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 19 (19.0%) zerosZeros
평균 속도(km/hr) has 19 (19.0%) zerosZeros
TSP(g/km) has 19 (19.0%) zerosZeros
PM10(g/km) has 20 (20.0%) zerosZeros

Reproduction

Analysis started2023-12-10 11:10:59.514263
Analysis finished2023-12-10 11:11:12.944718
Duration13.43 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
2023-12-10T20:11:13.063224image/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
2023-12-10T20:11:13.259946image/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

2023-12-10T20:11:13.422039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:11:13.557332image/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 length14.2
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-0010-2695C-6 6
 
6.0%
Other values (5) 26
26.0%

Length

2023-12-10T20:11:13.745557image/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-0010-2695c-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
53 
E
47 

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 53
53.0%
E 47
47.0%

Length

2023-12-10T20:11:13.936831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:11:14.042915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
s 53
53.0%
e 47
47.0%

차선
Categorical

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

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
1 27
27.0%
2 27
27.0%
3 27
27.0%
4 12
12.0%
5 7
 
7.0%

Length

2023-12-10T20:11:14.153691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:11:14.272829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 27
27.0%
2 27
27.0%
3 27
27.0%
4 12
12.0%
5 7
 
7.0%

측정구간
Categorical

HIGH CORRELATION 

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

Length

Max length10
Median length9
Mean length9.28
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 (%)
안성JC-안성IC 5
 
5.0%
북천안IC-천안IC 5
 
5.0%
천안IC-북천안IC 5
 
5.0%
천안IC-천안JC 5
 
5.0%
남이JC-청주JC 5
 
5.0%
청주JC-남이JC 5
 
5.0%
안성IC-안성JC 5
 
5.0%
옥천IC-금강IC 4
 
4.0%
청주JC-남청주IC 4
 
4.0%
천안JC-천안IC 4
 
4.0%
Other values (17) 53
53.0%

Length

2023-12-10T20:11:14.431992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
안성jc-안성ic 5
 
5.0%
천안ic-북천안ic 5
 
5.0%
천안ic-천안jc 5
 
5.0%
남이jc-청주jc 5
 
5.0%
청주jc-남이jc 5
 
5.0%
안성ic-안성jc 5
 
5.0%
북천안ic-천안ic 5
 
5.0%
동대구jc-경산ic 4
 
4.0%
경산ic-동대구jc 4
 
4.0%
천안jc-천안ic 4
 
4.0%
Other values (17) 53
53.0%

장비이정(km)
Real number (ℝ)

HIGH CORRELATION 

Distinct15
Distinct (%)15.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean242.5868
Minimum8.3
Maximum361.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:11:14.614784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8.3
5-th percentile8.3
Q1118.5
median276.1
Q3335.2
95-th percentile361.3
Maximum361.3
Range353
Interquartile range (IQR)216.7

Descriptive statistics

Standard deviation110.00748
Coefficient of variation (CV)0.45347676
Kurtosis-0.62198834
Mean242.5868
Median Absolute Deviation (MAD)69.1
Skewness-0.85121911
Sum24258.68
Variance12101.645
MonotonicityIncreasing
2023-12-10T20:11:14.800395image/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%
269.5 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%
269.5 6
6.0%
276.1 6
6.0%
295.3 4
4.0%
297.9 5
5.0%
ValueCountFrequency (%)
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%
269.5 6
6.0%
258.3 7
7.0%

측정일
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
20210101
100 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20210101
2nd row20210101
3rd row20210101
4th row20210101
5th row20210101

Common Values

ValueCountFrequency (%)
20210101 100
100.0%

Length

2023-12-10T20:11:15.082091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:11:15.226166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20210101 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

2023-12-10T20:11:15.366885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:11:15.479975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 100
100.0%

차량통과수(대)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct54
Distinct (%)54.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.59
Minimum0
Maximum112
Zeros19
Zeros (%)19.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:11:15.638051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median19.5
Q348
95-th percentile83.25
Maximum112
Range112
Interquartile range (IQR)44

Descriptive statistics

Standard deviation28.18485
Coefficient of variation (CV)1.0215603
Kurtosis0.70936082
Mean27.59
Median Absolute Deviation (MAD)18.5
Skewness1.1365451
Sum2759
Variance794.38576
MonotonicityNot monotonic
2023-12-10T20:11:15.813704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 19
 
