Overview

Dataset statistics

Number of variables10
Number of observations200
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory16.7 KiB
Average record size in memory85.7 B

Variable types

Text3
Categorical4
Numeric3

Dataset

DescriptionSample
Author(재)인천테크노파크
URLhttps://www.bigdata-telecom.kr/invoke/SOKBP2603/?goodsCode=ICTSPOTDTCTDD0000001

Alerts

위도X좌표 has constant value ""Constant
경도Y좌표 has constant value ""Constant
도로교통량값 is highly overall correlated with 도로점유율High correlation
도로점유율 is highly overall correlated with 도로교통량값 and 1 other fieldsHigh correlation
도로지점지역명 is highly overall correlated with 도로점유율High correlation
도로점유율 has 6 (3.0%) zerosZeros

Reproduction

Analysis started2023-12-10 06:24:16.257090
Analysis finished2023-12-10 06:24:18.704777
Duration2.45 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

도로지점시설물ID
Text

Distinct69
Distinct (%)34.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2023-12-10T15:24:18.918850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length15
Mean length15
Min length15

Characters and Unicode

Total characters3000
Distinct characters17
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSDTRVCT00100011
2nd rowSDTRVCT00100012
3rd rowSDTRVCT00100013
4th rowSDTRVCT00100014
5th rowSDTRVCT00100015
ValueCountFrequency (%)
sdtrvct00100011 3
 
1.5%
crtrvds00100016 3
 
1.5%
sdtrvct00100008 3
 
1.5%
crtrvds00100006 3
 
1.5%
crtrvds00100007 3
 
1.5%
crtrvds00100008 3
 
1.5%
crtrvds00100009 3
 
1.5%
crtrvds00100010 3
 
1.5%
crtrvds00100011 3
 
1.5%
crtrvds00100012 3
 
1.5%
Other values (59) 170
85.0%
2023-12-10T15:24:19.456661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1098
36.6%
R 310
 
10.3%
1 290
 
9.7%
T 248
 
8.3%
D 242
 
8.1%
S 200
 
6.7%
V 200
 
6.7%
C 158
 
5.3%
2 53
 
1.8%
3 42
 
1.4%
Other values (7) 159
 
5.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1600
53.3%
Uppercase Letter 1400
46.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1098
68.6%
1 290
 
18.1%
2 53
 
3.3%
3 42
 
2.6%
4 25
 
1.6%
6 20
 
1.2%
5 20
 
1.2%
7 18
 
1.1%
9 17
 
1.1%
8 17
 
1.1%
Uppercase Letter
ValueCountFrequency (%)
R 310
22.1%
T 248
17.7%
D 242
17.3%
S 200
14.3%
V 200
14.3%
C 158
11.3%
M 42
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1600
53.3%
Latin 1400
46.7%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1098
68.6%
1 290
 
18.1%
2 53
 
3.3%
3 42
 
2.6%
4 25
 
1.6%
6 20
 
1.2%
5 20
 
1.2%
7 18
 
1.1%
9 17
 
1.1%
8 17
 
1.1%
Latin
ValueCountFrequency (%)
R 310
22.1%
T 248
17.7%
D 242
17.3%
S 200
14.3%
V 200
14.3%
C 158
11.3%
M 42
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1098
36.6%
R 310
 
10.3%
1 290
 
9.7%
T 248
 
8.3%
D 242
 
8.1%
S 200
 
6.7%
V 200
 
6.7%
C 158
 
5.3%
2 53
 
1.8%
3 42
 
1.4%
Other values (7) 159
 
5.3%

생성일시
Categorical

Distinct3
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2019-12-18 15:00:00
69 
2019-12-19 15:00:00
69 
2019-12-12 15:00:00
62 

Length

Max length19
Median length19
Mean length19
Min length19

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2019-12-18 15:00:00
2nd row2019-12-18 15:00:00
3rd row2019-12-18 15:00:00
4th row2019-12-18 15:00:00
5th row2019-12-18 15:00:00

Common Values

ValueCountFrequency (%)
2019-12-18 15:00:00 69
34.5%
2019-12-19 15:00:00 69
34.5%
2019-12-12 15:00:00 62
31.0%

Length

2023-12-10T15:24:19.659584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:24:19.846631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
15:00:00 200
50.0%
2019-12-18 69
 
17.2%
2019-12-19 69
 
17.2%
2019-12-12 62
 
15.5%

도로교통량값
Real number (ℝ)

HIGH CORRELATION 

Distinct197
Distinct (%)98.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6187.47
Minimum3
Maximum38244
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-10T15:24:20.081254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile71.7
Q1821.5
median4384.5
Q38737.5
95-th percentile19765.35
Maximum38244
Range38241
Interquartile range (IQR)7916

