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 HC(g/km)High 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 5 other fieldsHigh correlation
NOX(g/km) is highly overall correlated with 차량통과수(대) and 4 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 4 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)High correlation
측정구간 is highly overall correlated with 기본키 and 7 other fieldsHigh correlation
기본키 has unique valuesUnique
차량통과수(대) has 5 (5.0%) zerosZeros
평균 속도(km) has 5 (5.0%) zerosZeros
CO(g/km) has 5 (5.0%) zerosZeros
NOX(g/km) has 5 (5.0%) zerosZeros
HC(g/km) has 7 (7.0%) zerosZeros
PM(g/km) has 56 (56.0%) zerosZeros
CO2(g/km) has 5 (5.0%) zerosZeros

Reproduction

Analysis started2023-12-10 11:56:38.634970
Analysis finished2023-12-10 11:56:59.591361
Duration20.96 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
2023-12-10T20:56:59.687777image/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
2023-12-10T20:56:59.879140image/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

2023-12-10T20:57:00.057304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:57:00.201986image/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

2023-12-10T20:57:00.317715image/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

2023-12-10T20:57:00.519730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:57:00.689138image/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

2023-12-10T20:57:00.846248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:57:01.005081image/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

2023-12-10T20:57:01.162269image/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
2023-12-10T20:57:01.311017image/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
2023-12-10T20:57:01.488499image/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

2023-12-10T20:57:01.642185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:57:01.754065image/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

2023-12-10T20:57:01.903340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:57:02.014666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 100
100.0%

차량통과수(대)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct30
Distinct (%)30.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.08
Minimum0
Maximum40
Zeros5
Zeros (%)5.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:57:02.122952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.95
Q14
median7
Q314.25
95-th percentile25.2
Maximum40
Range40
Interquartile range (IQR)10.25

Descriptive statistics

Standard deviation8.6452883
Coefficient of variation (CV)0.85766749
Kurtosis1.4910949
Mean10.08
Median Absolute Deviation (MAD)5
Skewness1.270854
Sum1008
Variance74.74101
MonotonicityNot monotonic
2023-12-10T20:57:02.301261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
6 10
 
10.0%
5 9
 
9.0%
2 8
 
8.0%
4 8
 
8.0%
1 6
 
6.0%
10 5
 
5.0%
0 5
 
5.0%
12 5
 
5.0%
7 4
 
4.0%
21 4
 
4.0%
Other values (20) 36
36.0%
ValueCountFrequency (%)
0 5
5.0%
1 6
6.0%
2 8
8.0%
3 2
 
2.0%
4 8
8.0%
5 9
9.0%
6 10
10.0%
7 4
 
4.0%
8 2
 
2.0%
9 3
 
3.0%
ValueCountFrequency (%)
40 1
 
1.0%
38 1
 
1.0%
32 1
 
1.0%
31 1
 
1.0%
29 1
 
1.0%
25 1
 
1.0%
24 2
2.0%
23 2
2.0%
21 4
4.0%
20 1
 
1.0%

평균 속도(km)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct81
Distinct (%)81.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean97.2536
Minimum0
Maximum165
Zeros5
Zeros (%)5.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:57:02.486625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile19
Q187.105
median97.25
Q3117.0975
95-th percentile133.1165
Maximum165
Range165
Interquartile range (IQR)29.9925

Descriptive statistics

Standard deviation31.054266
Coefficient of variation (CV)0.31931226
Kurtosis3.2477492
Mean97.2536
Median Absolute Deviation (MAD)14.915
Skewness-1.3942658
Sum9725.36
Variance964.36746
MonotonicityNot monotonic
2023-12-10T20:57:02.669525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 5
 
5.0%
89.0 3
 
3.0%
93.0 3
 
3.0%
104.0 3
 
3.0%
79.67 2
 
2.0%
133.0 2
 
2.0%
114.0 2
 
2.0%
110.33 2
 
2.0%
91.5 2
 
2.0%
106.0 2
 
2.0%
Other values (71) 74
74.0%
ValueCountFrequency (%)
0.0 5
5.0%
20.0 1
 
1.0%
52.32 1
 
1.0%
63.98 1
 
1.0%
66.29 1
 
1.0%
69.24 1
 
1.0%
72.66 1
 
1.0%
74.5 1
 
1.0%
79.67 2
 
2.0%
79.8 1
 
1.0%
ValueCountFrequency (%)
165.0 1
1.0%
158.82 1
1.0%
137.0 2
2.0%
135.33 1
1.0%
133.0 2
2.0%
132.5 1
1.0%
131.04 1
1.0%
131.0 1
1.0%
130.91 1
1.0%
128.75 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
2023-12-10T20:57:02.814698image/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
2023-12-10T20:57:02.986800image/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
2023-12-10T20:57:03.136162image/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
2023-12-10T20:57:03.289917image/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
2023-12-10T20:57:03.472476image/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
2023-12-10T20:57:03.661596image/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  ZEROS 

