Overview

Dataset statistics

Number of variables6
Number of observations464
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory22.3 KiB
Average record size in memory49.3 B

Variable types

Text4
Categorical1
Numeric1

Dataset

Description대구시교육청 소속 초등학교, 중학교, 고등학교, 특수학교 현황(일람표)에 대한 정보로 학교명, 주소, 우편번호, 연락처, 팩스번호를 제공합니다.
URLhttps://www.data.go.kr/data/15015254/fileData.do

Alerts

우편번호 is highly overall correlated with 관할구군청High correlation
관할구군청 is highly overall correlated with 우편번호High correlation
학교명 has unique valuesUnique
전화번호 has unique valuesUnique

Reproduction

Analysis started2023-12-12 13:07:13.121795
Analysis finished2023-12-12 13:07:14.026918
Duration0.91 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

학교명
Text

UNIQUE 

Distinct464
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size3.8 KiB
2023-12-12T22:07:14.241997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length8
Mean length7.1659483
Min length5

Characters and Unicode

Total characters3325
Distinct characters177
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

Unique464 ?
Unique (%)100.0%

Sample

1st row경북대학교사범대학부설초등학교
2nd row대구교육대학교대구부설초등학교
3rd row대구지묘초등학교
4th row대구해서초등학교
5th row대구해안초등학교
ValueCountFrequency (%)
대구공업고등학교 2
 
0.4%
경북대학교사범대학부설초등학교 1
 
0.2%
관음중학교 1
 
0.2%
도원중학교 1
 
0.2%
대서중학교 1
 
0.2%
대곡중학교 1
 
0.2%
대건중학교 1
 
0.2%
구남중학교 1
 
0.2%
월암중학교 1
 
0.2%
새본리중학교 1
 
0.2%
Other values (455) 455
97.6%
2023-12-12T22:07:14.636215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
학 480
14.4%
교 472
14.2%
등 329
 
9.9%
대 309
 
9.3%
구 287
 
8.6%
초 233
 
7.0%
중 131
 
3.9%
고 98
 
2.9%
성 47
 
1.4%
동 44
 
1.3%
Other values (167) 895
26.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3323
99.9%
Space Separator 2
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
학 480
14.4%
교 472
14.2%
등 329
 
9.9%
대 309
 
9.3%
구 287
 
8.6%
초 233
 
7.0%
중 131
 
3.9%
고 98
 
2.9%
성 47
 
1.4%
동 44
 
1.3%
Other values (166) 893
26.9%
Space Separator
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3323
99.9%
Common 2
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
학 480
14.4%
교 472
14.2%
등 329
 
9.9%
대 309
 
9.3%
구 287
 
8.6%
초 233
 
7.0%
중 131
 
3.9%
고 98
 
2.9%
성 47
 
1.4%
동 44
 
1.3%
Other values (166) 893
26.9%
Common
ValueCountFrequency (%)
2
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3323
99.9%
ASCII 2
 
0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
학 480
14.4%
교 472
14.2%
등 329
 
9.9%
대 309
 
9.3%
구 287
 
8.6%
초 233
 
7.0%
중 131
 
3.9%
고 98
 
2.9%
성 47
 
1.4%
동 44
 
1.3%
Other values (166) 893
26.9%
ASCII
ValueCountFrequency (%)
2
100.0%

관할구군청
Categorical

HIGH CORRELATION 

Distinct8
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size3.8 KiB
달서구
109 
북구
82 
수성구
75 
달성군
61 
동구
58 
Other values (3)
79 

Length

Max length3
Median length3
Mean length2.5280172
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row중구
2nd row달서구
3rd row동구
4th row동구
5th row동구

Common Values

ValueCountFrequency (%)
달서구 109
23.5%
북구 82
17.7%
수성구 75
16.2%
달성군 61
13.1%
동구 58
12.5%
서구 31
 
