Overview

Dataset statistics

Number of variables8
Number of observations41
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.9 KiB
Average record size in memory72.2 B

Variable types

Categorical2
Numeric5
Text1

Dataset

Description대전광역시에서 학교체육시설 지원현황입니다 지원학교명, 지원연도,지원금액,기타 지원내용에 대하여 자료 제공합니다.
URLhttps://www.data.go.kr/data/15081317/fileData.do

Alerts

구분 is highly overall correlated with 교육청지원(백만원단위) and 1 other fieldsHigh correlation
지원형태 is highly overall correlated with 국비지원액(백만원단위) and 1 other fieldsHigh correlation
국비지원액(백만원단위) is highly overall correlated with 시비지원액(백만원단위) and 1 other fieldsHigh correlation
시비지원액(백만원단위) is highly overall correlated with 국비지원액(백만원단위) and 1 other fieldsHigh correlation
교육청지원(백만원단위) is highly overall correlated with 시비지원액(백만원단위) and 1 other fieldsHigh correlation
학교명 has unique valuesUnique
국비지원액(백만원단위) has 20 (48.8%) zerosZeros
시비지원액(백만원단위) has 8 (19.5%) zerosZeros
교육청지원(백만원단위) has 10 (24.4%) zerosZeros

Reproduction

Analysis started2023-12-12 06:56:10.344604
Analysis finished2023-12-12 06:56:13.570768
Duration3.23 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Memory size460.0 B
다목적체육관
21 
운동장
20 

Length

Max length6
Median length6
Mean length4.5365854
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
다목적체육관 21
51.2%
운동장 20
48.8%

Length

2023-12-12T15:56:13.648728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:56:13.752734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
다목적체육관 21
51.2%
운동장 20
48.8%

지원형태
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)12.2%
Missing0
Missing (%)0.0%
Memory size460.0 B
문체부 공모
10 
교육행정협의회
10 
균 특
10 
교육행정 및 기타
10 
문체부
 
1

Length

Max length9
Median length7
Mean length6.1707317
Min length3

Unique

Unique1 ?
Unique (%)2.4%

Sample

1st row문체부 공모
2nd row문체부 공모
3rd row문체부 공모
4th row문체부 공모
5th row문체부 공모

Common Values

ValueCountFrequency (%)
문체부 공모 10
24.4%
교육행정협의회 10
24.4%
균 특 10
24.4%
교육행정 및 기타 10
24.4%
문체부 1
 
2.4%

Length

2023-12-12T15:56:13.866567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:56:13.999366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
문체부 11
13.6%
공모 10
12.3%
교육행정협의회 10
12.3%
균 10
12.3%
특 10
12.3%
교육행정 10
12.3%
및 10
12.3%
기타 10
12.3%

지원년도
Real number (ℝ)

Distinct13
Distinct (%)31.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2015.439
Minimum2010
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size501.0 B
2023-12-12T15:56:14.106586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2010
5-th percentile2010
Q12012
median2016
Q32018
95-th percentile2020
Maximum2022
Range12
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.5570267
Coefficient of variation (CV)0.0017648893
Kurtosis-1.2422348
Mean2015.439
Median Absolute Deviation (MAD)3
Skewness-0.047907888
Sum82633
Variance12.652439
MonotonicityNot monotonic
2023-12-12T15:56:14.232146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
2010 4
9.8%
2011 4
9.8%
2013 4
9.8%
2016 4
9.8%
2017 4
9.8%
2018 4
9.8%
2019 4
9.8%
2020 4
9.8%
2012 3
7.3%
2015 2
4.9%
Other values (3) 4
9.8%
ValueCountFrequency (%)
2010 4
9.8%
2011 4
9.8%
2012 3
7.3%
2013 4
9.8%
2014 2
4.9%
2015 2
4.9%
2016 4
9.8%
2017 4
9.8%
2018 4
9.8%
2019 4
9.8%
ValueCountFrequency (%)
2022 1
 
2.4%
2021 1
 
2.4%
2020 4
9.8%
2019 4
9.8%
2018 4
9.8%
2017 4
9.8%
2016 4
9.8%
2015 2
4.9%
2014 2
4.9%
2013 4
9.8%

지원학교수
Real number (ℝ)

