Overview

Dataset statistics

Number of variables13
Number of observations179
Missing cells123
Missing cells (%)5.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory19.0 KiB
Average record size in memory108.7 B

Variable types

Text5
Categorical5
Numeric3

Dataset

Description시설명,시설코드,시설종류명(시설유형),시설종류상세명(시설종류),자치구(시)구분,시설장명,시군구코드,시군구명,시설주소,정원(수용인원),현인원,전화번호,우편번호
Author동작구
URLhttps://data.seoul.go.kr/dataList/OA-20396/S/1/datasetView.do

Alerts

자치구(시)구분 has constant value ""Constant
시군구코드 has constant value ""Constant
시군구명 has constant value ""Constant
시설종류명(시설유형) is highly overall correlated with 정원(수용인원) and 2 other fieldsHigh correlation
시설종류상세명(시설종류) is highly overall correlated with 시설종류명(시설유형)High correlation
정원(수용인원) is highly overall correlated with 시설종류명(시설유형)High correlation
현인원 is highly overall correlated with 시설종류명(시설유형)High correlation
정원(수용인원) has 35 (19.6%) missing valuesMissing
현인원 has 88 (49.2%) missing valuesMissing
시설코드 has unique valuesUnique
정원(수용인원) has 17 (9.5%) zerosZeros
현인원 has 9 (5.0%) zerosZeros

Reproduction

Analysis started2024-05-10 23:11:23.275155
Analysis finished2024-05-10 23:11:29.334160
Duration6.06 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시설명
Text

Distinct170
Distinct (%)95.0%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
2024-05-10T23:11:29.733388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length25
Median length19
Mean length9.5027933
Min length2

Characters and Unicode

Total characters1701
Distinct characters215
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique161 ?
Unique (%)89.9%

Sample

1st row동작실버센터
2nd row청운노인복지센터
3rd row한국씨니어노인복지센터
4th row약수데이케어센터
5th row정성요양원
ValueCountFrequency (%)
우리동네키움센터 14
 
6.6%
동작 3
 
1.4%
꿈터 2
 
0.9%
재가노인복지센터 2
 
0.9%
지역아동센터 2
 
0.9%
재가복지센터 2
 
0.9%
선한지역아동센터 2
 
0.9%
대방지역아동센터 2
 
0.9%
상도푸른지역아동센터 2
 
0.9%
흑석지역아동센터 2
 
0.9%
Other values (174) 178
84.4%
2024-05-10T23:11:30.975882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
터 119
 
7.0%
센 117
 
6.9%
동 97
 
5.7%
지 86
 
5.1%
아 45
 
2.6%
복 44
 
2.6%
역 38
 
2.2%
재 36
 
2.1%
33
 
1.9%
작 32
 
1.9%
Other values (205) 1054
62.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1624
95.5%
Decimal Number 37
 
2.2%
Space Separator 33
 
1.9%
Open Punctuation 2
 
0.1%
Close Punctuation 2
 
0.1%
Other Punctuation 2
 
0.1%
Uppercase Letter 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
터 119
 
7.3%
센 117
 
7.2%
동 97
 
6.0%
지 86
 
5.3%
아 45
 
2.8%
복 44
 
2.7%
역 38
 
2.3%
재 36
 
2.2%
작 32
 
2.0%
가 31
 
1.9%
Other values (194) 979
60.3%
Decimal Number
ValueCountFrequency (%)
1 13
35.1%
2 10
27.0%
3 7
18.9%
0 3
 
8.1%
5 2
 
5.4%
4 2
 
5.4%
Space Separator
ValueCountFrequency (%)
33
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Other Punctuation
ValueCountFrequency (%)
' 2
100.0%
Uppercase Letter
ValueCountFrequency (%)
A 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1624
95.5%
Common 76
 
