Overview

Dataset statistics

Number of variables10
Number of observations139
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory12.1 KiB
Average record size in memory88.9 B

Variable types

Categorical2
Numeric7
Text1

Dataset

Description고속국도, 일반국도, 지방도, 국가지원지방도의 교통량을 조사하여 도로의 계획과 건설, 유지관리 및 도로행정등에 필요한 기본자료와 각종 연구에 필요한 차종별 평균일교통량과 그 구성비에 대한 데이터를 제공합니다.
Author국토교통부
URLhttps://www.data.go.kr/data/15113590/fileData.do

Alerts

계 is highly overall correlated with 승용차 and 2 other fieldsHigh correlation
승용차 is highly overall correlated with 계 and 2 other fieldsHigh correlation
승용차비율 is highly overall correlated with 화물차비율High correlation
버스 is highly overall correlated with 계 and 2 other fieldsHigh correlation
화물차 is highly overall correlated with 계 and 2 other fieldsHigh correlation
화물차비율 is highly overall correlated with 승용차비율High correlation
버스비율 is highly imbalanced (89.1%)Imbalance
노선명 has unique valuesUnique
계 has unique valuesUnique
승용차 has unique valuesUnique
화물차 has unique valuesUnique

Reproduction

Analysis started2023-12-12 18:06:49.615644
Analysis finished2023-12-12 18:06:55.639579
Duration6.02 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

도로등급
Categorical

Distinct4
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
일반국도
51 
고속국도
49 
국가지원지방도
30 
지방도
9 

Length

Max length7
Median length4
Mean length4.5827338
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
일반국도 51
36.7%
고속국도 49
35.3%
국가지원지방도 30
21.6%
지방도 9
 
6.5%

Length

2023-12-13T03:06:55.723850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T03:06:55.862547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
일반국도 51
36.7%
고속국도 49
35.3%
국가지원지방도 30
21.6%
지방도 9
 
6.5%

노선번호
Real number (ℝ)

Distinct91
Distinct (%)65.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean69.978417
Minimum1
Maximum600
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-13T03:06:55.990351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3.9
Q121.5
median40
Q379
95-th percentile257.7
Maximum600
Range599
Interquartile range (IQR)57.5

Descriptive statistics

Standard deviation96.711619
Coefficient of variation (CV)1.3820207
Kurtosis13.025429
Mean69.978417
Median Absolute Deviation (MAD)25
Skewness3.3965345
Sum9727
Variance9353.1372
MonotonicityNot monotonic
2023-12-13T03:06:56.146952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 3
 
2.2%
25 3
 
2.2%
37 3
 
2.2%
32 3
 
2.2%
30 3
 
2.2%
55 3
 
2.2%
60 3
 
2.2%
20 3
 
2.2%
17 3
 
2.2%
110 3
 
2.2%
Other values (81) 109
78.4%
ValueCountFrequency (%)
1 3
2.2%
2 2
1.4%
3 2
1.4%
4 2
1.4%
5 2
1.4%
6 2
1.4%
7 2
1.4%
8 1
 
0.7%
9 1
 
0.7%
10 1
 
0.7%
ValueCountFrequency (%)
600 1
0.7%
551 1
0.7%
451 1
0.7%
400 2
1.4%
301 1
0.7%
300 1
0.7%
253 1
0.7%
251 1
0.7%
171 2
1.4%
153 1
0.7%

노선명
Text

UNIQUE 

Distinct139
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
2023-12-13T03:06:56.432178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length9
Mean length7.6330935
Min length3

Characters and Unicode

Total characters1061
Distinct characters117
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique139 ?
Unique (%)100.0%

Sample

1st row경부선
2nd row남해선
3rd row광주대구선
4th row서해안선
5th row울산선
ValueCountFrequency (%)
79
 
