Overview

Dataset statistics

Number of variables5
Number of observations25
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.1 KiB
Average record size in memory45.3 B

Variable types

Categorical3
Text2

Dataset

Description원주시 중학교 선배정학교군 자료를 지역별로 구분하여 안내(선배정학교군, 초등학교명, 해당지역, 중학교명)
Author강원도교육청
URLhttps://www.data.go.kr/data/15049008/fileData.do

Alerts

선배정 학교군 has constant value ""Constant
중학교명 is highly overall correlated with 비 고High correlation
비 고 is highly overall correlated with 중학교명High correlation
비 고 is highly imbalanced (75.8%)Imbalance

Reproduction

Analysis started2023-12-12 02:51:32.929976
Analysis finished2023-12-12 02:51:33.382635
Duration0.45 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

선배정 학교군
Categorical

CONSTANT 

Distinct1
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
원주시
25 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row원주시
2nd row원주시
3rd row원주시
4th row원주시
5th row원주시

Common Values

ValueCountFrequency (%)
원주시 25
100.0%

Length

2023-12-12T11:51:33.474415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:51:33.605393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
원주시 25
100.0%

초등학교명
Text

Distinct23
Distinct (%)92.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
2023-12-12T11:51:33.784347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length3
Mean length3.28
Min length3

Characters and Unicode

Total characters82
Distinct characters40
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

Unique21 ?
Unique (%)84.0%

Sample

1st row교학초
2nd row둔둔초
3rd row소초초
4th row흥양초
5th row장양초
ValueCountFrequency (%)
원주초 2
 
7.4%
서곡초 2
 
7.4%
무실초 1
 
3.7%
전체 1
 
3.7%
원주시내 1
 
3.7%
교학초 1
 
3.7%
신평초 1
 
3.7%
매지초 1
 
3.7%
흥업초 1
 
3.7%
버들초 1
 
3.7%
Other values (15) 15
55.6%
2023-12-12T11:51:34.145133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
초 26
31.7%
원 3
 
3.7%
곡 3
 
3.7%
주 3
 
3.7%
서 2
 
2.4%
둔 2
 
2.4%
학 2
 
2.4%
흥 2
 
2.4%
양 2
 
2.4%
태 2
 
2.4%
Other values (30) 35
42.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 80
97.6%
Space Separator 2
 
2.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
초 26
32.5%
원 3
 
3.8%
곡 3
 
3.8%
주 3
 
3.8%
서 2
 
2.5%
둔 2
 
2.5%
학 2
 
2.5%
흥 2
 
2.5%
양 2
 
2.5%
태 2
 
2.5%
Other values (29) 33
41.2%
Space Separator
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 80
97.6%
Common 2
 
2.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
초 26
32.5%
원 3
 
3.8%
곡 3
 
3.8%
주 3
 
3.8%
서 2
 
2.5%
둔 2
 
2.5%
학 2
 
2.5%
흥 2
 
2.5%
양 2
 
2.5%
태 2
 
2.5%
Other values (29) 33
41.2%
Common
ValueCountFrequency (%)
2
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 80
97.6%
ASCII 2
 
2.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
초 26
32.5%
원 3
 
3.8%
곡 3
 
3.8%
주 3
 
3.8%
서 2
 
2.5%
둔 2
 
2.5%
학 2
 
2.5%
흥 2
 
2.5%
양 2
 
2.5%
태 2
 
2.5%
Other values (29) 33
41.2%
ASCII
ValueCountFrequency (%)
2
100.0%

해당 지역
Text

Distinct22
Distinct (%)88.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
2023-12-12T11:51:34.383752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length52
Median length26
Mean length19.76
Min length1

Characters and Unicode

Total characters494
Distinct characters71
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique21 ?
Unique (%)84.0%

Sample

1st row소초면
2nd row-
3rd row-
4th row-
5th row태장2동(21-26통), 소초면 장양1리 2～3반(화수동, 양촌), 장양2～9리
ValueCountFrequency (%)
1-2반 5
 
