Overview

Dataset statistics

Number of variables6
Number of observations49
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.5 KiB
Average record size in memory51.6 B

Variable types

Categorical2
Text3
Numeric1

Dataset

Description경기도남부경찰청 관내 자동차 운전전문학원 현황에 대한 데이터로 학원명, 연락처, 주소 관할서 등의 항목을 제공합니다
Author경찰청 경기도남부경찰청
URLhttps://www.data.go.kr/data/15113683/fileData.do

Alerts

구분 has constant value ""Constant
우편번호 is highly overall correlated with 관할서High correlation
관할서 is highly overall correlated with 우편번호High correlation
학원명 has unique valuesUnique
연락처 has unique valuesUnique
주소 has unique valuesUnique

Reproduction

Analysis started2024-03-14 19:30:00.430710
Analysis finished2024-03-14 19:30:01.564566
Duration1.13 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Categorical

CONSTANT 

Distinct1
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size520.0 B
전문학원
49 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
전문학원 49
100.0%

Length

2024-03-15T04:30:01.774337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T04:30:02.067955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
전문학원 49
100.0%

학원명
Text

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size520.0 B
2024-03-15T04:30:03.183132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length3.0612245
Min length2

Characters and Unicode

Total characters150
Distinct characters67
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique49 ?
Unique (%)100.0%

Sample

1st rowe동탄
2nd rowe삼성
3rd rowe편한
4th rowe현대
5th row공도삼성
ValueCountFrequency (%)
e동탄 1
 
2.0%
수원현대 1
 
2.0%
신동아 1
 
2.0%
국제분당 1
 
2.0%
신삼성 1
 
2.0%
신진중동 1
 
2.0%
신현대 1
 
2.0%
아시아 1
 
2.0%
안산대일 1
 
2.0%
안성신진 1
 
2.0%
Other values (39) 39
79.6%
2024-03-15T04:30:04.345675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
성 8
 
5.3%
신 8
 
5.3%
원 7
 
4.7%
대 6
 
4.0%
수 5
 
3.3%
동 5
 
3.3%
삼 5
 
3.3%
안 4
 
2.7%
제 4
 
2.7%
천 4
 
2.7%
Other values (57) 94
62.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 141
94.0%
Lowercase Letter 4
 
2.7%
Decimal Number 2
 
1.3%
Open Punctuation 1
 
0.7%
Uppercase Letter 1
 
0.7%
Close Punctuation 1
 
0.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
성 8
 
5.7%
신 8
 
5.7%
원 7
 
5.0%
대 6
 
4.3%
수 5
 
3.5%
동 5
 
3.5%
삼 5
 
3.5%
안 4
 
2.8%
제 4
 
2.8%
천 4
 
2.8%
Other values (51) 85
60.3%
Decimal Number
ValueCountFrequency (%)
2 1
50.0%
1 1
50.0%
Lowercase Letter
ValueCountFrequency (%)
e 4
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Uppercase Letter
ValueCountFrequency (%)
C 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 141
94.0%
Latin 5
 
3.3%
Common 4
 
2.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
성 8
 
5.7%
신 8
 
5.7%
원 7
 
5.0%
대 6
 
4.3%
수 5
 
3.5%
동 5
 
3.5%
삼 5
 
3.5%
안 4
 
2.8%
제 4
 
2.8%
천 4
 
2.8%
Other values (51) 85
60.3%
Common
ValueCountFrequency (%)
( 1
25.0%
2 1
25.0%
1 1
25.0%
) 1
25.0%
Latin
ValueCountFrequency (%)
e 4
80.0%
C 1
 
