Overview

Dataset statistics

Number of variables9
Number of observations462
Missing cells84
Missing cells (%)2.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory33.1 KiB
Average record size in memory73.3 B

Variable types

Numeric1
Text5
Categorical3

Dataset

Description부동산중개사무소 현황에 대한 자료로 등록번호, 행정처분상태, 사무소명, 중개업자명, 직위, 중개업자 구분, 사무소전화번호, 사무소주소 등의 정보를 제공
Author경기도 이천시
URLhttps://www.data.go.kr/data/3038198/fileData.do

Alerts

직위 has constant value ""Constant
행정처분상태 is highly imbalanced (97.8%)Imbalance
중개업자구분 is highly imbalanced (86.0%)Imbalance
사무소전화번호 has 84 (18.2%) missing valuesMissing
번호 has unique valuesUnique
등록번호 has unique valuesUnique

Reproduction

Analysis started2023-12-12 02:33:59.700036
Analysis finished2023-12-12 02:34:01.033341
Duration1.33 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

번호
Real number (ℝ)

UNIQUE 

Distinct462
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean231.5
Minimum1
Maximum462
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.2 KiB
2023-12-12T11:34:01.135805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile24.05
Q1116.25
median231.5
Q3346.75
95-th percentile438.95
Maximum462
Range461
Interquartile range (IQR)230.5

Descriptive statistics

Standard deviation133.51217
Coefficient of variation (CV)0.57672644
Kurtosis-1.2
Mean231.5
Median Absolute Deviation (MAD)115.5
Skewness0
Sum106953
Variance17825.5
MonotonicityStrictly increasing
2023-12-12T11:34:01.302114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.2%
319 1
 
0.2%
317 1
 
0.2%
316 1
 
0.2%
315 1
 
0.2%
314 1
 
0.2%
313 1
 
0.2%
312 1
 
0.2%
311 1
 
0.2%
310 1
 
0.2%
Other values (452) 452
97.8%
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 (%)
462 1
0.2%
461 1
0.2%
460 1
0.2%
459 1
0.2%
458 1
0.2%
457 1
0.2%
456 1
0.2%
455 1
0.2%
454 1
0.2%
453 1
0.2%

등록번호
Text

UNIQUE 

Distinct462
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
2023-12-12T11:34:01.576268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length16
Mean length12.883117
Min length8

Characters and Unicode

Total characters5952
Distinct characters14
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique462 ?
Unique (%)100.0%

Sample

1st row나 46-009
2nd row나-46-019
3rd row41500-2015-00016
4th row가-46-156
5th row가-46-1098
ValueCountFrequency (%)
나 1
 
0.2%
41500-2019-00032 1
 
0.2%
41500-2019-00030 1
 
0.2%
41500-2019-00028 1
 
0.2%
41500-2019-00025 1
 
0.2%
41500-2019-00024 1
 
0.2%
41500-2019-00023 1
 
0.2%
41500-2019-00022 1
 
0.2%
41500-2019-00016 1
 
0.2%
41500-2019-00008 1
 
0.2%
Other values (453) 453
97.8%
2023-12-12T11:34:01.956900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1763
29.6%
- 923
15.5%
1 733
12.3%
2 604
 
10.1%
4 557
 
9.4%
5 391
 
6.6%
6 309
 
5.2%
가 193
 
3.2%
3 172
 
2.9%
7 107
 
1.8%
Other values (4) 200
 
3.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4832
81.2%
Dash Punctuation 923
 
15.5%
Other Letter 196
 
3.3%
Space Separator 1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1763
36.5%
1 733
15.2%
2 604
 
12.5%
4 557
 
11.5%
5 391
 
8.1%
6 309
 
6.4%
3 172
 
3.6%
7 107
 
2.2%
8 104
 
2.2%
9 92
 
1.9%
Other Letter
ValueCountFrequency (%)
가 193
98.5%
나 3
 
1.5%
Dash Punctuation
ValueCountFrequency (%)
- 923
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 5756
96.7%
Hangul 196
 