19.0%
3 3
 
3.0%
8 3
 
3.0%
38 3
 
3.0%
18 3
 
3.0%
7 3
 
3.0%
14 2
 
2.0%
51 2
 
2.0%
49 2
 
2.0%
31 2
 
2.0%
Other values (44) 58
58.0%
ValueCountFrequency (%)
0 19
19.0%
1 2
 
2.0%
3 3
 
3.0%
4 2
 
2.0%
5 2
 
2.0%
6 2
 
2.0%
7 3
 
3.0%
8 3
 
3.0%
9 2
 
2.0%
11 1
 
1.0%
ValueCountFrequency (%)
112 1
1.0%
111 1
1.0%
100 1
1.0%
99 1
1.0%
88 1
1.0%
83 2
2.0%
76 1
1.0%
73 1
1.0%
68 1
1.0%
66 1
1.0%

평균 속도(km/hr)
Real number (ℝ)

ZEROS 

Distinct69
Distinct (%)69.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean76.5808
Minimum0
Maximum136
Zeros19
Zeros (%)19.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:11:15.994570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q175.125
median88.855
Q3100.285
95-th percentile121.075
Maximum136
Range136
Interquartile range (IQR)25.16

Descriptive statistics

Standard deviation39.523573
Coefficient of variation (CV)0.5161029
Kurtosis0.052381912
Mean76.5808
Median Absolute Deviation (MAD)12.355
Skewness-1.1735033
Sum7658.08
Variance1562.1128
MonotonicityNot monotonic
2023-12-10T20:11:16.312127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 19
 
19.0%
94.0 3
 
3.0%
106.0 3
 
3.0%
104.0 2
 
2.0%
92.0 2
 
2.0%
96.5 2
 
2.0%
91.5 2
 
2.0%
93.0 2
 
2.0%
124.0 2
 
2.0%
105.0 2
 
2.0%
Other values (59) 61
61.0%
ValueCountFrequency (%)
0.0 19
19.0%
56.83 1
 
1.0%
70.67 1
 
1.0%
71.43 1
 
1.0%
73.17 1
 
1.0%
73.86 1
 
1.0%
74.0 1
 
1.0%
75.5 1
 
1.0%
76.0 1
 
1.0%
77.0 1
 
1.0%
ValueCountFrequency (%)
136.0 1
1.0%
130.28 1
1.0%
124.0 2
2.0%
122.5 1
1.0%
121.0 1
1.0%
117.0 1
1.0%
115.0 1
1.0%
114.0 1
1.0%
111.6 1
1.0%
110.18 1
1.0%

위도(°)
Real number (ℝ)

HIGH CORRELATION 

Distinct15
Distinct (%)15.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.375861
Minimum35.306944
Maximum37.008889
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:11:16.536982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.306944
5-th percentile35.306944
Q135.904732
median36.38961
Q336.780833
95-th percentile37.008889
Maximum37.008889
Range1.7019444
Interquartile range (IQR)0.87610092

Descriptive statistics

Standard deviation0.47324885
Coefficient of variation (CV)0.01300997
Kurtosis-0.42349153
Mean36.375861
Median Absolute Deviation (MAD)0.39122333
Skewness-0.62330577
Sum3637.5861
Variance0.22396448
MonotonicityIncreasing
2023-12-10T20:11:16.712849image/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%
36.3475 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%
36.3475 6
6.0%
36.38961 6
6.0%
36.54 4
4.0%
36.57694444 5
5.0%
ValueCountFrequency (%)
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%
36.3475 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%
Mean127.79001
Minimum127.14917
Maximum129.18111
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:11:16.910805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum127.14917
5-th percentile127.14917
Q1127.18672
median127.42778
Q3128.56147
95-th percentile129.18111
Maximum129.18111
Range2.0319444
Interquartile range (IQR)1.3747494

Descriptive statistics

Standard deviation0.70924442
Coefficient of variation (CV)0.0055500774
Kurtosis-0.79158283
Mean127.79001
Median Absolute Deviation (MAD)0.2511111
Skewness0.91233757
Sum12779.001
Variance0.50302764
MonotonicityNot monotonic
2023-12-10T20:11:17.108258image/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%
127.4691667 6
 
6.0%
Other values (5) 26
26.0%
ValueCountFrequency (%)
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%
127.4691667 6
6.0%
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%
127.4691667 6
6.0%
127.4338889 4
4.0%
127.4277778 5
5.0%
127.4263889 5
5.0%

기울기(°)
Real number (ℝ)

HIGH CORRELATION 

Distinct27
Distinct (%)27.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.03454729
Minimum-13.24576
Maximum12.47809
Zeros0
Zeros (%)0.0%
Negative48
Negative (%)48.0%
Memory size1.0 KiB
2023-12-10T20:11:17.311723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-13.24576
5-th percentile-3.15276
Q1-1.252479
median0.166426
Q31.172023
95-th percentile3.071416
Maximum12.47809
Range25.72385
Interquartile range (IQR)2.424502

