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 규모별 면적 정보를 제공합니다.
Author충청남도
URLhttps://alldam.chungnam.go.kr/bigdata/collect/view.chungnam?menuCd=DOM_000000201001001000&apiIdx=2561

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 5 other fieldsHigh correlation
합계_면적 is highly overall correlated with 거래유형 and 8 other fieldsHigh correlation
규모1_면적 is highly overall correlated with 거래유형 and 8 other fieldsHigh correlation
규모2_면적 is highly overall correlated with 거래유형 and 8 other fieldsHigh correlation
규모3_면적 is highly overall correlated with 합계_면적 and 7 other fieldsHigh correlation
규모4_면적 is highly overall correlated with 합계_면적 and 7 other fieldsHigh correlation
규모5_면적 is highly overall correlated with 거래유형 and 8 other fieldsHigh correlation
규모6_면적 is highly overall correlated with 거래유형 and 8 other fieldsHigh correlation
규모7_면적 is highly overall correlated with 거래유형 and 8 other fieldsHigh correlation
규모8_면적 is highly overall correlated with 합계_면적 and 7 other fieldsHigh correlation
지역구분 레벨 is highly imbalanced (51.0%)Imbalance
규모9_면적 is highly skewed (γ1 = 38.37274129)Skewed
번호 has unique valuesUnique
규모1_면적 has 3128 (31.3%) zerosZeros
규모2_면적 has 351 (3.5%) zerosZeros
규모3_면적 has 167 (1.7%) zerosZeros
규모4_면적 has 186 (1.9%) zerosZeros
규모5_면적 has 1925 (19.2%) zerosZeros
규모6_면적 has 1136 (11.4%) zerosZeros
규모7_면적 has 2797 (28.0%) zerosZeros
규모8_면적 has 4861 (48.6%) zerosZeros
규모9_면적 has 7134 (71.3%) zerosZeros

Reproduction

Analysis started2024-01-09 21:51:51.451811
Analysis finished2024-01-09 21:52:12.164119
Duration20.71 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%
Mean12381.247
Minimum1
Maximum24792
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-10T06:52:12.221233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1196.95
Q16254.25
median12376
Q318543.25
95-th percentile23581.05
Maximum24792
Range24791
Interquartile range (IQR)12289

Descriptive statistics

Standard deviation7162.1294
Coefficient of variation (CV)0.57846591
Kurtosis-1.195307
Mean12381.247
Median Absolute Deviation (MAD)6145
Skewness0.0012594521
Sum1.2381247 × 108
Variance51296098
MonotonicityNot monotonic
2024-01-10T06:52:12.347242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17960 1
 
< 0.1%
10732 1
 
< 0.1%
13078 1
 
< 0.1%
3213 1
 
< 0.1%
18647 1
 
< 0.1%
1503 1
 
< 0.1%
16938 1
 
< 0.1%
21612 1
 
< 0.1%
11542 1
 
< 0.1%
2439 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
1 1
< 0.1%
3 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
11 1
< 0.1%
12 1
< 0.1%
14 1
< 0.1%
15 1
< 0.1%
16 1
< 0.1%
17 1
< 0.1%
ValueCountFrequency (%)
24792 1
< 0.1%
24790 1
< 0.1%
24788 1
< 0.1%
24786 1
< 0.1%
24785 1
< 0.1%
24782 1
< 0.1%
24780 1
< 0.1%
24778 1
< 0.1%
24777 1
< 0.1%
24773 1
< 0.1%

지역코드
Real number (ℝ)

HIGH CORRELATION 

Distinct18
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44415.179
Minimum44000
Maximum44825
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-10T06:52:12.474898image/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 deviation305.98135
Coefficient of variation (CV)0.0068891166
Kurtosis-1.7288753
Mean44415.179
Median Absolute Deviation (MAD)100
Skewness0.30711476
Sum4.4415179 × 108
Variance93624.584
MonotonicityNot monotonic
2024-01-10T06:52:12.581850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
44180 597
 
6.0%
44800 595
 
5.9%
44000 589
 
5.9%
44200 589
 
5.9%
44825 583
 
5.8%
44710 582
 
5.8%
44770 578
 
5.8%
44130 578
 
5.8%
44760 577
 
5.8%
44230 573
 
5.7%
Other values (8) 4159
41.6%
ValueCountFrequency (%)
44000 589
5.9%
44130 578
5.8%
44131 509
5.1%
44133 469
4.7%
44150 554
5.5%
44180 597
6.0%
44200 589
5.9%
44210 569
5.7%
44230 573
5.7%
44250 566
5.7%
ValueCountFrequency (%)
44825 583
5.8%
44810 554
5.5%
44800 595
5.9%
44790 559
5.6%
44770 578
5.8%
44760 577
5.8%
44710 582
5.8%
44270 379
3.8%
44250 566
5.7%
44230 573
5.7%

