Overview

Dataset statistics

Number of variables17
Number of observations958
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory135.8 KiB
Average record size in memory145.1 B

Variable types

Numeric4
Text3
Categorical10

Dataset

Description객체id,종명,과명,서울시보호,천연기념물,고유종,교란종,외래종,이동특성,이동특성,조사지역,조사연도,조사출처,참고문헌,x_value,y_value,조사지점
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-21155/S/1/datasetView.do

Alerts

조사출처 has constant value ""Constant
교란종 is highly overall correlated with 과명High correlation
이동특성 is highly overall correlated with 외래종High correlation
과명 is highly overall correlated with 서울시보호 and 1 other fieldsHigh correlation
외래종 is highly overall correlated with 이동특성High correlation
객체id is highly overall correlated with 참고문헌High correlation
조사연도 is highly overall correlated with 이동특성.1 and 1 other fieldsHigh correlation
x_value is highly overall correlated with 이동특성.1 and 1 other fieldsHigh correlation
y_value is highly overall correlated with 이동특성.1 and 1 other fieldsHigh correlation
서울시보호 is highly overall correlated with 과명High correlation
이동특성.1 is highly overall correlated with 조사연도 and 3 other fieldsHigh correlation
참고문헌 is highly overall correlated with 객체id and 4 other fieldsHigh correlation
과명 is highly imbalanced (51.8%)Imbalance
서울시보호 is highly imbalanced (87.8%)Imbalance
천연기념물 is highly imbalanced (97.8%)Imbalance
고유종 is highly imbalanced (52.7%)Imbalance
교란종 is highly imbalanced (93.0%)Imbalance
외래종 is highly imbalanced (70.0%)Imbalance
객체id has unique valuesUnique

Reproduction

Analysis started2024-05-11 09:50:15.324061
Analysis finished2024-05-11 09:50:24.239116
Duration8.92 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

객체id
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct958
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33115.5
Minimum32637
Maximum33594
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.5 KiB
2024-05-11T09:50:24.625005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum32637
5-th percentile32684.85
Q132876.25
median33115.5
Q333354.75
95-th percentile33546.15
Maximum33594
Range957
Interquartile range (IQR)478.5

Descriptive statistics

Standard deviation276.69508
Coefficient of variation (CV)0.0083554553
Kurtosis-1.2
Mean33115.5
Median Absolute Deviation (MAD)239.5
Skewness0
Sum31724649
Variance76560.167
MonotonicityStrictly increasing
2024-05-11T09:50:25.240776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
32637 1
 
0.1%
33281 1
 
0.1%
33269 1
 
0.1%
33270 1
 
0.1%
33271 1
 
0.1%
33272 1
 
0.1%
33273 1
 
0.1%
33274 1
 
0.1%
33275 1
 
0.1%
33276 1
 
0.1%
Other values (948) 948
99.0%
ValueCountFrequency (%)
32637 1
0.1%
32638 1
0.1%
32639 1
0.1%
32640 1
0.1%
32641 1
0.1%
32642 1
0.1%
32643 1
0.1%
32644 1
0.1%
32645 1
0.1%
32646 1
0.1%
ValueCountFrequency (%)
33594 1
0.1%
33593 1
0.1%
33592 1
0.1%
33591 1
0.1%
33590 1
0.1%
33589 1
0.1%
33588 1
0.1%
33587 1
0.1%
33586 1
0.1%
33585 1
0.1%

종명
Text

Distinct80
Distinct (%)8.4%
Missing0
Missing (%)0.0%
Memory size7.6 KiB
2024-05-11T09:50:25.796150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length3.0334029
Min length2

Characters and Unicode

Total characters2906
Distinct characters104
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

Unique7 ?
Unique (%)0.7%

Sample

1st row누치
2nd row대륙송사리
3rd row돌고기
4th row미꾸라지
5th row미꾸리
ValueCountFrequency (%)
붕어 68
 
7.1%
잉어 57
 
5.9%
참붕어 46
 
4.8%
피라미 41
 
4.3%
누치 36
 
3.8%
미꾸리 31
 
3.2%
밀어 31
 
3.2%
모래무지 28
 
2.9%
메기 26
 
2.7%
버들치 26
 
2.7%
Other values (71) 569
59.3%
2024-05-11T09:50:27.303255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
어 311
 
