Overview

Dataset statistics

Number of variables14
Number of observations4312
Missing cells0
Missing cells (%)0.0%
Duplicate rows463
Duplicate rows (%)10.7%
Total size in memory526.5 KiB
Average record size in memory125.0 B

Variable types

Categorical1
Text1
Numeric12

Dataset

Description한국동서발전의 2020년 월간운저시간 예측값 정보입니다. 월간운전시간은 연도, 발전기명, 1월~12월의 항목으로 구성됩니다.
Author한국동서발전(주)
URLhttps://www.data.go.kr/data/15089029/fileData.do

Alerts

연도 has constant value ""Constant
Dataset has 463 (10.7%) duplicate rowsDuplicates
1월 is highly overall correlated with 2월 and 7 other fieldsHigh correlation
2월 is highly overall correlated with 1월 and 5 other fieldsHigh correlation
3월 is highly overall correlated with 1월 and 7 other fieldsHigh correlation
4월 is highly overall correlated with 3월 and 6 other fieldsHigh correlation
5월 is highly overall correlated with 4월 and 4 other fieldsHigh correlation
6월 is highly overall correlated with 1월 and 10 other fieldsHigh correlation
7월 is highly overall correlated with 1월 and 10 other fieldsHigh correlation
8월 is highly overall correlated with 1월 and 10 other fieldsHigh correlation
9월 is highly overall correlated with 1월 and 8 other fieldsHigh correlation
10월 is highly overall correlated with 6월 and 5 other fieldsHigh correlation
11월 is highly overall correlated with 1월 and 8 other fieldsHigh correlation
12월 is highly overall correlated with 1월 and 8 other fieldsHigh correlation
1월 has 691 (16.0%) zerosZeros
2월 has 604 (14.0%) zerosZeros
3월 has 839 (19.5%) zerosZeros
4월 has 801 (18.6%) zerosZeros
5월 has 949 (22.0%) zerosZeros
6월 has 820 (19.0%) zerosZeros
7월 has 546 (12.7%) zerosZeros
8월 has 534 (12.4%) zerosZeros
9월 has 598 (13.9%) zerosZeros
10월 has 967 (22.4%) zerosZeros
11월 has 770 (17.9%) zerosZeros
12월 has 624 (14.5%) zerosZeros

Reproduction

Analysis started2023-12-12 07:03:31.799259
Analysis finished2023-12-12 07:03:51.717870
Duration19.92 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연도
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size33.8 KiB
2020
4312 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020
2nd row2020
3rd row2020
4th row2020
5th row2020

Common Values

ValueCountFrequency (%)
2020 4312
100.0%

Length

2023-12-12T16:03:51.780851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T16:03:51.883123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020 4312
100.0%
Distinct53
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size33.8 KiB
2023-12-12T16:03:52.130931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length4
Mean length5.5793135
Min length4

Characters and Unicode

Total characters24058
Distinct characters65
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)0.2%

Sample

1st rowRe당진#3
2nd rowRe당진#3
3rd rowRe당진#3
4th rowRe당진#3
5th rowRe당진#3
ValueCountFrequency (%)
호남#2 169
 
3.9%
당진#8 169
 
3.9%
일산복합2cc 169
 
3.9%
일산복합1cc 169
 
3.9%
당진#7 169
 
3.9%
당진#1 169
 
3.9%
호남#1 169
 
3.9%
동해#2 169
 
3.9%
동해#1 169
 
3.9%
당진#4 169
 
3.9%
Other values (44) 2645
61.0%
2023-12-12T16:03:52.628223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
# 3445
 
14.3%
C 1968
 
8.2%
1887
 
7.8%
1887
 
7.8%
1222
 
5.1%
1 1123
 
4.7%
994
 
4.1%
994
 
4.1%
882
 
3.7%
2 845
 
3.5%
Other values (55) 8811
36.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 13346
55.5%
Decimal Number 4204
 
17.5%
Other Punctuation 3468
 
14.4%
Uppercase Letter 2211
 
9.2%
Dash Punctuation 785
 
3.3%
Space Separator 23
 
0.1%
Lowercase Letter 21
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
1887
14.1%
1887
14.1%
1222
 