Descriptive statistics

Standard deviation6722.8847
Coefficient of variation (CV)1.0865321
Kurtosis3.523317
Mean6187.47
Median Absolute Deviation (MAD)3716
Skewness1.7020562
Sum1237494
Variance45197179
MonotonicityNot monotonic
2023-12-10T15:24:20.317724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5 2
 
1.0%
325 2
 
1.0%
588 2
 
1.0%
1633 1
 
0.5%
9940 1
 
0.5%
8827 1
 
0.5%
11288 1
 
0.5%
9722 1
 
0.5%
5076 1
 
0.5%
5192 1
 
0.5%
Other values (187) 187
93.5%
ValueCountFrequency (%)
3 1
0.5%
5 2
1.0%
24 1
0.5%
33 1
0.5%
37 1
0.5%
52 1
0.5%
60 1
0.5%
61 1
0.5%
66 1
0.5%
72 1
0.5%
ValueCountFrequency (%)
38244 1
0.5%
30199 1
0.5%
28514 1
0.5%
28376 1
0.5%
25300 1
0.5%
21448 1
0.5%
21336 1
0.5%
20938 1
0.5%
20507 1
0.5%
20247 1
0.5%

차량속도값
Real number (ℝ)

Distinct51
Distinct (%)25.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.945
Minimum9
Maximum104
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-10T15:24:20.571809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile31.9
Q140.75
median52
Q358
95-th percentile72
Maximum104
Range95
Interquartile range (IQR)17.25

Descriptive statistics

Standard deviation14.84804
Coefficient of variation (CV)0.29145235
Kurtosis2.4046877
Mean50.945
Median Absolute Deviation (MAD)8
Skewness0.5715137
Sum10189
Variance220.4643
MonotonicityNot monotonic
2023-12-10T15:24:20.813965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
51 10
 
5.0%
39 9
 
4.5%
52 9
 
4.5%
43 9
 
4.5%
59 9
 
4.5%
58 9
 
4.5%
54 9
 
4.5%
60 8
 
4.0%
50 7
 
3.5%
33 7
 
3.5%
Other values (41) 114
57.0%
ValueCountFrequency (%)
9 2
 
1.0%
10 1
 
0.5%
20 3
1.5%
28 1
 
0.5%
30 3
1.5%
32 2
 
1.0%
33 7
3.5%
34 1
 
0.5%
36 6
3.0%
37 6
3.0%
ValueCountFrequency (%)
104 1
 
0.5%
102 1
 
0.5%
100 1
 
0.5%
99 2
1.0%
84 1
 
0.5%
83 1
 
0.5%
81 1
 
0.5%
73 1
 
0.5%
72 2
1.0%
71 4
2.0%

도로점유율
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct10
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.86
Minimum0
Maximum11
Zeros6
Zeros (%)3.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-10T15:24:21.016153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q32
95-th percentile5
Maximum11
Range11
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.5820602
Coefficient of variation (CV)0.85057002
Kurtosis10.059402
Mean1.86
Median Absolute Deviation (MAD)0
Skewness2.6941525
Sum372
Variance2.5029146
MonotonicityNot monotonic
2023-12-10T15:24:21.203238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 114
57.0%
2 36
 
18.0%
3 20
 
10.0%
4 12
 
6.0%
5 6
 
3.0%
0 6
 
3.0%
6 3
 
1.5%
9 1
 
0.5%
11 1
 
0.5%
10 1
 
0.5%
ValueCountFrequency (%)
0 6
 
3.0%
1 114
57.0%
2 36
 
18.0%
3 20
 
10.0%
4 12
 
6.0%
5 6
 
3.0%
6 3
 
1.5%
9 1
 
0.5%
10 1
 
0.5%
11 1
 
0.5%
ValueCountFrequency (%)
11 1
 
0.5%
10 1
 
0.5%
9 1
 
0.5%
6 3
 
1.5%
5 6
 
3.0%
4 12
 
6.0%
3 20
 
10.0%
2 36
 
18.0%
1 114
57.0%
0 6
 
3.0%

도로지점시설물명
Text

Distinct69
Distinct (%)34.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2023-12-10T15:24:21.581441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length14
Mean length14.09
Min length14

Characters and Unicode

Total characters2818
Distinct characters28
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowVDS_송도#011(통신부)
2nd rowVDS_송도#012(통신부)
3rd rowVDS_송도#013(통신부)
4th rowVDS_송도#014(통신부)
5th rowVDS_송도#015(통신부)
ValueCountFrequency (%)
vds_송도#011(통신부 3
 
