Overview

Dataset statistics

Number of variables10
Number of observations134
Missing cells356
Missing cells (%)26.6%
Duplicate rows1
Duplicate rows (%)0.7%
Total size in memory10.7 KiB
Average record size in memory82.0 B

Variable types

Text5
Categorical4
Unsupported1

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 (0.7%) duplicate rowsDuplicates
Unnamed: 7 is highly overall correlated with Unnamed: 1 and 1 other fieldsHigh correlation
Unnamed: 2 is highly overall correlated with Unnamed: 1 and 1 other fieldsHigh correlation
Unnamed: 1 is highly overall correlated with Unnamed: 2 and 2 other fieldsHigh correlation
Unnamed: 5 is highly overall correlated with Unnamed: 1High correlation
Unnamed: 1 is highly imbalanced (55.5%)Imbalance
Unnamed: 7 is highly imbalanced (51.3%)Imbalance
사회복지법인 현황 (시설법인,지원법인) has 21 (15.7%) missing valuesMissing
Unnamed: 3 has 22 (16.4%) missing valuesMissing
Unnamed: 4 has 23 (17.2%) missing valuesMissing
Unnamed: 6 has 23 (17.2%) missing valuesMissing
Unnamed: 8 has 133 (99.3%) missing valuesMissing
Unnamed: 9 has 134 (100.0%) missing valuesMissing
Unnamed: 9 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2024-03-14 00:36:40.329604
Analysis finished2024-03-14 00:36:41.056099
Duration0.73 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

사회복지법인 현황 (시설법인,지원법인)
Text

MISSING 

Distinct113
Distinct (%)100.0%
Missing21
Missing (%)15.7%
Memory size1.2 KiB
2024-03-14T09:36:41.255405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length41
Median length2
Mean length2.3628319
Min length1

Characters and Unicode

Total characters267
Distinct characters38
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique113 ?
Unique (%)100.0%

Sample

1st row※ 2015.6.30 기준 사회복지법인 / 보육시설만을 운영하는 법인 제외
2nd row연번
3rd row총계
4th row1
5th row2
ValueCountFrequency (%)
24 1
 
0.8%
59 1
 
0.8%
79 1
 
0.8%
78 1
 
0.8%
77 1
 
0.8%
76 1
 
0.8%
75 1
 
0.8%
74 1
 
0.8%
73 1
 
0.8%
72 1
 
0.8%
Other values (111) 111
91.7%
2024-03-14T09:36:41.649255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 34
12.7%
0 23
8.6%
2 22
8.2%
3 22
8.2%
5 22
8.2%
6 22
8.2%
4 21
7.9%
7 21
7.9%
8 21
7.9%
9 21
7.9%
Other values (28) 38
14.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 229
85.8%
Other Letter 26
 
9.7%
Space Separator 8
 
3.0%
Other Punctuation 4
 
1.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
법 2
 
7.7%
인 2
 
7.7%
운 1
 
3.8%
영 1
 
3.8%
보 1
 
3.8%
을 1
 
3.8%
만 1
 
3.8%
는 1
 
3.8%
설 1
 
3.8%
시 1
 
3.8%
Other values (14) 14
53.8%
Decimal Number
ValueCountFrequency (%)
1 34
14.8%
0 23
10.0%
2 22
9.6%
3 22
9.6%
5 22
9.6%
6 22
9.6%
4 21
9.2%
7 21
9.2%
8 21
9.2%
9 21
9.2%
Other Punctuation
ValueCountFrequency (%)
. 2
50.0%
/ 1
25.0%
※ 1
25.0%
Space Separator
ValueCountFrequency (%)
8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 241
90.3%
Hangul 26
 
9.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
법 2
 
7.7%
인 2
 
7.7%
운 1
 
3.8%
영 1
 
3.8%
보 1
 
3.8%
을 1
 
3.8%
만 1
 
3.8%
는 1
 
3.8%
설 1
 
3.8%
시 1
 
3.8%
Other values (14) 14
53.8%
Common
ValueCountFrequency (%)
1 34
14.1%
0 23
9.5%
2 22
9.1%
3 22
9.1%
5 22
9.1%
6 22
9.1%
4 21
8.7%
7 21
8.7%
8 21
8.7%
9 21
8.7%
Other values (4) 12
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 240
89.9%
Hangul 26
 
