Overview

Dataset statistics

Number of variables11
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.0 MiB
Average record size in memory106.0 B

Variable types

Numeric9
Categorical2

Dataset

Description한국부동산원(구.한국감정원)에서 제공하는 부동산 거래 통계를 조회할 수 있는 서비스로 충남의 매입자거주지별 부동산 거래 면적 정보를 제공합니다.
Author충청남도
URLhttps://alldam.chungnam.go.kr/bigdata/collect/view.chungnam?menuCd=DOM_000000201001001000&apiIdx=2557

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 4 other fieldsHigh correlation
관할시군구내_면적 is highly overall correlated with 거래유형 and 4 other fieldsHigh correlation
관할시도내_면적 is highly overall correlated with 거래유형 and 4 other fieldsHigh correlation
관할시도외서울면적 is highly overall correlated with 거래유형 and 4 other fieldsHigh correlation
관할시도외기타면적 is highly overall correlated with 거래유형 and 4 other fieldsHigh correlation
지역구분 레벨 is highly imbalanced (52.1%)Imbalance
번호 has unique valuesUnique
관할시도내_면적 has 587 (5.9%) zerosZeros
관할시도외서울면적 has 685 (6.9%) zerosZeros
관할시도외기타면적 has 175 (1.8%) zerosZeros

Reproduction

Analysis started2024-01-09 21:21:01.755909
Analysis finished2024-01-09 21:21:11.168262
Duration9.41 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%
Mean12454.689
Minimum1
Maximum24792
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-10T06:21:11.230238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1303.8
Q16319.5
median12428.5
Q318629.25
95-th percentile23626.1
Maximum24792
Range24791
Interquartile range (IQR)12309.75

Descriptive statistics

Standard deviation7143.6805
Coefficient of variation (CV)0.57357357
Kurtosis-1.1906703
Mean12454.689
Median Absolute Deviation (MAD)6150
Skewness5.9123866 × 10-5
Sum1.2454689 × 108
Variance51032172
MonotonicityNot monotonic
2024-01-10T06:21:11.347969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17452 1
 
< 0.1%
16637 1
 
< 0.1%
519 1
 
< 0.1%
24162 1
 
< 0.1%
19746 1
 
< 0.1%
8566 1
 
< 0.1%
10358 1
 
< 0.1%
11142 1
 
< 0.1%
9741 1
 
< 0.1%
8660 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
5 1
< 0.1%
7 1
< 0.1%
9 1
< 0.1%
12 1
< 0.1%
13 1
< 0.1%
15 1
< 0.1%
18 1
< 0.1%
21 1
< 0.1%
ValueCountFrequency (%)
24792 1
< 0.1%
24786 1
< 0.1%
24785 1
< 0.1%
24784 1
< 0.1%
24782 1
< 0.1%
24780 1
< 0.1%
24779 1
< 0.1%
24775 1
< 0.1%
24771 1
< 0.1%
24770 1
< 0.1%

지역코드
Real number (ℝ)

HIGH CORRELATION 

Distinct18
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44417.464
Minimum44000
Maximum44825
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-10T06:21:11.450640image/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 deviation305.64645
Coefficient of variation (CV)0.0068812224
Kurtosis-1.7377555
Mean44417.464
Median Absolute Deviation (MAD)100
Skewness0.29995452
Sum4.4417464 × 108
Variance93419.751
MonotonicityNot monotonic
2024-01-10T06:21:11.534815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
44825 609
 
6.1%
44230 595
 
5.9%
44710 590
 
5.9%
44210 590
 
5.9%
44760 584
 
5.8%
44800 584
 
5.8%
44150 582
 
5.8%
44250 577
 
5.8%
44180 576
 
5.8%
44200 574
 
5.7%
Other values (8) 4139
41.4%
ValueCountFrequency (%)
44000 553
5.5%
44130 573
5.7%
44131 503
5.0%
44133 464
4.6%
44150 582
5.8%
44180 576
5.8%
44200 574
5.7%
44210 590
5.9%
44230 595
5.9%
44250 577
5.8%
ValueCountFrequency (%)
44825 609
6.1%
44810 560
5.6%
44800 584
5.8%
44790 554
5.5%
44770 573
5.7%
44760 584
5.8%
44710 590
5.9%
44270 359
3.6%
44250 577
5.8%
44230 595
5.9%

