Overview

Dataset statistics

Number of variables6
Number of observations29
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.5 KiB
Average record size in memory54.6 B

Variable types

Numeric2
Text2
Categorical2

Dataset

Description서대문구 공영주차장 목록 및 구획수, 설치형태, 운영방법 정보
Author서울특별시서대문구도시관리공단
URLhttps://www.data.go.kr/data/15074493/fileData.do

Alerts

구획수 is highly overall correlated with 설치형태High correlation
설치형태 is highly overall correlated with 구획수 and 1 other fieldsHigh correlation
운영방법 is highly overall correlated with 설치형태High correlation
연번 has unique valuesUnique
주차장명 has unique valuesUnique
주차장명(위치) has unique valuesUnique

Reproduction

Analysis started2023-12-12 13:36:57.795869
Analysis finished2023-12-12 13:36:58.677339
Duration0.88 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

UNIQUE 

Distinct29
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15
Minimum1
Maximum29
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size393.0 B
2023-12-12T22:36:58.737494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.4
Q18
median15
Q322
95-th percentile27.6
Maximum29
Range28
Interquartile range (IQR)14

Descriptive statistics

Standard deviation8.5146932
Coefficient of variation (CV)0.56764621
Kurtosis-1.2
Mean15
Median Absolute Deviation (MAD)7
Skewness0
Sum435
Variance72.5
MonotonicityStrictly increasing
2023-12-12T22:36:58.884433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
1 1
 
3.4%
2 1
 
3.4%
29 1
 
3.4%
28 1
 
3.4%
27 1
 
3.4%
26 1
 
3.4%
25 1
 
3.4%
24 1
 
3.4%
23 1
 
3.4%
22 1
 
3.4%
Other values (19) 19
65.5%
ValueCountFrequency (%)
1 1
3.4%
2 1
3.4%
3 1
3.4%
4 1
3.4%
5 1
3.4%
6 1
3.4%
7 1
3.4%
8 1
3.4%
9 1
3.4%
10 1
3.4%
ValueCountFrequency (%)
29 1
3.4%
28 1
3.4%
27 1
3.4%
26 1
3.4%
25 1
3.4%
24 1
3.4%
23 1
3.4%
22 1
3.4%
21 1
3.4%
20 1
3.4%

주차장명
Text

UNIQUE 

Distinct29
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size364.0 B
2023-12-12T22:36:59.061707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length10.275862
Min length6

Characters and Unicode

Total characters298
Distinct characters41
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique29 ?
Unique (%)100.0%

Sample

1st row충현동 제 1공영
2nd row미근동 공영
3rd row충현동 제 2공영
4th row북아현동 제 2공영
5th row신촌동 제 1공영
ValueCountFrequency (%)
제 24
24.7%
1공영 10
10.3%
2공영 7
 
7.2%
2동 7
 
7.2%
홍은 6
 
6.2%
연희동 6
 
6.2%
1동 5
 
5.2%
홍제 4
 
4.1%
공영 3
 
3.1%
3공영 3
 
3.1%
Other values (17) 22
22.7%
2023-12-12T22:36:59.395310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
68
22.8%
제 29
9.7%
공 28
9.4%
영 28
9.4%
동 26
 
8.7%
2 16
 
5.4%
1 15
 
5.0%
홍 11
 
3.7%
연 7
 
2.3%
은 6
 
2.0%
Other values (31) 64
21.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 190
63.8%
Space Separator 68
 
22.8%
Decimal Number 40
 
13.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
제 29
15.3%
공 28
14.7%
영 28
14.7%
동 26
13.7%
홍 11
 
5.8%
연 7
 
3.7%
은 6
 
3.2%
희 6
 
3.2%
가 5
 
2.6%
좌 5
 
2.6%
Other values (24) 39
20.5%
Decimal Number
ValueCountFrequency (%)
2 16
40.0%
1 15
37.5%
3 5
 
12.5%
4 2
 
5.0%
5 1
 
2.5%
6 1
 
2.5%
Space Separator
ValueCountFrequency (%)
68
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 190
63.8%
Common 108
36.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
제 29
15.3%
공 28
14.7%
영 28
14.7%
동 26
13.7%
홍 11
 
5.8%
연 7
 
3.7%
은 6
 
3.2%
희 6
 
3.2%
가 5
 
2.6%
좌 5
 
2.6%
Other values (24) 39
20.5%
Common
ValueCountFrequency (%)
68
63.0%
2 16
 
14.8%
1 15
 
13.9%
3 5
 
4.6%
4 2
 
1.9%
5 1
 
0.9%
6 1
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 190
63.8%
ASCII 108
36.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
68
63.0%
2 16
 
14.8%
1 15
 
13.9%
3 5
 
4.6%
4 2
 
1.9%
5 1
 
0.9%
6 1
 
0.9%
Hangul
ValueCountFrequency (%)
제 29
15.3%
공 28
14.7%
영 28
14.7%
동 26
13.7%
홍 11
 
