Overview

Dataset statistics

Number of variables12
Number of observations30
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.1 KiB
Average record size in memory104.4 B

Variable types

Numeric3
Categorical6
Text3

Dataset

Description샘플 데이터
Author국토연구원
URLhttps://bigdata-region.kr/#/dataset/2cd51889-037e-491b-9325-8fee91de5d88

Alerts

관리명 has constant value ""Constant
시도명 is highly overall correlated with 관리번호 and 1 other fieldsHigh correlation
승인자명 is highly overall correlated with 관리번호 and 1 other fieldsHigh correlation
관리번호 is highly overall correlated with 시도명 and 1 other fieldsHigh correlation
기준년도 is highly overall correlated with 승인년도 and 1 other fieldsHigh correlation
승인년도 is highly overall correlated with 기준년도 and 2 other fieldsHigh correlation
목표년도 is highly overall correlated with 기준년도 and 1 other fieldsHigh correlation
진행상태명 is highly overall correlated with 승인년도High correlation
관리번호 has unique valuesUnique
파일명 has unique valuesUnique

Reproduction

Analysis started2023-12-10 14:13:38.580059
Analysis finished2023-12-10 14:13:41.960765
Duration3.38 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

관리번호
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean98.633333
Minimum1
Maximum125
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:13:42.082178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile10.45
Q1103.25
median110.5
Q3117.75
95-th percentile123.55
Maximum125
Range124
Interquartile range (IQR)14.5

Descriptive statistics

Standard deviation36.688327
Coefficient of variation (CV)0.37196682
Kurtosis3.0000834
Mean98.633333
Median Absolute Deviation (MAD)7.5
Skewness-2.1097444
Sum2959
Variance1346.0333
MonotonicityNot monotonic
2023-12-10T23:13:42.339488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
1 1
 
3.3%
113 1
 
3.3%
125 1
 
3.3%
124 1
 
3.3%
123 1
 
3.3%
122 1
 
3.3%
121 1
 
3.3%
120 1
 
3.3%
12 1
 
3.3%
119 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
1 1
3.3%
10 1
3.3%
11 1
3.3%
12 1
3.3%
100 1
3.3%
101 1
3.3%
102 1
3.3%
103 1
3.3%
104 1
3.3%
105 1
3.3%
ValueCountFrequency (%)
125 1
3.3%
124 1
3.3%
123 1
3.3%
122 1
3.3%
121 1
3.3%
120 1
3.3%
119 1
3.3%
118 1
3.3%
117 1
3.3%
116 1
3.3%

관리명
Categorical

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
계획개발_도시군기본계획
30 

Length

Max length12
Median length12
Mean length12
Min length12

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
계획개발_도시군기본계획 30
100.0%

Length

2023-12-10T23:13:42.560475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:13:42.755712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
계획개발_도시군기본계획 30
100.0%

시도명
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)16.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
경상북도
16 
전라남도
10 
경기도
2 
서울특별시
 
1
세종특별자치시
 
1

Length

Max length7
Median length5
Mean length4.9333333
Min length3

Unique

Unique2 ?
Unique (%)6.7%

Sample

1st row서울특별시
2nd row세종특별자치시
3rd row전라남도
4th row전라남도
5th row전라남도

Common Values

ValueCountFrequency (%)
경상북도 16
53.3%
전라남도 10
33.3%
경기도 2
 
6.7%
서울특별시 1
 
3.3%
세종특별자치시 1
 
3.3%

Length

2023-12-10T23:13:42.949631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:13:43.174944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경상북도 16
53.3%
전라남도 10
33.3%
경기도 2
 
6.7%
서울특별시 1
 
3.3%
세종특별자치시 1
 
3.3%

시군구명
Text

Distinct25
Distinct (%)83.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-10T23:13:43.514467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.9333333
Min length2

Characters and Unicode

Total characters88
Distinct characters37
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

Unique21 ?
Unique (%)70.0%

Sample

1st row전체
2nd row전체
3rd row순천시
4th row나주시
5th row광양시
ValueCountFrequency (%)
영주시 3
 
