Overview

Dataset statistics

Number of variables20
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory17.2 KiB
Average record size in memory176.3 B

Variable types

Numeric12
Categorical8

Alerts

도로종류 has constant value ""Constant
측정일 has constant value ""Constant
측정시간 has constant value ""Constant
주소 is highly overall correlated with 기본키 and 6 other fieldsHigh correlation
지점 is highly overall correlated with 기본키 and 6 other fieldsHigh correlation
기본키 is highly overall correlated with 경도(°) and 3 other fieldsHigh correlation
장비이정(km) is highly overall correlated with 지점 and 2 other fieldsHigh correlation
차량통과수(대) is highly overall correlated with 경도(°) and 5 other fieldsHigh correlation
평균 속도(km) is highly overall correlated with CO(g/km) and 4 other fieldsHigh correlation
위도(°) is highly overall correlated with 경도(°) and 3 other fieldsHigh correlation
경도(°) is highly overall correlated with 기본키 and 5 other fieldsHigh correlation
기울기(°) is highly overall correlated with 지점 and 2 other fieldsHigh correlation
CO(g/km) is highly overall correlated with 차량통과수(대) and 5 other fieldsHigh correlation
NOX(g/km) is highly overall correlated with 차량통과수(대) and 5 other fieldsHigh correlation
HC(g/km) is highly overall correlated with 차량통과수(대) and 5 other fieldsHigh correlation
PM(g/km) is highly overall correlated with 차량통과수(대) and 5 other fieldsHigh correlation
CO2(g/km) is highly overall correlated with 차량통과수(대) and 5 other fieldsHigh correlation
방향 is highly overall correlated with 측정구간High correlation
측정구간 is highly overall correlated with 기본키 and 7 other fieldsHigh correlation
기본키 has unique valuesUnique
차량통과수(대) has 3 (3.0%) zerosZeros
평균 속도(km) has 3 (3.0%) zerosZeros
기울기(°) has 7 (7.0%) zerosZeros
CO(g/km) has 3 (3.0%) zerosZeros
NOX(g/km) has 3 (3.0%) zerosZeros
HC(g/km) has 3 (3.0%) zerosZeros
PM(g/km) has 27 (27.0%) zerosZeros
CO2(g/km) has 3 (3.0%) zerosZeros

Reproduction

Analysis started2023-12-10 11:56:08.774432
Analysis finished2023-12-10 11:56:28.784579
Duration20.01 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기본키
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.5
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:56:28.892746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5.95
Q125.75
median50.5
Q375.25
95-th percentile95.05
Maximum100
Range99
Interquartile range (IQR)49.5

Descriptive statistics

Standard deviation29.011492
Coefficient of variation (CV)0.57448499
Kurtosis-1.2
Mean50.5
Median Absolute Deviation (MAD)25
Skewness0
Sum5050
Variance841.66667
MonotonicityStrictly increasing
2023-12-10T20:56:29.086321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.0%
65 1
 
1.0%
75 1
 
1.0%
74 1
 
1.0%
73 1
 
1.0%
72 1
 
1.0%
71 1
 
1.0%
70 1
 
1.0%
69 1
 
1.0%
68 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
1 1
1.0%
2 1
1.0%
3 1
1.0%
4 1
1.0%
5 1
1.0%
6 1
1.0%
7 1
1.0%
8 1
1.0%
9 1
1.0%
10 1
1.0%
ValueCountFrequency (%)
100 1
1.0%
99 1
1.0%
98 1
1.0%
97 1
1.0%
96 1
1.0%
95 1
1.0%
94 1
1.0%
93 1
1.0%
92 1
1.0%
91 1
1.0%

도로종류
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
도로공사
100 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
도로공사 100
100.0%

Length

2023-12-10T20:56:29.238013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:56:29.334270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
도로공사 100
100.0%

지점
Categorical

HIGH CORRELATION 

Distinct22
Distinct (%)22.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
A-0100-0698S-8
A-0251-0721E-7
A-5510-0116E-6
 
6
A-0010-0083E-6
 
6
A-0120-0062E-6
 
6
Other values (17)
67 

Length

Max length14
Median length14
Mean length14
Min length14

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA-5510-0116E-6
2nd rowA-5510-0116E-6
3rd rowA-5510-0116E-6
4th rowA-5510-0116E-6
5th rowA-5510-0116E-6

Common Values

ValueCountFrequency (%)
A-0100-0698S-8 8
 
8.0%
A-0251-0721E-7 7
 
7.0%
A-5510-0116E-6 6
 
6.0%
A-0010-0083E-6 6
 
6.0%
A-0120-0062E-6 6
 
6.0%
A-0121-0066S-4 4
 
4.0%
A-6000-0265E-4 4
 
4.0%
A-0100-0407S-4 4
 
4.0%
A-0160-0058E-4 4
 
4.0%
A-0450-0480S-4 4
 
4.0%
Other values (12) 47
47.0%

Length

2023-12-10T20:56:29.500274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
a-0100-0698s-8 8
 
