Overview

Dataset statistics

Number of variables9
Number of observations253
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory19.9 KiB
Average record size in memory80.5 B

Variable types

Numeric8
Categorical1

Dataset

Description공매정보포털 온비드(Onbid)의 지역별 통계분석 자료입니다.(2013년부터 2023년까지의 데이터 수록)
Author한국자산관리공사
URLhttps://www.data.go.kr/data/15054749/fileData.do

Alerts

감정가(백만원) is highly overall correlated with 최저입찰가(백만원) and 2 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 1 other fieldsHigh correlation
낙찰가율(최저입찰가 대비 낙찰가) is highly overall correlated with 낙찰가율(감정가 대비 낙찰가) and 1 other fieldsHigh correlation
입찰 참가자수(명) is highly overall correlated with 감정가(백만원) and 2 other fieldsHigh correlation
경쟁률(낙찰자수 대비 입찰 참가자수) is highly overall correlated with 낙찰가율(감정가 대비 낙찰가) and 1 other fieldsHigh correlation

Reproduction

Analysis started2024-03-14 17:40:04.306011
Analysis finished2024-03-14 17:40:21.526265
Duration17.22 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연도
Real number (ℝ)

Distinct11
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2018
Minimum2013
Maximum2023
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2024-03-15T02:40:21.691925image/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 deviation3.1685458
Coefficient of variation (CV)0.0015701416
Kurtosis-1.2203627
Mean2018
Median Absolute Deviation (MAD)3
Skewness0
Sum510554
Variance10.039683
MonotonicityIncreasing
2024-03-15T02:40:22.043748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
2013 23
9.1%
2014 23
9.1%
2015 23
9.1%
2016 23
9.1%
2017 23
9.1%
2018 23
9.1%
2019 23
9.1%
2020 23
9.1%
2021 23
9.1%
2022 23
9.1%
ValueCountFrequency (%)
2013 23
9.1%
2014 23
9.1%
2015 23
9.1%
2016 23
9.1%
2017 23
9.1%
2018 23
9.1%
2019 23
9.1%
2020 23
9.1%
2021 23
9.1%
2022 23
9.1%
ValueCountFrequency (%)
2023 23
9.1%
2022 23
9.1%
2021 23
9.1%
2020 23
9.1%
2019 23
9.1%
2018 23
9.1%
2017 23
9.1%
2016 23
9.1%
2015 23
9.1%
2014 23
9.1%

지역
Categorical

Distinct23
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
전체
 
11
서울
 
11
인천/경기
 
11
인천
 
11
경기
 
11
Other values (18)
198 

Length

Max length8
Median length2
Mean length3.1304348
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row전체
2nd row서울
3rd row인천/경기
4th row인천
5th row경기

Common Values

ValueCountFrequency (%)
전체 11
 
4.3%
서울 11
 
4.3%
인천/경기 11
 
4.3%
인천 11
 
4.3%
경기 11
 
4.3%
강원 11
 
4.3%
충북 11
 
4.3%
대전/충남 11
 
4.3%
대전 11
 
4.3%
충남 11
 
4.3%
Other values (13) 143
56.5%

Length

2024-03-15T02:40:22.456095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
전체 11
 
4.3%
경북 11
 
4.3%
제주 11
 
4.3%
전남 11
 
4.3%
광주 11
 
4.3%
광주/전남/제주 11
 
4.3%
전북 11
 
4.3%
경남 11
 
4.3%
울산 11
 
4.3%
부산 11
 
4.3%
Other values (13) 143
56.5%

감정가(백만원)
Real number (ℝ)

HIGH CORRELATION 

Distinct252
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean120768.77
Minimum1168
Maximum1186399
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2024-03-15T02:40:22.717067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1168
5-th percentile6737.2
Q127086
median50534
Q387702
95-th percentile446302.2
Maximum1186399
Range1185231
Interquartile range (IQR)60616

Descriptive statistics

Standard deviation215816.74
Coefficient of variation (CV)1.7870244
Kurtosis12.510863
Mean120768.77
Median Absolute Deviation (MAD)28196
Skewness3.4904317
Sum30554499
Variance4.6576865 × 1010
MonotonicityNot monotonic
2024-03-15T02:40:23.125091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31867 2
 
