Overview

Dataset statistics

Number of variables16
Number of observations3613
Missing cells24
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory494.1 KiB
Average record size in memory140.0 B

Variable types

Numeric12
Categorical2
Text2

Dataset

Description행정동(읍면동)별 주민등록 세대수 현황에 대한 데이터입니다.행정동은 주민들이 거주하는 지역을 행정능률과 주민편의를 위하여 구분한 행정구역 단위입니다.
Author행정안전부
URLhttps://www.data.go.kr/data/15097974/fileData.do

Alerts

기준연월 has constant value ""Constant
행정기관코드 is highly overall correlated with 3인세대 and 1 other fieldsHigh correlation
전체세대수 is highly overall correlated with 1인세대 and 8 other fieldsHigh correlation
1인세대 is highly overall correlated with 전체세대수 and 7 other fieldsHigh correlation
2인세대 is highly overall correlated with 전체세대수 and 8 other fieldsHigh correlation
3인세대 is highly overall correlated with 행정기관코드 and 9 other fieldsHigh correlation
4인세대 is highly overall correlated with 전체세대수 and 8 other fieldsHigh correlation
5인세대 is highly overall correlated with 전체세대수 and 8 other fieldsHigh correlation
6인세대 is highly overall correlated with 전체세대수 and 8 other fieldsHigh correlation
7인세대 is highly overall correlated with 전체세대수 and 8 other fieldsHigh correlation
8인세대 is highly overall correlated with 전체세대수 and 7 other fieldsHigh correlation
9인세대 is highly overall correlated with 전체세대수 and 6 other fieldsHigh correlation
시도명 is highly overall correlated with 행정기관코드High correlation
행정기관코드 has unique valuesUnique
6인세대 has 50 (1.4%) zerosZeros
7인세대 has 307 (8.5%) zerosZeros
8인세대 has 1085 (30.0%) zerosZeros
9인세대 has 2134 (59.1%) zerosZeros
10인이상세대 has 2120 (58.7%) zerosZeros

Reproduction

Analysis started2024-04-06 08:19:43.352463
Analysis finished2024-04-06 08:20:21.001927
Duration37.65 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

행정기관코드
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct3613
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.8327467 × 109
Minimum1.1110515 × 109
Maximum5.280042 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.9 KiB
2024-04-06T17:20:21.165617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.1110515 × 109
5-th percentile1.1380557 × 109
Q12.8260544 × 109
median4.311135 × 109
Q34.773033 × 109
95-th percentile5.2134532 × 109
Maximum5.280042 × 109
Range4.1689905 × 109
Interquartile range (IQR)1.9469786 × 109

Descriptive statistics

Standard deviation1.2616173 × 109
Coefficient of variation (CV)0.32916794
Kurtosis-0.20220735
Mean3.8327467 × 109
Median Absolute Deviation (MAD)5.21921 × 108
Skewness-0.98268915
Sum1.3847714 × 1013
Variance1.5916783 × 1018
MonotonicityStrictly increasing
2024-04-06T17:20:21.470718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1111051500 1
 
< 0.1%
4681040000 1
 
< 0.1%
4684025800 1
 
< 0.1%
4684032000 1
 
< 0.1%
4684033000 1
 
< 0.1%
4684034000 1
 
< 0.1%
4684035000 1
 
< 0.1%
4684036000 1
 
< 0.1%
4684037000 1
 
< 0.1%
4686025000 1
 
< 0.1%
Other values (3603) 3603
99.7%
ValueCountFrequency (%)
1111051500 1
< 0.1%
1111053000 1
< 0.1%
1111054000 1
< 0.1%
1111055000 1
< 0.1%
1111056000 1
< 0.1%
1111057000 1
< 0.1%
1111058000 1
< 0.1%
1111060000 1
< 0.1%
1111061500 1
< 0.1%
1111063000 1
< 0.1%
ValueCountFrequency (%)
5280042000 1
< 0.1%
5280041000 1
< 0.1%
5280040000 1
< 0.1%
5280039000 1
< 0.1%
5280038000 1
< 0.1%
5280037000 1
< 0.1%
5280036000 1
< 0.1%
5280035000 1
< 0.1%
5280034000 1
< 0.1%
5280033000 1
< 0.1%

기준연월
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size28.4 KiB
2024-03-31
3613 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2024-03-31
2nd row2024-03-31
3rd row2024-03-31
4th row2024-03-31
5th row2024-03-31

Common Values

ValueCountFrequency (%)
2024-03-31 3613
100.0%

Length

2024-04-06T17:20:21.724847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T17:20:21.924771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2024-03-31 3613
100.0%

