Overview

Dataset statistics

Number of variables11
Number of observations24
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.4 KiB
Average record size in memory100.5 B

Variable types

Numeric6
Text2
Categorical3

Dataset

Description충청남도 천안시 관내 지하(차)도 현황에 대한 데이터로 연장, 폭, 높이, 차선, 준공연도 등 관련 제원을 제공 합니다.
Author충청남도
URLhttps://alldam.chungnam.go.kr/index.chungnam?menuCd=DOM_000000201001001001&st=&cds=&orgCd=&apiType=&isOpen=Y&pageIndex=26&beforeMenuCd=DOM_000000201001001000&publicdatapk=15119300

Alerts

연번 is highly overall correlated with 준공연도 and 3 other fieldsHigh correlation
준공연도 is highly overall correlated with 연번 and 3 other fieldsHigh correlation
연장(미터) is highly overall correlated with 연번 and 4 other fieldsHigh correlation
폭(미터) is highly overall correlated with 높이(미터) and 2 other fieldsHigh correlation
높이(미터) is highly overall correlated with 준공연도 and 4 other fieldsHigh correlation
비고(경과년수) is highly overall correlated with 연번 and 3 other fieldsHigh correlation
종별 is highly overall correlated with 연장(미터)High correlation
시설물종류 is highly overall correlated with 연번 and 2 other fieldsHigh correlation
차선 is highly overall correlated with 폭(미터)High correlation
연번 has unique valuesUnique
시설물명 has unique valuesUnique
주소 has unique valuesUnique

Reproduction

Analysis started2024-01-09 21:39:33.834564
Analysis finished2024-01-09 21:39:36.813079
Duration2.98 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.5
Minimum1
Maximum24
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2024-01-10T06:39:36.859736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.15
Q16.75
median12.5
Q318.25
95-th percentile22.85
Maximum24
Range23
Interquartile range (IQR)11.5

Descriptive statistics

Standard deviation7.0710678
Coefficient of variation (CV)0.56568542
Kurtosis-1.2
Mean12.5
Median Absolute Deviation (MAD)6
Skewness0
Sum300
Variance50
MonotonicityStrictly increasing
2024-01-10T06:39:36.948532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
1 1
 
4.2%
14 1
 
4.2%
24 1
 
4.2%
23 1
 
4.2%
22 1
 
4.2%
21 1
 
4.2%
20 1
 
4.2%
19 1
 
4.2%
18 1
 
4.2%
17 1
 
4.2%
Other values (14) 14
58.3%
ValueCountFrequency (%)
1 1
4.2%
2 1
4.2%
3 1
4.2%
4 1
4.2%
5 1
4.2%
6 1
4.2%
7 1
4.2%
8 1
4.2%
9 1
4.2%
10 1
4.2%
ValueCountFrequency (%)
24 1
4.2%
23 1
4.2%
22 1
4.2%
21 1
4.2%
20 1
4.2%
19 1
4.2%
18 1
4.2%
17 1
4.2%
16 1
4.2%
15 1
4.2%

시설물명
Text

UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size324.0 B
2024-01-10T06:39:37.090892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length7.5
Mean length6
Min length5

Characters and Unicode

Total characters144
Distinct characters45
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

Unique24 ?
Unique (%)100.0%

Sample

1st row쌍용지하도
2nd row성정지하차도
3rd row구상골지하도
4th row미라골지하도
5th row백석지하도
ValueCountFrequency (%)
지하차도 2
 
7.7%
쌍용지하도 1
 
3.8%
판정지하차도 1
 
3.8%
오룡지하차도 1
 
3.8%
청당지하차도 1
 
3.8%
청수지하차도 1
 
3.8%
신방지하차도 1
 
3.8%
천안지하도 1
 
3.8%
봉서지하도 1
 
3.8%
일봉지하도 1
 
3.8%
Other values (15) 15
57.7%
2024-01-10T06:39:37.355195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
지 24
16.7%
하 24
16.7%
도 24
16.7%
차 14
 
