Overview

Dataset statistics

Number of variables9
Number of observations10000
Missing cells70
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 주민등록 평균연령 현황입니다.법정동은 시 또는 구의 하위구역으로 법률로 지정한 구역을 말합니다.
Author행정안전부
URLhttps://www.data.go.kr/data/15099195/fileData.do

Alerts

기준연월 has constant value ""Constant
법정동코드 is highly overall correlated with 시도명High correlation
전체 평균연령 is highly overall correlated with 남자 평균연령 and 1 other fieldsHigh correlation
남자 평균연령 is highly overall correlated with 전체 평균연령 and 1 other fieldsHigh correlation
여자 평균연령 is highly overall correlated with 전체 평균연령 and 1 other fieldsHigh correlation
시도명 is highly overall correlated with 법정동코드High correlation
법정동코드 has unique valuesUnique

Reproduction

Analysis started2024-04-06 08:36:15.620744
Analysis finished2024-04-06 08:36:21.804694
Duration6.18 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%
Mean4.4580576 × 109
Minimum1.1110102 × 109
Maximum5.280042 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-06T17:36:22.010787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.1110102 × 109
5-th percentile2.7710259 × 109
Q14.37204 × 109
median4.683036 × 109
Q34.827032 × 109
95-th percentile5.221042 × 109
Maximum5.280042 × 109
Range4.1690318 × 109
Interquartile range (IQR)4.54992 × 108

Descriptive statistics

Standard deviation7.9105476 × 108
Coefficient of variation (CV)0.17744382
Kurtosis6.3340062
Mean4.4580576 × 109
Median Absolute Deviation (MAD)2.62002 × 108
Skewness-2.3572762
Sum4.4580576 × 1013
Variance6.2576763 × 1017
MonotonicityNot monotonic
2024-04-06T17:36:22.484547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4683025024 1
 
< 0.1%
4886035024 1
 
< 0.1%
4613033024 1
 
< 0.1%
5279033036 1
 
< 0.1%
5221043032 1
 
< 0.1%
4131010500 1
 
< 0.1%
4479036023 1
 
< 0.1%
4137011200 1
 
< 0.1%
4725043025 1
 
< 0.1%
4824036034 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
1111010200 1
< 0.1%
1111010600 1
< 0.1%
1111010800 1
< 0.1%
1111011100 1
< 0.1%
1111011200 1
< 0.1%
1111011300 1
< 0.1%
1111011500 1
< 0.1%
1111012000 1
< 0.1%
1111012100 1
< 0.1%
1111012200 1
< 0.1%
ValueCountFrequency (%)
5280042028 1
< 0.1%
5280042027 1
< 0.1%
5280042025 1
< 0.1%
5280042024 1
< 0.1%
5280042022 1
< 0.1%
5280041027 1
< 0.1%
5280041026 1
< 0.1%
5280041023 1
< 0.1%
5280040025 1
< 0.1%
5280040023 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
2024-04-06T17:36:22.739534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:22.958214image/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
1612 
전라남도
1461 
경상남도
1189 
충청남도
1105 
경기도
993 
Other values (12)
3640 

Length

Max length7
Median length4
Mean length4.5478
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row전라남도
2nd row서울특별시
3rd row인천광역시
4th row전라남도
5th row경상북도

Common Values

ValueCountFrequency (%)
경상북도 1612
16.1%
전라남도 1461
14.6%
경상남도 1189
11.9%
충청남도 1105
11.1%
경기도 993
9.9%
전북특별자치도 954
9.5%
충청북도 805
8.1%
강원특별자치도 691
6.9%
서울특별시 249
 
2.5%
대구광역시 202
 
2.0%
Other values (7) 739
7.4%

Length

2024-04-06T17:36:23.276597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경상북도 1612
16.1%
전라남도 1461
14.6%
경상남도 1189
11.9%
충청남도 1105
11.1%
경기도 993
9.9%
전북특별자치도 954
9.5%
충청북도 805
8.1%
강원특별자치도 691
6.9%
서울특별시 249
 
2.5%
대구광역시 202
 
2.0%
Other values (7) 739
7.4%

시군구명
Text

Distinct227
Distinct (%)2.3%
Missing70
Missing (%)0.7%
Memory size156.2 KiB
2024-04-06T17:36:24.045207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length3
Mean length3.2957704
Min length2

