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 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 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 30 (30.0%) zerosZeros
평균 속도(km/hr) has 30 (30.0%) zerosZeros
TSP(g/km) has 30 (30.0%) zerosZeros
PM10(g/km) has 33 (33.0%) zerosZeros

Reproduction

Analysis started2023-12-10 11:08:19.811519
Analysis finished2023-12-10 11:08:30.219677
Duration10.41 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
2023-12-10T20:08:30.300267image/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
2023-12-10T20:08:30.459555image/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

2023-12-10T20:08:30.616754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:08:30.728987image/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 length14.42
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-0010-1880E-6 6
 
6.0%
A-0010-2761E-6 6
 
6.0%
A-0010-3019E-6 6
 
6.0%
Other values (5) 21
21.0%

Length

2023-12-10T20:08:30.860125image/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-0010-1880e-6 6
 
6.0%
a-0010-2761e-6 6
 
6.0%
a-0010-3019e-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
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

2023-12-10T20:08:31.051519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:08:31.159237image/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
26 
2
26 
3
25 
4
13 
5
10 

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 13
13.0%
5 10
 
10.0%

Length

2023-12-10T20:08:31.286207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:08:31.404680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 26
26.0%
2 26
26.0%
3 25
25.0%
4 13
13.0%
5 10
 
10.0%

측정구간
Categorical

HIGH CORRELATION 

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

Length

Max length11
Median length9
Mean length9.28
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 (%)
동탄JC-오산IC 5
 
5.0%
청주JC-남이JC 5
 
5.0%
오산IC-동탄JC 5
 
5.0%
천안IC-북천안IC 5
 
5.0%
북천안IC-천안IC 5
 
5.0%
천안IC-천안JC 5
 
5.0%
안성IC-안성JC 5
 
5.0%
안성JC-안성IC 5
 
5.0%
안성JC-오산IC 5
 
5.0%
오산IC-안성JC 5
 
5.0%
Other values (16) 50
50.0%

Length

2023-12-10T20:08:31.550908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
동탄jc-오산ic 5
 
5.0%
안성ic-안성jc 5
 
5.0%
청주jc-남이jc 5
 
5.0%
안성jc-오산ic 5
 
5.0%
안성jc-안성ic 5
 
5.0%
오산ic-안성jc 5
 
5.0%
천안ic-천안jc 5
 
5.0%
북천안ic-천안ic 5
 
5.0%
천안ic-북천안ic 5
 
5.0%
오산ic-동탄jc 5
 
5.0%
Other values (16) 50
50.0%

장비이정(km)
Real number (ℝ)

HIGH CORRELATION 

Distinct15
Distinct (%)15.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean290.3432
Minimum8.3
Maximum384.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:08:31.674197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8.3
5-th percentile8.3
Q1295.3
median335.2
Q3361.3
95-th percentile380.1
Maximum384.5
Range376.2
Interquartile range (IQR)66

Descriptive statistics

Standard deviation107.54373
Coefficient of variation (CV)0.37040208
Kurtosis1.6545666
Mean290.3432
Median Absolute Deviation (MAD)37.03
Skewness-1.6607473
Sum29034.32
Variance11565.653
MonotonicityIncreasing
2023-12-10T20:08:32.095128image/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%
188.02 6
 
6.0%
276.1 6
 
6.0%
301.9 6
 
6.0%
Other values (5) 21
21.0%
ValueCountFrequency (%)
8.3 6
6.0%
53.4 6
6.0%
188.02 6
6.0%
276.1 6
6.0%
295.3 4
4.0%
295.6 4
4.0%
297.9 5
5.0%
301.9 6
6.0%
306.8 6
6.0%
335.2 9
9.0%
ValueCountFrequency (%)
384.5 2
 
2.0%
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%
297.9 5
5.0%
295.6 4
 
4.0%

측정일
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
20210901
100 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20210901
2nd row20210901
3rd row20210901
4th row20210901
5th row20210901

Common Values

ValueCountFrequency (%)
20210901 100
100.0%

Length

2023-12-10T20:08:32.308545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:08:32.410591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20210901 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

