Overview

Dataset statistics

Number of variables12
Number of observations60
Missing cells85
Missing cells (%)11.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.9 KiB
Average record size in memory101.2 B

Variable types

Text6
Categorical3
Numeric3

Dataset

Description(부보금융회사 종합정보)파산절차에 의하여 총파산채권자에게 배당되어야 할 파산금융회사의 재산 중 공매 대상 매물(부동산)에 관한 정보[유의사항]각 매물정보는 데이터 기준일에 따른 정보이며, 공고 및 매각 등의 사유로 변동될 수 있으므로현 매각상황 및 물건 특징 등은 예보공매정보(http://www.kdic.or.kr/k-assets)를 확인하시기 바랍니다.
Author예금보험공사
URLhttps://www.data.go.kr/data/15083239/fileData.do

Alerts

토지면적 is highly overall correlated with 건물면적High correlation
건물면적 is highly overall correlated with 토지면적 and 1 other fieldsHigh correlation
최저공매가 is highly overall correlated with 건물면적High correlation
토지면적 has 7 (11.7%) missing valuesMissing
건물면적 has 28 (46.7%) missing valuesMissing
최저공매가 has 7 (11.7%) missing valuesMissing
매물특징 has 43 (71.7%) missing valuesMissing

Reproduction

Analysis started2024-04-29 22:49:26.310915
Analysis finished2024-04-29 22:49:29.974747
Duration3.66 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

부동산고유번호
Text

Distinct59
Distinct (%)98.3%
Missing0
Missing (%)0.0%
Memory size612.0 B
2024-04-30T07:49:30.120690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length3
Mean length4.0333333
Min length2

Characters and Unicode

Total characters242
Distinct characters29
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique58 ?
Unique (%)96.7%

Sample

1st row742
2nd row380
3rd row581
4th row606
5th row729
ValueCountFrequency (%)
581 2
 
3.3%
서울-002 1
 
1.7%
더블유-016 1
 
1.7%
580 1
 
1.7%
211 1
 
1.7%
684 1
 
1.7%
603 1
 
1.7%
376 1
 
1.7%
570 1
 
1.7%
133 1
 
1.7%
Other values (49) 49
81.7%
2024-04-30T07:49:30.465310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 34
14.0%
0 31
12.8%
3 19
 
7.9%
- 19
 
7.9%
5 17
 
7.0%
6 16
 
6.6%
4 14
 
5.8%
7 13
 
5.4%
9 13
 
5.4%
8 11
 
4.5%
Other values (19) 55
22.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 178
73.6%
Other Letter 45
 
18.6%
Dash Punctuation 19
 
7.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
래 7
15.6%
미 7
15.6%
한 5
11.1%
국 5
11.1%
토 4
8.9%
마 2
 
4.4%
블 2
 
4.4%
더 2
 
4.4%
유 2
 
4.4%
릿 1
 
2.2%
Other values (8) 8
17.8%
Decimal Number
ValueCountFrequency (%)
1 34
19.1%
0 31
17.4%
3 19
10.7%
5 17
9.6%
6 16
9.0%
4 14
7.9%
7 13
 
7.3%
9 13
 
7.3%
8 11
 
6.2%
2 10
 
5.6%
Dash Punctuation
ValueCountFrequency (%)
- 19
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 197
81.4%
Hangul 45
 
18.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
래 7
15.6%
미 7
15.6%
한 5
11.1%
국 5
11.1%
토 4
8.9%
마 2
 
4.4%
블 2
 
4.4%
더 2
 
4.4%
유 2
 
4.4%
릿 1
 
2.2%
Other values (8) 8
17.8%
Common
ValueCountFrequency (%)
1 34
17.3%
0 31
15.7%
3 19
9.6%
- 19
9.6%
5 17
8.6%
6 16
8.1%
4 14
7.1%
7 13
 
6.6%
9 13
 
6.6%
8 11
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 197
81.4%
Hangul 45
 
18.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 34
17.3%
0 31
15.7%
3 19
9.6%
- 19
9.6%
5 17
8.6%
6 16
8.1%
4 14
7.1%
7 13
 
6.6%
9 13
 
6.6%
8 11
 
5.6%
Hangul
ValueCountFrequency (%)
래 7
15.6%
미 7
15.6%
한 5
11.1%
국 5
11.1%
토 4
8.9%
마 2
 
