Overview

Dataset statistics

Number of variables13
Number of observations263
Missing cells14
Missing cells (%)0.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory27.6 KiB
Average record size in memory107.5 B

Variable types

Text6
Categorical4
Numeric3

Dataset

Description시설명,시설코드,시설종류명(시설유형),시설종류상세명(시설종류),자치구(시)구분,시설장명,시군구코드,시군구명,시설주소,정원(수용인원),현인원,전화번호,우편번호
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-20425/S/1/datasetView.do

Alerts

시설종류상세명(시설종류) has constant value ""Constant
자치구(시)구분 has constant value ""Constant
시군구코드 is highly overall correlated with 시군구명High correlation
정원(수용인원) is highly overall correlated with 현인원High correlation
현인원 is highly overall correlated with 정원(수용인원) and 1 other fieldsHigh correlation
시설종류명(시설유형) is highly overall correlated with 현인원High correlation
시군구명 is highly overall correlated with 시군구코드High correlation
시설주소 has 3 (1.1%) missing valuesMissing
정원(수용인원) has 4 (1.5%) missing valuesMissing
현인원 has 7 (2.7%) missing valuesMissing
시설코드 has unique valuesUnique

Reproduction

Analysis started2024-05-11 09:23:40.043427
Analysis finished2024-05-11 09:23:45.096024
Duration5.05 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시설명
Text

Distinct261
Distinct (%)99.2%
Missing0
Missing (%)0.0%
Memory size2.2 KiB
2024-05-11T09:23:45.505987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length17
Mean length7.7794677
Min length2

Characters and Unicode

Total characters2046
Distinct characters260
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

Unique259 ?
Unique (%)98.5%

Sample

1st row대린원
2nd row동천의집
3rd row누림홈
4th row삼성농아원
5th row다니엘복지원
ValueCountFrequency (%)
집 11
 
3.7%
공동생활가정 3
 
1.0%
라파엘의집 2
 
0.7%
소망의집 2
 
0.7%
헬렌켈러의집 2
 
0.7%
2호 2
 
0.7%
행복이가득한집 2
 
0.7%
행복한 2
 
0.7%
충현의집2호 1
 
0.3%
임마누엘등촌공동체 1
 
0.3%
Other values (268) 268
90.5%
2024-05-11T09:23:46.713845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 99
 
4.8%
공 78
 
3.8%
가 70
 
3.4%
집 68
 
3.3%
활 66
 
3.2%
정 66
 
3.2%
생 64
 
3.1%
의 55
 
2.7%
호 49
 
2.4%
애 38
 
1.9%
Other values (250) 1393
68.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1937
94.7%
Decimal Number 57
 
2.8%
Space Separator 33
 
1.6%
Open Punctuation 6
 
0.3%
Close Punctuation 6
 
0.3%
Uppercase Letter 6
 
0.3%
Other Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 99
 
5.1%
공 78
 
4.0%
가 70
 
3.6%
집 68
 
3.5%
활 66
 
3.4%
정 66
 
3.4%
생 64
 
3.3%
의 55
 
2.8%
호 49
 
2.5%
애 38
 
2.0%
Other values (233) 1284
66.3%
Decimal Number
ValueCountFrequency (%)
2 18
31.6%
1 16
28.1%
3 8
14.0%
4 7
 
12.3%
5 4
 
7.0%
6 2
 
3.5%
8 1
 
1.8%
7 1
 
1.8%
Uppercase Letter
ValueCountFrequency (%)
S 2
33.3%
H 1
16.7%
C 1
16.7%
R 1
16.7%
G 1
16.7%
Space Separator
ValueCountFrequency (%)
33
100.0%
Open Punctuation
ValueCountFrequency (%)
( 6
100.0%
Close Punctuation
ValueCountFrequency (%)
) 6
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1937
94.7%
Common 103
 
5.0%
Latin 6
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 99
 
5.1%
공 78
 
4.0%
가 70
 
3.6%
집 68
 
3.5%
활 66
 
3.4%
정 66
 
3.4%
생 64
 
3.3%
의 55
 
2.8%
호 49
 
2.5%
애 38
 
2.0%
Other values (233) 1284
66.3%
Common
ValueCountFrequency (%)
33
32.0%
2 18
17.5%
1 16
15.5%
3 8
 
