Overview

Dataset statistics

Number of variables10
Number of observations551
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory44.3 KiB
Average record size in memory82.2 B

Variable types

Categorical2
Text6
Numeric2

Dataset

Description연평균일교통량 데이터 제공 (노선,지점번호,구간명,연장,차로,2017,2018, 2019,2020,2021)
URLhttps://www.data.go.kr/data/15062249/fileData.do

Alerts

차로 is highly imbalanced (51.0%)Imbalance
지점번호 has unique valuesUnique
구간명 has unique valuesUnique

Reproduction

Analysis started2023-12-12 18:58:40.782931
Analysis finished2023-12-12 18:58:42.880481
Duration2.1 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

노선
Categorical

Distinct33
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Memory size4.4 KiB
제1호(경부선)
58 
제 35 호(중부·통영대전선)
41 
제 10 호(남해선)
41 
제 15 호(서해안선)
39 
제 50 호(영동선)
34 
Other values (28)
338 

Length

Max length18
Median length17
Mean length12.702359
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row제1호(경부선)
2nd row제1호(경부선)
3rd row제1호(경부선)
4th row제1호(경부선)
5th row제1호(경부선)

Common Values

ValueCountFrequency (%)
제1호(경부선) 58
 
10.5%
제 35 호(중부·통영대전선) 41
 
7.4%
제 10 호(남해선) 41
 
7.4%
제 15 호(서해안선) 39
 
7.1%
제 50 호(영동선) 34
 
6.2%
제 55 호(중앙선) 31
 
5.6%
제 25 호(호남선) 30
 
5.4%
제 45 호(중부내륙선) 29
 
5.3%
제 30 호(당진영덕선) 27
 
4.9%
제 100 호(수도권제1순환선) 26
 
4.7%
Other values (23) 195
35.4%

Length

2023-12-13T03:58:43.009664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
제 493
32.1%
제1호(경부선 58
 
3.8%
35 41
 
2.7%
호(중부·통영대전선 41
 
2.7%
10 41
 
2.7%
호(남해선 41
 
2.7%
15 39
 
2.5%
호(서해안선 39
 
2.5%
50 34
 
2.2%
호(영동선 34
 
2.2%
Other values (55) 676
44.0%

지점번호
Text

UNIQUE 

Distinct551
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size4.4 KiB
2023-12-13T03:58:43.529760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length5
Mean length5.5372051
Min length5

Characters and Unicode

Total characters3051
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

Unique551 ?
Unique (%)100.0%

Sample

1st row00101
2nd row00101-1
3rd row00101-2
4th row00102
5th row00103
ValueCountFrequency (%)
00101 1
 
0.2%
05003-1 1
 
0.2%
05501 1
 
0.2%
05029 1
 
0.2%
05028 1
 
0.2%
05027 1
 
0.2%
05026 1
 
0.2%
05025 1
 
0.2%
05024-1 1
 
0.2%
06007 1
 
0.2%
Other values (541) 541
98.2%
2023-12-13T03:58:44.286871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1155
37.9%
1 554
18.2%
5 353
 
11.6%
2 273
 
8.9%
3 181
 
5.9%
- 145
 
4.8%
4 124
 
4.1%
6 107
 
3.5%
7 66
 
2.2%
9 49
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2906
95.2%
Dash Punctuation 145
 
4.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1155
39.7%
1 554
19.1%
5 353
 
12.1%
2 273
 
9.4%
3 181
 
6.2%
4 124
 
4.3%
6 107
 
3.7%
7 66
 
2.3%
9 49
 
1.7%
8 44
 
1.5%
Dash Punctuation
ValueCountFrequency (%)
- 145
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3051
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1155
37.9%
1 554
18.2%
5 353
 
11.6%
2 273
 
8.9%
3 181
 
5.9%
- 145
 
4.8%
4 124
 
4.1%
6 107
 
3.5%
7 66
 
2.2%
9 49
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3051
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1155
37.9%
1 554
18.2%
5 353
 
11.6%
2 273
 
8.9%
3 181
 
5.9%
- 145
 
4.8%
4 124
 
4.1%
6 107
 
3.5%
7 66
 
2.2%
9 49
 
1.6%

구간명
Text

UNIQUE 

Distinct551
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size4.4 KiB
2023-12-13T03:58:44.739424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length12
Mean length7.5880218
Min length5

