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 (86.4%)Imbalance
일련번호 has unique valuesUnique
리코드 has 1636 (16.4%) zerosZeros
부번 has 3181 (31.8%) zerosZeros

Reproduction

Analysis started2024-01-09 22:49:40.833130
Analysis finished2024-01-09 22:49:43.682268
Duration2.85 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%
Mean9211.2099
Minimum1
Maximum18464
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-10T07:49:43.740853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile894.85
Q14478.75
median9210
Q313899.25
95-th percentile17555.1
Maximum18464
Range18463
Interquartile range (IQR)9420.5

Descriptive statistics

Standard deviation5386.2172
Coefficient of variation (CV)0.58474589
Kurtosis-1.2230973
Mean9211.2099
Median Absolute Deviation (MAD)4705.5
Skewness0.0059573087
Sum92112099
Variance29011335
MonotonicityNot monotonic
2024-01-10T07:49:43.854926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2355 1
 
< 0.1%
13798 1
 
< 0.1%
14762 1
 
< 0.1%
14188 1
 
< 0.1%
14259 1
 
< 0.1%
16877 1
 
< 0.1%
1724 1
 
< 0.1%
5450 1
 
< 0.1%
13895 1
 
< 0.1%
10717 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
11 1
< 0.1%
12 1
< 0.1%
17 1
< 0.1%
19 1
< 0.1%
ValueCountFrequency (%)
18464 1
< 0.1%
18462 1
< 0.1%
18460 1
< 0.1%
18459 1
< 0.1%
18458 1
< 0.1%
18457 1
< 0.1%
18456 1
< 0.1%
18453 1
< 0.1%
18450 1
< 0.1%
18449 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

2024-01-10T07:49:43.991399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T07:49:44.077923image/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%
Mean288.5994
Minimum101
Maximum390
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-10T07:49:44.169638image/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 deviation93.246093
Coefficient of variation (CV)0.32309871
Kurtosis-0.2030764
Mean288.5994
Median Absolute Deviation (MAD)60
Skewness-1.010251
Sum2885994
Variance8694.8338
MonotonicityNot monotonic
2024-01-10T07:49:44.268259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
253 1228
12.3%
250 943
9.4%
320 837
8.4%
380 795
8.0%
310 746
 
7.5%
390 715
 
7.1%
360 712
 
7.1%
340 700
 
7.0%
370 687
 
6.9%
101 679
 
6.8%
Other values (12) 1958
19.6%
ValueCountFrequency (%)
101 679
6.8%
102 174
 
1.7%
103 80
 
0.8%
104 117
 
1.2%
105 112
 
1.1%
106 64
 
0.6%
107 191
 
1.9%
108 54
 
0.5%
109 65
 
0.7%
110 63
 
0.6%
ValueCountFrequency (%)
390 715
7.1%
380 795
8.0%
370 687
6.9%
360 712
7.1%
350 550
5.5%
340 700
7.0%
330 451
 
4.5%
320 837
8.4%
310 746
7.5%
253 1228
12.3%

리코드
Real number (ℝ)

ZEROS 

Distinct23
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.2674
Minimum0
Maximum42
Zeros1636
Zeros (%)16.4%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-10T07:49:44.389952image/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 deviation10.865988
Coefficient of variation (CV)0.48797742
Kurtosis0.32456939
Mean22.2674
Median Absolute Deviation (MAD)3
Skewness-1.0647418
Sum222674
Variance118.0697
MonotonicityNot monotonic
2024-01-10T07:49:44.491864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0 1636
16.4%
21 1380
13.8%
22 795
 
8.0%
23 726
 
7.3%
24 671
 
6.7%
25 636
 
6.4%
27 546
 
5.5%
26 527
 
5.3%
31 465
 
4.7%
29 459
 
4.6%
Other values (13) 2159
21.6%
ValueCountFrequency (%)
0 1636
16.4%
21 1380
13.8%
22 795
8.0%
23 726
7.3%
24 671
6.7%
25 636
 