6.7%
남구 29
 
6.2%
중구 19
 
4.1%

Length

2023-12-12T22:07:14.785335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T22:07:14.908556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
달서구 109
23.5%
북구 82
17.7%
수성구 75
16.2%
달성군 61
13.1%
동구 58
12.5%
서구 31
 
6.7%
남구 29
 
6.2%
중구 19
 
4.1%

주소
Text

Distinct429
Distinct (%)92.5%
Missing0
Missing (%)0.0%
Memory size3.8 KiB
2023-12-12T22:07:15.185368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length26
Mean length18.43319
Min length15

Characters and Unicode

Total characters8553
Distinct characters163
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique398 ?
Unique (%)85.8%

Sample

1st row대구광역시 중구 달구벌대로 2150
2nd row대구광역시 달서구 도원남로 19
3rd row대구광역시 동구 파계로6길 54-3
4th row대구광역시 동구 팔공로 220-4
5th row대구광역시 동구 옻골로 50
ValueCountFrequency (%)
대구광역시 463
24.1%
달서구 109
 
5.7%
북구 82
 
4.3%
수성구 75
 
3.9%
달성군 61
 
3.2%
동구 58
 
3.0%
서구 31
 
1.6%
남구 29
 
1.5%
중구 19
 
1.0%
다사읍 16
 
0.8%
Other values (521) 975
50.8%
2023-12-12T22:07:15.616357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1454
17.0%
구 913
 
10.7%
대 538
 
6.3%
역 469
 
5.5%
시 465
 
5.4%
광 463
 
5.4%
로 444
 
5.2%
1 270
 
3.2%
길 240
 
2.8%
2 214
 
2.5%
Other values (153) 3083
36.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 5543
64.8%
Decimal Number 1528
 
17.9%
Space Separator 1454
 
17.0%
Dash Punctuation 28
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 913
16.5%
대 538
 
9.7%
역 469
 
8.5%
시 465
 
8.4%
광 463
 
8.4%
로 444
 
8.0%
길 240
 
4.3%
달 206
 
3.7%
성 166
 
3.0%
서 166
 
3.0%
Other values (141) 1473
26.6%
Decimal Number
ValueCountFrequency (%)
1 270
17.7%
2 214
14.0%
3 196
12.8%
5 156
10.2%
0 155
10.1%
4 131
8.6%
7 124
8.1%
6 120
7.9%
8 92
 
6.0%
9 70
 
4.6%
Space Separator
ValueCountFrequency (%)
1454
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 28
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 5543
64.8%
Common 3010
35.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 913
16.5%
대 538
 
9.7%
역 469
 
8.5%
시 465
 
8.4%
광 463
 
8.4%
로 444
 
8.0%
길 240
 
4.3%
달 206
 
3.7%
성 166
 
3.0%
서 166
 
3.0%
Other values (141) 1473
26.6%
Common
ValueCountFrequency (%)
1454
48.3%
1 270
 
9.0%
2 214
 
7.1%
3 196
 
6.5%
5 156
 
5.2%
0 155
 
5.1%
4 131
 
4.4%
7 124
 
4.1%
6 120
 
4.0%
8 92
 
3.1%
Other values (2) 98
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 5543
64.8%
ASCII 3010
35.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1454
48.3%
1 270
 
9.0%
2 214
 
7.1%
3 196
 
6.5%
5 156
 
5.2%
0 155
 
5.1%
4 131
 
4.4%
7 124
 
4.1%
6 120
 
4.0%
8 92
 
3.1%
Other values (2) 98
 
3.3%
Hangul
ValueCountFrequency (%)
구 913
16.5%
대 538
 
9.7%
역 469
 
8.5%
시 465
 
8.4%
광 463
 
8.4%
로 444
 
8.0%
길 240
 
4.3%
달 206
 
3.7%
성 166
 
3.0%
서 166
 
3.0%
Other values (141) 1473
26.6%

우편번호
Real number (ℝ)

