Overview

Dataset statistics

Number of variables10
Number of observations517
Missing cells189
Missing cells (%)3.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory41.0 KiB
Average record size in memory81.3 B

Variable types

Numeric1
Text5
Categorical4

Dataset

Description인천광역시 계양구 관내 등록개설된 부동산공인중개사의 현황(등록번호,사무소명,중개업소종별,중개업자명,중개업자구분 등)
Author인천광역시 계양구
URLhttps://www.data.go.kr/data/15007140/fileData.do

Alerts

행정처분상태 has constant value ""Constant
직위 has constant value ""Constant
중개업소종별 is highly overall correlated with 중개업자구분High correlation
중개업자구분 is highly overall correlated with 중개업소종별High correlation
중개업소종별 is highly imbalanced (89.4%)Imbalance
중개업자구분 is highly imbalanced (89.7%)Imbalance
사무소전화번호 has 33 (6.4%) missing valuesMissing
팩스번호 has 156 (30.2%) missing valuesMissing
연번 has unique valuesUnique

Reproduction

Analysis started2023-12-12 07:02:15.057836
Analysis finished2023-12-12 07:02:16.200654
Duration1.14 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

UNIQUE 

Distinct517
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean259
Minimum1
Maximum517
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.7 KiB
2023-12-12T16:02:16.303600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile26.8
Q1130
median259
Q3388
95-th percentile491.2
Maximum517
Range516
Interquartile range (IQR)258

Descriptive statistics

Standard deviation149.38931
Coefficient of variation (CV)0.57679271
Kurtosis-1.2
Mean259
Median Absolute Deviation (MAD)129
Skewness0
Sum133903
Variance22317.167
MonotonicityStrictly increasing
2023-12-12T16:02:16.511827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.2%
325 1
 
0.2%
355 1
 
0.2%
354 1
 
0.2%
353 1
 
0.2%
352 1
 
0.2%
351 1
 
0.2%
350 1
 
0.2%
349 1
 
0.2%
348 1
 
0.2%
Other values (507) 507
98.1%
ValueCountFrequency (%)
1 1
0.2%
2 1
0.2%
3 1
0.2%
4 1
0.2%
5 1
0.2%
6 1
0.2%
7 1
0.2%
8 1
0.2%
9 1
0.2%
10 1
0.2%
ValueCountFrequency (%)
517 1
0.2%
516 1
0.2%
515 1
0.2%
514 1
0.2%
513 1
0.2%
512 1
0.2%
511 1
0.2%
510 1
0.2%
509 1
0.2%
508 1
0.2%

사무소명
Text

Distinct390
Distinct (%)75.4%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
2023-12-12T16:02:16.761369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length10
Mean length11.017408
Min length7

Characters and Unicode

Total characters5696
Distinct characters274
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique321 ?
Unique (%)62.1%

Sample

1st row태산부동산중개사무소
2nd row믿음공인중개사사무소
3rd row신진공인중개사사무소
4th row두산부동산공인중개사사무소
5th row신도공인중개사사무소
ValueCountFrequency (%)
삼성공인중개사사무소 10
 
1.9%
현대공인중개사사무소 9
 
1.7%
제일공인중개사사무소 8
 
1.5%
하나공인중개사사무소 7
 
1.3%
공인중개사사무소 5
 
1.0%
현대부동산공인중개사사무소 5
 
1.0%
탑공인중개사사무소 5
 
1.0%
우리공인중개사사무소 5
 
1.0%
황금공인중개사사무소 4
 
0.8%
은혜공인중개사사무소 4
 
0.8%
Other values (383) 462
88.2%
2023-12-12T16:02:17.172235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
사 1021
17.9%
인 526
9.2%
중 522
9.2%
개 518
9.1%
소 517
9.1%
무 514
9.0%
공 507
 
8.9%
동 111
 
1.9%
산 102
 
1.8%
부 90
 
1.6%
Other values (264) 1268
22.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 5624
98.7%
Uppercase Letter 26
 
