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 지점 and 2 other fieldsHigh correlation
장비이정(km) is highly overall correlated with 지점 and 2 other fieldsHigh correlation
차량통과수(대) is highly overall correlated with CO(g/km) and 4 other fieldsHigh correlation
평균 속도(km) is highly overall correlated with CO(g/km) and 3 other fieldsHigh correlation
위도(°) is highly overall correlated with 지점 and 2 other fieldsHigh correlation
경도(°) is highly overall correlated with 지점 and 2 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 6 other fieldsHigh correlation
PM(g/km) is highly overall correlated with 차량통과수(대) and 6 other fieldsHigh correlation
CO2(g/km) is highly overall correlated with 차량통과수(대) and 4 other fieldsHigh correlation
방향 is highly overall correlated with 측정구간High correlation
차선 is highly overall correlated with CO(g/km) and 3 other fieldsHigh correlation
측정구간 is highly overall correlated with 기본키 and 7 other fieldsHigh correlation
기본키 has unique valuesUnique
PM(g/km) has 30 (30.0%) zerosZeros

Reproduction

Analysis started2023-12-10 10:43:32.415189
Analysis finished2023-12-10 10:43:58.856005
Duration26.44 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
2023-12-10T19:43:58.976604image/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
2023-12-10T19:43:59.243294image/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

2023-12-10T19:43:59.488086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:43:59.641148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
도로공사 100
100.0%

지점
Categorical

HIGH CORRELATION 

Distinct21
Distinct (%)21.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
A-0010-1612E-8
A-0010-1688E-8
A-0010-1441E-8
A-0010-0728S-6
 
6
A-0120-1726E-6
 
6
Other values (16)
64 

Length

Max length14
Median length14
Mean length14
Min length14

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA-0550-1107S-4
2nd rowA-0550-1107S-4
3rd rowA-0550-1107S-4
4th rowA-0550-1107S-4
5th rowA-0120-0957E-4

Common Values

ValueCountFrequency (%)
A-0010-1612E-8 8
 
8.0%
A-0010-1688E-8 8
 
8.0%
A-0010-1441E-8 8
 
8.0%
A-0010-0728S-6 6
 
6.0%
A-0120-1726E-6 6
 
6.0%
A-0200-0364S-6 6
 
6.0%
A-0010-1185E-8 5
 
5.0%
A-0550-1107S-4 4
 
4.0%
A-0200-0116S-4 4
 
4.0%
A-0120-1129E-4 4
 
4.0%
Other values (11) 41
41.0%

Length

2023-12-10T19:43:59.800473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
a-0010-1612e-8 8
 
8.0%
a-0010-1441e-8 8
 
8.0%
a-0010-1688e-8 8
 
8.0%
a-0010-0728s-6 6
 
6.0%
a-0120-1726e-6 6
 
6.0%
a-0200-0364s-6 6
 
6.0%
a-0010-1185e-8 5
 
5.0%
a-0300-1450s-4 4
 
4.0%
a-0300-1002s-4 4
 
4.0%
a-0300-1910e-4 4
 
4.0%
Other values (11) 41
41.0%

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 rowE
4th rowE
5th rowS

Common Values

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

Length

2023-12-10T19:44:00.003373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:44:00.165238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
s 51
51.0%
e 49
49.0%

차선
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
1
40 
2
39 
3
14 
4

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
1 40
40.0%
2 39
39.0%
3 14
 
14.0%
4 7
 
7.0%

Length

2023-12-10T19:44:00.323086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:44:00.479291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 40
40.0%
2 39
39.0%
3 14
 
14.0%
4 7
 
7.0%

측정구간
Categorical

HIGH CORRELATION 

Distinct40
Distinct (%)40.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
금호JC-칠곡물류IC
 
4
왜관IC-남구미IC
 
4
남구미IC-왜관IC
 
4
남구미IC-구미IC
 
4
구미IC-남구미IC
 
4
Other values (35)
80 

Length

Max length12
Median length11.5
Mean length10.02
Min length9

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st row칠곡IC-금호JC
2nd row칠곡IC-금호JC
3rd row금호JC-칠곡IC
4th row금호JC-칠곡IC
5th row함양JC-함양IC

Common Values

ValueCountFrequency (%)
금호JC-칠곡물류IC 4
 
4.0%
왜관IC-남구미IC 4
 
4.0%
남구미IC-왜관IC 4
 
4.0%
남구미IC-구미IC 4
 
4.0%
구미IC-남구미IC 4
 
4.0%
칠곡물류IC-금호JC 4
 
4.0%
동대구JC-경산IC 4
 
4.0%
옥포JC-고령JC 3
 
3.0%
경주IC-건천IC 3
 
3.0%
동고령IC-고령JC 3
 
3.0%
Other values (30) 63
63.0%

Length

2023-12-10T19:44:00.687529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
금호jc-칠곡물류ic 4
 