3.3%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1763
30.6%
- 923
16.0%
1 733
12.7%
2 604
 
10.5%
4 557
 
9.7%
5 391
 
6.8%
6 309
 
5.4%
3 172
 
3.0%
7 107
 
1.9%
8 104
 
1.8%
Other values (2) 93
 
1.6%
Hangul
ValueCountFrequency (%)
가 193
98.5%
나 3
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5756
96.7%
Hangul 196
 
3.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1763
30.6%
- 923
16.0%
1 733
12.7%
2 604
 
10.5%
4 557
 
9.7%
5 391
 
6.8%
6 309
 
5.4%
3 172
 
3.0%
7 107
 
1.9%
8 104
 
1.8%
Other values (2) 93
 
1.6%
Hangul
ValueCountFrequency (%)
가 193
98.5%
나 3
 
1.5%

행정처분상태
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
영업중
461 
휴업
 
1

Length

Max length3
Median length3
Mean length2.9978355
Min length2

Unique

Unique1 ?
Unique (%)0.2%

Sample

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

Common Values

ValueCountFrequency (%)
영업중 461
99.8%
휴업 1
 
0.2%

Length

2023-12-12T11:34:02.100620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:34:02.204308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
영업중 461
99.8%
휴업 1
 
0.2%

사무소명
Text

Distinct438
Distinct (%)94.8%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
2023-12-12T11:34:02.386204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length19
Mean length11.318182
Min length9

Characters and Unicode

Total characters5229
Distinct characters312
Distinct categories8 ?
Distinct scripts4 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique417 ?
Unique (%)90.3%

Sample

1st row광동부동산중개사무소
2nd row동우부동산중개사무소
3rd row114부동산중개인사무소
4th row남일부동산중개사무소
5th row행운부동산중개사무소
ValueCountFrequency (%)
대성공인중개사사무소 3
 
0.6%
삼성공인중개사사무소 3
 
0.6%
제일공인중개사사무소 3
 
0.6%
우리공인중개사사무소 2
 
0.4%
행운공인중개사사무소 2
 
0.4%
현대공인중개사사무소 2
 
0.4%
황금공인중개사사무소 2
 
0.4%
길공인중개사사무소 2
 
0.4%
이천공인중개사사무소 2
 
0.4%
토박이공인중개사사무소 2
 
0.4%
Other values (431) 443
95.1%
2023-12-12T11:34:02.720034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
사 911
17.4%
중 469
 
9.0%
소 465
 
8.9%
개 463
 
8.9%
무 463
 
8.9%
인 449
 
8.6%
공 441
 
8.4%
이 88
 
1.7%
부 86
 
1.6%
산 85
 
1.6%
Other values (302) 1309
25.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 5151
98.5%
Uppercase Letter 28
 
0.5%
Decimal Number 24
 
0.5%
Lowercase Letter 11
 
0.2%
Space Separator 5
 
0.1%
Open Punctuation 4
 
0.1%
Close Punctuation 4
 
0.1%
Other Punctuation 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
사 911
17.7%
중 469
 
9.1%
소 465
 
9.0%
개 463
 
9.0%
무 463
 
9.0%
인 449
 
8.7%
공 441
 
8.6%
이 88
 
1.7%
부 86
 
1.7%
산 85
 
1.7%
Other values (274) 1231
23.9%
Uppercase Letter
ValueCountFrequency (%)
K 7
25.0%
S 5
17.9%
C 4
14.3%
D 4
14.3%
T 2
 
7.1%
I 2
 
7.1%
J 1
 
3.6%
O 1
 
3.6%
M 1
 
3.6%
N 1
 
3.6%
Decimal Number
ValueCountFrequency (%)
1 8
33.3%
2 5
20.8%
0 5
20.8%
4 2
 
8.3%
8 1
 
4.2%
6 1
 
4.2%
5 1
 
4.2%
3 1
 
4.2%
Lowercase Letter
ValueCountFrequency (%)
e 4
36.4%
h 2
18.2%
n 2
18.2%
i 2
18.2%
t 1
 
