Overview

Dataset statistics

Number of variables9
Number of observations10000
Missing cells77
Missing cells (%)0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory820.3 KiB
Average record size in memory84.0 B

Variable types

Numeric4
DateTime1
Categorical1
Text3

Dataset

Description법정동(읍면동리) 성별 출생등록자수 현황입니다.법정동은 시 또는 구의 하위 행정구역으로 법률로 지정한 구역을 말합니다.
Author행정안전부
URLhttps://www.data.go.kr/data/15099161/fileData.do

Alerts

통계년월 has constant value ""Constant
법정동코드 is highly overall correlated with 시도명High correlation
계 is highly overall correlated with 남자 and 1 other fieldsHigh correlation
남자 is highly overall correlated with 계 and 1 other fieldsHigh correlation
여자 is highly overall correlated with 계 and 1 other fieldsHigh correlation
시도명 is highly overall correlated with 법정동코드High correlation
법정동코드 has unique valuesUnique
계 has 8390 (83.9%) zerosZeros
남자 has 8774 (87.7%) zerosZeros
여자 has 8767 (87.7%) zerosZeros

Reproduction

Analysis started2024-04-06 08:07:29.753951
Analysis finished2024-04-06 08:07:36.120509
Duration6.37 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%
Mean4.458139 × 109
Minimum1.1110101 × 109
Maximum5.280042 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-06T17:07:36.276462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.1110101 × 109
5-th percentile2.771033 × 109
Q14.372039 × 109
median4.68204 × 109
Q34.824036 × 109
95-th percentile5.221035 × 109
Maximum5.280042 × 109
Range4.1690319 × 109
Interquartile range (IQR)4.5199699 × 108

Descriptive statistics

Standard deviation7.7730782 × 108
Coefficient of variation (CV)0.17435702
Kurtosis6.6049605
Mean4.458139 × 109
Median Absolute Deviation (MAD)2.6100401 × 108
Skewness-2.3862277
Sum4.458139 × 1013
Variance6.0420745 × 1017
MonotonicityNot monotonic
2024-04-06T17:07:36.647929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4715011000 1
 
< 0.1%
4679040024 1
 
< 0.1%
5175025027 1
 
< 0.1%
4782037050 1
 
< 0.1%
5273035023 1
 
< 0.1%
4773039048 1
 
< 0.1%
4711110400 1
 
< 0.1%
4723039035 1
 
< 0.1%
4790035034 1
 
< 0.1%
4717042032 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
1111010100 1
< 0.1%
1111010600 1
< 0.1%
1111010700 1
< 0.1%
1111010800 1
< 0.1%
1111010900 1
< 0.1%
1111011000 1
< 0.1%
1111011200 1
< 0.1%
1111011400 1
< 0.1%
1111011600 1
< 0.1%
1111011800 1
< 0.1%
ValueCountFrequency (%)
5280042026 1
< 0.1%
5280042025 1
< 0.1%
5280042021 1
< 0.1%
5280041026 1
< 0.1%
5280041025 1
< 0.1%
5280041023 1
< 0.1%
5280041022 1
< 0.1%
5280040025 1
< 0.1%
5280040021 1
< 0.1%
5280039027 1
< 0.1%

통계년월
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
2024-04-06T17:07:36.983957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:07:37.234433image/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
1635 
전라남도
1480 
충청남도
1154 
경상남도
1146 
경기도
1033 
Other values (12)
3552 

Length

Max length7
Median length4
Mean length4.5229
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row경상북도
2nd row강원특별자치도
3rd row경상남도
4th row서울특별시
5th row충청북도

Common Values

ValueCountFrequency (%)
경상북도 1635
16.4%
전라남도 1480
14.8%
충청남도 1154
11.5%
경상남도 1146
11.5%
경기도 1033
10.3%
전북특별자치도 912
9.1%
충청북도 810
8.1%
강원특별자치도 665
6.7%
서울특별시 239
 
