Overview

Dataset statistics

Number of variables11
Number of observations29
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.7 KiB
Average record size in memory96.6 B

Variable types

Numeric3
Categorical5
Text3

Dataset

Description샘플 데이터
Author국토연구원
URLhttps://www.bigdata-region.kr/#/dataset/33fe8ba1-8533-4f5a-a1ea-fd5ec7ea552e

Alerts

승인자명 is highly overall correlated with 관리번호 and 1 other fieldsHigh correlation
시도명 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 승인년도High correlation
승인년도 is highly overall correlated with 기준년도High correlation
관리번호 has unique valuesUnique
계획명 has unique valuesUnique
파일명 has unique valuesUnique

Reproduction

Analysis started2023-12-10 14:19:19.237955
Analysis finished2023-12-10 14:19:21.719706
Duration2.48 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

관리번호
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct29
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean72.689655
Minimum7
Maximum134
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size393.0 B
2023-12-10T23:19:21.800297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile8.4
Q126
median88
Q3113
95-th percentile130.6
Maximum134
Range127
Interquartile range (IQR)87

Descriptive statistics

Standard deviation45.807909
Coefficient of variation (CV)0.63018471
Kurtosis-1.6723766
Mean72.689655
Median Absolute Deviation (MAD)39
Skewness-0.16935554
Sum2108
Variance2098.3645
MonotonicityNot monotonic
2023-12-10T23:19:21.948625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
111 1
 
3.4%
114 1
 
3.4%
113 1
 
3.4%
112 1
 
3.4%
107 1
 
3.4%
101 1
 
3.4%
97 1
 
3.4%
92 1
 
3.4%
9 1
 
3.4%
88 1
 
3.4%
Other values (19) 19
65.5%
ValueCountFrequency (%)
7 1
3.4%
8 1
3.4%
9 1
3.4%
14 1
3.4%
17 1
3.4%
19 1
3.4%
23 1
3.4%
26 1
3.4%
30 1
3.4%
37 1
3.4%
ValueCountFrequency (%)
134 1
3.4%
131 1
3.4%
130 1
3.4%
126 1
3.4%
123 1
3.4%
116 1
3.4%
114 1
3.4%
113 1
3.4%
112 1
3.4%
111 1
3.4%

시도명
Categorical

HIGH CORRELATION 

Distinct11
Distinct (%)37.9%
Missing0
Missing (%)0.0%
Memory size364.0 B
경상북도
9 
경기도
8 
강원도
2 
전라북도
2 
전라남도
2 
Other values (6)
6 

Length

Max length5
Median length5
Mean length4.3103448
Min length3

Unique

Unique6 ?
Unique (%)20.7%

Sample

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

Common Values

ValueCountFrequency (%)
경상북도 9
31.0%
경기도 8
27.6%
강원도 2
 
6.9%
전라북도 2
 
6.9%
전라남도 2
 
6.9%
경상남도 1
 
3.4%
광주광역시 1
 
3.4%
충청북도 1
 
3.4%
대전광역시 1
 
3.4%
충청남도 1
 
3.4%

Length

2023-12-10T23:19:22.134681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경상북도 9
31.0%
경기도 8
27.6%
강원도 2
 
6.9%
전라북도 2
 
6.9%
전라남도 2
 
6.9%
경상남도 1
 
3.4%
광주광역시 1
 
3.4%
충청북도 1
 
3.4%
대전광역시 1
 
3.4%
충청남도 1
 
3.4%

시군구명
Text

Distinct26
Distinct (%)89.7%
Missing0
Missing (%)0.0%
Memory size364.0 B
2023-12-10T23:19:22.355627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.8965517
Min length2

Characters and Unicode

Total characters84
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

Unique24 ?
Unique (%)82.8%

Sample

1st row경주시
2nd row안동시
3rd row구미시
4th row경산시
5th row고령군
ValueCountFrequency (%)
전체 3
 
10.3%
김천시 2
 
6.9%
안동시 1
 
3.4%
무안군 1
 
3.4%
나주시 1
 
3.4%
무주군 1
 
3.4%
익산시 1
 
3.4%
홍성군 1
 
3.4%
영동군 1
 
3.4%
화천군 1
 
3.4%
Other values (16) 16
55.2%
2023-12-10T23:19:22.752043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 18
21.4%
군 9
 
