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 3 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 기울기(°) and 1 other fieldsHigh 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 23 (23.0%) zerosZeros
PM10(g/km) has 24 (24.0%) zerosZeros

Reproduction

Analysis started2023-12-10 11:06:56.214909
Analysis finished2023-12-10 11:07:14.949085
Duration18.73 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기본키
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.5
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:07:15.172280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5.95
Q125.75
median50.5
Q375.25
95-th percentile95.05
Maximum100
Range99
Interquartile range (IQR)49.5

Descriptive statistics

Standard deviation29.011492
Coefficient of variation (CV)0.57448499
Kurtosis-1.2
Mean50.5
Median Absolute Deviation (MAD)25
Skewness0
Sum5050
Variance841.66667
MonotonicityStrictly increasing
2023-12-10T20:07:15.514255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.0%
65 1
 
1.0%
75 1
 
1.0%
74 1
 
1.0%
73 1
 
1.0%
72 1
 
1.0%
71 1
 
1.0%
70 1
 
1.0%
69 1
 
1.0%
68 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
1 1
1.0%
2 1
1.0%
3 1
1.0%
4 1
1.0%
5 1
1.0%
6 1
1.0%
7 1
1.0%
8 1
1.0%
9 1
1.0%
10 1
1.0%
ValueCountFrequency (%)
100 1
1.0%
99 1
1.0%
98 1
1.0%
97 1
1.0%
96 1
1.0%
95 1
1.0%
94 1
1.0%
93 1
1.0%
92 1
1.0%
91 1
1.0%

도로종류
Categorical

CONSTANT 

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

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
도로공사 100
100.0%

Length

2023-12-10T20:07:15.825096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:07:16.036777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
도로공사 100
100.0%

지점
Categorical

HIGH CORRELATION 

Distinct16
Distinct (%)16.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
A-0010-3352E-9
9 
A-0010-1185E-8
8 
A-0010-1305E-8
8 
A-0010-1357E-8
8 
A-0010-3452S-10
7 
Other values (11)
60 

Length

Max length15
Median length14
Mean length14.07
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-0010-3352E-9 9
 
9.0%
A-0010-1185E-8 8
 
8.0%
A-0010-1305E-8 8
 
8.0%
A-0010-1357E-8 8
 
8.0%
A-0010-3452S-10 7
 
7.0%
A-0010-0083E-6 6
 
6.0%
A-0010-0538E-6 6
 
6.0%
A-0010-0728S-6 6
 
6.0%
A-0010-1073E-6 6
 
6.0%
A-0010-2761E-6 6
 
6.0%
Other values (6) 30
30.0%

Length

2023-12-10T20:07:16.287677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
a-0010-3352e-9 9
 
9.0%
a-0010-1185e-8 8
 
8.0%
a-0010-1305e-8 8
 
8.0%
a-0010-1357e-8 8
 
8.0%
a-0010-3452s-10 7
 
7.0%
a-0010-0083e-6 6
 
6.0%
a-0010-0538e-6 6
 
6.0%
a-0010-0728s-6 6
 
6.0%
a-0010-1073e-6 6
 
6.0%
a-0010-2761e-6 6
 
6.0%
Other values (6) 30
30.0%

방향
Categorical

HIGH CORRELATION 

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

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowE
2nd rowE
3rd rowE
4th rowS
5th rowS

Common Values

ValueCountFrequency (%)
E 51
51.0%
S 49
49.0%

Length

2023-12-10T20:07:16.598371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:07:16.775799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
e 51
51.0%
s 49
49.0%

차선
Categorical

Distinct5
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
1
28 
2
28 
3
27 
4
13 
5
4 

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 28
28.0%
2 28
28.0%
3 27
27.0%
4 13
13.0%
5 4
 
4.0%

Length

2023-12-10T20:07:16.970306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:07:17.179838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 28
28.0%
2 28
28.0%
3 27
27.0%
4 13
13.0%
5 4
 
4.0%

측정구간
Categorical

HIGH CORRELATION 

Distinct28
Distinct (%)28.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
천안IC-북천안IC
 
