Overview

Dataset statistics

Number of variables13
Number of observations28
Missing cells51
Missing cells (%)14.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.3 KiB
Average record size in memory120.7 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://data.incheon.go.kr/findData/publicDataDetail?dataId=15064954&srcSe=7661IVAWM27C61E190

Alerts

2021-01-01 is highly overall correlated with 2021-02-01 and 10 other fieldsHigh correlation
2021-02-01 is highly overall correlated with 2021-01-01 and 10 other fieldsHigh correlation
2021-03-01 is highly overall correlated with 2021-01-01 and 10 other fieldsHigh correlation
2021-04-01 is highly overall correlated with 2021-01-01 and 10 other fieldsHigh correlation
2021-05-01 is highly overall correlated with 2021-01-01 and 10 other fieldsHigh correlation
2021-06-01 is highly overall correlated with 2021-01-01 and 10 other fieldsHigh correlation
2021-07-01 is highly overall correlated with 2021-01-01 and 10 other fieldsHigh correlation
2021-08-01 is highly overall correlated with 2021-01-01 and 10 other fieldsHigh correlation
2021-09-01 is highly overall correlated with 2021-01-01 and 10 other fieldsHigh correlation
2021-10-01 is highly overall correlated with 2021-01-01 and 10 other fieldsHigh correlation
2021-11-01 is highly overall correlated with 2021-01-01 and 10 other fieldsHigh correlation
2021-12-01 is highly overall correlated with 2021-01-01 and 10 other fieldsHigh correlation
2021-01-01 has 5 (17.9%) missing valuesMissing
2021-02-01 has 5 (17.9%) missing valuesMissing
2021-03-01 has 5 (17.9%) missing valuesMissing
2021-04-01 has 4 (14.3%) missing valuesMissing
2021-05-01 has 4 (14.3%) missing valuesMissing
2021-06-01 has 4 (14.3%) missing valuesMissing
2021-07-01 has 4 (14.3%) missing valuesMissing
2021-08-01 has 4 (14.3%) missing valuesMissing
2021-09-01 has 4 (14.3%) missing valuesMissing
2021-10-01 has 4 (14.3%) missing valuesMissing
2021-11-01 has 4 (14.3%) missing valuesMissing
2021-12-01 has 4 (14.3%) missing valuesMissing
제조업 중분류별 전력사용량(MWh) has unique valuesUnique
2021-01-01 has 1 (3.6%) zerosZeros
2021-02-01 has 1 (3.6%) zerosZeros
2021-03-01 has 1 (3.6%) zerosZeros
2021-11-01 has 1 (3.6%) zerosZeros

Reproduction

Analysis started2024-01-28 07:17:07.838891
Analysis finished2024-01-28 07:17:19.382592
Duration11.54 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Distinct28
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size356.0 B
2024-01-28T16:17:19.510423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length9
Mean length5.1785714
Min length2

Characters and Unicode

Total characters145
Distinct characters70
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique28 ?
Unique (%)100.0%

Sample

1st row식음료품
2nd row담배제조업
3rd row섬유제품
4th row의복, 모피
5th row가죽, 가방
ValueCountFrequency (%)
제조 2
 
4.7%
기타제품 2
 
4.7%
가구 2
 
4.7%
자동차 1
 
2.3%
금속가공 1
 
2.3%
영상 1
 
2.3%
음향 1
 
2.3%
통신 1
 
2.3%
의료,광학 1
 
2.3%
전기장비 1
 
2.3%
Other values (30) 30
69.8%
2024-01-28T16:17:19.788317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
15
 
10.3%
12
 
8.3%
, 10
 
6.9%
7
 
4.8%
5
 
3.4%
5
 
3.4%
4
 
2.8%
4
 
2.8%
4
 
2.8%
3
 
2.1%
Other values (60) 76
52.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 119
82.1%
Space Separator 15
 
10.3%
Other Punctuation 10
 
6.9%
Decimal Number 1
 
0.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
12
 
10.1%
7
 
5.9%
5
 
4.2%
5
 
4.2%
4
 
3.4%
4
 
3.4%
4
 
3.4%
3
 
2.5%
3
 
2.5%
3
 
2.5%
Other values (57) 69
58.0%
Space Separator
ValueCountFrequency (%)
15
100.0%
Other Punctuation
ValueCountFrequency (%)
, 10
100.0%
Decimal Number
ValueCountFrequency (%)
1 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 119
82.1%
Common 26
 
