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 1 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 10~12차종 교통량(대)High correlation
8~9차종 교통량(대) is highly imbalanced (60.8%)Imbalance
기본키 has unique valuesUnique
차량통과수(대) has 21 (21.0%) zerosZeros
5~7차종 교통량(대) has 50 (50.0%) zerosZeros
10~12차종 교통량(대) has 59 (59.0%) zerosZeros

Reproduction

Analysis started2023-12-10 10:55:45.902007
Analysis finished2023-12-10 10:55:58.989350
Duration13.09 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
2023-12-10T19:55:59.196545image/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
2023-12-10T19:55:59.462481image/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

2023-12-10T19:55:59.678368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:55:59.844404image/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-0010-3352E-9
9 
A-0010-1185E-8
8 
A-0010-1688E-8
8 
A-0010-3613E-10
8 
Other values (10)
57 

Length

Max length15
Median length14
Mean length14.18
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-0010-1688E-8 8
 
8.0%
A-0010-3613E-10 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-0010-1880E-6 6
 
6.0%
A-0010-2695C-6 6
 
6.0%
Other values (5) 26
26.0%

Length

2023-12-10T19:56:00.023628image/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-0010-1688e-8 8
 
8.0%
a-0010-3613e-10 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-0010-1880e-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
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

2023-12-10T19:56:00.244582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:56:00.413235image/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
27 
2
27 
3
27 
4
13 
5
6 

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 13
13.0%
5 6
 
6.0%

Length

2023-12-10T19:56:00.615797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:56:00.810100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 27
27.0%
2 27
27.0%
3 27
27.0%
4 13
13.0%
5 6
 
6.0%

측정구간
Categorical

HIGH CORRELATION 

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

Length

Max length10
Median length9
Mean length9.36
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 (%)
안성IC-안성JC 5
 
5.0%
천안IC-북천안IC 5
 
5.0%
천안IC-천안JC 5
 
5.0%
남이JC-청주JC 5
 
5.0%
청주JC-남이JC 5
 
5.0%
북천안IC-천안IC 5
 
5.0%
구미IC-남구미IC 4
 
4.0%
옥천IC-금강IC 4
 
4.0%
천안JC-천안IC 4
 
4.0%
남구미IC-구미IC 4
 
4.0%
Other values (17) 54
54.0%

Length

2023-12-10T19:56:01.055919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
안성ic-안성jc 5
 
5.0%
천안ic-천안jc 5
 
5.0%
남이jc-청주jc 5
 
5.0%
청주jc-남이jc 5
 
5.0%
북천안ic-천안ic 5
 
5.0%
천안ic-북천안ic 5
 
5.0%
동대구jc-경산ic 4
 
4.0%
청주jc-남청주ic 4
 
4.0%
경산ic-동대구jc 4
 
4.0%
남구미ic-구미ic 4
 
4.0%
Other values (17) 54
54.0%

장비이정(km)
Real number (ℝ)

HIGH CORRELATION 

Distinct15
Distinct (%)15.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean242.4334
Minimum8.3
Maximum361.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:56:01.264406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8.3
5-th percentile8.3
Q1168.89
median276.1
Q3335.2
95-th percentile361.3
Maximum361.3
Range353
Interquartile range (IQR)166.31

Descriptive statistics

Standard deviation105.57805
Coefficient of variation (CV)0.435493
Kurtosis-0.37331657
Mean242.4334
Median Absolute Deviation (MAD)69.1
Skewness-0.8857268
Sum24243.34
Variance11146.724
MonotonicityIncreasing
2023-12-10T19:56:01.484330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
345.2 10
 
10.0%
335.2 9
 
9.0%
118.5 8
 
8.0%
168.89 8
 
8.0%
361.3 8
 
8.0%
258.3 7
 
7.0%
8.3 6
 
6.0%
53.4 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%
118.5 8
8.0%
168.89 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 8
8.0%
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
20200901
100 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20200901
2nd row20200901
3rd row20200901
4th row20200901
5th row20200901

Common Values

ValueCountFrequency (%)
20200901 100
100.0%

Length

2023-12-10T19:56:01.729390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:56:01.877798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20200901 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

2023-12-10T19:56:02.011594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:56:02.148239image/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%
Mean35.07
Minimum0
Maximum151
Zeros21
Zeros (%)21.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:56:02.314707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13.75
median28.5
Q361.5
95-th percentile90.35
Maximum151
Range151
Interquartile range (IQR)57.75