7.8%
4 7
 
6.8%
( 6
 
5.8%
) 6
 
5.8%
5 4
 
3.9%
6 2
 
1.9%
, 1
 
1.0%
Other values (2) 2
 
1.9%
Latin
ValueCountFrequency (%)
S 2
33.3%
H 1
16.7%
C 1
16.7%
R 1
16.7%
G 1
16.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1937
94.7%
ASCII 109
 
5.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 99
 
5.1%
공 78
 
4.0%
가 70
 
3.6%
집 68
 
3.5%
활 66
 
3.4%
정 66
 
3.4%
생 64
 
3.3%
의 55
 
2.8%
호 49
 
2.5%
애 38
 
2.0%
Other values (233) 1284
66.3%
ASCII
ValueCountFrequency (%)
33
30.3%
2 18
16.5%
1 16
14.7%
3 8
 
7.3%
4 7
 
6.4%
( 6
 
5.5%
) 6
 
5.5%
5 4
 
3.7%
6 2
 
1.8%
S 2
 
1.8%
Other values (7) 7
 
6.4%

시설코드
Text

UNIQUE 

Distinct263
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size2.2 KiB
2024-05-11T09:23:47.839041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length5.0190114
Min length5

Characters and Unicode

Total characters1320
Distinct characters12
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

Unique263 ?
Unique (%)100.0%

Sample

1st rowC0001
2nd rowC0003
3rd rowC0005
4th rowC0012
5th rowC0014
ValueCountFrequency (%)
c0001 1
 
0.4%
c8166 1
 
0.4%
c7008 1
 
0.4%
c7037 1
 
0.4%
c7039 1
 
0.4%
c7040 1
 
0.4%
c7108 1
 
0.4%
c7109 1
 
0.4%
c7110 1
 
0.4%
c7111 1
 
0.4%
Other values (253) 253
96.2%
2024-05-11T09:23:49.624848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
C 258
19.5%
0 171
13.0%
1 170
12.9%
8 129
9.8%
7 98
 
7.4%
2 93
 
7.0%
9 92
 
7.0%
5 82
 
6.2%
4 81
 
6.1%
3 72
 
5.5%
Other values (2) 74
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1057
80.1%
Uppercase Letter 263
 
19.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 171
16.2%
1 170
16.1%
8 129
12.2%
7 98
9.3%
2 93
8.8%
9 92
8.7%
5 82
7.8%
4 81
7.7%
3 72
6.8%
6 69
6.5%
Uppercase Letter
ValueCountFrequency (%)
C 258
98.1%
F 5
 
1.9%

Most occurring scripts

ValueCountFrequency (%)
Common 1057
80.1%
Latin 263
 
19.9%

Most frequent character per script

Common
ValueCountFrequency (%)
0 171
16.2%
1 170
16.1%
8 129
12.2%
7 98
9.3%
2 93
8.8%
9 92
8.7%
5 82
7.8%
4 81
7.7%
3 72
6.8%
6 69
6.5%
Latin
ValueCountFrequency (%)
C 258
98.1%
F 5
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1320
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
C 258
19.5%
0 171
13.0%
1 170
12.9%
8 129
9.8%
7 98
 
7.4%
2 93
 
7.0%
9 92
 
7.0%
5 82
 
6.2%
4 81
 
6.1%
3 72
 
5.5%
Other values (2) 74
 
5.6%

시설종류명(시설유형)
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Memory size2.2 KiB
(장애인) 장애인공동생활가정
173 
(장애인) 장애인단기거주시설
44 
(장애인) 중증장애인거주시설
23 
(장애인) 장애유형별거주시설
20 
(장애인) 장애영유아거주시설
 
2

Length

Max length15
Median length15
Mean length14.992395
Min length13

Unique

Unique1 ?
Unique (%)0.4%

Sample

1st row(장애인) 장애유형별거주시설
2nd row(장애인) 장애유형별거주시설
3rd row(장애인) 장애유형별거주시설
4th row(장애인) 장애유형별거주시설
5th row(장애인) 장애유형별거주시설

Common Values

ValueCountFrequency (%)
(장애인) 장애인공동생활가정 173
65.8%
(장애인) 장애인단기거주시설 44
 
16.7%
(장애인) 중증장애인거주시설 23
 
8.7%
(장애인) 장애유형별거주시설 20
 
7.6%
(장애인) 장애영유아거주시설 2
 
0.8%
(장애인) 피해장애인쉼터 1
 
0.4%

Length

2024-05-11T09:23:50.124751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T09:23:50.589242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
장애인 263
50.0%
장애인공동생활가정 173
32.9%
장애인단기거주시설 44
 
