Overview

Dataset statistics

Number of variables13
Number of observations25
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.0 KiB
Average record size in memory121.3 B

Variable types

Text1
Numeric12

Dataset

Description월별 제조업 중분류별(음식료품,담배,섬유제퓸,봉제의복,가죽 목재 등)항목값의 데이터에 대한 전력 사용량에 대한 정보를 제공합니다.
Author인천광역시
URLhttps://www.data.go.kr/data/15064954/fileData.do

Alerts

2023-01-01 is highly overall correlated with 2023-02-01 and 10 other fieldsHigh correlation
2023-02-01 is highly overall correlated with 2023-01-01 and 10 other fieldsHigh correlation
2023-03-01 is highly overall correlated with 2023-01-01 and 10 other fieldsHigh correlation
2023-04-01 is highly overall correlated with 2023-01-01 and 10 other fieldsHigh correlation
2023-05-01 is highly overall correlated with 2023-01-01 and 10 other fieldsHigh correlation
2023-06-01 is highly overall correlated with 2023-01-01 and 10 other fieldsHigh correlation
2023-07-01 is highly overall correlated with 2023-01-01 and 10 other fieldsHigh correlation
2023-08-01 is highly overall correlated with 2023-01-01 and 10 other fieldsHigh correlation
2023-09-01 is highly overall correlated with 2023-01-01 and 10 other fieldsHigh correlation
2023-10-01 is highly overall correlated with 2023-01-01 and 10 other fieldsHigh correlation
2023-11-01 is highly overall correlated with 2023-01-01 and 10 other fieldsHigh correlation
2023-12-01 is highly overall correlated with 2023-01-01 and 10 other fieldsHigh correlation
제조업 중분류별 전력사용량(MWh) has unique valuesUnique
2023-01-01 has unique valuesUnique
2023-02-01 has unique valuesUnique
2023-03-01 has unique valuesUnique
2023-04-01 has unique valuesUnique
2023-05-01 has unique valuesUnique
2023-06-01 has unique valuesUnique
2023-07-01 has unique valuesUnique
2023-08-01 has unique valuesUnique
2023-09-01 has unique valuesUnique
2023-10-01 has unique valuesUnique
2023-11-01 has unique valuesUnique
2023-12-01 has unique valuesUnique
2023-04-01 has 1 (4.0%) zerosZeros
2023-05-01 has 1 (4.0%) zerosZeros
2023-06-01 has 1 (4.0%) zerosZeros
2023-07-01 has 1 (4.0%) zerosZeros
2023-08-01 has 1 (4.0%) zerosZeros
2023-09-01 has 1 (4.0%) zerosZeros
2023-10-01 has 1 (4.0%) zerosZeros
2023-11-01 has 1 (4.0%) zerosZeros

Reproduction

Analysis started2024-04-06 08:36:20.332487
Analysis finished2024-04-06 08:36:57.970769
Duration37.64 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Distinct25
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
2024-04-06T17:36:58.460521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length5
Mean length5.04
Min length4

Characters and Unicode

Total characters126
Distinct characters61
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique25 ?
Unique (%)100.0%

Sample

1st row식 료 품
2nd row음 료
3rd row담 배
4th row섬유제품
5th row의복·모피
ValueCountFrequency (%)
2
 
5.4%
1
 
2.7%
1차 1
 
2.7%
금속 1
 
2.7%
금속가공 1
 
2.7%
전자·통신 1
 
2.7%
의료·광학 1
 
2.7%
전기장비 1
 
2.7%
기타기계 1
 
2.7%
1
 
2.7%
Other values (26) 26
70.3%
2024-04-06T17:36:59.503012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
28
22.2%
· 9
 
7.1%
6
 
4.8%
4
 
3.2%
4
 
3.2%
4
 
3.2%
3
 
2.4%
3
 
2.4%
3
 
2.4%
3
 
2.4%
Other values (51) 59
46.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 88
69.8%
Space Separator 28
 
