Overview

Dataset statistics

Number of variables17
Number of observations10000
Missing cells38220
Missing cells (%)22.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.4 MiB
Average record size in memory149.0 B

Variable types

Text7
Categorical3
DateTime2
Boolean1
Numeric4

Dataset

Description공공데이터 제공 표준데이터 속성정보(허용값, 표현형식/단위 등)는 [공공데이터 제공 표준] 전문을 참고하시기 바랍니다.(공공데이터포털>정보공유>자료실)
Author지방자치단체
URLhttps://www.data.go.kr/data/15107745/standard.do

Alerts

소속공인중개사수 is highly overall correlated with 중개보조원수 and 2 other fieldsHigh correlation
홈페이지주소 is highly overall correlated with 위도 and 5 other fieldsHigh correlation
공제가입유무 is highly overall correlated with 중개보조원수 and 2 other fieldsHigh correlation
위도 is highly overall correlated with 홈페이지주소High correlation
경도 is highly overall correlated with 홈페이지주소High correlation
중개보조원수 is highly overall correlated with 공제가입유무 and 2 other fieldsHigh correlation
제공기관코드 is highly overall correlated with 홈페이지주소High correlation
공제가입유무 is highly imbalanced (97.7%)Imbalance
소속공인중개사수 is highly imbalanced (87.7%)Imbalance
홈페이지주소 is highly imbalanced (93.3%)Imbalance
소재지도로명주소 has 168 (1.7%) missing valuesMissing
소재지지번주소 has 6177 (61.8%) missing valuesMissing
전화번호 has 6652 (66.5%) missing valuesMissing
위도 has 7941 (79.4%) missing valuesMissing
경도 has 7941 (79.4%) missing valuesMissing
중개보조원수 has 9341 (93.4%) missing valuesMissing
중개보조원수 has 268 (2.7%) zerosZeros

Reproduction

Analysis started2024-05-11 10:09:03.731438
Analysis finished2024-05-11 10:09:15.087953
Duration11.36 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

중개사무소명
Text

Distinct6802
Distinct (%)68.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-11T10:09:15.526458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length19
Mean length11.6321
Min length4

Characters and Unicode

Total characters116321
Distinct characters765
Distinct categories13 ?
Distinct scripts5 ?
Distinct blocks7 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5803 ?
Unique (%)58.0%

Sample

1st row거성공인중개사사무소
2nd row목화공인중개사사무소
3rd row용인SK소망공인중개사사무소
4th row뉴지리산공인중개사사무소
5th row원one부동산공인중개사사무소
ValueCountFrequency (%)
공인중개사사무소 325
 
3.1%
삼성공인중개사사무소 51
 
0.5%
우리공인중개사사무소 43
 
0.4%
현대공인중개사사무소 43
 
0.4%
미래공인중개사사무소 31
 
0.3%
강남공인중개사사무소 31
 
0.3%
주식회사 30
 
0.3%
하나공인중개사사무소 28
 
0.3%
에이스공인중개사사무소 26
 
0.2%
양지공인중개사사무소 25
 
0.2%
Other values (6830) 9860
94.0%
2024-05-11T10:09:16.560719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
사 18972
16.3%
중 10078
 
8.7%
개 10034
 
8.6%
소 9566
 
8.2%
인 9555
 
8.2%
무 9524
 
8.2%
공 9243
 
7.9%
동 2747
 
2.4%
산 2572
 
2.2%
부 2481
 
2.1%
Other values (755) 31549
27.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 113696
97.7%
Decimal Number 808
 
0.7%
Uppercase Letter 790
 
0.7%
Space Separator 493
 
0.4%
Lowercase Letter 185
 
0.2%
Close Punctuation 121
 
0.1%
Open Punctuation 120
 
0.1%
Dash Punctuation 67
 
0.1%
Other Punctuation 33
 
< 0.1%
Other Symbol 4
 
< 0.1%
Other values (3) 4
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
사 18972
16.7%
중 10078
 
8.9%
개 10034
 
8.8%
소 9566
 
8.4%
인 9555
 
8.4%
무 9524
 
8.4%
공 9243
 
8.1%
동 2747
 
2.4%
산 2572
 
2.3%
부 2481
 
2.2%
Other values (684) 28924
25.4%
Uppercase Letter
ValueCountFrequency (%)
K 160
20.3%
S 81
 
10.3%
O 70
 
8.9%
T 54
 
6.8%
L 47
 
5.9%
A 38
 
4.8%
C 38
 
4.8%
I 35
 
4.4%
B 32
 
4.1%
G 31
 
3.9%
Other values (16) 204
25.8%
Lowercase Letter
ValueCountFrequency (%)
e 71
38.4%
h 26
 
14.1%
i 11
 
5.9%
n 10
 
5.4%
w 8
 
4.3%
a 8
 
4.3%
c 7
 
3.8%
k 7
 
3.8%
o 6
 
3.2%
t 5
 
2.7%
Other values (10) 26
 
14.1%
Decimal Number
ValueCountFrequency (%)
1 218
27.0%
0 109
13.5%
2 97
12.0%
4 83
 
10.3%
3 72
 
8.9%
5 62
 
7.7%
8 55
 
6.8%
6 39
 
4.8%
9 37
 
4.6%
7 36
 
4.5%
Other Punctuation
ValueCountFrequency (%)
& 14
42.4%
. 10
30.3%
, 3
 
9.1%
· 2
 
6.1%
? 2
 
6.1%
/ 1
 
3.0%
＆ 1
 
3.0%
Space Separator
ValueCountFrequency (%)
493
100.0%
Close Punctuation
ValueCountFrequency (%)
) 121
100.0%
Open Punctuation
ValueCountFrequency (%)
( 120
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 67
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 4
100.0%
Math Symbol
ValueCountFrequency (%)
+ 2
100.0%
Final Punctuation
ValueCountFrequency (%)
’ 1
100.0%
Letter Number
ValueCountFrequency (%)
Ⅱ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 113677
97.7%
Common 1645
 
1.4%
Latin 976
 
0.8%
Han 22
 
< 0.1%
Katakana 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
사 18972
16.7%
중 10078
 
