Overview

Dataset statistics

Number of variables17
Number of observations10000
Missing cells93
Missing cells (%)0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.5 MiB
Average record size in memory156.0 B

Variable types

Numeric12
DateTime1
Categorical1
Text3

Dataset

Description법정동(읍면동리) 세대원수별 주민등록 세대수에 대한 데이터입니다.법정동은 시 또는 구의 하위 행정구역으로 법률로 지정한 구역을 말합니다.
Author행정안전부
URLhttps://www.data.go.kr/data/15099159/fileData.do

Alerts

기준연월 has constant value ""Constant
법정동코드 is highly overall correlated with 시도명High correlation
전체세대수 is highly overall correlated with 1인세대 and 6 other fieldsHigh correlation
1인세대 is highly overall correlated with 전체세대수 and 6 other fieldsHigh correlation
2인세대 is highly overall correlated with 전체세대수 and 6 other fieldsHigh correlation
3인세대 is highly overall correlated with 전체세대수 and 6 other fieldsHigh correlation
4인세대 is highly overall correlated with 전체세대수 and 6 other fieldsHigh correlation
5인세대 is highly overall correlated with 전체세대수 and 6 other fieldsHigh correlation
6인세대 is highly overall correlated with 전체세대수 and 6 other fieldsHigh correlation
7인세대 is highly overall correlated with 전체세대수 and 7 other fieldsHigh correlation
8인세대 is highly overall correlated with 7인세대High correlation
시도명 is highly overall correlated with 법정동코드High correlation
법정동코드 has unique valuesUnique
3인세대 has 154 (1.5%) zerosZeros
4인세대 has 680 (6.8%) zerosZeros
5인세대 has 2004 (20.0%) zerosZeros
6인세대 has 4450 (44.5%) zerosZeros
7인세대 has 6849 (68.5%) zerosZeros
8인세대 has 8447 (84.5%) zerosZeros
9인세대 has 9251 (92.5%) zerosZeros
10인이상세대 has 9138 (91.4%) zerosZeros

Reproduction

Analysis started2024-04-06 08:24:06.473584
Analysis finished2024-04-06 08:24:51.663737
Duration45.19 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

법정동코드
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.4503997 × 109
Minimum1.1110102 × 109
Maximum5.280042 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-06T17:24:51.837669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.1110102 × 109
5-th percentile2.771038 × 109
Q14.315032 × 109
median4.681034 × 109
Q34.824031 × 109
95-th percentile5.221034 × 109
Maximum5.280042 × 109
Range4.1690318 × 109
Interquartile range (IQR)5.0899898 × 108

Descriptive statistics

Standard deviation7.7397522 × 108
Coefficient of variation (CV)0.1739114
Kurtosis6.3965677
Mean4.4503997 × 109
Median Absolute Deviation (MAD)2.60995 × 108
Skewness-2.33892
Sum4.4503997 × 1013
Variance5.9903765 × 1017
MonotonicityNot monotonic
2024-04-06T17:24:52.252609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5271034021 1
 
< 0.1%
4680032028 1
 
< 0.1%
4376036050 1
 
< 0.1%
2872031021 1
 
< 0.1%
5277037025 1
 
< 0.1%
4420037032 1
 
< 0.1%
4679031027 1
 
< 0.1%
4723038026 1
 
< 0.1%
4615031028 1
 
< 0.1%
4777033035 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
1111010200 1
< 0.1%
1111010300 1
< 0.1%
1111010400 1
< 0.1%
1111010500 1
< 0.1%
1111010900 1
< 0.1%
1111011000 1
< 0.1%
1111011100 1
< 0.1%
1111011200 1
< 0.1%
1111011300 1
< 0.1%
1111011400 1
< 0.1%
ValueCountFrequency (%)
5280042028 1
< 0.1%
5280042026 1
< 0.1%
5280042025 1
< 0.1%
5280042024 1
< 0.1%
5280041025 1
< 0.1%
5280041023 1
< 0.1%
5280041022 1
< 0.1%
5280041021 1
< 0.1%
5280040025 1
< 0.1%
5280040024 1
< 0.1%

기준연월
Date

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2024-03-31 00:00:00
Maximum2024-03-31 00:00:00
2024-04-06T17:24:52.614722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:24:52.835508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

시도명
Categorical

HIGH CORRELATION 

Distinct17
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
경상북도
1595 
전라남도
1471 
경상남도
1149 
충청남도
1106 
경기도
1084 
Other values (12)
3595 

Length

Max length7
Median length4
Mean length4.516
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row전북특별자치도
2nd row울산광역시
3rd row경상북도
4th row전라남도
5th row경기도

Common Values

ValueCountFrequency (%)
경상북도 1595
16.0%
전라남도 1471
14.7%
경상남도 1149
11.5%
충청남도 1106
11.1%
경기도 1084
10.8%
전북특별자치도 881
8.8%
충청북도 849
8.5%
강원특별자치도 653
6.5%
서울특별시 229
 