5
남이JC-청주JC
 
5
청주JC-남이JC
 
5
천안IC-천안JC
 
5
북대구IC-금호JC
 
4
Other values (23)
76 

Length

Max length10
Median length9
Mean length9.39
Min length9

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
천안IC-북천안IC 5
 
5.0%
남이JC-청주JC 5
 
5.0%
청주JC-남이JC 5
 
5.0%
천안IC-천안JC 5
 
5.0%
북대구IC-금호JC 4
 
4.0%
천안JC-천안IC 4
 
4.0%
경산IC-동대구JC 4
 
4.0%
북대구IC-도동JC 4
 
4.0%
남청주IC-청주JC 4
 
4.0%
금호JC-북대구IC 4
 
4.0%
Other values (18) 56
56.0%

Length

2023-12-10T20:07:17.482918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
천안ic-북천안ic 5
 
5.0%
청주jc-남이jc 5
 
5.0%
천안ic-천안jc 5
 
5.0%
남이jc-청주jc 5
 
5.0%
남청주ic-청주jc 4
 
4.0%
동대구jc-경산ic 4
 
4.0%
도동jc-북대구ic 4
 
4.0%
금호jc-북대구ic 4
 
4.0%
청주jc-남청주ic 4
 
4.0%
북대구ic-도동jc 4
 
4.0%
Other values (18) 56
56.0%

장비이정(km)
Real number (ℝ)

HIGH CORRELATION 

Distinct16
Distinct (%)16.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean206.1706
Minimum8.3
Maximum345.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:07:17.822528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8.3
5-th percentile8.3
Q1118.5
median276.1
Q3301.9
95-th percentile345.2
Maximum345.2
Range336.9
Interquartile range (IQR)183.4

Descriptive statistics

Standard deviation113.09306
Coefficient of variation (CV)0.54854115
Kurtosis-1.5532634
Mean206.1706
Median Absolute Deviation (MAD)69.1
Skewness-0.24567357
Sum20617.06
Variance12790.04
MonotonicityIncreasing
2023-12-10T20:07:18.129506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
335.2 9
 
9.0%
118.5 8
 
8.0%
130.5 8
 
8.0%
135.7 8
 
8.0%
345.2 7
 
7.0%
8.3 6
 
6.0%
72.8 6
 
6.0%
107.31 6
 
6.0%
276.1 6
 
6.0%
53.4 6
 
6.0%
Other values (6) 30
30.0%
ValueCountFrequency (%)
8.3 6
6.0%
53.4 6
6.0%
72.8 6
6.0%
107.31 6
6.0%
118.5 8
8.0%
130.5 8
8.0%
135.7 8
8.0%
276.1 6
6.0%
295.3 4
4.0%
295.6 4
4.0%
ValueCountFrequency (%)
345.2 7
7.0%
335.2 9
9.0%
306.8 6
6.0%
301.9 6
6.0%
298.7 5
5.0%
297.9 5
5.0%
295.6 4
4.0%
295.3 4
4.0%
276.1 6
6.0%
135.7 8
8.0%

측정일
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
20220101
100 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20220101
2nd row20220101
3rd row20220101
4th row20220101
5th row20220101

Common Values

ValueCountFrequency (%)
20220101 100
100.0%

Length

2023-12-10T20:07:18.504838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:07:18.761798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20220101 100
100.0%

측정시간
Categorical

CONSTANT 

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

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 100
100.0%

Length

2023-12-10T20:07:18.979356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:07:19.165847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 100
100.0%

차량통과수(대)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.76
Minimum0
Maximum139
Zeros23
Zeros (%)23.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:07:19.392483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median19.5
Q350
95-th percentile88.1
Maximum139
Range139
Interquartile range (IQR)49

Descriptive statistics

Standard deviation32.060272
Coefficient of variation (CV)1.0422715
Kurtosis1.4336155
Mean30.76
Median Absolute Deviation (MAD)19.5
Skewness1.291847
Sum3076
Variance1027.861
MonotonicityNot monotonic
2023-12-10T20:07:19.673529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 23
23.0%
24 5
 