10.0%
김천시 2
 
6.7%
전체 2
 
6.7%
구미시 2
 
6.7%
포항시 1
 
3.3%
경주시 1
 
3.3%
군위군 1
 
3.3%
경산시 1
 
3.3%
문경시 1
 
3.3%
상주시 1
 
3.3%
Other values (15) 15
50.0%
2023-12-10T23:13:44.314875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 19
21.6%
군 10
 
11.4%
주 6
 
6.8%
영 5
 
5.7%
천 4
 
4.5%
경 3
 
3.4%
김 2
 
2.3%
양 2
 
2.3%
안 2
 
2.3%
남 2
 
2.3%
Other values (27) 33
37.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 88
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 19
21.6%
군 10
 
11.4%
주 6
 
6.8%
영 5
 
5.7%
천 4
 
4.5%
경 3
 
3.4%
김 2
 
2.3%
양 2
 
2.3%
안 2
 
2.3%
남 2
 
2.3%
Other values (27) 33
37.5%

Most occurring scripts

ValueCountFrequency (%)
Hangul 88
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 19
21.6%
군 10
 
11.4%
주 6
 
6.8%
영 5
 
5.7%
천 4
 
4.5%
경 3
 
3.4%
김 2
 
2.3%
양 2
 
2.3%
안 2
 
2.3%
남 2
 
2.3%
Other values (27) 33
37.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 88
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 19
21.6%
군 10
 
11.4%
주 6
 
6.8%
영 5
 
5.7%
천 4
 
4.5%
경 3
 
3.4%
김 2
 
2.3%
양 2
 
2.3%
안 2
 
2.3%
남 2
 
2.3%
Other values (27) 33
37.5%

계획명
Text

Distinct29
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-10T23:13:44.709223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length18
Mean length15.1
Min length11

Characters and Unicode

Total characters453
Distinct characters54
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

Unique28 ?
Unique (%)93.3%

Sample

1st row2030 서울도시기본계획
2nd row2030 세종도시기본계획
3rd row2030년 순천도시기본계획
4th row2030년 나주 도시기본계획(변경)
5th row2030년 광양 도시기본계획
ValueCountFrequency (%)
2020년 12
16.2%
2030년 8
 
10.8%
도시기본계획 6
 
8.1%
일부변경 4
 
5.4%
2030 4
 
5.4%
군기본계획 3
 
4.1%
도시기본계획(변경 3
 
4.1%
2025년 2
 
2.7%
김천도시기본계획 2
 
2.7%
2020년영주 2
 
2.7%
Other values (28) 28
37.8%
2023-12-10T23:13:45.405516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 58
12.8%
2 47
10.4%
44
 
9.7%
기 30
 
6.6%
본 30
 
6.6%
계 30
 
6.6%
획 30
 
6.6%
년 25
 
5.5%
도 22
 
4.9%
시 21
 
4.6%
Other values (44) 116
25.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 281
62.0%
Decimal Number 120
26.5%
Space Separator 44
 
9.7%
Open Punctuation 4
 
0.9%
Close Punctuation 4
 
0.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
기 30
10.7%
본 30
10.7%
계 30
10.7%
획 30
10.7%
년 25
 
8.9%
도 22
 
7.8%
시 21
 
7.5%
경 11
 
3.9%
군 11
 
3.9%
변 8
 
2.8%
Other values (37) 63
22.4%
Decimal Number
ValueCountFrequency (%)
0 58
48.3%
2 47
39.2%
3 13
 
10.8%
5 2
 
1.7%
Space Separator
ValueCountFrequency (%)
44
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 281
62.0%
Common 172
38.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
기 30
10.7%
본 30
10.7%
계 30
10.7%
획 30
10.7%
년 25
 
8.9%
도 22
 
7.8%
시 21
 
7.5%
경 11
 
3.9%
군 11
 
3.9%
변 8
 
2.8%
Other values (37) 63
22.4%
Common
ValueCountFrequency (%)
0 58
33.7%
2 47
27.3%
44
25.6%
3 13
 
7.6%
( 4
 
2.3%
) 4
 
2.3%
5 2
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 281
62.0%
ASCII 172
38.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 58
33.7%
2 47
27.3%
44
25.6%
3 13
 
