Overview

Dataset statistics

Number of variables6
Number of observations109
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.3 KiB
Average record size in memory50.2 B

Variable types

Text2
Categorical3
Numeric1

Dataset

Description서울시내 시영주차장 현황 자료를 제공하는 데이터입니다. 운영방식, 구획 수, 주소, 종류, 자동화정산여부를 제공합니다.
Author서울시설공단
URLhttps://www.data.go.kr/data/15108885/fileData.do

Alerts

운영방식 is highly overall correlated with 종류 and 1 other fieldsHigh correlation
종류 is highly overall correlated with 운영방식 and 1 other fieldsHigh correlation
자동화정산여부 is highly overall correlated with 운영방식 and 1 other fieldsHigh correlation
주차장명 has unique valuesUnique

Reproduction

Analysis started2024-04-13 11:34:38.837152
Analysis finished2024-04-13 11:34:42.236714
Duration3.4 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

주차장명
Text

UNIQUE 

Distinct109
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1000.0 B
2024-04-13T20:34:43.010042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length10
Mean length4.8623853
Min length2

Characters and Unicode

Total characters530
Distinct characters157
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

Unique109 ?
Unique (%)100.0%

Sample

1st row개화산역
2nd row개화역
3rd row구로디지털단지역
4th row구파발역
5th row가양라이품
ValueCountFrequency (%)
남대문 3
 
2.5%
금천교 2
 
1.7%
남산공원 2
 
1.7%
여의서로 1
 
0.8%
영동6교밑 1
 
0.8%
압구정고가2 1
 
0.8%
압구정고가1 1
 
0.8%
성내역(잠실나루역 1
 
0.8%
명일동 1
 
0.8%
동호대교(남 1
 
0.8%
Other values (105) 105
88.2%
2024-04-13T20:34:44.366876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
역 27
 
5.1%
동 23
 
4.3%
로 18
 
3.4%
남 15
 
2.8%
산 15
 
2.8%
) 13
 
2.5%
( 13
 
2.5%
대 12
 
2.3%
10
 
1.9%
청 10
 
1.9%
Other values (147) 374
70.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 467
88.1%
Decimal Number 21
 
4.0%
Close Punctuation 13
 
2.5%
Open Punctuation 13
 
2.5%
Space Separator 10
 
1.9%
Uppercase Letter 6
 
1.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
역 27
 
5.8%
동 23
 
4.9%
로 18
 
3.9%
남 15
 
3.2%
산 15
 
3.2%
대 12
 
2.6%
청 10
 
2.1%
가 10
 
2.1%
계 10
 
2.1%
화 9
 
1.9%
Other values (134) 318
68.1%
Decimal Number
ValueCountFrequency (%)
1 5
23.8%
2 4
19.0%
6 3
14.3%
3 3
14.3%
8 2
 
9.5%
5 2
 
9.5%
7 1
 
4.8%
4 1
 
4.8%
Uppercase Letter
ValueCountFrequency (%)
D 4
66.7%
P 2
33.3%
Close Punctuation
ValueCountFrequency (%)
) 13
100.0%
Open Punctuation
ValueCountFrequency (%)
( 13
100.0%
Space Separator
ValueCountFrequency (%)
10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 467
88.1%
Common 57
 
10.8%
Latin 6
 
1.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
역 27
 
5.8%
동 23
 
4.9%
로 18
 
3.9%
남 15
 
3.2%
산 15
 
3.2%
대 12
 
2.6%
청 10
 
2.1%
가 10
 
2.1%
계 10
 
2.1%
화 9
 
1.9%
Other values (134) 318
68.1%
Common
ValueCountFrequency (%)
) 13
22.8%
( 13
22.8%
10
17.5%
1 5
 
8.8%
2 4
 
7.0%
6 3
 
5.3%
3 3
 
5.3%
8 2
 
3.5%
5 2
 
3.5%
7 1
 
1.8%
Latin
ValueCountFrequency (%)
D 4
66.7%
P 2
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 467
88.1%
ASCII 63
 
11.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
역 27
 
5.8%
동 23
 
4.9%
로 18
 
3.9%
남 15
 
3.2%
산 15
 
3.2%
대 12
 
2.6%
청 10
 
2.1%
가 10
 
2.1%
계 10
 
2.1%
화 9
 
1.9%
Other values (134) 318
68.1%
ASCII
ValueCountFrequency (%)
) 13
20.6%
( 13
20.6%
10
15.9%
1 5
 
7.9%
2 4
 
6.3%
D 4
 
6.3%
6 3
 
4.8%
3 3
 
4.8%
P 2
 
3.2%
8 2
 
3.2%
Other values (3) 4
 
6.3%

운영방식
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size1000.0 B
민간위탁
64 
직영
45 

