Overview

Dataset statistics

Number of variables5
Number of observations40
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.7 KiB
Average record size in memory43.3 B

Variable types

Categorical1
Text4

Dataset

Description대한적십자사의 사회봉사관 정보입니다. 컬럼 내용으로는 지역, 기관명, 주소, 전화 번호, 팩스 번호로 구성되어 있습니다.
URLhttps://www.data.go.kr/data/3046946/fileData.do

Alerts

주 소 has unique valuesUnique
전 화 번 호 has unique valuesUnique
팩 스 번 호 has unique valuesUnique

Reproduction

Analysis started2023-12-12 09:00:31.434636
Analysis finished2023-12-12 09:00:31.899723
Duration0.47 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Categorical

Distinct11
Distinct (%)27.5%
Missing0
Missing (%)0.0%
Memory size452.0 B
경기
10 
충남
6 
광주전남
5 
서울
4 
강원
4 
Other values (6)
11 

Length

Max length4
Median length2
Mean length2.25
Min length2

Unique

Unique2 ?
Unique (%)5.0%

Sample

1st row서울
2nd row서울
3rd row서울
4th row서울
5th row인천

Common Values

ValueCountFrequency (%)
경기 10
25.0%
충남 6
15.0%
광주전남 5
12.5%
서울 4
 
10.0%
강원 4
 
10.0%
전북 3
 
7.5%
충북 2
 
5.0%
경북 2
 
5.0%
경남 2
 
5.0%
인천 1
 
2.5%

Length

2023-12-12T18:00:31.999160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기 10
25.0%
충남 6
15.0%
광주전남 5
12.5%
서울 4
 
10.0%
강원 4
 
10.0%
전북 3
 
7.5%
충북 2
 
5.0%
경북 2
 
5.0%
경남 2
 
5.0%
인천 1
 
2.5%

기관명
Text

Distinct27
Distinct (%)67.5%
Missing0
Missing (%)0.0%
Memory size452.0 B
2023-12-12T18:00:32.210813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length2
Mean length2.075
Min length2

Characters and Unicode

Total characters83
Distinct characters32
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

Unique22 ?
Unique (%)55.0%

Sample

1st row서부
2nd row남부
3rd row중앙
4th row북부
5th row북부
ValueCountFrequency (%)
서부 6
 
15.0%
북부 4
 
10.0%
동부 3
 
7.5%
남부 3
 
7.5%
중앙 2
 
5.0%
속초 1
 
2.5%
천안 1
 
2.5%
여수 1
 
2.5%
남원 1
 
2.5%
군산 1
 
2.5%
Other values (17) 17
42.5%
2023-12-12T18:00:32.576263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
부 19
22.9%
서 9
 
10.8%
남 7
 
8.4%
북 6
 
7.2%
동 5
 
6.0%
중 4
 
4.8%
주 3
 
3.6%
산 3
 
3.6%
영 2
 
2.4%
천 2
 
2.4%
Other values (22) 23
27.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 83
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
부 19
22.9%
서 9
 
10.8%
남 7
 
8.4%
북 6
 
7.2%
동 5
 
6.0%
중 4
 
4.8%
주 3
 
3.6%
산 3
 
3.6%
영 2
 
2.4%
천 2
 
2.4%
Other values (22) 23
27.7%

Most occurring scripts

ValueCountFrequency (%)
Hangul 83
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
부 19
22.9%
서 9
 
10.8%
남 7
 
8.4%
북 6
 
7.2%
동 5
 
6.0%
중 4
 
4.8%
주 3
 
3.6%
산 3
 
3.6%
영 2
 
2.4%
천 2
 
2.4%
Other values (22) 23
27.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 83
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
부 19
22.9%
서 9
 
10.8%
남 7
 
8.4%
북 6
 
7.2%
동 5
 
6.0%
중 4
 
4.8%
주 3
 
3.6%
산 3
 
3.6%
영 2
 
2.4%
천 2
 
2.4%
Other values (22) 23
27.7%

주 소
Text

UNIQUE 

Distinct40
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size452.0 B
2023-12-12T18:00:32.930717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length37
Median length27
Mean length19.775
Min length12

Characters and Unicode

Total characters791
Distinct characters137
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique40 ?
Unique (%)100.0%

Sample

1st row서울시 양청구 중앙로 345
2nd row서울시 관악구 성현로 28
3rd row서울시 종로구 종로58길 27
4th row서울시 노원구 동일로211길 16
5th row인천시 계양구 계산천동로 7번길 9, 소바우빌딩 2층
ValueCountFrequency (%)
경기도 10
 
5.0%
충남 6
 
3.0%
서울시 4
 
2.0%
2층 4
 
2.0%
전남 4
 
2.0%
강원도 4
 
2.0%
전북 3
 
1.5%
경북 2
 
1.0%
북구 2
 
1.0%
경남 2
 
1.0%
Other values (154) 160
79.6%
2023-12-12T18:00:33.404150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
162
 
