Overview

Dataset statistics

Number of variables10
Number of observations275
Missing cells382
Missing cells (%)13.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory22.2 KiB
Average record size in memory82.5 B

Variable types

Numeric1
Text5
Categorical2
DateTime2

Dataset

Description변경된 도로명 275건에 대한 자료(2013~2015 기준)
Author행정안전부
URLhttps://www.data.go.kr/data/15050426/fileData.do

Alerts

변경사유 has 111 (40.4%) missing valuesMissing
<유형분류> has 271 (98.5%) missing valuesMissing
순번 has unique valuesUnique
변경 전 이름 has unique valuesUnique

Reproduction

Analysis started2023-12-12 08:50:31.548818
Analysis finished2023-12-12 08:50:32.653302
Duration1.1 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

순번
Real number (ℝ)

UNIQUE 

Distinct275
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean138
Minimum1
Maximum275
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2023-12-12T17:50:32.834064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile14.7
Q169.5
median138
Q3206.5
95-th percentile261.3
Maximum275
Range274
Interquartile range (IQR)137

Descriptive statistics

Standard deviation79.529869
Coefficient of variation (CV)0.5763034
Kurtosis-1.2
Mean138
Median Absolute Deviation (MAD)69
Skewness0
Sum37950
Variance6325
MonotonicityStrictly increasing
2023-12-12T17:50:33.029946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.4%
183 1
 
0.4%
189 1
 
0.4%
188 1
 
0.4%
187 1
 
0.4%
186 1
 
0.4%
185 1
 
0.4%
184 1
 
0.4%
182 1
 
0.4%
174 1
 
0.4%
Other values (265) 265
96.4%
ValueCountFrequency (%)
1 1
0.4%
2 1
0.4%
3 1
0.4%
4 1
0.4%
5 1
0.4%
6 1
0.4%
7 1
0.4%
8 1
0.4%
9 1
0.4%
10 1
0.4%
ValueCountFrequency (%)
275 1
0.4%
274 1
0.4%
273 1
0.4%
272 1
0.4%
271 1
0.4%
270 1
0.4%
269 1
0.4%
268 1
0.4%
267 1
0.4%
266 1
0.4%

변경 전 이름
Text

UNIQUE 

Distinct275
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
2023-12-12T17:50:33.418504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length9
Mean length6.1018182
Min length3

Characters and Unicode

Total characters1678
Distinct characters201
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

Unique275 ?
Unique (%)100.0%

Sample

1st row낭성갈산2길
2nd row낭성갈산3길
3rd row강외제방길
4th row비토로
5th row비토해안길
ValueCountFrequency (%)
낭성갈산2길 1
 
0.4%
사파이어로140번길 1
 
0.4%
신화역사로1136번길 1
 
0.4%
재경골길 1
 
0.4%
듬북길 1
 
0.4%
한내로161번길 1
 
0.4%
한내로72번길 1
 
0.4%
명사십리1길 1
 
0.4%
백월로74번길 1
 
0.4%
강뚝길 1
 
0.4%
Other values (265) 265
96.4%
2023-12-12T17:50:33.908670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
길 284
 
16.9%
로 127
 
7.6%
번 126
 
7.5%
2 84
 
5.0%
1 72
 
4.3%
3 38
 
2.3%
4 37
 
2.2%
7 37
 
2.2%
5 34
 
2.0%
8 27
 
1.6%
Other values (191) 812
48.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1273
75.9%
Decimal Number 405
 
24.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
길 284
22.3%
로 127
 
10.0%
번 126
 
9.9%
남 21
 
1.6%
천 20
 
1.6%
동 19
 
1.5%
백 18
 
1.4%
율 17
 
1.3%
한 16
 
1.3%
내 15
 
1.2%
Other values (181) 610
47.9%
Decimal Number
ValueCountFrequency (%)
2 84
20.7%
1 72
17.8%
3 38
9.4%
4 37
9.1%
7 37
9.1%
5 34
8.4%
8 27
 