7.6%
( 4
 
2.3%
) 4
 
2.3%
5 2
 
1.2%
Hangul
ValueCountFrequency (%)
기 30
10.7%
본 30
10.7%
계 30
10.7%
획 30
10.7%
년 25
 
8.9%
도 22
 
7.8%
시 21
 
7.5%
경 11
 
3.9%
군 11
 
3.9%
변 8
 
2.8%
Other values (37) 63
22.4%

기준년도
Real number (ℝ)

HIGH CORRELATION 

Distinct11
Distinct (%)36.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2007.8667
Minimum2001
Maximum2015
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:13:45.693029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2001
5-th percentile2001
Q12003
median2010
Q32012
95-th percentile2014.55
Maximum2015
Range14
Interquartile range (IQR)9

Descriptive statistics

Standard deviation4.9878012
Coefficient of variation (CV)0.0024841297
Kurtosis-1.5977132
Mean2007.8667
Median Absolute Deviation (MAD)4
Skewness-0.13140542
Sum60236
Variance24.878161
MonotonicityNot monotonic
2023-12-10T23:13:45.885287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
2010 7
23.3%
2003 5
16.7%
2001 4
13.3%
2012 3
10.0%
2014 3
10.0%
2015 2
 
6.7%
2002 2
 
6.7%
2013 1
 
3.3%
2006 1
 
3.3%
2011 1
 
3.3%
ValueCountFrequency (%)
2001 4
13.3%
2002 2
 
6.7%
2003 5
16.7%
2005 1
 
3.3%
2006 1
 
3.3%
2010 7
23.3%
2011 1
 
3.3%
2012 3
10.0%
2013 1
 
3.3%
2014 3
10.0%
ValueCountFrequency (%)
2015 2
 
6.7%
2014 3
10.0%
2013 1
 
3.3%
2012 3
10.0%
2011 1
 
3.3%
2010 7
23.3%
2006 1
 
3.3%
2005 1
 
3.3%
2003 5
16.7%
2002 2
 
6.7%

목표년도
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
2020
16 
2030
12 
2025
2 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2030
2nd row2030
3rd row2030
4th row2030
5th row2030

Common Values

ValueCountFrequency (%)
2020 16
53.3%
2030 12
40.0%
2025 2
 
6.7%

Length

2023-12-10T23:13:46.050288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:13:46.221275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020 16
53.3%
2030 12
40.0%
2025 2
 
6.7%

승인자명
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)13.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
경상북도지사
16 
전라남도지사
10 
경기도지사
3 
서울특별시장
 
1

Length

Max length6
Median length6
Mean length5.9
Min length5

Unique

Unique1 ?
Unique (%)3.3%

Sample

1st row서울특별시장
2nd row경기도지사
3rd row전라남도지사
4th row전라남도지사
5th row전라남도지사

Common Values

ValueCountFrequency (%)
경상북도지사 16
53.3%
전라남도지사 10
33.3%
경기도지사 3
 
10.0%
서울특별시장 1
 
3.3%

Length

2023-12-10T23:13:46.397057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:13:46.586606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경상북도지사 16
53.3%
전라남도지사 10
33.3%
경기도지사 3
 
10.0%
서울특별시장 1
 
3.3%

승인년도
Real number (ℝ)

HIGH CORRELATION 

Distinct10
Distinct (%)33.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2013.1667
Minimum2006
Maximum2018
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:13:46.751247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2006
5-th percentile2006
Q12008.25
median2015
Q32017
95-th percentile2018
Maximum2018
Range12
Interquartile range (IQR)8.75

Descriptive statistics

Standard deviation4.5794393
Coefficient of variation (CV)0.0022747443
Kurtosis-1.3331037
Mean2013.1667
Median Absolute Deviation (MAD)2
Skewness-0.64600589
Sum60395
Variance20.971264
MonotonicityNot monotonic
2023-12-10T23:13:46.924294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
2017 8
26.7%
2006 5
16.7%
2014 4
13.3%
2016 3
 
