Overview

Dataset statistics

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

Variable types

Numeric9
Categorical8

Alerts

도로종류 has constant value ""Constant
측정일 has constant value ""Constant
측정시간 has constant value ""Constant
지점 is highly overall correlated with 기본키 and 6 other fieldsHigh correlation
측정구간 is highly overall correlated with 기본키 and 7 other fieldsHigh correlation
주소 is highly overall correlated with 기본키 and 6 other fieldsHigh correlation
기본키 is highly overall correlated with 장비이정(km) and 5 other fieldsHigh correlation
장비이정(km) is highly overall correlated with 기본키 and 5 other fieldsHigh correlation
차량통과수(대) is highly overall correlated with TSP(g/km) and 1 other fieldsHigh correlation
위도(°) is highly overall correlated with 기본키 and 5 other fieldsHigh correlation
경도(°) is highly overall correlated with 기본키 and 5 other fieldsHigh correlation
기울기(°) is highly overall correlated with 지점 and 2 other fieldsHigh correlation
TSP(g/km) is highly overall correlated with 차량통과수(대) and 1 other fieldsHigh correlation
PM10(g/km) is highly overall correlated with 차량통과수(대) and 1 other fieldsHigh correlation
방향 is highly overall correlated with 측정구간High correlation
기본키 has unique valuesUnique
차량통과수(대) has 17 (17.0%) zerosZeros
평균 속도(km/hr) has 17 (17.0%) zerosZeros
TSP(g/km) has 17 (17.0%) zerosZeros
PM10(g/km) has 20 (20.0%) zerosZeros

Reproduction

Analysis started2023-12-10 11:12:41.815842
Analysis finished2023-12-10 11:12:53.757768
Duration11.94 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기본키
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.5
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:12:53.854108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

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

Descriptive statistics

Standard deviation29.011492
Coefficient of variation (CV)0.57448499
Kurtosis-1.2
Mean50.5
Median Absolute Deviation (MAD)25
Skewness0
Sum5050
Variance841.66667
MonotonicityStrictly increasing
2023-12-10T20:12:54.028999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.0%
65 1
 
1.0%
75 1
 
1.0%
74 1
 
1.0%
73 1
 
1.0%
72 1
 
1.0%
71 1
 
1.0%
70 1
 
1.0%
69 1
 
1.0%
68 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
1 1
1.0%
2 1
1.0%
3 1
1.0%
4 1
1.0%
5 1
1.0%
6 1
1.0%
7 1
1.0%
8 1
1.0%
9 1
1.0%
10 1
1.0%
ValueCountFrequency (%)
100 1
1.0%
99 1
1.0%
98 1
1.0%
97 1
1.0%
96 1
1.0%
95 1
1.0%
94 1
1.0%
93 1
1.0%
92 1
1.0%
91 1
1.0%

도로종류
Categorical

CONSTANT 

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

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
도로공사 100
100.0%

Length

2023-12-10T20:12:54.236453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:12:54.350605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
도로공사 100
100.0%

지점
Categorical

HIGH CORRELATION 

Distinct15
Distinct (%)15.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
A-0010-3452S-10
10 
A-0010-3613E-10
10 
A-0010-3801E-10
10 
A-0010-3352E-9
9 
A-0010-1185E-8
8 
Other values (10)
53 

Length

Max length15
Median length14
Mean length14.32
Min length14

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
A-0010-3452S-10 10
10.0%
A-0010-3613E-10 10
10.0%
A-0010-3801E-10 10
10.0%
A-0010-3352E-9 9
9.0%
A-0010-1185E-8 8
 
8.0%
A-0010-2583E-7 7
 
7.0%
A-0010-0083E-6 6
 
6.0%
A-0010-0538E-6 6
 
6.0%
A-0010-2695C-6 6
 
6.0%
A-0010-2761E-6 6
 
6.0%
Other values (5) 22
22.0%

Length

2023-12-10T20:12:54.483726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
a-0010-3452s-10 10
10.0%
a-0010-3613e-10 10
10.0%
a-0010-3801e-10 10
10.0%
a-0010-3352e-9 9
9.0%
a-0010-1185e-8 8
 
