Overview

Dataset statistics

Number of variables6
Number of observations179
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory8.9 KiB
Average record size in memory50.7 B

Variable types

Numeric2
Text1
Categorical2
DateTime1

Dataset

Description인천광역시 중구 공영주차장 현황입니다. 항목은 위치, 면수, 종류(노상, 노외), 요금(유료,무료)으로 구성되어 있습니다.
URLhttps://www.data.go.kr/data/15103451/fileData.do

Alerts

데이터 기준일자 has constant value ""Constant
연번 is highly overall correlated with 종류High correlation
면수 is highly overall correlated with 요금High correlation
종류 is highly overall correlated with 연번High correlation
요금 is highly overall correlated with 면수High correlation
연번 has unique valuesUnique

Reproduction

Analysis started2023-12-12 19:21:13.326071
Analysis finished2023-12-12 19:21:14.237353
Duration0.91 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct179
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean92.480447
Minimum1
Maximum183
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-13T04:21:14.341173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile9.9
Q145.5
median94
Q3138.5
95-th percentile174.1
Maximum183
Range182
Interquartile range (IQR)93

Descriptive statistics

Standard deviation53.444116
Coefficient of variation (CV)0.57789638
Kurtosis-1.2251129
Mean92.480447
Median Absolute Deviation (MAD)47
Skewness-0.024931088
Sum16554
Variance2856.2735
MonotonicityStrictly increasing
2023-12-13T04:21:14.537603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.6%
118 1
 
0.6%
120 1
 
0.6%
121 1
 
0.6%
122 1
 
0.6%
123 1
 
0.6%
124 1
 
0.6%
125 1
 
0.6%
126 1
 
0.6%
127 1
 
0.6%
Other values (169) 169
94.4%
ValueCountFrequency (%)
1 1
0.6%
2 1
0.6%
3 1
0.6%
4 1
0.6%
5 1
0.6%
6 1
0.6%
7 1
0.6%
8 1
0.6%
9 1
0.6%
10 1
0.6%
ValueCountFrequency (%)
183 1
0.6%
182 1
0.6%
181 1
0.6%
180 1
0.6%
179 1
0.6%
178 1
0.6%
177 1
0.6%
176 1
0.6%
175 1
0.6%
174 1
0.6%

위치
Text

Distinct148
Distinct (%)82.7%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
2023-12-13T04:21:14.887889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length26
Median length23
Mean length19.374302
Min length14

Characters and Unicode

Total characters3468
Distinct characters90
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

Unique123 ?
Unique (%)68.7%

Sample

1st row인천광역시 중구 신포동 답동사거리
2nd row인천광역시 중구 신포동 제물량로 241번길
3rd row인천광역시 중구 신포동 신포로35번길
4th row인천광역시 중구 신포동 자유공원남로
5th row인천광역시 중구 신포동 신포로39번길
ValueCountFrequency (%)
인천광역시 179
24.5%
중구 179
24.5%
신흥동 36
 
4.9%
신포동 29
 
4.0%
북성동 21
 
2.9%
연안동 20
 
2.7%
율목동 19
 
2.6%
동인천동 19
 
2.6%
송월동 18
 
2.5%
운서동 11
 
1.5%
Other values (151) 200
27.4%
2023-12-13T04:21:15.418153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
553
15.9%
인 201
 
5.8%
동 201
 
5.8%
천 198
 
5.7%
중 182
 
5.2%
시 181
 
5.2%
역 179
 
5.2%
광 179
 
5.2%
구 179
 
5.2%
로 110
 
3.2%
Other values (80) 1305
37.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2445
70.5%
Space Separator 553
 
15.9%
Decimal Number 411
 
11.9%
Dash Punctuation 56
 
1.6%
Other Punctuation 3
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
인 201
 
8.2%
동 201
 
8.2%
천 198
 
8.1%
중 182
 
7.4%
시 181
 
7.4%
역 179
 
7.3%
광 179
 
7.3%
구 179
 
7.3%
로 110
 
4.5%
번 80
 
3.3%
Other values (67) 755
30.9%
Decimal Number
ValueCountFrequency (%)
1 83
20.2%
2 63
15.3%
3 51
12.4%
5 43
10.5%
4 34
8.3%
0 33
 
8.0%
8 28
 
6.8%
9 27
 
6.6%
7 27
 
6.6%
6 22
 
5.4%
Space Separator
ValueCountFrequency (%)
553
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 56
100.0%
Other Punctuation
ValueCountFrequency (%)
, 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2445
70.5%
Common 1023
29.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
인 201
 
8.2%
동 201
 
8.2%
천 198
 
8.1%
중 182
 
7.4%
시 181
 
7.4%
역 179
 
7.3%
광 179
 
7.3%
구 179
 
7.3%
로 110
 
4.5%
번 80
 
3.3%
Other values (67) 755
30.9%
Common
ValueCountFrequency (%)
553
54.1%
1 83
 
8.1%
2 63
 
6.2%
- 56
 
5.5%
3 51
 
5.0%
5 43
 
4.2%
4 34
 
3.3%
0 33
 
3.2%
8 28
 
2.7%
9 27
 
2.6%
Other values (3) 52
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2445
70.5%
ASCII 1023
29.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
553
54.1%
1 83
 
