Overview

Dataset statistics

Number of variables13
Number of observations113
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory12.5 KiB
Average record size in memory113.2 B

Variable types

Text4
Numeric8
Categorical1

Dataset

Description어선이 안전하게 출입 정박하고 어획물의 양륙, 선수품의 공급 및 기상악화 시 어선이 안전 대피할 수 있는 어업활동의 근거지인 어항에 관한 정보
Author해양수산부
URLhttps://www.data.go.kr/data/3083027/fileData.do

Alerts

전체가구 is highly overall correlated with 전체인구 and 2 other fieldsHigh correlation
전체인구 is highly overall correlated with 전체가구 and 2 other fieldsHigh correlation
어업가구 is highly overall correlated with 전체가구 and 2 other fieldsHigh correlation
배후어업인구 is highly overall correlated with 전체가구 and 2 other fieldsHigh correlation
어항명 has unique valuesUnique

Reproduction

Analysis started2023-12-12 04:46:48.963288
Analysis finished2023-12-12 04:46:56.663286
Duration7.7 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

어항명
Text

UNIQUE 

Distinct113
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
2023-12-12T13:46:56.949072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length3
Mean length3.4513274
Min length3

Characters and Unicode

Total characters390
Distinct characters116
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

Unique113 ?
Unique (%)100.0%

Sample

1st row다대포항
2nd row천성항
3rd row대변항
4th row어유정항
5th row덕적도항
ValueCountFrequency (%)
다대포항 1
 
0.9%
소안항 1
 
0.9%
감포항 1
 
0.9%
양포항 1
 
0.9%
대보항 1
 
0.9%
구계항 1
 
0.9%
축산항 1
 
0.9%
대진항 1
 
0.9%
구산항 1
 
0.9%
사동항 1
 
0.9%
Other values (103) 103
91.2%
2023-12-12T13:46:57.456715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
항 114
29.2%
포 22
 
5.6%
도 18
 
4.6%
진 12
 
3.1%
산 8
 
2.1%
남 8
 
2.1%
대 8
 
2.1%
전 6
 
1.5%
천 5
 
1.3%
( 5
 
1.3%
Other values (106) 184
47.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 380
97.4%
Open Punctuation 5
 
1.3%
Close Punctuation 5
 
1.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
항 114
30.0%
포 22
 
5.8%
도 18
 
4.7%
진 12
 
3.2%
산 8
 
2.1%
남 8
 
2.1%
대 8
 
2.1%
전 6
 
1.6%
천 5
 
1.3%
동 5
 
1.3%
Other values (104) 174
45.8%
Open Punctuation
ValueCountFrequency (%)
( 5
100.0%
Close Punctuation
ValueCountFrequency (%)
) 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 380
97.4%
Common 10
 
2.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
항 114
30.0%
포 22
 
5.8%
도 18
 
4.7%
진 12
 
3.2%
산 8
 
2.1%
남 8
 
2.1%
대 8
 
2.1%
전 6
 
1.6%
천 5
 
1.3%
동 5
 
1.3%
Other values (104) 174
45.8%
Common
ValueCountFrequency (%)
( 5
50.0%
) 5
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 380
97.4%
ASCII 10
 
2.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
항 114
30.0%
포 22
 
5.8%
도 18
 
4.7%
진 12
 
3.2%
산 8
 
2.1%
남 8
 
2.1%
대 8
 
2.1%
전 6
 
1.6%
천 5
 
1.3%
동 5
 
1.3%
Other values (104) 174
45.8%
ASCII
ValueCountFrequency (%)
( 5
50.0%
) 5
50.0%

연도
Real number (ℝ)

Distinct17
Distinct (%)15.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1983.531
Minimum1971
Maximum2017
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-12T13:46:57.591374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1971
5-th percentile1971
Q11971
median1983
Q31991
95-th percentile2008
Maximum2017
Range46
Interquartile range (IQR)20

