Overview

Dataset statistics

Number of variables11
Number of observations4535
Missing cells4865
Missing cells (%)9.8%
Duplicate rows17
Duplicate rows (%)0.4%
Total size in memory398.7 KiB
Average record size in memory90.0 B

Variable types

Numeric1
Categorical4
Text3
DateTime3

Dataset

Description대구광역시_부동산중개업 정보_2017.4월
Author대구광역시
URLhttp://data.daegu.go.kr/open/data/dataView.do?dataSetId=15052624&dataSetDetailId=1505262418edbdd1e25e8&provdMethod=FILE

Alerts

데이터기준일자 has constant value ""Constant
Dataset has 17 (0.4%) duplicate rowsDuplicates
상태구분코드 is highly overall correlated with 상태구분명High correlation
상태구분명 is highly overall correlated with 상태구분코드High correlation
법정동코드 is highly overall correlated with 법정동명High correlation
법정동명 is highly overall correlated with 법정동코드High correlation
상태구분코드 is highly imbalanced (97.3%)Imbalance
상태구분명 is highly imbalanced (97.3%)Imbalance
법인등록번호 has 4514 (99.5%) missing valuesMissing
사업자상호 has 62 (1.4%) missing valuesMissing
중개업자명 has 103 (2.3%) missing valuesMissing
보증설정시작일 has 93 (2.1%) missing valuesMissing
보증설정종료일 has 93 (2.1%) missing valuesMissing

Reproduction

Analysis started2024-04-21 07:14:13.787616
Analysis finished2024-04-21 07:14:16.441100
Duration2.65 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

법정동코드
Real number (ℝ)

HIGH CORRELATION 

Distinct8
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27291.35
Minimum27110
Maximum27710
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size40.0 KiB
2024-04-21T16:14:16.591777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum27110
5-th percentile27140
Q127200
median27260
Q327290
95-th percentile27710
Maximum27710
Range600
Interquartile range (IQR)90

Descriptive statistics

Standard deviation175.12182
Coefficient of variation (CV)0.0064167521
Kurtosis1.6120174
Mean27291.35
Median Absolute Deviation (MAD)30
Skewness1.6919667
Sum1.2376627 × 108
Variance30667.654
MonotonicityNot monotonic
2024-04-21T16:14:16.938393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
27290 983
21.7%
27260 868
19.1%
27230 730
16.1%
27140 635
14.0%
27710 619
13.6%
27170 256
 
5.6%
27200 243
 
5.4%
27110 201
 
4.4%
ValueCountFrequency (%)
27110 201
 
4.4%
27140 635
14.0%
27170 256
 
5.6%
27200 243
 
5.4%
27230 730
16.1%
27260 868
19.1%
27290 983
21.7%
27710 619
13.6%
ValueCountFrequency (%)
27710 619
13.6%
27290 983
21.7%
27260 868
19.1%
27230 730
16.1%
27200 243
 
5.4%
27170 256
 
5.6%
27140 635
14.0%
27110 201
 
4.4%

법정동명
Categorical

HIGH CORRELATION 

Distinct8
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size35.6 KiB
대구광역시 달서구
983 
대구광역시 수성구
868 
대구광역시 북구
730 
대구광역시 동구
635 
대구광역시 달성군
619 
Other values (3)
700 

Length

Max length9
Median length9
Mean length8.5446527
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row대구광역시 동구
2nd row대구광역시 동구
3rd row대구광역시 서구
4th row대구광역시 동구
5th row대구광역시 서구

Common Values

ValueCountFrequency (%)
대구광역시 달서구 983
21.7%
대구광역시 수성구 868
19.1%
대구광역시 북구 730
16.1%
대구광역시 동구 635
14.0%
대구광역시 달성군 619
13.6%
대구광역시 서구 256
 
5.6%
대구광역시 남구 243
 
5.4%
대구광역시 중구 201
 
4.4%

Length

2024-04-21T16:14:17.323470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T16:14:17.666662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
대구광역시 4535
50.0%
달서구 983
 
