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
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 3 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 기울기(°) and 1 other fieldsHigh correlation
측정구간 is highly overall correlated with 기본키 and 7 other fieldsHigh correlation
기본키 has unique valuesUnique
차량통과수(대) has 9 (9.0%) zerosZeros
평균 속도(km) has 9 (9.0%) zerosZeros
CO(g/km) has 9 (9.0%) zerosZeros
NOX(g/km) has 9 (9.0%) zerosZeros
HC(g/km) has 9 (9.0%) zerosZeros
PM(g/km) has 21 (21.0%) zerosZeros
CO2(g/km) has 9 (9.0%) zerosZeros

Reproduction

Analysis started2023-12-10 11:57:09.050473
Analysis finished2023-12-10 11:57:29.427625
Duration20.38 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
2023-12-10T20:57:29.552596image/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
2023-12-10T20:57:30.120851image/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

2023-12-10T20:57:30.306571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:57:30.434058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
도로공사 100
100.0%

지점
Categorical

HIGH CORRELATION 

Distinct20
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
A-0100-1312S-8
A-0100-1592S-6
A-0100-1080S-8
A-0010-0083E-6
 
6
A-0100-1501S-6
 
6
Other values (15)
64 

Length

Max length14
Median length14
Mean length14
Min length14

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st rowA-0351-0045S-4
2nd rowA-0351-0045S-4
3rd rowA-0351-0045S-4
4th rowA-0351-0045S-4
5th rowA-0010-0538E-6

Common Values

ValueCountFrequency (%)
A-0100-1312S-8 8
 
8.0%
A-0100-1592S-6 8
 
8.0%
A-0100-1080S-8 8
 
8.0%
A-0010-0083E-6 6
 
6.0%
A-0100-1501S-6 6
 
6.0%
A-0550-0058E-6 6
 
6.0%
A-0010-0538E-6 6
 
6.0%
A-5510-0116E-6 6
 
6.0%
A-6000-0396E-4 5
 
5.0%
A-0351-0165S-4 4
 
4.0%
Other values (10) 37
37.0%

Length

2023-12-10T20:57:30.574818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
a-0100-1312s-8 8
 
8.0%
a-0100-1080s-8 8
 
8.0%
a-0100-1592s-6 8
 
8.0%
a-0010-0083e-6 6
 
6.0%
a-0100-1501s-6 6
 
6.0%
a-0550-0058e-6 6
 
6.0%
a-0010-0538e-6 6
 
6.0%
a-5510-0116e-6 6
 
6.0%
a-6000-0396e-4 5
 
5.0%
a-5510-0085e-4 4
 
4.0%
Other values (10) 37
37.0%

방향
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
E
51 
S
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 (%)
E 51
51.0%
S 49
49.0%

Length

2023-12-10T20:57:30.746344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:57:30.890612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
e 51
51.0%
s 49
49.0%

차선
Categorical

Distinct5
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
1
39 
2
38 
3
17 
4
5
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)1.0%

Sample

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

Common Values

ValueCountFrequency (%)
1 39
39.0%
2 38
38.0%
3 17
17.0%
4 5
 
5.0%
5 1
 
1.0%

Length

2023-12-10T20:57:31.061361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:57:31.231251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 39
39.0%
2 38
38.0%
3 17
17.0%
4 5
 
5.0%
5 1
 
1.0%

측정구간
Categorical

HIGH CORRELATION 

Distinct37
Distinct (%)37.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
산인JC-함안IC
 
6
함안IC-산인JC
 
6
김해JC-북부산TG
 
5
동창원IC-창원JC
 
4
창원JC-동창원IC
 
4
Other values (32)
75 

Length

Max length11
Median length10
Mean length9.64
Min length9

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st row동고성IC-북통영IC
2nd row동고성IC-북통영IC
3rd row북통영IC-동고성IC
4th row북통영IC-동고성IC
5th row활천IC-언양JC

Common Values

ValueCountFrequency (%)
산인JC-함안IC 6
 
6.0%
함안IC-산인JC 6
 
6.0%
김해JC-북부산TG 5
 
5.0%
동창원IC-창원JC 4
 
4.0%
창원JC-동창원IC 4
 
4.0%
대동IC-초정IC 3
 
3.0%
양산JC-노포JC 3
 
3.0%
금정IC-기장철마IC 3
 
3.0%
노포JC-양산JC 3
 
3.0%
냉정JC-서김해IC 3
 
3.0%
Other values (27) 60
60.0%

Length

2023-12-10T20:57:31.420180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
산인jc-함안ic 6
 
