Overview

Dataset statistics

Number of variables7
Number of observations54
Missing cells87
Missing cells (%)23.0%
Duplicate rows4
Duplicate rows (%)7.4%
Total size in memory3.1 KiB
Average record size in memory58.4 B

Variable types

Text3
Unsupported4

Dataset

Description세종, 서울, 과천, 대전, 대구, 인천, 광주, 경남, 제주, 춘천, 고양, 충남, 경북 13개 정부청사의 입주기관별 위치, 연면적, 공무원 및 공무직 등 상시 근무자 현황 관련 데이터(청사별 한 시트로 구성)를 제공합니다.
Author행정안전부 정부청사관리본부
URLhttps://www.data.go.kr/data/3033692/fileData.do

Alerts

Dataset has 4 (7.4%) duplicate rowsDuplicates
정부세종청사 입주기관 현황 has 50 (92.6%) missing valuesMissing
Unnamed: 1 has 5 (9.3%) missing valuesMissing
Unnamed: 2 has 10 (18.5%) missing valuesMissing
Unnamed: 3 has 1 (1.9%) missing valuesMissing
Unnamed: 4 has 7 (13.0%) missing valuesMissing
Unnamed: 5 has 7 (13.0%) missing valuesMissing
Unnamed: 6 has 7 (13.0%) missing valuesMissing
Unnamed: 3 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

Reproduction

Analysis started2024-04-21 02:40:19.417570
Analysis finished2024-04-21 02:40:20.909878
Duration1.49 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

정부세종청사 입주기관 현황
Text

MISSING 

Distinct4
Distinct (%)100.0%
Missing50
Missing (%)92.6%
Memory size564.0 B
2024-04-21T11:40:20.998224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length7
Mean length8.75
Min length3

Characters and Unicode

Total characters35
Distinct characters19
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

Unique4 ?
Unique (%)100.0%

Sample

1st row기관명(소속기관)
2nd row37개(중앙22개, 소속15개)
3rd row1~17동
4th row중앙동
ValueCountFrequency (%)
기관명(소속기관 1
20.0%
37개(중앙22개 1
20.0%
소속15개 1
20.0%
1~17동 1
20.0%
중앙동 1
20.0%
2024-04-21T11:40:21.249000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
개 3
 
8.6%
1 3
 
8.6%
7 2
 
5.7%
2
 
5.7%
2 2
 
5.7%
앙 2
 
5.7%
중 2
 
5.7%
관 2
 
5.7%
기 2
 
5.7%
) 2
 
5.7%
Other values (9) 13
37.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 18
51.4%
Decimal Number 9
25.7%
Space Separator 2
 
5.7%
Close Punctuation 2
 
5.7%
Open Punctuation 2
 
5.7%
Other Punctuation 1
 
2.9%
Math Symbol 1
 
2.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
개 3
16.7%
앙 2
11.1%
중 2
11.1%
관 2
11.1%
기 2
11.1%
속 2
11.1%
소 2
11.1%
동 2
11.1%
명 1
 
5.6%
Decimal Number
ValueCountFrequency (%)
1 3
33.3%
7 2
22.2%
2 2
22.2%
3 1
 
11.1%
5 1
 
11.1%
Space Separator
ValueCountFrequency (%)
2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 18
51.4%
Common 17
48.6%

Most frequent character per script

Common
ValueCountFrequency (%)
1 3
17.6%
7 2
11.8%
2
11.8%
2 2
11.8%
) 2
11.8%
( 2
11.8%
3 1
 
5.9%
, 1
 
5.9%
5 1
 
5.9%
~ 1
 
5.9%
Hangul
ValueCountFrequency (%)
개 3
16.7%
앙 2
11.1%
중 2
11.1%
관 2
11.1%
기 2
11.1%
속 2
11.1%
소 2
11.1%
동 2
11.1%
명 1
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 18
51.4%
ASCII 17
48.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
개 3
16.7%
앙 2
11.1%
중 2
11.1%
관 2
11.1%
기 2
11.1%
속 2
11.1%
소 2
11.1%
동 2
11.1%
명 1
 
