Overview

Dataset statistics

Number of variables12
Number of observations4918
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory494.8 KiB
Average record size in memory103.0 B

Variable types

Numeric5
Categorical6
Text1

Dataset

Description대전광역시 유성구 주민등록 인구 현황에 대한 데이터로 행정동코드, 행정동이름, 출생년도, 인구수 등의 항목을 제공합니다.
Author대전광역시 유성구
URLhttps://www.data.go.kr/data/15108901/fileData.do

Alerts

기준년월 has constant value ""Constant
시도코드 has constant value ""Constant
시도이름 has constant value ""Constant
시군구코드 has constant value ""Constant
시군구이름 has constant value ""Constant
행정동코드 is highly overall correlated with 법정동코드 and 1 other fieldsHigh correlation
법정동코드 is highly overall correlated with 행정동코드 and 1 other fieldsHigh correlation
행정동이름 is highly overall correlated with 행정동코드 and 1 other fieldsHigh correlation
번호 has unique valuesUnique

Reproduction

Analysis started2023-12-12 08:18:09.603948
Analysis finished2023-12-12 08:18:13.589111
Duration3.99 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

번호
Real number (ℝ)

UNIQUE 

Distinct4918
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2461.6004
Minimum1
Maximum4921
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size43.4 KiB
2023-12-12T17:18:13.667529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile246.85
Q11231.25
median2462.5
Q33691.75
95-th percentile4675.15
Maximum4921
Range4920
Interquartile range (IQR)2460.5

Descriptive statistics

Standard deviation1420.8358
Coefficient of variation (CV)0.57720002
Kurtosis-1.2000983
Mean2461.6004
Median Absolute Deviation (MAD)1230.5
Skewness-0.0007726105
Sum12106151
Variance2018774.5
MonotonicityStrictly increasing
2023-12-12T17:18:13.813176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
< 0.1%
3281 1
 
< 0.1%
3288 1
 
< 0.1%
3287 1
 
< 0.1%
3286 1
 
< 0.1%
3285 1
 
< 0.1%
3284 1
 
< 0.1%
3283 1
 
< 0.1%
3282 1
 
< 0.1%
3280 1
 
< 0.1%
Other values (4908) 4908
99.8%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
4921 1
< 0.1%
4920 1
< 0.1%
4919 1
< 0.1%
4918 1
< 0.1%
4917 1
< 0.1%
4916 1
< 0.1%
4915 1
< 0.1%
4914 1
< 0.1%
4913 1
< 0.1%
4912 1
< 0.1%

기준년월
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.6 KiB
Jan-22
4918 

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowJan-22
2nd rowJan-22
3rd rowJan-22
4th rowJan-22
5th rowJan-22

Common Values

ValueCountFrequency (%)
Jan-22 4918
100.0%

Length

2023-12-12T17:18:13.956892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T17:18:14.050712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
jan-22 4918
100.0%

시도코드
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.6 KiB
3000000000
4918 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3000000000
2nd row3000000000
3rd row3000000000
4th row3000000000
5th row3000000000

Common Values

ValueCountFrequency (%)
3000000000 4918
100.0%

Length

2023-12-12T17:18:14.148588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T17:18:14.244644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3000000000 4918
100.0%

시도이름
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.6 KiB
대전광역시
4918 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
대전광역시 4918
100.0%

Length

2023-12-12T17:18:14.346293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T17:18:14.448885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
대전광역시 4918
100.0%

시군구코드
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.6 KiB
3020000000
4918 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3020000000
2nd row3020000000
3rd row3020000000
4th row3020000000
5th row3020000000

Common Values

ValueCountFrequency (%)
3020000000 4918
100.0%

Length

2023-12-12T17:18:14.543394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T17:18:14.643554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3020000000 4918
100.0%

시군구이름
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.6 KiB
유성구
4918 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
유성구 4918
100.0%

Length

2023-12-12T17:18:14.750356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T17:18:14.839270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
유성구 4918
100.0%

행정동코드
Real number (ℝ)

HIGH CORRELATION 

Distinct13
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.020055 × 109
Minimum3.020052 × 109
Maximum3.020061 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size43.4 KiB
2023-12-12T17:18:14.953524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.020052 × 109
5-th percentile3.020052 × 109
Q13.0200527 × 109
median3.0200547 × 109
Q33.020055 × 109
95-th percentile3.02006 × 109
Maximum3.020061 × 109
Range9000
Interquartile range (IQR)2300

