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
측정구간 is highly overall correlated with 기본키 and 7 other fieldsHigh correlation
지점 is highly overall correlated with 기본키 and 6 other fieldsHigh correlation
기본키 is highly overall correlated with 장비이정(km) and 5 other fieldsHigh correlation
장비이정(km) is highly overall correlated with 기본키 and 4 other fieldsHigh correlation
차량통과수(대) is highly overall correlated with CO(g/km) and 4 other fieldsHigh correlation
위도(°) is highly overall correlated with 기본키 and 4 other fieldsHigh correlation
경도(°) is highly overall correlated with 기본키 and 3 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 4 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 4 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
기본키 has unique valuesUnique
차량통과수(대) has 13 (13.0%) zerosZeros
평균 속도(km) has 13 (13.0%) zerosZeros
CO(g/km) has 13 (13.0%) zerosZeros
NOX(g/km) has 13 (13.0%) zerosZeros
HC(g/km) has 13 (13.0%) zerosZeros
PM(g/km) has 20 (20.0%) zerosZeros
CO2(g/km) has 13 (13.0%) zerosZeros

Reproduction

Analysis started2023-12-10 10:41:06.057139
Analysis finished2023-12-10 10:41:39.713080
Duration33.66 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
2023-12-10T19:41:39.856306image/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
2023-12-10T19:41:40.116168image/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

2023-12-10T19:41:40.377921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:41:40.553898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
도로공사 100
100.0%

지점
Categorical

HIGH CORRELATION 

Distinct14
Distinct (%)14.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
A-0010-3801E-10
10 
A-0010-3880E-10
10 
A-1000-1038S-9
A-1100-0249S-9
A-1000-0893S-8
Other values (9)
54 

Length

Max length15
Median length14
Mean length14.2
Min length14

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA-1000-0893S-8
2nd rowA-1000-0893S-8
3rd rowA-1000-0893S-8
4th rowA-1000-0893S-8
5th rowA-1000-0893S-8

Common Values

ValueCountFrequency (%)
A-0010-3801E-10 10
10.0%
A-0010-3880E-10 10
10.0%
A-1000-1038S-9 9
9.0%
A-1100-0249S-9 9
9.0%
A-1000-0893S-8 8
8.0%
A-1200-0149E-8 8
8.0%
A-1200-0178S-8 8
8.0%
A-1000-1002S-8 8
8.0%
A-1100-0054E-6 6
 
6.0%
A-1100-0129S-6 6
 
6.0%
Other values (4) 18
18.0%

Length

2023-12-10T19:41:40.774417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
a-0010-3801e-10 10
10.0%
a-0010-3880e-10 10
10.0%
a-1000-1038s-9 9
9.0%
a-1100-0249s-9 9
9.0%
a-1000-0893s-8 8
8.0%
a-1200-0149e-8 8
8.0%
a-1200-0178s-8 8
8.0%
a-1000-1002s-8 8
8.0%
a-1100-0054e-6 6
 
6.0%
a-1100-0129s-6 6
 
6.0%
Other values (4) 18
18.0%

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 rowS
5th rowE

Common Values

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

Length

2023-12-10T19:41:41.077626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:41:41.257151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
s 51
51.0%
e 49
49.0%

차선
Categorical

Distinct5
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
1
27 
2
27 
3
24 
4
16 
5

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row1
3rd row2
4th row3
5th row1

Common Values

ValueCountFrequency (%)
1 27
27.0%
2 27
27.0%
3 24
24.0%
4 16
16.0%
5 6
 
6.0%

Length

2023-12-10T19:41:41.452543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:41:41.699385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 27
27.0%
2 27
27.0%
3 24
24.0%
4 16
16.0%
5 6
 
6.0%

측정구간
Categorical

HIGH CORRELATION 

Distinct27
Distinct (%)27.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
동탄JC-오산IC
 
5
조남JC-도리JC
 
5
기흥IC-수원신갈IC
 
5
수원신갈IC-기흥IC
 
5
오산IC-동탄JC
 
5
Other values (22)
75 

Length

Max length11
Median length9
Mean length9.34
Min length9

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row장수IC-송내IC
2nd row장수IC-송내IC
3rd row장수IC-송내IC
4th row장수IC-송내IC
5th row송내IC-장수IC

