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 23 (23.0%) zerosZeros
평균 속도(km/hr) has 23 (23.0%) zerosZeros
TSP(g/km) has 24 (24.0%) zerosZeros
PM10(g/km) has 25 (25.0%) zerosZeros

Reproduction

Analysis started2023-12-10 11:09:12.950090
Analysis finished2023-12-10 11:09:26.069215
Duration13.12 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
2023-12-10T20:09:26.248410image/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
2023-12-10T20:09:26.464152image/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

2023-12-10T20:09:26.676590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:09:26.837657image/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-0010-3722E-10
10 
A-0010-3801E-10
10 
A-0010-3352E-9
A-0010-0083E-6
Other values (10)
55 

Length

Max length15
Median length14
Mean length14.36
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-0010-3722E-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-0010-2695C-6 6
 
6.0%
A-0010-2761E-6 6
 
6.0%
A-0010-3019E-6 6
 
6.0%
Other values (5) 25
25.0%

Length

2023-12-10T20:09:27.032840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
a-0010-3452s-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-0010-2695c-6 6
 
6.0%
a-0010-2761e-6 6
 
6.0%
a-0010-3019e-6 6
 
6.0%
Other values (5) 25
25.0%

방향
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
E
54 
S
46 

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 54
54.0%
S 46
46.0%

Length

2023-12-10T20:09:27.324893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:09:27.538187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
e 54
54.0%
s 46
46.0%

차선
Categorical

Distinct5
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
1
27 
2
26 
3
26 
4
12 
5

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row3
4th row1
5th row2

Common Values

ValueCountFrequency (%)
1 27
27.0%
2 26
26.0%
3 26
26.0%
4 12
12.0%
5 9
 
9.0%

Length

2023-12-10T20:09:27.710604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:09:27.889559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 27
27.0%
2 26
26.0%
3 26
26.0%
4 12
12.0%
5 9
 
9.0%

측정구간
Categorical

HIGH CORRELATION 

Distinct27
Distinct (%)27.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
동탄JC-기흥동탄IC
 
5
오산IC-동탄JC
 
5
오산IC-안성JC
 
5
안성JC-오산IC
 
5
북천안IC-천안IC
 
5
Other values (22)
75 

Length

Max length11
Median length9
Mean length9.36
Min length9

Unique

Unique1 ?
Unique (%)1.0%

Sample

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

Common Values

ValueCountFrequency (%)
동탄JC-기흥동탄IC 5
 
5.0%
오산IC-동탄JC 5
 
5.0%
오산IC-안성JC 5
 
5.0%
안성JC-오산IC 5
 
5.0%
북천안IC-천안IC 5
 
5.0%
천안IC-북천안IC 5
 
5.0%
천안IC-천안JC 5
 
5.0%
청주JC-남이JC 5
 
5.0%
동탄JC-오산IC 5
 
5.0%
천안JC-천안IC 4
 
4.0%
Other values (17) 51
51.0%

Length

2023-12-10T20:09:28.123919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
동탄jc-기흥동탄ic 5
 
5.0%
오산ic-안성jc 5
 
5.0%
안성jc-오산ic 5
 
5.0%
북천안ic-천안ic 5
 
5.0%
천안ic-북천안ic 5
 
5.0%
천안ic-천안jc 5
 
5.0%
청주jc-남이jc 5
 
5.0%
동탄jc-오산ic 5
 
5.0%
오산ic-동탄jc 5
 
5.0%
남청주ic-청주jc 4
 
4.0%
Other values (17) 51
51.0%

장비이정(km)
Real number (ℝ)

HIGH CORRELATION 

Distinct15
Distinct (%)15.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean285.7632
Minimum8.3
Maximum384.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:09:28.326426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8.3
5-th percentile8.3
Q1276.1
median306.8
Q3372.23
95-th percentile384.5
Maximum384.5
Range376.2
Interquartile range (IQR)96.13

Descriptive statistics

Standard deviation106.86638
Coefficient of variation (CV)0.37396832
Kurtosis1.4903591
Mean285.7632
Median Absolute Deviation (MAD)38.4
Skewness-1.5636812
Sum28576.32
Variance11420.424
MonotonicityIncreasing
2023-12-10T20:09:28.560326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
345.2 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%
269.5 6
 