8.4%
중증장애인거주시설 23
 
4.4%
장애유형별거주시설 20
 
3.8%
장애영유아거주시설 2
 
0.4%
피해장애인쉼터 1
 
0.2%

시설종류상세명(시설종류)
Categorical

CONSTANT 

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size2.2 KiB
장애인거주시설
263 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
장애인거주시설 263
100.0%

Length

2024-05-11T09:23:51.129569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T09:23:51.418887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
장애인거주시설 263
100.0%

자치구(시)구분
Categorical

CONSTANT 

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size2.2 KiB
자치구
263 

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 263
100.0%

Length

2024-05-11T09:23:51.794413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T09:23:52.170510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
자치구 263
100.0%

시설장명
Text

Distinct169
Distinct (%)64.3%
Missing0
Missing (%)0.0%
Memory size2.2 KiB
2024-05-11T09:23:53.113155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length2.9961977
Min length2

Characters and Unicode

Total characters788
Distinct characters119
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

Unique128 ?
Unique (%)48.7%

Sample

1st row임은희
2nd row김영문
3rd row김재훈
4th row엄종숙
5th row지승현
ValueCountFrequency (%)
장동국 15
 
5.7%
김영문 9
 
3.4%
강태인 5
 
1.9%
황규인 5
 
1.9%
하성도 5
 
1.9%
김경일 4
 
1.5%
김한덕 4
 
1.5%
김윤례 4
 
1.5%
김인숙 4
 
1.5%
정혜선 4
 
1.5%
Other values (159) 204
77.6%
2024-05-11T09:23:54.979789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
김 72
 
9.1%
이 40
 
5.1%
영 34
 
4.3%
정 29
 
3.7%
경 24
 
3.0%
동 18
 
2.3%
성 18
 
2.3%
수 17
 
2.2%
장 16
 
2.0%
희 16
 
2.0%
Other values (109) 504
64.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 788
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
김 72
 
9.1%
이 40
 
5.1%
영 34
 
4.3%
정 29
 
3.7%
경 24
 
3.0%
동 18
 
2.3%
성 18
 
2.3%
수 17
 
2.2%
장 16
 
2.0%
희 16
 
2.0%
Other values (109) 504
64.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 788
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
김 72
 
9.1%
이 40
 
5.1%
영 34
 
4.3%
정 29
 
3.7%
경 24
 
3.0%
동 18
 
2.3%
성 18
 
2.3%
수 17
 
2.2%
장 16
 
2.0%
희 16
 
2.0%
Other values (109) 504
64.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 788
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
김 72
 
9.1%
이 40
 
5.1%
영 34
 
4.3%
정 29
 
3.7%
경 24
 
3.0%
동 18
 
2.3%
성 18
 
2.3%
수 17
 
2.2%
장 16
 
2.0%
희 16
 
2.0%
Other values (109) 504
64.0%

시군구코드
Real number (ℝ)

HIGH CORRELATION 

Distinct26
Distinct (%)9.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1491293 × 109
Minimum1.1 × 109
Maximum1.174 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 KiB
2024-05-11T09:23:55.480462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.1 × 109
5-th percentile1.12 × 109
Q11.135 × 109
median1.15 × 109
Q31.165 × 109
95-th percentile1.174 × 109
Maximum1.174 × 109
Range74000000
Interquartile range (IQR)30000000

Descriptive statistics

Standard deviation17323549
Coefficient of variation (CV)0.01507537
Kurtosis-0.67097835
Mean1.1491293 × 109
Median Absolute Deviation (MAD)15000000
Skewness-0.30324867
Sum3.02221 × 1011
Variance3.0010536 × 1014
MonotonicityNot monotonic
2024-05-11T09:23:56.003063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
1150000000 29
 
