Overview

Dataset statistics

Number of variables7
Number of observations46
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.7 KiB
Average record size in memory60.9 B

Variable types

Numeric2
Categorical2
Text3

Dataset

Description행정,공공기관 정보시스템을 구축 운영관리하고 있는 연면적 기준 500 평방미터 이상의 데이터센터에 대한 데이터로 기관명, 데이터센터명, 설립연도, 운영형태, 위치 정보 등을 포함하고 있습니다.
Author행정안전부
URLhttps://www.data.go.kr/data/15080581/fileData.do

Alerts

번호 is highly overall correlated with 상위기관High correlation
상위기관 is highly overall correlated with 번호High correlation
번호 has unique valuesUnique

Reproduction

Analysis started2024-03-16 04:14:25.015549
Analysis finished2024-03-16 04:14:28.025797
Duration3.01 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

번호
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct46
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.5
Minimum1
Maximum46
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size546.0 B
2024-03-16T13:14:28.152427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3.25
Q112.25
median23.5
Q334.75
95-th percentile43.75
Maximum46
Range45
Interquartile range (IQR)22.5

Descriptive statistics

Standard deviation13.422618
Coefficient of variation (CV)0.57117522
Kurtosis-1.2
Mean23.5
Median Absolute Deviation (MAD)11.5
Skewness0
Sum1081
Variance180.16667
MonotonicityStrictly increasing
2024-03-16T13:14:28.371779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
1 1
 
2.2%
36 1
 
2.2%
27 1
 
2.2%
28 1
 
2.2%
29 1
 
2.2%
30 1
 
2.2%
31 1
 
2.2%
32 1
 
2.2%
33 1
 
2.2%
34 1
 
2.2%
Other values (36) 36
78.3%
ValueCountFrequency (%)
1 1
2.2%
2 1
2.2%
3 1
2.2%
4 1
2.2%
5 1
2.2%
6 1
2.2%
7 1
2.2%
8 1
2.2%
9 1
2.2%
10 1
2.2%
ValueCountFrequency (%)
46 1
2.2%
45 1
2.2%
44 1
2.2%
43 1
2.2%
42 1
2.2%
41 1
2.2%
40 1
2.2%
39 1
2.2%
38 1
2.2%
37 1
2.2%

상위기관
Categorical

HIGH CORRELATION 

Distinct20
Distinct (%)43.5%
Missing0
Missing (%)0.0%
Memory size500.0 B
국토교통부
5 
금융위원회
5 
보건복지부
5 
고용노동부
3 
산업통상자원부
3 
Other values (15)
25 

Length

Max length9
Median length5
Mean length5.0869565
Min length3

Unique

Unique7 ?
Unique (%)15.2%

Sample

1st row고용노동부
2nd row고용노동부
3rd row고용노동부
4th row과학기술정보통신부
5th row과학기술정보통신부

Common Values

ValueCountFrequency (%)
국토교통부 5
 
10.9%
금융위원회 5
 
10.9%
보건복지부 5
 
10.9%
고용노동부 3
 
6.5%
산업통상자원부 3
 
6.5%
행정안전부 3
 
6.5%
서울특별시 3
 
6.5%
인천광역시 2
 
4.3%
과학기술정보통신부 2
 
4.3%
대구광역시 2
 
4.3%
Other values (10) 13
28.3%

Length

2024-03-16T13:14:28.616433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
국토교통부 5
 
10.9%
금융위원회 5
 
10.9%
보건복지부 5
 
10.9%
고용노동부 3
 
6.5%
산업통상자원부 3
 
6.5%
행정안전부 3
 
6.5%
서울특별시 3
 
6.5%
충청남도 2
 
4.3%
경기도 2
 
4.3%
법무부 2
 
4.3%
Other values (10) 13
28.3%

기관명
Text

Distinct42
Distinct (%)91.3%
Missing0
Missing (%)0.0%
Memory size500.0 B
2024-03-16T13:14:29.011070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length10
Mean length7.2391304
Min length3

