Overview

Dataset statistics

Number of variables11
Number of observations1856
Missing cells4311
Missing cells (%)21.1%
Duplicate rows114
Duplicate rows (%)6.1%
Total size in memory163.3 KiB
Average record size in memory90.1 B

Variable types

Categorical1
Text8
Numeric2

Dataset

Description청주시 내의 개발행위허가(협의) 신청에 따른 개발행위 허가를 득하여 사업을 진행 중인 허가지 현황.
Author충청북도 청주시
URLhttps://www.data.go.kr/data/15014966/fileData.do

Alerts

Dataset has 114 (6.1%) duplicate rowsDuplicates
리 has 503 (27.1%) missing valuesMissing
허가면적( ㎡ ) has 1082 (58.3%) missing valuesMissing
부지 has 1084 (58.4%) missing valuesMissing
도로 has 1634 (88.0%) missing valuesMissing
도로 has 20 (1.1%) zerosZeros

Reproduction

Analysis started2023-12-12 12:54:18.540849
Analysis finished2023-12-12 12:54:20.105095
Duration1.56 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Categorical

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size14.6 KiB
청원구
548 
흥덕구
534 
상당구
387 
서원구
387 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
청원구 548
29.5%
흥덕구 534
28.8%
상당구 387
20.9%
서원구 387
20.9%

Length

2023-12-12T21:54:20.172625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T21:54:20.295092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
청원구 548
29.5%
흥덕구 534
28.8%
상당구 387
20.9%
서원구 387
20.9%

허가일자
Text

Distinct264
Distinct (%)14.2%
Missing0
Missing (%)0.0%
Memory size14.6 KiB
2023-12-12T21:54:20.715174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length10
Mean length9.9768319
Min length4

Characters and Unicode

Total characters18517
Distinct characters15
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique24 ?
Unique (%)1.3%

Sample

1st row2015-01-02
2nd row2015-01-02
3rd row2015-01-02
4th row2015-01-04
5th row2015-01-05
ValueCountFrequency (%)
2015-03-26 34
 
1.8%
2015-01-22 31
 
1.7%
2015-02-06 28
 
1.5%
2015-04-27 26
 
1.4%
2015-04-13 23
 
1.2%
2015-09-01 20
 
1.1%
2015-03-02 19
 
1.0%
2015-12-01 19
 
1.0%
2015-10-12 19
 
1.0%
2015-06-25 18
 
1.0%
Other values (254) 1619
87.2%
2023-12-12T21:54:21.296097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 4153
22.4%
- 3695
20.0%
1 3434
18.5%
2 2935
15.9%
5 2169
11.7%
3 477
 
2.6%
6 423
 
2.3%
4 369
 
2.0%
7 332
 
1.8%
9 303
 
1.6%
Other values (5) 227
 
1.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14816
80.0%
Dash Punctuation 3695
 
20.0%
Other Punctuation 3
 
< 0.1%
Lowercase Letter 2
 
< 0.1%
Uppercase Letter 1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4153
28.0%
1 3434
23.2%
2 2935
19.8%
5 2169
14.6%
3 477
 
3.2%
6 423
 
2.9%
4 369
 
2.5%
7 332
 
2.2%
9 303
 
2.0%
8 221
 
1.5%
Lowercase Letter
ValueCountFrequency (%)
a 1
50.0%
n 1
50.0%
Dash Punctuation
ValueCountFrequency (%)
- 3695
100.0%
Other Punctuation
ValueCountFrequency (%)
. 3
100.0%
Uppercase Letter
ValueCountFrequency (%)
J 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 18514
> 99.9%
Latin 3
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4153
22.4%
- 3695
20.0%
1 3434
18.5%
2 2935
15.9%
5 2169
11.7%
3 477
 
2.6%
6 423
 
2.3%
4 369
 
2.0%
7 332
 
1.8%
9 303
 
1.6%
Other values (2) 224
 
1.2%
Latin
ValueCountFrequency (%)
J 1
33.3%
a 1
33.3%
n 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 18517
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4153
22.4%
- 3695
20.0%
1 3434
18.5%
2 2935
15.9%
5 2169
11.7%
3 477
 
