Overview

Dataset statistics

Number of variables4
Number of observations404
Missing cells129
Missing cells (%)8.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory12.8 KiB
Average record size in memory32.3 B

Variable types

Text4

Dataset

Description대구광역시 서구 쓰레기종량제봉투관리시스템의 지정판매소 현황입니다. 2023.6.8.기준 자료이며, 판매소명, 도로명 주소 등의 데이터를 포함하고 있습니다.
URLhttps://www.data.go.kr/data/15084561/fileData.do

Alerts

전화번호 has 129 (31.9%) missing valuesMissing

Reproduction

Analysis started2023-12-12 22:59:14.450128
Analysis finished2023-12-12 22:59:14.909875
Duration0.46 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

대표자
Text

Distinct390
Distinct (%)96.5%
Missing0
Missing (%)0.0%
Memory size3.3 KiB
2023-12-13T07:59:15.150947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length3
Mean length3.0544554
Min length2

Characters and Unicode

Total characters1234
Distinct characters175
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique377 ?
Unique (%)93.3%

Sample

1st row김영신
2nd row권태암
3rd row최영희
4th row정영철
5th row김한철
ValueCountFrequency (%)
한윤수 3
 
0.7%
김정환 2
 
0.5%
정승인 2
 
0.5%
안정호 2
 
0.5%
김정애 2
 
0.5%
이명숙 2
 
0.5%
황태경 2
 
0.5%
장혜선 2
 
0.5%
이수진 2
 
0.5%
오정숙 2
 
0.5%
Other values (383) 386
94.8%
2023-12-13T07:59:15.578544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
김 86
 
7.0%
정 72
 
5.8%
이 63
 
5.1%
영 30
 
2.4%
수 27
 
2.2%
박 27
 
2.2%
경 27
 
2.2%
숙 27
 
2.2%
희 24
 
1.9%
최 23
 
1.9%
Other values (165) 828
67.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1226
99.4%
Space Separator 3
 
0.2%
Other Punctuation 2
 
0.2%
Other Symbol 1
 
0.1%
Close Punctuation 1
 
0.1%
Open Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
김 86
 
7.0%
정 72
 
5.9%
이 63
 
5.1%
영 30
 
2.4%
수 27
 
2.2%
박 27
 
2.2%
경 27
 
2.2%
숙 27
 
2.2%
희 24
 
2.0%
최 23
 
1.9%
Other values (159) 820
66.9%
Other Punctuation
ValueCountFrequency (%)
· 1
50.0%
, 1
50.0%
Space Separator
ValueCountFrequency (%)
3
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1227
99.4%
Common 7
 
0.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
김 86
 
7.0%
정 72
 
5.9%
이 63
 
5.1%
영 30
 
2.4%
수 27
 
2.2%
박 27
 
2.2%
경 27
 
2.2%
숙 27
 
2.2%
희 24
 
2.0%
최 23
 
1.9%
Other values (160) 821
66.9%
Common
ValueCountFrequency (%)
3
42.9%
) 1
 
14.3%
( 1
 
14.3%
· 1
 
14.3%
, 1
 
14.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1226
99.4%
ASCII 6
 
0.5%
None 2
 
0.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
김 86
 
7.0%
정 72
 
5.9%
이 63
 
5.1%
영 30
 
2.4%
수 27
 
2.2%
박 27
 
2.2%
경 27
 
2.2%
숙 27
 
2.2%
희 24
 
2.0%
최 23
 
1.9%
Other values (159) 820
66.9%
ASCII
ValueCountFrequency (%)
3
50.0%
) 1
 
16.7%
( 1
 
16.7%
, 1
 
16.7%
None
ValueCountFrequency (%)
㈜ 1
50.0%
· 1
50.0%

판매소 명
Text

Distinct373
Distinct (%)92.3%
Missing0
Missing (%)0.0%
Memory size3.3 KiB
2023-12-13T07:59:15.806098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length14
Mean length7.8514851
Min length3

Characters and Unicode

Total characters3172
Distinct characters282
Distinct categories9 ?
Distinct scripts4 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique351 ?
Unique (%)86.9%