9.1%
Other Punctuation
ValueCountFrequency (%)
· 1
50.0%
& 1
50.0%
Space Separator
ValueCountFrequency (%)
5
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 5148
98.5%
Common 39
 
0.7%
Latin 39
 
0.7%
Han 3
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
사 911
17.7%
중 469
 
9.1%
소 465
 
9.0%
개 463
 
9.0%
무 463
 
9.0%
인 449
 
8.7%
공 441
 
8.6%
이 88
 
1.7%
부 86
 
1.7%
산 85
 
1.7%
Other values (271) 1228
23.9%
Latin
ValueCountFrequency (%)
K 7
17.9%
S 5
12.8%
e 4
10.3%
C 4
10.3%
D 4
10.3%
h 2
 
5.1%
n 2
 
5.1%
i 2
 
5.1%
T 2
 
5.1%
I 2
 
5.1%
Other values (5) 5
12.8%
Common
ValueCountFrequency (%)
1 8
20.5%
5
12.8%
2 5
12.8%
0 5
12.8%
( 4
10.3%
) 4
10.3%
4 2
 
5.1%
8 1
 
2.6%
6 1
 
2.6%
5 1
 
2.6%
Other values (3) 3
 
7.7%
Han
ValueCountFrequency (%)
興 1
33.3%
大 1
33.3%
愛 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 5148
98.5%
ASCII 77
 
1.5%
CJK 3
 
0.1%
None 1
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
사 911
17.7%
중 469
 
9.1%
소 465
 
9.0%
개 463
 
9.0%
무 463
 
9.0%
인 449
 
8.7%
공 441
 
8.6%
이 88
 
1.7%
부 86
 
1.7%
산 85
 
1.7%
Other values (271) 1228
23.9%
ASCII
ValueCountFrequency (%)
1 8
 
10.4%
K 7
 
9.1%
5
 
6.5%
S 5
 
6.5%
2 5
 
6.5%
0 5
 
6.5%
e 4
 
5.2%
C 4
 
5.2%
( 4
 
5.2%
) 4
 
5.2%
Other values (17) 26
33.8%
CJK
ValueCountFrequency (%)
興 1
33.3%
大 1
33.3%
愛 1
33.3%
None
ValueCountFrequency (%)
· 1
100.0%

중개업자명
Text

Distinct458
Distinct (%)99.1%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
2023-12-12T11:34:03.032688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length2.995671
Min length2

Characters and Unicode

Total characters1384
Distinct characters173
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

Unique454 ?
Unique (%)98.3%

Sample

1st row권오상
2nd row황학수
3rd row심영희
4th row남윤일
5th row김영식
ValueCountFrequency (%)
최영미 2
 
0.4%
김은영 2
 
0.4%
이희성 2
 
0.4%
김성자 2
 
0.4%
김지연 1
 
0.2%
조재상 1
 
0.2%
박서영 1
 
0.2%
이선자 1
 
0.2%
손종관 1
 
0.2%
장은애 1
 
0.2%
Other values (448) 448
97.0%
2023-12-12T11:34:03.488618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
김 80
 
5.8%
이 71
 
5.1%
영 50
 
3.6%
정 46
 
3.3%
희 38
 
2.7%
경 37
 
2.7%
박 34
 
2.5%
성 32
 
2.3%
미 29
 
2.1%
은 29
 
2.1%
Other values (163) 938
67.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1384
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
김 80
 
5.8%
이 71
 
5.1%
영 50
 
3.6%
정 46
 
3.3%
희 38
 
2.7%
경 37
 
2.7%
박 34
 
2.5%
성 32
 
2.3%
미 29
 
2.1%
은 29
 
2.1%
Other values (163) 938
67.8%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1384
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
김 80
 
