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

Numeric7
Categorical10

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
위도(°) is highly overall correlated with 기본키 and 5 other fieldsHigh correlation
경도(°) is highly overall correlated with 기본키 and 5 other fieldsHigh correlation
기울기(°) is highly overall correlated with 지점 and 3 other fieldsHigh correlation
방향 is highly overall correlated with 기울기(°) and 1 other fieldsHigh correlation
8~9차종 교통량(대) is highly imbalanced (67.3%)Imbalance
10~12차종 교통량(대) is highly imbalanced (72.2%)Imbalance
기본키 has unique valuesUnique
차량통과수(대) has 19 (19.0%) zerosZeros
5~7차종 교통량(대) has 66 (66.0%) zerosZeros

Reproduction

Analysis started2023-12-10 10:54:59.388003
Analysis finished2023-12-10 10:55:09.817344
Duration10.43 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
2023-12-10T19:55:09.956882image/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
2023-12-10T19:55:10.264694image/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

2023-12-10T19:55:10.519252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:55:10.728002image/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-0010-3801E-10
10 
A-0010-3352E-9
9 
A-0010-1185E-8
8 
Other values (10)
53 

Length

Max length15
Median length14
Mean length14.32
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-0010-3801E-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-0010-2695C-6 6
 
6.0%
A-0010-2761E-6 6
 
6.0%
Other values (5) 22
22.0%

Length

2023-12-10T19:55:10.924670image/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-0010-3801e-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-0010-2695c-6 6
 
6.0%
a-0010-2761e-6 6
 
6.0%
Other values (5) 22
22.0%

방향
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
S
52 
E
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 (%)
S 52
52.0%
E 48
48.0%

Length

2023-12-10T19:55:11.154056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:55:11.353518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
s 52
52.0%
e 48
48.0%

차선
Categorical

Distinct5
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
1
26 
2
26 
3
25 
4
14 
5
9 

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 26
26.0%
2 26
26.0%
3 25
25.0%
4 14
14.0%
5 9
 
9.0%

Length

2023-12-10T19:55:11.562154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:55:11.760468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 26
26.0%
2 26
26.0%
3 25
25.0%
4 14
14.0%
5 9
 
9.0%

측정구간
Categorical

HIGH CORRELATION 

Distinct26
Distinct (%)26.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
청주JC-남이JC
 
5
오산IC-동탄JC
 
5
안성JC-안성IC
 
5
안성IC-안성JC
 
5
북천안IC-천안IC
 
5
Other values (21)
75 

Length

Max length11
Median length9
Mean length9.26
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 (%)
청주JC-남이JC 5
 
5.0%
오산IC-동탄JC 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%
남이JC-청주JC 5
 
5.0%
동탄JC-오산IC 5
 
5.0%
옥천IC-금강IC 4
 
4.0%
Other values (16) 51
51.0%

Length

2023-12-10T19:55:12.036761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
청주jc-남이jc 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%
남이jc-청주jc 5
 
5.0%
동탄jc-오산ic 5
 
5.0%
오산ic-동탄jc 5
 
5.0%
천안jc-천안ic 4
 
4.0%
Other values (16) 51
51.0%

장비이정(km)
Real number (ℝ)

HIGH CORRELATION 

Distinct15
Distinct (%)15.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean270.637
Minimum8.3
Maximum388
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:55:12.268702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8.3
5-th percentile8.3
Q1258.3
median298.7
Q3345.2
95-th percentile380.1
Maximum388
Range379.7
Interquartile range (IQR)86.9

Descriptive statistics

Standard deviation111.15128
Coefficient of variation (CV)0.41070247
Kurtosis0.35501158
Mean270.637
Median Absolute Deviation (MAD)46.5
Skewness-1.254172
Sum27063.7
Variance12354.608
MonotonicityIncreasing
2023-12-10T19:55:12.492140image/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%
380.1 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%
269.5 6
 
6.0%
276.1 6
 
6.0%
Other values (5) 22
22.0%
ValueCountFrequency (%)
8.3 6
6.0%
53.4 6
6.0%
118.5 8
8.0%
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%
298.7 5
5.0%
306.8 6
6.0%
ValueCountFrequency (%)
388.0 2
 
