Overview

Dataset statistics

Number of variables5
Number of observations30
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.4 KiB
Average record size in memory48.4 B

Variable types

Numeric4
Text1

Dataset

Description태양광 발전 현황에 대한 공공데이터 개방에 대한 데이터로 준공연도, 설치장소, 설비용량(MWp), 발전용량(MWh/년), CO2절감량(ton/년)으로 구성되어 있으며 년단위 데이터가 갱신됩니다.
Author여수광양항만공사
URLhttps://www.data.go.kr/data/15029223/fileData.do

Alerts

설비용량(MWp) is highly overall correlated with 발전용량(MWh_년) and 1 other fieldsHigh correlation
발전용량(MWh_년) is highly overall correlated with 설비용량(MWp) and 1 other fieldsHigh correlation
이산화탄소절감량(ton_년) is highly overall correlated with 설비용량(MWp) and 1 other fieldsHigh correlation
설치장소 has unique valuesUnique
설비용량(MWp) has 1 (3.3%) zerosZeros

Reproduction

Analysis started2023-12-11 22:52:34.293717
Analysis finished2023-12-11 22:52:35.973424
Duration1.68 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

준공연도
Real number (ℝ)

Distinct10
Distinct (%)33.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2018.9667
Minimum2011
Maximum2023
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-12T07:52:36.019983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2011
5-th percentile2013.45
Q12017.25
median2020
Q32021
95-th percentile2022
Maximum2023
Range12
Interquartile range (IQR)3.75

Descriptive statistics

Standard deviation3.1236951
Coefficient of variation (CV)0.0015471752
Kurtosis0.24055758
Mean2018.9667
Median Absolute Deviation (MAD)2
Skewness-1.0069425
Sum60569
Variance9.7574713
MonotonicityIncreasing
2023-12-12T07:52:36.124994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
2022 6
20.0%
2021 5
16.7%
2019 4
13.3%
2020 4
13.3%
2014 3
10.0%
2017 3
10.0%
2018 2
 
6.7%
2011 1
 
3.3%
2013 1
 
3.3%
2023 1
 
3.3%
ValueCountFrequency (%)
2011 1
 
3.3%
2013 1
 
3.3%
2014 3
10.0%
2017 3
10.0%
2018 2
 
6.7%
2019 4
13.3%
2020 4
13.3%
2021 5
16.7%
2022 6
20.0%
2023 1
 
3.3%
ValueCountFrequency (%)
2023 1
 
3.3%
2022 6
20.0%
2021 5
16.7%
2020 4
13.3%
2019 4
13.3%
2018 2
 
6.7%
2017 3
10.0%
2014 3
10.0%
2013 1
 
3.3%
2011 1
 
3.3%

설치장소
Text

UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-12T07:52:36.331339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length9
Mean length7.7
Min length2

Characters and Unicode

Total characters231
Distinct characters101
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique30 ?
Unique (%)100.0%

Sample

1st rowCFS 및 국제물류센터
2nd rowCJ대한통운
3rd row황금물류센터
4th rowLME창고
5th rowKCTC광양지점
ValueCountFrequency (%)
물류창고 2
 
5.3%
월드마린센터 2
 
5.3%
어울림 1
 
2.6%
광양항 1
 
2.6%
자전거도로 1
 
2.6%
㈜우인(배후단지 1
 
2.6%
㈜광양냉장(배후단지 1
 
2.6%
태웅글로벌㈜(배후단지 1
 
2.6%
현경물류(배후단지 1
 
2.6%
cfs 1
 
2.6%
Other values (26) 26
68.4%
2023-12-12T07:52:36.649109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
지 12
 
5.2%
) 9
 
3.9%
( 9
 
3.9%
8
 
3.5%
단 8
 
3.5%
후 8
 
3.5%
배 8
 
3.5%
물 7
 
3.0%
류 7
 
3.0%
터 6
 
2.6%
Other values (91) 149
64.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 187
81.0%
Uppercase Letter 12
 
5.2%
Close Punctuation 9
 
3.9%
Open Punctuation 9
 
3.9%
Space Separator 8
 
3.5%
Other Symbol 6
 
2.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
지 12
 
6.4%
단 8
 
4.3%
후 8
 
4.3%
배 8
 
4.3%
물 7
 
3.7%
류 7
 
3.7%
터 6
 
3.2%
센 5
 
2.7%
창 5
 
2.7%
고 5
 
2.7%
Other values (78) 116
62.0%
Uppercase Letter
ValueCountFrequency (%)
C 4
33.3%
S 1
 