17.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
12
 
10.1%
7
 
5.9%
5
 
4.2%
5
 
4.2%
4
 
3.4%
4
 
3.4%
4
 
3.4%
3
 
2.5%
3
 
2.5%
3
 
2.5%
Other values (57) 69
58.0%
Common
ValueCountFrequency (%)
15
57.7%
, 10
38.5%
1 1
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 119
82.1%
ASCII 26
 
17.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
15
57.7%
, 10
38.5%
1 1
 
3.8%
Hangul
ValueCountFrequency (%)
12
 
10.1%
7
 
5.9%
5
 
4.2%
5
 
4.2%
4
 
3.4%
4
 
3.4%
4
 
3.4%
3
 
2.5%
3
 
2.5%
3
 
2.5%
Other values (57) 69
58.0%

2021-01-01
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct23
Distinct (%)100.0%
Missing5
Missing (%)17.9%
Infinite0
Infinite (%)0.0%
Mean43157.13
Minimum0
Maximum169015
Zeros1
Zeros (%)3.6%
Negative0
Negative (%)0.0%
Memory size384.0 B
2024-01-28T16:17:19.880325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile379.9
Q14738.5
median26300
Q357946
95-th percentile160692.8
Maximum169015
Range169015
Interquartile range (IQR)53207.5

Descriptive statistics

Standard deviation50061.742
Coefficient of variation (CV)1.1599877
Kurtosis1.5881761
Mean43157.13
Median Absolute Deviation (MAD)25028
Skewness1.4462627
Sum992614
Variance2.506178 × 109
MonotonicityNot monotonic
2024-01-28T16:17:19.973530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0 1
 
3.6%
1949 1
 
3.6%
333 1
 
3.6%
50311 1
 
3.6%
16852 1
 
3.6%
5291 1
 
3.6%
51792 1
 
3.6%
87795 1
 
3.6%
169015 1
 
3.6%
109184 1
 
3.6%
Other values (13) 13
46.4%
(Missing) 5
 
17.9%
ValueCountFrequency (%)
0 1
3.6%
333 1
3.6%
802 1
3.6%
1272 1
3.6%
1949 1
3.6%
4586 1
3.6%
4891 1
3.6%
5291 1
3.6%
8605 1
3.6%
16335 1
3.6%
ValueCountFrequency (%)
169015 1
3.6%
166416 1
3.6%
109184 1
3.6%
87795 1
3.6%
71786 1
3.6%
61369 1
3.6%
54523 1
3.6%
51792 1
3.6%
50311 1
3.6%
49404 1
3.6%

2021-02-01
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct23
Distinct (%)100.0%
Missing5
Missing (%)17.9%
Infinite0
Infinite (%)0.0%
Mean39396.174
Minimum0
Maximum159021
Zeros1
Zeros (%)3.6%
Negative0
Negative (%)0.0%
Memory size384.0 B
2024-01-28T16:17:20.065550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile379
Q14348.5
median24388
Q352982.5
95-th percentile142383.5
Maximum159021
Range159021
Interquartile range (IQR)48634

Descriptive statistics

Standard deviation45707.807
Coefficient of variation (CV)1.1602093
Kurtosis1.6544181
Mean39396.174
Median Absolute Deviation (MAD)22496
Skewness1.4598643
Sum906112
Variance2.0892036 × 109
MonotonicityNot monotonic
2024-01-28T16:17:20.150571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0 1
 
3.6%
1892 1
 
3.6%
337 1
 
3.6%
46877 1
 
3.6%
16118 1
 
3.6%
4603 1
 
3.6%
44514 1
 
3.6%
81147 1
 
3.6%
159021 1
 
3.6%
99602 1
 
3.6%
Other values (13) 13
46.4%
(Missing) 5
 
17.9%
ValueCountFrequency (%)
0 1
3.6%
337 1
3.6%
757 1
3.6%
1249 1
3.6%
1892 1
3.6%
4094 1
3.6%
4603 1
3.6%
4741 1
3.6%
7778 1
3.6%
15047 1
3.6%
ValueCountFrequency (%)
159021 1
3.6%
147137 1
3.6%
99602 1
3.6%
81147 1
3.6%
65097 1
3.6%
56569 1
3.6%
49396 1
3.6%
46877 1
3.6%
44514 1
3.6%
44365 1
3.6%

