Overview

Dataset statistics

Number of variables13
Number of observations596
Missing cells131
Missing cells (%)1.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory63.0 KiB
Average record size in memory108.2 B

Variable types

Text5
Categorical4
Numeric4

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-20413/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 현인원 and 1 other fieldsHigh correlation
현인원 is highly overall correlated with 정원(수용인원) and 1 other fieldsHigh correlation
시설종류명(시설유형) is highly overall correlated with 정원(수용인원) and 1 other fieldsHigh correlation
시군구명 is highly overall correlated with 시군구코드High correlation
정원(수용인원) has 10 (1.7%) missing valuesMissing
현인원 has 118 (19.8%) missing valuesMissing
시설코드 has unique valuesUnique

Reproduction

Analysis started2024-05-04 02:41:39.138650
Analysis finished2024-05-04 02:41:46.503761
Duration7.37 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시설명
Text

Distinct545
Distinct (%)91.4%
Missing0
Missing (%)0.0%
Memory size4.8 KiB
2024-05-04T02:41:46.989432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length25
Median length21
Mean length7.9865772
Min length3

Characters and Unicode

Total characters4760
Distinct characters322
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique500 ?
Unique (%)83.9%

Sample

1st row서울꽃동네신내노인요양원
2nd row청운노인요양원
3rd row천사노인요양원
4th row서울특별시립 남부노인전문요양원
5th row노인요양센터 인영실버
ValueCountFrequency (%)
요양원 12
 
1.8%
늘푸른요양원 6
 
0.9%
2호점 6
 
0.9%
a 5
 
0.7%
노인요양공동생활가정 5
 
0.7%
신림요양원 3
 
0.4%
예원요양원 3
 
0.4%
가족사랑요양원 3
 
0.4%
사랑과섬김요양원 3
 
0.4%
난향요양원2 3
 
0.4%
Other values (564) 618
92.7%
2024-05-04T02:41:48.309769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
양 468
 
9.8%
요 465
 
9.8%
원 418
 
8.8%
노 149
 
3.1%
인 136
 
2.9%
센 110
 
2.3%
터 109
 
2.3%
버 92
 
1.9%
실 87
 
1.8%
72
 
1.5%
Other values (312) 2654
55.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4490
94.3%
Decimal Number 131
 
2.8%
Space Separator 72
 
1.5%
Uppercase Letter 25
 
0.5%
Close Punctuation 14
 
0.3%
Open Punctuation 14
 
0.3%
Lowercase Letter 8
 
0.2%
Other Punctuation 4
 
0.1%
Letter Number 1
 
< 0.1%
Dash Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
양 468
 
10.4%
요 465
 
10.4%
원 418
 
9.3%
노 149
 
3.3%
인 136
 
3.0%
센 110
 
2.4%
터 109
 
2.4%
버 92
 
2.0%
실 87
 
1.9%
랑 67
 
1.5%
Other values (284) 2389
53.2%
Uppercase Letter
ValueCountFrequency (%)
A 9
36.0%
B 5
20.0%
C 3
 
12.0%
I 2
 
8.0%
K 2
 
8.0%
P 1
 
4.0%
O 1
 
4.0%
T 1
 
4.0%
D 1
 
4.0%
Decimal Number
ValueCountFrequency (%)
2 63
48.1%
1 43
32.8%
3 13
 
9.9%
0 5
 
3.8%
4 5
 
3.8%
8 2
 
1.5%
Lowercase Letter
ValueCountFrequency (%)
v 2
25.0%
e 2
25.0%
i 1
12.5%
p 1
12.5%
o 1
12.5%
l 1
12.5%
Other Punctuation
ValueCountFrequency (%)
' 3
75.0%
* 1
 
25.0%
Space Separator
ValueCountFrequency (%)
72
100.0%
Close Punctuation
ValueCountFrequency (%)
) 14
100.0%
Open Punctuation
ValueCountFrequency (%)
( 14
100.0%
Letter Number
ValueCountFrequency (%)
Ⅱ 1
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4490
94.3%
Common 236
 
