Overview

Dataset statistics

Number of variables10
Number of observations36
Missing cells58
Missing cells (%)16.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.0 KiB
Average record size in memory84.7 B

Variable types

Unsupported6
Text3
Categorical1

Dataset

Description개발촉진지구투자현황20163월말
Author전라북도
URLhttps://www.bigdatahub.go.kr/opendata/dataSet/detail.nm?contentId=37&rlik=49451aebf056b486&serviceId=202675

Alerts

Unnamed: 9 is highly imbalanced (77.0%)Imbalance
Unnamed: 0 has 36 (100.0%) missing valuesMissing
개발촉진지구(낙후지역 개발사업) 투자현황 및 계획 (단위:백만원) has 22 (61.1%) missing valuesMissing
Unnamed: 0 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 4 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 5 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 6 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 7 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 8 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2024-03-14 01:29:28.927871
Analysis finished2024-03-14 01:29:29.490787
Duration0.56 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Unnamed: 0
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing36
Missing (%)100.0%
Memory size456.0 B

개발촉진지구(낙후지역 개발사업) 투자현황 및 계획 (단위:백만원)
Text

MISSING 

Distinct14
Distinct (%)100.0%
Missing22
Missing (%)61.1%
Memory size420.0 B
2024-03-14T10:29:29.609733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length2
Mean length2.7857143
Min length1

Characters and Unicode

Total characters39
Distinct characters33
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

Unique14 ?
Unique (%)100.0%

Sample

1st row시군
2nd row계
3rd row진안 임실
4th row장수
5th row순창
ValueCountFrequency (%)
시군 1
 
5.6%
계 1
 
5.6%
투자 1
 
5.6%
수요 1
 
5.6%
지역 1
 
5.6%
티브 1
 
5.6%
인센 1
 
5.6%
정읍 1
 
5.6%
부안 1
 
5.6%
김제 1
 
5.6%
Other values (8) 8
44.4%
2024-03-14T10:29:29.859532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4
 
10.3%
수 2
 
5.1%
안 2
 
5.1%
창 2
 
5.1%
지 1
 
2.6%
읍 1
 
2.6%
인 1
 
2.6%
센 1
 
2.6%
티 1
 
2.6%
브 1
 
2.6%
Other values (23) 23
59.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 35
89.7%
Control 4
 
10.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
수 2
 
5.7%
안 2
 
5.7%
창 2
 
5.7%
지 1
 
2.9%
읍 1
 
2.9%
인 1
 
2.9%
센 1
 
2.9%
티 1
 
2.9%
브 1
 
2.9%
시 1
 
2.9%
Other values (22) 22
62.9%
Control
ValueCountFrequency (%)
4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 35
89.7%
Common 4
 
10.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
수 2
 
5.7%
안 2
 
5.7%
창 2
 
5.7%
지 1
 
2.9%
읍 1
 
2.9%
인 1
 
2.9%
센 1
 
2.9%
티 1
 
2.9%
브 1
 
2.9%
시 1
 
2.9%
Other values (22) 22
62.9%
Common
ValueCountFrequency (%)
4
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 35
89.7%
ASCII 4
 
10.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4
100.0%
Hangul
ValueCountFrequency (%)
수 2
 
5.7%
안 2
 
5.7%
창 2
 
5.7%
지 1
 
2.9%
읍 1
 
2.9%
인 1
 
2.9%
센 1
 
2.9%
티 1
 
2.9%
브 1
 
2.9%
시 1
 
2.9%
Other values (22) 22
62.9%

Unnamed: 2
Text

Distinct32
Distinct (%)88.9%
Missing0
Missing (%)0.0%
Memory size420.0 B
2024-03-14T10:29:30.061022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length17
Mean length9.75
Min length1

Characters and Unicode

Total characters351
Distinct characters124
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

Unique31 ?
Unique (%)86.1%

Sample

1st row사 업 명
2nd row10개 시군
3rd row진안~관촌간도로
4th row장수관광순환도로 등
5th row모정~고원간도로 등
ValueCountFrequency (%)
계 5
 
6.7%
정비 4
 
5.3%
도로 3
 
4.0%
등 3
 
4.0%
조성사업 2
 
2.7%
탐방로 2
 
2.7%
연결도로 2
 
2.7%
임실 2
 
2.7%
기반시설 2
 
2.7%
명품경관정비사업 1
 
1.3%
Other values (49) 49
65.3%
2024-03-14T10:29:30.640351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
39
 
11.1%
로 19
 
5.4%
도 17
 
4.8%
지 11
 
3.1%
업 9
 
2.6%
관 9
 
2.6%
사 8
 
2.3%
산 7
 
2.0%
계 6
 
1.7%
마 6
 
1.7%
Other values (114) 220
62.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 305
86.9%
Space Separator 39
 