8.3%
T 1
 
8.3%
K 1
 
8.3%
E 1
 
8.3%
M 1
 
8.3%
L 1
 
8.3%
F 1
 
8.3%
J 1
 
8.3%
Close Punctuation
ValueCountFrequency (%)
) 9
100.0%
Open Punctuation
ValueCountFrequency (%)
( 9
100.0%
Space Separator
ValueCountFrequency (%)
8
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 193
83.5%
Common 26
 
11.3%
Latin 12
 
5.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
지 12
 
6.2%
단 8
 
4.1%
후 8
 
4.1%
배 8
 
4.1%
물 7
 
3.6%
류 7
 
3.6%
터 6
 
3.1%
㈜ 6
 
3.1%
센 5
 
2.6%
창 5
 
2.6%
Other values (79) 121
62.7%
Latin
ValueCountFrequency (%)
C 4
33.3%
S 1
 
8.3%
T 1
 
8.3%
K 1
 
8.3%
E 1
 
8.3%
M 1
 
8.3%
L 1
 
8.3%
F 1
 
8.3%
J 1
 
8.3%
Common
ValueCountFrequency (%)
) 9
34.6%
( 9
34.6%
8
30.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 187
81.0%
ASCII 38
 
16.5%
None 6
 
2.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
지 12
 
6.4%
단 8
 
4.3%
후 8
 
4.3%
배 8
 
4.3%
물 7
 
3.7%
류 7
 
3.7%
터 6
 
3.2%
센 5
 
2.7%
창 5
 
2.7%
고 5
 
2.7%
Other values (78) 116
62.0%
ASCII
ValueCountFrequency (%)
) 9
23.7%
( 9
23.7%
8
21.1%
C 4
10.5%
S 1
 
2.6%
T 1
 
2.6%
K 1
 
2.6%
E 1
 
2.6%
M 1
 
2.6%
L 1
 
2.6%
Other values (2) 2
 
5.3%
None
ValueCountFrequency (%)
㈜ 6
100.0%

설비용량(MWp)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct19
Distinct (%)63.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.19
Minimum0
Maximum3
Zeros1
Zeros (%)3.3%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-12T07:52:36.784531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.2
Q10.725
median1
Q31.65
95-th percentile2.41
Maximum3
Range3
Interquartile range (IQR)0.925

Descriptive statistics

Standard deviation0.72604265
Coefficient of variation (CV)0.61011987
Kurtosis0.005973272
Mean1.19
Median Absolute Deviation (MAD)0.4
Skewness0.64519631
Sum35.7
Variance0.52713793
MonotonicityNot monotonic
2023-12-12T07:52:36.968684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
1.0 6
20.0%
2.0 4
13.3%
0.2 2
 
6.7%
0.8 2
 
6.7%
0.6 2
 
6.7%
2.3 1
 
3.3%
0.7 1
 
3.3%
0.4 1
 
3.3%
2.5 1
 
3.3%
0.0 1
 
3.3%
Other values (9) 9
30.0%
ValueCountFrequency (%)
0.0 1
 
3.3%
0.2 2
 
6.7%
0.4 1
 
3.3%
0.5 1
 
3.3%
0.6 2
 
6.7%
0.7 1
 
3.3%
0.8 2
 
6.7%
0.9 1
 
3.3%
1.0 6
20.0%
1.1 1
 
3.3%
ValueCountFrequency (%)
3.0 1
 
3.3%
2.5 1
 
3.3%
2.3 1
 
3.3%
2.0 4
13.3%
1.7 1
 
3.3%
1.5 1
 
3.3%
1.4 1
 
3.3%
1.3 1
 
3.3%
1.2 1
 
3.3%
1.1 1
 
3.3%

발전용량(MWh_년)
Real number (ℝ)

HIGH CORRELATION 

Distinct24
Distinct (%)80.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1618.9
Minimum3
Maximum4177
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-12T07:52:37.130749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile278
Q11015
median1392
Q32304
95-th percentile3169.6
Maximum4177
Range4174
Interquartile range (IQR)1289

Descriptive statistics

Standard deviation990.06264
Coefficient of variation (CV)0.61156504
Kurtosis0.095317203
Mean1618.9
Median Absolute Deviation (MAD)606.5
Skewness0.65049208
Sum48567
Variance980224.02
MonotonicityNot monotonic
2023-12-12T07:52:37.295302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
1252 3
 
