Overview

Dataset statistics

Number of variables6
Number of observations347
Missing cells170
Missing cells (%)8.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory16.4 KiB
Average record size in memory48.4 B

Variable types

Categorical1
Text4
DateTime1

Dataset

Description이 데이터는 충청남도 금산군 경로당에 대한 데이터(행정구역, 행정리, 경로당명, 연락처, 도로명주소, 데이터기준일자)를 제공하고 있습니다.
Author충청남도 금산군
URLhttps://www.data.go.kr/data/15099753/fileData.do

Alerts

데이터기준일자 has constant value ""Constant
연락처 has 169 (48.7%) missing valuesMissing

Reproduction

Analysis started2024-04-21 02:06:02.558025
Analysis finished2024-04-21 02:06:03.461187
Duration0.9 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
68 
금성면
39 
진산면
35 
제원면
34 
부리면
33 
Other values (5)
138 

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 68
19.6%
금성면 39
11.2%
진산면 35
10.1%
제원면 34
9.8%
부리면 33
9.5%
남이면 30
8.6%
복수면 29
8.4%
추부면 28
8.1%
군북면 26
 
7.5%
남일면 25
 
7.2%

Length

2024-04-21T11:06:03.526134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T11:06:03.633979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
금산읍 68
19.6%
금성면 39
11.2%
진산면 35
10.1%
제원면 34
9.8%
부리면 33
9.5%
남이면 30
8.6%
복수면 29
8.4%
추부면 28
8.1%
군북면 26
 
7.5%
남일면 25
 
7.2%

행정리
Text

Distinct254
Distinct (%)73.4%
Missing1
Missing (%)0.3%
Memory size2.8 KiB
2024-04-21T11:06:03.939567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length4
Mean length3.8843931
Min length2

Characters and Unicode

Total characters1344
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

Unique173 ?
Unique (%)50.0%

Sample

1st row신대1리
2nd row신대1리
3rd row신대2리
4th row신대2리
5th row중도1리
ValueCountFrequency (%)
신대1리 4
 
1.1%
신대2리 4
 
1.1%
하옥4리 4
 
1.1%
음대리 3
 
0.9%
아인1리 3
 
0.9%
음지2리 3
 
0.9%
양전3리 3
 
0.9%
상1리 3
 
0.9%
창평2 2
 
0.6%
어재1 2
 
0.6%
Other values (246) 319
91.1%
2024-04-21T11:06:04.381642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
리 312
23.2%
1 129
 
9.6%
2 116
 
8.6%
3 37
 
2.8%
곡 35
 
2.6%
상 32
 
2.4%
신 28
 
2.1%
대 25
 
1.9%
도 22
 
1.6%
지 18
 
1.3%
Other values (112) 590
43.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1003
74.6%
Decimal Number 325
 
24.2%
Other Punctuation 4
 
0.3%
Space Separator 4
 
0.3%
Open Punctuation 4
 
0.3%
Close Punctuation 4
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
리 312
31.1%
곡 35
 
3.5%
상 32
 
3.2%
신 28
 
2.8%
대 25
 
2.5%
도 22
 
2.2%
지 18
 
1.8%
중 18
 
1.8%
천 17
 
1.7%
하 17
 
1.7%
Other values (98) 479
47.8%
Decimal Number
ValueCountFrequency (%)
1 129
39.7%
2 116
35.7%
3 37
 
11.4%
4 14
 
4.3%
5 8
 
2.5%
6 5
 
1.5%
7 5
 
1.5%
9 4
 
1.2%
8 4
 
1.2%
0 3
 
0.9%
Other Punctuation
ValueCountFrequency (%)
, 4
100.0%
Space Separator
ValueCountFrequency (%)
4
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1003
74.6%
Common 341
 
25.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
리 312
31.1%
곡 35
 
3.5%
상 32
 
3.2%
신 28
 
2.8%
대 25
 
2.5%
도 22
 
2.2%
지 18
 
1.8%
중 18
 
1.8%
천 17
 
1.7%
하 17
 
1.7%
Other values (98) 479
47.8%
Common
ValueCountFrequency (%)
1 129
37.8%
2 116
34.0%
3 37
 
10.9%
4 14
 
4.1%
5 8
 
2.3%
6 5
 
1.5%
7 5
 
1.5%
, 4
 
1.2%
4
 
1.2%
( 4
 
1.2%
Other values (4) 15
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1003
74.6%
ASCII 341
 
25.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
리 312
31.1%
곡 35
 
3.5%
상 32
 
3.2%
신 28
 
2.8%
대 25
 
2.5%
도 22
 
2.2%
지 18
 
1.8%
중 18
 
1.8%
천 17
 
1.7%
하 17
 
1.7%
Other values (98) 479
47.8%
ASCII
ValueCountFrequency (%)
1 129
37.8%
2 116
34.0%
3 37
 