6.7%
0 27
 
6.7%
9 26
 
6.4%
6 23
 
5.7%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1273
75.9%
Common 405
 
24.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
길 284
22.3%
로 127
 
10.0%
번 126
 
9.9%
남 21
 
1.6%
천 20
 
1.6%
동 19
 
1.5%
백 18
 
1.4%
율 17
 
1.3%
한 16
 
1.3%
내 15
 
1.2%
Other values (181) 610
47.9%
Common
ValueCountFrequency (%)
2 84
20.7%
1 72
17.8%
3 38
9.4%
4 37
9.1%
7 37
9.1%
5 34
8.4%
8 27
 
6.7%
0 27
 
6.7%
9 26
 
6.4%
6 23
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1273
75.9%
ASCII 405
 
24.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
길 284
22.3%
로 127
 
10.0%
번 126
 
9.9%
남 21
 
1.6%
천 20
 
1.6%
동 19
 
1.5%
백 18
 
1.4%
율 17
 
1.3%
한 16
 
1.3%
내 15
 
1.2%
Other values (181) 610
47.9%
ASCII
ValueCountFrequency (%)
2 84
20.7%
1 72
17.8%
3 38
9.4%
4 37
9.1%
7 37
9.1%
5 34
8.4%
8 27
 
6.7%
0 27
 
6.7%
9 26
 
6.4%
6 23
 
5.7%

변경 후 이름
Text

Distinct274
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
2023-12-12T17:50:34.261139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length9
Mean length5.7236364
Min length3

Characters and Unicode

Total characters1574
Distinct characters204
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

Unique273 ?
Unique (%)99.3%

Sample

1st row갈산2길
2nd row갈산3길
3rd row미호천길
4th row용궁로
5th row거북길
ValueCountFrequency (%)
도곡길 2
 
0.7%
동읍로267번다호길 1
 
0.4%
신화역사로726번길 1
 
0.4%
자작이길 1
 
0.4%
봉화길 1
 
0.4%
표절사길 1
 
0.4%
청라한내로161번길 1
 
0.4%
청라사파이어로140번길 1
 
0.4%
백월로74번기곡길 1
 
0.4%
초옥길 1
 
0.4%
Other values (264) 264
96.0%
2023-12-12T17:50:34.814077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
길 271
 
17.2%
로 104
 
6.6%
번 91
 
5.8%
1 71
 
4.5%
2 65
 
4.1%
7 28
 
1.8%
동 27
 
1.7%
5 26
 
1.7%
한 24
 
1.5%
4 24
 
1.5%
Other values (194) 843
53.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1265
80.4%
Decimal Number 309
 
19.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
길 271
21.4%
로 104
 
8.2%
번 91
 
7.2%
동 27
 
2.1%
한 24
 
1.9%
청 20
 
1.6%
천 18
 
1.4%
산 17
 
1.3%
계 17
 
1.3%
송 16
 
1.3%
Other values (184) 660
52.2%
Decimal Number
ValueCountFrequency (%)
1 71
23.0%
2 65
21.0%
7 28
 
9.1%
5 26
 
8.4%
4 24
 
7.8%
6 24
 
7.8%
3 21
 
6.8%
8 19
 
6.1%
0 19
 
6.1%
9 12
 
3.9%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1265
80.4%
Common 309
 
19.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
길 271
21.4%
로 104
 
8.2%
번 91
 
7.2%
동 27
 
2.1%
한 24
 
1.9%
청 20
 
1.6%
천 18
 
1.4%
산 17
 
1.3%
계 17
 
1.3%
송 16
 
1.3%
Other values (184) 660
52.2%
Common
ValueCountFrequency (%)
1 71
23.0%
2 65
21.0%
7 28
 
9.1%
5 26
 
8.4%
4 24
 
7.8%
6 24
 
7.8%
3 21
 
6.8%
8 19
 
6.1%
0 19
 
6.1%
9 12
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1265
80.4%
ASCII 309
 
19.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
길 271
21.4%
로 104
 
8.2%
번 91
 
7.2%
동 27
 
2.1%
한 24
 
1.9%
청 20
 
1.6%
천 18
 
1.4%
산 17
 
1.3%
계 17
 
1.3%
송 16
 
1.3%
Other values (184) 660
52.2%
ASCII
ValueCountFrequency (%)
1 71
23.0%
2 65
21.0%
7 28
 