4.0%
남구미ic-왜관ic 4
 
4.0%
남구미ic-구미ic 4
 
4.0%
구미ic-남구미ic 4
 
4.0%
칠곡물류ic-금호jc 4
 
4.0%
동대구jc-경산ic 4
 
4.0%
왜관ic-남구미ic 4
 
4.0%
건천ic-경주ic 3
 
3.0%
북영천ic-임고hi 3
 
3.0%
고령jc-옥포jc 3
 
3.0%
Other values (30) 63
63.0%

장비이정(km)
Real number (ℝ)

HIGH CORRELATION 

Distinct21
Distinct (%)21.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean123.4323
Minimum11.6
Maximum191
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:44:00.880416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11.6
5-th percentile36.47
Q195.71
median132.58
Q3162.3325
95-th percentile172.63
Maximum191
Range179.4
Interquartile range (IQR)66.6225

Descriptive statistics

Standard deviation46.298893
Coefficient of variation (CV)0.37509544
Kurtosis-0.17467907
Mean123.4323
Median Absolute Deviation (MAD)33.09
Skewness-0.74672492
Sum12343.23
Variance2143.5875
MonotonicityNot monotonic
2023-12-10T19:44:01.070244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
161.22 8
 
8.0%
144.11 8
 
8.0%
168.89 8
 
8.0%
36.47 6
 
6.0%
72.8 6
 
6.0%
172.63 6
 
6.0%
118.5 5
 
5.0%
100.2 4
 
4.0%
191.0 4
 
4.0%
172.3 4
 
4.0%
Other values (11) 41
41.0%
ValueCountFrequency (%)
11.6 4
4.0%
36.47 6
6.0%
70.35 4
4.0%
72.8 6
6.0%
91.4 2
 
2.0%
95.71 4
4.0%
100.2 4
4.0%
110.7 4
4.0%
112.98 4
4.0%
118.5 5
5.0%
ValueCountFrequency (%)
191.0 4
4.0%
172.63 6
6.0%
172.3 4
4.0%
168.89 8
8.0%
165.67 3
 
3.0%
161.22 8
8.0%
145.0 4
4.0%
144.11 8
8.0%
133.22 4
4.0%
132.58 4
4.0%

측정일
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
20200301
100 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20200301
2nd row20200301
3rd row20200301
4th row20200301
5th row20200301

Common Values

ValueCountFrequency (%)
20200301 100
100.0%

Length

2023-12-10T19:44:01.275118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:44:01.430793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20200301 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

2023-12-10T19:44:01.596028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:44:01.742392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 100
100.0%

차량통과수(대)
Real number (ℝ)

HIGH CORRELATION 

Distinct60
Distinct (%)60.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.4
Minimum0
Maximum128
Zeros1
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:44:01.913530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q112.75
median25
Q352.5
95-th percentile94.3
Maximum128
Range128
Interquartile range (IQR)39.75

Descriptive statistics

Standard deviation30.07667
Coefficient of variation (CV)0.84962344
Kurtosis0.81229434
Mean35.4
Median Absolute Deviation (MAD)17
Skewness1.1915111
Sum3540
Variance904.60606
MonotonicityNot monotonic
2023-12-10T19:44:02.166645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 5
 
5.0%
20 4
 
4.0%
12 4
 
4.0%
13 4
 
4.0%
42 3
 
3.0%
11 3
 
3.0%
4 3
 
3.0%
56 3
 
3.0%
8 3
 
3.0%
14 3
 
3.0%
Other values (50) 65
65.0%
ValueCountFrequency (%)
0 1
 
1.0%
2 2
 
2.0%
3 1
 
1.0%
4 3
3.0%
5 1
 
1.0%
6 5
5.0%
8 3
3.0%
9 1
 
1.0%
10 1
 
1.0%
11 3
3.0%
ValueCountFrequency (%)
128 1
1.0%
119 1
1.0%
115 1
1.0%
112 1
1.0%
100 1
1.0%
94 2
2.0%
90 1
1.0%
89 1
1.0%
88 1
1.0%
84 1
1.0%

평균 속도(km)
Real number (ℝ)

HIGH CORRELATION 

Distinct99
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean102.3629
Minimum0
Maximum171.94
Zeros1
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:44:02.431703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile68.7085
Q189.4125
median100.245
Q3116.78
95-th percentile133.3765
Maximum171.94
Range171.94
Interquartile range (IQR)27.3675