8.9%
개 10034
 
8.8%
소 9566
 
8.4%
인 9555
 
8.4%
무 9524
 
8.4%
공 9243
 
8.1%
동 2747
 
2.4%
산 2572
 
2.3%
부 2481
 
2.2%
Other values (664) 28905
25.4%
Latin
ValueCountFrequency (%)
K 160
16.4%
S 81
 
8.3%
e 71
 
7.3%
O 70
 
7.2%
T 54
 
5.5%
L 47
 
4.8%
A 38
 
3.9%
C 38
 
3.9%
I 35
 
3.6%
B 32
 
3.3%
Other values (37) 350
35.9%
Common
ValueCountFrequency (%)
493
30.0%
1 218
13.3%
) 121
 
7.4%
( 120
 
7.3%
0 109
 
6.6%
2 97
 
5.9%
4 83
 
5.0%
3 72
 
4.4%
- 67
 
4.1%
5 62
 
3.8%
Other values (13) 203
12.3%
Han
ValueCountFrequency (%)
土 2
 
9.1%
吾 2
 
9.1%
秀 1
 
4.5%
斌 1
 
4.5%
坪 1
 
4.5%
适 1
 
4.5%
煞 1
 
4.5%
瀯 1
 
4.5%
濚 1
 
4.5%
安 1
 
4.5%
Other values (10) 10
45.5%
Katakana
ValueCountFrequency (%)
ゼ 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 113673
97.7%
ASCII 2616
 
2.2%
CJK 22
 
< 0.1%
None 7
 
< 0.1%
Katakana 1
 
< 0.1%
Punctuation 1
 
< 0.1%
Number Forms 1
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
사 18972
16.7%
중 10078
 
8.9%
개 10034
 
8.8%
소 9566
 
8.4%
인 9555
 
8.4%
무 9524
 
8.4%
공 9243
 
8.1%
동 2747
 
2.4%
산 2572
 
2.3%
부 2481
 
2.2%
Other values (663) 28901
25.4%
ASCII
ValueCountFrequency (%)
493
18.8%
1 218
 
8.3%
K 160
 
6.1%
) 121
 
4.6%
( 120
 
4.6%
0 109
 
4.2%
2 97
 
3.7%
4 83
 
3.2%
S 81
 
3.1%
3 72
 
2.8%
Other values (56) 1062
40.6%
None
ValueCountFrequency (%)
㈜ 4
57.1%
· 2
28.6%
＆ 1
 
14.3%
CJK
ValueCountFrequency (%)
土 2
 
9.1%
吾 2
 
9.1%
秀 1
 
4.5%
斌 1
 
4.5%
坪 1
 
4.5%
适 1
 
4.5%
煞 1
 
4.5%
瀯 1
 
4.5%
濚 1
 
4.5%
安 1
 
4.5%
Other values (10) 10
45.5%
Katakana
ValueCountFrequency (%)
ゼ 1
100.0%
Punctuation
ValueCountFrequency (%)
’ 1
100.0%
Number Forms
ValueCountFrequency (%)
Ⅱ 1
100.0%

개설등록번호
Text

Distinct9882
Distinct (%)98.8%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-11T10:09:17.117337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length16
Mean length15.2445
Min length7

Characters and Unicode

Total characters152445
Distinct characters13
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

Unique9804 ?
Unique (%)98.0%

Sample

1st row가3629-1911
2nd row11680-2020-00558
3rd row41461-2020-00080
4th row48870-2015-00009
5th row11500-2022-00131
ValueCountFrequency (%)
44210-0000-00000 23
 
0.2%
26290-0000-00000 21
 
0.2%
42110-2022-00013 2
 
< 0.1%
45730-2018-00002 2
 
< 0.1%
42130-2020-00070 2
 
< 0.1%
42130-2005-00034 2
 
< 0.1%
42130-2016-00169 2
 
< 0.1%
가3722-480 2
 
< 0.1%
42130-2022-00029 2
 
< 0.1%
42170-2018-00003 2
 
< 0.1%
Other values (9872) 9940
99.4%
2024-05-11T10:09:18.081679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 45068
29.6%
2 23086
15.1%
1 22114
14.5%
- 18813
12.3%
4 9413
 
6.2%
3 7659
 
5.0%
6 5828
 
3.8%
5 5720
 
3.8%
8 5117
 
3.4%
7 4565
 
3.0%
Other values (3) 5062
 
3.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 132445
86.9%
Dash Punctuation 18813
 
12.3%
Other Letter 1187
 
0.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 45068
34.0%
2 23086
17.4%
1 22114
16.7%
4 9413
 
7.1%
3 7659
 
5.8%
6 5828
 
4.4%
5 5720
 
4.3%
8 5117
 
3.9%
7 4565
 
3.4%
9 3875
 
2.9%
Other Letter
ValueCountFrequency (%)
가 1153
97.1%
나 34
 
2.9%
Dash Punctuation
ValueCountFrequency (%)
- 18813
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 151258
99.2%
Hangul 1187
 
0.8%

Most frequent character per script

Common
ValueCountFrequency (%)
0 45068
29.8%
2 23086
15.3%
1 22114
14.6%
- 18813
12.4%
4 9413
 
6.2%
3 7659
 
5.1%
6 5828
 
3.9%
5 5720
 
3.8%
8 5117
 
3.4%
7 4565
 
3.0%
Hangul
ValueCountFrequency (%)
가 1153
97.1%
나 34
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 151258
99.2%
Hangul 1187
 
0.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 45068
29.8%
2 23086
15.3%
1 22114
14.6%
- 18813
12.4%
4 9413
 
6.2%
3 7659
 
5.1%
6 5828
 
3.9%
5 5720
 
3.8%
8 5117
 
3.4%
7 4565
 
3.0%
Hangul
ValueCountFrequency (%)
가 1153
97.1%
나 34
 
2.9%

개업공인중개사종별구분
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
공인중개사
7899 
개업공인중개사
1854 
법인
 
247

Length

Max length7
Median length5
Mean length5.2967
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row공인중개사
2nd row공인중개사
3rd row공인중개사
4th row개업공인중개사
5th row공인중개사

