Overview

Dataset statistics

Number of variables5
Number of observations74
Missing cells19
Missing cells (%)5.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.2 KiB
Average record size in memory43.7 B

Variable types

Categorical2
Text1
Numeric2

Dataset

Description종합관광안내표지 설치 현황(시군, 위치, 설치년도, 규격 등)전북특별자치도 내 종합 관광 안내표지가 설치된 시, 군의 이름우리기관에서는 더 이상 생성 불가 데이터입니다.
Author전북특별자치도
URLhttps://www.data.go.kr/data/3084459/fileData.do

Alerts

정비년도 has 19 (25.7%) missing valuesMissing

Reproduction

Analysis started2024-03-14 18:14:10.185502
Analysis finished2024-03-14 18:14:11.982456
Duration1.8 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군명
Categorical

Distinct14
Distinct (%)18.9%
Missing0
Missing (%)0.0%
Memory size720.0 B
군산(10)
10 
진안(8)
8 
무주(8)
8 
익산(7)
7 
남원(7)
7 
Other values (9)
34 

Length

Max length6
Median length5
Mean length5.1351351
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row전주(4)
2nd row전주(4)
3rd row전주(4)
4th row전주(4)
5th row군산(10)

Common Values

ValueCountFrequency (%)
군산(10) 10
13.5%
진안(8) 8
10.8%
무주(8) 8
10.8%
익산(7) 7
9.5%
남원(7) 7
9.5%
임실(6) 6
8.1%
전주(4) 4
 
5.4%
정읍(4) 4
 
5.4%
김제(4) 4
 
5.4%
순창(4) 4
 
5.4%
Other values (4) 12
16.2%

Length

2024-03-15T03:14:12.205388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
군산(10 10
13.5%
진안(8 8
10.8%
무주(8 8
10.8%
익산(7 7
9.5%
남원(7 7
9.5%
임실(6 6
8.1%
전주(4 4
 
5.4%
정읍(4 4
 
5.4%
김제(4 4
 
5.4%
순창(4 4
 
5.4%
Other values (4) 12
16.2%

위 치
Text

Distinct73
Distinct (%)98.6%
Missing0
Missing (%)0.0%
Memory size720.0 B
2024-03-15T03:14:13.289164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length10
Mean length7.3783784
Min length3

Characters and Unicode

Total characters546
Distinct characters174
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique72 ?
Unique (%)97.3%

Sample

1st row덕진공원 입구
2nd row고속버스터미널
3rd row시외버스터미널
4th row전주국립박물관
5th row금강호휴게소
ValueCountFrequency (%)
주차장 6
 
6.0%
내 4
 
4.0%
휴게소 3
 
3.0%
입구 3
 
3.0%
터미널 2
 
2.0%
시외버스터미널 2
 
2.0%
서해고인돌(하)휴게소 1
 
1.0%
자연환경연수원 1
 
1.0%
라제통문 1
 
1.0%
장터 1
 
1.0%
Other values (76) 76
76.0%
2024-03-15T03:14:14.783028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
26
 
4.8%
장 19
 
3.5%
산 18
 
3.3%
소 15
 
2.7%
휴 14
 
2.6%
주 13
 
2.4%
관 13
 
2.4%
게 11
 
2.0%
차 10
 
1.8%
원 10
 
1.8%
Other values (164) 397
72.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 502
91.9%
Space Separator 26
 
4.8%
Open Punctuation 8
 
1.5%
Close Punctuation 8
 
1.5%
Decimal Number 2
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
장 19
 
3.8%
산 18
 
3.6%
소 15
 
3.0%
휴 14
 
2.8%
주 13
 
2.6%
관 13
 
2.6%
게 11
 
2.2%
차 10
 
2.0%
원 10
 
2.0%
사 9
 
1.8%
Other values (159) 370
73.7%
Decimal Number
ValueCountFrequency (%)
5 1
50.0%
3 1
50.0%
Space Separator
ValueCountFrequency (%)
26
100.0%
Open Punctuation
ValueCountFrequency (%)
( 8
100.0%
Close Punctuation
ValueCountFrequency (%)
) 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 502
91.9%
Common 44
 
