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
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 5~7차종 교통량(대)High correlation
위도(°) is highly overall correlated with 지점 and 2 other fieldsHigh correlation
경도(°) is highly overall correlated with 지점 and 2 other fieldsHigh correlation
기울기(°) is highly overall correlated with 지점 and 2 other fieldsHigh correlation
5~7차종 교통량(대) is highly overall correlated with 차량통과수(대) and 2 other fieldsHigh correlation
10~12차종 교통량(대) is highly overall correlated with 5~7차종 교통량(대) and 1 other fieldsHigh correlation
방향 is highly overall correlated with 측정구간High correlation
측정구간 is highly overall correlated with 기본키 and 7 other fieldsHigh correlation
8~9차종 교통량(대) is highly overall correlated with 5~7차종 교통량(대) and 1 other fieldsHigh correlation
8~9차종 교통량(대) is highly imbalanced (53.0%)Imbalance
기본키 has unique valuesUnique
차량통과수(대) has 21 (21.0%) zerosZeros
5~7차종 교통량(대) has 44 (44.0%) zerosZeros
10~12차종 교통량(대) has 64 (64.0%) zerosZeros

Reproduction

Analysis started2023-12-10 10:56:09.114299
Analysis finished2023-12-10 10:56:24.388801
Duration15.27 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기본키
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.5
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:56:24.545250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5.95
Q125.75
median50.5
Q375.25
95-th percentile95.05
Maximum100
Range99
Interquartile range (IQR)49.5

Descriptive statistics

Standard deviation29.011492
Coefficient of variation (CV)0.57448499
Kurtosis-1.2
Mean50.5
Median Absolute Deviation (MAD)25
Skewness0
Sum5050
Variance841.66667
MonotonicityStrictly increasing
2023-12-10T19:56:24.949210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.0%
65 1
 
1.0%
75 1
 
1.0%
74 1
 
1.0%
73 1
 
1.0%
72 1
 
1.0%
71 1
 
1.0%
70 1
 
1.0%
69 1
 
1.0%
68 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
1 1
1.0%
2 1
1.0%
3 1
1.0%
4 1
1.0%
5 1
1.0%
6 1
1.0%
7 1
1.0%
8 1
1.0%
9 1
1.0%
10 1
1.0%
ValueCountFrequency (%)
100 1
1.0%
99 1
1.0%
98 1
1.0%
97 1
1.0%
96 1
1.0%
95 1
1.0%
94 1
1.0%
93 1
1.0%
92 1
1.0%
91 1
1.0%

도로종류
Categorical

CONSTANT 

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

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
도로공사 100
100.0%

Length

2023-12-10T19:56:25.205027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:56:25.359173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
도로공사 100
100.0%

지점
Categorical

HIGH CORRELATION 

Distinct23
Distinct (%)23.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
A-0500-0763E-8
12 
A-0400-0568S-6
 
6
A-0400-1082S-4
 
4
A-0500-0844S-8
 
4
A-0400-0834S-4
 
4
Other values (18)
70 

Length

Max length14
Median length14
Mean length14
Min length14

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA-0400-0708S-4
2nd rowA-0400-0708S-4
3rd rowA-0400-0708S-4
4th rowA-0400-0708S-4
5th rowA-0400-1082S-4

Common Values

ValueCountFrequency (%)
A-0500-0763E-8 12
 
12.0%
A-0400-0568S-6 6
 
6.0%
A-0400-1082S-4 4
 
4.0%
A-0500-0844S-8 4
 
4.0%
A-0400-0834S-4 4
 
4.0%
A-0450-2107S-4 4
 
4.0%
A-0400-0862S-4 4
 
4.0%
A-0400-1025S-4 4
 
4.0%
A-0352-2643E-4 4
 
4.0%
A-0450-2601E-4 4
 
4.0%
Other values (13) 50
50.0%

Length

2023-12-10T19:56:25.530818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
a-0500-0763e-8 12
 
