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 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
위도(°) is highly overall correlated with 경도(°) and 3 other fieldsHigh correlation
경도(°) is highly overall correlated with 위도(°) 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 15 (15.0%) zerosZeros
평균 속도(km) has 15 (15.0%) zerosZeros
CO(g/km) has 15 (15.0%) zerosZeros
NOX(g/km) has 15 (15.0%) zerosZeros
HC(g/km) has 15 (15.0%) zerosZeros
PM(g/km) has 16 (16.0%) zerosZeros
CO2(g/km) has 15 (15.0%) zerosZeros

Reproduction

Analysis started2023-12-10 10:44:48.508279
Analysis finished2023-12-10 10:45:15.945579
Duration27.44 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
2023-12-10T19:45:16.085221image/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
2023-12-10T19:45:16.683286image/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

2023-12-10T19:45:16.886776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:45:17.052622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
도로공사 100
100.0%

지점
Categorical

HIGH CORRELATION 

Distinct15
Distinct (%)15.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
A-1000-1180S-10
10 
A-0010-3880E-10
10 
A-1000-0754E-9
A-1000-0072S-8
A-1000-0893S-8
Other values (10)
55 

Length

Max length15
Median length14
Mean length14.21
Min length14

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st rowA-0352-3462E-4
2nd rowA-0352-3462E-4
3rd rowA-0352-3462E-4
4th rowA-0352-3462E-4
5th rowA-0500-0022E-6

Common Values

ValueCountFrequency (%)
A-1000-1180S-10 10
10.0%
A-0010-3880E-10 10
10.0%
A-1000-0754E-9 9
9.0%
A-1000-0072S-8 8
 
8.0%
A-1000-0893S-8 8
 
8.0%
A-1200-0149E-8 8
 
8.0%
A-1200-0178S-8 8
 
8.0%
A-0500-0022E-6 6
 
6.0%
A-0500-0138E-6 6
 
6.0%
A-0500-0285S-6 6
 
6.0%
Other values (5) 21
21.0%

Length

2023-12-10T19:45:17.217916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
a-1000-1180s-10 10
10.0%
a-0010-3880e-10 10
10.0%
a-1000-0754e-9 9
9.0%
a-1000-0072s-8 8
 
8.0%
a-1000-0893s-8 8
 
8.0%
a-1200-0149e-8 8
 
8.0%
a-1200-0178s-8 8
 
8.0%
a-0500-0022e-6 6
 
6.0%
a-0500-0138e-6 6
 
6.0%
a-0500-0285s-6 6
 
6.0%
Other values (5) 21
21.0%

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

2023-12-10T19:45:17.424998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:45:17.579950image/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
29 
2
28 
3
24 
4
14 
5

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 29
29.0%
2 28
28.0%
3 24
24.0%
4 14
14.0%
5 5
 
5.0%

Length

2023-12-10T19:45:17.846264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:45:18.035271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 29
29.0%
2 28
28.0%
3 24
24.0%
4 14
14.0%
5 5
 
5.0%

측정구간
Categorical

HIGH CORRELATION 

Distinct29
Distinct (%)29.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
김포TG-김포IC
 
5
기흥IC-수원신갈IC
 
5
평촌IC-학의JC
 
5
수원신갈IC-기흥IC
 
5
학의JC-평촌IC
 
5
Other values (24)
75 

Length

Max length13
Median length9
Mean length9.64
Min length9

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st row경기광주IC-경기광주JC
2nd row경기광주IC-경기광주JC
3rd row경기광주JC-경기광주IC
4th row경기광주JC-경기광주IC
5th row월곶JC-서창JC

Common Values

ValueCountFrequency (%)
김포TG-김포IC 5
 
5.0%
기흥IC-수원신갈IC 5
 
5.0%
평촌IC-학의JC 5
 
5.0%
수원신갈IC-기흥IC 5
 
5.0%
학의JC-평촌IC 5
 
5.0%
인천TG-부평IC 4
 
4.0%
송파IC-성남TG 4
 
4.0%
성남TG-송파IC 4
 
4.0%
장수IC-송내IC 4
 
4.0%
부평IC-인천TG 4
 
4.0%
Other values (19) 55
55.0%

Length

2023-12-10T19:45:18.271892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
김포tg-김포ic 5
 
5.0%
평촌ic-학의jc 5
 
5.0%
수원신갈ic-기흥ic 5
 
5.0%
학의jc-평촌ic 5
 
5.0%
기흥ic-수원신갈ic 5
 
5.0%
부천ic-서운jc 4
 
4.0%
송내ic-장수ic 4
 
4.0%
김포ic-김포tg 4
 
4.0%
서운jc-부천ic 4
 
4.0%
부평ic-인천tg 4
 
4.0%
Other values (19) 55
55.0%

장비이정(km)
Real number (ℝ)