Characters and Unicode

Total characters32727
Distinct characters140
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

Unique3 ?
Unique (%)< 0.1%

Sample

1st row영암군
2nd row서대문구
3rd row동구
4th row영암군
5th row예천군
ValueCountFrequency (%)
창원시 170
 
1.6%
청주시 170
 
1.6%
중구 145
 
1.4%
포항시 131
 
1.2%
상주시 127
 
1.2%
순천시 118
 
1.1%
북구 117
 
1.1%
영천시 116
 
1.1%
합천군 115
 
1.1%
안동시 114
 
1.1%
Other values (227) 9384
87.6%
2024-04-06T17:36:25.037869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
군 4799
 
14.7%
시 4527
 
13.8%
구 1672
 
5.1%
주 1376
 
4.2%
천 1288
 
3.9%
산 1022
 
3.1%
성 1003
 
3.1%
777
 
2.4%
양 761
 
2.3%
안 653
 
2.0%
Other values (130) 14849
45.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 31950
97.6%
Space Separator 777
 
2.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
군 4799
 
15.0%
시 4527
 
14.2%
구 1672
 
5.2%
주 1376
 
4.3%
천 1288
 
4.0%
산 1022
 
3.2%
성 1003
 
3.1%
양 761
 
2.4%
안 653
 
2.0%
영 609
 
1.9%
Other values (129) 14240
44.6%
Space Separator
ValueCountFrequency (%)
777
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 31950
97.6%
Common 777
 
2.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
군 4799
 
15.0%
시 4527
 
14.2%
구 1672
 
5.2%
주 1376
 
4.3%
천 1288
 
4.0%
산 1022
 
3.2%
성 1003
 
3.1%
양 761
 
2.4%
안 653
 
2.0%
영 609
 
1.9%
Other values (129) 14240
44.6%
Common
ValueCountFrequency (%)
777
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 31950
97.6%
ASCII 777
 
2.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
군 4799
 
15.0%
시 4527
 
14.2%
구 1672
 
5.2%
주 1376
 
4.3%
천 1288
 
4.0%
산 1022
 
3.2%
성 1003
 
3.1%
양 761
 
2.4%
안 653
 
2.0%
영 609
 
1.9%
Other values (129) 14240
44.6%
ASCII
ValueCountFrequency (%)
777
100.0%

읍면동명
Text

Distinct2812
Distinct (%)28.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-04-06T17:36:25.680118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length3
Mean length3.0464
Min length2

Characters and Unicode

Total characters30464
Distinct characters344
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

Unique1403 ?
Unique (%)14.0%

Sample

1st row영암읍
2nd row봉원동
3rd row화평동
4th row군서면
5th row예천읍
ValueCountFrequency (%)
남면 62
 
0.6%
북면 39
 
0.4%
서면 38
 
0.4%
동면 32
 
0.3%
금성면 29
 
0.3%
성산면 26
 
0.3%
옥산면 26
 
0.3%
오창읍 25
 
0.2%
대산면 23
 
0.2%
봉산면 23
 
0.2%
Other values (2802) 9677
96.8%
2024-04-06T17:36:26.724209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
면 6471
 
21.2%
동 2335
 
7.7%
읍 1633
 
5.4%
산 974
 
3.2%
성 552
 
1.8%
남 514
 
1.7%
천 485
 
1.6%
가 437
 
1.4%
양 419
 
1.4%
대 369
 
1.2%
Other values (334) 16275
53.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 30215
99.2%
Decimal Number 249
 
0.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
면 6471
21.4%
동 2335
 
7.7%
읍 1633
 
5.4%
산 974
 
3.2%
성 552
 
1.8%
남 514
 
1.7%
천 485
 
1.6%
가 437
 
1.4%
양 419
 
1.4%
대 369
 
1.2%
Other values (326) 16026
53.0%
Decimal Number
ValueCountFrequency (%)
2 79
31.7%
1 72
28.9%
3 48
19.3%
4 23
 
9.2%
5 15
 
6.0%
6 7
 
2.8%
7 4
 
1.6%
8 1
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Hangul 30215
99.2%
Common 249
 
0.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
면 6471
21.4%
동 2335
 
7.7%
읍 1633
 
5.4%
산 974
 
3.2%
성 552
 
1.8%
남 514
 
1.7%
천 485
 
1.6%
가 437
 
1.4%
양 419
 
1.4%
대 369
 
1.2%
Other values (326) 16026
53.0%
Common
ValueCountFrequency (%)
2 79
31.7%
1 72
28.9%
3 48
19.3%
4 23
 
