Overview

Dataset statistics

Number of variables9
Number of observations115
Missing cells135
Missing cells (%)13.0%
Duplicate rows1
Duplicate rows (%)0.9%
Total size in memory8.2 KiB
Average record size in memory73.1 B

Variable types

Unsupported3
Categorical2
Text4

Dataset

Description전라북도사회복지법인현황2015
Author전라북도
URLhttps://www.bigdatahub.go.kr/opendata/dataSet/detail.nm?contentId=37&rlik=49451aebf056b486&serviceId=202403

Alerts

Unnamed: 8 has constant value ""Constant
Dataset has 1 (0.9%) duplicate rowsDuplicates
Unnamed: 2 is highly overall correlated with Unnamed: 1High correlation
Unnamed: 1 is highly overall correlated with Unnamed: 2High correlation
Unnamed: 1 is highly imbalanced (84.5%)Imbalance
사회복지법인 현황 (시설법인,지원법인) has 2 (1.7%) missing valuesMissing
Unnamed: 3 has 3 (2.6%) missing valuesMissing
Unnamed: 4 has 4 (3.5%) missing valuesMissing
Unnamed: 5 has 4 (3.5%) missing valuesMissing
Unnamed: 6 has 4 (3.5%) missing valuesMissing
Unnamed: 7 has 4 (3.5%) missing valuesMissing
Unnamed: 8 has 114 (99.1%) missing valuesMissing
사회복지법인 현황 (시설법인,지원법인) is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 5 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

Reproduction

Analysis started2024-03-14 00:36:45.628540
Analysis finished2024-03-14 00:36:46.258354
Duration0.63 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

사회복지법인 현황 (시설법인,지원법인)
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing2
Missing (%)1.7%
Memory size1.0 KiB

Unnamed: 1
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
전북
111 
<NA>
 
3
시도
 
1

Length

Max length4
Median length2
Mean length2.0521739
Min length2

Unique

Unique1 ?
Unique (%)0.9%

Sample

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

Common Values

ValueCountFrequency (%)
전북 111
96.5%
<NA> 3
 
2.6%
시도 1
 
0.9%

Length

2024-03-14T09:36:46.322848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T09:36:46.433878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
전북 111
96.5%
na 3
 
2.6%
시도 1
 
0.9%

Unnamed: 2
Categorical

HIGH CORRELATION 

Distinct15
Distinct (%)13.0%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
전주시
28 
익산시
17 
군산시
13 
완주군
13 
남원시
10 
Other values (10)
34 

Length

Max length4
Median length3
Mean length3.0347826
Min length3

Unique

Unique2 ?
Unique (%)1.7%

Sample

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

Common Values

ValueCountFrequency (%)
전주시 28
24.3%
익산시 17
14.8%
군산시 13
11.3%
완주군 13
11.3%
남원시 10
 
8.7%
정읍시 8
 
7.0%
김제시 6
 
5.2%
<NA> 4
 
3.5%
고창군 4
 
3.5%
임실군 3
 
2.6%
Other values (5) 9
 
7.8%

Length

2024-03-14T09:36:46.541697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
전주시 28
24.3%
익산시 17
14.8%
군산시 13
11.3%
완주군 13
11.3%
남원시 10
 
8.7%
정읍시 8
 
7.0%
김제시 6
 
5.2%
na 4
 
3.5%
고창군 4
 
3.5%
임실군 3
 
2.6%
Other values (5) 9
 
7.8%

Unnamed: 3
Text

MISSING 

Distinct112
Distinct (%)100.0%
Missing3
Missing (%)2.6%
Memory size1.0 KiB
2024-03-14T09:36:46.777958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length12
Mean length5.6696429
Min length1

Characters and Unicode

Total characters635
Distinct characters165
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

Unique112 ?
Unique (%)100.0%

Sample

1st row법 인 명
2nd row110개 법인
3rd row전라북도사회복지협의회
4th row참사랑복지회
5th row천주교성가복지회
ValueCountFrequency (%)
법 1
 