16.8%
산 20
 
4.2%
천 16
 
3.4%
양 14
 
3.0%
주 13
 
2.8%
도 12
 
2.5%
안 11
 
2.3%
남 10
 
2.1%
부 10
 
2.1%
해 9
 
1.9%
Other values (135) 277
58.8%
2023-12-13T03:06:56.850670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
332
31.3%
- 79
 
7.4%
선 53
 
5.0%
산 31
 
2.9%
천 27
 
2.5%
부 23
 
2.2%
주 22
 
2.1%
도 19
 
1.8%
남 19
 
1.8%
양 19
 
1.8%
Other values (107) 437
41.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 628
59.2%
Space Separator 332
31.3%
Dash Punctuation 79
 
7.4%
Decimal Number 10
 
0.9%
Close Punctuation 6
 
0.6%
Open Punctuation 6
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
선 53
 
8.4%
산 31
 
4.9%
천 27
 
4.3%
부 23
 
3.7%
주 22
 
3.5%
도 19
 
3.0%
남 19
 
3.0%
양 19
 
3.0%
제 17
 
2.7%
해 14
 
2.2%
Other values (101) 384
61.1%
Decimal Number
ValueCountFrequency (%)
2 7
70.0%
1 3
30.0%
Space Separator
ValueCountFrequency (%)
332
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 79
100.0%
Close Punctuation
ValueCountFrequency (%)
) 6
100.0%
Open Punctuation
ValueCountFrequency (%)
( 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 628
59.2%
Common 433
40.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
선 53
 
8.4%
산 31
 
4.9%
천 27
 
4.3%
부 23
 
3.7%
주 22
 
3.5%
도 19
 
3.0%
남 19
 
3.0%
양 19
 
3.0%
제 17
 
2.7%
해 14
 
2.2%
Other values (101) 384
61.1%
Common
ValueCountFrequency (%)
332
76.7%
- 79
 
18.2%
2 7
 
1.6%
) 6
 
1.4%
( 6
 
1.4%
1 3
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 628
59.2%
ASCII 433
40.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
332
76.7%
- 79
 
18.2%
2 7
 
1.6%
) 6
 
1.4%
( 6
 
1.4%
1 3
 
0.7%
Hangul
ValueCountFrequency (%)
선 53
 
8.4%
산 31
 
4.9%
천 27
 
4.3%
부 23
 
3.7%
주 22
 
3.5%
도 19
 
3.0%
남 19
 
3.0%
양 19
 
3.0%
제 17
 
2.7%
해 14
 
2.2%
Other values (101) 384
61.1%

계
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct139
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27026
Minimum1229
Maximum178516
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-13T03:06:56.994162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1229
5-th percentile3427.3
Q17366.5
median16696
Q340196.5
95-th percentile76378.2
Maximum178516
Range177287
Interquartile range (IQR)32830

Descriptive statistics

Standard deviation30677.088
Coefficient of variation (CV)1.1350954
Kurtosis7.9198294
Mean27026
Median Absolute Deviation (MAD)10932
Skewness2.4927323
Sum3756614
Variance9.410837 × 108
MonotonicityNot monotonic
2023-12-13T03:06:57.134531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
90042 1
 
0.7%
10045 1
 
0.7%
1821 1
 
0.7%
6122 1
 
0.7%
2195 1
 
0.7%
10999 1
 
0.7%
3167 1
 
0.7%
16778 1
 
0.7%
32283 1
 
0.7%
25199 1
 
0.7%
Other values (129) 129
92.8%
ValueCountFrequency (%)
1229 1
0.7%
1238 1
0.7%
1821 1
0.7%
2195 1
0.7%
2696 1
0.7%
3167 1
0.7%
3304 1
0.7%
3441 1
0.7%
3541 1
0.7%
3577 1
0.7%
ValueCountFrequency (%)
178516 1
0.7%
166077 1
0.7%
148727 1
0.7%
118009 1
0.7%
98278 1
0.7%
90042 1
0.7%
87117 1
0.7%
75185 1
0.7%
73750 1
0.7%
68160 1
0.7%