지역명
Categorical

HIGH CORRELATION 

Distinct18
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
태안군
 
609
논산시
 
595
금산군
 
590
서산시
 
590
부여군
 
584
Other values (13)
7032 

Length

Max length3
Median length3
Mean length2.9447
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row부여군
2nd row서천군
3rd row금산군
4th row서천군
5th row금산군

Common Values

ValueCountFrequency (%)
태안군 609
 
6.1%
논산시 595
 
5.9%
금산군 590
 
5.9%
서산시 590
 
5.9%
부여군 584
 
5.8%
홍성군 584
 
5.8%
공주시 582
 
5.8%
계룡시 577
 
5.8%
보령시 576
 
5.8%
아산시 574
 
5.7%
Other values (8) 4139
41.4%

Length

2024-01-10T06:21:11.625254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
태안군 609
 
6.1%
논산시 595
 
5.9%
금산군 590
 
5.9%
서산시 590
 
5.9%
부여군 584
 
5.8%
홍성군 584
 
5.8%
공주시 582
 
5.8%
계룡시 577
 
5.8%
보령시 576
 
5.8%
아산시 574
 
5.7%
Other values (8) 4139
41.4%

조사일자
Real number (ℝ)

Distinct198
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean201428.83
Minimum200601
Maximum202206
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-10T06:21:11.722231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum200601
5-th percentile200612
Q1201009
median201411
Q3201812
95-th percentile202110
Maximum202206
Range1605
Interquartile range (IQR)803

Descriptive statistics

Standard deviation475.23115
Coefficient of variation (CV)0.0023593006
Kurtosis-1.1809514
Mean201428.83
Median Absolute Deviation (MAD)401
Skewness-0.091874354
Sum2.0142883 × 109
Variance225844.65
MonotonicityNot monotonic
2024-01-10T06:21:11.836555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
202007 70
 
0.7%
202009 70
 
0.7%
202010 67
 
0.7%
202108 67
 
0.7%
202111 66
 
0.7%
202204 65
 
0.7%
202006 64
 
0.6%
201508 63
 
0.6%
202106 63
 
0.6%
202202 63
 
0.6%
Other values (188) 9342
93.4%
ValueCountFrequency (%)
200601 42
0.4%
200602 39
0.4%
200603 43
0.4%
200604 41
0.4%
200605 45
0.4%
200606 48
0.5%
200607 41
0.4%
200608 45
0.4%
200609 43
0.4%
200610 47
0.5%
ValueCountFrequency (%)
202206 59
0.6%
202205 60
0.6%
202204 65
0.7%
202203 58
0.6%
202202 63
0.6%
202201 54
0.5%
202112 56
0.6%
202111 66
0.7%
202110 45
0.4%
202109 63
0.6%

거래유형
Real number (ℝ)

HIGH CORRELATION 

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.1271
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-10T06:21:11.939297image/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 deviation2.0973282
Coefficient of variation (CV)0.50818448
Kurtosis-1.1964517
Mean4.1271
Median Absolute Deviation (MAD)2
Skewness0.032739881
Sum41271
Variance4.3987855
MonotonicityNot monotonic
2024-01-10T06:21:12.027726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
7 1416
14.2%
1 1413
14.1%
4 1390
13.9%
6 1387
13.9%
2 1379
13.8%
5 1363
13.6%
3 1345
13.5%
8 307
 
3.1%
ValueCountFrequency (%)
1 1413
14.1%
2 1379
13.8%
3 1345
13.5%
4 1390
13.9%
5 1363
13.6%
6 1387
13.9%
7 1416
14.2%
8 307
 
3.1%
ValueCountFrequency (%)
8 307
 
3.1%
7 1416
14.2%
6 1387
13.9%
5 1363
13.6%
4 1390
13.9%
3 1345
13.5%
2 1379
13.8%
1 1413
14.1%