9.1%
5 26
 
8.4%
4 24
 
7.8%
6 24
 
7.8%
3 21
 
6.8%
8 19
 
6.1%
0 19
 
6.1%
9 12
 
3.9%

위치
Categorical

Distinct14
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
경기도
54 
충청북도
47 
경상북도
45 
경상남도
28 
전라남도
17 
Other values (9)
84 

Length

Max length7
Median length4
Mean length3.9890909
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row충청북도
2nd row충청북도
3rd row충청북도
4th row경상남도
5th row경상남도

Common Values

ValueCountFrequency (%)
경기도 54
19.6%
충청북도 47
17.1%
경상북도 45
16.4%
경상남도 28
10.2%
전라남도 17
 
6.2%
전라북도 16
 
5.8%
충청남도 15
 
5.5%
인천광역시 15
 
5.5%
울산광역시 10
 
3.6%
부산광역시 9
 
3.3%
Other values (4) 19
 
6.9%

Length

2023-12-12T17:50:35.011819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기도 54
19.6%
충청북도 47
17.1%
경상북도 45
16.4%
경상남도 28
10.2%
전라남도 17
 
6.2%
전라북도 16
 
5.8%
충청남도 15
 
5.5%
인천광역시 15
 
5.5%
울산광역시 10
 
3.6%
부산광역시 9
 
3.3%
Other values (4) 19
 
6.9%

시군구
Text

Distinct77
Distinct (%)28.0%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
2023-12-12T17:50:35.385366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length3
Mean length3.08
Min length2

Characters and Unicode

Total characters847
Distinct characters75
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

Unique26 ?
Unique (%)9.5%

Sample

1st row청원군
2nd row청원군
3rd row청원군
4th row사천시
5th row사천시
ValueCountFrequency (%)
포항시 19
 
6.7%
서구 15
 
5.3%
경주시 14
 
4.9%
보은군 13
 
4.6%
성남시 11
 
3.9%
울주군 10
 
3.5%
강서구 9
 
3.2%
남구 9
 
3.2%
제천시 8
 
2.8%
청주시 7
 
2.5%
Other values (67) 169
59.5%
2023-12-12T17:50:35.968556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 153
18.1%
군 99
 
11.7%
주 52
 
6.1%
남 38
 
4.5%
구 37
 
4.4%
천 35
 
4.1%
서 29
 
3.4%
포 25
 
3.0%
성 24
 
2.8%
항 19
 
2.2%
Other values (65) 336
39.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 838
98.9%
Space Separator 9
 
1.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 153
18.3%
군 99
 
11.8%
주 52
 
6.2%
남 38
 
4.5%
구 37
 
4.4%
천 35
 
4.2%
서 29
 
3.5%
포 25
 
3.0%
성 24
 
2.9%
항 19
 
2.3%
Other values (64) 327
39.0%
Space Separator
ValueCountFrequency (%)
9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 838
98.9%
Common 9
 
1.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 153
18.3%
군 99
 
11.8%
주 52
 
6.2%
남 38
 
4.5%
구 37
 
4.4%
천 35
 
4.2%
서 29
 
3.5%
포 25
 
3.0%
성 24
 
2.9%
항 19
 
2.3%
Other values (64) 327
39.0%
Common
ValueCountFrequency (%)
9
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 838
98.9%
ASCII 9
 
1.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 153
18.3%
군 99
 
11.8%
주 52
 
6.2%
남 38
 
4.5%
구 37
 
4.4%
천 35
 
4.2%
서 29
 
3.5%
포 25
 
3.0%
성 24
 
2.9%
항 19
 
2.3%
Other values (64) 327
39.0%
ASCII
ValueCountFrequency (%)
9
100.0%

변경일자
Date

Distinct124
Distinct (%)45.1%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
Minimum2013-01-18 00:00:00
Maximum2015-12-31 00:00:00
2023-12-12T17:50:36.177228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:50:36.387792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

고시일
Date

Distinct120
Distinct (%)43.6%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
Minimum2009-01-20 00:00:00
Maximum2015-12-31 00:00:00
2023-12-12T17:50:36.575878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:50:36.751990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

