Overview

Dataset statistics

Number of variables17
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory14.6 KiB
Average record size in memory149.3 B

Variable types

Numeric9
Categorical8

Alerts

도로종류 has constant value ""Constant
측정일 has constant value ""Constant
측정시간 has constant value ""Constant
지점 is highly overall correlated with 기본키 and 6 other fieldsHigh correlation
측정구간 is highly overall correlated with 기본키 and 7 other fieldsHigh correlation
주소 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 5 other fieldsHigh correlation
차량통과수(대) is highly overall correlated with TSP(g/km) and 1 other fieldsHigh correlation
위도(°) is highly overall correlated with 기본키 and 5 other fieldsHigh correlation
경도(°) is highly overall correlated with 기본키 and 5 other fieldsHigh correlation
기울기(°) is highly overall correlated with 지점 and 2 other fieldsHigh correlation
TSP(g/km) is highly overall correlated with 차량통과수(대) and 1 other fieldsHigh correlation
PM10(g/km) is highly overall correlated with 차량통과수(대) and 1 other fieldsHigh correlation
방향 is highly overall correlated with 측정구간High correlation
기본키 has unique valuesUnique
차량통과수(대) has 16 (16.0%) zerosZeros
평균 속도(km/hr) has 16 (16.0%) zerosZeros
TSP(g/km) has 16 (16.0%) zerosZeros
PM10(g/km) has 17 (17.0%) zerosZeros

Reproduction

Analysis started2023-12-10 11:09:57.088312
Analysis finished2023-12-10 11:10:09.326196
Duration12.24 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
2023-12-10T20:10:09.437679image/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
2023-12-10T20:10:09.616497image/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

2023-12-10T20:10:09.773979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:10:09.883379image/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-0010-3452S-10
10 
A-0010-3613E-10
10 
A-0010-3801E-10
10 
A-0010-3352E-9
9 
A-0010-0083E-6
6 
Other values (10)
55 

Length

Max length15
Median length14
Mean length14.3
Min length14

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA-0010-0083E-6
2nd rowA-0010-0083E-6
3rd rowA-0010-0083E-6
4th rowA-0010-0083E-6
5th rowA-0010-0083E-6

Common Values

ValueCountFrequency (%)
A-0010-3452S-10 10
 
10.0%
A-0010-3613E-10 10
 
10.0%
A-0010-3801E-10 10
 
10.0%
A-0010-3352E-9 9
 
9.0%
A-0010-0083E-6 6
 
6.0%
A-0010-0538E-6 6
 
6.0%
A-0010-1073E-6 6
 
6.0%
A-0010-1880E-6 6
 
6.0%
A-0010-2695C-6 6
 
6.0%
A-0010-2761E-6 6
 
6.0%
Other values (5) 25
25.0%

Length

2023-12-10T20:10:10.061677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
a-0010-3452s-10 10
 
10.0%
a-0010-3613e-10 10
 
10.0%
a-0010-3801e-10 10
 
10.0%
a-0010-3352e-9 9
 
9.0%
a-0010-0083e-6 6
 
6.0%
a-0010-0538e-6 6
 
6.0%
a-0010-1073e-6 6
 
6.0%
a-0010-1880e-6 6
 
6.0%
a-0010-2695c-6 6
 
6.0%
a-0010-2761e-6 6
 
6.0%
Other values (5) 25
25.0%

방향
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
E
52 
S
48 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowE
2nd rowE
3rd rowE
4th rowS
5th rowS

Common Values

ValueCountFrequency (%)
E 52
52.0%
S 48
48.0%

Length

2023-12-10T20:10:10.237982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:10:10.386143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
e 52
52.0%
s 48
48.0%

차선
Categorical

Distinct5
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
1
27 
2
27 
3
27 
4
11 
5
8 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
1 27
27.0%
2 27
27.0%
3 27
27.0%
4 11
11.0%
5 8
 
8.0%

Length

2023-12-10T20:10:10.520800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:10:10.674283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 27
27.0%
2 27
27.0%
3 27
27.0%
4 11
11.0%
5 8
 
8.0%

측정구간
Categorical

HIGH CORRELATION 

Distinct27
Distinct (%)27.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
동탄JC-오산IC
 