9.2%
994
 
7.4%
994
 
7.4%
882
 
6.6%
676
 
5.1%
548
 
4.1%
531
 
4.0%
525
 
3.9%
Other values (33) 3200
24.0%
Decimal Number
ValueCountFrequency (%)
1 1123
26.7%
2 845
20.1%
5 467
11.1%
6 336
 
8.0%
3 334
 
7.9%
4 324
 
7.7%
9 224
 
5.3%
7 202
 
4.8%
0 180
 
4.3%
8 169
 
4.0%
Uppercase Letter
ValueCountFrequency (%)
C 1968
89.0%
S 69
 
3.1%
G 65
 
2.9%
T 42
 
1.9%
K 23
 
1.0%
E 23
 
1.0%
R 21
 
0.9%
Other Punctuation
ValueCountFrequency (%)
# 3445
99.3%
& 23
 
0.7%
Dash Punctuation
ValueCountFrequency (%)
- 785
100.0%
Space Separator
ValueCountFrequency (%)
23
100.0%
Lowercase Letter
ValueCountFrequency (%)
e 21
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 13346
55.5%
Common 8480
35.2%
Latin 2232
 
9.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
1887
14.1%
1887
14.1%
1222
 
9.2%
994
 
7.4%
994
 
7.4%
882
 
6.6%
676
 
5.1%
548
 
4.1%
531
 
4.0%
525
 
3.9%
Other values (33) 3200
24.0%
Common
ValueCountFrequency (%)
# 3445
40.6%
1 1123
 
13.2%
2 845
 
10.0%
- 785
 
9.3%
5 467
 
5.5%
6 336
 
4.0%
3 334
 
3.9%
4 324
 
3.8%
9 224
 
2.6%
7 202
 
2.4%
Other values (4) 395
 
4.7%
Latin
ValueCountFrequency (%)
C 1968
88.2%
S 69
 
3.1%
G 65
 
2.9%
T 42
 
1.9%
K 23
 
1.0%
E 23
 
1.0%
R 21
 
0.9%
e 21
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 13346
55.5%
ASCII 10712
44.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
# 3445
32.2%
C 1968
18.4%
1 1123
 
10.5%
2 845
 
7.9%
- 785
 
7.3%
5 467
 
4.4%
6 336
 
3.1%
3 334
 
3.1%
4 324
 
3.0%
9 224
 
2.1%
Other values (12) 861
 
8.0%
Hangul
ValueCountFrequency (%)
1887
14.1%
1887
14.1%
1222
 
9.2%
994
 
7.4%
994
 
7.4%
882
 
6.6%
676
 
5.1%
548
 
4.1%
531
 
4.0%
525
 
3.9%
Other values (33) 3200
24.0%

1월
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct292
Distinct (%)6.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean531.38474
Minimum0
Maximum744
Zeros691
Zeros (%)16.0%
Negative0
Negative (%)0.0%
Memory size38.0 KiB
2023-12-12T16:03:52.772774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1385
median729
Q3744
95-th percentile744
Maximum744
Range744
Interquartile range (IQR)359

Descriptive statistics

Standard deviation295.00897
Coefficient of variation (CV)0.55517019
Kurtosis-0.74076471
Mean531.38474
Median Absolute Deviation (MAD)15
Skewness-1.0088559
Sum2291331
Variance87030.29
MonotonicityNot monotonic
2023-12-12T16:03:52.940379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
744 2093
48.5%
0 691
 
16.0%
403 140
 
3.2%
648 99
 
2.3%
612 74
 
1.7%
96 41
 
1.0%
456 32
 
0.7%
24 29
 
0.7%
729 27
 
0.6%
72 27
 
0.6%
Other values (282) 1059
24.6%
ValueCountFrequency (%)
0 691
16.0%
2 7
 
0.2%
3 8
 
0.2%
4 1
 
< 0.1%
5 2
 
< 0.1%
6 1
 
< 0.1%
9 3
 
0.1%
10 2
 
< 0.1%
11 1
 
< 0.1%
12 1
 
< 0.1%
ValueCountFrequency (%)
744 2093
48.5%
743 1
 
< 0.1%
735 4
 
0.1%
732 10
 
0.2%
731 23
 
0.5%
730 6
 
0.1%
729 27
 
0.6%
728 7
 
0.2%
727 1
 
< 0.1%
726 3
 
0.1%

2월
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct255
Distinct (%)5.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean489.19481
Minimum0
Maximum696
Zeros604
Zeros (%)14.0%
Negative0
Negative (%)0.0%
Memory size38.0 KiB
2023-12-12T16:03:53.117764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1312
median672
Q3696
95-th percentile696
Maximum696
Range696
Interquartile range (IQR)384

