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 25 (25.0%) zerosZeros
평균 속도(km/hr) has 25 (25.0%) zerosZeros
TSP(g/km) has 25 (25.0%) zerosZeros
PM10(g/km) has 26 (26.0%) zerosZeros

Reproduction

Analysis started2023-12-10 11:07:27.145074
Analysis finished2023-12-10 11:07:43.354456
Duration16.21 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
2023-12-10T20:07:43.491569image/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
2023-12-10T20:07:43.790961image/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

2023-12-10T20:07:44.030250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:07:44.212588image/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-0010-3722E-10
10 
A-0010-3801E-10
10 
A-0010-3352E-9
Other values (10)
51 

Length

Max length15
Median length14
Mean length14.43
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-0010-3722E-10 10
10.0%
A-0010-3801E-10 10
10.0%
A-0010-3352E-9 9
9.0%
A-0010-0083E-6 6
 
6.0%
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%
Other values (5) 21
21.0%

Length

2023-12-10T20:07:44.397789image/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-0010-3722e-10 10
10.0%
a-0010-3801e-10 10
10.0%
a-0010-3352e-9 9
9.0%
a-0010-0083e-6 6
 
6.0%
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%
Other values (5) 21
21.0%

방향
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
E
51 
S
49 

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 51
51.0%
S 49
49.0%

Length

2023-12-10T20:07:44.694074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:07:44.876918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
e 51
51.0%
s 49
49.0%

차선
Categorical

Distinct5
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
1
25 
2
25 
3
25 
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 25
25.0%
2 25
25.0%
3 25
25.0%
4 14
14.0%
5 11
11.0%

Length

2023-12-10T20:07:45.049967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:07:45.252098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 25
25.0%
2 25
25.0%
3 25
25.0%
4 14
14.0%
5 11
11.0%

측정구간
Categorical

HIGH CORRELATION 

Distinct25
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
천안IC-천안JC
 
5
동탄JC-오산IC
 
5
오산IC-동탄JC
 
5
오산IC-안성JC
 
5
안성JC-오산IC
 
5
Other values (20)
75 

Length

Max length11
Median length9
Mean length9.24
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 (%)
천안IC-천안JC 5
 
5.0%
동탄JC-오산IC 5
 
5.0%
오산IC-동탄JC 5
 
5.0%
오산IC-안성JC 5
 
5.0%
안성JC-오산IC 5
 
5.0%
청주JC-남이JC 5
 
5.0%
남이JC-청주JC 5
 
5.0%
안성JC-안성IC 5
 
5.0%
안성IC-안성JC 5
 
5.0%
북천안IC-천안IC 5
 
5.0%
Other values (15) 50
50.0%

Length

2023-12-10T20:07:45.500914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
천안ic-천안jc 5
 
5.0%
동탄jc-오산ic 5
 
5.0%
오산ic-동탄jc 5
 
5.0%
오산ic-안성jc 5
 
5.0%
안성jc-오산ic 5
 
5.0%
청주jc-남이jc 5
 
5.0%
남이jc-청주jc 5
 
5.0%
안성jc-안성ic 5
 
5.0%
안성ic-안성jc 5
 
5.0%
북천안ic-천안ic 5
 
5.0%
Other values (15) 50
50.0%

장비이정(km)
Real number (ℝ)

HIGH CORRELATION 

Distinct15
Distinct (%)15.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean297.947
Minimum8.3
Maximum388
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:07:45.780326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8.3
5-th percentile8.3
Q1295.6
median335.2
Q3361.3
95-th percentile380.1
Maximum388
Range379.7
Interquartile range (IQR)65.7

Descriptive statistics

Standard deviation104.81472
Coefficient of variation (CV)0.35178981
Kurtosis2.6756852
Mean297.947
Median Absolute Deviation (MAD)37.03
Skewness-1.9487163
Sum29794.7
Variance10986.125
MonotonicityIncreasing
2023-12-10T20:07:46.183940image/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%
372.23 10
10.0%
380.1 10
10.0%
335.2 9
9.0%
8.3 6
 