2021-03-01
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct23
Distinct (%)100.0%
Missing5
Missing (%)17.9%
Infinite0
Infinite (%)0.0%
Mean42855.261
Minimum0
Maximum216789
Zeros1
Zeros (%)3.6%
Negative0
Negative (%)0.0%
Memory size384.0 B
2024-01-28T16:17:20.251885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile283.9
Q13788
median23831
Q351929
95-th percentile162904.2
Maximum216789
Range216789
Interquartile range (IQR)48141

Descriptive statistics

Standard deviation55729.392
Coefficient of variation (CV)1.3004096
Kurtosis4.0558617
Mean42855.261
Median Absolute Deviation (MAD)22246
Skewness1.9929014
Sum985671
Variance3.1057651 × 109
MonotonicityNot monotonic
2024-01-28T16:17:20.331062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0 1
 
3.6%
2080 1
 
3.6%
248 1
 
3.6%
44924 1
 
3.6%
19048 1
 
3.6%
4301 1
 
3.6%
44959 1
 
3.6%
74976 1
 
3.6%
216789 1
 
3.6%
101526 1
 
3.6%
Other values (13) 13
46.4%
(Missing) 5
 
17.9%
ValueCountFrequency (%)
0 1
3.6%
248 1
3.6%
607 1
3.6%
997 1
3.6%
2080 1
3.6%
3387 1
3.6%
4189 1
3.6%
4301 1
3.6%
7620 1
3.6%
15657 1
3.6%
ValueCountFrequency (%)
216789 1
3.6%
169724 1
3.6%
101526 1
3.6%
74976 1
3.6%
71651 1
3.6%
53440 1
3.6%
50418 1
3.6%
46077 1
3.6%
44959 1
3.6%
44924 1
3.6%

2021-04-01
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct24
Distinct (%)100.0%
Missing4
Missing (%)14.3%
Infinite0
Infinite (%)0.0%
Mean40174.583
Minimum1
Maximum185727
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2024-01-28T16:17:20.409859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile330.9
Q14144.75
median24055
Q352330.75
95-th percentile157959.1
Maximum185727
Range185726
Interquartile range (IQR)48186

Descriptive statistics

Standard deviation50178.052
Coefficient of variation (CV)1.2489999
Kurtosis3.2437537
Mean40174.583
Median Absolute Deviation (MAD)20457
Skewness1.8694524
Sum964190
Variance2.5178369 × 109
MonotonicityNot monotonic
2024-01-28T16:17:20.492723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
167917 1
 
3.6%
282 1
 
3.6%
33720 1
 
3.6%
9842 1
 
3.6%
4162 1
 
3.6%
41138 1
 
3.6%
73513 1
 
3.6%
15409 1
 
3.6%
185727 1
 
3.6%
101531 1
 
3.6%
Other values (14) 14
50.0%
(Missing) 4
 
14.3%
ValueCountFrequency (%)
1 1
3.6%
282 1
3.6%
608 1
3.6%
977 1
3.6%
3103 1
3.6%
4093 1
3.6%
4162 1
3.6%
7233 1
3.6%
9842 1
3.6%
15409 1
3.6%
ValueCountFrequency (%)
185727 1
3.6%
167917 1
3.6%
101531 1
3.6%
73513 1
3.6%
73383 1
3.6%
52567 1
3.6%
52252 1
3.6%
42174 1
3.6%
41138 1
3.6%
33720 1
3.6%

2021-05-01
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct24
Distinct (%)100.0%
Missing4
Missing (%)14.3%
Infinite0
Infinite (%)0.0%
Mean40094.25
Minimum1
Maximum200548
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2024-01-28T16:17:20.791247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile306.95
Q13991
median24066.5
Q349149.75
95-th percentile161243.05
Maximum200548
Range200547
Interquartile range (IQR)45158.75