Common Values

ValueCountFrequency (%)
동탄JC-오산IC 5
 
5.0%
조남JC-도리JC 5
 
5.0%
기흥IC-수원신갈IC 5
 
5.0%
수원신갈IC-기흥IC 5
 
5.0%
오산IC-동탄JC 5
 
5.0%
일직JC-석수IC 5
 
5.0%
인천TG-부평IC 4
 
4.0%
송내IC-장수IC 4
 
4.0%
서운JC-부천IC 4
 
4.0%
부평IC-인천TG 4
 
4.0%
Other values (17) 54
54.0%

Length

2023-12-10T19:41:41.953066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
동탄jc-오산ic 5
 
5.0%
기흥ic-수원신갈ic 5
 
5.0%
수원신갈ic-기흥ic 5
 
5.0%
오산ic-동탄jc 5
 
5.0%
일직jc-석수ic 5
 
5.0%
조남jc-도리jc 5
 
5.0%
도리jc-안현jc 4
 
4.0%
장수ic-송내ic 4
 
4.0%
석수ic-일직jc 4
 
4.0%
안현jc-도리jc 4
 
4.0%
Other values (17) 54
54.0%

장비이정(km)
Real number (ℝ)

HIGH CORRELATION 

Distinct14
Distinct (%)14.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean124.2982
Minimum5.45
Maximum388
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:41:42.221155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5.45
5-th percentile5.45
Q115.3
median25.4
Q3103.8
95-th percentile388
Maximum388
Range382.55
Interquartile range (IQR)88.5

Descriptive statistics

Standard deviation148.05947
Coefficient of variation (CV)1.1911634
Kurtosis-0.64583396
Mean124.2982
Median Absolute Deviation (MAD)19.95
Skewness1.0691193
Sum12429.82
Variance21921.606
MonotonicityNot monotonic
2023-12-10T19:41:42.526320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
380.1 10
10.0%
388.0 10
10.0%
103.8 9
9.0%
25.4 9
9.0%
89.32 8
8.0%
14.91 8
8.0%
17.84 8
8.0%
100.2 8
8.0%
5.45 6
 
6.0%
12.9 6
 
6.0%
Other values (4) 18
18.0%
ValueCountFrequency (%)
5.45 6
6.0%
12.9 6
6.0%
14.91 8
8.0%
15.3 6
6.0%
17.84 8
8.0%
22.48 2
 
2.0%
22.8 6
6.0%
25.4 9
9.0%
89.32 8
8.0%
100.2 8
8.0%
ValueCountFrequency (%)
388.0 10
10.0%
380.1 10
10.0%
356.05 4
 
4.0%
103.8 9
9.0%
100.2 8
8.0%
89.32 8
8.0%
25.4 9
9.0%
22.8 6
6.0%
22.48 2
 
2.0%
17.84 8
8.0%

측정일
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
20200601
100 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20200601
2nd row20200601
3rd row20200601
4th row20200601
5th row20200601

Common Values

ValueCountFrequency (%)
20200601 100
100.0%

Length

2023-12-10T19:41:42.790634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:41:42.994791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20200601 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

2023-12-10T19:41:43.321102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:41:43.494934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 100
100.0%

차량통과수(대)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct80
Distinct (%)80.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean242.34
Minimum0
Maximum798
Zeros13
Zeros (%)13.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:41:43.698720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1112.25
median213
Q3326.5
95-th percentile625.85
Maximum798
Range798
Interquartile range (IQR)214.25

Descriptive statistics

Standard deviation194.5757
Coefficient of variation (CV)0.80290375
Kurtosis0.13294106
Mean242.34
Median Absolute Deviation (MAD)115
Skewness0.82620465
Sum24234
Variance37859.701
MonotonicityNot monotonic
2023-12-10T19:41:43.997694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 13
 
13.0%
295 2
 
2.0%
229 2
 
2.0%
121 2
 
2.0%
142 2
 
2.0%
282 2
 
2.0%
207 2
 
2.0%
152 2
 
2.0%
6 2
 
2.0%
682 1
 
1.0%
Other values (70) 70
70.0%
ValueCountFrequency (%)
0 13
13.0%
6 2
 
2.0%
14 1
 
1.0%
36 1
 
1.0%
38 1
 
1.0%
43 1
 
1.0%
61 1
 
1.0%
77 1
 
1.0%
84 1
 
1.0%
86 1
 
1.0%
ValueCountFrequency (%)
798 1
1.0%
752 1
1.0%
682 1
1.0%
656 1
1.0%
642 1
1.0%
625 1
1.0%
616 1
1.0%
596 1
1.0%
575 1
1.0%
553 1
1.0%

평균 속도(km)
Real number (ℝ)

ZEROS 

Distinct87
Distinct (%)87.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean77.3846
Minimum0
Maximum126.5
Zeros13
Zeros (%)13.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:41:44.354089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q177.1475
median84.825
Q394.19
95-th percentile109.594
Maximum126.5
Range126.5
Interquartile range (IQR)17.0425