6.0%
함안ic-산인jc 6
 
6.0%
김해jc-북부산tg 5
 
5.0%
동창원ic-창원jc 4
 
4.0%
창원jc-동창원ic 4
 
4.0%
초정ic-대동ic 3
 
3.0%
언양jc-활천ic 3
 
3.0%
서김해ic-냉정jc 3
 
3.0%
북부산tg-김해jc 3
 
3.0%
대동jc-물금ic 3
 
3.0%
Other values (27) 60
60.0%

장비이정(km)
Real number (ℝ)

HIGH CORRELATION 

Distinct20
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean65.5403
Minimum4.5
Maximum159.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:57:31.599498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4.5
5-th percentile5.8
Q111.6
median52.4
Q3112.4
95-th percentile159.2
Maximum159.2
Range154.7
Interquartile range (IQR)100.8

Descriptive statistics

Standard deviation55.274561
Coefficient of variation (CV)0.84336753
Kurtosis-1.3625316
Mean65.5403
Median Absolute Deviation (MAD)46.17
Skewness0.43057227
Sum6554.03
Variance3055.2771
MonotonicityNot monotonic
2023-12-10T20:57:31.766454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
131.2 8
 
8.0%
108.0 8
 
8.0%
159.2 8
 
8.0%
8.3 6
 
6.0%
150.1 6
 
6.0%
5.8 6
 
6.0%
53.4 6
 
6.0%
11.6 6
 
6.0%
39.61 5
 
5.0%
4.5 4
 
4.0%
Other values (10) 37
37.0%
ValueCountFrequency (%)
4.5 4
4.0%
5.8 6
6.0%
6.23 4
4.0%
8.3 6
6.0%
8.5 4
4.0%
11.6 6
6.0%
16.5 4
4.0%
26.69 4
4.0%
39.0 4
4.0%
39.61 5
5.0%
ValueCountFrequency (%)
159.2 8
8.0%
150.1 6
6.0%
131.2 8
8.0%
112.4 4
4.0%
108.0 8
8.0%
107.8 4
4.0%
64.2 4
4.0%
53.4 6
6.0%
52.4 4
4.0%
40.7 1
 
1.0%

측정일
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
20200201
100 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20200201
2nd row20200201
3rd row20200201
4th row20200201
5th row20200201

Common Values

ValueCountFrequency (%)
20200201 100
100.0%

Length

2023-12-10T20:57:31.951421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:57:32.077027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20200201 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

2023-12-10T20:57:32.245646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:57:32.406828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 100
100.0%

차량통과수(대)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.12
Minimum0
Maximum73
Zeros9
Zeros (%)9.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:57:32.534832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q113.75
median28
Q341
95-th percentile69.05
Maximum73
Range73
Interquartile range (IQR)27.25

Descriptive statistics

Standard deviation19.554265
Coefficient of variation (CV)0.67150636
Kurtosis-0.34271147
Mean29.12
Median Absolute Deviation (MAD)13.5
Skewness0.49131987
Sum2912
Variance382.36929
MonotonicityNot monotonic
2023-12-10T20:57:32.755229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 9
 
9.0%
30 4
 
4.0%
21 4
 
4.0%
41 4
 
4.0%
28 4
 
4.0%
13 4
 
4.0%
24 3
 
3.0%
8 3
 
3.0%
29 3
 
3.0%
35 3
 
3.0%
Other values (40) 59
59.0%
ValueCountFrequency (%)
0 9
9.0%
1 1
 
1.0%
3 2
 
2.0%
7 1
 
1.0%
8 3
 
3.0%
9 1
 
1.0%
10 1
 
1.0%
11 1
 
1.0%
12 2
 
2.0%
13 4
4.0%
ValueCountFrequency (%)
73 1
1.0%
72 2
2.0%
71 1
1.0%
70 1
1.0%
69 1
1.0%
66 1
1.0%
65 1
1.0%
63 1
1.0%
62 1
1.0%
58 1
1.0%

평균 속도(km)
Real number (ℝ)

ZEROS 

Distinct76
Distinct (%)76.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean88.5755
Minimum0
Maximum140
Zeros9
Zeros (%)9.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:57:33.004890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q184.7125
median93.985
Q3103.33
95-th percentile120.329
Maximum140
Range140
Interquartile range (IQR)18.6175

Descriptive statistics

Standard deviation30.737922
Coefficient of variation (CV)0.3470251
Kurtosis3.8211749
Mean88.5755
Median Absolute Deviation (MAD)9.345
Skewness-2.0036792
Sum8857.55
Variance944.81986
MonotonicityNot monotonic
2023-12-10T20:57:33.272969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 9
 