Descriptive statistics

Standard deviation272.97715
Coefficient of variation (CV)0.55801318
Kurtosis-0.88963135
Mean489.19481
Median Absolute Deviation (MAD)24
Skewness-0.90957674
Sum2109408
Variance74516.523
MonotonicityNot monotonic
2023-12-12T16:03:53.280988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
696 1976
45.8%
0 604
 
14.0%
552 154
 
3.6%
377 146
 
3.4%
672 138
 
3.2%
600 98
 
2.3%
676 75
 
1.7%
312 72
 
1.7%
456 69
 
1.6%
72 67
 
1.6%
Other values (245) 913
21.2%
ValueCountFrequency (%)
0 604
14.0%
2 1
 
< 0.1%
3 17
 
0.4%
4 1
 
< 0.1%
8 2
 
< 0.1%
9 1
 
< 0.1%
15 1
 
< 0.1%
16 5
 
0.1%
18 1
 
< 0.1%
19 2
 
< 0.1%
ValueCountFrequency (%)
696 1976
45.8%
694 12
 
0.3%
693 1
 
< 0.1%
692 1
 
< 0.1%
691 2
 
< 0.1%
687 1
 
< 0.1%
684 4
 
0.1%
683 1
 
< 0.1%
682 3
 
0.1%
681 2
 
< 0.1%

3월
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct280
Distinct (%)6.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean455.48864
Minimum0
Maximum744
Zeros839
Zeros (%)19.5%
Negative0
Negative (%)0.0%
Memory size38.0 KiB
2023-12-12T16:03:53.437902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1137
median556
Q3744
95-th percentile744
Maximum744
Range744
Interquartile range (IQR)607

Descriptive statistics

Standard deviation301.81782
Coefficient of variation (CV)0.66262427
Kurtosis-1.4313922
Mean455.48864
Median Absolute Deviation (MAD)188
Skewness-0.47230034
Sum1964067
Variance91093.998
MonotonicityNot monotonic
2023-12-12T16:03:53.634464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
744 1658
38.5%
0 839
19.5%
403 177
 
4.1%
624 145
 
3.4%
432 83
 
1.9%
480 80
 
1.9%
137 67
 
1.6%
456 64
 
1.5%
284 62
 
1.4%
324 53
 
1.2%
Other values (270) 1084
25.1%
ValueCountFrequency (%)
0 839
19.5%
2 3
 
0.1%
4 5
 
0.1%
5 3
 
0.1%
7 9
 
0.2%
8 4
 
0.1%
9 4
 
0.1%
10 3
 
0.1%
11 2
 
< 0.1%
12 2
 
< 0.1%
ValueCountFrequency (%)
744 1658
38.5%
742 1
 
< 0.1%
740 3
 
0.1%
737 1
 
< 0.1%
736 1
 
< 0.1%
735 1
 
< 0.1%
732 5
 
0.1%
730 4
 
0.1%
729 1
 
< 0.1%
727 2
 
< 0.1%

4월
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct285
Distinct (%)6.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean424.55589
Minimum0
Maximum720
Zeros801
Zeros (%)18.6%
Negative0
Negative (%)0.0%
Memory size38.0 KiB
2023-12-12T16:03:53.823643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q198
median508.5
Q3720
95-th percentile720
Maximum720
Range720
Interquartile range (IQR)622

Descriptive statistics

Standard deviation298.10052
Coefficient of variation (CV)0.70214671
Kurtosis-1.5685917
Mean424.55589
Median Absolute Deviation (MAD)211.5
Skewness-0.36556116
Sum1830685
Variance88863.922
MonotonicityNot monotonic
2023-12-12T16:03:53.974011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
720 1561
36.2%
0 801
18.6%
390 177
 