0.8%
1114925 1
 
0.4%
48200 1
 
0.4%
958786 1
 
0.4%
173584 1
 
0.4%
304062 1
 
0.4%
33239 1
 
0.4%
270822 1
 
0.4%
34267 1
 
0.4%
33430 1
 
0.4%
Other values (242) 242
95.7%
ValueCountFrequency (%)
1168 1
0.4%
1867 1
0.4%
2411 1
0.4%
4011 1
0.4%
4206 1
0.4%
4256 1
0.4%
4641 1
0.4%
4840 1
0.4%
5005 1
0.4%
5162 1
0.4%
ValueCountFrequency (%)
1186399 1
0.4%
1161251 1
0.4%
1146102 1
0.4%
1143919 1
0.4%
1114925 1
0.4%
1064937 1
0.4%
958786 1
0.4%
944246 1
0.4%
881660 1
0.4%
830760 1
0.4%

최저입찰가(백만원)
Real number (ℝ)

HIGH CORRELATION 

Distinct252
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean76695.692
Minimum839
Maximum795835
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2024-03-15T02:40:23.474297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum839
5-th percentile5033.8
Q117538
median31878
Q355395
95-th percentile285660.4
Maximum795835
Range794996
Interquartile range (IQR)37857

Descriptive statistics

Standard deviation138233.28
Coefficient of variation (CV)1.8023604
Kurtosis13.582783
Mean76695.692
Median Absolute Deviation (MAD)17740
Skewness3.5993955
Sum19404010
Variance1.9108439 × 1010
MonotonicityNot monotonic
2024-03-15T02:40:23.761305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17752 2
 
0.8%
670401 1
 
0.4%
29976 1
 
0.4%
591132 1
 
0.4%
122735 1
 
0.4%
192230 1
 
0.4%
24150 1
 
0.4%
168078 1
 
0.4%
21100 1
 
0.4%
19256 1
 
0.4%
Other values (242) 242
95.7%
ValueCountFrequency (%)
839 1
0.4%
1183 1
0.4%
1831 1
0.4%
2689 1
0.4%
2936 1
0.4%
3194 1
0.4%
3232 1
0.4%
3496 1
0.4%
3617 1
0.4%
4229 1
0.4%
ValueCountFrequency (%)
795835 1
0.4%
778199 1
0.4%
763057 1
0.4%
747502 1
0.4%
728473 1
0.4%
670401 1
0.4%
599149 1
0.4%
591132 1
0.4%
529759 1
0.4%
479750 1
0.4%

낙찰가(백만원)
Real number (ℝ)

HIGH CORRELATION 

Distinct251
Distinct (%)99.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean87339.708
Minimum931
Maximum1132261
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2024-03-15T02:40:24.140568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum931
5-th percentile6281.6
Q119464
median34810
Q362960
95-th percentile402525
Maximum1132261
Range1131330
Interquartile range (IQR)43496

Descriptive statistics

Standard deviation161308.74
Coefficient of variation (CV)1.8469118
Kurtosis14.959777
Mean87339.708
Median Absolute Deviation (MAD)19690
Skewness3.709168
Sum22096946
Variance2.6020509 × 1010
MonotonicityNot monotonic
2024-03-15T02:40:24.719157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
45229 2
 
0.8%
20395 2
 
0.8%
724464 1
 
0.4%
23620 1
 
0.4%
131428 1
 
0.4%
213223 1
 
0.4%
25707 1
 
0.4%
187515 1
 
0.4%
26908 1
 
0.4%
21142 1
 
0.4%
Other values (241) 241
95.3%
ValueCountFrequency (%)
931 1
0.4%
1243 1
0.4%
2013 1
0.4%
3047 1
0.4%
3288 1
0.4%
3961 1
0.4%
4034 1
0.4%
4715 1
0.4%
4871 1
0.4%
5197 1
0.4%
ValueCountFrequency (%)
1132261 1
0.4%
854967 1
0.4%
830161 1
0.4%
804160 1
0.4%
798860 1
0.4%
724464 1
0.4%
688561 1
0.4%
655232 1
0.4%
569318 1
0.4%
564259 1
0.4%