10.7%
붕 165
 
5.7%
리 154
 
5.3%
치 130
 
4.5%
미 92
 
3.2%
지 91
 
3.1%
납 79
 
2.7%
개 73
 
2.5%
잉 71
 
2.4%
참 68
 
2.3%
Other values (94) 1672
57.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2902
99.9%
Other Punctuation 3
 
0.1%
Space Separator 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
어 311
 
10.7%
붕 165
 
5.7%
리 154
 
5.3%
치 130
 
4.5%
미 92
 
3.2%
지 91
 
3.1%
납 79
 
2.7%
개 73
 
2.5%
잉 71
 
2.4%
참 68
 
2.3%
Other values (92) 1668
57.5%
Other Punctuation
ValueCountFrequency (%)
? 3
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2902
99.9%
Common 4
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
어 311
 
10.7%
붕 165
 
5.7%
리 154
 
5.3%
치 130
 
4.5%
미 92
 
3.2%
지 91
 
3.1%
납 79
 
2.7%
개 73
 
2.5%
잉 71
 
2.4%
참 68
 
2.3%
Other values (92) 1668
57.5%
Common
ValueCountFrequency (%)
? 3
75.0%
1
 
25.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2902
99.9%
ASCII 4
 
0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
어 311
 
10.7%
붕 165
 
5.7%
리 154
 
5.3%
치 130
 
4.5%
미 92
 
3.2%
지 91
 
3.1%
납 79
 
2.7%
개 73
 
2.5%
잉 71
 
2.4%
참 68
 
2.3%
Other values (92) 1668
57.5%
ASCII
ValueCountFrequency (%)
? 3
75.0%
1
 
25.0%

과명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct21
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size7.6 KiB
잉어과
641 
망둥어과
78 
미꾸리과
 
48
메기과
 
26
동자개과
 
24
Other values (16)
141 

Length

Max length5
Median length3
Mean length3.2557411
Min length3

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row잉어과
2nd row송사리과
3rd row잉어과
4th row미꾸리과
5th row미꾸리과

Common Values

ValueCountFrequency (%)
잉어과 641
66.9%
망둥어과 78
 
8.1%
미꾸리과 48
 
5.0%
메기과 26
 
2.7%
동자개과 24
 
2.5%
송사리과 23
 
2.4%
숭어과 19
 
2.0%
동사리과 19
 
2.0%
뱀장어과 11
 
1.1%
꺽지과 11
 
1.1%
Other values (11) 58
 
6.1%

Length

2024-05-11T09:50:27.895730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
잉어과 641
66.9%
망둥어과 78
 
8.1%
미꾸리과 48
 
5.0%
메기과 26
 
2.7%
동자개과 24
 
2.5%
송사리과 23
 
2.4%
숭어과 19
 
2.0%
동사리과 19
 
2.0%
뱀장어과 11
 
1.1%
꺽지과 11
 
1.1%
Other values (11) 58
 
6.1%

서울시보호
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.6 KiB
0
942 
1
 
16

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 942
98.3%
1 16
 
1.7%

Length

2024-05-11T09:50:28.297423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T09:50:28.626029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 942
98.3%
1 16
 
1.7%

천연기념물
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.6 KiB
0
956 
1
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 956
99.8%
1 2
 
0.2%

Length

2024-05-11T09:50:28.974828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T09:50:29.283331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 956
99.8%
1 2
 
0.2%

고유종
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.6 KiB
0
861 
1
97 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 861
89.9%
1 97
 
10.1%

Length

2024-05-11T09:50:29.596302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T09:50:29.896181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 861
89.9%
1 97
 
10.1%

교란종
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.6 KiB
0
950 
1
 
8

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 950
99.2%
1 8
 
0.8%

Length

2024-05-11T09:50:30.374162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T09:50:30.722339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 950
99.2%
1 8
 
0.8%

외래종
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.6 KiB
0
907 
1
 
51

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 907
94.7%
1 51
 
5.3%

Length

2024-05-11T09:50:31.066785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T09:50:31.386890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 907
94.7%
1 51
 