Characters and Unicode

Total characters4181
Distinct characters211
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique551 ?
Unique (%)100.0%

Sample

1st row구서~노포
2nd row노포~노포JCT
3rd row노포JCT~양산JCT
4th row양산JCT~양산
5th row양산~통도사하이패스
ValueCountFrequency (%)
구서~노포 1
 
0.2%
군자jct~서안산 1
 
0.2%
시점~대저jct 1
 
0.2%
대관령~강릉jct 1
 
0.2%
진부~대관령 1
 
0.2%
속사~진부 1
 
0.2%
평창~속사 1
 
0.2%
면온~평창 1
 
0.2%
동둔내하이패스~면온 1
 
0.2%
설악~강촌 1
 
0.2%
Other values (541) 541
98.2%
2023-12-13T03:58:45.516815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
~ 550
 
13.2%
C 317
 
7.6%
T 317
 
7.6%
J 317
 
7.6%
주 120
 
2.9%
산 110
 
2.6%
서 110
 
2.6%
동 100
 
2.4%
천 99
 
2.4%
남 82
 
2.0%
Other values (201) 2059
49.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2675
64.0%
Uppercase Letter 951
 
22.7%
Math Symbol 551
 
13.2%
Other Punctuation 2
 
< 0.1%
Close Punctuation 1
 
< 0.1%
Open Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
주 120
 
4.5%
산 110
 
4.1%
서 110
 
4.1%
동 100
 
3.7%
천 99
 
3.7%
남 82
 
3.1%
양 78
 
2.9%
성 59
 
2.2%
안 55
 
2.1%
북 51
 
1.9%
Other values (193) 1811
67.7%
Uppercase Letter
ValueCountFrequency (%)
C 317
33.3%
T 317
33.3%
J 317
33.3%
Math Symbol
ValueCountFrequency (%)
~ 550
99.8%
∼ 1
 
0.2%
Other Punctuation
ValueCountFrequency (%)
· 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2675
64.0%
Latin 951
 
22.7%
Common 555
 
13.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
주 120
 
4.5%
산 110
 
4.1%
서 110
 
4.1%
동 100
 
3.7%
천 99
 
3.7%
남 82
 
3.1%
양 78
 
2.9%
성 59
 
2.2%
안 55
 
2.1%
북 51
 
1.9%
Other values (193) 1811
67.7%
Common
ValueCountFrequency (%)
~ 550
99.1%
· 2
 
0.4%
) 1
 
0.2%
( 1
 
0.2%
∼ 1
 
0.2%
Latin
ValueCountFrequency (%)
C 317
33.3%
T 317
33.3%
J 317
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2675
64.0%
ASCII 1503
35.9%
None 2
 
< 0.1%
Math Operators 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
~ 550
36.6%
C 317
21.1%
T 317
21.1%
J 317
21.1%
) 1
 
0.1%
( 1
 
0.1%
Hangul
ValueCountFrequency (%)
주 120
 
4.5%
산 110
 
4.1%
서 110
 
4.1%
동 100
 
3.7%
천 99
 
3.7%
남 82
 
3.1%
양 78
 
2.9%
성 59
 
2.2%
안 55
 
2.1%
북 51
 
1.9%
Other values (193) 1811
67.7%
None
ValueCountFrequency (%)
· 2
100.0%
Math Operators
ValueCountFrequency (%)
∼ 1
100.0%

연장
Real number (ℝ)

Distinct175
Distinct (%)31.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.6646098
Minimum0.1
Maximum26.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.0 KiB
2023-12-13T03:58:45.804682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile1.8
Q13.7
median6.2
Q310.5
95-th percentile17.9
Maximum26.9
Range26.8
Interquartile range (IQR)6.8

Descriptive statistics

Standard deviation5.176991
Coefficient of variation (CV)0.67544091
Kurtosis0.91739572
Mean7.6646098
Median Absolute Deviation (MAD)3.1
Skewness1.0982979
Sum4223.2
Variance26.801236
MonotonicityNot monotonic
2023-12-13T03:58:46.092710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.1 11
 
