Overview

Dataset statistics

Number of variables6
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows3
Duplicate rows (%)< 0.1%
Total size in memory546.9 KiB
Average record size in memory56.0 B

Variable types

Categorical3
Text2
DateTime1

Dataset

Description관계법령 : 공간정보의 구축 및 관리 등에 관한 법률광주광역시 지적측량기준점 정보에 대한 데이터로 지적기준점의 명칭, 위치, 종류, 소재지 등에 관한 정보
Author광주광역시
URLhttps://www.data.go.kr/data/15001640/fileData.do

Alerts

좌표계 has constant value ""Constant
데이터기준일자 has constant value ""Constant
Dataset has 3 (< 0.1%) duplicate rowsDuplicates
점종류 is highly imbalanced (98.8%)Imbalance

Reproduction

Analysis started2024-03-14 20:05:49.301833
Analysis finished2024-03-14 20:05:50.301696
Duration1 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
광산구
3908 
북구
2159 
서구
1575 
남구
1301 
동구
1057 

Length

Max length3
Median length2
Mean length2.3908
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row광산구
2nd row광산구
3rd row북구
4th row광산구
5th row광산구

Common Values

ValueCountFrequency (%)
광산구 3908
39.1%
북구 2159
21.6%
서구 1575
15.8%
남구 1301
 
13.0%
동구 1057
 
10.6%

Length

2024-03-15T05:05:50.419515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T05:05:50.662914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
광산구 3908
39.1%
북구 2159
21.6%
서구 1575
15.8%
남구 1301
 
13.0%
동구 1057
 
10.6%

점종류
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
도근점
9982 
삼각보조점
 
17
삼각점
 
1

Length

Max length5
Median length3
Mean length3.0034
Min length3

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
도근점 9982
99.8%
삼각보조점 17
 
0.2%
삼각점 1
 
< 0.1%

Length

2024-03-15T05:05:51.017031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T05:05:51.363178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
도근점 9982
99.8%
삼각보조점 17
 
0.2%
삼각점 1
 
< 0.1%

기준점명
Text

Distinct7639
Distinct (%)76.4%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-03-15T05:05:52.978606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length4
Mean length4.2017
Min length1

Characters and Unicode

Total characters42017
Distinct characters16
Distinct categories3 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5735 ?
Unique (%)57.4%

Sample

1st row21917
2nd row21252
3rd row1722
4th row22431
5th row3431
ValueCountFrequency (%)
3565 4
 
< 0.1%
3036 4
 
< 0.1%
3115 4
 
< 0.1%
3524 4
 
< 0.1%
3260 4
 
< 0.1%
3479 4
 
< 0.1%
3517 4
 
< 0.1%
3228 4
 
< 0.1%
4022 4
 
< 0.1%
3283 4
 
< 0.1%
Other values (7629) 9960
99.6%
2024-03-15T05:05:55.261757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 6613
15.7%
1 5979
14.2%
3 4971
11.8%
0 4614
11.0%
8 3521
8.4%
4 3383
8.1%
5 3092
7.4%
7 3088
7.3%
6 2882
6.9%
9 2864
6.8%
Other values (6) 1010
 
2.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 41007
97.6%
Uppercase Letter 983
 
2.3%
Other Letter 27
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 6613
16.1%
1 5979
14.6%
3 4971
12.1%
0 4614
11.3%
8 3521
8.6%
4 3383
8.2%
5 3092
7.5%
7 3088
7.5%
6 2882
7.0%
9 2864
7.0%
Other Letter
ValueCountFrequency (%)
보 16
59.3%
북 9
33.3%
광 1
 
3.7%
주 1
 
3.7%
Uppercase Letter
ValueCountFrequency (%)
G 950
96.6%
W 33
 
3.4%

Most occurring scripts

ValueCountFrequency (%)
Common 41007
97.6%
Latin 983
 
2.3%
Hangul 27
 
0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
2 6613
16.1%
1 5979
14.6%
3 4971
12.1%
0 4614
11.3%
8 3521
8.6%
4 3383
8.2%
5 3092
7.5%
7 3088
7.5%
6 2882
7.0%
9 2864
7.0%
Hangul
ValueCountFrequency (%)
보 16
59.3%
북 9
33.3%
광 1
 
3.7%
주 1
 
3.7%
Latin
ValueCountFrequency (%)
G 950
96.6%
W 33
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 41990
99.9%
Hangul 27
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 6613
15.7%
1 5979
14.2%
3 4971
11.8%
0 4614
11.0%
8 3521
8.4%
4 3383
8.1%
5 3092
7.4%
7 3088
7.4%
6 2882
6.9%
9 2864
6.8%
Other values (2) 983
 
2.3%
Hangul
ValueCountFrequency (%)
보 16
59.3%
북 9
33.3%
광 1
 
3.7%
주 1
 
3.7%

소재지
Text

Distinct192
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-03-15T05:05:57.019146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length2.9611
Min length1

