Overview

Dataset statistics

Number of variables12
Number of observations444
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory41.8 KiB
Average record size in memory96.3 B

Variable types

Text11
Categorical1

Alerts

비고 is highly imbalanced (77.1%)Imbalance

Reproduction

Analysis started2024-03-14 01:55:01.468990
Analysis finished2024-03-14 01:55:02.570614
Duration1.1 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Text

Distinct106
Distinct (%)23.9%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
2024-03-14T10:55:02.751248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length2
Mean length1.75
Min length1

Characters and Unicode

Total characters777
Distinct characters13
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique22 ?
Unique (%)5.0%

Sample

1st row총계
2nd row소계
3rd row1
4th row2
5th row3
ValueCountFrequency (%)
소계 14
 
3.2%
3 14
 
3.2%
4 14
 
3.2%
1 14
 
3.2%
2 14
 
3.2%
5 13
 
2.9%
8 12
 
2.7%
7 12
 
2.7%
6 12
 
2.7%
9 11
 
2.5%
Other values (96) 314
70.7%
2024-03-14T10:55:03.166639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 136
17.5%
2 113
14.5%
3 85
10.9%
4 76
9.8%
5 72
9.3%
6 61
7.9%
7 60
7.7%
8 54
 
6.9%
9 48
 
6.2%
0 42
 
5.4%
Other values (3) 30
 
3.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 747
96.1%
Other Letter 30
 
3.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 136
18.2%
2 113
15.1%
3 85
11.4%
4 76
10.2%
5 72
9.6%
6 61
8.2%
7 60
8.0%
8 54
 
7.2%
9 48
 
6.4%
0 42
 
5.6%
Other Letter
ValueCountFrequency (%)
계 15
50.0%
소 14
46.7%
총 1
 
3.3%

Most occurring scripts

ValueCountFrequency (%)
Common 747
96.1%
Hangul 30
 
3.9%

Most frequent character per script

Common
ValueCountFrequency (%)
1 136
18.2%
2 113
15.1%
3 85
11.4%
4 76
10.2%
5 72
9.6%
6 61
8.2%
7 60
8.0%
8 54
 
7.2%
9 48
 
6.4%
0 42
 
5.6%
Hangul
ValueCountFrequency (%)
계 15
50.0%
소 14
46.7%
총 1
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 747
96.1%
Hangul 30
 
3.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 136
18.2%
2 113
15.1%
3 85
11.4%
4 76
10.2%
5 72
9.6%
6 61
8.2%
7 60
8.0%
8 54
 
7.2%
9 48
 
6.4%
0 42
 
5.6%
Hangul
ValueCountFrequency (%)
계 15
50.0%
소 14
46.7%
총 1
 
3.3%

시설구분
Text

Distinct51
Distinct (%)11.5%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
2024-03-14T10:55:03.326870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length10
Mean length7.509009
Min length3

Characters and Unicode

Total characters3334
Distinct characters78
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique27 ?
Unique (%)6.1%

Sample

1st row전라북도
2nd row전주시
3rd row사회복귀시설(종합시설)
4th row사회복귀시설(공동생활)
5th row사회복귀시설(종합시설)
ValueCountFrequency (%)
노인요양시설 148
31.0%
노인요양공동생활가정 76
15.9%
장애인거주시설 51
 
10.7%
아동공동생활가정 31
 
6.5%
장애인 19
 
4.0%
공동생활가정 17
 
3.6%
아동양육시설 12
 
2.5%
노인양로시설 12
 
2.5%
사회복귀시설(종합시설 10
 
2.1%
아동 9
 
1.9%
Other values (43) 93
19.5%
2024-03-14T10:55:03.602873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
인 327
 
9.8%
시 298
 
8.9%
설 292
 
8.8%
양 256
 
7.7%
노 253
 
7.6%
요 230
 
6.9%
동 215
 
6.4%
활 159
 
4.8%
공 156
 
4.7%
생 155
 
4.6%
Other values (68) 993
29.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3250
97.5%
Space Separator 38
 
1.1%
Close Punctuation 23
 
0.7%
Open Punctuation 23
 
0.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
인 327
 
10.1%
시 298
 
9.2%
설 292
 
9.0%
양 256
 
7.9%
노 253
 
7.8%
요 230
 
7.1%
동 215
 
6.6%
활 159
 
4.9%
공 156
 
4.8%
생 155
 
4.8%
Other values (65) 909
28.0%
Space Separator
ValueCountFrequency (%)
38
100.0%
Close Punctuation
ValueCountFrequency (%)
) 23
100.0%
Open Punctuation
ValueCountFrequency (%)
( 23
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3250
97.5%
Common 84
 
