Overview

Dataset statistics

Number of variables7
Number of observations2924
Missing cells7
Missing cells (%)< 0.1%
Duplicate rows2
Duplicate rows (%)0.1%
Total size in memory160.0 KiB
Average record size in memory56.0 B

Variable types

Categorical2
Text5

Dataset

Description충청북도내 11개 시군에 운영중인 부동산 중개업소에 대한 데이터로 등록번호, 상호, 지역, 소재지, 대표자, 상태구분 등과 같은 정보를 제공합니다.
URLhttps://www.data.go.kr/data/3077415/fileData.do

Alerts

Dataset has 2 (0.1%) duplicate rowsDuplicates
중개업소 구분 is highly imbalanced (85.3%)Imbalance

Reproduction

Analysis started2023-12-12 02:19:23.542598
Analysis finished2023-12-12 02:19:24.791889
Duration1.25 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군구명
Categorical

Distinct20
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size23.0 KiB
충청북도 청주시흥덕구
771 
충청북도 충주시
363 
충청북도 청주시청원구
358 
충청북도 청주시서원구
347 
충청북도 청주시상당구
339 
Other values (15)
746 

Length

Max length11
Median length11
Mean length9.8628591
Min length7

Unique

Unique5 ?
Unique (%)0.2%

Sample

1st row충청북도 청주시상당구
2nd row충청북도 청주시상당구
3rd row충청북도 청주시상당구
4th row충청북도 청주시상당구
5th row충청북도 청주시상당구

Common Values

ValueCountFrequency (%)
충청북도 청주시흥덕구 771
26.4%
충청북도 충주시 363
12.4%
충청북도 청주시청원구 358
12.2%
충청북도 청주시서원구 347
11.9%
충청북도 청주시상당구 339
11.6%
충청북도 음성군 192
 
6.6%
충청북도 제천시 180
 
6.2%
충청북도 진천군 144
 
4.9%
충청북도 증평군 53
 
1.8%
충청북도 괴산군 42
 
1.4%
Other values (10) 135
 
4.6%

Length

2023-12-12T11:19:24.879967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
충청북도 2917
49.9%
청주시흥덕구 771
 
13.2%
충주시 363
 
6.2%
청주시청원구 358
 
6.1%
청주시서원구 347
 
5.9%
청주시상당구 339
 
5.8%
음성군 192
 
3.3%
제천시 180
 
3.1%
진천군 144
 
2.5%
증평군 53
 
0.9%
Other values (14) 184
 
3.1%

법정동
Text

Distinct175
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Memory size23.0 KiB
2023-12-12T11:19:25.180791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length3.0266758
Min length2

Characters and Unicode

Total characters8850
Distinct characters130
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

Unique35 ?
Unique (%)1.2%

Sample

1st row문의면
2nd row영운동
3rd row수동
4th row영운동
5th row남주동
ValueCountFrequency (%)
오송읍 198
 
6.8%
복대동 159
 
5.4%
용암동 144
 
4.9%
오창읍 129
 
4.4%
가경동 104
 
3.6%
봉명동 89
 
3.0%
율량동 86
 
2.9%
연수동 76
 
2.6%
진천읍 63
 
2.2%
산남동 56
 
1.9%
Other values (165) 1820
62.2%
2023-12-12T11:19:25.750437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 1874
21.2%
읍 722
 
8.2%
면 399
 
4.5%
오 327
 
3.7%
송 244
 
2.8%
대 234
 
2.6%
암 222
 
2.5%
산 219
 
2.5%
용 198
 
2.2%
천 187
 
2.1%
Other values (120) 4224
47.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 8826
99.7%
Decimal Number 24
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 1874
21.2%
읍 722
 
8.2%
면 399
 
4.5%
오 327
 
3.7%
송 244
 
2.8%
대 234
 
2.7%
암 222
 
2.5%
산 219
 
2.5%
용 198
 
2.2%
천 187
 
2.1%
Other values (117) 4200
47.6%
Decimal Number
ValueCountFrequency (%)
1 11
45.8%
2 10
41.7%
3 3
 
12.5%

Most occurring scripts

ValueCountFrequency (%)
Hangul 8826
99.7%
Common 24
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 1874
21.2%
읍 722
 
8.2%
면 399
 
4.5%
오 327
 
3.7%
송 244
 
2.8%
대 234
 
2.7%
암 222
 
2.5%
산 219
 
2.5%
용 198
 
2.2%
천 187
 
2.1%
Other values (117) 4200
47.6%
Common
ValueCountFrequency (%)
1 11
45.8%
2 10
41.7%
3 3
 
