Overview

Dataset statistics

Number of variables5
Number of observations66
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.9 KiB
Average record size in memory45.0 B

Variable types

DateTime1
Categorical1
Numeric3

Dataset

Description한국동서발전의 월별 우드펠릿 사용현황 정보입니다. 우드펠릿 사용현황은 년월(월별), 사업소, 국내산, 수입산 등의 항목으로 구성됩니다.
URLhttps://www.data.go.kr/data/15004131/fileData.do

Alerts

사업소 has constant value ""Constant
수입산 is highly overall correlated with 총사용량High correlation
총사용량 is highly overall correlated with 수입산High correlation
년월(월별) has unique valuesUnique
국내산 has 47 (71.2%) zerosZeros
수입산 has 40 (60.6%) zerosZeros
총사용량 has 30 (45.5%) zerosZeros

Reproduction

Analysis started2023-12-11 23:22:57.775894
Analysis finished2023-12-11 23:22:59.275905
Duration1.5 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

년월(월별)
Date

UNIQUE 

Distinct66
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size660.0 B
Minimum2018-01-31 00:00:00
Maximum2023-06-30 00:00:00
2023-12-12T08:22:59.339044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:22:59.514710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

사업소
Categorical

CONSTANT 

Distinct1
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size660.0 B
당진
66 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
당진 66
100.0%

Length

2023-12-12T08:22:59.691414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T08:22:59.802204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
당진 66
100.0%

국내산
Real number (ℝ)

ZEROS 

Distinct20
Distinct (%)30.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean986264.24
Minimum0
Maximum8979000
Zeros47
Zeros (%)71.2%
Negative0
Negative (%)0.0%
Memory size726.0 B
2023-12-12T08:22:59.892157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3796750
95-th percentile4867405
Maximum8979000
Range8979000
Interquartile range (IQR)796750

Descriptive statistics

Standard deviation1949464.8
Coefficient of variation (CV)1.9766151
Kurtosis4.4479345
Mean986264.24
Median Absolute Deviation (MAD)0
Skewness2.1488402
Sum65093440
Variance3.8004129 × 1012
MonotonicityNot monotonic
2023-12-12T08:23:00.014530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0 47
71.2%
4366000 1
 
1.5%
4927660 1
 
1.5%
2104000 1
 
1.5%
3750000 1
 
1.5%
1069640 1
 
1.5%
718000 1
 
1.5%
4814620 1
 
1.5%
2832520 1
 
1.5%
3771000 1
 
1.5%
Other values (10) 10
 
15.2%
ValueCountFrequency (%)
0 47
71.2%
3000 1
 
1.5%
718000 1
 
1.5%
823000 1
 
1.5%
1069640 1
 
1.5%
1985000 1
 
1.5%
2104000 1
 
1.5%
2469000 1
 
1.5%
2662000 1
 
1.5%
2832520 1
 
1.5%
ValueCountFrequency (%)
8979000 1
1.5%
6923000 1
1.5%
4927660 1
1.5%
4885000 1
1.5%
4814620 1
1.5%
4366000 1
1.5%
4068000 1
1.5%
3943000 1
1.5%
3771000 1
1.5%
3750000 1
1.5%

수입산
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct27
Distinct (%)40.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2827060.6
Minimum0
Maximum14975000
Zeros40
Zeros (%)60.6%
Negative0
Negative (%)0.0%
Memory size726.0 B
2023-12-12T08:23:00.121656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33417250
95-th percentile12598750
Maximum14975000
Range14975000
Interquartile range (IQR)3417250

Descriptive statistics

Standard deviation4569293.1
Coefficient of variation (CV)1.6162699
Kurtosis0.83925765
Mean2827060.6
Median Absolute Deviation (MAD)0
Skewness1.4965962
Sum1.86586 × 108
Variance2.0878439 × 1013
MonotonicityNot monotonic
2023-12-12T08:23:00.257589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0 40
60.6%
7526000 1
 
