Overview

Dataset statistics

Number of variables10
Number of observations21
Missing cells8
Missing cells (%)3.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.8 KiB
Average record size in memory89.3 B

Variable types

Categorical3
Text5
Numeric2

Dataset

Description전라북도 사회복지시설(정신요양, 노숙인, 생활, 이용, 자활, 복지관, 복지 법인 등) 안내 및 기초수급자, 차상위계층 통계
Author전라북도
URLhttps://www.data.go.kr/data/3081365/fileData.do

Alerts

정원 is highly overall correlated with 생활 and 2 other fieldsHigh correlation
생활 is highly overall correlated with 정원 and 3 other fieldsHigh correlation
시군 is highly overall correlated with 생활High correlation
시설종류 is highly overall correlated with 정원 and 2 other fieldsHigh correlation
이용 is highly overall correlated with 정원 and 2 other fieldsHigh correlation
원장 has 4 (19.0%) missing valuesMissing
연락처 has 1 (4.8%) missing valuesMissing
생활 has 3 (14.3%) missing valuesMissing
시설명 has unique valuesUnique

Reproduction

Analysis started2023-12-12 19:22:49.677410
Analysis finished2023-12-12 19:22:51.059409
Duration1.38 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군
Categorical

HIGH CORRELATION 

Distinct10
Distinct (%)47.6%
Missing0
Missing (%)0.0%
Memory size300.0 B
전주시
7 
익산시
5 
군산시
2 
정읍시
1 
남원시
1 
Other values (5)
5 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique7 ?
Unique (%)33.3%

Sample

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

Common Values

ValueCountFrequency (%)
전주시 7
33.3%
익산시 5
23.8%
군산시 2
 
9.5%
정읍시 1
 
4.8%
남원시 1
 
4.8%
김제시 1
 
4.8%
완주군 1
 
4.8%
진안군 1
 
4.8%
장수군 1
 
4.8%
임실군 1
 
4.8%

Length

2023-12-13T04:22:51.141439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:22:51.323092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
전주시 7
33.3%
익산시 5
23.8%
군산시 2
 
9.5%
정읍시 1
 
4.8%
남원시 1
 
4.8%
김제시 1
 
4.8%
완주군 1
 
4.8%
진안군 1
 
4.8%
장수군 1
 
4.8%
임실군 1
 
4.8%

시설명
Text

UNIQUE 

Distinct21
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size300.0 B
2023-12-13T04:22:51.531232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length6
Mean length6.047619
Min length2

Characters and Unicode

Total characters127
Distinct characters60
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

Unique21 ?
Unique (%)100.0%

Sample

1st row마음건강복지관
2nd row마음건강 회복홈
3rd row마음건강 힐링홈
4th row아름다운세상
5th row아름다운집
ValueCountFrequency (%)
꿈 2
 
7.4%
둥근나래 2
 
7.4%
마음건강 2
 
7.4%
여성홈 1
 
3.7%
장수보건복지센터 1
 
3.7%
소망의집 1
 
3.7%
한사랑 1
 
3.7%
서로돕는마을 1
 
3.7%
성일유엔아이 1
 
3.7%
마음사랑의집 1
 
3.7%
Other values (14) 14
51.9%
2023-12-13T04:22:51.916962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
마 7
 
5.5%
6
 
4.7%
음 6
 
4.7%
건 5
 
3.9%
집 5
 
3.9%
강 4
 
3.1%
복 4
 
3.1%
의 4
 
3.1%
터 4
 
3.1%
홈 4
 
3.1%
Other values (50) 78
61.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 121
95.3%
Space Separator 6
 
4.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
마 7
 
5.8%
음 6
 
5.0%
건 5
 
4.1%
집 5
 
4.1%
강 4
 
3.3%
복 4
 
3.3%
의 4
 
3.3%
터 4
 
3.3%
홈 4
 
3.3%
근 3
 
2.5%
Other values (49) 75
62.0%
Space Separator
ValueCountFrequency (%)
6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 121
95.3%
Common 6
 