Descriptive statistics

Standard deviation22.962499
Coefficient of variation (CV)0.22432443
Kurtosis4.7288357
Mean102.3629
Median Absolute Deviation (MAD)13.035
Skewness-0.9834657
Sum10236.29
Variance527.27637
MonotonicityNot monotonic
2023-12-10T19:44:02.668203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
120.5 2
 
2.0%
114.5 1
 
1.0%
130.6 1
 
1.0%
98.17 1
 
1.0%
129.5 1
 
1.0%
89.44 1
 
1.0%
79.06 1
 
1.0%
113.6 1
 
1.0%
99.33 1
 
1.0%
119.46 1
 
1.0%
Other values (89) 89
89.0%
ValueCountFrequency (%)
0.0 1
1.0%
20.0 1
1.0%
62.77 1
1.0%
63.62 1
1.0%
64.12 1
1.0%
68.95 1
1.0%
79.06 1
1.0%
79.86 1
1.0%
82.74 1
1.0%
82.75 1
1.0%
ValueCountFrequency (%)
171.94 1
1.0%
139.2 1
1.0%
138.5 1
1.0%
137.25 1
1.0%
133.5 1
1.0%
133.37 1
1.0%
131.8 1
1.0%
130.6 1
1.0%
130.5 1
1.0%
129.88 1
1.0%

위도(°)
Real number (ℝ)

HIGH CORRELATION 

Distinct21
Distinct (%)21.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.0483
Minimum35.532156
Maximum36.928333
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:44:02.859826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.532156
5-th percentile35.621053
Q135.818274
median36.0365
Q336.381222
95-th percentile36.466241
Maximum36.928333
Range1.3961773
Interquartile range (IQR)0.56294797

Descriptive statistics

Standard deviation0.31624066
Coefficient of variation (CV)0.0087726926
Kurtosis0.54285163
Mean36.0483
Median Absolute Deviation (MAD)0.218226
Skewness0.82084076
Sum3604.83
Variance0.10000815
MonotonicityNot monotonic
2023-12-10T19:44:03.006902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
36.0365 8
 
8.0%
35.913806 8
 
8.0%
36.07555556 8
 
8.0%
36.05805556 6
 
6.0%
35.818274 6
 
6.0%
35.78527778 6
 
6.0%
35.90473241 5
 
5.0%
36.409391 4
 
4.0%
36.38122197 4
 
4.0%
36.466241 4
 
4.0%
Other values (11) 41
41.0%
ValueCountFrequency (%)
35.532156 4
4.0%
35.621053 4
4.0%
35.707368 4
4.0%
35.75616248 3
 
3.0%
35.78527778 6
6.0%
35.818274 6
6.0%
35.82583333 4
4.0%
35.90473241 5
5.0%
35.913806 8
8.0%
35.95416667 4
4.0%
ValueCountFrequency (%)
36.92833333 4
4.0%
36.466241 4
4.0%
36.43761 4
4.0%
36.423098 4
4.0%
36.409391 4
4.0%
36.38549848 2
 
2.0%
36.38122197 4
4.0%
36.08611111 4
4.0%
36.07555556 8
8.0%
36.05805556 6
6.0%

경도(°)
Real number (ℝ)

HIGH CORRELATION 

Distinct21
Distinct (%)21.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean128.56312
Minimum127.75714
Maximum129.36221
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:44:03.141984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum127.75714
5-th percentile127.89019
Q1128.3625
median128.48863
Q3128.76778
95-th percentile129.20123
Maximum129.36221
Range1.6050703
Interquartile range (IQR)0.4052778

Descriptive statistics

Standard deviation0.38412084
Coefficient of variation (CV)0.0029877995
Kurtosis-0.091494216
Mean128.56312
Median Absolute Deviation (MAD)0.1263847
Skewness0.16555005
Sum12856.312
Variance0.14754882
MonotonicityNot monotonic
2023-12-10T19:44:03.400939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
128.4138333 8
 
8.0%
128.488629 8
 
8.0%
128.3625 8
 
8.0%
129.0011111 6
 
6.0%
129.140095 6
 
6.0%
128.4580556 6
 
6.0%
128.5614716 5
 
5.0%
128.454677 4
 
4.0%
129.3622143 4
 
4.0%
129.201228 4
 
4.0%
Other values (11) 41
41.0%
ValueCountFrequency (%)
127.757144 4
4.0%
127.89019 4
4.0%
128.044625 4
4.0%
128.3469444 4
4.0%
128.3619886 2
 
2.0%
128.3625 8
8.0%
128.36667590000002 3
 
3.0%
128.4138333 8
8.0%
128.454677 4
4.0%
128.4580556 6
6.0%
ValueCountFrequency (%)
129.3622143 4
4.0%
129.201228 4
4.0%
129.140095 6
6.0%
129.0011111 6
6.0%
128.925734 4
4.0%
128.7677778 4
4.0%
128.673649 4
4.0%
128.5614716 5
5.0%
128.535 4
4.0%
128.5322222 4
4.0%