시도명
Categorical

HIGH CORRELATION 

Distinct17
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size28.4 KiB
경기도
602 
서울특별시
426 
경상북도
335 
전라남도
323 
경상남도
310 
Other values (12)
1617 

Length

Max length7
Median length5
Mean length4.5773595
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경기도 602
16.7%
서울특별시 426
11.8%
경상북도 335
9.3%
전라남도 323
8.9%
경상남도 310
8.6%
전북특별자치도 243
6.7%
충청남도 210
 
5.8%
부산광역시 205
 
5.7%
강원특별자치도 194
 
5.4%
인천광역시 160
 
4.4%
Other values (7) 605
16.7%

Length

2024-04-06T17:20:22.146960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기도 602
16.7%
서울특별시 426
11.8%
경상북도 335
9.3%
전라남도 323
8.9%
경상남도 310
8.6%
전북특별자치도 243
6.7%
충청남도 210
 
5.8%
부산광역시 205
 
5.7%
강원특별자치도 194
 
5.4%
인천광역시 160
 
4.4%
Other values (7) 605
16.7%
Distinct229
Distinct (%)6.4%
Missing24
Missing (%)0.7%
Memory size28.4 KiB
2024-04-06T17:20:22.755880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length3
Mean length3.4664252
Min length2

Characters and Unicode

Total characters12441
Distinct characters143
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

Unique0 ?
Unique (%)0.0%

Sample

1st row종로구
2nd row종로구
3rd row종로구
4th row종로구
5th row종로구
ValueCountFrequency (%)
서구 95
 
2.3%
북구 87
 
2.1%
동구 83
 
2.0%
중구 77
 
1.9%
남구 75
 
1.9%
창원시 55
 
1.4%
성남시 50
 
1.2%
수원시 44
 
1.1%
고양시 44
 
1.1%
청주시 43
 
1.1%
Other values (229) 3399
83.9%
2024-04-06T17:20:23.643425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1641
 
13.2%
1632
 
13.1%
908
 
7.3%
463
 
3.7%
409
 
3.3%
343
 
2.8%
342
 
2.7%
297
 
2.4%
293
 
2.4%
274
 
2.2%
Other values (133) 5839
46.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 11978
96.3%
Space Separator 463
 
3.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
1641
 
13.7%
1632
 
13.6%
908
 
7.6%
409
 
3.4%
343
 
2.9%
342
 
2.9%
297
 
2.5%
293
 
2.4%
274
 
2.3%
263
 
2.2%
Other values (132) 5576
46.6%
Space Separator
ValueCountFrequency (%)
463
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 11978
96.3%
Common 463
 
3.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
1641
 
13.7%
1632
 
13.6%
908
 
7.6%
409
 
3.4%
343
 
2.9%
342
 
2.9%
297
 
2.5%
293
 
2.4%
274
 
2.3%
263
 
2.2%
Other values (132) 5576
46.6%
Common
ValueCountFrequency (%)
463
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 11978
96.3%
ASCII 463
 
3.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
1641
 
13.7%
1632
 
13.6%
908
 
7.6%
409
 
3.4%
343
 
2.9%
342
 
2.9%
297
 
2.5%
293
 
2.4%
274
 
2.3%
263
 
2.2%
Other values (132) 5576
46.6%
ASCII
ValueCountFrequency (%)
463
100.0%
Distinct3277
Distinct (%)90.7%
Missing0
Missing (%)0.0%
Memory size28.4 KiB
2024-04-06T17:20:24.235727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length3
Mean length3.5203432
Min length2

Characters and Unicode

Total characters12719
Distinct characters348
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

Unique3056 ?
Unique (%)84.6%

Sample

1st row청운효자동
2nd row사직동
3rd row삼청동
4th row부암동
5th row평창동
ValueCountFrequency (%)
중앙동 31
 
0.9%
남면 12
 
0.3%
서면 9
 
0.2%
북면 8
 
0.2%
송정동 7
 
0.2%
금성면 5
 
0.1%
신흥동 5
 
0.1%
동면 5
 
0.1%
교동 5
 
0.1%
성산면 4
 
0.1%
Other values (3267) 3522
97.5%
2024-04-06T17:20:25.028547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2293
 
18.0%
1212
 
9.5%
1 399
 
3.1%
2 388
 
3.1%
354
 
2.8%
298
 
2.3%
258
 
2.0%
3 169
 
1.3%
159
 
1.3%
158
 
1.2%
Other values (338) 7031
55.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 11568
91.0%
Decimal Number 1118
 
8.8%
Other Punctuation 33
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
2293
 
