Overview

Dataset statistics

Number of variables5
Number of observations289
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory11.7 KiB
Average record size in memory41.5 B

Variable types

Numeric1
Text2
Categorical2

Dataset

Description파일 다운로드
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-15194/F/1/datasetView.do

Alerts

담당자 전화번호 is highly overall correlated with 관할관청High correlation
관할관청 is highly overall correlated with 담당자 전화번호High correlation
연번 has unique valuesUnique
업체명 has unique valuesUnique

Reproduction

Analysis started2024-03-23 03:18:01.396457
Analysis finished2024-03-23 03:18:02.506101
Duration1.11 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

UNIQUE 

Distinct289
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean145
Minimum1
Maximum289
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2024-03-23T12:18:02.669159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile15.4
Q173
median145
Q3217
95-th percentile274.6
Maximum289
Range288
Interquartile range (IQR)144

Descriptive statistics

Standard deviation83.571327
Coefficient of variation (CV)0.57635398
Kurtosis-1.2
Mean145
Median Absolute Deviation (MAD)72
Skewness0
Sum41905
Variance6984.1667
MonotonicityStrictly increasing
2024-03-23T12:18:03.015157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.3%
218 1
 
0.3%
198 1
 
0.3%
197 1
 
0.3%
196 1
 
0.3%
195 1
 
0.3%
194 1
 
0.3%
193 1
 
0.3%
192 1
 
0.3%
191 1
 
0.3%
Other values (279) 279
96.5%
ValueCountFrequency (%)
1 1
0.3%
2 1
0.3%
3 1
0.3%
4 1
0.3%
5 1
0.3%
6 1
0.3%
7 1
0.3%
8 1
0.3%
9 1
0.3%
10 1
0.3%
ValueCountFrequency (%)
289 1
0.3%
288 1
0.3%
287 1
0.3%
286 1
0.3%
285 1
0.3%
284 1
0.3%
283 1
0.3%
282 1
0.3%
281 1
0.3%
280 1
0.3%

업체명
Text

UNIQUE 

Distinct289
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size2.4 KiB
2024-03-23T12:18:03.408733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length17
Mean length8.5640138
Min length5

Characters and Unicode

Total characters2475
Distinct characters229
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique289 ?
Unique (%)100.0%

Sample

1st row(주)26렌트카
2nd row345렌트카(주)
3rd row(주)OK모터스
4th rowSK네트웍스(주)SK렌터카
5th row(주)가야렌트카
ValueCountFrequency (%)
주)26렌트카 1
 
0.3%
에스케이렌터카(주 1
 
0.3%
주)전진렌터카 1
 
0.3%
인천공항렌트카(주 1
 
0.3%
인지카(주 1
 
0.3%
주)인모션 1
 
0.3%
주)인렌트카 1
 
0.3%
이카모빌리티(주 1
 
0.3%
제로렌트카(주 1
 
0.3%
주)이지웨이렌트카 1
 
0.3%
Other values (279) 279
96.5%
2024-03-23T12:18:04.608799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
( 289
 
11.7%
) 289
 
11.7%
주 284
 
11.5%
카 208
 
8.4%
렌 190
 
7.7%
트 144
 
5.8%
이 89
 
3.6%
스 72
 
2.9%
터 52
 
2.1%
에 32
 
1.3%
Other values (219) 826
33.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1886
76.2%
Open Punctuation 289
 
11.7%
Close Punctuation 289
 
11.7%
Uppercase Letter 6
 
0.2%
Decimal Number 5
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
주 284
 
15.1%
카 208
 
11.0%
렌 190
 
10.1%
트 144
 
7.6%
이 89
 
4.7%
스 72
 
3.8%
터 52
 
2.8%
에 32
 
1.7%
리 31
 
1.6%
아 28
 
1.5%
Other values (209) 756
40.1%
Decimal Number
ValueCountFrequency (%)
3 1
20.0%
2 1
20.0%
6 1
20.0%
4 1
20.0%
5 1
20.0%
Uppercase Letter
ValueCountFrequency (%)
K 3
50.0%
S 2
33.3%
O 1
 