20.5%
시 39
 
4.9%
로 36
 
4.6%
2 34
 
4.3%
1 23
 
2.9%
남 19
 
2.4%
길 18
 
2.3%
구 17
 
2.1%
도 16
 
2.0%
4 15
 
1.9%
Other values (127) 412
52.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 466
58.9%
Space Separator 162
 
20.5%
Decimal Number 152
 
19.2%
Dash Punctuation 5
 
0.6%
Other Punctuation 3
 
0.4%
Uppercase Letter 3
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 39
 
8.4%
로 36
 
7.7%
남 19
 
4.1%
길 18
 
3.9%
구 17
 
3.6%
도 16
 
3.4%
경 15
 
3.2%
북 13
 
2.8%
원 12
 
2.6%
충 11
 
2.4%
Other values (111) 270
57.9%
Decimal Number
ValueCountFrequency (%)
2 34
22.4%
1 23
15.1%
4 15
9.9%
3 15
9.9%
6 13
 
8.6%
0 13
 
8.6%
8 12
 
7.9%
5 10
 
6.6%
7 9
 
5.9%
9 8
 
5.3%
Uppercase Letter
ValueCountFrequency (%)
A 1
33.3%
P 1
33.3%
T 1
33.3%
Space Separator
ValueCountFrequency (%)
162
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5
100.0%
Other Punctuation
ValueCountFrequency (%)
, 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 466
58.9%
Common 322
40.7%
Latin 3
 
0.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 39
 
8.4%
로 36
 
7.7%
남 19
 
4.1%
길 18
 
3.9%
구 17
 
3.6%
도 16
 
3.4%
경 15
 
3.2%
북 13
 
2.8%
원 12
 
2.6%
충 11
 
2.4%
Other values (111) 270
57.9%
Common
ValueCountFrequency (%)
162
50.3%
2 34
 
10.6%
1 23
 
7.1%
4 15
 
4.7%
3 15
 
4.7%
6 13
 
4.0%
0 13
 
4.0%
8 12
 
3.7%
5 10
 
3.1%
7 9
 
2.8%
Other values (3) 16
 
5.0%
Latin
ValueCountFrequency (%)
A 1
33.3%
P 1
33.3%
T 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 466
58.9%
ASCII 325
41.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
162
49.8%
2 34
 
10.5%
1 23
 
7.1%
4 15
 
4.6%
3 15
 
4.6%
6 13
 
4.0%
0 13
 
4.0%
8 12
 
3.7%
5 10
 
3.1%
7 9
 
2.8%
Other values (6) 19
 
5.8%
Hangul
ValueCountFrequency (%)
시 39
 
8.4%
로 36
 
7.7%
남 19
 
4.1%
길 18
 
3.9%
구 17
 
3.6%
도 16
 
3.4%
경 15
 
3.2%
북 13
 
2.8%
원 12
 
2.6%
충 11
 
2.4%
Other values (111) 270
57.9%

전 화 번 호
Text

UNIQUE 

Distinct40
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size452.0 B
2023-12-12T18:00:33.629560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length12
Mean length12.925
Min length11

Characters and Unicode

Total characters517
Distinct characters14
Distinct categories5 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique40 ?
Unique (%)100.0%

Sample

1st row02-2181-3151~6
2nd row02-889-9580
3rd row02-2238-3101~3
4th row02-951-0470~1
5th row032-555-9582,9580
ValueCountFrequency (%)
02-2181-3151~6 1
 
2.4%
063-855-3126 1
 
2.4%
043-847-7989 1
 
2.4%
063-462-3814 1
 
2.4%
041-576-2266 1
 
2.4%
041-854-1210 1
 
2.4%
041-631-3525 1
 
2.4%
041-936-1452 1
 
2.4%
041-732-9599 1
 
2.4%
041-356-9504 1
 
2.4%
Other values (32) 32
76.2%
2023-12-12T18:00:33.967370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 80
15.5%
0 57
11.0%
3 57
11.0%
5 56
10.8%
1 54
10.4%
2 50
9.7%
6 37
7.2%
4 37
7.2%
8 33
6.4%
9 22
 
4.3%
Other values (4) 34
6.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 421
81.4%
Dash Punctuation 80
 
15.5%
Math Symbol 11
 
2.1%
Other Punctuation 3
 
0.6%
Space Separator 2
 
0.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 57
13.5%
3 57
13.5%
5 56
13.3%
1 54
12.8%
2 50
11.9%
6 37
8.8%
4 37
8.8%
8 33
7.8%
9 22
 
5.2%
7 18
 
4.3%
Dash Punctuation
ValueCountFrequency (%)
- 80
100.0%
Math Symbol
ValueCountFrequency (%)
~ 11
100.0%
Other Punctuation
ValueCountFrequency (%)
, 3
100.0%
Space Separator
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 517
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 80
15.5%
0 57
11.0%
3 57
11.0%
5 56
10.8%
1 54
10.4%
2 50
9.7%
6 37
7.2%
4 37
7.2%
8 33
6.4%
9 22
 