2.3%
대구광역시 195
 
1.9%
Other values (7) 788
7.9%

Length

2024-04-06T17:24:53.071453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경상북도 1595
16.0%
전라남도 1471
14.7%
경상남도 1149
11.5%
충청남도 1106
11.1%
경기도 1084
10.8%
전북특별자치도 881
8.8%
충청북도 849
8.5%
강원특별자치도 653
6.5%
서울특별시 229
 
2.3%
대구광역시 195
 
1.9%
Other values (7) 788
7.9%
Distinct228
Distinct (%)2.3%
Missing93
Missing (%)0.9%
Memory size156.2 KiB
2024-04-06T17:24:53.724631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length3
Mean length3.3006965
Min length2

Characters and Unicode

Total characters32700
Distinct characters141
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)0.1%

Sample

1st row완주군
2nd row울주군
3rd row영천시
4th row함평군
5th row광주시
ValueCountFrequency (%)
청주시 181
 
1.7%
창원시 167
 
1.6%
중구 143
 
1.3%
상주시 137
 
1.3%
북구 131
 
1.2%
포항시 125
 
1.2%
안동시 122
 
1.1%
나주시 116
 
1.1%
논산시 116
 
1.1%
안성시 115
 
1.1%
Other values (228) 9348
87.4%
2024-04-06T17:24:54.664193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4712
 
14.4%
4600
 
14.1%
1670
 
5.1%
1456
 
4.5%
1224
 
3.7%
1017
 
3.1%
997
 
3.0%
794
 
2.4%
741
 
2.3%
670
 
2.0%
Other values (131) 14819
45.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 31906
97.6%
Space Separator 794
 
2.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
4712
 
14.8%
4600
 
14.4%
1670
 
5.2%
1456
 
4.6%
1224
 
3.8%
1017
 
3.2%
997
 
3.1%
741
 
2.3%
670
 
2.1%
571
 
1.8%
Other values (130) 14248
44.7%
Space Separator
ValueCountFrequency (%)
794
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 31906
97.6%
Common 794
 
2.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
4712
 
14.8%
4600
 
14.4%
1670
 
5.2%
1456
 
4.6%
1224
 
3.8%
1017
 
3.2%
997
 
3.1%
741
 
2.3%
670
 
2.1%
571
 
1.8%
Other values (130) 14248
44.7%
Common
ValueCountFrequency (%)
794
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 31906
97.6%
ASCII 794
 
2.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
4712
 
14.8%
4600
 
14.4%
1670
 
5.2%
1456
 
4.6%
1224
 
3.8%
1017
 
3.2%
997
 
3.1%
741
 
2.3%
670
 
2.1%
571
 
1.8%
Other values (130) 14248
44.7%
ASCII
ValueCountFrequency (%)
794
100.0%
Distinct2836
Distinct (%)28.4%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-04-06T17:24:55.321086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length3
Mean length3.0412
Min length2

Characters and Unicode

Total characters30412
Distinct characters339
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1410 ?
Unique (%)14.1%

Sample

1st row소양면
2nd row웅촌면
3rd row청통면
4th row대동면
5th row추자동
ValueCountFrequency (%)
남면 56
 
0.6%
서면 45
 
0.4%
북면 38
 
0.4%
동면 29
 
0.3%
금성면 28
 
0.3%
청산면 23
 
0.2%
옥산면 23
 
0.2%
금남면 22
 
0.2%
대강면 22
 
0.2%
이서면 21
 
0.2%
Other values (2826) 9693
96.9%
2024-04-06T17:24:56.308447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
6425
 
21.1%
2428
 
8.0%
1630
 
5.4%
972
 
3.2%
516
 
1.7%
498
 
1.6%
453
 
1.5%
436
 
1.4%
393
 
1.3%
360
 
1.2%
Other values (329) 16301
53.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 30194
99.3%
Decimal Number 218
 
0.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
6425
 
21.3%
2428
 
8.0%
1630
 
5.4%
972
 
3.2%
516
 
1.7%
498
 
1.6%
453
 
1.5%
436
 
1.4%
393
 
1.3%
360
 
1.2%
Other values (321) 16083
53.3%
Decimal Number
ValueCountFrequency (%)
1 63
28.9%
2 61
28.0%
3 47
21.6%
4 20
 
9.2%
5 18
 
8.3%
7 4
 
1.8%
6 4
 
1.8%
8 1
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
Hangul 30194
99.3%
Common 218
 
0.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
6425
 
21.3%
2428
 
8.0%
1630
 
5.4%
972
 
3.2%
516
 
1.7%
498
 
1.6%
453
 
1.5%
436
 
1.4%
393
 
1.3%
360
 
1.2%
Other values (321) 16083
53.3%
Common
ValueCountFrequency (%)
1 63
28.9%
2 61
28.0%
3 47
21.6%
4 20
 
