Overview

Dataset statistics

Number of variables8
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory781.2 KiB
Average record size in memory80.0 B

Variable types

Categorical2
Numeric6

Dataset

Description시군구코드 읍면동코드 리코드 토지구분 본번 부번 동일련번호 주택공시가격
Author충청남도 태안군
URLhttps://www.data.go.kr/data/15013655/fileData.do

Alerts

시군구코드 has constant value ""Constant
토지구분 is highly imbalanced (94.6%)Imbalance
부번 has 3140 (31.4%) zerosZeros
동일련번호 has 3784 (37.8%) zerosZeros

Reproduction

Analysis started2024-04-17 14:43:59.148508
Analysis finished2024-04-17 14:44:03.131345
Duration3.98 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
44825
10000 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row44825
2nd row44825
3rd row44825
4th row44825
5th row44825

Common Values

ValueCountFrequency (%)
44825 10000
100.0%

Length

2024-04-17T23:44:03.177875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T23:44:03.243864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
44825 10000
100.0%

읍면동코드
Real number (ℝ)

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean299.7631
Minimum250
Maximum360
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-17T23:44:03.305730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum250
5-th percentile250
Q1253
median320
Q3340
95-th percentile360
Maximum360
Range110
Interquartile range (IQR)87

Descriptive statistics

Standard deviation42.545778
Coefficient of variation (CV)0.14193134
Kurtosis-1.7070327
Mean299.7631
Median Absolute Deviation (MAD)30
Skewness-0.10571967
Sum2997631
Variance1810.1432
MonotonicityNot monotonic
2024-04-17T23:44:03.381850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
250 2262
22.6%
253 1887
18.9%
320 1175
11.8%
340 1155
11.6%
330 1021
10.2%
350 991
9.9%
310 820
 
8.2%
360 689
 
6.9%
ValueCountFrequency (%)
250 2262
22.6%
253 1887
18.9%
310 820
 
8.2%
320 1175
11.8%
330 1021
10.2%
340 1155
11.6%
350 991
9.9%
360 689
 
6.9%
ValueCountFrequency (%)
360 689
 
6.9%
350 991
9.9%
340 1155
11.6%
330 1021
10.2%
320 1175
11.8%
310 820
 
8.2%
253 1887
18.9%
250 2262
22.6%

리코드
Real number (ℝ)

Distinct13
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.0237
Minimum21
Maximum33
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-17T23:44:03.469398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile21
Q122
median23
Q326
95-th percentile29
Maximum33
Range12
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.8491315
Coefficient of variation (CV)0.1185967
Kurtosis0.22614014
Mean24.0237
Median Absolute Deviation (MAD)2
Skewness0.92457763
Sum240237
Variance8.1175501
MonotonicityNot monotonic
2024-04-17T23:44:03.557818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
21 2264
22.6%
22 1612
16.1%
23 1583
15.8%
24 912
9.1%
26 773
 
7.7%
27 757
 
7.6%
25 710
 
7.1%
28 665
 
6.7%
29 296
 
3.0%
30 140
 
1.4%
Other values (3) 288
 
2.9%
ValueCountFrequency (%)
21 2264
22.6%
22 1612
16.1%
23 1583
15.8%
24 912
9.1%
25 710
 
7.1%
26 773
 
7.7%
27 757
 
7.6%
28 665
 
6.7%
29 296
 
3.0%
30 140
 
1.4%
ValueCountFrequency (%)
33 132
 
1.3%
32 49
 
0.5%
31 107
 
1.1%
30 140
 
1.4%
29 296
 
3.0%
28 665
6.7%
27 757
7.6%
26 773
7.7%
25 710
7.1%
24 912
9.1%

토지구분
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
9939 
2
 
61

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 9939
99.4%
2 61
 
0.6%

Length

2024-04-17T23:44:03.671982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T23:44:03.746964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 9939
99.4%
2 61
 
0.6%

본번
Real number (ℝ)

Distinct1551
Distinct (%)15.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean583.256
Minimum1
Maximum3483
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-17T23:44:03.828266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile42
Q1269
median481
Q3785
95-th percentile1370.05
Maximum3483
Range3482
Interquartile range (IQR)516

Descriptive statistics

Standard deviation467.76723
Coefficient of variation (CV)0.80199301
Kurtosis5.6693863
Mean583.256
Median Absolute Deviation (MAD)248
Skewness1.8178712
Sum5832560
Variance218806.19
MonotonicityNot monotonic
2024-04-17T23:44:03.943010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
339 71
 
