Overview

Dataset statistics

Number of variables11
Number of observations23
Missing cells32
Missing cells (%)12.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.2 KiB
Average record size in memory98.7 B

Variable types

Numeric5
Text4
DateTime2

Dataset

Description주택연금 가입 대상 노인복지주택 리스트를 제공하며, 노인복지주택번호, 노인복지주택명, 법정동코드, 주소, 주소 본·부번, 시설설치일자, 시설폐지일자 등의 데이터 항목을 포함하고 있습니다.
Author한국주택금융공사
URLhttps://www.data.go.kr/data/15122871/fileData.do

Alerts

노인복지주택번호 is highly overall correlated with 법정동코드 and 1 other fieldsHigh correlation
법정동코드 is highly overall correlated with 노인복지주택번호 and 2 other fieldsHigh correlation
주소본번 is highly overall correlated with 법정동코드 and 2 other fieldsHigh correlation
주소부번 is highly overall correlated with 주소본번High correlation
도로명주소 우편번호 is highly overall correlated with 노인복지주택번호 and 2 other fieldsHigh correlation
주소부번 has 11 (47.8%) missing valuesMissing
시설폐지일자 has 21 (91.3%) missing valuesMissing
노인복지주택번호 has unique valuesUnique
노인복지주택명 has unique valuesUnique
상세주소 has unique valuesUnique
시설설치일자 has unique valuesUnique
도로명주소 has unique valuesUnique
주소부번 has 1 (4.3%) zerosZeros

Reproduction

Analysis started2023-12-12 11:14:13.963055
Analysis finished2023-12-12 11:14:19.442280
Duration5.48 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

노인복지주택번호
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean266092
Minimum110001
Maximum450003
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-12T20:14:19.545724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum110001
5-th percentile110002.1
Q1110006.5
median280001
Q3410006.5
95-th percentile450001.9
Maximum450003
Range340002
Interquartile range (IQR)300000

Descriptive statistics

Standard deviation155992.69
Coefficient of variation (CV)0.58623593
Kurtosis-2.1059561
Mean266092
Median Absolute Deviation (MAD)169992
Skewness0.0089038852
Sum6120116
Variance2.433372 × 1010
MonotonicityNot monotonic
2023-12-12T20:14:19.766443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
410004 1
 
4.3%
110001 1
 
4.3%
410008 1
 
4.3%
410007 1
 
4.3%
410006 1
 
4.3%
450001 1
 
4.3%
410005 1
 
4.3%
410001 1
 
4.3%
110008 1
 
4.3%
110006 1
 
4.3%
Other values (13) 13
56.5%
ValueCountFrequency (%)
110001 1
4.3%
110002 1
4.3%
110003 1
4.3%
110004 1
4.3%
110005 1
4.3%
110006 1
4.3%
110007 1
4.3%
110008 1
4.3%
110009 1
4.3%
110010 1
4.3%
ValueCountFrequency (%)
450003 1
4.3%
450002 1
4.3%
450001 1
4.3%
410009 1
4.3%
410008 1
4.3%
410007 1
4.3%
410006 1
4.3%
410005 1
4.3%
410004 1
4.3%
410003 1
4.3%

노인복지주택명
Text

UNIQUE 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size316.0 B
2023-12-12T20:14:20.162239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length12
Mean length8.4782609
Min length5

Characters and Unicode

Total characters195
Distinct characters96
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

Unique23 ?
Unique (%)100.0%

Sample

1st row더 헤리티지
2nd row둔촌동후성누리움
3rd row그레이스힐
4th row노블레스타워 노인복지주택
5th row시니어스 하우징 더 골든팰리스
ValueCountFrequency (%)
더 2
 
6.1%
시니어스 2
 
6.1%
노블레스타워2 1
 
3.0%
광교 1
 
3.0%
광교아르데코 1
 
3.0%
정원속궁전 1
 
3.0%
내장산실버아파트 1
 
3.0%
더클래식 1
 
3.0%
블루밍 1
 
3.0%
서울시니어스분당타워 1
 
3.0%
Other values (21) 21
63.6%
2023-12-12T20:14:20.743916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
스 12
 