4.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
마 7
 
5.8%
음 6
 
5.0%
건 5
 
4.1%
집 5
 
4.1%
강 4
 
3.3%
복 4
 
3.3%
의 4
 
3.3%
터 4
 
3.3%
홈 4
 
3.3%
근 3
 
2.5%
Other values (49) 75
62.0%
Common
ValueCountFrequency (%)
6
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 121
95.3%
ASCII 6
 
4.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
마 7
 
5.8%
음 6
 
5.0%
건 5
 
4.1%
집 5
 
4.1%
강 4
 
3.3%
복 4
 
3.3%
의 4
 
3.3%
터 4
 
3.3%
홈 4
 
3.3%
근 3
 
2.5%
Other values (49) 75
62.0%
ASCII
ValueCountFrequency (%)
6
100.0%

시설종류
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)33.3%
Missing0
Missing (%)0.0%
Memory size300.0 B
종합시설(입소, 주간)
7 
공동생활가정
7 
주간재활시설
2 
입소생활시설
2 
종합시설(주거, 주간)
1 
Other values (2)
2 

Length

Max length12
Median length6
Mean length8.2857143
Min length6

Unique

Unique3 ?
Unique (%)14.3%

Sample

1st row종합시설(입소, 주간)
2nd row공동생활가정
3rd row공동생활가정
4th row종합시설(입소, 주간)
5th row공동생활가정

Common Values

ValueCountFrequency (%)
종합시설(입소, 주간) 7
33.3%
공동생활가정 7
33.3%
주간재활시설 2
 
9.5%
입소생활시설 2
 
9.5%
종합시설(주거, 주간) 1
 
4.8%
주거제공시설 1
 
4.8%
직업재활시설 1
 
4.8%

Length

2023-12-13T04:22:52.103153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:22:52.267838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
주간 8
27.6%
종합시설(입소 7
24.1%
공동생활가정 7
24.1%
주간재활시설 2
 
6.9%
입소생활시설 2
 
6.9%
종합시설(주거 1
 
3.4%
주거제공시설 1
 
3.4%
직업재활시설 1
 
3.4%

원장
Text

MISSING 

Distinct17
Distinct (%)100.0%
Missing4
Missing (%)19.0%
Memory size300.0 B
2023-12-13T04:22:52.482898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters51
Distinct characters37
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

Unique17 ?
Unique (%)100.0%

Sample

1st row박헌수
2nd row최유영
3rd row김미경
4th row강경희
5th row윤규열
ValueCountFrequency (%)
박헌수 1
 
5.9%
손동혁 1
 
5.9%
박경원 1
 
5.9%
정종성 1
 
5.9%
이창영 1
 
5.9%
김승재 1
 
5.9%
백현숙 1
 
5.9%
박지영 1
 
5.9%
이미경 1
 
5.9%
김미경 1
 
5.9%
Other values (7) 7
41.2%
2023-12-13T04:22:52.855088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
경 5
 
9.8%
영 4
 
7.8%
박 3
 
5.9%
이 3
 
5.9%
김 2
 
3.9%
미 2
 
3.9%
윤 2
 
3.9%
백 1
 
2.0%
현 1
 
2.0%
숙 1
 
2.0%
Other values (27) 27
52.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 51
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
경 5
 
9.8%
영 4
 
7.8%
박 3
 
5.9%
이 3
 
5.9%
김 2
 
3.9%
미 2
 
3.9%
윤 2
 
3.9%
백 1
 
2.0%
현 1
 
2.0%
숙 1
 
2.0%
Other values (27) 27
52.9%

Most occurring scripts

ValueCountFrequency (%)
Hangul 51
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
경 5
 
9.8%
영 4
 
7.8%
박 3
 
5.9%
이 3
 
5.9%
김 2
 
3.9%
미 2
 
3.9%
윤 2
 
3.9%
백 1
 
2.0%
현 1
 
2.0%
숙 1
 
2.0%
Other values (27) 27
52.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 51
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
경 5
 
