Overview

Dataset statistics

Number of variables7
Number of observations129
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.6 KiB
Average record size in memory60.0 B

Variable types

Categorical4
Numeric3

Dataset

Description중소벤처기업진흥공단에서 시행한 지역별 감정평가 정보 관련 데이터
Author중소벤처기업진흥공단
URLhttps://www.data.go.kr/data/15040519/fileData.do

Alerts

담보대분류 has constant value ""Constant
감정금액 is highly overall correlated with 사정면적 and 1 other fieldsHigh correlation
사정면적 is highly overall correlated with 감정금액 and 1 other fieldsHigh correlation
감정건수 is highly overall correlated with 감정금액 and 1 other fieldsHigh correlation
담보중분류 is highly overall correlated with 담보소분류High correlation
담보소분류 is highly overall correlated with 담보중분류High correlation
감정금액 has unique valuesUnique
사정면적 has unique valuesUnique

Reproduction

Analysis started2023-12-12 11:27:33.378692
Analysis finished2023-12-12 11:27:36.120757
Duration2.74 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

지역
Categorical

Distinct32
Distinct (%)24.8%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
서울북부지부
10 
대구지역본부
 
7
서울동남부지부
 
7
인천서부지부
 
7
경남동부지부
 
6
Other values (27)
92 

Length

Max length8
Median length6
Mean length6.1007752
Min length6

Unique

Unique1 ?
Unique (%)0.8%

Sample

1st row강원영동지부
2nd row강원영동지부
3rd row강원지역본부
4th row강원지역본부
5th row강원지역본부

Common Values

ValueCountFrequency (%)
서울북부지부 10
 
7.8%
대구지역본부 7
 
5.4%
서울동남부지부 7
 
5.4%
인천서부지부 7
 
5.4%
경남동부지부 6
 
4.7%
충북북부지부 6
 
4.7%
울산지역본부 5
 
3.9%
부산동부지부 5
 
3.9%
강원지역본부 5
 
3.9%
경북동부지부 5
 
3.9%
Other values (22) 66
51.2%

Length

2023-12-12T20:27:36.244495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
서울북부지부 10
 
7.8%
서울동남부지부 7
 
5.4%
인천서부지부 7
 
5.4%
대구지역본부 7
 
5.4%
경남동부지부 6
 
4.7%
충북북부지부 6
 
4.7%
경북동부지부 5
 
3.9%
전북서부지부 5
 
3.9%
충남지역본부 5
 
3.9%
강원지역본부 5
 
3.9%
Other values (22) 66
51.2%

담보대분류
Categorical

CONSTANT 

Distinct1
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
부동산
129 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
부동산 129
100.0%

Length

2023-12-12T20:27:36.470366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T20:27:36.639492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
부동산 129
100.0%

담보중분류
Categorical

HIGH CORRELATION 

Distinct8
Distinct (%)6.2%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
공장
60 
공장저당
29 
공장(아파트형)
18 
상가
10 
단독주택
 
4
Other values (3)
8 

Length

Max length8
Median length2
Mean length3.4263566
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row공장
2nd row공장
3rd row공장
4th row공장
5th row공장저당

Common Values

ValueCountFrequency (%)
공장 60
46.5%
공장저당 29
22.5%
공장(아파트형) 18
 
14.0%
상가 10
 
7.8%
단독주택 4
 
3.1%
기타건물 3
 
2.3%
빌딩 3
 
2.3%
복합건물 2
 
1.6%

Length

2023-12-12T20:27:36.815565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T20:27:37.008935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
공장 60
46.5%
공장저당 29
22.5%
공장(아파트형 18
 
14.0%
상가 10
 
7.8%
단독주택 4
 
3.1%
기타건물 3
 
2.3%
빌딩 3
 
2.3%
복합건물 2
 
1.6%

담보소분류
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
건물
57 
토지
54 
공장(아파트형)
18 

Length

Max length8
Median length2
Mean length2.8372093
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row건물
2nd row토지
3rd row건물
4th row토지
5th row건물

