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 12 (12.0%) zerosZeros
평균 속도(km/hr) has 12 (12.0%) zerosZeros
TSP(g/km) has 12 (12.0%) zerosZeros
PM10(g/km) has 13 (13.0%) zerosZeros

Reproduction

Analysis started2023-12-10 11:10:17.853702
Analysis finished2023-12-10 11:10:29.687255
Duration11.83 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
2023-12-10T20:10:29.809570image/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
2023-12-10T20:10:30.104103image/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

2023-12-10T20:10:30.301439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:10:30.435113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
도로공사 100
100.0%

지점
Categorical

HIGH CORRELATION 

Distinct16
Distinct (%)16.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
A-0010-3452S-10
10 
A-0010-3613E-10
10 
A-0010-3352E-9
9 
A-0010-1185E-8
8 
A-0010-0083E-6
6 
Other values (11)
57 

Length

Max length15
Median length14
Mean length14.22
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-0010-3352E-9 9
 
9.0%
A-0010-1185E-8 8
 
8.0%
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 (6) 27
27.0%

Length

2023-12-10T20:10:30.604957image/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-0010-3352e-9 9
 
9.0%
a-0010-1185e-8 8
 
8.0%
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 (6) 27
27.0%

방향
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
E
53 
S
47 

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 53
53.0%
S 47
47.0%

Length

2023-12-10T20:10:30.787863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:10:30.927239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
e 53
53.0%
s 47
47.0%

차선
Categorical

Distinct5
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
1
28 
2
28 
3
27 
4
11 
5
6 

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 28
28.0%
2 28
28.0%
3 27
27.0%
4 11
 
11.0%
5 6
 
6.0%

Length

2023-12-10T20:10:31.097976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:10:31.239176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 28
28.0%
2 28
28.0%
3 27
27.0%
4 11
 
11.0%
5 6
 
6.0%

측정구간
Categorical

HIGH CORRELATION 

Distinct28
Distinct (%)28.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
안성JC-안성IC
 
5
북천안IC-천안IC
 
5
천안IC-북천안IC
 
5
천안IC-천안JC
 
5
청주JC-남이JC
 
5
Other values (23)
75 

Length

Max length10
Median length9
Mean length9.32
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 (%)
안성JC-안성IC 5
 
5.0%
북천안IC-천안IC 5
 
5.0%
천안IC-북천안IC 5
 
5.0%
천안IC-천안JC 5
 
5.0%
청주JC-남이JC 5
 
5.0%
안성IC-안성JC 5
 
5.0%
경산IC-동대구JC 4
 
4.0%
천안JC-천안IC 4
 
4.0%
동대구JC-경산IC 4
 
4.0%
남청주IC-청주JC 4
 
4.0%
Other values (18) 54
54.0%

Length

2023-12-10T20:10:31.393995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
안성jc-안성ic 5
 
5.0%
천안ic-북천안ic 5
 
5.0%
천안ic-천안jc 5
 
5.0%
청주jc-남이jc 5
 
5.0%
안성ic-안성jc 5
 
5.0%
북천안ic-천안ic 5
 
5.0%
동대구jc-경산ic 4
 
4.0%
청주jc-남청주ic 4
 
4.0%
남청주ic-청주jc 4
 
4.0%
천안jc-천안ic 4
 
4.0%
Other values (18) 54
54.0%

장비이정(km)
Real number (ℝ)

HIGH CORRELATION 

Distinct16
Distinct (%)16.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean247.1108
Minimum8.3
Maximum380.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:10:31.547138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8.3
5-th percentile8.3
Q1118.5
median295.6
Q3335.2
95-th percentile361.3
Maximum380.1
Range371.8
Interquartile range (IQR)216.7

Descriptive statistics

Standard deviation112.30883
Coefficient of variation (CV)0.45448775
Kurtosis-0.62931136
Mean247.1108
Median Absolute Deviation (MAD)49.6
Skewness-0.87867397
Sum24711.08
Variance12613.274
MonotonicityIncreasing
2023-12-10T20:10:31.748695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
345.2 10
 