4.1%
24 136
 
3.2%
120 99
 
2.3%
480 95
 
2.2%
624 92
 
2.1%
384 79
 
1.8%
312 75
 
1.7%
102 67
 
1.6%
Other values (275) 1130
26.2%
ValueCountFrequency (%)
0 801
18.6%
2 1
 
< 0.1%
4 2
 
< 0.1%
6 6
 
0.1%
8 1
 
< 0.1%
9 1
 
< 0.1%
10 16
 
0.4%
13 2
 
< 0.1%
16 1
 
< 0.1%
18 3
 
0.1%
ValueCountFrequency (%)
720 1561
36.2%
708 4
 
0.1%
707 7
 
0.2%
706 5
 
0.1%
705 1
 
< 0.1%
704 5
 
0.1%
703 15
 
0.3%
702 2
 
< 0.1%
701 5
 
0.1%
700 4
 
0.1%

5월
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct347
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean434.41651
Minimum0
Maximum744
Zeros949
Zeros (%)22.0%
Negative0
Negative (%)0.0%
Memory size38.0 KiB
2023-12-12T16:03:54.155690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1122
median461
Q3744
95-th percentile744
Maximum744
Range744
Interquartile range (IQR)622

Descriptive statistics

Standard deviation304.60029
Coefficient of variation (CV)0.70117107
Kurtosis-1.5346448
Mean434.41651
Median Absolute Deviation (MAD)283
Skewness-0.31723076
Sum1873204
Variance92781.337
MonotonicityNot monotonic
2023-12-12T16:03:54.341239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
744 1644
38.1%
0 949
22.0%
403 198
 
4.6%
289 116
 
2.7%
624 73
 
1.7%
127 65
 
1.5%
168 47
 
1.1%
336 39
 
0.9%
404 39
 
0.9%
312 36
 
0.8%
Other values (337) 1106
25.6%
ValueCountFrequency (%)
0 949
22.0%
1 21
 
0.5%
2 2
 
< 0.1%
3 1
 
< 0.1%
4 4
 
0.1%
5 1
 
< 0.1%
6 3
 
0.1%
7 6
 
0.1%
10 1
 
< 0.1%
11 1
 
< 0.1%
ValueCountFrequency (%)
744 1644
38.1%
742 1
 
< 0.1%
738 1
 
< 0.1%
737 2
 
< 0.1%
736 1
 
< 0.1%
735 1
 
< 0.1%
734 1
 
< 0.1%
732 9
 
0.2%
731 4
 
0.1%
730 1
 
< 0.1%

6월
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct317
Distinct (%)7.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean468.75023
Minimum0
Maximum720
Zeros820
Zeros (%)19.0%
Negative0
Negative (%)0.0%
Memory size38.0 KiB
2023-12-12T16:03:54.556481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1159
median624
Q3720
95-th percentile720
Maximum720
Range720
Interquartile range (IQR)561

Descriptive statistics

Standard deviation293.54096
Coefficient of variation (CV)0.6262204
Kurtosis-1.2841393
Mean468.75023
Median Absolute Deviation (MAD)96
Skewness-0.64587729
Sum2021251
Variance86166.294
MonotonicityNot monotonic
2023-12-12T16:03:54.740065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
720 1813
42.0%
0 820
19.0%
624 181
 
4.2%
390 177
 
4.1%
240 74
 
1.7%
140 66
 
1.5%
211 60
 
1.4%
504 43
 
1.0%
288 40
 
0.9%
714 32
 
0.7%
Other values (307) 1006
23.3%
ValueCountFrequency (%)
0 820
19.0%
1 3
 
0.1%
2 8
 
0.2%
3 1
 
< 0.1%
4 7
 
0.2%
6 5
 
0.1%
7 2
 
< 0.1%
8 2
 
< 0.1%
9 22
 
0.5%
11 1
 
< 0.1%
ValueCountFrequency (%)
720 1813
42.0%
719 27
 
0.6%
718 9
 
0.2%
715 27
 
0.6%
714 32
 
0.7%
713 16
 
0.4%
712 1
 
< 0.1%
711 1
 
< 0.1%
708 18
 
0.4%
707 2
 
< 0.1%

7월
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct307
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean515.84647
Minimum0
Maximum744
Zeros546
Zeros (%)12.7%
Negative0
Negative (%)0.0%
Memory size38.0 KiB
2023-12-12T16:03:54.938644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1130
median744
Q3744
95-th percentile744
Maximum744
Range744
Interquartile range (IQR)614