5.0%
13 4
 
4.0%
37 4
 
4.0%
18 3
 
3.0%
17 3
 
3.0%
1 3
 
3.0%
16 3
 
3.0%
14 3
 
3.0%
51 3
 
3.0%
Other values (40) 46
46.0%
ValueCountFrequency (%)
0 23
23.0%
1 3
 
3.0%
2 1
 
1.0%
3 1
 
1.0%
7 1
 
1.0%
10 1
 
1.0%
11 1
 
1.0%
12 1
 
1.0%
13 4
 
4.0%
14 3
 
3.0%
ValueCountFrequency (%)
139 1
1.0%
127 1
1.0%
118 1
1.0%
115 1
1.0%
109 1
1.0%
87 2
2.0%
81 1
1.0%
77 1
1.0%
76 1
1.0%
75 1
1.0%

평균 속도(km/hr)
Real number (ℝ)

ZEROS 

Distinct64
Distinct (%)64.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean72.5369
Minimum0
Maximum129.15
Zeros23
Zeros (%)23.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:07:19.973659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q169.6725
median89
Q399
95-th percentile116.499
Maximum129.15
Range129.15
Interquartile range (IQR)29.3275

Descriptive statistics

Standard deviation41.536872
Coefficient of variation (CV)0.57263092
Kurtosis-0.55283379
Mean72.5369
Median Absolute Deviation (MAD)11.75
Skewness-1.0135177
Sum7253.69
Variance1725.3117
MonotonicityNot monotonic
2023-12-10T20:07:20.300006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 23
23.0%
101.0 3
 
3.0%
89.0 3
 
3.0%
90.0 3
 
3.0%
91.0 2
 
2.0%
110.0 2
 
2.0%
87.0 2
 
2.0%
118.0 2
 
2.0%
99.0 2
 
2.0%
93.0 2
 
2.0%
Other values (54) 56
56.0%
ValueCountFrequency (%)
0.0 23
23.0%
62.3 1
 
1.0%
65.69 1
 
1.0%
71.0 1
 
1.0%
76.6 1
 
1.0%
76.88 1
 
1.0%
77.0 1
 
1.0%
78.71 1
 
1.0%
79.57 1
 
1.0%
80.23 1
 
1.0%
ValueCountFrequency (%)
129.15 1
1.0%
123.0 1
1.0%
121.0 1
1.0%
118.0 2
2.0%
116.42 1
1.0%
115.67 1
1.0%
114.65 1
1.0%
114.0 1
1.0%
110.87 1
1.0%
110.0 2
2.0%

위도(°)
Real number (ℝ)

HIGH CORRELATION 

Distinct15
Distinct (%)15.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.230602
Minimum35.306944
Maximum36.868903
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:07:20.522731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.306944
5-th percentile35.306944
Q135.904732
median36.38961
Q336.606111
95-th percentile36.868903
Maximum36.868903
Range1.5619583
Interquartile range (IQR)0.7013787

Descriptive statistics

Standard deviation0.45811501
Coefficient of variation (CV)0.012644422
Kurtosis-1.1579042
Mean36.230602
Median Absolute Deviation (MAD)0.47338646
Skewness-0.26110044
Sum3623.0602
Variance0.20986936
MonotonicityNot monotonic
2023-12-10T20:07:20.815541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
35.90473241 16
16.0%
36.78083333 9
 
9.0%
35.91622354 8
 
8.0%
36.86890278 7
 
7.0%
35.30694444 6
 
6.0%
35.68194444 6
 
6.0%
35.818274 6
 
6.0%
35.88230609 6
 
6.0%
36.38961 6
 
6.0%
36.60611111 6
 
6.0%
Other values (5) 24
24.0%
ValueCountFrequency (%)
35.30694444 6
 
6.0%
35.68194444 6
 
6.0%
35.818274 6
 
6.0%
35.88230609 6
 
6.0%
35.90473241 16
16.0%
35.91622354 8
8.0%
36.38961 6
 
6.0%
36.54 4
 
4.0%
36.55638889 4
 
4.0%
36.57694444 5
 
5.0%
ValueCountFrequency (%)
36.86890278 7
7.0%
36.78083333 9
9.0%
36.64019722 6
6.0%
36.60611111 6
6.0%
36.58444444 5
5.0%
36.57694444 5
5.0%
36.55638889 4
4.0%
36.54 4
4.0%
36.38961 6
6.0%
35.91622354 8
8.0%

경도(°)
Real number (ℝ)