Descriptive statistics

Standard deviation32.106186
Coefficient of variation (CV)0.41489116
Kurtosis1.8495744
Mean77.3846
Median Absolute Deviation (MAD)8.645
Skewness-1.6625394
Sum7738.46
Variance1030.8072
MonotonicityNot monotonic
2023-12-10T19:41:44.759273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 13
 
13.0%
97.25 2
 
2.0%
97.19 1
 
1.0%
80.7 1
 
1.0%
85.3 1
 
1.0%
75.85 1
 
1.0%
94.4 1
 
1.0%
92.75 1
 
1.0%
90.92 1
 
1.0%
91.0 1
 
1.0%
Other values (77) 77
77.0%
ValueCountFrequency (%)
0.0 13
13.0%
68.77 1
 
1.0%
68.81 1
 
1.0%
68.97 1
 
1.0%
70.66 1
 
1.0%
71.78 1
 
1.0%
71.88 1
 
1.0%
72.88 1
 
1.0%
75.59 1
 
1.0%
75.85 1
 
1.0%
ValueCountFrequency (%)
126.5 1
1.0%
121.88 1
1.0%
114.25 1
1.0%
113.62 1
1.0%
111.38 1
1.0%
109.5 1
1.0%
109.38 1
1.0%
108.25 1
1.0%
108.0 1
1.0%
105.25 1
1.0%

위도(°)
Real number (ℝ)

HIGH CORRELATION 

Distinct14
Distinct (%)14.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.490094
Minimum37.158778
Maximum37.877222
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:41:45.001031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.158778
5-th percentile37.158778
Q137.38814
median37.428889
Q337.720556
95-th percentile37.877222
Maximum37.877222
Range0.71844444
Interquartile range (IQR)0.33241556

Descriptive statistics

Standard deviation0.21836956
Coefficient of variation (CV)0.0058247268
Kurtosis-0.8497602
Mean37.490094
Median Absolute Deviation (MAD)0.09542222
Skewness0.32316117
Sum3749.0094
Variance0.047685263
MonotonicityNot monotonic
2023-12-10T19:41:45.217573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
37.15877778 10
10.0%
37.22611111 10
10.0%
37.38814 9
9.0%
37.42611111 9
9.0%
37.81555556 8
8.0%
37.87722222 8
8.0%
37.52431111 8
8.0%
37.41777778 8
8.0%
37.72527778 6
 
6.0%
37.72055556 6
 
6.0%
Other values (4) 18
18.0%
ValueCountFrequency (%)
37.15877778 10
10.0%
37.22611111 10
10.0%
37.38814 9
9.0%
37.41777778 8
8.0%
37.42611111 9
9.0%
37.42888889 6
6.0%
37.42906868 6
6.0%
37.47277778 4
 
4.0%
37.51972222 2
 
2.0%
37.52431111 8
8.0%
ValueCountFrequency (%)
37.87722222 8
8.0%
37.81555556 8
8.0%
37.72527778 6
6.0%
37.72055556 6
6.0%
37.52431111 8
8.0%
37.51972222 2
 
2.0%
37.47277778 4
4.0%
37.42906868 6
6.0%
37.42888889 6
6.0%
37.42611111 9
9.0%

경도(°)
Real number (ℝ)

HIGH CORRELATION 

Distinct14
Distinct (%)14.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.017
Minimum126.76357
Maximum127.28778
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:41:45.432276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.76357
5-th percentile126.76357
Q1126.85489
median127.08833
Q3127.23167
95-th percentile127.28778
Maximum127.28778
Range0.5242111
Interquartile range (IQR)0.3767767

Descriptive statistics

Standard deviation0.18430757
Coefficient of variation (CV)0.0014510464
Kurtosis-1.6130395
Mean127.017
Median Absolute Deviation (MAD)0.1922222
Skewness0.083838002
Sum12701.7
Variance0.033969281
MonotonicityNot monotonic
2023-12-10T19:41:45.638921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
127.0883333 10
10.0%
127.10833329999998 10
10.0%
126.85489 9
9.0%
126.8961111 9
9.0%
127.2547222 8
8.0%
127.23166670000002 8
8.0%
126.7635667 8
8.0%
126.8294444 8
8.0%
127.1530556 6
 
6.0%
127.2877778 6
 
6.0%
Other values (4) 18
18.0%
ValueCountFrequency (%)
126.7635667 8
8.0%
126.7997222 6
6.0%
126.8177778 2
 