HIGH CORRELATION 

Distinct15
Distinct (%)15.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean104.1636
Minimum2.2
Maximum393.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:45:18.493886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.2
5-th percentile2.2
Q113.8
median28.58
Q3118.16
95-th percentile388
Maximum393.2
Range391
Interquartile range (IQR)104.36

Descriptive statistics

Standard deviation135.9977
Coefficient of variation (CV)1.3056164
Kurtosis0.21828598
Mean104.1636
Median Absolute Deviation (MAD)26.38
Skewness1.3662192
Sum10416.36
Variance18495.375
MonotonicityNot monotonic
2023-12-10T19:45:18.855785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
118.16 10
10.0%
388.0 10
10.0%
75.4 9
9.0%
7.29 8
 
8.0%
89.32 8
 
8.0%
14.91 8
 
8.0%
17.84 8
 
8.0%
2.2 6
 
6.0%
13.8 6
 
6.0%
28.58 6
 
6.0%
Other values (5) 21
21.0%
ValueCountFrequency (%)
2.2 6
6.0%
7.29 8
8.0%
12.9 6
6.0%
13.8 6
6.0%
14.91 8
8.0%
15.3 6
6.0%
17.84 8
8.0%
28.58 6
6.0%
75.4 9
9.0%
89.32 8
8.0%
ValueCountFrequency (%)
393.2 1
 
1.0%
388.0 10
10.0%
356.05 4
 
4.0%
346.8 4
 
4.0%
118.16 10
10.0%
89.32 8
8.0%
75.4 9
9.0%
28.58 6
6.0%
17.84 8
8.0%
15.3 6
6.0%

측정일
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
20200101
100 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20200101
2nd row20200101
3rd row20200101
4th row20200101
5th row20200101

Common Values

ValueCountFrequency (%)
20200101 100
100.0%

Length

2023-12-10T19:45:19.084210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:45:19.250781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20200101 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

2023-12-10T19:45:19.403702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:45:19.545432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 100
100.0%

차량통과수(대)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct77
Distinct (%)77.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean249.83
Minimum0
Maximum964
Zeros15
Zeros (%)15.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:45:19.711366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1150
median244
Q3348.75
95-th percentile514
Maximum964
Range964
Interquartile range (IQR)198.75

Descriptive statistics

Standard deviation175.56622
Coefficient of variation (CV)0.70274273
Kurtosis1.8495516
Mean249.83
Median Absolute Deviation (MAD)105
Skewness0.75082234
Sum24983
Variance30823.496
MonotonicityNot monotonic
2023-12-10T19:45:19.951713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 15
 
15.0%
216 3
 
3.0%
393 2
 
2.0%
261 2
 
2.0%
310 2
 
2.0%
221 2
 
2.0%
198 2
 
2.0%
277 2
 
2.0%
155 2
 
2.0%
609 1
 
1.0%
Other values (67) 67
67.0%
ValueCountFrequency (%)
0 15
15.0%
1 1
 
1.0%
5 1
 
1.0%
13 1
 
1.0%
80 1
 
1.0%
107 1
 
1.0%
108 1
 
1.0%
116 1
 
1.0%
133 1
 
1.0%
134 1
 
1.0%
ValueCountFrequency (%)
964 1
1.0%
661 1
1.0%
648 1
1.0%
609 1
1.0%
590 1
1.0%
510 1
1.0%
502 1
1.0%
462 1
1.0%
444 1
1.0%
438 1
1.0%

평균 속도(km)
Real number (ℝ)

ZEROS 

Distinct83
Distinct (%)83.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean76.4275
Minimum0
Maximum124.38
Zeros15
Zeros (%)15.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:45:20.297950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q177.8
median85.9
Q395.4125
95-th percentile106.476
Maximum124.38
Range124.38
Interquartile range (IQR)17.6125

Descriptive statistics

Standard deviation33.718311
Coefficient of variation (CV)0.44118035
Kurtosis1.3152584
Mean76.4275
Median Absolute Deviation (MAD)8.865
Skewness-1.6277094
Sum7642.75
Variance1136.9245
MonotonicityNot monotonic
2023-12-10T19:45:20.631729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 15
 