낙찰가율(감정가 대비 낙찰가)
Real number (ℝ)

HIGH CORRELATION 

Distinct242
Distinct (%)95.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean78.475534
Minimum41.42
Maximum115.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2024-03-15T02:40:24.970274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum41.42
5-th percentile63.68
Q171.75
median77.23
Q384.82
95-th percentile95.568
Maximum115.5
Range74.08
Interquartile range (IQR)13.07

Descriptive statistics

Standard deviation9.8170208
Coefficient of variation (CV)0.12509658
Kurtosis1.1217711
Mean78.475534
Median Absolute Deviation (MAD)6
Skewness0.34231069
Sum19854.31
Variance96.373897
MonotonicityNot monotonic
2024-03-15T02:40:25.345415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
77.13 2
 
0.8%
79.61 2
 
0.8%
75.74 2
 
0.8%
84.3 2
 
0.8%
77.65 2
 
0.8%
79.85 2
 
0.8%
71.43 2
 
0.8%
83.13 2
 
0.8%
78.6 2
 
0.8%
84.96 2
 
0.8%
Other values (232) 233
92.1%
ValueCountFrequency (%)
41.42 1
0.4%
56.24 1
0.4%
58.56 1
0.4%
60.34 1
0.4%
60.61 1
0.4%
60.65 1
0.4%
60.97 1
0.4%
61.39 1
0.4%
61.78 1
0.4%
62.14 1
0.4%
ValueCountFrequency (%)
115.5 1
0.4%
107.04 1
0.4%
106.67 1
0.4%
105.04 1
0.4%
100.28 1
0.4%
98.37 1
0.4%
97.99 1
0.4%
97.22 1
0.4%
97.2 1
0.4%
97.01 1
0.4%

낙찰가율(최저입찰가 대비 낙찰가)
Real number (ℝ)

HIGH CORRELATION 

Distinct219
Distinct (%)86.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean109.73435
Minimum104.86
Maximum122.81
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2024-03-15T02:40:25.767716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum104.86
5-th percentile106.202
Q1107.61
median109.04
Q3110.85
95-th percentile115.21
Maximum122.81
Range17.95
Interquartile range (IQR)3.24

Descriptive statistics

Standard deviation3.0502143
Coefficient of variation (CV)0.02779635
Kurtosis3.6997933
Mean109.73435
Median Absolute Deviation (MAD)1.67
Skewness1.6055763
Sum27762.79
Variance9.3038072
MonotonicityNot monotonic
2024-03-15T02:40:26.216431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
107.2 3
 
1.2%
110.04 3
 
1.2%
108.49 2
 
0.8%
110.61 2
 
0.8%
110.77 2
 
0.8%
108.65 2
 
0.8%
108.7 2
 
0.8%
112.85 2
 
0.8%
107.37 2
 
0.8%
109.44 2
 
0.8%
Other values (209) 231
91.3%
ValueCountFrequency (%)
104.86 1
0.4%
104.99 1
0.4%
105.57 1
0.4%
105.77 1
0.4%
105.89 1
0.4%
105.9 1
0.4%
105.92 1
0.4%
106.01 1
0.4%
106.1 1
0.4%
106.12 1
0.4%
ValueCountFrequency (%)
122.81 1
0.4%
122.74 1
0.4%
122.26 1
0.4%
121.52 1
0.4%
119.45 1
0.4%
118.28 1
0.4%
117.3 1
0.4%
116.62 1
0.4%
116.1 1
0.4%
115.82 1
0.4%

입찰 참가자수(명)
Real number (ℝ)

HIGH CORRELATION 

Distinct247
Distinct (%)97.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2386.7352
Minimum26
Maximum37064
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2024-03-15T02:40:26.592706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum26
5-th percentile146.2
Q1401
median1323
Q32305
95-th percentile6465.8
Maximum37064
Range37038
Interquartile range (IQR)1904

Descriptive statistics

Standard deviation4271.1715
Coefficient of variation (CV)1.7895456
Kurtosis26.443643
Mean2386.7352
Median Absolute Deviation (MAD)923
Skewness4.6860618
Sum603844
Variance18242906
MonotonicityNot monotonic
2024-03-15T02:40:26.842969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
332 2
 
