Overview

Dataset statistics

Number of variables10
Number of observations41
Missing cells8
Missing cells (%)2.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.5 KiB
Average record size in memory87.2 B

Variable types

Numeric4
Categorical4
Text2

Alerts

설치년도 is highly overall correlated with 이정 and 2 other fieldsHigh correlation
이정 is highly overall correlated with 설치년도 and 2 other fieldsHigh correlation
위도 is highly overall correlated with 지사 and 1 other fieldsHigh correlation
경도 is highly overall correlated with 본부 and 2 other fieldsHigh correlation
본부 is highly overall correlated with 이정 and 3 other fieldsHigh correlation
지사 is highly overall correlated with 설치년도 and 5 other fieldsHigh correlation
노선 is highly overall correlated with 설치년도 and 5 other fieldsHigh correlation
방향 is highly overall correlated with 노선High correlation
위도 has 4 (9.8%) missing valuesMissing
경도 has 4 (9.8%) missing valuesMissing

Reproduction

Analysis started2023-12-10 21:00:14.400477
Analysis finished2023-12-10 21:00:16.971872
Duration2.57 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

설치년도
Real number (ℝ)

HIGH CORRELATION 

Distinct9
Distinct (%)22.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2014.439
Minimum2011
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size501.0 B
2023-12-11T06:00:17.044333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2011
5-th percentile2011
Q12012
median2014
Q32015
95-th percentile2020
Maximum2022
Range11
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.0826026
Coefficient of variation (CV)0.0015302536
Kurtosis0.45689817
Mean2014.439
Median Absolute Deviation (MAD)2
Skewness1.0927564
Sum82592
Variance9.502439
MonotonicityDecreasing
2023-12-11T06:00:17.177141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
2015 10
24.4%
2012 10
24.4%
2011 5
12.2%
2020 4
 
9.8%
2013 4
 
9.8%
2014 3
 
7.3%
2022 2
 
4.9%
2016 2
 
4.9%
2017 1
 
2.4%
ValueCountFrequency (%)
2011 5
12.2%
2012 10
24.4%
2013 4
 
9.8%
2014 3
 
7.3%
2015 10
24.4%
2016 2
 
4.9%
2017 1
 
2.4%
2020 4
 
9.8%
2022 2
 
4.9%
ValueCountFrequency (%)
2022 2
 
4.9%
2020 4
 
9.8%
2017 1
 
2.4%
2016 2
 
4.9%
2015 10
24.4%
2014 3
 
7.3%
2013 4
 
9.8%
2012 10
24.4%
2011 5
12.2%

본부
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)7.3%
Missing0
Missing (%)0.0%
Memory size460.0 B
수도권
27 
강원
9 
충북
5 

Length

Max length3
Median length3
Mean length2.6585366
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row강원
2nd row강원
3rd row수도권
4th row수도권
5th row수도권

Common Values

ValueCountFrequency (%)
수도권 27
65.9%
강원 9
 
22.0%
충북 5
 
12.2%

Length

2023-12-11T06:00:17.366697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T06:00:17.530716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
수도권 27
65.9%
강원 9
 
22.0%
충북 5
 
12.2%

지사
Categorical

HIGH CORRELATION 

Distinct12
Distinct (%)29.3%
Missing0
Missing (%)0.0%
Memory size460.0 B
경기광주
6 
동서울
5 
이천
4 
시흥
4 
수원
4 
Other values (7)
18 

Length

Max length4
Median length2
Mean length2.4146341
Min length2

Unique

Unique1 ?
Unique (%)2.4%

Sample

1st row춘천
2nd row춘천
3rd row화성
4th row화성
5th row동서울

Common Values

ValueCountFrequency (%)
경기광주 6
14.6%
동서울 5
12.2%
이천 4
9.8%
시흥 4
9.8%
수원 4
9.8%
진천 4
9.8%
화성 3
7.3%
군포 3
7.3%
원주 3
7.3%
춘천 2
 
4.9%
Other values (2) 3
7.3%

Length

2023-12-11T06:00:17.702502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기광주 6
14.6%
동서울 5
12.2%
이천 4
9.8%
시흥 4
9.8%
수원 4
9.8%
진천 4
9.8%
화성 3
7.3%
군포 3
7.3%
원주 3
7.3%
춘천 2
 
4.9%
Other values (2) 3
7.3%

노선
Categorical

HIGH CORRELATION 

Distinct10
Distinct (%)24.4%
Missing0
Missing (%)0.0%
Memory size460.0 B
영동선
8 
중부선
8 
수도권제1순환선
7 
경부선
4 
중부내륙선
3 
Other values (5)
11 

