Overview

Dataset statistics

Number of variables9
Number of observations198
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory15.6 KiB
Average record size in memory80.6 B

Variable types

Numeric8
Categorical1

Dataset

Description공매정보포털 온비드(Onbid)의 용도별 통계분석 자료입니다.(2013년부터 2023년까지의 데이터 수록)
Author한국자산관리공사
URLhttps://www.data.go.kr/data/15054750/fileData.do

Alerts

감정가(백만원) is highly overall correlated with 최저입찰가(백만원) and 3 other fieldsHigh correlation
최저입찰가(백만원) is highly overall correlated with 감정가(백만원) and 2 other fieldsHigh correlation
낙찰가(백만원) is highly overall correlated with 감정가(백만원) and 2 other fieldsHigh correlation
낙찰가율(감정가 대비 낙찰가) is highly overall correlated with 낙찰가율(최저입찰가 대비 낙찰가) and 2 other fieldsHigh correlation
낙찰가율(최저입찰가 대비 낙찰가) is highly overall correlated with 낙찰가율(감정가 대비 낙찰가) and 1 other fieldsHigh correlation
입찰참가자수(명) is highly overall correlated with 감정가(백만원) and 5 other fieldsHigh correlation
경쟁률(낙찰자수 대비 입찰 참가자수) is highly overall correlated with 낙찰가율(감정가 대비 낙찰가) and 1 other fieldsHigh correlation
용도 is highly overall correlated with 감정가(백만원)High correlation
감정가(백만원) has unique valuesUnique
최저입찰가(백만원) has unique valuesUnique
낙찰가(백만원) has unique valuesUnique

Reproduction

Analysis started2024-03-14 18:12:18.854395
Analysis finished2024-03-14 18:12:35.281124
Duration16.43 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연도
Real number (ℝ)

Distinct11
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2018
Minimum2013
Maximum2023
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2024-03-15T03:12:35.377961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2013
5-th percentile2013
Q12015
median2018
Q32021
95-th percentile2023
Maximum2023
Range10
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.1702936
Coefficient of variation (CV)0.0015710077
Kurtosis-1.2204516
Mean2018
Median Absolute Deviation (MAD)3
Skewness0
Sum399564
Variance10.050761
MonotonicityIncreasing
2024-03-15T03:12:35.571787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
2013 18
9.1%
2014 18
9.1%
2015 18
9.1%
2016 18
9.1%
2017 18
9.1%
2018 18
9.1%
2019 18
9.1%
2020 18
9.1%
2021 18
9.1%
2022 18
9.1%
ValueCountFrequency (%)
2013 18
9.1%
2014 18
9.1%
2015 18
9.1%
2016 18
9.1%
2017 18
9.1%
2018 18
9.1%
2019 18
9.1%
2020 18
9.1%
2021 18
9.1%
2022 18
9.1%
ValueCountFrequency (%)
2023 18
9.1%
2022 18
9.1%
2021 18
9.1%
2020 18
9.1%
2019 18
9.1%
2018 18
9.1%
2017 18
9.1%
2016 18
9.1%
2015 18
9.1%
2014 18
9.1%

용도
Categorical

HIGH CORRELATION 

Distinct18
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
전체
 
11
주거용건물
 
11
아파트
 
11
단독주택/다가구
 
11
연립주택/다세대/빌라
 
11
Other values (13)
143 

Length

Max length12
Median length7
Mean length5.0555556
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row전체
2nd row주거용건물
3rd row아파트
4th row단독주택/다가구
5th row연립주택/다세대/빌라

Common Values

ValueCountFrequency (%)
전체 11
 
5.6%
주거용건물 11
 
5.6%
아파트 11
 
5.6%
단독주택/다가구 11
 
5.6%
연립주택/다세대/빌라 11
 
5.6%
기타주거용건물 11
 
5.6%
비주거용건물 11
 
5.6%
판매및영업시설 11
 
5.6%
근린생활시설 11
 
5.6%
숙박시설 11
 
5.6%
Other values (8) 88
44.4%

Length

2024-03-15T03:12:35.839350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
전체 11
 