0.8%
1850 2
 
0.8%
328 2
 
0.8%
2110 2
 
0.8%
2305 2
 
0.8%
351 2
 
0.8%
3433 1
 
0.4%
624 1
 
0.4%
1256 1
 
0.4%
1549 1
 
0.4%
Other values (237) 237
93.7%
ValueCountFrequency (%)
26 1
0.4%
36 1
0.4%
50 1
0.4%
59 1
0.4%
61 1
0.4%
65 1
0.4%
69 1
0.4%
76 1
0.4%
79 1
0.4%
84 1
0.4%
ValueCountFrequency (%)
37064 1
0.4%
27917 1
0.4%
21235 1
0.4%
20505 1
0.4%
19116 1
0.4%
17961 1
0.4%
17849 1
0.4%
17078 1
0.4%
15188 1
0.4%
14551 1
0.4%

경쟁률(낙찰자수 대비 입찰 참가자수)
Real number (ℝ)

HIGH CORRELATION 

Distinct145
Distinct (%)57.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5260474
Minimum1.24
Maximum6.76
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2024-03-15T02:40:27.097431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.24
5-th percentile1.696
Q11.97
median2.25
Q32.8
95-th percentile4.124
Maximum6.76
Range5.52
Interquartile range (IQR)0.83

Descriptive statistics

Standard deviation0.87100755
Coefficient of variation (CV)0.34481045
Kurtosis5.2330008
Mean2.5260474
Median Absolute Deviation (MAD)0.33
Skewness2.0400717
Sum639.09
Variance0.75865416
MonotonicityNot monotonic
2024-03-15T02:40:27.509776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.17 6
 
2.4%
2.32 5
 
2.0%
1.94 5
 
2.0%
2.09 4
 
1.6%
1.78 4
 
1.6%
1.77 4
 
1.6%
2.45 4
 
1.6%
2.19 4
 
1.6%
2.2 4
 
1.6%
1.59 4
 
1.6%
Other values (135) 209
82.6%
ValueCountFrequency (%)
1.24 1
 
0.4%
1.41 1
 
0.4%
1.57 1
 
0.4%
1.59 4
1.6%
1.62 1
 
0.4%
1.64 1
 
0.4%
1.65 1
 
0.4%
1.66 2
0.8%
1.69 1
 
0.4%
1.7 1
 
0.4%
ValueCountFrequency (%)
6.76 1
0.4%
6.43 1
0.4%
6.16 1
0.4%
5.78 1
0.4%
5.3 1
0.4%
5.24 1
0.4%
5.1 1
0.4%
4.78 1
0.4%
4.61 1
0.4%
4.46 1
0.4%

