Overview

Dataset statistics

Number of variables7
Number of observations57
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.4 KiB
Average record size in memory60.3 B

Variable types

Numeric2
Text2
Categorical3

Dataset

Description인천광역시 서구의 공영(노외) 주차장 현황에 관한 데이터로, 연번, 주소, 주차장명, 행정동, 주차면수, 항목을 제공합니다.
Author인천광역시 서구
URLhttps://data.incheon.go.kr/findData/publicDataDetail?dataId=15105204&srcSe=7661IVAWM27C61E190

Alerts

행정동 is highly overall correlated with 연번 and 1 other fieldsHigh correlation
주차요금 is highly overall correlated with 구분High correlation
구분 is highly overall correlated with 연번 and 3 other fieldsHigh correlation
연번 is highly overall correlated with 행정동 and 1 other fieldsHigh correlation
주차면수 is highly overall correlated with 구분High correlation
주차요금 is highly imbalanced (52.5%)Imbalance
연번 has unique valuesUnique
주소 has unique valuesUnique

Reproduction

Analysis started2024-01-28 07:57:35.739180
Analysis finished2024-01-28 07:57:36.493981
Duration0.75 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct57
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29
Minimum1
Maximum57
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size645.0 B
2024-01-28T16:57:36.548359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3.8
Q115
median29
Q343
95-th percentile54.2
Maximum57
Range56
Interquartile range (IQR)28

Descriptive statistics

Standard deviation16.598193
Coefficient of variation (CV)0.57235147
Kurtosis-1.2
Mean29
Median Absolute Deviation (MAD)14
Skewness0
Sum1653
Variance275.5
MonotonicityStrictly increasing
2024-01-28T16:57:36.936614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.8%
44 1
 
1.8%
32 1
 
1.8%
33 1
 
1.8%
34 1
 
1.8%
35 1
 
1.8%
36 1
 
1.8%
37 1
 
1.8%
38 1
 
1.8%
39 1
 
1.8%
Other values (47) 47
82.5%
ValueCountFrequency (%)
1 1
1.8%
2 1
1.8%
3 1
1.8%
4 1
1.8%
5 1
1.8%
6 1
1.8%
7 1
1.8%
8 1
1.8%
9 1
1.8%
10 1
1.8%
ValueCountFrequency (%)
57 1
1.8%
56 1
1.8%
55 1
1.8%
54 1
1.8%
53 1
1.8%
52 1
1.8%
51 1
1.8%
50 1
1.8%
49 1
1.8%
48 1
1.8%

주소
Text

UNIQUE 

Distinct57
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size588.0 B
2024-01-28T16:57:37.157739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length22
Mean length18.54386
Min length15

Characters and Unicode

Total characters1057
Distinct characters52
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique57 ?
Unique (%)100.0%

Sample

1st row인천광역시 서구 가정동 530-4
2nd row인천광역시 서구 가정동 산 140-6
3rd row인천광역시 서구 가좌동 197-30
4th row인천광역시 서구 가좌동 217-31 일원
5th row인천광역시 서구 가좌동 409-8
ValueCountFrequency (%)
인천광역시 57
24.6%
서구 57
24.6%
석남동 8
 
3.4%
가좌동 8
 
3.4%
청라동 8
 
3.4%
마전동 5
 
2.2%
연희동 5
 
2.2%
당하동 4
 
1.7%
검암동 3
 
1.3%
오류동 3
 
1.3%
Other values (68) 74
31.9%
2024-01-28T16:57:37.478339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
177
16.7%
1 61
 
5.8%
서 59
 
5.6%
인 57
 
5.4%
동 57
 
5.4%
구 57
 
5.4%
천 57
 
5.4%
시 57
 
5.4%
광 57
 
5.4%
역 57
 
5.4%
Other values (42) 361
34.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 580
54.9%
Decimal Number 250
23.7%
Space Separator 177
 
16.7%
Dash Punctuation 50
 
4.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
서 59
10.2%
인 57
9.8%
동 57
9.8%
구 57
9.8%
천 57
9.8%
시 57
9.8%
광 57
9.8%
역 57
9.8%
가 10
 
1.7%
남 8
 
1.4%
Other values (30) 104
17.9%
Decimal Number
ValueCountFrequency (%)
1 61
24.4%
3 28
11.2%
5 25
10.0%
2 25
10.0%
7 24
 
9.6%
0 23
 
9.2%
4 19
 
7.6%
6 16
 
6.4%
8 15
 
6.0%
9 14
 
5.6%
Space Separator
ValueCountFrequency (%)
177
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 50
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 580
54.9%
Common 477
45.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
서 59
10.2%
인 57
9.8%
동 57
9.8%
구 57
9.8%
천 57
9.8%
시 57
9.8%
광 57
9.8%
역 57
9.8%
가 10
 
