Overview

Dataset statistics

Number of variables12
Number of observations434
Missing cells132
Missing cells (%)2.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory40.8 KiB
Average record size in memory96.3 B

Variable types

Text10
Categorical2

Dataset

Description사회복지생활시설현황전라북도2015
Author전라북도
URLhttps://www.bigdatahub.go.kr/opendata/dataSet/detail.nm?contentId=37&rlik=49451aebf056b486&serviceId=202406

Alerts

전라북도 사회복지(생활)시설 현황 is highly overall correlated with Unnamed: 11High correlation
Unnamed: 11 is highly overall correlated with 전라북도 사회복지(생활)시설 현황High correlation
Unnamed: 11 is highly imbalanced (77.1%)Imbalance
Unnamed: 3 has 17 (3.9%) missing valuesMissing
Unnamed: 4 has 17 (3.9%) missing valuesMissing
Unnamed: 5 has 17 (3.9%) missing valuesMissing
Unnamed: 6 has 15 (3.5%) missing valuesMissing
Unnamed: 7 has 16 (3.7%) missing valuesMissing
Unnamed: 8 has 15 (3.5%) missing valuesMissing
Unnamed: 9 has 16 (3.7%) missing valuesMissing
Unnamed: 10 has 16 (3.7%) missing valuesMissing

Reproduction

Analysis started2024-03-14 00:54:26.981691
Analysis finished2024-03-14 00:54:28.474806
Duration1.49 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Unnamed: 0
Text

Distinct99
Distinct (%)22.9%
Missing2
Missing (%)0.5%
Memory size3.5 KiB
2024-03-14T09:54:28.651538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length2
Median length2
Mean length1.7291667
Min length1

Characters and Unicode

Total characters747
Distinct characters15
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

Unique19 ?
Unique (%)4.4%

Sample

1st row연번
2nd row총계
3rd row소계
4th row1
5th row2
ValueCountFrequency (%)
소계 14
 
3.2%
1 14
 
3.2%
2 14
 
3.2%
3 14
 
3.2%
4 14
 
3.2%
5 13
 
3.0%
6 13
 
3.0%
7 12
 
2.8%
8 12
 
2.8%
9 11
 
2.5%
Other values (89) 301
69.7%
2024-03-14T09:54:28.992695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 129
17.3%
2 110
14.7%
3 82
11.0%
4 74
9.9%
5 71
9.5%
6 62
8.3%
7 59
7.9%
8 49
 
6.6%
9 44
 
5.9%
0 35
 
4.7%
Other values (5) 32
 
4.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 715
95.7%
Other Letter 32
 
4.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 129
18.0%
2 110
15.4%
3 82
11.5%
4 74
10.3%
5 71
9.9%
6 62
8.7%
7 59
8.3%
8 49
 
6.9%
9 44
 
6.2%
0 35
 
4.9%
Other Letter
ValueCountFrequency (%)
계 15
46.9%
소 14
43.8%
연 1
 
3.1%
번 1
 
3.1%
총 1
 
3.1%

Most occurring scripts

ValueCountFrequency (%)
Common 715
95.7%
Hangul 32
 
4.3%

Most frequent character per script

Common
ValueCountFrequency (%)
1 129
18.0%
2 110
15.4%
3 82
11.5%
4 74
10.3%
5 71
9.9%
6 62
8.7%
7 59
8.3%
8 49
 
6.9%
9 44
 
6.2%
0 35
 
4.9%
Hangul
ValueCountFrequency (%)
계 15
46.9%
소 14
43.8%
연 1
 
3.1%
번 1
 
3.1%
총 1
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 715
95.7%
Hangul 32
 
4.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 129
18.0%
2 110
15.4%
3 82
11.5%
4 74
10.3%
5 71
9.9%
6 62
8.7%
7 59
8.3%
8 49
 
6.9%
9 44
 
6.2%
0 35
 
4.9%
Hangul
ValueCountFrequency (%)
계 15
46.9%
소 14
43.8%
연 1
 
3.1%
번 1
 
3.1%
총 1
 
3.1%

전라북도 사회복지(생활)시설 현황
Categorical

HIGH CORRELATION 

Distinct49
Distinct (%)11.3%
Missing0
Missing (%)0.0%
Memory size3.5 KiB
노인요양시설
150 
노인요양공동생활가정
75 
장애인거주시설
51 
아동공동생활가정
34 
장애인 공동생활가정
16 
Other values (44)
108 

Length

Max length12
Median length10
Mean length7.4262673
Min length3

Unique

Unique28 ?
Unique (%)6.5%

Sample

1st row<NA>
2nd row시설구분
3rd row<NA>
4th row전라북도
5th row전주시

Common Values

ValueCountFrequency (%)
노인요양시설 150
34.6%
노인요양공동생활가정 75
17.3%
장애인거주시설 51
 
11.8%
아동공동생활가정 34
 
7.8%
장애인 공동생활가정 16
 
3.7%
아동양육시설 11
 
2.5%
노인양로시설 11
 
2.5%
사회복귀시설(종합시설) 10
 
2.3%
아동 공동생활시설 9
 
2.1%
한부모가족복지시설 7
 
1.6%
Other values (39) 60
 
13.8%

Length

2024-03-14T09:54:29.109924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
노인요양시설 150
32.1%
노인요양공동생활가정 75
16.1%
장애인거주시설 51
 