5.0%
Latin 34
 
0.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
양 468
 
10.4%
요 465
 
10.4%
원 418
 
9.3%
노 149
 
3.3%
인 136
 
3.0%
센 110
 
2.4%
터 109
 
2.4%
버 92
 
2.0%
실 87
 
1.9%
랑 67
 
1.5%
Other values (284) 2389
53.2%
Latin
ValueCountFrequency (%)
A 9
26.5%
B 5
14.7%
C 3
 
8.8%
v 2
 
5.9%
I 2
 
5.9%
K 2
 
5.9%
e 2
 
5.9%
i 1
 
2.9%
Ⅱ 1
 
2.9%
p 1
 
2.9%
Other values (6) 6
17.6%
Common
ValueCountFrequency (%)
72
30.5%
2 63
26.7%
1 43
18.2%
) 14
 
5.9%
( 14
 
5.9%
3 13
 
5.5%
0 5
 
2.1%
4 5
 
2.1%
' 3
 
1.3%
8 2
 
0.8%
Other values (2) 2
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4490
94.3%
ASCII 269
 
5.7%
Number Forms 1
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
양 468
 
10.4%
요 465
 
10.4%
원 418
 
9.3%
노 149
 
3.3%
인 136
 
3.0%
센 110
 
2.4%
터 109
 
2.4%
버 92
 
2.0%
실 87
 
1.9%
랑 67
 
1.5%
Other values (284) 2389
53.2%
ASCII
ValueCountFrequency (%)
72
26.8%
2 63
23.4%
1 43
16.0%
) 14
 
5.2%
( 14
 
5.2%
3 13
 
4.8%
A 9
 
3.3%
B 5
 
1.9%
0 5
 
1.9%
4 5
 
1.9%
Other values (17) 26
 
9.7%
Number Forms
ValueCountFrequency (%)
Ⅱ 1
100.0%

시설코드
Text

UNIQUE 

Distinct596
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size4.8 KiB
2024-05-04T02:41:50.005432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length5.0184564
Min length5

Characters and Unicode

Total characters2991
Distinct characters17
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

Unique596 ?
Unique (%)100.0%

Sample

1st rowA0001
2nd rowA0003
3rd rowA0007
4th rowA0016
5th rowA0099
ValueCountFrequency (%)
a0001 1
 
0.2%
g4652 1
 
0.2%
g4983 1
 
0.2%
g4758 1
 
0.2%
g4798 1
 
0.2%
g4819 1
 
0.2%
g4842 1
 
0.2%
g4859 1
 
0.2%
g4960 1
 
0.2%
g5001 1
 
0.2%
Other values (586) 586
98.3%
2024-05-04T02:41:52.015775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 286
9.6%
G 279
9.3%
3 269
9.0%
1 262
8.8%
2 255
8.5%
4 239
8.0%
7 238
8.0%
8 235
7.9%
6 220
7.4%
5 209
7.0%
Other values (7) 499
16.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2395
80.1%
Uppercase Letter 596
 
19.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 286
11.9%
3 269
11.2%
1 262
10.9%
2 255
10.6%
4 239
10.0%
7 238
9.9%
8 235
9.8%
6 220
9.2%
5 209
8.7%
9 182
7.6%
Uppercase Letter
ValueCountFrequency (%)
G 279
46.8%
A 185
31.0%
P 50
 
8.4%
J 37
 
6.2%
M 33
 
5.5%
F 11
 
1.8%
B 1
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common 2395
80.1%
Latin 596
 
19.9%

Most frequent character per script

Common
ValueCountFrequency (%)
0 286
11.9%
3 269
11.2%
1 262
10.9%
2 255
10.6%
4 239
10.0%
7 238
9.9%
8 235
9.8%
6 220
9.2%
5 209
8.7%
9 182
7.6%
Latin
ValueCountFrequency (%)
G 279
46.8%
A 185
31.0%
P 50
 
