Overview

Dataset statistics

Number of variables13
Number of observations1245
Missing cells1352
Missing cells (%)8.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory130.2 KiB
Average record size in memory107.1 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-20431/S/1/datasetView.do

Alerts

시설종류명(시설유형) has constant value ""Constant
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)High correlation
시군구명 is highly overall correlated with 시군구코드High correlation
정원(수용인원) has 609 (48.9%) missing valuesMissing
현인원 has 739 (59.4%) missing valuesMissing
시설코드 has unique valuesUnique
정원(수용인원) has 59 (4.7%) zerosZeros
현인원 has 58 (4.7%) zerosZeros

Reproduction

Analysis started2024-05-11 08:17:56.069089
Analysis finished2024-05-11 08:18:02.995260
Duration6.93 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시설명
Text

Distinct1200
Distinct (%)96.4%
Missing0
Missing (%)0.0%
Memory size9.9 KiB
2024-05-11T08:18:03.514773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length31
Median length27
Mean length10.677912
Min length3

Characters and Unicode

Total characters13294
Distinct characters438
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1157 ?
Unique (%)92.9%

Sample

1st row참사랑데이케어센터
2nd row우리사랑재가노인지원센터
3rd row동대문실버데이케어센터
4th row한국씨니어노인복지센터
5th row목동종합사회복지관병설목동노인복지센터
ValueCountFrequency (%)
재가복지센터 36
 
2.2%
방문요양센터 26
 
1.6%
데이케어센터 22
 
1.4%
재가노인복지센터 22
 
1.4%
방문요양 15
 
0.9%
a)비지팅엔젤스 13
 
0.8%
아리아케어 11
 
0.7%
병설 10
 
0.6%
a 10
 
0.6%
재단법인 7
 
0.4%
Other values (1302) 1452
89.4%
2024-05-11T08:18:05.262506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
센 1163
 
8.7%
터 1159
 
8.7%
지 555
 
4.2%
어 522
 
3.9%
복 515
 
3.9%
가 508
 
3.8%
케 472
 
3.6%
재 455
 
3.4%
이 444
 
3.3%
데 393
 
3.0%
Other values (428) 7108
53.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 12526
94.2%
Space Separator 382
 
2.9%
Close Punctuation 80
 
0.6%
Open Punctuation 79
 
0.6%
Uppercase Letter 68
 
0.5%
Decimal Number 64
 
0.5%
Other Punctuation 51
 
0.4%
Lowercase Letter 27
 
0.2%
Math Symbol 15
 
0.1%
Modifier Symbol 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
센 1163
 
9.3%
터 1159
 
9.3%
지 555
 
4.4%
어 522
 
4.2%
복 515
 
4.1%
가 508
 
4.1%
케 472
 
3.8%
재 455
 
3.6%
이 444
 
3.5%
데 393
 
3.1%
Other values (385) 6340
50.6%
Uppercase Letter
ValueCountFrequency (%)
A 46
67.6%
B 4
 
5.9%
K 3
 
4.4%
T 3
 
4.4%
C 3
 
4.4%
H 2
 
2.9%
S 1
 
1.5%
O 1
 
1.5%
Y 1
 
1.5%
G 1
 
1.5%
Other values (3) 3
 
4.4%
Decimal Number
ValueCountFrequency (%)
1 17
26.6%
2 9
14.1%
0 9
14.1%
5 7
10.9%
9 6
 
9.4%
3 6
 
9.4%
8 4
 
6.2%
6 3
 
4.7%
4 2
 
3.1%
7 1
 
1.6%
Other Punctuation
ValueCountFrequency (%)
' 36
70.6%
; 4
 
7.8%
& 4
 
7.8%
? 2
 
3.9%
. 2
 
3.9%
/ 1
 
2.0%
, 1
 
2.0%
* 1
 
2.0%
Lowercase Letter
ValueCountFrequency (%)
e 6
22.2%
a 5
18.5%
s 5
18.5%
o 4
14.8%
p 4
14.8%
h 2
 
7.4%
r 1
 
3.7%
Space Separator
ValueCountFrequency (%)
382
100.0%
Close Punctuation
ValueCountFrequency (%)
) 80
100.0%
Open Punctuation
ValueCountFrequency (%)
( 79
100.0%
Math Symbol
ValueCountFrequency (%)
+ 15
100.0%
Modifier Symbol
ValueCountFrequency (%)
` 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 12526
94.2%
Common 673
 