12.0%
a-0400-0568s-6 6
 
6.0%
a-0300-0040s-4 4
 
4.0%
a-0400-0708s-4 4
 
4.0%
a-0300-0245s-4 4
 
4.0%
a-0550-3119s-4 4
 
4.0%
a-0550-2644e-4 4
 
4.0%
a-0450-2483e-4 4
 
4.0%
a-0450-1719e-4 4
 
4.0%
a-0450-1398e-4 4
 
4.0%
Other values (13) 50
50.0%

방향
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
S
50 
E
50 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
S 50
50.0%
E 50
50.0%

Length

2023-12-10T19:56:25.754088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:56:25.974595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
s 50
50.0%
e 50
50.0%

차선
Real number (ℝ)

Distinct8
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.86
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:56:26.214123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q32
95-th percentile4
Maximum8
Range7
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.2228465
Coefficient of variation (CV)0.65744435
Kurtosis9.505553
Mean1.86
Median Absolute Deviation (MAD)1
Skewness2.7419668
Sum186
Variance1.4953535
MonotonicityNot monotonic
2023-12-10T19:56:26.550651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1 44
44.0%
2 44
44.0%
3 4
 
4.0%
4 4
 
4.0%
5 1
 
1.0%
6 1
 
1.0%
7 1
 
1.0%
8 1
 
1.0%
ValueCountFrequency (%)
1 44
44.0%
2 44
44.0%
3 4
 
4.0%
4 4
 
4.0%
5 1
 
1.0%
6 1
 
1.0%
7 1
 
1.0%
8 1
 
1.0%
ValueCountFrequency (%)
8 1
 
1.0%
7 1
 
1.0%
6 1
 
1.0%
5 1
 
1.0%
4 4
 
4.0%
3 4
 
4.0%
2 44
44.0%
1 44
44.0%

측정구간
Categorical

HIGH CORRELATION 

Distinct44
Distinct (%)44.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
호법JC-이천IC
8 
북진천IC-대소JC
 
4
여주JC-이천IC
 
4
이천IC-호법JC
 
4
금왕꽃동네IC-대소JC
 
2
Other values (39)
78 

Length

Max length13
Median length9
Mean length9.8
Min length9

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row음성IC-금왕꽃동네IC
2nd row음성IC-금왕꽃동네IC
3rd row금왕꽃동네IC-음성IC
4th row금왕꽃동네IC-음성IC
5th row제천JC-동충주IC

Common Values

ValueCountFrequency (%)
호법JC-이천IC 8
 
8.0%
북진천IC-대소JC 4
 
4.0%
여주JC-이천IC 4
 
4.0%
이천IC-호법JC 4
 
4.0%
금왕꽃동네IC-대소JC 2
 
2.0%
제천JC-동충주IC 2
 
2.0%
동충주IC-제천JC 2
 
2.0%
충주JC-서충주IC 2
 
2.0%
서충주IC-충주JC 2
 
2.0%
충주IC-괴산IC 2
 
2.0%
Other values (34) 68
68.0%

Length

2023-12-10T19:56:26.958201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
호법jc-이천ic 8
 
8.0%
이천ic-호법jc 4
 
4.0%
북진천ic-대소jc 4
 
4.0%
여주jc-이천ic 4
 
4.0%
신림ic-남원주ic 2
 
2.0%
보은ic-회인ic 2
 
2.0%
낙동jc-상주ic 2
 
2.0%
회인ic-보은ic 2
 
2.0%
화서ic-구병산hi 2
 
2.0%
낙동jc-남상주ic 2
 
2.0%
Other values (34) 68
68.0%

장비이정(km)
Real number (ℝ)

HIGH CORRELATION 

Distinct22
Distinct (%)22.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean140.5704
Minimum4
Maximum320.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:56:27.248824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile24.5
Q176.3
median86.28
Q3248.33
95-th percentile311.9
Maximum320.5
Range316.5
Interquartile range (IQR)172.03