12.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 8826
99.7%
ASCII 24
 
0.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 1874
21.2%
읍 722
 
8.2%
면 399
 
4.5%
오 327
 
3.7%
송 244
 
2.8%
대 234
 
2.7%
암 222
 
2.5%
산 219
 
2.5%
용 198
 
2.2%
천 187
 
2.1%
Other values (117) 4200
47.6%
ASCII
ValueCountFrequency (%)
1 11
45.8%
2 10
41.7%
3 3
 
12.5%

등록번호
Text

Distinct2918
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Memory size23.0 KiB
2023-12-12T11:19:26.047556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length16
Mean length14.555746
Min length7

Characters and Unicode

Total characters42561
Distinct characters15
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

Unique2912 ?
Unique (%)99.6%

Sample

1st row가-3820-659
2nd row43111-2016-00023
3rd row나-38020000-49
4th row가-38020000-487
5th row가-38020000-253
ValueCountFrequency (%)
43770-2020-00006 2
 
0.1%
43770-2021-00011 2
 
0.1%
43770-2020-00008 2
 
0.1%
43770-2019-00024 2
 
0.1%
43770-2020-00005 2
 
0.1%
43770-2021-00007 2
 
0.1%
가 2
 
0.1%
3804-271 1
 
< 0.1%
3804-151 1
 
< 0.1%
3804-262 1
 
< 0.1%
Other values (2909) 2909
99.4%
2023-12-12T11:19:26.494729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 12419
29.2%
1 5644
13.3%
- 5415
12.7%
2 5135
12.1%
3 5025
11.8%
4 3181
 
7.5%
8 1665
 
3.9%
7 1154
 
2.7%
5 990
 
2.3%
6 789
 
1.9%
Other values (5) 1144
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 36646
86.1%
Dash Punctuation 5415
 
12.7%
Other Letter 498
 
1.2%
Space Separator 2
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 12419
33.9%
1 5644
15.4%
2 5135
14.0%
3 5025
13.7%
4 3181
 
8.7%
8 1665
 
4.5%
7 1154
 
3.1%
5 990
 
2.7%
6 789
 
2.2%
9 644
 
1.8%
Other Letter
ValueCountFrequency (%)
가 476
95.6%
나 21
 
4.2%
공 1
 
0.2%
Dash Punctuation
ValueCountFrequency (%)
- 5415
100.0%
Space Separator
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 42063
98.8%
Hangul 498
 
1.2%

Most frequent character per script

Common
ValueCountFrequency (%)
0 12419
29.5%
1 5644
13.4%
- 5415
12.9%
2 5135
12.2%
3 5025
11.9%
4 3181
 
7.6%
8 1665
 
4.0%
7 1154
 
2.7%
5 990
 
2.4%
6 789
 
1.9%
Other values (2) 646
 
1.5%
Hangul
ValueCountFrequency (%)
가 476
95.6%
나 21
 
4.2%
공 1
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 42063
98.8%
Hangul 498
 
1.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 12419
29.5%
1 5644
13.4%
- 5415
12.9%
2 5135
12.2%
3 5025
11.9%
4 3181
 
7.6%
8 1665
 
4.0%
7 1154
 
2.7%
5 990
 
2.4%
6 789
 
1.9%
Other values (2) 646
 
1.5%
Hangul
ValueCountFrequency (%)
가 476
95.6%
나 21
 
4.2%
공 1
 
0.2%

중개업소 구분
Categorical

IMBALANCE 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size23.0 KiB
공인중개사
2828 
중개인
 
76
법인
 
20

Length

Max length5
Median length5
Mean length4.9274966
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
공인중개사 2828
96.7%
중개인 76
 
2.6%
법인 20
 
0.7%

Length

2023-12-12T11:19:26.691974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:19:26.822727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
공인중개사 2828
96.7%
중개인 76
 
2.6%
법인 20
 
0.7%

중개업자명
Text

Distinct2670
Distinct (%)91.3%
Missing0
Missing (%)0.0%
Memory size23.0 KiB
2023-12-12T11:19:27.123635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length2.995212
Min length2

Characters and Unicode

Total characters8758
Distinct characters240
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

Unique2481 ?
Unique (%)84.8%

Sample

1st row황재춘
2nd row윤재식
3rd row이용석
4th row이응복
5th row류성열
ValueCountFrequency (%)
김은경 6
 