8.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
장 19
 
3.8%
산 18
 
3.6%
소 15
 
3.0%
휴 14
 
2.8%
주 13
 
2.6%
관 13
 
2.6%
게 11
 
2.2%
차 10
 
2.0%
원 10
 
2.0%
사 9
 
1.8%
Other values (159) 370
73.7%
Common
ValueCountFrequency (%)
26
59.1%
( 8
 
18.2%
) 8
 
18.2%
5 1
 
2.3%
3 1
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 502
91.9%
ASCII 44
 
8.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
26
59.1%
( 8
 
18.2%
) 8
 
18.2%
5 1
 
2.3%
3 1
 
2.3%
Hangul
ValueCountFrequency (%)
장 19
 
3.8%
산 18
 
3.6%
소 15
 
3.0%
휴 14
 
2.8%
주 13
 
2.6%
관 13
 
2.6%
게 11
 
2.2%
차 10
 
2.0%
원 10
 
2.0%
사 9
 
1.8%
Other values (159) 370
73.7%

설치년도
Real number (ℝ)

Distinct13
Distinct (%)17.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2006.6216
Minimum2001
Maximum2016
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size794.0 B
2024-03-15T03:14:15.136473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2001
5-th percentile2002
Q12003
median2004
Q32012
95-th percentile2015.35
Maximum2016
Range15
Interquartile range (IQR)9

Descriptive statistics

Standard deviation4.8333961
Coefficient of variation (CV)0.0024087232
Kurtosis-1.116995
Mean2006.6216
Median Absolute Deviation (MAD)2
Skewness0.72466188
Sum148490
Variance23.361718
MonotonicityNot monotonic
2024-03-15T03:14:15.517028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
2002 14
18.9%
2004 12
16.2%
2003 12
16.2%
2005 9
12.2%
2012 6
8.1%
2014 5
 
6.8%
2013 4
 
5.4%
2016 4
 
5.4%
2011 3
 
4.1%
2010 2
 
2.7%
Other values (3) 3
 
4.1%
ValueCountFrequency (%)
2001 1
 
1.4%
2002 14
18.9%
2003 12
16.2%
2004 12
16.2%
2005 9
12.2%
2006 1
 
1.4%
2010 2
 
2.7%
2011 3
 
4.1%
2012 6
8.1%
2013 4
 
5.4%
ValueCountFrequency (%)
2016 4
 
5.4%
2015 1
 
1.4%
2014 5
6.8%
2013 4
 
5.4%
2012 6
8.1%
2011 3
 
4.1%
2010 2
 
2.7%
2006 1
 
1.4%
2005 9
12.2%
2004 12
16.2%

정비년도
Real number (ℝ)

MISSING 

Distinct8
Distinct (%)14.5%
Missing19
Missing (%)25.7%
Infinite0
Infinite (%)0.0%
Mean2013.7455
Minimum2009
Maximum2016
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size794.0 B
2024-03-15T03:14:15.979665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2009
5-th percentile2009
Q12012
median2015
Q32016
95-th percentile2016
Maximum2016
Range7
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.2543635
Coefficient of variation (CV)0.0011194878
Kurtosis-0.68414967
Mean2013.7455
Median Absolute Deviation (MAD)1
Skewness-0.73723021
Sum110756
Variance5.0821549
MonotonicityNot monotonic
2024-03-15T03:14:16.307705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
2016 16
21.6%
2015 13
17.6%
2011 7
 