1.5%
vds_청라#016(제어 3
 
1.5%
vds_송도#007(통신 3
 
1.5%
vds_청라#006(제어 3
 
1.5%
vds_청라#007(제어 3
 
1.5%
vds_청라#008(제어 3
 
1.5%
vds_청라#009(제어 3
 
1.5%
vds_청라#010(제어 3
 
1.5%
vds_청라#011(제어 3
 
1.5%
vds_청라#012(제어 3
 
1.5%
Other values (59) 170
85.0%
2023-12-10T15:24:22.538305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 298
 
10.6%
V 200
 
7.1%
( 200
 
7.1%
S 200
 
7.1%
_ 200
 
7.1%
D 200
 
7.1%
# 200
 
7.1%
) 200
 
7.1%
제 152
 
5.4%
어 152
 
5.4%
Other values (18) 816
29.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 818
29.0%
Decimal Number 600
21.3%
Uppercase Letter 600
21.3%
Open Punctuation 200
 
7.1%
Connector Punctuation 200
 
7.1%
Other Punctuation 200
 
7.1%
Close Punctuation 200
 
7.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
제 152
18.6%
어 152
18.6%
청 110
13.4%
라 110
13.4%
신 48
 
5.9%
도 48
 
5.9%
송 48
 
5.9%
통 48
 
5.9%
미 42
 
5.1%
단 42
 
5.1%
Decimal Number
ValueCountFrequency (%)
0 298
49.7%
1 90
 
15.0%
2 53
 
8.8%
3 42
 
7.0%
4 25
 
4.2%
5 20
 
3.3%
6 20
 
3.3%
7 18
 
3.0%
8 17
 
2.8%
9 17
 
2.8%
Uppercase Letter
ValueCountFrequency (%)
V 200
33.3%
S 200
33.3%
D 200
33.3%
Open Punctuation
ValueCountFrequency (%)
( 200
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 200
100.0%
Other Punctuation
ValueCountFrequency (%)
# 200
100.0%
Close Punctuation
ValueCountFrequency (%)
) 200
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1400
49.7%
Hangul 818
29.0%
Latin 600
21.3%

Most frequent character per script

Common
ValueCountFrequency (%)
0 298
21.3%
( 200
14.3%
_ 200
14.3%
# 200
14.3%
) 200
14.3%
1 90
 
6.4%
2 53
 
3.8%
3 42
 
3.0%
4 25
 
1.8%
5 20
 
1.4%
Other values (4) 72
 
5.1%
Hangul
ValueCountFrequency (%)
제 152
18.6%
어 152
18.6%
청 110
13.4%
라 110
13.4%
신 48
 
5.9%
도 48
 
5.9%
송 48
 
5.9%
통 48
 
5.9%
미 42
 
5.1%
단 42
 
5.1%
Latin
ValueCountFrequency (%)
V 200
33.3%
S 200
33.3%
D 200
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2000
71.0%
Hangul 818
29.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 298
14.9%
V 200
10.0%
( 200
10.0%
S 200
10.0%
_ 200
10.0%
D 200
10.0%
# 200
10.0%
) 200
10.0%
1 90
 
4.5%
2 53
 
2.6%
Other values (7) 159
8.0%
Hangul
ValueCountFrequency (%)
제 152
18.6%
어 152
18.6%
청 110
13.4%
라 110
13.4%
신 48
 
5.9%
도 48
 
5.9%
송 48
 
5.9%
통 48
 
5.9%
미 42
 
5.1%
단 42
 
5.1%

설치위치명
Text

Distinct62
Distinct (%)31.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2023-12-10T15:24:23.022581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length39
Median length36
Mean length27.04
Min length9

Characters and Unicode

Total characters5408
Distinct characters115
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row송도동201-2 삼성바이오로직스(가칭)
2nd row송도동203-5 BRC연구소(가칭)
3rd row송도동168-1 현대프리미엄아울렛(가칭)
4th row송도동238 연세대학교국제캠퍼스(가칭)
5th row송도동165 제4교(가칭)
ValueCountFrequency (%)
청라동 110
 
11.6%
하행 56
 
5.9%
상행 54
 
5.7%
인천광역시 42
 
4.4%
운북동 42
 
4.4%
중구 42
 
4.4%
1301 18
 
1.9%
상행1차로 14
 
1.5%
하행1차로 13
 
1.4%
하행2차로 12
 
1.3%
Other values (132) 548
57.6%
2023-12-10T15:24:23.690231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
751
 
