Overview

Dataset statistics

Number of variables9
Number of observations174
Missing cells0
Missing cells (%)0.0%
Duplicate rows1
Duplicate rows (%)0.6%
Total size in memory12.7 KiB
Average record size in memory74.8 B

Variable types

Categorical3
Text3
Numeric2
DateTime1

Dataset

Description제주특별자치도 서귀포시 관내 지정벽보판 현황에 관한 데이터로 지역, 설치 위치, 규격, 위도, 경도 등 정보를 제공합니다.
Author제주특별자치도 서귀포시
URLhttps://www.data.go.kr/data/15056353/fileData.do

Alerts

데이터기준일자 has constant value ""Constant
Dataset has 1 (0.6%) duplicate rowsDuplicates
지역 is highly overall correlated with 경도 and 2 other fieldsHigh correlation
읍면동 is highly overall correlated with 위도 and 3 other fieldsHigh correlation
위도 is highly overall correlated with 경도 and 1 other fieldsHigh correlation
경도 is highly overall correlated with 위도 and 2 other fieldsHigh correlation
규격 is highly overall correlated with 읍면동 and 1 other fieldsHigh correlation

Reproduction

Analysis started2023-12-12 17:31:36.321442
Analysis finished2023-12-12 17:31:37.206423
Duration0.88 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

읍면동
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
읍면
122 
동
52 

Length

Max length2
Median length2
Mean length1.7011494
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
읍면 122
70.1%
동 52
29.9%

Length

2023-12-13T02:31:37.257670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:31:37.334458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
읍면 122
70.1%
동 52
29.9%

지역
Categorical

HIGH CORRELATION 

Distinct15
Distinct (%)8.6%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
남원읍
36 
안덕면
29 
성산읍
24 
대정읍
19 
표선면
14 
Other values (10)
52 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique1 ?
Unique (%)0.6%

Sample

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

Common Values

ValueCountFrequency (%)
남원읍 36
20.7%
안덕면 29
16.7%
성산읍 24
13.8%
대정읍 19
10.9%
표선면 14
 
8.0%
대천동 10
 
5.7%
중문동 7
 
4.0%
예래동 7
 
4.0%
대륜동 6
 
3.4%
효돈동 5
 
2.9%
Other values (5) 17
9.8%

Length

2023-12-13T02:31:37.413473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
남원읍 36
20.7%
안덕면 29
16.7%
성산읍 24
13.8%
대정읍 19
10.9%
표선면 14
 
8.0%
대천동 10
 
5.7%
중문동 7
 
4.0%
예래동 7
 
4.0%
대륜동 6
 
3.4%
효돈동 5
 
2.9%
Other values (5) 17
9.8%

도로명
Text

Distinct114
Distinct (%)65.5%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
2023-12-13T02:31:37.636699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length10
Mean length4.3850575
Min length3

Characters and Unicode

Total characters763
Distinct characters127
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

Unique84 ?
Unique (%)48.3%

Sample

1st row상모대서로
2nd row동일하모로
3rd row암반수마농로329번길
4th row중산간서로
5th row서삼중로
ValueCountFrequency (%)
태위로 10
 
5.6%
일주동로 9
 
5.1%
일주서로 5
 
2.8%
중산간동로 5
 
2.8%
중산간서로 4
 
2.2%
한창로 4
 
2.2%
화순서서로 3
 
1.7%
이어도로 3
 
1.7%
토평로 3
 
1.7%
남한로 3
 
1.7%
Other values (105) 129
72.5%
2023-12-13T02:31:37.985403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
로 174
22.8%
중 33
 
4.3%
서 32
 
4.2%
동 27
 
3.5%
신 22
 
2.9%
산 20
 
2.6%
번 18
 
2.4%
길 18
 
2.4%
일 17
 
2.2%
하 15
 
2.0%
Other values (117) 387
50.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 713
93.4%
Decimal Number 46
 