2.0%
3.1 10
 
1.8%
4.2 10
 
1.8%
6.0 9
 
1.6%
3.8 9
 
1.6%
2.4 9
 
1.6%
3.6 8
 
1.5%
5.2 8
 
1.5%
2.9 8
 
1.5%
4.4 8
 
1.5%
Other values (165) 461
83.7%
ValueCountFrequency (%)
0.1 1
 
0.2%
0.4 1
 
0.2%
0.5 1
 
0.2%
0.9 1
 
0.2%
1.0 3
0.5%
1.1 4
0.7%
1.2 3
0.5%
1.3 1
 
0.2%
1.4 4
0.7%
1.6 2
0.4%
ValueCountFrequency (%)
26.9 1
0.2%
26.3 1
0.2%
25.6 1
0.2%
25.3 1
0.2%
24.3 1
0.2%
24.2 1
0.2%
23.7 1
0.2%
22.9 1
0.2%
22.6 1
0.2%
21.8 1
0.2%

차로
Categorical

IMBALANCE 

Distinct12
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size4.4 KiB
4
340 
6
90 
8
73 
8*
 
17
10
 
12
Other values (7)
 
19

Length

Max length3
Median length1
Mean length1.0852995
Min length1

Unique

Unique4 ?
Unique (%)0.7%

Sample

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

Common Values

ValueCountFrequency (%)
4 340
61.7%
6 90
 
16.3%
8 73
 
13.2%
8* 17
 
3.1%
10 12
 
2.2%
4* 11
 
2.0%
6* 2
 
0.4%
10* 2
 
0.4%
11 1
 
0.2%
5 1
 
0.2%
Other values (2) 2
 
0.4%

Length

2023-12-13T03:58:46.359605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
4 351
63.7%
6 92
 
16.7%
8 90
 
16.3%
10 14
 
2.5%
11 1
 
0.2%
5 1
 
0.2%
9 1
 
0.2%
7 1
 
0.2%

2017
Text

Distinct530
Distinct (%)96.2%
Missing0
Missing (%)0.0%
Memory size4.4 KiB
2023-12-13T03:58:46.913830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length5.0562613
Min length1

Characters and Unicode

Total characters2786
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

Unique519 ?
Unique (%)94.2%

Sample

1st row77857
2nd row77553
3rd row80723
4th row83196
5th row74491
ValueCountFrequency (%)
12
 
2.2%
38923 2
 
0.4%
52469 2
 
0.4%
73870 2
 
0.4%
103053 2
 
0.4%
63420 2
 
0.4%
29695 2
 
0.4%
164381 2
 
0.4%
93603 2
 
0.4%
144688 2
 
0.4%
Other values (520) 521
94.6%
2023-12-13T03:58:47.733564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 381
13.7%
3 333
12.0%
2 328
11.8%
4 296
10.6%
6 267
9.6%
7 263
9.4%
9 252
9.0%
8 221
7.9%
0 220
7.9%
5 213
7.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2774
99.6%
Dash Punctuation 12
 
0.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 381
13.7%
3 333
12.0%
2 328
11.8%
4 296
10.7%
6 267
9.6%
7 263
9.5%
9 252
9.1%
8 221
8.0%
0 220
7.9%
5 213
7.7%
Dash Punctuation
ValueCountFrequency (%)
- 12
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2786
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 381
13.7%
3 333
12.0%
2 328
11.8%
4 296
10.6%
6 267
9.6%
7 263
9.4%
9 252
9.0%
8 221
7.9%
0 220
7.9%
5 213
7.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2786
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 381
13.7%
3 333
12.0%
2 328
11.8%
4 296
10.6%
6 267
9.6%
7 263
9.4%
9 252
9.0%
8 221
7.9%
0 220
7.9%
5 213
7.6%

2018
Text

Distinct531
Distinct (%)96.4%
Missing0
Missing (%)0.0%
Memory size4.4 KiB
2023-12-13T03:58:48.343664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length5.076225
Min length1

Characters and Unicode

Total characters2797
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

Unique520 ?
Unique (%)94.4%

Sample

1st row76055
2nd row72053
3rd row88769
4th row93815
5th row75998
ValueCountFrequency (%)
11
 
2.0%
69585 2
 
0.4%
78833 2
 
0.4%
31387 2
 
0.4%
40556 2
 
0.4%
148596 2
 
0.4%
107877 2
 
0.4%
93303 2
 
0.4%
52994 2
 
0.4%
49646 2
 
0.4%
Other values (521) 522
94.7%
2023-12-13T03:58:49.167548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 355
12.7%
2 321
11.5%
3 299
10.7%
5 295
10.5%
4 279
10.0%
7 251
9.0%
8 250
8.9%
0 249
8.9%
6 249
8.9%
9 238
8.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2786
99.6%
Dash Punctuation 11
 
