Overview

Dataset statistics

Number of variables10
Number of observations93
Missing cells181
Missing cells (%)19.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.5 KiB
Average record size in memory82.4 B

Variable types

Categorical4
Numeric1
Text5

Dataset

Description충청남도 청양군의 농어촌버스의 읍면, 노선번호, 기점, 경유지, 종점 등 운행 노선 정보 데이터를 제공하고 있습니다.
Author충청남도
URLhttps://alldam.chungnam.go.kr/index.chungnam?menuCd=DOM_000000201001001001&st=&cds=&orgCd=&apiType=&isOpen=Y&pageIndex=428&beforeMenuCd=DOM_000000201001001000&publicdatapk=15029224

Alerts

노선번호 is highly overall correlated with 읍면High correlation
읍면 is highly overall correlated with 노선번호 and 1 other fieldsHigh correlation
기점 is highly overall correlated with 방향High correlation
종점 is highly overall correlated with 읍면High correlation
방향 is highly overall correlated with 기점High correlation
기점 is highly imbalanced (57.6%)Imbalance
경유지1 has 1 (1.1%) missing valuesMissing
경유지2 has 13 (14.0%) missing valuesMissing
경유지3 has 39 (41.9%) missing valuesMissing
경유지4 has 58 (62.4%) missing valuesMissing
경유지5 has 70 (75.3%) missing valuesMissing
노선번호 has unique valuesUnique

Reproduction

Analysis started2024-01-09 20:41:00.998356
Analysis finished2024-01-09 20:41:02.001779
Duration1 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

읍면
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)7.5%
Missing0
Missing (%)0.0%
Memory size876.0 B
대치 정산 장평
24 
남양
22 
운곡
13 
정산 목면 청남 장평
13 
화성
10 
Other values (2)
11 

Length

Max length16
Median length2
Mean length5.7634409
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row청양
2nd row청양
3rd row청양
4th row운곡
5th row운곡

Common Values

ValueCountFrequency (%)
대치 정산 장평 24
25.8%
남양 22
23.7%
운곡 13
14.0%
정산 목면 청남 장평 13
14.0%
화성 10
10.8%
비봉 8
 
8.6%
청양 3
 
3.2%

Length

2024-01-10T05:41:02.066560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T05:41:02.168179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
정산 37
20.6%
장평 37
20.6%
대치 24
13.3%
남양 22
12.2%
운곡 13
 
7.2%
목면 13
 
7.2%
청남 13
 
7.2%
화성 10
 
5.6%
비봉 8
 
4.4%
청양 3
 
1.7%

노선번호
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct93
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean453.25806
Minimum100
Maximum909
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size969.0 B
2024-01-10T05:41:02.307233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum100
5-th percentile104.6
Q1216
median331
Q3800
95-th percentile904.4
Maximum909
Range809
Interquartile range (IQR)584

Descriptive statistics

Standard deviation269.23313
Coefficient of variation (CV)0.59399523
Kurtosis-1.1845059
Mean453.25806
Median Absolute Deviation (MAD)128
Skewness0.50633458
Sum42153
Variance72486.476
MonotonicityNot monotonic
2024-01-10T05:41:02.458347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
112 1
 
1.1%
700 1
 
1.1%
810 1
 
1.1%
806 1
 
1.1%
805 1
 
1.1%
804 1
 
1.1%
803 1
 
1.1%
802 1
 
1.1%
801 1
 
1.1%
800 1
 
1.1%
Other values (83) 83
89.2%
ValueCountFrequency (%)
100 1
1.1%
101 1
1.1%
102 1
1.1%
103 1
1.1%
104 1
1.1%
105 1
1.1%
110 1
1.1%
111 1
1.1%
112 1
1.1%
113 1
1.1%
ValueCountFrequency (%)
909 1
1.1%
908 1
1.1%
907 1
1.1%
906 1
1.1%
905 1
1.1%
904 1
1.1%
903 1
1.1%
902 1
1.1%
901 1
1.1%
900 1
1.1%

기점
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct9
Distinct (%)9.7%
Missing0
Missing (%)0.0%
Memory size876.0 B
청양
70 
정산
13 
신대
 