6.0%
Space Separator 4
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 174
24.4%
중 33
 
4.6%
서 32
 
4.5%
동 27
 
3.8%
신 22
 
3.1%
산 20
 
2.8%
번 18
 
2.5%
길 18
 
2.5%
일 17
 
2.4%
하 15
 
2.1%
Other values (106) 337
47.3%
Decimal Number
ValueCountFrequency (%)
2 10
21.7%
1 8
17.4%
9 6
13.0%
7 6
13.0%
4 6
13.0%
5 5
10.9%
0 2
 
4.3%
8 1
 
2.2%
3 1
 
2.2%
6 1
 
2.2%
Space Separator
ValueCountFrequency (%)
4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 713
93.4%
Common 50
 
6.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 174
24.4%
중 33
 
4.6%
서 32
 
4.5%
동 27
 
3.8%
신 22
 
3.1%
산 20
 
2.8%
번 18
 
2.5%
길 18
 
2.5%
일 17
 
2.4%
하 15
 
2.1%
Other values (106) 337
47.3%
Common
ValueCountFrequency (%)
2 10
20.0%
1 8
16.0%
9 6
12.0%
7 6
12.0%
4 6
12.0%
5 5
10.0%
4
 
8.0%
0 2
 
4.0%
8 1
 
2.0%
3 1
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 713
93.4%
ASCII 50
 
6.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
로 174
24.4%
중 33
 
4.6%
서 32
 
4.5%
동 27
 
3.8%
신 22
 
3.1%
산 20
 
2.8%
번 18
 
2.5%
길 18
 
2.5%
일 17
 
2.4%
하 15
 
2.1%
Other values (106) 337
47.3%
ASCII
ValueCountFrequency (%)
2 10
20.0%
1 8
16.0%
9 6
12.0%
7 6
12.0%
4 6
12.0%
5 5
10.0%
4
 
8.0%
0 2
 
4.0%
8 1
 
2.0%
3 1
 
2.0%

위치
Text

Distinct172
Distinct (%)98.9%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
2023-12-13T02:31:38.208232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length16
Mean length10.356322
Min length6

Characters and Unicode

Total characters1802
Distinct characters207
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique170 ?
Unique (%)97.7%

Sample

1st row상모1리 사무소
2nd row동일1리마을회관 앞
3rd row동일2리마을회관 앞
4th row무릉2리사무소
5th row무릉문화의집 앞
ValueCountFrequency (%)
앞 91
 
22.4%
옆 13
 
3.2%
사무소 13
 
3.2%
버스정류장 10
 
2.5%
마을회관 7
 
1.7%
맞은편 7
 
1.7%
내 5
 
1.2%
사거리 4
 
1.0%
하례1리 4
 
1.0%
안 3
 
0.7%
Other values (220) 249
61.3%
2023-12-13T02:31:38.576626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
353
 
19.6%
리 102
 
5.7%
앞 91
 
5.0%
사 79
 
4.4%
소 76
 
4.2%
무 66
 
3.7%
동 39
 
2.2%
1 31
 
1.7%
신 27
 
1.5%
마 26
 
1.4%
Other values (197) 912
50.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1362
75.6%
Space Separator 353
 
19.6%
Decimal Number 80
 
4.4%
Lowercase Letter 4
 
0.2%
Dash Punctuation 2
 
0.1%
Close Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
리 102
 
7.5%
앞 91
 
6.7%
사 79
 
5.8%
소 76
 
5.6%
무 66
 
4.8%
동 39
 
2.9%
신 27
 
2.0%
마 26
 
1.9%
회 25
 
1.8%
관 23
 
1.7%
Other values (181) 808
59.3%
Decimal Number
ValueCountFrequency (%)
1 31
38.8%
2 22
27.5%
3 8
 