4.4%
블 2
 
4.4%
더 2
 
4.4%
유 2
 
4.4%
릿 1
 
2.2%
Other values (8) 8
17.8%

문의처
Categorical

Distinct23
Distinct (%)38.3%
Missing0
Missing (%)0.0%
Memory size612.0 B
하나자산신탁
7 
교보자산신탁
7 
미래저축은행
7 
KB부동산신탁
6 
한국저축은행
5 
Other values (18)
28 

Length

Max length12
Median length6
Mean length6.3166667
Min length4

Unique

Unique11 ?
Unique (%)18.3%

Sample

1st row한국토지신탁
2nd row우리자산신탁
3rd row코람코자산신탁
4th rowkb부동산신탁
5th rowKB부동산신탁

Common Values

ValueCountFrequency (%)
하나자산신탁 7
11.7%
교보자산신탁 7
11.7%
미래저축은행 7
11.7%
KB부동산신탁 6
 
10.0%
한국저축은행 5
 
8.3%
코람코자산신탁 3
 
5.0%
아시아신탁 3
 
5.0%
신한자산신탁 3
 
5.0%
더블유저축은행 2
 
3.3%
토마토저축은행 2
 
3.3%
Other values (13) 15
25.0%

Length

2024-04-30T07:49:30.668035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
kb부동산신탁 8
13.1%
하나자산신탁 7
11.5%
미래저축은행 7
11.5%
교보자산신탁 7
11.5%
한국저축은행 5
 
8.2%
코람코자산신탁 3
 
4.9%
아시아신탁 3
 
4.9%
신한자산신탁 3
 
4.9%
더블유저축은행 2
 
3.3%
토마토저축은행 2
 
3.3%
Other values (13) 14
23.0%

문의처 연락처
Text

Distinct42
Distinct (%)70.0%
Missing0
Missing (%)0.0%
Memory size612.0 B
2024-04-30T07:49:30.868941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length11.866667
Min length11

Characters and Unicode

Total characters712
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

Unique32 ?
Unique (%)53.3%

Sample

1st row02-3451-1086
2nd row02-6202-3029
3rd row02-787-0108
4th row02-2190-9899
5th row02-2190-9956
ValueCountFrequency (%)
02-6711-5555 7
 
11.7%
02-3287-4730 4
 
6.7%
02-519-7045 3
 
5.0%
02-2190-9899 2
 
3.3%
02-519-7046 2
 
3.3%
02-3404-3565 2
 
3.3%
02-3015-6023 2
 
3.3%
02-3490-5813 2
 
3.3%
031-720-1500 2
 
3.3%
02-3490-5878 2
 
3.3%
Other values (32) 32
53.3%
2024-04-30T07:49:31.209818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 123
17.3%
- 120
16.9%
2 94
13.2%
5 69
9.7%
1 57
8.0%
3 53
7.4%
7 44
 
6.2%
4 44
 
6.2%
9 41
 
5.8%
6 38
 
5.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 592
83.1%
Dash Punctuation 120
 
16.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 123
20.8%
2 94
15.9%
5 69
11.7%
1 57
9.6%
3 53
9.0%
7 44
 
7.4%
4 44
 
7.4%
9 41
 
6.9%
6 38
 
6.4%
8 29
 
4.9%
Dash Punctuation
ValueCountFrequency (%)
- 120
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 712
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 123
17.3%
- 120
16.9%
2 94
13.2%
5 69
9.7%
1 57
8.0%
3 53
7.4%
7 44
 
6.2%
4 44
 
6.2%
9 41
 
5.8%
6 38
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 712
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 123
17.3%
- 120
16.9%
2 94
13.2%
5 69
9.7%
1 57
8.0%
3 53
7.4%
7 44
 
6.2%
4 44
 
6.2%
9 41
 
5.8%
6 38
 
5.3%

소재지 시도
Categorical

Distinct11
Distinct (%)18.3%
Missing0
Missing (%)0.0%
Memory size612.0 B
경기도
12 
충청남도
12 
강원도
8 
서울특별시
7 
대전광역시
6 
Other values (6)
15 

Length

Max length5
Median length4
Mean length3.9833333
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row울산광역시
2nd row경기도
3rd row강원도
4th row충청남도
5th row부산광역시