10.9%
아동공동생활가정 34
 
7.3%
장애인 18
 
3.9%
공동생활가정 16
 
3.4%
아동양육시설 11
 
2.4%
노인양로시설 11
 
2.4%
사회복귀시설(종합시설 10
 
2.1%
아동 9
 
1.9%
Other values (41) 82
17.6%

Unnamed: 2
Text

Distinct418
Distinct (%)96.5%
Missing1
Missing (%)0.2%
Memory size3.5 KiB
2024-03-14T09:54:29.275652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length15
Mean length6.0831409
Min length2

Characters and Unicode

Total characters2634
Distinct characters311
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique404 ?
Unique (%)93.3%

Sample

1st row시 설 일 반 현 황
2nd row시설명
3rd row416
4th row96
5th row마음건강복지관
ValueCountFrequency (%)
집 8
 
1.7%
벧엘요양원 3
 
0.6%
자림공동생활가정 3
 
0.6%
우리집 3
 
0.6%
소망요양원 2
 
0.4%
그룹홈 2
 
0.4%
효도의집 2
 
0.4%
쉼터 2
 
0.4%
1호 2
 
0.4%
노인요양원 2
 
0.4%
Other values (427) 440
93.8%
2024-03-14T09:54:29.601488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
원 181
 
6.9%
양 114
 
4.3%
집 109
 
4.1%
요 103
 
3.9%
의 100
 
3.8%
인 62
 
2.4%
56
 
2.1%
노 53
 
2.0%
사 46
 
1.7%
동 39
 
1.5%
Other values (301) 1771
67.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2539
96.4%
Space Separator 56
 
2.1%
Decimal Number 33
 
1.3%
Uppercase Letter 4
 
0.2%
Other Punctuation 1
 
< 0.1%
Control 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
원 181
 
7.1%
양 114
 
4.5%
집 109
 
4.3%
요 103
 
4.1%
의 100
 
3.9%
인 62
 
2.4%
노 53
 
2.1%
사 46
 
1.8%
동 39
 
1.5%
홈 39
 
1.5%
Other values (285) 1693
66.7%
Decimal Number
ValueCountFrequency (%)
1 9
27.3%
3 5
15.2%
6 4
12.1%
2 4
12.1%
4 4
12.1%
9 3
 
9.1%
8 2
 
6.1%
7 1
 
3.0%
0 1
 
3.0%
Uppercase Letter
ValueCountFrequency (%)
A 1
25.0%
C 1
25.0%
W 1
25.0%
Y 1
25.0%
Space Separator
ValueCountFrequency (%)
56
100.0%
Other Punctuation
ValueCountFrequency (%)
? 1
100.0%
Control
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2539
96.4%
Common 91
 
3.5%
Latin 4
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
원 181
 
7.1%
양 114
 
4.5%
집 109
 
4.3%
요 103
 
4.1%
의 100
 
3.9%
인 62
 
2.4%
노 53
 
2.1%
사 46
 
1.8%
동 39
 
1.5%
홈 39
 
1.5%
Other values (285) 1693
66.7%
Common
ValueCountFrequency (%)
56
61.5%
1 9
 
9.9%
3 5
 
5.5%
6 4
 
4.4%
2 4
 
4.4%
4 4
 
4.4%
9 3
 
3.3%
8 2
 
2.2%
? 1
 
1.1%
1
 
1.1%
Other values (2) 2
 
2.2%
Latin
ValueCountFrequency (%)
A 1
25.0%
C 1
25.0%
W 1
25.0%
Y 1
25.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2539
96.4%
ASCII 95
 
3.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
원 181
 
7.1%
양 114
 
4.5%
집 109
 
4.3%
요 103
 
4.1%
의 100
 
3.9%
인 62
 
2.4%
노 53
 
2.1%
사 46
 
1.8%
동 39
 
1.5%
홈 39
 
1.5%
Other values (285) 1693
66.7%
ASCII
ValueCountFrequency (%)
56
58.9%
1 9
 
9.5%
3 5
 
5.3%
6 4
 
4.2%
2 4
 
4.2%
4 4
 
4.2%
9 3
 
3.2%
8 2
 
2.1%
? 1
 
1.1%
A 1
 
1.1%
Other values (6) 6
 
6.3%

Unnamed: 3
Text

MISSING 

Distinct378
Distinct (%)90.6%
Missing17
Missing (%)3.9%
Memory size3.5 KiB
2024-03-14T09:54:29.942596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length8
Mean length8.0047962
Min length5

Characters and Unicode

Total characters3338
Distinct characters18
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

