Overview

Dataset statistics

Number of variables10
Number of observations10000
Missing cells10752
Missing cells (%)10.8%
Duplicate rows17
Duplicate rows (%)0.2%
Total size in memory869.1 KiB
Average record size in memory89.0 B

Variable types

Categorical4
Text4
DateTime1
Unsupported1

Dataset

Description부동산 중개업 사무소 정보 현황
Author경기도
URLhttps://data.gg.go.kr/portal/data/service/selectServicePage.do?&infId=ZEQJ2E0QZGFYG1IS7LVU23076487&infSeq=2

Alerts

데이터기준일자 has constant value ""Constant
Dataset has 17 (0.2%) duplicate rowsDuplicates
시군명 is highly overall correlated with 시군구명High correlation
시군구명 is highly overall correlated with 시군명High correlation
상태구분명 is highly imbalanced (97.8%)Imbalance
사업자상호정보 has 142 (1.4%) missing valuesMissing
중개업자명 has 170 (1.7%) missing valuesMissing
전화번호정보 has 10000 (100.0%) missing valuesMissing
보증설정시작일 has 220 (2.2%) missing valuesMissing
보증설정종료일 has 220 (2.2%) missing valuesMissing
전화번호정보 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2023-12-10 22:04:42.760922
Analysis finished2023-12-10 22:04:44.138364
Duration1.38 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군명
Categorical

HIGH CORRELATION 

Distinct31
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
수원시
928 
화성시
869 
성남시
758 
고양시
691 
용인시
670 
Other values (26)
6084 

Length

Max length4
Median length3
Mean length3.0734
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row성남시
2nd row부천시
3rd row남양주시
4th row광명시
5th row화성시

Common Values

ValueCountFrequency (%)
수원시 928
 
9.3%
화성시 869
 
8.7%
성남시 758
 
7.6%
고양시 691
 
6.9%
용인시 670
 
6.7%
부천시 650
 
6.5%
평택시 591
 
5.9%
남양주시 442
 
4.4%
안산시 440
 
4.4%
안양시 428
 
4.3%
Other values (21) 3533
35.3%

Length

2023-12-11T07:04:44.212670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
수원시 928
 
9.3%
화성시 869
 
8.7%
성남시 758
 
7.6%
고양시 691
 
6.9%
용인시 670
 
6.7%
부천시 650
 
6.5%
평택시 591
 
5.9%
남양주시 442
 
4.4%
안산시 440
 
4.4%
안양시 428
 
4.3%
Other values (21) 3533
35.3%

시군구명
Categorical

HIGH CORRELATION 

Distinct43
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
경기도 화성시
869 
경기도 부천시
 
650
경기도 평택시
 
591
경기도 남양주시
 
442
경기도 김포시
 
406
Other values (38)
7042 

Length

Max length12
Median length7
Mean length8.6803
Min length7

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row경기도 성남시 수정구
2nd row경기도 부천시
3rd row경기도 남양주시
4th row경기도 광명시
5th row경기도 화성시

Common Values

ValueCountFrequency (%)
경기도 화성시 869
 
8.7%
경기도 부천시 650
 
6.5%
경기도 평택시 591
 
5.9%
경기도 남양주시 442
 
4.4%
경기도 김포시 406
 
4.1%
경기도 시흥시 404
 
4.0%
경기도 성남시 분당구 370
 
3.7%
경기도 수원시 영통구 313
 
3.1%
경기도 파주시 283
 
2.8%
경기도 하남시 280
 
2.8%
Other values (33) 5392
53.9%

Length

2023-12-11T07:04:44.351383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기도 10000
41.8%
수원시 928
 
3.9%
화성시 869
 
3.6%
성남시 758
 
3.2%
고양시 691
 
2.9%
용인시 670
 
2.8%
부천시 650
 
2.7%
평택시 591
 
2.5%
남양주시 442
 
1.8%
안산시 440
 
1.8%
Other values (39) 7875
32.9%

사업자상호정보
Text

MISSING 

Distinct6236
Distinct (%)63.3%
Missing142
Missing (%)1.4%
Memory size156.2 KiB
2023-12-11T07:04:44.662255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length29
Median length25
Mean length11.526679
Min length1

