Overview

Dataset statistics

Number of variables6
Number of observations341
Missing cells100
Missing cells (%)4.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory16.1 KiB
Average record size in memory48.4 B

Variable types

Categorical2
Text4

Dataset

Description이 데이터는 충청남도 금산군 경로당에 대한 데이터(행정구역, 행정리, 경로당명, 연락처, 도로명주소, 데이터기준일자)를 제공하고 있습니다.
Author충청남도
URLhttps://alldam.chungnam.go.kr/index.chungnam?menuCd=DOM_000000201001001001&st=&cds=&orgCd=&apiType=&isOpen=Y&pageIndex=104&beforeMenuCd=DOM_000000201001001000&publicdatapk=15099753

Alerts

데이터기준일자 has constant value ""Constant
연락처 has 100 (29.3%) missing valuesMissing

Reproduction

Analysis started2024-01-09 21:53:31.193686
Analysis finished2024-01-09 21:53:31.604767
Duration0.41 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

행정구역
Categorical

Distinct10
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
금산읍
63 
금성면
39 
진산면
35 
제원면
34 
부리면
33 
Other values (5)
137 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row금산읍
2nd row금산읍
3rd row금산읍
4th row금산읍
5th row금산읍

Common Values

ValueCountFrequency (%)
금산읍 63
18.5%
금성면 39
11.4%
진산면 35
10.3%
제원면 34
10.0%
부리면 33
9.7%
남이면 30
8.8%
복수면 29
8.5%
추부면 28
8.2%
군북면 25
 
7.3%
남일면 25
 
7.3%

Length

2024-01-10T06:53:31.657504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T06:53:31.752680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
금산읍 63
18.5%
금성면 39
11.4%
진산면 35
10.3%
제원면 34
10.0%
부리면 33
9.7%
남이면 30
8.8%
복수면 29
8.5%
추부면 28
8.2%
군북면 25
 
7.3%
남일면 25
 
7.3%

행정리
Text

Distinct251
Distinct (%)73.6%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
2024-01-10T06:53:32.022516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length4
Mean length3.8914956
Min length2

Characters and Unicode

Total characters1327
Distinct characters122
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

Unique172 ?
Unique (%)50.4%

Sample

1st row신대1리
2nd row신대1리
3rd row신대2리
4th row신대2리
5th row중도1리
ValueCountFrequency (%)
신대1리 4
 
1.2%
하옥4리 4
 
1.2%
신대2리 4
 
1.2%
음대리 3
 
0.9%
아인1리 3
 
0.9%
음지2리 3
 
0.9%
상1리 3
 
0.9%
양전3리 3
 
0.9%
창평2 2
 
0.6%
신동1리 2
 
0.6%
Other values (243) 314
91.0%
2024-01-10T06:53:32.383039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
리 308
23.2%
1 127
 
9.6%
2 116
 
8.7%
3 36
 
2.7%
곡 35
 
2.6%
상 30
 
2.3%
신 28
 
2.1%
대 26
 
2.0%
도 20
 
1.5%
지 18
 
1.4%
Other values (112) 583
43.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 991
74.7%
Decimal Number 320
 
24.1%
Open Punctuation 4
 
0.3%
Close Punctuation 4
 
0.3%
Other Punctuation 4
 
0.3%
Space Separator 4
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
리 308
31.1%
곡 35
 
3.5%
상 30
 
3.0%
신 28
 
2.8%
대 26
 
2.6%
도 20
 
2.0%
지 18
 
1.8%
천 17
 
1.7%
두 16
 
1.6%
양 16
 
1.6%
Other values (98) 477
48.1%
Decimal Number
ValueCountFrequency (%)
1 127
39.7%
2 116
36.2%
3 36
 
