Overview

Dataset statistics

Number of variables5
Number of observations245
Missing cells19
Missing cells (%)1.6%
Duplicate rows4
Duplicate rows (%)1.6%
Total size in memory9.7 KiB
Average record size in memory40.5 B

Variable types

Categorical2
Text2
DateTime1

Dataset

Description파일 다운로드
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-21723/S/1/datasetView.do

Alerts

Dataset has 4 (1.6%) duplicate rowsDuplicates
소재지 has 19 (7.8%) missing valuesMissing

Reproduction

Analysis started2024-04-29 21:10:58.450461
Analysis finished2024-04-29 21:10:59.180550
Duration0.73 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Categorical

Distinct23
Distinct (%)9.4%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
금천구
31 
성동구
30 
강서구
21 
종로구
17 
강동구
14 
Other values (18)
132 

Length

Max length4
Median length3
Mean length3.0979592
Min length2

Unique

Unique1 ?
Unique (%)0.4%

Sample

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

Common Values

ValueCountFrequency (%)
금천구 31
12.7%
성동구 30
12.2%
강서구 21
 
8.6%
종로구 17
 
6.9%
강동구 14
 
5.7%
동대문구 14
 
5.7%
서초구 13
 
5.3%
구로구 13
 
5.3%
송파구 12
 
4.9%
도봉구 11
 
4.5%
Other values (13) 69
28.2%

Length

2024-04-30T06:10:59.242135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
금천구 31
12.7%
성동구 30
12.2%
강서구 21
 
8.6%
종로구 17
 
6.9%
강동구 14
 
5.7%
동대문구 14
 
5.7%
서초구 13
 
5.3%
구로구 13
 
5.3%
송파구 12
 
4.9%
도봉구 11
 
4.5%
Other values (13) 69
28.2%

사업장명
Text

Distinct224
Distinct (%)91.4%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
2024-04-30T06:10:59.449083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length29
Median length19
Mean length7.7142857
Min length2

Characters and Unicode

Total characters1890
Distinct characters312
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

Unique205 ?
Unique (%)83.7%

Sample

1st row아세아자동차
2nd row디더블유에스제23호전문투자형사모부동산투자 유한회사
3rd row매직터치
4th row서울대학교병원
5th row가톨릭대학교
ValueCountFrequency (%)
주식회사 5
 
1.8%
강남베드로병원 3
 
1.1%
에스제이삼일모터스(주 3
 
1.1%
달인세차장 2
 
0.7%
애니카랜드 2
 
0.7%
기아오토큐 2
 
0.7%
주)세스코 2
 
0.7%
주)시온카독크 2
 
0.7%
오토앤셀프세차장 2
 
0.7%
디.버틀러(d.butler 2
 
0.7%
Other values (237) 251
90.9%
2024-04-30T06:10:59.797168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
주 80
 
4.2%
) 73
 
3.9%
( 73
 
3.9%
차 66
 
3.5%
스 59
 
3.1%
동 55
 
2.9%
자 46
 
2.4%
사 37
 
2.0%
터 31
 
1.6%
세 31
 
1.6%
Other values (302) 1339
70.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1629
86.2%
Close Punctuation 73
 
3.9%
Open Punctuation 73
 
3.9%
Uppercase Letter 49
 
2.6%
Space Separator 31
 
1.6%
Lowercase Letter 16
 
0.8%
Decimal Number 14
 
0.7%
Other Punctuation 4
 
0.2%
Dash Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
주 80
 
4.9%
차 66
 
4.1%
스 59
 
3.6%
동 55
 
3.4%
자 46
 
2.8%
사 37
 
2.3%
터 31
 
1.9%
세 31
 
1.9%
장 29
 
1.8%
서 28
 
1.7%
Other values (260) 1167
71.6%
Uppercase Letter
ValueCountFrequency (%)
T 6
12.2%
A 5
10.2%
U 5
10.2%
S 4
 
8.2%
B 4
 
8.2%
O 3
 
6.1%
R 3
 
6.1%
L 3
 
6.1%
D 3
 
6.1%
J 2
 
4.1%
Other values (8) 11
22.4%
Lowercase Letter
ValueCountFrequency (%)
o 4
25.0%
r 2
12.5%
e 2
12.5%
s 1
 
6.2%
t 1
 
6.2%
m 1
 
6.2%
a 1
 
6.2%
k 1
 
6.2%
n 1
 
6.2%
i 1
 
6.2%
Decimal Number
ValueCountFrequency (%)
1 4
28.6%
2 3
21.4%
4 2
14.3%
8 1
 
7.1%
5 1
 
7.1%
7 1
 
7.1%
9 1
 
7.1%
3 1
 
7.1%
Close Punctuation
ValueCountFrequency (%)
) 73
100.0%
Open Punctuation
ValueCountFrequency (%)
( 73
100.0%
Space Separator
ValueCountFrequency (%)
31
100.0%
Other Punctuation
ValueCountFrequency (%)
. 4
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1629
86.2%
Common 196
 