2.6%
6 423
 
2.3%
4 369
 
2.0%
7 332
 
1.8%
9 303
 
1.6%
Other values (5) 227
 
1.2%

준공 예정일
Text

Distinct102
Distinct (%)5.5%
Missing4
Missing (%)0.2%
Memory size14.6 KiB
2023-12-12T21:54:21.604156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length10
Mean length10.00216
Min length8

Characters and Unicode

Total characters18524
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique26 ?
Unique (%)1.4%

Sample

1st row2016-12-31
2nd row2015-12-31
3rd row2015-12-31
4th row2016-12-31
5th row2016-01-31
ValueCountFrequency (%)
2016-12-31 303
 
16.4%
2015-12-31 107
 
5.8%
2017-03-31 81
 
4.4%
2017-06-30 80
 
4.3%
2017-02-28 76
 
4.1%
2017-12-31 65
 
3.5%
2017-04-30 65
 
3.5%
2016-05-31 64
 
3.5%
2016-03-31 54
 
2.9%
2017-08-31 49
 
2.6%
Other values (92) 908
49.0%
2023-12-12T21:54:21.995813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 3839
20.7%
0 3738
20.2%
- 3700
20.0%
2 2660
14.4%
3 1859
10.0%
6 1020
 
5.5%
7 872
 
4.7%
5 303
 
1.6%
8 276
 
1.5%
4 131
 
0.7%
Other values (2) 126
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14818
80.0%
Dash Punctuation 3700
 
20.0%
Other Punctuation 6
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 3839
25.9%
0 3738
25.2%
2 2660
18.0%
3 1859
12.5%
6 1020
 
6.9%
7 872
 
5.9%
5 303
 
2.0%
8 276
 
1.9%
4 131
 
0.9%
9 120
 
0.8%
Dash Punctuation
ValueCountFrequency (%)
- 3700
100.0%
Other Punctuation
ValueCountFrequency (%)
. 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 18524
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 3839
20.7%
0 3738
20.2%
- 3700
20.0%
2 2660
14.4%
3 1859
10.0%
6 1020
 
5.5%
7 872
 
4.7%
5 303
 
1.6%
8 276
 
1.5%
4 131
 
0.7%
Other values (2) 126
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 18524
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 3839
20.7%
0 3738
20.2%
- 3700
20.0%
2 2660
14.4%
3 1859
10.0%
6 1020
 
5.5%
7 872
 
4.7%
5 303
 
1.6%
8 276
 
1.5%
4 131
 
0.7%
Other values (2) 126
 
0.7%

읍/면
Text

Distinct91
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Memory size14.6 KiB
2023-12-12T21:54:22.253448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length2
Mean length2.1508621
Min length2

Characters and Unicode

Total characters3992
Distinct characters65
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

Unique19 ?
Unique (%)1.0%

Sample

1st row현도
2nd row장성
3rd row장성
4th row남이
5th row남이
ValueCountFrequency (%)
남이 506
27.2%
오창 208
 
11.2%
내수 154
 
8.3%
북이 80
 
4.3%
미평 72
 
3.9%
강내 71
 
3.8%
옥산 69
 
3.7%
오송 68
 
3.7%
현도 66
 
3.5%
송절동 38
 
2.0%
Other values (72) 528
28.4%
2023-12-12T21:54:22.655454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
이 587
14.7%
남 519
 
13.0%
오 297
 
7.4%
동 262
 
6.6%
내 259
 
6.5%
창 210
 
5.3%
수 160
 
4.0%
북 121
 
3.0%
송 119
 
3.0%
평 107
 
2.7%
Other values (55) 1351
33.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3968
99.4%
Space Separator 23
 
0.6%
Other Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
이 587
14.8%
남 519
 
13.1%
오 297
 
7.5%
동 262
 
6.6%
내 259
 
6.5%
창 210
 
5.3%
수 160
 
4.0%
북 121
 
3.0%
송 119
 
3.0%
평 107
 
2.7%
Other values (53) 1327
33.4%
Space Separator
ValueCountFrequency (%)
23
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3968
99.4%
Common 24
 
0.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
이 587
14.8%
남 519
 
13.1%
오 297
 
7.5%
동 262
 
6.6%
내 259
 
6.5%
창 210
 
5.3%
수 160
 
4.0%
북 121
 
3.0%
송 119
 
3.0%
평 107
 
2.7%
Other values (53) 1327
33.4%
Common
ValueCountFrequency (%)
23
95.8%
/ 1
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3968
99.4%
ASCII 24
 