10.0%
361.3 10
 
10.0%
335.2 9
 
9.0%
118.5 8
 
8.0%
8.3 6
 
6.0%
107.31 6
 
6.0%
188.02 6
 
6.0%
269.5 6
 
6.0%
276.1 6
 
6.0%
53.4 6
 
6.0%
Other values (6) 27
27.0%
ValueCountFrequency (%)
8.3 6
6.0%
53.4 6
6.0%
107.31 6
6.0%
118.5 8
8.0%
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%
ValueCountFrequency (%)
380.1 2
 
2.0%
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
20210301
100 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20210301
2nd row20210301
3rd row20210301
4th row20210301
5th row20210301

Common Values

ValueCountFrequency (%)
20210301 100
100.0%

Length

2023-12-10T20:10:31.910247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:10:32.030803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20210301 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

2023-12-10T20:10:32.165471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:10:32.322615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 100
100.0%

차량통과수(대)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct62
Distinct (%)62.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.52
Minimum0
Maximum260
Zeros12
Zeros (%)12.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:10:32.484674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q111
median31.5
Q369.75
95-th percentile154.4
Maximum260
Range260
Interquartile range (IQR)58.75

Descriptive statistics

Standard deviation56.196891
Coefficient of variation (CV)1.1123692
Kurtosis4.3696646
Mean50.52
Median Absolute Deviation (MAD)24
Skewness1.9991814
Sum5052
Variance3158.0905
MonotonicityNot monotonic
2023-12-10T20:10:32.715990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 12
 
12.0%
45 4
 
4.0%
11 4
 
4.0%
30 3
 
3.0%
28 3
 
3.0%
25 3
 
3.0%
20 2
 
2.0%
42 2
 
2.0%
48 2
 
2.0%
41 2
 
2.0%
Other values (52) 63
63.0%
ValueCountFrequency (%)
0 12
12.0%
1 1
 
1.0%
4 1
 
1.0%
5 1
 
1.0%
6 2
 
2.0%
7 2
 
2.0%
8 2
 
2.0%
9 1
 
1.0%
10 1
 
1.0%
11 4
 
4.0%
ValueCountFrequency (%)
260 2
2.0%
227 1
1.0%
222 1
1.0%
219 1
1.0%
151 1
1.0%
137 1
1.0%
126 1
1.0%
124 1
1.0%
116 1
1.0%
115 1
1.0%

평균 속도(km/hr)
Real number (ℝ)

ZEROS 

Distinct70
Distinct (%)70.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean84.0688
Minimum0
Maximum131
Zeros12
Zeros (%)12.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:10:32.924970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q182.0825
median92
Q3100.3325
95-th percentile121.7305
Maximum131
Range131
Interquartile range (IQR)18.25

Descriptive statistics

Standard deviation34.031006
Coefficient of variation (CV)0.40479947
Kurtosis1.9841797
Mean84.0688
Median Absolute Deviation (MAD)9.5
Skewness-1.6840998
Sum8406.88
Variance1158.1094
MonotonicityNot monotonic
2023-12-10T20:10:33.123287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 12
 
12.0%
92.0 5
 
5.0%
88.0 3
 
3.0%
82.0 3
 
3.0%
82.67 2
 
2.0%
82.71 2
 
2.0%
93.0 2
 
2.0%
90.0 2
 
2.0%
100.0 2
 
2.0%
94.0 2
 
2.0%
Other values (60) 65
65.0%
ValueCountFrequency (%)
0.0 12
12.0%
33.23 1
 
1.0%
77.0 2
 
2.0%
79.0 1
 
1.0%
79.24 1
 
1.0%
79.57 1
 
1.0%
79.83 1
 
1.0%
80.0 1
 
1.0%
80.44 1
 
1.0%
80.71 1
 
1.0%
ValueCountFrequency (%)
131.0 1
1.0%
128.0 1
1.0%
124.0 1
1.0%
123.42 1
1.0%
122.5 1
1.0%
121.69 1
1.0%
118.67 1
1.0%
118.5 1
1.0%
117.0 1
1.0%
114.89 1
1.0%

위도(°)
Real number (ℝ)

