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 지점 and 2 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)High correlation
위도(°) is highly overall correlated with 장비이정(km) and 4 other fieldsHigh correlation
경도(°) is highly overall correlated with 장비이정(km) and 4 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 27 (27.0%) zerosZeros
평균 속도(km/hr) has 27 (27.0%) zerosZeros
TSP(g/km) has 27 (27.0%) zerosZeros
PM10(g/km) has 29 (29.0%) zerosZeros

Reproduction

Analysis started2023-12-10 10:56:45.228530
Analysis finished2023-12-10 10:57:06.813463
Duration21.58 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
2023-12-10T19:57:07.026801image/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
2023-12-10T19:57:07.414270image/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

2023-12-10T19:57:07.751995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:57:07.997272image/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-0010-3801E-10
10 
A-0010-3932E-10
10 
A-0010-4105E-10
10 
A-0010-2583E-7
7 
Other values (11)
53 

Length

Max length15
Median length14
Mean length14.4
Min length14

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
A-0010-3452S-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-0010-0538E-6 6
 
6.0%
A-0010-2761E-6 6
 
6.0%
A-0010-3019E-6 6
 
6.0%
A-0010-3068E-6 6
 
6.0%
A-0010-2626S-6 6
 
6.0%
Other values (6) 23
23.0%

Length

2023-12-10T19:57:08.225245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
a-0010-3452s-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-0010-0538e-6 6
 
6.0%
a-0010-2761e-6 6
 
6.0%
a-0010-3019e-6 6
 
6.0%
a-0010-3068e-6 6
 
6.0%
a-0010-2626s-6 6
 
6.0%
Other values (6) 23
23.0%

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

2023-12-10T19:57:08.569471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:57:08.865725image/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
23 
4
12 
5
9 

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 23
23.0%
4 12
12.0%
5 9
 
9.0%

Length

2023-12-10T19:57:09.057101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:57:09.254717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 28
28.0%
2 28
28.0%
3 23
23.0%
4 12
12.0%
5 9
 
9.0%

측정구간
Categorical

HIGH CORRELATION 

Distinct28
Distinct (%)28.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
북천안IC-천안IC
 
5
신갈JC-수원신갈IC
 
5
오산IC-동탄JC
 
5
동탄JC-오산IC
 
5
수원신갈IC-신갈JC
 
5
Other values (23)
75 

Length

Max length11
Median length9
Mean length9.46
Min length9

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row언양JC-활천IC
2nd row언양JC-활천IC
3rd row언양JC-활천IC
4th row활천IC-언양JC
5th row활천IC-언양JC

Common Values

ValueCountFrequency (%)
북천안IC-천안IC 5
 
5.0%
신갈JC-수원신갈IC 5
 
5.0%
오산IC-동탄JC 5
 
5.0%
동탄JC-오산IC 5
 
5.0%
수원신갈IC-신갈JC 5
 
5.0%
천안IC-북천안IC 5
 
5.0%
금토JC-양재IC 5
 
5.0%
청주JC-남이JC 5
 
5.0%
양재IC-금토JC 5
 
5.0%
옥천IC-금강IC 4
 
4.0%
Other values (18) 51
51.0%

Length

2023-12-10T19:57:09.628752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
북천안ic-천안ic 5
 
5.0%
천안ic-북천안ic 5
 
5.0%
신갈jc-수원신갈ic 5
 
5.0%
청주jc-남이jc 5
 
5.0%
금토jc-양재ic 5
 
5.0%
양재ic-금토jc 5
 
5.0%
수원신갈ic-신갈jc 5
 
5.0%
동탄jc-오산ic 5
 
5.0%
오산ic-동탄jc 5
 
5.0%
옥천ic-금강ic 4
 
4.0%
Other values (18) 51
51.0%

장비이정(km)
Real number (ℝ)

HIGH CORRELATION 

Distinct16
Distinct (%)16.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean283.041
Minimum1.6
Maximum410.41
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:57:10.041967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.6
5-th percentile15.3
Q1262.6
median301.9
Q3380.1
95-th percentile410.41
Maximum410.41
Range408.81
Interquartile range (IQR)117.5

Descriptive statistics

Standard deviation121.80477
Coefficient of variation (CV)0.43034319
Kurtosis0.53356437
Mean283.041
Median Absolute Deviation (MAD)43.6
Skewness-1.2633367
Sum28304.1
Variance14836.402
MonotonicityNot monotonic
2023-12-10T19:57:10.371001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
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%
262.6 6
 