9.2%
5 18
 
8.3%
7 4
 
1.8%
6 4
 
1.8%
8 1
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 30194
99.3%
ASCII 218
 
0.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
6425
 
21.3%
2428
 
8.0%
1630
 
5.4%
972
 
3.2%
516
 
1.7%
498
 
1.6%
453
 
1.5%
436
 
1.4%
393
 
1.3%
360
 
1.2%
Other values (321) 16083
53.3%
ASCII
ValueCountFrequency (%)
1 63
28.9%
2 61
28.0%
3 47
21.6%
4 20
 
9.2%
5 18
 
8.3%
7 4
 
1.8%
6 4
 
1.8%
8 1
 
0.5%

리명
Text

Distinct6158
Distinct (%)61.6%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-04-06T17:24:57.006265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length3
Mean length3.0292
Min length2

Characters and Unicode

Total characters30292
Distinct characters379
Distinct categories4 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4561 ?
Unique (%)45.6%

Sample

1st row신교리
2nd row통천리
3rd row우천리
4th row백호리
5th row추자동
ValueCountFrequency (%)
신촌리 23
 
0.2%
대곡리 23
 
0.2%
덕산리 22
 
0.2%
신흥리 21
 
0.2%
송정리 21
 
0.2%
용산리 21
 
0.2%
금곡리 20
 
0.2%
오산리 20
 
0.2%
마산리 19
 
0.2%
읍내리 19
 
0.2%
Other values (6148) 9791
97.9%
2024-04-06T17:24:57.963653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
8080
26.7%
2374
 
7.8%
765
 
2.5%
590
 
1.9%
529
 
1.7%
437
 
1.4%
395
 
1.3%
394
 
1.3%
388
 
1.3%
353
 
1.2%
Other values (369) 15987
52.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 30066
99.3%
Decimal Number 220
 
0.7%
Close Punctuation 3
 
< 0.1%
Open Punctuation 3
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
8080
26.9%
2374
 
7.9%
765
 
2.5%
590
 
2.0%
529
 
1.8%
437
 
1.5%
395
 
1.3%
394
 
1.3%
388
 
1.3%
353
 
1.2%
Other values (359) 15761
52.4%
Decimal Number
ValueCountFrequency (%)
1 64
29.1%
2 62
28.2%
3 47
21.4%
4 20
 
9.1%
5 18
 
8.2%
6 4
 
1.8%
7 4
 
1.8%
8 1
 
0.5%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 30060
99.2%
Common 226
 
0.7%
Han 6
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
8080
26.9%
2374
 
7.9%
765
 
2.5%
590
 
2.0%
529
 
1.8%
437
 
1.5%
395
 
1.3%
394
 
1.3%
388
 
1.3%
353
 
1.2%
Other values (354) 15755
52.4%
Common
ValueCountFrequency (%)
1 64
28.3%
2 62
27.4%
3 47
20.8%
4 20
 
8.8%
5 18
 
8.0%
6 4
 
1.8%
7 4
 
1.8%
) 3
 
1.3%
( 3
 
1.3%
8 1
 
0.4%
Han
ValueCountFrequency (%)
2
33.3%
1
16.7%
1
16.7%
1
16.7%
1
16.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 30060
99.2%
ASCII 226
 
0.7%
CJK 6
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
8080
26.9%
2374
 
7.9%
765
 
2.5%
590
 
2.0%
529
 
1.8%
437
 
1.5%
395
 
1.3%
394
 
1.3%
388
 
1.3%
353
 
1.2%
Other values (354) 15755
52.4%
ASCII
ValueCountFrequency (%)
1 64
28.3%
2 62
27.4%
3 47
20.8%
4 20
 
8.8%
5 18
 
8.0%
6 4
 
1.8%
7 4
 
1.8%
) 3
 
1.3%
( 3
 
1.3%
8 1
 
0.4%
CJK
ValueCountFrequency (%)
2
33.3%
1
16.7%
1
16.7%
1
16.7%
1
16.7%

전체세대수
Real number (ℝ)

HIGH CORRELATION 

Distinct2129
Distinct (%)21.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1271.922
Minimum1
Maximum108737
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-06T17:24:58.293036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile41
Q192
median156
Q3345.25
95-th percentile6816.95
Maximum108737
Range108736
Interquartile range (IQR)253.25

Descriptive statistics

Standard deviation4590.2781
Coefficient of variation (CV)3.6089305
Kurtosis95.789559
Mean1271.922
Median Absolute Deviation (MAD)83
Skewness8.0224284
Sum12719220
Variance21070653
MonotonicityNot monotonic
2024-04-06T17:24:58.550500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
88 59
 