Descriptive statistics

Standard deviation3.4502267
Coefficient of variation (CV)-99.869677
Kurtosis9.4780715
Mean-0.03454729
Median Absolute Deviation (MAD)1.066093
Skewness-0.26131262
Sum-3.454729
Variance11.904064
MonotonicityNot monotonic
2023-12-10T20:11:17.503304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
-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%
-1.758683 5
 
5.0%
-0.807188 4
 
4.0%
0.166426 4
 
4.0%
-0.131348 4
 
4.0%
Other values (17) 53
53.0%
ValueCountFrequency (%)
-13.24576 3
3.0%
-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%
-0.624001 5
5.0%
ValueCountFrequency (%)
12.47809 3
3.0%
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%
0.61369 5
5.0%
0.409237 3
3.0%

TSP(g/km)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct76
Distinct (%)76.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.8736
Minimum0
Maximum11.97
Zeros19
Zeros (%)19.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:11:17.679824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.0775
median0.85
Q32.5075
95-th percentile6.122
Maximum11.97
Range11.97
Interquartile range (IQR)2.43

Descriptive statistics

Standard deviation2.6097411
Coefficient of variation (CV)1.3929019
Kurtosis4.5067369
Mean1.8736
Median Absolute Deviation (MAD)0.85
Skewness2.0727856
Sum187.36
Variance6.8107485
MonotonicityNot monotonic
2023-12-10T20:11:17.878121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 19
 
19.0%
3.46 2
 
2.0%
0.21 2
 
2.0%
0.03 2
 
2.0%
1.56 2
 
2.0%
0.09 2
 
2.0%
0.19 2
 
2.0%
2.45 1
 
1.0%
2.79 1
 
1.0%
0.55 1
 
1.0%
Other values (66) 66
66.0%
ValueCountFrequency (%)
0.0 19
19.0%
0.01 1
 
1.0%
0.03 2
 
2.0%
0.04 1
 
1.0%
0.06 1
 
1.0%
0.07 1
 
1.0%
0.08 1
 
1.0%
0.09 2
 
2.0%
0.12 1
 
1.0%
0.16 1
 
1.0%
ValueCountFrequency (%)
11.97 1
1.0%
11.34 1
1.0%
11.23 1
1.0%
9.3 1
1.0%
8.25 1
1.0%
6.01 1
1.0%
6.0 1
1.0%
5.78 1
1.0%
5.42 1
1.0%
5.29 1
1.0%

PM10(g/km)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct64
Distinct (%)64.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.8241
Minimum0
Maximum5.27
Zeros20
Zeros (%)20.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:11:18.126023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.03
median0.375
Q31.105
95-th percentile2.6895
Maximum5.27
Range5.27
Interquartile range (IQR)1.075

Descriptive statistics

Standard deviation1.148575
Coefficient of variation (CV)1.3937325
Kurtosis4.5053663
Mean0.8241
Median Absolute Deviation (MAD)0.375
Skewness2.0721804
Sum82.41
Variance1.3192244
MonotonicityNot monotonic
2023-12-10T20:11:18.349514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 20
 
20.0%
1.52 3
 
3.0%
0.08 3
 
3.0%
0.43 2
 
2.0%
0.37 2
 
2.0%
0.31 2
 
2.0%
0.82 2
 
2.0%
0.03 2
 
2.0%
0.45 2
 
2.0%
0.68 2
 
2.0%
Other values (54) 60
60.0%
ValueCountFrequency (%)
0.0 20
20.0%
0.01 2
 
2.0%
0.02 2
 
2.0%
0.03 2
 
2.0%
0.04 2
 
2.0%
0.05 1
 
1.0%
0.07 2
 
2.0%
0.08 3
 
3.0%
0.09 2
 
2.0%
0.1 1
 
1.0%
ValueCountFrequency (%)
5.27 1
1.0%
4.99 1
1.0%
4.94 1
1.0%
4.09 1
1.0%
3.63 1
1.0%
2.64 2
2.0%
2.54 1
1.0%
2.39 1
1.0%
2.33 1
1.0%
2.26 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 length11.7
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

2023-12-10T20:11:18.535515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
충북 27
 