6.0%
276.1 6
 
6.0%
301.9 6
 
6.0%
Other values (5) 25
25.0%
ValueCountFrequency (%)
8.3 6
6.0%
53.4 6
6.0%
188.02 6
6.0%
269.5 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%
ValueCountFrequency (%)
384.5 6
6.0%
380.1 10
10.0%
372.23 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%
295.3 4
 
4.0%

측정일
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
20210601
100 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20210601
2nd row20210601
3rd row20210601
4th row20210601
5th row20210601

Common Values

ValueCountFrequency (%)
20210601 100
100.0%

Length

2023-12-10T20:09:28.767790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:09:28.918686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20210601 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

2023-12-10T20:09:29.069056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:09:29.225065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 100
100.0%

차량통과수(대)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct56
Distinct (%)56.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.4
Minimum0
Maximum153
Zeros23
Zeros (%)23.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:09:29.406352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median28.5
Q363.5
95-th percentile112.2
Maximum153
Range153
Interquartile range (IQR)62.5

Descriptive statistics

Standard deviation37.844178
Coefficient of variation (CV)0.98552547
Kurtosis0.10149596
Mean38.4
Median Absolute Deviation (MAD)28.5
Skewness0.90398846
Sum3840
Variance1432.1818
MonotonicityNot monotonic
2023-12-10T20:09:29.660163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 23
23.0%
1 4
 
4.0%
92 3
 
3.0%
30 3
 
3.0%
23 3
 
3.0%
24 3
 
3.0%
16 2
 
2.0%
31 2
 
2.0%
49 2
 
2.0%
53 2
 
2.0%
Other values (46) 53
53.0%
ValueCountFrequency (%)
0 23
23.0%
1 4
 
4.0%
2 1
 
1.0%
3 1
 
1.0%
5 1
 
1.0%
6 2
 
2.0%
13 2
 
2.0%
16 2
 
2.0%
18 1
 
1.0%
21 1
 
1.0%
ValueCountFrequency (%)
153 1
 
1.0%
133 1
 
1.0%
129 1
 
1.0%
126 1
 
1.0%
116 1
 
1.0%
112 1
 
1.0%
106 1
 
1.0%
100 1
 
1.0%
97 1
 
1.0%
92 3
3.0%

평균 속도(km/hr)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct71
Distinct (%)71.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean69.6094
Minimum0
Maximum132
Zeros23
Zeros (%)23.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:09:29.876624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q156.75
median84.37
Q396.4375
95-th percentile115.125
Maximum132
Range132
Interquartile range (IQR)39.6875

Descriptive statistics

Standard deviation40.864837
Coefficient of variation (CV)0.58705918
Kurtosis-0.6865454
Mean69.6094
Median Absolute Deviation (MAD)14.165
Skewness-0.88907613
Sum6960.94
Variance1669.9349
MonotonicityNot monotonic
2023-12-10T20:09:30.114408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 23
 
23.0%
88.67 2
 
2.0%
99.0 2
 
2.0%
84.0 2
 
2.0%
85.0 2
 
2.0%
74.71 2
 
2.0%
111.0 2
 
2.0%
99.5 2
 
2.0%
79.5 1
 
1.0%
101.0 1
 
1.0%
Other values (61) 61
61.0%
ValueCountFrequency (%)
0.0 23
23.0%
31.0 1
 
1.0%
50.0 1
 
1.0%
59.0 1
 
1.0%
59.2 1
 
1.0%
70.67 1
 
1.0%
73.5 1
 
1.0%
74.71 2
 
2.0%
74.78 1
 
1.0%
76.17 1
 
1.0%
ValueCountFrequency (%)
132.0 1
1.0%
126.0 1
1.0%
125.0 1
1.0%
118.33 1
1.0%
117.5 1
1.0%
115.0 1
1.0%
111.0 2
2.0%
110.0 1
1.0%
109.5 1
1.0%
109.0 1
1.0%

위도(°)
Real number (ℝ)

