Overview

Dataset statistics

Number of variables5
Number of observations428
Missing cells1
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory16.8 KiB
Average record size in memory40.3 B

Variable types

Text4
DateTime1

Dataset

Description대전광역시 중구 관내 부동산중개업소에 관한 데이터로 등록번호, 상호명, 대표자, 사업장소재지, 등록년월일을 제공합니다
URLhttps://www.data.go.kr/data/15035654/fileData.do

Alerts

등록번호 has unique valuesUnique

Reproduction

Analysis started2023-12-12 07:32:57.436363
Analysis finished2023-12-12 07:32:58.014778
Duration0.58 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

등록번호
Text

UNIQUE 

Distinct428
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size3.5 KiB
2023-12-12T16:32:58.157525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length16
Mean length12.378505
Min length4

Characters and Unicode

Total characters5298
Distinct characters14
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

Unique428 ?
Unique (%)100.0%

Sample

1st row중구 015
2nd row중구 021
3rd row중구40
4th row중구12
5th row중구 039
ValueCountFrequency (%)
중구 104
 
19.5%
30140-2019-00011 1
 
0.2%
30140-2019-00006 1
 
0.2%
30140-2019-00005 1
 
0.2%
30140-2019-00002 1
 
0.2%
30140-2020-00033 1
 
0.2%
30140-2018-00076 1
 
0.2%
30140-2018-00074 1
 
0.2%
30140-2018-00073 1
 
0.2%
30140-2018-00071 1
 
0.2%
Other values (419) 419
78.8%
2023-12-12T16:32:58.544057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1759
33.2%
1 656
 
12.4%
2 564
 
10.6%
- 539
 
10.2%
3 382
 
7.2%
4 381
 
7.2%
중 158
 
3.0%
구 158
 
3.0%
7 129
 
2.4%
5 126
 
2.4%
Other values (4) 446
 
8.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4339
81.9%
Dash Punctuation 539
 
10.2%
Other Letter 316
 
6.0%
Space Separator 104
 
2.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1759
40.5%
1 656
 
15.1%
2 564
 
13.0%
3 382
 
8.8%
4 381
 
8.8%
7 129
 
3.0%
5 126
 
2.9%
6 122
 
2.8%
9 118
 
2.7%
8 102
 
2.4%
Other Letter
ValueCountFrequency (%)
중 158
50.0%
구 158
50.0%
Dash Punctuation
ValueCountFrequency (%)
- 539
100.0%
Space Separator
ValueCountFrequency (%)
104
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4982
94.0%
Hangul 316
 
6.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1759
35.3%
1 656
 
13.2%
2 564
 
11.3%
- 539
 
10.8%
3 382
 
7.7%
4 381
 
7.6%
7 129
 
2.6%
5 126
 
2.5%
6 122
 
2.4%
9 118
 
2.4%
Other values (2) 206
 
4.1%
Hangul
ValueCountFrequency (%)
중 158
50.0%
구 158
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4982
94.0%
Hangul 316
 
6.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1759
35.3%
1 656
 
13.2%
2 564
 
11.3%
- 539
 
10.8%
3 382
 
7.7%
4 381
 
7.6%
7 129
 
2.6%
5 126
 
2.5%
6 122
 
2.4%
9 118
 
2.4%
Other values (2) 206
 
4.1%
Hangul
ValueCountFrequency (%)
중 158
50.0%
구 158
50.0%

상호명
Text

Distinct427
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Memory size3.5 KiB
2023-12-12T16:32:58.815589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length17
Mean length11.163551
Min length7

Characters and Unicode

Total characters4778
Distinct characters289
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique426 ?
Unique (%)99.5%

Sample

1st row현대부동산중개사무소
2nd row중앙부동산중개사무소
3rd row일신부동산중개사무소
4th row일부동산중개사무소
5th row성신부동산중개사무소
ValueCountFrequency (%)
행운공인중개사사무소 2
 
