Overview

Dataset statistics

Number of variables6
Number of observations28
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.5 KiB
Average record size in memory55.7 B

Variable types

Numeric3
Text3

Dataset

Description전라북도 임실군에 위치한 학교현황 데이터 입니다. 데이터 세부내역에는 순번, 학교명, 학생수, 학급수, 전화번호, 주소(도로명)를 포함하여 제공하고 있습니다.
Author전라북도
URLhttps://www.bigdatahub.go.kr/index.jeonbuk?startPage=2&menuCd=DOM_000000103007001000&pListTypeStr=&pId=15054838

Alerts

학생수(CLASS) is highly overall correlated with 학급수High correlation
학급수 is highly overall correlated with 학생수(CLASS)High correlation
순번 has unique valuesUnique
학교명 has unique valuesUnique
전화번호 has unique valuesUnique
주소(도로명) has unique valuesUnique

Reproduction

Analysis started2024-03-13 23:52:14.014386
Analysis finished2024-03-13 23:52:15.055378
Duration1.04 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

순번
Real number (ℝ)

UNIQUE 

Distinct28
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.5
Minimum1
Maximum28
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2024-03-14T08:52:15.115014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.35
Q17.75
median14.5
Q321.25
95-th percentile26.65
Maximum28
Range27
Interquartile range (IQR)13.5

Descriptive statistics

Standard deviation8.2259751
Coefficient of variation (CV)0.56730863
Kurtosis-1.2
Mean14.5
Median Absolute Deviation (MAD)7
Skewness0
Sum406
Variance67.666667
MonotonicityStrictly increasing
2024-03-14T08:52:15.233467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
1 1
 
3.6%
16 1
 
3.6%
28 1
 
3.6%
27 1
 
3.6%
26 1
 
3.6%
25 1
 
3.6%
24 1
 
3.6%
23 1
 
3.6%
22 1
 
3.6%
21 1
 
3.6%
Other values (18) 18
64.3%
ValueCountFrequency (%)
1 1
3.6%
2 1
3.6%
3 1
3.6%
4 1
3.6%
5 1
3.6%
6 1
3.6%
7 1
3.6%
8 1
3.6%
9 1
3.6%
10 1
3.6%
ValueCountFrequency (%)
28 1
3.6%
27 1
3.6%
26 1
3.6%
25 1
3.6%
24 1
3.6%
23 1
3.6%
22 1
3.6%
21 1
3.6%
20 1
3.6%
19 1
3.6%

학교명
Text

UNIQUE 

Distinct28
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size356.0 B
2024-03-14T08:52:15.405458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length6
Mean length5.9642857
Min length5

Characters and Unicode

Total characters167
Distinct characters42
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

Unique28 ?
Unique (%)100.0%

Sample

1st row예원예술대학교
2nd row임실고등학교
3rd row한국치즈과학고등학교
4th row오수고등학교
5th row임실동중학교
ValueCountFrequency (%)
예원예술대학교 1
 
3.6%
임실고등학교 1
 
3.6%
덕치초등학교 1
 
3.6%
갈담초등학교 1
 
3.6%
관촌초등학교 1
 
3.6%
삼계초등학교 1
 
3.6%
신덕초등학교 1
 
3.6%
오수초등학교 1
 
3.6%
성수초등학교 1
 
3.6%
대리초등학교 1
 
3.6%
Other values (18) 18
64.3%
2024-03-14T08:52:15.682624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
학 29
17.4%
교 28
16.8%
등 18
 
10.8%
초 15
 
9.0%
중 9
 
5.4%
수 5
 
3.0%
임 4
 
2.4%
실 4
 
2.4%
고 3
 
1.8%
암 3
 
1.8%
Other values (32) 49
29.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 167
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
학 29
17.4%
교 28
16.8%
등 18
 