6.0%
53.4 6
 
6.0%
276.1 6
 
6.0%
301.9 6
 
6.0%
306.8 6
 
6.0%
Other values (5) 21
21.0%
ValueCountFrequency (%)
8.3 6
6.0%
53.4 6
6.0%
276.1 6
6.0%
295.3 4
4.0%
295.6 4
4.0%
297.9 5
5.0%
298.7 5
5.0%
301.9 6
6.0%
306.8 6
6.0%
335.2 9
9.0%
ValueCountFrequency (%)
388.0 3
 
3.0%
380.1 10
10.0%
372.23 10
10.0%
361.3 10
10.0%
345.2 10
10.0%
335.2 9
9.0%
306.8 6
6.0%
301.9 6
6.0%
298.7 5
5.0%
297.9 5
5.0%

측정일
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
20211201
100 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20211201
2nd row20211201
3rd row20211201
4th row20211201
5th row20211201

Common Values

ValueCountFrequency (%)
20211201 100
100.0%

Length

2023-12-10T20:07:46.439988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:07:46.616400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20211201 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

2023-12-10T20:07:46.814414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:07:46.982769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 100
100.0%

차량통과수(대)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct57
Distinct (%)57.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.77
Minimum0
Maximum131
Zeros25
Zeros (%)25.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:07:47.167468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.75
median27.5
Q363
95-th percentile112
Maximum131
Range131
Interquartile range (IQR)62.25

Descriptive statistics

Standard deviation36.126001
Coefficient of variation (CV)0.95647343
Kurtosis-0.42866425
Mean37.77
Median Absolute Deviation (MAD)27.5
Skewness0.72876136
Sum3777
Variance1305.088
MonotonicityNot monotonic
2023-12-10T20:07:47.809495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 25
25.0%
20 3
 
3.0%
93 3
 
3.0%
15 2
 
2.0%
112 2
 
2.0%
90 2
 
2.0%
55 2
 
2.0%
39 2
 
2.0%
48 2
 
2.0%
63 2
 
2.0%
Other values (47) 55
55.0%
ValueCountFrequency (%)
0 25
25.0%
1 1
 
1.0%
3 2
 
2.0%
4 1
 
1.0%
5 1
 
1.0%
7 1
 
1.0%
11 1
 
1.0%
12 2
 
2.0%
15 2
 
2.0%
18 1
 
1.0%
ValueCountFrequency (%)
131 1
 
1.0%
123 1
 
1.0%
122 1
 
1.0%
119 1
 
1.0%
112 2
2.0%
93 3
3.0%
90 2
2.0%
89 1
 
1.0%
86 1
 
1.0%
84 1
 
1.0%

평균 속도(km/hr)
Real number (ℝ)

ZEROS 

Distinct70
Distinct (%)70.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean68.3556
Minimum0
Maximum143.5
Zeros25
Zeros (%)25.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:07:48.046057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q150.8725
median84.055
Q395.7825
95-th percentile110
Maximum143.5
Range143.5
Interquartile range (IQR)44.91

Descriptive statistics

Standard deviation41.214115
Coefficient of variation (CV)0.60293693
Kurtosis-0.7560643
Mean68.3556
Median Absolute Deviation (MAD)12.415
Skewness-0.89800248
Sum6835.56
Variance1698.6033
MonotonicityNot monotonic
2023-12-10T20:07:48.319466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 25
25.0%
111.0 3
 