Descriptive statistics

Standard deviation12.806995
Coefficient of variation (CV)0.006456665
Kurtosis-1.0931823
Mean1983.531
Median Absolute Deviation (MAD)12
Skewness0.43492821
Sum224139
Variance164.01912
MonotonicityNot monotonic
2023-12-12T13:46:57.711859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
1971 48
42.5%
1991 22
19.5%
1999 13
 
11.5%
1986 6
 
5.3%
2008 6
 
5.3%
1995 5
 
4.4%
1978 2
 
1.8%
1972 2
 
1.8%
2001 1
 
0.9%
2017 1
 
0.9%
Other values (7) 7
 
6.2%
ValueCountFrequency (%)
1971 48
42.5%
1972 2
 
1.8%
1975 1
 
0.9%
1976 1
 
0.9%
1978 2
 
1.8%
1979 1
 
0.9%
1981 1
 
0.9%
1983 1
 
0.9%
1986 6
 
5.3%
1991 22
19.5%
ValueCountFrequency (%)
2017 1
 
0.9%
2008 6
 
5.3%
2001 1
 
0.9%
1999 13
11.5%
1998 1
 
0.9%
1995 5
 
4.4%
1993 1
 
0.9%
1991 22
19.5%
1986 6
 
5.3%
1983 1
 
0.9%

어항주소
Text

Distinct112
Distinct (%)99.1%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
2023-12-12T13:46:58.063845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length32
Median length28
Mean length21.424779
Min length15

Characters and Unicode

Total characters2421
Distinct characters182
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

Unique111 ?
Unique (%)98.2%

Sample

1st row부산광역시 사하구 다대로605번길 67
2nd row부산광역시 강서구 가덕해안로 761
3rd row부산광역시 기장군 기장읍 기장해안로 623
4th row인천광역시 강화군 삼산면 어류정길177번길 117
5th row인천광역시 옹진군 덕적면 덕적북로518번길 77
ValueCountFrequency (%)
전라남도 33
 
6.0%
경상남도 20
 
3.6%
강원도 14
 
2.5%
경상북도 14
 
2.5%
완도군 8
 
1.4%
충청남도 8
 
1.4%
전라북도 7
 
1.3%
거제시 7
 
1.3%
여수시 6
 
1.1%
고성군 6
 
1.1%
Other values (340) 431
77.8%
2023-12-12T13:46:58.637803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
446
 
18.4%
도 137
 
5.7%
1 85
 
3.5%
면 81
 
3.3%
남 80
 
3.3%
길 74
 
3.1%
군 64
 
2.6%
시 60
 
2.5%
2 50
 
2.1%
- 43
 
1.8%
Other values (172) 1301
53.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1572
64.9%
Space Separator 446
 
18.4%
Decimal Number 360
 
14.9%
Dash Punctuation 43
 
1.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 137
 
8.7%
면 81
 
5.2%
남 80
 
5.1%
길 74
 
4.7%
군 64
 
4.1%
시 60
 
3.8%
로 42
 
2.7%
전 41
 
2.6%
라 41
 
2.6%
경 37
 
2.4%
Other values (160) 915
58.2%
Decimal Number
ValueCountFrequency (%)
1 85
23.6%
2 50
13.9%
6 38
10.6%
4 37
10.3%
7 32
 
8.9%
3 31
 
8.6%
0 29
 
8.1%
5 24
 
6.7%
9 18
 
5.0%
8 16
 
4.4%
Space Separator
ValueCountFrequency (%)
446
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 43
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1572
64.9%
Common 849
35.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 137
 
8.7%
면 81
 
5.2%
남 80
 
5.1%
길 74
 
4.7%
군 64
 
4.1%
시 60
 
3.8%
로 42
 
2.7%
전 41
 
2.6%
라 41
 
2.6%
경 37
 
2.4%
Other values (160) 915
58.2%
Common
ValueCountFrequency (%)
446
52.5%
1 85
 
10.0%
2 50
 
5.9%
- 43
 
5.1%
6 38
 
4.5%
4 37
 
4.4%
7 32
 
3.8%
3 31
 
3.7%
0 29
 
3.4%
5 24
 
2.8%
Other values (2) 34
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1572
64.9%
ASCII 849
35.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
446
52.5%
1 85
 