5.1%
Latin 95
 
0.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
센 1163
 
9.3%
터 1159
 
9.3%
지 555
 
4.4%
어 522
 
4.2%
복 515
 
4.1%
가 508
 
4.1%
케 472
 
3.8%
재 455
 
3.6%
이 444
 
3.5%
데 393
 
3.1%
Other values (385) 6340
50.6%
Common
ValueCountFrequency (%)
382
56.8%
) 80
 
11.9%
( 79
 
11.7%
' 36
 
5.3%
1 17
 
2.5%
+ 15
 
2.2%
2 9
 
1.3%
0 9
 
1.3%
5 7
 
1.0%
9 6
 
0.9%
Other values (13) 33
 
4.9%
Latin
ValueCountFrequency (%)
A 46
48.4%
e 6
 
6.3%
a 5
 
5.3%
s 5
 
5.3%
o 4
 
4.2%
p 4
 
4.2%
B 4
 
4.2%
K 3
 
3.2%
T 3
 
3.2%
C 3
 
3.2%
Other values (10) 12
 
12.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 12526
94.2%
ASCII 768
 
5.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
센 1163
 
9.3%
터 1159
 
9.3%
지 555
 
4.4%
어 522
 
4.2%
복 515
 
4.1%
가 508
 
4.1%
케 472
 
3.8%
재 455
 
3.6%
이 444
 
3.5%
데 393
 
3.1%
Other values (385) 6340
50.6%
ASCII
ValueCountFrequency (%)
382
49.7%
) 80
 
10.4%
( 79
 
10.3%
A 46
 
6.0%
' 36
 
4.7%
1 17
 
2.2%
+ 15
 
2.0%
2 9
 
1.2%
0 9
 
1.2%
5 7
 
0.9%
Other values (33) 88
 
11.5%

시설코드
Text

UNIQUE 

Distinct1245
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size9.9 KiB
2024-05-11T08:18:06.337892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length5.0160643
Min length5

Characters and Unicode

Total characters6245
Distinct characters19
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

Unique1245 ?
Unique (%)100.0%

Sample

1st rowA0166
2nd rowA0564
3rd rowA0638
4th rowA0681
5th rowA0707
ValueCountFrequency (%)
a0166 1
 
0.1%
j1827 1
 
0.1%
j7699 1
 
0.1%
j7590 1
 
0.1%
j7410 1
 
0.1%
j7329 1
 
0.1%
j7204 1
 
0.1%
j6647 1
 
0.1%
j6600 1
 
0.1%
j5143 1
 
0.1%
Other values (1235) 1235
99.2%
2024-05-11T08:18:07.828549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
G 614
9.8%
0 575
9.2%
1 552
8.8%
3 534
8.6%
7 520
8.3%
4 495
7.9%
2 494
7.9%
8 486
7.8%
9 454
7.3%
5 454
7.3%
Other values (9) 1067
17.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5000
80.1%
Uppercase Letter 1245
 
19.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 575
11.5%
1 552
11.0%
3 534
10.7%
7 520
10.4%
4 495
9.9%
2 494
9.9%
8 486
9.7%
9 454
9.1%
5 454
9.1%
6 436
8.7%
Uppercase Letter
ValueCountFrequency (%)
G 614
49.3%
P 387
31.1%
A 180
 
14.5%
J 29
 
2.3%
F 20
 
1.6%
M 10
 
0.8%
I 2
 
0.2%
Z 2
 
0.2%
X 1
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 5000
80.1%
Latin 1245
 
19.9%

Most frequent character per script

Common
ValueCountFrequency (%)
0 575
11.5%
1 552
11.0%
3 534
10.7%
7 520
10.4%
4 495
9.9%
2 494
9.9%
8 486
9.7%
9 454
9.1%
5 454
9.1%
6 436
8.7%
Latin
ValueCountFrequency (%)
G 614
49.3%
P 387
31.1%
A 180
 
14.5%
J 29
 
2.3%
F 20
 
1.6%
M 10
 
0.8%
I 2
 
0.2%
Z 2
 
0.2%
X 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6245
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
G 614
9.8%
0 575
9.2%
1 552
8.8%
3 534
8.6%
7 520
8.3%
4 495
7.9%
2 494
7.9%
8 486
7.8%
9 454
7.3%
5 454
7.3%
Other values (9) 1067
17.1%