11.1%
Math Symbol 5
 
1.4%
Decimal Number 2
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 19
 
6.2%
도 17
 
5.6%
지 11
 
3.6%
업 9
 
3.0%
관 9
 
3.0%
사 8
 
2.6%
산 7
 
2.3%
계 6
 
2.0%
마 6
 
2.0%
비 6
 
2.0%
Other values (110) 207
67.9%
Decimal Number
ValueCountFrequency (%)
0 1
50.0%
1 1
50.0%
Space Separator
ValueCountFrequency (%)
39
100.0%
Math Symbol
ValueCountFrequency (%)
~ 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 305
86.9%
Common 46
 
13.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 19
 
6.2%
도 17
 
5.6%
지 11
 
3.6%
업 9
 
3.0%
관 9
 
3.0%
사 8
 
2.6%
산 7
 
2.3%
계 6
 
2.0%
마 6
 
2.0%
비 6
 
2.0%
Other values (110) 207
67.9%
Common
ValueCountFrequency (%)
39
84.8%
~ 5
 
10.9%
0 1
 
2.2%
1 1
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 305
86.9%
ASCII 46
 
13.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
39
84.8%
~ 5
 
10.9%
0 1
 
2.2%
1 1
 
2.2%
Hangul
ValueCountFrequency (%)
로 19
 
6.2%
도 17
 
5.6%
지 11
 
3.6%
업 9
 
3.0%
관 9
 
3.0%
사 8
 
2.6%
산 7
 
2.3%
계 6
 
2.0%
마 6
 
2.0%
비 6
 
2.0%
Other values (110) 207
67.9%

Unnamed: 3
Text

Distinct35
Distinct (%)97.2%
Missing0
Missing (%)0.0%
Memory size420.0 B
2024-03-14T10:29:30.944933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length32
Median length29
Mean length12.611111
Min length4

Characters and Unicode

Total characters454
Distinct characters75
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique34 ?
Unique (%)94.4%

Sample

1st row사업개요
2nd row38개 사업
3rd row1노선/13.1km B=10m
4th row3노선/21.9km
5th row3노선/22.2km
ValueCountFrequency (%)
1.4km/12m 2
 
3.4%
등 2
 
3.4%
5노선/18.4km 1
 
1.7%
7.7km/10m 1
 
1.7%
0.77km/15~25m 1
 
1.7%
1.9m/10m 1
 
1.7%
1.6km/18m 1
 
1.7%
2노선/3.35km 1
 
1.7%
관광시설 1
 
1.7%
4.74km/18.5~25m 1
 
1.7%
Other values (46) 46
79.3%
2024-03-14T10:29:31.243893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
m 50
 
11.0%
1 33
 
7.3%
. 32
 
7.0%
k 28
 
6.2%
/ 25
 
5.5%
2 24
 
5.3%
22
 
4.8%
5 21
 
4.6%
3 19
 
4.2%
4 14
 
3.1%
Other values (65) 186
41.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 157
34.6%
Other Letter 113
24.9%
Lowercase Letter 78
17.2%
Other Punctuation 70
15.4%
Space Separator 26
 
5.7%
Math Symbol 6
 
1.3%
Other Symbol 3
 
0.7%
Uppercase Letter 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
선 9
 
8.0%
노 9
 
8.0%
로 7
 
6.2%
장 4
 
3.5%
주 4
 
3.5%
차 4
 
3.5%
설 4
 
3.5%
개 4
 
3.5%
교 4
 
3.5%
등 3
 
2.7%
Other values (44) 61
54.0%
Decimal Number
ValueCountFrequency (%)
1 33
21.0%
2 24
15.3%
5 21
13.4%
3 19
12.1%
4 14
8.9%
0 12
 
7.6%
9 12
 
7.6%
7 11
 
7.0%
6 6
 
3.8%
8 5
 
3.2%
Other Punctuation
ValueCountFrequency (%)
. 32
45.7%
/ 25
35.7%
, 13
18.6%
Lowercase Letter
ValueCountFrequency (%)
m 50
64.1%
k 28
35.9%
Space Separator
ValueCountFrequency (%)
22
84.6%
　 4
 
15.4%
Math Symbol
ValueCountFrequency (%)
~ 5
83.3%
= 1
 
16.7%
Other Symbol
ValueCountFrequency (%)
㎡ 3
100.0%
Uppercase Letter
ValueCountFrequency (%)
B 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 262
57.7%
Hangul 113
24.9%
Latin 79
 
17.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
선 9
 
8.0%
노 9
 
8.0%
로 7
 
6.2%
장 4
 
3.5%
주 4
 
3.5%
차 4
 
3.5%
설 4
 
3.5%
개 4
 
3.5%
교 4
 
3.5%
등 3
 
2.7%
Other values (44) 61
54.0%
Common
ValueCountFrequency (%)
1 33
12.6%
. 32
12.2%
/ 25
9.5%
2 24
9.2%
22
8.4%
5 21
8.0%
3 19
7.3%
4 14
 