Descriptive statistics

Standard deviation52538.94
Coefficient of variation (CV)1.3103859
Kurtosis3.9661333
Mean40094.25
Median Absolute Deviation (MAD)20885.5
Skewness2.0306902
Sum962262
Variance2.7603402 × 109
MonotonicityNot monotonic
2024-01-28T16:17:20.879086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
171835 1
 
3.6%
266 1
 
3.6%
30697 1
 
3.6%
8806 1
 
3.6%
4067 1
 
3.6%
37609 1
 
3.6%
67281 1
 
3.6%
15838 1
 
3.6%
200548 1
 
3.6%
101222 1
 
3.6%
Other values (14) 14
50.0%
(Missing) 4
 
14.3%
ValueCountFrequency (%)
1 1
3.6%
266 1
3.6%
539 1
3.6%
836 1
3.6%
2599 1
3.6%
3763 1
3.6%
4067 1
3.6%
6588 1
3.6%
8806 1
3.6%
15567 1
3.6%
ValueCountFrequency (%)
200548 1
3.6%
171835 1
3.6%
101222 1
3.6%
76234 1
3.6%
67281 1
3.6%
52365 1
3.6%
48078 1
3.6%
41544 1
3.6%
37609 1
3.6%
30697 1
3.6%

2021-06-01
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct24
Distinct (%)100.0%
Missing4
Missing (%)14.3%
Infinite0
Infinite (%)0.0%
Mean41199.458
Minimum1
Maximum194438
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2024-01-28T16:17:20.971838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile306.85
Q14050.25
median25779.5
Q351776.75
95-th percentile158876.75
Maximum194438
Range194437
Interquartile range (IQR)47726.5

Descriptive statistics

Standard deviation51596.269
Coefficient of variation (CV)1.2523531
Kurtosis3.344613
Mean41199.458
Median Absolute Deviation (MAD)22352.5
Skewness1.8858249
Sum988787
Variance2.662175 × 109
MonotonicityNot monotonic
2024-01-28T16:17:21.065944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
167744 1
 
3.6%
265 1
 
3.6%
33260 1
 
3.6%
9068 1
 
3.6%
4065 1
 
3.6%
43301 1
 
3.6%
71246 1
 
3.6%
19829 1
 
3.6%
194438 1
 
3.6%
108629 1
 
3.6%
Other values (14) 14
50.0%
(Missing) 4
 
14.3%
ValueCountFrequency (%)
1 1
3.6%
265 1
3.6%
544 1
3.6%
872 1
3.6%
2848 1
3.6%
4006 1
3.6%
4065 1
3.6%
6795 1
3.6%
9068 1
3.6%
16399 1
3.6%
ValueCountFrequency (%)
194438 1
3.6%
167744 1
3.6%
108629 1
3.6%
75058 1
3.6%
71246 1
3.6%
54785 1
3.6%
50774 1
3.6%
43301 1
3.6%
43133 1
3.6%
33260 1
3.6%

2021-07-01
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct24
Distinct (%)100.0%
Missing4
Missing (%)14.3%
Infinite0
Infinite (%)0.0%
Mean42127.458
Minimum1
Maximum185415
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2024-01-28T16:17:21.173463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile345.05
Q14325.75
median27345.5
Q354438.5
95-th percentile163230.95
Maximum185415
Range185414
Interquartile range (IQR)50112.75

Descriptive statistics

Standard deviation51724.304
Coefficient of variation (CV)1.227805
Kurtosis2.5437563
Mean42127.458
Median Absolute Deviation (MAD)23753.5
Skewness1.7294387
Sum1011059
Variance2.6754036 × 109
MonotonicityNot monotonic
2024-01-28T16:17:21.262964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
171137 1
 
3.6%
311 1
 
3.6%
34836 1
 
3.6%
9374 1
 
3.6%
4378 1
 
3.6%
44588 1
 
3.6%
75417 1
 
3.6%
13843 1
 
3.6%
185415 1
 
3.6%
118430 1
 
3.6%
Other values (14) 14
50.0%
(Missing) 4
 
14.3%
ValueCountFrequency (%)
1 1
3.6%
311 1
3.6%
538 1
3.6%
1054 1
3.6%
3015 1
3.6%
4169 1
3.6%
4378 1
3.6%
7589 1
3.6%
9374 1
3.6%
13843 1
3.6%
ValueCountFrequency (%)
185415 1
3.6%
171137 1
3.6%
118430 1
3.6%
79148 1
3.6%
75417 1
3.6%
59336 1
3.6%
52806 1
3.6%
44588 1
3.6%
40889 1
3.6%
34836 1
3.6%

