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 위도(°) and 3 other fieldsHigh correlation
장비이정(km) is highly overall correlated with 위도(°) and 4 other fieldsHigh correlation
차량통과수(대) is highly overall correlated with 평균 속도(km/hr) and 2 other fieldsHigh correlation
평균 속도(km/hr) is highly overall correlated with 차량통과수(대) and 2 other fieldsHigh correlation
위도(°) is highly overall correlated with 기본키 and 5 other fieldsHigh correlation
경도(°) is highly overall correlated with 장비이정(km) and 4 other fieldsHigh correlation
기울기(°) is highly overall correlated with 지점 and 3 other fieldsHigh correlation
TSP(g/km) is highly overall correlated with 차량통과수(대) and 2 other fieldsHigh correlation
PM10(g/km) is highly overall correlated with 차량통과수(대) and 2 other fieldsHigh correlation
방향 is highly overall correlated with 기울기(°) and 1 other fieldsHigh correlation
기본키 has unique valuesUnique
차량통과수(대) has 26 (26.0%) zerosZeros
평균 속도(km/hr) has 26 (26.0%) zerosZeros
TSP(g/km) has 26 (26.0%) zerosZeros
PM10(g/km) has 27 (27.0%) zerosZeros

Reproduction

Analysis started2023-12-10 10:58:21.309068
Analysis finished2023-12-10 10:58:40.941374
Duration19.63 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
2023-12-10T19:58:41.140558image/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
2023-12-10T19:58:41.432865image/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

2023-12-10T19:58:41.807834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:58:41.986828image/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-0010-3801E-10
10 
A-0010-3932E-10
10 
A-0010-4105E-10
10 
Other values (11)
50 

Length

Max length15
Median length14.5
Mean length14.5
Min length14

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA-0010-2583E-7
2nd rowA-0010-2583E-7
3rd rowA-0010-2583E-7
4th rowA-0010-2583E-7
5th rowA-0010-2583E-7

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-0010-3932E-10 10
10.0%
A-0010-4105E-10 10
10.0%
A-0010-2583E-7 7
 
7.0%
A-0010-2761E-6 6
 
6.0%
A-0010-3019E-6 6
 
6.0%
A-0010-3068E-6 6
 
6.0%
A-0010-2979E-5 5
 
5.0%
Other values (6) 20
20.0%

Length

2023-12-10T19:58:42.367174image/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-0010-3932e-10 10
10.0%
a-0010-4105e-10 10
10.0%
a-0010-2583e-7 7
 
7.0%
a-0010-2761e-6 6
 
6.0%
a-0010-3019e-6 6
 
6.0%
a-0010-3068e-6 6
 
6.0%
a-0010-2979e-5 5
 
5.0%
Other values (6) 20
20.0%

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

2023-12-10T19:58:42.680644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:58:42.848135image/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
21 
4
14 
5
11 

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 21
21.0%
4 14
14.0%
5 11
11.0%

Length

2023-12-10T19:58:43.195307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:58:43.445695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 27
27.0%
2 27
27.0%
3 21
21.0%
4 14
14.0%
5 11
11.0%

측정구간
Categorical

HIGH CORRELATION 

Distinct27
Distinct (%)27.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
안성IC-안성JC
 
5
양재IC-금토JC
 
5
금토JC-양재IC
 
5
청주JC-남이JC
 
5
신갈JC-수원신갈IC
 
5
Other values (22)
75 

Length

Max length11
Median length9
Mean length9.44
Min length9

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row금강IC-옥천IC
2nd row금강IC-옥천IC
3rd row금강IC-옥천IC
4th row옥천IC-금강IC
5th row옥천IC-금강IC

Common Values

ValueCountFrequency (%)
안성IC-안성JC 5
 
5.0%
양재IC-금토JC 5
 
5.0%
금토JC-양재IC 5
 
5.0%
청주JC-남이JC 5
 
5.0%
신갈JC-수원신갈IC 5
 
5.0%
수원신갈IC-신갈JC 5
 
5.0%
동탄JC-오산IC 5
 
5.0%
오산IC-동탄JC 5
 
5.0%
천안IC-북천안IC 5
 
5.0%
북천안IC-천안IC 5
 
5.0%
Other values (17) 50
50.0%

Length

2023-12-10T19:58:43.726351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
안성ic-안성jc 5
 