2.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
인 327
 
10.1%
시 298
 
9.2%
설 292
 
9.0%
양 256
 
7.9%
노 253
 
7.8%
요 230
 
7.1%
동 215
 
6.6%
활 159
 
4.9%
공 156
 
4.8%
생 155
 
4.8%
Other values (65) 909
28.0%
Common
ValueCountFrequency (%)
38
45.2%
) 23
27.4%
( 23
27.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3250
97.5%
ASCII 84
 
2.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
인 327
 
10.1%
시 298
 
9.2%
설 292
 
9.0%
양 256
 
7.9%
노 253
 
7.8%
요 230
 
7.1%
동 215
 
6.6%
활 159
 
4.9%
공 156
 
4.8%
생 155
 
4.8%
Other values (65) 909
28.0%
ASCII
ValueCountFrequency (%)
38
45.2%
) 23
27.4%
( 23
27.4%

시설명
Text

Distinct430
Distinct (%)96.8%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
2024-03-14T10:55:03.789724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length14
Mean length6.0608108
Min length2

Characters and Unicode

Total characters2691
Distinct characters313
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique416 ?
Unique (%)93.7%

Sample

1st row429
2nd row104
3rd row마음건강복지관
4th row마음건강회복홈
5th row아름다운세상
ValueCountFrequency (%)
집 9
 
1.9%
우리집 4
 
0.8%
행복한집 2
 
0.4%
노인요양원 2
 
0.4%
쉼터 2
 
0.4%
효도의집 2
 
0.4%
행복한 2
 
0.4%
1호 2
 
0.4%
자림공동생활가정 2
 
0.4%
은혜의집 2
 
0.4%
Other values (435) 447
93.9%
2024-03-14T10:55:04.095908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
원 179
 
6.7%
집 116
 
4.3%
양 106
 
3.9%
의 105
 
3.9%
요 95
 
3.5%
인 60
 
2.2%
57
 
2.1%
노 50
 
1.9%
사 48
 
1.8%
홈 44
 
1.6%
Other values (303) 1831
68.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2595
96.4%
Space Separator 57
 
2.1%
Decimal Number 34
 
1.3%
Uppercase Letter 4
 
0.1%
Other Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
원 179
 
6.9%
집 116
 
4.5%
양 106
 
4.1%
의 105
 
4.0%
요 95
 
3.7%
인 60
 
2.3%
노 50
 
1.9%
사 48
 
1.8%
홈 44
 
1.7%
랑 39
 
1.5%
Other values (288) 1753
67.6%
Decimal Number
ValueCountFrequency (%)
1 9
26.5%
4 5
14.7%
3 5
14.7%
2 5
14.7%
8 3
 
8.8%
5 2
 
5.9%
0 2
 
5.9%
9 2
 
5.9%
6 1
 
2.9%
Uppercase Letter
ValueCountFrequency (%)
Y 1
25.0%
W 1
25.0%
C 1
25.0%
A 1
25.0%
Space Separator
ValueCountFrequency (%)
57
100.0%
Other Punctuation
ValueCountFrequency (%)
? 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2595
96.4%
Common 92
 
3.4%
Latin 4
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
원 179
 
6.9%
집 116
 
4.5%
양 106
 
4.1%
의 105
 
4.0%
요 95
 
3.7%
인 60
 
2.3%
노 50
 
1.9%
사 48
 
1.8%
홈 44
 
1.7%
랑 39
 
1.5%
Other values (288) 1753
67.6%
Common
ValueCountFrequency (%)
57
62.0%
1 9
 
9.8%
4 5
 
5.4%
3 5
 
5.4%
2 5
 
5.4%
8 3
 
3.3%
5 2
 
2.2%
0 2
 
2.2%
9 2
 
2.2%
? 1
 
1.1%
Latin
ValueCountFrequency (%)
Y 1
25.0%
W 1
25.0%
C 1
25.0%
A 1
25.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2595
96.4%
ASCII 96
 
3.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
원 179
 
6.9%
집 116
 
4.5%
양 106
 
4.1%
의 105
 
4.0%
요 95
 
3.7%
인 60
 
2.3%
노 50
 
1.9%
사 48
 
1.8%
홈 44
 
1.7%
랑 39
 
1.5%
Other values (288) 1753
67.6%
ASCII
ValueCountFrequency (%)
57
59.4%
1 9
 
9.4%
4 5
 
5.2%
3 5
 
5.2%
2 5
 
5.2%
8 3
 
3.1%
5 2
 
2.1%
0 2
 
2.1%
9 2
 
2.1%
Y 1
 
1.0%
Other values (5) 5
 
5.2%

설치신고일
Text

Distinct397
Distinct (%)89.4%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
2024-03-14T10:55:04.335656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length10
Mean length9.2364865
Min length1