4.3%
Other values (4) 34
6.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 517
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 80
15.5%
0 57
11.0%
3 57
11.0%
5 56
10.8%
1 54
10.4%
2 50
9.7%
6 37
7.2%
4 37
7.2%
8 33
6.4%
9 22
 
4.3%
Other values (4) 34
6.6%

팩 스 번 호
Text

UNIQUE 

Distinct40
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size452.0 B
2023-12-12T18:00:34.182967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length11.95
Min length11

Characters and Unicode

Total characters478
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

Unique40 ?
Unique (%)100.0%

Sample

1st row02-2181-3159
2nd row02-878-6794
3rd row02-2238-3106
4th row02-951-0469
5th row032-555-9583
ValueCountFrequency (%)
02-2181-3159 1
 
2.5%
02-878-6794 1
 
2.5%
063-462-3840 1
 
2.5%
041-573-2720 1
 
2.5%
041-858-1211 1
 
2.5%
041-631-3524 1
 
2.5%
041-931-1452 1
 
2.5%
041-732-9447 1
 
2.5%
041-357-9504 1
 
2.5%
063-855-3156 1
 
2.5%
Other values (30) 30
75.0%
2023-12-12T18:00:34.571128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 80
16.7%
3 62
13.0%
0 57
11.9%
5 54
11.3%
1 48
10.0%
4 43
9.0%
2 36
7.5%
6 32
 
6.7%
8 23
 
4.8%
7 23
 
4.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 398
83.3%
Dash Punctuation 80
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 62
15.6%
0 57
14.3%
5 54
13.6%
1 48
12.1%
4 43
10.8%
2 36
9.0%
6 32
8.0%
8 23
 
5.8%
7 23
 
5.8%
9 20
 
5.0%
Dash Punctuation
ValueCountFrequency (%)
- 80
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 478
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 80
16.7%
3 62
13.0%
0 57
11.9%
5 54
11.3%
1 48
10.0%
4 43
9.0%
2 36
7.5%
6 32
 
6.7%
8 23
 
4.8%
7 23
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 478
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 80
16.7%
3 62
13.0%
0 57
11.9%
5 54
11.3%
1 48
10.0%
4 43
9.0%
2 36
7.5%
6 32
 
6.7%
8 23
 
4.8%
7 23
 
4.8%

Correlations

2023-12-12T18:00:34.689978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분기관명주 소전 화 번 호팩 스 번 호
구분1.0000.0001.0001.0001.000
기관명0.0001.0001.0001.0001.000
주 소1.0001.0001.0001.0001.000
전 화 번 호1.0001.0001.0001.0001.000
팩 스 번 호1.0001.0001.0001.0001.000

Missing values

2023-12-12T18:00:31.754672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T18:00:31.861043image/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

구분기관명주 소전 화 번 호팩 스 번 호
0서울서부서울시 양청구 중앙로 34502-2181-3151~602-2181-3159
1서울남부서울시 관악구 성현로 2802-889-958002-878-6794
2서울중앙서울시 종로구 종로58길 2702-2238-3101~302-2238-3106
3서울북부서울시 노원구 동일로211길 1602-951-0470~102-951-0469
4인천북부인천시 계양구 계산천동로 7번길 9, 소바우빌딩 2층032-555-9582,9580032-555-9583
5대구서부대구시 서구 달설로 52, 4층053-638-9512053-638-2450
6경기중부경기도 성남시 수정구 성남대로 1480번길 12031-759-5082~3031-759-5059
7경기북부경기도 의정부시 금오로 68031-874-3848031-875-9444
8경기동부경기도 이천시 경충대로 2493번길 2-21031-635-6274, 6285031-638-0314
9경기남부경기도 평택시 도일유통길 13-2031-653-1318, 1328031-653-1317
구분기관명주 소전 화 번 호팩 스 번 호
30전북남원전북 남원시 충정로 373063-626-0628063-631-0628
31광주전남여수전남 여수시 문수북5길 27061-651-5258061-651-5205
32광주전남서부전남 목포시 양을로 220번길 7-3061-272-2807061-272-0498
33광주전남동부전남 순천시 고지2길 3061-753-3952061-753-3953
34광주전남광주광주시 북구 서림로 21062-526-0548062-526-0549
35광주전남남부전남 해남군 해남읍 중앙1로 61061-537-0541061-537-0542
36경북동부경북 포항시 북구 죽도로 84054-247-8793054-247-8795
37경북서부경북 구미시 구미중앙로 160, 2층054-452-1884~5054-452-1886
38경남서부경남 진주시 솔밭로 80번길 7055-762-2252055-762-2253
39경남북부경남 밀양시 밀양대로 2089055-351-4401055-351-4403