10.4%
Latin 65
 
3.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
주 80
 
4.9%
차 66
 
4.1%
스 59
 
3.6%
동 55
 
3.4%
자 46
 
2.8%
사 37
 
2.3%
터 31
 
1.9%
세 31
 
1.9%
장 29
 
1.8%
서 28
 
1.7%
Other values (260) 1167
71.6%
Latin
ValueCountFrequency (%)
T 6
 
9.2%
A 5
 
7.7%
U 5
 
7.7%
S 4
 
6.2%
o 4
 
6.2%
B 4
 
6.2%
O 3
 
4.6%
R 3
 
4.6%
L 3
 
4.6%
D 3
 
4.6%
Other values (19) 25
38.5%
Common
ValueCountFrequency (%)
) 73
37.2%
( 73
37.2%
31
15.8%
1 4
 
2.0%
. 4
 
2.0%
2 3
 
1.5%
4 2
 
1.0%
- 1
 
0.5%
8 1
 
0.5%
5 1
 
0.5%
Other values (3) 3
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1629
86.2%
ASCII 261
 
13.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
주 80
 
4.9%
차 66
 
4.1%
스 59
 
3.6%
동 55
 
3.4%
자 46
 
2.8%
사 37
 
2.3%
터 31
 
1.9%
세 31
 
1.9%
장 29
 
1.8%
서 28
 
1.7%
Other values (260) 1167
71.6%
ASCII
ValueCountFrequency (%)
) 73
28.0%
( 73
28.0%
31
11.9%
T 6
 
2.3%
A 5
 
1.9%
U 5
 
1.9%
S 4
 
1.5%
o 4
 
1.5%
1 4
 
1.5%
B 4
 
1.5%
Other values (32) 52
19.9%

소재지
Text

MISSING 

Distinct202
Distinct (%)89.4%
Missing19
Missing (%)7.8%
Memory size2.0 KiB
2024-04-30T06:11:00.099459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length43
Median length35
Mean length22.933628
Min length17

Characters and Unicode

Total characters5183
Distinct characters201
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

Unique181 ?
Unique (%)80.1%

Sample

1st row서울특별시 종로구 홍지동 97-5번지 우성빌딩
2nd row서울특별시 종로구 신문로1가 115번지 금호아시아나본관
3rd row서울특별시 종로구 필운동 217-1번지
4th row서울특별시 종로구 연건동 28-21번지
5th row서울특별시 종로구 연지동 270번지
ValueCountFrequency (%)
서울특별시 226
 
23.5%
금천구 31
 
3.2%
성동구 24
 
2.5%
성수동2가 20
 
2.1%
강서구 18
 
1.9%
독산동 17
 
1.8%
종로구 16
 
1.7%
동대문구 14
 
1.5%
구로구 12
 
1.2%
강동구 12
 
1.2%
Other values (362) 570
59.4%
2024-04-30T06:11:00.542296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
734
 
14.2%
동 278
 
5.4%
서 263
 
5.1%
구 245
 
4.7%
지 240
 
4.6%
시 232
 
4.5%
울 228
 
4.4%
특 226
 
4.4%
별 226
 
4.4%
번 226
 
4.4%
Other values (191) 2285
44.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3207
61.9%
Decimal Number 1020
 
19.7%
Space Separator 734
 
14.2%
Dash Punctuation 206
 
4.0%
Other Punctuation 7
 
0.1%
Uppercase Letter 7
 
0.1%
Close Punctuation 1
 
< 0.1%
Open Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 278
 
8.7%
서 263
 
8.2%
구 245
 
7.6%
지 240
 
7.5%
시 232
 
7.2%
울 228
 
7.1%
특 226
 
7.0%
별 226
 
7.0%
번 226
 
7.0%
성 59
 
1.8%
Other values (169) 984
30.7%
Decimal Number
ValueCountFrequency (%)
1 183
17.9%
2 172
16.9%
3 125
12.3%
4 107
10.5%
5 93
9.1%
7 83
8.1%
6 72
 
7.1%
0 67
 
6.6%
9 66
 
6.5%
8 52
 
5.1%
Uppercase Letter
ValueCountFrequency (%)
S 2
28.6%
I 1
14.3%
T 1
14.3%
B 1
14.3%
J 1
14.3%
K 1
14.3%
Other Punctuation
ValueCountFrequency (%)
, 6
85.7%
. 1
 
14.3%
Space Separator
ValueCountFrequency (%)
734
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 206
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3207
61.9%
Common 1969
38.0%
Latin 7
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 278
 
8.7%
서 263
 
8.2%
구 245
 
7.6%
지 240
 
7.5%
시 232
 
7.2%
울 228
 
7.1%
특 226
 
7.0%
별 226
 
7.0%
번 226
 
7.0%
성 59
 
1.8%
Other values (169) 984
30.7%
Common
ValueCountFrequency (%)
734
37.3%
- 206
 
10.5%
1 183
 
9.3%
2 172
 
8.7%
3 125
 
6.3%
4 107
 
5.4%
5 93
 
4.7%
7 83
 
4.2%
6 72
 
3.7%
0 67
 
3.4%
Other values (6) 127
 
6.4%
Latin
ValueCountFrequency (%)
S 2
28.6%
I 1
14.3%
T 1
14.3%
B 1
14.3%
J 1
14.3%
K 1
14.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3207
61.9%
ASCII 1976
38.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
734
37.1%
- 206
 