합계_면적
Real number (ℝ)

HIGH CORRELATION 

Distinct9703
Distinct (%)97.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean609697.47
Minimum0
Maximum43982861
Zeros3
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-10T06:21:12.138329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1216
Q16845.5
median32768
Q3566381.18
95-th percentile1647644.5
Maximum43982861
Range43982861
Interquartile range (IQR)559535.68

Descriptive statistics

Standard deviation2275831.3
Coefficient of variation (CV)3.7327222
Kurtosis71.726365
Mean609697.47
Median Absolute Deviation (MAD)30959.906
Skewness7.7914659
Sum6.0969747 × 109
Variance5.1794082 × 1012
MonotonicityNot monotonic
2024-01-10T06:21:12.274483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60.0 7
 
0.1%
145.0 5
 
0.1%
1093.0 4
 
< 0.1%
349.0 4
 
< 0.1%
1716.0 4
 
< 0.1%
1143.0 3
 
< 0.1%
7624.0 3
 
< 0.1%
245.0 3
 
< 0.1%
291.0 3
 
< 0.1%
5248.0 3
 
< 0.1%
Other values (9693) 9961
99.6%
ValueCountFrequency (%)
0.0 3
< 0.1%
59.0 1
 
< 0.1%
60.0 7
0.1%
84.0 2
 
< 0.1%
84.906 2
 
< 0.1%
85.0 2
 
< 0.1%
89.37 1
 
< 0.1%
99.9193 2
 
< 0.1%
101.2843 1
 
< 0.1%
102.0 1
 
< 0.1%
ValueCountFrequency (%)
43982861.0 1
< 0.1%
34722814.0 1
< 0.1%
31096804.0 1
< 0.1%
29915611.0 1
< 0.1%
29694030.0 1
< 0.1%
25895643.0 1
< 0.1%
25442673.539975 1
< 0.1%
24797329.0 1
< 0.1%
24279139.692247 1
< 0.1%
24250119.0 1
< 0.1%

관할시군구내_면적
Real number (ℝ)

HIGH CORRELATION 

Distinct9509
Distinct (%)95.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean250539.32
Minimum0
Maximum33540910
Zeros8
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-10T06:21:12.406638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile833.8
Q13993
median17368
Q3224741.5
95-th percentile671278.2
Maximum33540910
Range33540910
Interquartile range (IQR)220748.5

Descriptive statistics

Standard deviation966100.95
Coefficient of variation (CV)3.8560851
Kurtosis195.45401
Mean250539.32
Median Absolute Deviation (MAD)16142.621
Skewness10.513156
Sum2.5053932 × 109
Variance9.3335105 × 1011
MonotonicityNot monotonic
2024-01-10T06:21:12.552289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 8
 
0.1%
60.0 6
 
0.1%
1904.0 5
 
0.1%
85.0 5
 
0.1%
1536.0 4
 
< 0.1%
246.0 4
 
< 0.1%
2019.0 4
 
< 0.1%
2909.0 4
 
< 0.1%
1665.0 4
 
< 0.1%
2247.0 4
 
< 0.1%
Other values (9499) 9952
99.5%
ValueCountFrequency (%)
0.0 8
0.1%
29.7 1
 
< 0.1%
36.0 2
 
< 0.1%
36.44 1
 
< 0.1%
49.95 1
 
< 0.1%
49.98 2
 
< 0.1%
59.0 1
 
< 0.1%
59.64 1
 
< 0.1%
60.0 6
0.1%
65.21 1
 
< 0.1%
ValueCountFrequency (%)
33540910.0 1
< 0.1%
15746083.0 1
< 0.1%
14176410.0 1
< 0.1%
14102263.0 1
< 0.1%
11532779.0 1
< 0.1%
11164575.0 1
< 0.1%
11100762.840522 1
< 0.1%
11053018.0 1
< 0.1%
10780804.0 1
< 0.1%
10609402.862246 1
< 0.1%

