Overview

Dataset statistics

Number of variables7
Number of observations23
Missing cells8
Missing cells (%)5.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.5 KiB
Average record size in memory64.7 B

Variable types

Text3
Numeric3
DateTime1

Dataset

Description서산시에 소재한 자동차 대여(렌트카)업체 등록현황으로 업체명, 대표자, 주소, 전화번호, 위치에 대한 정보를 제공합니다.
Author충청남도
URLhttps://alldam.chungnam.go.kr/index.chungnam?menuCd=DOM_000000201001001001&st=&cds=&orgCd=&apiType=&isOpen=Y&pageIndex=447&beforeMenuCd=DOM_000000201001001000&publicdatapk=15000680

Alerts

데이터기준일 has constant value ""Constant
업체명 has 1 (4.3%) missing valuesMissing
소재지 has 1 (4.3%) missing valuesMissing
차량보유대수 has 1 (4.3%) missing valuesMissing
전화번호 has 2 (8.7%) missing valuesMissing
경도(WGS84좌표) has 1 (4.3%) missing valuesMissing
위도(WGS84좌표) has 1 (4.3%) missing valuesMissing
데이터기준일 has 1 (4.3%) missing valuesMissing

Reproduction

Analysis started2024-01-09 20:41:14.814483
Analysis finished2024-01-09 20:41:16.318476
Duration1.5 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

업체명
Text

MISSING 

Distinct22
Distinct (%)100.0%
Missing1
Missing (%)4.3%
Memory size316.0 B
2024-01-10T05:41:16.426201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length5
Mean length7.1363636
Min length4

Characters and Unicode

Total characters157
Distinct characters51
Distinct categories4 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique22 ?
Unique (%)100.0%

Sample

1st row미래렌트카
2nd row구진렌트카
3rd row청훈렌트카
4th row기프트월드
5th row에이스렌트카 서산1영업소
ValueCountFrequency (%)
서산1영업소 3
 
10.7%
서산2영업소 3
 
10.7%
에이스렌트카 2
 
7.1%
기풍렌트카 2
 
7.1%
금강렌트카 2
 
7.1%
미래렌트카 1
 
3.6%
아주오토렌탈 1
 
3.6%
우송렌트카 1
 
3.6%
우주렌트카 1
 
3.6%
스카이렌트카 1
 
3.6%
Other values (11) 11
39.3%
2024-01-10T05:41:16.691002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
렌 20
 
12.7%
카 19
 
12.1%
트 18
 
11.5%
서 6
 
3.8%
소 6
 
3.8%
영 6
 
3.8%
6
 
3.8%
업 6
 
3.8%
산 6
 
3.8%
이 5
 
3.2%
Other values (41) 59
37.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 143
91.1%
Space Separator 6
 
3.8%
Decimal Number 6
 
3.8%
Uppercase Letter 2
 
1.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
렌 20
14.0%
카 19
 
13.3%
트 18
 
12.6%
서 6
 
4.2%
소 6
 
4.2%
영 6
 
4.2%
업 6
 
4.2%
산 6
 
4.2%
이 5
 
3.5%
에 3
 
2.1%
Other values (36) 48
33.6%
Decimal Number
ValueCountFrequency (%)
1 3
50.0%
2 3
50.0%
Uppercase Letter
ValueCountFrequency (%)
C 1
50.0%
S 1
50.0%
Space Separator
ValueCountFrequency (%)
6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 143
91.1%
Common 12
 
7.6%
Latin 2
 
1.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
렌 20
14.0%
카 19
 
13.3%
트 18
 
12.6%
서 6
 
4.2%
소 6
 
4.2%
영 6
 
4.2%
업 6
 
4.2%
산 6
 
4.2%
이 5
 
3.5%
에 3
 
2.1%
Other values (36) 48
33.6%
Common
ValueCountFrequency (%)
6
50.0%
1 3
25.0%
2 3
25.0%
Latin
ValueCountFrequency (%)
C 1
50.0%
S 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 143
91.1%
ASCII 14
 