Characters and Unicode

Total characters113630
Distinct characters703
Distinct categories12 ?
Distinct scripts5 ?
Distinct blocks5 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5257 ?
Unique (%)53.3%

Sample

1st row호성공동부동산중개사무소
2nd row리치랜드공인중개사사무소
3rd row다산하이공인중개사사무소
4th row광명사거리역애플부동산 공인중개사사무소
5th row다감공인중개사사무소
ValueCountFrequency (%)
공인중개사사무소 1477
 
12.6%
사무소 109
 
0.9%
삼성공인중개사사무소 81
 
0.7%
공인중개사 66
 
0.6%
우리공인중개사사무소 61
 
0.5%
현대공인중개사사무소 56
 
0.5%
미래공인중개사사무소 49
 
0.4%
행운공인중개사사무소 48
 
0.4%
하나공인중개사사무소 45
 
0.4%
중앙공인중개사사무소 43
 
0.4%
Other values (6225) 9683
82.6%
2023-12-11T07:04:45.205470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
사 19298
17.0%
중 9985
 
8.8%
개 9877
 
8.7%
소 9738
 
8.6%
인 9710
 
8.5%
무 9673
 
8.5%
공 9521
 
8.4%
동 2004
 
1.8%
1865
 
1.6%
산 1778
 
1.6%
Other values (693) 30181
26.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 109889
96.7%
Space Separator 1865
 
1.6%
Uppercase Letter 756
 
0.7%
Decimal Number 679
 
0.6%
Lowercase Letter 193
 
0.2%
Open Punctuation 93
 
0.1%
Close Punctuation 93
 
0.1%
Other Punctuation 27
 
< 0.1%
Dash Punctuation 17
 
< 0.1%
Math Symbol 9
 
< 0.1%
Other values (2) 9
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
사 19298
17.6%
중 9985
 
9.1%
개 9877
 
9.0%
소 9738
 
8.9%
인 9710
 
8.8%
무 9673
 
8.8%
공 9521
 
8.7%
동 2004
 
1.8%
산 1778
 
1.6%
부 1683
 
1.5%
Other values (617) 26622
24.2%
Uppercase Letter
ValueCountFrequency (%)
K 144
19.0%
O 72
9.5%
S 68
 
9.0%
L 63
 
8.3%
G 54
 
7.1%
A 45
 
6.0%
C 39
 
5.2%
I 34
 
4.5%
B 32
 
4.2%
E 28
 
3.7%
Other values (16) 177
23.4%
Lowercase Letter
ValueCountFrequency (%)
e 65
33.7%
a 16
 
8.3%
o 14
 
7.3%
h 13
 
6.7%
i 13
 
6.7%
t 10
 
5.2%
n 8
 
4.1%
k 8
 
4.1%
s 7
 
3.6%
r 7
 
3.6%
Other values (12) 32
16.6%
Decimal Number
ValueCountFrequency (%)
1 320
47.1%
4 107
 
15.8%
2 74
 
10.9%
9 40
 
5.9%
8 32
 
4.7%
5 30
 
4.4%
3 26
 
3.8%
0 21
 
3.1%
6 20
 
2.9%
7 9
 
1.3%
Other Punctuation
ValueCountFrequency (%)
. 13
48.1%
! 5
 
18.5%
, 4
 
14.8%
& 2
 
7.4%
？ 2
 
7.4%
@ 1
 
3.7%
Letter Number
ValueCountFrequency (%)
Ⅱ 4
57.1%
Ⅰ 2
28.6%
Ⅲ 1
 
14.3%
Open Punctuation
ValueCountFrequency (%)
( 92
98.9%
[ 1
 
1.1%
Close Punctuation
ValueCountFrequency (%)
) 92
98.9%
] 1
 
1.1%
Math Symbol
ValueCountFrequency (%)
+ 8
88.9%
~ 1
 
11.1%
Space Separator
ValueCountFrequency (%)
1865
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 17
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 109873
96.7%
Common 2783
 
2.4%
Latin 955
 
0.8%
Han 18
 
< 0.1%
Greek 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
사 19298
17.6%
중 9985
 
9.1%
개 9877
 
9.0%
소 9738
 
8.9%
인 9710
 
8.8%
무 9673
 
8.8%
공 9521
 
8.7%
동 2004
 
1.8%
산 1778
 
1.6%
부 1683
 
1.5%
Other values (602) 26606
24.2%
Latin
ValueCountFrequency (%)
K 144
15.1%
O 72
 