9.0%
100.0 4
 
4.0%
87.0 3
 
3.0%
109.0 2
 
2.0%
99.0 2
 
2.0%
114.0 2
 
2.0%
103.33 2
 
2.0%
103.0 2
 
2.0%
101.25 2
 
2.0%
91.0 2
 
2.0%
Other values (66) 70
70.0%
ValueCountFrequency (%)
0.0 9
9.0%
74.4 1
 
1.0%
75.14 1
 
1.0%
75.5 1
 
1.0%
77.25 1
 
1.0%
78.37 1
 
1.0%
79.29 1
 
1.0%
80.8 1
 
1.0%
81.0 1
 
1.0%
81.38 1
 
1.0%
ValueCountFrequency (%)
140.0 1
1.0%
128.0 1
1.0%
125.0 1
1.0%
124.2 1
1.0%
120.5 1
1.0%
120.32 1
1.0%
120.13 1
1.0%
117.0 2
2.0%
115.5 1
1.0%
114.0 2
2.0%

위도(°)
Real number (ℝ)

HIGH CORRELATION 

Distinct19
Distinct (%)19.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.332211
Minimum34.889167
Maximum35.840833
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:57:33.470734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum34.889167
5-th percentile34.983889
Q135.223611
median35.274167
Q335.306944
95-th percentile35.835819
Maximum35.840833
Range0.95166666
Interquartile range (IQR)0.08333333

Descriptive statistics

Standard deviation0.24742538
Coefficient of variation (CV)0.0070028275
Kurtosis0.0033303182
Mean35.332211
Median Absolute Deviation (MAD)0.05055556
Skewness0.78425807
Sum3533.2211
Variance0.061219318
MonotonicityNot monotonic
2023-12-10T20:57:33.661178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
35.22361111 14
14.0%
35.2725 8
 
8.0%
35.27888889 8
 
8.0%
35.30694444 6
 
6.0%
35.2425 6
 
6.0%
35.68194444 6
 
6.0%
35.29388889 6
 
6.0%
35.84083333 5
 
5.0%
34.88916667 4
 
4.0%
35.27694444 4
 
4.0%
Other values (9) 33
33.0%
ValueCountFrequency (%)
34.88916667 4
 
4.0%
34.98388889 4
 
4.0%
35.02027778 1
 
1.0%
35.09166667 4
 
4.0%
35.16055556 4
 
4.0%
35.22361111 14
14.0%
35.23972222 4
 
4.0%
35.2425 6
6.0%
35.2725 8
8.0%
35.27416667 4
 
4.0%
ValueCountFrequency (%)
35.84083333 5
5.0%
35.83555556 4
4.0%
35.77111111 4
4.0%
35.68194444 6
6.0%
35.55305556 4
4.0%
35.30694444 6
6.0%
35.29388889 6
6.0%
35.27888889 8
8.0%
35.27694444 4
4.0%
35.27416667 4
4.0%

경도(°)
Real number (ℝ)

HIGH CORRELATION 

Distinct20
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean128.81692
Minimum127.76028
Maximum130.08667
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:57:33.853217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum127.76028
5-th percentile127.98349
Q1128.42486
median128.835
Q3129.10132
95-th percentile129.83139
Maximum130.08667
Range2.3263889
Interquartile range (IQR)0.6764583

Descriptive statistics

Standard deviation0.57729773
Coefficient of variation (CV)0.0044815365
Kurtosis-0.35026142
Mean128.81692
Median Absolute Deviation (MAD)0.4061111
Skewness0.27015462
Sum12881.692
Variance0.33327267
MonotonicityNot monotonic
2023-12-10T20:57:34.054680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
128.6713889 8
 
8.0%
128.4288889 8
 
8.0%
128.835 8
 
8.0%
129.0747222 6
 
6.0%
129.8313889 6
 
6.0%
128.9838889 6
 
6.0%
129.1811111 6
 
6.0%
129.0072222 6
 
6.0%
129.3355556 5
 
5.0%
128.4127778 4
 
4.0%
Other values (10) 37
37.0%
ValueCountFrequency (%)
127.7602778 4
4.0%
127.8597222 1
 
1.0%
127.99 4
4.0%
128.0652778 4
4.0%
128.1725 4
4.0%
128.3697222 4
4.0%
128.4127778 4
4.0%
128.4288889 8
8.0%
128.45138889999998 4
4.0%
128.6713889 8
8.0%
ValueCountFrequency (%)
130.08666670000002 4
4.0%
129.8313889 6
6.0%
129.51305559999997 4
4.0%
129.3355556 5
5.0%
129.1811111 6
6.0%
129.0747222 6
6.0%
129.0072222 6
6.0%
128.9838889 6
6.0%
128.9788889 4
4.0%
128.835 8
8.0%