0.5%
Decimal Number 24
 
0.4%
Space Separator 7
 
0.1%
Open Punctuation 6
 
0.1%
Close Punctuation 6
 
0.1%
Lowercase Letter 2
 
< 0.1%
Other Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
사 1021
18.2%
인 526
9.4%
중 522
9.3%
개 518
9.2%
소 517
9.2%
무 514
9.1%
공 507
9.0%
동 111
 
2.0%
산 102
 
1.8%
부 90
 
1.6%
Other values (242) 1196
21.3%
Uppercase Letter
ValueCountFrequency (%)
K 8
30.8%
S 4
15.4%
L 2
 
7.7%
O 2
 
7.7%
G 2
 
7.7%
W 1
 
3.8%
E 1
 
3.8%
N 1
 
3.8%
B 1
 
3.8%
R 1
 
3.8%
Other values (3) 3
 
11.5%
Decimal Number
ValueCountFrequency (%)
1 14
58.3%
4 5
 
20.8%
2 3
 
12.5%
3 2
 
8.3%
Space Separator
ValueCountFrequency (%)
7
100.0%
Open Punctuation
ValueCountFrequency (%)
( 6
100.0%
Close Punctuation
ValueCountFrequency (%)
) 6
100.0%
Lowercase Letter
ValueCountFrequency (%)
e 2
100.0%
Other Punctuation
ValueCountFrequency (%)
! 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 5624
98.7%
Common 44
 
0.8%
Latin 28
 
0.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
사 1021
18.2%
인 526
9.4%
중 522
9.3%
개 518
9.2%
소 517
9.2%
무 514
9.1%
공 507
9.0%
동 111
 
2.0%
산 102
 
1.8%
부 90
 
1.6%
Other values (242) 1196
21.3%
Latin
ValueCountFrequency (%)
K 8
28.6%
S 4
14.3%
L 2
 
7.1%
O 2
 
7.1%
e 2
 
7.1%
G 2
 
7.1%
W 1
 
3.6%
E 1
 
3.6%
N 1
 
3.6%
B 1
 
3.6%
Other values (4) 4
14.3%
Common
ValueCountFrequency (%)
1 14
31.8%
7
15.9%
( 6
13.6%
) 6
13.6%
4 5
 
11.4%
2 3
 
6.8%
3 2
 
4.5%
! 1
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 5624
98.7%
ASCII 72
 
1.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
사 1021
18.2%
인 526
9.4%
중 522
9.3%
개 518
9.2%
소 517
9.2%
무 514
9.1%
공 507
9.0%
동 111
 
2.0%
산 102
 
1.8%
부 90
 
1.6%
Other values (242) 1196
21.3%
ASCII
ValueCountFrequency (%)
1 14
19.4%
K 8
11.1%
7
9.7%
( 6
 
8.3%
) 6
 
8.3%
4 5
 
6.9%
S 4
 
5.6%
2 3
 
4.2%
3 2
 
2.8%
L 2
 
2.8%
Other values (12) 15
20.8%

행정처분상태
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
영업중
517 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
영업중 517
100.0%

Length

2023-12-12T16:02:17.358803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T16:02:17.463325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
영업중 517
100.0%

중개업소종별
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
공인중개사
506 
중개인
 
7
법인
 
4

Length

Max length5
Median length5
Mean length4.9497099
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
공인중개사 506
97.9%
중개인 7
 
1.4%
법인 4
 
0.8%

Length

2023-12-12T16:02:17.577431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T16:02:17.698251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
공인중개사 506
97.9%
중개인 7
 
1.4%
법인 4
 
0.8%

중개업자명
Text

Distinct505
Distinct (%)97.7%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
2023-12-12T16:02:18.098717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length2.9825919
Min length2

Characters and Unicode

Total characters1542
Distinct characters170
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

Unique493 ?
Unique (%)95.4%

Sample

1st row박태완
2nd row이재희
3rd row김연숙
4th row원용희
5th row박재택
ValueCountFrequency (%)
이은주 2
 
