Overview

Dataset statistics

Number of variables14
Number of observations383
Missing cells51
Missing cells (%)1.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory46.5 KiB
Average record size in memory124.3 B

Variable types

Numeric7
Categorical6
Text1

Dataset

Description경상남도 도로대장전산화 시스템 데이터의 중장기개방계획에 따른 데이터입니다. 시스템 상에서의 각 도로별 시설물 기본정보를 가지고 있으며, 도로대장의 정차대 데이터를 포함하고있습니다.
Author경상남도
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=15091921

Alerts

관리기관 has constant value ""Constant
도로종류 has constant value ""Constant
비고 is highly overall correlated with 식별번호 and 7 other fieldsHigh correlation
이력코드 is highly overall correlated with 비고High correlation
식별번호 is highly overall correlated with 관리번호 and 4 other fieldsHigh correlation
관리번호 is highly overall correlated with 식별번호 and 2 other fieldsHigh correlation
노선번호 is highly overall correlated with 식별번호 and 2 other fieldsHigh correlation
구간번호 is highly overall correlated with 비고High correlation
위치 is highly overall correlated with 비고High correlation
연장 is highly overall correlated with 식별번호 and 2 other fieldsHigh correlation
폭 is highly overall correlated with 연장 and 1 other fieldsHigh correlation
위치_방향 is highly overall correlated with 비고High correlation
대기소 is highly overall correlated with 식별번호 and 3 other fieldsHigh correlation
이력코드 is highly imbalanced (88.4%)Imbalance
비고 is highly imbalanced (90.5%)Imbalance
사진 has 51 (13.3%) missing valuesMissing
식별번호 has unique valuesUnique
관리번호 has 10 (2.6%) zerosZeros
연장 has 124 (32.4%) zerosZeros
폭 has 114 (29.8%) zerosZeros

Reproduction

Analysis started2023-12-10 23:26:04.339129
Analysis finished2023-12-10 23:26:09.733510
Duration5.39 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

식별번호
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct383
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean192
Minimum1
Maximum383
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 KiB
2023-12-11T08:26:09.810307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile20.1
Q196.5
median192
Q3287.5
95-th percentile363.9
Maximum383
Range382
Interquartile range (IQR)191

Descriptive statistics

Standard deviation110.70682
Coefficient of variation (CV)0.57659802
Kurtosis-1.2
Mean192
Median Absolute Deviation (MAD)96
Skewness0
Sum73536
Variance12256
MonotonicityStrictly increasing
2023-12-11T08:26:09.969268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.3%
2 1
 
0.3%
263 1
 
0.3%
262 1
 
0.3%
261 1
 
0.3%
260 1
 
0.3%
259 1
 
0.3%
258 1
 
0.3%
257 1
 
0.3%
256 1
 
0.3%
Other values (373) 373
97.4%
ValueCountFrequency (%)
1 1
0.3%
2 1
0.3%
3 1
0.3%
4 1
0.3%
5 1
0.3%
6 1
0.3%
7 1
0.3%
8 1
0.3%
9 1
0.3%
10 1
0.3%
ValueCountFrequency (%)
383 1
0.3%
382 1
0.3%
381 1
0.3%
380 1
0.3%
379 1
0.3%
378 1
0.3%
377 1
0.3%
376 1
0.3%
375 1
0.3%
374 1
0.3%

관리번호
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct366
Distinct (%)95.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8403014
Minimum0
Maximum10990017
Zeros10
Zeros (%)2.6%
Negative0
Negative (%)0.0%
Memory size3.5 KiB
2023-12-11T08:26:10.121482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile370009.1
Q110010020
median10180002
Q310420010
95-th percentile10890003
Maximum10990017
Range10990017
Interquartile range (IQR)409990

Descriptive statistics

Standard deviation3928418.4
Coefficient of variation (CV)0.46750111
Kurtosis0.35349727
Mean8403014
Median Absolute Deviation (MAD)220000
Skewness-1.520052
Sum3.2183544 × 109
Variance1.5432471 × 1013
MonotonicityNot monotonic
2023-12-11T08:26:10.268055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 10
 