6.2%
10
 
5.1%
시 8
 
4.1%
워 7
 
3.6%
어 7
 
3.6%
니 7
 
3.6%
타 7
 
3.6%
서 6
 
3.1%
리 5
 
2.6%
울 5
 
2.6%
Other values (86) 121
62.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 184
94.4%
Space Separator 10
 
5.1%
Decimal Number 1
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
스 12
 
6.5%
시 8
 
4.3%
워 7
 
3.8%
어 7
 
3.8%
니 7
 
3.8%
타 7
 
3.8%
서 6
 
3.3%
리 5
 
2.7%
울 5
 
2.7%
카 3
 
1.6%
Other values (84) 117
63.6%
Space Separator
ValueCountFrequency (%)
10
100.0%
Decimal Number
ValueCountFrequency (%)
2 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 184
94.4%
Common 11
 
5.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
스 12
 
6.5%
시 8
 
4.3%
워 7
 
3.8%
어 7
 
3.8%
니 7
 
3.8%
타 7
 
3.8%
서 6
 
3.3%
리 5
 
2.7%
울 5
 
2.7%
카 3
 
1.6%
Other values (84) 117
63.6%
Common
ValueCountFrequency (%)
10
90.9%
2 1
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 184
94.4%
ASCII 11
 
5.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
스 12
 
6.5%
시 8
 
4.3%
워 7
 
3.8%
어 7
 
3.8%
니 7
 
3.8%
타 7
 
3.8%
서 6
 
3.3%
리 5
 
2.7%
울 5
 
2.7%
카 3
 
1.6%
Other values (84) 117
63.6%
ASCII
ValueCountFrequency (%)
10
90.9%
2 1
 
9.1%

법정동코드
Real number (ℝ)

HIGH CORRELATION 

Distinct20
Distinct (%)87.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6953341 × 109
Minimum1.1110187 × 109
Maximum4.579025 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-12T20:14:20.952222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.1110187 × 109
5-th percentile1.1155164 × 109
Q11.1470114 × 109
median2.8260114 × 109
Q34.129261 × 109
95-th percentile4.5173214 × 109
Maximum4.579025 × 109
Range3.4680063 × 109
Interquartile range (IQR)2.9822496 × 109

Descriptive statistics

Standard deviation1.5536256 × 109
Coefficient of variation (CV)0.57641301
Kurtosis-2.1022988
Mean2.6953341 × 109
Median Absolute Deviation (MAD)1.6760012 × 109
Skewness0.011168239
Sum6.1992685 × 1010
Variance2.4137526 × 1018
MonotonicityNot monotonic
2023-12-12T20:14:21.164917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
1150010200 3
 
13.0%
1129013500 2
 
8.7%
4113511100 1
 
4.3%
1138010200 1
 
4.3%
4111710200 1
 
4.3%
4111710300 1
 
4.3%
4113510300 1
 
4.3%
4518011200 1
 
4.3%
4145010600 1
 
4.3%
4113511400 1
 
4.3%
Other values (10) 10
43.5%
ValueCountFrequency (%)
1111018700 1
 
4.3%
1114016700 1
 
4.3%
1129013500 2
8.7%
1138010200 1
 
4.3%
1144012700 1
 
4.3%
1150010200 3
13.0%
1168011200 1
 
4.3%
1174010600 1
 
4.3%
2826011400 1
 
4.3%
4111710200 1
 
4.3%
ValueCountFrequency (%)
4579025025 1
4.3%
4518011200 1
4.3%
4511113500 1
4.3%
4146311600 1
4.3%
4146110400 1
4.3%
4145010600 1
4.3%
4113511400 1
4.3%
4113511100 1
4.3%
4113510300 1
4.3%
4111710300 1
4.3%

주소
Text

Distinct20
Distinct (%)87.0%
Missing0
Missing (%)0.0%
Memory size316.0 B
2023-12-12T20:14:21.511814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length15
Mean length13.521739
Min length11

Characters and Unicode

Total characters311
Distinct characters66
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