9.7%
Punctuation 1
 
0.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 34
14.2%
0 23
9.6%
2 22
9.2%
3 22
9.2%
5 22
9.2%
6 22
9.2%
4 21
8.8%
7 21
8.8%
8 21
8.8%
9 21
8.8%
Other values (3) 11
 
4.6%
Hangul
ValueCountFrequency (%)
법 2
 
7.7%
인 2
 
7.7%
운 1
 
3.8%
영 1
 
3.8%
보 1
 
3.8%
을 1
 
3.8%
만 1
 
3.8%
는 1
 
3.8%
설 1
 
3.8%
시 1
 
3.8%
Other values (14) 14
53.8%
Punctuation
ValueCountFrequency (%)
※ 1
100.0%

Unnamed: 1
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
전북
111 
<NA>
22 
시도
 
1

Length

Max length4
Median length2
Mean length2.3283582
Min length2

Unique

Unique1 ?
Unique (%)0.7%

Sample

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

Common Values

ValueCountFrequency (%)
전북 111
82.8%
<NA> 22
 
16.4%
시도 1
 
0.7%

Length

2024-03-14T09:36:41.793017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T09:36:41.880617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
전북 111
82.8%
na 22
 
16.4%
시도 1
 
0.7%

Unnamed: 2
Categorical

HIGH CORRELATION 

Distinct15
Distinct (%)11.2%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
전주시
28 
<NA>
23 
익산시
17 
군산시
13 
완주군
13 
Other values (10)
40 

Length

Max length4
Median length3
Mean length3.1716418
Min length3

Unique

Unique2 ?
Unique (%)1.5%

Sample

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

Common Values

ValueCountFrequency (%)
전주시 28
20.9%
<NA> 23
17.2%
익산시 17
12.7%
군산시 13
9.7%
완주군 13
9.7%
남원시 10
 
7.5%
정읍시 8
 
6.0%
김제시 6
 
4.5%
고창군 4
 
3.0%
임실군 3
 
2.2%
Other values (5) 9
 
6.7%

Length

2024-03-14T09:36:41.998202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
전주시 28
20.9%
na 23
17.2%
익산시 17
12.7%
군산시 13
9.7%
완주군 13
9.7%
남원시 10
 
7.5%
정읍시 8
 
6.0%
김제시 6
 
4.5%
고창군 4
 
3.0%
임실군 3
 
2.2%
Other values (5) 9
 
6.7%

Unnamed: 3
Text

MISSING 

Distinct112
Distinct (%)100.0%
Missing22
Missing (%)16.4%
Memory size1.2 KiB
2024-03-14T09:36:42.239151image/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%
2024-03-14T09:36:42.600229image/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%
Missing23
Missing (%)17.2%
Memory size1.2 KiB
2024-03-14T09:36:42.845494image/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%
2024-03-14T09:36:43.212891image/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
Categorical

HIGH CORRELATION 

Distinct45
Distinct (%)33.6%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
<NA>
23 
2006
14 
2004
11 
2007
9 
2005
 
7
Other values (40)
70 

Length

Max length10
Median length4
Mean length4.0447761
Min length4

Unique

Unique24 ?
Unique (%)17.9%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 23
17.2%
2006 14
 
10.4%
2004 11
 
8.2%
2007 9
 
6.7%
2005 7
 
5.2%
1999 7
 
5.2%
2003 5
 
3.7%
2000 4
 
3.0%
1997 3
 
2.2%
1982 3
 
2.2%
Other values (35) 48
35.8%

Length

2024-03-14T09:36:43.323899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 23
17.2%
2006 14
 