7.5%
S 68
 
7.1%
e 65
 
6.8%
L 63
 
6.6%
G 54
 
5.7%
A 45
 
4.7%
C 39
 
4.1%
I 34
 
3.6%
B 32
 
3.4%
Other values (40) 339
35.5%
Common
ValueCountFrequency (%)
1865
67.0%
1 320
 
11.5%
4 107
 
3.8%
( 92
 
3.3%
) 92
 
3.3%
2 74
 
2.7%
9 40
 
1.4%
8 32
 
1.1%
5 30
 
1.1%
3 26
 
0.9%
Other values (14) 105
 
3.8%
Han
ValueCountFrequency (%)
大 2
 
11.1%
秀 2
 
11.1%
安 1
 
5.6%
家 1
 
5.6%
山 1
 
5.6%
美 1
 
5.6%
愛 1
 
5.6%
潤 1
 
5.6%
正 1
 
5.6%
惠 1
 
5.6%
Other values (6) 6
33.3%
Greek
ValueCountFrequency (%)
Ι 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 109871
96.7%
ASCII 3729
 
3.3%
CJK 18
 
< 0.1%
Number Forms 7
 
< 0.1%
None 5
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
사 19298
17.6%
중 9985
 
9.1%
개 9877
 
9.0%
소 9738
 
8.9%
인 9710
 
8.8%
무 9673
 
8.8%
공 9521
 
8.7%
동 2004
 
1.8%
산 1778
 
1.6%
부 1683
 
1.5%
Other values (601) 26604
24.2%
ASCII
ValueCountFrequency (%)
1865
50.0%
1 320
 
8.6%
K 144
 
3.9%
4 107
 
2.9%
( 92
 
2.5%
) 92
 
2.5%
2 74
 
2.0%
O 72
 
1.9%
S 68
 
1.8%
e 65
 
1.7%
Other values (60) 830
22.3%
Number Forms
ValueCountFrequency (%)
Ⅱ 4
57.1%
Ⅰ 2
28.6%
Ⅲ 1
 
14.3%
CJK
ValueCountFrequency (%)
大 2
 
11.1%
秀 2
 
11.1%
安 1
 
5.6%
家 1
 
5.6%
山 1
 
5.6%
美 1
 
5.6%
愛 1
 
5.6%
潤 1
 
5.6%
正 1
 
5.6%
惠 1
 
5.6%
Other values (6) 6
33.3%
None
ValueCountFrequency (%)
？ 2
40.0%
㈜ 2
40.0%
Ι 1
20.0%

중개업자명
Text

MISSING 

Distinct7981
Distinct (%)81.2%
Missing170
Missing (%)1.7%
Memory size156.2 KiB
2023-12-11T07:04:45.548617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length3
Mean length2.9976602
Min length2

Characters and Unicode

Total characters29467
Distinct characters311
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

Unique6951 ?
Unique (%)70.7%

Sample

1st row박홍성
2nd row김미숙
3rd row김미옥
4th row이준석
5th row임서목
ValueCountFrequency (%)
김미숙 20
 
0.2%
김미경 17
 
0.2%
김경희 15
 
0.2%
김정희 12
 
0.1%
이미영 12
 
0.1%
김정숙 11
 
0.1%
김현숙 11
 
0.1%
김영숙 11
 
0.1%
김경숙 10
 
0.1%
김은정 10
 
0.1%
Other values (7976) 9706
98.7%
2023-12-11T07:04:46.357469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
김 1934
 
6.6%
이 1662
 
5.6%
정 1210
 
4.1%
영 971
 
3.3%
희 835
 
2.8%
박 786
 
2.7%
숙 718
 
2.4%
경 687
 
2.3%
미 686
 
2.3%
현 560
 
1.9%
Other values (301) 19418
65.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 29416
99.8%
Uppercase Letter 44
 