20.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 141
94.0%
ASCII 9
 
6.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
성 8
 
5.7%
신 8
 
5.7%
원 7
 
5.0%
대 6
 
4.3%
수 5
 
3.5%
동 5
 
3.5%
삼 5
 
3.5%
안 4
 
2.8%
제 4
 
2.8%
천 4
 
2.8%
Other values (51) 85
60.3%
ASCII
ValueCountFrequency (%)
e 4
44.4%
( 1
 
11.1%
2 1
 
11.1%
1 1
 
11.1%
C 1
 
11.1%
) 1
 
11.1%

연락처
Text

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size520.0 B
2024-03-15T04:30:05.374117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length11.795918
Min length9

Characters and Unicode

Total characters578
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

Unique49 ?
Unique (%)100.0%

Sample

1st row031-333-9995
2nd row031-335-3805
3rd row031-656-2004
4th row031-433-5577
5th row031-655-6525
ValueCountFrequency (%)
031-333-9995 1
 
2.0%
031-211-3431 1
 
2.0%
031-663-5300 1
 
2.0%
031-333-7917 1
 
2.0%
1644-7088 1
 
2.0%
032-323-3388 1
 
2.0%
031-421-5000 1
 
2.0%
031-239-8477 1
 
2.0%
031-413-1100 1
 
2.0%
031-676-5454 1
 
2.0%
Other values (39) 39
79.6%
2024-03-15T04:30:06.715277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 95
16.4%
3 90
15.6%
0 89
15.4%
1 78
13.5%
6 45
7.8%
5 37
 
6.4%
7 37
 
6.4%
2 30
 
5.2%
8 28
 
4.8%
9 27
 
4.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 483
83.6%
Dash Punctuation 95
 
16.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 90
18.6%
0 89
18.4%
1 78
16.1%
6 45
9.3%
5 37
7.7%
7 37
7.7%
2 30
 
6.2%
8 28
 
5.8%
9 27
 
5.6%
4 22
 
4.6%
Dash Punctuation
ValueCountFrequency (%)
- 95
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 578
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 95
16.4%
3 90
15.6%
0 89
15.4%
1 78
13.5%
6 45
7.8%
5 37
 
6.4%
7 37
 
6.4%
2 30
 
5.2%
8 28
 
4.8%
9 27
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 578
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 95
16.4%
3 90
15.6%
0 89
15.4%
1 78
13.5%
6 45
7.8%
5 37
 
6.4%
7 37
 
6.4%
2 30
 
5.2%
8 28
 
4.8%
9 27
 
4.7%

주소
Text

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size520.0 B
2024-03-15T04:30:07.941466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length31
Median length26
Mean length21.897959
Min length12

Characters and Unicode

Total characters1073
Distinct characters135
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique49 ?
Unique (%)100.0%

Sample

1st row용인시 처인구 남사읍 경기동로 121 (북리)
2nd row용인시 처인구 평옥로 5 (남동)
3rd row평택시 죽백1길 210
4th row시흥시 서해안로 584-1 (정왕동)
5th row안성시 공도읍 봉기길 87 (양기리)
ValueCountFrequency (%)
평택시 7
 
3.0%
용인시 6
 
2.6%
광주시 5
 
2.1%
이천시 4
 
1.7%
수원시 4
 
1.7%
처인구 4
 
1.7%
화성시 3
 
1.3%
안산시 3
 
1.3%
단원구 3
 
1.3%
김포시 3
 
1.3%
Other values (177) 193
82.1%
2024-03-15T04:30:09.594174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
186
 
17.3%
시 49
 
4.6%
) 48
 
4.5%
( 48
 
4.5%
로 41
 
3.8%
1 34
 
3.2%
동 33
 
3.1%
2 25
 
2.3%
3 21
 
2.0%
안 21
 
2.0%
Other values (125) 567
52.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 595
55.5%
Space Separator 186
 
