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 경도(°) and 3 other fieldsHigh correlation
장비이정(km) is highly overall correlated with 위도(°) and 4 other fieldsHigh correlation
차량통과수(대) is highly overall correlated with TSP(g/km) and 1 other fieldsHigh correlation
위도(°) is highly overall correlated with 장비이정(km) and 4 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 9 (9.0%) zerosZeros
평균 속도(km/hr) has 9 (9.0%) zerosZeros
TSP(g/km) has 9 (9.0%) zerosZeros
PM10(g/km) has 10 (10.0%) zerosZeros

Reproduction

Analysis started2023-12-10 11:10:38.026411
Analysis finished2023-12-10 11:10:50.763754
Duration12.74 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
2023-12-10T20:10:50.883237image/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
2023-12-10T20:10:51.102156image/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

2023-12-10T20:10:51.278557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:10:51.422379image/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-0010-3452S-10
10 
A-0010-3352E-9
9 
A-0010-1185E-8
8 
A-0100-0698S-8
8 
A-0010-0083E-6
6 
Other values (11)
59 

Length

Max length15
Median length14
Mean length14.1
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-0010-3352E-9 9
 
9.0%
A-0010-1185E-8 8
 
8.0%
A-0100-0698S-8 8
 
8.0%
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%
A-0010-2761E-6 6
 
6.0%
Other values (6) 29
29.0%

Length

2023-12-10T20:10:51.565638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
a-0010-3452s-10 10
 
10.0%
a-0010-3352e-9 9
 
9.0%
a-0010-1185e-8 8
 
8.0%
a-0100-0698s-8 8
 
8.0%
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%
a-0010-2761e-6 6
 
6.0%
Other values (6) 29
29.0%

방향
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
E
52 
S
48 

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 52
52.0%
S 48
48.0%

Length

2023-12-10T20:10:51.770886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:10:51.920463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
e 52
52.0%
s 48
48.0%

차선
Categorical

Distinct5
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
1
29 
2
29 
3
27 
4
11 
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 29
29.0%
2 29
29.0%
3 27
27.0%
4 11
 
11.0%
5 4
 
4.0%

Length

2023-12-10T20:10:52.076011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:10:52.228037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 29
29.0%
2 29
29.0%
3 27
27.0%
4 11
 
11.0%
5 4
 
4.0%

측정구간
Categorical

HIGH CORRELATION 

Distinct29
Distinct (%)29.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
북천안IC-천안IC
 
5
천안IC-천안JC
 
5
청주JC-남이JC
 
5
천안IC-북천안IC
 
5
진주JC-진주IC
 
4
Other values (24)
76 

Length

Max length10
Median length9
Mean length9.32
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 (%)
북천안IC-천안IC 5
 
5.0%
천안IC-천안JC 5
 
5.0%
청주JC-남이JC 5
 
5.0%
천안IC-북천안IC 5
 
5.0%
진주JC-진주IC 4
 
4.0%
천안JC-천안IC 4
 
4.0%
경산IC-동대구JC 4
 
4.0%
동대구JC-경산IC 4
 
4.0%
진주IC-진주JC 4
 
4.0%
남청주IC-청주JC 4
 
4.0%
Other values (19) 56
56.0%

Length

2023-12-10T20:10:52.391654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
북천안ic-천안ic 5
 
5.0%
청주jc-남이jc 5
 
5.0%
천안ic-북천안ic 5
 
5.0%
천안ic-천안jc 5
 
5.0%
동대구jc-경산ic 4
 
4.0%
청주jc-남청주ic 4
 
4.0%
진주ic-진주jc 4
 
4.0%
남청주ic-청주jc 4
 
4.0%
경산ic-동대구jc 4
 
4.0%
천안jc-천안ic 4
 
4.0%
Other values (19) 56
56.0%

장비이정(km)
Real number (ℝ)

