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 25 (25.0%) zerosZeros
평균 속도(km/hr) has 25 (25.0%) zerosZeros
TSP(g/km) has 25 (25.0%) zerosZeros
PM10(g/km) has 25 (25.0%) zerosZeros

Reproduction

Analysis started2023-12-10 11:11:21.599724
Analysis finished2023-12-10 11:11:33.451017
Duration11.85 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
2023-12-10T20:11:33.551806image/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
2023-12-10T20:11:33.777595image/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

2023-12-10T20:11:33.990243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:11:34.127738image/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-0010-3352E-9
A-0010-1185E-8
A-0010-2583E-7
Other values (10)
56 

Length

Max length15
Median length14
Mean length14.26
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-0010-3352E-9 9
 
9.0%
A-0010-1185E-8 8
 
8.0%
A-0010-2583E-7 7
 
7.0%
A-0010-0083E-6 6
 
6.0%
A-0010-0538E-6 6
 
6.0%
A-0010-1073E-6 6
 
6.0%
A-0010-1880E-6 6
 
6.0%
A-0010-2761E-6 6
 
6.0%
Other values (5) 26
26.0%

Length

2023-12-10T20:11:34.280307image/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-0010-3352e-9 9
 
9.0%
a-0010-1185e-8 8
 
8.0%
a-0010-2583e-7 7
 
7.0%
a-0010-0083e-6 6
 
6.0%
a-0010-0538e-6 6
 
6.0%
a-0010-1073e-6 6
 
6.0%
a-0010-1880e-6 6
 
6.0%
a-0010-2761e-6 6
 
6.0%
Other values (5) 26
26.0%

방향
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
S
51 
E
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 (%)
S 51
51.0%
E 49
49.0%

Length

2023-12-10T20:11:34.464241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:11:34.602014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
s 51
51.0%
e 49
49.0%

차선
Categorical

Distinct5
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
1
27 
2
26 
3
26 
4
13 
5

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
1 27
27.0%
2 26
26.0%
3 26
26.0%
4 13
13.0%
5 8
 
8.0%

Length

2023-12-10T20:11:34.761277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:11:34.935648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 27
27.0%
2 26
26.0%
3 26
26.0%
4 13
13.0%
5 8
 
8.0%

측정구간
Categorical

HIGH CORRELATION 

Distinct27
Distinct (%)27.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
남이JC-청주JC
 
5
안성JC-안성IC
 
5
안성IC-안성JC
 
5
북천안IC-천안IC
 
5
천안IC-북천안IC
 
5
Other values (22)
75 

Length

Max length10
Median length9
Mean length9.28
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 (%)
남이JC-청주JC 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-동탄JC 5
 
5.0%
옥천IC-금강IC 4
 
4.0%
청주JC-남청주IC 4
 
4.0%
Other values (17) 52
52.0%

Length

2023-12-10T20:11:35.109545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
남이jc-청주jc 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-동탄jc 5
 
5.0%
안성jc-안성ic 5
 
5.0%
천안jc-천안ic 4
 
4.0%
경산ic-동대구jc 4
 
4.0%
Other values (17) 52
52.0%

장비이정(km)
Real number (ℝ)

HIGH CORRELATION 

Distinct15
Distinct (%)15.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean249.2228
Minimum8.3
Maximum380.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:11:35.236458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8.3
5-th percentile8.3
Q1118.5
median297.9
Q3345.2
95-th percentile380.1
Maximum380.1
Range371.8
Interquartile range (IQR)226.7

Descriptive statistics

Standard deviation114.71407
Coefficient of variation (CV)0.46028723
Kurtosis-0.69371872
Mean249.2228
Median Absolute Deviation (MAD)47.3
Skewness-0.82223934
Sum24922.28
Variance13159.319
MonotonicityIncreasing
2023-12-10T20:11:35.367289image/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%
335.2 9
 