Common Values

ValueCountFrequency (%)
공인중개사 7899
79.0%
개업공인중개사 1854
 
18.5%
법인 247
 
2.5%

Length

2024-05-11T10:09:18.532595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T10:09:19.077332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
공인중개사 7899
79.0%
개업공인중개사 1854
 
18.5%
법인 247
 
2.5%

소재지도로명주소
Text

MISSING 

Distinct9622
Distinct (%)97.9%
Missing168
Missing (%)1.7%
Memory size156.2 KiB
2024-05-11T10:09:19.789664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length71
Median length53
Mean length30.092453
Min length13

Characters and Unicode

Total characters295869
Distinct characters698
Distinct categories12 ?
Distinct scripts4 ?
Distinct blocks6 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9449 ?
Unique (%)96.1%

Sample

1st row경기도 화성시 메타폴리스로 44 102호(반송동, 거성프라자)
2nd row서울특별시 강남구 테헤란로 113 3층 306호(역삼동, 목화밀라트)
3rd row경기도 용인시 처인구 이동읍 이원로 93, (천리)
4th row경상남도 함양군 휴천면 송전길 134
5th row서울특별시 강서구 공항대로 200 806호(마곡동)
ValueCountFrequency (%)
경기도 3322
 
5.7%
서울특별시 1735
 
3.0%
1층 934
 
1.6%
상가동 856
 
1.5%
부산광역시 837
 
1.4%
경상남도 777
 
1.3%
수원시 564
 
1.0%
화성시 514
 
0.9%
인천광역시 471
 
0.8%
용인시 456
 
0.8%
Other values (13916) 47819
82.0%
2024-05-11T10:09:21.124471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
48580
 
16.4%
1 15497
 
5.2%
시 9947
 
3.4%
동 9515
 
3.2%
로 8991
 
3.0%
2 6829
 
2.3%
도 6745
 
2.3%
0 6725
 
2.3%
구 6319
 
2.1%
) 5886
 
2.0%
Other values (688) 170835
57.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 176165
59.5%
Decimal Number 51977
 
17.6%
Space Separator 48580
 
16.4%
Close Punctuation 6018
 
2.0%
Open Punctuation 6018
 
2.0%
Other Punctuation 4377
 
1.5%
Dash Punctuation 1701
 
0.6%
Uppercase Letter 899
 
0.3%
Lowercase Letter 119
 
< 0.1%
Other Symbol 5
 
< 0.1%
Other values (2) 10
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 9947
 
5.6%
동 9515
 
5.4%
로 8991
 
5.1%
도 6745
 
3.8%
구 6319
 
3.6%
호 5298
 
3.0%
경 4704
 
2.7%
길 4075
 
2.3%
기 3753
 
2.1%
남 3484
 
2.0%
Other values (615) 113334
64.3%
Uppercase Letter
ValueCountFrequency (%)
B 294
32.7%
A 131
14.6%
S 64
 
7.1%
C 61
 
6.8%
K 44
 
4.9%
L 39
 
4.3%
E 31
 
3.4%
T 27
 
3.0%
R 25
 
2.8%
I 24
 
2.7%
Other values (16) 159
17.7%
Lowercase Letter
ValueCountFrequency (%)
e 46
38.7%
i 7
 
5.9%
c 7
 
5.9%
a 7
 
5.9%
r 7
 
5.9%
s 6
 
5.0%
h 5
 
4.2%
t 5
 
4.2%
l 5
 
4.2%
o 5
 
4.2%
Other values (8) 19
16.0%
Decimal Number
ValueCountFrequency (%)
1 15497
29.8%
2 6829
13.1%
0 6725
12.9%
3 4769
 
9.2%
4 3846
 
7.4%
5 3587
 
6.9%
6 3039
 
5.8%
7 2831
 
5.4%
8 2495
 
4.8%
9 2359
 
4.5%
Other Punctuation
ValueCountFrequency (%)
, 4307
98.4%
. 37
 
0.8%
@ 21
 
0.5%
· 4
 
0.1%
? 4
 
0.1%
/ 3
 
0.1%
？ 1
 
< 0.1%
Math Symbol
ValueCountFrequency (%)
~ 3
60.0%
< 1
 
20.0%
> 1
 
20.0%
Close Punctuation
ValueCountFrequency (%)
) 5886
97.8%
] 132
 
2.2%
Open Punctuation
ValueCountFrequency (%)
( 5886
97.8%
[ 132
 
2.2%
Letter Number
ValueCountFrequency (%)
Ⅱ 4
80.0%
Ⅰ 1
 
20.0%
Space Separator
ValueCountFrequency (%)
48580
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1701
100.0%
Other Symbol
ValueCountFrequency (%)
㈕ 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 176168
59.5%
Common 118676
40.1%
Latin 1023
 
0.3%
Han 2
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 9947
 
5.6%
동 9515
 
5.4%
로 8991
 
5.1%
도 6745
 
3.8%
구 6319
 
3.6%
호 5298
 
3.0%
경 4704
 
2.7%
길 4075
 
2.3%
기 3753
 
2.1%
남 3484
 
2.0%
Other values (614) 113337
64.3%
Latin
ValueCountFrequency (%)
B 294
28.7%
A 131
12.8%
S 64
 
6.3%
C 61
 
6.0%
e 46
 
4.5%
K 44
 
4.3%
L 39
 
3.8%
E 31
 
3.0%
T 27
 
2.6%
R 25
 
2.4%
Other values (36) 261
25.5%
Common
ValueCountFrequency (%)
48580
40.9%
1 15497
 
13.1%
2 6829
 
5.8%
0 6725
 
5.7%
) 5886
 
5.0%
( 5886
 
5.0%
3 4769
 
4.0%
, 4307
 
3.6%
4 3846
 
3.2%
5 3587
 
3.0%
Other values (16) 12764
 
10.8%
Han
ValueCountFrequency (%)
氷 1
50.0%
努 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 176161
59.5%
ASCII 119689
40.5%
None 10
 
< 0.1%
Number Forms 5
 
< 0.1%
Compat Jamo 2
 
< 0.1%
CJK 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
48580
40.6%
1 15497
 