9.8%
영 4
 
7.8%
박 3
 
5.9%
이 3
 
5.9%
김 2
 
3.9%
미 2
 
3.9%
윤 2
 
3.9%
백 1
 
2.0%
현 1
 
2.0%
숙 1
 
2.0%
Other values (27) 27
52.9%

연락처
Text

MISSING 

Distinct20
Distinct (%)100.0%
Missing1
Missing (%)4.8%
Memory size300.0 B
2023-12-13T04:22:53.045637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length12.2
Min length12

Characters and Unicode

Total characters244
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique20 ?
Unique (%)100.0%

Sample

1st row063-232-5558
2nd row063-224-7032
3rd row063-904-4334
4th row063-244-2816
5th row070-8201-2816
ValueCountFrequency (%)
063-232-5558 1
 
5.0%
063-224-7032 1
 
5.0%
063-351-7130 1
 
5.0%
063-432-2194 1
 
5.0%
063-232-7567 1
 
5.0%
063-544-3380 1
 
5.0%
063-634-2344 1
 
5.0%
063-533-8233 1
 
5.0%
063-857-4031 1
 
5.0%
063-837-6446 1
 
5.0%
Other values (10) 10
50.0%
2023-12-13T04:22:53.349983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 40
16.4%
3 38
15.6%
0 34
13.9%
4 32
13.1%
6 27
11.1%
2 18
7.4%
7 14
 
5.7%
5 12
 
4.9%
1 12
 
4.9%
8 9
 
3.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 204
83.6%
Dash Punctuation 40
 
16.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 38
18.6%
0 34
16.7%
4 32
15.7%
6 27
13.2%
2 18
8.8%
7 14
 
6.9%
5 12
 
5.9%
1 12
 
5.9%
8 9
 
4.4%
9 8
 
3.9%
Dash Punctuation
ValueCountFrequency (%)
- 40
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 244
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 40
16.4%
3 38
15.6%
0 34
13.9%
4 32
13.1%
6 27
11.1%
2 18
7.4%
7 14
 
5.7%
5 12
 
4.9%
1 12
 
4.9%
8 9
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 244
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 40
16.4%
3 38
15.6%
0 34
13.9%
4 32
13.1%
6 27
11.1%
2 18
7.4%
7 14
 
5.7%
5 12
 
4.9%
1 12
 
4.9%
8 9
 
3.7%

주소(도로명)
Text

Distinct20
Distinct (%)95.2%
Missing0
Missing (%)0.0%
Memory size300.0 B
2023-12-13T04:22:53.563658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length30
Median length22
Mean length16.952381
Min length10

Characters and Unicode

Total characters356
Distinct characters76
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique19 ?
Unique (%)90.5%

Sample

1st row전주시 완산구 물왕멀2길20-29
2nd row전주시 완산구 물왕멀2길 25
3rd row전주시 완산구 물왕멀2길 20-17
4th row전주시 덕진구 아중7길 9-5
5th row전주시 덕진구 인교9길 11, 401호
ValueCountFrequency (%)
전주시 7
 
9.5%
익산시 5
 
6.8%
덕진구 4
 
5.4%
3층 3
 
4.1%
완산구 3
 
4.1%
목천로229 3
 
4.1%
물왕멀2길 2
 
2.7%
군산시 2
 
2.7%
사매면 1
 
1.4%
춘향로 1
 
1.4%
Other values (43) 43
58.1%
2023-12-13T04:22:53.896385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
55
 
15.4%
2 24
 
6.7%
시 17
 
4.8%
1 16
 
4.5%
산 11
 
3.1%
로 11
 
3.1%
길 11
 
3.1%
3 9
 
2.5%
- 9
 
2.5%
, 8
 
2.2%
Other values (66) 185
52.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 188
52.8%
Decimal Number 85
23.9%
Space Separator 55
 
15.4%
Dash Punctuation 9
 
2.5%
Other Punctuation 8
 
2.2%
Close Punctuation 5
 
1.4%
Open Punctuation 5
 
1.4%
Uppercase Letter 1
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 17
 
9.0%
산 11
 
5.9%
로 11
 
5.9%
길 11
 
5.9%
주 8
 
4.3%
구 8
 
4.3%
전 7
 
3.7%
군 6
 
3.2%
진 6
 
3.2%
익 5
 
2.7%
Other values (50) 98
52.1%
Decimal Number
ValueCountFrequency (%)
2 24
28.2%
1 16
18.8%
3 9
 
