Overview

Dataset statistics

Number of variables11
Number of observations10000
Missing cells20172
Missing cells (%)18.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory986.3 KiB
Average record size in memory101.0 B

Variable types

Numeric5
Categorical3
Text3

Dataset

Description부산광역시_한국도로공사연계고속도로정보_20230828
Author부산광역시
URLhttp://data.busan.go.kr/dataSet/detail.nm?contentId=10&publicdatapk=15121231

Alerts

일련번호 is highly overall correlated with 고속도로명High correlation
고속도로(ID) is highly overall correlated with 고속도로명High correlation
경도 is highly overall correlated with 방향코드High correlation
위도 is highly overall correlated with 방향코드High correlation
고속도로명 is highly overall correlated with 일련번호 and 1 other fieldsHigh correlation
방향코드 is highly overall correlated with 경도 and 1 other fieldsHigh correlation
고속도로대상종류 is highly imbalanced (54.1%)Imbalance
고속도로대상(ID) has 6583 (65.8%) missing valuesMissing
고속도로대상명 has 6592 (65.9%) missing valuesMissing
방향 has 6969 (69.7%) missing valuesMissing
경도 is highly skewed (γ1 = -99.21892447)Skewed
위도 is highly skewed (γ1 = -98.80741614)Skewed
일련번호 has unique valuesUnique

Reproduction

Analysis started2024-04-21 12:18:47.440643
Analysis finished2024-04-21 12:18:54.439640
Duration7 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

일련번호
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29376.916
Minimum1
Maximum59194
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-21T21:18:54.565143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3005.95
Q114583.25
median29110.5
Q344298
95-th percentile56278.05
Maximum59194
Range59193
Interquartile range (IQR)29714.75

Descriptive statistics

Standard deviation17134.668
Coefficient of variation (CV)0.5832698
Kurtosis-1.1986745
Mean29376.916
Median Absolute Deviation (MAD)14822
Skewness0.028516002
Sum2.9376916 × 108
Variance2.9359685 × 108
MonotonicityNot monotonic
2024-04-21T21:18:54.810181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37080 1
 
< 0.1%
8493 1
 
< 0.1%
23816 1
 
< 0.1%
19404 1
 
< 0.1%
10864 1
 
< 0.1%
12234 1
 
< 0.1%
19866 1
 
< 0.1%
12256 1
 
< 0.1%
17454 1
 
< 0.1%
34859 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
1 1
< 0.1%
12 1
< 0.1%
17 1
< 0.1%
24 1
< 0.1%
26 1
< 0.1%
39 1
< 0.1%
41 1
< 0.1%
47 1
< 0.1%
49 1
< 0.1%
56 1
< 0.1%
ValueCountFrequency (%)
59194 1
< 0.1%
59190 1
< 0.1%
59181 1
< 0.1%
59179 1
< 0.1%
59177 1
< 0.1%
59174 1
< 0.1%
59164 1
< 0.1%
59145 1
< 0.1%
59140 1
< 0.1%
59133 1
< 0.1%

고속도로대상종류
Categorical

IMBALANCE 

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
이정
6583 
표지판
1836 
CCTV
923 
중분대개구부
 
245
터널
 
146
Other values (6)
 
267

Length

Max length6
Median length2
Mean length2.5166
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row이정
2nd row표지판
3rd row이정
4th row이정
5th row이정

Common Values

ValueCountFrequency (%)
이정 6583
65.8%
표지판 1836
 
18.4%
CCTV 923
 
9.2%
중분대개구부 245
 
2.5%
터널 146
 
1.5%
출입시설 81
 
0.8%
비상연결로 52
 
0.5%
영업소 49
 
0.5%
휴게소 44
 
0.4%
졸음쉼터 30
 
0.3%

Length

2024-04-21T21:18:55.073706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
이정 6583
65.8%
표지판 1836
 
