Overview

Dataset statistics

Number of variables6
Number of observations107
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.4 KiB
Average record size in memory51.2 B

Variable types

Numeric2
Categorical2
Text2

Dataset

Description2024년 광진구시설관리공단 거주자우선주차 구획은 노상 및 노외로 구분되어 있습니다.또한 지역 구역별 내 상세주소를 확인할 수 있으며 시설명열에서 해당 명칭을 확인할 수 있고, 주차면수 등을 확인할 수 있습니다.자세한 내용은 주차사업팀 2049-4540 으로 문의하여 주시기 바랍니다.
Author광진구시설관리공단
URLhttps://www.data.go.kr/data/15112974/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
관리번호 has unique valuesUnique

Reproduction

Analysis started2024-03-30 02:25:59.420533
Analysis finished2024-03-30 02:26:01.813354
Duration2.39 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

관리번호
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct107
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54
Minimum1
Maximum107
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-03-30T02:26:02.042899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6.3
Q127.5
median54
Q380.5
95-th percentile101.7
Maximum107
Range106
Interquartile range (IQR)53

Descriptive statistics

Standard deviation31.032241
Coefficient of variation (CV)0.57467114
Kurtosis-1.2
Mean54
Median Absolute Deviation (MAD)27
Skewness0
Sum5778
Variance963
MonotonicityStrictly increasing
2024-03-30T02:26:02.487161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.9%
69 1
 
0.9%
80 1
 
0.9%
79 1
 
0.9%
78 1
 
0.9%
77 1
 
0.9%
76 1
 
0.9%
75 1
 
0.9%
74 1
 
0.9%
73 1
 
0.9%
Other values (97) 97
90.7%
ValueCountFrequency (%)
1 1
0.9%
2 1
0.9%
3 1
0.9%
4 1
0.9%
5 1
0.9%
6 1
0.9%
7 1
0.9%
8 1
0.9%
9 1
0.9%
10 1
0.9%
ValueCountFrequency (%)
107 1
0.9%
106 1
0.9%
105 1
0.9%
104 1
0.9%
103 1
0.9%
102 1
0.9%
101 1
0.9%
100 1
0.9%
99 1
0.9%
98 1
0.9%

구역별
Categorical

HIGH CORRELATION 

Distinct19
Distinct (%)17.8%
Missing0
Missing (%)0.0%
Memory size988.0 B
중곡4동
13 
광장동
9 
중곡3동
8 
자양2동
8 
군자동
7 
Other values (14)
62 

Length

Max length5
Median length4
Mean length3.8130841
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
중곡4동 13
 
12.1%
광장동 9
 
8.4%
중곡3동 8
 
7.5%
자양2동 8
 
7.5%
군자동 7
 
6.5%
구의1동 6
 
5.6%
자양1동 6
 
5.6%
구의2동 6
 
5.6%
중곡1동 5
 
4.7%
중곡2동 5
 
4.7%
Other values (9) 34
31.8%

Length

2024-03-30T02:26:02.952281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
중곡4동 13
12.1%
구의2동 10
 
9.3%
광장동 9
 
8.4%
구의1동 9
 
8.4%
중곡3동 8
 
7.5%
자양2동 8
 
7.5%
군자동 7
 
6.5%
화양동 7
 
6.5%
자양1동 6
 
5.6%
중곡1동 5
 
4.7%
Other values (5) 25
23.4%

상세주소
Text

Distinct95
Distinct (%)88.8%
Missing0
Missing (%)0.0%
Memory size988.0 B
2024-03-30T02:26:03.529641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length20
Mean length16.785047
Min length10

Characters and Unicode

Total characters1796
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

Unique87 ?
Unique (%)81.3%

Sample

1st row서울시 광진구 광장동
2nd row서울시 광진구 광장동 126
3rd row서울시 광진구 광장동 126
4th row서울시 광진구 광장동 275-5
5th row서울시 광진구 광장동 394-48
ValueCountFrequency (%)
서울시 107
26.0%
광진구 107
26.0%
중곡4동 13
 
3.2%
구의2동 10
 
2.4%
광장동 9
 
2.2%
구의1동 9
 
2.2%
중곡3동 8
 
1.9%
자양2동 8
 
1.9%
화양동 7
 
1.7%
군자동 7
 
1.7%
Other values (89) 126
30.7%
2024-03-30T02:26:04.653558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
304
16.9%
구 131
 
7.3%
광 116
 
6.5%
서 107
 
6.0%
동 107
 
6.0%
시 107
 
6.0%
진 107
 
6.0%
울 107
 
6.0%
1 95
 
5.3%
- 76
 
4.2%
Other values (20) 539
30.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 959
53.4%
Decimal Number 454
25.3%
Space Separator 304
 
16.9%
Dash Punctuation 76
 
4.2%
Other Punctuation 3
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 131
13.7%
광 116
12.1%
서 107
11.2%
동 107
11.2%
시 107
11.2%
진 107
11.2%
울 107
11.2%
자 31
 
