Overview

Dataset statistics

Number of variables16
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.4 MiB
Average record size in memory151.0 B

Variable types

Numeric14
Categorical2

Dataset

Description한국부동산원(구.한국감정원)에서 제공하는 부동산 거래 통계를 조회할 수 있는 서비스로 충남의 거래주체별 면적 정보를 제공합니다.
Author충청남도
URLhttps://alldam.chungnam.go.kr/bigdata/collect/view.chungnam?menuCd=DOM_000000201001001000&apiIdx=2559

Alerts

지역명 is highly overall correlated with 번호 and 2 other fieldsHigh correlation
지역구분 레벨 is highly overall correlated with 번호 and 2 other fieldsHigh correlation
번호 is highly overall correlated with 지역코드 and 2 other fieldsHigh correlation
지역코드 is highly overall correlated with 번호 and 2 other fieldsHigh correlation
거래유형 is highly overall correlated with 합계_면적 and 4 other fieldsHigh correlation
합계_면적 is highly overall correlated with 거래유형 and 8 other fieldsHigh correlation
개인->개인_면적 is highly overall correlated with 거래유형 and 8 other fieldsHigh correlation
개인->법인_면적 is highly overall correlated with 거래유형 and 8 other fieldsHigh correlation
개인->기타_면적 is highly overall correlated with 거래유형 and 8 other fieldsHigh correlation
법인->개인_면적 is highly overall correlated with 합계_면적 and 6 other fieldsHigh correlation
법인->법인_면적 is highly overall correlated with 합계_면적 and 6 other fieldsHigh correlation
법인->기타_면적 is highly overall correlated with 법인->개인_면적High correlation
기타->개인_면적 is highly overall correlated with 거래유형 and 8 other fieldsHigh correlation
기타->법인_면적 is highly overall correlated with 합계_면적 and 5 other fieldsHigh correlation
기타->기타_면적 is highly overall correlated with 합계_면적 and 4 other fieldsHigh correlation
지역구분 레벨 is highly imbalanced (51.9%)Imbalance
법인->개인_면적 is highly skewed (γ1 = 21.03763203)Skewed
법인->기타_면적 is highly skewed (γ1 = 22.18462461)Skewed
기타->기타_면적 is highly skewed (γ1 = 23.67805741)Skewed
번호 has unique valuesUnique
개인->법인_면적 has 2416 (24.2%) zerosZeros
개인->기타_면적 has 3745 (37.5%) zerosZeros
법인->개인_면적 has 1020 (10.2%) zerosZeros
법인->법인_면적 has 4310 (43.1%) zerosZeros
법인->기타_면적 has 7654 (76.5%) zerosZeros
기타->개인_면적 has 4380 (43.8%) zerosZeros
기타->법인_면적 has 7709 (77.1%) zerosZeros
기타->기타_면적 has 7397 (74.0%) zerosZeros

Reproduction

Analysis started2024-01-09 20:33:48.258051
Analysis finished2024-01-09 20:34:13.451577
Duration25.19 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

번호
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12410.643
Minimum1
Maximum24789
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-10T05:34:13.548796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1276.85
Q16289.5
median12335
Q318562.5
95-th percentile23582.1
Maximum24789
Range24788
Interquartile range (IQR)12273

Descriptive statistics

Standard deviation7137.6136
Coefficient of variation (CV)0.57512036
Kurtosis-1.1895414
Mean12410.643
Median Absolute Deviation (MAD)6130
Skewness0.0053490621
Sum1.2410643 × 108
Variance50945528
MonotonicityNot monotonic
2024-01-10T05:34:13.686138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11690 1
 
< 0.1%
4617 1
 
< 0.1%
19006 1
 
< 0.1%
11942 1
 
< 0.1%
6532 1
 
< 0.1%
10884 1
 
< 0.1%
16241 1
 
< 0.1%
14379 1
 
< 0.1%
15891 1
 
< 0.1%
21932 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
1 1
< 0.1%
4 1
< 0.1%
9 1
< 0.1%
12 1
< 0.1%
16 1
< 0.1%
17 1
< 0.1%
19 1
< 0.1%
20 1
< 0.1%
22 1
< 0.1%
26 1
< 0.1%
ValueCountFrequency (%)
24789 1
< 0.1%
24788 1
< 0.1%
24785 1
< 0.1%
24781 1
< 0.1%
24778 1
< 0.1%
24777 1
< 0.1%
24773 1
< 0.1%
24771 1
< 0.1%
24770 1
< 0.1%
24768 1
< 0.1%

지역코드
Real number (ℝ)

HIGH CORRELATION 

Distinct18
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44415.12
Minimum44000
Maximum44825
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-10T05:34:13.908634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum44000
5-th percentile44000
Q144150
median44230
Q344770
95-th percentile44825
Maximum44825
Range825
Interquartile range (IQR)620

Descriptive statistics

Standard deviation305.3483
Coefficient of variation (CV)0.0068748726
Kurtosis-1.7267159
Mean44415.12
Median Absolute Deviation (MAD)100
Skewness0.315491
Sum4.441512 × 108
Variance93237.582
MonotonicityNot monotonic
2024-01-10T05:34:14.088496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
44760 599
 