2.0%
126.8294444 8
8.0%
126.85489 9
9.0%
126.8777058 6
6.0%
126.8961111 9
9.0%
127.0883333 10
10.0%
127.10833329999998 10
10.0%
127.1530556 6
6.0%
ValueCountFrequency (%)
127.2877778 6
6.0%
127.2547222 8
8.0%
127.2486111 4
 
4.0%
127.23166670000002 8
8.0%
127.1530556 6
6.0%
127.10833329999998 10
10.0%
127.0883333 10
10.0%
126.8961111 9
9.0%
126.8777058 6
6.0%
126.85489 9
9.0%

기울기(°)
Real number (ℝ)

HIGH CORRELATION 

Distinct26
Distinct (%)26.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.0365
Minimum-3.25
Maximum3.12
Zeros0
Zeros (%)0.0%
Negative51
Negative (%)51.0%
Memory size1.0 KiB
2023-12-10T19:41:45.877766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-3.25
5-th percentile-2.21
Q1-0.66
median-0.01
Q30.595
95-th percentile2.33
Maximum3.12
Range6.37
Interquartile range (IQR)1.255

Descriptive statistics

Standard deviation1.3042513
Coefficient of variation (CV)-35.732912
Kurtosis0.041175536
Mean-0.0365
Median Absolute Deviation (MAD)0.65
Skewness0.0079625423
Sum-3.65
Variance1.7010715
MonotonicityNot monotonic
2023-12-10T19:41:46.114119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
-1.51 7
 
7.0%
0.57 5
 
5.0%
-2.21 5
 
5.0%
-0.55 5
 
5.0%
0.56 5
 
5.0%
-0.66 5
 
5.0%
0.67 5
 
5.0%
1.46 4
 
4.0%
2.33 4
 
4.0%
1.45 4
 
4.0%
Other values (16) 51
51.0%
ValueCountFrequency (%)
-3.25 2
 
2.0%
-2.21 5
5.0%
-2.03 3
3.0%
-1.51 7
7.0%
-1.35 4
4.0%
-0.83 2
 
2.0%
-0.66 5
5.0%
-0.55 5
5.0%
-0.54 4
4.0%
-0.3 3
3.0%
ValueCountFrequency (%)
3.12 2
 
2.0%
2.33 4
4.0%
2.0 3
3.0%
1.46 4
4.0%
1.45 4
4.0%
1.24 3
3.0%
0.67 5
5.0%
0.57 5
5.0%
0.56 5
5.0%
0.26 3
3.0%

CO(g/km)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct88
Distinct (%)88.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean112.9818
Minimum0
Maximum445.25
Zeros13
Zeros (%)13.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:41:46.398913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q136.4275
median93.77
Q3163.0275
95-th percentile298.8855
Maximum445.25
Range445.25
Interquartile range (IQR)126.6

Descriptive statistics

Standard deviation100.22916
Coefficient of variation (CV)0.88712658
Kurtosis0.83874106
Mean112.9818
Median Absolute Deviation (MAD)62.525
Skewness1.0868488
Sum11298.18
Variance10045.884
MonotonicityNot monotonic
2023-12-10T19:41:46.757549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 13
 
13.0%
280.66 1
 
1.0%
291.04 1
 
1.0%
110.52 1
 
1.0%
136.68 1
 
1.0%
306.78 1
 
1.0%
222.08 1
 
1.0%
270.48 1
 
1.0%
224.08 1
 
1.0%
1.94 1
 
1.0%
Other values (78) 78
78.0%
ValueCountFrequency (%)
0.0 13
13.0%
1.94 1
 
1.0%
2.18 1
 
1.0%
2.28 1
 
1.0%
11.66 1
 
1.0%
22.8 1
 
1.0%
23.8 1
 
1.0%
26.26 1
 
1.0%
27.16 1
 
1.0%
27.26 1
 
1.0%
ValueCountFrequency (%)
445.25 1
1.0%
394.3 1
1.0%
375.06 1
1.0%
336.28 1
1.0%
306.78 1
1.0%
298.47 1
1.0%
291.04 1
1.0%
280.66 1
1.0%
270.48 1
1.0%
269.38 1
1.0%

NOX(g/km)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct88
Distinct (%)88.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.8882
Minimum0
Maximum780.58
Zeros13
Zeros (%)13.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:41:47.035719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q126.91
median89.305
Q3161.5475
95-th percentile434.4175
Maximum780.58
Range780.58
Interquartile range (IQR)134.6375