관할시도내_면적
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7712
Distinct (%)77.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean74401.377
Minimum0
Maximum6523634
Zeros587
Zeros (%)5.9%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-10T06:21:12.696426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1408.69405
median3007.4537
Q343075.483
95-th percentile237711.84
Maximum6523634
Range6523634
Interquartile range (IQR)42666.789

Descriptive statistics

Standard deviation298650.57
Coefficient of variation (CV)4.0140462
Kurtosis98.794828
Mean74401.377
Median Absolute Deviation (MAD)3007.4537
Skewness8.6889
Sum7.4401377 × 108
Variance8.9192162 × 1010
MonotonicityNot monotonic
2024-01-10T06:21:12.835652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 587
 
5.9%
60.0 50
 
0.5%
85.0 39
 
0.4%
50.0 33
 
0.3%
40.0 27
 
0.3%
59.0 12
 
0.1%
145.0 12
 
0.1%
185.0 12
 
0.1%
47.0 11
 
0.1%
120.0 10
 
0.1%
Other values (7702) 9207
92.1%
ValueCountFrequency (%)
0.0 587
5.9%
16.0 2
 
< 0.1%
17.0 2
 
< 0.1%
21.0 1
 
< 0.1%
22.0 1
 
< 0.1%
25.49 1
 
< 0.1%
26.0 1
 
< 0.1%
27.0 1
 
< 0.1%
28.0 1
 
< 0.1%
30.0 1
 
< 0.1%
ValueCountFrequency (%)
6523634.0 1
< 0.1%
5050908.0 1
< 0.1%
4890866.0 1
< 0.1%
4814525.0 1
< 0.1%
4371509.0 1
< 0.1%
4330576.0 1
< 0.1%
4232883.0 1
< 0.1%
3923921.0 1
< 0.1%
3830023.0 1
< 0.1%
3756803.0 1
< 0.1%

관할시도외서울면적
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7520
Distinct (%)75.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean74720.094
Minimum0
Maximum6267421
Zeros685
Zeros (%)6.9%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-10T06:21:12.972584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1313
median2097.7355
Q338273.375
95-th percentile236374.56
Maximum6267421
Range6267421
Interquartile range (IQR)37960.375

Descriptive statistics

Standard deviation312867.33
Coefficient of variation (CV)4.1871913
Kurtosis99.662341
Mean74720.094
Median Absolute Deviation (MAD)2062.7355
Skewness8.8210008
Sum7.4720094 × 108
Variance9.7885965 × 1010
MonotonicityNot monotonic
2024-01-10T06:21:13.111705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 685
 
6.9%
85.0 55
 
0.5%
60.0 53
 
0.5%
40.0 25
 
0.2%
145.0 23
 
0.2%
84.0 16
 
0.2%
185.0 15
 
0.1%
170.0 13
 
0.1%
50.0 13
 
0.1%
143.0 12
 
0.1%
Other values (7510) 9090
90.9%
ValueCountFrequency (%)
0.0 685
6.9%
2.0 2
 
< 0.1%
6.0 1
 
< 0.1%
16.0 1
 
< 0.1%
17.0123 1
 
< 0.1%
20.0 1
 
< 0.1%
21.0 1
 
< 0.1%
24.0 2
 
< 0.1%
28.08 2
 
< 0.1%
29.0 4
 
< 0.1%
ValueCountFrequency (%)
6267421.0 1
< 0.1%
6266762.0 1
< 0.1%
5495883.0 1
< 0.1%
4529069.0 1
< 0.1%
4196136.28256 1
< 0.1%
4098619.0 1
< 0.1%
4086322.352413 1
< 0.1%
4040286.707169 1
< 0.1%
3783564.677501 1
< 0.1%
3755930.0 1
< 0.1%

관할시도외기타면적
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8788
Distinct (%)87.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean210036.68
Minimum0
Maximum16989458
Zeros175
Zeros (%)1.8%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-10T06:21:13.477677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile165
Q11509.5255
median7470.2269
Q3160116.28
95-th percentile607558.55
Maximum16989458
Range16989458
Interquartile range (IQR)158606.75