19.8%
1212
 
10.5%
354
 
3.1%
298
 
2.6%
258
 
2.2%
159
 
1.4%
158
 
1.4%
153
 
1.3%
148
 
1.3%
136
 
1.2%
Other values (327) 6399
55.3%
Decimal Number
ValueCountFrequency (%)
1 399
35.7%
2 388
34.7%
3 169
15.1%
4 80
 
7.2%
5 35
 
3.1%
6 22
 
2.0%
7 11
 
1.0%
8 7
 
0.6%
9 5
 
0.4%
0 2
 
0.2%
Other Punctuation
ValueCountFrequency (%)
. 33
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 11568
91.0%
Common 1151
 
9.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
2293
 
19.8%
1212
 
10.5%
354
 
3.1%
298
 
2.6%
258
 
2.2%
159
 
1.4%
158
 
1.4%
153
 
1.3%
148
 
1.3%
136
 
1.2%
Other values (327) 6399
55.3%
Common
ValueCountFrequency (%)
1 399
34.7%
2 388
33.7%
3 169
14.7%
4 80
 
7.0%
5 35
 
3.0%
. 33
 
2.9%
6 22
 
1.9%
7 11
 
1.0%
8 7
 
0.6%
9 5
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 11568
91.0%
ASCII 1151
 
9.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
2293
 
19.8%
1212
 
10.5%
354
 
3.1%
298
 
2.6%
258
 
2.2%
159
 
1.4%
158
 
1.4%
153
 
1.3%
148
 
1.3%
136
 
1.2%
Other values (327) 6399
55.3%
ASCII
ValueCountFrequency (%)
1 399
34.7%
2 388
33.7%
3 169
14.7%
4 80
 
7.0%
5 35
 
3.0%
. 33
 
2.9%
6 22
 
1.9%
7 11
 
1.0%
8 7
 
0.6%
9 5
 
0.4%

전체세대수
Real number (ℝ)

HIGH CORRELATION 

Distinct3123
Distinct (%)86.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6643.235
Minimum33
Maximum49083
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.9 KiB
2024-04-06T17:20:25.309435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum33
5-th percentile841.6
Q11905
median5276
Q39854
95-th percentile17423
Maximum49083
Range49050
Interquartile range (IQR)7949

Descriptive statistics

Standard deviation5660.2389
Coefficient of variation (CV)0.85203051
Kurtosis3.4067597
Mean6643.235
Median Absolute Deviation (MAD)3662
Skewness1.4354419
Sum24002008
Variance32038304
MonotonicityNot monotonic
2024-04-06T17:20:25.580418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1118 5
 
0.1%
1241 4
 
0.1%
1683 4
 
0.1%
11464 4
 
0.1%
1658 4
 
0.1%
1009 4
 
0.1%
601 4
 
0.1%
1129 3
 
0.1%
1002 3
 
0.1%
1832 3
 
0.1%
Other values (3113) 3575
98.9%
ValueCountFrequency (%)
33 1
< 0.1%
43 1
< 0.1%
60 1
< 0.1%
93 1
< 0.1%
110 1
< 0.1%
124 1
< 0.1%
131 1
< 0.1%
133 1
< 0.1%
135 1
< 0.1%
139 1
< 0.1%
ValueCountFrequency (%)
49083 1
< 0.1%
41281 1
< 0.1%
40910 1
< 0.1%
40393 1
< 0.1%
40173 1
< 0.1%
39650 1
< 0.1%
34967 1
< 0.1%
33362 1
< 0.1%
32248 1
< 0.1%
31896 1
< 0.1%

1인세대
Real number (ℝ)

HIGH CORRELATION 

Distinct2647
Distinct (%)73.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2773.7097
Minimum22
Maximum18547
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.9 KiB
2024-04-06T17:20:25.811913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum22
5-th percentile467.6
Q1975
median2107
Q33794
95-th percentile7484.8
Maximum18547
Range18525
Interquartile range (IQR)2819

Descriptive statistics

Standard deviation2398.4837
Coefficient of variation (CV)0.86472053
Kurtosis5.2820727
Mean2773.7097
Median Absolute Deviation (MAD)1280
Skewness1.8874408
Sum10021413
Variance5752724
MonotonicityNot monotonic
2024-04-06T17:20:26.071481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
711 8
 
