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 15 (15.0%) zerosZeros
평균 속도(km/hr) has 15 (15.0%) zerosZeros
TSP(g/km) has 15 (15.0%) zerosZeros
PM10(g/km) has 16 (16.0%) zerosZeros

Reproduction

Analysis started2023-12-10 11:09:35.462214
Analysis finished2023-12-10 11:09:48.830347
Duration13.37 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
2023-12-10T20:09:48.963927image/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
2023-12-10T20:09:49.194469image/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

2023-12-10T20:09:49.388822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:09:49.508188image/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-0010-3352E-9
9 
A-0010-0083E-6
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-0010-3613E-10 10
 
10.0%
A-0010-3722E-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

2023-12-10T20:09:49.709087image/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-0010-3352e-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

2023-12-10T20:09:49.903981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:09:50.049206image/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
9 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

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

Length

2023-12-10T20:09:50.201462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:09:50.353574image/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
오산IC-동탄JC
 
5
안성JC-오산IC
 
5
안성JC-안성IC
 
5
안성IC-안성JC
 
5
북천안IC-천안IC
 
5
Other values (22)
75 

Length

Max length10
Median length9
Mean length9.24
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 (%)
오산IC-동탄JC 5
 
5.0%
안성JC-오산IC 5
 
5.0%
안성JC-안성IC 5
 
5.0%
안성IC-안성JC 5
 
5.0%
북천안IC-천안IC 5
 
5.0%
천안IC-북천안IC 5
 
5.0%
천안IC-천안JC 5
 
5.0%
청주JC-남이JC 5
 
5.0%
오산IC-안성JC 5
 
5.0%
천안JC-천안IC 4
 
4.0%
Other values (17) 51
51.0%

Length

2023-12-10T20:09:50.541520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
오산ic-동탄jc 5
 
5.0%
안성jc-안성ic 5
 
5.0%
안성ic-안성jc 5
 
5.0%
북천안ic-천안ic 5
 
5.0%
천안ic-북천안ic 5
 
5.0%
천안ic-천안jc 5
 
5.0%
청주jc-남이jc 5
 
5.0%
오산ic-안성jc 5
 
5.0%
안성jc-오산ic 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%
Mean283.6192
Minimum8.3
Maximum380.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:09:50.683527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8.3
5-th percentile8.3
Q1276.1
median306.8
Q3361.3
95-th percentile380.1
Maximum380.1
Range371.8
Interquartile range (IQR)85.2

Descriptive statistics

Standard deviation105.07952
Coefficient of variation (CV)0.3704951
Kurtosis1.6199761
Mean283.6192
Median Absolute Deviation (MAD)38.4
Skewness-1.6251982
Sum28361.92
Variance11041.706
MonotonicityIncreasing
2023-12-10T20:09:50.846830image/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%
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 (%)
380.1 6
6.0%
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%
295.3 4
 
4.0%

측정일
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
20210501
100 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20210501
2nd row20210501
3rd row20210501
4th row20210501
5th row20210501

Common Values

ValueCountFrequency (%)
20210501 100
100.0%

Length

2023-12-10T20:09:51.039791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:09:51.172882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20210501 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

2023-12-10T20:09:51.307228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:09:51.461989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 100
100.0%

차량통과수(대)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct72
Distinct (%)72.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64.54
Minimum0
Maximum221
Zeros15
Zeros (%)15.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:09:51.593208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q116.5
median53.5
Q3100.25
95-th percentile160.4
Maximum221
Range221
Interquartile range (IQR)83.75

Descriptive statistics

Standard deviation55.651408
Coefficient of variation (CV)0.86227778
Kurtosis-0.43460059
Mean64.54
Median Absolute Deviation (MAD)40
Skewness0.70447671
Sum6454
Variance3097.0792
MonotonicityNot monotonic
2023-12-10T20:09:51.754964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 15
 
15.0%
13 4
 
4.0%
59 2
 
2.0%
73 2
 
2.0%
60 2
 
2.0%
136 2
 
2.0%
2 2
 
2.0%
55 2
 
2.0%
34 2
 
2.0%
38 2
 
2.0%
Other values (62) 65
65.0%
ValueCountFrequency (%)
0 15
15.0%
1 1
 
1.0%
2 2
 
2.0%
8 1
 
1.0%
13 4
 
4.0%
14 1
 
1.0%
15 1
 
1.0%
17 1
 
1.0%
18 1
 
1.0%
20 1
 
1.0%
ValueCountFrequency (%)
221 1
1.0%
190 1
1.0%
182 1
1.0%
179 1
1.0%
168 1
1.0%
160 1
1.0%
157 1
1.0%
155 1
1.0%
154 1
1.0%
149 1
1.0%