13.9%
1 343
 
6.3%
로 330
 
6.1%
행 220
 
4.1%
청 178
 
3.3%
동 176
 
3.3%
3 176
 
3.3%
2 173
 
3.2%
라 146
 
2.7%
- 134
 
2.5%
Other values (105) 2781
51.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2770
51.2%
Decimal Number 1152
21.3%
Space Separator 751
 
13.9%
Uppercase Letter 345
 
6.4%
Dash Punctuation 134
 
2.5%
Open Punctuation 128
 
2.4%
Close Punctuation 128
 
2.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 330
 
11.9%
행 220
 
7.9%
청 178
 
6.4%
동 176
 
6.4%
라 146
 
5.3%
하 111
 
4.0%
차 110
 
4.0%
상 109
 
3.9%
중 89
 
3.2%
제 68
 
2.5%
Other values (85) 1233
44.5%
Decimal Number
ValueCountFrequency (%)
1 343
29.8%
3 176
15.3%
2 173
15.0%
0 92
 
8.0%
5 87
 
7.6%
8 68
 
5.9%
4 66
 
5.7%
6 59
 
5.1%
7 47
 
4.1%
9 41
 
3.6%
Uppercase Letter
ValueCountFrequency (%)
C 113
32.8%
S 110
31.9%
V 110
31.9%
D 6
 
1.7%
B 3
 
0.9%
R 3
 
0.9%
Space Separator
ValueCountFrequency (%)
751
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 134
100.0%
Open Punctuation
ValueCountFrequency (%)
( 128
100.0%
Close Punctuation
ValueCountFrequency (%)
) 128
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2770
51.2%
Common 2293
42.4%
Latin 345
 
6.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 330
 
11.9%
행 220
 
7.9%
청 178
 
6.4%
동 176
 
6.4%
라 146
 
5.3%
하 111
 
4.0%
차 110
 
4.0%
상 109
 
3.9%
중 89
 
3.2%
제 68
 
2.5%
Other values (85) 1233
44.5%
Common
ValueCountFrequency (%)
751
32.8%
1 343
15.0%
3 176
 
7.7%
2 173
 
7.5%
- 134
 
5.8%
( 128
 
5.6%
) 128
 
5.6%
0 92
 
4.0%
5 87
 
3.8%
8 68
 
3.0%
Other values (4) 213
 
9.3%
Latin
ValueCountFrequency (%)
C 113
32.8%
S 110
31.9%
V 110
31.9%
D 6
 
1.7%
B 3
 
0.9%
R 3
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2770
51.2%
ASCII 2638
48.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
751
28.5%
1 343
13.0%
3 176
 
6.7%
2 173
 
6.6%
- 134
 
5.1%
( 128
 
4.9%
) 128
 
4.9%
C 113
 
4.3%
S 110
 
4.2%
V 110
 
4.2%
Other values (10) 472
17.9%
Hangul
ValueCountFrequency (%)
로 330
 
11.9%
행 220
 
7.9%
청 178
 
6.4%
동 176
 
6.4%
라 146
 
5.3%
하 111
 
4.0%
차 110
 
4.0%
상 109
 
3.9%
중 89
 
3.2%
제 68
 
2.5%
Other values (85) 1233
44.5%

도로지점지역명
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
청라
110 
송도
48 
미단
42 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
청라 110
55.0%
송도 48
24.0%
미단 42
 
21.0%

Length

2023-12-10T15:24:23.939265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:24:24.125358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
청라 110
55.0%
송도 48
24.0%
미단 42
 
21.0%

위도X좌표
Categorical

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
37
200 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row37
2nd row37
3rd row37
4th row37
5th row37

Common Values

ValueCountFrequency (%)
37 200
100.0%

Length

2023-12-10T15:24:24.302464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:24:24.460973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
37 200
100.0%

경도Y좌표
Categorical

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
126
200 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row126
2nd row126
3rd row126
4th row126
5th row126

Common Values

ValueCountFrequency (%)
126 200
100.0%

Length

2023-12-10T15:24:24.629727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:24:24.832537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
126 200
100.0%