Characters and Unicode

Total characters333
Distinct characters106
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique38 ?
Unique (%)82.6%

Sample

1st row근로복지공단
2nd row노사발전재단
3rd row한국고용정보원
4th row한국생명공학연구원
5th row기초과학연구원
ValueCountFrequency (%)
건강보험심사평가원 2
 
4.3%
국가정보자원관리원 2
 
4.3%
서울특별시 2
 
4.3%
인천공항출입국·외국인청 2
 
4.3%
마포구시설관리공단 1
 
2.2%
인천광역시 1
 
2.2%
근로복지공단 1
 
2.2%
한국수력원자력(주 1
 
2.2%
한국전력공사 1
 
2.2%
한국여성인권진흥원 1
 
2.2%
Other values (32) 32
69.6%
2024-03-16T13:14:29.602694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
국 25
 
7.5%
공 18
 
5.4%
한 16
 
4.8%
원 16
 
4.8%
사 12
 
3.6%
시 10
 
3.0%
보 9
 
2.7%
단 9
 
2.7%
인 8
 
2.4%
관 7
 
2.1%
Other values (96) 203
61.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 319
95.8%
Close Punctuation 4
 
1.2%
Open Punctuation 4
 
1.2%
Uppercase Letter 4
 
1.2%
Other Punctuation 2
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
국 25
 
7.8%
공 18
 
5.6%
한 16
 
5.0%
원 16
 
5.0%
사 12
 
3.8%
시 10
 
3.1%
보 9
 
2.8%
단 9
 
2.8%
인 8
 
2.5%
관 7
 
2.2%
Other values (89) 189
59.2%
Uppercase Letter
ValueCountFrequency (%)
A 1
25.0%
P 1
25.0%
E 1
25.0%
C 1
25.0%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%
Other Punctuation
ValueCountFrequency (%)
· 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 319
95.8%
Common 10
 
3.0%
Latin 4
 
1.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
국 25
 
7.8%
공 18
 
5.6%
한 16
 
5.0%
원 16
 
5.0%
사 12
 
3.8%
시 10
 
3.1%
보 9
 
2.8%
단 9
 
2.8%
인 8
 
2.5%
관 7
 
2.2%
Other values (89) 189
59.2%
Latin
ValueCountFrequency (%)
A 1
25.0%
P 1
25.0%
E 1
25.0%
C 1
25.0%
Common
ValueCountFrequency (%)
) 4
40.0%
( 4
40.0%
· 2
20.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 319
95.8%
ASCII 12
 
3.6%
None 2
 
0.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
국 25
 
7.8%
공 18
 
5.6%
한 16
 
5.0%
원 16
 
5.0%
사 12
 
3.8%
시 10
 
3.1%
보 9
 
2.8%
단 9
 
2.8%
인 8
 
2.5%
관 7
 
2.2%
Other values (89) 189
59.2%
ASCII
ValueCountFrequency (%)
) 4
33.3%
( 4
33.3%
A 1
 
8.3%
P 1
 
8.3%
E 1
 
8.3%
C 1
 
8.3%
None
ValueCountFrequency (%)
· 2
100.0%

데이터센터명
Text

Distinct43
Distinct (%)93.5%
Missing0
Missing (%)0.0%
Memory size500.0 B
2024-03-16T13:14:30.022826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length15
Mean length11.304348
Min length5

Characters and Unicode

Total characters520
Distinct characters134
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique41 ?
Unique (%)89.1%

Sample

1st row근로복지공단 정보화본부 전산실(서울)
2nd row노사발전재단 세종IDC
3rd row한국고용정보원 전산실
4th row통합전산센터
5th row기초과학연구원 본원 데이터센터
ValueCountFrequency (%)
전산실 6
 