Descriptive statistics

Standard deviation96.832161
Coefficient of variation (CV)0.68885171
Kurtosis-1.1434071
Mean140.5704
Median Absolute Deviation (MAD)35.63
Skewness0.59205781
Sum14057.04
Variance9376.4673
MonotonicityNot monotonic
2023-12-10T19:56:27.496121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
76.3 16
 
16.0%
56.8 6
 
6.0%
70.8 4
 
4.0%
24.5 4
 
4.0%
84.43 4
 
4.0%
311.9 4
 
4.0%
264.4 4
 
4.0%
248.33 4
 
4.0%
171.91 4
 
4.0%
139.85 4
 
4.0%
Other values (12) 46
46.0%
ValueCountFrequency (%)
4.0 4
 
4.0%
24.5 4
 
4.0%
44.5 2
 
2.0%
56.8 6
 
6.0%
60.2 4
 
4.0%
70.8 4
 
4.0%
76.3 16
16.0%
83.4 4
 
4.0%
84.43 4
 
4.0%
86.28 4
 
4.0%
ValueCountFrequency (%)
320.5 4
4.0%
311.9 4
4.0%
285.2 4
4.0%
264.4 4
4.0%
264.3 4
4.0%
260.1 4
4.0%
248.33 4
4.0%
210.73 4
4.0%
171.91 4
4.0%
139.85 4
4.0%

측정일
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
20200801
100 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20200801
2nd row20200801
3rd row20200801
4th row20200801
5th row20200801

Common Values

ValueCountFrequency (%)
20200801 100
100.0%

Length

2023-12-10T19:56:27.738495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:56:27.908888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20200801 100
100.0%

측정시간
Categorical

CONSTANT 

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

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 100
100.0%

Length

2023-12-10T19:56:28.124002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:56:28.318600image/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%
Mean58.85
Minimum0
Maximum313
Zeros21
Zeros (%)21.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:56:28.517057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q114.5
median44
Q383.25
95-th percentile181.65
Maximum313
Range313
Interquartile range (IQR)68.75

Descriptive statistics

Standard deviation60.46426
Coefficient of variation (CV)1.0274301
Kurtosis3.8352763
Mean58.85
Median Absolute Deviation (MAD)37.5
Skewness1.6988757
Sum5885
Variance3655.9268
MonotonicityNot monotonic
2023-12-10T19:56:28.795247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 21
 
21.0%
72 3
 
3.0%
39 3
 
3.0%
20 2
 
2.0%
86 2
 
2.0%
101 2
 
2.0%
34 2
 
2.0%
124 2
 
2.0%
112 2
 
2.0%
80 2
 
2.0%
Other values (52) 59
59.0%
ValueCountFrequency (%)
0 21
21.0%
3 1
 
1.0%
4 1
 
1.0%
5 1
 
1.0%
13 1
 
1.0%
15 1
 
1.0%
17 1
 
1.0%
18 1
 
1.0%
20 2
 
2.0%
21 1
 
1.0%
ValueCountFrequency (%)
313 1
1.0%
277 1
1.0%
213 1
1.0%
201 1
1.0%
194 1
1.0%
181 1
1.0%
166 1
1.0%
153 1
1.0%
134 1
1.0%
125 1
1.0%

위도(°)
Real number (ℝ)

HIGH CORRELATION 

Distinct22
Distinct (%)22.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.933785
Minimum36.368358
Maximum37.410278
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:56:29.051930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.368358
5-th percentile36.374167
Q136.741389
median37.003333
Q337.112222
95-th percentile37.234167
Maximum37.410278
Range1.0419195
Interquartile range (IQR)0.37083333

Descriptive statistics

Standard deviation0.28347815
Coefficient of variation (CV)0.0076753072
Kurtosis-0.47929357
Mean36.933785
Median Absolute Deviation (MAD)0.15111111
Skewness-0.67249399
Sum3693.3785
Variance0.080359862
MonotonicityNot monotonic
2023-12-10T19:56:29.306308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
37.23416667 12
 
