Overview

Dataset statistics

Number of variables13
Number of observations616
Missing cells4206
Missing cells (%)52.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory63.3 KiB
Average record size in memory105.2 B

Variable types

Numeric1
DateTime1
Categorical1
Text7
Unsupported3

Dataset

Description2018년부터 2022년 현재까지 제주특별자치도 서귀포시 도로점용(굴착) 허가 자료입니다. (자료: 2018~2022 제주특별자치도 서귀포시 도로점용(굴착) 자료 요청건)
Author제주특별자치도 서귀포시
URLhttps://www.data.go.kr/data/15111561/fileData.do

Alerts

Unnamed: 8 has constant value ""Constant
Unnamed: 9 has constant value ""Constant
Unnamed: 10 has constant value ""Constant
Unnamed: 11 has constant value ""Constant
점 용 목 적 has 314 (51.0%) missing valuesMissing
Unnamed: 5 has 485 (78.7%) missing valuesMissing
Unnamed: 6 has 182 (29.5%) missing valuesMissing
Unnamed: 7 has 185 (30.0%) missing valuesMissing
Unnamed: 8 has 615 (99.8%) missing valuesMissing
Unnamed: 9 has 615 (99.8%) missing valuesMissing
Unnamed: 10 has 615 (99.8%) missing valuesMissing
Unnamed: 11 has 615 (99.8%) missing valuesMissing
Unnamed: 12 has 574 (93.2%) missing valuesMissing
점 용 목 적 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 6 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 7 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2023-12-13 00:27:20.536225
Analysis finished2023-12-13 00:27:21.360914
Duration0.82 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Unnamed: 0
Real number (ℝ)

Distinct614
Distinct (%)100.0%
Missing2
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean307.5
Minimum1
Maximum614
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.5 KiB
2023-12-13T09:27:21.415277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile31.65
Q1154.25
median307.5
Q3460.75
95-th percentile583.35
Maximum614
Range613
Interquartile range (IQR)306.5

Descriptive statistics

Standard deviation177.39081
Coefficient of variation (CV)0.57688069
Kurtosis-1.2
Mean307.5
Median Absolute Deviation (MAD)153.5
Skewness0
Sum188805
Variance31467.5
MonotonicityStrictly increasing
2023-12-13T09:27:21.516003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
414 1
 
0.2%
407 1
 
0.2%
408 1
 
0.2%
409 1
 
0.2%
410 1
 
0.2%
411 1
 
0.2%
412 1
 
0.2%
413 1
 
0.2%
415 1
 
0.2%
386 1
 
0.2%
Other values (604) 604
98.1%
(Missing) 2
 
0.3%
ValueCountFrequency (%)
1 1
0.2%
2 1
0.2%
3 1
0.2%
4 1
0.2%
5 1
0.2%
6 1
0.2%
7 1
0.2%
8 1
0.2%
9 1
0.2%
10 1
0.2%
ValueCountFrequency (%)
614 1
0.2%
613 1
0.2%
612 1
0.2%
611 1
0.2%
610 1
0.2%
609 1
0.2%
608 1
0.2%
607 1
0.2%
606 1
0.2%
605 1
0.2%

허가일
Date

Distinct172
Distinct (%)28.0%
Missing2
Missing (%)0.3%
Memory size4.9 KiB
Minimum2001-02-18 00:00:00
Maximum2031-12-18 00:00:00
2023-12-13T09:27:21.622368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:27:21.757769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

업무구분
Categorical

Distinct3
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size4.9 KiB
건축
443 
일반
171 
<NA>
 
2

Length

Max length4
Median length2
Mean length2.0064935
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row<NA>
3rd row건축
4th row건축
5th row건축

Common Values

ValueCountFrequency (%)
건축 443
71.9%
일반 171
 
27.8%
<NA> 2
 
0.3%

Length

2023-12-13T09:27:21.862066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T09:27:21.943112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
건축 443
71.9%
일반 171
 
27.8%
na 2
 
0.3%

점용지
Text

Distinct430
Distinct (%)70.0%
Missing2
Missing (%)0.3%
Memory size4.9 KiB
2023-12-13T09:27:22.116701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length175
Median length163
Mean length13.21987
Min length9