4.5%
Latin 1
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
터 119
 
7.3%
센 117
 
7.2%
동 97
 
6.0%
지 86
 
5.3%
아 45
 
2.8%
복 44
 
2.7%
역 38
 
2.3%
재 36
 
2.2%
작 32
 
2.0%
가 31
 
1.9%
Other values (194) 979
60.3%
Common
ValueCountFrequency (%)
33
43.4%
1 13
 
17.1%
2 10
 
13.2%
3 7
 
9.2%
0 3
 
3.9%
5 2
 
2.6%
4 2
 
2.6%
( 2
 
2.6%
) 2
 
2.6%
' 2
 
2.6%
Latin
ValueCountFrequency (%)
A 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1624
95.5%
ASCII 77
 
4.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
터 119
 
7.3%
센 117
 
7.2%
동 97
 
6.0%
지 86
 
5.3%
아 45
 
2.8%
복 44
 
2.7%
역 38
 
2.3%
재 36
 
2.2%
작 32
 
2.0%
가 31
 
1.9%
Other values (194) 979
60.3%
ASCII
ValueCountFrequency (%)
33
42.9%
1 13
 
16.9%
2 10
 
13.0%
3 7
 
9.1%
0 3
 
3.9%
5 2
 
2.6%
4 2
 
2.6%
( 2
 
2.6%
) 2
 
2.6%
' 2
 
2.6%

시설코드
Text

UNIQUE 

Distinct179
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
2024-05-10T23:11:31.992665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length4.9497207
Min length4

Characters and Unicode

Total characters886
Distinct characters22
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique179 ?
Unique (%)100.0%

Sample

1st rowA0347
2nd rowA0607
3rd rowA0681
4th rowA1609
5th rowA1704
ValueCountFrequency (%)
a0347 1
 
0.6%
g7983 1
 
0.6%
k1425 1
 
0.6%
k1287 1
 
0.6%
k1296 1
 
0.6%
k1322 1
 
0.6%
k1358 1
 
0.6%
k1362 1
 
0.6%
k1391 1
 
0.6%
k1392 1
 
0.6%
Other values (169) 169
94.4%
2024-05-10T23:11:33.607773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 92
10.4%
0 85
9.6%
1 83
9.4%
3 73
8.2%
5 69
 
7.8%
9 68
 
7.7%
4 67
 
7.6%
7 59
 
6.7%
6 56
 
6.3%
8 55
 
6.2%
Other values (12) 179
20.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 707
79.8%
Uppercase Letter 179
 
20.2%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
K 32
17.9%
G 31
17.3%
B 29
16.2%
C 19
10.6%
A 17
9.5%
P 15
8.4%
T 15
8.4%
Z 8
 
4.5%
J 5
 
2.8%
F 4
 
2.2%
Other values (2) 4
 
2.2%
Decimal Number
ValueCountFrequency (%)
2 92
13.0%
0 85
12.0%
1 83
11.7%
3 73
10.3%
5 69
9.8%
9 68
9.6%
4 67
9.5%
7 59
8.3%
6 56
7.9%
8 55
7.8%

Most occurring scripts

ValueCountFrequency (%)
Common 707
79.8%
Latin 179
 
20.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
K 32
17.9%
G 31
17.3%
B 29
16.2%
C 19
10.6%
A 17
9.5%
P 15
8.4%
T 15
8.4%
Z 8
 
4.5%
J 5
 
2.8%
F 4
 
2.2%
Other values (2) 4
 
2.2%
Common
ValueCountFrequency (%)
2 92
13.0%
0 85
12.0%
1 83
11.7%
3 73
10.3%
5 69
9.8%
9 68
9.6%
4 67
9.5%
7 59
8.3%
6 56
7.9%
8 55
7.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 886
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 92
10.4%
0 85
9.6%
1 83
9.4%
3 73
8.2%
5 69
 
7.8%
9 68
 
7.7%
4 67
 
7.6%
7 59
 
6.7%
6 56
 
6.3%
8 55
 
6.2%
Other values (12) 179
20.2%

시설종류명(시설유형)
Categorical

HIGH CORRELATION 

Distinct32
Distinct (%)17.9%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
(노인) 재가노인복지시설
52 
(아동) 지역아동센터
37 
(아동) 다함께돌봄센터
13 
(일반) 사회복지관
9 
(장애인) 장애인주간보호시설
7 
Other values (27)
61 