Characters and Unicode

Total characters4101
Distinct characters14
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique372 ?
Unique (%)83.8%

Sample

1st row-
2nd row-
3rd row2000.12.08
4th row2011.07.04
5th row2002.11.21
ValueCountFrequency (%)
18
 
4.0%
2006.11.10 5
 
1.1%
2011.07.04 3
 
0.7%
07.03.09 3
 
0.7%
02.03.27 3
 
0.7%
2008.12.31 2
 
0.4%
2008.06.25 2
 
0.4%
2006.12.29 2
 
0.4%
2006.05.10 2
 
0.4%
2007.12.28 2
 
0.4%
Other values (389) 406
90.6%
2024-03-14T10:55:04.710403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1078
26.3%
. 852
20.8%
1 615
15.0%
2 573
14.0%
9 177
 
4.3%
3 157
 
3.8%
8 153
 
3.7%
6 140
 
3.4%
7 120
 
2.9%
5 104
 
2.5%
Other values (4) 132
 
3.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3212
78.3%
Other Punctuation 855
 
20.8%
Dash Punctuation 18
 
0.4%
Space Separator 16
 
0.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1078
33.6%
1 615
19.1%
2 573
17.8%
9 177
 
5.5%
3 157
 
4.9%
8 153
 
4.8%
6 140
 
4.4%
7 120
 
3.7%
5 104
 
3.2%
4 95
 
3.0%
Other Punctuation
ValueCountFrequency (%)
. 852
99.6%
, 3
 
0.4%
Dash Punctuation
ValueCountFrequency (%)
- 18
100.0%
Space Separator
ValueCountFrequency (%)
16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4101
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1078
26.3%
. 852
20.8%
1 615
15.0%
2 573
14.0%
9 177
 
4.3%
3 157
 
3.8%
8 153
 
3.7%
6 140
 
3.4%
7 120
 
2.9%
5 104
 
2.5%
Other values (4) 132
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4101
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1078
26.3%
. 852
20.8%
1 615
15.0%
2 573
14.0%
9 177
 
4.3%
3 157
 
3.8%
8 153
 
3.7%
6 140
 
3.4%
7 120
 
2.9%
5 104
 
2.5%
Other values (4) 132
 
3.2%

시설장
Text

Distinct403
Distinct (%)90.8%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
2024-03-14T10:55:04.964642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length3
Mean length2.981982
Min length1

Characters and Unicode

Total characters1324
Distinct characters184
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique383 ?
Unique (%)86.3%

Sample

1st row-
2nd row-
3rd row박헌수
4th row김구중
5th row오미화
ValueCountFrequency (%)
18
 
4.0%
박미숙 4
 
0.9%
이건중 3
 
0.7%
김은경 3
 
0.7%
진숙선 3
 
0.7%
윤하람 2
 
0.4%
김기성 2
 
0.4%
권영조 2
 
0.4%
서철승 2
 
0.4%
이순자 2
 
0.4%
Other values (398) 409
90.9%
2024-03-14T10:55:05.356458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
김 92
 
6.9%
이 58
 
4.4%
정 51
 
3.9%
영 47
 
3.5%
숙 40
 
3.0%
희 35
 
2.6%
박 35
 
2.6%
성 31
 
2.3%
최 29
 
2.2%
미 29
 
2.2%
Other values (174) 877
66.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1282
96.8%
Space Separator 23
 
1.7%
Dash Punctuation 18
 
1.4%
Decimal Number 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
김 92
 
7.2%
이 58
 
4.5%
정 51
 
4.0%
영 47
 
3.7%
숙 40
 
3.1%
희 35
 
2.7%
박 35
 
2.7%
성 31
 
2.4%
최 29
 
2.3%
미 29
 
2.3%
Other values (171) 835
65.1%
Space Separator
ValueCountFrequency (%)
23
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 18
100.0%
Decimal Number
ValueCountFrequency (%)
1 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1282
96.8%
Common 42
 