5.6%
주거용건물 11
 
5.6%
기타토지 11
 
5.6%
대지 11
 
5.6%
답 11
 
5.6%
전 11
 
5.6%
임야 11
 
5.6%
토지 11
 
5.6%
기타비주거용건물 11
 
5.6%
숙박시설 11
 
5.6%
Other values (8) 88
44.4%

감정가(백만원)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct198
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean165579.87
Minimum2379
Maximum1186399
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2024-03-15T03:12:36.395586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2379
5-th percentile16503.65
Q140838.25
median81874.5
Q3165052.5
95-th percentile811008.55
Maximum1186399
Range1184020
Interquartile range (IQR)124214.25

Descriptive statistics

Standard deviation236512.16
Coefficient of variation (CV)1.4283871
Kurtosis8.4466208
Mean165579.87
Median Absolute Deviation (MAD)48429.5
Skewness2.8995335
Sum32784815
Variance5.5938002 × 1010
MonotonicityNot monotonic
2024-03-15T03:12:36.879777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1114925 1
 
0.5%
60894 1
 
0.5%
219267 1
 
0.5%
109634 1
 
0.5%
27814 1
 
0.5%
38625 1
 
0.5%
43191 1
 
0.5%
252797 1
 
0.5%
16695 1
 
0.5%
126797 1
 
0.5%
Other values (188) 188
94.9%
ValueCountFrequency (%)
2379 1
0.5%
4215 1
0.5%
4263 1
0.5%
6572 1
0.5%
9297 1
0.5%
11287 1
0.5%
12336 1
0.5%
13033 1
0.5%
15863 1
0.5%
16377 1
0.5%
ValueCountFrequency (%)
1186399 1
0.5%
1161251 1
0.5%
1146102 1
0.5%
1143919 1
0.5%
1114925 1
0.5%
1064937 1
0.5%
958786 1
0.5%
944246 1
0.5%
881660 1
0.5%
830760 1
0.5%

최저입찰가(백만원)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct198
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean105604.41
Minimum1252
Maximum795835
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2024-03-15T03:12:37.494824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1252
5-th percentile7429.55
Q127503.75
median54423.5
Q3108421.75
95-th percentile476367.85
Maximum795835
Range794583
Interquartile range (IQR)80918

Descriptive statistics

Standard deviation151967.35
Coefficient of variation (CV)1.4390247
Kurtosis9.1598536
Mean105604.41
Median Absolute Deviation (MAD)31224.5
Skewness2.9739586
Sum20909673
Variance2.3094077 × 1010
MonotonicityNot monotonic
2024-03-15T03:12:38.012476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
670401 1
 
0.5%
33919 1
 
0.5%
161124 1
 
0.5%
87234 1
 
0.5%
18415 1
 
0.5%
25945 1
 
0.5%
29527 1
 
0.5%
122494 1
 
0.5%
5854 1
 
0.5%
79230 1
 
0.5%
Other values (188) 188
94.9%
ValueCountFrequency (%)
1252 1
0.5%
2241 1
0.5%
2564 1
0.5%
3734 1
0.5%
4132 1
0.5%
4338 1
0.5%
5456 1
0.5%
5568 1
0.5%
5854 1
0.5%
6781 1
0.5%
ValueCountFrequency (%)
795835 1
0.5%
778199 1
0.5%
763057 1
0.5%
747502 1
0.5%
728473 1
0.5%
670401 1
0.5%
599149 1
0.5%
591132 1
0.5%
529759 1
0.5%
479750 1
0.5%

낙찰가(백만원)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct198
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean119950.53
Minimum1343
Maximum1132261
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2024-03-15T03:12:38.567439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1343
5-th percentile8056.55
Q129816.25
median62079.5
Q3118301.25
95-th percentile529420.45
Maximum1132261
Range1130918
Interquartile range (IQR)88485

Descriptive statistics

Standard deviation176758.83
Coefficient of variation (CV)1.4735977
Kurtosis10.345344
Mean119950.53
Median Absolute Deviation (MAD)35793
Skewness3.0628424
Sum23750205
Variance3.1243685 × 1010
MonotonicityNot monotonic
2024-03-15T03:12:39.038567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
724464 1
 