11.2%
4 13
 
4.1%
5 8
 
2.5%
7 5
 
1.6%
6 5
 
1.6%
8 4
 
1.2%
9 4
 
1.2%
0 2
 
0.6%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%
Other Punctuation
ValueCountFrequency (%)
, 4
100.0%
Space Separator
ValueCountFrequency (%)
4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 991
74.7%
Common 336
 
25.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
리 308
31.1%
곡 35
 
3.5%
상 30
 
3.0%
신 28
 
2.8%
대 26
 
2.6%
도 20
 
2.0%
지 18
 
1.8%
천 17
 
1.7%
두 16
 
1.6%
양 16
 
1.6%
Other values (98) 477
48.1%
Common
ValueCountFrequency (%)
1 127
37.8%
2 116
34.5%
3 36
 
10.7%
4 13
 
3.9%
5 8
 
2.4%
7 5
 
1.5%
6 5
 
1.5%
8 4
 
1.2%
( 4
 
1.2%
) 4
 
1.2%
Other values (4) 14
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 991
74.7%
ASCII 336
 
25.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
리 308
31.1%
곡 35
 
3.5%
상 30
 
3.0%
신 28
 
2.8%
대 26
 
2.6%
도 20
 
2.0%
지 18
 
1.8%
천 17
 
1.7%
두 16
 
1.6%
양 16
 
1.6%
Other values (98) 477
48.1%
ASCII
ValueCountFrequency (%)
1 127
37.8%
2 116
34.5%
3 36
 
10.7%
4 13
 
3.9%
5 8
 
2.4%
7 5
 
1.5%
6 5
 
1.5%
8 4
 
1.2%
( 4
 
1.2%
) 4
 
1.2%
Other values (4) 14
 
4.2%

경로당명
Text

Distinct339
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
2024-01-10T06:53:32.816441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length13
Mean length7.6041056
Min length5

Characters and Unicode

Total characters2593
Distinct characters191
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

Unique337 ?
Unique (%)98.8%

Sample

1st row신대1리경로당
2nd row신대1리(엄정리)경로당
3rd row신대2리경로당
4th row신대2리(하영처)경로당
5th row중도1리경로당
ValueCountFrequency (%)
경로당 194
35.3%
여자경로당 4
 
0.7%
남자경로당 4
 
0.7%
신촌 2
 
0.4%
제원1리 2
 
0.4%
상촌 2
 
0.4%
남자 2
 
0.4%
명암리 2
 
0.4%
제원2리 2
 
0.4%
대산1리 2
 
0.4%
Other values (332) 333
60.7%
2024-01-10T06:53:33.118438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
당 349
13.5%
경 344
13.3%
로 341
 
13.2%
리 227
 
8.8%
211
 
8.1%
1 83
 
3.2%
2 78
 
3.0%
여 37
 
1.4%
성 37
 
1.4%
상 28
 
1.1%
Other values (181) 858
33.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2136
82.4%
Space Separator 211
 
8.1%
Decimal Number 207
 
8.0%
Close Punctuation 19
 
0.7%
Open Punctuation 19
 
0.7%
Other Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
당 349
16.3%
경 344
16.1%
로 341
16.0%
리 227
 
10.6%
여 37
 
1.7%
성 37
 
1.7%
상 28
 
1.3%
신 25
 
1.2%
대 24
 
1.1%
곡 24
 
1.1%
Other values (168) 700
32.8%
Decimal Number
ValueCountFrequency (%)
1 83
40.1%
2 78
37.7%
3 23
 
11.1%
5 4
 
1.9%
7 4
 
1.9%
9 4
 
1.9%
4 4
 
1.9%
6 4
 
1.9%
8 3
 
1.4%
Space Separator
ValueCountFrequency (%)
211
100.0%
Close Punctuation
ValueCountFrequency (%)
) 19
100.0%
Open Punctuation
ValueCountFrequency (%)
( 19
100.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2136
82.4%
Common 457
 
17.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
당 349
16.3%
경 344
16.1%
로 341
16.0%
리 227
 
