Overview

Dataset statistics

Number of variables5
Number of observations234
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory10.0 KiB
Average record size in memory43.6 B

Variable types

Numeric1
Text2
Categorical2

Dataset

Description서울특별시 영등포구 관내 이면도로에 설치된 제설함 위치 등 현황입니다 제공내용: 관리번호, 위치, 염화칼슘, 바가지
URLhttps://www.data.go.kr/data/15048832/fileData.do

Alerts

염화칼슘(포) has constant value ""Constant
연번 is highly overall correlated with 바가지High correlation
바가지 is highly overall correlated with 연번High correlation
바가지 is highly imbalanced (92.9%)Imbalance
연번 has unique valuesUnique
관리번호 has unique valuesUnique

Reproduction

Analysis started2023-12-11 23:02:41.857289
Analysis finished2023-12-11 23:02:42.222412
Duration0.37 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct234
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean117.5
Minimum1
Maximum234
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2023-12-12T08:02:42.281951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile12.65
Q159.25
median117.5
Q3175.75
95-th percentile222.35
Maximum234
Range233
Interquartile range (IQR)116.5

Descriptive statistics

Standard deviation67.694165
Coefficient of variation (CV)0.57612055
Kurtosis-1.2
Mean117.5
Median Absolute Deviation (MAD)58.5
Skewness0
Sum27495
Variance4582.5
MonotonicityStrictly increasing
2023-12-12T08:02:42.453176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.4%
162 1
 
0.4%
150 1
 
0.4%
151 1
 
0.4%
152 1
 
0.4%
153 1
 
0.4%
154 1
 
0.4%
155 1
 
0.4%
156 1
 
0.4%
157 1
 
0.4%
Other values (224) 224
95.7%
ValueCountFrequency (%)
1 1
0.4%
2 1
0.4%
3 1
0.4%
4 1
0.4%
5 1
0.4%
6 1
0.4%
7 1
0.4%
8 1
0.4%
9 1
0.4%
10 1
0.4%
ValueCountFrequency (%)
234 1
0.4%
233 1
0.4%
232 1
0.4%
231 1
0.4%
230 1
0.4%
229 1
0.4%
228 1
0.4%
227 1
0.4%
226 1
0.4%
225 1
0.4%

관리번호
Text

UNIQUE 

Distinct234
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
2023-12-12T08:02:42.775650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length6
Mean length6.3205128
Min length5

Characters and Unicode

Total characters1479
Distinct characters27
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

Unique234 ?
Unique (%)100.0%

Sample

1st row영등포본동-1
2nd row영등포본동-2
3rd row영등포본동-3
4th row영등포본동-4
5th row영등포본동-5
ValueCountFrequency (%)
영등포본동-1 1
 
0.4%
신길5동-2 1
 
0.4%
신길4동-15 1
 
0.4%
신길6동-8 1
 
0.4%
신길4동-16 1
 
0.4%
신길4동-17 1
 
0.4%
신길4동-18 1
 
0.4%
신길4동-19 1
 
0.4%
신길4동-20 1
 
0.4%
신길4동-21 1
 
0.4%
Other values (224) 224
95.7%
2023-12-12T08:02:43.202220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 234
15.8%
- 234
15.8%
1 145
9.8%
길 120
 
8.1%
신 120
 
8.1%
2 75
 
5.1%
3 66
 
4.5%
4 51
 
3.4%
림 50
 
3.4%
6 48
 
3.2%
Other values (17) 336
22.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 743
50.2%
Decimal Number 502
33.9%
Dash Punctuation 234
 
15.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 234
31.5%
길 120
16.2%
신 120
16.2%
림 50
 
6.7%
대 29
 
3.9%
등 26
 
3.5%
포 26
 
3.5%
영 26
 
3.5%
도 21
 
2.8%
본 15
 
2.0%
Other values (6) 76
 
10.2%
Decimal Number
ValueCountFrequency (%)
1 145
28.9%
2 75
14.9%
3 66
13.1%
4 51
 
10.2%
6 48
 
9.6%
7 33
 
6.6%
5 32
 
6.4%
8 20
 
4.0%
9 17
 
3.4%
0 15
 
3.0%
Dash Punctuation
ValueCountFrequency (%)
- 234
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 743
50.2%
Common 736
49.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 234
31.5%
길 120
16.2%
신 120
16.2%
림 50
 
6.7%
대 29
 
3.9%
등 26
 
3.5%
포 26
 
3.5%
영 26
 
3.5%
도 21
 
2.8%
본 15
 
2.0%
Other values (6) 76
 
10.2%
Common
ValueCountFrequency (%)
- 234
31.8%
1 145
19.7%
2 75
 
10.2%
3 66
 
9.0%
4 51
 
6.9%
6 48
 
6.5%
7 33
 
4.5%
5 32
 
4.3%
8 20
 
2.7%
9 17
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 743
50.2%
ASCII 736
49.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 234
31.5%
길 120
16.2%
신 120
16.2%
림 50
 
6.7%
대 29
 
3.9%
등 26
 
3.5%
포 26
 
3.5%
영 26
 
3.5%
도 21
 
2.8%
본 15
 
2.0%
Other values (6) 76
 
10.2%
ASCII
ValueCountFrequency (%)
- 234
31.8%
1 145
19.7%
2 75
 
10.2%
3 66
 
9.0%
4 51
 
6.9%
6 48
 
6.5%
7 33
 
4.5%
5 32
 
4.3%
8 20
 
2.7%
9 17
 
2.3%

위치
Text

Distinct233
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
2023-12-12T08:02:43.525102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length33
Median length24
Mean length12.034188
Min length6