7.8%
청원군 20
 
5.8%
천안시 19
 
5.5%
남이면 14
 
4.1%
경북 12
 
3.5%
대전 12
 
3.5%
대덕구 12
 
3.5%
충남 10
 
2.9%
서북구 10
 
2.9%
성거읍 10
 
2.9%
Other values (28) 198
57.6%

Interactions

2023-12-10T20:11:10.837458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:11:00.410986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:11:01.636044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:11:02.825574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:11:04.471473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:11:05.851303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:11:07.073900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:11:08.245881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:11:09.575240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:11:10.964494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:11:00.504802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:11:01.770152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:11:02.954508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:11:04.622663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:11:05.995096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:11:07.182095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:11:08.385475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:11:09.708656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:11:11.110058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:11:00.609952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:11:01.916499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:11:03.099940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:11:04.767298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:11:06.146642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:11:07.333525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:11:08.518991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:11:09.864713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:11:11.257132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:11:00.717327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:11:02.055572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:11:03.236757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:11:04.903367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:11:06.280819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:11:07.449607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:11:08.661111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:11:09.990862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:11:11.405193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:11:00.864816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:11:02.184786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:11:03.394506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:11:05.075300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:11:06.414538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:11:07.562787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:11:08.802181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:11:10.138071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:11:11.561518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:11:01.086098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:11:02.303560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:11:03.559641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:11:05.241747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:11:06.555830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:11:07.722560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:11:08.955519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:11:10.282057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:11:11.716552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:11:01.252698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:11:02.443331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:11:03.736547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:11:05.415848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:11:06.715172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:11:07.950302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:11:09.142025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:11:10.429626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:11:11.829884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:11:01.377255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:11:02.560289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:11:03.863135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:11:05.556229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:11:06.835678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:11:08.052769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:11:09.292563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:11:10.561822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:11:11.945759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:11:01.506741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:11:02.695051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:11:04.347830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:11:05.716337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:11:06.960514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:11:08.145519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:11:09.448876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:11:10.706378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T20:11:18.698771image/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
기본키1.0000.9920.0000.0000.9900.9070.5450.1700.9400.9160.6330.0000.0000.980
지점0.9921.0000.0000.0001.0001.0000.5410.3901.0001.0000.8630.2060.2061.000
방향0.0000.0001.0000.0001.0000.0000.2970.1440.0000.0000.4860.0000.0000.000
차선0.0000.0000.0001.0000.0000.0000.3920.5130.0000.0000.0000.4210.4210.000
측정구간0.9901.0001.0000.0001.0001.0000.3960.4761.0001.0001.0000.0000.0001.000
장비이정(km)0.9071.0000.0000.0001.0001.0000.2680.2930.9730.9700.6570.0000.0001.000
차량통과수(대)0.5450.5410.2970.3920.3960.2681.0000.3160.4360.3430.3590.6460.6460.524
평균 속도(km/hr)0.1700.3900.1440.5130.4760.2930.3161.0000.3210.0000.4230.2850.2850.489
위도(°)0.9401.0000.0000.0001.0000.9730.4360.3211.0000.9570.7560.0000.0001.000
경도(°)0.9161.0000.0000.0001.0000.9700.3430.0000.9571.0000.4570.0000.0001.000
기울기(°)0.6330.8630.4860.0001.0000.6570.3590.4230.7560.4571.0000.1490.1490.849
TSP(g/km)0.0000.2060.0000.4210.0000.0000.6460.2850.0000.0000.1491.0001.0000.000
PM10(g/km)0.0000.2060.0000.4210.0000.0000.6460.2850.0000.0000.1491.0001.0000.000
주소0.9801.0000.0000.0001.0001.0000.5240.4891.0001.0000.8490.0000.0001.000
2023-12-10T20:11:18.962101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지점차선측정구간방향주소
지점1.0000.0000.9270.0000.994
차선0.0001.0000.0000.0000.000
측정구간0.9270.0001.0000.8630.921
방향0.0000.0000.8631.0000.000
주소0.9940.0000.9210.0001.000
2023-12-10T20:11:19.168517image/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
기본키1.0000.9970.069-0.3420.997-0.9740.021-0.023-0.0200.8840.0000.0000.8400.890
장비이정(km)0.9971.0000.064-0.3241.000-0.9770.014-0.030-0.0270.9610.0000.0000.8910.967
차량통과수(대)0.0690.0641.0000.2930.064-0.119-0.2040.8520.8510.2240.2160.1660.1280.233
평균 속도(km/hr)-0.342-0.3240.2931.000-0.3240.300-0.1930.1600.1500.1760.1480.3570.1880.191
위도(°)0.9971.0000.064-0.3241.000-0.9770.014-0.030-0.0270.9660.0000.0000.8960.972
경도(°)-0.974-0.977-0.1190.300-0.9771.000-0.025-0.014-0.0160.9560.0000.0000.8860.962
기울기(°)0.0210.014-0.204-0.1930.014-0.0251.000-0.209-0.2080.5950.3430.0000.8810.604
TSP(g/km)-0.023-0.0300.8520.160-0.030-0.014-0.2091.0000.9990.0720.0000.2520.0000.000
PM10(g/km)-0.020-0.0270.8510.150-0.027-0.016-0.2080.9991.0000.0720.0000.2520.0000.000
지점0.8840.9610.2240.1760.9660.9560.5950.0720.0721.0000.0000.0000.9270.994
방향0.0000.0000.2160.1480.0000.0000.3430.0000.0000.0001.0000.0000.8630.000
차선0.0000.0000.1660.3570.0000.0000.0000.2520.2520.0000.0001.0000.0000.000
측정구간0.8400.8910.1280.1880.8960.8860.8810.0000.0000.9270.8630.0001.0000.921
주소0.8900.9670.2330.1910.9720.9620.6040.0000.0000.9940.0000.0000.9211.000