15.0%
78.71 2
 
2.0%
95.46 2
 
2.0%
109.25 2
 
2.0%
81.28 1
 
1.0%
79.82 1
 
1.0%
95.41 1
 
1.0%
111.6 1
 
1.0%
70.43 1
 
1.0%
72.59 1
 
1.0%
Other values (73) 73
73.0%
ValueCountFrequency (%)
0.0 15
15.0%
69.14 1
 
1.0%
70.43 1
 
1.0%
72.59 1
 
1.0%
75.96 1
 
1.0%
76.21 1
 
1.0%
76.48 1
 
1.0%
76.52 1
 
1.0%
76.95 1
 
1.0%
77.12 1
 
1.0%
ValueCountFrequency (%)
124.38 1
1.0%
111.6 1
1.0%
110.75 1
1.0%
109.25 2
2.0%
106.33 1
1.0%
105.1 1
1.0%
104.5 1
1.0%
104.38 1
1.0%
104.17 1
1.0%
103.48 1
1.0%

위도(°)
Real number (ℝ)

HIGH CORRELATION 

Distinct15
Distinct (%)15.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.575671
Minimum37.226111
Maximum37.999444
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:45:20.887707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.226111
5-th percentile37.226111
Q137.384167
median37.524311
Q337.774097
95-th percentile37.999444
Maximum37.999444
Range0.77333333
Interquartile range (IQR)0.38993056

Descriptive statistics

Standard deviation0.24030875
Coefficient of variation (CV)0.0063953281
Kurtosis-1.1610909
Mean37.575671
Median Absolute Deviation (MAD)0.19972917
Skewness0.2937806
Sum3757.5671
Variance0.057748294
MonotonicityNot monotonic
2023-12-10T19:45:21.103585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
37.38416667 10
10.0%
37.22611111 10
10.0%
37.99944444 9
9.0%
37.76027778 8
 
8.0%
37.81555556 8
 
8.0%
37.87722222 8
 
8.0%
37.52431111 8
 
8.0%
37.41758333 6
 
6.0%
37.58333333 6
 
6.0%
37.32109722 6
 
6.0%
Other values (5) 21
21.0%
ValueCountFrequency (%)
37.22611111 10
10.0%
37.27944444 1
 
1.0%
37.32109722 6
6.0%
37.38416667 10
10.0%
37.41277778 4
 
4.0%
37.41758333 6
6.0%
37.42888889 6
6.0%
37.47277778 4
 
4.0%
37.52431111 8
8.0%
37.58333333 6
6.0%
ValueCountFrequency (%)
37.99944444 9
9.0%
37.87722222 8
8.0%
37.81555556 8
8.0%
37.76027778 8
8.0%
37.72055556 6
6.0%
37.58333333 6
6.0%
37.52431111 8
8.0%
37.47277778 4
4.0%
37.42888889 6
6.0%
37.41758333 6
6.0%

경도(°)
Real number (ℝ)

HIGH CORRELATION 

Distinct15
Distinct (%)15.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.10629
Minimum126.73836
Maximum127.37306
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:45:21.331239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.73836
5-th percentile126.73836
Q1126.96676
median127.21528
Q3127.26299
95-th percentile127.37306
Maximum127.37306
Range0.6346917
Interquartile range (IQR)0.2962278

Descriptive statistics

Standard deviation0.20416754
Coefficient of variation (CV)0.0016062741
Kurtosis-0.99858881
Mean127.10629
Median Absolute Deviation (MAD)0.1069445
Skewness-0.63664313
Sum12710.629
Variance0.041684383
MonotonicityNot monotonic
2023-12-10T19:45:21.553286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
126.9908333 10
10.0%
127.10833329999998 10
10.0%
127.2897222 9
9.0%
127.2152778 8
 
8.0%
127.2547222 8
 
8.0%
127.23166670000002 8
 
8.0%
126.7635667 8
 
8.0%
126.7383639 6
 
6.0%
127.3730556 6
 
6.0%
126.96675829999998 6
 
6.0%
Other values (5) 21
21.0%
ValueCountFrequency (%)
126.7383639 6
6.0%
126.7635667 8
8.0%
126.7997222 6
6.0%
126.96675829999998 6
6.0%
126.9908333 10
10.0%
127.1058333 1
 
1.0%
127.10833329999998 10
10.0%
127.2152778 8
8.0%
127.23166670000002 8
8.0%
127.2486111 4
 
4.0%
ValueCountFrequency (%)
127.3730556 6
6.0%
127.3033333 4
 
4.0%
127.2897222 9
9.0%
127.2877778 6
6.0%
127.2547222 8
8.0%
127.2486111 4
 
4.0%
127.23166670000002 8
8.0%
127.2152778 8
8.0%
127.10833329999998 10
10.0%
127.1058333 1
 
1.0%

기울기(°)
Real number (ℝ)

HIGH CORRELATION 

Distinct27
Distinct (%)27.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.0108
Minimum-4.97
Maximum4.99
Zeros0
Zeros (%)0.0%
Negative51
Negative (%)51.0%
Memory size1.0 KiB
2023-12-10T19:45:21.761628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-4.97
5-th percentile-3.336
Q1-0.55
median-0.01
Q30.56
95-th percentile3.2135
Maximum4.99
Range9.96
Interquartile range (IQR)1.11