Characters and Unicode

Total characters8117
Distinct characters71
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique333 ?
Unique (%)54.2%

Sample

1st row중문동 1322-2번지
2nd row하효동 1213-4번지
3rd row동홍동 1330-3번지
4th row대포동 12607번지
5th row서호동 791-1번지
ValueCountFrequency (%)
동홍동 77
 
5.3%
서귀동 68
 
4.7%
토평동 63
 
4.3%
강정동 57
 
3.9%
서호동 56
 
3.8%
서홍동 50
 
3.4%
법환동 41
 
2.8%
호근동 34
 
2.3%
하효동 28
 
1.9%
중문동 28
 
1.9%
Other values (505) 953
65.5%
2023-12-13T09:27:22.425498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 757
 
9.3%
748
 
9.2%
번 705
 
8.7%
지 705
 
8.7%
1 602
 
7.4%
- 524
 
6.5%
2 382
 
4.7%
4 347
 
4.3%
3 338
 
4.2%
5 313
 
3.9%
Other values (61) 2696
33.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3638
44.8%
Decimal Number 3105
38.3%
Space Separator 748
 
9.2%
Dash Punctuation 524
 
6.5%
Control 94
 
1.2%
Math Symbol 4
 
< 0.1%
Open Punctuation 2
 
< 0.1%
Close Punctuation 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 757
20.8%
번 705
19.4%
지 705
19.4%
서 179
 
4.9%
홍 128
 
3.5%
호 90
 
2.5%
정 70
 
1.9%
평 69
 
1.9%
귀 68
 
1.9%
하 66
 
1.8%
Other values (45) 801
22.0%
Decimal Number
ValueCountFrequency (%)
1 602
19.4%
2 382
12.3%
4 347
11.2%
3 338
10.9%
5 313
10.1%
6 259
8.3%
7 240
 
7.7%
9 219
 
7.1%
8 214
 
6.9%
0 191
 
6.2%
Space Separator
ValueCountFrequency (%)
748
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 524
100.0%
Control
ValueCountFrequency (%)
94
100.0%
Math Symbol
ValueCountFrequency (%)
~ 4
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4479
55.2%
Hangul 3638
44.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 757
20.8%
번 705
19.4%
지 705
19.4%
서 179
 
4.9%
홍 128
 
3.5%
호 90
 
2.5%
정 70
 
1.9%
평 69
 
1.9%
귀 68
 
1.9%
하 66
 
1.8%
Other values (45) 801
22.0%
Common
ValueCountFrequency (%)
748
16.7%
1 602
13.4%
- 524
11.7%
2 382
8.5%
4 347
7.7%
3 338
7.5%
5 313
7.0%
6 259
 
5.8%
7 240
 
5.4%
9 219
 
4.9%
Other values (6) 507
11.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4479
55.2%
Hangul 3638
44.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 757
20.8%
번 705
19.4%
지 705
19.4%
서 179
 
4.9%
홍 128
 
3.5%
호 90
 
2.5%
정 70
 
1.9%
평 69
 
1.9%
귀 68
 
1.9%
하 66
 
1.8%
Other values (45) 801
22.0%
ASCII
ValueCountFrequency (%)
748
16.7%
1 602
13.4%
- 524
11.7%
2 382
8.5%
4 347
7.7%
3 338
7.5%
5 313
7.0%
6 259
 
5.8%
7 240
 
5.4%
9 219
 
4.9%
Other values (6) 507
11.3%

점 용 목 적
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing314
Missing (%)51.0%
Memory size4.9 KiB

Unnamed: 5
Text

MISSING 

Distinct84
Distinct (%)64.1%
Missing485
Missing (%)78.7%
Memory size4.9 KiB
2023-12-13T09:27:22.673002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length16
Mean length6.740458
Min length2

Characters and Unicode

Total characters883
Distinct characters33
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

Unique73 ?
Unique (%)55.7%

Sample

1st row주택외/ 연면적
2nd row436/ 770
3rd row485/ 650
4th row70/ 150
5th row1245/ 1843
ValueCountFrequency (%)
주택 33
 
