Overview

Dataset statistics

Number of variables6
Number of observations190
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory9.4 KiB
Average record size in memory50.7 B

Variable types

Numeric1
Categorical2
Text2
Boolean1

Dataset

Description순번,시군구명,주소,상세위치,거치대 유무,거치대 크기
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-21710/S/1/datasetView.do

Alerts

순번 is highly overall correlated with 시군구명 and 2 other fieldsHigh correlation
시군구명 is highly overall correlated with 순번 and 2 other fieldsHigh correlation
거치대 유무 is highly overall correlated with 순번 and 2 other fieldsHigh correlation
거치대 크기 is highly overall correlated with 순번 and 2 other fieldsHigh correlation
순번 has unique valuesUnique

Reproduction

Analysis started2024-04-20 20:56:05.333657
Analysis finished2024-04-20 20:56:06.737932
Duration1.4 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

순번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct190
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean95.5
Minimum1
Maximum190
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2024-04-21T05:56:07.164208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile10.45
Q148.25
median95.5
Q3142.75
95-th percentile180.55
Maximum190
Range189
Interquartile range (IQR)94.5

Descriptive statistics

Standard deviation54.992424
Coefficient of variation (CV)0.5758369
Kurtosis-1.2
Mean95.5
Median Absolute Deviation (MAD)47.5
Skewness0
Sum18145
Variance3024.1667
MonotonicityStrictly increasing
2024-04-21T05:56:07.602055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.5%
132 1
 
0.5%
123 1
 
0.5%
124 1
 
0.5%
125 1
 
0.5%
126 1
 
0.5%
127 1
 
0.5%
128 1
 
0.5%
129 1
 
0.5%
130 1
 
0.5%
Other values (180) 180
94.7%
ValueCountFrequency (%)
1 1
0.5%
2 1
0.5%
3 1
0.5%
4 1
0.5%
5 1
0.5%
6 1
0.5%
7 1
0.5%
8 1
0.5%
9 1
0.5%
10 1
0.5%
ValueCountFrequency (%)
190 1
0.5%
189 1
0.5%
188 1
0.5%
187 1
0.5%
186 1
0.5%
185 1
0.5%
184 1
0.5%
183 1
0.5%
182 1
0.5%
181 1
0.5%

시군구명
Categorical

HIGH CORRELATION 

Distinct23
Distinct (%)12.1%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
서초구
49 
강서구
21 
종로구
17 
동대문구
14 
마포구
10 
Other values (18)
79 

Length

Max length4
Median length3
Mean length3.0578947
Min length2

Unique

Unique3 ?
Unique (%)1.6%

Sample

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

Common Values

ValueCountFrequency (%)
서초구 49
25.8%
강서구 21
11.1%
종로구 17
 
8.9%
동대문구 14
 
7.4%
마포구 10
 
5.3%
강남구 10
 
5.3%
강북구 9
 
4.7%
송파구 7
 
3.7%
관악구 6
 
3.2%
강동구 5
 
2.6%
Other values (13) 42
22.1%

Length

2024-04-21T05:56:08.073390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
서초구 49
25.8%
강서구 21
11.1%
종로구 17
 
8.9%
동대문구 14
 
7.4%
마포구 10
 
5.3%
강남구 10
 
5.3%
강북구 9
 
4.7%
송파구 7
 
3.7%
관악구 6
 
3.2%
용산구 5
 
2.6%
Other values (13) 42
22.1%

주소
Text

Distinct178
Distinct (%)93.7%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
2024-04-21T05:56:09.345892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length13
Mean length9.1
Min length5

Characters and Unicode

Total characters1729
Distinct characters137
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

Unique169 ?
Unique (%)88.9%

Sample

1st row팔판동 115-63
2nd row연건동 218-1
3rd row연건동 178-3
4th row동승동 1-24
5th row와룡동 75-4
ValueCountFrequency (%)
서초동 29
 
7.4%
마곡동 8
 
2.1%
반포동 7
 
1.8%
방배동 6
 
1.5%
양재동 6
 
1.5%
미아동 5
 
1.3%
등촌동 5
 
1.3%
남부순환로 5
 
1.3%
전농동 5
 
1.3%
을지로 4
 
1.0%
Other values (260) 310
79.5%
2024-04-21T05:56:11.042417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
200
 
11.6%
동 173
 
10.0%
1 168
 
9.7%
- 140
 
8.1%
2 88
 
5.1%
4 88
 
5.1%
7 77
 
4.5%
3 72
 
4.2%
5 68
 
3.9%
6 59
 
3.4%
Other values (127) 596
34.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 769
44.5%
Other Letter 620
35.9%
Space Separator 200
 