12.9%
2 6829
 
5.7%
0 6725
 
5.6%
) 5886
 
4.9%
( 5886
 
4.9%
3 4769
 
4.0%
, 4307
 
3.6%
4 3846
 
3.2%
5 3587
 
3.0%
Other values (58) 13777
 
11.5%
Hangul
ValueCountFrequency (%)
시 9947
 
5.6%
동 9515
 
5.4%
로 8991
 
5.1%
도 6745
 
3.8%
구 6319
 
3.6%
호 5298
 
3.0%
경 4704
 
2.7%
길 4075
 
2.3%
기 3753
 
2.1%
남 3484
 
2.0%
Other values (612) 113330
64.3%
None
ValueCountFrequency (%)
㈕ 5
50.0%
· 4
40.0%
？ 1
 
10.0%
Number Forms
ValueCountFrequency (%)
Ⅱ 4
80.0%
Ⅰ 1
 
20.0%
Compat Jamo
ValueCountFrequency (%)
ㆍ 2
100.0%
CJK
ValueCountFrequency (%)
氷 1
50.0%
努 1
50.0%

소재지지번주소
Text

MISSING 

Distinct3749
Distinct (%)98.1%
Missing6177
Missing (%)61.8%
Memory size156.2 KiB
2024-05-11T10:09:21.931949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length63
Median length52
Mean length26.953963
Min length11

Characters and Unicode

Total characters103045
Distinct characters490
Distinct categories11 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3686 ?
Unique (%)96.4%

Sample

1st row경기도 화성시 반송동 103-5 거성프라자 102호
2nd row경기도 용인시 처인구 이동읍 천리 61-9번지
3rd row경상남도 김해시 진영읍 여래리 969-1
4th row경상남도 진주시 충무공동 275 상가1동 107호
5th row부산광역시 서구 서대신동3가 153-15 (서대신동3가)
ValueCountFrequency (%)
경기도 1457
 
6.8%
화성시 510
 
2.4%
경상남도 430
 
2.0%
1층 417
 
1.9%
용인시 323
 
1.5%
상가동 309
 
1.4%
인천광역시 299
 
1.4%
강원도 284
 
1.3%
원주시 274
 
1.3%
김해시 254
 
1.2%
Other values (5834) 16914
78.8%
2024-05-11T10:09:23.467937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
17677
 
17.2%
1 6071
 
5.9%
동 5251
 
5.1%
시 3782
 
3.7%
도 3322
 
3.2%
- 2729
 
2.6%
2 2558
 
2.5%
0 2511
 
2.4%
경 1952
 
1.9%
3 1946
 
1.9%
Other values (480) 55246
53.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 57914
56.2%
Decimal Number 21806
 
21.2%
Space Separator 17677
 
17.2%
Dash Punctuation 2729
 
2.6%
Open Punctuation 931
 
0.9%
Close Punctuation 930
 
0.9%
Other Punctuation 707
 
0.7%
Uppercase Letter 332
 
0.3%
Lowercase Letter 14
 
< 0.1%
Letter Number 4
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 5251
 
9.1%
시 3782
 
6.5%
도 3322
 
5.7%
경 1952
 
3.4%
호 1737
 
3.0%
기 1685
 
2.9%
구 1554
 
2.7%
상 1297
 
2.2%
남 1247
 
2.2%
산 1123
 
1.9%
Other values (432) 34964
60.4%
Uppercase Letter
ValueCountFrequency (%)
B 99
29.8%
A 55
16.6%
C 30
 
9.0%
S 24
 
7.2%
L 22
 
6.6%
E 16
 
4.8%
T 10
 
3.0%
R 9
 
2.7%
D 9
 
2.7%
K 8
 
2.4%
Other values (14) 50
15.1%
Decimal Number
ValueCountFrequency (%)
1 6071
27.8%
2 2558
11.7%
0 2511
11.5%
3 1946
 
8.9%
4 1717
 
7.9%
5 1639
 
7.5%
6 1527
 
7.0%
8 1330
 
6.1%
7 1311
 
6.0%
9 1196
 
5.5%
Other Punctuation
ValueCountFrequency (%)
, 668
94.5%
@ 27
 
3.8%
. 10
 
1.4%
/ 2
 
0.3%
Lowercase Letter
ValueCountFrequency (%)
e 12
85.7%
s 1
 
7.1%
u 1
 
7.1%
Letter Number
ValueCountFrequency (%)
Ⅱ 3
75.0%
Ⅰ 1
 
25.0%
Space Separator
ValueCountFrequency (%)
17677
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2729
100.0%
Open Punctuation
ValueCountFrequency (%)
( 931
100.0%
Close Punctuation
ValueCountFrequency (%)
) 930
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 57914
56.2%
Common 44781
43.5%
Latin 350
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 5251
 
9.1%
시 3782
 
6.5%
도 3322
 
5.7%
경 1952
 
3.4%
호 1737
 
3.0%
기 1685
 
2.9%
구 1554
 
2.7%
상 1297
 
2.2%
남 1247
 
2.2%
산 1123
 
1.9%
Other values (432) 34964
60.4%
Latin
ValueCountFrequency (%)
B 99
28.3%
A 55
15.7%
C 30
 
8.6%
S 24
 
6.9%
L 22
 
6.3%
E 16
 
4.6%
e 12
 
3.4%
T 10
 
2.9%
R 9
 
2.6%
D 9
 
2.6%
Other values (19) 64
18.3%
Common
ValueCountFrequency (%)
17677
39.5%
1 6071
 
13.6%
- 2729
 
6.1%
2 2558
 
5.7%
0 2511
 
5.6%
3 1946
 
4.3%
4 1717
 
3.8%
5 1639
 
3.7%
6 1527
 
3.4%
8 1330
 
3.0%
Other values (9) 5076
 
11.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 57914
56.2%
ASCII 45127
43.8%
Number Forms 4
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
17677
39.2%
1 6071
 