Sample

1st rowGS25 내당점
2nd row미니마트
3rd row알뜰슈퍼
4th row미카엘소비센터
5th row생큐마트 비타민25
ValueCountFrequency (%)
gs25 38
 
6.2%
cu 36
 
5.9%
세븐일레븐 35
 
5.7%
이마트24 19
 
3.1%
대백마트 10
 
1.6%
신우유통 7
 
1.1%
코리아세븐 6
 
1.0%
원마트 6
 
1.0%
드림마트 5
 
0.8%
코사마트 5
 
0.8%
Other values (374) 445
72.7%
2023-12-13T07:59:16.161228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
208
 
6.6%
점 185
 
5.8%
트 168
 
5.3%
마 167
 
5.3%
대 152
 
4.8%
구 129
 
4.1%
븐 76
 
2.4%
리 75
 
2.4%
2 68
 
2.1%
퍼 53
 
1.7%
Other values (272) 1891
59.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2554
80.5%
Space Separator 208
 
6.6%
Uppercase Letter 189
 
6.0%
Decimal Number 159
 
5.0%
Close Punctuation 29
 
0.9%
Open Punctuation 29
 
0.9%
Other Symbol 2
 
0.1%
Other Punctuation 1
 
< 0.1%
Dash Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
점 185
 
7.2%
트 168
 
6.6%
마 167
 
6.5%
대 152
 
6.0%
구 129
 
5.1%
븐 76
 
3.0%
리 75
 
2.9%
퍼 53
 
2.1%
일 49
 
1.9%
세 47
 
1.8%
Other values (246) 1453
56.9%
Uppercase Letter
ValueCountFrequency (%)
S 46
24.3%
G 42
22.2%
C 41
21.7%
U 37
19.6%
K 9
 
4.8%
O 3
 
1.6%
I 3
 
1.6%
M 2
 
1.1%
X 2
 
1.1%
T 2
 
1.1%
Decimal Number
ValueCountFrequency (%)
2 68
42.8%
5 44
27.7%
4 27
 
17.0%
0 8
 
5.0%
3 4
 
2.5%
7 4
 
2.5%
8 2
 
1.3%
9 1
 
0.6%
1 1
 
0.6%
Space Separator
ValueCountFrequency (%)
208
100.0%
Close Punctuation
ValueCountFrequency (%)
) 29
100.0%
Open Punctuation
ValueCountFrequency (%)
( 29
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 2
100.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2555
80.5%
Common 427
 
13.5%
Latin 189
 
6.0%
Han 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
점 185
 
7.2%
트 168
 
6.6%
마 167
 
6.5%
대 152
 
5.9%
구 129
 
5.0%
븐 76
 
3.0%
리 75
 
2.9%
퍼 53
 
2.1%
일 49
 
1.9%
세 47
 
1.8%
Other values (246) 1454
56.9%
Common
ValueCountFrequency (%)
208
48.7%
2 68
 
15.9%
5 44
 
10.3%
) 29
 
6.8%
( 29
 
6.8%
4 27
 
6.3%
0 8
 
1.9%
3 4
 
0.9%
7 4
 
0.9%
8 2
 
0.5%
Other values (4) 4
 
0.9%
Latin
ValueCountFrequency (%)
S 46
24.3%
G 42
22.2%
C 41
21.7%
U 37
19.6%
K 9
 
4.8%
O 3
 
1.6%
I 3
 
1.6%
M 2
 
1.1%
X 2
 
1.1%
T 2
 
1.1%
Han
ValueCountFrequency (%)
家 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2553
80.5%
ASCII 616
 
19.4%
None 2
 
0.1%
CJK 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
208
33.8%
2 68
 
11.0%
S 46
 
7.5%
5 44
 
7.1%
G 42
 
6.8%
C 41
 
6.7%
U 37
 
6.0%
) 29
 
4.7%
( 29
 
4.7%
4 27
 
4.4%
Other values (15) 45
 
7.3%
Hangul
ValueCountFrequency (%)
점 185
 
7.2%
트 168
 
6.6%
마 167
 
6.5%
대 152
 
6.0%
구 129
 
5.1%
븐 76
 
3.0%
리 75
 
2.9%
퍼 53
 
2.1%
일 49
 
1.9%
세 47
 
1.8%
Other values (245) 1452
56.9%
None
ValueCountFrequency (%)
㈜ 2
100.0%
CJK
ValueCountFrequency (%)
家 1
100.0%