3.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
김 92
 
7.2%
이 58
 
4.5%
정 51
 
4.0%
영 47
 
3.7%
숙 40
 
3.1%
희 35
 
2.7%
박 35
 
2.7%
성 31
 
2.4%
최 29
 
2.3%
미 29
 
2.3%
Other values (171) 835
65.1%
Common
ValueCountFrequency (%)
23
54.8%
- 18
42.9%
1 1
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1282
96.8%
ASCII 42
 
3.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
김 92
 
7.2%
이 58
 
4.5%
정 51
 
4.0%
영 47
 
3.7%
숙 40
 
3.1%
희 35
 
2.7%
박 35
 
2.7%
성 31
 
2.4%
최 29
 
2.3%
미 29
 
2.3%
Other values (171) 835
65.1%
ASCII
ValueCountFrequency (%)
23
54.8%
- 18
42.9%
1 1
 
2.4%

주 소
Text

Distinct412
Distinct (%)92.8%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
2024-03-14T10:55:05.640933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length37
Median length30
Mean length16.581081
Min length1

Characters and Unicode

Total characters7362
Distinct characters278
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

Unique394 ?
Unique (%)88.7%

Sample

1st row-
2nd row-
3rd row전주시 완산구 물왕멀2길 20-29
4th row전주시 완산구 물왕멀2길 25
5th row전주시 덕진구 아중7길 9-5
ValueCountFrequency (%)
전주시 101
 
6.2%
익산시 81
 
5.0%
완산구 61
 
3.8%
전북 46
 
2.8%
덕진구 38
 
2.3%
정읍시 28
 
1.7%
군산시 28
 
1.7%
남원시 23
 
1.4%
완주군 21
 
1.3%
16
 
1.0%
Other values (862) 1177
72.7%
2024-03-14T10:55:06.348397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1203
 
16.3%
1 380
 
5.2%
시 296
 
4.0%
산 264
 
3.6%
길 240
 
3.3%
2 229
 
3.1%
- 209
 
2.8%
로 186
 
2.5%
3 179
 
2.4%
면 177
 
2.4%
Other values (268) 3999
54.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4069
55.3%
Decimal Number 1644
22.3%
Space Separator 1203
 
16.3%
Dash Punctuation 209
 
2.8%
Close Punctuation 85
 
1.2%
Open Punctuation 85
 
1.2%
Other Punctuation 57
 
0.8%
Uppercase Letter 6
 
0.1%
Lowercase Letter 4
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 296
 
7.3%
산 264
 
6.5%
길 240
 
5.9%
로 186
 
4.6%
면 177
 
4.3%
전 155
 
3.8%
주 152
 
3.7%
군 131
 
3.2%
동 124
 
3.0%
구 120
 
2.9%
Other values (246) 2224
54.7%
Decimal Number
ValueCountFrequency (%)
1 380
23.1%
2 229
13.9%
3 179
10.9%
4 156
9.5%
0 133
 
8.1%
6 125
 
7.6%
7 119
 
7.2%
5 119
 
7.2%
8 103
 
6.3%
9 101
 
6.1%
Other Punctuation
ValueCountFrequency (%)
? 19
33.3%
, 12
21.1%
/ 10
17.5%
. 9
15.8%
@ 7
 
12.3%
Uppercase Letter
ValueCountFrequency (%)
A 4
66.7%
B 2
33.3%
Space Separator
ValueCountFrequency (%)
1203
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 209
100.0%
Close Punctuation
ValueCountFrequency (%)
) 85
100.0%
Open Punctuation
ValueCountFrequency (%)
( 85
100.0%
Lowercase Letter
ValueCountFrequency (%)
a 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4069
55.3%
Common 3283
44.6%
Latin 10
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 296
 
7.3%
산 264
 
6.5%
길 240
 
5.9%
로 186
 
4.6%
면 177
 
4.3%
전 155
 
3.8%
주 152
 
3.7%
군 131
 
3.2%
동 124
 
3.0%
구 120
 
2.9%
Other values (246) 2224
54.7%
Common
ValueCountFrequency (%)
1203
36.6%
1 380
 
11.6%
2 229
 
7.0%
- 209
 
6.4%
3 179
 
5.5%
4 156
 
4.8%
0 133
 
4.1%
6 125
 
3.8%
7 119
 
3.6%
5 119
 
3.6%
Other values (9) 431
 
13.1%
Latin
ValueCountFrequency (%)
A 4
40.0%
a 4
40.0%
B 2
20.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4069
55.3%
ASCII 3293
44.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1203
36.5%
1 380
 