Descriptive statistics

Standard deviation32.993496
Coefficient of variation (CV)0.94078975
Kurtosis0.43904149
Mean35.07
Median Absolute Deviation (MAD)27.5
Skewness0.8881194
Sum3507
Variance1088.5708
MonotonicityNot monotonic
2023-12-10T19:56:02.558009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 21
 
21.0%
36 4
 
4.0%
26 4
 
4.0%
29 4
 
4.0%
70 3
 
3.0%
14 2
 
2.0%
1 2
 
2.0%
57 2
 
2.0%
67 2
 
2.0%
42 2
 
2.0%
Other values (45) 54
54.0%
ValueCountFrequency (%)
0 21
21.0%
1 2
 
2.0%
2 1
 
1.0%
3 1
 
1.0%
4 1
 
1.0%
5 1
 
1.0%
6 1
 
1.0%
7 2
 
2.0%
9 1
 
1.0%
10 1
 
1.0%
ValueCountFrequency (%)
151 1
1.0%
118 1
1.0%
108 1
1.0%
103 1
1.0%
97 1
1.0%
90 1
1.0%
86 1
1.0%
84 1
1.0%
82 1
1.0%
80 1
1.0%

위도(°)
Real number (ℝ)

HIGH CORRELATION 

Distinct15
Distinct (%)15.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.36879
Minimum35.306944
Maximum37.008889
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:56:02.776344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.306944
5-th percentile35.306944
Q136.075556
median36.38961
Q336.780833
95-th percentile37.008889
Maximum37.008889
Range1.7019444
Interquartile range (IQR)0.70527777

Descriptive statistics

Standard deviation0.45644567
Coefficient of variation (CV)0.012550477
Kurtosis-0.20280413
Mean36.36879
Median Absolute Deviation (MAD)0.31405444
Skewness-0.64756216
Sum3636.879
Variance0.20834265
MonotonicityIncreasing
2023-12-10T19:56:02.977410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
36.86890278 10
 
10.0%
36.78083333 9
 
9.0%
35.90473241 8
 
8.0%
36.07555556 8
 
8.0%
37.00888889 8
 
8.0%
36.30722222 7
 
7.0%
35.30694444 6
 
6.0%
35.68194444 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%
35.90473241 8
8.0%
36.07555556 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 8
8.0%
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%
Mean127.78509
Minimum127.14917
Maximum129.18111
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:56:03.243196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum127.14917
5-th percentile127.14917
Q1127.18672
median127.43389
Q3128.3625
95-th percentile129.18111
Maximum129.18111
Range2.0319444
Interquartile range (IQR)1.1757778

Descriptive statistics

Standard deviation0.67323653
Coefficient of variation (CV)0.005268506
Kurtosis-0.59742052
Mean127.78509
Median Absolute Deviation (MAD)0.2572222
Skewness0.91694457
Sum12778.509
Variance0.45324743
MonotonicityNot monotonic
2023-12-10T19:56:03.469368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
127.1867222 10
 
10.0%
127.1766667 9
 
9.0%
128.5614716 8
 
8.0%
128.3625 8
 
8.0%
127.1491667 8
 
8.0%
127.5744444 7
 
7.0%
129.0747222 6
 
6.0%
129.1811111 6
 
6.0%
128.2132778 6
 
6.0%
127.4691667 6
 
6.0%
Other values (5) 26
26.0%
ValueCountFrequency (%)
127.1491667 8
8.0%
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%
128.5614716 8
8.0%
128.3625 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%
Mean0.00024924
Minimum-13.24576
Maximum12.47809
Zeros0
Zeros (%)0.0%
Negative47
Negative (%)47.0%
Memory size1.0 KiB
2023-12-10T19:56:03.714413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-13.24576
5-th percentile-3.15276
Q1-1.2647142
median0.166426
Q31.293015
95-th percentile3.071416
Maximum12.47809
Range25.72385
Interquartile range (IQR)2.5577292

Descriptive statistics

Standard deviation3.4698987
Coefficient of variation (CV)13921.917
Kurtosis9.2093115
Mean0.00024924
Median Absolute Deviation (MAD)1.27099
Skewness-0.28473741
Sum0.024924
Variance12.040197
MonotonicityNot monotonic
2023-12-10T19:56:03.918782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0.61369 5
 
