Overview

Dataset statistics

Number of variables3
Number of observations3506
Missing cells3489
Missing cells (%)33.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory85.7 KiB
Average record size in memory25.0 B

Variable types

Text2
Numeric1

Dataset

Description충청남도 천안시 도시계획정보시스템(UPIS)권장용도 현황으로 현황도형 관리번호, 구분코드 등의 항목을 제공합니다.
Author충청남도
URLhttps://alldam.chungnam.go.kr/index.chungnam?menuCd=DOM_000000201001001001&st=&cds=&orgCd=&apiType=&isOpen=Y&pageIndex=13&beforeMenuCd=DOM_000000201001001000&publicdatapk=15123830

Alerts

용도 설명 has 3489 (99.5%) missing valuesMissing

Reproduction

Analysis started2024-01-09 20:51:09.675544
Analysis finished2024-01-09 20:51:10.060443
Duration0.38 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

현황도형 관리번호
Text

Distinct191
Distinct (%)5.4%
Missing0
Missing (%)0.0%
Memory size27.5 KiB
2024-01-10T05:51:10.251995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length24
Mean length24
Min length24

Characters and Unicode

Total characters84144
Distinct characters14
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique36 ?
Unique (%)1.0%

Sample

1st row44130UQ161PS201005310003
2nd row44130UQ161PS201005310003
3rd row44130UQ161PS201005310003
4th row44130UQ161PS201005310003
5th row44130UQ161PS201005310003
ValueCountFrequency (%)
44130uq161ps200511300001 786
22.4%
44130uq161ps201306200001 343
 
9.8%
44130uq161ps200002120001 154
 
4.4%
44130uq161ps200404300001 154
 
4.4%
44130uq161ps201305010004 97
 
2.8%
44130uq161ps201302120001 88
 
2.5%
44130uq161ps199701040002 82
 
2.3%
44130uq161ps200901200001 70
 
2.0%
44130uq161ps201207050001 69
 
2.0%
44130uq161ps202211210001 53
 
1.5%
Other values (181) 1610
45.9%
2024-01-10T05:51:10.591791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 24182
28.7%
1 18708
22.2%
4 7981
 
9.5%
2 6096
 
7.2%
3 5893
 
7.0%
6 4328
 
5.1%
U 3506
 
4.2%
Q 3506
 
4.2%
P 3506
 
4.2%
S 3506
 
4.2%
Other values (4) 2932
 
3.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 70120
83.3%
Uppercase Letter 14024
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 24182
34.5%
1 18708
26.7%
4 7981
 
11.4%
2 6096
 
8.7%
3 5893
 
8.4%
6 4328
 
6.2%
5 1470
 
2.1%
9 636
 
0.9%
7 546
 
0.8%
8 280
 
0.4%
Uppercase Letter
ValueCountFrequency (%)
U 3506
25.0%
Q 3506
25.0%
P 3506
25.0%
S 3506
25.0%

Most occurring scripts

ValueCountFrequency (%)
Common 70120
83.3%
Latin 14024
 
16.7%

Most frequent character per script

Common
ValueCountFrequency (%)
0 24182
34.5%
1 18708
26.7%
4 7981
 
11.4%
2 6096
 
8.7%
3 5893
 
8.4%
6 4328
 
6.2%
5 1470
 
2.1%
9 636
 
0.9%
7 546
 
0.8%
8 280
 
0.4%
Latin
ValueCountFrequency (%)
U 3506
25.0%
Q 3506
25.0%
P 3506
25.0%
S 3506
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 84144
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 24182
28.7%
1 18708
22.2%
4 7981
 
9.5%
2 6096
 
7.2%
3 5893
 
7.0%
6 4328
 
5.1%
U 3506
 
4.2%
Q 3506
 
4.2%
P 3506
 
4.2%
S 3506
 
4.2%
Other values (4) 2932
 
3.5%

구분 코드
Real number (ℝ)