10.9%
4 14
 
4.1%
5 8
 
2.3%
6 5
 
1.5%
7 5
 
1.5%
, 4
 
1.2%
4
 
1.2%
( 4
 
1.2%
Other values (4) 15
 
4.4%

경로당명
Text

Distinct345
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
2024-04-21T11:06:04.616583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length14
Mean length7.6051873
Min length5

Characters and Unicode

Total characters2639
Distinct characters197
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

Unique343 ?
Unique (%)98.8%

Sample

1st row신대1리경로당
2nd row신대1리(엄정리)경로당
3rd row신대2리경로당
4th row신대2리(하영처)경로당
5th row중도1리경로당
ValueCountFrequency (%)
경로당 195
35.1%
남자경로당 4
 
0.7%
여자경로당 4
 
0.7%
상촌 2
 
0.4%
제원2리 2
 
0.4%
제원1리 2
 
0.4%
대산1리 2
 
0.4%
성곡1리 2
 
0.4%
남자 2
 
0.4%
신촌 2
 
0.4%
Other values (338) 339
61.0%
2024-04-21T11:06:04.970994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
당 355
13.5%
경 350
 
13.3%
로 347
 
13.1%
리 230
 
8.7%
212
 
8.0%
1 84
 
3.2%
2 78
 
3.0%
성 38
 
1.4%
여 37
 
1.4%
상 31
 
1.2%
Other values (187) 877
33.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2177
82.5%
Space Separator 212
 
8.0%
Decimal Number 210
 
8.0%
Close Punctuation 19
 
0.7%
Open Punctuation 19
 
0.7%
Lowercase Letter 1
 
< 0.1%
Other Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
당 355
16.3%
경 350
16.1%
로 347
15.9%
리 230
 
10.6%
성 38
 
1.7%
여 37
 
1.7%
상 31
 
1.4%
신 25
 
1.1%
곡 24
 
1.1%
대 24
 
1.1%
Other values (173) 716
32.9%
Decimal Number
ValueCountFrequency (%)
1 84
40.0%
2 78
37.1%
3 24
 
11.4%
4 5
 
2.4%
6 4
 
1.9%
9 4
 
1.9%
5 4
 
1.9%
7 4
 
1.9%
8 3
 
1.4%
Space Separator
ValueCountFrequency (%)
212
100.0%
Close Punctuation
ValueCountFrequency (%)
) 19
100.0%
Open Punctuation
ValueCountFrequency (%)
( 19
100.0%
Lowercase Letter
ValueCountFrequency (%)
e 1
100.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2177
82.5%
Common 461
 
17.5%
Latin 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
당 355
16.3%
경 350
16.1%
로 347
15.9%
리 230
 
10.6%
성 38
 
1.7%
여 37
 
1.7%
상 31
 
1.4%
신 25
 
1.1%
곡 24
 
1.1%
대 24
 
1.1%
Other values (173) 716
32.9%
Common
ValueCountFrequency (%)
212
46.0%
1 84
 
18.2%
2 78
 
16.9%
3 24
 
5.2%
) 19
 
4.1%
( 19
 
4.1%
4 5
 
1.1%
6 4
 
0.9%
9 4
 
0.9%
5 4
 
0.9%
Other values (3) 8
 
1.7%
Latin
ValueCountFrequency (%)
e 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2177
82.5%
ASCII 462
 
17.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
당 355
16.3%
경 350
16.1%
로 347
15.9%
리 230
 
10.6%
성 38
 
1.7%
여 37
 
1.7%
상 31
 
1.4%
신 25
 
1.1%
곡 24
 
1.1%
대 24
 
1.1%
Other values (173) 716
32.9%
ASCII
ValueCountFrequency (%)
212
45.9%
1 84
 
18.2%
2 78
 
16.9%
3 24
 
5.2%
) 19
 
4.1%
( 19
 
4.1%
4 5
 
1.1%
6 4
 
0.9%
9 4
 
0.9%
5 4
 
0.9%
Other values (4) 9
 
1.9%

연락처
Text

MISSING 

Distinct173
Distinct (%)97.2%
Missing169
Missing (%)48.7%
Memory size2.8 KiB
2024-04-21T11:06:05.233860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length11.882022
Min length1

Characters and Unicode

Total characters2115
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

Unique168 ?
Unique (%)94.4%

Sample

1st row041-751-6010
2nd row041-753-8850
3rd row041-754-1030
4th row041-751-3677
5th row041-754-2515
ValueCountFrequency (%)
041-751-4882 2
 
1.1%
041-754-7599 2
 
1.1%
041-752-2647 2
 
1.1%
041-753-6115 2
 
1.1%
041-752-8893 1
 
0.6%
041-754-7535 1
 
0.6%
041-752-0218 1
 
0.6%
041-752-2587 1
 
0.6%
041-754-9339 1
 
0.6%
041-752-1129 1
 
0.6%
Other values (162) 162
92.0%
2024-04-21T11:06:05.695210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 352
16.6%
1 283
13.4%
4 271
12.8%
0 257
12.2%
5 249
11.8%
7 244
11.5%
3 124
 