10.6%
여 37
 
1.7%
성 37
 
1.7%
상 28
 
1.3%
신 25
 
1.2%
대 24
 
1.1%
곡 24
 
1.1%
Other values (168) 700
32.8%
Common
ValueCountFrequency (%)
211
46.2%
1 83
 
18.2%
2 78
 
17.1%
3 23
 
5.0%
) 19
 
4.2%
( 19
 
4.2%
5 4
 
0.9%
7 4
 
0.9%
9 4
 
0.9%
4 4
 
0.9%
Other values (3) 8
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2136
82.4%
ASCII 457
 
17.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
당 349
16.3%
경 344
16.1%
로 341
16.0%
리 227
 
10.6%
여 37
 
1.7%
성 37
 
1.7%
상 28
 
1.3%
신 25
 
1.2%
대 24
 
1.1%
곡 24
 
1.1%
Other values (168) 700
32.8%
ASCII
ValueCountFrequency (%)
211
46.2%
1 83
 
18.2%
2 78
 
17.1%
3 23
 
5.0%
) 19
 
4.2%
( 19
 
4.2%
5 4
 
0.9%
7 4
 
0.9%
9 4
 
0.9%
4 4
 
0.9%
Other values (3) 8
 
1.8%

연락처
Text

MISSING 

Distinct141
Distinct (%)58.5%
Missing100
Missing (%)29.3%
Memory size2.8 KiB
2024-01-10T06:53:33.311665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters2892
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

Unique43 ?
Unique (%)17.8%

Sample

1st row041-751-6010
2nd row041-753-8850
3rd row041-751-3677
4th row041-754-8890
5th row041-754-4450
ValueCountFrequency (%)
041-753-6115 4
 
1.7%
041-752-2587 2
 
0.8%
041-753-7941 2
 
0.8%
041-753-5386 2
 
0.8%
041-752-2148 2
 
0.8%
041-753-0958 2
 
0.8%
041-753-1488 2
 
0.8%
041-753-8791 2
 
0.8%
041-753-6414 2
 
0.8%
041-754-7535 2
 
0.8%
Other values (131) 219
90.9%
2024-01-10T06:53:33.591840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 482
16.7%
1 388
13.4%
4 353
12.2%
5 345
11.9%
0 338
11.7%
7 330
11.4%
3 181
 
6.3%
2 177
 
6.1%
9 121
 
4.2%
8 96
 
3.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2410
83.3%
Dash Punctuation 482
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 388
16.1%
4 353
14.6%
5 345
14.3%
0 338
14.0%
7 330
13.7%
3 181
7.5%
2 177
7.3%
9 121
 
5.0%
8 96
 
4.0%
6 81
 
3.4%
Dash Punctuation
ValueCountFrequency (%)
- 482
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2892
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 482
16.7%
1 388
13.4%
4 353
12.2%
5 345
11.9%
0 338
11.7%
7 330
11.4%
3 181
 
6.3%
2 177
 
6.1%
9 121
 
4.2%
8 96
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2892
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 482
16.7%
1 388
13.4%
4 353
12.2%
5 345
11.9%
0 338
11.7%
7 330
11.4%
3 181
 
6.3%
2 177
 
6.1%
9 121
 
4.2%
8 96
 
3.3%

도로명주소
Text

Distinct316
Distinct (%)92.7%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
2024-01-10T06:53:33.903379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length37
Median length34
Mean length20.278592
Min length17

Characters and Unicode

Total characters6915
Distinct characters240
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique291 ?
Unique (%)85.3%

Sample

1st row충청남도 금산군 금산읍 증티길 6
2nd row충청남도 금산군 금산읍 엄나무길 15
3rd row충청남도 금산군 금산읍 상영처길 19-2
4th row충청남도 금산군 금산읍 하영처길 58
5th row충청남도 금산군 금산읍 건삼전1길 26
ValueCountFrequency (%)
충청남도 341
20.3%
금산군 341
20.3%
금산읍 63
 