Interactions

2024-03-15T02:40:18.902414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:40:04.724630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:40:06.316352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:40:08.540939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:40:10.849131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:40:13.305360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:40:15.225120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:40:16.908425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:40:19.152184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:40:04.964634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:40:06.564562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:40:08.804842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:40:11.133149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:40:13.561961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:40:15.384915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:40:17.109457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:40:19.475141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:40:05.230880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:40:06.824491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:40:09.086328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:40:11.395432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:40:13.834723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:40:15.553904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:40:17.359804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:40:19.746701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:40:05.393795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:40:07.097129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:40:09.368344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:40:11.674281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:40:14.020077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:40:15.822411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:40:17.639440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:40:19.998827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:40:05.541788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:40:07.349954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:40:09.696841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:40:11.934366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:40:14.180144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:40:16.081254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:40:17.949731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:40:20.267351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:40:05.700035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:40:07.701817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:40:10.054969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:40:12.261624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:40:14.370292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:40:16.355586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:40:18.235381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:40:20.495718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:40:05.868316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:40:07.990742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:40:10.298326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:40:12.565634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:40:14.839060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:40:16.526597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:40:18.528096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:40:20.662937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:40:06.059332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:40:08.269260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:40:10.564338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:40:12.991948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:40:15.019028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:40:16.698995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:40:18.716464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-15T02:40:27.963500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도지역감정가(백만원)최저입찰가(백만원)낙찰가(백만원)낙찰가율(감정가 대비 낙찰가)낙찰가율(최저입찰가 대비 낙찰가)입찰 참가자수(명)경쟁률(낙찰자수 대비 입찰 참가자수)
연도1.0000.0000.0000.0000.0000.3490.3610.2020.335
지역0.0001.0000.8180.8110.7720.5440.6130.6810.498
감정가(백만원)0.0000.8181.0000.9750.9170.0000.0000.7780.000
최저입찰가(백만원)0.0000.8110.9751.0000.9090.0000.0000.8650.000
낙찰가(백만원)0.0000.7720.9170.9091.0000.0000.0000.9490.000
낙찰가율(감정가 대비 낙찰가)0.3490.5440.0000.0000.0001.0000.8210.0000.743
낙찰가율(최저입찰가 대비 낙찰가)0.3610.6130.0000.0000.0000.8211.0000.0000.858
입찰 참가자수(명)0.2020.6810.7780.8650.9490.0000.0001.0000.230
경쟁률(낙찰자수 대비 입찰 참가자수)0.3350.4980.0000.0000.0000.7430.8580.2301.000
2024-03-15T02:40:28.307831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도감정가(백만원)최저입찰가(백만원)낙찰가(백만원)낙찰가율(감정가 대비 낙찰가)낙찰가율(최저입찰가 대비 낙찰가)입찰 참가자수(명)경쟁률(낙찰자수 대비 입찰 참가자수)지역
연도1.000-0.088-0.125-0.1200.0460.061-0.0560.1460.000
감정가(백만원)-0.0881.0000.9870.985-0.269-0.2230.740-0.0860.477
최저입찰가(백만원)-0.1250.9871.0000.998-0.196-0.1730.746-0.0210.467
낙찰가(백만원)-0.1200.9850.9981.000-0.177-0.1540.757-0.0050.430
낙찰가율(감정가 대비 낙찰가)0.046-0.269-0.196-0.1771.0000.8650.1210.7790.236
낙찰가율(최저입찰가 대비 낙찰가)0.061-0.223-0.173-0.1540.8651.0000.2370.7270.270
입찰 참가자수(명)-0.0560.7400.7460.7570.1210.2371.0000.3050.342
경쟁률(낙찰자수 대비 입찰 참가자수)0.146-0.086-0.021-0.0050.7790.7270.3051.0000.202
지역0.0000.4770.4670.4300.2360.2700.3420.2021.000

Missing values

2024-03-15T02:40:20.875999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-15T02:40:21.331105image/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

연도지역감정가(백만원)최저입찰가(백만원)낙찰가(백만원)낙찰가율(감정가 대비 낙찰가)낙찰가율(최저입찰가 대비 낙찰가)입찰 참가자수(명)경쟁률(낙찰자수 대비 입찰 참가자수)
02013전체111492567040172446469.97108.01151881.9
12013서울18643712289912914471.47106.839472.19
22013인천/경기38992021812423402561.39105.9232691.83
32013인천47170311973535166.67106.015871.99
42013경기34274918692619867460.34105.926821.8
52013강원44710256622873170.22109.6513341.79
62013충북42849246942654272.18107.38271.77
72013대전/충남121022596616296056.24106.113741.57
82013대전33316162421729441.42104.863091.41
92013충남87704434184566660.97106.4910651.62
연도지역감정가(백만원)최저입찰가(백만원)낙찰가(백만원)낙찰가율(감정가 대비 낙찰가)낙찰가율(최저입찰가 대비 낙찰가)입찰 참가자수(명)경쟁률(낙찰자수 대비 입찰 참가자수)
2432023부산/울산/경남92252510625451069.35107.6213231.85
2442023부산29680176511880171.75107.53142.17
2452023울산24384124041334864.71107.371831.78
2462023경남38188210062235969.66107.718261.76
2472023전북33862179472004975.92108.228801.93
2482023광주/전남/제주54693316463458577.41110.022422.54
2492023광주77795171542179.78108.541962.39
2502023전남31583188572105579.61110.7718392.68
2512023제주153297617810862.8106.582071.82
2522023세종특별자치시42562936821077.24108.27651.59