0.6%
85 58
 
0.6%
99 54
 
0.5%
104 53
 
0.5%
93 52
 
0.5%
65 51
 
0.5%
84 51
 
0.5%
80 50
 
0.5%
101 50
 
0.5%
76 50
 
0.5%
Other values (2119) 9472
94.7%
ValueCountFrequency (%)
1 19
0.2%
2 14
0.1%
3 12
0.1%
4 6
 
0.1%
5 4
 
< 0.1%
6 7
 
0.1%
7 4
 
< 0.1%
8 4
 
< 0.1%
9 7
 
0.1%
10 9
0.1%
ValueCountFrequency (%)
108737 1
< 0.1%
86421 1
< 0.1%
72616 1
< 0.1%
70143 1
< 0.1%
68003 1
< 0.1%
61749 1
< 0.1%
60835 1
< 0.1%
59626 1
< 0.1%
55507 1
< 0.1%
55157 1
< 0.1%

1인세대
Real number (ℝ)

HIGH CORRELATION 

Distinct1625
Distinct (%)16.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean531.0129
Minimum0
Maximum51307
Zeros6
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-06T17:24:58.810973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile22
Q149
median83
Q3183
95-th percentile2552.2
Maximum51307
Range51307
Interquartile range (IQR)134

Descriptive statistics

Standard deviation1910.6003
Coefficient of variation (CV)3.59803
Kurtosis128.99604
Mean531.0129
Median Absolute Deviation (MAD)44
Skewness9.1990273
Sum5310129
Variance3650393.6
MonotonicityNot monotonic
2024-04-06T17:24:59.081825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
56 105
 
1.1%
54 98
 
1.0%
49 94
 
0.9%
58 91
 
0.9%
60 90
 
0.9%
52 89
 
0.9%
45 88
 
0.9%
34 88
 
0.9%
48 86
 
0.9%
50 86
 
0.9%
Other values (1615) 9085
90.8%
ValueCountFrequency (%)
0 6
 
0.1%
1 23
0.2%
2 16
0.2%
3 12
0.1%
4 9
 
0.1%
5 12
0.1%
6 10
0.1%
7 9
 
0.1%
8 13
0.1%
9 14
0.1%
ValueCountFrequency (%)
51307 1
< 0.1%
33571 1
< 0.1%
31849 1
< 0.1%
31421 1
< 0.1%
30659 1
< 0.1%
29988 1
< 0.1%
29388 1
< 0.1%
26817 1
< 0.1%
25634 1
< 0.1%
25446 1
< 0.1%

2인세대
Real number (ℝ)

HIGH CORRELATION 

Distinct1324
Distinct (%)13.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean313.9139
Minimum0
Maximum26037
Zeros48
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-06T17:24:59.390359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile11
Q128
median48
Q397
95-th percentile1661.1
Maximum26037
Range26037
Interquartile range (IQR)69

Descriptive statistics

Standard deviation1101.5688
Coefficient of variation (CV)3.509143
Kurtosis104.70535
Mean313.9139
Median Absolute Deviation (MAD)25
Skewness8.2956551
Sum3139139
Variance1213453.7
MonotonicityNot monotonic
2024-04-06T17:24:59.673630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29 154
 
1.5%
24 149
 
1.5%
30 145
 
1.5%
23 143
 
1.4%
26 142
 
1.4%
37 142
 
1.4%
25 141
 
1.4%
35 139
 
1.4%
27 138
 
1.4%
34 137
 
1.4%
Other values (1314) 8570
85.7%
ValueCountFrequency (%)
0 48
0.5%
1 35
0.4%
2 22
 
0.2%
3 29
0.3%
4 25
0.2%
5 39
0.4%
6 39
0.4%
7 42
0.4%
8 61
0.6%
9 48
0.5%
ValueCountFrequency (%)
26037 1
< 0.1%
22779 1
< 0.1%
18740 1
< 0.1%
18707 1
< 0.1%
16868 1
< 0.1%
15646 1
< 0.1%
14501 1
< 0.1%
14130 1
< 0.1%
13897 1
< 0.1%
12915 1
< 0.1%

3인세대
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1119
Distinct (%)11.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean213.6368
Minimum0
Maximum17565
Zeros154
Zeros (%)1.5%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-06T17:24:59.933331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q17
median15
Q336
95-th percentile1160.2
Maximum17565
Range17565
Interquartile range (IQR)29

Descriptive statistics

Standard deviation840.19657
Coefficient of variation (CV)3.932827
Kurtosis81.66126
Mean213.6368
Median Absolute Deviation (MAD)10
Skewness7.6296651
Sum2136368
Variance705930.27
MonotonicityNot monotonic
2024-04-06T17:25:00.204252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 453
 