Descriptive statistics

Standard deviation1.925827
Coefficient of variation (CV)-178.31731
Kurtosis2.1947986
Mean-0.0108
Median Absolute Deviation (MAD)0.57
Skewness0.013322563
Sum-1.08
Variance3.7088095
MonotonicityNot monotonic
2023-12-10T19:45:21.976725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
1.46 7
 
7.0%
-0.14 6
 
6.0%
-0.55 5
 
5.0%
-4.97 5
 
5.0%
4.99 5
 
5.0%
0.56 5
 
5.0%
-1.51 4
 
4.0%
0.2 4
 
4.0%
0.92 4
 
4.0%
-1.15 4
 
4.0%
Other values (17) 51
51.0%
ValueCountFrequency (%)
-4.97 5
5.0%
-3.25 2
 
2.0%
-2.1 3
3.0%
-1.51 4
4.0%
-1.5 2
 
2.0%
-1.39 3
3.0%
-1.15 4
4.0%
-0.55 5
5.0%
-0.3 3
3.0%
-0.23 4
4.0%
ValueCountFrequency (%)
4.99 5
5.0%
3.12 2
 
2.0%
2.12 3
3.0%
1.5 2
 
2.0%
1.46 7
7.0%
0.92 4
4.0%
0.56 5
5.0%
0.26 3
3.0%
0.2 4
4.0%
0.18 4
4.0%

CO(g/km)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct86
Distinct (%)86.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean116.5288
Minimum0
Maximum640.14
Zeros15
Zeros (%)15.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:45:22.210493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q161.375
median102.7
Q3173.5075
95-th percentile278.6385
Maximum640.14
Range640.14
Interquartile range (IQR)112.1325

Descriptive statistics

Standard deviation97.655369
Coefficient of variation (CV)0.83803634
Kurtosis7.693351
Mean116.5288
Median Absolute Deviation (MAD)62
Skewness1.8980086
Sum11652.88
Variance9536.571
MonotonicityNot monotonic
2023-12-10T19:45:22.483656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 15
 
15.0%
186.18 1
 
1.0%
95.09 1
 
1.0%
176.49 1
 
1.0%
179.57 1
 
1.0%
101.02 1
 
1.0%
155.41 1
 
1.0%
214.94 1
 
1.0%
195.22 1
 
1.0%
129.89 1
 
1.0%
Other values (76) 76
76.0%
ValueCountFrequency (%)
0.0 15
15.0%
1.64 1
 
1.0%
3.28 1
 
1.0%
5.89 1
 
1.0%
23.73 1
 
1.0%
23.74 1
 
1.0%
37.58 1
 
1.0%
38.45 1
 
1.0%
49.4 1
 
1.0%
51.03 1
 
1.0%
ValueCountFrequency (%)
640.14 1
1.0%
397.0 1
1.0%
347.21 1
1.0%
303.19 1
1.0%
281.84 1
1.0%
278.47 1
1.0%
245.69 1
1.0%
217.68 1
1.0%
214.94 1
1.0%
211.89 1
1.0%

NOX(g/km)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct86
Distinct (%)86.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean125.0201
Minimum0
Maximum431.32
Zeros15
Zeros (%)15.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:45:22.770107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q144.205
median104.37
Q3173.26
95-th percentile377.6735
Maximum431.32
Range431.32
Interquartile range (IQR)129.055

Descriptive statistics

Standard deviation108.34938
Coefficient of variation (CV)0.86665569
Kurtosis0.51871565
Mean125.0201
Median Absolute Deviation (MAD)62.76
Skewness1.0000441
Sum12502.01
Variance11739.588
MonotonicityNot monotonic
2023-12-10T19:45:23.054434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 15
 
15.0%
128.69 1
 
1.0%
149.67 1
 
1.0%
161.28 1
 
1.0%
156.12 1
 
1.0%
83.27 1
 
1.0%
138.12 1
 
1.0%
296.42 1
 
1.0%
170.7 1
 
1.0%
108.95 1
 
1.0%
Other values (76) 76
76.0%
ValueCountFrequency (%)
0.0 15
15.0%
3.56 1
 
1.0%
4.16 1
 
1.0%
5.5 1
 
1.0%
16.78 1
 
1.0%
19.3 1
 
1.0%
23.2 1
 
1.0%
24.99 1
 
1.0%
41.09 1
 
1.0%
42.3 1
 
1.0%
ValueCountFrequency (%)
431.32 1
1.0%
395.34 1
1.0%
392.48 1
1.0%
382.06 1
1.0%
378.69 1
1.0%
377.62 1
1.0%
344.51 1
1.0%
338.31 1
1.0%
334.37 1
1.0%
313.11 1
1.0%

