Overview

Dataset statistics

Number of variables6
Number of observations62
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.3 KiB
Average record size in memory55.1 B

Variable types

DateTime1
Numeric5

Dataset

Description한국수력원자력 전원별(원자력,수력,양수,신재생) 전력판매량 현황 관련입니다.- 단위: GWh- 시운전 판매량 포함- PPA(Power Purchase Agreement, 한전직거래) 판매량 제외
Author한국수력원자력(주)
URLhttps://www.data.go.kr/data/15061364/fileData.do

Alerts

원자력(GWh) is highly overall correlated with 신재생(GWh) and 1 other fieldsHigh correlation
신재생(GWh) is highly overall correlated with 원자력(GWh) and 1 other fieldsHigh correlation
합계(GWh) is highly overall correlated with 원자력(GWh) and 1 other fieldsHigh correlation
연월 has unique valuesUnique
원자력(GWh) has unique valuesUnique
합계(GWh) has unique valuesUnique

Reproduction

Analysis started2024-04-06 08:54:12.016552
Analysis finished2024-04-06 08:54:20.005170
Duration7.99 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연월
Date

UNIQUE 

Distinct62
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size628.0 B
Minimum2019-01-01 00:00:00
Maximum2024-02-01 00:00:00
2024-04-06T17:54:20.156080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:54:20.439726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

원자력(GWh)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct62
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13052.613
Minimum8835
Maximum15741
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size690.0 B
2024-04-06T17:54:20.728016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8835
5-th percentile10092.5
Q112098.5
median13325.5
Q314174.75
95-th percentile15318.65
Maximum15741
Range6906
Interquartile range (IQR)2076.25

Descriptive statistics

Standard deviation1557.1116
Coefficient of variation (CV)0.11929501
Kurtosis0.0029623078
Mean13052.613
Median Absolute Deviation (MAD)1106.5
Skewness-0.59235192
Sum809262
Variance2424596.5
MonotonicityNot monotonic
2024-04-06T17:54:21.087008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11663 1
 
1.6%
14498 1
 
1.6%
12904 1
 
1.6%
15741 1
 
1.6%
15331 1
 
1.6%
13314 1
 
1.6%
13198 1
 
1.6%
12748 1
 
1.6%
13912 1
 
1.6%
14014 1
 
1.6%
Other values (52) 52
83.9%
ValueCountFrequency (%)
8835 1
1.6%
9724 1
1.6%
9803 1
1.6%
10071 1
1.6%
10501 1
1.6%
10514 1
1.6%
10793 1
1.6%
11110 1
1.6%
11315 1
1.6%
11560 1
1.6%
ValueCountFrequency (%)
15741 1
1.6%
15493 1
1.6%
15487 1
1.6%
15331 1
1.6%
15084 1
1.6%
15030 1
1.6%
14958 1
1.6%
14914 1
1.6%
14590 1
1.6%
14555 1
1.6%

수력(GWh)
Real number (ℝ)

Distinct49
Distinct (%)79.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean88.258065
Minimum37
Maximum224
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size690.0 B
2024-04-06T17:54:21.386683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37
5-th percentile43.15
Q156.25
median71.5
Q3106.5
95-th percentile201.85
Maximum224
Range187
Interquartile range (IQR)50.25

Descriptive statistics

Standard deviation47.23639
Coefficient of variation (CV)0.53520764
Kurtosis1.8002002
Mean88.258065
Median Absolute Deviation (MAD)17
Skewness1.5529037
Sum5472
Variance2231.2766
MonotonicityNot monotonic
2024-04-06T17:54:21.652042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
63 4
 
6.5%
55 3
 
4.8%
47 2
 
3.2%
71 2
 
3.2%
42 2
 
3.2%
76 2
 
3.2%
48 2
 
3.2%
57 2
 
3.2%
75 2
 
3.2%
87 2
 
3.2%
Other values (39) 39
62.9%
ValueCountFrequency (%)
37 1
 
1.6%
42 2
3.2%
43 1
 
1.6%
46 1
 
1.6%
47 2
3.2%
48 2
3.2%
50 1
 
1.6%
52 1
 
1.6%
54 1
 
1.6%
55 3
4.8%
ValueCountFrequency (%)
224 1
1.6%
222 1
1.6%
217 1
1.6%
202 1
1.6%
199 1
1.6%
155 1
1.6%
150 1
1.6%
140 1
1.6%
137 1
1.6%
136 1
1.6%

양수(GWh)
Real number (ℝ)

Distinct48
Distinct (%)77.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean299.8871
Minimum222
Maximum377
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size690.0 B
2024-04-06T17:54:21.936959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum222
5-th percentile236.35
Q1275.25
median301.5
Q3323
95-th percentile357.6
Maximum377
Range155
Interquartile range (IQR)47.75

Descriptive statistics

Standard deviation36.056924
Coefficient of variation (CV)0.120235
Kurtosis-0.17121517
Mean299.8871
Median Absolute Deviation (MAD)23.5
Skewness-0.16060418
Sum18593
Variance1300.1018
MonotonicityNot monotonic
2024-04-06T17:54:22.222497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
323 3
 
4.8%
343 3
 
4.8%
312 3
 
4.8%
266 2
 
3.2%
286 2
 
3.2%
319 2
 
3.2%
302 2
 
3.2%
271 2
 
3.2%
313 2
 
3.2%
222 2
 
3.2%
Other values (38) 39
62.9%
ValueCountFrequency (%)
222 2
3.2%
224 1
1.6%
236 1
1.6%
243 1
1.6%
250 1
1.6%
255 1
1.6%
257 1
1.6%
259 1
1.6%
262 1
1.6%
266 2
3.2%
ValueCountFrequency (%)
377 1
 