지역명
Categorical

HIGH CORRELATION 

Distinct18
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
보령시
 
597
홍성군
 
595
충남
 
589
아산시
 
589
태안군
 
583
Other values (13)
7047 

Length

Max length3
Median length3
Mean length2.9411
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row서천군
2nd row청양군
3rd row동남구
4th row동남구
5th row부여군

Common Values

ValueCountFrequency (%)
보령시 597
 
6.0%
홍성군 595
 
5.9%
충남 589
 
5.9%
아산시 589
 
5.9%
태안군 583
 
5.8%
금산군 582
 
5.8%
서천군 578
 
5.8%
천안시 578
 
5.8%
부여군 577
 
5.8%
논산시 573
 
5.7%
Other values (8) 4159
41.6%

Length

2024-01-10T06:52:12.697386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
보령시 597
 
6.0%
홍성군 595
 
5.9%
충남 589
 
5.9%
아산시 589
 
5.9%
태안군 583
 
5.8%
금산군 582
 
5.8%
서천군 578
 
5.8%
천안시 578
 
5.8%
부여군 577
 
5.8%
논산시 573
 
5.7%
Other values (8) 4159
41.6%

조사분기
Real number (ℝ)

Distinct198
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean201427.17
Minimum200601
Maximum202206
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-10T06:52:12.815764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum200601
5-th percentile200612
Q1201009
median201410
Q3201811
95-th percentile202110
Maximum202206
Range1605
Interquartile range (IQR)802

Descriptive statistics

Standard deviation473.20696
Coefficient of variation (CV)0.0023492707
Kurtosis-1.1794812
Mean201427.17
Median Absolute Deviation (MAD)401
Skewness-0.10198935
Sum2.0142717 × 109
Variance223924.83
MonotonicityNot monotonic
2024-01-10T06:52:12.962164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
202002 69
 
0.7%
201909 67
 
0.7%
201904 66
 
0.7%
201906 66
 
0.7%
202006 65
 
0.7%
201811 64
 
0.6%
202010 64
 
0.6%
202106 64
 
0.6%
201905 63
 
0.6%
201902 62
 
0.6%
Other values (188) 9350
93.5%
ValueCountFrequency (%)
200601 46
0.5%
200602 34
0.3%
200603 37
0.4%
200604 43
0.4%
200605 43
0.4%
200606 37
0.4%
200607 36
0.4%
200608 42
0.4%
200609 51
0.5%
200610 50
0.5%
ValueCountFrequency (%)
202206 46
0.5%
202205 60
0.6%
202204 54
0.5%
202203 54
0.5%
202202 57
0.6%
202201 54
0.5%
202112 62
0.6%
202111 59
0.6%
202110 58
0.6%
202109 59
0.6%

거래유형
Real number (ℝ)

HIGH CORRELATION 

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.1229
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-10T06:52:13.087337image/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 deviation2.0907015
Coefficient of variation (CV)0.50709488
Kurtosis-1.1919046
Mean4.1229
Median Absolute Deviation (MAD)2
Skewness0.023118442
Sum41229
Variance4.3710327
MonotonicityNot monotonic
2024-01-10T06:52:13.197422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1 1430
14.3%
6 1420
14.2%
5 1409
14.1%
7 1380
13.8%
3 1366
13.7%
4 1355
13.6%
2 1344
13.4%
8 296
 
3.0%
ValueCountFrequency (%)
1 1430
14.3%
2 1344
13.4%
3 1366
13.7%
4 1355
13.6%
5 1409
14.1%
6 1420
14.2%
7 1380
13.8%
8 296
 
3.0%
ValueCountFrequency (%)
8 296
 
3.0%
7 1380
13.8%
6 1420
14.2%
5 1409
14.1%
4 1355
13.6%
3 1366
13.7%
2 1344
13.4%
1 1430
14.3%

합계_면적
Real number (ℝ)

HIGH CORRELATION 

Distinct9689
Distinct (%)96.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean611996.98
Minimum0
Maximum31692484
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-10T06:52:13.332902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1257.95
Q16840.8558
median33366.971
Q3554019.83
95-th percentile1630100.9
Maximum31692484
Range31692484
Interquartile range (IQR)547178.98

Descriptive statistics

Standard deviation2277563.4
Coefficient of variation (CV)3.7215272
Kurtosis64.742723
Mean611996.98
Median Absolute Deviation (MAD)31477.62
Skewness7.5658284
Sum6.1199698 × 109
Variance5.1872949 × 1012
MonotonicityNot monotonic
2024-01-10T06:52:13.449189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60.0 6
 