8.0%
a-0010-2583e-7 7
 
7.0%
a-0010-0083e-6 6
 
6.0%
a-0010-0538e-6 6
 
6.0%
a-0010-2695c-6 6
 
6.0%
a-0010-2761e-6 6
 
6.0%
Other values (5) 22
22.0%

방향
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
S
52 
E
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 (%)
S 52
52.0%
E 48
48.0%

Length

2023-12-10T20:12:54.686917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:12:54.849302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
s 52
52.0%
e 48
48.0%

차선
Categorical

Distinct5
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
1
26 
2
26 
3
25 
4
14 
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 26
26.0%
2 26
26.0%
3 25
25.0%
4 14
14.0%
5 9
 
9.0%

Length

2023-12-10T20:12:55.010538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:12:55.184134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 26
26.0%
2 26
26.0%
3 25
25.0%
4 14
14.0%
5 9
 
9.0%

측정구간
Categorical

HIGH CORRELATION 

Distinct26
Distinct (%)26.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
청주JC-남이JC
 
5
오산IC-동탄JC
 
5
안성JC-안성IC
 
5
안성IC-안성JC
 
5
북천안IC-천안IC
 
5
Other values (21)
75 

Length

Max length11
Median length9
Mean length9.26
Min length9

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
청주JC-남이JC 5
 
5.0%
오산IC-동탄JC 5
 
5.0%
안성JC-안성IC 5
 
5.0%
안성IC-안성JC 5
 
5.0%
북천안IC-천안IC 5
 
5.0%
천안IC-북천안IC 5
 
5.0%
천안IC-천안JC 5
 
5.0%
남이JC-청주JC 5
 
5.0%
동탄JC-오산IC 5
 
5.0%
옥천IC-금강IC 4
 
4.0%
Other values (16) 51
51.0%

Length

2023-12-10T20:12:55.353185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
청주jc-남이jc 5
 
5.0%
안성jc-안성ic 5
 
5.0%
안성ic-안성jc 5
 
5.0%
북천안ic-천안ic 5
 
5.0%
천안ic-북천안ic 5
 
5.0%
천안ic-천안jc 5
 
5.0%
남이jc-청주jc 5
 
5.0%
동탄jc-오산ic 5
 
5.0%
오산ic-동탄jc 5
 
5.0%
천안jc-천안ic 4
 
4.0%
Other values (16) 51
51.0%

장비이정(km)
Real number (ℝ)

HIGH CORRELATION 

Distinct15
Distinct (%)15.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean270.741
Minimum8.3
Maximum393.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:12:55.502909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8.3
5-th percentile8.3
Q1258.3
median298.7
Q3345.2
95-th percentile380.1
Maximum393.2
Range384.9
Interquartile range (IQR)86.9

Descriptive statistics

Standard deviation111.26456
Coefficient of variation (CV)0.41096308
Kurtosis0.35088339
Mean270.741
Median Absolute Deviation (MAD)46.5
Skewness-1.2498465
Sum27074.1
Variance12379.801
MonotonicityIncreasing
2023-12-10T20:12:55.664088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
345.2 10
10.0%
361.3 10
10.0%
380.1 10
10.0%
335.2 9
9.0%
118.5 8
 
8.0%
258.3 7
 
7.0%
8.3 6
 
6.0%
53.4 6
 
6.0%
269.5 6
 
6.0%
276.1 6
 
6.0%
Other values (5) 22
22.0%
ValueCountFrequency (%)
8.3 6
6.0%
53.4 6
6.0%
118.5 8
8.0%
258.3 7
7.0%
269.5 6
6.0%
276.1 6
6.0%
295.3 4
4.0%
297.9 5
5.0%
298.7 5
5.0%
306.8 6
6.0%
ValueCountFrequency (%)
393.2 2
 