10.0%
0 6
 
7.5%
6 4
 
5.0%
5 2
 
2.5%
8 2
 
2.5%
9 2
 
2.5%
7 2
 
2.5%
4 1
 
1.2%
Lowercase Letter
ValueCountFrequency (%)
m 2
50.0%
k 1
25.0%
s 1
25.0%
Space Separator
ValueCountFrequency (%)
353
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1362
75.6%
Common 436
 
24.2%
Latin 4
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
리 102
 
7.5%
앞 91
 
6.7%
사 79
 
5.8%
소 76
 
5.6%
무 66
 
4.8%
동 39
 
2.9%
신 27
 
2.0%
마 26
 
1.9%
회 25
 
1.8%
관 23
 
1.7%
Other values (181) 808
59.3%
Common
ValueCountFrequency (%)
353
81.0%
1 31
 
7.1%
2 22
 
5.0%
3 8
 
1.8%
0 6
 
1.4%
6 4
 
0.9%
5 2
 
0.5%
8 2
 
0.5%
9 2
 
0.5%
- 2
 
0.5%
Other values (3) 4
 
0.9%
Latin
ValueCountFrequency (%)
m 2
50.0%
k 1
25.0%
s 1
25.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1362
75.6%
ASCII 440
 
24.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
353
80.2%
1 31
 
7.0%
2 22
 
5.0%
3 8
 
1.8%
0 6
 
1.4%
6 4
 
0.9%
5 2
 
0.5%
8 2
 
0.5%
9 2
 
0.5%
- 2
 
0.5%
Other values (6) 8
 
1.8%
Hangul
ValueCountFrequency (%)
리 102
 
7.5%
앞 91
 
6.7%
사 79
 
5.8%
소 76
 
5.6%
무 66
 
4.8%
동 39
 
2.9%
신 27
 
2.0%
마 26
 
1.9%
회 25
 
1.8%
관 23
 
1.7%
Other values (181) 808
59.3%

소재지
Text

Distinct170
Distinct (%)97.7%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
2023-12-13T02:31:38.950795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length26
Mean length24.982759
Min length19

Characters and Unicode

Total characters4347
Distinct characters93
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

Unique166 ?
Unique (%)95.4%

Sample

1st row제주특별자치도 서귀포시 대정읍 상모리 2652-2
2nd row제주특별자치도 서귀포시 대정읍 동일리 2822-2
3rd row제주특별자치도 서귀포시 대정읍 동일리 273-4
4th row제주특별자치도 서귀포시 대정읍 무릉리 629-1
5th row제주특별자치도 서귀포시 대정읍 무릉리 3314-1
ValueCountFrequency (%)
제주특별자치도 174
21.3%
서귀포시 174
21.3%
남원읍 36
 
4.4%
안덕면 29
 
3.5%
성산읍 24
 
2.9%
대정읍 19
 
2.3%
표선면 14
 
1.7%
화순리 8
 
1.0%
하례리 7
 
0.9%
사계리 7
 
0.9%
Other values (232) 326
39.9%
2023-12-13T02:31:39.468338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
647
 
14.9%
서 185
 
4.3%
귀 181
 
4.2%
도 180
 
4.1%
시 176
 
4.0%
제 174
 
4.0%
특 174
 
4.0%
별 174
 
4.0%
자 174
 
4.0%
치 174
 
4.0%
Other values (83) 2108
48.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2805
64.5%
Decimal Number 750
 
17.3%
Space Separator 647
 
14.9%
Dash Punctuation 145
 
3.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
서 185
 
6.6%
귀 181
 
6.5%
도 180
 
6.4%
시 176
 
6.3%
제 174
 
6.2%
특 174
 
6.2%
별 174
 
6.2%
자 174
 
6.2%
치 174
 
6.2%
포 174
 
6.2%
Other values (71) 1039
37.0%
Decimal Number
ValueCountFrequency (%)
1 171
22.8%
2 108
14.4%
3 81
10.8%
4 79
10.5%
7 60
 