평균 속도(km/hr)
Real number (ℝ)

ZEROS 

Distinct76
Distinct (%)76.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean79.1708
Minimum0
Maximum134
Zeros15
Zeros (%)15.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:09:51.933725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q179.5575
median86.96
Q399.0825
95-th percentile116
Maximum134
Range134
Interquartile range (IQR)19.525

Descriptive statistics

Standard deviation35.767051
Coefficient of variation (CV)0.45177074
Kurtosis1.0271427
Mean79.1708
Median Absolute Deviation (MAD)9.665
Skewness-1.4623224
Sum7917.08
Variance1279.2819
MonotonicityNot monotonic
2023-12-10T20:09:52.163671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 15
 
15.0%
86.0 3
 
3.0%
107.67 2
 
2.0%
84.0 2
 
2.0%
85.0 2
 
2.0%
77.0 2
 
2.0%
116.0 2
 
2.0%
99.67 2
 
2.0%
99.0 2
 
2.0%
113.0 2
 
2.0%
Other values (66) 66
66.0%
ValueCountFrequency (%)
0.0 15
15.0%
42.64 1
 
1.0%
75.63 1
 
1.0%
76.55 1
 
1.0%
77.0 2
 
2.0%
78.25 1
 
1.0%
78.57 1
 
1.0%
78.86 1
 
1.0%
79.0 1
 
1.0%
79.22 1
 
1.0%
ValueCountFrequency (%)
134.0 1
1.0%
126.5 1
1.0%
124.5 1
1.0%
121.67 1
1.0%
116.0 2
2.0%
113.0 2
2.0%
112.0 1
1.0%
110.9 1
1.0%
108.0 1
1.0%
107.67 2
2.0%

위도(°)
Real number (ℝ)

HIGH CORRELATION 

Distinct15
Distinct (%)15.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.578058
Minimum35.306944
Maximum37.158778
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:09:52.339617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.306944
5-th percentile35.306944
Q136.38961
median36.640197
Q337.008889
95-th percentile37.158778
Maximum37.158778
Range1.8518333
Interquartile range (IQR)0.61927889

Descriptive statistics

Standard deviation0.49215798
Coefficient of variation (CV)0.013455006
Kurtosis0.85972789
Mean36.578058
Median Absolute Deviation (MAD)0.27164222
Skewness-1.1654497
Sum3657.8058
Variance0.24221947
MonotonicityIncreasing
2023-12-10T20:09:52.510289image/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%
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 (%)
37.15877778 6
6.0%
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%
36.54 4
 
4.0%

경도(°)
Real number (ℝ)

HIGH CORRELATION 

Distinct15
Distinct (%)15.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.55159
Minimum127.08833
Maximum129.18111
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:09:52.677543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum127.08833
5-th percentile127.08833
Q1127.14917
median127.37813
Q3127.43389
95-th percentile129.18111
Maximum129.18111
Range2.0927778
Interquartile range (IQR)0.2847222

Descriptive statistics

Standard deviation0.6395088
Coefficient of variation (CV)0.0050137265
Kurtosis1.8479606
Mean127.55159
Median Absolute Deviation (MAD)0.2014611
Skewness1.8046885
Sum12755.159
Variance0.40897151
MonotonicityNot monotonic
2023-12-10T20:09:52.843688image/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%
127.1766667 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 6
6.0%
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%
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-0.10244468
Minimum-13.24576
Maximum12.47809
Zeros0
Zeros (%)0.0%
Negative51
Negative (%)51.0%
Memory size1.0 KiB
2023-12-10T20:09:52.981991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-13.24576
5-th percentile-3.15276
Q1-1.30142
median-0.141433
Q31.0850327
95-th percentile3.071416
Maximum12.47809
Range25.72385
Interquartile range (IQR)2.3864528