8.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
렌 20
14.0%
카 19
 
13.3%
트 18
 
12.6%
서 6
 
4.2%
소 6
 
4.2%
영 6
 
4.2%
업 6
 
4.2%
산 6
 
4.2%
이 5
 
3.5%
에 3
 
2.1%
Other values (36) 48
33.6%
ASCII
ValueCountFrequency (%)
6
42.9%
1 3
21.4%
2 3
21.4%
C 1
 
7.1%
S 1
 
7.1%

소재지
Text

MISSING 

Distinct22
Distinct (%)100.0%
Missing1
Missing (%)4.3%
Memory size316.0 B
2024-01-10T05:41:16.876045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length21
Mean length19.681818
Min length16

Characters and Unicode

Total characters433
Distinct characters57
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique22 ?
Unique (%)100.0%

Sample

1st row충청남도 서산시 해미면 한티로 337
2nd row충청남도 서산시 대산읍 대산리 98-6
3rd row충청남도 서산시 해미면 한티로 10
4th row충청남도 서산시 중앙로 94, 1107호
5th row충청남도 서산시 고북면 기포리 299
ValueCountFrequency (%)
충청남도 22
22.0%
서산시 22
22.0%
대산읍 4
 
4.0%
석림동 3
 
3.0%
해미면 2
 
2.0%
한티로 2
 
2.0%
대산리 2
 
2.0%
서해로 2
 
2.0%
성연면 2
 
2.0%
46-7 1
 
1.0%
Other values (38) 38
38.0%
2024-01-10T05:41:17.193950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
79
18.2%
산 29
 
6.7%
남 24
 
5.5%
서 24
 
5.5%
충 23
 
5.3%
도 22
 
5.1%
시 22
 
5.1%
청 22
 
5.1%
1 18
 
4.2%
3 13
 
3.0%
Other values (47) 157
36.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 252
58.2%
Decimal Number 89
 
20.6%
Space Separator 79
 
18.2%
Dash Punctuation 11
 
2.5%
Other Punctuation 2
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 29
11.5%
남 24
 
9.5%
서 24
 
9.5%
충 23
 
9.1%
도 22
 
8.7%
시 22
 
8.7%
청 22
 
8.7%
로 8
 
3.2%
리 7
 
2.8%
대 6
 
2.4%
Other values (34) 65
25.8%
Decimal Number
ValueCountFrequency (%)
1 18
20.2%
3 13
14.6%
4 11
12.4%
7 10
11.2%
0 7
 
7.9%
2 7
 
7.9%
8 7
 
7.9%
6 6
 
6.7%
9 6
 
6.7%
5 4
 
4.5%
Space Separator
ValueCountFrequency (%)
79
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 11
100.0%
Other Punctuation
ValueCountFrequency (%)
, 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 252
58.2%
Common 181
41.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
산 29
11.5%
남 24
 
9.5%
서 24
 
9.5%
충 23
 
9.1%
도 22
 
8.7%
시 22
 
8.7%
청 22
 
8.7%
로 8
 
3.2%
리 7
 
2.8%
대 6
 
2.4%
Other values (34) 65
25.8%
Common
ValueCountFrequency (%)
79
43.6%
1 18
 
9.9%
3 13
 
7.2%
- 11
 
6.1%
4 11
 
6.1%
7 10
 
5.5%
0 7
 
3.9%
2 7
 
3.9%
8 7
 
3.9%
6 6
 
3.3%
Other values (3) 12
 
6.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 252
58.2%
ASCII 181
41.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
79
43.6%
1 18
 