6.4%
26 527
 
5.3%
27 546
 
5.5%
28 416
 
4.2%
29 459
 
4.6%
ValueCountFrequency (%)
42 104
 
1.0%
41 40
 
0.4%
40 18
 
0.2%
39 36
 
0.4%
38 122
 
1.2%
37 165
1.7%
36 79
 
0.8%
35 60
 
0.6%
34 168
1.7%
33 393
3.9%

토지구분
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
9809 
2
 
191

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 9809
98.1%
2 191
 
1.9%

Length

2024-01-10T07:49:44.590418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T07:49:44.666406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 9809
98.1%
2 191
 
1.9%

본번
Real number (ℝ)

Distinct1329
Distinct (%)13.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean407.8834
Minimum1
Maximum2374
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-10T07:49:44.755075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile28
Q1157
median322
Q3557
95-th percentile1087
Maximum2374
Range2373
Interquartile range (IQR)400

Descriptive statistics

Standard deviation340.15052
Coefficient of variation (CV)0.83394059
Kurtosis2.1515759
Mean407.8834
Median Absolute Deviation (MAD)186
Skewness1.41107
Sum4078834
Variance115702.38
MonotonicityNot monotonic
2024-01-10T07:49:45.219890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 84
 
0.8%
194 60
 
0.6%
247 58
 
0.6%
92 34
 
0.3%
273 34
 
0.3%
219 33
 
0.3%
2 32
 
0.3%
255 32
 
0.3%
153 32
 
0.3%
304 32
 
0.3%
Other values (1319) 9569
95.7%
ValueCountFrequency (%)
1 84
0.8%
2 32
 
0.3%
3 24
 
0.2%
4 15
 
0.1%
5 11
 
0.1%
6 16
 
0.2%
7 15
 
0.1%
8 13
 
0.1%
9 13
 
0.1%
10 13
 
0.1%
ValueCountFrequency (%)
2374 1
< 0.1%
1891 1
< 0.1%
1832 1
< 0.1%
1812 1
< 0.1%
1785 1
< 0.1%
1777 1
< 0.1%
1776 1
< 0.1%
1771 1
< 0.1%
1765 1
< 0.1%
1752 1
< 0.1%

부번
Real number (ℝ)

ZEROS 

Distinct243
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.26
Minimum0
Maximum1519
Zeros3181
Zeros (%)31.8%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-10T07:49:45.336291image/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 deviation91.587417
Coefficient of variation (CV)6.0017967
Kurtosis197.52242
Mean15.26
Median Absolute Deviation (MAD)2
Skewness13.564635
Sum152600
Variance8388.255
MonotonicityNot monotonic
2024-01-10T07:49:45.463480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3181
31.8%
1 1367
13.7%
2 1057
 
10.6%
3 612
 
6.1%
4 467
 
4.7%
5 353
 
3.5%
6 297
 
3.0%
7 239
 
2.4%
8 218
 
2.2%
10 177
 
1.8%
Other values (233) 2032
20.3%
ValueCountFrequency (%)
0 3181
31.8%
1 1367
13.7%
2 1057
 
10.6%
3 612
 
6.1%
4 467
 
4.7%
5 353
 
3.5%
6 297
 
3.0%
7 239
 
2.4%
8 218
 
2.2%
9 158
 
1.6%
ValueCountFrequency (%)
1519 1
< 0.1%
1510 1
< 0.1%
1506 1
< 0.1%
1490 1
< 0.1%
1484 1
< 0.1%
1477 1
< 0.1%
1476 1
< 0.1%
1466 1
< 0.1%
1465 1
< 0.1%
1464 1
< 0.1%

주택공시가격
Text

Distinct1688
Distinct (%)16.9%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-01-10T07:49:45.711772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length8
Mean length8.1685
Min length4

Characters and Unicode

Total characters81685
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

Unique361 ?
Unique (%)3.6%

Sample

1st row266000000
2nd row45600000
3rd row382000000
4th row21200000
5th row12200000
ValueCountFrequency (%)
100000000 38
 