Distinct9
Distinct (%)22.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.7073171
Minimum1
Maximum11
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size501.0 B
2023-12-12T15:56:14.341911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q36
95-th percentile10
Maximum11
Range10
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.891746
Coefficient of variation (CV)0.61430874
Kurtosis-0.36661464
Mean4.7073171
Median Absolute Deviation (MAD)2
Skewness0.67247505
Sum193
Variance8.3621951
MonotonicityNot monotonic
2023-12-12T15:56:14.446626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
2 7
17.1%
6 7
17.1%
4 7
17.1%
1 5
12.2%
10 5
12.2%
5 4
9.8%
3 3
7.3%
7 2
 
4.9%
11 1
 
2.4%
ValueCountFrequency (%)
1 5
12.2%
2 7
17.1%
3 3
7.3%
4 7
17.1%
5 4
9.8%
6 7
17.1%
7 2
 
4.9%
10 5
12.2%
11 1
 
2.4%
ValueCountFrequency (%)
11 1
 
2.4%
10 5
12.2%
7 2
 
4.9%
6 7
17.1%
5 4
9.8%
4 7
17.1%
3 3
7.3%
2 7
17.1%
1 5
12.2%

국비지원액(백만원단위)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct14
Distinct (%)34.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean756.92683
Minimum0
Maximum2250
Zeros20
Zeros (%)48.8%
Negative0
Negative (%)0.0%
Memory size501.0 B
2023-12-12T15:56:14.546393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median450
Q31750
95-th percentile2100
Maximum2250
Range2250
Interquartile range (IQR)1750

Descriptive statistics

Standard deviation834.24278
Coefficient of variation (CV)1.1021446
Kurtosis-1.5077995
Mean756.92683
Median Absolute Deviation (MAD)450
Skewness0.44627196
Sum31034
Variance695961.02
MonotonicityNot monotonic
2023-12-12T15:56:14.657183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0 20
48.8%
1800 6
 
14.6%
900 2
 
4.9%
1400 2
 
4.9%
1750 2
 
4.9%
752 1
 
2.4%
680 1
 
2.4%
2250 1
 
2.4%
2200 1
 
2.4%
1050 1
 
2.4%
Other values (4) 4
 
9.8%
ValueCountFrequency (%)
0 20
48.8%
450 1
 
2.4%
680 1
 
2.4%
752 1
 
2.4%
900 2
 
4.9%
1050 1
 
2.4%
1150 1
 
2.4%
1400 2
 
4.9%
1502 1
 
2.4%
1750 2
 
4.9%
ValueCountFrequency (%)
2250 1
 
2.4%
2200 1
 
2.4%
2100 1
 
2.4%
1800 6
14.6%
1750 2
 
4.9%
1502 1
 
2.4%
1400 2
 
4.9%
1150 1
 
2.4%
1050 1
 
2.4%
900 2
 
4.9%

시비지원액(백만원단위)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct23
Distinct (%)56.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1649.2195
Minimum0
Maximum8100
Zeros8
Zeros (%)19.5%
Negative0
Negative (%)0.0%
Memory size501.0 B
2023-12-12T15:56:14.770555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1412
median900
Q31800
95-th percentile4860
Maximum8100
Range8100
Interquartile range (IQR)1388

Descriptive statistics

Standard deviation2028.0242
Coefficient of variation (CV)1.2296872
Kurtosis3.3288462
Mean1649.2195
Median Absolute Deviation (MAD)700
Skewness1.8578525
Sum67618
Variance4112882.2
MonotonicityNot monotonic
2023-12-12T15:56:14.930253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0 8
19.5%
1200 6
14.6%
800 4
 
9.8%
600 2
 
4.9%
8100 2
 
4.9%
4000 2
 
4.9%
780 1
 
2.4%
1500 1
 
2.4%
1229 1
 
2.4%
905 1
 
2.4%
Other values (13) 13
31.7%
ValueCountFrequency (%)
0 8
19.5%
150 1
 
2.4%
200 1
 
2.4%
412 1
 
2.4%
453 1
 
2.4%
502 1
 
2.4%
600 2
 
4.9%
780 1
 
2.4%
800 4
9.8%
900 1
 
2.4%
ValueCountFrequency (%)
8100 2
4.9%
4860 1
2.4%
4400 1
2.4%
4000 2
4.9%
3992 1
2.4%
3600 1
2.4%
3435 1
2.4%
2700 1
2.4%
1800 1
2.4%
1500 1
2.4%

교육청지원(백만원단위)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct22
Distinct (%)53.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3789
Minimum0
Maximum27585
Zeros10
Zeros (%)24.4%
Negative0
Negative (%)0.0%
Memory size501.0 B
2023-12-12T15:56:15.056006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1150
median1500
Q34200
95-th percentile11340
Maximum27585
Range27585
Interquartile range (IQR)4050