0.1%
1027.0 4
 
< 0.1%
7624.0 4
 
< 0.1%
1842.0 4
 
< 0.1%
7567.0 3
 
< 0.1%
6197.0 3
 
< 0.1%
2748.0 3
 
< 0.1%
5151.0 3
 
< 0.1%
1352.0 3
 
< 0.1%
5044.0 3
 
< 0.1%
Other values (9679) 9964
99.6%
ValueCountFrequency (%)
0.0 1
 
< 0.1%
29.82 1
 
< 0.1%
49.95 2
 
< 0.1%
59.0 2
 
< 0.1%
59.64 1
 
< 0.1%
60.0 6
0.1%
99.9193 2
 
< 0.1%
101.2843 1
 
< 0.1%
102.0 1
 
< 0.1%
109.3166 1
 
< 0.1%
ValueCountFrequency (%)
31692484.0 1
< 0.1%
31096804.0 1
< 0.1%
30049933.0 1
< 0.1%
29915611.0 1
< 0.1%
29694030.0 1
< 0.1%
29377450.0 1
< 0.1%
28907440.0 1
< 0.1%
27028567.124984 1
< 0.1%
26446324.0 1
< 0.1%
26075118.985427 1
< 0.1%

규모1_면적
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct5789
Distinct (%)57.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26602.349
Minimum0
Maximum1105262.4
Zeros3128
Zeros (%)31.3%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-10T06:52:13.558968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median209.52
Q320582.25
95-th percentile100904.31
Maximum1105262.4
Range1105262.4
Interquartile range (IQR)20582.25

Descriptive statistics

Standard deviation85067.703
Coefficient of variation (CV)3.1977515
Kurtosis47.864748
Mean26602.349
Median Absolute Deviation (MAD)209.52
Skewness6.3923845
Sum2.6602349 × 108
Variance7.2365141 × 109
MonotonicityNot monotonic
2024-01-10T06:52:13.664517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 3128
31.3%
20.0 61
 
0.6%
17.0 59
 
0.6%
19.0 49
 
0.5%
38.0 37
 
0.4%
18.0 36
 
0.4%
16.0 30
 
0.3%
15.0 30
 
0.3%
13.0 23
 
0.2%
11.0 19
 
0.2%
Other values (5779) 6528
65.3%
ValueCountFrequency (%)
0.0 3128
31.3%
0.5 1
 
< 0.1%
0.558 1
 
< 0.1%
0.59 1
 
< 0.1%
0.59369 1
 
< 0.1%
0.5992 1
 
< 0.1%
0.674441 2
 
< 0.1%
0.7247 2
 
< 0.1%
0.7273 1
 
< 0.1%
0.7779 1
 
< 0.1%
ValueCountFrequency (%)
1105262.395733 1
< 0.1%
1049641.42827 1
< 0.1%
967630.580124 1
< 0.1%
948661.594893 1
< 0.1%
941996.879766 1
< 0.1%
923908.523789 1
< 0.1%
894180.13351 1
< 0.1%
883880.213493 1
< 0.1%
860379.032401 1
< 0.1%
852558.0 1
< 0.1%

규모2_면적
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7691
Distinct (%)76.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28462.259
Minimum0
Maximum1219283.9
Zeros351
Zeros (%)3.5%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-10T06:52:13.772656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile37.398
Q1285
median2737.1312
Q328542.23
95-th percentile79693.593
Maximum1219283.9
Range1219283.9
Interquartile range (IQR)28257.23

Descriptive statistics

Standard deviation97721.305
Coefficient of variation (CV)3.4333644
Kurtosis53.782328
Mean28462.259
Median Absolute Deviation (MAD)2657.1312
Skewness7.0877895
Sum2.8462259 × 108
Variance9.5494535 × 109
MonotonicityNot monotonic
2024-01-10T06:52:13.883565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 351
 
3.5%
40.0 60
 
0.6%
80.0 46
 
0.5%
120.0 43
 
0.4%
160.0 41
 
0.4%
200.0 27
 
0.3%
36.0 20
 
0.2%
35.0 17
 
0.2%
240.0 15
 
0.1%
75.0 15
 
0.1%
Other values (7681) 9365
93.7%
ValueCountFrequency (%)
0.0 351
3.5%
22.0 2
 
< 0.1%
23.1 2
 
< 0.1%
23.52 1
 
< 0.1%
24.0 4
 
< 0.1%
24.18 2
 
< 0.1%
25.49 1
 
< 0.1%
26.0 4
 
< 0.1%
26.45 1
 
< 0.1%
27.0 1
 
< 0.1%
ValueCountFrequency (%)
1219283.871137 1
< 0.1%
1057182.569201 1
< 0.1%
1054049.655005 1
< 0.1%
1047473.907767 1
< 0.1%
1040894.681237 1
< 0.1%
1024705.509892 1
< 0.1%
1016471.303362 1
< 0.1%
1003354.180531 1
< 0.1%
981719.0 1
< 0.1%
968187.0 1
< 0.1%