Descriptive statistics

Standard deviation142.79542
Coefficient of variation (CV)1.1253641
Kurtosis4.7062062
Mean126.8882
Median Absolute Deviation (MAD)65.005
Skewness1.9723567
Sum12688.82
Variance20390.533
MonotonicityNot monotonic
2023-12-10T19:41:47.407213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 13
 
13.0%
305.15 1
 
1.0%
225.83 1
 
1.0%
266.4 1
 
1.0%
157.76 1
 
1.0%
535.82 1
 
1.0%
555.36 1
 
1.0%
231.44 1
 
1.0%
222.39 1
 
1.0%
1.2 1
 
1.0%
Other values (78) 78
78.0%
ValueCountFrequency (%)
0.0 13
13.0%
1.2 1
 
1.0%
1.71 1
 
1.0%
1.74 1
 
1.0%
7.2 1
 
1.0%
12.92 1
 
1.0%
17.48 1
 
1.0%
17.66 1
 
1.0%
18.27 1
 
1.0%
23.89 1
 
1.0%
ValueCountFrequency (%)
780.58 1
1.0%
555.36 1
1.0%
535.82 1
1.0%
452.05 1
1.0%
450.14 1
1.0%
433.59 1
1.0%
425.06 1
1.0%
398.2 1
1.0%
355.58 1
1.0%
321.64 1
1.0%

HC(g/km)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct87
Distinct (%)87.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.1412
Minimum0
Maximum81.28
Zeros13
Zeros (%)13.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:41:47.740473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12.9225
median10.21
Q320.22
95-th percentile48.422
Maximum81.28
Range81.28
Interquartile range (IQR)17.2975

Descriptive statistics

Standard deviation15.541233
Coefficient of variation (CV)1.0990038
Kurtosis3.7650263
Mean14.1412
Median Absolute Deviation (MAD)7.79
Skewness1.8206618
Sum1414.12
Variance241.52993
MonotonicityNot monotonic
2023-12-10T19:41:48.151007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 13
 
13.0%
0.14 2
 
2.0%
38.85 1
 
1.0%
23.8 1
 
1.0%
16.84 1
 
1.0%
61.31 1
 
1.0%
55.96 1
 
1.0%
23.44 1
 
1.0%
20.17 1
 
1.0%
81.28 1
 
1.0%
Other values (77) 77
77.0%
ValueCountFrequency (%)
0.0 13
13.0%
0.14 2
 
2.0%
0.18 1
 
1.0%
0.83 1
 
1.0%
1.69 1
 
1.0%
1.9 1
 
1.0%
1.91 1
 
1.0%
1.99 1
 
1.0%
2.06 1
 
1.0%
2.22 1
 
1.0%
ValueCountFrequency (%)
81.28 1
1.0%
61.31 1
1.0%
55.96 1
1.0%
49.15 1
1.0%
48.65 1
1.0%
48.41 1
1.0%
46.36 1
1.0%
45.69 1
1.0%
44.22 1
1.0%
38.85 1
1.0%

PM(g/km)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct76
Distinct (%)76.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.3055
Minimum0
Maximum79.94
Zeros20
Zeros (%)20.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:41:48.453998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.2175
median3.685
Q310.0625
95-th percentile28.037
Maximum79.94
Range79.94
Interquartile range (IQR)9.845

Descriptive statistics

Standard deviation11.720797
Coefficient of variation (CV)1.6043798
Kurtosis16.615767
Mean7.3055
Median Absolute Deviation (MAD)3.585
Skewness3.5200446
Sum730.55
Variance137.37707
MonotonicityNot monotonic
2023-12-10T19:41:49.283648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 20
 
20.0%
6.5 2
 
2.0%
0.12 2
 
2.0%
5.38 2
 
2.0%
10.06 2
 
2.0%
0.1 2
 
2.0%
17.01 1
 
1.0%
6.75 1
 
1.0%
17.1 1
 
1.0%
10.07 1
 
1.0%
Other values (66) 66
66.0%
ValueCountFrequency (%)
0.0 20
20.0%
0.1 2
 
2.0%
0.12 2
 
2.0%
0.21 1
 
1.0%
0.22 1
 
1.0%
0.24 1
 
1.0%
0.33 1
 
1.0%
0.36 1
 
1.0%
0.38 1
 
1.0%
0.45 1
 
1.0%
ValueCountFrequency (%)
79.94 1
1.0%
54.7 1
1.0%
35.15 1
1.0%
32.18 1
1.0%
29.31 1
1.0%
27.97 1
1.0%
24.33 1
1.0%
24.23 1
1.0%
22.42 1
1.0%
18.94 1
1.0%