7.1%
데이터센터 4
 
4.8%
통합전산센터 3
 
3.6%
지역정보통합센터 2
 
2.4%
정보시스템실 2
 
2.4%
정보통신실 2
 
2.4%
국가정보자원관리원 2
 
2.4%
idc센터 2
 
2.4%
idc 2
 
2.4%
건강보험심사평가원 2
 
2.4%
Other values (57) 57
67.9%
2024-03-16T13:14:30.591075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
터 41
 
7.9%
38
 
7.3%
센 33
 
6.3%
보 19
 
3.7%
전 18
 
3.5%
산 14
 
2.7%
정 14
 
2.7%
실 13
 
2.5%
C 13
 
2.5%
I 13
 
2.5%
Other values (124) 304
58.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 409
78.7%
Uppercase Letter 52
 
10.0%
Space Separator 38
 
7.3%
Open Punctuation 6
 
1.2%
Close Punctuation 6
 
1.2%
Decimal Number 6
 
1.2%
Other Punctuation 1
 
0.2%
Other Symbol 1
 
0.2%
Dash Punctuation 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
터 41
 
10.0%
센 33
 
8.1%
보 19
 
4.6%
전 18
 
4.4%
산 14
 
3.4%
정 14
 
3.4%
실 13
 
3.2%
원 12
 
2.9%
통 11
 
2.7%
이 9
 
2.2%
Other values (106) 225
55.0%
Uppercase Letter
ValueCountFrequency (%)
C 13
25.0%
I 13
25.0%
T 10
19.2%
D 7
13.5%
K 3
 
5.8%
S 2
 
3.8%
B 1
 
1.9%
E 1
 
1.9%
P 1
 
1.9%
A 1
 
1.9%
Decimal Number
ValueCountFrequency (%)
1 4
66.7%
2 2
33.3%
Space Separator
ValueCountFrequency (%)
38
100.0%
Open Punctuation
ValueCountFrequency (%)
( 6
100.0%
Close Punctuation
ValueCountFrequency (%)
) 6
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 1
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 410
78.8%
Common 58
 
11.2%
Latin 52
 
10.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
터 41
 
10.0%
센 33
 
8.0%
보 19
 
4.6%
전 18
 
4.4%
산 14
 
3.4%
정 14
 
3.4%
실 13
 
3.2%
원 12
 
2.9%
통 11
 
2.7%
이 9
 
2.2%
Other values (107) 226
55.1%
Latin
ValueCountFrequency (%)
C 13
25.0%
I 13
25.0%
T 10
19.2%
D 7
13.5%
K 3
 
5.8%
S 2
 
3.8%
B 1
 
1.9%
E 1
 
1.9%
P 1
 
1.9%
A 1
 
1.9%
Common
ValueCountFrequency (%)
38
65.5%
( 6
 
10.3%
) 6
 
10.3%
1 4
 
6.9%
2 2
 
3.4%
, 1
 
1.7%
- 1
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 409
78.7%
ASCII 110
 
21.2%
None 1
 
0.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
터 41
 
10.0%
센 33
 
8.1%
보 19
 
4.6%
전 18
 
4.4%
산 14
 
3.4%
정 14
 
3.4%
실 13
 
3.2%
원 12
 
2.9%
통 11
 
2.7%
이 9
 
2.2%
Other values (106) 225
55.0%
ASCII
ValueCountFrequency (%)
38
34.5%
C 13
 
11.8%
I 13
 
11.8%
T 10
 
9.1%
D 7
 
6.4%
( 6
 
5.5%
) 6
 
5.5%
1 4
 
3.6%
K 3
 
2.7%
S 2
 
1.8%
Other values (7) 8
 
7.3%
None
ValueCountFrequency (%)
㈜ 1
100.0%

설립연도
Real number (ℝ)

Distinct22
Distinct (%)47.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2010.0435
Minimum1989
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size546.0 B
2024-03-16T13:14:30.798850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1989
5-th percentile1991.25
Q12005
median2014
Q32016
95-th percentile2020
Maximum2022
Range33
Interquartile range (IQR)11