Descriptive statistics

Standard deviation5650.4035
Coefficient of variation (CV)1.4912651
Kurtosis7.6981203
Mean3789
Median Absolute Deviation (MAD)1500
Skewness2.5361629
Sum155349
Variance31927059
MonotonicityNot monotonic
2023-12-12T15:56:15.179395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
0 10
24.4%
3000 6
14.6%
800 4
 
9.8%
1500 3
 
7.3%
6300 1
 
2.4%
18900 1
 
2.4%
27585 1
 
2.4%
1649 1
 
2.4%
1400 1
 
2.4%
700 1
 
2.4%
Other values (12) 12
29.3%
ValueCountFrequency (%)
0 10
24.4%
150 1
 
2.4%
700 1
 
2.4%
800 4
 
9.8%
960 1
 
2.4%
1133 1
 
2.4%
1254 1
 
2.4%
1400 1
 
2.4%
1500 3
 
7.3%
1649 1
 
2.4%
ValueCountFrequency (%)
27585 1
2.4%
18900 1
2.4%
11340 1
2.4%
9744 1
2.4%
9319 1
2.4%
9301 1
2.4%
9208 1
2.4%
8400 1
2.4%
8106 1
2.4%
6300 1
2.4%

학교명
Text

UNIQUE 

Distinct41
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size460.0 B
2023-12-12T15:56:15.526244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length54
Median length33
Mean length23.341463
Min length2

Characters and Unicode

Total characters957
Distinct characters130
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique41 ?
Unique (%)100.0%

Sample

1st row서원초
2nd row서중
3rd row어은초
4th row대전중
5th row송강초, 복수초
ValueCountFrequency (%)
가오중 2
 
1.0%
송촌고 2
 
1.0%
도마초 2
 
1.0%
와동초 2
 
1.0%
충남여중 2
 
1.0%
중앙초 2
 
1.0%
용전초 2
 
1.0%
진잠초 2
 
1.0%
성남초 2
 
1.0%
대청중 2
 
1.0%
Other values (157) 174
89.7%
2023-12-12T15:56:16.127233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
156
16.3%
, 152
15.9%
초 90
 
9.4%
중 66
 
6.9%
고 38
 
4.0%
대 30
 
3.1%
동 22
 
2.3%
성 16
 
1.7%
전 15
 
1.6%
남 12
 
1.3%
Other values (120) 360
37.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 639
66.8%
Space Separator 156
 
16.3%
Other Punctuation 152
 
15.9%
Open Punctuation 4
 
0.4%
Close Punctuation 4
 
0.4%
Uppercase Letter 2
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
초 90
 
14.1%
중 66
 
10.3%
고 38
 
5.9%
대 30
 
4.7%
동 22
 
3.4%
성 16
 
2.5%
전 15
 
2.3%
남 12
 
1.9%
정 10
 
1.6%
덕 10
 
1.6%
Other values (114) 330
51.6%
Uppercase Letter
ValueCountFrequency (%)
S 1
50.0%
W 1
50.0%
Space Separator
ValueCountFrequency (%)
156
100.0%
Other Punctuation
ValueCountFrequency (%)
, 152
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 639
66.8%
Common 316
33.0%
Latin 2
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
초 90
 
14.1%
중 66
 
10.3%
고 38
 
5.9%
대 30
 
4.7%
동 22
 
3.4%
성 16
 
2.5%
전 15
 
2.3%
남 12
 
1.9%
정 10
 
1.6%
덕 10
 
1.6%
Other values (114) 330
51.6%
Common
ValueCountFrequency (%)
156
49.4%
, 152
48.1%
( 4
 
1.3%
) 4
 
1.3%
Latin
ValueCountFrequency (%)
S 1
50.0%
W 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 639
66.8%
ASCII 318
33.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
156
49.1%
, 152
47.8%
( 4
 