2.6%
10420008 2
 
0.5%
10420009 2
 
0.5%
600006 2
 
0.5%
600005 2
 
0.5%
600004 2
 
0.5%
600003 2
 
0.5%
600002 2
 
0.5%
600001 2
 
0.5%
10110005 1
 
0.3%
Other values (356) 356
93.0%
ValueCountFrequency (%)
0 10
2.6%
300001 1
 
0.3%
370001 1
 
0.3%
370002 1
 
0.3%
370003 1
 
0.3%
370004 1
 
0.3%
370005 1
 
0.3%
370006 1
 
0.3%
370007 1
 
0.3%
370008 1
 
0.3%
ValueCountFrequency (%)
10990017 1
0.3%
10990016 1
0.3%
10990015 1
0.3%
10990014 1
0.3%
10990013 1
0.3%
10990012 1
0.3%
10990011 1
0.3%
10990010 1
0.3%
10990009 1
0.3%
10990008 1
0.3%

관리기관
Categorical

CONSTANT 

Distinct1
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
1683
383 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1683
2nd row1683
3rd row1683
4th row1683
5th row1683

Common Values

ValueCountFrequency (%)
1683 383
100.0%

Length

2023-12-11T08:26:10.414045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:26:10.519280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1683 383
100.0%

도로종류
Categorical

CONSTANT 

Distinct1
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
1504
383 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1504
2nd row1504
3rd row1504
4th row1504
5th row1504

Common Values

ValueCountFrequency (%)
1504 383
100.0%

Length

2023-12-11T08:26:10.614388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:26:10.725351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1504 383
100.0%

노선번호
Real number (ℝ)

HIGH CORRELATION 

Distinct36
Distinct (%)9.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean867.7389
Minimum30
Maximum1099
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 KiB
2023-12-11T08:26:10.827322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile37
Q11001
median1018
Q31042
95-th percentile1089
Maximum1099
Range1069
Interquartile range (IQR)41

Descriptive statistics

Standard deviation369.11855
Coefficient of variation (CV)0.42537974
Kurtosis1.0872594
Mean867.7389
Median Absolute Deviation (MAD)22
Skewness-1.7415781
Sum332344
Variance136248.5
MonotonicityNot monotonic
2023-12-11T08:26:10.962806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
1001 62
16.2%
1084 29
 
7.6%
1042 26
 
6.8%
60 22
 
5.7%
1020 21
 
5.5%
37 20
 
5.2%
1018 19
 
5.0%
69 17
 
4.4%
1099 17
 
4.4%
1080 15
 
3.9%
Other values (26) 135
35.2%
ValueCountFrequency (%)
30 1
 
0.3%
37 20
 
5.2%
58 2
 
0.5%
60 22
 
5.7%
67 3
 
0.8%
69 17
 
4.4%
907 1
 
0.3%
1001 62
16.2%
1002 7
 
1.8%
1003 1
 
0.3%
ValueCountFrequency (%)
1099 17
4.4%
1089 5
 
1.3%
1084 29
7.6%
1080 15
3.9%
1077 4
 
1.0%
1051 10
 
2.6%
1049 8
 
2.1%
1047 3
 
0.8%
1042 26
6.8%
1041 4
 
1.0%

구간번호
Real number (ℝ)

HIGH CORRELATION 

Distinct17
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.154047
Minimum1
Maximum19
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 KiB
2023-12-11T08:26:11.125196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q37
95-th percentile14
Maximum19
Range18
Interquartile range (IQR)5

Descriptive statistics

Standard deviation4.0745382
Coefficient of variation (CV)0.79055124
Kurtosis1.3501477
Mean5.154047
Median Absolute Deviation (MAD)2
Skewness1.2917408
Sum1974
Variance16.601862
MonotonicityNot monotonic
2023-12-11T08:26:11.268735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
1 70
18.3%
2 52
13.6%
3 47
12.3%
6 44
11.5%
5 35
9.1%
4 34
8.9%
7 26
 
6.8%
11 20
 
5.2%
9 13
 
3.4%
13 9
 
2.3%
Other values (7) 33
8.6%
ValueCountFrequency (%)
1 70
18.3%
2 52
13.6%
3 47
12.3%
4 34
8.9%
5 35
9.1%
6 44
11.5%
7 26
 
6.8%
8 7
 
1.8%
9 13
 
3.4%
10 2
 
0.5%
ValueCountFrequency (%)
19 6
 
1.6%
16 5
 
1.3%
15 2
 
0.5%
14 8
 
2.1%
13 9
2.3%
12 3
 
0.8%
11 20
5.2%
10 2
 
0.5%
9 13
3.4%
8 7
 
1.8%

이력코드
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
0
377 
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 (%)
0 377
98.4%
1 6
 
1.6%

Length

2023-12-11T08:26:11.424557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:26:11.530092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 377
98.4%
1 6
 
1.6%

위치
Real number (ℝ)