2.0%
380.1 10
10.0%
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%

측정일
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
20201101
100 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20201101
2nd row20201101
3rd row20201101
4th row20201101
5th row20201101

Common Values

ValueCountFrequency (%)
20201101 100
100.0%

Length

2023-12-10T19:55:12.750726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:55:12.918544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20201101 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

2023-12-10T19:55:13.102764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:55:13.274601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 100
100.0%

차량통과수(대)
Real number (ℝ)

ZEROS 

Distinct57
Distinct (%)57.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44.94
Minimum0
Maximum326
Zeros19
Zeros (%)19.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:55:13.465859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13.75
median30
Q368.5
95-th percentile131.4
Maximum326
Range326
Interquartile range (IQR)64.75

Descriptive statistics

Standard deviation51.209181
Coefficient of variation (CV)1.1395011
Kurtosis8.238046
Mean44.94
Median Absolute Deviation (MAD)27
Skewness2.1959212
Sum4494
Variance2622.3802
MonotonicityNot monotonic
2023-12-10T19:55:13.701583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 19
 
19.0%
3 6
 
6.0%
25 3
 
3.0%
30 3
 
3.0%
15 3
 
3.0%
33 3
 
3.0%
87 2
 
2.0%
111 2
 
2.0%
10 2
 
2.0%
53 2
 
2.0%
Other values (47) 55
55.0%
ValueCountFrequency (%)
0 19
19.0%
3 6
 
6.0%
4 1
 
1.0%
7 1
 
1.0%
8 2
 
2.0%
9 2
 
2.0%
10 2
 
2.0%
13 1
 
1.0%
14 1
 
1.0%
15 3
 
3.0%
ValueCountFrequency (%)
326 1
1.0%
178 1
1.0%
165 1
1.0%
141 1
1.0%
139 1
1.0%
131 1
1.0%
115 1
1.0%
111 2
2.0%
110 1
1.0%
107 2
2.0%

위도(°)
Real number (ℝ)

HIGH CORRELATION 

Distinct15
Distinct (%)15.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.51398
Minimum35.306944
Maximum37.226111
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:55:13.939067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.306944
5-th percentile35.306944
Q136.307222
median36.584444
Q336.868903
95-th percentile37.158778
Maximum37.226111
Range1.9191667
Interquartile range (IQR)0.56168056

Descriptive statistics

Standard deviation0.51333631
Coefficient of variation (CV)0.014058624
Kurtosis-0.035793781
Mean36.51398
Median Absolute Deviation (MAD)0.28445834
Skewness-0.81294171
Sum3651.398
Variance0.26351417
MonotonicityIncreasing
2023-12-10T19:55:14.158094image/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%
37.15877778 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%
36.3475 6
 
6.0%
36.38961 6
 
6.0%
Other values (5) 22
22.0%
ValueCountFrequency (%)
35.30694444 6
6.0%
35.68194444 6
6.0%
35.90473241 8
8.0%
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%
36.58444444 5
5.0%
36.64019722 6
6.0%
ValueCountFrequency (%)
37.22611111 2
 
2.0%
37.15877778 10
10.0%
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%

경도(°)
Real number (ℝ)

HIGH CORRELATION 

Distinct15
Distinct (%)15.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.61728
Minimum127.08833
Maximum129.18111
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:55:14.371845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum127.08833
5-th percentile127.08833
Q1127.17667
median127.42351
Q3127.57444
95-th percentile129.18111
Maximum129.18111
Range2.0927778
Interquartile range (IQR)0.3977777

Descriptive statistics

Standard deviation0.67320788
Coefficient of variation (CV)0.0052752094
Kurtosis0.63986465
Mean127.61728
Median Absolute Deviation (MAD)0.2468413
Skewness1.4755816
Sum12761.728
Variance0.45320884
MonotonicityNot monotonic
2023-12-10T19:55:14.576323image/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%
127.0883333 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%
127.4691667 6
 
6.0%
127.423508 6
 
6.0%
Other values (5) 22
22.0%
ValueCountFrequency (%)
127.0883333 10
10.0%
127.1083333 2
 
2.0%
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%
ValueCountFrequency (%)
129.1811111 6
6.0%
129.0747222 6
6.0%
128.5614716 8
8.0%
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%
127.423508 6
6.0%
127.3781278 6
6.0%