0.5%
삼부탑공인중개사사무소 1
 
0.2%
경원공인중개사사무소 1
 
0.2%
부부공인중개사사무소 1
 
0.2%
연합공인중개사사무소 1
 
0.2%
태종공인중개사사무소 1
 
0.2%
삼부랜드공인중개사사무소 1
 
0.2%
한결공인중개사사무소 1
 
0.2%
동남공인중개사사무소 1
 
0.2%
큐브공인중개사사무소 1
 
0.2%
Other values (421) 421
97.5%
2023-12-12T16:32:59.208186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
사 823
17.2%
중 435
 
9.1%
개 429
 
9.0%
소 426
 
8.9%
무 423
 
8.9%
인 398
 
8.3%
공 394
 
8.2%
동 80
 
1.7%
산 67
 
1.4%
부 67
 
1.4%
Other values (279) 1236
25.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4713
98.6%
Decimal Number 23
 
0.5%
Uppercase Letter 23
 
0.5%
Lowercase Letter 10
 
0.2%
Space Separator 6
 
0.1%
Dash Punctuation 1
 
< 0.1%
Close Punctuation 1
 
< 0.1%
Open Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
사 823
17.5%
중 435
 
9.2%
개 429
 
9.1%
소 426
 
9.0%
무 423
 
9.0%
인 398
 
8.4%
공 394
 
8.4%
동 80
 
1.7%
산 67
 
1.4%
부 67
 
1.4%
Other values (254) 1171
24.8%
Uppercase Letter
ValueCountFrequency (%)
K 8
34.8%
O 4
17.4%
S 3
 
13.0%
T 3
 
13.0%
B 1
 
4.3%
M 1
 
4.3%
N 1
 
4.3%
E 1
 
4.3%
H 1
 
4.3%
Decimal Number
ValueCountFrequency (%)
1 11
47.8%
4 5
21.7%
2 3
 
13.0%
3 2
 
8.7%
8 1
 
4.3%
5 1
 
4.3%
Lowercase Letter
ValueCountFrequency (%)
e 4
40.0%
h 2
20.0%
y 1
 
10.0%
k 1
 
10.0%
s 1
 
10.0%
w 1
 
10.0%
Space Separator
ValueCountFrequency (%)
6
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4713
98.6%
Latin 33
 
0.7%
Common 32
 
0.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
사 823
17.5%
중 435
 
9.2%
개 429
 
9.1%
소 426
 
9.0%
무 423
 
9.0%
인 398
 
8.4%
공 394
 
8.4%
동 80
 
1.7%
산 67
 
1.4%
부 67
 
1.4%
Other values (254) 1171
24.8%
Latin
ValueCountFrequency (%)
K 8
24.2%
O 4
12.1%
e 4
12.1%
S 3
 
9.1%
T 3
 
9.1%
h 2
 
6.1%
B 1
 
3.0%
y 1
 
3.0%
M 1
 
3.0%
k 1
 
3.0%
Other values (5) 5
15.2%
Common
ValueCountFrequency (%)
1 11
34.4%
6
18.8%
4 5
15.6%
2 3
 
9.4%
3 2
 
6.2%
8 1
 
3.1%
- 1
 
3.1%
5 1
 
3.1%
) 1
 
3.1%
( 1
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4713
98.6%
ASCII 65
 
1.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
사 823
17.5%
중 435
 
9.2%
개 429
 
9.1%
소 426
 
9.0%
무 423
 
9.0%
인 398
 
8.4%
공 394
 
8.4%
동 80
 
1.7%
산 67
 
1.4%
부 67
 
1.4%
Other values (254) 1171
24.8%
ASCII
ValueCountFrequency (%)
1 11
16.9%
K 8
12.3%
6
 