2
부여
 
2
신왕
 
2
Other values (4)
 
4

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique4 ?
Unique (%)4.3%

Sample

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

Common Values

ValueCountFrequency (%)
청양 70
75.3%
정산 13
 
14.0%
신대 2
 
2.2%
부여 2
 
2.2%
신왕 2
 
2.2%
정신 1
 
1.1%
졍산 1
 
1.1%
외산 1
 
1.1%
매곡 1
 
1.1%

Length

2024-01-10T05:41:02.595400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T05:41:02.694588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
청양 70
75.3%
정산 13
 
14.0%
신대 2
 
2.2%
부여 2
 
2.2%
신왕 2
 
2.2%
정신 1
 
1.1%
졍산 1
 
1.1%
외산 1
 
1.1%
매곡 1
 
1.1%

경유지1
Text

MISSING 

Distinct47
Distinct (%)51.1%
Missing1
Missing (%)1.1%
Memory size876.0 B
2024-01-10T05:41:02.879719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length2
Mean length2.1304348
Min length2

Characters and Unicode

Total characters196
Distinct characters67
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique25 ?
Unique (%)27.2%

Sample

1st row군청앞
2nd row체육관
3rd row체육관
4th row운곡
5th row운곡
ValueCountFrequency (%)
운곡 8
 
8.7%
비봉 6
 
6.5%
화성 6
 
6.5%
금정 4
 
4.3%
수석 4
 
4.3%
온직 3
 
3.3%
구봉 3
 
3.3%
미당 3
 
3.3%
대치 3
 
3.3%
구치 3
 
3.3%
Other values (37) 49
53.3%
2024-01-10T05:41:03.221497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
곡 14
 
7.1%
봉 9
 
4.6%
정 9
 
4.6%
운 8
 
4.1%
화 7
 
3.6%
성 7
 
3.6%
치 6
 
3.1%
대 6
 
3.1%
구 6
 
3.1%
비 6
 
3.1%
Other values (57) 118
60.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 194
99.0%
Space Separator 2
 
1.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
곡 14
 
7.2%
봉 9
 
4.6%
정 9
 
4.6%
운 8
 
4.1%
화 7
 
3.6%
성 7
 
3.6%
치 6
 
3.1%
대 6
 
3.1%
구 6
 
3.1%
비 6
 
3.1%
Other values (56) 116
59.8%
Space Separator
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 194
99.0%
Common 2
 
1.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
곡 14
 
7.2%
봉 9
 
4.6%
정 9
 
4.6%
운 8
 
4.1%
화 7
 
3.6%
성 7
 
3.6%
치 6
 
3.1%
대 6
 
3.1%
구 6
 
3.1%
비 6
 
3.1%
Other values (56) 116
59.8%
Common
ValueCountFrequency (%)
2
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 194
99.0%
ASCII 2
 
1.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
곡 14
 
7.2%
봉 9
 
4.6%
정 9
 
4.6%
운 8
 
4.1%
화 7
 
3.6%
성 7
 
3.6%
치 6
 
3.1%
대 6
 
3.1%
구 6
 
3.1%
비 6
 
3.1%
Other values (56) 116
59.8%
ASCII
ValueCountFrequency (%)
2
100.0%

경유지2
Text

MISSING 

Distinct43
Distinct (%)53.8%
Missing13
Missing (%)14.0%
Memory size876.0 B
2024-01-10T05:41:03.433612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length2
Mean length2.1
Min length2

Characters and Unicode

Total characters168
Distinct characters67
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique23 ?
Unique (%)28.7%

Sample

1st row학당
2nd row학당
3rd row광암
4th row광암
5th row광암
ValueCountFrequency (%)
장평 5
 
6.2%
청남 5
 
6.2%
광암 4
 
5.0%
양사 4
 
5.0%
남양 4
 
5.0%
시전 4
 
5.0%
낙지 3
 
3.8%
옥계 3
 
3.8%
천장호 3
 
3.8%
한재 3
 
3.8%
Other values (32) 42
52.5%
2024-01-10T05:41:03.742029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
남 9
 
5.4%
장 8
 
4.8%
양 8
 
4.8%
지 7
 
4.2%
암 7
 
4.2%
청 6
 
3.6%
천 6
 
3.6%
산 5
 
3.0%
평 5
 
3.0%
화 5
 
3.0%
Other values (57) 102
60.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 167
99.4%
Space Separator 1
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
남 9
 