5.0%
금토jc-양재ic 5
 
5.0%
청주jc-남이jc 5
 
5.0%
신갈jc-수원신갈ic 5
 
5.0%
수원신갈ic-신갈jc 5
 
5.0%
동탄jc-오산ic 5
 
5.0%
오산ic-동탄jc 5
 
5.0%
천안ic-북천안ic 5
 
5.0%
북천안ic-천안ic 5
 
5.0%
안성jc-안성ic 5
 
5.0%
Other values (17) 50
50.0%

장비이정(km)
Real number (ℝ)

HIGH CORRELATION 

Distinct16
Distinct (%)16.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean303.687
Minimum2.2
Maximum410.41
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:58:43.931924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.2
5-th percentile24.5
Q1290.5
median326
Q3380.1
95-th percentile410.41
Maximum410.41
Range408.21
Interquartile range (IQR)89.6

Descriptive statistics

Standard deviation108.12257
Coefficient of variation (CV)0.35603291
Kurtosis1.8540012
Mean303.687
Median Absolute Deviation (MAD)49.9
Skewness-1.6151505
Sum30368.7
Variance11690.49
MonotonicityNot monotonic
2023-12-10T19:58:44.139323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
361.3 10
10.0%
345.2 10
10.0%
410.41 10
10.0%
393.2 10
10.0%
380.1 10
10.0%
258.3 7
 
7.0%
306.8 6
 
6.0%
301.9 6
 
6.0%
276.1 6
 
6.0%
297.9 5
 
5.0%
Other values (6) 20
20.0%
ValueCountFrequency (%)
2.2 2
 
2.0%
24.5 4
4.0%
44.5 2
 
2.0%
76.3 4
4.0%
258.3 7
7.0%
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 (%)
410.41 10
10.0%
393.2 10
10.0%
380.1 10
10.0%
361.3 10
10.0%
345.2 10
10.0%
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%

측정일
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
20221101
100 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20221101
2nd row20221101
3rd row20221101
4th row20221101
5th row20221101

Common Values

ValueCountFrequency (%)
20221101 100
100.0%

Length

2023-12-10T19:58:44.362174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:58:44.529443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20221101 100
100.0%

측정시간
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
15
100 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row15
2nd row15
3rd row15
4th row15
5th row15

Common Values

ValueCountFrequency (%)
15 100
100.0%

Length

2023-12-10T19:58:44.717203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:58:44.899244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
15 100
100.0%

차량통과수(대)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct59
Distinct (%)59.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45.36
Minimum0
Maximum180
Zeros26
Zeros (%)26.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:58:45.108953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median32.5
Q369.25
95-th percentile139.3
Maximum180
Range180
Interquartile range (IQR)69.25

Descriptive statistics

Standard deviation46.65812
Coefficient of variation (CV)1.0286182
Kurtosis-0.054156185
Mean45.36
Median Absolute Deviation (MAD)32.5
Skewness0.91215068
Sum4536
Variance2176.9802
MonotonicityNot monotonic
2023-12-10T19:58:45.381287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 26
26.0%
62 3
 
3.0%
29 3
 
3.0%
1 3
 
3.0%
81 3
 
3.0%
100 2
 
2.0%
26 2
 
2.0%
54 2
 
2.0%
16 2
 
2.0%
47 2
 
2.0%
Other values (49) 52
52.0%
ValueCountFrequency (%)
0 26
26.0%
1 3
 
3.0%
2 1
 
1.0%
3 1
 
1.0%
4 1
 
1.0%
5 1
 
1.0%
6 1
 
1.0%
13 1
 
1.0%
15 1
 
1.0%
16 2
 
2.0%
ValueCountFrequency (%)
180 1
1.0%
157 1
1.0%
151 1
1.0%
148 1
1.0%
145 1
1.0%
139 1
1.0%
134 1
1.0%
123 1
1.0%
122 1
1.0%
121 2
2.0%