10.4%
1 183
 
9.3%
2 172
 
8.7%
3 125
 
6.3%
4 107
 
5.4%
5 93
 
4.7%
7 83
 
4.2%
6 72
 
3.6%
0 67
 
3.4%
Other values (12) 134
 
6.8%
Hangul
ValueCountFrequency (%)
동 278
 
8.7%
서 263
 
8.2%
구 245
 
7.6%
지 240
 
7.5%
시 232
 
7.2%
울 228
 
7.1%
특 226
 
7.0%
별 226
 
7.0%
번 226
 
7.0%
성 59
 
1.8%
Other values (169) 984
30.7%

업종
Categorical

Distinct46
Distinct (%)18.8%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
<NA>
112 
자동차 종합 수리업
26 
자동차 세차업
18 
자동차 수리 및 세차업
14 
도장 및 기타 피막처리업
 
8
Other values (41)
67 

Length

Max length18
Median length17
Mean length6.9183673
Min length2

Unique

Unique30 ?
Unique (%)12.2%

Sample

1st row자동차 수리 및 세차업
2nd row<NA>
3rd row<NA>
4th row보건업
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 112
45.7%
자동차 종합 수리업 26
 
10.6%
자동차 세차업 18
 
7.3%
자동차 수리 및 세차업 14
 
5.7%
도장 및 기타 피막처리업 8
 
3.3%
자동차 수리업 7
 
2.9%
택시 운송업 5
 
2.0%
종합 병원 4
 
1.6%
자동차 전문 수리업 4
 
1.6%
주유소 운영업 4
 
1.6%
Other values (36) 43
 
17.6%

Length

2024-04-30T06:11:00.692984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 112
22.6%
자동차 69
13.9%
수리업 37
 
7.5%
및 37
 
7.5%
세차업 32
 
6.5%
종합 30
 
6.1%
기타 19
 
3.8%
수리 14
 
2.8%
제조업 13
 
2.6%
도장 8
 
1.6%
Other values (69) 124
25.1%

점검일자
Date

Distinct132
Distinct (%)53.9%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
Minimum2018-10-24 00:00:00
Maximum2020-01-15 00:00:00
2024-04-30T06:11:00.822503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T06:11:00.969294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Correlations

2024-04-30T06:11:01.055691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분업종
구분1.0000.847
업종0.8471.000
2024-04-30T06:11:01.132634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업종구분
업종1.0000.313
구분0.3131.000
2024-04-30T06:11:01.199434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분업종
구분1.0000.313
업종0.3131.000

Missing values

2024-04-30T06:10:59.065253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-30T06:10:59.147588image/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

구분사업장명소재지업종점검일자
0종로구아세아자동차서울특별시 종로구 홍지동 97-5번지 우성빌딩자동차 수리 및 세차업2019.04.22
1종로구디더블유에스제23호전문투자형사모부동산투자 유한회사서울특별시 종로구 신문로1가 115번지 금호아시아나본관<NA>2019.04.30
2종로구매직터치서울특별시 종로구 필운동 217-1번지<NA>2019.09.24
3종로구서울대학교병원서울특별시 종로구 연건동 28-21번지보건업2019.10.30
4종로구가톨릭대학교<NA><NA>2019.12.03
5종로구(재)두산연강재단서울특별시 종로구 연지동 270번지<NA>2019.05.07
6종로구로터스피에프브이 주식회사서울특별시 종로구 관훈동 155-2번지호텔업2019.10.23
7종로구이지스제81호 사모부동산투자유한회사서울특별시 종로구 종로2가 6번지<NA>2019.11.29
8종로구효성주얼리시티상가운영위원회서울특별시 종로구 인의동 48-2번지 효성주얼리시티<NA>2019.07.22
9종로구골드존서울특별시 종로구 돈의동 50번지<NA>2019.07.15
구분사업장명소재지업종점검일자
235강동구고려덴트서울특별시 강동구 성내동 520번지<NA>2019.12.04
236강동구(주)선우지에스엠서울특별시 강동구 길동 368-5번지<NA>2019.03.15
237강동구강동JJ24시셀프세차장서울특별시 강동구 성내동 423-3번지<NA>2019.10.25
238강동구르노삼성자동차서비스코너천호점서울특별시 강동구 천호동 326-24번지<NA>2019.08.26
239강동구한스크루서울특별시 강동구 길동 344-6번지<NA>2019.07.24
240강동구우진쥬얼리서울특별시 강동구 천호동 317-2번지<NA>2019.03.15
241강동구korea motors 상일점서울특별시 강동구 상일동 458-2번지<NA>2019.10.24
242강동구코스모셀프세차타운서울특별시 강동구 둔촌동 63-1번지<NA>2019.03.25
243강동구(주)세스코<NA><NA>2019.03.15
244강동구(주)세스코 시험분석센터<NA><NA>2019.03.15

Duplicate rows

Most frequently occurring

구분사업장명소재지업종점검일자# duplicates
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