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 위도(°) and 6 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 차량통과수(대) and 4 other fieldsHigh correlation
경도(°) is highly overall correlated with 기본키 and 8 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 6 other fieldsHigh correlation
NOX(g/km) is highly overall correlated with 차량통과수(대) and 6 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 6 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 unique valuesUnique
CO(g/km) has unique valuesUnique
NOX(g/km) has unique valuesUnique
HC(g/km) has unique valuesUnique
PM(g/km) has unique valuesUnique
CO2(g/km) has unique valuesUnique
기울기(°) has 7 (7.0%) zerosZeros

Reproduction

Analysis started2023-12-10 13:00:07.921414
Analysis finished2023-12-10 13:00:27.823118
Duration19.9 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
2023-12-10T22:00:27.915444image/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
2023-12-10T22:00:28.102675image/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

2023-12-10T22:00:28.287758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:00:28.414077image/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

2023-12-10T22:00:28.553251image/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

2023-12-10T22:00:28.712571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:00:28.839757image/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

2023-12-10T22:00:28.982530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:00:29.118883image/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

2023-12-10T22:00:29.293432image/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
2023-12-10T22:00:29.454871image/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
2023-12-10T22:00:29.601488image/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

2023-12-10T22:00:29.759525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:00:29.884335image/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

2023-12-10T22:00:29.991383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:00:30.109217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 100
100.0%

차량통과수(대)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6110.72
Minimum143
Maximum16882
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:00:30.269289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum143
5-th percentile744.35
Q13489.75
median6107.5
Q37875.5
95-th percentile12435
Maximum16882
Range16739
Interquartile range (IQR)4385.75

Descriptive statistics

Standard deviation3556.1843
Coefficient of variation (CV)0.58195831
Kurtosis0.71117442
Mean6110.72
Median Absolute Deviation (MAD)2191
Skewness0.62265441
Sum611072
Variance12646447
MonotonicityNot monotonic
2023-12-10T22:00:30.432267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13195 1
 
1.0%
1414 1
 
1.0%
6889 1
 
1.0%
6018 1
 
1.0%
6360 1
 
1.0%
6408 1
 
1.0%
6094 1
 
1.0%
3320 1
 
1.0%
1854 1
 
1.0%
3261 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
143 1
1.0%
163 1
1.0%
293 1
1.0%
320 1
1.0%
428 1
1.0%
761 1
1.0%
870 1
1.0%
880 1
1.0%
1414 1
1.0%
1712 1
1.0%
ValueCountFrequency (%)
16882 1
1.0%
16027 1
1.0%
15971 1
1.0%
14052 1
1.0%
13195 1
1.0%
12395 1
1.0%
12180 1
1.0%
11170 1
1.0%
10540 1
1.0%
10245 1
1.0%

평균 속도(km)
Real number (ℝ)

HIGH CORRELATION 

Distinct95
Distinct (%)95.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean94.1594
Minimum72.34
Maximum122.32
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:00:30.610063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum72.34
5-th percentile79.5635
Q184.5525
median90.665
Q3103.365
95-th percentile116.2955
Maximum122.32
Range49.98
Interquartile range (IQR)18.8125

Descriptive statistics

Standard deviation12.285126
Coefficient of variation (CV)0.13047159
Kurtosis-0.79282947
Mean94.1594
Median Absolute Deviation (MAD)8.27
Skewness0.46725388
Sum9415.94
Variance150.92433
MonotonicityNot monotonic
2023-12-10T22:00:30.797976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
87.36 2
 