규모3_면적
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8449
Distinct (%)84.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32047.542
Minimum0
Maximum1252665.7
Zeros167
Zeros (%)1.7%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-10T06:52:14.023423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile231.9715
Q11049
median3811.6214
Q334517.75
95-th percentile82848.897
Maximum1252665.7
Range1252665.7
Interquartile range (IQR)33468.75

Descriptive statistics

Standard deviation102751.61
Coefficient of variation (CV)3.2062244
Kurtosis53.597317
Mean32047.542
Median Absolute Deviation (MAD)3539.6214
Skewness7.0654539
Sum3.2047542 × 108
Variance1.0557894 × 1010
MonotonicityNot monotonic
2024-01-10T06:52:14.180390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 167
 
1.7%
60.0 26
 
0.3%
120.0 15
 
0.1%
300.0 11
 
0.1%
210.0 8
 
0.1%
269.0 8
 
0.1%
280.0 8
 
0.1%
1233.0 7
 
0.1%
213.0 7
 
0.1%
646.0 6
 
0.1%
Other values (8439) 9737
97.4%
ValueCountFrequency (%)
0.0 167
1.7%
42.0 1
 
< 0.1%
45.0 1
 
< 0.1%
46.98 1
 
< 0.1%
47.76 1
 
< 0.1%
49.95 2
 
< 0.1%
50.0 3
 
< 0.1%
52.0 1
 
< 0.1%
59.0 4
 
< 0.1%
59.4 2
 
< 0.1%
ValueCountFrequency (%)
1252665.718227 1
< 0.1%
1153319.638227 1
< 0.1%
1125565.956312 1
< 0.1%
1090003.351126 1
< 0.1%
1072870.124176 1
< 0.1%
1070375.0 1
< 0.1%
1067530.0 1
< 0.1%
1042291.560003 1
< 0.1%
1035255.0 1
< 0.1%
1016776.0 1
< 0.1%

규모4_면적
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8712
Distinct (%)87.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean78599.123
Minimum0
Maximum3143834.5
Zeros186
Zeros (%)1.9%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-10T06:52:14.321739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile335
Q11831.75
median8272
Q388466.75
95-th percentile192459.65
Maximum3143834.5
Range3143834.5
Interquartile range (IQR)86635

Descriptive statistics

Standard deviation261072.47
Coefficient of variation (CV)3.3215697
Kurtosis56.321891
Mean78599.123
Median Absolute Deviation (MAD)7850
Skewness7.2195999
Sum7.8599123 × 108
Variance6.8158834 × 1010
MonotonicityNot monotonic
2024-01-10T06:52:14.466200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 186
 
1.9%
85.0 43
 
0.4%
170.0 21
 
0.2%
340.0 16
 
0.2%
254.0 13
 
0.1%
595.0 12
 
0.1%
169.0 9
 
0.1%
2380.0 8
 
0.1%
255.0 8
 
0.1%
425.0 8
 
0.1%
Other values (8702) 9676
96.8%
ValueCountFrequency (%)
0.0 186
1.9%
64.0 3
 
< 0.1%
67.0 1
 
< 0.1%
68.0 1
 
< 0.1%
70.3296 1
 
< 0.1%
74.0 1
 
< 0.1%
76.0 2
 
< 0.1%
76.95 1
 
< 0.1%
77.0 1
 
< 0.1%
78.0 1
 
< 0.1%
ValueCountFrequency (%)
3143834.511682 1
< 0.1%
3117207.0 1
< 0.1%
3062023.0 1
< 0.1%
3055426.121197 1
< 0.1%
2970461.110597 1
< 0.1%
2832156.0 1
< 0.1%
2804352.0 1
< 0.1%
2785238.0 1
< 0.1%
2710510.0 1
< 0.1%
2682695.0 1
< 0.1%

규모5_면적
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6268
Distinct (%)62.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean129043.91
Minimum0
Maximum6898930
Zeros1925
Zeros (%)19.2%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-10T06:52:14.606799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1189
median1233.7804
Q3122321.5
95-th percentile370522.73
Maximum6898930
Range6898930
Interquartile range (IQR)122132.5

