Overview

Dataset statistics

Number of variables5
Number of observations40
Missing cells16
Missing cells (%)8.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.7 KiB
Average record size in memory44.3 B

Variable types

Numeric1
Text4

Dataset

Description서울특별시 용산구 소독업소현황(순번, 소독업소명칭, 사무실소재지(도로명), 우편번호(도로명))에 대한 데이터를 제공합니다
URLhttps://www.data.go.kr/data/3077902/fileData.do

Alerts

순번 has 1 (2.5%) missing valuesMissing
소독업소명칭 has 1 (2.5%) missing valuesMissing
사무실소재지(도로명) has 1 (2.5%) missing valuesMissing
사무실우편번호(도로명) has 1 (2.5%) missing valuesMissing
영업소전화번호 has 12 (30.0%) missing valuesMissing

Reproduction

Analysis started2023-12-12 20:29:08.516050
Analysis finished2023-12-12 20:29:09.200802
Duration0.68 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

순번
Real number (ℝ)

MISSING 

Distinct39
Distinct (%)100.0%
Missing1
Missing (%)2.5%
Infinite0
Infinite (%)0.0%
Mean20
Minimum1
Maximum39
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size492.0 B
2023-12-13T05:29:09.274942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.9
Q110.5
median20
Q329.5
95-th percentile37.1
Maximum39
Range38
Interquartile range (IQR)19

Descriptive statistics

Standard deviation11.401754
Coefficient of variation (CV)0.57008771
Kurtosis-1.2
Mean20
Median Absolute Deviation (MAD)10
Skewness0
Sum780
Variance130
MonotonicityStrictly increasing
2023-12-13T05:29:09.422583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
1 1
 
2.5%
2 1
 
2.5%
23 1
 
2.5%
24 1
 
2.5%
25 1
 
2.5%
26 1
 
2.5%
27 1
 
2.5%
28 1
 
2.5%
29 1
 
2.5%
30 1
 
2.5%
Other values (29) 29
72.5%
ValueCountFrequency (%)
1 1
2.5%
2 1
2.5%
3 1
2.5%
4 1
2.5%
5 1
2.5%
6 1
2.5%
7 1
2.5%
8 1
2.5%
9 1
2.5%
10 1
2.5%
ValueCountFrequency (%)
39 1
2.5%
38 1
2.5%
37 1
2.5%
36 1
2.5%
35 1
2.5%
34 1
2.5%
33 1
2.5%
32 1
2.5%
31 1
2.5%
30 1
2.5%

소독업소명칭
Text

MISSING 

Distinct39
Distinct (%)100.0%
Missing1
Missing (%)2.5%
Memory size452.0 B
2023-12-13T05:29:09.640282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length13
Mean length8.2564103
Min length2

Characters and Unicode

Total characters322
Distinct characters135
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

Unique39 ?
Unique (%)100.0%

Sample

1st row(주)이은종합환경
2nd row에스엠씨
3rd row새니테크 방제
4th row(주)세방여행
5th row성심
ValueCountFrequency (%)
주식회사 4
 
7.4%
주)이은종합환경 1
 
1.9%
주)코리아휴먼테크 1
 
1.9%
한양청소 1
 
1.9%
및 1
 
1.9%
건물관리 1
 
1.9%
협동조합 1
 
1.9%
주)세스코 1
 
1.9%
용산지사 1
 
1.9%
주)휴먼종합관리 1
 
1.9%
Other values (41) 41
75.9%
2023-12-13T05:29:10.015111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
주 19
 
5.9%
( 17
 
5.3%
) 17
 
5.3%
15
 
4.7%
스 14
 
4.3%
에 9
 
2.8%
산 6
 
1.9%
방 6
 
1.9%
크 6
 
1.9%
리 5
 
1.6%
Other values (125) 208
64.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 251
78.0%
Open Punctuation 17
 
5.3%
Close Punctuation 17
 
5.3%
Space Separator 15
 
4.7%
Uppercase Letter 12
 
3.7%
Lowercase Letter 8
 
2.5%
Other Punctuation 1
 
0.3%
Decimal Number 1
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
주 19
 
7.6%
스 14
 
5.6%
에 9
 
3.6%
산 6
 
2.4%
방 6
 
2.4%
크 6
 
2.4%
리 5
 
2.0%
이 5
 
2.0%
사 5
 
2.0%
린 5
 
2.0%
Other values (103) 171
68.1%
Uppercase Letter
ValueCountFrequency (%)
N 2
16.7%
E 2
16.7%
U 1
8.3%
C 1
8.3%
I 1
8.3%
L 1
8.3%
F 1
8.3%
R 1
8.3%
G 1
8.3%
S 1
8.3%
Lowercase Letter
ValueCountFrequency (%)
p 2
25.0%
b 1
12.5%
a 1
12.5%
t 1
12.5%
r 1
12.5%
o 1
12.5%
u 1
12.5%
Open Punctuation
ValueCountFrequency (%)
( 17
100.0%
Close Punctuation
ValueCountFrequency (%)
) 17
100.0%
Space Separator
ValueCountFrequency (%)
15
100.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%
Decimal Number
ValueCountFrequency (%)
5 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 251
78.0%
Common 51
 
