Overview

Dataset statistics

Number of variables8
Number of observations169
Missing cells3
Missing cells (%)0.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory11.4 KiB
Average record size in memory68.8 B

Variable types

Numeric1
Categorical4
Text3

Dataset

Description서울특별시 영등포구 관내 간선도로에 설치된 제설함 위치 등 현황입니다. 제공내용: 관리자, 도로명, 위치, 장소, 염화칼슘, 모래주머니, 삽
URLhttps://www.data.go.kr/data/15048833/fileData.do

Alerts

관리자 has constant value ""Constant
염화칼슘(포) has constant value ""Constant
삽(개) has constant value ""Constant
연번 is highly overall correlated with 제설모래주머니(개)High correlation
제설모래주머니(개) is highly overall correlated with 연번High correlation
장소 has 2 (1.2%) missing valuesMissing
연번 has unique valuesUnique

Reproduction

Analysis started2023-12-12 20:24:57.133054
Analysis finished2023-12-12 20:24:57.892096
Duration0.76 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct169
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean85
Minimum1
Maximum169
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-12-13T05:24:57.957210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile9.4
Q143
median85
Q3127
95-th percentile160.6
Maximum169
Range168
Interquartile range (IQR)84

Descriptive statistics

Standard deviation48.930222
Coefficient of variation (CV)0.57564968
Kurtosis-1.2
Mean85
Median Absolute Deviation (MAD)42
Skewness0
Sum14365
Variance2394.1667
MonotonicityStrictly increasing
2023-12-13T05:24:58.094208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.6%
117 1
 
0.6%
109 1
 
0.6%
110 1
 
0.6%
111 1
 
0.6%
112 1
 
0.6%
113 1
 
0.6%
114 1
 
0.6%
115 1
 
0.6%
116 1
 
0.6%
Other values (159) 159
94.1%
ValueCountFrequency (%)
1 1
0.6%
2 1
0.6%
3 1
0.6%
4 1
0.6%
5 1
0.6%
6 1
0.6%
7 1
0.6%
8 1
0.6%
9 1
0.6%
10 1
0.6%
ValueCountFrequency (%)
169 1
0.6%
168 1
0.6%
167 1
0.6%
166 1
0.6%
165 1
0.6%
164 1
0.6%
163 1
0.6%
162 1
0.6%
161 1
0.6%
160 1
0.6%

관리자
Categorical

CONSTANT 

Distinct1
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
도로과
169 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
도로과 169
100.0%

Length

2023-12-13T05:24:58.553987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:24:58.701323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
도로과 169
100.0%

도로명
Text

Distinct76
Distinct (%)45.0%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
2023-12-13T05:24:58.954789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length8
Mean length4.6331361
Min length3

Characters and Unicode

Total characters783
Distinct characters65
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

Unique45 ?
Unique (%)26.6%

Sample

1st row경인로
2nd row여의대로
3rd row여의동로
4th row여의동로
5th row여의동로
ValueCountFrequency (%)
가마산로 11
 
6.5%
영등포로 8
 
4.7%
경인로 8
 
4.7%
신길로 8
 
4.7%
국회대로 7
 
4.1%
도림로 7
 
4.1%
여의대방로 6
 
3.6%
선유로 6
 
3.6%
여의동로 6
 
3.6%
여의서로 4
 
2.4%
Other values (66) 98
58.0%
2023-12-13T05:24:59.404803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
로 168
21.5%
길 59
 
7.5%
대 35
 
4.5%
산 34
 
4.3%
의 29
 
3.7%
여 27
 
3.4%
신 24
 
3.1%
가 20
 
2.6%
영 19
 
2.4%
3 19
 
2.4%
Other values (55) 349
44.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 667
85.2%
Decimal Number 115
 
14.7%
Dash Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 168
25.2%
길 59
 
8.8%
대 35
 
5.2%
산 34
 
5.1%
의 29
 
4.3%
여 27
 
4.0%
신 24
 
3.6%
가 20
 
3.0%
영 19
 
2.8%
도 18
 
2.7%
Other values (44) 234
35.1%
Decimal Number
ValueCountFrequency (%)
3 19
16.5%
2 17
14.8%
1 17
14.8%
4 15
13.0%
5 12
10.4%
9 10
8.7%
7 9
7.8%
6 8
7.0%
0 5
 