0.4%
김영애 2
 
0.4%
이은경 2
 
0.4%
김지연 2
 
0.4%
김옥자 2
 
0.4%
김복순 2
 
0.4%
박은숙 2
 
0.4%
김정화 2
 
0.4%
이명희 2
 
0.4%
김민주 2
 
0.4%
Other values (495) 497
96.1%
2023-12-12T16:02:18.783761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
김 117
 
7.6%
이 89
 
5.8%
정 70
 
4.5%
영 48
 
3.1%
경 38
 
2.5%
박 38
 
2.5%
희 35
 
2.3%
숙 32
 
2.1%
최 31
 
2.0%
순 30
 
1.9%
Other values (160) 1014
65.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1542
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
김 117
 
7.6%
이 89
 
5.8%
정 70
 
4.5%
영 48
 
3.1%
경 38
 
2.5%
박 38
 
2.5%
희 35
 
2.3%
숙 32
 
2.1%
최 31
 
2.0%
순 30
 
1.9%
Other values (160) 1014
65.8%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1542
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
김 117
 
7.6%
이 89
 
5.8%
정 70
 
4.5%
영 48
 
3.1%
경 38
 
2.5%
박 38
 
2.5%
희 35
 
2.3%
숙 32
 
2.1%
최 31
 
2.0%
순 30
 
1.9%
Other values (160) 1014
65.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1542
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
김 117
 
7.6%
이 89
 
5.8%
정 70
 
4.5%
영 48
 
3.1%
경 38
 
2.5%
박 38
 
2.5%
희 35
 
2.3%
숙 32
 
2.1%
최 31
 
2.0%
순 30
 
1.9%
Other values (160) 1014
65.8%

직위
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
대표
517 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
대표 517
100.0%

Length

2023-12-12T16:02:18.957660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T16:02:19.053383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
대표 517
100.0%

중개업자구분
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
공인중개사
510 
중개인
 
7

Length

Max length5
Median length5
Mean length4.9729207
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
공인중개사 510
98.6%
중개인 7
 
1.4%

Length

2023-12-12T16:02:19.201272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T16:02:19.342656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
공인중개사 510
98.6%
중개인 7
 
1.4%

사무소전화번호
Text

MISSING 

Distinct475
Distinct (%)98.1%
Missing33
Missing (%)6.4%
Memory size4.2 KiB
2023-12-12T16:02:19.594738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length11.997934
Min length9

Characters and Unicode

Total characters5807
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

Unique466 ?
Unique (%)96.3%

Sample

1st row032-541-5757
2nd row032-548-5558
3rd row032-541-1113
4th row032-551-4700
5th row032-548-3030
ValueCountFrequency (%)
032-555-5997 2
 
0.4%
032-546-7777 2
 
0.4%
032-553-8900 2
 
0.4%
032-541-7100 2
 
0.4%
032-552-3322 2
 
0.4%
032-549-8985 2
 
0.4%
032-553-8181 2
 
0.4%
032-542-3600 2
 
0.4%
032-548-9900 2
 
0.4%
032-545-4000 1
 
0.2%
Other values (465) 465
96.1%
2023-12-12T16:02:20.096693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 967
16.7%
5 955
16.4%
0 923
15.9%
2 716
12.3%
3 670
11.5%
4 458
7.9%
8 256
 
4.4%
1 253
 
4.4%
9 236
 
4.1%
7 193
 
3.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4840
83.3%
Dash Punctuation 967
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 955
19.7%
0 923
19.1%
2 716
14.8%
3 670
13.8%
4 458
9.5%
8 256
 
5.3%
1 253
 
5.2%
9 236
 
4.9%
7 193
 
4.0%
6 180
 
3.7%
Dash Punctuation
ValueCountFrequency (%)
- 967
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 5807
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 967
16.7%
5 955
16.4%
0 923
15.9%
2 716
12.3%
3 670
11.5%
4 458
7.9%
8 256
 
