Overview

Dataset statistics

Number of variables6
Number of observations30
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.6 KiB
Average record size in memory54.4 B

Variable types

Numeric2
Text2
Categorical2

Dataset

Description소프트웨어(SW) 영재학급 사업 운영 정보로 해당 학교, 수혜자 학생 수 등 기초데이터를 담음
Author한국과학창의재단
URLhttps://www.data.go.kr/data/15091282/fileData.do

Alerts

수혜학생수 is highly overall correlated with 학교급High correlation
학교급 is highly overall correlated with 수혜학생수High correlation
구분 has unique valuesUnique
사업기간 has unique valuesUnique

Reproduction

Analysis started2023-12-12 13:07:07.486911
Analysis finished2023-12-12 13:07:08.557998
Duration1.07 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Real number (ℝ)

UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.5
Minimum1
Maximum30
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-12T22:07:08.626468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.45
Q18.25
median15.5
Q322.75
95-th percentile28.55
Maximum30
Range29
Interquartile range (IQR)14.5

Descriptive statistics

Standard deviation8.8034084
Coefficient of variation (CV)0.56796183
Kurtosis-1.2
Mean15.5
Median Absolute Deviation (MAD)7.5
Skewness0
Sum465
Variance77.5
MonotonicityStrictly increasing
2023-12-12T22:07:08.757095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
1 1
 
3.3%
17 1
 
3.3%
30 1
 
3.3%
29 1
 
3.3%
28 1
 
3.3%
27 1
 
3.3%
26 1
 
3.3%
25 1
 
3.3%
24 1
 
3.3%
23 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
1 1
3.3%
2 1
3.3%
3 1
3.3%
4 1
3.3%
5 1
3.3%
6 1
3.3%
7 1
3.3%
8 1
3.3%
9 1
3.3%
10 1
3.3%
ValueCountFrequency (%)
30 1
3.3%
29 1
3.3%
28 1
3.3%
27 1
3.3%
26 1
3.3%
25 1
3.3%
24 1
3.3%
23 1
3.3%
22 1
3.3%
21 1
3.3%

학교명
Text

Distinct29
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-12T22:07:08.939744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6.5
Mean length4.0666667
Min length3

Characters and Unicode

Total characters122
Distinct characters66
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

Unique28 ?
Unique (%)93.3%

Sample

1st row경민여중
2nd row광명북중
3rd row광주동신여중
4th row김해내동초
5th row대전지족중
ValueCountFrequency (%)
홍광초 2
 
6.7%
경민여중 1
 
3.3%
예당고 1
 
3.3%
회룡초 1
 
3.3%
회덕초 1
 
3.3%
화도초 1
 
3.3%
학운초 1
 
3.3%
포산중 1
 
3.3%
제산초 1
 
3.3%
장복초 1
 
3.3%
Other values (19) 19
63.3%
2023-12-12T22:07:09.308349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
초 19
 
15.6%
중 7
 
5.7%
동 5
 
4.1%
광 4
 
3.3%
인 4
 
3.3%
포 3
 
2.5%
고 3
 
2.5%
천 3
 
2.5%
산 3
 
2.5%
홍 2
 
1.6%
Other values (56) 69
56.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 120
98.4%
Decimal Number 2
 
1.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
초 19
 
15.8%
중 7
 
5.8%
동 5
 
4.2%
광 4
 
3.3%
인 4
 
3.3%
포 3
 
2.5%
고 3
 
2.5%
천 3
 
2.5%
산 3
 
2.5%
홍 2
 
1.7%
Other values (54) 67
55.8%
Decimal Number
ValueCountFrequency (%)
2 1
50.0%
1 1
50.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 120
98.4%
Common 2
 
1.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
초 19
 
15.8%
중 7
 
5.8%
동 5
 
4.2%
광 4
 
3.3%
인 4
 
3.3%
포 3
 
2.5%
고 3
 
2.5%
천 3
 
2.5%
산 3
 
2.5%
홍 2
 
1.7%
Other values (54) 67
55.8%
Common
ValueCountFrequency (%)
2 1
50.0%
1 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 120
98.4%
ASCII 2
 
