Overview

Dataset statistics

Number of variables7
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory673.8 KiB
Average record size in memory69.0 B

Variable types

Numeric5
Categorical2

Dataset

Description개별공시지가는 국토교통부장관이 매년 공시하는 표준지공시지가를 기준으로 구청장이 조사한 개별토지의 특성과 비교표준지의 특성을 비교하여 국토교통부장관이 작성·공급한 「표준지와 지가산정대상토지의 지가형성 요인에 관한 표준적인 비교표(토지가격비준표)」 상의 토지특성 차이에 따른 가격배율을 산출하고 이를 표준지공시지가에 곱하여 산정한 후 감정평가법인등의 검증을 받아 토지소유자 등의 의견수렴과 구 부동산가격공시위원회 심의 등의 절차를 거쳐 구청장이 결정·공시하는 개별토지의 당위면적당 가격(원/m2)
Author부산광역시 연제구
URLhttps://www.data.go.kr/data/15039887/fileData.do

Alerts

연번 is highly overall correlated with 행정동 and 2 other fieldsHigh correlation
행정동 is highly overall correlated with 연번 and 1 other fieldsHigh correlation
본번 is highly overall correlated with 연번High correlation
법정동 is highly overall correlated with 연번 and 1 other fieldsHigh correlation
구분 is highly imbalanced (85.5%)Imbalance
연번 has unique valuesUnique
부번 has 234 (2.3%) zerosZeros

Reproduction

Analysis started2023-10-09 19:13:47.595216
Analysis finished2023-10-09 19:13:56.377056
Duration8.78 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12523.117
Minimum5
Maximum24980
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-10-10T04:13:56.514939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile1211.75
Q16282.75
median12550.5
Q318755.25
95-th percentile23734.15
Maximum24980
Range24975
Interquartile range (IQR)12472.5

Descriptive statistics

Standard deviation7224.2514
Coefficient of variation (CV)0.57687328
Kurtosis-1.1977471
Mean12523.117
Median Absolute Deviation (MAD)6238
Skewness-0.011268923
Sum1.2523117 × 108
Variance52189809
MonotonicityNot monotonic
2023-10-10T04:13:56.803856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9283 1
 
< 0.1%
14059 1
 
< 0.1%
20092 1
 
< 0.1%
21469 1
 
< 0.1%
12268 1
 
< 0.1%
8629 1
 
< 0.1%
19265 1
 
< 0.1%
21334 1
 
< 0.1%
10405 1
 
< 0.1%
18435 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
5 1
< 0.1%
6 1
< 0.1%
10 1
< 0.1%
12 1
< 0.1%
13 1
< 0.1%
14 1
< 0.1%
19 1
< 0.1%
23 1
< 0.1%
26 1
< 0.1%
27 1
< 0.1%
ValueCountFrequency (%)
24980 1
< 0.1%
24979 1
< 0.1%
24977 1
< 0.1%
24976 1
< 0.1%
24975 1
< 0.1%
24974 1
< 0.1%
24971 1
< 0.1%
24970 1
< 0.1%
24969 1
< 0.1%
24967 1
< 0.1%

법정동
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
연산동
6726 
거제동
3274 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row연산동
2nd row거제동
3rd row연산동
4th row연산동
5th row연산동

Common Values

ValueCountFrequency (%)
연산동 6726
67.3%
거제동 3274
32.7%

Length

2023-10-10T04:13:57.042313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-10T04:13:57.217987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
연산동 6726
67.3%
거제동 3274
32.7%

행정동
Real number (ℝ)

HIGH CORRELATION 

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean668.367
Minimum610
Maximum730
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-10-10T04:13:57.384066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum610
5-th percentile610
Q1630
median670
Q3700
95-th percentile730
Maximum730
Range120
Interquartile range (IQR)70

