Overview

Dataset statistics

Number of variables8
Number of observations195
Missing cells99
Missing cells (%)6.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory12.5 KiB
Average record size in memory65.7 B

Variable types

Numeric1
Text6
Categorical1

Dataset

Description인천광역시에서 유지관리하는 도로에 관한 데이터로 도로명, 도로폭, 도로연장, 도로면적, 기점, 총점 등의 항목을 제공합니다.
URLhttps://www.data.go.kr/data/15041791/fileData.do

Alerts

비 고 has 99 (50.8%) missing valuesMissing
번호 has unique valuesUnique

Reproduction

Analysis started2023-12-12 04:03:50.108092
Analysis finished2023-12-12 04:03:50.918897
Duration0.81 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

번호
Real number (ℝ)

UNIQUE 

Distinct195
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean98
Minimum1
Maximum195
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2023-12-12T13:03:51.038160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile10.7
Q149.5
median98
Q3146.5
95-th percentile185.3
Maximum195
Range194
Interquartile range (IQR)97

Descriptive statistics

Standard deviation56.435804
Coefficient of variation (CV)0.57587555
Kurtosis-1.2
Mean98
Median Absolute Deviation (MAD)49
Skewness0
Sum19110
Variance3185
MonotonicityStrictly increasing
2023-12-12T13:03:51.207119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.5%
124 1
 
0.5%
126 1
 
0.5%
127 1
 
0.5%
128 1
 
0.5%
129 1
 
0.5%
130 1
 
0.5%
131 1
 
0.5%
132 1
 
0.5%
133 1
 
0.5%
Other values (185) 185
94.9%
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 (%)
195 1
0.5%
194 1
0.5%
193 1
0.5%
192 1
0.5%
191 1
0.5%
190 1
0.5%
189 1
0.5%
188 1
0.5%
187 1
0.5%
186 1
0.5%

도로명(노선수)
Text

Distinct194
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2023-12-12T13:03:51.586626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length15
Mean length6.5897436
Min length3

Characters and Unicode

Total characters1285
Distinct characters170
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique193 ?
Unique (%)99.0%

Sample

1st row가람로(검단산단)
2nd row가석로156번길
3rd row가정로151번길
4th row가정로152번길
5th row가정로98번길
ValueCountFrequency (%)
드림로 2
 
1.0%
매소홀로 2
 
1.0%
장제로 1
 
0.5%
장승남로 1
 
0.5%
이든로(2)(검단산단 1
 
0.5%
원당대로 1
 
0.5%
원석로 1
 
0.5%
원인재로 1
 
0.5%
원적로 1
 
0.5%
월미로 1
 
0.5%
Other values (185) 185
93.9%
2023-12-12T13:03:52.161978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
로 194
 
15.1%
) 77
 
6.0%
( 77
 
6.0%
1 49
 
3.8%
단 49
 
3.8%
길 42
 
3.3%
대 41
 
3.2%
서 41
 
3.2%
번 39
 
3.0%
지 33
 
2.6%
Other values (160) 643
50.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 970
75.5%
Decimal Number 157
 
12.2%
Close Punctuation 77
 
6.0%
Open Punctuation 77
 
6.0%
Dash Punctuation 2
 
0.2%
Space Separator 2
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 194
20.0%
단 49
 
5.1%
길 42
 
4.3%
대 41
 
4.2%
서 41
 
4.2%
번 39
 
4.0%
지 33
 
3.4%
물 25
 
2.6%
류 24
 
2.5%
정 20
 
2.1%
Other values (146) 462
47.6%
Decimal Number
ValueCountFrequency (%)
1 49
31.2%
2 32
20.4%
6 15
 
9.6%
8 11
 
7.0%
3 10
 
6.4%
9 10
 
6.4%
7 9
 
5.7%
5 8
 
5.1%
0 7
 
4.5%
4 6
 
3.8%
Close Punctuation
ValueCountFrequency (%)
) 77
100.0%
Open Punctuation
ValueCountFrequency (%)
( 77
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%
Space Separator
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 970
75.5%
Common 315
 
