Overview

Dataset statistics

Number of variables8
Number of observations306
Missing cells41
Missing cells (%)1.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory19.9 KiB
Average record size in memory66.4 B

Variable types

Text2
Numeric2
DateTime4

Dataset

Description김해시 하천점용현황에 대한 자료로 소재지, 점용면적, 위도, 경도, 허가(연장)일자, 최초허가일, 점용시작일, 점용종료일에 대한 항목을 제공합니다.
Author경상남도 김해시
URLhttps://www.data.go.kr/data/15092370/fileData.do

Alerts

점용종료일 has 41 (13.4%) missing valuesMissing

Reproduction

Analysis started2023-12-12 13:59:01.835727
Analysis finished2023-12-12 13:59:03.024343
Duration1.19 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

소재지
Text

Distinct209
Distinct (%)68.3%
Missing0
Missing (%)0.0%
Memory size2.5 KiB
2023-12-12T22:59:03.185131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length40
Median length24
Mean length21.565359
Min length14

Characters and Unicode

Total characters6599
Distinct characters101
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

Unique177 ?
Unique (%)57.8%

Sample

1st row경상남도 김해시 한림면 병동리 905번지 1호 한림면 병동리 905-1
2nd row경상남도 김해시 한림면 병동리 906번지 5호 한림면 병동리 906-5
3rd row경상남도 김해시 한림면 병동리 906번지 9호 한림면 병동리 906-9
4th row경상남도 김해시 강동 455
5th row경상남도 김해시 대동면 덕산리 268
ValueCountFrequency (%)
경상남도 306
20.8%
김해시 306
20.8%
대동면 47
 
3.2%
진례면 47
 
3.2%
주촌면 38
 
2.6%
상동면 37
 
2.5%
예안리 32
 
2.2%
한림면 30
 
2.0%
1294-313 26
 
1.8%
생림면 21
 
1.4%
Other values (278) 583
39.6%
2023-12-12T22:59:03.532635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1239
18.8%
상 343
 
5.2%
1 337
 
5.1%
시 308
 
4.7%
해 306
 
4.6%
경 306
 
4.6%
김 306
 
4.6%
도 306
 
4.6%
남 306
 
4.6%
리 246
 
3.7%
Other values (91) 2596
39.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3795
57.5%
Decimal Number 1343
 
20.4%
Space Separator 1239
 
18.8%
Dash Punctuation 222
 
3.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
상 343
 
9.0%
시 308
 
8.1%
해 306
 
8.1%
경 306
 
8.1%
김 306
 
8.1%
도 306
 
8.1%
남 306
 
8.1%
리 246
 
6.5%
면 226
 
6.0%
동 170
 
4.5%
Other values (79) 972
25.6%
Decimal Number
ValueCountFrequency (%)
1 337
25.1%
2 154
11.5%
3 143
10.6%
9 134
 
10.0%
0 119
 
8.9%
4 115
 
8.6%
6 92
 
6.9%
8 87
 
6.5%
5 84
 
6.3%
7 78
 
5.8%
Space Separator
ValueCountFrequency (%)
1239
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 222
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3795
57.5%
Common 2804
42.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
상 343
 
9.0%
시 308
 
8.1%
해 306
 
8.1%
경 306
 
8.1%
김 306
 
8.1%
도 306
 
8.1%
남 306
 
8.1%
리 246
 
6.5%
면 226
 
6.0%
동 170
 
4.5%
Other values (79) 972
25.6%
Common
ValueCountFrequency (%)
1239
44.2%
1 337
 
12.0%
- 222
 
7.9%
2 154
 
5.5%
3 143
 
5.1%
9 134
 
4.8%
0 119
 
4.2%
4 115
 
4.1%
6 92
 
3.3%
8 87
 
3.1%
Other values (2) 162
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3795
57.5%
ASCII 2804
42.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1239
44.2%
1 337
 
12.0%
- 222
 
7.9%
2 154
 
5.5%
3 143
 
5.1%
9 134
 
4.8%
0 119
 
4.2%
4 115
 
4.1%
6 92
 
3.3%
8 87
 
3.1%
Other values (2) 162
 
5.8%
Hangul
ValueCountFrequency (%)
상 343
 
9.0%
시 308
 
8.1%
해 306
 
8.1%
경 306
 
8.1%
김 306
 
8.1%
도 306
 
8.1%
남 306
 
8.1%
리 246
 
6.5%
면 226
 
6.0%
동 170
 
4.5%
Other values (79) 972
25.6%

점용면적
Text

Distinct238
Distinct (%)77.8%
Missing0
Missing (%)0.0%
Memory size2.5 KiB
2023-12-12T22:59:03.881304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length2.7026144
Min length1

Characters and Unicode

Total characters827
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique192 ?
Unique (%)62.7%