Common Values

ValueCountFrequency (%)
경기도 12
20.0%
충청남도 12
20.0%
강원도 8
13.3%
서울특별시 7
11.7%
대전광역시 6
10.0%
경상남도 5
8.3%
울산광역시 2
 
3.3%
부산광역시 2
 
3.3%
경상북도 2
 
3.3%
인천광역시 2
 
3.3%

Length

2024-04-30T07:49:31.354288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기도 12
20.0%
충청남도 12
20.0%
강원도 8
13.3%
서울특별시 7
11.7%
대전광역시 6
10.0%
경상남도 5
8.3%
울산광역시 2
 
3.3%
부산광역시 2
 
3.3%
경상북도 2
 
3.3%
인천광역시 2
 
3.3%

소재지 구군
Text

Distinct39
Distinct (%)65.0%
Missing0
Missing (%)0.0%
Memory size612.0 B
2024-04-30T07:49:31.536291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length3
Mean length3.5833333
Min length2

Characters and Unicode

Total characters215
Distinct characters57
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

Unique25 ?
Unique (%)41.7%

Sample

1st row남구
2nd row용인시 처인구
3rd row정선군
4th row천안시 동남구
5th row수영구
ValueCountFrequency (%)
대덕구 5
 
7.2%
용인시 4
 
5.8%
원주시 3
 
4.3%
중구 3
 
4.3%
기흥구 3
 
4.3%
남양주시 3
 
4.3%
정선군 2
 
2.9%
아산시 2
 
2.9%
보령시 2
 
2.9%
팔달구 2
 
2.9%
Other values (33) 40
58.0%
2024-04-30T07:49:31.915287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
구 31
 
14.4%
시 30
 
14.0%
군 11
 
5.1%
9
 
4.2%
주 9
 
4.2%
천 7
 
3.3%
남 7
 
3.3%
산 6
 
2.8%
덕 5
 
2.3%
서 5
 
2.3%
Other values (47) 95
44.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 206
95.8%
Space Separator 9
 
4.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 31
 
15.0%
시 30
 
14.6%
군 11
 
5.3%
주 9
 
4.4%
천 7
 
3.4%
남 7
 
3.4%
산 6
 
2.9%
덕 5
 
2.4%
서 5
 
2.4%
대 5
 
2.4%
Other values (46) 90
43.7%
Space Separator
ValueCountFrequency (%)
9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 206
95.8%
Common 9
 
4.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 31
 
15.0%
시 30
 
14.6%
군 11
 
5.3%
주 9
 
4.4%
천 7
 
3.4%
남 7
 
3.4%
산 6
 
2.9%
덕 5
 
2.4%
서 5
 
2.4%
대 5
 
2.4%
Other values (46) 90
43.7%
Common
ValueCountFrequency (%)
9
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 206
95.8%
ASCII 9
 
4.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
구 31
 
15.0%
시 30
 
14.6%
군 11
 
5.3%
주 9
 
4.4%
천 7
 
3.4%
남 7
 
3.4%
산 6
 
2.9%
덕 5
 
2.4%
서 5
 
2.4%
대 5
 
2.4%
Other values (46) 90
43.7%
ASCII
ValueCountFrequency (%)
9
100.0%

소재지 지번주소
Text

Distinct58
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Memory size612.0 B
2024-04-30T07:49:32.302185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length67
Median length34
Mean length18.183333
Min length7

Characters and Unicode

Total characters1091
Distinct characters130
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique56 ?
Unique (%)93.3%

Sample

1st row야음동 675-7
2nd row양지면 남곡리 156-7 외
3rd row사북읍 사북리 432-8 외 45필지
4th row신부동 462-6
5th row광안동 143-1
ValueCountFrequency (%)
외 13
 
5.6%
송촌스포렉스 5
 
2.1%
510 5
 
2.1%
송촌동 5
 
2.1%
상가 3
 
1.3%
442-10 3
 
1.3%
보정동 3
 
1.3%
3개호 3
 
1.3%
산 3
 
1.3%
등 2
 
0.9%
Other values (172) 188
80.7%
2024-04-30T07:49:32.798626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
180
 