10.0%
2 50
 
5.9%
- 43
 
5.1%
6 38
 
4.5%
4 37
 
4.4%
7 32
 
3.8%
3 31
 
3.7%
0 29
 
3.4%
5 24
 
2.8%
Other values (2) 34
 
4.0%
Hangul
ValueCountFrequency (%)
도 137
 
8.7%
면 81
 
5.2%
남 80
 
5.1%
길 74
 
4.7%
군 64
 
4.1%
시 60
 
3.8%
로 42
 
2.7%
전 41
 
2.6%
라 41
 
2.6%
경 37
 
2.4%
Other values (160) 915
58.2%

위도
Real number (ℝ)

Distinct100
Distinct (%)88.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.631593
Minimum33.22
Maximum38.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-12T13:46:58.838659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum33.22
5-th percentile34.026
Q134.52
median35.1
Q336.78
95-th percentile37.996
Maximum38.5
Range5.28
Interquartile range (IQR)2.26

Descriptive statistics

Standard deviation1.3488809
Coefficient of variation (CV)0.037856317
Kurtosis-0.925589
Mean35.631593
Median Absolute Deviation (MAD)0.76
Skewness0.48058389
Sum4026.37
Variance1.8194796
MonotonicityNot monotonic
2023-12-12T13:46:59.008900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.62 3
 
2.7%
34.48 3
 
2.7%
36.08 2
 
1.8%
34.8 2
 
1.8%
34.73 2
 
1.8%
34.43 2
 
1.8%
34.18 2
 
1.8%
34.37 2
 
1.8%
34.52 2
 
1.8%
34.34 2
 
1.8%
Other values (90) 91
80.5%
ValueCountFrequency (%)
33.22 1
0.9%
33.27 1
0.9%
33.51 1
0.9%
33.56 1
0.9%
33.94 1
0.9%
33.99 1
0.9%
34.05 1
0.9%
34.13 1
0.9%
34.18 2
1.8%
34.24 1
0.9%
ValueCountFrequency (%)
38.5 1
0.9%
38.45 1
0.9%
38.35 1
0.9%
38.27 1
0.9%
38.17 1
0.9%
38.08 1
0.9%
37.94 1
0.9%
37.84 1
0.9%
37.83 1
0.9%
37.77 1
0.9%

경도
Real number (ℝ)

Distinct95
Distinct (%)84.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.71071
Minimum124.72
Maximum130.91
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-12T13:46:59.136426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum124.72
5-th percentile126.056
Q1126.48
median127.72
Q3128.72
95-th percentile129.486
Maximum130.91
Range6.19
Interquartile range (IQR)2.24

Descriptive statistics

Standard deviation1.3367596
Coefficient of variation (CV)0.010467091
Kurtosis-0.86571602
Mean127.71071
Median Absolute Deviation (MAD)1.18
Skewness0.20851693
Sum14431.31
Variance1.7869263
MonotonicityNot monotonic
2023-12-12T13:46:59.272124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.47 3
 
2.7%
129.45 3
 
2.7%
126.13 2
 
1.8%
128.05 2
 
1.8%
129.47 2
 
1.8%
129.42 2
 
1.8%
128.63 2
 
1.8%
127.8 2
 
1.8%
128.5 2
 
1.8%
127.72 2
 
1.8%
Other values (85) 91
80.5%
ValueCountFrequency (%)
124.72 1
0.9%
125.13 1
0.9%
125.86 1
0.9%
125.92 1
0.9%
125.98 1
0.9%
126.02 1
0.9%
126.08 1
0.9%
126.12 2
1.8%
126.13 2
1.8%
126.14 1
0.9%
ValueCountFrequency (%)
130.91 1
 
0.9%
130.83 2
1.8%
129.56 1
 
0.9%
129.52 1
 
0.9%
129.51 1
 
0.9%
129.47 2
1.8%
129.45 3
2.7%
129.43 2
1.8%
129.42 2
1.8%
129.38 1
 
0.9%

인근어항명
Text

Distinct109
Distinct (%)96.5%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
2023-12-12T13:46:59.551199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length3
Mean length3.5044248
Min length3