8.0%
a-0251-0721e-7 7
 
7.0%
a-5510-0116e-6 6
 
6.0%
a-0010-0083e-6 6
 
6.0%
a-0120-0062e-6 6
 
6.0%
a-0150-0290s-4 4
 
4.0%
a-0140-0273s-4 4
 
4.0%
a-0251-0652s-4 4
 
4.0%
a-0140-0388e-4 4
 
4.0%
a-0251-1042e-4 4
 
4.0%
Other values (12) 47
47.0%

방향
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
S
51 
E
49 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowS
2nd rowS
3rd rowS
4th rowE
5th rowE

Common Values

ValueCountFrequency (%)
S 51
51.0%
E 49
49.0%

Length

2023-12-10T20:56:29.692568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:56:29.815860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
s 51
51.0%
e 49
49.0%

차선
Categorical

Distinct4
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
1
44 
2
43 
3
10 
4
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row1
3rd row2
4th row1
5th row2

Common Values

ValueCountFrequency (%)
1 44
44.0%
2 43
43.0%
3 10
 
10.0%
4 3
 
3.0%

Length

2023-12-10T20:56:29.945432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:56:30.083509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 44
44.0%
2 43
43.0%
3 10
 
10.0%
4 3
 
3.0%

측정구간
Categorical

HIGH CORRELATION 

Distinct44
Distinct (%)44.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
문흥JC-동광주TG
 
4
진주IC-진주JC
 
4
진주JC-진주IC
 
4
담양JC-고서JC
 
3
양산JC-노포JC
 
3
Other values (39)
82 

Length

Max length12
Median length9
Mean length9.5
Min length9

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st row물금IC-대동JC
2nd row물금IC-대동JC
3rd row물금IC-대동JC
4th row대동JC-물금IC
5th row대동JC-물금IC

Common Values

ValueCountFrequency (%)
문흥JC-동광주TG 4
 
4.0%
진주IC-진주JC 4
 
4.0%
진주JC-진주IC 4
 
4.0%
담양JC-고서JC 3
 
3.0%
양산JC-노포JC 3
 
3.0%
물금IC-대동JC 3
 
3.0%
노포JC-양산JC 3
 
3.0%
고서JC-담양JC 3
 
3.0%
대동JC-물금IC 3
 
3.0%
동광주TG-문흥JC 3
 
3.0%
Other values (34) 67
67.0%

Length

2023-12-10T20:56:30.244182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
문흥jc-동광주tg 4
 
4.0%
진주jc-진주ic 4
 
4.0%
진주ic-진주jc 4
 
4.0%
노포jc-양산jc 3
 
3.0%
동광주tg-문흥jc 3
 
3.0%
고서jc-담양jc 3
 
3.0%
대동jc-물금ic 3
 
3.0%
물금ic-대동jc 3
 
3.0%
양산jc-노포jc 3
 
3.0%
담양jc-고서jc 3
 
3.0%
Other values (34) 67
67.0%

장비이정(km)
Real number (ℝ)

HIGH CORRELATION 

Distinct22
Distinct (%)22.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.6756
Minimum2.7
Maximum104.15
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:56:30.405077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.7
5-th percentile4.4
Q17.8675
median26.69
Q348
95-th percentile72.1
Maximum104.15
Range101.45
Interquartile range (IQR)40.1325

Descriptive statistics

Standard deviation28.150571
Coefficient of variation (CV)0.88871468
Kurtosis-0.22175483
Mean31.6756
Median Absolute Deviation (MAD)20.12
Skewness0.89674988
Sum3167.56
Variance792.45462
MonotonicityNot monotonic
2023-12-10T20:56:30.566694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
69.8 8
 
8.0%
72.1 7
 
7.0%
11.6 6
 
6.0%
8.3 6
 
6.0%
6.15 6
 
6.0%
27.3 4
 
4.0%
65.2 4
 
4.0%
38.8 4
 
4.0%
104.15 4
 
4.0%
44.75 4
 
4.0%
Other values (12) 47
47.0%
ValueCountFrequency (%)
2.7 4
4.0%
4.4 3
3.0%
5.8 4
4.0%
6.15 6
6.0%
6.23 4
4.0%
6.57 4
4.0%
8.3 6
6.0%
9.64 4
4.0%
11.6 6
6.0%
13.0 4
4.0%
ValueCountFrequency (%)
104.15 4
4.0%
72.1 7
7.0%
69.8 8
8.0%
65.2 4
4.0%
48.0 4
4.0%
44.75 4
4.0%
40.7 4
4.0%
38.8 4
4.0%
28.91 4
4.0%
27.3 4
4.0%

측정일
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
20200401
100 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20200401
2nd row20200401
3rd row20200401
4th row20200401
5th row20200401

Common Values

ValueCountFrequency (%)
20200401 100
100.0%

Length

2023-12-10T20:56:30.730734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:56:30.860362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20200401 100
100.0%

측정시간
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
0
100 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 100
100.0%