5.4%
장 8
 
4.8%
양 8
 
4.8%
지 7
 
4.2%
암 7
 
4.2%
청 6
 
3.6%
천 6
 
3.6%
산 5
 
3.0%
평 5
 
3.0%
화 5
 
3.0%
Other values (56) 101
60.5%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 167
99.4%
Common 1
 
0.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
남 9
 
5.4%
장 8
 
4.8%
양 8
 
4.8%
지 7
 
4.2%
암 7
 
4.2%
청 6
 
3.6%
천 6
 
3.6%
산 5
 
3.0%
평 5
 
3.0%
화 5
 
3.0%
Other values (56) 101
60.5%
Common
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 167
99.4%
ASCII 1
 
0.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
남 9
 
5.4%
장 8
 
4.8%
양 8
 
4.8%
지 7
 
4.2%
암 7
 
4.2%
청 6
 
3.6%
천 6
 
3.6%
산 5
 
3.0%
평 5
 
3.0%
화 5
 
3.0%
Other values (56) 101
60.5%
ASCII
ValueCountFrequency (%)
1
100.0%

경유지3
Text

MISSING 

Distinct38
Distinct (%)70.4%
Missing39
Missing (%)41.9%
Memory size876.0 B
2024-01-10T05:41:03.930558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length2
Mean length2.0925926
Min length2

Characters and Unicode

Total characters113
Distinct characters53
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique31 ?
Unique (%)57.4%

Sample

1st row신양
2nd row야광
3rd row광암
4th row미량
5th row위라리
ValueCountFrequency (%)
낙지 5
 
9.3%
용당 4
 
7.4%
남양 4
 
7.4%
장평 4
 
7.4%
화암 2
 
3.7%
흥산 2
 
3.7%
남천 2
 
3.7%
개곡 2
 
3.7%
기덕 1
 
1.9%
미량 1
 
1.9%
Other values (27) 27
50.0%
2024-01-10T05:41:04.246342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
장 7
 
6.2%
남 6
 
5.3%
용 6
 
5.3%
양 6
 
5.3%
낙 5
 
4.4%
산 5
 
4.4%
지 5
 
4.4%
당 5
 
4.4%
평 5
 
4.4%
화 4
 
3.5%
Other values (43) 59
52.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 112
99.1%
Space Separator 1
 
0.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
장 7
 
6.2%
남 6
 
5.4%
용 6
 
5.4%
양 6
 
5.4%
낙 5
 
4.5%
산 5
 
4.5%
지 5
 
4.5%
당 5
 
4.5%
평 5
 
4.5%
화 4
 
3.6%
Other values (42) 58
51.8%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 112
99.1%
Common 1
 
0.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
장 7
 
6.2%
남 6
 
5.4%
용 6
 
5.4%
양 6
 
5.4%
낙 5
 
4.5%
산 5
 
4.5%
지 5
 
4.5%
당 5
 
4.5%
평 5
 
4.5%
화 4
 
3.6%
Other values (42) 58
51.8%
Common
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 112
99.1%
ASCII 1
 
0.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
장 7
 
6.2%
남 6
 
5.4%
용 6
 
5.4%
양 6
 
5.4%
낙 5
 
4.5%
산 5
 
4.5%
지 5
 
4.5%
당 5
 
4.5%
평 5
 
4.5%
화 4
 
3.6%
Other values (42) 58
51.8%
ASCII
ValueCountFrequency (%)
1
100.0%

경유지4
Text

MISSING 

Distinct27
Distinct (%)77.1%
Missing58
Missing (%)62.4%
Memory size876.0 B
2024-01-10T05:41:04.441046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length2
Mean length2.0285714
Min length2

Characters and Unicode

Total characters71
Distinct characters34
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique20 ?
Unique (%)57.1%

Sample

1st row야광
2nd row청남
3rd row장평
4th row구치
5th row장곡사
ValueCountFrequency (%)
청남 3
 
8.6%
장평 2
 
5.7%
구치 2
 
5.7%
용당 2
 
5.7%
용천 2
 
5.7%
정탁 2
 
5.7%
비암 2
 
5.7%
야광 1
 
2.9%
비봉 1
 
2.9%
장곡 1
 
2.9%
Other values (17) 17
48.6%
2024-01-10T05:41:04.773701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
장 5
 
