Overview

Dataset statistics

Number of variables12
Number of observations96
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory10.1 KiB
Average record size in memory107.4 B

Variable types

Categorical2
DateTime1
Numeric9

Dataset

Description서울도시고속도로 노선별 요일별 속도자료- 공간적 범위 : 해당 노선의 전구간 평균속도- 시간적 범위 : 활동시간대(07시~22시) 평균값
Author서울시설공단
URLhttps://www.data.go.kr/data/15070003/fileData.do

Alerts

년도 has constant value ""Constant
평일 평균 교통량 is highly overall correlated with 도로명High correlation
평일 평균 속도 is highly overall correlated with 일요일(휴일포함) and 7 other fieldsHigh correlation
일요일(휴일포함) is highly overall correlated with 평일 평균 속도 and 7 other fieldsHigh correlation
월요일 is highly overall correlated with 평일 평균 속도 and 7 other fieldsHigh correlation
화요일 is highly overall correlated with 평일 평균 속도 and 7 other fieldsHigh correlation
수요일 is highly overall correlated with 평일 평균 속도 and 7 other fieldsHigh correlation
목요일 is highly overall correlated with 평일 평균 속도 and 7 other fieldsHigh correlation
금요일 is highly overall correlated with 평일 평균 속도 and 7 other fieldsHigh correlation
토요일 is highly overall correlated with 평일 평균 속도 and 7 other fieldsHigh correlation
도로명 is highly overall correlated with 평일 평균 교통량 and 8 other fieldsHigh correlation
평일 평균 교통량 has unique valuesUnique

Reproduction

Analysis started2024-04-21 01:38:41.831335
Analysis finished2024-04-21 01:38:50.704676
Duration8.87 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

년도
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size900.0 B
2023
96 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023
2nd row2023
3rd row2023
4th row2023
5th row2023

Common Values

ValueCountFrequency (%)
2023 96
100.0%

Length

2024-04-21T10:38:50.759889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T10:38:50.846307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023 96
100.0%


Date

Distinct12
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Memory size900.0 B
Minimum2023-01-01 00:00:00
Maximum2023-12-01 00:00:00
2024-04-21T10:38:50.925548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:38:51.012648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)

도로명
Categorical

HIGH CORRELATION 

Distinct8
Distinct (%)8.3%
Missing0
Missing (%)0.0%
Memory size900.0 B
강변북로
12 
경부고속도로
12 
내부순환로
12 
동부간선도로
12 
북부간선도로
12 
Other values (3)
36 

Length

Max length6
Median length5.5
Mean length5.25
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
강변북로 12
12.5%
경부고속도로 12
12.5%
내부순환로 12
12.5%
동부간선도로 12
12.5%
북부간선도로 12
12.5%
분당수서로 12
12.5%
올림픽대로 12
12.5%
강남순환로 12
12.5%

Length

2024-04-21T10:38:51.131608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T10:38:51.249295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
강변북로 12
12.5%
경부고속도로 12
12.5%
내부순환로 12
12.5%
동부간선도로 12
12.5%
북부간선도로 12
12.5%
분당수서로 12
12.5%
올림픽대로 12
12.5%
강남순환로 12
12.5%

평일 평균 교통량
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct96
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean167696.27
Minimum106040
Maximum251217
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size996.0 B
2024-04-21T10:38:51.382214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum106040
5-th percentile108562.25
Q1134493.5
median147639
Q3203675
95-th percentile246329.75
Maximum251217
Range145177
Interquartile range (IQR)69181.5

Descriptive statistics

Standard deviation46328.244
Coefficient of variation (CV)0.27626282
Kurtosis-1.1238651
Mean167696.27
Median Absolute Deviation (MAD)29855.5
Skewness0.53371701
Sum16098842
Variance2.1463062 × 109
MonotonicityNot monotonic
2024-04-21T10:38:51.533477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
230723 1
 
1.0%
108778 1
 
1.0%
131065 1
 
1.0%
133291 1
 
1.0%
134837 1
 
1.0%
133829 1
 
1.0%
131085 1
 
1.0%
131442 1
 
1.0%
134571 1
 
1.0%
136707 1
 
1.0%
Other values (86) 86
89.6%
ValueCountFrequency (%)
106040 1
1.0%
107072 1
1.0%
107133 1
1.0%
108088 1
1.0%
108407 1
1.0%
108614 1
1.0%
108778 1
1.0%
109247 1
1.0%
109325 1
1.0%
110291 1
1.0%
ValueCountFrequency (%)
251217 1
1.0%
250307 1
1.0%
250087 1
1.0%
248727 1
1.0%
247445 1
1.0%
245958 1
1.0%
245726 1
1.0%
245333 1
1.0%
243725 1
1.0%
242683 1
1.0%