Descriptive statistics

Standard deviation8.7151376
Coefficient of variation (CV)0.0043357956
Kurtosis0.096183741
Mean2010.0435
Median Absolute Deviation (MAD)5
Skewness-0.9092865
Sum92462
Variance75.953623
MonotonicityNot monotonic
2024-03-16T13:14:31.034320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
2005 5
 
10.9%
2016 5
 
10.9%
2014 4
 
8.7%
2017 4
 
8.7%
2015 4
 
8.7%
2001 3
 
6.5%
2020 2
 
4.3%
2019 2
 
4.3%
2007 2
 
4.3%
2012 2
 
4.3%
Other values (12) 13
28.3%
ValueCountFrequency (%)
1989 2
 
4.3%
1990 1
 
2.2%
1995 1
 
2.2%
1998 1
 
2.2%
2000 1
 
2.2%
2001 3
6.5%
2004 1
 
2.2%
2005 5
10.9%
2007 2
 
4.3%
2008 1
 
2.2%
ValueCountFrequency (%)
2022 1
 
2.2%
2021 1
 
2.2%
2020 2
 
4.3%
2019 2
 
4.3%
2018 1
 
2.2%
2017 4
8.7%
2016 5
10.9%
2015 4
8.7%
2014 4
8.7%
2012 2
 
4.3%

운영형태
Categorical

Distinct4
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Memory size500.0 B
혼합운영
30 
자체운영
9 
위탁운영
6 
자가운영
 
1

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique1 ?
Unique (%)2.2%

Sample

1st row혼합운영
2nd row혼합운영
3rd row혼합운영
4th row자체운영
5th row혼합운영

Common Values

ValueCountFrequency (%)
혼합운영 30
65.2%
자체운영 9
 
19.6%
위탁운영 6
 
13.0%
자가운영 1
 
2.2%

Length

2024-03-16T13:14:31.290493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-16T13:14:31.494006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
혼합운영 30
65.2%
자체운영 9
 
19.6%
위탁운영 6
 
13.0%
자가운영 1
 
2.2%

위치
Text

Distinct32
Distinct (%)69.6%
Missing0
Missing (%)0.0%
Memory size500.0 B
2024-03-16T13:14:31.785228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length10
Mean length8.1521739
Min length6

Characters and Unicode

Total characters375
Distinct characters56
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

Unique25 ?
Unique (%)54.3%

Sample

1st row서울특별시 영등포구
2nd row경기도 용인시
3rd row충청북도 음성군
4th row대전광역시 유성구
5th row대전광역시 유성구
ValueCountFrequency (%)
경기도 9
 
9.7%
서울특별시 9
 
9.7%
대전광역시 6
 
6.5%
유성구 5
 
5.4%
인천광역시 4
 
4.3%
마포구 4
 
4.3%
강원도 3
 
3.2%
원주시 3
 
3.2%
중구 3
 
3.2%
부산광역시 3
 
3.2%
Other values (36) 44
47.3%
2024-03-16T13:14:32.329658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
47
 
12.5%
시 43
 
11.5%
구 29
 
7.7%
도 18
 
4.8%
광 18
 
4.8%
역 16
 
4.3%
경 13
 
3.5%
서 12
 
3.2%
성 11
 
2.9%
남 10
 
2.7%
Other values (46) 158
42.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 328
87.5%
Space Separator 47
 
12.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 43
 
13.1%
구 29
 
8.8%
도 18
 
5.5%
광 18
 
5.5%
역 16
 
4.9%
경 13
 
4.0%
서 12
 
3.7%
성 11
 
3.4%
남 10
 
3.0%
대 9
 
2.7%
Other values (45) 149
45.4%
Space Separator
ValueCountFrequency (%)
47
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 328
87.5%
Common 47
 