11.0%
1168000000 25
 
9.5%
1135000000 21
 
8.0%
1174000000 20
 
7.6%
1171000000 20
 
7.6%
1130500000 18
 
6.8%
1138000000 16
 
6.1%
1132000000 12
 
4.6%
1147000000 11
 
4.2%
1165000000 11
 
4.2%
Other values (16) 80
30.4%
ValueCountFrequency (%)
1100000000 2
 
0.8%
1111000000 5
 
1.9%
1114000000 2
 
0.8%
1117000000 3
 
1.1%
1120000000 4
 
1.5%
1121500000 1
 
0.4%
1123000000 1
 
0.4%
1126000000 2
 
0.8%
1129000000 5
 
1.9%
1130500000 18
6.8%
ValueCountFrequency (%)
1174000000 20
7.6%
1171000000 20
7.6%
1168000000 25
9.5%
1165000000 11
 
4.2%
1162000000 8
 
3.0%
1159000000 7
 
2.7%
1156000000 7
 
2.7%
1154500000 9
 
3.4%
1153000000 9
 
3.4%
1150000000 29
11.0%

시군구명
Categorical

HIGH CORRELATION 

Distinct26
Distinct (%)9.9%
Missing0
Missing (%)0.0%
Memory size2.2 KiB
강서구
29 
강남구
25 
노원구
21 
강동구
20 
송파구
20 
Other values (21)
148 

Length

Max length5
Median length3
Mean length3.0646388
Min length2

Unique

Unique2 ?
Unique (%)0.8%

Sample

1st row노원구
2nd row노원구
3rd row양천구
4th row동작구
5th row서초구

Common Values

ValueCountFrequency (%)
강서구 29
 
11.0%
강남구 25
 
9.5%
노원구 21
 
8.0%
강동구 20
 
7.6%
송파구 20
 
7.6%
강북구 18
 
6.8%
은평구 16
 
6.1%
도봉구 12
 
4.6%
서초구 11
 
4.2%
양천구 11
 
4.2%
Other values (16) 80
30.4%

Length

2024-05-11T09:23:56.658151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
강서구 29
 
11.0%
강남구 25
 
9.5%
노원구 21
 
8.0%
강동구 20
 
7.6%
송파구 20
 
7.6%
강북구 18
 
6.8%
은평구 16
 
6.1%
도봉구 12
 
4.6%
서초구 11
 
4.2%
양천구 11
 
4.2%
Other values (16) 80
30.4%

시설주소
Text

MISSING 

Distinct257
Distinct (%)98.8%
Missing3
Missing (%)1.1%
Memory size2.2 KiB
2024-05-11T09:23:57.728338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length60
Median length43
Mean length29.388462
Min length10

Characters and Unicode

Total characters7641
Distinct characters276
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

Unique256 ?
Unique (%)98.5%

Sample

1st row서울특별시 노원구 동일로248길 30(상계1동)
2nd row서울특별시 노원구 노원로 18길 41 (하계동 288-1)(하계1동)
3rd row경기도 김포시 대곶면 율생중앙로 83
4th row서울특별시 동작구 양녕로30길 19-4(동작구)
5th row서울특별시 서초구 헌릉로468길 21-16
ValueCountFrequency (%)
서울특별시 242
 
18.8%
강서구 30
 
2.3%
강남구 24
 
1.9%
노원구 20
 
1.6%
강동구 19
 
1.5%
송파구 17
 
1.3%
은평구 16
 
1.2%
강북구 16
 
1.2%
도봉구 12
 
0.9%
경기도 11
 
0.9%
Other values (644) 878
68.3%
2024-05-11T09:23:59.184337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1042
 
13.6%
1 443
 
5.8%
서 296
 
3.9%
구 267
 
3.5%
시 266
 
3.5%
2 258
 
3.4%
로 256
 
3.4%
동 256
 
3.4%
울 243
 
3.2%
별 242
 
3.2%
Other values (266) 4072
53.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4317
56.5%
Decimal Number 1700
 
22.2%
Space Separator 1042
 
13.6%
Open Punctuation 175
 
2.3%
Close Punctuation 175
 
2.3%
Dash Punctuation 136
 
1.8%
Other Punctuation 78
 
1.0%
Uppercase Letter 16
 
0.2%
Lowercase Letter 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
서 296
 
6.9%
구 267
 
6.2%
시 266
 
6.2%
로 256
 
5.9%
동 256
 
5.9%
울 243
 
5.6%
별 242
 
5.6%
특 242
 
5.6%
길 222
 
5.1%
호 156
 
3.6%
Other values (242) 1871
43.3%
Decimal Number
ValueCountFrequency (%)
1 443
26.1%
2 258
15.2%
0 230
13.5%
3 177
 