9.2%
4 5
 
7.7%
O 4
 
6.2%
e 4
 
6.2%
S 3
 
4.6%
T 3
 
4.6%
2 3
 
4.6%
3 2
 
3.1%
Other values (15) 16
24.6%

성명(대표자)
Text

Distinct421
Distinct (%)98.4%
Missing0
Missing (%)0.0%
Memory size3.5 KiB
2023-12-12T16:32:59.589273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.9953271
Min length2

Characters and Unicode

Total characters1282
Distinct characters161
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique414 ?
Unique (%)96.7%

Sample

1st row장동호
2nd row권용윤
3rd row강호석
4th row유일준
5th row송순자
ValueCountFrequency (%)
김종윤 2
 
0.5%
이기정 2
 
0.5%
이승희 2
 
0.5%
김재식 2
 
0.5%
오순희 2
 
0.5%
박종호 2
 
0.5%
이석주 2
 
0.5%
현상완 1
 
0.2%
빈정현 1
 
0.2%
이영수 1
 
0.2%
Other values (411) 411
96.0%
2023-12-12T16:33:00.204935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
김 82
 
6.4%
이 74
 
5.8%
정 40
 
3.1%
영 39
 
3.0%
박 35
 
2.7%
희 32
 
2.5%
진 30
 
2.3%
현 29
 
2.3%
숙 26
 
2.0%
경 26
 
2.0%
Other values (151) 869
67.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1282
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
김 82
 
6.4%
이 74
 
5.8%
정 40
 
3.1%
영 39
 
3.0%
박 35
 
2.7%
희 32
 
2.5%
진 30
 
2.3%
현 29
 
2.3%
숙 26
 
2.0%
경 26
 
2.0%
Other values (151) 869
67.8%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1282
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
김 82
 
6.4%
이 74
 
5.8%
정 40
 
3.1%
영 39
 
3.0%
박 35
 
2.7%
희 32
 
2.5%
진 30
 
2.3%
현 29
 
2.3%
숙 26
 
2.0%
경 26
 
2.0%
Other values (151) 869
67.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1282
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
김 82
 
6.4%
이 74
 
5.8%
정 40
 
3.1%
영 39
 
3.0%
박 35
 
2.7%
희 32
 
2.5%
진 30
 
2.3%
현 29
 
2.3%
숙 26
 
2.0%
경 26
 
2.0%
Other values (151) 869
67.8%

주소(대표자)
Text

Distinct418
Distinct (%)97.9%
Missing1
Missing (%)0.2%
Memory size3.5 KiB
2023-12-12T16:33:00.523061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length62
Median length47
Mean length28.9274
Min length16

Characters and Unicode

Total characters12352
Distinct characters187
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique409 ?
Unique (%)95.8%

Sample

1st row대전광역시 중구 목중로 41 (목동)
2nd row대전광역시 중구 목척2길 40 (은행동)
3rd row대전광역시 중구 보문로337번길 41
4th row대전광역시 중구 보문로 309 (선화동)
5th row대전광역시 중구 보문로337번길 41 (선화동)
ValueCountFrequency (%)
대전광역시 427
 
18.0%
중구 427
 
18.0%
상가동 50
 
2.1%
1층 44
 
1.9%
대종로 29
 
1.2%
선화동 25
 
1.1%
유천동 22
 
0.9%
태평로 20
 
0.8%
보문로 19
 
0.8%
대둔산로 17
 
0.7%
Other values (591) 1298
54.6%
2023-12-12T16:33:01.039770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1970
 
15.9%
1 719
 
5.8%
대 568
 
4.6%
동 503
 
4.1%
중 489
 
4.0%
전 436
 
3.5%
시 433
 
3.5%
역 429
 
3.5%
광 428
 
3.5%
구 428
 
3.5%
Other values (177) 5949
48.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 7224
58.5%
Decimal Number 2187
 
17.7%
Space Separator 1970
 
15.9%
Open Punctuation 389
 
3.1%
Close Punctuation 389
 
3.1%
Other Punctuation 119
 
1.0%
Dash Punctuation 61
 
0.5%
Uppercase Letter 13
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
대 568
 
