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) and 2 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 3 other fieldsHigh correlation
TSP(g/km) is highly overall correlated with 차량통과수(대) and 2 other fieldsHigh correlation
PM10(g/km) is highly overall correlated with 차량통과수(대) and 2 other fieldsHigh correlation
방향 is highly overall correlated with 기울기(°) and 1 other fieldsHigh correlation
기본키 has unique valuesUnique
차량통과수(대) has 27 (27.0%) zerosZeros
평균 속도(km/hr) has 27 (27.0%) zerosZeros
TSP(g/km) has 27 (27.0%) zerosZeros
PM10(g/km) has 28 (28.0%) zerosZeros

Reproduction

Analysis started2023-12-10 10:58:53.134752
Analysis finished2023-12-10 10:59:10.349911
Duration17.22 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
2023-12-10T19:59:10.535028image/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
2023-12-10T19:59:10.841222image/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

2023-12-10T19:59:11.209587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:59:11.439921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
도로공사 100
100.0%

지점
Categorical

HIGH CORRELATION 

Distinct14
Distinct (%)14.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
A-0010-3452S-10
10 
A-0010-3613E-10
10 
A-0010-3801E-10
10 
A-0010-3880E-10
10 
A-0010-3932E-10
10 
Other values (9)
50 

Length

Max length15
Median length15
Mean length14.56
Min length14

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA-0010-0538E-6
2nd rowA-0010-0538E-6
3rd rowA-0010-0538E-6
4th rowA-0010-0538E-6
5th rowA-0010-0538E-6

Common Values

ValueCountFrequency (%)
A-0010-3452S-10 10
10.0%
A-0010-3613E-10 10
10.0%
A-0010-3801E-10 10
10.0%
A-0010-3880E-10 10
10.0%
A-0010-3932E-10 10
10.0%
A-0010-2583E-7 7
 
7.0%
A-0010-0538E-6 6
 
6.0%
A-0010-2761E-6 6
 
6.0%
A-0010-3019E-6 6
 
6.0%
A-0010-3068E-6 6
 
6.0%
Other values (4) 19
19.0%

Length

2023-12-10T19:59:12.048938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
a-0010-3452s-10 10
10.0%
a-0010-3613e-10 10
10.0%
a-0010-3801e-10 10
10.0%
a-0010-3880e-10 10
10.0%
a-0010-3932e-10 10
10.0%
a-0010-2583e-7 7
 
7.0%
a-0010-0538e-6 6
 
6.0%
a-0010-2761e-6 6
 
6.0%
a-0010-3019e-6 6
 
6.0%
a-0010-3068e-6 6
 
6.0%
Other values (4) 19
19.0%

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

2023-12-10T19:59:12.294605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:59:12.468932image/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
25 
2
24 
3
24 
4
15 
5
12 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
1 25
25.0%
2 24
24.0%
3 24
24.0%
4 15
15.0%
5 12
12.0%

Length

2023-12-10T19:59:12.678173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:59:12.970035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 25
25.0%
2 24
24.0%
3 24
24.0%
4 15
15.0%
5 12
12.0%

측정구간
Categorical

HIGH CORRELATION 

Distinct25
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
안성JC-안성IC
 
5
신갈JC-수원신갈IC
 
5
수원신갈IC-신갈JC
 
5
청주JC-남이JC
 
5
수원신갈IC-기흥IC
 
5
Other values (20)
75 

Length

Max length11
Median length9
Mean length9.58
Min length9

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st row언양JC-활천IC
2nd row언양JC-활천IC
3rd row언양JC-활천IC
4th row활천IC-언양JC
5th row활천IC-언양JC

Common Values

ValueCountFrequency (%)
안성JC-안성IC 5
 
5.0%
신갈JC-수원신갈IC 5
 
5.0%
수원신갈IC-신갈JC 5
 
5.0%
청주JC-남이JC 5
 
5.0%
수원신갈IC-기흥IC 5
 
5.0%
기흥IC-수원신갈IC 5
 
5.0%
동탄JC-오산IC 5
 
5.0%
오산IC-동탄JC 5
 
5.0%
천안IC-북천안IC 5
 
5.0%
북천안IC-천안IC 5
 
5.0%
Other values (15) 50
50.0%

Length

2023-12-10T19:59:13.227670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
안성jc-안성ic 5
 