5.0%
-1.758683 5
 
5.0%
1.437416 5
 
5.0%
1.293015 5
 
5.0%
1.544389 5
 
5.0%
-0.624001 5
 
5.0%
-1.359001 4
 
4.0%
0.2594 4
 
4.0%
-0.807188 4
 
4.0%
-1.377274 4
 
4.0%
Other values (17) 54
54.0%
ValueCountFrequency (%)
-13.24576 3
3.0%
-3.15276 3
3.0%
-2.788625 3
3.0%
-1.758683 5
5.0%
-1.377274 4
4.0%
-1.359001 4
4.0%
-1.30142 3
3.0%
-1.252479 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%
1.594453 4
4.0%
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%

5~7차종 교통량(대)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct30
Distinct (%)30.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.56
Minimum0
Maximum49
Zeros50
Zeros (%)50.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:56:04.126177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.5
Q310.5
95-th percentile37.15
Maximum49
Range49
Interquartile range (IQR)10.5

Descriptive statistics

Standard deviation12.291345
Coefficient of variation (CV)1.6258393
Kurtosis2.6792091
Mean7.56
Median Absolute Deviation (MAD)0.5
Skewness1.8732814
Sum756
Variance151.07717
MonotonicityNot monotonic
2023-12-10T19:56:04.384844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
0 50
50.0%
3 5
 
5.0%
6 4
 
4.0%
4 4
 
4.0%
12 3
 
3.0%
1 3
 
3.0%
24 2
 
2.0%
10 2
 
2.0%
5 2
 
2.0%
13 2
 
2.0%
Other values (20) 23
23.0%
ValueCountFrequency (%)
0 50
50.0%
1 3
 
3.0%
2 2
 
2.0%
3 5
 
5.0%
4 4
 
4.0%
5 2
 
2.0%
6 4
 
4.0%
7 2
 
2.0%
9 1
 
1.0%
10 2
 
2.0%
ValueCountFrequency (%)
49 1
1.0%
46 1
1.0%
45 1
1.0%
42 1
1.0%
40 1
1.0%
37 1
1.0%
34 1
1.0%
32 1
1.0%
31 1
1.0%
30 1
1.0%

8~9차종 교통량(대)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
0
82 
1
13 
5
 
2
2
 
2
4
 
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 82
82.0%
1 13
 
13.0%
5 2
 
2.0%
2 2
 
2.0%
4 1
 
1.0%

Length

2023-12-10T19:56:04.602206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:56:04.771174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 82
82.0%
1 13
 
13.0%
5 2
 
2.0%
2 2
 
2.0%
4 1
 
1.0%

10~12차종 교통량(대)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct16
Distinct (%)16.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.45
Minimum0
Maximum31
Zeros59
Zeros (%)59.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:56:04.949864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33
95-th percentile11.05
Maximum31
Range31
Interquartile range (IQR)3

Descriptive statistics

Standard deviation4.778688
Coefficient of variation (CV)1.9504849
Kurtosis13.486077
Mean2.45
Median Absolute Deviation (MAD)0
Skewness3.1975776
Sum245
Variance22.835859
MonotonicityNot monotonic
2023-12-10T19:56:05.139164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0 59
59.0%
2 9
 
9.0%
1 6
 
6.0%
4 5
 
5.0%
7 4
 
4.0%
3 3
 
3.0%
9 2
 
2.0%
6 2
 
2.0%
17 2
 
2.0%
8 2
 
2.0%
Other values (6) 6
 
6.0%
ValueCountFrequency (%)
0 59
59.0%
1 6
 
6.0%
2 9
 
9.0%
3 3
 
3.0%
4 5
 
5.0%
5 1
 
1.0%
6 2
 
2.0%
7 4
 
4.0%
8 2
 
2.0%
9 2
 
2.0%
ValueCountFrequency (%)
31 1
 
1.0%
17 2
2.0%
15 1
 
1.0%
12 1
 
1.0%
11 1
 
1.0%
10 1
 
1.0%
9 2
2.0%
8 2
2.0%
7 4
4.0%
6 2
2.0%

주소
Categorical

HIGH CORRELATION 

Distinct14
Distinct (%)14.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
충남 천안시 서북구 성거읍 송남리
10 
충북 청원군 남이면
9 
충청 천안시 구성동
9 
대구 동구 안심3동
8 
경북 구미시 임오동
8 
Other values (9)
56 