HIGH CORRELATION 

Distinct16
Distinct (%)16.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.406931
Minimum35.306944
Maximum37.158778
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:10:33.296831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.306944
5-th percentile35.306944
Q135.904732
median36.556389
Q336.780833
95-th percentile37.008889
Maximum37.158778
Range1.8518333
Interquartile range (IQR)0.87610092

Descriptive statistics

Standard deviation0.48720811
Coefficient of variation (CV)0.01338229
Kurtosis-0.41751351
Mean36.406931
Median Absolute Deviation (MAD)0.31251389
Skewness-0.67641322
Sum3640.6931
Variance0.23737174
MonotonicityIncreasing
2023-12-10T20:10:33.461128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
36.86890278 10
 
10.0%
37.00888889 10
 
10.0%
36.78083333 9
 
9.0%
35.90473241 8
 
8.0%
35.30694444 6
 
6.0%
35.88230609 6
 
6.0%
36.15572222 6
 
6.0%
36.3475 6
 
6.0%
36.38961 6
 
6.0%
35.68194444 6
 
6.0%
Other values (6) 27
27.0%
ValueCountFrequency (%)
35.30694444 6
6.0%
35.68194444 6
6.0%
35.88230609 6
6.0%
35.90473241 8
8.0%
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%
ValueCountFrequency (%)
37.15877778 2
 
2.0%
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 

Distinct16
Distinct (%)16.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.77204
Minimum127.08833
Maximum129.18111
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:10:33.626935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum127.08833
5-th percentile127.14917
Q1127.18672
median127.42778
Q3128.56147
95-th percentile129.18111
Maximum129.18111
Range2.0927778
Interquartile range (IQR)1.3747494

Descriptive statistics

Standard deviation0.71880292
Coefficient of variation (CV)0.0056256667
Kurtosis-0.79585146
Mean127.77204
Median Absolute Deviation (MAD)0.2511111
Skewness0.92805468
Sum12777.204
Variance0.51667763
MonotonicityNot monotonic
2023-12-10T20:10:33.822839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
127.1867222 10
 
10.0%
127.1491667 10
 
10.0%
127.1766667 9
 
9.0%
128.5614716 8
 
8.0%
129.0747222 6
 
6.0%
128.8488161 6
 
6.0%
128.2132778 6
 
6.0%
127.4691667 6
 
6.0%
127.423508 6
 
6.0%
129.1811111 6
 
6.0%
Other values (6) 27
27.0%
ValueCountFrequency (%)
127.0883333 2
 
2.0%
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%
128.5614716 8
8.0%
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%

기울기(°)
Real number (ℝ)

HIGH CORRELATION 

Distinct28
Distinct (%)28.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.08585103
Minimum-13.24576
Maximum12.47809
Zeros0
Zeros (%)0.0%
Negative50
Negative (%)50.0%
Memory size1.0 KiB
2023-12-10T20:10:33.997739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-13.24576
5-th percentile-3.15276
Q1-1.30142
median0.017539
Q31.0850327
95-th percentile3.071416
Maximum12.47809
Range25.72385
Interquartile range (IQR)2.3864528

Descriptive statistics

Standard deviation3.467457
Coefficient of variation (CV)-40.38923
Kurtosis9.2180258
Mean-0.08585103
Median Absolute Deviation (MAD)1.270018
Skewness-0.21449214
Sum-8.585103
Variance12.023258
MonotonicityNot monotonic
2023-12-10T20:10:34.173977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
-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%
-1.758683 5
 
5.0%
-0.807188 4
 
4.0%
1.056036 4
 
4.0%
0.166426 4
 
4.0%
-0.131348 4
 
4.0%
Other values (18) 54
54.0%
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%
0.61369 5
5.0%

TSP(g/km)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct80
Distinct (%)80.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.2433
Minimum0
Maximum24.57
Zeros12
Zeros (%)12.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:10:34.387397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.2875
median1.12
Q33.0025
95-th percentile9.003
Maximum24.57
Range24.57
Interquartile range (IQR)2.715