10.8%
수성구 868
 
9.6%
북구 730
 
8.0%
동구 635
 
7.0%
달성군 619
 
6.8%
서구 256
 
2.8%
남구 243
 
2.7%
중구 201
 
2.2%

법인등록번호
Text

MISSING 

Distinct21
Distinct (%)100.0%
Missing4514
Missing (%)99.5%
Memory size35.6 KiB
2024-04-21T16:14:18.442924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length24
Mean length24
Min length24

Characters and Unicode

Total characters504
Distinct characters65
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique21 ?
Unique (%)100.0%

Sample

1st rowx3LK5c7DSGuT/IV/5LB/0Q==
2nd rowuVn3Egm1WIvBz0s19wjN9g==
3rd rowjLg+QJ1KF6du6F9G311UjQ==
4th rowCRusQTRxVfdNZ7s2Z+tyaQ==
5th rowWB23TK+dHu8P9rKdZZr/ng==
ValueCountFrequency (%)
x3lk5c7dsgut/iv/5lb/0q 1
 
4.8%
gzarszonxm4qs0hsfzwsfw 1
 
4.8%
q+agciuuprnqzietjy2wma 1
 
4.8%
m4i7x4mfq37cfmxjonffra 1
 
4.8%
sslkzqsq+fzr/h9p0g2ljw 1
 
4.8%
07myxuy3scbgammukvlwq 1
 
4.8%
potgiwdpne6jrvzsm6ih2a 1
 
4.8%
os5cvau9jr/tzlmodiy4wa 1
 
4.8%
u33spkozmsykyxwoqnme2g 1
 
4.8%
tmswjjiiuzvccgjpafy4q 1
 
4.8%
Other values (11) 11
52.4%
2024-04-21T16:14:19.814309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
= 42
 
8.3%
Q 16
 
3.2%
g 14
 
2.8%
Z 14
 
2.8%
y 12
 
2.4%
+ 11
 
2.2%
K 11
 
2.2%
M 11
 
2.2%
S 11
 
2.2%
u 11
 
2.2%
Other values (55) 351
69.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 193
38.3%
Lowercase Letter 182
36.1%
Decimal Number 67
 
13.3%
Math Symbol 53
 
10.5%
Other Punctuation 9
 
1.8%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
Q 16
 
8.3%
Z 14
 
7.3%
K 11
 
5.7%
M 11
 
5.7%
S 11
 
5.7%
G 9
 
4.7%
A 9
 
4.7%
R 8
 
4.1%
N 8
 
4.1%
J 8
 
4.1%
Other values (16) 88
45.6%
Lowercase Letter
ValueCountFrequency (%)
g 14
 
7.7%
y 12
 
6.6%
u 11
 
6.0%
s 10
 
5.5%
f 10
 
5.5%
w 10
 
5.5%
j 10
 
5.5%
m 10
 
5.5%
d 9
 
4.9%
p 8
 
4.4%
Other values (16) 78
42.9%
Decimal Number
ValueCountFrequency (%)
7 9
13.4%
9 8
11.9%
3 8
11.9%
1 8
11.9%
2 7
10.4%
5 7
10.4%
6 6
9.0%
0 5
7.5%
4 5
7.5%
8 4
6.0%
Math Symbol
ValueCountFrequency (%)
= 42
79.2%
+ 11
 
20.8%
Other Punctuation
ValueCountFrequency (%)
/ 9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 375
74.4%
Common 129
 
25.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
Q 16
 
4.3%
g 14
 
3.7%
Z 14
 
3.7%
y 12
 
3.2%
K 11
 
2.9%
M 11
 
2.9%
S 11
 
2.9%
u 11
 
2.9%
s 10
 
2.7%
f 10
 
2.7%
Other values (42) 255
68.0%
Common
ValueCountFrequency (%)
= 42
32.6%
+ 11
 
8.5%
/ 9
 
7.0%
7 9
 
7.0%
9 8
 
6.2%
3 8
 
6.2%
1 8
 
6.2%
2 7
 
5.4%
5 7
 
5.4%
6 6
 
4.7%
Other values (3) 14
 
10.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 504
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
= 42
 