Length

2023-12-10T20:56:31.003942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:56:31.120852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 100
100.0%

차량통과수(대)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct40
Distinct (%)40.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.64
Minimum0
Maximum65
Zeros3
Zeros (%)3.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:56:31.255755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q17
median13
Q324
95-th percentile42
Maximum65
Range65
Interquartile range (IQR)17

Descriptive statistics

Standard deviation13.511327
Coefficient of variation (CV)0.81197879
Kurtosis1.2576486
Mean16.64
Median Absolute Deviation (MAD)8
Skewness1.1483894
Sum1664
Variance182.55596
MonotonicityNot monotonic
2023-12-10T20:56:31.445496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
2 8
 
8.0%
8 6
 
6.0%
12 6
 
6.0%
10 5
 
5.0%
11 5
 
5.0%
1 4
 
4.0%
5 4
 
4.0%
24 4
 
4.0%
13 4
 
4.0%
20 3
 
3.0%
Other values (30) 51
51.0%
ValueCountFrequency (%)
0 3
 
3.0%
1 4
4.0%
2 8
8.0%
3 2
 
2.0%
4 1
 
1.0%
5 4
4.0%
6 2
 
2.0%
7 2
 
2.0%
8 6
6.0%
9 1
 
1.0%
ValueCountFrequency (%)
65 1
 
1.0%
58 1
 
1.0%
48 1
 
1.0%
43 1
 
1.0%
42 2
2.0%
40 3
3.0%
39 1
 
1.0%
35 2
2.0%
34 1
 
1.0%
33 1
 
1.0%

평균 속도(km)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct85
Distinct (%)85.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean95.4352
Minimum0
Maximum154.5
Zeros3
Zeros (%)3.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:56:31.632517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile77.5805
Q184.735
median93.625
Q3108.25
95-th percentile129.075
Maximum154.5
Range154.5
Interquartile range (IQR)23.515

Descriptive statistics

Standard deviation23.59069
Coefficient of variation (CV)0.24719066
Kurtosis6.6378545
Mean95.4352
Median Absolute Deviation (MAD)11.335
Skewness-1.5337221
Sum9543.52
Variance556.52066
MonotonicityNot monotonic
2023-12-10T20:56:31.834661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
111.0 4
 
4.0%
0.0 3
 
3.0%
88.0 3
 
3.0%
115.0 2
 
2.0%
84.6 2
 
2.0%
129.0 2
 
2.0%
95.0 2
 
2.0%
100.0 2
 
2.0%
81.0 2
 
2.0%
107.0 2
 
2.0%
Other values (75) 76
76.0%
ValueCountFrequency (%)
0.0 3
3.0%
71.33 1
 
1.0%
75.88 1
 
1.0%
77.67 1
 
1.0%
78.83 1
 
1.0%
79.2 1
 
1.0%
79.5 1
 
1.0%
79.67 1
 
1.0%
80.11 1
 
1.0%
80.62 1
 
1.0%
ValueCountFrequency (%)
154.5 1
1.0%
140.0 1
1.0%
139.5 1
1.0%
132.0 1
1.0%
130.5 1
1.0%
129.0 2
2.0%
128.0 1
1.0%
126.0 1
1.0%
122.0 1
1.0%
121.0 1
1.0%

위도(°)
Real number (ℝ)

HIGH CORRELATION 

Distinct21
Distinct (%)21.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.267575
Minimum34.959722
Maximum35.835556
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:56:32.017545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum34.959722
5-th percentile34.978889
Q135.056389
median35.247222
Q335.306944
95-th percentile35.771111
Maximum35.835556
Range0.87583334
Interquartile range (IQR)0.25055555

Descriptive statistics

Standard deviation0.22777727
Coefficient of variation (CV)0.0064585465
Kurtosis0.52524235
Mean35.267575
Median Absolute Deviation (MAD)0.08138889
Skewness0.93333801
Sum3526.7575
Variance0.051882485
MonotonicityNot monotonic
2023-12-10T20:56:32.158228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
35.02027778 8
 
8.0%
35.26972222 8
 
8.0%
35.19972222 7
 
7.0%
35.29388889 6
 
6.0%
35.30694444 6
 
6.0%
35.23666667 6
 
6.0%
35.32861111 4
 
4.0%
35.22361111 4
 
4.0%
35.24722222 4
 
4.0%
35.37083333 4
 
4.0%
Other values (11) 43
43.0%
ValueCountFrequency (%)
34.95972222 4
4.0%
34.97888889 3
 
3.0%
35.00805556 4
4.0%
35.01694444 4
4.0%
35.02027778 8
8.0%
35.05638889 4
4.0%
35.19972222 7
7.0%
35.22361111 4
4.0%
35.23666667 6
6.0%
35.23777778 4
4.0%
ValueCountFrequency (%)
35.83555556 4
4.0%
35.77111111 4
4.0%
35.63805556 4
4.0%
35.56055556 4
4.0%
35.37083333 4
4.0%
35.32861111 4
4.0%
35.30694444 6
6.0%
35.29388889 6
6.0%
35.26972222 8
8.0%
35.265 4
4.0%