Length

Max length8
Median length5
Mean length4.4634146
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row서울양양선
2nd row서울양양선
3rd row평택제천선
4th row평택제천선
5th row구리포천선

Common Values

ValueCountFrequency (%)
영동선 8
19.5%
중부선 8
19.5%
수도권제1순환선 7
17.1%
경부선 4
9.8%
중부내륙선 3
 
7.3%
서해안선 3
 
7.3%
서울양양선 2
 
4.9%
평택제천선 2
 
4.9%
구리포천선 2
 
4.9%
제2중부선 2
 
4.9%

Length

2023-12-11T06:00:17.888983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T06:00:18.063132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
영동선 8
19.5%
중부선 8
19.5%
수도권제1순환선 7
17.1%
경부선 4
9.8%
중부내륙선 3
 
7.3%
서해안선 3
 
7.3%
서울양양선 2
 
4.9%
평택제천선 2
 
4.9%
구리포천선 2
 
4.9%
제2중부선 2
 
4.9%

이정
Real number (ℝ)

HIGH CORRELATION 

Distinct28
Distinct (%)68.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean188.75122
Minimum5.1
Maximum379.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size501.0 B
2023-12-11T06:00:18.253123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5.1
5-th percentile13
Q145.1
median121.5
Q3327.5
95-th percentile371.8
Maximum379.6
Range374.5
Interquartile range (IQR)282.4

Descriptive statistics

Standard deviation143.50462
Coefficient of variation (CV)0.76028445
Kurtosis-1.8556487
Mean188.75122
Median Absolute Deviation (MAD)116.4
Skewness0.031181592
Sum7738.8
Variance20593.575
MonotonicityNot monotonic
2023-12-11T06:00:18.416210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
13.0 2
 
4.9%
379.6 2
 
4.9%
18.3 2
 
4.9%
351.8 2
 
4.9%
76.0 2
 
4.9%
305.9 2
 
4.9%
336.2 2
 
4.9%
99.0 2
 
4.9%
95.8 2
 
4.9%
327.5 2
 
4.9%
Other values (18) 21
51.2%
ValueCountFrequency (%)
5.1 1
2.4%
10.5 1
2.4%
13.0 2
4.9%
18.3 2
4.9%
26.9 1
2.4%
30.2 1
2.4%
31.6 2
4.9%
45.1 1
2.4%
76.0 2
4.9%
76.8 1
2.4%
ValueCountFrequency (%)
379.6 2
4.9%
371.8 1
2.4%
361.1 1
2.4%
353.4 1
2.4%
353.3 1
2.4%
351.8 2
4.9%
336.2 2
4.9%
327.5 2
4.9%
319.0 2
4.9%
305.9 2
4.9%

방향
Categorical

HIGH CORRELATION 

Distinct16
Distinct (%)39.0%
Missing0
Missing (%)0.0%
Memory size460.0 B
강릉
5 
하남
5 
통영
5 
서울
4 
외측
4 
Other values (11)
18 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique6 ?
Unique (%)14.6%

Sample

1st row양양
2nd row서울
3rd row평택
4th row제천
5th row구리

Common Values

ValueCountFrequency (%)
강릉 5
12.2%
하남 5
12.2%
통영 5
12.2%
서울 4
9.8%
외측 4
9.8%
인천 3
7.3%
내측 3
7.3%
내서 2
 
4.9%
목포 2
 
4.9%
부산 2
 
4.9%
Other values (6) 6
14.6%

Length

2023-12-11T06:00:18.616871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
강릉 5
12.2%
하남 5
12.2%
통영 5
12.2%
서울 4
9.8%
외측 4
9.8%
인천 3
7.3%
내측 3
7.3%
내서 2
 
4.9%
목포 2
 
4.9%
부산 2
 
4.9%
Other values (6) 6
14.6%

명칭
Text

Distinct26
Distinct (%)63.4%
Missing0
Missing (%)0.0%
Memory size460.0 B
2023-12-11T06:00:18.836611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length2
Mean length2.2195122
Min length2

Characters and Unicode

Total characters91
Distinct characters47
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

Unique11 ?
Unique (%)26.8%

Sample

1st row화도
2nd row화도
3rd row송탄
4th row송탄
5th row소흘
ValueCountFrequency (%)
화도 2
 
4.9%
여주 2
 
4.9%
적금 2
 
4.9%
송탄 2
 
4.9%
일죽 2
 
4.9%
상번천 2
 
4.9%
번천 2
 
4.9%
오산 2
 
4.9%
도척 2
 
4.9%
이천 2
 
4.9%
Other values (16) 21
51.2%
2023-12-11T06:00:19.273566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
천 6
 