2023-12-10T20:08:32.508435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:08:32.612980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 100
100.0%

차량통과수(대)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.5
Minimum0
Maximum120
Zeros30
Zeros (%)30.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:08:32.736841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median25.5
Q358.5
95-th percentile97
Maximum120
Range120
Interquartile range (IQR)58.5

Descriptive statistics

Standard deviation34.39183
Coefficient of variation (CV)1.0266218
Kurtosis-0.27628155
Mean33.5
Median Absolute Deviation (MAD)25.5
Skewness0.81569508
Sum3350
Variance1182.798
MonotonicityNot monotonic
2023-12-10T20:08:32.896874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 30
30.0%
1 3
 
3.0%
60 3
 
3.0%
74 3
 
3.0%
28 2
 
2.0%
89 2
 
2.0%
32 2
 
2.0%
37 2
 
2.0%
120 2
 
2.0%
66 2
 
2.0%
Other values (40) 49
49.0%
ValueCountFrequency (%)
0 30
30.0%
1 3
 
3.0%
4 1
 
1.0%
5 2
 
2.0%
7 1
 
1.0%
10 2
 
2.0%
12 1
 
1.0%
16 1
 
1.0%
19 2
 
2.0%
20 1
 
1.0%
ValueCountFrequency (%)
120 2
2.0%
119 1
 
1.0%
118 1
 
1.0%
97 2
2.0%
91 2
2.0%
89 2
2.0%
84 1
 
1.0%
83 1
 
1.0%
74 3
3.0%
72 1
 
1.0%

평균 속도(km/hr)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct63
Distinct (%)63.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean63.0251
Minimum0
Maximum131
Zeros30
Zeros (%)30.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:08:33.029442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median82.535
Q391.91
95-th percentile108.175
Maximum131
Range131
Interquartile range (IQR)91.91

Descriptive statistics

Standard deviation42.803809
Coefficient of variation (CV)0.67915496
Kurtosis-1.2629067
Mean63.0251
Median Absolute Deviation (MAD)14.125
Skewness-0.67850624
Sum6302.51
Variance1832.1661
MonotonicityNot monotonic
2023-12-10T20:08:33.163688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 30
30.0%
103.0 3
 
3.0%
101.0 2
 
2.0%
91.0 2
 
2.0%
88.83 2
 
2.0%
87.75 2
 
2.0%
73.33 2
 
2.0%
92.0 2
 
2.0%
91.33 1
 
1.0%
95.57 1
 
1.0%
Other values (53) 53
53.0%
ValueCountFrequency (%)
0.0 30
30.0%
62.0 1
 
1.0%
68.75 1
 
1.0%
69.71 1
 
1.0%
72.75 1
 
1.0%
73.33 2
 
2.0%
73.44 1
 
1.0%
73.83 1
 
1.0%
75.33 1
 
1.0%
75.43 1
 
1.0%
ValueCountFrequency (%)
131.0 1
 
1.0%
115.0 1
 
1.0%
113.0 1
 
1.0%
112.0 1
 
1.0%
111.5 1
 
1.0%
108.0 1
 
1.0%
106.0 1
 
1.0%
105.5 1
 
1.0%
104.0 1
 
1.0%
103.0 3
3.0%

위도(°)
Real number (ℝ)

HIGH CORRELATION 

Distinct15
Distinct (%)15.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.627512
Minimum35.306944
Maximum37.197661
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:08:33.291895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.306944
5-th percentile35.306944
Q136.54
median36.780833
Q337.008889
95-th percentile37.158778
Maximum37.197661
Range1.8907167
Interquartile range (IQR)0.46888889

Descriptive statistics

Standard deviation0.50784722
Coefficient of variation (CV)0.013865185
Kurtosis0.96008182
Mean36.627512
Median Absolute Deviation (MAD)0.23444445
Skewness-1.2549226
Sum3662.7512
Variance0.2579088
MonotonicityIncreasing
2023-12-10T20:08:33.415576image/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%
36.15572222 6
 