0.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
이 587
14.8%
남 519
 
13.1%
오 297
 
7.5%
동 262
 
6.6%
내 259
 
6.5%
창 210
 
5.3%
수 160
 
4.0%
북 121
 
3.0%
송 119
 
3.0%
평 107
 
2.7%
Other values (53) 1327
33.4%
ASCII
ValueCountFrequency (%)
23
95.8%
/ 1
 
4.2%

리
Text

MISSING 

Distinct148
Distinct (%)10.9%
Missing503
Missing (%)27.1%
Memory size14.6 KiB
2023-12-12T21:54:22.995664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length2
Mean length1.9911308
Min length1

Characters and Unicode

Total characters2694
Distinct characters122
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

Unique23 ?
Unique (%)1.7%

Sample

1st row상삼
2nd row가마
3rd row양촌
4th row가좌
5th row부용외천
ValueCountFrequency (%)
95
 
7.0%
수대 74
 
5.5%
팔봉 72
 
5.3%
양촌 70
 
5.2%
성산 65
 
4.8%
가좌 59
 
4.4%
부용외천 28
 
2.1%
석판 28
 
2.1%
묵방 27
 
2.0%
상발 26
 
1.9%
Other values (137) 809
59.8%
2023-12-12T21:54:23.804358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
산 119
 
4.4%
대 103
 
3.8%
수 102
 
3.8%
성 99
 
3.7%
- 95
 
3.5%
가 95
 
3.5%
촌 91
 
3.4%
양 82
 
3.0%
봉 82
 
3.0%
팔 72
 
2.7%
Other values (112) 1754
65.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2596
96.4%
Dash Punctuation 95
 
3.5%
Space Separator 2
 
0.1%
Other Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 119
 
4.6%
대 103
 
4.0%
수 102
 
3.9%
성 99
 
3.8%
가 95
 
3.7%
촌 91
 
3.5%
양 82
 
3.2%
봉 82
 
3.2%
팔 72
 
2.8%
동 71
 
2.7%
Other values (109) 1680
64.7%
Dash Punctuation
ValueCountFrequency (%)
- 95
100.0%
Space Separator
ValueCountFrequency (%)
2
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2596
96.4%
Common 98
 
3.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
산 119
 
4.6%
대 103
 
4.0%
수 102
 
3.9%
성 99
 
3.8%
가 95
 
3.7%
촌 91
 
3.5%
양 82
 
3.2%
봉 82
 
3.2%
팔 72
 
2.8%
동 71
 
2.7%
Other values (109) 1680
64.7%
Common
ValueCountFrequency (%)
- 95
96.9%
2
 
2.0%
/ 1
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2596
96.4%
ASCII 98
 
3.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
산 119
 
4.6%
대 103
 
4.0%
수 102
 
3.9%
성 99
 
3.8%
가 95
 
3.7%
촌 91
 
3.5%
양 82
 
3.2%
봉 82
 
3.2%
팔 72
 
2.8%
동 71
 
2.7%
Other values (109) 1680
64.7%
ASCII
ValueCountFrequency (%)
- 95
96.9%
2
 
2.0%
/ 1
 
1.0%

지목
Text

Distinct157
Distinct (%)8.5%
Missing4
Missing (%)0.2%
Memory size14.6 KiB
2023-12-12T21:54:24.148896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length1
Mean length1.4886609
Min length1

Characters and Unicode

Total characters2757
Distinct characters28
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

Unique97 ?
Unique (%)5.2%

Sample

1st row답
2nd row답
3rd row답
4th row답
5th row창
ValueCountFrequency (%)
임 571
29.3%
답 449
23.1%
전 432
22.2%
대 88
 
4.5%
임,전 47
 
2.4%
전,임 27
 
1.4%
장 25
 
1.3%
답답 16
 
0.8%
과 16
 
0.8%
묘 15
 
0.8%
Other values (118) 260
13.4%
2023-12-12T21:54:24.682512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
임 760
27.6%
전 680
24.7%
답 572
20.7%
, 284
 
