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
Author충청남도
URLhttps://alldam.chungnam.go.kr/index.chungnam?menuCd=DOM_000000201001001001&st=&cds=&orgCd=&apiType=&isOpen=Y&pageIndex=446&beforeMenuCd=DOM_000000201001001000&publicdatapk=15013647

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 (85.2%)Imbalance
일련번호 has unique valuesUnique
리코드 has 1590 (15.9%) zerosZeros
부번 has 3168 (31.7%) zerosZeros

Reproduction

Analysis started2024-01-09 22:49:47.056484
Analysis finished2024-01-09 22:49:49.984565
Duration2.93 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%
Mean63844.111
Minimum2
Maximum901081
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-10T07:49:50.050737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile1022.95
Q16056.75
median11748.5
Q317096.25
95-th percentile900156.1
Maximum901081
Range901079
Interquartile range (IQR)11039.5

Descriptive statistics

Standard deviation210543.53
Coefficient of variation (CV)3.2977752
Kurtosis11.852093
Mean63844.111
Median Absolute Deviation (MAD)5493
Skewness3.7195236
Sum6.3844111 × 108
Variance4.4328578 × 1010
MonotonicityNot monotonic
2024-01-10T07:49:50.162899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9226 1
 
< 0.1%
9052 1
 
< 0.1%
900583 1
 
< 0.1%
18792 1
 
< 0.1%
11227 1
 
< 0.1%
1025 1
 
< 0.1%
8044 1
 
< 0.1%
1870 1
 
< 0.1%
12113 1
 
< 0.1%
3450 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
2 1
< 0.1%
3 1
< 0.1%
6 1
< 0.1%
8 1
< 0.1%
11 1
< 0.1%
12 1
< 0.1%
13 1
< 0.1%
14 1
< 0.1%
15 1
< 0.1%
17 1
< 0.1%
ValueCountFrequency (%)
901081 1
< 0.1%
901080 1
< 0.1%
901078 1
< 0.1%
901077 1
< 0.1%
901076 1
< 0.1%
901075 1
< 0.1%
901074 1
< 0.1%
901073 1
< 0.1%
901071 1
< 0.1%
901070 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

2024-01-10T07:49:50.275486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T07:49:50.350666image/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%
Mean289.7195
Minimum101
Maximum390
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-10T07:49:50.439702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum101
5-th percentile101
Q1250
median320
Q3360
95-th percentile390
Maximum390
Range289
Interquartile range (IQR)110

Descriptive statistics

Standard deviation92.346461
Coefficient of variation (CV)0.31874437
Kurtosis-0.11576637
Mean289.7195
Median Absolute Deviation (MAD)60
Skewness-1.0421252
Sum2897195
Variance8527.8688
MonotonicityNot monotonic
2024-01-10T07:49:50.552297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
253 1213
12.1%
250 921
9.2%
320 914
9.1%
380 779
7.8%
310 761
 
7.6%
360 714
 
7.1%
390 706
 
7.1%
370 698
 
7.0%
340 692
 
6.9%
101 660
 
6.6%
Other values (12) 1942
19.4%
ValueCountFrequency (%)
101 660
6.6%
102 193
 
1.9%
103 84
 
0.8%
104 106
 
1.1%
105 101
 
1.0%
106 47
 
0.5%
107 186
 
1.9%
108 53
 
0.5%
109 66
 
0.7%
110 57
 
0.6%
ValueCountFrequency (%)
390 706
7.1%
380 779
7.8%
370 698
7.0%
360 714
7.1%
350 548
5.5%
340 692
6.9%
330 464
 
4.6%
320 914
9.1%
310 761
7.6%
253 1213
12.1%

리코드
Real number (ℝ)

ZEROS 

Distinct23
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.3466
Minimum0
Maximum42
Zeros1590
Zeros (%)15.9%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-10T07:49:50.664998image/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 deviation10.741611
Coefficient of variation (CV)0.48068212
Kurtosis0.41714244
Mean22.3466
Median Absolute Deviation (MAD)3
Skewness-1.0891805
Sum223466
Variance115.38221
MonotonicityNot monotonic
2024-01-10T07:49:50.755959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0 1590
15.9%
21 1392
13.9%
22 790
 
