Overview

Dataset statistics

Number of variables7
Number of observations252
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory15.1 KiB
Average record size in memory61.5 B

Variable types

Categorical1
Text1
Numeric5

Dataset

Description한국토지정보시스템에서 전국에 있는 중개인, 법인, 공인중개사, 분사무소 정보를 시도, 시군구 별로 구분하여 통계 정보를 제공합니다.
Author국토교통부
URLhttps://www.data.go.kr/data/15063946/fileData.do

Alerts

중개인 is highly overall correlated with 공인중개사 and 2 other fieldsHigh correlation
공인중개사 is highly overall correlated with 중개인 and 3 other fieldsHigh correlation
법인 is highly overall correlated with 중개인 and 3 other fieldsHigh correlation
계 is highly overall correlated with 중개인 and 3 other fieldsHigh correlation
분사무소 is highly overall correlated with 공인중개사 and 2 other fieldsHigh correlation
중개인 has 24 (9.5%) zerosZeros
법인 has 83 (32.9%) zerosZeros
분사무소 has 177 (70.2%) zerosZeros

Reproduction

Analysis started2023-12-12 09:54:53.858512
Analysis finished2023-12-12 09:54:57.202882
Duration3.34 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

Distinct17
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
경기도
44 
서울특별시
25 
경상북도
23 
전라남도
22 
경상남도
22 
Other values (12)
116 

Length

Max length7
Median length5
Mean length4.3730159
Min length3

Unique

Unique1 ?
Unique (%)0.4%

Sample

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

Common Values

ValueCountFrequency (%)
경기도 44
17.5%
서울특별시 25
9.9%
경상북도 23
9.1%
전라남도 22
8.7%
경상남도 22
8.7%
강원특별자치도 18
7.1%
부산광역시 16
 
6.3%
충청남도 16
 
6.3%
전라북도 15
 
6.0%
충청북도 14
 
5.6%
Other values (7) 37
14.7%

Length

2023-12-12T18:54:57.309670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기도 44
17.5%
서울특별시 25
9.9%
경상북도 23
9.1%
전라남도 22
8.7%
경상남도 22
8.7%
강원특별자치도 18
7.1%
부산광역시 16
 
6.3%
충청남도 16
 
6.3%
전라북도 15
 
6.0%
충청북도 14
 
5.6%
Other values (7) 37
14.7%

시군구명
Text

Distinct230
Distinct (%)91.3%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
2023-12-12T18:54:57.665684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length3
Mean length3.3373016
Min length2

Characters and Unicode

Total characters841
Distinct characters142
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

Unique223 ?
Unique (%)88.5%

Sample

1st row종로구
2nd row중구
3rd row용산구
4th row성동구
5th row광진구
ValueCountFrequency (%)
동구 6
 
2.4%
중구 6
 
2.4%
서구 5
 
2.0%
남구 4
 
1.6%
북구 4
 
1.6%
고성군 2
 
0.8%
강서구 2
 
0.8%
나주시 1
 
0.4%
화순군 1
 
0.4%
보성군 1
 
0.4%
Other values (220) 220
87.3%
2023-12-12T18:54:58.141315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
구 106
 
12.6%
시 102
 
12.1%
군 85
 
10.1%
주 24
 
2.9%
양 23
 
2.7%
천 23
 
2.7%
산 23
 
2.7%
성 21
 
2.5%
동 20
 
2.4%
안 18
 
2.1%
Other values (132) 396
47.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 841
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 106
 
12.6%
시 102
 
12.1%
군 85
 
10.1%
주 24
 
2.9%
양 23
 
2.7%
천 23
 
2.7%
산 23
 
2.7%
성 21
 
2.5%
동 20
 
2.4%
안 18
 
2.1%
Other values (132) 396
47.1%

Most occurring scripts

ValueCountFrequency (%)
Hangul 841
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 106
 
12.6%
시 102
 
12.1%
군 85
 
10.1%
주 24
 
2.9%
양 23
 
2.7%
천 23
 
2.7%
산 23
 
2.7%
성 21
 
2.5%
동 20
 
2.4%
안 18
 
2.1%
Other values (132) 396
47.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 841
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
구 106
 
12.6%
시 102
 
12.1%
군 85
 
10.1%
주 24
 
2.9%
양 23
 
2.7%
천 23
 
2.7%
산 23
 
2.7%
성 21
 
2.5%
동 20
 
2.4%
안 18
 
2.1%
Other values (132) 396
47.1%

중개인
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct40
Distinct (%)15.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.9047619
Minimum0
Maximum67
Zeros24
Zeros (%)9.5%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2023-12-12T18:54:58.287502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q312
95-th percentile34
Maximum67
Range67
Interquartile range (IQR)10