HIGH CORRELATION 

Distinct367
Distinct (%)95.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.6456136
Minimum0.011
Maximum14.627
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 KiB
2023-12-11T08:26:11.678861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.011
5-th percentile0.4601
Q11.9855
median3.985
Q37.0195
95-th percentile10.804
Maximum14.627
Range14.616
Interquartile range (IQR)5.034

Descriptive statistics

Standard deviation3.3013977
Coefficient of variation (CV)0.71064837
Kurtosis-0.55662569
Mean4.6456136
Median Absolute Deviation (MAD)2.375
Skewness0.62414965
Sum1779.27
Variance10.899227
MonotonicityNot monotonic
2023-12-11T08:26:11.820460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.02 2
 
0.5%
9.27 2
 
0.5%
1.12 2
 
0.5%
1.023 2
 
0.5%
9.01 2
 
0.5%
6.18 2
 
0.5%
6.34 2
 
0.5%
2.162 2
 
0.5%
0.47 2
 
0.5%
7.745 2
 
0.5%
Other values (357) 363
94.8%
ValueCountFrequency (%)
0.011 1
0.3%
0.035 1
0.3%
0.086 1
0.3%
0.1 1
0.3%
0.117 1
0.3%
0.118 1
0.3%
0.127 1
0.3%
0.145 1
0.3%
0.15 1
0.3%
0.179 1
0.3%
ValueCountFrequency (%)
14.627 1
0.3%
13.947 1
0.3%
13.76 1
0.3%
12.809 1
0.3%
12.5 1
0.3%
12.099 1
0.3%
11.882 1
0.3%
11.468 1
0.3%
11.298 1
0.3%
11.194 1
0.3%

위치_방향
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
1
195 
0
188 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 195
50.9%
0 188
49.1%

Length

2023-12-11T08:26:11.953570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:26:12.045619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 195
50.9%
0 188
49.1%

연장
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct96
Distinct (%)25.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.508355
Minimum0
Maximum216
Zeros124
Zeros (%)32.4%
Negative0
Negative (%)0.0%
Memory size3.5 KiB
2023-12-11T08:26:12.149132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median30
Q357.5
95-th percentile94.9
Maximum216
Range216
Interquartile range (IQR)57.5

Descriptive statistics

Standard deviation35.908154
Coefficient of variation (CV)1.0405641
Kurtosis1.0828084
Mean34.508355
Median Absolute Deviation (MAD)30
Skewness0.91575467
Sum13216.7
Variance1289.3955
MonotonicityNot monotonic
2023-12-11T08:26:12.297775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 124
32.4%
55.0 36
 
9.4%
60.0 20
 
5.2%
4.0 20
 
5.2%
5.0 8
 
2.1%
53.0 7
 
1.8%
3.2 7
 
1.8%
75.0 6
 
1.6%
54.0 6
 
1.6%
51.0 5
 
1.3%
Other values (86) 144
37.6%
ValueCountFrequency (%)
0.0 124
32.4%
2.2 1
 
0.3%
3.2 7
 
1.8%
3.5 4
 
1.0%
4.0 20
 
5.2%
4.5 2
 
0.5%
5.0 8
 
2.1%
6.2 1
 
0.3%
8.5 1
 
0.3%
11.0 1
 
0.3%
ValueCountFrequency (%)
216.0 1
0.3%
158.0 1
0.3%
141.0 1
0.3%
140.0 1
0.3%
131.0 1
0.3%
122.0 1
0.3%
120.0 1
0.3%
118.0 1
0.3%
117.0 1
0.3%
116.0 1
0.3%

폭
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct61
Distinct (%)15.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0443864
Minimum0
Maximum25
Zeros114
Zeros (%)29.8%
Negative0
Negative (%)0.0%
Memory size3.5 KiB
2023-12-11T08:26:12.453939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3
Q34
95-th percentile8.09
Maximum25
Range25
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.1854074
Coefficient of variation (CV)1.0463217
Kurtosis10.365366
Mean3.0443864
Median Absolute Deviation (MAD)2
Skewness2.3203494
Sum1166
Variance10.14682
MonotonicityNot monotonic
2023-12-11T08:26:12.578547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 114
29.8%
3.0 46
12.0%
2.0 33
 