16.7%
Open Punctuation
ValueCountFrequency (%)
( 289
100.0%
Close Punctuation
ValueCountFrequency (%)
) 289
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1886
76.2%
Common 583
 
23.6%
Latin 6
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
주 284
 
15.1%
카 208
 
11.0%
렌 190
 
10.1%
트 144
 
7.6%
이 89
 
4.7%
스 72
 
3.8%
터 52
 
2.8%
에 32
 
1.7%
리 31
 
1.6%
아 28
 
1.5%
Other values (209) 756
40.1%
Common
ValueCountFrequency (%)
( 289
49.6%
) 289
49.6%
3 1
 
0.2%
2 1
 
0.2%
6 1
 
0.2%
4 1
 
0.2%
5 1
 
0.2%
Latin
ValueCountFrequency (%)
K 3
50.0%
S 2
33.3%
O 1
 
16.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1886
76.2%
ASCII 589
 
23.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
( 289
49.1%
) 289
49.1%
K 3
 
0.5%
S 2
 
0.3%
O 1
 
0.2%
3 1
 
0.2%
2 1
 
0.2%
6 1
 
0.2%
4 1
 
0.2%
5 1
 
0.2%
Hangul
ValueCountFrequency (%)
주 284
 
15.1%
카 208
 
11.0%
렌 190
 
10.1%
트 144
 
7.6%
이 89
 
4.7%
스 72
 
3.8%
터 52
 
2.8%
에 32
 
1.7%
리 31
 
1.6%
아 28
 
1.5%
Other values (209) 756
40.1%

주사무소 주소
Text

Distinct284
Distinct (%)98.3%
Missing0
Missing (%)0.0%
Memory size2.4 KiB
2024-03-23T12:18:05.135693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length50
Median length39
Mean length30.197232
Min length14

Characters and Unicode

Total characters8727
Distinct characters329
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

Unique279 ?
Unique (%)96.5%

Sample

1st row서울 중랑구 동일로 604 2층
2nd row서울 강남구 헌릉로745길 37 비동 203호(율현동,강남자동차매매장)
3rd row서울 강남구 자곡로 201 , 강남라르고빌딩 116호
4th row서울 종로구 청계천로 85 , 20층(관철동, 삼일빌딩)
5th row서울 은평구 연서로 110 , 403호(역촌동)
ValueCountFrequency (%)
서울 289
 
15.6%
155
 
8.4%
강남구 41
 
2.2%
서초구 26
 
1.4%
송파구 23
 
1.2%
성동구 23
 
1.2%
강서구 21
 
1.1%
2층 17
 
0.9%
광진구 16
 
0.9%
강동구 15
 
0.8%
Other values (833) 1230
66.3%
2024-03-23T12:18:05.900588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1584
 
18.2%
1 412
 
4.7%
서 375
 
4.3%
동 324
 
3.7%
구 319
 
3.7%
로 304
 
3.5%
울 291
 
3.3%
, 275
 
3.2%
2 270
 
3.1%
0 225
 
2.6%
Other values (319) 4348
49.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4597
52.7%
Decimal Number 1748
 
20.0%
Space Separator 1584
 
18.2%
Other Punctuation 276
 
3.2%
Open Punctuation 211
 
2.4%
Close Punctuation 211
 
2.4%
Dash Punctuation 56
 
0.6%
Uppercase Letter 44
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
서 375
 
8.2%
동 324
 
7.0%
구 319
 
6.9%
로 304
 
6.6%
울 291
 
6.3%
호 203
 
4.4%
층 116
 
2.5%
길 115
 
2.5%
강 95
 
2.1%
대 85
 
1.8%
Other values (286) 2370
51.6%
Uppercase Letter
ValueCountFrequency (%)
B 9
20.5%
A 8
18.2%
C 5
11.4%
T 3
 