Common Values

ValueCountFrequency (%)
건물 57
44.2%
토지 54
41.9%
공장(아파트형) 18
 
14.0%

Length

2023-12-12T20:27:37.178068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T20:27:37.317867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
건물 57
44.2%
토지 54
41.9%
공장(아파트형 18
 
14.0%

감정금액
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct129
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2932652 × 109
Minimum11690940
Maximum1.8891427 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-12-12T20:27:37.555193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11690940
5-th percentile1.660792 × 108
Q16.2939304 × 108
median1.8344128 × 109
Q34.007309 × 109
95-th percentile1.3345657 × 1010
Maximum1.8891427 × 1010
Range1.8879736 × 1010
Interquartile range (IQR)3.377916 × 109

Descriptive statistics

Standard deviation3.9620528 × 109
Coefficient of variation (CV)1.2030773
Kurtosis2.9366244
Mean3.2932652 × 109
Median Absolute Deviation (MAD)1.3537754 × 109
Skewness1.8566432
Sum4.2483122 × 1011
Variance1.5697862 × 1019
MonotonicityNot monotonic
2023-12-12T20:27:37.735332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1681380700 1
 
0.8%
11414132000 1
 
0.8%
2335000000 1
 
0.8%
309346200 1
 
0.8%
1970994000 1
 
0.8%
921840000 1
 
0.8%
1591000000 1
 
0.8%
7340739500 1
 
0.8%
3368750360 1
 
0.8%
1970701400 1
 
0.8%
Other values (119) 119
92.2%
ValueCountFrequency (%)
11690940 1
0.8%
17911300 1
0.8%
67775840 1
0.8%
72000000 1
0.8%
89378000 1
0.8%
130489800 1
0.8%
165968000 1
0.8%
166246000 1
0.8%
183113000 1
0.8%
184000000 1
0.8%
ValueCountFrequency (%)
18891426600 1
0.8%
15199797100 1
0.8%
14692203970 1
0.8%
14053189900 1
0.8%
14016034470 1
0.8%
13618345000 1
0.8%
13483000000 1
0.8%
13139642004 1
0.8%
12201576590 1
0.8%
11550000000 1
0.8%

사정면적
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct129
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7261.6747
Minimum42.025
Maximum82691.02
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-12-12T20:27:37.953975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum42.025
5-th percentile205.86
Q1885.2
median3178
Q39198.99
95-th percentile29582.908
Maximum82691.02
Range82648.995
Interquartile range (IQR)8313.79

Descriptive statistics

Standard deviation11655.584
Coefficient of variation (CV)1.6050821
Kurtosis16.946681
Mean7261.6747
Median Absolute Deviation (MAD)2580.7
Skewness3.6172914
Sum936756.04
Variance1.3585264 × 108
MonotonicityNot monotonic
2023-12-12T20:27:38.161022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2324.64 1
 
0.8%
5631.5 1
 
0.8%
1868.0 1
 
0.8%
1592.64 1
 
0.8%
3691.0 1
 
0.8%
1191.6 1
 
0.8%
617.4 1
 
0.8%
10092.5 1
 
0.8%
6303.655 1
 
0.8%
9399.8 1
 
0.8%
Other values (119) 119
92.2%
ValueCountFrequency (%)
42.025 1
0.8%
104.84 1
0.8%
156.1 1
0.8%
157.29 1
0.8%
165.46 1
0.8%
192.0 1
0.8%
198.3 1
0.8%
217.2 1
0.8%
245.59 1
0.8%
283.6 1
0.8%
ValueCountFrequency (%)
82691.02 1
0.8%
60930.8 1
0.8%
43172.34 1
0.8%
38013.7 1
0.8%
36472.0 1
0.8%
30793.78 1
0.8%
30679.8 1
0.8%
27937.57 1
0.8%
24937.27 1
0.8%
24591.455 1
0.8%

감정건수
Real number (ℝ)