5.6%
ASCII
ValueCountFrequency (%)
1 3
17.6%
7 2
11.8%
2
11.8%
2 2
11.8%
) 2
11.8%
( 2
11.8%
3 1
 
5.9%
, 1
 
5.9%
5 1
 
5.9%
~ 1
 
5.9%

Unnamed: 1
Text

MISSING 

Distinct45
Distinct (%)91.8%
Missing5
Missing (%)9.3%
Memory size564.0 B
2024-04-21T11:40:21.448295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length16
Mean length7.1632653
Min length3

Characters and Unicode

Total characters351
Distinct characters121
Distinct categories8 ?
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 (%)83.7%

Sample

1st row34개(중앙20개, 소속13개)
2nd row국무조정실
3rd row(조세심판원)
4th row(국제개발협력본부)
5th row국무총리비서실
ValueCountFrequency (%)
소계 4
 
7.3%
공용면적 2
 
3.6%
배정면적 2
 
3.6%
기타 2
 
3.6%
지원시설 2
 
3.6%
정부청사관리본부)4동 1
 
1.8%
인사혁신처 1
 
1.8%
무역위원회 1
 
1.8%
전기위원회 1
 
1.8%
교육부 1
 
1.8%
Other values (38) 38
69.1%
2024-04-21T11:40:21.787636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
부 20
 
5.7%
) 18
 
5.1%
( 18
 
5.1%
원 15
 
4.3%
정 12
 
3.4%
10
 
2.8%
회 9
 
2.6%
소 9
 
2.6%
위 9
 
2.6%
국 8
 
2.3%
Other values (111) 223
63.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 288
82.1%
Close Punctuation 18
 
5.1%
Open Punctuation 18
 
5.1%
Space Separator 10
 
2.8%
Decimal Number 10
 
2.8%
Other Punctuation 3
 
0.9%
Uppercase Letter 3
 
0.9%
Other Number 1
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
부 20
 
6.9%
원 15
 
5.2%
정 12
 
4.2%
회 9
 
3.1%
소 9
 
3.1%
위 9
 
3.1%
국 8
 
2.8%
개 7
 
2.4%
사 6
 
2.1%
청 6
 
2.1%
Other values (97) 187
64.9%
Decimal Number
ValueCountFrequency (%)
2 3
30.0%
4 3
30.0%
3 2
20.0%
0 1
 
10.0%
1 1
 
10.0%
Uppercase Letter
ValueCountFrequency (%)
K 1
33.3%
T 1
33.3%
V 1
33.3%
Other Punctuation
ValueCountFrequency (%)
, 2
66.7%
/ 1
33.3%
Close Punctuation
ValueCountFrequency (%)
) 18
100.0%
Open Punctuation
ValueCountFrequency (%)
( 18
100.0%
Space Separator
ValueCountFrequency (%)
10
100.0%
Other Number
ValueCountFrequency (%)
② 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 288
82.1%
Common 60
 
17.1%
Latin 3
 
0.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
부 20
 
6.9%
원 15
 
5.2%
정 12
 
4.2%
회 9
 
3.1%
소 9
 
3.1%
위 9
 
3.1%
국 8
 
2.8%
개 7
 
2.4%
사 6
 
2.1%
청 6
 
2.1%
Other values (97) 187
64.9%
Common
ValueCountFrequency (%)
) 18
30.0%
( 18
30.0%
10
16.7%
2 3
 
5.0%
4 3
 
5.0%
3 2
 
3.3%
, 2
 
3.3%
/ 1
 
1.7%
0 1
 
1.7%
1 1
 
1.7%
Latin
ValueCountFrequency (%)
K 1
33.3%
T 1
33.3%
V 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 288
82.1%
ASCII 62
 