5.3%

이동특성
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size7.6 KiB
회유성
671 
정착성
231 
0
 
56

Length

Max length3
Median length3
Mean length2.8830898
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row회유성
2nd row정착성
3rd row정착성
4th row정착성
5th row정착성

Common Values

ValueCountFrequency (%)
회유성 671
70.0%
정착성 231
 
24.1%
0 56
 
5.8%

Length

2024-05-11T09:50:31.759657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T09:50:32.106526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
회유성 671
70.0%
정착성 231
 
24.1%
0 56
 
5.8%

이동특성.1
Categorical

HIGH CORRELATION 

Distinct32
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size7.6 KiB
마포구
95 
성동구
74 
강동구
71 
강남구, 송파구
71 
강서구
64 
Other values (27)
583 

Length

Max length26
Median length24
Mean length7.6638831
Min length3

Unique

Unique4 ?
Unique (%)0.4%

Sample

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

Common Values

ValueCountFrequency (%)
마포구 95
 
9.9%
성동구 74
 
7.7%
강동구 71
 
7.4%
강남구, 송파구 71
 
7.4%
강서구 64
 
6.7%
강서구, 마포구 63
 
6.6%
영등포구 54
 
5.6%
용산구, 마포구, 영등포구, 서초구, 성동구 등 52
 
5.4%
강남구 50
 
5.2%
종로구, 성동구 42
 
4.4%
Other values (22) 322
33.6%

Length

2024-05-11T09:50:32.432598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
마포구 246
13.4%
성동구 195
10.6%
송파구 178
9.7%
강남구 170
9.2%
영등포구 167
9.1%
강서구 146
7.9%
강동구 110
 
6.0%
서초구 102
 
5.5%
용산구 90
 
4.9%
종로구 73
 
4.0%
Other values (15) 362
19.7%

조사지역
Text

Distinct68
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Memory size7.6 KiB
2024-05-11T09:50:33.045257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length15
Mean length7.6294363
Min length2

Characters and Unicode

Total characters7309
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

Unique14 ?
Unique (%)1.5%

Sample

1st row월드컵공원
2nd row월드컵공원
3rd row월드컵공원
4th row월드컵공원
5th row월드컵공원
ValueCountFrequency (%)
합류부 153
 
8.9%
139
 
8.1%
행주대교 100
 
5.8%
월드컵공원 93
 
5.4%
홍제천 81
 
4.7%
반포천 70
 
4.1%
길동생태공원 69
 
4.0%
잠실수중보 64
 
3.7%
탄천 63
 
3.7%
주변 59
 
3.4%
Other values (85) 833
48.3%
2024-05-11T09:50:33.954179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
766
 
10.5%
천 495
 
6.8%
~ 298
 
4.1%
교 274
 
3.7%
류 271
 
3.7%
합 258
 
3.5%
부 245
 
3.4%
주 186
 
2.5%
중 184
 
2.5%
보 174
 
2.4%
Other values (134) 4158
56.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 6141
84.0%
Space Separator 766
 
10.5%
Math Symbol 298
 
4.1%
Decimal Number 62
 
0.8%
Uppercase Letter 26
 
0.4%
Other Punctuation 6
 
0.1%
Open Punctuation 5
 
0.1%
Close Punctuation 5
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
천 495
 
8.1%
교 274
 
4.5%
류 271
 
4.4%
합 258
 
4.2%
부 245
 
4.0%
주 186
 
3.0%
중 184
 
3.0%
보 174
 
2.8%
원 166
 
2.7%
공 163
 
2.7%
Other values (121) 3725
60.7%
Decimal Number
ValueCountFrequency (%)
2 17
27.4%
5 15
24.2%
3 15
24.2%
4 15
24.2%
Uppercase Letter
ValueCountFrequency (%)
D 8
30.8%
C 6
23.1%
A 6
23.1%
B 6
23.1%
Space Separator
ValueCountFrequency (%)
766
100.0%
Math Symbol
ValueCountFrequency (%)
~ 298
100.0%
Other Punctuation
ValueCountFrequency (%)
, 6
100.0%
Open Punctuation
ValueCountFrequency (%)
( 5
100.0%
Close Punctuation
ValueCountFrequency (%)
) 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 6141
84.0%
Common 1142
 
