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

Numeric13
Categorical3

Dataset

Description한국부동산원(구.한국감정원)에서 제공하는 부동산 거래 통계를 조회할 수 있는 서비스로 충남의 용도지역별 면적 정보를 제공합니다.
Author충청남도
URLhttps://alldam.chungnam.go.kr/bigdata/collect/view.chungnam?menuCd=DOM_000000201001001000&apiIdx=2553

Alerts

지역명 is highly overall correlated with 번호 and 2 other fieldsHigh correlation
지역구분 레벨 is highly overall correlated with 번호 and 6 other fieldsHigh correlation
번호 is highly overall correlated with 지역코드 and 4 other fieldsHigh correlation
지역코드 is highly overall correlated with 번호 and 4 other fieldsHigh correlation
용도지역별합계_면적 is highly overall correlated with 도시지역녹지지역면적 and 3 other fieldsHigh correlation
도시지역주거지역면적 is highly overall correlated with 번호 and 4 other fieldsHigh correlation
도시지역상업지역면적 is highly overall correlated with 번호 and 2 other fieldsHigh correlation
도시지역공업지역면적 is highly overall correlated with 도시지역주거지역면적 and 1 other fieldsHigh correlation
도시지역녹지지역면적 is highly overall correlated with 용도지역별합계_면적 and 5 other fieldsHigh correlation
관리지역소계_면적 is highly overall correlated with 용도지역별합계_면적 and 3 other fieldsHigh correlation
농림지역_면적 is highly overall correlated with 용도지역별합계_면적 and 3 other fieldsHigh correlation
지역구분 레벨 is highly imbalanced (51.0%)Imbalance
도시지역주거지역면적 is highly skewed (γ1 = 36.02692811)Skewed
도시지역상업지역면적 is highly skewed (γ1 = 26.84861131)Skewed
도시지역개발제한구역면적 is highly skewed (γ1 = 23.15153807)Skewed
도시지역용도미지정면적 is highly skewed (γ1 = 20.59298021)Skewed
번호 has unique valuesUnique
도시지역상업지역면적 has 943 (9.4%) zerosZeros
도시지역공업지역면적 has 4599 (46.0%) zerosZeros
도시지역녹지지역면적 has 269 (2.7%) zerosZeros
도시지역개발제한구역면적 has 8764 (87.6%) zerosZeros
도시지역용도미지정면적 has 6346 (63.5%) zerosZeros
관리지역소계_면적 has 583 (5.8%) zerosZeros
농림지역_면적 has 845 (8.5%) zerosZeros
자연환경보전지역_면적 has 6214 (62.1%) zerosZeros

Reproduction

Analysis started2024-01-09 20:44:40.444474
Analysis finished2024-01-09 20:45:02.538739
Duration22.09 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%
Mean5531.281
Minimum1
Maximum11057
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-10T05:45:02.604841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile546.95
Q12761.5
median5545
Q38302
95-th percentile10501.05
Maximum11057
Range11056
Interquartile range (IQR)5540.5

Descriptive statistics

Standard deviation3199.0277
Coefficient of variation (CV)0.57835205
Kurtosis-1.2038521
Mean5531.281
Median Absolute Deviation (MAD)2772.5
Skewness-0.0028243654
Sum55312810
Variance10233778
MonotonicityNot monotonic
2024-01-10T05:45:02.723291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1587 1
 
< 0.1%
10050 1
 
< 0.1%
10069 1
 
< 0.1%
2266 1
 
< 0.1%
10560 1
 
< 0.1%
1367 1
 
< 0.1%
7528 1
 
< 0.1%
902 1
 
< 0.1%
7740 1
 
< 0.1%
5845 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
5 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
12 1
< 0.1%
14 1
< 0.1%
ValueCountFrequency (%)
11057 1
< 0.1%
11056 1
< 0.1%
11054 1
< 0.1%
11053 1
< 0.1%
11052 1
< 0.1%
11051 1
< 0.1%
11050 1
< 0.1%
11049 1
< 0.1%
11047 1
< 0.1%
11046 1
< 0.1%

지역코드
Real number (ℝ)

HIGH CORRELATION 

Distinct18
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44415.287
Minimum44000
Maximum44825
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-10T05:45:02.827003image/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 deviation306.0433
Coefficient of variation (CV)0.0068904947
Kurtosis-1.728559
Mean44415.287
Median Absolute Deviation (MAD)100
Skewness0.30807649
Sum4.4415287 × 108
Variance93662.499
MonotonicityNot monotonic
2024-01-10T05:45:02.925230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
44810 586
 