HIGH CORRELATION 

Distinct16
Distinct (%)16.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean211.1008
Minimum8.3
Maximum345.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:10:52.541232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8.3
5-th percentile8.3
Q1107.31
median266.05
Q3300.125
95-th percentile345.2
Maximum345.2
Range336.9
Interquartile range (IQR)192.815

Descriptive statistics

Standard deviation112.85135
Coefficient of variation (CV)0.53458516
Kurtosis-1.4061257
Mean211.1008
Median Absolute Deviation (MAD)78.03
Skewness-0.39314881
Sum21110.08
Variance12735.428
MonotonicityNot monotonic
2023-12-10T20:10:52.683781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
345.2 10
 
10.0%
335.2 9
 
9.0%
69.8 8
 
8.0%
118.5 8
 
8.0%
8.3 6
 
6.0%
107.31 6
 
6.0%
188.02 6
 
6.0%
269.5 6
 
6.0%
276.1 6
 
6.0%
53.4 6
 
6.0%
Other values (6) 29
29.0%
ValueCountFrequency (%)
8.3 6
6.0%
53.4 6
6.0%
69.8 8
8.0%
107.31 6
6.0%
112.4 4
4.0%
118.5 8
8.0%
188.02 6
6.0%
262.6 6
6.0%
269.5 6
6.0%
276.1 6
6.0%
ValueCountFrequency (%)
345.2 10
10.0%
335.2 9
9.0%
306.8 6
6.0%
297.9 5
5.0%
295.6 4
 
4.0%
295.3 4
 
4.0%
276.1 6
6.0%
269.5 6
6.0%
262.6 6
6.0%
188.02 6
6.0%

측정일
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
20210201
100 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20210201
2nd row20210201
3rd row20210201
4th row20210201
5th row20210201

Common Values

ValueCountFrequency (%)
20210201 100
100.0%

Length

2023-12-10T20:10:52.841996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:10:52.943719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20210201 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

2023-12-10T20:10:53.070320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:10:53.204106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 100
100.0%

차량통과수(대)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct55
Distinct (%)55.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.97
Minimum0
Maximum148
Zeros9
Zeros (%)9.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:10:53.378585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q110
median24
Q340
95-th percentile87.25
Maximum148
Range148
Interquartile range (IQR)30

Descriptive statistics

Standard deviation27.31095
Coefficient of variation (CV)0.91127627
Kurtosis3.1277866
Mean29.97
Median Absolute Deviation (MAD)15
Skewness1.5563597
Sum2997
Variance745.88798
MonotonicityNot monotonic
2023-12-10T20:10:53.602476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 9
 
9.0%
15 4
 
4.0%
7 4
 
4.0%
24 3
 
3.0%
34 3
 
3.0%
14 3
 
3.0%
48 3
 
3.0%
5 3
 
3.0%
29 3
 
3.0%
16 3
 
3.0%
Other values (45) 62
62.0%
ValueCountFrequency (%)
0 9
9.0%
1 2
 
2.0%
2 1
 
1.0%
5 3
 
3.0%
6 2
 
2.0%
7 4
4.0%
8 2
 
2.0%
9 1
 
1.0%
10 2
 
2.0%
12 2
 
2.0%
ValueCountFrequency (%)
148 1
1.0%
101 1
1.0%
100 1
1.0%
93 1
1.0%
92 1
1.0%
87 1
1.0%
81 1
1.0%
76 1
1.0%
73 1
1.0%
68 1
1.0%

평균 속도(km/hr)
Real number (ℝ)

ZEROS 

Distinct80
Distinct (%)80.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean86.1858
Minimum0
Maximum130
Zeros9
Zeros (%)9.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:10:53.800315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q182.15
median91.375
Q3101.5475
95-th percentile120.0355
Maximum130
Range130
Interquartile range (IQR)19.3975

Descriptive statistics

Standard deviation30.57291
Coefficient of variation (CV)0.35473257
Kurtosis3.3051366
Mean86.1858
Median Absolute Deviation (MAD)9.79
Skewness-1.8857777
Sum8618.58
Variance934.70284
MonotonicityNot monotonic
2023-12-10T20:10:54.366663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 9
 