7.1%
4
 
5.7%
제외 2
 
2.9%
반곡관설동(7통 2
 
2.9%
소초면 2
 
2.9%
반곡관설동(2통 1
 
1.4%
반곡관설동(1통 1
 
1.4%
신평2-3리 1
 
1.4%
가곡2리 1
 
1.4%
1반 1
 
1.4%
Other values (50) 50
71.4%
2023-12-12T11:51:34.774424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
47
 
9.5%
통 33
 
6.7%
1 31
 
6.3%
, 30
 
6.1%
- 28
 
5.7%
2 28
 
5.7%
( 28
 
5.7%
) 28
 
5.7%
동 21
 
4.3%
반 19
 
3.8%
Other values (61) 201
40.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 218
44.1%
Decimal Number 108
21.9%
Space Separator 47
 
9.5%
Other Punctuation 30
 
6.1%
Dash Punctuation 28
 
5.7%
Open Punctuation 28
 
5.7%
Close Punctuation 28
 
5.7%
Math Symbol 7
 
1.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
통 33
15.1%
동 21
 
9.6%
반 19
 
8.7%
리 16
 
7.3%
면 14
 
6.4%
곡 9
 
4.1%
장 9
 
4.1%
관 6
 
2.8%
태 6
 
2.8%
설 6
 
2.8%
Other values (42) 79
36.2%
Decimal Number
ValueCountFrequency (%)
1 31
28.7%
2 28
25.9%
3 15
13.9%
4 9
 
8.3%
7 7
 
6.5%
6 5
 
4.6%
8 4
 
3.7%
5 3
 
2.8%
0 3
 
2.8%
9 3
 
2.8%
Math Symbol
ValueCountFrequency (%)
> 2
28.6%
< 2
28.6%
～ 2
28.6%
~ 1
14.3%
Space Separator
ValueCountFrequency (%)
47
100.0%
Other Punctuation
ValueCountFrequency (%)
, 30
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 28
100.0%
Open Punctuation
ValueCountFrequency (%)
( 28
100.0%
Close Punctuation
ValueCountFrequency (%)
) 28
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 276
55.9%
Hangul 218
44.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
통 33
15.1%
동 21
 
9.6%
반 19
 
8.7%
리 16
 
7.3%
면 14
 
6.4%
곡 9
 
4.1%
장 9
 
4.1%
관 6
 
2.8%
태 6
 
2.8%
설 6
 
2.8%
Other values (42) 79
36.2%
Common
ValueCountFrequency (%)
47
17.0%
1 31
11.2%
, 30
10.9%
- 28
10.1%
2 28
10.1%
( 28
10.1%
) 28
10.1%
3 15
 
5.4%
4 9
 
3.3%
7 7
 
2.5%
Other values (9) 25
9.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 274
55.5%
Hangul 218
44.1%
None 2
 
0.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
47
17.2%
1 31
11.3%
, 30
10.9%
- 28
10.2%
2 28
10.2%
( 28
10.2%
) 28
10.2%
3 15
 
5.5%
4 9
 
3.3%
7 7
 
2.6%
Other values (8) 23
8.4%
Hangul
ValueCountFrequency (%)
통 33
15.1%
동 21
 
9.6%
반 19
 
8.7%
리 16
 
7.3%
면 14
 
6.4%
곡 9
 
4.1%
장 9
 
4.1%
관 6
 
2.8%
태 6
 
2.8%
설 6
 
2.8%
Other values (42) 79
36.2%
None
ValueCountFrequency (%)
～ 2
100.0%

중학교명
Categorical

HIGH CORRELATION 

Distinct11
Distinct (%)44.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
진광중, 북원중, 태장중
7 
원주중, 상지여중
3 
육민관중
3 
치악중
2 
태장중
2 
Other values (6)
8 

Length

Max length13
Median length9
Mean length6.96
Min length3

Unique

Unique4 ?
Unique (%)16.0%

Sample

1st row진광중, 북원중, 태장중
2nd row진광중, 북원중, 태장중
3rd row진광중, 북원중, 태장중
4th row진광중, 북원중, 태장중
5th row진광중, 북원중, 태장중