24.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 194
20.0%
단 49
 
5.1%
길 42
 
4.3%
대 41
 
4.2%
서 41
 
4.2%
번 39
 
4.0%
지 33
 
3.4%
물 25
 
2.6%
류 24
 
2.5%
정 20
 
2.1%
Other values (146) 462
47.6%
Common
ValueCountFrequency (%)
) 77
24.4%
( 77
24.4%
1 49
15.6%
2 32
10.2%
6 15
 
4.8%
8 11
 
3.5%
3 10
 
3.2%
9 10
 
3.2%
7 9
 
2.9%
5 8
 
2.5%
Other values (4) 17
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 970
75.5%
ASCII 315
 
24.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
로 194
20.0%
단 49
 
5.1%
길 42
 
4.3%
대 41
 
4.2%
서 41
 
4.2%
번 39
 
4.0%
지 33
 
3.4%
물 25
 
2.6%
류 24
 
2.5%
정 20
 
2.1%
Other values (146) 462
47.6%
ASCII
ValueCountFrequency (%)
) 77
24.4%
( 77
24.4%
1 49
15.6%
2 32
10.2%
6 15
 
4.8%
8 11
 
3.5%
3 10
 
3.2%
9 10
 
3.2%
7 9
 
2.9%
5 8
 
2.5%
Other values (4) 17
 
5.4%

폭원(미터)
Categorical

Distinct35
Distinct (%)17.9%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
25
45 
30
33 
35
21 
21
12 
40
11 
Other values (30)
73 

Length

Max length5
Median length2
Mean length2.625641
Min length2

Unique

Unique14 ?
Unique (%)7.2%

Sample

1st row25
2nd row21
3rd row25
4th row25
5th row25

Common Values

ValueCountFrequency (%)
25 45
23.1%
30 33
16.9%
35 21
10.8%
21 12
 
6.2%
40 11
 
5.6%
22 8
 
4.1%
25~30 7
 
3.6%
26 6
 
3.1%
23 4
 
2.1%
25.5 4
 
2.1%
Other values (25) 44
22.6%

Length

2023-12-12T13:03:52.360434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
25 45
23.1%
30 33
16.9%
35 21
10.8%
21 12
 
6.2%
40 11
 
5.6%
22 8
 
4.1%
25~30 7
 
3.6%
26 6
 
3.1%
33 4
 
2.1%
25.5 4
 
2.1%
Other values (25) 44
22.6%

연장(미터)
Text

Distinct182
Distinct (%)93.3%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2023-12-12T13:03:52.915045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length4.1333333
Min length3

Characters and Unicode

Total characters806
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique172 ?
Unique (%)88.2%

Sample

1st row542
2nd row530
3rd row365
4th row330
5th row320
ValueCountFrequency (%)
440 4
 
2.1%
650 3
 
1.5%
1,400 2
 
1.0%
3,350 2
 
1.0%
800 2
 
1.0%
117 2
 
1.0%
1,110 2
 
1.0%
1,430 2
 
1.0%
640 2
 
1.0%
13,000 2
 
1.0%
Other values (172) 172
88.2%
2023-12-12T13:03:53.575703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 151
18.7%
1 105
13.0%
, 103
12.8%
5 79
9.8%
3 74
9.2%
4 69
8.6%
2 60
 
7.4%
7 49
 
6.1%
9 43
 
5.3%
6 42
 
5.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 703
87.2%
Other Punctuation 103
 
12.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 151
21.5%
1 105
14.9%
5 79
11.2%
3 74
10.5%
4 69
9.8%
2 60
 
8.5%
7 49
 
7.0%
9 43
 
6.1%
6 42
 
6.0%
8 31
 
4.4%
Other Punctuation
ValueCountFrequency (%)
, 103
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 806
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 151
18.7%
1 105
13.0%
, 103
12.8%
5 79
9.8%
3 74
9.2%
4 69
8.6%
2 60
 