9.7%
성 5
 
3.5%
청 3
 
2.1%
정 3
 
2.1%
룡 2
 
1.4%
봉 2
 
1.4%
용 2
 
1.4%
Other values (35) 41
28.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 142
98.6%
Space Separator 2
 
1.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
지 24
16.9%
하 24
16.9%
도 24
16.9%
차 14
 
9.9%
성 5
 
3.5%
청 3
 
2.1%
정 3
 
2.1%
룡 2
 
1.4%
봉 2
 
1.4%
용 2
 
1.4%
Other values (34) 39
27.5%
Space Separator
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 142
98.6%
Common 2
 
1.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
지 24
16.9%
하 24
16.9%
도 24
16.9%
차 14
 
9.9%
성 5
 
3.5%
청 3
 
2.1%
정 3
 
2.1%
룡 2
 
1.4%
봉 2
 
1.4%
용 2
 
1.4%
Other values (34) 39
27.5%
Common
ValueCountFrequency (%)
2
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 142
98.6%
ASCII 2
 
1.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
지 24
16.9%
하 24
16.9%
도 24
16.9%
차 14
 
9.9%
성 5
 
3.5%
청 3
 
2.1%
정 3
 
2.1%
룡 2
 
1.4%
봉 2
 
1.4%
용 2
 
1.4%
Other values (34) 39
27.5%
ASCII
ValueCountFrequency (%)
2
100.0%

주소
Text

UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size324.0 B
2024-01-10T06:39:37.523688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length33
Median length30
Mean length25.291667
Min length20

Characters and Unicode

Total characters607
Distinct characters56
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

Unique24 ?
Unique (%)100.0%

Sample

1st row충청남도 천안시 서북구 쌍용동 1178번지 일원
2nd row충청남도 천안시 서북구 성정동 609-199번지 일원
3rd row충청남도 천안시 서북구 성정동 795번지 일원
4th row충청남도 천안시 서북구 쌍용동 998번지 일원
5th row충청남도 천안시 서북구 성정동 940번지 일원
ValueCountFrequency (%)
충청남도 24
17.4%
천안시 24
17.4%
일원 14
 
10.1%
서북구 14
 
10.1%
동남구 10
 
7.2%
쌍용동 3
 
2.2%
두정동 3
 
2.2%
성정동 3
 
2.2%
신부동 2
 
1.4%
다가동 2
 
1.4%
Other values (39) 39
28.3%
2024-01-10T06:39:37.788888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
117
19.3%
남 34
 
5.6%
동 31
 
5.1%
청 26
 
4.3%
구 25
 
4.1%
시 25
 
4.1%
충 24
 
4.0%
안 24
 
4.0%
천 24
 
4.0%
도 24
 
4.0%
Other values (46) 253
41.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 388
63.9%
Space Separator 117
 
19.3%
Decimal Number 90
 
14.8%
Dash Punctuation 10
 
1.6%
Open Punctuation 1
 
0.2%
Close Punctuation 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
남 34
 
8.8%
동 31
 
8.0%
청 26
 
6.7%
구 25
 
6.4%
시 25
 
6.4%
충 24
 
6.2%
안 24
 
6.2%
천 24
 
6.2%
도 24
 
6.2%
번 18
 
4.6%
Other values (32) 133
34.3%
Decimal Number
ValueCountFrequency (%)
1 15
16.7%
5 12
13.3%
9 11
12.2%
2 9
10.0%
4 9
10.0%
0 8
8.9%
8 8
8.9%
7 8
8.9%
6 6
 
6.7%
3 4
 
4.4%
Space Separator
ValueCountFrequency (%)
117
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 10
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 388
63.9%
Common 219
36.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
남 34
 
8.8%
동 31
 
8.0%
청 26
 
6.7%
구 25
 
6.4%
시 25
 
6.4%
충 24
 
6.2%
안 24
 
6.2%
천 24
 
6.2%
도 24
 
6.2%
번 18
 
4.6%
Other values (32) 133
34.3%
Common
ValueCountFrequency (%)
117
53.4%
1 15
 
6.8%
5 12
 
5.5%
9 11
 
5.0%
- 10
 
4.6%
2 9
 
4.1%
4 9
 
4.1%
0 8
 
3.7%
8 8
 
3.7%
7 8
 
3.7%
Other values (4) 12
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 388
63.9%
ASCII 219
36.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
117
53.4%
1 15
 
