Overview

Dataset statistics

Number of variables4
Number of observations82
Missing cells156
Missing cells (%)47.6%
Duplicate rows7
Duplicate rows (%)8.5%
Total size in memory2.8 KiB
Average record size in memory34.6 B

Variable types

Unsupported1
Text3

Dataset

Description※ 귀하께서 요청하신 대전광역시 소방서 및 안전센터의 위치와 관련한 자료는 붙임과 같습니다. 현재 요청하신 자료는 대전광역시 행정기구 및 정원조례(시행규칙)으로 규정되어 있는 사항으로 법제처의 국가법령정보센터 내 자치법규를 검색하시면 됩니다. 이 조례, 규칙은 행정안전부에서 제공하고 있습니다.
Author대전광역시
URLhttps://www.data.go.kr/data/15090017/fileData.do

Alerts

Dataset has 7 (8.5%) duplicate rowsDuplicates
Unnamed: 0 has 82 (100.0%) missing valuesMissing
소방서 명 칭 has 51 (62.2%) missing valuesMissing
위 치 has 10 (12.2%) missing valuesMissing
Unnamed: 3 has 13 (15.9%) missing valuesMissing
Unnamed: 0 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2023-12-12 00:23:28.061747
Analysis finished2023-12-12 00:23:28.452891
Duration0.39 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Unnamed: 0
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing82
Missing (%)100.0%
Memory size870.0 B

소방서 명 칭
Text

MISSING 

Distinct17
Distinct (%)54.8%
Missing51
Missing (%)62.2%
Memory size788.0 B
2023-12-12T09:23:28.548103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length1
Mean length2.0322581
Min length1

Characters and Unicode

Total characters63
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13 ?
Unique (%)41.9%

Sample

1st row동 부 소방서
2nd row둔 산 소방서
3rd row대 덕 소방서
4th row유 성 소방서
5th row서 부 소방서
ValueCountFrequency (%)
서 7
17.1%
소방서 6
14.6%
소 5
12.2%
방 5
12.2%
부 4
9.8%
동 2
 
4.9%
둔 2
 
4.9%
산 2
 
4.9%
대 2
 
4.9%
덕 2
 
4.9%
Other values (2) 4
9.8%
2023-12-12T09:23:28.815150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
서 13
20.6%
소 11
17.5%
방 11
17.5%
5
 
7.9%
5
 
7.9%
부 4
 
6.3%
동 2
 
3.2%
둔 2
 
3.2%
산 2
 
3.2%
대 2
 
3.2%
Other values (3) 6
9.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 53
84.1%
Space Separator 5
 
7.9%
Control 5
 
7.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
서 13
24.5%
소 11
20.8%
방 11
20.8%
부 4
 
7.5%
동 2
 
3.8%
둔 2
 
3.8%
산 2
 
3.8%
대 2
 
3.8%
덕 2
 
3.8%
유 2
 
3.8%
Space Separator
ValueCountFrequency (%)
5
100.0%
Control
ValueCountFrequency (%)
5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 53
84.1%
Common 10
 
15.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
서 13
24.5%
소 11
20.8%
방 11
20.8%
부 4
 
7.5%
동 2
 
3.8%
둔 2
 
3.8%
산 2
 
3.8%
대 2
 
3.8%
덕 2
 
3.8%
유 2
 
3.8%
Common
ValueCountFrequency (%)
5
50.0%
5
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 53
84.1%
ASCII 10
 
15.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
서 13
24.5%
소 11
20.8%
방 11
20.8%
부 4
 
7.5%
동 2
 
3.8%
둔 2
 
3.8%
산 2
 
3.8%
대 2
 
3.8%
덕 2
 
3.8%
유 2
 
3.8%
ASCII
ValueCountFrequency (%)
5
50.0%
5
50.0%

위 치
Text

MISSING 

Distinct39
Distinct (%)54.2%
Missing10
Missing (%)12.2%
Memory size788.0 B
2023-12-12T09:23:29.047807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length15
Mean length6.0138889
Min length3

Characters and Unicode

Total characters433
Distinct characters68
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique36 ?
Unique (%)50.0%

Sample

1st row대전광역시 동구 계족로 300(가양동)
2nd row대전광역시 서구 갈마중로
3rd row15(갈마동)
4th row대전광역시 대덕구 계족로
5th row682(법동)
ValueCountFrequency (%)
119안전센터 26
23.9%
대전광역시 5
 
