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

Numeric8
Categorical9

Alerts

도로종류 has constant value ""Constant
측정일 has constant value ""Constant
측정시간 has constant value ""Constant
측정구간 is highly overall correlated with 기본키 and 7 other fieldsHigh correlation
주소 is highly overall correlated with 기본키 and 6 other fieldsHigh correlation
지점 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 5~7차종 교통량(대) 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
5~7차종 교통량(대) is highly overall correlated with 차량통과수(대) and 2 other fieldsHigh correlation
10~12차종 교통량(대) is highly overall correlated with 차량통과수(대) and 2 other fieldsHigh correlation
방향 is highly overall correlated with 측정구간High correlation
8~9차종 교통량(대) is highly overall correlated with 5~7차종 교통량(대) and 1 other fieldsHigh correlation
8~9차종 교통량(대) is highly imbalanced (63.5%)Imbalance
기본키 has unique valuesUnique
차량통과수(대) has 25 (25.0%) zerosZeros
5~7차종 교통량(대) has 54 (54.0%) zerosZeros
10~12차종 교통량(대) has 63 (63.0%) zerosZeros

Reproduction

Analysis started2023-12-10 10:54:36.175363
Analysis finished2023-12-10 10:54:48.614084
Duration12.44 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기본키
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.5
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:54:48.789412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

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

Descriptive statistics

Standard deviation29.011492
Coefficient of variation (CV)0.57448499
Kurtosis-1.2
Mean50.5
Median Absolute Deviation (MAD)25
Skewness0
Sum5050
Variance841.66667
MonotonicityStrictly increasing
2023-12-10T19:54:49.076169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.0%
65 1
 
1.0%
75 1
 
1.0%
74 1
 
1.0%
73 1
 
1.0%
72 1
 
1.0%
71 1
 
1.0%
70 1
 
1.0%
69 1
 
1.0%
68 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
1 1
1.0%
2 1
1.0%
3 1
1.0%
4 1
1.0%
5 1
1.0%
6 1
1.0%
7 1
1.0%
8 1
1.0%
9 1
1.0%
10 1
1.0%
ValueCountFrequency (%)
100 1
1.0%
99 1
1.0%
98 1
1.0%
97 1
1.0%
96 1
1.0%
95 1
1.0%
94 1
1.0%
93 1
1.0%
92 1
1.0%
91 1
1.0%

도로종류
Categorical

CONSTANT 

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

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
도로공사 100
100.0%

Length

2023-12-10T19:54:49.312725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:54:49.522983image/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
9 
A-0010-1185E-8
8 
A-0010-2583E-7
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

2023-12-10T19:54:49.742854image/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

2023-12-10T19:54:49.993268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:54:50.169274image/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
8 

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

2023-12-10T19:54:50.363965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:54:50.575846image/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

2023-12-10T19:54:50.828341image/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
2023-12-10T19:54:51.107283image/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
2023-12-10T19:54:51.381096image/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

2023-12-10T19:54:51.652496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:54:51.887698image/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

2023-12-10T19:54:52.101855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:54:52.287169image/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
2023-12-10T19:54:52.555356image/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
2023-12-10T19:54:52.870972image/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%

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
2023-12-10T19:54:53.146401image/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
2023-12-10T19:54:53.422379image/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
2023-12-10T19:54:53.688474image/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
2023-12-10T19:54:53.980994image/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
2023-12-10T19:54:54.189856image/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
2023-12-10T19:54:54.401677image/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%

5~7차종 교통량(대)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct21
Distinct (%)21.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.94
Minimum0
Maximum47
Zeros54
Zeros (%)54.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:54:54.631533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q35.25
95-th percentile24
Maximum47
Range47
Interquartile range (IQR)5.25

Descriptive statistics

Standard deviation9.2111263
Coefficient of variation (CV)1.8646005
Kurtosis6.1663075
Mean4.94
Median Absolute Deviation (MAD)0
Skewness2.4488953
Sum494
Variance84.844848
MonotonicityNot monotonic
2023-12-10T19:54:54.939454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0 54
54.0%
1 8
 