2.0%
380.1 10
10.0%
361.3 10
10.0%
345.2 10
10.0%
335.2 9
9.0%
306.8 6
6.0%
298.7 5
5.0%
297.9 5
5.0%
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
20201001
100 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20201001
2nd row20201001
3rd row20201001
4th row20201001
5th row20201001

Common Values

ValueCountFrequency (%)
20201001 100
100.0%

Length

2023-12-10T20:12:55.811793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:12:55.919728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20201001 100
100.0%

측정시간
Categorical

CONSTANT 

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

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 100
100.0%

Length

2023-12-10T20:12:56.018266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:12:56.114527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 100
100.0%

차량통과수(대)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct60
Distinct (%)60.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.47
Minimum0
Maximum176
Zeros17
Zeros (%)17.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:12:56.225991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median22
Q364.25
95-th percentile131.1
Maximum176
Range176
Interquartile range (IQR)60.25

Descriptive statistics

Standard deviation43.758394
Coefficient of variation (CV)1.137468
Kurtosis0.6526922
Mean38.47
Median Absolute Deviation (MAD)21
Skewness1.2566556
Sum3847
Variance1914.7971
MonotonicityNot monotonic
2023-12-10T20:12:56.377708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 17
 
17.0%
4 5
 
5.0%
29 3
 
3.0%
1 3
 
3.0%
12 3
 
3.0%
10 3
 
3.0%
18 3
 
3.0%
33 2
 
2.0%
15 2
 
2.0%
24 2
 
2.0%
Other values (50) 57
57.0%
ValueCountFrequency (%)
0 17
17.0%
1 3
 
3.0%
2 2
 
2.0%
3 2
 
2.0%
4 5
 
5.0%
5 1
 
1.0%
8 1
 
1.0%
9 1
 
1.0%
10 3
 
3.0%
12 3
 
3.0%
ValueCountFrequency (%)
176 1
1.0%
156 1
1.0%
147 1
1.0%
141 1
1.0%
133 1
1.0%
131 1
1.0%
126 1
1.0%
116 1
1.0%
111 1
1.0%
110 1
1.0%

평균 속도(km/hr)
Real number (ℝ)

ZEROS 

Distinct67
Distinct (%)67.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean81.958
Minimum0
Maximum140
Zeros17
Zeros (%)17.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:12:56.547805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q181.4475
median93.5
Q3103.25
95-th percentile127.15
Maximum140
Range140
Interquartile range (IQR)21.8025

Descriptive statistics

Standard deviation39.802854
Coefficient of variation (CV)0.48564941
Kurtosis0.47182491
Mean81.958
Median Absolute Deviation (MAD)10.665
Skewness-1.2759338
Sum8195.8
Variance1584.2672
MonotonicityNot monotonic
2023-12-10T20:12:56.814189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 17
 
17.0%
100.0 4
 
4.0%
118.0 3
 
3.0%
85.0 2
 
2.0%
116.0 2
 
2.0%
81.0 2
 
2.0%
83.0 2
 
2.0%
81.5 2
 
2.0%
96.0 2
 
2.0%
104.0 2
 
2.0%
Other values (57) 62
62.0%
ValueCountFrequency (%)
0.0 17
17.0%
64.58 1
 
1.0%
70.0 1
 
1.0%
78.75 1
 
1.0%
79.33 1
 
1.0%
80.0 1
 
1.0%
81.0 2
 
2.0%
81.29 1
 
1.0%
81.5 2
 
2.0%
83.0 2
 
2.0%
ValueCountFrequency (%)
140.0 1
 
1.0%
139.0 1
 
1.0%
136.0 1
 
1.0%
131.0 1
 
1.0%
130.0 1
 
1.0%
127.0 1
 
1.0%
120.27 1
 
1.0%
118.5 1
 
1.0%
118.0 3
3.0%
116.0 2
2.0%

위도(°)
Real number (ℝ)