시설종류명(시설유형)
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size9.9 KiB
(노인) 재가노인복지시설
1245 

Length

Max length13
Median length13
Mean length13
Min length13

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
(노인) 재가노인복지시설 1245
100.0%

Length

2024-05-11T08:18:08.321294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T08:18:08.743886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
노인 1245
50.0%
재가노인복지시설 1245
50.0%

시설종류상세명(시설종류)
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size9.9 KiB
재가노인복지시설
1245 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
재가노인복지시설 1245
100.0%

Length

2024-05-11T08:18:09.077099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T08:18:09.386745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
재가노인복지시설 1245
100.0%

자치구(시)구분
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size9.9 KiB
자치구
1245 

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 1245
100.0%

Length

2024-05-11T08:18:09.677273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T08:18:09.947892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
자치구 1245
100.0%

시설장명
Text

Distinct1146
Distinct (%)92.2%
Missing2
Missing (%)0.2%
Memory size9.9 KiB
2024-05-11T08:18:10.754787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length2.9919549
Min length2

Characters and Unicode

Total characters3719
Distinct characters199
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

Unique1067 ?
Unique (%)85.8%

Sample

1st row최신영
2nd row정춘선
3rd row이동수
4th row반명규
5th row유영덕
ValueCountFrequency (%)
김영숙 5
 
0.4%
김진희 5
 
0.4%
김지연 4
 
0.3%
김정희 4
 
0.3%
이지은 3
 
0.2%
정영숙 3
 
0.2%
황연화 3
 
0.2%
박지은 3
 
0.2%
장진숙 3
 
0.2%
이현정 3
 
0.2%
Other values (1136) 1207
97.1%
2024-05-11T08:18:12.163088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
김 259
 
7.0%
이 196
 
5.3%
정 162
 
4.4%
영 132
 
3.5%
박 106
 
2.9%
희 105
 
2.8%
숙 92
 
2.5%
미 89
 
2.4%
진 77
 
2.1%
경 71
 
1.9%
Other values (189) 2430
65.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3719
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
김 259
 
7.0%
이 196
 
5.3%
정 162
 
4.4%
영 132
 
3.5%
박 106
 
2.9%
희 105
 
2.8%
숙 92
 
2.5%
미 89
 
2.4%
진 77
 
2.1%
경 71
 
1.9%
Other values (189) 2430
65.3%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3719
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
김 259
 
7.0%
이 196
 
5.3%
정 162
 
4.4%
영 132
 
3.5%
박 106
 
2.9%
희 105
 
2.8%
숙 92
 
2.5%
미 89
 
2.4%
진 77
 
2.1%
경 71
 
1.9%
Other values (189) 2430
65.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3719
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
김 259
 
7.0%
이 196
 
5.3%
정 162
 
4.4%
영 132
 
3.5%
박 106
 
2.9%
희 105
 
2.8%
숙 92
 
2.5%
미 89
 
2.4%
진 77
 
2.1%
경 71
 
1.9%
Other values (189) 2430
65.3%

시군구코드
Real number (ℝ)

HIGH CORRELATION 

Distinct26
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1432747 × 109
Minimum1.1 × 109
Maximum1.174 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.1 KiB
2024-05-11T08:18:12.735519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.1 × 109
5-th percentile1.12 × 109
Q11.129 × 109
median1.141 × 109
Q31.156 × 109
95-th percentile1.171 × 109
Maximum1.174 × 109
Range74000000
Interquartile range (IQR)27000000

Descriptive statistics

Standard deviation17117444
Coefficient of variation (CV)0.014972293
Kurtosis-1.0192736
Mean1.1432747 × 109
Median Absolute Deviation (MAD)13500000
Skewness0.17412507
Sum1.423377 × 1012
Variance2.9300688 × 1014
MonotonicityNot monotonic
2024-05-11T08:18:13.460711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
1126000000 88
 