7.4%
7 49
 
6.1%
9 43
 
5.3%
6 42
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 806
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 151
18.7%
1 105
13.0%
, 103
12.8%
5 79
9.8%
3 74
9.2%
4 69
8.6%
2 60
 
7.4%
7 49
 
6.1%
9 43
 
5.3%
6 42
 
5.2%

면적(제곱미터)
Text

Distinct191
Distinct (%)97.9%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2023-12-12T13:03:54.093799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length6.0871795
Min length5

Characters and Unicode

Total characters1187
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique187 ?
Unique (%)95.9%

Sample

1st row13,550
2nd row11,130
3rd row9,125
4th row8,250
5th row8,000
ValueCountFrequency (%)
11,000 2
 
1.0%
42,000 2
 
1.0%
27,750 2
 
1.0%
2,574 2
 
1.0%
8,375 1
 
0.5%
6,402 1
 
0.5%
511,410 1
 
0.5%
3,608 1
 
0.5%
427,200 1
 
0.5%
32,550 1
 
0.5%
Other values (181) 181
92.8%
2023-12-12T13:03:54.807031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 261
22.0%
, 195
16.4%
1 122
10.3%
5 122
10.3%
2 117
9.9%
3 73
 
6.1%
7 72
 
6.1%
8 63
 
5.3%
4 58
 
4.9%
9 54
 
4.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 992
83.6%
Other Punctuation 195
 
16.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 261
26.3%
1 122
12.3%
5 122
12.3%
2 117
11.8%
3 73
 
7.4%
7 72
 
7.3%
8 63
 
6.4%
4 58
 
5.8%
9 54
 
5.4%
6 50
 
5.0%
Other Punctuation
ValueCountFrequency (%)
, 195
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1187
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 261
22.0%
, 195
16.4%
1 122
10.3%
5 122
10.3%
2 117
9.9%
3 73
 
6.1%
7 72
 
6.1%
8 63
 
5.3%
4 58
 
4.9%
9 54
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1187
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 261
22.0%
, 195
16.4%
1 122
10.3%
5 122
10.3%
2 117
9.9%
3 73
 
6.1%
7 72
 
6.1%
8 63
 
5.3%
4 58
 
4.9%
9 54
 
4.5%

기 점
Text

Distinct145
Distinct (%)74.4%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2023-12-12T13:03:55.152320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length12
Mean length6.6
Min length2

Characters and Unicode

Total characters1287
Distinct characters145
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

Unique115 ?
Unique (%)59.0%

Sample

1st row오류동 1434
2nd row고속도로측도교차점
3rd row염곡로교차점
4th row가정로교차점
5th row가정로교차점
ValueCountFrequency (%)
대로 19
 
7.4%
중로 9
 
3.5%
축항대로교차점 8
 
3.1%
서창동 8
 
3.1%
대 6
 
2.3%
3-23 5
 
1.9%
광로 5
 
1.9%
2-1 4
 
1.6%
1-2 4
 
1.6%
교차점 4
 
1.6%
Other values (147) 185
72.0%
2023-12-12T13:03:55.688598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
로 114
 
8.9%
대 66
 
5.1%
교 66
 
5.1%
차 63
 
4.9%
점 62
 
4.8%
62
 
4.8%
- 60
 
4.7%
1 59
 
4.6%
리 45
 
3.5%
3 43
 
3.3%
Other values (135) 647
50.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 936
72.7%
Decimal Number 208
 
16.2%
Space Separator 62
 
4.8%
Dash Punctuation 60
 
4.7%
Close Punctuation 9
 
0.7%
Open Punctuation 9
 
0.7%
Uppercase Letter 2
 
0.2%
Other Symbol 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 114
 
12.2%
대 66
 
7.1%
교 66
 
7.1%
차 63
 
6.7%
점 62
 
6.6%
리 45
 
4.8%
거 41
 
4.4%
사 23
 
2.5%
서 21
 
2.2%
동 20
 
2.1%
Other values (118) 415
44.3%
Decimal Number
ValueCountFrequency (%)
1 59
28.4%
3 43
20.7%
2 33
15.9%
4 18
 