6.0%
53.4 6
 
6.0%
301.9 6
 
6.0%
276.1 6
 
6.0%
Other values (6) 23
23.0%
ValueCountFrequency (%)
1.6 4
4.0%
15.3 4
4.0%
40.7 2
 
2.0%
53.4 6
6.0%
258.3 7
7.0%
262.6 6
6.0%
276.1 6
6.0%
295.3 4
4.0%
295.6 4
4.0%
297.9 5
5.0%
ValueCountFrequency (%)
410.41 10
10.0%
393.2 10
10.0%
380.1 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%
276.1 6
6.0%

측정일
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
20230201
100 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20230201
2nd row20230201
3rd row20230201
4th row20230201
5th row20230201

Common Values

ValueCountFrequency (%)
20230201 100
100.0%

Length

2023-12-10T19:57:10.675368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:57:10.961107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20230201 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

2023-12-10T19:57:11.645123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:57:11.821736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 100
100.0%

차량통과수(대)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct52
Distinct (%)52.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42.31
Minimum0
Maximum187
Zeros27
Zeros (%)27.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:57:12.188664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median26
Q362.5
95-th percentile149.05
Maximum187
Range187
Interquartile range (IQR)62.5

Descriptive statistics

Standard deviation49.067403
Coefficient of variation (CV)1.1597117
Kurtosis0.42366419
Mean42.31
Median Absolute Deviation (MAD)26
Skewness1.1790713
Sum4231
Variance2407.61
MonotonicityNot monotonic
2023-12-10T19:57:12.481710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 27
27.0%
2 3
 
3.0%
1 3
 
3.0%
119 3
 
3.0%
17 3
 
3.0%
16 3
 
3.0%
150 2
 
2.0%
30 2
 
2.0%
40 2
 
2.0%
79 2
 
2.0%
Other values (42) 50
50.0%
ValueCountFrequency (%)
0 27
27.0%
1 3
 
3.0%
2 3
 
3.0%
5 2
 
2.0%
7 1
 
1.0%
8 1
 
1.0%
10 2
 
2.0%
13 1
 
1.0%
16 3
 
3.0%
17 3
 
3.0%
ValueCountFrequency (%)
187 1
 
1.0%
174 1
 
1.0%
158 1
 
1.0%
150 2
2.0%
149 1
 
1.0%
147 1
 
1.0%
132 1
 
1.0%
131 1
 
1.0%
122 1
 
1.0%
119 3
3.0%

평균 속도(km/hr)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct66
Distinct (%)66.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean65.4194
Minimum0
Maximum124.25
Zeros27
Zeros (%)27.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:57:12.765008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median81.69
Q392.5625
95-th percentile108.2
Maximum124.25
Range124.25
Interquartile range (IQR)92.5625

Descriptive statistics

Standard deviation41.348383
Coefficient of variation (CV)0.63205078
Kurtosis-0.98476311
Mean65.4194
Median Absolute Deviation (MAD)12.06
Skewness-0.81884331
Sum6541.94
Variance1709.6888
MonotonicityNot monotonic
2023-12-10T19:57:13.054754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 27
27.0%
75.0 3
 
3.0%
99.0 2
 
2.0%
94.5 2
 
2.0%
104.5 2
 
2.0%
82.0 2
 
2.0%
83.2 2
 
2.0%
106.0 2
 
2.0%
81.38 1
 
1.0%
80.71 1
 
1.0%
Other values (56) 56
56.0%
ValueCountFrequency (%)
0.0 27
27.0%
70.4 1
 
1.0%
72.8 1
 
1.0%
73.0 1
 
1.0%
75.0 3
 
3.0%
75.25 1
 
1.0%
75.62 1
 
1.0%
76.67 1
 
1.0%
76.78 1
 
1.0%
76.83 1
 
1.0%
ValueCountFrequency (%)
124.25 1
1.0%
123.5 1
1.0%
118.0 1
1.0%
113.0 1
1.0%
112.0 1
1.0%
108.0 1
1.0%
106.0 2
2.0%
104.5 2
2.0%
104.25 1
1.0%
104.0 1
1.0%

위도(°)
Real number (ℝ)

HIGH CORRELATION 

Distinct16
Distinct (%)16.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.580438
Minimum34.959722
Maximum37.42325
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:57:13.312562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum34.959722
5-th percentile35.020278
Q136.332222
median36.606111
Q337.158778
95-th percentile37.42325
Maximum37.42325
Range2.4635278
Interquartile range (IQR)0.82655556