2021-08-01
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct24
Distinct (%)100.0%
Missing4
Missing (%)14.3%
Infinite0
Infinite (%)0.0%
Mean41660.417
Minimum1
Maximum202843
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2024-01-28T16:17:21.351527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile326.15
Q14194
median26099.5
Q351404
95-th percentile156368.5
Maximum202843
Range202842
Interquartile range (IQR)47210

Descriptive statistics

Standard deviation53037.883
Coefficient of variation (CV)1.2731002
Kurtosis3.3510965
Mean41660.417
Median Absolute Deviation (MAD)22327
Skewness1.8884129
Sum999850
Variance2.813017 × 109
MonotonicityNot monotonic
2024-01-28T16:17:21.442836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
163249 1
 
3.6%
287 1
 
3.6%
34351 1
 
3.6%
8954 1
 
3.6%
4197 1
 
3.6%
39044 1
 
3.6%
70614 1
 
3.6%
13174 1
 
3.6%
202843 1
 
3.6%
117379 1
 
3.6%
Other values (14) 14
50.0%
(Missing) 4
 
14.3%
ValueCountFrequency (%)
1 1
3.6%
287 1
3.6%
548 1
3.6%
1195 1
3.6%
2981 1
3.6%
4185 1
3.6%
4197 1
3.6%
7157 1
3.6%
8954 1
3.6%
13174 1
3.6%
ValueCountFrequency (%)
202843 1
3.6%
163249 1
3.6%
117379 1
3.6%
80742 1
3.6%
70614 1
3.6%
59099 1
3.6%
48839 1
3.6%
40327 1
3.6%
39044 1
3.6%
34351 1
3.6%

2021-09-01
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct24
Distinct (%)100.0%
Missing4
Missing (%)14.3%
Infinite0
Infinite (%)0.0%
Mean38282.417
Minimum1
Maximum148939
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2024-01-28T16:17:21.530146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile282.1
Q14170
median25865.5
Q350469.5
95-th percentile142928.95
Maximum148939
Range148938
Interquartile range (IQR)46299.5

Descriptive statistics

Standard deviation44623.162
Coefficient of variation (CV)1.1656307
Kurtosis1.5066132
Mean38282.417
Median Absolute Deviation (MAD)22182.5
Skewness1.49107
Sum918778
Variance1.9912266 × 109
MonotonicityNot monotonic
2024-01-28T16:17:21.618068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
147886 1
 
3.6%
241 1
 
3.6%
33659 1
 
3.6%
8844 1
 
3.6%
4029 1
 
3.6%
37794 1
 
3.6%
70855 1
 
3.6%
14534 1
 
3.6%
148939 1
 
3.6%
114839 1
 
3.6%
Other values (14) 14
50.0%
(Missing) 4
 
14.3%
ValueCountFrequency (%)
1 1
3.6%
241 1
3.6%
515 1
3.6%
1098 1
3.6%
2817 1
3.6%
4029 1
3.6%
4217 1
3.6%
6873 1
3.6%
8844 1
3.6%
14534 1
3.6%
ValueCountFrequency (%)
148939 1
3.6%
147886 1
3.6%
114839 1
3.6%
77145 1
3.6%
70855 1
3.6%
56696 1
3.6%
48394 1
3.6%
41402 1
3.6%
37794 1
3.6%
33659 1
3.6%

2021-10-01
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct24
Distinct (%)100.0%
Missing4
Missing (%)14.3%
Infinite0
Infinite (%)0.0%
Mean38015.625
Minimum1
Maximum169567
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2024-01-28T16:17:21.718542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile254.15
Q13856
median23517
Q347150.25
95-th percentile148366.25
Maximum169567
Range169566
Interquartile range (IQR)43294.25