승용차
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct139
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19700.727
Minimum686
Maximum134784
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-13T03:06:57.319026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum686
5-th percentile2287.7
Q15210
median12380
Q326842.5
95-th percentile59655.5
Maximum134784
Range134098
Interquartile range (IQR)21632.5

Descriptive statistics

Standard deviation23183.852
Coefficient of variation (CV)1.1768019
Kurtosis9.0727387
Mean19700.727
Median Absolute Deviation (MAD)8188
Skewness2.6910334
Sum2738401
Variance5.3749099 × 108
MonotonicityNot monotonic
2023-12-13T03:06:57.761508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
61262 1
 
0.7%
8250 1
 
0.7%
1316 1
 
0.7%
4332 1
 
0.7%
1525 1
 
0.7%
8947 1
 
0.7%
2679 1
 
0.7%
13182 1
 
0.7%
21679 1
 
0.7%
20424 1
 
0.7%
Other values (129) 129
92.8%
ValueCountFrequency (%)
686 1
0.7%
952 1
0.7%
1316 1
0.7%
1525 1
0.7%
1748 1
0.7%
2146 1
0.7%
2195 1
0.7%
2298 1
0.7%
2562 1
0.7%
2569 1
0.7%
ValueCountFrequency (%)
134784 1
0.7%
133056 1
0.7%
105187 1
0.7%
90862 1
0.7%
89371 1
0.7%
65862 1
0.7%
61262 1
0.7%
59477 1
0.7%
54380 1
0.7%
52025 1
0.7%

승용차비율
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.7323741
Minimum0.2
Maximum0.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-13T03:06:57.909311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.2
5-th percentile0.6
Q10.7
median0.7
Q30.8
95-th percentile0.8
Maximum0.9
Range0.7
Interquartile range (IQR)0.1

Descriptive statistics

Standard deviation0.084455483
Coefficient of variation (CV)0.11531741
Kurtosis10.520008
Mean0.7323741
Median Absolute Deviation (MAD)0.1
Skewness-1.9200078
Sum101.8
Variance0.0071327286
MonotonicityNot monotonic
2023-12-13T03:06:58.022269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0.7 65
46.8%
0.8 56
40.3%
0.6 12
 
8.6%
0.9 4
 
2.9%
0.2 1
 
0.7%
0.5 1
 
0.7%
ValueCountFrequency (%)
0.2 1
 
0.7%
0.5 1
 
0.7%
0.6 12
 
8.6%
0.7 65
46.8%
0.8 56
40.3%
0.9 4
 
2.9%
ValueCountFrequency (%)
0.9 4
 
2.9%
0.8 56
40.3%
0.7 65
46.8%
0.6 12
 
8.6%
0.5 1
 
0.7%
0.2 1
 
0.7%

버스
Real number (ℝ)

HIGH CORRELATION 

Distinct124
Distinct (%)89.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean378.41007
Minimum24
Maximum3126
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-13T03:06:58.159369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum24
5-th percentile55.9
Q1116.5
median213
Q3458.5
95-th percentile1054
Maximum3126
Range3102
Interquartile range (IQR)342

Descriptive statistics

Standard deviation470.69261
Coefficient of variation (CV)1.2438691
Kurtosis14.447154
Mean378.41007
Median Absolute Deviation (MAD)127
Skewness3.3252911
Sum52599
Variance221551.53
MonotonicityNot monotonic
2023-12-13T03:06:58.306185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
121 3
 