22.2%
Other Punctuation 9
 
7.1%
Decimal Number 1
 
0.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
6
 
6.8%
4
 
4.5%
4
 
4.5%
4
 
4.5%
3
 
3.4%
3
 
3.4%
3
 
3.4%
3
 
3.4%
2
 
2.3%
2
 
2.3%
Other values (48) 54
61.4%
Space Separator
ValueCountFrequency (%)
28
100.0%
Other Punctuation
ValueCountFrequency (%)
· 9
100.0%
Decimal Number
ValueCountFrequency (%)
1 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 88
69.8%
Common 38
30.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
6
 
6.8%
4
 
4.5%
4
 
4.5%
4
 
4.5%
3
 
3.4%
3
 
3.4%
3
 
3.4%
3
 
3.4%
2
 
2.3%
2
 
2.3%
Other values (48) 54
61.4%
Common
ValueCountFrequency (%)
28
73.7%
· 9
 
23.7%
1 1
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 88
69.8%
ASCII 29
 
23.0%
None 9
 
7.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
28
96.6%
1 1
 
3.4%
None
ValueCountFrequency (%)
· 9
100.0%
Hangul
ValueCountFrequency (%)
6
 
6.8%
4
 
4.5%
4
 
4.5%
4
 
4.5%
3
 
3.4%
3
 
3.4%
3
 
3.4%
3
 
3.4%
2
 
2.3%
2
 
2.3%
Other values (48) 54
61.4%

2023-01-01
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct25
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40016.68
Minimum1
Maximum162209
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size357.0 B
2024-04-06T17:36:59.855920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile497.4
Q15062
median24787
Q355386
95-th percentile150778.2
Maximum162209
Range162208
Interquartile range (IQR)50324

Descriptive statistics

Standard deviation47085.97
Coefficient of variation (CV)1.1766586
Kurtosis1.8553043
Mean40016.68
Median Absolute Deviation (MAD)21348
Skewness1.5580594
Sum1000417
Variance2.2170885 × 109
MonotonicityNot monotonic
2024-04-06T17:37:00.289016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
55386 1
 
4.0%
1184 1
 
4.0%
449 1
 
4.0%
34381 1
 
4.0%
11456 1
 
4.0%
5164 1
 
4.0%
45925 1
 
4.0%
85319 1
 
4.0%
18670 1
 
4.0%
162209 1
 
4.0%
Other values (15) 15
60.0%
ValueCountFrequency (%)
1 1
4.0%
449 1
4.0%
691 1
4.0%
1184 1
4.0%
1340 1
4.0%
4205 1
4.0%
5062 1
4.0%
5164 1
4.0%
7980 1
4.0%
11456 1
4.0%
ValueCountFrequency (%)
162209 1
4.0%
159177 1
4.0%
117183 1
4.0%
85319 1
4.0%
70840 1
4.0%
56189 1
4.0%
55386 1
4.0%
46135 1
4.0%
45925 1
4.0%
34616 1
4.0%

2023-02-01
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct25
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38654.32
Minimum1
Maximum170894
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size357.0 B
2024-04-06T17:37:00.599534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile451.4
Q14507
median23422
Q351473
95-th percentile150508.6
Maximum170894
Range170893
Interquartile range (IQR)46966

Descriptive statistics

Standard deviation47233.327
Coefficient of variation (CV)1.2219417
Kurtosis2.7636346
Mean38654.32
Median Absolute Deviation (MAD)20012
Skewness1.7637725
Sum966358
Variance2.2309872 × 109
MonotonicityNot monotonic
2024-04-06T17:37:00.967906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
51473 1
 
4.0%
1135 1
 
4.0%
402 1
 
4.0%
32766 1
 
4.0%
10713 1
 
4.0%
4507 1
 
4.0%
43434 1
 
4.0%
79563 1
 
4.0%
20817 1
 
4.0%
170894 1
 
4.0%
Other values (15) 15
60.0%
ValueCountFrequency (%)
1 1
4.0%
402 1
4.0%
649 1
4.0%
1135 1
4.0%
1261 1
4.0%
3935 1
4.0%
4507 1
4.0%
4810 1
4.0%
7568 1
4.0%
10713 1
4.0%
ValueCountFrequency (%)
170894 1
4.0%
161502 1
4.0%
106535 1
4.0%
79563 1
4.0%
64483 1
4.0%
53018 1
4.0%
51473 1
4.0%
43434 1
4.0%
41868 1
4.0%
32766 1
4.0%