2.0%
79.63 2
 
2.0%
82.72 2
 
2.0%
105.86 2
 
2.0%
86.2 2
 
2.0%
115.89 1
 
1.0%
89.8 1
 
1.0%
91.18 1
 
1.0%
107.33 1
 
1.0%
89.15 1
 
1.0%
Other values (85) 85
85.0%
ValueCountFrequency (%)
72.34 1
1.0%
74.16 1
1.0%
74.39 1
1.0%
74.76 1
1.0%
78.3 1
1.0%
79.63 2
2.0%
79.68 1
1.0%
80.5 1
1.0%
80.65 1
1.0%
81.08 1
1.0%
ValueCountFrequency (%)
122.32 1
1.0%
118.3 1
1.0%
117.4 1
1.0%
117.05 1
1.0%
116.4 1
1.0%
116.29 1
1.0%
115.89 1
1.0%
115.84 1
1.0%
115.39 1
1.0%
114.93 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
2023-12-10T22:00:30.963369image/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
2023-12-10T22:00:31.108150image/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
2023-12-10T22:00:31.244910image/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
2023-12-10T22:00:31.411293image/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
2023-12-10T22:00:31.580802image/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
2023-12-10T22:00:31.766804image/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  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3679.4173
Minimum35.76
Maximum13894.57
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:00:31.901317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.76
5-th percentile163.818
Q11642.7275
median2831.865
Q35014.3425
95-th percentile9686.8345
Maximum13894.57
Range13858.81
Interquartile range (IQR)3371.615

Descriptive statistics

Standard deviation2996.0964
Coefficient of variation (CV)0.81428558
Kurtosis1.2468691
Mean3679.4173
Median Absolute Deviation (MAD)1791.85
Skewness1.200356
Sum367941.73
Variance8976593.9
MonotonicityNot monotonic
2023-12-10T22:00:32.031110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11111.0 1
 
1.0%
476.26 1
 
1.0%
2380.22 1
 
1.0%
5168.55 1
 
1.0%
2178.7 1
 
1.0%
4592.61 1
 
1.0%
2115.1 1
 
1.0%
2695.27 1
 
1.0%
442.62 1
 
1.0%
2679.45 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
35.76 1
1.0%
42.89 1
1.0%
104.01 1
1.0%
132.21 1
1.0%
153.52 1
1.0%
164.36 1
1.0%
230.25 1
1.0%
341.88 1
1.0%
407.42 1
1.0%
442.62 1
1.0%
ValueCountFrequency (%)
13894.57 1
1.0%
12334.51 1
1.0%
11169.54 1
1.0%
11111.0 1
1.0%
9826.57 1
1.0%
9679.48 1
1.0%
9649.81 1
1.0%
9301.11 1
1.0%
8691.35 1
1.0%
8689.11 1
1.0%

NOX(g/km)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9171.3694
Minimum33.65
Maximum58273.29
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:00:32.157991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum33.65
5-th percentile198.2695
Q11910.7675
median5237.65
Q313610.233
95-th percentile30755.712
Maximum58273.29
Range58239.64
Interquartile range (IQR)11699.465

Descriptive statistics

Standard deviation10706.17
Coefficient of variation (CV)1.167347
Kurtosis4.6259556
Mean9171.3694
Median Absolute Deviation (MAD)4316.035
Skewness1.9536766
Sum917136.94
Variance1.1462208 × 108
MonotonicityNot monotonic
2023-12-10T22:00:32.325529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
32069.14 1
 
1.0%
516.4 1
 
1.0%
2766.72 1
 
1.0%
18633.85 1
 
1.0%
2774.74 1
 
1.0%
15128.88 1
 
1.0%
3220.23 1
 
1.0%
7115.0 1
 
1.0%
584.18 1
 
1.0%
6080.68 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
33.65 1
1.0%
43.71 1
1.0%
86.91 1
1.0%
181.1 1
1.0%
187.24 1
1.0%
198.85 1
1.0%
261.67 1
1.0%
363.15 1
1.0%
516.4 1
1.0%
567.26 1
1.0%
ValueCountFrequency (%)
58273.29 1
1.0%
44523.37 1
1.0%
37136.03 1
1.0%
32069.14 1
1.0%
32015.83 1
1.0%
30689.39 1
1.0%
27490.8 1
1.0%
26778.33 1
1.0%
26489.16 1
1.0%
24718.12 1
1.0%

HC(g/km)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean831.2508
Minimum2.84
Maximum4538.57
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:00:32.507928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.84
5-th percentile23.8885
Q1184.02
median474.915
Q31225.3275
95-th percentile2788.4875
Maximum4538.57
Range4535.73
Interquartile range (IQR)1041.3075

