Overview

Dataset statistics

Number of variables7
Number of observations74
Missing cells74
Missing cells (%)14.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.4 KiB
Average record size in memory60.8 B

Variable types

Numeric2
Categorical2
Text2
Unsupported1

Dataset

Description광진구시설관리공단에서 관리하고 있는 거주자우선주차구획 현황
Author광진구시설관리공단
URLhttps://www.data.go.kr/data/15044586/fileData.do

Alerts

관리번호 is highly overall correlated with 구역별High correlation
주차면수 is highly overall correlated with 구분High correlation
구역별 is highly overall correlated with 관리번호High correlation
구분 is highly overall correlated with 주차면수High correlation
비고 has 74 (100.0%) missing valuesMissing
관리번호 has unique valuesUnique
상세주소 has unique valuesUnique
비고 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2023-12-12 01:45:37.617525
Analysis finished2023-12-12 01:45:38.822151
Duration1.2 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

관리번호
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct74
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.5
Minimum1
Maximum74
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size798.0 B
2023-12-12T10:45:38.910667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4.65
Q119.25
median37.5
Q355.75
95-th percentile70.35
Maximum74
Range73
Interquartile range (IQR)36.5

Descriptive statistics

Standard deviation21.505813
Coefficient of variation (CV)0.57348835
Kurtosis-1.2
Mean37.5
Median Absolute Deviation (MAD)18.5
Skewness0
Sum2775
Variance462.5
MonotonicityStrictly increasing
2023-12-12T10:45:39.073669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.4%
57 1
 
1.4%
55 1
 
1.4%
54 1
 
1.4%
53 1
 
1.4%
52 1
 
1.4%
51 1
 
1.4%
50 1
 
1.4%
49 1
 
1.4%
48 1
 
1.4%
Other values (64) 64
86.5%
ValueCountFrequency (%)
1 1
1.4%
2 1
1.4%
3 1
1.4%
4 1
1.4%
5 1
1.4%
6 1
1.4%
7 1
1.4%
8 1
1.4%
9 1
1.4%
10 1
1.4%
ValueCountFrequency (%)
74 1
1.4%
73 1
1.4%
72 1
1.4%
71 1
1.4%
70 1
1.4%
69 1
1.4%
68 1
1.4%
67 1
1.4%
66 1
1.4%
65 1
1.4%

구역별
Categorical

HIGH CORRELATION 

Distinct19
Distinct (%)25.7%
Missing0
Missing (%)0.0%
Memory size724.0 B
중곡4동
9 
구의1동
6 
구의2동
6 
광장동
5 
중곡3동
5 
Other values (14)
43 

Length

Max length5
Median length4
Mean length3.8108108
Min length2

Unique

Unique2 ?
Unique (%)2.7%

Sample

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

Common Values

ValueCountFrequency (%)
중곡4동 9
12.2%
구의1동 6
 
8.1%
구의2동 6
 
8.1%
광장동 5
 
6.8%
중곡3동 5
 
6.8%
군자동 5
 
6.8%
자양2동 5
 
6.8%
구의2동 4
 
5.4%
능동 4
 
5.4%
자양1동 4
 
5.4%
Other values (9) 21
28.4%

Length

2023-12-12T10:45:39.239656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
구의2동 10
13.5%
중곡4동 9
12.2%
구의1동 8
10.8%
화양동 7
9.5%
광장동 5
6.8%
중곡3동 5
6.8%
군자동 5
6.8%
자양2동 5
6.8%
능동 4
 
5.4%
자양1동 4
 
5.4%
Other values (5) 12
16.2%

상세주소
Text

UNIQUE 

Distinct74
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size724.0 B
2023-12-12T10:45:39.524038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length19
Mean length16.554054
Min length10

Characters and Unicode

Total characters1225
Distinct characters30
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

Unique74 ?
Unique (%)100.0%

Sample

1st row서울시 광진구 광장동
2nd row서울시 광진구 광장동 126
3rd row서울시 광진구 광장동 275-5
4th row서울시 광진구 광장동 394-48
5th row서울시 광진구 광장동 401
ValueCountFrequency (%)
서울시 74
26.1%
광진구 74
26.1%
구의2동 10
 
3.5%
중곡4동 9
 
3.2%
구의1동 8
 
2.8%
화양동 7
 
2.5%
군자동 5
 
1.8%
광장동 5
 
1.8%
자양2동 5
 
1.8%
중곡3동 5
 
1.8%
Other values (68) 81
28.6%
2023-12-12T10:45:40.075668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
209
17.1%
구 96
 
7.8%
광 79
 
6.4%
서 74
 
6.0%
시 74
 
6.0%
진 74
 
6.0%
동 74
 
6.0%
울 74
 
6.0%
1 67
 
5.5%
- 49
 
4.0%
Other values (20) 355
29.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 663
54.1%
Decimal Number 301
24.6%
Space Separator 209
 
17.1%
Dash Punctuation 49
 
4.0%
Other Punctuation 3
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 96
14.5%
광 79
11.9%
서 74
11.2%
시 74
11.2%
진 74
11.2%
동 74
11.2%
울 74
11.2%
의 22
 