Unique348 ?
Unique (%)83.5%

Sample

1st row설치신고일
2nd row00.12.08
3rd row02.11.21
4th row85.01.11
5th row82.03.04
ValueCountFrequency (%)
06.11.10 5
 
1.2%
06.12.26 4
 
1.0%
10.03.15 3
 
0.7%
08.07.31 3
 
0.7%
08.06.30 3
 
0.7%
07.03.09 3
 
0.7%
06.12.29 3
 
0.7%
11.04.28 2
 
0.5%
05.04.07 2
 
0.5%
10.03.16 2
 
0.5%
Other values (367) 388
92.8%
2024-03-14T09:54:30.308581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 833
25.0%
0 792
23.7%
1 558
16.7%
2 271
 
8.1%
3 151
 
4.5%
8 148
 
4.4%
6 140
 
4.2%
7 114
 
3.4%
9 113
 
3.4%
5 111
 
3.3%
Other values (8) 107
 
3.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2492
74.7%
Other Punctuation 835
 
25.0%
Space Separator 6
 
0.2%
Other Letter 5
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 792
31.8%
1 558
22.4%
2 271
 
10.9%
3 151
 
6.1%
8 148
 
5.9%
6 140
 
5.6%
7 114
 
4.6%
9 113
 
4.5%
5 111
 
4.5%
4 94
 
3.8%
Other Letter
ValueCountFrequency (%)
설 1
20.0%
치 1
20.0%
신 1
20.0%
고 1
20.0%
일 1
20.0%
Other Punctuation
ValueCountFrequency (%)
. 833
99.8%
, 2
 
0.2%
Space Separator
ValueCountFrequency (%)
6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3333
99.9%
Hangul 5
 
0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
. 833
25.0%
0 792
23.8%
1 558
16.7%
2 271
 
8.1%
3 151
 
4.5%
8 148
 
4.4%
6 140
 
4.2%
7 114
 
3.4%
9 113
 
3.4%
5 111
 
3.3%
Other values (3) 102
 
3.1%
Hangul
ValueCountFrequency (%)
설 1
20.0%
치 1
20.0%
신 1
20.0%
고 1
20.0%
일 1
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3333
99.9%
Hangul 5
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 833
25.0%
0 792
23.8%
1 558
16.7%
2 271
 
8.1%
3 151
 
4.5%
8 148
 
4.4%
6 140
 
4.2%
7 114
 
3.4%
9 113
 
3.4%
5 111
 
3.3%
Other values (3) 102
 
3.1%
Hangul
ValueCountFrequency (%)
설 1
20.0%
치 1
20.0%
신 1
20.0%
고 1
20.0%
일 1
20.0%

Unnamed: 4
Text

MISSING 

Distinct396
Distinct (%)95.0%
Missing17
Missing (%)3.9%
Memory size3.5 KiB
2024-03-14T09:54:30.592528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length3
Mean length3.0527578
Min length2

Characters and Unicode

Total characters1273
Distinct characters181
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

Unique379 ?
Unique (%)90.9%

Sample

1st row시설장
2nd row김구중
3rd row오미화
4th row문미애
5th row심근자
ValueCountFrequency (%)
박미숙 4
 
0.9%
김은경 3
 
0.7%
진숙선 3
 
0.7%
신막래 2
 
0.5%
윤하람 2
 
0.5%
김혜란 2
 
0.5%
김진숙 2
 
0.5%
김인숙 2
 
0.5%
이미숙 2
 
0.5%
김선자 2
 
0.5%
Other values (391) 398
94.3%
2024-03-14T09:54:30.970896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
김 89
 
7.0%
이 58
 
4.6%
정 46
 
3.6%
영 43
 
3.4%
숙 39
 
3.1%
박 36
 
2.8%
희 30
 
2.4%
순 29
 
2.3%
성 28
 
2.2%
최 27
 
2.1%
Other values (171) 848
66.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1254
98.5%
Space Separator 18
 
1.4%
Decimal Number 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
김 89
 
7.1%
이 58
 
4.6%
정 46
 
3.7%
영 43
 
3.4%
숙 39
 
3.1%
박 36
 
2.9%
희 30
 
2.4%
순 29
 
2.3%
성 28
 
2.2%
최 27
 
2.2%
Other values (169) 829
66.1%
Space Separator
ValueCountFrequency (%)
18
100.0%
Decimal Number
ValueCountFrequency (%)
1 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1254
98.5%
Common 19
 
1.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
김 89
 
7.1%
이 58
 
4.6%
정 46
 
3.7%
영 43
 
3.4%
숙 39
 
3.1%
박 36
 
2.9%
희 30
 
2.4%
순 29
 
2.3%
성 28
 
2.2%
최 27
 
2.2%
Other values (169) 829
66.1%
Common
ValueCountFrequency (%)
18
94.7%
1 1
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1254
98.5%
ASCII 19
 