18.4%
cctv 923
 
9.2%
중분대개구부 245
 
2.5%
터널 146
 
1.5%
출입시설 81
 
0.8%
비상연결로 52
 
0.5%
영업소 49
 
0.5%
휴게소 44
 
0.4%
졸음쉼터 30
 
0.3%

고속도로대상(ID)
Text

MISSING 

Distinct3417
Distinct (%)100.0%
Missing6583
Missing (%)65.8%
Memory size156.2 KiB
2024-04-21T21:18:55.856021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length35
Median length5
Mean length12.770266
Min length5

Characters and Unicode

Total characters43636
Distinct characters35
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3417 ?
Unique (%)100.0%

Sample

1st row24667
2nd row3106A0
3rd row42686
4th row0300T0171
5th row46166
ValueCountFrequency (%)
26853 1
 
< 0.1%
0400t0611 1
 
< 0.1%
24632 1
 
< 0.1%
0120t0921 1
 
< 0.1%
35765 1
 
< 0.1%
25952 1
 
< 0.1%
000000gbrh00000000000000tc0552392s 1
 
< 0.1%
000000gbrh00000000000000tc0200499e 1
 
< 0.1%
43630 1
 
< 0.1%
30089 1
 
< 0.1%
Other values (3407) 3407
99.7%
2024-04-21T21:18:56.859622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 22258
51.0%
1 2562
 
5.9%
2 2353
 
5.4%
4 2006
 
4.6%
3 1985
 
4.5%
5 1879
 
4.3%
6 1332
 
3.1%
h 1140
 
2.6%
7 1134
 
2.6%
8 1065
 
2.4%
Other values (25) 5922
 
13.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 37579
86.1%
Lowercase Letter 3697
 
8.5%
Uppercase Letter 2357
 
5.4%
Open Punctuation 1
 
< 0.1%
Close Punctuation 1
 
< 0.1%
Other Punctuation 1
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
C 790
33.5%
T 589
25.0%
E 426
18.1%
S 398
16.9%
I 61
 
2.6%
H 42
 
1.8%
J 21
 
0.9%
M 11
 
0.5%
O 11
 
0.5%
A 5
 
0.2%
Other values (2) 3
 
0.1%
Decimal Number
ValueCountFrequency (%)
0 22258
59.2%
1 2562
 
6.8%
2 2353
 
6.3%
4 2006
 
5.3%
3 1985
 
5.3%
5 1879
 
5.0%
6 1332
 
3.5%
7 1134
 
3.0%
8 1065
 
2.8%
9 1005
 
2.7%
Lowercase Letter
ValueCountFrequency (%)
h 1140
30.8%
r 918
24.8%
g 648
17.5%
n 355
 
9.6%
c 192
 
5.2%
b 183
 
4.9%
w 177
 
4.8%
j 69
 
1.9%
e 10
 
0.3%
m 5
 
0.1%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 37582
86.1%
Latin 6054
 
13.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
h 1140
18.8%
r 918
15.2%
C 790
13.0%
g 648
10.7%
T 589
9.7%
E 426
 
7.0%
S 398
 
6.6%
n 355
 
5.9%
c 192
 
3.2%
b 183
 
3.0%
Other values (12) 415
 
6.9%
Common
ValueCountFrequency (%)
0 22258
59.2%
1 2562
 
6.8%
2 2353
 
6.3%
4 2006
 
5.3%
3 1985
 
5.3%
5 1879
 
5.0%
6 1332
 
3.5%
7 1134
 
3.0%
8 1065
 
2.8%
9 1005
 
2.7%
Other values (3) 3
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 43636
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 22258
51.0%
1 2562
 
5.9%
2 2353
 
5.4%
4 2006
 
4.6%
3 1985
 
4.5%
5 1879
 
4.3%
6 1332
 
3.1%
h 1140
 
2.6%
7 1134
 
2.6%
8 1065
 
2.4%
Other values (25) 5922
 
13.6%

고속도로대상명
Text

MISSING 

Distinct3226
Distinct (%)94.7%
Missing6592
Missing (%)65.9%
Memory size156.2 KiB
2024-04-21T21:18:57.558041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length47
Median length39
Mean length12.139671
Min length2