12.0%
36.86916667 6
 
6.0%
36.9425 6
 
6.0%
37.11222222 6
 
6.0%
36.98055556 4
 
4.0%
36.46971667 4
 
4.0%
37.41027778 4
 
4.0%
37.03138889 4
 
4.0%
36.62861111 4
 
4.0%
36.37416667 4
 
4.0%
Other values (12) 46
46.0%
ValueCountFrequency (%)
36.36835833 4
4.0%
36.37416667 4
4.0%
36.444525 2
 
2.0%
36.46971667 4
4.0%
36.548875 4
4.0%
36.62861111 4
4.0%
36.74138889 4
4.0%
36.86916667 6
6.0%
36.9425 6
6.0%
36.94388889 4
4.0%
ValueCountFrequency (%)
37.41027778 4
 
4.0%
37.23416667 12
12.0%
37.1885605 4
 
4.0%
37.17138889 4
 
4.0%
37.11222222 6
6.0%
37.07186389 4
 
4.0%
37.065 4
 
4.0%
37.03944444 4
 
4.0%
37.03138889 4
 
4.0%
37.02611111 4
 
4.0%

경도(°)
Real number (ℝ)

HIGH CORRELATION 

Distinct22
Distinct (%)22.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.73052
Minimum127.44083
Maximum128.2625
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:56:29.554134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum127.44083
5-th percentile127.44806
Q1127.46389
median127.68551
Q3127.91361
95-th percentile128.23944
Maximum128.2625
Range0.8216667
Interquartile range (IQR)0.4497222

Descriptive statistics

Standard deviation0.27376387
Coefficient of variation (CV)0.0021432926
Kurtosis-0.90927999
Mean127.73052
Median Absolute Deviation (MAD)0.22402775
Skewness0.6139353
Sum12773.052
Variance0.074946658
MonotonicityNot monotonic
2023-12-10T19:56:29.811431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
127.4480556 12
 
12.0%
127.9094444 6
 
6.0%
127.4613889 6
 
6.0%
127.7019444 6
 
6.0%
127.6047222 4
 
4.0%
127.6690722 4
 
4.0%
127.9258333 4
 
4.0%
128.2625 4
 
4.0%
128.1472222 4
 
4.0%
128.2394444 4
 
4.0%
Other values (12) 46
46.0%
ValueCountFrequency (%)
127.4408333 4
 
4.0%
127.4480556 12
12.0%
127.4613889 6
6.0%
127.4638889 4
 
4.0%
127.4675 4
 
4.0%
127.4731028 4
 
4.0%
127.4988889 4
 
4.0%
127.6047222 4
 
4.0%
127.6126927 4
 
4.0%
127.6690722 4
 
4.0%
ValueCountFrequency (%)
128.2625 4
4.0%
128.2394444 4
4.0%
128.2008222 4
4.0%
128.1472222 4
4.0%
127.9739528 4
4.0%
127.9258333 4
4.0%
127.9136111 4
4.0%
127.9094444 6
6.0%
127.8848333 2
 
2.0%
127.7497222 4
4.0%

기울기(°)
Real number (ℝ)

HIGH CORRELATION 

Distinct23
Distinct (%)23.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.30097746
Minimum-6.688
Maximum4.25421
Zeros0
Zeros (%)0.0%
Negative58
Negative (%)58.0%
Memory size1.0 KiB
2023-12-10T19:56:30.034110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-6.688
5-th percentile-2.72685
Q1-0.785921
median-0.4315305
Q30.5068
95-th percentile1.99427
Maximum4.25421
Range10.94221
Interquartile range (IQR)1.292721

Descriptive statistics

Standard deviation1.8811455
Coefficient of variation (CV)-6.2501208
Kurtosis4.3993061
Mean-0.30097746
Median Absolute Deviation (MAD)0.742774
Skewness-0.97674872
Sum-30.097746
Variance3.5387083
MonotonicityNot monotonic
2023-12-10T19:56:30.247199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
-0.785921 12
 