8.7%
6 14
 
6.7%
0 10
 
4.8%
5 10
 
4.8%
9 8
 
3.8%
7 7
 
3.4%
8 6
 
2.9%
Uppercase Letter
ValueCountFrequency (%)
J 1
50.0%
C 1
50.0%
Space Separator
ValueCountFrequency (%)
62
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 60
100.0%
Close Punctuation
ValueCountFrequency (%)
) 9
100.0%
Open Punctuation
ValueCountFrequency (%)
( 9
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 937
72.8%
Common 348
 
27.0%
Latin 2
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 114
 
12.2%
대 66
 
7.0%
교 66
 
7.0%
차 63
 
6.7%
점 62
 
6.6%
리 45
 
4.8%
거 41
 
4.4%
사 23
 
2.5%
서 21
 
2.2%
동 20
 
2.1%
Other values (119) 416
44.4%
Common
ValueCountFrequency (%)
62
17.8%
- 60
17.2%
1 59
17.0%
3 43
12.4%
2 33
9.5%
4 18
 
5.2%
6 14
 
4.0%
0 10
 
2.9%
5 10
 
2.9%
) 9
 
2.6%
Other values (4) 30
8.6%
Latin
ValueCountFrequency (%)
J 1
50.0%
C 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 936
72.7%
ASCII 350
 
27.2%
None 1
 
0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
로 114
 
12.2%
대 66
 
7.1%
교 66
 
7.1%
차 63
 
6.7%
점 62
 
6.6%
리 45
 
4.8%
거 41
 
4.4%
사 23
 
2.5%
서 21
 
2.2%
동 20
 
2.1%
Other values (118) 415
44.3%
ASCII
ValueCountFrequency (%)
62
17.7%
- 60
17.1%
1 59
16.9%
3 43
12.3%
2 33
9.4%
4 18
 
5.1%
6 14
 
4.0%
0 10
 
2.9%
5 10
 
2.9%
) 9
 
2.6%
Other values (6) 32
9.1%
None
ValueCountFrequency (%)
㈜ 1
100.0%

종 점
Text

Distinct150
Distinct (%)76.9%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2023-12-12T13:03:56.088754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length13
Mean length6.8512821
Min length2

Characters and Unicode

Total characters1336
Distinct characters152
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

Unique126 ?
Unique (%)64.6%

Sample

1st row서측구역계
2nd row건지로348번길
3rd row가정로교차점
4th row고속도로측도교차점
5th row고속도로측도교차점
ValueCountFrequency (%)
대로 23
 
8.9%
1-1 10
 
3.9%
서창동 8
 
3.1%
부천시계 7
 
2.7%
중로 7
 
2.7%
대 5
 
1.9%
은봉로교차점 4
 
1.6%
소래로교차점 4
 
1.6%
김포시 3
 
1.2%
3-1 3
 
1.2%
Other values (154) 184
71.3%
2023-12-12T13:03:56.659580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
로 109
 
8.2%
1 88
 
6.6%
63
 
4.7%
- 63
 
4.7%
대 59
 
4.4%
교 55
 
4.1%
점 52
 
3.9%
차 52
 
3.9%
3 50
 
3.7%
리 29
 
2.2%
Other values (142) 716
53.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 932
69.8%
Decimal Number 246
 
18.4%
Space Separator 63
 
4.7%
Dash Punctuation 63
 
4.7%
Open Punctuation 12
 
0.9%
Close Punctuation 12
 
0.9%
Uppercase Letter 7
 
0.5%
Other Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 109
 
11.7%
대 59
 
6.3%
교 55
 
5.9%
점 52
 
5.6%
차 52
 
5.6%
리 29
 
3.1%
거 27
 
2.9%
동 22
 
2.4%
서 20
 
2.1%
계 19
 
2.0%
Other values (122) 488
52.4%
Decimal Number
ValueCountFrequency (%)
1 88
35.8%
3 50
20.3%
2 24
 
