Overview

Dataset statistics

Number of variables10
Number of observations74
Missing cells127
Missing cells (%)17.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.9 KiB
Average record size in memory81.8 B

Variable types

Unsupported5
Categorical2
Text3

Alerts

Unnamed: 3 has 48 (64.9%) missing valuesMissing
Unnamed: 4 has 1 (1.4%) missing valuesMissing
Unnamed: 5 has 1 (1.4%) missing valuesMissing
Unnamed: 6 has 1 (1.4%) missing valuesMissing
Unnamed: 8 has 4 (5.4%) missing valuesMissing
Unnamed: 9 has 72 (97.3%) missing valuesMissing
2015년 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
Unnamed: 8 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2024-03-14 02:50:01.719592
Analysis finished2024-03-14 02:50:02.272966
Duration0.55 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

2015년 구급대별 구급활동실적
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size724.0 B

Unnamed: 1
Categorical

Distinct11
Distinct (%)14.9%
Missing0
Missing (%)0.0%
Memory size724.0 B
전주덕진소방서
10 
무진장소방서
10 
전주완산소방서
9 
군산소방서
8 
익산소방서
8 
Other values (6)
29 

Length

Max length7
Median length5
Mean length5.6216216
Min length3

Unique

Unique1 ?
Unique (%)1.4%

Sample

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

Common Values

ValueCountFrequency (%)
전주덕진소방서 10
13.5%
무진장소방서 10
13.5%
전주완산소방서 9
12.2%
군산소방서 8
10.8%
익산소방서 8
10.8%
남원소방서 8
10.8%
정읍소방서 6
8.1%
고창소방서 5
6.8%
부안소방서 5
6.8%
김제소방서 4
 
5.4%

Length

2024-03-14T11:50:02.335083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
전주덕진소방서 10
13.5%
무진장소방서 10
13.5%
전주완산소방서 9
12.2%
군산소방서 8
10.8%
익산소방서 8
10.8%
남원소방서 8
10.8%
정읍소방서 6
8.1%
고창소방서 5
6.8%
부안소방서 5
6.8%
김제소방서 4
 
5.4%

Unnamed: 2
Text

Distinct50
Distinct (%)67.6%
Missing0
Missing (%)0.0%
Memory size724.0 B
2024-03-14T11:50:02.575628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length9
Mean length9.0135135
Min length8

Characters and Unicode

Total characters667
Distinct characters77
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

Unique34 ?
Unique (%)45.9%

Sample

1st row119안전센터명
2nd row금암119안전센터
3rd row팔복119안전센터
4th row전미119안전센터
5th row삼례119안전센터
ValueCountFrequency (%)
효자119안전센터 4
 
5.4%
교동119안전센터 3
 
4.1%
순창119안전센터 3
 
4.1%
부안119안전센터 3
 
4.1%
식정119안전센터 3
 
4.1%
진안119안전센터 3
 
4.1%
고산119안전센터 3
 
4.1%
무주119안전센터 2
 
2.7%
장계119안전센터 2
 
2.7%
무장119안전센터 2
 
2.7%
Other values (40) 46
62.2%
2024-03-14T11:50:02.955833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 147
22.0%
안 80
12.0%
전 75
11.2%
센 74
11.1%
터 74
11.1%
9 73
10.9%
무 5
 
0.7%
금 5
 
0.7%
산 5
 
0.7%
동 5
 
0.7%
Other values (67) 124
18.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 445
66.7%
Decimal Number 222
33.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
안 80
18.0%
전 75
16.9%
센 74
16.6%
터 74
16.6%
무 5
 
1.1%
금 5
 
1.1%
산 5
 
1.1%
동 5
 
1.1%
장 5
 
1.1%
정 4
 
0.9%
Other values (63) 113
25.4%
Decimal Number
ValueCountFrequency (%)
1 147
66.2%
9 73
32.9%
0 1
 
0.5%
2 1
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
Hangul 445
66.7%
Common 222
33.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
안 80
18.0%
전 75
16.9%
센 74
16.6%
터 74
16.6%
무 5
 
1.1%
금 5
 
1.1%
산 5
 
1.1%
동 5
 
1.1%
장 5
 
1.1%
정 4
 
0.9%
Other values (63) 113
25.4%
Common
ValueCountFrequency (%)
1 147
66.2%
9 73
32.9%
0 1
 