Unique18 ?
Unique (%)78.3%

Sample

1st row경기도 성남시 분당구 금곡동
2nd row서울특별시 강동구 둔촌동
3rd row서울특별시 강서구 등촌동
4th row서울특별시 성북구 종암동
5th row서울특별시 종로구 무악동
ValueCountFrequency (%)
서울특별시 11
 
14.1%
경기도 8
 
10.3%
강서구 3
 
3.8%
분당구 3
 
3.8%
성남시 3
 
3.8%
등촌동 3
 
3.8%
전라북도 3
 
3.8%
종암동 2
 
2.6%
영통구 2
 
2.6%
성북구 2
 
2.6%
Other values (36) 38
48.7%
2023-12-12T20:14:22.089381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
55
17.7%
동 23
 
7.4%
시 22
 
7.1%
구 21
 
6.8%
서 15
 
4.8%
도 11
 
3.5%
울 11
 
3.5%
별 11
 
3.5%
특 11
 
3.5%
기 9
 
2.9%
Other values (56) 122
39.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 256
82.3%
Space Separator 55
 
17.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 23
 
9.0%
시 22
 
8.6%
구 21
 
8.2%
서 15
 
5.9%
도 11
 
4.3%
울 11
 
4.3%
별 11
 
4.3%
특 11
 
4.3%
기 9
 
3.5%
경 8
 
3.1%
Other values (55) 114
44.5%
Space Separator
ValueCountFrequency (%)
55
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 256
82.3%
Common 55
 
17.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 23
 
9.0%
시 22
 
8.6%
구 21
 
8.2%
서 15
 
5.9%
도 11
 
4.3%
울 11
 
4.3%
별 11
 
4.3%
특 11
 
4.3%
기 9
 
3.5%
경 8
 
3.1%
Other values (55) 114
44.5%
Common
ValueCountFrequency (%)
55
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 256
82.3%
ASCII 55
 
17.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
55
100.0%
Hangul
ValueCountFrequency (%)
동 23
 
9.0%
시 22
 
8.6%
구 21
 
8.2%
서 15
 
5.9%
도 11
 
4.3%
울 11
 
4.3%
별 11
 
4.3%
특 11
 
4.3%
기 9
 
3.5%
경 8
 
3.1%
Other values (55) 114
44.5%

상세주소
Text

UNIQUE 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size316.0 B
2023-12-12T20:14:22.406523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length16
Mean length7.3043478
Min length5

Characters and Unicode

Total characters168
Distinct characters38
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique23 ?
Unique (%)100.0%

Sample

1st row305-2번지
2nd row79-23번지
3rd row717번지
4th row3-91번지
5th row66-3번지
ValueCountFrequency (%)
305-2번지 1
 
3.7%
스프링카운티자이 1
 
3.7%
1358번지 1
 
3.7%
209번지 1
 
3.7%
906-8번지 1
 
3.7%
517번지 1
 
3.7%
297-2번지 1
 
3.7%
15-6번지 1
 
3.7%
91-7번지 1
 
3.7%
서울시니어스가양타워 1
 
3.7%
Other values (17) 17
63.0%
2023-12-12T20:14:22.996222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
번 23
13.7%
지 23
13.7%
1 16
 
9.5%
3 11
 
6.5%
- 11
 
6.5%
7 11
 
6.5%
6 11
 
6.5%
5 8
 
4.8%
0 7
 
4.2%
9 7
 
4.2%
Other values (28) 40
23.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 82
48.8%
Other Letter 71
42.3%
Dash Punctuation 11
 
6.5%
Space Separator 4
 
2.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
번 23
32.4%
지 23
32.4%
스 2
 
2.8%
보 1
 
1.4%
자 1
 
1.4%
타 1
 
1.4%
양 1
 
1.4%
가 1
 
1.4%
어 1
 
1.4%
니 1
 
1.4%
Other values (16) 16
22.5%
Decimal Number
ValueCountFrequency (%)
1 16
19.5%
3 11
13.4%
7 11
13.4%
6 11
13.4%
5 8
9.8%
0 7
8.5%
9 7
8.5%
2 5
 
