Overview

Dataset statistics

Number of variables8
Number of observations10000
Missing cells10000
Missing cells (%)12.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory781.2 KiB
Average record size in memory80.0 B

Variable types

Categorical2
Numeric5
Unsupported1

Dataset

Description개별주택가격은 국토교통부장관이 매년 공시하는 표준주택가격을 기준으로 시장·군수·구청장이 조사한 개별주택의 특성과 비교표준주택의 특성을 비교하여 국토교통부장관이 작성·공급한 「주택가격비준표」 상의 주택특성 차이에 따른 가격배율을 산출하고 이를 표준주택가격에 곱하여 산정한 후 한국부동산원의 검증을 받아 주택소유자 등의 의견수렴과 시·군·구 부동산가격공시위원회 심의 등의 절차를 거쳐 시장·군수 ·구청장이 결정 공시하는 개별주택의 가격
Author경상남도 하동군
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=15013457

Alerts

시군구코드 has constant value ""Constant
토지구분 is highly imbalanced (94.6%)Imbalance
Unnamed: 7 has 10000 (100.0%) missing valuesMissing
Unnamed: 7 is an unsupported type, check if it needs cleaning or further analysisUnsupported
부번 has 4503 (45.0%) zerosZeros

Reproduction

Analysis started2023-12-11 00:31:09.114859
Analysis finished2023-12-11 00:31:13.089683
Duration3.97 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군구코드
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
48850
10000 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row48850
2nd row48850
3rd row48850
4th row48850
5th row48850

Common Values

ValueCountFrequency (%)
48850 10000
100.0%

Length

2023-12-11T09:31:13.154339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:31:13.256253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
48850 10000
100.0%

읍면동코드
Real number (ℝ)

Distinct13
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean346.733
Minimum250
Maximum420
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T09:31:13.359729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum250
5-th percentile250
Q1320
median350
Q3390
95-th percentile420
Maximum420
Range170
Interquartile range (IQR)70

Descriptive statistics

Standard deviation50.832028
Coefficient of variation (CV)0.1466028
Kurtosis-0.54034715
Mean346.733
Median Absolute Deviation (MAD)30
Skewness-0.53457587
Sum3467330
Variance2583.8951
MonotonicityNot monotonic
2023-12-11T09:31:13.496657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
250 1400
14.0%
320 1083
10.8%
410 1015
10.2%
370 962
9.6%
360 914
9.1%
310 817
8.2%
350 623
6.2%
420 599
6.0%
380 580
5.8%
340 565
 
5.7%
Other values (3) 1442
14.4%
ValueCountFrequency (%)
250 1400
14.0%
310 817
8.2%
320 1083
10.8%
330 534
 
5.3%
340 565
5.7%
350 623
6.2%
360 914
9.1%
370 962
9.6%
380 580
5.8%
390 518
 
5.2%
ValueCountFrequency (%)
420 599
6.0%
410 1015
10.2%
400 390
 
3.9%
390 518
5.2%
380 580
5.8%
370 962
9.6%
360 914
9.1%
350 623
6.2%
340 565
5.7%
330 534
5.3%

리코드
Real number (ℝ)

Distinct15
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.7601
Minimum21
Maximum35
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T09:31:13.660571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile21
Q122
median24
Q327
95-th percentile31
Maximum35
Range14
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.2089839
Coefficient of variation (CV)0.12960303
Kurtosis0.28838921
Mean24.7601
Median Absolute Deviation (MAD)2
Skewness0.87867006
Sum247601
Variance10.297578
MonotonicityNot monotonic
2023-12-11T09:31:13.849867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
21 1788
17.9%
24 1409
14.1%
22 1160
11.6%
23 1068
10.7%
25 1047
10.5%
26 917
9.2%
27 827
8.3%
28 487
 
4.9%
30 322
 
3.2%
29 321
 
3.2%
Other values (5) 654
 
6.5%
ValueCountFrequency (%)
21 1788
17.9%
22 1160
11.6%
23 1068
10.7%
24 1409
14.1%
25 1047
10.5%
26 917
9.2%
27 827
8.3%
28 487
 
