Overview

Dataset statistics

Number of variables6
Number of observations68
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.4 KiB
Average record size in memory51.9 B

Variable types

Categorical3
Text1
Numeric2

Dataset

Description경상남도 김해시 개발행위 허가 정보에 대한 데이터로 구분,지번주소,허가목적,용도지역,위도,경도 등의 항목을 제공합니다
Author경상남도 김해시
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=15036946

Alerts

경도 is highly overall correlated with 구분High correlation
구분 is highly overall correlated with 경도High correlation

Reproduction

Analysis started2023-12-10 23:37:37.916667
Analysis finished2023-12-10 23:37:38.636781
Duration0.72 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size676.0 B
개발행위변경허가
39 
개발행위허가
29 

Length

Max length8
Median length8
Mean length7.1470588
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
개발행위변경허가 39
57.4%
개발행위허가 29
42.6%

Length

2023-12-11T08:37:38.756913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:37:38.850920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
개발행위변경허가 39
57.4%
개발행위허가 29
42.6%

지번주소
Text

Distinct67
Distinct (%)98.5%
Missing0
Missing (%)0.0%
Memory size676.0 B
2023-12-11T08:37:38.979677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length57
Median length47
Mean length27.264706
Min length15

Characters and Unicode

Total characters1854
Distinct characters76
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique66 ?
Unique (%)97.1%

Sample

1st row경상남도 김해시 진례면 청천리 102-5
2nd row경상남도 김해시 생림면 마사리1522-8
3rd row경상남도 김해시 안동-583-11
4th row경상남도 김해시 한림면 명동리24
5th row경상남도 김해시 상동면 여차리714,716
ValueCountFrequency (%)
경상남도 68
26.4%
김해시 68
26.4%
한림면 14
 
5.4%
상동면 11
 
4.3%
대동면 9
 
3.5%
생림면 9
 
3.5%
진례면 4
 
1.6%
주촌면 2
 
0.8%
담안리485-24 2
 
0.8%
나전리12,13-2,18-1,18-2,19,21-1,23-9 1
 
0.4%
Other values (70) 70
27.1%
2023-12-11T08:37:39.266619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
191
 
10.3%
1 133
 
7.2%
- 127
 
6.9%
, 84
 
4.5%
상 79
 
4.3%
3 72
 
3.9%
2 70
 
3.8%
경 68
 
3.7%
해 68
 
3.7%
남 68
 
3.7%
Other values (66) 894
48.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 871
47.0%
Decimal Number 581
31.3%
Space Separator 191
 
10.3%
Dash Punctuation 127
 
6.9%
Other Punctuation 84
 
4.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
상 79
 
9.1%
경 68
 
7.8%
해 68
 
7.8%
남 68
 
7.8%
김 68
 
7.8%
도 68
 
7.8%
시 68
 
7.8%
리 57
 
6.5%
면 49
 
5.6%
동 45
 
5.2%
Other values (53) 233
26.8%
Decimal Number
ValueCountFrequency (%)
1 133
22.9%
3 72
12.4%
2 70
12.0%
8 59
10.2%
5 50
 
8.6%
0 48
 
8.3%
7 43
 
7.4%
4 39
 
6.7%
6 38
 
6.5%
9 29
 
5.0%
Space Separator
ValueCountFrequency (%)
191
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 127
100.0%
Other Punctuation
ValueCountFrequency (%)
, 84
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 983
53.0%
Hangul 871
47.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
상 79
 
9.1%
경 68
 
7.8%
해 68
 
7.8%
남 68
 
7.8%
김 68
 
7.8%
도 68
 
7.8%
시 68
 
7.8%
리 57
 
6.5%
면 49
 
5.6%
동 45
 
5.2%
Other values (53) 233
26.8%
Common
ValueCountFrequency (%)
191
19.4%
1 133
13.5%
- 127
12.9%
, 84
8.5%
3 72
 