6.1%
8 4
 
4.9%
4 2
 
2.4%
Dash Punctuation
ValueCountFrequency (%)
- 11
100.0%
Space Separator
ValueCountFrequency (%)
4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 97
57.7%
Hangul 71
42.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
번 23
32.4%
지 23
32.4%
스 2
 
2.8%
보 1
 
1.4%
자 1
 
1.4%
타 1
 
1.4%
양 1
 
1.4%
가 1
 
1.4%
어 1
 
1.4%
니 1
 
1.4%
Other values (16) 16
22.5%
Common
ValueCountFrequency (%)
1 16
16.5%
3 11
11.3%
- 11
11.3%
7 11
11.3%
6 11
11.3%
5 8
8.2%
0 7
7.2%
9 7
7.2%
2 5
 
5.2%
8 4
 
4.1%
Other values (2) 6
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 97
57.7%
Hangul 71
42.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
번 23
32.4%
지 23
32.4%
스 2
 
2.8%
보 1
 
1.4%
자 1
 
1.4%
타 1
 
1.4%
양 1
 
1.4%
가 1
 
1.4%
어 1
 
1.4%
니 1
 
1.4%
Other values (16) 16
22.5%
ASCII
ValueCountFrequency (%)
1 16
16.5%
3 11
11.3%
- 11
11.3%
7 11
11.3%
6 11
11.3%
5 8
8.2%
0 7
7.2%
9 7
7.2%
2 5
 
5.2%
8 4
 
4.1%
Other values (2) 6
 
6.2%

주소본번
Real number (ℝ)

HIGH CORRELATION 

Distinct22
Distinct (%)95.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean606.3913
Minimum3
Maximum1641
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-12T20:14:23.293500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile4.2
Q1150
median583
Q3868
95-th percentile1584.8
Maximum1641
Range1638
Interquartile range (IQR)718

Descriptive statistics

Standard deviation503.75903
Coefficient of variation (CV)0.8307491
Kurtosis-0.40739225
Mean606.3913
Median Absolute Deviation (MAD)374
Skewness0.63734601
Sum13947
Variance253773.16
MonotonicityNot monotonic
2023-12-12T20:14:23.514099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
3 2
 
8.7%
305 1
 
4.3%
669 1
 
4.3%
556 1
 
4.3%
1358 1
 
4.3%
209 1
 
4.3%
906 1
 
4.3%
517 1
 
4.3%
297 1
 
4.3%
15 1
 
4.3%
Other values (12) 12
52.2%
ValueCountFrequency (%)
3 2
8.7%
15 1
4.3%
66 1
4.3%
79 1
4.3%
91 1
4.3%
209 1
4.3%
297 1
4.3%
305 1
4.3%
517 1
4.3%
556 1
4.3%
ValueCountFrequency (%)
1641 1
4.3%
1610 1
4.3%
1358 1
4.3%
1147 1
4.3%
1077 1
4.3%
906 1
4.3%
830 1
4.3%
717 1
4.3%
669 1
4.3%
637 1
4.3%

주소부번
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct10
Distinct (%)83.3%
Missing11
Missing (%)47.8%
Infinite0
Infinite (%)0.0%
Mean137.75
Minimum0
Maximum1507
Zeros1
Zeros (%)4.3%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-12T20:14:23.727064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.55
Q12
median4.5
Q311.75
95-th percentile728.2
Maximum1507
Range1507
Interquartile range (IQR)9.75

Descriptive statistics

Standard deviation431.94426
Coefficient of variation (CV)3.1357115
Kurtosis11.892159
Mean137.75
Median Absolute Deviation (MAD)3
Skewness3.4433327
Sum1653
Variance186575.84
MonotonicityNot monotonic
2023-12-12T20:14:23.910239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
2 2
 
8.7%
3 2
 
8.7%
23 1
 
4.3%
91 1
 
4.3%
1507 1
 
4.3%
1 1
 
4.3%
0 1
 
4.3%
7 1
 
4.3%
6 1
 
4.3%
8 1
 
4.3%
(Missing) 11
47.8%
ValueCountFrequency (%)
0 1
4.3%
1 1
4.3%
2 2
8.7%
3 2
8.7%
6 1
4.3%
7 1
4.3%
8 1
4.3%
23 1
4.3%
91 1
4.3%
1507 1
4.3%
ValueCountFrequency (%)
1507 1
4.3%
91 1
4.3%
23 1
4.3%
8 1
4.3%
7 1
4.3%
6 1
4.3%
3 2
8.7%
2 2
8.7%
1 1
4.3%
0 1
4.3%