Characters and Unicode

Total characters2816
Distinct characters195
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

Unique232 ?
Unique (%)99.1%

Sample

1st row영등포로62라길 2-12
2nd row영등포로60길 5
3rd row신길로62길 17-12
4th row신길로60길 36
5th row도신로53길 19-1
ValueCountFrequency (%)
앞 22
 
3.9%
신길로 14
 
2.5%
9 10
 
1.8%
신길로42길 9
 
1.6%
가마산로 8
 
1.4%
7 7
 
1.2%
도신로 7
 
1.2%
뒤 6
 
1.1%
대방천로 6
 
1.1%
13 6
 
1.1%
Other values (349) 473
83.3%
2023-12-12T08:02:43.952607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
349
 
12.4%
로 234
 
8.3%
길 223
 
7.9%
1 213
 
7.6%
2 115
 
4.1%
4 102
 
3.6%
신 100
 
3.6%
3 92
 
3.3%
5 67
 
2.4%
9 66
 
2.3%
Other values (185) 1255
44.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1461
51.9%
Decimal Number 851
30.2%
Space Separator 349
 
12.4%
Close Punctuation 51
 
1.8%
Open Punctuation 51
 
1.8%
Dash Punctuation 44
 
1.6%
Other Punctuation 6
 
0.2%
Uppercase Letter 3
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 234
 
16.0%
길 223
 
15.3%
신 100
 
6.8%
도 59
 
4.0%
대 46
 
3.1%
영 37
 
2.5%
림 36
 
2.5%
방 33
 
2.3%
가 29
 
2.0%
의 26
 
1.8%
Other values (166) 638
43.7%
Decimal Number
ValueCountFrequency (%)
1 213
25.0%
2 115
13.5%
4 102
12.0%
3 92
10.8%
5 67
 
7.9%
9 66
 
7.8%
6 55
 
6.5%
7 51
 
6.0%
8 50
 
5.9%
0 40
 
4.7%
Uppercase Letter
ValueCountFrequency (%)
D 1
33.3%
S 1
33.3%
G 1
33.3%
Other Punctuation
ValueCountFrequency (%)
@ 4
66.7%
, 2
33.3%
Space Separator
ValueCountFrequency (%)
349
100.0%
Close Punctuation
ValueCountFrequency (%)
) 51
100.0%
Open Punctuation
ValueCountFrequency (%)
( 51
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 44
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1461
51.9%
Common 1352
48.0%
Latin 3
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 234
 
16.0%
길 223
 
15.3%
신 100
 
6.8%
도 59
 
4.0%
대 46
 
3.1%
영 37
 
2.5%
림 36
 
2.5%
방 33
 
2.3%
가 29
 
2.0%
의 26
 
1.8%
Other values (166) 638
43.7%
Common
ValueCountFrequency (%)
349
25.8%
1 213
15.8%
2 115
 
8.5%
4 102
 
7.5%
3 92
 
6.8%
5 67
 
5.0%
9 66
 
4.9%
6 55
 
4.1%
) 51
 
3.8%
7 51
 
3.8%
Other values (6) 191
14.1%
Latin
ValueCountFrequency (%)
D 1
33.3%
S 1
33.3%
G 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1461
51.9%
ASCII 1355
48.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
349
25.8%
1 213
15.7%
2 115
 
8.5%
4 102
 
7.5%
3 92
 
6.8%
5 67
 
4.9%
9 66
 
4.9%
6 55
 
4.1%
) 51
 
3.8%
7 51
 
3.8%
Other values (9) 194
14.3%
Hangul
ValueCountFrequency (%)
로 234
 
16.0%
길 223
 
15.3%
신 100
 
6.8%
도 59
 
4.0%
대 46
 
3.1%
영 37
 
2.5%
림 36
 
2.5%
방 33
 
2.3%
가 29
 
2.0%
의 26
 
1.8%
Other values (166) 638
43.7%

염화칼슘(포)
Categorical

CONSTANT 

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
3
234 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3 234
100.0%

Length

2023-12-12T08:02:44.074212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T08:02:44.152282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3 234
100.0%

바가지
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
1
232 
<NA>
 
2

Length

Max length4
Median length1
Mean length1.025641
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
1 232
99.1%
<NA> 2
 
0.9%

Length

2023-12-12T08:02:44.242472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T08:02:44.322044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 232
99.1%
na 2
 
0.9%

Interactions

2023-12-12T08:02:42.024353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T08:02:44.371643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번
연번1.000
2023-12-12T08:02:44.446087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번바가지
연번1.0001.000
바가지1.0001.000

Missing values

2023-12-12T08:02:42.109646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T08:02:42.189738image/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

연번관리번호위치염화칼슘(포)바가지
01영등포본동-1영등포로62라길 2-1231
12영등포본동-2영등포로60길 531
23영등포본동-3신길로62길 17-1231
34영등포본동-4신길로60길 3631
45영등포본동-5도신로53길 19-131
56영등포본동-6신길로60길 2031
67영등포본동-7신길로60가길 231
78영등포본동-8도신로47가길 6-131
89영등포본동-9신길로54길 1031
910영등포본동-10영신로8길 7-231
연번관리번호위치염화칼슘(포)바가지
224225대림3동-4도림로31길2831
225226대림3동-5대림로 31길4031
226227대림3동-6가마산로 312(신동아@,정문)31
227228대림3동-7가마산로31길2631
228229대림3동-8가마산로31길3231
229230대림3동-9도신로4길10(현대2차@,정문)31
230231대림3동-10도림로47가길431
231232대림3동-11도림로47가길 931
232233대림3동-12도영로2-5(코오롱@정문)31
233234대림3동-13도영로2-5(코오롱@후문)31