Overview

Dataset statistics

Number of variables8
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory771.5 KiB
Average record size in memory79.0 B

Variable types

Numeric5
Categorical2
Text1

Dataset

Description충청남도 당진시에서 관리하는 개별주택가격 공시자료입니다 (일련번호, 시군구코드, 읍면동코드, 리코드, 토지구분, 본번, 부번)
URLhttps://www.data.go.kr/data/15013647/fileData.do

Alerts

시군구코드 has constant value ""Constant
일련번호 is highly overall correlated with 읍면동코드High correlation
읍면동코드 is highly overall correlated with 일련번호High correlation
토지구분 is highly imbalanced (85.5%)Imbalance
일련번호 has unique valuesUnique
리코드 has 1568 (15.7%) zerosZeros
부번 has 3168 (31.7%) zerosZeros

Reproduction

Analysis started2023-12-12 02:12:22.217033
Analysis finished2023-12-12 02:12:26.778299
Duration4.56 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

일련번호
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9275.2534
Minimum1
Maximum18462
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T11:12:26.878766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile920.9
Q14678.75
median9293.5
Q313904.25
95-th percentile17531.05
Maximum18462
Range18461
Interquartile range (IQR)9225.5

Descriptive statistics

Standard deviation5330.9054
Coefficient of variation (CV)0.57474499
Kurtosis-1.1938622
Mean9275.2534
Median Absolute Deviation (MAD)4612.5
Skewness-0.00920073
Sum92752534
Variance28418553
MonotonicityNot monotonic
2023-12-12T11:12:27.081983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4073 1
 
< 0.1%
5391 1
 
< 0.1%
6876 1
 
< 0.1%
584 1
 
< 0.1%
14479 1
 
< 0.1%
10463 1
 
< 0.1%
4094 1
 
< 0.1%
16237 1
 
< 0.1%
4059 1
 
< 0.1%
9831 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
6 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
11 1
< 0.1%
14 1
< 0.1%
16 1
< 0.1%
ValueCountFrequency (%)
18462 1
< 0.1%
18458 1
< 0.1%
18457 1
< 0.1%
18456 1
< 0.1%
18455 1
< 0.1%
18454 1
< 0.1%
18453 1
< 0.1%
18450 1
< 0.1%
18449 1
< 0.1%
18448 1
< 0.1%

시군구코드
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
44270
10000 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row44270
2nd row44270
3rd row44270
4th row44270
5th row44270

Common Values

ValueCountFrequency (%)
44270 10000
100.0%

Length

2023-12-12T11:12:27.268973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:12:27.363902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
44270 10000
100.0%

읍면동코드
Real number (ℝ)

HIGH CORRELATION 

Distinct22
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean290.2278
Minimum101
Maximum390
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T11:12:27.466055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum101
5-th percentile101
Q1253
median320
Q3360
95-th percentile390
Maximum390
Range289
Interquartile range (IQR)107

Descriptive statistics

Standard deviation92.001634
Coefficient of variation (CV)0.31699801
Kurtosis-0.085996965
Mean290.2278
Median Absolute Deviation (MAD)60
Skewness-1.050323
Sum2902278
Variance8464.3007
MonotonicityNot monotonic
2023-12-12T11:12:27.596650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
253 1238
12.4%
250 908
9.1%
320 888
8.9%
380 795
8.0%
310 765
 
7.6%
360 737
 
7.4%
390 706
 
7.1%
340 688
 
6.9%
370 687
 
6.9%
101 653
 
6.5%
Other values (12) 1935
19.4%
ValueCountFrequency (%)
101 653
6.5%
102 177
 
1.8%
103 74
 
0.7%
104 131
 
1.3%
105 100
 
1.0%
106 48
 
0.5%
107 173
 
1.7%
108 45
 
0.4%
109 65
 
0.7%
110 55
 
0.5%
ValueCountFrequency (%)
390 706
7.1%
380 795
8.0%
370 687
6.9%
360 737
7.4%
350 536
5.4%
340 688
6.9%
330 484
 
4.8%
320 888
8.9%
310 765
7.6%
253 1238
12.4%

리코드
Real number (ℝ)

ZEROS 

Distinct23
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.4099
Minimum0
Maximum42
Zeros1568
Zeros (%)15.7%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T11:12:27.732879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q121
median24
Q329
95-th percentile36
Maximum42
Range42
Interquartile range (IQR)8