1.7%
남 8
 
1.4%
Other values (30) 104
17.9%
Common
ValueCountFrequency (%)
177
37.1%
1 61
 
12.8%
- 50
 
10.5%
3 28
 
5.9%
5 25
 
5.2%
2 25
 
5.2%
7 24
 
5.0%
0 23
 
4.8%
4 19
 
4.0%
6 16
 
3.4%
Other values (2) 29
 
6.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 580
54.9%
ASCII 477
45.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
177
37.1%
1 61
 
12.8%
- 50
 
10.5%
3 28
 
5.9%
5 25
 
5.2%
2 25
 
5.2%
7 24
 
5.0%
0 23
 
4.8%
4 19
 
4.0%
6 16
 
3.4%
Other values (2) 29
 
6.1%
Hangul
ValueCountFrequency (%)
서 59
10.2%
인 57
9.8%
동 57
9.8%
구 57
9.8%
천 57
9.8%
시 57
9.8%
광 57
9.8%
역 57
9.8%
가 10
 
1.7%
남 8
 
1.4%
Other values (30) 104
17.9%

주차장명
Text

Distinct56
Distinct (%)98.2%
Missing0
Missing (%)0.0%
Memory size588.0 B
2024-01-28T16:57:37.675755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length13
Mean length10.157895
Min length5

Characters and Unicode

Total characters579
Distinct characters90
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique55 ?
Unique (%)96.5%

Sample

1st row정서진중앙시장 주차장
2nd row가정동주차장
3rd row가좌3동 주차장
4th row가좌동 217-31 주차장
5th row가좌쌈지주차장
ValueCountFrequency (%)
주차장 31
25.6%
청라동 8
 
6.6%
연희동 4
 
3.3%
석남동 4
 
3.3%
오류지구 3
 
2.5%
검암 3
 
2.5%
마전동 3
 
2.5%
제4주차장 2
 
1.7%
제1주차장 2
 
1.7%
가좌쌈지주차장 2
 
1.7%
Other values (58) 59
48.8%
2024-01-28T16:57:38.029034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
64
 
11.1%
장 56
 
9.7%
주 53
 
9.2%
차 52
 
9.0%
1 33
 
5.7%
동 29
 
5.0%
- 23
 
4.0%
3 16
 
2.8%
지 13
 
2.2%
7 12
 
2.1%
Other values (80) 228
39.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 371
64.1%
Decimal Number 119
 
20.6%
Space Separator 64
 
11.1%
Dash Punctuation 23
 
4.0%
Close Punctuation 1
 
0.2%
Open Punctuation 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
장 56
15.1%
주 53
14.3%
차 52
 
14.0%
동 29
 
7.8%
지 13
 
3.5%
청 9
 
2.4%
라 8
 
2.2%
제 7
 
1.9%
남 6
 
1.6%
가 6
 
1.6%
Other values (66) 132
35.6%
Decimal Number
ValueCountFrequency (%)
1 33
27.7%
3 16
13.4%
7 12
 
10.1%
2 12
 
10.1%
0 10
 
8.4%
5 10
 
8.4%
8 8
 
6.7%
6 7
 
5.9%
4 6
 
5.0%
9 5
 
4.2%
Space Separator
ValueCountFrequency (%)
64
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 23
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 371
64.1%
Common 208
35.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
장 56
15.1%
주 53
14.3%
차 52
 
14.0%
동 29
 
7.8%
지 13
 
3.5%
청 9
 
2.4%
라 8
 
2.2%
제 7
 
1.9%
남 6
 
1.6%
가 6
 
1.6%
Other values (66) 132
35.6%
Common
ValueCountFrequency (%)
64
30.8%
1 33
15.9%
- 23
 
11.1%
3 16
 
7.7%
7 12
 
5.8%
2 12
 
5.8%
0 10
 
4.8%
5 10
 
4.8%
8 8
 
3.8%
6 7
 
3.4%
Other values (4) 13
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 371
64.1%
ASCII 208
35.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
64
30.8%
1 33
15.9%
- 23
 
11.1%
3 16
 
7.7%
7 12
 
5.8%
2 12
 
5.8%
0 10
 
4.8%
5 10
 
4.8%
8 8
 
3.8%
6 7
 
3.4%
Other values (4) 13
 
6.2%
Hangul
ValueCountFrequency (%)
장 56
15.1%
주 53
14.3%
차 52
 
14.0%
동 29
 
7.8%
지 13
 
3.5%
청 9
 
2.4%
라 8
 
2.2%
제 7
 
1.9%
남 6
 
1.6%
가 6
 
1.6%
Other values (66) 132
35.6%

행정동
Categorical

HIGH CORRELATION 

Distinct23
Distinct (%)40.4%
Missing0
Missing (%)0.0%
Memory size588.0 B
연희동
6 
청라1동
5 
검단4동
5 
석남1동
4 
검단1동
 