기울기(°)
Real number (ℝ)

HIGH CORRELATION 

Distinct26
Distinct (%)26.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.04250919
Minimum-13.24576
Maximum12.47809
Zeros0
Zeros (%)0.0%
Negative49
Negative (%)49.0%
Memory size1.0 KiB
2023-12-10T19:55:14.768169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-13.24576
5-th percentile-3.15276
Q1-0.807188
median0.166426
Q30.688696
95-th percentile3.071416
Maximum12.47809
Range25.72385
Interquartile range (IQR)1.495884

Descriptive statistics

Standard deviation3.4431155
Coefficient of variation (CV)-80.996967
Kurtosis9.578913
Mean-0.04250919
Median Absolute Deviation (MAD)0.973614
Skewness-0.25577771
Sum-4.250919
Variance11.855044
MonotonicityNot monotonic
2023-12-10T19:55:14.986702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
-1.758683 5
 
5.0%
0.671977 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%
0.166426 4
 
4.0%
Other values (16) 51
51.0%
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%
-0.807188 4
4.0%
-0.688696 3
3.0%
-0.660004 5
5.0%
-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%
0.688696 3
3.0%
0.671977 5
5.0%
0.61369 5
5.0%
0.2594 4
4.0%

5~7차종 교통량(대)
Real number (ℝ)

ZEROS 

Distinct8
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.09
Minimum0
Maximum9
Zeros66
Zeros (%)66.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:55:15.152498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile6
Maximum9
Range9
Interquartile range (IQR)1

Descriptive statistics

Standard deviation2.0601804
Coefficient of variation (CV)1.8900738
Kurtosis5.2872847
Mean1.09
Median Absolute Deviation (MAD)0
Skewness2.3238838
Sum109
Variance4.2443434
MonotonicityNot monotonic
2023-12-10T19:55:15.324376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 66
66.0%
1 11
 
11.0%
3 10
 
10.0%
4 4
 
4.0%
2 3
 
3.0%
9 2
 
2.0%
6 2
 
2.0%
8 2
 
2.0%
ValueCountFrequency (%)
0 66
66.0%
1 11
 
11.0%
2 3
 
3.0%
3 10
 
10.0%
4 4
 
4.0%
6 2
 
2.0%
8 2
 
2.0%
9 2
 
2.0%
ValueCountFrequency (%)
9 2
 
2.0%
8 2
 
2.0%
6 2
 
2.0%
4 4
 
4.0%
3 10
 
10.0%
2 3
 
3.0%
1 11
 
11.0%
0 66
66.0%

8~9차종 교통량(대)
Categorical

IMBALANCE 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
0
94 
1
 
6

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 94
94.0%
1 6
 
6.0%

Length

2023-12-10T19:55:15.524587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:55:15.693306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 94
94.0%
1 6
 
6.0%

10~12차종 교통량(대)
Categorical

IMBALANCE 

Distinct5
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
0
90 
1
 
5
2
 
2
4
 
2
3
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)1.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 90
90.0%
1 5
 
5.0%
2 2
 
2.0%
4 2
 
2.0%
3 1
 
1.0%

Length

2023-12-10T19:55:15.863256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:55:16.045408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 90
90.0%
1 5
 
5.0%
2 2
 
2.0%
4 2
 
2.0%
3 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 
경기 화성시 동탄면 송리
10 
충북 청원군 남이면
9 
충청 천안시 구성동
9 
Other values (9)
52 

Length

Max length18
Median length10
Mean length11.84
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%
경기 화성시 동탄면 송리 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%
대전 대덕구 비래동 6
 
6.0%
Other values (4) 19
19.0%

Length

2023-12-10T19:55:16.253633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
충북 27
 