Descriptive statistics

Standard deviation832478.82
Coefficient of variation (CV)3.9634926
Kurtosis83.977421
Mean210036.68
Median Absolute Deviation (MAD)7155.2269
Skewness8.2217564
Sum2.1003668 × 109
Variance6.9302099 × 1011
MonotonicityNot monotonic
2024-01-10T06:21:13.584638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 175
 
1.8%
85.0 29
 
0.3%
60.0 27
 
0.3%
84.0 10
 
0.1%
80.0 9
 
0.1%
210.0 7
 
0.1%
170.0 7
 
0.1%
40.0 6
 
0.1%
220.0 6
 
0.1%
59.0 6
 
0.1%
Other values (8778) 9718
97.2%
ValueCountFrequency (%)
0.0 175
1.8%
17.0 2
 
< 0.1%
19.0 1
 
< 0.1%
27.62 1
 
< 0.1%
29.7 1
 
< 0.1%
36.0 2
 
< 0.1%
38.0 1
 
< 0.1%
39.72 3
 
< 0.1%
39.99 2
 
< 0.1%
40.0 6
 
0.1%
ValueCountFrequency (%)
16989458.0 1
< 0.1%
15047933.0 1
< 0.1%
12988293.0 1
< 0.1%
12292163.0 1
< 0.1%
11837223.0 1
< 0.1%
11648404.0 1
< 0.1%
9387053.765878 1
< 0.1%
8720818.360286 1
< 0.1%
8685258.0 1
< 0.1%
8635651.0 1
< 0.1%

지역구분 레벨
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
8480 
2
967 
0
 
553

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 8480
84.8%
2 967
 
9.7%
0 553
 
5.5%

Length

2024-01-10T06:21:13.691099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T06:21:13.766139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 8480
84.8%
2 967
 
9.7%
0 553
 
5.5%

Interactions

2024-01-10T06:21:10.220850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:21:03.798133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:21:04.576221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:21:05.270370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:21:06.040331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:21:06.736796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:21:07.565933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:21:08.500937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:21:09.464396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:21:10.313467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:21:03.866952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:21:04.649701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:21:05.350147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:21:06.114085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:21:06.828762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:21:07.673171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:21:08.594945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:21:09.539454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:21:10.389160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:21:03.937733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:21:04.718359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:21:05.426244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:21:06.187261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:21:06.902856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:21:07.773643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:21:08.665171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:21:09.612581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:21:10.473095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:21:04.017670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:21:04.797190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:21:05.512235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:21:06.267713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:21:06.985637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:21:07.860720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:21:08.747681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:21:09.697889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:21:10.556830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:21:04.101034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:21:04.870988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:21:05.599952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:21:06.346136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:21:07.066682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:21:07.974199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:21:08.828925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:21:09.785744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:21:10.631067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:21:04.191406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:21:04.940542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:21:05.692362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:21:06.417125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:21:07.145685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:21:08.076718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:21:08.899686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:21:09.866369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:21:10.717209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:21:04.295418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:21:05.025281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:21:05.785006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:21:06.499246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:21:07.255514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:21:08.185729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:21:09.213923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:21:09.950306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:21:10.797303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:21:04.392113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:21:05.109354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:21:05.870009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:21:06.575931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:21:07.351458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:21:08.289009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:21:09.300456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:21:10.032587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:21:10.879407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:21:04.491723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:21:05.191534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:21:05.952221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:21:06.653180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:21:07.448689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:21:08.394249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:21:09.381047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:21:10.112000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-10T06:21:13.834324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
번호지역코드지역명조사일자거래유형합계_면적관할시군구내_면적관할시도내_면적관할시도외서울면적관할시도외기타면적지역구분 레벨
번호1.0000.9940.9810.2120.0520.3030.3280.2760.3740.3960.865
지역코드0.9941.0001.0000.1060.0000.0110.0110.0460.0370.0360.517
지역명0.9811.0001.0000.1320.0000.5040.5240.4630.3970.4231.000
조사일자0.2120.1060.1321.0000.2370.0640.0430.0570.1190.0940.123
거래유형0.0520.0000.0000.2371.0000.1620.1610.1770.1830.1650.019
합계_면적0.3030.0110.5040.0640.1621.0000.9230.8400.7840.8370.674
관할시군구내_면적0.3280.0110.5240.0430.1610.9231.0000.7220.7050.7570.695
관할시도내_면적0.2760.0460.4630.0570.1770.8400.7221.0000.6180.6570.627
관할시도외서울면적0.3740.0370.3970.1190.1830.7840.7050.6181.0000.8910.504
관할시도외기타면적0.3960.0360.4230.0940.1650.8370.7570.6570.8911.0000.536
지역구분 레벨0.8650.5171.0000.1230.0190.6740.6950.6270.5040.5361.000
2024-01-10T06:21:13.971571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지역명지역구분 레벨
지역명1.0000.999
지역구분 레벨0.9991.000
2024-01-10T06:21:14.063211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
번호지역코드조사일자거래유형합계_면적관할시군구내_면적관할시도내_면적관할시도외서울면적관할시도외기타면적지역명지역구분 레벨
번호1.0000.9980.0280.020-0.386-0.402-0.399-0.343-0.3560.9000.792
지역코드0.9981.000-0.0230.010-0.385-0.400-0.399-0.341-0.3570.9990.835
조사일자0.028-0.0231.0000.0680.1150.1000.1210.0890.1570.0510.075
거래유형0.0200.0100.0681.000-0.626-0.607-0.560-0.609-0.6270.0000.012
합계_면적-0.386-0.3850.115-0.6261.0000.9860.9430.9470.9760.1900.385
관할시군구내_면적-0.402-0.4000.100-0.6070.9861.0000.9270.9290.9470.2360.378
관할시도내_면적-0.399-0.3990.121-0.5600.9430.9271.0000.9060.9120.1710.346
관할시도외서울면적-0.343-0.3410.089-0.6090.9470.9290.9061.0000.9260.1630.350
관할시도외기타면적-0.356-0.3570.157-0.6270.9760.9470.9120.9261.0000.1760.381
지역명0.9000.9990.0510.0000.1900.2360.1710.1630.1761.0000.999
지역구분 레벨0.7920.8350.0750.0120.3850.3780.3460.3500.3810.9991.000