HC(g/km)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct85
Distinct (%)85.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.8345
Minimum0
Maximum64.37
Zeros15
Zeros (%)15.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:45:23.317543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14.7825
median11.52
Q319.5475
95-th percentile36.8435
Maximum64.37
Range64.37
Interquartile range (IQR)14.765

Descriptive statistics

Standard deviation12.51648
Coefficient of variation (CV)0.90472949
Kurtosis2.5081666
Mean13.8345
Median Absolute Deviation (MAD)7.055
Skewness1.374704
Sum1383.45
Variance156.66227
MonotonicityNot monotonic
2023-12-10T19:45:23.578880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 15
 
15.0%
19.66 2
 
2.0%
11.89 1
 
1.0%
16.34 1
 
1.0%
19.51 1
 
1.0%
16.84 1
 
1.0%
8.45 1
 
1.0%
17.94 1
 
1.0%
34.25 1
 
1.0%
18.59 1
 
1.0%
Other values (75) 75
75.0%
ValueCountFrequency (%)
0.0 15
15.0%
0.49 1
 
1.0%
0.59 1
 
1.0%
0.81 1
 
1.0%
1.56 1
 
1.0%
1.61 1
 
1.0%
2.67 1
 
1.0%
2.71 1
 
1.0%
4.48 1
 
1.0%
4.51 1
 
1.0%
ValueCountFrequency (%)
64.37 1
1.0%
49.72 1
1.0%
49.09 1
1.0%
44.29 1
1.0%
37.86 1
1.0%
36.79 1
1.0%
35.51 1
1.0%
34.25 1
1.0%
32.93 1
1.0%
30.32 1
1.0%

PM(g/km)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct83
Distinct (%)83.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.5264
Minimum0
Maximum80.62
Zeros16
Zeros (%)16.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:45:23.844780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.6175
median5.4
Q311.3825
95-th percentile25.8765
Maximum80.62
Range80.62
Interquartile range (IQR)10.765

Descriptive statistics

Standard deviation11.569813
Coefficient of variation (CV)1.3569399
Kurtosis14.941338
Mean8.5264
Median Absolute Deviation (MAD)5.105
Skewness3.1252142
Sum852.64
Variance133.86057
MonotonicityNot monotonic
2023-12-10T19:45:24.111186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 16
 
16.0%
0.92 2
 
2.0%
7.48 2
 
2.0%
2.42 1
 
1.0%
25.39 1
 
1.0%
2.83 1
 
1.0%
1.48 1
 
1.0%
13.91 1
 
1.0%
8.38 1
 
1.0%
10.47 1
 
1.0%
Other values (73) 73
73.0%
ValueCountFrequency (%)
0.0 16
16.0%
0.11 1
 
1.0%
0.12 1
 
1.0%
0.13 1
 
1.0%
0.21 1
 
1.0%
0.26 1
 
1.0%
0.44 1
 
1.0%
0.45 1
 
1.0%
0.49 1
 
1.0%
0.55 1
 
1.0%
ValueCountFrequency (%)
80.62 1
1.0%
38.9 1
1.0%
38.6 1
1.0%
37.1 1
1.0%
27.9 1
1.0%
25.77 1
1.0%
25.39 1
1.0%
24.52 1
1.0%
24.06 1
1.0%
23.43 1
1.0%

CO2(g/km)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct86
Distinct (%)86.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36362.52
Minimum0
Maximum151457.45
Zeros15
Zeros (%)15.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:45:24.467136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q121732.503
median34291.35
Q348949.605
95-th percentile80622.389
Maximum151457.45
Range151457.45
Interquartile range (IQR)27217.102

Descriptive statistics

Standard deviation27659.197
Coefficient of variation (CV)0.76065125
Kurtosis2.7790434
Mean36362.52
Median Absolute Deviation (MAD)14680.4
Skewness1.1128944
Sum3636252
Variance7.6503116 × 108
MonotonicityNot monotonic
2023-12-10T19:45:24.783526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 15
 