9.0%
118.5 8
 
8.0%
258.3 7
 
7.0%
8.3 6
 
6.0%
53.4 6
 
6.0%
107.31 6
 
6.0%
188.02 6
 
6.0%
276.1 6
 
6.0%
Other values (5) 26
26.0%
ValueCountFrequency (%)
8.3 6
6.0%
53.4 6
6.0%
107.31 6
6.0%
118.5 8
8.0%
188.02 6
6.0%
258.3 7
7.0%
276.1 6
6.0%
295.3 4
4.0%
297.9 5
5.0%
298.7 5
5.0%
ValueCountFrequency (%)
380.1 6
6.0%
361.3 10
10.0%
345.2 10
10.0%
335.2 9
9.0%
306.8 6
6.0%
298.7 5
5.0%
297.9 5
5.0%
295.3 4
 
4.0%
276.1 6
6.0%
258.3 7
7.0%

측정일
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
20201201
100 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20201201
2nd row20201201
3rd row20201201
4th row20201201
5th row20201201

Common Values

ValueCountFrequency (%)
20201201 100
100.0%

Length

2023-12-10T20:11:35.539832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:11:35.651400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20201201 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

2023-12-10T20:11:35.778586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:11:35.892915image/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%
Mean26.39
Minimum0
Maximum111
Zeros25
Zeros (%)25.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:11:36.040305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12.25
median16
Q337.75
95-th percentile86.25
Maximum111
Range111
Interquartile range (IQR)35.5

Descriptive statistics

Standard deviation29.291601
Coefficient of variation (CV)1.1099508
Kurtosis0.34092495
Mean26.39
Median Absolute Deviation (MAD)16
Skewness1.1711014
Sum2639
Variance857.99788
MonotonicityNot monotonic
2023-12-10T20:11:36.237303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 25
25.0%
16 6
 
6.0%
15 3
 
3.0%
8 3
 
3.0%
5 3
 
3.0%
18 3
 
3.0%
12 2
 
2.0%
96 2
 
2.0%
35 2
 
2.0%
84 2
 
2.0%
Other values (40) 49
49.0%
ValueCountFrequency (%)
0 25
25.0%
3 2
 
2.0%
4 2
 
2.0%
5 3
 
3.0%
6 1
 
1.0%
7 1
 
1.0%
8 3
 
3.0%
9 1
 
1.0%
10 1
 
1.0%
11 1
 
1.0%
ValueCountFrequency (%)
111 1
1.0%
101 1
1.0%
96 2
2.0%
91 1
1.0%
86 1
1.0%
84 2
2.0%
81 1
1.0%
76 1
1.0%
72 1
1.0%
71 1
1.0%

평균 속도(km/hr)
Real number (ℝ)

ZEROS 

Distinct64
Distinct (%)64.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean70.3941
Minimum0
Maximum124
Zeros25
Zeros (%)25.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:11:36.412829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q147.5725
median87.12
Q397.5525
95-th percentile118.0665
Maximum124
Range124
Interquartile range (IQR)49.98

Descriptive statistics

Standard deviation42.670534
Coefficient of variation (CV)0.60616634
Kurtosis-0.81908956
Mean70.3941
Median Absolute Deviation (MAD)11.89
Skewness-0.87638708
Sum7039.41
Variance1820.7745
MonotonicityNot monotonic
2023-12-10T20:11:36.618091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 25
25.0%
124.0 4
 
4.0%
106.0 3
 
3.0%
79.0 2
 
2.0%
84.0 2
 
2.0%
118.0 2
 
2.0%
99.0 2
 
2.0%
95.0 2
 
2.0%
96.0 2
 
2.0%
92.0 2
 
2.0%
Other values (54) 54
54.0%
ValueCountFrequency (%)
0.0 25
25.0%
63.43 1
 
1.0%
65.78 1
 
1.0%
67.56 1
 
1.0%
75.22 1
 
1.0%
75.89 1
 
1.0%
76.75 1
 
1.0%
77.86 1
 
1.0%
78.0 1
 
1.0%
78.83 1
 
1.0%
ValueCountFrequency (%)
124.0 4
4.0%
119.33 1
 
1.0%
118.0 2
2.0%
117.0 1
 
1.0%
115.0 1
 
1.0%
112.33 1
 
1.0%
112.0 1
 
1.0%
111.0 1
 
1.0%
108.5 1
 
1.0%
107.0 1
 
1.0%

위도(°)
Real number (ℝ)

