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://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=15069086

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 (75.6%)Imbalance
일련번호 has unique valuesUnique
본번 is an unsupported type, check if it needs cleaning or further analysisUnsupported
행정동 has 4727 (47.3%) zerosZeros
리 has 5205 (52.0%) zerosZeros
부번 has 2374 (23.7%) zerosZeros

Reproduction

Analysis started2023-12-10 23:35:26.234313
Analysis finished2023-12-10 23:35:31.712915
Duration5.48 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%
Mean50083.654
Minimum8
Maximum99998
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T08:35:31.806544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile4766.75
Q125276
median50123
Q375014.25
95-th percentile95087.5
Maximum99998
Range99990
Interquartile range (IQR)49738.25

Descriptive statistics

Standard deviation28934.746
Coefficient of variation (CV)0.57772833
Kurtosis-1.192026
Mean50083.654
Median Absolute Deviation (MAD)24854.5
Skewness-0.0082440493
Sum5.0083654 × 108
Variance8.3721952 × 108
MonotonicityNot monotonic
2023-12-11T08:35:31.982351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
91023 1
 
< 0.1%
53845 1
 
< 0.1%
25870 1
 
< 0.1%
80800 1
 
< 0.1%
92709 1
 
< 0.1%
25566 1
 
< 0.1%
51885 1
 
< 0.1%
48079 1
 
< 0.1%
51223 1
 
< 0.1%
2760 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
8 1
< 0.1%
26 1
< 0.1%
29 1
< 0.1%
33 1
< 0.1%
38 1
< 0.1%
42 1
< 0.1%
45 1
< 0.1%
46 1
< 0.1%
49 1
< 0.1%
64 1
< 0.1%
ValueCountFrequency (%)
99998 1
< 0.1%
99997 1
< 0.1%
99990 1
< 0.1%
99984 1
< 0.1%
99967 1
< 0.1%
99933 1
< 0.1%
99928 1
< 0.1%
99922 1
< 0.1%
99917 1
< 0.1%
99911 1
< 0.1%

법정동
Real number (ℝ)

HIGH CORRELATION 

Distinct30
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean197.2511
Minimum101
Maximum320
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T08:35:32.137939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum101
5-th percentile103
Q1116
median127
Q3310
95-th percentile320
Maximum320
Range219
Interquartile range (IQR)194

Descriptive statistics

Standard deviation88.728586
Coefficient of variation (CV)0.44982556
Kurtosis-1.7500661
Mean197.2511
Median Absolute Deviation (MAD)25
Skewness0.23514505
Sum1972511
Variance7872.7619
MonotonicityNot monotonic
2023-12-11T08:35:32.275685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
250 1997
20.0%
310 1989
19.9%
320 809
 
8.1%
116 536
 
5.4%
126 421
 
4.2%
127 414
 
4.1%
118 310
 
3.1%
111 283
 
2.8%
103 275
 
2.8%
109 254
 
2.5%
Other values (20) 2712
27.1%
ValueCountFrequency (%)
101 141
1.4%
102 201
2.0%
103 275
2.8%
104 215
2.1%
105 35
 
0.4%
106 124
1.2%
107 173
1.7%
108 220
2.2%
109 254
2.5%
110 117
1.2%
ValueCountFrequency (%)
320 809
8.1%
310 1989
19.9%
250 1997
20.0%
127 414
 
4.1%
126 421
 
4.2%
125 101
 
1.0%
124 219
 
2.2%
123 184
 
1.8%
122 96
 
1.0%
121 114
 
1.1%

행정동
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct33
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean287.6657
Minimum0
Maximum595
Zeros4727
Zeros (%)47.3%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T08:35:32.388722image/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 deviation276.44369
Coefficient of variation (CV)0.96098942
Kurtosis-1.9582512
Mean287.6657
Median Absolute Deviation (MAD)85
Skewness-0.051022444
Sum2876657
Variance76421.116
MonotonicityNot monotonic
2023-12-11T08:35:32.546371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
0 4727
47.3%
595 1417
 