Distinct81
Distinct (%)81.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.4454
Minimum0
Maximum22.98
Zeros5
Zeros (%)5.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:57:03.865025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0475
Q10.98
median2.735
Q36.92
95-th percentile13.4975
Maximum22.98
Range22.98
Interquartile range (IQR)5.94

Descriptive statistics

Standard deviation4.8904406
Coefficient of variation (CV)1.1001126
Kurtosis2.8156877
Mean4.4454
Median Absolute Deviation (MAD)2.14
Skewness1.6571824
Sum444.54
Variance23.916409
MonotonicityNot monotonic
2023-12-10T20:57:04.052342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.3 7
 
7.0%
0.0 5
 
5.0%
0.33 2
 
2.0%
0.98 2
 
2.0%
3.2 2
 
2.0%
0.65 2
 
2.0%
2.85 2
 
2.0%
1.62 2
 
2.0%
1.52 2
 
2.0%
7.45 2
 
2.0%
Other values (71) 72
72.0%
ValueCountFrequency (%)
0.0 5
5.0%
0.05 1
 
1.0%
0.07 1
 
1.0%
0.12 1
 
1.0%
0.16 1
 
1.0%
0.22 1
 
1.0%
0.25 1
 
1.0%
0.33 2
 
2.0%
0.37 1
 
1.0%
0.43 2
 
2.0%
ValueCountFrequency (%)
22.98 1
1.0%
21.17 1
1.0%
19.6 1
1.0%
15.42 1
1.0%
15.16 1
1.0%
13.41 1
1.0%
12.75 1
1.0%
12.43 1
1.0%
12.16 1
1.0%
11.93 1
1.0%

NOX(g/km)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct84
Distinct (%)84.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.7419
Minimum0
Maximum57.95
Zeros5
Zeros (%)5.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:57:04.243151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.057
Q10.7325
median2.025
Q35.6475
95-th percentile32.9085
Maximum57.95
Range57.95
Interquartile range (IQR)4.915

Descriptive statistics

Standard deviation11.775217
Coefficient of variation (CV)1.7465725
Kurtosis7.9060495
Mean6.7419
Median Absolute Deviation (MAD)1.72
Skewness2.7925951
Sum674.19
Variance138.65573
MonotonicityNot monotonic
2023-12-10T20:57:04.443364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 5
 
5.0%
0.91 4
 
4.0%
0.37 2
 
2.0%
1.06 2
 
2.0%
1.0 2
 
2.0%
0.3 2
 
2.0%
0.8 2
 
2.0%
1.1 2
 
2.0%
16.12 2
 
2.0%
2.76 2
 
2.0%
Other values (74) 75
75.0%
ValueCountFrequency (%)
0.0 5
5.0%
0.06 1
 
1.0%
0.07 1
 
1.0%
0.1 1
 
1.0%
0.12 1
 
1.0%
0.15 1
 
1.0%
0.2 1
 
1.0%
0.25 2
 
2.0%
0.3 2
 
2.0%
0.31 1
 
1.0%
ValueCountFrequency (%)
57.95 1
1.0%
52.75 1
1.0%
50.3 1
1.0%
48.55 1
1.0%
36.68 1
1.0%
32.71 1
1.0%
27.05 1
1.0%
23.71 1
1.0%
20.97 1
1.0%
18.44 1
1.0%

HC(g/km)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct61
Distinct (%)61.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.8051
Minimum0
Maximum7.41
Zeros7
Zeros (%)7.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:57:04.620825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.06
median0.22
Q30.8475
95-th percentile3.5905
Maximum7.41
Range7.41
Interquartile range (IQR)0.7875

Descriptive statistics

Standard deviation1.3810372
Coefficient of variation (CV)1.715361
Kurtosis8.9525904
Mean0.8051
Median Absolute Deviation (MAD)0.19
Skewness2.7813418
Sum80.51
Variance1.9072636
MonotonicityNot monotonic
2023-12-10T20:57:04.803139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 7
 