Descriptive statistics

Standard deviation3.4224712
Coefficient of variation (CV)1.5256413
Kurtosis18.724622
Mean2.2433
Median Absolute Deviation (MAD)1.045
Skewness3.655456
Sum224.33
Variance11.713309
MonotonicityNot monotonic
2023-12-10T20:10:34.645010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 12
 
12.0%
0.89 3
 
3.0%
0.3 2
 
2.0%
0.29 2
 
2.0%
1.54 2
 
2.0%
0.49 2
 
2.0%
0.25 2
 
2.0%
2.93 2
 
2.0%
0.45 2
 
2.0%
1.36 1
 
1.0%
Other values (70) 70
70.0%
ValueCountFrequency (%)
0.0 12
12.0%
0.01 1
 
1.0%
0.04 1
 
1.0%
0.05 1
 
1.0%
0.06 1
 
1.0%
0.07 1
 
1.0%
0.08 1
 
1.0%
0.09 1
 
1.0%
0.11 1
 
1.0%
0.21 1
 
1.0%
ValueCountFrequency (%)
24.57 1
1.0%
13.14 1
1.0%
10.98 1
1.0%
10.28 1
1.0%
9.06 1
1.0%
9.0 1
1.0%
6.66 1
1.0%
6.19 1
1.0%
6.09 1
1.0%
5.67 1
1.0%

PM10(g/km)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct71
Distinct (%)71.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.9867
Minimum0
Maximum10.81
Zeros13
Zeros (%)13.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:10:34.870932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.1275
median0.495
Q31.325
95-th percentile3.9615
Maximum10.81
Range10.81
Interquartile range (IQR)1.1975

Descriptive statistics

Standard deviation1.5056963
Coefficient of variation (CV)1.525992
Kurtosis18.730269
Mean0.9867
Median Absolute Deviation (MAD)0.465
Skewness3.6560221
Sum98.67
Variance2.2671213
MonotonicityNot monotonic
2023-12-10T20:10:35.103383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 13
 
13.0%
0.13 4
 
4.0%
0.39 3
 
3.0%
0.03 3
 
3.0%
0.17 2
 
2.0%
2.49 2
 
2.0%
0.68 2
 
2.0%
0.22 2
 
2.0%
0.11 2
 
2.0%
0.02 2
 
2.0%
Other values (61) 65
65.0%
ValueCountFrequency (%)
0.0 13
13.0%
0.02 2
 
2.0%
0.03 3
 
3.0%
0.04 1
 
1.0%
0.05 1
 
1.0%
0.09 1
 
1.0%
0.1 1
 
1.0%
0.11 2
 
2.0%
0.12 1
 
1.0%
0.13 4
 
4.0%
ValueCountFrequency (%)
10.81 1
1.0%
5.78 1
1.0%
4.83 1
1.0%
4.52 1
1.0%
3.99 1
1.0%
3.96 1
1.0%
2.93 1
1.0%
2.72 1
1.0%
2.68 1
1.0%
2.49 2
2.0%

주소
Categorical

HIGH CORRELATION 

Distinct14
Distinct (%)14.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
충북 청원군 남이면
13 
충남 천안시 서북구 성거읍 송남리
10 
경기 안성시 원곡면
10 
충청 천안시 구성동
9 
대구 동구 안심3동
8 
Other values (9)
50 

Length

Max length18
Median length10
Mean length11.58
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%
충청 천안시 구성동 9
9.0%
대구 동구 안심3동 8
 
8.0%
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 (4) 20
20.0%

Length

2023-12-10T20:10:35.320323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
충북 25
 