8.4%
J 37
 
6.2%
M 33
 
5.5%
F 11
 
1.8%
B 1
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2991
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 286
9.6%
G 279
9.3%
3 269
9.0%
1 262
8.8%
2 255
8.5%
4 239
8.0%
7 238
8.0%
8 235
7.9%
6 220
7.4%
5 209
7.0%
Other values (7) 499
16.7%

시설종류명(시설유형)
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size4.8 KiB
(노인) 노인요양공동생활가정
346 
(노인) 노인요양시설
250 

Length

Max length15
Median length15
Mean length13.322148
Min length11

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
(노인) 노인요양공동생활가정 346
58.1%
(노인) 노인요양시설 250
41.9%

Length

2024-05-04T02:41:52.695887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T02:41:53.107271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
노인 596
50.0%
노인요양공동생활가정 346
29.0%
노인요양시설 250
21.0%

시설종류상세명(시설종류)
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.8 KiB
노인의료복지시설
596 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
노인의료복지시설 596
100.0%

Length

2024-05-04T02:41:53.743262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T02:41:54.129922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
노인의료복지시설 596
100.0%

자치구(시)구분
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.8 KiB
자치구
596 

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 596
100.0%

Length

2024-05-04T02:41:54.536422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T02:41:54.905584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
자치구 596
100.0%

시설장명
Text

Distinct550
Distinct (%)92.7%
Missing3
Missing (%)0.5%
Memory size4.8 KiB
2024-05-04T02:41:56.481355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.9881956
Min length2

Characters and Unicode

Total characters1772
Distinct characters181
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

Unique512 ?
Unique (%)86.3%

Sample

1st row최은숙
2nd row이종후
3rd row김샛별
4th row한철수
5th row이희법
ValueCountFrequency (%)
김현주 4
 
0.7%
박형준 3
 
0.5%
이정희 3
 
0.5%
김표남 3
 
0.5%
이지택 2
 
0.3%
이춘석 2
 
0.3%
김판석 2
 
0.3%
김미경 2
 
0.3%
이병란 2
 
0.3%
이은경 2
 
0.3%
Other values (540) 568
95.8%
2024-05-04T02:41:57.928780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
김 135
 
7.6%
이 94
 
5.3%
정 72
 
4.1%
희 63
 
3.6%
영 55
 
3.1%
박 51
 
2.9%
숙 41
 
2.3%
미 41
 
2.3%
경 38
 
2.1%
현 37
 
2.1%
Other values (171) 1145
64.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1772
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
김 135
 
7.6%
이 94
 
5.3%
정 72
 
4.1%
희 63
 
3.6%
영 55
 
3.1%
박 51
 
2.9%
숙 41
 
2.3%
미 41
 
2.3%
경 38
 
2.1%
현 37
 
2.1%
Other values (171) 1145
64.6%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1772
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
김 135
 
7.6%
이 94
 
5.3%
정 72
 
4.1%
희 63
 
3.6%
영 55
 
3.1%
박 51
 
2.9%
숙 41
 
2.3%
미 41
 
2.3%
경 38
 
2.1%
현 37
 
2.1%
Other values (171) 1145
64.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1772
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
김 135
 
7.6%
이 94
 
5.3%
정 72
 
4.1%
희 63
 
3.6%
영 55
 
3.1%
박 51
 
2.9%
숙 41
 
2.3%
미 41
 
2.3%
경 38
 
2.1%
현 37
 
2.1%
Other values (171) 1145
64.6%

시군구코드
Real number (ℝ)

HIGH CORRELATION 

Distinct26
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1419874 × 109
Minimum1.1 × 109
Maximum1.174 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.4 KiB
2024-05-04T02:41:58.323093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.1 × 109
5-th percentile1.1215 × 109
Q11.129 × 109
median1.138 × 109
Q31.1545 × 109
95-th percentile1.171 × 109
Maximum1.174 × 109
Range74000000
Interquartile range (IQR)25500000