Characters and Unicode

Total characters41372
Distinct characters363
Distinct categories11 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3066 ?
Unique (%)90.0%

Sample

1st row진주JCT(1km 전방)
2nd row북진천영업소
3rd row곤양IC(1km 전방)
4th row수리티터널(상주)
5th row서충주IC(본선부)
ValueCountFrequency (%)
전방 534
 
11.6%
노즈부 39
 
0.8%
ic 18
 
0.4%
2 18
 
0.4%
교통표지판 18
 
0.4%
1 18
 
0.4%
2km 16
 
0.3%
진입부 14
 
0.3%
sta 14
 
0.3%
150m 13
 
0.3%
Other values (3375) 3912
84.8%
2024-04-21T21:18:58.712883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
) 2755
 
6.7%
( 2754
 
6.7%
C 2298
 
5.6%
I 1748
 
4.2%
1282
 
3.1%
m 1154
 
2.8%
1 1066
 
2.6%
전 986
 
2.4%
방 957
 
2.3%
0 933
 
2.3%
Other values (353) 25439
61.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 20224
48.9%
Uppercase Letter 5582
 
13.5%
Decimal Number 4742
 
11.5%
Close Punctuation 3304
 
8.0%
Open Punctuation 3303
 
8.0%
Lowercase Letter 1784
 
4.3%
Space Separator 1282
 
3.1%
Other Punctuation 602
 
1.5%
Dash Punctuation 383
 
0.9%
Math Symbol 160
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
전 986
 
4.9%
방 957
 
4.7%
터 795
 
3.9%
널 755
 
3.7%
천 589
 
2.9%
산 558
 
2.8%
주 487
 
2.4%
부 472
 
2.3%
양 391
 
1.9%
소 380
 
1.9%
Other values (301) 13854
68.5%
Uppercase Letter
ValueCountFrequency (%)
C 2298
41.2%
I 1748
31.3%
T 604
 
10.8%
J 549
 
9.8%
A 109
 
2.0%
R 62
 
1.1%
S 50
 
0.9%
M 43
 
0.8%
G 33
 
0.6%
K 23
 
0.4%
Other values (6) 63
 
1.1%
Lowercase Letter
ValueCountFrequency (%)
m 1154
64.7%
k 600
33.6%
a 8
 
0.4%
c 6
 
0.3%
t 5
 
0.3%
i 5
 
0.3%
p 3
 
0.2%
s 1
 
0.1%
r 1
 
0.1%
l 1
 
0.1%
Decimal Number
ValueCountFrequency (%)
1 1066
22.5%
0 933
19.7%
2 795
16.8%
5 552
11.6%
3 407
 
8.6%
4 303
 
6.4%
6 204
 
4.3%
8 177
 
3.7%
7 158
 
3.3%
9 147
 
3.1%
Other Punctuation
ValueCountFrequency (%)
. 278
46.2%
/ 188
31.2%
, 116
19.3%
# 19
 
3.2%
· 1
 
0.2%
Math Symbol
ValueCountFrequency (%)
+ 131
81.9%
~ 16
 
10.0%
→ 9
 
5.6%
> 4
 
2.5%
Close Punctuation
ValueCountFrequency (%)
) 2755
83.4%
] 549
 
16.6%
Open Punctuation
ValueCountFrequency (%)
( 2754
83.4%
[ 549
 
16.6%
Space Separator
ValueCountFrequency (%)
1282
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 383
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 20224
48.9%
Common 13782
33.3%
Latin 7366
 
17.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
전 986
 
4.9%
방 957
 
4.7%
터 795
 
3.9%
널 755
 
3.7%
천 589
 
2.9%
산 558
 
2.8%
주 487
 
2.4%
부 472
 
2.3%
양 391
 
1.9%
소 380
 
1.9%
Other values (301) 13854
68.5%
Common
ValueCountFrequency (%)
) 2755
20.0%
( 2754
20.0%
1282
9.3%
1 1066
 