5.9%
2 115
 
5.4%
9 83
 
3.9%
6 70
 
3.3%
Other values (2) 67
 
3.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1760
83.2%
Dash Punctuation 352
 
16.6%
Space Separator 3
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 283
16.1%
4 271
15.4%
0 257
14.6%
5 249
14.1%
7 244
13.9%
3 124
7.0%
2 115
6.5%
9 83
 
4.7%
6 70
 
4.0%
8 64
 
3.6%
Dash Punctuation
ValueCountFrequency (%)
- 352
100.0%
Space Separator
ValueCountFrequency (%)
3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2115
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 352
16.6%
1 283
13.4%
4 271
12.8%
0 257
12.2%
5 249
11.8%
7 244
11.5%
3 124
 
5.9%
2 115
 
5.4%
9 83
 
3.9%
6 70
 
3.3%
Other values (2) 67
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2115
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 352
16.6%
1 283
13.4%
4 271
12.8%
0 257
12.2%
5 249
11.8%
7 244
11.5%
3 124
 
5.9%
2 115
 
5.4%
9 83
 
3.9%
6 70
 
3.3%
Other values (2) 67
 
3.2%

도로명주소
Text

Distinct323
Distinct (%)93.1%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
2024-04-21T11:06:06.043994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length37
Median length35
Mean length20.270893
Min length17

Characters and Unicode

Total characters7034
Distinct characters239
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

Unique299 ?
Unique (%)86.2%

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 347
20.3%
금산군 347
20.3%
금산읍 68
 
4.0%
금성면 39
 
2.3%
진산면 35
 
2.0%
제원면 34
 
2.0%
부리면 33
 
1.9%
남이면 30
 
1.8%
복수면 29
 
1.7%
추부면 28
 
1.6%
Other values (456) 720
42.1%
2024-04-21T11:06:06.596716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1369
19.5%
산 474
 
6.7%
금 467
 
6.6%
남 403
 
5.7%
군 381
 
5.4%
청 351
 
5.0%
도 349
 
5.0%
충 347
 
4.9%
길 282
 
4.0%
면 279
 
4.0%
Other values (229) 2332
33.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4709
66.9%
Space Separator 1369
 
19.5%
Decimal Number 883
 
12.6%
Dash Punctuation 64
 
0.9%
Other Punctuation 6
 
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 474
 
10.1%
금 467
 
9.9%
남 403
 
8.6%
군 381
 
8.1%
청 351
 
7.5%
도 349
 
7.4%
충 347
 
7.4%
길 282
 
6.0%
면 279
 
5.9%
읍 68
 
1.4%
Other values (213) 1308
27.8%
Decimal Number
ValueCountFrequency (%)
1 209
23.7%
2 129
14.6%
3 105
11.9%
4 86
9.7%
5 83
 
9.4%
6 71
 
8.0%
7 54
 
6.1%
0 50
 
5.7%
8 50
 
5.7%
9 46
 
5.2%
Space Separator
ValueCountFrequency (%)
1369
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 64
100.0%
Other Punctuation
ValueCountFrequency (%)
, 6
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 4709
66.9%
Common 2324
33.0%
Latin 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
산 474
 
10.1%
금 467
 
9.9%
남 403
 
8.6%
군 381
 
8.1%
청 351
 
7.5%
도 349
 
7.4%
충 347
 
7.4%
길 282
 
6.0%
면 279
 
5.9%
읍 68
 
1.4%
Other values (213) 1308
27.8%
Common
ValueCountFrequency (%)
1369
58.9%
1 209
 
9.0%
2 129
 
5.6%
3 105
 
4.5%
4 86
 
3.7%
5 83
 
3.6%
6 71
 
3.1%
- 64
 
2.8%
7 54
 
2.3%
0 50
 
2.2%
Other values (5) 104
 
4.5%
Latin
ValueCountFrequency (%)
A 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4709
66.9%
ASCII 2325
33.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1369
58.9%
1 209
 
9.0%
2 129
 
5.5%
3 105
 
4.5%
4 86
 
3.7%
5 83
 
3.6%
6 71
 
3.1%
- 64
 
2.8%
7 54
 
2.3%
0 50
 
2.2%
Other values (6) 105
 
4.5%
Hangul
ValueCountFrequency (%)
산 474
 
10.1%
금 467
 
9.9%
남 403
 
8.6%
군 381
 
8.1%
청 351
 
7.5%
도 349
 
7.4%
충 347
 
7.4%
길 282
 
6.0%
면 279
 
5.9%
읍 68
 
1.4%
Other values (213) 1308
27.8%

데이터기준일자
Date

CONSTANT 

Distinct1
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
Minimum2024-03-31 00:00:00
Maximum2024-03-31 00:00:00
2024-04-21T11:06:06.698032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:06:06.791098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Missing values

2024-04-21T11:06:03.231524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-21T11:06:03.331053image/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.
2024-04-21T11:06:03.418987image/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

행정구역행정리경로당명연락처도로명주소데이터기준일자
0금산읍신대1리신대1리경로당<NA>충청남도 금산군 금산읍 증티길 62024-03-31
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