8.0%
3 6
 
6.0%
22 4
 
4.0%
7 3
 
3.0%
5 3
 
3.0%
13 2
 
2.0%
24 2
 
2.0%
4 2
 
2.0%
9 2
 
2.0%
Other values (11) 14
 
14.0%
ValueCountFrequency (%)
0 54
54.0%
1 8
 
8.0%
2 2
 
2.0%
3 6
 
6.0%
4 2
 
2.0%
5 3
 
3.0%
6 2
 
2.0%
7 3
 
3.0%
8 1
 
1.0%
9 2
 
2.0%
ValueCountFrequency (%)
47 1
 
1.0%
37 1
 
1.0%
36 1
 
1.0%
30 1
 
1.0%
24 2
2.0%
22 4
4.0%
18 1
 
1.0%
16 1
 
1.0%
14 1
 
1.0%
13 2
2.0%

8~9차종 교통량(대)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
0
87 
1
 
8
2
 
3
3
 
2

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 87
87.0%
1 8
 
8.0%
2 3
 
3.0%
3 2
 
2.0%

Length

2023-12-10T19:54:55.201712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:54:55.366724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 87
87.0%
1 8
 
8.0%
2 3
 
3.0%
3 2
 
2.0%

10~12차종 교통량(대)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct14
Distinct (%)14.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.94
Minimum0
Maximum28
Zeros63
Zeros (%)63.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:54:55.573840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile11.05
Maximum28
Range28
Interquartile range (IQR)2

Descriptive statistics

Standard deviation4.3247467
Coefficient of variation (CV)2.2292509
Kurtosis15.453337
Mean1.94
Median Absolute Deviation (MAD)0
Skewness3.5624779
Sum194
Variance18.703434
MonotonicityNot monotonic
2023-12-10T19:54:55.789592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0 63
63.0%
2 11
 
11.0%
1 8
 
8.0%
4 4
 
4.0%
5 3
 
3.0%
8 2
 
2.0%
7 2
 
2.0%
28 1
 
1.0%
6 1
 
1.0%
12 1
 
1.0%
Other values (4) 4
 
4.0%
ValueCountFrequency (%)
0 63
63.0%
1 8
 
8.0%
2 11
 
11.0%
4 4
 
4.0%
5 3
 
3.0%
6 1
 
1.0%
7 2
 
2.0%
8 2
 
2.0%
11 1
 
1.0%
12 1
 
1.0%
ValueCountFrequency (%)
28 1
 
1.0%
19 1
 
1.0%
14 1
 
1.0%
13 1
 
1.0%
12 1
 
1.0%
11 1
 
1.0%
8 2
2.0%
7 2
2.0%
6 1
 
1.0%
5 3
3.0%

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
9 
충청 천안시 구성동
9 
대구 동구 안심3동
8 
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

2023-12-10T19:54:56.024717image/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