12.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 43
 
13.1%
구 29
 
8.8%
도 18
 
5.5%
광 18
 
5.5%
역 16
 
4.9%
경 13
 
4.0%
서 12
 
3.7%
성 11
 
3.4%
남 10
 
3.0%
대 9
 
2.7%
Other values (45) 149
45.4%
Common
ValueCountFrequency (%)
47
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 328
87.5%
ASCII 47
 
12.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
47
100.0%
Hangul
ValueCountFrequency (%)
시 43
 
13.1%
구 29
 
8.8%
도 18
 
5.5%
광 18
 
5.5%
역 16
 
4.9%
경 13
 
4.0%
서 12
 
3.7%
성 11
 
3.4%
남 10
 
3.0%
대 9
 
2.7%
Other values (45) 149
45.4%

Interactions

2024-03-16T13:14:27.104151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:14:26.425986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:14:27.313247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:14:26.801907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-16T13:14:32.504483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
번호상위기관기관명데이터센터명설립연도운영형태위치
번호1.0000.9940.9840.7190.4130.0000.854
상위기관0.9941.0001.0000.8110.7460.0000.845
기관명0.9841.0001.0000.9200.9060.9760.994
데이터센터명0.7190.8110.9201.0000.9390.9460.971
설립연도0.4130.7460.9060.9391.0000.3240.905
운영형태0.0000.0000.9760.9460.3241.0000.985
위치0.8540.8450.9940.9710.9050.9851.000
2024-03-16T13:14:32.671449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
상위기관운영형태
상위기관1.0000.000
운영형태0.0001.000
2024-03-16T13:14:32.810632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
번호설립연도상위기관운영형태
번호1.000-0.2170.7890.000
설립연도-0.2171.0000.2680.187
상위기관0.7890.2681.0000.000
운영형태0.0000.1870.0001.000

Missing values

2024-03-16T13:14:27.699550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-16T13:14:27.941468image/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

번호상위기관기관명데이터센터명설립연도운영형태위치
01고용노동부근로복지공단근로복지공단 정보화본부 전산실(서울)1995혼합운영서울특별시 영등포구
12고용노동부노사발전재단노사발전재단 세종IDC2015혼합운영경기도 용인시
23고용노동부한국고용정보원한국고용정보원 전산실2014혼합운영충청북도 음성군
34과학기술정보통신부한국생명공학연구원통합전산센터2009자체운영대전광역시 유성구
45과학기술정보통신부기초과학연구원기초과학연구원 본원 데이터센터2017혼합운영대전광역시 유성구
56국토교통부한국도로공사ICT운영센터(동탄)2014자가운영경기도 화성시
67국토교통부한국철도공사통합전산센터2005혼합운영대전광역시 동구
78국토교통부인천국제공항공사서버실-12017위탁운영인천광역시 중구
89국토교통부주택관리공단(주)주택관리공단 서버실2020혼합운영경상남도 진주시
910국토교통부코레일관광개발(주)SK IDC 서초1센터2005혼합운영서울특별시 서초구
번호상위기관기관명데이터센터명설립연도운영형태위치
3637인천광역시인천문화재단㈜퍼플스톤즈 IDC 보라매센터2001위탁운영서울특별시 동작구
3738부산광역시부산광역시부산데이터센터(BDC)1998혼합운영부산광역시 연제구
3839대전광역시대전광역시지역정보통합센터2014혼합운영대전광역시 유성구
3940대구광역시대구광역시통합전산센터1989혼합운영대구광역시 수성구
4041대구광역시대구달성군시설관리공단KT IDC센터2001위탁운영대구광역시 남구
4142경기도경기도경기도청 전산실1989자체운영경기도 수원시
4243경기도수원시도시안전통합센터 정보통신실2012혼합운영경기도 수원시
4344충청남도충청남도충청남도 통합정보센터2012혼합운영충남 홍성군
4445충청남도천안시천안아산 도시통합운영센터2018자체운영충남 천안시
4546경상북도경상북도종합데이터센터, 정보통신망운영센터2016혼합운영경상북도 안동시