6.0%
44230 598
 
6.0%
44210 594
 
5.9%
44150 593
 
5.9%
44825 591
 
5.9%
44200 587
 
5.9%
44810 582
 
5.8%
44130 575
 
5.8%
44250 574
 
5.7%
44790 568
 
5.7%
Other values (8) 4139
41.4%
ValueCountFrequency (%)
44000 558
5.6%
44130 575
5.8%
44131 493
4.9%
44133 482
4.8%
44150 593
5.9%
44180 562
5.6%
44200 587
5.9%
44210 594
5.9%
44230 598
6.0%
44250 574
5.7%
ValueCountFrequency (%)
44825 591
5.9%
44810 582
5.8%
44800 558
5.6%
44790 568
5.7%
44770 555
5.5%
44760 599
6.0%
44710 560
5.6%
44270 371
3.7%
44250 574
5.7%
44230 598
6.0%

지역명
Categorical

HIGH CORRELATION 

Distinct18
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
부여군
 
599
논산시
 
598
서산시
 
594
공주시
 
593
태안군
 
591
Other values (13)
7025 

Length

Max length3
Median length3
Mean length2.9442
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row논산시
2nd row아산시
3rd row보령시
4th row태안군
5th row천안시

Common Values

ValueCountFrequency (%)
부여군 599
 
6.0%
논산시 598
 
6.0%
서산시 594
 
5.9%
공주시 593
 
5.9%
태안군 591
 
5.9%
아산시 587
 
5.9%
예산군 582
 
5.8%
천안시 575
 
5.8%
계룡시 574
 
5.7%
청양군 568
 
5.7%
Other values (8) 4139
41.4%

Length

2024-01-10T05:34:14.232064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
부여군 599
 
6.0%
논산시 598
 
6.0%
서산시 594
 
5.9%
공주시 593
 
5.9%
태안군 591
 
5.9%
아산시 587
 
5.9%
예산군 582
 
5.8%
천안시 575
 
5.8%
계룡시 574
 
5.7%
청양군 568
 
5.7%
Other values (8) 4139
41.4%

조사일자
Real number (ℝ)

Distinct198
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean201426.97
Minimum200601
Maximum202206
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-10T05:34:14.356452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum200601
5-th percentile200701
Q1201009
median201411
Q3201811
95-th percentile202110
Maximum202206
Range1605
Interquartile range (IQR)802

Descriptive statistics

Standard deviation472.67844
Coefficient of variation (CV)0.0023466492
Kurtosis-1.175917
Mean201426.97
Median Absolute Deviation (MAD)401
Skewness-0.089875826
Sum2.0142697 × 109
Variance223424.91
MonotonicityNot monotonic
2024-01-10T05:34:14.498170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
202112 71
 
0.7%
202201 68
 
0.7%
202106 66
 
0.7%
202006 66
 
0.7%
201311 66
 
0.7%
201910 65
 
0.7%
202004 65
 
0.7%
202103 65
 
0.7%
201901 65
 
0.7%
201708 64
 
0.6%
Other values (188) 9339
93.4%
ValueCountFrequency (%)
200601 44
0.4%
200602 40
0.4%
200603 42
0.4%
200604 35
0.4%
200605 36
0.4%
200606 42
0.4%
200607 39
0.4%
200608 39
0.4%
200609 43
0.4%
200610 38
0.4%
ValueCountFrequency (%)
202206 51
0.5%
202205 59
0.6%
202204 45
0.4%
202203 63
0.6%
202202 58
0.6%
202201 68
0.7%
202112 71
0.7%
202111 52
0.5%
202110 58
0.6%
202109 52
0.5%

거래유형
Real number (ℝ)

HIGH CORRELATION 

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.0959
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-10T05:34:14.617316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q36
95-th percentile7
Maximum8
Range7
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.0924008
Coefficient of variation (CV)0.5108525
Kurtosis-1.1936583
Mean4.0959
Median Absolute Deviation (MAD)2
Skewness0.047384707
Sum40959
Variance4.378141
MonotonicityNot monotonic
2024-01-10T05:34:14.735946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1 1440
14.4%
4 1402
14.0%
2 1397
14.0%
7 1392
13.9%
5 1366
13.7%
6 1363
13.6%
3 1351
13.5%
8 289
 
2.9%
ValueCountFrequency (%)
1 1440
14.4%
2 1397
14.0%
3 1351
13.5%
4 1402
14.0%
5 1366
13.7%
6 1363
13.6%
7 1392
13.9%
8 289
 
2.9%
ValueCountFrequency (%)
8 289
 
2.9%
7 1392
13.9%
6 1363
13.6%
5 1366
13.7%
4 1402
14.0%
3 1351
13.5%
2 1397
14.0%
1 1440
14.4%

합계_면적
Real number (ℝ)

HIGH CORRELATION 

Distinct9683
Distinct (%)96.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean591361.92
Minimum0
Maximum34722814
Zeros4
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-10T05:34:14.890741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1266.55
Q16808.936
median33974.817
Q3571391
95-th percentile1640540.1
Maximum34722814
Range34722814
Interquartile range (IQR)564582.06