17.7%
Enclosed Alphanum 1
 
0.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
부 20
 
6.9%
원 15
 
5.2%
정 12
 
4.2%
회 9
 
3.1%
소 9
 
3.1%
위 9
 
3.1%
국 8
 
2.8%
개 7
 
2.4%
사 6
 
2.1%
청 6
 
2.1%
Other values (97) 187
64.9%
ASCII
ValueCountFrequency (%)
) 18
29.0%
( 18
29.0%
10
16.1%
2 3
 
4.8%
4 3
 
4.8%
3 2
 
3.2%
, 2
 
3.2%
/ 1
 
1.6%
K 1
 
1.6%
T 1
 
1.6%
Other values (3) 3
 
4.8%
Enclosed Alphanum
ValueCountFrequency (%)
② 1
100.0%

Unnamed: 2
Text

MISSING 

Distinct41
Distinct (%)93.2%
Missing10
Missing (%)18.5%
Memory size564.0 B
2024-04-21T11:40:21.999331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length27
Median length18
Mean length11.863636
Min length2

Characters and Unicode

Total characters522
Distinct characters54
Distinct categories8 ?
Distinct scripts4 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique38 ?
Unique (%)86.4%

Sample

1st row위치
2nd row1동(1,2,3), 2동(4), 8동(7)
3rd row4동(3)
4th row1동(4)
5th row1동(4)
ValueCountFrequency (%)
12동(4 3
 
4.3%
4동(3 3
 
4.3%
공용회의실 2
 
2.9%
식당 2
 
2.9%
등 2
 
2.9%
11동(4 2
 
2.9%
1동(4 2
 
2.9%
민원동(4 2
 
2.9%
강당 2
 
2.9%
13동(5 2
 
2.9%
Other values (47) 48
68.6%
2024-04-21T11:40:22.323595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
) 59
11.3%
( 59
11.3%
동 58
11.1%
1 54
10.3%
, 50
9.6%
4 32
 
6.1%
3 31
 
5.9%
26
 
5.0%
~ 21
 
4.0%
2 18
 
3.4%
Other values (44) 114
21.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 184
35.2%
Other Letter 115
22.0%
Close Punctuation 59
 
11.3%
Open Punctuation 59
 
11.3%
Other Punctuation 50
 
9.6%
Space Separator 26
 
5.0%
Math Symbol 21
 
4.0%
Uppercase Letter 8
 
1.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 58
50.4%
당 4
 
3.5%
민 4
 
3.5%
원 4
 
3.5%
무 3
 
2.6%
의 3
 
2.6%
업 3
 
2.6%
공 2
 
1.7%
용 2
 
1.7%
등 2
 
1.7%
Other values (26) 30
26.1%
Decimal Number
ValueCountFrequency (%)
1 54
29.3%
4 32
17.4%
3 31
16.8%
2 18
 
9.8%
7 16
 
8.7%
5 13
 
7.1%
6 12
 
6.5%
8 4
 
2.2%
9 2
 
1.1%
0 2
 
1.1%
Uppercase Letter
ValueCountFrequency (%)
B 5
62.5%
C 2
 
25.0%
S 1
 
12.5%
Close Punctuation
ValueCountFrequency (%)
) 59
100.0%
Open Punctuation
ValueCountFrequency (%)
( 59
100.0%
Other Punctuation
ValueCountFrequency (%)
, 50
100.0%
Space Separator
ValueCountFrequency (%)
26
100.0%
Math Symbol
ValueCountFrequency (%)
~ 21
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 399
76.4%
Hangul 114
 
21.8%
Latin 8
 
1.5%
Han 1
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 58
50.9%
당 4
 
3.5%
민 4
 
3.5%
원 4
 
3.5%
무 3
 
2.6%
의 3
 
2.6%
업 3
 
2.6%
공 2
 
1.8%
용 2
 
1.8%
등 2
 
1.8%
Other values (25) 29
25.4%
Common
ValueCountFrequency (%)
) 59
14.8%
( 59
14.8%
1 54
13.5%
, 50
12.5%
4 32
8.0%
3 31
7.8%
26
6.5%
~ 21
 