Descriptive statistics

Standard deviation0.66094705
Coefficient of variation (CV)0.01806832
Kurtosis0.29370224
Mean36.580438
Median Absolute Deviation (MAD)0.29888889
Skewness-0.8749892
Sum3658.0438
Variance0.436851
MonotonicityNot monotonic
2023-12-10T19:57:13.608974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
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%
36.33222222 6
 
6.0%
35.68194444 6
 
6.0%
36.60611111 6
 
6.0%
36.38961 6
 
6.0%
Other values (6) 23
23.0%
ValueCountFrequency (%)
34.95972222 4
4.0%
35.02027778 2
 
2.0%
35.384807 4
4.0%
35.68194444 6
6.0%
36.30722222 7
7.0%
36.33222222 6
6.0%
36.38961 6
6.0%
36.54 4
4.0%
36.55638889 4
4.0%
36.57694444 5
5.0%
ValueCountFrequency (%)
37.42325 10
10.0%
37.27944444 10
10.0%
37.15877778 10
10.0%
36.86890278 10
10.0%
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%
Mean127.4293
Minimum127.07728
Maximum129.18111
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:57:13.836172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum127.07728
5-th percentile127.07728
Q1127.10583
median127.39323
Q3127.45896
95-th percentile129.18111
Maximum129.18111
Range2.1038333
Interquartile range (IQR)0.35312505

Descriptive statistics

Standard deviation0.48577983
Coefficient of variation (CV)0.0038121517
Kurtosis7.8966413
Mean127.4293
Median Absolute Deviation (MAD)0.20650835
Skewness2.7990904
Sum12742.93
Variance0.23598204
MonotonicityNot monotonic
2023-12-10T19:57:14.228373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
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%
127.5341667 6
 
6.0%
129.1811111 6
 
6.0%
127.4083333 6
 
6.0%
127.423508 6
 
6.0%
Other values (6) 23
23.0%
ValueCountFrequency (%)
127.0772778 10
10.0%
127.0883333 10
10.0%
127.1058333 10
10.0%
127.1867222 10
10.0%
127.234671 4
 
4.0%
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%
ValueCountFrequency (%)
129.1811111 6
6.0%
127.8597222 2
 
2.0%
127.6283333 4
4.0%
127.5744444 7
7.0%
127.5341667 6
6.0%
127.4338889 4
4.0%
127.4325 4
4.0%
127.4277778 5
5.0%
127.423508 6
6.0%
127.4083333 6
6.0%

기울기(°)
Real number (ℝ)

HIGH CORRELATION 

Distinct28
Distinct (%)28.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.08162426
Minimum-2.788625
Maximum2.703849
Zeros0
Zeros (%)0.0%
Negative50
Negative (%)50.0%
Memory size1.0 KiB
2023-12-10T19:57:14.465224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-2.788625
5-th percentile-1.7606739
Q1-0.73138
median0.0438985
Q30.671977
95-th percentile1.7272469
Maximum2.703849
Range5.492474
Interquartile range (IQR)1.403357

Descriptive statistics

Standard deviation1.1388355
Coefficient of variation (CV)-13.95217
Kurtosis0.20605462
Mean-0.08162426
Median Absolute Deviation (MAD)0.685901
Skewness-0.0036625342
Sum-8.162426
Variance1.2969464
MonotonicityNot monotonic
2023-12-10T19:57:14.737379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
0.61369 5
 
5.0%
-0.660004 5
 
5.0%
-0.73138 5
 
5.0%
-1.758683 5
 
5.0%
0.69298 5
 
5.0%
-0.139436 5
 
5.0%
0.151187 5
 
5.0%
0.671977 5
 
5.0%
-0.624001 5
 
5.0%
0.2594 4
 
4.0%
Other values (18) 51
51.0%
ValueCountFrequency (%)
-2.788625 3
3.0%
-1.7985 2
 
2.0%
-1.758683 5
5.0%
-1.717451 3
3.0%
-1.259975 2
 
2.0%
-1.22799 3
3.0%
-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%
1.91337 2
 
2.0%
1.717451 3
3.0%
1.22799 3
3.0%
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%
0.440648 2
 
2.0%

TSP(g/km)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct71
Distinct (%)71.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.1574
Minimum0
Maximum20.2
Zeros27
Zeros (%)27.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:57:15.240897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1.065
Q35.705
95-th percentile9.2915
Maximum20.2
Range20.2
Interquartile range (IQR)5.705