7.9%
23 778
 
7.8%
25 666
 
6.7%
24 656
 
6.6%
26 551
 
5.5%
27 544
 
5.4%
29 467
 
4.7%
31 450
 
4.5%
Other values (13) 2116
21.2%
ValueCountFrequency (%)
0 1590
15.9%
21 1392
13.9%
22 790
7.9%
23 778
7.8%
24 656
6.6%
25 666
6.7%
26 551
 
5.5%
27 544
 
5.4%
28 370
 
3.7%
29 467
 
4.7%
ValueCountFrequency (%)
42 91
 
0.9%
41 36
 
0.4%
40 27
 
0.3%
39 37
 
0.4%
38 120
 
1.2%
37 172
1.7%
36 76
 
0.8%
35 51
 
0.5%
34 183
1.8%
33 399
4.0%

토지구분
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
9788 
2
 
212

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 9788
97.9%
2 212
 
2.1%

Length

2024-01-10T07:49:50.851278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T07:49:50.925315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 9788
97.9%
2 212
 
2.1%

본번
Real number (ℝ)

Distinct1316
Distinct (%)13.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean409.6984
Minimum1
Maximum1832
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-10T07:49:51.009836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile30
Q1159
median321
Q3557
95-th percentile1091.05
Maximum1832
Range1831
Interquartile range (IQR)398

Descriptive statistics

Standard deviation342.21924
Coefficient of variation (CV)0.83529553
Kurtosis2.1054442
Mean409.6984
Median Absolute Deviation (MAD)185
Skewness1.4174054
Sum4096984
Variance117114.01
MonotonicityNot monotonic
2024-01-10T07:49:51.126371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
247 63
 
0.6%
1 63
 
0.6%
194 62
 
0.6%
366 35
 
0.4%
386 34
 
0.3%
304 33
 
0.3%
246 33
 
0.3%
24 31
 
0.3%
263 31
 
0.3%
92 30
 
0.3%
Other values (1306) 9585
95.9%
ValueCountFrequency (%)
1 63
0.6%
2 25
 
0.2%
3 27
0.3%
4 9
 
0.1%
5 14
 
0.1%
6 18
 
0.2%
7 9
 
0.1%
8 13
 
0.1%
9 12
 
0.1%
10 18
 
0.2%
ValueCountFrequency (%)
1832 1
< 0.1%
1819 1
< 0.1%
1803 1
< 0.1%
1799 1
< 0.1%
1777 1
< 0.1%
1776 1
< 0.1%
1765 1
< 0.1%
1762 1
< 0.1%
1748 1
< 0.1%
1746 1
< 0.1%

부번
Real number (ℝ)

ZEROS 

Distinct240
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.5829
Minimum0
Maximum1506
Zeros3168
Zeros (%)31.7%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-10T07:49:51.503435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q37
95-th percentile44.05
Maximum1506
Range1506
Interquartile range (IQR)7

Descriptive statistics

Standard deviation84.116307
Coefficient of variation (CV)5.7681467
Kurtosis223.20368
Mean14.5829
Median Absolute Deviation (MAD)2
Skewness14.26746
Sum145829
Variance7075.5531
MonotonicityNot monotonic
2024-01-10T07:49:51.613292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3168
31.7%
1 1356
13.6%
2 1043
 
10.4%
3 633
 
6.3%
4 466
 
4.7%
5 350
 
3.5%
6 315
 
3.1%
7 231
 
2.3%
8 191
 
1.9%
10 159
 
1.6%
Other values (230) 2088
20.9%
ValueCountFrequency (%)
0 3168
31.7%
1 1356
13.6%
2 1043
 
10.4%
3 633
 
6.3%
4 466
 
4.7%
5 350
 
3.5%
6 315
 
3.1%
7 231
 
2.3%
8 191
 
1.9%
9 153
 
1.5%
ValueCountFrequency (%)
1506 1
< 0.1%
1490 1
< 0.1%
1485 1
< 0.1%
1476 1
< 0.1%
1469 1
< 0.1%
1465 1
< 0.1%
1463 1
< 0.1%
1460 1
< 0.1%
1458 1
< 0.1%
1456 1
< 0.1%

주택공시가격
Text

Distinct1702
Distinct (%)17.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-01-10T07:49:51.822705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length11
Mean length11.1619
Min length5

Characters and Unicode

Total characters111619
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique357 ?
Unique (%)3.6%

Sample

1st row7980000.00
2nd row44300000.00
3rd row48100000.00
4th row51000000.00
5th row100000000.00
ValueCountFrequency (%)
100000000.00 42
 