5
안성JC-안성IC
 
5
안성IC-안성JC
 
5
북천안IC-천안IC
 
5
천안IC-북천안IC
 
5
Other values (22)
75 

Length

Max length10
Median length9
Mean length9.24
Min length9

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row노포JC-양산JC
2nd row노포JC-양산JC
3rd row노포JC-양산JC
4th row양산JC-노포JC
5th row양산JC-노포JC

Common Values

ValueCountFrequency (%)
동탄JC-오산IC 5
 
5.0%
안성JC-안성IC 5
 
5.0%
안성IC-안성JC 5
 
5.0%
북천안IC-천안IC 5
 
5.0%
천안IC-북천안IC 5
 
5.0%
천안IC-천안JC 5
 
5.0%
청주JC-남이JC 5
 
5.0%
오산IC-동탄JC 5
 
5.0%
남청주IC-청주JC 4
 
4.0%
천안JC-천안IC 4
 
4.0%
Other values (17) 52
52.0%

Length

2023-12-10T20:10:10.852903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
동탄jc-오산ic 5
 
5.0%
안성ic-안성jc 5
 
5.0%
북천안ic-천안ic 5
 
5.0%
천안ic-북천안ic 5
 
5.0%
천안ic-천안jc 5
 
5.0%
청주jc-남이jc 5
 
5.0%
오산ic-동탄jc 5
 
5.0%
안성jc-안성ic 5
 
5.0%
청주jc-남청주ic 4
 
4.0%
천안jc-천안ic 4
 
4.0%
Other values (17) 52
52.0%

장비이정(km)
Real number (ℝ)

HIGH CORRELATION 

Distinct15
Distinct (%)15.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean268.0388
Minimum8.3
Maximum380.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:10:11.037164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8.3
5-th percentile8.3
Q1269.5
median301.9
Q3345.2
95-th percentile380.1
Maximum380.1
Range371.8
Interquartile range (IQR)75.7

Descriptive statistics

Standard deviation110.74035
Coefficient of variation (CV)0.41315045
Kurtosis0.27477031
Mean268.0388
Median Absolute Deviation (MAD)43.3
Skewness-1.2300407
Sum26803.88
Variance12263.425
MonotonicityIncreasing
2023-12-10T20:10:11.243235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
345.2 10
 
10.0%
361.3 10
 
10.0%
380.1 10
 
10.0%
335.2 9
 
9.0%
8.3 6
 
6.0%
53.4 6
 
6.0%
107.31 6
 
6.0%
188.02 6
 
6.0%
269.5 6
 
6.0%
276.1 6
 
6.0%
Other values (5) 25
25.0%
ValueCountFrequency (%)
8.3 6
6.0%
53.4 6
6.0%
107.31 6
6.0%
188.02 6
6.0%
269.5 6
6.0%
276.1 6
6.0%
295.3 4
4.0%
295.6 4
4.0%
297.9 5
5.0%
301.9 6
6.0%
ValueCountFrequency (%)
380.1 10
10.0%
361.3 10
10.0%
345.2 10
10.0%
335.2 9
9.0%
306.8 6
6.0%
301.9 6
6.0%
297.9 5
5.0%
295.6 4
 
4.0%
295.3 4
 
4.0%
276.1 6
6.0%

측정일
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
20210401
100 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20210401
2nd row20210401
3rd row20210401
4th row20210401
5th row20210401

Common Values

ValueCountFrequency (%)
20210401 100
100.0%

Length

2023-12-10T20:10:11.423036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:10:11.533197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20210401 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

2023-12-10T20:10:11.641599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:10:11.766106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 100
100.0%

차량통과수(대)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct64
Distinct (%)64.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.84
Minimum0
Maximum127
Zeros16
Zeros (%)16.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:10:11.970921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q110.75
median33.5
Q361.25
95-th percentile103.25
Maximum127
Range127
Interquartile range (IQR)50.5

Descriptive statistics

Standard deviation33.514273
Coefficient of variation (CV)0.8412217
Kurtosis-0.25185079
Mean39.84
Median Absolute Deviation (MAD)24.5
Skewness0.69973059
Sum3984
Variance1123.2065
MonotonicityNot monotonic
2023-12-10T20:10:12.308699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 16
 