15.1%
농경지 6
 
2.7%
793 3
 
1.4%
1236 3
 
1.4%
71 3
 
1.4%
384 3
 
1.4%
1180 2
 
0.9%
188 2
 
0.9%
112 2
 
0.9%
282 2
 
0.9%
Other values (140) 160
73.1%
2023-12-13T09:27:23.029301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 100
11.3%
/ 88
10.0%
88
10.0%
2 84
9.5%
3 74
 
8.4%
4 59
 
6.7%
8 56
 
6.3%
0 54
 
6.1%
7 43
 
4.9%
9 42
 
4.8%
Other values (23) 195
22.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 587
66.5%
Other Punctuation 108
 
12.2%
Other Letter 100
 
11.3%
Control 88
 
10.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
택 34
34.0%
주 34
34.0%
경 6
 
6.0%
농 6
 
6.0%
지 6
 
6.0%
아 1
 
1.0%
면 1
 
1.0%
연 1
 
1.0%
외 1
 
1.0%
트 1
 
1.0%
Other values (9) 9
 
9.0%
Decimal Number
ValueCountFrequency (%)
1 100
17.0%
2 84
14.3%
3 74
12.6%
4 59
10.1%
8 56
9.5%
0 54
9.2%
7 43
7.3%
9 42
7.2%
5 41
7.0%
6 34
 
5.8%
Other Punctuation
ValueCountFrequency (%)
/ 88
81.5%
. 19
 
17.6%
* 1
 
0.9%
Control
ValueCountFrequency (%)
88
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 783
88.7%
Hangul 100
 
11.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
택 34
34.0%
주 34
34.0%
경 6
 
6.0%
농 6
 
6.0%
지 6
 
6.0%
아 1
 
1.0%
면 1
 
1.0%
연 1
 
1.0%
외 1
 
1.0%
트 1
 
1.0%
Other values (9) 9
 
9.0%
Common
ValueCountFrequency (%)
1 100
12.8%
/ 88
11.2%
88
11.2%
2 84
10.7%
3 74
9.5%
4 59
7.5%
8 56
7.2%
0 54
6.9%
7 43
5.5%
9 42
5.4%
Other values (4) 95
12.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 783
88.7%
Hangul 100
 
11.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 100
12.8%
/ 88
11.2%
88
11.2%
2 84
10.7%
3 74
9.5%
4 59
7.5%
8 56
7.2%
0 54
6.9%
7 43
5.5%
9 42
5.4%
Other values (4) 95
12.1%
Hangul
ValueCountFrequency (%)
택 34
34.0%
주 34
34.0%
경 6
 
6.0%
농 6
 
6.0%
지 6
 
6.0%
아 1
 
1.0%
면 1
 
1.0%
연 1
 
1.0%
외 1
 
1.0%
트 1
 
1.0%
Other values (9) 9
 
9.0%

Unnamed: 6
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing182
Missing (%)29.5%
Memory size4.9 KiB

Unnamed: 7
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing185
Missing (%)30.0%
Memory size4.9 KiB

Unnamed: 8
Text

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing615
Missing (%)99.8%
Memory size4.9 KiB
2023-12-13T09:27:23.124997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters6
Distinct characters6
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row비계 (㎡)
ValueCountFrequency (%)
비계 1
50.0%
㎡ 1
50.0%
2023-12-13T09:27:23.510033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
비 1
16.7%
계 1
16.7%
1
16.7%
( 1
16.7%
㎡ 1
16.7%
) 1
16.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2
33.3%
Control 1
16.7%
Open Punctuation 1
16.7%
Other Symbol 1
16.7%
Close Punctuation 1
16.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
비 1
50.0%
계 1
50.0%
Control
ValueCountFrequency (%)
1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Other Symbol
ValueCountFrequency (%)
㎡ 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4
66.7%
Hangul 2
33.3%

Most frequent character per script

Common
ValueCountFrequency (%)
1
25.0%
( 1
25.0%
㎡ 1
25.0%
) 1
25.0%
Hangul
ValueCountFrequency (%)
비 1
50.0%
계 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3
50.0%
Hangul 2
33.3%
CJK Compat 1
 