10.4%
2004 11
 
8.2%
2007 9
 
6.7%
2005 7
 
5.2%
1999 7
 
5.2%
2003 5
 
3.7%
2000 4
 
3.0%
1997 3
 
2.2%
1982 3
 
2.2%
Other values (35) 48
35.8%

Unnamed: 6
Text

MISSING 

Distinct111
Distinct (%)100.0%
Missing23
Missing (%)17.2%
Memory size1.2 KiB
2024-03-14T09:36:43.638928image/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%
2024-03-14T09:36:44.069696image/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
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
1
104 
<NA>
23 
2
 
6
시설 유형 * 시설법인 1 * 지원법인 2
 
1

Length

Max length24
Median length1
Mean length1.6865672
Min length1

Unique

Unique1 ?
Unique (%)0.7%

Sample

1st row<NA>
2nd row<NA>
3rd row시설 유형 * 시설법인 1 * 지원법인 2
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
1 104
77.6%
<NA> 23
 
17.2%
2 6
 
4.5%
시설 유형 * 시설법인 1 * 지원법인 2 1
 
0.7%

Length

2024-03-14T09:36:44.186208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T09:36:44.302013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 105
74.5%
na 23
 
16.3%
2 7
 
5.0%
2
 
1.4%
시설 1
 
0.7%
유형 1
 
0.7%
시설법인 1
 
0.7%
지원법인 1
 
0.7%

Unnamed: 8
Text

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing133
Missing (%)99.3%
Memory size1.2 KiB
2024-03-14T09:36:44.368414image/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%
2024-03-14T09:36:44.580145image/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%

Unnamed: 9
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing134
Missing (%)100.0%
Memory size1.3 KiB

Correlations

2024-03-14T09:36:44.645283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Unnamed: 1Unnamed: 2Unnamed: 5Unnamed: 7
Unnamed: 11.0001.0001.0001.000
Unnamed: 21.0001.0000.7560.828
Unnamed: 51.0000.7561.0000.761
Unnamed: 71.0000.8280.7611.000
2024-03-14T09:36:44.721219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Unnamed: 5Unnamed: 7Unnamed: 2Unnamed: 1
Unnamed: 51.0000.4160.2660.784
Unnamed: 70.4161.0000.6520.995
Unnamed: 20.2660.6521.0000.943
Unnamed: 10.7840.9950.9431.000
2024-03-14T09:36:44.797789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Unnamed: 1Unnamed: 2Unnamed: 5Unnamed: 7
Unnamed: 11.0000.9430.7840.995
Unnamed: 20.9431.0000.2660.652
Unnamed: 50.7840.2661.0000.416
Unnamed: 70.9950.6520.4161.000

Missing values

2024-03-14T09:36:40.718688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T09:36:40.850422image/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.
2024-03-14T09:36:40.977681image/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: 8Unnamed: 9
0※ 2015.6.30 기준 사회복지법인 / 보육시설만을 운영하는 법인 제외<NA><NA><NA><NA><NA><NA><NA><NA><NA>
1<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
2연번시도시군구법 인 명대표자명설립연도주소지시설 유형 * 시설법인 1 * 지원법인 2비고<NA>
3<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
4총계전북<NA>110개 법인<NA><NA><NA><NA><NA><NA>
51전북전주시전라북도사회복지협의회차종선1999전주시 덕진구 전주천동로 4832<NA><NA>
62전북전주시참사랑복지회양기승1982전주시 완산구 바람쐬는길 1521<NA><NA>
73전북전주시천주교성가복지회이병호1999전주시 완산구 서노송동 560-61<NA><NA>
84전북전주시전주시사회복지협의회김정석2007전주시 완산구 전주객사 2길 12-82<NA><NA>
95전북전주시한빛원임석기1997전주시 완산구 선너머로 35-31<NA><NA>
사회복지법인 현황 (시설법인,지원법인)Unnamed: 1Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 6Unnamed: 7Unnamed: 8Unnamed: 9
124<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
125<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
126<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
127<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
128<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
129<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
130<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
131<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
132<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
133<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>

Duplicate rows

Most frequently occurring

사회복지법인 현황 (시설법인,지원법인)Unnamed: 1Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 6Unnamed: 7Unnamed: 8# duplicates
0<NA><NA><NA><NA><NA><NA><NA><NA><NA>21