17.3%
Decimal Number 181
 
16.9%
Close Punctuation 48
 
4.5%
Open Punctuation 48
 
4.5%
Dash Punctuation 14
 
1.3%
Other Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 49
 
8.2%
로 41
 
6.9%
동 33
 
5.5%
안 21
 
3.5%
리 20
 
3.4%
구 17
 
2.9%
길 17
 
2.9%
읍 17
 
2.9%
평 14
 
2.4%
원 14
 
2.4%
Other values (110) 352
59.2%
Decimal Number
ValueCountFrequency (%)
1 34
18.8%
2 25
13.8%
3 21
11.6%
5 19
10.5%
7 16
8.8%
4 15
8.3%
9 14
7.7%
6 14
7.7%
0 12
 
6.6%
8 11
 
6.1%
Space Separator
ValueCountFrequency (%)
186
100.0%
Close Punctuation
ValueCountFrequency (%)
) 48
100.0%
Open Punctuation
ValueCountFrequency (%)
( 48
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 14
100.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 595
55.5%
Common 478
44.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 49
 
8.2%
로 41
 
6.9%
동 33
 
5.5%
안 21
 
3.5%
리 20
 
3.4%
구 17
 
2.9%
길 17
 
2.9%
읍 17
 
2.9%
평 14
 
2.4%
원 14
 
2.4%
Other values (110) 352
59.2%
Common
ValueCountFrequency (%)
186
38.9%
) 48
 
10.0%
( 48
 
10.0%
1 34
 
7.1%
2 25
 
5.2%
3 21
 
4.4%
5 19
 
4.0%
7 16
 
3.3%
4 15
 
3.1%
9 14
 
2.9%
Other values (5) 52
 
10.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 595
55.5%
ASCII 478
44.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
186
38.9%
) 48
 
10.0%
( 48
 
10.0%
1 34
 
7.1%
2 25
 
5.2%
3 21
 
4.4%
5 19
 
4.0%
7 16
 
3.3%
4 15
 
3.1%
9 14
 
2.9%
Other values (5) 52
 
10.9%
Hangul
ValueCountFrequency (%)
시 49
 
8.2%
로 41
 
6.9%
동 33
 
5.5%
안 21
 
3.5%
리 20
 
3.4%
구 17
 
2.9%
길 17
 
2.9%
읍 17
 
2.9%
평 14
 
2.4%
원 14
 
2.4%
Other values (110) 352
59.2%

우편번호
Real number (ℝ)

HIGH CORRELATION 

Distinct47
Distinct (%)95.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15765.878
Minimum10033
Maximum18564
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size569.0 B
2024-03-15T04:30:09.893783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10033
5-th percentile11064.6
Q114246
median16634
Q317596
95-th percentile18220.6
Maximum18564
Range8531
Interquartile range (IQR)3350

Descriptive statistics

Standard deviation2357.6334
Coefficient of variation (CV)0.14954026
Kurtosis0.039364342
Mean15765.878
Median Absolute Deviation (MAD)1242
Skewness-0.97797607
Sum772528
Variance5558435.4
MonotonicityNot monotonic
2024-03-15T04:30:10.147559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
16520 2
 
4.1%
15008 2
 
4.1%
17068 1
 
2.0%
16885 1
 
2.0%
18112 1
 
2.0%
14502 1
 
2.0%
16072 1
 
2.0%
18412 1
 
2.0%
15456 1
 
2.0%
17596 1
 
2.0%
Other values (37) 37
75.5%
ValueCountFrequency (%)
10033 1
2.0%
10053 1
2.0%
10065 1
2.0%
12564 1
2.0%
12646 1
2.0%
12739 1
2.0%
12764 1
2.0%
12775 1
2.0%
12800 1
2.0%
12820 1
2.0%
ValueCountFrequency (%)
18564 1
2.0%
18412 1
2.0%
18293 1
2.0%
18112 1
2.0%
18103 1
2.0%
17981 1
2.0%
17934 1
2.0%
17876 1
2.0%
17829 1
2.0%
17752 1
2.0%

관할서
Categorical

HIGH CORRELATION 

Distinct21
Distinct (%)42.9%
Missing0
Missing (%)0.0%
Memory size520.0 B
평택
7 
광주
5 
용인동부
5 
이천
4 
김포
3 
Other values (16)
25 