시설설치일자
Date

UNIQUE 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size316.0 B
Minimum2003-03-28 00:00:00
Maximum2019-10-10 00:00:00
2023-12-12T20:14:24.116408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:14:24.302661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)

시설폐지일자
Date

MISSING 

Distinct2
Distinct (%)100.0%
Missing21
Missing (%)91.3%
Memory size316.0 B
Minimum2016-03-23 00:00:00
Maximum2019-11-06 00:00:00
2023-12-12T20:14:24.502526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:14:24.671106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=2)

도로명주소 우편번호
Real number (ℝ)

HIGH CORRELATION 

Distinct22
Distinct (%)95.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15918.696
Minimum2797
Maximum56451
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-12T20:14:24.861154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2797
5-th percentile2820.3
Q14941.5
median12950
Q316747.5
95-th percentile56090.8
Maximum56451
Range53654
Interquartile range (IQR)11806

Descriptive statistics

Standard deviation16829.207
Coefficient of variation (CV)1.0571976
Kurtosis2.4151671
Mean15918.696
Median Absolute Deviation (MAD)6577
Skewness1.8476337
Sum366130
Variance2.832222 × 108
MonotonicityNot monotonic
2023-12-12T20:14:25.110372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
2797 2
 
8.7%
13552 1
 
4.3%
7591 1
 
4.3%
16500 1
 
4.3%
16495 1
 
4.3%
13606 1
 
4.3%
56196 1
 
4.3%
12950 1
 
4.3%
13619 1
 
4.3%
4518 1
 
4.3%
Other values (12) 12
52.2%
ValueCountFrequency (%)
2797 2
8.7%
3030 1
4.3%
3382 1
4.3%
3911 1
4.3%
4518 1
4.3%
5365 1
4.3%
6373 1
4.3%
7550 1
4.3%
7575 1
4.3%
7591 1
4.3%
ValueCountFrequency (%)
56451 1
4.3%
56196 1
4.3%
55144 1
4.3%
22675 1
4.3%
17058 1
4.3%
16995 1
4.3%
16500 1
4.3%
16495 1
4.3%
13619 1
4.3%
13606 1
4.3%

도로명주소
Text

UNIQUE 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size316.0 B
2023-12-12T20:14:25.493080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length33
Median length24
Mean length21.217391
Min length16

Characters and Unicode

Total characters488
Distinct characters103
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique23 ?
Unique (%)100.0%

Sample

1st row경기도 성남시 분당구 대왕판교로 155
2nd row서울특별시 강동구 명일로 135
3rd row서울특별시 강서구 양천로 470
4th row서울특별시 성북구 종암로 90
5th row서울특별시 종로구 통일로16길 4-1
ValueCountFrequency (%)
서울특별시 11
 
10.5%
경기도 8
 
7.6%
분당구 3
 
2.9%
전라북도 3
 
2.9%
성남시 3
 
2.9%
강서구 3
 
2.9%
수원시 2
 
1.9%
종암로 2
 
1.9%
성북구 2
 
1.9%
영통구 2
 
1.9%
Other values (65) 66
62.9%
2023-12-12T20:14:26.184720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
82
 
16.8%
시 23
 
4.7%
구 21
 
4.3%
로 21
 
4.3%
1 21
 
4.3%
서 16
 
3.3%
울 12
 
2.5%
도 11
 
2.3%
2 11
 
2.3%
0 11
 
2.3%
Other values (93) 259
53.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 307
62.9%
Decimal Number 87
 
17.8%
Space Separator 82
 
16.8%
Dash Punctuation 6
 
1.2%
Open Punctuation 3
 
0.6%
Close Punctuation 3
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 23
 