Length

Max length4
Median length4
Mean length3.1743119
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
민간위탁 64
58.7%
직영 45
41.3%

Length

2024-04-13T20:34:44.607966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-13T20:34:44.799255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
민간위탁 64
58.7%
직영 45
41.3%

구획수
Real number (ℝ)

Distinct81
Distinct (%)74.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean153.77982
Minimum2
Maximum1431
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-04-13T20:34:45.022475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile5
Q119
median47
Q3103
95-th percentile751.8
Maximum1431
Range1429
Interquartile range (IQR)84

Descriptive statistics

Standard deviation285.83692
Coefficient of variation (CV)1.8587415
Kurtosis9.0692247
Mean153.77982
Median Absolute Deviation (MAD)33
Skewness3.0230913
Sum16762
Variance81702.747
MonotonicityNot monotonic
2024-04-13T20:34:45.455487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16 4
 
3.7%
21 3
 
2.8%
79 3
 
2.8%
20 3
 
2.8%
2 3
 
2.8%
13 3
 
2.8%
19 3
 
2.8%
39 3
 
2.8%
28 3
 
2.8%
11 3
 
2.8%
Other values (71) 78
71.6%
ValueCountFrequency (%)
2 3
2.8%
3 1
 
0.9%
4 1
 
0.9%
5 2
1.8%
6 1
 
0.9%
7 1
 
0.9%
9 1
 
0.9%
10 1
 
0.9%
11 3
2.8%
12 1
 
0.9%
ValueCountFrequency (%)
1431 1
0.9%
1317 1
0.9%
1260 1
0.9%
1181 1
0.9%
1092 1
0.9%
873 1
0.9%
570 1
0.9%
568 1
0.9%
561 1
0.9%
503 1
0.9%

주소
Text

Distinct105
Distinct (%)96.3%
Missing0
Missing (%)0.0%
Memory size1000.0 B
2024-04-13T20:34:46.494826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length34
Median length28
Mean length16.706422
Min length9

Characters and Unicode

Total characters1821
Distinct characters163
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique101 ?
Unique (%)92.7%

Sample

1st row강서구 방화동 845
2nd row강서구 개화동 664(개화동로8길 19)
3rd row구로구 구로동 810-3
4th row은평구 진관동 66-30
5th row강서구 가양동 1457-1
ValueCountFrequency (%)
중구 21
 
5.6%
영등포구 12
 
3.2%
종로구 11
 
3.0%
강서구 9
 
2.4%
강남구 8
 
2.2%
구로구 8
 
2.2%
용산구 6
 
1.6%
구로동 5
 
1.3%
송파구 4
 
1.1%
동대문구 4
 
1.1%
Other values (230) 284
76.3%
2024-04-13T20:34:47.746575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
263
 
14.4%
구 132
 
7.2%
1 116
 
6.4%
동 114
 
6.3%
- 87
 
4.8%
2 61
 
3.3%
로 57
 
3.1%
4 53
 
2.9%
3 45
 
2.5%
8 41
 
2.3%
Other values (153) 852
46.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 915
50.2%
Decimal Number 483
26.5%
Space Separator 263
 
14.4%
Dash Punctuation 87
 
4.8%
Close Punctuation 35
 
1.9%
Open Punctuation 35
 
1.9%
Other Punctuation 2
 
0.1%
Math Symbol 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 132
 
14.4%
동 114
 
12.5%
로 57
 
6.2%
가 28
 
3.1%
대 24
 
2.6%
강 23
 
2.5%
중 23
 
2.5%
남 20
 
2.2%
신 20
 
2.2%
산 19
 
2.1%
Other values (136) 455
49.7%
Decimal Number
ValueCountFrequency (%)
1 116
24.0%
2 61
12.6%
4 53
11.0%
3 45
 
9.3%
8 41
 
8.5%
6 38
 
7.9%
7 36
 
7.5%
5 35
 
7.2%
9 30
 
6.2%
0 28
 
5.8%
Other Punctuation
ValueCountFrequency (%)
, 1
50.0%
. 1
50.0%
Space Separator
ValueCountFrequency (%)
263
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 87
100.0%
Close Punctuation
ValueCountFrequency (%)
) 35
100.0%
Open Punctuation
ValueCountFrequency (%)
( 35
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 915
50.2%
Common 906
49.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 132
 
14.4%
동 114
 
12.5%
로 57
 
6.2%
가 28
 
3.1%
대 24
 
2.6%
강 23
 
2.5%
중 23
 
2.5%
남 20
 
2.2%
신 20
 
2.2%
산 19
 
2.1%
Other values (136) 455
49.7%
Common
ValueCountFrequency (%)
263
29.0%
1 116
12.8%
- 87
 