Distinct786
Distinct (%)22.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean123.5
Minimum1
Maximum786
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size30.9 KiB
2024-01-10T05:51:10.721910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q19
median31
Q3140.75
95-th percentile610.75
Maximum786
Range785
Interquartile range (IQR)131.75

Descriptive statistics

Standard deviation189.93704
Coefficient of variation (CV)1.5379518
Kurtosis2.7147782
Mean123.5
Median Absolute Deviation (MAD)28
Skewness1.9200302
Sum432991
Variance36076.08
MonotonicityNot monotonic
2024-01-10T05:51:10.876302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 190
 
5.4%
2 155
 
4.4%
3 131
 
3.7%
4 101
 
2.9%
5 90
 
2.6%
6 75
 
2.1%
7 70
 
2.0%
8 62
 
1.8%
9 59
 
1.7%
10 55
 
1.6%
Other values (776) 2518
71.8%
ValueCountFrequency (%)
1 190
5.4%
2 155
4.4%
3 131
3.7%
4 101
2.9%
5 90
2.6%
6 75
 
2.1%
7 70
 
2.0%
8 62
 
1.8%
9 59
 
1.7%
10 55
 
1.6%
ValueCountFrequency (%)
786 1
< 0.1%
785 1
< 0.1%
784 1
< 0.1%
783 1
< 0.1%
782 1
< 0.1%
781 1
< 0.1%
780 1
< 0.1%
779 1
< 0.1%
778 1
< 0.1%
777 1
< 0.1%

용도 설명
Text

MISSING 

Distinct11
Distinct (%)64.7%
Missing3489
Missing (%)99.5%
Memory size27.5 KiB
2024-01-10T05:51:11.113506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length195
Median length104
Mean length85.588235
Min length6

Characters and Unicode

Total characters1455
Distinct characters141
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)41.2%

Sample

1st row부대복리시설
2nd row근린생활시설 / 제1종근린생활시설 중 가, 나, 다(목욕탕제외), 라, 마항 / 제2종근린생할시설 중 가, 나, 다, 바, 사, 자, 타항
3rd row부대복리시설
4th row공장 제조시설 및 부대시설
5th row세분류(2511) 구조용 금속판제품 및 금속공작물 제조업
ValueCountFrequency (%)
72
22.2%
중 17
 
5.2%
사무소 12
 
3.7%
「건축법 9
 
2.8%
건축법시행령 9
 
2.8%
별표1 9
 
2.8%
시행령」별표1 9
 
2.8%
제4호 8
 
2.5%
및 8
 
2.5%
부대시설 7
 
2.2%
Other values (85) 165
50.8%
2024-01-10T05:51:11.455937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
311
21.4%
시 90
 
6.2%
설 67
 
4.6%
제 54
 
3.7%
1 43
 
3.0%
/ 43
 
3.0%
호 34
 
2.3%
, 29
 
2.0%
- 29
 
2.0%
( 23
 
1.6%
Other values (131) 732
50.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 856
58.8%
Space Separator 311
 
21.4%
Decimal Number 97
 
6.7%
Other Punctuation 87
 
6.0%
Open Punctuation 32
 
2.2%
Close Punctuation 32
 
2.2%
Dash Punctuation 29
 
2.0%
Uppercase Letter 7
 
0.5%
Final Punctuation 2
 
0.1%
Initial Punctuation 2
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 90
 
10.5%
설 67
 
7.8%
제 54
 
6.3%
호 34
 
4.0%
무 23
 
2.7%
축 23
 
2.7%
건 23
 
2.7%
중 22
 
2.6%
행 20
 
2.3%
별 19
 
2.2%
Other values (105) 481
56.2%
Decimal Number
ValueCountFrequency (%)
1 43
44.3%
4 18
18.6%
2 12
 
