Overview

Dataset statistics

Number of variables7
Number of observations76
Missing cells50
Missing cells (%)9.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.3 KiB
Average record size in memory57.7 B

Variable types

Unsupported4
Categorical1
Text2

Alerts

Unnamed: 3 has 50 (65.8%) missing valuesMissing
2016년 구급대별 구급활동실적 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 started2024-03-14 02:58:48.349346
Analysis finished2024-03-14 02:58:48.616740
Duration0.27 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

2016년 구급대별 구급활동실적
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size740.0 B

Unnamed: 1
Categorical

Distinct11
Distinct (%)14.5%
Missing0
Missing (%)0.0%
Memory size740.0 B
무진장소방서
11 
전주덕진소방서
10 
전주완산소방서
9 
남원소방서
9 
군산소방서
8 
Other values (6)
29 

Length

Max length7
Median length5
Mean length5.6184211
Min length3

Unique

Unique1 ?
Unique (%)1.3%

Sample

1st row관서명
2nd row전주덕진소방서
3rd row전주덕진소방서
4th row전주덕진소방서
5th row전주덕진소방서

Common Values

ValueCountFrequency (%)
무진장소방서 11
14.5%
전주덕진소방서 10
13.2%
전주완산소방서 9
11.8%
남원소방서 9
11.8%
군산소방서 8
10.5%
익산소방서 8
10.5%
정읍소방서 6
7.9%
고창소방서 5
6.6%
부안소방서 5
6.6%
김제소방서 4
 
5.3%

Length

2024-03-14T11:58:48.691023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
무진장소방서 11
14.5%
전주덕진소방서 10
13.2%
전주완산소방서 9
11.8%
남원소방서 9
11.8%
군산소방서 8
10.5%
익산소방서 8
10.5%
정읍소방서 6
7.9%
고창소방서 5
6.6%
부안소방서 5
6.6%
김제소방서 4
 
5.3%

Unnamed: 2
Text

Distinct50
Distinct (%)65.8%
Missing0
Missing (%)0.0%
Memory size740.0 B
2024-03-14T11:58:48.893420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length9
Mean length9.0131579
Min length8

Characters and Unicode

Total characters685
Distinct characters76
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

Unique33 ?
Unique (%)43.4%

Sample

1st row119안전센터명
2nd row금암119안전센터
3rd row팔복119안전센터
4th row전미119안전센터
5th row삼례119안전센터
ValueCountFrequency (%)
순창119안전센터 4
 
5.3%
임실119안전센터 3
 
3.9%
교동119안전센터 3
 
3.9%
부안119안전센터 3
 
3.9%
진안119안전센터 3
 
3.9%
무주119안전센터 3
 
3.9%
고산119안전센터 3
 
3.9%
식정119안전센터 3
 
3.9%
함열119안전센터 2
 
2.6%
아중119안전센터 2
 
2.6%
Other values (40) 47
61.8%
2024-03-14T11:58:49.220313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 152
22.2%
안 82
12.0%
전 77
11.2%
9 76
11.1%
센 76
11.1%
터 76
11.1%
장 6
 
0.9%
무 5
 
0.7%
창 5
 
0.7%
금 5
 
0.7%
Other values (66) 125
18.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 457
66.7%
Decimal Number 228
33.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
안 82
17.9%
전 77
16.8%
센 76
16.6%
터 76
16.6%
장 6
 
1.3%
무 5
 
1.1%
창 5
 
1.1%
금 5
 
1.1%
산 5
 
1.1%
동 5
 
1.1%
Other values (64) 115
25.2%
Decimal Number
ValueCountFrequency (%)
1 152
66.7%
9 76
33.3%

Most occurring scripts

ValueCountFrequency (%)
Hangul 457
66.7%
Common 228
33.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
안 82
17.9%
전 77
16.8%
센 76
16.6%
터 76
16.6%
장 6
 
1.3%
무 5
 
1.1%
창 5
 
1.1%
금 5
 
1.1%
산 5
 
1.1%
동 5
 
1.1%
Other values (64) 115
25.2%
Common
ValueCountFrequency (%)
1 152
66.7%
9 76
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 457
66.7%
ASCII 228
33.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 152
66.7%
9 76
33.3%
Hangul
ValueCountFrequency (%)
안 82
17.9%
전 77
16.8%
센 76
16.6%
터 76
16.6%
장 6
 