0.2%
이영숙 5
 
0.2%
김정숙 5
 
0.2%
김영미 5
 
0.2%
길갑영 5
 
0.2%
이미숙 5
 
0.2%
이미선 5
 
0.2%
김지영 4
 
0.1%
김정미 4
 
0.1%
이혜숙 4
 
0.1%
Other values (2660) 2876
98.4%
2023-12-12T11:19:27.558855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
김 597
 
6.8%
이 474
 
5.4%
정 317
 
3.6%
영 315
 
3.6%
박 217
 
2.5%
경 201
 
2.3%
희 188
 
2.1%
미 186
 
2.1%
숙 181
 
2.1%
현 162
 
1.8%
Other values (230) 5920
67.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 8758
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
김 597
 
6.8%
이 474
 
5.4%
정 317
 
3.6%
영 315
 
3.6%
박 217
 
2.5%
경 201
 
2.3%
희 188
 
2.1%
미 186
 
2.1%
숙 181
 
2.1%
현 162
 
1.8%
Other values (230) 5920
67.6%

Most occurring scripts

ValueCountFrequency (%)
Hangul 8758
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
김 597
 
6.8%
이 474
 
5.4%
정 317
 
3.6%
영 315
 
3.6%
박 217
 
2.5%
경 201
 
2.3%
희 188
 
2.1%
미 186
 
2.1%
숙 181
 
2.1%
현 162
 
1.8%
Other values (230) 5920
67.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 8758
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
김 597
 
6.8%
이 474
 
5.4%
정 317
 
3.6%
영 315
 
3.6%
박 217
 
2.5%
경 201
 
2.3%
희 188
 
2.1%
미 186
 
2.1%
숙 181
 
2.1%
현 162
 
1.8%
Other values (230) 5920
67.6%

중개업소명
Text

Distinct2327
Distinct (%)79.6%
Missing0
Missing (%)0.0%
Memory size23.0 KiB
2023-12-12T11:19:27.811412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length25
Median length21
Mean length11.298906
Min length6

Characters and Unicode

Total characters33038
Distinct characters544
Distinct categories11 ?
Distinct scripts4 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1997 ?
Unique (%)68.3%

Sample

1st row문의부동산중개사무소
2nd row뉴평화부동산중개인사무소
3rd row보은부동산중개인사무소
4th row신영부동산중개인사무소
5th row유성부동산중개인사무소
ValueCountFrequency (%)
공인중개사사무소 11
 
0.4%
사무소 9
 
0.3%
드림공인중개사사무소 8
 
0.3%
우리공인중개사사무소 8
 
0.3%
미소공인중개사사무소 7
 
0.2%
일등공인중개사사무소 7
 
0.2%
대박공인중개사사무소 7
 
0.2%
삼성공인중개사사무소 7
 
0.2%
행운공인중개사사무소 7
 
0.2%
신한공인중개사사무소 7
 
0.2%
Other values (2328) 2890
97.4%
2023-12-12T11:19:28.814095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
사 5652
17.1%
중 2951
 
8.9%
개 2931
 
8.9%
소 2885
 
8.7%
무 2862
 
8.7%
인 2861
 
8.7%
공 2775
 
8.4%
동 685
 
2.1%
산 575
 
1.7%
부 545
 
1.6%
Other values (534) 8316
25.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 32564
98.6%
Uppercase Letter 220
 
0.7%
Decimal Number 140
 
0.4%
Space Separator 44
 
0.1%
Lowercase Letter 34
 
0.1%
Close Punctuation 13
 
< 0.1%
Open Punctuation 13
 
< 0.1%
Other Punctuation 6
 
< 0.1%
Dash Punctuation 2
 
< 0.1%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
사 5652
17.4%
중 2951
 
9.1%
개 2931
 
9.0%
소 2885
 
8.9%
무 2862
 
8.8%
인 2861
 
8.8%
공 2775
 
8.5%
동 685
 
2.1%
산 575
 
1.8%
부 545
 
1.7%
Other values (483) 7842
24.1%
Uppercase Letter
ValueCountFrequency (%)
K 28
12.7%
N 18
 
8.2%
S 18
 
8.2%
B 17
 
7.7%
L 17
 
7.7%
O 16
 
7.3%
T 14
 
6.4%
A 14
 
6.4%
E 13
 
5.9%
W 9
 
4.1%
Other values (12) 56
25.5%
Decimal Number
ValueCountFrequency (%)
1 66
47.1%
4 22
 