0.5%
36179 1
 
0.5%
175240 1
 
0.5%
95217 1
 
0.5%
19843 1
 
0.5%
28278 1
 
0.5%
31900 1
 
0.5%
137736 1
 
0.5%
6317 1
 
0.5%
84459 1
 
0.5%
Other values (188) 188
94.9%
ValueCountFrequency (%)
1343 1
0.5%
2542 1
0.5%
2798 1
0.5%
4050 1
0.5%
4290 1
0.5%
4696 1
0.5%
5864 1
0.5%
6317 1
0.5%
6676 1
0.5%
7391 1
0.5%
ValueCountFrequency (%)
1132261 1
0.5%
854967 1
0.5%
830161 1
0.5%
804160 1
0.5%
798860 1
0.5%
724464 1
0.5%
688561 1
0.5%
655232 1
0.5%
569318 1
0.5%
532517 1
0.5%

낙찰가율(감정가 대비 낙찰가)
Real number (ℝ)

HIGH CORRELATION 

Distinct190
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean74.050909
Minimum44.85
Maximum99.75
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2024-03-15T03:12:39.548090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum44.85
5-th percentile55.921
Q169.0425
median74.49
Q380.5125
95-th percentile88.6425
Maximum99.75
Range54.9
Interquartile range (IQR)11.47

Descriptive statistics

Standard deviation9.8595844
Coefficient of variation (CV)0.13314603
Kurtosis0.011317213
Mean74.050909
Median Absolute Deviation (MAD)5.87
Skewness-0.30422864
Sum14662.08
Variance97.211404
MonotonicityNot monotonic
2024-03-15T03:12:40.023193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
83.94 2
 
1.0%
73.39 2
 
1.0%
70.83 2
 
1.0%
73.96 2
 
1.0%
74.3 2
 
1.0%
61.05 2
 
1.0%
81.17 2
 
1.0%
72.29 2
 
1.0%
69.97 1
 
0.5%
82.8 1
 
0.5%
Other values (180) 180
90.9%
ValueCountFrequency (%)
44.85 1
0.5%
49.34 1
0.5%
52.5 1
0.5%
52.76 1
0.5%
52.81 1
0.5%
53.13 1
0.5%
54.68 1
0.5%
55.01 1
0.5%
55.16 1
0.5%
55.53 1
0.5%
ValueCountFrequency (%)
99.75 1
0.5%
98.02 1
0.5%
93.81 1
0.5%
91.93 1
0.5%
91.39 1
0.5%
91.23 1
0.5%
90.98 1
0.5%
90.28 1
0.5%
89.3 1
0.5%
88.94 1
0.5%

낙찰가율(최저입찰가 대비 낙찰가)
Real number (ℝ)

HIGH CORRELATION 

Distinct169
Distinct (%)85.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean108.28268
Minimum103.98
Maximum115.31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2024-03-15T03:12:40.333631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum103.98
5-th percentile105.437
Q1106.505
median108.055
Q3109.6875
95-th percentile112.2365
Maximum115.31
Range11.33
Interquartile range (IQR)3.1825

Descriptive statistics

Standard deviation2.2338232
Coefficient of variation (CV)0.020629552
Kurtosis0.070763977
Mean108.28268
Median Absolute Deviation (MAD)1.6
Skewness0.63401832
Sum21439.97
Variance4.9899659
MonotonicityNot monotonic
2024-03-15T03:12:40.660053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
106.58 4
 
2.0%
106.88 3
 
1.5%
105.93 3
 
1.5%
107.36 2
 
1.0%
106.94 2
 
1.0%
109.78 2
 
1.0%
106.52 2
 
1.0%
108.06 2
 
1.0%
110.04 2
 
1.0%
105.42 2
 
1.0%
Other values (159) 174
87.9%
ValueCountFrequency (%)
103.98 1
0.5%
104.23 1
0.5%
104.47 1
0.5%
104.55 1
0.5%
104.72 1
0.5%
104.78 1
0.5%
104.9 1
0.5%
105.0 1
0.5%
105.42 2
1.0%
105.44 1
0.5%
ValueCountFrequency (%)
115.31 1
0.5%
115.24 1
0.5%
114.15 1
0.5%
113.9 1
0.5%
113.0 1
0.5%
112.95 1
0.5%
112.87 1
0.5%
112.74 1
0.5%
112.6 1
0.5%
112.5 1
0.5%

입찰참가자수(명)
Real number (ℝ)

HIGH CORRELATION 

Distinct190
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3349.3838
Minimum101
Maximum37064
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2024-03-15T03:12:40.940670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum101
5-th percentile175.9
Q1369.25
median1279.5
Q32895.75
95-th percentile15808.95
Maximum37064
Range36963
Interquartile range (IQR)2526.5