7.0%
용 4
 
5.6%
천 4
 
5.6%
청 3
 
4.2%
화 3
 
4.2%
비 3
 
4.2%
암 3
 
4.2%
구 3
 
4.2%
산 3
 
4.2%
정 3
 
4.2%
Other values (24) 37
52.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 71
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
장 5
 
7.0%
용 4
 
5.6%
천 4
 
5.6%
청 3
 
4.2%
화 3
 
4.2%
비 3
 
4.2%
암 3
 
4.2%
구 3
 
4.2%
산 3
 
4.2%
정 3
 
4.2%
Other values (24) 37
52.1%

Most occurring scripts

ValueCountFrequency (%)
Hangul 71
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
장 5
 
7.0%
용 4
 
5.6%
천 4
 
5.6%
청 3
 
4.2%
화 3
 
4.2%
비 3
 
4.2%
암 3
 
4.2%
구 3
 
4.2%
산 3
 
4.2%
정 3
 
4.2%
Other values (24) 37
52.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 71
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
장 5
 
7.0%
용 4
 
5.6%
천 4
 
5.6%
청 3
 
4.2%
화 3
 
4.2%
비 3
 
4.2%
암 3
 
4.2%
구 3
 
4.2%
산 3
 
4.2%
정 3
 
4.2%
Other values (24) 37
52.1%

경유지5
Text

MISSING 

Distinct16
Distinct (%)69.6%
Missing70
Missing (%)75.3%
Memory size876.0 B
2024-01-10T05:41:04.958501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length2
Mean length2.173913
Min length1

Characters and Unicode

Total characters50
Distinct characters31
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10 ?
Unique (%)43.5%

Sample

1st row청남
2nd row장곡사
3rd row광금
4th row미당
5th row치천
ValueCountFrequency (%)
신원 3
13.6%
옥산 2
 
9.1%
장곡사 2
 
9.1%
광금 2
 
9.1%
미당 2
 
9.1%
주정 2
 
9.1%
은산 1
 
4.5%
치천 1
 
4.5%
온직 1
 
4.5%
청남 1
 
4.5%
Other values (5) 5
22.7%
2024-01-10T05:41:05.276274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
산 4
 
8.0%
정 4
 
8.0%
신 3
 
6.0%
원 3
 
6.0%
금 2
 
4.0%
주 2
 
4.0%
미 2
 
4.0%
당 2
 
4.0%
광 2
 
4.0%
사 2
 
4.0%
Other values (21) 24
48.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 49
98.0%
Space Separator 1
 
2.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 4
 
8.2%
정 4
 
8.2%
신 3
 
6.1%
원 3
 
6.1%
금 2
 
4.1%
주 2
 
4.1%
미 2
 
4.1%
당 2
 
4.1%
광 2
 
4.1%
사 2
 
4.1%
Other values (20) 23
46.9%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 49
98.0%
Common 1
 
2.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
산 4
 
8.2%
정 4
 
8.2%
신 3
 
6.1%
원 3
 
6.1%
금 2
 
4.1%
주 2
 
4.1%
미 2
 
4.1%
당 2
 
4.1%
광 2
 
4.1%
사 2
 
4.1%
Other values (20) 23
46.9%
Common
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 49
98.0%
ASCII 1
 
2.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
산 4
 
8.2%
정 4
 
8.2%
신 3
 
6.1%
원 3
 
6.1%
금 2
 
4.1%
주 2
 
4.1%
미 2
 
4.1%
당 2
 
4.1%
광 2
 
4.1%
사 2
 
4.1%
Other values (20) 23
46.9%
ASCII
ValueCountFrequency (%)
1
100.0%

종점
Categorical

HIGH CORRELATION 

Distinct35
Distinct (%)37.6%
Missing0
Missing (%)0.0%
Memory size876.0 B
청양
29 
정산
10 
광암
 
3
추광
 
3
신대
 
3
Other values (30)
45 

Length

Max length3
Median length2
Mean length2.0537634
Min length2

Unique

Unique19 ?
Unique (%)20.4%

Sample

1st row청수
2nd row청수
3rd row수영장
4th row예산
5th row광암

Common Values

ValueCountFrequency (%)
청양 29
31.2%
정산 10
 
10.8%
광암 3
 
3.2%
추광 3
 
3.2%
신대 3
 
3.2%
백금 3
 
3.2%
부여 3
 
3.2%
형산 3
 
3.2%
상갑 3
 
3.2%
외산 2
 
2.2%
Other values (25) 31
33.3%

Length

2024-01-10T05:41:05.393132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
청양 29
31.2%
정산 10
 
