Overview

Dataset statistics

Number of variables11
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1015.6 KiB
Average record size in memory104.0 B

Variable types

Categorical3
Text1
Numeric7

Dataset

Description국토 소유 연령별 집계 현황
Author경기도
URLhttps://data.gg.go.kr/portal/data/service/selectServicePage.do?&infId=5ZI7JQ6ZGLI7FFG849TG24361643&infSeq=1

Alerts

소유자수(명) is highly overall correlated with 단독소유지번수(개) and 3 other fieldsHigh correlation
평균지번수(개) is highly overall correlated with 평균면적(㎡)High correlation
평균면적(㎡) is highly overall correlated with 평균지번수(개) and 1 other fieldsHigh correlation
단독소유지번수(개) is highly overall correlated with 소유자수(명) and 3 other fieldsHigh correlation
단독소유면적(㎡) is highly overall correlated with 소유자수(명) and 4 other fieldsHigh correlation
공동소유지번수(개) is highly overall correlated with 소유자수(명) and 3 other fieldsHigh correlation
공동소유면적(㎡) is highly overall correlated with 소유자수(명) and 3 other fieldsHigh correlation
평균면적(㎡) is highly skewed (γ1 = 26.30059764)Skewed
평균지번수(개) has 371 (3.7%) zerosZeros
단독소유지번수(개) has 1165 (11.7%) zerosZeros
단독소유면적(㎡) has 1165 (11.7%) zerosZeros
공동소유지번수(개) has 633 (6.3%) zerosZeros
공동소유면적(㎡) has 633 (6.3%) zerosZeros

Reproduction

Analysis started2023-12-16 06:26:11.314794
Analysis finished2023-12-16 06:27:01.281186
Duration49.97 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기준년월
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
201212
5028 
201712
4972 

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row201712
2nd row201212
3rd row201712
4th row201212
5th row201712

Common Values

ValueCountFrequency (%)
201212 5028
50.3%
201712 4972
49.7%

Length

2023-12-16T06:27:01.866502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-16T06:27:02.566901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
201212 5028
50.3%
201712 4972
49.7%

시군명
Categorical

Distinct31
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
안성시
935 
화성시
904 
여주시
716 
평택시
695 
이천시
668 
Other values (26)
6082 

Length

Max length4
Median length3
Mean length3.0436
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row양평군
2nd row화성시
3rd row양평군
4th row성남시
5th row남양주시

Common Values

ValueCountFrequency (%)
안성시 935
 
9.3%
화성시 904
 
9.0%
여주시 716
 
7.2%
평택시 695
 
7.0%
이천시 668
 
6.7%
파주시 633
 
6.3%
용인시 543
 
5.4%
양평군 536
 
5.4%
포천시 447
 
4.5%
광주시 421
 
4.2%
Other values (21) 3502
35.0%

Length

2023-12-16T06:27:03.356044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
안성시 935
 
9.3%
화성시 904
 
9.0%
여주시 716
 
7.2%
평택시 695
 
7.0%
이천시 668
 
6.7%
파주시 633
 
6.3%
용인시 543
 
5.4%
양평군 536
 
5.4%
포천시 447
 
4.5%
광주시 421
 
4.2%
Other values (21) 3502
35.0%

법정동명
Text

Distinct2195
Distinct (%)21.9%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-16T06:27:04.805940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length15
Mean length14.484
Min length10

Characters and Unicode

Total characters144840
Distinct characters315
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique135 ?
Unique (%)1.4%

Sample

1st row경기도 양평군 용문면 오촌리
2nd row경기도 화성시 활초동
3rd row경기도 양평군 강하면 성덕리
4th row경기도 성남시 분당구 구미동
5th row경기도 남양주시 화도읍 답내리
ValueCountFrequency (%)
경기도 10000
25.9%
안성시 935
 
2.4%
화성시 904
 
2.3%
평택시 695
 
1.8%
이천시 668
 
1.7%
파주시 633
 
1.6%
용인시 543
 
1.4%
양평군 536
 
1.4%
포천시 447
 
1.2%
광주시 421
 
1.1%
Other values (1971) 22807
59.1%
2023-12-16T06:27:07.728218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
28589
19.7%
도 10427
 