4.5%
7 416
 
4.2%
9 391
 
3.9%
5 388
 
3.9%
8 381
 
3.8%
4 362
 
3.6%
10 359
 
3.6%
12 355
 
3.5%
3 345
 
3.5%
11 333
 
3.3%
Other values (1109) 6217
62.2%
ValueCountFrequency (%)
0 154
 
1.5%
1 191
1.9%
2 266
2.7%
3 345
3.5%
4 362
3.6%
5 388
3.9%
6 453
4.5%
7 416
4.2%
8 381
3.8%
9 391
3.9%
ValueCountFrequency (%)
17565 1
< 0.1%
16652 1
< 0.1%
11665 1
< 0.1%
11307 1
< 0.1%
11296 1
< 0.1%
10850 1
< 0.1%
10158 1
< 0.1%
9980 1
< 0.1%
9776 1
< 0.1%
9604 1
< 0.1%

4인세대
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1005
Distinct (%)10.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean164.6025
Minimum0
Maximum12977
Zeros680
Zeros (%)6.8%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-06T17:25:00.878057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median6
Q317
95-th percentile880.2
Maximum12977
Range12977
Interquartile range (IQR)14

Descriptive statistics

Standard deviation687.02257
Coefficient of variation (CV)4.1738283
Kurtosis71.421532
Mean164.6025
Median Absolute Deviation (MAD)4
Skewness7.3910081
Sum1646025
Variance472000.01
MonotonicityNot monotonic
2024-04-06T17:25:01.153769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 942
 
9.4%
1 876
 
8.8%
3 847
 
8.5%
4 756
 
7.6%
0 680
 
6.8%
5 611
 
6.1%
6 499
 
5.0%
7 445
 
4.5%
8 364
 
3.6%
9 289
 
2.9%
Other values (995) 3691
36.9%
ValueCountFrequency (%)
0 680
6.8%
1 876
8.8%
2 942
9.4%
3 847
8.5%
4 756
7.6%
5 611
6.1%
6 499
5.0%
7 445
4.5%
8 364
 
3.6%
9 289
 
2.9%
ValueCountFrequency (%)
12977 1
< 0.1%
11414 1
< 0.1%
10091 1
< 0.1%
9046 1
< 0.1%
8852 1
< 0.1%
8556 1
< 0.1%
8403 1
< 0.1%
8010 1
< 0.1%
7872 1
< 0.1%
7768 1
< 0.1%

5인세대
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct579
Distinct (%)5.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.8709
Minimum0
Maximum2564
Zeros2004
Zeros (%)20.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-06T17:25:01.435164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q37
95-th percentile210.05
Maximum2564
Range2564
Interquartile range (IQR)6

Descriptive statistics

Standard deviation149.64886
Coefficient of variation (CV)3.8498944
Kurtosis61.215767
Mean38.8709
Median Absolute Deviation (MAD)2
Skewness6.843237
Sum388709
Variance22394.781
MonotonicityNot monotonic
2024-04-06T17:25:01.682470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2004
20.0%
1 1939
19.4%
2 1280
12.8%
3 932
9.3%
4 609
 
6.1%
5 396
 
4.0%
6 295
 
2.9%
7 209
 
2.1%
8 162
 
1.6%
9 121
 
1.2%
Other values (569) 2053
20.5%
ValueCountFrequency (%)
0 2004
20.0%
1 1939
19.4%
2 1280
12.8%
3 932
9.3%
4 609
 
6.1%
5 396
 
4.0%
6 295
 
2.9%
7 209
 
2.1%
8 162
 
1.6%
9 121
 
1.2%
ValueCountFrequency (%)
2564 1
< 0.1%
2512 1
< 0.1%
2300 1
< 0.1%
1772 1
< 0.1%
1743 1
< 0.1%
1712 1
< 0.1%
1680 1
< 0.1%
1635 1
< 0.1%
1626 1
< 0.1%
1618 1
< 0.1%

6인세대
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct213
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.4939
Minimum0
Maximum546
Zeros4450
Zeros (%)44.5%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-06T17:25:01.931927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile39
Maximum546
Range546
Interquartile range (IQR)2

Descriptive statistics

Standard deviation27.917684
Coefficient of variation (CV)3.7253879
Kurtosis73.484399
Mean7.4939
Median Absolute Deviation (MAD)1
Skewness7.358939
Sum74939
Variance779.3971
MonotonicityNot monotonic
2024-04-06T17:25:02.199250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4450
44.5%
1 2193
21.9%
2 960
 