16.0%
22 4
 
4.0%
18 3
 
3.0%
55 3
 
3.0%
44 2
 
2.0%
25 2
 
2.0%
9 2
 
2.0%
38 2
 
2.0%
43 2
 
2.0%
31 2
 
2.0%
Other values (54) 62
62.0%
ValueCountFrequency (%)
0 16
16.0%
2 2
 
2.0%
3 1
 
1.0%
5 1
 
1.0%
7 1
 
1.0%
8 1
 
1.0%
9 2
 
2.0%
10 1
 
1.0%
11 1
 
1.0%
13 1
 
1.0%
ValueCountFrequency (%)
127 1
1.0%
125 1
1.0%
120 1
1.0%
111 1
1.0%
108 1
1.0%
103 1
1.0%
97 1
1.0%
96 2
2.0%
93 1
1.0%
88 1
1.0%

평균 속도(km/hr)
Real number (ℝ)

ZEROS 

Distinct79
Distinct (%)79.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean78.294
Minimum0
Maximum134
Zeros16
Zeros (%)16.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:10:12.544281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q178.3625
median86.405
Q399.4725
95-th percentile121.064
Maximum134
Range134
Interquartile range (IQR)21.11

Descriptive statistics

Standard deviation36.97683
Coefficient of variation (CV)0.47228179
Kurtosis0.65094579
Mean78.294
Median Absolute Deviation (MAD)10.475
Skewness-1.303545
Sum7829.4
Variance1367.286
MonotonicityNot monotonic
2023-12-10T20:10:13.118996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 16
 
16.0%
93.0 2
 
2.0%
79.0 2
 
2.0%
123.0 2
 
2.0%
110.0 2
 
2.0%
87.8 2
 
2.0%
107.0 2
 
2.0%
112.0 1
 
1.0%
73.67 1
 
1.0%
78.67 1
 
1.0%
Other values (69) 69
69.0%
ValueCountFrequency (%)
0.0 16
16.0%
63.7 1
 
1.0%
68.0 1
 
1.0%
68.32 1
 
1.0%
73.67 1
 
1.0%
75.75 1
 
1.0%
76.11 1
 
1.0%
76.2 1
 
1.0%
76.9 1
 
1.0%
77.44 1
 
1.0%
ValueCountFrequency (%)
134.0 1
1.0%
127.89 1
1.0%
123.0 2
2.0%
122.28 1
1.0%
121.0 1
1.0%
119.0 1
1.0%
118.0 1
1.0%
116.0 1
1.0%
115.13 1
1.0%
112.0 1
1.0%

위도(°)
Real number (ℝ)

HIGH CORRELATION 

Distinct15
Distinct (%)15.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.507255
Minimum35.306944
Maximum37.158778
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:10:13.351277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.306944
5-th percentile35.306944
Q136.3475
median36.606111
Q336.868903
95-th percentile37.158778
Maximum37.158778
Range1.8518333
Interquartile range (IQR)0.52140278

Descriptive statistics

Standard deviation0.50249756
Coefficient of variation (CV)0.013764321
Kurtosis0.089821452
Mean36.507255
Median Absolute Deviation (MAD)0.26279167
Skewness-0.87740084
Sum3650.7255
Variance0.2525038
MonotonicityIncreasing
2023-12-10T20:10:13.498684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
36.86890278 10
 
10.0%
37.00888889 10
 
10.0%
37.15877778 10
 
10.0%
36.78083333 9
 
9.0%
35.30694444 6
 
6.0%
35.68194444 6
 
6.0%
35.88230609 6
 
6.0%
36.15572222 6
 
6.0%
36.3475 6
 
6.0%
36.38961 6
 
6.0%
Other values (5) 25
25.0%
ValueCountFrequency (%)
35.30694444 6
6.0%
35.68194444 6
6.0%
35.88230609 6
6.0%
36.15572222 6
6.0%
36.3475 6
6.0%
36.38961 6
6.0%
36.54 4
4.0%
36.55638889 4
4.0%
36.57694444 5
5.0%
36.60611111 6
6.0%
ValueCountFrequency (%)
37.15877778 10
10.0%
37.00888889 10
10.0%
36.86890278 10
10.0%
36.78083333 9
9.0%
36.64019722 6
6.0%
36.60611111 6
6.0%
36.57694444 5
5.0%
36.55638889 4
 
4.0%
36.54 4
 
4.0%
36.38961 6
6.0%

경도(°)
Real number (ℝ)

HIGH CORRELATION 

Distinct15
Distinct (%)15.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.65419
Minimum127.08833
Maximum129.18111
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:10:13.692708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum127.08833
5-th percentile127.08833
Q1127.17667
median127.40833
Q3127.46917
95-th percentile129.18111
Maximum129.18111
Range2.0927778
Interquartile range (IQR)0.2925