5.3%
, 13
 
5.0%
0 12
 
4.6%
Other values (8) 47
17.9%
Latin
ValueCountFrequency (%)
m 50
63.3%
k 28
35.4%
B 1
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 334
73.6%
Hangul 113
 
24.9%
None 4
 
0.9%
CJK Compat 3
 
0.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
m 50
15.0%
1 33
9.9%
. 32
9.6%
k 28
8.4%
/ 25
 
7.5%
2 24
 
7.2%
22
 
6.6%
5 21
 
6.3%
3 19
 
5.7%
4 14
 
4.2%
Other values (9) 66
19.8%
Hangul
ValueCountFrequency (%)
선 9
 
8.0%
노 9
 
8.0%
로 7
 
6.2%
장 4
 
3.5%
주 4
 
3.5%
차 4
 
3.5%
설 4
 
3.5%
개 4
 
3.5%
교 4
 
3.5%
등 3
 
2.7%
Other values (44) 61
54.0%
None
ValueCountFrequency (%)
　 4
100.0%
CJK Compat
ValueCountFrequency (%)
㎡ 3
100.0%

Unnamed: 4
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size420.0 B

Unnamed: 5
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size420.0 B

Unnamed: 6
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size420.0 B

Unnamed: 7
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size420.0 B

Unnamed: 8
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size420.0 B

Unnamed: 9
Categorical

IMBALANCE 

Distinct3
Distinct (%)8.3%
Missing0
Missing (%)0.0%
Memory size420.0 B
 
34 
비고
 
1
<NA>
 
1

Length

Max length4
Median length2
Mean length2.0555556
Min length2

Unique

Unique2 ?
Unique (%)5.6%

Sample

1st row비고
2nd row 
3rd row 
4th row 
5th row 

Common Values

ValueCountFrequency (%)
  34
94.4%
비고 1
 
2.8%
<NA> 1
 
2.8%

Length

2024-03-14T10:29:31.356373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T10:29:31.443828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
비고 1
50.0%
na 1
50.0%

Correlations

2024-03-14T10:29:31.494879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
개발촉진지구(낙후지역 개발사업) 투자현황 및 계획 (단위:백만원)Unnamed: 2Unnamed: 3Unnamed: 9
개발촉진지구(낙후지역 개발사업) 투자현황 및 계획 (단위:백만원)1.0001.0001.0001.000
Unnamed: 21.0001.0000.9741.000
Unnamed: 31.0000.9741.0001.000
Unnamed: 91.0001.0001.0001.000

Missing values

2024-03-14T10:29:29.262732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T10:29:29.424691image/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

Unnamed: 0개발촉진지구(낙후지역 개발사업) 투자현황 및 계획 (단위:백만원)Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 6Unnamed: 7Unnamed: 8Unnamed: 9
0<NA>시군사 업 명사업개요총사업비‘14까지‘15예산‘16예산‘17이후비고
1<NA>계10개 시군38개 사업468536371072315751341752472
2<NA>진안 임실진안~관촌간도로1노선/13.1km B=10m5021850218000
3<NA>장수장수관광순환도로 등3노선/21.9km6160061600000
4<NA>순창모정~고원간도로 등3노선/22.2km5068850688000
5<NA>고창구시포~두어리간도로 등6노선/31.1km5878758787000
6<NA>무주계2노선/7.35km571975248941475610
7<NA><NA>오산~당산간 도로3.79km/15m323413021515655610
8<NA><NA>덕지~삼거간 도로3.56km/9~9.5m2485622274258200
9<NA>남원계5노선/18.4km46561348731015015380
Unnamed: 0개발촉진지구(낙후지역 개발사업) 투자현황 및 계획 (단위:백만원)Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 6Unnamed: 7Unnamed: 8Unnamed: 9
26<NA>정읍계2노선/3.35km, 관광시설 등324570090031557
27<NA><NA>구절초테마파크 기반시설 조성사업탐방로1.9km,출렁다리 97m, 주차장29,700㎡4145005003645
28<NA><NA>오감만족체험 축산테마파크 기반시설조성사업도로 0.41km, 진입교량 55m, 주차장5,620㎡2770004002370
29<NA><NA>영원 고분군마을 기반시설 조성사업탐방로개설4.3km, 주차장19,557㎡, 간위쉼터1식 등26050002605
30<NA><NA>내장산리조트 연결도로 확장사업도로2.94km, 교량35m, 터널334m2293700022937
31<NA>인센 티브개발계획 미반영 사업김제 백석초등학교 진입로 개설47402502240
32<NA>지역 수요임실 오지마을 교통나눔 서비스수요응답형 대중교통100001000
33<NA><NA>임실 전통시장활성화사업다기능 주차장119700600597
34<NA><NA>고창 명품경관정비사업가로경관정비1757002971460
35<NA>투자 선도순창 한국전통 발효문화산업 투자선도지구진입도로, 내부기반시설10000003009700