6.8%
5 12
 
5.5%
9 11
 
5.0%
- 10
 
4.6%
2 9
 
4.1%
4 9
 
4.1%
0 8
 
3.7%
8 8
 
3.7%
7 8
 
3.7%
Other values (4) 12
 
5.5%
Hangul
ValueCountFrequency (%)
남 34
 
8.8%
동 31
 
8.0%
청 26
 
6.7%
구 25
 
6.4%
시 25
 
6.4%
충 24
 
6.2%
안 24
 
6.2%
천 24
 
6.2%
도 24
 
6.2%
번 18
 
4.6%
Other values (32) 133
34.3%

종별
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Memory size324.0 B
3종
18 
2종
5 
1종
 
1

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique1 ?
Unique (%)4.2%

Sample

1st row3종
2nd row3종
3rd row3종
4th row3종
5th row3종

Common Values

ValueCountFrequency (%)
3종 18
75.0%
2종 5
 
20.8%
1종 1
 
4.2%

Length

2024-01-10T06:39:37.890736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T06:39:37.961706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3종 18
75.0%
2종 5
 
20.8%
1종 1
 
4.2%

시설물종류
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)8.3%
Missing0
Missing (%)0.0%
Memory size324.0 B
지하도
13 
지하차도
11 

Length

Max length4
Median length3
Mean length3.4583333
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
지하도 13
54.2%
지하차도 11
45.8%

Length

2024-01-10T06:39:38.044746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T06:39:38.118335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
지하도 13
54.2%
지하차도 11
45.8%

준공연도
Real number (ℝ)

HIGH CORRELATION 

Distinct13
Distinct (%)54.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2001.625
Minimum1968
Maximum2017
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2024-01-10T06:39:38.184180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1968
5-th percentile1977.9
Q11993
median2007
Q32010
95-th percentile2012.7
Maximum2017
Range49
Interquartile range (IQR)17

Descriptive statistics

Standard deviation12.558775
Coefficient of variation (CV)0.0062742896
Kurtosis0.93798347
Mean2001.625
Median Absolute Deviation (MAD)4
Skewness-1.2423418
Sum48039
Variance157.72283
MonotonicityNot monotonic
2024-01-10T06:39:38.290895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
2010 6
25.0%
2007 4
16.7%
1993 3
12.5%
2011 2
 
8.3%
1990 1
 
4.2%
1983 1
 
4.2%
1996 1
 
4.2%
1997 1
 
4.2%
1968 1
 
4.2%
2009 1
 
4.2%
Other values (3) 3
12.5%
ValueCountFrequency (%)
1968 1
 
4.2%
1977 1
 
4.2%
1983 1
 
4.2%
1990 1
 
4.2%
1993 3
12.5%
1996 1
 
4.2%
1997 1
 
4.2%
2007 4
16.7%
2009 1
 
4.2%
2010 6
25.0%
ValueCountFrequency (%)
2017 1
 
4.2%
2013 1
 
4.2%
2011 2
 
8.3%
2010 6
25.0%
2009 1
 
4.2%
2007 4
16.7%
1997 1
 
4.2%
1996 1
 
4.2%
1993 3
12.5%
1990 1
 
4.2%

연장(미터)
Real number (ℝ)

HIGH CORRELATION 

Distinct23
Distinct (%)95.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean224.30417
Minimum21.4
Maximum880
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2024-01-10T06:39:38.400082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum21.4
5-th percentile25.75
Q150.25
median83.5
Q3313.75
95-th percentile853
Maximum880
Range858.6
Interquartile range (IQR)263.5

Descriptive statistics

Standard deviation265.62202
Coefficient of variation (CV)1.1842046
Kurtosis1.5532192
Mean224.30417
Median Absolute Deviation (MAD)57.5
Skewness1.5925485
Sum5383.3
Variance70555.059
MonotonicityNot monotonic
2024-01-10T06:39:38.490085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
880.0 2
 