평일 평균 속도
Real number (ℝ)

HIGH CORRELATION 

Distinct29
Distinct (%)30.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.65625
Minimum30
Maximum83
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size996.0 B
2024-04-21T10:38:51.661663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile33
Q144
median47
Q352
95-th percentile81.25
Maximum83
Range53
Interquartile range (IQR)8

Descriptive statistics

Standard deviation12.74431
Coefficient of variation (CV)0.25665067
Kurtosis1.5910139
Mean49.65625
Median Absolute Deviation (MAD)4
Skewness1.3840536
Sum4767
Variance162.41743
MonotonicityNot monotonic
2024-04-21T10:38:51.808211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
45 10
 
10.4%
47 9
 
9.4%
52 8
 
8.3%
46 7
 
7.3%
53 6
 
6.2%
51 5
 
5.2%
49 5
 
5.2%
48 5
 
5.2%
44 5
 
5.2%
82 4
 
4.2%
Other values (19) 32
33.3%
ValueCountFrequency (%)
30 1
 
1.0%
32 2
 
2.1%
33 3
3.1%
34 4
4.2%
35 2
 
2.1%
39 1
 
1.0%
40 2
 
2.1%
42 4
4.2%
43 3
3.1%
44 5
5.2%
ValueCountFrequency (%)
83 1
 
1.0%
82 4
4.2%
81 1
 
1.0%
80 1
 
1.0%
79 1
 
1.0%
78 1
 
1.0%
76 1
 
1.0%
75 1
 
1.0%
74 1
 
1.0%
55 1
 
1.0%

일요일(휴일포함)
Real number (ℝ)

HIGH CORRELATION 

Distinct33
Distinct (%)34.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean60.9375
Minimum37
Maximum89
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size996.0 B
2024-04-21T10:38:51.930509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37
5-th percentile44.5
Q155
median60
Q364
95-th percentile87
Maximum89
Range52
Interquartile range (IQR)9

Descriptive statistics

Standard deviation11.621045
Coefficient of variation (CV)0.19070433
Kurtosis0.89456158
Mean60.9375
Median Absolute Deviation (MAD)4
Skewness0.87697612
Sum5850
Variance135.04868
MonotonicityNot monotonic
2024-04-21T10:38:52.060520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
64 10
 
10.4%
60 8
 
8.3%
62 7
 
7.3%
63 6
 
6.2%
58 6
 
6.2%
61 6
 
6.2%
54 4
 
4.2%
55 4
 
4.2%
59 4
 
4.2%
56 3
 
3.1%
Other values (23) 38
39.6%
ValueCountFrequency (%)
37 1
 
1.0%
40 1
 
1.0%
42 1
 
1.0%
43 2
2.1%
45 2
2.1%
46 3
3.1%
47 1
 
1.0%
48 1
 
1.0%
49 1
 
1.0%
50 1
 
1.0%
ValueCountFrequency (%)
89 1
 
1.0%
88 3
3.1%
87 3
3.1%
86 1
 
1.0%
85 2
2.1%
84 1
 
1.0%
83 1
 
1.0%
67 1
 
1.0%
66 1
 
1.0%
65 3
3.1%

월요일
Real number (ℝ)

HIGH CORRELATION 

Distinct31
Distinct (%)32.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52.1875
Minimum32
Maximum84
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size996.0 B
2024-04-21T10:38:52.197092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum32
5-th percentile35.75
Q146.75
median50
Q354
95-th percentile82.25
Maximum84
Range52
Interquartile range (IQR)7.25

Descriptive statistics

Standard deviation12.406545
Coefficient of variation (CV)0.23773021
Kurtosis1.3191431
Mean52.1875
Median Absolute Deviation (MAD)4
Skewness1.2247135
Sum5010
Variance153.92237
MonotonicityNot monotonic
2024-04-21T10:38:52.315520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
48 9
 
9.4%
50 7
 
7.3%
51 7
 
7.3%
49 6
 
6.2%
54 6
 
6.2%
47 5
 
5.2%
52 5
 
5.2%
56 4
 
4.2%
83 4
 
4.2%
53 4
 
4.2%
Other values (21) 39
40.6%
ValueCountFrequency (%)
32 1
 
1.0%
33 2
2.1%
35 2
2.1%
36 3
3.1%
37 2
2.1%
38 2
2.1%
42 2
2.1%
43 2
2.1%
44 3
3.1%
45 3
3.1%
ValueCountFrequency (%)
84 1
 