7.7%
경기 22
 
6.3%
청원군 20
 
5.7%
천안시 19
 
5.4%
남이면 14
 
4.0%
대덕구 12
 
3.4%
대전 12
 
3.4%
동탄면 10
 
2.9%
충남 10
 
2.9%
화성시 10
 
2.9%
Other values (28) 194
55.4%

Interactions

2023-12-10T19:55:07.728789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:00.681610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:01.636130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:02.970980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:04.206064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:05.407516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:06.695664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:07.870272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:00.817660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:01.781830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:03.170205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:04.373458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:05.539676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:06.847145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:08.027442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:00.966388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:01.946926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:03.374913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:04.563990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:05.812119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:07.004046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:08.161721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:01.107200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:02.232806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:03.546968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:04.732431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:05.967766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:07.148513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:08.309695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:01.255393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:02.435428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:03.755884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:04.905918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:06.182160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:07.313234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:08.451022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:01.385924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:02.571024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:03.913526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:05.119285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:06.414017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:07.460823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:08.587083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:01.509772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:02.692270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:04.063095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:05.264029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:06.554084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:07.598979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T19:55:16.422486image/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
기본키1.0000.9800.0000.0000.9830.9320.3270.9440.9940.6940.2230.0000.2710.966
지점0.9801.0000.1720.0001.0001.0000.6151.0001.0000.8670.0000.0000.4211.000
방향0.0000.1721.0000.0001.0000.0000.1340.0000.0000.7130.0000.0940.0000.000
차선0.0000.0000.0001.0000.0000.0000.2130.0000.0000.0000.4530.2270.4370.000
측정구간0.9831.0001.0000.0001.0001.0000.5921.0001.0001.0000.4210.0000.4441.000
장비이정(km)0.9321.0000.0000.0001.0001.0000.3180.9640.9410.6780.0730.0000.1591.000
차량통과수(대)0.3270.6150.1340.2130.5920.3181.0000.3780.2630.0000.3700.0000.0000.715
위도(°)0.9441.0000.0000.0001.0000.9640.3781.0000.9320.6600.3240.0000.1211.000
경도(°)0.9941.0000.0000.0001.0000.9410.2630.9321.0000.4990.0000.0000.0001.000
기울기(°)0.6940.8670.7130.0001.0000.6780.0000.6600.4991.0000.0000.1350.2930.852
5~7차종 교통량(대)0.2230.0000.0000.4530.4210.0730.3700.3240.0000.0001.0000.4860.6430.135
8~9차종 교통량(대)0.0000.0000.0940.2270.0000.0000.0000.0000.0000.1350.4861.0000.3680.000
10~12차종 교통량(대)0.2710.4210.0000.4370.4440.1590.0000.1210.0000.2930.6430.3681.0000.397
주소0.9661.0000.0000.0001.0001.0000.7151.0001.0000.8520.1350.0000.3971.000
2023-12-10T19:55:16.694009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
측정구간10~12차종 교통량(대)주소지점8~9차종 교통량(대)차선방향
측정구간1.0000.1970.9280.9330.0000.0000.869
10~12차종 교통량(대)0.1971.0000.2060.1780.4420.1740.000
주소0.9280.2061.0000.9940.0000.0000.000
지점0.9330.1780.9941.0000.0000.0000.141
8~9차종 교통량(대)0.0000.4420.0000.0001.0000.2730.059
차선0.0000.1740.0000.0000.2731.0000.000
방향0.8690.0000.0000.1410.0590.0001.000
2023-12-10T19:55:16.885609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기본키장비이정(km)차량통과수(대)위도(°)경도(°)기울기(°)5~7차종 교통량(대)지점방향차선측정구간8~9차종 교통량(대)10~12차종 교통량(대)주소
기본키1.0000.9970.2510.997-0.9730.0270.0600.8300.0000.0000.8070.0000.1090.834
장비이정(km)0.9971.0000.2531.000-0.9750.0100.0350.9560.0000.0000.8920.0000.0980.962
차량통과수(대)0.2510.2531.0000.253-0.3190.0070.3010.3180.1370.1340.2620.0000.0000.333
위도(°)0.9971.0000.2531.000-0.9750.0100.0350.9610.0000.0000.8970.0000.0680.967
경도(°)-0.973-0.975-0.319-0.9751.000-0.024-0.0600.9460.0000.0000.8830.0000.0000.951
기울기(°)0.0270.0100.0070.010-0.0241.000-0.0850.6000.5150.0000.8870.0930.2010.609
5~7차종 교통량(대)0.0600.0350.3010.035-0.060-0.0851.0000.0000.0000.2930.1550.3530.4580.043
지점0.8300.9560.3180.9610.9460.6000.0001.0000.1410.0000.9330.0000.1780.994
방향0.0000.0000.1370.0000.0000.5150.0000.1411.0000.0000.8690.0590.0000.000
차선0.0000.0000.1340.0000.0000.0000.2930.0000.0001.0000.0000.2730.1740.000
측정구간0.8070.8920.2620.8970.8830.8870.1550.9330.8690.0001.0000.0000.1970.928
8~9차종 교통량(대)0.0000.0000.0000.0000.0000.0930.3530.0000.0590.2730.0001.0000.4420.000
10~12차종 교통량(대)0.1090.0980.0000.0680.0000.2010.4580.1780.0000.1740.1970.4421.0000.206
주소0.8340.9620.3330.9670.9510.6090.0430.9940.0000.0000.9280.0000.2061.000