1.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
김 89
 
7.1%
이 58
 
4.6%
정 46
 
3.7%
영 43
 
3.4%
숙 39
 
3.1%
박 36
 
2.9%
희 30
 
2.4%
순 29
 
2.3%
성 28
 
2.2%
최 27
 
2.2%
Other values (169) 829
66.1%
ASCII
ValueCountFrequency (%)
18
94.7%
1 1
 
5.3%

Unnamed: 5
Text

MISSING 

Distinct403
Distinct (%)96.6%
Missing17
Missing (%)3.9%
Memory size3.5 KiB
2024-03-14T09:54:31.250207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length36
Median length30
Mean length17.203837
Min length3

Characters and Unicode

Total characters7174
Distinct characters277
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

Unique389 ?
Unique (%)93.3%

Sample

1st row주 소
2nd row전주시 완산구 물왕멀2길 20-29
3rd row전주시 덕진구 아중7길 9-5
4th row전주시 완산구 바람쐬는길152(대성동)
5th row전주시 덕진구 동부대로 926
ValueCountFrequency (%)
전주시 93
 
6.0%
익산시 78
 
5.0%
완산구 57
 
3.7%
전북 45
 
2.9%
덕진구 34
 
2.2%
군산시 28
 
1.8%
정읍시 27
 
1.7%
남원시 23
 
1.5%
완주군 21
 
1.4%
진안군 14
 
0.9%
Other values (840) 1132
72.9%
2024-03-14T09:54:31.656398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1159
 
16.2%
1 388
 
5.4%
시 285
 
4.0%
산 259
 
3.6%
길 238
 
3.3%
2 220
 
3.1%
로 187
 
2.6%
- 185
 
2.6%
면 174
 
2.4%
3 173
 
2.4%
Other values (267) 3906
54.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3970
55.3%
Decimal Number 1613
22.5%
Space Separator 1159
 
16.2%
Dash Punctuation 185
 
2.6%
Close Punctuation 87
 
1.2%
Open Punctuation 87
 
1.2%
Other Punctuation 69
 
1.0%
Lowercase Letter 2
 
< 0.1%
Uppercase Letter 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 285
 
7.2%
산 259
 
6.5%
길 238
 
6.0%
로 187
 
4.7%
면 174
 
4.4%
전 146
 
3.7%
주 140
 
3.5%
군 130
 
3.3%
동 126
 
3.2%
구 113
 
2.8%
Other values (245) 2172
54.7%
Decimal Number
ValueCountFrequency (%)
1 388
24.1%
2 220
13.6%
3 173
10.7%
4 153
 
9.5%
0 127
 
7.9%
6 121
 
7.5%
7 118
 
7.3%
5 113
 
7.0%
8 102
 
6.3%
9 98
 
6.1%
Other Punctuation
ValueCountFrequency (%)
, 21
30.4%
? 19
27.5%
/ 13
18.8%
@ 11
15.9%
. 5
 
7.2%
Uppercase Letter
ValueCountFrequency (%)
B 1
50.0%
A 1
50.0%
Space Separator
ValueCountFrequency (%)
1159
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 185
100.0%
Close Punctuation
ValueCountFrequency (%)
) 87
100.0%
Open Punctuation
ValueCountFrequency (%)
( 87
100.0%
Lowercase Letter
ValueCountFrequency (%)
a 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3970
55.3%
Common 3200
44.6%
Latin 4
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 285
 
7.2%
산 259
 
6.5%
길 238
 
6.0%
로 187
 
4.7%
면 174
 
4.4%
전 146
 
3.7%
주 140
 
3.5%
군 130
 
3.3%
동 126
 
3.2%
구 113
 
2.8%
Other values (245) 2172
54.7%
Common
ValueCountFrequency (%)
1159
36.2%
1 388
 
12.1%
2 220
 
6.9%
- 185
 
5.8%
3 173
 
5.4%
4 153
 
4.8%
0 127
 
4.0%
6 121
 
3.8%
7 118
 
3.7%
5 113
 
3.5%
Other values (9) 443
 
13.8%
Latin
ValueCountFrequency (%)
a 2
50.0%
B 1
25.0%
A 1
25.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3970
55.3%
ASCII 3204
44.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1159
36.2%
1 388
 
12.1%
2 220
 
6.9%
- 185
 
5.8%
3 173
 
5.4%
4 153
 
4.8%
0 127
 
4.0%
6 121
 
3.8%
7 118
 
3.7%
5 113
 
3.5%
Other values (12) 447
 
14.0%
Hangul
ValueCountFrequency (%)
시 285
 
7.2%
산 259
 
6.5%
길 238
 
6.0%
로 187
 
4.7%
면 174
 
4.4%
전 146
 
3.7%
주 140
 
3.5%
군 130
 
3.3%
동 126
 
3.2%
구 113
 
2.8%
Other values (245) 2172
54.7%

Unnamed: 6
Text

MISSING 

Distinct61
Distinct (%)14.6%
Missing15
Missing (%)3.5%
Memory size3.5 KiB
2024-03-14T09:54:31.829091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length3
Mean length3.4821002
Min length2

Characters and Unicode

Total characters1459
Distinct characters18
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