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%
충북 청원군 남이면 9
 
9.0%
충청 천안시 구성동 9
 
9.0%
대구 동구 안심3동 8
 
8.0%
경북 구미시 임오동 8
 
8.0%
경기 안성시 원곡면 8
 
8.0%
충북 옥천군 옥천읍 삼양리 7
 
7.0%
경남 양산시 동면 6
 
6.0%
울산 울주군 두서면 활천리 6
 
6.0%
경북 김천시 아포읍 봉산리 6
 
6.0%
Other values (4) 23
23.0%

Length

2023-12-10T19:56:05.368320image/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 14
 
4.1%
대덕구 12
 
3.5%
대전 12
 
3.5%
충남 10
 
2.9%
송남리 10
 
2.9%
성거읍 10
 
2.9%
Other values (28) 196
57.0%

Interactions

2023-12-10T19:55:56.847604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:47.370582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:48.662761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:50.073905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:51.485951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:52.945263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:54.298486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:55.430228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:56.990485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:47.526438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:48.811363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:50.232887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:51.664464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:53.108294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:54.426935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:55.560905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:57.136894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:47.696477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:48.983755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:50.410886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:51.847812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:53.275816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:54.579218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:55.720485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:57.482827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:47.873492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:49.168705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:50.581821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:52.051314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:53.463617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:54.722764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:55.862998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:57.672837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:48.074802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:49.336737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:50.750905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:52.233660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:53.651835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:54.859207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:56.009224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:57.870079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:48.232935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:49.520518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:50.936536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:52.426712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:53.842230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:55.009654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:56.482761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:58.009817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:48.367128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:49.700808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:51.108170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:52.601158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:53.988921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:55.116764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:56.591682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:58.169093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:48.511632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:49.889572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:51.318202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:52.777561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:54.143252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:55.300118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:56.708327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T19:56:05.521790image/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
기본키1.0000.9790.0000.0000.9860.9170.4680.9330.9480.6750.2910.0460.1490.963
지점0.9791.0000.0930.0001.0001.0000.4631.0001.0000.8630.2540.3800.1881.000
방향0.0000.0931.0000.0001.0000.0000.0000.0000.0000.4900.0000.1270.2270.000
차선0.0000.0000.0001.0000.0000.0000.2440.0000.0000.0000.5000.0000.2240.000
측정구간0.9861.0001.0000.0001.0001.0000.1821.0001.0001.0000.3040.5110.3981.000
장비이정(km)0.9171.0000.0000.0001.0001.0000.3010.9730.9670.6560.0000.0000.0001.000
차량통과수(대)0.4680.4630.0000.2440.1820.3011.0000.6100.4830.2480.5990.5170.5760.459
위도(°)0.9331.0000.0000.0001.0000.9730.6101.0001.0000.7630.0000.0000.0001.000
경도(°)0.9481.0000.0000.0001.0000.9670.4831.0001.0000.6850.0000.0000.0001.000
기울기(°)0.6750.8630.4900.0001.0000.6560.2480.7630.6851.0000.0000.4890.3000.849
5~7차종 교통량(대)0.2910.2540.0000.5000.3040.0000.5990.0000.0000.0001.0000.8010.7990.141
8~9차종 교통량(대)0.0460.3800.1270.0000.5110.0000.5170.0000.0000.4890.8011.0000.6980.367
10~12차종 교통량(대)0.1490.1880.2270.2240.3980.0000.5760.0000.0000.3000.7990.6981.0000.171
주소0.9631.0000.0000.0001.0001.0000.4591.0001.0000.8490.1410.3670.1711.000
2023-12-10T19:56:05.776183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
측정구간주소지점8~9차종 교통량(대)차선방향
측정구간1.0000.9210.9270.2360.0000.863
주소0.9211.0000.9940.1880.0000.000
지점0.9270.9941.0000.1580.0000.069
8~9차종 교통량(대)0.2360.1880.1581.0000.0000.152
차선0.0000.0000.0000.0001.0000.000
방향0.8630.0000.0690.1520.0001.000
2023-12-10T19:56:06.419008image/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
기본키1.0000.9970.2660.997-0.9740.0420.0110.0520.8240.0000.0000.8200.0000.825
장비이정(km)0.9971.0000.2681.000-0.9770.038-0.0030.0470.9610.0000.0000.8910.0000.967
차량통과수(대)0.2660.2681.0000.268-0.296-0.0150.6650.5770.1970.0000.1370.0140.3230.204
위도(°)0.9971.0000.2681.000-0.9770.038-0.0030.0470.9660.0000.0000.8960.0000.972
경도(°)-0.974-0.977-0.296-0.9771.000-0.051-0.033-0.0960.9510.0000.0000.8810.0000.957
기울기(°)0.0420.038-0.0150.038-0.0511.000-0.0190.0150.5950.3460.0000.8810.3550.604
5~7차종 교통량(대)0.011-0.0030.665-0.003-0.033-0.0191.0000.8490.0860.0000.2220.0880.4450.041
10~12차종 교통량(대)0.0520.0470.5770.047-0.0960.0150.8491.0000.0720.2360.1410.1490.5400.049
지점0.8240.9610.1970.9660.9510.5950.0860.0721.0000.0690.0000.9270.1580.994
방향0.0000.0000.0000.0000.0000.3460.0000.2360.0691.0000.0000.8630.1520.000
차선0.0000.0000.1370.0000.0000.0000.2220.1410.0000.0001.0000.0000.0000.000
측정구간0.8200.8910.0140.8960.8810.8810.0880.1490.9270.8630.0001.0000.2360.921
8~9차종 교통량(대)0.0000.0000.3230.0000.0000.3550.4450.5400.1580.1520.0000.2361.0000.188
주소0.8250.9670.2040.9720.9570.6040.0410.0490.9940.0000.0000.9210.1881.000

