Overview

Dataset statistics

Number of variables18
Number of observations68
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory10.4 KiB
Average record size in memory156.9 B

Variable types

Numeric7
Text3
Categorical8

Dataset

Description객체id,출현종,출현종수,서울시보호,천연기념물,고유종,교란종,외래종,정착성,회유성,하천명,자치구명,조사지역,조사연도,x_value,y_value,종수범례,조사시점
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-21157/S/1/datasetView.do

Alerts

외래종 is highly overall correlated with 출현종수 and 4 other fieldsHigh correlation
교란종 is highly overall correlated with 출현종수 and 6 other fieldsHigh correlation
객체id is highly overall correlated with 자치구명High correlation
출현종수 is highly overall correlated with 고유종 and 6 other fieldsHigh correlation
고유종 is highly overall correlated with 출현종수 and 7 other fieldsHigh correlation
정착성 is highly overall correlated with 출현종수 and 5 other fieldsHigh correlation
회유성 is highly overall correlated with 출현종수 and 6 other fieldsHigh correlation
x_value is highly overall correlated with 자치구명High correlation
y_value is highly overall correlated with 자치구명High correlation
서울시보호 is highly overall correlated with 출현종수 and 3 other fieldsHigh correlation
천연기념물 is highly overall correlated with 고유종 and 3 other fieldsHigh correlation
자치구명 is highly overall correlated with 객체id and 5 other fieldsHigh correlation
조사연도 is highly overall correlated with 천연기념물High correlation
종수범례 is highly overall correlated with 출현종수 and 3 other fieldsHigh correlation
서울시보호 is highly imbalanced (64.7%)Imbalance
천연기념물 is highly imbalanced (88.9%)Imbalance
객체id has unique valuesUnique
조사지역 has unique valuesUnique
y_value has unique valuesUnique
조사시점 has unique valuesUnique
고유종 has 34 (50.0%) zerosZeros
정착성 has 21 (30.9%) zerosZeros

Reproduction

Analysis started2024-05-11 08:37:06.419120
Analysis finished2024-05-11 08:37:32.063074
Duration25.64 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

객체id
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct68
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10236.382
Minimum9977
Maximum10300
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size744.0 B
2024-05-11T08:37:32.302659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9977
5-th percentile9980.35
Q110249.75
median10266.5
Q310283.25
95-th percentile10296.65
Maximum10300
Range323
Interquartile range (IQR)33.5

Descriptive statistics

Standard deviation95.549007
Coefficient of variation (CV)0.0093342554
Kurtosis3.6234645
Mean10236.382
Median Absolute Deviation (MAD)17
Skewness-2.2888712
Sum696074
Variance9129.6128
MonotonicityStrictly increasing
2024-05-11T08:37:32.800657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9977 1
 
1.5%
10277 1
 
1.5%
10283 1
 
1.5%
10282 1
 
1.5%
10281 1
 
1.5%
10280 1
 
1.5%
10279 1
 
1.5%
10278 1
 
1.5%
10276 1
 
1.5%
10268 1
 
1.5%
Other values (58) 58
85.3%
ValueCountFrequency (%)
9977 1
1.5%
9978 1
1.5%
9979 1
1.5%
9980 1
1.5%
9981 1
1.5%
9982 1
1.5%
9983 1
1.5%
9984 1
1.5%
10241 1
1.5%
10242 1
1.5%
ValueCountFrequency (%)
10300 1
1.5%
10299 1
1.5%
10298 1
1.5%
10297 1
1.5%
10296 1
1.5%
10295 1
1.5%
10294 1
1.5%
10293 1
1.5%
10292 1
1.5%
10291 1
1.5%

출현종
Text

Distinct55
Distinct (%)80.9%
Missing0
Missing (%)0.0%
Memory size676.0 B
2024-05-11T08:37:33.396412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length29
Median length25
Mean length17.676471
Min length1

Characters and Unicode

Total characters1202
Distinct characters74
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique48 ?
Unique (%)70.6%

Sample

1st row붕어, 잉어
2nd row버들치, 피라미, 모래무지, 꺽지, 돌고기
3rd row버들치, 참붕어, 붕어
4th row가물치, 가숭어, 가시납지리, 각시붕어, 갈문망둑 등
5th row가시납지리, 누치, 돌고기, 메기, 모래무지 등
ValueCountFrequency (%)
등 39
 
13.5%
붕어 26
 
9.0%
잉어 16
 
5.5%
누치 16
 
5.5%
강준치 15
 
5.2%
버들치 13
 
4.5%
가시납지리 13
 
4.5%
가숭어 10
 
3.5%
각시붕어 9
 
3.1%
모래무지 9
 
3.1%
Other values (34) 123
42.6%
2024-05-11T08:37:34.436283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
223
18.6%
, 183
15.2%
어 83
 