HIGH CORRELATION 

Distinct15
Distinct (%)15.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean128.05285
Minimum127.17667
Maximum129.18111
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:07:21.036609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum127.17667
5-th percentile127.17667
Q1127.40833
median127.43389
Q3128.61741
95-th percentile129.18111
Maximum129.18111
Range2.0044444
Interquartile range (IQR)1.2090796

Descriptive statistics

Standard deviation0.76696061
Coefficient of variation (CV)0.005989407
Kurtosis-1.7247028
Mean128.05285
Median Absolute Deviation (MAD)0.2572222
Skewness0.21479524
Sum12805.285
Variance0.58822858
MonotonicityNot monotonic
2023-12-10T20:07:21.244097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
128.5614716 16
16.0%
127.1766667 9
 
9.0%
128.6174129 8
 
8.0%
127.1867222 7
 
7.0%
129.0747222 6
 
6.0%
129.1811111 6
 
6.0%
129.140095 6
 
6.0%
128.8488161 6
 
6.0%
127.423508 6
 
6.0%
127.4083333 6
 
6.0%
Other values (5) 24
24.0%
ValueCountFrequency (%)
127.1766667 9
9.0%
127.1867222 7
7.0%
127.3781278 6
 
6.0%
127.4083333 6
 
6.0%
127.423508 6
 
6.0%
127.4263889 5
 
5.0%
127.4277778 5
 
5.0%
127.4325 4
 
4.0%
127.4338889 4
 
4.0%
128.5614716 16
16.0%
ValueCountFrequency (%)
129.1811111 6
 
6.0%
129.140095 6
 
6.0%
129.0747222 6
 
6.0%
128.8488161 6
 
6.0%
128.6174129 8
8.0%
128.5614716 16
16.0%
127.4338889 4
 
4.0%
127.4325 4
 
4.0%
127.4277778 5
 
5.0%
127.4263889 5
 
5.0%

기울기(°)
Real number (ℝ)

HIGH CORRELATION 

Distinct26
Distinct (%)26.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0084955
Minimum-3.15276
Maximum3.071416
Zeros0
Zeros (%)0.0%
Negative48
Negative (%)48.0%
Memory size1.0 KiB
2023-12-10T20:07:21.465212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-3.15276
5-th percentile-2.788625
Q1-0.67275475
median0.166426
Q30.688696
95-th percentile2.703849
Maximum3.071416
Range6.224176
Interquartile range (IQR)1.3614507

Descriptive statistics

Standard deviation1.3702415
Coefficient of variation (CV)161.29027
Kurtosis0.19685704
Mean0.0084955
Median Absolute Deviation (MAD)0.833867
Skewness-0.078346497
Sum0.84955
Variance1.8775618
MonotonicityNot monotonic
2023-12-10T20:07:21.730658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
0.166426 8
 
8.0%
-0.131348 8
 
8.0%
-1.758683 5
 
5.0%
-0.624001 5
 
5.0%
1.544389 5
 
5.0%
1.437416 5
 
5.0%
-1.359001 4
 
4.0%
0.685727 4
 
4.0%
-0.667441 4
 
4.0%
-0.807188 4
 
4.0%
Other values (16) 48
48.0%
ValueCountFrequency (%)
-3.15276 3
3.0%
-2.788625 3
3.0%
-1.758683 5
5.0%
-1.717451 3
3.0%
-1.359001 4
4.0%
-0.807188 4
4.0%
-0.688696 3
3.0%
-0.667441 4
4.0%
-0.624001 5
5.0%
-0.550996 3
3.0%
ValueCountFrequency (%)
3.071416 3
3.0%
2.703849 3
3.0%
1.717451 3
3.0%
1.544389 5
5.0%
1.437416 5
5.0%
1.056036 4
4.0%
0.688696 3
3.0%
0.685727 4
4.0%
0.61369 2
 
2.0%
0.540588 3
3.0%

TSP(g/km)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct68
Distinct (%)68.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.6028
Minimum0
Maximum9.65
Zeros23
Zeros (%)23.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:07:22.025317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.065
median0.825
Q32.45
95-th percentile5.2535
Maximum9.65
Range9.65
Interquartile range (IQR)2.385

Descriptive statistics

Standard deviation2.0385131
Coefficient of variation (CV)1.2718449
Kurtosis2.9565597
Mean1.6028
Median Absolute Deviation (MAD)0.825
Skewness1.681275
Sum160.28
Variance4.1555355
MonotonicityNot monotonic
2023-12-10T20:07:22.375801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 23
 