기울기(°)
Real number (ℝ)

HIGH CORRELATION 

Distinct37
Distinct (%)37.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.2927
Minimum-5.44
Maximum3.07
Zeros0
Zeros (%)0.0%
Negative51
Negative (%)51.0%
Memory size1.0 KiB
2023-12-10T20:57:34.241065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-5.44
5-th percentile-5.44
Q1-0.62
median-0.02
Q30.75
95-th percentile2.495
Maximum3.07
Range8.51
Interquartile range (IQR)1.37

Descriptive statistics

Standard deviation1.8286744
Coefficient of variation (CV)-6.2476066
Kurtosis2.0372228
Mean-0.2927
Median Absolute Deviation (MAD)0.63
Skewness-1.1184795
Sum-29.27
Variance3.3440502
MonotonicityNot monotonic
2023-12-10T20:57:34.434058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
0.19 7
 
7.0%
-5.44 6
 
6.0%
-0.61 5
 
5.0%
-0.38 4
 
4.0%
-0.13 4
 
4.0%
3.07 3
 
3.0%
2.48 3
 
3.0%
0.75 3
 
3.0%
-0.31 3
 
3.0%
0.3 3
 
3.0%
Other values (27) 59
59.0%
ValueCountFrequency (%)
-5.44 6
6.0%
-3.15 3
3.0%
-2.74 2
 
2.0%
-2.38 2
 
2.0%
-1.61 2
 
2.0%
-1.54 2
 
2.0%
-1.48 2
 
2.0%
-1.07 2
 
2.0%
-0.7 2
 
2.0%
-0.65 2
 
2.0%
ValueCountFrequency (%)
3.07 3
3.0%
2.78 2
2.0%
2.48 3
3.0%
1.51 2
2.0%
1.45 2
2.0%
1.09 2
2.0%
0.99 2
2.0%
0.85 2
2.0%
0.84 1
 
1.0%
0.82 2
2.0%

CO(g/km)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct91
Distinct (%)91.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.6922
Minimum0
Maximum58.07
Zeros9
Zeros (%)9.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:57:34.620180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14.6725
median12.82
Q322.7825
95-th percentile35.374
Maximum58.07
Range58.07
Interquartile range (IQR)18.11

Descriptive statistics

Standard deviation12.409563
Coefficient of variation (CV)0.84463616
Kurtosis0.96680574
Mean14.6922
Median Absolute Deviation (MAD)8.545
Skewness1.0397496
Sum1469.22
Variance153.99726
MonotonicityNot monotonic
2023-12-10T20:57:34.818603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 9
 
9.0%
7.24 2
 
2.0%
18.58 1
 
1.0%
33.43 1
 
1.0%
8.61 1
 
1.0%
13.26 1
 
1.0%
48.9 1
 
1.0%
20.66 1
 
1.0%
15.46 1
 
1.0%
28.84 1
 
1.0%
Other values (81) 81
81.0%
ValueCountFrequency (%)
0.0 9
9.0%
0.61 1
 
1.0%
0.65 1
 
1.0%
1.3 1
 
1.0%
1.31 1
 
1.0%
2.14 1
 
1.0%
2.28 1
 
1.0%
2.7 1
 
1.0%
2.87 1
 
1.0%
3.16 1
 
1.0%
ValueCountFrequency (%)
58.07 1
1.0%
49.64 1
1.0%
48.9 1
1.0%
39.64 1
1.0%
36.21 1
1.0%
35.33 1
1.0%
34.24 1
1.0%
33.43 1
1.0%
33.42 1
1.0%
32.2 1
1.0%

NOX(g/km)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct90
Distinct (%)90.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.3088
Minimum0
Maximum265.59
Zeros9
Zeros (%)9.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:57:35.017903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15.4625
median14.88
Q338.5575
95-th percentile124.426
Maximum265.59
Range265.59
Interquartile range (IQR)33.095

Descriptive statistics

Standard deviation51.366783
Coefficient of variation (CV)1.4971897
Kurtosis7.5438825
Mean34.3088
Median Absolute Deviation (MAD)12.905
Skewness2.6506292
Sum3430.88
Variance2638.5464
MonotonicityNot monotonic
2023-12-10T20:57:35.218035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 9
 