7.1%
1135000000 87
 
7.0%
1138000000 79
 
6.3%
1171000000 78
 
6.3%
1150000000 74
 
5.9%
1123000000 64
 
5.1%
1147000000 61
 
4.9%
1130500000 59
 
4.7%
1132000000 59
 
4.7%
1162000000 54
 
4.3%
Other values (16) 542
43.5%
ValueCountFrequency (%)
1100000000 3
 
0.2%
1111000000 16
 
1.3%
1114000000 19
 
1.5%
1117000000 13
 
1.0%
1120000000 32
 
2.6%
1121500000 34
 
2.7%
1123000000 64
5.1%
1126000000 88
7.1%
1129000000 49
3.9%
1130500000 59
4.7%
ValueCountFrequency (%)
1174000000 47
3.8%
1171000000 78
6.3%
1168000000 42
3.4%
1165000000 31
 
2.5%
1162000000 54
4.3%
1159000000 52
4.2%
1156000000 45
3.6%
1154500000 25
 
2.0%
1153000000 46
3.7%
1150000000 74
5.9%

시군구명
Categorical

HIGH CORRELATION 

Distinct26
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size9.9 KiB
중랑구
88 
노원구
87 
은평구
 
79
송파구
 
78
강서구
 
74
Other values (21)
839 

Length

Max length5
Median length3
Mean length3.113253
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row송파구
2nd row동대문구
3rd row서울특별시
4th row동작구
5th row양천구

Common Values

ValueCountFrequency (%)
중랑구 88
 
7.1%
노원구 87
 
7.0%
은평구 79
 
6.3%
송파구 78
 
6.3%
강서구 74
 
5.9%
동대문구 64
 
5.1%
양천구 61
 
4.9%
강북구 59
 
4.7%
도봉구 59
 
4.7%
관악구 54
 
4.3%
Other values (16) 542
43.5%

Length

2024-05-11T08:18:14.055534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
중랑구 88
 
7.1%
노원구 87
 
7.0%
은평구 79
 
6.3%
송파구 78
 
6.3%
강서구 74
 
5.9%
동대문구 64
 
5.1%
양천구 61
 
4.9%
강북구 59
 
4.7%
도봉구 59
 
4.7%
관악구 54
 
4.3%
Other values (16) 542
43.5%

시설주소
Text

Distinct1227
Distinct (%)98.6%
Missing1
Missing (%)0.1%
Memory size9.9 KiB
2024-05-11T08:18:14.997405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length63
Median length51
Mean length31.201768
Min length16

Characters and Unicode

Total characters38815
Distinct characters401
Distinct categories11 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1211 ?
Unique (%)97.3%

Sample

1st row서울특별시 송파구 마천로 89
2nd row서울특별시 동대문구 휘경로12길 83(휘경동)
3rd row서울특별시 동대문구 약령시로5길 223층 동대문실버데이케어센터
4th row서울특별시 동작구 상도로30길 82층
5th row서울특별시 양천구 목동중앙북로8길 104목동종합사회복지관 2층
ValueCountFrequency (%)
서울특별시 1243
 
16.7%
2층 195
 
2.6%
1층 138
 
1.9%
3층 118
 
1.6%
중랑구 87
 
1.2%
노원구 87
 
1.2%
4층 82
 
1.1%
송파구 78
 
1.0%
은평구 78
 
1.0%
강서구 74
 
1.0%
Other values (2290) 5268
70.7%
2024-05-11T08:18:16.729067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
6212
 
16.0%
동 1571
 
4.0%
서 1471
 
3.8%
1 1422
 
3.7%
구 1345
 
3.5%
시 1283
 
3.3%
로 1281
 
3.3%
울 1249
 
3.2%
별 1246
 
3.2%
특 1244
 
3.2%
Other values (391) 20491
52.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 22115
57.0%
Decimal Number 6567
 
16.9%
Space Separator 6212
 
16.0%
Other Punctuation 1213
 
3.1%
Close Punctuation 1174
 
3.0%
Open Punctuation 1174
 
3.0%
Dash Punctuation 248
 
0.6%
Uppercase Letter 59
 
0.2%
Math Symbol 28
 
0.1%
Lowercase Letter 24
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 1571
 
7.1%
서 1471
 
6.7%
구 1345
 
6.1%
시 1283
 
5.8%
로 1281
 
5.8%
울 1249
 
5.6%
별 1246
 
5.6%
특 1244
 
5.6%
층 794
 
3.6%
길 659
 
3.0%
Other values (346) 9972
45.1%
Uppercase Letter
ValueCountFrequency (%)
B 37
62.7%
A 6
 