5.3%
2 18
 
4.5%
7 16
 
4.0%
Other values (5) 33
8.3%
Latin
ValueCountFrequency (%)
B 5
62.5%
C 2
 
25.0%
S 1
 
12.5%
Han
ValueCountFrequency (%)
舊 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 407
78.0%
Hangul 114
 
21.8%
CJK 1
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
) 59
14.5%
( 59
14.5%
1 54
13.3%
, 50
12.3%
4 32
7.9%
3 31
7.6%
26
6.4%
~ 21
 
5.2%
2 18
 
4.4%
7 16
 
3.9%
Other values (8) 41
10.1%
Hangul
ValueCountFrequency (%)
동 58
50.9%
당 4
 
3.5%
민 4
 
3.5%
원 4
 
3.5%
무 3
 
2.6%
의 3
 
2.6%
업 3
 
2.6%
공 2
 
1.8%
용 2
 
1.8%
등 2
 
1.8%
Other values (25) 29
25.4%
CJK
ValueCountFrequency (%)
舊 1
100.0%

Unnamed: 3
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1
Missing (%)1.9%
Memory size564.0 B

Unnamed: 4
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing7
Missing (%)13.0%
Memory size564.0 B

Unnamed: 5
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing7
Missing (%)13.0%
Memory size564.0 B

Unnamed: 6
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing7
Missing (%)13.0%
Memory size564.0 B

Correlations

2024-04-21T11:40:22.409002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
정부세종청사 입주기관 현황Unnamed: 1Unnamed: 2
정부세종청사 입주기관 현황1.0000.000NaN
Unnamed: 10.0001.0001.000
Unnamed: 2NaN1.0001.000

Missing values

2024-04-21T11:40:20.588998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-21T11:40:20.716172image/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.
2024-04-21T11:40:20.832865image/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

정부세종청사 입주기관 현황Unnamed: 1Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 6
0기관명(소속기관)<NA>위치면적정원정원외현원
1<NA><NA><NA>NaN(공무원)(파견,공무직등)(실 근무인원)
237개(중앙22개, 소속15개)<NA><NA>84885715262400519464
31~17동34개(중앙20개, 소속13개)<NA>71436712367331315929
4<NA>국무조정실1동(1,2,3), 2동(4), 8동(7)8244254402695
5<NA>(조세심판원)4동(3)285112112131
6<NA>(국제개발협력본부)1동(4)462281237
7<NA>국무총리비서실1동(4)30959740104
8<NA>공정거래위원회2동(1,3,4), 12동(4)926848955538
9<NA>과학기술정보통신부4동(3~6)1525482655889
정부세종청사 입주기관 현황Unnamed: 1Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 6
44<NA>기타 소계<NA>494663---
45중앙동②4개(중앙2개, 소속2개)<NA>13449028956923535
46<NA>기획재정부업무동(4~9), 민원동(4)1895010831301188
47<NA>(복권위원회)민원동(4)29726-23
48<NA>행정안전부업무동(1~4,10~14), 민원동(2,3)2299016861441829
49<NA>(정부청사관리본부)중앙동업무동(2), 민원동(2)198100418495
50<NA>배정면적 소계<NA>4243528956923535
51<NA>지원시설공용회의실, 강당, 식당 등11123NaNNaNNaN
52<NA>공용면적<NA>80932NaNNaNNaN
53<NA>기타 소계<NA>92055---

Duplicate rows

Most frequently occurring

정부세종청사 입주기관 현황Unnamed: 1Unnamed: 2# duplicates
0<NA>공용면적<NA>2
1<NA>기타 소계<NA>2
2<NA>배정면적 소계<NA>2
3<NA>지원시설공용회의실, 강당, 식당 등2