15.0%
54610.49 1
 
1.0%
28374.36 1
 
1.0%
50907.48 1
 
1.0%
59639.08 1
 
1.0%
39560.7 1
 
1.0%
40504.72 1
 
1.0%
58565.41 1
 
1.0%
65862.62 1
 
1.0%
48927.46 1
 
1.0%
Other values (76) 76
76.0%
ValueCountFrequency (%)
0.0 15
15.0%
262.5 1
 
1.0%
714.6 1
 
1.0%
1709.35 1
 
1.0%
9920.41 1
 
1.0%
11459.79 1
 
1.0%
12026.79 1
 
1.0%
12802.92 1
 
1.0%
13979.37 1
 
1.0%
18556.01 1
 
1.0%
ValueCountFrequency (%)
151457.45 1
1.0%
126992.34 1
1.0%
101038.59 1
1.0%
84212.24 1
1.0%
82452.08 1
1.0%
80526.09 1
1.0%
78070.61 1
1.0%
76873.18 1
1.0%
70002.5 1
1.0%
69793.39 1
1.0%

주소
Categorical

HIGH CORRELATION 

Distinct15
Distinct (%)15.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
경기 의왕시 청계동
10 
경기 용인시 기흥구 기흥동
10 
경기 김포시 고촌읍 신곡리
경기 성남시 수정구 복정동
인천 부평구 일신동
Other values (10)
55 

Length

Max length14
Median length10
Mean length11.54
Min length9

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st row경기 광주시 초월읍
2nd row경기 광주시 초월읍
3rd row경기 광주시 초월읍
4th row경기 광주시 초월읍
5th row인천 연수구 선학동

Common Values

ValueCountFrequency (%)
경기 의왕시 청계동 10
10.0%
경기 용인시 기흥구 기흥동 10
10.0%
경기 김포시 고촌읍 신곡리 9
9.0%
경기 성남시 수정구 복정동 8
 
8.0%
인천 부평구 일신동 8
 
8.0%
인천 부평구 갈산동 8
 
8.0%
경기 부천시오정구 삼정동 8
 
8.0%
인천 연수구 선학동 6
 
6.0%
경기 안산시 단원구 선부동 6
 
6.0%
경기 의왕시 삼동 6
 
6.0%
Other values (5) 21
21.0%

Length

2023-12-10T19:45:25.022010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기 78
23.4%
인천 22
 
