Overview

Dataset statistics

Number of variables9
Number of observations30
Missing cells1
Missing cells (%)0.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.2 KiB
Average record size in memory76.4 B

Variable types

Text9

Dataset

Description기초수급자및차상위계층통계201712
Author전라북도
URLhttps://www.bigdatahub.go.kr/opendata/dataSet/detail.nm?contentId=37&rlik=49451aebf056b486&serviceId=202002

Alerts

시설수급자 has 1 (3.3%) missing valuesMissing
시군명 has unique valuesUnique
합계(기초수급자수) has unique valuesUnique
일반수급자 has unique valuesUnique
소계(법정차상위) has unique valuesUnique
차상위장애인 has unique valuesUnique

Reproduction

Analysis started2024-03-14 03:06:36.275377
Analysis finished2024-03-14 03:06:36.756229
Duration0.48 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군명
Text

UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
2024-03-14T12:06:36.837614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length11
Mean length9.5
Min length7

Characters and Unicode

Total characters285
Distinct characters31
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

Unique30 ?
Unique (%)100.0%

Sample

1st row 계(가구)
2nd row (인원)
3rd row 전주시 계(가구)
4th row전주시 (인원)
5th row 군산시 계(가구)
ValueCountFrequency (%)
계(가구 15
25.9%
인원 15
25.9%
전주시 2
 
3.4%
군산시 2
 
3.4%
익산시 2
 
3.4%
정읍시 2
 
3.4%
남원시 2
 
3.4%
김제시 2
 
3.4%
완주군 2
 
3.4%
진안군 2
 
3.4%
Other values (6) 12
20.7%
2024-03-14T12:06:37.101380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
66
23.2%
( 30
10.5%
) 30
10.5%
군 18
 
6.3%
원 17
 
6.0%
가 15
 
5.3%
구 15
 
5.3%
인 15
 
5.3%
계 15
 
5.3%
시 12
 
4.2%
Other values (21) 52
18.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 159
55.8%
Space Separator 66
23.2%
Open Punctuation 30
 
10.5%
Close Punctuation 30
 
10.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
군 18
11.3%
원 17
10.7%
가 15
 
9.4%
구 15
 
9.4%
인 15
 
9.4%
계 15
 
9.4%
시 12
 
7.5%
주 6
 
3.8%
안 4
 
2.5%
산 4
 
2.5%
Other values (18) 38
23.9%
Space Separator
ValueCountFrequency (%)
66
100.0%
Open Punctuation
ValueCountFrequency (%)
( 30
100.0%
Close Punctuation
ValueCountFrequency (%)
) 30
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 159
55.8%
Common 126
44.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
군 18
11.3%
원 17
10.7%
가 15
 
9.4%
구 15
 
9.4%
인 15
 
9.4%
계 15
 
9.4%
시 12
 
7.5%
주 6
 
3.8%
안 4
 
2.5%
산 4
 
2.5%
Other values (18) 38
23.9%
Common
ValueCountFrequency (%)
66
52.4%
( 30
23.8%
) 30
23.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 159
55.8%
ASCII 126
44.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
66
52.4%
( 30
23.8%
) 30
23.8%
Hangul
ValueCountFrequency (%)
군 18
11.3%
원 17
10.7%
가 15
 
9.4%
구 15
 
9.4%
인 15
 
9.4%
계 15
 
9.4%
시 12
 
7.5%
주 6
 
3.8%
안 4
 
2.5%
산 4
 
2.5%
Other values (18) 38
23.9%

합계(기초수급자수)
Text

UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
2024-03-14T12:06:37.261481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length4.9666667
Min length3

Characters and Unicode

Total characters149
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

Unique30 ?
Unique (%)100.0%

Sample

1st row64,251
2nd row89,275
3rd row17,510
4th row26,548
5th row9,344
ValueCountFrequency (%)
64,251 1
 
3.3%
89,275 1
 
3.3%
2,195 1
 
3.3%
2,579 1
 
3.3%
2,136 1
 
3.3%
1,255 1
 
3.3%
936 1
 
3.3%
1,581 1
 
3.3%
1,157 1
 
3.3%
1,297 1
 
3.3%
Other values (20) 20
66.7%
2024-03-14T12:06:37.535693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 26
17.4%
1 23
15.4%
5 17
11.4%
2 16
10.7%
6 13
8.7%
4 11
7.4%
9 11
7.4%
7 10
 