Characters and Unicode

Total characters396
Distinct characters123
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

Unique105 ?
Unique (%)92.9%

Sample

1st row눌차항
2nd row대항항
3rd row신암항
4th row장곳항
5th row서포리항
ValueCountFrequency (%)
국화항 2
 
1.8%
기성항 2
 
1.8%
대진항(삼척 2
 
1.8%
서외항 2
 
1.8%
죽포항 1
 
0.9%
골장항 1
 
0.9%
가곡항 1
 
0.9%
전촌항 1
 
0.9%
모포항 1
 
0.9%
대보1리항 1
 
0.9%
Other values (99) 99
87.6%
2023-12-12T13:47:00.010632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
항 116
29.3%
도 13
 
3.3%
포 10
 
2.5%
진 9
 
2.3%
( 8
 
2.0%
) 8
 
2.0%
동 8
 
2.0%
산 7
 
1.8%
대 7
 
1.8%
정 5
 
1.3%
Other values (113) 205
51.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 377
95.2%
Open Punctuation 8
 
2.0%
Close Punctuation 8
 
2.0%
Decimal Number 3
 
0.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
항 116
30.8%
도 13
 
3.4%
포 10
 
2.7%
진 9
 
2.4%
동 8
 
2.1%
산 7
 
1.9%
대 7
 
1.9%
정 5
 
1.3%
사 5
 
1.3%
하 5
 
1.3%
Other values (109) 192
50.9%
Decimal Number
ValueCountFrequency (%)
1 2
66.7%
2 1
33.3%
Open Punctuation
ValueCountFrequency (%)
( 8
100.0%
Close Punctuation
ValueCountFrequency (%)
) 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 377
95.2%
Common 19
 
4.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
항 116
30.8%
도 13
 
3.4%
포 10
 
2.7%
진 9
 
2.4%
동 8
 
2.1%
산 7
 
1.9%
대 7
 
1.9%
정 5
 
1.3%
사 5
 
1.3%
하 5
 
1.3%
Other values (109) 192
50.9%
Common
ValueCountFrequency (%)
( 8
42.1%
) 8
42.1%
1 2
 
10.5%
2 1
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 377
95.2%
ASCII 19
 
4.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
항 116
30.8%
도 13
 
3.4%
포 10
 
2.7%
진 9
 
2.4%
동 8
 
2.1%
산 7
 
1.9%
대 7
 
1.9%
정 5
 
1.3%
사 5
 
1.3%
하 5
 
1.3%
Other values (109) 192
50.9%
ASCII
ValueCountFrequency (%)
( 8
42.1%
) 8
42.1%
1 2
 
10.5%
2 1
 
5.3%

인근어항항종
Categorical

Distinct3
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
지방어항
51 
어촌정주어항
50 
국가어항
12 

Length

Max length6
Median length4
Mean length4.8849558
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row지방어항
2nd row지방어항
3rd row지방어항
4th row지방어항
5th row어촌정주어항

Common Values

ValueCountFrequency (%)
지방어항 51
45.1%
어촌정주어항 50
44.2%
국가어항 12
 
10.6%

Length

2023-12-12T13:47:00.164573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T13:47:00.287094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
지방어항 51
45.1%
어촌정주어항 50
44.2%
국가어항 12
 
10.6%

인근어항과의거리
Real number (ℝ)

Distinct72
Distinct (%)63.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.7327434
Minimum0.5
Maximum35.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-12T13:47:00.420694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.5
5-th percentile1
Q12.1
median3.9
Q36.6
95-th percentile14.58
Maximum35.8
Range35.3
Interquartile range (IQR)4.5

Descriptive statistics

Standard deviation6.1115295
Coefficient of variation (CV)1.0660742
Kurtosis9.7378446
Mean5.7327434
Median Absolute Deviation (MAD)2
Skewness2.7897356
Sum647.8
Variance37.350793
MonotonicityNot monotonic
2023-12-12T13:47:00.566707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.5 4
 