9.2%
5 15
 
6.0%
6 7
 
2.8%
7 4
 
1.6%
8 1
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 30215
99.2%
ASCII 249
 
0.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
면 6471
21.4%
동 2335
 
7.7%
읍 1633
 
5.4%
산 974
 
3.2%
성 552
 
1.8%
남 514
 
1.7%
천 485
 
1.6%
가 437
 
1.4%
양 419
 
1.4%
대 369
 
1.2%
Other values (326) 16026
53.0%
ASCII
ValueCountFrequency (%)
2 79
31.7%
1 72
28.9%
3 48
19.3%
4 23
 
9.2%
5 15
 
6.0%
6 7
 
2.8%
7 4
 
1.6%
8 1
 
0.4%

리명
Text

Distinct6184
Distinct (%)61.8%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-04-06T17:36:27.404174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length3
Mean length3.0322
Min length2

Characters and Unicode

Total characters30322
Distinct characters381
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

Unique4639 ?
Unique (%)46.4%

Sample

1st row동무리
2nd row봉원동
3rd row화평동
4th row도갑리
5th row통명리
ValueCountFrequency (%)
용산리 30
 
0.3%
덕산리 22
 
0.2%
신기리 21
 
0.2%
금곡리 20
 
0.2%
신월리 20
 
0.2%
대곡리 20
 
0.2%
읍내리 19
 
0.2%
용암리 19
 
0.2%
신촌리 18
 
0.2%
금산리 18
 
0.2%
Other values (6174) 9793
97.9%
2024-04-06T17:36:28.351365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
리 8123
26.8%
동 2296
 
7.6%
산 788
 
2.6%
곡 538
 
1.8%
신 503
 
1.7%
대 449
 
1.5%
가 430
 
1.4%
천 394
 
1.3%
정 382
 
1.3%
성 366
 
1.2%
Other values (371) 16053
52.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 30062
99.1%
Decimal Number 252
 
0.8%
Open Punctuation 4
 
< 0.1%
Close Punctuation 4
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
리 8123
27.0%
동 2296
 
7.6%
산 788
 
2.6%
곡 538
 
1.8%
신 503
 
1.7%
대 449
 
1.5%
가 430
 
1.4%
천 394
 
1.3%
정 382
 
1.3%
성 366
 
1.2%
Other values (361) 15793
52.5%
Decimal Number
ValueCountFrequency (%)
2 81
32.1%
1 73
29.0%
3 48
19.0%
4 23
 
9.1%
5 15
 
6.0%
6 7
 
2.8%
7 4
 
1.6%
8 1
 
0.4%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 30054
99.1%
Common 260
 
0.9%
Han 8
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
리 8123
27.0%
동 2296
 
7.6%
산 788
 
2.6%
곡 538
 
1.8%
신 503
 
1.7%
대 449
 
1.5%
가 430
 
1.4%
천 394
 
1.3%
정 382
 
1.3%
성 366
 
1.2%
Other values (354) 15785
52.5%
Common
ValueCountFrequency (%)
2 81
31.2%
1 73
28.1%
3 48
18.5%
4 23
 
8.8%
5 15
 
5.8%
6 7
 
2.7%
7 4
 
1.5%
( 4
 
1.5%
) 4
 
1.5%
8 1
 
0.4%
Han
ValueCountFrequency (%)
沙 2
25.0%
山 1
12.5%
坪 1
12.5%
華 1
12.5%
岐 1
12.5%
岩 1
12.5%
平 1
12.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 30054
99.1%
ASCII 260
 
0.9%
CJK 8
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
리 8123
27.0%
동 2296
 
7.6%
산 788
 
2.6%
곡 538
 
1.8%
신 503
 
1.7%
대 449
 
1.5%
가 430
 
1.4%
천 394
 
1.3%
정 382
 
1.3%
성 366
 
1.2%
Other values (354) 15785
52.5%
ASCII
ValueCountFrequency (%)
2 81
31.2%
1 73
28.1%
3 48
18.5%
4 23
 