0.1%
Space Separator 5
 
< 0.1%
Close Punctuation 1
 
< 0.1%
Open Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
김 1934
 
6.6%
이 1662
 
5.6%
정 1210
 
4.1%
영 971
 
3.3%
희 835
 
2.8%
박 786
 
2.7%
숙 718
 
2.4%
경 687
 
2.3%
미 686
 
2.3%
현 560
 
1.9%
Other values (282) 19367
65.8%
Uppercase Letter
ValueCountFrequency (%)
A 5
11.4%
N 5
11.4%
U 5
11.4%
I 5
11.4%
H 5
11.4%
M 4
9.1%
K 3
6.8%
Y 3
6.8%
S 2
 
4.5%
G 1
 
2.3%
Other values (6) 6
13.6%
Space Separator
ValueCountFrequency (%)
5
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 29416
99.8%
Latin 44
 
0.1%
Common 7
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
김 1934
 
6.6%
이 1662
 
5.6%
정 1210
 
4.1%
영 971
 
3.3%
희 835
 
2.8%
박 786
 
2.7%
숙 718
 
2.4%
경 687
 
2.3%
미 686
 
2.3%
현 560
 
1.9%
Other values (282) 19367
65.8%
Latin
ValueCountFrequency (%)
A 5
11.4%
N 5
11.4%
U 5
11.4%
I 5
11.4%
H 5
11.4%
M 4
9.1%
K 3
6.8%
Y 3
6.8%
S 2
 
4.5%
G 1
 
2.3%
Other values (6) 6
13.6%
Common
ValueCountFrequency (%)
5
71.4%
) 1
 
14.3%
( 1
 
14.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 29416
99.8%
ASCII 51
 
0.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
김 1934
 
6.6%
이 1662
 
5.6%
정 1210
 
4.1%
영 971
 
3.3%
희 835
 
2.8%
박 786
 
2.7%
숙 718
 
2.4%
경 687
 
2.3%
미 686
 
2.3%
현 560
 
1.9%
Other values (282) 19367
65.8%
ASCII
ValueCountFrequency (%)
A 5
9.8%
N 5
9.8%
U 5
9.8%
I 5
9.8%
H 5
9.8%
5
9.8%
M 4
7.8%
K 3
 
5.9%
Y 3
 
5.9%
S 2
 
3.9%
Other values (9) 9
17.6%

상태구분명
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
영업중
9967 
휴업
 
25
업무정지
 
8

Length

Max length4
Median length3
Mean length2.9983
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
영업중 9967
99.7%
휴업 25
 
0.2%
업무정지 8
 
0.1%

Length

2023-12-11T07:04:46.501879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T07:04:46.604939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
영업중 9967
99.7%
휴업 25
 
0.2%
업무정지 8
 
0.1%

등록일자
Date

Distinct3853
Distinct (%)38.5%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum1984-05-11 00:00:00
Maximum2019-12-06 00:00:00
2023-12-11T07:04:46.728853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:04:46.894589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

전화번호정보
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size166.0 KiB

보증설정시작일
Text

MISSING 

Distinct626
Distinct (%)6.4%
Missing220
Missing (%)2.2%
Memory size156.2 KiB
2023-12-11T07:04:47.158860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length9.999591
Min length8

Characters and Unicode

Total characters97796
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

Unique159 ?
Unique (%)1.6%

Sample

1st row2019-06-30
2nd row2019-03-06
3rd row2019-12-01
4th row2018-10-24
5th row2019-01-16
ValueCountFrequency (%)
2019-06-30 121
 
1.2%
2019-01-04 53
 
0.5%
2019-05-02 51
 
0.5%
2019-03-15 47
 
0.5%
2019-01-02 46
 
0.5%
2019-03-08 45
 
0.5%
2019-04-25 44
 
0.4%
2019-04-04 44
 
0.4%
2019-01-05 44
 
0.4%
2019-02-02 43
 
0.4%
Other values (617) 9243
94.5%
2023-12-11T07:04:47.561410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 21992
22.5%
- 19556
20.0%
1 18066
18.5%
2 15585
15.9%
9 10620
10.9%
8 2459
 
2.5%
3 2380
 
2.4%
4 1893
 
1.9%
6 1810
 
1.9%
5 1727
 
1.8%
Other values (2) 1708
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 78239
80.0%
Dash Punctuation 19556
 
20.0%
Space Separator 1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 21992
28.1%
1 18066
23.1%
2 15585
19.9%
9 10620
13.6%
8 2459
 