Descriptive statistics

Standard deviation499210.77
Coefficient of variation (CV)3.8685341
Kurtosis63.007708
Mean129043.91
Median Absolute Deviation (MAD)1233.7804
Skewness7.5343243
Sum1.2904391 × 109
Variance2.4921139 × 1011
MonotonicityNot monotonic
2024-01-10T06:52:14.976046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 1925
 
19.2%
100.0 67
 
0.7%
99.0 49
 
0.5%
89.0 44
 
0.4%
200.0 31
 
0.3%
97.0 25
 
0.2%
98.0 24
 
0.2%
267.0 19
 
0.2%
90.0 19
 
0.2%
178.0 18
 
0.2%
Other values (6258) 7779
77.8%
ValueCountFrequency (%)
0.0 1925
19.2%
85.0 1
 
< 0.1%
85.02 1
 
< 0.1%
85.05 1
 
< 0.1%
86.0 8
 
0.1%
86.6864 1
 
< 0.1%
86.7592 1
 
< 0.1%
86.9867 1
 
< 0.1%
87.0 9
 
0.1%
88.0 13
 
0.1%
ValueCountFrequency (%)
6898930.0 1
< 0.1%
6812161.0 1
< 0.1%
6163822.0 1
< 0.1%
6083538.0 1
< 0.1%
5844718.0 1
< 0.1%
5794982.0 1
< 0.1%
5778359.0 1
< 0.1%
5729383.0 1
< 0.1%
5690277.687632 1
< 0.1%
5665552.690003 1
< 0.1%

규모6_면적
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7192
Distinct (%)71.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean62370.178
Minimum0
Maximum3444407
Zeros1136
Zeros (%)11.4%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-10T06:52:15.088017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1545.8498
median3338.46
Q349781.75
95-th percentile182255.65
Maximum3444407
Range3444407
Interquartile range (IQR)49235.9

Descriptive statistics

Standard deviation234331.47
Coefficient of variation (CV)3.7571076
Kurtosis64.715871
Mean62370.178
Median Absolute Deviation (MAD)3338.46
Skewness7.5687109
Sum6.2370178 × 108
Variance5.4911237 × 1010
MonotonicityNot monotonic
2024-01-10T06:52:15.197859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 1136
 
11.4%
122.0 23
 
0.2%
129.0 20
 
0.2%
125.0 19
 
0.2%
128.0 14
 
0.1%
120.0 14
 
0.1%
109.0 12
 
0.1%
102.0 12
 
0.1%
250.0 11
 
0.1%
108.0 11
 
0.1%
Other values (7182) 8728
87.3%
ValueCountFrequency (%)
0.0 1136
11.4%
100.16 1
 
< 0.1%
101.7129 2
 
< 0.1%
101.75 2
 
< 0.1%
101.9596 2
 
< 0.1%
101.984 4
 
< 0.1%
102.0 12
 
0.1%
102.53 1
 
< 0.1%
103.0 8
 
0.1%
103.05 5
 
0.1%
ValueCountFrequency (%)
3444407.0 1
< 0.1%
3289712.0 1
< 0.1%
3089945.0 1
< 0.1%
3040723.0 1
< 0.1%
2977678.0 1
< 0.1%
2902845.0 1
< 0.1%
2865438.0 1
< 0.1%
2824794.0 1
< 0.1%
2818350.0 1
< 0.1%
2741047.524279 1
< 0.1%

규모7_면적
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct5195
Distinct (%)51.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean106464.13
Minimum0
Maximum10071671
Zeros2797
Zeros (%)28.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-10T06:52:15.314421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median739.695
Q363799.75
95-th percentile304654.26
Maximum10071671
Range10071671
Interquartile range (IQR)63799.75

Descriptive statistics

Standard deviation464925.46
Coefficient of variation (CV)4.366968
Kurtosis126.59173
Mean106464.13
Median Absolute Deviation (MAD)739.695
Skewness9.6771262
Sum1.0646413 × 109
Variance2.1615568 × 1011
MonotonicityNot monotonic
2024-01-10T06:52:15.425653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 2797
28.0%
137.0 34
 
0.3%
138.0 33
 
0.3%
150.0 32
 
0.3%
143.0 31
 
0.3%
152.0 27
 
0.3%
149.0 26
 
0.3%
151.0 25
 
0.2%
156.0 24
 
0.2%
162.0 21
 
0.2%
Other values (5185) 6950
69.5%
ValueCountFrequency (%)
0.0 2797
28.0%
135.0 2
 