6.7%
8 9
 
6.0%
0 7
 
4.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 123
82.6%
Other Punctuation 26
 
17.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 23
18.7%
5 17
13.8%
2 16
13.0%
6 13
10.6%
4 11
8.9%
9 11
8.9%
7 10
8.1%
8 9
 
7.3%
0 7
 
5.7%
3 6
 
4.9%
Other Punctuation
ValueCountFrequency (%)
, 26
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 149
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
, 26
17.4%
1 23
15.4%
5 17
11.4%
2 16
10.7%
6 13
8.7%
4 11
7.4%
9 11
7.4%
7 10
 
6.7%
8 9
 
6.0%
0 7
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 149
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
, 26
17.4%
1 23
15.4%
5 17
11.4%
2 16
10.7%
6 13
8.7%
4 11
7.4%
9 11
7.4%
7 10
 
6.7%
8 9
 
6.0%
0 7
 
4.7%

일반수급자
Text

UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
2024-03-14T12:06:37.696305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length4.9333333
Min length3

Characters and Unicode

Total characters148
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

Unique30 ?
Unique (%)100.0%

Sample

1st row58,887
2nd row89,275
3rd row16,565
4th row26,548
5th row8,557
ValueCountFrequency (%)
58,887 1
 
3.3%
89,275 1
 
3.3%
2,138 1
 
3.3%
2,579 1
 
3.3%
1,904 1
 
3.3%
1,255 1
 
3.3%
843 1
 
3.3%
1,581 1
 
3.3%
1,070 1
 
3.3%
1,297 1
 
3.3%
Other values (20) 20
66.7%
2024-03-14T12:06:37.968287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 26
17.6%
5 18
12.2%
8 16
10.8%
1 15
10.1%
6 15
10.1%
2 13
8.8%
7 12
8.1%
4 10
 
6.8%
9 9
 
6.1%
3 7
 
4.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 122
82.4%
Other Punctuation 26
 
17.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 18
14.8%
8 16
13.1%
1 15
12.3%
6 15
12.3%
2 13
10.7%
7 12
9.8%
4 10
8.2%
9 9
7.4%
3 7
 
5.7%
0 7
 
5.7%
Other Punctuation
ValueCountFrequency (%)
, 26
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 148
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
, 26
17.6%
5 18
12.2%
8 16
10.8%
1 15
10.1%
6 15
10.1%
2 13
8.8%
7 12
8.1%
4 10
 
6.8%
9 9
 
6.1%
3 7
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 148
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
, 26
17.6%
5 18
12.2%
8 16
10.8%
1 15
10.1%
6 15
10.1%
2 13
8.8%
7 12
8.1%
4 10
 
6.8%
9 9
 
6.1%
3 7
 
4.7%

시설수급자
Text

MISSING 

Distinct16
Distinct (%)55.2%
Missing1
Missing (%)3.3%
Memory size372.0 B
2024-03-14T12:06:38.094933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length2
Min length1

Characters and Unicode

Total characters58
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique15 ?
Unique (%)51.7%

Sample

1st row5,364
2nd row945
3rd row-
4th row787
5th row-
ValueCountFrequency (%)
14
48.3%
206 1
 
3.4%
945 1
 
3.4%
787 1
 
3.4%
1,486 1
 
3.4%
339 1
 
3.4%
433 1
 
3.4%
102 1
 
3.4%
5,364 1
 
3.4%
59 1
 
3.4%
Other values (6) 6
20.7%
2024-03-14T12:06:38.464230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 14
24.1%
3 8
13.8%
5 5
 
8.6%
7 5
 
8.6%
2 4
 
6.9%
9 4
 
6.9%
4 4
 
6.9%
0 3
 
5.2%
6 3
 
5.2%
8 3
 
5.2%
Other values (2) 5
 
8.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 42
72.4%
Dash Punctuation 14
 
24.1%
Other Punctuation 2
 
3.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 8
19.0%
5 5
11.9%
7 5
11.9%
2 4
9.5%
9 4
9.5%
4 4
9.5%
0 3
 