16.5%
1 68
 
6.2%
- 58
 
5.3%
2 57
 
5.2%
0 56
 
5.1%
4 48
 
4.4%
3 47
 
4.3%
동 44
 
4.0%
5 34
 
3.1%
7 29
 
2.7%
Other values (120) 470
43.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 421
38.6%
Decimal Number 391
35.8%
Space Separator 180
16.5%
Dash Punctuation 58
 
5.3%
Other Punctuation 29
 
2.7%
Open Punctuation 5
 
0.5%
Close Punctuation 5
 
0.5%
Math Symbol 2
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 44
 
10.5%
리 23
 
5.5%
호 18
 
4.3%
면 15
 
3.6%
외 14
 
3.3%
송 14
 
3.3%
스 13
 
3.1%
산 12
 
2.9%
개 11
 
2.6%
촌 10
 
2.4%
Other values (104) 247
58.7%
Decimal Number
ValueCountFrequency (%)
1 68
17.4%
2 57
14.6%
0 56
14.3%
4 48
12.3%
3 47
12.0%
5 34
8.7%
7 29
7.4%
6 22
 
5.6%
8 17
 
4.3%
9 13
 
3.3%
Space Separator
ValueCountFrequency (%)
180
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 58
100.0%
Other Punctuation
ValueCountFrequency (%)
, 29
100.0%
Open Punctuation
ValueCountFrequency (%)
( 5
100.0%
Close Punctuation
ValueCountFrequency (%)
) 5
100.0%
Math Symbol
ValueCountFrequency (%)
~ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 670
61.4%
Hangul 421
38.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 44
 
10.5%
리 23
 
5.5%
호 18
 
4.3%
면 15
 
3.6%
외 14
 
3.3%
송 14
 
3.3%
스 13
 
3.1%
산 12
 
2.9%
개 11
 
2.6%
촌 10
 
2.4%
Other values (104) 247
58.7%
Common
ValueCountFrequency (%)
180
26.9%
1 68
 
10.1%
- 58
 
8.7%
2 57
 
8.5%
0 56
 
8.4%
4 48
 
7.2%
3 47
 
7.0%
5 34
 
5.1%
7 29
 
4.3%
, 29
 
4.3%
Other values (6) 64
 
9.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 670
61.4%
Hangul 421
38.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
180
26.9%
1 68
 
10.1%
- 58
 
8.7%
2 57
 
8.5%
0 56
 
8.4%
4 48
 
7.2%
3 47
 
7.0%
5 34
 
5.1%
7 29
 
4.3%
, 29
 
4.3%
Other values (6) 64
 
9.6%
Hangul
ValueCountFrequency (%)
동 44
 
10.5%
리 23
 
5.5%
호 18
 
4.3%
면 15
 
3.6%
외 14
 
3.3%
송 14
 
3.3%
스 13
 
3.1%
산 12
 
2.9%
개 11
 
2.6%
촌 10
 
2.4%
Other values (104) 247
58.7%

용도
Categorical

Distinct18
Distinct (%)30.0%
Missing0
Missing (%)0.0%
Memory size612.0 B
기타근린생활시설
11 
임야
11 
대지
10 
근린생활시설
7 
상업용빌딩
3 
Other values (13)
18 

Length

Max length8
Median length7
Mean length4.2166667
Min length1

Unique

Unique9 ?
Unique (%)15.0%

Sample

1st row대지
2nd row대지
3rd row대지
4th row기타근린생활시설
5th row대지

Common Values

ValueCountFrequency (%)
기타근린생활시설 11
18.3%
임야 11
18.3%
대지 10
16.7%
근린생활시설 7
11.7%
상업용빌딩 3
 
5.0%
단독주택 3
 
5.0%
전 2
 
3.3%
여관 2
 
3.3%
유통쇼핑센타 2
 
3.3%
아파트 등 1
 
1.7%
Other values (8) 8
13.3%

Length

2024-04-30T07:49:32.942434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
기타근린생활시설 11
18.0%
임야 11
18.0%
대지 10
16.4%
근린생활시설 7
11.5%
상업용빌딩 3
 
4.9%
단독주택 3
 
4.9%
유통쇼핑센타 2
 
3.3%
아파트 2
 
3.3%
여관 2
 
3.3%
전 2
 
3.3%
Other values (8) 8
13.1%

토지면적
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct52
Distinct (%)98.1%
Missing7
Missing (%)11.7%
Infinite0
Infinite (%)0.0%
Mean168050.18
Minimum13.021
Maximum6939260
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size672.0 B
2024-04-30T07:49:33.082191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum13.021
5-th percentile30.0576
Q1162
median836.16
Q34834
95-th percentile258228.4
Maximum6939260
Range6939247
Interquartile range (IQR)4672