HIGH CORRELATION 

Distinct15
Distinct (%)15.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.424538
Minimum35.306944
Maximum37.158778
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:11:36.822556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.306944
5-th percentile35.306944
Q135.904732
median36.576944
Q336.868903
95-th percentile37.158778
Maximum37.158778
Range1.8518333
Interquartile range (IQR)0.96417037

Descriptive statistics

Standard deviation0.50859745
Coefficient of variation (CV)0.013963045
Kurtosis-0.56132503
Mean36.424538
Median Absolute Deviation (MAD)0.29195834
Skewness-0.5704546
Sum3642.4538
Variance0.25867137
MonotonicityIncreasing
2023-12-10T20:11:37.014932image/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%
36.78083333 9
 
9.0%
35.90473241 8
 
8.0%
36.30722222 7
 
7.0%
35.30694444 6
 
6.0%
35.68194444 6
 
6.0%
35.88230609 6
 
6.0%
36.15572222 6
 
6.0%
36.38961 6
 
6.0%
Other values (5) 26
26.0%
ValueCountFrequency (%)
35.30694444 6
6.0%
35.68194444 6
6.0%
35.88230609 6
6.0%
35.90473241 8
8.0%
36.15572222 6
6.0%
36.30722222 7
7.0%
36.38961 6
6.0%
36.54 4
4.0%
36.57694444 5
5.0%
36.58444444 5
5.0%
ValueCountFrequency (%)
37.15877778 6
6.0%
37.00888889 10
10.0%
36.86890278 10
10.0%
36.78083333 9
9.0%
36.64019722 6
6.0%
36.58444444 5
5.0%
36.57694444 5
5.0%
36.54 4
 
4.0%
36.38961 6
6.0%
36.30722222 7
7.0%

경도(°)
Real number (ℝ)

HIGH CORRELATION 

Distinct15
Distinct (%)15.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.76716
Minimum127.08833
Maximum129.18111
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:11:37.208022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum127.08833
5-th percentile127.08833
Q1127.18421
median127.42639
Q3128.56147
95-th percentile129.18111
Maximum129.18111
Range2.0927778
Interquartile range (IQR)1.3772633

Descriptive statistics

Standard deviation0.72532794
Coefficient of variation (CV)0.0056769515
Kurtosis-0.82349525
Mean127.76716
Median Absolute Deviation (MAD)0.2497222
Skewness0.89881755
Sum12776.716
Variance0.52610063
MonotonicityNot monotonic
2023-12-10T20:11:37.387139image/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%
127.1766667 9
 
9.0%
128.5614716 8
 
8.0%
127.5744444 7
 
7.0%
129.0747222 6
 
6.0%
129.1811111 6
 
6.0%
128.8488161 6
 
6.0%
128.2132778 6
 
6.0%
127.423508 6
 
6.0%
Other values (5) 26
26.0%
ValueCountFrequency (%)
127.0883333 6
6.0%
127.1491667 10
10.0%
127.1766667 9
9.0%
127.1867222 10
10.0%
127.3781278 6
6.0%
127.423508 6
6.0%
127.4263889 5
5.0%
127.4277778 5
5.0%
127.4338889 4
 
4.0%
127.5744444 7
7.0%
ValueCountFrequency (%)
129.1811111 6
6.0%
129.0747222 6
6.0%
128.8488161 6
6.0%
128.5614716 8
8.0%
128.2132778 6
6.0%
127.5744444 7
7.0%
127.4338889 4
4.0%
127.4277778 5
5.0%
127.4263889 5
5.0%
127.423508 6
6.0%