Length

Max length27
Median length21
Mean length12.743017
Min length10

Unique

Unique17 ?
Unique (%)9.5%

Sample

1st row(노인) 노인요양시설
2nd row(노인) 노인요양시설
3rd row(노인) 재가노인복지시설
4th row(노인) 재가노인복지시설
5th row(노인) 노인요양공동생활가정

Common Values

ValueCountFrequency (%)
(노인) 재가노인복지시설 52
29.1%
(아동) 지역아동센터 37
20.7%
(아동) 다함께돌봄센터 13
 
7.3%
(일반) 사회복지관 9
 
5.0%
(장애인) 장애인주간보호시설 7
 
3.9%
(노인) 노인요양공동생활가정 7
 
3.9%
(노인) 노인복지관(소규모) 6
 
3.4%
(노인) 노인복지관 6
 
3.4%
(장애인) 장애인공동생활가정 5
 
2.8%
(노인) 노인요양시설 5
 
2.8%
Other values (22) 32
17.9%

Length

2024-05-10T23:11:34.097331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
노인 77
21.5%
아동 60
16.8%
재가노인복지시설 52
14.5%
지역아동센터 37
10.3%
장애인 24
 
6.7%
다함께돌봄센터 15
 
4.2%
일반 9
 
2.5%
사회복지관 9
 
2.5%
장애인주간보호시설 7
 
2.0%
노인요양공동생활가정 7
 
2.0%
Other values (32) 61
17.0%

시설종류상세명(시설종류)
Categorical

HIGH CORRELATION 

Distinct19
Distinct (%)10.6%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
아동복지시설
60 
재가노인복지시설
52 
노인의료복지시설
12 
노인여가복지시설
12 
장애인지역사회재활시설
12 
Other values (14)
31 

Length

Max length11
Median length9
Mean length7.4469274
Min length4

Unique

Unique11 ?
Unique (%)6.1%

Sample

1st row노인의료복지시설
2nd row노인의료복지시설
3rd row재가노인복지시설
4th row재가노인복지시설
5th row노인의료복지시설

Common Values

ValueCountFrequency (%)
아동복지시설 60
33.5%
재가노인복지시설 52
29.1%
노인의료복지시설 12
 
6.7%
노인여가복지시설 12
 
6.7%
장애인지역사회재활시설 12
 
6.7%
일반사회복지시설 9
 
5.0%
장애인거주시설 7
 
3.9%
장애인직업재활시설 4
 
2.2%
다함께돌봄센터 1
 
0.6%
한부모가족복지시설 1
 
0.6%
Other values (9) 9
 
5.0%

Length

2024-05-10T23:11:34.578889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
아동복지시설 60
33.5%
재가노인복지시설 52
29.1%
노인의료복지시설 12
 
6.7%
노인여가복지시설 12
 
6.7%
장애인지역사회재활시설 12
 
6.7%
일반사회복지시설 9
 
5.0%
장애인거주시설 7
 
3.9%
장애인직업재활시설 4
 
2.2%
성매매피해지원시설 1
 
0.6%
자활시설 1
 
0.6%
Other values (9) 9
 
5.0%

자치구(시)구분
Categorical

CONSTANT 

Distinct1
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
자치구
179 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
자치구 179
100.0%

Length

2024-05-10T23:11:35.113262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-10T23:11:35.458335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
자치구 179
100.0%

시설장명
Text

Distinct136
Distinct (%)76.0%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
2024-05-10T23:11:36.063196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length3.1340782
Min length3

Characters and Unicode

Total characters561
Distinct characters118
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

Unique106 ?
Unique (%)59.2%

Sample

1st row이재희
2nd row손창숙
3rd row반명규
4th row강경희
5th row김대경
ValueCountFrequency (%)
재단테스트 12
 
6.7%
이혜옥 3
 
1.7%
유병욱 3
 
1.7%
김종가 3
 
1.7%
심후석 2
 
1.1%
박미경 2
 
1.1%
이수영 2
 
1.1%
손창숙 2
 
1.1%
조미선 2
 
1.1%
이숙희 2
 
1.1%
Other values (126) 146
81.6%
2024-05-10T23:11:37.356206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
김 37
 