HIGH CORRELATION 

Distinct328
Distinct (%)70.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42127.349
Minimum41000
Maximum43019
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.2 KiB
2023-12-12T22:07:15.793758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum41000
5-th percentile41089
Q141521
median42176
Q342726
95-th percentile42974
Maximum43019
Range2019
Interquartile range (IQR)1205

Descriptive statistics

Standard deviation628.06744
Coefficient of variation (CV)0.014908781
Kurtosis-1.2900448
Mean42127.349
Median Absolute Deviation (MAD)583
Skewness-0.2332172
Sum19547090
Variance394468.71
MonotonicityNot monotonic
2023-12-12T22:07:15.984981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
42748 7
 
1.5%
42726 6
 
1.3%
42400 5
 
1.1%
42655 4
 
0.9%
42974 4
 
0.9%
42083 4
 
0.9%
42507 4
 
0.9%
42606 4
 
0.9%
41578 3
 
0.6%
41522 3
 
0.6%
Other values (318) 420
90.5%
ValueCountFrequency (%)
41000 2
0.4%
41001 1
 
0.2%
41002 1
 
0.2%
41003 2
0.4%
41009 2
0.4%
41021 3
0.6%
41023 1
 
0.2%
41027 1
 
0.2%
41042 2
0.4%
41055 1
 
0.2%
ValueCountFrequency (%)
43019 2
0.4%
43015 2
0.4%
43010 2
0.4%
43008 2
0.4%
43007 1
0.2%
43005 1
0.2%
43001 2
0.4%
42997 1
0.2%
42995 2
0.4%
42993 1
0.2%

전화번호
Text

UNIQUE 

Distinct464
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size3.8 KiB
2023-12-12T22:07:16.277539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length12
Mean length12.025862
Min length12

Characters and Unicode

Total characters5580
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique464 ?
Unique (%)100.0%

Sample

1st row053-232-5804
2nd row053-234-5332
3rd row053-232-2311
4th row053-232-1103
5th row053-232-0951
ValueCountFrequency (%)
053-232-5804 1
 
0.2%
053-233-7151 1
 
0.2%
053-234-7070 1
 
0.2%
053-234-6281 1
 
0.2%
053-234-6805 1
 
0.2%
053-234-8808 1
 
0.2%
053-234-8816 1
 
0.2%
053-234-8280 1
 
0.2%
053-234-7668 1
 
0.2%
053-234-8444 1
 
0.2%
Other values (454) 454
97.8%
2023-12-12T22:07:16.637116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3 1188
21.3%
0 946
17.0%
- 928
16.6%
5 736
13.2%
2 715
12.8%
4 264
 
4.7%
1 244
 
4.4%
7 158
 
2.8%
8 149
 
2.7%
6 144
 
2.6%
Other values (3) 108
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4649
83.3%
Dash Punctuation 928
 
16.6%
Other Punctuation 2
 
< 0.1%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 1188
25.6%
0 946
20.3%
5 736
15.8%
2 715
15.4%
4 264
 
5.7%
1 244
 
5.2%
7 158
 
3.4%
8 149
 
3.2%
6 144
 
3.1%
9 105
 
2.3%
Dash Punctuation
ValueCountFrequency (%)
- 928
100.0%
Other Punctuation
ValueCountFrequency (%)
, 2
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 5580
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 1188
21.3%
0 946
17.0%
- 928
16.6%
5 736
13.2%
2 715
12.8%
4 264
 
4.7%
1 244
 
4.4%
7 158
 
2.8%
8 149
 
2.7%
6 144
 
2.6%
Other values (3) 108
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5580
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 1188
21.3%
0 946
17.0%
- 928
16.6%
5 736
13.2%
2 715
12.8%
4 264
 
4.7%
1 244
 
4.4%
7 158
 
2.8%
8 149
 
2.7%
6 144
 
2.6%
Other values (3) 108
 
1.9%

팩스번호
Text

Distinct463
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Memory size3.8 KiB
2023-12-12T22:07:16.872605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters5568
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique462 ?
Unique (%)99.6%