Descriptive statistics

Standard deviation2173129.1
Coefficient of variation (CV)3.674787
Kurtosis73.21364
Mean591361.92
Median Absolute Deviation (MAD)32124.306
Skewness7.9476152
Sum5.9136192 × 109
Variance4.7224901 × 1012
MonotonicityNot monotonic
2024-01-10T05:34:15.043895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
145.0 5
 
0.1%
60.0 4
 
< 0.1%
1082.0 4
 
< 0.1%
0.0 4
 
< 0.1%
4538.0 3
 
< 0.1%
160.0 3
 
< 0.1%
691.0 3
 
< 0.1%
5248.0 3
 
< 0.1%
291.0 3
 
< 0.1%
7624.0 3
 
< 0.1%
Other values (9673) 9965
99.7%
ValueCountFrequency (%)
0.0 4
< 0.1%
49.95 1
 
< 0.1%
59.0 1
 
< 0.1%
59.64 1
 
< 0.1%
60.0 4
< 0.1%
84.0 2
< 0.1%
84.906 1
 
< 0.1%
85.0 1
 
< 0.1%
99.9193 1
 
< 0.1%
102.0 1
 
< 0.1%
ValueCountFrequency (%)
34722814.0 1
< 0.1%
31096804.0 1
< 0.1%
30803717.0 1
< 0.1%
30782343.0 1
< 0.1%
30049933.0 1
< 0.1%
29694030.0 1
< 0.1%
28996216.0 1
< 0.1%
28907440.0 1
< 0.1%
26446324.0 1
< 0.1%
25998025.0 1
< 0.1%

개인->개인_면적
Real number (ℝ)

HIGH CORRELATION 

Distinct9619
Distinct (%)96.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean366939.28
Minimum0
Maximum18277019
Zeros6
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-10T05:34:15.180633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile986.9
Q15309.6788
median21316
Q3383744.77
95-th percentile964939.53
Maximum18277019
Range18277019
Interquartile range (IQR)378435.09

Descriptive statistics

Standard deviation1324784.8
Coefficient of variation (CV)3.6103653
Kurtosis61.803955
Mean366939.28
Median Absolute Deviation (MAD)19963.5
Skewness7.5303512
Sum3.6693928 × 109
Variance1.7550548 × 1012
MonotonicityNot monotonic
2024-01-10T05:34:15.323741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 6
 
0.1%
831.0 5
 
0.1%
60.0 5
 
0.1%
458.0 4
 
< 0.1%
349.0 4
 
< 0.1%
145.0 4
 
< 0.1%
805.0 4
 
< 0.1%
3311.0 4
 
< 0.1%
778.0 4
 
< 0.1%
3645.0 3
 
< 0.1%
Other values (9609) 9957
99.6%
ValueCountFrequency (%)
0.0 6
0.1%
17.0 2
 
< 0.1%
49.95 1
 
< 0.1%
49.98 1
 
< 0.1%
50.0 1
 
< 0.1%
59.0 1
 
< 0.1%
59.64 2
 
< 0.1%
60.0 5
0.1%
84.0 2
 
< 0.1%
84.906 1
 
< 0.1%
ValueCountFrequency (%)
18277019.0 1
< 0.1%
17643281.0 1
< 0.1%
16376269.0 1
< 0.1%
15824458.0 1
< 0.1%
15274618.0 1
< 0.1%
15035290.0 1
< 0.1%
14637808.019225 1
< 0.1%
14611041.638358 1
< 0.1%
14270619.0 1
< 0.1%
14210964.021321 1
< 0.1%

개인->법인_면적
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct5725
Distinct (%)57.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean76899.831
Minimum0
Maximum10339747
Zeros2416
Zeros (%)24.2%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-10T05:34:15.501892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q139.51
median549
Q327659.5
95-th percentile272641.1
Maximum10339747
Range10339747
Interquartile range (IQR)27619.99

Descriptive statistics

Standard deviation379757.8
Coefficient of variation (CV)4.9383438
Kurtosis304.72396
Mean76899.831
Median Absolute Deviation (MAD)549
Skewness14.508173
Sum7.6899831 × 108
Variance1.4421599 × 1011
MonotonicityNot monotonic
2024-01-10T05:34:15.660994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 2416
 
24.2%
85.0 128
 
1.3%
60.0 104
 
1.0%
40.0 30
 
0.3%
170.0 28
 
0.3%
145.0 28
 
0.3%
84.0 27
 
0.3%
50.0 23
 
0.2%
59.0 20
 
0.2%
47.0 19
 
0.2%
Other values (5715) 7177
71.8%
ValueCountFrequency (%)
0.0 2416
24.2%
6.4489 1
 