0.7%
600 39
 
0.4%
1 38
 
0.4%
353 37
 
0.4%
1525 35
 
0.4%
63 34
 
0.3%
333 33
 
0.3%
8 32
 
0.3%
172 31
 
0.3%
680 31
 
0.3%
Other values (1541) 9619
96.2%
ValueCountFrequency (%)
1 38
0.4%
2 14
 
0.1%
3 5
 
0.1%
4 18
0.2%
5 16
0.2%
6 17
0.2%
7 6
 
0.1%
8 32
0.3%
9 11
 
0.1%
10 10
 
0.1%
ValueCountFrequency (%)
3483 1
< 0.1%
3456 1
< 0.1%
3452 1
< 0.1%
3446 1
< 0.1%
3282 2
< 0.1%
3271 2
< 0.1%
3268 1
< 0.1%
3267 1
< 0.1%
3266 1
< 0.1%
3265 1
< 0.1%

부번
Real number (ℝ)

ZEROS 

Distinct249
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.3117
Minimum0
Maximum645
Zeros3140
Zeros (%)31.4%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-17T23:44:04.065216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q38
95-th percentile52
Maximum645
Range645
Interquartile range (IQR)8

Descriptive statistics

Standard deviation40.989515
Coefficient of variation (CV)3.329314
Kurtosis82.024416
Mean12.3117
Median Absolute Deviation (MAD)2
Skewness7.9561189
Sum123117
Variance1680.1404
MonotonicityNot monotonic
2024-04-17T23:44:04.170613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3140
31.4%
1 1389
13.9%
2 954
 
9.5%
3 603
 
6.0%
4 488
 
4.9%
5 357
 
3.6%
6 285
 
2.9%
7 256
 
2.6%
8 196
 
2.0%
9 160
 
1.6%
Other values (239) 2172
21.7%
ValueCountFrequency (%)
0 3140
31.4%
1 1389
13.9%
2 954
 
9.5%
3 603
 
6.0%
4 488
 
4.9%
5 357
 
3.6%
6 285
 
2.9%
7 256
 
2.6%
8 196
 
2.0%
9 160
 
1.6%
ValueCountFrequency (%)
645 3
< 0.1%
638 2
 
< 0.1%
637 1
 
< 0.1%
564 1
 
< 0.1%
549 1
 
< 0.1%
519 1
 
< 0.1%
510 5
0.1%
509 1
 
< 0.1%
469 1
 
< 0.1%
467 1
 
< 0.1%

동일련번호
Real number (ℝ)

ZEROS 

Distinct16
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.7451
Minimum0
Maximum21
Zeros3784
Zeros (%)37.8%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-17T23:44:04.263731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile2
Maximum21
Range21
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.90233442
Coefficient of variation (CV)1.2110246
Kurtosis93.102139
Mean0.7451
Median Absolute Deviation (MAD)0
Skewness6.310187
Sum7451
Variance0.81420741
MonotonicityNot monotonic
2024-04-17T23:44:04.344803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
1 5573
55.7%
0 3784
37.8%
2 408
 
4.1%
3 108
 
1.1%
4 61
 
0.6%
5 29
 
0.3%
10 10
 
0.1%
6 9
 
0.1%
7 4
 
< 0.1%
8 4
 
< 0.1%
Other values (6) 10
 
0.1%
ValueCountFrequency (%)
0 3784
37.8%
1 5573
55.7%
2 408
 
4.1%
3 108
 
1.1%
4 61
 
0.6%
5 29
 
0.3%
6 9
 
0.1%
7 4
 
< 0.1%
8 4
 
< 0.1%
9 3
 
< 0.1%
ValueCountFrequency (%)
21 1
 
< 0.1%
20 2
 
< 0.1%
13 1
 
< 0.1%
12 1
 
< 0.1%
11 2
 
< 0.1%
10 10
0.1%
9 3
 
< 0.1%
8 4
 
< 0.1%
7 4
 
< 0.1%
6 9
0.1%

주택공시가격
Real number (ℝ)

Distinct1451
Distinct (%)14.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean59555583
Minimum1580000
Maximum6.13 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-17T23:44:04.446570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1580000
5-th percentile10400000
Q124000000
median44400000
Q375600000
95-th percentile1.53 × 108
Maximum6.13 × 108
Range6.1142 × 108
Interquartile range (IQR)51600000