2023-12-10T19:54:46.727720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:37.416798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:38.690735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:40.030450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:41.386106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:42.772615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:43.978251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:45.122258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:46.876417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:37.552459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:38.845562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:40.161730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:41.582680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:42.915078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:44.131723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:45.262007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:47.020792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:37.751240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:39.021010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:40.304131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:41.743630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:43.068945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:44.285815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:45.422576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:47.163133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:37.888994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:39.216064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:40.419642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:41.903021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:43.255728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:44.416811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:45.583995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:47.337261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:38.077312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:39.398361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:40.632877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:42.123520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:43.423908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:44.573331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:45.820240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:47.474361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:38.271375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:39.556615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:40.806508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:42.283013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:43.559594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:44.705764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:45.952887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:47.614386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:38.409803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:39.715661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:40.965857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:42.452593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:43.693734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:44.839627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:46.432171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:47.756479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:38.557245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:39.864109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:41.123297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:42.618706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:43.821017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:44.975109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:46.574022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T19:54:56.176360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기본키지점방향차선측정구간장비이정(km)차량통과수(대)위도(°)경도(°)기울기(°)5~7차종 교통량(대)8~9차종 교통량(대)10~12차종 교통량(대)주소
기본키1.0000.9680.0000.0000.9830.9140.5290.9120.9100.7020.0510.1690.1750.946
지점0.9681.0000.2010.0001.0001.0000.5311.0001.0000.9030.2250.0000.4251.000
방향0.0000.2011.0000.0001.0000.0000.0000.0000.0000.5270.0000.0000.0000.000
차선0.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.4310.1470.2190.000
측정구간0.9831.0001.0000.0001.0001.0000.4921.0001.0001.0000.0000.0000.0001.000
장비이정(km)0.9141.0000.0000.0001.0001.0000.3440.9820.9530.8590.0000.0000.3501.000
차량통과수(대)0.5290.5310.0000.0000.4920.3441.0000.3480.3430.2890.6860.5670.6950.520
위도(°)0.9121.0000.0000.0001.0000.9820.3481.0000.9220.7510.0000.0000.2821.000
경도(°)0.9101.0000.0000.0001.0000.9530.3430.9221.0000.6320.0000.0000.0001.000
기울기(°)0.7020.9030.5270.0001.0000.8590.2890.7510.6321.0000.0000.3080.1570.875
5~7차종 교통량(대)0.0510.2250.0000.4310.0000.0000.6860.0000.0000.0001.0000.8070.8920.000
8~9차종 교통량(대)0.1690.0000.0000.1470.0000.0000.5670.0000.0000.3080.8071.0000.9230.000
10~12차종 교통량(대)0.1750.4250.0000.2190.0000.3500.6950.2820.0000.1570.8920.9231.0000.352
주소0.9461.0000.0000.0001.0001.0000.5201.0001.0000.8750.0000.0000.3521.000
2023-12-10T19:54:56.807379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
측정구간주소지점8~9차종 교통량(대)차선방향
측정구간1.0000.9210.9270.0000.0000.863
주소0.9211.0000.9940.0000.0000.000
지점0.9270.9941.0000.0000.0000.167
8~9차종 교통량(대)0.0000.0000.0001.0000.1180.000
차선0.0000.0000.0000.1181.0000.000
방향0.8630.0000.1670.0000.0001.000
2023-12-10T19:54:57.030253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기본키장비이정(km)차량통과수(대)위도(°)경도(°)기울기(°)5~7차종 교통량(대)10~12차종 교통량(대)지점방향차선측정구간8~9차종 교통량(대)주소
기본키1.0000.9970.0810.997-0.974-0.016-0.107-0.0390.7810.0000.0000.8020.0940.766
장비이정(km)0.9971.0000.0741.000-0.977-0.030-0.126-0.0530.9610.0000.0000.8910.0000.967
차량통과수(대)0.0810.0741.0000.074-0.135-0.1570.6670.6210.2180.0000.0000.1730.3630.231
위도(°)0.9971.0000.0741.000-0.977-0.030-0.126-0.0530.9660.0000.0000.8960.0000.972
경도(°)-0.974-0.977-0.135-0.9771.0000.0210.071-0.0070.9560.0000.0000.8860.0000.962
기울기(°)-0.016-0.030-0.157-0.0300.0211.000-0.061-0.0850.6560.3850.0000.8910.1360.618
5~7차종 교통량(대)-0.107-0.1260.667-0.1260.071-0.0611.0000.8760.0810.0000.2590.0000.6530.000
10~12차종 교통량(대)-0.039-0.0530.621-0.053-0.007-0.0850.8761.0000.1780.0000.1660.0000.5150.132
지점0.7810.9610.2180.9660.9560.6560.0810.1781.0000.1670.0000.9270.0000.994
방향0.0000.0000.0000.0000.0000.3850.0000.0000.1671.0000.0000.8630.0000.000
차선0.0000.0000.0000.0000.0000.0000.2590.1660.0000.0001.0000.0000.1180.000
측정구간0.8020.8910.1730.8960.8860.8910.0000.0000.9270.8630.0001.0000.0000.921
8~9차종 교통량(대)0.0940.0000.3630.0000.0000.1360.6530.5150.0000.0000.1180.0001.0000.000
주소0.7660.9670.2310.9720.9620.6180.0000.1320.9940.0000.0000.9210.0001.000