변경사유
Text

MISSING 

Distinct127
Distinct (%)77.4%
Missing111
Missing (%)40.4%
Memory size2.3 KiB
2023-12-12T17:50:37.064037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length77
Median length36
Mean length21.012195
Min length4

Characters and Unicode

Total characters3446
Distinct characters281
Distinct categories11 ?
Distinct scripts4 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique109 ?
Unique (%)66.5%

Sample

1st row주민들 요구(사용의 편리성에 따라 변경)
2nd row주민들 요구(사용의 편리성에 따라 변경)
3rd row주민 요청에 의한 도로명 변경
4th row위계변경
5th row위계변경
ValueCountFrequency (%)
변경 40
 
5.4%
도로명 23
 
3.1%
도로 23
 
3.1%
분기되는 19
 
2.6%
의한 11
 
1.5%
마을 11
 
1.5%
약 10
 
1.4%
반영 10
 
1.4%
부여 9
 
1.2%
신청에 8
 
1.1%
Other values (336) 572
77.7%
2023-12-12T17:50:37.639040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
578
 
16.8%
로 167
 
4.8%
명 109
 
3.2%
도 90
 
2.6%
에 85
 
2.5%
을 77
 
2.2%
지 67
 
1.9%
변 63
 
1.8%
경 63
 
1.8%
의 48
 
1.4%
Other values (271) 2099
60.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2689
78.0%
Space Separator 578
 
16.8%
Decimal Number 99
 
2.9%
Open Punctuation 19
 
0.6%
Close Punctuation 19
 
0.6%
Other Punctuation 15
 
0.4%
Lowercase Letter 11
 
0.3%
Dash Punctuation 5
 
0.1%
Math Symbol 5
 
0.1%
Initial Punctuation 3
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 167
 
6.2%
명 109
 
4.1%
도 90
 
3.3%
에 85
 
3.2%
을 77
 
2.9%
지 67
 
2.5%
변 63
 
2.3%
경 63
 
2.3%
의 48
 
1.8%
이 48
 
1.8%
Other values (249) 1872
69.6%
Decimal Number
ValueCountFrequency (%)
0 24
24.2%
1 22
22.2%
2 16
16.2%
3 7
 
7.1%
7 7
 
7.1%
8 6
 
6.1%
5 6
 
6.1%
4 5
 
5.1%
6 4
 
4.0%
9 2
 
2.0%
Other Punctuation
ValueCountFrequency (%)
, 8
53.3%
. 4
26.7%
' 2
 
13.3%
: 1
 
6.7%
Space Separator
ValueCountFrequency (%)
578
100.0%
Open Punctuation
ValueCountFrequency (%)
( 19
100.0%
Close Punctuation
ValueCountFrequency (%)
) 19
100.0%
Lowercase Letter
ValueCountFrequency (%)
m 11
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5
100.0%
Math Symbol
ValueCountFrequency (%)
> 5
100.0%
Initial Punctuation
ValueCountFrequency (%)
“ 3
100.0%
Final Punctuation
ValueCountFrequency (%)
” 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2685
77.9%
Common 746
 
21.6%
Latin 11
 
0.3%
Han 4
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 167
 
6.2%
명 109
 
4.1%
도 90
 
3.4%
에 85
 
3.2%
을 77
 
2.9%
지 67
 
2.5%
변 63
 
2.3%
경 63
 
2.3%
의 48
 
1.8%
이 48
 
1.8%
Other values (247) 1868
69.6%
Common
ValueCountFrequency (%)
578
77.5%
0 24
 
3.2%
1 22
 
2.9%
( 19
 
2.5%
) 19
 
2.5%
2 16
 
2.1%
, 8
 
1.1%
3 7
 
0.9%
7 7
 
0.9%
8 6
 
0.8%
Other values (11) 40
 
5.4%
Han
ValueCountFrequency (%)
陶 2
50.0%
深 2
50.0%
Latin
ValueCountFrequency (%)
m 11
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2685
77.9%
ASCII 751
 