15.6%
Latin 26
 
0.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
천 495
 
8.1%
교 274
 
4.5%
류 271
 
4.4%
합 258
 
4.2%
부 245
 
4.0%
주 186
 
3.0%
중 184
 
3.0%
보 174
 
2.8%
원 166
 
2.7%
공 163
 
2.7%
Other values (121) 3725
60.7%
Common
ValueCountFrequency (%)
766
67.1%
~ 298
 
26.1%
2 17
 
1.5%
5 15
 
1.3%
3 15
 
1.3%
4 15
 
1.3%
, 6
 
0.5%
( 5
 
0.4%
) 5
 
0.4%
Latin
ValueCountFrequency (%)
D 8
30.8%
C 6
23.1%
A 6
23.1%
B 6
23.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 6141
84.0%
ASCII 1168
 
16.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
766
65.6%
~ 298
 
25.5%
2 17
 
1.5%
5 15
 
1.3%
3 15
 
1.3%
4 15
 
1.3%
D 8
 
0.7%
C 6
 
0.5%
A 6
 
0.5%
, 6
 
0.5%
Other values (3) 16
 
1.4%
Hangul
ValueCountFrequency (%)
천 495
 
8.1%
교 274
 
4.5%
류 271
 
4.4%
합 258
 
4.2%
부 245
 
4.0%
주 186
 
3.0%
중 184
 
3.0%
보 174
 
2.8%
원 166
 
2.7%
공 163
 
2.7%
Other values (121) 3725
60.7%

조사연도
Real number (ℝ)

HIGH CORRELATION 

Distinct12
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2007.2171
Minimum1998
Maximum2012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.5 KiB
2024-05-11T09:50:34.336355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1998
5-th percentile2002
Q12003
median2007
Q32012
95-th percentile2012
Maximum2012
Range14
Interquartile range (IQR)9

Descriptive statistics

Standard deviation4.307273
Coefficient of variation (CV)0.0021458929
Kurtosis-1.375847
Mean2007.2171
Median Absolute Deviation (MAD)5
Skewness-0.18479318
Sum1922914
Variance18.552601
MonotonicityNot monotonic
2024-05-11T09:50:34.659111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
2012 354
37.0%
2007 180
18.8%
2002 167
17.4%
2003 86
 
9.0%
2004 36
 
3.8%
2005 27
 
2.8%
2006 23
 
2.4%
1998 22
 
2.3%
2001 21
 
2.2%
2010 14
 
1.5%
Other values (2) 28
 
2.9%
ValueCountFrequency (%)
1998 22
 
2.3%
2001 21
 
2.2%
2002 167
17.4%
2003 86
9.0%
2004 36
 
3.8%
2005 27
 
2.8%
2006 23
 
2.4%
2007 180
18.8%
2009 14
 
1.5%
2010 14
 
1.5%
ValueCountFrequency (%)
2012 354
37.0%
2011 14
 
1.5%
2010 14
 
1.5%
2009 14
 
1.5%
2007 180
18.8%
2006 23
 
2.4%
2005 27
 
2.8%
2004 36
 
3.8%
2003 86
 
9.0%
2002 167
17.4%

조사출처
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size7.6 KiB
문헌조사
958 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
문헌조사 958
100.0%

Length

2024-05-11T09:50:35.151358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T09:50:35.517713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
문헌조사 958
100.0%

참고문헌
Categorical

HIGH CORRELATION 

Distinct21
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size7.6 KiB
제7차 한강 생태계 조사연구
371 
2007년 한강생태계 조사
143 
한강생태계 조사연구
133 
길동생태공원 운영결과보고서
69 
월드컵공원 자연생태계 모니터링 및 관리방안
51 
Other values (16)
191 

Length

Max length28
Median length27
Mean length15.265136
Min length1

Unique

Unique6 ?
Unique (%)0.6%

Sample

1st row월드컵공원 자연생태계 모니터링
2nd row월드컵공원 자연생태계 모니터링
3rd row월드컵공원 자연생태계 모니터링
4th row월드컵공원 자연생태계 모니터링
5th row월드컵공원 자연생태계 모니터링

