Overview

Dataset statistics

Number of variables11
Number of observations443
Missing cells190
Missing cells (%)3.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory38.2 KiB
Average record size in memory88.3 B

Variable types

Text10
Categorical1

Dataset

Description공공데이터한부모가족복지시설현황
Author전라북도
URLhttps://www.bigdatahub.go.kr/opendata/dataSet/detail.nm?contentId=37&rlik=49451aebf056b486&serviceId=202041

Alerts

Unnamed: 3 has 18 (4.1%) missing valuesMissing
Unnamed: 4 has 22 (5.0%) missing valuesMissing
Unnamed: 5 has 18 (4.1%) missing valuesMissing
Unnamed: 6 has 29 (6.5%) missing valuesMissing
Unnamed: 7 has 30 (6.8%) missing valuesMissing
Unnamed: 8 has 25 (5.6%) missing valuesMissing
Unnamed: 9 has 26 (5.9%) missing valuesMissing
Unnamed: 10 has 17 (3.8%) missing valuesMissing

Reproduction

Analysis started2024-03-14 00:22:16.686910
Analysis finished2024-03-14 00:22:17.973346
Duration1.29 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Distinct103
Distinct (%)23.4%
Missing3
Missing (%)0.7%
Memory size3.6 KiB
2024-03-14T09:22:18.148485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length2
Mean length1.7295455
Min length1

Characters and Unicode

Total characters761
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 (%)4.3%

Sample

1st row연번
2nd row총계
3rd row소계
4th row1
5th row2
ValueCountFrequency (%)
소계 14
 
3.2%
4 14
 
3.2%
6 14
 
3.2%
5 14
 
3.2%
1 14
 
3.2%
3 14
 
3.2%
2 14
 
3.2%
7 13
 
3.0%
8 12
 
2.7%
9 11
 
2.5%
Other values (93) 306
69.5%
2024-03-14T09:22:18.575178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 131
17.2%
2 108
14.2%
3 83
10.9%
4 72
9.5%
5 72
9.5%
6 64
8.4%
7 60
7.9%
8 53
7.0%
9 48
 
6.3%
0 38
 
5.0%
Other values (5) 32
 
4.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 729
95.8%
Other Letter 32
 
4.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 131
18.0%
2 108
14.8%
3 83
11.4%
4 72
9.9%
5 72
9.9%
6 64
8.8%
7 60
8.2%
8 53
7.3%
9 48
 
6.6%
0 38
 
5.2%
Other Letter
ValueCountFrequency (%)
15
46.9%
14
43.8%
1
 
3.1%
1
 
3.1%
1
 
3.1%

Most occurring scripts

ValueCountFrequency (%)
Common 729
95.8%
Hangul 32
 
4.2%

Most frequent character per script

Common
ValueCountFrequency (%)
1 131
18.0%
2 108
14.8%
3 83
11.4%
4 72
9.9%
5 72
9.9%
6 64
8.8%
7 60
8.2%
8 53
7.3%
9 48
 
6.6%
0 38
 
5.2%
Hangul
ValueCountFrequency (%)
15
46.9%
14
43.8%
1
 
3.1%
1
 
3.1%
1
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 729
95.8%
Hangul 32
 
4.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 131
18.0%
2 108
14.8%
3 83
11.4%
4 72
9.9%
5 72
9.9%
6 64
8.8%
7 60
8.2%
8 53
7.3%
9 48
 
6.6%
0 38
 
5.2%
Hangul
ValueCountFrequency (%)
15
46.9%
14
43.8%
1
 
3.1%
1
 
3.1%
1
 
3.1%

Unnamed: 1
Categorical

Distinct45
Distinct (%)10.2%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
노인요양시설
162 
노인요양공동생활가정
66 
장애인거주시설
51 
아동공동생활가정
35 
사회복귀시설
19 
Other values (40)
110 

Length

Max length10
Median length9
Mean length7.1512415
Min length3

Unique

Unique26 ?
Unique (%)5.9%

Sample

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

Common Values

ValueCountFrequency (%)
노인요양시설 162
36.6%
노인요양공동생활가정 66
14.9%
장애인거주시설 51
 
11.5%
아동공동생활가정 35
 
7.9%
사회복귀시설 19
 
4.3%
장애인 공동생활가정 17
 
3.8%
아동양육시설 14
 
3.2%
노인양로시설 11
 
2.5%
아동 공동생활시설 10
 
2.3%
한부모가족복지시설 7
 
1.6%
Other values (35) 51
 
11.5%

Length

2024-03-14T09:22:18.700025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
노인요양시설 162
33.9%
노인요양공동생활가정 66
13.8%
장애인거주시설 51
 