8.0%
6 54
 
7.2%
9 51
 
6.8%
8 49
 
6.5%
0 49
 
6.5%
5 48
 
6.4%
Space Separator
ValueCountFrequency (%)
647
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 145
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2805
64.5%
Common 1542
35.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
서 185
 
6.6%
귀 181
 
6.5%
도 180
 
6.4%
시 176
 
6.3%
제 174
 
6.2%
특 174
 
6.2%
별 174
 
6.2%
자 174
 
6.2%
치 174
 
6.2%
포 174
 
6.2%
Other values (71) 1039
37.0%
Common
ValueCountFrequency (%)
647
42.0%
1 171
 
11.1%
- 145
 
9.4%
2 108
 
7.0%
3 81
 
5.3%
4 79
 
5.1%
7 60
 
3.9%
6 54
 
3.5%
9 51
 
3.3%
8 49
 
3.2%
Other values (2) 97
 
6.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2805
64.5%
ASCII 1542
35.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
647
42.0%
1 171
 
11.1%
- 145
 
9.4%
2 108
 
7.0%
3 81
 
5.3%
4 79
 
5.1%
7 60
 
3.9%
6 54
 
3.5%
9 51
 
3.3%
8 49
 
3.2%
Other values (2) 97
 
6.3%
Hangul
ValueCountFrequency (%)
서 185
 
6.6%
귀 181
 
6.5%
도 180
 
6.4%
시 176
 
6.3%
제 174
 
6.2%
특 174
 
6.2%
별 174
 
6.2%
자 174
 
6.2%
치 174
 
6.2%
포 174
 
6.2%
Other values (71) 1039
37.0%

규격
Categorical

HIGH CORRELATION 

Distinct22
Distinct (%)12.6%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
2.7*2.1
101 
2.4*1.2
27 
3.0*2.2
 
5
2.2*1.9
 
5
2.7×2.1
 
5
Other values (17)
31 

Length

Max length8
Median length7
Mean length6.8505747
Min length3

Unique

Unique10 ?
Unique (%)5.7%

Sample

1st row2.7*2.1
2nd row2.7*2.1
3rd row2.7*2.1
4th row2.7*2.1
5th row2.7*2.1

Common Values

ValueCountFrequency (%)
2.7*2.1 101
58.0%
2.4*1.2 27
 
15.5%
3.0*2.2 5
 
2.9%
2.2*1.9 5
 
2.9%
2.7×2.1 5
 
2.9%
<NA> 4
 
2.3%
2*2 4
 
2.3%
1.0*2.5 4
 
2.3%
2.4*1.3 3
 
1.7%
2.7*1.9 2
 
1.1%
Other values (12) 14
 
8.0%

Length

2023-12-13T02:31:39.584529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2.7*2.1 101
58.0%
2.4*1.2 27
 
15.5%
3.0*2.2 5
 
2.9%
2.2*1.9 5
 
2.9%
2.7×2.1 5
 
2.9%
na 4
 
2.3%
2*2 4
 
2.3%
1.0*2.5 4
 
2.3%
2.7*1.9 4
 
2.3%
2.4*1.3 3
 
1.7%
Other values (11) 12
 
6.9%

위도
Real number (ℝ)

HIGH CORRELATION 

Distinct170
Distinct (%)97.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.29065
Minimum33.215629
Maximum33.474205
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-13T02:31:39.690646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum33.215629
5-th percentile33.230877
Q133.251718
median33.26755
Q333.307403
95-th percentile33.442291
Maximum33.474205
Range0.25857592
Interquartile range (IQR)0.055684792

Descriptive statistics

Standard deviation0.061112065
Coefficient of variation (CV)0.0018357126
Kurtosis1.6452743
Mean33.29065
Median Absolute Deviation (MAD)0.019622225
Skewness1.5622434
Sum5792.5731
Variance0.0037346845
MonotonicityNot monotonic
2023-12-13T02:31:39.804848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33.27109723 2
 