Characters and Unicode

Total characters29611
Distinct characters121
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

Unique2 ?
Unique (%)< 0.1%

Sample

1st row도덕동
2nd row삼도동
3rd row용봉동
4th row장덕동
5th row하남동
ValueCountFrequency (%)
우산동 209
 
2.1%
쌍촌동 200
 
2.0%
용봉동 183
 
1.8%
수완동 167
 
1.7%
장덕동 164
 
1.6%
두암동 163
 
1.6%
신창동 152
 
1.5%
지산동 150
 
1.5%
화정동 150
 
1.5%
풍암동 149
 
1.5%
Other values (181) 8310
83.1%
2024-03-15T05:05:59.157519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 10284
34.7%
산 1541
 
5.2%
월 886
 
3.0%
암 684
 
2.3%
용 672
 
2.3%
촌 552
 
1.9%
신 474
 
1.6%
곡 473
 
1.6%
정 449
 
1.5%
수 412
 
1.4%
Other values (111) 13184
44.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 29581
99.9%
Decimal Number 27
 
0.1%
Space Separator 3
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 10284
34.8%
산 1541
 
5.2%
월 886
 
3.0%
암 684
 
2.3%
용 672
 
2.3%
촌 552
 
1.9%
신 474
 
1.6%
곡 473
 
1.6%
정 449
 
1.5%
수 412
 
1.4%
Other values (105) 13154
44.5%
Decimal Number
ValueCountFrequency (%)
5 14
51.9%
4 6
22.2%
1 3
 
11.1%
2 3
 
11.1%
3 1
 
3.7%
Space Separator
ValueCountFrequency (%)
3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 29581
99.9%
Common 30
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 10284
34.8%
산 1541
 
5.2%
월 886
 
3.0%
암 684
 
2.3%
용 672
 
2.3%
촌 552
 
1.9%
신 474
 
1.6%
곡 473
 
1.6%
정 449
 
1.5%
수 412
 
1.4%
Other values (105) 13154
44.5%
Common
ValueCountFrequency (%)
5 14
46.7%
4 6
20.0%
1 3
 
10.0%
3
 
10.0%
2 3
 
10.0%
3 1
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 29581
99.9%
ASCII 30
 
0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 10284
34.8%
산 1541
 
5.2%
월 886
 
3.0%
암 684
 
2.3%
용 672
 
2.3%
촌 552
 
1.9%
신 474
 
1.6%
곡 473
 
1.6%
정 449
 
1.5%
수 412
 
1.4%
Other values (105) 13154
44.5%
ASCII
ValueCountFrequency (%)
5 14
46.7%
4 6
20.0%
1 3
 
10.0%
3
 
10.0%
2 3
 
10.0%
3 1
 
3.3%

좌표계
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
세계
10000 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
세계 10000
100.0%

Length

2024-03-15T05:05:59.563922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T05:05:59.853449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
세계 10000
100.0%

데이터기준일자
Date

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2024-02-20 00:00:00
Maximum2024-02-20 00:00:00
2024-03-15T05:06:00.115617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:06:00.377476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Correlations

2024-03-15T05:06:00.580438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분점종류
구분1.0000.071
점종류0.0711.000
2024-03-15T05:06:00.804790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분점종류
구분1.0000.053
점종류0.0531.000
2024-03-15T05:06:01.037264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분점종류
구분1.0000.053
점종류0.0531.000

Missing values

2024-03-15T05:05:49.918263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-15T05:05:50.203318image/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

구분점종류기준점명소재지좌표계데이터기준일자
17792광산구도근점21917도덕동세계2024-02-20
17129광산구도근점21252삼도동세계2024-02-20
8123북구도근점1722용봉동세계2024-02-20
18306광산구도근점22431장덕동세계2024-02-20
13468광산구도근점3431하남동세계2024-02-20
994동구도근점3661운림동세계2024-02-20
5776남구도근점G1431행암동세계2024-02-20
450동구도근점3109산수동세계2024-02-20
3401서구도근점2556세하동세계2024-02-20
642동구도근점3308황금동세계2024-02-20
구분점종류기준점명소재지좌표계데이터기준일자
6013남구도근점G1685임암동세계2024-02-20
10423북구도근점8571두암동세계2024-02-20
5227남구도근점623노대동세계2024-02-20
12258광산구도근점1954월계동세계2024-02-20
16703광산구도근점20804용동세계2024-02-20
11498광산구도근점382우산동세계2024-02-20
9056북구도근점3276중흥동세계2024-02-20
4848남구도근점10서동세계2024-02-20
10175북구도근점8338생용동세계2024-02-20
15223광산구도근점8651산막동세계2024-02-20

Duplicate rows

Most frequently occurring

구분점종류기준점명소재지좌표계데이터기준일자# duplicates
0북구도근점3096연제동세계2024-02-202
1서구도근점1836매월동세계2024-02-202
2서구도근점1891매월동세계2024-02-202