2.4%
대구광역시 186
 
1.9%
Other values (7) 740
7.4%

Length

2024-04-06T17:07:37.547141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경상북도 1635
16.4%
전라남도 1480
14.8%
충청남도 1154
11.5%
경상남도 1146
11.5%
경기도 1033
10.3%
전북특별자치도 912
9.1%
충청북도 810
8.1%
강원특별자치도 665
6.7%
서울특별시 239
 
2.4%
대구광역시 186
 
1.9%
Other values (7) 740
7.4%

시군구명
Text

Distinct226
Distinct (%)2.3%
Missing77
Missing (%)0.8%
Memory size156.2 KiB
2024-04-06T17:07:38.149880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length3
Mean length3.2868084
Min length2

Characters and Unicode

Total characters32615
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 row김천시
2nd row삼척시
3rd row밀양시
4th row종로구
5th row영동군
ValueCountFrequency (%)
창원시 168
 
1.6%
청주시 164
 
1.5%
상주시 130
 
1.2%
중구 128
 
1.2%
공주시 126
 
1.2%
안동시 123
 
1.2%
북구 121
 
1.1%
영천시 115
 
1.1%
경주시 114
 
1.1%
포항시 111
 
1.0%
Other values (226) 9372
87.8%
2024-04-06T17:07:39.188988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
군 4736
 
14.5%
시 4578
 
14.0%
구 1636
 
5.0%
주 1462
 
4.5%
천 1254
 
3.8%
산 1058
 
3.2%
성 955
 
2.9%
양 787
 
2.4%
749
 
2.3%
안 627
 
1.9%
Other values (133) 14773
45.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 31866
97.7%
Space Separator 749
 
2.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
군 4736
 
14.9%
시 4578
 
14.4%
구 1636
 
5.1%
주 1462
 
4.6%
천 1254
 
3.9%
산 1058
 
3.3%
성 955
 
3.0%
양 787
 
2.5%
안 627
 
2.0%
영 579
 
1.8%
Other values (132) 14194
44.5%
Space Separator
ValueCountFrequency (%)
749
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 31866
97.7%
Common 749
 
2.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
군 4736
 
14.9%
시 4578
 
14.4%
구 1636
 
5.1%
주 1462
 
4.6%
천 1254
 
3.9%
산 1058
 
3.3%
성 955
 
3.0%
양 787
 
2.5%
안 627
 
2.0%
영 579
 
1.8%
Other values (132) 14194
44.5%
Common
ValueCountFrequency (%)
749
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 31866
97.7%
ASCII 749
 
2.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
군 4736
 
14.9%
시 4578
 
14.4%
구 1636
 
5.1%
주 1462
 
4.6%
천 1254
 
3.9%
산 1058
 
3.3%
성 955
 
3.0%
양 787
 
2.5%
안 627
 
2.0%
영 579
 
1.8%
Other values (132) 14194
44.5%
ASCII
ValueCountFrequency (%)
749
100.0%

읍면동명
Text

Distinct2830
Distinct (%)28.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-04-06T17:07:39.961994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length3
Mean length3.0393
Min length2

Characters and Unicode

Total characters30393
Distinct characters346
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

Unique1416 ?
Unique (%)14.2%

Sample

1st row삼락동
2nd row미로면
3rd row삼문동
4th row원남동
5th row양강면
ValueCountFrequency (%)
남면 64
 
0.6%
서면 46
 
0.5%
북면 36
 
0.4%
금성면 29
 
0.3%
화산면 28
 
0.3%
동면 26
 
0.3%
옥산면 24
 
0.2%
적성면 23
 
0.2%
군북면 22
 
0.2%
청산면 22
 
0.2%
Other values (2820) 9680
96.8%
2024-04-06T17:07:41.123563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
면 6498
 
21.4%
동 2341
 
7.7%
읍 1609
 
5.3%
산 973
 
3.2%
성 539
 
1.8%
남 497
 
1.6%
천 482
 
1.6%
양 404
 
1.3%
가 399
 
1.3%
북 368
 
1.2%
Other values (336) 16283
53.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 30181
99.3%
Decimal Number 212
 