15.8%
Latin 20
 
6.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
주 19
 
7.6%
스 14
 
5.6%
에 9
 
3.6%
산 6
 
2.4%
방 6
 
2.4%
크 6
 
2.4%
리 5
 
2.0%
이 5
 
2.0%
사 5
 
2.0%
린 5
 
2.0%
Other values (103) 171
68.1%
Latin
ValueCountFrequency (%)
N 2
 
10.0%
E 2
 
10.0%
p 2
 
10.0%
U 1
 
5.0%
C 1
 
5.0%
I 1
 
5.0%
L 1
 
5.0%
F 1
 
5.0%
R 1
 
5.0%
G 1
 
5.0%
Other values (7) 7
35.0%
Common
ValueCountFrequency (%)
( 17
33.3%
) 17
33.3%
15
29.4%
. 1
 
2.0%
5 1
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 251
78.0%
ASCII 71
 
22.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
주 19
 
7.6%
스 14
 
5.6%
에 9
 
3.6%
산 6
 
2.4%
방 6
 
2.4%
크 6
 
2.4%
리 5
 
2.0%
이 5
 
2.0%
사 5
 
2.0%
린 5
 
2.0%
Other values (103) 171
68.1%
ASCII
ValueCountFrequency (%)
( 17
23.9%
) 17
23.9%
15
21.1%
N 2
 
2.8%
E 2
 
2.8%
p 2
 
2.8%
U 1
 
1.4%
. 1
 
1.4%
C 1
 
1.4%
I 1
 
1.4%
Other values (12) 12
16.9%

사무실소재지(도로명)
Text

MISSING 

Distinct39
Distinct (%)100.0%
Missing1
Missing (%)2.5%
Memory size452.0 B
2023-12-13T05:29:10.312909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length48
Median length38
Mean length34.076923
Min length23

Characters and Unicode

Total characters1329
Distinct characters110
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

Unique39 ?
Unique (%)100.0%

Sample

1st row서울특별시 용산구 이촌로22길 14, 우리은행 1층 (이촌동)
2nd row서울특별시 용산구 이촌로 1, 1116호 (한강로3가, 지에스 한강에클라트)
3rd row서울특별시 용산구 이촌로14길 16, 5동 105호 (이촌동, 시범아파트)
4th row서울특별시 용산구 한강대로98길 3, KCC IT빌딩 4층 (갈월동)
5th row서울특별시 용산구 임정로13길 10-2 (효창동)
ValueCountFrequency (%)
서울특별시 39
 
15.5%
용산구 39
 
15.5%
한강로3가 5
 
2.0%
2층 5
 
2.0%
한강대로 4
 
1.6%
원효로 4
 
1.6%
한남동 3
 
1.2%
남영동 3
 
1.2%
원효로3가 3
 
1.2%
4층 3
 
1.2%
Other values (119) 144
57.1%
2023-12-13T05:29:10.730196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
213
 
16.0%
1 59
 
4.4%
로 55
 
4.1%
서 44
 
3.3%
용 42
 
3.2%
산 42
 
3.2%
, 41
 
3.1%
울 41
 
3.1%
( 40
 
3.0%
) 40
 
3.0%
Other values (100) 712
53.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 757
57.0%
Decimal Number 220
 
16.6%
Space Separator 213
 
16.0%
Other Punctuation 41
 
3.1%
Open Punctuation 40
 
3.0%
Close Punctuation 40
 
3.0%
Dash Punctuation 10
 
0.8%
Uppercase Letter 7
 
0.5%
Lowercase Letter 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 55
 
7.3%
서 44
 
5.8%
용 42
 
5.5%
산 42
 
5.5%
울 41
 
5.4%
시 40
 
5.3%
특 39
 
5.2%
별 39
 
5.2%
구 39
 
5.2%
동 28
 
3.7%
Other values (78) 348
46.0%
Decimal Number
ValueCountFrequency (%)
1 59
26.8%
3 34
15.5%
2 31
14.1%
4 20
 