Descriptive statistics

Standard deviation0.70004513
Coefficient of variation (CV)0.0054839181
Kurtosis0.15063974
Mean127.65419
Median Absolute Deviation (MAD)0.2316666
Skewness1.3298762
Sum12765.419
Variance0.49006318
MonotonicityNot monotonic
2023-12-10T20:10:13.867407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
127.1867222 10
 
10.0%
127.1491667 10
 
10.0%
127.0883333 10
 
10.0%
127.1766667 9
 
9.0%
129.0747222 6
 
6.0%
129.1811111 6
 
6.0%
128.8488161 6
 
6.0%
128.2132778 6
 
6.0%
127.4691667 6
 
6.0%
127.423508 6
 
6.0%
Other values (5) 25
25.0%
ValueCountFrequency (%)
127.0883333 10
10.0%
127.1491667 10
10.0%
127.1766667 9
9.0%
127.1867222 10
10.0%
127.3781278 6
6.0%
127.4083333 6
6.0%
127.423508 6
6.0%
127.4277778 5
5.0%
127.4325 4
 
4.0%
127.4338889 4
 
4.0%
ValueCountFrequency (%)
129.1811111 6
6.0%
129.0747222 6
6.0%
128.8488161 6
6.0%
128.2132778 6
6.0%
127.4691667 6
6.0%
127.4338889 4
4.0%
127.4325 4
4.0%
127.4277778 5
5.0%
127.423508 6
6.0%
127.4083333 6
6.0%

기울기(°)
Real number (ℝ)

HIGH CORRELATION 

Distinct27
Distinct (%)27.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.07345542
Minimum-13.24576
Maximum12.47809
Zeros0
Zeros (%)0.0%
Negative49
Negative (%)49.0%
Memory size1.0 KiB
2023-12-10T20:10:14.045053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-13.24576
5-th percentile-3.15276
Q1-1.30142
median0.19158
Q31.0850327
95-th percentile3.071416
Maximum12.47809
Range25.72385
Interquartile range (IQR)2.3864528

Descriptive statistics

Standard deviation3.4726708
Coefficient of variation (CV)-47.275896
Kurtosis9.1476521
Mean-0.07345542
Median Absolute Deviation (MAD)1.101435
Skewness-0.22407437
Sum-7.345542
Variance12.059442
MonotonicityNot monotonic
2023-12-10T20:10:14.240255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
-1.758683 5
 
5.0%
0.671977 5
 
5.0%
-0.660004 5
 
5.0%
-1.30142 5
 
5.0%
1.293015 5
 
5.0%
0.61369 5
 
5.0%
-0.624001 5
 
5.0%
1.544389 5
 
5.0%
-1.359001 4
 
4.0%
-0.807188 4
 
4.0%
Other values (17) 52
52.0%
ValueCountFrequency (%)
-13.24576 3
3.0%
-3.15276 3
3.0%
-2.788625 3
3.0%
-1.758683 5
5.0%
-1.717451 3
3.0%
-1.359001 4
4.0%
-1.30142 5
5.0%
-1.252479 3
3.0%
-0.807188 4
4.0%
-0.688696 3
3.0%
ValueCountFrequency (%)
12.47809 3
3.0%
3.071416 3
3.0%
2.703849 3
3.0%
1.717451 3
3.0%
1.544389 5
5.0%
1.293015 5
5.0%
1.172023 3
3.0%
1.056036 4
4.0%
0.688696 3
3.0%
0.671977 5
5.0%

TSP(g/km)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct82
Distinct (%)82.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.5631
Minimum0
Maximum15.42
Zeros16
Zeros (%)16.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:10:14.433260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.2425
median2.02
Q35.8575
95-th percentile11.685
Maximum15.42
Range15.42
Interquartile range (IQR)5.615

Descriptive statistics

Standard deviation4.0287644
Coefficient of variation (CV)1.1306908
Kurtosis0.58144417
Mean3.5631
Median Absolute Deviation (MAD)2.02
Skewness1.1655639
Sum356.31
Variance16.230943
MonotonicityNot monotonic
2023-12-10T20:10:14.675613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 16
 