4.3%
8 3
 
2.6%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 667
85.2%
Common 116
 
14.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 168
25.2%
길 59
 
8.8%
대 35
 
5.2%
산 34
 
5.1%
의 29
 
4.3%
여 27
 
4.0%
신 24
 
3.6%
가 20
 
3.0%
영 19
 
2.8%
도 18
 
2.7%
Other values (44) 234
35.1%
Common
ValueCountFrequency (%)
3 19
16.4%
2 17
14.7%
1 17
14.7%
4 15
12.9%
5 12
10.3%
9 10
8.6%
7 9
7.8%
6 8
6.9%
0 5
 
4.3%
8 3
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 667
85.2%
ASCII 116
 
14.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
로 168
25.2%
길 59
 
8.8%
대 35
 
5.2%
산 34
 
5.1%
의 29
 
4.3%
여 27
 
4.0%
신 24
 
3.6%
가 20
 
3.0%
영 19
 
2.8%
도 18
 
2.7%
Other values (44) 234
35.1%
ASCII
ValueCountFrequency (%)
3 19
16.4%
2 17
14.7%
1 17
14.7%
4 15
12.9%
5 12
10.3%
9 10
8.6%
7 9
7.8%
6 8
6.9%
0 5
 
4.3%
8 3
 
2.6%

위치
Text

Distinct152
Distinct (%)90.5%
Missing1
Missing (%)0.6%
Memory size1.4 KiB
2023-12-13T05:24:59.705751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length12
Mean length6.9285714
Min length2

Characters and Unicode

Total characters1164
Distinct characters175
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique138 ?
Unique (%)82.1%

Sample

1st row서울교북단
2nd row여의버스 환승센타
3rd row마포대교입구
4th row여의도상류 IC
5th row여의상류IC여의동진입
ValueCountFrequency (%)
대림3동 3
 
1.7%
신길동 3
 
1.7%
도림초등학교 2
 
1.1%
여의2교 2
 
1.1%
영등포동7가 2
 
1.1%
영등포구청 2
 
1.1%
당중초등학교 2
 
1.1%
목동교(동 2
 
1.1%
대방초등학교 2
 
1.1%
대길초등학교 2
 
1.1%
Other values (150) 155
87.6%
2023-12-13T05:25:00.127087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
교 62
 
5.3%
동 54
 
4.6%
등 52
 
4.5%
신 33
 
2.8%
영 33
 
2.8%
초 33
 
2.8%
학 30
 
2.6%
길 29
 
2.5%
대 25
 
2.1%
가 25
 
2.1%
Other values (165) 788
67.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 983
84.5%
Decimal Number 85
 
7.3%
Uppercase Letter 44
 
3.8%
Other Punctuation 18
 
1.5%
Close Punctuation 11
 
0.9%
Open Punctuation 10
 
0.9%
Space Separator 9
 
0.8%
Dash Punctuation 4
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
교 62
 
6.3%
동 54
 
5.5%
등 52
 
5.3%
신 33
 
3.4%
영 33
 
3.4%
초 33
 
3.4%
학 30
 
3.1%
길 29
 
3.0%
대 25
 
2.5%
가 25
 
2.5%
Other values (140) 607
61.7%
Decimal Number
ValueCountFrequency (%)
3 18
21.2%
2 14
16.5%
1 12
14.1%
4 10
11.8%
0 8
9.4%
5 7
 
8.2%
7 5
 
5.9%
6 5
 
5.9%
8 3
 
3.5%
9 3
 
3.5%
Uppercase Letter
ValueCountFrequency (%)
P 10
22.7%
T 10
22.7%
A 10
22.7%
R 3
 
6.8%
C 3
 
6.8%
K 2
 
4.5%
S 2
 
4.5%
I 2
 
4.5%
M 1
 
2.3%
B 1
 
2.3%
Other Punctuation
ValueCountFrequency (%)
. 18
100.0%
Close Punctuation
ValueCountFrequency (%)
) 11
100.0%
Open Punctuation
ValueCountFrequency (%)
( 10
100.0%
Space Separator
ValueCountFrequency (%)
9
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 983
84.5%
Common 137
 
11.8%
Latin 44
 
3.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
교 62
 
6.3%
동 54
 
5.5%
등 52
 
5.3%
신 33
 
3.4%
영 33
 
3.4%
초 33
 
3.4%
학 30
 
3.1%
길 29
 
3.0%
대 25
 
2.5%
가 25
 
2.5%
Other values (140) 607
61.7%
Common
ValueCountFrequency (%)
3 18
13.1%
. 18
13.1%
2 14
10.2%
1 12
8.8%
) 11
8.0%
4 10
7.3%
( 10
7.3%
9
6.6%
0 8
 