8.6%
4.0 29
 
7.6%
3.5 22
 
5.7%
5.0 19
 
5.0%
6.0 12
 
3.1%
2.5 12
 
3.1%
8.0 7
 
1.8%
7.0 5
 
1.3%
Other values (51) 84
21.9%
ValueCountFrequency (%)
0.0 114
29.8%
1.1 3
 
0.8%
1.2 4
 
1.0%
1.3 2
 
0.5%
1.5 1
 
0.3%
1.6 2
 
0.5%
1.8 1
 
0.3%
1.9 2
 
0.5%
2.0 33
 
8.6%
2.3 3
 
0.8%
ValueCountFrequency (%)
25.0 1
 
0.3%
22.7 1
 
0.3%
17.0 1
 
0.3%
16.0 1
 
0.3%
13.3 1
 
0.3%
12.0 2
0.5%
11.6 1
 
0.3%
11.3 1
 
0.3%
10.4 1
 
0.3%
10.0 3
0.8%

대기소
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
1
217 
0
166 

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 (%)
1 217
56.7%
0 166
43.3%

Length

2023-12-11T08:26:12.678189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:26:12.755785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 217
56.7%
0 166
43.3%

사진
Text

MISSING 

Distinct332
Distinct (%)100.0%
Missing51
Missing (%)13.3%
Memory size3.1 KiB
2023-12-11T08:26:12.893203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length13
Mean length13
Min length13

Characters and Unicode

Total characters4316
Distinct characters13
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique332 ?
Unique (%)100.0%

Sample

1st row006703C02151U
2nd row006703C01112D
3rd row006703C01112U
4th row003705C08070U
5th row003705C06894D
ValueCountFrequency (%)
003702c10876u 1
 
0.3%
100106c07780d 1
 
0.3%
100109c03457d 1
 
0.3%
100109c05916d 1
 
0.3%
100109c07848u 1
 
0.3%
100107c02793d 1
 
0.3%
100107c03427u 1
 
0.3%
100107c04340u 1
 
0.3%
100107c06836u 1
 
0.3%
100106c09742u 1
 
0.3%
Other values (322) 322
97.0%
2023-12-11T08:26:13.152462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1358
31.5%
1 659
15.3%
C 332
 
7.7%
2 243
 
5.6%
4 236
 
5.5%
9 200
 
4.6%
5 199
 
4.6%
8 198
 
4.6%
3 196
 
4.5%
6 192
 
4.4%
Other values (3) 503
 
11.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3652
84.6%
Uppercase Letter 664
 
15.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1358
37.2%
1 659
18.0%
2 243
 
6.7%
4 236
 
6.5%
9 200
 
5.5%
5 199
 
5.4%
8 198
 
5.4%
3 196
 
5.4%
6 192
 
5.3%
7 171
 
4.7%
Uppercase Letter
ValueCountFrequency (%)
C 332
50.0%
D 174
26.2%
U 158
23.8%

Most occurring scripts

ValueCountFrequency (%)
Common 3652
84.6%
Latin 664
 
15.4%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1358
37.2%
1 659
18.0%
2 243
 
6.7%
4 236
 
6.5%
9 200
 
5.5%
5 199
 
5.4%
8 198
 
5.4%
3 196
 
5.4%
6 192
 
5.3%
7 171
 
4.7%
Latin
ValueCountFrequency (%)
C 332
50.0%
D 174
26.2%
U 158
23.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4316
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1358
31.5%
1 659
15.3%
C 332
 
7.7%
2 243
 
5.6%
4 236
 
5.5%
9 200
 
4.6%
5 199
 
4.6%
8 198
 
4.6%
3 196
 
4.5%
6 192
 
4.4%
Other values (3) 503
 
11.7%

비고
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct6
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
<NA>
372 
공사중
 
5
공항버스
 
2
대기소
 
2
통근버스
 
1

Length

Max length4
Median length4
Mean length3.9765013
Min length2

Unique

Unique2 ?
Unique (%)0.5%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 372
97.1%
공사중 5
 
1.3%
공항버스 2
 
0.5%
대기소 2
 
0.5%
통근버스 1
 
0.3%
세로 1
 
0.3%

Length

2023-12-11T08:26:13.272470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:26:13.373865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 372
97.1%
공사중 5
 