Descriptive statistics

Standard deviation56565771
Coefficient of variation (CV)0.94979795
Kurtosis16.935426
Mean59555583
Median Absolute Deviation (MAD)23800000
Skewness3.2420416
Sum5.9555583 × 1011
Variance3.1996864 × 1015
MonotonicityNot monotonic
2024-04-17T23:44:04.558202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
102000000 38
 
0.4%
122000000 33
 
0.3%
11500000 32
 
0.3%
105000000 31
 
0.3%
104000000 30
 
0.3%
101000000 30
 
0.3%
100000000 30
 
0.3%
108000000 28
 
0.3%
106000000 27
 
0.3%
112000000 26
 
0.3%
Other values (1441) 9695
97.0%
ValueCountFrequency (%)
1580000 1
< 0.1%
2390000 1
< 0.1%
2460000 1
< 0.1%
2560000 1
< 0.1%
3170000 1
< 0.1%
3240000 1
< 0.1%
3330000 1
< 0.1%
3650000 2
< 0.1%
3660000 1
< 0.1%
3730000 1
< 0.1%
ValueCountFrequency (%)
613000000 1
< 0.1%
583000000 1
< 0.1%
568000000 1
< 0.1%
559000000 1
< 0.1%
525000000 1
< 0.1%
522000000 1
< 0.1%
520000000 1
< 0.1%
512000000 1
< 0.1%
506000000 1
< 0.1%
502000000 1
< 0.1%

Interactions

2024-04-17T23:44:02.504209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:43:59.930706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:44:00.408562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:44:01.105672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:44:01.580639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:44:02.037992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:44:02.578398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:43:59.998363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:44:00.486299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:44:01.180150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:44:01.654161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:44:02.107731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:44:02.655147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:44:00.076567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:44:00.559620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:44:01.259164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:44:01.727977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:44:02.178778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:44:02.733266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:44:00.150933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:44:00.641355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:44:01.341640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:44:01.809134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:44:02.264599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:44:02.810661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:44:00.239253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:44:00.734424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:44:01.417561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:44:01.881600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:44:02.337023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:44:02.887730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:44:00.314195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:44:01.033051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:44:01.493984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:44:01.959868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:44:02.412250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-17T23:44:04.637845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
읍면동코드리코드토지구분본번부번동일련번호주택공시가격
읍면동코드1.0000.5490.0710.3420.1680.0870.189
리코드0.5491.0000.0650.3390.1630.0530.125
토지구분0.0710.0651.0000.1330.0520.0740.056
본번0.3420.3390.1331.0000.1470.1330.177
부번0.1680.1630.0520.1471.0000.1910.200
동일련번호0.0870.0530.0740.1330.1911.0000.000
주택공시가격0.1890.1250.0560.1770.2000.0001.000
2024-04-17T23:44:04.726087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
읍면동코드리코드본번부번동일련번호주택공시가격토지구분
읍면동코드1.0000.243-0.036-0.222-0.192-0.1960.062
리코드0.2431.000-0.054-0.0900.029-0.1130.051
본번-0.036-0.0541.000-0.007-0.0640.0760.102
부번-0.222-0.090-0.0071.0000.1020.1860.040
동일련번호-0.1920.029-0.0640.1021.0000.1340.055
주택공시가격-0.196-0.1130.0760.1860.1341.0000.043
토지구분0.0620.0510.1020.0400.0550.0431.000

Missing values

2024-04-17T23:44:02.992322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-17T23:44:03.089364image/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

시군구코드읍면동코드리코드토지구분본번부번동일련번호주택공시가격
3503448252532319217039000000
1438448252502411160149800000
298448252502113611126700000
1664482525021132812155100000
54844482532023137617132400000
499344825310231167330158000000
243444825253211249541153000000
5778448253202514710125400000
5620448253202416597119200000
2485448252532113393211302000000
시군구코드읍면동코드리코드토지구분본번부번동일련번호주택공시가격
8636448253502216190012800000
1855448252502812762132900000
4297448253102113093019200000
3052448252532212123020500000
32264482525322111965021900000
7014482525021181220182000000
9273448253502912000012000000
9427448253503111444044500000
989644825360231519027150000
388844825253241118320131000000