3.0%
92.33 2
 
2.0%
110.0 2
 
2.0%
105.5 2
 
2.0%
86.0 2
 
2.0%
81.75 1
 
1.0%
75.5 1
 
1.0%
79.33 1
 
1.0%
92.5 1
 
1.0%
Other values (60) 60
60.0%
ValueCountFrequency (%)
0.0 25
25.0%
67.83 1
 
1.0%
71.67 1
 
1.0%
74.6 1
 
1.0%
75.0 1
 
1.0%
75.5 1
 
1.0%
75.57 1
 
1.0%
76.5 1
 
1.0%
76.71 1
 
1.0%
77.0 1
 
1.0%
ValueCountFrequency (%)
143.5 1
 
1.0%
111.0 3
3.0%
110.0 2
2.0%
109.0 1
 
1.0%
107.67 1
 
1.0%
107.5 1
 
1.0%
107.0 1
 
1.0%
106.5 1
 
1.0%
105.5 2
2.0%
105.0 1
 
1.0%

위도(°)
Real number (ℝ)

HIGH CORRELATION 

Distinct15
Distinct (%)15.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.660221
Minimum35.306944
Maximum37.226111
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:07:48.670516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.306944
5-th percentile35.306944
Q136.556389
median36.780833
Q337.008889
95-th percentile37.158778
Maximum37.226111
Range1.9191667
Interquartile range (IQR)0.4525

Descriptive statistics

Standard deviation0.49771766
Coefficient of variation (CV)0.013576505
Kurtosis1.6505027
Mean36.660221
Median Absolute Deviation (MAD)0.22805556
Skewness-1.459139
Sum3666.0221
Variance0.24772287
MonotonicityIncreasing
2023-12-10T20:07:49.154805image/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%
37.100926 10
10.0%
37.15877778 10
10.0%
36.78083333 9
9.0%
35.30694444 6
 
6.0%
35.68194444 6
 
6.0%
36.38961 6
 
6.0%
36.60611111 6
 
6.0%
36.64019722 6
 
6.0%
Other values (5) 21
21.0%
ValueCountFrequency (%)
35.30694444 6
6.0%
35.68194444 6
6.0%
36.38961 6
6.0%
36.54 4
4.0%
36.55638889 4
4.0%
36.57694444 5
5.0%
36.58444444 5
5.0%
36.60611111 6
6.0%
36.64019722 6
6.0%
36.78083333 9
9.0%
ValueCountFrequency (%)
37.22611111 3
 
3.0%
37.15877778 10
10.0%
37.100926 10
10.0%
37.00888889 10
10.0%
36.86890278 10
10.0%
36.78083333 9
9.0%
36.64019722 6
6.0%
36.60611111 6
6.0%
36.58444444 5
5.0%
36.57694444 5
5.0%

경도(°)
Real number (ℝ)

HIGH CORRELATION 

Distinct15
Distinct (%)15.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.47875
Minimum127.08833
Maximum129.18111
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:07:49.462668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum127.08833
5-th percentile127.08833
Q1127.14917
median127.18672
Q3127.42674
95-th percentile129.18111
Maximum129.18111
Range2.0927778
Interquartile range (IQR)0.27756943

Descriptive statistics

Standard deviation0.62616419
Coefficient of variation (CV)0.0049119104
Kurtosis3.193034
Mean127.47875
Median Absolute Deviation (MAD)0.0983889
Skewness2.179382
Sum12747.875
Variance0.39208159
MonotonicityNot monotonic
2023-12-10T20:07:49.680272image/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%
127.118642 10
10.0%
127.0883333 10
10.0%
127.1766667 9
9.0%
129.0747222 6
 
6.0%
129.1811111 6
 
6.0%
127.423508 6
 
6.0%
127.4083333 6
 
6.0%
127.3781278 6
 
6.0%
Other values (5) 21
21.0%
ValueCountFrequency (%)
127.0883333 10
10.0%
127.1083333 3
 
3.0%
127.118642 10
10.0%
127.1491667 10
10.0%
127.1766667 9
9.0%
127.1867222 10
10.0%
127.3781278 6
6.0%
127.4083333 6
6.0%
127.423508 6
6.0%
127.4263889 5
5.0%
ValueCountFrequency (%)
129.1811111 6
6.0%
129.0747222 6
6.0%
127.4338889 4
 