0.2%
608 6
 
0.2%
590 6
 
0.2%
748 6
 
0.2%
651 5
 
0.1%
522 5
 
0.1%
701 5
 
0.1%
518 5
 
0.1%
657 5
 
0.1%
2925 5
 
0.1%
Other values (2637) 3557
98.5%
ValueCountFrequency (%)
22 1
< 0.1%
29 1
< 0.1%
40 1
< 0.1%
50 1
< 0.1%
53 1
< 0.1%
66 1
< 0.1%
71 1
< 0.1%
74 1
< 0.1%
91 1
< 0.1%
92 1
< 0.1%
ValueCountFrequency (%)
18547 1
< 0.1%
18032 1
< 0.1%
17650 1
< 0.1%
17645 1
< 0.1%
17044 1
< 0.1%
16491 1
< 0.1%
15622 1
< 0.1%
15369 1
< 0.1%
15362 1
< 0.1%
15192 1
< 0.1%

2인세대
Real number (ℝ)

HIGH CORRELATION 

Distinct2259
Distinct (%)62.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1635.6596
Minimum2
Maximum11360
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.9 KiB
2024-04-06T17:20:26.517541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile248.6
Q1553
median1340
Q32385
95-th percentile4084.2
Maximum11360
Range11358
Interquartile range (IQR)1832

Descriptive statistics

Standard deviation1298.4241
Coefficient of variation (CV)0.79382297
Kurtosis3.383657
Mean1635.6596
Median Absolute Deviation (MAD)858
Skewness1.3924246
Sum5909638
Variance1685905.2
MonotonicityNot monotonic
2024-04-06T17:20:26.818594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
533 7
 
0.2%
328 7
 
0.2%
357 7
 
0.2%
384 6
 
0.2%
296 6
 
0.2%
408 6
 
0.2%
299 6
 
0.2%
467 6
 
0.2%
535 6
 
0.2%
277 6
 
0.2%
Other values (2249) 3550
98.3%
ValueCountFrequency (%)
2 1
 
< 0.1%
6 1
 
< 0.1%
23 1
 
< 0.1%
24 1
 
< 0.1%
25 3
0.1%
26 1
 
< 0.1%
30 1
 
< 0.1%
32 1
 
< 0.1%
33 1
 
< 0.1%
36 1
 
< 0.1%
ValueCountFrequency (%)
11360 1
< 0.1%
10434 1
< 0.1%
9694 1
< 0.1%
8743 1
< 0.1%
8621 1
< 0.1%
8348 1
< 0.1%
8236 1
< 0.1%
8109 1
< 0.1%
7928 1
< 0.1%
7449 1
< 0.1%

3인세대
Real number (ℝ)

HIGH CORRELATION 

Distinct1963
Distinct (%)54.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1118.3424
Minimum0
Maximum10482
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size31.9 KiB
2024-04-06T17:20:27.095373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile66
Q1182
median760
Q31707
95-th percentile3284.8
Maximum10482
Range10482
Interquartile range (IQR)1525

Descriptive statistics

Standard deviation1136.2404
Coefficient of variation (CV)1.0160041
Kurtosis4.1501743
Mean1118.3424
Median Absolute Deviation (MAD)634
Skewness1.6110605
Sum4040571
Variance1291042.2
MonotonicityNot monotonic
2024-04-06T17:20:27.378640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
82 13
 
0.4%
77 13
 
0.4%
93 13
 
0.4%
108 13
 
0.4%
84 12
 
0.3%
111 11
 
0.3%
143 11
 
0.3%
95 11
 
0.3%
73 11
 
0.3%
152 11
 
0.3%
Other values (1953) 3494
96.7%
ValueCountFrequency (%)
0 1
 
< 0.1%
2 1
 
< 0.1%
3 1
 
< 0.1%
4 1
 
< 0.1%
5 1
 
< 0.1%
7 1
 
< 0.1%
8 2
 
0.1%
10 1
 
< 0.1%
11 6
0.2%
12 1
 
< 0.1%
ValueCountFrequency (%)
10482 1
< 0.1%
8652 1
< 0.1%
8144 1
< 0.1%
7760 1
< 0.1%
7291 1
< 0.1%
6822 1
< 0.1%
6721 1
< 0.1%
6566 1
< 0.1%
6547 1
< 0.1%
6502 1
< 0.1%

4인세대
Real number (ℝ)

HIGH CORRELATION 

Distinct1707
Distinct (%)47.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean861.75671
Minimum0
Maximum9235
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size31.9 KiB
2024-04-06T17:20:27.642882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile24.6
Q180
median480
Q31292
95-th percentile2994
Maximum9235
Range9235
Interquartile range (IQR)1212

Descriptive statistics

Standard deviation1032.4802
Coefficient of variation (CV)1.198111
Kurtosis5.9250902
Mean861.75671
Median Absolute Deviation (MAD)434
Skewness2.0122853
Sum3113527
Variance1066015.3
MonotonicityNot monotonic
2024-04-06T17:20:28.389858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40 25
 