Missing values

2023-12-10T19:54:47.987490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T19:54:48.441798image/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)위도(°)경도(°)기울기(°)5~7차종 교통량(대)8~9차종 교통량(대)10~12차종 교통량(대)주소
01도로공사A-0010-0083E-6E1노포JC-양산JC8.32020120101235.306944129.074722-3.15276000경남 양산시 동면
12도로공사A-0010-0083E-6E2노포JC-양산JC8.32020120104035.306944129.074722-3.15276000경남 양산시 동면
23도로공사A-0010-0083E-6E3노포JC-양산JC8.32020120102335.306944129.074722-3.15276100경남 양산시 동면
34도로공사A-0010-0083E-6S1양산JC-노포JC8.32020120101335.306944129.0747223.071416000경남 양산시 동면
45도로공사A-0010-0083E-6S2양산JC-노포JC8.32020120103735.306944129.0747223.071416100경남 양산시 동면
56도로공사A-0010-0083E-6S3양산JC-노포JC8.32020120102035.306944129.0747223.071416302경남 양산시 동면
67도로공사A-0010-0538E-6E1언양JC-활천IC53.4202012010435.681944129.1811110.22545000울산 울주군 두서면 활천리
78도로공사A-0010-0538E-6E2언양JC-활천IC53.42020120101635.681944129.1811110.22545100울산 울주군 두서면 활천리
89도로공사A-0010-0538E-6E3언양JC-활천IC53.42020120101835.681944129.1811110.22545704울산 울주군 두서면 활천리
910도로공사A-0010-0538E-6S1활천IC-언양JC53.4202012010935.681944129.1811110.19158000울산 울주군 두서면 활천리
기본키도로종류지점방향차선측정구간장비이정(km)측정일측정시간차량통과수(대)위도(°)경도(°)기울기(°)5~7차종 교통량(대)8~9차종 교통량(대)10~12차종 교통량(대)주소
9091도로공사A-0010-3613E-10S2안성JC-안성IC361.32020120108137.008889127.149167-1.30142001경기 안성시 원곡면
9192도로공사A-0010-3613E-10S3안성JC-안성IC361.32020120106637.008889127.149167-1.301421612경기 안성시 원곡면
9293도로공사A-0010-3613E-10S4안성JC-안성IC361.32020120108637.008889127.149167-1.301422201경기 안성시 원곡면
9394도로공사A-0010-3613E-10S5안성JC-안성IC361.32020120104537.008889127.149167-1.30142701경기 안성시 원곡면
9495도로공사A-0010-3801E-10E1오산IC-동탄JC380.12020120104537.158778127.088333-0.660004000경기 화성시 동탄면 송리
9596도로공사A-0010-3801E-10E2오산IC-동탄JC380.120201201011137.158778127.088333-0.660004000경기 화성시 동탄면 송리
9697도로공사A-0010-3801E-10E3오산IC-동탄JC380.12020120109637.158778127.088333-0.6600041402경기 화성시 동탄면 송리
9798도로공사A-0010-3801E-10E4오산IC-동탄JC380.12020120109137.158778127.088333-0.6600042408경기 화성시 동탄면 송리
9899도로공사A-0010-3801E-10E5오산IC-동탄JC380.1202012010837.158778127.088333-0.660004000경기 화성시 동탄면 송리
99100도로공사A-0010-3801E-10S1동탄JC-오산IC380.12020120103137.158778127.0883330.671977000경기 화성시 동탄면 송리