0.8%
일봉복지재단 1
 
0.8%
성암복지원 1
 
0.8%
김제가나안복지재단 1
 
0.8%
시온회 1
 
0.8%
길보른재단 1
 
0.8%
햇빛 1
 
0.8%
우리원 1
 
0.8%
예닮문화복지재단 1
 
0.8%
서남행복원 1
 
0.8%
Other values (113) 113
91.9%
2024-03-14T09:36:47.094610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
복 62
 
9.8%
지 59
 
9.3%
재 43
 
6.8%
단 43
 
6.8%
원 38
 
6.0%
회 21
 
3.3%
17
 
2.7%
사 15
 
2.4%
성 11
 
1.7%
산 8
 
1.3%
Other values (155) 318
50.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 611
96.2%
Space Separator 17
 
2.7%
Decimal Number 3
 
0.5%
Close Punctuation 2
 
0.3%
Open Punctuation 2
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
복 62
 
10.1%
지 59
 
9.7%
재 43
 
7.0%
단 43
 
7.0%
원 38
 
6.2%
회 21
 
3.4%
사 15
 
2.5%
성 11
 
1.8%
산 8
 
1.3%
전 8
 
1.3%
Other values (150) 303
49.6%
Decimal Number
ValueCountFrequency (%)
1 2
66.7%
0 1
33.3%
Space Separator
ValueCountFrequency (%)
17
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 611
96.2%
Common 24
 
3.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
복 62
 
10.1%
지 59
 
9.7%
재 43
 
7.0%
단 43
 
7.0%
원 38
 
6.2%
회 21
 
3.4%
사 15
 
2.5%
성 11
 
1.8%
산 8
 
1.3%
전 8
 
1.3%
Other values (150) 303
49.6%
Common
ValueCountFrequency (%)
17
70.8%
) 2
 
8.3%
1 2
 
8.3%
( 2
 
8.3%
0 1
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 611
96.2%
ASCII 24
 
3.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
복 62
 
10.1%
지 59
 
9.7%
재 43
 
7.0%
단 43
 
7.0%
원 38
 
6.2%
회 21
 
3.4%
사 15
 
2.5%
성 11
 
1.8%
산 8
 
1.3%
전 8
 
1.3%
Other values (150) 303
49.6%
ASCII
ValueCountFrequency (%)
17
70.8%
) 2
 
8.3%
1 2
 
8.3%
( 2
 
8.3%
0 1
 
4.2%

Unnamed: 4
Text

MISSING 

Distinct109
Distinct (%)98.2%
Missing4
Missing (%)3.5%
Memory size1.0 KiB
2024-03-14T09:36:47.342519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.018018
Min length2

Characters and Unicode

Total characters335
Distinct characters113
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

Unique108 ?
Unique (%)97.3%

Sample

1st row대표자명
2nd row차종선
3rd row양기승
4th row이병호
5th row김정석
ValueCountFrequency (%)
이병호 3
 
2.7%
오세현 1
 
0.9%
유재천 1
 
0.9%
권혁일 1
 
0.9%
한규택 1
 
0.9%
김영식 1
 
0.9%
온주현 1
 
0.9%
최재식 1
 
0.9%
조종남 1
 
0.9%
최규순 1
 
0.9%
Other values (101) 101
89.4%
2024-03-14T09:36:47.715892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
김 25
 
7.5%
이 15
 
4.5%
박 14
 
4.2%
영 11
 
3.3%
정 10
 
3.0%
병 8
 
2.4%
인 7
 
2.1%
순 7
 
2.1%
종 6
 
1.8%
최 6
 
1.8%
Other values (103) 226
67.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 331
98.8%
Space Separator 4
 
1.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
김 25
 
7.6%
이 15
 
4.5%
박 14
 
4.2%
영 11
 
3.3%
정 10
 
3.0%
병 8
 
2.4%
인 7
 
2.1%
순 7
 
2.1%
종 6
 
1.8%
최 6
 
1.8%
Other values (102) 222
67.1%
Space Separator
ValueCountFrequency (%)
4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 331
98.8%
Common 4
 