9.0%
98.0 3
 
3.0%
100.0 3
 
3.0%
101.0 3
 
3.0%
95.0 2
 
2.0%
104.0 2
 
2.0%
93.33 2
 
2.0%
86.6 2
 
2.0%
90.0 2
 
2.0%
104.5 2
 
2.0%
Other values (70) 70
70.0%
ValueCountFrequency (%)
0.0 9
9.0%
41.26 1
 
1.0%
71.79 1
 
1.0%
72.0 1
 
1.0%
73.0 1
 
1.0%
76.43 1
 
1.0%
77.75 1
 
1.0%
78.0 1
 
1.0%
79.14 1
 
1.0%
79.56 1
 
1.0%
ValueCountFrequency (%)
130.0 1
1.0%
125.57 1
1.0%
122.0 1
1.0%
121.0 1
1.0%
120.71 1
1.0%
120.0 1
1.0%
119.0 1
1.0%
118.0 1
1.0%
117.0 1
1.0%
116.5 1
1.0%

위도(°)
Real number (ℝ)

HIGH CORRELATION 

Distinct16
Distinct (%)16.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.169322
Minimum35.149019
Maximum36.868903
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:10:54.540328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.149019
5-th percentile35.149019
Q135.882306
median36.339861
Q336.592758
95-th percentile36.868903
Maximum36.868903
Range1.7198838
Interquartile range (IQR)0.71045154

Descriptive statistics

Standard deviation0.55674663
Coefficient of variation (CV)0.015392786
Kurtosis-0.9426791
Mean36.169322
Median Absolute Deviation (MAD)0.4351287
Skewness-0.54903171
Sum3616.9322
Variance0.30996681
MonotonicityNot monotonic
2023-12-10T20:10:54.698503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
36.86890278 10
 
10.0%
36.78083333 9
 
9.0%
35.149019 8
 
8.0%
35.90473241 8
 
8.0%
35.30694444 6
 
6.0%
35.88230609 6
 
6.0%
36.15572222 6
 
6.0%
36.3475 6
 
6.0%
36.38961 6
 
6.0%
35.68194444 6
 
6.0%
Other values (6) 29
29.0%
ValueCountFrequency (%)
35.149019 8
8.0%
35.27416667 4
4.0%
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%
36.33222222 6
6.0%
36.3475 6
6.0%
36.38961 6
6.0%
ValueCountFrequency (%)
36.86890278 10
10.0%
36.78083333 9
9.0%
36.64019722 6
6.0%
36.57694444 5
5.0%
36.55638889 4
 
4.0%
36.54 4
 
4.0%
36.38961 6
6.0%
36.3475 6
6.0%
36.33222222 6
6.0%
36.15572222 6
6.0%

경도(°)
Real number (ℝ)

HIGH CORRELATION 

Distinct16
Distinct (%)16.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.90933
Minimum127.17667
Maximum129.18111
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:10:54.829918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum127.17667
5-th percentile127.17667
Q1127.41216
median127.50167
Q3128.56147
95-th percentile129.18111
Maximum129.18111
Range2.0044444
Interquartile range (IQR)1.1493087

Descriptive statistics

Standard deviation0.6870234
Coefficient of variation (CV)0.005371175
Kurtosis-1.1445523
Mean127.90933
Median Absolute Deviation (MAD)0.325
Skewness0.59588992
Sum12790.933
Variance0.47200115
MonotonicityNot monotonic
2023-12-10T20:10:54.997700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
127.1867222 10
 
10.0%
127.1766667 9
 
9.0%
128.104573 8
 
8.0%
128.5614716 8
 
8.0%
129.0747222 6
 
6.0%
128.8488161 6
 
6.0%
128.2132778 6
 
6.0%
127.4691667 6
 
6.0%
127.423508 6
 
6.0%
129.1811111 6
 
6.0%
Other values (6) 29
29.0%
ValueCountFrequency (%)
127.1766667 9
9.0%
127.1867222 10
10.0%
127.3781278 6
6.0%
127.423508 6
6.0%
127.4277778 5
5.0%
127.4325 4
 