6.9%
치 54
 
4.5%
붕 51
 
4.2%
리 47
 
3.9%
등 39
 
3.2%
지 32
 
2.7%
가 31
 
2.6%
납 25
 
2.1%
Other values (64) 434
36.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 796
66.2%
Space Separator 223
 
18.6%
Other Punctuation 183
 
15.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
어 83
 
10.4%
치 54
 
6.8%
붕 51
 
6.4%
리 47
 
5.9%
등 39
 
4.9%
지 32
 
4.0%
가 31
 
3.9%
납 25
 
3.1%
시 22
 
2.8%
잉 18
 
2.3%
Other values (62) 394
49.5%
Space Separator
ValueCountFrequency (%)
223
100.0%
Other Punctuation
ValueCountFrequency (%)
, 183
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 796
66.2%
Common 406
33.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
어 83
 
10.4%
치 54
 
6.8%
붕 51
 
6.4%
리 47
 
5.9%
등 39
 
4.9%
지 32
 
4.0%
가 31
 
3.9%
납 25
 
3.1%
시 22
 
2.8%
잉 18
 
2.3%
Other values (62) 394
49.5%
Common
ValueCountFrequency (%)
223
54.9%
, 183
45.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 796
66.2%
ASCII 406
33.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
223
54.9%
, 183
45.1%
Hangul
ValueCountFrequency (%)
어 83
 
10.4%
치 54
 
6.8%
붕 51
 
6.4%
리 47
 
5.9%
등 39
 
4.9%
지 32
 
4.0%
가 31
 
3.9%
납 25
 
3.1%
시 22
 
2.8%
잉 18
 
2.3%
Other values (62) 394
49.5%

출현종수
Real number (ℝ)

HIGH CORRELATION 

Distinct28
Distinct (%)41.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.161765
Minimum1
Maximum49
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size744.0 B
2024-05-11T08:37:34.834334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median7
Q315
95-th percentile34.65
Maximum49
Range48
Interquartile range (IQR)13

Descriptive statistics

Standard deviation11.770961
Coefficient of variation (CV)1.0545789
Kurtosis0.88534442
Mean11.161765
Median Absolute Deviation (MAD)6
Skewness1.323624
Sum759
Variance138.55553
MonotonicityNot monotonic
2024-05-11T08:37:35.371583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
1 15
22.1%
3 6
 
8.8%
7 5
 
7.4%
8 4
 
5.9%
6 4
 
5.9%
15 4
 
5.9%
2 4
 
5.9%
11 3
 
4.4%
12 2
 
2.9%
5 2
 
2.9%
Other values (18) 19
27.9%
ValueCountFrequency (%)
1 15
22.1%
2 4
 
5.9%
3 6
 
8.8%
4 1
 
1.5%
5 2
 
2.9%
6 4
 
5.9%
7 5
 
7.4%
8 4
 
5.9%
9 1
 
1.5%
11 3
 
4.4%
ValueCountFrequency (%)
49 1
1.5%
39 1
1.5%
36 1
1.5%
35 1
1.5%
34 1
1.5%
33 1
1.5%
31 1
1.5%
30 1
1.5%
29 2
2.9%
28 1
1.5%

서울시보호
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)5.9%
Missing0
Missing (%)0.0%
Memory size676.0 B
0
60 
3
 
3
1
 
3
2
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row3
5th row0

Common Values

ValueCountFrequency (%)
0 60
88.2%
3 3
 
4.4%
1 3
 
4.4%
2 2
 
2.9%

Length

2024-05-11T08:37:35.867681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T08:37:36.223527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 60
88.2%
3 3
 
4.4%
1 3
 
4.4%
2 2
 
2.9%

천연기념물
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size676.0 B
0
67 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)1.5%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 67
98.5%
1 1
 
1.5%

Length

2024-05-11T08:37:36.664285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T08:37:36.988207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 67
98.5%
1 1
 
1.5%

고유종
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8
Distinct (%)11.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1764706
Minimum0
Maximum9
Zeros34
Zeros (%)50.0%
Negative0
Negative (%)0.0%
Memory size744.0 B
2024-05-11T08:37:37.288808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.5
Q32
95-th percentile4.65
Maximum9
Range9
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.7866936
Coefficient of variation (CV)1.5186895
Kurtosis5.4915643
Mean1.1764706
Median Absolute Deviation (MAD)0.5
Skewness2.1849244
Sum80
Variance3.1922739
MonotonicityNot monotonic
2024-05-11T08:37:37.664971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 34
50.0%
1 16
23.5%
2 8
 