기울기(°)
Real number (ℝ)

HIGH CORRELATION 

Distinct27
Distinct (%)27.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.03779762
Minimum-3.15276
Maximum3.071416
Zeros0
Zeros (%)0.0%
Negative50
Negative (%)50.0%
Memory size1.0 KiB
2023-12-10T20:11:37.962413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-3.15276
5-th percentile-2.788625
Q1-0.807188
median0.051518
Q30.688696
95-th percentile2.703849
Maximum3.071416
Range6.224176
Interquartile range (IQR)1.495884

Descriptive statistics

Standard deviation1.3767542
Coefficient of variation (CV)-36.424363
Kurtosis0.079415208
Mean-0.03779762
Median Absolute Deviation (MAD)0.858706
Skewness9.6031432 × 10-5
Sum-3.779762
Variance1.8954522
MonotonicityNot monotonic
2023-12-10T20:11:38.274064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
-1.758683 5
 
5.0%
-0.660004 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%
1.437416 5
 
5.0%
-0.807188 4
 
4.0%
0.166426 4
 
4.0%
Other values (17) 52
52.0%
ValueCountFrequency (%)
-3.15276 3
3.0%
-2.788625 3
3.0%
-1.758683 5
5.0%
-1.359001 4
4.0%
-1.30142 5
5.0%
-1.252479 3
3.0%
-0.807188 4
4.0%
-0.688696 3
3.0%
-0.660004 5
5.0%
-0.624001 5
5.0%
ValueCountFrequency (%)
3.071416 3
3.0%
2.703849 3
3.0%
1.544389 5
5.0%
1.437416 5
5.0%
1.293015 5
5.0%
1.172023 3
3.0%
0.688696 3
3.0%
0.671977 1
 
1.0%
0.61369 5
5.0%
0.409237 3
3.0%

TSP(g/km)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct71
Distinct (%)71.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5286
Minimum0
Maximum20.85
Zeros25
Zeros (%)25.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:11:38.517282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.0225
median0.65
Q33.3175
95-th percentile10.26
Maximum20.85
Range20.85
Interquartile range (IQR)3.295

Descriptive statistics

Standard deviation3.8689032
Coefficient of variation (CV)1.5300574
Kurtosis5.3105607
Mean2.5286
Median Absolute Deviation (MAD)0.65
Skewness2.1694482
Sum252.86
Variance14.968412
MonotonicityNot monotonic
2023-12-10T20:11:38.740674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 25
25.0%
0.04 3
 
3.0%
0.51 2
 
2.0%
0.03 2
 
2.0%
0.18 2
 
2.0%
2.1 1
 
1.0%
11.56 1
 
1.0%
9.5 1
 
1.0%
20.85 1
 
1.0%
5.26 1
 
1.0%
Other values (61) 61
61.0%
ValueCountFrequency (%)
0.0 25
25.0%
0.03 2
 
2.0%
0.04 3
 
3.0%
0.08 1
 
1.0%
0.09 1
 
1.0%
0.1 1
 
1.0%
0.13 1
 
1.0%
0.15 1
 
1.0%
0.17 1
 
1.0%
0.18 2
 
2.0%
ValueCountFrequency (%)
20.85 1
1.0%
14.02 1
1.0%
13.6 1
1.0%
11.56 1
1.0%
11.21 1
1.0%
10.21 1
1.0%
9.87 1
1.0%
9.5 1
1.0%
9.42 1
1.0%
9.0 1
1.0%

PM10(g/km)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct67
Distinct (%)67.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1127
Minimum0
Maximum9.17
Zeros25
Zeros (%)25.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:11:38.945709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.0075
median0.285
Q31.46
95-th percentile4.512
Maximum9.17
Range9.17
Interquartile range (IQR)1.4525