HIGH CORRELATION 

Distinct15
Distinct (%)15.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.59538
Minimum35.306944
Maximum37.197661
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:09:30.293966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.306944
5-th percentile35.306944
Q136.38961
median36.640197
Q337.100926
95-th percentile37.197661
Maximum37.197661
Range1.8907167
Interquartile range (IQR)0.711316

Descriptive statistics

Standard deviation0.50996099
Coefficient of variation (CV)0.013935119
Kurtosis0.63388146
Mean36.59538
Median Absolute Deviation (MAD)0.27164222
Skewness-1.0419698
Sum3659.538
Variance0.26006021
MonotonicityIncreasing
2023-12-10T20:09:30.803810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
36.86890278 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%
36.3475 6
 
6.0%
36.38961 6
 
6.0%
36.60611111 6
 
6.0%
Other values (5) 25
25.0%
ValueCountFrequency (%)
35.30694444 6
6.0%
35.68194444 6
6.0%
36.15572222 6
6.0%
36.3475 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%
ValueCountFrequency (%)
37.19766111 6
6.0%
37.15877778 10
10.0%
37.100926 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%
36.54 4
 
4.0%

경도(°)
Real number (ℝ)

HIGH CORRELATION 

Distinct15
Distinct (%)15.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.54585
Minimum127.08833
Maximum129.18111
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:09:31.018781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum127.08833
5-th percentile127.08833
Q1127.11864
median127.37813
Q3127.43389
95-th percentile129.18111
Maximum129.18111
Range2.0927778
Interquartile range (IQR)0.3152469

Descriptive statistics

Standard deviation0.64338158
Coefficient of variation (CV)0.0050443161
Kurtosis1.807567
Mean127.54585
Median Absolute Deviation (MAD)0.2014611
Skewness1.7872007
Sum12754.585
Variance0.41393986
MonotonicityNot monotonic
2023-12-10T20:09:31.219456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
127.1867222 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%
127.4691667 6
 
6.0%
127.423508 6
 
6.0%
127.4083333 6
 
6.0%
Other values (5) 25
25.0%
ValueCountFrequency (%)
127.0883333 10
10.0%
127.0940278 6
6.0%
127.118642 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%
127.4325 4
 
4.0%
ValueCountFrequency (%)
129.1811111 6
6.0%
129.0747222 6
6.0%
128.2132778 6
6.0%
127.4691667 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%

기울기(°)
Real number (ℝ)

HIGH CORRELATION 

Distinct27
Distinct (%)27.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.02413127
Minimum-13.24576
Maximum12.47809
Zeros0
Zeros (%)0.0%
Negative47
Negative (%)47.0%
Memory size1.0 KiB
2023-12-10T20:09:31.434456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-13.24576
5-th percentile-3.15276
Q1-0.89864525
median0.09761
Q31.0850327
95-th percentile3.071416
Maximum12.47809
Range25.72385
Interquartile range (IQR)1.983678

Descriptive statistics

Standard deviation3.4610736
Coefficient of variation (CV)-143.42691
Kurtosis9.3262758
Mean-0.02413127
Median Absolute Deviation (MAD)1.0164195
Skewness-0.26829937
Sum-2.413127
Variance11.979031
MonotonicityNot monotonic
2023-12-10T20:09:31.638793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
1.254885 5
 
5.0%
0.671977 5
 
5.0%
-0.660004 5
 
5.0%
-0.141433 5
 
5.0%
0.09761 5
 
5.0%
0.61369 5
 
5.0%
-0.624001 5
 
5.0%
1.544389 5
 
5.0%
-1.758683 5
 
5.0%
-0.807188 4
 
4.0%
Other values (17) 51
51.0%
ValueCountFrequency (%)
-13.24576 3
3.0%
-3.15276 3
3.0%
-2.788625 3
3.0%
-1.758683 5
5.0%
-1.717451 3
3.0%
-1.359001 4
4.0%
-1.252479 3
3.0%
-1.173017 1
 
1.0%
-0.807188 4
4.0%
-0.688696 3
3.0%
ValueCountFrequency (%)
12.47809 3
3.0%
3.071416 3
3.0%
2.703849 3
3.0%
1.717451 3
3.0%
1.544389 5
5.0%
1.254885 5
5.0%
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 

