Overview

Dataset statistics

Number of variables3
Number of observations27
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory807.0 B
Average record size in memory29.9 B

Variable types

Text2
Numeric1

Dataset

Description경상남도 내 향교 현황 정보입니다.
Author경상남도
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=3083305

Alerts

향교명 has unique valuesUnique
소재지 has unique valuesUnique
유림수 has unique valuesUnique

Reproduction

Analysis started2023-12-10 22:55:53.688160
Analysis finished2023-12-10 22:55:54.016933
Duration0.33 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

향교명
Text

UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size348.0 B
2023-12-11T07:55:54.137088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters108
Distinct characters38
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

Unique27 ?
Unique (%)100.0%

Sample

1st row창원향교
2nd row마산향교
3rd row진주향교
4th row통영향교
5th row사천향교
ValueCountFrequency (%)
창원향교 1
 
3.7%
영산향교 1
 
3.7%
합천향교 1
 
3.7%
삼가향교 1
 
3.7%
강양향교 1
 
3.7%
거창향교 1
 
3.7%
안의향교 1
 
3.7%
함양향교 1
 
3.7%
단성향교 1
 
3.7%
산청향교 1
 
3.7%
Other values (17) 17
63.0%
2023-12-11T07:55:54.440269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
향 27
25.0%
교 27
25.0%
양 5
 
4.6%
산 4
 
3.7%
창 3
 
2.8%
성 2
 
1.9%
안 2
 
1.9%
함 2
 
1.9%
원 2
 
1.9%
거 2
 
1.9%
Other values (28) 32
29.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 108
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
향 27
25.0%
교 27
25.0%
양 5
 
4.6%
산 4
 
3.7%
창 3
 
2.8%
성 2
 
1.9%
안 2
 
1.9%
함 2
 
1.9%
원 2
 
1.9%
거 2
 
1.9%
Other values (28) 32
29.6%

Most occurring scripts

ValueCountFrequency (%)
Hangul 108
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
향 27
25.0%
교 27
25.0%
양 5
 
4.6%
산 4
 
3.7%
창 3
 
2.8%
성 2
 
1.9%
안 2
 
1.9%
함 2
 
1.9%
원 2
 
1.9%
거 2
 
1.9%
Other values (28) 32
29.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 108
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
향 27
25.0%
교 27
25.0%
양 5
 
4.6%
산 4
 
3.7%
창 3
 
2.8%
성 2
 
1.9%
안 2
 
1.9%
함 2
 
1.9%
원 2
 
1.9%
거 2
 
1.9%
Other values (28) 32
29.6%

소재지
Text

UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size348.0 B
2023-12-11T07:55:54.670759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length19
Mean length16.222222
Min length11

Characters and Unicode

Total characters438
Distinct characters81
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique27 ?
Unique (%)100.0%

Sample

1st row창원시 의창구 의안로 59번길 10
2nd row창원시 마산합포구 진동면 교동1길 86
3rd row진주시 향교로 99-3
4th row통영시 광도면 향교길 82
5th row사천시 사천읍 사천향교로 25
ValueCountFrequency (%)
향교길 5
 
4.7%
합천군 4
 
3.7%
10 3
 
2.8%
창원시 2
 
1.9%
교동1길 2
 
1.9%
산청군 2
 
1.9%
사천시 2
 
1.9%
함안군 2
 
1.9%
함양군 2
 
1.9%
창녕군 2
 
1.9%
Other values (81) 81
75.7%
2023-12-11T07:55:54.996133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
80
 
18.3%
길 25
 
5.7%
1 23
 
5.3%
군 17
 
3.9%
교 16
 
3.7%
면 12
 
2.7%
5 12
 
2.7%
2 11
 
2.5%
- 10
 
2.3%
3 10
 
2.3%
Other values (71) 222
50.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 257
58.7%
Decimal Number 87
 
19.9%
Space Separator 80
 
18.3%
Dash Punctuation 10
 
2.3%
Close Punctuation 2
 
0.5%
Open Punctuation 2
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
길 25
 
9.7%
군 17
 
6.6%
교 16
 
6.2%
면 12
 
4.7%
로 10
 
3.9%
읍 10
 
3.9%
시 10
 
3.9%
향 9
 
3.5%
천 9
 
3.5%
동 9
 
3.5%
Other values (57) 130
50.6%
Decimal Number
ValueCountFrequency (%)
1 23
26.4%
5 12
13.8%
2 11
12.6%
3 10
11.5%
0 9
 