Missing values

2023-12-10T20:11:12.445100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T20:11:12.811317image/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-양산JC8.320210101014104.035.306944129.074722-3.152762.451.08경남 양산시 동면
12도로공사A-0010-0083E-6E2노포JC-양산JC8.320210101031104.035.306944129.074722-3.152761.020.45경남 양산시 동면
23도로공사A-0010-0083E-6E3노포JC-양산JC8.32021010102194.035.306944129.074722-3.152761.210.53경남 양산시 동면
34도로공사A-0010-0083E-6S1양산JC-노포JC8.320210101019124.035.306944129.0747223.0714160.210.09경남 양산시 동면
45도로공사A-0010-0083E-6S2양산JC-노포JC8.320210101050104.3335.306944129.0747223.0714161.080.47경남 양산시 동면
56도로공사A-0010-0083E-6S3양산JC-노포JC8.320210101030105.035.306944129.0747223.0714164.912.16경남 양산시 동면
67도로공사A-0010-0538E-6E1언양JC-활천IC53.42021010104110.035.681944129.1811110.225450.040.02울산 울주군 두서면 활천리
78도로공사A-0010-0538E-6E2언양JC-활천IC53.42021010102884.035.681944129.1811110.225450.680.3울산 울주군 두서면 활천리
89도로공사A-0010-0538E-6E3언양JC-활천IC53.42021010101471.4335.681944129.1811110.225451.560.68울산 울주군 두서면 활천리
910도로공사A-0010-0538E-6S1활천IC-언양JC53.420210101024110.035.681944129.1811110.191580.270.12울산 울주군 두서면 활천리
기본키도로종류지점방향차선측정구간장비이정(km)측정일측정시간차량통과수(대)평균 속도(km/hr)위도(°)경도(°)기울기(°)TSP(g/km)PM10(g/km)주소
9091도로공사A-0010-3613E-10E1안성IC-안성JC361.320210101000.037.008889127.1491671.2930150.00.0경기 안성시 원곡면
9192도로공사A-0010-3613E-10E2안성IC-안성JC361.320210101000.037.008889127.1491671.2930150.00.0경기 안성시 원곡면
9293도로공사A-0010-3613E-10E3안성IC-안성JC361.32021010103108.537.008889127.1491671.2930150.190.08경기 안성시 원곡면
9394도로공사A-0010-3613E-10E4안성IC-안성JC361.320210101000.037.008889127.1491671.2930150.00.0경기 안성시 원곡면
9495도로공사A-0010-3613E-10E5안성IC-안성JC361.3202101010587.3337.008889127.1491671.2930150.840.37경기 안성시 원곡면
9596도로공사A-0010-3613E-10S1안성JC-안성IC361.320210101049122.537.008889127.149167-1.301420.710.31경기 안성시 원곡면
9697도로공사A-0010-3613E-10S2안성JC-안성IC361.3202101010111100.6737.008889127.149167-1.301422.080.91경기 안성시 원곡면
9798도로공사A-0010-3613E-10S3안성JC-안성IC361.32021010107390.2537.008889127.149167-1.301424.662.05경기 안성시 원곡면
9899도로공사A-0010-3613E-10S4안성JC-안성IC361.32021010108882.537.008889127.149167-1.301424.341.91경기 안성시 원곡면
99100도로공사A-0010-3613E-10S5안성JC-안성IC361.32021010103074.037.008889127.149167-1.301420.860.38경기 안성시 원곡면