1.0%
83 4
4.2%
82 1
 
1.0%
81 1
 
1.0%
80 1
 
1.0%
79 1
 
1.0%
77 2
2.1%
75 1
 
1.0%
58 3
3.1%
57 3
3.1%

화요일
Real number (ℝ)

HIGH CORRELATION 

Distinct29
Distinct (%)30.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.6875
Minimum28
Maximum83
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size996.0 B
2024-04-21T10:38:52.426247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum28
5-th percentile33.75
Q144
median47
Q352
95-th percentile81.25
Maximum83
Range55
Interquartile range (IQR)8

Descriptive statistics

Standard deviation12.774409
Coefficient of variation (CV)0.25709502
Kurtosis1.5460247
Mean49.6875
Median Absolute Deviation (MAD)4
Skewness1.3546034
Sum4770
Variance163.18553
MonotonicityNot monotonic
2024-04-21T10:38:52.548222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
46 11
 
11.5%
45 8
 
8.3%
52 7
 
7.3%
53 6
 
6.2%
49 5
 
5.2%
47 5
 
5.2%
44 5
 
5.2%
43 5
 
5.2%
82 4
 
4.2%
51 4
 
4.2%
Other values (19) 36
37.5%
ValueCountFrequency (%)
28 1
 
1.0%
32 1
 
1.0%
33 3
3.1%
34 4
4.2%
35 3
3.1%
38 2
 
2.1%
39 1
 
1.0%
42 3
3.1%
43 5
5.2%
44 5
5.2%
ValueCountFrequency (%)
83 1
 
1.0%
82 4
4.2%
81 1
 
1.0%
80 2
2.1%
78 1
 
1.0%
76 1
 
1.0%
74 1
 
1.0%
73 1
 
1.0%
55 1
 
1.0%
54 3
3.1%

수요일
Real number (ℝ)

HIGH CORRELATION 

Distinct33
Distinct (%)34.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.010417
Minimum30
Maximum83
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size996.0 B
2024-04-21T10:38:52.693728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile34
Q144.75
median47
Q352.25
95-th percentile81.25
Maximum83
Range53
Interquartile range (IQR)7.5

Descriptive statistics

Standard deviation12.750641
Coefficient of variation (CV)0.2549597
Kurtosis1.5215011
Mean50.010417
Median Absolute Deviation (MAD)5
Skewness1.3732995
Sum4801
Variance162.57884
MonotonicityNot monotonic
2024-04-21T10:38:52.812119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
46 10
 
10.4%
47 10
 
10.4%
53 8
 
8.3%
45 8
 
8.3%
52 7
 
7.3%
35 4
 
4.2%
49 4
 
4.2%
48 4
 
4.2%
44 4
 
4.2%
50 4
 
4.2%
Other values (23) 33
34.4%
ValueCountFrequency (%)
30 1
 
1.0%
31 1
 
1.0%
33 1
 
1.0%
34 3
3.1%
35 4
4.2%
36 1
 
1.0%
37 2
2.1%
38 1
 
1.0%
39 1
 
1.0%
41 3
3.1%
ValueCountFrequency (%)
83 2
2.1%
82 3
3.1%
81 1
 
1.0%
80 1
 
1.0%
79 1
 
1.0%
78 1
 
1.0%
77 1
 
1.0%
76 1
 
1.0%
75 1
 
1.0%
55 3
3.1%

목요일
Real number (ℝ)

HIGH CORRELATION 

Distinct31
Distinct (%)32.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.260417
Minimum30
Maximum83
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size996.0 B
2024-04-21T10:38:52.934954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile32.75
Q143
median47
Q351.25
95-th percentile81
Maximum83
Range53
Interquartile range (IQR)8.25

Descriptive statistics

Standard deviation12.854854
Coefficient of variation (CV)0.26095706
Kurtosis1.5946833
Mean49.260417
Median Absolute Deviation (MAD)4
Skewness1.3980478
Sum4729
Variance165.24726
MonotonicityNot monotonic
2024-04-21T10:38:53.046898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
45 8
 