Descriptive statistics

Standard deviation10.705529
Coefficient of variation (CV)0.47771425
Kurtosis0.46108483
Mean22.4099
Median Absolute Deviation (MAD)3
Skewness-1.0914094
Sum224099
Variance114.60834
MonotonicityNot monotonic
2023-12-12T11:12:27.868019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0 1568
15.7%
21 1403
14.0%
22 785
 
7.8%
23 758
 
7.6%
25 698
 
7.0%
24 665
 
6.7%
27 554
 
5.5%
26 537
 
5.4%
29 444
 
4.4%
31 441
 
4.4%
Other values (13) 2147
21.5%
ValueCountFrequency (%)
0 1568
15.7%
21 1403
14.0%
22 785
7.8%
23 758
7.6%
24 665
6.7%
25 698
7.0%
26 537
 
5.4%
27 554
 
5.5%
28 388
 
3.9%
29 444
 
4.4%
ValueCountFrequency (%)
42 104
 
1.0%
41 43
 
0.4%
40 17
 
0.2%
39 49
 
0.5%
38 110
 
1.1%
37 164
1.6%
36 85
 
0.9%
35 43
 
0.4%
34 194
1.9%
33 384
3.8%

토지구분
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
9793 
2
 
207

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 9793
97.9%
2 207
 
2.1%

Length

2023-12-12T11:12:28.011109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:12:28.109901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 9793
97.9%
2 207
 
2.1%

본번
Real number (ℝ)

Distinct1328
Distinct (%)13.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean407.8393
Minimum1
Maximum2374
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T11:12:28.228426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile29
Q1159
median320
Q3558
95-th percentile1088
Maximum2374
Range2373
Interquartile range (IQR)399

Descriptive statistics

Standard deviation340.35104
Coefficient of variation (CV)0.83452243
Kurtosis2.2278638
Mean407.8393
Median Absolute Deviation (MAD)184
Skewness1.4316411
Sum4078393
Variance115838.83
MonotonicityNot monotonic
2023-12-12T11:12:28.374946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 78
 
0.8%
194 60
 
0.6%
247 53
 
0.5%
219 42
 
0.4%
304 36
 
0.4%
349 34
 
0.3%
300 33
 
0.3%
263 33
 
0.3%
373 32
 
0.3%
145 31
 
0.3%
Other values (1318) 9568
95.7%
ValueCountFrequency (%)
1 78
0.8%
2 31
 
0.3%
3 21
 
0.2%
4 13
 
0.1%
5 19
 
0.2%
6 17
 
0.2%
7 12
 
0.1%
8 13
 
0.1%
9 9
 
0.1%
10 18
 
0.2%
ValueCountFrequency (%)
2374 1
< 0.1%
1891 1
< 0.1%
1845 1
< 0.1%
1841 2
< 0.1%
1826 1
< 0.1%
1812 1
< 0.1%
1800 1
< 0.1%
1785 1
< 0.1%
1777 1
< 0.1%
1776 1
< 0.1%

부번
Real number (ℝ)

ZEROS 

Distinct236
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.1576
Minimum0
Maximum1519
Zeros3168
Zeros (%)31.7%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T11:12:28.539436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q37
95-th percentile45
Maximum1519
Range1519
Interquartile range (IQR)7

Descriptive statistics

Standard deviation81.92066
Coefficient of variation (CV)5.786338
Kurtosis232.87203
Mean14.1576
Median Absolute Deviation (MAD)2
Skewness14.565036
Sum141576
Variance6710.9945
MonotonicityNot monotonic
2023-12-12T11:12:28.700080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3168
31.7%
1 1323
13.2%
2 1066
 
10.7%
3 631
 
6.3%
4 479
 
4.8%
5 361
 
3.6%
6 297
 
3.0%
7 229
 
2.3%
8 218
 
2.2%
10 163
 
1.6%
Other values (226) 2065
20.6%
ValueCountFrequency (%)
0 3168
31.7%
1 1323
13.2%
2 1066
 
10.7%
3 631
 
6.3%
4 479
 
4.8%
5 361
 
3.6%
6 297
 
3.0%
7 229
 
2.3%
8 218
 
2.2%
9 162
 
1.6%
ValueCountFrequency (%)
1519 1
< 0.1%
1484 1
< 0.1%
1477 1
< 0.1%
1466 1
< 0.1%
1465 1
< 0.1%
1464 1
< 0.1%
1463 1
< 0.1%
1457 1
< 0.1%
1456 1
< 0.1%
1455 1
< 0.1%