9.5%
2013 6
 
8.1%
2012 4
 
5.4%
2014 4
 
5.4%
2009 4
 
5.4%
2010 1
 
1.4%
(Missing) 19
25.7%
ValueCountFrequency (%)
2009 4
 
5.4%
2010 1
 
1.4%
2011 7
9.5%
2012 4
 
5.4%
2013 6
 
8.1%
2014 4
 
5.4%
2015 13
17.6%
2016 16
21.6%
ValueCountFrequency (%)
2016 16
21.6%
2015 13
17.6%
2014 4
 
5.4%
2013 6
 
8.1%
2012 4
 
5.4%
2011 7
9.5%
2010 1
 
1.4%
2009 4
 
5.4%

규 격
Categorical

Distinct8
Distinct (%)10.8%
Missing0
Missing (%)0.0%
Memory size720.0 B
3600×2400
44 
3900×2400
16 
3000×2400
 
4
3600×2320
 
4
2480×1880
 
2
Other values (3)
 
4

Length

Max length9
Median length9
Mean length9
Min length9

Unique

Unique2 ?
Unique (%)2.7%

Sample

1st row3900×2400
2nd row3500×2970
3rd row3600×2400
4th row3600×2400
5th row3600×2400

Common Values

ValueCountFrequency (%)
3600×2400 44
59.5%
3900×2400 16
 
21.6%
3000×2400 4
 
5.4%
3600×2320 4
 
5.4%
2480×1880 2
 
2.7%
3500×2400 2
 
2.7%
3500×2970 1
 
1.4%
3600×2300 1
 
1.4%

Length

2024-03-15T03:14:16.534597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T03:14:16.747357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3600×2400 44
59.5%
3900×2400 16
 
21.6%
3000×2400 4
 
5.4%
3600×2320 4
 
5.4%
2480×1880 2
 
2.7%
3500×2400 2
 
2.7%
3500×2970 1
 
1.4%
3600×2300 1
 
1.4%

Interactions

2024-03-15T03:14:11.038285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:14:10.505358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:14:11.298427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:14:10.779771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-15T03:14:16.914357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군명위 치설치년도정비년도규 격
시군명1.0000.9110.0000.8580.258
위 치0.9111.0000.9831.0000.858
설치년도0.0000.9831.0000.0000.560
정비년도0.8581.0000.0001.0000.000
규 격0.2580.8580.5600.0001.000
2024-03-15T03:14:17.081128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군명규 격
시군명1.0000.098
규 격0.0981.000
2024-03-15T03:14:17.230663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
설치년도정비년도시군명규 격
설치년도1.0000.4860.0000.315
정비년도0.4861.0000.4250.000
시군명0.0000.4251.0000.098
규 격0.3150.0000.0981.000

Missing values

2024-03-15T03:14:11.645225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-15T03:14:11.862234image/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

시군명위 치설치년도정비년도규 격
0전주(4)덕진공원 입구200420123900×2400
1전주(4)고속버스터미널2012<NA>3500×2970
2전주(4)시외버스터미널2012<NA>3600×2400
3전주(4)전주국립박물관201120163600×2400
4군산(10)금강호휴게소2013<NA>3600×2400
5군산(10)군산공항201220163600×2400
6군산(10)고속도군산(하)휴게소200220133600×2400
7군산(10)여객선터미널(중)2011<NA>2480×1880
8군산(10)여객선 터미널200320122480×1880
9군산(10)은파관광지 내200420153900×2400
시군명위 치설치년도정비년도규 격
64순창(4)시외버스터미널200320093600×2320
65순창(4)순창고추장마을2012<NA>3600×2400
66순창(4)회문산자연휴양림200420163900×2400
67고창(4)선운산관리사무소200220153600×2400
68고창(4)석정 휴스파200220153600×2400
69고창(4)서해고인돌(하)휴게소200220153600×2400
70고창(4)서해고인돌(상)휴게소200320113600×2400
71부안(3)부안영상테마파크200620163600×2400
72부안(3)변산해수욕장200420103900×2400
73부안(3)내소사 주차장2016<NA>3600×2400