5.9%
44000 585
 
5.9%
44800 585
 
5.9%
44250 583
 
5.8%
44130 578
 
5.8%
44200 577
 
5.8%
44180 577
 
5.8%
44825 576
 
5.8%
44760 573
 
5.7%
44230 572
 
5.7%
Other values (8) 4208
42.1%
ValueCountFrequency (%)
44000 585
5.9%
44130 578
5.8%
44131 501
5.0%
44133 485
4.9%
44150 571
5.7%
44180 577
5.8%
44200 577
5.8%
44210 558
5.6%
44230 572
5.7%
44250 583
5.8%
ValueCountFrequency (%)
44825 576
5.8%
44810 586
5.9%
44800 585
5.9%
44790 572
5.7%
44770 563
5.6%
44760 573
5.7%
44710 570
5.7%
44270 388
3.9%
44250 583
5.8%
44230 572
5.7%

지역명
Categorical

HIGH CORRELATION 

Distinct18
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
예산군
 
586
홍성군
 
585
충남
 
585
계룡시
 
583
천안시
 
578
Other values (13)
7083 

Length

Max length3
Median length3
Mean length2.9415
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row동남구
2nd row동남구
3rd row청양군
4th row충남
5th row천안시

Common Values

ValueCountFrequency (%)
예산군 586
 
5.9%
홍성군 585
 
5.9%
충남 585
 
5.9%
계룡시 583
 
5.8%
천안시 578
 
5.8%
아산시 577
 
5.8%
보령시 577
 
5.8%
태안군 576
 
5.8%
부여군 573
 
5.7%
청양군 572
 
5.7%
Other values (8) 4208
42.1%

Length

2024-01-10T05:45:03.026348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
예산군 586
 
5.9%
충남 585
 
5.9%
홍성군 585
 
5.9%
계룡시 583
 
5.8%
천안시 578
 
5.8%
아산시 577
 
5.8%
보령시 577
 
5.8%
태안군 576
 
5.8%
부여군 573
 
5.7%
논산시 572
 
5.7%
Other values (8) 4208
42.1%

조사일자
Real number (ℝ)

Distinct198
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean201445.25
Minimum200601
Maximum202206
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-10T05:45:03.135364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum200601
5-th percentile200701
Q1201009
median201502
Q3201904
95-th percentile202111
Maximum202206
Range1605
Interquartile range (IQR)895

Descriptive statistics

Standard deviation481.2159
Coefficient of variation (CV)0.0023888173
Kurtosis-1.21705
Mean201445.25
Median Absolute Deviation (MAD)404
Skewness-0.13032389
Sum2.0144525 × 109
Variance231568.75
MonotonicityNot monotonic
2024-01-10T05:45:03.277688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
202111 69
 
0.7%
202005 69
 
0.7%
202112 68
 
0.7%
201901 68
 
0.7%
202101 68
 
0.7%
202001 68
 
0.7%
201903 68
 
0.7%
202102 67
 
0.7%
201907 67
 
0.7%
202003 67
 
0.7%
Other values (188) 9321
93.2%
ValueCountFrequency (%)
200601 42
0.4%
200602 39
0.4%
200603 37
0.4%
200604 43
0.4%
200605 40
0.4%
200606 41
0.4%
200607 38
0.4%
200608 43
0.4%
200609 42
0.4%
200610 39
0.4%
ValueCountFrequency (%)
202206 62
0.6%
202205 65
0.7%
202204 63
0.6%
202203 66
0.7%
202202 62
0.6%
202201 64
0.6%
202112 68
0.7%
202111 69
0.7%
202110 65
0.7%
202109 65
0.7%

거래유형
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2
3110 
3
3109 
1
3106 
8
675 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
2 3110
31.1%
3 3109
31.1%
1 3106
31.1%
8 675
 
6.8%

Length

2024-01-10T05:45:03.656885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T05:45:03.741889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 3110
31.1%
3 3109
31.1%
1 3106
31.1%
8 675
 
6.8%

용도지역별합계_면적
Real number (ℝ)

HIGH CORRELATION 

Distinct9898
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1298569.6
Minimum636
Maximum43982861
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-10T05:45:03.851360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum636
5-th percentile8714.9
Q163558.5
median655743.5
Q31090677.2
95-th percentile3093596.4
Maximum43982861
Range43982225
Interquartile range (IQR)1027118.8

Descriptive statistics

Standard deviation3175037
Coefficient of variation (CV)2.4450264
Kurtosis29.517701
Mean1298569.6
Median Absolute Deviation (MAD)541547.5
Skewness5.1204534
Sum1.2985696 × 1010
Variance1.008086 × 1013
MonotonicityNot monotonic
2024-01-10T05:45:03.975179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16414 3
 