8.8%
5 15
 
5.8%
6 7
 
2.7%
7 4
 
1.5%
( 4
 
1.5%
) 4
 
1.5%
8 1
 
0.4%
CJK
ValueCountFrequency (%)
沙 2
25.0%
山 1
12.5%
坪 1
12.5%
華 1
12.5%
岐 1
12.5%
岩 1
12.5%
平 1
12.5%

전체 평균연령
Real number (ℝ)

HIGH CORRELATION 

Distinct412
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean57.3826
Minimum21.5
Maximum77.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-06T17:36:28.718390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum21.5
5-th percentile42.095
Q154.3
median59.3
Q362.4
95-th percentile66.2
Maximum77.5
Range56
Interquartile range (IQR)8.1

Descriptive statistics

Standard deviation7.4252413
Coefficient of variation (CV)0.12939883
Kurtosis0.80990729
Mean57.3826
Median Absolute Deviation (MAD)3.7
Skewness-1.0709872
Sum573826
Variance55.134209
MonotonicityNot monotonic
2024-04-06T17:36:29.493025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
61.0 104
 
1.0%
60.8 96
 
1.0%
61.3 95
 
0.9%
61.7 92
 
0.9%
60.2 92
 
0.9%
60.6 89
 
0.9%
62.1 89
 
0.9%
60.7 88
 
0.9%
61.9 88
 
0.9%
61.2 88
 
0.9%
Other values (402) 9079
90.8%
ValueCountFrequency (%)
21.5 1
< 0.1%
24.0 1
< 0.1%
26.1 1
< 0.1%
26.2 1
< 0.1%
26.5 1
< 0.1%
27.3 2
< 0.1%
27.5 1
< 0.1%
27.8 1
< 0.1%
29.2 1
< 0.1%
30.1 1
< 0.1%
ValueCountFrequency (%)
77.5 1
 
< 0.1%
74.5 1
 
< 0.1%
74.4 1
 
< 0.1%
74.0 2
< 0.1%
73.7 3
< 0.1%
73.5 3
< 0.1%
73.1 1
 
< 0.1%
72.2 2
< 0.1%
72.1 1
 
< 0.1%
72.0 1
 
< 0.1%

남자 평균연령
Real number (ℝ)

HIGH CORRELATION 

Distinct401
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean55.11816
Minimum21.5
Maximum77.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-06T17:36:29.752568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum21.5
5-th percentile41.1
Q152.175
median56.7
Q359.725
95-th percentile63.7
Maximum77.5
Range56
Interquartile range (IQR)7.55

Descriptive statistics

Standard deviation6.8888844
Coefficient of variation (CV)0.12498393
Kurtosis0.73352339
Mean55.11816
Median Absolute Deviation (MAD)3.5
Skewness-0.96408043
Sum551181.6
Variance47.456728
MonotonicityNot monotonic
2024-04-06T17:36:30.139131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
57.8 103
 
1.0%
57.9 100
 
1.0%
58.6 97
 
1.0%
58.2 93
 
0.9%
57.6 93
 
0.9%
58.3 92
 
0.9%
57.4 92
 
0.9%
59.5 89
 
0.9%
59.1 87
 
0.9%
57.3 87
 
0.9%
Other values (391) 9067
90.7%
ValueCountFrequency (%)
21.5 1
< 0.1%
24.0 1
< 0.1%
26.0 1
< 0.1%
26.1 1
< 0.1%
26.5 1
< 0.1%
27.3 1
< 0.1%
27.8 1
< 0.1%
27.9 1
< 0.1%
28.8 1
< 0.1%
29.1 1
< 0.1%
ValueCountFrequency (%)
77.5 1
< 0.1%
76.5 1
< 0.1%
74.5 2
< 0.1%
74.4 1
< 0.1%
72.9 1
< 0.1%
71.6 1
< 0.1%
71.2 2
< 0.1%
71.0 1
< 0.1%
70.9 1
< 0.1%
70.8 1
< 0.1%

여자 평균연령
Real number (ℝ)

HIGH CORRELATION 

Distinct455
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean59.64213
Minimum0
Maximum86.5
Zeros33
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-06T17:36:30.451220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile42.6
Q156.2
median61.9
Q365.4
95-th percentile70
Maximum86.5
Range86.5
Interquartile range (IQR)9.2

Descriptive statistics

Standard deviation8.8889411
Coefficient of variation (CV)0.14903795
Kurtosis6.2705881
Mean59.64213
Median Absolute Deviation (MAD)4.3
Skewness-1.7234695
Sum596421.3
Variance79.013273
MonotonicityNot monotonic
2024-04-06T17:36:30.907544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
64.4 85
 