7.1%
6 3
 
7.1%
8 3
 
7.1%
1 3
 
7.1%
Dash Punctuation
ValueCountFrequency (%)
- 14
100.0%
Other Punctuation
ValueCountFrequency (%)
, 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 58
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 14
24.1%
3 8
13.8%
5 5
 
8.6%
7 5
 
8.6%
2 4
 
6.9%
9 4
 
6.9%
4 4
 
6.9%
0 3
 
5.2%
6 3
 
5.2%
8 3
 
5.2%
Other values (2) 5
 
8.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 58
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 14
24.1%
3 8
13.8%
5 5
 
8.6%
7 5
 
8.6%
2 4
 
6.9%
9 4
 
6.9%
4 4
 
6.9%
0 3
 
5.2%
6 3
 
5.2%
8 3
 
5.2%
Other values (2) 5
 
8.6%

소계(법정차상위)
Text

UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
2024-03-14T12:06:38.629426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length4.4333333
Min length3

Characters and Unicode

Total characters133
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

Unique30 ?
Unique (%)100.0%

Sample

1st row23,409
2nd row46,049
3rd row4,981
4th row12,677
5th row2,944
ValueCountFrequency (%)
23,409 1
 
3.3%
46,049 1
 
3.3%
1,255 1
 
3.3%
2,449 1
 
3.3%
1,571 1
 
3.3%
618 1
 
3.3%
412 1
 
3.3%
953 1
 
3.3%
604 1
 
3.3%
603 1
 
3.3%
Other values (20) 20
66.7%
2024-03-14T12:06:38.883917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 20
15.0%
4 17
12.8%
9 17
12.8%
2 15
11.3%
3 14
10.5%
1 11
8.3%
7 11
8.3%
0 9
6.8%
6 8
 
6.0%
5 7
 
5.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 113
85.0%
Other Punctuation 20
 
15.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 17
15.0%
9 17
15.0%
2 15
13.3%
3 14
12.4%
1 11
9.7%
7 11
9.7%
0 9
8.0%
6 8
7.1%
5 7
6.2%
8 4
 
3.5%
Other Punctuation
ValueCountFrequency (%)
, 20
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 133
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
, 20
15.0%
4 17
12.8%
9 17
12.8%
2 15
11.3%
3 14
10.5%
1 11
8.3%
7 11
8.3%
0 9
6.8%
6 8
 
6.0%
5 7
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 133
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
, 20
15.0%
4 17
12.8%
9 17
12.8%
2 15
11.3%
3 14
10.5%
1 11
8.3%
7 11
8.3%
0 9
6.8%
6 8
 
6.0%
5 7
 
5.3%

한부모가족
Text

Distinct28
Distinct (%)93.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
2024-03-14T12:06:39.044013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length2.8333333
Min length2

Characters and Unicode

Total characters85
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

Unique26 ?
Unique (%)86.7%

Sample

1st row1,982
2nd row8,322
3rd row716
4th row3,101
5th row238
ValueCountFrequency (%)
24 2
 
6.7%
95 2
 
6.7%
1,982 1
 
3.3%
329 1
 
3.3%
39 1
 
3.3%
258 1
 
3.3%
62 1
 
3.3%
88 1
 
3.3%
16 1
 
3.3%
77 1
 
3.3%
Other values (18) 18
60.0%
2024-03-14T12:06:39.374485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 14
16.5%
1 12
14.1%
9 10
11.8%
3 9
10.6%
8 8
9.4%
4 7
8.2%
5 7
8.2%
7 5
 
5.9%
6 5
 
5.9%
, 5
 
5.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 80
94.1%
Other Punctuation 5
 
5.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 14
17.5%
1 12
15.0%
9 10
12.5%
3 9
11.2%
8 8
10.0%
4 7
8.8%
5 7
8.8%
7 5
 
6.2%
6 5
 
6.2%
0 3
 
3.8%
Other Punctuation
ValueCountFrequency (%)
, 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 85
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 14
16.5%
1 12
14.1%
9 10
11.8%
3 9
10.6%
8 8
9.4%
4 7
8.2%
5 7
8.2%
7 5
 