0.5%
2 1
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 445
66.7%
ASCII 222
33.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 147
66.2%
9 73
32.9%
0 1
 
0.5%
2 1
 
0.5%
Hangul
ValueCountFrequency (%)
안 80
18.0%
전 75
16.9%
센 74
16.6%
터 74
16.6%
무 5
 
1.1%
금 5
 
1.1%
산 5
 
1.1%
동 5
 
1.1%
장 5
 
1.1%
정 4
 
0.9%
Other values (63) 113
25.4%

Unnamed: 3
Text

MISSING 

Distinct26
Distinct (%)100.0%
Missing48
Missing (%)64.9%
Memory size724.0 B
2024-03-14T11:50:03.139177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length2
Mean length2.0769231
Min length2

Characters and Unicode

Total characters54
Distinct characters42
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 row선발
ValueCountFrequency (%)
사매 1
 
3.8%
소양 1
 
3.8%
용성 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%
줄포 1
 
3.8%
Other values (16) 16
61.5%
2024-03-14T11:50:03.392840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
천 4
 
7.4%
성 2
 
3.7%
화 2
 
3.7%
산 2
 
3.7%
안 2
 
3.7%
주 2
 
3.7%
발 2
 
3.7%
동 2
 
3.7%
용 2
 
3.7%
구 2
 
3.7%
Other values (32) 32
59.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 54
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
천 4
 
7.4%
성 2
 
3.7%
화 2
 
3.7%
산 2
 
3.7%
안 2
 
3.7%
주 2
 
3.7%
발 2
 
3.7%
동 2
 
3.7%
용 2
 
3.7%
구 2
 
3.7%
Other values (32) 32
59.3%

Most occurring scripts

ValueCountFrequency (%)
Hangul 54
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
천 4
 
7.4%
성 2
 
3.7%
화 2
 
3.7%
산 2
 
3.7%
안 2
 
3.7%
주 2
 
3.7%
발 2
 
3.7%
동 2
 
3.7%
용 2
 
3.7%
구 2
 
3.7%
Other values (32) 32
59.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 54
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
천 4
 
7.4%
성 2
 
3.7%
화 2
 
3.7%
산 2
 
3.7%
안 2
 
3.7%
주 2
 
3.7%
발 2
 
3.7%
동 2
 
3.7%
용 2
 
3.7%
구 2
 
3.7%
Other values (32) 32
59.3%

Unnamed: 4
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1
Missing (%)1.4%
Memory size724.0 B

Unnamed: 5
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1
Missing (%)1.4%
Memory size724.0 B

Unnamed: 6
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1
Missing (%)1.4%
Memory size724.0 B

Unnamed: 7
Categorical

Distinct32
Distinct (%)43.2%
Missing0
Missing (%)0.0%
Memory size724.0 B
2011.10.04
8 
2011.09.07
5 
2013.07.23
5 
2012.07.04
5 
2010.07.02
5 
Other values (27)
46 

Length

Max length11
Median length10
Mean length9.6351351
Min length2

Unique

Unique16 ?
Unique (%)21.6%

Sample

1st row등록
2nd row2012.08.23
3rd row2011.09.08
4th row2012.07.05
5th row2009.02.26

Common Values

ValueCountFrequency (%)
2011.10.04 8
 
10.8%
2011.09.07 5
 
6.8%
2013.07.23 5
 
6.8%
2012.07.04 5
 
6.8%
2010.07.02 5
 
6.8%
2012.08.23 4
 
5.4%
<NA> 4
 
5.4%
2012.07.05 4
 
5.4%
2009.11.04 3
 
4.1%
2011.09.08 3
 
4.1%
Other values (22) 28
37.8%

Length

2024-03-14T11:50:03.497711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2011.10.04 8
 
10.8%
2013.07.23 5
 
6.8%
2012.07.04 5
 
6.8%
2010.07.02 5
 
6.8%
2011.09.07 5
 
6.8%
2012.08.23 4
 
5.4%
na 4
 
5.4%
2012.07.05 4
 
5.4%
2009.11.04 3
 
4.1%
2011.09.08 3
 
4.1%
Other values (20) 28
37.8%

Unnamed: 8
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing4
Missing (%)5.4%
Memory size724.0 B