4.0%
127.4325 4
 
4.0%
127.4277778 5
5.0%
127.4263889 5
5.0%
127.423508 6
6.0%
127.4083333 6
6.0%
127.3781278 6
6.0%
127.1867222 10
10.0%

기울기(°)
Real number (ℝ)

HIGH CORRELATION 

Distinct25
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.00516618
Minimum-3.15276
Maximum3.071416
Zeros0
Zeros (%)0.0%
Negative48
Negative (%)48.0%
Memory size1.0 KiB
2023-12-10T20:07:49.880880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-3.15276
5-th percentile-2.788625
Q1-0.807188
median0.09761
Q31.056036
95-th percentile2.703849
Maximum3.071416
Range6.224176
Interquartile range (IQR)1.863224

Descriptive statistics

Standard deviation1.4302631
Coefficient of variation (CV)276.8512
Kurtosis-0.24886454
Mean0.00516618
Median Absolute Deviation (MAD)0.958426
Skewness-0.063408923
Sum0.516618
Variance2.0456526
MonotonicityNot monotonic
2023-12-10T20:07:50.105469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
-0.624001 5
 
5.0%
0.671977 5
 
5.0%
-0.660004 5
 
5.0%
-0.141433 5
 
5.0%
-1.758683 5
 
5.0%
1.437416 5
 
5.0%
0.09761 5
 
5.0%
-1.30142 5
 
5.0%
1.293015 5
 
5.0%
0.61369 5
 
5.0%
Other values (15) 50
50.0%
ValueCountFrequency (%)
-3.15276 3
3.0%
-2.788625 3
3.0%
-1.758683 5
5.0%
-1.717451 3
3.0%
-1.359001 4
4.0%
-1.30142 5
5.0%
-0.807188 4
4.0%
-0.688696 3
3.0%
-0.660004 5
5.0%
-0.624001 5
5.0%
ValueCountFrequency (%)
3.071416 3
3.0%
2.703849 3
3.0%
1.717451 3
3.0%
1.544389 5
5.0%
1.437416 5
5.0%
1.293015 5
5.0%
1.056036 4
4.0%
0.688696 3
3.0%
0.671977 5
5.0%
0.61369 5
5.0%

TSP(g/km)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct70
Distinct (%)70.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.1111
Minimum0
Maximum13.37
Zeros25
Zeros (%)25.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:07:50.411373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.0075
median1.315
Q36.3225
95-th percentile9.504
Maximum13.37
Range13.37
Interquartile range (IQR)6.315

Descriptive statistics

Standard deviation3.5826529
Coefficient of variation (CV)1.1515711
Kurtosis-0.35361541
Mean3.1111
Median Absolute Deviation (MAD)1.315
Skewness0.9521024
Sum311.11
Variance12.835402
MonotonicityNot monotonic
2023-12-10T20:07:50.692252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 25
25.0%
0.35 3
 
3.0%
0.38 2
 
2.0%
0.97 2
 
2.0%
3.74 2
 
2.0%
6.42 2
 
2.0%
1.71 1
 
1.0%
3.27 1
 
1.0%
8.2 1
 
1.0%
2.41 1
 
1.0%
Other values (60) 60
60.0%
ValueCountFrequency (%)
0.0 25
25.0%
0.01 1
 
1.0%
0.03 1
 
1.0%
0.11 1
 
1.0%
0.24 1
 
1.0%
0.35 3
 
3.0%
0.37 1
 
1.0%
0.38 2
 
2.0%
0.39 1
 
1.0%
0.43 1
 
1.0%
ValueCountFrequency (%)
13.37 1
1.0%
11.45 1
1.0%
10.91 1
1.0%
10.45 1
1.0%
10.34 1
1.0%
9.46 1
1.0%
9.27 1
1.0%
8.99 1
1.0%
8.82 1
1.0%
8.75 1
1.0%

