Overview

Dataset statistics

Number of variables10
Number of observations29
Missing cells95
Missing cells (%)32.8%
Duplicate rows4
Duplicate rows (%)13.8%
Total size in memory2.4 KiB
Average record size in memory84.6 B

Variable types

Text2
Unsupported8

Dataset

Description세종, 서울, 과천, 대전 4개 정부청사의 일반, 장애인, 임산부, 경차, 전기차, 버스, 유아동승, 방문객 등 주차장 현황 데이터를 제공합니다.
Author행정안전부 정부청사관리본부
URLhttps://www.data.go.kr/data/15081543/fileData.do

Alerts

Dataset has 4 (13.8%) duplicate rowsDuplicates
주차장현황 has 6 (20.7%) missing valuesMissing
Unnamed: 1 has 25 (86.2%) missing valuesMissing
Unnamed: 2 has 8 (27.6%) missing valuesMissing
Unnamed: 3 has 8 (27.6%) missing valuesMissing
Unnamed: 4 has 8 (27.6%) missing valuesMissing
Unnamed: 5 has 8 (27.6%) missing valuesMissing
Unnamed: 6 has 8 (27.6%) missing valuesMissing
Unnamed: 7 has 8 (27.6%) missing valuesMissing
Unnamed: 8 has 8 (27.6%) missing valuesMissing
Unnamed: 9 has 8 (27.6%) missing valuesMissing
Unnamed: 2 is an unsupported type, check if it needs cleaning or further analysisUnsupported
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
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
Unnamed: 9 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2024-04-21 01:55:10.993108
Analysis finished2024-04-21 01:55:12.467622
Duration1.47 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

주차장현황
Text

MISSING 

Distinct17
Distinct (%)73.9%
Missing6
Missing (%)20.7%
Memory size364.0 B
2024-04-21T10:55:12.585282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length4
Mean length3.6086957
Min length1

Characters and Unicode

Total characters83
Distinct characters41
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

Unique14 ?
Unique (%)60.9%

Sample

1st row세종청사
2nd row구 분
3rd row계
4th row내 부
5th row외 부
ValueCountFrequency (%)
계 4
 
10.8%
부 4
 
10.8%
구분 3
 
8.1%
외 2
 
5.4%
관 2
 
5.4%
내 2
 
5.4%
지 2
 
5.4%
창성동 2
 
5.4%
별관은 1
 
2.7%
세종청사 1
 
2.7%
Other values (14) 14
37.8%
2024-04-21T10:55:12.878881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
13
 
15.7%
사 5
 
6.0%
계 4
 
4.8%
분 4
 
4.8%
부 4
 
4.8%
청 4
 
4.8%
구 4
 
4.8%
관 3
 
3.6%
지 2
 
2.4%
* 2
 
2.4%
Other values (31) 38
45.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 66
79.5%
Space Separator 13
 
15.7%
Other Punctuation 2
 
2.4%
Control 2
 
2.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
사 5
 
7.6%
계 4
 
6.1%
분 4
 
6.1%
부 4
 
6.1%
청 4
 
6.1%
구 4
 
6.1%
관 3
 
4.5%
지 2
 
3.0%
창 2
 
3.0%
성 2
 
3.0%
Other values (28) 32
48.5%
Space Separator
ValueCountFrequency (%)
13
100.0%
Other Punctuation
ValueCountFrequency (%)
* 2
100.0%
Control
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 66
79.5%
Common 17
 
20.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
사 5
 
7.6%
계 4
 
6.1%
분 4
 
6.1%
부 4
 
6.1%
청 4
 
6.1%
구 4
 
6.1%
관 3
 
4.5%
지 2
 
3.0%
창 2
 
3.0%
성 2
 
3.0%
Other values (28) 32
48.5%
Common
ValueCountFrequency (%)
13
76.5%
* 2
 
11.8%
2
 
11.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 66
79.5%
ASCII 17
 
20.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
13
76.5%
* 2
 
11.8%
2
 
11.8%
Hangul
ValueCountFrequency (%)
사 5
 
7.6%
계 4
 
6.1%
분 4
 
6.1%
부 4
 
6.1%
청 4
 
6.1%
구 4
 
6.1%
관 3
 
4.5%
지 2
 
3.0%
창 2
 
3.0%
성 2
 
3.0%
Other values (28) 32
48.5%

Unnamed: 1
Text

MISSING 

Distinct4
Distinct (%)100.0%
Missing25
Missing (%)86.2%
Memory size364.0 B
2024-04-21T10:55:13.026899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5.5
Mean length4.25
Min length3