10.8%
초 15
 
9.0%
중 9
 
5.4%
수 5
 
3.0%
임 4
 
2.4%
실 4
 
2.4%
고 3
 
1.8%
암 3
 
1.8%
Other values (32) 49
29.3%

Most occurring scripts

ValueCountFrequency (%)
Hangul 167
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
학 29
17.4%
교 28
16.8%
등 18
 
10.8%
초 15
 
9.0%
중 9
 
5.4%
수 5
 
3.0%
임 4
 
2.4%
실 4
 
2.4%
고 3
 
1.8%
암 3
 
1.8%
Other values (32) 49
29.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 167
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
학 29
17.4%
교 28
16.8%
등 18
 
10.8%
초 15
 
9.0%
중 9
 
5.4%
수 5
 
3.0%
임 4
 
2.4%
실 4
 
2.4%
고 3
 
1.8%
암 3
 
1.8%
Other values (32) 49
29.3%

학생수(CLASS)
Real number (ℝ)

HIGH CORRELATION 

Distinct24
Distinct (%)85.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean104.85714
Minimum18
Maximum500
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2024-03-14T08:52:15.784025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile23.35
Q132
median51.5
Q3133.5
95-th percentile296.9
Maximum500
Range482
Interquartile range (IQR)101.5

Descriptive statistics

Standard deviation111.57449
Coefficient of variation (CV)1.0640619
Kurtosis4.9427283
Mean104.85714
Median Absolute Deviation (MAD)28
Skewness2.0829411
Sum2936
Variance12448.868
MonotonicityNot monotonic
2024-03-14T08:52:15.876060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
32 3
 
10.7%
24 2
 
7.1%
36 2
 
7.1%
500 1
 
3.6%
35 1
 
3.6%
83 1
 
3.6%
156 1
 
3.6%
43 1
 
3.6%
38 1
 
3.6%
233 1
 
3.6%
Other values (14) 14
50.0%
ValueCountFrequency (%)
18 1
 
3.6%
23 1
 
3.6%
24 2
7.1%
29 1
 
3.6%
32 3
10.7%
35 1
 
3.6%
36 2
7.1%
38 1
 
3.6%
42 1
 
3.6%
43 1
 
3.6%
ValueCountFrequency (%)
500 1
3.6%
320 1
3.6%
254 1
3.6%
233 1
3.6%
205 1
3.6%
156 1
3.6%
144 1
3.6%
130 1
3.6%
127 1
3.6%
122 1
3.6%

학급수
Real number (ℝ)

HIGH CORRELATION 

Distinct7
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.5357143
Minimum3
Maximum13
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2024-03-14T08:52:15.974237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile3
Q16
median6
Q37
95-th percentile11.95
Maximum13
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation2.6733547
Coefficient of variation (CV)0.40903788
Kurtosis0.87295836
Mean6.5357143
Median Absolute Deviation (MAD)1
Skewness0.86547236
Sum183
Variance7.1468254
MonotonicityNot monotonic
2024-03-14T08:52:16.057853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
6 12
42.9%
3 5
17.9%
7 4
 
14.3%
10 2
 
7.1%
9 2
 
7.1%
13 2
 
7.1%
4 1
 
3.6%
ValueCountFrequency (%)
3 5
17.9%
4 1
 
3.6%
6 12
42.9%
7 4
 
14.3%
9 2
 
7.1%
10 2
 
7.1%
13 2
 
7.1%
ValueCountFrequency (%)
13 2
 
7.1%
10 2
 
7.1%
9 2
 
7.1%
7 4
 
14.3%
6 12
42.9%
4 1
 
3.6%
3 5
17.9%

전화번호
Text

UNIQUE 

Distinct28
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size356.0 B
2024-03-14T08:52:16.209537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters336
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

Unique28 ?
Unique (%)100.0%

Sample

1st row063-640-7114
2nd row063-644-7512
3rd row063-643-1308
4th row063-642-2949
5th row063-642-2537
ValueCountFrequency (%)
063-640-7114 1
 