7.9%
동 503
 
7.0%
중 489
 
6.8%
전 436
 
6.0%
시 433
 
6.0%
역 429
 
5.9%
광 428
 
5.9%
구 428
 
5.9%
로 425
 
5.9%
호 164
 
2.3%
Other values (157) 2921
40.4%
Decimal Number
ValueCountFrequency (%)
1 719
32.9%
0 259
 
11.8%
2 213
 
9.7%
3 201
 
9.2%
4 169
 
7.7%
5 161
 
7.4%
6 127
 
5.8%
8 123
 
5.6%
7 121
 
5.5%
9 94
 
4.3%
Other Punctuation
ValueCountFrequency (%)
, 111
93.3%
@ 7
 
5.9%
/ 1
 
0.8%
Uppercase Letter
ValueCountFrequency (%)
A 7
53.8%
B 5
38.5%
D 1
 
7.7%
Space Separator
ValueCountFrequency (%)
1970
100.0%
Open Punctuation
ValueCountFrequency (%)
( 389
100.0%
Close Punctuation
ValueCountFrequency (%)
) 389
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 61
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 7224
58.5%
Common 5115
41.4%
Latin 13
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
대 568
 
7.9%
동 503
 
7.0%
중 489
 
6.8%
전 436
 
6.0%
시 433
 
6.0%
역 429
 
5.9%
광 428
 
5.9%
구 428
 
5.9%
로 425
 
5.9%
호 164
 
2.3%
Other values (157) 2921
40.4%
Common
ValueCountFrequency (%)
1970
38.5%
1 719
 
14.1%
( 389
 
7.6%
) 389
 
7.6%
0 259
 
5.1%
2 213
 
4.2%
3 201
 
3.9%
4 169
 
3.3%
5 161
 
3.1%
6 127
 
2.5%
Other values (7) 518
 
10.1%
Latin
ValueCountFrequency (%)
A 7
53.8%
B 5
38.5%
D 1
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 7224
58.5%
ASCII 5128
41.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1970
38.4%
1 719
 
14.0%
( 389
 
7.6%
) 389
 
7.6%
0 259
 
5.1%
2 213
 
4.2%
3 201
 
3.9%
4 169
 
3.3%
5 161
 
3.1%
6 127
 
2.5%
Other values (10) 531
 
10.4%
Hangul
ValueCountFrequency (%)
대 568
 
7.9%
동 503
 
7.0%
중 489
 
6.8%
전 436
 
6.0%
시 433
 
6.0%
역 429
 
5.9%
광 428
 
5.9%
구 428
 
5.9%
로 425
 
5.9%
호 164
 
2.3%
Other values (157) 2921
40.4%

등록년월일
Date

Distinct390
Distinct (%)91.1%
Missing0
Missing (%)0.0%
Memory size3.5 KiB
Minimum1984-05-21 00:00:00
Maximum2023-03-24 00:00:00
2023-12-12T16:33:01.235795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:33:01.744672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Missing values

2023-12-12T16:32:57.846965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T16:32:57.966102image/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

등록번호상호명성명(대표자)주소(대표자)등록년월일
0중구 015현대부동산중개사무소장동호대전광역시 중구 목중로 41 (목동)1984-05-21
1중구 021중앙부동산중개사무소권용윤대전광역시 중구 목척2길 40 (은행동)1984-05-23
2중구40일신부동산중개사무소강호석대전광역시 중구 보문로337번길 411984-05-29
3중구12일부동산중개사무소유일준대전광역시 중구 보문로 309 (선화동)1984-05-29
4중구 039성신부동산중개사무소송순자대전광역시 중구 보문로337번길 41 (선화동)1984-05-29
5중구52단골부동산중개사무소송인서대전광역시 중구 문화로133번길 91 (유천동)1984-06-02
6중구51신한부동산중개사무소윤규오대전광역시 중구 대종로 201-1 (석교동)1984-06-02
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