평균 속도(km/hr)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct62
Distinct (%)62.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean65.1505
Minimum0
Maximum132.5
Zeros26
Zeros (%)26.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:58:45.703892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median80.165
Q389.525
95-th percentile109.1
Maximum132.5
Range132.5
Interquartile range (IQR)89.525

Descriptive statistics

Standard deviation40.457328
Coefficient of variation (CV)0.62098261
Kurtosis-0.87831411
Mean65.1505
Median Absolute Deviation (MAD)12.19
Skewness-0.81698801
Sum6515.05
Variance1636.7954
MonotonicityNot monotonic
2023-12-10T19:58:46.086318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 26
26.0%
75.0 6
 
6.0%
100.0 3
 
3.0%
69.33 2
 
2.0%
83.0 2
 
2.0%
103.0 2
 
2.0%
96.0 2
 
2.0%
108.5 2
 
2.0%
88.5 2
 
2.0%
112.5 1
 
1.0%
Other values (52) 52
52.0%
ValueCountFrequency (%)
0.0 26
26.0%
69.33 2
 
2.0%
70.0 1
 
1.0%
70.67 1
 
1.0%
72.0 1
 
1.0%
74.0 1
 
1.0%
74.43 1
 
1.0%
75.0 6
 
6.0%
76.22 1
 
1.0%
77.43 1
 
1.0%
ValueCountFrequency (%)
132.5 1
1.0%
123.67 1
1.0%
112.5 1
1.0%
111.25 1
1.0%
111.0 1
1.0%
109.0 1
1.0%
108.5 2
2.0%
105.0 1
1.0%
103.25 1
1.0%
103.0 2
2.0%

위도(°)
Real number (ℝ)

HIGH CORRELATION 

Distinct16
Distinct (%)16.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.825348
Minimum36.307222
Maximum37.42325
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:58:46.343123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.307222
5-th percentile36.307222
Q136.54
median36.850562
Q337.158778
95-th percentile37.42325
Maximum37.42325
Range1.1160278
Interquartile range (IQR)0.61877778

Descriptive statistics

Standard deviation0.36475994
Coefficient of variation (CV)0.0099051323
Kurtosis-1.3186764
Mean36.825348
Median Absolute Deviation (MAD)0.30821528
Skewness0.21583666
Sum3682.5348
Variance0.13304981
MonotonicityNot monotonic
2023-12-10T19:58:46.587917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
37.00888889 10
10.0%
36.86890278 10
10.0%
37.42325 10
10.0%
37.27944444 10
10.0%
37.15877778 10
10.0%
36.30722222 7
 
7.0%
36.64019722 6
 
6.0%
36.60611111 6
 
6.0%
36.38961 6
 
6.0%
36.57694444 5
 
5.0%
Other values (6) 20
20.0%
ValueCountFrequency (%)
36.30722222 7
7.0%
36.36835833 4
4.0%
36.38961 6
6.0%
36.444525 2
 
2.0%
36.46971667 4
4.0%
36.54 4
4.0%
36.55638889 4
4.0%
36.57694444 5
5.0%
36.60611111 6
6.0%
36.64019722 6
6.0%
ValueCountFrequency (%)
37.42325 10
10.0%
37.27944444 10
10.0%
37.15877778 10
10.0%
37.00888889 10
10.0%
36.86890278 10
10.0%
36.83222222 2
 
2.0%
36.64019722 6
6.0%
36.60611111 6
6.0%
36.57694444 5
5.0%
36.55638889 4
 
4.0%

경도(°)
Real number (ℝ)

HIGH CORRELATION 

Distinct16
Distinct (%)16.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.31476
Minimum126.63417
Maximum128.20082
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:58:46.828335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.63417
5-th percentile127.07728
Q1127.10583
median127.18672
Q3127.42896
95-th percentile127.88483
Maximum128.20082
Range1.5666555
Interquartile range (IQR)0.32312505

Descriptive statistics

Standard deviation0.28366283
Coefficient of variation (CV)0.0022280435
Kurtosis2.3635411
Mean127.31476
Median Absolute Deviation (MAD)0.150425
Skewness1.106415
Sum12731.476
Variance0.0804646
MonotonicityNot monotonic
2023-12-10T19:58:47.047123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
127.1491667 10
10.0%
127.1867222 10
10.0%
127.0772778 10
10.0%
127.1058333 10
10.0%
127.0883333 10
10.0%
127.5744444 7
 