10.3%
대 175
 
6.3%
94
 
3.4%
장 39
 
1.4%
잡 30
 
1.1%
과 20
 
0.7%
창 20
 
0.7%
Other values (18) 83
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2376
86.2%
Other Punctuation 285
 
10.3%
Space Separator 94
 
3.4%
Dash Punctuation 1
 
< 0.1%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
임 760
32.0%
전 680
28.6%
답 572
24.1%
대 175
 
7.4%
장 39
 
1.6%
잡 30
 
1.3%
과 20
 
0.8%
창 20
 
0.8%
묘 17
 
0.7%
목 17
 
0.7%
Other values (13) 46
 
1.9%
Other Punctuation
ValueCountFrequency (%)
, 284
99.6%
? 1
 
0.4%
Space Separator
ValueCountFrequency (%)
94
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%
Math Symbol
ValueCountFrequency (%)
> 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2376
86.2%
Common 381
 
13.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
임 760
32.0%
전 680
28.6%
답 572
24.1%
대 175
 
7.4%
장 39
 
1.6%
잡 30
 
1.3%
과 20
 
0.8%
창 20
 
0.8%
묘 17
 
0.7%
목 17
 
0.7%
Other values (13) 46
 
1.9%
Common
ValueCountFrequency (%)
, 284
74.5%
94
 
24.7%
- 1
 
0.3%
> 1
 
0.3%
? 1
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2376
86.2%
ASCII 381
 
13.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
임 760
32.0%
전 680
28.6%
답 572
24.1%
대 175
 
7.4%
장 39
 
1.6%
잡 30
 
1.3%
과 20
 
0.8%
창 20
 
0.8%
묘 17
 
0.7%
목 17
 
0.7%
Other values (13) 46
 
1.9%
ASCII
ValueCountFrequency (%)
, 284
74.5%
94
 
24.7%
- 1
 
0.3%
> 1
 
0.3%
? 1
 
0.3%

용 도 지 역
Text

Distinct89
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Memory size14.6 KiB
2023-12-12T21:54:24.886306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length26
Median length4
Mean length4.6821121
Min length2

Characters and Unicode

Total characters8690
Distinct characters50
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique37 ?
Unique (%)2.0%

Sample

1st row계획관리
2nd row자연녹지
3rd row자연녹지
4th row자연녹지
5th row자연녹지
ValueCountFrequency (%)
자연녹지 765
38.2%
계획관리 411
20.5%
보전녹지 198
 
9.9%
생산녹지 136
 
6.8%
보전관리 126
 
6.3%
생산관리 83
 
4.2%
농림지역 57
 
2.9%
농림 56
 
2.8%
제2종일반주거지역 16
 
0.8%
제1종일반주거 14
 
0.7%
Other values (50) 138
 
6.9%
2023-12-12T21:54:25.269631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
지 1290
14.8%
녹 1174
13.5%
자 804
9.3%
연 802
9.2%
관 666
7.7%
리 666
7.7%
계 446
 
5.1%
획 443
 
5.1%
보 370
 
4.3%
전 370
 
4.3%
Other values (40) 1659
19.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 8366
96.3%
Space Separator 144
 
1.7%
Other Punctuation 109
 
1.3%
Decimal Number 65
 
0.7%
Close Punctuation 3
 
< 0.1%
Open Punctuation 3
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
지 1290
15.4%
녹 1174
14.0%
자 804
9.6%
연 802
9.6%
관 666
8.0%
리 666
8.0%
계 446
 
5.3%
획 443
 
5.3%
보 370
 
4.4%
전 370
 
4.4%
Other values (32) 1335
16.0%
Other Punctuation
ValueCountFrequency (%)
, 84
77.1%
/ 24
 
22.0%
. 1
 
0.9%
Decimal Number
ValueCountFrequency (%)
1 37
56.9%
2 28
43.1%
Space Separator
ValueCountFrequency (%)
144
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 8366
96.3%
Common 324
 
3.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
지 1290
15.4%
녹 1174
14.0%
자 804
9.6%
연 802
9.6%
관 666
8.0%
리 666
8.0%
계 446
 
5.3%
획 443
 
5.3%
보 370
 
4.4%
전 370
 
4.4%
Other values (32) 1335
16.0%
Common
ValueCountFrequency (%)
144
44.4%
, 84
25.9%
1 37
 