Descriptive statistics

Standard deviation303.33855
Coefficient of variation (CV)0.58804037
Kurtosis-1.1395406
Mean515.84647
Median Absolute Deviation (MAD)0
Skewness-0.80704341
Sum2224330
Variance92014.276
MonotonicityNot monotonic
2023-12-12T16:03:55.473228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
744 2299
53.3%
0 546
 
12.7%
96 157
 
3.6%
403 141
 
3.3%
648 69
 
1.6%
48 60
 
1.4%
130 36
 
0.8%
504 36
 
0.8%
78 35
 
0.8%
451 32
 
0.7%
Other values (297) 901
 
20.9%
ValueCountFrequency (%)
0 546
12.7%
1 11
 
0.3%
2 6
 
0.1%
3 2
 
< 0.1%
4 11
 
0.3%
6 1
 
< 0.1%
7 1
 
< 0.1%
8 1
 
< 0.1%
9 17
 
0.4%
10 5
 
0.1%
ValueCountFrequency (%)
744 2299
53.3%
743 3
 
0.1%
742 1
 
< 0.1%
741 1
 
< 0.1%
738 8
 
0.2%
734 1
 
< 0.1%
732 9
 
0.2%
730 8
 
0.2%
729 4
 
0.1%
728 8
 
0.2%

8월
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct365
Distinct (%)8.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean505.28896
Minimum0
Maximum744
Zeros534
Zeros (%)12.4%
Negative0
Negative (%)0.0%
Memory size38.0 KiB
2023-12-12T16:03:55.638510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1137.75
median739.5
Q3744
95-th percentile744
Maximum744
Range744
Interquartile range (IQR)606.25

Descriptive statistics

Standard deviation301.4227
Coefficient of variation (CV)0.5965353
Kurtosis-1.2515037
Mean505.28896
Median Absolute Deviation (MAD)4.5
Skewness-0.72073113
Sum2178806
Variance90855.645
MonotonicityNot monotonic
2023-12-12T16:03:55.796086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
744 2127
49.3%
0 534
 
12.4%
120 139
 
3.2%
403 138
 
3.2%
48 110
 
2.6%
624 99
 
2.3%
117 70
 
1.6%
720 34
 
0.8%
528 22
 
0.5%
726 22
 
0.5%
Other values (355) 1017
23.6%
ValueCountFrequency (%)
0 534
12.4%
1 2
 
< 0.1%
2 5
 
0.1%
3 1
 
< 0.1%
4 3
 
0.1%
5 2
 
< 0.1%
8 4
 
0.1%
9 2
 
< 0.1%
10 3
 
0.1%
11 6
 
0.1%
ValueCountFrequency (%)
744 2127
49.3%
743 1
 
< 0.1%
742 3
 
0.1%
741 21
 
0.5%
740 4
 
0.1%
739 1
 
< 0.1%
738 1
 
< 0.1%
737 1
 
< 0.1%
734 1
 
< 0.1%
732 10
 
0.2%

9월
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct412
Distinct (%)9.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean445.77968
Minimum0
Maximum720
Zeros598
Zeros (%)13.9%
Negative0
Negative (%)0.0%
Memory size38.0 KiB
2023-12-12T16:03:55.935064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q196
median553
Q3720
95-th percentile720
Maximum720
Range720
Interquartile range (IQR)624

Descriptive statistics

Standard deviation289.13534
Coefficient of variation (CV)0.64860591
Kurtosis-1.4631864
Mean445.77968
Median Absolute Deviation (MAD)167
Skewness-0.47519224
Sum1922202
Variance83599.243
MonotonicityNot monotonic
2023-12-12T16:03:56.131288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
720 1399
32.4%
0 598
 
