Overview

Dataset statistics

Number of variables4
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows1
Duplicate rows (%)1.0%
Total size in memory3.4 KiB
Average record size in memory34.3 B

Variable types

Numeric1
Categorical1
Text2

Dataset

Description수학교육 발전을 위해 기여한 교원을 발굴·포상하고 우수사례를 확산함으로써 수학교육 내실화를 추구하는 대한민국 수학교육상 목록입니다. 해당 데이터가 보유한 컬럼은 다음과 같습니다. 컬럼명 : 수상연도, 지역, 학교, 교사명
Author한국과학창의재단
URLhttps://www.data.go.kr/data/15093497/fileData.do

Alerts

Dataset has 1 (1.0%) duplicate rowsDuplicates

Reproduction

Analysis started2023-12-12 04:27:12.153920
Analysis finished2023-12-12 04:27:12.920927
Duration0.77 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

수상연도
Real number (ℝ)

Distinct7
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2016.26
Minimum2014
Maximum2020
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-12T13:27:12.958513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2014
5-th percentile2014
Q12015
median2015.5
Q32018
95-th percentile2020
Maximum2020
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.0480096
Coefficient of variation (CV)0.0010157468
Kurtosis-1.0759847
Mean2016.26
Median Absolute Deviation (MAD)1.5
Skewness0.5475777
Sum201626
Variance4.1943434
MonotonicityNot monotonic
2023-12-12T13:27:13.039704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2015 26
26.0%
2014 24
24.0%
2018 10
 
10.0%
2020 10
 
10.0%
2019 10
 
10.0%
2016 10
 
10.0%
2017 10
 
10.0%
ValueCountFrequency (%)
2014 24
24.0%
2015 26
26.0%
2016 10
 
10.0%
2017 10
 
10.0%
2018 10
 
10.0%
2019 10
 
10.0%
2020 10
 
10.0%
ValueCountFrequency (%)
2020 10
 
10.0%
2019 10
 
10.0%
2018 10
 
10.0%
2017 10
 
10.0%
2016 10
 
10.0%
2015 26
26.0%
2014 24
24.0%

지역
Categorical

Distinct17
Distinct (%)17.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
경기
17 
서울
11 
대구
11 
대전
11 
인천
10 
Other values (12)
40 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique3 ?
Unique (%)3.0%

Sample

1st row대전
2nd row인천
3rd row경기
4th row서울
5th row광주

Common Values

ValueCountFrequency (%)
경기 17
17.0%
서울 11
11.0%
대구 11
11.0%
대전 11
11.0%
인천 10
10.0%
광주 6
 
6.0%
부산 6
 
6.0%
강원 5
 
5.0%
경북 4
 
4.0%
경남 4
 
4.0%
Other values (7) 15
15.0%

Length

2023-12-12T13:27:13.132479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기 17
17.0%
대구 11
11.0%
대전 11
11.0%
서울 11
11.0%
인천 10
10.0%
광주 6
 
6.0%
부산 6
 
6.0%
강원 5
 
5.0%
충남 4
 
4.0%
경북 4
 
4.0%
Other values (7) 15
15.0%

학교
Text

Distinct98
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-12T13:27:13.325320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length12
Mean length6.68
Min length5

Characters and Unicode

Total characters668
Distinct characters122
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

Unique96 ?
Unique (%)96.0%

Sample

1st row대신고등학교
2nd row작전여자고등학교
3rd row가온고등학교
4th row반포고등학교
5th row전남대학교사범대학 부설중학교
ValueCountFrequency (%)
어등초등학교 2
 
1.8%
부설중학교 2
 
1.8%
강원체육고등학교 2
 
1.8%
초등학교 2
 
1.8%
가온고등학교 1
 
0.9%
반포고등학교 1
 
0.9%
대전탄방중학교 1
 
0.9%
김천중학교 1
 
0.9%
문지중학교 1
 
0.9%
어은중학교 1
 
0.9%
Other values (98) 98
87.5%
2023-12-12T13:27:13.631753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
학 109
16.3%
교 104
15.6%
등 63
 
9.4%
중 39
 
5.8%
초 31
 
4.6%
고 31
 
4.6%
대 19
 
2.8%
12
 
1.8%
동 11
 
1.6%
자 8
 
1.2%
Other values (112) 241
36.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 656
98.2%
Space Separator 12
 
1.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
학 109
16.6%
교 104
15.9%
등 63
 
9.6%
중 39
 
5.9%
초 31
 
4.7%
고 31
 
4.7%
대 19
 
2.9%
동 11
 
1.7%
자 8
 
1.2%
남 8
 
1.2%
Other values (111) 233
35.5%
Space Separator
ValueCountFrequency (%)
12
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 656
98.2%
Common 12
 