12.4%
5 6
 
6.2%
8 6
 
6.2%
0 4
 
4.1%
3 3
 
3.1%
7 3
 
3.1%
9 1
 
1.0%
6 1
 
1.0%
Other Punctuation
ValueCountFrequency (%)
/ 43
49.4%
, 29
33.3%
: 8
 
9.2%
. 4
 
4.6%
· 3
 
3.4%
Uppercase Letter
ValueCountFrequency (%)
C 5
71.4%
S 1
 
14.3%
O 1
 
14.3%
Open Punctuation
ValueCountFrequency (%)
( 23
71.9%
「 9
 
28.1%
Close Punctuation
ValueCountFrequency (%)
) 23
71.9%
」 9
 
28.1%
Space Separator
ValueCountFrequency (%)
311
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 29
100.0%
Final Punctuation
ValueCountFrequency (%)
” 2
100.0%
Initial Punctuation
ValueCountFrequency (%)
“ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 856
58.8%
Common 592
40.7%
Latin 7
 
0.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 90
 
10.5%
설 67
 
7.8%
제 54
 
6.3%
호 34
 
4.0%
무 23
 
2.7%
축 23
 
2.7%
건 23
 
2.7%
중 22
 
2.6%
행 20
 
2.3%
별 19
 
2.2%
Other values (105) 481
56.2%
Common
ValueCountFrequency (%)
311
52.5%
1 43
 
7.3%
/ 43
 
7.3%
, 29
 
4.9%
- 29
 
4.9%
( 23
 
3.9%
) 23
 
3.9%
4 18
 
3.0%
2 12
 
2.0%
「 9
 
1.5%
Other values (13) 52
 
8.8%
Latin
ValueCountFrequency (%)
C 5
71.4%
S 1
 
14.3%
O 1
 
14.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 856
58.8%
ASCII 574
39.5%
None 21
 
1.4%
Punctuation 4
 
0.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
311
54.2%
1 43
 
7.5%
/ 43
 
7.5%
, 29
 
5.1%
- 29
 
5.1%
( 23
 
4.0%
) 23
 
4.0%
4 18
 
3.1%
2 12
 
2.1%
: 8
 
1.4%
Other values (11) 35
 
6.1%
Hangul
ValueCountFrequency (%)
시 90
 
10.5%
설 67
 
7.8%
제 54
 
6.3%
호 34
 
4.0%
무 23
 
2.7%
축 23
 
2.7%
건 23
 
2.7%
중 22
 
2.6%
행 20
 
2.3%
별 19
 
2.2%
Other values (105) 481
56.2%
None
ValueCountFrequency (%)
「 9
42.9%
」 9
42.9%
· 3
 
14.3%
Punctuation
ValueCountFrequency (%)
” 2
50.0%
“ 2
50.0%

Interactions

2024-01-10T05:51:09.831630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-10T05:51:11.540115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분 코드용도 설명
구분 코드1.000NaN
용도 설명NaN1.000

Missing values

2024-01-10T05:51:09.956703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-10T05:51:10.024509image/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

현황도형 관리번호구분 코드용도 설명
044130UQ161PS20100531000314<NA>
144130UQ161PS20100531000315<NA>
244130UQ161PS20100531000316<NA>
344130UQ161PS20100531000317<NA>
444130UQ161PS20100531000318<NA>
544130UQ161PS20100531000319<NA>
644130UQ161PS20100531000320<NA>
744130UQ161PS20100531000321<NA>
844130UQ161PS20100531000322<NA>
944130UQ161PS20100531000323<NA>
현황도형 관리번호구분 코드용도 설명
349644130UQ161PS201306200001146<NA>
349744130UQ161PS201306200001147<NA>
349844130UQ161PS201306200001148<NA>
349944130UQ161PS201306200001149<NA>
350044130UQ161PS201306200001150<NA>
350144130UQ161PS201306200001151<NA>
350244130UQ161PS201306200001152<NA>
350344130UQ161PS201306200001153<NA>
350444130UQ161PS201306200001154<NA>
350544130UQ161PS201306200001155<NA>