7.0%
127.3781278 6
 
6.0%
127.4083333 6
 
6.0%
127.423508 6
 
6.0%
127.4277778 5
 
5.0%
Other values (6) 20
20.0%
ValueCountFrequency (%)
126.6341667 2
 
2.0%
127.0772778 10
10.0%
127.0883333 10
10.0%
127.1058333 10
10.0%
127.1491667 10
10.0%
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%
ValueCountFrequency (%)
128.2008222 4
4.0%
127.8848333 2
 
2.0%
127.6690722 4
4.0%
127.5744444 7
7.0%
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%
127.3781278 6
6.0%

기울기(°)
Real number (ℝ)

HIGH CORRELATION 

Distinct27
Distinct (%)27.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.09707377
Minimum-2.788625
Maximum2.703849
Zeros0
Zeros (%)0.0%
Negative52
Negative (%)52.0%
Memory size1.0 KiB
2023-12-10T19:58:47.265994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-2.788625
5-th percentile-1.7593238
Q1-0.73138
median-0.06339
Q30.67615675
95-th percentile1.718283
Maximum2.703849
Range5.492474
Interquartile range (IQR)1.4075368

Descriptive statistics

Standard deviation1.1686855
Coefficient of variation (CV)-12.039149
Kurtosis-0.091092428
Mean-0.09707377
Median Absolute Deviation (MAD)0.735367
Skewness0.022211902
Sum-9.707377
Variance1.3658259
MonotonicityNot monotonic
2023-12-10T19:58:47.939568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
-1.30142 5
 
5.0%
-0.73138 5
 
5.0%
0.69298 5
 
5.0%
-1.758683 5
 
5.0%
-0.139436 5
 
5.0%
0.151187 5
 
5.0%
0.671977 5
 
5.0%
-0.660004 5
 
5.0%
-0.624001 5
 
5.0%
0.61369 5
 
5.0%
Other values (17) 50
50.0%
ValueCountFrequency (%)
-2.788625 3
3.0%
-1.7715 2
 
2.0%
-1.758683 5
5.0%
-1.717451 3
3.0%
-1.30142 5
5.0%
-1.29918 2
 
2.0%
-0.807188 4
4.0%
-0.73138 5
5.0%
-0.688696 3
3.0%
-0.660004 5
5.0%
ValueCountFrequency (%)
2.703849 3
3.0%
1.73409 2
 
2.0%
1.717451 3
3.0%
1.293015 5
5.0%
1.056036 4
4.0%
0.69298 5
5.0%
0.688696 3
3.0%
0.671977 5
5.0%
0.61369 5
5.0%
0.5068 2
 
2.0%

TSP(g/km)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct71
Distinct (%)71.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4084
Minimum0
Maximum15.2
Zeros26
Zeros (%)26.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:58:48.370030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1.2
Q36.3275
95-th percentile12.611
Maximum15.2
Range15.2
Interquartile range (IQR)6.3275

Descriptive statistics

Standard deviation4.2680594
Coefficient of variation (CV)1.2522179
Kurtosis0.21616442
Mean3.4084
Median Absolute Deviation (MAD)1.2
Skewness1.1490412
Sum340.84
Variance18.216331
MonotonicityNot monotonic
2023-12-10T19:58:48.663517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 26
26.0%
0.14 2
 
2.0%
0.03 2
 
2.0%
0.02 2
 
2.0%
1.59 2
 
2.0%
8.96 1
 
1.0%
7.04 1
 
1.0%
2.24 1
 
1.0%
1.25 1
 
1.0%
8.91 1
 
1.0%
Other values (61) 61
61.0%
ValueCountFrequency (%)
0.0 26
26.0%
0.01 1
 
1.0%
0.02 2
 
2.0%
0.03 2
 
2.0%
0.07 1
 
1.0%
0.09 1
 
1.0%
0.12 1
 
1.0%
0.14 2
 
2.0%
0.16 1
 
1.0%
0.17 1
 
1.0%
ValueCountFrequency (%)
15.2 1
1.0%
15.13 1
1.0%
14.3 1
1.0%
12.91 1
1.0%
12.63 1
1.0%
12.61 1
1.0%
11.35 1
1.0%
11.2 1
1.0%
10.82 1
1.0%
10.47 1
1.0%