Descriptive statistics

Standard deviation4.1740213
Coefficient of variation (CV)1.3219805
Kurtosis3.109834
Mean3.1574
Median Absolute Deviation (MAD)1.065
Skewness1.6740709
Sum315.74
Variance17.422454
MonotonicityNot monotonic
2023-12-10T19:57:15.583575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 27
27.0%
0.02 2
 
2.0%
0.01 2
 
2.0%
1.9 2
 
2.0%
3.75 1
 
1.0%
1.05 1
 
1.0%
8.37 1
 
1.0%
7.61 1
 
1.0%
7.76 1
 
1.0%
2.8 1
 
1.0%
Other values (61) 61
61.0%
ValueCountFrequency (%)
0.0 27
27.0%
0.01 2
 
2.0%
0.02 2
 
2.0%
0.07 1
 
1.0%
0.08 1
 
1.0%
0.09 1
 
1.0%
0.1 1
 
1.0%
0.11 1
 
1.0%
0.15 1
 
1.0%
0.18 1
 
1.0%
ValueCountFrequency (%)
20.2 1
1.0%
17.27 1
1.0%
15.52 1
1.0%
13.31 1
1.0%
9.89 1
1.0%
9.26 1
1.0%
9.21 1
1.0%
9.12 1
1.0%
8.6 1
1.0%
8.38 1
1.0%

PM10(g/km)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct65
Distinct (%)65.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3889
Minimum0
Maximum8.89
Zeros29
Zeros (%)29.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:57:16.085027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.465
Q32.51
95-th percentile4.084
Maximum8.89
Range8.89
Interquartile range (IQR)2.51

Descriptive statistics

Standard deviation1.8367032
Coefficient of variation (CV)1.3224157
Kurtosis3.1149184
Mean1.3889
Median Absolute Deviation (MAD)0.465
Skewness1.6749966
Sum138.89
Variance3.3734786
MonotonicityNot monotonic
2023-12-10T19:57:16.355474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 29
29.0%
0.01 2
 
2.0%
0.04 2
 
2.0%
0.84 2
 
2.0%
0.93 2
 
2.0%
0.44 2
 
2.0%
0.03 2
 
2.0%
0.36 2
 
2.0%
2.48 1
 
1.0%
8.89 1
 
1.0%
Other values (55) 55
55.0%
ValueCountFrequency (%)
0.0 29
29.0%
0.01 2
 
2.0%
0.03 2
 
2.0%
0.04 2
 
2.0%
0.05 1
 
1.0%
0.06 1
 
1.0%
0.08 1
 
1.0%
0.1 1
 
1.0%
0.14 1
 
1.0%
0.15 1
 
1.0%
ValueCountFrequency (%)
8.89 1
1.0%
7.6 1
1.0%
6.83 1
1.0%
5.86 1
1.0%
4.35 1
1.0%
4.07 1
1.0%
4.05 1
1.0%
4.01 1
1.0%
3.79 1
1.0%
3.69 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 화성시 동탄면 송리
10 
경기 용인시 기흥구 신갈동
10 
경기 성남시 수정구 금토동
10 
Other values (9)
47 

Length

Max length18
Median length15
Mean length13.24
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row울산 울주군 두서면 활천리
2nd row울산 울주군 두서면 활천리
3rd row울산 울주군 두서면 활천리
4th row울산 울주군 두서면 활천리
5th row울산 울주군 두서면 활천리

Common Values

ValueCountFrequency (%)
충북 청원군 남이면 13
13.0%
충남 천안시 서북구 성거읍 송남리 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%
울산 울주군 두서면 활천리 6
 
6.0%
대전 대덕구 연축동 6
 
6.0%
충북 청주시 흥덕구 강서1동 6
 
6.0%
충북 청원군 강내면 6
 
6.0%
Other values (4) 16
16.0%

Length

2023-12-10T19:57:16.619480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
충북 38
 