6.0%
36.38961 6
 
6.0%
36.60611111 6
 
6.0%
Other values (5) 21
21.0%
ValueCountFrequency (%)
35.30694444 6
6.0%
35.68194444 6
6.0%
36.15572222 6
6.0%
36.38961 6
6.0%
36.54 4
4.0%
36.55638889 4
4.0%
36.57694444 5
5.0%
36.60611111 6
6.0%
36.64019722 6
6.0%
36.78083333 9
9.0%
ValueCountFrequency (%)
37.19766111 2
 
2.0%
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%
36.57694444 5
5.0%
36.55638889 4
 
4.0%

경도(°)
Real number (ℝ)

HIGH CORRELATION 

Distinct15
Distinct (%)15.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.52886
Minimum127.08833
Maximum129.18111
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:08:33.558790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum127.08833
5-th percentile127.08833
Q1127.14917
median127.18672
Q3127.4325
95-th percentile129.18111
Maximum129.18111
Range2.0927778
Interquartile range (IQR)0.2833333

Descriptive statistics

Standard deviation0.64879756
Coefficient of variation (CV)0.0050874569
Kurtosis1.8443249
Mean127.52886
Median Absolute Deviation (MAD)0.0983889
Skewness1.8134878
Sum12752.886
Variance0.42093827
MonotonicityNot monotonic
2023-12-10T20:08:33.679797image/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%
128.2132778 6
 
6.0%
127.423508 6
 
6.0%
127.4083333 6
 
6.0%
Other values (5) 21
21.0%
ValueCountFrequency (%)
127.0883333 10
10.0%
127.0940278 2
 
2.0%
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%
127.4277778 5
5.0%
ValueCountFrequency (%)
129.1811111 6
6.0%
129.0747222 6
6.0%
128.2132778 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%
127.3781278 6
6.0%
127.1867222 10
10.0%

기울기(°)
Real number (ℝ)

HIGH CORRELATION 

Distinct26
Distinct (%)26.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.0274378
Minimum-3.15276
Maximum3.071416
Zeros0
Zeros (%)0.0%
Negative48
Negative (%)48.0%
Memory size1.0 KiB
2023-12-10T20:08:33.800004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-3.15276
5-th percentile-2.788625
Q1-1.252479
median0.09761
Q31.056036
95-th percentile2.703849
Maximum3.071416
Range6.224176
Interquartile range (IQR)2.308515

Descriptive statistics

Standard deviation1.4325792
Coefficient of variation (CV)-52.211883
Kurtosis-0.28232948
Mean-0.0274378
Median Absolute Deviation (MAD)1.0164195
Skewness-0.033619583
Sum-2.74378
Variance2.0522832
MonotonicityNot monotonic
2023-12-10T20:08:33.937720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
0.671977 5
 
5.0%
-0.660004 5
 
5.0%
-0.141433 5
 
5.0%
0.09761 5
 
5.0%
-1.30142 5
 
5.0%
1.293015 5
 
5.0%
-1.758683 5
 
5.0%
0.61369 5
 
5.0%
-0.624001 5
 
5.0%
1.544389 5
 
5.0%
Other values (16) 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%
-1.252479 3
3.0%
-0.807188 4
4.0%
-0.688696 3
3.0%
-0.660004 5
5.0%
ValueCountFrequency (%)
3.071416 3
3.0%
2.703849 3
3.0%
1.717451 3
3.0%
1.544389 5
5.0%
1.293015 5
5.0%
1.254885 2
 
2.0%
1.172023 3
3.0%
1.056036 4
4.0%
0.688696 3
3.0%
0.671977 5
5.0%

TSP(g/km)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct66
Distinct (%)66.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6159
Minimum0
Maximum13.17
Zeros30
Zeros (%)30.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:08:34.099134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.605
Q34.6975
95-th percentile9.533
Maximum13.17
Range13.17
Interquartile range (IQR)4.6975