0.7%
47 23
 
0.6%
30 23
 
0.6%
29 23
 
0.6%
65 21
 
0.6%
26 20
 
0.6%
53 19
 
0.5%
43 18
 
0.5%
36 18
 
0.5%
49 17
 
0.5%
Other values (1697) 3406
94.3%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 1
 
< 0.1%
2 3
 
0.1%
3 3
 
0.1%
4 4
0.1%
5 8
0.2%
6 5
0.1%
7 5
0.1%
8 4
0.1%
9 7
0.2%
ValueCountFrequency (%)
9235 1
< 0.1%
8316 1
< 0.1%
7296 1
< 0.1%
6961 1
< 0.1%
6885 1
< 0.1%
6763 1
< 0.1%
6470 1
< 0.1%
6365 1
< 0.1%
6165 1
< 0.1%
6078 2
0.1%

5인세대
Real number (ℝ)

HIGH CORRELATION 

Distinct738
Distinct (%)20.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean201.98339
Minimum0
Maximum2121
Zeros9
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size31.9 KiB
2024-04-06T17:20:28.652139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8
Q130
median126
Q3301
95-th percentile656
Maximum2121
Range2121
Interquartile range (IQR)271

Descriptive statistics

Standard deviation228.00082
Coefficient of variation (CV)1.1288097
Kurtosis7.3926926
Mean201.98339
Median Absolute Deviation (MAD)107
Skewness2.1333846
Sum729766
Variance51984.375
MonotonicityNot monotonic
2024-04-06T17:20:28.946277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12 48
 
1.3%
20 47
 
1.3%
17 45
 
1.2%
13 45
 
1.2%
10 43
 
1.2%
14 43
 
1.2%
16 42
 
1.2%
15 38
 
1.1%
18 38
 
1.1%
11 36
 
1.0%
Other values (728) 3188
88.2%
ValueCountFrequency (%)
0 9
 
0.2%
1 10
 
0.3%
2 12
 
0.3%
3 13
 
0.4%
4 19
0.5%
5 28
0.8%
6 28
0.8%
7 30
0.8%
8 34
0.9%
9 33
0.9%
ValueCountFrequency (%)
2121 1
< 0.1%
1998 1
< 0.1%
1782 1
< 0.1%
1772 1
< 0.1%
1740 1
< 0.1%
1570 1
< 0.1%
1554 1
< 0.1%
1530 1
< 0.1%
1452 1
< 0.1%
1412 1
< 0.1%

6인세대
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct200
Distinct (%)5.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.184334
Minimum0
Maximum385
Zeros50
Zeros (%)1.4%
Negative0
Negative (%)0.0%
Memory size31.9 KiB
2024-04-06T17:20:29.264121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q18
median27
Q358
95-th percentile116
Maximum385
Range385
Interquartile range (IQR)50

Descriptive statistics

Standard deviation40.118268
Coefficient of variation (CV)1.0238344
Kurtosis6.7363439
Mean39.184334
Median Absolute Deviation (MAD)21
Skewness1.9892186
Sum141573
Variance1609.4754
MonotonicityNot monotonic
2024-04-06T17:20:29.536613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5 129
 
3.6%
6 116
 
3.2%
2 115
 
3.2%
7 113
 
3.1%
8 112
 
3.1%
4 110
 
3.0%
3 101
 
2.8%
9 72
 
2.0%
12 72
 
2.0%
13 67
 
1.9%
Other values (190) 2606
72.1%
ValueCountFrequency (%)
0 50
 
1.4%
1 63
1.7%
2 115
3.2%
3 101
2.8%
4 110
3.0%
5 129
3.6%
6 116
3.2%
7 113
3.1%
8 112
3.1%
9 72
2.0%
ValueCountFrequency (%)
385 1
< 0.1%
356 1
< 0.1%
307 1
< 0.1%
298 1
< 0.1%
285 1
< 0.1%
275 1
< 0.1%
270 1
< 0.1%
268 1
< 0.1%
251 1
< 0.1%
248 1
< 0.1%

7인세대
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct62
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.8649322
Minimum0
Maximum109
Zeros307
Zeros (%)8.5%
Negative0
Negative (%)0.0%
Memory size31.9 KiB
2024-04-06T17:20:29.892363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median6
Q313
95-th percentile26
Maximum109
Range109
Interquartile range (IQR)11