4.0%
127.4338889 4
 
4.0%
127.4691667 6
6.0%
127.5341667 6
6.0%
128.104573 8
8.0%
ValueCountFrequency (%)
129.1811111 6
6.0%
129.0747222 6
6.0%
128.8488161 6
6.0%
128.5614716 8
8.0%
128.4513889 4
4.0%
128.2132778 6
6.0%
128.104573 8
8.0%
127.5341667 6
6.0%
127.4691667 6
6.0%
127.4338889 4
4.0%

기울기(°)
Real number (ℝ)

HIGH CORRELATION 

Distinct29
Distinct (%)29.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.16641478
Minimum-13.24576
Maximum12.47809
Zeros0
Zeros (%)0.0%
Negative49
Negative (%)49.0%
Memory size1.0 KiB
2023-12-10T20:10:55.294701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-13.24576
5-th percentile-3.2671246
Q1-1.22799
median0.123751
Q30.852122
95-th percentile3.071416
Maximum12.47809
Range25.72385
Interquartile range (IQR)2.080112

Descriptive statistics

Standard deviation3.514413
Coefficient of variation (CV)-21.118395
Kurtosis8.6607135
Mean-0.16641478
Median Absolute Deviation (MAD)0.931612
Skewness-0.20973552
Sum-16.641478
Variance12.351099
MonotonicityNot monotonic
2023-12-10T20:10:55.483752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
-1.758683 5
 
5.0%
0.61369 5
 
5.0%
-0.624001 5
 
5.0%
1.544389 5
 
5.0%
1.056036 4
 
4.0%
0.123751 4
 
4.0%
-0.184388 4
 
4.0%
0.166426 4
 
4.0%
-0.131348 4
 
4.0%
-1.359001 4
 
4.0%
Other values (19) 56
56.0%
ValueCountFrequency (%)
-13.24576 3
3.0%
-5.440052 2
 
2.0%
-3.15276 3
3.0%
-2.788625 3
3.0%
-1.758683 5
5.0%
-1.359001 4
4.0%
-1.252479 3
3.0%
-1.22799 3
3.0%
-0.807188 4
4.0%
-0.688696 3
3.0%
ValueCountFrequency (%)
12.47809 3
3.0%
3.071416 3
3.0%
2.703849 3
3.0%
1.544389 5
5.0%
1.22799 3
3.0%
1.172023 3
3.0%
1.056036 4
4.0%
0.852122 2
 
2.0%
0.688696 3
3.0%
0.61369 5
5.0%

TSP(g/km)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct78
Distinct (%)78.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6177
Minimum0
Maximum18.69
Zeros9
Zeros (%)9.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:10:55.706503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.1775
median1.19
Q33.3425
95-th percentile9.5565
Maximum18.69
Range18.69
Interquartile range (IQR)3.165

Descriptive statistics

Standard deviation3.55227
Coefficient of variation (CV)1.3570195
Kurtosis4.5040535
Mean2.6177
Median Absolute Deviation (MAD)1.13
Skewness2.0362813
Sum261.77
Variance12.618622
MonotonicityNot monotonic
2023-12-10T20:10:55.938156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 9
 
9.0%
0.06 3
 
3.0%
0.17 3
 
3.0%
0.87 2
 
2.0%
0.08 2
 
2.0%
0.44 2
 
2.0%
0.38 2
 
2.0%
11.77 2
 
2.0%
0.28 2
 
2.0%
8.84 2
 
2.0%
Other values (68) 71
71.0%
ValueCountFrequency (%)
0.0 9
9.0%
0.01 1
 
1.0%
0.02 1
 
1.0%
0.06 3
 
3.0%
0.07 2
 
2.0%
0.08 2
 
2.0%
0.09 1
 
1.0%
0.13 1
 
1.0%
0.14 1
 
1.0%
0.16 1
 
1.0%
ValueCountFrequency (%)
18.69 1
1.0%
12.66 1
1.0%
12.38 1
1.0%
11.77 2
2.0%
9.44 1
1.0%
8.92 1
1.0%
8.84 2
2.0%
8.68 1
1.0%
8.52 1
1.0%
7.95 1
1.0%