1.3%
무 5
 
1.1%
창 5
 
1.1%
금 5
 
1.1%
산 5
 
1.1%
동 5
 
1.1%
Other values (64) 115
25.2%

Unnamed: 3
Text

MISSING 

Distinct26
Distinct (%)100.0%
Missing50
Missing (%)65.8%
Memory size740.0 B
2024-03-14T11:58:49.393065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length2
Mean length2.0769231
Min length2

Characters and Unicode

Total characters54
Distinct characters41
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique26 ?
Unique (%)100.0%

Sample

1st row지역대
2nd row소양
3rd row화산
4th row운주
5th row신덕
ValueCountFrequency (%)
동계 1
 
3.8%
소양 1
 
3.8%
쌍치 1
 
3.8%
설천 1
 
3.8%
구천동 1
 
3.8%
주천 1
 
3.8%
안천 1
 
3.8%
안성 1
 
3.8%
변산 1
 
3.8%
계화 1
 
3.8%
Other values (16) 16
61.5%
2024-03-14T11:58:49.722671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
천 4
 
7.4%
동 3
 
5.6%
용 2
 
3.7%
성 2
 
3.7%
계 2
 
3.7%
화 2
 
3.7%
산 2
 
3.7%
주 2
 
3.7%
안 2
 
3.7%
구 2
 
3.7%
Other values (31) 31
57.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 54
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
천 4
 
7.4%
동 3
 
5.6%
용 2
 
3.7%
성 2
 
3.7%
계 2
 
3.7%
화 2
 
3.7%
산 2
 
3.7%
주 2
 
3.7%
안 2
 
3.7%
구 2
 
3.7%
Other values (31) 31
57.4%

Most occurring scripts

ValueCountFrequency (%)
Hangul 54
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
천 4
 
7.4%
동 3
 
5.6%
용 2
 
3.7%
성 2
 
3.7%
계 2
 
3.7%
화 2
 
3.7%
산 2
 
3.7%
주 2
 
3.7%
안 2
 
3.7%
구 2
 
3.7%
Other values (31) 31
57.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 54
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
천 4
 
7.4%
동 3
 
5.6%
용 2
 
3.7%
성 2
 
3.7%
계 2
 
3.7%
화 2
 
3.7%
산 2
 
3.7%
주 2
 
3.7%
안 2
 
3.7%
구 2
 
3.7%
Other values (31) 31
57.4%

Unnamed: 4
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size740.0 B

Unnamed: 5
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size740.0 B

Unnamed: 6
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size740.0 B

Correlations

2024-03-14T11:58:49.794859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Unnamed: 1Unnamed: 2Unnamed: 3
Unnamed: 11.0000.9971.000
Unnamed: 20.9971.0001.000
Unnamed: 31.0001.0001.000

Missing values

2024-03-14T11:58:48.492243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T11:58:48.581024image/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

2016년 구급대별 구급활동실적Unnamed: 1Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 6
0연번관서명119안전센터명지역대출동건수이송건수이송인원
11전주덕진소방서금암119안전센터<NA>473130943160
22전주덕진소방서팔복119안전센터<NA>258215971638
33전주덕진소방서전미119안전센터<NA>246116211662
44전주덕진소방서삼례119안전센터<NA>159010461100
55전주덕진소방서봉동119안전센터<NA>15349961032
66전주덕진소방서아중119안전센터<NA>359822532294
77전주덕진소방서아중119안전센터소양646431443
88전주덕진소방서고산119안전센터<NA>971575588
99전주덕진소방서고산119안전센터화산447289302
2016년 구급대별 구급활동실적Unnamed: 1Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 6
6666무진장소방서장계119안전센터안성537347358
6767무진장소방서장수119안전센터<NA>911538555
6868무진장소방서진안119안전센터<NA>1130729777
6969무진장소방서진안119안전센터안천526354359
7070무진장소방서진안119안전센터주천401197197
7171무진장소방서무주119안전센터<NA>967664697
7272무진장소방서무주119안전센터구천동584286290
7373무진장소방서무주119안전센터설천466270275
7474무진장소방서마령119안전센터<NA>850567598
7575무진장소방서장수119안전센터번암375227238