Descriptive statistics

Standard deviation1.7020326
Coefficient of variation (CV)1.5296419
Kurtosis5.3007455
Mean1.1127
Median Absolute Deviation (MAD)0.285
Skewness2.1676299
Sum111.27
Variance2.8969149
MonotonicityNot monotonic
2023-12-10T20:11:39.146273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 25
25.0%
0.08 3
 
3.0%
0.02 3
 
3.0%
0.06 2
 
2.0%
0.22 2
 
2.0%
0.04 2
 
2.0%
0.73 2
 
2.0%
0.01 2
 
2.0%
0.92 1
 
1.0%
1.18 1
 
1.0%
Other values (57) 57
57.0%
ValueCountFrequency (%)
0.0 25
25.0%
0.01 2
 
2.0%
0.02 3
 
3.0%
0.03 1
 
1.0%
0.04 2
 
2.0%
0.06 2
 
2.0%
0.08 3
 
3.0%
0.1 1
 
1.0%
0.12 1
 
1.0%
0.13 1
 
1.0%
ValueCountFrequency (%)
9.17 1
1.0%
6.17 1
1.0%
5.98 1
1.0%
5.08 1
1.0%
4.93 1
1.0%
4.49 1
1.0%
4.34 1
1.0%
4.18 1
1.0%
4.15 1
1.0%
3.96 1
1.0%

주소
Categorical

HIGH CORRELATION 

Distinct14
Distinct (%)14.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
충남 천안시 서북구 성거읍 송남리
10 
경기 안성시 원곡면
10 
충북 청원군 남이면
충청 천안시 구성동
대구 동구 안심3동
Other values (9)
54 

Length

Max length18
Median length10
Mean length11.88
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 (%)
충남 천안시 서북구 성거읍 송남리 10
10.0%
경기 안성시 원곡면 10
10.0%
충북 청원군 남이면 9
 
9.0%
충청 천안시 구성동 9
 
9.0%
대구 동구 안심3동 8
 
8.0%
충북 옥천군 옥천읍 삼양리 7
 
7.0%
경남 양산시 동면 6
 
6.0%
울산 울주군 두서면 활천리 6
 
6.0%
경북 경산시 진량읍 6
 
6.0%
경북 김천시 아포읍 봉산리 6
 
6.0%
Other values (4) 23
23.0%

Length

2023-12-10T20:11:39.355356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
충북 27
 