11.4%
2 28
 
8.6%
/ 24
 
7.4%
) 3
 
0.9%
( 3
 
0.9%
. 1
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 8366
96.3%
ASCII 324
 
3.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
지 1290
15.4%
녹 1174
14.0%
자 804
9.6%
연 802
9.6%
관 666
8.0%
리 666
8.0%
계 446
 
5.3%
획 443
 
5.3%
보 370
 
4.4%
전 370
 
4.4%
Other values (32) 1335
16.0%
ASCII
ValueCountFrequency (%)
144
44.4%
, 84
25.9%
1 37
 
11.4%
2 28
 
8.6%
/ 24
 
7.4%
) 3
 
0.9%
( 3
 
0.9%
. 1
 
0.3%

허 가 목 적
Text

Distinct395
Distinct (%)21.3%
Missing0
Missing (%)0.0%
Memory size14.6 KiB
2023-12-12T21:54:25.618915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length56
Median length31
Mean length10.57597
Min length2

Characters and Unicode

Total characters19629
Distinct characters202
Distinct categories11 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique274 ?
Unique (%)14.8%

Sample

1st row제2종근린생활시설(차체 및 특장차 제조업)
2nd row제1종근린생활시설(소매점)및제2종근린생활시설(사무소)
3rd row제1종근린생활시설(소매점)및제3종근린생활시설(사무소)
4th row제1종근린생활시설(소매점)
5th row제1종근린생활시설(소매점)
ValueCountFrequency (%)
단독주택 445
 
14.6%
부지조성 227
 
7.4%
따른 220
 
7.2%
건축물의건축에따른부지조성 147
 
4.8%
제2종근생(제조업소 112
 
3.7%
제1종근생(소매점 101
 
3.3%
건립에 98
 
3.2%
건축물 92
 
3.0%
건축에 87
 
2.9%
제조업소 69
 
2.3%
Other values (367) 1449
47.6%
2023-12-12T21:54:26.136347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1238
 
6.3%
건 856
 
4.4%
제 816
 
4.2%
) 768
 
3.9%
( 768
 
3.9%
조 719
 
3.7%
종 630
 
3.2%
생 628
 
3.2%
근 628
 
3.2%
주 593
 
3.0%
Other values (192) 11985
61.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 16087
82.0%
Space Separator 1238
 
6.3%
Close Punctuation 769
 
3.9%
Open Punctuation 769
 
3.9%
Decimal Number 662
 
3.4%
Other Punctuation 57
 
0.3%
Dash Punctuation 19
 
0.1%
Math Symbol 13
 
0.1%
Connector Punctuation 7
 
< 0.1%
Other Number 5
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
건 856
 
5.3%
제 816
 
5.1%
조 719
 
4.5%
종 630
 
3.9%
생 628
 
3.9%
근 628
 
3.9%
주 593
 
3.7%
축 592
 
3.7%
택 582
 
3.6%
소 581
 
3.6%
Other values (163) 9462
58.8%
Decimal Number
ValueCountFrequency (%)
1 332
50.2%
2 312
47.1%
7 5
 
0.8%
5 3
 
0.5%
3 3
 
0.5%
4 2
 
0.3%
6 2
 
0.3%
9 1
 
0.2%
0 1
 
0.2%
8 1
 
0.2%
Other Number
ValueCountFrequency (%)
③ 1
20.0%
⑥ 1
20.0%
⑦ 1
20.0%
⑤ 1
20.0%
④ 1
20.0%
Other Punctuation
ValueCountFrequency (%)
, 47
82.5%
· 6
 
10.5%
. 3
 
5.3%
/ 1
 
1.8%
Close Punctuation
ValueCountFrequency (%)
) 768
99.9%
] 1
 
0.1%
Open Punctuation
ValueCountFrequency (%)
( 768
99.9%
[ 1
 
0.1%
Uppercase Letter
ValueCountFrequency (%)
H 2
66.7%
L 1
33.3%
Space Separator
ValueCountFrequency (%)
1238
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 19
100.0%
Math Symbol
ValueCountFrequency (%)
> 13
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 16087
82.0%
Common 3539
 