Descriptive statistics

Standard deviation11.18403
Coefficient of variation (CV)1.2559606
Kurtosis6.4621257
Mean8.9047619
Median Absolute Deviation (MAD)4
Skewness2.3518153
Sum2244
Variance125.08253
MonotonicityNot monotonic
2023-12-12T18:54:58.427186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
2 30
11.9%
1 29
 
11.5%
0 24
 
9.5%
3 18
 
7.1%
4 18
 
7.1%
6 17
 
6.7%
5 14
 
5.6%
7 14
 
5.6%
9 8
 
3.2%
16 7
 
2.8%
Other values (30) 73
29.0%
ValueCountFrequency (%)
0 24
9.5%
1 29
11.5%
2 30
11.9%
3 18
7.1%
4 18
7.1%
5 14
5.6%
6 17
6.7%
7 14
5.6%
8 7
 
2.8%
9 8
 
3.2%
ValueCountFrequency (%)
67 1
 
0.4%
60 1
 
0.4%
54 1
 
0.4%
52 1
 
0.4%
46 1
 
0.4%
44 1
 
0.4%
43 1
 
0.4%
40 1
 
0.4%
37 3
1.2%
35 1
 
0.4%

공인중개사
Real number (ℝ)

HIGH CORRELATION 

Distinct213
Distinct (%)84.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean444.40873
Minimum1
Maximum2610
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2023-12-12T18:54:58.597281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile14.55
Q162
median330.5
Q3729.75
95-th percentile1226.2
Maximum2610
Range2609
Interquartile range (IQR)667.75

Descriptive statistics

Standard deviation453.09708
Coefficient of variation (CV)1.0195504
Kurtosis3.328575
Mean444.40873
Median Absolute Deviation (MAD)286
Skewness1.5233215
Sum111991
Variance205296.97
MonotonicityNot monotonic
2023-12-12T18:54:58.778963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
32 5
 
2.0%
10 4
 
1.6%
17 3
 
1.2%
28 3
 
1.2%
56 2
 
0.8%
63 2
 
0.8%
111 2
 
0.8%
64 2
 
0.8%
166 2
 
0.8%
506 2
 
0.8%
Other values (203) 225
89.3%
ValueCountFrequency (%)
1 1
 
0.4%
2 1
 
0.4%
5 1
 
0.4%
7 1
 
0.4%
10 4
1.6%
11 2
0.8%
12 2
0.8%
14 1
 
0.4%
15 1
 
0.4%
16 1
 
0.4%
ValueCountFrequency (%)
2610 1
0.4%
2539 1
0.4%
1913 1
0.4%
1786 1
0.4%
1663 1
0.4%
1616 1
0.4%
1537 1
0.4%
1528 1
0.4%
1458 1
0.4%
1355 1
0.4%

법인
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct34
Distinct (%)13.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.0912698
Minimum0
Maximum413
Zeros83
Zeros (%)32.9%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2023-12-12T18:54:58.916154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3
Q38
95-th percentile28.45
Maximum413
Range413
Interquartile range (IQR)8

Descriptive statistics

Standard deviation28.882293
Coefficient of variation (CV)3.5695625
Kurtosis157.40194
Mean8.0912698
Median Absolute Deviation (MAD)3
Skewness11.682454
Sum2039
Variance834.18686
MonotonicityNot monotonic
2023-12-12T18:54:59.074200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
0 83
32.9%
3 24
 
9.5%
1 24
 
9.5%
2 17
 
6.7%
4 13
 
5.2%
9 9
 
3.6%
5 9
 
3.6%
8 8
 
3.2%
6 8
 
3.2%
13 8
 
3.2%
Other values (24) 49
19.4%
ValueCountFrequency (%)
0 83
32.9%
1 24
 
9.5%
2 17
 
6.7%
3 24
 
9.5%
4 13
 
5.2%
5 9
 
3.6%
6 8
 
3.2%
7 6
 
2.4%
8 8
 
3.2%
9 9
 
3.6%
ValueCountFrequency (%)
413 1
0.4%
152 1
0.4%
90 1
0.4%
42 1
0.4%
41 1
0.4%
40 1
0.4%
35 2
0.8%
34 1
0.4%
31 1
0.4%
30 2
0.8%

계
Real number (ℝ)

HIGH CORRELATION 

Distinct219
Distinct (%)86.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean461.40476
Minimum1
Maximum3077
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2023-12-12T18:54:59.226298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile15
Q163.75
median338
Q3745.75
95-th percentile1259.35
Maximum3077
Range3076
Interquartile range (IQR)682