5.8%
이 71
 
5.1%
영 50
 
3.6%
정 46
 
3.3%
희 38
 
2.7%
경 37
 
2.7%
박 34
 
2.5%
성 32
 
2.3%
미 29
 
2.1%
은 29
 
2.1%
Other values (163) 938
67.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1384
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
김 80
 
5.8%
이 71
 
5.1%
영 50
 
3.6%
정 46
 
3.3%
희 38
 
2.7%
경 37
 
2.7%
박 34
 
2.5%
성 32
 
2.3%
미 29
 
2.1%
은 29
 
2.1%
Other values (163) 938
67.8%

직위
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
대표
462 

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 462
100.0%

Length

2023-12-12T11:34:03.610961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:34:03.686694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
대표 462
100.0%

중개업자구분
Categorical

IMBALANCE 

Distinct3
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
공인중개사
448 
중개인
 
10
법인
 
4

Length

Max length5
Median length5
Mean length4.9307359
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
공인중개사 448
97.0%
중개인 10
 
2.2%
법인 4
 
0.9%

Length

2023-12-12T11:34:03.771660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:34:03.869596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
공인중개사 448
97.0%
중개인 10
 
2.2%
법인 4
 
0.9%

사무소전화번호
Text

MISSING 

Distinct376
Distinct (%)99.5%
Missing84
Missing (%)18.2%
Memory size3.7 KiB
2023-12-12T11:34:04.085469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length64
Median length12
Mean length13.079365
Min length9

Characters and Unicode

Total characters4944
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

Unique374 ?
Unique (%)98.9%

Sample

1st row031-634-4989
2nd row031-631-4031
3rd row031-641-2114
4th row031-641-3053
5th row031-638-7367
ValueCountFrequency (%)
031-637-4401 2
 
0.5%
031-637-0990 2
 
0.5%
031-634-4989 1
 
0.3%
031-631-8949 1
 
0.3%
031-632-2987 1
 
0.3%
031-636-9944 1
 
0.3%
031-638-8249 1
 
0.3%
031-638-4988 1
 
0.3%
031-631-4000 1
 
0.3%
031-638-8666 1
 
0.3%
Other values (366) 366
96.8%
2023-12-12T11:34:04.471310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3 956
19.3%
- 817
16.5%
0 680
13.8%
1 600
12.1%
6 566
11.4%
4 283
 
5.7%
8 256
 
5.2%
9 221
 
4.5%
5 194
 
3.9%
2 182
 
3.7%
Other values (2) 189
 
3.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4095
82.8%
Dash Punctuation 817
 
16.5%
Other Punctuation 32
 
0.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 956
23.3%
0 680
16.6%
1 600
14.7%
6 566
13.8%
4 283
 
6.9%
8 256
 
6.3%
9 221
 
5.4%
5 194
 
4.7%
2 182
 
4.4%
7 157
 
3.8%
Dash Punctuation
ValueCountFrequency (%)
- 817
100.0%
Other Punctuation
ValueCountFrequency (%)
, 32
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4944
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 956
19.3%
- 817
16.5%
0 680
13.8%
1 600
12.1%
6 566
11.4%
4 283
 
5.7%
8 256
 
5.2%
9 221
 
4.5%
5 194
 
3.9%
2 182
 
3.7%
Other values (2) 189
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4944
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 956
19.3%
- 817
16.5%
0 680
13.8%
1 600
12.1%
6 566
11.4%
4 283
 
5.7%
8 256
 
5.2%
9 221
 
4.5%
5 194
 
3.9%
2 182
 
3.7%
Other values (2) 189
 
3.8%

사무소주소
Text

Distinct436
Distinct (%)94.4%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
2023-12-12T11:34:04.788531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length58
Median length46
Mean length25.606061
Min length14

Characters and Unicode

Total characters11830
Distinct characters203
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