6.6%
의왕시 16
 
4.8%
부평구 16
 
4.8%
시흥시 12
 
3.6%
용인시 11
 
3.3%
기흥구 11
 
3.3%
청계동 10
 
3.0%
기흥동 10
 
3.0%
김포시 9
 
2.7%
Other values (21) 139
41.6%

Interactions

2023-12-10T19:45:13.065925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:50.137994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:52.012693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:54.057925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:55.963770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:58.350929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:45:00.872500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:45:03.214742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:45:05.277649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:45:07.402472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:45:09.310858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:45:11.221671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:45:13.207702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:50.275492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:52.174315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:54.207458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:56.122672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:58.550058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:45:01.035392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:45:03.370915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:45:05.447057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:45:07.567773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:45:09.427826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:45:11.381317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:45:13.360276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:50.451033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:52.358917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:54.383241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:56.299084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:58.890111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:45:01.204612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:45:03.542333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:45:05.636508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:45:08.036958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:45:09.574625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:45:11.529868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:45:13.523108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:50.605791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:52.512754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:54.528285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:56.456719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:59.120687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:45:01.344599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:45:03.728721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:45:05.791489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:45:08.160262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:45:09.714391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:45:11.670628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:45:13.678531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:50.751909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:52.665207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:54.678096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:56.606822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:59.330163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:45:01.566710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:45:03.896915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:45:05.965843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:45:08.291097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:45:09.853814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:45:11.829127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:45:13.852472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:50.920678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:52.856591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:54.851596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:57.149834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:59.529468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:45:01.750984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:45:04.079581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:45:06.207567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:45:08.438363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:45:10.042828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:45:12.008855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:45:14.020596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:51.081331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:53.021212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:55.045305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:57.317248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:59.709220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:45:01.942575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:45:04.256565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:45:06.382973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:45:08.577894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:45:10.194324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:45:12.170064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:45:14.188882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:51.221878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:53.177412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:55.193693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:57.478548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:59.877251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:45:02.101332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:45:04.405190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:45:06.556163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:45:08.707957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:45:10.328927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:45:12.303010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:45:14.386186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:51.368146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:53.324021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:55.344314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:57.642681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:45:00.098754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:45:02.295859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:45:04.602739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:45:06.737049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:45:08.833318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:45:10.484096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:45:12.441056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:45:14.570074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:51.517372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:53.473428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:55.477406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:57.794606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:45:00.288393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:45:02.470746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:45:04.742863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:45:06.894936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:45:08.918540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:45:10.627269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:45:12.583899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:45:14.757721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:51.671697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:53.652998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:55.621871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:57.979521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:45:00.475359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:45:02.668576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:45:04.910310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:45:07.053986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:45:09.035461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:45:10.762580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:45:12.736578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:45:14.974063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:51.855746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:53.881374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:55.810032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:58.163271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:45:00.681390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:45:02.914809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:45:05.089626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:45:07.231415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:45:09.187127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:45:10.928261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:45:12.897763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T19:45:25.600788image/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
기본키1.0000.9930.0000.0000.9930.9850.4830.3100.9330.9270.8710.3640.3400.0510.3000.3300.993
지점0.9931.0000.0000.0001.0001.0000.5970.4631.0001.0000.8920.3700.3440.2880.4090.3501.000
방향0.0000.0001.0000.0001.0000.0000.0000.0560.0000.0000.5510.0000.0000.0000.0000.0000.000
차선0.0000.0000.0001.0000.0000.0000.3830.5450.0000.0000.0000.3670.6420.4380.3110.4110.000
측정구간0.9931.0001.0000.0001.0001.0000.6520.5331.0001.0001.0000.0000.2490.0000.0000.0001.000
장비이정(km)0.9851.0000.0000.0001.0001.0000.3240.3870.8850.7160.8060.2540.3180.0000.0920.2081.000
차량통과수(대)0.4830.5970.0000.3830.6520.3241.0000.6650.5810.3960.3640.9550.6880.7800.4080.8690.597
평균 속도(km)0.3100.4630.0560.5450.5330.3870.6651.0000.4010.4460.0000.6100.6950.6700.5180.6780.463
위도(°)0.9331.0000.0000.0001.0000.8850.5810.4011.0000.9630.7510.4360.4010.4870.4900.5511.000
경도(°)0.9271.0000.0000.0001.0000.7160.3960.4460.9631.0000.7960.2830.2630.0000.2730.1971.000
기울기(°)0.8710.8920.5510.0001.0000.8060.3640.0000.7510.7961.0000.0000.2430.0000.1040.0000.892
CO(g/km)0.3640.3700.0000.3670.0000.2540.9550.6100.4360.2830.0001.0000.8090.8840.7680.9550.370
NOX(g/km)0.3400.3440.0000.6420.2490.3180.6880.6950.4010.2630.2430.8091.0000.8750.7750.7770.344
HC(g/km)0.0510.2880.0000.4380.0000.0000.7800.6700.4870.0000.0000.8840.8751.0000.8030.9600.288
PM(g/km)0.3000.4090.0000.3110.0000.0920.4080.5180.4900.2730.1040.7680.7750.8031.0000.8270.409
CO2(g/km)0.3300.3500.0000.4110.0000.2080.8690.6780.5510.1970.0000.9550.7770.9600.8271.0000.350
주소0.9931.0000.0000.0001.0001.0000.5970.4631.0001.0000.8920.3700.3440.2880.4090.3501.000
2023-12-10T19:45:25.860458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
차선주소측정구간지점방향
차선1.0000.0000.0000.0000.000
주소0.0001.0000.9141.0000.000
측정구간0.0000.9141.0000.9140.851
지점0.0001.0000.9141.0000.000
방향0.0000.0000.8510.0001.000
2023-12-10T19:45:26.055058image/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
기본키1.0000.4120.073-0.173-0.080-0.161-0.0460.0630.0450.0260.0170.0820.8890.0000.0000.8370.889
장비이정(km)0.4121.000-0.145-0.199-0.4440.083-0.014-0.136-0.096-0.133-0.145-0.1350.9460.0000.0000.8650.946
차량통과수(대)0.073-0.1451.0000.3040.193-0.1450.0540.9290.7510.7610.6320.9460.2910.0000.2410.2810.291
평균 속도(km)-0.173-0.1990.3041.0000.1100.058-0.0680.1100.0310.0050.0970.1840.2220.0320.4050.2270.222
위도(°)-0.080-0.4440.1930.1101.0000.537-0.0080.1700.0800.1320.1970.1830.9660.0000.0000.8830.966
경도(°)-0.1610.083-0.1450.0580.5371.000-0.008-0.124-0.083-0.0850.042-0.1200.9510.0000.0000.8690.951
기울기(°)-0.046-0.0140.054-0.068-0.008-0.0081.0000.0590.0470.0590.0050.0610.5920.4120.0000.8880.592
CO(g/km)0.063-0.1360.9290.1100.170-0.1240.0591.0000.9170.9300.8190.9890.1570.0000.2300.0000.157
NOX(g/km)0.045-0.0960.7510.0310.080-0.0830.0470.9171.0000.9890.9330.8970.1250.0000.3100.0580.125
HC(g/km)0.026-0.1330.7610.0050.132-0.0850.0590.9300.9891.0000.9280.9030.1100.0000.2640.0000.110
PM(g/km)0.017-0.1450.6320.0970.1970.0420.0050.8190.9330.9281.0000.8120.1910.0000.2140.0000.191
CO2(g/km)0.082-0.1350.9460.1840.183-0.1200.0610.9890.8970.9030.8121.0000.1400.0000.2450.0000.140
지점0.8890.9460.2910.2220.9660.9510.5920.1570.1250.1100.1910.1401.0000.0000.0000.9141.000
방향0.0000.0000.0000.0320.0000.0000.4120.0000.0000.0000.0000.0000.0001.0000.0000.8510.000
차선0.0000.0000.2410.4050.0000.0000.0000.2300.3100.2640.2140.2450.0000.0001.0000.0000.000
측정구간0.8370.8650.2810.2270.8830.8690.8880.0000.0580.0000.0000.0000.9140.8510.0001.0000.914
주소0.8890.9460.2910.2220.9660.9510.5920.1570.1250.1100.1910.1401.0000.0000.0000.9141.000

