Overview

Dataset statistics

Number of variables7
Number of observations34
Missing cells58
Missing cells (%)24.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.0 KiB
Average record size in memory59.9 B

Variable types

Categorical1
Text6

Dataset

Description안전한보행환경조성사업추진현황20156
Author전라북도
URLhttps://www.bigdatahub.go.kr/opendata/dataSet/detail.nm?contentId=37&rlik=49451aebf056b486&serviceId=202596

Alerts

Unnamed: 6 has constant value ""Constant
Unnamed: 1 has 9 (26.5%) missing valuesMissing
Unnamed: 2 has 3 (8.8%) missing valuesMissing
Unnamed: 3 has 3 (8.8%) missing valuesMissing
Unnamed: 4 has 9 (26.5%) missing valuesMissing
Unnamed: 5 has 1 (2.9%) missing valuesMissing
Unnamed: 6 has 33 (97.1%) missing valuesMissing

Reproduction

Analysis started2024-03-14 03:00:30.502496
Analysis finished2024-03-14 03:00:31.064153
Duration0.56 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Unnamed: 0
Categorical

Distinct9
Distinct (%)26.5%
Missing0
Missing (%)0.0%
Memory size404.0 B
2010
8 
2011
8 
2012
6 
2013
4 
2014
3 
Other values (4)
5 

Length

Max length30
Median length4
Mean length4.6470588
Min length1

Unique

Unique3 ?
Unique (%)8.8%

Sample

1st row2010~2015년 안전한 보행환경 조성사업 추진 현황
2nd row<NA>
3rd row년도별
4th row<NA>
5th row계

Common Values

ValueCountFrequency (%)
2010 8
23.5%
2011 8
23.5%
2012 6
17.6%
2013 4
11.8%
2014 3
 
8.8%
<NA> 2
 
5.9%
2010~2015년 안전한 보행환경 조성사업 추진 현황 1
 
2.9%
년도별 1
 
2.9%
계 1
 
2.9%

Length

2024-03-14T12:00:31.121990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T12:00:31.231875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2010 8
20.5%
2011 8
20.5%
2012 6
15.4%
2013 4
10.3%
2014 3
 
7.7%
na 2
 
5.1%
2010~2015년 1
 
2.6%
안전한 1
 
2.6%
보행환경 1
 
2.6%
조성사업 1
 
2.6%
Other values (4) 4
10.3%

Unnamed: 1
Text

MISSING 

Distinct24
Distinct (%)96.0%
Missing9
Missing (%)26.5%
Memory size404.0 B
2024-03-14T12:00:31.406487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length25
Median length21
Mean length20.6
Min length3

Characters and Unicode

Total characters515
Distinct characters75
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique23 ?
Unique (%)92.0%

Sample

1st row사업명
2nd row안전한 보행환경 조성사업 (순창 농암)
3rd row안전한 보행환경 조성사업 (부안 모산)
4th row안전한 보행환경 조성사업 (고창 선운)
5th row안전한 보행환경 조성사업 (정읍 운학)
ValueCountFrequency (%)
안전한 24
20.0%
조성사업 24
20.0%
보행환경 24
20.0%
익산 5
 
4.2%
완주 4
 
3.3%
정읍 2
 
1.7%
무주 2
 
1.7%
김제 2
 
1.7%
순창 2
 
1.7%
남원 2
 
1.7%
Other values (28) 29
24.2%
2024-03-14T12:00:31.746015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
71
 
13.8%
안 26
 
5.0%
사 25
 
4.9%
업 25
 
4.9%
한 24
 
4.7%
보 24
 
4.7%
행 24
 
4.7%
환 24
 
4.7%
경 24
 
4.7%
조 24
 
4.7%
Other values (65) 224
43.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 368
71.5%
Space Separator 71
 
13.8%
Control 24
 
4.7%
Open Punctuation 24
 
4.7%
Close Punctuation 24
 
4.7%
Dash Punctuation 2
 
0.4%
Decimal Number 2
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
안 26
 
7.1%
사 25
 
6.8%
업 25
 
6.8%
한 24
 
6.5%
보 24
 
6.5%
행 24
 
6.5%
환 24
 
6.5%
경 24
 
6.5%
조 24
 
6.5%
성 24
 
6.5%
Other values (58) 124
33.7%
Decimal Number
ValueCountFrequency (%)
1 1
50.0%
2 1
50.0%
Space Separator
ValueCountFrequency (%)
71
100.0%
Control
ValueCountFrequency (%)
24
100.0%
Open Punctuation
ValueCountFrequency (%)
( 24
100.0%
Close Punctuation
ValueCountFrequency (%)
) 24
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 368
71.5%
Common 147
 