HIGH CORRELATION 

Distinct15
Distinct (%)15.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.515046
Minimum35.306944
Maximum37.279444
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:12:56.996279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.306944
5-th percentile35.306944
Q136.307222
median36.584444
Q336.868903
95-th percentile37.158778
Maximum37.279444
Range1.9725
Interquartile range (IQR)0.56168056

Descriptive statistics

Standard deviation0.51488352
Coefficient of variation (CV)0.014100585
Kurtosis-0.039204354
Mean36.515046
Median Absolute Deviation (MAD)0.28445834
Skewness-0.79878217
Sum3651.5046
Variance0.26510504
MonotonicityIncreasing
2023-12-10T20:12:57.156871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
36.86890278 10
10.0%
37.00888889 10
10.0%
37.15877778 10
10.0%
36.78083333 9
9.0%
35.90473241 8
 
8.0%
36.30722222 7
 
7.0%
35.30694444 6
 
6.0%
35.68194444 6
 
6.0%
36.3475 6
 
6.0%
36.38961 6
 
6.0%
Other values (5) 22
22.0%
ValueCountFrequency (%)
35.30694444 6
6.0%
35.68194444 6
6.0%
35.90473241 8
8.0%
36.30722222 7
7.0%
36.3475 6
6.0%
36.38961 6
6.0%
36.54 4
4.0%
36.57694444 5
5.0%
36.58444444 5
5.0%
36.64019722 6
6.0%
ValueCountFrequency (%)
37.27944444 2
 
2.0%
37.15877778 10
10.0%
37.00888889 10
10.0%
36.86890278 10
10.0%
36.78083333 9
9.0%
36.64019722 6
6.0%
36.58444444 5
5.0%
36.57694444 5
5.0%
36.54 4
 
4.0%
36.38961 6
6.0%

경도(°)
Real number (ℝ)

HIGH CORRELATION 

Distinct15
Distinct (%)15.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.61723
Minimum127.08833
Maximum129.18111
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:12:57.300200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum127.08833
5-th percentile127.08833
Q1127.17667
median127.42351
Q3127.57444
95-th percentile129.18111
Maximum129.18111
Range2.0927778
Interquartile range (IQR)0.3977777

Descriptive statistics

Standard deviation0.67324615
Coefficient of variation (CV)0.0052755114
Kurtosis0.63961097
Mean127.61723
Median Absolute Deviation (MAD)0.2468413
Skewness1.4754254
Sum12761.723
Variance0.45326038
MonotonicityNot monotonic
2023-12-10T20:12:57.458596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
127.1867222 10
10.0%
127.1491667 10
10.0%
127.0883333 10
10.0%
127.1766667 9
9.0%
128.5614716 8
 
8.0%
127.5744444 7
 
7.0%
129.0747222 6
 
6.0%
129.1811111 6
 
6.0%
127.4691667 6
 
6.0%
127.423508 6
 
6.0%
Other values (5) 22
22.0%
ValueCountFrequency (%)
127.0883333 10
10.0%
127.1058333 2
 
2.0%
127.1491667 10
10.0%
127.1766667 9
9.0%
127.1867222 10
10.0%
127.3781278 6
6.0%
127.423508 6
6.0%
127.4263889 5
5.0%
127.4277778 5
5.0%
127.4338889 4
 
4.0%
ValueCountFrequency (%)
129.1811111 6
6.0%
129.0747222 6
6.0%
128.5614716 8
8.0%
127.5744444 7
7.0%
127.4691667 6
6.0%
127.4338889 4
4.0%
127.4277778 5
5.0%
127.4263889 5
5.0%
127.423508 6
6.0%
127.3781278 6
6.0%

기울기(°)
Real number (ℝ)

HIGH CORRELATION 

Distinct26
Distinct (%)26.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.02843025
Minimum-13.24576
Maximum12.47809
Zeros0
Zeros (%)0.0%
Negative47
Negative (%)47.0%
Memory size1.0 KiB
2023-12-10T20:12:57.623529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-13.24576
5-th percentile-3.15276
Q1-0.807188
median0.166426
Q30.688696
95-th percentile3.071416
Maximum12.47809
Range25.72385
Interquartile range (IQR)1.495884

