Overview

Dataset statistics

Number of variables11
Number of observations200
Missing cells200
Missing cells (%)9.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory18.5 KiB
Average record size in memory94.7 B

Variable types

Text3
Categorical2
Numeric5
Unsupported1

Dataset

DescriptionSample
Author(재)인천테크노파크
URLhttps://www.bigdata-telecom.kr/invoke/SOKBP2603/?goodsCode=ICTSPOTDTCT150000001

Alerts

경도 Y좌표 is highly overall correlated with 도로 교통량 값 and 1 other fieldsHigh correlation
도로 교통량 값 is highly overall correlated with 경도 Y좌표High correlation
위도 X좌표 is highly overall correlated with 도로 지점 지역 명High correlation
도로 지점 지역 명 is highly overall correlated with 경도 Y좌표 and 1 other fieldsHigh correlation
도로 파티션 구분자 여부 has 200 (100.0%) missing valuesMissing
도로 파티션 구분자 여부 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2023-12-10 06:21:20.846700
Analysis finished2023-12-10 06:21:26.675207
Duration5.83 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

도로 지점 시설물 ID
Text

Distinct68
Distinct (%)34.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2023-12-10T15:21:27.106944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length15
Mean length15
Min length15

Characters and Unicode

Total characters3000
Distinct characters19
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 rowMDTRVDS00100002
2nd rowSDTRVCT00100009
3rd rowCRTRVDS00100024
4th rowSDTRVCT00100007
5th rowCRTRVDS00100026
ValueCountFrequency (%)
mdtrvds00100002 3
 
1.5%
sdtrvct00100009 3
 
1.5%
crtrvds00100027 3
 
1.5%
crtrvds00100019 3
 
1.5%
crtrvds00100035 3
 
1.5%
sdtrvct00100016 3
 
1.5%
crtrvds00100039 3
 
1.5%
mdtrvds00100010 3
 
1.5%
crtrvds00100012 3
 
1.5%
crtrvds00100029 3
 
1.5%
Other values (58) 170
85.0%
2023-12-10T15:21:27.883411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1087
36.2%
R 316
 
10.5%
1 289
 
9.6%
T 245
 
8.2%
D 219
 
7.3%
V 200
 
6.7%
S 200
 
6.7%
C 161
 
5.4%
2 60
 
2.0%
3 55
 
1.8%
Other values (9) 168
 
5.6%

Most occurring categories

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

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1087
67.9%
1 289
 
18.1%
2 60
 
3.8%
3 55
 
3.4%
5 21
 
1.3%
4 20
 
1.2%
6 20
 
1.2%
9 18
 
1.1%
8 15
 
0.9%
7 15
 
0.9%
Uppercase Letter
ValueCountFrequency (%)
R 316
22.6%
T 245
17.5%
D 219
15.6%
V 200
14.3%
S 200
14.3%
C 161
11.5%
J 20
 
1.4%
Y 20
 
1.4%
M 19
 
1.4%

Most occurring scripts

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

Most frequent character per script

Common
ValueCountFrequency (%)
0 1087
67.9%
1 289
 
18.1%
2 60
 
3.8%
3 55
 
3.4%
5 21
 
1.3%
4 20
 
1.2%
6 20
 
1.2%
9 18
 
1.1%
8 15
 
0.9%
7 15
 
0.9%
Latin
ValueCountFrequency (%)
R 316
22.6%
T 245
17.5%
D 219
15.6%
V 200
14.3%
S 200
14.3%
C 161
11.5%
J 20
 
1.4%
Y 20
 
1.4%
M 19
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1087
36.2%
R 316
 
10.5%
1 289
 
9.6%
T 245
 
8.2%
D 219
 
7.3%
V 200
 
6.7%
S 200
 
6.7%
C 161
 
5.4%
2 60
 
2.0%
3 55
 
1.8%
Other values (9) 168
 
5.6%

생성 일시
Categorical

Distinct4
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2020-06-15 15:00
67 
2020-06-15 15:15
66 
2020-06-15 15:30
66 
2020-06-15 16:15
 
