Overview

Dataset statistics

Number of variables4
Number of observations50
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.7 KiB
Average record size in memory34.6 B

Variable types

Categorical1
Text3

Dataset

Description2022년 노후관개량 및 블록구축정비사업 추진실적 자료입니다. 내용: 담당부서별 사업명, 사업량(매설관로 관경 및 연장), 추진실적 현황(총 연장)
URLhttps://www.data.go.kr/data/15081148/fileData.do

Alerts

사업명 has unique valuesUnique

Reproduction

Analysis started2023-12-12 15:40:32.984856
Analysis finished2023-12-12 15:40:33.383559
Duration0.4 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

부서
Categorical

Distinct6
Distinct (%)12.0%
Missing0
Missing (%)0.0%
Memory size532.0 B
동부사업소
19 
수도시설관리사업소
9 
서부사업소
7 
중부사업소
6 
유성사업소
6 

Length

Max length9
Median length5
Mean length5.72
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
동부사업소 19
38.0%
수도시설관리사업소 9
18.0%
서부사업소 7
 
14.0%
중부사업소 6
 
12.0%
유성사업소 6
 
12.0%
대덕사업소 3
 
6.0%

Length

2023-12-13T00:40:33.513335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T00:40:33.679594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
동부사업소 19
38.0%
수도시설관리사업소 9
18.0%
서부사업소 7
 
14.0%
중부사업소 6
 
12.0%
유성사업소 6
 
12.0%
대덕사업소 3
 
6.0%

사업명
Text

UNIQUE 

Distinct50
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size532.0 B
2023-12-13T00:40:33.951560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length36
Median length30
Mean length22.44
Min length13

Characters and Unicode

Total characters1122
Distinct characters88
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique50 ?
Unique (%)100.0%

Sample

1st rowY42 블록구축 정비공사
2nd rowJ35외 1개소(J66) 블록구축 정비공사
3rd rowS16외 1개소(S75) 블록구축 정비공사
4th row서구 둔산동 1545번지 일원 노후관 개량공사
5th row대학로(보건환경연구원~국립중앙과학관) 일원 노후관 개량공사
ValueCountFrequency (%)
노후관 45
18.7%
개량공사 44
18.3%
일원 33
 
13.7%
가양동 7
 
2.9%
용전동 4
 
1.7%
1개소 4
 
1.7%
외 4
 
1.7%
정림동 3
 
1.2%
블록구축 3
 
1.2%
갈마동 3
 
1.2%
Other values (81) 91
37.8%
2023-12-13T00:40:34.332787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
191
 
17.0%
사 52
 
4.6%
개 50
 
4.5%
공 50
 
4.5%
관 48
 
4.3%
후 45
 
4.0%
노 45
 
4.0%
량 44
 
3.9%
동 43
 
3.8%
원 38
 
3.4%
Other values (78) 516
46.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 705
62.8%
Space Separator 191
 
17.0%
Decimal Number 168
 
15.0%
Dash Punctuation 24
 
2.1%
Uppercase Letter 17
 
1.5%
Open Punctuation 7
 
0.6%
Close Punctuation 7
 
0.6%
Math Symbol 3
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
사 52
 
7.4%
개 50
 
7.1%
공 50
 
7.1%
관 48
 
6.8%
후 45
 
6.4%
노 45
 
6.4%
량 44
 
6.2%
동 43
 
6.1%
원 38
 
5.4%
일 36
 
5.1%
Other values (60) 254
36.0%
Decimal Number
ValueCountFrequency (%)
1 38
22.6%
2 27
16.1%
4 21
12.5%
6 20
11.9%
5 17
10.1%
3 15
 
8.9%
7 11
 
6.5%
9 8
 
4.8%
8 8
 
4.8%
0 3
 
1.8%
Uppercase Letter
ValueCountFrequency (%)
Y 6
35.3%
S 6
35.3%
J 5
29.4%
Space Separator
ValueCountFrequency (%)
191
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 24
100.0%
Open Punctuation
ValueCountFrequency (%)
( 7
100.0%
Close Punctuation
ValueCountFrequency (%)
) 7
100.0%
Math Symbol
ValueCountFrequency (%)
~ 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 705
62.8%
Common 400
35.7%
Latin 17
 
1.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
사 52
 
7.4%
개 50
 
7.1%
공 50
 
7.1%
관 48
 
6.8%
후 45
 
6.4%
노 45
 
6.4%
량 44
 
6.2%
동 43
 
6.1%
원 38
 
5.4%
일 36
 
5.1%
Other values (60) 254
36.0%
Common
ValueCountFrequency (%)
191
47.8%
1 38
 
9.5%
2 27
 
6.8%
- 24
 
6.0%
4 21
 
5.2%
6 20
 
5.0%
5 17
 
4.2%
3 15
 
3.8%
7 11
 
2.8%
9 8
 
2.0%
Other values (5) 28
 
7.0%
Latin
ValueCountFrequency (%)
Y 6
35.3%
S 6
35.3%
J 5
29.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 705
62.8%
ASCII 417
37.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
191
45.8%
1 38
 