Descriptive statistics

Standard deviation47442.019
Coefficient of variation (CV)1.247961
Kurtosis2.5017789
Mean38015.625
Median Absolute Deviation (MAD)20115
Skewness1.7346336
Sum912375
Variance2.2507452 × 109
MonotonicityNot monotonic
2024-01-28T16:17:21.808789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
169567 1
 
3.6%
227 1
 
3.6%
30814 1
 
3.6%
8259 1
 
3.6%
3941 1
 
3.6%
34355 1
 
3.6%
64738 1
 
3.6%
12600 1
 
3.6%
155627 1
 
3.6%
107222 1
 
3.6%
Other values (14) 14
50.0%
(Missing) 4
 
14.3%
ValueCountFrequency (%)
1 1
3.6%
227 1
3.6%
408 1
3.6%
859 1
3.6%
2343 1
3.6%
3601 1
3.6%
3941 1
3.6%
6627 1
3.6%
8259 1
3.6%
12600 1
3.6%
ValueCountFrequency (%)
169567 1
3.6%
155627 1
3.6%
107222 1
3.6%
80505 1
3.6%
64738 1
3.6%
57108 1
3.6%
43831 1
3.6%
39327 1
3.6%
34355 1
3.6%
30814 1
3.6%

2021-11-01
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct24
Distinct (%)100.0%
Missing4
Missing (%)14.3%
Infinite0
Infinite (%)0.0%
Mean37698.125
Minimum0
Maximum165882
Zeros1
Zeros (%)3.6%
Negative0
Negative (%)0.0%
Memory size384.0 B
2024-01-28T16:17:21.906368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile325.15
Q14114.75
median23898
Q351792.5
95-th percentile120897.7
Maximum165882
Range165882
Interquartile range (IQR)47677.75

Descriptive statistics

Standard deviation43828.441
Coefficient of variation (CV)1.1626159
Kurtosis2.1762118
Mean37698.125
Median Absolute Deviation (MAD)20456.5
Skewness1.5753213
Sum904755
Variance1.9209322 × 109
MonotonicityNot monotonic
2024-01-28T16:17:21.997166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
165882 1
 
3.6%
298 1
 
3.6%
33696 1
 
3.6%
9507 1
 
3.6%
4039 1
 
3.6%
39833 1
 
3.6%
73375 1
 
3.6%
13654 1
 
3.6%
122929 1
 
3.6%
109387 1
 
3.6%
Other values (14) 14
50.0%
(Missing) 4
 
14.3%
ValueCountFrequency (%)
0 1
3.6%
298 1
3.6%
479 1
3.6%
960 1
3.6%
2844 1
3.6%
4039 1
3.6%
4140 1
3.6%
7409 1
3.6%
9507 1
3.6%
13654 1
3.6%
ValueCountFrequency (%)
165882 1
3.6%
122929 1
3.6%
109387 1
3.6%
75298 1
3.6%
73375 1
3.6%
54905 1
3.6%
50755 1
3.6%
40043 1
3.6%
39833 1
3.6%
33696 1
3.6%

2021-12-01
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct24
Distinct (%)100.0%
Missing4
Missing (%)14.3%
Infinite0
Infinite (%)0.0%
Mean41229.333
Minimum1
Maximum176382
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2024-01-28T16:17:22.087022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile395.3
Q14824.5
median25191.5
Q356619.5
95-th percentile145853.75
Maximum176382
Range176381
Interquartile range (IQR)51795

Descriptive statistics

Standard deviation48423.444
Coefficient of variation (CV)1.1744901
Kurtosis2.0600523
Mean41229.333
Median Absolute Deviation (MAD)21073.5
Skewness1.5916323
Sum989504
Variance2.3448299 × 109
MonotonicityNot monotonic
2024-01-28T16:17:22.195565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
176382 1
 
3.6%
365 1
 
3.6%
37185 1
 
3.6%
10852 1
 
3.6%
4836 1
 
3.6%
42774 1
 
3.6%
81639 1
 
3.6%
14346 1
 
3.6%
150983 1
 
3.6%
116788 1
 
3.6%
Other values (14) 14
50.0%
(Missing) 4
 
14.3%
ValueCountFrequency (%)
1 1
3.6%
365 1
3.6%
567 1
3.6%
1149 1
3.6%
3446 1
3.6%
4790 1
3.6%
4836 1
3.6%
8038 1
3.6%
10852 1
3.6%
14346 1
3.6%
ValueCountFrequency (%)
176382 1
3.6%
150983 1
3.6%
116788 1
3.6%
81639 1
3.6%
75450 1
3.6%
57686 1
3.6%
56264 1
3.6%
44583 1
3.6%
42774 1
3.6%
37185 1
3.6%