14.2%
570 1246
 
12.5%
510 1082
 
10.8%
550 580
 
5.8%
520 559
 
5.6%
530 246
 
2.5%
310 43
 
0.4%
250 35
 
0.4%
320 10
 
0.1%
Other values (23) 55
 
0.5%
ValueCountFrequency (%)
0 4727
47.3%
101 3
 
< 0.1%
102 3
 
< 0.1%
103 10
 
0.1%
104 2
 
< 0.1%
106 1
 
< 0.1%
107 1
 
< 0.1%
108 3
 
< 0.1%
109 3
 
< 0.1%
110 3
 
< 0.1%
ValueCountFrequency (%)
595 1417
14.2%
570 1246
12.5%
550 580
5.8%
530 246
 
2.5%
520 559
 
5.6%
510 1082
10.8%
320 10
 
0.1%
310 43
 
0.4%
250 35
 
0.4%
127 2
 
< 0.1%

리
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.2764
Minimum0
Maximum31
Zeros5205
Zeros (%)52.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T08:35:32.694073image/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 deviation12.990831
Coefficient of variation (CV)1.0581954
Kurtosis-1.8543302
Mean12.2764
Median Absolute Deviation (MAD)0
Skewness0.17319846
Sum122764
Variance168.76168
MonotonicityNot monotonic
2023-12-11T08:35:32.824371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 5205
52.0%
22 644
 
6.4%
21 592
 
5.9%
28 534
 
5.3%
24 487
 
4.9%
29 417
 
4.2%
25 413
 
4.1%
30 387
 
3.9%
31 375
 
3.8%
23 337
 
3.4%
Other values (2) 609
 
6.1%
ValueCountFrequency (%)
0 5205
52.0%
21 592
 
5.9%
22 644
 
6.4%
23 337
 
3.4%
24 487
 
4.9%
25 413
 
4.1%
26 323
 
3.2%
27 286
 
2.9%
28 534
 
5.3%
29 417
 
4.2%
ValueCountFrequency (%)
31 375
3.8%
30 387
3.9%
29 417
4.2%
28 534
5.3%
27 286
2.9%
26 323
3.2%
25 413
4.1%
24 487
4.9%
23 337
3.4%
22 644
6.4%

구분
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
9249 
2
 
749
5
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
1 9249
92.5%
2 749
 
7.5%
5 2
 
< 0.1%

Length

2023-12-11T08:35:32.943346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:35:33.038608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 9249
92.5%
2 749
 
7.5%
5 2
 
< 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%
Mean8.2231
Minimum0
Maximum442
Zeros2374
Zeros (%)23.7%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T08:35:33.146216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q37
95-th percentile31
Maximum442
Range442
Interquartile range (IQR)6

Descriptive statistics

Standard deviation25.247936
Coefficient of variation (CV)3.0703671
Kurtosis136.22033
Mean8.2231
Median Absolute Deviation (MAD)2
Skewness10.263602
Sum82231
Variance637.45827
MonotonicityNot monotonic
2023-12-11T08:35:33.296185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2374
23.7%
1 1511
15.1%
2 1231
12.3%
3 842
 
8.4%
4 591
 
5.9%
5 467
 
4.7%
6 356
 
3.6%
7 309
 
3.1%
8 246
 
2.5%
9 209
 
2.1%
Other values (164) 1864
18.6%
ValueCountFrequency (%)
0 2374
23.7%
1 1511
15.1%
2 1231
12.3%
3 842
 
8.4%
4 591
 
5.9%
5 467
 
4.7%
6 356
 
3.6%
7 309
 
3.1%
8 246
 
2.5%
9 209
 
2.1%
ValueCountFrequency (%)
442 1
< 0.1%
439 1
< 0.1%
438 1
< 0.1%
436 1
< 0.1%
432 1
< 0.1%
428 1
< 0.1%
421 1
< 0.1%
420 1
< 0.1%
413 1
< 0.1%
409 1
< 0.1%

결정지가
Real number (ℝ)