23.0%
0.16 3
 
3.0%
0.21 2
 
2.0%
0.82 2
 
2.0%
1.7 2
 
2.0%
0.2 2
 
2.0%
0.29 2
 
2.0%
0.83 2
 
2.0%
2.38 2
 
2.0%
0.15 2
 
2.0%
Other values (58) 58
58.0%
ValueCountFrequency (%)
0.0 23
23.0%
0.01 1
 
1.0%
0.02 1
 
1.0%
0.08 1
 
1.0%
0.12 1
 
1.0%
0.15 2
 
2.0%
0.16 3
 
3.0%
0.19 1
 
1.0%
0.2 2
 
2.0%
0.21 2
 
2.0%
ValueCountFrequency (%)
9.65 1
1.0%
8.96 1
1.0%
6.99 1
1.0%
5.77 1
1.0%
5.32 1
1.0%
5.25 1
1.0%
5.11 1
1.0%
5.1 1
1.0%
4.81 1
1.0%
4.72 1
1.0%

PM10(g/km)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct60
Distinct (%)60.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.7048
Minimum0
Maximum4.25
Zeros24
Zeros (%)24.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:07:22.725605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.025
median0.365
Q31.075
95-th percentile2.3115
Maximum4.25
Range4.25
Interquartile range (IQR)1.05

Descriptive statistics

Standard deviation0.89718694
Coefficient of variation (CV)1.2729667
Kurtosis2.9558061
Mean0.7048
Median Absolute Deviation (MAD)0.365
Skewness1.6807254
Sum70.48
Variance0.8049444
MonotonicityNot monotonic
2023-12-10T20:07:22.982600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 24
24.0%
0.09 4
 
4.0%
0.07 3
 
3.0%
0.37 2
 
2.0%
0.16 2
 
2.0%
0.58 2
 
2.0%
0.75 2
 
2.0%
1.87 2
 
2.0%
0.06 2
 
2.0%
0.46 2
 
2.0%
Other values (50) 55
55.0%
ValueCountFrequency (%)
0.0 24
24.0%
0.01 1
 
1.0%
0.03 1
 
1.0%
0.05 1
 
1.0%
0.06 2
 
2.0%
0.07 3
 
3.0%
0.08 1
 
1.0%
0.09 4
 
4.0%
0.11 1
 
1.0%
0.12 1
 
1.0%
ValueCountFrequency (%)
4.25 1
1.0%
3.94 1
1.0%
3.07 1
1.0%
2.54 1
1.0%
2.34 1
1.0%
2.31 1
1.0%
2.25 1
1.0%
2.24 1
1.0%
2.12 1
1.0%
2.08 1
1.0%

주소
Categorical

HIGH CORRELATION 

Distinct14
Distinct (%)14.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
충북 청원군 남이면
13 
충청 천안시 구성동
9 
대구 동구 안심3동
8 
대구 북구 검단동
8 
대구 북구 관문동
8 
Other values (9)
54 

Length

Max length18
Median length15
Mean length11.32
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%
충청 천안시 구성동 9
 
9.0%
대구 동구 안심3동 8
 
8.0%
대구 북구 검단동 8
 
8.0%
대구 북구 관문동 8
 
8.0%
충남 천안시 서북구 성거읍 송남리 7
 
7.0%
경남 양산시 동면 6
 
6.0%
울산 울주군 두서면 활천리 6
 
6.0%
경북 경주시 건천읍 모량리 6
 
6.0%
경북 경산시 진량읍 6
 
6.0%
Other values (4) 23
23.0%

Length

2023-12-10T20:07:23.304914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
충북 30
 