8.3%
219.0 1
 
4.2%
72.4 1
 
4.2%
80.0 1
 
4.2%
150.0 1
 
4.2%
430.0 1
 
4.2%
480.0 1
 
4.2%
87.0 1
 
4.2%
33.0 1
 
4.2%
355.0 1
 
4.2%
Other values (13) 13
54.2%
ValueCountFrequency (%)
21.4 1
4.2%
25.0 1
4.2%
30.0 1
4.2%
33.0 1
4.2%
43.0 1
4.2%
45.0 1
4.2%
52.0 1
4.2%
54.5 1
4.2%
61.0 1
4.2%
72.4 1
4.2%
ValueCountFrequency (%)
880.0 2
8.3%
700.0 1
4.2%
480.0 1
4.2%
430.0 1
4.2%
355.0 1
4.2%
300.0 1
4.2%
219.0 1
4.2%
170.0 1
4.2%
150.0 1
4.2%
140.0 1
4.2%

폭(미터)
Real number (ℝ)

HIGH CORRELATION 

Distinct17
Distinct (%)70.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.7125
Minimum3
Maximum45
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2024-01-10T06:39:38.571859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile4.3
Q16
median13.9
Q318.125
95-th percentile23
Maximum45
Range42
Interquartile range (IQR)12.125

Descriptive statistics

Standard deviation9.3205389
Coefficient of variation (CV)0.67971113
Kurtosis4.2369843
Mean13.7125
Median Absolute Deviation (MAD)6.85
Skewness1.6009656
Sum329.1
Variance86.872446
MonotonicityNot monotonic
2024-01-10T06:39:38.650912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
6.0 6
25.0%
23.0 2
 
8.3%
18.0 2
 
8.3%
18.1 1
 
4.2%
3.0 1
 
4.2%
18.2 1
 
4.2%
19.0 1
 
4.2%
8.5 1
 
4.2%
19.8 1
 
4.2%
8.0 1
 
4.2%
Other values (7) 7
29.2%
ValueCountFrequency (%)
3.0 1
 
4.2%
4.0 1
 
4.2%
6.0 6
25.0%
6.1 1
 
4.2%
8.0 1
 
4.2%
8.5 1
 
4.2%
12.8 1
 
4.2%
15.0 1
 
4.2%
16.5 1
 
4.2%
17.1 1
 
4.2%
ValueCountFrequency (%)
45.0 1
4.2%
23.0 2
8.3%
19.8 1
4.2%
19.0 1
4.2%
18.2 1
4.2%
18.1 1
4.2%
18.0 2
8.3%
17.1 1
4.2%
16.5 1
4.2%
15.0 1
4.2%

차선
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Memory size324.0 B
4
9 
<NA>
7 
2
4 
6
2 
3
1 

Length

Max length4
Median length1
Mean length1.875
Min length1

Unique

Unique2 ?
Unique (%)8.3%

Sample

1st row4
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
4 9
37.5%
<NA> 7
29.2%
2 4
16.7%
6 2
 
8.3%
3 1
 
4.2%
8 1
 
4.2%

Length

2024-01-10T06:39:38.742824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T06:39:38.827646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
4 9
37.5%
na 7
29.2%
2 4
16.7%
6 2
 
8.3%
3 1
 
4.2%
8 1
 
4.2%

높이(미터)
Real number (ℝ)

HIGH CORRELATION 

Distinct13
Distinct (%)54.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.4041667
Minimum2.3
Maximum7.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2024-01-10T06:39:38.903534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.3
5-th percentile2.5
Q13
median4.5
Q35.125
95-th percentile7.3
Maximum7.4
Range5.1
Interquartile range (IQR)2.125

Descriptive statistics

Standard deviation1.6469944
Coefficient of variation (CV)0.37396278
Kurtosis-0.72370891
Mean4.4041667
Median Absolute Deviation (MAD)1.5
Skewness0.55763686
Sum105.7
Variance2.7125906
MonotonicityNot monotonic
2024-01-10T06:39:38.985390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
3.0 6
25.0%
4.5 3
12.5%
4.7 2
 