Descriptive statistics

Standard deviation3.4680429
Coefficient of variation (CV)-33.852835
Kurtosis9.2056122
Mean-0.10244468
Median Absolute Deviation (MAD)1.178728
Skewness-0.19984158
Sum-10.244468
Variance12.027321
MonotonicityNot monotonic
2023-12-10T20:09:53.213276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
-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%
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%
-1.30142 5
5.0%
-1.252479 3
3.0%
-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%
1.293015 5
5.0%
1.172023 3
3.0%
1.056036 4
4.0%
0.688696 3
3.0%
0.671977 1
 
1.0%

TSP(g/km)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct84
Distinct (%)84.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.5195
Minimum0
Maximum17.77
Zeros15
Zeros (%)15.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:09:53.361162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.47
median2.205
Q35.645
95-th percentile10.777
Maximum17.77
Range17.77
Interquartile range (IQR)5.175

Descriptive statistics

Standard deviation3.8139262
Coefficient of variation (CV)1.0836557
Kurtosis1.9578387
Mean3.5195
Median Absolute Deviation (MAD)2.065
Skewness1.4214711
Sum351.95
Variance14.546033
MonotonicityNot monotonic
2023-12-10T20:09:53.570717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 15
 
15.0%
0.15 2
 
2.0%
0.47 2
 
2.0%
6.72 1
 
1.0%
5.33 1
 
1.0%
1.8 1
 
1.0%
0.02 1
 
1.0%
8.1 1
 
1.0%
8.63 1
 
1.0%
1.57 1
 
1.0%
Other values (74) 74
74.0%
ValueCountFrequency (%)
0.0 15
15.0%
0.01 1
 
1.0%
0.02 1
 
1.0%
0.15 2
 
2.0%
0.17 1
 
1.0%
0.24 1
 
1.0%
0.25 1
 
1.0%
0.28 1
 
1.0%
0.43 1
 
1.0%
0.47 2
 
2.0%
ValueCountFrequency (%)
17.77 1
1.0%
16.14 1
1.0%
12.3 1
1.0%
12.16 1
1.0%
11.67 1
1.0%
10.73 1
1.0%
10.41 1
1.0%
9.73 1
1.0%
9.4 1
1.0%
9.14 1
1.0%

PM10(g/km)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct84
Distinct (%)84.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5483
Minimum0
Maximum7.82
Zeros16
Zeros (%)16.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:09:53.809946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.2075
median0.97
Q32.485
95-th percentile4.7405
Maximum7.82
Range7.82
Interquartile range (IQR)2.2775

Descriptive statistics

Standard deviation1.6783374
Coefficient of variation (CV)1.0839872
Kurtosis1.9553205
Mean1.5483
Median Absolute Deviation (MAD)0.91
Skewness1.4205787
Sum154.83
Variance2.8168163
MonotonicityNot monotonic
2023-12-10T20:09:54.058259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 16
 
16.0%
0.06 2
 
2.0%
0.07 1
 
1.0%
2.34 1
 
1.0%
0.79 1
 
1.0%
0.01 1
 
1.0%
3.56 1
 
1.0%
3.8 1
 
1.0%
0.69 1
 
1.0%
0.11 1
 
1.0%
Other values (74) 74
74.0%
ValueCountFrequency (%)
0.0 16
16.0%
0.01 1
 
1.0%
0.06 2
 
2.0%
0.07 1
 
1.0%
0.1 1
 
1.0%
0.11 1
 
1.0%
0.12 1
 
1.0%
0.19 1
 
1.0%
0.2 1
 
1.0%
0.21 1
 
1.0%
ValueCountFrequency (%)
7.82 1
1.0%
7.1 1
1.0%
5.41 1
1.0%
5.35 1
1.0%
5.13 1
1.0%
4.72 1
1.0%
4.58 1
1.0%
4.28 1
1.0%
4.14 1
1.0%
4.02 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 
충청 천안시 구성동
9 
Other values (8)
48 

Length

Max length18
Median length16.5
Mean length12.5
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%
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

2023-12-10T20:09:54.251959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기 26
 