9.9%
경기 30
 
7.8%
청원군 19
 
5.0%
남이면 13
 
3.4%
옥천군 13
 
3.4%
송리 10
 
2.6%
금토동 10
 
2.6%
수정구 10
 
2.6%
성남시 10
 
2.6%
기흥구 10
 
2.6%
Other values (34) 220
57.4%

Interactions

2023-12-10T19:57:04.075240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:56:48.500759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:56:50.095665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:56:51.979479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:56:53.828221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:56:55.374300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:56:57.166036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:56:59.657096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:57:01.554473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:57:04.248837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:56:48.670205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:56:50.250335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:56:52.198300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:56:53.990110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:56:55.609771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:56:57.749845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:56:59.854519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:57:01.783087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:57:04.431618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:56:48.833722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:56:50.443092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:56:52.402727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:56:54.169281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:56:55.790087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:56:57.960254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:57:00.074663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:57:02.066039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:57:04.594915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:56:49.001270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:56:50.742136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:56:52.643338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:56:54.343453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:56:55.976388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:56:58.148514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:57:00.326318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:57:02.423298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:57:04.790596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:56:49.179299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:56:50.973234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:56:52.912138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:56:54.525550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:56:56.149491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:56:58.336422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:57:00.500428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:57:02.658022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:57:05.024199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:56:49.427415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:56:51.254238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:56:53.110788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:56:54.683507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:56:56.328226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:56:58.609002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:57:00.678339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:57:02.919938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:57:05.218534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:56:49.578298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:56:51.447124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:56:53.262992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:56:54.838354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:56:56.629013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:56:58.881162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:57:00.861732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:57:03.240060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:57:05.407549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:56:49.733333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:56:51.634373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:56:53.443138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:56:55.006365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:56:56.784508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:56:59.113122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:57:01.160160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:57:03.448024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:57:05.638905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:56:49.909630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:56:51.822654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:56:53.657672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:56:55.203971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:56:57.017216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:56:59.467992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:57:01.372110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:57:03.858683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T19:57:16.836649image/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
기본키1.0000.9500.0000.0000.9840.9300.6580.5150.9000.9360.8710.0000.0000.946
지점0.9501.0000.0000.0001.0001.0000.4850.6151.0001.0000.9110.2150.2151.000
방향0.0000.0001.0000.0001.0000.0000.3450.1200.0000.0000.3120.0000.0000.000
차선0.0000.0000.0001.0000.0000.1350.0000.3600.0000.0000.0000.4010.4010.000
측정구간0.9841.0001.0000.0001.0001.0000.5280.6361.0001.0001.0000.0000.0001.000
장비이정(km)0.9301.0000.0000.1351.0001.0000.4750.5271.0000.8750.7040.0000.0001.000
차량통과수(대)0.6580.4850.3450.0000.5280.4751.0000.4540.4810.4980.3800.6660.6660.486
평균 속도(km/hr)0.5150.6150.1200.3600.6360.5270.4541.0000.4010.3530.4240.5490.5490.590
위도(°)0.9001.0000.0000.0001.0001.0000.4810.4011.0000.9040.7040.0000.0001.000
경도(°)0.9361.0000.0000.0001.0000.8750.4980.3530.9041.0000.8800.0000.0001.000
기울기(°)0.8710.9110.3120.0001.0000.7040.3800.4240.7040.8801.0000.0880.0880.885
TSP(g/km)0.0000.2150.0000.4010.0000.0000.6660.5490.0000.0000.0881.0001.0000.000
PM10(g/km)0.0000.2150.0000.4010.0000.0000.6660.5490.0000.0000.0881.0001.0000.000
주소0.9461.0000.0000.0001.0001.0000.4860.5901.0001.0000.8850.0000.0001.000
2023-12-10T19:57:17.116665image/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
2023-12-10T19:57:17.334229image/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
기본키1.0000.2580.2710.1810.257-0.426-0.0100.1580.1450.7590.0000.0000.8000.766
장비이정(km)0.2581.0000.242-0.1730.999-0.8950.029-0.0020.0050.9450.0000.0880.8750.957
차량통과수(대)0.2710.2421.0000.5310.234-0.2110.0060.8700.8670.2020.2520.0000.1880.211
평균 속도(km/hr)0.181-0.1730.5311.000-0.1720.098-0.0020.4620.4450.3310.0820.2510.2990.322
위도(°)0.2570.9990.234-0.1721.000-0.9090.032-0.007-0.0020.9560.0000.0000.8850.967
경도(°)-0.426-0.895-0.2110.098-0.9091.000-0.0490.0300.0360.9400.0000.0000.8710.951
기울기(°)-0.0100.0290.006-0.0020.032-0.0491.000-0.102-0.0990.6480.2270.0000.8940.612
TSP(g/km)0.158-0.0020.8700.462-0.0070.030-0.1021.0000.9980.0760.0000.2380.0000.000
PM10(g/km)0.1450.0050.8670.445-0.0020.036-0.0990.9981.0000.0760.0000.2380.0000.000
지점0.7590.9450.2020.3310.9560.9400.6480.0760.0761.0000.0000.0000.9260.988
방향0.0000.0000.2520.0820.0000.0000.2270.0000.0000.0001.0000.0000.8570.000
차선0.0000.0880.0000.2510.0000.0000.0000.2380.2380.0000.0001.0000.0000.000
측정구간0.8000.8750.1880.2990.8850.8710.8940.0000.0000.9260.8570.0001.0000.915
주소0.7660.9570.2110.3220.9670.9510.6120.0000.0000.9880.0000.0000.9151.000