5.9%
6 5
 
5.9%
, 5
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 85
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 14
16.5%
1 12
14.1%
9 10
11.8%
3 9
10.6%
8 8
9.4%
4 7
8.2%
5 7
8.2%
7 5
 
5.9%
6 5
 
5.9%
, 5
 
5.9%

차상위본인부담경감대상자
Text

Distinct29
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
2024-03-14T12:06:39.519487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length4.1333333
Min length3

Characters and Unicode

Total characters124
Distinct characters10
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

Unique28 ?
Unique (%)93.3%

Sample

1st row11,464
2nd row25,646
3rd row2,272
4th row7,113
5th row1,456
ValueCountFrequency (%)
170 2
 
6.7%
11,464 1
 
3.3%
1,435 1
 
3.3%
738 1
 
3.3%
1,382 1
 
3.3%
832 1
 
3.3%
270 1
 
3.3%
444 1
 
3.3%
220 1
 
3.3%
200 1
 
3.3%
Other values (19) 19
63.3%
2024-03-14T12:06:39.785643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 23
18.5%
2 21
16.9%
, 16
12.9%
3 14
11.3%
4 12
9.7%
0 10
8.1%
7 8
 
6.5%
6 7
 
5.6%
8 7
 
5.6%
5 6
 
4.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 108
87.1%
Other Punctuation 16
 
12.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 23
21.3%
2 21
19.4%
3 14
13.0%
4 12
11.1%
0 10
9.3%
7 8
 
7.4%
6 7
 
6.5%
8 7
 
6.5%
5 6
 
5.6%
Other Punctuation
ValueCountFrequency (%)
, 16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 124
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 23
18.5%
2 21
16.9%
, 16
12.9%
3 14
11.3%
4 12
9.7%
0 10
8.1%
7 8
 
6.5%
6 7
 
5.6%
8 7
 
5.6%
5 6
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 124
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 23
18.5%
2 21
16.9%
, 16
12.9%
3 14
11.3%
4 12
9.7%
0 10
8.1%
7 8
 
6.5%
6 7
 
5.6%
8 7
 
5.6%
5 6
 
4.8%

차상위자활사업참여자
Text

Distinct28
Distinct (%)93.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
2024-03-14T12:06:39.949805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length2
Mean length2.2666667
Min length1

Characters and Unicode

Total characters68
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

Unique26 ?
Unique (%)86.7%

Sample

1st row819
2nd row1,102
3rd row214
4th row264
5th row80
ValueCountFrequency (%)
32 2
 
6.7%
61 2
 
6.7%
819 1
 
3.3%
12 1
 
3.3%
35 1
 
3.3%
90 1
 
3.3%
67 1
 
3.3%
13 1
 
3.3%
5 1
 
3.3%
48 1
 
3.3%
Other values (18) 18
60.0%
2024-03-14T12:06:40.212699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 14
20.6%
2 10
14.7%
4 9
13.2%
5 7
10.3%
6 5
 
7.4%
8 5
 
7.4%
9 5
 
7.4%
3 4
 
5.9%
0 4
 
5.9%
7 4
 
5.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 67
98.5%
Other Punctuation 1
 
1.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 14
20.9%
2 10
14.9%
4 9
13.4%
5 7
10.4%
6 5
 
7.5%
8 5
 
7.5%
9 5
 
7.5%
3 4
 
6.0%
0 4
 
6.0%
7 4
 
6.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 68
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 14
20.6%
2 10
14.7%
4 9
13.2%
5 7
10.3%
6 5
 
7.4%
8 5
 
7.4%
9 5
 
7.4%
3 4
 
5.9%
0 4
 
5.9%
7 4
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 68
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 14
20.6%
2 10
14.7%
4 9
13.2%
5 7
10.3%
6 5
 
7.4%
8 5
 
7.4%
9 5
 
7.4%
3 4
 
5.9%
0 4
 
5.9%
7 4
 
5.9%

차상위장애인
Text

UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
2024-03-14T12:06:40.412153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length3
Mean length3.5666667
Min length3

Characters and Unicode

Total characters107
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

Unique30 ?
Unique (%)100.0%

Sample

1st row9,144
2nd row10,979
3rd row1,779
4th row2,199
5th row1,170
ValueCountFrequency (%)
9,144 1
 