10.7%
아동공동생활가정 35
 
7.3%
사회복귀시설 19
 
4.0%
장애인 19
 
4.0%
공동생활가정 17
 
3.6%
아동양육시설 14
 
2.9%
노인양로시설 11
 
2.3%
아동 10
 
2.1%
Other values (37) 74
15.5%
Distinct428
Distinct (%)97.1%
Missing2
Missing (%)0.5%
Memory size3.6 KiB
2024-03-14T09:22:18.875213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length14
Mean length6.5963719
Min length2

Characters and Unicode

Total characters2909
Distinct characters311
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique415 ?
Unique (%)94.1%

Sample

1st row시 설 일 반 현 황
2nd row시설명
3rd row424
4th row100
5th row마음건강복지관
ValueCountFrequency (%)
13
 
2.7%
1호 3
 
0.6%
벧엘요양원 3
 
0.6%
자림공동생활가정 3
 
0.6%
그룹홈 3
 
0.6%
행복한집 3
 
0.6%
우리집 3
 
0.6%
느티나무 2
 
0.4%
다솜요양원 2
 
0.4%
행복의집 2
 
0.4%
Other values (427) 446
92.3%
2024-03-14T09:22:19.195615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
331
 
11.4%
192
 
6.6%
118
 
4.1%
108
 
3.7%
108
 
3.7%
96
 
3.3%
57
 
2.0%
49
 
1.7%
43
 
1.5%
41
 
1.4%
Other values (301) 1766
60.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2536
87.2%
Space Separator 331
 
11.4%
Decimal Number 35
 
1.2%
Uppercase Letter 4
 
0.1%
Control 1
 
< 0.1%
Close Punctuation 1
 
< 0.1%
Open Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
192
 
7.6%
118
 
4.7%
108
 
4.3%
108
 
4.3%
96
 
3.8%
57
 
2.2%
49
 
1.9%
43
 
1.7%
41
 
1.6%
36
 
1.4%
Other values (284) 1688
66.6%
Decimal Number
ValueCountFrequency (%)
1 11
31.4%
3 8
22.9%
2 5
14.3%
6 3
 
8.6%
8 2
 
5.7%
4 2
 
5.7%
0 2
 
5.7%
9 1
 
2.9%
7 1
 
2.9%
Uppercase Letter
ValueCountFrequency (%)
Y 1
25.0%
C 1
25.0%
W 1
25.0%
A 1
25.0%
Space Separator
ValueCountFrequency (%)
331
100.0%
Control
ValueCountFrequency (%)
1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2536
87.2%
Common 369
 
12.7%
Latin 4
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
192
 
7.6%
118
 
4.7%
108
 
4.3%
108
 
4.3%
96
 
3.8%
57
 
2.2%
49
 
1.9%
43
 
1.7%
41
 
1.6%
36
 
1.4%
Other values (284) 1688
66.6%
Common
ValueCountFrequency (%)
331
89.7%
1 11
 
3.0%
3 8
 
2.2%
2 5
 
1.4%
6 3
 
0.8%
8 2
 
0.5%
4 2
 
0.5%
0 2
 
0.5%
1
 
0.3%
9 1
 
0.3%
Other values (3) 3
 
0.8%
Latin
ValueCountFrequency (%)
Y 1
25.0%
C 1
25.0%
W 1
25.0%
A 1
25.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2536
87.2%
ASCII 373
 
12.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
331
88.7%
1 11
 
2.9%
3 8
 
2.1%
2 5
 
1.3%
6 3
 
0.8%
8 2
 
0.5%
4 2
 
0.5%
0 2
 
0.5%
Y 1
 
0.3%
1
 
0.3%
Other values (7) 7
 
1.9%
Hangul
ValueCountFrequency (%)
192
 
7.6%
118
 
4.7%
108
 
4.3%
108
 
4.3%
96
 
3.8%
57
 
2.2%
49
 
1.9%
43
 
1.7%
41
 
1.6%
36
 
1.4%
Other values (284) 1688
66.6%

Unnamed: 3
Text

MISSING 

Distinct398
Distinct (%)93.6%
Missing18
Missing (%)4.1%
Memory size3.6 KiB
2024-03-14T09:22:19.436797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length25
Median length14
Mean length9.7505882
Min length5