Descriptive statistics

Standard deviation961049.03
Coefficient of variation (CV)5.7188219
Kurtosis50.023806
Mean168050.18
Median Absolute Deviation (MAD)772.86
Skewness7.0018937
Sum8906659.4
Variance9.2361524 × 1011
MonotonicityNot monotonic
2024-04-30T07:49:33.210923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
63.3 2
 
3.3%
210.0 1
 
1.7%
487.37 1
 
1.7%
3147.0 1
 
1.7%
444.5 1
 
1.7%
59.43 1
 
1.7%
451141.0 1
 
1.7%
1715.35 1
 
1.7%
31984.0 1
 
1.7%
58736.0 1
 
1.7%
Other values (42) 42
70.0%
(Missing) 7
 
11.7%
ValueCountFrequency (%)
13.021 1
1.7%
23.55 1
1.7%
24.45 1
1.7%
33.796 1
1.7%
36.0 1
1.7%
49.44 1
1.7%
59.43 1
1.7%
63.3 2
3.3%
85.4 1
1.7%
86.0 1
1.7%
ValueCountFrequency (%)
6939260.0 1
1.7%
1066809.0 1
1.7%
451141.0 1
1.7%
129620.0 1
1.7%
71224.0 1
1.7%
58736.0 1
1.7%
37568.0 1
1.7%
31984.0 1
1.7%
31340.0 1
1.7%
24835.0 1
1.7%

건물면적
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct30
Distinct (%)93.8%
Missing28
Missing (%)46.7%
Infinite0
Infinite (%)0.0%
Mean1838.2512
Minimum28.6
Maximum11577.88
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size672.0 B
2024-04-30T07:49:33.330745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum28.6
5-th percentile76.401
Q1248.01
median1157.26
Q32120.8375
95-th percentile5813.0215
Maximum11577.88
Range11549.28
Interquartile range (IQR)1872.8275

Descriptive statistics

Standard deviation2427.3755
Coefficient of variation (CV)1.3204809
Kurtosis7.7536882
Mean1838.2512
Median Absolute Deviation (MAD)909.25
Skewness2.5313682
Sum58824.037
Variance5892151.8
MonotonicityNot monotonic
2024-04-30T07:49:33.445246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
248.01 2
 
3.3%
354.45 2
 
3.3%
2065.45 1
 
1.7%
28.6 1
 
1.7%
715.85 1
 
1.7%
1314.81 1
 
1.7%
1502.55 1
 
1.7%
813.22 1
 
1.7%
999.71 1
 
1.7%
1432.04 1
 
1.7%
Other values (20) 20
33.3%
(Missing) 28
46.7%
ValueCountFrequency (%)
28.6 1
1.7%
70.78 1
1.7%
81.0 1
1.7%
110.49 1
1.7%
148.8 1
1.7%
154.857 1
1.7%
230.5 1
1.7%
248.01 2
3.3%
354.45 2
3.3%
588.35 1
1.7%
ValueCountFrequency (%)
11577.88 1
1.7%
6468.0 1
1.7%
5277.13 1
1.7%
4869.65 1
1.7%
4311.57 1
1.7%
3021.28 1
1.7%
2659.93 1
1.7%
2287.0 1
1.7%
2065.45 1
1.7%
1824.99 1
1.7%

최근감정가
Text

Distinct59
Distinct (%)98.3%
Missing0
Missing (%)0.0%
Memory size612.0 B
2024-04-30T07:49:33.712426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length8
Mean length6.4333333
Min length4

Characters and Unicode

Total characters386
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

Unique58 ?
Unique (%)96.7%

Sample

1st row43851
2nd row122032
3rd row4370284
4th row1934000
5th row316000
ValueCountFrequency (%)
1934000 2
 
3.3%
1866528 1
 
1.7%
26670 1
 
1.7%
4528300 1
 
1.7%
2413861 1
 
1.7%
15252760 1
 
1.7%
12634000 1
 
1.7%
35600 1
 
1.7%
8,080,173 1
 
1.7%
48227000 1
 
1.7%
Other values (49) 49
81.7%
2024-04-30T07:49:34.050326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 109
28.2%
2 39
 