7.2%
기 10199
 
7.0%
경 10023
 
6.9%
시 8610
 
5.9%
리 7110
 
4.9%
면 5100
 
3.5%
동 3737
 
2.6%
성 2739
 
1.9%
주 2541
 
1.8%
Other values (305) 55765
38.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 116217
80.2%
Space Separator 28589
 
19.7%
Decimal Number 34
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 10427
 
9.0%
기 10199
 
8.8%
경 10023
 
8.6%
시 8610
 
7.4%
리 7110
 
6.1%
면 5100
 
4.4%
동 3737
 
3.2%
성 2739
 
2.4%
주 2541
 
2.2%
양 2289
 
2.0%
Other values (301) 53442
46.0%
Decimal Number
ValueCountFrequency (%)
2 14
41.2%
3 11
32.4%
1 9
26.5%
Space Separator
ValueCountFrequency (%)
28589
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 116217
80.2%
Common 28623
 
19.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 10427
 
9.0%
기 10199
 
8.8%
경 10023
 
8.6%
시 8610
 
7.4%
리 7110
 
6.1%
면 5100
 
4.4%
동 3737
 
3.2%
성 2739
 
2.4%
주 2541
 
2.2%
양 2289
 
2.0%
Other values (301) 53442
46.0%
Common
ValueCountFrequency (%)
28589
99.9%
2 14
 
< 0.1%
3 11
 
< 0.1%
1 9
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 116217
80.2%
ASCII 28623
 
19.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
28589
99.9%
2 14
 
< 0.1%
3 11
 
< 0.1%
1 9
 
< 0.1%
Hangul
ValueCountFrequency (%)
도 10427
 
9.0%
기 10199
 
8.8%
경 10023
 
8.6%
시 8610
 
7.4%
리 7110
 
6.1%
면 5100
 
4.4%
동 3737
 
3.2%
성 2739
 
2.4%
주 2541
 
2.2%
양 2289
 
2.0%
Other values (301) 53442
46.0%

연령대구분명
Categorical

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
70대
1040 
80대
1029 
20대
1010 
40대
1009 
60대
993 
Other values (6)
4919 

Length

Max length6
Median length3
Mean length3.3131
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row40대
2nd row50대
3rd row90대
4th row90대
5th row80대

Common Values

ValueCountFrequency (%)
70대 1040
10.4%
80대 1029
10.3%
20대 1010
10.1%
40대 1009
10.1%
60대 993
9.9%
90대 991
9.9%
50대 989
9.9%
30대 978
9.8%
100세이상 805
8.1%
10대 798
8.0%

Length

2023-12-16T06:27:08.673898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
70대 1040
10.4%
80대 1029
10.3%
20대 1010
10.1%
40대 1009
10.1%
60대 993
9.9%
90대 991
9.9%
50대 989
9.9%
30대 978
9.8%
100세이상 805
8.1%
10대 798
8.0%

소유자수(명)
Real number (ℝ)

HIGH CORRELATION 

Distinct1221
Distinct (%)12.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean241.4587
Minimum1
Maximum13317
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-16T06:27:09.508139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q18
median44
Q3162
95-th percentile1052.05
Maximum13317
Range13316
Interquartile range (IQR)154

Descriptive statistics

Standard deviation757.46502
Coefficient of variation (CV)3.1370376
Kurtosis81.324966
Mean241.4587
Median Absolute Deviation (MAD)41
Skewness7.665649
Sum2414587
Variance573753.26
MonotonicityNot monotonic
2023-12-16T06:27:10.407965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 612
 
6.1%
2 461
 
4.6%
3 390
 
3.9%
4 325
 
3.2%
5 285
 
2.9%
6 209
 
2.1%
8 202
 
2.0%
7 201
 
2.0%
9 183
 
1.8%
11 126
 
1.3%
Other values (1211) 7006
70.1%
ValueCountFrequency (%)
1 612
6.1%
2 461
4.6%
3 390
3.9%
4 325
3.2%
5 285
2.9%
6 209
 