2.2%
116 3
 
2.2%
63 3
 
2.2%
142 3
 
2.2%
85 2
 
1.4%
68 2
 
1.4%
138 2
 
1.4%
231 2
 
1.4%
129 2
 
1.4%
115 2
 
1.4%
Other values (114) 115
82.7%
ValueCountFrequency (%)
24 1
 
0.7%
29 1
 
0.7%
39 1
 
0.7%
42 1
 
0.7%
49 1
 
0.7%
52 1
 
0.7%
55 1
 
0.7%
56 1
 
0.7%
58 1
 
0.7%
63 3
2.2%
ValueCountFrequency (%)
3126 1
0.7%
2934 1
0.7%
2038 1
0.7%
1576 1
0.7%
1536 1
0.7%
1198 1
0.7%
1162 1
0.7%
1042 1
0.7%
1026 1
0.7%
983 1
0.7%

버스비율
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
0.0
137 
0.1
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 137
98.6%
0.1 2
 
1.4%

Length

2023-12-13T03:06:58.463782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T03:06:58.559846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 137
98.6%
0.1 2
 
1.4%

화물차
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct139
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6946.8633
Minimum262
Maximum42156
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-13T03:06:58.664604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum262
5-th percentile918.3
Q11766
median3457
Q39222.5
95-th percentile22809.3
Maximum42156
Range41894
Interquartile range (IQR)7456.5

Descriptive statistics

Standard deviation7974.9991
Coefficient of variation (CV)1.148
Kurtosis4.8878168
Mean6946.8633
Median Absolute Deviation (MAD)2145
Skewness2.0898389
Sum965614
Variance63600610
MonotonicityNot monotonic
2023-12-13T03:06:58.800130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
26742 1
 
0.7%
1695 1
 
0.7%
463 1
 
0.7%
1741 1
 
0.7%
641 1
 
0.7%
1936 1
 
0.7%
433 1
 
0.7%
3403 1
 
0.7%
10126 1
 
0.7%
4221 1
 
0.7%
Other values (129) 129
92.8%
ValueCountFrequency (%)
262 1
0.7%
433 1
0.7%
457 1
0.7%
463 1
0.7%
641 1
0.7%
829 1
0.7%
885 1
0.7%
922 1
0.7%
997 1
0.7%
1046 1
0.7%
ValueCountFrequency (%)
42156 1
0.7%
40414 1
0.7%
32133 1
0.7%
31460 1
0.7%
26742 1
0.7%
26105 1
0.7%
24324 1
0.7%
22641 1
0.7%
20328 1
0.7%
19711 1
0.7%

화물차비율
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.25755396
Minimum0.1
Maximum0.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-13T03:06:58.912825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.2
Q10.2
median0.2
Q30.3
95-th percentile0.4
Maximum0.7
Range0.6
Interquartile range (IQR)0.1

Descriptive statistics

Standard deviation0.080737917
Coefficient of variation (CV)0.31347962
Kurtosis5.8166402
Mean0.25755396
Median Absolute Deviation (MAD)0.1
Skewness1.4273976
Sum35.8
Variance0.0065186112
MonotonicityNot monotonic
2023-12-13T03:06:59.031628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0.2 65
46.8%
0.3 57
41.0%
0.4 10
 