7.1%
충북 25
 
6.9%
청원군 19
 
5.2%
천안시 19
 
5.2%
남이면 13
 
3.6%
대전 12
 
3.3%
대덕구 12
 
3.3%
송남리 10
 
2.7%
서북구 10
 
2.7%
충남 10
 
2.7%
Other values (29) 208
57.1%

Interactions

2023-12-10T20:09:46.393977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:36.302537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:37.604501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:38.787932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:40.129960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:41.573063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:42.919816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:44.080784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:45.256694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:46.525415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:36.419293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:37.745300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:38.926677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:40.600341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:41.725322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:43.061164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:44.204766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:45.394364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:46.648216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:36.552144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:37.879754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:39.163114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:40.742182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:41.875825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:43.199187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:44.343445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:45.521174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:46.777374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:36.752221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:37.974969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:39.289851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:40.880560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:42.000902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:43.320728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:44.470992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:45.636814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:46.972629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:36.912707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:38.072252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:39.442527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:40.989132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:42.141379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:43.461067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:44.610778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:45.749665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:47.143666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:37.054955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:38.221771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:39.567154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:41.138200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:42.302357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:43.602523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:44.746353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:45.885840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:47.316452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:37.189915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:38.367809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:39.711836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:41.259964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:42.460024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:43.716811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:44.857429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:45.995158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:47.511412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:37.318869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:38.477213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:39.844160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:41.351811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:42.587519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:43.810526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:44.969903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:46.094651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:47.675581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:37.470272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:38.625623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:39.978826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:41.445755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:42.742740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:43.942878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:45.108828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:09:46.234492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T20:09:54.374590image/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
기본키1.0000.9660.0000.0000.9840.9070.6550.4330.9220.9430.6210.3450.3450.942
지점0.9661.0000.1310.0001.0001.0000.2220.3631.0001.0000.8610.3680.3681.000
방향0.0000.1311.0000.0001.0000.0000.2310.1100.0000.0000.5740.0000.0000.000
차선0.0000.0000.0001.0000.0000.0000.4780.5470.0000.1300.0000.4330.4330.000
측정구간0.9841.0001.0000.0001.0001.0000.0000.4251.0001.0001.0000.4600.4601.000
장비이정(km)0.9071.0000.0000.0001.0001.0000.2690.2810.9870.9980.7930.0000.0001.000
차량통과수(대)0.6550.2220.2310.4780.0000.2691.0000.4670.3780.3590.0000.6280.6280.299
평균 속도(km/hr)0.4330.3630.1100.5470.4250.2810.4671.0000.3230.3060.3980.4610.4610.367
위도(°)0.9221.0000.0000.0001.0000.9870.3780.3231.0000.9990.7030.2530.2531.000
경도(°)0.9431.0000.0000.1301.0000.9980.3590.3060.9991.0000.5240.0000.0001.000
기울기(°)0.6210.8610.5740.0001.0000.7930.0000.3980.7030.5241.0000.1670.1670.834
TSP(g/km)0.3450.3680.0000.4330.4600.0000.6280.4610.2530.0000.1671.0001.0000.210
PM10(g/km)0.3450.3680.0000.4330.4600.0000.6280.4610.2530.0000.1671.0001.0000.210
주소0.9421.0000.0000.0001.0001.0000.2990.3671.0001.0000.8340.2100.2101.000
2023-12-10T20:09:54.576366image/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
2023-12-10T20:09:54.747287image/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
기본키1.0000.9970.267-0.1400.997-0.9750.0050.0570.0540.7740.0000.0000.8090.771
장비이정(km)0.9971.0000.272-0.1171.000-0.978-0.0050.0480.0460.9510.0000.0000.8810.962
차량통과수(대)0.2670.2721.0000.3900.272-0.311-0.0980.6630.6620.0720.1670.2100.0000.121
평균 속도(km/hr)-0.140-0.1170.3901.000-0.1170.071-0.0760.0920.0910.1610.1120.3860.1620.170
위도(°)0.9971.0000.272-0.1171.000-0.978-0.0050.0480.0460.9610.0000.0000.8910.972
경도(°)-0.975-0.978-0.3110.071-0.9781.000-0.006-0.054-0.0510.9410.0000.1040.8720.952
기울기(°)0.005-0.005-0.098-0.076-0.005-0.0061.000-0.108-0.1070.5920.4070.0000.8810.586
TSP(g/km)0.0570.0480.6630.0920.048-0.054-0.1081.0001.0000.1560.0000.2780.1730.086
PM10(g/km)0.0540.0460.6620.0910.046-0.051-0.1071.0001.0000.1560.0000.2780.1730.086
지점0.7740.9510.0720.1610.9610.9410.5920.1560.1561.0000.1050.0000.9270.988
방향0.0000.0000.1670.1120.0000.0000.4070.0000.0000.1051.0000.0000.8630.000
차선0.0000.0000.2100.3860.0000.1040.0000.2780.2780.0000.0001.0000.0000.000
측정구간0.8090.8810.0000.1620.8910.8720.8810.1730.1730.9270.8630.0001.0000.916
주소0.7710.9620.1210.1700.9720.9520.5860.0860.0860.9880.0000.0000.9161.000