10.1%
1 35
 
9.1%
4 35
 
9.1%
3 32
 
8.3%
6 30
 
7.8%
8 28
 
7.3%
5 25
 
6.5%
7 24
 
6.2%
9 23
 
6.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 380
98.4%
Other Punctuation 6
 
1.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 109
28.7%
2 39
 
10.3%
1 35
 
9.2%
4 35
 
9.2%
3 32
 
8.4%
6 30
 
7.9%
8 28
 
7.4%
5 25
 
6.6%
7 24
 
6.3%
9 23
 
6.1%
Other Punctuation
ValueCountFrequency (%)
, 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 386
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 109
28.2%
2 39
 
10.1%
1 35
 
9.1%
4 35
 
9.1%
3 32
 
8.3%
6 30
 
7.8%
8 28
 
7.3%
5 25
 
6.5%
7 24
 
6.2%
9 23
 
6.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 386
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 109
28.2%
2 39
 
10.1%
1 35
 
9.1%
4 35
 
9.1%
3 32
 
8.3%
6 30
 
7.8%
8 28
 
7.3%
5 25
 
6.5%
7 24
 
6.2%
9 23
 
6.0%

최저공매가
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct52
Distinct (%)98.1%
Missing7
Missing (%)11.7%
Infinite0
Infinite (%)0.0%
Mean1503831.4
Minimum1905
Maximum12115000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size672.0 B
2024-04-30T07:49:34.431468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1905
5-th percentile8825.2
Q193535
median422928
Q31380435
95-th percentile7876836
Maximum12115000
Range12113095
Interquartile range (IQR)1286900

Descriptive statistics

Standard deviation2687158.4
Coefficient of variation (CV)1.7868747
Kurtosis6.0981082
Mean1503831.4
Median Absolute Deviation (MAD)395450
Skewness2.5565011
Sum79703065
Variance7.2208201 × 1012
MonotonicityNot monotonic
2024-04-30T07:49:34.561809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
966000 2
 
3.3%
168413 1
 
1.7%
7682268 1
 
1.7%
6317000 1
 
1.7%
14800 1
 
1.7%
383900 1
 
1.7%
12115000 1
 
1.7%
160000 1
 
1.7%
203000 1
 
1.7%
27478 1
 
1.7%
Other values (42) 42
70.0%
(Missing) 7
 
11.7%
ValueCountFrequency (%)
1905 1
1.7%
2150 1
1.7%
2320 1
1.7%
13162 1
1.7%
13335 1
1.7%
14800 1
1.7%
27478 1
1.7%
29160 1
1.7%
30826 1
1.7%
32000 1
1.7%
ValueCountFrequency (%)
12115000 1
1.7%
10003000 1
1.7%
8168688 1
1.7%
7682268 1
1.7%
6317000 1
1.7%
6021000 1
1.7%
3876279 1
1.7%
2270500 1
1.7%
2185142 1
1.7%
2001105 1
1.7%

매물특징
Text

MISSING 

Distinct11
Distinct (%)64.7%
Missing43
Missing (%)71.7%
Memory size612.0 B
2024-04-30T07:49:34.727111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length29
Median length21
Mean length18.529412
Min length3

Characters and Unicode

Total characters315
Distinct characters57
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)52.9%

Sample

1st row타임몰 쇼핑센타 지하4~지상5층
2nd row등기외 건물 5평
3rd row1개호(제비219-2호)
4th row9개호
5th row121개호
ValueCountFrequency (%)
2024년 9
11.5%
기준 9
11.5%
최저공매가 9
11.5%
3월 8
10.3%
25일 8
10.3%
물건 5
 
6.4%
5
 
6.4%
분할 5
 
6.4%
2월 2
 
2.6%
22일 1
 
1.3%
Other values (17) 17
21.8%
2024-04-30T07:49:35.024093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
61
19.4%
2 36
 
11.4%
4 13
 
4.1%
기 10
 
3.2%
5 10
 
3.2%
월 10
 
3.2%
일 10
 
3.2%
0 9
 
2.9%
저 9
 
2.9%
년 9
 
2.9%
Other values (47) 138
43.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 155
49.2%
Decimal Number 88
27.9%
Space Separator 61
 