0.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
면 6498
21.5%
동 2341
 
7.8%
읍 1609
 
5.3%
산 973
 
3.2%
성 539
 
1.8%
남 497
 
1.6%
천 482
 
1.6%
양 404
 
1.3%
가 399
 
1.3%
북 368
 
1.2%
Other values (330) 16071
53.2%
Decimal Number
ValueCountFrequency (%)
2 74
34.9%
1 56
26.4%
3 41
19.3%
4 22
 
10.4%
5 14
 
6.6%
6 5
 
2.4%

Most occurring scripts

ValueCountFrequency (%)
Hangul 30181
99.3%
Common 212
 
0.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
면 6498
21.5%
동 2341
 
7.8%
읍 1609
 
5.3%
산 973
 
3.2%
성 539
 
1.8%
남 497
 
1.6%
천 482
 
1.6%
양 404
 
1.3%
가 399
 
1.3%
북 368
 
1.2%
Other values (330) 16071
53.2%
Common
ValueCountFrequency (%)
2 74
34.9%
1 56
26.4%
3 41
19.3%
4 22
 
10.4%
5 14
 
6.6%
6 5
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 30181
99.3%
ASCII 212
 
0.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
면 6498
21.5%
동 2341
 
7.8%
읍 1609
 
5.3%
산 973
 
3.2%
성 539
 
1.8%
남 497
 
1.6%
천 482
 
1.6%
양 404
 
1.3%
가 399
 
1.3%
북 368
 
1.2%
Other values (330) 16071
53.2%
ASCII
ValueCountFrequency (%)
2 74
34.9%
1 56
26.4%
3 41
19.3%
4 22
 
10.4%
5 14
 
6.6%
6 5
 
2.4%

리명
Text

Distinct6206
Distinct (%)62.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-04-06T17:07:41.910950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length3
Mean length3.0276
Min length2

Characters and Unicode

Total characters30276
Distinct characters385
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

Unique4632 ?
Unique (%)46.3%

Sample

1st row삼락동
2nd row내미로리
3rd row삼문동
4th row원남동
5th row산막리
ValueCountFrequency (%)
금곡리 26
 
0.3%
대곡리 24
 
0.2%
덕산리 22
 
0.2%
용산리 22
 
0.2%
남산리 18
 
0.2%
용암리 18
 
0.2%
동산리 17
 
0.2%
신흥리 17
 
0.2%
봉산리 16
 
0.2%
월산리 16
 
0.2%
Other values (6196) 9804
98.0%
2024-04-06T17:07:42.807192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
리 8122
26.8%
동 2303
 
7.6%
산 839
 
2.8%
곡 548
 
1.8%
신 502
 
1.7%
대 418
 
1.4%
가 394
 
1.3%
천 390
 
1.3%
정 377
 
1.2%
성 352
 
1.2%
Other values (375) 16031
52.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 30059
99.3%
Decimal Number 213
 
0.7%
Open Punctuation 2
 
< 0.1%
Close Punctuation 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
리 8122
27.0%
동 2303
 
7.7%
산 839
 
2.8%
곡 548
 
1.8%
신 502
 
1.7%
대 418
 
1.4%
가 394
 
1.3%
천 390
 
1.3%
정 377
 
1.3%
성 352
 
1.2%
Other values (367) 15814
52.6%
Decimal Number
ValueCountFrequency (%)
2 75
35.2%
1 56
26.3%
3 41
19.2%
4 22
 
10.3%
5 14
 
6.6%
6 5
 
2.3%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 30055
99.3%
Common 217
 
0.7%
Han 4
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
리 8122
27.0%
동 2303
 
7.7%
산 839
 
2.8%
곡 548
 
1.8%
신 502
 
1.7%
대 418
 
1.4%
가 394
 
1.3%
천 390
 
1.3%
정 377
 
1.3%
성 352
 
1.2%
Other values (363) 15810
52.6%
Common
ValueCountFrequency (%)
2 75
34.6%
1 56
25.8%
3 41
18.9%
4 22
 