Descriptive statistics

Standard deviation2492.3104
Coefficient of variation (CV)8.2525331 × 10-7
Kurtosis0.019948611
Mean3.020055 × 109
Median Absolute Deviation (MAD)2000
Skewness0.89350433
Sum1.485263 × 1013
Variance6211610.9
MonotonicityNot monotonic
2023-12-12T17:18:15.066702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
3020052000 734
14.9%
3020055000 694
14.1%
3020054700 519
10.6%
3020054000 444
9.0%
3020058000 426
8.7%
3020052600 383
7.8%
3020054600 361
7.3%
3020060000 289
 
5.9%
3020057000 286
 
5.8%
3020053000 202
 
4.1%
Other values (3) 580
11.8%
ValueCountFrequency (%)
3020052000 734
14.9%
3020052600 383
7.8%
3020052700 177
 
3.6%
3020053000 202
 
4.1%
3020054000 444
9.0%
3020054600 361
7.3%
3020054700 519
10.6%
3020054800 202
 
4.1%
3020055000 694
14.1%
3020057000 286
 
5.8%
ValueCountFrequency (%)
3020061000 201
 
4.1%
3020060000 289
5.9%
3020058000 426
8.7%
3020057000 286
5.8%
3020055000 694
14.1%
3020054800 202
 
4.1%
3020054700 519
10.6%
3020054600 361
7.3%
3020054000 444
9.0%
3020053000 202
 
4.1%

행정동이름
Categorical

HIGH CORRELATION 

Distinct13
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size38.6 KiB
진잠동
734 
신성동
694 
노은2동
519 
온천2동
444 
구즉동
426 
Other values (8)
2101 

Length

Max length4
Median length3
Mean length3.3922326
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row노은2동
2nd row전민동
3rd row온천1동
4th row학하동
5th row구즉동

Common Values

ValueCountFrequency (%)
진잠동 734
14.9%
신성동 694
14.1%
노은2동 519
10.6%
온천2동 444
9.0%
구즉동 426
8.7%
학하동 383
7.8%
노은1동 361
7.3%
관평동 289
 
5.9%
전민동 286
 
5.8%
온천1동 202
 
4.1%
Other values (3) 580
11.8%

Length

2023-12-12T17:18:15.193274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
진잠동 734
14.9%
신성동 694
14.1%
노은2동 519
10.6%
온천2동 444
9.0%
구즉동 426
8.7%
학하동 383
7.8%
노은1동 361
7.3%
관평동 289
 
5.9%
전민동 286
 
5.8%
온천1동 202
 
4.1%
Other values (3) 580
11.8%

법정동코드
Real number (ℝ)

HIGH CORRELATION 

Distinct53
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0200124 × 109
Minimum3.0200101 × 109
Maximum3.0200153 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size43.4 KiB
2023-12-12T17:18:15.343105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.0200101 × 109
5-th percentile3.0200103 × 109
Q13.0200113 × 109
median3.0200121 × 109
Q33.0200137 × 109
95-th percentile3.0200147 × 109
Maximum3.0200153 × 109
Range5200
Interquartile range (IQR)2400

Descriptive statistics

Standard deviation1427.6904
Coefficient of variation (CV)4.7274323 × 10-7
Kurtosis-1.0820355
Mean3.0200124 × 109
Median Absolute Deviation (MAD)1100
Skewness0.23315463
Sum1.4852421 × 1013
Variance2038299.9
MonotonicityNot monotonic
2023-12-12T17:18:15.536428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3020012000 301
 
6.1%
3020012100 259
 
5.3%
3020011100 199
 
4.0%
3020011600 168
 
3.4%
3020013200 165
 
3.4%
3020013900 123
 
2.5%
3020011400 102
 
2.1%
3020011900 102
 
2.1%
3020011200 102
 
2.1%
3020014100 101
 
2.1%
Other values (43) 3296
67.0%
ValueCountFrequency (%)
3020010100 100
2.0%
3020010200 97
2.0%
3020010300 98
2.0%
3020010400 98
2.0%
3020010500 94
1.9%
3020010600 98
2.0%
3020010700 88
1.8%
3020010800 81
1.6%
3020010900 85
1.7%
3020011000 87
1.8%
ValueCountFrequency (%)
3020015300 61
1.2%
3020015200 19
 
0.4%
3020015100 4
 
0.1%
3020015000 59
1.2%
3020014900 78
1.6%
3020014800 4
 
0.1%
3020014700 101
2.1%
3020014600 101
2.1%
3020014500 100
2.0%
3020014400 98
2.0%