10.7%
주 5
 
6.0%
천 4
 
4.8%
전 3
 
3.6%
체 3
 
3.6%
안 3
 
3.6%
김 3
 
3.6%
경 2
 
2.4%
무 2
 
2.4%
Other values (27) 32
38.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 84
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 18
21.4%
군 9
 
10.7%
주 5
 
6.0%
천 4
 
4.8%
전 3
 
3.6%
체 3
 
3.6%
안 3
 
3.6%
김 3
 
3.6%
경 2
 
2.4%
무 2
 
2.4%
Other values (27) 32
38.1%

Most occurring scripts

ValueCountFrequency (%)
Hangul 84
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 18
21.4%
군 9
 
10.7%
주 5
 
6.0%
천 4
 
4.8%
전 3
 
3.6%
체 3
 
3.6%
안 3
 
3.6%
김 3
 
3.6%
경 2
 
2.4%
무 2
 
2.4%
Other values (27) 32
38.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 84
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 18
21.4%
군 9
 
10.7%
주 5
 
6.0%
천 4
 
4.8%
전 3
 
3.6%
체 3
 
3.6%
안 3
 
3.6%
김 3
 
3.6%
경 2
 
2.4%
무 2
 
2.4%
Other values (27) 32
38.1%

계획명
Text

UNIQUE 

Distinct29
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size364.0 B
2023-12-10T23:19:22.973981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length19
Mean length15.137931
Min length11

Characters and Unicode

Total characters439
Distinct characters56
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

Unique29 ?
Unique (%)100.0%

Sample

1st row2030년 경주도시기본계획
2nd row2030년 안동도시기본계획
3rd row2020년 구미 도시기본계획(변경)
4th row2030년 경산도시기본계획
5th row2020 고령군기본계획 수정계획
ValueCountFrequency (%)
2020년 12
 
16.9%
2030년 9
 
12.7%
변경 4
 
5.6%
도시기본계획 4
 
5.6%
2025년 3
 
4.2%
2035년 2
 
2.8%
김천도시기본계획 2
 
2.8%
도시기본계획(변경 2
 
2.8%
2020 1
 
1.4%
군기본게획 1
 
1.4%
Other values (31) 31
43.7%
2023-12-10T23:19:23.350347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 53
12.1%
2 45
 
10.3%
42
 
9.6%
획 30
 
6.8%
기 29
 
6.6%
본 29
 
6.6%
계 29
 
6.6%
년 26
 
5.9%
시 22
 
5.0%
도 21
 
4.8%
Other values (46) 113
25.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 272
62.0%
Decimal Number 117
26.7%
Space Separator 42
 
9.6%
Close Punctuation 4
 
0.9%
Open Punctuation 4
 
0.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
획 30
11.0%
기 29
10.7%
본 29
10.7%
계 29
10.7%
년 26
9.6%
시 22
 
8.1%
도 21
 
7.7%
경 10
 
3.7%
군 9
 
3.3%
변 8
 
2.9%
Other values (38) 59
21.7%
Decimal Number
ValueCountFrequency (%)
0 53
45.3%
2 45
38.5%
3 13
 
11.1%
5 5
 
4.3%
1 1
 
0.9%
Space Separator
ValueCountFrequency (%)
42
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 272
62.0%
Common 167
38.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
획 30
11.0%
기 29
10.7%
본 29
10.7%
계 29
10.7%
년 26
9.6%
시 22
 
8.1%
도 21
 
7.7%
경 10
 
3.7%
군 9
 
3.3%
변 8
 
2.9%
Other values (38) 59
21.7%
Common
ValueCountFrequency (%)
0 53
31.7%
2 45
26.9%
42
25.1%
3 13
 
7.8%
5 5
 
3.0%
) 4
 
2.4%
( 4
 
2.4%
1 1
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 272
62.0%
ASCII 167
38.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 53
31.7%
2 45
26.9%
42
25.1%
3 13
 
7.8%
5 5
 
3.0%
) 4
 
2.4%
( 4
 
2.4%
1 1
 
0.6%
Hangul
ValueCountFrequency (%)
획 30
11.0%
기 29
10.7%
본 29
10.7%
계 29
10.7%
년 26
9.6%
시 22
 
