Overview

Dataset statistics

Number of variables3
Number of observations38
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.1 KiB
Average record size in memory28.5 B

Variable types

Text2
Numeric1

Dataset

Description1년 간 수상 레저 면제 교육을 받은 인원 현황에 대한 데이터로 수상레저 교육기관, 교육기관 위치, 22년 수상레저 교육 이수자 등의 항목을 제공합니다.
URLhttps://www.data.go.kr/data/15053873/fileData.do

Alerts

22년 has 2 (5.3%) zerosZeros

Reproduction

Analysis started2023-12-12 15:31:04.996490
Analysis finished2023-12-12 15:31:05.392829
Duration0.4 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

교육기관
Text

Distinct20
Distinct (%)52.6%
Missing0
Missing (%)0.0%
Memory size436.0 B
2023-12-13T00:31:05.570743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length9.9210526
Min length5

Characters and Unicode

Total characters377
Distinct characters60
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

Unique16 ?
Unique (%)42.1%

Sample

1st row(사)한국수상레저안전협회
2nd row(사)한국수상레저안전협회
3rd row(사)한국수상레저안전협회
4th row(사)한국수상레저안전협회
5th row(사)한국수상레저안전협회
ValueCountFrequency (%)
사)한국수상레저안전협회 9
23.7%
사)한국수상레저안전연합회 8
21.1%
대한수상안전교육협회 3
 
7.9%
한국해양소년단 2
 
5.3%
목포해양대학교 1
 
2.6%
부산소방학교 1
 
2.6%
경남거제시 1
 
2.6%
경북울진군 1
 
2.6%
경남통영시 1
 
2.6%
한서대학교 1
 
2.6%
Other values (10) 10
26.3%
2023-12-13T00:31:05.961490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
한 28
 
7.4%
회 24
 
6.4%
국 23
 
6.1%
수 23
 
6.1%
전 22
 
5.8%
상 20
 
5.3%
안 20
 
5.3%
레 19
 
5.0%
저 19
 
5.0%
) 18
 
4.8%
Other values (50) 161
42.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 349
92.6%
Close Punctuation 18
 
4.8%
Open Punctuation 10
 
2.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
한 28
 
8.0%
회 24
 
6.9%
국 23
 
6.6%
수 23
 
6.6%
전 22
 
6.3%
상 20
 
5.7%
안 20
 
5.7%
레 19
 
5.4%
저 19
 
5.4%
사 18
 
5.2%
Other values (48) 133
38.1%
Close Punctuation
ValueCountFrequency (%)
) 18
100.0%
Open Punctuation
ValueCountFrequency (%)
( 10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 349
92.6%
Common 28
 
7.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
한 28
 
8.0%
회 24
 
6.9%
국 23
 
6.6%
수 23
 
6.6%
전 22
 
6.3%
상 20
 
5.7%
안 20
 
5.7%
레 19
 
5.4%
저 19
 
5.4%
사 18
 
5.2%
Other values (48) 133
38.1%
Common
ValueCountFrequency (%)
) 18
64.3%
( 10
35.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 349
92.6%
ASCII 28
 
7.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
한 28
 
8.0%
회 24
 
6.9%
국 23
 
6.6%
수 23
 
6.6%
전 22
 
6.3%
상 20
 
5.7%
안 20
 
5.7%
레 19
 
5.4%
저 19
 
5.4%
사 18
 
5.2%
Other values (48) 133
38.1%
ASCII
ValueCountFrequency (%)
) 18
64.3%
( 10
35.7%

면제교육장
Text

Distinct35
Distinct (%)92.1%
Missing0
Missing (%)0.0%
Memory size436.0 B
2023-12-13T00:31:06.201801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length4
Mean length4.3684211
Min length4

Characters and Unicode

Total characters166
Distinct characters57
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

Unique32 ?
Unique (%)84.2%

Sample

1st row경기시흥
2nd row경기화성
3rd row경북구미
4th row경남합천
5th row경남진해
ValueCountFrequency (%)
경남통영 2
 
5.3%
경남고성 2
 
5.3%
경남거제 2
 
5.3%
한라대학교 1
 
2.6%
해군특수전전단 1
 
2.6%
부산소방 1
 
2.6%
강원대학교 1
 
2.6%
목포해양대학교 1
 
2.6%
한서대학교 1
 
2.6%
경기시흥 1
 
2.6%
Other values (25) 25
65.8%
2023-12-13T00:31:06.572171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
경 18
 