11.8%
4 4
 
5.9%
6 2
 
2.9%
3 2
 
2.9%
9 1
 
1.5%
5 1
 
1.5%
ValueCountFrequency (%)
0 34
50.0%
1 16
23.5%
2 8
 
11.8%
3 2
 
2.9%
4 4
 
5.9%
5 1
 
1.5%
6 2
 
2.9%
9 1
 
1.5%
ValueCountFrequency (%)
9 1
 
1.5%
6 2
 
2.9%
5 1
 
1.5%
4 4
 
5.9%
3 2
 
2.9%
2 8
 
11.8%
1 16
23.5%
0 34
50.0%

교란종
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size676.0 B
0
60 
1
8 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 60
88.2%
1 8
 
11.8%

Length

2024-05-11T08:37:38.189229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T08:37:38.558346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 60
88.2%
1 8
 
11.8%

외래종
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)7.4%
Missing0
Missing (%)0.0%
Memory size676.0 B
0
46 
1
10 
2
5 
4
 
4
3
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row3
5th row0

Common Values

ValueCountFrequency (%)
0 46
67.6%
1 10
 
14.7%
2 5
 
7.4%
4 4
 
5.9%
3 3
 
4.4%

Length

2024-05-11T08:37:39.082354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T08:37:39.421184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 46
67.6%
1 10
 
14.7%
2 5
 
7.4%
4 4
 
5.9%
3 3
 
4.4%

정착성
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct11
Distinct (%)16.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3382353
Minimum0
Maximum14
Zeros21
Zeros (%)30.9%
Negative0
Negative (%)0.0%
Memory size744.0 B
2024-05-11T08:37:39.823753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q33
95-th percentile8
Maximum14
Range14
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.7891597
Coefficient of variation (CV)1.1928482
Kurtosis4.616927
Mean2.3382353
Median Absolute Deviation (MAD)2
Skewness1.935836
Sum159
Variance7.7794118
MonotonicityNot monotonic
2024-05-11T08:37:40.203857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 21
30.9%
2 13
19.1%
1 11
16.2%
3 8
 
11.8%
4 4
 
5.9%
5 4
 
5.9%
8 3
 
4.4%
14 1
 
1.5%
6 1
 
1.5%
11 1
 
1.5%
ValueCountFrequency (%)
0 21
30.9%
1 11
16.2%
2 13
19.1%
3 8
 
11.8%
4 4
 
5.9%
5 4
 
5.9%
6 1
 
1.5%
7 1
 
1.5%
8 3
 
4.4%
11 1
 
1.5%
ValueCountFrequency (%)
14 1
 
1.5%
11 1
 
1.5%
8 3
 
4.4%
7 1
 
1.5%
6 1
 
1.5%
5 4
 
5.9%
4 4
 
5.9%
3 8
11.8%
2 13
19.1%
1 11
16.2%

회유성
Real number (ℝ)

HIGH CORRELATION 

Distinct22
Distinct (%)32.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.1029412
Minimum1
Maximum32
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size744.0 B
2024-05-11T08:37:40.663076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median5
Q311
95-th percentile26.65
Maximum32
Range31
Interquartile range (IQR)9

Descriptive statistics

Standard deviation8.474967
Coefficient of variation (CV)1.0459124
Kurtosis0.9067856
Mean8.1029412
Median Absolute Deviation (MAD)4
Skewness1.3927407
Sum551
Variance71.825066
MonotonicityNot monotonic
2024-05-11T08:37:41.162498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
1 15
22.1%
2 8
11.8%
5 7
10.3%
11 5
 
7.4%
4 4
 
5.9%
6 4
 
5.9%
3 4
 
5.9%
10 3
 
4.4%
22 2
 
2.9%
27 2
 
2.9%
Other values (12) 14
20.6%
ValueCountFrequency (%)
1 15
22.1%
2 8
11.8%
3 4
 
5.9%
4 4
 
5.9%
5 7
10.3%
6 4
 
5.9%
7 1
 
1.5%
8 2
 
2.9%
9 2
 
2.9%
10 3
 
4.4%
ValueCountFrequency (%)
32 1
1.5%
31 1
1.5%
27 2
2.9%
26 1
1.5%
25 1
1.5%
24 1
1.5%
22 2
2.9%
21 1
1.5%
18 1
1.5%
17 1
1.5%

하천명
Categorical

Distinct23
Distinct (%)33.8%
Missing0
Missing (%)0.0%
Memory size676.0 B
15 
한강
11 
중랑천
7 
청계천
6 
탄천
5 
Other values (18)
24 