10.6%
9 7
 
8.2%
6 7
 
8.2%
0 7
 
8.2%
5 6
 
7.1%
4 5
 
5.9%
7 3
 
3.5%
8 1
 
1.2%
Space Separator
ValueCountFrequency (%)
55
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 9
100.0%
Other Punctuation
ValueCountFrequency (%)
, 8
100.0%
Close Punctuation
ValueCountFrequency (%)
) 5
100.0%
Open Punctuation
ValueCountFrequency (%)
( 5
100.0%
Uppercase Letter
ValueCountFrequency (%)
B 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 188
52.8%
Common 167
46.9%
Latin 1
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 17
 
9.0%
산 11
 
5.9%
로 11
 
5.9%
길 11
 
5.9%
주 8
 
4.3%
구 8
 
4.3%
전 7
 
3.7%
군 6
 
3.2%
진 6
 
3.2%
익 5
 
2.7%
Other values (50) 98
52.1%
Common
ValueCountFrequency (%)
55
32.9%
2 24
14.4%
1 16
 
9.6%
3 9
 
5.4%
- 9
 
5.4%
, 8
 
4.8%
9 7
 
4.2%
6 7
 
4.2%
0 7
 
4.2%
5 6
 
3.6%
Other values (5) 19
 
11.4%
Latin
ValueCountFrequency (%)
B 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 188
52.8%
ASCII 168
47.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
55
32.7%
2 24
14.3%
1 16
 
9.5%
3 9
 
5.4%
- 9
 
5.4%
, 8
 
4.8%
9 7
 
4.2%
6 7
 
4.2%
0 7
 
4.2%
5 6
 
3.6%
Other values (6) 20
 
11.9%
Hangul
ValueCountFrequency (%)
시 17
 
9.0%
산 11
 
5.9%
로 11
 
5.9%
길 11
 
5.9%
주 8
 
4.3%
구 8
 
4.3%
전 7
 
3.7%
군 6
 
3.2%
진 6
 
3.2%
익 5
 
2.7%
Other values (50) 98
52.1%

정원
Real number (ℝ)

HIGH CORRELATION 

Distinct9
Distinct (%)42.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.904762
Minimum4
Maximum123
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-13T04:22:54.011489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile4
Q14
median25
Q350
95-th percentile50
Maximum123
Range119
Interquartile range (IQR)46

Descriptive statistics

Standard deviation28.941155
Coefficient of variation (CV)1.0012591
Kurtosis4.4298887
Mean28.904762
Median Absolute Deviation (MAD)21
Skewness1.7362617
Sum607
Variance837.59048
MonotonicityNot monotonic
2023-12-13T04:22:54.104323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
4 7
33.3%
50 5
23.8%
10 2
 
9.5%
30 2
 
9.5%
123 1
 
4.8%
41 1
 
4.8%
45 1
 
4.8%
25 1
 
4.8%
15 1
 
4.8%
ValueCountFrequency (%)
4 7
33.3%
10 2
 
9.5%
15 1
 
4.8%
25 1
 
4.8%
30 2
 
9.5%
41 1
 
4.8%
45 1
 
4.8%
50 5
23.8%
123 1
 
4.8%
ValueCountFrequency (%)
123 1
 
4.8%
50 5
23.8%
45 1
 
4.8%
41 1
 
4.8%
30 2
 
9.5%
25 1
 
4.8%
15 1
 
4.8%
10 2
 
9.5%
4 7
33.3%

생활
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct7
Distinct (%)38.9%
Missing3
Missing (%)14.3%
Infinite0
Infinite (%)0.0%
Mean13.111111
Minimum4
Maximum30
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-13T04:22:54.219751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile4
Q14
median12.5
Q320
95-th percentile25.75
Maximum30
Range26
Interquartile range (IQR)16