3.3%
10,979 1
 
3.3%
443 1
 
3.3%
719 1
 
3.3%
610 1
 
3.3%
247 1
 
3.3%
221 1
 
3.3%
384 1
 
3.3%
328 1
 
3.3%
261 1
 
3.3%
Other values (20) 20
66.7%
2024-03-14T12:06:40.792554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 17
15.9%
1 16
15.0%
9 12
11.2%
7 11
10.3%
4 10
9.3%
, 8
7.5%
0 8
7.5%
3 8
7.5%
8 7
6.5%
6 7
6.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 99
92.5%
Other Punctuation 8
 
7.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 17
17.2%
1 16
16.2%
9 12
12.1%
7 11
11.1%
4 10
10.1%
0 8
8.1%
3 8
8.1%
8 7
7.1%
6 7
7.1%
5 3
 
3.0%
Other Punctuation
ValueCountFrequency (%)
, 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 107
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 17
15.9%
1 16
15.0%
9 12
11.2%
7 11
10.3%
4 10
9.3%
, 8
7.5%
0 8
7.5%
3 8
7.5%
8 7
6.5%
6 7
6.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 107
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 17
15.9%
1 16
15.0%
9 12
11.2%
7 11
10.3%
4 10
9.3%
, 8
7.5%
0 8
7.5%
3 8
7.5%
8 7
6.5%
6 7
6.5%

Correlations

2024-03-14T12:06:40.874233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군명합계(기초수급자수)일반수급자시설수급자소계(법정차상위)한부모가족차상위본인부담경감대상자차상위자활사업참여자차상위장애인
시군명1.0001.0001.0001.0001.0001.0001.0001.0001.000
합계(기초수급자수)1.0001.0001.0001.0001.0001.0001.0001.0001.000
일반수급자1.0001.0001.0001.0001.0001.0001.0001.0001.000
시설수급자1.0001.0001.0001.0001.0000.3270.9580.9271.000
소계(법정차상위)1.0001.0001.0001.0001.0001.0001.0001.0001.000
한부모가족1.0001.0001.0000.3271.0001.0000.9710.9811.000
차상위본인부담경감대상자1.0001.0001.0000.9581.0000.9711.0000.9711.000
차상위자활사업참여자1.0001.0001.0000.9271.0000.9810.9711.0001.000
차상위장애인1.0001.0001.0001.0001.0001.0001.0001.0001.000

Missing values

2024-03-14T12:06:36.593995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T12:06:36.712981image/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.

Sample

시군명합계(기초수급자수)일반수급자시설수급자소계(법정차상위)한부모가족차상위본인부담경감대상자차상위자활사업참여자차상위장애인
0계(가구)64,25158,8875,36423,4091,98211,4648199,144
1(인원)89,27589,275<NA>46,0498,32225,6461,10210,979
2전주시 계(가구)17,51016,5659454,9817162,2722141,779
3전주시 (인원)26,54826,548-12,6773,1017,1132642,199
4군산시 계(가구)9,3448,5577872,9442381,456801,170
5군산시 (인원)12,67212,672-5,9941,1273,3301121,425
6익산시 계(가구)11,2519,7651,4863,3394231,5611251,230
7익산시 (인원)14,86314,863-7,1721,6493,8881511,484
8정읍시 계(가구)5,1964,8573392,3791401,37445820
9정읍시 (인원)7,1947,194-3,9965482,42259967
시군명합계(기초수급자수)일반수급자시설수급자소계(법정차상위)한부모가족차상위본인부담경감대상자차상위자활사업참여자차상위장애인
20장수군 계(가구)887856313842211532215
21장수군 (인원)1,2971,297-6039520047261
22임실군 계(가구)1,1571,070876042422032328
23임실군 (인원)1,5811,581-9537744448384
24순창군 계(가구)93684393412161705221
25순창군 (인원)1,2551,255-6188827013247
26고창군 계(가구)2,1361,9042321,5716283267610
27고창군 (인원)2,5792,579-2,4492581,38290719
28부안군 계(가구)2,1952,138571,2553973835443
29부안군 (인원)2,9562,956-1,9971801,23157529