10.2%
I 4
 
6.8%
M 2
 
3.4%
E 2
 
3.4%
D 1
 
1.7%
K 1
 
1.7%
R 1
 
1.7%
P 1
 
1.7%
S 1
 
1.7%
Other values (3) 3
 
5.1%
Lowercase Letter
ValueCountFrequency (%)
s 4
16.7%
e 4
16.7%
l 2
8.3%
t 2
8.3%
a 2
8.3%
h 2
8.3%
p 2
8.3%
y 1
 
4.2%
u 1
 
4.2%
o 1
 
4.2%
Other values (3) 3
12.5%
Decimal Number
ValueCountFrequency (%)
1 1422
21.7%
2 1153
17.6%
3 840
12.8%
4 644
9.8%
0 634
9.7%
5 503
 
7.7%
6 403
 
6.1%
7 330
 
5.0%
9 327
 
5.0%
8 311
 
4.7%
Other Punctuation
ValueCountFrequency (%)
, 1211
99.8%
. 1
 
0.1%
@ 1
 
0.1%
Space Separator
ValueCountFrequency (%)
6212
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1174
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1174
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 248
100.0%
Math Symbol
ValueCountFrequency (%)
~ 28
100.0%
Letter Number
ValueCountFrequency (%)
Ⅱ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 22115
57.0%
Common 16616
42.8%
Latin 84
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 1571
 
7.1%
서 1471
 
6.7%
구 1345
 
6.1%
시 1283
 
5.8%
로 1281
 
5.8%
울 1249
 
5.6%
별 1246
 
5.6%
특 1244
 
5.6%
층 794
 
3.6%
길 659
 
3.0%
Other values (346) 9972
45.1%
Latin
ValueCountFrequency (%)
B 37
44.0%
A 6
 
7.1%
I 4
 
4.8%
s 4
 
4.8%
e 4
 
4.8%
M 2
 
2.4%
l 2
 
2.4%
t 2
 
2.4%
a 2
 
2.4%
h 2
 
2.4%
Other values (17) 19
22.6%
Common
ValueCountFrequency (%)
6212
37.4%
1 1422
 
8.6%
, 1211
 
7.3%
) 1174
 
7.1%
( 1174
 
7.1%
2 1153
 
6.9%
3 840
 
5.1%
4 644
 
3.9%
0 634
 
3.8%
5 503
 
3.0%
Other values (8) 1649
 
9.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 22115
57.0%
ASCII 16699
43.0%
Number Forms 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
6212
37.2%
1 1422
 
8.5%
, 1211
 
7.3%
) 1174
 
7.0%
( 1174
 
7.0%
2 1153
 
6.9%
3 840
 
5.0%
4 644
 
3.9%
0 634
 
3.8%
5 503
 
3.0%
Other values (34) 1732
 
10.4%
Hangul
ValueCountFrequency (%)
동 1571
 
7.1%
서 1471
 
6.7%
구 1345
 
6.1%
시 1283
 
5.8%
로 1281
 
5.8%
울 1249
 
5.6%
별 1246
 
5.6%
특 1244
 
5.6%
층 794
 
3.6%
길 659
 
3.0%
Other values (346) 9972
45.1%
Number Forms
ValueCountFrequency (%)
Ⅱ 1
100.0%

정원(수용인원)
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct81
Distinct (%)12.7%
Missing609
Missing (%)48.9%
Infinite0
Infinite (%)0.0%
Mean33.149371
Minimum0
Maximum1374
Zeros59
Zeros (%)4.7%
Negative0
Negative (%)0.0%
Memory size11.1 KiB
2024-05-11T08:18:17.181623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q117
median24
Q335
95-th percentile80
Maximum1374
Range1374
Interquartile range (IQR)18

Descriptive statistics

Standard deviation68.248721
Coefficient of variation (CV)2.058824
Kurtosis260.69057
Mean33.149371
Median Absolute Deviation (MAD)8
Skewness14.586186
Sum21083
Variance4657.8879
MonotonicityNot monotonic
2024-05-11T08:18:17.758801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21 68
 