9.6%
3 475
 
4.8%
4 263
 
2.6%
5 167
 
1.7%
6 131
 
1.3%
7 89
 
0.9%
8 71
 
0.7%
9 60
 
0.6%
Other values (203) 1141
 
11.4%
ValueCountFrequency (%)
0 4450
44.5%
1 2193
21.9%
2 960
 
9.6%
3 475
 
4.8%
4 263
 
2.6%
5 167
 
1.7%
6 131
 
1.3%
7 89
 
0.9%
8 71
 
0.7%
9 60
 
0.6%
ValueCountFrequency (%)
546 1
< 0.1%
461 1
< 0.1%
455 1
< 0.1%
382 1
< 0.1%
362 1
< 0.1%
359 1
< 0.1%
351 1
< 0.1%
344 1
< 0.1%
342 1
< 0.1%
332 1
< 0.1%

7인세대
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct77
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7001
Minimum0
Maximum151
Zeros6849
Zeros (%)68.5%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-06T17:25:02.501291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile8
Maximum151
Range151
Interquartile range (IQR)1

Descriptive statistics

Standard deviation6.3875066
Coefficient of variation (CV)3.7571358
Kurtosis94.007578
Mean1.7001
Median Absolute Deviation (MAD)0
Skewness8.048492
Sum17001
Variance40.80024
MonotonicityNot monotonic
2024-04-06T17:25:02.849128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 6849
68.5%
1 1552
 
15.5%
2 480
 
4.8%
3 198
 
2.0%
4 135
 
1.4%
5 97
 
1.0%
6 75
 
0.8%
7 67
 
0.7%
8 49
 
0.5%
10 48
 
0.5%
Other values (67) 450
 
4.5%
ValueCountFrequency (%)
0 6849
68.5%
1 1552
 
15.5%
2 480
 
4.8%
3 198
 
2.0%
4 135
 
1.4%
5 97
 
1.0%
6 75
 
0.8%
7 67
 
0.7%
8 49
 
0.5%
9 44
 
0.4%
ValueCountFrequency (%)
151 1
< 0.1%
111 1
< 0.1%
110 1
< 0.1%
90 1
< 0.1%
88 1
< 0.1%
85 1
< 0.1%
82 1
< 0.1%
80 1
< 0.1%
74 2
< 0.1%
71 1
< 0.1%

8인세대
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct29
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4275
Minimum0
Maximum42
Zeros8447
Zeros (%)84.5%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-06T17:25:03.253452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum42
Range42
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.7520419
Coefficient of variation (CV)4.0983436
Kurtosis104.31507
Mean0.4275
Median Absolute Deviation (MAD)0
Skewness8.4959678
Sum4275
Variance3.0696507
MonotonicityNot monotonic
2024-04-06T17:25:03.524581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
0 8447
84.5%
1 878
 
8.8%
2 239
 
2.4%
3 124
 
1.2%
4 80
 
0.8%
5 44
 
0.4%
6 38
 
0.4%
7 28
 
0.3%
8 27
 
0.3%
10 16
 
0.2%
Other values (19) 79
 
0.8%
ValueCountFrequency (%)
0 8447
84.5%
1 878
 
8.8%
2 239
 
2.4%
3 124
 
1.2%
4 80
 
0.8%
5 44
 
0.4%
6 38
 
0.4%
7 28
 
0.3%
8 27
 
0.3%
9 13
 
0.1%
ValueCountFrequency (%)
42 1
 
< 0.1%
32 1
 
< 0.1%
31 1
 
< 0.1%
25 1
 
< 0.1%
24 2
 
< 0.1%
23 1
 
< 0.1%
22 1
 
< 0.1%
21 5
0.1%
20 2
 
< 0.1%
19 3
< 0.1%

9인세대
Real number (ℝ)

ZEROS 

Distinct16
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1351
Minimum0
Maximum16
Zeros9251
Zeros (%)92.5%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-06T17:25:03.798565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum16
Range16
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.66640258
Coefficient of variation (CV)4.9326616
Kurtosis138.48612
Mean0.1351
Median Absolute Deviation (MAD)0
Skewness9.6413995
Sum1351
Variance0.4440924
MonotonicityNot monotonic
2024-04-06T17:25:04.036437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0 9251
92.5%
1 492
 
4.9%
2 131
 
1.3%
3 52
 
0.5%
4 28
 
0.3%
5 15
 
0.1%
6 11
 
0.1%
7 7
 
0.1%
8 4
 
< 0.1%
9 2
 
< 0.1%
Other values (6) 7
 
0.1%
ValueCountFrequency (%)
0 9251
92.5%
1 492
 
4.9%
2 131
 
1.3%
3 52
 
0.5%
4 28
 
0.3%
5 15
 
0.1%
6 11
 
0.1%
7 7
 
0.1%
8 4
 
< 0.1%
9 2
 
< 0.1%
ValueCountFrequency (%)
16 1
 
< 0.1%
15 1
 
< 0.1%
13 1
 
< 0.1%
12 2
 
< 0.1%
11 1
 
< 0.1%
10 1
 
< 0.1%
9 2
 
< 0.1%
8 4
 
< 0.1%
7 7
0.1%
6 11
0.1%

10인이상세대
Real number (ℝ)