Descriptive statistics

Standard deviation38.436585
Coefficient of variation (CV)0.057508202
Kurtosis-1.1846321
Mean668.367
Median Absolute Deviation (MAD)30
Skewness0.067259151
Sum6683670
Variance1477.371
MonotonicityNot monotonic
2023-10-10T04:13:57.662719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
700 1193
11.9%
620 1113
11.1%
730 1096
11.0%
670 856
8.6%
680 851
8.5%
610 848
8.5%
660 798
8.0%
690 718
7.2%
630 702
7.0%
720 635
6.3%
Other values (2) 1190
11.9%
ValueCountFrequency (%)
610 848
8.5%
620 1113
11.1%
630 702
7.0%
640 611
6.1%
650 579
5.8%
660 798
8.0%
670 856
8.6%
680 851
8.5%
690 718
7.2%
700 1193
11.9%
ValueCountFrequency (%)
730 1096
11.0%
720 635
6.3%
700 1193
11.9%
690 718
7.2%
680 851
8.5%
670 856
8.6%
660 798
8.0%
650 579
5.8%
640 611
6.1%
630 702
7.0%

구분
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
일반
9793 
산
 
207

Length

Max length2
Median length2
Mean length1.9793
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
일반 9793
97.9%
산 207
 
2.1%

Length

2023-10-10T04:13:57.932175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-10T04:13:58.334270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
일반 9793
97.9%
산 207
 
2.1%

본번
Real number (ℝ)

HIGH CORRELATION 

Distinct1305
Distinct (%)13.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean946.622
Minimum1
Maximum2380
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-10-10T04:13:58.658089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile70
Q1462
median777.5
Q31397
95-th percentile2063
Maximum2380
Range2379
Interquartile range (IQR)935

Descriptive statistics

Standard deviation626.12111
Coefficient of variation (CV)0.66142675
Kurtosis-0.88521538
Mean946.622
Median Absolute Deviation (MAD)432.5
Skewness0.5028971
Sum9466220
Variance392027.65
MonotonicityNot monotonic
2023-10-10T04:13:59.061324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1811 300
 
3.0%
2022 212
 
2.1%
676 149
 
1.5%
1876 94
 
0.9%
1941 92
 
0.9%
643 81
 
0.8%
802 70
 
0.7%
1135 60
 
0.6%
766 59
 
0.6%
815 59
 
0.6%
Other values (1295) 8824
88.2%
ValueCountFrequency (%)
1 38
0.4%
2 31
0.3%
3 2
 
< 0.1%
5 1
 
< 0.1%
6 1
 
< 0.1%
7 1
 
< 0.1%
8 1
 
< 0.1%
10 18
0.2%
11 8
 
0.1%
12 2
 
< 0.1%
ValueCountFrequency (%)
2380 1
< 0.1%
2379 1
< 0.1%
2376 1
< 0.1%
2375 1
< 0.1%
2373 1
< 0.1%
2372 1
< 0.1%
2370 1
< 0.1%
2364 1
< 0.1%
2361 1
< 0.1%
2360 1
< 0.1%

부번
Real number (ℝ)

ZEROS 

Distinct595
Distinct (%)5.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52.5289
Minimum0
Maximum891
Zeros234
Zeros (%)2.3%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-10-10T04:13:59.379879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q17
median20
Q344.25
95-th percentile232
Maximum891
Range891
Interquartile range (IQR)37.25

Descriptive statistics

Standard deviation107.47058
Coefficient of variation (CV)2.0459324
Kurtosis21.656355
Mean52.5289
Median Absolute Deviation (MAD)15
Skewness4.3453893
Sum525289
Variance11549.926
MonotonicityNot monotonic
2023-10-10T04:13:59.755866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 423
 
4.2%
2 392
 
3.9%
3 351
 
3.5%
4 327
 
3.3%
5 297
 
3.0%
6 289
 
2.9%
9 250
 
2.5%
7 244
 
2.4%
0 234
 
2.3%
8 229
 
2.3%
Other values (585) 6964
69.6%
ValueCountFrequency (%)
0 234
2.3%
1 423
4.2%
2 392
3.9%
3 351
3.5%
4 327
3.3%
5 297
3.0%
6 289
2.9%
7 244
2.4%
8 229
2.3%
9 250
2.5%
ValueCountFrequency (%)
891 1
< 0.1%
890 1
< 0.1%
889 1
< 0.1%
881 1
< 0.1%
879 1
< 0.1%
870 1
< 0.1%
869 1
< 0.1%
868 1
< 0.1%
864 1
< 0.1%
860 1
< 0.1%

결정지가
Real number (ℝ)

Distinct2691
Distinct (%)26.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1637967.8
Minimum1480
Maximum17580000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-10-10T04:14:00.022712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1480
5-th percentile397300
Q1990400
median1433000
Q31883000
95-th percentile3650300
Maximum17580000
Range17578520
Interquartile range (IQR)892600