1

Length

Max length16
Median length16
Mean length16
Min length16

Unique

Unique1 ?
Unique (%)0.5%

Sample

1st row2020-06-15 16:15
2nd row2020-06-15 15:15
3rd row2020-06-15 15:15
4th row2020-06-15 15:15
5th row2020-06-15 15:15

Common Values

ValueCountFrequency (%)
2020-06-15 15:00 67
33.5%
2020-06-15 15:15 66
33.0%
2020-06-15 15:30 66
33.0%
2020-06-15 16:15 1
 
0.5%

Length

2023-12-10T15:21:28.171243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:21:28.435535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020-06-15 200
50.0%
15:00 67
 
16.8%
15:15 66
 
16.5%
15:30 66
 
16.5%
16:15 1
 
0.2%

경도 Y좌표
Real number (ℝ)

HIGH CORRELATION 

Distinct68
Distinct (%)34.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.62054
Minimum126.50284
Maximum126.67557
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-10T15:21:28.717159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.50284
5-th percentile126.50452
Q1126.62392
median126.63931
Q3126.65211
95-th percentile126.66233
Maximum126.67557
Range0.1727223
Interquartile range (IQR)0.02818535

Descriptive statistics

Standard deviation0.049509317
Coefficient of variation (CV)0.0003910054
Kurtosis0.67636709
Mean126.62054
Median Absolute Deviation (MAD)0.0131977
Skewness-1.4545548
Sum25324.109
Variance0.0024511725
MonotonicityNot monotonic
2023-12-10T15:21:29.017220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.5093455 3
 
1.5%
126.6597135 3
 
1.5%
126.6405004 3
 
1.5%
126.6389643 3
 
1.5%
126.6522902 3
 
1.5%
126.6639226 3
 
1.5%
126.6600716 3
 
1.5%
126.5364467 3
 
1.5%
126.6294164 3
 
1.5%
126.6493549 3
 
1.5%
Other values (58) 170
85.0%
ValueCountFrequency (%)
126.5028435 3
1.5%
126.5028863 3
1.5%
126.5042142 3
1.5%
126.5045154 3
1.5%
126.5093455 3
1.5%
126.5121607 3
1.5%
126.5173366 2
1.0%
126.5176543 3
1.5%
126.5363838 2
1.0%
126.5364467 3
1.5%
ValueCountFrequency (%)
126.6755658 3
1.5%
126.6717596 3
1.5%
126.6639226 3
1.5%
126.662331 3
1.5%
126.6602014 3
1.5%
126.6600716 3
1.5%
126.6600094 3
1.5%
126.6597135 3
1.5%
126.6596897 3
1.5%
126.659616 3
1.5%

도로 교통량 값
Real number (ℝ)

HIGH CORRELATION 

Distinct63
Distinct (%)31.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.9
Minimum1
Maximum143
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-10T15:21:29.425358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q16
median17
Q332
95-th percentile99
Maximum143
Range142
Interquartile range (IQR)26

Descriptive statistics

Standard deviation27.73094
Coefficient of variation (CV)1.1136924
Kurtosis4.2350787
Mean24.9
Median Absolute Deviation (MAD)12
Skewness2.0671042
Sum4980
Variance769.00503
MonotonicityNot monotonic
2023-12-10T15:21:30.245607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 14
 
7.0%
2 13
 
6.5%
6 10
 
5.0%
16 8
 
4.0%
5 7
 
3.5%
4 7
 
3.5%
11 6
 
3.0%
3 6
 
3.0%
17 6
 
3.0%
18 6
 
3.0%
Other values (53) 117
58.5%
ValueCountFrequency (%)
1 14
7.0%
2 13
6.5%
3 6
3.0%
4 7
3.5%
5 7
3.5%
6 10
5.0%
7 2
 
1.0%
9 6
3.0%
10 2
 
1.0%
11 6
3.0%
ValueCountFrequency (%)
143 1
0.5%
123 1
0.5%
118 1
0.5%
117 1
0.5%
113 1
0.5%
108 1
0.5%
106 1
0.5%
105 1
0.5%
104 1
0.5%
99 2
1.0%