5.0%
수원신갈ic-신갈jc 5
 
5.0%
청주jc-남이jc 5
 
5.0%
수원신갈ic-기흥ic 5
 
5.0%
기흥ic-수원신갈ic 5
 
5.0%
동탄jc-오산ic 5
 
5.0%
오산ic-동탄jc 5
 
5.0%
천안ic-북천안ic 5
 
5.0%
북천안ic-천안ic 5
 
5.0%
안성ic-안성jc 5
 
5.0%
Other values (15) 50
50.0%

장비이정(km)
Real number (ℝ)

HIGH CORRELATION 

Distinct14
Distinct (%)14.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean324.3086
Minimum53.4
Maximum410.41
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:59:13.475501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum53.4
5-th percentile53.4
Q1295.6
median345.2
Q3388
95-th percentile410.41
Maximum410.41
Range357.01
Interquartile range (IQR)92.4

Descriptive statistics

Standard deviation82.9675
Coefficient of variation (CV)0.25582886
Kurtosis4.4560042
Mean324.3086
Median Absolute Deviation (MAD)43.3
Skewness-1.9914723
Sum32430.86
Variance6883.6061
MonotonicityIncreasing
2023-12-10T19:59:13.697589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
345.2 10
10.0%
361.3 10
10.0%
380.1 10
10.0%
388.0 10
10.0%
393.2 10
10.0%
258.3 7
 
7.0%
53.4 6
 
6.0%
276.1 6
 
6.0%
301.9 6
 
6.0%
306.8 6
 
6.0%
Other values (4) 19
19.0%
ValueCountFrequency (%)
53.4 6
6.0%
258.3 7
7.0%
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%
345.2 10
10.0%
361.3 10
10.0%
ValueCountFrequency (%)
410.41 6
6.0%
393.2 10
10.0%
388.0 10
10.0%
380.1 10
10.0%
361.3 10
10.0%
345.2 10
10.0%
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
20221001
100 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20221001
2nd row20221001
3rd row20221001
4th row20221001
5th row20221001

Common Values

ValueCountFrequency (%)
20221001 100
100.0%

Length

2023-12-10T19:59:13.925277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:59:14.109740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20221001 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

2023-12-10T19:59:14.292775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:59:14.518706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 100
100.0%

차량통과수(대)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct64
Distinct (%)64.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean86.53
Minimum0
Maximum259
Zeros27
Zeros (%)27.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:59:14.752552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median61
Q3164
95-th percentile229.3
Maximum259
Range259
Interquartile range (IQR)164

Descriptive statistics

Standard deviation82.153768
Coefficient of variation (CV)0.94942526
Kurtosis-1.2186489
Mean86.53
Median Absolute Deviation (MAD)61
Skewness0.44693417
Sum8653
Variance6749.2415
MonotonicityNot monotonic
2023-12-10T19:59:15.027836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 27
27.0%
61 2
 
2.0%
103 2
 
2.0%
39 2
 
2.0%
55 2
 
2.0%
235 2
 
2.0%
6 2
 
2.0%
47 2
 
2.0%
155 2
 
2.0%
5 2
 
2.0%
Other values (54) 55
55.0%
ValueCountFrequency (%)
0 27
27.0%
1 1
 
1.0%
5 2
 
2.0%
6 2
 
2.0%
7 1
 
1.0%
12 1
 
1.0%
18 1
 
1.0%
21 1
 
1.0%
25 1
 
1.0%
39 2
 
2.0%
ValueCountFrequency (%)
259 1
1.0%
247 1
1.0%
237 1
1.0%
235 2
2.0%
229 1
1.0%
226 1
1.0%
220 1
1.0%
210 1
1.0%
205 1
1.0%
203 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%
Mean63.5461
Minimum0
Maximum109.33
Zeros27
Zeros (%)27.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:59:15.308556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median81.555
Q390.625
95-th percentile103.3635
Maximum109.33
Range109.33
Interquartile range (IQR)90.625