PM10(g/km)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct65
Distinct (%)65.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3693
Minimum0
Maximum5.88
Zeros26
Zeros (%)26.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:07:50.985418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.58
Q32.7825
95-th percentile4.1795
Maximum5.88
Range5.88
Interquartile range (IQR)2.7825

Descriptive statistics

Standard deviation1.576555
Coefficient of variation (CV)1.1513584
Kurtosis-0.35545558
Mean1.3693
Median Absolute Deviation (MAD)0.58
Skewness0.95150517
Sum136.93
Variance2.4855258
MonotonicityNot monotonic
2023-12-10T20:07:51.354243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 26
26.0%
0.16 3
 
3.0%
0.17 3
 
3.0%
0.43 2
 
2.0%
2.82 2
 
2.0%
3.41 2
 
2.0%
3.51 2
 
2.0%
1.65 2
 
2.0%
0.19 2
 
2.0%
1.16 1
 
1.0%
Other values (55) 55
55.0%
ValueCountFrequency (%)
0.0 26
26.0%
0.01 1
 
1.0%
0.05 1
 
1.0%
0.1 1
 
1.0%
0.15 1
 
1.0%
0.16 3
 
3.0%
0.17 3
 
3.0%
0.19 2
 
2.0%
0.22 1
 
1.0%
0.23 1
 
1.0%
ValueCountFrequency (%)
5.88 1
1.0%
5.04 1
1.0%
4.8 1
1.0%
4.6 1
1.0%
4.55 1
1.0%
4.16 1
1.0%
4.08 1
1.0%
3.96 1
1.0%
3.88 1
1.0%
3.85 1
1.0%

주소
Categorical

HIGH CORRELATION 

Distinct13
Distinct (%)13.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
충북 청원군 남이면
13 
충남 천안시 서북구 성거읍 송남리
10 
경기 안성시 원곡면
10 
경기 용인시 처인구 남사면 진목리
10 
경기 화성시 동탄면 송리
10 
Other values (8)
47 

Length

Max length18
Median length15
Mean length12.7
Min length9

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
충북 청원군 남이면 13
13.0%
충남 천안시 서북구 성거읍 송남리 10
10.0%
경기 안성시 원곡면 10
10.0%
경기 용인시 처인구 남사면 진목리 10
10.0%
경기 화성시 동탄면 송리 10
10.0%
충청 천안시 구성동 9
9.0%
경남 양산시 동면 6
6.0%
울산 울주군 두서면 활천리 6
6.0%
대전 대덕구 연축동 6
6.0%
충북 청주시 흥덕구 강서1동 6
6.0%
Other values (3) 14
14.0%

Length

2023-12-10T20:07:51.660334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기 33
 