< 0.1%
7.0 1
 
< 0.1%
7.0519 1
 
< 0.1%
8.0 1
 
< 0.1%
9.612 1
 
< 0.1%
10.0 1
 
< 0.1%
12.0 1
 
< 0.1%
13.0 1
 
< 0.1%
15.2092 1
 
< 0.1%
ValueCountFrequency (%)
10339747.0 1
< 0.1%
10208587.0 1
< 0.1%
10164330.0 1
< 0.1%
9815766.0 1
< 0.1%
9788023.0 1
< 0.1%
8371553.0 1
< 0.1%
6786678.0 1
< 0.1%
6680081.0 1
< 0.1%
5540254.0 1
< 0.1%
4339460.0 1
< 0.1%

개인->기타_면적
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct4258
Distinct (%)42.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33594.359
Minimum0
Maximum4104039
Zeros3745
Zeros (%)37.5%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-10T05:34:15.802647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median103.555
Q32354.5075
95-th percentile126343.25
Maximum4104039
Range4104039
Interquartile range (IQR)2354.5075

Descriptive statistics

Standard deviation184815.22
Coefficient of variation (CV)5.5013768
Kurtosis165.91809
Mean33594.359
Median Absolute Deviation (MAD)103.555
Skewness11.405387
Sum3.3594359 × 108
Variance3.4156667 × 1010
MonotonicityNot monotonic
2024-01-10T05:34:15.939301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 3745
37.5%
85.0 149
 
1.5%
60.0 98
 
1.0%
40.0 46
 
0.5%
59.0 30
 
0.3%
58.0 26
 
0.3%
47.0 20
 
0.2%
50.0 19
 
0.2%
120.0 19
 
0.2%
55.0 16
 
0.2%
Other values (4248) 5832
58.3%
ValueCountFrequency (%)
0.0 3745
37.5%
3.0 1
 
< 0.1%
4.0 1
 
< 0.1%
6.0 2
 
< 0.1%
8.0 1
 
< 0.1%
10.0 2
 
< 0.1%
12.0 1
 
< 0.1%
13.3 1
 
< 0.1%
14.0 1
 
< 0.1%
17.0123 1
 
< 0.1%
ValueCountFrequency (%)
4104039.0 1
< 0.1%
3504528.0 1
< 0.1%
3491564.0 1
< 0.1%
3485205.0 1
< 0.1%
3242062.0 1
< 0.1%
3175592.0 1
< 0.1%
3166163.0 1
< 0.1%
3159593.0 1
< 0.1%
3155977.0 1
< 0.1%
3040073.0 1
< 0.1%

법인->개인_면적
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct7306
Distinct (%)73.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41732.233
Minimum0
Maximum8798696
Zeros1020
Zeros (%)10.2%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-10T05:34:16.077769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1347.9526
median3213.3657
Q325148.067
95-th percentile138926.37
Maximum8798696
Range8798696
Interquartile range (IQR)24800.115

Descriptive statistics

Standard deviation208349.87
Coefficient of variation (CV)4.9925405
Kurtosis674.55578
Mean41732.233
Median Absolute Deviation (MAD)3213.3657
Skewness21.037632
Sum4.1732233 × 108
Variance4.3409667 × 1010
MonotonicityNot monotonic
2024-01-10T05:34:16.239466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 1020
 
10.2%
85.0 66
 
0.7%
60.0 35
 
0.4%
170.0 33
 
0.3%
255.0 29
 
0.3%
40.0 18
 
0.2%
59.0 12
 
0.1%
510.0 11
 
0.1%
162.0 11
 
0.1%
324.0 10
 
0.1%
Other values (7296) 8755
87.5%
ValueCountFrequency (%)
0.0 1020
10.2%
5.0 1
 
< 0.1%
7.0 2
 
< 0.1%
14.0 1
 
< 0.1%
15.0 2
 
< 0.1%
16.0 2
 
< 0.1%
18.0 1
 
< 0.1%
19.65 1
 
< 0.1%
26.0 1
 
< 0.1%
29.0 2
 
< 0.1%
ValueCountFrequency (%)
8798696.0 1
< 0.1%
8085556.0 1
< 0.1%
5986968.0 1
< 0.1%
4939243.0 1
< 0.1%
3520794.0 1
< 0.1%
3234107.0 1
< 0.1%
2929821.0 1
< 0.1%
2864784.0 1
< 0.1%
2619437.0 1
< 0.1%
2611641.0 1
< 0.1%

법인->법인_면적
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct4376
Distinct (%)43.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39215.505
Minimum0
Maximum8796149
Zeros4310
Zeros (%)43.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-10T05:34:16.392601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median120
Q37303.6332
95-th percentile149863.05
Maximum8796149
Range8796149
Interquartile range (IQR)7303.6332

Descriptive statistics

Standard deviation208741.44
Coefficient of variation (CV)5.3229314
Kurtosis443.31458
Mean39215.505
Median Absolute Deviation (MAD)120
Skewness15.867499
Sum3.9215505 × 108
Variance4.3572991 × 1010
MonotonicityNot monotonic
2024-01-10T05:34:16.530358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 4310
43.1%
85.0 95
 