16.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
비 1
50.0%
계 1
50.0%
ASCII
ValueCountFrequency (%)
1
33.3%
( 1
33.3%
) 1
33.3%
CJK Compat
ValueCountFrequency (%)
㎡ 1
100.0%

Unnamed: 9
Text

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing615
Missing (%)99.8%
Memory size4.9 KiB
2023-12-13T09:27:23.603493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters8
Distinct characters8
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row사설안내 표지판
ValueCountFrequency (%)
사설안내 1
50.0%
표지판 1
50.0%
2023-12-13T09:27:23.788177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
사 1
12.5%
설 1
12.5%
안 1
12.5%
내 1
12.5%
1
12.5%
표 1
12.5%
지 1
12.5%
판 1
12.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 7
87.5%
Control 1
 
12.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
사 1
14.3%
설 1
14.3%
안 1
14.3%
내 1
14.3%
표 1
14.3%
지 1
14.3%
판 1
14.3%
Control
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 7
87.5%
Common 1
 
12.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
사 1
14.3%
설 1
14.3%
안 1
14.3%
내 1
14.3%
표 1
14.3%
지 1
14.3%
판 1
14.3%
Common
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 7
87.5%
ASCII 1
 
12.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
사 1
14.3%
설 1
14.3%
안 1
14.3%
내 1
14.3%
표 1
14.3%
지 1
14.3%
판 1
14.3%
ASCII
ValueCountFrequency (%)
1
100.0%

Unnamed: 10
Text

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing615
Missing (%)99.8%
Memory size4.9 KiB
2023-12-13T09:27:23.890166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters8
Distinct characters7
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

Unique1 ?
Unique (%)100.0%

Sample

1st row전주 (강관주)
ValueCountFrequency (%)
전주 1
50.0%
강관주 1
50.0%
2023-12-13T09:27:24.078014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
주 2
25.0%
전 1
12.5%
1
12.5%
( 1
12.5%
강 1
12.5%
관 1
12.5%
) 1
12.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 5
62.5%
Control 1
 
12.5%
Open Punctuation 1
 
12.5%
Close Punctuation 1
 
12.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
주 2
40.0%
전 1
20.0%
강 1
20.0%
관 1
20.0%
Control
ValueCountFrequency (%)
1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 5
62.5%
Common 3
37.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
주 2
40.0%
전 1
20.0%
강 1
20.0%
관 1
20.0%
Common
ValueCountFrequency (%)
1
33.3%
( 1
33.3%
) 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 5
62.5%
ASCII 3
37.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
주 2
40.0%
전 1
20.0%
강 1
20.0%
관 1
20.0%
ASCII
ValueCountFrequency (%)
1
33.3%
( 1
33.3%
) 1
33.3%

Unnamed: 11
Text

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing615
Missing (%)99.8%
Memory size4.9 KiB
2023-12-13T09:27:24.187056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters8
Distinct characters8
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row무선전화 기지국
ValueCountFrequency (%)
무선전화 1
50.0%
기지국 1
50.0%
2023-12-13T09:27:24.371554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
무 1
12.5%
선 1
12.5%
전 1
12.5%
화 1
12.5%
1
12.5%
기 1
12.5%
지 1
12.5%
국 1
12.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 7
87.5%
Control 1
 
12.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
무 1
14.3%
선 1
14.3%
전 1
14.3%
화 1
14.3%
기 1
14.3%
지 1
14.3%
국 1
14.3%
Control
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 7
87.5%
Common 1
 
12.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
무 1
14.3%
선 1
14.3%
전 1
14.3%
화 1
14.3%
기 1
14.3%
지 1
14.3%
국 1
14.3%
Common
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 7
87.5%
ASCII 1
 
12.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
무 1
14.3%
선 1
14.3%
전 1
14.3%
화 1
14.3%
기 1
14.3%
지 1
14.3%
국 1
14.3%
ASCII
ValueCountFrequency (%)
1
100.0%