Descriptive statistics

Standard deviation3.4424326
Coefficient of variation (CV)-121.08344
Kurtosis9.5934258
Mean-0.02843025
Median Absolute Deviation (MAD)0.973614
Skewness-0.26837909
Sum-2.843025
Variance11.850342
MonotonicityNot monotonic
2023-12-10T20:12:57.785501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
-1.758683 5
 
5.0%
0.671977 5
 
5.0%
-0.660004 5
 
5.0%
-1.30142 5
 
5.0%
1.293015 5
 
5.0%
0.61369 5
 
5.0%
-0.624001 5
 
5.0%
1.544389 5
 
5.0%
1.437416 5
 
5.0%
0.166426 4
 
4.0%
Other values (16) 51
51.0%
ValueCountFrequency (%)
-13.24576 3
3.0%
-3.15276 3
3.0%
-2.788625 3
3.0%
-1.758683 5
5.0%
-1.359001 4
4.0%
-1.30142 5
5.0%
-0.807188 4
4.0%
-0.688696 3
3.0%
-0.660004 5
5.0%
-0.624001 5
5.0%
ValueCountFrequency (%)
12.47809 3
3.0%
3.071416 3
3.0%
2.703849 3
3.0%
1.544389 5
5.0%
1.437416 5
5.0%
1.293015 5
5.0%
0.688696 3
3.0%
0.671977 5
5.0%
0.61369 5
5.0%
0.2594 4
4.0%

TSP(g/km)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct71
Distinct (%)71.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.8387
Minimum0
Maximum19.05
Zeros17
Zeros (%)17.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:12:57.949188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.11
median0.625
Q32.1475
95-th percentile9.8725
Maximum19.05
Range19.05
Interquartile range (IQR)2.0375

Descriptive statistics

Standard deviation3.3963455
Coefficient of variation (CV)1.847145
Kurtosis11.616149
Mean1.8387
Median Absolute Deviation (MAD)0.625
Skewness3.2735215
Sum183.87
Variance11.535163
MonotonicityNot monotonic
2023-12-10T20:12:58.153289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 17
 
17.0%
0.01 3
 
3.0%
0.15 2
 
2.0%
0.43 2
 
2.0%
0.3 2
 
2.0%
2.75 2
 
2.0%
2.22 2
 
2.0%
10.68 2
 
2.0%
0.11 2
 
2.0%
1.37 2
 
2.0%
Other values (61) 64
64.0%
ValueCountFrequency (%)
0.0 17
17.0%
0.01 3
 
3.0%
0.02 1
 
1.0%
0.03 1
 
1.0%
0.04 1
 
1.0%
0.06 1
 
1.0%
0.11 2
 
2.0%
0.13 2
 
2.0%
0.15 2
 
2.0%
0.16 1
 
1.0%
ValueCountFrequency (%)
19.05 1
1.0%
17.38 1
1.0%
13.21 1
1.0%
10.68 2
2.0%
9.83 1
1.0%
8.83 1
1.0%
5.44 1
1.0%
5.19 1
1.0%
5.02 1
1.0%
4.15 1
1.0%

PM10(g/km)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct58
Distinct (%)58.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.8086
Minimum0
Maximum8.38
Zeros20
Zeros (%)20.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:12:58.323258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.05
median0.275
Q30.945
95-th percentile4.3485
Maximum8.38
Range8.38
Interquartile range (IQR)0.895

Descriptive statistics

Standard deviation1.4947274
Coefficient of variation (CV)1.8485375
Kurtosis11.608262
Mean0.8086
Median Absolute Deviation (MAD)0.275
Skewness3.2726995
Sum80.86
Variance2.2342101
MonotonicityNot monotonic
2023-12-10T20:12:58.520642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 20
 