8.1%
도 21
 
7.7%
경 10
 
3.7%
군 9
 
3.3%
변 8
 
2.9%
Other values (38) 59
21.7%

기준년도
Real number (ℝ)

HIGH CORRELATION 

Distinct12
Distinct (%)41.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2008.3448
Minimum2001
Maximum2015
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size393.0 B
2023-12-10T23:19:23.477702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2001
5-th percentile2002.4
Q12004
median2010
Q32013
95-th percentile2014
Maximum2015
Range14
Interquartile range (IQR)9

Descriptive statistics

Standard deviation4.5847251
Coefficient of variation (CV)0.0022828376
Kurtosis-1.5276616
Mean2008.3448
Median Absolute Deviation (MAD)4
Skewness-0.031062004
Sum58242
Variance21.019704
MonotonicityNot monotonic
2023-12-10T23:19:23.591547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
2010 6
20.7%
2014 6
20.7%
2003 5
17.2%
2005 3
10.3%
2006 2
 
6.9%
2012 1
 
3.4%
2001 1
 
3.4%
2002 1
 
3.4%
2015 1
 
3.4%
2004 1
 
3.4%
Other values (2) 2
 
6.9%
ValueCountFrequency (%)
2001 1
 
3.4%
2002 1
 
3.4%
2003 5
17.2%
2004 1
 
3.4%
2005 3
10.3%
2006 2
 
6.9%
2009 1
 
3.4%
2010 6
20.7%
2012 1
 
3.4%
2013 1
 
3.4%
ValueCountFrequency (%)
2015 1
 
3.4%
2014 6
20.7%
2013 1
 
3.4%
2012 1
 
3.4%
2010 6
20.7%
2009 1
 
3.4%
2006 2
 
6.9%
2005 3
10.3%
2004 1
 
3.4%
2003 5
17.2%

목표년도
Categorical

Distinct4
Distinct (%)13.8%
Missing0
Missing (%)0.0%
Memory size364.0 B
2020
14 
2030
10 
2025
3 
2035
2 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2030
2nd row2020
3rd row2020
4th row2030
5th row2020

Common Values

ValueCountFrequency (%)
2020 14
48.3%
2030 10
34.5%
2025 3
 
10.3%
2035 2
 
6.9%

Length

2023-12-10T23:19:23.722043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:19:23.864974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020 14
48.3%
2030 10
34.5%
2025 3
 
10.3%
2035 2
 
6.9%

승인자명
Categorical

HIGH CORRELATION 

Distinct11
Distinct (%)37.9%
Missing0
Missing (%)0.0%
Memory size364.0 B
경상북도지사
9 
경기도지사
8 
강원도지사
2 
전라북도지사
2 
전라남도지사
2 
Other values (6)
6 

Length

Max length8
Median length6
Mean length5.7241379
Min length5

Unique

Unique6 ?
Unique (%)20.7%

Sample

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

Common Values

ValueCountFrequency (%)
경상북도지사 9
31.0%
경기도지사 8
27.6%
강원도지사 2
 
6.9%
전라북도지사 2
 
6.9%
전라남도지사 2
 
6.9%
경상남도지사 1
 
3.4%
광주광역시장 1
 
3.4%
충청북도지사 1
 
3.4%
울산광역시장 1
 
3.4%
충청남도지사 1
 
3.4%

Length

2023-12-10T23:19:24.038144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경상북도지사 9
31.0%
경기도지사 8
27.6%
강원도지사 2
 
6.9%
전라북도지사 2
 
6.9%
전라남도지사 2
 
6.9%
경상남도지사 1
 
3.4%
광주광역시장 1
 
3.4%
충청북도지사 1
 
3.4%
울산광역시장 1
 
3.4%
충청남도지사 1
 
3.4%

승인년도
Real number (ℝ)

HIGH CORRELATION 

Distinct12
Distinct (%)41.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2013.2759
Minimum2006
Maximum2018
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size393.0 B
2023-12-10T23:19:24.198543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2006
5-th percentile2007
Q12009
median2015
Q32017
95-th percentile2017
Maximum2018
Range12
Interquartile range (IQR)8