Unnamed: 9
Text

MISSING 

Distinct2
Distinct (%)100.0%
Missing72
Missing (%)97.3%
Memory size724.0 B
2024-03-14T11:50:03.614686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3
Min length2

Characters and Unicode

Total characters6
Distinct characters6
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

Unique2 ?
Unique (%)100.0%

Sample

1st row비고
2nd row중환자용
ValueCountFrequency (%)
비고 1
50.0%
중환자용 1
50.0%
2024-03-14T11:50:03.841791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
비 1
16.7%
고 1
16.7%
중 1
16.7%
환 1
16.7%
자 1
16.7%
용 1
16.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 6
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
비 1
16.7%
고 1
16.7%
중 1
16.7%
환 1
16.7%
자 1
16.7%
용 1
16.7%

Most occurring scripts

ValueCountFrequency (%)
Hangul 6
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
비 1
16.7%
고 1
16.7%
중 1
16.7%
환 1
16.7%
자 1
16.7%
용 1
16.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 6
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
비 1
16.7%
고 1
16.7%
중 1
16.7%
환 1
16.7%
자 1
16.7%
용 1
16.7%

Correlations

2024-03-14T11:50:03.917911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Unnamed: 1Unnamed: 2Unnamed: 3Unnamed: 7Unnamed: 9
Unnamed: 11.0000.9971.0000.8620.000
Unnamed: 20.9971.0001.0000.6890.000
Unnamed: 31.0001.0001.0001.000NaN
Unnamed: 70.8620.6891.0001.0000.000
Unnamed: 90.0000.000NaN0.0001.000
2024-03-14T11:50:04.009072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Unnamed: 7Unnamed: 1
Unnamed: 71.0000.410
Unnamed: 10.4101.000
2024-03-14T11:50:04.079987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Unnamed: 1Unnamed: 7
Unnamed: 11.0000.410
Unnamed: 70.4101.000

Missing values

2024-03-14T11:50:01.965354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T11:50:02.081565image/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.
2024-03-14T11:50:02.182945image/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

2015년 구급대별 구급활동실적Unnamed: 1Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 6Unnamed: 7Unnamed: 8Unnamed: 9
0연번관서명119안전센터명지역대출동건수이송건수이송인원등록주행거리비고
11전주덕진소방서금암119안전센터<NA>4836312331942012.08.2347078<NA>
22전주덕진소방서팔복119안전센터<NA>2698171417672011.09.0885533<NA>
33전주덕진소방서전미119안전센터<NA>2336148915332012.07.0559987<NA>
44전주덕진소방서삼례119안전센터<NA>1541100110462009.02.26118021<NA>
55전주덕진소방서봉동119안전센터<NA>154095510062009.11.06227372<NA>
66전주덕진소방서아중119안전센터<NA>3504221122782010.07.0192827<NA>
77전주덕진소방서아중119안전센터소양5633713872006.06.1988627<NA>
88전주덕진소방서고산119안전센터<NA>9445585672011.09.0881276<NA>
99전주덕진소방서고산119안전센터화산5423363562011.09.0868805<NA>
2015년 구급대별 구급활동실적Unnamed: 1Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 6Unnamed: 7Unnamed: 8Unnamed: 9
6464무진장소방서장계119안전센터<NA>10317197472010.07.06164082<NA>
6565무진장소방서장계119안전센터안성5163263402011.10.0491740<NA>
6666무진장소방서장수119안전센터<NA>10665886232011.10.04109880<NA>
6767무진장소방서진안119안전센터<NA>11507747982010.07.02213641<NA>
6868무진장소방서진안119안전센터안천5904104192006.06.20134742<NA>
6969무진장소방서진안119안전센터주천3561921992010.07.02147876<NA>
7070무진장소방서무주119안전센터<NA>9776236632012.8.24.64495<NA>
7171무진장소방서무주119안전센터구천동5753053182011.10.0489200<NA>
7272무진장소방서무주120안전센터설천490296310<NA>NaN<NA>
7373무진장소방서마령119안전센터<NA>9536216502013.06.0428640<NA>