10.3%
4 8
 
9.2%
9 6
 
6.9%
8 3
 
3.4%
6 3
 
3.4%
7 2
 
2.3%
Space Separator
ValueCountFrequency (%)
80
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 10
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 257
58.7%
Common 181
41.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
길 25
 
9.7%
군 17
 
6.6%
교 16
 
6.2%
면 12
 
4.7%
로 10
 
3.9%
읍 10
 
3.9%
시 10
 
3.9%
향 9
 
3.5%
천 9
 
3.5%
동 9
 
3.5%
Other values (57) 130
50.6%
Common
ValueCountFrequency (%)
80
44.2%
1 23
 
12.7%
5 12
 
6.6%
2 11
 
6.1%
- 10
 
5.5%
3 10
 
5.5%
0 9
 
5.0%
4 8
 
4.4%
9 6
 
3.3%
8 3
 
1.7%
Other values (4) 9
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 257
58.7%
ASCII 181
41.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
80
44.2%
1 23
 
12.7%
5 12
 
6.6%
2 11
 
6.1%
- 10
 
5.5%
3 10
 
5.5%
0 9
 
5.0%
4 8
 
4.4%
9 6
 
3.3%
8 3
 
1.7%
Other values (4) 9
 
5.0%
Hangul
ValueCountFrequency (%)
길 25
 
9.7%
군 17
 
6.6%
교 16
 
6.2%
면 12
 
4.7%
로 10
 
3.9%
읍 10
 
3.9%
시 10
 
3.9%
향 9
 
3.5%
천 9
 
3.5%
동 9
 
3.5%
Other values (57) 130
50.6%

유림수
Real number (ℝ)

UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean529.11111
Minimum112
Maximum2156
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-11T07:55:55.100661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum112
5-th percentile133.8
Q1266.5
median471
Q3612.5
95-th percentile1138.9
Maximum2156
Range2044
Interquartile range (IQR)346

Descriptive statistics

Standard deviation425.91849
Coefficient of variation (CV)0.80496986
Kurtosis7.5572176
Mean529.11111
Median Absolute Deviation (MAD)202
Skewness2.3634285
Sum14286
Variance181406.56
MonotonicityNot monotonic
2023-12-11T07:55:55.202844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
973 1
 
3.7%
650 1
 
3.7%
160 1
 
3.7%
332 1
 
3.7%
420 1
 
3.7%
269 1
 
3.7%
2156 1
 
3.7%
500 1
 
3.7%
557 1
 
3.7%
541 1
 
3.7%
Other values (17) 17
63.0%
ValueCountFrequency (%)
112 1
3.7%
132 1
3.7%
138 1
3.7%
140 1
3.7%
160 1
3.7%
255 1
3.7%
264 1
3.7%
269 1
3.7%
284 1
3.7%
332 1
3.7%
ValueCountFrequency (%)
2156 1
3.7%
1210 1
3.7%
973 1
3.7%
904 1
3.7%
804 1
3.7%
688 1
3.7%
650 1
3.7%
575 1
3.7%
557 1
3.7%
541 1
3.7%

Interactions

2023-12-11T07:55:53.808332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T07:55:55.273541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
향교명소재지유림수
향교명1.0001.0001.000
소재지1.0001.0001.000
유림수1.0001.0001.000

Missing values

2023-12-11T07:55:53.931177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T07:55:53.989927image/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

향교명소재지유림수
0창원향교창원시 의창구 의안로 59번길 10973
1마산향교창원시 마산합포구 진동면 교동1길 86650
2진주향교진주시 향교로 99-3575
3통영향교통영시 광도면 향교길 82380
4사천향교사천시 사천읍 사천향교로 25132
5곤양향교사천시 곤양면 향교길 43-31138
6김해향교김해시 가락로 150번길 21(대성동)1210
7밀양향교밀양시 밀양향교3길 19(교동)904
8거제향교거제시 거제면 기성로7길 10804
9양산향교양산시 교동1길 10480
향교명소재지유림수
17하동향교하동군 하동읍 향교2길 5520
18산청향교산청군 산청읍 중앙로59번길 20-5471
19단성향교산청군 단성면 교동길 13-19541
20함양향교함양군 함양읍 원교길 50557
21안의향교함양군 안의면 향교길 15500
22거창향교거창군 거창읍 성산길 342156
23강양향교합천군 합천읍 충효로1길 5-12269
24삼가향교합천군 삼가면 소오2길 24-2420
25합천향교합천군 야로면 향교길 17-3332
26초계향교합천군 초계면 교촌3길 18-4160