8.3%
Q 16
 
3.2%
g 14
 
2.8%
Z 14
 
2.8%
y 12
 
2.4%
+ 11
 
2.2%
K 11
 
2.2%
M 11
 
2.2%
S 11
 
2.2%
u 11
 
2.2%
Other values (55) 351
69.6%

사업자상호
Text

MISSING 

Distinct3433
Distinct (%)76.7%
Missing62
Missing (%)1.4%
Memory size35.6 KiB
2024-04-21T16:14:20.711801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length27
Median length21
Mean length11.077129
Min length5

Characters and Unicode

Total characters49548
Distinct characters560
Distinct categories10 ?
Distinct scripts4 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2901 ?
Unique (%)64.9%

Sample

1st row안심지구부동산중개
2nd row광장공인중개사사무소
3rd row한영공인중개사사무소
4th row뉴대왕공인중개사사무소
5th rowM플러스 공인중개사 사무소
ValueCountFrequency (%)
사무소 174
 
3.5%
공인중개사 132
 
2.7%
부동산중개 30
 
0.6%
부동산 25
 
0.5%
중개 16
 
0.3%
삼성공인중개사사무소 11
 
0.2%
중개사무소 11
 
0.2%
제일공인중개사사무소 9
 
0.2%
행복한공인중개사사무소 8
 
0.2%
대구공인중개사사무소 8
 
0.2%
Other values (3444) 4488
91.4%
2024-04-21T16:14:21.770400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
사 8166
16.5%
중 4489
 
9.1%
개 4481
 
9.0%
소 4218
 
8.5%
무 4201
 
8.5%
인 4074
 
8.2%
공 3985
 
8.0%
동 1008
 
2.0%
산 918
 
1.9%
부 875
 
1.8%
Other values (550) 13133
26.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 48434
97.8%
Space Separator 439
 
0.9%
Uppercase Letter 328
 
0.7%
Decimal Number 172
 
0.3%
Lowercase Letter 131
 
0.3%
Dash Punctuation 13
 
< 0.1%
Close Punctuation 12
 
< 0.1%
Open Punctuation 11
 
< 0.1%
Other Punctuation 7
 
< 0.1%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
사 8166
16.9%
중 4489
 
9.3%
개 4481
 
9.3%
소 4218
 
8.7%
무 4201
 
8.7%
인 4074
 
8.4%
공 3985
 
8.2%
동 1008
 
2.1%
산 918
 
1.9%
부 875
 
1.8%
Other values (491) 12019
24.8%
Uppercase Letter
ValueCountFrequency (%)
K 58
17.7%
A 43
13.1%
L 25
 
7.6%
S 24
 
7.3%
O 23
 
7.0%
B 20
 
6.1%
N 18
 
5.5%
E 14
 
4.3%
M 14
 
4.3%
T 13
 
4.0%
Other values (14) 76
23.2%
Lowercase Letter
ValueCountFrequency (%)
e 75
57.3%
w 14
 
10.7%
h 11
 
8.4%
n 6
 
4.6%
t 6
 
4.6%
s 3
 
2.3%
i 3
 
2.3%
k 3
 
2.3%
u 2
 
1.5%
r 2
 
1.5%
Other values (5) 6
 
4.6%
Decimal Number
ValueCountFrequency (%)
1 68
39.5%
3 25
 
14.5%
2 22
 
12.8%
4 16
 
9.3%
5 13
 
7.6%
6 10
 
5.8%
8 5
 
2.9%
9 5
 
2.9%
7 5
 
2.9%
0 3
 
1.7%
Other Punctuation
ValueCountFrequency (%)
& 2
28.6%
. 2
28.6%
· 1
14.3%
, 1
14.3%
# 1
14.3%
Space Separator
ValueCountFrequency (%)
439
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 13
100.0%
Close Punctuation
ValueCountFrequency (%)
) 12
100.0%
Open Punctuation
ValueCountFrequency (%)
( 11
100.0%
Math Symbol
ValueCountFrequency (%)
+ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 48425
97.7%
Common 655
 
1.3%
Latin 459
 
0.9%
Han 9
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
사 8166
16.9%
중 4489
 