차량 속도 값
Real number (ℝ)

Distinct168
Distinct (%)84.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean55.432
Minimum10.9
Maximum128.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-10T15:21:30.513046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10.9
5-th percentile28.35
Q142.775
median51.65
Q362.75
95-th percentile103.74
Maximum128.3
Range117.4
Interquartile range (IQR)19.975

Descriptive statistics

Standard deviation21.888128
Coefficient of variation (CV)0.39486447
Kurtosis2.1262871
Mean55.432
Median Absolute Deviation (MAD)10.4
Skewness1.2814819
Sum11086.4
Variance479.09013
MonotonicityNot monotonic
2023-12-10T15:21:30.749432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
46.3 3
 
1.5%
39.5 3
 
1.5%
43.0 3
 
1.5%
52.0 2
 
1.0%
42.8 2
 
1.0%
61.6 2
 
1.0%
40.2 2
 
1.0%
57.7 2
 
1.0%
53.4 2
 
1.0%
45.8 2
 
1.0%
Other values (158) 177
88.5%
ValueCountFrequency (%)
10.9 1
0.5%
11.1 1
0.5%
12.6 1
0.5%
22.2 1
0.5%
22.7 1
0.5%
23.2 1
0.5%
23.6 1
0.5%
24.4 1
0.5%
24.5 1
0.5%
25.5 1
0.5%
ValueCountFrequency (%)
128.3 1
0.5%
128.0 1
0.5%
123.3 1
0.5%
120.0 1
0.5%
119.4 1
0.5%
119.2 1
0.5%
118.0 1
0.5%
116.4 1
0.5%
113.6 1
0.5%
104.5 1
0.5%

도로 파티션 구분자 여부
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing200
Missing (%)100.0%
Memory size1.9 KiB

도로 점유 율
Real number (ℝ)

Distinct37
Distinct (%)18.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.6945
Minimum0.5
Maximum5.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-10T15:21:30.997819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.5
5-th percentile0.8
Q11.1
median1.5
Q32
95-th percentile3.805
Maximum5.2
Range4.7
Interquartile range (IQR)0.9

Descriptive statistics

Standard deviation0.87271295
Coefficient of variation (CV)0.51502682
Kurtosis3.4483271
Mean1.6945
Median Absolute Deviation (MAD)0.5
Skewness1.7487393
Sum338.9
Variance0.76162789
MonotonicityNot monotonic
2023-12-10T15:21:31.280907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
1.0 35
17.5%
1.1 16
 
8.0%
1.5 13
 
6.5%
1.4 12
 
6.0%
2.0 12
 
6.0%
1.6 12
 
6.0%
1.2 10
 
5.0%
1.7 9
 
4.5%
1.3 9
 
4.5%
1.8 8
 
4.0%
Other values (27) 64
32.0%
ValueCountFrequency (%)
0.5 3
 
1.5%
0.6 3
 
1.5%
0.7 3
 
1.5%
0.8 2
 
1.0%
0.9 2
 
1.0%
1.0 35
17.5%
1.1 16
8.0%
1.2 10
 
5.0%
1.3 9
 
4.5%
1.4 12
 
6.0%
ValueCountFrequency (%)
5.2 1
0.5%
5.0 1
0.5%
4.7 1
0.5%
4.6 1
0.5%
4.3 2
1.0%
4.2 1
0.5%
4.1 1
0.5%
3.9 2
1.0%
3.8 1
0.5%
3.4 1
0.5%

도로 지점 시설물 명
Text

Distinct68
Distinct (%)34.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2023-12-10T15:21:31.785315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length14
Mean length14.09
Min length14

Characters and Unicode

Total characters2818
Distinct characters30
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_미단#002(제어)
2nd rowVDS_송도#009(통신)
3rd rowVDS_청라#024(제어)
4th rowVDS_송도#006(통신)
5th rowVDS_청라#026(제어)
ValueCountFrequency (%)
vds_미단#002(제어 3
 