CO2(g/km)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct88
Distinct (%)88.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34364.377
Minimum0
Maximum125930.89
Zeros13
Zeros (%)13.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:41:49.598182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q113995.162
median29969.345
Q345668.12
95-th percentile93803.006
Maximum125930.89
Range125930.89
Interquartile range (IQR)31672.957

Descriptive statistics

Standard deviation29363.046
Coefficient of variation (CV)0.85446178
Kurtosis0.68558568
Mean34364.377
Median Absolute Deviation (MAD)16028.48
Skewness1.0276252
Sum3436437.7
Variance8.6218849 × 108
MonotonicityNot monotonic
2023-12-10T19:41:49.912710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 13
 
13.0%
75672.57 1
 
1.0%
93770.22 1
 
1.0%
34236.1 1
 
1.0%
42667.92 1
 
1.0%
83032.63 1
 
1.0%
64851.52 1
 
1.0%
85085.79 1
 
1.0%
70155.77 1
 
1.0%
662.22 1
 
1.0%
Other values (78) 78
78.0%
ValueCountFrequency (%)
0.0 13
13.0%
662.22 1
 
1.0%
801.64 1
 
1.0%
1102.16 1
 
1.0%
3973.32 1
 
1.0%
6096.5 1
 
1.0%
6136.52 1
 
1.0%
9881.14 1
 
1.0%
9899.84 1
 
1.0%
10949.29 1
 
1.0%
ValueCountFrequency (%)
125930.89 1
1.0%
116261.42 1
1.0%
109850.81 1
1.0%
104777.69 1
1.0%
94425.94 1
1.0%
93770.22 1
1.0%
85085.79 1
1.0%
83032.63 1
1.0%
82434.66 1
1.0%
78495.07 1
1.0%

주소
Categorical

HIGH CORRELATION 

Distinct14
Distinct (%)14.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
경기 화성시 동탄면 송리
10 
경기 용인시 기흥구 기흥동
10 
경기 시흥시 논곡동
경기 안양시 만안구 석수동
인천 부평구 일신동
Other values (9)
54 

Length

Max length14
Median length10
Mean length11.3
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row인천 부평구 일신동
2nd row인천 부평구 일신동
3rd row인천 부평구 일신동
4th row인천 부평구 일신동
5th row인천 부평구 일신동

Common Values

ValueCountFrequency (%)
경기 화성시 동탄면 송리 10
10.0%
경기 용인시 기흥구 기흥동 10
10.0%
경기 시흥시 논곡동 9
9.0%
경기 안양시 만안구 석수동 9
9.0%
인천 부평구 일신동 8
8.0%
인천 부평구 갈산동 8
8.0%
경기 부천시오정구 삼정동 8
8.0%
경기 시흥시 매화동 8
8.0%
인천 연수구 선학동 6
 
6.0%
경기 시흥시 신천동 6
 
6.0%
Other values (4) 18
18.0%

Length

2023-12-10T19:41:50.211894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기 78
23.7%
시흥시 29
 