2.1%
7 201
 
2.0%
8 202
 
2.0%
9 183
 
1.8%
10 122
 
1.2%
ValueCountFrequency (%)
13317 1
< 0.1%
13129 1
< 0.1%
13024 1
< 0.1%
12910 1
< 0.1%
12257 1
< 0.1%
11253 1
< 0.1%
10432 1
< 0.1%
9839 1
< 0.1%
9679 1
< 0.1%
8991 1
< 0.1%

평균지번수(개)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct25
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.04
Minimum0
Maximum70
Zeros371
Zeros (%)3.7%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-16T06:27:11.564922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q33
95-th percentile4
Maximum70
Range70
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.5232964
Coefficient of variation (CV)0.74671394
Kurtosis457.36873
Mean2.04
Median Absolute Deviation (MAD)1
Skewness13.408564
Sum20400
Variance2.320432
MonotonicityNot monotonic
2023-12-16T06:27:12.242531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
2 3953
39.5%
1 2981
29.8%
3 2049
20.5%
4 416
 
4.2%
0 371
 
3.7%
5 120
 
1.2%
6 39
 
0.4%
7 19
 
0.2%
8 15
 
0.1%
10 6
 
0.1%
Other values (15) 31
 
0.3%
ValueCountFrequency (%)
0 371
 
3.7%
1 2981
29.8%
2 3953
39.5%
3 2049
20.5%
4 416
 
4.2%
5 120
 
1.2%
6 39
 
0.4%
7 19
 
0.2%
8 15
 
0.1%
9 4
 
< 0.1%
ValueCountFrequency (%)
70 1
 
< 0.1%
32 1
 
< 0.1%
28 1
 
< 0.1%
27 3
< 0.1%
26 1
 
< 0.1%
21 1
 
< 0.1%
19 1
 
< 0.1%
18 1
 
< 0.1%
17 1
 
< 0.1%
16 1
 
< 0.1%

평균면적(㎡)
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct9796
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2969.7537
Minimum0.16
Maximum375831
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-16T06:27:13.003338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.16
5-th percentile58.2405
Q1499.91
median1738.705
Q33527.4725
95-th percentile8710.787
Maximum375831
Range375830.84
Interquartile range (IQR)3027.5625

Descriptive statistics

Standard deviation7455.321
Coefficient of variation (CV)2.5104172
Kurtosis1084.8301
Mean2969.7537
Median Absolute Deviation (MAD)1398.915
Skewness26.300598
Sum29697537
Variance55581811
MonotonicityNot monotonic
2023-12-16T06:27:13.728908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
52.0 3
 
< 0.1%
182.0 3
 
< 0.1%
109.5 3
 
< 0.1%
792.64 3
 
< 0.1%
106.0 3
 
< 0.1%
99.0 3
 
< 0.1%
55.04 3
 
< 0.1%
1.0 3
 
< 0.1%
3.0 3
 
< 0.1%
44.96 3
 
< 0.1%
Other values (9786) 9970
99.7%
ValueCountFrequency (%)
0.16 1
 
< 0.1%
0.2 1
 
< 0.1%
0.33 1
 
< 0.1%
0.75 1
 
< 0.1%
1.0 3
< 0.1%
1.2 1
 
< 0.1%
1.38 1
 
< 0.1%
1.4 1
 
< 0.1%
1.5 1
 
< 0.1%
2.13 1
 
< 0.1%
ValueCountFrequency (%)
375831.0 1
< 0.1%
329256.0 1
< 0.1%
200127.93 1
< 0.1%
142662.33 1
< 0.1%
142239.75 1
< 0.1%
112573.4 1
< 0.1%
104529.0 1
< 0.1%
95647.62 1
< 0.1%
93327.84 1
< 0.1%
82780.0 1
< 0.1%

단독소유지번수(개)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct734
Distinct (%)7.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean116.7054
Minimum0
Maximum2337
Zeros1165
Zeros (%)11.7%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-16T06:27:14.560167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median39
Q3175
95-th percentile452
Maximum2337
Range2337
Interquartile range (IQR)171

Descriptive statistics

Standard deviation171.1803
Coefficient of variation (CV)1.4667728
Kurtosis15.67514
Mean116.7054
Median Absolute Deviation (MAD)39
Skewness2.8698185
Sum1167054
Variance29302.696
MonotonicityNot monotonic
2023-12-16T06:27:15.594569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1165
 