Descriptive statistics

Standard deviation5831.6824
Coefficient of variation (CV)1.7411209
Kurtosis10.148436
Mean3349.3838
Median Absolute Deviation (MAD)992.5
Skewness3.0144825
Sum663178
Variance34008519
MonotonicityNot monotonic
2024-03-15T03:12:41.229210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
359 3
 
1.5%
205 2
 
1.0%
369 2
 
1.0%
468 2
 
1.0%
240 2
 
1.0%
303 2
 
1.0%
178 2
 
1.0%
271 1
 
0.5%
1723 1
 
0.5%
399 1
 
0.5%
Other values (180) 180
90.9%
ValueCountFrequency (%)
101 1
0.5%
110 1
0.5%
126 1
0.5%
134 1
0.5%
135 1
0.5%
138 1
0.5%
152 1
0.5%
153 1
0.5%
162 1
0.5%
164 1
0.5%
ValueCountFrequency (%)
37064 1
0.5%
31209 1
0.5%
27917 1
0.5%
21592 1
0.5%
21235 1
0.5%
20505 1
0.5%
19116 1
0.5%
17961 1
0.5%
17849 1
0.5%
17078 1
0.5%

경쟁률(낙찰자수 대비 입찰 참가자수)
Real number (ℝ)

HIGH CORRELATION 

Distinct139
Distinct (%)70.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3276768
Minimum1.25
Maximum6.72
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2024-03-15T03:12:41.480532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.25
5-th percentile1.457
Q11.71
median2.13
Q32.6675
95-th percentile3.9615
Maximum6.72
Range5.47
Interquartile range (IQR)0.9575

Descriptive statistics

Standard deviation0.83097927
Coefficient of variation (CV)0.35699943
Kurtosis4.8664605
Mean2.3276768
Median Absolute Deviation (MAD)0.47
Skewness1.7710851
Sum460.88
Variance0.69052655
MonotonicityNot monotonic
2024-03-15T03:12:41.847400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.66 5
 
2.5%
1.57 4
 
2.0%
1.9 3
 
1.5%
1.65 3
 
1.5%
1.99 3
 
1.5%
2.36 3
 
1.5%
2.21 3
 
1.5%
2.94 3
 
1.5%
2.4 3
 
1.5%
1.52 3
 
1.5%
Other values (129) 165
83.3%
ValueCountFrequency (%)
1.25 1
0.5%
1.27 1
0.5%
1.28 1
0.5%
1.32 1
0.5%
1.36 1
0.5%
1.37 1
0.5%
1.42 2
1.0%
1.43 1
0.5%
1.44 1
0.5%
1.46 2
1.0%
ValueCountFrequency (%)
6.72 1
0.5%
5.78 1
0.5%
4.75 1
0.5%
4.61 2
1.0%
4.24 1
0.5%
4.19 1
0.5%
4.08 1
0.5%
4.02 1
0.5%
3.97 1
0.5%
3.96 1
0.5%