Unique410 ?
Unique (%)88.7%

Sample

1st row경기도 이천시 남천로 23
2nd row경기도 이천시 신둔면 경충대로 3187 (신둔면)
3rd row경기도 이천시 율면 고당로 131
4th row경기도 이천시 장호원읍 장터로70번길 5
5th row경기도 이천시 마장면 서이천로 47
ValueCountFrequency (%)
경기도 462
 
18.2%
이천시 461
 
18.2%
부발읍 82
 
3.2%
경충대로 44
 
1.7%
신둔면 39
 
1.5%
마장면 34
 
1.3%
1층 32
 
1.3%
대월면 30
 
1.2%
애련정로 28
 
1.1%
상가동 27
 
1.1%
Other values (592) 1294
51.1%
2023-12-12T11:34:05.321909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2210
18.7%
1 623
 
5.3%
천 553
 
4.7%
경 541
 
4.6%
이 531
 
4.5%
시 470
 
4.0%
도 463
 
3.9%
기 463
 
3.9%
로 462
 
3.9%
2 320
 
2.7%
Other values (193) 5194
43.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 6809
57.6%
Decimal Number 2268
 
19.2%
Space Separator 2210
 
18.7%
Open Punctuation 201
 
1.7%
Close Punctuation 201
 
1.7%
Dash Punctuation 82
 
0.7%
Other Punctuation 37
 
0.3%
Uppercase Letter 22
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
천 553
 
8.1%
경 541
 
7.9%
이 531
 
7.8%
시 470
 
6.9%
도 463
 
6.8%
기 463
 
6.8%
로 462
 
6.8%
동 215
 
3.2%
호 200
 
2.9%
대 179
 
2.6%
Other values (172) 2732
40.1%
Decimal Number
ValueCountFrequency (%)
1 623
27.5%
2 320
14.1%
0 286
12.6%
3 195
 
8.6%
4 160
 
7.1%
5 147
 
6.5%
8 146
 
6.4%
9 137
 
6.0%
7 129
 
5.7%
6 125
 
5.5%
Uppercase Letter
ValueCountFrequency (%)
B 6
27.3%
A 6
27.3%
C 4
18.2%
R 4
18.2%
G 1
 
4.5%
K 1
 
4.5%
Space Separator
ValueCountFrequency (%)
2210
100.0%
Open Punctuation
ValueCountFrequency (%)
( 201
100.0%
Close Punctuation
ValueCountFrequency (%)
) 201
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 82
100.0%
Other Punctuation
ValueCountFrequency (%)
, 37
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 6809
57.6%
Common 4999
42.3%
Latin 22
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
천 553
 
8.1%
경 541
 
7.9%
이 531
 
7.8%
시 470
 
6.9%
도 463
 
6.8%
기 463
 
6.8%
로 462
 
6.8%
동 215
 
3.2%
호 200
 
2.9%
대 179
 
2.6%
Other values (172) 2732
40.1%
Common
ValueCountFrequency (%)
2210
44.2%
1 623
 
12.5%
2 320
 
6.4%
0 286
 
5.7%
( 201
 
4.0%
) 201
 
4.0%
3 195
 
3.9%
4 160
 
3.2%
5 147
 
2.9%
8 146
 
2.9%
Other values (5) 510
 
10.2%
Latin
ValueCountFrequency (%)
B 6
27.3%
A 6
27.3%
C 4
18.2%
R 4
18.2%
G 1
 
4.5%
K 1
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 6809
57.6%
ASCII 5021
42.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2210
44.0%
1 623
 