< 0.1%
7923 3
 
< 0.1%
31407 2
 
< 0.1%
25041 2
 
< 0.1%
9232 2
 
< 0.1%
10076 2
 
< 0.1%
5888 2
 
< 0.1%
9466 2
 
< 0.1%
21392 2
 
< 0.1%
8789 2
 
< 0.1%
Other values (9888) 9978
99.8%
ValueCountFrequency (%)
636 1
< 0.1%
798 1
< 0.1%
831 1
< 0.1%
998 1
< 0.1%
1011 1
< 0.1%
1155 1
< 0.1%
1167 1
< 0.1%
1347 1
< 0.1%
1350 1
< 0.1%
1438 1
< 0.1%
ValueCountFrequency (%)
43982861 1
< 0.1%
34722814 1
< 0.1%
31692484 1
< 0.1%
31096804 1
< 0.1%
30803717 1
< 0.1%
30782343 1
< 0.1%
30049933 1
< 0.1%
29915611 1
< 0.1%
28907440 1
< 0.1%
27028567 1
< 0.1%

도시지역주거지역면적
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct9636
Distinct (%)96.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean136085.2
Minimum0
Maximum40597591
Zeros8
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-10T05:45:04.105386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1714.9681
Q16502.406
median17305
Q382681.329
95-th percentile561119.5
Maximum40597591
Range40597591
Interquartile range (IQR)76178.924

Descriptive statistics

Standard deviation597566.61
Coefficient of variation (CV)4.3911211
Kurtosis2176.5579
Mean136085.2
Median Absolute Deviation (MAD)14290.6
Skewness36.026928
Sum1.360852 × 109
Variance3.5708585 × 1011
MonotonicityNot monotonic
2024-01-10T05:45:04.240831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 8
 
0.1%
1846.0 3
 
< 0.1%
3022.0 3
 
< 0.1%
1825.0 3
 
< 0.1%
3854.0 3
 
< 0.1%
12726.0 3
 
< 0.1%
17063.0 3
 
< 0.1%
2591.0 3
 
< 0.1%
7845.0 3
 
< 0.1%
2519.0 3
 
< 0.1%
Other values (9626) 9965
99.7%
ValueCountFrequency (%)
0.0 8
0.1%
6.0 1
 
< 0.1%
43.0 1
 
< 0.1%
52.0 1
 
< 0.1%
58.0 1
 
< 0.1%
68.0 1
 
< 0.1%
89.0 1
 
< 0.1%
122.0 1
 
< 0.1%
130.0 1
 
< 0.1%
144.0 1
 
< 0.1%
ValueCountFrequency (%)
40597591.0 1
< 0.1%
14727505.0 1
< 0.1%
9643425.0 1
< 0.1%
9627705.0 1
< 0.1%
9169379.0 1
< 0.1%
8917943.0 1
< 0.1%
8822998.0 1
< 0.1%
8027832.0 1
< 0.1%
7274446.0 1
< 0.1%
6326400.0 1
< 0.1%

도시지역상업지역면적
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct6468
Distinct (%)64.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5805.0231
Minimum0
Maximum1046581
Zeros943
Zeros (%)9.4%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-10T05:45:04.391801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1238.94945
median961.5
Q33421.4157
95-th percentile31249.611
Maximum1046581
Range1046581
Interquartile range (IQR)3182.4662

Descriptive statistics

Standard deviation20850.712
Coefficient of variation (CV)3.5918396
Kurtosis1226.4053
Mean5805.0231
Median Absolute Deviation (MAD)903.955
Skewness26.848611
Sum58050231
Variance4.3475219 × 108
MonotonicityNot monotonic
2024-01-10T05:45:04.522149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 943
 
9.4%
43.0 17
 
0.2%
66.0 14
 
0.1%
38.0 13
 
0.1%
14.0 12
 
0.1%
20.0 12
 
0.1%
48.0 11
 
0.1%
22.0 11
 
0.1%
61.0 10
 
0.1%
489.0 10
 
0.1%
Other values (6458) 8947
89.5%
ValueCountFrequency (%)
0.0 943
9.4%
1.0 2
 
< 0.1%
1.4 1
 
< 0.1%
2.0 2
 
< 0.1%
3.0 4
 
< 0.1%
3.53195 1
 
< 0.1%
3.71065 2
 
< 0.1%
4.0 1
 
< 0.1%
5.0 5
 
0.1%
6.0 5
 
0.1%
ValueCountFrequency (%)
1046581.0 1
< 0.1%
1030991.0 1
< 0.1%
338973.0 1
< 0.1%
321586.0 1
< 0.1%
280130.0 1
< 0.1%
247474.7569 1
< 0.1%
194209.0 1
< 0.1%
181762.0 1
< 0.1%
176948.9262 1
< 0.1%
164162.0 1
< 0.1%

도시지역공업지역면적
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct3751
Distinct (%)37.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25588.946
Minimum0
Maximum1626159
Zeros4599
Zeros (%)46.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-10T05:45:04.647222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median109.8
Q38243.8
95-th percentile124659.75
Maximum1626159
Range1626159
Interquartile range (IQR)8243.8