3.8%
금성면 39
 
2.3%
진산면 35
 
2.1%
제원면 34
 
2.0%
부리면 33
 
2.0%
남이면 30
 
1.8%
복수면 29
 
1.7%
추부면 28
 
1.7%
Other values (449) 704
42.0%
2024-01-10T06:53:34.332736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1342
19.4%
산 461
 
6.7%
금 454
 
6.6%
남 397
 
5.7%
군 374
 
5.4%
청 345
 
5.0%
도 343
 
5.0%
충 341
 
4.9%
면 278
 
4.0%
길 278
 
4.0%
Other values (230) 2302
33.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4639
67.1%
Space Separator 1342
 
19.4%
Decimal Number 860
 
12.4%
Dash Punctuation 64
 
0.9%
Other Punctuation 7
 
0.1%
Open Punctuation 1
 
< 0.1%
Uppercase Letter 1
 
< 0.1%
Close Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 461
 
9.9%
금 454
 
9.8%
남 397
 
8.6%
군 374
 
8.1%
청 345
 
7.4%
도 343
 
7.4%
충 341
 
7.4%
면 278
 
6.0%
길 278
 
6.0%
부 66
 
1.4%
Other values (214) 1302
28.1%
Decimal Number
ValueCountFrequency (%)
1 206
24.0%
2 124
14.4%
3 102
11.9%
4 82
 
9.5%
5 81
 
9.4%
6 71
 
8.3%
7 53
 
6.2%
8 49
 
5.7%
0 47
 
5.5%
9 45
 
5.2%
Space Separator
ValueCountFrequency (%)
1342
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 64
100.0%
Other Punctuation
ValueCountFrequency (%)
, 7
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Uppercase Letter
ValueCountFrequency (%)
A 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4639
67.1%
Common 2275
32.9%
Latin 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
산 461
 
9.9%
금 454
 
9.8%
남 397
 
8.6%
군 374
 
8.1%
청 345
 
7.4%
도 343
 
7.4%
충 341
 
7.4%
면 278
 
6.0%
길 278
 
6.0%
부 66
 
1.4%
Other values (214) 1302
28.1%
Common
ValueCountFrequency (%)
1342
59.0%
1 206
 
9.1%
2 124
 
5.5%
3 102
 
4.5%
4 82
 
3.6%
5 81
 
3.6%
6 71
 
3.1%
- 64
 
2.8%
7 53
 
2.3%
8 49
 
2.2%
Other values (5) 101
 
4.4%
Latin
ValueCountFrequency (%)
A 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4639
67.1%
ASCII 2276
32.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1342
59.0%
1 206
 
9.1%
2 124
 
5.4%
3 102
 
4.5%
4 82
 
3.6%
5 81
 
3.6%
6 71
 
3.1%
- 64
 
2.8%
7 53
 
2.3%
8 49
 
2.2%
Other values (6) 102
 
4.5%
Hangul
ValueCountFrequency (%)
산 461
 
9.9%
금 454
 
9.8%
남 397
 
8.6%
군 374
 
8.1%
청 345
 
7.4%
도 343
 
7.4%
충 341
 
7.4%
면 278
 
6.0%
길 278
 
6.0%
부 66
 
1.4%
Other values (214) 1302
28.1%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
2022-03-31
341 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022-03-31
2nd row2022-03-31
3rd row2022-03-31
4th row2022-03-31
5th row2022-03-31

Common Values

ValueCountFrequency (%)
2022-03-31 341
100.0%

Length

2024-01-10T06:53:34.437661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T06:53:34.510687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022-03-31 341
100.0%

Missing values

2024-01-10T06:53:31.495295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-10T06:53:31.573366image/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

행정구역행정리경로당명연락처도로명주소데이터기준일자
0금산읍신대1리신대1리경로당<NA>충청남도 금산군 금산읍 증티길 62022-03-31
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