1.5%
vds_송도#009(통신 3
 
1.5%
vds_청라#027(제어 3
 
1.5%
vds_청라#019(제어 3
 
1.5%
vds_청라#035(제어 3
 
1.5%
vds_송도#016(통신부 3
 
1.5%
vds_청라#039(제어 3
 
1.5%
vds_미단#010(제어 3
 
1.5%
vds_청라#012(제어 3
 
1.5%
vds_청라#029(제어 3
 
1.5%
Other values (58) 170
85.0%
2023-12-10T15:21:32.578897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 287
 
10.2%
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 155
 
5.5%
어 155
 
5.5%
Other values (20) 821
29.1%

Most occurring categories

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

Most frequent character per category

Other Letter
ValueCountFrequency (%)
제 155
18.9%
어 155
18.9%
라 116
14.2%
청 116
14.2%
통 45
 
5.5%
신 45
 
5.5%
도 45
 
5.5%
송 45
 
5.5%
영 20
 
2.4%
종 20
 
2.4%
Other values (3) 56
 
6.8%
Decimal Number
ValueCountFrequency (%)
0 287
47.8%
1 86
 
14.3%
2 63
 
10.5%
3 55
 
9.2%
5 21
 
3.5%
4 20
 
3.3%
6 20
 
3.3%
9 18
 
3.0%
8 15
 
2.5%
7 15
 
2.5%
Uppercase Letter
ValueCountFrequency (%)
V 200
33.3%
S 200
33.3%
D 200
33.3%
Close Punctuation
ValueCountFrequency (%)
) 200
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 200
100.0%
Other Punctuation
ValueCountFrequency (%)
# 200
100.0%
Open 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 (%)
0 287
20.5%
) 200
14.3%
_ 200
14.3%
# 200
14.3%
( 200
14.3%
1 86
 
6.1%
2 63
 
4.5%
3 55
 
3.9%
5 21
 
1.5%
4 20
 
1.4%
Other values (4) 68
 
4.9%
Hangul
ValueCountFrequency (%)
제 155
18.9%
어 155
18.9%
라 116
14.2%
청 116
14.2%
통 45
 
5.5%
신 45
 
5.5%
도 45
 
5.5%
송 45
 
5.5%
영 20
 
2.4%
종 20
 
2.4%
Other values (3) 56
 
6.8%
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 (%)
0 287
14.3%
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 86
 
4.3%
2 63
 
3.1%
Other values (7) 164
8.2%
Hangul
ValueCountFrequency (%)
제 155
18.9%
어 155
18.9%
라 116
14.2%
청 116
14.2%
통 45
 
5.5%
신 45
 
5.5%
도 45
 
5.5%
송 45
 
5.5%
영 20
 
2.4%
종 20
 
2.4%
Other values (3) 56
 
6.8%

설치 위치 명
Text

Distinct61
Distinct (%)30.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2023-12-10T15:21:33.037990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length39
Median length36
Mean length26.18
Min length4

Characters and Unicode

Total characters5236
Distinct characters120
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 row인천광역시 중구 운북동 1368-3
2nd row송도 애니파크 대각 맞은편
3rd row청라동 139-5(청라루비로 상행2차로 CVS24 루비로1 상행)
4th row인천송도 힐스테이트4단지 앞
5th row청라동 155-26(청라루비로 상행2차로 CVS26 루비로2 상행)
ValueCountFrequency (%)
청라동 116
 
12.8%
상행 59
 
6.5%
하행 57
 
6.3%
인천광역시 19
 
2.1%
운북동 19
 
2.1%
중구 19
 
2.1%
상행1차로 18
 
2.0%
하행1차로 18
 
2.0%
앞 12
 
1.3%
상행2차로 12
 
1.3%
Other values (131) 554
61.4%
2023-12-10T15:21:33.971148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
703
 
13.4%
로 368
 
7.0%
1 318
 
6.1%
행 232
 
4.4%
청 188
 
3.6%
2 170
 
3.2%
3 168
 
3.2%
동 159
 
3.0%
라 152
 
2.9%
( 134
 
2.6%
Other values (110) 2644
50.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2671
51.0%
Decimal Number 1100
21.0%
Space Separator 703
 