13.9%
96 259
 
6.0%
390 106
 
2.5%
703 88
 
2.0%
624 87
 
2.0%
260 71
 
1.6%
702 57
 
1.3%
240 49
 
1.1%
552 47
 
1.1%
Other values (402) 1551
36.0%
ValueCountFrequency (%)
0 598
13.9%
1 2
 
< 0.1%
2 2
 
< 0.1%
3 3
 
0.1%
4 16
 
0.4%
6 6
 
0.1%
7 17
 
0.4%
8 8
 
0.2%
9 1
 
< 0.1%
10 5
 
0.1%
ValueCountFrequency (%)
720 1399
32.4%
719 20
 
0.5%
718 19
 
0.4%
717 17
 
0.4%
716 12
 
0.3%
715 1
 
< 0.1%
714 1
 
< 0.1%
708 4
 
0.1%
707 6
 
0.1%
706 2
 
< 0.1%

10월
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct377
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean430.13057
Minimum0
Maximum744
Zeros967
Zeros (%)22.4%
Negative0
Negative (%)0.0%
Memory size38.0 KiB
2023-12-12T16:03:56.295983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q150
median558
Q3744
95-th percentile744
Maximum744
Range744
Interquartile range (IQR)694

Descriptive statistics

Standard deviation313.50073
Coefficient of variation (CV)0.72885016
Kurtosis-1.6444676
Mean430.13057
Median Absolute Deviation (MAD)186
Skewness-0.33007064
Sum1854723
Variance98282.708
MonotonicityNot monotonic
2023-12-12T16:03:56.446754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
744 1288
29.9%
0 967
22.4%
96 175
 
4.1%
648 140
 
3.2%
403 107
 
2.5%
696 100
 
2.3%
364 71
 
1.6%
689 66
 
1.5%
708 60
 
1.4%
720 55
 
1.3%
Other values (367) 1283
29.8%
ValueCountFrequency (%)
0 967
22.4%
2 10
 
0.2%
4 11
 
0.3%
5 1
 
< 0.1%
6 6
 
0.1%
8 4
 
0.1%
10 3
 
0.1%
14 2
 
< 0.1%
16 4
 
0.1%
19 1
 
< 0.1%
ValueCountFrequency (%)
744 1288
29.9%
739 1
 
< 0.1%
738 1
 
< 0.1%
737 1
 
< 0.1%
736 1
 
< 0.1%
732 1
 
< 0.1%
730 10
 
0.2%
729 1
 
< 0.1%
728 7
 
0.2%
727 10
 
0.2%

11월
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct357
Distinct (%)8.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean450.49629
Minimum0
Maximum720
Zeros770
Zeros (%)17.9%
Negative0
Negative (%)0.0%
Memory size38.0 KiB
2023-12-12T16:03:56.572262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1120
median600
Q3720
95-th percentile720
Maximum720
Range720
Interquartile range (IQR)600

Descriptive statistics

Standard deviation301.3686
Coefficient of variation (CV)0.66897021
Kurtosis-1.5086831
Mean450.49629
Median Absolute Deviation (MAD)120
Skewness-0.49887654
Sum1942540
Variance90823.032
MonotonicityNot monotonic
2023-12-12T16:03:56.683513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
720 1889
43.8%
0 770
17.9%
390 176
 
4.1%
600 130
 
3.0%
120 92
 
2.1%
417 41
 
1.0%
144 38
 
0.9%
68 27
 
0.6%
570 26
 
0.6%
67 26
 
0.6%
Other values (347) 1097
25.4%
ValueCountFrequency (%)
0 770
17.9%
1 11
 
0.3%
2 11
 
0.3%
6 6
 
0.1%
7 1
 
< 0.1%
8 1
 
< 0.1%
9 1
 
< 0.1%
10 1
 
< 0.1%
11 3
 
0.1%
12 1
 
< 0.1%
ValueCountFrequency (%)
720 1889
43.8%
715 2
 
< 0.1%
713 1
 
< 0.1%
711 3
 
0.1%
708 3
 
0.1%
707 11
 
0.3%
705 16
 
0.4%
704 13
 
0.3%
703 4
 
0.1%
702 3
 
0.1%

12월
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct354
Distinct (%)8.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean518.96962
Minimum0
Maximum744
Zeros624
Zeros (%)14.5%
Negative0
Negative (%)0.0%
Memory size38.0 KiB
2023-12-12T16:03:56.797122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1288
median732
Q3744
95-th percentile744
Maximum744
Range744
Interquartile range (IQR)456

Descriptive statistics

Standard deviation293.4155
Coefficient of variation (CV)0.56538087
Kurtosis-0.96224474
Mean518.96962
Median Absolute Deviation (MAD)12
Skewness-0.85656612
Sum2237797
Variance86092.654
MonotonicityNot monotonic
2023-12-12T16:03:56.914731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
744 2112
49.0%
0 624
 