6.6%
이 28
 
5.0%
희 18
 
3.2%
정 18
 
3.2%
숙 17
 
3.0%
재 16
 
2.9%
영 16
 
2.9%
경 13
 
2.3%
순 13
 
2.3%
미 12
 
2.1%
Other values (108) 373
66.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 561
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
김 37
 
6.6%
이 28
 
5.0%
희 18
 
3.2%
정 18
 
3.2%
숙 17
 
3.0%
재 16
 
2.9%
영 16
 
2.9%
경 13
 
2.3%
순 13
 
2.3%
미 12
 
2.1%
Other values (108) 373
66.5%

Most occurring scripts

ValueCountFrequency (%)
Hangul 561
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
김 37
 
6.6%
이 28
 
5.0%
희 18
 
3.2%
정 18
 
3.2%
숙 17
 
3.0%
재 16
 
2.9%
영 16
 
2.9%
경 13
 
2.3%
순 13
 
2.3%
미 12
 
2.1%
Other values (108) 373
66.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 561
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
김 37
 
6.6%
이 28
 
5.0%
희 18
 
3.2%
정 18
 
3.2%
숙 17
 
3.0%
재 16
 
2.9%
영 16
 
2.9%
경 13
 
2.3%
순 13
 
2.3%
미 12
 
2.1%
Other values (108) 373
66.5%

시군구코드
Categorical

CONSTANT 

Distinct1
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
1159000000
179 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1159000000
2nd row1159000000
3rd row1159000000
4th row1159000000
5th row1159000000

Common Values

ValueCountFrequency (%)
1159000000 179
100.0%

Length

2024-05-10T23:11:37.861513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-10T23:11:38.213397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1159000000 179
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
동작구
179 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
동작구 179
100.0%

Length

2024-05-10T23:11:38.599957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-10T23:11:39.033660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
동작구 179
100.0%

시설주소
Text

Distinct160
Distinct (%)89.4%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
2024-05-10T23:11:39.673429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length59
Median length45
Mean length29.206704
Min length16

Characters and Unicode

Total characters5228
Distinct characters153
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique156 ?
Unique (%)87.2%

Sample

1st row서울특별시 동작구 노량진로32길 53 (본동)
2nd row서울특별시 동작구 국사봉1길 145(상도동)
3rd row서울특별시 동작구 상도로30길 82층
4th row서울특별시 동작구 양녕로36길 132층(상도동)
5th row서울특별시 동작구 보라매로 89-0거성빌딩 6층
ValueCountFrequency (%)
서울특별시 178
 
18.6%
동작구 178
 
18.6%
상도동 28
 
2.9%
사당동 27
 
2.8%
신대방동 20
 
2.1%
본동 17
 
1.8%
대방동 17
 
1.8%
노량진로32길79 16
 
1.7%
2층 15
 
1.6%
3층 12
 
1.3%
Other values (306) 448
46.9%
2024-05-10T23:11:41.017431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
785
 
15.0%
동 356
 
6.8%
작 194
 
3.7%
2 183
 
3.5%
서 182
 
3.5%
구 180
 
3.4%
울 180
 
3.4%
시 180
 
3.4%
별 178
 
3.4%
특 178
 
3.4%
Other values (143) 2632
50.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3121
59.7%
Decimal Number 893
 
17.1%
Space Separator 785
 
15.0%
Close Punctuation 145
 
2.8%
Open Punctuation 145
 
2.8%
Other Punctuation 94
 
1.8%
Dash Punctuation 39
 
0.7%
Uppercase Letter 4
 
0.1%
Math Symbol 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 356
 
11.4%
작 194
 
6.2%
서 182
 
5.8%
구 180
 
5.8%
울 180
 
5.8%
시 180
 
5.8%
별 178
 
5.7%
특 178
 
5.7%
로 149
 
4.8%
길 123
 
3.9%
Other values (125) 1221
39.1%
Decimal Number
ValueCountFrequency (%)
2 183
20.5%
1 163
18.3%
3 117
13.1%
0 115
12.9%
4 83
9.3%
9 56
 
