Overview

Dataset statistics

Number of variables4
Number of observations205
Missing cells55
Missing cells (%)6.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.5 KiB
Average record size in memory32.6 B

Variable types

Categorical1
Text3

Dataset

Description의왕도시공사 종량제 봉투 판매소의 위치 및 전화번호 자료입니다.
Author의왕도시공사
URLhttps://www.data.go.kr/data/15005539/fileData.do

Alerts

전화번호 has 54 (26.3%) missing valuesMissing
상호 has unique valuesUnique

Reproduction

Analysis started2023-12-12 11:21:29.365468
Analysis finished2023-12-12 11:21:30.144234
Duration0.78 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

지역
Categorical

Distinct13
Distinct (%)6.3%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
내손동
47 
오전동
46 
삼동
37 
포일동
19 
관외
16 
Other values (8)
40 

Length

Max length3
Median length3
Mean length2.7268293
Min length2

Unique

Unique2 ?
Unique (%)1.0%

Sample

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

Common Values

ValueCountFrequency (%)
내손동 47
22.9%
오전동 46
22.4%
삼동 37
18.0%
포일동 19
9.3%
관외 16
 
7.8%
고천동 10
 
4.9%
왕곡동 8
 
3.9%
학의동 8
 
3.9%
청계동 7
 
3.4%
이동 3
 
1.5%
Other values (3) 4
 
2.0%

Length

2023-12-12T20:21:30.681111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
내손동 47
22.9%
오전동 46
22.4%
삼동 37
18.0%
포일동 19
9.3%
관외 16
 
7.8%
고천동 10
 
4.9%
왕곡동 8
 
3.9%
학의동 8
 
3.9%
청계동 7
 
3.4%
이동 3
 
1.5%
Other values (3) 4
 
2.0%

주소
Text

Distinct202
Distinct (%)99.0%
Missing1
Missing (%)0.5%
Memory size1.7 KiB
2023-12-12T20:21:31.109679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length37
Median length34
Mean length18.617647
Min length11

Characters and Unicode

Total characters3798
Distinct characters159
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique200 ?
Unique (%)98.0%

Sample

1st row경기도 의왕시 고천동 299-14
2nd row경기도 의왕시 고천동 414
3rd row경기도 의왕시 고천동 280-7
4th row경기도 의왕시 고천동 295-26
5th row경기도 의왕시 고천동 332-33
ValueCountFrequency (%)
경기도 197
21.9%
의왕시 187
20.8%
오전동 40
 
4.4%
삼동 31
 
3.4%
내손동 26
 
2.9%
안양시 14
 
1.6%
포일동 14
 
1.6%
동안구 13
 
1.4%
1층 11
 
1.2%
고천동 9
 
1.0%
Other values (294) 358
39.8%
2023-12-12T20:21:31.834762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
697
18.4%
시 205
 
5.4%
경 204
 
5.4%
기 203
 
5.3%
도 198
 
5.2%
왕 196
 
5.2%
의 193
 
5.1%
동 179
 
4.7%
1 161
 
4.2%
2 112
 
2.9%
Other values (149) 1450
38.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2164
57.0%
Decimal Number 803
 
21.1%
Space Separator 697
 
18.4%
Dash Punctuation 109
 
2.9%
Uppercase Letter 8
 
0.2%
Open Punctuation 7
 
0.2%
Close Punctuation 7
 
0.2%
Other Punctuation 2
 
0.1%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 205
 
9.5%
경 204
 
9.4%
기 203
 
9.4%
도 198
 
9.1%
왕 196
 
9.1%
의 193
 
8.9%
동 179
 
8.3%
오 43
 
2.0%
전 43
 
2.0%
내 43
 
2.0%
Other values (128) 657
30.4%
Decimal Number
ValueCountFrequency (%)
1 161
20.0%
2 112
13.9%
6 91
11.3%
4 78
9.7%
3 74
9.2%
0 69
8.6%
8 65
8.1%
5 52
 
6.5%
9 51
 
6.4%
7 50
 
6.2%
Uppercase Letter
ValueCountFrequency (%)
A 3
37.5%
B 3
37.5%
L 1
 
12.5%
D 1
 
12.5%
Other Punctuation
ValueCountFrequency (%)
/ 1
50.0%
@ 1
50.0%
Space Separator
ValueCountFrequency (%)
697
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 109
100.0%
Open Punctuation
ValueCountFrequency (%)
( 7
100.0%
Close Punctuation
ValueCountFrequency (%)
) 7
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2164
57.0%
Common 1626
42.8%
Latin 8
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 205
 