Unique17 ?
Unique (%)4.1%

Sample

1st row종사자
2nd row정원
3rd row 5,954
4th row 15
5th row 11
ValueCountFrequency (%)
2 52
 
12.4%
4 31
 
7.4%
5 29
 
6.9%
6 25
 
6.0%
7 21
 
5.0%
1 15
 
3.6%
12 14
 
3.3%
9 13
 
3.1%
11 13
 
3.1%
10 13
 
3.1%
Other values (51) 193
46.1%
2024-03-14T09:54:32.166321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
834
57.2%
2 128
 
8.8%
1 124
 
8.5%
4 66
 
4.5%
3 62
 
4.2%
5 58
 
4.0%
6 57
 
3.9%
7 36
 
2.5%
0 32
 
2.2%
9 27
 
1.9%
Other values (8) 35
 
2.4%

Most occurring categories

ValueCountFrequency (%)
Space Separator 834
57.2%
Decimal Number 611
41.9%
Dash Punctuation 8
 
0.5%
Other Letter 5
 
0.3%
Other Punctuation 1
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 128
20.9%
1 124
20.3%
4 66
10.8%
3 62
10.1%
5 58
9.5%
6 57
9.3%
7 36
 
5.9%
0 32
 
5.2%
9 27
 
4.4%
8 21
 
3.4%
Other Letter
ValueCountFrequency (%)
정 1
20.0%
원 1
20.0%
종 1
20.0%
사 1
20.0%
자 1
20.0%
Space Separator
ValueCountFrequency (%)
834
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 8
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1454
99.7%
Hangul 5
 
0.3%

Most frequent character per script

Common
ValueCountFrequency (%)
834
57.4%
2 128
 
8.8%
1 124
 
8.5%
4 66
 
4.5%
3 62
 
4.3%
5 58
 
4.0%
6 57
 
3.9%
7 36
 
2.5%
0 32
 
2.2%
9 27
 
1.9%
Other values (3) 30
 
2.1%
Hangul
ValueCountFrequency (%)
정 1
20.0%
원 1
20.0%
종 1
20.0%
사 1
20.0%
자 1
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1454
99.7%
Hangul 5
 
0.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
834
57.4%
2 128
 
8.8%
1 124
 
8.5%
4 66
 
4.5%
3 62
 
4.3%
5 58
 
4.0%
6 57
 
3.9%
7 36
 
2.5%
0 32
 
2.2%
9 27
 
1.9%
Other values (3) 30
 
2.1%
Hangul
ValueCountFrequency (%)
정 1
20.0%
원 1
20.0%
종 1
20.0%
사 1
20.0%
자 1
20.0%

Unnamed: 7
Text

MISSING 

Distinct58
Distinct (%)13.9%
Missing16
Missing (%)3.7%
Memory size3.5 KiB
2024-03-14T09:54:32.305202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length3
Mean length3.4808612
Min length2

Characters and Unicode

Total characters1455
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

Unique17 ?
Unique (%)4.1%

Sample

1st row현원
2nd row 5,769
3rd row 15
4th row 11
5th row 31
ValueCountFrequency (%)
2 55
 
13.2%
5 32
 
7.7%
4 31
 
7.4%
6 25
 
6.0%
1 16
 
3.8%
7 16
 
3.8%
10 15
 
3.6%
11 14
 
3.3%
3 14
 
3.3%
9 13
 
3.1%
Other values (48) 187
44.7%
2024-03-14T09:54:32.542727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
834
57.3%
1 134
 
9.2%
2 127
 
8.7%
4 63
 
4.3%
5 61
 
4.2%
3 54
 
3.7%
6 52
 
3.6%
0 35
 
2.4%
7 34
 
2.3%
8 28
 
1.9%
Other values (5) 33
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Space Separator 834
57.3%
Decimal Number 614
42.2%
Dash Punctuation 4
 
0.3%
Other Letter 2
 
0.1%
Other Punctuation 1
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 134
21.8%
2 127
20.7%
4 63
10.3%
5 61
9.9%
3 54
8.8%
6 52
 
8.5%
0 35
 
5.7%
7 34
 
5.5%
8 28
 
4.6%
9 26
 
4.2%
Other Letter
ValueCountFrequency (%)
현 1
50.0%
원 1
50.0%
Space Separator
ValueCountFrequency (%)
834
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1453
99.9%
Hangul 2
 
0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
834
57.4%
1 134
 
9.2%
2 127
 
8.7%
4 63
 
4.3%
5 61
 
4.2%
3 54
 
3.7%
6 52
 
3.6%
0 35
 
2.4%
7 34
 
2.3%
8 28
 
1.9%
Other values (3) 31
 
2.1%
Hangul
ValueCountFrequency (%)
현 1
50.0%
원 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1453
99.9%
Hangul 2
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
834
57.4%
1 134
 