6.3%
5 47
 
5.3%
7 45
 
5.0%
8 44
 
4.9%
6 40
 
4.5%
Uppercase Letter
ValueCountFrequency (%)
A 2
50.0%
B 2
50.0%
Space Separator
ValueCountFrequency (%)
785
100.0%
Close Punctuation
ValueCountFrequency (%)
) 145
100.0%
Open Punctuation
ValueCountFrequency (%)
( 145
100.0%
Other Punctuation
ValueCountFrequency (%)
, 94
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 39
100.0%
Math Symbol
ValueCountFrequency (%)
~ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3121
59.7%
Common 2103
40.2%
Latin 4
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 356
 
11.4%
작 194
 
6.2%
서 182
 
5.8%
구 180
 
5.8%
울 180
 
5.8%
시 180
 
5.8%
별 178
 
5.7%
특 178
 
5.7%
로 149
 
4.8%
길 123
 
3.9%
Other values (125) 1221
39.1%
Common
ValueCountFrequency (%)
785
37.3%
2 183
 
8.7%
1 163
 
7.8%
) 145
 
6.9%
( 145
 
6.9%
3 117
 
5.6%
0 115
 
5.5%
, 94
 
4.5%
4 83
 
3.9%
9 56
 
2.7%
Other values (6) 217
 
10.3%
Latin
ValueCountFrequency (%)
A 2
50.0%
B 2
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3121
59.7%
ASCII 2107
40.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
785
37.3%
2 183
 
8.7%
1 163
 
7.7%
) 145
 
6.9%
( 145
 
6.9%
3 117
 
5.6%
0 115
 
5.5%
, 94
 
4.5%
4 83
 
3.9%
9 56
 
2.7%
Other values (8) 221
 
10.5%
Hangul
ValueCountFrequency (%)
동 356
 
11.4%
작 194
 
6.2%
서 182
 
5.8%
구 180
 
5.8%
울 180
 
5.8%
시 180
 
5.8%
별 178
 
5.7%
특 178
 
5.7%
로 149
 
4.8%
길 123
 
3.9%
Other values (125) 1221
39.1%

정원(수용인원)
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct42
Distinct (%)29.2%
Missing35
Missing (%)19.6%
Infinite0
Infinite (%)0.0%
Mean57.131944
Minimum0
Maximum3448
Zeros17
Zeros (%)9.5%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2024-05-10T23:11:41.507685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q114.5
median21
Q330
95-th percentile78.2
Maximum3448
Range3448
Interquartile range (IQR)15.5

Descriptive statistics

Standard deviation296.02928
Coefficient of variation (CV)5.1815018
Kurtosis122.80661
Mean57.131944
Median Absolute Deviation (MAD)9
Skewness10.800039
Sum8227
Variance87633.332
MonotonicityNot monotonic
2024-05-10T23:11:41.983887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
17 18
 
10.1%
0 17
 
9.5%
30 10
 
5.6%
19 9
 
5.0%
9 7
 
3.9%
24 7
 
3.9%
35 7
 
3.9%
20 6
 
3.4%
21 5
 
2.8%
4 5
 
2.8%
Other values (32) 53
29.6%
(Missing) 35
19.6%
ValueCountFrequency (%)
0 17
9.5%
4 5
 
2.8%
5 2
 
1.1%
7 1
 
0.6%
9 7
 
3.9%
10 3
 
1.7%
13 1
 
0.6%
15 2
 
1.1%
17 18
10.1%
19 9
5.0%
ValueCountFrequency (%)
3448 1
0.6%
752 1
0.6%
635 1
0.6%
150 1
0.6%
100 1
0.6%
90 2
1.1%
80 1
0.6%
68 1
0.6%
65 1
0.6%
63 1
0.6%

현인원
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct50
Distinct (%)54.9%
Missing88
Missing (%)49.2%
Infinite0
Infinite (%)0.0%
Mean113.71429
Minimum0
Maximum1034
Zeros9
Zeros (%)5.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2024-05-10T23:11:42.505944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q19
median22
Q335
95-th percentile701
Maximum1034
Range1034
Interquartile range (IQR)26