13.5%
- 2729
 
6.0%
2 2558
 
5.7%
0 2511
 
5.6%
3 1946
 
4.3%
4 1717
 
3.8%
5 1639
 
3.6%
6 1527
 
3.4%
8 1330
 
2.9%
Other values (36) 5422
 
12.0%
Hangul
ValueCountFrequency (%)
동 5251
 
9.1%
시 3782
 
6.5%
도 3322
 
5.7%
경 1952
 
3.4%
호 1737
 
3.0%
기 1685
 
2.9%
구 1554
 
2.7%
상 1297
 
2.2%
남 1247
 
2.2%
산 1123
 
1.9%
Other values (432) 34964
60.4%
Number Forms
ValueCountFrequency (%)
Ⅱ 3
75.0%
Ⅰ 1
 
25.0%

전화번호
Text

MISSING 

Distinct3213
Distinct (%)96.0%
Missing6652
Missing (%)66.5%
Memory size156.2 KiB
2024-05-11T10:09:24.303894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length11.949522
Min length9

Characters and Unicode

Total characters40007
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

Unique3181 ?
Unique (%)95.0%

Sample

1st row031-8003-3555
2nd row02-6949-3777
3rd row051-255-2480
4th row031-376-4589
5th row031-356-9796
ValueCountFrequency (%)
02-0000-0000 71
 
2.1%
000-000-0000 35
 
1.0%
061-000-0000 3
 
0.1%
033-651-8919 2
 
0.1%
051-756-1488 2
 
0.1%
033-434-6164 2
 
0.1%
033-435-4466 2
 
0.1%
033-522-3555 2
 
0.1%
041-669-0015 2
 
0.1%
033-574-0600 2
 
0.1%
Other values (3203) 3225
96.3%
2024-05-11T10:09:25.940382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 7159
17.9%
- 6687
16.7%
3 4678
11.7%
1 3428
8.6%
5 3088
7.7%
2 2974
7.4%
4 2736
 
6.8%
7 2549
 
6.4%
8 2393
 
6.0%
6 2273
 
5.7%
Other values (2) 2042
 
5.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 33315
83.3%
Dash Punctuation 6687
 
16.7%
Math Symbol 5
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 7159
21.5%
3 4678
14.0%
1 3428
10.3%
5 3088
9.3%
2 2974
8.9%
4 2736
 
8.2%
7 2549
 
7.7%
8 2393
 
7.2%
6 2273
 
6.8%
9 2037
 
6.1%
Dash Punctuation
ValueCountFrequency (%)
- 6687
100.0%
Math Symbol
ValueCountFrequency (%)
+ 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 40007
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 7159
17.9%
- 6687
16.7%
3 4678
11.7%
1 3428
8.6%
5 3088
7.7%
2 2974
7.4%
4 2736
 
6.8%
7 2549
 
6.4%
8 2393
 
6.0%
6 2273
 
5.7%
Other values (2) 2042
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 40007
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 7159
17.9%
- 6687
16.7%
3 4678
11.7%
1 3428
8.6%
5 3088
7.7%
2 2974
7.4%
4 2736
 
6.8%
7 2549
 
6.4%
8 2393
 
6.0%
6 2273
 
5.7%
Other values (2) 2042
 
5.1%

개설등록일자
Date

Distinct3465
Distinct (%)34.6%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum1984-05-11 00:00:00
Maximum2023-12-28 00:00:00
2024-05-11T10:09:26.668989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:09:27.255756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

공제가입유무
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size87.9 KiB
True
9978 
False
 
22
ValueCountFrequency (%)
True 9978
99.8%
False 22
 
0.2%
2024-05-11T10:09:27.717343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

대표자명
Text

Distinct8132
Distinct (%)81.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-11T10:09:28.499361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length3
Mean length2.9957
Min length2

Characters and Unicode

Total characters29957
Distinct characters308
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7023 ?
Unique (%)70.2%

Sample

1st row김미혜
2nd row최욱형
3rd row제종상
4th row이혜영
5th row송회영
ValueCountFrequency (%)
이경희 12
 
0.1%
김경희 12
 
0.1%
김미정 11
 
0.1%
김영희 11
 
0.1%
김정희 10
 
0.1%
김은정 10
 
0.1%
김도연 10
 
0.1%
이명희 10
 
0.1%
김민정 9
 
0.1%
이은주 9
 
0.1%
Other values (8127) 9901
99.0%
2024-05-11T10:09:29.906916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
김 2048
 
6.8%
이 1539
 
5.1%
정 1328
 
4.4%
영 952
 
3.2%
박 827
 
2.8%
희 732
 
2.4%
경 646
 
2.2%
미 635
 
2.1%
현 625
 
2.1%
숙 550
 
1.8%
Other values (298) 20075
67.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 29914
99.9%
Uppercase Letter 31
 
0.1%
Lowercase Letter 6
 
< 0.1%
Space Separator 5
 
< 0.1%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
김 2048
 
6.8%
이 1539
 
5.1%
정 1328
 
4.4%
영 952
 
3.2%
박 827
 
2.8%
희 732
 
2.4%
경 646
 
2.2%
미 635
 
2.1%
현 625
 
2.1%
숙 550
 
1.8%
Other values (278) 20032
67.0%
Uppercase Letter
ValueCountFrequency (%)
H 5
16.1%
I 5
16.1%
N 4
12.9%
U 3
9.7%
C 3
9.7%
S 2
 
6.5%
A 2
 
6.5%
J 2
 
6.5%
M 1
 
3.2%
E 1
 
3.2%
Other values (3) 3
9.7%
Lowercase Letter
ValueCountFrequency (%)
a 2
33.3%
n 1
16.7%
u 1
16.7%
m 1
16.7%
e 1
16.7%
Space Separator
ValueCountFrequency (%)
5
100.0%
Math Symbol
ValueCountFrequency (%)
+ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 29914
99.9%
Latin 37
 
0.1%
Common 6
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
김 2048
 
6.8%
이 1539
 
5.1%
정 1328
 
4.4%
영 952
 
3.2%
박 827
 
2.8%
희 732
 
2.4%
경 646
 
2.2%
미 635
 
2.1%
현 625
 
2.1%
숙 550
 
1.8%
Other values (278) 20032
67.0%
Latin
ValueCountFrequency (%)
H 5
13.5%
I 5
13.5%
N 4
10.8%
U 3
 