8.9%
대구 24
 
7.1%
청원군 24
 
7.1%
남이면 18
 
5.3%
천안시 16
 
4.7%
북구 16
 
4.7%
경북 12
 
3.6%
충청 9
 
2.7%
구성동 9
 
2.7%
동구 8
 
2.4%
Other values (27) 171
50.7%

Interactions

2023-12-10T20:07:12.217902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:57.662400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:59.269636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:01.056108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:03.052161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:04.433709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:06.652879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:08.593696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:10.363762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:12.398681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:57.821329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:59.461952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:01.248410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:03.188947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:04.697445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:06.847813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:08.754628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:10.600959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:12.580446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:58.001331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:59.636749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:01.427469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:03.357781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:05.020618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:07.061859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:08.920647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:10.853690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:12.763132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:58.172484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:59.835545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:01.595206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:03.522722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:05.224537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:07.256325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:09.153022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:11.027405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:12.931144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:58.335633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:00.005079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:01.783706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:03.644618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:05.587986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:07.488930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:09.432509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:11.294636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:13.094688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:58.518203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:00.259973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:02.340673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:03.778416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:05.863662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:07.720549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:09.630495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:11.479000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:13.686687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:58.736415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:00.459791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:02.560418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:03.967229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:06.092556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:07.973112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:09.825254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:11.678947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:13.853431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:58.894920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:00.671952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:02.739043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:04.125253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:06.312454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:08.189384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:09.995129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:11.855310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:14.016552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:59.075806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:00.875326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:02.898494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:04.282666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:06.483759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:08.384856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:10.179850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:12.058577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T20:07:23.578980image/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
기본키1.0000.9660.0000.0000.9830.8910.4170.3820.8840.9250.7120.4510.4510.955
지점0.9661.0000.2880.0001.0001.0000.2980.4801.0001.0000.9580.6670.6671.000
방향0.0000.2881.0000.0001.0000.0000.2300.0000.0000.0000.8860.2160.2160.000
차선0.0000.0000.0001.0000.0000.0000.3290.4250.0000.0000.0000.3840.3840.000
측정구간0.9831.0001.0000.0001.0001.0000.4420.3641.0001.0001.0000.2450.2451.000
장비이정(km)0.8911.0000.0000.0001.0001.0000.2310.5190.9910.9440.7290.2300.2301.000
차량통과수(대)0.4170.2980.2300.3290.4420.2311.0000.5650.2440.3830.2980.6430.6430.358
평균 속도(km/hr)0.3820.4800.0000.4250.3640.5190.5651.0000.3700.3500.0000.5290.5290.575
위도(°)0.8841.0000.0000.0001.0000.9910.2440.3701.0000.8390.7680.3220.3220.999
경도(°)0.9251.0000.0000.0001.0000.9440.3830.3500.8391.0000.6490.3210.3211.000
기울기(°)0.7120.9580.8860.0001.0000.7290.2980.0000.7680.6491.0000.2810.2810.860
TSP(g/km)0.4510.6670.2160.3840.2450.2300.6430.5290.3220.3210.2811.0001.0000.515
PM10(g/km)0.4510.6670.2160.3840.2450.2300.6430.5290.3220.3210.2811.0001.0000.515
주소0.9551.0000.0000.0001.0001.0000.3580.5750.9991.0000.8600.5150.5151.000
2023-12-10T20:07:24.360830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지점차선측정구간방향주소
지점1.0000.0000.9260.2060.988
차선0.0001.0000.0000.0000.000
측정구간0.9260.0001.0000.8570.915
방향0.2060.0000.8571.0000.000
주소0.9880.0000.9150.0001.000
2023-12-10T20:07:24.584200image/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
기본키1.0000.998-0.243-0.3310.987-0.9580.002-0.254-0.2550.8150.0000.0000.7920.795
장비이정(km)0.9981.000-0.245-0.3120.989-0.960-0.018-0.257-0.2590.9500.0000.0000.8800.962
차량통과수(대)-0.243-0.2451.0000.403-0.2470.246-0.0370.8180.8170.1100.1660.1350.1460.144
평균 속도(km/hr)-0.331-0.3120.4031.000-0.2970.262-0.1810.2840.2720.2280.0000.2850.1160.237
위도(°)0.9870.989-0.247-0.2971.000-0.952-0.017-0.260-0.2620.9500.0000.0000.8800.912
경도(°)-0.958-0.9600.2460.262-0.9521.0000.0090.2510.2520.9450.0000.0000.8750.957
기울기(°)0.002-0.018-0.037-0.181-0.0170.0091.000-0.0030.0010.6620.6890.0000.8850.591
TSP(g/km)-0.254-0.2570.8180.284-0.2600.251-0.0031.0000.9990.2810.1550.2420.0670.245
PM10(g/km)-0.255-0.2590.8170.272-0.2620.2520.0010.9991.0000.2810.1550.2420.0670.245
지점0.8150.9500.1100.2280.9500.9450.6620.2810.2811.0000.2060.0000.9260.988
방향0.0000.0000.1660.0000.0000.0000.6890.1550.1550.2061.0000.0000.8570.000
차선0.0000.0000.1350.2850.0000.0000.0000.2420.2420.0000.0001.0000.0000.000
측정구간0.7920.8800.1460.1160.8800.8750.8850.0670.0670.9260.8570.0001.0000.915
주소0.7950.9620.1440.2370.9120.9570.5910.2450.2450.9880.0000.0000.9151.000