PM10(g/km)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct66
Distinct (%)66.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4997
Minimum0
Maximum6.69
Zeros27
Zeros (%)27.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:58:48.960116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.53
Q32.7825
95-th percentile5.5505
Maximum6.69
Range6.69
Interquartile range (IQR)2.7825

Descriptive statistics

Standard deviation1.8782619
Coefficient of variation (CV)1.2524251
Kurtosis0.21624099
Mean1.4997
Median Absolute Deviation (MAD)0.53
Skewness1.1488521
Sum149.97
Variance3.5278676
MonotonicityNot monotonic
2023-12-10T19:58:49.251496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 27
27.0%
0.01 4
 
4.0%
1.02 2
 
2.0%
0.31 2
 
2.0%
0.7 2
 
2.0%
0.06 2
 
2.0%
0.07 2
 
2.0%
3.61 1
 
1.0%
1.01 1
 
1.0%
6.69 1
 
1.0%
Other values (56) 56
56.0%
ValueCountFrequency (%)
0.0 27
27.0%
0.01 4
 
4.0%
0.03 1
 
1.0%
0.04 1
 
1.0%
0.05 1
 
1.0%
0.06 2
 
2.0%
0.07 2
 
2.0%
0.09 1
 
1.0%
0.1 1
 
1.0%
0.13 1
 
1.0%
ValueCountFrequency (%)
6.69 1
1.0%
6.66 1
1.0%
6.29 1
1.0%
5.68 1
1.0%
5.56 1
1.0%
5.55 1
1.0%
4.99 1
1.0%
4.93 1
1.0%
4.76 1
1.0%
4.61 1
1.0%

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 
경기 화성시 동탄면 송리
10 
경기 용인시 기흥구 신갈동
10 
Other values (9)
47 

Length

Max length18
Median length15
Mean length12.58
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row충북 옥천군 옥천읍 삼양리
2nd row충북 옥천군 옥천읍 삼양리
3rd row충북 옥천군 옥천읍 삼양리
4th row충북 옥천군 옥천읍 삼양리
5th row충북 옥천군 옥천읍 삼양리

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%
경기 용인시 기흥구 신갈동 10
10.0%
경기 성남시 수정구 금토동 10
10.0%
충북 옥천군 옥천읍 삼양리 7
7.0%
대전 대덕구 연축동 6
6.0%
충북 청주시 흥덕구 강서1동 6
6.0%
충북 청원군 강내면 6
6.0%
Other values (4) 12
12.0%

Length

2023-12-10T19:58:49.550401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기 40
 