Descriptive statistics

Standard deviation8.8176887
Coefficient of variation (CV)0.67253558
Kurtosis-1.2780568
Mean13.111111
Median Absolute Deviation (MAD)8.5
Skewness0.33121384
Sum236
Variance77.751634
MonotonicityNot monotonic
2023-12-13T04:22:54.313974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
4 7
33.3%
20 4
19.0%
10 2
 
9.5%
15 2
 
9.5%
23 1
 
4.8%
30 1
 
4.8%
25 1
 
4.8%
(Missing) 3
14.3%
ValueCountFrequency (%)
4 7
33.3%
10 2
 
9.5%
15 2
 
9.5%
20 4
19.0%
23 1
 
4.8%
25 1
 
4.8%
30 1
 
4.8%
ValueCountFrequency (%)
30 1
 
4.8%
25 1
 
4.8%
23 1
 
4.8%
20 4
19.0%
15 2
 
9.5%
10 2
 
9.5%
4 7
33.3%

이용
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)28.6%
Missing0
Missing (%)0.0%
Memory size300.0 B
<NA>
10 
30
7 
100
 
1
31
 
1
10
 
1

Length

Max length4
Median length3
Mean length3
Min length2

Unique

Unique4 ?
Unique (%)19.0%

Sample

1st row100
2nd row<NA>
3rd row<NA>
4th row30
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 10
47.6%
30 7
33.3%
100 1
 
4.8%
31 1
 
4.8%
10 1
 
4.8%
20 1
 
4.8%

Length

2023-12-13T04:22:54.449920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:22:54.559087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 10
47.6%
30 7
33.3%
100 1
 
4.8%
31 1
 
4.8%
10 1
 
4.8%
20 1
 
4.8%

운영주체
Text

Distinct13
Distinct (%)61.9%
Missing0
Missing (%)0.0%
Memory size300.0 B
2023-12-13T04:22:54.705948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length6
Mean length6.047619
Min length3

Characters and Unicode

Total characters127
Distinct characters38
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

Unique9 ?
Unique (%)42.9%

Sample

1st row마음건강복지재단
2nd row마음건강복지재단
3rd row마음건강복지재단
4th row인산의료재단
5th row인산의료재단
ValueCountFrequency (%)
인산의료재단 4
19.0%
마음건강복지재단 3
14.3%
삼동회 3
14.3%
규란복지재단 2
9.5%
제은복지재단 1
 
4.8%
보배복지재단 1
 
4.8%
샘골복지재단 1
 
4.8%
성일의료법인 1
 
4.8%
일봉복지재단 1
 
4.8%
한국장로교복지재단 1
 
4.8%
Other values (3) 3
14.3%
2023-12-13T04:22:54.995051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
재 17
13.4%
단 17
13.4%
복 13
 
10.2%
지 13
 
10.2%
인 5
 
3.9%
의 5
 
3.9%
료 5
 
3.9%
산 4
 
3.1%
회 3
 
2.4%
삼 3
 
2.4%
Other values (28) 42
33.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 127
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
재 17
13.4%
단 17
13.4%
복 13
 
10.2%
지 13
 
10.2%
인 5
 
3.9%
의 5
 
3.9%
료 5
 
3.9%
산 4
 
3.1%
회 3
 
2.4%
삼 3
 
2.4%
Other values (28) 42
33.1%

Most occurring scripts

ValueCountFrequency (%)
Hangul 127
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
재 17
13.4%
단 17
13.4%
복 13
 
10.2%
지 13
 
10.2%
인 5
 
3.9%
의 5
 
3.9%
료 5
 
3.9%
산 4
 
3.1%
회 3
 
2.4%
삼 3
 
2.4%
Other values (28) 42
33.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 127
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
재 17
13.4%
단 17
13.4%
복 13
 