Common Values

ValueCountFrequency (%)
제7차 한강 생태계 조사연구 371
38.7%
2007년 한강생태계 조사 143
 
14.9%
한강생태계 조사연구 133
 
13.9%
길동생태공원 운영결과보고서 69
 
7.2%
월드컵공원 자연생태계 모니터링 및 관리방안 51
 
5.3%
월드컵공원 자연생태계 모니터링 42
 
4.4%
양재천 자연형 하천 조성사례 41
 
4.3%
한강 지류천 생태계 조사연구 - 중랑천?탄천 - 22
 
2.3%
청계천 생태계모니터링 학술연구용역 21
 
2.2%
탄천 생태경관보전지역 일반생태변화관찰 및 관리대책 17
 
1.8%
Other values (11) 48
 
5.0%

Length

2024-05-11T09:50:35.870728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
조사연구 541
16.0%
생태계 414
12.3%
한강 402
11.9%
제7차 371
11.0%
한강생태계 276
 
8.2%
조사 155
 
4.6%
2007년 143
 
4.2%
모니터링 106
 
3.1%
월드컵공원 93
 
2.8%
자연생태계 93
 
2.8%
Other values (37) 780
23.1%

x_value
Real number (ℝ)

HIGH CORRELATION 

Distinct67
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean199248.8
Minimum182882.13
Maximum213611
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.5 KiB
2024-05-11T09:50:36.324687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum182882.13
5-th percentile182949.6
Q1190514.83
median202702.81
Q3206587.19
95-th percentile213611
Maximum213611
Range30728.869
Interquartile range (IQR)16072.36

Descriptive statistics

Standard deviation9075.4523
Coefficient of variation (CV)0.045548342
Kurtosis-1.121451
Mean199248.8
Median Absolute Deviation (MAD)7304.964
Skewness-0.22519865
Sum1.9088035 × 108
Variance82363835
MonotonicityNot monotonic
2024-05-11T09:50:36.754003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
190514.829981 93
 
9.7%
213611.0 69
 
7.2%
204171.6 57
 
5.9%
198658.9 52
 
5.4%
208246.3 46
 
4.8%
210007.778 39
 
4.1%
187406.515 36
 
3.8%
194491.875 36
 
3.8%
202760.826 34
 
3.5%
182882.131 33
 
3.4%
Other values (57) 463
48.3%
ValueCountFrequency (%)
182882.131 33
3.4%
182949.6 31
3.2%
184303.098552 1
 
0.1%
185160.6 27
2.8%
187406.515 36
3.8%
187514.269 12
 
1.3%
187722.062408 2
 
0.2%
188434.915494 1
 
0.1%
189205.712 3
 
0.3%
189443.6 19
2.0%
ValueCountFrequency (%)
213611.0 69
7.2%
213198.248142 1
 
0.1%
212619.906 1
 
0.1%
210777.498467 3
 
0.3%
210007.778 39
4.1%
209339.656029 17
 
1.8%
209005.941741 4
 
0.4%
208856.618 6
 
0.6%
208603.096 8
 
0.8%
208246.3 46
4.8%

y_value
Real number (ℝ)

HIGH CORRELATION 

Distinct68
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean449410.15
Minimum437375.5
Maximum464087.82
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.5 KiB
2024-05-11T09:50:37.195835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum437375.5
5-th percentile442884.19
Q1446888.7
median449327.43
Q3451949.16
95-th percentile455395.72
Maximum464087.82
Range26712.322
Interquartile range (IQR)5060.463

Descriptive statistics

Standard deviation4202.4517
Coefficient of variation (CV)0.0093510387
Kurtosis0.29566684
Mean449410.15
Median Absolute Deviation (MAD)2621.73
Skewness0.28075876
Sum4.3053492 × 108
Variance17660600
MonotonicityNot monotonic
2024-05-11T09:50:37.573536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
451203.416188 93
 