3.2%
양 31
 
3.2%
중 31
 
3.2%
Other values (7) 84
8.8%
Decimal Number
ValueCountFrequency (%)
1 95
20.9%
2 67
14.8%
3 64
14.1%
4 51
11.2%
6 46
10.1%
5 42
9.3%
7 27
 
5.9%
0 25
 
5.5%
9 20
 
4.4%
8 17
 
3.7%
Space Separator
ValueCountFrequency (%)
304
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 76
100.0%
Other Punctuation
ValueCountFrequency (%)
, 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 959
53.4%
Common 837
46.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 131
13.7%
광 116
12.1%
서 107
11.2%
동 107
11.2%
시 107
11.2%
진 107
11.2%
울 107
11.2%
자 31
 
3.2%
양 31
 
3.2%
중 31
 
3.2%
Other values (7) 84
8.8%
Common
ValueCountFrequency (%)
304
36.3%
1 95
 
11.4%
- 76
 
9.1%
2 67
 
8.0%
3 64
 
7.6%
4 51
 
6.1%
6 46
 
5.5%
5 42
 
5.0%
7 27
 
3.2%
0 25
 
3.0%
Other values (3) 40
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 959
53.4%
ASCII 837
46.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
304
36.3%
1 95
 
11.4%
- 76
 
9.1%
2 67
 
8.0%
3 64
 
7.6%
4 51
 
6.1%
6 46
 
5.5%
5 42
 
5.0%
7 27
 
3.2%
0 25
 
3.0%
Other values (3) 40
 
4.8%
Hangul
ValueCountFrequency (%)
구 131
13.7%
광 116
12.1%
서 107
11.2%
동 107
11.2%
시 107
11.2%
진 107
11.2%
울 107
11.2%
자 31
 
3.2%
양 31
 
3.2%
중 31
 
3.2%
Other values (7) 84
8.8%

구분
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size988.0 B
노외
92 
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 92
86.0%
노상 15
 
14.0%

Length

2024-03-30T02:26:05.054805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-30T02:26:05.435148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
노외 92
86.0%
노상 15
 
14.0%

시설명
Text

Distinct92
Distinct (%)86.0%
Missing0
Missing (%)0.0%
Memory size988.0 B
2024-03-30T02:26:05.940863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length9
Mean length5.2990654
Min length2

Characters and Unicode

Total characters567
Distinct characters147
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique90 ?
Unique (%)84.1%

Sample

1st row노상주차장
2nd row버스차고지
3rd row하늘뜻교회
4th row장로신학대
5th row광장공동
ValueCountFrequency (%)
노상주차장 15
 
12.3%
자투리땅 7
 
5.7%
주차장 2
 
1.6%
공원 2
 
1.6%
영존 1
 
0.8%
경도빌딩 1
 
0.8%
제2호 1
 
0.8%
제11호 1
 
0.8%
용마빌 1
 
0.8%
동원빌라 1
 
0.8%
Other values (90) 90
73.8%
2024-03-30T02:26:07.151036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
장 28
 
4.9%
주 27
 
4.8%
차 27
 
4.8%
노 16
 
2.8%
자 16
 
2.8%
상 16
 
2.8%
15
 
2.6%
빌 14
 
2.5%
공 14
 
2.5%
리 13
 
2.3%
Other values (137) 381
67.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 503
88.7%
Decimal Number 39
 
6.9%
Space Separator 15
 
2.6%
Dash Punctuation 4
 
0.7%
Uppercase Letter 4
 
0.7%
Open Punctuation 1
 
0.2%
Close Punctuation 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
장 28
 
5.6%
주 27
 
5.4%
차 27
 
5.4%
노 16
 
3.2%
자 16
 
3.2%
상 16
 
3.2%
빌 14
 
2.8%
공 14
 
2.8%
리 13
 
2.6%
스 12
 
2.4%
Other values (120) 320
63.6%
Decimal Number
ValueCountFrequency (%)
1 10
25.6%
3 7
17.9%
0 5
12.8%
2 4
 
10.3%
5 3
 
7.7%
4 3
 
7.7%
7 2
 
5.1%
9 2
 
5.1%
8 2
 
5.1%
6 1
 
2.6%
Uppercase Letter
ValueCountFrequency (%)
H 2
50.0%
L 1
25.0%
S 1
25.0%
Space Separator
ValueCountFrequency (%)
15
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 503
88.7%
Common 60
 
10.6%
Latin 4
 
0.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
장 28
 
5.6%
주 27
 
5.4%
차 27
 
5.4%
노 16
 
3.2%
자 16
 
3.2%
상 16
 
3.2%
빌 14
 
2.8%
공 14
 
2.8%
리 13
 
2.6%
스 12
 
2.4%
Other values (120) 320
63.6%
Common
ValueCountFrequency (%)
15
25.0%
1 10
16.7%
3 7
11.7%
0 5
 