Interactions

2024-01-28T16:17:18.154209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:17:08.167229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:17:09.209354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:17:10.096394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:17:10.941310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:17:11.768289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:17:12.876951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:17:13.722164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:17:14.558750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:17:15.459804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:17:16.303170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:17:17.327939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:17:18.223345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:17:08.237047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:17:09.275546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:17:10.156274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:17:11.010396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:17:11.841206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:17:12.946913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:17:13.789917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:17:14.625580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:17:15.527853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:17:16.373418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:17:17.396174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:17:18.295726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:17:08.307080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:17:09.353583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:17:10.225016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:17:11.078511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:17:11.920372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:17:13.017666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:17:13.859074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:17:14.696155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:17:15.597341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:17:16.652593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:17:17.480955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:17:18.353619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:17:08.365726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:17:09.419574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:17:10.277755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:17:11.139273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:17:11.991272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:17:13.078994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:17:13.921991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:17:14.761653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:17:15.655620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:17:16.710666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:17:17.539717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:17:18.419976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:17:08.649756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:17:09.492822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:17:10.355850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:17:11.205056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:17:12.076157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:17:13.149983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:17:13.988628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:17:14.844148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:17:15.722703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:17:16.778080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:17:17.607162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:17:18.490203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:17:08.718139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:17:09.567289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:17:10.431752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:17:11.275362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:17:12.149281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:17:13.224416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:17:14.057948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:17:14.932685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:17:15.806879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:17:16.859279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:17:17.675866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:17:18.561058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:17:08.786475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:17:09.645549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:17:10.517869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:17:11.355099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:17:12.224649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:17:13.298853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:17:14.129659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:17:15.019620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:17:15.877679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:17:16.932404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:17:17.761166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:17:18.627762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:17:08.849678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:17:09.719191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:17:10.579447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:17:11.432503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:17:12.298750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:17:13.369532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:17:14.194444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:17:15.085680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:17:15.942991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:17:16.998911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:17:17.831193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:17:18.695404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:17:08.926142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:17:09.798409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:17:10.645570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:17:11.501863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:17:12.369724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:17:13.438487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:17:14.266406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:17:15.156899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:17:16.014848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:17:17.067545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:17:17.899180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:17:18.763826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:17:09.008022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:17:09.871716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:17:10.711526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:17:11.569517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:17:12.646261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:17:13.508509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:17:14.344463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:17:15.225713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:17:16.081661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:17:17.133832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:17:17.963092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:17:18.832445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:17:09.081985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:17:09.951162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:17:10.791903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:17:11.638217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:17:12.711923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:17:13.579623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:17:14.424375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:17:15.307779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:17:16.151421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:17:17.199059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:17:18.028572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:17:18.900460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:17:09.143291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:17:10.026114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:17:10.859251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:17:11.702692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:17:12.790007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:17:13.649902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:17:14.492004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:17:15.380154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:17:16.230719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:17:17.260346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:17:18.088269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-28T16:17:22.280955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
제조업 중분류별 전력사용량(MWh)2021-01-012021-02-012021-03-012021-04-012021-05-012021-06-012021-07-012021-08-012021-09-012021-10-012021-11-012021-12-01
제조업 중분류별 전력사용량(MWh)1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
2021-01-011.0001.0000.9960.9501.0000.9130.9440.9510.9090.9860.9690.9710.971
2021-02-011.0000.9961.0000.9271.0000.9220.9440.9630.9440.9950.9750.9890.989
2021-03-011.0000.9500.9271.0001.0000.9981.0000.9930.9960.8810.9860.9750.975
2021-04-011.0001.0001.0001.0001.0000.9680.9761.0000.9520.9570.9700.9670.967
2021-05-011.0000.9130.9220.9980.9681.0000.9980.9890.9990.9150.9800.9710.971
2021-06-011.0000.9440.9441.0000.9760.9981.0000.9910.9960.8900.9830.9610.961
2021-07-011.0000.9510.9630.9931.0000.9890.9911.0000.9880.9230.9970.9330.933
2021-08-011.0000.9090.9440.9960.9520.9990.9960.9881.0000.9410.9750.9870.987
2021-09-011.0000.9860.9950.8810.9570.9150.8900.9230.9411.0000.9380.9910.991
2021-10-011.0000.9690.9750.9860.9700.9800.9830.9970.9750.9381.0000.8990.899
2021-11-011.0000.9710.9890.9750.9670.9710.9610.9330.9870.9910.8991.0001.000
2021-12-011.0000.9710.9890.9750.9670.9710.9610.9330.9870.9910.8991.0001.000
2024-01-28T16:17:22.393256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2021-01-012021-02-012021-03-012021-04-012021-05-012021-06-012021-07-012021-08-012021-09-012021-10-012021-11-012021-12-01
2021-01-011.0000.9980.9970.9960.9950.9960.9960.9950.9930.9930.9910.995
2021-02-010.9981.0000.9950.9950.9930.9950.9950.9930.9950.9910.9930.993
2021-03-010.9970.9951.0000.9980.9960.9950.9950.9960.9950.9950.9930.996
2021-04-010.9960.9950.9981.0000.9980.9970.9970.9980.9980.9970.9970.999
2021-05-010.9950.9930.9960.9981.0000.9990.9970.9980.9980.9970.9970.997
2021-06-010.9960.9950.9950.9970.9991.0000.9980.9970.9970.9970.9970.997
2021-07-010.9960.9950.9950.9970.9970.9981.0000.9990.9970.9980.9970.997
2021-08-010.9950.9930.9960.9980.9980.9970.9991.0000.9980.9990.9970.997
2021-09-010.9930.9950.9950.9980.9980.9970.9970.9981.0000.9970.9990.997
2021-10-010.9930.9910.9950.9970.9970.9970.9980.9990.9971.0000.9980.998
2021-11-010.9910.9930.9930.9970.9970.9970.9970.9970.9990.9981.0000.998
2021-12-010.9950.9930.9960.9990.9970.9970.9970.9970.9970.9980.9981.000