Descriptive statistics

Standard deviation3.5238352
Coefficient of variation (CV)1.3470833
Kurtosis0.59743091
Mean2.6159
Median Absolute Deviation (MAD)0.605
Skewness1.2824831
Sum261.59
Variance12.417414
MonotonicityNot monotonic
2023-12-10T20:08:34.332646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 30
30.0%
0.01 3
 
3.0%
0.04 2
 
2.0%
0.76 2
 
2.0%
0.26 2
 
2.0%
1.06 1
 
1.0%
0.17 1
 
1.0%
5.48 1
 
1.0%
4.78 1
 
1.0%
0.67 1
 
1.0%
Other values (56) 56
56.0%
ValueCountFrequency (%)
0.0 30
30.0%
0.01 3
 
3.0%
0.04 2
 
2.0%
0.06 1
 
1.0%
0.08 1
 
1.0%
0.11 1
 
1.0%
0.17 1
 
1.0%
0.2 1
 
1.0%
0.26 2
 
2.0%
0.31 1
 
1.0%
ValueCountFrequency (%)
13.17 1
1.0%
12.75 1
1.0%
12.05 1
1.0%
10.56 1
1.0%
9.78 1
1.0%
9.52 1
1.0%
9.5 1
1.0%
9.17 1
1.0%
8.43 1
1.0%
8.28 1
1.0%

PM10(g/km)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct63
Distinct (%)63.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.151
Minimum0
Maximum5.79
Zeros33
Zeros (%)33.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:08:34.456984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.265
Q32.0625
95-th percentile4.1955
Maximum5.79
Range5.79
Interquartile range (IQR)2.0625

Descriptive statistics

Standard deviation1.5502926
Coefficient of variation (CV)1.3469093
Kurtosis0.59461769
Mean1.151
Median Absolute Deviation (MAD)0.265
Skewness1.2817913
Sum115.1
Variance2.4034071
MonotonicityNot monotonic
2023-12-10T20:08:34.585203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 33
33.0%
0.02 3
 
3.0%
0.2 2
 
2.0%
0.18 2
 
2.0%
0.24 2
 
2.0%
4.03 1
 
1.0%
4.19 1
 
1.0%
1.54 1
 
1.0%
0.33 1
 
1.0%
1.43 1
 
1.0%
Other values (53) 53
53.0%
ValueCountFrequency (%)
0.0 33
33.0%
0.02 3
 
3.0%
0.03 1
 
1.0%
0.05 1
 
1.0%
0.08 1
 
1.0%
0.09 1
 
1.0%
0.11 1
 
1.0%
0.12 1
 
1.0%
0.14 1
 
1.0%
0.16 1
 
1.0%
ValueCountFrequency (%)
5.79 1
1.0%
5.61 1
1.0%
5.3 1
1.0%
4.65 1
1.0%
4.3 1
1.0%
4.19 1
1.0%
4.18 1
1.0%
4.03 1
1.0%
3.71 1
1.0%
3.64 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 length12.62
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%
경북 김천시 아포읍 봉산리 6
6.0%
대전 대덕구 연축동 6
6.0%
Other values (3) 14
14.0%

Length

2023-12-10T20:08:34.727241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기 32
 