Characters and Unicode

Total characters4144
Distinct characters21
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

Unique375 ?
Unique (%)88.2%

Sample

1st row설치신고일
2nd row00.12.08
3rd row11.07.04
4th row15.09.11
5th row02.11.21
ValueCountFrequency (%)
2006.11.10 5
 
1.2%
07.03.09 3
 
0.7%
2010.03.01 3
 
0.7%
2009.12.29 3
 
0.7%
11.04.28 2
 
0.5%
2010.03.11 2
 
0.5%
05.04.06 2
 
0.5%
11.06.13 2
 
0.5%
08.07.31 2
 
0.5%
1997.03.21 2
 
0.5%
Other values (386) 400
93.9%
2024-03-14T09:22:19.800703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1006
24.3%
. 854
20.6%
1 599
14.5%
2 501
12.1%
276
 
6.7%
6 154
 
3.7%
3 149
 
3.6%
8 144
 
3.5%
9 126
 
3.0%
7 118
 
2.8%
Other values (11) 217
 
5.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3004
72.5%
Other Punctuation 856
 
20.7%
Space Separator 276
 
6.7%
Other Letter 5
 
0.1%
Control 1
 
< 0.1%
Open Punctuation 1
 
< 0.1%
Close Punctuation 1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1006
33.5%
1 599
19.9%
2 501
16.7%
6 154
 
5.1%
3 149
 
5.0%
8 144
 
4.8%
9 126
 
4.2%
7 118
 
3.9%
5 112
 
3.7%
4 95
 
3.2%
Other Letter
ValueCountFrequency (%)
1
20.0%
1
20.0%
1
20.0%
1
20.0%
1
20.0%
Other Punctuation
ValueCountFrequency (%)
. 854
99.8%
, 2
 
0.2%
Space Separator
ValueCountFrequency (%)
276
100.0%
Control
ValueCountFrequency (%)
1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4139
99.9%
Hangul 5
 
0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1006
24.3%
. 854
20.6%
1 599
14.5%
2 501
12.1%
276
 
6.7%
6 154
 
3.7%
3 149
 
3.6%
8 144
 
3.5%
9 126
 
3.0%
7 118
 
2.9%
Other values (6) 212
 
5.1%
Hangul
ValueCountFrequency (%)
1
20.0%
1
20.0%
1
20.0%
1
20.0%
1
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4139
99.9%
Hangul 5
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1006
24.3%
. 854
20.6%
1 599
14.5%
2 501
12.1%
276
 
6.7%
6 154
 
3.7%
3 149
 
3.6%
8 144
 
3.5%
9 126
 
3.0%
7 118
 
2.9%
Other values (6) 212
 
5.1%
Hangul
ValueCountFrequency (%)
1
20.0%
1
20.0%
1
20.0%
1
20.0%
1
20.0%

Unnamed: 4
Text

MISSING 

Distinct396
Distinct (%)94.1%
Missing22
Missing (%)5.0%
Memory size3.6 KiB
2024-03-14T09:22:20.124341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length3
Mean length3.064133
Min length2

Characters and Unicode

Total characters1290
Distinct characters176
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

Unique372 ?
Unique (%)88.4%

Sample

1st row시설장
2nd row박헌수
3rd row최유영
4th row김미경
5th row김성은
ValueCountFrequency (%)
정란희 3
 
0.7%
추교인 2
 
0.5%
김혜란 2
 
0.5%
김인숙 2
 
0.5%
강옥선 2
 
0.5%
김혜자 2
 
0.5%
김은희 2
 
0.5%
김선자 2
 
0.5%
정영순 2
 
0.5%
박용민 2
 
0.5%
Other values (391) 405
95.1%
2024-03-14T09:22:20.575851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
92
 
7.1%
63
 
4.9%
51
 
4.0%
47
 
3.6%
37
 
2.9%
32
 
2.5%
31
 
2.4%
29
 
2.2%
29
 
2.2%
26
 
2.0%
Other values (166) 853
66.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1266
98.1%
Space Separator 24
 
1.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
92
 
7.3%
63
 
5.0%
51
 
4.0%
47
 
3.7%
37
 
2.9%
32
 
2.5%
31
 
2.4%
29
 
2.3%
29
 
2.3%
26
 
2.1%
Other values (165) 829
65.5%
Space Separator
ValueCountFrequency (%)
24
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1266
98.1%
Common 24
 