9.9%
3 13
 
7.2%
- 11
 
6.1%
4 11
 
6.1%
7 10
 
5.5%
0 7
 
3.9%
2 7
 
3.9%
8 7
 
3.9%
6 6
 
3.3%
Other values (3) 12
 
6.6%
Hangul
ValueCountFrequency (%)
산 29
11.5%
남 24
 
9.5%
서 24
 
9.5%
충 23
 
9.1%
도 22
 
8.7%
시 22
 
8.7%
청 22
 
8.7%
로 8
 
3.2%
리 7
 
2.8%
대 6
 
2.4%
Other values (34) 65
25.8%

차량보유대수
Real number (ℝ)

MISSING 

Distinct19
Distinct (%)86.4%
Missing1
Missing (%)4.3%
Infinite0
Infinite (%)0.0%
Mean46.909091
Minimum4
Maximum547
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2024-01-10T05:41:17.318887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile5.05
Q17.25
median14
Q342.75
95-th percentile53
Maximum547
Range543
Interquartile range (IQR)35.5

Descriptive statistics

Standard deviation113.21278
Coefficient of variation (CV)2.4134508
Kurtosis20.670591
Mean46.909091
Median Absolute Deviation (MAD)8.5
Skewness4.4886858
Sum1032
Variance12817.134
MonotonicityNot monotonic
2024-01-10T05:41:17.449118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
53 2
 
8.7%
6 2
 
8.7%
7 2
 
8.7%
52 1
 
4.3%
37 1
 
4.3%
15 1
 
4.3%
9 1
 
4.3%
8 1
 
4.3%
19 1
 
4.3%
43 1
 
4.3%
Other values (9) 9
39.1%
ValueCountFrequency (%)
4 1
4.3%
5 1
4.3%
6 2
8.7%
7 2
8.7%
8 1
4.3%
9 1
4.3%
10 1
4.3%
11 1
4.3%
13 1
4.3%
15 1
4.3%
ValueCountFrequency (%)
547 1
4.3%
53 2
8.7%
52 1
4.3%
50 1
4.3%
43 1
4.3%
42 1
4.3%
37 1
4.3%
35 1
4.3%
19 1
4.3%
15 1
4.3%

전화번호
Text

MISSING 

Distinct21
Distinct (%)100.0%
Missing2
Missing (%)8.7%
Memory size316.0 B
2024-01-10T05:41:17.606498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length12.047619
Min length12

Characters and Unicode

Total characters253
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 (%)100.0%

Sample

1st row041-688-5650
2nd row041-688-6111
3rd row070-4655-4554
4th row041-663-8228
5th row041-669-7885
ValueCountFrequency (%)
041-688-5650 1
 
4.8%
041-667-2082 1
 
4.8%
041-666-1122 1
 
4.8%
041-667-4044 1
 
4.8%
080-225-7777 1
 
4.8%
041-668-7922 1
 
4.8%
041-681-5507 1
 
4.8%
041-668-2847 1
 
4.8%
041-681-4113 1
 
4.8%
041-665-3323 1
 
4.8%
Other values (11) 11
52.4%
2024-01-10T05:41:17.881256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 42
16.6%
6 38
15.0%
1 34
13.4%
4 31
12.3%
0 30
11.9%
8 19
7.5%
2 18
7.1%
5 14
 
5.5%
7 14
 
5.5%
3 9
 
3.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 211
83.4%
Dash Punctuation 42
 
16.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
6 38
18.0%
1 34
16.1%
4 31
14.7%
0 30
14.2%
8 19
9.0%
2 18
8.5%
5 14
 
6.6%
7 14
 
6.6%
3 9
 
4.3%
9 4
 
1.9%
Dash Punctuation
ValueCountFrequency (%)
- 42
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 253
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 42
16.6%
6 38
15.0%
1 34
13.4%
4 31
12.3%
0 30
11.9%
8 19
7.5%
2 18
7.1%
5 14
 
5.5%
7 14
 
5.5%
3 9
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 253
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 42
16.6%
6 38
15.0%
1 34
13.4%
4 31
12.3%
0 30
11.9%
8 19
7.5%
2 18
7.1%
5 14
 