Missing values

2023-12-10T19:57:05.955526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T19:57:06.596745image/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-0010-0538E-6E1언양JC-활천IC53.4202302010299.035.681944129.1811110.225450.020.01울산 울주군 두서면 활천리
12도로공사A-0010-0538E-6E2언양JC-활천IC53.42023020103090.235.681944129.1811110.225451.00.44울산 울주군 두서면 활천리
23도로공사A-0010-0538E-6E3언양JC-활천IC53.42023020102478.3835.681944129.1811110.225453.331.46울산 울주군 두서면 활천리
34도로공사A-0010-0538E-6S1활천IC-언양JC53.4202302010899.035.681944129.1811110.191580.070.03울산 울주군 두서면 활천리
45도로공사A-0010-0538E-6S2활천IC-언양JC53.42023020103292.535.681944129.1811110.191580.970.43울산 울주군 두서면 활천리
56도로공사A-0010-0538E-6S3활천IC-언양JC53.42023020103176.6735.681944129.1811110.191584.441.95울산 울주군 두서면 활천리
67도로공사A-0010-2583E-7E1금강IC-옥천IC258.32023020101775.036.307222127.574444-0.063390.10.04충북 옥천군 옥천읍 삼양리
78도로공사A-0010-2583E-7E2금강IC-옥천IC258.32023020103388.6236.307222127.574444-0.063395.92.6충북 옥천군 옥천읍 삼양리
89도로공사A-0010-2583E-7E3금강IC-옥천IC258.32023020103775.6236.307222127.574444-0.063398.383.69충북 옥천군 옥천읍 삼양리
910도로공사A-0010-2583E-7S1옥천IC-금강IC258.320230201010118.036.307222127.5744440.25940.110.05충북 옥천군 옥천읍 삼양리
기본키도로종류지점방향차선측정구간장비이정(km)측정일측정시간차량통과수(대)평균 속도(km/hr)위도(°)경도(°)기울기(°)TSP(g/km)PM10(g/km)주소
9091도로공사A-0100-0016S-4E1서순천IC-순천IC1.62023020105106.035.384807127.2346711.913370.310.14전남 순천시 서면 죽평리
9192도로공사A-0100-0016S-4E2서순천IC-순천IC1.62023020101783.235.384807127.2346711.913372.120.93전남 순천시 서면 죽평리
9293도로공사A-0100-0016S-4S1순천IC-서순천IC1.62023020101101.035.384807127.234671-1.79850.010.0전남 순천시 서면 죽평리
9394도로공사A-0100-0016S-4S2순천IC-서순천IC1.62023020101082.435.384807127.234671-1.79851.950.86전남 순천시 서면 죽평리
9495도로공사A-0100-0153S-4E1광양IC-동광양IC15.32023020107494.534.959722127.628333-0.3373732.371.04전남 광양시 광양읍 죽림리
9596도로공사A-0100-0153S-4E2광양IC-동광양IC15.32023020106480.2234.959722127.628333-0.3373736.732.96전남 광양시 광양읍 죽림리
9697도로공사A-0100-0153S-4S1동광양IC-광양IC15.32023020103195.634.959722127.6283330.4406482.010.88전남 광양시 광양읍 죽림리
9798도로공사A-0100-0153S-4S2동광양IC-광양IC15.32023020105386.6734.959722127.6283330.4406487.363.24전남 광양시 광양읍 죽림리
9899도로공사A-0100-0407S-4E1하동IC-진교IC40.720230201016123.535.020278127.859722-1.2599750.340.15경남 하동군 고전면
99100도로공사A-0100-0407S-4E2하동IC-진교IC40.72023020104181.2935.020278127.859722-1.2599754.31.89경남 하동군 고전면