9.1%
2 27
 
6.5%
- 24
 
5.8%
4 21
 
5.0%
6 20
 
4.8%
5 17
 
4.1%
3 15
 
3.6%
7 11
 
2.6%
9 8
 
1.9%
Other values (8) 45
 
10.8%
Hangul
ValueCountFrequency (%)
사 52
 
7.4%
개 50
 
7.1%
공 50
 
7.1%
관 48
 
6.8%
후 45
 
6.4%
노 45
 
6.4%
량 44
 
6.2%
동 43
 
6.1%
원 38
 
5.4%
일 36
 
5.1%
Other values (60) 254
36.0%

사업량
Text

Distinct49
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Memory size532.0 B
2023-12-13T00:40:34.546774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length19
Mean length15.7
Min length12

Characters and Unicode

Total characters785
Distinct characters20
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique48 ?
Unique (%)96.0%

Sample

1st rowD100mm, L=877m
2nd rowD100mm, L=367m
3rd rowD300~600mm. L=371m
4th rowD400mm, L=270m
5th rowD600mm L=668m
ValueCountFrequency (%)
d=100mm 8
 
8.0%
d100mm 8
 
8.0%
d100~150mm 7
 
7.0%
d150mm 4
 
4.0%
d600mm 4
 
4.0%
l=270m 3
 
3.0%
d100~300mm 3
 
3.0%
l=96m 2
 
2.0%
d=150mm 2
 
2.0%
l=176m 2
 
2.0%
Other values (57) 57
57.0%
2023-12-13T00:40:34.900963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 142
18.1%
m 142
18.1%
1 69
8.8%
= 61
7.8%
, 54
 
6.9%
51
 
6.5%
D 50
 
6.4%
L 50
 
6.4%
5 29
 
3.7%
2 26
 
3.3%
Other values (10) 111
14.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 354
45.1%
Lowercase Letter 142
18.1%
Uppercase Letter 100
 
12.7%
Math Symbol 78
 
9.9%
Other Punctuation 56
 
7.1%
Space Separator 51
 
6.5%
Dash Punctuation 2
 
0.3%
Other Symbol 2
 
0.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 142
40.1%
1 69
19.5%
5 29
 
8.2%
2 26
 
7.3%
6 19
 
5.4%
3 17
 
4.8%
7 15
 
4.2%
4 14
 
4.0%
8 14
 
4.0%
9 9
 
2.5%
Math Symbol
ValueCountFrequency (%)
= 61
78.2%
~ 17
 
21.8%
Other Punctuation
ValueCountFrequency (%)
, 54
96.4%
. 2
 
3.6%
Uppercase Letter
ValueCountFrequency (%)
D 50
50.0%
L 50
50.0%
Lowercase Letter
ValueCountFrequency (%)
m 142
100.0%
Space Separator
ValueCountFrequency (%)
51
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%
Other Symbol
ValueCountFrequency (%)
㎜ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 543
69.2%
Latin 242
30.8%

Most frequent character per script

Common
ValueCountFrequency (%)
0 142
26.2%
1 69
12.7%
= 61
11.2%
, 54
 
9.9%
51
 
9.4%
5 29
 
5.3%
2 26
 
4.8%
6 19
 
3.5%
~ 17
 
3.1%
3 17
 
3.1%
Other values (7) 58
10.7%
Latin
ValueCountFrequency (%)
m 142
58.7%
D 50
 
20.7%
L 50
 
20.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 783
99.7%
CJK Compat 2
 
0.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 142
18.1%
m 142
18.1%
1 69
8.8%
= 61
7.8%
, 54
 
6.9%
51
 
6.5%
D 50
 
6.4%
L 50
 
6.4%
5 29
 
3.7%
2 26
 
3.3%
Other values (9) 109
13.9%
CJK Compat
ValueCountFrequency (%)
㎜ 2
100.0%

추진실적
Text

Distinct46
Distinct (%)92.0%
Missing0
Missing (%)0.0%
Memory size532.0 B
2023-12-13T00:40:35.096107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length6
Mean length6.22
Min length5

Characters and Unicode

Total characters311
Distinct characters16
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique43 ?
Unique (%)86.0%

Sample

1st rowL=877m
2nd rowL=367m
3rd rowL=371m
4th rowL=270m
5th rowL=668m
ValueCountFrequency (%)
l=270m 3
 