기울기(°)
Real number (ℝ)

HIGH CORRELATION 

Distinct39
Distinct (%)39.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0813
Minimum-3.43
Maximum4.74
Zeros0
Zeros (%)0.0%
Negative52
Negative (%)52.0%
Memory size1.0 KiB
2023-12-10T19:44:03.610816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-3.43
5-th percentile-2.89
Q1-0.9975
median-0.13
Q31.59
95-th percentile3.02
Maximum4.74
Range8.17
Interquartile range (IQR)2.5875

Descriptive statistics

Standard deviation1.8755151
Coefficient of variation (CV)23.069066
Kurtosis-0.44269566
Mean0.0813
Median Absolute Deviation (MAD)1.25
Skewness0.1658115
Sum8.13
Variance3.5175569
MonotonicityNot monotonic
2023-12-10T19:44:04.114378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
0.54 5
 
5.0%
-1.38 4
 
4.0%
-0.13 4
 
4.0%
-0.87 4
 
4.0%
0.9 4
 
4.0%
1.59 4
 
4.0%
-0.49 4
 
4.0%
0.55 4
 
4.0%
-0.66 3
 
3.0%
0.62 3
 
3.0%
Other values (29) 61
61.0%
ValueCountFrequency (%)
-3.43 2
2.0%
-3.3 2
2.0%
-2.89 2
2.0%
-2.64 3
3.0%
-2.54 2
2.0%
-2.39 2
2.0%
-2.34 2
2.0%
-1.9 2
2.0%
-1.79 2
2.0%
-1.78 2
2.0%
ValueCountFrequency (%)
4.74 2
2.0%
3.43 2
2.0%
3.02 2
2.0%
2.93 2
2.0%
2.63 2
2.0%
2.54 2
2.0%
2.41 2
2.0%
2.27 3
3.0%
1.79 2
2.0%
1.77 2
2.0%

CO(g/km)
Real number (ℝ)

HIGH CORRELATION 

Distinct98
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.4094
Minimum0
Maximum63.8
Zeros1
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:44:04.372504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.8035
Q13.3
median10.895
Q321.6925
95-th percentile48.969
Maximum63.8
Range63.8
Interquartile range (IQR)18.3925

Descriptive statistics

Standard deviation15.545992
Coefficient of variation (CV)1.0088642
Kurtosis1.0806035
Mean15.4094
Median Absolute Deviation (MAD)8.12
Skewness1.3116298
Sum1540.94
Variance241.67787
MonotonicityNot monotonic
2023-12-10T19:44:04.677626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19.06 2
 
2.0%
0.49 2
 
2.0%
12.13 1
 
1.0%
1.28 1
 
1.0%
21.64 1
 
1.0%
2.04 1
 
1.0%
28.56 1
 
1.0%
6.01 1
 
1.0%
6.7 1
 
1.0%
0.43 1
 
1.0%
Other values (88) 88
88.0%
ValueCountFrequency (%)
0.0 1
1.0%
0.16 1
1.0%
0.43 1
1.0%
0.49 2
2.0%
0.82 1
1.0%
0.93 1
1.0%
0.98 1
1.0%
1.11 1
1.0%
1.28 1
1.0%
1.3 1
1.0%
ValueCountFrequency (%)
63.8 1
1.0%
58.23 1
1.0%
57.49 1
1.0%
55.31 1
1.0%
49.71 1
1.0%
48.93 1
1.0%
48.54 1
1.0%
48.47 1
1.0%
44.91 1
1.0%
41.22 1
1.0%

NOX(g/km)
Real number (ℝ)

HIGH CORRELATION 

Distinct99
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.0726
Minimum0
Maximum201.24
Zeros1
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:44:04.945397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.6095
Q12.6575
median12.56
Q328.5525
95-th percentile125.2225
Maximum201.24
Range201.24
Interquartile range (IQR)25.895

Descriptive statistics

Standard deviation37.600346
Coefficient of variation (CV)1.4996588
Kurtosis7.2106593
Mean25.0726
Median Absolute Deviation (MAD)10.775
Skewness2.6173004
Sum2507.26
Variance1413.786
MonotonicityNot monotonic
2023-12-10T19:44:05.208658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.41 2
 
