Overview

Dataset statistics

Number of variables7
Number of observations114
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.4 KiB
Average record size in memory57.2 B

Variable types

Text1
DateTime6

Dataset

Description철도분야 2016년 이후 종합시험관리대장으로 일반국민에게 사전점검시작 및 종료일자를 안내하고 영업시운전시작일시 및 시운전 종료일자 안내 및 개통일자를 안내하여 시운전에 대한 종합적인 개략정보를 안내
URLhttps://www.data.go.kr/data/15020859/fileData.do

Reproduction

Analysis started2023-12-12 06:24:37.883447
Analysis finished2023-12-12 06:24:38.353859
Duration0.47 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

사업명
Text

Distinct66
Distinct (%)57.9%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
2023-12-12T15:24:38.605966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length21
Mean length11.780702
Min length5

Characters and Unicode

Total characters1343
Distinct characters154
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique43 ?
Unique (%)37.7%

Sample

1st row호남권내륙화물기지 인입철도
2nd row동부전동차사무소건설(기지 턴키공사)
3rd row조치원~대구 전철화
4th row대구선철도이설(동대구~청천)
5th row청량리-덕소 복선전철(중앙선)
ValueCountFrequency (%)
복선전철 36
 
15.1%
일반철도시설개량 8
 
3.4%
철도건설 7
 
2.9%
신설 7
 
2.9%
경부고속철도 6
 
2.5%
12년5월 5
 
2.1%
오리~수원 5
 
2.1%
복선전철(ver4.9 5
 
2.1%
2단계(rev7.4 5
 
2.1%
용산~문산 5
 
2.1%
Other values (99) 149
62.6%
2023-12-12T15:24:39.181935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
124
 
9.2%
철 99
 
7.4%
선 79
 
5.9%
전 58
 
4.3%
복 54
 
4.0%
~ 50
 
3.7%
도 49
 
3.6%
설 47
 
3.5%
산 29
 
2.2%
원 26
 
1.9%
Other values (144) 728
54.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1041
77.5%
Space Separator 124
 
9.2%
Math Symbol 52
 
3.9%
Decimal Number 45
 
3.4%
Lowercase Letter 20
 
1.5%
Open Punctuation 16
 
1.2%
Close Punctuation 16
 
1.2%
Other Punctuation 11
 
0.8%
Uppercase Letter 10
 
0.7%
Dash Punctuation 8
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
철 99
 
9.5%
선 79
 
7.6%
전 58
 
5.6%
복 54
 
5.2%
도 49
 
4.7%
설 47
 
4.5%
산 29
 
2.8%
원 26
 
2.5%
천 25
 
2.4%
건 19
 
1.8%
Other values (125) 556
53.4%
Decimal Number
ValueCountFrequency (%)
2 14
31.1%
4 10
22.2%
1 6
13.3%
7 5
 
11.1%
5 5
 
11.1%
9 5
 
11.1%
Lowercase Letter
ValueCountFrequency (%)
e 10
50.0%
v 5
25.0%
r 5
25.0%
Math Symbol
ValueCountFrequency (%)
~ 50
96.2%
∼ 2
 
3.8%
Other Punctuation
ValueCountFrequency (%)
. 10
90.9%
, 1
 
9.1%
Uppercase Letter
ValueCountFrequency (%)
R 5
50.0%
V 5
50.0%
Space Separator
ValueCountFrequency (%)
124
100.0%
Open Punctuation
ValueCountFrequency (%)
( 16
100.0%
Close Punctuation
ValueCountFrequency (%)
) 16
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1041
77.5%
Common 272
 
20.3%
Latin 30
 
2.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
철 99
 
9.5%
선 79
 
7.6%
전 58
 
5.6%
복 54
 
5.2%
도 49
 
4.7%
설 47
 
4.5%
산 29
 
2.8%
원 26
 
2.5%
천 25
 
2.4%
건 19
 
1.8%
Other values (125) 556
53.4%
Common
ValueCountFrequency (%)
124
45.6%
~ 50
18.4%
( 16
 
5.9%
) 16
 
5.9%
2 14
 
5.1%
. 10
 
3.7%
4 10
 
3.7%
- 8
 
2.9%
1 6
 
2.2%
7 5
 
1.8%
Other values (4) 13
 
4.8%
Latin
ValueCountFrequency (%)
e 10
33.3%
R 5
16.7%
v 5
16.7%
V 5
16.7%
r 5
16.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1041
77.5%
ASCII 300
 
22.3%
Math Operators 2
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
124
41.3%
~ 50
16.7%
( 16
 