8.8%
인천 22
 
6.7%
부평구 16
 
4.9%
동탄면 10
 
3.0%
송리 10
 
3.0%
용인시 10
 
3.0%
기흥구 10
 
3.0%
기흥동 10
 
3.0%
화성시 10
 
3.0%
Other values (19) 124
37.7%

Interactions

2023-12-10T19:41:35.914990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:07.981639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:10.168811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:12.587102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:15.244430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:17.925965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:20.599607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:23.416816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:25.796984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:27.984363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:30.850115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:33.387166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:36.123761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:08.133601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:10.301594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:12.953266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:15.399925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:18.118182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:20.751847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:23.585182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:25.947524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:28.182759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:31.023712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:33.560137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:36.316845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:08.324553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:10.460440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:13.177320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:15.581612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:18.386829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:20.949655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:23.782539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:26.157492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:28.766876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:31.222211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:33.747925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:36.508148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:08.515491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:10.619371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:13.371476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:15.747713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:18.558724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:21.150183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:24.011207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:26.434104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:28.994460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:31.400329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:33.960030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:36.699342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:08.673779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:10.774534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:13.594986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:15.934961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:18.737820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:21.470117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:24.176818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:26.596658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:29.159328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:31.583401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:34.126991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:36.960230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:08.852992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:10.940428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:13.802957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:16.126837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:18.920840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:21.818465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:24.359175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:26.778311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:29.361920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:31.833081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:34.316063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:37.154717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:09.042553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:11.189423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:13.982386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:16.816740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:19.138407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:22.077683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:24.539485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:26.937462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:29.544106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:32.131942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:34.497422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:37.352448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:09.230436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:11.546878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:14.155630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:16.978144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:19.347560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:22.325361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:24.779034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:27.103834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:29.778369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:32.374880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:34.684962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:37.515555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:09.421764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:11.708713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:14.304859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:17.122902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:19.577922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:22.590297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:25.014056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:27.281470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:29.971451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:32.631402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:34.933239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:37.716980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:09.605760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:11.893825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:14.514586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:17.299238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:19.927406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:22.859116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:25.192581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:27.461380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:30.157166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:32.830407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:35.267919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:38.005663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:09.773417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:12.112234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:14.824992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:17.518795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:20.230107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:23.050866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:25.391160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:27.656973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:30.371420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:32.999660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:35.460328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:38.172722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:09.954878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:12.293557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:15.019210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:17.707725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:20.423227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:23.237625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:25.625550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:27.829841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:30.603775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:33.182379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:35.686418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T19:41:50.433237image/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
기본키1.0000.9690.0000.0000.9880.9210.4920.5020.8750.8840.8640.3160.1490.2030.0000.3840.969
지점0.9691.0000.0000.0001.0001.0000.4320.5431.0001.0000.9030.2380.0000.0000.0000.3701.000
방향0.0000.0001.0000.0001.0000.0000.0000.0000.0000.0000.3070.0000.1050.0000.0000.0900.000
차선0.0000.0000.0001.0000.0000.0930.5540.5730.0000.0000.0000.5790.5030.5200.4080.6250.000
측정구간0.9881.0001.0000.0001.0001.0000.3710.3491.0001.0001.0000.3070.0000.0000.0000.4921.000
장비이정(km)0.9211.0000.0000.0931.0001.0000.4220.5110.9620.8720.7610.3180.3190.3740.3150.4511.000
차량통과수(대)0.4920.4320.0000.5540.3710.4221.0000.5770.4460.3130.2770.9010.6640.6200.6160.9390.432
평균 속도(km)0.5020.5430.0000.5730.3490.5110.5771.0000.3560.2610.0000.5520.5150.5200.0970.5300.543
위도(°)0.8751.0000.0000.0001.0000.9620.4460.3561.0000.8750.7900.3160.0000.0000.0000.4051.000
경도(°)0.8841.0000.0000.0001.0000.8720.3130.2610.8751.0000.6840.2460.0000.0910.2260.3201.000
기울기(°)0.8640.9030.3070.0001.0000.7610.2770.0000.7900.6841.0000.0000.0000.0000.1850.0930.903
CO(g/km)0.3160.2380.0000.5790.3070.3180.9010.5520.3160.2460.0001.0000.8190.8100.7780.9670.238
NOX(g/km)0.1490.0000.1050.5030.0000.3190.6640.5150.0000.0000.0000.8191.0000.9870.8770.7570.000
HC(g/km)0.2030.0000.0000.5200.0000.3740.6200.5200.0000.0910.0000.8100.9871.0000.8850.7560.000
PM(g/km)0.0000.0000.0000.4080.0000.3150.6160.0970.0000.2260.1850.7780.8770.8851.0000.7610.000
CO2(g/km)0.3840.3700.0900.6250.4920.4510.9390.5300.4050.3200.0930.9670.7570.7560.7611.0000.370
주소0.9691.0000.0000.0001.0001.0000.4320.5431.0001.0000.9030.2380.0000.0000.0000.3701.000
2023-12-10T19:41:50.829817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
차선주소측정구간지점방향
차선1.0000.0000.0000.0000.000
주소0.0001.0000.9211.0000.000
측정구간0.0000.9211.0000.9210.863
지점0.0001.0000.9211.0000.000
방향0.0000.0000.8630.0001.000
2023-12-10T19:41:51.166179image/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
기본키1.0000.528-0.157-0.140-0.706-0.506-0.032-0.135-0.116-0.115-0.141-0.1370.8440.0000.0000.8330.844
장비이정(km)0.5281.0000.033-0.131-0.799-0.051-0.0090.0450.0360.028-0.0110.0510.9420.0000.0660.8680.942
차량통과수(대)-0.1570.0331.0000.2420.0660.0280.1050.9590.8290.8380.6880.9750.1820.0000.2530.1170.182
평균 속도(km)-0.140-0.1310.2421.0000.079-0.1150.0640.0830.006-0.020-0.0930.1360.2880.0000.4310.1320.288
위도(°)-0.706-0.7990.0660.0791.0000.3700.0050.0500.0210.0470.0910.0400.9570.0000.0000.8810.957
경도(°)-0.506-0.0510.028-0.1150.3701.0000.0310.0580.0440.0520.0880.0390.9620.0000.0000.8860.962
기울기(°)-0.032-0.0090.1050.0640.0050.0311.0000.0670.0280.0400.0590.0680.6510.2240.0000.9010.651
CO(g/km)-0.1350.0450.9590.0830.0500.0580.0671.0000.9380.9490.8310.9940.0880.0000.2690.0900.088
NOX(g/km)-0.1160.0360.8290.0060.0210.0440.0280.9381.0000.9920.9460.9210.0000.0970.3120.0000.000
HC(g/km)-0.1150.0280.838-0.0200.0470.0520.0400.9490.9921.0000.9360.9260.0000.0000.3250.0000.000
PM(g/km)-0.141-0.0110.688-0.0930.0910.0880.0590.8310.9460.9361.0000.8060.0000.0000.2720.0000.000
CO2(g/km)-0.1370.0510.9750.1360.0400.0390.0680.9940.9210.9260.8061.0000.1500.0580.2980.1730.150
지점0.8440.9420.1820.2880.9570.9620.6510.0880.0000.0000.0000.1501.0000.0000.0000.9211.000
방향0.0000.0000.0000.0000.0000.0000.2240.0000.0970.0000.0000.0580.0001.0000.0000.8630.000
차선0.0000.0660.2530.4310.0000.0000.0000.2690.3120.3250.2720.2980.0000.0001.0000.0000.000
측정구간0.8330.8680.1170.1320.8810.8860.9010.0900.0000.0000.0000.1730.9210.8630.0001.0000.921
주소0.8440.9420.1820.2880.9570.9620.6510.0880.0000.0000.0000.1501.0000.0000.0000.9211.000