9.7%
448693.0 69
 
7.2%
445458.0 52
 
5.4%
443827.0 46
 
4.8%
451564.6 42
 
4.4%
449327.433 39
 
4.1%
452048.734 36
 
3.8%
447623.42 36
 
3.8%
448311.431 34
 
3.5%
455395.72 33
 
3.4%
Other values (58) 478
49.9%
ValueCountFrequency (%)
437375.5 1
 
0.1%
440315.288 6
 
0.6%
440375.123173 1
 
0.1%
440699.611315 3
 
0.3%
442266.3 15
1.6%
442303.8 15
1.6%
442360.774931 1
 
0.1%
442884.194 12
1.3%
443013.45831 15
1.6%
443236.323775 17
1.8%
ValueCountFrequency (%)
464087.821895 1
 
0.1%
462337.416 1
 
0.1%
461506.866 1
 
0.1%
461398.5 15
1.6%
460623.33 3
 
0.3%
459571.841 1
 
0.1%
459456.994 8
0.8%
458136.4 1
 
0.1%
456808.729705 1
 
0.1%
455488.33407 1
 
0.1%

조사지점
Text

Distinct68
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Memory size7.6 KiB
2024-05-11T09:50:37.961277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters4790
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

Unique14 ?
Unique (%)1.5%

Sample

1st rowp0198
2nd rowp0198
3rd rowp0198
4th rowp0198
5th rowp0198
ValueCountFrequency (%)
p0198 93
 
9.7%
p0050 69
 
7.2%
p0271 52
 
5.4%
p0246 42
 
4.4%
p0326 39
 
4.1%
p0340 36
 
3.8%
p0318 36
 
3.8%
p0329 34
 
3.5%
p0251 34
 
3.5%
p0339 33
 
3.4%
Other values (58) 490
51.1%
2024-05-11T09:50:38.775489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1262
26.3%
p 958
20.0%
2 480
 
10.0%
3 451
 
9.4%
1 428
 
8.9%
5 228
 
4.8%
7 214
 
4.5%
9 213
 
4.4%
6 199
 
4.2%
8 182
 
3.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3832
80.0%
Lowercase Letter 958
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1262
32.9%
2 480
 
12.5%
3 451
 
11.8%
1 428
 
11.2%
5 228
 
5.9%
7 214
 
5.6%
9 213
 
5.6%
6 199
 
5.2%
8 182
 
4.7%
4 175
 
4.6%
Lowercase Letter
ValueCountFrequency (%)
p 958
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3832
80.0%
Latin 958
 
20.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1262
32.9%
2 480
 
12.5%
3 451
 
11.8%
1 428
 
11.2%
5 228
 
5.9%
7 214
 
5.6%
9 213
 
5.6%
6 199
 
5.2%
8 182
 
4.7%
4 175
 
4.6%
Latin
ValueCountFrequency (%)
p 958
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4790
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1262
26.3%
p 958
20.0%
2 480
 