18.0%
Latin 3
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
건 856
 
5.3%
제 816
 
5.1%
조 719
 
4.5%
종 630
 
3.9%
생 628
 
3.9%
근 628
 
3.9%
주 593
 
3.7%
축 592
 
3.7%
택 582
 
3.6%
소 581
 
3.6%
Other values (163) 9462
58.8%
Common
ValueCountFrequency (%)
1238
35.0%
) 768
21.7%
( 768
21.7%
1 332
 
9.4%
2 312
 
8.8%
, 47
 
1.3%
- 19
 
0.5%
> 13
 
0.4%
_ 7
 
0.2%
· 6
 
0.2%
Other values (17) 29
 
0.8%
Latin
ValueCountFrequency (%)
H 2
66.7%
L 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 16087
82.0%
ASCII 3531
 
18.0%
None 6
 
< 0.1%
Enclosed Alphanum 5
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1238
35.1%
) 768
21.8%
( 768
21.8%
1 332
 
9.4%
2 312
 
8.8%
, 47
 
1.3%
- 19
 
0.5%
> 13
 
0.4%
_ 7
 
0.2%
7 5
 
0.1%
Other values (13) 22
 
0.6%
Hangul
ValueCountFrequency (%)
건 856
 
5.3%
제 816
 
5.1%
조 719
 
4.5%
종 630
 
3.9%
생 628
 
3.9%
근 628
 
3.9%
주 593
 
3.7%
축 592
 
3.7%
택 582
 
3.6%
소 581
 
3.6%
Other values (163) 9462
58.8%
None
ValueCountFrequency (%)
· 6
100.0%
Enclosed Alphanum
ValueCountFrequency (%)
③ 1
20.0%
⑥ 1
20.0%
⑦ 1
20.0%
⑤ 1
20.0%
④ 1
20.0%

허가면적( ㎡ )
Real number (ℝ)

MISSING 

Distinct323
Distinct (%)41.7%
Missing1082
Missing (%)58.3%
Infinite0
Infinite (%)0.0%
Mean1341.876
Minimum2
Maximum13403
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.4 KiB
2023-12-12T21:54:26.331594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile253
Q1502.75
median796
Q31536.25
95-th percentile4300
Maximum13403
Range13401
Interquartile range (IQR)1033.5

Descriptive statistics

Standard deviation1505.9385
Coefficient of variation (CV)1.1222636
Kurtosis15.163165
Mean1341.876
Median Absolute Deviation (MAD)380
Skewness3.1566963
Sum1038612
Variance2267850.8
MonotonicityNot monotonic
2023-12-12T21:54:26.550757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
550 16
 
0.9%
660 10
 
0.5%
348 6
 
0.3%
910 6
 
0.3%
495 6
 
0.3%
340 6
 
0.3%
332 6
 
0.3%
684 6
 
0.3%
1000 6
 
0.3%
540 6
 
0.3%
Other values (313) 700
37.7%
(Missing) 1082
58.3%
ValueCountFrequency (%)
2 2
0.1%
13 2
0.1%
22 2
0.1%
49 2
0.1%
66 2
0.1%
67 2
0.1%
68 2
0.1%
80 2
0.1%
100 2
0.1%
111 2
0.1%
ValueCountFrequency (%)
13403 2
0.1%
9560 2
0.1%
8927 2
0.1%
8089 2
0.1%
6954 2
0.1%
6950 2
0.1%
5089 2
0.1%
5043 2
0.1%
5007 2
0.1%
4960 2
0.1%

부지
Text

MISSING 

Distinct322
Distinct (%)41.7%
Missing1084
Missing (%)58.4%
Memory size14.6 KiB
2023-12-12T21:54:27.057014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length3
Mean length3.3432642
Min length1

Characters and Unicode

Total characters2581
Distinct characters18
Distinct categories6 ?
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 (%)0.3%

Sample

1st row1967
2nd row1449
3rd row1458
4th row625
5th row1477
ValueCountFrequency (%)
550 16
 
2.1%
660 8
 
1.0%
332 8
 
1.0%
348 6
 
0.8%
328 6
 
0.8%
540 6
 
0.8%
505 6
 
0.8%
316 4
 
0.5%
2574 4
 
0.5%
218 4
 
0.5%
Other values (314) 706
91.2%
2023-12-12T21:54:27.647312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3 342
13.3%
1 282
10.9%
5 276
10.7%
2 276
10.7%
4 260
10.1%
0 256
9.9%
6 235
9.1%
9 229
8.9%
8 217
8.4%
7 195
7.6%
Other values (8) 13
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2568
99.5%
Other Punctuation 6
 