5.5%
0 59
 
4.7%
20 50
 
4.0%
24 49
 
3.9%
17 34
 
2.7%
9 26
 
2.1%
28 26
 
2.1%
25 16
 
1.3%
15 15
 
1.2%
35 14
 
1.1%
Other values (71) 279
22.4%
(Missing) 609
48.9%
ValueCountFrequency (%)
0 59
4.7%
1 2
 
0.2%
2 1
 
0.1%
5 1
 
0.1%
6 4
 
0.3%
7 4
 
0.3%
8 12
 
1.0%
9 26
2.1%
10 1
 
0.1%
11 3
 
0.2%
ValueCountFrequency (%)
1374 1
 
0.1%
802 1
 
0.1%
300 3
0.2%
200 1
 
0.1%
150 1
 
0.1%
140 1
 
0.1%
134 1
 
0.1%
130 1
 
0.1%
129 1
 
0.1%
120 2
0.2%

현인원
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct76
Distinct (%)15.0%
Missing739
Missing (%)59.4%
Infinite0
Infinite (%)0.0%
Mean29.513834
Minimum0
Maximum1374
Zeros58
Zeros (%)4.7%
Negative0
Negative (%)0.0%
Memory size11.1 KiB
2024-05-11T08:18:18.333781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q113.25
median20
Q327
95-th percentile80
Maximum1374
Range1374
Interquartile range (IQR)13.75

Descriptive statistics

Standard deviation78.35798
Coefficient of variation (CV)2.6549577
Kurtosis198.76612
Mean29.513834
Median Absolute Deviation (MAD)7
Skewness13.093687
Sum14934
Variance6139.9731
MonotonicityNot monotonic
2024-05-11T08:18:18.864396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 58
 
4.7%
21 51
 
4.1%
20 46
 
3.7%
24 34
 
2.7%
17 28
 
2.2%
15 21
 
1.7%
28 16
 
1.3%
18 14
 
1.1%
8 13
 
1.0%
9 12
 
1.0%
Other values (66) 213
 
17.1%
(Missing) 739
59.4%
ValueCountFrequency (%)
0 58
4.7%
1 5
 
0.4%
2 4
 
0.3%
3 3
 
0.2%
4 3
 
0.2%
5 3
 
0.2%
6 5
 
0.4%
7 6
 
0.5%
8 13
 
1.0%
9 12
 
1.0%
ValueCountFrequency (%)
1374 1
0.1%
803 1
0.1%
671 1
0.1%
150 1
0.1%
140 1
0.1%
137 1
0.1%
134 1
0.1%
131 1
0.1%
129 1
0.1%
122 1
0.1%

전화번호
Text

Distinct1205
Distinct (%)96.9%
Missing1
Missing (%)0.1%
Memory size9.9 KiB
2024-05-11T08:18:19.706291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length10.02492
Min length8

Characters and Unicode

Total characters12471
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

Unique1167 ?
Unique (%)93.8%

Sample

1st row02-3401-5558
2nd row02-2215-8027
3rd row02-920-4547
4th row02-815-1922
5th row02-2651-0809
ValueCountFrequency (%)
024021005 3
 
0.2%
025770091 2
 
0.2%
02-000-0000 2
 
0.2%
02-962-3009 2
 
0.2%
024366900 2
 
0.2%
028671533 2
 
0.2%
028323355 2
 
0.2%
029650366 2
 
0.2%
0269540004 2
 
0.2%
02-448-8400 2
 
0.2%
Other values (1195) 1223
98.3%
2024-05-11T08:18:21.065688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 2378
19.1%
2 2220
17.8%
9 1049
8.4%
3 1016
8.1%
8 910
 
7.3%
6 880
 
7.1%
5 860
 
6.9%
1 851
 
6.8%
7 827
 
6.6%
4 783
 
6.3%
Other values (2) 697
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 11774
94.4%
Dash Punctuation 694
 
5.6%
Close Punctuation 3
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2378
20.2%
2 2220
18.9%
9 1049
8.9%
3 1016
8.6%
8 910
 
7.7%
6 880
 
7.5%
5 860
 
7.3%
1 851
 
7.2%
7 827
 
7.0%
4 783
 
6.7%
Dash Punctuation
ValueCountFrequency (%)
- 694
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 12471
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2378
19.1%
2 2220
17.8%
9 1049
8.4%
3 1016
8.1%
8 910
 