11.5%
2 229
 
7.0%
- 209
 
6.3%
3 179
 
5.4%
4 156
 
4.7%
0 133
 
4.0%
6 125
 
3.8%
7 119
 
3.6%
5 119
 
3.6%
Other values (12) 441
 
13.4%
Hangul
ValueCountFrequency (%)
시 296
 
7.3%
산 264
 
6.5%
길 240
 
5.9%
로 186
 
4.6%
면 177
 
4.3%
전 155
 
3.8%
주 152
 
3.7%
군 131
 
3.2%
동 124
 
3.0%
구 120
 
2.9%
Other values (246) 2224
54.7%

정원(종사자)
Text

Distinct58
Distinct (%)13.1%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
2024-03-14T10:55:06.512915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length3
Mean length3.4459459
Min length1

Characters and Unicode

Total characters1530
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique14 ?
Unique (%)3.2%

Sample

1st row 5,785
2nd row-
3rd row 14
4th row 1
5th row 11
ValueCountFrequency (%)
2 42
 
9.5%
5 33
 
7.4%
4 32
 
7.2%
1 28
 
6.3%
6 26
 
5.9%
24
 
5.4%
3 21
 
4.7%
7 17
 
3.8%
10 13
 
2.9%
12 13
 
2.9%
Other values (47) 195
43.9%
2024-03-14T10:55:06.840041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
886
57.9%
1 137
 
9.0%
2 123
 
8.0%
4 70
 
4.6%
3 65
 
4.2%
5 61
 
4.0%
6 54
 
3.5%
7 29
 
1.9%
8 29
 
1.9%
0 27
 
1.8%
Other values (3) 49
 
3.2%

Most occurring categories

ValueCountFrequency (%)
Space Separator 886
57.9%
Decimal Number 619
40.5%
Dash Punctuation 24
 
1.6%
Other Punctuation 1
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 137
22.1%
2 123
19.9%
4 70
11.3%
3 65
10.5%
5 61
9.9%
6 54
 
8.7%
7 29
 
4.7%
8 29
 
4.7%
0 27
 
4.4%
9 24
 
3.9%
Space Separator
ValueCountFrequency (%)
886
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 24
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1530
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
886
57.9%
1 137
 
9.0%
2 123
 
8.0%
4 70
 
4.6%
3 65
 
4.2%
5 61
 
4.0%
6 54
 
3.5%
7 29
 
1.9%
8 29
 
1.9%
0 27
 
1.8%
Other values (3) 49
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1530
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
886
57.9%
1 137
 
9.0%
2 123
 
8.0%
4 70
 
4.6%
3 65
 
4.2%
5 61
 
4.0%
6 54
 
3.5%
7 29
 
1.9%
8 29
 
1.9%
0 27
 
1.8%
Other values (3) 49
 
3.2%

현원(종사자)
Text

Distinct56
Distinct (%)12.6%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
2024-03-14T10:55:07.005114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length3
Mean length3.4527027
Min length1

Characters and Unicode

Total characters1533
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16 ?
Unique (%)3.6%

Sample

1st row 5,683
2nd row-
3rd row 14
4th row 1
5th row 11
ValueCountFrequency (%)
2 48
 
10.8%
4 34
 
7.7%
5 31
 
7.0%
1 30
 
6.8%
6 27
 
6.1%
21
 
4.7%
3 18
 
4.1%
10 16
 
3.6%
11 13
 
2.9%
12 12
 
2.7%
Other values (45) 194
43.7%
2024-03-14T10:55:07.292113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
886
57.8%
1 150
 
9.8%
2 127
 
8.3%
4 72
 
4.7%
5 58
 
3.8%
3 57
 
3.7%
6 51
 
3.3%
8 31
 
2.0%
0 28
 
1.8%
7 26
 
1.7%
Other values (3) 47
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Space Separator 886
57.8%
Decimal Number 625
40.8%
Dash Punctuation 21
 
1.4%
Other Punctuation 1
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 150
24.0%
2 127
20.3%
4 72
11.5%
5 58
 
9.3%
3 57
 
9.1%
6 51
 
8.2%
8 31
 
5.0%
0 28
 
4.5%
7 26
 
4.2%
9 25
 
4.0%
Space Separator
ValueCountFrequency (%)
886
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 21
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1533
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
886
57.8%
1 150
 
9.8%
2 127
 
8.3%
4 72
 
4.7%
5 58
 
3.8%
3 57
 
3.7%
6 51
 
3.3%
8 31
 
2.0%
0 28
 
1.8%
7 26
 
1.7%
Other values (3) 47
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1533
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
886
57.8%
1 150
 