9.0%
21.48 2
 
2.0%
2.6 2
 
2.0%
6.8 1
 
1.0%
32.5 1
 
1.0%
241.07 1
 
1.0%
43.5 1
 
1.0%
22.32 1
 
1.0%
28.74 1
 
1.0%
28.49 1
 
1.0%
Other values (80) 80
80.0%
ValueCountFrequency (%)
0.0 9
9.0%
0.45 1
 
1.0%
0.61 1
 
1.0%
0.99 1
 
1.0%
1.43 1
 
1.0%
1.74 1
 
1.0%
1.84 1
 
1.0%
1.93 1
 
1.0%
2.02 1
 
1.0%
2.6 2
 
2.0%
ValueCountFrequency (%)
265.59 1
1.0%
241.07 1
1.0%
212.76 1
1.0%
201.02 1
1.0%
124.92 1
1.0%
124.4 1
1.0%
121.84 1
1.0%
119.92 1
1.0%
117.11 1
1.0%
112.3 1
1.0%

HC(g/km)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct82
Distinct (%)82.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.8818
Minimum0
Maximum15.24
Zeros9
Zeros (%)9.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:57:35.458502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.37
median1.405
Q33.6675
95-th percentile9.9845
Maximum15.24
Range15.24
Interquartile range (IQR)3.2975

Descriptive statistics

Standard deviation3.5614403
Coefficient of variation (CV)1.2358388
Kurtosis2.7606545
Mean2.8818
Median Absolute Deviation (MAD)1.295
Skewness1.7901168
Sum288.18
Variance12.683857
MonotonicityNot monotonic
2023-12-10T20:57:35.740236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 9
 
9.0%
0.14 2
 
2.0%
0.37 2
 
2.0%
3.62 2
 
2.0%
1.06 2
 
2.0%
0.83 2
 
2.0%
0.69 2
 
2.0%
0.73 2
 
2.0%
2.1 2
 
2.0%
0.28 2
 
2.0%
Other values (72) 73
73.0%
ValueCountFrequency (%)
0.0 9
9.0%
0.04 1
 
1.0%
0.08 2
 
2.0%
0.12 1
 
1.0%
0.14 2
 
2.0%
0.2 1
 
1.0%
0.21 1
 
1.0%
0.22 1
 
1.0%
0.28 2
 
2.0%
0.3 1
 
1.0%
ValueCountFrequency (%)
15.24 1
1.0%
14.35 1
1.0%
14.29 1
1.0%
12.95 1
1.0%
11.78 1
1.0%
9.89 1
1.0%
9.67 1
1.0%
9.58 1
1.0%
8.88 1
1.0%
8.63 1
1.0%

PM(g/km)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct68
Distinct (%)68.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.2269
Minimum0
Maximum17.14
Zeros21
Zeros (%)21.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:57:35.973384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.1875
median0.785
Q32.9725
95-th percentile7.3365
Maximum17.14
Range17.14
Interquartile range (IQR)2.785

Descriptive statistics

Standard deviation3.3323813
Coefficient of variation (CV)1.4964216
Kurtosis7.7697696
Mean2.2269
Median Absolute Deviation (MAD)0.785
Skewness2.5804323
Sum222.69
Variance11.104765
MonotonicityNot monotonic
2023-12-10T20:57:36.190764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 21
 
21.0%
0.21 6
 
6.0%
0.12 3
 
3.0%
0.42 2
 
2.0%
0.45 2
 
2.0%
7.19 2
 
2.0%
2.03 2
 
2.0%
0.33 2
 
2.0%
3.53 1
 
1.0%
16.98 1
 
1.0%
Other values (58) 58
58.0%
ValueCountFrequency (%)
0.0 21
21.0%
0.1 1
 
1.0%
0.12 3
 
3.0%
0.21 6
 
6.0%
0.22 1
 
1.0%
0.31 1
 
1.0%
0.32 1
 
1.0%
0.33 2
 
2.0%
0.34 1
 
1.0%
0.36 1
 
1.0%
ValueCountFrequency (%)
17.14 1
1.0%
16.98 1
1.0%
12.76 1
1.0%
12.22 1
1.0%
8.03 1
1.0%
7.3 1
1.0%
7.19 2
2.0%
7.14 1
1.0%
6.88 1
1.0%
6.38 1
1.0%

CO2(g/km)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct92
Distinct (%)92.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5513.1294
Minimum0
Maximum29414.35
Zeros9
Zeros (%)9.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:57:36.399997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11768.7375
median4334.12
Q37634.4025
95-th percentile13594.402
Maximum29414.35
Range29414.35
Interquartile range (IQR)5865.665