9.8%
5 19
 
7.7%
4 16
 
6.5%
9 13
 
5.3%
6 12
 
4.9%
7 11
 
4.5%
0 7
 
2.8%
8 6
 
2.4%
Uppercase Letter
ValueCountFrequency (%)
C 2
28.6%
I 2
28.6%
P 1
14.3%
A 1
14.3%
T 1
14.3%
Space Separator
ValueCountFrequency (%)
63
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 63
100.0%
Open Punctuation
ValueCountFrequency (%)
( 12
100.0%
Close Punctuation
ValueCountFrequency (%)
) 12
100.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 932
69.8%
Common 397
29.7%
Latin 7
 
0.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 109
 
11.7%
대 59
 
6.3%
교 55
 
5.9%
점 52
 
5.6%
차 52
 
5.6%
리 29
 
3.1%
거 27
 
2.9%
동 22
 
2.4%
서 20
 
2.1%
계 19
 
2.0%
Other values (122) 488
52.4%
Common
ValueCountFrequency (%)
1 88
22.2%
63
15.9%
- 63
15.9%
3 50
12.6%
2 24
 
6.0%
5 19
 
4.8%
4 16
 
4.0%
9 13
 
3.3%
6 12
 
3.0%
( 12
 
3.0%
Other values (5) 37
9.3%
Latin
ValueCountFrequency (%)
C 2
28.6%
I 2
28.6%
P 1
14.3%
A 1
14.3%
T 1
14.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 932
69.8%
ASCII 404
30.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
로 109
 
11.7%
대 59
 
6.3%
교 55
 
5.9%
점 52
 
5.6%
차 52
 
5.6%
리 29
 
3.1%
거 27
 
2.9%
동 22
 
2.4%
서 20
 
2.1%
계 19
 
2.0%
Other values (122) 488
52.4%
ASCII
ValueCountFrequency (%)
1 88
21.8%
63
15.6%
- 63
15.6%
3 50
12.4%
2 24
 
5.9%
5 19
 
4.7%
4 16
 
4.0%
9 13
 
3.2%
6 12
 
3.0%
( 12
 
3.0%
Other values (10) 44
10.9%

비 고
Text

MISSING 

Distinct93
Distinct (%)96.9%
Missing99
Missing (%)50.8%
Memory size1.7 KiB
2023-12-12T13:03:57.017772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length17
Mean length6.1354167
Min length4

Characters and Unicode

Total characters589
Distinct characters33
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique90 ?
Unique (%)93.8%

Sample

1st row대3-114
2nd row중1-174
3rd row대3-3
4th row대3-5
5th row대3-4
ValueCountFrequency (%)
중 4
 
3.8%
대 3
 
2.9%
대3-2 2
 
1.9%
3-9 2
 
1.9%
광3-8 2
 
1.9%
대3-180 1
 
1.0%
대3-109 1
 
1.0%
광3-7 1
 
1.0%
중1-368 1
 
1.0%
중1-369 1
 
1.0%
Other values (87) 87
82.9%
2023-12-12T13:03:57.512310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 107
18.2%
1 100
17.0%
대 65
11.0%
3 65
11.0%
2 37
 
6.3%
중 31
 
5.3%
6 26
 
4.4%
5 20
 
3.4%
8 19
 
3.2%
4 16
 
2.7%
Other values (23) 103
17.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 322
54.7%
Other Letter 126
 
21.4%
Dash Punctuation 107
 
18.2%
Other Punctuation 13
 
2.2%
Space Separator 9
 
1.5%
Close Punctuation 6
 
1.0%
Open Punctuation 6
 
1.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
대 65
51.6%
중 31
24.6%
광 10
 
7.9%
단 3
 
2.4%
서 2
 
1.6%
운 2
 
1.6%
산 2
 
1.6%
북 1
 
0.8%
항 1
 
0.8%
배 1
 
0.8%
Other values (8) 8
 
6.3%
Decimal Number
ValueCountFrequency (%)
1 100
31.1%
3 65
20.2%
2 37
 
11.5%
6 26
 
8.1%
5 20
 
6.2%
8 19
 
5.9%
4 16
 
5.0%
0 15
 
4.7%
7 14
 
4.3%
9 10
 
3.1%
Dash Punctuation
ValueCountFrequency (%)
- 107
100.0%
Other Punctuation
ValueCountFrequency (%)
, 13
100.0%
Space Separator
ValueCountFrequency (%)
9
100.0%
Close Punctuation
ValueCountFrequency (%)
) 6
100.0%
Open Punctuation
ValueCountFrequency (%)
( 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 463
78.6%
Hangul 126
 