10.1%
5 14
 
6.5%
6 5
 
2.3%
( 2
 
0.9%
) 2
 
0.9%
Han
ValueCountFrequency (%)
平 1
25.0%
沙 1
25.0%
花 1
25.0%
山 1
25.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 30055
99.3%
ASCII 217
 
0.7%
CJK 4
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
리 8122
27.0%
동 2303
 
7.7%
산 839
 
2.8%
곡 548
 
1.8%
신 502
 
1.7%
대 418
 
1.4%
가 394
 
1.3%
천 390
 
1.3%
정 377
 
1.3%
성 352
 
1.2%
Other values (363) 15810
52.6%
ASCII
ValueCountFrequency (%)
2 75
34.6%
1 56
25.8%
3 41
18.9%
4 22
 
10.1%
5 14
 
6.5%
6 5
 
2.3%
( 2
 
0.9%
) 2
 
0.9%
CJK
ValueCountFrequency (%)
平 1
25.0%
沙 1
25.0%
花 1
25.0%
山 1
25.0%

계
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct54
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0115
Minimum0
Maximum108
Zeros8390
Zeros (%)83.9%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-06T17:07:43.147716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile6
Maximum108
Range108
Interquartile range (IQR)0

Descriptive statistics

Standard deviation4.3586543
Coefficient of variation (CV)4.3090997
Kurtosis105.63242
Mean1.0115
Median Absolute Deviation (MAD)0
Skewness8.3853569
Sum10115
Variance18.997868
MonotonicityNot monotonic
2024-04-06T17:07:43.418867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 8390
83.9%
1 608
 
6.1%
2 208
 
2.1%
3 122
 
1.2%
4 85
 
0.9%
5 66
 
0.7%
7 56
 
0.6%
10 46
 
0.5%
9 46
 
0.5%
6 45
 
0.4%
Other values (44) 328
 
3.3%
ValueCountFrequency (%)
0 8390
83.9%
1 608
 
6.1%
2 208
 
2.1%
3 122
 
1.2%
4 85
 
0.9%
5 66
 
0.7%
6 45
 
0.4%
7 56
 
0.6%
8 37
 
0.4%
9 46
 
0.5%
ValueCountFrequency (%)
108 1
 
< 0.1%
81 1
 
< 0.1%
80 1
 
< 0.1%
63 1
 
< 0.1%
59 1
 
< 0.1%
55 3
< 0.1%
54 2
< 0.1%
52 2
< 0.1%
50 2
< 0.1%
47 1
 
< 0.1%

남자
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct34
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5147
Minimum0
Maximum54
Zeros8774
Zeros (%)87.7%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-06T17:07:43.922521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile3
Maximum54
Range54
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.2714532
Coefficient of variation (CV)4.4131596
Kurtosis98.963845
Mean0.5147
Median Absolute Deviation (MAD)0
Skewness8.2039241
Sum5147
Variance5.1594999
MonotonicityNot monotonic
2024-04-06T17:07:44.243259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
0 8774
87.7%
1 483
 
4.8%
2 188
 
1.9%
3 111
 
1.1%
5 77
 
0.8%
4 76
 
0.8%
6 55
 
0.5%
7 41
 
0.4%
9 31
 
0.3%
8 28
 
0.3%
Other values (24) 136
 
1.4%
ValueCountFrequency (%)
0 8774
87.7%
1 483
 
4.8%
2 188
 
1.9%
3 111
 
1.1%
4 76
 
0.8%
5 77
 
0.8%
6 55
 
0.5%
7 41
 
0.4%
8 28
 
0.3%
9 31
 
0.3%
ValueCountFrequency (%)
54 1
 
< 0.1%
42 1
 
< 0.1%
41 1
 
< 0.1%
31 2
 
< 0.1%
29 1
 
< 0.1%
28 1
 
< 0.1%
27 5
0.1%
26 3
< 0.1%
25 1
 
< 0.1%
24 2
 
< 0.1%

여자
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct33
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4968
Minimum0
Maximum54
Zeros8767
Zeros (%)87.7%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-06T17:07:44.595542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile3
Maximum54
Range54
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.1968323
Coefficient of variation (CV)4.4219653
Kurtosis106.32505
Mean0.4968
Median Absolute Deviation (MAD)0
Skewness8.4562924
Sum4968
Variance4.8260724
MonotonicityNot monotonic
2024-04-06T17:07:45.414171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
0 8767
87.7%
1 490
 