7.7%
0 933
 
6.8%
2 795
 
5.8%
5 552
 
4.0%
] 549
 
4.0%
[ 549
 
4.0%
3 407
 
3.0%
Other values (16) 2140
15.5%
Latin
ValueCountFrequency (%)
C 2298
31.2%
I 1748
23.7%
m 1154
15.7%
T 604
 
8.2%
k 600
 
8.1%
J 549
 
7.5%
A 109
 
1.5%
R 62
 
0.8%
S 50
 
0.7%
M 43
 
0.6%
Other values (16) 149
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21138
51.1%
Hangul 20224
48.9%
Arrows 9
 
< 0.1%
None 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
) 2755
13.0%
( 2754
13.0%
C 2298
 
10.9%
I 1748
 
8.3%
1282
 
6.1%
m 1154
 
5.5%
1 1066
 
5.0%
0 933
 
4.4%
2 795
 
3.8%
T 604
 
2.9%
Other values (40) 5749
27.2%
Hangul
ValueCountFrequency (%)
전 986
 
4.9%
방 957
 
4.7%
터 795
 
3.9%
널 755
 
3.7%
천 589
 
2.9%
산 558
 
2.8%
주 487
 
2.4%
부 472
 
2.3%
양 391
 
1.9%
소 380
 
1.9%
Other values (301) 13854
68.5%
Arrows
ValueCountFrequency (%)
→ 9
100.0%
None
ValueCountFrequency (%)
· 1
100.0%

고속도로(ID)
Real number (ℝ)

HIGH CORRELATION 

Distinct36
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean451.3793
Minimum10
Maximum5510
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-21T21:18:58.927342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile10
Q1140
median351
Q3550
95-th percentile1200
Maximum5510
Range5500
Interquartile range (IQR)410

Descriptive statistics

Standard deviation637.15195
Coefficient of variation (CV)1.4115666
Kurtosis26.311276
Mean451.3793
Median Absolute Deviation (MAD)201
Skewness4.6268814
Sum4513793
Variance405962.6
MonotonicityNot monotonic
2024-04-21T21:18:59.141685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
10 1068
 
10.7%
351 824
 
8.2%
150 764
 
7.6%
550 740
 
7.4%
450 722
 
7.2%
500 589
 
5.9%
120 494
 
4.9%
100 466
 
4.7%
251 451
 
4.5%
655 415
 
4.2%
Other values (26) 3467
34.7%
ValueCountFrequency (%)
10 1068
10.7%
100 466
4.7%
101 302
 
3.0%
120 494
4.9%
121 97
 
1.0%
140 129
 
1.3%
150 764
7.6%
160 45
 
0.4%
170 47
 
0.5%
200 198
 
2.0%
ValueCountFrequency (%)
5510 30
 
0.3%
4510 70
0.7%
4000 32
 
0.3%
3000 48
 
0.5%
2510 123
1.2%
1510 153
1.5%
1200 62
0.6%
1100 79
0.8%
1040 39
 
0.4%
1020 45
 
0.4%

고속도로명
Categorical

HIGH CORRELATION 

Distinct41
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
경부선
1068 
서해안선
764 
중앙선
740 
중부내륙선
722 
중부선,통영대전선
 
598
Other values (36)
6108 

Length

Max length14
Median length9
Mean length5.0033
Min length3

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row서울양양
2nd row남해선(순천부산)
3rd row중부선,통영대전선
4th row중앙선
5th row순천완주선

Common Values

ValueCountFrequency (%)
경부선 1068
 
10.7%
서해안선 764
 
7.6%
중앙선 740
 
7.4%
중부내륙선 722
 
7.2%
중부선,통영대전선 598
 
6.0%
영동선 589
 
5.9%
광주대구선 493
 
4.9%
논산천안선,호남선 451
 
4.5%
부산포항선 415
 
4.2%
평택제천선 325
 
3.2%
Other values (31) 3835
38.4%

Length

2024-04-21T21:18:59.360858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경부선 1068
 