1.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
92
 
7.3%
63
 
5.0%
51
 
4.0%
47
 
3.7%
37
 
2.9%
32
 
2.5%
31
 
2.4%
29
 
2.3%
29
 
2.3%
26
 
2.1%
Other values (165) 829
65.5%
Common
ValueCountFrequency (%)
24
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1266
98.1%
ASCII 24
 
1.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
92
 
7.3%
63
 
5.0%
51
 
4.0%
47
 
3.7%
37
 
2.9%
32
 
2.5%
31
 
2.4%
29
 
2.3%
29
 
2.3%
26
 
2.1%
Other values (165) 829
65.5%
ASCII
ValueCountFrequency (%)
24
100.0%

Unnamed: 5
Text

MISSING 

Distinct411
Distinct (%)96.7%
Missing18
Missing (%)4.1%
Memory size3.6 KiB
2024-03-14T09:22:20.844364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length36
Median length30
Mean length15.830588
Min length3

Characters and Unicode

Total characters6728
Distinct characters275
Distinct categories11 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique397 ?
Unique (%)93.4%

Sample

1st row주 소
2nd row전주시 완산구 물왕멀2길 20-29
3rd row전주시 완산구 물왕멀2길 25
4th row전주시 완산구 물왕멀2길 20-17
5th row전주시 덕진구 아중7길 9-5
ValueCountFrequency (%)
익산시 82
 
5.7%
완산구 60
 
4.1%
전주시 57
 
3.9%
덕진구 35
 
2.4%
군산시 29
 
2.0%
전북 21
 
1.4%
완주군 18
 
1.2%
정읍시 10
 
0.7%
소양면 9
 
0.6%
남원시 9
 
0.6%
Other values (835) 1119
77.2%
2024-03-14T09:22:21.237567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1063
 
15.8%
1 390
 
5.8%
267
 
4.0%
2 246
 
3.7%
231
 
3.4%
202
 
3.0%
186
 
2.8%
3 183
 
2.7%
- 180
 
2.7%
175
 
2.6%
Other values (265) 3605
53.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3565
53.0%
Decimal Number 1664
24.7%
Space Separator 1063
 
15.8%
Dash Punctuation 180
 
2.7%
Close Punctuation 95
 
1.4%
Open Punctuation 95
 
1.4%
Other Punctuation 58
 
0.9%
Control 3
 
< 0.1%
Math Symbol 2
 
< 0.1%
Uppercase Letter 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
267
 
7.5%
231
 
6.5%
202
 
5.7%
186
 
5.2%
175
 
4.9%
126
 
3.5%
119
 
3.3%
92
 
2.6%
92
 
2.6%
86
 
2.4%
Other values (241) 1989
55.8%
Decimal Number
ValueCountFrequency (%)
1 390
23.4%
2 246
14.8%
3 183
11.0%
4 146
 
8.8%
0 135
 
8.1%
5 128
 
7.7%
7 119
 
7.2%
6 117
 
7.0%
8 102
 
6.1%
9 98
 
5.9%
Other Punctuation
ValueCountFrequency (%)
, 27
46.6%
/ 13
22.4%
@ 11
19.0%
. 5
 
8.6%
? 2
 
3.4%
Uppercase Letter
ValueCountFrequency (%)
A 1
50.0%
B 1
50.0%
Space Separator
ValueCountFrequency (%)
1063
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 180
100.0%
Close Punctuation
ValueCountFrequency (%)
) 95
100.0%
Open Punctuation
ValueCountFrequency (%)
( 95
100.0%
Control
ValueCountFrequency (%)
3
100.0%
Math Symbol
ValueCountFrequency (%)
~ 2
100.0%
Lowercase Letter
ValueCountFrequency (%)
a 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3565
53.0%
Common 3160
47.0%
Latin 3
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
267
 
7.5%
231
 
6.5%
202
 
5.7%
186
 
5.2%
175
 
4.9%
126
 
3.5%
119
 
3.3%
92
 
2.6%
92
 
2.6%
86
 
2.4%
Other values (241) 1989
55.8%
Common
ValueCountFrequency (%)
1063
33.6%
1 390
 
12.3%
2 246
 
7.8%
3 183
 
5.8%
- 180
 
5.7%
4 146
 
4.6%
0 135
 
4.3%
5 128
 
4.1%
7 119
 
3.8%
6 117
 
3.7%
Other values (11) 453
14.3%
Latin
ValueCountFrequency (%)
a 1
33.3%
A 1
33.3%
B 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3565
53.0%
ASCII 3163
47.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1063
33.6%
1 390
 