10.0%
3 451
 
9.4%
1 428
 
8.9%
5 228
 
4.8%
7 214
 
4.5%
9 213
 
4.4%
6 199
 
4.2%
8 182
 
3.8%

Interactions

2024-05-11T09:50:21.396886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:50:17.913071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:50:19.043910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:50:20.253806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:50:21.683963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:50:18.179128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:50:19.320786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:50:20.514740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:50:22.200670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:50:18.464070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:50:19.593843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:50:20.819802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:50:22.486194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:50:18.748123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:50:19.967277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:50:21.113406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-11T09:50:39.027241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
객체id종명과명서울시보호천연기념물고유종교란종외래종이동특성이동특성.1조사지역조사연도참고문헌x_valuey_value조사지점
객체id1.0000.7750.4700.1360.1270.1600.1380.1430.2150.7530.8360.7940.8490.6820.3890.836
종명0.7751.0001.0001.0001.0001.0001.0001.0001.0000.5290.0000.0000.7330.5200.3300.000
과명0.4701.0001.0000.9340.4580.1811.0000.4680.7650.0000.0000.0000.0000.2520.1540.000
서울시보호0.1361.0000.9341.0000.0000.0000.0000.0000.0110.1950.0000.0000.0000.1610.1200.000
천연기념물0.1271.0000.4580.0001.0000.0000.0000.0000.0000.0000.0000.0430.0900.1620.0000.000
고유종0.1601.0000.1810.0000.0001.0000.0000.1010.1160.1130.1780.0000.2250.0510.0330.178
교란종0.1381.0001.0000.0000.0000.0001.0000.5360.2240.0000.0000.0000.0000.0000.0000.000
외래종0.1431.0000.4680.0000.0000.1010.5361.0000.7060.0970.0000.0140.0000.0680.0000.000
이동특성0.2151.0000.7650.0110.0000.1160.2240.7061.0000.2790.0000.1420.3000.2210.0000.000
이동특성.10.7530.5290.0000.1950.0000.1130.0000.0970.2791.0001.0000.8860.9590.9860.9691.000
조사지역0.8360.0000.0000.0000.0000.1780.0000.0000.0001.0001.0000.9300.9941.0001.0001.000
조사연도0.7940.0000.0000.0000.0430.0000.0000.0140.1420.8860.9301.0000.9650.6770.4930.930
참고문헌0.8490.7330.0000.0000.0900.2250.0000.0000.3000.9590.9940.9651.0000.8970.8440.994
x_value0.6820.5200.2520.1610.1620.0510.0000.0680.2210.9861.0000.6770.8971.0000.7501.000
y_value0.3890.3300.1540.1200.0000.0330.0000.0000.0000.9691.0000.4930.8440.7501.0001.000
조사지점0.8360.0000.0000.0000.0000.1780.0000.0000.0001.0001.0000.9300.9941.0001.0001.000
2024-05-11T09:50:39.436705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
교란종고유종천연기념물이동특성.1이동특성과명참고문헌외래종서울시보호
교란종1.0000.0000.0000.0000.3660.9900.0000.3600.000
고유종0.0001.0000.0000.0880.1920.1570.1950.0640.000
천연기념물0.0000.0001.0000.0000.0000.3990.0780.0000.000
이동특성.10.0000.0880.0001.0000.1430.0000.6420.0750.152
이동특성0.3660.1920.0000.1431.0000.4950.1430.9510.017
과명0.9900.1570.3990.0000.4951.0000.0000.4080.889
참고문헌0.0000.1950.0780.6420.1430.0001.0000.0000.000
외래종0.3600.0640.0000.0750.9510.4080.0001.0000.000
서울시보호0.0000.0000.0000.1520.0170.8890.0000.0001.000
2024-05-11T09:50:39.758399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
객체id조사연도x_valuey_value과명서울시보호천연기념물고유종교란종외래종이동특성이동특성.1참고문헌
객체id1.000-0.3110.160-0.1210.1920.1040.0970.1220.1050.1090.1310.3770.518
조사연도-0.3111.000-0.151-0.0030.0000.0000.0140.0000.0000.0230.0930.6030.837
x_value0.160-0.1511.000-0.4050.1060.1490.1250.0450.0000.0480.1350.8940.592
y_value-0.121-0.003-0.4051.0000.0290.1150.0000.0130.0000.0000.0000.8380.503
과명0.1920.0000.1060.0291.0000.8890.3990.1570.9900.4080.4950.0000.000
서울시보호0.1040.0000.1490.1150.8891.0000.0000.0000.0000.0000.0170.1520.000
천연기념물0.0970.0140.1250.0000.3990.0001.0000.0000.0000.0000.0000.0000.078
고유종0.1220.0000.0450.0130.1570.0000.0001.0000.0000.0640.1920.0880.195
교란종0.1050.0000.0000.0000.9900.0000.0000.0001.0000.3600.3660.0000.000
외래종0.1090.0230.0480.0000.4080.0000.0000.0640.3601.0000.9510.0750.000
이동특성0.1310.0930.1350.0000.4950.0170.0000.1920.3660.9511.0000.1430.143
이동특성.10.3770.6030.8940.8380.0000.1520.0000.0880.0000.0750.1431.0000.642
참고문헌0.5180.8370.5920.5030.0000.0000.0780.1950.0000.0000.1430.6421.000

Missing values

2024-05-11T09:50:23.013116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-11T09:50:23.883616image/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

객체id종명과명서울시보호천연기념물고유종교란종외래종이동특성이동특성.1조사지역조사연도조사출처참고문헌x_valuey_value조사지점
032637누치잉어과00000회유성마포구월드컵공원2010문헌조사월드컵공원 자연생태계 모니터링190514.829981451203.416188p0198
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