Descriptive statistics

Standard deviation39.737288
Coefficient of variation (CV)0.62533009
Kurtosis-0.98075141
Mean63.5461
Median Absolute Deviation (MAD)9.615
Skewness-0.8954976
Sum6354.61
Variance1579.0521
MonotonicityNot monotonic
2023-12-10T19:59:15.637601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 27
27.0%
75.0 4
 
4.0%
86.0 3
 
3.0%
102.0 2
 
2.0%
101.5 2
 
2.0%
82.75 2
 
2.0%
91.0 2
 
2.0%
95.5 2
 
2.0%
73.0 2
 
2.0%
85.62 1
 
1.0%
Other values (53) 53
53.0%
ValueCountFrequency (%)
0.0 27
27.0%
70.71 1
 
1.0%
73.0 2
 
2.0%
73.43 1
 
1.0%
75.0 4
 
4.0%
75.29 1
 
1.0%
75.62 1
 
1.0%
76.12 1
 
1.0%
76.56 1
 
1.0%
76.83 1
 
1.0%
ValueCountFrequency (%)
109.33 1
1.0%
108.0 1
1.0%
107.33 1
1.0%
106.0 1
1.0%
104.0 1
1.0%
103.33 1
1.0%
102.33 1
1.0%
102.0 2
2.0%
101.5 2
2.0%
100.0 1
1.0%

위도(°)
Real number (ℝ)

HIGH CORRELATION 

Distinct14
Distinct (%)14.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.812888
Minimum35.681944
Maximum37.42325
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:59:15.880018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.681944
5-th percentile35.681944
Q136.556389
median36.868903
Q337.226111
95-th percentile37.42325
Maximum37.42325
Range1.7413056
Interquartile range (IQR)0.66972222

Descriptive statistics

Standard deviation0.44432902
Coefficient of variation (CV)0.012069931
Kurtosis0.29171888
Mean36.812888
Median Absolute Deviation (MAD)0.31251389
Skewness-0.79441893
Sum3681.2888
Variance0.19742828
MonotonicityIncreasing
2023-12-10T19:59:16.091336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
36.86890278 10
10.0%
37.00888889 10
10.0%
37.15877778 10
10.0%
37.22611111 10
10.0%
37.27944444 10
10.0%
36.30722222 7
 
7.0%
35.68194444 6
 
6.0%
36.38961 6
 
6.0%
36.60611111 6
 
6.0%
36.64019722 6
 
6.0%
Other values (4) 19
19.0%
ValueCountFrequency (%)
35.68194444 6
6.0%
36.30722222 7
7.0%
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%
36.86890278 10
10.0%
37.00888889 10
10.0%
ValueCountFrequency (%)
37.42325 6
6.0%
37.27944444 10
10.0%
37.22611111 10
10.0%
37.15877778 10
10.0%
37.00888889 10
10.0%
36.86890278 10
10.0%
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 

Distinct14
Distinct (%)14.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.3682
Minimum127.07728
Maximum129.18111
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:59:16.313445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum127.07728
5-th percentile127.07728
Q1127.10583
median127.18672
Q3127.42778
95-th percentile129.18111
Maximum129.18111
Range2.1038333
Interquartile range (IQR)0.3219445

Descriptive statistics

Standard deviation0.48784858
Coefficient of variation (CV)0.0038302229
Kurtosis9.2334642
Mean127.3682
Median Absolute Deviation (MAD)0.10391665
Skewness3.1007595
Sum12736.82
Variance0.23799624
MonotonicityNot monotonic
2023-12-10T19:59:16.525754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
127.1867222 10
10.0%
127.1491667 10
10.0%
127.0883333 10
10.0%
127.1083333 10
10.0%
127.1058333 10
10.0%
127.5744444 7
 
7.0%
129.1811111 6
 
6.0%
127.423508 6
 
6.0%
127.4083333 6
 
6.0%
127.3781278 6
 
6.0%
Other values (4) 19
19.0%
ValueCountFrequency (%)
127.0772778 6
6.0%
127.0883333 10
10.0%
127.1058333 10
10.0%
127.1083333 10
10.0%
127.1491667 10
10.0%
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%
127.5744444 7
7.0%
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%
127.1491667 10
10.0%