9.8%
2 127
 
8.3%
4 72
 
4.7%
5 58
 
3.8%
3 57
 
3.7%
6 51
 
3.3%
8 31
 
2.0%
0 28
 
1.8%
7 26
 
1.7%
Other values (3) 47
 
3.1%

정원(생활인)
Text

Distinct87
Distinct (%)19.6%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
2024-03-14T10:55:07.488647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length4
Mean length3.6869369
Min length1

Characters and Unicode

Total characters1637
Distinct characters15
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique40 ?
Unique (%)9.0%

Sample

1st row 14,132
2nd row-
3rd row 26
4th row 4
5th row 20
ValueCountFrequency (%)
9 59
 
13.3%
7 48
 
10.8%
4 21
 
4.7%
29 20
 
4.5%
50 19
 
4.3%
19
 
4.3%
80 14
 
3.2%
10 12
 
2.7%
40 11
 
2.5%
16 11
 
2.5%
Other values (76) 210
47.3%
2024-03-14T10:55:07.795448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
886
54.1%
0 117
 
7.1%
9 96
 
5.9%
2 90
 
5.5%
1 87
 
5.3%
5 75
 
4.6%
7 71
 
4.3%
4 59
 
3.6%
6 49
 
3.0%
8 46
 
2.8%
Other values (5) 61
 
3.7%

Most occurring categories

ValueCountFrequency (%)
Space Separator 886
54.1%
Decimal Number 721
44.0%
Dash Punctuation 19
 
1.2%
Other Letter 10
 
0.6%
Other Punctuation 1
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 117
16.2%
9 96
13.3%
2 90
12.5%
1 87
12.1%
5 75
10.4%
7 71
9.8%
4 59
8.2%
6 49
6.8%
8 46
 
6.4%
3 31
 
4.3%
Other Letter
ValueCountFrequency (%)
세 5
50.0%
대 5
50.0%
Space Separator
ValueCountFrequency (%)
886
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 19
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1627
99.4%
Hangul 10
 
0.6%

Most frequent character per script

Common
ValueCountFrequency (%)
886
54.5%
0 117
 
7.2%
9 96
 
5.9%
2 90
 
5.5%
1 87
 
5.3%
5 75
 
4.6%
7 71
 
4.4%
4 59
 
3.6%
6 49
 
3.0%
8 46
 
2.8%
Other values (3) 51
 
3.1%
Hangul
ValueCountFrequency (%)
세 5
50.0%
대 5
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1627
99.4%
Hangul 10
 
0.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
886
54.5%
0 117
 
7.2%
9 96
 
5.9%
2 90
 
5.5%
1 87
 
5.3%
5 75
 
4.6%
7 71
 
4.4%
4 59
 
3.6%
6 49
 
3.0%
8 46
 
2.8%
Other values (3) 51
 
3.1%
Hangul
ValueCountFrequency (%)
세 5
50.0%
대 5
50.0%

현원(생활인)
Text

Distinct103
Distinct (%)23.2%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
2024-03-14T10:55:08.018907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length4
Mean length3.5923423
Min length1

Characters and Unicode

Total characters1595
Distinct characters15
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique43 ?
Unique (%)9.7%

Sample

1st row 11,274
2nd row-
3rd row 26
4th row 4
5th row 20
ValueCountFrequency (%)
34
 
7.7%
4 30
 
6.8%
9 26
 
5.9%
5 22
 
5.0%
7 22
 
5.0%
6 22
 
5.0%
8 17
 
3.8%
10 13
 
2.9%
20 12
 
2.7%
2 11
 
2.5%
Other values (89) 235
52.9%
2024-03-14T10:55:08.347428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
876
54.9%
2 104
 
6.5%
1 93
 
5.8%
4 92
 
5.8%
5 67
 
4.2%
7 60
 
3.8%
6 60
 
3.8%
3 60
 
3.8%
9 49
 
3.1%
0 49
 
3.1%
Other values (5) 85
 
5.3%

Most occurring categories

ValueCountFrequency (%)
Space Separator 876
54.9%
Decimal Number 674
42.3%
Dash Punctuation 34
 
2.1%
Other Letter 10
 
0.6%
Other Punctuation 1
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 104
15.4%
1 93
13.8%
4 92
13.6%
5 67
9.9%
7 60
8.9%
6 60
8.9%
3 60
8.9%
9 49
7.3%
0 49
7.3%
8 40
 
5.9%
Other Letter
ValueCountFrequency (%)
세 5
50.0%
대 5
50.0%
Space Separator
ValueCountFrequency (%)
876
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 34
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1585
99.4%
Hangul 10
 