0.9%
62.8 84
 
0.8%
62.9 84
 
0.8%
64.0 82
 
0.8%
62.2 79
 
0.8%
65.2 75
 
0.8%
65.3 74
 
0.7%
62.7 74
 
0.7%
62.6 74
 
0.7%
65.1 73
 
0.7%
Other values (445) 9216
92.2%
ValueCountFrequency (%)
0.0 33
0.3%
25.5 1
 
< 0.1%
26.5 2
 
< 0.1%
28.9 1
 
< 0.1%
29.4 1
 
< 0.1%
29.5 1
 
< 0.1%
30.6 1
 
< 0.1%
30.7 1
 
< 0.1%
31.0 1
 
< 0.1%
31.1 1
 
< 0.1%
ValueCountFrequency (%)
86.5 1
 
< 0.1%
82.5 1
 
< 0.1%
79.6 1
 
< 0.1%
78.7 1
 
< 0.1%
78.5 1
 
< 0.1%
77.5 1
 
< 0.1%
77.4 1
 
< 0.1%
77.0 1
 
< 0.1%
76.8 1
 
< 0.1%
76.2 3
< 0.1%

Interactions

2024-04-06T17:36:20.560088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:17.983689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:18.930038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:19.755892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:20.756168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:18.229458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:19.123456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:19.943069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:20.943725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:18.539746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:19.350684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:20.149549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:21.138459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:18.738624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:19.569777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:20.366678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-06T17:36:31.090008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
법정동코드시도명전체 평균연령남자 평균연령여자 평균연령
법정동코드1.0000.9900.3520.3230.337
시도명0.9901.0000.4360.4120.446
전체 평균연령0.3520.4361.0000.9790.848
남자 평균연령0.3230.4120.9791.0000.787
여자 평균연령0.3370.4460.8480.7871.000
2024-04-06T17:36:31.358155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
법정동코드전체 평균연령남자 평균연령여자 평균연령시도명
법정동코드1.0000.2810.2470.2930.968
전체 평균연령0.2811.0000.9550.9540.185
남자 평균연령0.2470.9551.0000.8410.174
여자 평균연령0.2930.9540.8411.0000.195
시도명0.9680.1850.1740.1951.000

Missing values

2024-04-06T17:36:21.370872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-06T17:36:21.662868image/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

법정동코드기준연월시도명시군구명읍면동명리명전체 평균연령남자 평균연령여자 평균연령
940146830250242024-03-31전라남도영암군영암읍동무리49.248.150.3
30511410115002024-03-31서울특별시서대문구봉원동봉원동49.050.148.1
113428140104002024-03-31인천광역시동구화평동화평동55.553.957.2
948746830360292024-03-31전라남도영암군군서면도갑리57.253.061.4
1295847900250332024-03-31경상북도예천군예천읍통명리62.460.164.8
1778352710250262024-03-31전북특별자치도완주군삼례읍석전리57.456.258.7
1486448850330212024-03-31경상남도하동군적량면관리59.555.763.8
1372948170460232024-03-31경상남도진주시수곡면대천리62.359.065.3
282941500250332024-03-31경기도이천시장호원읍노탑리50.749.252.2
181031710256272024-03-31울산광역시울주군온양읍내광리61.059.862.2
법정동코드기준연월시도명시군구명읍면동명리명전체 평균연령남자 평균연령여자 평균연령
1057947130250232024-03-31경상북도경주시감포읍전촌리60.659.062.1
1308047900400312024-03-31경상북도예천군지보면상월리70.066.973.2
429443114310342024-03-31충청북도청주시 청원구북이면광암리64.762.068.7
1297147900253272024-03-31경상북도예천군호명읍형호리68.867.770.3
315441570256272024-03-31경기도김포시양촌읍유현리57.456.658.5
293741550104002024-03-31경기도안성시봉남동봉남동49.448.650.2
1167247250360232024-03-31경상북도상주시외남면신촌리64.561.766.8
1772952210410232024-03-31전북특별자치도김제시진봉면가실리64.359.569.0
402943111320272024-03-31충청북도청주시 상당구미원면운교리63.361.266.1
1090947170121002024-03-31경상북도안동시당북동당북동42.541.343.6