9.2%
2 127
 
8.7%
4 63
 
4.3%
5 61
 
4.2%
3 54
 
3.7%
6 52
 
3.6%
0 35
 
2.4%
7 34
 
2.3%
8 28
 
1.9%
Other values (3) 31
 
2.1%
Hangul
ValueCountFrequency (%)
현 1
50.0%
원 1
50.0%

Unnamed: 8
Text

MISSING 

Distinct81
Distinct (%)19.3%
Missing15
Missing (%)3.5%
Memory size3.5 KiB
2024-03-14T09:54:32.722695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length4
Mean length3.699284
Min length2

Characters and Unicode

Total characters1550
Distinct characters18
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

Unique38 ?
Unique (%)9.1%

Sample

1st row생활인
2nd row정원
3rd row 14,059
4th row 26
5th row 20
ValueCountFrequency (%)
9 60
 
14.3%
7 48
 
11.5%
29 20
 
4.8%
50 19
 
4.5%
4 16
 
3.8%
80 16
 
3.8%
20 14
 
3.3%
28 11
 
2.6%
40 11
 
2.6%
16 11
 
2.6%
Other values (71) 193
46.1%
2024-03-14T09:54:33.019069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
834
53.8%
0 118
 
7.6%
9 98
 
6.3%
2 91
 
5.9%
1 81
 
5.2%
5 73
 
4.7%
7 69
 
4.5%
4 54
 
3.5%
6 47
 
3.0%
8 47
 
3.0%
Other values (8) 38
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Space Separator 834
53.8%
Decimal Number 707
45.6%
Other Letter 5
 
0.3%
Dash Punctuation 3
 
0.2%
Other Punctuation 1
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 118
16.7%
9 98
13.9%
2 91
12.9%
1 81
11.5%
5 73
10.3%
7 69
9.8%
4 54
7.6%
6 47
 
6.6%
8 47
 
6.6%
3 29
 
4.1%
Other Letter
ValueCountFrequency (%)
생 1
20.0%
활 1
20.0%
인 1
20.0%
정 1
20.0%
원 1
20.0%
Space Separator
ValueCountFrequency (%)
834
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1545
99.7%
Hangul 5
 
0.3%

Most frequent character per script

Common
ValueCountFrequency (%)
834
54.0%
0 118
 
7.6%
9 98
 
6.3%
2 91
 
5.9%
1 81
 
5.2%
5 73
 
4.7%
7 69
 
4.5%
4 54
 
3.5%
6 47
 
3.0%
8 47
 
3.0%
Other values (3) 33
 
2.1%
Hangul
ValueCountFrequency (%)
생 1
20.0%
활 1
20.0%
인 1
20.0%
정 1
20.0%
원 1
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1545
99.7%
Hangul 5
 
0.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
834
54.0%
0 118
 
7.6%
9 98
 
6.3%
2 91
 
5.9%
1 81
 
5.2%
5 73
 
4.7%
7 69
 
4.5%
4 54
 
3.5%
6 47
 
3.0%
8 47
 
3.0%
Other values (3) 33
 
2.1%
Hangul
ValueCountFrequency (%)
생 1
20.0%
활 1
20.0%
인 1
20.0%
정 1
20.0%
원 1
20.0%

Unnamed: 9
Text

MISSING 

Distinct93
Distinct (%)22.2%
Missing16
Missing (%)3.7%
Memory size3.5 KiB
2024-03-14T09:54:33.228852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length4
Mean length3.6028708
Min length1

Characters and Unicode

Total characters1506
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

Unique34 ?
Unique (%)8.1%

Sample

1st row현원
2nd row 11,359
3rd row 26
4th row 20
5th row 169
ValueCountFrequency (%)
4 34
 
8.1%
9 33
 
7.9%
5 24
 
5.7%
6 23
 
5.5%
7 19
 
4.5%
8 13
 
3.1%
15 13
 
3.1%
20 12
 
2.9%
10 11
 
2.6%
16 10
 
2.4%
Other values (80) 226
54.1%
2024-03-14T09:54:33.527723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
824
54.7%
1 97
 
6.4%
2 84
 
5.6%
4 80
 
5.3%
5 77
 
5.1%
6 72
 
4.8%
9 63
 
4.2%
7 54
 
3.6%
3 53
 
3.5%
0 52
 
3.5%
Other values (5) 50
 
3.3%

Most occurring categories

ValueCountFrequency (%)
Space Separator 824
54.7%
Decimal Number 670
44.5%
Dash Punctuation 9
 
0.6%
Other Letter 2
 
0.1%
Other Punctuation 1
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 97
14.5%
2 84
12.5%
4 80
11.9%
5 77
11.5%
6 72
10.7%
9 63
9.4%
7 54
8.1%
3 53
7.9%
0 52
7.8%
8 38
 
5.7%
Other Letter
ValueCountFrequency (%)
현 1
50.0%
원 1
50.0%
Space Separator
ValueCountFrequency (%)
824
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 9
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1504
99.9%
Hangul 2
 