7.0%
0.1 4
 
4.0%
0.01 4
 
4.0%
0.12 4
 
4.0%
0.08 4
 
4.0%
0.06 4
 
4.0%
0.04 3
 
3.0%
0.05 3
 
3.0%
0.03 3
 
3.0%
0.22 3
 
3.0%
Other values (51) 61
61.0%
ValueCountFrequency (%)
0.0 7
7.0%
0.01 4
4.0%
0.02 3
3.0%
0.03 3
3.0%
0.04 3
3.0%
0.05 3
3.0%
0.06 4
4.0%
0.07 2
 
2.0%
0.08 4
4.0%
0.09 2
 
2.0%
ValueCountFrequency (%)
7.41 1
1.0%
7.17 1
1.0%
4.36 1
1.0%
3.95 1
1.0%
3.6 1
1.0%
3.59 1
1.0%
3.58 1
1.0%
3.06 1
1.0%
2.98 1
1.0%
2.46 1
1.0%

PM(g/km)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct33
Distinct (%)33.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3654
Minimum0
Maximum3.47
Zeros56
Zeros (%)56.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:57:05.310281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.3425
95-th percentile2.0295
Maximum3.47
Range3.47
Interquartile range (IQR)0.3425

Descriptive statistics

Standard deviation0.72230051
Coefficient of variation (CV)1.9767392
Kurtosis7.0539545
Mean0.3654
Median Absolute Deviation (MAD)0
Skewness2.6575757
Sum36.54
Variance0.52171802
MonotonicityNot monotonic
2023-12-10T20:57:05.480146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
0.0 56
56.0%
0.12 7
 
7.0%
0.25 2
 
2.0%
0.24 2
 
2.0%
0.1 2
 
2.0%
1.2 2
 
2.0%
0.22 2
 
2.0%
0.57 2
 
2.0%
2.4 1
 
1.0%
0.33 1
 
1.0%
Other values (23) 23
23.0%
ValueCountFrequency (%)
0.0 56
56.0%
0.1 2
 
2.0%
0.12 7
 
7.0%
0.13 1
 
1.0%
0.21 1
 
1.0%
0.22 2
 
2.0%
0.24 2
 
2.0%
0.25 2
 
2.0%
0.33 1
 
1.0%
0.34 1
 
1.0%
ValueCountFrequency (%)
3.47 1
1.0%
3.26 1
1.0%
2.89 1
1.0%
2.5 1
1.0%
2.4 1
1.0%
2.01 1
1.0%
1.59 1
1.0%
1.56 1
1.0%
1.38 1
1.0%
1.28 1
1.0%

CO2(g/km)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct88
Distinct (%)88.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1335.698
Minimum0
Maximum5502.53
Zeros5
Zeros (%)5.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:57:05.638679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile42.845
Q1429.5175
median756.11
Q31899.045
95-th percentile4536.537
Maximum5502.53
Range5502.53
Interquartile range (IQR)1469.5275

Descriptive statistics

Standard deviation1308.7677
Coefficient of variation (CV)0.97983801
Kurtosis1.7079566
Mean1335.698
Median Absolute Deviation (MAD)645.465
Skewness1.4308521
Sum133569.8
Variance1712872.8
MonotonicityNot monotonic
2023-12-10T20:57:05.803010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 5
 
5.0%
542.94 4
 
4.0%
633.43 2
 
2.0%
180.98 2
 
2.0%
441.48 2
 
2.0%
157.45 2
 
2.0%
551.85 2
 
2.0%
1615.36 1
 
1.0%
5384.89 1
 
1.0%
342.46 1
 
1.0%
Other values (78) 78
78.0%
ValueCountFrequency (%)
0.0 5
5.0%
45.1 1
 
1.0%
51.84 1
 
1.0%
68.49 1
 
1.0%
78.73 1
 
1.0%
90.49 1
 
1.0%
136.98 1
 
1.0%
157.45 2
 
2.0%
180.98 2
 
2.0%
205.48 1
 
1.0%
ValueCountFrequency (%)
5502.53 1
1.0%
5384.89 1
1.0%
5122.2 1
1.0%
4796.03 1
1.0%
4566.5 1
1.0%
4534.96 1
1.0%
3860.04 1
1.0%
3389.55 1
1.0%
3302.24 1
1.0%
3264.24 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

2023-12-10T20:57:05.980411image/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