8.3%
- 4
 
6.7%
2 4
 
6.7%
5 3
 
5.0%
4 3
 
5.0%
7 2
 
3.3%
9 2
 
3.3%
Other values (4) 5
 
8.3%
Latin
ValueCountFrequency (%)
H 2
50.0%
L 1
25.0%
S 1
25.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 503
88.7%
ASCII 64
 
11.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
장 28
 
5.6%
주 27
 
5.4%
차 27
 
5.4%
노 16
 
3.2%
자 16
 
3.2%
상 16
 
3.2%
빌 14
 
2.8%
공 14
 
2.8%
리 13
 
2.6%
스 12
 
2.4%
Other values (120) 320
63.6%
ASCII
ValueCountFrequency (%)
15
23.4%
1 10
15.6%
3 7
10.9%
0 5
 
7.8%
- 4
 
6.2%
2 4
 
6.2%
5 3
 
4.7%
4 3
 
4.7%
H 2
 
3.1%
7 2
 
3.1%
Other values (7) 9
14.1%

주차면수
Real number (ℝ)

HIGH CORRELATION 

Distinct50
Distinct (%)46.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.224299
Minimum1
Maximum374
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-03-30T02:26:07.587412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q14.5
median10
Q334.5
95-th percentile197.4
Maximum374
Range373
Interquartile range (IQR)30

Descriptive statistics

Standard deviation73.368456
Coefficient of variation (CV)1.6973891
Kurtosis5.604661
Mean43.224299
Median Absolute Deviation (MAD)7
Skewness2.3832642
Sum4625
Variance5382.9303
MonotonicityNot monotonic
2024-03-30T02:26:08.078624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 11
 
10.3%
3 10
 
9.3%
5 9
 
8.4%
2 8
 
7.5%
4 5
 
4.7%
6 5
 
4.7%
1 4
 
3.7%
7 3
 
2.8%
25 3
 
2.8%
14 3
 
2.8%
Other values (40) 46
43.0%
ValueCountFrequency (%)
1 4
 
3.7%
2 8
7.5%
3 10
9.3%
4 5
4.7%
5 9
8.4%
6 5
4.7%
7 3
 
2.8%
8 2
 
1.9%
9 2
 
1.9%
10 11
10.3%
ValueCountFrequency (%)
374 1
0.9%
304 1
0.9%
275 1
0.9%
262 1
0.9%
199 1
0.9%
198 1
0.9%
196 1
0.9%
184 1
0.9%
173 1
0.9%
172 1
0.9%

Interactions

2024-03-30T02:26:00.643219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:26:00.117747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:26:00.921584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:26:00.380738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-30T02:26:08.384161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관리번호구역별상세주소구분시설명주차면수
관리번호1.0000.9710.9980.0000.3820.000
구역별0.9711.0001.0000.0000.0000.000
상세주소0.9981.0001.0000.8200.0000.624
구분0.0000.0000.8201.0001.0000.922
시설명0.3820.0000.0001.0001.0000.000
주차면수0.0000.0000.6240.9220.0001.000
2024-03-30T02:26:08.687145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분구역별
구분1.0000.000
구역별0.0001.000
2024-03-30T02:26:08.944871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관리번호주차면수구역별구분
관리번호1.000-0.1720.8040.000
주차면수-0.1721.0000.0000.736
구역별0.8040.0001.0000.000
구분0.0000.7360.0001.000

Missing values

2024-03-30T02:26:01.276396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-30T02:26:01.700139image/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

관리번호구역별상세주소구분시설명주차면수
01광장동서울시 광진구 광장동노상노상주차장199
12광장동서울시 광진구 광장동 126노외버스차고지13
23광장동서울시 광진구 광장동 126노외하늘뜻교회6
34광장동서울시 광진구 광장동 275-5노외장로신학대20
45광장동서울시 광진구 광장동 394-48노외광장공동8
56광장동서울시 광진구 광장동 401노외행정차고지10
67광장동서울시 광진구 광장동 413-20노외홈타운5
78광장동서울시 광진구 광장동 114노외코레스코10
89광장동서울시 광진구 광장동 275-1노외세촌7
910구의1동서울시 광진구 구의1동노상노상주차장84
관리번호구역별상세주소구분시설명주차면수
9798중곡4동서울시 광진구 중곡4동 76-58노외긴고랑5
9899중곡4동서울시 광진구 중곡4동 75-39노외75-39주택2
99100중곡4동서울시 광진구 중곡4동 30-44노외30-44주택1
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101102화양동서울시 광진구 화양동 110-33노외화양동 공영주차장27
102103화양동서울시 광진구 화양동 195-25노외마을마당6
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104105화양동서울시 광진구 화양동 111-255노외자투리땅 제11호3
105106화양동서울시 광진구 화양동 132-8노외자투리땅 제2호4
106107화양동서울시 광진구 화양동 12-2노외화양초교25