28.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
안 26
 
7.1%
사 25
 
6.8%
업 25
 
6.8%
한 24
 
6.5%
보 24
 
6.5%
행 24
 
6.5%
환 24
 
6.5%
경 24
 
6.5%
조 24
 
6.5%
성 24
 
6.5%
Other values (58) 124
33.7%
Common
ValueCountFrequency (%)
71
48.3%
24
 
16.3%
( 24
 
16.3%
) 24
 
16.3%
- 2
 
1.4%
1 1
 
0.7%
2 1
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 368
71.5%
ASCII 147
 
28.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
71
48.3%
24
 
16.3%
( 24
 
16.3%
) 24
 
16.3%
- 2
 
1.4%
1 1
 
0.7%
2 1
 
0.7%
Hangul
ValueCountFrequency (%)
안 26
 
7.1%
사 25
 
6.8%
업 25
 
6.8%
한 24
 
6.5%
보 24
 
6.5%
행 24
 
6.5%
환 24
 
6.5%
경 24
 
6.5%
조 24
 
6.5%
성 24
 
6.5%
Other values (58) 124
33.7%

Unnamed: 2
Text

MISSING 

Distinct28
Distinct (%)90.3%
Missing3
Missing (%)8.8%
Memory size404.0 B
2024-03-14T12:00:31.916728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length11
Mean length8.2903226
Min length3

Characters and Unicode

Total characters257
Distinct characters63
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

Unique26 ?
Unique (%)83.9%

Sample

1st row위 치
2nd row25개소
3rd row7개소
4th row순창 복흥
5th row부안 부안
ValueCountFrequency (%)
익산 7
 
12.1%
완주 4
 
6.9%
부안 3
 
5.2%
금마(지722 3
 
5.2%
김제 2
 
3.4%
정읍 2
 
3.4%
남원 2
 
3.4%
순창 2
 
3.4%
7개소 2
 
3.4%
3개소 1
 
1.7%
Other values (30) 30
51.7%
2024-03-14T12:00:32.205669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
27
 
10.5%
지 19
 
7.4%
) 18
 
7.0%
( 18
 
7.0%
7 18
 
7.0%
산 15
 
5.8%
2 10
 
3.9%
4 7
 
2.7%
주 7
 
2.7%
익 7
 
2.7%
Other values (53) 111
43.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 133
51.8%
Decimal Number 60
23.3%
Space Separator 27
 
10.5%
Close Punctuation 18
 
7.0%
Open Punctuation 18
 
7.0%
Other Punctuation 1
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
지 19
 
14.3%
산 15
 
11.3%
주 7
 
5.3%
익 7
 
5.3%
개 6
 
4.5%
소 6
 
4.5%
완 4
 
3.0%
안 4
 
3.0%
창 3
 
2.3%
마 3
 
2.3%
Other values (39) 59
44.4%
Decimal Number
ValueCountFrequency (%)
7 18
30.0%
2 10
16.7%
4 7
 
11.7%
1 6
 
10.0%
6 5
 
8.3%
5 4
 
6.7%
3 4
 
6.7%
0 2
 
3.3%
9 2
 
3.3%
8 2
 
3.3%
Space Separator
ValueCountFrequency (%)
27
100.0%
Close Punctuation
ValueCountFrequency (%)
) 18
100.0%
Open Punctuation
ValueCountFrequency (%)
( 18
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 133
51.8%
Common 124
48.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
지 19
 
14.3%
산 15
 
11.3%
주 7
 
5.3%
익 7
 
5.3%
개 6
 
4.5%
소 6
 
4.5%
완 4
 
3.0%
안 4
 
3.0%
창 3
 
2.3%
마 3
 
2.3%
Other values (39) 59
44.4%
Common
ValueCountFrequency (%)
27
21.8%
) 18
14.5%
( 18
14.5%
7 18
14.5%
2 10
 
8.1%
4 7
 
5.6%
1 6
 
4.8%
6 5
 
4.0%
5 4
 
3.2%
3 4
 
3.2%
Other values (4) 7
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 133
51.8%
ASCII 124
48.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
27
21.8%
) 18
14.5%
( 18
14.5%
7 18
14.5%
2 10
 