주택공시가격
Text

Distinct1696
Distinct (%)17.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T11:12:28.995652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length8
Mean length8.1648
Min length4

Characters and Unicode

Total characters81648
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique363 ?
Unique (%)3.6%

Sample

1st row34200000
2nd row17200000
3rd row162000000
4th row632000000
5th row19500000
ValueCountFrequency (%)
100000000 33
 
0.3%
108000000 33
 
0.3%
112000000 32
 
0.3%
103000000 31
 
0.3%
113000000 28
 
0.3%
104000000 28
 
0.3%
101000000 28
 
0.3%
109000000 26
 
0.3%
102000000 26
 
0.3%
119000000 25
 
0.3%
Other values (1685) 9683
97.1%
2023-12-12T11:12:29.852805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 53773
65.9%
1 4524
 
5.5%
3 3537
 
4.3%
2 3435
 
4.2%
4 3335
 
4.1%
5 2956
 
3.6%
6 2745
 
3.4%
7 2556
 
3.1%
8 2349
 
2.9%
9 2330
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 81540
99.9%
Space Separator 108
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 53773
65.9%
1 4524
 
5.5%
3 3537
 
4.3%
2 3435
 
4.2%
4 3335
 
4.1%
5 2956
 
3.6%
6 2745
 
3.4%
7 2556
 
3.1%
8 2349
 
2.9%
9 2330
 
2.9%
Space Separator
ValueCountFrequency (%)
108
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 81648
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 53773
65.9%
1 4524
 
5.5%
3 3537
 
4.3%
2 3435
 
4.2%
4 3335
 
4.1%
5 2956
 
3.6%
6 2745
 
3.4%
7 2556
 
3.1%
8 2349
 
2.9%
9 2330
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 81648
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 53773
65.9%
1 4524
 
5.5%
3 3537
 
4.3%
2 3435
 
4.2%
4 3335
 
4.1%
5 2956
 
3.6%
6 2745
 
3.4%
7 2556
 
3.1%
8 2349
 
2.9%
9 2330
 
2.9%

Interactions

2023-12-12T11:12:25.811115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:12:23.371873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:12:24.029780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:12:24.580026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:12:25.224402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:12:25.935550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:12:23.526484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:12:24.149789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:12:24.718393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:12:25.326892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:12:26.069380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:12:23.664611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:12:24.252115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:12:24.853750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:12:25.454767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:12:26.188280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:12:23.782045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:12:24.355576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:12:24.985707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:12:25.597875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:12:26.355316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:12:23.897300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:12:24.450872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:12:25.102688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:12:25.696417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T11:12:29.963735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일련번호읍면동코드리코드토지구분본번부번
일련번호1.0000.9960.7760.0930.3660.159
읍면동코드0.9961.0000.7030.0410.3600.132
리코드0.7760.7031.0000.0740.3670.253
토지구분0.0930.0410.0741.0000.1810.000
본번0.3660.3600.3670.1811.0000.092
부번0.1590.1320.2530.0000.0921.000
2023-12-12T11:12:30.088391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일련번호읍면동코드리코드본번부번토지구분
일련번호1.0000.9970.407-0.179-0.0620.071
읍면동코드0.9971.0000.362-0.181-0.0580.050
리코드0.4070.3621.000-0.2500.0260.054
본번-0.179-0.181-0.2501.000-0.1060.180
부번-0.062-0.0580.026-0.1061.0000.000
토지구분0.0710.0500.0540.1800.0001.000

Missing values

2023-12-12T11:12:26.544370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T11:12:26.711935image/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

일련번호시군구코드읍면동코드리코드토지구분본번부번주택공시가격
4072407344270250261436834200000
114121141344270340281391017200000
1303913040442703503014452162000000
83883944270101017530632000000
166281662944270380271475219500000
75727573442703102411091320900000
11567115684427034030151768620000
1324132544270102012495296000000
162591626044270380231494028900000
18311832442701040110292147000000
일련번호시군구코드읍면동코드리코드토지구분본번부번주택공시가격
114641146544270340291266023800000
140861408744270360281296213700000
7976797744270310271215024300000
8591859244270320221277177500000
10418104194427033025162250800000
258425854427010801396061300000
53153244270101014893182000000
8493849444270320211799122900000
11506115074427034030141019400000
1722917230442703902113171730100000