15.7%
2 15
 
10.7%
9 14
 
10.0%
3 7
 
5.0%
8 7
 
5.0%
5 4
 
2.9%
0 3
 
2.1%
6 2
 
1.4%
Lowercase Letter
ValueCountFrequency (%)
e 17
50.0%
h 6
 
17.6%
w 5
 
14.7%
s 1
 
2.9%
m 1
 
2.9%
n 1
 
2.9%
k 1
 
2.9%
b 1
 
2.9%
a 1
 
2.9%
Other Punctuation
ValueCountFrequency (%)
# 2
33.3%
, 1
16.7%
/ 1
16.7%
. 1
16.7%
& 1
16.7%
Space Separator
ValueCountFrequency (%)
44
100.0%
Close Punctuation
ValueCountFrequency (%)
) 13
100.0%
Open Punctuation
ValueCountFrequency (%)
( 13
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%
Math Symbol
ValueCountFrequency (%)
+ 1
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 32554
98.5%
Latin 254
 
0.8%
Common 219
 
0.7%
Han 11
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
사 5652
17.4%
중 2951
 
9.1%
개 2931
 
9.0%
소 2885
 
8.9%
무 2862
 
8.8%
인 2861
 
8.8%
공 2775
 
8.5%
동 685
 
2.1%
산 575
 
1.8%
부 545
 
1.7%
Other values (474) 7832
24.1%
Latin
ValueCountFrequency (%)
K 28
 
11.0%
N 18
 
7.1%
S 18
 
7.1%
e 17
 
6.7%
B 17
 
6.7%
L 17
 
6.7%
O 16
 
6.3%
T 14
 
5.5%
A 14
 
5.5%
E 13
 
5.1%
Other values (21) 82
32.3%
Common
ValueCountFrequency (%)
1 66
30.1%
44
20.1%
4 22
 
10.0%
2 15
 
6.8%
9 14
 
6.4%
) 13
 
5.9%
( 13
 
5.9%
3 7
 
3.2%
8 7
 
3.2%
5 4
 
1.8%
Other values (9) 14
 
6.4%
Han
ValueCountFrequency (%)
山 2
18.2%
川 1
9.1%
作 1
9.1%
時 1
9.1%
潤 1
9.1%
新 1
9.1%
鎬 1
9.1%
正 1
9.1%
林 1
9.1%
松 1
9.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 32553
98.5%
ASCII 473
 
1.4%
CJK 11
 
< 0.1%
None 1
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
사 5652
17.4%
중 2951
 
9.1%
개 2931
 
9.0%
소 2885
 
8.9%
무 2862
 
8.8%
인 2861
 
8.8%
공 2775
 
8.5%
동 685
 
2.1%
산 575
 
1.8%
부 545
 
1.7%
Other values (473) 7831
24.1%
ASCII
ValueCountFrequency (%)
1 66
 
14.0%
44
 
9.3%
K 28
 
5.9%
4 22
 
4.7%
N 18
 
3.8%
S 18
 
3.8%
e 17
 
3.6%
B 17
 
3.6%
L 17
 
3.6%
O 16
 
3.4%
Other values (40) 210
44.4%
CJK
ValueCountFrequency (%)
山 2
18.2%
川 1
9.1%
作 1
9.1%
時 1
9.1%
潤 1
9.1%
新 1
9.1%
鎬 1
9.1%
正 1
9.1%
林 1
9.1%
松 1
9.1%
None
ValueCountFrequency (%)
㈜ 1
100.0%

도로명주소(지번)
Text

Distinct2769
Distinct (%)94.9%
Missing7
Missing (%)0.2%
Memory size23.0 KiB
2023-12-12T11:19:29.371521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length63
Median length52
Mean length29.836476
Min length16

Characters and Unicode

Total characters87033
Distinct characters410
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

Unique2628 ?
Unique (%)90.1%

Sample

1st row충청북도 청주시 상당구 문의면 문의시내로 22
2nd row충청북도 청주시 상당구 단재로77번길 1 (영운동)
3rd row충청북도 청주시 상당구 대성로 200 (수동)
4th row충청북도 청주시 상당구 영운로 8
5th row충청북도 청주시 상당구 남사로112번길 62-2 (남주동)
ValueCountFrequency (%)
충청북도 2910
 