기울기(°)
Real number (ℝ)

HIGH CORRELATION 

Distinct25
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.03138456
Minimum-2.788625
Maximum2.703849
Zeros0
Zeros (%)0.0%
Negative47
Negative (%)47.0%
Memory size1.0 KiB
2023-12-10T19:59:16.745419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-2.788625
5-th percentile-1.758683
Q1-0.660004
median0.151187
Q30.671977
95-th percentile1.717451
Maximum2.703849
Range5.492474
Interquartile range (IQR)1.331981

Descriptive statistics

Standard deviation1.1016177
Coefficient of variation (CV)-35.100626
Kurtosis0.41265709
Mean-0.03138456
Median Absolute Deviation (MAD)0.703947
Skewness-0.1369889
Sum-3.138456
Variance1.2135616
MonotonicityNot monotonic
2023-12-10T19:59:16.966704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
-0.624001 5
 
5.0%
1.293015 5
 
5.0%
0.69298 5
 
5.0%
-0.139436 5
 
5.0%
0.151187 5
 
5.0%
-1.758683 5
 
5.0%
0.564123 5
 
5.0%
-0.55276 5
 
5.0%
0.671977 5
 
5.0%
-0.660004 5
 
5.0%
Other values (15) 50
50.0%
ValueCountFrequency (%)
-2.788625 3
3.0%
-1.758683 5
5.0%
-1.717451 3
3.0%
-1.30142 5
5.0%
-0.807188 4
4.0%
-0.73138 1
 
1.0%
-0.688696 3
3.0%
-0.660004 5
5.0%
-0.624001 5
5.0%
-0.55276 5
5.0%
ValueCountFrequency (%)
2.703849 3
3.0%
1.717451 3
3.0%
1.293015 5
5.0%
1.056036 4
4.0%
0.69298 5
5.0%
0.688696 3
3.0%
0.671977 5
5.0%
0.61369 5
5.0%
0.564123 5
5.0%
0.2594 4
4.0%

TSP(g/km)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct74
Distinct (%)74.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.1674
Minimum0
Maximum24.64
Zeros27
Zeros (%)27.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:59:17.209696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2.41
Q37.01
95-th percentile14.24
Maximum24.64
Range24.64
Interquartile range (IQR)7.01

Descriptive statistics

Standard deviation4.9940546
Coefficient of variation (CV)1.1983622
Kurtosis2.2785171
Mean4.1674
Median Absolute Deviation (MAD)2.41
Skewness1.4682713
Sum416.74
Variance24.940581
MonotonicityNot monotonic
2023-12-10T19:59:17.533187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 27
27.0%
0.13 1
 
1.0%
1.8 1
 
1.0%
0.04 1
 
1.0%
8.81 1
 
1.0%
5.84 1
 
1.0%
7.0 1
 
1.0%
0.11 1
 
1.0%
9.36 1
 
1.0%
7.26 1
 
1.0%
Other values (64) 64
64.0%
ValueCountFrequency (%)
0.0 27
27.0%
0.01 1
 
1.0%
0.03 1
 
1.0%
0.04 1
 
1.0%
0.05 1
 
1.0%
0.11 1
 
1.0%
0.13 1
 
1.0%
0.31 1
 
1.0%
0.37 1
 
1.0%
0.4 1
 
1.0%
ValueCountFrequency (%)
24.64 1
1.0%
17.69 1
1.0%
16.42 1
1.0%
15.49 1
1.0%
14.81 1
1.0%
14.21 1
1.0%
12.97 1
1.0%
12.79 1
1.0%
12.73 1
1.0%
11.87 1
1.0%

PM10(g/km)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct72
Distinct (%)72.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.8334
Minimum0
Maximum10.84
Zeros28
Zeros (%)28.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:59:17.822233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1.06
Q33.085
95-th percentile6.2635
Maximum10.84
Range10.84
Interquartile range (IQR)3.085