7.5%
구 21
 
6.8%
로 21
 
6.8%
서 16
 
5.2%
울 12
 
3.9%
도 11
 
3.6%
별 11
 
3.6%
특 11
 
3.6%
길 11
 
3.6%
기 9
 
2.9%
Other values (79) 161
52.4%
Decimal Number
ValueCountFrequency (%)
1 21
24.1%
2 11
12.6%
0 11
12.6%
3 10
11.5%
7 8
 
9.2%
5 7
 
8.0%
4 7
 
8.0%
6 5
 
5.7%
8 4
 
4.6%
9 3
 
3.4%
Space Separator
ValueCountFrequency (%)
82
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 6
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 307
62.9%
Common 181
37.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 23
 
7.5%
구 21
 
6.8%
로 21
 
6.8%
서 16
 
5.2%
울 12
 
3.9%
도 11
 
3.6%
별 11
 
3.6%
특 11
 
3.6%
길 11
 
3.6%
기 9
 
2.9%
Other values (79) 161
52.4%
Common
ValueCountFrequency (%)
82
45.3%
1 21
 
11.6%
2 11
 
6.1%
0 11
 
6.1%
3 10
 
5.5%
7 8
 
4.4%
5 7
 
3.9%
4 7
 
3.9%
- 6
 
3.3%
6 5
 
2.8%
Other values (4) 13
 
7.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 307
62.9%
ASCII 181
37.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
82
45.3%
1 21
 
11.6%
2 11
 
6.1%
0 11
 
6.1%
3 10
 
5.5%
7 8
 
4.4%
5 7
 
3.9%
4 7
 
3.9%
- 6
 
3.3%
6 5
 
2.8%
Other values (4) 13
 
7.2%
Hangul
ValueCountFrequency (%)
시 23
 
7.5%
구 21
 
6.8%
로 21
 
6.8%
서 16
 
5.2%
울 12
 
3.9%
도 11
 
3.6%
별 11
 
3.6%
특 11
 
3.6%
길 11
 
3.6%
기 9
 
2.9%
Other values (79) 161
52.4%

Interactions

2023-12-12T20:14:17.465952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:14:14.523252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:14:15.212742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:14:15.944177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:14:16.739896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:14:17.605010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:14:14.681984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:14:15.359215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:14:16.106654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:14:16.883426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:14:17.747388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:14:14.808165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:14:15.509537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:14:16.288214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:14:17.041861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:14:18.359877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:14:14.951338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:14:15.619978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:14:16.433641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:14:17.182115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:14:18.601486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:14:15.089617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:14:15.773745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:14:16.601766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:14:17.336297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T20:14:26.353179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
노인복지주택번호노인복지주택명법정동코드주소상세주소주소본번주소부번시설설치일자시설폐지일자도로명주소 우편번호도로명주소
노인복지주택번호1.0001.0001.0001.0001.0000.9470.0001.000NaN1.0001.000
노인복지주택명1.0001.0001.0001.0001.0001.0001.0001.0000.0001.0001.000
법정동코드1.0001.0001.0001.0001.0000.9470.0001.000NaN1.0001.000
주소1.0001.0001.0001.0001.0000.9550.0001.0000.0001.0001.000
상세주소1.0001.0001.0001.0001.0001.0001.0001.0000.0001.0001.000
주소본번0.9471.0000.9470.9551.0001.0000.0001.0000.0000.7071.000
주소부번0.0001.0000.0000.0001.0000.0001.0001.000NaNNaN1.000
시설설치일자1.0001.0001.0001.0001.0001.0001.0001.0000.0001.0001.000
시설폐지일자NaN0.000NaN0.0000.0000.000NaN0.0001.000NaN0.000
도로명주소 우편번호1.0001.0001.0001.0001.0000.707NaN1.000NaN1.0001.000
도로명주소1.0001.0001.0001.0001.0001.0001.0001.0000.0001.0001.000
2023-12-12T20:14:26.569464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
노인복지주택번호법정동코드주소본번주소부번도로명주소 우편번호
노인복지주택번호1.0000.8000.4580.0210.776
법정동코드0.8001.0000.523-0.2570.900
주소본번0.4580.5231.000-0.5980.639
주소부번0.021-0.257-0.5981.000-0.499
도로명주소 우편번호0.7760.9000.639-0.4991.000