12.4%
2 320
 
6.4%
0 286
 
5.7%
( 201
 
4.0%
) 201
 
4.0%
3 195
 
3.9%
4 160
 
3.2%
5 147
 
2.9%
8 146
 
2.9%
Other values (11) 532
 
10.6%
Hangul
ValueCountFrequency (%)
천 553
 
8.1%
경 541
 
7.9%
이 531
 
7.8%
시 470
 
6.9%
도 463
 
6.8%
기 463
 
6.8%
로 462
 
6.8%
동 215
 
3.2%
호 200
 
2.9%
대 179
 
2.6%
Other values (172) 2732
40.1%

Interactions

2023-12-12T11:34:00.298652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T11:34:05.448279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
번호행정처분상태중개업자구분
번호1.0000.0150.498
행정처분상태0.0151.0000.000
중개업자구분0.4980.0001.000
2023-12-12T11:34:05.561491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정처분상태중개업자구분
행정처분상태1.0000.000
중개업자구분0.0001.000
2023-12-12T11:34:05.643810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
번호행정처분상태중개업자구분
번호1.0000.0100.342
행정처분상태0.0101.0000.000
중개업자구분0.3420.0001.000

Missing values

2023-12-12T11:34:00.797088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T11:34:00.962254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

번호등록번호행정처분상태사무소명중개업자명직위중개업자구분사무소전화번호사무소주소
01나 46-009영업중광동부동산중개사무소권오상대표중개인031-634-4989경기도 이천시 남천로 23
12나-46-019영업중동우부동산중개사무소황학수대표중개인031-631-4031경기도 이천시 신둔면 경충대로 3187 (신둔면)
2341500-2015-00016영업중114부동산중개인사무소심영희대표중개인031-641-2114경기도 이천시 율면 고당로 131
34가-46-156영업중남일부동산중개사무소남윤일대표중개인031-641-3053경기도 이천시 장호원읍 장터로70번길 5
45가-46-1098영업중행운부동산중개사무소김영식대표중개인031-638-7367경기도 이천시 마장면 서이천로 47
56가-46-157영업중송도부동산중개사무소설문수대표중개인031-633-6647경기도 이천시 이섭대천로 1200
67가-46-165영업중부발부동산중개사무소나인균대표중개인031-634-6400경기도 이천시 부발읍 무촌로 128 (부발읍)
78가-46-067영업중고려부동산중개인사무소이재광대표중개인<NA>경기도 이천시 남천로 73
89나-46-016영업중부국부동산중개사무소최장도대표중개인031-632-5575경기도 이천시 이섭대천로 1106 (진리동)
910가-46-127영업중삼일부동산중개사무소이규동대표중개인031-633-9777경기도 이천시 마장면 서이천로 481-3
번호등록번호행정처분상태사무소명중개업자명직위중개업자구분사무소전화번호사무소주소
45245341500-2023-00013영업중희망공인중개사사무소장미경대표공인중개사<NA>경기도 이천시 남천로 46
45345441500-2023-00014영업중미스터홈즈부동산공인중개사사무소이천부발센터김애경대표공인중개사<NA>경기도 이천시 부발읍 경충대로2092번길 39-33 101동 상가110호 (이천신일해피트리트리빌 1단지)
45445541500-2023-00015영업중삼성공인중개사사무소오영성대표공인중개사<NA>경기도 이천시 경충대로 3039
45545641500-2023-00016영업중행운공인중개사사무소강순영대표공인중개사031-634-4980경기도 이천시 영창로 240
45645741500-2023-00017영업중부경공인중개사사무소윤주은대표공인중개사031-634-5888경기도 이천시 영창로227번길 30 102호 (창전동)
45745841500-2023-00018영업중대박부동산공인중개사사무소이상훈대표공인중개사<NA>경기도 이천시 중리천로 59 제1호 (창전동)
45845941500-2017-00033영업중행운알파부동산중개주식회사유점상대표법인031-631-5028경기도 이천시 경충대로 2866 , A01호(사음동)
459460가-46-841영업중(주)한길공인중개사사무소천병기대표법인031-694-1000,031-694-2000,031-694-3000경기도 이천시 설봉로 40 (중리동)
46046141500-2019-00020영업중(주)한길공인중개사사무소성남분사무소조문숙대표법인031-741-9000,031-742-3000경기도 성남시 중원구 산성대로80번길 10 (성남동)
46146241500-2018-00055영업중(주)디유부동산중개법인박은경대표법인031-636-2442경기도 이천시 서희로 16-4 1층 101호(중리동)