Descriptive statistics

Standard deviation16961531
Coefficient of variation (CV)0.014852643
Kurtosis-0.95636604
Mean1.1419874 × 109
Median Absolute Deviation (MAD)12000000
Skewness0.30227512
Sum6.806245 × 1011
Variance2.8769354 × 1014
MonotonicityNot monotonic
2024-05-04T02:41:58.711929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
1126000000 57
 
9.6%
1132000000 57
 
9.6%
1162000000 43
 
7.2%
1150000000 39
 
6.5%
1130500000 34
 
5.7%
1123000000 33
 
5.5%
1135000000 32
 
5.4%
1174000000 29
 
4.9%
1154500000 27
 
4.5%
1138000000 27
 
4.5%
Other values (16) 218
36.6%
ValueCountFrequency (%)
1100000000 2
 
0.3%
1111000000 11
 
1.8%
1114000000 3
 
0.5%
1117000000 5
 
0.8%
1120000000 7
 
1.2%
1121500000 22
 
3.7%
1123000000 33
5.5%
1126000000 57
9.6%
1129000000 26
4.4%
1130500000 34
5.7%
ValueCountFrequency (%)
1174000000 29
4.9%
1171000000 24
4.0%
1168000000 12
 
2.0%
1165000000 7
 
1.2%
1162000000 43
7.2%
1159000000 12
 
2.0%
1156000000 21
3.5%
1154500000 27
4.5%
1153000000 17
 
2.9%
1150000000 39
6.5%

시군구명
Categorical

HIGH CORRELATION 

Distinct26
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Memory size4.8 KiB
중랑구
57 
도봉구
57 
관악구
43 
강서구
39 
강북구
 
34
Other values (21)
366 

Length

Max length5
Median length3
Mean length3.1241611
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row중랑구
2nd row종로구
3rd row강서구
4th row영등포구
5th row금천구

Common Values

ValueCountFrequency (%)
중랑구 57
 
9.6%
도봉구 57
 
9.6%
관악구 43
 
7.2%
강서구 39
 
6.5%
강북구 34
 
5.7%
동대문구 33
 
5.5%
노원구 32
 
5.4%
강동구 29
 
4.9%
금천구 27
 
4.5%
은평구 27
 
4.5%
Other values (16) 218
36.6%

Length

2024-05-04T02:41:59.143479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
중랑구 57
 
9.6%
도봉구 57
 
9.6%
관악구 43
 
7.2%
강서구 39
 
6.5%
강북구 34
 
5.7%
동대문구 33
 
5.5%
노원구 32
 
5.4%
강동구 29
 
4.9%
금천구 27
 
4.5%
은평구 27
 
4.5%
Other values (16) 218
36.6%

시설주소
Text

Distinct547
Distinct (%)91.8%
Missing0
Missing (%)0.0%
Memory size4.8 KiB
2024-05-04T02:41:59.980068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length53
Median length45.5
Mean length28.238255
Min length15

Characters and Unicode

Total characters16830
Distinct characters288
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

Unique501 ?
Unique (%)84.1%

Sample

1st row서울특별시 중랑구 신내로 194
2nd row서울특별시 종로구 비봉길 76 (구기동)
3rd row서울특별시 강서구 강서로45다길 30-22
4th row경기도 군포시 고산로 589
5th row서울특별시 금천구 금하로 596-0
ValueCountFrequency (%)
서울특별시 589
 
18.1%
4층 59
 
1.8%
중랑구 57
 
1.8%
도봉구 57
 
1.8%
3층 56
 
1.7%
2층 48
 
1.5%
관악구 43
 
1.3%
5층 43
 
1.3%
강서구 39
 
1.2%
신림동 37
 
1.1%
Other values (1015) 2219
68.3%
2024-05-04T02:42:01.595899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2661
 
15.8%
서 686
 
4.1%
동 663
 
3.9%
구 634
 
3.8%
시 627
 
3.7%
로 602
 
3.6%
특 590
 
3.5%
별 590
 
3.5%
울 590
 
3.5%
) 522
 
3.1%
Other values (278) 8665
51.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 9886
58.7%
Decimal Number 2700
 