Length

Max length7
Median length4
Mean length2.3970588
Min length1

Unique

Unique15 ?
Unique (%)22.1%

Sample

1st row욱천
2nd row
3rd row중학천
4th row한강
5th row청계천

Common Values

ValueCountFrequency (%)
15
22.1%
한강 11
16.2%
중랑천 7
10.3%
청계천 6
 
8.8%
탄천 5
 
7.4%
안양천 4
 
5.9%
양재천 3
 
4.4%
불광천 2
 
2.9%
세곡천 1
 
1.5%
중학천 1
 
1.5%
Other values (13) 13
19.1%

Length

2024-05-11T08:37:41.625651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
한강 11
20.4%
중랑천 7
13.0%
청계천 6
11.1%
탄천 6
11.1%
안양천 4
 
7.4%
양재천 4
 
7.4%
불광천 2
 
3.7%
당현천 1
 
1.9%
욱천 1
 
1.9%
목감천 1
 
1.9%
Other values (11) 11
20.4%

자치구명
Categorical

HIGH CORRELATION 

Distinct32
Distinct (%)47.1%
Missing0
Missing (%)0.0%
Memory size676.0 B
성동구
9 
종로구
6 
강남구
5 
영등포구
 
4
구로구
 
3
Other values (27)
41 

Length

Max length18
Median length3
Mean length5.3529412
Min length3

Unique

Unique16 ?
Unique (%)23.5%

Sample

1st row용산구
2nd row강북구
3rd row종로구
4th row용산구, 마포구, 영등포구, 서초
5th row종로구

Common Values

ValueCountFrequency (%)
성동구 9
 
13.2%
종로구 6
 
8.8%
강남구 5
 
7.4%
영등포구 4
 
5.9%
구로구 3
 
4.4%
강동구 3
 
4.4%
강북구 3
 
4.4%
강남구, 송파구 3
 
4.4%
노원구 2
 
2.9%
서대문구, 은평구 2
 
2.9%
Other values (22) 28
41.2%

Length

2024-05-11T08:37:42.024381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
성동구 12
 
12.2%
강남구 10
 
10.2%
영등포구 8
 
8.2%
송파구 7
 
7.1%
종로구 7
 
7.1%
마포구 6
 
6.1%
강서구 5
 
5.1%
강북구 4
 
4.1%
강동구 4
 
4.1%
동대문구 4
 
4.1%
Other values (17) 31
31.6%

조사지역
Text

UNIQUE 

Distinct68
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size676.0 B
2024-05-11T08:37:42.617142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length15
Mean length8.1764706
Min length2

Characters and Unicode

Total characters556
Distinct characters144
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique68 ?
Unique (%)100.0%

Sample

1st row욱천
2nd row북한산
3rd row중학천
4th row한강
5th row청계광장~배오개다리
ValueCountFrequency (%)
생태경관보전지역 5
 
3.8%
합류부 5
 
3.8%
서울숲 4
 
3.1%
하류 4
 
3.1%
4
 
3.1%
행주대교 3
 
2.3%
상류 3
 
2.3%
중랑천 3
 
2.3%
홍제천 3
 
2.3%
주변 2
 
1.5%
Other values (85) 94
72.3%
2024-05-11T08:37:44.177909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
62
 
11.2%
천 37
 
6.7%
류 22
 
4.0%
교 22
 
4.0%
구 20
 
3.6%
동 19
 
3.4%
~ 16
 
2.9%
합 14
 
2.5%
부 13
 
2.3%
중 9
 
1.6%
Other values (134) 322
57.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 464
83.5%
Space Separator 62
 
11.2%
Math Symbol 16
 
2.9%
Decimal Number 5
 
0.9%
Uppercase Letter 4
 
0.7%
Open Punctuation 2
 
0.4%
Close Punctuation 2
 
0.4%
Other Punctuation 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
천 37
 
8.0%
류 22
 
4.7%
교 22
 
4.7%
구 20
 
4.3%
동 19
 
4.1%
합 14
 
3.0%
부 13
 
2.8%
중 9
 
1.9%
보 9
 
1.9%
역 8
 
1.7%
Other values (121) 291
62.7%
Decimal Number
ValueCountFrequency (%)
2 2
40.0%
3 1
20.0%
4 1
20.0%
5 1
20.0%
Uppercase Letter
ValueCountFrequency (%)
A 1
25.0%
B 1
25.0%
C 1
25.0%
D 1
25.0%
Space Separator
ValueCountFrequency (%)
62
100.0%
Math Symbol
ValueCountFrequency (%)
~ 16
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 464
83.5%
Common 88
 
