Overview

Dataset statistics

Number of variables11
Number of observations215
Missing cells345
Missing cells (%)14.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory18.6 KiB
Average record size in memory88.6 B

Variable types

Unsupported6
Categorical1
Text4

Alerts

Unnamed: 3 has 18 (8.4%) missing valuesMissing
Unnamed: 4 has 17 (7.9%) missing valuesMissing
Unnamed: 5 has 17 (7.9%) missing valuesMissing
Unnamed: 6 has 16 (7.4%) missing valuesMissing
Unnamed: 7 has 17 (7.9%) missing valuesMissing
Unnamed: 8 has 29 (13.5%) missing valuesMissing
Unnamed: 9 has 16 (7.4%) missing valuesMissing
Unnamed: 10 has 212 (98.6%) missing valuesMissing
전라북도 사회복지(이용)시설 현황 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 2 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 6 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 7 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 8 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 9 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2024-03-14 03:18:16.621265
Analysis finished2024-03-14 03:18:17.418894
Duration0.8 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

전라북도 사회복지(이용)시설 현황
Unsupported

REJECTED  UNSUPPORTED 

Missing2
Missing (%)0.9%
Memory size1.8 KiB

Unnamed: 1
Categorical

Distinct43
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
장애인주간보호시설
26 
여성폭력피해보호시설
23 
지역자활센터
18 
장애인보호작업장
17 
장애인수화통역센터
15 
Other values (38)
116 

Length

Max length16
Median length12
Mean length7.4883721
Min length3

Unique

Unique22 ?
Unique (%)10.2%

Sample

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

Common Values

ValueCountFrequency (%)
장애인주간보호시설 26
12.1%
여성폭력피해보호시설 23
10.7%
지역자활센터 18
 
8.4%
장애인보호작업장 17
 
7.9%
장애인수화통역센터 15
 
7.0%
장애인심부름센터 15
 
7.0%
사회복지관 14
 
6.5%
다문화가족지원센터 14
 
6.5%
노인복지관 13
 
6.0%
장애인복지관 12
 
5.6%
Other values (33) 48
22.3%

Length

2024-03-14T12:18:17.482234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
장애인주간보호시설 26
11.9%
여성폭력피해보호시설 23
10.5%
지역자활센터 18
 
8.2%
장애인보호작업장 17
 
7.8%
장애인수화통역센터 15
 
6.8%
장애인심부름센터 15
 
6.8%
노인복지관 15
 
6.8%
사회복지관 14
 
6.4%
다문화가족지원센터 14
 
6.4%
장애인복지관 12
 
5.5%
Other values (32) 50
22.8%

Unnamed: 2
Unsupported

REJECTED  UNSUPPORTED 

Missing1
Missing (%)0.5%
Memory size1.8 KiB

Unnamed: 3
Text

MISSING 

Distinct182
Distinct (%)92.4%
Missing18
Missing (%)8.4%
Memory size1.8 KiB
2024-03-14T12:18:17.717314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length12
Mean length8.7817259
Min length5

Characters and Unicode

Total characters1730
Distinct characters20
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique170 ?
Unique (%)86.3%

Sample

1st row설치신고일
2nd row88.12.27
3rd row92.12.28
4th row95.12.29
5th row04.04.01
ValueCountFrequency (%)
2001.07.01 5
 
2.2%
1 4
 
1.8%
09 4
 
1.8%
06 3
 
1.3%
2009.02.13 2
 
0.9%
6 2
 
0.9%
2001.7.1 2
 
0.9%
1993 2
 
0.9%
07 2
 
0.9%
05 2
 
0.9%
Other values (188) 198
87.6%
2024-03-14T12:18:18.079696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 394
22.8%
0 380
22.0%
1 272
15.7%
2 227
13.1%
9 110
 
6.4%
3 79
 
4.6%
7 55
 
3.2%
8 51
 
2.9%
5 48
 
2.8%
4 43
 
2.5%
Other values (10) 71
 
4.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1300
75.1%
Other Punctuation 394
 
22.8%
Space Separator 28
 
1.6%
Other Letter 5
 
0.3%
Control 1
 
0.1%
Open Punctuation 1
 
0.1%
Close Punctuation 1
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 380
29.2%
1 272
20.9%
2 227
17.5%
9 110
 
8.5%
3 79
 
6.1%
7 55
 
4.2%
8 51
 
3.9%
5 48
 
3.7%
4 43
 
3.3%
6 35
 
2.7%
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 (%)
. 394
100.0%
Space Separator
ValueCountFrequency (%)
28
100.0%
Control
ValueCountFrequency (%)
1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1725
99.7%
Hangul 5
 
0.3%

Most frequent character per script

Common
ValueCountFrequency (%)
. 394
22.8%
0 380
22.0%
1 272
15.8%
2 227
13.2%
9 110
 
6.4%
3 79
 
4.6%
7 55
 
3.2%
8 51
 
3.0%
5 48
 
2.8%
4 43
 
2.5%
Other values (5) 66
 
3.8%
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 1725
99.7%
Hangul 5
 
0.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 394
22.8%
0 380
22.0%
1 272
15.8%
2 227
13.2%
9 110
 