0.9%
170.0 39
 
0.4%
60.0 22
 
0.2%
40.0 20
 
0.2%
425.0 13
 
0.1%
84.0 12
 
0.1%
180.0 10
 
0.1%
360.0 10
 
0.1%
29.0 10
 
0.1%
Other values (4366) 5459
54.6%
ValueCountFrequency (%)
0.0 4310
43.1%
3.0 2
 
< 0.1%
3.6391 3
 
< 0.1%
4.0 3
 
< 0.1%
4.1981 3
 
< 0.1%
5.0 1
 
< 0.1%
5.4167 1
 
< 0.1%
5.935 1
 
< 0.1%
6.0 2
 
< 0.1%
6.5 1
 
< 0.1%
ValueCountFrequency (%)
8796149.0 1
< 0.1%
6026214.0 1
< 0.1%
4236253.0 1
< 0.1%
3541105.0 1
< 0.1%
2853190.1951 1
< 0.1%
2822837.0 1
< 0.1%
2623989.0 1
< 0.1%
2606361.0 1
< 0.1%
2556465.187285 1
< 0.1%
2494437.9414 1
< 0.1%

법인->기타_면적
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct1495
Distinct (%)14.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4705.6007
Minimum0
Maximum2275281
Zeros7654
Zeros (%)76.5%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-10T05:34:16.668358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile5516.423
Maximum2275281
Range2275281
Interquartile range (IQR)0

Descriptive statistics

Standard deviation48071.22
Coefficient of variation (CV)10.215746
Kurtosis712.73572
Mean4705.6007
Median Absolute Deviation (MAD)0
Skewness22.184625
Sum47056007
Variance2.3108422 × 109
MonotonicityNot monotonic
2024-01-10T05:34:16.810970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 7654
76.5%
85.0 73
 
0.7%
60.0 22
 
0.2%
170.0 22
 
0.2%
50.0 12
 
0.1%
84.0 12
 
0.1%
180.0 11
 
0.1%
330.0 8
 
0.1%
240.0 8
 
0.1%
59.91 8
 
0.1%
Other values (1485) 2170
 
21.7%
ValueCountFrequency (%)
0.0 7654
76.5%
0.92 2
 
< 0.1%
1.0 2
 
< 0.1%
2.0 5
 
0.1%
3.0 2
 
< 0.1%
3.3125 1
 
< 0.1%
3.4408 1
 
< 0.1%
4.0 2
 
< 0.1%
4.1714 1
 
< 0.1%
4.8 1
 
< 0.1%
ValueCountFrequency (%)
2275281.0 1
< 0.1%
1242295.0 2
< 0.1%
1108799.0 2
< 0.1%
806650.9789 1
< 0.1%
758671.6125 1
< 0.1%
733589.0 1
< 0.1%
733525.0 1
< 0.1%
723871.0 1
< 0.1%
710736.4509 1
< 0.1%
708389.3 1
< 0.1%

기타->개인_면적
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct3957
Distinct (%)39.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12892.936
Minimum0
Maximum3117412
Zeros4380
Zeros (%)43.8%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-10T05:34:17.206061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median82.63
Q33052
95-th percentile43412.45
Maximum3117412
Range3117412
Interquartile range (IQR)3052

Descriptive statistics

Standard deviation79374.311
Coefficient of variation (CV)6.1564187
Kurtosis480.30509
Mean12892.936
Median Absolute Deviation (MAD)82.63
Skewness18.209352
Sum1.2892936 × 108
Variance6.3002813 × 109
MonotonicityNot monotonic
2024-01-10T05:34:17.332603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 4380
43.8%
85.0 83
 
0.8%
60.0 68
 
0.7%
40.0 31
 
0.3%
50.0 30
 
0.3%
39.0 18
 
0.2%
46.0 17
 
0.2%
59.0 17
 
0.2%
170.0 15
 
0.1%
144.0 14
 
0.1%
Other values (3947) 5327
53.3%
ValueCountFrequency (%)
0.0 4380
43.8%
1.0 1
 
< 0.1%
4.0 1
 
< 0.1%
11.7 1
 
< 0.1%
16.0 2
 
< 0.1%
16.0999 2
 
< 0.1%
16.551 1
 
< 0.1%
17.0 2
 
< 0.1%
18.633333 2
 
< 0.1%
18.84 1
 
< 0.1%
ValueCountFrequency (%)
3117412.0 1
< 0.1%
2428979.0 1
< 0.1%
2009197.0 1
< 0.1%
1910699.389134 1
< 0.1%
1697399.0 1
< 0.1%
1478316.0 1
< 0.1%
1468888.0 1
< 0.1%
1345221.0 1
< 0.1%
1225069.0 1
< 0.1%
1151109.0 1
< 0.1%

기타->법인_면적
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1684
Distinct (%)16.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8031.0592
Minimum0
Maximum1756929
Zeros7709
Zeros (%)77.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-10T05:34:17.465336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile20697.027
Maximum1756929
Range1756929
Interquartile range (IQR)0

Descriptive statistics

Standard deviation62058.127
Coefficient of variation (CV)7.7272656
Kurtosis340.83986
Mean8031.0592
Median Absolute Deviation (MAD)0
Skewness16.539549
Sum80310592
Variance3.8512112 × 109
MonotonicityNot monotonic
2024-01-10T05:34:17.611746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 7709
77.1%
85.0 12
 