Distinct76
Distinct (%)76.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.1568
Minimum0
Maximum16.35
Zeros24
Zeros (%)24.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:09:31.858310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.025
median0.895
Q34.505
95-th percentile12.2685
Maximum16.35
Range16.35
Interquartile range (IQR)4.48

Descriptive statistics

Standard deviation4.2950589
Coefficient of variation (CV)1.3605737
Kurtosis1.138124
Mean3.1568
Median Absolute Deviation (MAD)0.895
Skewness1.4620831
Sum315.68
Variance18.447531
MonotonicityNot monotonic
2023-12-10T20:09:32.137912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 24
 
24.0%
3.61 2
 
2.0%
0.24 1
 
1.0%
0.23 1
 
1.0%
2.83 1
 
1.0%
1.37 1
 
1.0%
0.9 1
 
1.0%
0.27 1
 
1.0%
1.91 1
 
1.0%
0.03 1
 
1.0%
Other values (66) 66
66.0%
ValueCountFrequency (%)
0.0 24
24.0%
0.01 1
 
1.0%
0.03 1
 
1.0%
0.05 1
 
1.0%
0.09 1
 
1.0%
0.1 1
 
1.0%
0.12 1
 
1.0%
0.15 1
 
1.0%
0.22 1
 
1.0%
0.23 1
 
1.0%
ValueCountFrequency (%)
16.35 1
1.0%
15.69 1
1.0%
15.22 1
1.0%
13.8 1
1.0%
13.19 1
1.0%
12.22 1
1.0%
11.62 1
1.0%
10.72 1
1.0%
10.38 1
1.0%
9.72 1
1.0%

PM10(g/km)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct69
Distinct (%)69.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3892
Minimum0
Maximum7.19
Zeros25
Zeros (%)25.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:09:32.377737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.0075
median0.395
Q31.9875
95-th percentile5.401
Maximum7.19
Range7.19
Interquartile range (IQR)1.98

Descriptive statistics

Standard deviation1.8900423
Coefficient of variation (CV)1.3605257
Kurtosis1.1325538
Mean1.3892
Median Absolute Deviation (MAD)0.395
Skewness1.4605087
Sum138.92
Variance3.57226
MonotonicityNot monotonic
2023-12-10T20:09:32.650723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 25
25.0%
0.04 2
 
2.0%
0.4 2
 
2.0%
0.19 2
 
2.0%
1.59 2
 
2.0%
0.1 2
 
2.0%
0.31 2
 
2.0%
0.6 2
 
2.0%
1.4 1
 
1.0%
0.51 1
 
1.0%
Other values (59) 59
59.0%
ValueCountFrequency (%)
0.0 25
25.0%
0.01 1
 
1.0%
0.02 1
 
1.0%
0.04 2
 
2.0%
0.05 1
 
1.0%
0.06 1
 
1.0%
0.09 1
 
1.0%
0.1 2
 
2.0%
0.11 1
 
1.0%
0.12 1
 
1.0%
ValueCountFrequency (%)
7.19 1
1.0%
6.9 1
1.0%
6.7 1
1.0%
6.07 1
1.0%
5.8 1
1.0%
5.38 1
1.0%
5.11 1
1.0%
4.72 1
1.0%
4.57 1
1.0%
4.28 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 용인시 처인구 남사면 진목리
10 
경기 화성시 동탄면 송리
10 
충청 천안시 구성동
Other values (8)
48 

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 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 비래동 6
 
6.0%
대전 대덕구 연축동 6
 
6.0%
Other values (3) 18
18.0%

Length

2023-12-10T20:09:32.892303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기 26
 