Descriptive statistics

Standard deviation2.197079
Coefficient of variation (CV)1.1983631
Kurtosis2.2766742
Mean1.8334
Median Absolute Deviation (MAD)1.06
Skewness1.4678876
Sum183.34
Variance4.827156
MonotonicityNot monotonic
2023-12-10T19:59:18.101932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 28
28.0%
0.02 2
 
2.0%
0.06 1
 
1.0%
3.19 1
 
1.0%
2.99 1
 
1.0%
3.88 1
 
1.0%
2.57 1
 
1.0%
3.08 1
 
1.0%
0.05 1
 
1.0%
4.12 1
 
1.0%
Other values (62) 62
62.0%
ValueCountFrequency (%)
0.0 28
28.0%
0.01 1
 
1.0%
0.02 2
 
2.0%
0.05 1
 
1.0%
0.06 1
 
1.0%
0.14 1
 
1.0%
0.16 1
 
1.0%
0.18 1
 
1.0%
0.24 1
 
1.0%
0.31 1
 
1.0%
ValueCountFrequency (%)
10.84 1
1.0%
7.78 1
1.0%
7.22 1
1.0%
6.81 1
1.0%
6.52 1
1.0%
6.25 1
1.0%
5.71 1
1.0%
5.63 1
1.0%
5.6 1
1.0%
5.22 1
1.0%

주소
Categorical

HIGH CORRELATION 

Distinct12
Distinct (%)12.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
충북 청원군 남이면
13 
충남 천안시 서북구 성거읍 송남리
10 
경기 안성시 원곡면
10 
경기 화성시 동탄면 송리
10 
경기 용인시 기흥구 기흥동
10 
Other values (7)
47 

Length

Max length18
Median length15
Mean length12.96
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row울산 울주군 두서면 활천리
2nd row울산 울주군 두서면 활천리
3rd row울산 울주군 두서면 활천리
4th row울산 울주군 두서면 활천리
5th row울산 울주군 두서면 활천리

Common Values

ValueCountFrequency (%)
충북 청원군 남이면 13
13.0%
충남 천안시 서북구 성거읍 송남리 10
10.0%
경기 안성시 원곡면 10
10.0%
경기 화성시 동탄면 송리 10
10.0%
경기 용인시 기흥구 기흥동 10
10.0%
경기 용인시 기흥구 신갈동 10
10.0%
충북 옥천군 옥천읍 삼양리 7
7.0%
울산 울주군 두서면 활천리 6
6.0%
대전 대덕구 연축동 6
6.0%
충북 청주시 흥덕구 강서1동 6
6.0%
Other values (2) 12
12.0%

Length

2023-12-10T19:59:18.373583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기 46
 