Descriptive statistics

Standard deviation103889.98
Coefficient of variation (CV)4.0599553
Kurtosis81.925862
Mean25588.946
Median Absolute Deviation (MAD)109.8
Skewness8.0266283
Sum2.5588946 × 108
Variance1.0793127 × 1010
MonotonicityNot monotonic
2024-01-10T05:45:04.784469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 4599
46.0%
44.0 32
 
0.3%
85.0 22
 
0.2%
41.0 16
 
0.2%
60.0 15
 
0.1%
84.0 10
 
0.1%
44.46 9
 
0.1%
145.0 9
 
0.1%
1653.0 8
 
0.1%
106.0 8
 
0.1%
Other values (3741) 5272
52.7%
ValueCountFrequency (%)
0.0 4599
46.0%
2.0 3
 
< 0.1%
4.0 2
 
< 0.1%
6.0 2
 
< 0.1%
7.0 1
 
< 0.1%
10.0 3
 
< 0.1%
11.0 1
 
< 0.1%
12.0 2
 
< 0.1%
14.0 1
 
< 0.1%
16.0 2
 
< 0.1%
ValueCountFrequency (%)
1626159.0 1
< 0.1%
1618424.0 1
< 0.1%
1615842.0 1
< 0.1%
1610258.0 1
< 0.1%
1459392.0 1
< 0.1%
1453391.0 1
< 0.1%
1444878.0 1
< 0.1%
1396593.0 1
< 0.1%
1385565.0 1
< 0.1%
1310155.0 1
< 0.1%

도시지역녹지지역면적
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8764
Distinct (%)87.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean86433.492
Minimum0
Maximum3251794
Zeros269
Zeros (%)2.7%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-10T05:45:04.916471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile115.9055
Q12726.7516
median23618.805
Q361699.123
95-th percentile375102.44
Maximum3251794
Range3251794
Interquartile range (IQR)58972.372

Descriptive statistics

Standard deviation218206.97
Coefficient of variation (CV)2.524565
Kurtosis30.464326
Mean86433.492
Median Absolute Deviation (MAD)22607.209
Skewness4.9473101
Sum8.6433492 × 108
Variance4.7614283 × 1010
MonotonicityNot monotonic
2024-01-10T05:45:05.038081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 269
 
2.7%
72.4905 9
 
0.1%
71.0 7
 
0.1%
226.0 6
 
0.1%
85.0 6
 
0.1%
48.0 6
 
0.1%
92.0 5
 
0.1%
152.0 4
 
< 0.1%
73.0 4
 
< 0.1%
68.0 4
 
< 0.1%
Other values (8754) 9680
96.8%
ValueCountFrequency (%)
0.0 269
2.7%
1.0 1
 
< 0.1%
4.4 1
 
< 0.1%
13.0 1
 
< 0.1%
15.0 1
 
< 0.1%
15.96 1
 
< 0.1%
18.0 1
 
< 0.1%
20.0 1
 
< 0.1%
23.94 1
 
< 0.1%
25.0 1
 
< 0.1%
ValueCountFrequency (%)
3251794.0 1
< 0.1%
2754891.0 1
< 0.1%
2403671.930071 1
< 0.1%
2333450.941301 1
< 0.1%
2176834.975733 1
< 0.1%
2104457.632343 1
< 0.1%
2095708.03108 1
< 0.1%
2069928.935102 1
< 0.1%
2025443.232602 1
< 0.1%
2016997.13138 1
< 0.1%

도시지역개발제한구역면적
Real number (ℝ)

SKEWED  ZEROS 

Distinct651
Distinct (%)6.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2685.129
Minimum0
Maximum968532
Zeros8764
Zeros (%)87.6%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-10T05:45:05.165602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile6671.0029
Maximum968532
Range968532
Interquartile range (IQR)0

Descriptive statistics

Standard deviation26984.308
Coefficient of variation (CV)10.049539
Kurtosis682.88302
Mean2685.129
Median Absolute Deviation (MAD)0
Skewness23.151538
Sum26851290
Variance7.281529 × 108
MonotonicityNot monotonic
2024-01-10T05:45:05.310153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 8764
87.6%
793.0 6
 
0.1%
3203.0 6
 
0.1%
6671.0584 6
 
0.1%
453.0 6
 
0.1%
10191.3333 6
 
0.1%
1382.0 6
 
0.1%
1000.0 6
 
0.1%
1073.0 6
 
0.1%
2273.0 6
 
0.1%
Other values (641) 1182
 
11.8%
ValueCountFrequency (%)
0.0 8764
87.6%
36.0 1
 
< 0.1%
37.0 1
 
< 0.1%
38.0 1
 
< 0.1%
40.0 1
 
< 0.1%
49.275 2
 
< 0.1%
54.0 1
 
< 0.1%
57.0 1
 
< 0.1%
59.0 3
 
< 0.1%
60.0 2
 
< 0.1%
ValueCountFrequency (%)
968532.0 1
 
< 0.1%
967726.0 2
< 0.1%
772519.0 2
< 0.1%
460418.9385 1
 
< 0.1%
433100.852 2
< 0.1%
431906.0 2
< 0.1%
429336.0 1
 
< 0.1%
326617.0 2
< 0.1%
321098.0 1
 
< 0.1%
320886.0 3
< 0.1%

도시지역용도미지정면적
Real number (ℝ)