16.0%
Space Separator 2661
 
15.8%
Close Punctuation 522
 
3.1%
Open Punctuation 521
 
3.1%
Other Punctuation 409
 
2.4%
Dash Punctuation 108
 
0.6%
Math Symbol 15
 
0.1%
Uppercase Letter 8
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
서 686
 
6.9%
동 663
 
6.7%
구 634
 
6.4%
시 627
 
6.3%
로 602
 
6.1%
특 590
 
6.0%
별 590
 
6.0%
울 590
 
6.0%
층 366
 
3.7%
길 251
 
2.5%
Other values (257) 4287
43.4%
Decimal Number
ValueCountFrequency (%)
1 450
16.7%
2 411
15.2%
4 342
12.7%
3 340
12.6%
5 233
8.6%
6 220
8.1%
0 210
7.8%
7 190
7.0%
8 154
 
5.7%
9 150
 
5.6%
Uppercase Letter
ValueCountFrequency (%)
B 3
37.5%
S 2
25.0%
A 2
25.0%
M 1
 
12.5%
Other Punctuation
ValueCountFrequency (%)
, 403
98.5%
. 6
 
1.5%
Space Separator
ValueCountFrequency (%)
2661
100.0%
Close Punctuation
ValueCountFrequency (%)
) 522
100.0%
Open Punctuation
ValueCountFrequency (%)
( 521
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 108
100.0%
Math Symbol
ValueCountFrequency (%)
~ 15
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 9886
58.7%
Common 6936
41.2%
Latin 8
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
서 686
 
6.9%
동 663
 
6.7%
구 634
 
6.4%
시 627
 
6.3%
로 602
 
6.1%
특 590
 
6.0%
별 590
 
6.0%
울 590
 
6.0%
층 366
 
3.7%
길 251
 
2.5%
Other values (257) 4287
43.4%
Common
ValueCountFrequency (%)
2661
38.4%
) 522
 
7.5%
( 521
 
7.5%
1 450
 
6.5%
2 411
 
5.9%
, 403
 
5.8%
4 342
 
4.9%
3 340
 
4.9%
5 233
 
3.4%
6 220
 
3.2%
Other values (7) 833
 
12.0%
Latin
ValueCountFrequency (%)
B 3
37.5%
S 2
25.0%
A 2
25.0%
M 1
 
12.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 9886
58.7%
ASCII 6944
41.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2661
38.3%
) 522
 
7.5%
( 521
 
7.5%
1 450
 
6.5%
2 411
 
5.9%
, 403
 
5.8%
4 342
 
4.9%
3 340
 
4.9%
5 233
 
3.4%
6 220
 
3.2%
Other values (11) 841
 
12.1%
Hangul
ValueCountFrequency (%)
서 686
 
6.9%
동 663
 
6.7%
구 634
 
6.4%
시 627
 
6.3%
로 602
 
6.1%
특 590
 
6.0%
별 590
 
6.0%
울 590
 
6.0%
층 366
 
3.7%
길 251
 
2.5%
Other values (257) 4287
43.4%

정원(수용인원)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct94
Distinct (%)16.0%
Missing10
Missing (%)1.7%
Infinite0
Infinite (%)0.0%
Mean30.073379
Minimum5
Maximum296
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.4 KiB
2024-05-04T02:42:02.148836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile8
Q19
median9
Q329
95-th percentile117.25
Maximum296
Range291
Interquartile range (IQR)20

Descriptive statistics

Standard deviation41.132761
Coefficient of variation (CV)1.3677466
Kurtosis10.865278
Mean30.073379
Median Absolute Deviation (MAD)0
Skewness2.9635277
Sum17623
Variance1691.904
MonotonicityNot monotonic
2024-05-04T02:42:02.653205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9 310
52.0%
29 26
 