Descriptive statistics

Standard deviation9.3038425
Coefficient of variation (CV)1.0495109
Kurtosis9.1166536
Mean8.8649322
Median Absolute Deviation (MAD)5
Skewness2.1857608
Sum32029
Variance86.561486
MonotonicityNot monotonic
2024-04-06T17:20:30.399061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 368
 
10.2%
2 339
 
9.4%
3 311
 
8.6%
0 307
 
8.5%
4 218
 
6.0%
5 198
 
5.5%
8 160
 
4.4%
6 157
 
4.3%
7 150
 
4.2%
9 137
 
3.8%
Other values (52) 1268
35.1%
ValueCountFrequency (%)
0 307
8.5%
1 368
10.2%
2 339
9.4%
3 311
8.6%
4 218
6.0%
5 198
5.5%
6 157
4.3%
7 150
4.2%
8 160
4.4%
9 137
 
3.8%
ValueCountFrequency (%)
109 1
< 0.1%
86 1
< 0.1%
75 1
< 0.1%
71 1
< 0.1%
63 1
< 0.1%
62 1
< 0.1%
60 1
< 0.1%
58 1
< 0.1%
56 1
< 0.1%
54 1
< 0.1%

8인세대
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct21
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3401605
Minimum0
Maximum22
Zeros1085
Zeros (%)30.0%
Negative0
Negative (%)0.0%
Memory size31.9 KiB
2024-04-06T17:20:30.856885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile8
Maximum22
Range22
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.7952926
Coefficient of variation (CV)1.1944875
Kurtosis5.0596435
Mean2.3401605
Median Absolute Deviation (MAD)1
Skewness1.9427298
Sum8455
Variance7.8136608
MonotonicityNot monotonic
2024-04-06T17:20:31.164924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0 1085
30.0%
1 763
21.1%
2 530
14.7%
3 365
 
10.1%
4 253
 
7.0%
5 190
 
5.3%
6 127
 
3.5%
7 80
 
2.2%
8 68
 
1.9%
9 48
 
1.3%
Other values (11) 104
 
2.9%
ValueCountFrequency (%)
0 1085
30.0%
1 763
21.1%
2 530
14.7%
3 365
 
10.1%
4 253
 
7.0%
5 190
 
5.3%
6 127
 
3.5%
7 80
 
2.2%
8 68
 
1.9%
9 48
 
1.3%
ValueCountFrequency (%)
22 1
 
< 0.1%
20 2
 
0.1%
18 1
 
< 0.1%
17 5
 
0.1%
16 1
 
< 0.1%
15 5
 
0.1%
14 9
0.2%
13 15
0.4%
12 14
0.4%
11 22
0.6%

9인세대
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct10
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.71132023
Minimum0
Maximum9
Zeros2134
Zeros (%)59.1%
Negative0
Negative (%)0.0%
Memory size31.9 KiB
2024-04-06T17:20:31.388349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile3
Maximum9
Range9
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1457837
Coefficient of variation (CV)1.6107847
Kurtosis7.776593
Mean0.71132023
Median Absolute Deviation (MAD)0
Skewness2.3922905
Sum2570
Variance1.3128203
MonotonicityNot monotonic
2024-04-06T17:20:31.587672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 2134
59.1%
1 881
24.4%
2 335
 
9.3%
3 136
 
3.8%
4 74
 
2.0%
5 25
 
0.7%
6 15
 
0.4%
7 6
 
0.2%
8 5
 
0.1%
9 2
 
0.1%
ValueCountFrequency (%)
0 2134
59.1%
1 881
24.4%
2 335
 
9.3%
3 136
 
3.8%
4 74
 
2.0%
5 25
 
0.7%
6 15
 
0.4%
7 6
 
0.2%
8 5
 
0.1%
9 2
 
0.1%
ValueCountFrequency (%)
9 2
 
0.1%
8 5
 
0.1%
7 6
 
0.2%
6 15
 
0.4%
5 25
 
0.7%
4 74
 
2.0%
3 136
 
3.8%
2 335
 
9.3%
1 881
24.4%
0 2134
59.1%

10인이상세대
Real number (ℝ)

ZEROS 

Distinct12
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.68253529
Minimum0
Maximum12
Zeros2120
Zeros (%)58.7%
Negative0
Negative (%)0.0%
Memory size31.9 KiB
2024-04-06T17:20:31.845728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile3
Maximum12
Range12
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.0815154
Coefficient of variation (CV)1.584556
Kurtosis11.857437
Mean0.68253529
Median Absolute Deviation (MAD)0
Skewness2.633906
Sum2466
Variance1.1696755
MonotonicityNot monotonic
2024-04-06T17:20:32.081633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 2120
58.7%
1 910
25.2%
2 365
 