Sample

1st row053-427-9455
2nd row053-234-5336
3rd row053-985-6324
4th row053-983-1705
5th row053-981-6588
ValueCountFrequency (%)
053-981-8003 2
 
0.4%
053-641-2207 1
 
0.2%
053-651-3374 1
 
0.2%
053-634-8559 1
 
0.2%
053-634-0967 1
 
0.2%
053-234-6777 1
 
0.2%
053-637-8401 1
 
0.2%
053-625-8054 1
 
0.2%
053-635-2309 1
 
0.2%
053-527-6301 1
 
0.2%
Other values (453) 453
97.6%
2023-12-12T22:07:17.218723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 928
16.7%
5 897
16.1%
3 847
15.2%
0 704
12.6%
6 404
7.3%
2 345
 
6.2%
4 327
 
5.9%
7 294
 
5.3%
1 282
 
5.1%
9 273
 
4.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4640
83.3%
Dash Punctuation 928
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 897
19.3%
3 847
18.3%
0 704
15.2%
6 404
8.7%
2 345
 
7.4%
4 327
 
7.0%
7 294
 
6.3%
1 282
 
6.1%
9 273
 
5.9%
8 267
 
5.8%
Dash Punctuation
ValueCountFrequency (%)
- 928
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 5568
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 928
16.7%
5 897
16.1%
3 847
15.2%
0 704
12.6%
6 404
7.3%
2 345
 
6.2%
4 327
 
5.9%
7 294
 
5.3%
1 282
 
5.1%
9 273
 
4.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5568
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 928
16.7%
5 897
16.1%
3 847
15.2%
0 704
12.6%
6 404
7.3%
2 345
 
6.2%
4 327
 
5.9%
7 294
 
5.3%
1 282
 
5.1%
9 273
 
4.9%

Interactions

2023-12-12T22:07:13.395358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T22:07:17.308394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관할구군청우편번호
관할구군청1.0000.958
우편번호0.9581.000
2023-12-12T22:07:17.379048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
우편번호관할구군청
우편번호1.0000.869
관할구군청0.8691.000

Missing values

2023-12-12T22:07:13.521440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T22:07:13.975034image/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

학교명관할구군청주소우편번호전화번호팩스번호
0경북대학교사범대학부설초등학교중구대구광역시 중구 달구벌대로 215041959053-232-5804053-427-9455
1대구교육대학교대구부설초등학교달서구대구광역시 달서구 도원남로 1942831053-234-5332053-234-5336
2대구지묘초등학교동구대구광역시 동구 파계로6길 54-341002053-232-2311053-985-6324
3대구해서초등학교동구대구광역시 동구 팔공로 220-441027053-232-1103053-983-1705
4대구해안초등학교동구대구광역시 동구 옻골로 5041055053-232-0951053-981-6588
5대구효동초등학교동구대구광역시 동구 효동로 78-741171053-232-3703053-959-4744
6대구효목초등학교동구대구광역시 동구 화랑로27길 2541240053-232-1666053-742-1438
7대구효신초등학교동구대구광역시 동구 동부로34길 7041247053-232-1913053-743-0514
8동대구초등학교동구대구광역시 동구 신암남로 4541211053-232-1404053-939-1404
9영신초등학교동구대구광역시 동구 팔공로50길 3241021053-232-5900053-232-5920
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455대구성보학교북구대구광역시 북구 동북로71길 9-4541528053-231-3168053-943-0556
456대구이룸고등학교북구대구광역시 북구 복현로8길 16-5441528053-233-7596053-233-7605
457대구광명학교남구대구광역시 남구 성당로50길 3342400053-231-3300053-624-5836
458대구영화학교남구대구광역시 남구 성당로50길 3342400053-231-3390053-628-6629
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460대구덕희학교남구대구광역시 남구 성당로50길 3342400053-231-3770053-626-3061
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462대구세명학교달서구대구광역시 달서구 새방로 6242607053-231-9560053-585-2575
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