4.9%
29 321
 
3.2%
30 322
 
3.2%
ValueCountFrequency (%)
35 28
 
0.3%
34 142
 
1.4%
33 111
 
1.1%
32 168
 
1.7%
31 205
 
2.1%
30 322
 
3.2%
29 321
 
3.2%
28 487
4.9%
27 827
8.3%
26 917
9.2%

토지구분
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
9938 
2
 
62

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 9938
99.4%
2 62
 
0.6%

Length

2023-12-11T09:31:14.010898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:31:14.104756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 9938
99.4%
2 62
 
0.6%

본번
Real number (ℝ)

Distinct1515
Distinct (%)15.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean543.8583
Minimum1
Maximum2109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T09:31:14.213107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile69
Q1259
median475
Q3759
95-th percentile1246
Maximum2109
Range2108
Interquartile range (IQR)500

Descriptive statistics

Standard deviation367.44213
Coefficient of variation (CV)0.67562107
Kurtosis0.28678604
Mean543.8583
Median Absolute Deviation (MAD)250
Skewness0.81394668
Sum5438583
Variance135013.72
MonotonicityNot monotonic
2023-12-11T09:31:14.390157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
924 62
 
0.6%
302 61
 
0.6%
1113 55
 
0.5%
221 36
 
0.4%
116 35
 
0.4%
299 28
 
0.3%
124 27
 
0.3%
100 27
 
0.3%
1337 25
 
0.2%
109 25
 
0.2%
Other values (1505) 9619
96.2%
ValueCountFrequency (%)
1 1
 
< 0.1%
2 3
 
< 0.1%
3 10
0.1%
4 4
 
< 0.1%
5 10
0.1%
6 1
 
< 0.1%
7 5
0.1%
8 5
0.1%
9 4
 
< 0.1%
11 2
 
< 0.1%
ValueCountFrequency (%)
2109 1
< 0.1%
2108 1
< 0.1%
2018 1
< 0.1%
2015 1
< 0.1%
2012 2
< 0.1%
2011 1
< 0.1%
2010 1
< 0.1%
2008 1
< 0.1%
2007 1
< 0.1%
2004 1
< 0.1%

부번
Real number (ℝ)

ZEROS 

Distinct167
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.0286
Minimum0
Maximum479
Zeros4503
Zeros (%)45.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T09:31:14.579669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile16
Maximum479
Range479
Interquartile range (IQR)3

Descriptive statistics

Standard deviation21.675621
Coefficient of variation (CV)4.3104684
Kurtosis163.87964
Mean5.0286
Median Absolute Deviation (MAD)1
Skewness11.131531
Sum50286
Variance469.83257
MonotonicityNot monotonic
2023-12-11T09:31:14.730805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4503
45.0%
1 1668
 
16.7%
2 1050
 
10.5%
3 687
 
6.9%
4 403
 
4.0%
5 285
 
2.9%
6 213
 
2.1%
7 162
 
1.6%
8 109
 
1.1%
9 103
 
1.0%
Other values (157) 817
 
8.2%
ValueCountFrequency (%)
0 4503
45.0%
1 1668
 
16.7%
2 1050
 
10.5%
3 687
 
6.9%
4 403
 
4.0%
5 285
 
2.9%
6 213
 
2.1%
7 162
 
1.6%
8 109
 
1.1%
9 103
 
1.0%
ValueCountFrequency (%)
479 1
< 0.1%
452 1
< 0.1%
435 1
< 0.1%
434 1
< 0.1%
433 1
< 0.1%
428 1
< 0.1%
373 1
< 0.1%
345 1
< 0.1%
314 1
< 0.1%
293 1
< 0.1%

주택공시가격
Real number (ℝ)

Distinct1687
Distinct (%)16.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28692488
Minimum644000
Maximum5.03 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T09:31:14.880867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum644000
5-th percentile4480000
Q19977500
median20200000
Q338900000
95-th percentile79000000
Maximum5.03 × 108
Range5.02356 × 108
Interquartile range (IQR)28922500