5.8%
5 7
 
5.1%
Other values (5) 20
14.6%
Latin
ValueCountFrequency (%)
P 10
22.7%
T 10
22.7%
A 10
22.7%
R 3
 
6.8%
C 3
 
6.8%
K 2
 
4.5%
S 2
 
4.5%
I 2
 
4.5%
M 1
 
2.3%
B 1
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 982
84.4%
ASCII 181
 
15.5%
Compat Jamo 1
 
0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
교 62
 
6.3%
동 54
 
5.5%
등 52
 
5.3%
신 33
 
3.4%
영 33
 
3.4%
초 33
 
3.4%
학 30
 
3.1%
길 29
 
3.0%
대 25
 
2.5%
가 25
 
2.5%
Other values (139) 606
61.7%
ASCII
ValueCountFrequency (%)
3 18
 
9.9%
. 18
 
9.9%
2 14
 
7.7%
1 12
 
6.6%
) 11
 
6.1%
P 10
 
5.5%
4 10
 
5.5%
T 10
 
5.5%
( 10
 
5.5%
A 10
 
5.5%
Other values (15) 58
32.0%
Compat Jamo
ValueCountFrequency (%)
ㅣ 1
100.0%

장소
Text

MISSING 

Distinct159
Distinct (%)95.2%
Missing2
Missing (%)1.2%
Memory size1.4 KiB
2023-12-13T05:25:00.433349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length11
Mean length7.6107784
Min length3

Characters and Unicode

Total characters1271
Distinct characters204
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique155 ?
Unique (%)92.8%

Sample

1st row서울교여의동로입구교통섬
2nd row여의도버스환승정류장
3rd row마포대교남단 교통섬
4th row노들길진입로
5th row63B/D뒤
ValueCountFrequency (%)
육교 9
 
5.1%
2개 6
 
3.4%
영림초교후문 2
 
1.1%
영등포공원앞 2
 
1.1%
3개 2
 
1.1%
신길7동주민센터앞 1
 
0.6%
신길3동주민센터입구 1
 
0.6%
보라매역4번출구 1
 
0.6%
광야교회(동함 1
 
0.6%
대림공원사거리 1
 
0.6%
Other values (151) 151
85.3%
2023-12-13T05:25:00.857616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
교 60
 
4.7%
. 52
 
4.1%
초 30
 
2.4%
문 29
 
2.3%
동 26
 
2.0%
대 26
 
2.0%
앞 26
 
2.0%
T 25
 
2.0%
P 25
 
2.0%
A 25
 
2.0%
Other values (194) 947
74.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1025
80.6%
Uppercase Letter 87
 
6.8%
Other Punctuation 55
 
4.3%
Decimal Number 54
 
4.2%
Open Punctuation 19
 
1.5%
Close Punctuation 19
 
1.5%
Space Separator 10
 
0.8%
Math Symbol 2
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
교 60
 
5.9%
초 30
 
2.9%
문 29
 
2.8%
동 26
 
2.5%
대 26
 
2.5%
앞 26
 
2.5%
영 25
 
2.4%
리 22
 
2.1%
거 21
 
2.0%
신 21
 
2.0%
Other values (170) 739
72.1%
Decimal Number
ValueCountFrequency (%)
2 14
25.9%
1 13
24.1%
0 9
16.7%
3 7
13.0%
4 4
 
7.4%
6 3
 
5.6%
7 2
 
3.7%
5 1
 
1.9%
8 1
 
1.9%
Uppercase Letter
ValueCountFrequency (%)
T 25
28.7%
P 25
28.7%
A 25
28.7%
B 4
 
4.6%
D 3
 
3.4%
R 3
 
3.4%
S 1
 
1.1%
K 1
 
1.1%
Other Punctuation
ValueCountFrequency (%)
. 52
94.5%
/ 3
 
5.5%
Math Symbol
ValueCountFrequency (%)
< 1
50.0%
> 1
50.0%
Open Punctuation
ValueCountFrequency (%)
( 19
100.0%
Close Punctuation
ValueCountFrequency (%)
) 19
100.0%
Space Separator
ValueCountFrequency (%)
10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1025
80.6%
Common 159
 