20.0%
0.06 3
 
3.0%
0.13 3
 
3.0%
0.23 3
 
3.0%
0.36 2
 
2.0%
0.2 2
 
2.0%
4.7 2
 
2.0%
1.05 2
 
2.0%
0.02 2
 
2.0%
0.05 2
 
2.0%
Other values (48) 59
59.0%
ValueCountFrequency (%)
0.0 20
20.0%
0.01 2
 
2.0%
0.02 2
 
2.0%
0.05 2
 
2.0%
0.06 3
 
3.0%
0.07 2
 
2.0%
0.08 1
 
1.0%
0.09 2
 
2.0%
0.11 1
 
1.0%
0.13 3
 
3.0%
ValueCountFrequency (%)
8.38 1
1.0%
7.65 1
1.0%
5.81 1
1.0%
4.7 2
2.0%
4.33 1
1.0%
3.89 1
1.0%
2.39 1
1.0%
2.28 1
1.0%
2.21 1
1.0%
1.83 1
1.0%

주소
Categorical

HIGH CORRELATION 

Distinct14
Distinct (%)14.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
충남 천안시 서북구 성거읍 송남리
10 
경기 안성시 원곡면
10 
경기 화성시 동탄면 송리
10 
충북 청원군 남이면
9 
충청 천안시 구성동
9 
Other values (9)
52 

Length

Max length18
Median length10
Mean length11.84
Min length9

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
충남 천안시 서북구 성거읍 송남리 10
10.0%
경기 안성시 원곡면 10
10.0%
경기 화성시 동탄면 송리 10
10.0%
충북 청원군 남이면 9
9.0%
충청 천안시 구성동 9
9.0%
대구 동구 안심3동 8
8.0%
충북 옥천군 옥천읍 삼양리 7
 
7.0%
경남 양산시 동면 6
 
6.0%
울산 울주군 두서면 활천리 6
 
6.0%
대전 대덕구 비래동 6
 
6.0%
Other values (4) 19
19.0%

Length

2023-12-10T20:12:58.695956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
충북 27
 