SKEWED  ZEROS 

Distinct2863
Distinct (%)28.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15257.003
Minimum0
Maximum3497389
Zeros6346
Zeros (%)63.5%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-10T05:45:05.461306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31350.1324
95-th percentile72778.7
Maximum3497389
Range3497389
Interquartile range (IQR)1350.1324

Descriptive statistics

Standard deviation95662.95
Coefficient of variation (CV)6.270101
Kurtosis582.3824
Mean15257.003
Median Absolute Deviation (MAD)0
Skewness20.59298
Sum1.5257003 × 108
Variance9.1514 × 109
MonotonicityNot monotonic
2024-01-10T05:45:05.600540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 6346
63.5%
2653.0 10
 
0.1%
142.0 8
 
0.1%
660.0 7
 
0.1%
294.0 7
 
0.1%
66.0 7
 
0.1%
198.0 7
 
0.1%
85.0 6
 
0.1%
560.0 6
 
0.1%
254.0 6
 
0.1%
Other values (2853) 3590
35.9%
ValueCountFrequency (%)
0.0 6346
63.5%
3.0 3
 
< 0.1%
5.0 5
 
0.1%
6.0 2
 
< 0.1%
7.0 4
 
< 0.1%
10.0 3
 
< 0.1%
12.0 3
 
< 0.1%
15.6 6
 
0.1%
15.8206 1
 
< 0.1%
16.0 1
 
< 0.1%
ValueCountFrequency (%)
3497389.0 1
< 0.1%
3494173.0 1
< 0.1%
2660779.0 1
< 0.1%
2556136.0 1
< 0.1%
2551198.0 1
< 0.1%
1885942.0 2
< 0.1%
1710681.0 1
< 0.1%
1707364.0 1
< 0.1%
1679001.0 2
< 0.1%
1144396.0 1
< 0.1%

관리지역소계_면적
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct9172
Distinct (%)91.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean494517.57
Minimum0
Maximum14401236
Zeros583
Zeros (%)5.8%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-10T05:45:05.732886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q110414.251
median229659.8
Q3413156.65
95-th percentile1376018.4
Maximum14401236
Range14401236
Interquartile range (IQR)402742.4

Descriptive statistics

Standard deviation1248602.2
Coefficient of variation (CV)2.5248895
Kurtosis28.944371
Mean494517.57
Median Absolute Deviation (MAD)215606.42
Skewness5.0705287
Sum4.9451757 × 109
Variance1.5590074 × 1012
MonotonicityNot monotonic
2024-01-10T05:45:05.860230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 583
 
5.8%
6609.0 3
 
< 0.1%
372360.5273 2
 
< 0.1%
6836.0 2
 
< 0.1%
342264.09889 2
 
< 0.1%
4074.0 2
 
< 0.1%
181563.3707 2
 
< 0.1%
48682.8752 2
 
< 0.1%
1128121.703 2
 
< 0.1%
4250.0 2
 
< 0.1%
Other values (9162) 9398
94.0%
ValueCountFrequency (%)
0.0 583
5.8%
102.0 1
 
< 0.1%
106.0 1
 
< 0.1%
288.0 1
 
< 0.1%
298.0 1
 
< 0.1%
328.0 1
 
< 0.1%
371.0 1
 
< 0.1%
442.0 1
 
< 0.1%
475.0 1
 
< 0.1%
477.0 1
 
< 0.1%
ValueCountFrequency (%)
14401236.0 1
< 0.1%
14175652.0 1
< 0.1%
13585818.0 1
< 0.1%
13511285.0 1
< 0.1%
13020777.0 1
< 0.1%
13002144.0 1
< 0.1%
12322878.0 1
< 0.1%
12214238.0 1
< 0.1%
12107521.0 1
< 0.1%
10791255.378621 1
< 0.1%

농림지역_면적
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7909
Distinct (%)79.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean511194.37
Minimum0
Maximum19334427
Zeros845
Zeros (%)8.5%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-10T05:45:05.989841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11138.4275
median220506.5
Q3436896.53
95-th percentile1439814.4
Maximum19334427
Range19334427
Interquartile range (IQR)435758.1

Descriptive statistics

Standard deviation1321586.9
Coefficient of variation (CV)2.5852924
Kurtosis34.895351
Mean511194.37
Median Absolute Deviation (MAD)219276.5
Skewness5.360761
Sum5.1119437 × 109
Variance1.7465919 × 1012
MonotonicityNot monotonic
2024-01-10T05:45:06.122176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 845
 