Missing values

2024-01-10T06:21:10.983991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-10T06:21:11.107888image/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_면적관할시군구내_면적관할시도내_면적관할시도외서울면적관할시도외기타면적지역구분 레벨
174511745244760부여군2020028918246.0456607151.4003102109.382322860.5884186124.67461
182101821144770서천군2013015512.0428.00.00.084.01
161881618944710금산군20220511834583.608414315345.540667978617.610441398.1464499222.3109471
183831838444770서천군2014107956.0824.048.084.00.01
157291573044710금산군20161265496.30131620.4213192.15935.372748.361
145811458244270당진시202004519212.1325512879.44871046.02491477.178053809.48091
14514644000충남201310114058178.04808903.01740849.02708053.04800373.00
9012901344200아산시201602634912.017370.04457.03400.09685.01
218372183844800홍성군2021061962149.008043476460.33728854660.408265562.95325365465.3093051
122551225644230논산시20200268721.16165459.0199804.144189.92692268.07081
번호지역코드지역명조사일자거래유형합계_면적관할시군구내_면적관할시도내_면적관할시도외서울면적관할시도외기타면적지역구분 레벨
107551075644210서산시201907341842.69547724266.9851282450.09916564.6410678560.9701821
128631286444250계룡시200806416229.04299.01037.04879.06014.01
2639264044130천안시2020058869338.23195282069.877265876.4120557002.0399264389.9031
5409541044150공주시20060411968811.0747379.0136969.0146631.0937832.01
101521015344210서산시201210617157.010706.01159.02161.03131.01
149271492844710금산군2008081826930.0248859.0174967.0153427.0249677.01
141511415244270당진시201505348545.030521.04548.03574.09902.01
2913291444131동남구2012021464243.0154305.082774.036890.0190274.02
162221622344710금산군20220621041543.576615346817.4469675716.011676.6677333.5296481
144101441144270당진시201809610884.71925244.1257879.82021320.93993439.83341