6.8%
K 3
 
6.8%
S 2
 
4.5%
L 2
 
4.5%
N 2
 
4.5%
H 2
 
4.5%
G 1
 
2.3%
Other values (7) 7
15.9%
Decimal Number
ValueCountFrequency (%)
1 412
23.6%
2 270
15.4%
0 225
12.9%
3 181
10.4%
4 132
 
7.6%
6 129
 
7.4%
9 106
 
6.1%
5 105
 
6.0%
7 103
 
5.9%
8 85
 
4.9%
Other Punctuation
ValueCountFrequency (%)
, 275
99.6%
. 1
 
0.4%
Space Separator
ValueCountFrequency (%)
1584
100.0%
Open Punctuation
ValueCountFrequency (%)
( 211
100.0%
Close Punctuation
ValueCountFrequency (%)
) 211
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 56
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4597
52.7%
Common 4086
46.8%
Latin 44
 
0.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
서 375
 
8.2%
동 324
 
7.0%
구 319
 
6.9%
로 304
 
6.6%
울 291
 
6.3%
호 203
 
4.4%
층 116
 
2.5%
길 115
 
2.5%
강 95
 
2.1%
대 85
 
1.8%
Other values (286) 2370
51.6%
Latin
ValueCountFrequency (%)
B 9
20.5%
A 8
18.2%
C 5
11.4%
T 3
 
6.8%
K 3
 
6.8%
S 2
 
4.5%
L 2
 
4.5%
N 2
 
4.5%
H 2
 
4.5%
G 1
 
2.3%
Other values (7) 7
15.9%
Common
ValueCountFrequency (%)
1584
38.8%
1 412
 
10.1%
, 275
 
6.7%
2 270
 
6.6%
0 225
 
5.5%
( 211
 
5.2%
) 211
 
5.2%
3 181
 
4.4%
4 132
 
3.2%
6 129
 
3.2%
Other values (6) 456
 
11.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4597
52.7%
ASCII 4130
47.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1584
38.4%
1 412
 
10.0%
, 275
 
6.7%
2 270
 
6.5%
0 225
 
5.4%
( 211
 
5.1%
) 211
 
5.1%
3 181
 
4.4%
4 132
 
3.2%
6 129
 
3.1%
Other values (23) 500
 
12.1%
Hangul
ValueCountFrequency (%)
서 375
 
8.2%
동 324
 
7.0%
구 319
 
6.9%
로 304
 
6.6%
울 291
 
6.3%
호 203
 
4.4%
층 116
 
2.5%
길 115
 
2.5%
강 95
 
2.1%
대 85
 
1.8%
Other values (286) 2370
51.6%

관할관청
Categorical

HIGH CORRELATION 

Distinct25
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Memory size2.4 KiB
강남구
36 
서초구
25 
성동구
23 
송파구
23 
강서구
19 
Other values (20)
163 

Length

Max length4
Median length3
Mean length3.0346021
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row중랑구
2nd row강남구
3rd row강남구
4th row서울시
5th row은평구

Common Values

ValueCountFrequency (%)
강남구 36
12.5%
서초구 25
 
8.7%
성동구 23
 
8.0%
송파구 23
 
8.0%
강서구 19
 
6.6%
서울시 18
 
6.2%
광진구 16
 
5.5%
강동구 15
 
5.2%
마포구 13
 
4.5%
구로구 13
 
4.5%
Other values (15) 88
30.4%

Length

2024-03-23T12:18:06.212882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
강남구 36
12.5%
서초구 25
 
8.7%
성동구 23
 
8.0%
송파구 23
 
8.0%
강서구 19
 
6.6%
서울시 18
 
6.2%
광진구 16
 
5.5%
강동구 15
 
5.2%
마포구 13
 
4.5%
구로구 13
 
4.5%
Other values (15) 88
30.4%

담당자 전화번호
Categorical

HIGH CORRELATION 

Distinct25
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Memory size2.4 KiB
3423-6503
36 
2155-7179
25 
2286-5679
23 
2147-3151
23 
2600-4123
19 
Other values (20)
163 