2023-03-01
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct25
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37742.56
Minimum1
Maximum167055
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size357.0 B
2024-04-06T17:37:01.264817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile386
Q14080
median21460
Q348179
95-th percentile148196.2
Maximum167055
Range167054
Interquartile range (IQR)44099

Descriptive statistics

Standard deviation46704.28
Coefficient of variation (CV)1.2374434
Kurtosis2.5881131
Mean37742.56
Median Absolute Deviation (MAD)20410
Skewness1.7352781
Sum943564
Variance2.1812898 × 109
MonotonicityNot monotonic
2024-04-06T17:37:01.513409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
50023 1
 
4.0%
1050 1
 
4.0%
363 1
 
4.0%
30047 1
 
4.0%
9737 1
 
4.0%
4468 1
 
4.0%
41612 1
 
4.0%
70455 1
 
4.0%
15562 1
 
4.0%
167055 1
 
4.0%
Other values (15) 15
60.0%
ValueCountFrequency (%)
1 1
4.0%
363 1
4.0%
478 1
4.0%
968 1
4.0%
1050 1
4.0%
3133 1
4.0%
4080 1
4.0%
4468 1
4.0%
7272 1
4.0%
9737 1
4.0%
ValueCountFrequency (%)
167055 1
4.0%
158282 1
4.0%
107853 1
4.0%
75832 1
4.0%
70455 1
4.0%
50023 1
4.0%
48179 1
4.0%
42405 1
4.0%
41612 1
4.0%
36605 1
4.0%

2023-04-01
Real number (ℝ)

HIGH CORRELATION  UNIQUE  ZEROS 

Distinct25
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37453.64
Minimum0
Maximum173013
Zeros1
Zeros (%)4.0%
Negative0
Negative (%)0.0%
Memory size357.0 B
2024-04-06T17:37:01.801762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile332
Q13821
median20894
Q345121
95-th percentile155161.8
Maximum173013
Range173013
Interquartile range (IQR)41300

Descriptive statistics

Standard deviation48321.588
Coefficient of variation (CV)1.2901707
Kurtosis3.1048892
Mean37453.64
Median Absolute Deviation (MAD)19546
Skewness1.8629526
Sum936341
Variance2.3349758 × 109
MonotonicityNot monotonic
2024-04-06T17:37:02.072858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
51399 1
 
4.0%
1223 1
 
4.0%
305 1
 
4.0%
27415 1
 
4.0%
8764 1
 
4.0%
4261 1
 
4.0%
39687 1
 
4.0%
66855 1
 
4.0%
11820 1
 
4.0%
173013 1
 
4.0%
Other values (15) 15
60.0%
ValueCountFrequency (%)
0 1
4.0%
305 1
4.0%
440 1
4.0%
842 1
4.0%
1223 1
4.0%
2772 1
4.0%
3821 1
4.0%
4261 1
4.0%
6884 1
4.0%
8764 1
4.0%
ValueCountFrequency (%)
173013 1
4.0%
167576 1
4.0%
105505 1
4.0%
75351 1
4.0%
66855 1
4.0%
51399 1
4.0%
45121 1
4.0%
40440 1
4.0%
39687 1
4.0%
36572 1
4.0%

2023-05-01
Real number (ℝ)

HIGH CORRELATION  UNIQUE  ZEROS 

Distinct25
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37547.16
Minimum0
Maximum174942
Zeros1
Zeros (%)4.0%
Negative0
Negative (%)0.0%
Memory size357.0 B
2024-04-06T17:37:02.317800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile307.8
Q13418
median19648
Q343127
95-th percentile156128
Maximum174942
Range174942
Interquartile range (IQR)39709