16.0%
0.25 2
 
2.0%
0.27 2
 
2.0%
0.41 2
 
2.0%
0.46 1
 
1.0%
10.65 1
 
1.0%
4.08 1
 
1.0%
8.56 1
 
1.0%
13.41 1
 
1.0%
15.42 1
 
1.0%
Other values (72) 72
72.0%
ValueCountFrequency (%)
0.0 16
16.0%
0.01 1
 
1.0%
0.06 1
 
1.0%
0.08 1
 
1.0%
0.11 1
 
1.0%
0.12 1
 
1.0%
0.13 1
 
1.0%
0.19 1
 
1.0%
0.21 1
 
1.0%
0.22 1
 
1.0%
ValueCountFrequency (%)
15.42 1
1.0%
15.19 1
1.0%
14.52 1
1.0%
13.41 1
1.0%
11.97 1
1.0%
11.67 1
1.0%
10.65 1
1.0%
10.09 1
1.0%
9.71 1
1.0%
9.65 1
1.0%

PM10(g/km)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct80
Distinct (%)80.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5678
Minimum0
Maximum6.78
Zeros17
Zeros (%)17.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:10:14.922527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.1075
median0.885
Q32.58
95-th percentile5.137
Maximum6.78
Range6.78
Interquartile range (IQR)2.4725

Descriptive statistics

Standard deviation1.7722927
Coefficient of variation (CV)1.1304329
Kurtosis0.57891226
Mean1.5678
Median Absolute Deviation (MAD)0.885
Skewness1.1645332
Sum156.78
Variance3.1410214
MonotonicityNot monotonic
2023-12-10T20:10:15.126435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 17
 
17.0%
0.11 2
 
2.0%
0.12 2
 
2.0%
0.18 2
 
2.0%
0.05 2
 
2.0%
0.2 1
 
1.0%
4.69 1
 
1.0%
1.8 1
 
1.0%
3.76 1
 
1.0%
5.9 1
 
1.0%
Other values (70) 70
70.0%
ValueCountFrequency (%)
0.0 17
17.0%
0.02 1
 
1.0%
0.03 1
 
1.0%
0.05 2
 
2.0%
0.06 1
 
1.0%
0.08 1
 
1.0%
0.09 1
 
1.0%
0.1 1
 
1.0%
0.11 2
 
2.0%
0.12 2
 
2.0%
ValueCountFrequency (%)
6.78 1
1.0%
6.68 1
1.0%
6.39 1
1.0%
5.9 1
1.0%
5.27 1
1.0%
5.13 1
1.0%
4.69 1
1.0%
4.44 1
1.0%
4.27 1
1.0%
4.24 1
1.0%

주소
Categorical

HIGH CORRELATION 

Distinct13
Distinct (%)13.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
충북 청원군 남이면
13 
충남 천안시 서북구 성거읍 송남리
10 
경기 안성시 원곡면
10 
경기 화성시 동탄면 송리
10 
충청 천안시 구성동
9 
Other values (8)
48 

Length

Max length18
Median length10
Mean length11.82
Min length9

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row경남 양산시 동면
2nd row경남 양산시 동면
3rd row경남 양산시 동면
4th row경남 양산시 동면
5th row경남 양산시 동면

Common Values

ValueCountFrequency (%)
충북 청원군 남이면 13
13.0%
충남 천안시 서북구 성거읍 송남리 10
10.0%
경기 안성시 원곡면 10
10.0%
경기 화성시 동탄면 송리 10
10.0%
충청 천안시 구성동 9
9.0%
경남 양산시 동면 6
 
6.0%
울산 울주군 두서면 활천리 6
 
6.0%
경북 경산시 진량읍 6
 
6.0%
경북 김천시 아포읍 봉산리 6
 
6.0%
대전 대덕구 비래동 6
 
6.0%
Other values (3) 18
18.0%

Length

2023-12-10T20:10:15.302717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
충북 25
 