13.4%
Uppercase Letter 360
 
6.9%
Open Punctuation 134
 
2.6%
Dash Punctuation 134
 
2.6%
Close Punctuation 134
 
2.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 368
 
13.8%
행 232
 
8.7%
청 188
 
7.0%
동 159
 
6.0%
라 152
 
5.7%
하 125
 
4.7%
차 116
 
4.3%
상 116
 
4.3%
대 72
 
2.7%
제 70
 
2.6%
Other values (90) 1073
40.2%
Decimal Number
ValueCountFrequency (%)
1 318
28.9%
2 170
15.5%
3 168
15.3%
5 86
 
7.8%
0 81
 
7.4%
8 65
 
5.9%
6 65
 
5.9%
4 60
 
5.5%
7 45
 
4.1%
9 42
 
3.8%
Uppercase Letter
ValueCountFrequency (%)
C 119
33.1%
V 116
32.2%
S 116
32.2%
D 3
 
0.8%
B 3
 
0.8%
R 3
 
0.8%
Space Separator
ValueCountFrequency (%)
703
100.0%
Open Punctuation
ValueCountFrequency (%)
( 134
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 134
100.0%
Close Punctuation
ValueCountFrequency (%)
) 134
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2671
51.0%
Common 2205
42.1%
Latin 360
 
6.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 368
 
13.8%
행 232
 
8.7%
청 188
 
7.0%
동 159
 
6.0%
라 152
 
5.7%
하 125
 
4.7%
차 116
 
4.3%
상 116
 
4.3%
대 72
 
2.7%
제 70
 
2.6%
Other values (90) 1073
40.2%
Common
ValueCountFrequency (%)
703
31.9%
1 318
14.4%
2 170
 
7.7%
3 168
 
7.6%
( 134
 
6.1%
- 134
 
6.1%
) 134
 
6.1%
5 86
 
3.9%
0 81
 
3.7%
8 65
 
2.9%
Other values (4) 212
 
9.6%
Latin
ValueCountFrequency (%)
C 119
33.1%
V 116
32.2%
S 116
32.2%
D 3
 
0.8%
B 3
 
0.8%
R 3
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2671
51.0%
ASCII 2565
49.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
703
27.4%
1 318
12.4%
2 170
 
6.6%
3 168
 
6.5%
( 134
 
5.2%
- 134
 
5.2%
) 134
 
5.2%
C 119
 
4.6%
V 116
 
4.5%
S 116
 
4.5%
Other values (10) 453
17.7%
Hangul
ValueCountFrequency (%)
로 368
 
13.8%
행 232
 
8.7%
청 188
 
7.0%
동 159
 
6.0%
라 152
 
5.7%
하 125
 
4.7%
차 116
 
4.3%
상 116
 
4.3%
대 72
 
2.7%
제 70
 
2.6%
Other values (90) 1073
40.2%

도로 지점 지역 명
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
청라
116 
송도
45 
영종
20 
미단
19 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
청라 116
58.0%
송도 45
 
22.5%
영종 20
 
10.0%
미단 19
 
9.5%

Length

2023-12-10T15:21:34.230136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:21:34.475137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
청라 116
58.0%
송도 45
 
22.5%
영종 20
 
10.0%
미단 19
 
9.5%

위도 X좌표
Real number (ℝ)

HIGH CORRELATION 

Distinct67
Distinct (%)33.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.4952
Minimum37.364656
Maximum37.54802
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-10T15:21:34.745999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.364656
5-th percentile37.377793
Q137.486389
median37.529715
Q337.536224
95-th percentile37.545121
Maximum37.54802
Range0.1833647
Interquartile range (IQR)0.049835375

Descriptive statistics

Standard deviation0.062964283
Coefficient of variation (CV)0.0016792625
Kurtosis-0.44114598
Mean37.4952
Median Absolute Deviation (MAD)0.0099621
Skewness-1.1592002
Sum7499.04
Variance0.0039645009
MonotonicityNot monotonic
2023-12-10T15:21:35.035357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.5396769 6
 