5.9%
l=176m 2
 
3.9%
l=96m 2
 
3.9%
l=240m 1
 
2.0%
l=934m 1
 
2.0%
l=300m 1
 
2.0%
l=352m 1
 
2.0%
l=490m 1
 
2.0%
l=792m 1
 
2.0%
l=278m 1
 
2.0%
Other values (37) 37
72.5%
2023-12-13T00:40:35.413093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
L 50
16.1%
= 50
16.1%
m 50
16.1%
0 25
8.0%
2 22
7.1%
1 22
7.1%
7 15
 
4.8%
6 14
 
4.5%
4 12
 
3.9%
5 11
 
3.5%
Other values (6) 40
12.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 152
48.9%
Uppercase Letter 50
 
16.1%
Math Symbol 50
 
16.1%
Lowercase Letter 50
 
16.1%
Other Punctuation 8
 
2.6%
Space Separator 1
 
0.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 25
16.4%
2 22
14.5%
1 22
14.5%
7 15
9.9%
6 14
9.2%
4 12
7.9%
5 11
7.2%
3 11
7.2%
8 11
7.2%
9 9
 
5.9%
Other Punctuation
ValueCountFrequency (%)
, 7
87.5%
. 1
 
12.5%
Uppercase Letter
ValueCountFrequency (%)
L 50
100.0%
Math Symbol
ValueCountFrequency (%)
= 50
100.0%
Lowercase Letter
ValueCountFrequency (%)
m 50
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 211
67.8%
Latin 100
32.2%

Most frequent character per script

Common
ValueCountFrequency (%)
= 50
23.7%
0 25
11.8%
2 22
10.4%
1 22
10.4%
7 15
 
7.1%
6 14
 
6.6%
4 12
 
5.7%
5 11
 
5.2%
3 11
 
5.2%
8 11
 
5.2%
Other values (4) 18
 
8.5%
Latin
ValueCountFrequency (%)
L 50
50.0%
m 50
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 311
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
L 50
16.1%
= 50
16.1%
m 50
16.1%
0 25
8.0%
2 22
7.1%
1 22
7.1%
7 15
 
4.8%
6 14
 
4.5%
4 12
 
3.9%
5 11
 
3.5%
Other values (6) 40
12.9%

Correlations

2023-12-13T00:40:35.515211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
부서사업명사업량추진실적
부서1.0001.0001.0000.956
사업명1.0001.0001.0001.000
사업량1.0001.0001.0001.000
추진실적0.9561.0001.0001.000

Missing values

2023-12-13T00:40:33.210617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T00:40:33.335547image/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

부서사업명사업량추진실적
0수도시설관리사업소Y42 블록구축 정비공사D100mm, L=877mL=877m
1수도시설관리사업소J35외 1개소(J66) 블록구축 정비공사D100mm, L=367mL=367m
2수도시설관리사업소S16외 1개소(S75) 블록구축 정비공사D300~600mm. L=371mL=371m
3수도시설관리사업소서구 둔산동 1545번지 일원 노후관 개량공사D400mm, L=270mL=270m
4수도시설관리사업소대학로(보건환경연구원~국립중앙과학관) 일원 노후관 개량공사D600mm L=668mL=668m
5수도시설관리사업소계백로(정림동 9-6 ~정림동 267-7번지)일원 노후관 개량공사D600mm, L=600mL=600m
6수도시설관리사업소구성동 464번지 일원 노후관 개량공사D600mm, L=440mL=440m
7수도시설관리사업소대학로(구성동23번지)일원 노후관 갱생공사D600mm, L=270mL=270m
8수도시설관리사업소대전천서로(인창교~보문교)일원 노후관 개량공사D500mm, L=320mL=320m
9동부사업소가양동 321-17번지 일원 노후관 개량공사D100-150mm, L=1,947mL=1,947m
부서사업명사업량추진실적
40서부사업소정림동 125번지 일원 노후관 개량공사D100mm, L=882mL=882m
41유성사업소가정동 Y21블록 노후관 개량공사D300, L=270mL=270m
42유성사업소궁동 Y38블록 노후관 개량공사D100~200mm, L=322mL=322m
43유성사업소전민동 Y12블록 노후관 개량공사D80~200mm, L=546mL=546m
44유성사업소신성동 Y19블록 노후관 개량공사D200, L=520mL=520m
45유성사업소Y24블록(원촌동 49-2번지 일원) 노후관 개량공사D100~300mm, L=680mL=680m ,
46유성사업소화암동 59-3번지 일원 외 1개소 노후관 개량공사D100mm, L=240mL=240m
47대덕사업소대화로 52번길 154 일원 노후관 개량공사D100~300mm, L=660mL=660m
48대덕사업소대화로 132번길 외 1개소 일원 노후관 개량공사D150~300mm, L=912mL=912m
49대덕사업소대화로 52번안길 일원 노후관 개량공사D150mm, L=1,660mL=1,660m