15.8%
Latin 4
 
0.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
천 37
 
8.0%
류 22
 
4.7%
교 22
 
4.7%
구 20
 
4.3%
동 19
 
4.1%
합 14
 
3.0%
부 13
 
2.8%
중 9
 
1.9%
보 9
 
1.9%
역 8
 
1.7%
Other values (121) 291
62.7%
Common
ValueCountFrequency (%)
62
70.5%
~ 16
 
18.2%
( 2
 
2.3%
) 2
 
2.3%
2 2
 
2.3%
, 1
 
1.1%
3 1
 
1.1%
4 1
 
1.1%
5 1
 
1.1%
Latin
ValueCountFrequency (%)
A 1
25.0%
B 1
25.0%
C 1
25.0%
D 1
25.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 464
83.5%
ASCII 92
 
16.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
62
67.4%
~ 16
 
17.4%
( 2
 
2.2%
) 2
 
2.2%
2 2
 
2.2%
A 1
 
1.1%
B 1
 
1.1%
, 1
 
1.1%
C 1
 
1.1%
D 1
 
1.1%
Other values (3) 3
 
3.3%
Hangul
ValueCountFrequency (%)
천 37
 
8.0%
류 22
 
4.7%
교 22
 
4.7%
구 20
 
4.3%
동 19
 
4.1%
합 14
 
3.0%
부 13
 
2.8%
중 9
 
1.9%
보 9
 
1.9%
역 8
 
1.7%
Other values (121) 291
62.7%

조사연도
Categorical

HIGH CORRELATION 

Distinct15
Distinct (%)22.1%
Missing0
Missing (%)0.0%
Memory size676.0 B
2012
21 
2002
10 
2007
7 
2003
6 
1998
6 
Other values (10)
18 

Length

Max length34
Median length4
Mean length5.8529412
Min length4

Unique

Unique5 ?
Unique (%)7.4%

Sample

1st row2005
2nd row2001, 2002, 2003, 2004, 2005
3rd row2003, 2005
4th row2007
5th row2012

Common Values

ValueCountFrequency (%)
2012 21
30.9%
2002 10
14.7%
2007 7
 
10.3%
2003 6
 
8.8%
1998 6
 
8.8%
2007, 2012 4
 
5.9%
2005 3
 
4.4%
2003, 2005 2
 
2.9%
2002, 2007 2
 
2.9%
2006 2
 
2.9%
Other values (5) 5
 
7.4%

Length

2024-05-11T08:37:44.777947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2012 25
28.1%
2002 14
15.7%
2007 14
15.7%
2003 11
12.4%
2005 7
 
7.9%
1998 6
 
6.7%
2004 4
 
4.5%
2006 3
 
3.4%
2001 2
 
2.2%
2009 1
 
1.1%
Other values (2) 2
 
2.2%

x_value
Real number (ℝ)

HIGH CORRELATION 

Distinct67
Distinct (%)98.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean199796.3
Minimum182882.13
Maximum213611
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size744.0 B
2024-05-11T08:37:45.347351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum182882.13
5-th percentile185946.67
Q1193648.23
median202731.82
Q3205811.63
95-th percentile210508.1
Maximum213611
Range30728.869
Interquartile range (IQR)12163.402

Descriptive statistics

Standard deviation7976.6816
Coefficient of variation (CV)0.03992407
Kurtosis-0.76391683
Mean199796.3
Median Absolute Deviation (MAD)5531.3493
Skewness-0.41038527
Sum13586149
Variance63627450
MonotonicityNot monotonic
2024-05-11T08:37:45.806525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
204171.6 2
 
2.9%
197183.601391 1
 
1.5%
182949.6 1
 
1.5%
187722.062408 1
 
1.5%
187514.269 1
 
1.5%
187406.515 1
 
1.5%
185160.6 1
 
1.5%
184303.098552 1
 
1.5%
182882.131 1
 
1.5%
209005.941741 1
 
1.5%
Other values (57) 57
83.8%
ValueCountFrequency (%)
182882.131 1
1.5%
182949.6 1
1.5%
184303.098552 1
1.5%
185160.6 1
1.5%
187406.515 1
1.5%
187514.269 1
1.5%
187722.062408 1
1.5%
188434.915494 1
1.5%
189205.712 1
1.5%
189443.6 1
1.5%
ValueCountFrequency (%)
213611.0 1
1.5%
213198.248142 1
1.5%
212619.906 1
1.5%
210777.498467 1
1.5%
210007.778 1
1.5%
209339.656029 1
1.5%
209005.941741 1
1.5%
208856.618 1
1.5%
208603.096 1
1.5%
208246.3 1
1.5%

y_value
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct68
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean450185.2
Minimum437375.5
Maximum464087.82
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size744.0 B
2024-05-11T08:37:46.291767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum437375.5
5-th percentile441247.95
Q1446046.25
median449956.03
Q3453325.62
95-th percentile461127.19
Maximum464087.82
Range26712.322
Interquartile range (IQR)7279.3725