1.3%
공항버스 2
 
0.5%
대기소 2
 
0.5%
통근버스 1
 
0.3%
세로 1
 
0.3%

Interactions

2023-12-11T08:26:08.786757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:26:04.804579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:26:05.409675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:26:06.101516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:26:06.709305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:26:07.285014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:26:07.876963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:26:08.882390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:26:04.894000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:26:05.517459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:26:06.209418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:26:06.795698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:26:07.378997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:26:07.960515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:26:08.985856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:26:04.977127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:26:05.632831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:26:06.299835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:26:06.873157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:26:07.458538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:26:08.038439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:26:09.087664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:26:05.056314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:26:05.756766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:26:06.376932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:26:06.951406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:26:07.537369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:26:08.389491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:26:09.186730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:26:05.145860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:26:05.867251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:26:06.454202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:26:07.046371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:26:07.643797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:26:08.534057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:26:09.283255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:26:05.241845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:26:05.949911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:26:06.545448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:26:07.136128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:26:07.721956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:26:08.622216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:26:09.367166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:26:05.324911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:26:06.026406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:26:06.633702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:26:07.211026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:26:07.801855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:26:08.705045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T08:26:13.441459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
식별번호관리번호노선번호구간번호이력코드위치위치_방향연장폭대기소비고
식별번호1.0000.5740.6190.7520.4500.4160.1430.5550.5310.8831.000
관리번호0.5741.0000.9990.2910.0000.2310.0370.6170.3370.249NaN
노선번호0.6190.9991.0000.3190.0000.2390.0000.5860.2740.222NaN
구간번호0.7520.2910.3191.0000.1360.4460.1770.1310.0000.4330.598
이력코드0.4500.0000.0000.1361.0000.0880.0000.1550.1390.115NaN
위치0.4160.2310.2390.4460.0881.0000.1130.0000.0440.1120.856
위치_방향0.1430.0370.0000.1770.0000.1131.0000.0000.0000.0000.609
연장0.5550.6170.5860.1310.1550.0000.0001.0000.5830.6040.000
폭0.5310.3370.2740.0000.1390.0440.0000.5831.0000.7140.228
대기소0.8830.2490.2220.4330.1150.1120.0000.6040.7141.0000.698
비고1.000NaNNaN0.598NaN0.8560.6090.0000.2280.6981.000
2023-12-11T08:26:13.766371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
위치_방향비고대기소이력코드
위치_방향1.0000.5840.0000.000
비고0.5841.0000.6701.000
대기소0.0000.6701.0000.073
이력코드0.0001.0000.0731.000
2023-12-11T08:26:13.851795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
식별번호관리번호노선번호구간번호위치연장폭이력코드위치_방향대기소비고
식별번호1.0000.5440.5900.008-0.037-0.504-0.3930.3420.1080.7110.926
관리번호0.5441.0000.903-0.126-0.097-0.268-0.1480.0000.0610.4051.000
노선번호0.5900.9031.000-0.200-0.087-0.198-0.0680.0000.0000.3621.000
구간번호0.008-0.126-0.2001.0000.1610.009-0.0660.1030.1380.3270.572
위치-0.037-0.097-0.0870.1611.0000.1030.0690.0660.0860.0850.771
연장-0.504-0.268-0.1980.0090.1031.0000.7480.1530.0000.6040.000
폭-0.393-0.148-0.068-0.0660.0690.7481.0000.0920.0000.5350.000
이력코드0.3420.0000.0000.1030.0660.1530.0921.0000.0000.0731.000
위치_방향0.1080.0610.0000.1380.0860.0000.0000.0001.0000.0000.584
대기소0.7110.4050.3620.3270.0850.6040.5350.0730.0001.0000.670
비고0.9261.0001.0000.5720.7710.0000.0001.0000.5840.6701.000

Missing values

2023-12-11T08:26:09.482915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T08:26:09.654461image/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

식별번호관리번호관리기관도로종류노선번호구간번호이력코드위치위치_방향연장폭대기소사진비고
0110110005168315041011204.76160.06.80<NA>공사중
1210110004168315041011204.715120.02.30<NA>공사중
2310110003168315041011204.67040.06.00<NA>공사중
3410110002168315041011204.605155.06.40<NA>공사중
4510110001168315041011204.46175.08.00<NA>공사중
566700031683150467302.151038.05.01006703C02151U<NA>
676700021683150467301.112179.03.51006703C01112D<NA>
786700011683150467301.112079.03.51006703C01112U<NA>
893700201683150437508.07054.02.50003705C08070U<NA>
9103700191683150437506.894143.02.50003705C06894D<NA>
식별번호관리번호관리기관도로종류노선번호구간번호이력코드위치위치_방향연장폭대기소사진비고
37337410420017168315041042102.6600.04.00104200C02660U<NA>
37437510420018168315041042103.3200.04.00104200C03320U<NA>
3753760168315041077802.1660118.03.00<NA><NA>
37637701683150410401300.38135.03.51104013C00380D<NA>
37737810420019168315041042110.65054.03.51104201C00650U<NA>
37837910420020168315041042110.655155.03.51104201C00655D<NA>
37938010420024168315041042112.465055.03.51104201C02465U<NA>
38038110420022168315041042112.267160.03.51104201C02267D<NA>
38138210420021168315041042112.162058.03.51104201C02162U<NA>
38238310420023168315041042112.465155.03.51104201C02465D<NA>