11.6%
Dash Punctuation 140
 
8.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 173
27.9%
서 30
 
4.8%
초 29
 
4.7%
로 22
 
3.5%
가 13
 
2.1%
곡 13
 
2.1%
구 12
 
1.9%
마 11
 
1.8%
양 10
 
1.6%
방 10
 
1.6%
Other values (115) 297
47.9%
Decimal Number
ValueCountFrequency (%)
1 168
21.8%
2 88
11.4%
4 88
11.4%
7 77
10.0%
3 72
9.4%
5 68
8.8%
6 59
 
7.7%
8 56
 
7.3%
0 53
 
6.9%
9 40
 
5.2%
Space Separator
ValueCountFrequency (%)
200
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 140
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1109
64.1%
Hangul 620
35.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 173
27.9%
서 30
 
4.8%
초 29
 
4.7%
로 22
 
3.5%
가 13
 
2.1%
곡 13
 
2.1%
구 12
 
1.9%
마 11
 
1.8%
양 10
 
1.6%
방 10
 
1.6%
Other values (115) 297
47.9%
Common
ValueCountFrequency (%)
200
18.0%
1 168
15.1%
- 140
12.6%
2 88
7.9%
4 88
7.9%
7 77
 
6.9%
3 72
 
6.5%
5 68
 
6.1%
6 59
 
5.3%
8 56
 
5.0%
Other values (2) 93
8.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1109
64.1%
Hangul 620
35.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
200
18.0%
1 168
15.1%
- 140
12.6%
2 88
7.9%
4 88
7.9%
7 77
 
6.9%
3 72
 
6.5%
5 68
 
6.1%
6 59
 
5.3%
8 56
 
5.0%
Other values (2) 93
8.4%
Hangul
ValueCountFrequency (%)
동 173
27.9%
서 30
 
4.8%
초 29
 
4.7%
로 22
 
3.5%
가 13
 
2.1%
곡 13
 
2.1%
구 12
 
1.9%
마 11
 
1.8%
양 10
 
1.6%
방 10
 
1.6%
Other values (115) 297
47.9%

상세위치
Text

Distinct188
Distinct (%)98.9%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
2024-04-21T05:56:12.030715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length29
Median length25
Mean length13.284211
Min length6

Characters and Unicode

Total characters2524
Distinct characters299
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique187 ?
Unique (%)98.4%

Sample

1st row청와대 춘추문 맞은편 인근
2nd rowKT광화문 혜화지사 앞
3rd row홍익대학교 대학로 맞은편
4th row대학로 마로니에공원 앞
5th row연악사 맞은편
ValueCountFrequency (%)
출구 89
 
13.6%
앞 70
 
10.7%
4번 20
 
3.0%
인근 20
 
3.0%
1번 15
 
2.3%
2번 14
 
2.1%
측면 10
 
1.5%
옆 10
 
1.5%
5번 9
 
1.4%
3번 9
 
1.4%
Other values (274) 390
59.5%
2024-04-21T05:56:13.429122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
470
 
18.6%
구 146
 
5.8%
출 121
 
4.8%
번 118
 
4.7%
역 117
 
4.6%
앞 85
 
3.4%
대 37
 
1.5%
1 34
 
1.3%
( 31
 
1.2%
) 31
 
1.2%
Other values (289) 1334
52.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1789
70.9%
Space Separator 470
 
18.6%
Decimal Number 171
 
6.8%
Open Punctuation 31
 
1.2%
Close Punctuation 31
 
1.2%
Uppercase Letter 15
 
0.6%
Other Punctuation 14
 
0.6%
Dash Punctuation 1
 
< 0.1%
Lowercase Letter 1
 
< 0.1%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 146
 
8.2%
출 121
 
6.8%
번 118
 
6.6%
역 117
 
6.5%
앞 85
 
4.8%
대 37
 
2.1%
근 31
 
1.7%
사 29
 
1.6%
이 26
 
1.5%
스 24
 
1.3%
Other values (261) 1055
59.0%
Decimal Number
ValueCountFrequency (%)
1 34
19.9%
4 27
15.8%
2 25
14.6%
3 21
12.3%
5 18
10.5%
0 11
 
6.4%
7 11
 
6.4%
6 10
 
5.8%
8 8
 
4.7%
9 6
 
3.5%
Uppercase Letter
ValueCountFrequency (%)
G 3
20.0%
K 2
13.3%
T 2
13.3%
B 2
13.3%
L 2
13.3%
A 1
 
6.7%
Y 1
 
6.7%
M 1
 
6.7%
I 1
 
6.7%
Other Punctuation
ValueCountFrequency (%)
/ 7
50.0%
, 6
42.9%
? 1
 
7.1%
Space Separator
ValueCountFrequency (%)
470
100.0%
Open Punctuation
ValueCountFrequency (%)
( 31
100.0%
Close Punctuation
ValueCountFrequency (%)
) 31
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%
Lowercase Letter
ValueCountFrequency (%)
m 1
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1789
70.9%
Common 719
28.5%
Latin 16
 