16.0%
청주시 1814
 
9.9%
흥덕구 771
 
4.2%
충주시 363
 
2.0%
청원구 358
 
2.0%
서원구 347
 
1.9%
상당구 338
 
1.9%
상가동 229
 
1.3%
101호 204
 
1.1%
오송읍 198
 
1.1%
Other values (3070) 10710
58.7%
2023-12-12T11:19:29.979725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
16043
 
18.4%
청 5294
 
6.1%
1 4622
 
5.3%
충 3363
 
3.9%
북 2963
 
3.4%
도 2939
 
3.4%
로 2649
 
3.0%
시 2475
 
2.8%
주 2313
 
2.7%
2 1981
 
2.3%
Other values (400) 42391
48.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 52553
60.4%
Space Separator 16043
 
18.4%
Decimal Number 14652
 
16.8%
Close Punctuation 1309
 
1.5%
Open Punctuation 1308
 
1.5%
Dash Punctuation 523
 
0.6%
Other Punctuation 455
 
0.5%
Uppercase Letter 165
 
0.2%
Lowercase Letter 23
 
< 0.1%
Letter Number 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
청 5294
 
10.1%
충 3363
 
6.4%
북 2963
 
5.6%
도 2939
 
5.6%
로 2649
 
5.0%
시 2475
 
4.7%
주 2313
 
4.4%
동 1980
 
3.8%
구 1866
 
3.6%
호 1561
 
3.0%
Other values (362) 25150
47.9%
Uppercase Letter
ValueCountFrequency (%)
B 49
29.7%
A 49
29.7%
K 15
 
9.1%
L 12
 
7.3%
D 8
 
4.8%
C 7
 
4.2%
T 6
 
3.6%
S 6
 
3.6%
X 4
 
2.4%
P 3
 
1.8%
Other values (4) 6
 
3.6%
Decimal Number
ValueCountFrequency (%)
1 4622
31.5%
2 1981
13.5%
0 1923
13.1%
3 1334
 
9.1%
4 1059
 
7.2%
5 962
 
6.6%
6 826
 
5.6%
7 737
 
5.0%
8 648
 
4.4%
9 559
 
3.8%
Lowercase Letter
ValueCountFrequency (%)
e 7
30.4%
s 4
17.4%
y 3
13.0%
m 3
13.0%
j 3
13.0%
o 3
13.0%
Other Punctuation
ValueCountFrequency (%)
, 426
93.6%
@ 29
 
6.4%
Space Separator
ValueCountFrequency (%)
16043
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1309
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1308
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 523
100.0%
Letter Number
ValueCountFrequency (%)
Ⅱ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 52553
60.4%
Common 34290
39.4%
Latin 190
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
청 5294
 
10.1%
충 3363
 
6.4%
북 2963
 
5.6%
도 2939
 
5.6%
로 2649
 
5.0%
시 2475
 
4.7%
주 2313
 
4.4%
동 1980
 
3.8%
구 1866
 
3.6%
호 1561
 
3.0%
Other values (362) 25150
47.9%
Latin
ValueCountFrequency (%)
B 49
25.8%
A 49
25.8%
K 15
 
7.9%
L 12
 
6.3%
D 8
 
4.2%
C 7
 
3.7%
e 7
 
3.7%
T 6
 
3.2%
S 6
 
3.2%
X 4
 
2.1%
Other values (11) 27
14.2%
Common
ValueCountFrequency (%)
16043
46.8%
1 4622
 
13.5%
2 1981
 
5.8%
0 1923
 
5.6%
3 1334
 
3.9%
) 1309
 
3.8%
( 1308
 
3.8%
4 1059
 
3.1%
5 962
 
2.8%
6 826
 
2.4%
Other values (7) 2923
 
8.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 52553
60.4%
ASCII 34477
39.6%
Number Forms 2
 
< 0.1%
None 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
16043
46.5%
1 4622
 
13.4%
2 1981
 
5.7%
0 1923
 
5.6%
3 1334
 
3.9%
) 1309
 
3.8%
( 1308
 
3.8%
4 1059
 
3.1%
5 962
 
2.8%
6 826
 
2.4%
Other values (26) 3110
 
9.0%
Hangul
ValueCountFrequency (%)
청 5294
 
10.1%
충 3363
 
6.4%
북 2963
 
5.6%
도 2939
 
5.6%
로 2649
 
5.0%
시 2475
 
4.7%
주 2313
 
4.4%
동 1980
 
3.8%
구 1866
 
3.6%
호 1561
 
3.0%
Other values (362) 25150
47.9%
Number Forms
ValueCountFrequency (%)
Ⅱ 2
100.0%
None
ValueCountFrequency (%)
２ 1
100.0%