Distinct3559
Distinct (%)35.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean144731.7
Minimum231
Maximum2354000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T08:35:33.439407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum231
5-th percentile2170
Q125100
median69650
Q3179500
95-th percentile497400
Maximum2354000
Range2353769
Interquartile range (IQR)154400

Descriptive statistics

Standard deviation216638.83
Coefficient of variation (CV)1.4968306
Kurtosis21.121773
Mean144731.7
Median Absolute Deviation (MAD)55300
Skewness3.8554965
Sum1.447317 × 109
Variance4.6932384 × 1010
MonotonicityNot monotonic
2023-12-11T08:35:33.580657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23600 32
 
0.3%
20200 30
 
0.3%
29200 26
 
0.3%
49200 24
 
0.2%
32500 23
 
0.2%
16200 23
 
0.2%
27500 22
 
0.2%
16900 22
 
0.2%
12000 22
 
0.2%
81800 22
 
0.2%
Other values (3549) 9754
97.5%
ValueCountFrequency (%)
231 2
 
< 0.1%
260 2
 
< 0.1%
303 1
 
< 0.1%
306 5
0.1%
339 1
 
< 0.1%
386 2
 
< 0.1%
389 4
< 0.1%
396 2
 
< 0.1%
471 1
 
< 0.1%
478 1
 
< 0.1%
ValueCountFrequency (%)
2354000 1
< 0.1%
2235000 2
< 0.1%
2197000 1
< 0.1%
2182000 1
< 0.1%
2166000 1
< 0.1%
2079000 1
< 0.1%
2078000 1
< 0.1%
2038000 1
< 0.1%
2003000 1
< 0.1%
1894000 1
< 0.1%

Interactions

2023-12-11T08:35:30.863536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:35:27.204748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:35:27.889343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:35:28.615913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:35:29.323226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:35:30.275955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:35:30.951335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:35:27.327150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:35:28.022703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:35:28.767295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:35:29.434322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:35:30.373403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:35:31.040438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:35:27.436585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:35:28.161071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:35:28.875823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:35:29.551706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:35:30.476912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:35:31.153450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:35:27.529213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:35:28.286942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:35:29.001781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:35:29.673158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:35:30.569500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:35:31.247722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:35:27.637602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:35:28.410926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:35:29.119907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:35:30.083989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:35:30.685063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:35:31.350839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:35:27.773700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:35:28.515406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:35:29.217451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:35:30.179608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:35:30.774000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T08:35:33.666436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일련번호법정동행정동리구분부번결정지가
일련번호1.0000.9780.7860.9310.1340.2710.432
법정동0.9781.0000.7550.6780.0360.0960.220
행정동0.7860.7551.0000.6560.0210.1520.165
리0.9310.6780.6561.0000.1360.0960.311
구분0.1340.0360.0210.1361.0000.0000.146
부번0.2710.0960.1520.0960.0001.0000.443
결정지가0.4320.2200.1650.3110.1460.4431.000
2023-12-11T08:35:33.763759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일련번호법정동행정동리부번결정지가구분
일련번호1.0000.949-0.6520.815-0.111-0.2400.080
법정동0.9491.000-0.7050.780-0.140-0.2240.034
행정동-0.652-0.7051.000-0.8540.0560.0860.014
리0.8150.780-0.8541.000-0.120-0.3020.103
부번-0.111-0.1400.056-0.1201.0000.2880.000
결정지가-0.240-0.2240.086-0.3020.2881.0000.088
구분0.0800.0340.0140.1030.0000.0881.000

Missing values

2023-12-11T08:35:31.509094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T08:35:31.659012image/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
91022910233100301744012600
104001040112151001672115200
3044930450113570023301180
51115511161275950190013215900
780797808031002317752176200
7877878779310024193027500
222002220110955001322010400
5557655577250022139932359600
154511545210752001245467800
407024070312359501542638900
일련번호법정동행정동리구분본번부번결정지가
143311433210652001310073700
988109881132002513950392500
933449334532002115452103400
2267122672109550014941292600
60966097118510016110116900
3767737678116570011234137200
89701897023100301162155500
6507365074250028295451970
893138931431002921175702
96371963723200231333072500