11.7%
1 506
 
5.1%
2 361
 
3.6%
3 278
 
2.8%
4 277
 
2.8%
5 215
 
2.1%
6 207
 
2.1%
7 176
 
1.8%
9 131
 
1.3%
8 119
 
1.2%
Other values (724) 6565
65.6%
ValueCountFrequency (%)
0 1165
11.7%
1 506
5.1%
2 361
 
3.6%
3 278
 
2.8%
4 277
 
2.8%
5 215
 
2.1%
6 207
 
2.1%
7 176
 
1.8%
8 119
 
1.2%
9 131
 
1.3%
ValueCountFrequency (%)
2337 1
< 0.1%
2037 1
< 0.1%
1958 1
< 0.1%
1863 1
< 0.1%
1805 1
< 0.1%
1797 1
< 0.1%
1775 1
< 0.1%
1772 1
< 0.1%
1618 1
< 0.1%
1418 1
< 0.1%

단독소유면적(㎡)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8421
Distinct (%)84.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean167045.55
Minimum0
Maximum6320103
Zeros1165
Zeros (%)11.7%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-16T06:27:16.559645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13079.25
median48170.2
Q3233212
95-th percentile660803.6
Maximum6320103
Range6320103
Interquartile range (IQR)230132.75

Descriptive statistics

Standard deviation282792.54
Coefficient of variation (CV)1.6929067
Kurtosis67.725295
Mean167045.55
Median Absolute Deviation (MAD)48170.2
Skewness5.2616949
Sum1.6704555 × 109
Variance7.9971618 × 1010
MonotonicityNot monotonic
2023-12-16T06:27:17.407711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 1165
 
11.7%
3.0 10
 
0.1%
10.0 7
 
0.1%
99.0 7
 
0.1%
496.0 6
 
0.1%
198.0 6
 
0.1%
17.0 5
 
0.1%
86.0 5
 
0.1%
1653.0 5
 
0.1%
397.0 5
 
0.1%
Other values (8411) 8779
87.8%
ValueCountFrequency (%)
0.0 1165
11.7%
1.0 5
 
0.1%
2.0 2
 
< 0.1%
2.5 1
 
< 0.1%
3.0 10
 
0.1%
6.0 1
 
< 0.1%
7.0 2
 
< 0.1%
9.0 1
 
< 0.1%
10.0 7
 
0.1%
11.0 1
 
< 0.1%
ValueCountFrequency (%)
6320103.0 1
< 0.1%
5943887.0 1
< 0.1%
5786814.0 1
< 0.1%
3371866.0 1
< 0.1%
3358193.0 1
< 0.1%
2788600.0 1
< 0.1%
2692601.0 1
< 0.1%
2561258.0 1
< 0.1%
2559391.0 1
< 0.1%
2553898.0 1
< 0.1%

공동소유지번수(개)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct545
Distinct (%)5.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean75.1352
Minimum0
Maximum1729
Zeros633
Zeros (%)6.3%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-16T06:27:18.403165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q16
median30
Q3103
95-th percentile287
Maximum1729
Range1729
Interquartile range (IQR)97

Descriptive statistics

Standard deviation112.85028
Coefficient of variation (CV)1.5019628
Kurtosis24.858433
Mean75.1352
Median Absolute Deviation (MAD)28
Skewness3.5998653
Sum751352
Variance12735.185
MonotonicityNot monotonic
2023-12-16T06:27:19.147784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 633
 
6.3%
1 571
 
5.7%
2 419
 
4.2%
3 331
 
3.3%
4 277
 
2.8%
5 230
 
2.3%
6 209
 
2.1%
7 196
 
2.0%
8 182
 
1.8%
9 157
 
1.6%
Other values (535) 6795
68.0%
ValueCountFrequency (%)
0 633
6.3%
1 571
5.7%
2 419
4.2%
3 331
3.3%
4 277
2.8%
5 230
 
2.3%
6 209
 
2.1%
7 196
 
2.0%
8 182
 
1.8%
9 157
 
1.6%
ValueCountFrequency (%)
1729 1
< 0.1%
1606 1
< 0.1%
1558 1
< 0.1%
1433 1
< 0.1%
1415 1
< 0.1%
1178 1
< 0.1%
1124 1
< 0.1%
1103 1
< 0.1%
1082 1
< 0.1%
1055 1
< 0.1%