10.8%
광암 3
 
3.2%
추광 3
 
3.2%
신대 3
 
3.2%
백금 3
 
3.2%
부여 3
 
3.2%
형산 3
 
3.2%
상갑 3
 
3.2%
광천 2
 
2.2%
Other values (25) 31
33.3%

방향
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size876.0 B
상하행
52 
상 행
33 
하 행
8 

Length

Max length5
Median length3
Mean length3.8817204
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
상하행 52
55.9%
상 행 33
35.5%
하 행 8
 
8.6%

Length

2024-01-10T05:41:05.511696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T05:41:05.607020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
상하행 52
38.8%
행 41
30.6%
상 33
24.6%
하 8
 
6.0%

Interactions

2024-01-10T05:41:01.497926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-10T05:41:05.675772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
읍면노선번호기점경유지1경유지2경유지3경유지4경유지5종점방향
읍면1.0000.9570.5120.9920.9920.9480.9760.7990.9440.265
노선번호0.9571.0000.6590.9550.9380.9480.9690.8900.8640.416
기점0.5120.6591.0000.9720.9110.0000.0000.9170.0000.879
경유지10.9920.9550.9721.0000.9630.8810.7620.7590.7780.804
경유지20.9920.9380.9110.9631.0000.9090.8490.8670.9280.734
경유지30.9480.9480.0000.8810.9091.0000.9720.9710.9920.374
경유지40.9760.9690.0000.7620.8490.9721.0000.9160.9910.941
경유지50.7990.8900.9170.7590.8670.9710.9161.0000.9480.000
종점0.9440.8640.0000.7780.9280.9920.9910.9481.0000.000
방향0.2650.4160.8790.8040.7340.3740.9410.0000.0001.000
2024-01-10T05:41:05.786376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
방향읍면기점종점
방향1.0000.1780.5850.000
읍면0.1781.0000.2960.570
기점0.5850.2961.0000.000
종점0.0000.5700.0001.000
2024-01-10T05:41:06.121124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
노선번호읍면기점종점방향
노선번호1.0000.8940.2620.4440.193
읍면0.8941.0000.2960.5700.178
기점0.2620.2961.0000.0000.585
종점0.4440.5700.0001.0000.000
방향0.1930.1780.5850.0001.000

Missing values

2024-01-10T05:41:01.653778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-10T05:41:01.819056image/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.
2024-01-10T05:41:01.938145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

읍면노선번호기점경유지1경유지2경유지3경유지4경유지5종점방향
0청양112청양군청앞학당<NA><NA><NA>청수상하행
1청양113청양체육관학당<NA><NA><NA>청수상하행
2청양114청양체육관<NA><NA><NA><NA>수영장상하행
3운곡200청양운곡광암신양<NA><NA>예산상하행
4운곡201청양운곡<NA><NA><NA><NA>광암상하행
5운곡202청양운곡광암<NA><NA><NA>추광상하행
6운곡203청양운곡광암야광<NA><NA>추광상하행
7운곡204청양방주골운곡광암야광<NA>추광상 행
8운곡205청양운곡미량<NA><NA><NA>광암상 행
9운곡210청양운곡광암미량<NA><NA>신대상 행
읍면노선번호기점경유지1경유지2경유지3경유지4경유지5종점방향
83화성908청양군량리화성화암용당군량리청양상 행
84화성909청양화성<NA><NA><NA><NA>청양상 행
85비봉100청양용천옥계장곡<NA><NA>광천상하행
86비봉101청양비봉양사옥계장곡<NA>광천상하행
87비봉102청양신원옥계양사비봉<NA>청양상 행
88비봉103청양비봉양사용천<NA><NA>청양상 행
89비봉104청양비봉양사용당용천신원청양상하행
90비봉105청양용천옥계용화골양사배나무골청양상하행
91비봉110청양비봉천태금당리<NA><NA>홍성상하행
92비봉111청양비봉천태<NA><NA><NA>광시상하행