10.0%
278 2
 
6.7%
2785 2
 
6.7%
1114 2
 
6.7%
1392 2
 
6.7%
3202 1
 
3.3%
982 1
 
3.3%
751 1
 
3.3%
501 1
 
3.3%
2504 1
 
3.3%
Other values (14) 14
46.7%
ValueCountFrequency (%)
3 1
 
3.3%
278 2
6.7%
501 1
 
3.3%
696 1
 
3.3%
751 1
 
3.3%
835 1
 
3.3%
982 1
 
3.3%
1114 2
6.7%
1252 3
10.0%
1263 1
 
3.3%
ValueCountFrequency (%)
4177 1
3.3%
3202 1
3.3%
3130 1
3.3%
2785 2
6.7%
2784 1
3.3%
2504 1
3.3%
2375 1
3.3%
2091 1
3.3%
1964 1
3.3%
1810 1
3.3%

이산화탄소절감량(ton_년)
Real number (ℝ)

HIGH CORRELATION 

Distinct24
Distinct (%)80.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean686.4
Minimum1
Maximum1771
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-12T07:52:37.443147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile118
Q1430
median590
Q3977
95-th percentile1344.05
Maximum1771
Range1770
Interquartile range (IQR)547

Descriptive statistics

Standard deviation419.85692
Coefficient of variation (CV)0.61167966
Kurtosis0.093920084
Mean686.4
Median Absolute Deviation (MAD)257.5
Skewness0.65002299
Sum20592
Variance176279.83
MonotonicityNot monotonic
2023-12-12T07:52:37.586740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
531 3
 
10.0%
118 2
 
6.7%
1181 2
 
6.7%
472 2
 
6.7%
590 2
 
6.7%
1358 1
 
3.3%
416 1
 
3.3%
318 1
 
3.3%
212 1
 
3.3%
1062 1
 
3.3%
Other values (14) 14
46.7%
ValueCountFrequency (%)
1 1
 
3.3%
118 2
6.7%
212 1
 
3.3%
295 1
 
3.3%
318 1
 
3.3%
354 1
 
3.3%
416 1
 
3.3%
472 2
6.7%
531 3
10.0%
536 1
 
3.3%
ValueCountFrequency (%)
1771 1
3.3%
1358 1
3.3%
1327 1
3.3%
1181 2
6.7%
1180 1
3.3%
1062 1
3.3%
1007 1
3.3%
887 1
3.3%
833 1
3.3%
767 1
3.3%

Interactions

2023-12-12T07:52:35.397873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:52:34.472177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:52:34.781458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:52:35.115147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:52:35.483997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:52:34.546157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:52:34.864012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:52:35.184875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:52:35.611835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:52:34.618374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:52:34.942879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:52:35.259913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:52:35.707082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:52:34.686105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:52:35.028871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:52:35.323145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T07:52:37.709784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
준공연도설치장소설비용량(MWp)발전용량(MWh_년)이산화탄소절감량(ton_년)
준공연도1.0001.0000.0000.0000.000
설치장소1.0001.0001.0001.0001.000
설비용량(MWp)0.0001.0001.0000.9390.939
발전용량(MWh_년)0.0001.0000.9391.0001.000
이산화탄소절감량(ton_년)0.0001.0000.9391.0001.000
2023-12-12T07:52:37.818848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
준공연도설비용량(MWp)발전용량(MWh_년)이산화탄소절감량(ton_년)
준공연도1.000-0.286-0.331-0.331
설비용량(MWp)-0.2861.0000.9900.990
발전용량(MWh_년)-0.3310.9901.0001.000
이산화탄소절감량(ton_년)-0.3310.9901.0001.000

Missing values

2023-12-12T07:52:35.830311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T07:52:35.938056image/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

준공연도설치장소설비용량(MWp)발전용량(MWh_년)이산화탄소절감량(ton_년)
02011CFS 및 국제물류센터2.332021358
12013CJ대한통운1.723751007
22014황금물류센터1.11531649
32014LME창고1.52091887
42014KCTC광양지점2.027841180
52017대풍(배후단지)0.2278118
62017하포일반부두(세방)3.041771771
72017물류명가(배후단지)1.31810767
82018엠에스케이(배후단지)2.027851181
92018동부창고(배후단지)0.81114472
준공연도설치장소설비용량(MWp)발전용량(MWh_년)이산화탄소절감량(ton_년)
202021태웅글로벌㈜(배후단지)0.91263536
212021현경물류(배후단지)1.41964833
222021월드마린센터 옥상0.031
232022대평 물류창고2.531301327
242022동방광양물류센터1.01252531
252022한영이앤씨1.01252531
262022한진2.025041062
272022이에스꼬레아0.4501212
282022인터피드1.01252531
292023하포일반부두 주차장0.6751318