10.4%
4 142
 
8.4%
5 125
 
7.4%
6 86
 
5.1%
8 81
 
4.8%
7 80
 
4.7%
9 78
 
4.6%
Uppercase Letter
ValueCountFrequency (%)
B 4
25.0%
S 4
25.0%
J 3
18.8%
A 3
18.8%
C 1
 
6.2%
R 1
 
6.2%
Other Punctuation
ValueCountFrequency (%)
, 77
98.7%
. 1
 
1.3%
Lowercase Letter
ValueCountFrequency (%)
s 1
50.0%
j 1
50.0%
Space Separator
ValueCountFrequency (%)
1042
100.0%
Open Punctuation
ValueCountFrequency (%)
( 175
100.0%
Close Punctuation
ValueCountFrequency (%)
) 175
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 136
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4317
56.5%
Common 3306
43.3%
Latin 18
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
서 296
 
6.9%
구 267
 
6.2%
시 266
 
6.2%
로 256
 
5.9%
동 256
 
5.9%
울 243
 
5.6%
별 242
 
5.6%
특 242
 
5.6%
길 222
 
5.1%
호 156
 
3.6%
Other values (242) 1871
43.3%
Common
ValueCountFrequency (%)
1042
31.5%
1 443
13.4%
2 258
 
7.8%
0 230
 
7.0%
3 177
 
5.4%
( 175
 
5.3%
) 175
 
5.3%
4 142
 
4.3%
- 136
 
4.1%
5 125
 
3.8%
Other values (6) 403
 
12.2%
Latin
ValueCountFrequency (%)
B 4
22.2%
S 4
22.2%
J 3
16.7%
A 3
16.7%
s 1
 
5.6%
j 1
 
5.6%
C 1
 
5.6%
R 1
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4317
56.5%
ASCII 3324
43.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1042
31.3%
1 443
13.3%
2 258
 
7.8%
0 230
 
6.9%
3 177
 
5.3%
( 175
 
5.3%
) 175
 
5.3%
4 142
 
4.3%
- 136
 
4.1%
5 125
 
3.8%
Other values (14) 421
12.7%
Hangul
ValueCountFrequency (%)
서 296
 
6.9%
구 267
 
6.2%
시 266
 
6.2%
로 256
 
5.9%
동 256
 
5.9%
울 243
 
5.6%
별 242
 
5.6%
특 242
 
5.6%
길 222
 
5.1%
호 156
 
3.6%
Other values (242) 1871
43.3%

정원(수용인원)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct38
Distinct (%)14.7%
Missing4
Missing (%)1.5%
Infinite0
Infinite (%)0.0%
Mean15.374517
Minimum1
Maximum228
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 KiB
2024-05-11T09:23:59.780551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q14
median4
Q310
95-th percentile60
Maximum228
Range227
Interquartile range (IQR)6

Descriptive statistics

Standard deviation28.812053
Coefficient of variation (CV)1.8740135
Kurtosis22.986865
Mean15.374517
Median Absolute Deviation (MAD)0
Skewness4.3138414
Sum3982
Variance830.13439
MonotonicityNot monotonic
2024-05-11T09:24:00.412263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
4 152
57.8%
10 34
 
12.9%
30 7
 
2.7%
1 5
 
1.9%
60 4
 
1.5%
55 4
 
1.5%
3 4
 
1.5%
15 4
 
1.5%
25 3
 
1.1%
50 3
 
1.1%
Other values (28) 39
 
14.8%
(Missing) 4
 
1.5%
ValueCountFrequency (%)
1 5
 
1.9%
3 4
 
1.5%
4 152
57.8%
5 2
 
0.8%
6 3
 
1.1%
8 3
 
1.1%
9 2
 
0.8%
10 34
 
12.9%
12 2
 
0.8%
15 4
 
1.5%
ValueCountFrequency (%)
228 1
0.4%
200 1
0.4%
175 1
0.4%
150 1
0.4%
134 1
0.4%
86 1
0.4%
80 2
0.8%
77 1
0.4%
70 1
0.4%
65 1
0.4%

현인원
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct46
Distinct (%)18.0%
Missing7
Missing (%)2.7%
Infinite0
Infinite (%)0.0%
Mean13.503906
Minimum1
Maximum160
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 KiB
2024-05-11T09:24:01.015693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q14
median4
Q310
95-th percentile53.25
Maximum160
Range159
Interquartile range (IQR)6