9.5%
경 204
 
9.4%
기 203
 
9.4%
도 198
 
9.1%
왕 196
 
9.1%
의 193
 
8.9%
동 179
 
8.3%
오 43
 
2.0%
전 43
 
2.0%
내 43
 
2.0%
Other values (128) 657
30.4%
Common
ValueCountFrequency (%)
697
42.9%
1 161
 
9.9%
2 112
 
6.9%
- 109
 
6.7%
6 91
 
5.6%
4 78
 
4.8%
3 74
 
4.6%
0 69
 
4.2%
8 65
 
4.0%
5 52
 
3.2%
Other values (7) 118
 
7.3%
Latin
ValueCountFrequency (%)
A 3
37.5%
B 3
37.5%
L 1
 
12.5%
D 1
 
12.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2164
57.0%
ASCII 1634
43.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
697
42.7%
1 161
 
9.9%
2 112
 
6.9%
- 109
 
6.7%
6 91
 
5.6%
4 78
 
4.8%
3 74
 
4.5%
0 69
 
4.2%
8 65
 
4.0%
5 52
 
3.2%
Other values (11) 126
 
7.7%
Hangul
ValueCountFrequency (%)
시 205
 
9.5%
경 204
 
9.4%
기 203
 
9.4%
도 198
 
9.1%
왕 196
 
9.1%
의 193
 
8.9%
동 179
 
8.3%
오 43
 
2.0%
전 43
 
2.0%
내 43
 
2.0%
Other values (128) 657
30.4%

상호
Text

UNIQUE 

Distinct205
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2023-12-12T20:21:32.228492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length15
Mean length8.3756098
Min length3

Characters and Unicode

Total characters1717
Distinct characters252
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

Unique205 ?
Unique (%)100.0%

Sample

1st row24시슈퍼
2nd rowCU 의왕대로점
3rd rowGS25 의왕고천점
4th row명진슈퍼(고천동)
5th row미스터마트
ValueCountFrequency (%)
gs25 37
 
11.5%
cu 20
 
6.2%
세븐일레븐 11
 
3.4%
주)코리아세븐 7
 
2.2%
의왕점 6
 
1.9%
의왕오전점 5
 
1.5%
의왕삼동점 4
 
1.2%
의왕내손점 4
 
1.2%
미니스톱 4
 
1.2%
의왕왕곡점 3
 
0.9%
Other values (203) 222
68.7%
2023-12-12T20:21:32.909977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
118
 
6.9%
점 113
 
6.6%
왕 86
 
5.0%
의 84
 
4.9%
마 58
 
3.4%
트 55
 
3.2%
2 42
 
2.4%
5 41
 
2.4%
G 40
 
2.3%
S 37
 
2.2%
Other values (242) 1043
60.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1320
76.9%
Uppercase Letter 143
 
8.3%
Space Separator 118
 
6.9%
Decimal Number 89
 
5.2%
Close Punctuation 22
 
1.3%
Open Punctuation 22
 
1.3%
Other Punctuation 2
 
0.1%
Dash Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
점 113
 
8.6%
왕 86
 
6.5%
의 84
 
6.4%
마 58
 
4.4%
트 55
 
4.2%
븐 29
 
2.2%
일 28
 
2.1%
퍼 24
 
1.8%
슈 22
 
1.7%
세 21
 
1.6%
Other values (218) 800
60.6%
Uppercase Letter
ValueCountFrequency (%)
G 40
28.0%
S 37
25.9%
C 26
18.2%
U 22
15.4%
I 4
 
2.8%
T 3
 
2.1%
D 2
 
1.4%
A 2
 
1.4%
E 2
 
1.4%
R 1
 
0.7%
Other values (4) 4
 
2.8%
Decimal Number
ValueCountFrequency (%)
2 42
47.2%
5 41
46.1%
9 4
 
4.5%
4 2
 
2.2%
Other Punctuation
ValueCountFrequency (%)
" 1
50.0%
/ 1
50.0%
Space Separator
ValueCountFrequency (%)
118
100.0%
Close Punctuation
ValueCountFrequency (%)
) 22
100.0%
Open Punctuation
ValueCountFrequency (%)
( 22
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1320
76.9%
Common 254
 
14.8%
Latin 143
 
8.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
점 113
 
8.6%
왕 86
 
6.5%
의 84
 
6.4%
마 58
 
4.4%
트 55
 
4.2%
븐 29
 
2.2%
일 28
 
2.1%
퍼 24
 
1.8%
슈 22
 
1.7%
세 21
 
1.6%
Other values (218) 800
60.6%
Latin
ValueCountFrequency (%)
G 40
28.0%
S 37
25.9%
C 26
18.2%
U 22
15.4%
I 4
 
2.8%
T 3
 
2.1%
D 2
 
1.4%
A 2
 
1.4%
E 2
 
1.4%
R 1
 
0.7%
Other values (4) 4
 
2.8%
Common
ValueCountFrequency (%)
118
46.5%
2 42
 
16.5%
5 41
 
16.1%
) 22
 
8.7%
( 22
 
8.7%
9 4
 
1.6%
4 2
 
0.8%
" 1
 
0.4%
- 1
 
0.4%
/ 1
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1320
76.9%
ASCII 397
 