1.1%
33.31006665 2
 
1.1%
33.32826721 2
 
1.1%
33.26508687 2
 
1.1%
33.30872306 1
 
0.6%
33.38995599 1
 
0.6%
33.39067229 1
 
0.6%
33.41328113 1
 
0.6%
33.33211824 1
 
0.6%
33.30631354 1
 
0.6%
Other values (160) 160
92.0%
ValueCountFrequency (%)
33.21562901 1
0.6%
33.22025154 1
0.6%
33.22314232 1
0.6%
33.22421007 1
0.6%
33.22598953 1
0.6%
33.22637151 1
0.6%
33.22753375 1
0.6%
33.22853498 1
0.6%
33.23047633 1
0.6%
33.2310935 1
0.6%
ValueCountFrequency (%)
33.47420493 1
0.6%
33.47406784 1
0.6%
33.46417724 1
0.6%
33.46362823 1
0.6%
33.46148856 1
0.6%
33.46006644 1
0.6%
33.4458765 1
0.6%
33.44483648 1
0.6%
33.44263707 1
0.6%
33.44210496 1
0.6%

경도
Real number (ℝ)

HIGH CORRELATION 

Distinct170
Distinct (%)97.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.56243
Minimum126.18451
Maximum126.93405
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-13T02:31:39.924950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.18451
5-th percentile126.24982
Q1126.36659
median126.5661
Q3126.74085
95-th percentile126.90653
Maximum126.93405
Range0.749536
Interquartile range (IQR)0.37426197

Descriptive statistics

Standard deviation0.21390712
Coefficient of variation (CV)0.0016901313
Kurtosis-1.169722
Mean126.56243
Median Absolute Deviation (MAD)0.1925254
Skewness0.064982423
Sum22021.863
Variance0.045756257
MonotonicityNot monotonic
2023-12-13T02:31:40.071599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.5902216 2
 
1.1%
126.7143721 2
 
1.1%
126.7123115 2
 
1.1%
126.6407824 2
 
1.1%
126.7806244 1
 
0.6%
126.7977862 1
 
0.6%
126.8010008 1
 
0.6%
126.7719854 1
 
0.6%
126.7979895 1
 
0.6%
126.8017471 1
 
0.6%
Other values (160) 160
92.0%
ValueCountFrequency (%)
126.18451 1
0.6%
126.1917436 1
0.6%
126.1989618 1
0.6%
126.2029901 1
0.6%
126.2107624 1
0.6%
126.2347859 1
0.6%
126.2367864 1
0.6%
126.2397079 1
0.6%
126.2430704 1
0.6%
126.253458 1
0.6%
ValueCountFrequency (%)
126.934046 1
0.6%
126.9339317 1
0.6%
126.9308624 1
0.6%
126.9192662 1
0.6%
126.917446 1
0.6%
126.9164292 1
0.6%
126.9149889 1
0.6%
126.9120232 1
0.6%
126.9110612 1
0.6%
126.9040827 1
0.6%

데이터기준일자
Date

CONSTANT 

Distinct1
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
Minimum2023-10-30 00:00:00
Maximum2023-10-30 00:00:00
2023-12-13T02:31:40.181325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:31:40.512215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2023-12-13T02:31:36.887395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:31:36.743001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:31:36.950999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:31:36.806899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T02:31:40.568946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
읍면동지역규격위도경도
읍면동1.0001.0000.7000.6990.970
지역1.0001.0000.8810.7390.941
규격0.7000.8811.0000.0640.627
위도0.6990.7390.0641.0000.856
경도0.9700.9410.6270.8561.000
2023-12-13T02:31:40.663662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지역규격읍면동
지역1.0000.5040.961
규격0.5041.0000.590
읍면동0.9610.5901.000
2023-12-13T02:31:40.751222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
위도경도읍면동지역규격
위도1.0000.7910.5320.3760.000
경도0.7911.0000.8300.7060.276
읍면동0.5320.8301.0000.9610.590
지역0.3760.7060.9611.0000.504
규격0.0000.2760.5900.5041.000