Descriptive statistics

Standard deviation48944.258
Coefficient of variation (CV)1.3035409
Kurtosis3.0274155
Mean37547.16
Median Absolute Deviation (MAD)18911
Skewness1.8573319
Sum938679
Variance2.3955403 × 109
MonotonicityNot monotonic
2024-04-06T17:37:02.581812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
52787 1
 
4.0%
1244 1
 
4.0%
288 1
 
4.0%
26111 1
 
4.0%
7715 1
 
4.0%
4189 1
 
4.0%
38722 1
 
4.0%
63218 1
 
4.0%
12660 1
 
4.0%
174942 1
 
4.0%
Other values (15) 15
60.0%
ValueCountFrequency (%)
0 1
4.0%
288 1
4.0%
387 1
4.0%
737 1
4.0%
1244 1
4.0%
2409 1
4.0%
3418 1
4.0%
4189 1
4.0%
6977 1
4.0%
7715 1
4.0%
ValueCountFrequency (%)
174942 1
4.0%
167893 1
4.0%
109068 1
4.0%
79767 1
4.0%
63218 1
4.0%
52787 1
4.0%
43127 1
4.0%
40779 1
4.0%
38722 1
4.0%
38442 1
4.0%

2023-06-01
Real number (ℝ)

HIGH CORRELATION  UNIQUE  ZEROS 

Distinct25
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36446.08
Minimum0
Maximum161306
Zeros1
Zeros (%)4.0%
Negative0
Negative (%)0.0%
Memory size357.0 B
2024-04-06T17:37:03.049314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile288
Q13628
median20796
Q344852
95-th percentile129212.6
Maximum161306
Range161306
Interquartile range (IQR)41224

Descriptive statistics

Standard deviation44165.874
Coefficient of variation (CV)1.2118141
Kurtosis2.0063424
Mean36446.08
Median Absolute Deviation (MAD)20056
Skewness1.5913089
Sum911152
Variance1.9506244 × 109
MonotonicityNot monotonic
2024-04-06T17:37:03.342549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
56066 1
 
4.0%
1414 1
 
4.0%
266 1
 
4.0%
28699 1
 
4.0%
7769 1
 
4.0%
4255 1
 
4.0%
40911 1
 
4.0%
66111 1
 
4.0%
14687 1
 
4.0%
132614 1
 
4.0%
Other values (15) 15
60.0%
ValueCountFrequency (%)
0 1
4.0%
266 1
4.0%
376 1
4.0%
740 1
4.0%
1414 1
4.0%
2499 1
4.0%
3628 1
4.0%
4255 1
4.0%
7218 1
4.0%
7769 1
4.0%
ValueCountFrequency (%)
161306 1
4.0%
132614 1
4.0%
115607 1
4.0%
77742 1
4.0%
66111 1
4.0%
56066 1
4.0%
44852 1
4.0%
43338 1
4.0%
40911 1
4.0%
34133 1
4.0%

2023-07-01
Real number (ℝ)

HIGH CORRELATION  UNIQUE  ZEROS 

Distinct25
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38691.4
Minimum0
Maximum160826
Zeros1
Zeros (%)4.0%
Negative0
Negative (%)0.0%
Memory size357.0 B
2024-04-06T17:37:03.740166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile339.6
Q13828
median21679
Q349041
95-th percentile148239
Maximum160826
Range160826
Interquartile range (IQR)45213

Descriptive statistics

Standard deviation47152.072
Coefficient of variation (CV)1.2186706
Kurtosis1.754523
Mean38691.4
Median Absolute Deviation (MAD)20420
Skewness1.5670752
Sum967285
Variance2.2233179 × 109
MonotonicityNot monotonic
2024-04-06T17:37:04.124170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
59342 1
 