8.9%
충북 30
 
8.1%
청원군 24
 
6.5%
천안시 19
 
5.1%
남이면 18
 
4.9%
용인시 13
 
3.5%
충남 10
 
2.7%
서북구 10
 
2.7%
성거읍 10
 
2.7%
송남리 10
 
2.7%
Other values (27) 193
52.2%

Interactions

2023-12-10T20:07:41.296911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:28.413515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:29.731044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:31.255805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:32.699281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:34.490051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:36.305217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:38.044733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:39.615962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:41.440628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:28.538664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:29.883411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:31.418106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:32.854014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:34.724666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:36.450168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:38.233084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:39.774040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:41.610930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:28.702959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:30.058269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:31.584221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:33.033805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:34.898491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:36.629923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:38.432337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:39.975163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:41.759733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:28.842911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:30.224495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:31.733565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:33.195315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:35.063054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:36.788626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:38.610195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:40.145222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:41.924345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:28.985056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:30.374877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:31.892902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:33.372176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:35.296312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:37.277461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:38.789120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:40.337930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:42.101394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:29.157328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:30.565658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:32.082345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:33.563505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:35.627579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:37.399498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:38.972560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:40.555955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:42.251404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:29.301460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:30.728353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:32.213605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:33.700054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:35.786194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:37.497348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:39.138945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:40.725800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:42.405520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:29.446789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:30.894089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:32.372196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:33.864155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:35.932923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:37.641837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:39.307523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:40.929610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:42.576985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:29.586085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:31.083184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:32.535310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:34.144020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:36.131868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:37.808574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:39.453451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:41.118080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T20:07:51.847981image/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
기본키1.0000.9640.0000.0000.9810.9760.5500.3330.9050.9420.7270.2210.2210.943
지점0.9641.0000.3150.0001.0001.0000.4690.5041.0001.0000.9160.4080.4081.000
방향0.0000.3151.0000.0001.0000.0000.0000.0000.0000.0000.6430.1640.1640.000
차선0.0000.0000.0001.0000.0000.0000.5100.4440.0000.1110.0000.5640.5640.000
측정구간0.9811.0001.0000.0001.0001.0000.2120.5451.0001.0001.0000.2610.2611.000
장비이정(km)0.9761.0000.0000.0001.0001.0000.6290.4060.9531.0000.7100.4690.4691.000
차량통과수(대)0.5500.4690.0000.5100.2120.6291.0000.6450.4750.5510.2860.8250.8250.475
평균 속도(km/hr)0.3330.5040.0000.4440.5450.4060.6451.0000.3680.5480.3540.6420.6420.429
위도(°)0.9051.0000.0000.0001.0000.9530.4750.3681.0001.0000.7830.3100.3101.000
경도(°)0.9421.0000.0000.1111.0001.0000.5510.5481.0001.0000.7020.3720.3721.000
기울기(°)0.7270.9160.6430.0001.0000.7100.2860.3540.7830.7021.0000.1320.1320.865
TSP(g/km)0.2210.4080.1640.5640.2610.4690.8250.6420.3100.3720.1321.0001.0000.330
PM10(g/km)0.2210.4080.1640.5640.2610.4690.8250.6420.3100.3720.1321.0001.0000.330
주소0.9431.0000.0000.0001.0001.0000.4750.4291.0001.0000.8650.3300.3301.000
2023-12-10T20:07:52.116460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지점차선측정구간방향주소
지점1.0000.0000.9390.2650.988
차선0.0001.0000.0000.0000.000
측정구간0.9390.0001.0000.8750.928
방향0.2650.0000.8751.0000.000
주소0.9880.0000.9280.0001.000
2023-12-10T20:07:52.324763image/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
기본키1.0000.9970.190-0.1170.997-0.9660.006-0.066-0.0650.7690.0000.0000.7840.773
장비이정(km)0.9971.0000.198-0.0931.000-0.969-0.019-0.066-0.0650.9460.0000.0000.8890.957
차량통과수(대)0.1900.1981.0000.4820.198-0.221-0.1240.8540.8540.1850.0000.2280.0470.212
평균 속도(km/hr)-0.117-0.0930.4821.000-0.0930.063-0.2070.3210.3110.2470.0000.3180.2450.217
위도(°)0.9971.0000.198-0.0931.000-0.969-0.019-0.066-0.0650.9560.0000.0000.8980.967
경도(°)-0.966-0.969-0.2210.063-0.9691.0000.0080.0450.0440.9360.0000.0800.8790.947
기울기(°)0.006-0.019-0.124-0.207-0.0190.0081.000-0.133-0.1310.6860.4720.0000.9030.615
TSP(g/km)-0.066-0.0660.8540.321-0.0660.045-0.1331.0000.9990.1550.1170.2590.0700.136
PM10(g/km)-0.065-0.0650.8540.311-0.0650.044-0.1310.9991.0000.1550.1170.2590.0700.136
지점0.7690.9460.1850.2470.9560.9360.6860.1550.1551.0000.2650.0000.9390.988
방향0.0000.0000.0000.0000.0000.0000.4720.1170.1170.2651.0000.0000.8750.000
차선0.0000.0000.2280.3180.0000.0800.0000.2590.2590.0000.0001.0000.0000.000
측정구간0.7840.8890.0470.2450.8980.8790.9030.0700.0700.9390.8750.0001.0000.928
주소0.7730.9570.2120.2170.9670.9470.6150.1360.1360.9880.0000.0000.9281.000