7.7%
청원군 20
 
5.7%
천안시 19
 
5.4%
경기 16
 
4.6%
남이면 14
 
4.0%
경북 12
 
3.4%
충남 10
 
2.9%
서북구 10
 
2.9%
성거읍 10
 
2.9%
송남리 10
 
2.9%
Other values (30) 202
57.7%

Interactions

2023-12-10T20:11:31.917274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:11:22.419938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:11:23.519793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:11:24.742749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:11:25.857494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:11:27.031837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:11:28.339911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:11:29.273777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:11:30.780226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:11:32.036423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:11:22.525865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:11:23.607880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:11:24.867534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:11:25.982371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:11:27.167801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:11:28.445071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:11:29.399644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:11:30.902684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:11:32.157460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:11:22.686396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:11:23.713204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:11:24.990153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:11:26.104788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:11:27.314010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:11:28.567313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:11:29.586992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:11:31.035998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:11:32.274464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:11:22.809537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:11:23.849573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:11:25.099213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:11:26.231573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:11:27.445429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:11:28.677094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:11:29.713909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:11:31.143949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:11:32.382720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:11:22.943779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:11:24.015349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:11:25.224714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:11:26.359550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:11:27.567575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:11:28.767033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:11:29.854019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:11:31.280586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:11:32.507284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:11:23.097728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:11:24.167207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:11:25.356191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:11:26.517077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:11:27.719050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:11:28.870016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:11:30.012825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:11:31.435467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:11:32.622173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:11:23.203980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:11:24.302454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:11:25.490059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:11:26.668842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:11:27.837765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:11:28.945940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:11:30.416029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:11:31.560020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:11:32.731451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:11:23.321595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:11:24.459600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:11:25.622926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:11:26.810302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:11:28.101134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:11:29.039557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:11:30.545164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:11:31.701222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:11:32.827320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:11:23.429111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:11:24.612407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:11:25.737091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:11:26.922533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:11:28.222457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:11:29.162707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:11:30.653780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:11:31.828061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T20:11:39.497233image/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
기본키1.0000.9680.0000.0000.9830.9140.5290.4340.9120.9100.7020.0000.0000.946
지점0.9681.0000.2010.0001.0001.0000.5310.5581.0001.0000.9030.2310.2311.000
방향0.0000.2011.0000.0001.0000.0000.0000.0000.0000.0000.5270.1820.1820.000
차선0.0000.0000.0001.0000.0000.0000.0000.5380.0000.0000.0000.2140.2140.000
측정구간0.9831.0001.0000.0001.0001.0000.4920.5571.0001.0001.0000.0000.0001.000
장비이정(km)0.9141.0000.0000.0001.0001.0000.3440.3460.9820.9530.8590.0000.0001.000
차량통과수(대)0.5290.5310.0000.0000.4920.3441.0000.5490.3480.3430.2890.7470.7470.520
평균 속도(km/hr)0.4340.5580.0000.5380.5570.3460.5491.0000.4270.3620.0000.3820.3820.537
위도(°)0.9121.0000.0000.0001.0000.9820.3480.4271.0000.9220.7510.1660.1661.000
경도(°)0.9101.0000.0000.0001.0000.9530.3430.3620.9221.0000.6320.0000.0001.000
기울기(°)0.7020.9030.5270.0001.0000.8590.2890.0000.7510.6321.0000.0000.0000.875
TSP(g/km)0.0000.2310.1820.2140.0000.0000.7470.3820.1660.0000.0001.0001.0000.218
PM10(g/km)0.0000.2310.1820.2140.0000.0000.7470.3820.1660.0000.0001.0001.0000.218
주소0.9461.0000.0000.0001.0001.0000.5200.5371.0001.0000.8750.2180.2181.000
2023-12-10T20:11:39.752896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지점차선측정구간방향주소
지점1.0000.0000.9270.1670.994
차선0.0001.0000.0000.0000.000
측정구간0.9270.0001.0000.8630.921
방향0.1670.0000.8631.0000.000
주소0.9940.0000.9210.0001.000
2023-12-10T20:11:39.928938image/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
기본키1.0000.9970.081-0.2440.997-0.974-0.016-0.097-0.0980.7810.0000.0000.8020.766
장비이정(km)0.9971.0000.074-0.2211.000-0.977-0.030-0.105-0.1050.9610.0000.0000.8910.967
차량통과수(대)0.0810.0741.0000.4440.074-0.135-0.1570.8820.8820.2180.0000.0000.1730.231
평균 속도(km/hr)-0.244-0.2210.4441.000-0.2210.192-0.0180.3310.3310.2820.0000.3990.2440.284
위도(°)0.9971.0000.074-0.2211.000-0.977-0.030-0.105-0.1050.9660.0000.0000.8960.972
경도(°)-0.974-0.977-0.1350.192-0.9771.0000.0210.0450.0450.9560.0000.0000.8860.962
기울기(°)-0.016-0.030-0.157-0.018-0.0300.0211.000-0.105-0.1050.6560.3850.0000.8910.618
TSP(g/km)-0.097-0.1050.8820.331-0.1050.045-0.1051.0001.0000.0890.1290.1280.0000.086
PM10(g/km)-0.098-0.1050.8820.331-0.1050.045-0.1051.0001.0000.0890.1290.1280.0000.086
지점0.7810.9610.2180.2820.9660.9560.6560.0890.0891.0000.1670.0000.9270.994
방향0.0000.0000.0000.0000.0000.0000.3850.1290.1290.1671.0000.0000.8630.000
차선0.0000.0000.0000.3990.0000.0000.0000.1280.1280.0000.0001.0000.0000.000
측정구간0.8020.8910.1730.2440.8960.8860.8910.0000.0000.9270.8630.0001.0000.921
주소0.7660.9670.2310.2840.9720.9620.6180.0860.0860.9940.0000.0000.9211.000