Missing values

2023-12-10T19:55:09.247254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T19:55:09.649010image/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-양산JC8.32020110102635.306944129.074722-3.15276000경남 양산시 동면
12도로공사A-0010-0083E-6E2노포JC-양산JC8.32020110106335.306944129.074722-3.15276000경남 양산시 동면
23도로공사A-0010-0083E-6E3노포JC-양산JC8.32020110103535.306944129.074722-3.15276100경남 양산시 동면
34도로공사A-0010-0083E-6S1양산JC-노포JC8.32020110103835.306944129.0747223.071416000경남 양산시 동면
45도로공사A-0010-0083E-6S2양산JC-노포JC8.32020110105835.306944129.0747223.071416000경남 양산시 동면
56도로공사A-0010-0083E-6S3양산JC-노포JC8.32020110103035.306944129.0747223.071416010경남 양산시 동면
67도로공사A-0010-0538E-6E1언양JC-활천IC53.4202011010935.681944129.1811110.22545000울산 울주군 두서면 활천리
78도로공사A-0010-0538E-6E2언양JC-활천IC53.42020110104235.681944129.1811110.22545000울산 울주군 두서면 활천리
89도로공사A-0010-0538E-6E3언양JC-활천IC53.42020110101535.681944129.1811110.22545301울산 울주군 두서면 활천리
910도로공사A-0010-0538E-6S1활천IC-언양JC53.42020110101835.681944129.1811110.19158000울산 울주군 두서면 활천리
기본키도로종류지점방향차선측정구간장비이정(km)측정일측정시간차량통과수(대)위도(°)경도(°)기울기(°)5~7차종 교통량(대)8~9차종 교통량(대)10~12차종 교통량(대)주소
9091도로공사A-0010-3801E-10E3오산IC-동탄JC380.120201101016537.158778127.088333-0.660004100경기 화성시 동탄면 송리
9192도로공사A-0010-3801E-10E4오산IC-동탄JC380.120201101013137.158778127.088333-0.660004600경기 화성시 동탄면 송리
9293도로공사A-0010-3801E-10E5오산IC-동탄JC380.1202011010337.158778127.088333-0.660004000경기 화성시 동탄면 송리
9394도로공사A-0010-3801E-10S1동탄JC-오산IC380.12020110108737.158778127.0883330.671977000경기 화성시 동탄면 송리
9495도로공사A-0010-3801E-10S2동탄JC-오산IC380.120201101014137.158778127.0883330.671977000경기 화성시 동탄면 송리
9596도로공사A-0010-3801E-10S3동탄JC-오산IC380.120201101011137.158778127.0883330.671977000경기 화성시 동탄면 송리
9697도로공사A-0010-3801E-10S4동탄JC-오산IC380.12020110109437.158778127.0883330.671977400경기 화성시 동탄면 송리
9798도로공사A-0010-3801E-10S5동탄JC-오산IC380.1202011010437.158778127.0883330.671977000경기 화성시 동탄면 송리
9899도로공사A-0010-3880E-10E1기흥IC-수원신갈IC388.0202011010037.226111127.108333-0.55276000경기 용인시 기흥구 기흥동
99100도로공사A-0010-3880E-10E2기흥IC-수원신갈IC388.020201101032637.226111127.108333-0.55276100경기 용인시 기흥구 기흥동