0.1%
137.0 7
 
0.1%
58.92 7
 
0.1%
262.0 7
 
0.1%
60.0 6
 
0.1%
634.0 6
 
0.1%
119.34 6
 
0.1%
23.0 6
 
0.1%
303.6552 5
 
0.1%
Other values (1674) 2229
 
22.3%
ValueCountFrequency (%)
0.0 7709
77.1%
1.0 3
 
< 0.1%
3.0 2
 
< 0.1%
5.0 2
 
< 0.1%
6.0 1
 
< 0.1%
7.0 1
 
< 0.1%
8.0 2
 
< 0.1%
10.0 1
 
< 0.1%
11.0 1
 
< 0.1%
11.089286 1
 
< 0.1%
ValueCountFrequency (%)
1756929.0 1
< 0.1%
1560975.4 1
< 0.1%
1531044.0 1
< 0.1%
1486940.0 1
< 0.1%
1423285.0 1
< 0.1%
1387027.0 1
< 0.1%
1347525.0 1
< 0.1%
1318847.0 2
< 0.1%
1146567.0 1
< 0.1%
1146388.0 1
< 0.1%

기타->기타_면적
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct1782
Distinct (%)17.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7351.1177
Minimum0
Maximum3617824.7
Zeros7397
Zeros (%)74.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-10T05:34:17.761607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q340
95-th percentile12099
Maximum3617824.7
Range3617824.7
Interquartile range (IQR)40

Descriptive statistics

Standard deviation73275.684
Coefficient of variation (CV)9.967965
Kurtosis809.81711
Mean7351.1177
Median Absolute Deviation (MAD)0
Skewness23.678057
Sum73511177
Variance5.3693258 × 109
MonotonicityNot monotonic
2024-01-10T05:34:17.903476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 7397
74.0%
60.0 18
 
0.2%
85.0 17
 
0.2%
120.0 10
 
0.1%
100.0 10
 
0.1%
40.0 10
 
0.1%
74.0 9
 
0.1%
84.0 8
 
0.1%
222.0 8
 
0.1%
108.0 8
 
0.1%
Other values (1772) 2505
 
25.1%
ValueCountFrequency (%)
0.0 7397
74.0%
1.0 7
 
0.1%
2.0 1
 
< 0.1%
3.0 1
 
< 0.1%
4.0 2
 
< 0.1%
5.0 2
 
< 0.1%
7.0 1
 
< 0.1%
8.0 3
 
< 0.1%
8.816667 1
 
< 0.1%
9.4 2
 
< 0.1%
ValueCountFrequency (%)
3617824.688064 1
 
< 0.1%
1871657.0 1
 
< 0.1%
1746242.0 1
 
< 0.1%
1701192.0 1
 
< 0.1%
1692890.0 1
 
< 0.1%
1473873.4 1
 
< 0.1%
1305510.0 1
 
< 0.1%
1305436.0 1
 
< 0.1%
1267301.0 1
 
< 0.1%
1243162.0 3
< 0.1%

지역구분 레벨
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
8467 
2
975 
0
 
558

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 8467
84.7%
2 975
 
9.8%
0 558
 
5.6%

Length

2024-01-10T05:34:18.027619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T05:34:18.115116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 8467
84.7%
2 975
 