Descriptive statistics

Standard deviation23.863665
Coefficient of variation (CV)1.7671675
Kurtosis17.597367
Mean13.503906
Median Absolute Deviation (MAD)1
Skewness3.878978
Sum3457
Variance569.47449
MonotonicityNot monotonic
2024-05-11T09:24:01.516857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
4 124
47.1%
3 29
 
11.0%
10 29
 
11.0%
1 5
 
1.9%
11 5
 
1.9%
30 5
 
1.9%
2 5
 
1.9%
12 4
 
1.5%
50 4
 
1.5%
15 3
 
1.1%
Other values (36) 43
 
16.3%
(Missing) 7
 
2.7%
ValueCountFrequency (%)
1 5
 
1.9%
2 5
 
1.9%
3 29
 
11.0%
4 124
47.1%
5 1
 
0.4%
6 3
 
1.1%
7 1
 
0.4%
8 2
 
0.8%
9 1
 
0.4%
10 29
 
11.0%
ValueCountFrequency (%)
160 1
0.4%
151 1
0.4%
146 1
0.4%
145 1
0.4%
125 1
0.4%
80 1
0.4%
74 1
0.4%
70 1
0.4%
69 1
0.4%
58 1
0.4%

전화번호
Text

Distinct249
Distinct (%)94.7%
Missing0
Missing (%)0.0%
Memory size2.2 KiB
2024-05-11T09:24:02.333817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length11.254753
Min length9

Characters and Unicode

Total characters2960
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique240 ?
Unique (%)91.3%

Sample

1st row02-939-2298
2nd row02-974-9577
3rd row031-987-9324
4th row02-823-2234
5th row02-445-4892
ValueCountFrequency (%)
02-3665-3831 6
 
2.3%
02-6956-3046 3
 
1.1%
070-8839-3999 2
 
0.8%
02-3411-9581 2
 
0.8%
02-385-2046 2
 
0.8%
02-2290-3100 2
 
0.8%
02-2602-3880 2
 
0.8%
02-950-0152 2
 
0.8%
0269598100 2
 
0.8%
02-841-3831 1
 
0.4%
Other values (239) 239
90.9%
2024-05-11T09:24:03.681746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 490
16.6%
2 446
15.1%
- 446
15.1%
3 249
8.4%
8 217
7.3%
6 200
6.8%
9 197
6.7%
4 194
 
6.6%
5 189
 
6.4%
1 176
 
5.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2514
84.9%
Dash Punctuation 446
 
15.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 490
19.5%
2 446
17.7%
3 249
9.9%
8 217
8.6%
6 200
8.0%
9 197
7.8%
4 194
 
7.7%
5 189
 
7.5%
1 176
 
7.0%
7 156
 
6.2%
Dash Punctuation
ValueCountFrequency (%)
- 446
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2960
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 490
16.6%
2 446
15.1%
- 446
15.1%
3 249
8.4%
8 217
7.3%
6 200
6.8%
9 197
6.7%
4 194
 
6.6%
5 189
 
6.4%
1 176
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2960
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 490
16.6%
2 446
15.1%
- 446
15.1%
3 249
8.4%
8 217
7.3%
6 200
6.8%
9 197
6.7%
4 194
 
6.6%
5 189
 
6.4%
1 176
 
5.9%

우편번호
Text

Distinct211
Distinct (%)80.2%
Missing0
Missing (%)0.0%
Memory size2.2 KiB
2024-05-11T09:24:04.650044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length5.0722433
Min length5

Characters and Unicode

Total characters1334
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique173 ?
Unique (%)65.8%

Sample

1st row01623
2nd row01747
3rd row10040
4th row156841
5th row137180
ValueCountFrequency (%)
06378 4
 
1.5%
03428 4
 
1.5%
06024 4
 
1.5%
03650 3
 
1.1%
04723 3
 
1.1%
03451 3
 
1.1%
01090 3
 
1.1%
05226 3
 
1.1%
07724 3
 
1.1%
01747 3
 
1.1%
Other values (201) 230
87.5%
2024-05-11T09:24:06.142364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 338
25.3%
7 146
10.9%
3 141
10.6%
1 137
10.3%
2 118
 