7.3%
2 70
 
7.1%
8 59
 
6.0%
5 50
 
5.1%
0 48
 
4.9%
7 43
 
4.4%
Other values (3) 106
10.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 983
53.0%
Hangul 871
47.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
191
19.4%
1 133
13.5%
- 127
12.9%
, 84
8.5%
3 72
 
7.3%
2 70
 
7.1%
8 59
 
6.0%
5 50
 
5.1%
0 48
 
4.9%
7 43
 
4.4%
Other values (3) 106
10.8%
Hangul
ValueCountFrequency (%)
상 79
 
9.1%
경 68
 
7.8%
해 68
 
7.8%
남 68
 
7.8%
김 68
 
7.8%
도 68
 
7.8%
시 68
 
7.8%
리 57
 
6.5%
면 49
 
5.6%
동 45
 
5.2%
Other values (53) 233
26.8%

허가목적
Categorical

Distinct18
Distinct (%)26.5%
Missing0
Missing (%)0.0%
Memory size676.0 B
단독주택
29 
공장
10 
제2종 근린생활시설
4 
동물 및 식물 관련 시설
3 
제1종 근린생활시설(소매점)
3 
Other values (13)
19 

Length

Max length29
Median length4
Mean length6.8382353
Min length2

Unique

Unique8 ?
Unique (%)11.8%

Sample

1st row타용도
2nd row제2종 근린생활시설(제조업소)
3rd row제2종근린생활시설(사무소)
4th row제2종 근린생활시설
5th row단독주택

Common Values

ValueCountFrequency (%)
단독주택 29
42.6%
공장 10
 
14.7%
제2종 근린생활시설 4
 
5.9%
동물 및 식물 관련 시설 3
 
4.4%
제1종 근린생활시설(소매점) 3
 
4.4%
제2종 근린생활시설(제조업소) 3
 
4.4%
발전시설 2
 
2.9%
제1종근린생활시설(소매점),제2종근린생활시설(사무소) 2
 
2.9%
발전시설, 구조물설치 2
 
2.9%
제1종 근린생활시설 2
 
2.9%
Other values (8) 8
 
11.8%

Length

2023-12-11T08:37:39.394878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
단독주택 29
30.5%
공장 10
 
10.5%
제2종 7
 
7.4%
근린생활시설 6
 
6.3%
제1종 5
 
5.3%
발전시설 4
 
4.2%
구조물설치 3
 
3.2%
근린생활시설(소매점 3
 
3.2%
근린생활시설(제조업소 3
 
3.2%
시설 3
 
3.2%
Other values (12) 22
23.2%

용도지역
Categorical

Distinct15
Distinct (%)22.1%
Missing0
Missing (%)0.0%
Memory size676.0 B
계획관리지역
20 
자연녹지지역
9 
제1종일반주거지역
9 
일반공업지역
6 
제2종일반주거지역
6 
Other values (10)
18 

Length

Max length17
Median length6
Mean length7.6911765
Min length4

Unique

Unique5 ?
Unique (%)7.4%

Sample

1st row농림지역
2nd row농림지역
3rd row성장관리지역자연녹지지역
4th row계획관리지역
5th row보전관리지역

Common Values

ValueCountFrequency (%)
계획관리지역 20
29.4%
자연녹지지역 9
13.2%
제1종일반주거지역 9
13.2%
일반공업지역 6
 
8.8%
제2종일반주거지역 6
 
8.8%
보전관리지역 5
 
7.4%
농림지역 2
 
2.9%
취락지역(자연)지구 계획관리지역 2
 
2.9%
제1종일반주거지역 도시지역 2
 
2.9%
생산관리지역 2
 
2.9%
Other values (5) 5
 
7.4%

Length

2023-12-11T08:37:39.554573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
계획관리지역 23
30.3%
제1종일반주거지역 11
14.5%
자연녹지지역 10
13.2%
제2종일반주거지역 8
 