3.5%
1.0 4
 
3.5%
2.8 4
 
3.5%
2.5 4
 
3.5%
2.0 4
 
3.5%
2.6 4
 
3.5%
2.2 3
 
2.7%
4.3 3
 
2.7%
6.2 3
 
2.7%
11.6 2
 
1.8%
Other values (62) 78
69.0%
ValueCountFrequency (%)
0.5 1
 
0.9%
0.7 2
1.8%
0.8 1
 
0.9%
0.9 1
 
0.9%
1.0 4
3.5%
1.2 2
1.8%
1.3 2
1.8%
1.4 1
 
0.9%
1.5 2
1.8%
1.6 2
1.8%
ValueCountFrequency (%)
35.8 1
0.9%
35.1 1
0.9%
26.0 1
0.9%
21.5 1
0.9%
21.3 1
0.9%
15.0 1
0.9%
14.3 1
0.9%
14.2 1
0.9%
14.0 1
0.9%
12.9 1
0.9%

전체가구
Real number (ℝ)

HIGH CORRELATION 

Distinct108
Distinct (%)95.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1102.8584
Minimum18
Maximum38775
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-12T13:47:00.735789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile49.6
Q1145
median296
Q3566
95-th percentile3050.8
Maximum38775
Range38757
Interquartile range (IQR)421

Descriptive statistics

Standard deviation4146.7782
Coefficient of variation (CV)3.7600277
Kurtosis66.121753
Mean1102.8584
Median Absolute Deviation (MAD)173
Skewness7.831306
Sum124623
Variance17195769
MonotonicityNot monotonic
2023-12-12T13:47:00.882749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
564 2
 
1.8%
250 2
 
1.8%
300 2
 
1.8%
95 2
 
1.8%
287 2
 
1.8%
2617 1
 
0.9%
566 1
 
0.9%
697 1
 
0.9%
132 1
 
0.9%
681 1
 
0.9%
Other values (98) 98
86.7%
ValueCountFrequency (%)
18 1
0.9%
22 1
0.9%
33 1
0.9%
35 1
0.9%
40 1
0.9%
49 1
0.9%
50 1
0.9%
61 1
0.9%
64 1
0.9%
70 1
0.9%
ValueCountFrequency (%)
38775 1
0.9%
21035 1
0.9%
4800 1
0.9%
3797 1
0.9%
3474 1
0.9%
3091 1
0.9%
3024 1
0.9%
3000 1
0.9%
2642 1
0.9%
2617 1
0.9%

전체인구
Real number (ℝ)

HIGH CORRELATION 

Distinct107
Distinct (%)94.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2647.1327
Minimum33
Maximum106672
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-12T13:47:01.041205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum33
5-th percentile99.6
Q1304
median591
Q31205
95-th percentile7099.2
Maximum106672
Range106639
Interquartile range (IQR)901

Descriptive statistics

Standard deviation11055.398
Coefficient of variation (CV)4.1763671
Kurtosis73.799961
Mean2647.1327
Median Absolute Deviation (MAD)366
Skewness8.2683837
Sum299126
Variance1.2222183 × 108
MonotonicityNot monotonic
2023-12-12T13:47:01.274226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
508 2
 
1.8%
197 2
 
1.8%
1450 2
 
1.8%
915 2
 
1.8%
760 2
 
1.8%
400 2
 
1.8%
558 1
 
0.9%
1210 1
 
0.9%
6501 1
 
0.9%
1205 1
 
0.9%
Other values (97) 97
85.8%
ValueCountFrequency (%)
33 1
0.9%
40 1
0.9%
42 1
0.9%
62 1
0.9%
82 1
0.9%
90 1
0.9%
106 1
0.9%
124 1
0.9%
134 1
0.9%
150 1
0.9%
ValueCountFrequency (%)
106672 1
0.9%
49466 1
0.9%
13000 1
0.9%
8174 1
0.9%
7897 1
0.9%
7248 1
0.9%
7000 1
0.9%
6501 1
0.9%
6442 1
0.9%
6035 1
0.9%

어업가구
Real number (ℝ)