Missing values

2023-12-10T20:07:14.292144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T20:07:14.718369image/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-양산JC8.320220101016101.035.306944129.074722-3.152762.791.23경남 양산시 동면
12도로공사A-0010-0083E-6E2노포JC-양산JC8.32022010104098.6735.306944129.074722-3.152760.940.41경남 양산시 동면
23도로공사A-0010-0083E-6E3노포JC-양산JC8.32022010103089.035.306944129.074722-3.152761.530.67경남 양산시 동면
34도로공사A-0010-0083E-6S1양산JC-노포JC8.320220101019123.035.306944129.0747223.0714160.210.09경남 양산시 동면
45도로공사A-0010-0083E-6S2양산JC-노포JC8.32022010104795.3335.306944129.0747223.0714160.820.36경남 양산시 동면
56도로공사A-0010-0083E-6S3양산JC-노포JC8.320220101020100.035.306944129.0747223.0714163.341.47경남 양산시 동면
67도로공사A-0010-0538E-6E1언양JC-활천IC53.420220101014101.035.681944129.1811110.225450.160.07울산 울주군 두서면 활천리
78도로공사A-0010-0538E-6E2언양JC-활천IC53.42022010104085.535.681944129.1811110.225450.990.43울산 울주군 두서면 활천리
89도로공사A-0010-0538E-6E3언양JC-활천IC53.42022010101483.3335.681944129.1811110.225452.10.92울산 울주군 두서면 활천리
910도로공사A-0010-0538E-6S1활천IC-언양JC53.420220101026100.035.681944129.1811110.191580.290.13울산 울주군 두서면 활천리
기본키도로종류지점방향차선측정구간장비이정(km)측정일측정시간차량통과수(대)평균 속도(km/hr)위도(°)경도(°)기울기(°)TSP(g/km)PM10(g/km)주소
9091도로공사A-0010-3352E-9S3천안IC-천안JC335.220220101000.036.780833127.1766671.5443890.00.0충청 천안시 구성동
9192도로공사A-0010-3352E-9S4천안IC-천안JC335.220220101000.036.780833127.1766671.5443890.00.0충청 천안시 구성동
9293도로공사A-0010-3352E-9S5천안IC-천안JC335.220220101000.036.780833127.1766671.5443890.00.0충청 천안시 구성동
9394도로공사A-0010-3452S-10E1천안IC-북천안IC345.22022010101109.036.868903127.186722-0.6240010.20.09충남 천안시 서북구 성거읍 송남리
9495도로공사A-0010-3452S-10E2천안IC-북천안IC345.22022010101114.036.868903127.186722-0.6240010.010.0충남 천안시 서북구 성거읍 송남리
9596도로공사A-0010-3452S-10E3천안IC-북천안IC345.220220101000.036.868903127.186722-0.6240010.00.0충남 천안시 서북구 성거읍 송남리
9697도로공사A-0010-3452S-10E4천안IC-북천안IC345.220220101000.036.868903127.186722-0.6240010.00.0충남 천안시 서북구 성거읍 송남리
9798도로공사A-0010-3452S-10E5천안IC-북천안IC345.2202201010293.036.868903127.186722-0.6240010.020.01충남 천안시 서북구 성거읍 송남리
9899도로공사A-0010-3452S-10S1북천안IC-천안IC345.220220101018105.036.868903127.1867220.613690.20.09충남 천안시 서북구 성거읍 송남리
99100도로공사A-0010-3452S-10S2북천안IC-천안IC345.220220101000.036.868903127.1867220.613690.00.0충남 천안시 서북구 성거읍 송남리