9.6%
2 61
 
6.7%
4 53
 
5.8%
3 45
 
5.0%
8 41
 
4.5%
6 38
 
4.2%
7 36
 
4.0%
) 35
 
3.9%
Other values (7) 131
14.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 915
50.2%
ASCII 906
49.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
263
29.0%
1 116
12.8%
- 87
 
9.6%
2 61
 
6.7%
4 53
 
5.8%
3 45
 
5.0%
8 41
 
4.5%
6 38
 
4.2%
7 36
 
4.0%
) 35
 
3.9%
Other values (7) 131
14.5%
Hangul
ValueCountFrequency (%)
구 132
 
14.4%
동 114
 
12.5%
로 57
 
6.2%
가 28
 
3.1%
대 24
 
2.6%
강 23
 
2.5%
중 23
 
2.5%
남 20
 
2.2%
신 20
 
2.2%
산 19
 
2.1%
Other values (136) 455
49.7%

종류
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Memory size1000.0 B
노상
54 
노외
29 
노외(시설)
26 

Length

Max length6
Median length2
Mean length2.9541284
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row노외
2nd row노외(시설)
3rd row노외
4th row노외(시설)
5th row노외(시설)

Common Values

ValueCountFrequency (%)
노상 54
49.5%
노외 29
26.6%
노외(시설) 26
23.9%

Length

2024-04-13T20:34:48.180345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-13T20:34:48.516116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
노상 54
49.5%
노외 29
26.6%
노외(시설 26
23.9%

자동화정산여부
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Memory size1000.0 B
자동관제
50 
스마트폰
48 
무료
10 
-
 
1

Length

Max length4
Median length4
Mean length3.7981651
Min length2

Unique

Unique1 ?
Unique (%)0.9%

Sample

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

Common Values

ValueCountFrequency (%)
자동관제 50
45.9%
스마트폰 48
44.0%
무료 10
 
9.2%
- 1
 
0.9%

Length

2024-04-13T20:34:48.889233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-13T20:34:49.231261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
자동관제 50
45.9%
스마트폰 48
44.0%
무료 10
 
9.2%
1
 
0.9%

Interactions

2024-04-13T20:34:41.388645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-13T20:34:49.437819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
운영방식구획수종류자동화정산여부
운영방식1.0000.5860.3500.929
구획수0.5861.0000.5350.441
종류0.3500.5351.0000.636
자동화정산여부0.9290.4410.6361.000
2024-04-13T20:34:49.692999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
자동화정산여부운영방식종류
자동화정산여부1.0000.7520.654
운영방식0.7521.0000.556
종류0.6540.5561.000
2024-04-13T20:34:49.937321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구획수운영방식종류자동화정산여부
구획수1.0000.4300.3910.204
운영방식0.4301.0000.5560.752
종류0.3910.5561.0000.654
자동화정산여부0.2040.7520.6541.000

Missing values

2024-04-13T20:34:41.737030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-13T20:34:42.086910image/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

주차장명운영방식구획수주소종류자동화정산여부
0개화산역직영321강서구 방화동 845노외자동관제
1개화역직영483강서구 개화동 664(개화동로8길 19)노외(시설)자동관제
2구로디지털단지역직영91구로구 구로동 810-3노외자동관제
3구파발역직영399은평구 진관동 66-30노외(시설)자동관제
4가양라이품직영38강서구 가양동 1457-1노외(시설)자동관제
5도봉산역직영358도봉구 도봉동 288-19(도봉구 도봉로 955)노외(시설)자동관제
6동구로직영70구로구 가마산로 26길 27노외(시설)자동관제
7동대문직영1092중구 신당동 251-7노외(시설)자동관제
8마포유수지직영503마포구 마포동 36-1노외자동관제
9복정역직영362송파구 장지동 600-2노외자동관제
주차장명운영방식구획수주소종류자동화정산여부
99배오개길민간위탁20중구 예관동 22-1노상스마트폰
100을지로민간위탁89중구 을지로3가 282-8노상스마트폰
101청계8민간위탁103동대문구 신설동 109-2노상스마트폰
102청계8가민간위탁19중구 흥인동 162-1노상스마트폰
103당고개위민간위탁22노원구 상계동 111-567(도로양쪽)노상스마트폰
104도봉로 15길민간위탁16강북구 미아동 458-7노상스마트폰
105도봉산민간위탁142도봉구 도봉동 282-26노외자동관제
106석계역민간위탁70노원구 월계동 50-9노외자동관제
107창동역(서)민간위탁106도봉구 창동 330노외자동관제
108반포천민간위탁1181서초구 반포동 118-3노외(시설)자동관제