1.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
초 19
 
15.8%
중 7
 
5.8%
동 5
 
4.2%
광 4
 
3.3%
인 4
 
3.3%
포 3
 
2.5%
고 3
 
2.5%
천 3
 
2.5%
산 3
 
2.5%
홍 2
 
1.7%
Other values (54) 67
55.8%
ASCII
ValueCountFrequency (%)
2 1
50.0%
1 1
50.0%

지역
Categorical

Distinct11
Distinct (%)36.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
경기도
8 
경상남도
4 
전라남도
4 
충청북도
3 
대전
2 
Other values (6)
9 

Length

Max length4
Median length3
Mean length3.0666667
Min length2

Unique

Unique3 ?
Unique (%)10.0%

Sample

1st row경기도
2nd row경기도
3rd row광주
4th row경상남도
5th row대전

Common Values

ValueCountFrequency (%)
경기도 8
26.7%
경상남도 4
13.3%
전라남도 4
13.3%
충청북도 3
 
10.0%
대전 2
 
6.7%
서울 2
 
6.7%
인천 2
 
6.7%
대구 2
 
6.7%
광주 1
 
3.3%
부산 1
 
3.3%

Length

2023-12-12T22:07:09.459629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기도 8
26.7%
경상남도 4
13.3%
전라남도 4
13.3%
충청북도 3
 
10.0%
대전 2
 
6.7%
서울 2
 
6.7%
인천 2
 
6.7%
대구 2
 
6.7%
광주 1
 
3.3%
부산 1
 
3.3%

사업기간
Text

UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-12T22:07:09.654564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length21
Mean length21
Min length21

Characters and Unicode

Total characters630
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique30 ?
Unique (%)100.0%

Sample

1st row2021-03-01~2021-12-31
2nd row2021-03-01~2021-12-32
3rd row2021-03-01~2021-12-33
4th row2021-03-01~2021-12-34
5th row2021-03-01~2021-12-35
ValueCountFrequency (%)
2021-03-01~2021-12-31 1
 
3.3%
2021-03-01~2021-12-32 1
 
3.3%
2021-03-01~2021-12-59 1
 
3.3%
2021-03-01~2021-12-58 1
 
3.3%
2021-03-01~2021-12-57 1
 
3.3%
2021-03-01~2021-12-56 1
 
3.3%
2021-03-01~2021-12-55 1
 
3.3%
2021-03-01~2021-12-54 1
 
3.3%
2021-03-01~2021-12-53 1
 
3.3%
2021-03-01~2021-12-52 1
 
3.3%
Other values (20) 20
66.7%
2023-12-12T22:07:09.976016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 153
24.3%
0 123
19.5%
1 123
19.5%
- 120
19.0%
3 42
 
6.7%
~ 30
 
4.8%
4 13
 
2.1%
5 13
 
2.1%
6 4
 
0.6%
7 3
 
0.5%
Other values (2) 6
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 480
76.2%
Dash Punctuation 120
 
19.0%
Math Symbol 30
 
4.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 153
31.9%
0 123
25.6%
1 123
25.6%
3 42
 
8.8%
4 13
 
2.7%
5 13
 
2.7%
6 4
 
0.8%
7 3
 
0.6%
8 3
 
0.6%
9 3
 
0.6%
Dash Punctuation
ValueCountFrequency (%)
- 120
100.0%
Math Symbol
ValueCountFrequency (%)
~ 30
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 630
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 153
24.3%
0 123
19.5%
1 123
19.5%
- 120
19.0%
3 42
 
6.7%
~ 30
 
4.8%
4 13
 
2.1%
5 13
 
2.1%
6 4
 
0.6%
7 3
 
0.5%
Other values (2) 6
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 630
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 153
24.3%
0 123
19.5%
1 123
19.5%
- 120
19.0%
3 42
 
6.7%
~ 30
 
4.8%
4 13
 
2.1%
5 13
 
2.1%
6 4
 
0.6%
7 3
 
0.5%
Other values (2) 6
 
1.0%

학교급
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
초등
19 
중등
10 
초등.중등
 
1

Length

Max length5
Median length2
Mean length2.1
Min length2

Unique

Unique1 ?
Unique (%)3.3%

Sample

1st row중등
2nd row중등
3rd row중등
4th row초등
5th row중등

Common Values

ValueCountFrequency (%)
초등 19
63.3%
중등 10
33.3%
초등.중등 1
 
3.3%

Length

2023-12-12T22:07:10.130042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T22:07:10.253015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
초등 19
63.3%
중등 10
33.3%
초등.중등 1
 