4.9%
2 196
 
2.0%
3 116
 
1.2%
4 87
 
0.9%
5 68
 
0.7%
6 60
 
0.6%
7 43
 
0.4%
8 27
 
0.3%
10 21
 
0.2%
Other values (23) 125
 
1.2%
ValueCountFrequency (%)
0 8767
87.7%
1 490
 
4.9%
2 196
 
2.0%
3 116
 
1.2%
4 87
 
0.9%
5 68
 
0.7%
6 60
 
0.6%
7 43
 
0.4%
8 27
 
0.3%
9 19
 
0.2%
ValueCountFrequency (%)
54 1
 
< 0.1%
39 2
< 0.1%
36 1
 
< 0.1%
33 1
 
< 0.1%
30 1
 
< 0.1%
29 1
 
< 0.1%
28 2
< 0.1%
26 2
< 0.1%
25 1
 
< 0.1%
24 4
< 0.1%

Interactions

2024-04-06T17:07:34.760086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:07:32.343363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:07:33.185480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:07:34.044868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:07:35.001920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:07:32.521122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:07:33.377916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:07:34.239649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:07:35.207414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:07:32.796148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:07:33.582550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:07:34.414442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:07:35.394635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:07:33.004742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:07:33.817070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:07:34.573606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-06T17:07:45.587407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
법정동코드시도명계남자여자
법정동코드1.0000.9900.2110.2030.220
시도명0.9901.0000.2820.2760.282
계0.2110.2821.0000.9780.929
남자0.2030.2760.9781.0000.872
여자0.2200.2820.9290.8721.000
2024-04-06T17:07:45.778391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
법정동코드계남자여자시도명
법정동코드1.000-0.230-0.230-0.2320.968
계-0.2301.0000.8780.8800.123
남자-0.2300.8781.0000.6880.119
여자-0.2320.8800.6881.0000.116
시도명0.9680.1230.1190.1161.000

Missing values

2024-04-06T17:07:35.630664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-06T17:07:35.926867image/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

법정동코드통계년월시도명시군구명읍면동명리명계남자여자
1072847150110002024-03-31경상북도김천시삼락동삼락동321
1611351230340342024-03-31강원특별자치도삼척시미로면내미로리000
1402148270104002024-03-31경상남도밀양시삼문동삼문동945
5511110159002024-03-31서울특별시종로구원남동원남동000
497943740360262024-03-31충청북도영동군양강면산막리000
1641251760250352024-03-31강원특별자치도평창군평창읍도돈리000
315941570340242024-03-31경기도김포시대곶면상마리000
1125447210250292024-03-31경상북도영주시풍기읍서부리110
1622251720380352024-03-31강원특별자치도홍천군북방면도사곡리000
513043750370222024-03-31충청북도진천군광혜원면월성리000
법정동코드통계년월시도명시군구명읍면동명리명계남자여자
560344133250242024-03-31충청남도천안시 서북구성환읍율금리000
616944210320322024-03-31충청남도서산시부석면마룡리000
1200047290310212024-03-31경상북도경산시와촌면소월리101
421443114102002024-03-31충청북도청주시 청원구내덕동내덕동422
1063147130315232024-03-31경상북도경주시문무대왕면두산리000
1074647150250282024-03-31경상북도김천시아포읍국사리000
306841550360322024-03-31경기도안성시양성면미산리000
1781952710330282024-03-31전북특별자치도완주군이서면은교리000
1399248250340312024-03-31경상남도김해시한림면가산리000
181731710259242024-03-31울산광역시울주군범서읍굴화리1358