2.0%
11.36 1
 
1.0%
0.81 1
 
1.0%
21.63 1
 
1.0%
1.93 1
 
1.0%
31.93 1
 
1.0%
6.94 1
 
1.0%
19.21 1
 
1.0%
0.3 1
 
1.0%
3.87 1
 
1.0%
Other values (89) 89
89.0%
ValueCountFrequency (%)
0.0 1
1.0%
0.17 1
1.0%
0.3 1
1.0%
0.41 2
2.0%
0.62 1
1.0%
0.74 1
1.0%
0.8 1
1.0%
0.81 1
1.0%
0.92 1
1.0%
1.1 1
1.0%
ValueCountFrequency (%)
201.24 1
1.0%
161.84 1
1.0%
139.73 1
1.0%
132.21 1
1.0%
129.45 1
1.0%
125.0 1
1.0%
95.96 1
1.0%
95.84 1
1.0%
81.46 1
1.0%
77.46 1
1.0%

HC(g/km)
Real number (ℝ)

HIGH CORRELATION 

Distinct86
Distinct (%)86.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7243
Minimum0
Maximum17.64
Zeros1
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:44:05.455758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.049
Q10.22
median1.165
Q33.3325
95-th percentile12.932
Maximum17.64
Range17.64
Interquartile range (IQR)3.1125

Descriptive statistics

Standard deviation3.9848088
Coefficient of variation (CV)1.4626909
Kurtosis4.9599648
Mean2.7243
Median Absolute Deviation (MAD)1.05
Skewness2.2877038
Sum272.43
Variance15.878702
MonotonicityNot monotonic
2023-12-10T19:44:05.712807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.22 3
 
3.0%
0.12 3
 
3.0%
0.03 3
 
3.0%
1.08 2
 
2.0%
0.1 2
 
2.0%
0.69 2
 
2.0%
0.17 2
 
2.0%
0.18 2
 
2.0%
1.29 2
 
2.0%
2.61 2
 
2.0%
Other values (76) 77
77.0%
ValueCountFrequency (%)
0.0 1
 
1.0%
0.01 1
 
1.0%
0.03 3
3.0%
0.05 1
 
1.0%
0.06 2
2.0%
0.09 1
 
1.0%
0.1 2
2.0%
0.11 1
 
1.0%
0.12 3
3.0%
0.13 1
 
1.0%
ValueCountFrequency (%)
17.64 1
1.0%
17.22 1
1.0%
16.65 1
1.0%
14.41 1
1.0%
13.92 1
1.0%
12.88 1
1.0%
10.37 1
1.0%
10.26 1
1.0%
9.6 1
1.0%
8.42 1
1.0%

PM(g/km)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct61
Distinct (%)61.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4181
Minimum0
Maximum12.06
Zeros30
Zeros (%)30.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:44:05.965636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.435
Q31.55
95-th percentile7.9615
Maximum12.06
Range12.06
Interquartile range (IQR)1.55

Descriptive statistics

Standard deviation2.3833288
Coefficient of variation (CV)1.6806493
Kurtosis6.6687917
Mean1.4181
Median Absolute Deviation (MAD)0.435
Skewness2.5917622
Sum141.81
Variance5.6802559
MonotonicityNot monotonic
2023-12-10T19:44:06.216554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 30
30.0%
0.12 4
 
4.0%
0.38 3
 
3.0%
1.14 2
 
2.0%
1.19 2
 
2.0%
1.55 2
 
2.0%
0.1 2
 
2.0%
0.32 2
 
2.0%
1.22 1
 
1.0%
0.17 1
 
1.0%
Other values (51) 51
51.0%
ValueCountFrequency (%)
0.0 30
30.0%
0.1 2
 
2.0%
0.12 4
 
4.0%
0.13 1
 
1.0%
0.17 1
 
1.0%
0.21 1
 
1.0%
0.22 1
 
1.0%
0.32 2
 
2.0%
0.33 1
 
1.0%
0.37 1
 
1.0%
ValueCountFrequency (%)
12.06 1
1.0%
10.26 1
1.0%
8.65 1
1.0%
8.24 1
1.0%
7.99 1
1.0%
7.96 1
1.0%
6.37 1
1.0%
6.36 1
1.0%
5.39 1
1.0%
5.02 1
1.0%

CO2(g/km)
Real number (ℝ)

HIGH CORRELATION 

Distinct99
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4915.6795
Minimum0
Maximum18661.92
Zeros1
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:44:06.450597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile286.8615
Q11411.5175
median3050.05
Q37136.7325
95-th percentile14456.038
Maximum18661.92
Range18661.92
Interquartile range (IQR)5725.215

Descriptive statistics

Standard deviation4553.1254
Coefficient of variation (CV)0.92624537
Kurtosis0.56145551
Mean4915.6795
Median Absolute Deviation (MAD)2323.575
Skewness1.1612948
Sum491567.95
Variance20730951
MonotonicityNot monotonic
2023-12-10T19:44:06.697461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
273.97 2
 