0.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 355
12.7%
2 321
11.5%
3 299
10.7%
5 295
10.6%
4 279
10.0%
7 251
9.0%
8 250
9.0%
0 249
8.9%
6 249
8.9%
9 238
8.5%
Dash Punctuation
ValueCountFrequency (%)
- 11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2797
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 355
12.7%
2 321
11.5%
3 299
10.7%
5 295
10.5%
4 279
10.0%
7 251
9.0%
8 250
8.9%
0 249
8.9%
6 249
8.9%
9 238
8.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2797
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 355
12.7%
2 321
11.5%
3 299
10.7%
5 295
10.5%
4 279
10.0%
7 251
9.0%
8 250
8.9%
0 249
8.9%
6 249
8.9%
9 238
8.5%

2019
Text

Distinct531
Distinct (%)96.4%
Missing0
Missing (%)0.0%
Memory size4.4 KiB
2023-12-13T03:58:49.745661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length5.0707804
Min length1

Characters and Unicode

Total characters2794
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

Unique520 ?
Unique (%)94.4%

Sample

1st row71512
2nd row66817
3rd row86906
4th row89018
5th row71192
ValueCountFrequency (%)
11
 
2.0%
143988 2
 
0.4%
166623 2
 
0.4%
34035 2
 
0.4%
65305 2
 
0.4%
104824 2
 
0.4%
49756 2
 
0.4%
87598 2
 
0.4%
71994 2
 
0.4%
42082 2
 
0.4%
Other values (521) 522
94.7%
2023-12-13T03:58:50.401519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 356
12.7%
2 347
12.4%
4 290
10.4%
3 278
9.9%
5 270
9.7%
0 264
9.4%
6 260
9.3%
7 255
9.1%
9 235
8.4%
8 228
8.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2783
99.6%
Dash Punctuation 11
 
0.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 356
12.8%
2 347
12.5%
4 290
10.4%
3 278
10.0%
5 270
9.7%
0 264
9.5%
6 260
9.3%
7 255
9.2%
9 235
8.4%
8 228
8.2%
Dash Punctuation
ValueCountFrequency (%)
- 11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2794
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 356
12.7%
2 347
12.4%
4 290
10.4%
3 278
9.9%
5 270
9.7%
0 264
9.4%
6 260
9.3%
7 255
9.1%
9 235
8.4%
8 228
8.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2794
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 356
12.7%
2 347
12.4%
4 290
10.4%
3 278
9.9%
5 270
9.7%
0 264
9.4%
6 260
9.3%
7 255
9.1%
9 235
8.4%
8 228
8.2%

2020
Text

Distinct533
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Memory size4.4 KiB
2023-12-13T03:58:50.877897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length5.1016334
Min length1

Characters and Unicode

Total characters2811
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

Unique521 ?
Unique (%)94.6%

Sample

1st row73319
2nd row69907
3rd row91389
4th row98248
5th row79848
ValueCountFrequency (%)
8
 
1.5%
45798 2
 
0.4%
114976 2
 
0.4%
53038 2
 
0.4%
176403 2
 
0.4%
155725 2
 
0.4%
75639 2
 
0.4%
44607 2
 
0.4%
38463 2
 
0.4%
53160 2
 
0.4%
Other values (523) 525
95.3%
2023-12-13T03:58:51.609259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 369
13.1%
2 335
11.9%
3 310
11.0%
4 305
10.9%
6 271
9.6%
7 258
9.2%
5 255
9.1%
0 242
8.6%
8 234
8.3%
9 224
8.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2803
99.7%
Dash Punctuation 8
 
0.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 369
13.2%
2 335
12.0%
3 310
11.1%
4 305
10.9%
6 271
9.7%
7 258
9.2%
5 255
9.1%
0 242
8.6%
8 234
8.3%
9 224
8.0%
Dash Punctuation
ValueCountFrequency (%)
- 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2811
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 369
13.1%
2 335
11.9%
3 310
11.0%
4 305
10.9%
6 271
9.6%
7 258
9.2%
5 255
9.1%
0 242
8.6%
8 234
8.3%
9 224
8.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2811
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 369
13.1%
2 335
11.9%
3 310
11.0%
4 305
10.9%
6 271
9.6%
7 258
9.2%
5 255
9.1%
0 242
8.6%
8 234
8.3%
9 224
8.0%