3.1%
3 2380
 
3.0%
4 1893
 
2.4%
6 1810
 
2.3%
5 1727
 
2.2%
7 1707
 
2.2%
Dash Punctuation
ValueCountFrequency (%)
- 19556
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 97796
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 21992
22.5%
- 19556
20.0%
1 18066
18.5%
2 15585
15.9%
9 10620
10.9%
8 2459
 
2.5%
3 2380
 
2.4%
4 1893
 
1.9%
6 1810
 
1.9%
5 1727
 
1.8%
Other values (2) 1708
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 97796
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 21992
22.5%
- 19556
20.0%
1 18066
18.5%
2 15585
15.9%
9 10620
10.9%
8 2459
 
2.5%
3 2380
 
2.4%
4 1893
 
1.9%
6 1810
 
1.9%
5 1727
 
1.8%
Other values (2) 1708
 
1.7%

보증설정종료일
Text

MISSING 

Distinct627
Distinct (%)6.4%
Missing220
Missing (%)2.2%
Memory size156.2 KiB
2023-12-11T07:04:47.822074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters97800
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique168 ?
Unique (%)1.7%

Sample

1st row2020-06-29
2nd row2020-03-05
3rd row2020-11-30
4th row2019-10-23
5th row2020-01-15
ValueCountFrequency (%)
2020-06-29 122
 
1.2%
2020-01-03 54
 
0.6%
2020-05-01 51
 
0.5%
2020-03-14 46
 
0.5%
2020-01-01 46
 
0.5%
2020-03-07 45
 
0.5%
2020-04-03 44
 
0.4%
2020-01-04 44
 
0.4%
2020-04-24 44
 
0.4%
2020-02-01 43
 
0.4%
Other values (617) 9241
94.5%
2023-12-11T07:04:48.221559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 30932
31.6%
2 24534
25.1%
- 19560
20.0%
1 9122
 
9.3%
9 2445
 
2.5%
3 2438
 
2.5%
6 1834
 
1.9%
4 1805
 
1.8%
7 1728
 
1.8%
8 1712
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 78240
80.0%
Dash Punctuation 19560
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 30932
39.5%
2 24534
31.4%
1 9122
 
11.7%
9 2445
 
3.1%
3 2438
 
3.1%
6 1834
 
2.3%
4 1805
 
2.3%
7 1728
 
2.2%
8 1712
 
2.2%
5 1690
 
2.2%
Dash Punctuation
ValueCountFrequency (%)
- 19560
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 97800
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 30932
31.6%
2 24534
25.1%
- 19560
20.0%
1 9122
 
9.3%
9 2445
 
2.5%
3 2438
 
2.5%
6 1834
 
1.9%
4 1805
 
1.8%
7 1728
 
1.8%
8 1712
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 97800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 30932
31.6%
2 24534
25.1%
- 19560
20.0%
1 9122
 
9.3%
9 2445
 
2.5%
3 2438
 
2.5%
6 1834
 
1.9%
4 1805
 
1.8%
7 1728
 
1.8%
8 1712
 
1.8%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2019-12-09
10000 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2019-12-09
2nd row2019-12-09
3rd row2019-12-09
4th row2019-12-09
5th row2019-12-09

Common Values

ValueCountFrequency (%)
2019-12-09 10000
100.0%

Length

2023-12-11T07:04:48.371387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T07:04:48.457972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2019-12-09 10000
100.0%

Correlations

2023-12-11T07:04:48.516459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군명시군구명상태구분명
시군명1.0001.0000.000
시군구명1.0001.0000.000
상태구분명0.0000.0001.000
2023-12-11T07:04:48.603216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군명상태구분명시군구명
시군명1.0000.0000.999
상태구분명0.0001.0000.000
시군구명0.9990.0001.000
2023-12-11T07:04:48.690424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군명시군구명상태구분명
시군명1.0000.9990.000
시군구명0.9991.0000.000
상태구분명0.0000.0001.000

Missing values

2023-12-11T07:04:43.764582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T07:04:43.914779image/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.
2023-12-11T07:04:44.056786image/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