Descriptive statistics

Standard deviation27736562
Coefficient of variation (CV)0.96668375
Kurtosis24.784038
Mean28692488
Median Absolute Deviation (MAD)12300000
Skewness3.2338428
Sum2.8692488 × 1011
Variance7.6931684 × 1014
MonotonicityNot monotonic
2023-12-11T09:31:15.054395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10000000 45
 
0.4%
10200000 43
 
0.4%
11500000 42
 
0.4%
10800000 41
 
0.4%
11400000 41
 
0.4%
13000000 40
 
0.4%
10600000 40
 
0.4%
12900000 39
 
0.4%
11900000 39
 
0.4%
10300000 38
 
0.4%
Other values (1677) 9592
95.9%
ValueCountFrequency (%)
644000 1
< 0.1%
758000 1
< 0.1%
790000 1
< 0.1%
820000 1
< 0.1%
833000 1
< 0.1%
837000 1
< 0.1%
848000 1
< 0.1%
865000 1
< 0.1%
1000000 1
< 0.1%
1060000 1
< 0.1%
ValueCountFrequency (%)
503000000 1
< 0.1%
416000000 1
< 0.1%
372000000 1
< 0.1%
351000000 1
< 0.1%
330000000 1
< 0.1%
281000000 1
< 0.1%
278000000 1
< 0.1%
256000000 1
< 0.1%
248000000 1
< 0.1%
239000000 1
< 0.1%

Unnamed: 7
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size166.0 KiB

Interactions

2023-12-11T09:31:12.338617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:31:09.878436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:31:10.793580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:31:11.332066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:31:11.819456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:31:12.451712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:31:09.971553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:31:10.912294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:31:11.447354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:31:11.954764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:31:12.551575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:31:10.448633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:31:11.021864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:31:11.538393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:31:12.061083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:31:12.640955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:31:10.601327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:31:11.127393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:31:11.613175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:31:12.152918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:31:12.746536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:31:10.697793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:31:11.230305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:31:11.708891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:31:12.248366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T09:31:15.159606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
읍면동코드리코드토지구분본번부번주택공시가격
읍면동코드1.0000.3830.0460.2480.1580.085
리코드0.3831.0000.0330.3120.1330.046
토지구분0.0460.0331.0000.1790.0000.000
본번0.2480.3120.1791.0000.1720.000
부번0.1580.1330.0000.1721.0000.126
주택공시가격0.0850.0460.0000.0000.1261.000
2023-12-11T09:31:15.269883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
읍면동코드리코드본번부번주택공시가격토지구분
읍면동코드1.0000.030-0.031-0.003-0.0800.050
리코드0.0301.0000.030-0.114-0.0980.024
본번-0.0310.0301.000-0.0690.0220.137
부번-0.003-0.114-0.0691.0000.1740.000
주택공시가격-0.080-0.0980.0220.1741.0000.000
토지구분0.0500.0240.1370.0000.0001.000

Missing values

2023-12-11T09:31:12.865352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T09:31:13.024570image/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

시군구코드읍면동코드리코드토지구분본번부번주택공시가격Unnamed: 7
1511048850410321464271400000<NA>
1226948850390211100332100000<NA>
227748850310211162063600000<NA>
9488502502111001341400000<NA>
1369048850400251161505340000<NA>
1202348850380261539214900000<NA>
82484885036022175014900000<NA>
1547848850420211866022000000<NA>
1104748850370281756317200000<NA>
531048850320341429047300000<NA>
시군구코드읍면동코드리코드토지구분본번부번주택공시가격Unnamed: 7
4070488503202411025035800000<NA>
2625488503102313306124000000<NA>
143464885041026131709020000<NA>
1498148850410311574106420000<NA>
321348850310271141319600000<NA>
115604885038022183815530000<NA>
35614885031029113810125000000<NA>
4072488503202411065314700000<NA>
60848850250211945015400000<NA>
48224885032031134020000000<NA>