0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
824
54.8%
1 97
 
6.4%
2 84
 
5.6%
4 80
 
5.3%
5 77
 
5.1%
6 72
 
4.8%
9 63
 
4.2%
7 54
 
3.6%
3 53
 
3.5%
0 52
 
3.5%
Other values (3) 48
 
3.2%
Hangul
ValueCountFrequency (%)
현 1
50.0%
원 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1504
99.9%
Hangul 2
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
824
54.8%
1 97
 
6.4%
2 84
 
5.6%
4 80
 
5.3%
5 77
 
5.1%
6 72
 
4.8%
9 63
 
4.2%
7 54
 
3.6%
3 53
 
3.5%
0 52
 
3.5%
Other values (3) 48
 
3.2%
Hangul
ValueCountFrequency (%)
현 1
50.0%
원 1
50.0%

Unnamed: 10
Text

MISSING 

Distinct163
Distinct (%)39.0%
Missing16
Missing (%)3.7%
Memory size3.5 KiB
2024-03-14T09:54:33.698394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length25
Median length23
Mean length5.5837321
Min length2

Characters and Unicode

Total characters2334
Distinct characters201
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

Unique125 ?
Unique (%)29.9%

Sample

1st row2015년 6월말 현재
2nd row운영주체(법인명)
3rd row마음건강복지재단
4th row인산의료재단
5th row참사랑복지회
ValueCountFrequency (%)
개인 193
40.4%
사회복지법인 23
 
4.8%
사복 16
 
3.3%
삼동회 13
 
2.7%
사복)중도원 6
 
1.3%
한기장복지재단 6
 
1.3%
원광효도마을 6
 
1.3%
사복)자림복지재단 5
 
1.0%
한울안 5
 
1.0%
전주가톨릭사회복지회 4
 
0.8%
Other values (162) 201
42.1%
2024-03-14T09:54:33.989429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
인 260
 
11.1%
개 199
 
8.5%
복 198
 
8.5%
사 140
 
6.0%
지 140
 
6.0%
회 108
 
4.6%
95
 
4.1%
) 90
 
3.9%
단 77
 
3.3%
재 76
 
3.3%
Other values (191) 951
40.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2118
90.7%
Space Separator 95
 
4.1%
Close Punctuation 90
 
3.9%
Open Punctuation 19
 
0.8%
Decimal Number 5
 
0.2%
Uppercase Letter 4
 
0.2%
Other Punctuation 2
 
0.1%
Other Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
인 260
 
12.3%
개 199
 
9.4%
복 198
 
9.3%
사 140
 
6.6%
지 140
 
6.6%
회 108
 
5.1%
단 77
 
3.6%
재 76
 
3.6%
원 64
 
3.0%
법 50
 
2.4%
Other values (177) 806
38.1%
Decimal Number
ValueCountFrequency (%)
2 1
20.0%
0 1
20.0%
1 1
20.0%
5 1
20.0%
6 1
20.0%
Uppercase Letter
ValueCountFrequency (%)
A 1
25.0%
C 1
25.0%
W 1
25.0%
Y 1
25.0%
Space Separator
ValueCountFrequency (%)
95
100.0%
Close Punctuation
ValueCountFrequency (%)
) 90
100.0%
Open Punctuation
ValueCountFrequency (%)
( 19
100.0%
Other Punctuation
ValueCountFrequency (%)
, 2
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2119
90.8%
Common 211
 
9.0%
Latin 4
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
인 260
 
12.3%
개 199
 
9.4%
복 198
 
9.3%
사 140
 
6.6%
지 140
 
6.6%
회 108
 
5.1%
단 77
 
3.6%
재 76
 
3.6%
원 64
 
3.0%
법 50
 
2.4%
Other values (178) 807
38.1%
Common
ValueCountFrequency (%)
95
45.0%
) 90
42.7%
( 19
 
9.0%
, 2
 
0.9%
2 1
 
0.5%
0 1
 
0.5%
1 1
 
0.5%
5 1
 
0.5%
6 1
 
0.5%
Latin
ValueCountFrequency (%)
A 1
25.0%
C 1
25.0%
W 1
25.0%
Y 1
25.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2118
90.7%
ASCII 215
 
9.2%
None 1
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
인 260
 
12.3%
개 199
 
9.4%
복 198
 
9.3%
사 140
 
6.6%
지 140
 
6.6%
회 108
 
5.1%
단 77
 
3.6%
재 76
 
3.6%
원 64
 
3.0%
법 50
 
2.4%
Other values (177) 806
38.1%
ASCII
ValueCountFrequency (%)
95
44.2%
) 90
41.9%
( 19
 
8.8%
, 2
 
0.9%
A 1
 
0.5%
2 1
 
0.5%
C 1
 
0.5%
0 1
 
0.5%
W 1
 
0.5%
1 1
 
0.5%
Other values (3) 3
 
1.4%
None
ValueCountFrequency (%)
㈜ 1
100.0%

Unnamed: 11
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct19
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Memory size3.5 KiB
<NA>
378 
지적
 