Descriptive statistics

Standard deviation922.7952
Coefficient of variation (CV)1.1101285
Kurtosis3.06712
Mean831.2508
Median Absolute Deviation (MAD)381.335
Skewness1.7128709
Sum83125.08
Variance851550.98
MonotonicityNot monotonic
2023-12-10T22:00:32.697619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2783.92 1
 
1.0%
50.64 1
 
1.0%
256.14 1
 
1.0%
1396.96 1
 
1.0%
238.87 1
 
1.0%
1198.58 1
 
1.0%
252.84 1
 
1.0%
749.59 1
 
1.0%
58.41 1
 
1.0%
731.28 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
2.84 1
1.0%
3.6 1
1.0%
7.85 1
1.0%
17.29 1
1.0%
21.01 1
1.0%
24.04 1
1.0%
24.9 1
1.0%
31.54 1
1.0%
50.25 1
1.0%
50.64 1
1.0%
ValueCountFrequency (%)
4538.57 1
1.0%
4064.43 1
1.0%
2918.47 1
1.0%
2905.9 1
1.0%
2875.27 1
1.0%
2783.92 1
1.0%
2587.33 1
1.0%
2452.08 1
1.0%
2361.67 1
1.0%
2265.41 1
1.0%

PM(g/km)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean575.212
Minimum1.61
Maximum3513.51
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:00:32.832105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.61
5-th percentile13.1135
Q185.95
median304.205
Q3883.355
95-th percentile1979.2995
Maximum3513.51
Range3511.9
Interquartile range (IQR)797.405

Descriptive statistics

Standard deviation680.81893
Coefficient of variation (CV)1.1835965
Kurtosis3.6145104
Mean575.212
Median Absolute Deviation (MAD)268.735
Skewness1.7935112
Sum57521.2
Variance463514.41
MonotonicityNot monotonic
2023-12-10T22:00:32.986358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2091.58 1
 
1.0%
24.96 1
 
1.0%
158.29 1
 
1.0%
1197.96 1
 
1.0%
143.57 1
 
1.0%
947.63 1
 
1.0%
165.57 1
 
1.0%
455.61 1
 
1.0%
33.02 1
 
1.0%
390.67 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
1.61 1
1.0%
2.61 1
1.0%
4.49 1
1.0%
10.82 1
1.0%
12.8 1
1.0%
13.13 1
1.0%
14.81 1
1.0%
17.39 1
1.0%
24.55 1
1.0%
24.96 1
1.0%
ValueCountFrequency (%)
3513.51 1
1.0%
2810.55 1
1.0%
2273.96 1
1.0%
2104.42 1
1.0%
2091.58 1
1.0%
1973.39 1
1.0%
1756.34 1
1.0%
1689.24 1
1.0%
1653.78 1
1.0%
1591.95 1
1.0%

CO2(g/km)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1251893.1
Minimum14880.05
Maximum4912394.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:00:33.151958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum14880.05
5-th percentile72354.399
Q1572063.35
median1002718.1
Q31747734.2
95-th percentile3043177
Maximum4912394.8
Range4897514.8
Interquartile range (IQR)1175670.8

Descriptive statistics

Standard deviation980911.29
Coefficient of variation (CV)0.78354239
Kurtosis1.4357172
Mean1251893.1
Median Absolute Deviation (MAD)637138.16
Skewness1.1718808
Sum1.2518931 × 108
Variance9.6218695 × 1011
MonotonicityNot monotonic
2023-12-10T22:00:33.602661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3699184.46 1
 
1.0%
175962.81 1
 
1.0%
898471.34 1
 
1.0%
2014583.07 1
 
1.0%
828248.84 1
 
1.0%
1747294.47 1
 
1.0%
832607.77 1
 
1.0%
780657.61 1
 
1.0%
184936.08 1
 
1.0%
670653.48 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
14880.05 1
1.0%
18026.3 1
1.0%
43858.06 1
1.0%
44415.88 1
1.0%
44557.95 1
1.0%
73817.37 1
1.0%
94653.1 1
1.0%
142607.95 1
1.0%
156559.44 1
1.0%
175962.81 1
1.0%
ValueCountFrequency (%)
4912394.84 1
1.0%
3880371.71 1
1.0%
3699184.46 1
1.0%
3388181.36 1
1.0%
3359660.47 1
1.0%
3026519.96 1
1.0%
2995916.79 1
1.0%
2941859.93 1
1.0%
2936037.01 1
1.0%
2771984.8 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