HIGH CORRELATION 

Distinct24
Distinct (%)18.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.496124
Minimum1
Maximum51
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-12-12T20:27:38.318406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median3
Q36
95-th percentile18.6
Maximum51
Range50
Interquartile range (IQR)5

Descriptive statistics

Standard deviation7.1097688
Coefficient of variation (CV)1.2935969
Kurtosis14.034572
Mean5.496124
Median Absolute Deviation (MAD)2
Skewness3.1854286
Sum709
Variance50.548813
MonotonicityNot monotonic
2023-12-12T20:27:38.492654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
1 38
29.5%
2 23
17.8%
5 10
 
7.8%
6 10
 
7.8%
3 10
 
7.8%
4 9
 
7.0%
8 4
 
3.1%
10 4
 
3.1%
24 2
 
1.6%
16 2
 
1.6%
Other values (14) 17
13.2%
ValueCountFrequency (%)
1 38
29.5%
2 23
17.8%
3 10
 
7.8%
4 9
 
7.0%
5 10
 
7.8%
6 10
 
7.8%
7 2
 
1.6%
8 4
 
3.1%
9 2
 
1.6%
10 4
 
3.1%
ValueCountFrequency (%)
51 1
0.8%
30 1
0.8%
27 1
0.8%
24 2
1.6%
23 1
0.8%
19 1
0.8%
18 1
0.8%
17 1
0.8%
16 2
1.6%
15 2
1.6%

Interactions

2023-12-12T20:27:35.290179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:27:33.871894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:27:34.368499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:27:35.468010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:27:34.050448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:27:34.541788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:27:35.622591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:27:34.213072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:27:35.153224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T20:27:38.638576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지역담보중분류담보소분류감정금액사정면적감정건수
지역1.0000.4560.0000.7010.5510.579
담보중분류0.4561.0000.7760.0000.0000.000
담보소분류0.0000.7761.0000.2890.1440.275
감정금액0.7010.0000.2891.0000.7980.738
사정면적0.5510.0000.1440.7981.0000.610
감정건수0.5790.0000.2750.7380.6101.000
2023-12-12T20:27:38.804456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
담보중분류담보소분류지역
담보중분류1.0000.6760.154
담보소분류0.6761.0000.000
지역0.1540.0001.000
2023-12-12T20:27:38.938228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
감정금액사정면적감정건수지역담보중분류담보소분류
감정금액1.0000.7330.7050.2990.0000.177
사정면적0.7331.0000.6560.2000.0000.087
감정건수0.7050.6561.0000.2530.0000.182
지역0.2990.2000.2531.0000.1540.000
담보중분류0.0000.0000.0000.1541.0000.676
담보소분류0.1770.0870.1820.0000.6761.000

Missing values

2023-12-12T20:27:35.834668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T20:27:36.053490image/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

지역담보대분류담보중분류담보소분류감정금액사정면적감정건수
0강원영동지부부동산공장건물16813807002324.645
1강원영동지부부동산공장토지1831130002701.52
2강원지역본부부동산공장건물43139715519198.9911
3강원지역본부부동산공장토지339739260030679.84
4강원지역본부부동산공장저당건물4288278002387.245
5강원지역본부부동산공장저당토지4684012006597.22
6강원지역본부부동산기타건물건물166246000389.662
7경기남부지부부동산공장건물287277384721.091
8경기남부지부부동산공장토지5745489881290.01
9경기동부지부부동산공장건물75901249509277.0419
지역담보대분류담보중분류담보소분류감정금액사정면적감정건수
119충남지역본부부동산공장저당토지201288020011157.75
120충북북부지부부동산공장건물38569275007501.716
121충북북부지부부동산공장토지205598000016272.05
122충북북부지부부동산공장저당건물15018260001883.63
123충북북부지부부동산공장저당토지7440000004000.01
124충북북부지부부동산기타건물건물477840000597.31
125충북북부지부부동산기타건물토지893780001541.02
126충북지역본부부동산공장건물62134881009988.84
127충북지역본부부동산공장토지14096094007232.02
128충북지역본부부동산공장저당건물33287070004698.21