Descriptive statistics

Standard deviation5369.539
Coefficient of variation (CV)0.97395483
Kurtosis5.7676153
Mean5513.1294
Median Absolute Deviation (MAD)2723.37
Skewness2.0634682
Sum551312.94
Variance28831949
MonotonicityNot monotonic
2023-12-10T20:57:36.627057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 9
 
9.0%
3763.13 1
 
1.0%
3120.36 1
 
1.0%
4750.35 1
 
1.0%
26285.44 1
 
1.0%
7706.32 1
 
1.0%
6328.81 1
 
1.0%
11838.58 1
 
1.0%
10487.41 1
 
1.0%
4498.51 1
 
1.0%
Other values (82) 82
82.0%
ValueCountFrequency (%)
0.0 9
9.0%
196.37 1
 
1.0%
271.47 1
 
1.0%
579.97 1
 
1.0%
629.81 1
 
1.0%
1040.12 1
 
1.0%
1102.16 1
 
1.0%
1144.13 1
 
1.0%
1207.44 1
 
1.0%
1223.39 1
 
1.0%
ValueCountFrequency (%)
29414.35 1
1.0%
26285.44 1
1.0%
23103.48 1
1.0%
19279.19 1
1.0%
14697.97 1
1.0%
13536.32 1
1.0%
13479.19 1
1.0%
13040.53 1
1.0%
11838.58 1
1.0%
11776.89 1
1.0%

주소
Categorical

HIGH CORRELATION 

Distinct19
Distinct (%)19.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
경남 김해시 대동면
10 
경남 창원시 동읍 덕산리
경남 함안군 산인면 송정리
경남 김해시 불암동
경남 양산시 동면
 
6
Other values (14)
60 

Length

Max length14
Median length14
Mean length12.37
Min length9

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st row경남 통영시 광도면 노산리
2nd row경남 통영시 광도면 노산리
3rd row경남 통영시 광도면 노산리
4th row경남 통영시 광도면 노산리
5th row울산 울주군 두서면 활천리

Common Values

ValueCountFrequency (%)
경남 김해시 대동면 10
 
10.0%
경남 창원시 동읍 덕산리 8
 
8.0%
경남 함안군 산인면 송정리 8
 
8.0%
경남 김해시 불암동 8
 
8.0%
경남 양산시 동면 6
 
6.0%
경남 김해시 주촌면 망덕리 6
 
6.0%
울산 울주군 두서면 활천리 6
 
6.0%
경남 양산시 물금읍 6
 
6.0%
부산 금정구 선동 5
 
5.0%
경남 함안군 산인면 모곡리 4
 
4.0%
Other values (9) 33
33.0%

Length

2023-12-10T20:57:36.868432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경남 89
24.5%
김해시 32
 
