준)Unnamed: 1Unnamed: 2Unnamed: 3Unnamed: 4
0<NA><NA>NaN<NA><NA>
1지정현황 : 43개 지역, 364.726㎢(개선지역 및 주변관리지역 포함)<NA>NaN<NA><NA>
2<NA><NA>NaN<NA><NA>
3지역명위 치면적(㎢)특 징지정일자(람사르등록)
4환경부 지정 : 23개소, 126.772㎢<NA>NaN<NA><NA>
5낙동강하구부산 사하구 신평, 장림, 다대동 일원 해면 및 강서구 명지동 하단 해면37.718철새도래지1999.08.09
6대암산용늪강원 인제군 서화면 대암산의 큰용늪과 작은용늪 일원1.36우리나라 유일의 고층습원1999.08.09(`97.03.28)
7우포늪경남 창녕군 대합면, 이방면, 유어면, 대지면 일원8.609(개:0.062)우리나라 최고(最古)의 원시 자연늪1999.08.09(`98.03.02)
8무제치늪울산 울주군 삼동면 조일리 일원0.184산지습지1999.8.9(`07.12.20)
9제주 물영아리오름제주 서귀포시 남원읍 수망리0.309기생화산구2000.12.5
습지보호지역 지정현황(‘17.06월 기준)Unnamed: 1Unnamed: 2Unnamed: 3Unnamed: 4
44비금․도초도 갯벌전남 신안군 비금면, 도초면12.32염생식물, 철새 중간기착지 등 생물다양성이 풍부2015.12.30
45대부도갯벌경기 안산시 단원구 연안갯벌4.53멸종위기종인 저어새, 노랑부리백로, 알락꼬리마도요의 서식지이자 생물다양성이 풍부한 갯벌2017.03.22
46시·도지사 지정(7개소,8.254㎢)<NA>NaN<NA><NA>
47대구달성하천습지대구 달서구 호림동, 달성군 화원읍0.178흑두루미, 재두루미 등 철새도래지, 노랑어리연꽃,기생초 등 습지식물 발달2007.05.25
48대청호 추동습지대전 동구 추동 91번지0.346수달, 말똥가리, 흰목물떼새, 청딱따구리 등 희귀 동물 서식2008.12.26
49송도갯벌인천 연수구 송도동 일원6.11저어새, 검은머리갈매기, 말똥가리, 알락꼬리도요 등동아시아 철새이동경로2009.12.31.(`14.07.10)
50경포호·가시연습지강원 강릉시 운정동, 안현동, 초당동, 저동일원1.314(주0.007)동해안 대표 석호, 철새도래지 멸종위기종 가시연 서식2016.11.15
51순포호강원 강릉시 사천면 산대월리 일원0.133멸종위기종 Ⅱ급 순채서식,철새도래지이며 생물다양성이 풍부2016.11.15
52쌍호강원 양양군 손양면 오산리 일원0.139(주0.012)사구위에 형성된 소규모 석호, 동발 서식2016.11.15
53가평리습지강원 양양군 손양면 가평리 일원0.034해안충적지에 발달한 담수화된 석호로 꽃창포, 부채붓꽃, 털부처꽃 서식2016.11.15

Duplicate rows

Most frequently occurring

습지보호지역 지정현황(‘17.06월 기준)Unnamed: 1Unnamed: 3Unnamed: 4# duplicates
0<NA><NA><NA><NA>3