8.3%
2.5 2
 
8.3%
5.1 2
 
8.3%
7.3 2
 
8.3%
2.3 1
 
4.2%
7.4 1
 
4.2%
7.1 1
 
4.2%
2.7 1
 
4.2%
Other values (3) 3
12.5%
ValueCountFrequency (%)
2.3 1
 
4.2%
2.5 2
 
8.3%
2.7 1
 
4.2%
3.0 6
25.0%
4.5 3
12.5%
4.7 2
 
8.3%
5.0 1
 
4.2%
5.1 2
 
8.3%
5.2 1
 
4.2%
5.3 1
 
4.2%
ValueCountFrequency (%)
7.4 1
 
4.2%
7.3 2
 
8.3%
7.1 1
 
4.2%
5.3 1
 
4.2%
5.2 1
 
4.2%
5.1 2
 
8.3%
5.0 1
 
4.2%
4.7 2
 
8.3%
4.5 3
12.5%
3.0 6
25.0%

비고(경과년수)
Real number (ℝ)

HIGH CORRELATION 

Distinct13
Distinct (%)54.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.375
Minimum6
Maximum55
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2024-01-10T06:39:39.063976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile10.3
Q113
median16
Q330
95-th percentile45.1
Maximum55
Range49
Interquartile range (IQR)17

Descriptive statistics

Standard deviation12.558775
Coefficient of variation (CV)0.58754502
Kurtosis0.93798347
Mean21.375
Median Absolute Deviation (MAD)4
Skewness1.2423418
Sum513
Variance157.72283
MonotonicityNot monotonic
2024-01-10T06:39:39.141200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
13 6
25.0%
16 4
16.7%
30 3
12.5%
12 2
 
8.3%
33 1
 
4.2%
40 1
 
4.2%
27 1
 
4.2%
26 1
 
4.2%
55 1
 
4.2%
14 1
 
4.2%
Other values (3) 3
12.5%
ValueCountFrequency (%)
6 1
 
4.2%
10 1
 
4.2%
12 2
 
8.3%
13 6
25.0%
14 1
 
4.2%
16 4
16.7%
26 1
 
4.2%
27 1
 
4.2%
30 3
12.5%
33 1
 
4.2%
ValueCountFrequency (%)
55 1
 
4.2%
46 1
 
4.2%
40 1
 
4.2%
33 1
 
4.2%
30 3
12.5%
27 1
 
4.2%
26 1
 
4.2%
16 4
16.7%
14 1
 
4.2%
13 6
25.0%

Interactions

2024-01-10T06:39:36.195230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:39:34.182590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:39:34.733809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:39:35.130408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:39:35.492558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:39:35.819268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:39:36.269531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:39:34.240446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:39:34.791428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:39:35.208114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:39:35.547879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:39:35.878581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:39:36.326149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:39:34.298020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:39:34.848273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:39:35.271621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:39:35.606130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:39:35.935475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:39:36.393267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:39:34.355763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:39:34.902892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:39:35.326083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:39:35.659589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:39:35.991195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:39:36.467089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:39:34.408575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:39:34.968995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:39:35.378947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:39:35.709897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:39:36.048609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:39:36.548935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:39:34.465115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:39:35.054889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:39:35.434273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:39:35.764977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:39:36.118509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-10T06:39:39.217959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번시설물명주소종별시설물종류준공연도연장(미터)폭(미터)차선높이(미터)비고(경과년수)
연번1.0001.0001.0000.5090.9600.6700.5660.7330.4830.2480.702
시설물명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
주소1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
종별0.5091.0001.0001.0000.1610.1990.9340.3270.0000.5310.000
시설물종류0.9601.0001.0000.1611.0000.4950.4350.9070.0000.6670.704
준공연도0.6701.0001.0000.1990.4951.0000.6160.4460.1710.7141.000
연장(미터)0.5661.0001.0000.9340.4350.6161.0000.3530.3370.4850.687
폭(미터)0.7331.0001.0000.3270.9070.4460.3531.0000.7370.6290.623
차선0.4831.0001.0000.0000.0000.1710.3370.7371.0000.7690.000
높이(미터)0.2481.0001.0000.5310.6670.7140.4850.6290.7691.0000.729
비고(경과년수)0.7021.0001.0000.0000.7041.0000.6870.6230.0000.7291.000
2024-01-10T06:39:39.322268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시설물종류종별차선
시설물종류1.0000.2520.000
종별0.2521.0000.000
차선0.0000.0001.000
2024-01-10T06:39:39.414292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번준공연도연장(미터)폭(미터)높이(미터)비고(경과년수)종별시설물종류차선
연번1.0000.5700.5730.3400.451-0.5700.2610.6570.162
준공연도0.5701.0000.5870.4380.669-1.0000.0000.4480.000
연장(미터)0.5730.5871.0000.3980.565-0.5870.8180.2570.039
폭(미터)0.3400.4380.3981.0000.709-0.4380.0000.6340.597
높이(미터)0.4510.6690.5650.7091.000-0.6690.4400.7400.369
비고(경과년수)-0.570-1.000-0.587-0.438-0.6691.0000.0000.4480.000
종별0.2610.0000.8180.0000.4400.0001.0000.2520.000
시설물종류0.6570.4480.2570.6340.7400.4480.2521.0000.000
차선0.1620.0000.0390.5970.3690.0000.0000.0001.000