Interactions

2024-03-15T03:12:32.786466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:12:19.269988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:12:21.295554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:12:23.625144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:12:25.330184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:12:27.172491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:12:29.029869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:12:30.802481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:12:33.034163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:12:19.527140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:12:21.540426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:12:23.886232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:12:25.576334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:12:27.425474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:12:29.170818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:12:31.049565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:12:33.292497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:12:19.781926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:12:21.806883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:12:24.236605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:12:25.802572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:12:27.636437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:12:29.329483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:12:31.312320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:12:33.563572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:12:20.052143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:12:22.077604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:12:24.446043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:12:26.007879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:12:27.931012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:12:29.543190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:12:31.586080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:12:33.810703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:12:20.298598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:12:22.328883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:12:24.607448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:12:26.288162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:12:28.172131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:12:29.794121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:12:31.841980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:12:34.100782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:12:20.547401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:12:22.585709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:12:24.790536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:12:26.537641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:12:28.323807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:12:30.044854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:12:32.096270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:12:34.302290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:12:20.797855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:12:23.109241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:12:24.955983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:12:26.759445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:12:28.564157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:12:30.293726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:12:32.353705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:12:34.463086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:12:21.051427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:12:23.371609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:12:25.130442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:12:26.998713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:12:28.825862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:12:30.553969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:12:32.617579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-15T03:12:42.131728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도용도감정가(백만원)최저입찰가(백만원)낙찰가(백만원)낙찰가율(감정가 대비 낙찰가)낙찰가율(최저입찰가 대비 낙찰가)입찰참가자수(명)경쟁률(낙찰자수 대비 입찰 참가자수)
연도1.0000.0000.0000.0000.0000.3410.3920.0000.112
용도0.0001.0000.9100.8150.8570.6890.7070.7720.763
감정가(백만원)0.0000.9101.0000.9080.9770.0000.3060.8840.219
최저입찰가(백만원)0.0000.8150.9081.0000.9090.0000.5210.8230.365
낙찰가(백만원)0.0000.8570.9770.9091.0000.0000.2800.9240.072
낙찰가율(감정가 대비 낙찰가)0.3410.6890.0000.0000.0001.0000.8650.0000.665
낙찰가율(최저입찰가 대비 낙찰가)0.3920.7070.3060.5210.2800.8651.0000.4220.671
입찰참가자수(명)0.0000.7720.8840.8230.9240.0000.4221.0000.548
경쟁률(낙찰자수 대비 입찰 참가자수)0.1120.7630.2190.3650.0720.6650.6710.5481.000
2024-03-15T03:12:42.466040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도감정가(백만원)최저입찰가(백만원)낙찰가(백만원)낙찰가율(감정가 대비 낙찰가)낙찰가율(최저입찰가 대비 낙찰가)입찰참가자수(명)경쟁률(낙찰자수 대비 입찰 참가자수)용도
연도1.000-0.104-0.127-0.1200.0710.091-0.0720.0820.000
감정가(백만원)-0.1041.0000.9900.9880.2610.2200.7370.3790.552
최저입찰가(백만원)-0.1270.9901.0000.9970.3300.2260.7590.4400.474
낙찰가(백만원)-0.1200.9880.9971.0000.3420.2320.7670.4460.462
낙찰가율(감정가 대비 낙찰가)0.0710.2610.3300.3421.0000.6050.5770.8230.339
낙찰가율(최저입찰가 대비 낙찰가)0.0910.2200.2260.2320.6051.0000.5650.4460.354
입찰참가자수(명)-0.0720.7370.7590.7670.5770.5651.0000.5960.362
경쟁률(낙찰자수 대비 입찰 참가자수)0.0820.3790.4400.4460.8230.4460.5961.0000.354
용도0.0000.5520.4740.4620.3390.3540.3620.3541.000

Missing values

2024-03-15T03:12:34.929490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-15T03:12:35.182555image/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

연도용도감정가(백만원)최저입찰가(백만원)낙찰가(백만원)낙찰가율(감정가 대비 낙찰가)낙찰가율(최저입찰가 대비 낙찰가)입찰참가자수(명)경쟁률(낙찰자수 대비 입찰 참가자수)
02013전체111492567040172446469.97108.01151881.9
12013주거용건물23294816619317592477.58106.3934812.76
22013아파트13237710110310692283.35105.8722413.77
32013단독주택/다가구31648187331996670.83108.23461.66
42013연립주택/다세대/빌라34236214932278369.66106.225351.87
52013기타주거용건물34683248612625079.17106.233592.09
62013비주거용건물32633617009918041255.16106.4615121.57
72013판매및영업시설192808509903344.85106.423591.25
82013근린생활시설23764912106812797255.53105.936791.58
92013숙박시설207036781739154.68104.551971.49
연도용도감정가(백만원)최저입찰가(백만원)낙찰가(백만원)낙찰가율(감정가 대비 낙찰가)낙찰가율(최저입찰가 대비 낙찰가)입찰참가자수(명)경쟁률(낙찰자수 대비 입찰 참가자수)
1882023근린생활시설82357477165262560.73104.92661.32
1892023숙박시설163775568667666.15106.881531.46
1902023기타비주거용건물165268585911371.16105.691011.68
1912023토지37221820062422467474.3109.0997521.99
1922023임야143094733338551673.39109.1844742.41
1932023전53400299413214269.96108.2816831.58
1942023답47614311393381970.19107.6113051.52
1952023대지71043371924204388.07111.9912542.24
1962023기타토지57064290173115478.29109.8310361.84
1972023산업용및용도복합용건물등55525305353327666.3105.912051.68