5.8%
연 7
 
3.7%
은 6
 
3.2%
희 6
 
3.2%
가 5
 
2.6%
좌 5
 
2.6%
Other values (24) 39
20.5%

주차장명(위치)
Text

UNIQUE 

Distinct29
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size364.0 B
2023-12-12T22:36:59.999148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length21
Mean length17.344828
Min length8

Characters and Unicode

Total characters503
Distinct characters52
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

Unique29 ?
Unique (%)100.0%

Sample

1st row충정로2가 78-13(경기대로 72)
2nd row미근동 267-3
3rd row북아현동 3-6(북아현로 14길 24)
4th row북아현동 121-1(북아현로 6길 60)
5th row창천동 70-34(성산로 22길 40)
ValueCountFrequency (%)
2동 7
 
6.5%
홍은 6
 
5.6%
연희동 6
 
5.6%
1동 5
 
4.6%
외 5
 
4.6%
홍제 4
 
3.7%
북아현동 3
 
2.8%
39길 2
 
1.9%
남가좌 2
 
1.9%
3(연희로 2
 
1.9%
Other values (60) 66
61.1%
2023-12-12T22:37:00.457065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
79
15.7%
2 47
 
9.3%
1 35
 
7.0%
- 29
 
5.8%
동 28
 
5.6%
3 25
 
5.0%
6 19
 
3.8%
5 17
 
3.4%
홍 16
 
3.2%
4 16
 
3.2%
Other values (42) 192
38.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 195
38.8%
Other Letter 169
33.6%
Space Separator 79
15.7%
Dash Punctuation 29
 
5.8%
Close Punctuation 15
 
3.0%
Open Punctuation 15
 
3.0%
Other Punctuation 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 28
16.6%
홍 16
 
9.5%
로 13
 
7.7%
길 12
 
7.1%
연 9
 
5.3%
은 9
 
5.3%
희 8
 
4.7%
가 8
 
4.7%
북 8
 
4.7%
현 7
 
4.1%
Other values (27) 51
30.2%
Decimal Number
ValueCountFrequency (%)
2 47
24.1%
1 35
17.9%
3 25
12.8%
6 19
9.7%
5 17
 
8.7%
4 16
 
8.2%
0 13
 
6.7%
8 9
 
4.6%
7 9
 
4.6%
9 5
 
2.6%
Space Separator
ValueCountFrequency (%)
79
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 29
100.0%
Close Punctuation
ValueCountFrequency (%)
) 15
100.0%
Open Punctuation
ValueCountFrequency (%)
( 15
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 334
66.4%
Hangul 169
33.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 28
16.6%
홍 16
 
9.5%
로 13
 
7.7%
길 12
 
7.1%
연 9
 
5.3%
은 9
 
5.3%
희 8
 
4.7%
가 8
 
4.7%
북 8
 
4.7%
현 7
 
4.1%
Other values (27) 51
30.2%
Common
ValueCountFrequency (%)
79
23.7%
2 47
14.1%
1 35
10.5%
- 29
 
8.7%
3 25
 
7.5%
6 19
 
5.7%
5 17
 
5.1%
4 16
 
4.8%
) 15
 
4.5%
( 15
 
4.5%
Other values (5) 37
11.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 334
66.4%
Hangul 169
33.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
79
23.7%
2 47
14.1%
1 35
10.5%
- 29
 
8.7%
3 25
 
7.5%
6 19
 
5.7%
5 17
 
5.1%
4 16
 
4.8%
) 15
 
4.5%
( 15
 
4.5%
Other values (5) 37
11.1%
Hangul
ValueCountFrequency (%)
동 28
16.6%
홍 16
 
9.5%
로 13
 
7.7%
길 12
 
7.1%
연 9
 
5.3%
은 9
 
5.3%
희 8
 
4.7%
가 8
 
4.7%
북 8
 
4.7%
현 7
 
4.1%
Other values (27) 51
30.2%

구획수
Real number (ℝ)

HIGH CORRELATION 

Distinct24
Distinct (%)82.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44.068966
Minimum9
Maximum105
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size393.0 B
2023-12-12T22:37:00.586748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile9.4
Q127
median34
Q361
95-th percentile98
Maximum105
Range96
Interquartile range (IQR)34

Descriptive statistics

Standard deviation27.900245
Coefficient of variation (CV)0.63310414
Kurtosis-0.25639687
Mean44.068966
Median Absolute Deviation (MAD)17
Skewness0.81933247
Sum1278
Variance778.42365
MonotonicityNot monotonic
2023-12-12T22:37:00.715084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
27 3
 