경도(°)
Real number (ℝ)

HIGH CORRELATION 

Distinct22
Distinct (%)22.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.73953
Minimum126.41639
Maximum130.08667
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:56:32.322516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.41639
5-th percentile126.45639
Q1126.96139
median127.34583
Q3128.44472
95-th percentile129.51306
Maximum130.08667
Range3.6702778
Interquartile range (IQR)1.4833333

Descriptive statistics

Standard deviation1.0362326
Coefficient of variation (CV)0.0081120749
Kurtosis-0.73391239
Mean127.73953
Median Absolute Deviation (MAD)0.5138889
Skewness0.71213676
Sum12773.953
Variance1.073778
MonotonicityNot monotonic
2023-12-10T20:56:32.474377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
128.1897222 8
 
8.0%
126.9327778 7
 
7.0%
129.0072222 6
 
6.0%
129.0747222 6
 
6.0%
126.9613889 6
 
6.0%
126.8775 4
 
4.0%
126.9947222 4
 
4.0%
126.99944440000002 4
 
4.0%
126.8041667 4
 
4.0%
127.1933333 4
 
4.0%
Other values (12) 47
47.0%
ValueCountFrequency (%)
126.4163889 4
4.0%
126.4563889 4
4.0%
126.8041667 4
4.0%
126.8775 4
4.0%
126.9327778 7
7.0%
126.9613889 6
6.0%
126.9725 4
4.0%
126.97305559999998 4
4.0%
126.9947222 4
4.0%
126.99944440000002 4
4.0%
ValueCountFrequency (%)
130.08666670000002 4
4.0%
129.51305559999997 4
4.0%
129.1838889 4
4.0%
129.0747222 6
6.0%
129.0072222 6
6.0%
128.4447222 4
4.0%
128.1897222 8
8.0%
127.8597222 4
4.0%
127.62833329999998 4
4.0%
127.5488889 3
 
3.0%

기울기(°)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct37
Distinct (%)37.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.0161
Minimum-3.9
Maximum3.07
Zeros7
Zeros (%)7.0%
Negative46
Negative (%)46.0%
Memory size1.0 KiB
2023-12-10T20:56:32.674779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-3.9
5-th percentile-2.13
Q1-0.7
median0
Q30.6425
95-th percentile2.051
Maximum3.07
Range6.97
Interquartile range (IQR)1.3425

Descriptive statistics

Standard deviation1.3023591
Coefficient of variation (CV)-80.891869
Kurtosis0.93174304
Mean-0.0161
Median Absolute Deviation (MAD)0.7
Skewness-0.15769917
Sum-1.61
Variance1.6961392
MonotonicityNot monotonic
2023-12-10T20:56:32.851744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
0.0 7
 
7.0%
-0.5 5
 
5.0%
0.5 5
 
5.0%
0.62 4
 
4.0%
-1.0 4
 
4.0%
0.99 4
 
4.0%
0.44 4
 
4.0%
-0.18 4
 
4.0%
0.12 4
 
4.0%
0.3 3
 
3.0%
Other values (27) 56
56.0%
ValueCountFrequency (%)
-3.9 1
 
1.0%
-3.15 3
3.0%
-2.13 2
2.0%
-1.9 2
2.0%
-1.6 2
2.0%
-1.54 2
2.0%
-1.26 2
2.0%
-1.07 2
2.0%
-1.0 4
4.0%
-0.99 2
2.0%
ValueCountFrequency (%)
3.07 3
3.0%
2.64 2
2.0%
2.02 2
2.0%
1.87 2
2.0%
1.6 2
2.0%
1.09 2
2.0%
0.99 4
4.0%
0.98 2
2.0%
0.92 2
2.0%
0.84 2
2.0%

CO(g/km)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct95
Distinct (%)95.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.8595
Minimum0
Maximum91.51
Zeros3
Zeros (%)3.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:56:33.029778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.1875
Q12.135
median6.085
Q316.235
95-th percentile32.3405
Maximum91.51
Range91.51
Interquartile range (IQR)14.1

Descriptive statistics

Standard deviation15.357341
Coefficient of variation (CV)1.29494
Kurtosis9.8877815
Mean11.8595
Median Absolute Deviation (MAD)5.31
Skewness2.7129154
Sum1185.95
Variance235.84791
MonotonicityNot monotonic
2023-12-10T20:56:33.181059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 3
 