2021
Real number (ℝ)

Distinct550
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean65962.207
Minimum1654
Maximum273766
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.0 KiB
2023-12-13T03:58:51.874357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1654
5-th percentile13839
Q129380.5
median45604
Q382479
95-th percentile185845
Maximum273766
Range272112
Interquartile range (IQR)53098.5

Descriptive statistics

Standard deviation53964.238
Coefficient of variation (CV)0.81810844
Kurtosis1.7274468
Mean65962.207
Median Absolute Deviation (MAD)21590
Skewness1.5166894
Sum36345176
Variance2.912139 × 109
MonotonicityNot monotonic
2023-12-13T03:58:52.173498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
61848 2
 
0.4%
69680 1
 
0.2%
52719 1
 
0.2%
39611 1
 
0.2%
26553 1
 
0.2%
43881 1
 
0.2%
43390 1
 
0.2%
38900 1
 
0.2%
36152 1
 
0.2%
37018 1
 
0.2%
Other values (540) 540
98.0%
ValueCountFrequency (%)
1654 1
0.2%
3690 1
0.2%
7770 1
0.2%
8016 1
0.2%
8046 1
0.2%
8076 1
0.2%
9060 1
0.2%
9142 1
0.2%
9807 1
0.2%
10157 1
0.2%
ValueCountFrequency (%)
273766 1
0.2%
267009 1
0.2%
259458 1
0.2%
254355 1
0.2%
241914 1
0.2%
241549 1
0.2%
239886 1
0.2%
236666 1
0.2%
221395 1
0.2%
217073 1
0.2%

Interactions

2023-12-13T03:58:41.749473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:58:41.429596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:58:42.344725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:58:41.590031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T03:58:52.369789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
노선연장차로2021
노선1.0000.3370.6150.731
연장0.3371.0000.2100.420
차로0.6150.2101.0000.708
20210.7310.4200.7081.000
2023-12-13T03:58:52.559528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
차로노선
차로1.0000.242
노선0.2421.000
2023-12-13T03:58:52.725432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연장2021노선차로
연장1.000-0.4450.1220.089
2021-0.4451.0000.3520.392
노선0.1220.3521.0000.242
차로0.0890.3920.2421.000

Missing values

2023-12-13T03:58:42.554844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T03:58:42.787934image/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

노선지점번호구간명연장차로20172018201920202021
0제1호(경부선)00101구서~노포5.167785776055715127331969680
1제1호(경부선)00101-1노포~노포JCT0.567755372053668176990766539
2제1호(경부선)00101-2노포JCT~양산JCT7.268072388769869069138991692
3제1호(경부선)00102양산JCT~양산5.1883196938158901898248100392
4제1호(경부선)00103양산~통도사하이패스12.867449175998711927984882481
5제1호(경부선)00103-1통도사하이패스~통도사1.466437170004683527698179594
6제1호(경부선)00103-2통도사~서울주JCT1.966342069585653056981175209
7제1호(경부선)00104서울주JCT~서울산4.566342069585653056087060282
8제1호(경부선)00105서울산~언양JCT1.665834963165585855333453217
9제1호(경부선)00106언양JCT~활천17.264810049435494234951450772
노선지점번호구간명연장차로20172018201920202021
541제 600 호(부산외곽순환선)60008금정~기장철마4.244702351750530875747352170
542제 600 호(부산외곽순환선)60009기장철마~기장JCT5.644509449612523425592956046
543제 700 호(부산외곽순환선)70001달서~다사3.64----10563
544제 700 호(부산외곽순환선)70002다사~북다사4.24----15888
545제 700 호(부산외곽순환선)70003북다사~칠곡JCT3.74----19792
546제 700 호(부산외곽순환선)70004칠곡JCT~지천4.94----13008
547제 700 호(부산외곽순환선)70005지천~동명동호JCT3.64----12813
548제 700 호(부산외곽순환선)70006서변~파군재4.04----25585
549제 700 호(부산외곽순환선)70007파군재~둔산6.34----27148
550제 700 호(부산외곽순환선)70008둔산~상매JCT1.74----26475