1.3%
) 4
 
1.3%
S 1
 
0.3%
W 1
 
0.3%
Hangul
ValueCountFrequency (%)
초 90
 
14.1%
중 66
 
10.3%
고 38
 
5.9%
대 30
 
4.7%
동 22
 
3.4%
성 16
 
2.5%
전 15
 
2.3%
남 12
 
1.9%
정 10
 
1.6%
덕 10
 
1.6%
Other values (114) 330
51.6%

Interactions

2023-12-12T15:56:12.881761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:56:10.703461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:56:11.127309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:56:11.512033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:56:11.948194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:56:12.985413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:56:10.787578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:56:11.204071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:56:11.605578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:56:12.053051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:56:13.070415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:56:10.868957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:56:11.274409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:56:11.691661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:56:12.173412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:56:13.161045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:56:10.957662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:56:11.350078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:56:11.779562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:56:12.286103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:56:13.256543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:56:11.044590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:56:11.434072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:56:11.864237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:56:12.769522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T15:56:16.233384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분지원형태지원년도지원학교수국비지원액(백만원단위)시비지원액(백만원단위)교육청지원(백만원단위)학교명
구분1.0001.0000.0000.5150.5240.4620.6461.000
지원형태1.0001.0000.0000.6750.7740.6270.6241.000
지원년도0.0000.0001.0000.2440.0000.5340.4451.000
지원학교수0.5150.6750.2441.0000.7480.7080.6321.000
국비지원액(백만원단위)0.5240.7740.0000.7481.0000.0000.0001.000
시비지원액(백만원단위)0.4620.6270.5340.7080.0001.0000.9621.000
교육청지원(백만원단위)0.6460.6240.4450.6320.0000.9621.0001.000
학교명1.0001.0001.0001.0001.0001.0001.0001.000
2023-12-12T15:56:16.375090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분지원형태
구분1.0000.961
지원형태0.9611.000
2023-12-12T15:56:16.481967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지원년도지원학교수국비지원액(백만원단위)시비지원액(백만원단위)교육청지원(백만원단위)구분지원형태
지원년도1.0000.010-0.0400.1940.1760.0000.000
지원학교수0.0101.000-0.3770.4550.3020.4850.467
국비지원액(백만원단위)-0.040-0.3771.000-0.563-0.4650.3590.593
시비지원액(백만원단위)0.1940.455-0.5631.0000.9450.4590.452
교육청지원(백만원단위)0.1760.302-0.4650.9451.0000.6490.449
구분0.0000.4850.3590.4590.6491.0000.961
지원형태0.0000.4670.5930.4520.4490.9611.000

Missing values

2023-12-12T15:56:13.363587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T15:56:13.506200image/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

구분지원형태지원년도지원학교수국비지원액(백만원단위)시비지원액(백만원단위)교육청지원(백만원단위)학교명
0다목적체육관문체부 공모201019006001500서원초
1다목적체육관문체부 공모201119006001500서중
2다목적체육관문체부 공모201217525021254어은초
3다목적체육관문체부 공모201316804531133대전중
4다목적체육관문체부 공모20152180012003000송강초, 복수초
5다목적체육관문체부 공모20162180012003000탄방중, 정림초
6다목적체육관문체부 공모20172180012003000동문초, 유성여고
7다목적체육관문체부 공모20182180012003000대덕고, 생활과학고(경덕중)
8다목적체육관문체부 공모20192180012003000동대전초, 매봉중
9다목적체육관문체부 공모20202180012003000삼천초, 목상초
구분지원형태지원년도지원학교수국비지원액(백만원단위)시비지원액(백만원단위)교육청지원(백만원단위)학교명
31운동장교육행정 및 기타201040900700목원대, 도마초, 충남중, 정림초
32운동장교육행정 및 기타2011709051400흥룡초 등, 충남기공, 한남대, 한밭중, 유성중, 전민초, 경덕중
33운동장교육행정 및 기타20136012291649충남여중, 충남기공, 유성생명고, 서대전고, 대덕중, 이문고
34운동장교육행정 및 기타201640800800동신중, 한밭여중, 중리중, 유성생명과학고
35운동장교육행정 및 기타201740800800대전고, 문정중, 문화초, 버드내초
36운동장교육행정 및 기타201840800800판암초, 가양중, 국제통상고, 대성고
37운동장교육행정 및 기타201940800800성남초, 가양초, 용정초, 계룡디지텍고
38운동장교육행정 및 기타20206015001500서대전초, 글꽃중, 노은고, 외삼중, 용전초, 산내초
39운동장교육행정 및 기타2021100810027585관평초, 산서초, 법동중, 괴정중, 동화중, 전민중, 청란여중, 삼육중, 장동초, 자운중
40운동장교육행정 및 기타2022100810018900태평중, 신일여중, 동산초, 남선초, 신계중, 기성초, 성천초, 선암초, 동명초, 대성여중