법정동이름
Text

Distinct53
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size38.6 KiB
2023-12-12T17:18:15.810721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length2.8735258
Min length2

Characters and Unicode

Total characters14132
Distinct characters66
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
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 row하기동
2nd row전민동
3rd row구암동
4th row복용동
5th row금탄동
ValueCountFrequency (%)
지족동 301
 
6.1%
죽동 259
 
5.3%
봉명동 199
 
4.0%
복용동 168
 
3.4%
하기동 165
 
3.4%
반석동 123
 
2.5%
구암동 102
 
2.1%
원신흥동 102
 
2.1%
노은동 102
 
2.1%
장대동 101
 
2.1%
Other values (43) 3296
67.0%
2023-12-12T17:18:16.286047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 4918
34.8%
지 394
 
2.8%
산 383
 
2.7%
봉 381
 
2.7%
대 377
 
2.7%
용 364
 
2.6%
족 301
 
2.1%
명 299
 
2.1%
원 294
 
2.1%
신 287
 
2.0%
Other values (56) 6134
43.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 14132
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 4918
34.8%
지 394
 
2.8%
산 383
 
2.7%
봉 381
 
2.7%
대 377
 
2.7%
용 364
 
2.6%
족 301
 
2.1%
명 299
 
2.1%
원 294
 
2.1%
신 287
 
2.0%
Other values (56) 6134
43.4%

Most occurring scripts

ValueCountFrequency (%)
Hangul 14132
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 4918
34.8%
지 394
 
2.8%
산 383
 
2.7%
봉 381
 
2.7%
대 377
 
2.7%
용 364
 
2.6%
족 301
 
2.1%
명 299
 
2.1%
원 294
 
2.1%
신 287
 
2.0%
Other values (56) 6134
43.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 14132
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 4918
34.8%
지 394
 
2.8%
산 383
 
2.7%
봉 381
 
2.7%
대 377
 
2.7%
용 364
 
2.6%
족 301
 
2.1%
명 299
 
2.1%
원 294
 
2.1%
신 287
 
2.0%
Other values (56) 6134
43.4%

출생년도
Real number (ℝ)

Distinct107
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1973.4331
Minimum1915
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size43.4 KiB
2023-12-12T17:18:16.456122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1915
5-th percentile1930
Q11951
median1974
Q31996
95-th percentile2017
Maximum2022
Range107
Interquartile range (IQR)45

Descriptive statistics

Standard deviation27.451518
Coefficient of variation (CV)0.013910539
Kurtosis-1.1012287
Mean1973.4331
Median Absolute Deviation (MAD)23
Skewness-0.027446584
Sum9705344
Variance753.58582
MonotonicityNot monotonic
2023-12-12T17:18:16.607156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1982 58
 
1.2%
1985 57
 
1.2%
1992 57
 
1.2%
1988 57
 
1.2%
1993 57
 
1.2%
1979 56
 
1.1%
1989 56
 
1.1%
1955 55
 
1.1%
1952 55
 
1.1%
1961 55
 
1.1%
Other values (97) 4355
88.6%
ValueCountFrequency (%)
1915 2
 
< 0.1%
1916 1
 
< 0.1%
1918 1
 
< 0.1%
1919 3
 
0.1%
1920 4
 
0.1%
1921 12
0.2%
1922 13
0.3%
1923 19
0.4%
1924 16
0.3%
1925 26
0.5%
ValueCountFrequency (%)
2022 33
0.7%
2021 41
0.8%
2020 48
1.0%
2019 41
0.8%
2018 45
0.9%
2017 49
1.0%
2016 49
1.0%
2015 46
0.9%
2014 46
0.9%
2013 49
1.0%

인구수
Real number (ℝ)

Distinct423
Distinct (%)8.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean71.209435
Minimum1
Maximum666
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size43.4 KiB
2023-12-12T17:18:17.067301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median21
Q3113
95-th percentile280.3
Maximum666
Range665
Interquartile range (IQR)109

Descriptive statistics

Standard deviation99.127134
Coefficient of variation (CV)1.3920506
Kurtosis4.3943487
Mean71.209435
Median Absolute Deviation (MAD)20
Skewness1.9768311
Sum350208
Variance9826.1888
MonotonicityNot monotonic
2023-12-12T17:18:17.234003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 525
 
10.7%
2 383
 
7.8%
3 256
 
5.2%
4 204
 
4.1%
5 164
 
3.3%
6 142
 
2.9%
7 109
 
2.2%
8 103
 
2.1%
9 80
 
1.6%
10 68
 
1.4%
Other values (413) 2884
58.6%
ValueCountFrequency (%)
1 525
10.7%
2 383
7.8%
3 256
5.2%
4 204
 