10.0%
2018 3
 
10.0%
2007 2
 
6.7%
2015 2
 
6.7%
2009 1
 
3.3%
2010 1
 
3.3%
2008 1
 
3.3%
ValueCountFrequency (%)
2006 5
16.7%
2007 2
 
6.7%
2008 1
 
3.3%
2009 1
 
3.3%
2010 1
 
3.3%
2014 4
13.3%
2015 2
 
6.7%
2016 3
 
10.0%
2017 8
26.7%
2018 3
 
10.0%
ValueCountFrequency (%)
2018 3
 
10.0%
2017 8
26.7%
2016 3
 
10.0%
2015 2
 
6.7%
2014 4
13.3%
2010 1
 
3.3%
2009 1
 
3.3%
2008 1
 
3.3%
2007 2
 
6.7%
2006 5
16.7%

진행상태명
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
최종계획
26 
기정계획
4 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
최종계획 26
86.7%
기정계획 4
 
13.3%

Length

2023-12-10T23:13:47.138932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:13:47.332938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
최종계획 26
86.7%
기정계획 4
 
13.3%

파일유형명
Categorical

Distinct3
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
pdf
22 
zip
6 
hwp
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowpdf
2nd rowpdf
3rd rowzip
4th rowpdf
5th rowzip

Common Values

ValueCountFrequency (%)
pdf 22
73.3%
zip 6
 
20.0%
hwp 2
 
6.7%

Length

2023-12-10T23:13:47.542277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:13:47.737129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
pdf 22
73.3%
zip 6
 
20.0%
hwp 2
 
6.7%

파일명
Text

UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-10T23:13:48.172791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length24
Mean length23.933333
Min length23

Characters and Unicode

Total characters718
Distinct characters67
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique30 ?
Unique (%)100.0%

Sample

1st rowBASS_CTY_서울_전체_2014.pdf
2nd rowBASS_CTY_세종_전체_2014.pdf
3rd rowBASS_CTY_전남_순천시_2017.zip
4th rowBASS_CTY_전남_나주시_2017.pdf
5th rowBASS_CTY_전남_광양시_2016.zip
ValueCountFrequency (%)
bass_cty_서울_전체_2014.pdf 1
 
3.3%
bass_cty_세종_전체_2014.pdf 1
 
3.3%
bass_cty_경북_군위군_2015.hwp 1
 
3.3%
bass_cty_경북_경산시_2017.pdf 1
 
3.3%
bass_cty_경북_문경시_2008.pdf 1
 
3.3%
bass_cty_경북_상주시_2017.pdf 1
 
3.3%
bass_cty_경북_영천시_2016.zip 1
 
3.3%
bass_cty_경기_성남시_2018.pdf 1
 
3.3%
bass_cty_경북_영주시_2017.pdf 1
 
3.3%
bass_cty_경북_영주시_2010.pdf 1
 
3.3%
Other values (20) 20
66.7%
2023-12-10T23:13:49.038244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
_ 120
16.7%
S 60
 
8.4%
0 40
 
5.6%
B 30
 
4.2%
. 30
 
4.2%
C 30
 
4.2%
T 30
 
4.2%
Y 30
 
4.2%
p 30
 
4.2%
2 30
 
4.2%
Other values (57) 288
40.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 210
29.2%
Other Letter 148
20.6%
Connector Punctuation 120
16.7%
Decimal Number 120
16.7%
Lowercase Letter 90
12.5%
Other Punctuation 30
 
4.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
경 21
14.2%
시 19
12.8%
북 16
 
10.8%
남 12
 
8.1%
전 12
 
8.1%
군 10
 
6.8%
주 6
 
4.1%
영 5
 
3.4%
천 4
 
2.7%
구 2
 
1.4%
Other values (33) 41
27.7%
Decimal Number
ValueCountFrequency (%)
0 40
33.3%
2 30
25.0%
1 21
17.5%
7 10
 
8.3%
6 8
 
6.7%
4 4
 
3.3%
8 4
 
3.3%
5 2
 
1.7%
9 1
 
0.8%
Lowercase Letter
ValueCountFrequency (%)
p 30
33.3%
f 22
24.4%
d 22
24.4%
z 6
 