6.6%
남 5
 
5.5%
도 4
 
4.4%
서 4
 
4.4%
번 4
 
4.4%
주 3
 
3.3%
가 3
 
3.3%
이 3
 
3.3%
화 2
 
2.2%
금 2
 
2.2%
Other values (37) 55
60.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 91
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
천 6
 
6.6%
남 5
 
5.5%
도 4
 
4.4%
서 4
 
4.4%
번 4
 
4.4%
주 3
 
3.3%
가 3
 
3.3%
이 3
 
3.3%
화 2
 
2.2%
금 2
 
2.2%
Other values (37) 55
60.4%

Most occurring scripts

ValueCountFrequency (%)
Hangul 91
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
천 6
 
6.6%
남 5
 
5.5%
도 4
 
4.4%
서 4
 
4.4%
번 4
 
4.4%
주 3
 
3.3%
가 3
 
3.3%
이 3
 
3.3%
화 2
 
2.2%
금 2
 
2.2%
Other values (37) 55
60.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 91
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
천 6
 
6.6%
남 5
 
5.5%
도 4
 
4.4%
서 4
 
4.4%
번 4
 
4.4%
주 3
 
3.3%
가 3
 
3.3%
이 3
 
3.3%
화 2
 
2.2%
금 2
 
2.2%
Other values (37) 55
60.4%

소재지지번주소
Text

Distinct37
Distinct (%)90.2%
Missing0
Missing (%)0.0%
Memory size460.0 B
2023-12-11T06:00:19.647819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length25
Median length23
Mean length19.926829
Min length14

Characters and Unicode

Total characters817
Distinct characters88
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique33 ?
Unique (%)80.5%

Sample

1st row경기도 남양주시 화도읍 차산리 산67-28
2nd row경기도 남양주시 화도읍 차산리 산67-59
3rd row경기도 평택시 모곡동 171-1
4th row경기도 평택시 모곡동 171-1
5th row경기도 포천시 소흘읍 이가팔리 685
ValueCountFrequency (%)
경기도 41
 
21.1%
광주시 6
 
3.1%
여주시 6
 
3.1%
이천시 4
 
2.1%
남한산성면 4
 
2.1%
시흥시 3
 
1.5%
평택시 3
 
1.5%
오산시 2
 
1.0%
고담동 2
 
1.0%
남양주시 2
 
1.0%
Other values (88) 121
62.4%
2023-12-11T06:00:20.169007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
153
18.7%
도 45
 
5.5%
시 44
 
5.4%
기 42
 
5.1%
경 41
 
5.0%
1 34
 
4.2%
- 30
 
3.7%
리 25
 
3.1%
산 20
 
2.4%
8 17
 
2.1%
Other values (78) 366
44.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 488
59.7%
Space Separator 153
 
18.7%
Decimal Number 146
 
17.9%
Dash Punctuation 30
 
3.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 45
 
9.2%
시 44
 
9.0%
기 42
 
8.6%
경 41
 
8.4%
리 25
 
5.1%
산 20
 
4.1%
동 17
 
3.5%
면 14
 
2.9%
주 14
 
2.9%
천 13
 
2.7%
Other values (66) 213
43.6%
Decimal Number
ValueCountFrequency (%)
1 34
23.3%
8 17
11.6%
5 16
11.0%
7 15
10.3%
4 14
9.6%
6 13
 
8.9%
2 11
 
7.5%
0 11
 
7.5%
3 8
 
5.5%
9 7
 
4.8%
Space Separator
ValueCountFrequency (%)
153
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 30
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 488
59.7%
Common 329
40.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 45
 
9.2%
시 44
 
9.0%
기 42
 
8.6%
경 41
 
8.4%
리 25
 
5.1%
산 20
 
4.1%
동 17
 
3.5%
면 14
 
2.9%
주 14
 
2.9%
천 13
 
2.7%
Other values (66) 213
43.6%
Common
ValueCountFrequency (%)
153
46.5%
1 34
 
10.3%
- 30
 
9.1%
8 17
 
5.2%
5 16
 
4.9%
7 15
 
4.6%
4 14
 
4.3%
6 13
 
4.0%
2 11
 
3.3%
0 11
 
3.3%
Other values (2) 15
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 488
59.7%
ASCII 329
40.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
153
46.5%
1 34
 