1.6%
374 1
 
1.6%
365 1
 
1.6%
358 1
 
1.6%
350 1
 
1.6%
343 3
4.8%
340 1
 
1.6%
337 1
 
1.6%
335 1
 
1.6%
329 1
 
1.6%

신재생(GWh)
Real number (ℝ)

HIGH CORRELATION 

Distinct13
Distinct (%)21.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.3548387
Minimum2
Maximum21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size690.0 B
2024-04-06T17:54:22.479549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3
Q13
median6
Q37
95-th percentile17.85
Maximum21
Range19
Interquartile range (IQR)4

Descriptive statistics

Standard deviation4.1373817
Coefficient of variation (CV)0.65106006
Kurtosis4.087631
Mean6.3548387
Median Absolute Deviation (MAD)2
Skewness1.9832425
Sum394
Variance17.117927
MonotonicityNot monotonic
2024-04-06T17:54:22.701264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
3 15
24.2%
6 11
17.7%
4 8
12.9%
7 8
12.9%
9 6
 
9.7%
5 4
 
6.5%
2 2
 
3.2%
8 2
 
3.2%
18 2
 
3.2%
10 1
 
1.6%
Other values (3) 3
 
4.8%
ValueCountFrequency (%)
2 2
 
3.2%
3 15
24.2%
4 8
12.9%
5 4
 
6.5%
6 11
17.7%
7 8
12.9%
8 2
 
3.2%
9 6
 
9.7%
10 1
 
1.6%
15 1
 
1.6%
ValueCountFrequency (%)
21 1
 
1.6%
19 1
 
1.6%
18 2
 
3.2%
15 1
 
1.6%
10 1
 
1.6%
9 6
9.7%
8 2
 
3.2%
7 8
12.9%
6 11
17.7%
5 4
 
6.5%

합계(GWh)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct62
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13447.081
Minimum9291
Maximum16125
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size690.0 B
2024-04-06T17:54:22.971031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9291
5-th percentile10432.3
Q112478.5
median13673
Q314611.5
95-th percentile15699.8
Maximum16125
Range6834
Interquartile range (IQR)2133

Descriptive statistics

Standard deviation1567.3724
Coefficient of variation (CV)0.11655856
Kurtosis-0.038244389
Mean13447.081
Median Absolute Deviation (MAD)1093.5
Skewness-0.55458081
Sum833719
Variance2456656.3
MonotonicityNot monotonic
2024-04-06T17:54:23.412186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12069 1
 
1.6%
14874 1
 
1.6%
13207 1
 
1.6%
16125 1
 
1.6%
15709 1
 
1.6%
13680 1
 
1.6%
13611 1
 
1.6%
13077 1
 
1.6%
14290 1
 
1.6%
14411 1
 
1.6%
Other values (52) 52
83.9%
ValueCountFrequency (%)
9291 1
1.6%
10050 1
1.6%
10213 1
1.6%
10410 1
1.6%
10856 1
1.6%
10878 1
1.6%
11244 1
1.6%
11510 1
1.6%
11706 1
1.6%
11921 1
1.6%
ValueCountFrequency (%)
16125 1
1.6%
16044 1
1.6%
15893 1
1.6%
15709 1
1.6%
15525 1
1.6%
15489 1
1.6%
15386 1
1.6%
15297 1
1.6%
15107 1
1.6%
14927 1
1.6%

Interactions

2024-04-06T17:54:18.242191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:54:12.929481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:54:14.801053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:54:16.031294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:54:17.226960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:54:18.451648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:54:13.190788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:54:15.090950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:54:16.234078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:54:17.444952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:54:18.639156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:54:13.399142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:54:15.379173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:54:16.444794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:54:17.635988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:54:18.900648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:54:13.837653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:54:15.573566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:54:16.763990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:54:17.829775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:54:19.077401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:54:14.423546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:54:15.846098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:54:16.995566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:54:18.024564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-06T17:54:23.613648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연월원자력(GWh)수력(GWh)양수(GWh)신재생(GWh)합계(GWh)
연월1.0001.0001.0001.0001.0001.000
원자력(GWh)1.0001.0000.0000.3700.0000.999
수력(GWh)1.0000.0001.0000.0000.0680.330
양수(GWh)1.0000.3700.0001.0000.5390.340
신재생(GWh)1.0000.0000.0680.5391.0000.000
합계(GWh)1.0000.9990.3300.3400.0001.000
2024-04-06T17:54:23.810090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
원자력(GWh)수력(GWh)양수(GWh)신재생(GWh)합계(GWh)
원자력(GWh)1.0000.0330.2060.5270.998
수력(GWh)0.0331.000-0.1200.1930.038
양수(GWh)0.206-0.1201.0000.3620.231
신재생(GWh)0.5270.1930.3621.0000.542
합계(GWh)0.9980.0380.2310.5421.000

Missing values

2024-04-06T17:54:19.318882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-06T17:54:19.923347image/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

연월원자력(GWh)수력(GWh)양수(GWh)신재생(GWh)합계(GWh)
02019-01-011166363340312069
12019-02-011050154298310856
22019-03-011336947274413694
32019-04-011347166284313824
42019-05-011405184257414396
52019-06-011293476222313235
62019-07-011111091306311510
72019-08-0111597112313312025
82019-09-019803108299310213
92019-10-011007146290310410
연월원자력(GWh)수력(GWh)양수(GWh)신재생(GWh)합계(GWh)
522023-05-0113752137365914263
532023-06-0113468140277913894
542023-07-0115030217271715525
552023-08-0115084134262915489
562023-09-0114243118324914694
572023-10-0114396723771514860
582023-11-0114958753351815386
592023-12-0115493872951815893
602024-01-0114216553742114666
612024-02-0114008713501914448