Correlations

2023-12-12T11:19:30.131810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군구명중개업소 구분
시군구명1.0000.142
중개업소 구분0.1421.000
2023-12-12T11:19:30.229056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군구명중개업소 구분
시군구명1.0000.074
중개업소 구분0.0741.000
2023-12-12T11:19:30.311466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군구명중개업소 구분
시군구명1.0000.074
중개업소 구분0.0741.000

Missing values

2023-12-12T11:19:24.616448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T11:19:24.739335image/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

시군구명법정동등록번호중개업소 구분중개업자명중개업소명도로명주소(지번)
0충청북도 청주시상당구문의면가-3820-659중개인황재춘문의부동산중개사무소충청북도 청주시 상당구 문의면 문의시내로 22
1충청북도 청주시상당구영운동43111-2016-00023중개인윤재식뉴평화부동산중개인사무소충청북도 청주시 상당구 단재로77번길 1 (영운동)
2충청북도 청주시상당구수동나-38020000-49중개인이용석보은부동산중개인사무소충청북도 청주시 상당구 대성로 200 (수동)
3충청북도 청주시상당구영운동가-38020000-487중개인이응복신영부동산중개인사무소충청북도 청주시 상당구 영운로 8
4충청북도 청주시상당구남주동가-38020000-253중개인류성열유성부동산중개인사무소충청북도 청주시 상당구 남사로112번길 62-2 (남주동)
5충청북도 청주시상당구미원면가-3820-55중개인배석헌청원부동산중개사무소충청북도 청주시 상당구 미원면 미원초정로 8
6충청북도 청주시상당구문의면가-3820-9중개인홍성효대청부동산중개인사무소충청북도 청주시 상당구 문의면 문의시내로 42-1
7충청북도 청주시상당구북문로1가가-38020000-944중개인정대용공원부동산중개인사무소충청북도 청주시 상당구 사직대로 374 (북문로1가)
8충청북도 청주시상당구영운동가-38020000-2296공인중개사윤창규까치공인중개사사무소충청북도 청주시 상당구 영운천로51번길 35 105호(영운동,태암상가)
9충청북도 청주시상당구용암동가-38020000-2303공인중개사서은희가이드공인중개사사무소충청북도 청주시 상당구 무농정로 96 상가 103호(용암동,세원아파트)
시군구명법정동등록번호중개업소 구분중개업자명중개업소명도로명주소(지번)
2914충청북도 단양군단양읍43800-2021-00001공인중개사신지향좋은집부동산중개충청북도 단양군 단양읍 삼봉로 321-2
2915충청북도 단양군단양읍43800-2021-00002공인중개사김구현단양부동산공인중개사사무소충청북도 단양군 단양읍 삼봉로 99
2916충청북도 단양군단양읍43800-2022-00002공인중개사백재희한아름공인중개사사무소충청북도 단양군 단양읍 중앙2로 1
2917충청북도 단양군단양읍43800-2022-00003공인중개사김웅기대한부동산공인중개사사무소충청북도 단양군 단양읍 별곡1로 29
2918충청북도 단양군단양읍가38380000-61공인중개사김영길대명공인중개사사무소<NA>
2919충청북도 단양군단양읍가38380000-66공인중개사조창배삼성부동산공인중개사사무소<NA>
2920충청북도 단양군매포읍가38380000-71공인중개사이석재은혜공인중개사사무소<NA>
2921충청북도 단양군어상천면가38380000-83공인중개사연희어상천공인중개사사무소충청북도 단양군 어상천면 어상천로 925-1
2922충청북도 단양군단성면가38380000-73공인중개사김시연뉴스공인중개사사무소충청북도 단양군 단성면 충혼길 24
2923충청북도 단양군대강면가38380000-87공인중개사경당현단양팔경부동산공인중개사사무소<NA>

Duplicate rows

Most frequently occurring

시군구명법정동등록번호중개업소 구분중개업자명중개업소명도로명주소(지번)# duplicates
0경기도 평택시서정동43770-2021-00011공인중개사장덕영델타공인중개사사무소경기도 평택시 서정역로 3-1 201호(서정동)2
1충청북도 음성군금왕읍43770-2021-00007공인중개사최미소미소공인중개사사무소충청북도 음성군 금왕읍 대금로 15232