8.1%
4 7
 
5.6%
1 6
 
4.8%
6 5
 
4.0%
5 4
 
3.2%
3 4
 
3.2%
Other values (4) 7
 
5.6%
Hangul
ValueCountFrequency (%)
지 19
 
14.3%
산 15
 
11.3%
주 7
 
5.3%
익 7
 
5.3%
개 6
 
4.5%
소 6
 
4.5%
완 4
 
3.0%
안 4
 
3.0%
창 3
 
2.3%
마 3
 
2.3%
Other values (39) 59
44.4%

Unnamed: 3
Text

MISSING 

Distinct22
Distinct (%)71.0%
Missing3
Missing (%)8.8%
Memory size404.0 B
2024-03-14T12:00:32.348325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length5
Mean length5.4193548
Min length5

Characters and Unicode

Total characters168
Distinct characters22
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique18 ?
Unique (%)58.1%

Sample

1st row사 업 량 (km)
2nd rowL=13.4
3rd rowL=4.3
4th rowL=0.73
5th rowL=0.78
ValueCountFrequency (%)
l=0.4 5
 
14.7%
l=0.5 4
 
11.8%
l=1.0 2
 
5.9%
l=0.3 2
 
5.9%
l=0.73 1
 
2.9%
l=0.78 1
 
2.9%
l=0.51 1
 
2.9%
l=0.6 1
 
2.9%
l=0.2 1
 
2.9%
l=0.8 1
 
2.9%
Other values (15) 15
44.1%
2024-03-14T12:00:32.596659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
L 30
17.9%
. 30
17.9%
= 30
17.9%
0 25
14.9%
4 11
 
6.5%
1 9
 
5.4%
3 8
 
4.8%
5 5
 
3.0%
7 3
 
1.8%
8 3
 
1.8%
Other values (12) 14
8.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 68
40.5%
Uppercase Letter 30
17.9%
Other Punctuation 30
17.9%
Math Symbol 30
17.9%
Other Letter 3
 
1.8%
Space Separator 2
 
1.2%
Lowercase Letter 2
 
1.2%
Close Punctuation 1
 
0.6%
Open Punctuation 1
 
0.6%
Control 1
 
0.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 25
36.8%
4 11
16.2%
1 9
 
13.2%
3 8
 
11.8%
5 5
 
7.4%
7 3
 
4.4%
8 3
 
4.4%
2 2
 
2.9%
9 1
 
1.5%
6 1
 
1.5%
Other Letter
ValueCountFrequency (%)
업 1
33.3%
량 1
33.3%
사 1
33.3%
Lowercase Letter
ValueCountFrequency (%)
m 1
50.0%
k 1
50.0%
Uppercase Letter
ValueCountFrequency (%)
L 30
100.0%
Other Punctuation
ValueCountFrequency (%)
. 30
100.0%
Math Symbol
ValueCountFrequency (%)
= 30
100.0%
Space Separator
ValueCountFrequency (%)
2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Control
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 133
79.2%
Latin 32
 
19.0%
Hangul 3
 
1.8%

Most frequent character per script

Common
ValueCountFrequency (%)
. 30
22.6%
= 30
22.6%
0 25
18.8%
4 11
 
8.3%
1 9
 
6.8%
3 8
 
6.0%
5 5
 
3.8%
7 3
 
2.3%
8 3
 
2.3%
2
 
1.5%
Other values (6) 7
 
5.3%
Latin
ValueCountFrequency (%)
L 30
93.8%
m 1
 
3.1%
k 1
 
3.1%
Hangul
ValueCountFrequency (%)
업 1
33.3%
량 1
33.3%
사 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 165
98.2%
Hangul 3
 
1.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
L 30
18.2%
. 30
18.2%
= 30
18.2%
0 25
15.2%
4 11
 
6.7%
1 9
 
5.5%
3 8
 
4.8%
5 5
 
3.0%
7 3
 
1.8%
8 3
 
1.8%
Other values (9) 11
 
6.7%
Hangul
ValueCountFrequency (%)
업 1
33.3%
량 1
33.3%
사 1
33.3%

Unnamed: 4
Text

MISSING 

Distinct18
Distinct (%)72.0%
Missing9
Missing (%)26.5%
Memory size404.0 B
2024-03-14T12:00:32.742863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length15
Mean length14.56
Min length4

Characters and Unicode

Total characters364
Distinct characters16
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

Unique14 ?
Unique (%)56.0%

Sample

1st row사업기간
2nd row2010.05~2010.12
3rd row2010.05~2010.11
4th row2010.05~2010.12
5th row2010.05~2010.12
ValueCountFrequency (%)
2011.06~2012.02 3
 
