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

Numeric6
Categorical1
Unsupported1

Dataset

Description경상남도 사천시 개별공시지가(2016년 1월 1일 기준 자료)의 자료입니다.(읍면동,리,토지구분,본번,부번, 결정지가)
Author경상남도 사천시
URLhttps://www.data.go.kr/data/15069086/fileData.do

Alerts

일련번호 is highly overall correlated with 법정동 and 2 other fieldsHigh correlation
법정동 is highly overall correlated with 일련번호 and 2 other fieldsHigh correlation
행정동 is highly overall correlated with 일련번호 and 2 other fieldsHigh correlation
리 is highly overall correlated with 일련번호 and 2 other fieldsHigh correlation
구분 is highly imbalanced (80.8%)Imbalance
일련번호 has unique valuesUnique
본번 is an unsupported type, check if it needs cleaning or further analysisUnsupported
행정동 has 4670 (46.7%) zerosZeros
리 has 5260 (52.6%) zerosZeros
부번 has 2390 (23.9%) zerosZeros

Reproduction

Analysis started2023-12-12 12:35:21.866000
Analysis finished2023-12-12 12:35:26.299592
Duration4.43 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%
Mean49771.009
Minimum5
Maximum99999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T21:35:26.376787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile4697.85
Q124634.75
median49607.5
Q374867.25
95-th percentile95155.2
Maximum99999
Range99994
Interquartile range (IQR)50232.5

Descriptive statistics

Standard deviation28988.72
Coefficient of variation (CV)0.58244188
Kurtosis-1.2008149
Mean49771.009
Median Absolute Deviation (MAD)25134.5
Skewness0.0081006804
Sum4.9771009 × 108
Variance8.4034588 × 108
MonotonicityNot monotonic
2023-12-12T21:35:26.509249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14363 1
 
< 0.1%
48448 1
 
< 0.1%
14005 1
 
< 0.1%
45819 1
 
< 0.1%
89287 1
 
< 0.1%
16422 1
 
< 0.1%
45732 1
 
< 0.1%
17374 1
 
< 0.1%
96019 1
 
< 0.1%
84567 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
5 1
< 0.1%
13 1
< 0.1%
15 1
< 0.1%
16 1
< 0.1%
23 1
< 0.1%
59 1
< 0.1%
78 1
< 0.1%
81 1
< 0.1%
93 1
< 0.1%
101 1
< 0.1%
ValueCountFrequency (%)
99999 1
< 0.1%
99996 1
< 0.1%
99987 1
< 0.1%
99973 1
< 0.1%
99971 1
< 0.1%
99949 1
< 0.1%
99940 1
< 0.1%
99936 1
< 0.1%
99934 1
< 0.1%
99915 1
< 0.1%

법정동
Real number (ℝ)

HIGH CORRELATION 

Distinct30
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean196.2687
Minimum101
Maximum320
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T21:35:26.645486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum101
5-th percentile103
Q1115
median127
Q3310
95-th percentile320
Maximum320
Range219
Interquartile range (IQR)195

Descriptive statistics

Standard deviation88.721524
Coefficient of variation (CV)0.45204113
Kurtosis-1.7418399
Mean196.2687
Median Absolute Deviation (MAD)25
Skewness0.25504898
Sum1962687
Variance7871.5089
MonotonicityNot monotonic
2023-12-12T21:35:26.751843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
310 1972
19.7%
250 1971
19.7%
320 797
 
8.0%
116 558
 
5.6%
127 438
 
4.4%
126 387
 
3.9%
118 282
 
2.8%
103 269
 
2.7%
109 268
 
2.7%
111 266
 
2.7%
Other values (20) 2792
27.9%
ValueCountFrequency (%)
101 165
1.7%
102 193
1.9%
103 269
2.7%
104 242
2.4%
105 37
 
0.4%
106 125
1.2%
107 183
1.8%
108 215
2.1%
109 268
2.7%
110 120
1.2%
ValueCountFrequency (%)
320 797
8.0%
310 1972
19.7%
250 1971
19.7%
127 438
 
4.4%
126 387
 
3.9%
125 77
 
0.8%
124 207
 
2.1%
123 192
 
1.9%
122 120
 
1.2%
121 158
 
1.6%

행정동
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct33
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean290.33
Minimum0
Maximum595
Zeros4670
Zeros (%)46.7%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T21:35:26.871386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median510
Q3570
95-th percentile595
Maximum595
Range595
Interquartile range (IQR)570