4.0%
1522 1
 
4.0%
317 1
 
4.0%
29805 1
 
4.0%
8022 1
 
4.0%
4569 1
 
4.0%
42099 1
 
4.0%
69767 1
 
4.0%
14624 1
 
4.0%
153878 1
 
4.0%
Other values (15) 15
60.0%
ValueCountFrequency (%)
0 1
4.0%
317 1
4.0%
430 1
4.0%
871 1
4.0%
1522 1
4.0%
2683 1
4.0%
3828 1
4.0%
4569 1
4.0%
7915 1
4.0%
8022 1
4.0%
ValueCountFrequency (%)
160826 1
4.0%
153878 1
4.0%
125683 1
4.0%
80314 1
4.0%
69767 1
4.0%
59342 1
4.0%
49041 1
4.0%
46066 1
4.0%
42099 1
4.0%
36129 1
4.0%

2023-08-01
Real number (ℝ)

HIGH CORRELATION  UNIQUE  ZEROS 

Distinct25
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38920.88
Minimum0
Maximum171923
Zeros1
Zeros (%)4.0%
Negative0
Negative (%)0.0%
Memory size357.0 B
2024-04-06T17:37:04.672684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile354.2
Q14042
median20792
Q349990
95-th percentile143986.6
Maximum171923
Range171923
Interquartile range (IQR)45948

Descriptive statistics

Standard deviation48100.473
Coefficient of variation (CV)1.2358526
Kurtosis1.9914651
Mean38920.88
Median Absolute Deviation (MAD)19199
Skewness1.6189514
Sum973022
Variance2.3136555 × 109
MonotonicityNot monotonic
2024-04-06T17:37:05.060339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
61637 1
 
4.0%
1593 1
 
4.0%
334 1
 
4.0%
29857 1
 
4.0%
7827 1
 
4.0%
4568 1
 
4.0%
38845 1
 
4.0%
69156 1
 
4.0%
13999 1
 
4.0%
171923 1
 
4.0%
Other values (15) 15
60.0%
ValueCountFrequency (%)
0 1
4.0%
334 1
4.0%
435 1
4.0%
1006 1
4.0%
1593 1
4.0%
2740 1
4.0%
4042 1
4.0%
4568 1
4.0%
7827 1
4.0%
7978 1
4.0%
ValueCountFrequency (%)
171923 1
4.0%
147781 1
4.0%
128809 1
4.0%
80618 1
4.0%
69156 1
4.0%
61637 1
4.0%
49990 1
4.0%
45759 1
4.0%
38845 1
4.0%
36584 1
4.0%

2023-09-01
Real number (ℝ)

HIGH CORRELATION  UNIQUE  ZEROS 

Distinct25
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38049.64
Minimum0
Maximum160332
Zeros1
Zeros (%)4.0%
Negative0
Negative (%)0.0%
Memory size357.0 B
2024-04-06T17:37:05.424732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile338.4
Q14063
median21783
Q346081
95-th percentile141093.6
Maximum160332
Range160332
Interquartile range (IQR)42018

Descriptive statistics

Standard deviation46139.793
Coefficient of variation (CV)1.212621
Kurtosis1.7189878
Mean38049.64
Median Absolute Deviation (MAD)20135
Skewness1.5595804
Sum951241
Variance2.1288805 × 109
MonotonicityNot monotonic
2024-04-06T17:37:05.766118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
58702 1
 
4.0%
1648 1
 
4.0%
304 1
 
4.0%
28974 1
 
4.0%
8309 1
 
4.0%
4550 1
 
4.0%
39321 1
 
4.0%
71088 1
 
4.0%
15000 1
 
4.0%
160332 1
 
4.0%
Other values (15) 15
60.0%
ValueCountFrequency (%)
0 1
4.0%
304 1
4.0%
476 1
4.0%
979 1
4.0%
1648 1
4.0%
2757 1
4.0%
4063 1
4.0%
4550 1
4.0%
7837 1
4.0%
8309 1
4.0%
ValueCountFrequency (%)
160332 1
4.0%
144680 1
4.0%
126748 1
4.0%
77631 1
4.0%
71088 1
4.0%
58702 1
4.0%
46081 1
4.0%
44905 1
4.0%
39321 1
4.0%
37159 1
4.0%