Length

Max length9
Median length9
Mean length8.8512111
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2094-2578
2nd row3423-6503
3rd row3423-6503
4th row2133-2339
5th row351-7755

Common Values

ValueCountFrequency (%)
3423-6503 36
12.5%
2155-7179 25
 
8.7%
2286-5679 23
 
8.0%
2147-3151 23
 
8.0%
2600-4123 19
 
6.6%
2133-2339 18
 
6.2%
450-7916 16
 
5.5%
3425-6247 15
 
5.2%
3153-9682 13
 
4.5%
860-3194 13
 
4.5%
Other values (15) 88
30.4%

Length

2024-03-23T12:18:06.565346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
3423-6503 36
12.5%
2155-7179 25
 
8.7%
2286-5679 23
 
8.0%
2147-3151 23
 
8.0%
2600-4123 19
 
6.6%
2133-2339 18
 
6.2%
450-7916 16
 
5.5%
3425-6247 15
 
5.2%
3153-9682 13
 
4.5%
860-3194 13
 
4.5%
Other values (15) 88
30.4%

Interactions

2024-03-23T12:18:02.001687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-23T12:18:06.773856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번관할관청담당자 전화번호
연번1.0000.2650.265
관할관청0.2651.0001.000
담당자 전화번호0.2651.0001.000
2024-03-23T12:18:07.017628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
담당자 전화번호관할관청
담당자 전화번호1.0001.000
관할관청1.0001.000
2024-03-23T12:18:07.182789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번관할관청담당자 전화번호
연번1.0000.0920.092
관할관청0.0921.0001.000
담당자 전화번호0.0921.0001.000

Missing values

2024-03-23T12:18:02.237097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-23T12:18:02.421656image/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

연번업체명주사무소 주소관할관청담당자 전화번호
01(주)26렌트카서울 중랑구 동일로 604 2층중랑구2094-2578
12345렌트카(주)서울 강남구 헌릉로745길 37 비동 203호(율현동,강남자동차매매장)강남구3423-6503
23(주)OK모터스서울 강남구 자곡로 201 , 강남라르고빌딩 116호강남구3423-6503
34SK네트웍스(주)SK렌터카서울 종로구 청계천로 85 , 20층(관철동, 삼일빌딩)서울시2133-2339
45(주)가야렌트카서울 은평구 연서로 110 , 403호(역촌동)은평구351-7755
56(주)가을렌트카서울 강서구 양천로 400-12 , 더리브골드타워 311호강서구2600-4123
67(주)가인렌트카서울 송파구 중대로38길 12 , 4층 402호(오금동)송파구2147-3151
78강남렌트카(주)서울 서초구 바우뫼로41길 72 - 2 화인빌딩 301호 3층서초구2155-7179
89(주)거성렌트카서울 서초구 청계산로 217 자연누리오피스텔 2층, 254호서초구2155-7179
910건아렌트카(주)서울 용산구 독서당로 23 402호(한남동,전국빌딩)용산구2199-7748
연번업체명주사무소 주소관할관청담당자 전화번호
279280(주)해동렌트카서울 강서구 양천로 410, 2층 (가양동)강서구2600-4123
280281(주)해피네트웍스서울 성동구 자동차시장1길 94-47 ,701호(용답동,장안모터프라자)성동구2286-5679
281282(주)햇살렌트카서울 중랑구 망우로 316 1층 112호(상봉동, 이지팰리스)중랑구2094-2578
282283향우렌트카(주)서울 강서구 강서로56길 102 2층(등촌동, 세진빌딩)강서구2600-4123
283284(주)허니문렌트카서울 노원구 동일로 986 프레미어스엠코 102동 210호노원구2116-4036
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