5.5%
7 14
 
5.5%
3 9
 
3.6%

경도(WGS84좌표)
Real number (ℝ)

MISSING 

Distinct22
Distinct (%)100.0%
Missing1
Missing (%)4.3%
Infinite0
Infinite (%)0.0%
Mean126.48044
Minimum126.38698
Maximum126.68696
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2024-01-10T05:41:17.992545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.38698
5-th percentile126.43519
Q1126.44492
median126.45947
Q3126.47767
95-th percentile126.58143
Maximum126.68696
Range0.299984
Interquartile range (IQR)0.03275125

Descriptive statistics

Standard deviation0.066474067
Coefficient of variation (CV)0.00052556795
Kurtosis3.4640406
Mean126.48044
Median Absolute Deviation (MAD)0.0166985
Skewness1.7708687
Sum2782.5697
Variance0.0044188015
MonotonicityNot monotonic
2024-01-10T05:41:18.097445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
126.57701 1
 
4.3%
126.458875 1
 
4.3%
126.439804 1
 
4.3%
126.462965 1
 
4.3%
126.452628 1
 
4.3%
126.482297 1
 
4.3%
126.447069 1
 
4.3%
126.436406 1
 
4.3%
126.386976 1
 
4.3%
126.68696 1
 
4.3%
Other values (12) 12
52.2%
ValueCountFrequency (%)
126.386976 1
4.3%
126.435125 1
4.3%
126.436406 1
4.3%
126.439804 1
4.3%
126.441346 1
4.3%
126.444198 1
4.3%
126.447069 1
4.3%
126.449947 1
4.3%
126.452628 1
4.3%
126.454995 1
4.3%
ValueCountFrequency (%)
126.68696 1
4.3%
126.581658 1
4.3%
126.57701 1
4.3%
126.551782 1
4.3%
126.531897 1
4.3%
126.482297 1
4.3%
126.463777 1
4.3%
126.462974 1
4.3%
126.462965 1
4.3%
126.460977 1
4.3%

위도(WGS84좌표)
Real number (ℝ)

MISSING 

Distinct22
Distinct (%)100.0%
Missing1
Missing (%)4.3%
Infinite0
Infinite (%)0.0%
Mean36.800776
Minimum36.668057
Maximum36.976357
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2024-01-10T05:41:18.203241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.668057
5-th percentile36.690317
Q136.76813
median36.775835
Q336.810128
95-th percentile36.974452
Maximum36.976357
Range0.3083
Interquartile range (IQR)0.041997

Descriptive statistics

Standard deviation0.083741836
Coefficient of variation (CV)0.0022755454
Kurtosis0.56246177
Mean36.800776
Median Absolute Deviation (MAD)0.011556
Skewness0.98920788
Sum809.61707
Variance0.007012695
MonotonicityNot monotonic
2024-01-10T05:41:18.299914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
36.689405 1
 
4.3%
36.770649 1
 
4.3%
36.82265 1
 
4.3%
36.775801 1
 
4.3%
36.76789 1
 
4.3%
36.788515 1
 
4.3%
36.768852 1
 
4.3%
36.934862 1
 
4.3%
36.976357 1
 
4.3%
36.976316 1
 
4.3%
Other values (12) 12
52.2%
ValueCountFrequency (%)
36.668057 1
4.3%
36.689405 1
4.3%
36.707646 1
4.3%
36.765403 1
4.3%
36.767178 1
4.3%
36.76789 1
4.3%
36.768852 1
4.3%
36.770649 1
4.3%
36.773843 1
4.3%
36.774716 1
4.3%
ValueCountFrequency (%)
36.976357 1
4.3%
36.976316 1
4.3%
36.939045 1
4.3%
36.934862 1
4.3%
36.82265 1
4.3%
36.811379 1
4.3%
36.806373 1
4.3%
36.788515 1
4.3%
36.778511 1
4.3%
36.777756 1
4.3%