12.3%
충북 32
 
8.5%
기흥구 20
 
5.3%
용인시 20
 
5.3%
청원군 19
 
5.1%
남이면 13
 
3.5%
화성시 10
 
2.7%
기흥동 10
 
2.7%
송리 10
 
2.7%
동탄면 10
 
2.7%
Other values (25) 185
49.3%

Interactions

2023-12-10T19:59:07.829732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:58:54.569099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:58:56.169533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:58:57.941648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:58:59.465579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:59:01.316244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:59:02.887618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:59:04.636103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:59:06.314954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:59:07.979348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:58:54.735749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:58:56.402686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:58:58.127075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:58:59.626148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:59:01.495836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:59:03.023645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:59:04.809710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:59:06.492295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:59:08.148074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:58:54.909086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:58:56.578298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:58:58.339006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:58:59.794487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:59:01.676888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:59:03.198066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:59:04.994321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:59:06.689191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:59:08.318374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:58:55.055426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:58:56.767917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:58:58.491419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:58:59.942261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:59:01.835283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:59:03.358570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:59:05.156502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:59:06.863351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:59:08.469552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:58:55.197441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:58:56.957910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:58:58.644993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:59:00.119824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:59:02.027809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:59:03.516174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:59:05.328068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:59:07.029105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:59:08.660604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:58:55.362341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:58:57.206468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:58:58.809081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:59:00.303908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:59:02.200799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:59:03.857066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:59:05.527609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:59:07.206809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:59:08.893020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:58:55.516715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:58:57.396846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:58:58.952903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:59:00.450241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:59:02.372669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:59:04.117661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:59:05.811556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:59:07.354211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:59:09.149253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:58:55.720748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:58:57.576054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:58:59.108318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:59:00.975169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:59:02.539420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:59:04.317611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:59:05.992196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:59:07.520587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:59:09.340556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:58:55.957470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:58:57.743023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:58:59.315681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:59:01.155181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:59:02.721329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:59:04.471124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:59:06.141129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:59:07.671372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T19:59:18.574629image/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
기본키1.0000.9360.0000.0000.9860.8830.5900.5310.9000.8770.9070.2810.2810.930
지점0.9361.0000.2070.0001.0001.0000.3260.4141.0001.0000.9070.3600.3601.000
방향0.0000.2071.0000.0001.0000.0000.0000.0000.0000.0000.6720.0000.0000.000
차선0.0000.0000.0001.0000.0000.0000.3970.6210.0000.0000.0000.3550.3550.000
측정구간0.9861.0001.0000.0001.0001.0000.0000.3991.0001.0001.0000.0000.0001.000
장비이정(km)0.8831.0000.0000.0001.0001.0000.3830.3310.9601.0000.7510.2720.2721.000
차량통과수(대)0.5900.3260.0000.3970.0000.3831.0000.7280.4180.4000.0000.6790.6790.362
평균 속도(km/hr)0.5310.4140.0000.6210.3990.3310.7281.0000.3210.1860.0000.5740.5740.307
위도(°)0.9001.0000.0000.0001.0000.9600.4180.3211.0001.0000.8260.1950.1950.999
경도(°)0.8771.0000.0000.0001.0001.0000.4000.1861.0001.0000.7370.0000.0001.000
기울기(°)0.9070.9070.6720.0001.0000.7510.0000.0000.8260.7371.0000.0000.0000.876
TSP(g/km)0.2810.3600.0000.3550.0000.2720.6790.5740.1950.0000.0001.0001.0000.312
PM10(g/km)0.2810.3600.0000.3550.0000.2720.6790.5740.1950.0000.0001.0001.0000.312
주소0.9301.0000.0000.0001.0001.0000.3620.3070.9991.0000.8760.3120.3121.000
2023-12-10T19:59:18.854970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지점차선측정구간방향주소
지점1.0000.0000.9340.1470.989
차선0.0001.0000.0000.0000.000
측정구간0.9340.0001.0000.8750.923
방향0.1470.0000.8751.0000.000
주소0.9890.0000.9230.0001.000
2023-12-10T19:59:19.087284image/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
기본키1.0000.9970.4810.0060.997-0.9520.0980.2660.2680.7350.0000.0000.8080.741
장비이정(km)0.9971.0000.4840.0271.000-0.9550.0820.2660.2680.9570.0000.0000.8930.968
차량통과수(대)0.4810.4841.0000.6000.484-0.4720.1060.7670.7660.1290.0000.1680.0000.155
평균 속도(km/hr)0.0060.0270.6001.0000.027-0.038-0.1240.5220.5100.2170.0000.2730.1550.165
위도(°)0.9971.0000.4840.0271.000-0.9550.0820.2660.2680.9670.0000.0000.9030.945
경도(°)-0.952-0.955-0.472-0.038-0.9551.000-0.103-0.221-0.2230.9460.0000.0000.8840.957
기울기(°)0.0980.0820.106-0.1240.082-0.1031.000-0.037-0.0310.6600.5010.0000.9130.616
TSP(g/km)0.2660.2660.7670.5220.266-0.221-0.0371.0000.9990.1510.0000.2070.0000.131
PM10(g/km)0.2680.2680.7660.5100.268-0.223-0.0310.9991.0000.1510.0000.2070.0000.131
지점0.7350.9570.1290.2170.9670.9460.6600.1510.1511.0000.1470.0000.9340.989
방향0.0000.0000.0000.0000.0000.0000.5010.0000.0000.1471.0000.0000.8750.000
차선0.0000.0000.1680.2730.0000.0000.0000.2070.2070.0000.0001.0000.0000.000
측정구간0.8080.8930.0000.1550.9030.8840.9130.0000.0000.9340.8750.0001.0000.923
주소0.7410.9680.1550.1650.9450.9570.6160.1310.1310.9890.0000.0000.9231.000