12.3%
2 246
 
7.8%
3 183
 
5.8%
- 180
 
5.7%
4 146
 
4.6%
0 135
 
4.3%
5 128
 
4.0%
7 119
 
3.8%
6 117
 
3.7%
Other values (14) 456
14.4%
Hangul
ValueCountFrequency (%)
267
 
7.5%
231
 
6.5%
202
 
5.7%
186
 
5.2%
175
 
4.9%
126
 
3.5%
119
 
3.3%
92
 
2.6%
92
 
2.6%
86
 
2.4%
Other values (241) 1989
55.8%

Unnamed: 6
Text

MISSING 

Distinct65
Distinct (%)15.7%
Missing29
Missing (%)6.5%
Memory size3.6 KiB
2024-03-14T09:22:21.419780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length4
Mean length3.4589372
Min length1

Characters and Unicode

Total characters1432
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 6,121
4th row 10
5th row 1
ValueCountFrequency (%)
2 55
 
13.3%
4 28
 
6.8%
6 23
 
5.6%
5 23
 
5.6%
1 18
 
4.3%
12 17
 
4.1%
7 16
 
3.9%
8 15
 
3.6%
11 14
 
3.4%
9 12
 
2.9%
Other values (48) 193
46.6%
2024-03-14T09:22:21.657959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
804
56.1%
2 144
 
10.1%
1 124
 
8.7%
4 72
 
5.0%
3 64
 
4.5%
6 50
 
3.5%
5 48
 
3.4%
7 32
 
2.2%
0 30
 
2.1%
9 27
 
1.9%
Other values (8) 37
 
2.6%

Most occurring categories

ValueCountFrequency (%)
Space Separator 804
56.1%
Decimal Number 616
43.0%
Dash Punctuation 6
 
0.4%
Other Letter 5
 
0.3%
Other Punctuation 1
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 144
23.4%
1 124
20.1%
4 72
11.7%
3 64
10.4%
6 50
 
8.1%
5 48
 
7.8%
7 32
 
5.2%
0 30
 
4.9%
9 27
 
4.4%
8 25
 
4.1%
Other Letter
ValueCountFrequency (%)
1
20.0%
1
20.0%
1
20.0%
1
20.0%
1
20.0%
Space Separator
ValueCountFrequency (%)
804
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 6
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1427
99.7%
Hangul 5
 
0.3%

Most frequent character per script

Common
ValueCountFrequency (%)
804
56.3%
2 144
 
10.1%
1 124
 
8.7%
4 72
 
5.0%
3 64
 
4.5%
6 50
 
3.5%
5 48
 
3.4%
7 32
 
2.2%
0 30
 
2.1%
9 27
 
1.9%
Other values (3) 32
 
2.2%
Hangul
ValueCountFrequency (%)
1
20.0%
1
20.0%
1
20.0%
1
20.0%
1
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1427
99.7%
Hangul 5
 
0.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
804
56.3%
2 144
 
10.1%
1 124
 
8.7%
4 72
 
5.0%
3 64
 
4.5%
6 50
 
3.5%
5 48
 
3.4%
7 32
 
2.2%
0 30
 
2.1%
9 27
 
1.9%
Other values (3) 32
 
2.2%
Hangul
ValueCountFrequency (%)
1
20.0%
1
20.0%
1
20.0%
1
20.0%
1
20.0%

Unnamed: 7
Text

MISSING 

Distinct63
Distinct (%)15.3%
Missing30
Missing (%)6.8%
Memory size3.6 KiB
2024-03-14T09:22:21.816037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length4
Mean length3.4552058
Min length1

Characters and Unicode

Total characters1427
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

Unique15 ?
Unique (%)3.6%

Sample

1st row현원
2nd row 5,919
3rd row 10
4th row 1
5th row 7
ValueCountFrequency (%)
2 57
 
13.8%
4 26
 
6.3%
5 25
 
6.1%
6 24
 
5.8%
1 19
 
4.6%
11 16
 
3.9%
8 15
 
3.6%
7 15
 
3.6%
12 13
 
3.1%
10 12
 
2.9%
Other values (47) 191
46.2%
2024-03-14T09:22:22.078822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
804
56.3%
2 141
 
9.9%
1 138
 
9.7%
4 63
 
4.4%
3 55
 
3.9%
5 53
 
3.7%
6 43
 
3.0%
8 33
 
2.3%
7 32
 
2.2%
9 32
 
2.2%
Other values (5) 33
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Space Separator 804
56.3%
Decimal Number 617
43.2%
Dash Punctuation 3
 