7.7%
경기 22
 
6.3%
청원군 20
 
5.7%
천안시 19
 
5.4%
남이면 14
 
4.0%
대덕구 12
 
3.4%
대전 12
 
3.4%
동탄면 10
 
2.9%
충남 10
 
2.9%
화성시 10
 
2.9%
Other values (28) 194
55.4%

Interactions

2023-12-10T20:12:52.041003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:12:42.762634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:12:43.895994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:12:45.090401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:12:46.190605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:12:47.645215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:12:48.870645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:12:49.926490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:12:50.834003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:12:52.125411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:12:42.900057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:12:44.018241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:12:45.224100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:12:46.677359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:12:47.795949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:12:48.973435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:12:50.024936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:12:50.938691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:12:52.230139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:12:43.035089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:12:44.158216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:12:45.377163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:12:46.794605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:12:47.953022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:12:49.106783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:12:50.116642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:12:51.076801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:12:52.335068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:12:43.165440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:12:44.280857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:12:45.526206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:12:46.925483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:12:48.079997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:12:49.223547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:12:50.219371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:12:51.294399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:12:52.444078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:12:43.298653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:12:44.420542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:12:45.660735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:12:47.062163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:12:48.222895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:12:49.333109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:12:50.329156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:12:51.415339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:12:52.554778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:12:43.412478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:12:44.563043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:12:45.795498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:12:47.197806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:12:48.359037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:12:49.471589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:12:50.454617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:12:51.557049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:12:52.680391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:12:43.509689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:12:44.693540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:12:45.886348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:12:47.299468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:12:48.475702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:12:49.575709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:12:50.555198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:12:51.689848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:12:52.784676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:12:43.625281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:12:44.815490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:12:45.970182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:12:47.413435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:12:48.612710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:12:49.689883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:12:50.638624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:12:51.822968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:12:52.914054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:12:43.768048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:12:44.975161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:12:46.072003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:12:47.540844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:12:48.744777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:12:49.809150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:12:50.739694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:12:51.947592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T20:12:58.822367image/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
기본키1.0000.9800.0000.0000.9830.9380.3190.3010.9430.9940.6960.4110.4110.966
지점0.9801.0000.1720.0001.0001.0000.3260.2751.0001.0000.8650.5320.5321.000
방향0.0000.1721.0000.0001.0000.0000.1540.0000.0000.0000.6730.1590.1590.000
차선0.0000.0000.0001.0000.0000.0000.5210.4520.0000.0000.0000.0790.0790.000
측정구간0.9831.0001.0000.0001.0001.0000.0000.4331.0001.0001.0000.4500.4501.000
장비이정(km)0.9381.0000.0000.0001.0001.0000.2570.2161.0000.9250.6400.2530.2531.000
차량통과수(대)0.3190.3260.1540.5210.0000.2571.0000.3550.2330.1480.0000.7250.7250.351
평균 속도(km/hr)0.3010.2750.0000.4520.4330.2160.3551.0000.0800.2060.3750.3940.3940.330
위도(°)0.9431.0000.0000.0001.0001.0000.2330.0801.0000.9320.6560.2330.2331.000
경도(°)0.9941.0000.0000.0001.0000.9250.1480.2060.9321.0000.4940.0910.0911.000
기울기(°)0.6960.8650.6730.0001.0000.6400.0000.3750.6560.4941.0000.0000.0000.851
TSP(g/km)0.4110.5320.1590.0790.4500.2530.7250.3940.2330.0910.0001.0001.0000.637
PM10(g/km)0.4110.5320.1590.0790.4500.2530.7250.3940.2330.0910.0001.0001.0000.637
주소0.9661.0000.0000.0001.0001.0000.3510.3301.0001.0000.8510.6370.6371.000
2023-12-10T20:12:59.011533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지점차선측정구간방향주소
지점1.0000.0000.9330.1410.994
차선0.0001.0000.0000.0000.000
측정구간0.9330.0001.0000.8690.928
방향0.1410.0000.8691.0000.000
주소0.9940.0000.9280.0001.000
2023-12-10T20:12:59.166594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기본키장비이정(km)차량통과수(대)평균 속도(km/hr)위도(°)경도(°)기울기(°)TSP(g/km)PM10(g/km)지점방향차선측정구간주소
기본키1.0000.9970.243-0.0160.997-0.9730.0400.3350.3260.8300.0000.0000.8070.834
장비이정(km)0.9971.0000.2380.0081.000-0.9750.0240.3280.3200.9560.0000.0000.8920.962
차량통과수(대)0.2430.2381.0000.3860.238-0.259-0.0110.8550.8530.1490.0840.2370.0550.167
평균 속도(km/hr)-0.0160.0080.3861.0000.008-0.031-0.0070.3700.3550.0880.0000.3210.1580.090
위도(°)0.9971.0000.2380.0081.000-0.9750.0240.3280.3200.9610.0000.0000.8970.967
경도(°)-0.973-0.975-0.259-0.031-0.9751.000-0.036-0.324-0.3160.9460.0000.0000.8830.951
기울기(°)0.0400.024-0.011-0.0070.024-0.0361.000-0.033-0.0360.5980.4820.0000.8870.607
TSP(g/km)0.3350.3280.8550.3700.328-0.324-0.0331.0000.9980.2600.1640.0430.1790.276
PM10(g/km)0.3260.3200.8530.3550.320-0.316-0.0360.9981.0000.2600.1640.0430.1790.276
지점0.8300.9560.1490.0880.9610.9460.5980.2600.2601.0000.1410.0000.9330.994
방향0.0000.0000.0840.0000.0000.0000.4820.1640.1640.1411.0000.0000.8690.000
차선0.0000.0000.2370.3210.0000.0000.0000.0430.0430.0000.0001.0000.0000.000
측정구간0.8070.8920.0550.1580.8970.8830.8870.1790.1790.9330.8690.0001.0000.928
주소0.8340.9620.1670.0900.9670.9510.6070.2760.2760.9940.0000.0000.9281.000