7.2%
경기 20
 
5.7%
천안시 19
 
5.5%
청원군 19
 
5.5%
남이면 13
 
3.7%
대전 12
 
3.4%
경북 12
 
3.4%
대덕구 12
 
3.4%
화성시 10
 
2.9%
동탄면 10
 
2.9%
Other values (27) 196
56.3%

Interactions

2023-12-10T20:10:07.870156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:58.016536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:59.108933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:00.345745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:01.599325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:02.723977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:04.011606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:05.548486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:06.667166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:07.968546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:58.157233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:59.224801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:00.482361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:01.753514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:02.853753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:04.134219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:05.676653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:06.812905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:08.097918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:58.288099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:59.361862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:00.610795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:01.876083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:02.999716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:04.264234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:05.804881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:06.944465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:08.213696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:58.421036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:59.499445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:00.772371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:01.999768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:03.148379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:04.395187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:05.921522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:07.072365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:08.355282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:58.539174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:59.657439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:00.914271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:02.129832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:03.290318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:04.531171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:06.020323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:07.236932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:08.499630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:58.667250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:59.800191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:01.060615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:02.261967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:03.413011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:04.672505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:06.152967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:07.403159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:08.623064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:58.781146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:59.918882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:01.180751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:02.363672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:03.561408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:04.778788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:06.290483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:07.543550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:08.729460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:58.876276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:00.045324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:01.305565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:02.465780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:03.697093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:04.906584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:06.411483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:07.653201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:08.851976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:58.981865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:00.211384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:01.453241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:02.594157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:03.853100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:05.051281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:06.539728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:07.767604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T20:10:15.435068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기본키지점방향차선측정구간장비이정(km)차량통과수(대)평균 속도(km/hr)위도(°)경도(°)기울기(°)TSP(g/km)PM10(g/km)주소
기본키1.0000.9970.2620.0000.9940.8960.5190.2320.9340.9800.6490.0000.0000.976
지점0.9971.0000.0000.0001.0001.0000.3690.1721.0001.0000.8440.0000.0001.000
방향0.2620.0001.0000.0001.0000.0000.0000.0000.0000.0000.5430.0000.0000.000
차선0.0000.0000.0001.0000.0000.0000.6750.6050.0000.1590.0000.7020.6820.000
측정구간0.9941.0001.0000.0001.0001.0000.1210.2421.0001.0001.0000.0000.0001.000
장비이정(km)0.8961.0000.0000.0001.0001.0000.3420.2640.9790.9710.5750.0000.0001.000
차량통과수(대)0.5190.3690.0000.6750.1210.3421.0000.5770.4050.5110.0000.7320.7360.384
평균 속도(km/hr)0.2320.1720.0000.6050.2420.2640.5771.0000.2420.2600.4310.4850.4650.298
위도(°)0.9341.0000.0000.0001.0000.9790.4050.2421.0000.9760.7390.0000.0001.000
경도(°)0.9801.0000.0000.1591.0000.9710.5110.2600.9761.0000.4060.0000.0001.000
기울기(°)0.6490.8440.5430.0001.0000.5750.0000.4310.7390.4061.0000.0000.0000.816
TSP(g/km)0.0000.0000.0000.7020.0000.0000.7320.4850.0000.0000.0001.0001.0000.000
PM10(g/km)0.0000.0000.0000.6820.0000.0000.7360.4650.0000.0000.0001.0001.0000.000
주소0.9761.0000.0000.0001.0001.0000.3840.2981.0001.0000.8160.0000.0001.000
2023-12-10T20:10:15.653168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지점차선측정구간방향주소
지점1.0000.0000.9270.0000.988
차선0.0001.0000.0000.0000.000
측정구간0.9270.0001.0000.8630.916
방향0.0000.0000.8631.0000.000
주소0.9880.0000.9160.0001.000
2023-12-10T20:10:15.822768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기본키장비이정(km)차량통과수(대)평균 속도(km/hr)위도(°)경도(°)기울기(°)TSP(g/km)PM10(g/km)지점방향차선측정구간주소
기본키1.0000.9970.253-0.3360.997-0.9750.0450.0560.0520.9160.1900.0000.8640.891
장비이정(km)0.9971.0000.260-0.3121.000-0.9780.0320.0490.0460.9560.0000.0000.8860.967
차량통과수(대)0.2530.2601.0000.2490.260-0.309-0.0800.8220.8220.1370.0000.3340.0000.163
평균 속도(km/hr)-0.336-0.3120.2491.000-0.3120.261-0.0560.0690.0680.0640.0000.4410.0750.134
위도(°)0.9971.0000.260-0.3121.000-0.9780.0320.0490.0460.9660.0000.0000.8960.978
경도(°)-0.975-0.978-0.3090.261-0.9781.000-0.048-0.079-0.0750.9460.0000.0560.8770.957
기울기(°)0.0450.032-0.080-0.0560.032-0.0481.000-0.073-0.0750.5650.3840.0000.8810.560
TSP(g/km)0.0560.0490.8220.0690.049-0.079-0.0731.0001.0000.0000.0000.3550.0000.000
PM10(g/km)0.0520.0460.8220.0680.046-0.075-0.0751.0001.0000.0000.0000.3390.0000.000
지점0.9160.9560.1370.0640.9660.9460.5650.0000.0001.0000.0000.0000.9270.988
방향0.1900.0000.0000.0000.0000.0000.3840.0000.0000.0001.0000.0000.8630.000
차선0.0000.0000.3340.4410.0000.0560.0000.3550.3390.0000.0001.0000.0000.000
측정구간0.8640.8860.0000.0750.8960.8770.8810.0000.0000.9270.8630.0001.0000.916
주소0.8910.9670.1630.1340.9780.9570.5600.0000.0000.9880.0000.0000.9161.000