6.4%
3 79
 
4.6%
7 55
 
3.2%
8 51
 
3.0%
5 48
 
2.8%
4 43
 
2.5%
Other values (5) 66
 
3.8%
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 

Distinct179
Distinct (%)90.4%
Missing17
Missing (%)7.9%
Memory size1.8 KiB
2024-03-14T12:18:18.387675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3
Min length2

Characters and Unicode

Total characters594
Distinct characters135
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

Unique163 ?
Unique (%)82.3%

Sample

1st row시설장
2nd row문정훈
3rd row강기오
4th row노영웅
5th row정식수
ValueCountFrequency (%)
김 3
 
1.5%
이춘섭 3
 
1.5%
이영재 3
 
1.5%
진 3
 
1.5%
전윤주 2
 
1.0%
이희수 2
 
1.0%
배인재 2
 
1.0%
박승택 2
 
1.0%
한갑수 2
 
1.0%
김원경 2
 
1.0%
Other values (171) 178
88.1%
2024-03-14T12:18:18.854991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
김 39
 
6.6%
이 27
 
4.5%
영 22
 
3.7%
정 19
 
3.2%
박 18
 
3.0%
진 16
 
2.7%
희 14
 
2.4%
수 14
 
2.4%
성 13
 
2.2%
재 11
 
1.9%
Other values (125) 401
67.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 588
99.0%
Space Separator 4
 
0.7%
Open Punctuation 1
 
0.2%
Close Punctuation 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
김 39
 
6.6%
이 27
 
4.6%
영 22
 
3.7%
정 19
 
3.2%
박 18
 
3.1%
진 16
 
2.7%
희 14
 
2.4%
수 14
 
2.4%
성 13
 
2.2%
재 11
 
1.9%
Other values (122) 395
67.2%
Space Separator
ValueCountFrequency (%)
4
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 588
99.0%
Common 6
 
1.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
김 39
 
6.6%
이 27
 
4.6%
영 22
 
3.7%
정 19
 
3.2%
박 18
 
3.1%
진 16
 
2.7%
희 14
 
2.4%
수 14
 
2.4%
성 13
 
2.2%
재 11
 
1.9%
Other values (122) 395
67.2%
Common
ValueCountFrequency (%)
4
66.7%
( 1
 
16.7%
) 1
 
16.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 588
99.0%
ASCII 6
 
1.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
김 39
 
6.6%
이 27
 
4.6%
영 22
 
3.7%
정 19
 
3.2%
박 18
 
3.1%
진 16
 
2.7%
희 14
 
2.4%
수 14
 
2.4%
성 13
 
2.2%
재 11
 
1.9%
Other values (122) 395
67.2%
ASCII
ValueCountFrequency (%)
4
66.7%
( 1
 
16.7%
) 1
 
16.7%

Unnamed: 5
Text

MISSING 

Distinct176
Distinct (%)88.9%
Missing17
Missing (%)7.9%
Memory size1.8 KiB
2024-03-14T12:18:19.204777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length30
Median length25
Mean length15.560606
Min length3

Characters and Unicode

Total characters3081
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

Unique158 ?
Unique (%)79.8%

Sample

1st row주 소
2nd row전주시 완산구 흑석로 70
3rd row전주시 완산구 덕적골2길 10
4th row전주시 완산구 모악로 4726-1
5th row전주시 완산구 선너머로 54
ValueCountFrequency (%)
전주시 50
 
7.0%
완산구 36
 
5.0%
익산시 24
 
3.3%
군산시 20
 
2.8%
정읍시 14
 
1.9%
김제시 12
 
1.7%
덕진구 12
 
1.7%
임실읍 11
 
1.5%
남원시 11
 
1.5%
완주군 10
 
1.4%
Other values (353) 518
72.1%
2024-03-14T12:18:19.583681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
523
 
17.0%
1 149
 
4.8%
시 138
 
4.5%
로 116
 
3.8%
2 103
 
3.3%
산 102
 
3.3%
읍 79
 
2.6%
군 78
 
2.5%
주 76
 
2.5%
길 76
 
2.5%
Other values (191) 1641
53.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1765
57.3%
Decimal Number 665
 
21.6%
Space Separator 523
 
17.0%
Dash Punctuation 74
 
2.4%
Close Punctuation 23
 
0.7%
Open Punctuation 23
 
0.7%
Other Punctuation 4
 
0.1%
Uppercase Letter 4
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 138
 
7.8%
로 116
 
6.6%
산 102
 
5.8%
읍 79
 
4.5%
군 78
 
4.4%
주 76
 
4.3%
길 76
 
4.3%
전 58
 
3.3%
동 54
 
3.1%
구 51
 
2.9%
Other values (171) 937
53.1%
Decimal Number
ValueCountFrequency (%)
1 149
22.4%
2 103
15.5%
5 68
10.2%
4 66
9.9%
3 65
9.8%
7 57
 