Missing values

2023-12-10T20:12:53.067631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T20:12:53.638742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

기본키도로종류지점방향차선측정구간장비이정(km)측정일측정시간차량통과수(대)평균 속도(km/hr)위도(°)경도(°)기울기(°)TSP(g/km)PM10(g/km)주소
01도로공사A-0010-0083E-6E1노포JC-양산JC8.320201001013111.035.306944129.074722-3.152760.150.06경남 양산시 동면
12도로공사A-0010-0083E-6E2노포JC-양산JC8.320201001039116.035.306944129.074722-3.152760.890.39경남 양산시 동면
23도로공사A-0010-0083E-6E3노포JC-양산JC8.32020100102592.035.306944129.074722-3.152760.750.33경남 양산시 동면
34도로공사A-0010-0083E-6S1양산JC-노포JC8.320201001019131.035.306944129.0747223.0714160.210.09경남 양산시 동면
45도로공사A-0010-0083E-6S2양산JC-노포JC8.32020100105499.035.306944129.0747223.0714161.160.51경남 양산시 동면
56도로공사A-0010-0083E-6S3양산JC-노포JC8.32020100102578.7535.306944129.0747223.0714160.60.26경남 양산시 동면
67도로공사A-0010-0538E-6E1언양JC-활천IC53.420201001012100.035.681944129.1811110.225450.130.06울산 울주군 두서면 활천리
78도로공사A-0010-0538E-6E2언양JC-활천IC53.420201001040101.035.681944129.1811110.225450.530.23울산 울주군 두서면 활천리
89도로공사A-0010-0538E-6E3언양JC-활천IC53.4202010010990.035.681944129.1811110.225450.390.17울산 울주군 두서면 활천리
910도로공사A-0010-0538E-6S1활천IC-언양JC53.420201001018127.035.681944129.1811110.191580.20.09울산 울주군 두서면 활천리
기본키도로종류지점방향차선측정구간장비이정(km)측정일측정시간차량통과수(대)평균 속도(km/hr)위도(°)경도(°)기울기(°)TSP(g/km)PM10(g/km)주소
9091도로공사A-0010-3801E-10E3오산IC-동탄JC380.1202010010105100.537.158778127.088333-0.6600041.280.56경기 화성시 동탄면 송리
9192도로공사A-0010-3801E-10E4오산IC-동탄JC380.12020100109485.037.158778127.088333-0.6600045.442.39경기 화성시 동탄면 송리
9293도로공사A-0010-3801E-10E5오산IC-동탄JC380.1202010010170.037.158778127.088333-0.6600040.010.0경기 화성시 동탄면 송리
9394도로공사A-0010-3801E-10S1동탄JC-오산IC380.12020100102499.2537.158778127.0883330.6719772.381.05경기 화성시 동탄면 송리
9495도로공사A-0010-3801E-10S2동탄JC-오산IC380.120201001017697.6737.158778127.0883330.6719775.022.21경기 화성시 동탄면 송리
9596도로공사A-0010-3801E-10S3동탄JC-오산IC380.120201001015697.6737.158778127.0883330.6719772.751.21경기 화성시 동탄면 송리
9697도로공사A-0010-3801E-10S4동탄JC-오산IC380.120201001010283.037.158778127.0883330.6719773.161.39경기 화성시 동탄면 송리
9798도로공사A-0010-3801E-10S5동탄JC-오산IC380.120201001000.037.158778127.0883330.6719770.00.0경기 화성시 동탄면 송리
9899도로공사A-0010-3932E-10E1수원신갈IC-신갈JC393.22020100101592.537.279444127.1058330.1511872.140.94경기 용인시 기흥구 신갈동
99100도로공사A-0010-3932E-10E2수원신갈IC-신갈JC393.2202010010126100.6737.279444127.1058330.1511872.621.15경기 용인시 기흥구 신갈동