3.6%
063-644-7512 1
 
3.6%
063-643-0024 1
 
3.6%
063-643-1895 1
 
3.6%
063-642-0035 1
 
3.6%
063-642-7506 1
 
3.6%
063-643-0405 1
 
3.6%
063-642-5005 1
 
3.6%
063-642-9011 1
 
3.6%
063-642-0778 1
 
3.6%
Other values (18) 18
64.3%
2024-03-14T08:52:16.523987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
6 59
17.6%
0 58
17.3%
- 56
16.7%
3 44
13.1%
4 38
11.3%
2 25
7.4%
5 17
 
5.1%
1 15
 
4.5%
7 11
 
3.3%
9 8
 
2.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 280
83.3%
Dash Punctuation 56
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
6 59
21.1%
0 58
20.7%
3 44
15.7%
4 38
13.6%
2 25
8.9%
5 17
 
6.1%
1 15
 
5.4%
7 11
 
3.9%
9 8
 
2.9%
8 5
 
1.8%
Dash Punctuation
ValueCountFrequency (%)
- 56
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 336
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
6 59
17.6%
0 58
17.3%
- 56
16.7%
3 44
13.1%
4 38
11.3%
2 25
7.4%
5 17
 
5.1%
1 15
 
4.5%
7 11
 
3.3%
9 8
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 336
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
6 59
17.6%
0 58
17.3%
- 56
16.7%
3 44
13.1%
4 38
11.3%
2 25
7.4%
5 17
 
5.1%
1 15
 
4.5%
7 11
 
3.3%
9 8
 
2.4%

주소(도로명)
Text

UNIQUE 

Distinct28
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size356.0 B
2024-03-14T08:52:16.741580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length22
Mean length20.392857
Min length18

Characters and Unicode

Total characters571
Distinct characters60
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

Unique28 ?
Unique (%)100.0%

Sample

1st row전라북도 임실군 신평면 창인로 117
2nd row전라북도 임실군 임실읍 봉황로 257
3rd row전라북도 임실군 강진면 갈담3길 48
4th row전라북도 임실군 오수면 충효로 2099-23
5th row전라북도 임실군 임실읍 봉황로 147
ValueCountFrequency (%)
전라북도 28
20.0%
임실군 28
20.0%
임실읍 4
 
2.9%
봉황로 4
 
2.9%
충효로 4
 
2.9%
신평면 3
 
2.1%
운암면 3
 
2.1%
강진면 3
 
2.1%
오수면 3
 
2.1%
관촌면 2
 
1.4%
Other values (52) 58
41.4%
2024-03-14T08:52:17.031679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
112
19.6%
임 33
 
5.8%
실 32
 
5.6%
전 28
 
4.9%
라 28
 
4.9%
북 28
 
4.9%
도 28
 
4.9%
군 28
 
4.9%
면 24
 
4.2%
1 20
 
3.5%
Other values (50) 210
36.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 362
63.4%
Space Separator 112
 
19.6%
Decimal Number 92
 
16.1%
Dash Punctuation 5
 
0.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
임 33
 
9.1%
실 32
 
8.8%
전 28
 
7.7%
라 28
 
7.7%
북 28
 
7.7%
도 28
 
7.7%
군 28
 
7.7%
면 24
 
6.6%
로 19
 
5.2%
길 9
 
2.5%
Other values (38) 105
29.0%
Decimal Number
ValueCountFrequency (%)
1 20
21.7%
2 14
15.2%
3 9
9.8%
6 8
 
8.7%
9 8
 
8.7%
8 8
 
8.7%
7 7
 
7.6%
0 6
 
6.5%
4 6
 
6.5%
5 6
 
6.5%
Space Separator
ValueCountFrequency (%)
112
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 362
63.4%
Common 209
36.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
임 33
 