4.4%
1 253
 
4.4%
9 236
 
4.1%
7 193
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5807
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 967
16.7%
5 955
16.4%
0 923
15.9%
2 716
12.3%
3 670
11.5%
4 458
7.9%
8 256
 
4.4%
1 253
 
4.4%
9 236
 
4.1%
7 193
 
3.3%

팩스번호
Text

MISSING 

Distinct353
Distinct (%)97.8%
Missing156
Missing (%)30.2%
Memory size4.2 KiB
2023-12-12T16:02:20.414813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length12.01108
Min length12

Characters and Unicode

Total characters4336
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

Unique345 ?
Unique (%)95.6%

Sample

1st row032-541-5757
2nd row032-549-2225
3rd row032-551-4782
4th row032-548-3077
5th row032-545-0280
ValueCountFrequency (%)
032-553-8903 2
 
0.6%
032-552-4221 2
 
0.6%
032-555-2201 2
 
0.6%
032-555-5996 2
 
0.6%
032-544-8388 2
 
0.6%
032-553-0883 2
 
0.6%
032-552-7368 2
 
0.6%
032-541-7105 2
 
0.6%
032-555-2933 1
 
0.3%
032-553-0021 1
 
0.3%
Other values (343) 343
95.0%
2023-12-12T16:02:20.909487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 722
16.7%
5 708
16.3%
0 592
13.7%
3 540
12.5%
2 520
12.0%
4 341
7.9%
1 211
 
4.9%
8 199
 
4.6%
9 184
 
4.2%
6 166
 
3.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3614
83.3%
Dash Punctuation 722
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 708
19.6%
0 592
16.4%
3 540
14.9%
2 520
14.4%
4 341
9.4%
1 211
 
5.8%
8 199
 
5.5%
9 184
 
5.1%
6 166
 
4.6%
7 153
 
4.2%
Dash Punctuation
ValueCountFrequency (%)
- 722
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4336
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 722
16.7%
5 708
16.3%
0 592
13.7%
3 540
12.5%
2 520
12.0%
4 341
7.9%
1 211
 
4.9%
8 199
 
4.6%
9 184
 
4.2%
6 166
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4336
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 722
16.7%
5 708
16.3%
0 592
13.7%
3 540
12.5%
2 520
12.0%
4 341
7.9%
1 211
 
4.9%
8 199
 
4.6%
9 184
 
4.2%
6 166
 
3.8%

사무소주소
Text

Distinct511
Distinct (%)98.8%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
2023-12-12T16:02:21.295558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length53
Median length46
Mean length29.088975
Min length15

Characters and Unicode

Total characters15039
Distinct characters206
Distinct categories9 ?
Distinct scripts4 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique505 ?
Unique (%)97.7%

Sample

1st row인천광역시 계양구 용종로 23, 상가동 113호(계산동,은행마을태산아파트)
2nd row인천광역시 계양구 효서로 283(작전동)
3rd row인천광역시 계양구 안남로519번길 4, 101동 102호
4th row인천광역시 계양구 계양문화로 140, 상가동 101호 (용종동,초정마을두산.쌍용아파트)
5th row인천광역시 계양구 계양대로 215, 101동,106호(계산동, 신도브래뉴아파트)
ValueCountFrequency (%)
인천광역시 517
 
19.4%
계양구 517
 
19.4%
상가동 115
 
4.3%
1층 81
 
3.0%
효서로 42
 
1.6%
주부토로 20
 
0.7%
계산로 19
 
0.7%
101호 19
 
0.7%
장제로 18
 
0.7%
아나지로 16
 
0.6%
Other values (703) 1305
48.9%
2023-12-12T16:02:21.822775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2152
 
14.3%
1 809
 
5.4%
계 746
 
5.0%
양 610
 
4.1%
천 536
 
3.6%
동 535
 
3.6%
시 524
 
3.5%
인 522
 
3.5%
광 519
 
3.5%
역 518
 
3.4%
Other values (196) 7568
50.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 9124
60.7%
Decimal Number 2559
 