10.3%
- 30
 
9.1%
8 17
 
5.2%
5 16
 
4.9%
7 15
 
4.6%
4 14
 
4.3%
6 13
 
4.0%
2 11
 
3.3%
0 11
 
3.3%
Other values (2) 15
 
4.6%
Hangul
ValueCountFrequency (%)
도 45
 
9.2%
시 44
 
9.0%
기 42
 
8.6%
경 41
 
8.4%
리 25
 
5.1%
산 20
 
4.1%
동 17
 
3.5%
면 14
 
2.9%
주 14
 
2.9%
천 13
 
2.7%
Other values (66) 213
43.6%

위도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct33
Distinct (%)89.2%
Missing4
Missing (%)9.8%
Infinite0
Infinite (%)0.0%
Mean37.33996
Minimum37.001052
Maximum37.828841
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size501.0 B
2023-12-11T06:00:20.348531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.001052
5-th percentile37.032522
Q137.196532
median37.323
Q337.450323
95-th percentile37.670937
Maximum37.828841
Range0.82778864
Interquartile range (IQR)0.25379119

Descriptive statistics

Standard deviation0.20808877
Coefficient of variation (CV)0.0055728174
Kurtosis0.020467355
Mean37.33996
Median Absolute Deviation (MAD)0.12732327
Skewness0.54173543
Sum1381.5785
Variance0.043300938
MonotonicityNot monotonic
2023-12-11T06:00:20.502534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
37.03252159 2
 
4.9%
37.32299987 2
 
4.9%
37.08420098 2
 
4.9%
37.45277459 2
 
4.9%
37.25877534 1
 
2.4%
37.31869978 1
 
2.4%
37.00105195 1
 
2.4%
37.15670134 1
 
2.4%
37.15419707 1
 
2.4%
37.44804447 1
 
2.4%
Other values (23) 23
56.1%
(Missing) 4
 
9.8%
ValueCountFrequency (%)
37.00105195 1
2.4%
37.03252159 2
4.9%
37.08420098 2
4.9%
37.09586999 1
2.4%
37.15419707 1
2.4%
37.15670134 1
2.4%
37.16898405 1
2.4%
37.19653195 1
2.4%
37.23550154 1
2.4%
37.24172354 1
2.4%
ValueCountFrequency (%)
37.82884059 1
2.4%
37.82789539 1
2.4%
37.6316977 1
2.4%
37.63086526 1
2.4%
37.59225514 1
2.4%
37.5900192 1
2.4%
37.58694141 1
2.4%
37.45277459 2
4.9%
37.45032314 1
2.4%
37.44849647 1
2.4%

경도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct33
Distinct (%)89.2%
Missing4
Missing (%)9.8%
Infinite0
Infinite (%)0.0%
Mean127.22798
Minimum126.7606
Maximum127.71524
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size501.0 B
2023-12-11T06:00:20.674737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.7606
5-th percentile126.7783
Q1127.08165
median127.25591
Q3127.45637
95-th percentile127.64203
Maximum127.71524
Range0.9546363
Interquartile range (IQR)0.3747214

Descriptive statistics

Standard deviation0.26725339
Coefficient of variation (CV)0.0021005866
Kurtosis-0.72266642
Mean127.22798
Median Absolute Deviation (MAD)0.1873044
Skewness-0.047595734
Sum4707.4353
Variance0.071424374
MonotonicityNot monotonic
2023-12-11T06:00:20.855735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
127.0816457 2
 
4.9%
127.3384055 2
 
4.9%
127.4563671 2
 
4.9%
127.255907 2
 
4.9%
127.7152402 1
 
2.4%
126.9863093 1
 
2.4%
127.151941 1
 
2.4%
127.0869753 1
 
2.4%
127.0860832 1
 
2.4%
127.2751306 1
 
2.4%
Other values (23) 23
56.1%
(Missing) 4
 
9.8%
ValueCountFrequency (%)
126.7606039 1
2.4%
126.7683886 1
2.4%
126.780779 1
2.4%
126.8032104 1
2.4%
126.8619898 1
2.4%
126.8646742 1
2.4%
126.9863093 1
2.4%
127.0223237 1
2.4%
127.0816457 2
4.9%
127.0860832 1
2.4%
ValueCountFrequency (%)
127.7152402 1
2.4%
127.7037995 1
2.4%
127.626587 1
2.4%
127.6049459 1
2.4%
127.5467039 1
2.4%
127.5463703 1
2.4%
127.4670685 1
2.4%
127.4655408 1
2.4%
127.4563671 2
4.9%
127.4432114 1
2.4%