8.8%
대동면 14
 
3.8%
함안군 12
 
3.3%
산인면 12
 
3.3%
양산시 12
 
3.3%
진주시 12
 
3.3%
창원시 8
 
2.2%
동읍 8
 
2.2%
덕산리 8
 
2.2%
Other values (34) 157
43.1%

Interactions

2023-12-10T20:57:27.127855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:57:10.360590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:57:11.837175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:57:13.373280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:57:14.914667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:57:16.696696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:57:18.176293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:57:19.563956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:57:20.879653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:57:22.268546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:57:24.030195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:57:25.578734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:57:27.231206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:57:10.470534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:57:11.964532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:57:13.472432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:57:15.360015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:57:16.826100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:57:18.278637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:57:19.652057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:57:20.976960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:57:22.377274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:57:24.160772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:57:25.691108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:57:27.352461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:57:10.586818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:57:12.087995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:57:13.597229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:57:15.482928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:57:16.944119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:57:18.399785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:57:19.774033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:57:21.084181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:57:22.820999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:57:24.293366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:57:25.850152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:57:27.495058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:57:10.681020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:57:12.221005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:57:13.721498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:57:15.607331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:57:17.060428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:57:18.529655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:57:19.890798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:57:21.197702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:57:22.932301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:57:24.433918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:57:25.988694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:57:27.649042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:57:10.797620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:57:12.358196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:57:13.886277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:57:15.731696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:57:17.193652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:57:18.643100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:57:20.002181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:57:21.317113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:57:23.063357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:57:24.579710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:57:26.124750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:57:27.771843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:57:10.907637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:57:12.492248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:57:14.037371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:57:15.839453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:57:17.312078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:57:18.744246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:57:20.101494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:57:21.413811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:57:23.177216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:57:24.711985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:57:26.246048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:57:27.923308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:57:11.035784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:57:12.620474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:57:14.164818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:57:15.945719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:57:17.429147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:57:18.851163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:57:20.200656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:57:21.519312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:57:23.303350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:57:24.829748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:57:26.362019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:57:28.094105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:57:11.151496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:57:12.761907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:57:14.264050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:57:16.047142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:57:17.543972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:57:18.954023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:57:20.306347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:57:21.625979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:57:23.407242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:57:24.947200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:57:26.477972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:57:28.237502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:57:11.301000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:57:12.885253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:57:14.375299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:57:16.156356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:57:17.678761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:57:19.084043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:57:20.430616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:57:21.747578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:57:23.525971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:57:25.071251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:57:26.600637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:57:28.380503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:57:11.457069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:57:13.013513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:57:14.496335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:57:16.271554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:57:17.799095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:57:19.207496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:57:20.543727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:57:21.872994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:57:23.633588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:57:25.192506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:57:26.722708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:57:28.544064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:57:11.605311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:57:13.153271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:57:14.645231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:57:16.404803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:57:17.932729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:57:19.342032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:57:20.653602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:57:22.003575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:57:23.771474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:57:25.327418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:57:26.853456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:57:28.695803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:57:11.719256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:57:13.269936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:57:14.783853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:57:16.565701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:57:18.063619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:57:19.461046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:57:20.757041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:57:22.161892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:57:23.888834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:57:25.464443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:57:26.982440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T20:57:37.043424image/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
기본키1.0000.9970.0000.0000.9940.8470.2430.0000.8460.9580.6930.0000.0000.0000.0000.0000.979
지점0.9971.0000.0000.0000.9971.0000.4280.0001.0001.0000.8750.0000.0000.0000.0000.0001.000
방향0.0000.0001.0000.0001.0000.0000.1080.2070.0000.0000.6020.0000.0000.2470.0000.1380.000
차선0.0000.0000.0001.0000.0000.0000.5540.4910.0000.0000.0000.4950.3920.5560.2880.2750.000
측정구간0.9940.9971.0000.0001.0001.0000.0000.0001.0001.0000.9990.0000.0000.0000.0000.0000.997
장비이정(km)0.8471.0000.0000.0001.0001.0000.0000.0000.7850.9370.5870.0000.1180.0000.0000.0001.000
차량통과수(대)0.2430.4280.1080.5540.0000.0001.0000.5730.2020.2510.2300.6500.4160.6370.3810.6650.373
평균 속도(km)0.0000.0000.2070.4910.0000.0000.5731.0000.0000.0000.3080.5380.4100.5200.3380.4310.000
위도(°)0.8461.0000.0000.0001.0000.7850.2020.0001.0000.8890.6130.0000.0000.0000.0000.0000.996
경도(°)0.9581.0000.0000.0001.0000.9370.2510.0000.8891.0000.6250.0000.2470.0000.0910.0001.000
기울기(°)0.6930.8750.6020.0000.9990.5870.2300.3080.6130.6251.0000.0000.0000.0000.0000.0000.878
CO(g/km)0.0000.0000.0000.4950.0000.0000.6500.5380.0000.0000.0001.0000.7990.8200.7630.9600.000
NOX(g/km)0.0000.0000.0000.3920.0000.1180.4160.4100.0000.2470.0000.7991.0000.8990.9250.8990.000
HC(g/km)0.0000.0000.2470.5560.0000.0000.6370.5200.0000.0000.0000.8200.8991.0000.8330.7680.000
PM(g/km)0.0000.0000.0000.2880.0000.0000.3810.3380.0000.0910.0000.7630.9250.8331.0000.8550.000
CO2(g/km)0.0000.0000.1380.2750.0000.0000.6650.4310.0000.0000.0000.9600.8990.7680.8551.0000.000
주소0.9791.0000.0000.0000.9971.0000.3730.0000.9961.0000.8780.0000.0000.0000.0000.0001.000
2023-12-10T20:57:37.685200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
측정구간주소지점차선방향
측정구간1.0000.8430.8500.0000.802
주소0.8431.0000.9940.0000.000
지점0.8500.9941.0000.0000.000
차선0.0000.0000.0001.0000.000
방향0.8020.0000.0000.0001.000
2023-12-10T20:57:37.899101image/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
기본키1.000-0.2920.081-0.1240.3150.4910.0420.046-0.0090.0150.0220.0180.8540.0000.0000.7920.833
장비이정(km)-0.2921.0000.0880.060-0.166-0.136-0.1070.0690.0510.0300.0740.1160.9270.0000.0000.8230.933
차량통과수(대)0.0810.0881.0000.1000.0450.1470.1140.8450.6520.6700.5780.8380.1320.0740.2530.0000.134
평균 속도(km)-0.1240.0600.1001.000-0.257-0.0980.011-0.216-0.303-0.323-0.327-0.1550.0000.2020.3520.0000.000
위도(°)0.315-0.1660.045-0.2571.0000.5390.0230.1420.0670.1320.0330.0640.9330.0000.0000.8280.925
경도(°)0.491-0.1360.147-0.0980.5391.0000.0920.1240.0140.066-0.0060.0880.9430.0000.0000.8370.949
기울기(°)0.042-0.1070.1140.0110.0230.0921.0000.1000.0590.0710.0100.0640.5570.5840.0000.8220.561
CO(g/km)0.0460.0690.845-0.2160.1420.1240.1001.0000.9270.9450.8860.9740.0000.0000.3050.0000.000
NOX(g/km)-0.0090.0510.652-0.3030.0670.0140.0590.9271.0000.9890.9610.9230.0000.0000.2470.0000.000
HC(g/km)0.0150.0300.670-0.3230.1320.0660.0710.9450.9891.0000.9470.9180.0000.1790.2540.0000.000
PM(g/km)0.0220.0740.578-0.3270.033-0.0060.0100.8860.9610.9471.0000.8980.0000.0000.1850.0000.000
CO2(g/km)0.0180.1160.838-0.1550.0640.0880.0640.9740.9230.9180.8981.0000.0000.1300.1560.0000.000
지점0.8540.9270.1320.0000.9330.9430.5570.0000.0000.0000.0000.0001.0000.0000.0000.8500.994
방향0.0000.0000.0740.2020.0000.0000.5840.0000.0000.1790.0000.1300.0001.0000.0000.8020.000
차선0.0000.0000.2530.3520.0000.0000.0000.3050.2470.2540.1850.1560.0000.0001.0000.0000.000
측정구간0.7920.8230.0000.0000.8280.8370.8220.0000.0000.0000.0000.0000.8500.8020.0001.0000.843
주소0.8330.9330.1340.0000.9250.9490.5610.0000.0000.0000.0000.0000.9940.0000.0000.8431.000