17.0%
Space Separator 2152
 
14.3%
Other Punctuation 461
 
3.1%
Open Punctuation 341
 
2.3%
Close Punctuation 341
 
2.3%
Dash Punctuation 52
 
0.3%
Uppercase Letter 7
 
< 0.1%
Lowercase Letter 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
계 746
 
8.2%
양 610
 
6.7%
천 536
 
5.9%
동 535
 
5.9%
시 524
 
5.7%
인 522
 
5.7%
광 519
 
5.7%
역 518
 
5.7%
구 517
 
5.7%
로 507
 
5.6%
Other values (174) 3590
39.3%
Decimal Number
ValueCountFrequency (%)
1 809
31.6%
0 335
13.1%
2 269
 
10.5%
3 219
 
8.6%
4 201
 
7.9%
5 181
 
7.1%
7 163
 
6.4%
6 143
 
5.6%
9 137
 
5.4%
8 102
 
4.0%
Uppercase Letter
ValueCountFrequency (%)
B 2
28.6%
A 2
28.6%
C 2
28.6%
D 1
14.3%
Other Punctuation
ValueCountFrequency (%)
, 451
97.8%
. 8
 
1.7%
@ 2
 
0.4%
Space Separator
ValueCountFrequency (%)
2152
100.0%
Open Punctuation
ValueCountFrequency (%)
( 341
100.0%
Close Punctuation
ValueCountFrequency (%)
) 341
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 52
100.0%
Lowercase Letter
ValueCountFrequency (%)
e 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 9123
60.7%
Common 5906
39.3%
Latin 9
 
0.1%
Han 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
계 746
 
8.2%
양 610
 
6.7%
천 536
 
5.9%
동 535
 
5.9%
시 524
 
5.7%
인 522
 
5.7%
광 519
 
5.7%
역 518
 
5.7%
구 517
 
5.7%
로 507
 
5.6%
Other values (173) 3589
39.3%
Common
ValueCountFrequency (%)
2152
36.4%
1 809
 
13.7%
, 451
 
7.6%
( 341
 
5.8%
) 341
 
5.8%
0 335
 
5.7%
2 269
 
4.6%
3 219
 
3.7%
4 201
 
3.4%
5 181
 
3.1%
Other values (7) 607
 
10.3%
Latin
ValueCountFrequency (%)
B 2
22.2%
A 2
22.2%
C 2
22.2%
e 2
22.2%
D 1
11.1%
Han
ValueCountFrequency (%)
家 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 9123
60.7%
ASCII 5915
39.3%
CJK 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2152
36.4%
1 809
 
13.7%
, 451
 
7.6%
( 341
 
5.8%
) 341
 
5.8%
0 335
 
5.7%
2 269
 
4.5%
3 219
 
3.7%
4 201
 
3.4%
5 181
 
3.1%
Other values (12) 616
 
10.4%
Hangul
ValueCountFrequency (%)
계 746
 
8.2%
양 610
 
6.7%
천 536
 
5.9%
동 535
 
5.9%
시 524
 
5.7%
인 522
 
5.7%
광 519
 
5.7%
역 518
 
5.7%
구 517
 
5.7%
로 507
 
5.6%
Other values (173) 3589
39.3%
CJK
ValueCountFrequency (%)
家 1
100.0%

Interactions

2023-12-12T16:02:15.673161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T16:02:21.939932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번중개업소종별중개업자구분
연번1.0000.1730.218
중개업소종별0.1731.0001.000
중개업자구분0.2181.0001.000
2023-12-12T16:02:22.054354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
중개업소종별중개업자구분
중개업소종별1.0000.999
중개업자구분0.9991.000
2023-12-12T16:02:22.145853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번중개업소종별중개업자구분
연번1.0000.1030.166
중개업소종별0.1031.0000.999
중개업자구분0.1660.9991.000

Missing values

2023-12-12T16:02:15.819064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T16:02:15.996479image/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.
2023-12-12T16:02:16.124842image/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