Descriptive statistics

Standard deviation5884.8581
Coefficient of variation (CV)0.013072083
Kurtosis-0.26667196
Mean450185.2
Median Absolute Deviation (MAD)3417.4025
Skewness0.20189409
Sum30612593
Variance34631554
MonotonicityNot monotonic
2024-05-11T08:37:46.966955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
447973.185463 1
 
1.5%
455395.72 1
 
1.5%
452632.60315 1
 
1.5%
442884.194 1
 
1.5%
452048.734 1
 
1.5%
453093.7 1
 
1.5%
442360.774931 1
 
1.5%
454926.4 1
 
1.5%
446683.306 1
 
1.5%
443480.524943 1
 
1.5%
Other values (58) 58
85.3%
ValueCountFrequency (%)
437375.5 1
1.5%
440315.288 1
1.5%
440375.123173 1
1.5%
440699.611315 1
1.5%
442266.3 1
1.5%
442303.8 1
1.5%
442360.774931 1
1.5%
442884.194 1
1.5%
443013.45831 1
1.5%
443236.323775 1
1.5%
ValueCountFrequency (%)
464087.821895 1
1.5%
462337.416 1
1.5%
461506.866 1
1.5%
461398.5 1
1.5%
460623.33 1
1.5%
459571.841 1
1.5%
459456.994 1
1.5%
458136.4 1
1.5%
456808.729705 1
1.5%
455488.33407 1
1.5%

종수범례
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)7.4%
Missing0
Missing (%)0.0%
Memory size676.0 B
5종이하
28 
6~10종
14 
11~20종
13 
31종이상
7 
21~30종
6 

Length

Max length6
Median length5
Mean length4.8676471
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5종이하
2nd row5종이하
3rd row5종이하
4th row31종이상
5th row11~20종

Common Values

ValueCountFrequency (%)
5종이하 28
41.2%
6~10종 14
20.6%
11~20종 13
19.1%
31종이상 7
 
10.3%
21~30종 6
 
8.8%

Length

2024-05-11T08:37:47.632737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T08:37:48.140049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
5종이하 28
41.2%
6~10종 14
20.6%
11~20종 13
19.1%
31종이상 7
 
10.3%
21~30종 6
 
8.8%

조사시점
Text

UNIQUE 

Distinct68
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size676.0 B
2024-05-11T08:37:48.905443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters340
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

Unique68 ?
Unique (%)100.0%

Sample

1st rowp0302
2nd rowp0102
3rd rowp0226
4th rowp0271
5th rowp0331
ValueCountFrequency (%)
p0302 1
 
1.5%
p0279 1
 
1.5%
p0326 1
 
1.5%
p0064 1
 
1.5%
p0029 1
 
1.5%
p0050 1
 
1.5%
p0246 1
 
1.5%
p0102 1
 
1.5%
p0339 1
 
1.5%
p0300 1
 
1.5%
Other values (58) 58
85.3%
2024-05-11T08:37:50.392933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 86
25.3%
p 68
20.0%
3 42
12.4%
2 41
12.1%
1 32
 
9.4%
7 14
 
4.1%
6 14
 
4.1%
5 12
 
3.5%
9 11
 
3.2%
4 10
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 272
80.0%
Lowercase Letter 68
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 86
31.6%
3 42
15.4%
2 41
15.1%
1 32
 
11.8%
7 14
 
5.1%
6 14
 
5.1%
5 12
 
4.4%
9 11
 
4.0%
4 10
 
3.7%
8 10
 
3.7%
Lowercase Letter
ValueCountFrequency (%)
p 68
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 272
80.0%
Latin 68
 
20.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 86
31.6%
3 42
15.4%
2 41
15.1%
1 32
 
11.8%
7 14
 
5.1%
6 14
 
5.1%
5 12
 
4.4%
9 11
 
4.0%
4 10
 
3.7%
8 10
 
3.7%
Latin
ValueCountFrequency (%)
p 68
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 340
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 86
25.3%
p 68
20.0%
3 42
12.4%
2 41
12.1%
1 32
 