7.2%
0.1 5
 
3.6%
0.7 1
 
0.7%
0.5 1
 
0.7%
ValueCountFrequency (%)
0.1 5
 
3.6%
0.2 65
46.8%
0.3 57
41.0%
0.4 10
 
7.2%
0.5 1
 
0.7%
0.7 1
 
0.7%
ValueCountFrequency (%)
0.7 1
 
0.7%
0.5 1
 
0.7%
0.4 10
 
7.2%
0.3 57
41.0%
0.2 65
46.8%
0.1 5
 
3.6%

Interactions

2023-12-13T03:06:54.580702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:06:49.984273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:06:50.596601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:06:51.565829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:06:52.230083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:06:52.952475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:06:53.846080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:06:54.690221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:06:50.062558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:06:50.682564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:06:51.658402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:06:52.339216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:06:53.078876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:06:53.938393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:06:54.820771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:06:50.144436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:06:50.769790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:06:51.762249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:06:52.443712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:06:53.232545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:06:54.047751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:06:54.935161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:06:50.242146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:06:51.152596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:06:51.854817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:06:52.556430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:06:53.369633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:06:54.153199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:06:55.048199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:06:50.330319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:06:51.240119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:06:51.952634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:06:52.657259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:06:53.495022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:06:54.270067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:06:55.141919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:06:50.414395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:06:51.328984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:06:52.042204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:06:52.753570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:06:53.602516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:06:54.392138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:06:55.245250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:06:50.494426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:06:51.473037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:06:52.130549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:06:52.847236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:06:53.718325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:06:54.478427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T03:06:59.121298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
도로등급노선번호계승용차승용차비율버스버스비율화물차화물차비율
도로등급1.0000.5030.6030.7290.2940.6240.0000.6290.305
노선번호0.5031.0000.4100.5030.3330.1560.0000.5190.362
계0.6030.4101.0000.9470.2440.7980.0000.9420.263
승용차0.7290.5030.9471.0000.1550.8970.0480.7990.152
승용차비율0.2940.3330.2440.1551.0000.0000.0390.5190.998
버스0.6240.1560.7980.8970.0001.0000.5980.7600.074
버스비율0.0000.0000.0000.0480.0390.5981.0000.0000.126
화물차0.6290.5190.9420.7990.5190.7600.0001.0000.538
화물차비율0.3050.3620.2630.1520.9980.0740.1260.5381.000
2023-12-13T03:06:59.236154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
도로등급버스비율
도로등급1.0000.000
버스비율0.0001.000
2023-12-13T03:06:59.323969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
노선번호계승용차승용차비율버스화물차화물차비율도로등급버스비율
노선번호1.0000.3240.298-0.0600.2770.3340.1010.2390.000
계0.3241.0000.9910.1400.8950.968-0.0760.4260.000
승용차0.2980.9911.0000.2160.8790.938-0.1540.3940.000
승용차비율-0.0600.1400.2161.0000.105-0.049-0.9010.1910.023
버스0.2770.8950.8790.1051.0000.868-0.0830.3140.442
화물차0.3340.9680.938-0.0490.8681.0000.1180.4500.000
화물차비율0.101-0.076-0.154-0.901-0.0830.1181.0000.1990.088
도로등급0.2390.4260.3940.1910.3140.4500.1991.0000.000
버스비율0.0000.0000.0000.0230.4420.0000.0880.0001.000

Missing values

2023-12-13T03:06:55.380089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T03:06:55.575995image/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

도로등급노선번호노선명계승용차승용차비율버스버스비율화물차화물차비율
0고속국도1경부선90042612620.720380.0267420.3
1고속국도10남해선40271273130.74830.0124750.3
2고속국도12광주대구선19004130930.72290.056820.3
3고속국도15서해안선45520320680.74320.0130200.3
4고속국도16울산선46605294920.67300.0163830.4
5고속국도17평택화성선87117658620.89270.0203280.2
6고속국도17수원광명선67757543800.81980.0131790.2
7고속국도20익산포항선25456175690.72350.076520.3
8고속국도25호남선43234303670.78400.0120270.3
9고속국도25논산천안선52106342230.715360.0163470.3
도로등급노선번호노선명계승용차승용차비율버스버스비율화물차화물차비율
129국가지원지방도98수도권 순환선19046139880.73910.046670.2
130지방도1경 기 도14009105280.82130.032680.2
131지방도2강 원 도354125970.71150.08290.2
132지방도3충 청 북 도436131080.7580.011950.3
133지방도4충 청 남 도447332890.7850.010990.2
134지방도5전 라 북 도383025690.7850.011760.3
135지방도6전 라 남 도344121460.61050.011900.3
136지방도7경 상 북 도330421950.7630.010460.3
137지방도8경 상 남 도492336120.71130.011980.2
138지방도9제주특별자치도1207497080.83090.020570.2