4.4%
8 20
 
3.4%
7 11
 
1.8%
49 9
 
1.5%
25 9
 
1.5%
23 8
 
1.3%
20 8
 
1.3%
28 7
 
1.2%
45 6
 
1.0%
Other values (84) 172
28.9%
(Missing) 10
 
1.7%
ValueCountFrequency (%)
5 1
 
0.2%
6 4
 
0.7%
7 11
 
1.8%
8 20
 
3.4%
9 310
52.0%
10 2
 
0.3%
11 1
 
0.2%
13 3
 
0.5%
14 3
 
0.5%
15 4
 
0.7%
ValueCountFrequency (%)
296 1
0.2%
271 1
0.2%
270 1
0.2%
258 1
0.2%
234 1
0.2%
200 2
0.3%
190 1
0.2%
170 2
0.3%
168 2
0.3%
165 1
0.2%

현인원
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct105
Distinct (%)22.0%
Missing118
Missing (%)19.8%
Infinite0
Infinite (%)0.0%
Mean30.458159
Minimum0
Maximum296
Zeros4
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size5.4 KiB
2024-05-04T02:42:03.142467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7
Q19
median9
Q335.75
95-th percentile118
Maximum296
Range296
Interquartile range (IQR)26.75

Descriptive statistics

Standard deviation42.104558
Coefficient of variation (CV)1.3823737
Kurtosis11.688759
Mean30.458159
Median Absolute Deviation (MAD)1
Skewness3.0487133
Sum14559
Variance1772.7938
MonotonicityNot monotonic
2024-05-04T02:42:03.550633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9 215
36.1%
8 25
 
4.2%
7 18
 
3.0%
29 16
 
2.7%
21 8
 
1.3%
23 7
 
1.2%
24 6
 
1.0%
28 6
 
1.0%
20 6
 
1.0%
49 5
 
0.8%
Other values (95) 166
27.9%
(Missing) 118
19.8%
ValueCountFrequency (%)
0 4
 
0.7%
2 1
 
0.2%
3 1
 
0.2%
4 2
 
0.3%
5 3
 
0.5%
6 4
 
0.7%
7 18
 
3.0%
8 25
 
4.2%
9 215
36.1%
10 2
 
0.3%
ValueCountFrequency (%)
296 1
0.2%
284 1
0.2%
270 1
0.2%
256 1
0.2%
234 1
0.2%
200 1
0.2%
188 1
0.2%
184 1
0.2%
167 1
0.2%
165 2
0.3%

전화번호
Text

Distinct496
Distinct (%)83.2%
Missing0
Missing (%)0.0%
Memory size4.8 KiB
2024-05-04T02:42:04.226449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length10.181208
Min length9

Characters and Unicode

Total characters6068
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

Unique420 ?
Unique (%)70.5%

Sample

1st row02-490-2609
2nd row02-3217-0057
3rd row02-2602-2443
4th row031-390-1003
5th row02-804-6141
ValueCountFrequency (%)
028575882 4
 
0.7%
028666028 4
 
0.7%
024830707 4
 
0.7%
029622262 4
 
0.7%
028888833 4
 
0.7%
07081567366 4
 
0.7%
023051868 4
 
0.7%
024331221 3
 
0.5%
02-987-7600 3
 
0.5%
028564382 3
 
0.5%
Other values (486) 559
93.8%
2024-05-04T02:42:05.538299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1184
19.5%
2 1021
16.8%
- 510
8.4%
8 481
7.9%
3 467
 
7.7%
6 452
 
7.4%
9 450
 
7.4%
5 390
 
6.4%
7 388
 
6.4%
4 364
 
6.0%
Other values (2) 361
 
5.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5557
91.6%
Dash Punctuation 510
 
8.4%
Close Punctuation 1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1184
21.3%
2 1021
18.4%
8 481
8.7%
3 467
 
8.4%
6 452
 
8.1%
9 450
 
8.1%
5 390
 
7.0%
7 388
 
7.0%
4 364
 
6.6%
1 360
 
6.5%
Dash Punctuation
ValueCountFrequency (%)
- 510
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 6068
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1184
19.5%
2 1021
16.8%
- 510
8.4%
8 481
7.9%
3 467
 