Descriptive statistics

Standard deviation3.9901356
Coefficient of variation (CV)0.001981912
Kurtosis-1.2860496
Mean2013.2759
Median Absolute Deviation (MAD)2
Skewness-0.5769824
Sum58385
Variance15.921182
MonotonicityNot monotonic
2023-12-10T23:19:24.354569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
2017 9
31.0%
2008 4
13.8%
2015 3
 
10.3%
2014 2
 
6.9%
2013 2
 
6.9%
2016 2
 
6.9%
2007 2
 
6.9%
2018 1
 
3.4%
2010 1
 
3.4%
2009 1
 
3.4%
Other values (2) 2
 
6.9%
ValueCountFrequency (%)
2006 1
 
3.4%
2007 2
6.9%
2008 4
13.8%
2009 1
 
3.4%
2010 1
 
3.4%
2012 1
 
3.4%
2013 2
6.9%
2014 2
6.9%
2015 3
10.3%
2016 2
6.9%
ValueCountFrequency (%)
2018 1
 
3.4%
2017 9
31.0%
2016 2
 
6.9%
2015 3
 
10.3%
2014 2
 
6.9%
2013 2
 
6.9%
2012 1
 
3.4%
2010 1
 
3.4%
2009 1
 
3.4%
2008 4
13.8%

진행상태명
Categorical

Distinct2
Distinct (%)6.9%
Missing0
Missing (%)0.0%
Memory size364.0 B
최종계획
23 
기정계획
6 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
최종계획 23
79.3%
기정계획 6
 
20.7%

Length

2023-12-10T23:19:24.541945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:19:24.683477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
최종계획 23
79.3%
기정계획 6
 
20.7%

파일유형명
Categorical

Distinct3
Distinct (%)10.3%
Missing0
Missing (%)0.0%
Memory size364.0 B
pdf
22 
zip
6 
hwp
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique1 ?
Unique (%)3.4%

Sample

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

Common Values

ValueCountFrequency (%)
pdf 22
75.9%
zip 6
 
20.7%
hwp 1
 
3.4%

Length

2023-12-10T23:19:24.832587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:19:24.970684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
pdf 22
75.9%
zip 6
 
20.7%
hwp 1
 
3.4%

파일명
Text

UNIQUE 

Distinct29
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size364.0 B
2023-12-10T23:19:25.272494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length24
Mean length23.896552
Min length23

Characters and Unicode

Total characters693
Distinct characters68
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

Unique29 ?
Unique (%)100.0%

Sample

1st rowBASS_CTY_경북_경주시_2017.zip
2nd rowBASS_CTY_경북_안동시_2017.pdf
3rd rowBASS_CTY_경북_구미시_2014.zip
4th rowBASS_CTY_경북_경산시_2017.pdf
5th rowBASS_CTY_경북_고령군_2013.pdf
ValueCountFrequency (%)
bass_cty_경북_경주시_2017.zip 1
 
3.4%
bass_cty_경기_김포시_2015.pdf 1
 
3.4%
bass_cty_경북_김천시_2006.pdf 1
 
3.4%
bass_cty_전남_무안군_2017.pdf 1
 
3.4%
bass_cty_전남_나주시_2017.pdf 1
 
3.4%
bass_cty_전북_무주군_2017.pdf 1
 
3.4%
bass_cty_전북_익산시_2007.pdf 1
 
3.4%
bass_cty_울산_전체_2016.pdf 1
 
3.4%
bass_cty_충남_홍성군_2012.pdf 1
 
3.4%
bass_cty_대전_전체_2013.pdf 1
 
3.4%
Other values (19) 19
65.5%
2023-12-10T23:19:25.724152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
_ 116
16.7%
S 58
 
8.4%
0 38
 
5.5%
2 30
 
4.3%
B 29
 
4.2%
. 29
 
4.2%
C 29
 
4.2%
T 29
 
4.2%
Y 29
 
4.2%
A 29
 
4.2%
Other values (58) 277
40.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 203
29.3%
Other Letter 142
20.5%
Connector Punctuation 116
16.7%
Decimal Number 116
16.7%
Lowercase Letter 87
12.6%
Other Punctuation 29
 