0.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 146
 
8.2%
출 121
 
6.8%
번 118
 
6.6%
역 117
 
6.5%
앞 85
 
4.8%
대 37
 
2.1%
근 31
 
1.7%
사 29
 
1.6%
이 26
 
1.5%
스 24
 
1.3%
Other values (261) 1055
59.0%
Common
ValueCountFrequency (%)
470
65.4%
1 34
 
4.7%
( 31
 
4.3%
) 31
 
4.3%
4 27
 
3.8%
2 25
 
3.5%
3 21
 
2.9%
5 18
 
2.5%
0 11
 
1.5%
7 11
 
1.5%
Other values (8) 40
 
5.6%
Latin
ValueCountFrequency (%)
G 3
18.8%
K 2
12.5%
T 2
12.5%
B 2
12.5%
L 2
12.5%
m 1
 
6.2%
A 1
 
6.2%
Y 1
 
6.2%
M 1
 
6.2%
I 1
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1789
70.9%
ASCII 735
29.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
470
63.9%
1 34
 
4.6%
( 31
 
4.2%
) 31
 
4.2%
4 27
 
3.7%
2 25
 
3.4%
3 21
 
2.9%
5 18
 
2.4%
0 11
 
1.5%
7 11
 
1.5%
Other values (18) 56
 
7.6%
Hangul
ValueCountFrequency (%)
구 146
 
8.2%
출 121
 
6.8%
번 118
 
6.6%
역 117
 
6.5%
앞 85
 
4.8%
대 37
 
2.1%
근 31
 
1.7%
사 29
 
1.6%
이 26
 
1.5%
스 24
 
1.3%
Other values (261) 1055
59.0%

거치대 유무
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size318.0 B
False
130 
True
60 
ValueCountFrequency (%)
False 130
68.4%
True 60
31.6%
2024-04-21T05:56:13.778768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

거치대 크기
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
<NA>
109 
6
46 
8
15 
5
13 
4
 
6

Length

Max length4
Median length4
Mean length2.7210526
Min length1

Unique

Unique1 ?
Unique (%)0.5%

Sample

1st row6
2nd row6
3rd row6
4th row8
5th row8

Common Values

ValueCountFrequency (%)
<NA> 109
57.4%
6 46
24.2%
8 15
 
7.9%
5 13
 
6.8%
4 6
 
3.2%
3 1
 
0.5%

Length

2024-04-21T05:56:14.139893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T05:56:14.480844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 109
57.4%
6 46
24.2%
8 15
 
7.9%
5 13
 
6.8%
4 6
 
3.2%
3 1
 
0.5%

Interactions

2024-04-21T05:56:05.884219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-21T05:56:14.707638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번시군구명거치대 유무거치대 크기
순번1.0000.9510.9110.735
시군구명0.9511.0000.9560.818
거치대 유무0.9110.9561.0000.540
거치대 크기0.7350.8180.5401.000
2024-04-21T05:56:14.960139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군구명거치대 크기거치대 유무
시군구명1.0000.5650.873
거치대 크기0.5651.0000.641
거치대 유무0.8730.6411.000
2024-04-21T05:56:15.206075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번시군구명거치대 유무거치대 크기
순번1.0000.7380.7350.558
시군구명0.7381.0000.8730.565
거치대 유무0.7350.8731.0000.641
거치대 크기0.5580.5650.6411.000

Missing values

2024-04-21T05:56:06.236869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-21T05:56:06.601892image/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

순번시군구명주소상세위치거치대 유무거치대 크기
01종로구팔판동 115-63청와대 춘추문 맞은편 인근Y6
12종로구연건동 218-1KT광화문 혜화지사 앞Y6
23종로구연건동 178-3홍익대학교 대학로 맞은편Y6
34종로구동승동 1-24대학로 마로니에공원 앞Y8
45종로구와룡동 75-4연악사 맞은편Y8
56종로구명륜4가 96-4흥사단 동숭미술관 맞은편Y6
67종로구소격동 165-5국립현대미술관 앞Y6
78종로구신문로1가 5-4새문안교회 앞Y8
89종로구신문로 2가 58(구) 경찰박물관 앞Y8
910종로구무악동 41-7무악현대아파트 앞Y6
순번시군구명주소상세위치거치대 유무거치대 크기
180181송파구방이동 89-28올림픽공원역 4번 출구 측면Y8
181182송파구잠실동 50종합운동장역 4번 출구 앞Y8
182183강동구명일동 46-5고덕역 4번 출구 인근N<NA>
183184강동구명일동 303-1명일역 2번 출구 인근N<NA>
184185강동구암사동 524선사고등학교 앞Y8
185186강동구강일동 679-6강동공영차고지 버스정류장(25101) 인근Y6
186187강동구강일동 681강일리버파크3단지308동 버스정류장(25521) 인근Y6
187188구로구구로동 188-15구로G밸리비즈플자라 하나은행 앞Y6
188189동작구상도동 511숭실대입구 3번출구N<NA>
189190성북구길음동 877-66길음역 3번출구 뒷편N<NA>