Unnamed: 12
Text

MISSING 

Distinct40
Distinct (%)95.2%
Missing574
Missing (%)93.2%
Memory size4.9 KiB
2023-12-13T09:27:24.556090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length16
Mean length9.9047619
Min length1

Characters and Unicode

Total characters416
Distinct characters117
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique38 ?
Unique (%)90.5%

Sample

1st row기타
2nd row
3rd row가감속차로 513㎡
4th row가감속차로 148㎡
5th row교통신호기 4
ValueCountFrequency (%)
1 8
 
8.0%
무인교통단속장비 5
 
5.0%
2 4
 
4.0%
교통신호기 3
 
3.0%
4 3
 
3.0%
cctv 3
 
3.0%
설치 3
 
3.0%
5 3
 
3.0%
버스승차대 2
 
2.0%
비가림승차대 2
 
2.0%
Other values (57) 64
64.0%
2023-12-13T09:27:24.838651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
46
 
11.1%
1 20
 
4.8%
15
 
3.6%
장 12
 
2.9%
비 12
 
2.9%
3 11
 
2.6%
차 10
 
2.4%
교 10
 
2.4%
C 10
 
2.4%
속 8
 
1.9%
Other values (107) 262
63.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 256
61.5%
Decimal Number 62
 
14.9%
Space Separator 46
 
11.1%
Uppercase Letter 20
 
4.8%
Control 15
 
3.6%
Other Symbol 6
 
1.4%
Lowercase Letter 5
 
1.2%
Other Punctuation 4
 
1.0%
Math Symbol 2
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
장 12
 
4.7%
비 12
 
4.7%
차 10
 
3.9%
교 10
 
3.9%
속 8
 
3.1%
기 8
 
3.1%
통 8
 
3.1%
단 7
 
2.7%
설 7
 
2.7%
인 6
 
2.3%
Other values (84) 168
65.6%
Decimal Number
ValueCountFrequency (%)
1 20
32.3%
3 11
17.7%
5 8
 
12.9%
2 7
 
11.3%
4 5
 
8.1%
8 3
 
4.8%
6 2
 
3.2%
9 2
 
3.2%
7 2
 
3.2%
0 2
 
3.2%
Uppercase Letter
ValueCountFrequency (%)
C 10
50.0%
V 4
 
20.0%
T 4
 
20.0%
L 1
 
5.0%
B 1
 
5.0%
Lowercase Letter
ValueCountFrequency (%)
m 3
60.0%
v 1
 
20.0%
t 1
 
20.0%
Space Separator
ValueCountFrequency (%)
46
100.0%
Control
ValueCountFrequency (%)
15
100.0%
Other Symbol
ValueCountFrequency (%)
㎡ 6
100.0%
Other Punctuation
ValueCountFrequency (%)
. 4
100.0%
Math Symbol
ValueCountFrequency (%)
= 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 256
61.5%
Common 135
32.5%
Latin 25
 
6.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
장 12
 
4.7%
비 12
 
4.7%
차 10
 
3.9%
교 10
 
3.9%
속 8
 
3.1%
기 8
 
3.1%
통 8
 
3.1%
단 7
 
2.7%
설 7
 
2.7%
인 6
 
2.3%
Other values (84) 168
65.6%
Common
ValueCountFrequency (%)
46
34.1%
1 20
14.8%
15
 
11.1%
3 11
 
8.1%
5 8
 
5.9%
2 7
 
5.2%
㎡ 6
 
4.4%
4 5
 
3.7%
. 4
 
3.0%
8 3
 
2.2%
Other values (5) 10
 
7.4%
Latin
ValueCountFrequency (%)
C 10
40.0%
V 4
 
16.0%
T 4
 
16.0%
m 3
 
12.0%
L 1
 
4.0%
B 1
 
4.0%
v 1
 
4.0%
t 1
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 256
61.5%
ASCII 154
37.0%
CJK Compat 6
 