도로명 주소
Text

Distinct398
Distinct (%)98.5%
Missing0
Missing (%)0.0%
Memory size3.3 KiB
2023-12-13T07:59:16.503364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length26
Median length22
Mean length15.571782
Min length4

Characters and Unicode

Total characters6291
Distinct characters71
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique392 ?
Unique (%)97.0%

Sample

1st row통학로 46(내당1동)
2nd row평리로 358-1(내당1동)
3rd row서대구로10길 52(내당1동)
4th row서대구로6길 33(내당1동)
5th row달구벌대로361길 7(내당1동)
ValueCountFrequency (%)
국채보상로 27
 
3.1%
서대구로 19
 
2.2%
달서로 16
 
1.9%
북비산로 14
 
1.6%
당산로 11
 
1.3%
문화로 10
 
1.2%
평리로 9
 
1.0%
와룡로 9
 
1.0%
달구벌대로 8
 
0.9%
통학로 7
 
0.8%
Other values (568) 734
85.0%
2023-12-13T07:59:17.246619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
460
 
7.3%
로 399
 
6.3%
동 390
 
6.2%
) 382
 
6.1%
( 382
 
6.1%
1 366
 
5.8%
3 293
 
4.7%
길 253
 
4.0%
2 241
 
3.8%
4 232
 
3.7%
Other values (61) 2893
46.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3033
48.2%
Decimal Number 1895
30.1%
Space Separator 460
 
7.3%
Close Punctuation 382
 
6.1%
Open Punctuation 382
 
6.1%
Dash Punctuation 77
 
1.2%
Other Punctuation 61
 
1.0%
Uppercase Letter 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 399
13.2%
동 390
 
12.9%
길 253
 
8.3%
산 186
 
6.1%
리 182
 
6.0%
비 168
 
5.5%
평 142
 
4.7%
서 122
 
4.0%
대 104
 
3.4%
달 94
 
3.1%
Other values (42) 993
32.7%
Decimal Number
ValueCountFrequency (%)
1 366
19.3%
3 293
15.5%
2 241
12.7%
4 232
12.2%
7 179
9.4%
6 169
8.9%
5 162
8.5%
0 87
 
4.6%
9 84
 
4.4%
8 82
 
4.3%
Other Punctuation
ValueCountFrequency (%)
, 30
49.2%
· 28
45.9%
. 2
 
3.3%
/ 1
 
1.6%
Space Separator
ValueCountFrequency (%)
460
100.0%
Close Punctuation
ValueCountFrequency (%)
) 382
100.0%
Open Punctuation
ValueCountFrequency (%)
( 382
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 77
100.0%
Uppercase Letter
ValueCountFrequency (%)
B 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3257
51.8%
Hangul 3033
48.2%
Latin 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 399
13.2%
동 390
 
12.9%
길 253
 
8.3%
산 186
 
6.1%
리 182
 
6.0%
비 168
 
5.5%
평 142
 
4.7%
서 122
 
4.0%
대 104
 
3.4%
달 94
 
3.1%
Other values (42) 993
32.7%
Common
ValueCountFrequency (%)
460
14.1%
) 382
11.7%
( 382
11.7%
1 366
11.2%
3 293
9.0%
2 241
7.4%
4 232
7.1%
7 179
 
5.5%
6 169
 
5.2%
5 162
 
5.0%
Other values (8) 391
12.0%
Latin
ValueCountFrequency (%)
B 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3230
51.3%
Hangul 3033
48.2%
None 28
 
0.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
460
14.2%
) 382
11.8%
( 382
11.8%
1 366
11.3%
3 293
9.1%
2 241
7.5%
4 232
7.2%
7 179
 
5.5%
6 169
 
5.2%
5 162
 
5.0%
Other values (8) 364
11.3%
Hangul
ValueCountFrequency (%)
로 399
13.2%
동 390
 
12.9%
길 253
 
8.3%
산 186
 
6.1%
리 182
 
6.0%
비 168
 
5.5%
평 142
 
4.7%
서 122
 
4.0%
대 104
 
3.4%
달 94
 
3.1%
Other values (42) 993
32.7%
None
ValueCountFrequency (%)
· 28
100.0%

전화번호
Text

MISSING 

Distinct275
Distinct (%)100.0%
Missing129
Missing (%)31.9%
Memory size3.3 KiB
2023-12-13T07:59:17.544598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length18
Mean length17.985455
Min length14