11.0%
충북 36
 
9.9%
청원군 19
 
5.2%
남이면 13
 
3.6%
충남 10
 
2.7%
송리 10
 
2.7%
금토동 10
 
2.7%
수정구 10
 
2.7%
성남시 10
 
2.7%
신갈동 10
 
2.7%
Other values (30) 197
54.0%

Interactions

2023-12-10T19:58:38.509092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:58:23.080536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:58:24.610008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:58:27.691895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:58:29.388837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:58:31.064178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:58:32.687314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:58:34.686317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:58:36.376485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:58:38.695085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:58:23.223598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:58:24.815573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:58:27.930016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:58:29.583529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:58:31.193656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:58:32.958535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:58:34.847065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:58:36.565657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:58:38.880844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:58:23.389315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:58:25.033588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:58:28.144576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:58:29.769716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:58:31.426345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:58:33.200482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:58:35.037111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:58:36.741177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:58:39.074893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:58:23.611630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:58:25.705935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:58:28.335946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:58:30.007477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:58:31.607006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:58:33.405094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:58:35.208752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:58:37.367453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:58:39.255332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:58:23.818983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:58:26.035074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:58:28.505070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:58:30.186020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:58:31.761179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:58:33.665044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:58:35.391867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:58:37.568382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:58:39.421059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:58:23.966199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:58:26.308495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:58:28.664815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:58:30.342514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:58:31.909413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:58:33.859716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:58:35.587571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:58:37.727716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:58:39.635595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:58:24.129140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:58:26.623084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:58:28.866079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:58:30.522036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:58:32.158315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:58:34.055212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:58:35.787148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:58:37.921661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:58:39.811228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:58:24.272757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:58:26.930457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:58:29.036596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:58:30.729891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:58:32.342096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:58:34.234429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:58:35.958398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:58:38.137534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:58:39.993486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:58:24.446204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:58:27.297136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:58:29.213442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:58:30.893305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:58:32.508569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:58:34.488530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:58:36.178457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:58:38.321062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T19:58:49.740630image/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
기본키1.0000.9590.0000.0000.9810.9200.4870.0910.9100.8550.8930.3530.3530.946
지점0.9591.0000.1960.0001.0001.0000.2230.2921.0001.0000.9040.2900.2901.000
방향0.0000.1961.0000.0001.0000.0000.0000.0000.0000.0000.6940.1350.1350.000
차선0.0000.0000.0001.0000.0000.0880.4020.2630.0000.0000.0000.5760.5760.000
측정구간0.9811.0001.0000.0001.0001.0000.0000.3691.0001.0001.0000.3100.3101.000
장비이정(km)0.9201.0000.0000.0881.0001.0000.3850.0000.9720.9140.7470.3540.3541.000
차량통과수(대)0.4870.2230.0000.4020.0000.3851.0000.5610.4530.0000.0000.8320.8320.373
평균 속도(km/hr)0.0910.2920.0000.2630.3690.0000.5611.0000.0000.1670.0000.5410.5410.275
위도(°)0.9101.0000.0000.0001.0000.9720.4530.0001.0000.9030.7630.0000.0001.000
경도(°)0.8551.0000.0000.0001.0000.9140.0000.1670.9031.0000.7130.1910.1911.000
기울기(°)0.8930.9040.6940.0001.0000.7470.0000.0000.7630.7131.0000.1580.1580.877
TSP(g/km)0.3530.2900.1350.5760.3100.3540.8320.5410.0000.1910.1581.0001.0000.207
PM10(g/km)0.3530.2900.1350.5760.3100.3540.8320.5410.0000.1910.1581.0001.0000.207
주소0.9461.0000.0000.0001.0001.0000.3730.2751.0001.0000.8770.2070.2071.000
2023-12-10T19:58:50.022682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지점차선측정구간방향주소
지점1.0000.0000.9320.1370.988
차선0.0001.0000.0000.0000.000
측정구간0.9320.0001.0000.8630.921
방향0.1370.0000.8631.0000.000
주소0.9880.0000.9210.0001.000
2023-12-10T19:58:50.239152image/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
기본키1.0000.3600.2820.1850.543-0.4550.0410.1850.1860.7910.0000.0000.7940.766
장비이정(km)0.3601.0000.3430.0890.955-0.8590.0730.0360.0350.9450.0000.0550.8810.957
차량통과수(대)0.2820.3431.0000.6180.341-0.2550.0100.8540.8530.0000.0000.1230.0000.126
평균 속도(km/hr)0.1850.0890.6181.0000.102-0.030-0.1010.5610.5500.0990.0000.1630.1230.094
위도(°)0.5430.9550.3410.1021.000-0.9290.0770.0390.0400.9610.0000.0000.8960.972
경도(°)-0.455-0.859-0.255-0.030-0.9291.000-0.1190.0630.0610.9560.0000.0000.8910.967
기울기(°)0.0410.0730.010-0.1010.077-0.1191.000-0.137-0.1390.6330.5180.0000.9010.596
TSP(g/km)0.1850.0360.8540.5610.0390.063-0.1371.0000.9990.1060.0950.2660.0910.074
PM10(g/km)0.1860.0350.8530.5500.0400.061-0.1390.9991.0000.1060.0950.2660.0910.074
지점0.7910.9450.0000.0990.9610.9560.6330.1060.1061.0000.1370.0000.9320.988
방향0.0000.0000.0000.0000.0000.0000.5180.0950.0950.1371.0000.0000.8630.000
차선0.0000.0550.1230.1630.0000.0000.0000.2660.2660.0000.0001.0000.0000.000
측정구간0.7940.8810.0000.1230.8960.8910.9010.0910.0910.9320.8630.0001.0000.921
주소0.7660.9570.1260.0940.9720.9670.5960.0740.0740.9880.0000.0000.9211.000