1.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
김 25
 
7.6%
이 15
 
4.5%
박 14
 
4.2%
영 11
 
3.3%
정 10
 
3.0%
병 8
 
2.4%
인 7
 
2.1%
순 7
 
2.1%
종 6
 
1.8%
최 6
 
1.8%
Other values (102) 222
67.1%
Common
ValueCountFrequency (%)
4
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 331
98.8%
ASCII 4
 
1.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
김 25
 
7.6%
이 15
 
4.5%
박 14
 
4.2%
영 11
 
3.3%
정 10
 
3.0%
병 8
 
2.4%
인 7
 
2.1%
순 7
 
2.1%
종 6
 
1.8%
최 6
 
1.8%
Other values (102) 222
67.1%
ASCII
ValueCountFrequency (%)
4
100.0%

Unnamed: 5
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing4
Missing (%)3.5%
Memory size1.0 KiB

Unnamed: 6
Text

MISSING 

Distinct111
Distinct (%)100.0%
Missing4
Missing (%)3.5%
Memory size1.0 KiB
2024-03-14T09:36:48.050431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length19
Mean length14.972973
Min length3

Characters and Unicode

Total characters1662
Distinct characters170
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique111 ?
Unique (%)100.0%

Sample

1st row주소지
2nd row전주시 덕진구 전주천동로 483
3rd row전주시 완산구 바람쐬는길 152
4th row전주시 완산구 서노송동 560-6
5th row전주시 완산구 전주객사 2길 12-8
ValueCountFrequency (%)
전주시 28
 
6.8%
완산구 19
 
4.6%
익산시 16
 
3.9%
완주군 13
 
3.1%
군산시 13
 
3.1%
남원시 10
 
2.4%
덕진구 9
 
2.2%
정읍시 8
 
1.9%
김제시 6
 
1.4%
19 4
 
1.0%
Other values (250) 288
69.6%
2024-03-14T09:36:48.496172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
303
 
18.2%
시 81
 
4.9%
1 61
 
3.7%
산 61
 
3.7%
로 57
 
3.4%
길 54
 
3.2%
2 52
 
3.1%
주 48
 
2.9%
- 45
 
2.7%
4 45
 
2.7%
Other values (160) 855
51.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 956
57.5%
Decimal Number 354
 
21.3%
Space Separator 303
 
18.2%
Dash Punctuation 45
 
2.7%
Close Punctuation 2
 
0.1%
Open Punctuation 2
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 81
 
8.5%
산 61
 
6.4%
로 57
 
6.0%
길 54
 
5.6%
주 48
 
5.0%
군 39
 
4.1%
면 38
 
4.0%
전 33
 
3.5%
완 32
 
3.3%
구 31
 
3.2%
Other values (146) 482
50.4%
Decimal Number
ValueCountFrequency (%)
1 61
17.2%
2 52
14.7%
4 45
12.7%
3 36
10.2%
7 33
9.3%
6 32
9.0%
9 29
8.2%
5 26
7.3%
8 23
 
6.5%
0 17
 
4.8%
Space Separator
ValueCountFrequency (%)
303
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 45
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 956
57.5%
Common 706
42.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 81
 
8.5%
산 61
 
6.4%
로 57
 
6.0%
길 54
 
5.6%
주 48
 
5.0%
군 39
 
4.1%
면 38
 
4.0%
전 33
 
3.5%
완 32
 
3.3%
구 31
 
3.2%
Other values (146) 482
50.4%
Common
ValueCountFrequency (%)
303
42.9%
1 61
 
8.6%
2 52
 
7.4%
- 45
 
6.4%
4 45
 
6.4%
3 36
 
5.1%
7 33
 
4.7%
6 32
 
4.5%
9 29
 
4.1%
5 26
 
3.7%
Other values (4) 44
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 956
57.5%
ASCII 706
42.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
303
42.9%
1 61
 