5.3%
) 16
 
5.3%
2 14
 
4.7%
. 10
 
3.3%
4 10
 
3.3%
e 10
 
3.3%
- 8
 
2.7%
1 6
 
2.0%
Other values (8) 36
 
12.0%
Hangul
ValueCountFrequency (%)
철 99
 
9.5%
선 79
 
7.6%
전 58
 
5.6%
복 54
 
5.2%
도 49
 
4.7%
설 47
 
4.5%
산 29
 
2.8%
원 26
 
2.5%
천 25
 
2.4%
건 19
 
1.8%
Other values (125) 556
53.4%
Math Operators
ValueCountFrequency (%)
∼ 2
100.0%

사전점검 시작일자
Date

Distinct106
Distinct (%)93.0%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
Minimum2005-03-10 00:00:00
Maximum2021-09-16 00:00:00
2023-12-12T15:24:39.378028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:24:39.565470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

사전점검 종료일자
Date

Distinct106
Distinct (%)93.0%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
Minimum2005-03-10 00:00:00
Maximum2021-09-17 00:00:00
2023-12-12T15:24:39.709312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:24:39.864507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

영업시운전 시작일자
Date

Distinct103
Distinct (%)90.4%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
Minimum2005-03-11 00:00:00
Maximum2021-12-01 00:00:00
2023-12-12T15:24:40.014971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:24:40.163042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

영업시운전 종료일자
Date

Distinct104
Distinct (%)91.2%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
Minimum2005-03-11 00:00:00
Maximum2021-12-03 00:00:00
2023-12-12T15:24:40.304788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:24:40.450663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

시설물검증시험 시작일자
Date

Distinct108
Distinct (%)94.7%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
Minimum2005-03-11 00:00:00
Maximum2021-10-27 00:00:00
2023-12-12T15:24:40.601514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:24:40.737695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

시설물검증시험 종료일자
Date

Distinct109
Distinct (%)95.6%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
Minimum2005-03-11 00:00:00
Maximum2021-10-29 00:00:00
2023-12-12T15:24:40.891743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:24:41.075654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Missing values

2023-12-12T15:24:38.125242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T15:24:38.288839image/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

사업명사전점검 시작일자사전점검 종료일자영업시운전 시작일자영업시운전 종료일자시설물검증시험 시작일자시설물검증시험 종료일자
0호남권내륙화물기지 인입철도2005-03-102005-03-102005-03-112005-03-112005-03-112005-03-11
1동부전동차사무소건설(기지 턴키공사)2005-04-062005-04-082005-04-072005-04-072006-04-182005-04-19
2조치원~대구 전철화2005-04-262005-04-292005-04-272005-04-282005-05-192005-05-26
3대구선철도이설(동대구~청천)2005-10-052005-10-072005-10-072005-10-072005-10-102005-10-10
4청량리-덕소 복선전철(중앙선)2005-11-212005-11-232005-11-222005-11-232005-11-252005-11-25
5경인 2복선전철2005-11-242005-11-252005-11-242005-11-252005-11-282005-11-28
6경춘선복선전철2006-08-212006-08-282006-08-212006-08-212006-08-302006-08-31
7의정부~동안 복선전철2006-10-302006-11-032006-11-102006-11-152006-11-052006-11-15
8오리~수원 복선전철(Ver4.9)2007-10-122007-11-222007-12-032007-12-222007-10-182007-12-05
9철도교통관제시설 설계2007-10-232007-10-242007-12-012007-12-102007-10-292007-11-16
사업명사전점검 시작일자사전점검 종료일자영업시운전 시작일자영업시운전 종료일자시설물검증시험 시작일자시설물검증시험 종료일자
104도담~영천 복선전철2020-10-062020-10-142020-11-222020-11-232020-11-022020-11-05
105일반철도시설개량2020-08-182020-08-202020-10-062020-10-162020-10-062020-10-16
106대구선 복선전철사업2021-01-252021-02-032021-07-062021-07-092021-04-052021-04-28
107부산~울산 복선전철2021-03-152021-03-242021-09-132021-10-062021-06-232021-08-06
108장항선 탕정역 신설2021-04-052021-04-062021-09-062021-09-082021-07-212021-07-23
109울산~포항 복선전철2021-03-292021-04-122021-10-282021-11-262021-08-132021-10-01
110여주~문경 철도건설2021-05-102021-05-212021-10-282021-12-012021-07-262021-09-10
111영천~신경주 복선전철2021-04-292021-05-072021-10-282021-11-262021-08-132021-10-01
112문산~도라산 전철화2021-06-142021-06-162021-09-102021-09-142021-07-262021-07-29
113서대구 고속철도역 건설2021-09-162021-09-172021-12-012021-12-032021-10-272021-10-29