Missing values

2023-12-10T19:41:38.810484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T19:41:39.527737image/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-1000-0893S-8S4장수IC-송내IC89.3220200601051781.2837.815556127.2547221.46280.66305.1538.8517.0175672.57인천 부평구 일신동
12도로공사A-1000-0893S-8S1장수IC-송내IC89.32202006010387108.2537.815556127.2547221.46111.7472.527.860.3340654.54인천 부평구 일신동
23도로공사A-1000-0893S-8S2장수IC-송내IC89.3220200601064292.1737.815556127.2547221.46394.3398.249.1579.94125930.89인천 부평구 일신동
34도로공사A-1000-0893S-8S3장수IC-송내IC89.3220200601059685.0837.815556127.2547221.46298.47425.0645.6927.9794425.94인천 부평구 일신동
45도로공사A-1000-0893S-8E1송내IC-장수IC89.3220200601000.037.815556127.254722-1.510.00.00.00.00.0인천 부평구 일신동
56도로공사A-1000-0893S-8E2송내IC-장수IC89.3220200601045292.5837.815556127.254722-1.51180.13124.2315.076.558713.39인천 부평구 일신동
67도로공사A-1000-0893S-8E3송내IC-장수IC89.3220200601047779.4537.815556127.254722-1.51233.1192.6326.226.5764209.73인천 부평구 일신동
78도로공사A-1000-0893S-8E4송내IC-장수IC89.3220200601014282.6237.815556127.254722-1.5153.632.484.110.3617049.05인천 부평구 일신동
89도로공사A-1100-0054E-6S1남동IC-문학IC5.4520200601018499.1237.725278127.1530562.060.7241.154.480.4520747.89인천 연수구 선학동
910도로공사A-1100-0054E-6S2남동IC-문학IC5.4520200601032581.5937.725278127.1530562.0162.12213.2824.6310.0645332.17인천 연수구 선학동
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-1100-0249S-9S2석수IC-일직JC25.420200601020786.8337.426111126.896111-0.5484.9662.718.033.1526727.52경기 안양시 만안구 석수동
9192도로공사A-1100-0249S-9S3석수IC-일직JC25.420200601015272.8837.426111126.896111-0.5474.4561.677.72.7520796.97경기 안양시 만안구 석수동
9293도로공사A-1100-0249S-9S4석수IC-일직JC25.420200601013768.8137.426111126.896111-0.5476.0656.07.212.2319923.95경기 안양시 만안구 석수동
9394도로공사A-1100-0249S-9E1일직JC-석수IC25.420200601095108.037.426111126.8961110.5727.2617.481.90.09881.14경기 안양시 만안구 석수동
9495도로공사A-1100-0249S-9E2일직JC-석수IC25.420200601000.037.426111126.8961110.570.00.00.00.00.0경기 안양시 만안구 석수동
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