Missing values

2023-12-10T19:45:15.301417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T19:45:15.757327image/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-0352-3462E-4S1경기광주IC-경기광주JC346.8202001010224103.4837.412778127.3033331.571.169.136.262.4225771.18경기 광주시 초월읍
12도로공사A-0352-3462E-4S2경기광주IC-경기광주JC346.820200101022686.2137.412778127.3033331.5115.26182.6216.710.8735171.86경기 광주시 초월읍
23도로공사A-0352-3462E-4E1경기광주JC-경기광주IC346.8202001010134124.3837.412778127.303333-1.523.7419.31.610.6411459.79경기 광주시 초월읍
34도로공사A-0352-3462E-4E2경기광주JC-경기광주IC346.820200101015595.4637.412778127.303333-1.566.2696.269.267.7123842.25경기 광주시 초월읍
45도로공사A-0500-0022E-6S1월곶JC-서창JC2.220200101025099.537.417583126.738364-0.0882.9656.246.160.9228582.63인천 연수구 선학동
56도로공사A-0500-0022E-6S2월곶JC-서창JC2.2202001010188.037.417583126.738364-0.081.644.160.590.21262.5인천 연수구 선학동
67도로공사A-0500-0022E-6S3월곶JC-서창JC2.220200101028377.6537.417583126.738364-0.08172.99248.7930.3214.0245735.82인천 연수구 선학동
78도로공사A-0500-0022E-6E1서창JC-월곶JC2.220200101021894.0437.417583126.7383640.1174.153.776.061.0524993.23인천 연수구 선학동
89도로공사A-0500-0022E-6E2서창JC-월곶JC2.220200101042883.8437.417583126.7383640.11202.37166.6120.167.559505.76인천 연수구 선학동
910도로공사A-0500-0022E-6E3서창JC-월곶JC2.220200101028077.8537.417583126.7383640.11163.0221.4626.1413.4145001.54인천 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-0010-3880E-10S2수원신갈IC-기흥IC388.020200101000.037.226111127.1083330.560.00.00.00.00.0경기 용인시 기흥구 기흥동
9192도로공사A-0010-3880E-10S3수원신갈IC-기흥IC388.020200101040084.7437.226111127.1083330.56199.79338.3129.3517.4161163.18경기 용인시 기흥구 기흥동
9293도로공사A-0010-3880E-10S4수원신갈IC-기흥IC388.020200101015980.6137.226111127.1083330.56122.44215.824.0124.5236206.52경기 용인시 기흥구 기흥동
9394도로공사A-0010-3880E-10S5수원신갈IC-기흥IC388.020200101000.037.226111127.1083330.560.00.00.00.00.0경기 용인시 기흥구 기흥동
9495도로공사A-0010-3880E-10E1기흥IC-수원신갈IC388.020200101000.037.226111127.108333-0.550.00.00.00.00.0경기 용인시 기흥구 기흥동
9596도로공사A-0010-3880E-10E2기흥IC-수원신갈IC388.020200101051095.037.226111127.108333-0.55203.88205.0818.076.3965517.79경기 용인시 기흥구 기흥동
9697도로공사A-0010-3880E-10E3기흥IC-수원신갈IC388.020200101000.037.226111127.108333-0.550.00.00.00.00.0경기 용인시 기흥구 기흥동
9798도로공사A-0010-3880E-10E4기흥IC-수원신갈IC388.020200101044475.9637.226111127.108333-0.55278.47431.3249.7227.976873.18경기 용인시 기흥구 기흥동
9899도로공사A-0010-3880E-10E5기흥IC-수원신갈IC388.020200101000.037.226111127.108333-0.550.00.00.00.00.0경기 용인시 기흥구 기흥동
99100도로공사A-0010-3932E-10S1신갈JC-수원신갈IC393.220200101000.037.279444127.105833-0.140.00.00.00.00.0경기 용인시 기흥구 신갈동