Overview

Dataset statistics

Number of variables7
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows1
Duplicate rows (%)< 0.1%
Total size in memory693.4 KiB
Average record size in memory71.0 B

Variable types

Categorical2
Numeric5

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
Dataset has 1 (< 0.1%) duplicate rowsDuplicates
토지구분 is highly imbalanced (94.0%)Imbalance
부번 has 4475 (44.8%) zerosZeros

Reproduction

Analysis started2023-08-15 04:30:07.983163
Analysis finished2023-08-15 04:30:15.767774
Duration7.78 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

2023-08-15T13:30:15.913866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-15T13:30:16.124073image/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%
Mean346.88
Minimum250
Maximum420
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-08-15T13:30:16.344007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum250
5-th percentile250
Q1320
median350
Q3380
95-th percentile420
Maximum420
Range170
Interquartile range (IQR)60

Descriptive statistics

Standard deviation50.335663
Coefficient of variation (CV)0.14510973
Kurtosis-0.50001241
Mean346.88
Median Absolute Deviation (MAD)30
Skewness-0.54782802
Sum3468800
Variance2533.679
MonotonicityNot monotonic
2023-08-15T13:30:16.597904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
250 1366
13.7%
320 1092
10.9%
410 1007
10.1%
370 994
9.9%
360 911
9.1%
310 825
8.2%
350 675
6.8%
380 596
6.0%
420 561
 
5.6%
340 549
 
5.5%
Other values (3) 1424
14.2%
ValueCountFrequency (%)
250 1366
13.7%
310 825
8.2%
320 1092
10.9%
330 513
 
5.1%
340 549
5.5%
350 675
6.8%
360 911
9.1%
370 994
9.9%
380 596
6.0%
390 520
 
5.2%
ValueCountFrequency (%)
420 561
5.6%
410 1007
10.1%
400 391
 
3.9%
390 520
5.2%
380 596
6.0%
370 994
9.9%
360 911
9.1%
350 675
6.8%
340 549
5.5%
330 513
5.1%

리코드
Real number (ℝ)

Distinct15
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.7905
Minimum21
Maximum35
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-08-15T13:30:16.903594image/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 deviation3.2118906
Coefficient of variation (CV)0.12956135
Kurtosis0.30351391
Mean24.7905
Median Absolute Deviation (MAD)2
Skewness0.87922925
Sum247905
Variance10.316241
MonotonicityNot monotonic
2023-08-15T13:30:17.412546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
21 1727
17.3%
24 1396
14.0%
22 1188
11.9%
25 1065
10.7%
23 1063
10.6%
26 940
9.4%
27 791
7.9%
28 534
 
5.3%
29 329
 
3.3%
30 303
 
3.0%
Other values (5) 664
 
6.6%
ValueCountFrequency (%)
21 1727
17.3%
22 1188
11.9%
23 1063
10.6%
24 1396
14.0%
25 1065
10.7%
26 940
9.4%
27 791
7.9%
28 534
 
5.3%
29 329
 
3.3%
30 303
 
3.0%
ValueCountFrequency (%)
35 33
 
0.3%
34 144
 
1.4%
33 113
 
1.1%
32 170
 
1.7%
31 204
 
2.0%
30 303
 
3.0%
29 329
 
3.3%
28 534
5.3%
27 791
7.9%
26 940
9.4%

토지구분
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
9930 
2
 
70

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 9930
99.3%
2 70
 
0.7%

Length

2023-08-15T13:30:17.981615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-15T13:30:18.184229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 9930
99.3%
2 70
 
0.7%

본번
Real number (ℝ)

Distinct1508
Distinct (%)15.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean541.2357
Minimum1
Maximum2203
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-08-15T13:30:18.448148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile67
Q1257
median469
Q3757
95-th percentile1235.05
Maximum2203
Range2202
Interquartile range (IQR)500

Descriptive statistics

Standard deviation366.60116
Coefficient of variation (CV)0.67734105
Kurtosis0.28276235
Mean541.2357
Median Absolute Deviation (MAD)248
Skewness0.81488564
Sum5412357
Variance134396.41
MonotonicityNot monotonic
2023-08-15T13:30:18.727036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
924 66
 
0.7%
302 66
 
0.7%
1113 57
 
0.6%
221 35
 
0.4%
116 32
 
0.3%
299 30
 
0.3%
349 27
 
0.3%
250 25
 
0.2%
124 25
 
0.2%
186 23
 
0.2%
Other values (1498) 9614
96.1%
ValueCountFrequency (%)
1 3
 
< 0.1%
2 4
< 0.1%
3 7
0.1%
4 5
0.1%
5 8
0.1%
6 1
 
< 0.1%
7 1
 
< 0.1%
8 4
< 0.1%
9 2
 
< 0.1%
10 1
 
< 0.1%
ValueCountFrequency (%)
2203 1
< 0.1%
2198 1
< 0.1%
2109 1
< 0.1%
2018 1
< 0.1%
2015 1
< 0.1%
2012 1
< 0.1%
2008 1
< 0.1%
2007 1
< 0.1%
2004 1
< 0.1%
2003 1
< 0.1%

부번
Real number (ℝ)