Descriptive statistics

Standard deviation476.72702
Coefficient of variation (CV)1.0332078
Kurtosis4.6169061
Mean461.40476
Median Absolute Deviation (MAD)292.5
Skewness1.6952152
Sum116274
Variance227268.65
MonotonicityNot monotonic
2023-12-12T18:54:59.807072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11 4
 
1.6%
36 3
 
1.2%
138 2
 
0.8%
45 2
 
0.8%
57 2
 
0.8%
1244 2
 
0.8%
146 2
 
0.8%
174 2
 
0.8%
923 2
 
0.8%
17 2
 
0.8%
Other values (209) 229
90.9%
ValueCountFrequency (%)
1 1
 
0.4%
2 1
 
0.4%
5 1
 
0.4%
7 1
 
0.4%
10 1
 
0.4%
11 4
1.6%
13 2
0.8%
14 1
 
0.4%
15 2
0.8%
16 1
 
0.4%
ValueCountFrequency (%)
3077 1
0.4%
2601 1
0.4%
1972 1
0.4%
1859 1
0.4%
1799 1
0.4%
1787 1
0.4%
1575 1
0.4%
1547 1
0.4%
1485 1
0.4%
1389 1
0.4%

분사무소
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.61904762
Minimum0
Maximum10
Zeros177
Zeros (%)70.2%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2023-12-12T18:54:59.987839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile3
Maximum10
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.2740571
Coefficient of variation (CV)2.0580922
Kurtosis13.924331
Mean0.61904762
Median Absolute Deviation (MAD)0
Skewness3.1449841
Sum156
Variance1.6232214
MonotonicityNot monotonic
2023-12-12T18:55:00.118520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 177
70.2%
1 37
 
14.7%
2 18
 
7.1%
3 8
 
3.2%
4 7
 
2.8%
5 3
 
1.2%
10 1
 
0.4%
6 1
 
0.4%
ValueCountFrequency (%)
0 177
70.2%
1 37
 
14.7%
2 18
 
7.1%
3 8
 
3.2%
4 7
 
2.8%
5 3
 
1.2%
6 1
 
0.4%
10 1
 
0.4%
ValueCountFrequency (%)
10 1
 
0.4%
6 1
 
0.4%
5 3
 
1.2%
4 7
 
2.8%
3 8
 
3.2%
2 18
 
7.1%
1 37
 
14.7%
0 177
70.2%

Interactions

2023-12-12T18:54:56.418625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:54:54.167101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:54:54.714988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:54:55.198116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:54:55.806192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:54:56.540754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:54:54.289763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:54:54.822338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:54:55.353292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:54:55.919503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:54:56.633597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:54:54.399056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:54:54.925852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:54:55.492477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:54:56.103122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:54:56.718873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:54:54.502507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:54:55.015867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:54:55.596163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:54:56.197534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:54:56.834997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:54:54.615066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:54:55.108705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:54:55.703040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:54:56.316055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T18:55:00.222415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시도명중개인공인중개사법인계분사무소
시도명1.0000.5150.5070.0000.5570.454
중개인0.5151.0000.6630.7790.7150.708
공인중개사0.5070.6631.0000.7210.9780.606
법인0.0000.7790.7211.0000.7810.740
계0.5570.7150.9780.7811.0000.690
분사무소0.4540.7080.6060.7400.6901.000
2023-12-12T18:55:00.367799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
중개인공인중개사법인계분사무소시도명
중개인1.0000.7370.6490.7470.4570.224
공인중개사0.7371.0000.8761.0000.5250.225
법인0.6490.8761.0000.8810.5310.000
계0.7471.0000.8811.0000.5300.255
분사무소0.4570.5250.5310.5301.0000.190
시도명0.2240.2250.0000.2550.1901.000

Missing values

2023-12-12T18:54:56.976558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T18:54:57.143794image/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

시도명시군구명중개인공인중개사법인계분사무소
0서울특별시종로구37500295663
1서울특별시중구44527356063
2서울특별시용산구35834349034
3서울특별시성동구14809428654
4서울특별시광진구2492599581
5서울특별시동대문구4089399424
6서울특별시중랑구3775737974
7서울특별시성북구2984288793
8서울특별시강북구1860436250
9서울특별시도봉구1749555172
시도명시군구명중개인공인중개사법인계분사무소
242강원특별자치도횡성군2930950
243강원특별자치도영월군0320320
244강원특별자치도평창군1630640
245강원특별자치도정선군0170170
246강원특별자치도철원군0421430
247강원특별자치도화천군0160160
248강원특별자치도양구군1100110
249강원특별자치도인제군4230270
250강원특별자치도고성군0302320
251강원특별자치도양양군2370390