3.0%
0.33 2
 
2.0%
0.82 2
 
2.0%
0.57 2
 
2.0%
22.34 1
 
1.0%
13.38 1
 
1.0%
22.38 1
 
1.0%
2.81 1
 
1.0%
17.05 1
 
1.0%
3.74 1
 
1.0%
Other values (85) 85
85.0%
ValueCountFrequency (%)
0.0 3
3.0%
0.12 1
 
1.0%
0.14 1
 
1.0%
0.19 1
 
1.0%
0.22 1
 
1.0%
0.25 1
 
1.0%
0.29 1
 
1.0%
0.33 2
2.0%
0.49 1
 
1.0%
0.57 2
2.0%
ValueCountFrequency (%)
91.51 1
1.0%
79.45 1
1.0%
51.82 1
1.0%
47.54 1
1.0%
36.53 1
1.0%
32.12 1
1.0%
32.11 1
1.0%
30.03 1
1.0%
29.93 1
1.0%
29.65 1
1.0%

NOX(g/km)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct94
Distinct (%)94.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.0823
Minimum0
Maximum263.96
Zeros3
Zeros (%)3.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:56:33.427344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.1495
Q11.5925
median12.16
Q343.345
95-th percentile139.457
Maximum263.96
Range263.96
Interquartile range (IQR)41.7525

Descriptive statistics

Standard deviation51.052453
Coefficient of variation (CV)1.4552197
Kurtosis5.1710775
Mean35.0823
Median Absolute Deviation (MAD)11.91
Skewness2.1623443
Sum3508.23
Variance2606.353
MonotonicityNot monotonic
2023-12-10T20:56:33.615947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 3
 
3.0%
0.37 3
 
3.0%
0.25 2
 
2.0%
0.62 2
 
2.0%
93.85 1
 
1.0%
30.5 1
 
1.0%
65.62 1
 
1.0%
16.94 1
 
1.0%
46.02 1
 
1.0%
5.2 1
 
1.0%
Other values (84) 84
84.0%
ValueCountFrequency (%)
0.0 3
3.0%
0.1 1
 
1.0%
0.14 1
 
1.0%
0.15 1
 
1.0%
0.17 1
 
1.0%
0.18 1
 
1.0%
0.2 1
 
1.0%
0.25 2
2.0%
0.37 3
3.0%
0.55 1
 
1.0%
ValueCountFrequency (%)
263.96 1
1.0%
222.98 1
1.0%
172.0 1
1.0%
165.84 1
1.0%
145.1 1
1.0%
139.16 1
1.0%
123.03 1
1.0%
117.3 1
1.0%
117.01 1
1.0%
109.01 1
1.0%

HC(g/km)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct82
Distinct (%)82.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2266
Minimum0
Maximum31.41
Zeros3
Zeros (%)3.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:56:33.812115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.01
Q10.15
median1.17
Q34.31
95-th percentile10.731
Maximum31.41
Range31.41
Interquartile range (IQR)4.16

Descriptive statistics

Standard deviation5.1407686
Coefficient of variation (CV)1.5932463
Kurtosis11.843754
Mean3.2266
Median Absolute Deviation (MAD)1.12
Skewness3.0471708
Sum322.66
Variance26.427501
MonotonicityNot monotonic
2023-12-10T20:56:34.023427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.01 5
 
5.0%
0.02 3
 
3.0%
0.07 3
 
3.0%
0.14 3
 
3.0%
0.0 3
 
3.0%
0.73 2
 
2.0%
4.31 2
 
2.0%
0.04 2
 
2.0%
0.05 2
 
2.0%
0.15 2
 
2.0%
Other values (72) 73
73.0%
ValueCountFrequency (%)
0.0 3
3.0%
0.01 5
5.0%
0.02 3
3.0%
0.03 1
 
1.0%
0.04 2
 
2.0%
0.05 2
 
2.0%
0.06 1
 
1.0%
0.07 3
3.0%
0.1 1
 
1.0%
0.14 3
3.0%
ValueCountFrequency (%)
31.41 1
1.0%
25.48 1
1.0%
18.22 1
1.0%
15.62 1
1.0%
10.94 1
1.0%
10.72 1
1.0%
9.99 1
1.0%
9.93 1
1.0%
9.17 1
1.0%
9.13 1
1.0%

PM(g/km)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct69
Distinct (%)69.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1269
Minimum0
Maximum15.41
Zeros27
Zeros (%)27.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:56:34.224675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.7
Q32.6425
95-th percentile8.712
Maximum15.41
Range15.41
Interquartile range (IQR)2.6425

Descriptive statistics

Standard deviation3.0941129
Coefficient of variation (CV)1.4547524
Kurtosis4.4190055
Mean2.1269
Median Absolute Deviation (MAD)0.7
Skewness2.0503451
Sum212.69
Variance9.5735347
MonotonicityNot monotonic
2023-12-10T20:56:34.742628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 27
27.0%
6.83 2
 
2.0%
0.54 2
 
2.0%
0.13 2
 
2.0%
0.1 2
 
2.0%
0.12 2
 
2.0%
0.46 1
 
1.0%
0.21 1
 
1.0%
0.34 1
 
1.0%
4.19 1
 
1.0%
Other values (59) 59
59.0%
ValueCountFrequency (%)
0.0 27
27.0%
0.1 2
 
2.0%
0.11 1
 
1.0%
0.12 2
 
2.0%
0.13 2
 
2.0%
0.19 1
 
1.0%
0.21 1
 
1.0%
0.24 1
 
1.0%
0.32 1
 
1.0%
0.33 1
 
1.0%
ValueCountFrequency (%)
15.41 1
1.0%
13.29 1
1.0%
10.31 1
1.0%
10.14 1
1.0%
8.75 1
1.0%
8.71 1
1.0%
7.75 1
1.0%
7.12 1
1.0%
6.87 1
1.0%
6.83 2
2.0%