8.3%
47 7
 
7.3%
44 7
 
7.3%
52 6
 
6.2%
42 6
 
6.2%
46 5
 
5.2%
50 5
 
5.2%
51 5
 
5.2%
43 5
 
5.2%
49 4
 
4.2%
Other values (21) 38
39.6%
ValueCountFrequency (%)
30 1
 
1.0%
31 1
 
1.0%
32 3
3.1%
33 4
4.2%
35 2
 
2.1%
36 1
 
1.0%
39 1
 
1.0%
40 1
 
1.0%
41 2
 
2.1%
42 6
6.2%
ValueCountFrequency (%)
83 1
 
1.0%
82 3
3.1%
81 2
2.1%
80 1
 
1.0%
79 1
 
1.0%
78 1
 
1.0%
76 1
 
1.0%
75 1
 
1.0%
74 1
 
1.0%
54 3
3.1%

금요일
Real number (ℝ)

HIGH CORRELATION 

Distinct27
Distinct (%)28.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.927083
Minimum28
Maximum81
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size996.0 B
2024-04-21T10:38:53.152063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum28
5-th percentile30
Q140
median44
Q350
95-th percentile78.75
Maximum81
Range53
Interquartile range (IQR)10

Descriptive statistics

Standard deviation13.227758
Coefficient of variation (CV)0.28187897
Kurtosis1.5776831
Mean46.927083
Median Absolute Deviation (MAD)5
Skewness1.4249364
Sum4505
Variance174.97357
MonotonicityNot monotonic
2024-04-21T10:38:53.285379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
42 9
 
9.4%
40 6
 
6.2%
45 6
 
6.2%
43 6
 
6.2%
50 6
 
6.2%
51 6
 
6.2%
44 6
 
6.2%
81 5
 
5.2%
39 5
 
5.2%
41 4
 
4.2%
Other values (17) 37
38.5%
ValueCountFrequency (%)
28 1
 
1.0%
29 2
 
2.1%
30 3
3.1%
31 3
3.1%
32 2
 
2.1%
34 1
 
1.0%
37 1
 
1.0%
38 3
3.1%
39 5
5.2%
40 6
6.2%
ValueCountFrequency (%)
81 5
5.2%
78 2
 
2.1%
77 2
 
2.1%
75 1
 
1.0%
73 2
 
2.1%
52 2
 
2.1%
51 6
6.2%
50 6
6.2%
49 4
4.2%
48 3
3.1%

토요일
Real number (ℝ)

HIGH CORRELATION 

Distinct28
Distinct (%)29.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52.229167
Minimum33
Maximum84
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size996.0 B
2024-04-21T10:38:53.409771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum33
5-th percentile36.75
Q146
median50.5
Q353.25
95-th percentile82
Maximum84
Range51
Interquartile range (IQR)7.25

Descriptive statistics

Standard deviation12.373553
Coefficient of variation (CV)0.23690888
Kurtosis1.4683987
Mean52.229167
Median Absolute Deviation (MAD)3.5
Skewness1.3435174
Sum5014
Variance153.10482
MonotonicityNot monotonic
2024-04-21T10:38:53.713625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
53 13
 