3.3%

수혜학생수
Real number (ℝ)

HIGH CORRELATION 

Distinct12
Distinct (%)40.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.433333
Minimum7
Maximum20
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-12T22:07:10.375520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile9.45
Q114.25
median17.5
Q320
95-th percentile20
Maximum20
Range13
Interquartile range (IQR)5.75

Descriptive statistics

Standard deviation3.918861
Coefficient of variation (CV)0.23847024
Kurtosis-0.23878336
Mean16.433333
Median Absolute Deviation (MAD)2.5
Skewness-0.90512012
Sum493
Variance15.357471
MonotonicityNot monotonic
2023-12-12T22:07:10.505746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
20 11
36.7%
16 5
16.7%
12 2
 
6.7%
10 2
 
6.7%
18 2
 
6.7%
19 2
 
6.7%
14 1
 
3.3%
9 1
 
3.3%
13 1
 
3.3%
17 1
 
3.3%
Other values (2) 2
 
6.7%
ValueCountFrequency (%)
7 1
 
3.3%
9 1
 
3.3%
10 2
 
6.7%
12 2
 
6.7%
13 1
 
3.3%
14 1
 
3.3%
15 1
 
3.3%
16 5
16.7%
17 1
 
3.3%
18 2
 
6.7%
ValueCountFrequency (%)
20 11
36.7%
19 2
 
6.7%
18 2
 
6.7%
17 1
 
3.3%
16 5
16.7%
15 1
 
3.3%
14 1
 
3.3%
13 1
 
3.3%
12 2
 
6.7%
10 2
 
6.7%

Interactions

2023-12-12T22:07:07.923548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:07:07.752579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:07:08.023013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:07:07.833455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T22:07:10.602491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분학교명지역사업기간학교급수혜학생수
구분1.0001.0000.5641.0000.5230.000
학교명1.0001.0001.0001.0000.0000.430
지역0.5641.0001.0001.0000.3820.601
사업기간1.0001.0001.0001.0001.0001.000
학교급0.5230.0000.3821.0001.0000.860
수혜학생수0.0000.4300.6011.0000.8601.000
2023-12-12T22:07:10.717980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
학교급지역
학교급1.0000.172
지역0.1721.000
2023-12-12T22:07:10.813235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분수혜학생수지역학교급
구분1.0000.1330.2440.301
수혜학생수0.1331.0000.2720.672
지역0.2440.2721.0000.172
학교급0.3010.6720.1721.000

Missing values

2023-12-12T22:07:08.412075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T22:07:08.512657image/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

구분학교명지역사업기간학교급수혜학생수
01경민여중경기도2021-03-01~2021-12-31중등12
12광명북중경기도2021-03-01~2021-12-32중등14
23광주동신여중광주2021-03-01~2021-12-33중등9
34김해내동초경상남도2021-03-01~2021-12-34초등16
45대전지족중대전2021-03-01~2021-12-35중등10
56덕소중경기도2021-03-01~2021-12-36중등20
67동수영중부산2021-03-01~2021-12-37중등20
78목포동초전라남도2021-03-01~2021-12-38초등20
89목포석현초전라남도2021-03-01~2021-12-39초등20
910문경영재교육원경상북도2021-03-01~2021-12-40초등16
구분학교명지역사업기간학교급수혜학생수
2021장복초경상남도2021-03-01~2021-12-51초등20
2122제산초경상남도2021-03-01~2021-12-52초등19
2223포산중대구2021-03-01~2021-12-53중등10
2324학운초경기도2021-03-01~2021-12-54초등12
2425홍광초충청북도2021-03-01~2021-12-55초등16
2526홍광초충청북도2021-03-01~2021-12-56초등.중등7
2627화도초경기도2021-03-01~2021-12-57초등20
2728회덕초대전2021-03-01~2021-12-58초등20
2829회룡초경기도2021-03-01~2021-12-59초등18
2930효성초대구2021-03-01~2021-12-60초등19