CO2(g/km)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct95
Distinct (%)95.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3894.4118
Minimum0
Maximum20501.21
Zeros3
Zeros (%)3.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:56:34.990758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile103.0205
Q1793.38
median1965.455
Q35533.8425
95-th percentile13461.271
Maximum20501.21
Range20501.21
Interquartile range (IQR)4740.4625

Descriptive statistics

Standard deviation4439.452
Coefficient of variation (CV)1.1399544
Kurtosis2.4008302
Mean3894.4118
Median Absolute Deviation (MAD)1716.115
Skewness1.6335609
Sum389441.18
Variance19708734
MonotonicityNot monotonic
2023-12-10T20:56:35.182460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 3
 
3.0%
157.45 2
 
2.0%
393.63 2
 
2.0%
208.02 2
 
2.0%
9189.21 1
 
1.0%
2475.35 1
 
1.0%
6873.54 1
 
1.0%
2184.4 1
 
1.0%
5483.28 1
 
1.0%
1543.98 1
 
1.0%
Other values (85) 85
85.0%
ValueCountFrequency (%)
0.0 3
3.0%
68.49 1
 
1.0%
90.49 1
 
1.0%
103.68 1
 
1.0%
104.01 1
 
1.0%
119.18 1
 
1.0%
136.98 1
 
1.0%
157.45 2
2.0%
208.02 2
2.0%
236.18 1
 
1.0%
ValueCountFrequency (%)
20501.21 1
1.0%
17147.05 1
1.0%
16948.68 1
1.0%
15994.04 1
1.0%
13559.14 1
1.0%
13456.12 1
1.0%
11602.18 1
1.0%
11295.3 1
1.0%
11233.81 1
1.0%
11226.62 1
1.0%

주소
Categorical

HIGH CORRELATION 

Distinct22
Distinct (%)22.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
경남 진주시 가좌동
광주 북구 석곡동
경남 양산시 물금읍
 
6
경남 양산시 동면
 
6
전남 담양군 고서면 주산리
 
6
Other values (17)
67 

Length

Max length14
Median length14
Mean length12.27
Min length9

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row경남 양산시 물금읍
2nd row경남 양산시 물금읍
3rd row경남 양산시 물금읍
4th row경남 양산시 물금읍
5th row경남 양산시 물금읍

Common Values

ValueCountFrequency (%)
경남 진주시 가좌동 8
 
8.0%
광주 북구 석곡동 7
 
7.0%
경남 양산시 물금읍 6
 
6.0%
경남 양산시 동면 6
 
6.0%
전남 담양군 고서면 주산리 6
 
6.0%
전남 무안군 현경면 평산리 4
 
4.0%
경남 김해시 대동면 대감리 4
 
4.0%
경남 하동군 고전면 4
 
4.0%
울산 울주군 언양읍 반곡리 4
 
4.0%
대구 달성군 구지면 예천리 4
 
4.0%
Other values (12) 47
47.0%

Length

2023-12-10T20:56:35.357220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
전남 53
 