2.0%
5332.47 1
 
1.0%
287.54 1
 
1.0%
7327.94 1
 
1.0%
1071.69 1
 
1.0%
8413.48 1
 
1.0%
2373.28 1
 
1.0%
2870.46 1
 
1.0%
180.98 1
 
1.0%
1720.41 1
 
1.0%
Other values (89) 89
89.0%
ValueCountFrequency (%)
0.0 1
1.0%
135.31 1
1.0%
180.98 1
1.0%
273.97 2
2.0%
287.54 1
1.0%
393.63 1
1.0%
441.48 1
1.0%
472.36 1
1.0%
612.09 1
1.0%
616.43 1
1.0%
ValueCountFrequency (%)
18661.92 1
1.0%
17235.11 1
1.0%
16942.3 1
1.0%
15331.62 1
1.0%
14670.33 1
1.0%
14444.76 1
1.0%
14261.2 1
1.0%
12331.24 1
1.0%
12298.45 1
1.0%
12048.21 1
1.0%

주소
Categorical

HIGH CORRELATION 

Distinct21
Distinct (%)21.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
경북 칠곡군 석적읍 포남리
경북 구미시 임오동
경북 칠곡군 지천면
경북 경주시 건천읍 모량리
 
6
경북 달성군 옥포면 강림리
 
6
Other values (16)
64 

Length

Max length15
Median length14.5
Mean length11.86
Min length9

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row대구 북구 금호동
2nd row대구 북구 금호동
3rd row대구 북구 금호동
4th row대구 북구 금호동
5th row경남 함양군 함양읍 신관리

Common Values

ValueCountFrequency (%)
경북 칠곡군 석적읍 포남리 8
 
8.0%
경북 구미시 임오동 8
 
8.0%
경북 칠곡군 지천면 8
 
8.0%
경북 경주시 건천읍 모량리 6
 
6.0%
경북 달성군 옥포면 강림리 6
 
6.0%
경북 영천시 임고면 삼매2리 6
 
6.0%
대구 동구 안심3동 5
 
5.0%
대구 북구 금호동 4
 
4.0%
경북 경산시 와촌면 강학리 4
 
4.0%
경남 거창군 남상면 오계리 4
 
4.0%
Other values (11) 41
41.0%

Length

2023-12-10T19:44:06.935089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경북 79
22.8%
칠곡군 20
 