10.6%
서해안선 764
 
7.6%
중앙선 740
 
7.3%
중부내륙선 722
 
7.1%
중부선,통영대전선 598
 
5.9%
영동선 589
 
5.8%
광주대구선 493
 
4.9%
논산천안선,호남선 451
 
4.5%
부산포항선 415
 
4.1%
평택제천선 325
 
3.2%
Other values (32) 3946
39.0%

방향
Text

MISSING 

Distinct68
Distinct (%)2.2%
Missing6969
Missing (%)69.7%
Memory size156.2 KiB
2024-04-21T21:19:00.055767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length2
Mean length2.3170571
Min length2

Characters and Unicode

Total characters7023
Distinct characters83
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)0.1%

Sample

1st row부산
2nd row부산
3rd row상주
4th row제천
5th row통영
ValueCountFrequency (%)
부산 367
 
12.1%
서울 299
 
9.9%
순천 206
 
6.8%
양방향 195
 
6.4%
하남 112
 
3.7%
양평 95
 
3.1%
서창 88
 
2.9%
대구 88
 
2.9%
통영 85
 
2.8%
강릉 82
 
2.7%
Other values (58) 1414
46.7%
2024-04-21T21:19:01.011768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
산 538
 
7.7%
양 425
 
6.1%
천 413
 
5.9%
서 407
 
5.8%
부 367
 
5.2%
울 358
 
5.1%
0 312
 
4.4%
순 206
 
2.9%
방 195
 
2.8%
향 195
 
2.8%
Other values (73) 3607
51.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 6290
89.6%
Decimal Number 406
 
5.8%
Close Punctuation 113
 
1.6%
Open Punctuation 113
 
1.6%
Math Symbol 101
 
1.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 538
 
8.6%
양 425
 
6.8%
천 413
 
6.6%
서 407
 
6.5%
부 367
 
5.8%
울 358
 
5.7%
순 206
 
3.3%
방 195
 
3.1%
향 195
 
3.1%
주 165
 
2.6%
Other values (60) 3021
48.0%
Decimal Number
ValueCountFrequency (%)
0 312
76.8%
5 30
 
7.4%
3 17
 
4.2%
8 15
 
3.7%
1 8
 
2.0%
6 7
 
1.7%
2 5
 
1.2%
7 5
 
1.2%
9 4
 
1.0%
4 3
 
0.7%
Close Punctuation
ValueCountFrequency (%)
) 113
100.0%
Open Punctuation
ValueCountFrequency (%)
( 113
100.0%
Math Symbol
ValueCountFrequency (%)
+ 101
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 6290
89.6%
Common 733
 
10.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
산 538
 
8.6%
양 425
 
6.8%
천 413
 
6.6%
서 407
 
6.5%
부 367
 
5.8%
울 358
 
5.7%
순 206
 
3.3%
방 195
 
3.1%
향 195
 
3.1%
주 165
 
2.6%
Other values (60) 3021
48.0%
Common
ValueCountFrequency (%)
0 312
42.6%
) 113
 
15.4%
( 113
 
15.4%
+ 101
 
13.8%
5 30
 
4.1%
3 17
 
2.3%
8 15
 
2.0%
1 8
 
1.1%
6 7
 
1.0%
2 5
 
0.7%
Other values (3) 12
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 6290
89.6%
ASCII 733
 
10.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
산 538
 
8.6%
양 425
 
6.8%
천 413
 
6.6%
서 407
 
6.5%
부 367
 
5.8%
울 358
 
5.7%
순 206
 
3.3%
방 195
 
3.1%
향 195
 
3.1%
주 165
 
2.6%
Other values (60) 3021
48.0%
ASCII
ValueCountFrequency (%)
0 312
42.6%
) 113
 