0.2%
Other Letter 2
 
0.1%
Other Punctuation 1
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 141
22.9%
1 138
22.4%
4 63
10.2%
3 55
 
8.9%
5 53
 
8.6%
6 43
 
7.0%
8 33
 
5.3%
7 32
 
5.2%
9 32
 
5.2%
0 27
 
4.4%
Other Letter
ValueCountFrequency (%)
1
50.0%
1
50.0%
Space Separator
ValueCountFrequency (%)
804
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1425
99.9%
Hangul 2
 
0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
804
56.4%
2 141
 
9.9%
1 138
 
9.7%
4 63
 
4.4%
3 55
 
3.9%
5 53
 
3.7%
6 43
 
3.0%
8 33
 
2.3%
7 32
 
2.2%
9 32
 
2.2%
Other values (3) 31
 
2.2%
Hangul
ValueCountFrequency (%)
1
50.0%
1
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1425
99.9%
Hangul 2
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
804
56.4%
2 141
 
9.9%
1 138
 
9.7%
4 63
 
4.4%
3 55
 
3.9%
5 53
 
3.7%
6 43
 
3.0%
8 33
 
2.3%
7 32
 
2.2%
9 32
 
2.2%
Other values (3) 31
 
2.2%
Hangul
ValueCountFrequency (%)
1
50.0%
1
50.0%

Unnamed: 8
Text

MISSING 

Distinct91
Distinct (%)21.8%
Missing25
Missing (%)5.6%
Memory size3.6 KiB
2024-03-14T09:22:22.276234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length4
Mean length3.6507177
Min length1

Characters and Unicode

Total characters1526
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

Unique46 ?
Unique (%)11.0%

Sample

1st row생활인
2nd row정원
3rd row 13,821
4th row 26
5th row 4
ValueCountFrequency (%)
9 54
 
12.9%
7 48
 
11.5%
4 24
 
5.7%
29 21
 
5.0%
50 20
 
4.8%
80 15
 
3.6%
20 14
 
3.3%
16 13
 
3.1%
60 11
 
2.6%
28 10
 
2.4%
Other values (75) 188
45.0%
2024-03-14T09:22:22.607531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
812
53.2%
0 112
 
7.3%
9 90
 
5.9%
2 89
 
5.8%
1 81
 
5.3%
5 75
 
4.9%
7 68
 
4.5%
4 64
 
4.2%
8 49
 
3.2%
6 47
 
3.1%
Other values (8) 39
 
2.6%

Most occurring categories

ValueCountFrequency (%)
Space Separator 812
53.2%
Decimal Number 707
46.3%
Other Letter 5
 
0.3%
Other Punctuation 1
 
0.1%
Dash Punctuation 1
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 112
15.8%
9 90
12.7%
2 89
12.6%
1 81
11.5%
5 75
10.6%
7 68
9.6%
4 64
9.1%
8 49
6.9%
6 47
6.6%
3 32
 
4.5%
Other Letter
ValueCountFrequency (%)
1
20.0%
1
20.0%
1
20.0%
1
20.0%
1
20.0%
Space Separator
ValueCountFrequency (%)
812
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1521
99.7%
Hangul 5
 
0.3%

Most frequent character per script

Common
ValueCountFrequency (%)
812
53.4%
0 112
 
7.4%
9 90
 
5.9%
2 89
 
5.9%
1 81
 
5.3%
5 75
 
4.9%
7 68
 
4.5%
4 64
 
4.2%
8 49
 
3.2%
6 47
 
3.1%
Other values (3) 34
 
2.2%
Hangul
ValueCountFrequency (%)
1
20.0%
1
20.0%
1
20.0%
1
20.0%
1
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1521
99.7%
Hangul 5
 
0.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
812
53.4%
0 112
 
7.4%
9 90
 
5.9%
2 89
 
5.9%
1 81
 
5.3%
5 75
 
4.9%
7 68
 
4.5%
4 64
 
4.2%
8 49
 
3.2%
6 47
 
3.1%
Other values (3) 34
 
2.2%
Hangul
ValueCountFrequency (%)
1
20.0%
1
20.0%
1
20.0%
1
20.0%
1
20.0%

Unnamed: 9
Text

MISSING 

Distinct102
Distinct (%)24.5%
Missing26
Missing (%)5.9%
Memory size3.6 KiB
2024-03-14T09:22:22.833586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length4
Mean length3.5827338
Min length1