5.8%
경남 12
 
3.5%
의성군 10
 
2.9%
대구 9
 
2.6%
포남리 8
 
2.3%
영덕군 8
 
2.3%
구미시 8
 
2.3%
석적읍 8
 
2.3%
거창군 8
 
2.3%
Other values (38) 176
50.9%

Interactions

2023-12-10T19:43:56.036079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:34.146921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:36.062514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:37.879325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:39.981205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:42.002940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:43.849449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:46.004669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:48.319200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:50.210953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:52.182751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:54.022522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:56.199574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:34.329909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:36.207687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:38.012220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:40.142792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:42.149791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:44.053659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:46.168294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:48.447579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:50.364315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:52.325341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:54.163385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:56.352782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:34.466393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:36.353359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:38.141816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:40.296235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:42.308529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:44.225246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:46.344619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:48.614752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:50.511274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:52.483469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:54.423383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:56.844498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:34.609622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:36.500457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:38.282864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:40.475338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:42.453233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:44.395329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:46.511915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:48.773514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:50.656504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:52.640423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:54.562766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:56.993591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:34.776876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:36.638024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:38.427416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:40.652806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:42.616773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:44.574625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:46.682373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:48.930259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:50.792736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:52.803519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:54.722916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:57.136623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:34.975384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:36.771811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:38.609036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:40.821711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:42.773611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:44.749319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:46.843152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:49.084887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:50.995704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:52.962016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:54.873433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:57.267281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:35.151570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:36.917547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:38.737875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:40.992126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:42.940309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:44.934226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:47.022395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:49.257845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:51.162989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:53.132914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:55.045064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:57.438506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:35.305844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:37.067485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:38.850032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:41.169716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:43.104283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:45.115940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:47.183374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:49.429101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:51.334959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:53.275372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:55.216323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:57.586632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:35.449753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:37.270684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:38.966893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:41.311242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:43.235890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:45.298457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:47.330455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:49.585895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:51.468506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:53.411785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:55.369564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:57.744303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:35.607689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:37.416384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:39.108566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:41.494994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:43.377967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:45.476014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:47.491835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:49.750874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:51.700516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:53.563613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:55.549622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:57.900846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:35.778635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:37.579769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:39.688234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:41.668562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:43.534348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:45.667838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:47.671076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:49.904165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:51.862124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:53.737393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:55.704529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:58.059242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:35.920377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:37.738415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:39.833513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:41.845566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:43.682000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:45.832778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:47.828809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:50.050045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:52.018284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:53.880644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:55.866957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T19:44:07.074862image/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
기본키1.0000.9800.0000.1270.9980.8390.3240.2820.8300.8210.7110.2260.0000.0000.2210.3940.980
지점0.9801.0000.0000.0001.0001.0000.0000.6431.0001.0000.9070.0000.0000.0000.0000.0001.000
방향0.0000.0001.0000.0001.0000.0000.1710.2130.0000.0000.4080.4060.0880.0950.0000.3970.000
차선0.1270.0000.0001.0000.0000.0000.4360.6380.1150.0000.0000.7310.8840.7420.7160.5880.000
측정구간0.9981.0001.0000.0001.0001.0000.0000.5121.0001.0001.0000.0000.0000.0000.0000.0001.000
장비이정(km)0.8391.0000.0000.0001.0001.0000.3250.6650.8130.9630.6010.0000.0000.0000.0000.2181.000
차량통과수(대)0.3240.0000.1710.4360.0000.3251.0000.0000.4870.0000.0000.8650.5100.5990.4920.9230.000
평균 속도(km)0.2820.6430.2130.6380.5120.6650.0001.0000.2560.6700.4370.3810.3000.4070.0000.0000.643
위도(°)0.8301.0000.0000.1151.0000.8130.4870.2561.0000.8150.6110.3200.0000.1880.2000.3991.000
경도(°)0.8211.0000.0000.0001.0000.9630.0000.6700.8151.0000.6790.0000.0000.0000.0000.0001.000
기울기(°)0.7110.9070.4080.0001.0000.6010.0000.4370.6110.6791.0000.0000.0000.0000.0000.1510.907
CO(g/km)0.2260.0000.4060.7310.0000.0000.8650.3810.3200.0000.0001.0000.8310.8490.8240.9440.000
NOX(g/km)0.0000.0000.0880.8840.0000.0000.5100.3000.0000.0000.0000.8311.0000.9020.9600.7240.000
HC(g/km)0.0000.0000.0950.7420.0000.0000.5990.4070.1880.0000.0000.8490.9021.0000.9760.7240.000
PM(g/km)0.2210.0000.0000.7160.0000.0000.4920.0000.2000.0000.0000.8240.9600.9761.0000.7370.000
CO2(g/km)0.3940.0000.3970.5880.0000.2180.9230.0000.3990.0000.1510.9440.7240.7240.7371.0000.000
주소0.9801.0000.0000.0001.0001.0000.0000.6431.0001.0000.9070.0000.0000.0000.0000.0001.000
2023-12-10T19:44:07.328641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
차선주소측정구간지점방향
차선1.0000.0000.0000.0000.000
주소0.0001.0000.8711.0000.000
측정구간0.0000.8711.0000.8710.782
지점0.0001.0000.8711.0000.000
방향0.0000.0000.7820.0001.000
2023-12-10T19:44:07.550484image/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
기본키1.0000.257-0.174-0.0490.4290.3870.010-0.143-0.141-0.136-0.116-0.1470.8250.0000.0670.7520.825
장비이정(km)0.2571.000-0.113-0.1480.149-0.035-0.056-0.018-0.066-0.013-0.095-0.0920.9320.0000.0000.8120.932
차량통과수(대)-0.174-0.1131.000-0.230-0.119-0.030-0.0950.8690.7530.7370.5780.9300.0000.1220.2640.0000.000
평균 속도(km)-0.049-0.148-0.2301.0000.118-0.0190.025-0.592-0.619-0.668-0.621-0.4820.2860.2030.4550.1610.286
위도(°)0.4290.149-0.1190.1181.0000.4720.080-0.177-0.209-0.230-0.163-0.1380.9170.0000.0700.7990.917
경도(°)0.387-0.035-0.030-0.0190.4721.000-0.041-0.038-0.069-0.094-0.083-0.0240.9320.0000.0000.8120.932
기울기(°)0.010-0.056-0.0950.0250.080-0.0411.000-0.089-0.048-0.0610.000-0.1030.5930.2990.0000.8160.593
CO(g/km)-0.143-0.0180.869-0.592-0.177-0.038-0.0891.0000.9420.9550.8060.9720.0000.2970.5190.0000.000
NOX(g/km)-0.141-0.0660.753-0.619-0.209-0.069-0.0480.9421.0000.9850.9190.9180.0000.0580.5600.0000.000
HC(g/km)-0.136-0.0130.737-0.668-0.230-0.094-0.0610.9550.9851.0000.9000.9020.0000.0870.5680.0000.000
PM(g/km)-0.116-0.0950.578-0.621-0.163-0.0830.0000.8060.9190.9001.0000.7770.0000.0000.5370.0000.000
CO2(g/km)-0.147-0.0920.930-0.482-0.138-0.024-0.1030.9720.9180.9020.7771.0000.0000.2910.3800.0000.000
지점0.8250.9320.0000.2860.9170.9320.5930.0000.0000.0000.0000.0001.0000.0000.0000.8711.000
방향0.0000.0000.1220.2030.0000.0000.2990.2970.0580.0870.0000.2910.0001.0000.0000.7820.000
차선0.0670.0000.2640.4550.0700.0000.0000.5190.5600.5680.5370.3800.0000.0001.0000.0000.000
측정구간0.7520.8120.0000.1610.7990.8120.8160.0000.0000.0000.0000.0000.8710.7820.0001.0000.871
주소0.8250.9320.0000.2860.9170.9320.5930.0000.0000.0000.0000.0001.0000.0000.0000.8711.000