0.4%
104000000 33
 
0.3%
103000000 32
 
0.3%
108000000 32
 
0.3%
101000000 30
 
0.3%
132000000 29
 
0.3%
106000000 27
 
0.3%
109000000 26
 
0.3%
112000000 24
 
0.2%
41900000 23
 
0.2%
Other values (1677) 9684
97.1%
2024-01-10T07:49:46.066166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 53818
65.9%
1 4439
 
5.4%
3 3524
 
4.3%
2 3430
 
4.2%
4 3358
 
4.1%
5 3070
 
3.8%
6 2710
 
3.3%
7 2507
 
3.1%
8 2394
 
2.9%
9 2347
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 81597
99.9%
Space Separator 88
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 53818
66.0%
1 4439
 
5.4%
3 3524
 
4.3%
2 3430
 
4.2%
4 3358
 
4.1%
5 3070
 
3.8%
6 2710
 
3.3%
7 2507
 
3.1%
8 2394
 
2.9%
9 2347
 
2.9%
Space Separator
ValueCountFrequency (%)
88
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 81685
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 53818
65.9%
1 4439
 
5.4%
3 3524
 
4.3%
2 3430
 
4.2%
4 3358
 
4.1%
5 3070
 
3.8%
6 2710
 
3.3%
7 2507
 
3.1%
8 2394
 
2.9%
9 2347
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 81685
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 53818
65.9%
1 4439
 
5.4%
3 3524
 
4.3%
2 3430
 
4.2%
4 3358
 
4.1%
5 3070
 
3.8%
6 2710
 
3.3%
7 2507
 
3.1%
8 2394
 
2.9%
9 2347
 
2.9%

Interactions

2024-01-10T07:49:43.097692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:49:41.485622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:49:41.897417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:49:42.303066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:49:42.704986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:49:43.209529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:49:41.564406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:49:41.992225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:49:42.383577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:49:42.790584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:49:43.293338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:49:41.646126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:49:42.073940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:49:42.462897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:49:42.871678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:49:43.367248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:49:41.718883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:49:42.149987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:49:42.540732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:49:42.944926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:49:43.437956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:49:41.798365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:49:42.220320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:49:42.618333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:49:43.012991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-10T07:49:46.155412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일련번호읍면동코드리코드토지구분본번부번
일련번호1.0000.9960.7740.0930.3490.177
읍면동코드0.9961.0000.7010.0430.3520.136
리코드0.7740.7011.0000.0700.3610.260
토지구분0.0930.0430.0701.0000.1800.000
본번0.3490.3520.3610.1801.0000.101
부번0.1770.1360.2600.0000.1011.000
2024-01-10T07:49:46.245586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일련번호읍면동코드리코드본번부번토지구분
일련번호1.0000.9970.433-0.168-0.0550.072
읍면동코드0.9971.0000.389-0.171-0.0500.053
리코드0.4330.3891.000-0.2440.0150.050
본번-0.168-0.171-0.2441.000-0.1140.180
부번-0.055-0.0500.015-0.1141.0000.000
토지구분0.0720.0530.0500.1800.0001.000

Missing values

2024-01-10T07:49:43.540099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-10T07:49:43.637556image/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
235423554427010701118649266000000
176131761444270390251360345600000
97809781442703202613714382000000
121811218244270350211556221200000
8563856444270320221186012200000
9435943644270320251704100000000
17698176994427039026131528640000
10680106814427033028173216600000
165741657544270380271312028200000
201520164427010501700625100000
일련번호시군구코드읍면동코드리코드토지구분본번부번주택공시가격
17135171364427038033135077200000
8656865744270320221828028300000
114501145144270340291124015300000
123851238644270350221580144400000
982598264427032027167257500000
9869987044270320271285017100000
181091811044270390311118252400000
24542455442701070115900311000000
75637564442703102411039430500000
19819944270101012054121000000