4.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
경 20
14.1%
시 18
 
12.7%
북 12
 
8.5%
군 9
 
6.3%
기 8
 
5.6%
전 8
 
5.6%
주 6
 
4.2%
천 4
 
2.8%
남 4
 
2.8%
원 4
 
2.8%
Other values (33) 49
34.5%
Decimal Number
ValueCountFrequency (%)
0 38
32.8%
2 30
25.9%
1 21
18.1%
7 11
 
9.5%
8 5
 
4.3%
5 3
 
2.6%
6 3
 
2.6%
3 2
 
1.7%
4 2
 
1.7%
9 1
 
0.9%
Lowercase Letter
ValueCountFrequency (%)
p 29
33.3%
f 22
25.3%
d 22
25.3%
z 6
 
6.9%
i 6
 
6.9%
w 1
 
1.1%
h 1
 
1.1%
Uppercase Letter
ValueCountFrequency (%)
S 58
28.6%
B 29
14.3%
C 29
14.3%
T 29
14.3%
Y 29
14.3%
A 29
14.3%
Connector Punctuation
ValueCountFrequency (%)
_ 116
100.0%
Other Punctuation
ValueCountFrequency (%)
. 29
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 290
41.8%
Common 261
37.7%
Hangul 142
20.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
경 20
14.1%
시 18
 
12.7%
북 12
 
8.5%
군 9
 
6.3%
기 8
 
5.6%
전 8
 
5.6%
주 6
 
4.2%
천 4
 
2.8%
남 4
 
2.8%
원 4
 
2.8%
Other values (33) 49
34.5%
Latin
ValueCountFrequency (%)
S 58
20.0%
B 29
10.0%
C 29
10.0%
T 29
10.0%
Y 29
10.0%
A 29
10.0%
p 29
10.0%
f 22
 
7.6%
d 22
 
7.6%
z 6
 
2.1%
Other values (3) 8
 
2.8%
Common
ValueCountFrequency (%)
_ 116
44.4%
0 38
 
14.6%
2 30
 
11.5%
. 29
 
11.1%
1 21
 
8.0%
7 11
 
4.2%
8 5
 
1.9%
5 3
 
1.1%
6 3
 
1.1%
3 2
 
0.8%
Other values (2) 3
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 551
79.5%
Hangul 142
 
20.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
_ 116
21.1%
S 58
10.5%
0 38
 
6.9%
2 30
 
5.4%
B 29
 
5.3%
. 29
 
5.3%
C 29
 
5.3%
T 29
 
5.3%
Y 29
 
5.3%
A 29
 
5.3%
Other values (15) 135
24.5%
Hangul
ValueCountFrequency (%)
경 20
14.1%
시 18
 
12.7%
북 12
 
8.5%
군 9
 
6.3%
기 8
 
5.6%
전 8
 
5.6%
주 6
 
4.2%
천 4
 
2.8%
남 4
 
2.8%
원 4
 
2.8%
Other values (33) 49
34.5%

Interactions

2023-12-10T23:19:20.711668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:19:19.971654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:19:20.341428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:19:20.836720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:19:20.094415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:19:20.475146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:19:20.959160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:19:20.216199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:19:20.589706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T23:19:25.851772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관리번호시도명시군구명계획명기준년도목표년도승인자명승인년도진행상태명파일유형명파일명
관리번호1.0000.8431.0001.0000.6750.5440.8430.8600.2440.1071.000
시도명0.8431.0000.0001.0000.5810.4301.0000.7560.0000.0001.000
시군구명1.0000.0001.0001.0000.9831.0000.0000.8330.6081.0001.000
계획명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
기준년도0.6750.5810.9831.0001.0000.6900.5810.6480.4060.3691.000
목표년도0.5440.4301.0001.0000.6901.0000.4300.2580.4460.3391.000
승인자명0.8431.0000.0001.0000.5810.4301.0000.7560.0000.0001.000
승인년도0.8600.7560.8331.0000.6480.2580.7561.0000.6820.0001.000
진행상태명0.2440.0000.6081.0000.4060.4460.0000.6821.0000.1641.000
파일유형명0.1070.0001.0001.0000.3690.3390.0000.0000.1641.0001.000
파일명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
2023-12-10T23:19:25.999746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
파일유형명승인자명진행상태명시도명목표년도
파일유형명1.0000.0000.2610.0000.316
승인자명0.0001.0000.0001.0000.203
진행상태명0.2610.0001.0000.0000.283
시도명0.0001.0000.0001.0000.203
목표년도0.3160.2030.2830.2031.000
2023-12-10T23:19:26.143892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관리번호기준년도승인년도시도명목표년도승인자명진행상태명파일유형명
관리번호1.000-0.316-0.0400.5420.2930.5420.1170.000
기준년도-0.3161.0000.7620.3060.4730.3060.3130.167
승인년도-0.0400.7621.0000.4140.0000.4140.4810.000
시도명0.5420.3060.4141.0000.2031.0000.0000.000
목표년도0.2930.4730.0000.2031.0000.2030.2830.316
승인자명0.5420.3060.4141.0000.2031.0000.0000.000
진행상태명0.1170.3130.4810.0000.2830.0001.0000.261
파일유형명0.0000.1670.0000.0000.3160.0000.2611.000