Sample

1st row230
2nd row256
3rd row258
4th row33
5th row
ValueCountFrequency (%)
2 5
 
1.7%
5 4
 
1.3%
40 4
 
1.3%
20 4
 
1.3%
8 4
 
1.3%
55 3
 
1.0%
10 3
 
1.0%
30 3
 
1.0%
113 3
 
1.0%
141 3
 
1.0%
Other values (227) 262
87.9%
2023-12-12T22:59:04.351407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 153
18.5%
2 124
15.0%
3 76
9.2%
6 75
9.1%
4 72
8.7%
5 70
8.5%
0 69
8.3%
8 61
 
7.4%
7 59
 
7.1%
9 52
 
6.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 811
98.1%
Space Separator 16
 
1.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 153
18.9%
2 124
15.3%
3 76
9.4%
6 75
9.2%
4 72
8.9%
5 70
8.6%
0 69
8.5%
8 61
 
7.5%
7 59
 
7.3%
9 52
 
6.4%
Space Separator
ValueCountFrequency (%)
16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 827
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 153
18.5%
2 124
15.0%
3 76
9.2%
6 75
9.1%
4 72
8.7%
5 70
8.5%
0 69
8.3%
8 61
 
7.4%
7 59
 
7.1%
9 52
 
6.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 827
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 153
18.5%
2 124
15.0%
3 76
9.2%
6 75
9.1%
4 72
8.7%
5 70
8.5%
0 69
8.3%
8 61
 
7.4%
7 59
 
7.1%
9 52
 
6.3%

위도
Real number (ℝ)

Distinct204
Distinct (%)66.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.25752
Minimum35.174589
Maximum35.358794
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2023-12-12T22:59:04.491150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.174589
5-th percentile35.189366
Q135.227356
median35.239632
Q335.295441
95-th percentile35.32965
Maximum35.358794
Range0.18420537
Interquartile range (IQR)0.068085305

Descriptive statistics

Standard deviation0.043225112
Coefficient of variation (CV)0.0012259828
Kurtosis-0.89544631
Mean35.25752
Median Absolute Deviation (MAD)0.02531888
Skewness0.33628584
Sum10788.801
Variance0.0018684103
MonotonicityNot monotonic
2023-12-12T22:59:04.623101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.22735559 26
 
8.5%
35.23317197 10
 
3.3%
35.25364588 8
 
2.6%
35.30771571 7
 
2.3%
35.23251298 7
 
2.3%
35.23026039 6
 
2.0%
35.18478911 5
 
1.6%
35.31116104 4
 
1.3%
35.26943606 4
 
1.3%
35.25584923 4
 
1.3%
Other values (194) 225
73.5%
ValueCountFrequency (%)
35.17458871 1
 
0.3%
35.17554667 1
 
0.3%
35.17667369 1
 
0.3%
35.17694779 1
 
0.3%
35.17889322 1
 
0.3%
35.18247111 1
 
0.3%
35.18311828 1
 
0.3%
35.18413801 1
 
0.3%
35.18478911 5
1.6%
35.18705675 1
 
0.3%
ValueCountFrequency (%)
35.35879408 1
 
0.3%
35.34855484 1
 
0.3%
35.34472694 1
 
0.3%
35.34440361 1
 
0.3%
35.34386303 1
 
0.3%
35.34381711 2
0.7%
35.3433188 1
 
0.3%
35.34209728 3
1.0%
35.3416113 1
 
0.3%
35.34117925 1
 
0.3%

경도
Real number (ℝ)

Distinct204
Distinct (%)66.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean128.85161
Minimum128.70623
Maximum128.99825
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2023-12-12T22:59:04.763088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum128.70623
5-th percentile128.75365
Q1128.78574
median128.83973
Q3128.91197
95-th percentile128.96108
Maximum128.99825
Range0.2920111
Interquartile range (IQR)0.1262266

Descriptive statistics

Standard deviation0.072184317
Coefficient of variation (CV)0.00056021278
Kurtosis-1.1627239
Mean128.85161
Median Absolute Deviation (MAD)0.06781985
Skewness0.17236289
Sum39428.592
Variance0.0052105756
MonotonicityNot monotonic
2023-12-12T22:59:04.915189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
128.9610755 26
 
8.5%
128.767963 10
 
3.3%
128.8419147 8
 
2.6%
128.9297966 7
 
2.3%
128.9096449 7
 
2.3%
128.7536539 6
 
2.0%
128.7756757 5
 
1.6%
128.8752595 4
 
1.3%
128.769284 4
 
1.3%
128.7568692 4
 
1.3%
Other values (194) 225
73.5%
ValueCountFrequency (%)
128.7062349 1
0.3%
128.7128665 1
0.3%
128.7129693 1
0.3%
128.7187683 1
0.3%
128.7251904 1
0.3%
128.7254955 1
0.3%
128.7432718 1
0.3%
128.7528633 2
0.7%
128.752945 1
0.3%
128.7529526 1
0.3%
ValueCountFrequency (%)
128.998246 1
0.3%
128.9961795 1
0.3%
128.9822323 1
0.3%
128.9822174 1
0.3%
128.9778871 1
0.3%
128.9676963 1
0.3%
128.9675855 1
0.3%
128.9673109 1
0.3%
128.9655761 1
0.3%
128.9629057 1
0.3%