23.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
118
29.7%
2 42
 
10.6%
5 41
 
10.3%
G 40
 
10.1%
S 37
 
9.3%
C 26
 
6.5%
U 22
 
5.5%
) 22
 
5.5%
( 22
 
5.5%
I 4
 
1.0%
Other values (14) 23
 
5.8%
Hangul
ValueCountFrequency (%)
점 113
 
8.6%
왕 86
 
6.5%
의 84
 
6.4%
마 58
 
4.4%
트 55
 
4.2%
븐 29
 
2.2%
일 28
 
2.1%
퍼 24
 
1.8%
슈 22
 
1.7%
세 21
 
1.6%
Other values (218) 800
60.6%

전화번호
Text

MISSING 

Distinct149
Distinct (%)98.7%
Missing54
Missing (%)26.3%
Memory size1.7 KiB
2023-12-12T20:21:33.312986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length12.039735
Min length12

Characters and Unicode

Total characters1818
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

Unique147 ?
Unique (%)97.4%

Sample

1st row031-453-1337
2nd row031-429-3060
3rd row031-456-0305
4th row031-452-8813
5th row031-427-1735
ValueCountFrequency (%)
031-425-5373 2
 
1.3%
031-462-0111 2
 
1.3%
031-425-9579 1
 
0.7%
031-459-2502 1
 
0.7%
031-455-7361 1
 
0.7%
031-427-1011 1
 
0.7%
031-456-4676 1
 
0.7%
031-453-1337 1
 
0.7%
031-427-8240 1
 
0.7%
031-454-5835 1
 
0.7%
Other values (139) 139
92.1%
2023-12-12T20:21:33.891440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 302
16.6%
1 253
13.9%
0 237
13.0%
3 224
12.3%
4 200
11.0%
2 174
9.6%
5 100
 
5.5%
6 99
 
5.4%
7 83
 
4.6%
8 81
 
4.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1516
83.4%
Dash Punctuation 302
 
16.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 253
16.7%
0 237
15.6%
3 224
14.8%
4 200
13.2%
2 174
11.5%
5 100
 
6.6%
6 99
 
6.5%
7 83
 
5.5%
8 81
 
5.3%
9 65
 
4.3%
Dash Punctuation
ValueCountFrequency (%)
- 302
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1818
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 302
16.6%
1 253
13.9%
0 237
13.0%
3 224
12.3%
4 200
11.0%
2 174
9.6%
5 100
 
5.5%
6 99
 
5.4%
7 83
 
4.6%
8 81
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1818
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 302
16.6%
1 253
13.9%
0 237
13.0%
3 224
12.3%
4 200
11.0%
2 174
9.6%
5 100
 
5.5%
6 99
 
5.4%
7 83
 
4.6%
8 81
 
4.5%

Missing values

2023-12-12T20:21:29.790873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T20:21:29.937440image/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.
2023-12-12T20:21:30.067929image/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

지역주소상호전화번호
0고천동경기도 의왕시 고천동 299-1424시슈퍼031-453-1337
1고천동경기도 의왕시 고천동 414CU 의왕대로점031-429-3060
2고천동경기도 의왕시 고천동 280-7GS25 의왕고천점031-456-0305
3고천동경기도 의왕시 고천동 295-26명진슈퍼(고천동)031-452-8813
4고천동경기도 의왕시 고천동 332-33미스터마트031-427-1735
5왕곡동경기도 의왕시 왕곡동 592신안유통<NA>
6고천동경기도 의왕시 현충탑길 30영광슈퍼031-459-2035
7고천동경기도 의왕시 고천동 171의왕시청 구내매점031-345-2145
8고천동경기도 의왕시 고천동 295-11중앙쌀상회031-452-4853
9관외경기도 안양시 동안구 평촌동 45-1(주)코리아세븐 평촌신성점070-4038-9462
지역주소상호전화번호
195삼동경기도 의왕시 삼동 67-2세븐일레븐 파크푸르지오점<NA>
196월암동경기도 의왕시 월암동 624-1쿠키라인좋은나들가게<NA>
197청계동경기도 의왕시 청계동 976CU 청계점<NA>
198청게동경기도 의왕시 청계동 800-17GS25 청계사점031-425-9579
199포일동경기도 의왕시 포일동 662-2CU 포일프라자점031-422-2470
200학의동경기도 의왕시 학의동687-2GS25 백운밸리점031-422-6817
201학의동경기도 의왕시 학의동 10효성유통031-424-8949
202학의동경기도 의왕시 백운중앙로 88CU 의왕백운5단지점<NA>
203학의동경기도 의왕시 백운중앙로 99GS25 의왕이레점<NA>
204학의동경기도 의왕시 바라산로 75GS25 바라산점031-421-0892