9.4%
7 14
 
4.1%
6 14
 
4.1%
5 12
 
3.5%
9 11
 
3.2%
4 10
 
2.9%

Interactions

2024-05-11T08:37:28.834616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T08:37:15.700115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T08:37:17.405381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T08:37:19.394150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T08:37:21.294652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T08:37:23.546630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T08:37:26.071321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T08:37:29.200951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T08:37:16.031046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T08:37:17.667052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T08:37:19.662302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T08:37:21.665058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T08:37:23.926001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T08:37:26.412468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T08:37:29.549708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T08:37:16.264722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T08:37:17.898095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T08:37:19.912899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T08:37:22.011008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T08:37:24.580770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T08:37:26.766487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T08:37:29.784109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T08:37:16.453568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T08:37:18.164260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T08:37:20.149974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T08:37:22.344314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T08:37:24.806544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T08:37:27.133682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T08:37:30.074583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T08:37:16.689021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T08:37:18.541899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T08:37:20.404004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T08:37:22.662379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T08:37:25.078079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T08:37:27.553809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T08:37:30.366647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T08:37:16.934345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T08:37:18.840336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T08:37:20.644749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T08:37:22.913550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T08:37:25.389532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T08:37:27.962510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T08:37:30.616284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T08:37:17.176892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T08:37:19.147428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T08:37:20.959217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T08:37:23.176961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T08:37:25.752870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T08:37:28.362426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-11T08:37:50.867524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
객체id출현종출현종수서울시보호천연기념물고유종교란종외래종정착성회유성하천명자치구명조사지역조사연도x_valuey_value종수범례조사시점
객체id1.0000.5410.6330.0000.0000.0000.0750.0720.3490.3980.6780.8931.0000.6830.8900.3280.2351.000
출현종0.5411.0000.9900.8251.0000.9951.0001.0000.9980.9920.8530.9081.0000.7320.0000.0000.9931.000
출현종수0.6330.9901.0000.7990.3710.7970.8420.7670.8950.9400.0000.8471.0000.7290.1490.0000.9781.000
서울시보호0.0000.8250.7991.0000.0000.8840.8230.4240.7440.6910.0000.9861.0000.0000.3780.1800.5421.000
천연기념물0.0001.0000.3710.0001.0000.8390.0000.0001.0000.8110.0000.0001.0001.0000.4520.0000.2611.000
고유종0.0000.9950.7970.8840.8391.0000.9390.7030.8510.8120.0000.9341.0000.5870.0000.0000.6641.000
교란종0.0751.0000.8420.8230.0000.9391.0000.8000.6880.9360.6140.9801.0000.0000.0000.0640.6211.000
외래종0.0721.0000.7670.4240.0000.7030.8001.0000.7170.9120.0000.8671.0000.6660.2360.0000.7861.000
정착성0.3490.9980.8950.7441.0000.8510.6880.7171.0000.8570.0000.8031.0000.7730.3190.0000.7681.000
회유성0.3980.9920.9400.6910.8110.8120.9360.9120.8571.0000.0000.7761.0000.6990.5620.0000.9931.000
하천명0.6780.8530.0000.0000.0000.0000.6140.0000.0000.0001.0000.8731.0000.4740.7900.7210.6191.000
자치구명0.8930.9080.8470.9860.0000.9340.9800.8670.8030.7760.8731.0001.0000.7480.9520.8920.7151.000
조사지역1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
조사연도0.6830.7320.7290.0001.0000.5870.0000.6660.7730.6990.4740.7481.0001.0000.6510.2950.7391.000
x_value0.8900.0000.1490.3780.4520.0000.0000.2360.3190.5620.7900.9521.0000.6511.0000.4740.0001.000
y_value0.3280.0000.0000.1800.0000.0000.0640.0000.0000.0000.7210.8921.0000.2950.4741.0000.1631.000
종수범례0.2350.9930.9780.5420.2610.6640.6210.7860.7680.9930.6190.7151.0000.7390.0000.1631.0001.000
조사시점1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
2024-05-11T08:37:51.462739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
자치구명천연기념물외래종종수범례하천명서울시보호조사연도교란종
자치구명1.0000.0000.4740.3210.3560.6460.2500.662
천연기념물0.0001.0000.0000.3100.0000.0000.8960.000
외래종0.4740.0001.0000.4070.0000.3540.3150.908
종수범례0.3210.3100.4071.0000.2990.4650.3740.730
하천명0.3560.0000.0000.2991.0000.0000.1320.443
서울시보호0.6460.0000.3540.4650.0001.0000.0000.607
조사연도0.2500.8960.3150.3740.1320.0001.0000.000
교란종0.6620.0000.9080.7300.4430.6070.0001.000
2024-05-11T08:37:51.969853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
객체id출현종수고유종정착성회유성x_valuey_value서울시보호천연기념물교란종외래종하천명자치구명조사연도종수범례
객체id1.000-0.0380.096-0.054-0.045-0.382-0.1010.0000.0000.1210.0550.4190.5570.4060.192
출현종수-0.0381.0000.7420.9430.9930.012-0.2140.6330.3480.8240.5610.0000.3980.3780.935
고유종0.0960.7421.0000.6930.751-0.001-0.1870.5550.6280.7470.5170.0000.5010.2750.475
정착성-0.0540.9430.6931.0000.9100.101-0.2450.4240.9530.4740.5020.0000.3810.4560.636
회유성-0.0450.9930.7510.9101.0000.006-0.2130.4670.6030.7370.5850.0000.3090.3250.843
x_value-0.3820.012-0.0010.1010.0061.000-0.1990.2990.3260.0000.0650.3940.5970.2650.105
y_value-0.101-0.214-0.187-0.245-0.213-0.1991.0000.0000.0000.0000.0000.2910.5100.0000.000
서울시보호0.0000.6330.5550.4240.4670.2990.0001.0000.0000.6070.3540.0000.6460.0000.465
천연기념물0.0000.3480.6280.9530.6030.3260.0000.0001.0000.0000.0000.0000.0000.8960.310
교란종0.1210.8240.7470.4740.7370.0000.0000.6070.0001.0000.9080.4430.6620.0000.730
외래종0.0550.5610.5170.5020.5850.0650.0000.3540.0000.9081.0000.0000.4740.3150.407
하천명0.4190.0000.0000.0000.0000.3940.2910.0000.0000.4430.0001.0000.3560.1320.299
자치구명0.5570.3980.5010.3810.3090.5970.5100.6460.0000.6620.4740.3561.0000.2500.321
조사연도0.4060.3780.2750.4560.3250.2650.0000.0000.8960.0000.3150.1320.2501.0000.374
종수범례0.1920.9350.4750.6360.8430.1050.0000.4650.3100.7300.4070.2990.3210.3741.000