9.1%
실 32
 
8.8%
전 28
 
7.7%
라 28
 
7.7%
북 28
 
7.7%
도 28
 
7.7%
군 28
 
7.7%
면 24
 
6.6%
로 19
 
5.2%
길 9
 
2.5%
Other values (38) 105
29.0%
Common
ValueCountFrequency (%)
112
53.6%
1 20
 
9.6%
2 14
 
6.7%
3 9
 
4.3%
6 8
 
3.8%
9 8
 
3.8%
8 8
 
3.8%
7 7
 
3.3%
0 6
 
2.9%
4 6
 
2.9%
Other values (2) 11
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 362
63.4%
ASCII 209
36.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
112
53.6%
1 20
 
9.6%
2 14
 
6.7%
3 9
 
4.3%
6 8
 
3.8%
9 8
 
3.8%
8 8
 
3.8%
7 7
 
3.3%
0 6
 
2.9%
4 6
 
2.9%
Other values (2) 11
 
5.3%
Hangul
ValueCountFrequency (%)
임 33
 
9.1%
실 32
 
8.8%
전 28
 
7.7%
라 28
 
7.7%
북 28
 
7.7%
도 28
 
7.7%
군 28
 
7.7%
면 24
 
6.6%
로 19
 
5.2%
길 9
 
2.5%
Other values (38) 105
29.0%

Interactions

2024-03-14T08:52:14.675672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T08:52:14.234331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T08:52:14.488362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T08:52:14.752833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T08:52:14.344891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T08:52:14.551330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T08:52:14.827337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T08:52:14.431736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T08:52:14.611589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-14T08:52:17.134276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번학교명학생수(CLASS)학급수전화번호주소(도로명)
순번1.0001.0000.0680.4681.0001.000
학교명1.0001.0001.0001.0001.0001.000
학생수(CLASS)0.0681.0001.0000.8571.0001.000
학급수0.4681.0000.8571.0001.0001.000
전화번호1.0001.0001.0001.0001.0001.000
주소(도로명)1.0001.0001.0001.0001.0001.000
2024-03-14T08:52:17.216823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번학생수(CLASS)학급수
순번1.000-0.2440.030
학생수(CLASS)-0.2441.0000.855
학급수0.0300.8551.000

Missing values

2024-03-14T08:52:14.914363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T08:52:15.011925image/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

순번학교명학생수(CLASS)학급수전화번호주소(도로명)
01예원예술대학교50010063-640-7114전라북도 임실군 신평면 창인로 117
12임실고등학교2549063-644-7512전라북도 임실군 임실읍 봉황로 257
23한국치즈과학고등학교1276063-643-1308전라북도 임실군 강진면 갈담3길 48
34오수고등학교1309063-642-2949전라북도 임실군 오수면 충효로 2099-23
45임실동중학교2057063-642-2537전라북도 임실군 임실읍 봉황로 147
56관촌중학교1447063-642-0311전라북도 임실군 관촌면 사선10길 23
67청웅중학교243063-643-8020전라북도 임실군 청웅면 청웅로 131-7
78지사중학교183063-642-4683전라북도 임실군 지사면 충효로 2469
89섬진중학교804063-643-1101전라북도 임실군 강진면 강운로 87
910오수중학교1226063-642-5475전라북도 임실군 오수면 충효로 2099-15
순번학교명학생수(CLASS)학급수전화번호주소(도로명)
1819신평초등학교366063-643-7005전라북도 임실군 신평면 가덕로 683
1920대리초등학교606063-642-0778전라북도 임실군 신평면 대리로 187
2021성수초등학교367063-642-9011전라북도 임실군 성수면 임진로 189
2122오수초등학교23313063-642-5005전라북도 임실군 오수면 충효로 2059
2223신덕초등학교386063-643-0405전라북도 임실군 신덕면 수지로 56
2324삼계초등학교436063-642-7506전라북도 임실군 삼계면 삼계1길 20-3
2425관촌초등학교15610063-642-0035전라북도 임실군 관촌면 사선 6길 11
2526갈담초등학교837063-643-1895전라북도 임실군 강진면 호국로 65
2627덕치초등학교356063-643-0024전라북도 임실군 덕치면 인덕로1217
2728지사초등학교326063-642-4064전라북도 임실군 지사면 방계3길 10