2023-12-10T20:56:57.163923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:39.910827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:41.374740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:42.916257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:44.763562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:46.283642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:47.756449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:49.342040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:50.911537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:52.709592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:54.168882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:55.653780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:57.272798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:40.050637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:41.512635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:43.036215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:44.881703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:46.409532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:47.877119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:49.470162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:51.026544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:52.809923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:54.279251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:55.764100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:57.393240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:40.206993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:41.628408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:43.172894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:45.057175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:46.532199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:48.005986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:49.598727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:51.139180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:52.935730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:54.400072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:55.884821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:57.539011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:40.316910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:41.757548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:43.303544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:45.175949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:46.670494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:48.136707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:49.745520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:51.253973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:53.064904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:54.526722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:55.993079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:57.650039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:40.413809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:41.892670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:43.424195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:45.300662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:46.764533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:48.255889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:49.860378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:51.692865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:53.213221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:54.687330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:56.112586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:57.834801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:40.530394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:42.040180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:43.532986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:45.428818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:46.867292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:48.397389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:50.006866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:51.803406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:53.340620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:54.836979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:56.238843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:57.995974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:40.656064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:42.184880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:43.655942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:45.558002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:46.992487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:48.537876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:50.163195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:51.924591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:53.487872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:54.960495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:56.425920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:58.135481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:40.784222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:42.320368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:43.792018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:45.690220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:47.129937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:48.670496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:50.297640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:52.074708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:53.599650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:55.079084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:56.535673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:58.246336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:40.899991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:42.435910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:43.891861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:45.794112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:47.228640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:48.786289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:50.429534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:52.180914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:53.697051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:55.193701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:56.640842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:58.379562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:41.018881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:42.559479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:44.013839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:45.912165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:47.359942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:48.912474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:50.554230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:52.300222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:53.799002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:55.323782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:56.762445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:58.492008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:41.129432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:42.685818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:44.141895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:46.038200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:47.477237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:49.038543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:50.671194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:52.433583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:53.932117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:55.439367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:56.879205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:58.931449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:41.249862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:42.806144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:44.635825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:46.164568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:47.651122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:49.211142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:50.806423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:52.581578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:54.057278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:55.554711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:56:56.996965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T20:57:06.112794image/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
기본키1.0000.9800.0000.1270.9980.8390.2490.2330.8300.8210.7110.0000.0000.0000.4260.3550.980
지점0.9801.0000.0000.0001.0001.0000.0000.4771.0001.0000.9070.0000.0000.0000.0000.0001.000
방향0.0000.0001.0000.0001.0000.0000.1920.0000.0000.0000.4080.0000.0000.0000.0000.1910.000
차선0.1270.0000.0001.0000.0000.0000.5240.5660.1150.0000.0000.7220.6500.6420.6990.6350.000
측정구간0.9981.0001.0000.0001.0001.0000.0000.4181.0001.0001.0000.0000.0000.0000.0000.0001.000
장비이정(km)0.8391.0000.0000.0001.0001.0000.2970.6230.8130.9630.6010.2780.0000.0000.0000.2651.000
차량통과수(대)0.2490.0000.1920.5240.0000.2971.0000.1390.3930.0000.0000.8580.6960.6190.7880.9280.000
평균 속도(km)0.2330.4770.0000.5660.4180.6230.1391.0000.3260.5910.0000.6370.0000.3640.0000.2230.477
위도(°)0.8301.0000.0000.1151.0000.8130.3930.3261.0000.8150.6110.2850.0920.3190.0000.3981.000
경도(°)0.8211.0000.0000.0001.0000.9630.0000.5910.8151.0000.6790.0000.0000.0000.0000.0001.000
기울기(°)0.7110.9070.4080.0001.0000.6010.0000.0000.6110.6791.0000.0000.0000.1170.0000.2920.907
CO(g/km)0.0000.0000.0000.7220.0000.2780.8580.6370.2850.0000.0001.0000.9470.8470.8370.8620.000
NOX(g/km)0.0000.0000.0000.6500.0000.0000.6960.0000.0920.0000.0000.9471.0000.9100.9460.8150.000
HC(g/km)0.0000.0000.0000.6420.0000.0000.6190.3640.3190.0000.1170.8470.9101.0000.9280.7880.000
PM(g/km)0.4260.0000.0000.6990.0000.0000.7880.0000.0000.0000.0000.8370.9460.9281.0000.9370.000
CO2(g/km)0.3550.0000.1910.6350.0000.2650.9280.2230.3980.0000.2920.8620.8150.7880.9371.0000.000
주소0.9801.0000.0000.0001.0001.0000.0000.4771.0001.0000.9070.0000.0000.0000.0000.0001.000
2023-12-10T20:57:06.330523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
측정구간주소지점차선방향
측정구간1.0000.8710.8710.0000.782
주소0.8711.0001.0000.0000.000
지점0.8711.0001.0000.0000.000
차선0.0000.0000.0001.0000.000
방향0.7820.0000.0000.0001.000
2023-12-10T20:57:06.499546image/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
기본키1.0000.257-0.121-0.1010.4290.3870.010-0.096-0.122-0.099-0.123-0.1160.8250.0000.0670.7520.825
장비이정(km)0.2571.000-0.087-0.1790.149-0.035-0.0560.051-0.0030.051-0.114-0.0370.9320.0000.0000.8120.932
차량통과수(대)-0.121-0.0871.000-0.100-0.128-0.002-0.0340.8350.7870.7420.5250.9330.0000.1400.2510.0000.000
평균 속도(km)-0.101-0.179-0.1001.000-0.0130.004-0.014-0.479-0.460-0.528-0.483-0.3340.1860.0000.3880.1180.186
위도(°)0.4290.149-0.128-0.0131.0000.4720.080-0.197-0.241-0.229-0.243-0.1750.9170.0000.0700.7990.917
경도(°)0.387-0.035-0.0020.0040.4721.000-0.041-0.043-0.093-0.082-0.130-0.0160.9320.0000.0000.8120.932
기울기(°)0.010-0.056-0.034-0.0140.080-0.0411.000-0.051-0.044-0.051-0.054-0.0610.5930.2990.0000.8160.593
CO(g/km)-0.0960.0510.835-0.479-0.197-0.043-0.0511.0000.9740.9780.7770.9640.0000.0000.5440.0000.000
NOX(g/km)-0.122-0.0030.787-0.460-0.241-0.093-0.0440.9741.0000.9900.8530.9380.0000.0000.4670.0000.000
HC(g/km)-0.0990.0510.742-0.528-0.229-0.082-0.0510.9780.9901.0000.8410.9140.0000.0000.4940.0000.000
PM(g/km)-0.123-0.1140.525-0.483-0.243-0.130-0.0540.7770.8530.8411.0000.7210.0000.0000.4850.0000.000
CO2(g/km)-0.116-0.0370.933-0.334-0.175-0.016-0.0610.9640.9380.9140.7211.0000.0000.1370.4220.0000.000
지점0.8250.9320.0000.1860.9170.9320.5930.0000.0000.0000.0000.0001.0000.0000.0000.8711.000
방향0.0000.0000.1400.0000.0000.0000.2990.0000.0000.0000.0000.1370.0001.0000.0000.7820.000
차선0.0670.0000.2510.3880.0700.0000.0000.5440.4670.4940.4850.4220.0000.0001.0000.0000.000
측정구간0.7520.8120.0000.1180.7990.8120.8160.0000.0000.0000.0000.0000.8710.7820.0001.0000.871
주소0.8250.9320.0000.1860.9170.9320.5930.0000.0000.0000.0000.0001.0000.0000.0000.8711.000