HIGH CORRELATION 

Distinct96
Distinct (%)85.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean173.22124
Minimum11
Maximum1138
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-12T13:47:01.419203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile24.8
Q160
median105
Q3214
95-th percentile496.4
Maximum1138
Range1127
Interquartile range (IQR)154

Descriptive statistics

Standard deviation185.34282
Coefficient of variation (CV)1.0699774
Kurtosis9.1102589
Mean173.22124
Median Absolute Deviation (MAD)61
Skewness2.6593947
Sum19574
Variance34351.96
MonotonicityNot monotonic
2023-12-12T13:47:01.557728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 6
 
5.3%
40 2
 
1.8%
96 2
 
1.8%
101 2
 
1.8%
105 2
 
1.8%
98 2
 
1.8%
55 2
 
1.8%
56 2
 
1.8%
75 2
 
1.8%
112 2
 
1.8%
Other values (86) 89
78.8%
ValueCountFrequency (%)
11 1
0.9%
12 2
1.8%
14 1
0.9%
20 1
0.9%
23 1
0.9%
26 1
0.9%
28 1
0.9%
29 1
0.9%
30 1
0.9%
32 1
0.9%
ValueCountFrequency (%)
1138 1
0.9%
982 1
0.9%
702 1
0.9%
663 1
0.9%
602 1
0.9%
563 1
0.9%
452 1
0.9%
420 1
0.9%
418 1
0.9%
412 1
0.9%

배후어업인구
Real number (ℝ)

HIGH CORRELATION 

Distinct97
Distinct (%)85.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean275.12389
Minimum23
Maximum1901
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-12T13:47:01.686485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum23
5-th percentile31.2
Q198
median160
Q3358
95-th percentile845.6
Maximum1901
Range1878
Interquartile range (IQR)260

Descriptive statistics

Standard deviation303.71942
Coefficient of variation (CV)1.1039369
Kurtosis9.2710437
Mean275.12389
Median Absolute Deviation (MAD)93
Skewness2.6473569
Sum31089
Variance92245.485
MonotonicityNot monotonic
2023-12-12T13:47:01.830632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
97 3
 
2.7%
98 2
 
1.8%
35 2
 
1.8%
51 2
 
1.8%
230 2
 
1.8%
217 2
 
1.8%
70 2
 
1.8%
30 2
 
1.8%
108 2
 
1.8%
23 2
 
1.8%
Other values (87) 92
81.4%
ValueCountFrequency (%)
23 2
1.8%
27 1
0.9%
29 1
0.9%
30 2
1.8%
32 1
0.9%
35 2
1.8%
38 1
0.9%
39 1
0.9%
40 1
0.9%
41 1
0.9%
ValueCountFrequency (%)
1901 1
0.9%
1554 1
0.9%
1081 1
0.9%
1070 1
0.9%
880 1
0.9%
866 1
0.9%
832 1
0.9%
795 1
0.9%
790 1
0.9%
664 1
0.9%

어촌계명
Text

Distinct112
Distinct (%)99.1%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
2023-12-12T13:47:02.241453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length34
Median length27
Mean length6.2212389
Min length2

Characters and Unicode

Total characters703
Distinct characters159
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

Unique111 ?
Unique (%)98.2%

Sample

1st row다대, 홍치
2nd row천성
3rd row대변, 서암, 신암
4th row매음, 사하동
5th row덕적
ValueCountFrequency (%)
연도 2
 
0.9%
선창 2
 
0.9%
서부 2
 
0.9%
사동 2
 
0.9%
남당 2
 
0.9%
대포 2
 
0.9%
다대 2
 
0.9%
남촌 2
 
0.9%
모곡 1
 
0.5%
대진3리 1
 
0.5%
Other values (193) 193
91.5%
2023-12-12T13:47:02.817994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
104
 
14.8%
, 97
 
13.8%
리 30
 
4.3%
동 18
 
2.6%
도 17
 
2.4%
포 17
 
2.4%
구 15
 
2.1%
대 15
 
2.1%
진 15
 
2.1%
산 13
 
1.8%
Other values (149) 362
51.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 481
68.4%
Space Separator 104
 