12.0%
-0.7189909999999999 6
 
6.0%
1.99427 4
 
4.0%
4.25421 4
 
4.0%
-0.489801 4
 
4.0%
0.21004 4
 
4.0%
-0.37326 4
 
4.0%
-0.14187 4
 
4.0%
-6.688 4
 
4.0%
-1.203288 4
 
4.0%
Other values (13) 50
50.0%
ValueCountFrequency (%)
-6.688 4
 
4.0%
-2.72685 4
 
4.0%
-1.391058 4
 
4.0%
-1.344974 4
 
4.0%
-1.203288 4
 
4.0%
-1.13121 4
 
4.0%
-0.785921 12
12.0%
-0.7189909999999999 6
6.0%
-0.522334 4
 
4.0%
-0.489801 4
 
4.0%
ValueCountFrequency (%)
4.25421 4
4.0%
1.99427 4
4.0%
1.73409 4
4.0%
1.10171 4
4.0%
1.04309 2
2.0%
0.647992 4
4.0%
0.5068 4
4.0%
0.484469 4
4.0%
0.28226 4
4.0%
0.21004 4
4.0%

5~7차종 교통량(대)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct29
Distinct (%)29.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.28
Minimum0
Maximum43
Zeros44
Zeros (%)44.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:56:30.900423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q36.25
95-th percentile29.05
Maximum43
Range43
Interquartile range (IQR)6.25

Descriptive statistics

Standard deviation10.11857
Coefficient of variation (CV)1.6112372
Kurtosis2.226591
Mean6.28
Median Absolute Deviation (MAD)1
Skewness1.7898273
Sum628
Variance102.38545
MonotonicityNot monotonic
2023-12-10T19:56:31.147022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
0 44
44.0%
4 7
 
7.0%
1 7
 
7.0%
2 7
 
7.0%
3 6
 
6.0%
5 2
 
2.0%
11 2
 
2.0%
25 2
 
2.0%
13 2
 
2.0%
6 2
 
2.0%
Other values (19) 19
19.0%
ValueCountFrequency (%)
0 44
44.0%
1 7
 
7.0%
2 7
 
7.0%
3 6
 
6.0%
4 7
 
7.0%
5 2
 
2.0%
6 2
 
2.0%
7 1
 
1.0%
9 1
 
1.0%
10 1
 
1.0%
ValueCountFrequency (%)
43 1
1.0%
36 1
1.0%
33 1
1.0%
31 1
1.0%
30 1
1.0%
29 1
1.0%
28 1
1.0%
27 1
1.0%
26 1
1.0%
25 2
2.0%

8~9차종 교통량(대)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
0
84 
1
13 
2
 
3

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 84
84.0%
1 13
 
13.0%
2 3
 
3.0%

Length

2023-12-10T19:56:31.469624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:56:31.661535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 84
84.0%
1 13
 
13.0%
2 3
 
3.0%

10~12차종 교통량(대)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct11
Distinct (%)11.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.32
Minimum0
Maximum11
Zeros64
Zeros (%)64.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:56:31.857920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31.25
95-th percentile7
Maximum11
Range11
Interquartile range (IQR)1.25

Descriptive statistics

Standard deviation2.407396
Coefficient of variation (CV)1.8237849
Kurtosis4.0188515
Mean1.32
Median Absolute Deviation (MAD)0
Skewness2.1009345
Sum132
Variance5.7955556
MonotonicityNot monotonic
2023-12-10T19:56:32.050675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 64
64.0%
1 11
 