1.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
46
29.9%
1 20
13.0%
15
 
9.7%
3 11
 
7.1%
C 10
 
6.5%
5 8
 
5.2%
2 7
 
4.5%
4 5
 
3.2%
V 4
 
2.6%
. 4
 
2.6%
Other values (12) 24
15.6%
Hangul
ValueCountFrequency (%)
장 12
 
4.7%
비 12
 
4.7%
차 10
 
3.9%
교 10
 
3.9%
속 8
 
3.1%
기 8
 
3.1%
통 8
 
3.1%
단 7
 
2.7%
설 7
 
2.7%
인 6
 
2.3%
Other values (84) 168
65.6%
CJK Compat
ValueCountFrequency (%)
㎡ 6
100.0%

Interactions

2023-12-13T09:27:20.881109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T09:27:24.912632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Unnamed: 0업무구분Unnamed: 5Unnamed: 12
Unnamed: 01.0000.4220.5730.967
업무구분0.4221.0000.5571.000
Unnamed: 50.5730.5571.0000.000
Unnamed: 120.9671.0000.0001.000
2023-12-13T09:27:25.000051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Unnamed: 0업무구분
Unnamed: 01.0000.322
업무구분0.3221.000

Missing values

2023-12-13T09:27:20.980761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T09:27:21.117881image/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.
2023-12-13T09:27:21.247983image/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

Unnamed: 0허가일업무구분점용지점 용 목 적Unnamed: 5Unnamed: 6Unnamed: 7Unnamed: 8Unnamed: 9Unnamed: 10Unnamed: 11Unnamed: 12
0<NA><NA><NA><NA>차량\n진출입\n(㎡)주택외/ 연면적오ㆍ우수관(전선관)매설NaN비계 (㎡)사설안내 표지판전주 (강관주)무선전화 기지국기타
1<NA><NA><NA><NA>NaN<NA>구경\n(mm)길이\n(m)<NA><NA><NA><NA><NA>
2118.01.03건축중문동 1322-2번지NaN<NA>1001.4<NA><NA><NA><NA><NA>
3218.01.03건축하효동 1213-4번지7.1<NA>1501.5<NA><NA><NA><NA><NA>
4318.01.03건축동홍동 1330-3번지NaN<NA>20025<NA><NA><NA><NA><NA>
5418.01.04건축대포동 12607번지NaN<NA>100\n1002\n1<NA><NA><NA><NA><NA>
6518.01.04건축서호동 791-1번지NaN<NA>2004.6<NA><NA><NA><NA><NA>
7618.01.04건축서호동 1595번지NaN<NA>2003.9<NA><NA><NA><NA><NA>
8718.01.04건축상예동 4999번지30436/ 7701508<NA><NA><NA><NA><NA>
9818.01.04건축서귀동 341번지NaN<NA>1502<NA><NA><NA><NA><NA>
Unnamed: 0허가일업무구분점용지점 용 목 적Unnamed: 5Unnamed: 6Unnamed: 7Unnamed: 8Unnamed: 9Unnamed: 10Unnamed: 11Unnamed: 12
60660518.12.24건축서귀동 435-1번지6.8주택NaNNaN<NA><NA><NA><NA><NA>
60760618.12.26일반동홍동 657-2번지 동홍동 657-5번지 동홍동 1301-3번지 보목동 881-7번지 대포동 1599-4번지 호근동 1763-5번지NaN<NA>NaNNaN<NA><NA><NA><NA>우편물 중간보관함 6
60860718.12.26건축서귀동 340번지7.2289/ 331NaNNaN<NA><NA><NA><NA><NA>
60960818.12.26건축보목동 760-2번지7118/ 3211003.5<NA><NA><NA><NA><NA>
61060918.12.31일반동홍동 1683번지NaN<NA>NaNNaN<NA><NA><NA><NA>전기충전기 4기 분전반 2면
61161018.12.31건축토평동 3245번지NaN<NA>20045<NA><NA><NA><NA><NA>
61261118.12.31건축하효동 1460번지NaN<NA>1005<NA><NA><NA><NA><NA>
61361218.12.31건축서귀동 961번지12.5224/ 2851001<NA><NA><NA><NA><NA>
61461318.12.31건축강정동 1275-4번지NaN<NA>1252<NA><NA><NA><NA><NA>
61561418.12.31건축중문동 2774번지NaN<NA>200\n2004.7\n3.3<NA><NA><NA><NA><NA>