Missing values

2024-05-11T08:37:31.035450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-11T08:37:31.804193image/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

객체id출현종출현종수서울시보호천연기념물고유종교란종외래종정착성회유성하천명자치구명조사지역조사연도x_valuey_value종수범례조사시점
09977붕어, 잉어20000002욱천용산구욱천2005197183.601391447973.1854635종이하p0302
19978버들치, 피라미, 모래무지, 꺽지, 돌고기50010014강북구북한산2001, 2002, 2003, 2004, 2005198426.6461398.55종이하p0102
29979버들치, 참붕어, 붕어30000012중학천종로구중학천2003, 2005198029.83262451942.5532135종이하p0226
39980가물치, 가숭어, 가시납지리, 각시붕어, 갈문망둑 등49306131432한강용산구, 마포구, 영등포구, 서초한강2007198658.9445458.031종이상p0271
49981가시납지리, 누치, 돌고기, 메기, 모래무지 등1200100210청계천종로구청계광장~배오개다리2012198716.662451774.48111~20종p0331
59982버들치10000001성북구정릉공원2003200548.294598455488.334075종이하p0212
69983버들치10000001오리천강북구오리천2005200620.769459571.8415종이하p0301
79984돌고기, 밀어, 버들치, 붕어, 잉어 등80010026청계천종로구새벽다리~영도교2012200658.399451936.1376~10종p0320
810241버들치10000001백운천강북구백운천2005200772.54462337.4165종이하p0300
910242강준치, 금붕어, 누치, 돌고기, 몰개 등1500200311청계천성동구황학교~고산자교2012202702.814451949.16311~20종p0342
객체id출현종출현종수서울시보호천연기념물고유종교란종외래종정착성회유성하천명자치구명조사지역조사연도x_valuey_value종수범례조사시점
5810291끄리10000001불광천서대문구, 은평구한강합류부~불광천 상류2012191884.158453384.8835종이하p0333
5910292납지리, 누치, 대륙송사리, 떡붕어, 미꾸리 등130000238여의샛강영등포구여의샛강2007, 2012192809.208577446242.33136911~20종p0175
6010293가숭어, 가시납지리, 강준치, 끄리, 납지리 등2900314322한강영등포구밤섬 주변2002193544.8448375.021~30종p0173
6110294몰개10010001한강영등포구밤섬2007193682.7448498.25종이하p0088
6210295가시납지리, 몰개, 미꾸리, 배스, 버들치 등90020126홍제천서대문구홍제천2007, 2012193975.693726452946.9771316~10종p0286
6310296가물치, 가숭어, 가시납지리, 각시붕어, 강준치 등3620511431한강동작구, 영등포구, 마포구, 용산반포천 합류부 ~ 홍제천 합류부2012194491.875447623.4231종이상p0318
6410297긴몰개10010001은평구진관내동 생태경관보전지역2006194985.8461506.8665종이하p0229
6510298버들치10000001종로구종로구 구기동 관음사 하류2002196348.758771456808.7297055종이하p0135
6610299버들치10000001종로구종로구 구기동 승가사 하류2002196476.1458136.45종이하p0136
6710300버들치, 붕어, 참붕어30000012백운동천종로구백운동천2003, 2005196661.9453313.55종이하p0092