12.0%
2010.05~2010.12 3
 
12.0%
2012.05~2013.01 3
 
12.0%
2012.10~2013.01 2
 
8.0%
2011.09~2012.07 1
 
4.0%
2012.03~2012.07 1
 
4.0%
2013.01~2014.06 1
 
4.0%
2013.10~2013.12 1
 
4.0%
2013.01~2013.08 1
 
4.0%
2011.10~2012.02 1
 
4.0%
Other values (8) 8
32.0%
2024-03-14T12:00:32.985836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 103
28.3%
1 77
21.2%
2 70
19.2%
. 48
13.2%
~ 24
 
6.6%
3 13
 
3.6%
6 8
 
2.2%
5 7
 
1.9%
7 4
 
1.1%
4 3
 
0.8%
Other values (6) 7
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 288
79.1%
Other Punctuation 48
 
13.2%
Math Symbol 24
 
6.6%
Other Letter 4
 
1.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 103
35.8%
1 77
26.7%
2 70
24.3%
3 13
 
4.5%
6 8
 
2.8%
5 7
 
2.4%
7 4
 
1.4%
4 3
 
1.0%
9 2
 
0.7%
8 1
 
0.3%
Other Letter
ValueCountFrequency (%)
사 1
25.0%
업 1
25.0%
기 1
25.0%
간 1
25.0%
Other Punctuation
ValueCountFrequency (%)
. 48
100.0%
Math Symbol
ValueCountFrequency (%)
~ 24
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 360
98.9%
Hangul 4
 
1.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 103
28.6%
1 77
21.4%
2 70
19.4%
. 48
13.3%
~ 24
 
6.7%
3 13
 
3.6%
6 8
 
2.2%
5 7
 
1.9%
7 4
 
1.1%
4 3
 
0.8%
Other values (2) 3
 
0.8%
Hangul
ValueCountFrequency (%)
사 1
25.0%
업 1
25.0%
기 1
25.0%
간 1
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 360
98.9%
Hangul 4
 
1.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 103
28.6%
1 77
21.4%
2 70
19.4%
. 48
13.3%
~ 24
 
6.7%
3 13
 
3.6%
6 8
 
2.2%
5 7
 
1.9%
7 4
 
1.1%
4 3
 
0.8%
Other values (2) 3
 
0.8%
Hangul
ValueCountFrequency (%)
사 1
25.0%
업 1
25.0%
기 1
25.0%
간 1
25.0%

Unnamed: 5
Text

MISSING 

Distinct29
Distinct (%)87.9%
Missing1
Missing (%)2.9%
Memory size404.0 B
2024-03-14T12:00:33.142603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length3
Mean length3.5454545
Min length2

Characters and Unicode

Total characters117
Distinct characters24
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

Unique26 ?
Unique (%)78.8%

Sample

1st row(2015.6.30일 기준)
2nd row사업비
3rd row(백만원)
4th row8,790
5th row2,800
ValueCountFrequency (%)
90 3
 
8.8%
430 2
 
5.9%
340 2
 
5.9%
기준 1
 
2.9%
800 1
 
2.9%
373 1
 
2.9%
80 1
 
2.9%
510 1
 
2.9%
290 1
 
2.9%
280 1
 
2.9%
Other values (20) 20
58.8%
2024-03-14T12:00:33.416989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 34
29.1%
3 12
 
10.3%
2 9
 
7.7%
4 8
 
6.8%
1 8
 
6.8%
9 7
 
6.0%
8 6
 
5.1%
5 5
 
4.3%
6 4
 
3.4%
7 4
 
3.4%
Other values (14) 20
17.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 97
82.9%
Other Letter 9
 
7.7%
Other Punctuation 6
 
5.1%
Close Punctuation 2
 
1.7%
Open Punctuation 2
 
1.7%
Space Separator 1
 
0.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 34
35.1%
3 12
 
12.4%
2 9
 
9.3%
4 8
 
8.2%
1 8
 
8.2%
9 7
 
7.2%
8 6
 
6.2%
5 5
 
5.2%
6 4
 
4.1%
7 4
 
4.1%
Other Letter
ValueCountFrequency (%)
원 1
11.1%
만 1
11.1%
백 1
11.1%
비 1
11.1%
업 1
11.1%
사 1
11.1%
일 1
11.1%
기 1
11.1%
준 1
11.1%
Other Punctuation
ValueCountFrequency (%)
, 4
66.7%
. 2
33.3%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 108
92.3%
Hangul 9
 