8.5%
99.0 13
 
0.1%
198.0 9
 
0.1%
396.0 9
 
0.1%
199.0 8
 
0.1%
195.0 8
 
0.1%
196.0 8
 
0.1%
60.0 7
 
0.1%
165.0 7
 
0.1%
96.0 7
 
0.1%
Other values (7899) 9079
90.8%
ValueCountFrequency (%)
0.0 845
8.5%
10.0 1
 
< 0.1%
14.0 1
 
< 0.1%
15.0 1
 
< 0.1%
25.0 1
 
< 0.1%
28.0 1
 
< 0.1%
34.0 1
 
< 0.1%
35.0 1
 
< 0.1%
35.2667 1
 
< 0.1%
36.0 3
 
< 0.1%
ValueCountFrequency (%)
19334427.0 1
< 0.1%
19304374.0 1
< 0.1%
15261685.0 1
< 0.1%
15236015.0 1
< 0.1%
12844366.0 1
< 0.1%
12842533.0 1
< 0.1%
12810323.0 1
< 0.1%
12772469.0 1
< 0.1%
12705769.0 1
< 0.1%
12209381.0 1
< 0.1%

자연환경보전지역_면적
Real number (ℝ)

ZEROS 

Distinct2446
Distinct (%)24.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21009.771
Minimum0
Maximum3384506
Zeros6214
Zeros (%)62.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-10T05:45:06.255168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32109
95-th percentile91366.125
Maximum3384506
Range3384506
Interquartile range (IQR)2109

Descriptive statistics

Standard deviation116748.86
Coefficient of variation (CV)5.5568837
Kurtosis334.89683
Mean21009.771
Median Absolute Deviation (MAD)0
Skewness15.335144
Sum2.1009771 × 108
Variance1.3630296 × 1010
MonotonicityNot monotonic
2024-01-10T05:45:06.387007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 6214
62.1%
1079.0 12
 
0.1%
5154.0 10
 
0.1%
356.0 8
 
0.1%
2145.0 8
 
0.1%
133.0 8
 
0.1%
97.0 8
 
0.1%
50.0 7
 
0.1%
170.0 7
 
0.1%
3018.0 7
 
0.1%
Other values (2436) 3711
37.1%
ValueCountFrequency (%)
0.0 6214
62.1%
7.0 2
 
< 0.1%
13.0 2
 
< 0.1%
16.96 2
 
< 0.1%
18.0 1
 
< 0.1%
20.571429 2
 
< 0.1%
22.0 6
 
0.1%
22.01 1
 
< 0.1%
23.7 2
 
< 0.1%
25.0 2
 
< 0.1%
ValueCountFrequency (%)
3384506.0 1
< 0.1%
3383735.0 1
< 0.1%
3167898.0 2
< 0.1%
2793620.0 1
< 0.1%
1997209.0 1
< 0.1%
1969456.0 1
< 0.1%
1938021.0 1
< 0.1%
1815604.0 1
< 0.1%
1782699.0 2
< 0.1%
1439773.0 1
< 0.1%

지역구분 레벨
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
8429 
2
986 
0
 
585

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
1 8429
84.3%
2 986
 
9.9%
0 585
 
5.9%

Length

2024-01-10T05:45:06.506581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T05:45:06.589656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 8429
84.3%
2 986
 