데이터기준일
Date

CONSTANT  MISSING 

Distinct1
Distinct (%)4.5%
Missing1
Missing (%)4.3%
Memory size316.0 B
Minimum2016-03-21 00:00:00
Maximum2016-03-21 00:00:00
2024-01-10T05:41:18.386203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:41:18.462167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2024-01-10T05:41:15.520916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:41:15.077896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:41:15.292621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:41:15.597055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:41:15.146524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:41:15.366555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:41:15.924549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:41:15.218919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:41:15.445942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-10T05:41:18.527961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업체명소재지차량보유대수전화번호경도(WGS84좌표)위도(WGS84좌표)
업체명1.0001.0001.0001.0001.0001.000
소재지1.0001.0001.0001.0001.0001.000
차량보유대수1.0001.0001.0001.0000.0000.000
전화번호1.0001.0001.0001.0001.0001.000
경도(WGS84좌표)1.0001.0000.0001.0001.0000.838
위도(WGS84좌표)1.0001.0000.0001.0000.8381.000
2024-01-10T05:41:18.625861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
차량보유대수경도(WGS84좌표)위도(WGS84좌표)
차량보유대수1.000-0.023-0.195
경도(WGS84좌표)-0.0231.000-0.382
위도(WGS84좌표)-0.195-0.3821.000

Missing values

2024-01-10T05:41:16.021126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-10T05:41:16.125743image/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.
2024-01-10T05:41:16.235719image/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

업체명소재지차량보유대수전화번호경도(WGS84좌표)위도(WGS84좌표)데이터기준일
0미래렌트카충청남도 서산시 해미면 한티로 33752041-688-5650126.5770136.6894052016-03-21
1구진렌트카충청남도 서산시 대산읍 대산리 98-642<NA>126.43512536.9390452016-03-21
2청훈렌트카충청남도 서산시 해미면 한티로 1053041-688-6111126.55178236.7076462016-03-21
3기프트월드충청남도 서산시 중앙로 94, 1107호50070-4655-4554126.46006636.7777562016-03-21
4에이스렌트카 서산1영업소충청남도 서산시 고북면 기포리 2996041-663-8228126.53189736.6680572016-03-21
5에이스렌트카 서산2영업소충청남도 서산시 성연면 일람리 7874041-669-7885126.44134636.8063732016-03-21
6조은렌트카충청남도 서산시 예천동 49110041-663-0123126.44419836.7738432016-03-21
7하나로개발충청남도 서산시 운산면 용장리 364-1413041-669-4479126.58165836.8113792016-03-21
8우리렌트카충청남도 서산시 석남동 100-111041-664-1680126.45499536.7671782016-03-21
9고고렌트카충청남도 서산시 석림동 527-105041-668-3111126.46377736.7758682016-03-21
업체명소재지차량보유대수전화번호경도(WGS84좌표)위도(WGS84좌표)데이터기준일
13아주오토렌탈충청남도 서산시 석림동 647-1353041-665-3323126.46297436.7747162016-03-21
14CS렌트카충청남도 서산시 대산읍 기은리 598-326041-681-4113126.6869636.9763162016-03-21
15에이제이렌터카충청남도 서산시 벌말1길 46-7, 2층43041-668-2847126.38697636.9763572016-03-21
16기풍렌트카 서산1영업소충청남도 서산시 대산읍 대산리 148-77041-681-5507126.43640636.9348622016-03-21
17기풍렌트카 서산2영업소충청남도 서산시 예천1로 3219041-668-7922126.44706936.7688522016-03-21
18스카이렌트카충청남도 서산시 잠홍2길 1108080-225-7777126.48229736.7885152016-03-21
19우주렌트카충청남도 서산시 서해로 3364-207041-667-4044126.45262836.767892016-03-21
20우송렌트카충청남도 서산시 석림동 648-349041-666-1122126.46296536.7758012016-03-21
21동원렌트카충청남도 서산시 성연면 고남리 11315041-664-1770126.43980436.822652016-03-21
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