6.7%
i 6
 
6.7%
w 2
 
2.2%
h 2
 
2.2%
Uppercase Letter
ValueCountFrequency (%)
S 60
28.6%
B 30
14.3%
C 30
14.3%
T 30
14.3%
Y 30
14.3%
A 30
14.3%
Connector Punctuation
ValueCountFrequency (%)
_ 120
100.0%
Other Punctuation
ValueCountFrequency (%)
. 30
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 300
41.8%
Common 270
37.6%
Hangul 148
20.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
경 21
14.2%
시 19
12.8%
북 16
 
10.8%
남 12
 
8.1%
전 12
 
8.1%
군 10
 
6.8%
주 6
 
4.1%
영 5
 
3.4%
천 4
 
2.7%
구 2
 
1.4%
Other values (33) 41
27.7%
Latin
ValueCountFrequency (%)
S 60
20.0%
B 30
10.0%
C 30
10.0%
T 30
10.0%
Y 30
10.0%
p 30
10.0%
A 30
10.0%
f 22
 
7.3%
d 22
 
7.3%
z 6
 
2.0%
Other values (3) 10
 
3.3%
Common
ValueCountFrequency (%)
_ 120
44.4%
0 40
 
14.8%
. 30
 
11.1%
2 30
 
11.1%
1 21
 
7.8%
7 10
 
3.7%
6 8
 
3.0%
4 4
 
1.5%
8 4
 
1.5%
5 2
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 570
79.4%
Hangul 148
 
20.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
_ 120
21.1%
S 60
10.5%
0 40
 
7.0%
B 30
 
5.3%
. 30
 
5.3%
C 30
 
5.3%
T 30
 
5.3%
Y 30
 
5.3%
p 30
 
5.3%
2 30
 
5.3%
Other values (14) 140
24.6%
Hangul
ValueCountFrequency (%)
경 21
14.2%
시 19
12.8%
북 16
 
10.8%
남 12
 
8.1%
전 12
 
8.1%
군 10
 
6.8%
주 6
 
4.1%
영 5
 
3.4%
천 4
 
2.7%
구 2
 
1.4%
Other values (33) 41
27.7%

Interactions

2023-12-10T23:13:40.602280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:13:39.415176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:13:39.935646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:13:40.782013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:13:39.622362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:13:40.191927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:13:41.289427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:13:39.771022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:13:40.341568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T23:13:49.276361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관리번호시도명시군구명계획명기준년도목표년도승인자명승인년도진행상태명파일유형명파일명
관리번호1.0000.7450.9821.0000.0000.3260.9510.0000.0000.0001.000
시도명0.7451.0000.0001.0000.0000.1421.0000.0000.0310.0001.000
시군구명0.9820.0001.0001.0000.9831.0000.0000.7470.0001.0001.000
계획명1.0001.0001.0001.0001.0001.0001.0000.4200.0001.0001.000
기준년도0.0000.0000.9831.0001.0000.8790.0000.9360.0000.0001.000
목표년도0.3260.1421.0001.0000.8791.0000.1960.7980.1640.0001.000
승인자명0.9511.0000.0001.0000.0000.1961.0000.0000.2940.0001.000
승인년도0.0000.0000.7470.4200.9360.7980.0001.0000.9410.4161.000
진행상태명0.0000.0310.0000.0000.0000.1640.2940.9411.0000.0001.000
파일유형명0.0000.0001.0001.0000.0000.0000.0000.4160.0001.0001.000
파일명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
2023-12-10T23:13:49.701015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
진행상태명시도명파일유형명승인자명목표년도
진행상태명1.0000.0000.0000.1800.261
시도명0.0001.0000.0000.9810.075
파일유형명0.0000.0001.0000.0000.000
승인자명0.1800.9810.0001.0000.172
목표년도0.2610.0750.0000.1721.000
2023-12-10T23:13:49.911528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관리번호기준년도승인년도시도명목표년도승인자명진행상태명파일유형명
관리번호1.000-0.499-0.1560.6740.3030.7020.0000.000
기준년도-0.4991.0000.6190.0480.7890.0000.0000.000
승인년도-0.1560.6191.0000.0000.7100.1070.5230.000
시도명0.6740.0480.0001.0000.0750.9810.0000.000
목표년도0.3030.7890.7100.0751.0000.1720.2610.000
승인자명0.7020.0000.1070.9810.1721.0000.1800.000
진행상태명0.0000.0000.5230.0000.2610.1801.0000.000
파일유형명0.0000.0000.0000.0000.0000.0000.0001.000