7.1%
충북 25
 
6.8%
천안시 19
 
5.2%
청원군 19
 
5.2%
화성시 16
 
4.3%
남이면 13
 
3.5%
대전 12
 
3.3%
대덕구 12
 
3.3%
진목리 10
 
2.7%
성거읍 10
 
2.7%
Other values (28) 206
56.0%

Interactions

2023-12-10T20:09:24.289276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:13.954180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:15.265877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:16.359244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:17.437155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:18.560683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:19.919127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:21.161146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:22.819722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:24.416304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:14.055366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:15.357315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:16.487739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:17.562314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:18.709745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:20.033925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:21.291498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:22.981699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:24.528330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:14.179153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:15.471570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:16.616437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:17.704928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:18.866810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:20.173986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:21.447110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:23.148775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:24.648218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:14.319287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:15.595659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:16.721679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:17.835869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:19.036940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:20.296563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:21.625954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:23.315163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:24.759933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:14.456966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:15.735198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:16.820944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:17.964793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:19.195332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:20.429248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:21.769077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:23.483127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:24.927390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:14.900250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:15.876660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:16.971214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:18.098806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:19.349679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:20.598161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:22.259185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:23.677465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:25.040213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:15.003323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:15.994682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:17.073341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:18.200573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:19.484974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:20.736027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:22.388639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:23.840017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:25.175215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:15.090081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:16.104214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:17.184065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:18.285943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:19.613804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:20.865634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:22.523309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:23.974844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:25.317276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:15.180250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:16.224520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:17.318670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:18.417129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:19.776526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:21.014014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:22.676627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:24.142789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T20:09:33.031009image/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
기본키1.0000.9660.0000.0000.9840.8840.6210.3340.9260.9430.6510.3340.3340.942
지점0.9661.0000.1310.0001.0001.0000.4340.4011.0001.0000.8600.4730.4731.000
방향0.0000.1311.0000.0001.0000.0000.0980.0000.0000.0000.6620.2960.2960.000
차선0.0000.0000.0001.0000.0000.0000.5670.5740.0000.1300.0000.6730.6730.000
측정구간0.9841.0001.0000.0001.0001.0000.5340.5891.0001.0001.0000.2860.2861.000
장비이정(km)0.8841.0000.0000.0001.0001.0000.3990.4060.9661.0000.7650.2630.2631.000
차량통과수(대)0.6210.4340.0980.5670.5340.3991.0000.6080.3160.4160.3850.8250.8250.466
평균 속도(km/hr)0.3340.4010.0000.5740.5890.4060.6081.0000.2190.1200.7000.4220.4220.442
위도(°)0.9261.0000.0000.0001.0000.9660.3160.2191.0000.9990.7120.2250.2251.000
경도(°)0.9431.0000.0000.1301.0001.0000.4160.1200.9991.0000.5400.3080.3081.000
기울기(°)0.6510.8600.6620.0001.0000.7650.3850.7000.7120.5401.0000.0000.0000.832
TSP(g/km)0.3340.4730.2960.6730.2860.2630.8250.4220.2250.3080.0001.0001.0000.415
PM10(g/km)0.3340.4730.2960.6730.2860.2630.8250.4220.2250.3080.0001.0001.0000.415
주소0.9421.0000.0000.0001.0001.0000.4660.4421.0001.0000.8320.4150.4151.000
2023-12-10T20:09:33.264112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지점차선측정구간방향주소
지점1.0000.0000.9270.1050.988
차선0.0001.0000.0000.0000.000
측정구간0.9270.0001.0000.8630.916
방향0.1050.0000.8631.0000.000
주소0.9880.0000.9160.0001.000
2023-12-10T20:09:33.463107image/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
기본키1.0000.9970.023-0.2230.997-0.9700.100-0.160-0.1630.7740.0000.0000.8090.771
장비이정(km)0.9971.0000.038-0.2031.000-0.9720.080-0.157-0.1600.9560.0000.0000.8860.967
차량통과수(대)0.0230.0381.0000.5320.038-0.113-0.2290.8830.8810.1670.0650.2610.1950.206
평균 속도(km/hr)-0.223-0.2030.5321.000-0.2030.132-0.1510.3730.3680.1650.0000.3690.2060.199
위도(°)0.9971.0000.038-0.2031.000-0.9720.080-0.157-0.1600.9610.0000.0000.8910.972
경도(°)-0.970-0.972-0.1130.132-0.9721.000-0.0830.1000.1040.9410.0000.1040.8720.952
기울기(°)0.1000.080-0.229-0.1510.080-0.0831.000-0.251-0.2480.5890.4740.0000.8810.583
TSP(g/km)-0.160-0.1570.8830.373-0.1570.100-0.2511.0000.9990.1870.2160.3320.0800.179
PM10(g/km)-0.163-0.1600.8810.368-0.1600.104-0.2480.9991.0000.1870.2160.3320.0800.179
지점0.7740.9560.1670.1650.9610.9410.5890.1870.1871.0000.1050.0000.9270.988
방향0.0000.0000.0650.0000.0000.0000.4740.2160.2160.1051.0000.0000.8630.000
차선0.0000.0000.2610.3690.0000.1040.0000.3320.3320.0000.0001.0000.0000.000
측정구간0.8090.8860.1950.2060.8910.8720.8810.0800.0800.9270.8630.0001.0000.916
주소0.7710.9670.2060.1990.9720.9520.5830.1790.1790.9880.0000.0000.9161.000