14.8%
Other Punctuation 97
 
13.8%
Decimal Number 21
 
3.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
리 30
 
6.2%
동 18
 
3.7%
도 17
 
3.5%
포 17
 
3.5%
구 15
 
3.1%
대 15
 
3.1%
진 15
 
3.1%
산 13
 
2.7%
신 10
 
2.1%
장 9
 
1.9%
Other values (144) 322
66.9%
Decimal Number
ValueCountFrequency (%)
2 10
47.6%
1 8
38.1%
3 3
 
14.3%
Space Separator
ValueCountFrequency (%)
104
100.0%
Other Punctuation
ValueCountFrequency (%)
, 97
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 481
68.4%
Common 222
31.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
리 30
 
6.2%
동 18
 
3.7%
도 17
 
3.5%
포 17
 
3.5%
구 15
 
3.1%
대 15
 
3.1%
진 15
 
3.1%
산 13
 
2.7%
신 10
 
2.1%
장 9
 
1.9%
Other values (144) 322
66.9%
Common
ValueCountFrequency (%)
104
46.8%
, 97
43.7%
2 10
 
4.5%
1 8
 
3.6%
3 3
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 481
68.4%
ASCII 222
31.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
104
46.8%
, 97
43.7%
2 10
 
4.5%
1 8
 
3.6%
3 3
 
1.4%
Hangul
ValueCountFrequency (%)
리 30
 
6.2%
동 18
 
3.7%
도 17
 
3.5%
포 17
 
3.5%
구 15
 
3.1%
대 15
 
3.1%
진 15
 
3.1%
산 13
 
2.7%
신 10
 
2.1%
장 9
 
1.9%
Other values (144) 322
66.9%

Interactions

2023-12-12T13:46:55.427823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:46:49.828774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:46:50.440637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:46:51.115646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:46:51.967547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:46:52.875412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:46:53.785241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:46:54.650173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:46:55.513721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:46:49.894867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:46:50.510693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:46:51.201879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:46:52.093395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:46:52.972895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:46:53.887318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:46:54.739155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:46:55.604157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:46:49.966213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:46:50.580165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:46:51.295784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:46:52.215652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:46:53.077683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:46:54.000736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:46:54.818100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:46:55.697695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:46:50.043678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:46:50.651768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:46:51.404657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:46:52.341081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:46:53.194822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:46:54.108970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:46:54.910160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:46:56.019577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:46:50.125679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:46:50.746317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:46:51.518022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:46:52.438568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:46:53.298879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:46:54.234533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:46:55.035700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:46:56.114322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:46:50.200965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:46:50.841542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:46:51.625636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:46:52.549105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:46:53.420061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:46:54.333287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:46:55.143034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:46:56.196068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:46:50.268092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:46:50.920793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:46:51.725339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:46:52.644885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:46:53.523768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:46:54.427826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:46:55.235827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:46:56.272585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:46:50.351831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:46:50.997534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:46:51.853307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:46:52.753554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:46:53.649943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:46:54.551581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:46:55.321612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T13:47:02.942296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도위도경도인근어항항종인근어항과의거리전체가구전체인구어업가구배후어업인구
연도1.0000.3780.2880.4620.4811.0001.0000.5890.482
위도0.3781.0000.6610.4560.3520.0000.0000.0610.000
경도0.2880.6611.0000.5300.4710.0000.0000.2180.000
인근어항항종0.4620.4560.5301.0000.5280.0000.0000.0000.000
인근어항과의거리0.4810.3520.4710.5281.0000.7240.7240.0000.000
전체가구1.0000.0000.0000.0000.7241.0001.0000.0000.000
전체인구1.0000.0000.0000.0000.7241.0001.0000.0000.000
어업가구0.5890.0610.2180.0000.0000.0000.0001.0000.911
배후어업인구0.4820.0000.0000.0000.0000.0000.0000.9111.000
2023-12-12T13:47:03.109490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도위도경도인근어항과의거리전체가구전체인구어업가구배후어업인구인근어항항종
연도1.000-0.030-0.137-0.082-0.068-0.065-0.0360.0190.115
위도-0.0301.0000.340-0.0910.1530.1370.0820.0810.297
경도-0.1370.3401.000-0.3510.1230.101-0.083-0.1060.266
인근어항과의거리-0.082-0.091-0.3511.000-0.113-0.143-0.111-0.0830.384
전체가구-0.0680.1530.123-0.1131.0000.9600.7370.6970.000
전체인구-0.0650.1370.101-0.1430.9601.0000.7090.6980.000
어업가구-0.0360.082-0.083-0.1110.7370.7091.0000.9210.000
배후어업인구0.0190.081-0.106-0.0830.6970.6980.9211.0000.000
인근어항항종0.1150.2970.2660.3840.0000.0000.0000.0001.000