< 0.1%
135.06 3
 
< 0.1%
135.16 2
 
< 0.1%
135.2 4
 
< 0.1%
135.3 1
 
< 0.1%
135.36 1
 
< 0.1%
135.42 1
 
< 0.1%
135.5 1
 
< 0.1%
136.0 18
 
0.2%
ValueCountFrequency (%)
10071671.0 2
< 0.1%
9381929.0 1
< 0.1%
7658624.0 1
< 0.1%
7515519.0 1
< 0.1%
7340617.0 1
< 0.1%
7298862.0 1
< 0.1%
6992726.0 1
< 0.1%
6966437.0 1
< 0.1%
6011127.0 1
< 0.1%
5503475.0 1
< 0.1%

규모8_면적
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct3464
Distinct (%)34.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean146376.21
Minimum0
Maximum13052280
Zeros4861
Zeros (%)48.6%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-10T06:52:15.534824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median170.97
Q311246
95-th percentile535422.8
Maximum13052280
Range13052280
Interquartile range (IQR)11246

Descriptive statistics

Standard deviation652059.86
Coefficient of variation (CV)4.4546847
Kurtosis91.821076
Mean146376.21
Median Absolute Deviation (MAD)170.97
Skewness8.3903311
Sum1.4637621 × 109
Variance4.2518206 × 1011
MonotonicityNot monotonic
2024-01-10T06:52:15.648013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 4861
48.6%
184.0 27
 
0.3%
178.0 26
 
0.3%
198.0 25
 
0.2%
186.0 23
 
0.2%
197.0 18
 
0.2%
194.0 17
 
0.2%
172.0 17
 
0.2%
182.0 16
 
0.2%
176.0 16
 
0.2%
Other values (3454) 4954
49.5%
ValueCountFrequency (%)
0.0 4861
48.6%
165.0 2
 
< 0.1%
165.16 1
 
< 0.1%
165.32 1
 
< 0.1%
165.33 2
 
< 0.1%
165.36 1
 
< 0.1%
165.377 2
 
< 0.1%
165.58 1
 
< 0.1%
165.6781 3
 
< 0.1%
165.72 1
 
< 0.1%
ValueCountFrequency (%)
13052280.0 1
< 0.1%
11285143.0 2
< 0.1%
11096481.0 1
< 0.1%
10554376.0 1
< 0.1%
9374308.0 1
< 0.1%
7627402.45 2
< 0.1%
7529755.0 1
< 0.1%
7405734.0 1
< 0.1%
7358278.0 1
< 0.1%
7180079.0 1
< 0.1%

규모9_면적
Real number (ℝ)

SKEWED  ZEROS 

Distinct2257
Distinct (%)22.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2031.304
Minimum0
Maximum917807
Zeros7134
Zeros (%)71.3%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-10T06:52:15.756765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3397.5
95-th percentile8264.6
Maximum917807
Range917807
Interquartile range (IQR)397.5

Descriptive statistics

Standard deviation19171.718
Coefficient of variation (CV)9.4381336
Kurtosis1688.7709
Mean2031.304
Median Absolute Deviation (MAD)0
Skewness38.372741
Sum20313040
Variance3.6755478 × 108
MonotonicityNot monotonic
2024-01-10T06:52:15.860223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 7134
71.3%
217.0 15
 
0.1%
216.766 15
 
0.1%
199.0 13
 
0.1%
202.0 9
 
0.1%
421.548 8
 
0.1%
200.0 7
 
0.1%
494.0 6
 
0.1%
722.0 6
 
0.1%
422.0 6
 
0.1%
Other values (2247) 2781
 
27.8%
ValueCountFrequency (%)
0.0 7134
71.3%
198.0 1
 
< 0.1%
198.61 1
 
< 0.1%
198.85 1
 
< 0.1%
199.0 13
 
0.1%
199.06 1
 
< 0.1%
199.49 1
 
< 0.1%
199.53 1
 
< 0.1%
199.92 1
 
< 0.1%
200.0 7
 
0.1%
ValueCountFrequency (%)
917807.0 1
< 0.1%
851140.0 1
< 0.1%
849648.0 1
< 0.1%
847843.0 1
< 0.1%
356571.0 2
< 0.1%
115846.011 1
< 0.1%
87328.472 1
< 0.1%
82053.567 1
< 0.1%
81380.2 1
< 0.1%
79565.0 1
< 0.1%

지역구분 레벨
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
8433 
2
978 
0
 
589

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
1 8433
84.3%
2 978
 
9.8%
0 589
 
5.9%

Length

2024-01-10T06:52:15.956445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T06:52:16.027567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 8433
84.3%
2 978
 