21
중증
 
8
지적
 
5
지체
 
4
Other values (14)
 
18

Length

Max length18
Median length4
Mean length3.8571429
Min length2

Unique

Unique10 ?
Unique (%)2.3%

Sample

1st row<NA>
2nd row비고
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 378
87.1%
지적 21
 
4.8%
중증 8
 
1.8%
지적 5
 
1.2%
지체 4
 
0.9%
휴지 2
 
0.5%
중증실비 2
 
0.5%
추가 2
 
0.5%
신규 2
 
0.5%
지적(여) 1
 
0.2%
Other values (9) 9
 
2.1%

Length

2024-03-14T09:54:34.195878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 378
86.9%
지적 26
 
6.0%
중증 8
 
1.8%
지체 4
 
0.9%
휴지 3
 
0.7%
중증실비 2
 
0.5%
추가 2
 
0.5%
신규 2
 
0.5%
주거(이용 1
 
0.2%
15.7.15폐지 1
 
0.2%
Other values (8) 8
 
1.8%

Correlations

2024-03-14T09:54:34.284160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Unnamed: 0전라북도 사회복지(생활)시설 현황Unnamed: 6Unnamed: 7Unnamed: 8Unnamed: 9Unnamed: 11
Unnamed: 01.0000.7850.0000.0000.0000.0000.875
전라북도 사회복지(생활)시설 현황0.7851.0000.8480.6980.9340.8950.942
Unnamed: 60.0000.8481.0000.9970.9830.9870.740
Unnamed: 70.0000.6980.9971.0000.9830.9880.687
Unnamed: 80.0000.9340.9830.9831.0000.9900.826
Unnamed: 90.0000.8950.9870.9880.9901.0000.736
Unnamed: 110.8750.9420.7400.6870.8260.7361.000
2024-03-14T09:54:34.373857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
전라북도 사회복지(생활)시설 현황Unnamed: 11
전라북도 사회복지(생활)시설 현황1.0000.700
Unnamed: 110.7001.000
2024-03-14T09:54:34.439626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
전라북도 사회복지(생활)시설 현황Unnamed: 11
전라북도 사회복지(생활)시설 현황1.0000.700
Unnamed: 110.7001.000

Missing values

2024-03-14T09:54:28.122567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T09:54:28.243389image/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.
2024-03-14T09:54:28.366899image/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

Unnamed: 0전라북도 사회복지(생활)시설 현황Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 6Unnamed: 7Unnamed: 8Unnamed: 9Unnamed: 10Unnamed: 11
0<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>2015년 6월말 현재<NA>
1연번시설구분시 설 일 반 현 황<NA><NA><NA>종사자<NA>생활인<NA>운영주체(법인명)비고
2<NA><NA>시설명설치신고일시설장주 소정원현원정원현원<NA><NA>
3총계전라북도416<NA><NA><NA>5,9545,76914,05911,359<NA><NA>
4소계전주시96<NA><NA><NA><NA><NA><NA><NA><NA><NA>
51사회복귀시설(종합시설)마음건강복지관00.12.08김구중전주시 완산구 물왕멀2길 20-2915152626마음건강복지재단<NA>
62사회복귀시설(종합시설)아름다운세상02.11.21오미화전주시 덕진구 아중7길 9-511112020인산의료재단<NA>
73정신요양시설참사랑낙원85.01.11문미애전주시 완산구 바람쐬는길152(대성동)3131176169참사랑복지회<NA>
84노숙인 요양시설전주사랑의집82.03.04심근자전주시 덕진구 동부대로 92614156060전주가톨릭사회복지회<NA>
95노숙인 자활시설일꾼쉼터98.12.07임내규덕진구 하가1길 6441917대한성공회유지재단<NA>
Unnamed: 0전라북도 사회복지(생활)시설 현황Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 6Unnamed: 7Unnamed: 8Unnamed: 9Unnamed: 10Unnamed: 11
42411아동양육고창행복원52.07.05박지환전북 고창군 고창읍 모양성로 116-1318156041고창행복원<NA>
42512아동양육요엘원66.06.04양향환전북 고창군 무장면 학천로 22121166443아모스<NA>
42613아동보호치료시설희망샘학교73.08.01김정강전북 고창군 무장면 학천로226-1626237055아모스<NA>
427소계부안군6<NA><NA><NA><NA><NA><NA><NA><NA><NA>
4281노인요양시설송산효도마을05.09.08김은경부안군 주산면 화봉길 8-3049499079한울안<NA>
4292노인요양시설은총의집06.09.29주혜숙부안군 상서면 부안로 1539-2118182324개인<NA>
4303노인요양시설로댐실버케어11.07.29유희성부안군 하서면 고인돌로 34715151819개인<NA>
4314노인요양시설부안군노인요양원10.03.11송용기부안군 부안읍 봉두길 5230304041사회복지법인한국장로교복지재단<NA>
4325노인요양시설산타요양원15.2.13김성일부안군 부안읍 매창로 287-43772015개인<NA>
4336노인요양공동생활가정섬김요양원14.12.01박난주부안군 동진로 1718899개인<NA>