Descriptive statistics

Standard deviation241.19703
Coefficient of variation (CV)2.1210794
Kurtosis5.1161627
Mean113.71429
Median Absolute Deviation (MAD)13
Skewness2.4843964
Sum10348
Variance58176.006
MonotonicityNot monotonic
2024-05-10T23:11:42.970392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 9
 
5.0%
9 7
 
3.9%
4 5
 
2.8%
21 5
 
2.8%
24 4
 
2.2%
22 4
 
2.2%
17 3
 
1.7%
56 2
 
1.1%
3 2
 
1.1%
28 2
 
1.1%
Other values (40) 48
26.8%
(Missing) 88
49.2%
ValueCountFrequency (%)
0 9
5.0%
3 2
 
1.1%
4 5
2.8%
7 1
 
0.6%
9 7
3.9%
10 2
 
1.1%
11 1
 
0.6%
12 1
 
0.6%
13 1
 
0.6%
14 1
 
0.6%
ValueCountFrequency (%)
1034 1
0.6%
1019 1
0.6%
808 1
0.6%
788 1
0.6%
752 1
0.6%
650 1
0.6%
644 1
0.6%
600 1
0.6%
581 1
0.6%
530 1
0.6%

전화번호
Text

Distinct155
Distinct (%)86.6%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
2024-05-10T23:11:43.692548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length10.52514
Min length9

Characters and Unicode

Total characters1884
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique144 ?
Unique (%)80.4%

Sample

1st row02-821-8600
2nd row02-823-3833
3rd row02-815-1922
4th row02-825-7071
5th row02-822-8636
ValueCountFrequency (%)
070-7163-0907 15
 
8.4%
02-822-8636 2
 
1.1%
028168031 2
 
1.1%
02-823-3363 2
 
1.1%
02-811-0055 2
 
1.1%
02-826-2900 2
 
1.1%
028140287 2
 
1.1%
029245907 2
 
1.1%
07048483067 2
 
1.1%
028352924 2
 
1.1%
Other values (145) 146
81.6%
2024-05-10T23:11:45.276787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 388
20.6%
2 324
17.2%
8 182
9.7%
- 179
9.5%
7 155
 
8.2%
1 142
 
7.5%
3 116
 
6.2%
5 113
 
6.0%
6 102
 
5.4%
9 101
 
5.4%
Other values (2) 82
 
4.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1704
90.4%
Dash Punctuation 179
 
9.5%
Close Punctuation 1
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 388
22.8%
2 324
19.0%
8 182
10.7%
7 155
 
9.1%
1 142
 
8.3%
3 116
 
6.8%
5 113
 
6.6%
6 102
 
6.0%
9 101
 
5.9%
4 81
 
4.8%
Dash Punctuation
ValueCountFrequency (%)
- 179
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1884
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 388
20.6%
2 324
17.2%
8 182
9.7%
- 179
9.5%
7 155
 
8.2%
1 142
 
7.5%
3 116
 
6.2%
5 113
 
6.0%
6 102
 
5.4%
9 101
 
5.4%
Other values (2) 82
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1884
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 388
20.6%
2 324
17.2%
8 182
9.7%
- 179
9.5%
7 155
 
8.2%
1 142
 
7.5%
3 116
 
6.2%
5 113
 
6.0%
6 102
 
5.4%
9 101
 
5.4%
Other values (2) 82
 
4.4%

우편번호
Real number (ℝ)

Distinct92
Distinct (%)51.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12903.402
Minimum6904
Maximum156844
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2024-05-10T23:11:45.756766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6904
5-th percentile6908
Q16941.5
median6997
Q37040
95-th percentile7073
Maximum156844
Range149940
Interquartile range (IQR)98.5

Descriptive statistics

Standard deviation29094.007
Coefficient of variation (CV)2.2547547
Kurtosis21.192923
Mean12903.402
Median Absolute Deviation (MAD)51
Skewness4.7891976
Sum2309709
Variance8.4646127 × 108
MonotonicityNot monotonic
2024-05-10T23:11:46.216279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6908 19
 