ZEROS 

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1284
Minimum0
Maximum10
Zeros9138
Zeros (%)91.4%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-06T17:25:04.285233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum10
Range10
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.5341741
Coefficient of variation (CV)4.1602345
Kurtosis77.645862
Mean0.1284
Median Absolute Deviation (MAD)0
Skewness7.309394
Sum1284
Variance0.28534197
MonotonicityNot monotonic
2024-04-06T17:25:04.572218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 9138
91.4%
1 634
 
6.3%
2 144
 
1.4%
3 41
 
0.4%
4 16
 
0.2%
7 9
 
0.1%
5 9
 
0.1%
6 4
 
< 0.1%
8 3
 
< 0.1%
10 1
 
< 0.1%
ValueCountFrequency (%)
0 9138
91.4%
1 634
 
6.3%
2 144
 
1.4%
3 41
 
0.4%
4 16
 
0.2%
5 9
 
0.1%
6 4
 
< 0.1%
7 9
 
0.1%
8 3
 
< 0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
10 1
 
< 0.1%
9 1
 
< 0.1%
8 3
 
< 0.1%
7 9
 
0.1%
6 4
 
< 0.1%
5 9
 
0.1%
4 16
 
0.2%
3 41
 
0.4%
2 144
 
1.4%
1 634
6.3%

Interactions

2024-04-06T17:24:48.435830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:24:16.085203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:24:19.423142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:24:21.992873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:24:24.875350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:24:27.791038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:24:30.600276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:24:33.637566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:24:35.883969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:24:39.039844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:24:41.618058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:24:45.162112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:24:48.774881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:24:16.256154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:24:19.614885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:24:22.190725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:24:25.053237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:24:27.993271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:24:30.868842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:24:33.831857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:24:36.192566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:24:39.252044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:24:41.809075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:24:45.535303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:24:48.992105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:24:16.520433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:24:19.809101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:24:22.363261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:24:25.351017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:24:28.368647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:24:31.145911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:24:34.027758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:24:36.390677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:24:39.439590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:24:42.072149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:24:45.872531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:24:49.173119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:24:16.889777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:24:20.029092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:24:22.559453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:24:25.595303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:24:28.565289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:24:31.339002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:24:34.214589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:24:36.758615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:24:39.623326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:24:42.380992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:24:46.098258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:24:49.378193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:24:17.089303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:24:20.211359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:24:22.767609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:24:25.822150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:24:28.747135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:24:31.516312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:24:34.395244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:24:37.221603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:24:39.845806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:24:42.657586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:24:46.438636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:24:49.550573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:24:17.667458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:24:20.421091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:24:23.124224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:24:26.079988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:24:28.954362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:24:32.151493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:24:34.575659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:24:37.540572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:24:40.047117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:24:42.973430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:24:46.669013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:24:49.744415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:24:17.886327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:24:20.729353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:24:23.470282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:24:26.359721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:24:29.167084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:24:32.370435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:24:34.760235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:24:37.752946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:24:40.259007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:24:43.278793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:24:47.234339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:24:49.924539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:24:18.166783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:24:20.911085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:24:23.801990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:24:26.630739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:24:29.371980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:24:32.572712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:24:34.944857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:24:37.937103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:24:40.550888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:24:43.504349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:24:47.416393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:24:50.107337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:24:18.347358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:24:21.087310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:24:24.009284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:24:26.858644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:24:29.566867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:24:32.807439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:24:35.119734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:24:38.163627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:24:40.744755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:24:43.669677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:24:47.613863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:24:50.323800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:24:18.558247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:24:21.280670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:24:24.251598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:24:27.136255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:24:29.764055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:24:33.061427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:24:35.315248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:24:38.369873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:24:40.981159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:24:43.940630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:24:47.880531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:24:50.496262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:24:18.745389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:24:21.541518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:24:24.448788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:24:27.379735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:24:29.971024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:24:33.227850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:24:35.487265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:24:38.544046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:24:41.181292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:24:44.182848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:24:48.056532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:24:50.723159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:24:19.157680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:24:21.786575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:24:24.689416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:24:27.610418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:24:30.306713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:24:33.455307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:24:35.683081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:24:38.761250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:24:41.390277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:24:44.682212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:24:48.257488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-06T17:25:04.816791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
법정동코드시도명전체세대수1인세대2인세대3인세대4인세대5인세대6인세대7인세대8인세대9인세대10인이상세대
법정동코드1.0000.9900.2520.2400.2570.2380.2480.2370.2560.2470.2400.2380.221
시도명0.9901.0000.3230.3090.3070.3160.2930.3080.3250.3230.3000.2740.260
전체세대수0.2520.3231.0000.8920.9540.8930.8830.9200.9500.8540.7910.6980.639
1인세대0.2400.3090.8921.0000.8630.8970.8030.7100.7930.9070.8820.6680.595
2인세대0.2570.3070.9540.8631.0000.9180.9510.8160.8820.8680.8130.8410.770
3인세대0.2380.3160.8930.8970.9181.0000.8820.8800.8920.9280.8980.6770.570
4인세대0.2480.2930.8830.8030.9510.8821.0000.9320.8930.8810.8210.8510.782
5인세대0.2370.3080.9200.7100.8160.8800.9321.0000.9550.8250.7680.7000.609
6인세대0.2560.3250.9500.7930.8820.8920.8930.9551.0000.9230.8560.7950.619
7인세대0.2470.3230.8540.9070.8680.9280.8810.8250.9231.0000.9410.7920.634
8인세대0.2400.3000.7910.8820.8130.8980.8210.7680.8560.9411.0000.7950.603
9인세대0.2380.2740.6980.6680.8410.6770.8510.7000.7950.7920.7951.0000.721
10인이상세대0.2210.2600.6390.5950.7700.5700.7820.6090.6190.6340.6030.7211.000
2024-04-06T17:25:05.166222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
법정동코드전체세대수1인세대2인세대3인세대4인세대5인세대6인세대7인세대8인세대9인세대10인이상세대시도명
법정동코드1.000-0.227-0.206-0.216-0.250-0.252-0.238-0.236-0.228-0.222-0.183-0.1390.968
전체세대수-0.2271.0000.9830.9680.9350.8960.8210.7240.6080.4880.3710.3110.135
1인세대-0.2060.9831.0000.9220.8830.8450.7750.6880.5870.4770.3620.3080.135
2인세대-0.2160.9680.9221.0000.9280.8840.8160.7210.6050.4870.3690.3060.124
3인세대-0.2500.9350.8830.9281.0000.9000.8300.7340.6150.4900.3720.3040.138
4인세대-0.2520.8960.8450.8840.9001.0000.8210.7230.6120.4890.3720.3080.118
5인세대-0.2380.8210.7750.8160.8300.8211.0000.6960.5990.4800.3730.2960.128
6인세대-0.2360.7240.6880.7210.7340.7230.6961.0000.5810.4870.3830.3030.136
7인세대-0.2280.6080.5870.6050.6150.6120.5990.5811.0000.5030.4070.3230.141
8인세대-0.2220.4880.4770.4870.4900.4890.4800.4870.5031.0000.4590.3350.132
9인세대-0.1830.3710.3620.3690.3720.3720.3730.3830.4070.4591.0000.3120.110
10인이상세대-0.1390.3110.3080.3060.3040.3080.2960.3030.3230.3350.3121.0000.093
시도명0.9680.1350.1350.1240.1380.1180.1280.1360.1410.1320.1100.0931.000