10.5%
일반공업지역 6
 
7.9%
보전관리지역 5
 
6.6%
농림지역 3
 
3.9%
도시지역 3
 
3.9%
생산관리지역 3
 
3.9%
취락지역(자연)지구 2
 
2.6%
Other values (2) 2
 
2.6%

위도
Real number (ℝ)

Distinct64
Distinct (%)94.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.289747
Minimum35.213198
Maximum35.37296
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size744.0 B
2023-12-11T08:37:39.668954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.213198
5-th percentile35.225288
Q135.277084
median35.290974
Q335.306378
95-th percentile35.355193
Maximum35.37296
Range0.15976155
Interquartile range (IQR)0.029294203

Descriptive statistics

Standard deviation0.037491761
Coefficient of variation (CV)0.0010623981
Kurtosis-0.12094496
Mean35.289747
Median Absolute Deviation (MAD)0.01535608
Skewness-0.023775994
Sum2399.7028
Variance0.0014056321
MonotonicityNot monotonic
2023-12-11T08:37:40.102168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.29104816 3
 
4.4%
35.21864539 2
 
2.9%
35.25803871 2
 
2.9%
35.25616825 1
 
1.5%
35.28252025 1
 
1.5%
35.28223787 1
 
1.5%
35.28215727 1
 
1.5%
35.28232513 1
 
1.5%
35.28197008 1
 
1.5%
35.28211918 1
 
1.5%
Other values (54) 54
79.4%
ValueCountFrequency (%)
35.21319845 1
1.5%
35.21864539 2
2.9%
35.22518752 1
1.5%
35.22547529 1
1.5%
35.22704318 1
1.5%
35.2299166 1
1.5%
35.23519599 1
1.5%
35.23605524 1
1.5%
35.23721126 1
1.5%
35.24882551 1
1.5%
ValueCountFrequency (%)
35.37296 1
1.5%
35.36680944 1
1.5%
35.36342252 1
1.5%
35.35965553 1
1.5%
35.34690677 1
1.5%
35.34399698 1
1.5%
35.34308384 1
1.5%
35.34039809 1
1.5%
35.34031975 1
1.5%
35.331803 1
1.5%

경도
Real number (ℝ)

HIGH CORRELATION 

Distinct64
Distinct (%)94.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean128.85639
Minimum128.71091
Maximum128.99422
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size744.0 B
2023-12-11T08:37:40.277868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum128.71091
5-th percentile128.74497
Q1128.79976
median128.85202
Q3128.90295
95-th percentile128.99362
Maximum128.99422
Range0.2833085
Interquartile range (IQR)0.10318177

Descriptive statistics

Standard deviation0.07765481
Coefficient of variation (CV)0.00060264615
Kurtosis-0.68266257
Mean128.85639
Median Absolute Deviation (MAD)0.052258
Skewness0.23868528
Sum8762.2348
Variance0.0060302695
MonotonicityNot monotonic
2023-12-11T08:37:40.443869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
128.7997638 3
 
4.4%
128.9018194 2
 
2.9%
128.7759607 2
 
2.9%
128.7546583 1
 
1.5%
128.9942171 1
 
1.5%
128.9933407 1
 
1.5%
128.993442 1
 
1.5%
128.99324 1
 
1.5%
128.9936746 1
 
1.5%
128.9938444 1
 
1.5%
Other values (54) 54
79.4%
ValueCountFrequency (%)
128.7109086 1
1.5%
128.7110248 1
1.5%
128.7314887 1
1.5%
128.7447454 1
1.5%
128.7453777 1
1.5%
128.748834 1
1.5%
128.7546583 1
1.5%
128.7605489 1
1.5%
128.765969 1
1.5%
128.7712474 1
1.5%
ValueCountFrequency (%)
128.9942171 1
1.5%
128.9939977 1
1.5%
128.9938444 1
1.5%
128.9936746 1
1.5%
128.9935249 1
1.5%
128.993442 1
1.5%
128.9933884 1
1.5%
128.9933407 1
1.5%
128.99324 1
1.5%
128.9429461 1
1.5%