Descriptive statistics

Standard deviation275.9824
Coefficient of variation (CV)0.95058174
Kurtosis-1.954568
Mean290.33
Median Absolute Deviation (MAD)85
Skewness-0.071796371
Sum2903300
Variance76166.283
MonotonicityNot monotonic
2023-12-12T21:35:26.983508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
0 4670
46.7%
595 1397
 
14.0%
570 1255
 
12.6%
510 1100
 
11.0%
550 586
 
5.9%
520 569
 
5.7%
530 272
 
2.7%
310 48
 
0.5%
250 38
 
0.4%
108 10
 
0.1%
Other values (23) 55
 
0.5%
ValueCountFrequency (%)
0 4670
46.7%
101 2
 
< 0.1%
102 1
 
< 0.1%
103 5
 
0.1%
104 4
 
< 0.1%
106 1
 
< 0.1%
107 1
 
< 0.1%
108 10
 
0.1%
109 3
 
< 0.1%
110 1
 
< 0.1%
ValueCountFrequency (%)
595 1397
14.0%
570 1255
12.6%
550 586
5.9%
530 272
 
2.7%
520 569
5.7%
510 1100
11.0%
320 8
 
0.1%
310 48
 
0.5%
250 38
 
0.4%
127 3
 
< 0.1%

리
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.1528
Minimum0
Maximum31
Zeros5260
Zeros (%)52.6%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T21:35:27.091286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q325
95-th percentile30
Maximum31
Range31
Interquartile range (IQR)25

Descriptive statistics

Standard deviation12.99662
Coefficient of variation (CV)1.0694342
Kurtosis-1.8507139
Mean12.1528
Median Absolute Deviation (MAD)0
Skewness0.19256364
Sum121528
Variance168.91214
MonotonicityNot monotonic
2023-12-12T21:35:27.229354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 5260
52.6%
22 592
 
5.9%
21 570
 
5.7%
28 541
 
5.4%
24 487
 
4.9%
29 418
 
4.2%
25 405
 
4.0%
31 372
 
3.7%
30 365
 
3.6%
23 356
 
3.6%
Other values (2) 634
 
6.3%
ValueCountFrequency (%)
0 5260
52.6%
21 570
 
5.7%
22 592
 
5.9%
23 356
 
3.6%
24 487
 
4.9%
25 405
 
4.0%
26 337
 
3.4%
27 297
 
3.0%
28 541
 
5.4%
29 418
 
4.2%
ValueCountFrequency (%)
31 372
3.7%
30 365
3.6%
29 418
4.2%
28 541
5.4%
27 297
3.0%
26 337
3.4%
25 405
4.0%
24 487
4.9%
23 356
3.6%
22 592
5.9%

구분
Categorical

IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
9270 
2
 
721
5
 
8
4
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
1 9270
92.7%
2 721
 
7.2%
5 8
 
0.1%
4 1
 
< 0.1%

Length

2023-12-12T21:35:27.422105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T21:35:27.536073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 9270
92.7%
2 721
 
7.2%
5 8
 
0.1%
4 1
 
< 0.1%

본번
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size156.2 KiB

부번
Real number (ℝ)

ZEROS 

Distinct174
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.4717
Minimum0
Maximum441
Zeros2390
Zeros (%)23.9%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T21:35:27.651023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q37
95-th percentile34
Maximum441
Range441
Interquartile range (IQR)6

Descriptive statistics

Standard deviation25.356749
Coefficient of variation (CV)2.9931122
Kurtosis119.79065
Mean8.4717
Median Absolute Deviation (MAD)2
Skewness9.6418648
Sum84717
Variance642.9647
MonotonicityNot monotonic
2023-12-12T21:35:27.806134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2390
23.9%
1 1531
15.3%
2 1147
11.5%
3 852
 
8.5%
4 601
 
6.0%
5 498
 
5.0%
6 368
 
3.7%
7 271
 
2.7%
8 247
 
2.5%
9 182
 
1.8%
Other values (164) 1913
19.1%
ValueCountFrequency (%)
0 2390
23.9%
1 1531
15.3%
2 1147
11.5%
3 852
 
8.5%
4 601
 
6.0%
5 498
 
5.0%
6 368
 
3.7%
7 271
 
2.7%
8 247
 
2.5%
9 182
 
1.8%
ValueCountFrequency (%)
441 1
< 0.1%
438 1
< 0.1%
416 1
< 0.1%
396 1
< 0.1%
391 1
< 0.1%
386 1
< 0.1%
385 1
< 0.1%
384 1
< 0.1%
380 1
< 0.1%
375 1
< 0.1%