Interactions

2023-12-11T06:00:16.208137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:00:14.921700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:00:15.380213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:00:15.860824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:00:16.311241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:00:15.046273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:00:15.530749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:00:15.950780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:00:16.416596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:00:15.182992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:00:15.650547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:00:16.043014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:00:16.516612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:00:15.278108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:00:15.760669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:00:16.123525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T06:00:20.968345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
설치년도본부지사노선이정방향명칭소재지지번주소위도경도
설치년도1.0000.5340.8850.8790.8600.8780.9981.0000.8540.650
본부0.5341.0001.0000.7500.6670.5411.0001.0000.5250.770
지사0.8851.0001.0000.9220.9220.7881.0001.0000.8820.877
노선0.8790.7500.9221.0000.7710.9591.0001.0000.8660.918
이정0.8600.6670.9220.7711.0000.6101.0001.0000.7340.791
방향0.8780.5410.7880.9590.6101.0000.0000.7950.8520.424
명칭0.9981.0001.0001.0001.0000.0001.0001.0001.0001.000
소재지지번주소1.0001.0001.0001.0001.0000.7951.0001.0001.0001.000
위도0.8540.5250.8820.8660.7340.8521.0001.0001.0000.769
경도0.6500.7700.8770.9180.7910.4241.0001.0000.7691.000
2023-12-11T06:00:21.125747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
방향지사노선본부
방향1.0000.3780.7330.269
지사0.3781.0000.7010.874
노선0.7330.7011.0000.558
본부0.2690.8740.5581.000
2023-12-11T06:00:21.223306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
설치년도이정위도경도본부지사노선방향
설치년도1.000-0.7050.380-0.3150.4150.5860.6700.493
이정-0.7051.000-0.2930.1090.5050.6600.4900.201
위도0.380-0.2931.000-0.2270.3540.5730.6300.396
경도-0.3150.109-0.2271.0000.5740.5960.5310.104
본부0.4150.5050.3540.5741.0000.8740.5580.269
지사0.5860.6600.5730.5960.8741.0000.7010.378
노선0.6700.4900.6300.5310.5580.7011.0000.733
방향0.4930.2010.3960.1040.2690.3780.7331.000

Missing values

2023-12-11T06:00:16.663754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T06:00:16.808833image/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.
2023-12-11T06:00:16.910997image/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

설치년도본부지사노선이정방향명칭소재지지번주소위도경도
02022강원춘천서울양양선13.0양양화도경기도 남양주시 화도읍 차산리 산67-2837.631698127.29838
12022강원춘천서울양양선13.0서울화도경기도 남양주시 화도읍 차산리 산67-5937.630865127.29619
22020수도권화성평택제천선18.3평택송탄경기도 평택시 모곡동 171-137.032522127.081646
32020수도권화성평택제천선18.3제천송탄경기도 평택시 모곡동 171-137.032522127.081646
42020수도권동서울구리포천선31.6구리소흘경기도 포천시 소흘읍 이가팔리 68537.827895127.159109
52020수도권동서울구리포천선31.6포천소흘경기도 포천시 소흘읍 이가팔리 65037.828841127.159701
62017수도권군포영동선10.5인천군자경기도 시흥시 거모동 산62-10번지37.358755126.780779
72016강원이천중부내륙선279.0양평여주경기도 여주시 흥천면 귀백리 261-737.34224127.54637
82016강원이천중부내륙선279.0내서여주경기도 여주시 흥천면 귀백리 308-437.341544127.546704
92015수도권동서울수도권제1순환선26.9내측구리남양주경기도 구리시 토평동 8637.590019127.155951
설치년도본부지사노선이정방향명칭소재지지번주소위도경도
312012수도권경기광주제2중부선351.8통영상번천경기도 광주시 남한산성면 상번천리 315-237.448496127.273802
322012강원원주영동선99.0인천적금경기도 여주시 강천면 적금리 26937.25278127.7038
332012충북진천중부선305.9통영일죽경기도 안성시 일죽면 월정리 75537.084201127.456367
342012수도권경기광주중부선336.2하남도척경기도 광주시 도척면 진우리 482-137.323127.338405
352012수도권경기광주중부선336.2통영도척경기도 광주시 도척면 진우리 482-137.323127.338405
362011수도권경기광주중부선353.4통영번천경기도 광주시 남한산성면 광지원리 306-137.452775127.255907
372011수도권화성서해안선293.4목포향남경기도 화성시 향남읍 구문천리 144-11<NA><NA>
382011충북진천중부선305.9하남일죽경기도 안성시 일죽면 월정리 75537.084201127.456367
392011충북진천중부선319.0통영모가경기도 이천시 모가면 신갈리 587-137.196532127.443211
402011충북진천중부선319.0하남모가경기도 이천시 모가면 신갈리 25-11<NA><NA>