7.7%
6 452
 
7.4%
9 450
 
7.4%
5 390
 
6.4%
7 388
 
6.4%
4 364
 
6.0%
Other values (2) 361
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6068
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1184
19.5%
2 1021
16.8%
- 510
8.4%
8 481
7.9%
3 467
 
7.7%
6 452
 
7.4%
9 450
 
7.4%
5 390
 
6.4%
7 388
 
6.4%
4 364
 
6.0%
Other values (2) 361
 
5.9%

우편번호
Real number (ℝ)

Distinct363
Distinct (%)60.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21982.98
Minimum1015
Maximum158856
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.4 KiB
2024-05-04T02:42:06.080389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1015
5-th percentile1227.75
Q12182
median5109
Q38232.75
95-th percentile145605.25
Maximum158856
Range157841
Interquartile range (IQR)6050.75

Descriptive statistics

Standard deviation45956.037
Coefficient of variation (CV)2.0905281
Kurtosis3.3065009
Mean21982.98
Median Absolute Deviation (MAD)2957.5
Skewness2.2797387
Sum13101856
Variance2.1119573 × 109
MonotonicityNot monotonic
2024-05-04T02:42:06.642568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
131600 12
 
2.0%
8771 9
 
1.5%
151600 9
 
1.5%
2182 8
 
1.3%
7438 8
 
1.3%
157650 7
 
1.2%
1332 6
 
1.0%
2571 6
 
1.0%
2535 5
 
0.8%
7695 5
 
0.8%
Other values (353) 521
87.4%
ValueCountFrequency (%)
1015 1
 
0.2%
1022 1
 
0.2%
1033 1
 
0.2%
1034 1
 
0.2%
1046 4
0.7%
1048 1
 
0.2%
1055 2
0.3%
1070 2
0.3%
1081 2
0.3%
1082 1
 
0.2%
ValueCountFrequency (%)
158856 1
 
0.2%
158600 3
0.5%
157650 7
1.2%
156844 1
 
0.2%
156830 1
 
0.2%
156600 2
 
0.3%
153858 1
 
0.2%
153600 3
0.5%
151906 1
 
0.2%
151829 1
 
0.2%

Interactions

2024-05-04T02:41:44.107828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:41:40.750699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:41:41.913063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:41:43.038274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:41:44.406564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:41:41.051768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:41:42.208256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:41:43.312300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:41:44.696423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:41:41.326334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:41:42.481222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:41:43.581891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:41:44.975601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:41:41.598371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:41:42.749285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:41:43.834084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-04T02:42:07.042502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시설종류명(시설유형)시군구코드시군구명정원(수용인원)현인원우편번호
시설종류명(시설유형)1.0000.1770.4030.8410.8470.068
시군구코드0.1771.0001.0000.2180.1550.397
시군구명0.4031.0001.0000.5030.4770.497
정원(수용인원)0.8410.2180.5031.0000.9960.000
현인원0.8470.1550.4770.9961.0000.117
우편번호0.0680.3970.4970.0000.1171.000
2024-05-04T02:42:07.428293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시설종류명(시설유형)시군구명
시설종류명(시설유형)1.0000.313
시군구명0.3131.000
2024-05-04T02:42:07.740433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군구코드정원(수용인원)현인원우편번호시설종류명(시설유형)시군구명
시군구코드1.0000.0040.0350.4930.1830.986
정원(수용인원)0.0041.0000.9590.0230.6690.199
현인원0.0350.9591.0000.0120.6740.185
우편번호0.4930.0230.0121.0000.0830.259
시설종류명(시설유형)0.1830.6690.6740.0831.0000.313
시군구명0.9860.1990.1850.2590.3131.000