10.3%
32 2
 
6.9%
9 2
 
6.9%
54 2
 
6.9%
63 1
 
3.4%
10 1
 
3.4%
89 1
 
3.4%
100 1
 
3.4%
22 1
 
3.4%
95 1
 
3.4%
Other values (14) 14
48.3%
ValueCountFrequency (%)
9 2
6.9%
10 1
 
3.4%
14 1
 
3.4%
17 1
 
3.4%
22 1
 
3.4%
24 1
 
3.4%
27 3
10.3%
29 1
 
3.4%
30 1
 
3.4%
32 2
6.9%
ValueCountFrequency (%)
105 1
3.4%
100 1
3.4%
95 1
3.4%
89 1
3.4%
73 1
3.4%
71 1
3.4%
63 1
3.4%
61 1
3.4%
54 2
6.9%
50 1
3.4%

설치형태
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)6.9%
Missing0
Missing (%)0.0%
Memory size364.0 B
노외(건물식)
17 
노외
12 

Length

Max length7
Median length7
Mean length4.9310345
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
노외(건물식) 17
58.6%
노외 12
41.4%

Length

2023-12-12T22:37:00.856240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T22:37:00.975654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
노외(건물식 17
58.6%
노외 12
41.4%

운영방법
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)10.3%
Missing0
Missing (%)0.0%
Memory size364.0 B
구간제
18 
개별지정제
10 
개별
 
1

Length

Max length5
Median length3
Mean length3.6551724
Min length2

Unique

Unique1 ?
Unique (%)3.4%

Sample

1st row구간제
2nd row구간제
3rd row구간제
4th row구간제
5th row개별지정제

Common Values

ValueCountFrequency (%)
구간제 18
62.1%
개별지정제 10
34.5%
개별 1
 
3.4%

Length

2023-12-12T22:37:01.115066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T22:37:01.231551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
구간제 18
62.1%
개별지정제 10
34.5%
개별 1
 
3.4%

Interactions

2023-12-12T22:36:58.292056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:36:58.091816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:36:58.375726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:36:58.214431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T22:37:01.307833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번주차장명주차장명(위치)구획수설치형태운영방법
연번1.0001.0001.0000.0000.0000.531
주차장명1.0001.0001.0001.0001.0001.000
주차장명(위치)1.0001.0001.0001.0001.0001.000
구획수0.0001.0001.0001.0000.6250.377
설치형태0.0001.0001.0000.6251.0000.447
운영방법0.5311.0001.0000.3770.4471.000
2023-12-12T22:37:01.434258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
운영방법설치형태
운영방법1.0000.680
설치형태0.6801.000
2023-12-12T22:37:01.528730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번구획수설치형태운영방법
연번1.0000.1010.0000.326
구획수0.1011.0000.5370.121
설치형태0.0000.5371.0000.680
운영방법0.3260.1210.6801.000

Missing values

2023-12-12T22:36:58.507538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T22:36:58.633297image/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

연번주차장명주차장명(위치)구획수설치형태운영방법
01충현동 제 1공영충정로2가 78-13(경기대로 72)63노외(건물식)구간제
12미근동 공영미근동 267-327노외구간제
23충현동 제 2공영북아현동 3-6(북아현로 14길 24)38노외(건물식)구간제
34북아현동 제 2공영북아현동 121-1(북아현로 6길 60)50노외(건물식)구간제
45신촌동 제 1공영창천동 70-34(성산로 22길 40)32노외(건물식)개별지정제
56연희동 제 1공영연희동 151-3224노외개별지정제
67연희동 제 2공영연희동 105-839노외개별지정제
78연희동 제 3공영연희동 50-334노외개별지정제
89연희동 제 4공영연희동 522-4(홍제천로 54-19)14노외(건물식)구간제
910연희동 제 5공영연희동 522-2(홍제천로 2길 83)61노외(건물식)구간제
연번주차장명주차장명(위치)구획수설치형태운영방법
1920홍은 2동 제 2공영홍은 2동 265-26외 3(연희로 39길 12)54노외(건물식)구간제
2021홍은 2동 제 3공영홍은동 265-26외 3(연희로 39길 12)54노외(건물식)구간제
2122남가좌 1동 제 2공영남가좌 1동 152-83 외 132노외구간제
2223남가좌 2동 제 1공영남가좌 2동 64-8 외 2(가재울로 14길 22)71노외(건물식)구간제
2324남가좌2동 제2공영주차장남가좌동 3-7595노외(건물식)구간제
2425정원단지 회차 주차시설홍은2동 265-2209노외(건물식)개별
2526북가좌 2동 제 1공영북가좌 2동 73-1622노외개별지정제
2627북가좌 2동 제 2공영북가좌 2동 346-209노외구간제
2728북성초등학교 공영북아현동 215(북아현로5나길 84)100노외(건물식)구간제
2829홍연초등학교 공영홍은 2동 305(백현사길 55)89노외(건물식)구간제