2023-10-01
Real number (ℝ)

HIGH CORRELATION  UNIQUE  ZEROS 

Distinct25
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34181.2
Minimum0
Maximum145295
Zeros1
Zeros (%)4.0%
Negative0
Negative (%)0.0%
Memory size357.0 B
2024-04-06T17:37:07.029362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile276.6
Q13325
median17711
Q340853
95-th percentile124313
Maximum145295
Range145295
Interquartile range (IQR)37528

Descriptive statistics

Standard deviation41796.005
Coefficient of variation (CV)1.2227776
Kurtosis1.5469073
Mean34181.2
Median Absolute Deviation (MAD)17384
Skewness1.5225319
Sum854530
Variance1.746906 × 109
MonotonicityNot monotonic
2024-04-06T17:37:07.378764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
54913 1
 
4.0%
1408 1
 
4.0%
264 1
 
4.0%
24324 1
 
4.0%
7248 1
 
4.0%
4146 1
 
4.0%
37032 1
 
4.0%
58086 1
 
4.0%
10886 1
 
4.0%
126899 1
 
4.0%
Other values (15) 15
60.0%
ValueCountFrequency (%)
0 1
4.0%
264 1
4.0%
327 1
4.0%
699 1
4.0%
1408 1
4.0%
1891 1
4.0%
3325 1
4.0%
4146 1
4.0%
6589 1
4.0%
7248 1
4.0%
ValueCountFrequency (%)
145295 1
4.0%
126899 1
4.0%
113969 1
4.0%
80740 1
4.0%
58086 1
4.0%
54913 1
4.0%
40853 1
4.0%
39603 1
4.0%
37032 1
4.0%
36110 1
4.0%

2023-11-01
Real number (ℝ)

HIGH CORRELATION  UNIQUE  ZEROS 

Distinct25
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36624.84
Minimum0
Maximum156488
Zeros1
Zeros (%)4.0%
Negative0
Negative (%)0.0%
Memory size357.0 B
2024-04-06T17:37:07.771835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile366.2
Q14063
median21996
Q347990
95-th percentile125680.4
Maximum156488
Range156488
Interquartile range (IQR)43927

Descriptive statistics

Standard deviation43319.963
Coefficient of variation (CV)1.1828028
Kurtosis1.7167596
Mean36624.84
Median Absolute Deviation (MAD)19761
Skewness1.5196332
Sum915621
Variance1.8766192 × 109
MonotonicityNot monotonic
2024-04-06T17:37:08.188823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
54662 1
 
4.0%
1343 1
 
4.0%
343 1
 
4.0%
28467 1
 
4.0%
8765 1
 
4.0%
4586 1
 
4.0%
41757 1
 
4.0%
68116 1
 
4.0%
15874 1
 
4.0%
127406 1
 
4.0%
Other values (15) 15
60.0%
ValueCountFrequency (%)
0 1
4.0%
343 1
4.0%
459 1
4.0%
834 1
4.0%
1343 1
4.0%
2520 1
4.0%
4063 1
4.0%
4586 1
4.0%
7469 1
4.0%
8765 1
4.0%
ValueCountFrequency (%)
156488 1
4.0%
127406 1
4.0%
118778 1
4.0%
78207 1
4.0%
68116 1
4.0%
54662 1
4.0%
47990 1
4.0%
41757 1
4.0%
38140 1
4.0%
37637 1
4.0%

2023-12-01
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct25
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37552.84
Minimum1
Maximum148750
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size357.0 B
2024-04-06T17:37:08.707235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile430.8
Q14515
median22419
Q351012
95-th percentile130162.4
Maximum148750
Range148749
Interquartile range (IQR)46497

Descriptive statistics

Standard deviation43394.625
Coefficient of variation (CV)1.1555617
Kurtosis1.1637881
Mean37552.84
Median Absolute Deviation (MAD)21221
Skewness1.3966586
Sum938821
Variance1.8830934 × 109
MonotonicityNot monotonic
2024-04-06T17:37:09.261893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
53941 1
 