Missing values

2023-12-10T20:09:48.309007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T20:09:48.686085image/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-양산JC8.320210501015116.035.306944129.074722-3.152760.170.07경남 양산시 동면
12도로공사A-0010-0083E-6E2노포JC-양산JC8.32021050104799.6735.306944129.074722-3.152760.720.32경남 양산시 동면
23도로공사A-0010-0083E-6E3노포JC-양산JC8.32021050104191.8335.306944129.074722-3.152762.721.19경남 양산시 동면
34도로공사A-0010-0083E-6S1양산JC-노포JC8.320210501053113.035.306944129.0747223.0714160.60.26경남 양산시 동면
45도로공사A-0010-0083E-6S2양산JC-노포JC8.32021050108899.035.306944129.0747223.0714161.630.72경남 양산시 동면
56도로공사A-0010-0083E-6S3양산JC-노포JC8.32021050103584.6735.306944129.0747223.0714161.180.52경남 양산시 동면
67도로공사A-0010-0538E-6E1언양JC-활천IC53.42021050101399.035.681944129.1811110.225450.280.12울산 울주군 두서면 활천리
78도로공사A-0010-0538E-6E2언양JC-활천IC53.42021050103890.035.681944129.1811110.225451.430.63울산 울주군 두서면 활천리
89도로공사A-0010-0538E-6E3언양JC-활천IC53.42021050102378.8635.681944129.1811110.225452.41.06울산 울주군 두서면 활천리
910도로공사A-0010-0538E-6S1활천IC-언양JC53.420210501059108.035.681944129.1811110.191580.660.29울산 울주군 두서면 활천리
기본키도로종류지점방향차선측정구간장비이정(km)측정일측정시간차량통과수(대)평균 속도(km/hr)위도(°)경도(°)기울기(°)TSP(g/km)PM10(g/km)주소
9091도로공사A-0010-3722E-10S2오산IC-안성JC372.2320210501022199.3337.100926127.118642-0.1414333.461.52경기 용인시 처인구 남사면 진목리
9192도로공사A-0010-3722E-10S3오산IC-안성JC372.2320210501014292.8637.100926127.118642-0.1414336.843.01경기 용인시 처인구 남사면 진목리
9293도로공사A-0010-3722E-10S4오산IC-안성JC372.232021050109279.6737.100926127.118642-0.1414339.44.14경기 용인시 처인구 남사면 진목리
9394도로공사A-0010-3722E-10S5오산IC-안성JC372.23202105010179.037.100926127.118642-0.1414330.010.0경기 용인시 처인구 남사면 진목리
9495도로공사A-0010-3801E-10E1오산IC-동탄JC380.120210501013392.537.158778127.088333-0.6600041.550.68경기 화성시 동탄면 송리
9596도로공사A-0010-3801E-10E2오산IC-동탄JC380.120210501018297.6737.158778127.088333-0.6600043.111.37경기 화성시 동탄면 송리
9697도로공사A-0010-3801E-10E3오산IC-동탄JC380.120210501014789.7537.158778127.088333-0.6600046.072.67경기 화성시 동탄면 송리
9798도로공사A-0010-3801E-10E4오산IC-동탄JC380.120210501011481.1237.158778127.088333-0.6600048.133.58경기 화성시 동탄면 송리
9899도로공사A-0010-3801E-10E5오산IC-동탄JC380.12021050104085.037.158778127.088333-0.6600041.250.55경기 화성시 동탄면 송리
99100도로공사A-0010-3801E-10S1동탄JC-오산IC380.1202105010149103.7537.158778127.0883330.6719771.860.82경기 화성시 동탄면 송리