8.1%
C 3
 
8.1%
a 2
 
5.4%
S 2
 
5.4%
A 2
 
5.4%
J 2
 
5.4%
M 1
 
2.7%
Other values (8) 8
21.6%
Common
ValueCountFrequency (%)
5
83.3%
+ 1
 
16.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 29914
99.9%
ASCII 43
 
0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
김 2048
 
6.8%
이 1539
 
5.1%
정 1328
 
4.4%
영 952
 
3.2%
박 827
 
2.8%
희 732
 
2.4%
경 646
 
2.2%
미 635
 
2.1%
현 625
 
2.1%
숙 550
 
1.8%
Other values (278) 20032
67.0%
ASCII
ValueCountFrequency (%)
H 5
11.6%
I 5
11.6%
5
11.6%
N 4
 
9.3%
U 3
 
7.0%
C 3
 
7.0%
a 2
 
4.7%
S 2
 
4.7%
A 2
 
4.7%
J 2
 
4.7%
Other values (10) 10
23.3%

위도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct1823
Distinct (%)88.5%
Missing7941
Missing (%)79.4%
Infinite0
Infinite (%)0.0%
Mean36.830827
Minimum33.21149
Maximum38.445726
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T10:09:30.309539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum33.21149
5-th percentile35.176233
Q136.354696
median37.308843
Q337.463427
95-th percentile37.871501
Maximum38.445726
Range5.2342365
Interquartile range (IQR)1.1087307

Descriptive statistics

Standard deviation1.0546293
Coefficient of variation (CV)0.028634419
Kurtosis1.1271156
Mean36.830827
Median Absolute Deviation (MAD)0.17775822
Skewness-1.3914392
Sum75834.672
Variance1.112243
MonotonicityNot monotonic
2024-05-11T10:09:30.820198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.401605 11
 
0.1%
35.18430911 11
 
0.1%
37.415135 5
 
0.1%
37.27397354 5
 
0.1%
35.19202678 4
 
< 0.1%
37.385357 4
 
< 0.1%
35.18244487 4
 
< 0.1%
37.373787 4
 
< 0.1%
37.86947 4
 
< 0.1%
35.1741951 4
 
< 0.1%
Other values (1813) 2003
 
20.0%
(Missing) 7941
79.4%
ValueCountFrequency (%)
33.21149003 1
< 0.1%
33.21442636 1
< 0.1%
33.2257528 1
< 0.1%
33.24450388 1
< 0.1%
33.24490941 1
< 0.1%
33.24504991 1
< 0.1%
33.24571631 1
< 0.1%
33.24644611 1
< 0.1%
33.24819712 1
< 0.1%
33.24933809 1
< 0.1%
ValueCountFrequency (%)
38.4457265 1
< 0.1%
38.41139357 1
< 0.1%
38.3795863 2
< 0.1%
38.28974061 1
< 0.1%
38.25848943 1
< 0.1%
38.2540200044 1
< 0.1%
38.25295944 1
< 0.1%
38.24735082 1
< 0.1%
38.2464382 1
< 0.1%
38.2133205117 1
< 0.1%

경도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct1824
Distinct (%)88.6%
Missing7941
Missing (%)79.4%
Infinite0
Infinite (%)0.0%
Mean127.47981
Minimum126.11007
Maximum129.45408
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T10:09:31.285128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.11007
5-th percentile126.63286
Q1126.90705
median127.15124
Q3127.79962
95-th percentile129.07984
Maximum129.45408
Range3.3440043
Interquartile range (IQR)0.8925744

Descriptive statistics

Standard deviation0.8147349
Coefficient of variation (CV)0.0063910896
Kurtosis-0.54107108
Mean127.47981
Median Absolute Deviation (MAD)0.3825177
Skewness0.91566272
Sum262480.93
Variance0.66379295
MonotonicityNot monotonic
2024-05-11T10:09:31.923859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.631562 12
 
0.1%
129.0798432 11
 
0.1%
127.720764 6
 
0.1%
126.618423 5
 
0.1%
127.1192312 5
 
0.1%
126.617901 4
 
< 0.1%
128.8293012 4
 
< 0.1%
129.0955342 4
 
< 0.1%
126.647772 4
 
< 0.1%
129.0782648 4
 
< 0.1%
Other values (1814) 2000
 
20.0%
(Missing) 7941
79.4%
ValueCountFrequency (%)
126.1100709 1
< 0.1%
126.2516539 1
< 0.1%
126.2572418 1
< 0.1%
126.2574394 1
< 0.1%
126.2599696 1
< 0.1%
126.270302 1
< 0.1%
126.2743248 1
< 0.1%
126.2767595 1
< 0.1%
126.2787694 1
< 0.1%
126.281324 2
< 0.1%
ValueCountFrequency (%)
129.4540752 1
< 0.1%
129.4397014 1
< 0.1%
129.4161344 1
< 0.1%
129.4017011 1
< 0.1%
129.3977854 1
< 0.1%
129.3737927 1
< 0.1%
129.1215402 1
< 0.1%
129.1202451 1
< 0.1%
129.1143073 2
< 0.1%
129.1140861 1
< 0.1%

중개보조원수
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct12
Distinct (%)1.8%
Missing9341
Missing (%)93.4%
Infinite0
Infinite (%)0.0%
Mean0.94081942
Minimum0
Maximum17
Zeros268
Zeros (%)2.7%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T10:09:32.485622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile3
Maximum17
Range17
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.4167324
Coefficient of variation (CV)1.5058494
Kurtosis48.168053
Mean0.94081942
Median Absolute Deviation (MAD)1
Skewness5.3670398
Sum620
Variance2.0071306
MonotonicityNot monotonic
2024-05-11T10:09:32.976262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1 283
 