15.4%
( 113
 
15.4%
+ 101
 
13.8%
5 30
 
4.1%
3 17
 
2.3%
8 15
 
2.0%
1 8
 
1.1%
6 7
 
1.0%
2 5
 
0.7%
Other values (3) 12
 
1.6%

방향코드
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
6969 
S
1472 
E
1413 
0
 
145
Y
 
1

Length

Max length4
Median length4
Mean length3.0907
Min length1

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row<NA>
2nd rowE
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 6969
69.7%
S 1472
 
14.7%
E 1413
 
14.1%
0 145
 
1.5%
Y 1
 
< 0.1%

Length

2024-04-21T21:19:01.243747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:19:01.440366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 6969
69.7%
s 1472
 
14.7%
e 1413
 
14.1%
0 145
 
1.5%
y 1
 
< 0.1%

거리
Real number (ℝ)

Distinct4524
Distinct (%)45.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean117.99139
Minimum0
Maximum416.05
Zeros16
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-21T21:19:01.657647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5.838
Q133.8075
median85.2
Q3177.6
95-th percentile333.005
Maximum416.05
Range416.05
Interquartile range (IQR)143.7925

Descriptive statistics

Standard deviation103.5777
Coefficient of variation (CV)0.87784116
Kurtosis-0.11102987
Mean117.99139
Median Absolute Deviation (MAD)61.025
Skewness0.95524131
Sum1179913.9
Variance10728.34
MonotonicityNot monotonic
2024-04-21T21:19:01.902936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 16
 
0.2%
4.8 14
 
0.1%
19.5 13
 
0.1%
18.9 13
 
0.1%
28.5 12
 
0.1%
6.2 12
 
0.1%
4.2 11
 
0.1%
20.5 11
 
0.1%
2.8 11
 
0.1%
24.9 11
 
0.1%
Other values (4514) 9876
98.8%
ValueCountFrequency (%)
0.0 16
0.2%
0.05 1
 
< 0.1%
0.066 1
 
< 0.1%
0.1 7
0.1%
0.15 1
 
< 0.1%
0.2 6
 
0.1%
0.23 1
 
< 0.1%
0.26 1
 
< 0.1%
0.28 1
 
< 0.1%
0.3 6
 
0.1%
ValueCountFrequency (%)
416.05 1
< 0.1%
416.0 1
< 0.1%
415.5 1
< 0.1%
415.4 1
< 0.1%
415.0 1
< 0.1%
414.85 1
< 0.1%
414.1 1
< 0.1%
414.0 1
< 0.1%
413.8 1
< 0.1%
413.0 1
< 0.1%

경도
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct9342
Distinct (%)93.6%
Missing14
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean127.62809
Minimum-999
Maximum129.99984
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)< 0.1%
Memory size166.0 KiB
2024-04-21T21:19:02.151804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-999
5-th percentile126.65761
Q1127.1037
median127.59801
Q3128.34012
95-th percentile129.19155
Maximum129.99984
Range1128.9998
Interquartile range (IQR)1.2364198

Descriptive statistics

Standard deviation11.302165
Coefficient of variation (CV)0.088555468
Kurtosis9891.379
Mean127.62809
Median Absolute Deviation (MAD)0.6055149
Skewness-99.218924
Sum1274494.1
Variance127.73894
MonotonicityNot monotonic
2024-04-21T21:19:02.395050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
129.4241688 9
 
0.1%
129.3897736 7
 
0.1%
129.2494344 6
 
0.1%
129.2610217 6
 
0.1%
129.2364277 4
 
< 0.1%
127.1000723 4
 
< 0.1%
129.2818178 4
 
< 0.1%
129.2495687 4
 
< 0.1%
126.8103553 3
 
< 0.1%
129.1831454 3
 
< 0.1%
Other values (9332) 9936
99.4%
(Missing) 14
 
0.1%
ValueCountFrequency (%)
-999.0 1
< 0.1%
126.4013242 1
< 0.1%
126.402408 1
< 0.1%
126.4034917 1
< 0.1%
126.4119094 1
< 0.1%
126.4139251 2
< 0.1%
126.42419 1
< 0.1%
126.43509 1
< 0.1%
126.4434441 1
< 0.1%
126.4467248 1
< 0.1%
ValueCountFrequency (%)
129.99984 1
 