9.8%
0 589
 
5.9%

Interactions

2024-01-10T06:52:10.359709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:51:55.406553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:51:56.710053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:51:57.770938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:51:58.944653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:52:00.016766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:52:01.318401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:52:02.397609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:52:03.502403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:52:04.602427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:52:05.921075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:52:07.033684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:52:08.153589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:52:09.268314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:52:10.429917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:51:55.472409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:51:56.778778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:51:57.846245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:51:59.015527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:52:00.096353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:52:01.391931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:52:02.471120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:52:03.576009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:52:04.682261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:52:05.993680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:52:07.108682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:52:08.226208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:52:09.340922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:52:10.718431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:51:55.546795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:51:56.846514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:51:57.923967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:51:59.087291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:52:00.170212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:52:01.462342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:52:02.543971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:52:03.649741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:52:04.755369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:52:06.066553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:52:07.183274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:52:08.295678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:52:09.413139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:52:10.807206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:51:55.624318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:51:56.923833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:51:58.005302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:51:59.166846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:52:00.255022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:52:01.542642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:52:02.636351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:52:03.731894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:52:04.839640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:52:06.149462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:52:07.267292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:52:08.377576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:52:09.493134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:52:10.913927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:51:55.700008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:51:57.000511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:51:58.093506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:51:59.244327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:52:00.332390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:52:01.619402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:52:02.716255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:52:03.820810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:52:04.919950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:52:06.235730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:52:07.350617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:52:08.454669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:52:09.573050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:52:11.016958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:51:55.771967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:51:57.074372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:51:58.175210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:51:59.319432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:52:00.409099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:52:01.702087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:52:02.795897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:52:03.901171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:52:04.998880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:52:06.323046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:52:07.432157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:52:08.535131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:52:09.653338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:52:11.111703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:51:56.080342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:51:57.146995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:51:58.254932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:51:59.395228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:52:00.481756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:52:01.781821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:52:02.872279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:52:03.980082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:52:05.073914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:52:06.400706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:52:07.511374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:52:08.608268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:52:09.729182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:52:11.209033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:51:56.154501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:51:57.226736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:51:58.340988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:51:59.475336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:52:00.572818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:52:01.856972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:52:02.953093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:52:04.058439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:52:05.151914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:52:06.478817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:52:07.591053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:52:08.684261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:52:09.805634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:52:11.309300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:51:56.250494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:51:57.300688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:51:58.427368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:51:59.553240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:52:00.866040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:52:01.936039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:52:03.031080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:52:04.138065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:52:05.231925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:52:06.559070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:52:07.672176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:52:08.765018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:52:09.880553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