Common Values

ValueCountFrequency (%)
진광중, 북원중, 태장중 7
28.0%
원주중, 상지여중 3
12.0%
육민관중 3
12.0%
치악중 2
 
8.0%
태장중 2
 
8.0%
반곡중 2
 
8.0%
버들중 2
 
8.0%
진광중, 북원중 1
 
4.0%
대성중 1
 
4.0%
남원주중 1
 
4.0%

Length

2023-12-12T11:51:34.940950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
태장중 9
20.9%
진광중 8
18.6%
북원중 8
18.6%
원주중 3
 
7.0%
상지여중 3
 
7.0%
육민관중 3
 
7.0%
치악중 2
 
4.7%
반곡중 2
 
4.7%
버들중 2
 
4.7%
대성중 1
 
2.3%
Other values (2) 2
 
4.7%

비 고
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
-
24 
2020.3.1. 기업중 개교전까지
 
1

Length

Max length19
Median length1
Mean length1.72
Min length1

Unique

Unique1 ?
Unique (%)4.0%

Sample

1st row-
2nd row-
3rd row-
4th row-
5th row-

Common Values

ValueCountFrequency (%)
- 24
96.0%
2020.3.1. 기업중 개교전까지 1
 
4.0%

Length

2023-12-12T11:51:35.083348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:51:35.204750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
24
88.9%
2020.3.1 1
 
3.7%
기업중 1
 
3.7%
개교전까지 1
 
3.7%

Correlations

2023-12-12T11:51:35.275458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
초등학교명해당 지역중학교명비 고
초등학교명1.0000.8810.4341.000
해당 지역0.8811.0000.9961.000
중학교명0.4340.9961.0001.000
비 고1.0001.0001.0001.000
2023-12-12T11:51:35.385201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
비 고중학교명
비 고1.0000.780
중학교명0.7801.000
2023-12-12T11:51:35.472852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
중학교명비 고
중학교명1.0000.780
비 고0.7801.000

Missing values

2023-12-12T11:51:33.210193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T11:51:33.331564image/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

선배정 학교군초등학교명해당 지역중학교명비 고
0원주시교학초소초면진광중, 북원중, 태장중-
1원주시둔둔초-진광중, 북원중, 태장중-
2원주시소초초-진광중, 북원중, 태장중-
3원주시흥양초-진광중, 북원중, 태장중-
4원주시장양초태장2동(21-26통), 소초면 장양1리 2～3반(화수동, 양촌), 장양2～9리진광중, 북원중, 태장중-
5원주시태장초태장1동(7통), 태장2동(1-10통, 29-30통, 31통)진광중, 북원중, 태장중-
6원주시태봉초태장2동(11-20통, 27-28통, 32통)진광중, 북원중, 태장중-
7원주시우산초우산동(2-18통, 21통, 24통)진광중, 북원중-
8원주시금대초반곡관설동(7통 1-2반), 판부면(금대리)원주중, 상지여중-
9원주시관설초반곡관설동(7통 3-6반, 8-9통, 12통), 판부면(신촌리)원주중, 상지여중-
선배정 학교군초등학교명해당 지역중학교명비 고
15원주시원주초태장1동(4통 3-4반), 봉산동(17-21통)태장중-
16원주시학성초태장1동(1-3통, 4통 1-2반, 5-6통, 7통 4반, 8-16통)태장중-
17원주시원주초봉산동(1~3통)반곡중-
18원주시반곡초반곡관설동(5통), 행구동(3통3반)반곡중-
19원주시봉대초행구동(1-3통2반, 4-5통1반), 반곡관설동(1통)버들중-
20원주시버들초반곡관설동(2통)버들중-
21원주시흥업초흥업면<사제3리 1-2반 제외>육민관중-
22원주시매지초-육민관중-
23원주시서곡초판부면(서곡 2-7리)육민관중-
24원주시원주시내 전체 초교원주기업도시섬강중학교2020.3.1. 기업중 개교전까지