0.2%
Space Separator 2
 
0.1%
Uppercase Letter 2
 
0.1%
Other Symbol 2
 
0.1%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 342
13.3%
1 282
11.0%
5 276
10.7%
2 276
10.7%
4 260
10.1%
0 256
10.0%
6 235
9.2%
9 229
8.9%
8 217
8.5%
7 195
7.6%
Other Punctuation
ValueCountFrequency (%)
. 3
50.0%
, 3
50.0%
Uppercase Letter
ValueCountFrequency (%)
A 1
50.0%
T 1
50.0%
Other Symbol
ValueCountFrequency (%)
㎡ 1
50.0%
㎥ 1
50.0%
Space Separator
ValueCountFrequency (%)
2
100.0%
Math Symbol
ValueCountFrequency (%)
= 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2579
99.9%
Latin 2
 
0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
3 342
13.3%
1 282
10.9%
5 276
10.7%
2 276
10.7%
4 260
10.1%
0 256
9.9%
6 235
9.1%
9 229
8.9%
8 217
8.4%
7 195
7.6%
Other values (6) 11
 
0.4%
Latin
ValueCountFrequency (%)
A 1
50.0%
T 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2579
99.9%
CJK Compat 2
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 342
13.3%
1 282
10.9%
5 276
10.7%
2 276
10.7%
4 260
10.1%
0 256
9.9%
6 235
9.1%
9 229
8.9%
8 217
8.4%
7 195
7.6%
Other values (6) 11
 
0.4%
CJK Compat
ValueCountFrequency (%)
㎡ 1
50.0%
㎥ 1
50.0%

도로
Real number (ℝ)

MISSING  ZEROS 

Distinct88
Distinct (%)39.6%
Missing1634
Missing (%)88.0%
Infinite0
Infinite (%)0.0%
Mean174.94595
Minimum0
Maximum2257
Zeros20
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size16.4 KiB
2023-12-12T21:54:27.792976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q127.25
median73
Q3281.75
95-th percentile495.85
Maximum2257
Range2257
Interquartile range (IQR)254.5

Descriptive statistics

Standard deviation276.45257
Coefficient of variation (CV)1.5802171
Kurtosis28.715343
Mean174.94595
Median Absolute Deviation (MAD)67
Skewness4.4958274
Sum38838
Variance76426.024
MonotonicityNot monotonic
2023-12-12T21:54:27.936441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 20
 
1.1%
56 8
 
0.4%
41 6
 
0.3%
45 4
 
0.2%
7 4
 
0.2%
291 4
 
0.2%
1 4
 
0.2%
18 4
 
0.2%
66 4
 
0.2%
30 4
 
0.2%
Other values (78) 160
 
8.6%
(Missing) 1634
88.0%
ValueCountFrequency (%)
0 20
1.1%
1 4
 
0.2%
3 2
 
0.1%
4 2
 
0.1%
6 2
 
0.1%
7 4
 
0.2%
10 2
 
0.1%
11 2
 
0.1%
13 2
 
0.1%
14 2
 
0.1%
ValueCountFrequency (%)
2257 2
0.1%
1066 2
0.1%
939 2
0.1%
684 2
0.1%
599 2
0.1%
496 2
0.1%
493 2
0.1%
458 2
0.1%
423 2
0.1%
419 2
0.1%

Interactions

2023-12-12T21:54:19.500935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:54:19.307056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:54:19.606600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:54:19.399814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T21:54:28.026568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분읍/면용 도 지 역허가면적( ㎡ )도로
구분1.0000.9580.6340.0000.000
읍/면0.9581.0000.9360.3780.471
용 도 지 역0.6340.9361.0000.6570.559
허가면적( ㎡ )0.0000.3780.6571.0000.849
도로0.0000.4710.5590.8491.000
2023-12-12T21:54:28.113357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
허가면적( ㎡ )도로구분
허가면적( ㎡ )1.0000.1890.000
도로0.1891.0000.000
구분0.0000.0001.000