결정지가
Real number (ℝ)

Distinct3404
Distinct (%)34.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean143431.99
Minimum231
Maximum2627000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T21:35:28.246509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum231
5-th percentile2409.5
Q125100
median67700
Q3177600
95-th percentile505255
Maximum2627000
Range2626769
Interquartile range (IQR)152500

Descriptive statistics

Standard deviation217012.14
Coefficient of variation (CV)1.5129968
Kurtosis22.003839
Mean143431.99
Median Absolute Deviation (MAD)53300
Skewness3.9108402
Sum1.4343199 × 109
Variance4.709427 × 1010
MonotonicityNot monotonic
2023-12-12T21:35:28.381921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40800 37
 
0.4%
20200 29
 
0.3%
16200 27
 
0.3%
23600 27
 
0.3%
76200 25
 
0.2%
20800 23
 
0.2%
26200 21
 
0.2%
27500 21
 
0.2%
22800 21
 
0.2%
14200 21
 
0.2%
Other values (3394) 9748
97.5%
ValueCountFrequency (%)
231 1
 
< 0.1%
260 2
< 0.1%
294 3
< 0.1%
303 4
< 0.1%
306 1
 
< 0.1%
309 1
 
< 0.1%
311 1
 
< 0.1%
386 2
< 0.1%
389 2
< 0.1%
396 2
< 0.1%
ValueCountFrequency (%)
2627000 1
< 0.1%
2551000 1
< 0.1%
2345000 1
< 0.1%
2190000 1
< 0.1%
2099000 1
< 0.1%
2083000 1
< 0.1%
2079000 1
< 0.1%
2041000 1
< 0.1%
2038000 1
< 0.1%
1997000 1
< 0.1%

Interactions

2023-12-12T21:35:25.503176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:35:22.884570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:35:23.378171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:35:23.886186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:35:24.405721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:35:24.972964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:35:25.593658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:35:22.954979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:35:23.453509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:35:23.971275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:35:24.485826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:35:25.056381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:35:25.695331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:35:23.033426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:35:23.526445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:35:24.058773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:35:24.573051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:35:25.146293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:35:25.795583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:35:23.129482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:35:23.626486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:35:24.150688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:35:24.667421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:35:25.240350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:35:25.893715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:35:23.215352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:35:23.713028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:35:24.240172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:35:24.787279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:35:25.328764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:35:25.986063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:35:23.293099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:35:23.801353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:35:24.318836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:35:24.886131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:35:25.411300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T21:35:28.482286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일련번호법정동행정동리구분부번결정지가
일련번호1.0000.9760.7870.9280.1070.2770.404
법정동0.9761.0000.7550.6780.0980.0930.203
행정동0.7870.7551.0000.6560.0110.1390.156
리0.9280.6780.6561.0000.0900.1270.277
구분0.1070.0980.0110.0901.0000.0000.095
부번0.2770.0930.1390.1270.0001.0000.601
결정지가0.4040.2030.1560.2770.0950.6011.000
2023-12-12T21:35:28.620599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일련번호법정동행정동리부번결정지가구분
일련번호1.0000.948-0.6460.815-0.097-0.2470.064
법정동0.9481.000-0.7000.781-0.128-0.2390.039
행정동-0.646-0.7001.000-0.8530.0410.0950.007
리0.8150.781-0.8531.000-0.109-0.3020.074
부번-0.097-0.1280.041-0.1091.0000.2750.000
결정지가-0.247-0.2390.095-0.3020.2751.0000.057
구분0.0640.0390.0070.0740.0000.0571.000

Missing values

2023-12-12T21:35:26.114713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T21:35:26.251215image/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

일련번호법정동행정동리구분본번부번결정지가
143621436310652001329154300
11741175101510013764181500
300383003911357001315166500
2134121342108550014988381700
84196841973100271540611100
923429234332002111195108000
7013970140250031192316830
52944529452502502113001465700
57019570202500241870263000
840728407331002715082216900
일련번호법정동행정동리구분본번부번결정지가
38361383621225950189626200
39047390481225950218117500
1782417825104530012661290600
90864908653100301584217600
439464394712559501431923600
648536485425002822002290
385023850312259501190025500
83444834453100271267679900
800018000231002425301890
93726937273200211713061700