공동소유면적(㎡)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct9266
Distinct (%)92.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64159.21
Minimum0
Maximum6500232.8
Zeros633
Zeros (%)6.3%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-16T06:27:19.946788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12546.13
median19746.715
Q374384.423
95-th percentile259317.14
Maximum6500232.8
Range6500232.8
Interquartile range (IQR)71838.292

Descriptive statistics

Standard deviation137873.42
Coefficient of variation (CV)2.1489264
Kurtosis504.97488
Mean64159.21
Median Absolute Deviation (MAD)19456.52
Skewness13.990365
Sum6.415921 × 108
Variance1.900908 × 1010
MonotonicityNot monotonic
2023-12-16T06:27:20.715879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 633
 
6.3%
64.0 4
 
< 0.1%
182.0 4
 
< 0.1%
909.0 4
 
< 0.1%
587.0 3
 
< 0.1%
159.67 3
 
< 0.1%
7.8 3
 
< 0.1%
1.5 3
 
< 0.1%
248.0 3
 
< 0.1%
22.0 3
 
< 0.1%
Other values (9256) 9337
93.4%
ValueCountFrequency (%)
0.0 633
6.3%
0.14 1
 
< 0.1%
0.2 1
 
< 0.1%
0.33 1
 
< 0.1%
0.5 1
 
< 0.1%
0.7 1
 
< 0.1%
0.75 1
 
< 0.1%
1.0 2
 
< 0.1%
1.2 1
 
< 0.1%
1.25 1
 
< 0.1%
ValueCountFrequency (%)
6500232.76 1
< 0.1%
2131309.65 1
< 0.1%
1875655.18 1
< 0.1%
1865138.49 1
< 0.1%
1828978.58 1
< 0.1%
1630325.46 1
< 0.1%
1594886.03 1
< 0.1%
1466065.03 1
< 0.1%
1430270.52 1
< 0.1%
1400895.49 1
< 0.1%

Interactions

2023-12-16T06:26:52.900018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T06:26:24.056146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T06:26:28.749472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T06:26:32.988976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T06:26:37.381632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T06:26:41.644757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T06:26:45.534870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T06:26:53.418967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T06:26:24.990641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T06:26:29.210817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T06:26:33.554799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T06:26:37.956126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T06:26:42.074116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T06:26:46.223392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T06:26:54.156058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T06:26:25.805698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T06:26:30.178060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T06:26:34.496869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T06:26:38.599454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T06:26:42.680682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T06:26:46.849938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T06:26:54.951762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T06:26:26.317443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T06:26:30.842667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T06:26:34.938884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T06:26:39.560978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T06:26:43.274469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T06:26:47.756600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T06:26:55.955919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T06:26:26.988227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T06:26:31.353140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T06:26:35.673406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T06:26:40.114550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T06:26:43.784875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T06:26:48.860857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T06:26:57.217942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T06:26:27.531547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T06:26:31.792858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T06:26:36.198669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T06:26:40.674886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T06:26:44.457281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T06:26:50.424834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T06:26:58.378892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T06:26:28.114600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T06:26:32.295182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T06:26:36.779289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T06:26:41.156030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T06:26:45.022458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T06:26:51.765127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-16T06:27:21.451499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준년월시군명연령대구분명소유자수(명)평균지번수(개)평균면적(㎡)단독소유지번수(개)단독소유면적(㎡)공동소유지번수(개)공동소유면적(㎡)
기준년월1.0000.0000.0080.0000.0000.0080.0000.0250.0650.000
시군명0.0001.0000.0290.3600.0680.0610.2650.2120.2750.119
연령대구분명0.0080.0291.0000.1890.0650.0530.4810.2820.3990.096
소유자수(명)0.0000.3600.1891.0000.0000.0000.5600.0000.4400.048
평균지번수(개)0.0000.0680.0650.0001.0000.6990.0000.0000.0000.000
평균면적(㎡)0.0080.0610.0530.0000.6991.0000.0000.1170.0000.230
단독소유지번수(개)0.0000.2650.4810.5600.0000.0001.0000.4890.7710.340
단독소유면적(㎡)0.0250.2120.2820.0000.0000.1170.4891.0000.3860.343
공동소유지번수(개)0.0650.2750.3990.4400.0000.0000.7710.3861.0000.271
공동소유면적(㎡)0.0000.1190.0960.0480.0000.2300.3400.3430.2711.000
2023-12-16T06:27:22.121585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연령대구분명기준년월시군명
연령대구분명1.0000.0080.010
기준년월0.0081.0000.000
시군명0.0100.0001.000
2023-12-16T06:27:23.341968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
소유자수(명)평균지번수(개)평균면적(㎡)단독소유지번수(개)단독소유면적(㎡)공동소유지번수(개)공동소유면적(㎡)기준년월시군명연령대구분명
소유자수(명)1.000-0.0550.0090.8660.7610.8960.7540.0000.1340.081
평균지번수(개)-0.0551.0000.6350.2570.3580.1900.2650.0160.0170.032
평균면적(㎡)0.0090.6351.0000.3190.5370.1910.4740.0080.0250.026
단독소유지번수(개)0.8660.2570.3191.0000.9410.8720.7980.0000.0960.227
단독소유면적(㎡)0.7610.3580.5370.9411.0000.8040.8180.0270.0890.143
공동소유지번수(개)0.8960.1900.1910.8720.8041.0000.8790.0650.1060.193
공동소유면적(㎡)0.7540.2650.4740.7980.8180.8791.0000.0000.0560.053
기준년월0.0000.0160.0080.0000.0270.0650.0001.0000.0000.008
시군명0.1340.0170.0250.0960.0890.1060.0560.0001.0000.010
연령대구분명0.0810.0320.0260.2270.1430.1930.0530.0080.0101.000