8.1%
2 63
 
6.2%
- 56
 
5.5%
3 51
 
5.0%
5 43
 
4.2%
4 34
 
3.3%
0 33
 
3.2%
8 28
 
2.7%
9 27
 
2.6%
Other values (3) 52
 
5.1%
Hangul
ValueCountFrequency (%)
인 201
 
8.2%
동 201
 
8.2%
천 198
 
8.1%
중 182
 
7.4%
시 181
 
7.4%
역 179
 
7.3%
광 179
 
7.3%
구 179
 
7.3%
로 110
 
4.5%
번 80
 
3.3%
Other values (67) 755
30.9%

면수
Real number (ℝ)

HIGH CORRELATION 

Distinct68
Distinct (%)38.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.743017
Minimum2
Maximum282
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-13T04:21:15.614769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3
Q18
median18
Q338.5
95-th percentile102
Maximum282
Range280
Interquartile range (IQR)30.5

Descriptive statistics

Standard deviation41.726516
Coefficient of variation (CV)1.2743638
Kurtosis11.60239
Mean32.743017
Median Absolute Deviation (MAD)12
Skewness2.9751228
Sum5861
Variance1741.1021
MonotonicityNot monotonic
2023-12-13T04:21:15.803447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5 11
 
6.1%
6 10
 
5.6%
10 8
 
4.5%
8 7
 
3.9%
3 7
 
3.9%
4 7
 
3.9%
11 6
 
3.4%
12 5
 
2.8%
21 5
 
2.8%
16 5
 
2.8%
Other values (58) 108
60.3%
ValueCountFrequency (%)
2 3
 
1.7%
3 7
3.9%
4 7
3.9%
5 11
6.1%
6 10
5.6%
7 1
 
0.6%
8 7
3.9%
9 4
 
2.2%
10 8
4.5%
11 6
3.4%
ValueCountFrequency (%)
282 1
0.6%
231 1
0.6%
220 1
0.6%
186 1
0.6%
136 1
0.6%
110 1
0.6%
109 1
0.6%
103 1
0.6%
102 2
1.1%
101 1
0.6%

종류
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
노상
117 
노외
62 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
노상 117
65.4%
노외 62
34.6%

Length

2023-12-13T04:21:15.958230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:21:16.107954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
노상 117
65.4%
노외 62
34.6%

요금
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
무료
148 
유료
31 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row유료
2nd row무료
3rd row무료
4th row무료
5th row무료

Common Values

ValueCountFrequency (%)
무료 148
82.7%
유료 31
 
17.3%

Length

2023-12-13T04:21:16.278311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:21:16.418374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
무료 148
82.7%
유료 31
 
17.3%

데이터 기준일자
Date

CONSTANT 

Distinct1
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
Minimum2023-08-09 00:00:00
Maximum2023-08-09 00:00:00
2023-12-13T04:21:16.520593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:21:16.660892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2023-12-13T04:21:13.753241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:21:13.536836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:21:13.873343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:21:13.647615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T04:21:16.785196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번면수종류요금
연번1.0000.1310.9960.588
면수0.1311.0000.1800.560
종류0.9960.1801.0000.575
요금0.5880.5600.5751.000
2023-12-13T04:21:16.911873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
종류요금
종류1.0000.390
요금0.3901.000
2023-12-13T04:21:17.033358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번면수종류요금
연번1.0000.1710.9210.444
면수0.1711.0000.2030.567
종류0.9210.2031.0000.390
요금0.4440.5670.3901.000

Missing values

2023-12-13T04:21:14.018338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T04:21:14.168097image/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

연번위치면수종류요금데이터 기준일자
01인천광역시 중구 신포동 답동사거리12노상유료2023-08-09
12인천광역시 중구 신포동 제물량로 241번길46노상무료2023-08-09
23인천광역시 중구 신포동 신포로35번길16노상무료2023-08-09
34인천광역시 중구 신포동 자유공원남로3노상무료2023-08-09
45인천광역시 중구 신포동 신포로39번길21노상무료2023-08-09
56인천광역시 중구 신포동 신포로35번길15노상무료2023-08-09
67인천광역시 중구 신포동 자유공원남로4노상무료2023-08-09
78인천광역시 중구 신포동 신포로23번길26노상무료2023-08-09
89인천광역시 중구 신포동 제물량로232번안길9노상무료2023-08-09
910인천광역시 중구 신포동 신포로15번길13노상무료2023-08-09
연번위치면수종류요금데이터 기준일자
169174인천광역시 중구 영종동 464103노외유료2023-08-09
170175인천광역시 중구 운서동 2802-441노외유료2023-08-09
171176인천광역시 중구 운서동 2807-1282노외유료2023-08-09
172177인천광역시 중구 운서동 2898-817노외무료2023-08-09
173178인천광역시 중구 운서동 2916-125노외무료2023-08-09
174179인천광역시 중구 운서동 2680-129노외무료2023-08-09
175180인천광역시 중구 운서동 2724-119노외무료2023-08-09
176181인천광역시 중구 운서동 2741-120노외무료2023-08-09
177182인천광역시 중구 운서동 2705-129노외무료2023-08-09
178183인천광역시 중구 운서동 2772-157노외무료2023-08-09