Missing values

2023-12-13T02:31:37.054176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T02:31:37.168118image/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

읍면동지역도로명위치소재지규격위도경도데이터기준일자
0읍면대정읍상모대서로상모1리 사무소제주특별자치도 서귀포시 대정읍 상모리 2652-22.7*2.133.226372126.2694812023-10-30
1읍면대정읍동일하모로동일1리마을회관 앞제주특별자치도 서귀포시 대정읍 동일리 2822-22.7*2.133.228535126.243072023-10-30
2읍면대정읍암반수마농로329번길동일2리마을회관 앞제주특별자치도 서귀포시 대정읍 동일리 273-42.7*2.133.257837126.2367862023-10-30
3읍면대정읍중산간서로무릉2리사무소제주특별자치도 서귀포시 대정읍 무릉리 629-12.7*2.133.275145126.2347862023-10-30
4읍면대정읍서삼중로무릉문화의집 앞제주특별자치도 서귀포시 대정읍 무릉리 3314-12.7*2.133.27346126.1989622023-10-30
5읍면대정읍추사로보성상동 삼거리제주특별자치도 서귀포시 대정읍 보성리 1246-32.7*2.133.25261126.2729242023-10-30
6읍면대정읍상모로274번길상모2리사무소 앞제주특별자치도 서귀포시 대정읍 상모리 3792-12.7*2.133.22421126.2599442023-10-30
7읍면대정읍상모로279번길상모3리 비료창고 앞제주특별자치도 서귀포시 대정읍 상모리 4056-12.7*2.133.223142126.2596652023-10-30
8읍면대정읍도원로신도1리사무소 앞제주특별자치도 서귀포시 대정읍 신도리 1381-112.7*2.133.278431126.1917442023-10-30
9읍면대정읍비자낭로신도3리사무소 앞제주특별자치도 서귀포시 대정읍 신도리 2067-242.7*2.133.286701126.184512023-10-30
읍면동지역도로명위치소재지규격위도경도데이터기준일자
164동중문동중문상로 17번길중문오일시장 열린화장실 뒤제주특별자치도 서귀포시 중문동 2128-12*233.250296126.4240072023-10-30
165동중문동중문로41번길중문동 1583-4제주특별자치도 서귀포시 중문동 1583-42*233.255344126.4246142023-10-30
166동중문동중문상로중문동 1677-1제주특별자치도 서귀포시 중문동 1677-12*233.254868126.4276272023-10-30
167동예래동열리로정문동 사거리제주특별자치도 서귀포시 상예동 1309-22.4*1.233.249599126.3934712023-10-30
168동예래동색달로색달슈퍼 사거리제주특별자치도 서귀포시 색달동 1934-33.0*2.233.261518126.412432023-10-30
169동예래동하예로하예1동 마을회관 앞제주특별자치도 서귀포시 하예동 163-13.0*2.233.243646126.3862832023-10-30
170동예래동가가로상예2동 마을회관 앞제주특별자치도 서귀포시 상예동 4625-52.4*1.233.265555126.3773172023-10-30
171동예래동예래로하예하동 마을회관 앞제주특별자치도 서귀포시 하예동 1561-12.4*1.233.236031126.373422023-10-30
172동예래동예래로예래동 주민센터 앞제주특별자치도 서귀포시 상예동 518-42.4*1.233.254281126.397872023-10-30
173동예래동색달중앙로색달부녀회 창고 앞제주특별자치도 서귀포시 색달동 1927-12.4*1.233.260707126.4131532023-10-30

Duplicate rows

Most frequently occurring

읍면동지역도로명위치소재지규격위도경도데이터기준일자# duplicates
0읍면남원읍한신로의귀리 새마을금고 앞제주특별자치도 서귀포시 남원읍 의귀리 1483-152.7*2.133.310067126.7143722023-10-302