0.6%

Most frequent character per script

Common
ValueCountFrequency (%)
876
55.3%
2 104
 
6.6%
1 93
 
5.9%
4 92
 
5.8%
5 67
 
4.2%
7 60
 
3.8%
6 60
 
3.8%
3 60
 
3.8%
9 49
 
3.1%
0 49
 
3.1%
Other values (3) 75
 
4.7%
Hangul
ValueCountFrequency (%)
세 5
50.0%
대 5
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1585
99.4%
Hangul 10
 
0.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
876
55.3%
2 104
 
6.6%
1 93
 
5.9%
4 92
 
5.8%
5 67
 
4.2%
7 60
 
3.8%
6 60
 
3.8%
3 60
 
3.8%
9 49
 
3.1%
0 49
 
3.1%
Other values (3) 75
 
4.7%
Hangul
ValueCountFrequency (%)
세 5
50.0%
대 5
50.0%

운영주체(법인명)
Text

Distinct170
Distinct (%)38.3%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
2024-03-14T10:55:08.535897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length25
Median length23
Mean length5.4256757
Min length1

Characters and Unicode

Total characters2409
Distinct characters197
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique129 ?
Unique (%)29.1%

Sample

1st row-
2nd row-
3rd row마음건강복지재단
4th row마음건강복지재단
5th row인산의료재단
ValueCountFrequency (%)
개인 194
38.6%
사회복지법인 23
 
4.6%
18
 
3.6%
사복 17
 
3.4%
삼동회 14
 
2.8%
사복)중도원 6
 
1.2%
한기장복지재단 6
 
1.2%
원광효도마을 6
 
1.2%
한울안 5
 
1.0%
사복)자림복지재단 5
 
1.0%
Other values (166) 209
41.6%
2024-03-14T10:55:08.833071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
인 265
 
11.0%
복 201
 
8.3%
개 200
 
8.3%
지 145
 
6.0%
사 142
 
5.9%
회 109
 
4.5%
100
 
4.2%
) 92
 
3.8%
단 82
 
3.4%
재 80
 
3.3%
Other values (187) 993
41.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2173
90.2%
Space Separator 100
 
4.2%
Close Punctuation 92
 
3.8%
Open Punctuation 19
 
0.8%
Dash Punctuation 18
 
0.7%
Uppercase Letter 4
 
0.2%
Other Punctuation 2
 
0.1%
Other Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
인 265
 
12.2%
복 201
 
9.2%
개 200
 
9.2%
지 145
 
6.7%
사 142
 
6.5%
회 109
 
5.0%
단 82
 
3.8%
재 80
 
3.7%
원 63
 
2.9%
법 50
 
2.3%
Other values (177) 836
38.5%
Uppercase Letter
ValueCountFrequency (%)
A 1
25.0%
C 1
25.0%
W 1
25.0%
Y 1
25.0%
Space Separator
ValueCountFrequency (%)
100
100.0%
Close Punctuation
ValueCountFrequency (%)
) 92
100.0%
Open Punctuation
ValueCountFrequency (%)
( 19
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 18
100.0%
Other Punctuation
ValueCountFrequency (%)
, 2
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2174
90.2%
Common 231
 
9.6%
Latin 4
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
인 265
 
12.2%
복 201
 
9.2%
개 200
 
9.2%
지 145
 
6.7%
사 142
 
6.5%
회 109
 
5.0%
단 82
 
3.8%
재 80
 
3.7%
원 63
 
2.9%
법 50
 
2.3%
Other values (178) 837
38.5%
Common
ValueCountFrequency (%)
100
43.3%
) 92
39.8%
( 19
 
8.2%
- 18
 
7.8%
, 2
 
0.9%
Latin
ValueCountFrequency (%)
A 1
25.0%
C 1
25.0%
W 1
25.0%
Y 1
25.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2173
90.2%
ASCII 235
 
9.8%
None 1
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
인 265
 
12.2%
복 201
 
9.2%
개 200
 
9.2%
지 145
 
6.7%
사 142
 
6.5%
회 109
 
5.0%
단 82
 
3.8%
재 80
 
3.7%
원 63
 
2.9%
법 50
 
2.3%
Other values (177) 836
38.5%
ASCII
ValueCountFrequency (%)
100
42.6%
) 92
39.1%
( 19
 