8.7%
충북 25
 
6.8%
천안시 19
 
5.2%
청원군 19
 
5.2%
남이면 13
 
3.5%
화성시 12
 
3.3%
용인시 10
 
2.7%
동탄면 10
 
2.7%
진목리 10
 
2.7%
남사면 10
 
2.7%
Other values (29) 208
56.5%

Interactions

2023-12-10T20:08:28.879891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:08:20.827557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:08:21.907132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:08:22.930907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:08:23.764854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:08:24.693256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:08:25.860276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:08:26.832111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:08:27.863824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:08:28.972919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:08:20.936270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:08:22.022580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:08:23.024321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:08:23.854423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:08:24.785423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:08:25.979252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:08:26.941060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:08:27.966346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:08:29.111492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:08:21.060770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:08:22.142530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:08:23.123755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:08:23.975935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:08:24.890268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:08:26.088883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:08:27.077340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:08:28.084759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:08:29.244999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:08:21.164800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:08:22.248392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:08:23.228254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:08:24.094361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:08:24.975096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:08:26.197561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:08:27.206130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:08:28.211545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:08:29.356480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:08:21.287168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:08:22.376832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:08:23.326271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:08:24.188314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:08:25.060408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:08:26.306993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:08:27.329823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:08:28.325485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:08:29.470383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:08:21.431477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:08:22.494997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:08:23.422822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:08:24.295891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:08:25.149710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:08:26.428965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:08:27.436913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:08:28.459618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:08:29.576039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:08:21.543952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:08:22.602321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:08:23.503637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:08:24.393264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:08:25.232926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:08:26.534072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:08:27.539468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:08:28.560516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:08:29.675266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:08:21.670368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:08:22.713001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:08:23.590930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:08:24.494244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:08:25.326176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:08:26.642988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:08:27.636308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:08:28.673119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:08:29.785826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:08:21.787367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:08:22.829248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:08:23.682477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:08:24.594018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:08:25.432210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:08:26.733858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:08:27.747497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:08:28.779372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T20:08:34.814996image/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
기본키1.0000.9730.2400.0000.9800.9140.4840.3350.9020.9510.7240.2060.2060.945
지점0.9731.0000.1060.0001.0001.0000.2470.5101.0001.0000.9030.0000.0001.000
방향0.2400.1061.0000.0001.0000.0000.2590.0000.0000.0000.5330.0000.0000.000
차선0.0000.0000.0001.0000.0000.0000.4070.5380.0000.0900.0000.7220.7220.000
측정구간0.9801.0001.0000.0001.0001.0000.3900.5051.0001.0001.0000.0000.0001.000
장비이정(km)0.9141.0000.0000.0001.0001.0000.3890.3890.9931.0000.7110.0000.0001.000
차량통과수(대)0.4840.2470.2590.4070.3900.3891.0000.5430.3580.3830.3590.8300.8300.332
평균 속도(km/hr)0.3350.5100.0000.5380.5050.3890.5431.0000.4630.3760.4030.4260.4260.523
위도(°)0.9021.0000.0000.0001.0000.9930.3580.4631.0000.9990.8890.0000.0001.000
경도(°)0.9511.0000.0000.0901.0001.0000.3830.3760.9991.0000.8390.1990.1991.000
기울기(°)0.7240.9030.5330.0001.0000.7110.3590.4030.8890.8391.0000.0000.0000.857
TSP(g/km)0.2060.0000.0000.7220.0000.0000.8300.4260.0000.1990.0001.0001.0000.000
PM10(g/km)0.2060.0000.0000.7220.0000.0000.8300.4260.0000.1990.0001.0001.0000.000
주소0.9451.0000.0000.0001.0001.0000.3320.5231.0001.0000.8570.0000.0001.000
2023-12-10T20:08:34.962731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지점차선측정구간방향주소
지점1.0000.0000.9330.0820.988
차선0.0001.0000.0000.0000.000
측정구간0.9330.0001.0000.8690.922
방향0.0820.0000.8691.0000.000
주소0.9880.0000.9220.0001.000
2023-12-10T20:08:35.086170image/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
기본키1.0000.9970.155-0.2550.997-0.9740.080-0.041-0.0520.8000.1740.0000.7930.780
장비이정(km)0.9971.0000.173-0.2271.000-0.9770.061-0.034-0.0450.9510.0000.0000.8870.962
차량통과수(대)0.1550.1731.0000.5240.173-0.207-0.0880.8940.8890.0000.2240.1300.1240.076
평균 속도(km/hr)-0.255-0.2270.5241.000-0.2270.202-0.1480.4380.4190.2450.0000.3780.2090.264
위도(°)0.9971.0000.173-0.2271.000-0.9770.061-0.034-0.0450.9610.0000.0000.8970.972
경도(°)-0.974-0.977-0.2070.202-0.9771.000-0.071-0.0010.0070.9410.0000.0700.8780.952
기울기(°)0.0800.061-0.088-0.1480.061-0.0711.000-0.104-0.1050.6560.3880.0000.8970.601
TSP(g/km)-0.041-0.0340.8940.438-0.034-0.001-0.1041.0000.9950.0000.0000.3710.0000.000
PM10(g/km)-0.052-0.0450.8890.419-0.0450.007-0.1050.9951.0000.0000.0000.3710.0000.000
지점0.8000.9510.0000.2450.9610.9410.6560.0000.0001.0000.0820.0000.9330.988
방향0.1740.0000.2240.0000.0000.0000.3880.0000.0000.0821.0000.0000.8690.000
차선0.0000.0000.1300.3780.0000.0700.0000.3710.3710.0000.0001.0000.0000.000
측정구간0.7930.8870.1240.2090.8970.8780.8970.0000.0000.9330.8690.0001.0000.922
주소0.7800.9620.0760.2640.9720.9520.6010.0000.0000.9880.0000.0000.9221.000