허가(연장)일자
Date

Distinct168
Distinct (%)54.9%
Missing0
Missing (%)0.0%
Memory size2.5 KiB
Minimum2000-07-13 00:00:00
Maximum2024-03-10 00:00:00
2023-12-12T22:59:05.091363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:59:05.248850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

최초허가일자
Date

Distinct131
Distinct (%)42.8%
Missing0
Missing (%)0.0%
Memory size2.5 KiB
Minimum1900-01-01 00:00:00
Maximum2022-04-01 00:00:00
2023-12-12T22:59:05.581288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:59:05.782957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

점용시작일
Date

Distinct144
Distinct (%)47.1%
Missing0
Missing (%)0.0%
Memory size2.5 KiB
Minimum1900-01-01 00:00:00
Maximum2022-04-01 00:00:00
2023-12-12T22:59:05.932160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:59:06.075127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

점용종료일
Date

MISSING 

Distinct69
Distinct (%)26.0%
Missing41
Missing (%)13.4%
Memory size2.5 KiB
Minimum2014-12-31 00:00:00
Maximum2100-12-31 00:00:00
2023-12-12T22:59:06.205853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:59:06.347286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Interactions

2023-12-12T22:59:02.327712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:59:02.094332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:59:02.423958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:59:02.216851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T22:59:06.452077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
위도경도점용종료일
위도1.0000.7660.759
경도0.7661.0000.735
점용종료일0.7590.7351.000
2023-12-12T22:59:06.542157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
위도경도
위도1.0000.023
경도0.0231.000

Missing values

2023-12-12T22:59:02.568092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T22:59:02.977776image/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

소재지점용면적위도경도허가(연장)일자최초허가일자점용시작일점용종료일
0경상남도 김해시 한림면 병동리 905번지 1호 한림면 병동리 905-123035.290167128.790382022-04-012022-04-012022-04-012027-04-01
1경상남도 김해시 한림면 병동리 906번지 5호 한림면 병동리 906-525635.290888128.7904532022-04-012022-04-012022-04-012027-04-01
2경상남도 김해시 한림면 병동리 906번지 9호 한림면 병동리 906-925835.290888128.7904532022-04-012022-04-012022-04-012027-04-01
3경상남도 김해시 강동 4553335.219032128.8761022021-05-272021-05-272021-05-27<NA>
4경상남도 김해시 대동면 덕산리 26835.298935128.9778872021-03-172021-03-172021-03-172022-03-16
5경상남도 김해시 대동면 주동리 444-27835.239199128.9463832021-03-092021-03-092021-03-09<NA>
6경상남도 김해시 무계동 10-716735.197957128.8263052021-01-142021-01-142021-01-14<NA>
7경상남도 김해시 상동면 대감리 18-4535.3146128.9416652020-09-082020-09-092020-09-092025-09-09
8경상남도 김해시 한림면 용덕리 711-52235.289862128.8305272020-07-022020-07-022020-07-022100-12-31
9경상남도 김해시 한림면 병동리 936-216635.291052128.7887942020-04-032020-04-032020-04-012024-12-31
소재지점용면적위도경도허가(연장)일자최초허가일자점용시작일점용종료일
296경상남도 김해시 주촌면 원지리 1220-544735.246333128.8344332021-01-221900-01-012010-01-012030-12-31
297경상남도 김해시 생림면 사촌리 639-8105435.311161128.8752592013-11-191900-01-012004-01-012023-12-31
298경상남도 김해시 생림면 나전리 1229-6207535.311829128.8727042020-01-081900-01-011900-01-012024-12-31
299경상남도 김해시 생림면 사촌리 59-566135.311161128.8752592013-11-211900-01-012004-01-012023-12-31
300경상남도 김해시 생림면 사촌리 528-113135.319111128.8614442013-12-311900-01-012004-01-012023-12-31
301경상남도 김해시 생림면 봉림리 1262-3122835.324759128.8479762020-01-291900-01-011900-01-012024-12-31
302경상남도 김해시 생림면 사촌리 639-839035.311161128.8752592013-11-251900-01-012004-01-012023-12-31
303경상남도 김해시 생림면 나전리 1229-3170535.297048128.8774272004-01-011900-01-012010-01-012023-12-31
304경상남도 김해시 생림면 나전리 1229-637135.311829128.8727042013-12-101900-01-012004-01-012023-12-31
305경상남도 김해시 생림면 나전리 1229-6210135.311829128.8727042020-01-011900-01-011900-01-012024-12-31