Characters and Unicode

Total characters1494
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

Unique40 ?
Unique (%)9.6%

Sample

1st row현원
2nd row 11,223
3rd row 26
4th row 4
5th row 4
ValueCountFrequency (%)
4 39
 
9.4%
9 24
 
5.8%
5 22
 
5.3%
6 20
 
4.8%
7 18
 
4.3%
8 13
 
3.1%
10 13
 
3.1%
26 10
 
2.4%
16 10
 
2.4%
28 10
 
2.4%
Other values (81) 238
57.1%
2024-03-14T09:22:23.149751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
802
53.7%
1 115
 
7.7%
4 99
 
6.6%
2 90
 
6.0%
6 70
 
4.7%
5 63
 
4.2%
9 55
 
3.7%
3 52
 
3.5%
7 49
 
3.3%
8 44
 
2.9%
Other values (5) 55
 
3.7%

Most occurring categories

ValueCountFrequency (%)
Space Separator 802
53.7%
Decimal Number 681
45.6%
Dash Punctuation 8
 
0.5%
Other Letter 2
 
0.1%
Other Punctuation 1
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 115
16.9%
4 99
14.5%
2 90
13.2%
6 70
10.3%
5 63
9.3%
9 55
8.1%
3 52
7.6%
7 49
7.2%
8 44
 
6.5%
0 44
 
6.5%
Other Letter
ValueCountFrequency (%)
1
50.0%
1
50.0%
Space Separator
ValueCountFrequency (%)
802
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 8
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1492
99.9%
Hangul 2
 
0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
802
53.8%
1 115
 
7.7%
4 99
 
6.6%
2 90
 
6.0%
6 70
 
4.7%
5 63
 
4.2%
9 55
 
3.7%
3 52
 
3.5%
7 49
 
3.3%
8 44
 
2.9%
Other values (3) 53
 
3.6%
Hangul
ValueCountFrequency (%)
1
50.0%
1
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1492
99.9%
Hangul 2
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
802
53.8%
1 115
 
7.7%
4 99
 
6.6%
2 90
 
6.0%
6 70
 
4.7%
5 63
 
4.2%
9 55
 
3.7%
3 52
 
3.5%
7 49
 
3.3%
8 44
 
2.9%
Other values (3) 53
 
3.6%
Hangul
ValueCountFrequency (%)
1
50.0%
1
50.0%

Unnamed: 10
Text

MISSING 

Distinct160
Distinct (%)37.6%
Missing17
Missing (%)3.8%
Memory size3.6 KiB
2024-03-14T09:22:23.381197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length20
Mean length5.7370892
Min length2

Characters and Unicode

Total characters2444
Distinct characters192
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique124 ?
Unique (%)29.1%

Sample

1st row2016년 12월말
2nd row운영주체(법인명)
3rd row마음건강복지재단
4th row마음건강복지재단
5th row마음건강복지재단
ValueCountFrequency (%)
개인 193
40.0%
사회복지법인 35
 
7.3%
삼동회 9
 
1.9%
사복)중도원 6
 
1.2%
한기장복지재단 6
 
1.2%
원광효도마을 5
 
1.0%
5
 
1.0%
5
 
1.0%
5
 
1.0%
인산의료재단 4
 
0.8%
Other values (158) 209
43.4%
2024-03-14T09:22:23.723827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
327
 
13.4%
263
 
10.8%
198
 
8.1%
174
 
7.1%
133
 
5.4%
121
 
5.0%
102
 
4.2%
77
 
3.2%
77
 
3.2%
) 74
 
3.0%
Other values (182) 898
36.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2013
82.4%
Space Separator 327
 
13.4%
Close Punctuation 74
 
3.0%
Open Punctuation 19
 
0.8%
Decimal Number 6
 
0.2%
Uppercase Letter 4
 
0.2%
Other Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
263
 
13.1%
198
 
9.8%
174
 
8.6%
133
 
6.6%
121
 
6.0%
102
 
5.1%
77
 
3.8%
77
 
3.8%
60
 
3.0%
52
 
2.6%
Other values (170) 756
37.6%
Decimal Number
ValueCountFrequency (%)
2 2
33.3%
1 2
33.3%
0 1
16.7%
6 1
16.7%
Uppercase Letter
ValueCountFrequency (%)
W 1
25.0%
Y 1
25.0%
C 1
25.0%
A 1
25.0%
Space Separator
ValueCountFrequency (%)
327
100.0%
Close Punctuation
ValueCountFrequency (%)
) 74
100.0%
Open Punctuation
ValueCountFrequency (%)
( 19
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2013
82.4%
Common 427
 