Length

Max length4
Median length2
Mean length2.8163265
Min length2

Unique

Unique8 ?
Unique (%)16.3%

Sample

1st row용인동부
2nd row용인동부
3rd row평택
4th row시흥
5th row안성

Common Values

ValueCountFrequency (%)
평택 7
14.3%
광주 5
 
10.2%
용인동부 5
 
10.2%
이천 4
 
8.2%
김포 3
 
6.1%
안산단원 3
 
6.1%
안양만안 2
 
4.1%
수원남부 2
 
4.1%
화성서부 2
 
4.1%
안성 2
 
4.1%
Other values (11) 14
28.6%

Length

2024-03-15T04:30:10.477499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
평택 7
14.3%
용인동부 5
 
10.2%
광주 5
 
10.2%
이천 4
 
8.2%
김포 3
 
6.1%
안산단원 3
 
6.1%
안성 2
 
4.1%
오산 2
 
4.1%
수원서부 2
 
4.1%
시흥 2
 
4.1%
Other values (11) 14
28.6%

Interactions

2024-03-15T04:30:00.764436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-15T04:30:10.633385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
학원명연락처주소우편번호관할서
학원명1.0001.0001.0001.0001.000
연락처1.0001.0001.0001.0001.000
주소1.0001.0001.0001.0001.000
우편번호1.0001.0001.0001.0000.999
관할서1.0001.0001.0000.9991.000
2024-03-15T04:30:10.796328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
우편번호관할서
우편번호1.0000.823
관할서0.8231.000

Missing values

2024-03-15T04:30:01.108099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-15T04:30:01.423407image/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

구분학원명연락처주소우편번호관할서
0전문학원e동탄031-333-9995용인시 처인구 남사읍 경기동로 121 (북리)17118용인동부
1전문학원e삼성031-335-3805용인시 처인구 평옥로 5 (남동)17146용인동부
2전문학원e편한031-656-2004평택시 죽백1길 21017876평택
3전문학원e현대031-433-5577시흥시 서해안로 584-1 (정왕동)15008시흥
4전문학원공도삼성031-655-6525안성시 공도읍 봉기길 87 (양기리)17558안성
5전문학원광원031-771-7100양평군 양평읍 중앙로 244-26 (도곡리)12564양평
6전문학원광주제일031-761-1400광주시 곤지암읍 평촌길11번길 48 (삼리)12800광주
7전문학원국제031-718-7777광주시 오포읍 신현로 55-10 (신현리)12820광주
8전문학원한신1833-2122김포시 양촌읍 김포대로 1682 (석모리)10053김포
9전문학원뉴삼성031-296-7711수원시 권선구 매송고색로506번길 18 (오목천동)16634수원서부
구분학원명연락처주소우편번호관할서
39전문학원이천제일031-638-0707이천시 신둔면 원적로 512번길 256-23 (도봉리)17300이천
40전문학원장호원031-643-8000이천시 장호원읍 장여로 229-33 (노탑리)17416이천
41전문학원중앙031-656-0007평택시 울성길 35 (신대동)17829평택
42전문학원카카오1599-1101오산시 가장로 439 (가장동)18103오산
43전문학원평택삼성031-665-4770평택시 노루댕이1길 18-5 (장당동)17743평택
44전문학원평택안중031-682-1160평택시 안중읍 서동대로 1673-56 (안중리)17934평택
45전문학원하남21C031-763-2221광주시 회안대로 982 (송정동)12739광주
46전문학원한강031-996-6411김포시 통진읍 율마로 300번길 153 (가현리)10033김포
47전문학원한빛031-433-5500시흥시 봉화로 49 (정왕동)15008시흥
48전문학원화성향남031-351-0405화성시 우정읍 3.1만세로 199-17 (조암리)18564화성서부