Missing values

2023-12-10T20:56:59.120370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T20:56:59.458893image/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-금호JC110.720200301014114.536.928333128.532222-2.343.483.480.310.351615.36대구 북구 금호동
12도로공사A-0550-1107S-4S2칠곡IC-금호JC110.720200301025104.036.928333128.532222-2.347.455.120.550.122746.08대구 북구 금호동
23도로공사A-0550-1107S-4E1금호JC-칠곡IC110.72020030107119.036.928333128.5322222.411.521.060.10.0633.43대구 북구 금호동
34도로공사A-0550-1107S-4E2금호JC-칠곡IC110.72020030102196.036.928333128.5322222.416.84.20.480.02317.77대구 북구 금호동
45도로공사A-0120-0957E-4S1함양JC-함양IC95.712020030102111.535.532156127.757144-0.430.430.30.030.0180.98경남 함양군 함양읍 신관리
56도로공사A-0120-0957E-4S2함양JC-함양IC95.71202003010689.4235.532156127.757144-0.433.555.570.760.34900.35경남 함양군 함양읍 신관리
67도로공사A-0120-0957E-4E1함양IC-함양JC95.712020030102132.535.532156127.7571440.430.250.20.010.0136.98경남 함양군 함양읍 신관리
78도로공사A-0120-0957E-4E2함양IC-함양JC95.712020030101387.7835.532156127.7571440.438.615.32.20.821892.19경남 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-함양JC112.982020030103135.3335.621053127.890191.630.370.310.020.0205.48경남 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-왜관IC161.222020030101879.836.0365128.4138330.922.9850.37.412.893860.04경북 칠곡군 석적읍 포남리
9192도로공사A-0010-1612E-8E1왜관IC-남구미IC161.2220200301010131.036.0365128.413833-0.871.231.020.070.0684.92경북 칠곡군 석적읍 포남리
9293도로공사A-0010-1612E-8E2왜관IC-남구미IC161.2220200301015104.036.0365128.413833-0.874.32.760.30.01560.18경북 칠곡군 석적읍 포남리
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