0.4%
108000000.00 35
 
0.4%
104000000.00 30
 
0.3%
103000000.00 29
 
0.3%
113000000.00 27
 
0.3%
16300000.00 27
 
0.3%
107000000.00 26
 
0.3%
106000000.00 26
 
0.3%
123000000.00 24
 
0.2%
128000000.00 24
 
0.2%
Other values (1692) 9710
97.1%
2024-01-10T07:49:52.150611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 73753
66.1%
. 9977
 
8.9%
1 4428
 
4.0%
3 3521
 
3.2%
2 3445
 
3.1%
4 3349
 
3.0%
5 3041
 
2.7%
6 2741
 
2.5%
7 2578
 
2.3%
8 2377
 
2.1%
Other values (3) 2409
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 101527
91.0%
Other Punctuation 9977
 
8.9%
Space Separator 92
 
0.1%
Dash Punctuation 23
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 73753
72.6%
1 4428
 
4.4%
3 3521
 
3.5%
2 3445
 
3.4%
4 3349
 
3.3%
5 3041
 
3.0%
6 2741
 
2.7%
7 2578
 
2.5%
8 2377
 
2.3%
9 2294
 
2.3%
Other Punctuation
ValueCountFrequency (%)
. 9977
100.0%
Space Separator
ValueCountFrequency (%)
92
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 23
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 111619
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 73753
66.1%
. 9977
 
8.9%
1 4428
 
4.0%
3 3521
 
3.2%
2 3445
 
3.1%
4 3349
 
3.0%
5 3041
 
2.7%
6 2741
 
2.5%
7 2578
 
2.3%
8 2377
 
2.1%
Other values (3) 2409
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 111619
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 73753
66.1%
. 9977
 
8.9%
1 4428
 
4.0%
3 3521
 
3.2%
2 3445
 
3.1%
4 3349
 
3.0%
5 3041
 
2.7%
6 2741
 
2.5%
7 2578
 
2.3%
8 2377
 
2.1%
Other values (3) 2409
 
2.2%

Interactions

2024-01-10T07:49:49.389507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:49:47.728072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:49:48.139042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:49:48.559475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:49:48.971036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:49:49.467228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:49:47.809193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:49:48.215599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:49:48.634760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:49:49.055215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:49:49.550598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:49:47.898816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:49:48.304331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:49:48.712211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:49:49.137168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:49:49.630476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:49:47.968817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:49:48.385815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:49:48.786985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:49:49.217601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:49:49.719524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:49:48.049217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:49:48.478352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:49:48.886354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:49:49.299754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-10T07:49:52.254948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일련번호읍면동코드리코드토지구분본번부번
일련번호1.0000.0000.0000.0540.0420.000
읍면동코드0.0001.0000.7020.0440.4860.131
리코드0.0000.7021.0000.0840.3460.252
토지구분0.0540.0440.0841.0000.2820.000
본번0.0420.4860.3460.2821.0000.090
부번0.0000.1310.2520.0000.0901.000
2024-01-10T07:49:52.358858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일련번호읍면동코드리코드본번부번토지구분
일련번호1.0000.8840.370-0.162-0.0670.034
읍면동코드0.8841.0000.371-0.171-0.0610.054
리코드0.3700.3711.000-0.2400.0100.061
본번-0.162-0.171-0.2401.000-0.1050.216
부번-0.067-0.0610.010-0.1051.0000.000
토지구분0.0340.0540.0610.2160.0001.000

Missing values

2024-01-10T07:49:49.829826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-10T07:49:49.935242image/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
758592264427031024229187980000.00
179019924427010401764244300000.00
979511707442703202613738048100000.00
116301369144270340311561051000000.00
1578018326442703802111683100000000.00
1490617284442703702214161029500000.00
178702116544270390291621144000000.00
24512683442701070115850352000000.00
150911747344270370231192175100000.00
109671298844270340211409147600000.00
일련번호시군구코드읍면동코드리코드토지구분본번부번주택공시가격
32534844270101012442175300000.00
598173634427025335127083200000.00
85881040844270320221263647100000.00
146061697844270370211342561100000.00
110351306044270340221127016900000.00
17981213014427039030182057600000.00
532165714427025331118269000000.00
1212214212442703502111132153000000.00
3483744427010101272678100000.00
218823904427010701230172000000.00