2.8%
0 268
 
2.7%
2 58
 
0.6%
3 27
 
0.3%
4 10
 
0.1%
5 7
 
0.1%
7 1
 
< 0.1%
9 1
 
< 0.1%
6 1
 
< 0.1%
16 1
 
< 0.1%
Other values (2) 2
 
< 0.1%
(Missing) 9341
93.4%
ValueCountFrequency (%)
0 268
2.7%
1 283
2.8%
2 58
 
0.6%
3 27
 
0.3%
4 10
 
0.1%
5 7
 
0.1%
6 1
 
< 0.1%
7 1
 
< 0.1%
9 1
 
< 0.1%
10 1
 
< 0.1%
ValueCountFrequency (%)
17 1
 
< 0.1%
16 1
 
< 0.1%
10 1
 
< 0.1%
9 1
 
< 0.1%
7 1
 
< 0.1%
6 1
 
< 0.1%
5 7
 
0.1%
4 10
 
0.1%
3 27
0.3%
2 58
0.6%

소속공인중개사수
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9532 
0
 
373
1
 
68
2
 
21
3
 
5

Length

Max length4
Median length4
Mean length3.8596
Min length1

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 9532
95.3%
0 373
 
3.7%
1 68
 
0.7%
2 21
 
0.2%
3 5
 
0.1%
4 1
 
< 0.1%

Length

2024-05-11T10:09:33.519620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T10:09:33.872820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9532
95.3%
0 373
 
3.7%
1 68
 
0.7%
2 21
 
0.2%
3 5
 
< 0.1%
4 1
 
< 0.1%

홈페이지주소
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9792 
http://www.nsdi.go.kr/lxportal/?menuno=4085
 
191
-
 
14
X
 
2
http://hyo8777.cjbds.com/
 
1

Length

Max length43
Median length4
Mean length4.7422
Min length1

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 9792
97.9%
http://www.nsdi.go.kr/lxportal/?menuno=4085 191
 
1.9%
- 14
 
0.1%
X 2
 
< 0.1%
http://hyo8777.cjbds.com/ 1
 
< 0.1%

Length

2024-05-11T10:09:34.198545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T10:09:34.596824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9792
97.9%
http://www.nsdi.go.kr/lxportal/?menuno=4085 191
 
1.9%
14
 
0.1%
x 2
 
< 0.1%
http://hyo8777.cjbds.com 1
 
< 0.1%

데이터기준일자
Date

Distinct81
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2022-11-14 00:00:00
Maximum2024-03-25 00:00:00
2024-05-11T10:09:35.030185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:09:35.624591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

제공기관코드
Real number (ℝ)

HIGH CORRELATION 

Distinct140
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4110480.8
Minimum3020000
Maximum6520000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T10:09:36.237046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3020000
5-th percentile3130000
Q13400000
median3930000
Q34671000
95-th percentile5670000
Maximum6520000
Range3500000
Interquartile range (IQR)1271000

Descriptive statistics

Standard deviation850893.48
Coefficient of variation (CV)0.20700583
Kurtosis-0.54520503
Mean4110480.8
Median Absolute Deviation (MAD)560000
Skewness0.76572675
Sum4.1104808 × 1010
Variance7.2401972 × 1011
MonotonicityNot monotonic
2024-05-11T10:09:36.754483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3740000 564
 
5.6%
5530000 514
 
5.1%
4050000 456
 
4.6%
3220000 440
 
4.4%
3780000 320
 
3.2%
5670000 272
 
2.7%
5350000 254
 
2.5%
5710000 253
 
2.5%
3630000 248
 
2.5%
3210000 245
 
2.5%
Other values (130) 6434
64.3%
ValueCountFrequency (%)
3020000 132
1.3%
3050000 129
1.3%
3060000 88
 
0.9%
3070000 21
 
0.2%
3100000 81
 
0.8%
3130000 158
1.6%
3140000 98
 
1.0%
3150000 215
2.1%
3200000 128
1.3%
3210000 245
2.5%
ValueCountFrequency (%)
6520000 81
 
0.8%
5710000 253
2.5%
5700000 22
 
0.2%
5680000 66
 
0.7%
5670000 272
2.7%
5600000 59
 
0.6%
5540000 109
 
1.1%
5530000 514
5.1%
5480000 8
 
0.1%
5470000 8
 
0.1%

제공기관명
Text

Distinct140
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-11T10:09:37.713838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length11
Mean length8.2013
Min length7

Characters and Unicode

Total characters82013
Distinct characters98
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

Unique2 ?
Unique (%)< 0.1%

Sample

1st row경기도 화성시
2nd row서울특별시 강남구
3rd row경기도 용인시
4th row경상남도 함양군
5th row서울특별시 강서구
ValueCountFrequency (%)
경기도 3328
 
16.6%
서울특별시 1735
 
8.7%
부산광역시 837
 
4.2%
경상남도 777
 
3.9%
수원시 564
 
2.8%
화성시 514
 
2.6%
인천광역시 472
 
2.4%
용인시 456
 
2.3%
강남구 440
 
2.2%
강원특별자치도 397
 
2.0%
Other values (127) 10480
52.4%
2024-05-11T10:09:39.272644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
10000
 
12.2%
시 9470
 
11.5%
도 6190
 
7.5%
경 4464
 
5.4%
구 3790
 
4.6%
기 3402
 
4.1%
광 2805
 
3.4%
서 2526
 
3.1%
남 2523
 
3.1%
별 2394
 
2.9%
Other values (88) 34449
42.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 72013
87.8%
Space Separator 10000
 
12.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 9470
 
13.2%
도 6190
 
8.6%
경 4464
 
6.2%
구 3790
 
5.3%
기 3402
 
4.7%
광 2805
 
3.9%
서 2526
 
3.5%
남 2523
 
3.5%
별 2394
 
3.3%
특 2394
 
3.3%
Other values (87) 32055
44.5%
Space Separator
ValueCountFrequency (%)
10000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 72013
87.8%
Common 10000
 
12.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 9470
 
13.2%
도 6190
 
8.6%
경 4464
 
6.2%
구 3790
 
5.3%
기 3402
 
4.7%
광 2805
 
3.9%
서 2526
 
3.5%
남 2523
 
3.5%
별 2394
 
3.3%
특 2394
 
3.3%
Other values (87) 32055
44.5%
Common
ValueCountFrequency (%)
10000
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 72013
87.8%
ASCII 10000
 
12.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
10000
100.0%
Hangul
ValueCountFrequency (%)
시 9470
 