Missing values

2023-12-10T20:57:28.925207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T20:57:29.291653image/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-0351-0045S-4S1동고성IC-북통영IC4.520200201014120.3234.889167128.4127780.822.281.740.140.01102.16경남 통영시 광도면 노산리
12도로공사A-0351-0045S-4S2동고성IC-북통영IC4.520200201027105.434.889167128.4127780.828.849.310.770.453291.18경남 통영시 광도면 노산리
23도로공사A-0351-0045S-4E1북통영IC-동고성IC4.52020020108124.234.889167128.412778-0.71.30.990.080.0629.81경남 통영시 광도면 노산리
34도로공사A-0351-0045S-4E2북통영IC-동고성IC4.5202002010884.034.889167128.412778-0.75.0821.481.51.292395.02경남 통영시 광도면 노산리
45도로공사A-0010-0538E-6S1활천IC-언양JC53.420200201020114.035.681944129.1811110.194.343.020.280.01809.8울산 울주군 두서면 활천리
56도로공사A-0010-0538E-6S2활천IC-언양JC53.42020020105896.535.681944129.1811110.1920.0917.792.11.066992.33울산 울주군 두서면 활천리
67도로공사A-0010-0538E-6S3활천IC-언양JC53.42020020102181.3835.681944129.1811110.1924.04112.37.946.8810413.24울산 울주군 두서면 활천리
78도로공사A-0010-0538E-6E1언양JC-활천IC53.420200201011103.035.681944129.1811110.233.162.020.220.01144.13울산 울주군 두서면 활천리
89도로공사A-0010-0538E-6E2언양JC-활천IC53.42020020103194.235.681944129.1811110.2313.4217.92.030.764050.09울산 울주군 두서면 활천리
910도로공사A-0010-0538E-6E3언양JC-활천IC53.42020020102575.1435.681944129.1811110.2335.33102.8911.786.158168.98울산 울주군 두서면 활천리
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-6000-0396E-4E1금정IC-기장철마IC39.612020020103299.035.840833129.3355562.489.486.340.690.123442.83부산 금정구 선동
9192도로공사A-6000-0396E-4E2금정IC-기장철마IC39.612020020104180.835.840833129.3355562.4830.2249.627.143.196797.67부산 금정구 선동
9293도로공사A-6000-0396E-4E3금정IC-기장철마IC39.6120200201000.035.840833129.3355562.480.00.00.00.00.0부산 금정구 선동
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