Interactions

2023-12-11T08:37:38.312845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:37:38.152417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:37:38.394954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:37:38.234589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T08:37:40.565888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분지번주소허가목적용도지역위도경도
구분1.0001.0000.6030.5430.2050.684
지번주소1.0001.0000.0001.0001.0001.000
허가목적0.6030.0001.0000.7840.7260.634
용도지역0.5431.0000.7841.0000.7400.759
위도0.2051.0000.7260.7401.0000.610
경도0.6841.0000.6340.7590.6101.000
2023-12-11T08:37:40.665698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
허가목적구분용도지역
허가목적1.0000.4140.371
구분0.4141.0000.444
용도지역0.3710.4441.000
2023-12-11T08:37:40.748727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
위도경도구분허가목적용도지역
위도1.000-0.1550.1410.3490.362
경도-0.1551.0000.5060.2690.441
구분0.1410.5061.0000.4140.444
허가목적0.3490.2690.4141.0000.371
용도지역0.3620.4410.4440.3711.000

Missing values

2023-12-11T08:37:38.493433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T08:37:38.602850image/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

구분지번주소허가목적용도지역위도경도
0개발행위허가경상남도 김해시 진례면 청천리 102-5타용도농림지역35.256168128.754658
1개발행위허가경상남도 김해시 생림면 마사리1522-8제2종 근린생활시설(제조업소)농림지역35.37296128.825197
2개발행위허가경상남도 김해시 안동-583-11제2종근린생활시설(사무소)성장관리지역자연녹지지역35.225188128.91246
3개발행위허가경상남도 김해시 한림면 명동리24제2종 근린생활시설계획관리지역35.306374128.807374
4개발행위허가경상남도 김해시 상동면 여차리714,716단독주택보전관리지역35.340398128.891599
5개발행위허가경상남도 김해시 한림면 명동리산93-1제1종 근린생활시설계획관리지역35.282131128.821176
6개발행위허가경상남도 김해시 한림면 용덕리784-1,784-7,786-2제2종 근린생활시설계획관리지역35.296883128.83096
7개발행위허가경상남도 김해시 한림면 명동리1383-12공장일반공업지역35.291048128.799764
8개발행위허가경상남도 김해시 진례면 담안리485-24공동주택계획관리지역35.258039128.775961
9개발행위허가경상남도 김해시 진례면 담안리485-24제2종 근린생활시설(제조업소)계획관리지역35.258039128.775961
구분지번주소허가목적용도지역위도경도
58개발행위변경허가경상남도 김해시 진영읍본산리792-3,793-4,794,794-5,794-6,805-3단독주택자연녹지지역35.316388128.748834
59개발행위변경허가경상남도 김해시 상동면 묵방리154단독주택보전관리지역35.284918128.920201
60개발행위변경허가경상남도 김해시 한림면 명동리1048-11제2종 근린생활시설계획관리지역35.293557128.809387
61개발행위변경허가경상남도 김해시 상동면 묵방리963-3,산204-6단독주택보전관리지역35.284007128.891341
62개발행위변경허가경상남도 김해시 생림면 나전리12,13-2,18-1,18-2,19,21-1,23-9제1종근린생활시설(소매점),제2종근린생활시설(사무소)계획관리지역35.276388128.889601
63개발행위변경허가경상남도 김해시 주촌면 내삼리산11-9단독주택자연녹지지역35.237211128.82147
64개발행위변경허가경상남도 김해시 상동면 우계리1168,1169-1,1170-1,1313-1,1313-2단독주택계획관리지역35.306158128.888011
65개발행위변경허가경상남도 김해시 생림면 봉림리892,산97-8,산97-9공장계획관리지역35.34032128.851101
66개발행위변경허가경상남도 김해시 상동면 우계리113-3,113-4,113-5공장계획관리지역35.299807128.910486
67개발행위변경허가경상남도 김해시 진영읍신용리106-17단독주택자연녹지지역35.303833128.760549