7.4%
청원군 19
 
5.6%
천안시 19
 
5.6%
남이면 13
 
3.8%
경기 12
 
3.5%
대전 12
 
3.5%
대덕구 12
 
3.5%
경북 12
 
3.5%
충남 10
 
2.9%
서북구 10
 
2.9%
Other values (30) 196
57.6%

Interactions

2023-12-10T20:10:27.974805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:18.666867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:19.749214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:20.916485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:22.372569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:23.534848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:24.792293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:25.834179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:26.925900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:28.057568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:18.764688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:19.889853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:21.057196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:22.476216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:23.671966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:24.911741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:25.917767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:27.051620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:28.167686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:18.864193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:20.034575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:21.191958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:22.587746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:23.823173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:25.053606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:26.018407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:27.173996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:28.270522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:18.965738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:20.153913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:21.320298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:22.725196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:23.953999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:25.170023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:26.135729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:27.280325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:28.380686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:19.073819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:20.291998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:21.446629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:22.855749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:24.118769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:25.275823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:26.278107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:27.419041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:28.498445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:19.208117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:20.436620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:21.881849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:23.010854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:24.255728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:25.415437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:26.458820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:27.571996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:28.612566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:19.322620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:20.545883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:22.005579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:23.137476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:24.366730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:25.534588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:26.567858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:27.680538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:28.732763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:19.432345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:20.662833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:22.124443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:23.266071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:24.493417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:25.652535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:26.680971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:27.782301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:28.845585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:19.577142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:20.778861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:22.258576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:23.396955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:24.639143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:25.749748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:26.804050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:27.884942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T20:10:35.472680image/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
기본키1.0000.9610.2400.0000.9820.8960.4290.1780.9160.9100.6790.2930.2930.950
지점0.9611.0000.0000.0001.0001.0000.7320.4591.0001.0000.8430.3500.3501.000
방향0.2400.0001.0000.0001.0000.0000.2440.0000.0000.0000.4970.1450.1450.000
차선0.0000.0000.0001.0000.0000.0000.1570.5740.0000.0000.0000.1080.1080.000
측정구간0.9821.0001.0000.0001.0001.0000.7190.5521.0001.0001.0000.2270.2271.000
장비이정(km)0.8961.0000.0000.0001.0001.0000.3680.2790.9790.9320.5900.0000.0001.000
차량통과수(대)0.4290.7320.2440.1570.7190.3681.0000.2540.4400.2730.2050.4180.4180.653
평균 속도(km/hr)0.1780.4590.0000.5740.5520.2790.2541.0000.3110.2500.5230.2520.2520.548
위도(°)0.9161.0000.0000.0001.0000.9790.4400.3111.0000.9660.7490.1760.1761.000
경도(°)0.9101.0000.0000.0001.0000.9320.2730.2500.9661.0000.6370.0000.0001.000
기울기(°)0.6790.8430.4970.0001.0000.5900.2050.5230.7490.6371.0000.2980.2980.828
TSP(g/km)0.2930.3500.1450.1080.2270.0000.4180.2520.1760.0000.2981.0001.0000.000
PM10(g/km)0.2930.3500.1450.1080.2270.0000.4180.2520.1760.0000.2981.0001.0000.000
주소0.9501.0000.0000.0001.0001.0000.6530.5481.0001.0000.8280.0000.0001.000
2023-12-10T20:10:35.676296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지점차선측정구간방향주소
지점1.0000.0000.9260.0000.988
차선0.0001.0000.0000.0000.000
측정구간0.9260.0001.0000.8570.915
방향0.0000.0000.8571.0000.000
주소0.9880.0000.9150.0001.000
2023-12-10T20:10:35.835310image/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
기본키1.0000.9970.368-0.2110.997-0.976-0.0030.3210.3200.7990.1740.0000.7910.778
장비이정(km)0.9971.0000.387-0.1891.000-0.978-0.0120.3280.3270.9500.0000.0000.8800.962
차량통과수(대)0.3680.3871.0000.2400.387-0.417-0.1450.7790.7790.3220.1960.1100.3220.327
평균 속도(km/hr)-0.211-0.1890.2401.000-0.1890.1460.0280.0700.0660.2150.0000.4110.2050.222
위도(°)0.9971.0000.387-0.1891.000-0.978-0.0120.3280.3270.9610.0000.0000.8890.972
경도(°)-0.976-0.978-0.4170.146-0.9781.000-0.001-0.326-0.3260.9450.0000.0000.8750.957
기울기(°)-0.003-0.012-0.1450.028-0.012-0.0011.000-0.029-0.0290.5770.3510.0000.8750.572
TSP(g/km)0.3210.3280.7790.0700.328-0.326-0.0291.0001.0000.1550.1490.0630.0550.000
PM10(g/km)0.3200.3270.7790.0660.327-0.326-0.0291.0001.0000.1550.1490.0630.0550.000
지점0.7990.9500.3220.2150.9610.9450.5770.1550.1551.0000.0000.0000.9260.988
방향0.1740.0000.1960.0000.0000.0000.3510.1490.1490.0001.0000.0000.8570.000
차선0.0000.0000.1100.4110.0000.0000.0000.0630.0630.0000.0001.0000.0000.000
측정구간0.7910.8800.3220.2050.8890.8750.8750.0550.0550.9260.8570.0001.0000.915
주소0.7780.9620.3270.2220.9720.9570.5720.0000.0000.9880.0000.0000.9151.000