Missing values

2023-12-10T19:58:40.291203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T19:58:40.737607image/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-0010-2583E-7E1금강IC-옥천IC258.3202211011500.036.307222127.574444-0.063390.00.0충북 옥천군 옥천읍 삼양리
12도로공사A-0010-2583E-7E2금강IC-옥천IC258.320221101152669.3336.307222127.574444-0.063393.111.37충북 옥천군 옥천읍 삼양리
23도로공사A-0010-2583E-7E3금강IC-옥천IC258.3202211011500.036.307222127.574444-0.063390.00.0충북 옥천군 옥천읍 삼양리
34도로공사A-0010-2583E-7S1옥천IC-금강IC258.32022110115587.536.307222127.5744440.25940.220.1충북 옥천군 옥천읍 삼양리
45도로공사A-0010-2583E-7S2옥천IC-금강IC258.320221101152975.036.307222127.5744440.25940.160.07충북 옥천군 옥천읍 삼양리
56도로공사A-0010-2583E-7S3옥천IC-금강IC258.32022110115675.036.307222127.5744440.25940.030.01충북 옥천군 옥천읍 삼양리
67도로공사A-0010-2583E-7S4옥천IC-금강IC258.3202211011500.036.307222127.5744440.25940.00.0충북 옥천군 옥천읍 삼양리
78도로공사A-0010-2761E-6E1대전IC-회덕JC276.1202211011500.036.38961127.423508-2.7886250.00.0대전 대덕구 연축동
89도로공사A-0010-2761E-6E2대전IC-회덕JC276.120221101155788.936.38961127.423508-2.7886257.943.49대전 대덕구 연축동
910도로공사A-0010-2761E-6E3대전IC-회덕JC276.120221101156179.536.38961127.423508-2.78862511.354.99대전 대덕구 연축동
기본키도로종류지점방향차선측정구간장비이정(km)측정일측정시간차량통과수(대)평균 속도(km/hr)위도(°)경도(°)기울기(°)TSP(g/km)PM10(g/km)주소
9091도로공사A-0300-0245S-4S1보은IC-회인IC24.520221101155682.4436.469717127.6690720.50686.953.06충북 보은군 수한면
9192도로공사A-0300-0245S-4S2보은IC-회인IC24.520221101152977.4336.469717127.6690720.50684.271.88충북 보은군 수한면
9293도로공사A-0300-0445S-4S1화서IC-구병산Hi44.52022110115197.036.444525127.884833-1.299180.140.06경북 상주시 화남면
9394도로공사A-0300-0445S-4S2화서IC-구병산Hi44.5202211011555108.536.444525127.884833-1.2991812.915.68경북 상주시 화남면
9495도로공사A-0300-0763S-4E1남상주IC-낙동JC76.320221101152393.7136.368358128.200822-1.77152.331.02경북 상주시 낙동면
9596도로공사A-0300-0763S-4E2남상주IC-낙동JC76.320221101156282.836.368358128.200822-1.771512.635.56경북 상주시 낙동면
9697도로공사A-0300-0763S-4S1낙동JC-남상주IC76.320221101152983.036.368358128.2008221.734091.150.51경북 상주시 낙동면
9798도로공사A-0300-0763S-4S2낙동JC-남상주IC76.320221101155479.436.368358128.2008221.734098.453.72경북 상주시 낙동면
9899도로공사A-0301-0022S-4E1당진JC-면천IC2.22022110115475.036.832222126.6341670.2962120.020.01충청 당진군 면천면 사기소리
99100도로공사A-0301-0022S-4E2당진JC-면천IC2.2202211011500.036.832222126.6341670.2962120.00.0충청 당진군 면천면 사기소리