9.8%
0 558
 
5.6%

Interactions

2024-01-10T05:34:11.426427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:33:52.508809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:33:53.971158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:33:55.315249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:33:56.754155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:33:58.153241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:33:59.780128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:34:01.256672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:34:02.578797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:34:04.031827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:34:05.374042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:34:07.070008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:34:08.470390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:34:09.917577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:34:11.516865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:33:52.586445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:33:54.052977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:33:55.425870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:33:56.841955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:33:58.250119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:33:59.882192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:34:01.343781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:34:02.673340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:34:04.132541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:34:05.696792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:34:07.172857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:34:08.562353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:34:10.016536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:34:11.608813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:33:52.666167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:33:54.135695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:33:55.522120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:33:56.931364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:33:58.359432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:33:59.975276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:34:01.434711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:34:02.781619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:34:04.219455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:34:05.775325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:34:07.268591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:34:08.657743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:34:10.118340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:34:11.709683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:33:52.757312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:33:54.257434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:33:55.636214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:33:57.038319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:33:58.496239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:34:00.084884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:34:01.541062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:34:02.899986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:34:04.322709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:34:05.870986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:34:07.384751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:34:08.776199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:34:10.243935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:34:11.809052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:33:52.841273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:33:54.359934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:33:55.741020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:33:57.139500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:33:58.598326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:34:00.183441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:34:01.634671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:34:03.014268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:34:04.415500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:34:05.977320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:34:07.487707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:34:08.885560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:34:10.359585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:34:11.902022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:33:53.181300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:33:54.450551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:33:55.850950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:33:57.238194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:33:58.707365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:34:00.282114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:34:01.732738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:34:03.124823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:34:04.512651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:34:06.094400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:34:07.601183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:34:08.984001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:34:10.466200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:34:12.242906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:33:53.275743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:33:54.553267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:33:55.952728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:33:57.341755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:33:58.809438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:34:00.380726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:34:01.834357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:34:03.220380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:34:04.607022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:34:06.187585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:34:07.715893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:34:09.097576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:34:10.566208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:34:12.335002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:33:53.363918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:33:54.636929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:33:56.049997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:33:57.439400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:33:58.897103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:34:00.469117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:34:01.931079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:34:03.314118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:34:04.697955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:34:06.281874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:34:07.810502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:34:09.199407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:34:10.662296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:34:12.428447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:33:53.448777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:33:54.718556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:33:56.138283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:33:57.540549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:33:58.980749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:34:00.585026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:34:02.015477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:34:03.397261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:34:04.807728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:34:06.396049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:34:07.895631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:34:09.284351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:34:10.760854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:34:12.513570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:33:53.537925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:33:54.798712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:33:56.234744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:33:57.629151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:33:59.292899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:34:00.679278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:34:02.100989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:34:03.502028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:34:04.912048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:34:06.486339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:34:07.982511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:34:09.381685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:34:10.853241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:34:12.607004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:33:53.621817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:33:54.877901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:33:56.328810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:33:57.731495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:33:59.378492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:34:00.797554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:34:02.189445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:34:03.594263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:34:05.007704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:34:06.582673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:34:08.065714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:34:09.479203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:34:10.982746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:34:12.708896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:33:53.706409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:33:54.963726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:33:56.424859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:33:57.824579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:33:59.474778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:34:00.924475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:34:02.282590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:34:03.681144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:34:05.090678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:34:06.688434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:34:08.157342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:34:09.611437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:34:11.084469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:34:12.820456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:33:53.791659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:33:55.054235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:33:56.535312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:33:57.928247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:33:59.571557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:34:01.047622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:34:02.381688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:34:03.785786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:34:05.183118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:34:06.816663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:34:08.253112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:34:09.720992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:34:11.223933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:34:12.918090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:33:53.882212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:33:55.179271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:33:56.654784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:33:58.034852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:33:59.671950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:34:01.150587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:34:02.479753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:34:03.893689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:34:05.281986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:34:06.936591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:34:08.357084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:34:09.820848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:34:11.321937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-10T05:34:18.202153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
번호지역코드지역명조사일자거래유형합계_면적개인->개인_면적개인->법인_면적개인->기타_면적법인->개인_면적법인->법인_면적법인->기타_면적기타->개인_면적기타->법인_면적기타->기타_면적지역구분 레벨
번호1.0000.9940.9810.2170.0760.3910.3920.2660.2690.1550.1560.0800.1470.1970.0870.864
지역코드0.9941.0001.0000.1130.0000.0220.0320.0350.0600.0000.0390.0920.0860.0410.0470.516
지역명0.9811.0001.0000.1310.0000.4190.4240.4570.2970.2160.2380.1310.2580.2010.1311.000
조사일자0.2170.1130.1311.0000.2320.0810.0560.0550.0800.0520.0710.0420.0360.0780.0610.122
거래유형0.0760.0000.0000.2321.0000.1630.1650.1470.1620.1240.1110.0690.0880.1100.0750.027
합계_면적0.3910.0220.4190.0810.1631.0000.9200.8150.7860.6440.6850.3700.7800.7810.3430.531
개인->개인_면적0.3920.0320.4240.0560.1650.9201.0000.7910.7230.5450.4900.3300.5760.6350.3100.532
개인->법인_면적0.2660.0350.4570.0550.1470.8150.7911.0000.6240.5650.7080.2750.8780.6410.2770.625
개인->기타_면적0.2690.0600.2970.0800.1620.7860.7230.6241.0000.4170.4400.3890.5910.7210.2060.392
법인->개인_면적0.1550.0000.2160.0520.1240.6440.5450.5650.4171.0000.3120.1440.4400.4090.1760.272
법인->법인_면적0.1560.0390.2380.0710.1110.6850.4900.7080.4400.3121.0000.1870.5730.4910.3280.278
법인->기타_면적0.0800.0920.1310.0420.0690.3700.3300.2750.3890.1440.1871.0000.0810.1180.0000.157
기타->개인_면적0.1470.0860.2580.0360.0880.7800.5760.8780.5910.4400.5730.0811.0000.6790.2360.390
기타->법인_면적0.1970.0410.2010.0780.1100.7810.6350.6410.7210.4090.4910.1180.6791.0000.6800.284
기타->기타_면적0.0870.0470.1310.0610.0750.3430.3100.2770.2060.1760.3280.0000.2360.6801.0000.164
지역구분 레벨0.8640.5161.0000.1220.0270.5310.5320.6250.3920.2720.2780.1570.3900.2840.1641.000
2024-01-10T05:34:18.352993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지역명지역구분 레벨
지역명1.0000.999
지역구분 레벨0.9991.000
2024-01-10T05:34:18.452502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
번호지역코드조사일자거래유형합계_면적개인->개인_면적개인->법인_면적개인->기타_면적법인->개인_면적법인->법인_면적법인->기타_면적기타->개인_면적기타->법인_면적기타->기타_면적지역명지역구분 레벨
번호1.0000.9980.0180.021-0.383-0.384-0.318-0.253-0.430-0.408-0.302-0.235-0.236-0.2180.9000.791
지역코드0.9981.000-0.0320.006-0.379-0.381-0.317-0.249-0.427-0.407-0.302-0.233-0.233-0.2180.9990.834
조사일자0.018-0.0321.0000.0610.1190.1330.1680.1120.0470.0960.0730.1370.0740.0850.0510.074
거래유형0.0210.0060.0611.000-0.629-0.622-0.590-0.610-0.458-0.431-0.360-0.596-0.488-0.4750.0000.017
합계_면적-0.383-0.3790.119-0.6291.0000.9850.8970.7890.8320.7250.4880.8060.5980.5650.1740.375
개인->개인_면적-0.384-0.3810.133-0.6220.9851.0000.8900.7870.7890.6890.4650.8020.5870.5560.1760.376
개인->법인_면적-0.318-0.3170.168-0.5900.8970.8901.0000.7370.7420.6860.4660.7490.5940.5340.1680.345
개인->기타_면적-0.253-0.2490.112-0.6100.7890.7870.7371.0000.5930.5600.4300.7390.5520.5460.1180.256
법인->개인_면적-0.430-0.4270.047-0.4580.8320.7890.7420.5931.0000.7000.5060.6270.4960.4630.0920.179
법인->법인_면적-0.408-0.4070.096-0.4310.7250.6890.6860.5600.7001.0000.4810.5630.5080.4500.1080.194
법인->기타_면적-0.302-0.3020.073-0.3600.4880.4650.4660.4300.5060.4811.0000.4250.4270.4110.0590.106
기타->개인_면적-0.235-0.2330.137-0.5960.8060.8020.7490.7390.6270.5630.4251.0000.5750.5520.0880.187
기타->법인_면적-0.236-0.2330.074-0.4880.5980.5870.5940.5520.4960.5080.4270.5751.0000.4720.0780.177
기타->기타_면적-0.218-0.2180.085-0.4750.5650.5560.5340.5460.4630.4500.4110.5520.4721.0000.0580.111
지역명0.9000.9990.0510.0000.1740.1760.1680.1180.0920.1080.0590.0880.0780.0581.0000.999
지역구분 레벨0.7910.8340.0740.0170.3750.3760.3450.2560.1790.1940.1060.1870.1770.1110.9991.000