4.1%
5 164
 
3.3%
6 142
 
2.9%
7 109
 
2.2%
8 103
 
2.1%
9 80
 
1.6%
10 68
 
1.4%
ValueCountFrequency (%)
666 1
< 0.1%
662 1
< 0.1%
654 1
< 0.1%
633 1
< 0.1%
628 1
< 0.1%
594 1
< 0.1%
592 1
< 0.1%
591 1
< 0.1%
569 1
< 0.1%
547 1
< 0.1%

Interactions

2023-12-12T17:18:12.802902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:18:10.410050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:18:10.989213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:18:11.611012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:18:12.251286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:18:12.906484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:18:10.513150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:18:11.118850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:18:11.736228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:18:12.362504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:18:12.997795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:18:10.627844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:18:11.232799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:18:11.876718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:18:12.468379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:18:13.096076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:18:10.733806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:18:11.366179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:18:12.012373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:18:12.597094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:18:13.191662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:18:10.868452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:18:11.499333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:18:12.128610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:18:12.702079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T17:18:17.383081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
번호행정동코드행정동이름법정동코드법정동이름출생년도인구수
번호1.0000.0370.0410.0310.0000.0000.061
행정동코드0.0371.0001.0000.8910.9930.0000.267
행정동이름0.0411.0001.0000.9100.9920.0000.391
법정동코드0.0310.8910.9101.0001.0000.0450.401
법정동이름0.0000.9930.9921.0001.0000.0000.661
출생년도0.0000.0000.0000.0450.0001.0000.437
인구수0.0610.2670.3910.4010.6610.4371.000
2023-12-12T17:18:17.524720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
번호행정동코드법정동코드출생년도인구수행정동이름
번호1.000-0.017-0.013-0.002-0.0230.017
행정동코드-0.0171.0000.8050.0030.0490.999
법정동코드-0.0130.8051.0000.006-0.1000.695
출생년도-0.0020.0030.0061.0000.2490.000
인구수-0.0230.049-0.1000.2491.0000.173
행정동이름0.0170.9990.6950.0000.1731.000

Missing values

2023-12-12T17:18:13.325858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T17:18:13.508625image/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

번호기준년월시도코드시도이름시군구코드시군구이름행정동코드행정동이름법정동코드법정동이름출생년도인구수
01Jan-223000000000대전광역시3020000000유성구3020054700노은2동3020013200하기동194536
12Jan-223000000000대전광역시3020000000유성구3020057000전민동3020014100전민동193843
23Jan-223000000000대전광역시3020000000유성구3020053000온천1동3020011200구암동1968206
34Jan-223000000000대전광역시3020000000유성구3020052600학하동3020011600복용동202014
45Jan-223000000000대전광역시3020000000유성구3020058000구즉동3020015000금탄동19741
56Jan-223000000000대전광역시3020000000유성구3020054700노은2동3020012000지족동195467
67Jan-223000000000대전광역시3020000000유성구3020054000온천2동3020012100죽동1984272
78Jan-223000000000대전광역시3020000000유성구3020054800노은3동3020012000지족동194564
89Jan-223000000000대전광역시3020000000유성구3020052000진잠동3020010200교촌동198559
910Jan-223000000000대전광역시3020000000유성구3020052600학하동3020011300덕명동201962
번호기준년월시도코드시도이름시군구코드시군구이름행정동코드행정동이름법정동코드법정동이름출생년도인구수
49084912Jan-223000000000대전광역시3020000000유성구3020054800노은3동3020013900반석동2016140
49094913Jan-223000000000대전광역시3020000000유성구3020054800노은3동3020013900반석동1952104
49104914Jan-223000000000대전광역시3020000000유성구3020054000온천2동3020012200궁동1999379
49114915Jan-223000000000대전광역시3020000000유성구3020052600학하동3020011300덕명동200938
49124916Jan-223000000000대전광역시3020000000유성구3020054000온천2동3020012200궁동195545
49134917Jan-223000000000대전광역시3020000000유성구3020052000진잠동3020011000방동19764
49144918Jan-223000000000대전광역시3020000000유성구3020052600학하동3020010500학하동198318
49154919Jan-223000000000대전광역시3020000000유성구3020054600노은1동3020011800갑동20113
49164920Jan-223000000000대전광역시3020000000유성구3020052600학하동3020011600복용동199324
49174921Jan-223000000000대전광역시3020000000유성구3020054700노은2동3020012100죽동19334