Descriptive statistics

Standard deviation1169309.5
Coefficient of variation (CV)0.71387818
Kurtosis29.071125
Mean1637967.8
Median Absolute Deviation (MAD)450000
Skewness3.697671
Sum1.6379678 × 1010
Variance1.3672847 × 1012
MonotonicityNot monotonic
2023-10-10T04:14:00.476143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1883000 767
 
7.7%
1433000 256
 
2.6%
1836000 78
 
0.8%
733200 64
 
0.6%
709100 62
 
0.6%
549100 47
 
0.5%
816000 44
 
0.4%
2609000 38
 
0.4%
2223000 38
 
0.4%
1244000 35
 
0.4%
Other values (2681) 8571
85.7%
ValueCountFrequency (%)
1480 3
 
< 0.1%
1820 1
 
< 0.1%
1960 3
 
< 0.1%
2020 1
 
< 0.1%
2030 1
 
< 0.1%
3000 1
 
< 0.1%
3100 1
 
< 0.1%
3200 8
0.1%
4270 1
 
< 0.1%
4440 1
 
< 0.1%
ValueCountFrequency (%)
17580000 1
< 0.1%
16140000 2
< 0.1%
15990000 1
< 0.1%
15980000 2
< 0.1%
15520000 1
< 0.1%
14860000 1
< 0.1%
14410000 1
< 0.1%
11990000 1
< 0.1%
11750000 1
< 0.1%
11610000 2
< 0.1%

Interactions

2023-10-10T04:13:54.716919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-10T04:13:49.914706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-10T04:13:51.524896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-10T04:13:52.581135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-10T04:13:53.591605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-10T04:13:54.978654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-10T04:13:50.120109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-10T04:13:51.741188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-10T04:13:52.772997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-10T04:13:53.923895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-10T04:13:55.211328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-10T04:13:50.373638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-10T04:13:51.922738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-10T04:13:52.985468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-10T04:13:54.147948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-10T04:13:55.412236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-10T04:13:50.623331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-10T04:13:52.132297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-10T04:13:53.176095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-10T04:13:54.324142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-10T04:13:55.682230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-10T04:13:51.316742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-10T04:13:52.338905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-10T04:13:53.361204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-10T04:13:54.496053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-10-10T04:14:00.769270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번법정동행정동구분본번부번결정지가
연번1.0000.9970.8630.3870.9570.4840.314
법정동0.9971.0001.0000.0330.5780.1130.207
행정동0.8631.0001.0000.1230.7420.3650.466
구분0.3870.0330.1231.0000.5340.0580.072
본번0.9570.5780.7420.5341.0000.5090.285
부번0.4840.1130.3650.0580.5091.0000.119
결정지가0.3140.2070.4660.0720.2850.1191.000
2023-10-10T04:14:01.125908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분법정동
구분1.0000.021
법정동0.0211.000
2023-10-10T04:14:01.403852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번행정동본번부번결정지가법정동구분
연번1.0000.5980.6830.064-0.1280.9530.297
행정동0.5981.0000.0870.033-0.0941.0000.081
본번0.6830.0871.0000.058-0.0550.4460.412
부번0.0640.0330.0581.000-0.1550.0860.045
결정지가-0.128-0.094-0.055-0.1551.0000.2070.073
법정동0.9531.0000.4460.0860.2071.0000.021
구분0.2970.0810.4120.0450.0730.0211.000

Missing values

2023-10-10T04:13:56.067995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-10-10T04:13:56.283060image/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

연번법정동행정동구분본번부번결정지가
92829283연산동650일반305421042000
74417442거제동620일반14050961800
94589459연산동650일반312183004000
2315323154연산동670일반2022594463300
1840118402연산동660일반1306331113000
1150711508연산동730일반41448677100
71797180거제동620일반125691883000
22042205거제동630일반488103757000
2147321474연산동670일반1875751433000
2493024931연산동660산18185410
연번법정동행정동구분본번부번결정지가
1841218413연산동690일반130653754000
1899218993연산동690일반138632922000
660661거제동610일반4427551700
1884918850연산동690일반1358172501000
2270622707연산동670일반2022501433000
1786117862연산동680일반1233161183000
1961219613연산동660일반158872659000
37893790거제동640일반71429302900
84468447연산동730일반2299894700
71157116거제동620일반1253631883000