< 0.1%
129.4272066 2
 
< 0.1%
129.426958 1
 
< 0.1%
129.4267422 1
 
< 0.1%
129.4261318 1
 
< 0.1%
129.4258383 1
 
< 0.1%
129.4250152 1
 
< 0.1%
129.4241688 9
0.1%
129.4237095 1
 
< 0.1%
129.4214359 1
 
< 0.1%

위도
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct9349
Distinct (%)93.6%
Missing14
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean36.159135
Minimum-999
Maximum54.62644
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)< 0.1%
Memory size166.0 KiB
2024-04-21T21:19:02.648293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-999
5-th percentile35.003697
Q135.489494
median36.161345
Q337.064803
95-th percentile37.645033
Maximum54.62644
Range1053.6264
Interquartile range (IQR)1.5753089

Descriptive statistics

Standard deviation10.398938
Coefficient of variation (CV)0.28758812
Kurtosis9836.7822
Mean36.159135
Median Absolute Deviation (MAD)0.79154279
Skewness-98.807416
Sum361085.12
Variance108.1379
MonotonicityNot monotonic
2024-04-21T21:19:02.899782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.78191189 9
 
0.1%
35.9458158 7
 
0.1%
35.41408714 6
 
0.1%
35.51761795 6
 
0.1%
35.55390727 4
 
< 0.1%
35.47913425 4
 
< 0.1%
35.32755532 4
 
< 0.1%
37.4000478 4
 
< 0.1%
35.66805911 3
 
< 0.1%
35.36052019 3
 
< 0.1%
Other values (9339) 9936
99.4%
(Missing) 14
 
0.1%
ValueCountFrequency (%)
-999.0 1
< 0.1%
34.67996942 1
< 0.1%
34.68139442 1
< 0.1%
34.68160076 1
< 0.1%
34.68175213 1
< 0.1%
34.68333489 1
< 0.1%
34.68422267 1
< 0.1%
34.68431309 1
< 0.1%
34.68440084 1
< 0.1%
34.68501476 2
< 0.1%
ValueCountFrequency (%)
54.62644 1
< 0.1%
38.19991061 1
< 0.1%
38.19831023 1
< 0.1%
38.19614036 1
< 0.1%
38.18743191 1
< 0.1%
38.18032641 1
< 0.1%
38.1800968 1
< 0.1%
38.17986373 1
< 0.1%
38.17979447 1
< 0.1%
38.17771925 1
< 0.1%