Missing values

2024-01-28T16:17:19.023305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-28T16:17:19.155434image/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.
2024-01-28T16:17:19.274615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

제조업 중분류별 전력사용량(MWh)2021-01-012021-02-012021-03-012021-04-012021-05-012021-06-012021-07-012021-08-012021-09-012021-10-012021-11-012021-12-01
0식음료품545234939650418525675236554785593365909956696571085490557686
1담배제조업000111111101
2섬유제품458640943387310325992848301529812817234328443446
3의복, 모피127212499979778368721054119510988599601149
4가죽, 가방802757607608539544538548515408479567
5목재, 나무494044436546077421744154443133408894032741402393274004344583
6펄프, 종이489147414189409337634006416941854217360141404790
7인쇄, 매체860577787620723365886795758971576873662774098038
8연탄, 석유717866509771651733837623475058791488074277145805057529875450
9화학제품338033138329222298462784630168316213073830590267772993132998
제조업 중분류별 전력사용량(MWh)2021-01-012021-02-012021-03-012021-04-012021-05-012021-06-012021-07-012021-08-012021-09-012021-10-012021-11-012021-12-01
18기타기계877958114774976735136728171246754177061470855647387337581639
19자동차 제조517924451444959411383760943301445883904437794343553983342774
20기타운송529146034301416240674065437841974029394140394836
21가구<NA><NA><NA>9842880690689374895488448259950710852
22기타제품<NA><NA><NA>337203069733260348363435133659308143369637185
23산업기계<NA><NA><NA>282266265311287241227298365
24전기기기 제조168521611819048<NA><NA><NA><NA><NA><NA><NA><NA><NA>
25가구 및 기타제품503114687744924<NA><NA><NA><NA><NA><NA><NA><NA><NA>
26사무기기333337248<NA><NA><NA><NA><NA><NA><NA><NA><NA>
27재생재료 처리194918922080<NA><NA><NA><NA><NA><NA><NA><NA><NA>