Missing values

2023-12-10T20:07:42.814726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T20:07:43.207873image/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-양산JC8.320211201020104.535.306944129.074722-3.152763.331.47경남 양산시 동면
12도로공사A-0010-0083E-6E2노포JC-양산JC8.320211201018100.3335.306944129.074722-3.152762.781.22경남 양산시 동면
23도로공사A-0010-0083E-6E3노포JC-양산JC8.32021120102389.6735.306944129.074722-3.152762.741.21경남 양산시 동면
34도로공사A-0010-0083E-6S1양산JC-노포JC8.320211201019143.535.306944129.0747223.0714160.380.17경남 양산시 동면
45도로공사A-0010-0083E-6S2양산JC-노포JC8.320211201034107.535.306944129.0747223.0714160.990.44경남 양산시 동면
56도로공사A-0010-0083E-6S3양산JC-노포JC8.320211201022110.035.306944129.0747223.0714163.741.64경남 양산시 동면
67도로공사A-0010-0538E-6E1언양JC-활천IC53.4202112010396.035.681944129.1811110.225450.030.01울산 울주군 두서면 활천리
78도로공사A-0010-0538E-6E2언양JC-활천IC53.42021120103288.1735.681944129.1811110.225451.770.78울산 울주군 두서면 활천리
89도로공사A-0010-0538E-6E3언양JC-활천IC53.42021120102078.2935.681944129.1811110.225453.521.55울산 울주군 두서면 활천리
910도로공사A-0010-0538E-6S1활천IC-언양JC53.42021120101298.035.681944129.1811110.191580.110.05울산 울주군 두서면 활천리
기본키도로종류지점방향차선측정구간장비이정(km)측정일측정시간차량통과수(대)평균 속도(km/hr)위도(°)경도(°)기울기(°)TSP(g/km)PM10(g/km)주소
9091도로공사A-0010-3801E-10E4오산IC-동탄JC380.12021120108976.7137.158778127.088333-0.6600047.453.28경기 화성시 동탄면 송리
9192도로공사A-0010-3801E-10E5오산IC-동탄JC380.1202112010386.037.158778127.088333-0.6600040.380.17경기 화성시 동탄면 송리
9293도로공사A-0010-3801E-10S1동탄JC-오산IC380.120211201046111.037.158778127.0883330.6719770.680.3경기 화성시 동탄면 송리
9394도로공사A-0010-3801E-10S2동탄JC-오산IC380.120211201011995.2537.158778127.0883330.6719774.081.8경기 화성시 동탄면 송리
9495도로공사A-0010-3801E-10S3동탄JC-오산IC380.12021120109390.037.158778127.0883330.6719778.553.76경기 화성시 동탄면 송리
9596도로공사A-0010-3801E-10S4동탄JC-오산IC380.12021120109381.037.158778127.0883330.67197710.454.6경기 화성시 동탄면 송리
9697도로공사A-0010-3801E-10S5동탄JC-오산IC380.120211201000.037.158778127.0883330.6719770.00.0경기 화성시 동탄면 송리
9798도로공사A-0010-3880E-10E1기흥IC-수원신갈IC388.020211201086102.037.226111127.108333-0.552760.970.43경기 용인시 기흥구 기흥동
9899도로공사A-0010-3880E-10E2기흥IC-수원신갈IC388.020211201000.037.226111127.108333-0.552760.00.0경기 용인시 기흥구 기흥동
99100도로공사A-0010-3880E-10E3기흥IC-수원신갈IC388.020211201012381.7537.226111127.108333-0.552767.973.51경기 용인시 기흥구 기흥동