Interactions

2024-04-21T21:18:52.924672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T21:18:49.655136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T21:18:50.457516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T21:18:51.310459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T21:18:52.143377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T21:18:53.091070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T21:18:49.822047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T21:18:50.620085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T21:18:51.494212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T21:18:52.305649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T21:18:53.247434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T21:18:49.981618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T21:18:50.777414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T21:18:51.681357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T21:18:52.461987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T21:18:53.400877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T21:18:50.136957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T21:18:50.926348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T21:18:51.829661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T21:18:52.609562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T21:18:53.553616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T21:18:50.296053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T21:18:51.087314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T21:18:51.988592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T21:18:52.769159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-21T21:19:03.071180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일련번호고속도로대상종류고속도로(ID)고속도로명방향방향코드거리경도위도
일련번호1.0000.7630.4220.9490.7920.3830.491NaNNaN
고속도로대상종류0.7631.0000.0980.4200.6090.2890.085NaNNaN
고속도로(ID)0.4220.0981.0001.0000.9890.0000.320NaNNaN
고속도로명0.9490.4201.0001.0000.9940.5960.736NaNNaN
방향0.7920.6090.9890.9941.0000.9220.643NaNNaN
방향코드0.3830.2890.0000.5960.9221.0000.107NaNNaN
거리0.4910.0850.3200.7360.6430.1071.000NaNNaN
경도NaNNaNNaNNaNNaNNaNNaN1.000NaN
위도NaNNaNNaNNaNNaNNaNNaNNaN1.000
2024-04-21T21:19:03.278529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
고속도로명고속도로대상종류방향코드
고속도로명1.0000.1520.341
고속도로대상종류0.1521.0000.189
방향코드0.3410.1891.000
2024-04-21T21:19:03.434713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일련번호고속도로(ID)거리경도위도고속도로대상종류고속도로명방향코드
일련번호1.0000.2710.1200.0130.1780.4570.6690.157
고속도로(ID)0.2711.000-0.1910.1740.4450.0460.9980.000
거리0.120-0.1911.0000.0260.4060.0360.3300.064
경도0.0130.1740.0261.000-0.0540.1060.0001.000
위도0.1780.4450.406-0.0541.0000.1060.0001.000
고속도로대상종류0.4570.0460.0360.1060.1061.0000.1520.189
고속도로명0.6690.9980.3300.0000.0000.1521.0000.341
방향코드0.1570.0000.0641.0001.0000.1890.3411.000

Missing values

2024-04-21T21:18:53.767533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-21T21:18:54.047466image/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.
2024-04-21T21:18:54.288155image/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

일련번호고속도로대상종류고속도로대상(ID)고속도로대상명고속도로(ID)고속도로명방향방향코드거리경도위도
3278437080이정<NA><NA>600서울양양<NA><NA>20.1127.3695437.634275
1841414920표지판24667진주JCT(1km 전방)100남해선(순천부산)부산E66.49128.09444735.120937
4236552834이정<NA><NA>351중부선,통영대전선<NA><NA>243.7127.42977536.567593
5898255568이정<NA><NA>550중앙선<NA><NA>233.3128.50055436.869301
4747843367이정<NA><NA>270순천완주선<NA><NA>114.5127.18759835.857225
3710031802이정<NA><NA>301당진대전선<NA><NA>29.8126.82383936.648278
5365858513이정<NA><NA>170평택화성선<NA><NA>6.0126.9906637.062044
1333018951영업소3106A0북진천영업소400평택제천선<NA><NA>55.8127.4499436.94045
1901515244표지판42686곤양IC(1km 전방)100남해선(순천부산)부산E48.8127.93318835.054555
4167835183이정<NA><NA>650동해선<NA><NA>107.7128.58285538.105075
일련번호고속도로대상종류고속도로대상(ID)고속도로대상명고속도로(ID)고속도로명방향방향코드거리경도위도
3278637082이정<NA><NA>600서울양양<NA><NA>20.3127.37178837.634485
4595450174이정<NA><NA>4510중부내륙지선<NA><NA>8.1128.47676435.729937
1677019976이정<NA><NA>10경부선<NA><NA>11.4129.04982135.325264
3838232747이정<NA><NA>300당진상주선<NA><NA>32.7127.75459836.455019
1329218289표지판41372부안주차장휴게소(목포기점 105.1km)150서해안선무안S105.1126.73908835.680302
110467966CCTVccrh000000000000803[공주]부여1터널(공주)1510서천공주선양방향033.0126.86267636.305692
1509018389표지판33092청양IC(출구점 )1510서천공주선공주E43.9126.94901136.373396
26162770CCTV000000gbrh000000000000000C00102110S추풍령대교10경부선부산S211.0128.00722736.186144
5592256198이정<NA><NA>550중앙선<NA><NA>296.2128.06905937.237768
107876638CCTV000000ggrh00000000000000TC0500384E[강릉]광교방음터널(강릉 3)500영동선강릉E38.48127.06741237.295392