3
Other values (18)
34 

Length

Max length5
Median length4
Mean length3.7719298
Min length3

Unique

Unique8 ?
Unique (%)14.0%

Sample

1st row가정1동
2nd row가정1동
3rd row가좌3동
4th row가좌3동
5th row가좌4동

Common Values

ValueCountFrequency (%)
연희동 6
 
10.5%
청라1동 5
 
8.8%
검단4동 5
 
8.8%
석남1동 4
 
7.0%
검단1동 3
 
5.3%
석남동 3
 
5.3%
가좌3동 3
 
5.3%
오류왕길동 3
 
5.3%
검단3동 3
 
5.3%
검암동 3
 
5.3%
Other values (13) 19
33.3%

Length

2024-01-28T16:57:38.166213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
연희동 6
 
10.5%
검단4동 5
 
8.8%
청라1동 5
 
8.8%
석남1동 4
 
7.0%
검단1동 3
 
5.3%
석남동 3
 
5.3%
가좌3동 3
 
5.3%
오류왕길동 3
 
5.3%
검단3동 3
 
5.3%
검암동 3
 
5.3%
Other values (12) 19
33.3%

주차면수
Real number (ℝ)

HIGH CORRELATION 

Distinct42
Distinct (%)73.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42.403509
Minimum4
Maximum341
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size645.0 B
2024-01-28T16:57:38.284503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile4
Q116
median30
Q356
95-th percentile103.6
Maximum341
Range337
Interquartile range (IQR)40

Descriptive statistics

Standard deviation50.178915
Coefficient of variation (CV)1.1833671
Kurtosis22.252695
Mean42.403509
Median Absolute Deviation (MAD)19
Skewness4.0483657
Sum2417
Variance2517.9236
MonotonicityNot monotonic
2024-01-28T16:57:38.405276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
22 5
 
8.8%
4 4
 
7.0%
30 2
 
3.5%
7 2
 
3.5%
5 2
 
3.5%
18 2
 
3.5%
16 2
 
3.5%
42 2
 
3.5%
35 2
 
3.5%
58 2
 
3.5%
Other values (32) 32
56.1%
ValueCountFrequency (%)
4 4
7.0%
5 2
3.5%
6 1
 
1.8%
7 2
3.5%
8 1
 
1.8%
9 1
 
1.8%
10 1
 
1.8%
13 1
 
1.8%
14 1
 
1.8%
16 2
3.5%
ValueCountFrequency (%)
341 1
1.8%
136 1
1.8%
114 1
1.8%
101 1
1.8%
100 1
1.8%
85 1
1.8%
78 1
1.8%
71 1
1.8%
68 1
1.8%
67 1
1.8%

구분
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Memory size588.0 B
<NA>
45 
2급지
12 

Length

Max length4
Median length4
Mean length3.7894737
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2급지
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 45
78.9%
2급지 12
 
21.1%

Length

2024-01-28T16:57:38.522624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-28T16:57:38.603940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 45
78.9%
2급지 12
 
21.1%

주차요금
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Memory size588.0 B
무료
44 
유료
11 
공사중
 
1
유료
 
1

Length

Max length3
Median length2
Mean length2.0350877
Min length2

Unique

Unique2 ?
Unique (%)3.5%

Sample

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

Common Values

ValueCountFrequency (%)
무료 44
77.2%
유료 11
 
19.3%
공사중 1
 
1.8%
유료 1
 
1.8%

Length

2024-01-28T16:57:38.695796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-28T16:57:38.790466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
무료 44
77.2%
유료 12
 
21.1%
공사중 1
 
1.8%

Interactions

2024-01-28T16:57:36.190407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:57:36.049219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:57:36.266605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:57:36.114317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-28T16:57:38.851656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번주소주차장명행정동주차면수주차요금
연번1.0001.0001.0000.9360.5500.371
주소1.0001.0001.0001.0001.0001.000
주차장명1.0001.0001.0001.0001.0001.000
행정동0.9361.0001.0001.0000.7980.000
주차면수0.5501.0001.0000.7981.0000.260
주차요금0.3711.0001.0000.0000.2601.000
2024-01-28T16:57:38.941194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정동주차요금구분
행정동1.0000.0001.000
주차요금0.0001.0001.000
구분1.0001.0001.000
2024-01-28T16:57:39.030681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번주차면수행정동구분주차요금
연번1.000-0.1670.5751.0000.210
주차면수-0.1671.0000.4381.0000.209
행정동0.5750.4381.0001.0000.000
구분1.0001.0001.0001.0001.000
주차요금0.2100.2090.0001.0001.000

Missing values

2024-01-28T16:57:36.367066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-28T16:57:36.454862image/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

연번주소주차장명행정동주차면수구분주차요금
01인천광역시 서구 가정동 530-4정서진중앙시장 주차장가정1동782급지유료
12인천광역시 서구 가정동 산 140-6가정동주차장가정1동25<NA>무료
23인천광역시 서구 가좌동 197-30가좌3동 주차장가좌3동30<NA>무료
34인천광역시 서구 가좌동 217-31 일원가좌동 217-31 주차장가좌3동48<NA>무료
45인천광역시 서구 가좌동 409-8가좌쌈지주차장가좌4동4<NA>무료
56인천광역시 서구 가좌동 410-30가좌쌈지주차장가좌4동5<NA>무료
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