연번사무소명행정처분상태중개업소종별중개업자명직위중개업자구분사무소전화번호팩스번호사무소주소
01태산부동산중개사무소영업중중개인박태완대표중개인032-541-5757032-541-5757인천광역시 계양구 용종로 23, 상가동 113호(계산동,은행마을태산아파트)
12믿음공인중개사사무소영업중공인중개사이재희대표공인중개사032-548-5558032-549-2225인천광역시 계양구 효서로 283(작전동)
23신진공인중개사사무소영업중공인중개사김연숙대표공인중개사032-541-1113<NA>인천광역시 계양구 안남로519번길 4, 101동 102호
34두산부동산공인중개사사무소영업중공인중개사원용희대표공인중개사032-551-4700032-551-4782인천광역시 계양구 계양문화로 140, 상가동 101호 (용종동,초정마을두산.쌍용아파트)
45신도공인중개사사무소영업중공인중개사박재택대표공인중개사032-548-3030032-548-3077인천광역시 계양구 계양대로 215, 101동,106호(계산동, 신도브래뉴아파트)
56삼천리공인중개사사무소영업중공인중개사장준대표공인중개사032-549-6622032-545-0280인천광역시 계양구 주부토로 507, 상가동 1층 5호(계산동,삼천리열망아파트)
67금보공인중개사사무소영업중공인중개사김경옥대표공인중개사032-545-1800032-545-0777인천광역시 계양구 계산로112번길 6-1
78현대공인중개사사무소영업중공인중개사김미욱대표공인중개사032-554-3666032-554-3667인천광역시 계양구 주부토로 529(계산동)
89하나로부동산공인중개사사무소영업중공인중개사한종덕대표공인중개사032-547-8884032-547-8862인천광역시 계양구 계양대로205번길 3(계산동)
910그린공인중개사사무소영업중공인중개사우성웅대표공인중개사032-549-9933032-549-9935인천광역시 계양구 장제로875번길 6(임학동)
연번사무소명행정처분상태중개업소종별중개업자명직위중개업자구분사무소전화번호팩스번호사무소주소
507508라인부동산공인중개사사무소영업중공인중개사박정은대표공인중개사<NA><NA>인천광역시 계양구 계산천서로4번길 1, 1층
508509이음공인중개사사무소영업중공인중개사박미숙대표공인중개사032-555-1002032-556-1002인천광역시 계양구 아나지로 332, 101동 113호
509510등대공인중개사사무소영업중공인중개사유혜경대표공인중개사032-549-5200032-547-6895인천광역시 계양구 오조산로45번길 7, 104호
510511다원공인중개사사무소영업중공인중개사이진성대표공인중개사032-545-8982032-544-8982인천광역시 계양구 경명대로1045번길 31, 상가동 B102호 (계산동, 계양산파크루엘)
511512미소공인중개사사무소영업중공인중개사황병기대표공인중개사032-552-1588050-4021-9582인천광역시 계양구 계양문화로 140, 상가동 104호 (용종동)
512513파트너공인중개사사무소영업중공인중개사박기홍대표공인중개사02-2249-2224<NA>인천광역시 계양구 양지로 160, 3층 (귤현동, 양지빌딩)
513514NEW대박부동산 공인중개사사무소영업중공인중개사윤병철대표공인중개사<NA><NA>인천광역시 계양구 효서로145번길 7, 1층
514515현대부동산공인중개사사무소영업중공인중개사정옥란대표공인중개사032-551-6611032-551-4140인천광역시 계양구 주부토로413번길 18, 상가동 107호 (작전동, 현대아파트)
515516다온삼보부동산공인중개사사무소영업중공인중개사김민경대표공인중개사032-541-0001<NA>인천광역시 계양구 봉오대로743번길 29, 상가1동 102호 (작전동, 삼보아파트)
516517미소부동산공인중개사사무소영업중공인중개사추영은대표공인중개사032-543-0888032-543-0883인천광역시 계양구 경명대로1029번길 20, 1층 (계산동)