Missing values

2023-12-12T21:54:19.715672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T21:54:19.882845image/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.
2023-12-12T21:54:20.027271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

구분허가일자준공 예정일읍/면리지목용 도 지 역허 가 목 적허가면적( ㎡ )부지도로
0상당구2015-01-022016-12-31현도상삼답계획관리제2종근린생활시설(차체 및 특장차 제조업)19671967<NA>
1상당구2015-01-022015-12-31장성<NA>답자연녹지제1종근린생활시설(소매점)및제2종근린생활시설(사무소)14491449<NA>
2상당구2015-01-022015-12-31장성<NA>답자연녹지제1종근린생활시설(소매점)및제3종근린생활시설(사무소)1505145847
3상당구2015-01-042016-12-31남이가마답자연녹지제1종근린생활시설(소매점)625625<NA>
4상당구2015-01-052016-01-31남이양촌창자연녹지제1종근린생활시설(소매점)14771477<NA>
5상당구2015-01-052016-06-30남이가좌임보전녹지제1종근린생활시설(소매점)371371<NA>
6상당구2015-01-072016-12-30남이부용외천임보전관리제1종근린생활시설(소매점)806806<NA>
7상당구2015-01-122016-12-30남이산막묘대계획관리 생산관리버섯재배사31292890239
8상당구15-Jan2016-12-31남이가좌전보전녹지, 자연녹지단독주택72266656
9상당구2015-01-202016-11-30남이상발임보전녹지, 자연녹지제1종근린생활시설(소매점)10481048<NA>
구분허가일자준공 예정일읍/면리지목용 도 지 역허 가 목 적허가면적( ㎡ )부지도로
1846흥덕구2015-12-162016-12-31외북동<NA>답자연녹지단독주택<NA><NA><NA>
1847흥덕구2015-12-212016-12-31평동<NA>임자연녹지1종근생 (소매점)<NA><NA><NA>
1848흥덕구2015-12-172016-12-31오송읍공북리답농림동물 및 식물관련시설(축사)<NA><NA><NA>
1849흥덕구2015-12-182017-11-30가경동<NA>묘자연녹지단독주택<NA><NA><NA>
1850흥덕구2015-12-182017-11-30가경동<NA>묘자연녹지단독주택<NA><NA><NA>
1851흥덕구2015-12-182017-11-30가경동<NA>묘자연녹지단독주택<NA><NA><NA>
1852흥덕구2015-12-222017-11-30강내면저산리답농림, 계획관리농업용창고<NA><NA><NA>
1853흥덕구2015-12-242016-11-30옥산면덕촌리전계획관리농업인 주택<NA><NA><NA>
1854흥덕구2015-12-032016-12-31화계동 송절동<NA>임자연녹지매장문화재 표본조사<NA><NA><NA>
1855흥덕구2015-12-032016-12-31화계동 송절동<NA>임자연녹지매장문화재 표본조사<NA><NA><NA>

Duplicate rows

Most frequently occurring

구분허가일자준공 예정일읍/면리지목용 도 지 역허 가 목 적허가면적( ㎡ )부지도로# duplicates
17청원구2015-01-222016-05-31오창성산임보전관리단독주택<NA><NA><NA>27
44청원구2015-06-252016-04-30오창석우임,전계획관리제2종근생(제조업소)<NA><NA><NA>8
62청원구2015-12-222017-10-31내수세교임생산관리 농림지역단독주택<NA><NA><NA>8
21청원구2015-02-162015-12-31내수학평임, 전계획관리단독주택<NA><NA><NA>7
52청원구2015-10-122018-07-31내수구성임자연녹지단독주택<NA><NA><NA>7
59청원구2015-11-252017-10-30내수풍정전계획관리단독주택<NA><NA><NA>7
60청원구2015-12-032016-12-31내수묵방임보전녹지/ 생산녹지단독주택<NA><NA><NA>7
81흥덕구2015-03-252016-03-31송절동<NA>임자연녹지단독주택<NA><NA><NA>7
16청원구2015-01-152016-12-31내수덕암전자연녹지단독주택<NA><NA><NA>6
104흥덕구2015-10-032017-08-31송절동<NA>답자연녹지단독주택<NA><NA><NA>6