19.4%
Other Punctuation 7
 
2.2%
Close Punctuation 1
 
0.3%
Dash Punctuation 1
 
0.3%
Open Punctuation 1
 
0.3%
Math Symbol 1
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
기 10
 
6.5%
월 10
 
6.5%
일 10
 
6.5%
저 9
 
5.8%
년 9
 
5.8%
매 9
 
5.8%
가 9
 
5.8%
공 9
 
5.8%
준 9
 
5.8%
최 9
 
5.8%
Other values (32) 62
40.0%
Decimal Number
ValueCountFrequency (%)
2 36
40.9%
4 13
 
14.8%
5 10
 
11.4%
0 9
 
10.2%
3 9
 
10.2%
1 7
 
8.0%
8 2
 
2.3%
9 2
 
2.3%
Other Punctuation
ValueCountFrequency (%)
/ 6
85.7%
, 1
 
14.3%
Space Separator
ValueCountFrequency (%)
61
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 160
50.8%
Hangul 155
49.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
기 10
 
6.5%
월 10
 
6.5%
일 10
 
6.5%
저 9
 
5.8%
년 9
 
5.8%
매 9
 
5.8%
가 9
 
5.8%
공 9
 
5.8%
준 9
 
5.8%
최 9
 
5.8%
Other values (32) 62
40.0%
Common
ValueCountFrequency (%)
61
38.1%
2 36
22.5%
4 13
 
8.1%
5 10
 
6.2%
0 9
 
5.6%
3 9
 
5.6%
1 7
 
4.4%
/ 6
 
3.8%
8 2
 
1.2%
9 2
 
1.2%
Other values (5) 5
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 160
50.8%
Hangul 155
49.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
61
38.1%
2 36
22.5%
4 13
 
8.1%
5 10
 
6.2%
0 9
 
5.6%
3 9
 
5.6%
1 7
 
4.4%
/ 6
 
3.8%
8 2
 
1.2%
9 2
 
1.2%
Other values (5) 5
 
3.1%
Hangul
ValueCountFrequency (%)
기 10
 
6.5%
월 10
 
6.5%
일 10
 
6.5%
저 9
 
5.8%
년 9
 
5.8%
매 9
 
5.8%
가 9
 
5.8%
공 9
 
5.8%
준 9
 
5.8%
최 9
 
5.8%
Other values (32) 62
40.0%

Interactions

2024-04-30T07:49:29.282586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:49:28.689189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:49:29.005194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:49:29.379415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:49:28.840397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:49:29.092069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:49:29.468861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:49:28.924424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:49:29.173199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-30T07:49:35.119111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
부동산고유번호문의처문의처 연락처소재지 시도소재지 구군소재지 지번주소용도토지면적건물면적최근감정가최저공매가매물특징
부동산고유번호1.0001.0000.9671.0001.0000.9920.5581.0001.0000.9970.9721.000
문의처1.0001.0000.9980.8350.9820.9950.8520.0000.7411.0000.0000.974
문의처 연락처0.9670.9981.0000.9470.9860.9960.8181.0000.9661.0000.9820.997
소재지 시도1.0000.8350.9471.0000.9921.0000.5940.0000.6981.0000.0551.000
소재지 구군1.0000.9820.9860.9921.0001.0000.9381.0000.9281.0000.8350.998
소재지 지번주소0.9920.9950.9961.0001.0001.0000.9721.0000.9211.0000.8871.000
용도0.5580.8520.8180.5940.9380.9721.0000.0000.7321.0000.7690.964
토지면적1.0000.0001.0000.0001.0001.0000.0001.000NaN1.0001.000NaN
건물면적1.0000.7410.9660.6980.9280.9210.732NaN1.0001.0000.9490.901
최근감정가0.9971.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
최저공매가0.9720.0000.9820.0550.8350.8870.7691.0000.9491.0001.0000.891
매물특징1.0000.9740.9971.0000.9981.0000.964NaN0.9011.0000.8911.000
2024-04-30T07:49:35.256327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
문의처소재지 시도용도
문의처1.0000.4080.400
소재지 시도0.4081.0000.235
용도0.4000.2351.000
2024-04-30T07:49:35.345236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
토지면적건물면적최저공매가문의처소재지 시도용도
토지면적1.0000.8080.4620.0000.0000.000
건물면적0.8081.0000.8190.4180.2970.426
최저공매가0.4620.8191.0000.0000.0000.343
문의처0.0000.4180.0001.0000.4080.400
소재지 시도0.0000.2970.0000.4081.0000.235
용도0.0000.4260.3430.4000.2351.000