Missing values

2024-04-06T17:24:51.007810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-06T17:24:51.482062image/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

법정동코드기준연월시도명시군구명읍면동명리명전체세대수1인세대2인세대3인세대4인세대5인세대6인세대7인세대8인세대9인세대10인이상세대
1782452710340212024-03-31전북특별자치도완주군소양면신교리32411712244261113000
185531710340282024-03-31울산광역시울주군웅촌면통천리2814741000002
1139247230310222024-03-31경상북도영천시청통면우천리20311667117110000
968146860350232024-03-31전라남도함평군대동면백호리1368831104101100
340241610118002024-03-31경기도광주시추자동추자동197172252137326177121220
693944770310342024-03-31충청남도서천군마서면신포리1105732115410000
809546170400222024-03-31전라남도나주시산포면신도리2001265578310000
1366848170390242024-03-31경상남도진주시지수면압사리19911059157700100
821346710250332024-03-31전라남도담양군담양읍학동리1378335144010000
524043760390272024-03-31충청북도괴산군사리면이곡리1889669138200000
법정동코드기준연월시도명시군구명읍면동명리명전체세대수1인세대2인세대3인세대4인세대5인세대6인세대7인세대8인세대9인세대10인이상세대
714644790360222024-03-31충청남도청양군장평면은곡리97434074120000
1674551820253232024-03-31강원특별자치도고성군거진읍원당리38191450000000
1402348270106002024-03-31경상남도밀양시용평동용평동237112872213210000
975646870253312024-03-31전라남도영광군백수읍지산리17397402112300000
801146170320292024-03-31전라남도나주시왕곡면신원리1829963126110000
1491548850390252024-03-31경상남도하동군북천면서황리1196631174100000
1009746900330282024-03-31전라남도진도군의신면연주리90582264000000
1379648220360272024-03-31경상남도통영시한산면비진리97543290110000
392541830360222024-03-31경기도양평군단월면삼가리19086642410420000
454943150330212024-03-31충청북도제천시수산면수산리1839861184200000