21.8%
Punctuation 6
 
0.2%
CJK 4
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
578
77.0%
0 24
 
3.2%
1 22
 
2.9%
( 19
 
2.5%
) 19
 
2.5%
2 16
 
2.1%
m 11
 
1.5%
, 8
 
1.1%
3 7
 
0.9%
7 7
 
0.9%
Other values (10) 40
 
5.3%
Hangul
ValueCountFrequency (%)
로 167
 
6.2%
명 109
 
4.1%
도 90
 
3.4%
에 85
 
3.2%
을 77
 
2.9%
지 67
 
2.5%
변 63
 
2.3%
경 63
 
2.3%
의 48
 
1.8%
이 48
 
1.8%
Other values (247) 1868
69.6%
Punctuation
ValueCountFrequency (%)
“ 3
50.0%
” 3
50.0%
CJK
ValueCountFrequency (%)
陶 2
50.0%
深 2
50.0%

변경유형
Categorical

Distinct4
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
1
144 
4
84 
2
43 
3
 
4

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row1
3rd row1
4th row4
5th row4

Common Values

ValueCountFrequency (%)
1 144
52.4%
4 84
30.5%
2 43
 
15.6%
3 4
 
1.5%

Length

2023-12-12T17:50:37.868179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T17:50:38.137348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 144
52.4%
4 84
30.5%
2 43
 
15.6%
3 4
 
1.5%

<유형분류>
Text

MISSING 

Distinct4
Distinct (%)100.0%
Missing271
Missing (%)98.5%
Memory size2.3 KiB
2023-12-12T17:50:38.371908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length26
Median length24.5
Mean length21.5
Min length11

Characters and Unicode

Total characters86
Distinct characters40
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

Unique4 ?
Unique (%)100.0%

Sample

1st row1. 마을이름, 지역명칭 반영 변경 : 144건
2nd row2. 개발 및 구획정리에 따른 변경 : 43건
3rd row3. 부정적 어감 및 이미지 개선 : 4건
4th row4. 기타 : 84건
ValueCountFrequency (%)
4
 
14.8%
변경 2
 
7.4%
및 2
 
7.4%
1 1
 
3.7%
3 1
 
3.7%
기타 1
 
3.7%
4 1
 
3.7%
4건 1
 
3.7%
개선 1
 
3.7%
이미지 1
 
3.7%
Other values (12) 12
44.4%
2023-12-12T17:50:38.781464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
24
27.9%
4 6
 
7.0%
. 4
 
4.7%
건 4
 
4.7%
: 4
 
4.7%
1 2
 
2.3%
변 2
 
2.3%
3 2
 
2.3%
정 2
 
2.3%
및 2
 
2.3%
Other values (30) 34
39.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 41
47.7%
Space Separator 24
27.9%
Decimal Number 12
 
14.0%
Other Punctuation 9
 
10.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
건 4
 
9.8%
변 2
 
4.9%
정 2
 
4.9%
및 2
 
4.9%
경 2
 
4.9%
개 2
 
4.9%
지 2
 
4.9%
이 2
 
4.9%
반 1
 
2.4%
에 1
 
2.4%
Other values (21) 21
51.2%
Decimal Number
ValueCountFrequency (%)
4 6
50.0%
1 2
 
16.7%
3 2
 
16.7%
2 1
 
8.3%
8 1
 
8.3%
Other Punctuation
ValueCountFrequency (%)
. 4
44.4%
: 4
44.4%
, 1
 
11.1%
Space Separator
ValueCountFrequency (%)
24
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 45
52.3%
Hangul 41
47.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
건 4
 
9.8%
변 2
 
4.9%
정 2
 
4.9%
및 2
 
4.9%
경 2
 
4.9%
개 2
 
4.9%
지 2
 
4.9%
이 2
 
4.9%
반 1
 
2.4%
에 1
 
2.4%
Other values (21) 21
51.2%
Common
ValueCountFrequency (%)
24
53.3%
4 6
 
13.3%
. 4
 
8.9%
: 4
 
8.9%
1 2
 
4.4%
3 2
 
4.4%
2 1
 
2.2%
, 1
 
2.2%
8 1
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 45
52.3%
Hangul 41
47.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
24
53.3%
4 6
 