Missing values

2023-12-10T19:59:09.636831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T19:59:10.124822image/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-0010-0538E-6E1언양JC-활천IC53.420221001012102.035.681944129.1811110.225450.130.06울산 울주군 두서면 활천리
12도로공사A-0010-0538E-6E2언양JC-활천IC53.42022100104790.535.681944129.1811110.225451.580.69울산 울주군 두서면 활천리
23도로공사A-0010-0538E-6E3언양JC-활천IC53.42022100101882.5735.681944129.1811110.225452.431.07울산 울주군 두서면 활천리
34도로공사A-0010-0538E-6S1활천IC-언양JC53.420221001061102.035.681944129.1811110.191581.00.44울산 울주군 두서면 활천리
45도로공사A-0010-0538E-6S2활천IC-언양JC53.420221001010391.8335.681944129.1811110.191583.281.44울산 울주군 두서면 활천리
56도로공사A-0010-0538E-6S3활천IC-언양JC53.42022100103978.1135.681944129.1811110.191584.321.9울산 울주군 두서면 활천리
67도로공사A-0010-2583E-7E1금강IC-옥천IC258.320221001000.036.307222127.574444-0.063390.00.0충북 옥천군 옥천읍 삼양리
78도로공사A-0010-2583E-7E2금강IC-옥천IC258.32022100104782.7536.307222127.574444-0.063393.411.5충북 옥천군 옥천읍 삼양리
89도로공사A-0010-2583E-7E3금강IC-옥천IC258.320221001000.036.307222127.574444-0.063390.00.0충북 옥천군 옥천읍 삼양리
910도로공사A-0010-2583E-7S1옥천IC-금강IC258.320221001048107.3336.307222127.5744440.25941.440.63충북 옥천군 옥천읍 삼양리
기본키도로종류지점방향차선측정구간장비이정(km)측정일측정시간차량통과수(대)평균 속도(km/hr)위도(°)경도(°)기울기(°)TSP(g/km)PM10(g/km)주소
9091도로공사A-0010-3932E-10S2신갈JC-수원신갈IC393.220221001024782.7537.279444127.105833-0.1394366.452.84경기 용인시 기흥구 신갈동
9192도로공사A-0010-3932E-10S3신갈JC-수원신갈IC393.220221001021075.6237.279444127.105833-0.1394367.373.24경기 용인시 기흥구 신갈동
9293도로공사A-0010-3932E-10S4신갈JC-수원신갈IC393.220221001017876.5637.279444127.105833-0.1394368.723.83경기 용인시 기흥구 신갈동
9394도로공사A-0010-3932E-10S5신갈JC-수원신갈IC393.220221001016175.2937.279444127.105833-0.13943611.875.22경기 용인시 기흥구 신갈동
9495도로공사A-0010-4105E-10E1금토JC-양재IC410.41202210010220102.3337.42325127.0772780.692983.011.32경기 성남시 수정구 금토동
9596도로공사A-0010-4105E-10E2금토JC-양재IC410.4120221001025975.037.42325127.0772780.692981.460.64경기 성남시 수정구 금토동
9697도로공사A-0010-4105E-10E3금토JC-양재IC410.4120221001019475.037.42325127.0772780.692981.090.48경기 성남시 수정구 금토동
9798도로공사A-0010-4105E-10E4금토JC-양재IC410.4120221001015476.8337.42325127.0772780.692987.043.1경기 성남시 수정구 금토동
9899도로공사A-0010-4105E-10E5금토JC-양재IC410.412022100109870.7137.42325127.0772780.692983.361.48경기 성남시 수정구 금토동
99100도로공사A-0010-4105E-10S1양재IC-금토JC410.4120221001000.037.42325127.077278-0.731380.00.0경기 성남시 수정구 금토동