9.3%
개 4481
 
9.3%
소 4218
 
8.7%
무 4201
 
8.7%
인 4074
 
8.4%
공 3985
 
8.2%
동 1008
 
2.1%
산 918
 
1.9%
부 875
 
1.8%
Other values (483) 12010
24.8%
Latin
ValueCountFrequency (%)
e 75
16.3%
K 58
 
12.6%
A 43
 
9.4%
L 25
 
5.4%
S 24
 
5.2%
O 23
 
5.0%
B 20
 
4.4%
N 18
 
3.9%
E 14
 
3.1%
M 14
 
3.1%
Other values (29) 145
31.6%
Common
ValueCountFrequency (%)
439
67.0%
1 68
 
10.4%
3 25
 
3.8%
2 22
 
3.4%
4 16
 
2.4%
5 13
 
2.0%
- 13
 
2.0%
) 12
 
1.8%
( 11
 
1.7%
6 10
 
1.5%
Other values (10) 26
 
4.0%
Han
ValueCountFrequency (%)
地 2
22.2%
家 1
11.1%
山 1
11.1%
元 1
11.1%
天 1
11.1%
大 1
11.1%
新 1
11.1%
源 1
11.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 48425
97.7%
ASCII 1113
 
2.2%
CJK 9
 
< 0.1%
None 1
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
사 8166
16.9%
중 4489
 
9.3%
개 4481
 
9.3%
소 4218
 
8.7%
무 4201
 
8.7%
인 4074
 
8.4%
공 3985
 
8.2%
동 1008
 
2.1%
산 918
 
1.9%
부 875
 
1.8%
Other values (483) 12010
24.8%
ASCII
ValueCountFrequency (%)
439
39.4%
e 75
 
6.7%
1 68
 
6.1%
K 58
 
5.2%
A 43
 
3.9%
L 25
 
2.2%
3 25
 
2.2%
S 24
 
2.2%
O 23
 
2.1%
2 22
 
2.0%
Other values (48) 311
27.9%
CJK
ValueCountFrequency (%)
地 2
22.2%
家 1
11.1%
山 1
11.1%
元 1
11.1%
天 1
11.1%
大 1
11.1%
新 1
11.1%
源 1
11.1%
None
ValueCountFrequency (%)
· 1
100.0%

중개업자명
Text

MISSING 

Distinct3928
Distinct (%)88.6%
Missing103
Missing (%)2.3%
Memory size35.6 KiB
2024-04-21T16:14:23.011970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length2.9986462
Min length2

Characters and Unicode

Total characters13290
Distinct characters258
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

Unique3593 ?
Unique (%)81.1%

Sample

1st row김주영
2nd row조창현
3rd row신윤겸
4th row장기옥
5th row이재욱
ValueCountFrequency (%)
김명희 10
 
0.2%
김영희 8
 
0.2%
이영숙 6
 
0.1%
이은희 6
 
0.1%
김영숙 6
 
0.1%
김명자 6
 
0.1%
김정희 6
 
0.1%
박영희 5
 
0.1%
김진수 5
 
0.1%
박정희 5
 
0.1%
Other values (3920) 4371
98.6%
2024-04-21T16:14:24.684543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
김 946
 
7.1%
이 716
 
5.4%
정 541
 
4.1%
영 398
 
3.0%
희 393
 
3.0%
박 392
 
2.9%
경 297
 
2.2%
숙 284
 
2.1%
미 229
 
1.7%
현 225
 
1.7%
Other values (248) 8869
66.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 13288
> 99.9%
Space Separator 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
김 946
 
7.1%
이 716
 
5.4%
정 541
 
4.1%
영 398
 
3.0%
희 393
 
3.0%
박 392
 
3.0%
경 297
 
2.2%
숙 284
 
2.1%
미 229
 
1.7%
현 225
 
1.7%
Other values (247) 8867
66.7%
Space Separator
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 13288
> 99.9%
Common 2
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
김 946
 
7.1%
이 716
 
5.4%
정 541
 
4.1%
영 398
 
3.0%
희 393
 
3.0%
박 392
 
3.0%
경 297
 
2.2%
숙 284
 
2.1%
미 229
 
1.7%
현 225
 
1.7%
Other values (247) 8867
66.7%
Common
ValueCountFrequency (%)
2
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 13288
> 99.9%
ASCII 2
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
김 946
 