Missing values

2024-01-10T05:34:13.058821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-10T05:34:13.302248image/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

번호지역코드지역명조사일자거래유형합계_면적개인->개인_면적개인->법인_면적개인->기타_면적법인->개인_면적법인->법인_면적법인->기타_면적기타->개인_면적기타->법인_면적기타->기타_면적지역구분 레벨
116891169044230논산시20080772857.01769.00.00.01088.00.00.00.00.00.01
9003900444200아산시201601719880.019166.00.0196.0518.00.00.00.00.00.01
8104810544180보령시2022038540697.3661395564.30457091.308735266.6411733.00922208.00420.038834.10.00.01
245612456244825태안군2020028664936.2575395776.924567091.53161279.036170.1032702.3136.01625.4155.00.01
2733273444130천안시20210422801440.8371961499033.412404494141.950634842.975192034.2011320661.5602680.011450.8249030.937824245.01
2208220944130천안시201508754887.049379.02523.043.02663.0239.00.040.00.00.01
121651216644230논산시2019098454926.4558334155.061393603.10650.018930.6886873.60.01364.00.00.01
5546554744150공주시201307318090.016948.082.0360.0156.0330.00.0214.00.00.01
1278127944000충남2021034578755.151769443904.45396816039.53976800.946896663.16134191.09542373.335479.51280.03303.1118010
113441134544230논산시20070145756.05255.080.00.0263.085.00.073.00.00.01
번호지역코드지역명조사일자거래유형합계_면적개인->개인_면적개인->법인_면적개인->기타_면적법인->개인_면적법인->법인_면적법인->기타_면적기타->개인_면적기타->법인_면적기타->기타_면적지역구분 레벨
118841188544230논산시20160554839.61154695.94970.00.083.70430.059.95750.00.00.01
7182718344180보령시20070954771.03229.047.059.01436.00.00.00.00.00.01
7663766444180보령시20170164818.38494657.7449103.320.057.320.00.00.00.00.01
129521295344250계룡시20100269098.07172.085.00.01841.00.00.00.00.00.01
4482448344133서북구201212687660.081141.0645.0144.04091.01390.0202.047.00.00.02
237372373844825태안군20100644202.04015.00.00.0187.00.00.00.00.00.01
105491055044210서산시20170921047472.681903785916.106918120858.4082847075.68000176746.98671553.7369.72495.55112.647344.01
199391994044790청양군2016021553707.0474011.07080.048952.0471.00.00.023193.00.00.01
115301153144230논산시201009635491.05727.00.0128.029551.085.00.00.00.00.01
2345234644130천안시2017033241164.627098135665.8070984319.14681179.4188404.26689618.7099737.806539.481200.00.01