9.9%
0 585
 
5.9%

Interactions

2024-01-10T05:45:00.907522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:44:44.222673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:44:45.418028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:44:46.852494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:44:48.210798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:44:49.530995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:44:50.857946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:44:52.361220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:44:53.683572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:44:54.949359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:44:56.482575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:44:57.883120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:44:59.585515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:45:01.003784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:44:44.301844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:44:45.504336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:44:46.956044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:44:48.324112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:44:49.642734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:44:50.948421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:44:52.456011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:44:53.772206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:44:55.085911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:44:56.574167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:44:57.993319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:44:59.678059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:45:01.094653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:44:44.382034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:44:45.580641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:44:47.057806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:44:48.411476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:44:49.751185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:44:51.038499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:44:52.546890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:44:53.866748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:44:55.195549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:44:56.663215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:44:58.089142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:44:59.774230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:45:01.207711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:44:44.474322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:44:45.925553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:44:47.155166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:44:48.504846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:44:49.851757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:44:51.141682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:44:52.653976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:44:53.971495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:44:55.317449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:44:56.760737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:44:58.206177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:44:59.881301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:45:01.297225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:44:44.553195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:44:46.003705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:44:47.253143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:44:48.587595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:44:49.946376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:44:51.227869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:44:52.750394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:44:54.057507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:44:55.423445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:44:56.847632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:44:58.600859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:44:59.977499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:45:01.405137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:44:44.655398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:44:46.098123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:44:47.361416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:44:48.680736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:44:50.044839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:44:51.323359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:44:52.858888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:44:54.151777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:44:55.547916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:44:56.943986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:44:58.703323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:45:00.083023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:45:01.500238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:44:44.749013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:44:46.203036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:44:47.471399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:44:48.768302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:44:50.137078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:44:51.415393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:44:52.953686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:44:54.242163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:44:55.675143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:44:57.034803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:44:58.792465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:45:00.182155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:45:01.605823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:44:44.849942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:44:46.303884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:44:47.581583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:44:48.865624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:44:50.239199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:44:51.516260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:44:53.056046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:44:54.339510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:44:55.809205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:44:57.132661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:44:58.892037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:45:00.285609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:45:01.698224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:44:44.937755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:44:46.392078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:44:47.677304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:44:48.960172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:44:50.346911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:44:51.630366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:44:53.163991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:44:54.434450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:44:55.929249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:44:57.233484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:44:59.007083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:45:00.386389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:45:01.794888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:44:45.030051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:44:46.479386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:44:47.769252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:44:49.055969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:44:50.449687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:44:51.724145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:44:53.261479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:44:54.523144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:44:56.043304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:44:57.359902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:44:59.126661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:45:00.496671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:45:01.896013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:44:45.120759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:44:46.570513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:44:47.869730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:44:49.153311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:44:50.554795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:44:51.823300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:44:53.362959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:44:54.620299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:44:56.173273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:44:57.510261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:44:59.253508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:45:00.598560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:45:02.018360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:44:45.211505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:44:46.664872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:44:47.969633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:44:49.278182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:44:50.653625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:44:52.160588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:44:53.466722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:44:54.720811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:44:56.282933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:44:57.628840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:44:59.382078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:45:00.695486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:45:02.132117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:44:45.326358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:44:46.759887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:44:48.088500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:44:49.404451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:44:50.759220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:44:52.262943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:44:53.571070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:44:54.835980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:44:56.380763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:44:57.754649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:44:59.486476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:45:00.799679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-10T05:45:06.659971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
번호지역코드지역명조사일자거래유형용도지역별합계_면적도시지역주거지역면적도시지역상업지역면적도시지역공업지역면적도시지역녹지지역면적도시지역개발제한구역면적도시지역용도미지정면적관리지역소계_면적농림지역_면적자연환경보전지역_면적지역구분 레벨
번호1.0000.9950.9810.2080.0770.4330.1350.1520.3470.5410.0850.1290.5540.4220.1540.867
지역코드0.9951.0001.0000.1070.0000.0140.0140.0180.0890.0830.0560.0360.0580.0400.0230.521
지역명0.9811.0001.0000.1100.0000.6670.1110.1350.3790.5730.1320.2020.5850.6590.2391.000
조사일자0.2080.1070.1101.0000.4040.0730.0820.0770.1150.1320.0850.0890.1550.0670.0760.119
거래유형0.0770.0000.0000.4041.0000.1390.0330.0000.1240.1520.0300.0570.1670.1290.0900.000
용도지역별합계_면적0.4330.0140.6670.0730.1391.0000.7720.2360.4520.6830.3150.3920.8070.9130.3790.855
도시지역주거지역면적0.1350.0140.1110.0820.0330.7721.0000.5600.2340.4270.1970.3340.5450.2340.0210.117
도시지역상업지역면적0.1520.0180.1350.0770.0000.2360.5601.0000.2360.3270.0000.5250.1870.1430.0000.140
도시지역공업지역면적0.3470.0890.3790.1150.1240.4520.2340.2361.0000.5860.1790.1600.5750.4610.3200.469
도시지역녹지지역면적0.5410.0830.5730.1320.1520.6830.4270.3270.5861.0000.1830.4000.7870.6610.3060.685
도시지역개발제한구역면적0.0850.0560.1320.0850.0300.3150.1970.0000.1790.1831.0000.0520.2990.4660.2480.142
도시지역용도미지정면적0.1290.0360.2020.0890.0570.3920.3340.5250.1600.4000.0521.0000.4360.2840.5590.253
관리지역소계_면적0.5540.0580.5850.1550.1670.8070.5450.1870.5750.7870.2990.4361.0000.7340.3580.709
농림지역_면적0.4220.0400.6590.0670.1290.9130.2340.1430.4610.6610.4660.2840.7341.0000.3770.847
자연환경보전지역_면적0.1540.0230.2390.0760.0900.3790.0210.0000.3200.3060.2480.5590.3580.3771.0000.289
지역구분 레벨0.8670.5211.0000.1190.0000.8550.1170.1400.4690.6850.1420.2530.7090.8470.2891.000
2024-01-10T05:45:06.809066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지역명지역구분 레벨거래유형
지역명1.0000.9990.000
지역구분 레벨0.9991.0000.000
거래유형0.0000.0001.000
2024-01-10T05:45:06.913806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
번호지역코드조사일자용도지역별합계_면적도시지역주거지역면적도시지역상업지역면적도시지역공업지역면적도시지역녹지지역면적도시지역개발제한구역면적도시지역용도미지정면적관리지역소계_면적농림지역_면적자연환경보전지역_면적지역명거래유형지역구분 레벨
번호1.0000.9980.024-0.253-0.632-0.588-0.459-0.401-0.303-0.282-0.134-0.0820.0560.9010.0460.796
지역코드0.9981.000-0.026-0.258-0.638-0.591-0.464-0.406-0.305-0.288-0.137-0.0860.0610.9990.0000.836
조사일자0.024-0.0261.0000.0880.1550.1120.1280.1070.0180.1580.0540.069-0.1150.0430.2530.073
용도지역별합계_면적-0.253-0.2580.0881.0000.5000.2490.4570.7880.3540.3500.9130.8970.4220.2840.0890.572
도시지역주거지역면적-0.632-0.6380.1550.5001.0000.6080.5430.5760.1930.3710.3510.2790.1200.0570.0270.088
도시지역상업지역면적-0.588-0.5910.1120.2490.6081.0000.4960.3730.1400.3930.1700.108-0.0430.0690.0000.106
도시지역공업지역면적-0.459-0.4640.1280.4570.5430.4961.0000.5440.1830.4080.3640.3390.1270.1550.0740.320
도시지역녹지지역면적-0.401-0.4060.1070.7880.5760.3730.5441.0000.2610.3170.6690.6530.2320.2600.0910.539
도시지역개발제한구역면적-0.303-0.3050.0180.3540.1930.1400.1830.2611.0000.2340.3650.3210.3580.0590.0210.096
도시지역용도미지정면적-0.282-0.2880.1580.3500.3710.3930.4080.3170.2341.0000.3350.2640.2000.0860.0260.165
관리지역소계_면적-0.134-0.1370.0540.9130.3510.1700.3640.6690.3650.3351.0000.8570.4420.2680.1010.568
농림지역_면적-0.082-0.0860.0690.8970.2790.1080.3390.6530.3210.2640.8571.0000.4140.2790.0820.562
자연환경보전지역_면적0.0560.061-0.1150.4220.120-0.0430.1270.2320.3580.2000.4420.4141.0000.1020.0400.192
지역명0.9010.9990.0430.2840.0570.0690.1550.2600.0590.0860.2680.2790.1021.0000.0000.999
거래유형0.0460.0000.2530.0890.0270.0000.0740.0910.0210.0260.1010.0820.0400.0001.0000.000
지역구분 레벨0.7960.8360.0730.5720.0880.1060.3200.5390.0960.1650.5680.5620.1920.9990.0001.000