7.1%
이 716
 
5.4%
정 541
 
4.1%
영 398
 
3.0%
희 393
 
3.0%
박 392
 
3.0%
경 297
 
2.2%
숙 284
 
2.1%
미 229
 
1.7%
현 225
 
1.7%
Other values (247) 8867
66.7%
ASCII
ValueCountFrequency (%)
2
100.0%

상태구분코드
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size35.6 KiB
1
4511 
2
 
15
8
 
6
3
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 4511
99.5%
2 15
 
0.3%
8 6
 
0.1%
3 3
 
0.1%

Length

2024-04-21T16:14:24.928966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T16:14:25.112737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 4511
99.5%
2 15
 
0.3%
8 6
 
0.1%
3 3
 
0.1%

상태구분명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size35.6 KiB
영업중
4511 
휴업
 
15
업무정지
 
6
휴업연정
 
3

Length

Max length4
Median length3
Mean length2.998677
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
영업중 4511
99.5%
휴업 15
 
0.3%
업무정지 6
 
0.1%
휴업연정 3
 
0.1%

Length

2024-04-21T16:14:25.332765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T16:14:25.543322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
영업중 4511
99.5%
휴업 15
 
0.3%
업무정지 6
 
0.1%
휴업연정 3
 
0.1%

등록일자
Date

Distinct2291
Distinct (%)50.5%
Missing0
Missing (%)0.0%
Memory size35.6 KiB
Minimum1984-05-16 00:00:00
Maximum2017-03-02 00:00:00
2024-04-21T16:14:25.757705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T16:14:25.999795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

보증설정시작일
Date

MISSING 

Distinct462
Distinct (%)10.4%
Missing93
Missing (%)2.1%
Memory size35.6 KiB
Minimum2004-10-30 00:00:00
Maximum2031-11-27 00:00:00
2024-04-21T16:14:26.229661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T16:14:26.452792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

보증설정종료일
Date

MISSING 

Distinct461
Distinct (%)10.4%
Missing93
Missing (%)2.1%
Memory size35.6 KiB
Minimum2005-10-29 00:00:00
Maximum2209-04-18 00:00:00
2024-04-21T16:14:26.666713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T16:14:26.907819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size35.6 KiB
2017-03-03
4535 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2017-03-03
2nd row2017-03-03
3rd row2017-03-03
4th row2017-03-03
5th row2017-03-03

Common Values

ValueCountFrequency (%)
2017-03-03 4535
100.0%

Length

2024-04-21T16:14:27.133572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T16:14:27.281924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2017-03-03 4535
100.0%

Interactions

2024-04-21T16:14:14.957715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-21T16:14:27.380857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
법정동코드법정동명법인등록번호상태구분코드상태구분명
법정동코드1.0001.0001.0000.0000.000
법정동명1.0001.0001.0000.0000.000
법인등록번호1.0001.0001.000NaNNaN
상태구분코드0.0000.000NaN1.0001.000
상태구분명0.0000.000NaN1.0001.000
2024-04-21T16:14:27.556808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
상태구분코드상태구분명법정동명
상태구분코드1.0001.0000.000
상태구분명1.0001.0000.000
법정동명0.0000.0001.000
2024-04-21T16:14:27.705899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
법정동코드법정동명상태구분코드상태구분명
법정동코드1.0001.0000.0050.005
법정동명1.0001.0000.0000.000
상태구분코드0.0050.0001.0001.000
상태구분명0.0050.0001.0001.000

Missing values

2024-04-21T16:14:15.335728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-21T16:14:15.864061image/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.
2024-04-21T16:14:16.240577image/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