9.1%
0 19
 
8.6%
5 15
 
6.8%
8 12
 
5.5%
6 11
 
5.0%
9 10
 
4.5%
7 9
 
4.1%
Uppercase Letter
ValueCountFrequency (%)
C 2
28.6%
G 1
14.3%
K 1
14.3%
I 1
14.3%
T 1
14.3%
S 1
14.3%
Space Separator
ValueCountFrequency (%)
213
100.0%
Other Punctuation
ValueCountFrequency (%)
, 41
100.0%
Open Punctuation
ValueCountFrequency (%)
( 40
100.0%
Close Punctuation
ValueCountFrequency (%)
) 40
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 10
100.0%
Lowercase Letter
ValueCountFrequency (%)
b 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 757
57.0%
Common 564
42.4%
Latin 8
 
0.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 55
 
7.3%
서 44
 
5.8%
용 42
 
5.5%
산 42
 
5.5%
울 41
 
5.4%
시 40
 
5.3%
특 39
 
5.2%
별 39
 
5.2%
구 39
 
5.2%
동 28
 
3.7%
Other values (78) 348
46.0%
Common
ValueCountFrequency (%)
213
37.8%
1 59
 
10.5%
, 41
 
7.3%
( 40
 
7.1%
) 40
 
7.1%
3 34
 
6.0%
2 31
 
5.5%
4 20
 
3.5%
0 19
 
3.4%
5 15
 
2.7%
Other values (5) 52
 
9.2%
Latin
ValueCountFrequency (%)
C 2
25.0%
G 1
12.5%
K 1
12.5%
I 1
12.5%
T 1
12.5%
b 1
12.5%
S 1
12.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 757
57.0%
ASCII 572
43.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
213
37.2%
1 59
 
10.3%
, 41
 
7.2%
( 40
 
7.0%
) 40
 
7.0%
3 34
 
5.9%
2 31
 
5.4%
4 20
 
3.5%
0 19
 
3.3%
5 15
 
2.6%
Other values (12) 60
 
10.5%
Hangul
ValueCountFrequency (%)
로 55
 
7.3%
서 44
 
5.8%
용 42
 
5.5%
산 42
 
5.5%
울 41
 
5.4%
시 40
 
5.3%
특 39
 
5.2%
별 39
 
5.2%
구 39
 
5.2%
동 28
 
3.7%
Other values (78) 348
46.0%

사무실우편번호(도로명)
Text

MISSING 

Distinct28
Distinct (%)71.8%
Missing1
Missing (%)2.5%
Memory size452.0 B
2023-12-13T05:29:10.928684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length4
Mean length4.0769231
Min length4

Characters and Unicode

Total characters159
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

Unique21 ?
Unique (%)53.8%

Sample

1st row4374
2nd row4373
3rd row4374
4th row4334
5th row4319
ValueCountFrequency (%)
4374 3
 
7.7%
4363 3
 
7.7%
4373 3
 
7.7%
4352 3
 
7.7%
4379 2
 
5.1%
4409 2
 
5.1%
4320 2
 
5.1%
4382 1
 
2.6%
4303 1
 
2.6%
4365 1
 
2.6%
Other values (18) 18
46.2%
2023-12-13T05:29:11.269774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4 51
32.1%
3 43
27.0%
7 11
 
6.9%
1 11
 
6.9%
2 10
 
6.3%
6 9
 
5.7%
0 9
 
5.7%
9 7
 
4.4%
5 5
 
3.1%
8 2
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 158
99.4%
Dash Punctuation 1
 
0.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 51
32.3%
3 43
27.2%
7 11
 
7.0%
1 11
 
7.0%
2 10
 
6.3%
6 9
 
5.7%
0 9
 
5.7%
9 7
 
4.4%
5 5
 
3.2%
8 2
 
1.3%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 159
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4 51
32.1%
3 43
27.0%
7 11
 
6.9%
1 11
 
6.9%
2 10
 
6.3%
6 9
 
5.7%
0 9
 
5.7%
9 7
 
4.4%
5 5
 
3.1%
8 2
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 159
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 51
32.1%
3 43
27.0%
7 11
 
6.9%
1 11
 
6.9%
2 10
 
6.3%
6 9
 
5.7%
0 9
 
5.7%
9 7
 
4.4%
5 5
 
3.1%
8 2
 
1.3%

영업소전화번호
Text

MISSING 

Distinct28
Distinct (%)100.0%
Missing12
Missing (%)30.0%
Memory size452.0 B
2023-12-13T05:29:11.452607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length11
Mean length11.535714
Min length11

Characters and Unicode

Total characters323
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

Unique28 ?
Unique (%)100.0%

Sample

1st row070-7611-6167
2nd row02-2711-7882
3rd row02-792-8853
4th row02-793-4212
5th row02-6052-1410
ValueCountFrequency (%)
070-7611-6167 1
 