7.7%

Most frequent character per script

Common
ValueCountFrequency (%)
0 34
31.5%
3 12
 
11.1%
2 9
 
8.3%
4 8
 
7.4%
1 8
 
7.4%
9 7
 
6.5%
8 6
 
5.6%
5 5
 
4.6%
6 4
 
3.7%
7 4
 
3.7%
Other values (5) 11
 
10.2%
Hangul
ValueCountFrequency (%)
원 1
11.1%
만 1
11.1%
백 1
11.1%
비 1
11.1%
업 1
11.1%
사 1
11.1%
일 1
11.1%
기 1
11.1%
준 1
11.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 108
92.3%
Hangul 9
 
7.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 34
31.5%
3 12
 
11.1%
2 9
 
8.3%
4 8
 
7.4%
1 8
 
7.4%
9 7
 
6.5%
8 6
 
5.6%
5 5
 
4.6%
6 4
 
3.7%
7 4
 
3.7%
Other values (5) 11
 
10.2%
Hangul
ValueCountFrequency (%)
원 1
11.1%
만 1
11.1%
백 1
11.1%
비 1
11.1%
업 1
11.1%
사 1
11.1%
일 1
11.1%
기 1
11.1%
준 1
11.1%

Unnamed: 6
Text

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing33
Missing (%)97.1%
Memory size404.0 B
2024-03-14T12:00:33.513336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2
Distinct characters2
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

Unique1 ?
Unique (%)100.0%

Sample

1st row비고
ValueCountFrequency (%)
비고 1
100.0%
2024-03-14T12:00:33.692888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
비 1
50.0%
고 1
50.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
비 1
50.0%
고 1
50.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
비 1
50.0%
고 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
비 1
50.0%
고 1
50.0%

Correlations

2024-03-14T12:00:34.022403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Unnamed: 0Unnamed: 1Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5
Unnamed: 01.0000.9640.8270.9021.0000.729
Unnamed: 10.9641.0000.9590.9910.9850.944
Unnamed: 20.8270.9591.0000.8930.4360.990
Unnamed: 30.9020.9910.8931.0000.0000.879
Unnamed: 41.0000.9850.4360.0001.0000.431
Unnamed: 50.7290.9440.9900.8790.4311.000

Missing values

2024-03-14T12:00:30.783374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T12:00:30.874014image/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.
2024-03-14T12:00:30.991226image/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

Unnamed: 0Unnamed: 1Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 6
02010~2015년 안전한 보행환경 조성사업 추진 현황<NA><NA><NA><NA><NA><NA>
1<NA><NA><NA><NA><NA>(2015.6.30일 기준)<NA>
2년도별사업명위 치사 업 량 (km)사업기간사업비비고
3<NA><NA><NA><NA><NA>(백만원)<NA>
4계<NA>25개소L=13.4<NA>8,790<NA>
52010<NA>7개소L=4.3<NA>2,800<NA>
62010안전한 보행환경 조성사업 (순창 농암)순창 복흥L=0.732010.05~2010.12190<NA>
72010안전한 보행환경 조성사업 (부안 모산)부안 부안L=0.782010.05~2010.11430<NA>
82010안전한 보행환경 조성사업 (고창 선운)고창 부안L=0.512010.05~2010.12340<NA>
92010안전한 보행환경 조성사업 (정읍 운학)정읍 영원L=0.312010.05~2010.12170<NA>
Unnamed: 0Unnamed: 1Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 6
242012안전한 보행환경 조성사업 (김제 청하)김제 청하(지711)L=0.82012.05~2013.01430<NA>
252012안전한 보행환경 조성사업 (순창 복흥)순창 복흥(지897)L=0.52012.10~2013.01280<NA>
262012안전한 보행환경 조성사업 (완주 화산)완주 화산(지643)L=0.42012.10~2013.01290<NA>
272013<NA>3개소L=1.0<NA>510<NA>
282013안전한 보행환경 조성사업 (익산 석천)익산 낭산(지718)L=0.42013.01~2013.01340<NA>
292013안전한 보행환경 조성사업 (정읍 두지)정읍 두지(지736)L=0.22013.01~2013.0880<NA>
302013안전한 보행환경 조성사업 (익산 금마-1공구)익산 금마(지722)L=0.42013.10~2013.1290<NA>
312014<NA>2개소L=0.6<NA>373<NA>
322014안전한 보행환경 조성사업 (완주 어우)완주 고산(지741)L=0.32013.01~2014.06283<NA>
332014안전한 보행환경 조성사업 (익산 금마-2공구)익산 금마(지722)L=0.32014.01~2014.0790<NA>