법정동코드법정동명법인등록번호사업자상호중개업자명상태구분코드상태구분명등록일자보증설정시작일보증설정종료일데이터기준일자
027140대구광역시 동구<NA>안심지구부동산중개김주영1영업중2016-12-072016-12-012017-11-302017-03-03
127140대구광역시 동구<NA>광장공인중개사사무소조창현1영업중2016-05-112016-05-112017-05-102017-03-03
227170대구광역시 서구<NA>한영공인중개사사무소신윤겸1영업중2013-10-282016-10-282017-10-272017-03-03
327140대구광역시 동구<NA>뉴대왕공인중개사사무소장기옥1영업중2017-02-242017-02-242018-02-232017-03-03
427170대구광역시 서구<NA>M플러스 공인중개사 사무소이재욱1영업중2014-02-172017-02-172018-02-162017-03-03
527170대구광역시 서구<NA>하나공인중개사사무소박상돈1영업중2012-12-202016-12-202017-12-192017-03-03
627170대구광역시 서구<NA>금강부동산중개사무소구경미1영업중2015-01-062017-01-062018-01-052017-03-03
727170대구광역시 서구<NA>뉴대진부동산(주)사무소이승열1영업중2014-09-252016-09-252017-09-242017-03-03
827170대구광역시 서구<NA>황금 공인중개사 사무소이병준1영업중2014-06-022016-06-022017-06-012017-03-03
927170대구광역시 서구<NA>M공인중개사 사무소송호진1영업중2014-01-202017-01-202018-01-192017-03-03
법정동코드법정동명법인등록번호사업자상호중개업자명상태구분코드상태구분명등록일자보증설정시작일보증설정종료일데이터기준일자
452527710대구광역시 달성군<NA>화원역이진캐스빌공인중개사사무소조덕천1영업중2017-01-182017-01-182018-01-172017-03-03
452627710대구광역시 달성군<NA>건국공인중개사사무소이형식1영업중2017-01-232017-01-232018-01-222017-03-03
452727710대구광역시 달성군<NA>곽상헌공인중개사사무소곽상헌1영업중2017-01-252017-01-252018-01-242017-03-03
452827710대구광역시 달성군<NA>구지태양공인중개사사무소이수용1영업중2017-02-082017-02-082018-02-072017-03-03
452927710대구광역시 달성군<NA>북죽곡제일공인중개사사무소강병욱1영업중2017-02-082017-02-082018-02-072017-03-03
453027710대구광역시 달성군<NA>동원아이위시부동산중개전재곤1영업중2015-07-072016-07-072017-07-062017-03-03
453127710대구광역시 달성군<NA>화원이진캐스빌공인중개사사무소김광호1영업중2014-03-182016-03-202017-03-192017-03-03
453227710대구광역시 달성군<NA>이진캐스빌부동산중개김혜경1영업중2017-02-232017-02-232018-02-222017-03-03
453327710대구광역시 달성군<NA>화원대백부동산공인중개사사무소박세철1영업중2017-02-242017-02-242018-02-232017-03-03
453427710대구광역시 달성군<NA>스마일공인중개사사무소김병욱1영업중2017-02-272017-02-272018-02-262017-03-03

Duplicate rows

Most frequently occurring

법정동코드법정동명법인등록번호사업자상호중개업자명상태구분코드상태구분명등록일자보증설정시작일보증설정종료일데이터기준일자# duplicates
927290대구광역시 달서구<NA><NA><NA>1영업중2012-08-20<NA><NA>2017-03-0311
1327710대구광역시 달성군<NA><NA><NA>1영업중2014-02-18<NA><NA>2017-03-036
1127710대구광역시 달성군<NA><NA><NA>1영업중2013-10-29<NA><NA>2017-03-035
1027290대구광역시 달서구<NA><NA><NA>1영업중2013-10-02<NA><NA>2017-03-033
1527710대구광역시 달성군<NA><NA><NA>1영업중2014-08-14<NA><NA>2017-03-033
027110대구광역시 중구<NA>S 부동산 공인중개사 사무소강명미1영업중2016-04-262016-04-262017-04-252017-03-032
127140대구광역시 동구<NA><NA><NA>1영업중2013-12-18<NA><NA>2017-03-032
227200대구광역시 남구<NA><NA><NA>1영업중2014-01-20<NA><NA>2017-03-032
327200대구광역시 남구<NA><NA><NA>1영업중2014-02-07<NA><NA>2017-03-032
427200대구광역시 남구<NA><NA><NA>1영업중2014-09-23<NA><NA>2017-03-032