13.2%
도 6190
 
8.6%
경 4464
 
6.2%
구 3790
 
5.3%
기 3402
 
4.7%
광 2805
 
3.9%
서 2526
 
3.5%
남 2523
 
3.5%
별 2394
 
3.3%
특 2394
 
3.3%
Other values (87) 32055
44.5%

Interactions

2024-05-11T10:09:12.176128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:09:09.260781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:09:10.185645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:09:11.310028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:09:12.397930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:09:09.530154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:09:10.452936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:09:11.521287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:09:12.674963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:09:09.799061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:09:10.820702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:09:11.674419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:09:12.963638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:09:09.986279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:09:11.068584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:09:11.949671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-11T10:09:39.651567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
개업공인중개사종별구분공제가입유무위도경도중개보조원수소속공인중개사수홈페이지주소데이터기준일자제공기관코드
개업공인중개사종별구분1.0000.0000.6460.4430.0250.1100.0000.8130.491
공제가입유무0.0001.0000.0600.028NaNNaNNaN0.4830.106
위도0.6460.0601.0000.7820.2520.5480.6780.9890.946
경도0.4430.0280.7821.0000.1000.5270.9810.9520.837
중개보조원수0.025NaN0.2520.1001.0000.756NaN0.0000.000
소속공인중개사수0.110NaN0.5480.5270.7561.0001.0000.6260.321
홈페이지주소0.000NaN0.6780.981NaN1.0001.0001.0001.000
데이터기준일자0.8130.4830.9890.9520.0000.6261.0001.0000.974
제공기관코드0.4910.1060.9460.8370.0000.3211.0000.9741.000
2024-05-11T10:09:40.070934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
소속공인중개사수홈페이지주소개업공인중개사종별구분공제가입유무
소속공인중개사수1.0000.9660.0821.000
홈페이지주소0.9661.0000.0001.000
개업공인중개사종별구분0.0820.0001.0000.001
공제가입유무1.0001.0000.0011.000
2024-05-11T10:09:40.387287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
위도경도중개보조원수제공기관코드개업공인중개사종별구분공제가입유무소속공인중개사수홈페이지주소
위도1.000-0.3830.165-0.2560.3620.0600.4880.707
경도-0.3831.0000.0630.4200.2970.0220.4090.813
중개보조원수0.1650.0631.0000.1940.0161.0000.6171.000
제공기관코드-0.2560.4200.1941.0000.2510.1060.2110.998
개업공인중개사종별구분0.3620.2970.0160.2511.0000.0010.0820.000
공제가입유무0.0600.0221.0000.1060.0011.0001.0001.000
소속공인중개사수0.4880.4090.6170.2110.0821.0001.0000.966
홈페이지주소0.7070.8131.0000.9980.0001.0000.9661.000

Missing values

2024-05-11T10:09:13.435879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-11T10:09:14.126518image/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.
2024-05-11T10:09:14.788148image/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

중개사무소명개설등록번호개업공인중개사종별구분소재지도로명주소소재지지번주소전화번호개설등록일자공제가입유무대표자명위도경도중개보조원수소속공인중개사수홈페이지주소데이터기준일자제공기관코드제공기관명
40503거성공인중개사사무소가3629-1911공인중개사경기도 화성시 메타폴리스로 44 102호(반송동, 거성프라자)경기도 화성시 반송동 103-5 거성프라자 102호031-8003-35552010-03-26Y김미혜<NA><NA><NA><NA><NA>2023-10-045530000경기도 화성시
29473목화공인중개사사무소11680-2020-00558공인중개사서울특별시 강남구 테헤란로 113 3층 306호(역삼동, 목화밀라트)<NA><NA>2020-12-18Y최욱형<NA><NA><NA><NA><NA>2023-07-113220000서울특별시 강남구
795용인SK소망공인중개사사무소41461-2020-00080공인중개사경기도 용인시 처인구 이동읍 이원로 93, (천리)경기도 용인시 처인구 이동읍 천리 61-9번지<NA>2016-08-10Y제종상37.187746127.216733<NA><NA><NA>2023-09-154050000경기도 용인시
36942뉴지리산공인중개사사무소48870-2015-00009개업공인중개사경상남도 함양군 휴천면 송전길 134<NA><NA>2015-12-17Y이혜영<NA><NA>1<NA><NA>2023-07-105460000경상남도 함양군
18395원one부동산공인중개사사무소11500-2022-00131공인중개사서울특별시 강서구 공항대로 200 806호(마곡동)<NA>02-6949-37772022-04-21Y송회영<NA><NA><NA><NA><NA>2023-11-233150000서울특별시 강서구
42341유어홈즈공인중개사사무소가4370-0308공인중개사경상남도 김해시 진영읍 본산로 15-17경상남도 김해시 진영읍 여래리 969-1<NA>2014-01-23Y박정선35.306683128.736589<NA><NA><NA>2023-06-265350000경상남도 김해시
12546중흥6단지공인중개사사무소48170-2020-00049공인중개사경상남도 진주시 충의로 146 상가1동 107호경상남도 진주시 충무공동 275 상가1동 107호<NA>2020-07-01Y백은미<NA><NA><NA><NA><NA>2023-11-305310000경상남도 진주시
20819미소공인중개사사무소26230-2017-00041공인중개사부산광역시 부산진구 중앙대로970번길 18 101호 (양정동,더샵골드5)<NA><NA>2017-02-20Y신순복<NA><NA><NA><NA><NA>2023-11-173290000부산광역시 부산진구
19605한빛공인중개사사무소26140-2017-00019개업공인중개사부산광역시 서구 꽃마을로 42 (서대신동3가)부산광역시 서구 서대신동3가 153-15 (서대신동3가)051-255-24802017-04-25Y신유미<NA><NA><NA><NA><NA>2023-11-203260000부산광역시 서구
42496오케이공인중개사사무소48250-2016-00211공인중개사경상남도 김해시 금관대로1204번길 20경상남도 김해시 외동 1223-8<NA>2016-12-07Y박재화35.232951128.855839<NA><NA><NA>2023-06-265350000경상남도 김해시
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