8.8%
6 113
 
8.5%
5 106
 
7.9%
8 97
 
7.3%
4 83
 
6.2%
9 54
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1333
99.9%
Dash Punctuation 1
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 338
25.4%
7 146
11.0%
3 141
10.6%
1 137
10.3%
2 118
 
8.9%
6 113
 
8.5%
5 106
 
8.0%
8 97
 
7.3%
4 83
 
6.2%
9 54
 
4.1%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1334
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 338
25.3%
7 146
10.9%
3 141
10.6%
1 137
10.3%
2 118
 
8.8%
6 113
 
8.5%
5 106
 
7.9%
8 97
 
7.3%
4 83
 
6.2%
9 54
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1334
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 338
25.3%
7 146
10.9%
3 141
10.6%
1 137
10.3%
2 118
 
8.8%
6 113
 
8.5%
5 106
 
7.9%
8 97
 
7.3%
4 83
 
6.2%
9 54
 
4.0%

Interactions

2024-05-11T09:23:42.795089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:23:41.208559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:23:42.021006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:23:43.067419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:23:41.489522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:23:42.287006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:23:43.344108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:23:41.736742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:23:42.532441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-11T09:24:06.482217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시설종류명(시설유형)시군구코드시군구명정원(수용인원)현인원
시설종류명(시설유형)1.0000.2000.3100.7150.667
시군구코드0.2001.0001.0000.4020.207
시군구명0.3101.0001.0000.3110.319
정원(수용인원)0.7150.4020.3111.0000.926
현인원0.6670.2070.3190.9261.000
2024-05-11T09:24:06.877786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군구명시설종류명(시설유형)
시군구명1.0000.137
시설종류명(시설유형)0.1371.000
2024-05-11T09:24:07.233033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군구코드정원(수용인원)현인원시설종류명(시설유형)시군구명
시군구코드1.000-0.072-0.0390.0870.968
정원(수용인원)-0.0721.0000.9170.4500.119
현인원-0.0390.9171.0000.5080.135
시설종류명(시설유형)0.0870.4500.5081.0000.137
시군구명0.9680.1190.1350.1371.000

Missing values

2024-05-11T09:23:43.747851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-11T09:23:44.484822image/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.
2024-05-11T09:23:44.886504image/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
0대린원C0001(장애인) 장애유형별거주시설장애인거주시설자치구임은희1135000000노원구서울특별시 노원구 동일로248길 30(상계1동)543102-939-229801623
1동천의집C0003(장애인) 장애유형별거주시설장애인거주시설자치구김영문1135000000노원구서울특별시 노원구 노원로 18길 41 (하계동 288-1)(하계1동)474502-974-957701747
2누림홈C0005(장애인) 장애유형별거주시설장애인거주시설자치구김재훈1147000000양천구경기도 김포시 대곶면 율생중앙로 837058031-987-932410040
3삼성농아원C0012(장애인) 장애유형별거주시설장애인거주시설자치구엄종숙1159000000동작구서울특별시 동작구 양녕로30길 19-4(동작구)622802-823-2234156841
4다니엘복지원C0014(장애인) 장애유형별거주시설장애인거주시설자치구지승현1165000000서초구서울특별시 서초구 헌릉로468길 21-16807002-445-4892137180
5늘편한집C0029(장애인) 중증장애인거주시설장애인거주시설자치구안성균1135000000노원구서울특별시 노원구 중계로 163(중계본동)453902-933-522801736
6임마누엘집C0030(장애인) 장애유형별거주시설장애인거주시설자치구김경식1171000000송파구서울특별시 송파구 양산로10길 26555102-449-695605773
7아름해든집C0054(장애인) 중증장애인거주시설장애인거주시설자치구강순옥1174000000강동구서울특별시 강동구 고덕로 295-45(고덕동,나동)393502-3427-087005225
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시설명시설코드시설종류명(시설유형)시설종류상세명(시설종류)자치구(시)구분시설장명시군구코드시군구명시설주소정원(수용인원)현인원전화번호우편번호
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257성북피해장애인쉼터C9848(장애인) 피해장애인쉼터장애인거주시설자치구김정호1129000000성북구<NA>8<NA>026959810002793
258행복이가득한집2호F04657(장애인) 장애인공동생활가정장애인거주시설자치구김세룡1100000000서울특별시서울특별시 강서구 월정로18길 20401 (화곡동)(화곡동)<NA><NA>02-2236-908307775
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