Missing values

2024-01-10T05:45:02.264612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-10T05:45:02.451261image/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

번호지역코드지역명조사일자거래유형용도지역별합계_면적도시지역주거지역면적도시지역상업지역면적도시지역공업지역면적도시지역녹지지역면적도시지역개발제한구역면적도시지역용도미지정면적관리지역소계_면적농림지역_면적자연환경보전지역_면적지역구분 레벨
1586158744131동남구2017042747952255568.8770752713.065410353.025914.50.00.0283513.0352169889.0750.02
1470147144131동남구2013052731263415113.0983.00.026603.00.00.0123631.0164836.097.02
8770877144790청양군20070821578585960.00.00.00.00.00.0167323.0291624.01118678.01
56156244000충남2021032221151292417033.30612529234.87218037.03331442935.58075853476.6113471.54048122965.6982659449492.481924268481.96330
83984044130천안시201310223178801570587.06133.042628.075515.00.00.0322801.0300216.00.01
9291929244800홍성군2013103106487259.0230.00.0515.00.077.01868.0699.00.01
2782278344150공주시2017061189076066779.4077512867.7076097384.061691.1645675814.0769.8554718.5803761171668.95046719066.01
5578557944250계룡시2012081361106094.0472.01653.027891.00.00.00.00.00.01
2551255244150공주시200809388094159.02963.00.0127.00.00.01022.0401.0137.01
4429443044210서산시20130233428211534.01527.065.02021.00.00.018919.0216.00.01
번호지역코드지역명조사일자거래유형용도지역별합계_면적도시지역주거지역면적도시지역상업지역면적도시지역공업지역면적도시지역녹지지역면적도시지역개발제한구역면적도시지역용도미지정면적관리지역소계_면적농림지역_면적자연환경보전지역_면적지역구분 레벨
4867486844210서산시2021048170883552994.62864436.04619557.8209047.00650.00.0406369.5661016429.99260.01
9652965344800홍성군20190725221174686.02123.0430.06396.8333330.00.0281918.330467194725.14696932268.01
9752975344800홍성군202110150776455937.63443412.27912868.819540.90.00.0252498.879158828.25764677.01
8683868444790청양군200611368091382.03455.00.00.00.00.01773.0199.00.01
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