21.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
대 65
51.6%
중 31
24.6%
광 10
 
7.9%
단 3
 
2.4%
서 2
 
1.6%
운 2
 
1.6%
산 2
 
1.6%
북 1
 
0.8%
항 1
 
0.8%
배 1
 
0.8%
Other values (8) 8
 
6.3%
Common
ValueCountFrequency (%)
- 107
23.1%
1 100
21.6%
3 65
14.0%
2 37
 
8.0%
6 26
 
5.6%
5 20
 
4.3%
8 19
 
4.1%
4 16
 
3.5%
0 15
 
3.2%
7 14
 
3.0%
Other values (5) 44
9.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 463
78.6%
Hangul 126
 
21.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 107
23.1%
1 100
21.6%
3 65
14.0%
2 37
 
8.0%
6 26
 
5.6%
5 20
 
4.3%
8 19
 
4.1%
4 16
 
3.5%
0 15
 
3.2%
7 14
 
3.0%
Other values (5) 44
9.5%
Hangul
ValueCountFrequency (%)
대 65
51.6%
중 31
24.6%
광 10
 
7.9%
단 3
 
2.4%
서 2
 
1.6%
운 2
 
1.6%
산 2
 
1.6%
북 1
 
0.8%
항 1
 
0.8%
배 1
 
0.8%
Other values (8) 8
 
6.3%

Interactions

2023-12-12T13:03:50.569155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T13:03:57.619110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
번호폭원(미터)비 고
번호1.0000.6140.967
폭원(미터)0.6141.0000.999
비 고0.9670.9991.000
2023-12-12T13:03:57.707513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
번호폭원(미터)
번호1.0000.238
폭원(미터)0.2381.000

Missing values

2023-12-12T13:03:50.697759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T13:03:50.848164image/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

번호도로명(노선수)폭원(미터)연장(미터)면적(제곱미터)기 점종 점비 고
01가람로(검단산단)2554213,550오류동 1434서측구역계대3-114
12가석로156번길2153011,130고속도로측도교차점건지로348번길<NA>
23가정로151번길253659,125염곡로교차점가정로교차점<NA>
34가정로152번길253308,250가정로교차점고속도로측도교차점중1-174
45가정로98번길253208,000가정로교차점고속도로측도교차점<NA>
56갑문2로(물류단지)2653914,014대로 1-2대로 1-3대3-3
67갑문4로(1)(물류단지)261,09328,418대로 1-3대로 1-3대3-5
78갑문4로(2)(물류단지)2648612,636대로 1-2대로 1-3대3-4
89갑문로(물류단지)3543715,295중로 1-141대로 3-3대1-3
910거첨로(1)(물류단지)331,43047,190대로 3-50중로 1-141대2-49
번호도로명(노선수)폭원(미터)연장(미터)면적(제곱미터)기 점종 점비 고
185186도화로2번길25.51152,933대 2-32대 3-9대 3-6
186187숙골로87번길25.53508,925대 2-3대 3-1대 3-9
187188숙골로88번길25.574819,074대 2-3대 3-1대 3-9
188189숙골로112번길22.53096,953대 3-9대 2-92중 1-144
189190염전로168번길22.51773,983대 2-1중 3-317중 1-488
190191서운산업로231533,595광 2-5중 3-73중 1-658(서운산단)
191192서운산단로2342910,081광 3-9중 1-656중 1-657(서운산단)
192193대 2-1163060218,060대 2-40대 1-17북항배후단지
193194중1-176(정서진로)23.414,294334,480서구 시천동계양구 평동 1-1경인아라뱃길
194195드림로25.549012,495대㈜2-61김포시 경계<NA>