Missing values

2023-12-10T23:13:41.509629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T23:13:41.842809image/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

관리번호관리명시도명시군구명계획명기준년도목표년도승인자명승인년도진행상태명파일유형명파일명
01계획개발_도시군기본계획서울특별시전체2030 서울도시기본계획20102030서울특별시장2014최종계획pdfBASS_CTY_서울_전체_2014.pdf
110계획개발_도시군기본계획세종특별자치시전체2030 세종도시기본계획20122030경기도지사2014최종계획pdfBASS_CTY_세종_전체_2014.pdf
2100계획개발_도시군기본계획전라남도순천시2030년 순천도시기본계획20142030전라남도지사2017최종계획zipBASS_CTY_전남_순천시_2017.zip
3101계획개발_도시군기본계획전라남도나주시2030년 나주 도시기본계획(변경)20142030전라남도지사2017최종계획pdfBASS_CTY_전남_나주시_2017.pdf
4102계획개발_도시군기본계획전라남도광양시2030년 광양 도시기본계획20132030전라남도지사2016최종계획zipBASS_CTY_전남_광양시_2016.zip
5103계획개발_도시군기본계획전라남도담양군2030 담양 군기본계획20152030전라남도지사2018최종계획pdfBASS_CTY_전남_담양군_2018.pdf
6104계획개발_도시군기본계획전라남도화순군(2020년)화순군 군기본계획20032020전라남도지사2007최종계획pdfBASS_CTY_전남_화순군_2007.pdf
7105계획개발_도시군기본계획전라남도해남군2025년 해남군기본계획20062025전라남도지사2009최종계획pdfBASS_CTY_전남_해남군_2009.pdf
8106계획개발_도시군기본계획전라남도영암군2020년 영암 군기본계획20022020전라남도지사2006최종계획pdfBASS_CTY_전남_영암군_2006.pdf
9107계획개발_도시군기본계획전라남도무안군2030무안군기본계획20142030전라남도지사2017최종계획pdfBASS_CTY_전남_무안군_2017.pdf
관리번호관리명시도명시군구명계획명기준년도목표년도승인자명승인년도진행상태명파일유형명파일명
20117계획개발_도시군기본계획경상북도영주시2020년 영주 도시기본계획20012020경상북도지사2006기정계획pdfBASS_CTY_경북_영주시_2006.pdf
21118계획개발_도시군기본계획경상북도영주시2020년영주 도시기본계획 일부변경20012020경상북도지사2010기정계획pdfBASS_CTY_경북_영주시_2010.pdf
22119계획개발_도시군기본계획경상북도영주시2020년영주 도시기본계획 일부변경20012020경상북도지사2017최종계획pdfBASS_CTY_경북_영주시_2017.pdf
2312계획개발_도시군기본계획경기도성남시2020년 성남 도시기본계획(변경)20102020경기도지사2018최종계획pdfBASS_CTY_경기_성남시_2018.pdf
24120계획개발_도시군기본계획경상북도영천시2020년 영천 도시기본계획20112020경상북도지사2016최종계획zipBASS_CTY_경북_영천시_2016.zip
25121계획개발_도시군기본계획경상북도상주시2020년 상주도시기본계획 일부변경20032020경상북도지사2017최종계획pdfBASS_CTY_경북_상주시_2017.pdf
26122계획개발_도시군기본계획경상북도문경시2025년 문경도시기본계획20052025경상북도지사2008최종계획pdfBASS_CTY_경북_문경시_2008.pdf
27123계획개발_도시군기본계획경상북도경산시2030년 경산도시기본계획20122030경상북도지사2017최종계획pdfBASS_CTY_경북_경산시_2017.pdf
28124계획개발_도시군기본계획경상북도군위군2020년 군위군기본계획 일부변경20012020경상북도지사2015최종계획hwpBASS_CTY_경북_군위군_2015.hwp
29125계획개발_도시군기본계획경상북도청도군2020년 청도군기본계획20022020경상북도지사2006최종계획pdfBASS_CTY_경북_청도군_2006.pdf