Missing values

2023-12-10T23:19:21.436247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T23:19:21.652962image/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

관리번호시도명시군구명계획명기준년도목표년도승인자명승인년도진행상태명파일유형명파일명
0111경상북도경주시2030년 경주도시기본계획20102030경상북도지사2017최종계획zipBASS_CTY_경북_경주시_2017.zip
1114경상북도안동시2030년 안동도시기본계획20102020경상북도지사2017최종계획pdfBASS_CTY_경북_안동시_2017.pdf
2116경상북도구미시2020년 구미 도시기본계획(변경)20102020경상북도지사2014최종계획zipBASS_CTY_경북_구미시_2014.zip
3123경상북도경산시2030년 경산도시기본계획20122030경상북도지사2017최종계획pdfBASS_CTY_경북_경산시_2017.pdf
4126경상북도고령군2020 고령군기본계획 수정계획20012020경상북도지사2013기정계획pdfBASS_CTY_경북_고령군_2013.pdf
5130경상북도칠곡군2020년 칠곡군기본계획 변경20022020경상북도지사2015최종계획pdfBASS_CTY_경북_칠곡군_2015.pdf
6131경상북도울릉군2025년 울릉군기본계획20062025경상북도지사2008기정계획hwpBASS_CTY_경북_울릉군_2008.hwp
7134경상남도창원시2025년 창원도시기본계획 변경20102025경상남도지사2016최종계획pdfBASS_CTY_경남_창원시_2016.pdf
814경기도안양시2030년 안양도시기본계획20142030경기도지사2017최종계획pdfBASS_CTY_경기_안양시_2017.pdf
917경기도광명시2030년 광명 도시기본계획20142030경기도지사2017최종계획pdfBASS_CTY_경기_광명시_2017.pdf
관리번호시도명시군구명계획명기준년도목표년도승인자명승인년도진행상태명파일유형명파일명
1973충청북도영동군2020년 영동군 기본계획20062020충청북도지사2009최종계획zipBASS_CTY_충북_영동군_2009.zip
208대전광역시전체2030년 대전도시기본계획20102030울산광역시장2013최종계획pdfBASS_CTY_대전_전체_2013.pdf
2188충청남도홍성군2020년 홍성 군기본게획 변경(1차)20092020충청남도지사2012최종계획pdfBASS_CTY_충남_홍성군_2012.pdf
229울산광역시전체2030년 울산도시기본계획20142030세종특별자치시장2016최종계획pdfBASS_CTY_울산_전체_2016.pdf
2392전라북도익산시2025년 익산시 도시기본계획20052025전라북도지사2007최종계획pdfBASS_CTY_전북_익산시_2007.pdf
2497전라북도무주군2035년 무주군기본계획20132035전라북도지사2017최종계획pdfBASS_CTY_전북_무주군_2017.pdf
25101전라남도나주시2030년 나주 도시기본계획(변경)20142030전라남도지사2017최종계획pdfBASS_CTY_전남_나주시_2017.pdf
26107전라남도무안군2030무안군기본계획20142030전라남도지사2017최종계획pdfBASS_CTY_전남_무안군_2017.pdf
27112경상북도김천시2020년 김천도시기본계획20032020경상북도지사2006기정계획pdfBASS_CTY_경북_김천시_2006.pdf
28113경상북도김천시2020년 김천도시기본계획 변경20032020경상북도지사2015최종계획pdfBASS_CTY_경북_김천시_2015.pdf