Missing values

2024-05-04T02:41:45.399128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-04T02:41:45.968979image/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.
2024-05-04T02:41:46.348566image/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
0서울꽃동네신내노인요양원A0001(노인) 노인요양시설노인의료복지시설자치구최은숙1126000000중랑구서울특별시 중랑구 신내로 19423423402-490-26092052
1청운노인요양원A0003(노인) 노인요양시설노인의료복지시설자치구이종후1111000000종로구서울특별시 종로구 비봉길 76 (구기동)454502-3217-00573001
2천사노인요양원A0007(노인) 노인요양시설노인의료복지시설자치구김샛별1150000000강서구서울특별시 강서구 강서로45다길 30-2216115102-2602-24437704
3서울특별시립 남부노인전문요양원A0016(노인) 노인요양시설노인의료복지시설자치구한철수1156000000영등포구경기도 군포시 고산로 589190188031-390-100315820
4노인요양센터 인영실버A0099(노인) 노인요양시설노인의료복지시설자치구이희법1154500000금천구서울특별시 금천구 금하로 596-0968402-804-61418632
5동명노인복지센타A0100(노인) 노인요양시설노인의료복지시설자치구김병한1162000000관악구서울특별시 관악구 봉천로23라길 15(봉천동)909002-875-2770151829
6영기노인요양원A0131(노인) 노인요양시설노인의료복지시설자치구김원제1135000000노원구서울특별시 노원구 동일로248길 30 (상계동)838302-939-81111623
7은천노인요양센터A0343(노인) 노인요양시설노인의료복지시설자치구박애자1123000000동대문구서울특별시 동대문구 장한로27가길 66 (장안동)282902-2249-99802527
8동작실버센터A0347(노인) 노인요양시설노인의료복지시설자치구이재희1159000000동작구서울특별시 동작구 노량진로32길 53 (본동)686802-821-86006908
9구립서초노인요양센터A0368(노인) 노인요양시설노인의료복지시설자치구나종선1165000000서초구서울특별시 서초구 남부순환로340길 21 (서초동 380-4)20020002-597-50086756
시설명시설코드시설종류명(시설유형)시설종류상세명(시설종류)자치구(시)구분시설장명시군구코드시군구명시설주소정원(수용인원)현인원전화번호우편번호
586사랑초요양원P3544(노인) 노인요양시설노인의료복지시설자치구조요순1147000000양천구서울특별시 양천구 지양로 152 (신월동, 1층~4층)119<NA>02260611908033
587맑은요양원P3578(노인) 노인요양공동생활가정노인의료복지시설자치구신일규1150000000강서구서울특별시 강서구 화곡로225, 6층 (화곡동, 두산빌딩)8<NA>02366420127695
588예원요양원 2호점P3605(노인) 노인요양공동생활가정노인의료복지시설자치구김동숙1126000000중랑구서울특별시 중랑구 용마산로 554, 2층 (망우동)9<NA>0243312212059
589예원요양원P3606(노인) 노인요양공동생활가정노인의료복지시설자치구황현화1126000000중랑구서울특별시 중랑구 용마산로125길 92 (신내동, 석탑아파트 101동 210호)9<NA>0243312212058
590은미요양원P3623(노인) 노인요양시설노인의료복지시설자치구김춘환1159000000동작구서울특별시 동작구 상도로68길 1-20 (상도동, 4층)23<NA>0282502297040
591정릉노인요양원P3810(노인) 노인요양시설노인의료복지시설자치구조미정1129000000성북구서울특별시 성북구 정릉로12길 69 (정릉동)44<NA>02624330002812
592강동힐링요양원P3843(노인) 노인요양시설노인의료복지시설자치구김은경1174000000강동구서울특별시 강동구 천호대로 1240-9 (둔촌동)29<NA>0248547015359
593강서실버요양원P3867(노인) 노인요양공동생활가정노인의료복지시설자치구김미선1150000000강서구서울특별시 강서구 강서로45길 174, 4층 (내발산동, SM프라자)9902266788907638
594나오미요양원P3911(노인) 노인요양시설노인의료복지시설자치구이제숙1150000000강서구서울특별시 강서구 금낭화로 135, 8층 (방화동, 금강프라자)40<NA>02208857127510
595메디컬요양원P3919(노인) 노인요양시설노인의료복지시설자치구김원정1123000000동대문구서울특별시 동대문구 약령시로 124, 2, 4~10층 (청량리동)10<NA>02646775752573