Missing values

2023-12-10T20:11:32.977525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T20:11:33.322455image/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-양산JC8.320201201012101.035.306944129.074722-3.152762.10.92경남 양산시 동면
12도로공사A-0010-0083E-6E2노포JC-양산JC8.320201201040106.035.306944129.074722-3.152760.690.3경남 양산시 동면
23도로공사A-0010-0083E-6E3노포JC-양산JC8.32020120102397.535.306944129.074722-3.152761.410.62경남 양산시 동면
34도로공사A-0010-0083E-6S1양산JC-노포JC8.320201201013124.035.306944129.0747223.0714160.150.06경남 양산시 동면
45도로공사A-0010-0083E-6S2양산JC-노포JC8.32020120103792.035.306944129.0747223.0714161.030.45경남 양산시 동면
56도로공사A-0010-0083E-6S3양산JC-노포JC8.32020120102083.3335.306944129.0747223.0714162.060.91경남 양산시 동면
67도로공사A-0010-0538E-6E1언양JC-활천IC53.4202012010496.035.681944129.1811110.225450.040.02울산 울주군 두서면 활천리
78도로공사A-0010-0538E-6E2언양JC-활천IC53.42020120101694.6735.681944129.1811110.225450.820.36울산 울주군 두서면 활천리
89도로공사A-0010-0538E-6E3언양JC-활천IC53.42020120101881.5735.681944129.1811110.225452.881.27울산 울주군 두서면 활천리
910도로공사A-0010-0538E-6S1활천IC-언양JC53.42020120109107.035.681944129.1811110.191580.10.04울산 울주군 두서면 활천리
기본키도로종류지점방향차선측정구간장비이정(km)측정일측정시간차량통과수(대)평균 속도(km/hr)위도(°)경도(°)기울기(°)TSP(g/km)PM10(g/km)주소
9091도로공사A-0010-3613E-10S2안성JC-안성IC361.320201201081103.437.008889127.149167-1.301422.461.08경기 안성시 원곡면
9192도로공사A-0010-3613E-10S3안성JC-안성IC361.32020120106682.6737.008889127.149167-1.301426.062.67경기 안성시 원곡면
9293도로공사A-0010-3613E-10S4안성JC-안성IC361.32020120108687.5737.008889127.149167-1.301425.452.4경기 안성시 원곡면
9394도로공사A-0010-3613E-10S5안성JC-안성IC361.32020120104584.037.008889127.149167-1.301423.231.42경기 안성시 원곡면
9495도로공사A-0010-3801E-10E1오산IC-동탄JC380.120201201045111.037.158778127.088333-0.6600040.510.22경기 화성시 동탄면 송리
9596도로공사A-0010-3801E-10E2오산IC-동탄JC380.1202012010111103.3337.158778127.088333-0.6600041.90.84경기 화성시 동탄면 송리
9697도로공사A-0010-3801E-10E3오산IC-동탄JC380.12020120109686.6737.158778127.088333-0.6600047.93.48경기 화성시 동탄면 송리
9798도로공사A-0010-3801E-10E4오산IC-동탄JC380.12020120109179.037.158778127.088333-0.6600049.424.15경기 화성시 동탄면 송리
9899도로공사A-0010-3801E-10E5오산IC-동탄JC380.1202012010878.037.158778127.088333-0.6600040.040.02경기 화성시 동탄면 송리
99100도로공사A-0010-3801E-10S1동탄JC-오산IC380.120201201031117.037.158778127.0883330.6719770.510.23경기 화성시 동탄면 송리