Missing values

2023-12-10T20:08:29.930887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T20:08:30.140707image/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-양산JC8.320210901021105.535.306944129.074722-3.152760.730.32경남 양산시 동면
12도로공사A-0010-0083E-6E2노포JC-양산JC8.320210901037102.035.306944129.074722-3.152761.640.72경남 양산시 동면
23도로공사A-0010-0083E-6E3노포JC-양산JC8.32021090101095.435.306944129.074722-3.152761.190.52경남 양산시 동면
34도로공사A-0010-0083E-6S1양산JC-노포JC8.320210901037112.035.306944129.0747223.0714160.420.18경남 양산시 동면
45도로공사A-0010-0083E-6S2양산JC-노포JC8.320210901055108.035.306944129.0747223.0714161.250.55경남 양산시 동면
56도로공사A-0010-0083E-6S3양산JC-노포JC8.32021090103488.8335.306944129.0747223.0714164.311.9경남 양산시 동면
67도로공사A-0010-0538E-6E1언양JC-활천IC53.42021090107103.035.681944129.1811110.225450.080.03울산 울주군 두서면 활천리
78도로공사A-0010-0538E-6E2언양JC-활천IC53.42021090101987.7535.681944129.1811110.225450.660.29울산 울주군 두서면 활천리
89도로공사A-0010-0538E-6E3언양JC-활천IC53.42021090102378.035.681944129.1811110.225453.471.53울산 울주군 두서면 활천리
910도로공사A-0010-0538E-6S1활천IC-언양JC53.420210901012103.035.681944129.1811110.191580.260.12울산 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-동탄JC380.120210901011880.037.158778127.088333-0.6600047.323.22경기 화성시 동탄면 송리
9192도로공사A-0010-3801E-10E4오산IC-동탄JC380.12021090108969.7137.158778127.088333-0.6600047.013.09경기 화성시 동탄면 송리
9293도로공사A-0010-3801E-10E5오산IC-동탄JC380.120210901000.037.158778127.088333-0.6600040.00.0경기 화성시 동탄면 송리
9394도로공사A-0010-3801E-10S1동탄JC-오산IC380.12021090102290.037.158778127.0883330.6719770.20.09경기 화성시 동탄면 송리
9495도로공사A-0010-3801E-10S2동탄JC-오산IC380.12021090109188.537.158778127.0883330.6719773.441.51경기 화성시 동탄면 송리
9596도로공사A-0010-3801E-10S3동탄JC-오산IC380.12021090109182.2937.158778127.0883330.6719776.662.93경기 화성시 동탄면 송리
9697도로공사A-0010-3801E-10S4동탄JC-오산IC380.12021090107475.4337.158778127.0883330.6719777.23.17경기 화성시 동탄면 송리
9798도로공사A-0010-3801E-10S5동탄JC-오산IC380.1202109010162.037.158778127.0883330.6719770.010.0경기 화성시 동탄면 송리
9899도로공사A-0010-3845E-10E1동탄JC-기흥동탄IC384.520210901000.037.197661127.0940281.2548850.00.0경기 화성시 오산동
99100도로공사A-0010-3845E-10E2동탄JC-기흥동탄IC384.520210901000.037.197661127.0940281.2548850.00.0경기 화성시 오산동