8.6%
6 44
 
6.6%
8 43
 
6.5%
9 36
 
5.4%
0 34
 
5.1%
Uppercase Letter
ValueCountFrequency (%)
Y 1
25.0%
M 1
25.0%
C 1
25.0%
A 1
25.0%
Other Punctuation
ValueCountFrequency (%)
/ 2
50.0%
＠ 2
50.0%
Space Separator
ValueCountFrequency (%)
523
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 74
100.0%
Close Punctuation
ValueCountFrequency (%)
) 23
100.0%
Open Punctuation
ValueCountFrequency (%)
( 23
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1765
57.3%
Common 1312
42.6%
Latin 4
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 138
 
7.8%
로 116
 
6.6%
산 102
 
5.8%
읍 79
 
4.5%
군 78
 
4.4%
주 76
 
4.3%
길 76
 
4.3%
전 58
 
3.3%
동 54
 
3.1%
구 51
 
2.9%
Other values (171) 937
53.1%
Common
ValueCountFrequency (%)
523
39.9%
1 149
 
11.4%
2 103
 
7.9%
- 74
 
5.6%
5 68
 
5.2%
4 66
 
5.0%
3 65
 
5.0%
7 57
 
4.3%
6 44
 
3.4%
8 43
 
3.3%
Other values (6) 120
 
9.1%
Latin
ValueCountFrequency (%)
Y 1
25.0%
M 1
25.0%
C 1
25.0%
A 1
25.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1765
57.3%
ASCII 1314
42.6%
None 2
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
523
39.8%
1 149
 
11.3%
2 103
 
7.8%
- 74
 
5.6%
5 68
 
5.2%
4 66
 
5.0%
3 65
 
4.9%
7 57
 
4.3%
6 44
 
3.3%
8 43
 
3.3%
Other values (9) 122
 
9.3%
Hangul
ValueCountFrequency (%)
시 138
 
7.8%
로 116
 
6.6%
산 102
 
5.8%
읍 79
 
4.5%
군 78
 
4.4%
주 76
 
4.3%
길 76
 
4.3%
전 58
 
3.3%
동 54
 
3.1%
구 51
 
2.9%
Other values (171) 937
53.1%
None
ValueCountFrequency (%)
＠ 2
100.0%

Unnamed: 6
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing16
Missing (%)7.4%
Memory size1.8 KiB

Unnamed: 7
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing17
Missing (%)7.9%
Memory size1.8 KiB

Unnamed: 8
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing29
Missing (%)13.5%
Memory size1.8 KiB

Unnamed: 9
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing16
Missing (%)7.4%
Memory size1.8 KiB

Unnamed: 10
Text

MISSING 

Distinct3
Distinct (%)100.0%
Missing212
Missing (%)98.6%
Memory size1.8 KiB
2024-03-14T12:18:19.720956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.6666667
Min length2

Characters and Unicode

Total characters8
Distinct characters7
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

Unique3 ?
Unique (%)100.0%

Sample

1st row비고
2nd row휴지중
3rd row재지정
ValueCountFrequency (%)
비고 1
33.3%
휴지중 1
33.3%
재지정 1
33.3%
2024-03-14T12:18:20.144820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
지 2
25.0%
비 1
12.5%
고 1
12.5%
휴 1
12.5%
중 1
12.5%
재 1
12.5%
정 1
12.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 8
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
지 2
25.0%
비 1
12.5%
고 1
12.5%
휴 1
12.5%
중 1
12.5%
재 1
12.5%
정 1
12.5%

Most occurring scripts

ValueCountFrequency (%)
Hangul 8
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
지 2
25.0%
비 1
12.5%
고 1
12.5%
휴 1
12.5%
중 1
12.5%
재 1
12.5%
정 1
12.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 8
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
지 2
25.0%
비 1
12.5%
고 1
12.5%
휴 1
12.5%
중 1
12.5%
재 1
12.5%
정 1
12.5%

Correlations

2024-03-14T12:18:20.213086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Unnamed: 1Unnamed: 10
Unnamed: 11.0001.000
Unnamed: 101.0001.000

Missing values

2024-03-14T12:18:17.012996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T12:18:17.186346image/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.
2024-03-14T12:18:17.312112image/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

전라북도 사회복지(이용)시설 현황Unnamed: 1Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 6Unnamed: 7Unnamed: 8Unnamed: 9Unnamed: 10
0NaN<NA>NaN<NA><NA><NA>NaNNaNNaN2014년 6월말<NA>
1언번시설구분시 설 일 반 현 황<NA><NA><NA>종사자NaN월평균\n이용인원운영주체(법인명)비고
2NaN<NA>시설명설치신고일시설장주 소정원현원NaNNaN<NA>
3총계전라북도197<NA><NA><NA>15111504NaNNaN<NA>
4소계전주시50<NA><NA><NA>NaNNaNNaNNaN<NA>
51사회복지관전북종합사회복지관88.12.27문정훈전주시 완산구 흑석로 7011117220어린이재단<NA>
62사회복지관전주종합사회복지관92.12.28강기오전주시 완산구 덕적골2길 10101011000삼동회<NA>
73사회복지관학산종합사회복지관95.12.29노영웅전주시 완산구 모악로 4726-112129160한기장복지재단<NA>
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