14.7%
경남 32
 
8.9%
담양군 22
 
6.1%
고서면 14
 
3.9%
양산시 12
 
3.3%
진주시 8
 
2.2%
현경면 8
 
2.2%
무안군 8
 
2.2%
김해시 8
 
2.2%
가좌동 8
 
2.2%
Other values (43) 188
52.1%

Interactions

2023-12-10T20:56:26.579000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:10.053807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:11.453581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:12.842519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:14.585607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:16.171705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:17.486819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:19.043484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:20.517414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:22.217622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:23.586093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:25.025096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:26.731867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:10.151587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:11.570663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:12.954571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:14.693575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:16.289356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:17.579778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:19.167390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:20.978046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:22.322377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:23.707906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:25.159009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:26.856191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:10.254853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:11.680561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:13.081107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:14.820021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:16.404269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:17.761574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:19.270066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:21.074317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:22.430912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:23.832848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:25.281079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:26.989202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:10.369339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:11.790549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:13.195990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:14.952633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:16.505261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:17.893389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:19.376098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:21.178260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:22.544250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:23.958131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:25.410191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:27.123554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:10.480823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:11.909760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:13.331323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:15.107066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:16.615374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:18.028313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:19.511975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:21.322828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:22.671891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:24.084495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:25.544375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:27.229196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:10.556369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:12.032958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:13.445105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:15.233877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:16.708357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:18.139148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:19.613239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:21.423511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:22.773549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:24.195208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:25.640172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:27.361159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:10.656955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:12.178865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:13.852877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:15.362858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:16.838131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:18.287071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:19.764640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:21.539672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:22.885907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:24.320302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:25.758572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:27.472486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:10.787652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:12.298025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:13.978320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:15.487389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:16.952377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:18.422511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:19.874178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:21.652830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:22.994871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:24.439642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:25.896767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:27.567068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:10.981556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:12.405809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:14.075704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:15.620961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:17.055642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:18.541701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:19.999230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:21.753979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:23.096511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:24.531868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:26.021057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:27.686674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:11.117114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:12.510459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:14.192036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:15.757655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:17.167545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:18.666760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:20.106328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:21.869697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:23.219506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:24.635632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:26.158970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:27.813175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:11.232185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:12.624199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:14.309774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:15.904607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:17.279102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:18.794802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:20.253101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:21.984039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:23.330245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:24.758707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:26.302622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:27.930243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:11.354510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:12.740697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:14.447643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:16.048881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:17.395602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:18.927571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:20.382926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:22.117461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:23.467552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:24.909371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:26.444845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T20:56:35.506599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기본키지점방향차선측정구간장비이정(km)차량통과수(대)평균 속도(km)위도(°)경도(°)기울기(°)CO(g/km)NOX(g/km)HC(g/km)PM(g/km)CO2(g/km)주소
기본키1.0000.9860.0000.0000.9960.8060.5840.2310.7650.8730.7870.3640.0940.1610.0910.5060.986
지점0.9861.0000.0000.0001.0001.0000.4550.2231.0001.0000.8800.4580.0000.1760.0000.3151.000
방향0.0000.0001.0000.0001.0000.0000.0000.1700.0000.0000.5460.0700.0000.1800.2110.3510.000
차선0.0000.0000.0001.0000.0000.1730.3840.5290.0000.1410.0000.4550.4350.5090.4460.4820.000
측정구간0.9961.0001.0000.0001.0001.0000.0000.4791.0001.0001.0000.1850.1260.3370.0000.0001.000
장비이정(km)0.8061.0000.0000.1731.0001.0000.3140.0000.7910.7940.5430.4820.2650.3950.1950.4181.000
차량통과수(대)0.5840.4550.0000.3840.0000.3141.0000.2850.3980.5870.0000.8770.8380.8620.8440.9140.455
평균 속도(km)0.2310.2230.1700.5290.4790.0000.2851.0000.1270.0000.0000.5950.4270.3150.5690.4400.223
위도(°)0.7651.0000.0000.0001.0000.7910.3980.1271.0000.8630.5860.4780.4760.6650.4640.4251.000
경도(°)0.8731.0000.0000.1411.0000.7940.5870.0000.8631.0000.6640.4540.5690.3430.5680.3881.000
기울기(°)0.7870.8800.5460.0001.0000.5430.0000.0000.5860.6641.0000.0000.0000.0000.0000.0000.880
CO(g/km)0.3640.4580.0700.4550.1850.4820.8770.5950.4780.4540.0001.0000.8990.9370.8980.8690.458
NOX(g/km)0.0940.0000.0000.4350.1260.2650.8380.4270.4760.5690.0000.8991.0000.9330.9960.9320.000
HC(g/km)0.1610.1760.1800.5090.3370.3950.8620.3150.6650.3430.0000.9370.9331.0000.9120.8920.176
PM(g/km)0.0910.0000.2110.4460.0000.1950.8440.5690.4640.5680.0000.8980.9960.9121.0000.9370.000
CO2(g/km)0.5060.3150.3510.4820.0000.4180.9140.4400.4250.3880.0000.8690.9320.8920.9371.0000.315
주소0.9861.0000.0000.0001.0001.0000.4550.2231.0001.0000.8800.4580.0000.1760.0000.3151.000
2023-12-10T20:56:35.731485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
측정구간주소지점차선방향
측정구간1.0000.8470.8470.0000.756
주소0.8471.0001.0000.0000.000
지점0.8471.0001.0000.0000.000
차선0.0000.0000.0001.0000.000
방향0.7560.0000.0000.0001.000
2023-12-10T20:56:35.877966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기본키장비이정(km)차량통과수(대)평균 속도(km)위도(°)경도(°)기울기(°)CO(g/km)NOX(g/km)HC(g/km)PM(g/km)CO2(g/km)지점방향차선측정구간주소
기본키1.0000.321-0.3160.105-0.497-0.7300.025-0.182-0.134-0.122-0.076-0.2290.8560.0000.0000.7540.856
장비이정(km)0.3211.0000.250-0.0870.093-0.131-0.0370.2760.2840.2800.2780.2710.9160.0000.1150.7760.916
차량통과수(대)-0.3160.2501.000-0.4510.3610.514-0.0330.9240.8750.8540.8240.9490.1690.0000.2150.0000.169
평균 속도(km)0.105-0.087-0.4511.000-0.109-0.094-0.019-0.639-0.641-0.677-0.681-0.5840.0750.1760.3860.1540.075
위도(°)-0.4970.0930.361-0.1091.0000.541-0.0210.2550.2280.2140.1630.2910.9210.0000.0000.7800.921
경도(°)-0.730-0.1310.514-0.0940.5411.000-0.0020.4010.3710.3440.2910.4570.9260.0000.0830.7840.926
기울기(°)0.025-0.037-0.033-0.019-0.021-0.0021.0000.0090.0700.0480.0830.0380.5380.4020.0000.7890.538
CO(g/km)-0.1820.2760.924-0.6390.2550.4010.0091.0000.9750.9810.9470.9770.1910.0680.3230.0000.191
NOX(g/km)-0.1340.2840.875-0.6410.2280.3710.0700.9751.0000.9880.9790.9740.0000.0000.2830.0000.000
HC(g/km)-0.1220.2800.854-0.6770.2140.3440.0480.9810.9881.0000.9670.9520.0460.1280.2400.0780.046
PM(g/km)-0.0760.2780.824-0.6810.1630.2910.0830.9470.9790.9671.0000.9450.0000.2020.2920.0000.000
CO2(g/km)-0.2290.2710.949-0.5840.2910.4570.0380.9770.9740.9520.9451.0000.1030.2570.2970.0000.103
지점0.8560.9160.1690.0750.9210.9260.5380.1910.0000.0460.0000.1031.0000.0000.0000.8471.000
방향0.0000.0000.0000.1760.0000.0000.4020.0680.0000.1280.2020.2570.0001.0000.0000.7560.000
차선0.0000.1150.2150.3860.0000.0830.0000.3230.2830.2400.2920.2970.0000.0001.0000.0000.000
측정구간0.7540.7760.0000.1540.7800.7840.7890.0000.0000.0780.0000.0000.8470.7560.0001.0000.847
주소0.8560.9160.1690.0750.9210.9260.5380.1910.0000.0460.0000.1031.0000.0000.0000.8471.000