3.0%
37.5246634 3
 
1.5%
37.5322488 3
 
1.5%
37.5480204 3
 
1.5%
37.532583 3
 
1.5%
37.5296704 3
 
1.5%
37.4897444 3
 
1.5%
37.4863888 3
 
1.5%
37.535881 3
 
1.5%
37.3646557 3
 
1.5%
Other values (57) 167
83.5%
ValueCountFrequency (%)
37.3646557 3
1.5%
37.372195 3
1.5%
37.3748904 3
1.5%
37.377793 3
1.5%
37.3779874 3
1.5%
37.3807888 3
1.5%
37.3826247 3
1.5%
37.3827585 3
1.5%
37.383105 3
1.5%
37.3833786 3
1.5%
ValueCountFrequency (%)
37.5480204 3
1.5%
37.5480002 3
1.5%
37.5452038 3
1.5%
37.545121 3
1.5%
37.5419912 3
1.5%
37.5419662 2
1.0%
37.5416694 3
1.5%
37.5405361 3
1.5%
37.5405295 3
1.5%
37.5404769 3
1.5%

Interactions

2023-12-10T15:21:25.076219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:21:21.601817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:21:22.396477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:21:23.385702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:21:24.244910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:21:25.270767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:21:21.758126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:21:22.571741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:21:23.576752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:21:24.413153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:21:25.521604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:21:21.913628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:21:22.749513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:21:23.791446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:21:24.573952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:21:25.763874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:21:22.064723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:21:22.949032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:21:23.941501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:21:24.736676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:21:25.924611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:21:22.218328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:21:23.216758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:21:24.075260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:21:24.885691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T15:21:35.259632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
도로 지점 시설물 ID생성 일시경도 Y좌표도로 교통량 값차량 속도 값도로 점유 율도로 지점 시설물 명설치 위치 명도로 지점 지역 명위도 X좌표
도로 지점 시설물 ID1.0000.0001.0000.8320.9270.8341.0001.0001.0001.000
생성 일시0.0001.0000.0000.0000.0000.1430.0000.0000.1030.000
경도 Y좌표1.0000.0001.0000.2940.3370.2171.0000.9770.9620.712
도로 교통량 값0.8320.0000.2941.0000.0000.6130.8320.8430.3840.572
차량 속도 값0.9270.0000.3370.0001.0000.7560.9270.8980.5530.600
도로 점유 율0.8340.1430.2170.6130.7561.0000.8340.8520.4670.498
도로 지점 시설물 명1.0000.0001.0000.8320.9270.8341.0001.0001.0001.000
설치 위치 명1.0000.0000.9770.8430.8980.8521.0001.0001.0000.992
도로 지점 지역 명1.0000.1030.9620.3840.5530.4671.0001.0001.0000.928
위도 X좌표1.0000.0000.7120.5720.6000.4981.0000.9920.9281.000
2023-12-10T15:21:35.548794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
도로 지점 지역 명생성 일시
도로 지점 지역 명1.0000.040
생성 일시0.0401.000
2023-12-10T15:21:35.703494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
경도 Y좌표도로 교통량 값차량 속도 값도로 점유 율위도 X좌표생성 일시도로 지점 지역 명
경도 Y좌표1.0000.503-0.1180.422-0.1460.0000.735
도로 교통량 값0.5031.0000.0320.352-0.0750.0000.235
차량 속도 값-0.1180.0321.000-0.446-0.1430.0000.358
도로 점유 율0.4220.352-0.4461.000-0.3440.0840.292
위도 X좌표-0.146-0.075-0.143-0.3441.0000.0000.901
생성 일시0.0000.0000.0000.0840.0001.0000.040
도로 지점 지역 명0.7350.2350.3580.2920.9010.0401.000

Missing values

2023-12-10T15:21:26.214437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T15:21:26.547064image/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