1.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
학 109
16.6%
교 104
15.9%
등 63
 
9.6%
중 39
 
5.9%
초 31
 
4.7%
고 31
 
4.7%
대 19
 
2.9%
동 11
 
1.7%
자 8
 
1.2%
남 8
 
1.2%
Other values (111) 233
35.5%
Common
ValueCountFrequency (%)
12
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 656
98.2%
ASCII 12
 
1.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
학 109
16.6%
교 104
15.9%
등 63
 
9.6%
중 39
 
5.9%
초 31
 
4.7%
고 31
 
4.7%
대 19
 
2.9%
동 11
 
1.7%
자 8
 
1.2%
남 8
 
1.2%
Other values (111) 233
35.5%
ASCII
ValueCountFrequency (%)
12
100.0%

교사명
Text

Distinct98
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-12T13:27:13.885744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.99
Min length2

Characters and Unicode

Total characters299
Distinct characters90
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

Unique96 ?
Unique (%)96.0%

Sample

1st row하진수
2nd row이현진
3rd row신종환
4th row박지현
5th row이옥자
ValueCountFrequency (%)
신은희 2
 
2.0%
김정주 2
 
2.0%
김혜진 1
 
1.0%
박근영 1
 
1.0%
최수연 1
 
1.0%
미 1
 
1.0%
주 1
 
1.0%
조미영 1
 
1.0%
안영지 1
 
1.0%
김형식 1
 
1.0%
Other values (89) 89
88.1%
2023-12-12T13:27:14.251036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
김 28
 
9.4%
영 19
 
6.4%
정 14
 
4.7%
이 12
 
4.0%
은 11
 
3.7%
현 9
 
3.0%
최 8
 
2.7%
미 8
 
2.7%
박 7
 
2.3%
숙 7
 
2.3%
Other values (80) 176
58.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 298
99.7%
Space Separator 1
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
김 28
 
9.4%
영 19
 
6.4%
정 14
 
4.7%
이 12
 
4.0%
은 11
 
3.7%
현 9
 
3.0%
최 8
 
2.7%
미 8
 
2.7%
박 7
 
2.3%
숙 7
 
2.3%
Other values (79) 175
58.7%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 298
99.7%
Common 1
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
김 28
 
9.4%
영 19
 
6.4%
정 14
 
4.7%
이 12
 
4.0%
은 11
 
3.7%
현 9
 
3.0%
최 8
 
2.7%
미 8
 
2.7%
박 7
 
2.3%
숙 7
 
2.3%
Other values (79) 175
58.7%
Common
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 298
99.7%
ASCII 1
 
0.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
김 28
 
9.4%
영 19
 
6.4%
정 14
 
4.7%
이 12
 
4.0%
은 11
 
3.7%
현 9
 
3.0%
최 8
 
2.7%
미 8
 
2.7%
박 7
 
2.3%
숙 7
 
2.3%
Other values (79) 175
58.7%
ASCII
ValueCountFrequency (%)
1
100.0%

Interactions

2023-12-12T13:27:12.730578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T13:27:14.331446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
수상연도지역학교교사명
수상연도1.0000.0000.9651.000
지역0.0001.0001.0000.986
학교0.9651.0001.0001.000
교사명1.0000.9861.0001.000
2023-12-12T13:27:14.410873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
수상연도지역
수상연도1.0000.000
지역0.0001.000

Missing values

2023-12-12T13:27:12.825965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T13:27:12.893106image/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

수상연도지역학교교사명
02018대전대신고등학교하진수
12018인천작전여자고등학교이현진
22018경기가온고등학교신종환
32018서울반포고등학교박지현
42018광주전남대학교사범대학 부설중학교이옥자
52018경북석전중학교김희자
62018인천부평동중학교김정란
72018대구두산초등학교표명균
82018서울서빙고초등학교김주숙
92018서울불암초등학교김남준
수상연도지역학교교사명
902017광주어등초등학교양종현
912017서울서울오류초등학교임미인
922017충남한내여자중학교김미영
932017대구경북대학교사범대학 부설중학교김선혜
942017대전대전봉우중학교송라영
952017대구동원중학교임은영
962017충남공주 생명과학고등학교김은숙
972017경남안의고등학교박근영
982015대구덕원고등학교조치연
992015강원강원체육고등학교신은희

Duplicate rows

Most frequently occurring

수상연도지역학교교사명# duplicates
02015강원강원체육고등학교신은희2