Missing values

2023-12-10T19:55:58.418367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T19:55:58.845123image/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-양산JC8.32020090102135.306944129.074722-3.15276000경남 양산시 동면
12도로공사A-0010-0083E-6E2노포JC-양산JC8.32020090104235.306944129.074722-3.15276100경남 양산시 동면
23도로공사A-0010-0083E-6E3노포JC-양산JC8.32020090103635.306944129.074722-3.15276305경남 양산시 동면
34도로공사A-0010-0083E-6S1양산JC-노포JC8.32020090102635.306944129.0747223.071416000경남 양산시 동면
45도로공사A-0010-0083E-6S2양산JC-노포JC8.32020090105635.306944129.0747223.071416100경남 양산시 동면
56도로공사A-0010-0083E-6S3양산JC-노포JC8.32020090102335.306944129.0747223.071416302경남 양산시 동면
67도로공사A-0010-0538E-6E1언양JC-활천IC53.4202009010635.681944129.1811110.22545000울산 울주군 두서면 활천리
78도로공사A-0010-0538E-6E2언양JC-활천IC53.42020090102535.681944129.1811110.22545200울산 울주군 두서면 활천리
89도로공사A-0010-0538E-6E3언양JC-활천IC53.42020090102135.681944129.1811110.225451204울산 울주군 두서면 활천리
910도로공사A-0010-0538E-6S1활천IC-언양JC53.42020090101735.681944129.1811110.19158000울산 울주군 두서면 활천리
기본키도로종류지점방향차선측정구간장비이정(km)측정일측정시간차량통과수(대)위도(°)경도(°)기울기(°)5~7차종 교통량(대)8~9차종 교통량(대)10~12차종 교통량(대)주소
9091도로공사A-0010-3452S-10S4북천안IC-천안IC345.22020090104736.868903127.1867220.61369611충남 천안시 서북구 성거읍 송남리
9192도로공사A-0010-3452S-10S5북천안IC-천안IC345.2202009010236.868903127.1867220.61369000충남 천안시 서북구 성거읍 송남리
9293도로공사A-0010-3613E-10E1안성IC-안성JC361.3202009010137.008889127.1491671.293015000경기 안성시 원곡면
9394도로공사A-0010-3613E-10E2안성IC-안성JC361.3202009010037.008889127.1491671.293015000경기 안성시 원곡면
9495도로공사A-0010-3613E-10E3안성IC-안성JC361.3202009010137.008889127.1491671.293015000경기 안성시 원곡면
9596도로공사A-0010-3613E-10E4안성IC-안성JC361.3202009010037.008889127.1491671.293015000경기 안성시 원곡면
9697도로공사A-0010-3613E-10E5안성IC-안성JC361.32020090105737.008889127.1491671.29301524112경기 안성시 원곡면
9798도로공사A-0010-3613E-10S1안성JC-안성IC361.32020090104237.008889127.149167-1.30142000경기 안성시 원곡면
9899도로공사A-0010-3613E-10S2안성JC-안성IC361.32020090107437.008889127.149167-1.30142302경기 안성시 원곡면
99100도로공사A-0010-3613E-10S3안성JC-안성IC361.32020090108637.008889127.149167-1.301423701경기 안성시 원곡면