12.5%
Latin 87
 
6.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
교 60
 
5.9%
초 30
 
2.9%
문 29
 
2.8%
동 26
 
2.5%
대 26
 
2.5%
앞 26
 
2.5%
영 25
 
2.4%
리 22
 
2.1%
거 21
 
2.0%
신 21
 
2.0%
Other values (170) 739
72.1%
Common
ValueCountFrequency (%)
. 52
32.7%
( 19
 
11.9%
) 19
 
11.9%
2 14
 
8.8%
1 13
 
8.2%
10
 
6.3%
0 9
 
5.7%
3 7
 
4.4%
4 4
 
2.5%
/ 3
 
1.9%
Other values (6) 9
 
5.7%
Latin
ValueCountFrequency (%)
T 25
28.7%
P 25
28.7%
A 25
28.7%
B 4
 
4.6%
D 3
 
3.4%
R 3
 
3.4%
S 1
 
1.1%
K 1
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1025
80.6%
ASCII 246
 
19.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
교 60
 
5.9%
초 30
 
2.9%
문 29
 
2.8%
동 26
 
2.5%
대 26
 
2.5%
앞 26
 
2.5%
영 25
 
2.4%
리 22
 
2.1%
거 21
 
2.0%
신 21
 
2.0%
Other values (170) 739
72.1%
ASCII
ValueCountFrequency (%)
. 52
21.1%
T 25
10.2%
P 25
10.2%
A 25
10.2%
( 19
 
7.7%
) 19
 
7.7%
2 14
 
5.7%
1 13
 
5.3%
10
 
4.1%
0 9
 
3.7%
Other values (14) 35
14.2%

염화칼슘(포)
Categorical

CONSTANT 

Distinct1
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
5
169 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5
2nd row5
3rd row5
4th row5
5th row5

Common Values

ValueCountFrequency (%)
5 169
100.0%

Length

2023-12-13T05:25:01.037102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:25:01.159165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
5 169
100.0%

제설모래주머니(개)
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
15
140 
<NA>
29 

Length

Max length4
Median length2
Mean length2.3431953
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row15
2nd row15
3rd row15
4th row15
5th row15

Common Values

ValueCountFrequency (%)
15 140
82.8%
<NA> 29
 
17.2%

Length

2023-12-13T05:25:01.290773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:25:01.424714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
15 140
82.8%
na 29
 
17.2%

삽(개)
Categorical

CONSTANT 

Distinct1
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
1
169 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 169
100.0%

Length

2023-12-13T05:25:01.558943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:25:01.663260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 169
100.0%

Interactions

2023-12-13T05:24:57.459015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T05:25:01.733172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번도로명
연번1.0000.888
도로명0.8881.000
2023-12-13T05:25:01.824563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번제설모래주머니(개)
연번1.0001.000
제설모래주머니(개)1.0001.000

Missing values

2023-12-13T05:24:57.598887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T05:24:57.746570image/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.
2023-12-13T05:24:57.842819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

연번관리자도로명위치장소염화칼슘(포)제설모래주머니(개)삽(개)
01도로과경인로서울교북단서울교여의동로입구교통섬5151
12도로과여의대로여의버스 환승센타여의도버스환승정류장5151
23도로과여의동로마포대교입구마포대교남단 교통섬5151
34도로과여의동로여의도상류 IC노들길진입로5151
45도로과여의동로여의상류IC여의동진입63B/D뒤5151
56도로과여의동로원요대교남단원효대교진입로5151
67도로과여의동로원효대교남단원효대교서출구5151
78도로과여의나루로여의나루역4번출구앞목화아파트5151
89도로과여의서로초원A.P.T월편초원A.P.T월편5151
910도로과여의서로서강대교남교통섬서강대교남교통섬5151
연번관리자도로명위치장소염화칼슘(포)제설모래주머니(개)삽(개)
159160도로과문래로문래초등학교문래초교5<NA>1
160161도로과여의동로샛강다리육교 (6개)5151
161162도로과신길로우신초교육교 (2개)5151
162163도로과도영로영등포역육교 (3개)5151
163164도로과영등포로2길관악고교육교 (2개)5151
164165도로과양산로양평역육교 (2개)5151
165166도로과양평로21길롯데제과육교 (2개)5151
166167도로과노들로인공폭포육교 (2개)5151
167168도로과도영로쌍용A.P.T단지내육교 (2개)5151
168169도로과노들로선유도육교육교 (3개)5151