4.6%
119구급대 5
 
4.6%
119구조대 5
 
4.6%
평 3
 
2.8%
동 3
 
2.8%
계족로 2
 
1.8%
서구 2
 
1.8%
내 2
 
1.8%
산 2
 
1.8%
Other values (49) 54
49.5%
2023-12-12T09:23:29.496555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 74
17.1%
39
 
9.0%
9 36
 
8.3%
전 33
 
7.6%
안 27
 
6.2%
센 27
 
6.2%
터 27
 
6.2%
대 19
 
4.4%
구 16
 
3.7%
동 9
 
2.1%
Other values (58) 126
29.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 263
60.7%
Decimal Number 121
27.9%
Space Separator 39
 
9.0%
Close Punctuation 5
 
1.2%
Open Punctuation 5
 
1.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
전 33
 
12.5%
안 27
 
10.3%
센 27
 
10.3%
터 27
 
10.3%
대 19
 
7.2%
구 16
 
6.1%
동 9
 
3.4%
시 5
 
1.9%
역 5
 
1.9%
광 5
 
1.9%
Other values (46) 90
34.2%
Decimal Number
ValueCountFrequency (%)
1 74
61.2%
9 36
29.8%
6 3
 
2.5%
0 2
 
1.7%
5 2
 
1.7%
2 1
 
0.8%
3 1
 
0.8%
7 1
 
0.8%
8 1
 
0.8%
Space Separator
ValueCountFrequency (%)
39
100.0%
Close Punctuation
ValueCountFrequency (%)
) 5
100.0%
Open Punctuation
ValueCountFrequency (%)
( 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 263
60.7%
Common 170
39.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
전 33
 
12.5%
안 27
 
10.3%
센 27
 
10.3%
터 27
 
10.3%
대 19
 
7.2%
구 16
 
6.1%
동 9
 
3.4%
시 5
 
1.9%
역 5
 
1.9%
광 5
 
1.9%
Other values (46) 90
34.2%
Common
ValueCountFrequency (%)
1 74
43.5%
39
22.9%
9 36
21.2%
) 5
 
2.9%
( 5
 
2.9%
6 3
 
1.8%
0 2
 
1.2%
5 2
 
1.2%
2 1
 
0.6%
3 1
 
0.6%
Other values (2) 2
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 263
60.7%
ASCII 170
39.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 74
43.5%
39
22.9%
9 36
21.2%
) 5
 
2.9%
( 5
 
2.9%
6 3
 
1.8%
0 2
 
1.2%
5 2
 
1.2%
2 1
 
0.6%
3 1
 
0.6%
Other values (2) 2
 
1.2%
Hangul
ValueCountFrequency (%)
전 33
 
12.5%
안 27
 
10.3%
센 27
 
10.3%
터 27
 
10.3%
대 19
 
7.2%
구 16
 
6.1%
동 9
 
3.4%
시 5
 
1.9%
역 5
 
1.9%
광 5
 
1.9%
Other values (46) 90
34.2%

Unnamed: 3
Text

MISSING 

Distinct36
Distinct (%)52.2%
Missing13
Missing (%)15.9%
Memory size788.0 B
2023-12-12T09:23:29.784580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length16
Mean length11.144928
Min length3

Characters and Unicode

Total characters769
Distinct characters81
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique26 ?
Unique (%)37.7%

Sample

1st row위 치
2nd row대전광역시 동구 계족로 300(가양동)
3rd row대전광역시 동구
4th row태전로 134(삼성동)
5th row대전광역시 동구
ValueCountFrequency (%)
대전광역시 35
24.1%
서구 10
 
6.9%
유성구 8
 
5.5%
대덕구 7
 
4.8%
동구 7
 
4.8%
계족로 6
 
4.1%
중구 4
 
2.8%
15(갈마동 3
 
2.1%
300(가양동 3
 
2.1%
682(법동 3
 
2.1%
Other values (49) 59
40.7%
2023-12-12T09:23:30.247986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
79
 
10.3%
대 55
 
7.2%
동 48
 
6.2%
전 38
 
4.9%
구 37
 
4.8%
역 36
 
4.7%
광 36
 
4.7%
) 36
 
4.7%
( 36
 
4.7%
시 36
 
4.7%
Other values (71) 332
43.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 508
66.1%
Decimal Number 109
 