1.5%
3397000 1
 
1.5%
2718000 1
 
1.5%
3424000 1
 
1.5%
2281000 1
 
1.5%
4100000 1
 
1.5%
5714000 1
 
1.5%
2959000 1
 
1.5%
6193000 1
 
1.5%
Other values (17) 17
25.8%
ValueCountFrequency (%)
0 40
60.6%
1366000 1
 
1.5%
1428000 1
 
1.5%
1587000 1
 
1.5%
1830000 1
 
1.5%
2281000 1
 
1.5%
2718000 1
 
1.5%
2959000 1
 
1.5%
3335000 1
 
1.5%
3397000 1
 
1.5%
ValueCountFrequency (%)
14975000 1
1.5%
14926000 1
1.5%
13063000 1
1.5%
12657000 1
1.5%
12424000 1
1.5%
12307000 1
1.5%
11492000 1
1.5%
11482000 1
1.5%
10993000 1
1.5%
10373000 1
1.5%

총사용량
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct37
Distinct (%)56.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3813324.8
Minimum0
Maximum14975000
Zeros30
Zeros (%)45.5%
Negative0
Negative (%)0.0%
Memory size726.0 B
2023-12-12T08:23:00.389302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1507500
Q36698500
95-th percentile12961500
Maximum14975000
Range14975000
Interquartile range (IQR)6698500

Descriptive statistics

Standard deviation4792914
Coefficient of variation (CV)1.2568858
Kurtosis-0.41536228
Mean3813324.8
Median Absolute Deviation (MAD)1507500
Skewness1.0015944
Sum2.5167944 × 108
Variance2.2972024 × 1013
MonotonicityNot monotonic
2023-12-12T08:23:00.592502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
0 30
45.5%
7526000 1
 
1.5%
3771000 1
 
1.5%
10599000 1
 
1.5%
8043000 1
 
1.5%
6349000 1
 
1.5%
5409000 1
 
1.5%
2721000 1
 
1.5%
2662000 1
 
1.5%
4366000 1
 
1.5%
Other values (27) 27
40.9%
ValueCountFrequency (%)
0 30
45.5%
718000 1
 
1.5%
1069640 1
 
1.5%
1428000 1
 
1.5%
1587000 1
 
1.5%
1830000 1
 
1.5%
2104000 1
 
1.5%
2662000 1
 
1.5%
2721000 1
 
1.5%
2832520 1
 
1.5%
ValueCountFrequency (%)
14975000 1
1.5%
14926000 1
1.5%
13116000 1
1.5%
13063000 1
1.5%
12657000 1
1.5%
12424000 1
1.5%
12307000 1
1.5%
11492000 1
1.5%
11482000 1
1.5%
10993000 1
1.5%

Interactions

2023-12-12T08:22:58.746332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:22:57.905700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:22:58.169705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:22:58.876418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:22:57.984724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:22:58.257872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:22:58.983539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:22:58.074611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:22:58.634234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T08:23:00.738615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
년월(월별)국내산수입산총사용량
년월(월별)1.0001.0001.0001.000
국내산1.0001.0000.0000.709
수입산1.0000.0001.0000.962
총사용량1.0000.7090.9621.000
2023-12-12T08:23:00.844010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
국내산수입산총사용량
국내산1.0000.0210.428
수입산0.0211.0000.868
총사용량0.4280.8681.000

Missing values

2023-12-12T08:22:59.126162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T08:22:59.240238image/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

년월(월별)사업소국내산수입산총사용량
02018-01-31당진075260007526000
12018-02-28당진033970003397000
22018-03-31당진018300001830000
32018-04-30당진01037300010373000
42018-05-31당진01099300010993000
52018-06-30당진01148200011482000
62018-07-31당진033350003335000
72018-08-31당진01306300013063000
82018-09-30당진01230700012307000
92018-10-31당진01265700012657000
년월(월별)사업소국내산수입산총사용량
562022-09-30당진000
572022-10-31당진000
582022-11-30당진000
592022-12-31당진000
602023-01-31당진000
612023-02-28당진000
622023-03-31당진000
632023-04-30당진000
642023-05-31당진000
652023-06-30당진000