13.5%
49 9
 
9.4%
52 6
 
6.2%
54 6
 
6.2%
50 5
 
5.2%
46 5
 
5.2%
51 5
 
5.2%
48 4
 
4.2%
44 4
 
4.2%
38 4
 
4.2%
Other values (18) 35
36.5%
ValueCountFrequency (%)
33 2
2.1%
35 2
2.1%
36 1
 
1.0%
37 1
 
1.0%
38 4
4.2%
40 2
2.1%
41 3
3.1%
42 2
2.1%
44 4
4.2%
45 1
 
1.0%
ValueCountFrequency (%)
84 1
 
1.0%
83 3
3.1%
82 3
3.1%
80 2
 
2.1%
79 2
 
2.1%
77 1
 
1.0%
57 3
3.1%
56 1
 
1.0%
55 2
 
2.1%
54 6
6.2%

Interactions

2024-04-21T10:38:49.578759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:38:43.365596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:38:44.305635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:38:44.984280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:38:45.734257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:38:46.448174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:38:47.387691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:38:48.107976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:38:48.818020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:38:49.655258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:38:43.627111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:38:44.405568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:38:45.076635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:38:45.820881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:38:46.529892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:38:47.467261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:38:48.195377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:38:48.915019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:38:49.734112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:38:43.706001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:38:44.479602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:38:45.154463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:38:45.913179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:38:46.619988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:38:47.541768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:38:48.278684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:38:49.016414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:38:49.806482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:38:43.805923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:38:44.552835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:38:45.236434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:38:45.983128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:38:46.696695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:38:47.617574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:38:48.374822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:38:49.099332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:38:49.876876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:38:43.883236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:38:44.624092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:38:45.325333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:38:46.059347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:38:46.772568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:38:47.692210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:38:48.456426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:38:49.184279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:38:49.958178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:38:43.959730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:38:44.696074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:38:45.401194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:38:46.138339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:38:47.000073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:38:47.765794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:38:48.530089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:38:49.260237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:38:50.039249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:38:44.035028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:38:44.765579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:38:45.499667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:38:46.210435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:38:47.078703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:38:47.865070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:38:48.602415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:38:49.332062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:38:50.112635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:38:44.113991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:38:44.838362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:38:45.594275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:38:46.288179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:38:47.172225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:38:47.963524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:38:48.673158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:38:49.413113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:38:50.184863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:38:44.210232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:38:44.911600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:38:45.665461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:38:46.364240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:38:47.292810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:38:48.037473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:38:48.743871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:38:49.497655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-21T10:38:53.814029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
도로명평일 평균 교통량평일 평균 속도일요일(휴일포함)월요일화요일수요일목요일금요일토요일
1.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
도로명0.0001.0000.9480.8500.8920.8190.8400.8390.8230.8470.790
평일 평균 교통량0.0000.9481.0000.8920.7800.8740.8940.8780.8770.8940.810
평일 평균 속도0.0000.8500.8921.0000.7950.9840.9860.9940.9870.9870.937
일요일(휴일포함)0.0000.8920.7800.7951.0000.7780.7800.7840.8070.8040.827
월요일0.0000.8190.8740.9840.7781.0000.9740.9800.9740.9790.935
화요일0.0000.8400.8940.9860.7800.9741.0000.9880.9750.9790.919
수요일0.0000.8390.8780.9940.7840.9800.9881.0000.9850.9810.932
목요일0.0000.8230.8770.9870.8070.9740.9750.9851.0000.9810.958
금요일0.0000.8470.8940.9870.8040.9790.9790.9810.9811.0000.938
토요일0.0000.7900.8100.9370.8270.9350.9190.9320.9580.9381.000
2024-04-21T10:38:53.951406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
평일 평균 교통량평일 평균 속도일요일(휴일포함)월요일화요일수요일목요일금요일토요일도로명
평일 평균 교통량1.000-0.123-0.003-0.110-0.171-0.173-0.178-0.083-0.0310.832
평일 평균 속도-0.1231.0000.7800.9760.9760.9800.9650.9840.8520.664
일요일(휴일포함)-0.0030.7801.0000.7680.7570.7440.7790.7950.9500.509
월요일-0.1100.9760.7681.0000.9400.9480.9210.9570.8460.617
화요일-0.1710.9760.7570.9401.0000.9730.9440.9640.8060.642
수요일-0.1730.9800.7440.9480.9731.0000.9400.9620.8060.647
목요일-0.1780.9650.7790.9210.9440.9401.0000.9580.8500.623
금요일-0.0830.9840.7950.9570.9640.9620.9581.0000.8570.660
토요일-0.0310.8520.9500.8460.8060.8060.8500.8571.0000.575
도로명0.8320.6640.5090.6170.6420.6470.6230.6600.5751.000

Missing values

2024-04-21T10:38:50.310019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-21T10:38:50.648840image/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

년도도로명평일 평균 교통량평일 평균 속도일요일(휴일포함)월요일화요일수요일목요일금요일토요일
020232023-01강변북로2307234562484445464253
120232023-02강변북로2341214559484446454349
220232023-03강변북로2374084762514847474450
320232023-04강변북로2332494558484445444249
420232023-05강변북로2331004654514546424246
520232023-06강변북로2325234560484545444248
620232023-07강변북로2293244358494244413949
720232023-08강변북로2202074259444241433848
820232023-09강변북로2203293954423837393842
920232023-10강변북로2223454458474545424044
년도도로명평일 평균 교통량평일 평균 속도일요일(휴일포함)월요일화요일수요일목요일금요일토요일
8620232023-03강남순환로1697437686777677767579
8720232023-04강남순환로1654977987808079787780
8820232023-05강남순환로1592008285848282818182
8920232023-06강남순환로1599048288838282828183
9020232023-07강남순환로1560578187838181807883
9120232023-08강남순환로1562398087818080817882
9220232023-09강남순환로1612257885797878797779
9320232023-10강남순환로1554888288828282828183
9420232023-11강남순환로1547988389838283838182
9520232023-12강남순환로1533468288838383828184