:52:11.408197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:51:56.327960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:51:57.375667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:51:58.515865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:51:59.633684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:52:00.942798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:52:02.011920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:52:03.112298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:52:04.217441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:52:05.307942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:52:06.636179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:52:07.752051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:52:08.874863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:52:09.965662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:52:11.511629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:51:56.414431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:51:57.451839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:51:58.607275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:51:59.717014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:52:01.020258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:52:02.092149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:52:03.192445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:52:04.296757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:52:05.389371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:52:06.716849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:52:07.836316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:52:08.957497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:52:10.048993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:52:11.613188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:51:56.491372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:51:57.531398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:51:58.694697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:51:59.794775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:52:01.098941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:52:02.172706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:52:03.274973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:52:04.378359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:52:05.468656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:52:06.803235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:52:07.923258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:52:09.045273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:52:10.132007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:52:11.710305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:51:56.564124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:51:57.621796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:51:58.777893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:51:59.867095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:52:01.171872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:52:02.248796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:52:03.349389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:52:04.453526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:52:05.763504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:52:06.880592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:52:08.001656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:52:09.122902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:52:10.208143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:52:11.809768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:51:56.638575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:51:57.702733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:51:58.864370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:51:59.945918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:52:01.248932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:52:02.325225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:52:03.428092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:52:04.532394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:52:05.845273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:52:06.959371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:52:08.079680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:52:09.196249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:52:10.288685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-10T06:52:16.089626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
번호지역코드지역명조사분기거래유형합계_면적규모1_면적규모2_면적규모3_면적규모4_면적규모5_면적규모6_면적규모7_면적규모8_면적규모9_면적지역구분 레벨
번호1.0000.9940.9810.2090.0640.4000.4770.4030.4020.4010.3980.4000.2890.2640.0430.867
지역코드0.9941.0001.0000.1330.0000.0210.1210.0400.0470.0000.0120.0410.0270.1140.0100.522
지역명0.9811.0001.0000.1320.0000.4240.5090.4290.4330.4270.4230.4240.4750.4480.0631.000
조사분기0.2090.1330.1321.0000.2370.0890.1040.1090.1070.0920.0650.0870.0630.0610.0660.130
거래유형0.0640.0000.0000.2371.0000.1820.2530.1880.1630.1590.1660.1760.1680.1920.0000.000
합계_면적0.4000.0210.4240.0890.1821.0000.7720.8500.8980.9190.9290.9410.8320.7800.0000.533
규모1_면적0.4770.1210.5090.1040.2530.7721.0000.9040.8330.7880.7720.7820.6070.5750.0000.584
규모2_면적0.4030.0400.4290.1090.1880.8500.9041.0000.9460.8940.8640.8730.6840.6530.0000.537
규모3_면적0.4020.0470.4330.1070.1630.8980.8330.9461.0000.9570.8960.9100.7400.6810.0000.537
규모4_면적0.4010.0000.4270.0920.1590.9190.7880.8940.9571.0000.9530.9410.7570.7010.0000.540
규모5_면적0.3980.0120.4230.0650.1660.9290.7720.8640.8960.9531.0000.9680.7690.7060.0000.536
규모6_면적0.4000.0410.4240.0870.1760.9410.7820.8730.9100.9410.9681.0000.8000.7120.0000.535
규모7_면적0.2890.0270.4750.0630.1680.8320.6070.6840.7400.7570.7690.8001.0000.8620.0000.643
규모8_면적0.2640.1140.4480.0610.1920.7800.5750.6530.6810.7010.7060.7120.8621.0000.0000.606
규모9_면적0.0430.0100.0630.0660.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.050
지역구분 레벨0.8670.5221.0000.1300.0000.5330.5840.5370.5370.5400.5360.5350.6430.6060.0501.000
2024-01-10T06:52:16.222904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지역명지역구분 레벨
지역명1.0000.999
지역구분 레벨0.9991.000
2024-01-10T06:52:16.325816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
번호지역코드조사분기거래유형합계_면적규모1_면적규모2_면적규모3_면적규모4_면적규모5_면적규모6_면적규모7_면적규모8_면적규모9_면적지역명지역구분 레벨
번호1.0000.9980.0260.018-0.391-0.286-0.398-0.431-0.398-0.273-0.361-0.315-0.283-0.2170.9000.795
지역코드0.9981.000-0.0240.002-0.387-0.282-0.393-0.430-0.397-0.269-0.356-0.310-0.279-0.2220.9990.837
조사분기0.026-0.0241.0000.0830.0980.1310.0880.1140.1280.1150.0710.0650.0410.0370.0510.079
거래유형0.0180.0020.0831.000-0.617-0.702-0.623-0.412-0.463-0.657-0.544-0.513-0.3930.3000.0000.000
합계_면적-0.391-0.3870.098-0.6171.0000.8350.9400.8970.9260.9110.9300.8700.702-0.1140.1760.377
규모1_면적-0.286-0.2820.131-0.7020.8351.0000.8770.6250.6560.8440.7510.6720.502-0.3100.2210.428
규모2_면적-0.398-0.3930.088-0.6230.9400.8771.0000.8300.8340.8810.8740.8040.619-0.2010.1790.382
규모3_면적-0.431-0.4300.114-0.4120.8970.6250.8301.0000.9450.7910.8740.8470.6970.0260.1810.381
규모4_면적-0.398-0.3970.128-0.4630.9260.6560.8340.9451.0000.8210.8920.8600.699-0.0030.1780.384
규모5_면적-0.273-0.2690.115-0.6570.9110.8440.8810.7910.8211.0000.8540.8140.666-0.1180.1760.381
규모6_면적-0.361-0.3560.071-0.5440.9300.7510.8740.8740.8920.8541.0000.8590.671-0.0940.1770.380
규모7_면적-0.315-0.3100.065-0.5130.8700.6720.8040.8470.8600.8140.8591.0000.715-0.0410.1760.359
규모8_면적-0.283-0.2790.041-0.3930.7020.5020.6190.6970.6990.6660.6710.7151.0000.1380.1640.330
규모9_면적-0.217-0.2220.0370.300-0.114-0.310-0.2010.026-0.003-0.118-0.094-0.0410.1381.0000.0340.047
지역명0.9000.9990.0510.0000.1760.2210.1790.1810.1780.1760.1770.1760.1640.0341.0000.999
지역구분 레벨0.7950.8370.0790.0000.3770.4280.3820.3810.3840.3810.3800.3590.3300.0470.9991.000

Missing values

2024-01-10T06:52:11.931036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-10T06:52:12.094123image/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

번호지역코드지역명조사분기거래유형합계_면적규모1_면적규모2_면적규모3_면적규모4_면적규모5_면적규모6_면적규모7_면적규모8_면적규모9_면적지역구분 레벨
179591796044770서천군20120448297.00.0191.01438.02154.0552.02290.0310.0188.01174.01
194021940344790청양군2008094937.00.027.0109.0140.089.00.0150.00.0422.01
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