Missing values

2023-12-10T19:43:58.269714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T19:43:58.674937image/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-0550-1107S-4S1칠곡IC-금호JC110.720200301041114.536.928333128.532222-2.3412.1311.361.081.225332.47대구 북구 금호동
12도로공사A-0550-1107S-4S2칠곡IC-금호JC110.720200301084107.5436.928333128.532222-2.3429.2731.393.481.5510499.31대구 북구 금호동
23도로공사A-0550-1107S-4E1금호JC-칠곡IC110.720200301023124.2536.928333128.5322222.413.752.850.230.01810.7대구 북구 금호동
34도로공사A-0550-1107S-4E2금호JC-칠곡IC110.72020030107395.036.928333128.5322222.4127.7125.013.091.149002.11대구 북구 금호동
45도로공사A-0120-0957E-4S1함양JC-함양IC95.712020030104130.535.532156127.757144-0.430.490.410.030.0273.97경남 함양군 함양읍 신관리
56도로공사A-0120-0957E-4S2함양JC-함양IC95.712020030103188.9335.532156127.757144-0.4313.6216.242.111.194069.82경남 함양군 함양읍 신관리
67도로공사A-0120-0957E-4E1함양IC-함양JC95.712020030109131.835.532156127.7571440.431.110.920.060.0616.43경남 함양군 함양읍 신관리
78도로공사A-0120-0957E-4E2함양IC-함양JC95.712020030103488.6235.532156127.7571440.4319.6731.564.361.554819.54경남 함양군 함양읍 신관리
89도로공사A-0120-1129E-4S1거창IC-함양JC112.9820200301000.035.621053127.890191.630.00.00.00.00.0경남 거창군 남상면 오계리
910도로공사A-0120-1129E-4S2거창IC-함양JC112.9820200301018102.0635.621053127.890191.6312.2724.153.341.452780.83경남 거창군 남상면 오계리
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-0010-1612E-8S4남구미IC-왜관IC161.222020030105487.5336.0365128.4138330.958.23132.2117.648.2412045.52경북 칠곡군 석적읍 포남리
9192도로공사A-0010-1612E-8E1왜관IC-남구미IC161.2220200301026137.2536.0365128.413833-0.873.22.650.180.01780.79경북 칠곡군 석적읍 포남리
9293도로공사A-0010-1612E-8E2왜관IC-남구미IC161.2220200301070111.7536.0365128.413833-0.8716.9618.091.350.647027.66경북 칠곡군 석적읍 포남리
9394도로공사A-0010-1612E-8E3왜관IC-남구미IC161.222020030105699.3436.0365128.413833-0.8718.3514.261.520.956896.91경북 칠곡군 석적읍 포남리
9495도로공사A-0010-1612E-8E4왜관IC-남구미IC161.222020030102189.5636.0365128.413833-0.8718.3245.314.763.495371.07경북 칠곡군 석적읍 포남리
9596도로공사A-0010-1185E-8S1동대구JC-경산IC118.5202003010495.535.904732128.561472-0.131.30.80.090.0441.48대구 동구 안심3동
9697도로공사A-0010-1185E-8S2동대구JC-경산IC118.52020030105695.2535.904732128.561472-0.1318.1411.21.290.06180.72대구 동구 안심3동
9798도로공사A-0010-1185E-8S3동대구JC-경산IC118.52020030104386.5435.904732128.561472-0.1322.5833.854.291.815996.57대구 동구 안심3동
9899도로공사A-0010-1185E-8S4동대구JC-경산IC118.52020030102079.8635.904732128.561472-0.1317.5731.84.592.153676.64대구 동구 안심3동
99100도로공사A-0010-1185E-8E1경산IC-동대구JC118.52020030106106.035.904732128.5614720.171.721.10.120.0624.07대구 동구 안심3동