Missing values

2023-12-10T20:56:28.402678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T20:56:28.673539image/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

기본키도로종류지점방향차선측정구간장비이정(km)측정일측정시간차량통과수(대)평균 속도(km)위도(°)경도(°)기울기(°)CO(g/km)NOX(g/km)HC(g/km)PM(g/km)CO2(g/km)주소
01도로공사A-5510-0116E-6S3물금IC-대동JC11.62020040102377.6735.293889129.007222-0.3122.3493.856.885.669189.21경남 양산시 물금읍
12도로공사A-5510-0116E-6S1물금IC-대동JC11.620200401012111.035.293889129.007222-0.312.61.810.170.01085.88경남 양산시 물금읍
23도로공사A-5510-0116E-6S2물금IC-대동JC11.62020040103983.035.293889129.007222-0.3115.6626.181.91.636095.87경남 양산시 물금읍
34도로공사A-5510-0116E-6E1대동JC-물금IC11.620200401021111.035.293889129.0072220.34.563.170.290.01900.29경남 양산시 물금읍
45도로공사A-5510-0116E-6E2대동JC-물금IC11.62020040104887.035.293889129.0072220.322.1339.833.562.67732.82경남 양산시 물금읍
56도로공사A-5510-0116E-6E3대동JC-물금IC11.62020040104278.8335.293889129.0072220.330.0399.98.136.4411602.18경남 양산시 물금읍
67도로공사A-6000-0062E-4S1한림IC-진영IC6.23202004010894.035.835556129.513056-1.072.591.60.180.0882.96경남 김해시 진영읍 설창리
78도로공사A-6000-0062E-4S2한림IC-진영IC6.232020040102379.535.835556129.513056-1.0719.550.915.273.185391.48경남 김해시 진영읍 설창리
89도로공사A-6000-0062E-4E1진영IC-한림IC6.2320200401011111.035.835556129.5130561.093.936.060.730.321329.2경남 김해시 진영읍 설창리
910도로공사A-6000-0062E-4E2진영IC-한림IC6.232020040102981.035.835556129.5130561.0924.58104.076.826.8711233.81경남 김해시 진영읍 설창리
기본키도로종류지점방향차선측정구간장비이정(km)측정일측정시간차량통과수(대)평균 속도(km)위도(°)경도(°)기울기(°)CO(g/km)NOX(g/km)HC(g/km)PM(g/km)CO2(g/km)주소
9091도로공사A-0140-0388E-4S2대덕JC-담양JC38.82020040101088.035.247222126.999444-0.999.0642.952.822.554267.93전남 담양군 고서면 장화리
9192도로공사A-0140-0388E-4E1담양JC-대덕JC38.82020040102129.035.247222126.9994440.980.330.250.020.0157.45전남 담양군 고서면 장화리
9293도로공사A-0140-0388E-4E2담양JC-대덕JC38.8202004010781.2535.247222126.9994440.985.6910.451.530.611168.49전남 담양군 고서면 장화리
9394도로공사A-0251-0652S-4S1고서JC-창평IC65.22020040105113.035.223611126.9947220.51.080.760.070.0452.45전남 담양군 고서면
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