13.3%
. 4
 
8.9%
: 4
 
8.9%
1 2
 
4.4%
3 2
 
4.4%
2 1
 
2.2%
, 1
 
2.2%
8 1
 
2.2%
Hangul
ValueCountFrequency (%)
건 4
 
9.8%
변 2
 
4.9%
정 2
 
4.9%
및 2
 
4.9%
경 2
 
4.9%
개 2
 
4.9%
지 2
 
4.9%
이 2
 
4.9%
반 1
 
2.4%
에 1
 
2.4%
Other values (21) 21
51.2%

Interactions

2023-12-12T17:50:32.148742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T17:50:38.918692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번위치시군구변경유형<유형분류>
순번1.0000.6720.9500.520NaN
위치0.6721.0000.9980.5981.000
시군구0.9500.9981.0000.9231.000
변경유형0.5200.5980.9231.0001.000
<유형분류>NaN1.0001.0001.0001.000
2023-12-12T17:50:39.060596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
변경유형위치
변경유형1.0000.375
위치0.3751.000
2023-12-12T17:50:39.169095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번위치변경유형
순번1.0000.3450.334
위치0.3451.0000.375
변경유형0.3340.3751.000

Missing values

2023-12-12T17:50:32.280920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T17:50:32.451971image/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.
2023-12-12T17:50:32.591727image/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

순번변경 전 이름변경 후 이름위치시군구변경일자고시일변경사유변경유형<유형분류>
01낭성갈산2길갈산2길충청북도청원군2013-01-182011-03-16주민들 요구(사용의 편리성에 따라 변경)41. 마을이름, 지역명칭 반영 변경 : 144건
12낭성갈산3길갈산3길충청북도청원군2013-01-182013-01-18주민들 요구(사용의 편리성에 따라 변경)12. 개발 및 구획정리에 따른 변경 : 43건
23강외제방길미호천길충청북도청원군2013-01-182013-01-18주민 요청에 의한 도로명 변경13. 부정적 어감 및 이미지 개선 : 4건
34비토로용궁로경상남도사천시2013-01-242013-01-24<NA>44. 기타 : 84건
45비토해안길거북길경상남도사천시2013-01-242013-01-24<NA>4<NA>
56낭성갈산1길갈산1길충청북도청원군2013-01-282013-01-21<NA>1<NA>
67종가길하회종가길경상북도안동시2013-02-072013-02-07<NA>4<NA>
78북촌길하회북촌길경상북도안동시2013-02-072013-02-07<NA>4<NA>
89남촌길하회남촌길경상북도안동시2013-02-072013-02-07<NA>4<NA>
910초평길초평로경기도의왕시2013-02-142013-01-04위계변경4<NA>
순번변경 전 이름변경 후 이름위치시군구변경일자고시일변경사유변경유형<유형분류>
265266도장4길대안로6길충청북도옥천군2015-09-072015-09-07행정구역명(대안)을 인용하여 일련번호부여1<NA>
266267광치농공1길광치산업1길전라북도남원시2015-09-162015-09-16고유지명과지역특성을활용1<NA>
267268광치농공2길광치산업2길전라북도남원시2015-09-162015-09-16고유지명과지역특성을활용1<NA>
268269남창윗길남창태동길전라북도남원시2015-09-162015-09-16고유지명에방향성을부여1<NA>
269270고루포기산길안반데기2길강원도강릉시2015-10-012015-10-01마을형태가 안반처럼 평평하고 넓다는 의미로 붙여진 마을이름1<NA>
270271안반덕길안반데기길강원도강릉시2015-10-012015-10-01마을의형태가떡매로떡쌀을칠때밑에받치는안반처럼평평하게생겼다하여불리던옛지명에서유래1<NA>
271272큰안반덕길안반데기1길강원도강릉시2015-10-012015-10-01마을 형태가 안반처럼 평평하고 넓다는 의미로 붙여진 마을이름1<NA>
272273깃골길귓골길충청북도충주시2015-11-132015-11-13예부터 전해오는 마을 지명 반영1<NA>
273274분양동길분향동길경기도화성시2015-11-302015-11-30예전부터 불리어진 자연마을이름 반영1<NA>
274275교하로71번길청암로17번길경기도파주시2015-12-312015-12-31청암로 시점에서 170m 지점에 위치한 도로2<NA>