2023-12-10T22:00:33.778166image/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

2023-12-10T22:00:25.565479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:00:09.199891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:00:10.646019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:00:11.935901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:00:13.482806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:00:14.820996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:00:15.997458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:00:17.310928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:00:18.583839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:00:20.235996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:00:22.684893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:00:24.088601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:00:25.743384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:00:09.323281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:00:10.743905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:00:12.047221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:00:13.575224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:00:14.904631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:00:16.082798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:00:17.403715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:00:18.678137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:00:20.353641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:00:22.794476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:00:24.210441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:00:25.851861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:00:09.448387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:00:10.831941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:00:12.169623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:00:13.722222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:00:14.993985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:00:16.182020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:00:17.519244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:00:18.788008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:00:20.489576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:00:22.895634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:00:24.337594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:00:25.968690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:00:09.587823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:00:10.943973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:00:12.277317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:00:13.844425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:00:15.085025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:00:16.300162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:00:17.624838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:00:18.907739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:00:20.713340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:00:22.999821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:00:24.453377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:00:26.067674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:00:09.719101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:00:11.066360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:00:12.378230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:00:13.933240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:00:15.183193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:00:16.411497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:00:17.720270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:00:19.015559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:00:20.987662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:00:23.121996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:00:24.564426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:00:26.174633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:00:09.844286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:00:11.151976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:00:12.484080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:00:14.054544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:00:15.270818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:00:16.523691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:00:17.821392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:00:19.107293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:00:21.248533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:00:23.249807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:00:24.674509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:00:26.276620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:00:09.972725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:00:11.267523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:00:12.608716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:00:14.187241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:00:15.364599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:00:16.623530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:00:17.940104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:00:19.214792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:00:21.583250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:00:23.405324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:00:24.813558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:00:26.393593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:00:10.075486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:00:11.389827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:00:12.720816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:00:14.286663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:00:15.478842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:00:16.734882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:00:18.051197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:00:19.615116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:00:21.905303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:00:23.527341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:00:24.946516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:00:26.499060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:00:10.199865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:00:11.511770