10.6%
7035 7
 
3.9%
7053 6
 
3.4%
7062 5
 
2.8%
6942 5
 
2.8%
6968 4
 
2.2%
7038 4
 
2.2%
7043 4
 
2.2%
7040 3
 
1.7%
7005 3
 
1.7%
Other values (82) 119
66.5%
ValueCountFrequency (%)
6904 3
 
1.7%
6908 19
10.6%
6910 1
 
0.6%
6912 2
 
1.1%
6917 1
 
0.6%
6920 1
 
0.6%
6922 1
 
0.6%
6924 1
 
0.6%
6925 3
 
1.7%
6926 1
 
0.6%
ValueCountFrequency (%)
156844 1
0.6%
156841 1
0.6%
156830 1
0.6%
156808 1
0.6%
156600 2
1.1%
156070 1
0.6%
18338 1
0.6%
7073 2
1.1%
7070 1
0.6%
7069 2
1.1%

Interactions

2024-05-10T23:11:26.650512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:11:24.740343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:11:25.674552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:11:27.042790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:11:25.082806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:11:25.980103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:11:27.318240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:11:25.379381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:11:26.356468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-10T23:11:46.575864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시설종류명(시설유형)시설종류상세명(시설종류)정원(수용인원)현인원우편번호
시설종류명(시설유형)1.0001.0000.8460.8970.409
시설종류상세명(시설종류)1.0001.0000.0000.7700.067
정원(수용인원)0.8460.0001.0000.7130.000
현인원0.8970.7700.7131.0000.000
우편번호0.4090.0670.0000.0001.000
2024-05-10T23:11:46.984717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시설종류명(시설유형)시설종류상세명(시설종류)
시설종류명(시설유형)1.0000.959
시설종류상세명(시설종류)0.9591.000
2024-05-10T23:11:47.231756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
정원(수용인원)현인원우편번호시설종류명(시설유형)시설종류상세명(시설종류)
정원(수용인원)1.0000.4310.1260.5580.000
현인원0.4311.000-0.1820.5870.380
우편번호0.126-0.1821.0000.3000.070
시설종류명(시설유형)0.5580.5870.3001.0000.959
시설종류상세명(시설종류)0.0000.3800.0700.9591.000

Missing values

2024-05-10T23:11:27.735136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-10T23:11:28.702697image/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.
2024-05-10T23:11:29.128160image/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

시설명시설코드시설종류명(시설유형)시설종류상세명(시설종류)자치구(시)구분시설장명시군구코드시군구명시설주소정원(수용인원)현인원전화번호우편번호
0동작실버센터A0347(노인) 노인요양시설노인의료복지시설자치구이재희1159000000동작구서울특별시 동작구 노량진로32길 53 (본동)686802-821-86006908
1청운노인복지센터A0607(노인) 노인요양시설노인의료복지시설자치구손창숙1159000000동작구서울특별시 동작구 국사봉1길 145(상도동)10010002-823-3833156844
2한국씨니어노인복지센터A0681(노인) 재가노인복지시설재가노인복지시설자치구반명규1159000000동작구서울특별시 동작구 상도로30길 82층0002-815-19226964
3약수데이케어센터A1609(노인) 재가노인복지시설재가노인복지시설자치구강경희1159000000동작구서울특별시 동작구 양녕로36길 132층(상도동)242402-825-70716965
4정성요양원A1704(노인) 노인요양공동생활가정노인의료복지시설자치구김대경1159000000동작구서울특별시 동작구 보라매로 89-0거성빌딩 6층9902-822-86367060
5동작노인종합복지관A2509(노인) 노인복지관노인여가복지시설자치구김익환1159000000동작구서울특별시 동작구 상도로11길 7(대방동)0103402-823-00646953
6청남노인요양공동생활가정A2538(노인) 노인요양공동생활가정노인의료복지시설자치구강미수1159000000동작구서울특별시 동작구 강남초등8길 20-04층9902-566-6976156830
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8서울시립발달장애인복지관A5108(장애인) 장애인복지관장애인지역사회재활시설자치구최선자1159000000동작구서울특별시 동작구 여의대방로20길 33-0395(보라매공원내)75275202-840-15007062
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