PM10(g/km)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct72
Distinct (%)72.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1513
Minimum0
Maximum8.23
Zeros10
Zeros (%)10.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:10:56.170035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.08
median0.52
Q31.47
95-th percentile4.211
Maximum8.23
Range8.23
Interquartile range (IQR)1.39

Descriptive statistics

Standard deviation1.563971
Coefficient of variation (CV)1.3584392
Kurtosis4.5082421
Mean1.1513
Median Absolute Deviation (MAD)0.495
Skewness2.0367647
Sum115.13
Variance2.4460054
MonotonicityNot monotonic
2023-12-10T20:10:56.414079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 10
 
10.0%
0.03 5
 
5.0%
0.08 3
 
3.0%
0.07 3
 
3.0%
0.38 2
 
2.0%
1.42 2
 
2.0%
0.02 2
 
2.0%
3.89 2
 
2.0%
5.18 2
 
2.0%
0.17 2
 
2.0%
Other values (62) 67
67.0%
ValueCountFrequency (%)
0.0 10
10.0%
0.01 1
 
1.0%
0.02 2
 
2.0%
0.03 5
5.0%
0.04 1
 
1.0%
0.06 2
 
2.0%
0.07 3
 
3.0%
0.08 3
 
3.0%
0.11 1
 
1.0%
0.12 2
 
2.0%
ValueCountFrequency (%)
8.23 1
1.0%
5.57 1
1.0%
5.45 1
1.0%
5.18 2
2.0%
4.16 1
1.0%
3.92 1
1.0%
3.89 2
2.0%
3.82 1
1.0%
3.75 1
1.0%
3.5 1
1.0%

주소
Categorical

HIGH CORRELATION 

Distinct14
Distinct (%)14.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
충북 청원군 남이면
13 
충남 천안시 서북구 성거읍 송남리
10 
충청 천안시 구성동
9 
대구 동구 안심3동
8 
경남 진주시 가좌동
8 
Other values (9)
52 

Length

Max length18
Median length10
Mean length11.62
Min length9

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row경남 양산시 동면
2nd row경남 양산시 동면
3rd row경남 양산시 동면
4th row경남 양산시 동면
5th row경남 양산시 동면

Common Values

ValueCountFrequency (%)
충북 청원군 남이면 13
13.0%
충남 천안시 서북구 성거읍 송남리 10
10.0%
충청 천안시 구성동 9
9.0%
대구 동구 안심3동 8
 
8.0%
경남 진주시 가좌동 8
 
8.0%
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%
대전 대덕구 비래동 6
 
6.0%
Other values (4) 22
22.0%

Length

2023-12-10T20:10:56.637139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
충북 25
 