17.5%
Latin 4
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
263
 
13.1%
198
 
9.8%
174
 
8.6%
133
 
6.6%
121
 
6.0%
102
 
5.1%
77
 
3.8%
77
 
3.8%
60
 
3.0%
52
 
2.6%
Other values (170) 756
37.6%
Common
ValueCountFrequency (%)
327
76.6%
) 74
 
17.3%
( 19
 
4.4%
2 2
 
0.5%
1 2
 
0.5%
, 1
 
0.2%
0 1
 
0.2%
6 1
 
0.2%
Latin
ValueCountFrequency (%)
W 1
25.0%
Y 1
25.0%
C 1
25.0%
A 1
25.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2013
82.4%
ASCII 431
 
17.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
327
75.9%
) 74
 
17.2%
( 19
 
4.4%
2 2
 
0.5%
1 2
 
0.5%
W 1
 
0.2%
Y 1
 
0.2%
C 1
 
0.2%
A 1
 
0.2%
, 1
 
0.2%
Other values (2) 2
 
0.5%
Hangul
ValueCountFrequency (%)
263
 
13.1%
198
 
9.8%
174
 
8.6%
133
 
6.6%
121
 
6.0%
102
 
5.1%
77
 
3.8%
77
 
3.8%
60
 
3.0%
52
 
2.6%
Other values (170) 756
37.6%

Correlations

2024-03-14T09:22:23.812484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Unnamed: 1Unnamed: 6Unnamed: 7Unnamed: 8
Unnamed: 11.0000.9000.8540.955
Unnamed: 60.9001.0000.9970.987
Unnamed: 70.8540.9971.0000.986
Unnamed: 80.9550.9870.9861.000

Missing values

2024-03-14T09:22:17.557191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T09:22:17.716952image/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.
2024-03-14T09:22:17.857974image/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(생활)시설 현황Unnamed: 1Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 6Unnamed: 7Unnamed: 8Unnamed: 9Unnamed: 10
0<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>2016년 12월말
1연번시설구분시 설 일 반 현 황<NA><NA><NA>종사자<NA>생활인<NA>운영주체(법인명)
2<NA><NA>시설명설치신고일시설장주 소정원현원정원현원<NA>
3총계전라북도424<NA><NA><NA>6,1215,91913,82111,223<NA>
4소계전주시100<NA><NA><NA><NA><NA><NA><NA><NA>
51사회복귀시설마음건강복지관00.12.08박헌수전주시 완산구 물왕멀2길 20-2910102626마음건강복지재단
62사회복귀시설마음건강회복홈11.07.04최유영전주시 완산구 물왕멀2길 251144마음건강복지재단
73사회복귀시설마음건강힐링홈15.09.11<NA>전주시 완산구 물왕멀2길 20-17<NA><NA>44마음건강복지재단
84사회복귀시설아름다운세상02.11.21김미경전주시 덕진구 아중7길 9-5772021인산의료재단
95사회복귀시설아름다운집11.05.26김성은전주시 덕진구 인교9길 11 (401호)2243인산의료재단
전라북도 사회복지(생활)시설 현황Unnamed: 1Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 6Unnamed: 7Unnamed: 8Unnamed: 9Unnamed: 10
4331노인요양시설송산효도마을2005.09.08하정만주산면 화봉길 8-3049499079한 울 안
4342노인요양시설은총의 집2006.09.29주혜숙상서면 부안로 1539-2118182324개인
4353노인요양시설해성요양원2016.02.15.이병협해안면 월륜길 515151819개인
4364노인요양시설로댐실버케어2011.07.29유희성하서면 고인돌로 34730304041개인
4375노인요양시설부안군노인요양원2010.03.11송용기부안읍 봉두길 52772015사복)한국장로교복지재단
4386노인요양시설산타요양원2015.02.13김성일부안읍 매창로 287-43<NA><NA><NA><NA>개인
4397노인요양공동생활가정부안군재가노인지원센터(입소)2011.05.18이주재부안읍 봉두길 528899사복)한국장로교복지재단
4408노인요양공동생활가정섬김요양원2014.12.22박난주동진면 동진로 1718899개인
4419장애인거주시설둥근마음보금자리16.12.30하정만부안군 주산면 화봉길 8-1866--한울안
442<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>