7.3%
6 880
 
7.1%
5 860
 
6.9%
1 851
 
6.8%
7 827
 
6.6%
4 783
 
6.3%
Other values (2) 697
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12471
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2378
19.1%
2 2220
17.8%
9 1049
8.4%
3 1016
8.1%
8 910
 
7.3%
6 880
 
7.1%
5 860
 
6.9%
1 851
 
6.8%
7 827
 
6.6%
4 783
 
6.3%
Other values (2) 697
 
5.6%

우편번호
Text

Distinct977
Distinct (%)78.5%
Missing0
Missing (%)0.0%
Memory size9.9 KiB
2024-05-11T08:18:22.050675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length5.053012
Min length3

Characters and Unicode

Total characters6291
Distinct characters14
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

Unique787 ?
Unique (%)63.2%

Sample

1st row05655
2nd row130876
3rd row02476
4th row06964
5th row07949
ValueCountFrequency (%)
131600 7
 
0.6%
02535 6
 
0.5%
02139 5
 
0.4%
03502 5
 
0.4%
01682 5
 
0.4%
120650 5
 
0.4%
06534 5
 
0.4%
151600 4
 
0.3%
02182 4
 
0.3%
02480 4
 
0.3%
Other values (966) 1195
96.0%
2024-05-11T08:18:23.607144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1656
26.3%
1 629
 
10.0%
3 576
 
9.2%
7 560
 
8.9%
6 546
 
8.7%
2 543
 
8.6%
5 542
 
8.6%
8 475
 
7.6%
4 467
 
7.4%
9 293
 
4.7%
Other values (4) 4
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 6287
99.9%
Other Letter 3
 
< 0.1%
Space Separator 1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1656
26.3%
1 629
 
10.0%
3 576
 
9.2%
7 560
 
8.9%
6 546
 
8.7%
2 543
 
8.6%
5 542
 
8.6%
8 475
 
7.6%
4 467
 
7.4%
9 293
 
4.7%
Other Letter
ValueCountFrequency (%)
서 1
33.3%
울 1
33.3%
특 1
33.3%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 6288
> 99.9%
Hangul 3
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1656
26.3%
1 629
 
10.0%
3 576
 
9.2%
7 560
 
8.9%
6 546
 
8.7%
2 543
 
8.6%
5 542
 
8.6%
8 475
 
7.6%
4 467
 
7.4%
9 293
 
4.7%
Hangul
ValueCountFrequency (%)
서 1
33.3%
울 1
33.3%
특 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6288
> 99.9%
Hangul 3
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1656
26.3%
1 629
 
10.0%
3 576
 
9.2%
7 560
 
8.9%
6 546
 
8.7%
2 543
 
8.6%
5 542
 
8.6%
8 475
 
7.6%
4 467
 
7.4%
9 293
 
4.7%
Hangul
ValueCountFrequency (%)
서 1
33.3%
울 1
33.3%
특 1
33.3%

Interactions

2024-05-11T08:18:00.150560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T08:17:57.859082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T08:17:58.835723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T08:18:00.542016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T08:17:58.186075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T08:17:59.193018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T08:18:00.952396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T08:17:58.523611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T08:17:59.650806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-11T08:18:24.026665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군구코드시군구명정원(수용인원)현인원
시군구코드1.0001.0000.1440.000
시군구명1.0001.0000.0000.000
정원(수용인원)0.1440.0001.0000.998
현인원0.0000.0000.9981.000
2024-05-11T08:18:24.397036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군구코드정원(수용인원)현인원시군구명
시군구코드1.0000.0430.1530.994
정원(수용인원)0.0431.0000.6660.000
현인원0.1530.6661.0000.000
시군구명0.9940.0000.0001.000