14.2%
Space Separator 79
 
10.3%
Close Punctuation 36
 
4.7%
Open Punctuation 36
 
4.7%
Dash Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
대 55
 
10.8%
동 48
 
9.4%
전 38
 
7.5%
구 37
 
7.3%
역 36
 
7.1%
광 36
 
7.1%
시 36
 
7.1%
로 36
 
7.1%
서 14
 
2.8%
덕 10
 
2.0%
Other values (57) 162
31.9%
Decimal Number
ValueCountFrequency (%)
1 24
22.0%
6 17
15.6%
5 12
11.0%
8 12
11.0%
3 11
10.1%
0 10
9.2%
2 9
 
8.3%
7 7
 
6.4%
4 5
 
4.6%
9 2
 
1.8%
Space Separator
ValueCountFrequency (%)
79
100.0%
Close Punctuation
ValueCountFrequency (%)
) 36
100.0%
Open Punctuation
ValueCountFrequency (%)
( 36
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 508
66.1%
Common 261
33.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
대 55
 
10.8%
동 48
 
9.4%
전 38
 
7.5%
구 37
 
7.3%
역 36
 
7.1%
광 36
 
7.1%
시 36
 
7.1%
로 36
 
7.1%
서 14
 
2.8%
덕 10
 
2.0%
Other values (57) 162
31.9%
Common
ValueCountFrequency (%)
79
30.3%
) 36
13.8%
( 36
13.8%
1 24
 
9.2%
6 17
 
6.5%
5 12
 
4.6%
8 12
 
4.6%
3 11
 
4.2%
0 10
 
3.8%
2 9
 
3.4%
Other values (4) 15
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 508
66.1%
ASCII 261
33.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
79
30.3%
) 36
13.8%
( 36
13.8%
1 24
 
9.2%
6 17
 
6.5%
5 12
 
4.6%
8 12
 
4.6%
3 11
 
4.2%
0 10
 
3.8%
2 9
 
3.4%
Other values (4) 15
 
5.7%
Hangul
ValueCountFrequency (%)
대 55
 
10.8%
동 48
 
9.4%
전 38
 
7.5%
구 37
 
7.3%
역 36
 
7.1%
광 36
 
7.1%
시 36
 
7.1%
로 36
 
7.1%
서 14
 
2.8%
덕 10
 
2.0%
Other values (57) 162
31.9%

Correlations

2023-12-12T09:23:30.364275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
소방서 명 칭위 치Unnamed: 3
소방서\n명 칭1.0000.8690.887
위 치0.8691.0000.000
Unnamed: 30.8870.0001.000

Missing values

2023-12-12T09:23:28.224600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T09:23:28.301531image/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.
2023-12-12T09:23:28.399488image/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

Unnamed: 0소방서 명 칭위 치Unnamed: 3
0<NA>동 부 소방서대전광역시 동구 계족로 300(가양동)<NA>
1<NA><NA><NA><NA>
2<NA>둔 산 소방서대전광역시 서구 갈마중로<NA>
3<NA><NA>15(갈마동)<NA>
4<NA>대 덕 소방서대전광역시 대덕구 계족로<NA>
5<NA><NA>682(법동)<NA>
6<NA>유 성 소방서대전광역시 유성구 대덕대로<NA>
7<NA><NA>516(도룡동)<NA>
8<NA>서 부 소방서대전광역시 서구 복수서로<NA>
9<NA><NA>67(복수동)<NA>
Unnamed: 0소방서 명 칭위 치Unnamed: 3
72<NA>서산 성대전광역시 중구
73<NA><NA>119안전센터산성로 59(문화동)
74<NA><NA>가수원대전광역시 서구
75<NA><NA>119안전센터용소로 46(가수원동)
76<NA><NA>원 내대전광역시 유성구
77<NA><NA>119안전센터계백로801번길 17(원내동)
78<NA><NA>119구조대대전광역시 서구
79<NA><NA><NA>복수서로 67(복수동)
80<NA><NA>119구급대대전광역시 서구
81<NA><NA><NA>복수서로 67(복수동)

Duplicate rows

Most frequently occurring

소방서 명 칭위 치Unnamed: 3# duplicates
0<NA>119구급대대전광역시 서구2
1<NA>119구조대대전광역시 서구2
2<NA><NA>갈마중로 15(갈마동)2
3<NA><NA>계족로 682(법동)2
4<NA><NA>대덕대로 516(도룡동)2
5<NA><NA>복수서로 67(복수동)2
6<NA><NA><NA>2