도로 지점 시설물 ID생성 일시경도 Y좌표도로 교통량 값차량 속도 값도로 파티션 구분자 여부도로 점유 율도로 지점 시설물 명설치 위치 명도로 지점 지역 명위도 X좌표
0MDTRVDS001000022020-06-15 16:15126.509345155.0<NA>1.0VDS_미단#002(제어)인천광역시 중구 운북동 1368-3미단37.524663
1SDTRVCT001000092020-06-15 15:15126.641514351.0<NA>1.3VDS_송도#009(통신)송도 애니파크 대각 맞은편송도37.38382
2CRTRVDS001000242020-06-15 15:15126.640705937.2<NA>1.4VDS_청라#024(제어)청라동 139-5(청라루비로 상행2차로 CVS24 루비로1 상행)청라37.540536
3SDTRVCT001000072020-06-15 15:15126.65014111844.9<NA>5.0VDS_송도#006(통신)인천송도 힐스테이트4단지 앞송도37.395511
4CRTRVDS001000262020-06-15 15:15126.6407163137.9<NA>2.3VDS_청라#026(제어)청라동 155-26(청라루비로 상행2차로 CVS26 루비로2 상행)청라37.534014
5CRTRVDS001000182020-06-15 15:15126.6381651860.5<NA>1.2VDS_청라#018(제어)청라동 9-98(국제대로 상행3차로 CVS18 국제대로2 하행)청라37.541669
6CRTRVDS001000172020-06-15 15:15126.63813211100.5<NA>0.7VDS_청라#017(제어)청라동 9-92(국제대로 하행3차로 CVS17 국제대로2 상행)청라37.541991
7CRTRVDS001000362020-06-15 15:15126.6521053747.4<NA>1.4VDS_청라#036(제어)청라동 185-2(청중로 상행1차로 CVS36 청중로3 하행)청라37.529727
8CRTRVDS001000032020-06-15 15:15126.623039171.0<NA>1.0VDS_청라#003(제어)청라동 8-126(국제대로 하행5차로 CVS03 국제대로1 상행)청라37.541966
9CRTRVDS001000072020-06-15 15:15126.623676751.6<NA>1.2VDS_청라#007(제어)청라동 1-773(사파이어로 하행2차로 CVS07 사파이어로2 하행)청라37.533638
도로 지점 시설물 ID생성 일시경도 Y좌표도로 교통량 값차량 속도 값도로 파티션 구분자 여부도로 점유 율도로 지점 시설물 명설치 위치 명도로 지점 지역 명위도 X좌표
190SDTRVCT001000012020-06-15 15:00126.6506792757.8<NA>2.4VDS_송도#008(통신)한국생산기술연구원 맞은편송도37.382625
191YJTRVDS001000192020-06-15 15:00126.51216116123.3<NA>1.2VDS_영종#019(제어)하늘대로영종37.472037
192SDTRVCT001000032020-06-15 15:00126.6397267758.9<NA>4.3VDS_송도#002(통신)커낼워크 D4 오피스텔송도37.402632
193CRTRVDS001000252020-06-15 15:00126.6404352634.3<NA>2.0VDS_청라#025(제어)청라동 155-26(청라루비로 하행2차로 CVS25 루비로2 하행)청라37.533649
194CRTRVDS001000212020-06-15 15:00126.6393143455.5<NA>1.0VDS_청라#021(제어)청라동 155-28(청중로 하행3차로 CVS21 청중로2 상행)청라37.530071
195CRTRVDS001000062020-06-15 15:00126.625221638.6<NA>2.2VDS_청라#006(제어)청라동 55-5(사파이어로 상행3차로 CVS06 사파이어로1 상행)청라37.539677
196MDTRVDS001000012020-06-15 15:00126.504515249.0<NA>1.0VDS_미단#001(제어)인천광역시 중구 운북동 1368-2미단37.525889
197SDTRVCT001000042020-06-15 15:00126.640575257.7<NA>2.4VDS_송도#003(통신)송도시외버스 환승센터 앞 중앙녹지송도37.386938
198SDTRVCT001000062020-06-15 15:00126.6571039967.0<NA>2.6VDS_송도#005(통신)테크노파크역 1번출구송도37.382759
199YJTRVDS001000222020-06-15 15:00126.5028433854.7<NA>1.4VDS_영종#022(제어)영종대로영종37.489062