Missing values

2023-12-16T06:26:59.535612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-16T06:27:00.970445image/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

기준년월시군명법정동명연령대구분명소유자수(명)평균지번수(개)평균면적(㎡)단독소유지번수(개)단독소유면적(㎡)공동소유지번수(개)공동소유면적(㎡)
8971201712양평군경기도 양평군 용문면 오촌리40대10334131.04103251692.0185173805.01
39417201212화성시경기도 화성시 활초동50대23632854.59393551480.9224121491.26
8178201712양평군경기도 양평군 강하면 성덕리90대1233169.452120275.01217758.4
23825201212성남시경기도 성남시 분당구 구미동90대37160.081396.400.0
3585201712남양주시경기도 남양주시 화도읍 답내리80대4431466.265956375.0457823.16
5865201712안성시경기도 안성시 구포동60대441147.94375535.110798.0
11240201712연천군경기도 연천군 전곡읍 늘목리100세이상31535.53757.01849.5
28789201212양평군경기도 양평군 지평면 일신리10세미만21170.0700.02340.13
8826201712양평군경기도 양평군 양평읍 회현리40대2732923.33235136214.0398115747.75
6614201712안성시경기도 안성시 삼죽면 내강리60대11733829.43236275471.0145172572.08
기준년월시군명법정동명연령대구분명소유자수(명)평균지번수(개)평균면적(㎡)단독소유지번수(개)단독소유면적(㎡)공동소유지번수(개)공동소유면적(㎡)
7154201712안성시경기도 안성시 원곡면 성주리90대722042.21122.01114273.48
1226201712과천시경기도 과천시 문원동90대15219470.1519271013.0621039.25
7633201712양주시경기도 양주시 광적면 우고리30대4821748.614650277.03733656.21
2628201712김포시경기도 김포시 양촌읍 석모리20대1831300.33207014.02716391.85
35683201212평택시경기도 평택시 월곡동20대614697.03937.0527244.99
18254201712화성시경기도 화성시 동탄면 중리60대8236365.62101327803.0122194177.76
33575201212이천시경기도 이천시 율면 북두리70대5034310.14134192332.04023175.11
1616201712광주시경기도 광주시 남한산성면 광지원리30대3223655.212030691.05386275.57
20503201212고양시경기도 고양시 덕양구 고양동100세이상12440.02440.000.0
4775201712수원시경기도 수원시 장안구 조원동60대27361138.33505193009.928094192.51