11.0%
2 5
 
5.0%
3 4
 
4.0%
7 4
 
4.0%
5 4
 
4.0%
4 4
 
4.0%
11 1
 
1.0%
10 1
 
1.0%
6 1
 
1.0%
ValueCountFrequency (%)
0 64
64.0%
1 11
 
11.0%
2 5
 
5.0%
3 4
 
4.0%
4 4
 
4.0%
5 4
 
4.0%
6 1
 
1.0%
7 4
 
4.0%
8 1
 
1.0%
10 1
 
1.0%
ValueCountFrequency (%)
11 1
 
1.0%
10 1
 
1.0%
8 1
 
1.0%
7 4
 
4.0%
6 1
 
1.0%
5 4
 
4.0%
4 4
 
4.0%
3 4
 
4.0%
2 5
5.0%
1 11
11.0%

주소
Categorical

HIGH CORRELATION 

Distinct23
Distinct (%)23.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
경기 이천시 호법면
12 
충북 진천군 이월면 미잠리
 
6
충북 충주시 산척면 영덕리
 
4
경기 여주시 가남읍 정단리
 
4
충북 충주시 신니면 화석리
 
4
Other values (18)
70 

Length

Max length14
Median length14
Mean length12.6
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row충북 음성군 금왕읍 금석리
2nd row충북 음성군 금왕읍 금석리
3rd row충북 음성군 금왕읍 금석리
4th row충북 음성군 금왕읍 금석리
5th row충북 충주시 산척면 영덕리

Common Values

ValueCountFrequency (%)
경기 이천시 호법면 12
 
12.0%
충북 진천군 이월면 미잠리 6
 
6.0%
충북 충주시 산척면 영덕리 4
 
4.0%
경기 여주시 가남읍 정단리 4
 
4.0%
충북 충주시 신니면 화석리 4
 
4.0%
충북 괴산군 장연면 상리 4
 
4.0%
충북 충주시 노은면 문성리 4
 
4.0%
충북 충주시 엄정면 율릉리 4
 
4.0%
충북 청원군 오창읍 학소리 4
 
4.0%
경기 여주시 가남읍 4
 
4.0%
Other values (13) 50
50.0%

Length

2023-12-10T19:56:32.341039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
충북 58
 