Characters and Unicode

Total characters17
Distinct characters11
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

Unique4 ?
Unique (%)100.0%

Sample

1st row소 계
2nd row1~15동
3rd row16~17동
4th row중앙동
ValueCountFrequency (%)
소 1
20.0%
계 1
20.0%
1~15동 1
20.0%
16~17동 1
20.0%
중앙동 1
20.0%
2024-04-21T10:55:13.294354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 4
23.5%
동 3
17.6%
~ 2
11.8%
소 1
 
5.9%
1
 
5.9%
계 1
 
5.9%
5 1
 
5.9%
6 1
 
5.9%
7 1
 
5.9%
중 1
 
5.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7
41.2%
Other Letter 7
41.2%
Math Symbol 2
 
11.8%
Space Separator 1
 
5.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 3
42.9%
소 1
 
14.3%
계 1
 
14.3%
중 1
 
14.3%
앙 1
 
14.3%
Decimal Number
ValueCountFrequency (%)
1 4
57.1%
5 1
 
14.3%
6 1
 
14.3%
7 1
 
14.3%
Math Symbol
ValueCountFrequency (%)
~ 2
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 10
58.8%
Hangul 7
41.2%

Most frequent character per script

Common
ValueCountFrequency (%)
1 4
40.0%
~ 2
20.0%
1
 
10.0%
5 1
 
10.0%
6 1
 
10.0%
7 1
 
10.0%
Hangul
ValueCountFrequency (%)
동 3
42.9%
소 1
 
14.3%
계 1
 
14.3%
중 1
 
14.3%
앙 1
 
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10
58.8%
Hangul 7
41.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 4
40.0%
~ 2
20.0%
1
 
10.0%
5 1
 
10.0%
6 1
 
10.0%
7 1
 
10.0%
Hangul
ValueCountFrequency (%)
동 3
42.9%
소 1
 
14.3%
계 1
 
14.3%
중 1
 
14.3%
앙 1
 
14.3%

Unnamed: 2
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing8
Missing (%)27.6%
Memory size364.0 B

Unnamed: 3
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing8
Missing (%)27.6%
Memory size364.0 B

Unnamed: 4
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing8
Missing (%)27.6%
Memory size364.0 B

Unnamed: 5
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing8
Missing (%)27.6%
Memory size364.0 B

Unnamed: 6
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing8
Missing (%)27.6%
Memory size364.0 B

Unnamed: 7
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing8
Missing (%)27.6%
Memory size364.0 B

Unnamed: 8
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing8
Missing (%)27.6%
Memory size364.0 B

Unnamed: 9
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing8
Missing (%)27.6%
Memory size364.0 B

Correlations

2024-04-21T10:55:13.375645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
주차장현황Unnamed: 1
주차장현황1.000NaN
Unnamed: 1NaN1.000

Missing values

2024-04-21T10:55:12.054590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-21T10:55:12.222280image/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.
2024-04-21T10:55:12.357772image/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

주차장현황Unnamed: 1Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 6Unnamed: 7Unnamed: 8Unnamed: 9
0세종청사<NA>NaNNaNNaNNaNNaNNaNNaNNaN
1구 분<NA>계일반장애인임산부경차전기차버스방문객
2계<NA>10094921325217367785162
3내 부소 계4818409718915300505162
4<NA>1~15동300525811231318425574
5<NA>16~17동55745925-376-30
6<NA>중앙동125610574127919-58
7외 부<NA>3606356717-22---
8복합편의<NA>167015494624528--
9<NA><NA>NaNNaNNaNNaNNaNNaNNaNNaN
주차장현황Unnamed: 1Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 6Unnamed: 7Unnamed: 8Unnamed: 9
19구분<NA>계일반장애인임산부경차전기차버스국가유공자
20계<NA>2144186856155514802
21내 부<NA>1377110156155514802
22외 부<NA>767767------
23<NA><NA>NaNNaNNaNNaNNaNNaNNaNNaN
24대전청사<NA>NaNNaNNaNNaNNaNNaNNaNNaN
25구분<NA>계일반장애인임산부경차전기차버스방문객
26계<NA>2643214980101815918146
27지 상<NA>195214895961815318146
28지 하<NA>691660214-6--

Duplicate rows

Most frequently occurring

주차장현황Unnamed: 1# duplicates
0계<NA>4
1구분<NA>3
3<NA><NA>3
2외 부<NA>2