4.0%
1198 1
 
4.0%
396 1
 
4.0%
29851 1
 
4.0%
9742 1
 
4.0%
5017 1
 
4.0%
43908 1
 
4.0%
74639 1
 
4.0%
14122 1
 
4.0%
132407 1
 
4.0%
Other values (15) 15
60.0%
ValueCountFrequency (%)
1 1
4.0%
396 1
4.0%
570 1
4.0%
1075 1
4.0%
1198 1
4.0%
3082 1
4.0%
4515 1
4.0%
5017 1
4.0%
7753 1
4.0%
9742 1
4.0%
ValueCountFrequency (%)
148750 1
4.0%
132407 1
4.0%
121184 1
4.0%
81368 1
4.0%
74639 1
4.0%
53941 1
4.0%
51012 1
4.0%
43908 1
4.0%
39876 1
4.0%
38295 1
4.0%

Interactions

2024-04-06T17:36:53.849035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:20.943824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:24.088972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:26.446098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:29.202870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:31.826554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:34.292722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:36.974743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:39.952836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:42.732224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:45.257572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:50.272468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:54.162334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:21.150404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:24.278781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:26.648937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:29.394564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:32.081180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:34.624556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:37.189279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:40.156291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:42.947357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:45.501731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:50.547883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:54.405185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:21.320575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:24.455834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:26.848399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:29.575243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:32.268597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:34.890274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:37.366608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:40.394745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:43.128248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:45.782620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:51.315164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:54.665413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:21.492575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:24.629533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:27.031971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:29.765249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:32.498107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:35.086855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:37.997823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:40.667258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:43.399077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:46.051892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:51.601234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:54.998930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:21.788306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:24.823726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:27.634405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:29.955969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:32.745869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:35.355416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:38.205235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:40.877239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:43.589515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:46.429976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:51.905897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:55.287037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:22.047801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:24.999918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:27.806997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:30.165794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:32.962858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:35.553759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:38.380881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:41.070061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:43.819387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:47.331972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:52.136526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:55.495830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:22.484872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:25.198597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:27.983246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:30.383907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:33.146697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:35.751603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:38.581664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:41.274115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:44.008406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:48.178357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:52.345212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:55.729397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:22.723972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:25.387064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:28.165569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:30.653264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:33.355466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:35.945004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:38.779683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:41.472584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:44.225294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:48.620564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:52.541252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:55.957114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:23.022816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:25.579799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:28.382627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:30.987701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:33.538603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:36.183965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:39.021987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:41.756388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:44.496256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:49.070400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:52.789927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:56.230510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:23.282193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:25.787294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:28.594697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:31.219827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:33.734102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:36.387305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:39.263139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:42.044338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:44.674605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:49.416515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:53.025986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:56.634544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:23.507112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:26.005178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:28.779834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:31.429353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:33.907520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:36.584869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:39.453681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:42.283512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:44.899722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:49.685128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:53.284888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:56.921063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:23.832129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:26.253352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:29.018003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:31.648811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:34.091841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:36.783653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:39.737033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:42.487401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:45.074729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:49.958016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:53.585485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-06T17:37:09.508978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
제조업 중분류별 전력사용량(MWh)2023-01-012023-02-012023-03-012023-04-012023-05-012023-06-012023-07-012023-08-012023-09-012023-10-012023-11-012023-12-01
제조업 중분류별 전력사용량(MWh)1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
2023-01-011.0001.0000.9500.9540.9560.9320.9690.9060.9640.9260.9760.9920.944
2023-02-011.0000.9501.0000.9070.9880.9790.8820.9750.9000.9730.8820.9170.945
2023-03-011.0000.9540.9071.0000.9740.9531.0001.0000.9881.0000.9850.9670.908
2023-04-011.0000.9560.9880.9741.0000.9980.9340.9900.9510.9900.9560.9010.949
2023-05-011.0000.9320.9790.9530.9981.0000.9480.9880.9630.9940.9820.8880.938
2023-06-011.0000.9690.8821.0000.9340.9481.0000.9691.0001.0000.9980.9940.939
2023-07-011.0000.9060.9751.0000.9900.9880.9691.0000.9550.9980.9450.9190.965
2023-08-011.0000.9640.9000.9880.9510.9631.0000.9551.0000.9820.9950.9910.921
2023-09-011.0000.9260.9731.0000.9900.9941.0000.9980.9821.0000.9630.9380.975
2023-10-011.0000.9760.8820.9850.9560.9820.9980.9450.9950.9631.0000.9860.908
2023-11-011.0000.9920.9170.9670.9010.8880.9940.9190.9910.9380.9861.0000.965
2023-12-011.0000.9440.9450.9080.9490.9380.9390.9650.9210.9750.9080.9651.000
2024-04-06T17:37:09.896398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-01-012023-02-012023-03-012023-04-012023-05-012023-06-012023-07-012023-08-012023-09-012023-10-012023-11-012023-12-01
2023-01-011.0000.9980.9970.9960.9930.9920.9890.9890.9930.9890.9930.995
2023-02-010.9981.0000.9950.9930.9900.9890.9850.9850.9900.9850.9920.993
2023-03-010.9970.9951.0000.9990.9960.9950.9930.9930.9960.9930.9960.998
2023-04-010.9960.9930.9991.0000.9970.9960.9940.9940.9970.9940.9970.998
2023-05-010.9930.9900.9960.9971.0000.9990.9980.9981.0000.9980.9980.997
2023-06-010.9920.9890.9950.9960.9991.0000.9990.9980.9990.9990.9990.998
2023-07-010.9890.9850.9930.9940.9980.9991.0000.9980.9981.0000.9980.995
2023-08-010.9890.9850.9930.9940.9980.9980.9981.0000.9980.9980.9960.994
2023-09-010.9930.9900.9960.9971.0000.9990.9980.9981.0000.9980.9980.997
2023-10-010.9890.9850.9930.9940.9980.9991.0000.9980.9981.0000.9980.995
2023-11-010.9930.9920.9960.9970.9980.9990.9980.9960.9980.9981.0000.999
2023-12-010.9950.9930.9980.9980.9970.9980.9950.9940.9970.9950.9991.000