10.2%
지 13
 
10.2%
인 5
 
3.9%
의 5
 
3.9%
료 5
 
3.9%
산 4
 
3.1%
회 3
 
2.4%
삼 3
 
2.4%
Other values (28) 42
33.1%

Interactions

2023-12-13T04:22:50.414605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:22:50.175273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:22:50.531755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:22:50.301052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T04:22:55.088696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군시설명시설종류원장연락처주소(도로명)정원생활이용운영주체
시군1.0001.0000.6421.0001.0001.0000.7660.8820.4401.000
시설명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
시설종류0.6421.0001.0001.0001.0001.0000.8010.8190.7700.852
원장1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
연락처1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
주소(도로명)1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
정원0.7661.0000.8011.0001.0001.0001.0000.9430.7700.763
생활0.8821.0000.8191.0001.0001.0000.9431.0001.0000.899
이용0.4401.0000.7701.0001.0001.0000.7701.0001.0001.000
운영주체1.0001.0000.8521.0001.0001.0000.7630.8991.0001.000
2023-12-13T04:22:55.220677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시설종류시군이용
시설종류1.0000.3110.648
시군0.3111.0000.000
이용0.6480.0001.000
2023-12-13T04:22:55.300722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
정원생활시군시설종류이용
정원1.0000.9090.3170.6300.648
생활0.9091.0000.6500.6440.866
시군0.3170.6501.0000.3110.000
시설종류0.6300.6440.3111.0000.648
이용0.6480.8660.0000.6481.000

Missing values

2023-12-13T04:22:50.680422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T04:22:50.857878image/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.
2023-12-13T04:22:50.982882image/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

시군시설명시설종류원장연락처주소(도로명)정원생활이용운영주체
0전주시마음건강복지관종합시설(입소, 주간)박헌수063-232-5558전주시 완산구 물왕멀2길20-2912323100마음건강복지재단
1전주시마음건강 회복홈공동생활가정최유영063-224-7032전주시 완산구 물왕멀2길 2544<NA>마음건강복지재단
2전주시마음건강 힐링홈공동생활가정<NA>063-904-4334전주시 완산구 물왕멀2길 20-1744<NA>마음건강복지재단
3전주시아름다운세상종합시설(입소, 주간)김미경063-244-2816전주시 덕진구 아중7길 9-5502030인산의료재단
4전주시아름다운집공동생활가정강경희070-8201-2816전주시 덕진구 인교9길 11, 401호44<NA>인산의료재단
5전주시꿈이있는집공동생활가정<NA>070-7561-3714전주시 덕진구 아중1길 23-3, (퓨처빌B 402호)44<NA>인산의료재단
6전주시행복한집공동생활가정<NA>070-4141-0052전주시 덕진구 인교로35-26, (501호)44<NA>인산의료재단
7군산시희망의쉼터종합시설(주거, 주간)윤규열063-442-4599군산시 둔배미길6-2411031규란복지재단
8군산시희망의그루터기주거제공시설윤길준063-442-4597군산시 둔배미길6-61010<NA>규란복지재단
9익산시둥근마음종합시설(입소, 주간)이법영063-841-6446익산시 목천로229, (1,2층)502030삼동회
시군시설명시설종류원장연락처주소(도로명)정원생활이용운영주체
11익산시둥근나래 꿈 남성홈공동생활가정<NA><NA>익산시 목천로229, (3층)44<NA>삼동회
12익산시참마음직업재활시설이미경063-837-6446익산시 황등면 황등중앙로 11210<NA>10제은복지재단
13익산시보배정신건강상담센터주간재활시설박경원063-857-4031익산시 인북로2길5330<NA>30보배복지재단
14정읍시마음사랑의집주간재활시설손동혁063-533-8233정읍시 벚꽃로32330<NA>30샘골복지재단
15남원시성일유엔아이종합시설(입소, 주간)박지영063-634-2344남원시 사매면 춘향로 822-129503020성일의료법인
16김제시서로돕는마을종합시설(입소, 주간)백현숙063-544-3380김제시 금구면 낙산1길 46502030일봉복지재단
17완주군한사랑종합시설(입소, 주간)김승재063-232-7567완주군 상관면 신리로 61, 3층451530한국장로교복지재단
18진안군소망의집입소생활시설이창영063-432-2194진안군 진안읍 원반월 안길412525<NA>반월복지재단
19장수군장수보건복지센터입소생활시설정종성063-351-7130장수군 장수읍 장천로4001515<NA>장수복지재단
20임실군동행종합시설(입소, 주간)고병훈063-643-0764임실군 임실읍 호국로1716-15502030미리암복지재단