15.8%
경기 24
 
6.6%
충주시 16
 
4.4%
음성군 16
 
4.4%
경북 14
 
3.8%
호법면 12
 
3.3%
이천시 12
 
3.3%
상주시 10
 
2.7%
청원군 8
 
2.2%
낙동면 8
 
2.2%
Other values (43) 188
51.4%

Interactions

2023-12-10T19:56:22.488325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:56:10.833729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:56:12.375190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:56:13.814748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:56:15.200182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:56:16.621545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:56:18.018516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:56:19.418933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:56:21.154986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:56:22.625925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:56:10.983358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:56:12.532546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:56:13.975228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:56:15.349260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:56:16.733984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:56:18.155180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:56:19.953571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:56:21.297760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:56:22.751579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:56:11.161896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:56:12.683474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:56:14.142021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:56:15.523547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:56:16.886823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:56:18.320417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:56:20.107159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:56:21.446040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:56:22.901928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:56:11.338442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:56:12.829842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:56:14.297150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:56:15.677414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:56:17.043220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:56:18.491983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:56:20.264005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:56:21.608119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:56:23.077995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:56:11.509593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:56:13.001421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:56:14.463045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:56:15.863458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:56:17.210836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:56:18.665663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:56:20.424600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:56:21.752846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:56:23.226976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:56:11.675582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:56:13.149527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:56:14.611407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:56:16.014324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:56:17.377009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:56:18.810278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:56:20.597585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:56:21.905962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:56:23.363693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:56:11.853520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:56:13.293390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:56:14.757621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:56:16.167359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:56:17.546890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:56:18.956143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:56:20.739626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:56:22.056592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:56:23.487183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:56:12.024400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:56:13.445472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:56:14.896408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:56:16.318439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:56:17.694468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:56:19.131082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:56:20.880539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:56:22.191744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:56:23.632544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:56:12.177599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:56:13.632978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:56:15.045374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:56:16.463017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:56:17.868191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:56:19.271552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:56:21.015852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:56:22.345019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T19:56:32.552287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기본키지점방향차선측정구간장비이정(km)차량통과수(대)위도(°)경도(°)기울기(°)5~7차종 교통량(대)8~9차종 교통량(대)10~12차종 교통량(대)주소
기본키1.0000.9810.0000.1030.9950.9140.4650.9360.7470.7540.3400.0880.0820.981
지점0.9811.0000.0000.0001.0001.0000.6510.9970.9981.0000.4760.0000.5551.000
방향0.0000.0001.0000.0001.0000.0000.0930.0000.0000.0000.0000.0000.0000.000
차선0.1030.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.1860.0000.000
측정구간0.9951.0001.0000.0001.0001.0000.8051.0001.0001.0000.5600.2760.5481.000
장비이정(km)0.9141.0000.0000.0001.0001.0000.6030.9610.8270.8730.5690.3130.4151.000
차량통과수(대)0.4650.6510.0930.0000.8050.6031.0000.5900.3890.5130.6120.5450.4960.651
위도(°)0.9360.9970.0000.0001.0000.9610.5901.0000.8470.8550.6580.0000.2840.997
경도(°)0.7470.9980.0000.0001.0000.8270.3890.8471.0000.8440.2360.0000.1790.998
기울기(°)0.7541.0000.0000.0001.0000.8730.5130.8550.8441.0000.3260.0000.0001.000
5~7차종 교통량(대)0.3400.4760.0000.0000.5600.5690.6120.6580.2360.3261.0000.7600.7750.476
8~9차종 교통량(대)0.0880.0000.0000.1860.2760.3130.5450.0000.0000.0000.7601.0000.8370.000
10~12차종 교통량(대)0.0820.5550.0000.0000.5480.4150.4960.2840.1790.0000.7750.8371.0000.555
주소0.9811.0000.0000.0001.0001.0000.6510.9970.9981.0000.4760.0000.5551.000
2023-12-10T19:56:32.839809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
주소지점8~9차종 교통량(대)측정구간방향
주소1.0001.0000.0000.8530.000
지점1.0001.0000.0000.8530.000
8~9차종 교통량(대)0.0000.0001.0000.0840.000
측정구간0.8530.8530.0841.0000.756
방향0.0000.0000.0000.7561.000
2023-12-10T19:56:33.127883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기본키차선장비이정(km)차량통과수(대)위도(°)경도(°)기울기(°)5~7차종 교통량(대)10~12차종 교통량(대)지점방향측정구간8~9차종 교통량(대)주소
기본키1.0000.2620.114-0.0460.293-0.020-0.270-0.0520.0540.8220.0000.7490.0400.822
차선0.2621.000-0.149-0.2500.257-0.219-0.1240.2780.3040.0000.0000.0000.1130.000
장비이정(km)0.114-0.1491.0000.4020.2430.1990.1060.2120.0380.9250.0000.7890.1870.925
차량통과수(대)-0.046-0.2500.4021.000-0.3760.2590.0670.6770.3840.2880.0850.3420.2750.288
위도(°)0.2930.2570.243-0.3761.000-0.339-0.096-0.346-0.2660.9090.0000.7890.0000.909
경도(°)-0.020-0.2190.1990.259-0.3391.0000.0790.1940.1580.9040.0000.7800.0000.904
기울기(°)-0.270-0.1240.1060.067-0.0960.0791.0000.0100.0130.9100.0000.7760.0000.910
5~7차종 교통량(대)-0.0520.2780.2120.677-0.3460.1940.0101.0000.7190.1750.0000.1670.6110.175
10~12차종 교통량(대)0.0540.3040.0380.384-0.2660.1580.0130.7191.0000.2260.0000.1670.5320.226
지점0.8220.0000.9250.2880.9090.9040.9100.1750.2261.0000.0000.8530.0001.000
방향0.0000.0000.0000.0850.0000.0000.0000.0000.0000.0001.0000.7560.0000.000
측정구간0.7490.0000.7890.3420.7890.7800.7760.1670.1670.8530.7561.0000.0840.853
8~9차종 교통량(대)0.0400.1130.1870.2750.0000.0000.0000.6110.5320.0000.0000.0841.0000.000
주소0.8220.0000.9250.2880.9090.9040.9100.1750.2261.0000.0000.8530.0001.000