Missing values

2023-12-12T20:14:18.874252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T20:14:19.138275image/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.
2023-12-12T20:14:19.344585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

노인복지주택번호노인복지주택명법정동코드주소상세주소주소본번주소부번시설설치일자시설폐지일자도로명주소 우편번호도로명주소
0410004더 헤리티지4113511100경기도 성남시 분당구 금곡동305-2번지30522009-09-22<NA>13552경기도 성남시 분당구 대왕판교로 155
1110001둔촌동후성누리움1174010600서울특별시 강동구 둔촌동79-23번지79232007-04-26<NA>5365서울특별시 강동구 명일로 135
2110003그레이스힐1150010200서울특별시 강서구 등촌동717번지717<NA>2007-03-222019-11-067575서울특별시 강서구 양천로 470
3110005노블레스타워 노인복지주택1129013500서울특별시 성북구 종암동3-91번지3912008-04-14<NA>2797서울특별시 성북구 종암로 90
4110007시니어스 하우징 더 골든팰리스1111018700서울특별시 종로구 무악동66-3번지6632008-01-012016-03-233030서울특별시 종로구 통일로16길 4-1
5280001보미골드리즌빌2826011400인천광역시 서구 당하동1077-3번지 보미 골드리즌빌107732009-02-19<NA>22675인천광역시 서구 원당대로 628 (보미골드리즌빌)
6410003명지엘펜하임4146110400경기도 용인시 처인구 남동583번지583<NA>2006-12-20<NA>17058경기도 용인시 처인구 명지로116번길 9-70
7110010상암카이져펠리스클래식1144012700서울특별시 마포구 상암동1641번지1641<NA>2011-01-19<NA>3911서울특별시 마포구 월드컵북로47길 37
8450002옥성골든카운티4511113500전라북도 전주시 완산구 중인동1610번지1610<NA>2013-05-30<NA>55144전라북도 전주시 완산구 중인1길 136-20
9110011서울시니어스강남타워1168011200서울특별시 강남구 자곡동631번지631<NA>2015-04-28<NA>6373서울특별시 강남구 자곡로 100-2
노인복지주택번호노인복지주택명법정동코드주소상세주소주소본번주소부번시설설치일자시설폐지일자도로명주소 우편번호도로명주소
13110002서울시니어스강서타워1150010200서울특별시 강서구 등촌동669-1번지66912003-03-28<NA>7591서울특별시 강서구 공항대로 315
14110004서울 시니어스 가양타워1150010200서울특별시 강서구 등촌동637번지 서울시니어스가양타워63702008-01-30<NA>7550서울특별시 강서구 화곡로68길 102 (서울시니어스가양타워)
15110006시니어캐슬 클라시온1138010200서울특별시 은평구 녹번동91-7번지9172007-09-28<NA>3382서울특별시 은평구 은평로21길 34-5
16110008정동상림원1114016700서울특별시 중구 정동15-6번지1562008-11-10<NA>4518서울특별시 중구 정동길 21-31
17410001서울시니어스분당타워4113511400경기도 성남시 분당구 구미동297-2번지29722003-08-03<NA>13619경기도 성남시 분당구 구미로173번길 47
18410005블루밍 더클래식4145010600경기도 하남시 신장동517번지517<NA>2010-08-02<NA>12950경기도 하남시 하남대로 770
19450001내장산실버아파트4518011200전라북도 정읍시 금붕동906-8번지90682011-11-16<NA>56196전라북도 정읍시 금붕1길 190
20410006정원속궁전4113510300경기도 성남시 분당구 정자동209번지209<NA>2014-02-12<NA>13606경기도 성남시 분당구 불정로 112
21410007광교아르데코4111710300경기도 수원시 영통구 이의동1358번지1358<NA>2017-08-29<NA>16495경기도 수원시 영통구 광교로42번길 80
22410008광교 두산위브4111710200경기도 수원시 영통구 원천동556번지556<NA>2018-05-28<NA>16500경기도 수원시 영통구 광교중앙로 55