Missing values

2024-04-06T17:36:57.239980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-06T17:36:57.768327image/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

제조업 중분류별 전력사용량(MWh)2023-01-012023-02-012023-03-012023-04-012023-05-012023-06-012023-07-012023-08-012023-09-012023-10-012023-11-012023-12-01
0식 료 품553865147350023513995278756066593426163758702549135466253941
1음 료118411351050122312441414152215931648140813431198
2담 배111000000001
3섬유제품420539353133277224092499268327402757189125203082
4의복·모피1340126196884273774087110069796998341075
5가죽·가방691649478440387376430435476327459570
6목재·나무461354186842405404403844234133361293658437159361103763739876
7펄프·종이506248104080382134183628382840424063332540634515
8인쇄·매체798075687272688469777218791579787837658974697753
9연탄·석유708406448375832753517976777742803148061877631807407820781368
제조업 중분류별 전력사용량(MWh)2023-01-012023-02-012023-03-012023-04-012023-05-012023-06-012023-07-012023-08-012023-09-012023-10-012023-11-012023-12-01
15금속가공561895301848179451214312744852460664575946081396034799051012
16전자·통신117183106535107853105505109068115607125683128809126748113969118778121184
17의료·광학162209170894167055173013174942132614153878171923160332126899127406132407
18전기장비186702081715562118201266014687146241399915000108861587414122
19기타기계853197956370455668556321866111697676915671088580866811674639
20자 동 차459254343441612396873872240911420993884539321370324175743908
21기타운송516445074468426141894255456945684550414645865017
22가 구11456107139737876477157769802278278309724887659742
23기타 제품343813276630047274152611128699298052985728974243242846729851
24산업기계449402363305288266317334304264343396