10.1%
3 130
 
3.6%
4 50
 
1.4%
5 18
 
0.5%
6 9
 
0.2%
7 4
 
0.1%
8 3
 
0.1%
9 2
 
0.1%
Other values (2) 2
 
0.1%
ValueCountFrequency (%)
0 2120
58.7%
1 910
25.2%
2 365
 
10.1%
3 130
 
3.6%
4 50
 
1.4%
5 18
 
0.5%
6 9
 
0.2%
7 4
 
0.1%
8 3
 
0.1%
9 2
 
0.1%
ValueCountFrequency (%)
12 1
 
< 0.1%
10 1
 
< 0.1%
9 2
 
0.1%
8 3
 
0.1%
7 4
 
0.1%
6 9
 
0.2%
5 18
 
0.5%
4 50
 
1.4%
3 130
 
3.6%
2 365
10.1%

Interactions

2024-04-06T17:20:17.931571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:19:47.970991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:19:50.952094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:19:53.584310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:19:55.878543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:19:58.849995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:20:01.510645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:20:04.729888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:20:07.394793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:20:10.363531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:20:12.652785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:20:15.011595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:20:18.130117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:19:48.178676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:19:51.143500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:19:53.764849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:19:56.110226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:19:59.088513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:20:01.847501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:20:04.926926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:20:07.599924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:20:10.550757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:20:12.905882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:20:15.185036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:20:18.396821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:19:48.388867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:19:51.341326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:19:53.952264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:19:56.355635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:19:59.277938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:20:02.579915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:20:05.159614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:20:07.796886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:20:10.781153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:20:13.124742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:20:15.356314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:20:18.611609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:19:48.624425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:19:51.554452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:19:54.180210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:19:56.802628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:19:59.514380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:20:02.826292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:20:05.398528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:20:08.096866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:20:10.976398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:20:13.325811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:20:15.563863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:20:18.802486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:19:48.867133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:19:51.822239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:19:54.349388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:19:57.007187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:19:59.694774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:20:03.057750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:20:05.662758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:20:08.343921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:20:11.156669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:20:13.521837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:20:15.767302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:20:19.084613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:19:49.109446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:19:52.021856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:19:54.516316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:19:57.188902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:19:59.863078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:20:03.321479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:20:05.929639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:20:08.598228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:20:11.333091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:20:13.685825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:20:15.938318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:20:19.271699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:19:49.293110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:19:52.224982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:19:54.682693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:19:57.355656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:20:00.021960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:20:03.567486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:20:06.151955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:20:08.837401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:20:11.515160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:20:13.856822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:20:16.494507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:20:19.453617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:19:49.610299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:19:52.448471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:19:54.846188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:19:57.572014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:20:00.195806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:20:03.753623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:20:06.331500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:20:09.075966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:20:11.677636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:20:14.021312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:20:16.654557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:20:19.640687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:19:49.928515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:19:52.751254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:19:55.053360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:19:57.856562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:20:00.401420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:20:03.973883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:20:06.534280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:20:09.358970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:20:11.878828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:20:14.217686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:20:16.870334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:20:19.821682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:19:50.255158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:19:53.054934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:19:55.248614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:19:58.139092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:20:00.643293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:20:04.167370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:20:06.708413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:20:09.650821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:20:12.086325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:20:14.402303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:20:17.137827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:20:19.986885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:19:50.532347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:19:53.223144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:19:55.442435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:19:58.408600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:20:00.941320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:20:04.338387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:20:06.978593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:20:09.893803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:20:12.268214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:20:14.603682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:20:17.484312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:20:20.176705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:19:50.773028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:19:53.408106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:19:55.630105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:19:58.650661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:20:01.233618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:20:04.562342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:20:07.188142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:20:10.161106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:20:12.458068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:20:14.808706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:20:17.706609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-06T17:20:32.375295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정기관코드시도명전체세대수1인세대2인세대3인세대4인세대5인세대6인세대7인세대8인세대9인세대10인이상세대
행정기관코드1.0000.9920.4070.3800.3940.3820.3290.3250.3330.3000.3230.2700.068
시도명0.9921.0000.4500.4140.4340.4240.3960.3670.4140.3830.3680.3080.100
전체세대수0.4070.4501.0000.8810.9570.9560.7890.9230.7940.7570.7880.7290.421
1인세대0.3800.4140.8811.0000.8450.7660.5410.7500.6120.5400.6190.5030.251
2인세대0.3940.4340.9570.8451.0000.9500.7610.9090.7710.7170.8030.6770.429
3인세대0.3820.4240.9560.7660.9501.0000.9040.9660.8320.8220.8820.7490.479
4인세대0.3290.3960.7890.5410.7610.9041.0000.9120.8940.8700.7040.5480.304
5인세대0.3250.3670.9230.7500.9090.9660.9121.0000.8680.7870.8400.7190.428
6인세대0.3330.4140.7940.6120.7710.8320.8940.8681.0000.9380.7900.6680.372
7인세대0.3000.3830.7570.5400.7170.8220.8700.7870.9381.0000.8250.6190.542
8인세대0.3230.3680.7880.6190.8030.8820.7040.8400.7900.8251.0000.7840.536
9인세대0.2700.3080.7290.5030.6770.7490.5480.7190.6680.6190.7841.0000.512
10인이상세대0.0680.1000.4210.2510.4290.4790.3040.4280.3720.5420.5360.5121.000
2024-04-06T17:20:32.867877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정기관코드전체세대수1인세대2인세대3인세대4인세대5인세대6인세대7인세대8인세대9인세대10인이상세대시도명
행정기관코드1.000-0.499-0.472-0.470-0.502-0.491-0.448-0.442-0.412-0.352-0.279-0.1040.972
전체세대수-0.4991.0000.9490.9850.9710.9510.9470.9280.8630.7180.5380.2970.192
1인세대-0.4720.9491.0000.9230.8620.8270.8270.8160.7610.6220.4580.2730.174
2인세대-0.4700.9850.9231.0000.9700.9430.9430.9280.8630.7140.5280.3030.184
3인세대-0.5020.9710.8620.9701.0000.9920.9820.9550.8840.7370.5510.2910.179
4인세대-0.4910.9510.8270.9430.9921.0000.9880.9570.8840.7380.5520.2850.170
5인세대-0.4480.9470.8270.9430.9820.9881.0000.9690.9000.7500.5560.3050.152
6인세대-0.4420.9280.8160.9280.9550.9570.9691.0000.9110.7720.5710.3280.178
7인세대-0.4120.8630.7610.8630.8840.8840.9000.9111.0000.7420.5570.3380.163
8인세대-0.3520.7180.6220.7140.7370.7380.7500.7720.7421.0000.4860.3050.151
9인세대-0.2790.5380.4580.5280.5510.5520.5560.5710.5570.4861.0000.2300.125
10인이상세대-0.1040.2970.2730.3030.2910.2850.3050.3280.3380.3050.2301.0000.048
시도명0.9720.1920.1740.1840.1790.1700.1520.1780.1630.1510.1250.0481.000