Characters and Unicode

Total characters4946
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

Unique275 ?
Unique (%)100.0%

Sample

1st row053-566-0974
2nd row053-555-1555
3rd row053-557-0652
4th row053-551-7375
5th row053-525-8525
ValueCountFrequency (%)
053-521-8354 1
 
0.4%
053-561-8486 1
 
0.4%
053-553-1518 1
 
0.4%
053-552-2346 1
 
0.4%
053-663-2762 1
 
0.4%
053-565-4996 1
 
0.4%
053-571-2631 1
 
0.4%
053-553-9935 1
 
0.4%
053-573-3400 1
 
0.4%
053-566-8674 1
 
0.4%
Other values (265) 265
96.4%
2023-12-13T07:59:17.942055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1646
33.3%
5 756
15.3%
- 550
 
11.1%
3 491
 
9.9%
0 403
 
8.1%
2 224
 
4.5%
6 222
 
4.5%
7 163
 
3.3%
1 139
 
2.8%
8 128
 
2.6%
Other values (2) 224
 
4.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2750
55.6%
Space Separator 1646
33.3%
Dash Punctuation 550
 
11.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 756
27.5%
3 491
17.9%
0 403
14.7%
2 224
 
8.1%
6 222
 
8.1%
7 163
 
5.9%
1 139
 
5.1%
8 128
 
4.7%
4 114
 
4.1%
9 110
 
4.0%
Space Separator
ValueCountFrequency (%)
1646
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 550
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4946
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1646
33.3%
5 756
15.3%
- 550
 
11.1%
3 491
 
9.9%
0 403
 
8.1%
2 224
 
4.5%
6 222
 
4.5%
7 163
 
3.3%
1 139
 
2.8%
8 128
 
2.6%
Other values (2) 224
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4946
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1646
33.3%
5 756
15.3%
- 550
 
11.1%
3 491
 
9.9%
0 403
 
8.1%
2 224
 
4.5%
6 222
 
4.5%
7 163
 
3.3%
1 139
 
2.8%
8 128
 
2.6%
Other values (2) 224
 
4.5%

Missing values

2023-12-13T07:59:14.795725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T07:59:14.878160image/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

대표자판매소 명도로명 주소전화번호
0김영신GS25 내당점통학로 46(내당1동)053-566-0974
1권태암미니마트평리로 358-1(내당1동)053-555-1555
2최영희알뜰슈퍼서대구로10길 52(내당1동)053-557-0652
3정영철미카엘소비센터서대구로6길 33(내당1동)053-551-7375
4김한철생큐마트 비타민25달구벌대로361길 7(내당1동)053-525-8525
5오윤동파워마트서대구로8길 63(내당1동)053-571-1222
6최옥연이마트24R 대구내당중앙달구벌대로361길 25(내당1동)053-525-5566
7임지현대백마트통학로 54(내당1동)053-523-0010
8이근동GS25 서구내당역점서대구로2길 69, 1층(내당1동)<NA>
9한승원세븐일레븐 대구내당중앙점서대구로10길 6 (내당1동)053-521-8354
대표자판매소 명도로명 주소전화번호
394장성민대백마트 비산점통학로46길 37<NA>
395정용만지에스(GS)25 비산문화점문화로332-2<NA>
396김명진지에스(GS)25 평리동서점국채보상로42길 46<NA>
397최경호세븐일레븐 대구광장타운점달구벌대로 1707<NA>
398서병준세븐일레븐 대구평리청구점평리로57길 7<NA>
399류영효서대구농업협동조합서대구로 92<NA>
400김경애세븐일레븐 대구중리체육공원점당산로 67길 5<NA>
401박경민세븐일레븐 대구비산늘푸른점국채보상로 67길 36<NA>
402임현태나이스마트(원대점)고성로 27(원대동3가)<NA>
403이동현, 이승훈이마트24(대구 고성점)고성로 133<NA>