3.3%
양 21
 
3.2%
자 19
 
2.9%
Other values (7) 56
8.4%
Decimal Number
ValueCountFrequency (%)
1 67
22.3%
2 45
15.0%
3 40
13.3%
4 35
11.6%
6 31
10.3%
7 20
 
6.6%
5 20
 
6.6%
0 16
 
5.3%
9 15
 
5.0%
8 12
 
4.0%
Space Separator
ValueCountFrequency (%)
209
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 49
100.0%
Other Punctuation
ValueCountFrequency (%)
, 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 663
54.1%
Common 562
45.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 96
14.5%
광 79
11.9%
서 74
11.2%
시 74
11.2%
진 74
11.2%
동 74
11.2%
울 74
11.2%
의 22
 
3.3%
양 21
 
3.2%
자 19
 
2.9%
Other values (7) 56
8.4%
Common
ValueCountFrequency (%)
209
37.2%
1 67
 
11.9%
- 49
 
8.7%
2 45
 
8.0%
3 40
 
7.1%
4 35
 
6.2%
6 31
 
5.5%
7 20
 
3.6%
5 20
 
3.6%
0 16
 
2.8%
Other values (3) 30
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 663
54.1%
ASCII 562
45.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
209
37.2%
1 67
 
11.9%
- 49
 
8.7%
2 45
 
8.0%
3 40
 
7.1%
4 35
 
6.2%
6 31
 
5.5%
7 20
 
3.6%
5 20
 
3.6%
0 16
 
2.8%
Other values (3) 30
 
5.3%
Hangul
ValueCountFrequency (%)
구 96
14.5%
광 79
11.9%
서 74
11.2%
시 74
11.2%
진 74
11.2%
동 74
11.2%
울 74
11.2%
의 22
 
3.3%
양 21
 
3.2%
자 19
 
2.9%
Other values (7) 56
8.4%

구분
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Memory size724.0 B
노외
59 
노상
15 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
노외 59
79.7%
노상 15
 
20.3%

Length

2023-12-12T10:45:40.270837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T10:45:40.420539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
노외 59
79.7%
노상 15
 
20.3%

시설명
Text

Distinct58
Distinct (%)78.4%
Missing0
Missing (%)0.0%
Memory size724.0 B
2023-12-12T10:45:40.710047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length8
Mean length5.5675676
Min length2

Characters and Unicode

Total characters412
Distinct characters121
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

Unique55 ?
Unique (%)74.3%

Sample

1st row노상주차장
2nd row큰나루
3rd row세촌
4th row광장공동
5th row행정차고지
ValueCountFrequency (%)
노상주차장 15
 
16.9%
자투리땅 7
 
7.9%
주차장 3
 
3.4%
공원 2
 
2.2%
동사무소 2
 
2.2%
마을공원 1
 
1.1%
중곡체육센터 1
 
1.1%
자양초등학교 1
 
1.1%
자양힐아파트 1
 
1.1%
한국중앙교회 1
 
1.1%
Other values (55) 55
61.8%
2023-12-12T10:45:41.207146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
장 29
 
7.0%
차 28
 
6.8%
주 27
 
6.6%
상 16
 
3.9%
노 15
 
3.6%
15
 
3.6%
동 12
 
2.9%
자 12
 
2.9%
공 9
 
2.2%
호 8
 
1.9%
Other values (111) 241
58.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 377
91.5%
Decimal Number 16
 
3.9%
Space Separator 15
 
3.6%
Uppercase Letter 2
 
0.5%
Close Punctuation 1
 
0.2%
Open Punctuation 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
장 29
 
7.7%
차 28
 
7.4%
주 27
 
7.2%
상 16
 
4.2%
노 15
 
4.0%
동 12
 
3.2%
자 12
 
3.2%
공 9
 
2.4%
호 8
 
2.1%
제 8
 
2.1%
Other values (99) 213
56.5%
Decimal Number
ValueCountFrequency (%)
3 5
31.2%
2 4
25.0%
1 3
18.8%
7 1
 
6.2%
9 1
 
6.2%
8 1
 
6.2%
5 1
 
6.2%
Uppercase Letter
ValueCountFrequency (%)
S 1
50.0%
H 1
50.0%
Space Separator
ValueCountFrequency (%)
15
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 377
91.5%
Common 33
 
8.0%
Latin 2
 
0.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
장 29
 
7.7%
차 28
 
7.4%
주 27
 
7.2%
상 16
 
4.2%
노 15
 
4.0%
동 12
 
3.2%
자 12
 
3.2%
공 9
 
2.4%
호 8
 
2.1%
제 8
 
2.1%
Other values (99) 213
56.5%
Common
ValueCountFrequency (%)
15
45.5%
3 5
 
15.2%
2 4
 
12.1%
1 3
 
9.1%
7 1
 
3.0%
) 1
 
3.0%
( 1
 
3.0%
9 1
 
3.0%
8 1
 
3.0%
5 1
 
3.0%
Latin
ValueCountFrequency (%)
S 1
50.0%
H 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 377
91.5%
ASCII 35
 