10.8%
남 13
 
7.8%
북 8
 
4.8%
산 7
 
4.2%
전 6
 
3.6%
기 5
 
3.0%
교 5
 
3.0%
영 5
 
3.0%
학 5
 
3.0%
대 5
 
3.0%
Other values (47) 89
53.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 166
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
경 18
 
10.8%
남 13
 
7.8%
북 8
 
4.8%
산 7
 
4.2%
전 6
 
3.6%
기 5
 
3.0%
교 5
 
3.0%
영 5
 
3.0%
학 5
 
3.0%
대 5
 
3.0%
Other values (47) 89
53.6%

Most occurring scripts

ValueCountFrequency (%)
Hangul 166
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
경 18
 
10.8%
남 13
 
7.8%
북 8
 
4.8%
산 7
 
4.2%
전 6
 
3.6%
기 5
 
3.0%
교 5
 
3.0%
영 5
 
3.0%
학 5
 
3.0%
대 5
 
3.0%
Other values (47) 89
53.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 166
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
경 18
 
10.8%
남 13
 
7.8%
북 8
 
4.8%
산 7
 
4.2%
전 6
 
3.6%
기 5
 
3.0%
교 5
 
3.0%
영 5
 
3.0%
학 5
 
3.0%
대 5
 
3.0%
Other values (47) 89
53.6%

22년
Real number (ℝ)

ZEROS 

Distinct37
Distinct (%)97.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean261.28947
Minimum0
Maximum918
Zeros2
Zeros (%)5.3%
Negative0
Negative (%)0.0%
Memory size474.0 B
2023-12-13T00:31:06.753020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile11.9
Q181.5
median221
Q3384.25
95-th percentile656.35
Maximum918
Range918
Interquartile range (IQR)302.75

Descriptive statistics

Standard deviation224.74333
Coefficient of variation (CV)0.86013157
Kurtosis1.2722523
Mean261.28947
Median Absolute Deviation (MAD)150
Skewness1.1770746
Sum9929
Variance50509.563
MonotonicityNot monotonic
2023-12-13T00:31:06.915304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
0 2
 
5.3%
918 1
 
2.6%
78 1
 
2.6%
532 1
 
2.6%
834 1
 
2.6%
593 1
 
2.6%
257 1
 
2.6%
243 1
 
2.6%
61 1
 
2.6%
393 1
 
2.6%
Other values (27) 27
71.1%
ValueCountFrequency (%)
0 2
5.3%
14 1
2.6%
24 1
2.6%
26 1
2.6%
29 1
2.6%
61 1
2.6%
70 1
2.6%
72 1
2.6%
78 1
2.6%
92 1
2.6%
ValueCountFrequency (%)
918 1
2.6%
834 1
2.6%
625 1
2.6%
593 1
2.6%
532 1
2.6%
498 1
2.6%
431 1
2.6%
399 1
2.6%
393 1
2.6%
388 1
2.6%

Interactions

2023-12-13T00:31:05.154726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T00:31:07.006452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
교육기관면제교육장22년
교육기관1.0000.0000.000
면제교육장0.0001.0000.962
22년0.0000.9621.000

Missing values

2023-12-13T00:31:05.261581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T00:31:05.356545image/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

교육기관면제교육장22년
0(사)한국수상레저안전협회경기시흥918
1(사)한국수상레저안전협회경기화성72
2(사)한국수상레저안전협회경북구미262
3(사)한국수상레저안전협회경남합천103
4(사)한국수상레저안전협회경남진해353
5(사)한국수상레저안전협회부산강서373
6(사)한국수상레저안전협회제주이호232
7(사)한국수상레저안전협회충북진천498
8(사)한국수상레저안전협회경남고성0
9사)한국수상레저안전연합회서울마포399
교육기관면제교육장22년
28목포해양대학교목포해양대학교393
29강원대학교강원대학교78
30부산소방학교부산소방24
31해군특수전전단해군특수전전단26
32한라대학교한라대학교201
33한서대학교한서대학교388
34경남통영시경남통영129
35경북울진군경북울진115
36경남거제시경남거제14
37경남고성군경남고성0