Missing values

2024-04-06T17:20:20.440914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-06T17:20:20.842225image/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

행정기관코드기준연월시도명시군구명읍면동명전체세대수1인세대2인세대3인세대4인세대5인세대6인세대7인세대8인세대9인세대10인이상세대
011110515002024-03-31서울특별시종로구청운효자동5025195011819526971824212630
111110530002024-03-31서울특별시종로구사직동454523079256684831182513213
211110540002024-03-31서울특별시종로구삼청동111855526213211042124100
311110550002024-03-31서울특별시종로구부암동412916739947355311433811301
411110560002024-03-31서울특별시종로구평창동7105223418111457114534178211521
511110570002024-03-31서울특별시종로구무악동3014711751692673148287211
611110580002024-03-31서울특별시종로구교남동434016261042897623126158120
711110600002024-03-31서울특별시종로구가회동190091440332119050173101
811110615002024-03-31서울특별시종로구종로1.2.3.4가동4918393549526216445133010
911110630002024-03-31서울특별시종로구종로5.6가동355825564912971683763000
행정기관코드기준연월시도명시군구명읍면동명전체세대수1인세대2인세대3인세대4인세대5인세대6인세대7인세대8인세대9인세대10인이상세대
360352800330002024-03-31전북특별자치도부안군행안면1247730328119431872000
360452800340002024-03-31전북특별자치도부안군계화면18371034561145562883110
360552800350002024-03-31전북특별자치도부안군보안면147189243293361251000
360652800360002024-03-31전북특별자치도부안군변산면249914436582321074991000
360752800370002024-03-31전북특별자치도부안군진서면1365801393104461362000
360852800380002024-03-31전북특별자치도부안군백산면1517913446102361811000
360952800390002024-03-31전북특별자치도부안군상서면1268750342117361443110
361052800400002024-03-31전북특별자치도부안군하서면1513880445121461730100
361152800410002024-03-31전북특별자치도부안군줄포면1502876421128521644001
361252800420002024-03-31전북특별자치도부안군위도면7705431742517920000