시군명시군구명사업자상호정보중개업자명상태구분명등록일자전화번호정보보증설정시작일보증설정종료일데이터기준일자
9211성남시경기도 성남시 수정구호성공동부동산중개사무소박홍성영업중1984-06-20<NA>2019-06-302020-06-292019-12-09
7836부천시경기도 부천시리치랜드공인중개사사무소김미숙영업중2013-03-06<NA>2019-03-062020-03-052019-12-09
6291남양주시경기도 남양주시다산하이공인중개사사무소김미옥영업중2018-11-21<NA>2019-12-012020-11-302019-12-09
2848광명시경기도 광명시광명사거리역애플부동산 공인중개사사무소이준석영업중2017-10-24<NA>2018-10-242019-10-232019-12-09
28306화성시경기도 화성시다감공인중개사사무소임서목영업중2019-01-16<NA>2019-01-162020-01-152019-12-09
14968시흥시경기도 시흥시서우 공인중개사사무소윤서우영업중2016-01-06<NA>2019-01-062020-01-052019-12-09
8491부천시경기도 부천시부성2공인중개사사무소손은미영업중2017-11-14<NA>2019-11-152020-11-142019-12-09
16037안산시경기도 안산시 상록구신한공인중개사사무소박신자영업중2008-08-01<NA>2019-08-062020-08-052019-12-09
15865안산시경기도 안산시 상록구대동공인중개사사무소유충길영업중2013-01-24<NA>2019-01-242020-01-232019-12-09
9498성남시경기도 성남시 수정구미도공인중개사사무소장선숙영업중2009-03-25<NA>2019-03-252020-03-242019-12-09
시군명시군구명사업자상호정보중개업자명상태구분명등록일자전화번호정보보증설정시작일보증설정종료일데이터기준일자
23048파주시경기도 파주시성원공인중개사사무소노진숙영업중2005-12-09<NA>2019-09-122020-09-112019-12-09
19818용인시경기도 용인시 기흥구LG공인중개사사무소권종순영업중2004-03-29<NA>2019-03-292020-03-282019-12-09
609고양시경기도 고양시 일산동구코오롱1차송윤지 공인중개사사무소송윤지영업중2012-09-03<NA>2019-09-032020-09-022019-12-09
28221화성시경기도 화성시서해공인중개사사무소이항섭영업중2016-05-27<NA>2019-06-022020-06-012019-12-09
12312수원시경기도 수원시 팔달구경기공인중개사사무소김연옥영업중2007-04-16<NA>2019-04-162020-04-152019-12-09
28927화성시경기도 화성시우남OK공인중개사사무소고태미영업중2017-08-17<NA>2019-08-182020-08-172019-12-09
22538의정부시경기도 의정부시우리집공인중개사사무소조보람영업중2019-01-29<NA>2019-01-302020-01-292019-12-09
16410안산시경기도 안산시 단원구대신공인중개사사무소이선숙영업중2016-12-13<NA>2018-12-222019-12-212019-12-09
24235평택시경기도 평택시반도OK공인중개사사무소선원진영업중2014-02-17<NA>2019-02-182020-02-172019-12-09
17404안양시경기도 안양시 만안구신광공인중개사사무소구란아영업중2013-10-14<NA>2019-10-172020-10-162019-12-09

Duplicate rows

Most frequently occurring

시군명시군구명사업자상호정보중개업자명상태구분명등록일자보증설정시작일보증설정종료일데이터기준일자# duplicates
5화성시경기도 화성시<NA><NA>영업중1988-10-20<NA><NA>2019-12-0935
7화성시경기도 화성시<NA><NA>영업중2010-05-04<NA><NA>2019-12-0915
8화성시경기도 화성시<NA><NA>영업중2011-02-21<NA><NA>2019-12-0910
11화성시경기도 화성시<NA><NA>영업중2013-04-10<NA><NA>2019-12-099
4용인시경기도 용인시 기흥구<NA><NA>영업중2013-08-13<NA><NA>2019-12-096
12화성시경기도 화성시<NA><NA>영업중2014-01-15<NA><NA>2019-12-096
15화성시경기도 화성시<NA><NA>영업중2015-12-11<NA><NA>2019-12-096
9화성시경기도 화성시<NA><NA>영업중2012-11-06<NA><NA>2019-12-094
14화성시경기도 화성시<NA><NA>영업중2014-12-15<NA><NA>2019-12-093
0광명시경기도 광명시삼성공인중개사사무소<NA>영업중2019-01-23<NA><NA>2019-12-092