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:00:12.820162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:00:14.401237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:00:15.607130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:00:16.858170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:00:18.151559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:00:19.725706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:00:22.141797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:00:23.658241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:00:25.076203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:00:26.590439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:00:10.323034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:00:11.617324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:00:13.191723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:00:14.510164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:00:15.705866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:00:16.989300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:00:18.243361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:00:19.866680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:00:22.274558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:00:23.787273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:00:25.199468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:00:26.672549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:00:10.423759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:00:11.718032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:00:13.272284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:00:14.606176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:00:15.804664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:00:17.094344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:00:18.351983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:00:19.984901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:00:22.366942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:00:23.873828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:00:25.311183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:00:26.770945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:00:10.527474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:00:11.828501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:00:13.383141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:00:14.715829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:00:15.911517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:00:17.197169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:00:18.467935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:00:20.116014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:00:22.502049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:00:23.968930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:00:25.438445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T22:00:33.925677image/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
기본키1.0000.9860.0000.0000.9960.8060.8000.6050.7650.8730.7870.7360.4110.4850.4180.5540.986
지점0.9861.0000.0000.0001.0001.0000.8460.5061.0001.0000.8800.6970.4680.5030.5160.6851.000
방향0.0000.0001.0000.0001.0000.0000.0000.0000.0000.0000.5460.0000.1930.0000.1490.0000.000
차선0.0000.0000.0001.0000.0000.1730.2140.6440.0000.1410.0000.5100.4820.5010.5410.3460.000
측정구간0.9961.0001.0000.0001.0001.0000.7880.0001.0001.0001.0000.1110.0000.0000.0000.0001.000
장비이정(km)0.8061.0000.0000.1731.0001.0000.5320.0000.7910.7940.5430.4190.4010.3620.4690.3451.000
차량통과수(대)0.8000.8460.0000.2140.7880.5321.0000.6460.5680.6450.0000.8200.4780.5050.4600.7160.846
평균 속도(km)0.6050.5060.0000.6440.0000.0000.6461.0000.1520.3560.0000.7700.6590.6370.6620.5610.506
위도(°)0.7651.0000.0000.0001.0000.7910.5680.1521.0000.8630.5860.4690.4220.4410.4400.4711.000
경도(°)0.8731.0000.0000.1411.0000.7940.6450.3560.8631.0000.6640.6380.6630.7040.6670.7331.000
기울기(°)0.7870.8800.5460.0001.0000.5430.0000.0000.5860.6641.0000.0000.0000.0000.0000.0000.880
CO(g/km)0.7360.6970.0000.5100.1110.4190.8200.7700.4690.6380.0001.0000.8650.8760.8640.9370.697
NOX(g/km)0.4110.4680.1930.4820.0000.4010.4780.6590.4220.6630.0000.8651.0000.9870.9990.9480.468
HC(g/km)0.4850.5030.0000.5010.0000.3620.5050.6370.4410.7040.0000.8760.9871.0000.9880.9390.503
PM(g/km)0.4180.5160.1490.5410.0000.4690.4600.6620.4400.6670.0000.8640.9990.9881.0000.9550.516
CO2(g/km)0.5540.6850.0000.3460.0000.3450.7160.5610.4710.7330.0000.9370.9480.9390.9551.0000.685
주소0.9861.0000.0000.0001.0001.0000.8460.5061.0001.0000.8800.6970.4680.5030.5160.6851.000
2023-12-10T22:00:34.144408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
방향차선주소지점측정구간
방향1.0000.0000.0000.0000.756
차선0.0001.0000.0000.0000.000
주소0.0000.0001.0001.0000.847
지점0.0000.0001.0001.0000.847
측정구간0.7560.0000.8470.8471.000
2023-12-10T22:00:34.283815image/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
기본키1.0000.321-0.4910.255-0.497-0.7300.025-0.454-0.315-0.331-0.276-0.4420.8560.0000.0000.7540.856
장비이정(km)0.3211.0000.246-0.0960.093-0.131-0.0370.2130.2450.2220.2520.2370.9160.0000.1150.7760.916
차량통과수(대)-0.4910.2461.000-0.4540.5200.643-0.0130.8510.6880.6800.6490.8690.4870.0000.1350.3290.487
평균 속도(km)0.255-0.096-0.4541.000-0.226-0.321-0.121-0.799-0.874-0.894-0.886-0.7430.1930.0000.4300.0000.193
위도(°)-0.4970.0930.520-0.2261.0000.541-0.0210.4360.3100.3380.2960.4070.9210.0000.0000.7800.921
경도(°)-0.730-0.1310.643-0.3210.5411.000-0.0020.6030.5070.4990.4660.6270.9260.0000.0830.7840.926
기울기(°)0.025-0.037-0.013-0.121-0.021-0.0021.0000.0290.0180.0230.0280.0070.5380.4020.0000.7890.538
CO(g/km)-0.4540.2130.851-0.7990.4360.6030.0291.0000.9450.9500.9210.9810.3160.0000.3180.0000.316
NOX(g/km)-0.3150.2450.688-0.8740.3100.5070.0180.9451.0000.9940.9910.9380.1810.1840.3190.0000.181
HC(g/km)-0.3310.2220.680-0.8940.3380.4990.0230.9500.9941.0000.9870.9260.1990.0000.3340.0000.199
PM(g/km)-0.2760.2520.649-0.8860.2960.4660.0280.9210.9910.9871.0000.9130.2060.1410.3670.0000.206
CO2(g/km)-0.4420.2370.869-0.7430.4070.6270.0070.9810.9380.9260.9131.0000.3250.0000.2270.0000.325
지점0.8560.9160.4870.1930.9210.9260.5380.3160.1810.1990.2060.3251.0000.0000.0000.8471.000
방향0.0000.0000.0000.0000.0000.0000.4020.0000.1840.0000.1410.0000.0001.0000.0000.7560.000
차선0.0000.1150.1350.4300.0000.0830.0000.3180.3190.3340.3670.2270.0000.0001.0000.0000.000
측정구간0.7540.7760.3290.0000.7800.7840.7890.0000.0000.0000.0000.0000.8470.7560.0001.0000.847
주소0.8560.9160.4870.1930.9210.9260.5380.3160.1810.1990.2060.3251.0000.0000.0000.8471.000