8.6%
2 52
 
7.4%
- 45
 
6.4%
4 45
 
6.4%
3 36
 
5.1%
7 33
 
4.7%
6 32
 
4.5%
9 29
 
4.1%
5 26
 
3.7%
Other values (4) 44
 
6.2%
Hangul
ValueCountFrequency (%)
시 81
 
8.5%
산 61
 
6.4%
로 57
 
6.0%
길 54
 
5.6%
주 48
 
5.0%
군 39
 
4.1%
면 38
 
4.0%
전 33
 
3.5%
완 32
 
3.3%
구 31
 
3.2%
Other values (146) 482
50.4%

Unnamed: 7
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing4
Missing (%)3.5%
Memory size1.0 KiB

Unnamed: 8
Text

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing114
Missing (%)99.1%
Memory size1.0 KiB
2024-03-14T09:36:48.601217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2
Distinct characters2
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

Unique1 ?
Unique (%)100.0%

Sample

1st row비고
ValueCountFrequency (%)
비고 1
100.0%
2024-03-14T09:36:48.772820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
비 1
50.0%
고 1
50.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
비 1
50.0%
고 1
50.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
비 1
50.0%
고 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
비 1
50.0%
고 1
50.0%

Correlations

2024-03-14T09:36:49.087901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Unnamed: 1Unnamed: 2
Unnamed: 11.0001.000
Unnamed: 21.0001.000
2024-03-14T09:36:49.150965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Unnamed: 2Unnamed: 1
Unnamed: 21.0000.943
Unnamed: 10.9431.000
2024-03-14T09:36:49.215368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Unnamed: 1Unnamed: 2
Unnamed: 11.0000.943
Unnamed: 20.9431.000

Missing values

2024-03-14T09:36:45.939032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T09:36:46.075660image/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.
2024-03-14T09:36:46.180703image/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

사회복지법인 현황 (시설법인,지원법인)Unnamed: 1Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 6Unnamed: 7Unnamed: 8
0※ 2015.6.30 기준 사회복지법인 / 보육시설만을 운영하는 법인 제외<NA><NA><NA><NA>NaN<NA>NaN<NA>
1NaN<NA><NA><NA><NA>NaN<NA>NaN<NA>
2연번시도시군구법 인 명대표자명설립연도주소지시설 유형\n\n* 시설법인 1\n* 지원법인 2비고
3NaN<NA><NA><NA><NA>NaN<NA>NaN<NA>
4총계전북<NA>110개 법인<NA>NaN<NA>NaN<NA>
51전북전주시전라북도사회복지협의회차종선1999전주시 덕진구 전주천동로 4832<NA>
62전북전주시참사랑복지회양기승1982전주시 완산구 바람쐬는길 1521<NA>
73전북전주시천주교성가복지회이병호1999전주시 완산구 서노송동 560-61<NA>
84전북전주시전주시사회복지협의회김정석2007전주시 완산구 전주객사 2길 12-82<NA>
95전북전주시한빛원임석기1997전주시 완산구 선너머로 35-31<NA>
사회복지법인 현황 (시설법인,지원법인)Unnamed: 1Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 6Unnamed: 7Unnamed: 8
105101전북임실군섬김복지재단김경순2004임실군 오수면 삼일로 22-131<NA>
106102전북임실군크리스찬복지재단노 준1998임실군 신평면 석등슬치로 491-71<NA>
107103전북임실군미리암복지재단고병훈2006임실군 임실읍 호국로 1716-151<NA>
108104전북순창군순창군사회복지협의회민선홍2006순창군 순창읍 옥천로 662<NA>
109105전북순창군원산원서양원2009순창읍 남계리 638-51<NA>
110106전북순창군도실원김인영1999순창읍 장류로 289-31<NA>
111107전북고창군고창행복원전준구1986고창군 고창읍 모양성로 116-131<NA>
112108전북고창군아모스김성강1973고창군 무장면 학천로 2211<NA>
113109전북고창군아름다운마을김범일2004고창군 상하면 풍촌길 81<NA>
114110전북고창군선운사 복지재단(대한불교조계종)황찬연2011고창군 아산면 선운사로 2501<NA>

Duplicate rows

Most frequently occurring

Unnamed: 1Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 6Unnamed: 8# duplicates
0<NA><NA><NA><NA><NA><NA>3