14.5%
403 140
 
3.2%
96 74
 
1.7%
288 74
 
1.7%
672 73
 
1.7%
456 52
 
1.2%
648 46
 
1.1%
732 44
 
1.0%
260 35
 
0.8%
Other values (344) 1038
24.1%
ValueCountFrequency (%)
0 624
14.5%
1 5
 
0.1%
2 3
 
0.1%
3 1
 
< 0.1%
4 1
 
< 0.1%
5 1
 
< 0.1%
6 3
 
0.1%
8 3
 
0.1%
9 4
 
0.1%
10 3
 
0.1%
ValueCountFrequency (%)
744 2112
49.0%
742 3
 
0.1%
740 17
 
0.4%
739 1
 
< 0.1%
738 1
 
< 0.1%
736 1
 
< 0.1%
734 1
 
< 0.1%
733 1
 
< 0.1%
732 44
 
1.0%
731 4
 
0.1%

Interactions

2023-12-12T16:03:49.993709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:03:33.620656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:03:34.947368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:03:36.801916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:03:38.340135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:03:39.946396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:03:41.354757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:03:42.517017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:03:43.949406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:03:45.327040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:03:46.879597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:03:48.256305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:03:50.119219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:03:33.735377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:03:35.067779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:03:36.909539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:03:38.455869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:03:40.107532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:03:41.458425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:03:42.597882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:03:44.068345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:03:45.455047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:03:46.982699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:03:48.378769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:03:50.262086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:03:33.825042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:03:35.172003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:03:37.028635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:03:38.583605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:03:40.256147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:03:41.550042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:03:42.947831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:03:44.181601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:03:45.546670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:03:47.098962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:03:48.503933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:03:50.378798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:03:33.934924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:03:35.301870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:03:37.134209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:03:38.742704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:03:40.379776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:03:41.642672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:03:43.044929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:03:44.287408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:03:45.647022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:03:47.240088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:03:48.635292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:03:50.477327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:03:34.025882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:03:35.436704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:03:37.289359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:03:38.879064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:03:40.489741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:03:41.735716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:03:43.134666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:03:44.384703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:03:45.800360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:03:47.345629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:03:48.754339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:03:50.582023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:03:34.130692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:03:35.549981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:03:37.428031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:03:39.021626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:03:40.599674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:03:41.837004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:03:43.222748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:03:44.511009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:03:45.937116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:03:47.440384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:03:48.845332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:03:50.693218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:03:34.226928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:03:35.675800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:03:37.552244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:03:39.169039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:03:40.722423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:03:41.990668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:03:43.311080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:03:44.630035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:03:46.092790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:03:47.529756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:03:48.941807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:03:50.822356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:03:34.342081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:03:35.809516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:03:37.674655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:03:39.304758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:03:40.827426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:03:42.086387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:03:43.448347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:03:44.743055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:03:46.246950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:03:47.641786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:03:49.044799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:03:50.951667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:03:34.465018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:03:35.942901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:03:37.786432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:03:39.444303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:03:40.942697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:03:42.172085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:03:43.540135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:03:44.843624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:03:46.404341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:03:47.766288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:03:49.162906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:03:51.077027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:03:34.579447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:03:36.395833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:03:37.927062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:03:39.566515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:03:41.060160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:03:42.267396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:03:43.631736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:03:44.971938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:03:46.525179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:03:47.877939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:03:49.295222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:03:51.191577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:03:34.683025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:03:36.535326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:03:38.064041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:03:39.693425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:03:41.157235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:03:42.354532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:03:43.733269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:03:45.073116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:03:46.663258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:03:47.997925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:03:49.720367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:03:51.295378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:03:34.824282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:03:36.677976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:03:38.206924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:03:39.823922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:03:41.264352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:03:42.433472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:03:43.850793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:03:45.201593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:03:46.759367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:03:48.110623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:03:49.886091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T16:03:57.001588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
발전기명1월2월3월4월5월6월7월8월9월10월11월12월
발전기명1.0000.9050.9160.9070.8820.8600.8760.8920.8800.8530.8770.8570.866
1월0.9051.0000.9080.8080.7740.7340.7810.8050.8380.7300.7260.7890.834
2월0.9160.9081.0000.8810.7910.7400.7840.8190.8080.7150.6880.7540.814
3월0.9070.8080.8811.0000.7880.7930.7820.8000.8040.7030.7190.7430.752
4월0.8820.7740.7910.7881.0000.8840.8230.8120.7980.6780.6960.7050.710
5월0.8600.7340.7400.7930.8841.0000.8850.8050.7990.7180.7200.7460.757
6월0.8760.7810.7840.7820.8230.8851.0000.8810.8460.7210.7180.7470.745
7월0.8920.8050.8190.8000.8120.8050.8811.0000.9090.7930.7900.7720.786
8월0.8800.8380.8080.8040.7980.7990.8460.9091.0000.8370.8120.8010.818
9월0.8530.7300.7150.7030.6780.7180.7210.7930.8371.0000.8730.8270.782
10월0.8770.7260.6880.7190.6960.7200.7180.7900.8120.8731.0000.8590.757
11월0.8570.7890.7540.7430.7050.7460.7470.7720.8010.8270.8591.0000.822
12월0.8660.8340.8140.7520.7100.7570.7450.7860.8180.7820.7570.8221.000
2023-12-12T16:03:57.117260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
1월2월3월4월5월6월7월8월9월10월11월12월
1월1.0000.8310.5940.4740.4680.5760.6560.6890.5670.4910.5550.792
2월0.8311.0000.7230.4820.4470.5510.6090.6320.4780.3860.4790.705
3월0.5940.7231.0000.6330.4880.5690.5760.6350.4810.4360.5260.576
4월0.4740.4820.6331.0000.7880.6970.6300.6700.5300.4250.5220.487
5월0.4680.4470.4880.7881.0000.8020.6090.6410.5100.4180.4900.496
6월0.5760.5510.5690.6970.8021.0000.8450.7930.5910.5090.6000.594
7월0.6560.6090.5760.6300.6090.8451.0000.8950.6490.5650.6480.659
8월0.6890.6320.6350.6700.6410.7930.8951.0000.7730.6670.7390.729
9월0.5670.4780.4810.5300.5100.5910.6490.7731.0000.8500.7660.666
10월0.4910.3860.4360.4250.4180.5090.5650.6670.8501.0000.8310.598
11월0.5550.4790.5260.5220.4900.6000.6480.7390.7660.8311.0000.736
12월0.7920.7050.5760.4870.4960.5940.6590.7290.6660.5980.7361.000