7.3%
천안시 19
 
5.6%
청원군 19
 
5.6%
경남 18
 
5.3%
남이면 13
 
3.8%
대전 12
 
3.5%
대덕구 12
 
3.5%
경북 12
 
3.5%
서북구 10
 
2.9%
충남 10
 
2.9%
Other values (29) 192
56.1%

Interactions

2023-12-10T20:10:49.048284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:38.991202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:40.121376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:41.350300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:42.632871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:43.864780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:45.196200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:46.745571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:47.754117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:49.162987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:39.126847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:40.240465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:41.490578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:42.793666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:43.991391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:45.356150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:46.852307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:47.907109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:49.284533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:39.249161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:40.370626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:41.645245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:42.943141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:44.139218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:45.508279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:46.953768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:48.076897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:49.405901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:39.368584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:40.516320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:41.776376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:43.088638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:44.260054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:45.641074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:47.049165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:48.229311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:49.526249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:39.471825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:40.662972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:41.908194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:43.212919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:44.405989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:45.788630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:47.142744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:48.373986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:49.667442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:39.604927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:40.785289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:42.053097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:43.358500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:44.564798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:45.917730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:47.243153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:48.518841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:49.806833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:39.749211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:40.943294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:42.208716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:43.509216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:44.715438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:46.074376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:47.366451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:48.675008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:49.921110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:39.878744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:41.070043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:42.332538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:43.626280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:44.859725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:46.173295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:47.474953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:48.788059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:50.051184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:40.013655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:41.224710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:42.491353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:43.759969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:45.036648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:46.296280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:47.623801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:10:48.926932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T20:10:56.780218image/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
기본키1.0000.9590.2620.0000.9830.8820.4410.1430.8830.8880.6730.2070.2070.947
지점0.9591.0000.0000.0001.0001.0000.4710.4411.0001.0000.8500.3990.3991.000
방향0.2620.0001.0000.0001.0000.0000.3340.0000.0000.0000.5200.2520.2520.000
차선0.0000.0000.0001.0000.0000.0000.3090.5810.0000.0000.0000.3670.3670.000
측정구간0.9831.0001.0000.0001.0001.0000.3040.3891.0001.0001.0000.0000.0001.000
장비이정(km)0.8821.0000.0000.0001.0001.0000.3320.2530.9880.9720.6480.0000.0001.000
차량통과수(대)0.4410.4710.3340.3090.3040.3321.0000.4560.3920.2180.0000.7280.7280.309
평균 속도(km/hr)0.1430.4410.0000.5810.3890.2530.4561.0000.0000.3260.4630.4420.4420.401
위도(°)0.8831.0000.0000.0001.0000.9880.3920.0001.0000.9200.6460.2820.2821.000
경도(°)0.8881.0000.0000.0001.0000.9720.2180.3260.9201.0000.4220.0000.0001.000
기울기(°)0.6730.8500.5200.0001.0000.6480.0000.4630.6460.4221.0000.4980.4980.834
TSP(g/km)0.2070.3990.2520.3670.0000.0000.7280.4420.2820.0000.4981.0001.0000.138
PM10(g/km)0.2070.3990.2520.3670.0000.0000.7280.4420.2820.0000.4981.0001.0000.138
주소0.9471.0000.0000.0001.0001.0000.3090.4011.0001.0000.8340.1380.1381.000
2023-12-10T20:10:56.996294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지점차선측정구간방향주소
지점1.0000.0000.9190.0000.988
차선0.0001.0000.0000.0000.000
측정구간0.9190.0001.0000.8510.909
방향0.0000.0000.8511.0000.000
주소0.9880.0000.9090.0001.000
2023-12-10T20:10:57.187983image/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
기본키1.0000.4380.185-0.2260.261-0.578-0.0390.1120.1100.7900.1900.0000.7800.769
장비이정(km)0.4381.0000.170-0.2750.974-0.939-0.0160.0380.0370.9560.0000.0000.8780.967
차량통과수(대)0.1850.1701.000-0.0040.141-0.216-0.0470.7620.7620.1700.2420.1900.0950.131
평균 속도(km/hr)-0.226-0.275-0.0041.000-0.2410.240-0.014-0.231-0.2340.1870.0000.4060.1200.143
위도(°)0.2610.9740.141-0.2411.000-0.8830.0060.0160.0150.9560.0000.0000.8780.967
경도(°)-0.578-0.939-0.2160.240-0.8831.0000.012-0.068-0.0670.9500.0000.0000.8740.962
기울기(°)-0.039-0.016-0.047-0.0140.0060.0121.000-0.048-0.0500.5870.3680.0000.8690.580
TSP(g/km)0.1120.0380.762-0.2310.016-0.068-0.0481.0001.0000.1380.1810.2300.0000.045
PM10(g/km)0.1100.0370.762-0.2340.015-0.067-0.0501.0001.0000.1380.1810.2300.0000.045
지점0.7900.9560.1700.1870.9560.9500.5870.1380.1381.0000.0000.0000.9190.988
방향0.1900.0000.2420.0000.0000.0000.3680.1810.1810.0001.0000.0000.8510.000
차선0.0000.0000.1900.4060.0000.0000.0000.2300.2300.0000.0001.0000.0000.000
측정구간0.7800.8780.0950.1200.8780.8740.8690.0000.0000.9190.8510.0001.0000.909
주소0.7690.9670.1310.1430.9670.9620.5800.0450.0450.9880.0000.0000.9091.000