Missing values

2023-12-10T20:09:25.544278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T20:09:25.929988image/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-양산JC8.320210601021109.035.306944129.074722-3.152760.240.1경남 양산시 동면
12도로공사A-0010-0083E-6E2노포JC-양산JC8.32021060105399.035.306944129.074722-3.152761.610.71경남 양산시 동면
23도로공사A-0010-0083E-6E3노포JC-양산JC8.32021060102892.6735.306944129.074722-3.152761.360.6경남 양산시 동면
34도로공사A-0010-0083E-6S1양산JC-노포JC8.320210601022125.035.306944129.0747223.0714160.250.11경남 양산시 동면
45도로공사A-0010-0083E-6S2양산JC-노포JC8.32021060104199.535.306944129.0747223.0714160.70.31경남 양산시 동면
56도로공사A-0010-0083E-6S3양산JC-노포JC8.32021060101882.6735.306944129.0747223.0714160.720.32경남 양산시 동면
67도로공사A-0010-0538E-6E1언양JC-활천IC53.4202106010695.035.681944129.1811110.225450.050.02울산 울주군 두서면 활천리
78도로공사A-0010-0538E-6E2언양JC-활천IC53.42021060103087.835.681944129.1811110.225451.530.67울산 울주군 두서면 활천리
89도로공사A-0010-0538E-6E3언양JC-활천IC53.42021060102377.4335.681944129.1811110.225453.611.59울산 울주군 두서면 활천리
910도로공사A-0010-0538E-6S1활천IC-언양JC53.420210601013110.035.681944129.1811110.191580.150.06울산 울주군 두서면 활천리
기본키도로종류지점방향차선측정구간장비이정(km)측정일측정시간차량통과수(대)평균 속도(km/hr)위도(°)경도(°)기울기(°)TSP(g/km)PM10(g/km)주소
9091도로공사A-0010-3801E-10S2동탄JC-오산IC380.120210601011694.2537.158778127.0883330.6719773.171.4경기 화성시 동탄면 송리
9192도로공사A-0010-3801E-10S3동탄JC-오산IC380.120210601011288.037.158778127.0883330.6719779.684.26경기 화성시 동탄면 송리
9293도로공사A-0010-3801E-10S4동탄JC-오산IC380.12021060109281.7537.158778127.0883330.6719779.434.15경기 화성시 동탄면 송리
9394도로공사A-0010-3801E-10S5동탄JC-오산IC380.1202106010159.037.158778127.0883330.6719770.090.04경기 화성시 동탄면 송리
9495도로공사A-0010-3845E-10E1동탄JC-기흥동탄IC384.520210601000.037.197661127.0940281.2548850.00.0경기 화성시 오산동
9596도로공사A-0010-3845E-10E2동탄JC-기흥동탄IC384.520210601000.037.197661127.0940281.2548850.00.0경기 화성시 오산동
9697도로공사A-0010-3845E-10E3동탄JC-기흥동탄IC384.520210601000.037.197661127.0940281.2548850.00.0경기 화성시 오산동
9798도로공사A-0010-3845E-10E4동탄JC-기흥동탄IC384.520210601000.037.197661127.0940281.2548850.00.0경기 화성시 오산동
9899도로공사A-0010-3845E-10E5동탄JC-기흥동탄IC384.520210601000.037.197661127.0940281.2548850.00.0경기 화성시 오산동
99100도로공사A-0010-3845E-10S1기흥동탄IC-동탄JC384.520210601000.037.197661127.094028-1.1730170.00.0경기 화성시 오산동