Missing values

2023-12-10T20:10:09.010551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T20:10:09.219715image/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/hr)위도(°)경도(°)기울기(°)TSP(g/km)PM10(g/km)주소
01도로공사A-0010-0083E-6E1노포JC-양산JC8.320210401021112.035.306944129.074722-3.152760.460.2경남 양산시 동면
12도로공사A-0010-0083E-6E2노포JC-양산JC8.320210401044109.6735.306944129.074722-3.152760.890.39경남 양산시 동면
23도로공사A-0010-0083E-6E3노포JC-양산JC8.32021040104096.6735.306944129.074722-3.152762.250.99경남 양산시 동면
34도로공사A-0010-0083E-6S1양산JC-노포JC8.320210401024123.035.306944129.0747223.0714160.270.12경남 양산시 동면
45도로공사A-0010-0083E-6S2양산JC-노포JC8.32021040105693.3335.306944129.0747223.0714161.160.51경남 양산시 동면
56도로공사A-0010-0083E-6S3양산JC-노포JC8.32021040101888.535.306944129.0747223.0714160.580.26경남 양산시 동면
67도로공사A-0010-0538E-6E1언양JC-활천IC53.42021040105101.035.681944129.1811110.225450.060.02울산 울주군 두서면 활천리
78도로공사A-0010-0538E-6E2언양JC-활천IC53.42021040102286.5735.681944129.1811110.225451.990.87울산 울주군 두서면 활천리
89도로공사A-0010-0538E-6E3언양JC-활천IC53.42021040102381.035.681944129.1811110.225453.431.51울산 울주군 두서면 활천리
910도로공사A-0010-0538E-6S1활천IC-언양JC53.420210401011116.035.681944129.1811110.191580.120.05울산 울주군 두서면 활천리
기본키도로종류지점방향차선측정구간장비이정(km)측정일측정시간차량통과수(대)평균 속도(km/hr)위도(°)경도(°)기울기(°)TSP(g/km)PM10(g/km)주소
9091도로공사A-0010-3801E-10E1오산IC-동탄JC380.12021040108396.537.158778127.088333-0.6600040.970.43경기 화성시 동탄면 송리
9192도로공사A-0010-3801E-10E2오산IC-동탄JC380.120210401012094.6737.158778127.088333-0.6600042.971.31경기 화성시 동탄면 송리
9293도로공사A-0010-3801E-10E3오산IC-동탄JC380.120210401010887.837.158778127.088333-0.6600046.542.88경기 화성시 동탄면 송리
9394도로공사A-0010-3801E-10E4오산IC-동탄JC380.12021040109779.1237.158778127.088333-0.66000410.094.44경기 화성시 동탄면 송리
9495도로공사A-0010-3801E-10E5오산IC-동탄JC380.1202104010284.037.158778127.088333-0.6600040.130.06경기 화성시 동탄면 송리
9596도로공사A-0010-3801E-10S1동탄JC-오산IC380.120210401000.037.158778127.0883330.6719770.00.0경기 화성시 동탄면 송리
9697도로공사A-0010-3801E-10S2동탄JC-오산IC380.120210401012798.2537.158778127.0883330.6719773.341.47경기 화성시 동탄면 송리
9798도로공사A-0010-3801E-10S3동탄JC-오산IC380.120210401011186.037.158778127.0883330.6719779.714.27경기 화성시 동탄면 송리
9899도로공사A-0010-3801E-10S4동탄JC-오산IC380.12021040109677.4437.158778127.0883330.6719778.813.88경기 화성시 동탄면 송리
99100도로공사A-0010-3801E-10S5동탄JC-오산IC380.1202104010268.037.158778127.0883330.6719770.010.0경기 화성시 동탄면 송리