8.1%
- 18
 
7.7%
, 2
 
0.9%
A 1
 
0.4%
C 1
 
0.4%
W 1
 
0.4%
Y 1
 
0.4%
None
ValueCountFrequency (%)
㈜ 1
100.0%

비고
Categorical

IMBALANCE 

Distinct16
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
-
386 
지적
 
27
중증
 
9
지적
 
5
지체
 
5
Other values (11)
 
12

Length

Max length35
Median length1
Mean length1.3986486
Min length1

Unique

Unique10 ?
Unique (%)2.3%

Sample

1st row-
2nd row-
3rd row-
4th row-
5th row-

Common Values

ValueCountFrequency (%)
- 386
86.9%
지적 27
 
6.1%
중증 9
 
2.0%
지적 5
 
1.1%
지체 5
 
1.1%
중증실비 2
 
0.5%
지적(여) 1
 
0.2%
휴지 중(14.6.18~15.5.31) 1
 
0.2%
2013.101~2014.9.30휴지 1
 
0.2%
2014.5.01~2015.1.31 1
 
0.2%
Other values (6) 6
 
1.4%

Length

2024-03-14T10:55:08.973385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
386
86.4%
지적 32
 
7.2%
중증 9
 
2.0%
지체 5
 
1.1%
중증실비 2
 
0.4%
휴지 2
 
0.4%
시각 1
 
0.2%
휴지사유-건물개보수 1
 
0.2%
중 1
 
0.2%
휴업 1
 
0.2%
Other values (7) 7
 
1.6%

Correlations

2024-03-14T10:55:09.046681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시설구분정원(종사자)현원(종사자)정원(생활인)비고
시설구분1.0000.7730.6750.9010.585
정원(종사자)0.7731.0000.9950.9880.000
현원(종사자)0.6750.9951.0000.9880.000
정원(생활인)0.9010.9880.9881.0000.000
비고0.5850.0000.0000.0001.000

Missing values

2024-03-14T10:55:02.324315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T10:55:02.511048image/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.

Sample

연번시설구분시설명설치신고일시설장주 소정원(종사자)현원(종사자)정원(생활인)현원(생활인)운영주체(법인명)비고
0총계전라북도429---5,7855,68314,13211,274--
1소계전주시104---------
21사회복귀시설(종합시설)마음건강복지관2000.12.08박헌수전주시 완산구 물왕멀2길 20-2914142626마음건강복지재단-
32사회복귀시설(공동생활)마음건강회복홈2011.07.04김구중전주시 완산구 물왕멀2길 251144마음건강복지재단-
43사회복귀시설(종합시설)아름다운세상2002.11.21오미화전주시 덕진구 아중7길 9-511112020인산의료재단-
54사회복귀시설(공동생활)아름다운집2011.05.26강경희전주시 덕진구 인교9길 11 아중지1 401호1144인산의료재단-
65사회복귀시설(공동생활)꿈이있는집2012.05.08강경희전주시 덕진구 아중1길 23-3 퓨처빌B 402호1144인산의료재단-
76노숙인 요양시설전주사랑의집82.3.4심근자전주시 덕진구 동부대로 92614156060전주가톨릭사회복지회-
87노숙인 자활시설일꾼쉼터1998.12.7임내규덕진구 하가1길 6441911대한성공회유지재단-
98노숙인 자활시설희망의쉼터1998.5.7오해영완산구 현무1길 21-16442212재)대한예수교장로회총회유지재단-
연번시설구분시설명설치신고일시설장주 소정원(종사자)현원(종사자)정원(생활인)현원(생활인)운영주체(법인명)비고
43410노인요양공동생활가정에덴의마을2009.10.01이루리전북 고창군 해리면 해리송산길 1066699개인-
43511아동양육고창행복원1952.7.5강선자전북 고창군 고창읍 모양성로 116-1319157038고창행복원-
43612아동양육요엘원1966.6.4양향환전북 고창군 무장면 학천로 22121166442아모스-
43713아동보호치료시설희망샘학교1973.8.1김정강전북 고창군 무장면 학천로226-1622227049아모스-
438소계부안군5---------
4391노인공동생활가정배매골사랑방2010.02.20이정희부안군 주산면 주산북로 214339-개인-
4402노인요양시설송산효도마을2005.09.08김은경부안군 주산면 화봉길 8-3048489082한울안-
4413노인요양시설은총의집2006.09.29주혜숙부안군 상서면 부안로 1539-2117172323개인-
4424노인요양시설로댐실버케어2011.07.29유희성부안군 하서면 고인돌로 34711111817개인-
4435노인요양시설부안군노인요양원2010.03.11권란희부안군 부안읍 봉두길 5230304141사회복지법인한국장로교복지재단-