Missing values

2023-12-12T16:03:51.438280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T16:03:51.642328image/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

연도발전기명1월2월3월4월5월6월7월8월9월10월11월12월
02020Re당진#300000000000740
12020Re당진#300000000000744
22020Re당진#300000000000744
32020Re당진#300000000000744
42020Re당진#300000000000740
52020Re당진#300000000000740
62020Re당진#300000000000740
72020Re당진#300000000000740
82020Re당진#300000000000740
92020Re당진#300000000000725
연도발전기명1월2월3월4월5월6월7월8월9월10월11월12월
43022020호남#2744696432720744720744744720744720744
43032020호남#2744696432720744720744744720744720744
43042020호남#2744696432720744720744744720744720744
43052020호남#2744696432720744720744744720744720744
43062020호남#2744696432720744720744744720744720744
43072020호남#2744696432720744720744744720744720744
43082020호남#2744696432720744720744744720744720744
43092020호남#2744696432720744720744744720744720744
43102020호남#2744696432720744720744744720744720744
43112020호남#2744696432720744720744744720744720744

Duplicate rows

Most frequently occurring

연도발전기명1월2월3월4월5월6월7월8월9월10월11월12월# duplicates
4542020호남#274469643272074472074474472074472074483
4402020호남#174445645672074472074474472074472074463
1952020당진#974469674424021174474472074472074447
2652020신재생-동서-소수력74469674472074472074474472074472074444
2662020신재생-동서-연료전지74469674472074472074474472074472074444
2682020신재생-동서-풍력74469674472074472074474472074472074444
2642020신재생-동서-기타00062474472074474472074472074437
4222020일산복합2CC003241021271409612096961209637
2692020신재생-태양광140337740339040339040340339040339040335
2702020신재생-태양광440337740339040339040340339040339040335