Missing values

2024-01-10T06:39:36.657119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-10T06:39:36.768982image/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

연번시설물명주소종별시설물종류준공연도연장(미터)폭(미터)차선높이(미터)비고(경과년수)
01쌍용지하도충청남도 천안시 서북구 쌍용동 1178번지 일원3종지하도1990219.018.144.733
12성정지하차도충청남도 천안시 서북구 성정동 609-199번지 일원3종지하도198372.46.1<NA>2.540
23구상골지하도충청남도 천안시 서북구 성정동 795번지 일원3종지하도199325.06.0<NA>3.030
34미라골지하도충청남도 천안시 서북구 쌍용동 998번지 일원3종지하도199330.06.0<NA>3.030
45백석지하도충청남도 천안시 서북구 성정동 940번지 일원3종지하도199621.46.0<NA>3.027
56용암지하도충청남도 천안시 서북구 쌍용동 2012번지 일원3종지하도199743.06.0<NA>3.026
67북부지하차도충청남도 천안시 서북구 두정동 1078번지 일원3종지하차도200775.023.064.516
78영성지하도충청남도 천안시 서북구 두정동 1843번지 일원3종지하도200754.56.0<NA>3.016
89성환둔포지하도충청남도 천안시 서북구 성환읍 성환리 449-729번지 일원3종지하도200752.04.0<NA>2.316
910모시리지하차도충청남도 천안시 서북구 직산읍 모시리 2-2번지 일원3종지하차도200745.045.024.516
연번시설물명주소종별시설물종류준공연도연장(미터)폭(미터)차선높이(미터)비고(경과년수)
1415천안로 지하차도충청남도 천안시 동남구 신부동 115-73번지1종지하도2010700.016.547.313
1516나들목 지하차도충청남도 천안시 동남구 신부동 157번지2종지하도2010355.018.047.113
1617일봉지하도충청남도 천안시 동남구 다가8길 26 (다가동)3종지하도2009880.03.082.514
1718봉서지하도충청남도 천안시 동남구 봉명동 500번지3종지하도199333.06.063.030
1819천안지하도충청남도 천안시 동남구 다가동 565번지3종지하도197787.08.022.746
1920신방지하차도충청남도 천안시 동남구 신방동 778-13종지하차도2013480.019.844.710
2021청수지하차도충청남도 천안시 동남구 청수동 2542종지하차도2010430.08.525.013
2122청당지하차도충청남도 천안시 동남구 구룡동 564-43종지하차도2010150.023.045.313
2223오룡지하차도충청남도 천안시 동남구 오룡동 43-143종지하차도2010880.019.047.313
2324청룡지하차도충청남도 천안시 동남구 청당동 156-23종지하차도201780.018.245.26