Missing values

2024-04-30T07:49:29.600994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-30T07:49:29.779660image/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.
2024-04-30T07:49:29.908819image/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

부동산고유번호문의처문의처 연락처소재지 시도소재지 구군소재지 지번주소용도토지면적건물면적최근감정가최저공매가매물특징
0742한국토지신탁02-3451-1086울산광역시남구야음동 675-7대지109.0<NA>4385130826<NA>
1380우리자산신탁02-6202-3029경기도용인시 처인구양지면 남곡리 156-7 외대지464.0<NA>12203248813<NA>
2581코람코자산신탁02-787-0108강원도정선군사북읍 사북리 432-8 외 45필지대지37568.0<NA>43702842185142<NA>
3606kb부동산신탁02-2190-9899충청남도천안시 동남구신부동 462-6기타근린생활시설63.3354.451934000966000<NA>
4729KB부동산신탁02-2190-9956부산광역시수영구광안동 143-1대지85.4<NA>316000168000<NA>
5595하나자산신탁02-3287-4732서울특별시서초구반포동 19-1기타근린생활시설369.58588.355112860<NA><NA>
665광주은행062-239-5402충청남도서산시동문동 313-3 외 13필지유통쇼핑센타2101.185277.13204217208168688타임몰 쇼핑센타 지하4~지상5층
7394교보자산신탁02-3404-3565강원도평창군용평면 도사리 108콘도미니움17515.02065.4529150001458000등기외 건물 5평
8278하나자산신탁02-3287-4746경상남도김해시장유면 삼문리 445대지4834.0<NA>920636<NA><NA>
9352아시아신탁02-3490-5813경상남도사천시송포동 958-1 외 1아파트86.0<NA>43002150<NA>
부동산고유번호문의처문의처 연락처소재지 시도소재지 구군소재지 지번주소용도토지면적건물면적최근감정가최저공매가매물특징
50한국-140한국저축은행02-519-7045경기도용인시 기흥구보정동 442-10 702동단독주택<NA>248.01188488100443<NA>
51한국-141한국저축은행02-519-7045경기도용인시 기흥구보정동 442-10 703동단독주택<NA>248.01245034130735<NA>
52미래-012미래저축은행02-6711-5555충청남도서천군마서면 송내리 25-2,35-3여관916.01397.214489393636422024년 3월 25일 기준 최저공매가
53미래-013미래저축은행02-6711-5555충청남도서천군마서면 송내리 35-4여관755.01432.044209393409622024년 3월 25일 기준 최저공매가
54미래-014미래저축은행02-6711-5555대전광역시대덕구송촌동 510 송촌스포렉스 1층(102~108, 110~114, 116, 117, 120호 -15개호)근린생활시설688.5999.7130500002001105물건 분할 / 2024년 3월 25일 기준 최저공매가
55미래-015미래저축은행02-6711-5555대전광역시대덕구송촌동 510 송촌스포렉스 2층(202, 203호 -2개호)근린생활시설560.06813.221421000932319물건 분할 / 2024년 3월 25일 기준 최저공매가
56미래-016미래저축은행02-6711-5555대전광역시대덕구송촌동 510 송촌스포렉스 3층(301, 302, 303, 304, 305호 - 5개호)근린생활시설1034.81502.5524040001577265물건 분할 / 2024년 3월 25일 기준 최저공매가
57미래-017미래저축은행02-6711-5555대전광역시대덕구송촌동 510 송촌스포렉스 4층(401, 402, 403호 - 3개호)근린생활시설905.511314.8121040001380435물건 분할 / 2024년 3월 25일 기준 최저공매가
58미래-018미래저축은행02-6711-5555대전광역시대덕구송촌동 510 송촌스포렉스 5층(501, 502, 503호 -3개호)근린생활시설493.01715.85944000619359물건 분할 / 2024년 3월 25일 기준 최저공매가
59골든브릿지-010골든브릿지저축은행061-660-0211전라남도순천시가곡동 62-4전1051.8<NA>39968399682월 23일 온비드낙찰 후 계약 미체결