3.6%
02-2711-7882 1
 
3.6%
02-705-1005 1
 
3.6%
02-798-9906 1
 
3.6%
02-790-5946 1
 
3.6%
02-711-2010 1
 
3.6%
02-718-4711 1
 
3.6%
02-2617-3771~2 1
 
3.6%
02-704-3001 1
 
3.6%
02-3272-1488 1
 
3.6%
Other values (18) 18
64.3%
2023-12-13T05:29:11.801346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 58
18.0%
- 56
17.3%
2 49
15.2%
7 35
10.8%
1 31
9.6%
9 19
 
5.9%
8 18
 
5.6%
4 18
 
5.6%
3 15
 
4.6%
5 12
 
3.7%
Other values (2) 12
 
3.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 266
82.4%
Dash Punctuation 56
 
17.3%
Math Symbol 1
 
0.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 58
21.8%
2 49
18.4%
7 35
13.2%
1 31
11.7%
9 19
 
7.1%
8 18
 
6.8%
4 18
 
6.8%
3 15
 
5.6%
5 12
 
4.5%
6 11
 
4.1%
Dash Punctuation
ValueCountFrequency (%)
- 56
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 323
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 58
18.0%
- 56
17.3%
2 49
15.2%
7 35
10.8%
1 31
9.6%
9 19
 
5.9%
8 18
 
5.6%
4 18
 
5.6%
3 15
 
4.6%
5 12
 
3.7%
Other values (2) 12
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 323
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 58
18.0%
- 56
17.3%
2 49
15.2%
7 35
10.8%
1 31
9.6%
9 19
 
5.9%
8 18
 
5.6%
4 18
 
5.6%
3 15
 
4.6%
5 12
 
3.7%
Other values (2) 12
 
3.7%

Interactions

2023-12-13T05:29:08.807607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T05:29:11.895113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번소독업소명칭사무실소재지(도로명)사무실우편번호(도로명)영업소전화번호
순번1.0001.0001.0000.2271.000
소독업소명칭1.0001.0001.0001.0001.000
사무실소재지(도로명)1.0001.0001.0001.0001.000
사무실우편번호(도로명)0.2271.0001.0001.0001.000
영업소전화번호1.0001.0001.0001.0001.000

Missing values

2023-12-13T05:29:08.938118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T05:29:09.039122image/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.
2023-12-13T05:29:09.134720image/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

순번소독업소명칭사무실소재지(도로명)사무실우편번호(도로명)영업소전화번호
01(주)이은종합환경서울특별시 용산구 이촌로22길 14, 우리은행 1층 (이촌동)4374<NA>
12에스엠씨서울특별시 용산구 이촌로 1, 1116호 (한강로3가, 지에스 한강에클라트)4373070-7611-6167
23새니테크 방제서울특별시 용산구 이촌로14길 16, 5동 105호 (이촌동, 시범아파트)4374<NA>
34(주)세방여행서울특별시 용산구 한강대로98길 3, KCC IT빌딩 4층 (갈월동)4334<NA>
45성심서울특별시 용산구 임정로13길 10-2 (효창동)4319<NA>
56용산구립장애인보호작업장서울특별시 용산구 한강대로 331, 2층 (갈월동)432002-2711-7882
67홍지컴퍼니서울특별시 용산구 한강대로21가길 27, 영창빌딩 202호 (한강로3가)437902-792-8853
78삼성크린서울특별시 용산구 독서당로 29-1, 1층 (한남동)441002-793-4212
89GREEN F5 용산본부서울특별시 용산구 이태원로 148, 국제아케이드 308호 (이태원동)4391<NA>
910스마트 클린서울특별시 용산구 원효로41길 15, 502호 (원효로3가)436302-6052-1410
순번소독업소명칭사무실소재지(도로명)사무실우편번호(도로명)영업소전화번호
3031하우스키퍼서울특별시 용산구 백범로79길 99, 지하1층 (청파동3가)431202-3272-1488
3132(주)케이에스메이트서울특별시 용산구 한강대로62나길 17, 인월빌딩 2층 (용산동3가)438202-704-3001
3233(주)대청에스에치서울특별시 용산구 한강대로 405, 서울역(철도역) 서울역 신호실동 2층 (동자동)432002-2617-3771~2
3334미래방제시스템서울특별시 용산구 원효로41길 15, 401호 (원효로3가)436302-718-4711
3435거명개발(주)서울특별시 용산구 원효로 128 (원효로3가)436602-711-2010
3536향우산업(주)서울특별시 용산구 한강대로21길 35, 2층 (한강로3가)437902-790-5946
3637(주)유성시에스서울특별시 용산구 새창로 213, 석우빌딩 3층 (한강로2가)437602-798-9906
3738(주)성인개발서울특별시 용산구 이촌로 1, 1116호 (한강로3가, GS한강에클라트)437302-705-1005
3839제이마스터서울특별시 용산구 보광로 46, 501호 (보광동, 용호빌딩)441402-798-0711
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