Missing values

2023-12-12T13:46:56.398762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T13:46:56.602621image/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

어항명연도어항주소위도경도인근어항명인근어항항종인근어항과의거리전체가구전체인구어업가구배후어업인구어촌계명
0다대포항1971부산광역시 사하구 다대로605번길 6735.05128.99눌차항지방어항11.626175602278539다대, 홍치
1천성항2008부산광역시 강서구 가덕해안로 76135.03128.82대항항지방어항2.2369662214215천성
2대변항1971부산광역시 기장군 기장읍 기장해안로 62335.13129.23신암항지방어항1.07353120243880대변, 서암, 신암
3어유정항1971인천광역시 강화군 삼산면 어류정길177번길 11737.64126.34장곳항지방어항2.210022510185매음, 사하동
4덕적도항1971인천광역시 옹진군 덕적면 덕적북로518번길 7737.25126.12서포리항어촌정주어항5.88681450111181덕적
5선진포항1999인천광역시 옹진군 대청면 대청로 22-237.83124.72옥죽포항지방어항2.8432757180228선진, 옥죽
6진두항1986인천광역시 옹진군 영흥면 내리37.15126.29광명항지방어항14.3302460359821081영흥
7소래포구항2017인천광역시 남동구 장도로 6737.4126.74선재항어촌정주어항21.338775106672152375소래, 월곶
8정자항1971울산광역시 북구 정자1길 60-1235.62129.45당사항지방어항5.7407777144430정자, 제전, 판지
9방어진항1971울산광역시 동구 성끝4길 135.48129.43주전항지방어항8.92103549466353460방어진, 일산
어항명연도어항주소위도경도인근어항명인근어항항종인근어항과의거리전체가구전체인구어업가구배후어업인구어촌계명
103매물도항1991경상남도 통영시 한산면 당금길 3334.65128.57내항항지방어항12.918421223매죽
104신수항1971경상남도 사천시 신수서길 1-4834.9128.08대구항어촌정주어항1.2151304112110신수도, 대구
105물건항1986경상남도 남해군 삼동면 동부대로1030번길 42-2634.79128.05은점항지방어항1.32985984444물건
106미조항1971경상남도 남해군 미조면 미조로 23534.71128.05답하항(남해)어촌정주어항0.57371705321465본촌, 사항, 팔랑, 답하
107노량항2008경상남도 하동군 금남면 노량해안길 16-134.95127.86송문항어촌정주어항0.7510997169230신노량 , 구노량, 송문
108김녕항1991제주특별자치도 제주시 구좌읍 구좌해안로 229-1633.56126.74동김녕항어촌정주어항1.911122788602664김녕
109도두항1976제주특별자치도 제주시 도두항길 1833.51126.47도두사수항어촌정주어항1.613633052100105도두
110신양항1971제주특별자치도 제주시 추자면 추자로 576-433.94126.33보옥항국가어항26.0296580101123신양, 예초, 묵
111모슬포항1971제주특별자치도 서귀포시 대정읍 하모항구로 3033.22126.25산이수동항어촌정주어항4.426426442301430하모
112위미항1986제주특별자치도 서귀포시 남원읍 위미중앙로196번길 6-1333.27126.66하효항국가어항5.51106270283105위리1리, 위미2리