Missing values

2023-12-10T20:10:29.028929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T20:10:29.591702image/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-양산JC8.320210301027112.035.306944129.074722-3.152760.30.13경남 양산시 동면
12도로공사A-0010-0083E-6E2노포JC-양산JC8.320210301048104.035.306944129.074722-3.152761.090.48경남 양산시 동면
23도로공사A-0010-0083E-6E3노포JC-양산JC8.32021030103992.6735.306944129.074722-3.152762.070.91경남 양산시 동면
34도로공사A-0010-0083E-6S1양산JC-노포JC8.320210301019117.035.306944129.0747223.0714160.210.09경남 양산시 동면
45도로공사A-0010-0083E-6S2양산JC-노포JC8.320210301045107.535.306944129.0747223.0714160.890.39경남 양산시 동면
56도로공사A-0010-0083E-6S3양산JC-노포JC8.32021030102699.035.306944129.0747223.0714163.631.6경남 양산시 동면
67도로공사A-0010-0538E-6E1언양JC-활천IC53.4202103010897.035.681944129.1811110.225450.070.03울산 울주군 두서면 활천리
78도로공사A-0010-0538E-6E2언양JC-활천IC53.42021030103590.535.681944129.1811110.225450.450.2울산 울주군 두서면 활천리
89도로공사A-0010-0538E-6E3언양JC-활천IC53.42021030101379.8335.681944129.1811110.225451.170.51울산 울주군 두서면 활천리
910도로공사A-0010-0538E-6S1활천IC-언양JC53.420210301026114.035.681944129.1811110.191580.290.13울산 울주군 두서면 활천리
기본키도로종류지점방향차선측정구간장비이정(km)측정일측정시간차량통과수(대)평균 속도(km/hr)위도(°)경도(°)기울기(°)TSP(g/km)PM10(g/km)주소
9091도로공사A-0010-3613E-10E3안성IC-안성JC361.320210301000.037.008889127.1491671.2930150.00.0경기 안성시 원곡면
9192도로공사A-0010-3613E-10E4안성IC-안성JC361.320210301012480.7137.008889127.1491671.2930155.662.49경기 안성시 원곡면
9293도로공사A-0010-3613E-10E5안성IC-안성JC361.32021030102379.5737.008889127.1491671.2930151.80.79경기 안성시 원곡면
9394도로공사A-0010-3613E-10S1안성JC-안성IC361.320210301042118.6737.008889127.149167-1.301421.470.65경기 안성시 원곡면
9495도로공사A-0010-3613E-10S2안성JC-안성IC361.3202103010106100.6737.008889127.149167-1.301421.660.73경기 안성시 원곡면
9596도로공사A-0010-3613E-10S3안성JC-안성IC361.32021030108590.6737.008889127.149167-1.301422.931.29경기 안성시 원곡면
9697도로공사A-0010-3613E-10S4안성JC-안성IC361.32021030107882.6737.008889127.149167-1.301422.250.99경기 안성시 원곡면
9798도로공사A-0010-3613E-10S5안성JC-안성IC361.32021030104588.037.008889127.149167-1.301423.471.53경기 안성시 원곡면
9899도로공사A-0010-3801E-10E1오산IC-동탄JC380.120210301022292.6737.158778127.088333-0.6600044.131.82경기 화성시 동탄면 송리
99100도로공사A-0010-3801E-10E2오산IC-동탄JC380.120210301022793.6737.158778127.088333-0.6600042.81.23경기 화성시 동탄면 송리