Interactions

2023-12-10T15:24:17.860059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:24:16.967728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:24:17.446982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:24:18.018645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:24:17.136693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:24:17.603109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:24:18.138129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:24:17.274242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:24:17.721419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T15:24:24.969582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
도로지점시설물ID생성일시도로교통량값차량속도값도로점유율도로지점시설물명설치위치명도로지점지역명
도로지점시설물ID1.0000.0000.9290.9980.9641.0001.0001.000
생성일시0.0001.0000.0000.0000.0000.0000.0000.000
도로교통량값0.9290.0001.0000.2580.7770.9290.9360.776
차량속도값0.9980.0000.2581.0000.5510.9980.9940.612
도로점유율0.9640.0000.7770.5511.0000.9640.9670.668
도로지점시설물명1.0000.0000.9290.9980.9641.0001.0001.000
설치위치명1.0000.0000.9360.9940.9671.0001.0001.000
도로지점지역명1.0000.0000.7760.6120.6681.0001.0001.000
2023-12-10T15:24:25.160828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
도로지점지역명생성일시
도로지점지역명1.0000.000
생성일시0.0001.000
2023-12-10T15:24:25.308468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
도로교통량값차량속도값도로점유율생성일시도로지점지역명
도로교통량값1.000-0.0170.5330.0000.473
차량속도값-0.0171.000-0.1550.0000.328
도로점유율0.533-0.1551.0000.0000.540
생성일시0.0000.0000.0001.0000.000
도로지점지역명0.4730.3280.5400.0001.000

Missing values

2023-12-10T15:24:18.341319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T15:24:18.595389image/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

도로지점시설물ID생성일시도로교통량값차량속도값도로점유율도로지점시설물명설치위치명도로지점지역명위도X좌표경도Y좌표
0SDTRVCT001000112019-12-18 15:00:001633531VDS_송도#011(통신부)송도동201-2 삼성바이오로직스(가칭)송도37126
1SDTRVCT001000122019-12-18 15:00:006883503VDS_송도#012(통신부)송도동203-5 BRC연구소(가칭)송도37126
2SDTRVCT001000132019-12-18 15:00:0010274433VDS_송도#013(통신부)송도동168-1 현대프리미엄아울렛(가칭)송도37126
3SDTRVCT001000142019-12-18 15:00:003944592VDS_송도#014(통신부)송도동238 연세대학교국제캠퍼스(가칭)송도37126
4SDTRVCT001000152019-12-18 15:00:0014551713VDS_송도#015(통신부)송도동165 제4교(가칭)송도37126
5SDTRVCT001000162019-12-18 15:00:004535582VDS_송도#016(통신부)송도동187 글로벌캠퍼스(가칭)송도37126
6SDTRVCT001000012019-12-18 15:00:006622632VDS_송도#008(통신)한국생산기술연구원 맞은편송도37126
7SDTRVCT001000022019-12-18 15:00:0015844595VDS_송도#001(통신)커낼워크 D4 오피스텔 맞은편송도37126
8SDTRVCT001000032019-12-18 15:00:0013640574VDS_송도#002(통신)커낼워크 D4 오피스텔송도37126
9SDTRVCT001000042019-12-18 15:00:0010534553VDS_송도#003(통신)송도시외버스 환승센터 앞 중앙녹지송도37126
도로지점시설물ID생성일시도로교통량값차량속도값도로점유율도로지점시설물명설치위치명도로지점지역명위도X좌표경도Y좌표
190CRTRVDS001000232019-12-12 15:00:001197330VDS_청라#023(제어)청라동 139-5(청라루비로 하행2차로 CVS23 루비로1 하행)청라37126
191CRTRVDS001000242019-12-12 15:00:002840391VDS_청라#024(제어)청라동 139-5(청라루비로 상행2차로 CVS24 루비로1 상행)청라37126
192CRTRVDS001000252019-12-12 15:00:006975372VDS_청라#025(제어)청라동 155-26(청라루비로 하행2차로 CVS25 루비로2 하행)청라37126
193CRTRVDS001000262019-12-12 15:00:007985332VDS_청라#026(제어)청라동 155-26(청라루비로 상행2차로 CVS26 루비로2 상행)청라37126
194CRTRVDS001000272019-12-12 15:00:004359362VDS_청라#027(제어)청라동 173-7(청라루비로 하행1차로 CVS27 루비로3 하행)청라37126
195CRTRVDS001000282019-12-12 15:00:008252204VDS_청라#028(제어)청라동 173-7(청라루비로 상행1차로 CVS28 루비로3 상행)청라37126
196CRTRVDS001000292019-12-12 15:00:0019018401VDS_청라#029(제어)청라동 115-8(중봉대로 하행2차로 CVS29 중봉로 하행)청라37126
197CRTRVDS001000302019-12-12 15:00:0020247431VDS_청라#030(제어)청라동 162-13(중봉대로 상행2차로 CVS30 중봉로 상행)청라37126
198CRTRVDS001000312019-12-12 15:00:0013732511VDS_청라#031(제어)청라동 116-11(국제대로 하행1차로 CVS31 국제대로3 상행)청라37126
199CRTRVDS001000372019-12-12 15:00:005027501VDS_청라#037(제어)청라동 130-8(담지로 하행6차로 CVS37 담지로1 하행)청라37126