ZEROS 

Distinct169
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.101
Minimum0
Maximum479
Zeros4475
Zeros (%)44.8%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-08-15T13:30:18.997115image/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 deviation22.509546
Coefficient of variation (CV)4.4127712
Kurtosis176.30789
Mean5.101
Median Absolute Deviation (MAD)1
Skewness11.639143
Sum51010
Variance506.67967
MonotonicityNot monotonic
2023-08-15T13:30:19.346880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4475
44.8%
1 1666
 
16.7%
2 1037
 
10.4%
3 687
 
6.9%
4 421
 
4.2%
5 289
 
2.9%
6 220
 
2.2%
7 167
 
1.7%
8 115
 
1.1%
9 96
 
1.0%
Other values (159) 827
 
8.3%
ValueCountFrequency (%)
0 4475
44.8%
1 1666
 
16.7%
2 1037
 
10.4%
3 687
 
6.9%
4 421
 
4.2%
5 289
 
2.9%
6 220
 
2.2%
7 167
 
1.7%
8 115
 
1.1%
9 96
 
1.0%
ValueCountFrequency (%)
479 1
< 0.1%
452 1
< 0.1%
450 1
< 0.1%
436 1
< 0.1%
435 1
< 0.1%
434 1
< 0.1%
432 1
< 0.1%
428 1
< 0.1%
418 1
< 0.1%
373 1
< 0.1%

주택공시가격
Real number (ℝ)

Distinct1695
Distinct (%)17.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29021752
Minimum346000
Maximum4.97 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-08-15T13:30:19.722905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum346000
5-th percentile4579500
Q110100000
median20300000
Q339200000
95-th percentile80400000
Maximum4.97 × 108
Range4.96654 × 108
Interquartile range (IQR)29100000

Descriptive statistics

Standard deviation28424117
Coefficient of variation (CV)0.97940733
Kurtosis26.003287
Mean29021752
Median Absolute Deviation (MAD)12270000
Skewness3.3824863
Sum2.9021752 × 1011
Variance8.0793041 × 1014
MonotonicityNot monotonic
2023-08-15T13:30:20.119758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10600000 45
 
0.4%
11900000 42
 
0.4%
10300000 40
 
0.4%
11800000 39
 
0.4%
11200000 39
 
0.4%
10000000 38
 
0.4%
13400000 38
 
0.4%
11100000 37
 
0.4%
12400000 37
 
0.4%
12600000 37
 
0.4%
Other values (1685) 9608
96.1%
ValueCountFrequency (%)
346000 1
< 0.1%
515000 1
< 0.1%
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%
870000 1
< 0.1%
ValueCountFrequency (%)
497000000 1
< 0.1%
424000000 1
< 0.1%
398000000 1
< 0.1%
353000000 1
< 0.1%
351000000 1
< 0.1%
349000000 1
< 0.1%
310000000 1
< 0.1%
278000000 1
< 0.1%
256000000 1
< 0.1%
239000000 1
< 0.1%

Interactions

2023-08-15T13:30:14.372906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-15T13:30:09.465724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-15T13:30:10.862680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-15T13:30:12.301546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-15T13:30:13.262436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-15T13:30:14.579145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-15T13:30:09.786083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-15T13:30:11.097874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-15T13:30:12.477302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-15T13:30:13.484430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-15T13:30:14.788974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-15T13:30:10.153649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-15T13:30:11.344119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-15T13:30:12.665817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-15T13:30:13.701622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-15T13:30:14.963379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-15T13:30:10.343347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-15T13:30:11.531103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-15T13:30:12.830747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-15T13:30:13.855672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-15T13:30:15.151341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-15T13:30:10.623555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-15T13:30:12.111511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-15T13:30:13.008003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-15T13:30:14.085975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-08-15T13:30:20.365617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
읍면동코드리코드토지구분본번부번주택공시가격
읍면동코드1.0000.3880.0440.2460.1520.093
리코드0.3881.0000.0450.3060.1370.066
토지구분0.0440.0451.0000.1900.0000.000
본번0.2460.3060.1901.0000.1720.000
부번0.1520.1370.0000.1721.0000.629
주택공시가격0.0930.0660.0000.0000.6291.000
2023-08-15T13:30:20.610274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
읍면동코드리코드본번부번주택공시가격토지구분
읍면동코드1.0000.040-0.024-0.009-0.0870.058
리코드0.0401.0000.040-0.124-0.0960.034
본번-0.0240.0401.000-0.0750.0210.145
부번-0.009-0.124-0.0751.0000.1720.000
주택공시가격-0.087-0.0960.0210.1721.0000.000
토지구분0.0580.0340.1450.0000.0001.000

Missing values

2023-08-15T13:30:15.435615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-08-15T13:30:15.657441image/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

시군구코드읍면동코드리코드토지구분본번부번주택공시가격
1593648850420241165026100000
9220488503602711096390000000
73534885035022130309510000
1256748850390231659118700000
1329648850400221136602940000
31994885031027175010100000
356488502502113495246600000
376148850320221463133200000
297548850310251998172700000
331148850310271471966200000
시군구코드읍면동코드리코드토지구분본번부번주택공시가격
4648850250211109562400000
71074885034027161509800000
919348850360271782121100000
878448850360251551180300000
12474488503902211043044600000
124248850250231363233900000
854948850360241115055000000
724448850350211617045300000
533948850320341521011400000
1085748850370271504031300000

Duplicate rows

Most frequently occurring

시군구코드읍면동코드리코드토지구분본번부번주택공시가격# duplicates
048850370211694471000002