Missing values

2024-05-11T08:18:01.442724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-11T08:18:02.172980image/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.
2024-05-11T08:18:02.748291image/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
0참사랑데이케어센터A0166(노인) 재가노인복지시설재가노인복지시설자치구최신영1171000000송파구서울특별시 송파구 마천로 89171602-3401-555805655
1우리사랑재가노인지원센터A0564(노인) 재가노인복지시설재가노인복지시설자치구정춘선1123000000동대문구서울특별시 동대문구 휘경로12길 83(휘경동)403002-2215-8027130876
2동대문실버데이케어센터A0638(노인) 재가노인복지시설재가노인복지시설자치구이동수1100000000서울특별시서울특별시 동대문구 약령시로5길 223층 동대문실버데이케어센터232302-920-454702476
3한국씨니어노인복지센터A0681(노인) 재가노인복지시설재가노인복지시설자치구반명규1159000000동작구서울특별시 동작구 상도로30길 82층0002-815-192206964
4목동종합사회복지관병설목동노인복지센터A0707(노인) 재가노인복지시설재가노인복지시설자치구유영덕1147000000양천구서울특별시 양천구 목동중앙북로8길 104목동종합사회복지관 2층242402-2651-080907949
5효림재가노인지원센터A0738(노인) 재가노인복지시설재가노인복지시설자치구김동숙1141000000서대문구서울특별시 서대문구 경기대로9길 62-01-38<NA>67102-313-512403746
6용산재가노인지원센터A0773(노인) 재가노인복지시설재가노인복지시설자치구권용자1117000000용산구서울특별시 용산구 한강대로 43길 13 대우아이빌 712호(한강로동)13413402-792-788204376
7길음노인복지센터A0789(노인) 재가노인복지시설재가노인복지시설자치구장민균1129000000성북구서울특별시 성북구 삼양로2길 55 (길음동)171702-989-016102732
8남산실버복지센터A0839(노인) 재가노인복지시설재가노인복지시설자치구박창남1114000000중구서울특별시 중구 동호로5길 189 (신당동)343402-2238-994104595
9행복창조노인복지센터(방문요양)A0877(노인) 재가노인복지시설재가노인복지시설자치구김현훈1138000000은평구서울특별시 은평구 응암로21길 10(응암동)202302-382-144203456
시설명시설코드시설종류명(시설유형)시설종류상세명(시설종류)자치구(시)구분시설장명시군구코드시군구명시설주소정원(수용인원)현인원전화번호우편번호
1235조양방문요양센터P3997(노인) 재가노인복지시설재가노인복지시설자치구김영숙1120000000성동구서울특별시 성동구 성덕정길 102, 2층 3호 (성수동2가)<NA><NA>02464330104776
1236효케어방문요양재가센터P4001(노인) 재가노인복지시설재가노인복지시설자치구김민지1171000000송파구서울특별시 송파구 중대로 24, 제4동 209호 (문정동, 올림픽훼미리타운상가)<NA><NA>026369727705834
1237브라보 시니어케어P4007(노인) 재가노인복지시설재가노인복지시설자치구김춘추1168000000강남구서울특별시 강남구 테헤란로25길 20, 8층 819호 (역삼동)<NA><NA>0704060519606132
1238더케어방문요양센터P4011(노인) 재가노인복지시설재가노인복지시설자치구최태련1168000000강남구서울특별시 강남구 테헤란로 151, 8층 816호 (역삼동)<NA><NA>02539222706132
1239허니재가방문요양센터P4017(노인) 재가노인복지시설재가노인복지시설자치구송윤경1168000000강남구서울특별시 강남구 개포로642, 3층 303호 (일원동)<NA><NA>02451487606339
1240송파메디재가복지센터P4024(노인) 재가노인복지시설재가노인복지시설자치구노진아1171000000송파구서울특별시 송파구 문정로 195(오금동) 상가 107호<NA><NA>02443888005742
1241스마일시니어 잠실송파 방문요양센터P4060(노인) 재가노인복지시설재가노인복지시설자치구송승태1171000000송파구서울특별시 송파구 오금로 87, 1916호 (방이동, 잠실리시온)<NA><NA>026953566705542
1242사단법인나눔과보람복지회양천재가노인복지센터X0591(노인) 재가노인복지시설재가노인복지시설자치구임준희1147000000양천구서울특별시 양천구 목동로23길 34205호 (신정동, 양천시장주상복합)202002-2605-082507938
1243은천주간보호센터Z0144(노인) 재가노인복지시설재가노인복지시설자치구노선영1123000000동대문구서울특별시 동대문구 장한로27가길 66(장안2동)191902-2249-725302527
1244서초구립방배노인데이케어센터Z0630(노인) 재가노인복지시설재가노인복지시설자치구이후자1165000000서초구서울특별시 서초구 방배천로 48(방배동 455-11)202002-597-799206677