Missing values

2023-12-10T22:00:26.934995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T22:00:27.247981image/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-대동JC11.62020040101319574.3935.293889129.007222-0.3111111.032069.142783.922091.583699184.46경남 양산시 물금읍
12도로공사A-5510-0116E-6S1물금IC-대동JC11.62020040101405296.9435.293889129.007222-0.314792.533922.98412.8186.371635318.81경남 양산시 물금읍
23도로공사A-5510-0116E-6S2물금IC-대동JC11.62020040101602780.535.293889129.007222-0.319826.5718783.781907.611306.862995916.79경남 양산시 물금읍
34도로공사A-5510-0116E-6E1대동JC-물금IC11.62020040101688297.3735.293889129.0072220.36049.755275.45553.61236.782118934.4경남 양산시 물금읍
45도로공사A-5510-0116E-6E2대동JC-물금IC11.62020040101597179.6335.293889129.0072220.311169.5424317.132361.671466.43359660.47경남 양산시 물금읍
56도로공사A-5510-0116E-6E3대동JC-물금IC11.62020040101218072.3435.293889129.0072220.39649.8124718.122254.361591.953026519.96경남 양산시 물금읍
67도로공사A-6000-0062E-4S1한림IC-진영IC6.23202004010630290.0635.835556129.513056-1.072659.582523.39305.7988.15825775.62경남 김해시 진영읍 설창리
78도로공사A-6000-0062E-4S2한림IC-진영IC6.23202004010784581.6435.835556129.513056-1.076837.4717558.031832.771164.91939378.08경남 김해시 진영읍 설창리
89도로공사A-6000-0062E-4E1진영IC-한림IC6.23202004010716496.4135.835556129.5130561.092909.313614.21374.95205.361010084.68경남 김해시 진영읍 설창리
910도로공사A-6000-0062E-4E2진영IC-한림IC6.23202004010733881.0835.835556129.5130561.096901.7923779.552045.181502.72388714.66경남 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-담양JC38.8202004010336188.2335.247222126.999444-0.992225.8510075.32689.56601.661089506.27전남 담양군 고서면 장화리
9192도로공사A-0140-0388E-4E1담양JC-대덕JC38.82020040101980115.8435.247222126.9994440.98514.43640.3753.7232.03219946.44전남 담양군 고서면 장화리
9293도로공사A-0140-0388E-4E2담양JC-대덕JC38.8202004010329487.3635.247222126.9994440.982403.948865.32717.58572.61979730.9전남 담양군 고서면 장화리
9394도로공사A-0251-0652S-4S1고서JC-창평IC65.22020040106304107.2435.223611126.9947220.52044.342045.45186.24100.06766170.42전남 담양군 고서면
9495도로공사A-0251-0652S-4S2고서JC-창평IC65.2202004010718686.7335.223611126.9947220.53870.039603.93812.53629.721447148.73전남 담양군 고서면
9596도로공사A-0251-0652S-4E1창평IC-고서JC65.22020040106393106.2635.223611126.994722-0.52092.512159.39195.8110.89788254.96전남 담양군 고서면
9697도로공사A-0251-0652S-4E2창평IC-고서JC65.2202004010716685.8335.223611126.994722-0.53980.6410150.98859.79618.491439114.26전남 담양군 고서면
9798도로공사A-0270-0044S-4S1순천JC-동순천IC4.42020040102295105.8634.978889127.5488892.64692.571157.1397.6663.73283496.53전남 순천시 왕지동 왕지길
9899도로공사A-0270-0044S-4S2순천JC-동순천IC4.4202004010346584.9934.978889127.5488892.643032.899077.58903.06544.26918388.68전남 순천시 왕지동 왕지길
99100도로공사A-0270-0044S-4E1동순천IC-순천JC4.42020040102688102.2834.978889127.548889-3.9922.841110.86106.0452.27336934.33전남 순천시 왕지동 왕지길