Missing values

2023-12-10T19:56:23.835617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T19:56:24.228734image/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)위도(°)경도(°)기울기(°)5~7차종 교통량(대)8~9차종 교통량(대)10~12차종 교통량(대)주소
01도로공사A-0400-0708S-4S1음성IC-금왕꽃동네IC70.82020080102036.980556127.6047221.99427000충북 음성군 금왕읍 금석리
12도로공사A-0400-0708S-4S2음성IC-금왕꽃동네IC70.82020080103436.980556127.6047221.99427200충북 음성군 금왕읍 금석리
23도로공사A-0400-0708S-4E1금왕꽃동네IC-음성IC70.82020080102936.980556127.6047221.99427000충북 음성군 금왕읍 금석리
34도로공사A-0400-0708S-4E2금왕꽃동네IC-음성IC70.82020080104636.980556127.6047221.99427400충북 음성군 금왕읍 금석리
45도로공사A-0400-1082S-4S1제천JC-동충주IC108.24202008010337.071864127.9739534.25421000충북 충주시 산척면 영덕리
56도로공사A-0400-1082S-4S2제천JC-동충주IC108.242020080101337.071864127.9739534.25421102충북 충주시 산척면 영덕리
67도로공사A-0400-1082S-4E1동충주IC-제천JC108.242020080103937.071864127.9739534.25421000충북 충주시 산척면 영덕리
78도로공사A-0400-1082S-4E2동충주IC-제천JC108.242020080104137.071864127.9739534.25421600충북 충주시 산척면 영덕리
89도로공사A-0400-0834S-4S1충주JC-서충주IC83.4202008010037.026111127.723333-1.13121000충북 충주시 신니면 화석리
910도로공사A-0400-0834S-4S2충주JC-서충주IC83.4202008010037.026111127.723333-1.13121000충북 충주시 신니면 화석리
기본키도로종류지점방향차선측정구간장비이정(km)측정일측정시간차량통과수(대)위도(°)경도(°)기울기(°)5~7차종 교통량(대)8~9차종 교통량(대)10~12차종 교통량(대)주소
9091도로공사A-0500-0763E-8E3호법JC-이천IC76.3202008010037.234167127.448056-0.785921000경기 이천시 호법면
9192도로공사A-0500-0763E-8E4호법JC-이천IC76.3202008010037.234167127.448056-0.785921000경기 이천시 호법면
9293도로공사A-0500-0763E-8E5호법JC-이천IC76.3202008010037.234167127.448056-0.785921000경기 이천시 호법면
9394도로공사A-0500-0763E-8E6호법JC-이천IC76.3202008010037.234167127.448056-0.785921000경기 이천시 호법면
9495도로공사A-0500-0763E-8E7호법JC-이천IC76.3202008010037.234167127.448056-0.785921000경기 이천시 호법면
9596도로공사A-0500-0763E-8E8호법JC-이천IC76.3202008010037.234167127.448056-0.785921000경기 이천시 호법면
9697도로공사A-0500-0844S-8S1여주JC-이천IC84.43202008010037.410278127.925833-0.489801000경기 여주시 가남읍 정단리
9798도로공사A-0500-0844S-8S2여주JC-이천IC84.4320200801011637.410278127.925833-0.489801100경기 여주시 가남읍 정단리
9899도로공사A-0500-0844S-8S3여주JC-이천IC84.432020080107937.410278127.925833-0.4898011714경기 여주시 가남읍 정단리
99100도로공사A-0500-0844S-8S4여주JC-이천IC84.432020080102137.410278127.925833-0.4898011102경기 여주시 가남읍 정단리