8.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
장 29
 
7.7%
차 28
 
7.4%
주 27
 
7.2%
상 16
 
4.2%
노 15
 
4.0%
동 12
 
3.2%
자 12
 
3.2%
공 9
 
2.4%
호 8
 
2.1%
제 8
 
2.1%
Other values (99) 213
56.5%
ASCII
ValueCountFrequency (%)
15
42.9%
3 5
 
14.3%
2 4
 
11.4%
1 3
 
8.6%
S 1
 
2.9%
7 1
 
2.9%
) 1
 
2.9%
( 1
 
2.9%
H 1
 
2.9%
9 1
 
2.9%
Other values (2) 2
 
5.7%

주차면수
Real number (ℝ)

HIGH CORRELATION 

Distinct53
Distinct (%)71.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean78.891892
Minimum1
Maximum494
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size798.0 B
2023-12-12T10:45:41.404065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q19.25
median23
Q3103.75
95-th percentile294.25
Maximum494
Range493
Interquartile range (IQR)94.5

Descriptive statistics

Standard deviation112.62455
Coefficient of variation (CV)1.4275808
Kurtosis3.7570906
Mean78.891892
Median Absolute Deviation (MAD)19
Skewness1.9761534
Sum5838
Variance12684.29
MonotonicityNot monotonic
2023-12-12T10:45:41.627256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 7
 
9.5%
4 3
 
4.1%
9 3
 
4.1%
53 3
 
4.1%
8 3
 
4.1%
12 2
 
2.7%
1 2
 
2.7%
13 2
 
2.7%
3 2
 
2.7%
7 2
 
2.7%
Other values (43) 45
60.8%
ValueCountFrequency (%)
1 2
 
2.7%
2 1
 
1.4%
3 2
 
2.7%
4 3
4.1%
5 1
 
1.4%
6 2
 
2.7%
7 2
 
2.7%
8 3
4.1%
9 3
4.1%
10 7
9.5%
ValueCountFrequency (%)
494 1
1.4%
490 1
1.4%
352 1
1.4%
304 1
1.4%
289 1
1.4%
274 1
1.4%
266 1
1.4%
249 1
1.4%
239 1
1.4%
213 1
1.4%

비고
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing74
Missing (%)100.0%
Memory size798.0 B

Interactions

2023-12-12T10:45:38.130761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:45:37.939876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:45:38.550365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:45:38.040631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T10:45:41.755299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관리번호구역별상세주소구분시설명주차면수
관리번호1.0000.9631.0000.0970.5760.000
구역별0.9631.0001.0000.0000.0000.678
상세주소1.0001.0001.0001.0001.0001.000
구분0.0970.0001.0001.0001.0000.781
시설명0.5760.0001.0001.0001.0000.000
주차면수0.0000.6781.0000.7810.0001.000
2023-12-12T10:45:41.899691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구역별구분
구역별1.0000.000
구분0.0001.000
2023-12-12T10:45:42.002100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관리번호주차면수구역별구분
관리번호1.000-0.0640.7510.057
주차면수-0.0641.0000.3110.761
구역별0.7510.3111.0000.000
구분0.0570.7610.0001.000

Missing values

2023-12-12T10:45:38.661555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T10:45:38.777030image/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

관리번호구역별상세주소구분시설명주차면수비고
01광장동서울시 광진구 광장동노상노상주차장249<NA>
12광장동서울시 광진구 광장동 126노외큰나루13<NA>
23광장동서울시 광진구 광장동 275-5노외세촌7<NA>
34광장동서울시 광진구 광장동 394-48노외광장공동9<NA>
45광장동서울시 광진구 광장동 401노외행정차고지61<NA>
56구의1동서울시 광진구 구의1동노상노상주차장133<NA>
67구의1동서울시 광진구 구의1동 246-14노외크레신타워 3차10<NA>
78구의1동서울시 광진구 구의1동 251-34노외신도브래뉴14<NA>
89구의1동서울시 광진구 구의1동 656노외구의새한아파트10<NA>
910구의1동서울시 광진구 구의1동 230-7노외구의번영35<NA>
관리번호구역별상세주소구분시설명주차면수비고
6465중곡4동서울시 광진구 중곡4동 72-124노외긴고랑길 주차장53<NA>
6566중곡4동서울시 광진구 중곡4동 87-14노외해오름20<NA>
6667중곡4동서울시 광진구 중곡4동 98-3노외근린시설(한상문)3<NA>
6768화양동서울시 광진구 화양동노상노상주차장188<NA>
6869화양동서울시 광진구 화양동 110-33노외화양동 공영주차장27<NA>
6970화양동서울시 광진구 화양동 195-25노외마을마당6<NA>
7071화양동서울시 광진구 화양동 303-9노외구의초등학교45<NA>
7172화양동서울시 광진구 화양동 111-255노외자투리땅 제7호1<NA>
7273화양동서울시 광진구 화양동 132-8노외자투리땅 제2호6<NA>
7374화양동서울시 광진구 화양동 17-82노외자투리땅 제5호13<NA>