Missing values

2023-12-10T20:10:50.249275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T20:10:50.639207image/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-양산JC8.32021020105109.035.306944129.074722-3.152760.870.38경남 양산시 동면
12도로공사A-0010-0083E-6E2노포JC-양산JC8.320210201039100.035.306944129.074722-3.152760.440.19경남 양산시 동면
23도로공사A-0010-0083E-6E3노포JC-양산JC8.32021020101597.835.306944129.074722-3.152761.90.84경남 양산시 동면
34도로공사A-0010-0083E-6S1양산JC-노포JC8.320210201012130.035.306944129.0747223.0714160.130.06경남 양산시 동면
45도로공사A-0010-0083E-6S2양산JC-노포JC8.320210201033112.2535.306944129.0747223.0714161.210.53경남 양산시 동면
56도로공사A-0010-0083E-6S3양산JC-노포JC8.320210201016104.035.306944129.0747223.0714162.851.26경남 양산시 동면
67도로공사A-0010-0538E-6E1언양JC-활천IC53.4202102010798.035.681944129.1811110.225450.060.03울산 울주군 두서면 활천리
78도로공사A-0010-0538E-6E2언양JC-활천IC53.42021020102691.535.681944129.1811110.225450.360.16울산 울주군 두서면 활천리
89도로공사A-0010-0538E-6E3언양JC-활천IC53.42021020101780.1235.681944129.1811110.225452.661.17울산 울주군 두서면 활천리
910도로공사A-0010-0538E-6S1활천IC-언양JC53.420210201015113.035.681944129.1811110.191580.170.07울산 울주군 두서면 활천리
기본키도로종류지점방향차선측정구간장비이정(km)측정일측정시간차량통과수(대)평균 속도(km/hr)위도(°)경도(°)기울기(°)TSP(g/km)PM10(g/km)주소
9091도로공사A-0100-0698S-8E3진주JC-진주IC69.82021020102286.635.149019128.104573-0.1843881.450.64경남 진주시 가좌동
9192도로공사A-0100-0698S-8E4진주JC-진주IC69.82021020102993.3335.149019128.104573-0.1843882.951.3경남 진주시 가좌동
9293도로공사A-0100-0698S-8S1진주IC-진주JC69.820210201016121.035.149019128.1045730.1237510.180.08경남 진주시 가좌동
9394도로공사A-0100-0698S-8S2진주IC-진주JC69.820210201031101.035.149019128.1045730.1237510.870.38경남 진주시 가좌동
9495도로공사A-0100-0698S-8S3진주IC-진주JC69.82021020102784.6735.149019128.1045730.1237514.491.98경남 진주시 가좌동
9596도로공사A-0100-0698S-8S4진주IC-진주JC69.82021020102078.035.149019128.1045730.1237513.671.61경남 진주시 가좌동
9697도로공사A-0100-1124S-4E1함안IC-산인JC112.420210201034100.035.274167128.451389-5.4400520.380.17경남 함안군 산인면 모곡리
9798도로공사A-0100-1124S-4E2함안IC-산인JC112.42021020105172.035.274167128.451389-5.4400523.141.38경남 함안군 산인면 모곡리
9899도로공사A-0100-1124S-4S1산인JC-함안IC112.42021020102993.3335.274167128.4513890.8521220.560.25경남 함안군 산인면 모곡리
99100도로공사A-0100-1124S-4S2산인JC-함안IC112.42021020103085.2935.274167128.4513890.8521223.321.46경남 함안군 산인면 모곡리